Update parquet files (step 74 of 397)

spaces/1368565466ki/ZSTRD/commons.py

@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm

spaces/1368565466ki/ZSTRD/text/symbols.py

@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Commentaire Arabe Fifa 14 Pc Crack.md

@@ -1,155 +0,0 @@
-<br />
-<h1>Commentaire Arabe Fifa 14 Pc Crack</h1>
-<p>FIFA 14 is one of the most popular football simulation games ever released by EA Sports. It features realistic graphics, gameplay, physics, teams, players, stadiums, leagues, tournaments and more. It also supports various languages and commentaries, including Arabic, which is spoken by millions of football fans around the world.</p>
-<p>The Arabic commentary adds more excitement, emotion and authenticity to the game. It is voiced by famous Arab commentators such as Issam Chawali, Raouf Khlif, Hafid Derradji and others. They provide insightful analysis, witty remarks, catchy phrases and memorable reactions to the events on the pitch.</p>
-<h2>Commentaire Arabe Fifa 14 Pc Crack</h2><br /><p><b><b>DOWNLOAD</b> &raquo;&raquo;&raquo; <a href="https://byltly.com/2uKwbl">https://byltly.com/2uKwbl</a></b></p><br /><br />
-<p>However, not everyone can afford to buy FIFA 14 or its Arabic commentary pack from EA Sports. That's why some people resort to using a PC crack, which is a modified version of the game that bypasses its security measures and allows players to enjoy it for free. However, using a PC crack also comes with some risks and challenges.</p>
-<p>In this article, we will show you how to download and install FIFA 14 Arabic commentary and PC crack on your computer. We will also give you some tips on how to enjoy FIFA 14 with these additions. But before we proceed, we must warn you that using a PC crack or downloading pirated content is illegal and may harm your computer or expose you to viruses or malware. We do not condone or encourage such actions and we are not responsible for any consequences that may arise from them. This article is for informational purposes only.</p>
- <h2>How to Download and Install FIFA 14 Arabic Commentary</h2>
-<p>There are two ways to get FIFA 14 Arabic commentary on your PC: either buy it from EA Sports or download it from another source.</p>
- <h3>The official source of the Arabic commentary and how to purchase it</h3>
-<p>The official source of the Arabic commentary is EA Sports itself. You can buy it from their website or from their online store. However, you need to have a valid copy of FIFA 14 installed on your PC first. You also need to have an EA account and an internet connection.</p>
-<p>To purchase FIFA 14 Arabic commentary from EA Sports, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://www.easports.com/fifa">https://www.easports.com/fifa</a> and click on "FIFA 14".</li>
-<li>Click on "Buy Now" and choose your platform (PC).</li>
-<li>Select your region (Middle East) and your language (Arabic).</li>
-<li>Click on "Add to Cart" and proceed to checkout.</li>
-<li>Enter your payment details and confirm your order.</li>
-<li>Wait for the confirmation email and follow the instructions to download and install FIFA 14 Arabic commentary on your PC.</li>
-</ol>
- <h3>The alternative source of the Arabic commentary and how to download it for free</h3>
-<p>The alternative source of the Arabic commentary is another website that offers it for free. However, you need to be careful when choosing such a website as some of them may contain viruses or malware that can harm your computer or steal your personal information. You also need to have a torrent client such as uTorrent or BitTorrent installed on your PC.</p>
-<p>To download FIFA 14 Arabic commentary from another source for free, follow these steps:</p>
-<p>Télécharger commentaire arabe fifa 14 pc gratuit<br />
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-<ol>
-<li>Go to <a href="https://micano4u.blogspot.com/2015/03/fifa-14.html">https://micano4u.blogspot.com/2015/03/fifa-14.html</a>, which is one of the websites that offer FIFA 14 Arabic commentary for free.</li>
-<li>Scroll down until you see a link that says "رابط التحميل من هنا" (Download link from here).</li>
-<li>Click on that link and wait for a few seconds until you see another link that says "Download Now".</li>
-<li>Click on that link and you will be redirected to a torrent file.</li>
-<li>Open that file with your torrent client and start downloading FIFA 14 Arabic commentary.</li>
-<li>Wait until the download is complete.</li>
-</ol>
- <h3>The steps to install the Arabic commentary on PC</h3>
-<p>Once you have downloaded FIFA 14 Arabic commentary from either source, you need to install it on your PC. To do that, follow these steps:</p>
-<ol>
-<li>Extract the downloaded file using WinRAR or any other extraction tool.</li>
-<li>You will see two folders: one called "Data" and another called "FIFASetup". Copy both folders.</li>
-<li>Paste them into your FIFA 14 installation directory (usually C:\Program Files (x86)\Origin Games\FIFA 14).</li>
-<li>If prompted, replace any existing files or folders with the new ones.</li>
-<li>Run FIFASetup\fifaconfig.exe as administrator.</li>
-<li>Go to "Game Settings" tab and click on "Audio".</li>
-<li>Select "Arabic" as your language under "Commentary Language".</li>
-<li>Click on "Apply Settings" and close fifaconfig.exe.</li>
-<li>Run FIFA 14 as usual.</li>
-</ol>
- <h2>How to Download and Install FIFA 14 PC Crack</h2>
-<p>A PC crack is a modified version of a game that bypasses its security measures and allows players to enjoy it for free. However, using a PC crack also has some benefits and risks.</p>
- <h3>The benefits of using a PC crack for FIFA 14</h3>
-<p>Some of the benefits of using a PC crack for FIFA 14 are:</p>
-<ul>
-<li>You can play FIFA 14 without buying it from EA Sports or having a valid license key.</li>
-<li>You can play FIFA 14 offline without needing an internet connection or an EA account.</li>
-<li>You can play FIFA 14 with any language or commentary you want without needing to purchase them separately.</li>
-<li>You can play FIFA 14 with any mod or patch you want without worrying about compatibility issues or updates.</li>
-</ul>
- <h3>The risks of using a PC crack for FIFA 14</h3>
-<p>Some of the risks of using a PC crack for FIFA 14 are:</p>
-<ul>
-<li>You may violate EA Sports' terms of service or copyright laws by playing a pirated version of their game.</li>
-<li>You may expose your computer or personal information to viruses or malware by downloading or installing files from untrusted sources.</li>
-<li>You may experience errors, bugs, crashes or performance issues by playing an unofficial version of their game.</li>
-<li>You may not be able to access some features or modes of their game such as online multiplayer or Ultimate Team.</ul>
- <h3>The steps to install the PC crack on PC</h3>
-<p>Once you have downloaded the PC crack for FIFA 14 from SoccerFandom.com, you need to install it on your PC. To do that, follow these steps:</p>
-<ol>
-<li>Extract the downloaded file using WinRAR or any other extraction tool.</li>
-<li>You will see a folder called "FIFA 14 PC Crack". Copy that folder.</li>
-<li>Paste it into your FIFA 14 installation directory (usually C:\Program Files (x86)\Origin Games\FIFA 14).</li>
-<li>If prompted, replace any existing files or folders with the new ones.</li>
-<li>Run FIFA 14 as usual.</li>
-</ol>
- <h2>How to Enjoy FIFA 14 with Arabic Commentary and PC Crack</h2>
-<p>Now that you have installed FIFA 14 Arabic commentary and PC crack on your PC, you can enjoy the game with these additions. Here are some of the features and modes of FIFA 14 that you can play with Arabic commentary and PC crack:</p>
- <h3>Kick Off</h3>
-<p>This is the simplest and quickest way to play a match in FIFA 14. You can choose any team, stadium, weather, time and difficulty level. You can also customize the match settings such as half length, ball type, game speed and more. You can play against the computer or a friend on the same PC. You can also select Arabic as your commentary language and enjoy the game with the voices of your favorite Arab commentators.</p>
- <h3>Career Mode</h3>
-<p>This is the most immersive and realistic mode in FIFA 14. You can choose to be a player, a manager or both. You can create your own custom player or use an existing one. You can choose any club from any league in the world. You can also transfer players, negotiate contracts, scout talents, manage finances and more. You can play through multiple seasons and compete in various tournaments and cups. You can also experience the highs and lows of a football career such as injuries, suspensions, form, morale and more. You can also listen to Arabic commentary and get feedback on your performance and progress.</p>
- <h3>Ultimate Team</h3>
-<p>This is the most popular and addictive mode in FIFA 14. You can create your own dream team from scratch using players from different clubs and leagues. You can also buy, sell and trade players with other users online. You can earn coins by playing matches, completing challenges and objectives. You can use coins to buy packs that contain players, items and consumables. You can also customize your team's kits, badges, stadiums and more. You can play online or offline matches against other users or the computer. You can also participate in tournaments and seasons and win rewards and trophies. However, you need an internet connection and an EA account to access this mode. You may also not be able to use Arabic commentary in this mode due to compatibility issues.</p>
- <h2>Conclusion</h2>
-<p>FIFA 14 is a great game for football fans who want to experience the thrill and excitement of the beautiful game. It offers various features and modes that cater to different tastes and preferences. It also supports Arabic commentary that adds more flavor and authenticity to the game. However, not everyone can afford to buy FIFA 14 or its Arabic commentary pack from EA Sports. That's why some people use a PC crack that allows them to play FIFA 14 for free.</p>
-<p>In this article, we showed you how to download and install FIFA 14 Arabic commentary and PC crack on your computer. We also gave you some tips on how to enjoy FIFA 14 with these additions. However, we also warned you about the risks and challenges of using a PC crack or downloading pirated content. We do not condone or encourage such actions and we are not responsible for any consequences that may arise from them. This article is for informational purposes only.</p>
-<p>If you want to try out FIFA 14 with Arabic commentary and PC crack, you can follow the steps we provided in this article. But if you want to support EA Sports and their hard work, you can buy FIFA 14 and its Arabic commentary pack from their official sources. Either way, we hope you have fun playing FIFA 14.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about FIFA 14, Arabic commentary and PC crack:</p>
- <h4>Q: Is FIFA 14 compatible with Windows 10?</h4>
-<p>A: Yes, FIFA 14 is compatible with Windows 10. However, you may need to run it in compatibility mode or as administrator if you encounter any issues.</p>
- <h4>Q: How can I change the language of FIFA 14?</h4>
-<p>A: You can change the language of FIFA 14 by running fifaconfig.exe as administrator and going to "Game Settings" tab. There you can select your language under "Text Language" and "Audio Language". However, you need to have the corresponding language pack installed on your PC first.</p>
- <h4>Q: How can I update FIFA 14 to the latest version?</h4>
-<p>A: You can update FIFA 14 to the latest version by downloading and installing patches or updates from EA Sports or other sources. However, you may need to re-install or re-apply the PC crack after updating.</p>
- <h4>Q: How can I fix FIFA 14 errors or crashes?</h4>
-<p>A: You can fix FIFA 14 errors or crashes by following these steps:</p>
-<ol>
-<li>Make sure your PC meets the minimum system requirements for FIFA 14.</li>
-<li>Update your drivers, DirectX and Visual C++ redistributables.</li>
-<li>Disable any antivirus or firewall programs that may interfere with FIFA 14.</li>
-<li>Delete any corrupted or unnecessary files or folders in your FIFA 14 installation directory.</li>
-<li>Regenerate with Creation Master 14/i68Controller/FHL-BH-Editor.</li>
-<li>Run fifaconfig.exe as administrator and adjust your settings according to your preferences.</li>
-<li>Run FIFA 14 as administrator.</li>
-</ol>
- <h4>Q: How can I contact EA Sports for support or feedback?</h4>
-<p>A: You can contact EA Sports for support or feedback by visiting their website <a href="https://help.ea.com/en/fifa/fifa-2014/">https://help.ea.com/en/fifa/fifa-2014/</a> or their social media accounts such as Facebook <a href="https://www.facebook.com/easportsfifa/">https://www.facebook.com/easportsfifa/</a> or Twitter <a href="https://twitter.com/EASPORTSFIFA">https://twitter.com/EASPORTSFIFA</a>.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Driver Sagem Fst 3304 V2 Maroc Telecom Download and Update the Latest Version for Your Router.md

@@ -1,180 +0,0 @@
-
-<h1>Driver Sagem F@st 3304 V2 Maroc Telecom</h1>
- <p>If you are looking for a reliable and easy-to-use router for your home or office network, you might want to consider the Sagem F@st 3304 V2. This router is designed by Sagem, a French company that specializes in telecommunications equipment, and distributed by Maroc Telecom, the leading telecom operator in Morocco. In this article, we will give you an overview of what this router can do, how to install and configure it, and how to troubleshoot some common issues that you might encounter.</p>
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- <h2>What is Sagem F@st 3304 V2?</h2>
- <p>Sagem F@st 3304 V2 is a wireless router that supports ADSL2+ technology, which means it can provide high-speed internet access up to 24 Mbps. It also has four Ethernet ports for connecting wired devices, such as computers, printers, or game consoles. Additionally, it has a USB port for connecting external storage devices or printers. The router also has a built-in firewall and NAT function, which can protect your network from unauthorized access and enable multiple devices to share the same internet connection.</p>
- <h3>Features and specifications</h3>
- <p>Here are some of the main features and specifications of the Sagem F@st 3304 V2 router:</p>
- <ul>
-<li>Dimensions: 180 x 120 x 32 mm</li>
-<li>Weight: 300 g</li>
-<li>Power supply: 12V DC / 1A</li>
-<li>Operating temperature: 0°C to 40°C</li>
-<li>Operating humidity: 10% to 90% (non-condensing)</li>
-<li>ADSL standards: G.992.1 (G.dmt), G.992.2 (G.lite), G.992.3 (ADSL2), G.992.5 (ADSL2+)</li>
-<li>Data rates: up to 24 Mbps downstream and up to 1 Mbps upstream</li>
-<li>Ethernet ports: 4 x RJ-45 (10/100 Mbps)</li>
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-<li>Wireless standards: IEEE 802.11b/g/n</li>
-<li>Wireless frequency: 2.4 GHz</li>
-<li>Wireless data rates: up to 300 Mbps</li>
-<li>Wireless security: WEP, WPA, WPA2, WPS</li>
-<li>Wireless features: SSID broadcast, MAC filtering, WMM, WDS</li>
-<li>Firewall features: SPI, DoS protection, IP filtering, MAC filtering, URL filtering</li>
-<li>NAT features: NAPT, DMZ, virtual server, port forwarding, port triggering</li>
-<li>Management features: web-based interface, remote management, firmware upgrade, backup and restore settings</li>
-</ul>
- <h3>Benefits and drawbacks</h3>
- <p>The Sagem F@st 3304 V2 router has some benefits and drawbacks that you should be aware of before buying it. Here are some of them:</p>
- <table>
-<tr><th>Benefits</th><th>Drawbacks</th></tr>
-<tr><td>- Easy to install and configure</td><td>- Limited wireless range and coverage</td></tr>
-<tr><td>- High-speed internet access</td><td>- No dual-band support</td></tr>
-<tr><td>- Multiple connectivity options</td><td>- No gigabit Ethernet ports</td></tr>
-<tr><td>- Secure and reliable network</td><td>- No parental control features</td></tr>
-<tr><td>- Affordable price</td><td>- No VPN support</td></tr>
-</table>
- <h2>How to install and configure Sagem F@st 3304 V2?</h2>
- <p>To install and configure the Sagem F@st 3304 V2 router, you will need the following items:</p>
- <ul>
-<li>The router itself</li>
-<li>The power adapter</li>
-<li>The ADSL cable (RJ-11)</li>
-<li>The Ethernet cable (RJ-45)</li>
-<li>A computer with a web browser</li>
-<li>Your Maroc Telecom username and password (provided by your ISP)</li>
-</ul>
- <h3>Installation steps</h3>
- <p>To install the router, follow these steps:</p>
- <ol>
-<li>Connect the ADSL cable to the ADSL port on the back of the router and to the phone jack on the wall.</li>
-<li>Connect the Ethernet cable to one of the LAN ports on the back of the router and to the Ethernet port on your computer.</li>
-<li>Connect the power adapter to the power port on the back of the router and to an electrical outlet.</li>
-<li>Turn on the router by pressing the power button on the back.</li>
-<li>Wait for a few minutes until the power LED turns green and the ADSL LED turns solid green.</li>
-<li>If you want to use the wireless function of the router, you can also connect your wireless devices to the router using the default SSID (SAGEM_XXXX) and password (12345678), which are printed on a label on the bottom of the router.</li>
-</ol>
- <h3>Configuration steps</h3>
- <p>To configure the router, follow these steps:</p>
- <h4>Basic settings</h4>
- <ol>
-<li>Open your web browser and type http://192.168.1.1 in the address bar.</li>
-<li>You will see a login page asking for your username and password. Enter admin for both fields and click OK.</li>
-<li>You will see a welcome page with some basic information about your router. Click Next.</li>
-<li>You will see a page asking for your Maroc Telecom username and password. Enter them in the corresponding fields and click Next.</li>
-<li>You will see a page asking for your time zone settings. Select your time zone from the drop-down menu and click Next.</li>
-<li>You will see a page asking for your wireless settings. You can change the SSID (network name) and password if you want or leave them as default. Click Next.</li>
-<li>You will see a summary page showing your configuration settings. Click Save.</li>
-<li>Your router will reboot and apply your settings.</li>
-</ol>
- <h4>Wireless settings</h4>
- <p>If you want to change your wireless settings further, you can do so by following these steps:</p>
-<p>How to configure Sagem 3304 V2 router for Maroc Telecom internet<br />
-Sagem F@st 3304 V2 firmware update for Maroc Telecom customers<br />
-Sagem F@st 3304 V2 router troubleshooting guide for Maroc Telecom users<br />
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- <ol>
-<li>Login to your router's web interface as described above.</li>
-<li>Click on Wireless in the left menu.</li>
-<li>You will see several tabs with different wireless options. You can change them according to your preferences.</li>
-<ul><li>The Basic tab allows you to enable or disable wireless function, change SSID and channel number.</li><
- Some additional sentences are: - The Security tab allows you to change encryption type, password and WPS settings. - The MAC Filter tab allows you to restrict wireless access based on MAC addresses of devices. - The WMM tab allows you to enable or disable WMM function for better quality of service. - The WDS tab allows you to enable or disable WDS function for extending wireless coverage.</ul></ol>
- <h4>Advanced settings</h4>
- <p>If you want to change your advanced settings further, you can do so by following these steps:</p>
- <ol><
-- Login to your router's web interface as described above. - Click on Advanced in the left menu. - You will see several tabs with different advanced options. You can change them according to your preferences. <ul><
-- The Firewall tab allows you to enable or disable firewall function, set up IP filtering, MAC filtering and URL filtering rules. - The NAT tab allows you to enable or disable NAT function, set up DMZ host, virtual server, port forwarding and port triggering rules. - The Management tab allows you to change admin password, enable or disable remote management, upgrade firmware version, backup and restore settings. - The Status tab allows you to view system information, statistics, logs and diagnostics.</ul></ol>
- <h2>How to troubleshoot common issues with Sagem F@st 3304 V2?</h2>
- <p>If you encounter <p>If you encounter any problems with your router, you can try the following troubleshooting tips:</p>
- <h3>No internet connection</h3>
- <p>If you have no internet connection, you can check the following things:</p>
- <ul>
-<li>Make sure the ADSL cable is properly connected to the router and the phone jack.</li>
-<li>Make sure the power LED and the ADSL LED on the router are green and solid.</li>
-<li>Make sure your Maroc Telecom username and password are correct and entered in the router's web interface.</li>
-<li>Try to reboot your router by turning it off and on again.</li>
-<li>Try to reset your router to factory settings by pressing and holding the reset button on the back of the router for 10 seconds.</li>
-</ul>
- <h3>Slow internet speed</h3>
- <p>If you have slow internet speed, you can check the following things:</p>
- <ul>
-<li>Make sure your ADSL line is not noisy or interfered by other devices. You can try to use a filter or splitter to separate your phone line from your ADSL line.</li>
-<li>Make sure your router is not too far from your devices or blocked by walls or other obstacles. You can try to move your router to a central location or use a wireless extender to improve wireless signal.</li>
-<li>Make sure your devices are not overloaded with too many applications or downloads. You can try to close some programs or limit bandwidth usage.</li>
-<li>Make sure your wireless network is secure and not accessed by unauthorized users. You can change your wireless password or enable MAC filtering to prevent unwanted connections.</li>
-</ul>
- <h3>Wireless network not detected</h3>
- <p>If you cannot find your wireless network on your devices, you can check the following things:</p>
- <ul>
-<li>Make sure the wireless function on your router is enabled and the wireless LED is green and blinking.</li>
-<li>Make sure the SSID broadcast on your router is enabled and the SSID is not hidden or changed.</li>
-<li>Make sure your devices are compatible with the wireless standards and frequencies of your router. You can try to change the wireless mode or channel on your router's web interface.</li>
-<li>Make sure your devices are within the wireless range of your router and not affected by interference from other networks or devices. You can try to scan for available networks or change the wireless channel on your router's web interface.</li>
-</ul>
- <h3>Forgotten password or username</h3>
- <p>If you forget your admin password or username for accessing your router's web interface, you can do the following things:</p>
- <ul>
-<li>Try to use the default username and password (admin/admin) if you have not changed them before.</li>
-<li>Try to reset your router to factory settings by pressing and holding the reset button on the back of the router for 10 seconds. This will erase all your settings and restore the default username and password.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In this article, we have given you an overview of the Sagem F@st 3304 V2 router, how to install and configure it, and how to troubleshoot some common issues. We hope this article has been helpful for you and that you enjoy using this router for your home or office network. If you have any questions or feedback, please feel free to contact us or leave a comment below.</p>
- <h2>Frequently Asked Questions</h2>
- <ol>
-<li><b>Where can I find more information about Sagem F@st 3304 V2?</b></li>
-<p>You can find more information about this router on the official website of Sagem (https://www.safran-electronics-defense.com) or Maroc Telecom (https://www.iam.ma). You can also watch some video tutorials on YouTube (https://www.youtube.com/watch?v=kRMEvVC2elY).</p>
-<li><b>How can I update the firmware of Sagem F@st 3304 V2?</b></li>
-<p>You can update the firmware of this router by downloading the latest version from Sagem's website (https://www.safran-electronics-defense.com) and uploading it to your router's web interface. To do this, follow these steps:</p>
-<ul><
-- Login to your router's web interface as described above. - Click on Management in the left menu. - Click on Firmware Upgrade in the top menu. - Click on Browse and select the firmware file that you downloaded. - Click on Upgrade and wait for the process to complete. - Your router will reboot and apply the new firmware.</ul></ol>
- <ol start="3">
-<li><b>How can I change the language of Sagem F@st 3304 V2?</b></li>
-<p>You can change the language of this router by selecting one of the available options from a drop-down menu on the top right corner of your router's web interface. The available languages are English, French, Arabic, Spanish, Portuguese, German, Italian, Turkish, Russian, Polish, Dutch, Swedish, Norwegian, Danish, Finnish, Greek, Hungarian, Czech, Slovak, Romanian, Bulgarian, Croatian, Slovenian, Serbian, Lithuanian, Latvian, Estonian and Chinese.</p>
-<li><b>How can I contact Sagem or Maroc Telecom for technical support?</b></li>
-<p>You can contact Sagem or Maroc Telecom for technical support by using one of the following methods:</p>
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-- For Sagem: You can call their customer service number (+33 1 40 70 61 61) or send them an email ([email protected]). - For Maroc Telecom: You can call their customer service number (115) or visit their website (https://www.iam.ma).</ul></ol>
- <ol start="5">
-<li><b>How can I write a review or feedback about Sagem F@st 3304 V2?</b></li>
-<p>You can write a review or feedback about this router by leaving a comment below this article or on any online platform that allows user reviews. You can also rate this article by clicking on one of the stars below. We appreciate your opinion and suggestions.</p>
-</ol>
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-<meta name="description" content="Learn how to use Dead Island v.1.0.0 trainer to activate cheats and mods in the zombie survival game Dead Island: Definitive Edition.">
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-<p>These are some of the tips and tricks that will help you master Beach Buggy Racing 3. Of course, the best way to improve is to practice and have fun!</p>
- <h2>Conclusion and FAQs</h2>
-<p>Beach Buggy Racing 3 is a fantastic racing game that will keep you entertained for hours. It has amazing graphics, gameplay, characters, vehicles, modes, challenges, power-ups, and more. It is also easy to download and install on your Android device using the APK file. If you are looking for a fun and exciting racing game that will make you feel like you are on a real beach, then you should definitely try Beach Buggy Racing 3.</p>
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-<li><b>Is Beach Buggy Racing 3 safe to download and play?</b><br>Yes, Beach Buggy Racing 3 is safe to download and play, as long as you use a reliable source for the APK file. We recommend using one of the links we provided above , which are verified by APKCombo. APKCombo is a trusted website that scans all the APK files for viruses and malware before uploading them.</li>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bouncing Rainbow Mod APK Enjoy a Fun and Colorful Game on Your Android Device.md

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-<h1>Bouncing Rainbow Mod Apk: A Fun and Colorful Game for Android Users</h1>
-<p>If you are looking for a fun and colorful game that will keep you entertained for hours, you should try bouncing rainbow mod apk. This is a modded version of the original bouncing rainbow game, which is a casual arcade game where you control a bouncing ball that changes colors as it moves. The goal is to collect as many coins as possible while avoiding obstacles and enemies. The game is simple but addictive, and it has many features that make it more enjoyable.</p>
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-<li>The game has two modes: endless and level. In endless mode, you have to bounce as far as you can without hitting any obstacles or enemies. In level mode, you have to complete a series of levels with different objectives and difficulties.</li>
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-<li>To change your skin or theme, you need to go to the shop menu from the main menu. There you can see all the available options and their prices. You can also preview how they look before buying them.</li>
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spaces/1phancelerku/anime-remove-background/Beach Buggy Racing APK - Compete with Your Google Play Friends on Leaderboards and Achievements.md

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-<h1>Beach Buggy Racing APK 2023: The Amazing 3D Kart Racing Game</h1>
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-<p>Beach Buggy Racing APK 2023 is a game that offers 15 spectacular race tracks to explore and enjoy. Each track has its own theme, scenery, obstacles, and secrets. You can race on tracks such as:</p>
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-<p>Beach Buggy Racing APK 2023 is a game that lets you recruit and play with a team of racers, each with their own unique personality and special ability. You can unlock and choose from 12 racers, such as:</p>
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-<h3>Click on the download button and wait for the file to be downloaded</h3 <p>You can click on the download button on the website or Google Play store and wait for the file to be downloaded on your device. The file size is about 150 MB, so make sure you have enough space and a stable internet connection.</p>
-<h3>Enable unknown sources in your device settings</h3 <p>If you download the file from the website, you will need to enable unknown sources in your device settings. This will allow you to install apps from sources other than Google Play store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</p>
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-<h2>Conclusion</h2 <p>Beach Buggy Racing APK 2023 is a game that offers a lot of fun and excitement for kart racing fans. It has amazing graphics, gameplay, features, and modes that make it one of the best kart racers on Android. It is also free to download and play, with optional in-app purchases. If you are looking for a game that will keep you entertained for hours, you should definitely try Beach Buggy Racing APK 2023.</p>
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-<li>You can customize your character's name, gender, appearance, clothes, accessories, etc.</li>
-<li>You can choose from different dialogue options that affect your relationships and outcomes.</li>
-<li>You can explore different locations in the game, such as your dorm, campus, cafeteria, library, park, etc.</li>
-<li>You can participate in various activities and events in the game, such as classes, parties, dates, exams, etc.</li>
-<li>You can earn money and resources by doing tasks, jobs, or mini-games in the game.</li>
-<li>You can use money and resources to buy items, gifts, outfits, etc. in the game.</li>
-<li>You can unlock new scenes, chapters, stories, characters, etc. as you progress in the game.</li>
-</ul>
- <h2>Why use College Love Game Mod Apk?</h2>
-<p>While College Love Game is a free game to download and play, it also has some limitations and drawbacks that can affect your gaming experience. Some of them are:</p>
-<ul>
-<li>You have to watch ads to get extra money or resources in the game.</li>
-<li>You have to wait for energy to refill before you can continue playing the game.</li>
-<li>You have to spend real money to buy premium items or features in the game.</li>
-<li>You have to face compatibility issues or bugs in the game.</li>
-</ul>
-<p>That's why many players prefer using College Love Game Mod Apk, a modified version that removes all these problems and gives you more advantages. Some of them are:</p>
- <h3>Unlimited money and resources</h3>
-<p>With College Love Game Mod Apk, you don't have to worry about running out of money or resources in the game. You can get unlimited amounts of coins, gems, diamonds, tickets, etc. by using the mod apk. You can use them to buy anything you want in the game, such as outfits, gifts, scenes, etc. You can also skip the ads and the waiting time for energy by using the mod apk.</p>
- <h3>No ads and interruptions</h3>
-<p>With College Love Game Mod Apk, you don't have to watch annoying ads that pop up every now and then in the game. You can enjoy the game without any interruptions or distractions. You can also save your data and battery by using the mod apk.</p>
- <h3>Easy installation and compatibility</h3>
-<p>With College Love Game Mod Apk, you don't have to worry about compatibility issues or bugs in the game. The mod apk is designed to work on any Android device, regardless of the version or model. You can also install the mod apk easily and quickly by following some simple steps.</p>
- <h2>How to download and install College Love Game Mod Apk?</h2>
-<p>If you want to download and install College Love Game Mod Apk on your device, you have to follow these steps:</p>
- <h3>Step 1: Enable unknown sources on your device</h3>
-<p>Before you can install the mod apk file, you have to enable unknown sources on your device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</p>
- <h3>Step 2: Download the mod apk file from a trusted source</h3>
-<p>Next, you have to download the mod apk file from a trusted source. There are many websites that offer mod apk files for various games, but not all of them are safe and reliable. Some of them may contain viruses or malware that can harm your device or steal your data. Therefore, you have to be careful and choose a reputable source that provides authentic and updated mod apk files. One such source is [College Love Game Mod Apk Download], where you can find the latest version of the mod apk file for this game.</p>
- <h3>Step 3: Install the mod apk file and launch the game</h3>
-<p>Finally, you have to install the mod apk file and launch the game. To do this, locate the downloaded file on your device storage, tap on it, and follow the instructions on the screen. Once the installation is complete, you can open the game and enjoy it with all the benefits of the mod apk.</p>
- <h2>Conclusion</h2>
-<p>College Love Game is a fun and romantic simulation game that lets you live your college life as you want. You can customize your character, choose your love interest, interact with different people and events, and experience different endings in the game. However, if you want to enhance your gaming experience and get unlimited money, resources, and other advantages, you should use College Love Game Mod Apk, a modified version that removes all the limitations and drawbacks of the original game. You can download and install College Love Game Mod Apk easily and quickly by following some simple steps.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Is College Love Game Mod Apk safe to use?</b></li>
-<p>Yes, College Love Game Mod Apk is safe to use as long as you download it from a trusted source that provides authentic and updated mod apk files. You should also scan the file with an antivirus before installing it on your device.</p>
-<li><b>Do I need to root my device to use College Love Game Mod Apk?</b></li>
-<p>No, you don't need to root your device to use College Love Game Mod Apk. The mod apk works on any Android device without requiring root access.</p>
-<li><b>Will I get banned from playing College Love Game if I use College Love Game Mod Apk?</b></li>
-<p>No, you won't get banned from playing College Love Game if you use College Love Game Mod Apk. The mod apk is undetectable by the game servers and does not affect your account or progress in any way.</p>
-<li><b>Can I update College Love Game Mod Apk when a new version of the game is released?</b></li>
-<p>Yes, you can update College Love Game Mod Apk when a new version of the game is released. However, you have to download and install the new version of the mod apk file from the same source that you used before.</p>
-<li><b>Can I play College Love Game online with other players if I use College Love Game Mod Apk?</b></li>
-<p>Yes, you can play College Love Game online with other players if you use College Love Game Mod Apk. The mod apk does not affect your online connectivity or compatibility with other players. However, you should be careful and respectful when playing online, as some players may not like or appreciate the use of mod apk.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download GTA SA APK OBB 2.0 for Android Experience the Classic GTA Game with Improved Graphics and Features.md

@@ -1,132 +0,0 @@
-
-<h1>GTA SA APK OBB 2.0: How to Download and Play the Classic Game on Android</h1>
-<p>Grand Theft Auto: San Andreas is one of the most iconic games in history. Released in 2004 for PlayStation 2, it was later ported to PC, Xbox, and other platforms. The game follows the story of Carl Johnson (CJ), a former gangster who returns to his hometown of Los Santos after his mother's death. There, he gets involved in a series of events that take him across three cities: Los Santos, San Fierro, and Las Venturas.</p>
-<p>The game is widely praised for its open-world design, its gameplay variety, its characters, its soundtrack, and its cultural impact. However, it also has some limitations and flaws that prevent it from being fully enjoyed on modern devices. That's where GTA SA APK OBB 2.0 comes in.</p>
-<h2>gta sa apk obb 2.0</h2><br /><p><b><b>DOWNLOAD</b> &#9999; <a href="https://jinyurl.com/2uNPuv">https://jinyurl.com/2uNPuv</a></b></p><br /><br />
-<p>GTA SA APK OBB 2.0 is a modified version of Grand Theft Auto: San Andreas that is designed to run on Android devices. It is not an official release by Rockstar Games, but rather a fan-made project that aims to enhance and improve the original game. It has many features that make it more appealing and enjoyable for fans of the game.</p>
-<h2>Features of GTA SA APK OBB 2.0</h2>
-<h3>Enhanced Graphics and Performance</h3>
-<p>One of the main features of GTA SA APK OBB 2.0 is that it improves the graphics and the performance of the game. The mod uses high-resolution textures, better lighting effects, smoother animations, and more realistic shadows to make the game look more beautiful and immersive. It also fixes some of the bugs and glitches that were present in the original game.</p>
-<p>The <p>The mod also optimizes the game for Android devices, making it run faster and smoother. It reduces the loading times, the lag, and the crashes that often occur in the original game. It also supports different screen resolutions and aspect ratios, making it compatible with various devices.</p>
-<h3>Customizable Controls and Settings</h3>
-<p>Another feature of GTA SA APK OBB 2.0 is that it allows the player to customize the controls and the settings of the game. The mod provides different control schemes, such as touch, tilt, or external controllers, to suit the player's preference. It also enables the player to adjust the sensitivity, the vibration, and the feedback of the controls.</p>
-<p>The mod also gives the player more options to tweak the settings of the game, such as the audio, the language, the brightness, and the difficulty. It also adds a save and load feature, which lets the player save their progress at any time and resume from where they left off.</p>
-<h3>PS2 Features Restored</h3>
-<p>A third feature of GTA SA APK OBB 2.0 is that it restores some of the features that were present in the original PS2 version of the game, but were removed or altered in later versions. These include:</p>
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-<ul>
-<li>Gang wars: The mod allows the player to take over enemy territories by fighting against rival gangs. This adds more challenge and fun to the game, as well as rewards such as money, respect, and weapons.</li>
-<li>Car mods: The mod enables the player to modify their cars with various parts and accessories, such as spoilers, bumpers, rims, hydraulics, and more. This adds more variety and customization to the game, as well as improves the performance and appearance of the cars.</li>
-<li>More missions: The mod adds some of the missions that were cut or changed in later versions of the game, such as "The Truth is Out There", "Impounded", and "Doberman". These add more content and story to the game, as well as more opportunities to earn money and respect.</li>
-</ul>
-<h2>Installation Guide for GTA SA APK OBB 2.0</h2>
-<h3>System Requirements</h3>
-<p>Before downloading and installing GTA SA APK OBB 2.0, you need to make sure that your Android device meets the minimum and recommended system requirements for running the mod. These are:</p>
-<table>
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>Android version: 4.0 or higher</td><td>Android version: 6.0 or higher</td></tr>
-<tr><td>CPU: Dual-core 1 GHz or higher</td><td>CPU: Quad-core 1.4 GHz or higher</td></tr>
-<tr><td>RAM: 1 GB or higher</td><td>RAM: 2 GB or higher</td></tr>
-<tr><td>Storage: 4 GB or higher</td><td>Storage: 8 GB or higher</td></tr>
-<tr><td>GPU: Adreno 200 series or higher</td><td>GPU: Adreno 300 series or higher</td></tr>
-</table>
-<p>If your device does not meet these requirements, you may experience some issues or errors while playing GTA SA APK OBB 2.0.</p>
-<h3>Download Links and Instructions</h3>
-<p>To download and install GTA SA APK OBB 2.0 on your Android device, you need to follow these steps:</p>
-<ol>
-<li>Download GTA SA APK OBB 2.0 from one of these links:   . Make sure you download both the APK file and the OBB file.</li>
-<li>Enable unknown sources on your device by going to Settings > Security > Unknown Sources. This will allow you to install apps from sources other than Google Play Store.</li>
-<li>Locate the downloaded files on your device using a file manager app. Tap on the APK file and install it.</li>
-<li>Extract or copy the OBB file to your device's internal storage using a file manager app. You need to place it in this folder: Android/obb/com.rockstargames.gtasa/.</li>
-<li>Launch GTA SA APK OBB 2.0 from your app drawer and enjoy!</li>
-</ol>
-<h3>Troubleshooting Tips</h3>
-<p>If you encounter any problems or errors while playing GTA SA APK OBB 2.0, you can try these tips:</p>
-<ul>
-<li>Make sure you have enough storage space on your device before downloading and installing GTA SA APK OBB 2.0.</li>
-<li>Make sure you have a stable internet connection while downloading and installing GTA SA APK OBB 2.0. If the download is interrupted or corrupted, try again from a different source.</li>
-<li>Make sure you follow the installation instructions correctly and place the OBB file in the right folder. If the game does not launch or crashes, check if the OBB file is in the correct location.</li>
-<li>Make sure you have enough RAM and CPU resources on your device while playing GTA SA APK OBB 2.0. If the game lags or freezes, close other apps that are running in the background or lower the graphics settings in the game.</li>
-<li>Make sure you have the latest version of GTA SA APK OBB 2.0 installed on your device. If the game does not work properly or has bugs, check for updates or patches from the mod developers.</li>
-</ul>
-<h2>Tips and Tricks for GTA SA APK OBB 2.0</h2>
-<h3>Perks and Side Activities</h3>
-<p>GTA SA APK OBB 2.0 offers a lot of perks and benefits that can make your gameplay more enjoyable and rewarding. Some of these perks and benefits are unlocked by completing certain side activities, such as:</p>
-<ul>
-<li>Dating: You can date various girlfriends in the game, such as Denise, Michelle, Helena, Katie, Barbara, and Millie. Each girlfriend has her own preferences, personality, and rewards. By dating them, you can gain access to their cars, their houses, their special abilities, and more.</li>
-<li>Burglary: You can break into houses at night and steal items from them. This can earn you money, respect, and weapons. You can also find some rare items in some houses, such as jetpacks, miniguns, or samurai swords.</li>
-<li>Vehicle Missions: You can perform various missions with different vehicles in the game, such as taxis, ambulances, fire trucks, police cars, freight trains, and more. By completing these missions, you can earn money, respect, and bonuses. For example, by completing taxi missions, you can unlock nitro for all taxis in the game.</li>
-</ul>
-<h3>Weapons and Items</h3>
-<p>GTA SA APK OBB 2.0 also offers a lot of weapons and items that can help you in your missions and adventures. Some of these weapons and items are hidden or hard to find in the game, such as:</p>
-<ul>
-<li>Body Armor: You can find body armor in various locations in the game, such as police stations, hospitals, military bases, or gang hideouts. Body armor can protect you from bullets and explosions, making you more durable in combat.</li>
-<li>Shotguns: You can find shotguns in various locations in the game, such as Ammu-Nation stores, police cars, or gang members. Shotguns are powerful weapons that can deal a lot of damage at close range, making them ideal for clearing rooms or taking out enemies quickly.</li>
-<li>Health Pickups: You can find health pickups in various locations in the game, such as restaurants, bars, vending machines, or safe houses. Health pickups can restore your health when you are injured or low on health, making you more resilient in survival situations.</li>
-</ul>
-<h3>Cheats and Easter Eggs</h3>
-<p>GTA SA APK OBB 2.0 also offers a lot of fun and entertainment with its cheats and easter eggs. These are secret codes or hidden features that can alter the gameplay or reveal some surprises in the game, such as:</p>
-<ul>
-<li>Flying Cars: You can activate this cheat by entering this code on your device's keyboard: RIPAZHA. This will make all cars fly like planes when you accelerate them. This can be useful for traveling across the map quickly or escaping from enemies easily.</li>
-<li>Jetpack: You can activate this cheat by entering this code on your device's keyboard: YECGAA. This will give you a jetpack that you can use to fly around the map freely. This can be useful for exploring hard-to-reach areas or avoiding traffic jams.</li>
-<li>Aliens: You can find this easter egg by going to Area 69 (a parody of Area 51) in the desert. There, you can see some aliens inside glass tubes in a secret laboratory. You can also find a UFO on top of a building nearby.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>GTA SA APK OBB 2.0 is a great mod that enhances and improves Grand Theft Auto: San Andreas for Android devices. It has many features that make it more appealing and enjoyable for fans of the game. It also has many tips and tricks that can help you in your gameplay.</p>
-<p>If you want to experience GTA SA APK OBB 2.0 on your Android device, you can download it from the links and follow the instructions provided in this article. You will surely have a blast playing this classic game with a new twist.</p>
-<p>Do you have any questions or comments about GTA SA APK OBB 2.0? Feel free to share them below. We would love to hear from you!</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about GTA SA APK OBB 2.0:</p>
-<ol>
-<li>Is GTA SA APK OBB 2.0 safe to download and install?</li>
-<p>Yes, GTA SA APK OBB 2.0 is safe to download and install, as long as you get it from a trusted source and follow the installation guide correctly. However, you should always be careful when downloading and installing any mod or app from unknown sources, as they may contain viruses or malware that can harm your device.</p>
-<li>Is GTA SA APK OBB 2.0 legal to use?</li>
-<p>GTA SA APK OBB 2.0 is not an official release by Rockstar Games, but rather a fan-made project that modifies the original game. Therefore, it is not endorsed or supported by Rockstar Games, and may violate their terms of service or intellectual property rights. Use GTA SA APK OBB 2.0 at your own risk and discretion.</p>
-<li>Does GTA SA APK OBB 2.0 require root access or internet connection?</li>
-<p>No, GTA SA APK OBB 2.0 does not require root access or internet connection to run on your device. However, you may need internet connection to download the mod files and to access some of the online features of the game, such as multiplayer mode or cloud save.</p>
-<li>Can I play GTA SA APK OBB 2.0 with my friends online?</li>
-<p>Yes, GTA SA APK OBB 2.0 supports online multiplayer mode, which lets you play with your friends or other players around the world. You can join or host various game modes, such as deathmatch, race, co-op, or free roam. You can also chat and communicate with other players using voice or text messages.</p>
-<li>Can I use other mods or cheats with GTA SA APK OBB 2.0?</li>
-<p>Yes, GTA SA APK OBB 2.0 is compatible with most of the other mods or cheats that are available for Grand Theft Auto: San Andreas. You can use them to enhance or customize your gameplay further. However, you should be careful when using multiple mods or cheats together, as they may cause conflicts or errors that can affect your game performance or stability.</p>
-</ol></p> 401be4b1e0<br />
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spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -1,16 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# NOT IMPLEMENT YET!
-StableDiffusionKDiffusionPipeline = None

spaces/2023Liu2023/bingo/src/pages/api/image.ts

@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, {
-      IMAGE_BING_COOKIE: process.env.IMAGE_BING_COOKIE
-    })
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}

spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/models/__init__.py

@@ -1,3 +0,0 @@
-from .base_model import *
-from .transformer_model import *
-

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/_base_/datasets/deepfashion2.py

@@ -1,2660 +0,0 @@
-colors = dict(
-    sss=[255, 128, 0],  # short_sleeve_shirt
-    lss=[255, 0, 128],  # long_sleeved_shirt
-    sso=[128, 0, 255],  # short_sleeved_outwear
-    lso=[0, 128, 255],  # long_sleeved_outwear
-    vest=[0, 128, 128],  # vest
-    sling=[0, 0, 128],  # sling
-    shorts=[128, 128, 128],  # shorts
-    trousers=[128, 0, 128],  # trousers
-    skirt=[64, 128, 128],  # skirt
-    ssd=[64, 64, 128],  # short_sleeved_dress
-    lsd=[128, 64, 0],  # long_sleeved_dress
-    vd=[128, 64, 255],  # vest_dress
-    sd=[128, 64, 0],  # sling_dress
-)
-dataset_info = dict(
-    dataset_name='deepfashion2',
-    paper_info=dict(
-        author='Yuying Ge and Ruimao Zhang and Lingyun Wu '
-        'and Xiaogang Wang and Xiaoou Tang and Ping Luo',
-        title='DeepFashion2: A Versatile Benchmark for '
-        'Detection, Pose Estimation, Segmentation and '
-        'Re-Identification of Clothing Images',
-        container='Proceedings of IEEE Conference on Computer '
-        'Vision and Pattern Recognition (CVPR)',
-        year='2019',
-        homepage='https://github.com/switchablenorms/DeepFashion2',
-    ),
-    keypoint_info={
-        # short_sleeved_shirt
-        0:
-        dict(name='sss_kpt1', id=0, color=colors['sss'], type='', swap=''),
-        1:
-        dict(
-            name='sss_kpt2',
-            id=1,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt6'),
-        2:
-        dict(
-            name='sss_kpt3',
-            id=2,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt5'),
-        3:
-        dict(name='sss_kpt4', id=3, color=colors['sss'], type='', swap=''),
-        4:
-        dict(
-            name='sss_kpt5',
-            id=4,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt3'),
-        5:
-        dict(
-            name='sss_kpt6',
-            id=5,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt2'),
-        6:
-        dict(
-            name='sss_kpt7',
-            id=6,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt25'),
-        7:
-        dict(
-            name='sss_kpt8',
-            id=7,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt24'),
-        8:
-        dict(
-            name='sss_kpt9',
-            id=8,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt23'),
-        9:
-        dict(
-            name='sss_kpt10',
-            id=9,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt22'),
-        10:
-        dict(
-            name='sss_kpt11',
-            id=10,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt21'),
-        11:
-        dict(
-            name='sss_kpt12',
-            id=11,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt20'),
-        12:
-        dict(
-            name='sss_kpt13',
-            id=12,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt19'),
-        13:
-        dict(
-            name='sss_kpt14',
-            id=13,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt18'),
-        14:
-        dict(
-            name='sss_kpt15',
-            id=14,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt17'),
-        15:
-        dict(name='sss_kpt16', id=15, color=colors['sss'], type='', swap=''),
-        16:
-        dict(
-            name='sss_kpt17',
-            id=16,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt15'),
-        17:
-        dict(
-            name='sss_kpt18',
-            id=17,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt14'),
-        18:
-        dict(
-            name='sss_kpt19',
-            id=18,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt13'),
-        19:
-        dict(
-            name='sss_kpt20',
-            id=19,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt12'),
-        20:
-        dict(
-            name='sss_kpt21',
-            id=20,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt11'),
-        21:
-        dict(
-            name='sss_kpt22',
-            id=21,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt10'),
-        22:
-        dict(
-            name='sss_kpt23',
-            id=22,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt9'),
-        23:
-        dict(
-            name='sss_kpt24',
-            id=23,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt8'),
-        24:
-        dict(
-            name='sss_kpt25',
-            id=24,
-            color=colors['sss'],
-            type='',
-            swap='sss_kpt7'),
-        # long_sleeved_shirt
-        25:
-        dict(name='lss_kpt1', id=25, color=colors['lss'], type='', swap=''),
-        26:
-        dict(
-            name='lss_kpt2',
-            id=26,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt6'),
-        27:
-        dict(
-            name='lss_kpt3',
-            id=27,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt5'),
-        28:
-        dict(name='lss_kpt4', id=28, color=colors['lss'], type='', swap=''),
-        29:
-        dict(
-            name='lss_kpt5',
-            id=29,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt3'),
-        30:
-        dict(
-            name='lss_kpt6',
-            id=30,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt2'),
-        31:
-        dict(
-            name='lss_kpt7',
-            id=31,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt33'),
-        32:
-        dict(
-            name='lss_kpt8',
-            id=32,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt32'),
-        33:
-        dict(
-            name='lss_kpt9',
-            id=33,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt31'),
-        34:
-        dict(
-            name='lss_kpt10',
-            id=34,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt30'),
-        35:
-        dict(
-            name='lss_kpt11',
-            id=35,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt29'),
-        36:
-        dict(
-            name='lss_kpt12',
-            id=36,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt28'),
-        37:
-        dict(
-            name='lss_kpt13',
-            id=37,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt27'),
-        38:
-        dict(
-            name='lss_kpt14',
-            id=38,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt26'),
-        39:
-        dict(
-            name='lss_kpt15',
-            id=39,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt25'),
-        40:
-        dict(
-            name='lss_kpt16',
-            id=40,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt24'),
-        41:
-        dict(
-            name='lss_kpt17',
-            id=41,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt23'),
-        42:
-        dict(
-            name='lss_kpt18',
-            id=42,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt22'),
-        43:
-        dict(
-            name='lss_kpt19',
-            id=43,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt21'),
-        44:
-        dict(name='lss_kpt20', id=44, color=colors['lss'], type='', swap=''),
-        45:
-        dict(
-            name='lss_kpt21',
-            id=45,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt19'),
-        46:
-        dict(
-            name='lss_kpt22',
-            id=46,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt18'),
-        47:
-        dict(
-            name='lss_kpt23',
-            id=47,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt17'),
-        48:
-        dict(
-            name='lss_kpt24',
-            id=48,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt16'),
-        49:
-        dict(
-            name='lss_kpt25',
-            id=49,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt15'),
-        50:
-        dict(
-            name='lss_kpt26',
-            id=50,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt14'),
-        51:
-        dict(
-            name='lss_kpt27',
-            id=51,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt13'),
-        52:
-        dict(
-            name='lss_kpt28',
-            id=52,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt12'),
-        53:
-        dict(
-            name='lss_kpt29',
-            id=53,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt11'),
-        54:
-        dict(
-            name='lss_kpt30',
-            id=54,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt10'),
-        55:
-        dict(
-            name='lss_kpt31',
-            id=55,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt9'),
-        56:
-        dict(
-            name='lss_kpt32',
-            id=56,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt8'),
-        57:
-        dict(
-            name='lss_kpt33',
-            id=57,
-            color=colors['lss'],
-            type='',
-            swap='lss_kpt7'),
-        # short_sleeved_outwear
-        58:
-        dict(name='sso_kpt1', id=58, color=colors['sso'], type='', swap=''),
-        59:
-        dict(
-            name='sso_kpt2',
-            id=59,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt26'),
-        60:
-        dict(
-            name='sso_kpt3',
-            id=60,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt5'),
-        61:
-        dict(
-            name='sso_kpt4',
-            id=61,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt6'),
-        62:
-        dict(
-            name='sso_kpt5',
-            id=62,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt3'),
-        63:
-        dict(
-            name='sso_kpt6',
-            id=63,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt4'),
-        64:
-        dict(
-            name='sso_kpt7',
-            id=64,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt25'),
-        65:
-        dict(
-            name='sso_kpt8',
-            id=65,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt24'),
-        66:
-        dict(
-            name='sso_kpt9',
-            id=66,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt23'),
-        67:
-        dict(
-            name='sso_kpt10',
-            id=67,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt22'),
-        68:
-        dict(
-            name='sso_kpt11',
-            id=68,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt21'),
-        69:
-        dict(
-            name='sso_kpt12',
-            id=69,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt20'),
-        70:
-        dict(
-            name='sso_kpt13',
-            id=70,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt19'),
-        71:
-        dict(
-            name='sso_kpt14',
-            id=71,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt18'),
-        72:
-        dict(
-            name='sso_kpt15',
-            id=72,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt17'),
-        73:
-        dict(
-            name='sso_kpt16',
-            id=73,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt29'),
-        74:
-        dict(
-            name='sso_kpt17',
-            id=74,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt15'),
-        75:
-        dict(
-            name='sso_kpt18',
-            id=75,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt14'),
-        76:
-        dict(
-            name='sso_kpt19',
-            id=76,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt13'),
-        77:
-        dict(
-            name='sso_kpt20',
-            id=77,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt12'),
-        78:
-        dict(
-            name='sso_kpt21',
-            id=78,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt11'),
-        79:
-        dict(
-            name='sso_kpt22',
-            id=79,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt10'),
-        80:
-        dict(
-            name='sso_kpt23',
-            id=80,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt9'),
-        81:
-        dict(
-            name='sso_kpt24',
-            id=81,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt8'),
-        82:
-        dict(
-            name='sso_kpt25',
-            id=82,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt7'),
-        83:
-        dict(
-            name='sso_kpt26',
-            id=83,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt2'),
-        84:
-        dict(
-            name='sso_kpt27',
-            id=84,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt30'),
-        85:
-        dict(
-            name='sso_kpt28',
-            id=85,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt31'),
-        86:
-        dict(
-            name='sso_kpt29',
-            id=86,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt16'),
-        87:
-        dict(
-            name='sso_kpt30',
-            id=87,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt27'),
-        88:
-        dict(
-            name='sso_kpt31',
-            id=88,
-            color=colors['sso'],
-            type='',
-            swap='sso_kpt28'),
-        # long_sleeved_outwear
-        89:
-        dict(name='lso_kpt1', id=89, color=colors['lso'], type='', swap=''),
-        90:
-        dict(
-            name='lso_kpt2',
-            id=90,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt6'),
-        91:
-        dict(
-            name='lso_kpt3',
-            id=91,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt5'),
-        92:
-        dict(
-            name='lso_kpt4',
-            id=92,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt34'),
-        93:
-        dict(
-            name='lso_kpt5',
-            id=93,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt3'),
-        94:
-        dict(
-            name='lso_kpt6',
-            id=94,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt2'),
-        95:
-        dict(
-            name='lso_kpt7',
-            id=95,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt33'),
-        96:
-        dict(
-            name='lso_kpt8',
-            id=96,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt32'),
-        97:
-        dict(
-            name='lso_kpt9',
-            id=97,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt31'),
-        98:
-        dict(
-            name='lso_kpt10',
-            id=98,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt30'),
-        99:
-        dict(
-            name='lso_kpt11',
-            id=99,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt29'),
-        100:
-        dict(
-            name='lso_kpt12',
-            id=100,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt28'),
-        101:
-        dict(
-            name='lso_kpt13',
-            id=101,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt27'),
-        102:
-        dict(
-            name='lso_kpt14',
-            id=102,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt26'),
-        103:
-        dict(
-            name='lso_kpt15',
-            id=103,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt25'),
-        104:
-        dict(
-            name='lso_kpt16',
-            id=104,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt24'),
-        105:
-        dict(
-            name='lso_kpt17',
-            id=105,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt23'),
-        106:
-        dict(
-            name='lso_kpt18',
-            id=106,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt22'),
-        107:
-        dict(
-            name='lso_kpt19',
-            id=107,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt21'),
-        108:
-        dict(
-            name='lso_kpt20',
-            id=108,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt37'),
-        109:
-        dict(
-            name='lso_kpt21',
-            id=109,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt19'),
-        110:
-        dict(
-            name='lso_kpt22',
-            id=110,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt18'),
-        111:
-        dict(
-            name='lso_kpt23',
-            id=111,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt17'),
-        112:
-        dict(
-            name='lso_kpt24',
-            id=112,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt16'),
-        113:
-        dict(
-            name='lso_kpt25',
-            id=113,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt15'),
-        114:
-        dict(
-            name='lso_kpt26',
-            id=114,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt14'),
-        115:
-        dict(
-            name='lso_kpt27',
-            id=115,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt13'),
-        116:
-        dict(
-            name='lso_kpt28',
-            id=116,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt12'),
-        117:
-        dict(
-            name='lso_kpt29',
-            id=117,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt11'),
-        118:
-        dict(
-            name='lso_kpt30',
-            id=118,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt10'),
-        119:
-        dict(
-            name='lso_kpt31',
-            id=119,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt9'),
-        120:
-        dict(
-            name='lso_kpt32',
-            id=120,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt8'),
-        121:
-        dict(
-            name='lso_kpt33',
-            id=121,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt7'),
-        122:
-        dict(
-            name='lso_kpt34',
-            id=122,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt4'),
-        123:
-        dict(
-            name='lso_kpt35',
-            id=123,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt38'),
-        124:
-        dict(
-            name='lso_kpt36',
-            id=124,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt39'),
-        125:
-        dict(
-            name='lso_kpt37',
-            id=125,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt20'),
-        126:
-        dict(
-            name='lso_kpt38',
-            id=126,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt35'),
-        127:
-        dict(
-            name='lso_kpt39',
-            id=127,
-            color=colors['lso'],
-            type='',
-            swap='lso_kpt36'),
-        # vest
-        128:
-        dict(name='vest_kpt1', id=128, color=colors['vest'], type='', swap=''),
-        129:
-        dict(
-            name='vest_kpt2',
-            id=129,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt6'),
-        130:
-        dict(
-            name='vest_kpt3',
-            id=130,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt5'),
-        131:
-        dict(name='vest_kpt4', id=131, color=colors['vest'], type='', swap=''),
-        132:
-        dict(
-            name='vest_kpt5',
-            id=132,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt3'),
-        133:
-        dict(
-            name='vest_kpt6',
-            id=133,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt2'),
-        134:
-        dict(
-            name='vest_kpt7',
-            id=134,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt15'),
-        135:
-        dict(
-            name='vest_kpt8',
-            id=135,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt14'),
-        136:
-        dict(
-            name='vest_kpt9',
-            id=136,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt13'),
-        137:
-        dict(
-            name='vest_kpt10',
-            id=137,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt12'),
-        138:
-        dict(
-            name='vest_kpt11', id=138, color=colors['vest'], type='', swap=''),
-        139:
-        dict(
-            name='vest_kpt12',
-            id=139,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt10'),
-        140:
-        dict(
-            name='vest_kpt13', id=140, color=colors['vest'], type='', swap=''),
-        141:
-        dict(
-            name='vest_kpt14',
-            id=141,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt8'),
-        142:
-        dict(
-            name='vest_kpt15',
-            id=142,
-            color=colors['vest'],
-            type='',
-            swap='vest_kpt7'),
-        # sling
-        143:
-        dict(
-            name='sling_kpt1', id=143, color=colors['sling'], type='',
-            swap=''),
-        144:
-        dict(
-            name='sling_kpt2',
-            id=144,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt6'),
-        145:
-        dict(
-            name='sling_kpt3',
-            id=145,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt5'),
-        146:
-        dict(
-            name='sling_kpt4', id=146, color=colors['sling'], type='',
-            swap=''),
-        147:
-        dict(
-            name='sling_kpt5',
-            id=147,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt3'),
-        148:
-        dict(
-            name='sling_kpt6',
-            id=148,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt2'),
-        149:
-        dict(
-            name='sling_kpt7',
-            id=149,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt15'),
-        150:
-        dict(
-            name='sling_kpt8',
-            id=150,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt14'),
-        151:
-        dict(
-            name='sling_kpt9',
-            id=151,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt13'),
-        152:
-        dict(
-            name='sling_kpt10',
-            id=152,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt12'),
-        153:
-        dict(
-            name='sling_kpt11',
-            id=153,
-            color=colors['sling'],
-            type='',
-            swap=''),
-        154:
-        dict(
-            name='sling_kpt12',
-            id=154,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt10'),
-        155:
-        dict(
-            name='sling_kpt13',
-            id=155,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt9'),
-        156:
-        dict(
-            name='sling_kpt14',
-            id=156,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt8'),
-        157:
-        dict(
-            name='sling_kpt15',
-            id=157,
-            color=colors['sling'],
-            type='',
-            swap='sling_kpt7'),
-        # shorts
-        158:
-        dict(
-            name='shorts_kpt1',
-            id=158,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt3'),
-        159:
-        dict(
-            name='shorts_kpt2',
-            id=159,
-            color=colors['shorts'],
-            type='',
-            swap=''),
-        160:
-        dict(
-            name='shorts_kpt3',
-            id=160,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt1'),
-        161:
-        dict(
-            name='shorts_kpt4',
-            id=161,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt10'),
-        162:
-        dict(
-            name='shorts_kpt5',
-            id=162,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt9'),
-        163:
-        dict(
-            name='shorts_kpt6',
-            id=163,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt8'),
-        164:
-        dict(
-            name='shorts_kpt7',
-            id=164,
-            color=colors['shorts'],
-            type='',
-            swap=''),
-        165:
-        dict(
-            name='shorts_kpt8',
-            id=165,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt6'),
-        166:
-        dict(
-            name='shorts_kpt9',
-            id=166,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt5'),
-        167:
-        dict(
-            name='shorts_kpt10',
-            id=167,
-            color=colors['shorts'],
-            type='',
-            swap='shorts_kpt4'),
-        # trousers
-        168:
-        dict(
-            name='trousers_kpt1',
-            id=168,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt3'),
-        169:
-        dict(
-            name='trousers_kpt2',
-            id=169,
-            color=colors['trousers'],
-            type='',
-            swap=''),
-        170:
-        dict(
-            name='trousers_kpt3',
-            id=170,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt1'),
-        171:
-        dict(
-            name='trousers_kpt4',
-            id=171,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt14'),
-        172:
-        dict(
-            name='trousers_kpt5',
-            id=172,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt13'),
-        173:
-        dict(
-            name='trousers_kpt6',
-            id=173,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt12'),
-        174:
-        dict(
-            name='trousers_kpt7',
-            id=174,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt11'),
-        175:
-        dict(
-            name='trousers_kpt8',
-            id=175,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt10'),
-        176:
-        dict(
-            name='trousers_kpt9',
-            id=176,
-            color=colors['trousers'],
-            type='',
-            swap=''),
-        177:
-        dict(
-            name='trousers_kpt10',
-            id=177,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt8'),
-        178:
-        dict(
-            name='trousers_kpt11',
-            id=178,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt7'),
-        179:
-        dict(
-            name='trousers_kpt12',
-            id=179,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt6'),
-        180:
-        dict(
-            name='trousers_kpt13',
-            id=180,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt5'),
-        181:
-        dict(
-            name='trousers_kpt14',
-            id=181,
-            color=colors['trousers'],
-            type='',
-            swap='trousers_kpt4'),
-        # skirt
-        182:
-        dict(
-            name='skirt_kpt1',
-            id=182,
-            color=colors['skirt'],
-            type='',
-            swap='skirt_kpt3'),
-        183:
-        dict(
-            name='skirt_kpt2', id=183, color=colors['skirt'], type='',
-            swap=''),
-        184:
-        dict(
-            name='skirt_kpt3',
-            id=184,
-            color=colors['skirt'],
-            type='',
-            swap='skirt_kpt1'),
-        185:
-        dict(
-            name='skirt_kpt4',
-            id=185,
-            color=colors['skirt'],
-            type='',
-            swap='skirt_kpt8'),
-        186:
-        dict(
-            name='skirt_kpt5',
-            id=186,
-            color=colors['skirt'],
-            type='',
-            swap='skirt_kpt7'),
-        187:
-        dict(
-            name='skirt_kpt6', id=187, color=colors['skirt'], type='',
-            swap=''),
-        188:
-        dict(
-            name='skirt_kpt7',
-            id=188,
-            color=colors['skirt'],
-            type='',
-            swap='skirt_kpt5'),
-        189:
-        dict(
-            name='skirt_kpt8',
-            id=189,
-            color=colors['skirt'],
-            type='',
-            swap='skirt_kpt4'),
-        # short_sleeved_dress
-        190:
-        dict(name='ssd_kpt1', id=190, color=colors['ssd'], type='', swap=''),
-        191:
-        dict(
-            name='ssd_kpt2',
-            id=191,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt6'),
-        192:
-        dict(
-            name='ssd_kpt3',
-            id=192,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt5'),
-        193:
-        dict(name='ssd_kpt4', id=193, color=colors['ssd'], type='', swap=''),
-        194:
-        dict(
-            name='ssd_kpt5',
-            id=194,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt3'),
-        195:
-        dict(
-            name='ssd_kpt6',
-            id=195,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt2'),
-        196:
-        dict(
-            name='ssd_kpt7',
-            id=196,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt29'),
-        197:
-        dict(
-            name='ssd_kpt8',
-            id=197,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt28'),
-        198:
-        dict(
-            name='ssd_kpt9',
-            id=198,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt27'),
-        199:
-        dict(
-            name='ssd_kpt10',
-            id=199,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt26'),
-        200:
-        dict(
-            name='ssd_kpt11',
-            id=200,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt25'),
-        201:
-        dict(
-            name='ssd_kpt12',
-            id=201,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt24'),
-        202:
-        dict(
-            name='ssd_kpt13',
-            id=202,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt23'),
-        203:
-        dict(
-            name='ssd_kpt14',
-            id=203,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt22'),
-        204:
-        dict(
-            name='ssd_kpt15',
-            id=204,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt21'),
-        205:
-        dict(
-            name='ssd_kpt16',
-            id=205,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt20'),
-        206:
-        dict(
-            name='ssd_kpt17',
-            id=206,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt19'),
-        207:
-        dict(name='ssd_kpt18', id=207, color=colors['ssd'], type='', swap=''),
-        208:
-        dict(
-            name='ssd_kpt19',
-            id=208,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt17'),
-        209:
-        dict(
-            name='ssd_kpt20',
-            id=209,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt16'),
-        210:
-        dict(
-            name='ssd_kpt21',
-            id=210,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt15'),
-        211:
-        dict(
-            name='ssd_kpt22',
-            id=211,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt14'),
-        212:
-        dict(
-            name='ssd_kpt23',
-            id=212,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt13'),
-        213:
-        dict(
-            name='ssd_kpt24',
-            id=213,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt12'),
-        214:
-        dict(
-            name='ssd_kpt25',
-            id=214,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt11'),
-        215:
-        dict(
-            name='ssd_kpt26',
-            id=215,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt10'),
-        216:
-        dict(
-            name='ssd_kpt27',
-            id=216,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt9'),
-        217:
-        dict(
-            name='ssd_kpt28',
-            id=217,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt8'),
-        218:
-        dict(
-            name='ssd_kpt29',
-            id=218,
-            color=colors['ssd'],
-            type='',
-            swap='ssd_kpt7'),
-        # long_sleeved_dress
-        219:
-        dict(name='lsd_kpt1', id=219, color=colors['lsd'], type='', swap=''),
-        220:
-        dict(
-            name='lsd_kpt2',
-            id=220,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt6'),
-        221:
-        dict(
-            name='lsd_kpt3',
-            id=221,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt5'),
-        222:
-        dict(name='lsd_kpt4', id=222, color=colors['lsd'], type='', swap=''),
-        223:
-        dict(
-            name='lsd_kpt5',
-            id=223,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt3'),
-        224:
-        dict(
-            name='lsd_kpt6',
-            id=224,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt2'),
-        225:
-        dict(
-            name='lsd_kpt7',
-            id=225,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt37'),
-        226:
-        dict(
-            name='lsd_kpt8',
-            id=226,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt36'),
-        227:
-        dict(
-            name='lsd_kpt9',
-            id=227,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt35'),
-        228:
-        dict(
-            name='lsd_kpt10',
-            id=228,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt34'),
-        229:
-        dict(
-            name='lsd_kpt11',
-            id=229,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt33'),
-        230:
-        dict(
-            name='lsd_kpt12',
-            id=230,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt32'),
-        231:
-        dict(
-            name='lsd_kpt13',
-            id=231,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt31'),
-        232:
-        dict(
-            name='lsd_kpt14',
-            id=232,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt30'),
-        233:
-        dict(
-            name='lsd_kpt15',
-            id=233,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt29'),
-        234:
-        dict(
-            name='lsd_kpt16',
-            id=234,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt28'),
-        235:
-        dict(
-            name='lsd_kpt17',
-            id=235,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt27'),
-        236:
-        dict(
-            name='lsd_kpt18',
-            id=236,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt26'),
-        237:
-        dict(
-            name='lsd_kpt19',
-            id=237,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt25'),
-        238:
-        dict(
-            name='lsd_kpt20',
-            id=238,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt24'),
-        239:
-        dict(
-            name='lsd_kpt21',
-            id=239,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt23'),
-        240:
-        dict(name='lsd_kpt22', id=240, color=colors['lsd'], type='', swap=''),
-        241:
-        dict(
-            name='lsd_kpt23',
-            id=241,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt21'),
-        242:
-        dict(
-            name='lsd_kpt24',
-            id=242,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt20'),
-        243:
-        dict(
-            name='lsd_kpt25',
-            id=243,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt19'),
-        244:
-        dict(
-            name='lsd_kpt26',
-            id=244,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt18'),
-        245:
-        dict(
-            name='lsd_kpt27',
-            id=245,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt17'),
-        246:
-        dict(
-            name='lsd_kpt28',
-            id=246,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt16'),
-        247:
-        dict(
-            name='lsd_kpt29',
-            id=247,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt15'),
-        248:
-        dict(
-            name='lsd_kpt30',
-            id=248,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt14'),
-        249:
-        dict(
-            name='lsd_kpt31',
-            id=249,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt13'),
-        250:
-        dict(
-            name='lsd_kpt32',
-            id=250,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt12'),
-        251:
-        dict(
-            name='lsd_kpt33',
-            id=251,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt11'),
-        252:
-        dict(
-            name='lsd_kpt34',
-            id=252,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt10'),
-        253:
-        dict(
-            name='lsd_kpt35',
-            id=253,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt9'),
-        254:
-        dict(
-            name='lsd_kpt36',
-            id=254,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt8'),
-        255:
-        dict(
-            name='lsd_kpt37',
-            id=255,
-            color=colors['lsd'],
-            type='',
-            swap='lsd_kpt7'),
-        # vest_dress
-        256:
-        dict(name='vd_kpt1', id=256, color=colors['vd'], type='', swap=''),
-        257:
-        dict(
-            name='vd_kpt2',
-            id=257,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt6'),
-        258:
-        dict(
-            name='vd_kpt3',
-            id=258,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt5'),
-        259:
-        dict(name='vd_kpt4', id=259, color=colors['vd'], type='', swap=''),
-        260:
-        dict(
-            name='vd_kpt5',
-            id=260,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt3'),
-        261:
-        dict(
-            name='vd_kpt6',
-            id=261,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt2'),
-        262:
-        dict(
-            name='vd_kpt7',
-            id=262,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt19'),
-        263:
-        dict(
-            name='vd_kpt8',
-            id=263,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt18'),
-        264:
-        dict(
-            name='vd_kpt9',
-            id=264,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt17'),
-        265:
-        dict(
-            name='vd_kpt10',
-            id=265,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt16'),
-        266:
-        dict(
-            name='vd_kpt11',
-            id=266,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt15'),
-        267:
-        dict(
-            name='vd_kpt12',
-            id=267,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt14'),
-        268:
-        dict(name='vd_kpt13', id=268, color=colors['vd'], type='', swap=''),
-        269:
-        dict(
-            name='vd_kpt14',
-            id=269,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt12'),
-        270:
-        dict(
-            name='vd_kpt15',
-            id=270,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt11'),
-        271:
-        dict(
-            name='vd_kpt16',
-            id=271,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt10'),
-        272:
-        dict(
-            name='vd_kpt17',
-            id=272,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt9'),
-        273:
-        dict(
-            name='vd_kpt18',
-            id=273,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt8'),
-        274:
-        dict(
-            name='vd_kpt19',
-            id=274,
-            color=colors['vd'],
-            type='',
-            swap='vd_kpt7'),
-        # sling_dress
-        275:
-        dict(name='sd_kpt1', id=275, color=colors['sd'], type='', swap=''),
-        276:
-        dict(
-            name='sd_kpt2',
-            id=276,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt6'),
-        277:
-        dict(
-            name='sd_kpt3',
-            id=277,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt5'),
-        278:
-        dict(name='sd_kpt4', id=278, color=colors['sd'], type='', swap=''),
-        279:
-        dict(
-            name='sd_kpt5',
-            id=279,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt3'),
-        280:
-        dict(
-            name='sd_kpt6',
-            id=280,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt2'),
-        281:
-        dict(
-            name='sd_kpt7',
-            id=281,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt19'),
-        282:
-        dict(
-            name='sd_kpt8',
-            id=282,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt18'),
-        283:
-        dict(
-            name='sd_kpt9',
-            id=283,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt17'),
-        284:
-        dict(
-            name='sd_kpt10',
-            id=284,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt16'),
-        285:
-        dict(
-            name='sd_kpt11',
-            id=285,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt15'),
-        286:
-        dict(
-            name='sd_kpt12',
-            id=286,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt14'),
-        287:
-        dict(name='sd_kpt13', id=287, color=colors['sd'], type='', swap=''),
-        288:
-        dict(
-            name='sd_kpt14',
-            id=288,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt12'),
-        289:
-        dict(
-            name='sd_kpt15',
-            id=289,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt11'),
-        290:
-        dict(
-            name='sd_kpt16',
-            id=290,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt10'),
-        291:
-        dict(
-            name='sd_kpt17',
-            id=291,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt9'),
-        292:
-        dict(
-            name='sd_kpt18',
-            id=292,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt8'),
-        293:
-        dict(
-            name='sd_kpt19',
-            id=293,
-            color=colors['sd'],
-            type='',
-            swap='sd_kpt7'),
-    },
-    skeleton_info={
-        # short_sleeved_shirt
-        0:
-        dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]),
-        1:
-        dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]),
-        2:
-        dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]),
-        3:
-        dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]),
-        4:
-        dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]),
-        5:
-        dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]),
-        6:
-        dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]),
-        7:
-        dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]),
-        8:
-        dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]),
-        9:
-        dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]),
-        10:
-        dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]),
-        11:
-        dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]),
-        12:
-        dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]),
-        13:
-        dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]),
-        14:
-        dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]),
-        15:
-        dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]),
-        16:
-        dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]),
-        17:
-        dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]),
-        18:
-        dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]),
-        19:
-        dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]),
-        20:
-        dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]),
-        21:
-        dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]),
-        22:
-        dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]),
-        23:
-        dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]),
-        24:
-        dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]),
-        25:
-        dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]),
-        # long_sleeve_shirt
-        26:
-        dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]),
-        27:
-        dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]),
-        28:
-        dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]),
-        29:
-        dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]),
-        30:
-        dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]),
-        31:
-        dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]),
-        32:
-        dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]),
-        33:
-        dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]),
-        34:
-        dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]),
-        35:
-        dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]),
-        36:
-        dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]),
-        37:
-        dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]),
-        38:
-        dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]),
-        39:
-        dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]),
-        40:
-        dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]),
-        41:
-        dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]),
-        42:
-        dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]),
-        43:
-        dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]),
-        44:
-        dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]),
-        45:
-        dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]),
-        46:
-        dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]),
-        47:
-        dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]),
-        48:
-        dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]),
-        49:
-        dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]),
-        50:
-        dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]),
-        51:
-        dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]),
-        52:
-        dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]),
-        53:
-        dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]),
-        54:
-        dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]),
-        55:
-        dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]),
-        56:
-        dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]),
-        57:
-        dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]),
-        58:
-        dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]),
-        59:
-        dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]),
-        # short_sleeved_outwear
-        60:
-        dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]),
-        61:
-        dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]),
-        62:
-        dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]),
-        63:
-        dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]),
-        64:
-        dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]),
-        65:
-        dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]),
-        66:
-        dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]),
-        67:
-        dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]),
-        68:
-        dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]),
-        69:
-        dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]),
-        70:
-        dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]),
-        71:
-        dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]),
-        72:
-        dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]),
-        73:
-        dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]),
-        74:
-        dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]),
-        75:
-        dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]),
-        76:
-        dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]),
-        77:
-        dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]),
-        78:
-        dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]),
-        79:
-        dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]),
-        80:
-        dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]),
-        81:
-        dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]),
-        82:
-        dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]),
-        83:
-        dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]),
-        84:
-        dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]),
-        85:
-        dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]),
-        86:
-        dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]),
-        87:
-        dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]),
-        88:
-        dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]),
-        89:
-        dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]),
-        90:
-        dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]),
-        91:
-        dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]),
-        # long_sleeved_outwear
-        92:
-        dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]),
-        93:
-        dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]),
-        94:
-        dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]),
-        95:
-        dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]),
-        96:
-        dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]),
-        97:
-        dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]),
-        98:
-        dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]),
-        99:
-        dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]),
-        100:
-        dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]),
-        101:
-        dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]),
-        102:
-        dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]),
-        103:
-        dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]),
-        104:
-        dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]),
-        105:
-        dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]),
-        106:
-        dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]),
-        107:
-        dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]),
-        108:
-        dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]),
-        109:
-        dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]),
-        110:
-        dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]),
-        111:
-        dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]),
-        112:
-        dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]),
-        113:
-        dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]),
-        114:
-        dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]),
-        115:
-        dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]),
-        116:
-        dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]),
-        117:
-        dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]),
-        118:
-        dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]),
-        119:
-        dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]),
-        120:
-        dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]),
-        121:
-        dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]),
-        122:
-        dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]),
-        123:
-        dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]),
-        124:
-        dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]),
-        125:
-        dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]),
-        126:
-        dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]),
-        127:
-        dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]),
-        128:
-        dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]),
-        129:
-        dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]),
-        130:
-        dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]),
-        131:
-        dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]),
-        # vest
-        132:
-        dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]),
-        133:
-        dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]),
-        134:
-        dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]),
-        135:
-        dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]),
-        136:
-        dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]),
-        137:
-        dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]),
-        138:
-        dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]),
-        139:
-        dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]),
-        140:
-        dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]),
-        141:
-        dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]),
-        142:
-        dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]),
-        143:
-        dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]),
-        144:
-        dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]),
-        145:
-        dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]),
-        146:
-        dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]),
-        147:
-        dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]),
-        # sling
-        148:
-        dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]),
-        149:
-        dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]),
-        150:
-        dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]),
-        151:
-        dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]),
-        152:
-        dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]),
-        153:
-        dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]),
-        154:
-        dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]),
-        155:
-        dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]),
-        156:
-        dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]),
-        157:
-        dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]),
-        158:
-        dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]),
-        159:
-        dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]),
-        160:
-        dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]),
-        161:
-        dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]),
-        162:
-        dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]),
-        163:
-        dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]),
-        # shorts
-        164:
-        dict(
-            link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128,
-                                                                128]),
-        165:
-        dict(
-            link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128,
-                                                                128]),
-        166:
-        dict(
-            link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128,
-                                                                128]),
-        167:
-        dict(
-            link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128,
-                                                                128]),
-        168:
-        dict(
-            link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128,
-                                                                128]),
-        169:
-        dict(
-            link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128,
-                                                                128]),
-        170:
-        dict(
-            link=('shorts_kpt9', 'shorts_kpt10'),
-            id=170,
-            color=[128, 128, 128]),
-        171:
-        dict(
-            link=('shorts_kpt10', 'shorts_kpt3'),
-            id=171,
-            color=[128, 128, 128]),
-        172:
-        dict(
-            link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128,
-                                                                128]),
-        173:
-        dict(
-            link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128,
-                                                                128]),
-        # trousers
-        174:
-        dict(
-            link=('trousers_kpt1', 'trousers_kpt4'),
-            id=174,
-            color=[128, 0, 128]),
-        175:
-        dict(
-            link=('trousers_kpt4', 'trousers_kpt5'),
-            id=175,
-            color=[128, 0, 128]),
-        176:
-        dict(
-            link=('trousers_kpt5', 'trousers_kpt6'),
-            id=176,
-            color=[128, 0, 128]),
-        177:
-        dict(
-            link=('trousers_kpt6', 'trousers_kpt7'),
-            id=177,
-            color=[128, 0, 128]),
-        178:
-        dict(
-            link=('trousers_kpt7', 'trousers_kpt8'),
-            id=178,
-            color=[128, 0, 128]),
-        179:
-        dict(
-            link=('trousers_kpt8', 'trousers_kpt9'),
-            id=179,
-            color=[128, 0, 128]),
-        180:
-        dict(
-            link=('trousers_kpt9', 'trousers_kpt10'),
-            id=180,
-            color=[128, 0, 128]),
-        181:
-        dict(
-            link=('trousers_kpt10', 'trousers_kpt11'),
-            id=181,
-            color=[128, 0, 128]),
-        182:
-        dict(
-            link=('trousers_kpt11', 'trousers_kpt12'),
-            id=182,
-            color=[128, 0, 128]),
-        183:
-        dict(
-            link=('trousers_kpt12', 'trousers_kpt13'),
-            id=183,
-            color=[128, 0, 128]),
-        184:
-        dict(
-            link=('trousers_kpt13', 'trousers_kpt14'),
-            id=184,
-            color=[128, 0, 128]),
-        185:
-        dict(
-            link=('trousers_kpt14', 'trousers_kpt3'),
-            id=185,
-            color=[128, 0, 128]),
-        186:
-        dict(
-            link=('trousers_kpt3', 'trousers_kpt2'),
-            id=186,
-            color=[128, 0, 128]),
-        187:
-        dict(
-            link=('trousers_kpt2', 'trousers_kpt1'),
-            id=187,
-            color=[128, 0, 128]),
-        # skirt
-        188:
-        dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]),
-        189:
-        dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]),
-        190:
-        dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]),
-        191:
-        dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]),
-        192:
-        dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]),
-        193:
-        dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]),
-        194:
-        dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]),
-        195:
-        dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]),
-        # short_sleeved_dress
-        196:
-        dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]),
-        197:
-        dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]),
-        198:
-        dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]),
-        199:
-        dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]),
-        200:
-        dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]),
-        201:
-        dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]),
-        202:
-        dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]),
-        203:
-        dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]),
-        204:
-        dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]),
-        205:
-        dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]),
-        206:
-        dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]),
-        207:
-        dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]),
-        208:
-        dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]),
-        209:
-        dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]),
-        210:
-        dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]),
-        211:
-        dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]),
-        212:
-        dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]),
-        213:
-        dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]),
-        214:
-        dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]),
-        215:
-        dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]),
-        216:
-        dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]),
-        217:
-        dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]),
-        218:
-        dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]),
-        219:
-        dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]),
-        220:
-        dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]),
-        221:
-        dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]),
-        222:
-        dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]),
-        223:
-        dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]),
-        224:
-        dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]),
-        225:
-        dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]),
-        # long_sleeved_dress
-        226:
-        dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]),
-        227:
-        dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]),
-        228:
-        dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]),
-        229:
-        dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]),
-        230:
-        dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]),
-        231:
-        dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]),
-        232:
-        dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]),
-        233:
-        dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]),
-        234:
-        dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]),
-        235:
-        dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]),
-        236:
-        dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]),
-        237:
-        dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]),
-        238:
-        dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]),
-        239:
-        dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]),
-        240:
-        dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]),
-        241:
-        dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]),
-        242:
-        dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]),
-        243:
-        dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]),
-        244:
-        dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]),
-        245:
-        dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]),
-        246:
-        dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]),
-        247:
-        dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]),
-        248:
-        dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]),
-        249:
-        dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]),
-        250:
-        dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]),
-        251:
-        dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]),
-        252:
-        dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]),
-        253:
-        dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]),
-        254:
-        dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]),
-        255:
-        dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]),
-        256:
-        dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]),
-        257:
-        dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]),
-        258:
-        dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]),
-        259:
-        dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]),
-        260:
-        dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]),
-        261:
-        dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]),
-        262:
-        dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]),
-        263:
-        dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]),
-        # vest_dress
-        264:
-        dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]),
-        265:
-        dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]),
-        266:
-        dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]),
-        267:
-        dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]),
-        268:
-        dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]),
-        269:
-        dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]),
-        270:
-        dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]),
-        271:
-        dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]),
-        272:
-        dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]),
-        273:
-        dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]),
-        274:
-        dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]),
-        275:
-        dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]),
-        276:
-        dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]),
-        277:
-        dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]),
-        278:
-        dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]),
-        279:
-        dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]),
-        280:
-        dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]),
-        281:
-        dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]),
-        282:
-        dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]),
-        283:
-        dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]),
-        # sling_dress
-        284:
-        dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]),
-        285:
-        dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]),
-        286:
-        dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]),
-        287:
-        dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]),
-        288:
-        dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]),
-        289:
-        dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]),
-        290:
-        dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]),
-        291:
-        dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]),
-        292:
-        dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]),
-        293:
-        dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]),
-        294:
-        dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]),
-        295:
-        dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]),
-        296:
-        dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]),
-        297:
-        dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]),
-        298:
-        dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]),
-        299:
-        dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]),
-        300:
-        dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]),
-        301:
-        dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]),
-        302:
-        dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]),
-        303:
-        dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0]),
-    },
-    joint_weights=[1.] * 294,
-    sigmas=[])

spaces/Abhaykoul/Prompt_generator_for_helpingAI-tti/README.md

@@ -1,13 +0,0 @@
----
-title: Prompt Generator For HelpingAI-tti
-emoji: 🐢
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AchyuthGamer/OpenGPT/g4f/Provider/FastGpt.py

@@ -1,86 +0,0 @@
-from __future__ import annotations
-
-import json
-import random
-from abc import ABC, abstractmethod
-
-import requests
-
-from ..typing import Any, CreateResult
-
-
-class FastGpt(ABC):
-    url: str                = 'https://chat9.fastgpt.me/'
-    working                 = False
-    needs_auth              = False
-    supports_stream         = True
-    supports_gpt_35_turbo   = True
-    supports_gpt_4          = False
-
-    @staticmethod
-    @abstractmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool, **kwargs: Any) -> CreateResult:
-
-        headers = {
-            'authority'         : 'chat9.fastgpt.me',
-            'accept'            : 'text/event-stream',
-            'accept-language'   : 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-            'cache-control'     : 'no-cache',
-            'content-type'      : 'application/json',
-            'origin'            : 'https://chat9.fastgpt.me',
-            'plugins'           : '0',
-            'pragma'            : 'no-cache',
-            'referer'           : 'https://chat9.fastgpt.me/',
-            'sec-ch-ua'         : '"Not/A)Brand";v="99", "Google Chrome";v="115", "Chromium";v="115"',
-            'sec-ch-ua-mobile'  : '?0',
-            'sec-ch-ua-platform': '"macOS"',
-            'sec-fetch-dest'    : 'empty',
-            'sec-fetch-mode'    : 'cors',
-            'sec-fetch-site'    : 'same-origin',
-            'user-agent'        : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36',
-            'usesearch'         : 'false',
-            'x-requested-with'  : 'XMLHttpRequest',
-        }
-
-        json_data = {
-            'messages'          : messages,
-            'stream'            : stream,
-            'model'             : model,
-            'temperature'       : kwargs.get('temperature', 0.5),
-            'presence_penalty'  : kwargs.get('presence_penalty', 0),
-            'frequency_penalty' : kwargs.get('frequency_penalty', 0),
-            'top_p'             : kwargs.get('top_p', 1),
-        }
-        
-        subdomain = random.choice([
-            'jdaen979ew',
-            'chat9'
-        ])
-
-        response = requests.post(f'https://{subdomain}.fastgpt.me/api/openai/v1/chat/completions',
-                                 headers=headers, json=json_data, stream=stream)
-        
-        for line in response.iter_lines():
-            if line:
-                try:
-                    if b'content' in line:
-                            line_json = json.loads(line.decode('utf-8').split('data: ')[1])
-                            token = line_json['choices'][0]['delta'].get('content')
-                            if token:
-                                yield token
-                except:
-                    continue
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Provider.py

@@ -1,16 +0,0 @@
-import os
-from ..typing import sha256, Dict, get_type_hints
-
-url = None
-model = None
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    return
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Xiaor.py

@@ -1,39 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://xiaor.eu.org'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k',
-         'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/p1/v1/chat/completions',
-                             json=data, stream=True)
-
-    if stream:
-        for chunk in response.iter_content(chunk_size=None):
-            chunk = chunk.decode('utf-8')
-            if chunk.strip():
-                message = json.loads(chunk)['choices'][0]['message']['content']
-                yield message
-    else:
-        message = response.json()['choices'][0]['message']['content']
-        yield message
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AlexWang/lama/bin/paper_runfiles/generate_test_paris_256.sh

@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-# paths to data are valid for mml-ws01
-OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val_256"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in paris_eval_gt
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 segm_256
-    do
-        "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \
-         location.out_dir=$OUT_DIR cropping.out_square_crop=False cropping.out_min_size=256
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done

spaces/Alpaca233/SadTalker/src/facerender/modules/mapping.py

@@ -1,47 +0,0 @@
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class MappingNet(nn.Module):
-    def __init__(self, coeff_nc, descriptor_nc, layer, num_kp, num_bins):
-        super( MappingNet, self).__init__()
-
-        self.layer = layer
-        nonlinearity = nn.LeakyReLU(0.1)
-
-        self.first = nn.Sequential(
-            torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True))
-
-        for i in range(layer):
-            net = nn.Sequential(nonlinearity,
-                torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3))
-            setattr(self, 'encoder' + str(i), net)   
-
-        self.pooling = nn.AdaptiveAvgPool1d(1)
-        self.output_nc = descriptor_nc
-
-        self.fc_roll = nn.Linear(descriptor_nc, num_bins)
-        self.fc_pitch = nn.Linear(descriptor_nc, num_bins)
-        self.fc_yaw = nn.Linear(descriptor_nc, num_bins)
-        self.fc_t = nn.Linear(descriptor_nc, 3)
-        self.fc_exp = nn.Linear(descriptor_nc, 3*num_kp)
-
-    def forward(self, input_3dmm):
-        out = self.first(input_3dmm)
-        for i in range(self.layer):
-            model = getattr(self, 'encoder' + str(i))
-            out = model(out) + out[:,:,3:-3]
-        out = self.pooling(out)
-        out = out.view(out.shape[0], -1)
-        #print('out:', out.shape)
-
-        yaw = self.fc_yaw(out)
-        pitch = self.fc_pitch(out)
-        roll = self.fc_roll(out)
-        t = self.fc_t(out)
-        exp = self.fc_exp(out)
-
-        return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} 

spaces/Alycer/VITS-Umamusume-voice-synthesizer/ONNXVITS_utils.py

@@ -1,19 +0,0 @@
-import torch
-import numpy as np
-import random
-import onnxruntime as ort
-def set_random_seed(seed=0):
-    ort.set_seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
-    random.seed(seed)
-    np.random.seed(seed)
-
-def runonnx(model_path, **kwargs):
-    ort_session = ort.InferenceSession(model_path)
-    outputs = ort_session.run(
-        None,
-        kwargs
-    )
-    return outputs

spaces/Amrrs/DragGan-Inversion/PTI/training/coaches/multi_id_coach.py

@@ -1,73 +0,0 @@
-import os
-import sys
-sys.path.append('.')
-import torch
-from tqdm import tqdm
-
-from PTI.configs import paths_config, hyperparameters, global_config
-from PTI.training.coaches.base_coach import BaseCoach
-from PTI.utils.log_utils import log_images_from_w
-
-
-class MultiIDCoach(BaseCoach):
-
-    def __init__(self, data_loader, use_wandb):
-        super().__init__(data_loader, use_wandb)
-
-    def train(self):
-        self.G.synthesis.train()
-        self.G.mapping.train()
-
-        w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}'
-        os.makedirs(w_path_dir, exist_ok=True)
-        os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True)
-
-        use_ball_holder = True
-        w_pivots = []
-        images = []
-
-        for fname, image in self.data_loader:
-            if self.image_counter >= hyperparameters.max_images_to_invert:
-                break
-
-            image_name = fname[0]
-            if hyperparameters.first_inv_type == 'w+':
-                embedding_dir = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}'
-            else:
-                embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}'
-            os.makedirs(embedding_dir, exist_ok=True)
-
-            w_pivot = self.get_inversion(w_path_dir, image_name, image)
-            w_pivots.append(w_pivot)
-            images.append((image_name, image))
-            self.image_counter += 1
-
-        for i in tqdm(range(hyperparameters.max_pti_steps)):
-            self.image_counter = 0
-
-            for data, w_pivot in zip(images, w_pivots):
-                image_name, image = data
-
-                if self.image_counter >= hyperparameters.max_images_to_invert:
-                    break
-
-                real_images_batch = image.to(global_config.device)
-
-                generated_images = self.forward(w_pivot)
-                loss, l2_loss_val, loss_lpips = self.calc_loss(generated_images, real_images_batch, image_name,
-                                      self.G, use_ball_holder, w_pivot)
-
-                self.optimizer.zero_grad()
-                loss.backward()
-                self.optimizer.step()
-
-                use_ball_holder = global_config.training_step % hyperparameters.locality_regularization_interval == 0
-
-                global_config.training_step += 1
-                self.image_counter += 1
-
-        if self.use_wandb:
-            log_images_from_w(w_pivots, self.G, [image[0] for image in images])
-
-        torch.save(self.G,
-                   f'{paths_config.checkpoints_dir}/model_{global_config.run_name}_multi_id.pt')

spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/__init__.py

@@ -1,11 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-from .util import EasyDict, make_cache_dir_path

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_training.py

@@ -1,86 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import torch
-
-from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel
-from diffusers.training_utils import set_seed
-from diffusers.utils.testing_utils import slow
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-class TrainingTests(unittest.TestCase):
-    def get_model_optimizer(self, resolution=32):
-        set_seed(0)
-        model = UNet2DModel(sample_size=resolution, in_channels=3, out_channels=3)
-        optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)
-        return model, optimizer
-
-    @slow
-    def test_training_step_equality(self):
-        device = "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
-        ddpm_scheduler = DDPMScheduler(
-            num_train_timesteps=1000,
-            beta_start=0.0001,
-            beta_end=0.02,
-            beta_schedule="linear",
-            clip_sample=True,
-        )
-        ddim_scheduler = DDIMScheduler(
-            num_train_timesteps=1000,
-            beta_start=0.0001,
-            beta_end=0.02,
-            beta_schedule="linear",
-            clip_sample=True,
-        )
-
-        assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
-
-        # shared batches for DDPM and DDIM
-        set_seed(0)
-        clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(device) for _ in range(4)]
-        noise = [torch.randn((4, 3, 32, 32)).to(device) for _ in range(4)]
-        timesteps = [torch.randint(0, 1000, (4,)).long().to(device) for _ in range(4)]
-
-        # train with a DDPM scheduler
-        model, optimizer = self.get_model_optimizer(resolution=32)
-        model.train().to(device)
-        for i in range(4):
-            optimizer.zero_grad()
-            ddpm_noisy_images = ddpm_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
-            ddpm_noise_pred = model(ddpm_noisy_images, timesteps[i]).sample
-            loss = torch.nn.functional.mse_loss(ddpm_noise_pred, noise[i])
-            loss.backward()
-            optimizer.step()
-        del model, optimizer
-
-        # recreate the model and optimizer, and retry with DDIM
-        model, optimizer = self.get_model_optimizer(resolution=32)
-        model.train().to(device)
-        for i in range(4):
-            optimizer.zero_grad()
-            ddim_noisy_images = ddim_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
-            ddim_noise_pred = model(ddim_noisy_images, timesteps[i]).sample
-            loss = torch.nn.functional.mse_loss(ddim_noise_pred, noise[i])
-            loss.backward()
-            optimizer.step()
-        del model, optimizer
-
-        self.assertTrue(torch.allclose(ddpm_noisy_images, ddim_noisy_images, atol=1e-5))
-        self.assertTrue(torch.allclose(ddpm_noise_pred, ddim_noise_pred, atol=1e-5))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/evaluation/class_names.py

@@ -1,152 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-
-
-def cityscapes_classes():
-    """Cityscapes class names for external use."""
-    return [
-        'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
-        'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
-        'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
-        'bicycle'
-    ]
-
-
-def ade_classes():
-    """ADE20K class names for external use."""
-    return [
-        'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
-        'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
-        'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
-        'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
-        'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
-        'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
-        'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
-        'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
-        'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
-        'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
-        'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
-        'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
-        'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
-        'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
-        'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
-        'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
-        'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
-        'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
-        'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
-        'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
-        'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
-        'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
-        'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
-        'clock', 'flag'
-    ]
-
-
-def voc_classes():
-    """Pascal VOC class names for external use."""
-    return [
-        'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
-        'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
-        'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
-        'tvmonitor'
-    ]
-
-
-def cityscapes_palette():
-    """Cityscapes palette for external use."""
-    return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
-            [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
-            [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
-            [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100],
-            [0, 0, 230], [119, 11, 32]]
-
-
-def ade_palette():
-    """ADE20K palette for external use."""
-    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-            [102, 255, 0], [92, 0, 255]]
-
-
-def voc_palette():
-    """Pascal VOC palette for external use."""
-    return [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
-            [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
-            [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
-            [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
-            [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-
-
-dataset_aliases = {
-    'cityscapes': ['cityscapes'],
-    'ade': ['ade', 'ade20k'],
-    'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug']
-}
-
-
-def get_classes(dataset):
-    """Get class names of a dataset."""
-    alias2name = {}
-    for name, aliases in dataset_aliases.items():
-        for alias in aliases:
-            alias2name[alias] = name
-
-    if mmcv.is_str(dataset):
-        if dataset in alias2name:
-            labels = eval(alias2name[dataset] + '_classes()')
-        else:
-            raise ValueError(f'Unrecognized dataset: {dataset}')
-    else:
-        raise TypeError(f'dataset must a str, but got {type(dataset)}')
-    return labels
-
-
-def get_palette(dataset):
-    """Get class palette (RGB) of a dataset."""
-    alias2name = {}
-    for name, aliases in dataset_aliases.items():
-        for alias in aliases:
-            alias2name[alias] = name
-
-    if mmcv.is_str(dataset):
-        if dataset in alias2name:
-            labels = eval(alias2name[dataset] + '_palette()')
-        else:
-            raise ValueError(f'Unrecognized dataset: {dataset}')
-    else:
-        raise TypeError(f'dataset must a str, but got {type(dataset)}')
-    return labels

spaces/Arielliu/just_talk/app.py

@@ -1,58 +0,0 @@
-import openai, os
-import gradio as gr
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-class Conversation2:
-    def __init__(self, prompt,):
-        self.prompt = prompt
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append( {"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        num_of_tokens = response['usage']['total_tokens']
-        self.messages.append({"role": "assistant", "content": message})
-        
-         # 删除最开始的对话，直到Token数量不超标
-        while num_of_tokens > 2048:
-            del self.messages[1:3]
-            num_of_tokens = num_of_tokens - len(self.messages[1]["content"].split())
-
-        return message, num_of_tokens
-
-
-prompt = """你是一个计算机助手，你尽力回答一切问题。"""
-
-conv = Conversation2(prompt)
-
-def answer(question, history=[]):
-    history.append(question)
-    response,num_of_tokens = conv.ask(question)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-with gr.Blocks(css="#chatbot{height:600px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter")
-
-    txt.submit(answer, [txt, state], [chatbot, state])
-
-demo.launch()

spaces/Arnx/MusicGenXvAKN/audiocraft/modules/conditioners.py

@@ -1,990 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import defaultdict
-from copy import deepcopy
-from dataclasses import dataclass, field
-from itertools import chain
-import logging
-import math
-import random
-import re
-import typing as tp
-import warnings
-
-from einops import rearrange
-from num2words import num2words
-import spacy
-from transformers import T5EncoderModel, T5Tokenizer  # type: ignore
-import torchaudio
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-from torch.nn.utils.rnn import pad_sequence
-
-from .streaming import StreamingModule
-from .transformer import create_sin_embedding
-from ..data.audio_dataset import SegmentInfo
-from ..utils.autocast import TorchAutocast
-from ..utils.utils import hash_trick, length_to_mask, collate
-
-
-logger = logging.getLogger(__name__)
-TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
-ConditionType = tp.Tuple[Tensor, Tensor]  # condition, mask
-
-
-class WavCondition(tp.NamedTuple):
-    wav: Tensor
-    length: Tensor
-    path: tp.List[tp.Optional[str]] = []
-
-
-def nullify_condition(condition: ConditionType, dim: int = 1):
-    """This function transforms an input condition to a null condition.
-    The way it is done by converting it to a single zero vector similarly
-    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
-
-    Args:
-        condition (ConditionType): a tuple of condition and mask (tp.Tuple[Tensor, Tensor])
-        dim (int): the dimension that will be truncated (should be the time dimension)
-        WARNING!: dim should not be the batch dimension!
-    Returns:
-        ConditionType: a tuple of null condition and mask
-    """
-    assert dim != 0, "dim cannot be the batch dimension!"
-    assert type(condition) == tuple and \
-        type(condition[0]) == Tensor and \
-        type(condition[1]) == Tensor, "'nullify_condition' got an unexpected input type!"
-    cond, mask = condition
-    B = cond.shape[0]
-    last_dim = cond.dim() - 1
-    out = cond.transpose(dim, last_dim)
-    out = 0. * out[..., :1]
-    out = out.transpose(dim, last_dim)
-    mask = torch.zeros((B, 1), device=out.device).int()
-    assert cond.dim() == out.dim()
-    return out, mask
-
-
-def nullify_wav(wav: Tensor) -> WavCondition:
-    """Create a nullified WavCondition from a wav tensor with appropriate shape.
-
-    Args:
-        wav (Tensor): tensor of shape [B, T]
-    Returns:
-        WavCondition: wav condition with nullified wav.
-    """
-    null_wav, _ = nullify_condition((wav, torch.zeros_like(wav)), dim=wav.dim() - 1)
-    return WavCondition(
-        wav=null_wav,
-        length=torch.tensor([0] * wav.shape[0], device=wav.device),
-        path=['null_wav'] * wav.shape[0]
-    )
-
-
-@dataclass
-class ConditioningAttributes:
-    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
-    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
-
-    def __getitem__(self, item):
-        return getattr(self, item)
-
-    @property
-    def text_attributes(self):
-        return self.text.keys()
-
-    @property
-    def wav_attributes(self):
-        return self.wav.keys()
-
-    @property
-    def attributes(self):
-        return {"text": self.text_attributes, "wav": self.wav_attributes}
-
-    def to_flat_dict(self):
-        return {
-            **{f"text.{k}": v for k, v in self.text.items()},
-            **{f"wav.{k}": v for k, v in self.wav.items()},
-        }
-
-    @classmethod
-    def from_flat_dict(cls, x):
-        out = cls()
-        for k, v in x.items():
-            kind, att = k.split(".")
-            out[kind][att] = v
-        return out
-
-
-class SegmentWithAttributes(SegmentInfo):
-    """Base class for all dataclasses that are used for conditioning.
-    All child classes should implement `to_condition_attributes` that converts
-    the existing attributes to a dataclass of type ConditioningAttributes.
-    """
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        raise NotImplementedError()
-
-
-class Tokenizer:
-    """Base class for all tokenizers
-    (in case we want to introduce more advances tokenizers in the future).
-    """
-    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
-        raise NotImplementedError()
-
-
-class WhiteSpaceTokenizer(Tokenizer):
-    """This tokenizer should be used for natural language descriptions.
-    For example:
-    ["he didn't, know he's going home.", 'shorter sentence'] =>
-    [[78, 62, 31,  4, 78, 25, 19, 34],
-    [59, 77,  0,  0,  0,  0,  0,  0]]
-    """
-    PUNCTUATIONS = "?:!.,;"
-
-    def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
-                 lemma: bool = True, stopwords: bool = True) -> None:
-        self.n_bins = n_bins
-        self.pad_idx = pad_idx
-        self.lemma = lemma
-        self.stopwords = stopwords
-        try:
-            self.nlp = spacy.load(language)
-        except IOError:
-            spacy.cli.download(language)  # type: ignore
-            self.nlp = spacy.load(language)
-
-    @tp.no_type_check
-    def __call__(
-        self,
-        texts: tp.List[tp.Optional[str]],
-        return_text: bool = False
-    ) -> tp.Tuple[Tensor, Tensor]:
-        """Take a list of strings and convert them to a tensor of indices.
-
-        Args:
-            texts (tp.List[str]): List of strings.
-            return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
-        Returns:
-            tp.Tuple[Tensor, Tensor]:
-                - Indices of words in the LUT.
-                - And a mask indicating where the padding tokens are
-        """
-        output, lengths = [], []
-        texts = deepcopy(texts)
-        for i, text in enumerate(texts):
-            # if current sample doesn't have a certain attribute, replace with pad token
-            if text is None:
-                output.append(Tensor([self.pad_idx]))
-                lengths.append(0)
-                continue
-
-            # convert numbers to words
-            text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text)  # type: ignore
-            # normalize text
-            text = self.nlp(text)  # type: ignore
-            # remove stopwords
-            if self.stopwords:
-                text = [w for w in text if not w.is_stop]  # type: ignore
-            # remove punctuations
-            text = [w for w in text if w.text not in self.PUNCTUATIONS]  # type: ignore
-            # lemmatize if needed
-            text = [getattr(t, "lemma_" if self.lemma else "text") for t in text]  # type: ignore
-
-            texts[i] = " ".join(text)
-            lengths.append(len(text))
-            # convert to tensor
-            tokens = Tensor([hash_trick(w, self.n_bins) for w in text])
-            output.append(tokens)
-
-        mask = length_to_mask(torch.IntTensor(lengths)).int()
-        padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
-        if return_text:
-            return padded_output, mask, texts  # type: ignore
-        return padded_output, mask
-
-
-class NoopTokenizer(Tokenizer):
-    """This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
-    The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
-    strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
-    split it to ["Jeff", "Buckley"] and return an index per word.
-
-    For example:
-    ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
-    ["Metal", "Rock", "Classical"] => [0, 223, 51]
-    """
-    def __init__(self, n_bins: int, pad_idx: int = 0):
-        self.n_bins = n_bins
-        self.pad_idx = pad_idx
-
-    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
-        output, lengths = [], []
-        for text in texts:
-            # if current sample doesn't have a certain attribute, replace with pad token
-            if text is None:
-                output.append(self.pad_idx)
-                lengths.append(0)
-            else:
-                output.append(hash_trick(text, self.n_bins))
-                lengths.append(1)
-
-        tokens = torch.LongTensor(output).unsqueeze(1)
-        mask = length_to_mask(torch.IntTensor(lengths)).int()
-        return tokens, mask
-
-
-class BaseConditioner(nn.Module):
-    """Base model for all conditioner modules. We allow the output dim to be different
-    than the hidden dim for two reasons: 1) keep our LUTs small when the vocab is large;
-    2) make all condition dims consistent.
-
-    Args:
-        dim (int): Hidden dim of the model (text-encoder/LUT).
-        output_dim (int): Output dim of the conditioner.
-    """
-    def __init__(self, dim, output_dim):
-        super().__init__()
-        self.dim = dim
-        self.output_dim = output_dim
-        self.output_proj = nn.Linear(dim, output_dim)
-
-    def tokenize(self, *args, **kwargs) -> tp.Any:
-        """Should be any part of the processing that will lead to a synchronization
-        point, e.g. BPE tokenization with transfer to the GPU.
-
-        The returned value will be saved and return later when calling forward().
-        """
-        raise NotImplementedError()
-
-    def forward(self, inputs: tp.Any) -> ConditionType:
-        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
-        Outputs a ConditionType, after the input data was embedded as a dense vector.
-
-        Returns:
-            ConditionType:
-                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
-                  output embedding and D is the dimension of the embedding.
-                - And a mask indicating where the padding tokens.
-        """
-        raise NotImplementedError()
-
-
-class TextConditioner(BaseConditioner):
-    ...
-
-
-class LUTConditioner(TextConditioner):
-    """Lookup table TextConditioner.
-
-    Args:
-        n_bins (int): Number of bins.
-        dim (int): Hidden dim of the model (text-encoder/LUT).
-        output_dim (int): Output dim of the conditioner.
-        tokenizer (str): Name of the tokenizer.
-        pad_idx (int, optional): Index for padding token. Defaults to 0.
-    """
-    def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
-        super().__init__(dim, output_dim)
-        self.embed = nn.Embedding(n_bins, dim)
-        self.tokenizer: Tokenizer
-        if tokenizer == "whitespace":
-            self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
-        elif tokenizer == "noop":
-            self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
-        else:
-            raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
-
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-        device = self.embed.weight.device
-        tokens, mask = self.tokenizer(x)
-        tokens, mask = tokens.to(device), mask.to(device)
-        return tokens, mask
-
-    def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
-        tokens, mask = inputs
-        embeds = self.embed(tokens)
-        embeds = self.output_proj(embeds)
-        embeds = (embeds * mask.unsqueeze(-1))
-        return embeds, mask
-
-
-class T5Conditioner(TextConditioner):
-    """T5-based TextConditioner.
-
-    Args:
-        name (str): Name of the T5 model.
-        output_dim (int): Output dim of the conditioner.
-        finetune (bool): Whether to fine-tune T5 at train time.
-        device (str): Device for T5 Conditioner.
-        autocast_dtype (tp.Optional[str], optional): Autocast dtype.
-        word_dropout (float, optional): Word dropout probability.
-        normalize_text (bool, optional): Whether to apply text normalization.
-    """
-    MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
-              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
-              "google/flan-t5-xl", "google/flan-t5-xxl"]
-    MODELS_DIMS = {
-        "t5-small": 512,
-        "t5-base": 768,
-        "t5-large": 1024,
-        "t5-3b": 1024,
-        "t5-11b": 1024,
-        "google/flan-t5-small": 512,
-        "google/flan-t5-base": 768,
-        "google/flan-t5-large": 1024,
-        "google/flan-t5-3b": 1024,
-        "google/flan-t5-11b": 1024,
-    }
-
-    def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
-                 autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
-                 normalize_text: bool = False):
-        assert name in self.MODELS, f"unrecognized t5 model name (should in {self.MODELS})"
-        super().__init__(self.MODELS_DIMS[name], output_dim)
-        self.device = device
-        self.name = name
-        self.finetune = finetune
-        self.word_dropout = word_dropout
-
-        if autocast_dtype is None or self.device == 'cpu':
-            self.autocast = TorchAutocast(enabled=False)
-            if self.device != 'cpu':
-                logger.warning("T5 has no autocast, this might lead to NaN")
-        else:
-            dtype = getattr(torch, autocast_dtype)
-            assert isinstance(dtype, torch.dtype)
-            logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
-            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
-        # Let's disable logging temporarily because T5 will vomit some errors otherwise.
-        # thanks https://gist.github.com/simon-weber/7853144
-        previous_level = logging.root.manager.disable
-        logging.disable(logging.ERROR)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            try:
-                self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
-                t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
-            finally:
-                logging.disable(previous_level)
-        if finetune:
-            self.t5 = t5
-        else:
-            # this makes sure that the t5 models is not part
-            # of the saved checkpoint
-            self.__dict__["t5"] = t5.to(device)
-
-        self.normalize_text = normalize_text
-        if normalize_text:
-            self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
-
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
-        # if current sample doesn't have a certain attribute, replace with empty string
-        entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
-        if self.normalize_text:
-            _, _, entries = self.text_normalizer(entries, return_text=True)
-        if self.word_dropout > 0. and self.training:
-            new_entries = []
-            for entry in entries:
-                words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
-                new_entries.append(" ".join(words))
-            entries = new_entries
-
-        empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
-
-        inputs = self.t5_tokenizer(entries, return_tensors="pt", padding=True).to(self.device)
-        mask = inputs["attention_mask"]
-        mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
-        return inputs
-
-    def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
-        mask = inputs["attention_mask"]
-        with torch.set_grad_enabled(self.finetune), self.autocast:
-            embeds = self.t5(**inputs).last_hidden_state
-        embeds = self.output_proj(embeds.to(self.output_proj.weight))
-        embeds = (embeds * mask.unsqueeze(-1))
-        return embeds, mask
-
-
-class WaveformConditioner(BaseConditioner):
-    """Base class for all conditioners that take a waveform as input.
-    Classes that inherit must implement `_get_wav_embedding` that outputs
-    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
-    factor of the embedding model.
-
-    Args:
-        dim (int): The internal representation dimension.
-        output_dim (int): Output dimension.
-        device (tp.Union[torch.device, str]): Device.
-    """
-    def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
-        super().__init__(dim, output_dim)
-        self.device = device
-
-    def tokenize(self, wav_length: WavCondition) -> WavCondition:
-        wav, length, path = wav_length
-        assert length is not None
-        return WavCondition(wav.to(self.device), length.to(self.device), path)
-
-    def _get_wav_embedding(self, wav: Tensor) -> Tensor:
-        """Gets as input a wav and returns a dense vector of conditions."""
-        raise NotImplementedError()
-
-    def _downsampling_factor(self):
-        """Returns the downsampling factor of the embedding model."""
-        raise NotImplementedError()
-
-    def forward(self, inputs: WavCondition) -> ConditionType:
-        """
-        Args:
-            input (WavCondition): Tuple of (waveform, lengths).
-        Returns:
-            ConditionType: Dense vector representing the conditioning along with its' mask.
-        """
-        wav, lengths, path = inputs
-        with torch.no_grad():
-            embeds = self._get_wav_embedding(wav)
-        embeds = embeds.to(self.output_proj.weight)
-        embeds = self.output_proj(embeds)
-
-        if lengths is not None:
-            lengths = lengths / self._downsampling_factor()
-            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
-        else:
-            mask = torch.ones_like(embeds)
-        embeds = (embeds * mask.unsqueeze(2).to(self.device))
-
-        return embeds, mask
-
-
-class ChromaStemConditioner(WaveformConditioner):
-    """Chroma conditioner that uses DEMUCS to first filter out drums and bass. The is followed by
-    the insight the drums and bass often dominate the chroma, leading to the chroma not containing the
-    information about melody.
-
-    Args:
-        output_dim (int): Output dimension for the conditioner.
-        sample_rate (int): Sample rate for the chroma extractor.
-        n_chroma (int): Number of chroma for the chroma extractor.
-        radix2_exp (int): Radix2 exponent for the chroma extractor.
-        duration (float): Duration used during training. This is later used for correct padding
-            in case we are using chroma as prefix.
-        match_len_on_eval (bool, optional): If True then all chromas are padded to the training
-            duration. Defaults to False.
-        eval_wavs (str, optional): Path to a json egg with waveform, this waveforms are used as
-            conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
-            Defaults to None.
-        n_eval_wavs (int, optional): Limits the number of waveforms used for conditioning. Defaults to 0.
-        device (tp.Union[torch.device, str], optional): Device for the conditioner.
-        **kwargs: Additional parameters for the chroma extractor.
-    """
-    def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
-                 duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
-                 n_eval_wavs: int = 0, device: tp.Union[torch.device, str] = "cpu", **kwargs):
-        from demucs import pretrained
-        super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
-        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
-        self.sample_rate = sample_rate
-        self.match_len_on_eval = match_len_on_eval
-        self.duration = duration
-        self.__dict__["demucs"] = pretrained.get_model('htdemucs').to(device)
-        self.stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
-        self.stem_idx = torch.LongTensor([self.stem2idx['vocal'], self.stem2idx['other']]).to(device)
-        self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma, radix2_exp=radix2_exp,
-                                      device=device, **kwargs)
-        self.chroma_len = self._get_chroma_len()
-
-    def _downsampling_factor(self):
-        return self.chroma.winhop
-
-    def _get_chroma_len(self):
-        """Get length of chroma during training"""
-        dummy_wav = torch.zeros((1, self.sample_rate * self.duration), device=self.device)
-        dummy_chr = self.chroma(dummy_wav)
-        return dummy_chr.shape[1]
-
-    @torch.no_grad()
-    def _get_filtered_wav(self, wav):
-        from demucs.apply import apply_model
-        from demucs.audio import convert_audio
-        with self.autocast:
-            wav = convert_audio(wav, self.sample_rate, self.demucs.samplerate, self.demucs.audio_channels)
-            stems = apply_model(self.demucs, wav, device=self.device)
-            stems = stems[:, self.stem_idx]  # extract stem
-            stems = stems.sum(1)  # merge extracted stems
-            stems = stems.mean(1, keepdim=True)  # mono
-            stems = convert_audio(stems, self.demucs.samplerate, self.sample_rate, 1)
-            return stems
-
-    @torch.no_grad()
-    def _get_wav_embedding(self, wav):
-        # avoid 0-size tensors when we are working with null conds
-        if wav.shape[-1] == 1:
-            return self.chroma(wav)
-        stems = self._get_filtered_wav(wav)
-        chroma = self.chroma(stems)
-
-        if self.match_len_on_eval:
-            b, t, c = chroma.shape
-            if t > self.chroma_len:
-                chroma = chroma[:, :self.chroma_len]
-                logger.debug(f'chroma was truncated! ({t} -> {chroma.shape[1]})')
-            elif t < self.chroma_len:
-                # chroma = F.pad(chroma, (0, 0, 0, self.chroma_len - t))
-                n_repeat = int(math.ceil(self.chroma_len / t))
-                chroma = chroma.repeat(1, n_repeat, 1)
-                chroma = chroma[:, :self.chroma_len]
-                logger.debug(f'chroma was zero-padded! ({t} -> {chroma.shape[1]})')
-        return chroma
-
-
-class ChromaExtractor(nn.Module):
-    """Chroma extraction class, handles chroma extraction and quantization.
-
-    Args:
-        sample_rate (int): Sample rate.
-        n_chroma (int): Number of chroma to consider.
-        radix2_exp (int): Radix2 exponent.
-        nfft (tp.Optional[int], optional): Number of FFT.
-        winlen (tp.Optional[int], optional): Window length.
-        winhop (tp.Optional[int], optional): Window hop size.
-        argmax (bool, optional): Whether to use argmax. Defaults to False.
-        norm (float, optional): Norm for chroma normalization. Defaults to inf.
-        device (tp.Union[torch.device, str], optional): Device to use. Defaults to cpu.
-    """
-    def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12,
-                 nfft: tp.Optional[int] = None, winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None,
-                 argmax: bool = False, norm: float = torch.inf, device: tp.Union[torch.device, str] = "cpu"):
-        super().__init__()
-        from librosa import filters
-        self.device = device
-        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
-        self.winlen = winlen or 2 ** radix2_exp
-        self.nfft = nfft or self.winlen
-        self.winhop = winhop or (self.winlen // 4)
-        self.sr = sample_rate
-        self.n_chroma = n_chroma
-        self.norm = norm
-        self.argmax = argmax
-        self.window = torch.hann_window(self.winlen).to(device)
-        self.fbanks = torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
-                                                      n_chroma=self.n_chroma)).to(device)
-        self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
-                                                      hop_length=self.winhop, power=2, center=True,
-                                                      pad=0, normalized=True).to(device)
-
-    def forward(self, wav):
-        with self.autocast:
-            T = wav.shape[-1]
-            # in case we are getting a wav that was dropped out (nullified)
-            # make sure wav length is no less that nfft
-            if T < self.nfft:
-                pad = self.nfft - T
-                r = 0 if pad % 2 == 0 else 1
-                wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
-                assert wav.shape[-1] == self.nfft, f'expected len {self.nfft} but got {wav.shape[-1]}'
-            spec = self.spec(wav).squeeze(1)
-            raw_chroma = torch.einsum("cf,...ft->...ct", self.fbanks, spec)
-            norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
-            norm_chroma = rearrange(norm_chroma, "b d t -> b t d")
-
-            if self.argmax:
-                idx = norm_chroma.argmax(-1, keepdims=True)
-                norm_chroma[:] = 0
-                norm_chroma.scatter_(dim=-1, index=idx, value=1)
-
-            return norm_chroma
-
-
-def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str):
-    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
-    If the condition is of type "wav", then nullify it using "nullify_condition".
-    If the condition is of any other type, set its' value to None.
-    Works in-place.
-    """
-    if condition_type not in ["text", "wav"]:
-        raise ValueError(
-            "dropout_condition got an unexpected condition type!"
-            f" expected 'wav' or 'text' but got '{condition_type}'"
-        )
-
-    if condition not in getattr(sample, condition_type):
-        raise ValueError(
-            "dropout_condition received an unexpected condition!"
-            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
-            f"but got '{condition}' of type '{condition_type}'!"
-        )
-
-    if condition_type == "wav":
-        wav, length, path = sample.wav[condition]
-        sample.wav[condition] = nullify_wav(wav)
-    else:
-        sample.text[condition] = None
-
-    return sample
-
-
-class DropoutModule(nn.Module):
-    """Base class for all dropout modules."""
-    def __init__(self, seed: int = 1234):
-        super().__init__()
-        self.rng = torch.Generator()
-        self.rng.manual_seed(seed)
-
-
-class AttributeDropout(DropoutModule):
-    """Applies dropout with a given probability per attribute. This is different from the behavior of
-    ClassifierFreeGuidanceDropout as this allows for attributes to be dropped out separately. For example,
-    "artist" can be dropped while "genre" remains. This is in contrast to ClassifierFreeGuidanceDropout
-    where if "artist" is dropped "genre" must also be dropped.
-
-    Args:
-        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
-            ...
-            "genre": 0.1,
-            "artist": 0.5,
-            "wav": 0.25,
-            ...
-        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
-        seed (int, optional): Random seed.
-    """
-    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.active_on_eval = active_on_eval
-        # construct dict that return the values from p otherwise 0
-        self.p = {}
-        for condition_type, probs in p.items():
-            self.p[condition_type] = defaultdict(lambda: 0, probs)
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of conditions.
-        Returns:
-            tp.List[ConditioningAttributes]: List of conditions after certain attributes were set to None.
-        """
-        if not self.training and not self.active_on_eval:
-            return samples
-
-        samples = deepcopy(samples)
-
-        for condition_type, ps in self.p.items():  # for condition types [text, wav]
-            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
-                if torch.rand(1, generator=self.rng).item() < p:
-                    for sample in samples:
-                        dropout_condition(sample, condition_type, condition)
-
-        return samples
-
-    def __repr__(self):
-        return f"AttributeDropout({dict(self.p)})"
-
-
-class ClassifierFreeGuidanceDropout(DropoutModule):
-    """Applies Classifier Free Guidance dropout, meaning all attributes
-    are dropped with the same probability.
-
-    Args:
-        p (float): Probability to apply condition dropout during training.
-        seed (int): Random seed.
-    """
-    def __init__(self, p: float, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.p = p
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of conditions.
-        Returns:
-            tp.List[ConditioningAttributes]: List of conditions after all attributes were set to None.
-        """
-        if not self.training:
-            return samples
-
-        # decide on which attributes to drop in a batched fashion
-        drop = torch.rand(1, generator=self.rng).item() < self.p
-        if not drop:
-            return samples
-
-        # nullify conditions of all attributes
-        samples = deepcopy(samples)
-
-        for condition_type in ["wav", "text"]:
-            for sample in samples:
-                for condition in sample.attributes[condition_type]:
-                    dropout_condition(sample, condition_type, condition)
-
-        return samples
-
-    def __repr__(self):
-        return f"ClassifierFreeGuidanceDropout(p={self.p})"
-
-
-class ConditioningProvider(nn.Module):
-    """Main class to provide conditions given all the supported conditioners.
-
-    Args:
-        conditioners (dict): Dictionary of conditioners.
-        merge_text_conditions_p (float, optional): Probability to merge all text sources
-            into a single text condition. Defaults to 0.
-        drop_desc_p (float, optional): Probability to drop the original description
-            when merging all text sources into a single text condition. Defaults to 0.
-        device (tp.Union[torch.device, str], optional): Device for conditioners and output condition types.
-    """
-    def __init__(
-        self,
-        conditioners: tp.Dict[str, BaseConditioner],
-        merge_text_conditions_p: float = 0,
-        drop_desc_p: float = 0,
-        device: tp.Union[torch.device, str] = "cpu",
-    ):
-        super().__init__()
-        self.device = device
-        self.merge_text_conditions_p = merge_text_conditions_p
-        self.drop_desc_p = drop_desc_p
-        self.conditioners = nn.ModuleDict(conditioners)
-
-    @property
-    def text_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
-
-    @property
-    def wav_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
-
-    @property
-    def has_wav_condition(self):
-        return len(self.wav_conditions) > 0
-
-    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
-        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
-        This should be called before starting any real GPU work to avoid synchronization points.
-        This will return a dict matching conditioner names to their arbitrary tokenized representations.
-
-        Args:
-            inputs (list[ConditioningAttribres]): List of ConditioningAttributes objects containing
-                text and wav conditions.
-        """
-        assert all([type(x) == ConditioningAttributes for x in inputs]), \
-            "got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]" \
-            f" but types were {set([type(x) for x in inputs])}"
-
-        output = {}
-        text = self._collate_text(inputs)
-        wavs = self._collate_wavs(inputs)
-
-        assert set(text.keys() | wavs.keys()).issubset(set(self.conditioners.keys())), \
-            f"got an unexpected attribute! Expected {self.conditioners.keys()}, got {text.keys(), wavs.keys()}"
-
-        for attribute, batch in chain(text.items(), wavs.items()):
-            output[attribute] = self.conditioners[attribute].tokenize(batch)
-        return output
-
-    def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
-        """Compute pairs of `(embedding, mask)` using the configured conditioners
-        and the tokenized representations. The output is for example:
-
-            {
-                "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
-                "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
-                ...
-            }
-
-        Args:
-            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
-        """
-        output = {}
-        for attribute, inputs in tokenized.items():
-            condition, mask = self.conditioners[attribute](inputs)
-            output[attribute] = (condition, mask)
-        return output
-
-    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
-        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
-        are the attributes and the values are the aggregated input per attribute.
-        For example:
-        Input:
-        [
-            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
-            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
-        ]
-        Output:
-        {
-            "genre": ["Rock", "Hip-hop"],
-            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
-        }
-        """
-        batch_per_attribute: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
-
-        def _merge_conds(cond, merge_text_conditions_p=0, drop_desc_p=0):
-            def is_valid(k, v):
-                k_valid = k in ['key', 'bpm', 'genre', 'moods', 'instrument']
-                v_valid = v is not None and isinstance(v, (int, float, str, list))
-                return k_valid and v_valid
-
-            def process_value(v):
-                if isinstance(v, (int, float, str)):
-                    return v
-                if isinstance(v, list):
-                    return ", ".join(v)
-                else:
-                    RuntimeError(f"unknown type for text value! ({type(v), v})")
-
-            desc = cond.text['description']
-            meta_data = ""
-            if random.uniform(0, 1) < merge_text_conditions_p:
-                meta_pairs = [f'{k}: {process_value(v)}' for k, v in cond.text.items() if is_valid(k, v)]
-                random.shuffle(meta_pairs)
-                meta_data = ". ".join(meta_pairs)
-                desc = desc if not random.uniform(0, 1) < drop_desc_p else None
-
-            if desc is None:
-                desc = meta_data if len(meta_data) > 1 else None
-            else:
-                desc = desc.rstrip('.') + ". " + meta_data
-            cond.text['description'] = desc.strip() if desc else None
-
-        if self.training and self.merge_text_conditions_p:
-            for sample in samples:
-                _merge_conds(sample, self.merge_text_conditions_p, self.drop_desc_p)
-
-        texts = [x.text for x in samples]
-        for text in texts:
-            for condition in self.text_conditions:
-                batch_per_attribute[condition].append(text[condition])
-
-        return batch_per_attribute
-
-    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]):
-        """Generate a dict where the keys are attributes by which we fetch similar wavs,
-        and the values are Tensors of wavs according to said attribtues.
-
-        *Note*: by the time the samples reach this function, each sample should have some waveform
-        inside the "wav" attribute. It should be either:
-        1. A real waveform
-        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
-        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
-
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of ConditioningAttributes samples.
-        Returns:
-            dict: A dicionary mapping an attribute name to wavs.
-        """
-        wavs = defaultdict(list)
-        lens = defaultdict(list)
-        paths = defaultdict(list)
-        out = {}
-
-        for sample in samples:
-            for attribute in self.wav_conditions:
-                wav, length, path = sample.wav[attribute]
-                wavs[attribute].append(wav.flatten())
-                lens[attribute].append(length)
-                paths[attribute].append(path)
-
-        # stack all wavs to a single tensor
-        for attribute in self.wav_conditions:
-            stacked_wav, _ = collate(wavs[attribute], dim=0)
-            out[attribute] = WavCondition(stacked_wav.unsqueeze(1),
-                                          torch.cat(lens['self_wav']), paths[attribute])  # type: ignore
-
-        return out
-
-
-class ConditionFuser(StreamingModule):
-    """Condition fuser handles the logic to combine the different conditions
-    to the actual model input.
-
-    Args:
-        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
-            each condition. For example:
-            {
-                "prepend": ["description"],
-                "sum": ["genre", "bpm"],
-                "cross": ["description"],
-            }
-        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
-        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
-    """
-    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
-
-    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
-                 cross_attention_pos_emb_scale: float = 1.0):
-        super().__init__()
-        assert all(
-            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
-        ), f"got invalid fuse method, allowed methods: {self.FUSING_MEHTODS}"
-        self.cross_attention_pos_emb = cross_attention_pos_emb
-        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
-        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
-        self.cond2fuse: tp.Dict[str, str] = {}
-        for fuse_method, conditions in fuse2cond.items():
-            for condition in conditions:
-                self.cond2fuse[condition] = fuse_method
-
-    def forward(
-        self,
-        input: Tensor,
-        conditions: tp.Dict[str, ConditionType]
-    ) -> tp.Tuple[Tensor, tp.Optional[Tensor]]:
-        """Fuse the conditions to the provided model input.
-
-        Args:
-            input (Tensor): Transformer input.
-            conditions (tp.Dict[str, ConditionType]): Dict of conditions.
-        Returns:
-            tp.Tuple[Tensor, Tensor]: The first tensor is the transformer input
-                after the conditions have been fused. The second output tensor is the tensor
-                used for cross-attention or None if no cross attention inputs exist.
-        """
-        B, T, _ = input.shape
-
-        if 'offsets' in self._streaming_state:
-            first_step = False
-            offsets = self._streaming_state['offsets']
-        else:
-            first_step = True
-            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
-
-        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
-            f"given conditions contain unknown attributes for fuser, " \
-            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
-        cross_attention_output = None
-        for cond_type, (cond, cond_mask) in conditions.items():
-            op = self.cond2fuse[cond_type]
-            if op == "sum":
-                input += cond
-            elif op == "input_interpolate":
-                cond = rearrange(cond, "b t d -> b d t")
-                cond = F.interpolate(cond, size=input.shape[1])
-                input += rearrange(cond, "b d t -> b t d")
-            elif op == "prepend":
-                if first_step:
-                    input = torch.cat([cond, input], dim=1)
-            elif op == "cross":
-                if cross_attention_output is not None:
-                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
-                else:
-                    cross_attention_output = cond
-            else:
-                raise ValueError(f"unknown op ({op})")
-
-        if self.cross_attention_pos_emb and cross_attention_output is not None:
-            positions = torch.arange(
-                cross_attention_output.shape[1],
-                device=cross_attention_output.device
-            ).view(1, -1, 1)
-            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
-            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return input, cross_attention_output

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/provider.py

@@ -1,255 +0,0 @@
-import collections
-import math
-from typing import (
-    TYPE_CHECKING,
-    Dict,
-    Iterable,
-    Iterator,
-    Mapping,
-    Sequence,
-    TypeVar,
-    Union,
-)
-
-from pip._vendor.resolvelib.providers import AbstractProvider
-
-from .base import Candidate, Constraint, Requirement
-from .candidates import REQUIRES_PYTHON_IDENTIFIER
-from .factory import Factory
-
-if TYPE_CHECKING:
-    from pip._vendor.resolvelib.providers import Preference
-    from pip._vendor.resolvelib.resolvers import RequirementInformation
-
-    PreferenceInformation = RequirementInformation[Requirement, Candidate]
-
-    _ProviderBase = AbstractProvider[Requirement, Candidate, str]
-else:
-    _ProviderBase = AbstractProvider
-
-# Notes on the relationship between the provider, the factory, and the
-# candidate and requirement classes.
-#
-# The provider is a direct implementation of the resolvelib class. Its role
-# is to deliver the API that resolvelib expects.
-#
-# Rather than work with completely abstract "requirement" and "candidate"
-# concepts as resolvelib does, pip has concrete classes implementing these two
-# ideas. The API of Requirement and Candidate objects are defined in the base
-# classes, but essentially map fairly directly to the equivalent provider
-# methods. In particular, `find_matches` and `is_satisfied_by` are
-# requirement methods, and `get_dependencies` is a candidate method.
-#
-# The factory is the interface to pip's internal mechanisms. It is stateless,
-# and is created by the resolver and held as a property of the provider. It is
-# responsible for creating Requirement and Candidate objects, and provides
-# services to those objects (access to pip's finder and preparer).
-
-
-D = TypeVar("D")
-V = TypeVar("V")
-
-
-def _get_with_identifier(
-    mapping: Mapping[str, V],
-    identifier: str,
-    default: D,
-) -> Union[D, V]:
-    """Get item from a package name lookup mapping with a resolver identifier.
-
-    This extra logic is needed when the target mapping is keyed by package
-    name, which cannot be directly looked up with an identifier (which may
-    contain requested extras). Additional logic is added to also look up a value
-    by "cleaning up" the extras from the identifier.
-    """
-    if identifier in mapping:
-        return mapping[identifier]
-    # HACK: Theoretically we should check whether this identifier is a valid
-    # "NAME[EXTRAS]" format, and parse out the name part with packaging or
-    # some regular expression. But since pip's resolver only spits out three
-    # kinds of identifiers: normalized PEP 503 names, normalized names plus
-    # extras, and Requires-Python, we can cheat a bit here.
-    name, open_bracket, _ = identifier.partition("[")
-    if open_bracket and name in mapping:
-        return mapping[name]
-    return default
-
-
-class PipProvider(_ProviderBase):
-    """Pip's provider implementation for resolvelib.
-
-    :params constraints: A mapping of constraints specified by the user. Keys
-        are canonicalized project names.
-    :params ignore_dependencies: Whether the user specified ``--no-deps``.
-    :params upgrade_strategy: The user-specified upgrade strategy.
-    :params user_requested: A set of canonicalized package names that the user
-        supplied for pip to install/upgrade.
-    """
-
-    def __init__(
-        self,
-        factory: Factory,
-        constraints: Dict[str, Constraint],
-        ignore_dependencies: bool,
-        upgrade_strategy: str,
-        user_requested: Dict[str, int],
-    ) -> None:
-        self._factory = factory
-        self._constraints = constraints
-        self._ignore_dependencies = ignore_dependencies
-        self._upgrade_strategy = upgrade_strategy
-        self._user_requested = user_requested
-        self._known_depths: Dict[str, float] = collections.defaultdict(lambda: math.inf)
-
-    def identify(self, requirement_or_candidate: Union[Requirement, Candidate]) -> str:
-        return requirement_or_candidate.name
-
-    def get_preference(
-        self,
-        identifier: str,
-        resolutions: Mapping[str, Candidate],
-        candidates: Mapping[str, Iterator[Candidate]],
-        information: Mapping[str, Iterable["PreferenceInformation"]],
-        backtrack_causes: Sequence["PreferenceInformation"],
-    ) -> "Preference":
-        """Produce a sort key for given requirement based on preference.
-
-        The lower the return value is, the more preferred this group of
-        arguments is.
-
-        Currently pip considers the following in order:
-
-        * Prefer if any of the known requirements is "direct", e.g. points to an
-          explicit URL.
-        * If equal, prefer if any requirement is "pinned", i.e. contains
-          operator ``===`` or ``==``.
-        * If equal, calculate an approximate "depth" and resolve requirements
-          closer to the user-specified requirements first. If the depth cannot
-          by determined (eg: due to no matching parents), it is considered
-          infinite.
-        * Order user-specified requirements by the order they are specified.
-        * If equal, prefers "non-free" requirements, i.e. contains at least one
-          operator, such as ``>=`` or ``<``.
-        * If equal, order alphabetically for consistency (helps debuggability).
-        """
-        try:
-            next(iter(information[identifier]))
-        except StopIteration:
-            # There is no information for this identifier, so there's no known
-            # candidates.
-            has_information = False
-        else:
-            has_information = True
-
-        if has_information:
-            lookups = (r.get_candidate_lookup() for r, _ in information[identifier])
-            candidate, ireqs = zip(*lookups)
-        else:
-            candidate, ireqs = None, ()
-
-        operators = [
-            specifier.operator
-            for specifier_set in (ireq.specifier for ireq in ireqs if ireq)
-            for specifier in specifier_set
-        ]
-
-        direct = candidate is not None
-        pinned = any(op[:2] == "==" for op in operators)
-        unfree = bool(operators)
-
-        try:
-            requested_order: Union[int, float] = self._user_requested[identifier]
-        except KeyError:
-            requested_order = math.inf
-            if has_information:
-                parent_depths = (
-                    self._known_depths[parent.name] if parent is not None else 0.0
-                    for _, parent in information[identifier]
-                )
-                inferred_depth = min(d for d in parent_depths) + 1.0
-            else:
-                inferred_depth = math.inf
-        else:
-            inferred_depth = 1.0
-        self._known_depths[identifier] = inferred_depth
-
-        requested_order = self._user_requested.get(identifier, math.inf)
-
-        # Requires-Python has only one candidate and the check is basically
-        # free, so we always do it first to avoid needless work if it fails.
-        requires_python = identifier == REQUIRES_PYTHON_IDENTIFIER
-
-        # Prefer the causes of backtracking on the assumption that the problem
-        # resolving the dependency tree is related to the failures that caused
-        # the backtracking
-        backtrack_cause = self.is_backtrack_cause(identifier, backtrack_causes)
-
-        return (
-            not requires_python,
-            not direct,
-            not pinned,
-            not backtrack_cause,
-            inferred_depth,
-            requested_order,
-            not unfree,
-            identifier,
-        )
-
-    def find_matches(
-        self,
-        identifier: str,
-        requirements: Mapping[str, Iterator[Requirement]],
-        incompatibilities: Mapping[str, Iterator[Candidate]],
-    ) -> Iterable[Candidate]:
-        def _eligible_for_upgrade(identifier: str) -> bool:
-            """Are upgrades allowed for this project?
-
-            This checks the upgrade strategy, and whether the project was one
-            that the user specified in the command line, in order to decide
-            whether we should upgrade if there's a newer version available.
-
-            (Note that we don't need access to the `--upgrade` flag, because
-            an upgrade strategy of "to-satisfy-only" means that `--upgrade`
-            was not specified).
-            """
-            if self._upgrade_strategy == "eager":
-                return True
-            elif self._upgrade_strategy == "only-if-needed":
-                user_order = _get_with_identifier(
-                    self._user_requested,
-                    identifier,
-                    default=None,
-                )
-                return user_order is not None
-            return False
-
-        constraint = _get_with_identifier(
-            self._constraints,
-            identifier,
-            default=Constraint.empty(),
-        )
-        return self._factory.find_candidates(
-            identifier=identifier,
-            requirements=requirements,
-            constraint=constraint,
-            prefers_installed=(not _eligible_for_upgrade(identifier)),
-            incompatibilities=incompatibilities,
-        )
-
-    def is_satisfied_by(self, requirement: Requirement, candidate: Candidate) -> bool:
-        return requirement.is_satisfied_by(candidate)
-
-    def get_dependencies(self, candidate: Candidate) -> Sequence[Requirement]:
-        with_requires = not self._ignore_dependencies
-        return [r for r in candidate.iter_dependencies(with_requires) if r is not None]
-
-    @staticmethod
-    def is_backtrack_cause(
-        identifier: str, backtrack_causes: Sequence["PreferenceInformation"]
-    ) -> bool:
-        for backtrack_cause in backtrack_causes:
-            if identifier == backtrack_cause.requirement.name:
-                return True
-            if backtrack_cause.parent and identifier == backtrack_cause.parent.name:
-                return True
-        return False

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

@@ -1,4 +0,0 @@
-from .distro import main
-
-if __name__ == "__main__":
-    main()

spaces/Banbri/zcvzcv/src/lib/dirtyCaptionCleaner.ts

@@ -1,38 +0,0 @@
-export function dirtyCaptionCleaner({
-  panel,
-  instructions,
-  caption
-}: {
-  panel: number;
-  instructions: string;
-  caption: string
-}) {
-  let newCaption = caption.split(":").pop()?.trim() || ""
-  let newInstructions = (
-    // need to remove from LLM garbage here, too
-    (instructions.split(":").pop() || "")
-    .replaceAll("Show a", "")
-    .replaceAll("Show the", "")
-    .replaceAll("Opens with a", "")
-    .replaceAll("Opens with the", "")
-    .replaceAll("Opens with", "")
-    .replaceAll("Cut to a", "")
-    .replaceAll("Cut to the", "")
-    .replaceAll("Cut to", "")
-    .replaceAll("End with a", "")
-    .replaceAll("End with", "").trim() || ""
-  )
-
-  // we have to crop the instructions unfortunately, otherwise the style will disappear
-  // newInstructions = newInstructions.slice(0, 77)
-  // EDIT: well actually the instructions are already at the end of the prompt,
-  // so we can let SDXL do this cropping job for us
-
-  // american comic about brunette wood elf walks around a dark forrest and suddenly stops when hearing a strange noise, single panel, modern american comic, comicbook style, 2010s, digital print, color comicbook, color drawing, Full shot of the elf, her eyes widening in surprise, as a glowing, ethereal creature steps out of the shadows.",
-
-  return {
-    panel,
-    instructions: newInstructions,
-    caption: newCaption,
-  }
-}

spaces/Benson/text-generation/Examples/35 Apk.md

@@ -1,137 +0,0 @@
-
-<h1>35 apk: Una guía para descargar y usar el popular Android App Store</h1>
- <p>Si usted es un usuario de Android que le encanta explorar nuevas aplicaciones y juegos, es posible que haya oído hablar de 35 apk. Es una tienda de aplicaciones popular que ofrece una amplia gama de aplicaciones gratuitas y de pago para dispositivos Android. Pero, ¿qué es un archivo APK y cómo se instala en su dispositivo? ¿Cuáles son los beneficios y riesgos de usar archivos APK? En este artículo, vamos a responder a estas preguntas y le proporcionará una guía completa para descargar y usar 35 apk en su dispositivo Android. </p>
-<h2>35 apk</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://bltlly.com/2v6JOi">https://bltlly.com/2v6JOi</a></b></p><br /><br />
- <h2> ¿Qué es un archivo APK y por qué se utiliza para aplicaciones de Android</h2>
- <p>Un archivo APK es un formato de archivo de paquete utilizado para la distribución e instalación de aplicaciones Android. Es el nombre de Android Package Kit, y contiene todos los archivos, recursos y metadatos necesarios para que una aplicación se ejecute en un dispositivo Android. Piense en ello como un archivo zip que contiene un archivo ejecutable para Windows o un archivo dmg para macOS. </p>
- <p>Normalmente, cuando descarga una aplicación de Google Play u otras tiendas de aplicaciones oficiales, está descargando y ejecutando un archivo APK en segundo plano. Sin embargo, a veces es posible que desee instalar una aplicación que no está disponible en Google Play u otras fuentes. Esto puede suceder por varias razones, como restricciones geográficas, contenido para adultos, eliminación de desarrolladores o preferencias personales. En tales casos, puede descargar el archivo APK desde un sitio web o tienda de aplicaciones de terceros e instalarlo manualmente en su dispositivo. Este proceso se llama sideloading. </p>
- <h2>Cómo instalar archivos APK en dispositivos Android</h2>
- <p>Instalar archivos APK en dispositivos Android no es muy difícil, pero requiere algunos ajustes en la configuración del dispositivo. De forma predeterminada, Android bloquea la instalación de aplicaciones de fuentes desconocidas, lo que significa que cualquier fuente que no sea Google Play u otras tiendas de aplicaciones de confianza. Esta es una medida de seguridad para evitar que las aplicaciones maliciosas dañen su dispositivo o roben sus datos. </p>
-
- <ol>
-<li>Ir a la configuración del dispositivo y toque en Aplicaciones y notificaciones (o aplicaciones en versiones anteriores de Android). </li>
-<li>Toque los tres puntos en la esquina superior derecha y seleccione Acceso especial. </li>
-<li>Toque en Instalar aplicaciones desconocidas (o fuentes desconocidas en versiones anteriores de Android). </li>
-<li>Seleccione la aplicación que desea utilizar para descargar e instalar archivos APK, como Chrome, Administrador de archivos, o cualquier otro navegador o aplicación explorador de archivos. </li>
-<li> Alternar en el Permitir desde esta fuente (o fuentes desconocidas) opción. </li>
-</ol>
- <p>Una vez que haya hecho esto, puede descargar cualquier archivo APK de un sitio web de terceros o tienda de aplicaciones utilizando su aplicación preferida. Después de descargar el archivo, puede abrirlo con su aplicación del explorador de archivos o tocar en él en la barra de notificaciones. Verá un mensaje pidiéndole que confirme la instalación. Toque en Instalar en la parte inferior y espere a que termine el proceso. A continuación, puede iniciar la aplicación desde el cajón de la aplicación o la pantalla de inicio. </p>
- <h2>Beneficios y riesgos de los archivos APK</h2>
- <p>El uso de archivos APK tiene algunas ventajas y desventajas que usted debe tener en cuenta antes de decidir para sideload aplicaciones en su dispositivo. Estos son algunos de ellos:</p>
-<p></p>
- <h3>Beneficios</h3>
- <ul>
-<li> Puede acceder a aplicaciones que no están disponibles en Google Play u otras tiendas de aplicaciones oficiales debido a varias razones. </li>
-<h3>Riesgos</h3>
- <ul>
-<li>Puede exponer su dispositivo y datos a malware, spyware, adware u otro software dañino que puede dañar su dispositivo o robar su información. </li>
-<li>Puede violar los términos y condiciones de Google Play u otras tiendas de aplicaciones oficiales y correr el riesgo de perder el acceso a sus servicios o características. </li>
-<li>Puede infringir los derechos de propiedad intelectual de los desarrolladores o editores de aplicaciones y enfrentar consecuencias legales. </li>
-</ul>
-
- <h2>Lo que es 35 apk y lo que ofrece</h2>
- <p>35 apk es una de las tiendas de aplicaciones de terceros más populares para dispositivos Android. Ofrece una gran colección de aplicaciones y juegos gratuitos y de pago para varias categorías, como entretenimiento, educación, estilo de vida, productividad, deportes, herramientas y más. Puedes encontrar aplicaciones que no están disponibles en Google Play u otras tiendas de aplicaciones oficiales, así como aplicaciones que se modifican o hackean para proporcionar funciones o beneficios adicionales. </p>
- <p>Algunas de las aplicaciones y juegos más populares que se pueden descargar desde 35 apk son:</p>
- <tabla>
-<tr>
-<th>App/Juego</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Spotify Premium</td>
-<td>Una versión modificada de la aplicación de Spotify que te permite disfrutar de streaming de música ilimitado sin anuncios ni restricciones. </td>
-</tr>
-<tr>
-<td>Mod de Netflix</td>
-<td>Una versión modificada de la aplicación de Netflix que le permite ver películas y programas ilimitados sin pagar una cuota de suscripción. </td>
-</tr>
-<tr>
-<td>PUBG Mobile Lite</td>
-<td>Una versión más ligera del juego PUBG Mobile que funciona sin problemas en dispositivos de gama baja y consume menos datos y batería. </td>
-</tr>
-<tr>
-<td>Minecraft PE</td>
-<td>Una versión de pago del juego Minecraft que te permite crear y explorar mundos infinitos con varios modos y características. </td>
-</tr>
-<tr>
-<td>WhatsApp Plus</td>
-<td>Una versión modificada de la aplicación WhatsApp que le permite personalizar su interfaz, ocultar su estado en línea, enviar archivos más grandes, y más. </td>
-</tr>
-</tabla>
- <p>Estos son solo algunos ejemplos de los miles de aplicaciones y juegos que se pueden descargar desde 35 apk. Puede navegar a través de diferentes categorías o utilizar la función de búsqueda para encontrar su aplicación o juego deseado. También puedes consultar las valoraciones, reseñas, capturas de pantalla y descripciones de las aplicaciones y juegos antes de descargarlos. </p>
- <h2>Cómo descargar e instalar 35 apk en dispositivos Android</h2>
- <p>Para descargar e instalar 35 apk en su dispositivo Android, es necesario seguir estos pasos:</p>
- <ol>
-
-<li>Toque en el botón Descargar en la esquina superior derecha del sitio web. Verá una ventana emergente pidiéndole que confirme la descarga. Toque en Aceptar para comenzar a descargar el archivo APK. </li>
-<li>Una vez completada la descarga, abra el archivo con su aplicación explorador de archivos o toque en él en la barra de notificaciones. Verá un mensaje pidiéndole que confirme la instalación. Toque en Instalar en la parte inferior y espere a que el proceso termine. </li>
-<li>Después de la instalación se ha completado, puede iniciar 35 apk desde el cajón de la aplicación o la pantalla de inicio. Verá una pantalla de bienvenida con algunas instrucciones y consejos sobre cómo utilizar 35 apk. Toque en Siguiente para proceder. </li>
-<li>Ahora se puede acceder 35 apk y navegar a través de su colección de aplicaciones y juegos. También puede usar la función de búsqueda o las opciones de filtro para encontrar la aplicación o el juego que desee. Para descargar una aplicación o juego de 35 apk, toque en él y luego toque en Descargar en la parte inferior. Verá una ventana emergente que le pide que confirme la descarga. Toque en Aceptar para comenzar a descargar el archivo APK. </li>
-<h2>Características y beneficios de 35 apk</h2>
- <p>35 apk no es solo otra tienda de aplicaciones para dispositivos Android. Tiene algunas características y beneficios únicos que lo hacen destacar de otras tiendas de aplicaciones similares. Aquí están algunos de ellos:</p>
- <ul>
-<li> Tiene una interfaz sencilla y fácil de usar que facilita la navegación y el uso. </li>
-<li> Tiene una gran y diversa colección de aplicaciones y juegos para varias categorías y preferencias. </li>
-<li> Ofrece aplicaciones gratuitas y de pago y juegos que no están disponibles en Google Play u otras tiendas de aplicaciones oficiales. </li>
-<li>Proporciona aplicaciones y juegos modificados o hackeados que tienen características o beneficios adicionales, como monedas ilimitadas, gemas, vidas, etc.</li>
-<li>Actualiza sus aplicaciones y juegos regularmente para asegurarse de que son compatibles con las últimas versiones y dispositivos Android. </li>
-<li>Soporta múltiples idiomas, incluyendo inglés, chino, árabe, español, francés y más. </li>
-
-<li> Tiene un sistema de valoración y revisión que permite a los usuarios calificar y revisar las aplicaciones y juegos que descargan y usan. </li>
-<li> Tiene un equipo de servicio al cliente que responde a las consultas y comentarios de los usuarios de forma rápida y profesional. </li>
-</ul>
- <h2>Riesgos y precauciones de usar 35 apk</h2>
- <p>Mientras que 35 apk tiene muchas ventajas, también tiene algunos riesgos y desventajas que usted debe ser consciente de antes de usarlo. Aquí están algunos de ellos:</p>
- <ul>
-<li>No es una tienda de aplicaciones oficial y no está avalado o aprobado por Google o cualquier otra autoridad. Por lo tanto, puede violar los términos y condiciones de Google Play u otras tiendas de aplicaciones oficiales y correr el riesgo de perder el acceso a sus servicios o características. </li>
-<li>Puede contener malware, spyware, adware u otro software dañino que puede dañar su dispositivo o robar su información. Por lo tanto, siempre debe escanear los archivos APK antes de instalarlos y usar una aplicación antivirus confiable para proteger su dispositivo. </li>
-<li>Puede infringir los derechos de propiedad intelectual de los desarrolladores de aplicaciones o editores y enfrentar consecuencias legales. Por lo tanto, siempre debe respetar los derechos de los creadores originales y utilizar las aplicaciones y juegos para fines personales y no comerciales solamente. </li>
-<li>Puede que no funcione correctamente en algunos dispositivos o versiones de Android debido a problemas de compatibilidad. Por lo tanto, siempre debe comprobar la compatibilidad de las aplicaciones y juegos antes de descargarlos y seguir las instrucciones cuidadosamente para evitar errores o fallos. </li>
-</ul>
- <p>Para utilizar 35 apk de forma segura y eficaz, debe seguir estas precauciones:</p>
- <ul>
-<li> Solo descargar archivos APK de fuentes confiables y de buena reputación, como el sitio web oficial de 35 apk u otras tiendas de aplicaciones verificadas. </li>
-<li> Solo instalar aplicaciones y juegos que tienen calificaciones positivas, comentarios, capturas de pantalla, y descripciones de otros usuarios. </li>
-
-<li> Solo actualizar sus aplicaciones y juegos de 35 apk u otras fuentes de confianza y evitar la descarga de actualizaciones de fuentes desconocidas o no verificadas. </li>
-<li>Solo desinstale sus aplicaciones y juegos de la configuración del dispositivo o la aplicación del explorador de archivos y evite el uso de desinstaladores o limpiadores de terceros que pueden dejar archivos o datos residuales. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>y aplicaciones y juegos de pago para dispositivos Android. Le permite acceder a aplicaciones que no están disponibles en Google Play u otras tiendas de aplicaciones oficiales, así como aplicaciones que se modifican o hackean para proporcionar características o beneficios adicionales. Sin embargo, también tiene algunos riesgos y desventajas que debe tener en cuenta antes de usarlo. Siempre debe tener cuidado al descargar e instalar archivos APK de fuentes desconocidas y seguir las precauciones y consejos mencionados en este artículo para utilizar 35 apk de forma segura y eficaz. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre 35 apk:</p>
- <h3>¿Cuáles son algunas alternativas a 35 apk? </h3>
- <p>Si estás buscando otras tiendas de aplicaciones que ofrecen aplicaciones y juegos similares o diferentes para dispositivos Android, puedes probar algunas de estas alternativas:</p>
- <ul>
-<li>Aptoide: una tienda de aplicaciones descentralizada que permite a los usuarios crear y administrar sus propias tiendas de aplicaciones y descargar aplicaciones de otros usuarios. </li>
-<li>ACMarket: Una tienda de aplicaciones agrietada que ofrece aplicaciones y juegos modificados o hackeados de forma gratuita. </li>
-<li>APKPure: Una tienda de aplicaciones segura y segura que ofrece archivos APK puros sin modificaciones ni alteraciones. </li>
-<li>Appvn: Una tienda de aplicaciones vietnamita que ofrece una variedad de aplicaciones y juegos para dispositivos Android, iOS y Windows. </li>
-<li>HappyMod: Una tienda de aplicaciones modded que ofrece aplicaciones y juegos con varios mods, como dinero ilimitado, funciones desbloqueadas, etc.</li>
-</ul>
- <h3>Cómo actualizar 35 apk a la última versión? </h3>
- <p>Para actualizar 35 apk a la última versión, puede seguir estos pasos:</p>
- <ol>
-<li>Iniciar 35 apk desde el cajón de la aplicación o la pantalla de inicio. </li>
-
-<li>Toque en Configuración en la parte inferior del menú. </li>
-<li>Toque en Buscar actualizaciones en la sección General. </li>
-<li> Si hay una nueva versión disponible, verá una ventana emergente pidiéndole que confirme la actualización. Pulse en OK para comenzar a descargar el archivo APK. </li>
-<li>Una vez completada la descarga, abra el archivo con su aplicación explorador de archivos o toque en él en la barra de notificaciones. Verá un mensaje pidiéndole que confirme la instalación. Toque en Instalar en la parte inferior y espere a que el proceso termine. </li>
-<li> Ahora puede disfrutar de la última versión de 35 apk con nuevas características y mejoras. </li>
-</ol>
- <h3>Cómo desinstalar 35 apk de dispositivos Android? </h3>
- <p>Para desinstalar 35 apk de su dispositivo Android, puede seguir estos pasos:</p>
- <ol>
-<li>Ir a la configuración del dispositivo y toque en Aplicaciones y notificaciones (o aplicaciones en versiones anteriores de Android). </li>
-<li> Buscar y toque en 35 apk en la lista de aplicaciones. </li>
-<li>Toque en Desinstalar en la parte inferior de la pantalla de información de la aplicación. </li>
-<li> Verá una ventana emergente pidiéndole que confirme la desinstalación. Pulse en OK para continuar. </li>
-<li> Se ha desinstalado con éxito 35 apk de su dispositivo. También puede eliminar cualquier archivo APK o datos residuales relacionados con 35 apk de su dispositivo de almacenamiento utilizando la aplicación explorador de archivos. </li>
-</ol>
- <h3>Es 35 apk legal y seguro de usar? </h3>
-
- <h3>¿Cómo ponerse en contacto con los desarrolladores de 35 apk de apoyo o retroalimentación? </h3>
- <p>Si usted tiene alguna pregunta, problemas, sugerencias, o comentarios sobre 35 apk, puede ponerse en contacto con los desarrolladores de 35 apk a través de su sitio web oficial (https://www.35apk.com/) o sus cuentas de medios sociales (Facebook, Twitter, Instagram). También puede dejar un comentario o revisión en su página web o tienda de aplicaciones. Los desarrolladores de 35 apk están siempre abiertos a la retroalimentación de los usuarios y el apoyo y se esforzarán al máximo para responder a sus consultas y resolver sus problemas lo antes posible. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Carx Street Descargar La ltima Versin.md

@@ -1,58 +0,0 @@
-
-<h1>CarX Street: Cómo descargar la última versión del juego Open World Street Racing</h1>
-<p>Si eres un fanático de los juegos de carreras callejeras realistas y emocionantes, es posible que quieras ver CarX Street, un nuevo juego de los creadores de CarX Drift Racing 2. En este juego, puedes explorar un dinámico mundo abierto de Sunset City, donde puedes unirte a clubes, desafiar a los jefes, personalizar sus coches, y la carrera a la velocidad máxima o la deriva a través de turnos. En este artículo, te mostraremos cómo descargar la última versión de CarX Street para tus dispositivos Android o PC/Mac.</p>
-<h2>¿Qué es CarX Street? </h2>
-<p>CarX Street es un juego de carreras desarrollado por CarX Technologies, LLC, una empresa que se especializa en la creación de la física realista del automóvil y el comportamiento. El juego fue lanzado en junio de 2023 como una prueba beta abierta, y ya ha ganado más de 1 millón de descargas en Google Play Store. Características del juego:</p>
-<h2>carx street descargar la última versión</h2><br /><p><b><b>Download Zip</b> &harr; <a href="https://bltlly.com/2v6MUZ">https://bltlly.com/2v6MUZ</a></b></p><br /><br />
-<h3>Características de CarX Street</h3>
-<ul>
-<li>Un enorme mundo abierto de Sunset City, donde puedes explorar cada esquina y encontrar secretos ocultos. </li>
-<li>Carreras realistas en carreteras y calles de la ciudad, además de carreras de deriva de alta velocidad. </li>
-<li>Un sistema detallado de construcción de automóviles, donde puede intercambiar piezas y afinar su automóvil para una carrera específica. </li>
-<li>Un sistema visual de ajuste de coches, donde puede personalizar los espejos, faros, luces, falda, parachoques, llantas y mucho más. </li>
-<li>Un modo de carrera, donde puedes unirte a clubes, derrotar jefes y demostrar a todos que eres el mejor piloto de la ciudad. </li>
-<li>Un ciclo dinámico de día/noche, donde puedes correr a cualquier hora de la noche o del día. </li>
-</ul>
-<h3>Requisitos para CarX Street</h3>
-<p>Para jugar CarX Street en tu dispositivo Android, necesitas tener:</p>
-<ul>
-<li>Una versión de Android de 9.0 o superior. </li>
-<li>Al menos 1.19 GB de espacio de almacenamiento libre. </li>
-<li>Una conexión a Internet estable. </li>
-</ul>
-<h2>Cómo descargar CarX Street para dispositivos Android</h2>
-<p>Si quieres descargar CarX Street para tu dispositivo Android, puedes seguir estos sencillos pasos:</p>
-
-<p>Abre la aplicación Google Play Store en tu dispositivo y asegúrate de iniciar sesión con tu cuenta de Google. </p>
-<h3>Paso 2: Búsqueda de CarX Street</h3>
-<p>Escribe "CarX Street" en la barra de búsqueda y toca el primer resultado que aparece. También puedes usar este enlace para ir directamente a la página del juego. </p>
-<h3>Paso 3: Toque en Instalar</h3>
-<p>Toque en el botón verde Instalar y espere a que se complete el proceso de descarga e instalación. Es posible que necesite conceder algunos permisos para que el juego se ejecute correctamente. </p>
-<h2>Cómo descargar CarX Street para PC o Mac</h2>
-<p>Si quieres jugar CarX Street en tu PC o Mac, tendrás que usar un emulador de Android, que es un software que te permite ejecutar aplicaciones Android en tu ordenador. Hay muchos emuladores disponibles en línea, pero recomendamos usar BlueStacks, que es uno de los más populares y confiables. Estos son los pasos para descargar CarX Street para PC o Mac usando BlueStacks:</p>
-<p></p>
-<h3>Paso 1: Descargar e instalar un emulador de Android</ <h3>Paso 1: Descargar e instalar un emulador de Android</h3>
-<p>Ir a la página web oficial de BlueStacks y descargar la última versión del emulador para su PC o Mac. Siga las instrucciones en la pantalla para instalar el emulador en su computadora. Es posible que necesite habilitar la tecnología de virtualización en la configuración de su BIOS para un mejor rendimiento. </p>
-<h3>Paso 2: Inicie el emulador e inicie sesión con la cuenta de Google</h3>
-<p>Abra la aplicación BlueStacks en su computadora e inicie sesión con su cuenta de Google. Esto te permitirá acceder a Google Play Store y sincronizar tus datos con tu dispositivo Android. </p>
-<h3>Paso 3: Búsqueda de CarX Street en el emulador</h3>
-<p>Haga clic en el icono de Google Play en la pantalla de inicio de BlueStacks y escriba "CarX Street" en la barra de búsqueda. Toca el primer resultado que aparece y ve a la página del juego. </p>
-<h3>Paso 4: Instalar y reproducir CarX Street en PC o Mac</h3>
-
-<h2>Conclusión</h2>
-<p>CarX Street es un divertido y realista juego de carreras callejeras que te permite explorar un enorme mundo abierto, personalizar tus coches y competir con otros jugadores. Puede descargar la última versión de CarX Street para sus dispositivos Android o PC/ Mac siguiendo los pasos que hemos proporcionado en este artículo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
-<h2>Preguntas frecuentes</h2>
-<ul>
-<li><b>Q: ¿Cuánto cuesta CarX Street? </b></li>
-<li>A: CarX Street es gratis para descargar y jugar, pero contiene compras en la aplicación que pueden mejorar su experiencia de juego. </li>
-<li><b>Q: ¿Es CarX Street multijugador? </b></li>
-<li>A: Sí, CarX Street tiene un modo multijugador donde puedes competir con otros jugadores en línea o unirte a clubes y chatear con ellos. </li>
-<li><b>Q: ¿Puedo jugar CarX Street sin conexión? </b></li>
-<li>A: No, CarX Street requiere una conexión a Internet para jugar, ya que es un juego en línea. </li>
-<li><b>Q: ¿Cuáles son los mejores coches en CarX Street? </b></li>
-<li>A: No hay una respuesta definitiva a esta pregunta, ya que los diferentes coches tienen diferentes fortalezas y debilidades. Puede probar diferentes coches y ver cuáles se adaptan a su estilo de conducción y preferencias. </li>
-<li><b>Q: ¿Cómo puedo obtener más dinero y oro en CarX Street? </b></li>
-<li>A: Puedes ganar dinero y oro completando carreras, misiones, logros y tareas diarias. También puede comprarlos con dinero real o ver anuncios para obtener algunas recompensas gratuitas. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Delfn Emulador Trucos Descargar.md

@@ -1,94 +0,0 @@
-<br />
-<h1>Delfín emulador trucos Descargar: Cómo mejorar su experiencia de juego</h1>
-<p>Si eres un fan de los juegos de GameCube y Wii, es posible que hayas oído hablar de <strong>dolphin emulator</strong>, un software libre y de código abierto que te permite jugar a estos juegos en tu PC, Mac, Linux, Android o dispositivos Xbox. Dolphin emulador no solo mejora los gráficos y el rendimiento de los juegos, pero también ofrece muchas características que mejoran su experiencia de juego, tales como la compatibilidad con todos los controladores de PC, velocidad turbo, multijugador en red, e incluso más. Una de estas características es la capacidad de usar códigos de trucos <strong></strong>, lo que puede hacer que los juegos sean más fáciles, divertidos o desbloquear contenido oculto. En este artículo, te mostraremos cómo habilitar trucos en el emulador de delfines, cómo descargar códigos de trucos para juegos de emuladores de delfines, cómo usar códigos de trucos en el emulador de delfines y cuáles son algunos de los mejores trucos para juegos de emuladores de delfines. </p>
- <h2>Cómo activar trucos en Dolphin Emulator</h2>
-<p>Antes de poder usar códigos de trucos en el emulador de delfines, debe habilitar la opción de trucos en la configuración. Para hacer esto, siga estos pasos:</p>
-<h2>delfín emulador trucos descargar</h2><br /><p><b><b>DOWNLOAD</b> &#10084;&#10084;&#10084; <a href="https://bltlly.com/2v6MHT">https://bltlly.com/2v6MHT</a></b></p><br /><br />
-<ol>
-<li>Abra el emulador de delfines y haga clic en Opciones > Configurar.</li>
-<li>En la pestaña General, asegúrese de que la opción Habilitar trucos está marcada. </li>
-<li>Haga clic en Cerrar para guardar los cambios. </li>
-</ol>
- <h2>Cómo descargar códigos de trucos para Dolphin Emulator Juegos</h2>
-<p>Hay muchas fuentes donde puedes encontrar códigos de trucos para juegos de emuladores de delfines, como sitios web, foros o bases de datos. Una de las maneras más fáciles de descargar códigos de trucos es utilizar la función integrada en el emulador de delfines que le permite descargar códigos de la base de datos de Wiird. Para hacer esto, siga estos pasos:</p>
-<ol>
-<li>Haga clic derecho en el juego al que desea aplicar códigos de trucos y seleccione Propiedades.</li>
-<li>En la pestaña Códigos RA o Códigos Gecko, haga clic en Descargar códigos (base de datos WiiRD). </li>
-<li>Seleccione los códigos que desea y haga clic en Aplicar cambios.</li>
-</ol>
-
- <h2>Cómo usar códigos de trucos en Dolphin Emulator</h2>
-<p>Una vez que hayas descargado o añadido códigos de trucos para tu juego, puedes usarlos siguiendo estos pasos:</p>
-<ol>
-<li>Haga clic derecho en el juego que desea jugar y seleccione Propiedades.</li>
-<li> En la pestaña Códigos RA o Códigos Gecko, marque las casillas junto a los códigos que desea activar. </li>
-<li>Haga clic en Cerrar y comenzar el juego. </li>
-<li>Compruebe si el código de trucos funcionó jugando el juego. </li>
-</ol>
-<p>También puede desactivar o eliminar códigos de trucos desmarcando o eliminándolos de la ventana de propiedades del juego. </p>
- <h2>Los mejores trucos para Dolphin Emulator Juegos</h2>
-<p>Hay muchos códigos de trucos para juegos de emuladores de delfines que pueden mejorar su experiencia de juego de diferentes maneras. Algunos de ellos son:</p>
-<ul>
-<li><strong>Salud infinita, munición, vidas, tiempo, etc.</strong>: Estos trucos pueden hacer que los juegos sean más fáciles al evitar que te mueras, te quedes sin recursos o misiones fallidas. </li>
-<li><strong>Desbloquea todos los personajes, niveles, elementos, modos, etc.</strong>: Estos trucos pueden hacer que los juegos sean más divertidos al darte acceso a todo lo que los juegos tienen para ofrecer. </li>
-<li><strong>Modificar la física del juego, gráficos, sonido, etc.</strong>: Estos trucos pueden hacer los juegos más interesantes al cambiar su aspecto o comportamiento. </li>
-</ul>
-<p>Algunos ejemplos de códigos de trucos populares para juegos de emuladores de delfines son:</p> <table>
-<tr>
-<th>Juego</th>
-<th>Código de trucos</th>
-<th>Efecto</th>
-</tr>
-<tr>
-<td>Super Smash Bros. Melee</td>
-<td>044C0C70 00000063</td>
-<td>Desbloquear todos los caracteres</td>
-</tr>
-<tr>
-<td>Mario Kart Wii</td>
-<td>48000000 809BD730 DE000000 80008180 140F26A8 0000FFFF 140F26B0 0000FFFF 140F26B8 0000FFFF 140F26C0 0000FFFF E0000000 80008000</td>
-<td>Desbloquear todos los vehículos y cursos</td>
-</tr>
-<tr>
-<td>La leyenda de Zelda: Twilight Princess</td>
-
-<td>Desactivar el efecto de floración</td>
-</tr>
-<tr>
-<td>Pikmin</td>
-<td>0419A5A8 C03E8008 0419A5AC FC200050 0419A5B0 C03E800C 0419A5B4 FC200050 0419A5B8 C03E8010 0419A5BC FC200050 0419A5C0 C03E8014 0419A5C4 FC200050</td>
-<td>Aumentar la distancia de la cámara</td>
-</tr>
-<tr>
-<td>Metroid Prime</td>
-<td>042F69DC C0229FFC</td>
-<td>Habilitar el modo panorámico</td>
-</tr>
-</tabla>
- <h2>Conclusión: Resumen y Beneficios de Usar Trucos en Dolphin Emulator</h2>
-<p>En conclusión, el uso de códigos de trucos en el emulador de delfines puede mejorar su experiencia de juego haciendo los juegos más fáciles, más divertidos o más interesantes. Puedes habilitar trucos en el emulador de delfines marcando la opción en la configuración, descargando códigos de trucos de la base de datos de Wiird u otras fuentes, y usando códigos de trucos activándolos en la ventana de propiedades del juego. Algunos de los mejores trucos para los juegos de emulación de delfines son salud infinita, munición, vidas, tiempo, etc., desbloquear todos los personajes, niveles, artículos, modos, etc., y modificar la física del juego, gráficos, sonido, etc. Esperamos que este artículo te haya ayudado a aprender a usar códigos de trucos en el emulador de delfines y a disfrutar de tus juegos favoritos de GameCube y Wii en tus dispositivos. </p>
- <h2>Preguntas frecuentes: Cinco preguntas y respuestas comunes sobre Dolphin Emulator Trucos</h2>
-<p>Aquí están algunas de las preguntas y respuestas más frecuentes sobre los tramposos emuladores de delfines:</p>
-<p></p>
- <h3>Q: ¿Cómo sé si un código de trucos funciona para mi juego? </h3>
-<p>A: La mejor manera de saber si un código de trucos funciona para tu juego es probarlo y ver si tiene el efecto deseado. Sin embargo, también puede comprobar la compatibilidad del código de trucos con su juego mirando el ID del juego. El ID del juego es un código de seis dígitos que identifica tu versión del juego y tu región. Puedes encontrarlo haciendo clic derecho en el juego y seleccionando Propiedades. El ID del juego se muestra en la pestaña Información. Puede comparar el ID del juego con la fuente de código de trucos para ver si coinciden. </p>
- <h3>P: ¿Cómo puedo crear mis propios códigos de trucos para juegos de emulación de delfines? </h3>
-
- <h3>Q: ¿Cómo puedo actualizar o eliminar códigos de trucos para juegos de emulación de delfines? </h3>
-<p>A: Para actualizar o eliminar códigos de trucos para juegos de emuladores de delfines, debe acceder a los archivos de código de trucos que se almacenan en la carpeta del emulador de delfines. Los archivos de código de trucos llevan el nombre del ID del juego y tienen la extensión .ini. Puedes encontrarlos en las siguientes ubicaciones:</p>
- <ul>
-<li>Para códigos AR: Dolphin User GameSettings GXXXXXXX.ini (donde XXXXXXX es el ID del juego)</li>
-<li>Para códigos Gecko: Dolphin User GameSettings RSBE01.ini (para juegos de Wii) o Dolphin User GameSettings GXXXXXXX.ini (para juegos de GameCube)</li>
-</ul>
-<p>Puede abrir estos archivos con un editor de texto y editar o eliminar los códigos de trucos como desee. También puede descargar códigos de trucos actualizados de la base de datos de Wiird u otras fuentes y reemplazar los antiguos. </p>
- <h3>P: ¿Cómo comparto códigos de trucos para juegos de emulación de delfines con otros? </h3>
-<p>A: Para compartir códigos de trucos para juegos de emuladores de delfines con otros, puede enviarles los archivos de código de trucos que ha editado o creado, o publicarlos en sitios web, foros o bases de datos que albergan códigos de trucos para juegos de emuladores de delfines. También puedes usar la función Exportar a GCT en el emulador de delfín para crear un archivo GCT que contenga todos los códigos de trucos activados para un juego. A continuación, puede enviar o cargar este archivo a otros que pueden usarlo con el emulador de delfines. </p>
- <h3>P: ¿Hay algún riesgo o inconveniente de usar códigos de trucos en los juegos de emulación de delfines? </h3>
-<p>A: El uso de códigos de trucos en los juegos de emulación de delfines es generalmente seguro e inofensivo, siempre y cuando los use de manera responsable y con precaución. Sin embargo, hay algunos riesgos potenciales o inconvenientes que usted debe tener en cuenta, tales como:</p>
- <ul>
-
-<li><strong>Prohibición de servicios en línea</strong>: Algunos códigos de trucos pueden darle una ventaja injusta sobre otros jugadores en juegos multijugador en línea, lo que puede violar los términos de servicio del juego o el proveedor de servicios en línea. Para evitar esto, nunca debes usar códigos de trucos en juegos multijugador en línea, y deshabilitarlos si te unes a una sesión en línea. </li>
-<li><strong>Reducir el desafío y el disfrute del juego</strong>: Algunos códigos de trucos pueden hacer el juego demasiado fácil o aburrido al eliminar el desafío y la recompensa de jugar el juego. Para evitar esto, solo debes usar códigos de trucos que mejoren tu experiencia de juego, y no abusar de ellos para arruinar el juego. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/loaders.py

@@ -1,524 +0,0 @@
-# Copyright 2012-2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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.
-"""Module for loading various model files.
-
-This module provides the classes that are used to load models used
-by botocore.  This can include:
-
-    * Service models (e.g. the model for EC2, S3, DynamoDB, etc.)
-    * Service model extras which customize the service models
-    * Other models associated with a service (pagination, waiters)
-    * Non service-specific config (Endpoint data, retry config)
-
-Loading a module is broken down into several steps:
-
-    * Determining the path to load
-    * Search the data_path for files to load
-    * The mechanics of loading the file
-    * Searching for extras and applying them to the loaded file
-
-The last item is used so that other faster loading mechanism
-besides the default JSON loader can be used.
-
-The Search Path
-===============
-
-Similar to how the PATH environment variable is to finding executables
-and the PYTHONPATH environment variable is to finding python modules
-to import, the botocore loaders have the concept of a data path exposed
-through AWS_DATA_PATH.
-
-This enables end users to provide additional search paths where we
-will attempt to load models outside of the models we ship with
-botocore.  When you create a ``Loader``, there are two paths
-automatically added to the model search path:
-
-    * <botocore root>/data/
-    * ~/.aws/models
-
-The first value is the path where all the model files shipped with
-botocore are located.
-
-The second path is so that users can just drop new model files in
-``~/.aws/models`` without having to mess around with the AWS_DATA_PATH.
-
-The AWS_DATA_PATH using the platform specific path separator to
-separate entries (typically ``:`` on linux and ``;`` on windows).
-
-
-Directory Layout
-================
-
-The Loader expects a particular directory layout.  In order for any
-directory specified in AWS_DATA_PATH to be considered, it must have
-this structure for service models::
-
-    <root>
-      |
-      |-- servicename1
-      |   |-- 2012-10-25
-      |       |-- service-2.json
-      |-- ec2
-      |   |-- 2014-01-01
-      |   |   |-- paginators-1.json
-      |   |   |-- service-2.json
-      |   |   |-- waiters-2.json
-      |   |-- 2015-03-01
-      |       |-- paginators-1.json
-      |       |-- service-2.json
-      |       |-- waiters-2.json
-      |       |-- service-2.sdk-extras.json
-
-
-That is:
-
-    * The root directory contains sub directories that are the name
-      of the services.
-    * Within each service directory, there's a sub directory for each
-      available API version.
-    * Within each API version, there are model specific files, including
-      (but not limited to): service-2.json, waiters-2.json, paginators-1.json
-
-The ``-1`` and ``-2`` suffix at the end of the model files denote which version
-schema is used within the model.  Even though this information is available in
-the ``version`` key within the model, this version is also part of the filename
-so that code does not need to load the JSON model in order to determine which
-version to use.
-
-The ``sdk-extras`` and similar files represent extra data that needs to be
-applied to the model after it is loaded. Data in these files might represent
-information that doesn't quite fit in the original models, but is still needed
-for the sdk. For instance, additional operation parameters might be added here
-which don't represent the actual service api.
-"""
-import logging
-import os
-
-from botocore import BOTOCORE_ROOT
-from botocore.compat import HAS_GZIP, OrderedDict, json
-from botocore.exceptions import DataNotFoundError, UnknownServiceError
-from botocore.utils import deep_merge
-
-_JSON_OPEN_METHODS = {
-    '.json': open,
-}
-
-
-if HAS_GZIP:
-    from gzip import open as gzip_open
-
-    _JSON_OPEN_METHODS['.json.gz'] = gzip_open
-
-
-logger = logging.getLogger(__name__)
-
-
-def instance_cache(func):
-    """Cache the result of a method on a per instance basis.
-
-    This is not a general purpose caching decorator.  In order
-    for this to be used, it must be used on methods on an
-    instance, and that instance *must* provide a
-    ``self._cache`` dictionary.
-
-    """
-
-    def _wrapper(self, *args, **kwargs):
-        key = (func.__name__,) + args
-        for pair in sorted(kwargs.items()):
-            key += pair
-        if key in self._cache:
-            return self._cache[key]
-        data = func(self, *args, **kwargs)
-        self._cache[key] = data
-        return data
-
-    return _wrapper
-
-
-class JSONFileLoader:
-    """Loader JSON files.
-
-    This class can load the default format of models, which is a JSON file.
-
-    """
-
-    def exists(self, file_path):
-        """Checks if the file exists.
-
-        :type file_path: str
-        :param file_path: The full path to the file to load without
-            the '.json' extension.
-
-        :return: True if file path exists, False otherwise.
-
-        """
-        for ext in _JSON_OPEN_METHODS:
-            if os.path.isfile(file_path + ext):
-                return True
-        return False
-
-    def _load_file(self, full_path, open_method):
-        if not os.path.isfile(full_path):
-            return
-
-        # By default the file will be opened with locale encoding on Python 3.
-        # We specify "utf8" here to ensure the correct behavior.
-        with open_method(full_path, 'rb') as fp:
-            payload = fp.read().decode('utf-8')
-
-        logger.debug("Loading JSON file: %s", full_path)
-        return json.loads(payload, object_pairs_hook=OrderedDict)
-
-    def load_file(self, file_path):
-        """Attempt to load the file path.
-
-        :type file_path: str
-        :param file_path: The full path to the file to load without
-            the '.json' extension.
-
-        :return: The loaded data if it exists, otherwise None.
-
-        """
-        for (ext, open_method) in _JSON_OPEN_METHODS.items():
-            data = self._load_file(file_path + ext, open_method)
-            if data is not None:
-                return data
-        return None
-
-
-def create_loader(search_path_string=None):
-    """Create a Loader class.
-
-    This factory function creates a loader given a search string path.
-
-    :type search_string_path: str
-    :param search_string_path: The AWS_DATA_PATH value.  A string
-        of data path values separated by the ``os.path.pathsep`` value,
-        which is typically ``:`` on POSIX platforms and ``;`` on
-        windows.
-
-    :return: A ``Loader`` instance.
-
-    """
-    if search_path_string is None:
-        return Loader()
-    paths = []
-    extra_paths = search_path_string.split(os.pathsep)
-    for path in extra_paths:
-        path = os.path.expanduser(os.path.expandvars(path))
-        paths.append(path)
-    return Loader(extra_search_paths=paths)
-
-
-class Loader:
-    """Find and load data models.
-
-    This class will handle searching for and loading data models.
-
-    The main method used here is ``load_service_model``, which is a
-    convenience method over ``load_data`` and ``determine_latest_version``.
-
-    """
-
-    FILE_LOADER_CLASS = JSONFileLoader
-    # The included models in botocore/data/ that we ship with botocore.
-    BUILTIN_DATA_PATH = os.path.join(BOTOCORE_ROOT, 'data')
-    # For convenience we automatically add ~/.aws/models to the data path.
-    CUSTOMER_DATA_PATH = os.path.join(
-        os.path.expanduser('~'), '.aws', 'models'
-    )
-    BUILTIN_EXTRAS_TYPES = ['sdk']
-
-    def __init__(
-        self,
-        extra_search_paths=None,
-        file_loader=None,
-        cache=None,
-        include_default_search_paths=True,
-        include_default_extras=True,
-    ):
-        self._cache = {}
-        if file_loader is None:
-            file_loader = self.FILE_LOADER_CLASS()
-        self.file_loader = file_loader
-        if extra_search_paths is not None:
-            self._search_paths = extra_search_paths
-        else:
-            self._search_paths = []
-        if include_default_search_paths:
-            self._search_paths.extend(
-                [self.CUSTOMER_DATA_PATH, self.BUILTIN_DATA_PATH]
-            )
-
-        self._extras_types = []
-        if include_default_extras:
-            self._extras_types.extend(self.BUILTIN_EXTRAS_TYPES)
-
-        self._extras_processor = ExtrasProcessor()
-
-    @property
-    def search_paths(self):
-        return self._search_paths
-
-    @property
-    def extras_types(self):
-        return self._extras_types
-
-    @instance_cache
-    def list_available_services(self, type_name):
-        """List all known services.
-
-        This will traverse the search path and look for all known
-        services.
-
-        :type type_name: str
-        :param type_name: The type of the service (service-2,
-            paginators-1, waiters-2, etc).  This is needed because
-            the list of available services depends on the service
-            type.  For example, the latest API version available for
-            a resource-1.json file may not be the latest API version
-            available for a services-2.json file.
-
-        :return: A list of all services.  The list of services will
-            be sorted.
-
-        """
-        services = set()
-        for possible_path in self._potential_locations():
-            # Any directory in the search path is potentially a service.
-            # We'll collect any initial list of potential services,
-            # but we'll then need to further process these directories
-            # by searching for the corresponding type_name in each
-            # potential directory.
-            possible_services = [
-                d
-                for d in os.listdir(possible_path)
-                if os.path.isdir(os.path.join(possible_path, d))
-            ]
-            for service_name in possible_services:
-                full_dirname = os.path.join(possible_path, service_name)
-                api_versions = os.listdir(full_dirname)
-                for api_version in api_versions:
-                    full_load_path = os.path.join(
-                        full_dirname, api_version, type_name
-                    )
-                    if self.file_loader.exists(full_load_path):
-                        services.add(service_name)
-                        break
-        return sorted(services)
-
-    @instance_cache
-    def determine_latest_version(self, service_name, type_name):
-        """Find the latest API version available for a service.
-
-        :type service_name: str
-        :param service_name: The name of the service.
-
-        :type type_name: str
-        :param type_name: The type of the service (service-2,
-            paginators-1, waiters-2, etc).  This is needed because
-            the latest API version available can depend on the service
-            type.  For example, the latest API version available for
-            a resource-1.json file may not be the latest API version
-            available for a services-2.json file.
-
-        :rtype: str
-        :return: The latest API version.  If the service does not exist
-            or does not have any available API data, then a
-            ``DataNotFoundError`` exception will be raised.
-
-        """
-        return max(self.list_api_versions(service_name, type_name))
-
-    @instance_cache
-    def list_api_versions(self, service_name, type_name):
-        """List all API versions available for a particular service type
-
-        :type service_name: str
-        :param service_name: The name of the service
-
-        :type type_name: str
-        :param type_name: The type name for the service (i.e service-2,
-            paginators-1, etc.)
-
-        :rtype: list
-        :return: A list of API version strings in sorted order.
-
-        """
-        known_api_versions = set()
-        for possible_path in self._potential_locations(
-            service_name, must_exist=True, is_dir=True
-        ):
-            for dirname in os.listdir(possible_path):
-                full_path = os.path.join(possible_path, dirname, type_name)
-                # Only add to the known_api_versions if the directory
-                # contains a service-2, paginators-1, etc. file corresponding
-                # to the type_name passed in.
-                if self.file_loader.exists(full_path):
-                    known_api_versions.add(dirname)
-        if not known_api_versions:
-            raise DataNotFoundError(data_path=service_name)
-        return sorted(known_api_versions)
-
-    @instance_cache
-    def load_service_model(self, service_name, type_name, api_version=None):
-        """Load a botocore service model
-
-        This is the main method for loading botocore models (e.g. a service
-        model, pagination configs, waiter configs, etc.).
-
-        :type service_name: str
-        :param service_name: The name of the service (e.g ``ec2``, ``s3``).
-
-        :type type_name: str
-        :param type_name: The model type.  Valid types include, but are not
-            limited to: ``service-2``, ``paginators-1``, ``waiters-2``.
-
-        :type api_version: str
-        :param api_version: The API version to load.  If this is not
-            provided, then the latest API version will be used.
-
-        :type load_extras: bool
-        :param load_extras: Whether or not to load the tool extras which
-            contain additional data to be added to the model.
-
-        :raises: UnknownServiceError if there is no known service with
-            the provided service_name.
-
-        :raises: DataNotFoundError if no data could be found for the
-            service_name/type_name/api_version.
-
-        :return: The loaded data, as a python type (e.g. dict, list, etc).
-        """
-        # Wrapper around the load_data.  This will calculate the path
-        # to call load_data with.
-        known_services = self.list_available_services(type_name)
-        if service_name not in known_services:
-            raise UnknownServiceError(
-                service_name=service_name,
-                known_service_names=', '.join(sorted(known_services)),
-            )
-        if api_version is None:
-            api_version = self.determine_latest_version(
-                service_name, type_name
-            )
-        full_path = os.path.join(service_name, api_version, type_name)
-        model = self.load_data(full_path)
-
-        # Load in all the extras
-        extras_data = self._find_extras(service_name, type_name, api_version)
-        self._extras_processor.process(model, extras_data)
-
-        return model
-
-    def _find_extras(self, service_name, type_name, api_version):
-        """Creates an iterator over all the extras data."""
-        for extras_type in self.extras_types:
-            extras_name = f'{type_name}.{extras_type}-extras'
-            full_path = os.path.join(service_name, api_version, extras_name)
-
-            try:
-                yield self.load_data(full_path)
-            except DataNotFoundError:
-                pass
-
-    @instance_cache
-    def load_data_with_path(self, name):
-        """Same as ``load_data`` but returns file path as second return value.
-
-        :type name: str
-        :param name: The data path, i.e ``ec2/2015-03-01/service-2``.
-
-        :return: Tuple of the loaded data and the path to the data file
-            where the data was loaded from. If no data could be found then a
-            DataNotFoundError is raised.
-        """
-        for possible_path in self._potential_locations(name):
-            found = self.file_loader.load_file(possible_path)
-            if found is not None:
-                return found, possible_path
-
-        # We didn't find anything that matched on any path.
-        raise DataNotFoundError(data_path=name)
-
-    def load_data(self, name):
-        """Load data given a data path.
-
-        This is a low level method that will search through the various
-        search paths until it's able to load a value.  This is typically
-        only needed to load *non* model files (such as _endpoints and
-        _retry).  If you need to load model files, you should prefer
-        ``load_service_model``.  Use ``load_data_with_path`` to get the
-        data path of the data file as second return value.
-
-        :type name: str
-        :param name: The data path, i.e ``ec2/2015-03-01/service-2``.
-
-        :return: The loaded data. If no data could be found then
-            a DataNotFoundError is raised.
-        """
-        data, _ = self.load_data_with_path(name)
-        return data
-
-    def _potential_locations(self, name=None, must_exist=False, is_dir=False):
-        # Will give an iterator over the full path of potential locations
-        # according to the search path.
-        for path in self.search_paths:
-            if os.path.isdir(path):
-                full_path = path
-                if name is not None:
-                    full_path = os.path.join(path, name)
-                if not must_exist:
-                    yield full_path
-                else:
-                    if is_dir and os.path.isdir(full_path):
-                        yield full_path
-                    elif os.path.exists(full_path):
-                        yield full_path
-
-    def is_builtin_path(self, path):
-        """Whether a given path is within the package's data directory.
-
-        This method can be used together with load_data_with_path(name)
-        to determine if data has been loaded from a file bundled with the
-        package, as opposed to a file in a separate location.
-
-        :type path: str
-        :param path: The file path to check.
-
-        :return: Whether the given path is within the package's data directory.
-        """
-        path = os.path.expanduser(os.path.expandvars(path))
-        return path.startswith(self.BUILTIN_DATA_PATH)
-
-
-class ExtrasProcessor:
-    """Processes data from extras files into service models."""
-
-    def process(self, original_model, extra_models):
-        """Processes data from a list of loaded extras files into a model
-
-        :type original_model: dict
-        :param original_model: The service model to load all the extras into.
-
-        :type extra_models: iterable of dict
-        :param extra_models: A list of loaded extras models.
-        """
-        for extras in extra_models:
-            self._process(original_model, extras)
-
-    def _process(self, model, extra_model):
-        """Process a single extras model into a service model."""
-        if 'merge' in extra_model:
-            deep_merge(model, extra_model['merge'])

spaces/CVPR/LIVE/README.md

@@ -1,14 +0,0 @@
----
-title: LIVE
-emoji: 📊
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
-

spaces/CVPR/LIVE/thrust/thrust/detail/execute_with_allocator.h

@@ -1,148 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-#include <thrust/detail/execute_with_allocator_fwd.h>
-#include <thrust/pair.h>
-#include <thrust/detail/raw_pointer_cast.h>
-#include <thrust/detail/type_traits/pointer_traits.h>
-#include <thrust/detail/allocator/allocator_traits.h>
-#include <thrust/detail/integer_math.h>
-
-namespace thrust
-{
-namespace detail
-{
-
-template <
-    typename T
-  , typename Allocator
-  , template <typename> class BaseSystem
->
-__host__
-thrust::pair<T*, std::ptrdiff_t>
-get_temporary_buffer(
-    thrust::detail::execute_with_allocator<Allocator, BaseSystem>& system
-  , std::ptrdiff_t n
-    )
-{
-  typedef typename thrust::detail::remove_reference<Allocator>::type naked_allocator;
-  typedef typename thrust::detail::allocator_traits<naked_allocator> alloc_traits;
-  typedef typename alloc_traits::void_pointer                        void_pointer;
-  typedef typename alloc_traits::size_type                           size_type;
-  typedef typename alloc_traits::value_type                          value_type;
-
-  // How many elements of type value_type do we need to accommodate n elements
-  // of type T?
-  size_type num_elements = divide_ri(sizeof(T) * n, sizeof(value_type));
-
-  void_pointer ptr = alloc_traits::allocate(system.get_allocator(), num_elements);
-
-  // Return the pointer and the number of elements of type T allocated.
-  return thrust::make_pair(thrust::reinterpret_pointer_cast<T*>(ptr),n);
-}
-
-template <
-    typename Pointer
-  , typename Allocator
-  , template <typename> class BaseSystem
->
-__host__
-void
-return_temporary_buffer(
-    thrust::detail::execute_with_allocator<Allocator, BaseSystem>& system
-  , Pointer p
-  , std::ptrdiff_t n
-    )
-{
-  typedef typename thrust::detail::remove_reference<Allocator>::type naked_allocator;
-  typedef typename thrust::detail::allocator_traits<naked_allocator> alloc_traits;
-  typedef typename alloc_traits::pointer                             pointer;
-  typedef typename alloc_traits::size_type                           size_type;
-  typedef typename alloc_traits::value_type                          value_type;
-  typedef typename thrust::detail::pointer_traits<Pointer>::element_type T;
-
-  size_type num_elements = divide_ri(sizeof(T) * n, sizeof(value_type));
-
-  pointer to_ptr = thrust::reinterpret_pointer_cast<pointer>(p);
-  alloc_traits::deallocate(system.get_allocator(), to_ptr, num_elements);
-}
-
-#if THRUST_CPP_DIALECT >= 2011
-
-template <
-    typename T,
-    template <typename> class BaseSystem,
-    typename Allocator,
-    typename ...Dependencies
->
-__host__
-thrust::pair<T*, std::ptrdiff_t>
-get_temporary_buffer(
-    thrust::detail::execute_with_allocator_and_dependencies<Allocator, BaseSystem, Dependencies...>& system,
-    std::ptrdiff_t n
-    )
-{
-  typedef typename thrust::detail::remove_reference<Allocator>::type naked_allocator;
-  typedef typename thrust::detail::allocator_traits<naked_allocator> alloc_traits;
-  typedef typename alloc_traits::void_pointer                        void_pointer;
-  typedef typename alloc_traits::size_type                           size_type;
-  typedef typename alloc_traits::value_type                          value_type;
-
-  // How many elements of type value_type do we need to accommodate n elements
-  // of type T?
-  size_type num_elements = divide_ri(sizeof(T) * n, sizeof(value_type));
-
-  void_pointer ptr = alloc_traits::allocate(system.get_allocator(), num_elements);
-
-  // Return the pointer and the number of elements of type T allocated.
-  return thrust::make_pair(thrust::reinterpret_pointer_cast<T*>(ptr),n);
-}
-
-template <
-    typename Pointer,
-    template <typename> class BaseSystem,
-    typename Allocator,
-    typename ...Dependencies
->
-__host__
-void
-return_temporary_buffer(
-    thrust::detail::execute_with_allocator_and_dependencies<Allocator, BaseSystem, Dependencies...>& system,
-    Pointer p,
-    std::ptrdiff_t n
-    )
-{
-  typedef typename thrust::detail::remove_reference<Allocator>::type naked_allocator;
-  typedef typename thrust::detail::allocator_traits<naked_allocator> alloc_traits;
-  typedef typename alloc_traits::pointer                             pointer;
-  typedef typename alloc_traits::size_type                           size_type;
-  typedef typename alloc_traits::value_type                          value_type;
-  typedef typename thrust::detail::pointer_traits<Pointer>::element_type T;
-
-  size_type num_elements = divide_ri(sizeof(T) * n, sizeof(value_type));
-
-  pointer to_ptr = thrust::reinterpret_pointer_cast<pointer>(p);
-  alloc_traits::deallocate(system.get_allocator(), to_ptr, num_elements);
-}
-
-#endif
-
-}} // namespace thrust::detail
-

spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/binary_search.h

@@ -1,73 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/omp/detail/execution_policy.h>
-#include <thrust/system/detail/generic/binary_search.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace omp
-{
-namespace detail
-{
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
-ForwardIterator lower_bound(execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator begin,
-                            ForwardIterator end,
-                            const T& value, 
-                            StrictWeakOrdering comp)
-{
-    // omp prefers generic::lower_bound to cpp::lower_bound
-    return thrust::system::detail::generic::lower_bound(exec, begin, end, value, comp);
-}
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering, typename Backend>
-ForwardIterator upper_bound(execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator begin,
-                            ForwardIterator end,
-                            const T& value, 
-                            StrictWeakOrdering comp)
-{
-    // omp prefers generic::upper_bound to cpp::upper_bound
-    return thrust::system::detail::generic::upper_bound(exec, begin, end, value, comp);
-}
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
-bool binary_search(execution_policy<DerivedPolicy> &exec,
-                   ForwardIterator begin,
-                   ForwardIterator end,
-                   const T& value, 
-                   StrictWeakOrdering comp)
-{
-    // omp prefers generic::binary_search to cpp::binary_search
-    return thrust::system::detail::generic::binary_search(exec, begin, end, value, comp);
-}
-
-
-} // end detail
-} // end omp
-} // end system
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/system/omp/execution_policy.h

@@ -1,156 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-/*! \file thrust/system/omp/execution_policy.h
- *  \brief Execution policies for Thrust's OpenMP system.
- */
-
-#include <thrust/detail/config.h>
-
-// get the execution policies definitions first
-#include <thrust/system/omp/detail/execution_policy.h>
-
-// get the definition of par
-#include <thrust/system/omp/detail/par.h>
-
-// now get all the algorithm definitions
-
-#include <thrust/system/omp/detail/adjacent_difference.h>
-#include <thrust/system/omp/detail/assign_value.h>
-#include <thrust/system/omp/detail/binary_search.h>
-#include <thrust/system/omp/detail/copy.h>
-#include <thrust/system/omp/detail/copy_if.h>
-#include <thrust/system/omp/detail/count.h>
-#include <thrust/system/omp/detail/equal.h>
-#include <thrust/system/omp/detail/extrema.h>
-#include <thrust/system/omp/detail/fill.h>
-#include <thrust/system/omp/detail/find.h>
-#include <thrust/system/omp/detail/for_each.h>
-#include <thrust/system/omp/detail/gather.h>
-#include <thrust/system/omp/detail/generate.h>
-#include <thrust/system/omp/detail/get_value.h>
-#include <thrust/system/omp/detail/inner_product.h>
-#include <thrust/system/omp/detail/iter_swap.h>
-#include <thrust/system/omp/detail/logical.h>
-#include <thrust/system/omp/detail/malloc_and_free.h>
-#include <thrust/system/omp/detail/merge.h>
-#include <thrust/system/omp/detail/mismatch.h>
-#include <thrust/system/omp/detail/partition.h>
-#include <thrust/system/omp/detail/reduce.h>
-#include <thrust/system/omp/detail/reduce_by_key.h>
-#include <thrust/system/omp/detail/remove.h>
-#include <thrust/system/omp/detail/replace.h>
-#include <thrust/system/omp/detail/reverse.h>
-#include <thrust/system/omp/detail/scan.h>
-#include <thrust/system/omp/detail/scan_by_key.h>
-#include <thrust/system/omp/detail/scatter.h>
-#include <thrust/system/omp/detail/sequence.h>
-#include <thrust/system/omp/detail/set_operations.h>
-#include <thrust/system/omp/detail/sort.h>
-#include <thrust/system/omp/detail/swap_ranges.h>
-#include <thrust/system/omp/detail/tabulate.h>
-#include <thrust/system/omp/detail/transform.h>
-#include <thrust/system/omp/detail/transform_reduce.h>
-#include <thrust/system/omp/detail/transform_scan.h>
-#include <thrust/system/omp/detail/uninitialized_copy.h>
-#include <thrust/system/omp/detail/uninitialized_fill.h>
-#include <thrust/system/omp/detail/unique.h>
-#include <thrust/system/omp/detail/unique_by_key.h>
-
-
-// define these entities here for the purpose of Doxygenating them
-// they are actually defined elsewhere
-#if 0
-namespace thrust
-{
-namespace system
-{
-namespace omp
-{
-
-
-/*! \addtogroup execution_policies
- *  \{
- */
-
-
-/*! \p thrust::omp::execution_policy is the base class for all Thrust parallel execution
- *  policies which are derived from Thrust's OpenMP backend system.
- */
-template<typename DerivedPolicy>
-struct execution_policy : thrust::execution_policy<DerivedPolicy>
-{};
-
-
-/*! \p omp::tag is a type representing Thrust's standard C++ backend system in C++'s type system.
- *  Iterators "tagged" with a type which is convertible to \p omp::tag assert that they may be
- *  "dispatched" to algorithm implementations in the \p omp system.
- */
-struct tag : thrust::system::omp::execution_policy<tag> { unspecified };
-
-
-/*! \p thrust::omp::par is the parallel execution policy associated with Thrust's OpenMP
- *  backend system.
- *
- *  Instead of relying on implicit algorithm dispatch through iterator system tags, users may
- *  directly target Thrust's OpenMP backend system by providing \p thrust::omp::par as an algorithm
- *  parameter.
- *
- *  Explicit dispatch can be useful in avoiding the introduction of data copies into containers such
- *  as \p thrust::omp::vector.
- *
- *  The type of \p thrust::omp::par is implementation-defined.
- *
- *  The following code snippet demonstrates how to use \p thrust::omp::par to explicitly dispatch an
- *  invocation of \p thrust::for_each to the OpenMP backend system:
- *
- *  \code
- *  #include <thrust/for_each.h>
- *  #include <thrust/system/omp/execution_policy.h>
- *  #include <cstdio>
- *
- *  struct printf_functor
- *  {
- *    __host__ __device__
- *    void operator()(int x)
- *    {
- *      printf("%d\n", x);
- *    }
- *  };
- *  ...
- *  int vec[3];
- *  vec[0] = 0; vec[1] = 1; vec[2] = 2;
- *
- *  thrust::for_each(thrust::omp::par, vec.begin(), vec.end(), printf_functor());
- *
- *  // 0 1 2 is printed to standard output in some unspecified order
- *  \endcode
- */
-static const unspecified par;
-
-
-/*! \}
- */
-
-
-} // end cpp
-} // end system
-} // end thrust
-#endif
-
-

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/execution_policy.h

@@ -1,83 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/cpp/detail/execution_policy.h>
-#include <thrust/iterator/detail/any_system_tag.h>
-#include <thrust/detail/type_traits.h>
-
-namespace thrust
-{
-namespace system
-{
-// put the canonical tag in the same ns as the backend's entry points
-namespace tbb
-{
-namespace detail
-{
-
-// this awkward sequence of definitions arise
-// from the desire both for tag to derive
-// from execution_policy and for execution_policy
-// to convert to tag (when execution_policy is not
-// an ancestor of tag)
-
-// forward declaration of tag
-struct tag;
-
-// forward declaration of execution_policy
-template<typename> struct execution_policy;
-
-// specialize execution_policy for tag
-template<>
-  struct execution_policy<tag>
-    : thrust::system::cpp::detail::execution_policy<tag>
-{};
-
-// tag's definition comes before the
-// generic definition of execution_policy
-struct tag : execution_policy<tag> {};
-
-// allow conversion to tag when it is not a successor
-template<typename Derived>
-  struct execution_policy
-    : thrust::system::cpp::detail::execution_policy<Derived>
-{
-  typedef tag tag_type; 
-  operator tag() const { return tag(); }
-};
-
-} // end detail
-
-// alias execution_policy and tag here
-using thrust::system::tbb::detail::execution_policy;
-using thrust::system::tbb::detail::tag;
-
-} // end tbb
-} // end system
-
-// alias items at top-level
-namespace tbb
-{
-
-using thrust::system::tbb::execution_policy;
-using thrust::system::tbb::tag;
-
-} // end tbb
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/type_traits/is_contiguous_iterator.h

@@ -1,185 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file is_contiguous_iterator.h
- *  \brief An extensible type trait for determining if an iterator satisifies
- *         the <a href="https://en.cppreference.com/w/cpp/named_req/ContiguousIterator">ContiguousIterator</a>
- *         requirements (e.g. is pointer-like).
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/detail/type_traits/pointer_traits.h>
-
-#include <iterator>
-
-#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC && _MSC_VER < 1916 // MSVC 2017 version 15.9
-  #include <vector>
-  #include <string>
-  #include <array>
-
-  #if THRUST_CPP_DIALECT >= 2017
-    #include <string_view>
-  #endif
-#endif
-
-namespace thrust
-{
-
-namespace detail
-{
-
-template <typename Iterator>
-struct is_contiguous_iterator_impl;
-
-} // namespace detail
-
-/// Unary metafunction returns \c true_type if \c Iterator satisfies
-/// <a href="https://en.cppreference.com/w/cpp/named_req/ContiguousIterator">ContiguousIterator</a>,
-/// e.g. it points to elements that are contiguous in memory, and \c false_type
-/// otherwise.
-template <typename Iterator>
-#if THRUST_CPP_DIALECT >= 2011
-using is_contiguous_iterator =
-#else
-struct is_contiguous_iterator :
-#endif
-  detail::is_contiguous_iterator_impl<Iterator>
-#if THRUST_CPP_DIALECT < 2011
-{}
-#endif
-;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// <code>constexpr bool</code> that is \c true if \c Iterator satisfies
-/// <a href="https://en.cppreference.com/w/cpp/named_req/ContiguousIterator">ContiguousIterator</a>,
-/// e.g. it points to elements that are contiguous in memory, and \c false
-/// otherwise.
-template <typename Iterator>
-constexpr bool is_contiguous_iterator_v = is_contiguous_iterator<Iterator>::value;
-#endif
-
-/// Customization point that can be customized to indicate that an iterator
-/// type \c Iterator satisfies
-/// <a href="https://en.cppreference.com/w/cpp/named_req/ContiguousIterator">ContiguousIterator</a>,
-/// e.g. it points to elements that are contiguous in memory.
-template <typename Iterator>
-struct proclaim_contiguous_iterator : false_type {};
-
-/// Declares that the iterator \c Iterator is
-/// <a href="https://en.cppreference.com/w/cpp/named_req/ContiguousIterator">ContiguousIterator</a>
-/// by specializing `thrust::proclaim_contiguous_iterator`.
-#define THRUST_PROCLAIM_CONTIGUOUS_ITERATOR(Iterator)                         \
-  namespace thrust {                                                          \
-  template <>                                                                 \
-  struct proclaim_contiguous_iterator<Iterator> : ::thrust::true_type {};     \
-  } /* end namespace thrust */                                                \
-  /**/
-
-///////////////////////////////////////////////////////////////////////////////
-
-namespace detail
-{
-
-template <typename Iterator>
-struct is_libcxx_wrap_iter : false_type {};
-
-#if defined(_LIBCPP_VERSION)
-template <typename Iterator>
-struct is_libcxx_wrap_iter<
-  _VSTD::__wrap_iter<Iterator>
-> : true_type {};
-#endif
-
-template <typename Iterator>
-struct is_libstdcxx_normal_iterator : false_type {};
-
-#if defined(__GLIBCXX__)
-template <typename Iterator, typename Container>
-struct is_libstdcxx_normal_iterator<
-  ::__gnu_cxx::__normal_iterator<Iterator, Container>
-> : true_type {};
-#endif
-
-#if   _MSC_VER >= 1916 // MSVC 2017 version 15.9.
-template <typename Iterator>
-struct is_msvc_contiguous_iterator
-  : is_pointer<::std::_Unwrapped_t<Iterator> > {};
-#elif _MSC_VER >= 1700 // MSVC 2012.
-template <typename Iterator>
-struct is_msvc_contiguous_iterator : false_type {};
-
-template <typename Vector>
-struct is_msvc_contiguous_iterator<
-  ::std::_Vector_const_iterator<Vector>
-> : true_type {};
-
-template <typename Vector>
-struct is_msvc_contiguous_iterator<
-  ::std::_Vector_iterator<Vector>
-> : true_type {};
-
-template <typename String>
-struct is_msvc_contiguous_iterator<
-  ::std::_String_const_iterator<String>
-> : true_type {};
-
-template <typename String>
-struct is_msvc_contiguous_iterator<
-  ::std::_String_iterator<String>
-> : true_type {};
-
-template <typename T, std::size_t N>
-struct is_msvc_contiguous_iterator<
-  ::std::_Array_const_iterator<T, N>
-> : true_type {};
-
-template <typename T, std::size_t N>
-struct is_msvc_contiguous_iterator<
-  ::std::_Array_iterator<T, N>
-> : true_type {};
-
-#if THRUST_CPP_DIALECT >= 2017
-template <typename Traits>
-struct is_msvc_contiguous_iterator<
-  ::std::_String_view_iterator<Traits>
-> : true_type {};
-#endif
-#else
-template <typename Iterator>
-struct is_msvc_contiguous_iterator : false_type {};
-#endif
-
-
-template <typename Iterator>
-struct is_contiguous_iterator_impl
-  : integral_constant<
-      bool
-    ,    is_pointer<Iterator>::value
-      || is_thrust_pointer<Iterator>::value
-      || is_libcxx_wrap_iter<Iterator>::value
-      || is_libstdcxx_normal_iterator<Iterator>::value
-      || is_msvc_contiguous_iterator<Iterator>::value
-      || proclaim_contiguous_iterator<Iterator>::value
-    >
-{};
-
-} // namespace detail
-
-} // end namespace thrust
-

spaces/CVPR/Text2Human/Text2Human/models/losses/vqgan_loss.py

@@ -1,114 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-def calculate_adaptive_weight(recon_loss, g_loss, last_layer, disc_weight_max):
-    recon_grads = torch.autograd.grad(
-        recon_loss, last_layer, retain_graph=True)[0]
-    g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-
-    d_weight = torch.norm(recon_grads) / (torch.norm(g_grads) + 1e-4)
-    d_weight = torch.clamp(d_weight, 0.0, disc_weight_max).detach()
-    return d_weight
-
-
-def adopt_weight(weight, global_step, threshold=0, value=0.):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-@torch.jit.script
-def hinge_d_loss(logits_real, logits_fake):
-    loss_real = torch.mean(F.relu(1. - logits_real))
-    loss_fake = torch.mean(F.relu(1. + logits_fake))
-    d_loss = 0.5 * (loss_real + loss_fake)
-    return d_loss
-
-
-def DiffAugment(x, policy='', channels_first=True):
-    if policy:
-        if not channels_first:
-            x = x.permute(0, 3, 1, 2)
-        for p in policy.split(','):
-            for f in AUGMENT_FNS[p]:
-                x = f(x)
-        if not channels_first:
-            x = x.permute(0, 2, 3, 1)
-        x = x.contiguous()
-    return x
-
-
-def rand_brightness(x):
-    x = x + (
-        torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
-    return x
-
-
-def rand_saturation(x):
-    x_mean = x.mean(dim=1, keepdim=True)
-    x = (x - x_mean) * (torch.rand(
-        x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
-    return x
-
-
-def rand_contrast(x):
-    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
-    x = (x - x_mean) * (torch.rand(
-        x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
-    return x
-
-
-def rand_translation(x, ratio=0.125):
-    shift_x, shift_y = int(x.size(2) * ratio +
-                           0.5), int(x.size(3) * ratio + 0.5)
-    translation_x = torch.randint(
-        -shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
-    translation_y = torch.randint(
-        -shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
-    grid_batch, grid_x, grid_y = torch.meshgrid(
-        torch.arange(x.size(0), dtype=torch.long, device=x.device),
-        torch.arange(x.size(2), dtype=torch.long, device=x.device),
-        torch.arange(x.size(3), dtype=torch.long, device=x.device),
-    )
-    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
-    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
-    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
-    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x,
-                                               grid_y].permute(0, 3, 1, 2)
-    return x
-
-
-def rand_cutout(x, ratio=0.5):
-    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
-    offset_x = torch.randint(
-        0,
-        x.size(2) + (1 - cutout_size[0] % 2),
-        size=[x.size(0), 1, 1],
-        device=x.device)
-    offset_y = torch.randint(
-        0,
-        x.size(3) + (1 - cutout_size[1] % 2),
-        size=[x.size(0), 1, 1],
-        device=x.device)
-    grid_batch, grid_x, grid_y = torch.meshgrid(
-        torch.arange(x.size(0), dtype=torch.long, device=x.device),
-        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
-        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
-    )
-    grid_x = torch.clamp(
-        grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
-    grid_y = torch.clamp(
-        grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
-    mask = torch.ones(
-        x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
-    mask[grid_batch, grid_x, grid_y] = 0
-    x = x * mask.unsqueeze(1)
-    return x
-
-
-AUGMENT_FNS = {
-    'color': [rand_brightness, rand_saturation, rand_contrast],
-    'translation': [rand_translation],
-    'cutout': [rand_cutout],
-}

spaces/CartelFi/README/README.md

@@ -1,10 +0,0 @@
----
-title: README
-emoji: 🐨
-colorFrom: indigo
-colorTo: blue
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card 🔥

spaces/Clebersla/RVC_V2_Huggingface_Version/rmvpe.py

@@ -1,432 +0,0 @@
-import sys, torch, numpy as np, traceback, pdb
-import torch.nn as nn
-from time import time as ttime
-import torch.nn.functional as F
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # 帧长#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # 帧长，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # 帧长
-        devided = product_sum / weight_sum  # 帧长
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # 帧长
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)

spaces/CofAI/chat/client/css/hljs.css

@@ -1,68 +0,0 @@
-.hljs {
-	color: #e9e9f4;
-	background: #28293629;
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-	font-size: 15px;
-	word-wrap: break-word;
-	white-space: pre-wrap;
-}
-
-/* style for hljs copy */
-.hljs-copy-wrapper {
-	position: relative;
-	overflow: hidden;
-}
-
-.hljs-copy-wrapper:hover .hljs-copy-button,
-.hljs-copy-button:focus {
-	transform: translateX(0);
-}
-
-.hljs-copy-button {
-	position: absolute;
-	transform: translateX(calc(100% + 1.125em));
-	top: 1em;
-	right: 1em;
-	width: 2rem;
-	height: 2rem;
-	text-indent: -9999px;
-	color: #fff;
-	border-radius: 0.25rem;
-	border: 1px solid #ffffff22;
-	background-color: #2d2b57;
-	background-image: url('data:image/svg+xml;utf-8,<svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M6 5C5.73478 5 5.48043 5.10536 5.29289 5.29289C5.10536 5.48043 5 5.73478 5 6V20C5 20.2652 5.10536 20.5196 5.29289 20.7071C5.48043 20.8946 5.73478 21 6 21H18C18.2652 21 18.5196 20.8946 18.7071 20.7071C18.8946 20.5196 19 20.2652 19 20V6C19 5.73478 18.8946 5.48043 18.7071 5.29289C18.5196 5.10536 18.2652 5 18 5H16C15.4477 5 15 4.55228 15 4C15 3.44772 15.4477 3 16 3H18C18.7956 3 19.5587 3.31607 20.1213 3.87868C20.6839 4.44129 21 5.20435 21 6V20C21 20.7957 20.6839 21.5587 20.1213 22.1213C19.5587 22.6839 18.7957 23 18 23H6C5.20435 23 4.44129 22.6839 3.87868 22.1213C3.31607 21.5587 3 20.7957 3 20V6C3 5.20435 3.31607 4.44129 3.87868 3.87868C4.44129 3.31607 5.20435 3 6 3H8C8.55228 3 9 3.44772 9 4C9 4.55228 8.55228 5 8 5H6Z" fill="white"/><path fill-rule="evenodd" clip-rule="evenodd" d="M7 3C7 1.89543 7.89543 1 9 1H15C16.1046 1 17 1.89543 17 3V5C17 6.10457 16.1046 7 15 7H9C7.89543 7 7 6.10457 7 5V3ZM15 3H9V5H15V3Z" fill="white"/></svg>');
-	background-repeat: no-repeat;
-	background-position: center;
-	transition: background-color 200ms ease, transform 200ms ease-out;
-}
-
-.hljs-copy-button:hover {
-	border-color: #ffffff44;
-}
-
-.hljs-copy-button:active {
-	border-color: #ffffff66;
-}
-
-.hljs-copy-button[data-copied="true"] {
-	text-indent: 0;
-	width: auto;
-	background-image: none;
-}
-
-.hljs-copy-alert {
-	clip: rect(0 0 0 0);
-	clip-path: inset(50%);
-	height: 1px;
-	overflow: hidden;
-	position: absolute;
-	white-space: nowrap;
-	width: 1px;
-}
-
-@media (prefers-reduced-motion) {
-	.hljs-copy-button {
-		transition: none;
-	}
-}

spaces/CofAI/chat/client/css/label.css

@@ -1,16 +0,0 @@
-label {
-	cursor: pointer;
-	text-indent: -9999px;
-	width: 50px;
-	height: 30px;
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-	display: block;
-	border-radius: 100px;
-	position: relative;
-	overflow: hidden;
-	transition: 0.33s;
-}

spaces/Cran-May/ygVI/model.py

@@ -1,34 +0,0 @@
-
-from typing import Iterator
-
-
-
-model_id = 'xuqinyang/baichuan-13b-chat-ggml-int4'
-
-from huggingface_hub import snapshot_download,hf_hub_download
-#旧
-#snapshot_download(model_id, local_dir="./",revision="7f71a8abefa7b2eede3f74ce0564abe5fbe6874a")
-snapshot_download(model_id, local_dir="./",revision="b2414a0ceee68fe09c99ace44446cfc9a1c52b08")
-hf_hub_download(repo_id="baichuan-inc/Baichuan-13B-Chat",local_dir="./", filename="tokenizer.model")
-from llama_cpp import Llama
-llm = Llama(model_path="./ggml-model-q4_0.bin", n_ctx=4096,seed=-1)
-
-def run(message: str,
-        chat_history: list[tuple[str, str]],
-        system_prompt: str,
-        max_new_tokens: int = 1024,
-        temperature: float = 0.3,
-        top_p: float = 0.85,
-        top_k: int = 5) -> Iterator[str]:
-    history = []
-    print(chat_history)
-    result=""
-    for i in chat_history:
-        history.append({"role": "user", "content": i[0]})
-        history.append({"role": "assistant", "content": i[1]})
-    print(history)
-    history.append({"role": "user", "content": message})
-    for response in llm.create_chat_completion(history,stop=["</s>"],stream=True,max_tokens=-1,temperature=temperature,top_k=top_k,top_p=top_p,repeat_penalty=1.1):
-        if "content" in response["choices"][0]["delta"]:
-            result = result + response["choices"][0]["delta"]["content"]
-            yield result

spaces/Cropinky/esrgan/realesrgan/data/__init__.py

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import dataset modules for registry
-# scan all the files that end with '_dataset.py' under the data folder
-data_folder = osp.dirname(osp.abspath(__file__))
-dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
-# import all the dataset modules
-_dataset_modules = [importlib.import_module(f'realesrgan.data.{file_name}') for file_name in dataset_filenames]

spaces/Cvandi/remake/realesrgan/models/__init__.py

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]