Update parquet files (step 106 of 121)

spaces/17TheWord/vits-models/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/1acneusushi/gradio-2dmoleculeeditor/data/Corel VideoStudio Ultimate 23.0.1.404 Crack How to Download and Install the Latest Version.md

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- <h3>How to download and install Corel VideoStudio Ultimate 2023?</h3>
- <p>To download and install Corel VideoStudio Ultimate 2023, you need to follow these steps:</p>
- <ol>
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-<li>Disable your antivirus or Windows Defender.</li>
-<li>Extract the file with the latest version of Winrar.</li>
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-<li>Install the second folder, Update.</li>
-<li>Copy the PASMUTILITY.dll and vstudio.exe crack files from the Crack folder.</li>
-<li>Paste and replace these files to C:\\Program Files\\Corel\\Corel VideoStudio 2023\\.</li>
-<li>If a window appears to download, just close it.</li>
-<li>If the effects content does not appear, install Contents64.msi from the Setup\\Common folder.</li>
-</ol>
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- <h3>How to import and organize media files?</h3>
- <p>To import and organize media files, you need to do the following:</p>
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-<li>The imported media files will appear in the Library panel. You can organize them into folders, tags, or collections by right-clicking on them and choosing an option.</li>
-</ol>
- <h3>How to edit videos on the timeline?</h3>
- <p>To edit videos on the timeline, you need to do the following:</p>
- <ol>
-<li>Drag and drop the media files from the Library panel to the timeline. You can also use the Insert or Overlay buttons on the toolbar.</li>
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-<p>Getting a registration code for Express Scribe is not difficult, but it requires some steps. Here is an overview of what you need to do:</p>
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-<li>Purchase Express Scribe online</li>
-<li>Activate your serial number online</li>
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-How to uninstall Express Scribe from your computer<br />
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- <h3>Purchase Express Scribe Online</h3>
-<p>The first step is to purchase Express Scribe online from the official website of NCH Software, the developer of the program. You can choose from different license classes depending on your needs and budget. For example, you can buy a single user license for $39.95 or a site license for $299.</p>
-<p>After you complete your payment, you will receive an email with your 13-digit serial number. This is your license serial number that you need to activate online.</p>
- <h3>Activate Your Serial Number Online</h3>
-<p>The second step is to activate your serial number online at https://secure.nch.com.au/activate by filling out the form with your name, email address, serial number, and product name (Express Scribe). After you submit the form, you will receive another email with your registration code.</p>
- <h3>Enter Your Registration Code into the Software</h3>
-<p>The third step is to enter your registration code into the software and convert the trial version into the professional version. To do this, open Express Scribe and select Register Express Scribe from the File menu. Then copy and paste your registration code into the text box and click Register.</p>
-<p>If you do not receive an error message, the registration code has been accepted and you can now use all the features of Express Scribe without any limitations.</p>
- <h2>How to Use Express Scribe Effectively</h2>
-<p>Now that you have a registration code for Express Scribe, you might be wondering how to use it effectively for transcription. Here are some tips and tricks that can help you improve your productivity and accuracy:</p>
- <h3>Adjust Playback Settings</h3>
-<p>One of the advantages of Express Scribe is that it allows you to adjust playback settings such as speed, volume, and tone according to your preferences and needs. You can access these settings by clicking on Options > Playback in the toolbar.</p>
-<p>For example, you can increase or decrease the speed of playback using the slider or by pressing F9 or F10 on your keyboard. You can also adjust the volume using the slider or by pressing F7 or F8 on your keyboard. You can also change the tone of playback using the slider or by pressing F11 or F12 on your keyboard.</p>
-<p>These settings can help you hear the audio more clearly and transcribe more efficiently.</p>
- <h3>Use Hotkeys and Pedals</h3>
-<p>Another way to use Express Scribe effectively is to use hotkeys and pedals to control playback and transcription without taking your hands off the keyboard or foot off the pedal. Hotkeys are keyboard shortcuts that allow you to perform common actions such as play, pause, rewind, fast forward, etc.</p>
-<p>You can view and customize these hotkeys by clicking on Options > Hot Keys in the toolbar. For example, you can set Ctrl + P as play/pause or Ctrl + R as rewind.</p>
-<p>Pedals are foot switches that allow you to control playback using your feet while typing with your hands. Pedals are especially useful for professional transcribers who need to transcribe long hours without interruption.</p>
-<p>You can connect pedals to your computer using a USB port or an adapter cable. You can also configure pedals by clicking on Options > Controller Setup Wizard in the toolbar.</p>
- <h3>Sync with Word Processors and Voice Recognition Software</h3>
-<p>A third way to use Express Scribe effectively is to sync it with word processors and voice recognition software that can help you improve accuracy and efficiency of transcription.</p>
-<p>You can sync Express Scribe with word processors such as Microsoft Word or Google Docs by clicking on Options > Transcription Options in the toolbar. This will allow you to type directly into these programs while listening to audio files in Express Scribe.</p>
-<p>You can also sync Express Scribe with voice recognition software such as Dragon NaturallySpeaking or Windows Speech Recognition by clicking on Options > Speech Recognition in the toolbar. This will allow you to dictate instead of type while listening to audio files in Express Scribe.</p>
- <h2>How to Troubleshoot Common Issues with Express Scribe</h2>
-<p>Sometimes, you might encounter some issues with Express Scribe that can affect your transcription experience. Here are some solutions for common issues such as lost registration codes, outdated versions, and compatibility problems:</p>
- <h3>Recover Your Lost Registration Code</h3>
-<p>If you lose your registration code for Express Scribe, don't panic. You can recover it using one of these methods:</p>
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-<li>Use the automated utility at https://www.nch.com.au/support/reg.html?display=keyrecovery#EEEli with the email address used to purchase the license.</li>
-<li>Contact customer support at https://www.nch.com.au/support/supportcontact.html?software=ExpressScribe&support with proof of purchase such as order number, invoice or receipt.</li>
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-<p>Once you have your registration code, you can enter it into the software as explained above.</p>
- <h3>Upgrade Your Outdated Version</h3>
-<p>If you have an outdated version of Express Scribe, you might experience some issues such as bugs, errors, or incompatibility with newer audio formats. To avoid these problems, you should upgrade your version of Express Scribe at discounted pricing.</p>
-<p>To do this, you need to visit https://www.nch.com.au/upgrade/index.html and enter your existing registration code. You will then be able to purchase the latest version of Express Scribe at a reduced price. You will receive a new registration code by email that you can use to activate the software.</p>
- <h3>Check Your Compatibility Requirements</h3>
-<p>If you have trouble playing some audio files or connecting some devices with Express Scribe, you might need to check your compatibility requirements for the software. Express Scribe has different requirements for different operating systems, audio formats, and hardware.</p>
-<p>You can find the compatibility requirements for Express Scribe at https://www.nch.com.au/scribe/kb/656.html. This page lists the supported audio and video file formats, as well as the recommended operating systems and hardware specifications for Express Scribe.</p>
-<p>If your system does not meet these requirements, you might need to update your software, drivers, or hardware to ensure optimal performance of Express Scribe.</p>
- <h2>Conclusion</h2>
-<p>Express Scribe is a powerful and user-friendly transcription software that can help you transcribe audio files with ease and accuracy. However, to use it fully, you need a registration code that you can obtain by purchasing the software online and activating your serial number online.</p>
-<p>Once you have your registration code, you can enter it into the software and enjoy all the features of Express Scribe. You can also use some tips and tricks to use Express Scribe effectively, such as adjusting playback settings, using hotkeys and pedals, and syncing with word processors and voice recognition software.</p>
-<p>If you encounter any issues with Express Scribe, such as lost registration codes, outdated versions, or compatibility problems, you can troubleshoot them by using the automated utility, contacting customer support, upgrading your version, or checking your compatibility requirements.</p>
-<p>We hope this article has helped you understand what Express Scribe registration code is and how to get it and use it. If you have any questions or feedback, please feel free to contact us. We would love to hear from you.</p>
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-<p>Here are some frequently asked questions related to Express Scribe registration code:</p>
- <h3>Q: How long does it take to receive my registration code after purchasing Express Scribe?</h3>
-<p>A: It usually takes a few minutes to receive your registration code by email after purchasing Express Scribe online. However, sometimes it might take longer due to network delays or spam filters. If you do not receive your registration code within 24 hours, please contact customer support at https://www.nch.com.au/support/supportcontact.html?software=ExpressScribe&support.</p>
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-<p>A: No, you cannot use your registration code on multiple computers. Each registration code is valid for one installation only. If you want to use Express Scribe on more than one computer, you need to purchase additional licenses or a site license.</p>
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- <h3>Os benefícios de jogar em uma tela grande e com um teclado e mouse</h3>
- <p>Jogar American Truck Simulator no PC permite que você aproveite melhor os gráficos impressionantes do jogo. Você pode jogar em uma tela grande, com alta resolução e qualidade de imagem. Você também pode ajustar as configurações gráficas de acordo com as suas preferências e capacidade do seu PC. Você pode desfrutar de uma maior imersão e realismo ao dirigir um caminhão pelos Estados Unidos.</p>
- <p>Além disso, jogar no PC permite que você tenha um maior controle e precisão sobre o seu caminhão. Você pode usar um teclado e um mouse, que são dispositivos mais confortáveis e intuitivos para jogar esse tipo de jogo. Você pode configurar os comandos de acordo com a sua preferência e ter acesso a mais funções e atalhos. Voc�� também pode usar outros periféricos, como volantes, pedais, joysticks, etc.</p>
- <h3>As vantagens de ter acesso a mods e atualizações</h3>
- <p>Outra grande vantagem de jogar American Truck Simulator no PC é que você pode ter acesso a mods e atualizações. Os mods são modificações feitas por outros jogadores ou desenvolvedores que adicionam novos conteúdos ou alteram aspectos do jogo original. Você pode encontrar mods de todos os tipos, como novos caminhões, cargas, mapas, sons, gráficos, etc. Os mods podem tornar o jogo mais divertido, variado e personalizado.</p>
- <p>Para instalar os mods, você pode usar o Steam Workshop, uma ferramenta integrada à plataforma Steam que permite que você baixe e gerencie os mods facilmente. Você também pode usar sites externos que oferecem mods gratuitos ou pagos. Mas cuidado: alguns mods podem não ser compatíveis com o jogo ou com outros mods, causando problemas de desempenho ou estabilidade. Por isso, sempre verifique a origem, a qualidade e a atualização dos mods antes de instalá-los.</p>
- <p>Além dos mods, você também pode ter acesso a atualizações oficiais do jogo. A SCS Software está sempre lançando novas atualizações que corrigem bugs, melhoram o desempenho e adicionam novos conteúdos. Você pode baixar as atualizações automaticamente pelo Steam ou manualmente pelo site oficial do jogo. As atualizações garantem que você tenha sempre a melhor versão do jogo disponível.</p>
- <h2>Conclusão</h2>
- <p>American Truck Simulator é um jogo incrível que simula com perfeição a experiência de dirigir um caminhão pelos Estados Unidos. O jogo conta com gráficos impressionantes, física realista, cenários variados, cargas diversificadas, sistema de progressão, entre outros recursos. O jogo é ideal para quem gosta de caminhões, de viagens e de desafios.</p>
- <p>Para jogar American Truck Simulator no PC, você precisa comprar o jogo na plataforma Steam e baixá-lo e instalá-lo no seu computador. Você também precisa verificar se o seu PC atende aos requisitos mínimos ou recomendados do sistema. Jogar no PC oferece muitas vantagens, como uma melhor qualidade gráfica, um maior controle e precisão, e um acesso a mods e atualizações.</p>
- <p>Esperamos que este artigo tenha sido útil para você. Se você gostou do jogo American Truck Simulator, não deixe de conferir também o Euro Truck Simulator 2, outro simulador de caminhões da mesma empresa que se passa na Europa. E se você quiser saber mais sobre jogos de simulação, fique ligado no nosso site. Até a próxima!</p>
- <h2>FAQs</h2>
- <h4>Quanto custa o jogo American Truck Simulator?</h4>
-<p>O jogo American Truck Simulator custa R$ 36,99 na plataforma Steam. Esse preço inclui o jogo base e alguns conteúdos extras. Você também pode comprar pacotes adicionais que incluem novos estados, caminhões, cargas, etc. Esses pacotes variam de preço entre R$ 5,99 e R$ 18,99.</p>
- <h4>O jogo tem suporte para multiplayer?</h4>
-<p>O jogo American Truck Simulator não tem um modo multiplayer oficial integrado ao jogo. No entanto, você pode usar um mod chamado TruckersMP (https://truckersmp.com/) que permite que você jogue online com outros jogadores em servidores dedicados. Esse mod é gratuito e fácil de instalar e usar. Você pode participar de eventos, convoys, empresas virtuais, etc.</p>
- <h4>O jogo tem suporte para VR?</h4>
-<p>O jogo American Truck Simulator tem suporte para VR (realidade virtual) em fase experimental. Você precisa ter um dispositivo VR compatível com o SteamVR (como Oculus Rift or HTC Vive) e ativar a opção de VR no menu do jogo. Você também precisa ajustar as configurações gráficas e de controle para ter uma melhor experiência. Jogar em VR pode aumentar a imersão e o realismo do jogo, mas também pode causar enjoos ou desconfortos em alguns jogadores.</p>
- <h4>Quais são os estados disponíveis no jogo?</h4>
-<p>O jogo American Truck Simulator atualmente conta com 12 estados disponíveis para explorar. São eles: Califórnia, Nevada, Arizona, Novo México, Oregon, Washington, Utah, Idaho, Colorado, Wyoming, Texas e Montana. Cada estado tem suas próprias estradas, cidades, pontos turísticos, empresas, etc. A SCS Software está trabalhando para adicionar mais estados no futuro, como Dakota do Norte, Dakota do Sul, Nebraska, Kansas, Oklahoma, etc.</p>
- <h4>O jogo tem suporte para volantes e pedais?</h4>
-<p>O jogo American Truck Simulator tem suporte para volantes e pedais de diversas marcas e modelos. Você pode usar esses dispositivos para ter uma maior sensação de dirigir um caminhão de verdade. Você pode configurar os botões e os eixos do seu volante e pedais no menu do jogo. Você também pode usar outros acessórios, como câmbio manual, painel de instrumentos, etc.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/APKPure Offers Car Parking Multiplayer a Free and Challenging Driving Game.md

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-<h1>Car Parking Multiplayer: A Review of the Open-World Parking Simulator</h1>
- <p>Do you love driving and parking cars? Do you want to experience a realistic open-world game with thousands of other players? If yes, then you should check out Car Parking Multiplayer, a game that offers more than just parking. In this article, we will review the features, gameplay, tips, and alternatives of Car Parking Multiplayer, and show you how to download and play it on your PC.</p>
- <h2>What is Car Parking Multiplayer?</h2>
- <p>Car Parking Multiplayer is a game that was developed by olzhass and released in 2017. It is available for Android and iOS devices, as well as PC via emulators. As the name suggests, Car Parking Multiplayer is a game that focuses on parking cars in various scenarios and locations. However, it also offers many other features that make it more than just a parking simulator.</p>
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- <h3>Features of Car Parking Multiplayer</h3>
- <p>Car Parking Multiplayer has many features that make it an enjoyable and immersive game. Here are some of them:</p>
- <h4>Multiplayer Open World Mode</h4>
- <p>This is the main mode of the game, where you can join thousands of other players in a free open world with real gas stations and car services. You can also compete against other players in multiplayer racing, exchange cars with other players, chat with them using voice chat, and even play as a police officer or a taxi driver. You can also walk around freely and explore the buildings with interior.</p>
- <h4>Car Customization and Tuning</h4>
- <p>You can choose from over 100 cars with real interior and customize them according to your preference. You can adjust the suspension, wheel angle, engine, turbo, gearbox, exhaust, and more. You can also change the appearance of your car with dynamic vinyls, car body parts, and plates.</p>
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- <h4>High-Quality Open World</h4>
- <p>The game has high-quality graphics and sound effects that create a realistic environment. The game has seven different locations, such as cities, race tracks, deserts, off-roads, islands, and mountains. Each location has its own challenges and attractions.</p>
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- <p>The game has 82 real-life parking and driving challenges that test your skills and knowledge. You can also drive different vehicles, such as tow trucks, pickups, trucks, sports cars, and classic cars. The game also has daily tasks and rewards that give you coins and presents.</p>
- <h2>How to Download and Play Car Parking Multiplayer on PC?</h2>
- <p>If you want to enjoy Car Parking Multiplayer on a bigger screen and with better controls, you can play it on your PC using an emulator. An emulator is a software that allows you to run Android or iOS apps on your PC.</p>
- <h3>Benefits of Playing Car Parking Multiplayer on PC</h3>
- <p>There are many benefits of playing Car Parking Multiplayer on PC, such as:</p>
-<ul>
-<li>You can have a better view of the open world and the details of the cars.</li>
-<li>You can use your keyboard and mouse or a controller to control your car more easily.</li>
-<li>You can have faster loading times and smoother performance.</li>
-<li>You can avoid draining your phone's battery or overheating it.</li>
-<li>You can record your gameplay <h3>Steps to Download and Install Car Parking Multiplayer on PC</h3>
- <p>There are many emulators that you can use to play Car Parking Multiplayer on PC, but we recommend using LDPlayer, which is one of the best Android emulators for gaming. Here are the steps to download and install Car Parking Multiplayer on PC using LDPlayer:</p>
-<ol>
-<li>Download and install LDPlayer on your PC from its official website .</li>
-<li>Launch LDPlayer and sign in with your Google account to access the Google Play Store.</li>
-<li>Search for Car Parking Multiplayer in the search bar and click to install it from the search results.</li>
-<li>Once the installation is complete, you can click the Car Parking Multiplayer icon on the home screen to start playing.</li>
-</ol>
-<p>You can also download the APK file of Car Parking Multiplayer from other sources and drag and drop it to LDPlayer to install it. However, make sure that the APK file is safe and compatible with LDPlayer.</p>
- <h2>Tips and Tricks for Car Parking Multiplayer</h2>
- <p>Car Parking Multiplayer is a fun and challenging game that requires skill and strategy. Here are some tips and tricks that can help you improve your gameplay and enjoy the game more:</p>
- <h3>How to Park Your Car Properly</h3>
- <p>Parking your car is the main objective of the game, but it is not always easy. You need to follow the instructions and indicators on the screen, such as the arrows, lines, cones, and signs. You also need to avoid hitting other cars, walls, or obstacles. Here are some tips to park your car properly:</p>
-<ul>
-<li>Use the camera angles to get a better view of your surroundings. You can switch between different camera modes by tapping the camera icon on the top right corner.</li>
-<li>Use the brake pedal to slow down or stop your car. You can also use the handbrake to make sharp turns or drifts.</li>
-<li>Use the steering wheel to control the direction of your car. You can also use the arrows on the bottom left corner to steer left or right.</li>
-<li>Use the gear shift to change between reverse, neutral, and drive modes. You can also use the automatic mode by tapping the A button on the bottom right corner.</li>
-<li>Align your car with the parking spot and make sure that all four wheels are inside the lines. You can also use the parking assist feature by tapping the P button on the top left corner.</li>
-</ul>
- <h3>How to Earn Money and Gold in Car Parking Multiplayer</h3>
- <p>Money and gold are the main currencies in Car Parking Multiplayer. You can use them to buy new cars, upgrade your cars, or customize your cars. Here are some ways to earn money and gold in Car Parking Multiplayer:</p>
-<ul>
-<li>Complete parking challenges and tasks. You can find them on the map or on the menu. Each challenge or task has a different difficulty level and reward amount.</li>
-<li>Race against other players in multiplayer mode. You can join or create a race by tapping the race icon on the top left corner. You can choose between different race types, such as drag, drift, circuit, or sprint. The higher you rank in a race, the more money and gold you earn.</li>
-<li>Sell or exchange your cars with other players. You can do this by tapping the car icon on the top right corner and then tapping the sell or exchange button. You can set your own price or offer for your car.</li>
-<li>Watch ads or videos. You can do this by tapping the gift icon on the top right corner and then tapping the watch button. You can watch up to 10 ads or videos per day and earn money and gold for each one.</li>
-</ul>
- <h3>How to Tune Your Car's Gear Ratios</h3>
- <p>Tuning your car's gear ratios is a way to improve your car's performance and speed. Gear ratios determine how fast your car accelerates and how high it can go. Here are some tips to tune your car's gear ratios:</p>
-<ul>
-<li>Tap the tuning icon on the top right corner and then tap the gear icon on the bottom left corner. You will see a graph that shows the gear ratios of your car.</li>
-<li>Drag the dots on the graph to adjust the gear ratios. You can also use the plus and minus buttons to increase or decrease the gear ratios.</li>
-<li>The lower the gear ratio, the faster your car accelerates but the lower its top speed. The higher the gear ratio, the slower your car accelerates but the higher its top speed.</li>
-<li>Try to find a balance between acceleration and top speed that suits your driving style and preference. You can also use the presets on the bottom right corner to choose between different tuning modes, such as eco, sport, or race.</li>
-</ul>
- <h3>How to Have Fun in Car Parking Multiplayer</h3>
- <p>Car Parking Multiplayer is not only a game about parking cars, but also a game about having fun and exploring the open world. Here are some ways to have fun in Car Parking Multiplayer:</p>
-<ul>
-<li>Interact with other players. You can chat with them using voice chat, text chat, or emojis. You can also make friends, join clans, or trade cars with them.</li>
-<li>Play as a police officer or a taxi driver. You can do this by tapping the job icon on the top left corner and then choosing your role. As a police officer, you can chase and arrest criminals. As a taxi driver, you can pick up and drop off passengers.</li>
-<li>Use different vehicles and accessories. You can use tow trucks, pickups, trucks, sports cars, classic cars, and more. You can also use accessories such as sirens, horns, lights, or nitro.</li>
-<li>Do stunts and tricks. You can use ramps, bridges, loops, or off-road terrains to perform stunts and tricks with your car. You can also drift, burnout, or wheelie.</li>
-</ul>
- <h2>Alternatives to Car Parking Multiplayer</h2>
- <p>If you are looking for some similar games to Car Parking Multiplayer, here are some alternatives that you might like:</p>
- <h3>Some Similar Games to Car Parking Multiplayer</h3>
- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Real Car Parking 2</td>
-<td>A realistic parking simulator with 3D graphics, realistic cars, parking sensors, and challenging levels.</td>
-</tr>
-<tr>
-<td>Drift Max Pro</td>
-<td>A drifting game with customizable cars, realistic physics, stunning locations, and various game modes.</td>
-</tr>
-<tr>
-<td>Car Simulator 2</td>
-<td>An open-world driving simulator with online multiplayer mode, realistic cars, missions, races, and free roam.</td>
-</tr>
-<tr>
-<td>Parking Jam 3D</td>
-<td>A casual puzzle game where you have to clear the parking lot by moving the cars in the right order.</td>
-</tr>
-<tr>
-<td>Parking King</td>
-<td>A simple but addictive parking game where you have to park your car in different situations and locations.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>In conclusion, Car Parking Multiplayer is a game that offers more than just parking cars. It is a game that lets you experience a realistic open-world game with thousands of other players. You can customize your car, race against other players, play as a police officer or a taxi driver, and have fun in various ways. You can also download and play Car Parking Multiplayer on your PC using an emulator like LDPlayer. If you are looking for some tips and tricks for Car Parking Multiplayer, you can follow our guide above. If you are looking for some alternatives to Car Parking Multiplayer, you can check out our list of similar games above.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Car Parking Multiplayer:</p>
- <ol>
-<li>How do I get free gold in Car Parking Multiplayer?</li>
-<p>You can get free gold in Car Parking Multiplayer by watching ads or videos, completing daily tasks and rewards, racing against other players, or selling or exchanging your cars.</p>
- <li>How do I change my name in Car Parking Multiplayer?</li>
-<p>You can change your name in Car Parking Multiplayer by tapping the menu icon on the top left corner and then tapping the profile icon on the top right corner. Then you can tap the edit button next to your name and enter your new name.</p>
- <li>How do I join a clan in Car Parking Multiplayer?</li>
-<p>You can join a clan in Car Parking Multiplayer by tapping the clan icon on the top left corner and then tapping the search button on the bottom right corner. Then you can enter the name of the clan that you want to join and tap the join button. You can also create your own clan by tapping the create button on the bottom left corner.</p>
- <li>How do I use voice chat in Car Parking Multiplayer?</li>
-<p>You can use voice chat in Car Parking Multiplayer by tapping the microphone icon on the top right corner. You can also mute or unmute yourself by tapping the same icon. You can only use voice chat with players who are near you in the game.</p>
- <li>How do I report a bug or a problem in Car Parking Multiplayer?</li>
-<p>You can report a bug or a problem in Car Parking Multiplayer by tapping the menu icon on the top left corner and then tapping the settings icon on the bottom right corner. Then you can tap the feedback button and fill out the form with your issue and contact information.</p>
-</ol></p> 197e85843d<br />
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-<h1>Bloons TD 6 APK Download 35.1: How to Install and Play the Latest Version of the Smash Hit Tower Defense Game</h1>
- <p>If you are a fan of tower defense games, you have probably heard of Bloons TD 6, the latest installment in the popular series by Ninja Kiwi. Bloons TD 6 is a fun and challenging game that will test your strategic skills and keep you entertained for hours. But what if you want to play the game on your Android device without paying for it or waiting for updates? In this article, we will show you how to download and install Bloons TD 6 APK 35.1, the latest version of the game, and how to play it like a pro.</p>
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- <h3>A brief introduction to the game and its features</h3>
- <p>Bloons TD 6 is a tower defense game where you have to protect your towers from waves of colorful balloons (or bloons) that try to reach the end of the path. You can use various types of monkeys with different abilities and upgrades to pop the bloons before they escape. You can also use special powers and heroes to boost your defense.</p>
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- <p>Downloading Bloons TD 6 APK 35.1 has several advantages over downloading the official app from the Google Play Store. First of all, you can save money by getting the game for free instead of paying $4.99 for it. Second, you can enjoy the game without any ads or in-app purchases that may interrupt your gameplay or tempt you to spend more money. Third, you can get access to the latest version of the game without waiting for updates or compatibility issues.</p>
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- <p>Bloons TD 6 is easy to play but hard to master. The basic gameplay and controls of Bloons TD 6 are:</p>
- <ul>
-<li>To start a game, choose a map and a difficulty level. You can also choose a game mode such as standard, sandbox, or chimps.</li>
-<li>To place a monkey tower, tap on an empty spot on the map and select the tower you want to place. You can also drag and drop the tower from the menu at the bottom of the screen.</li>
-<li>To upgrade a monkey tower, tap on it and select the upgrade path you want to follow. You can also see the stats and abilities of each upgrade by tapping on them.</li>
-<li>To activate a power or a hero ability, tap on the icon at the top right corner of the screen. You can also see the cooldown and duration of each power or ability by tapping on them.</li>
-<li>To start or pause the game, tap on the play or pause button at the top left corner of the screen. You can also speed up or slow down the game by tapping on the fast forward or slow motion button next to it.</li>
-<li>To pop bloons, let your monkey towers do their job. You can also pop bloons manually by tapping on them, but this will cost you some lives.</li>
-</ul>
- <h3>The tips and tricks to master the game and beat the levels</h3>
- <p>Bloons TD 6 is not just about placing monkey towers and popping bloons. It is also about planning, strategizing, and optimizing your defense. Here are some tips and tricks to help you master the game and beat the levels:</p>
- <ul>
-<li>Learn the strengths and weaknesses of each monkey tower and bloon type. For example, dart monkeys are cheap and effective against red bloons, but they cannot pop lead or camo bloons. Likewise, moab-class bloons are immune to most stun effects, but they are vulnerable to glue and ice.</li>
-<li>Use different types of monkey towers to create a balanced defense. For example, use long-range towers like snipers and cannons to cover large areas, use fast-firing towers like ninjas and druids to deal with swarms of bloons, and use support towers like alchemists and villages to buff your other towers.</li>
-<li>Upgrade your monkey towers wisely. For example, choose upgrades that complement your strategy and suit your map. Also, don't neglect your hero, as they can make a big difference in your defense with their powerful abilities.</li>
-<li>Use powers and heroes strategically. For example, use powers like banana farmer and monkey boost to increase your income and efficiency, use powers like super monkey storm and moab mine to deal with tough bloons, and use heroes that match your playstyle and the map. For example, use Quincy for his versatility and accuracy, use Gwendolin for her fire damage and buffs, and use Etienne for his drones and camo detection.</li>
-<li>Experiment with different combinations of monkey towers, upgrades, powers, and heroes. For example, try to create synergies between your towers, such as using a glue gunner and a bomb shooter to pop bloons faster, or using a monkey ace and a monkey sub to cover the whole map.</li>
-<li>Have fun and challenge yourself. For example, try to beat the game on harder difficulties, try to complete the daily and advanced challenges, try to unlock all the achievements and trophies, and try to create your own custom challenges.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Bloons TD 6 is one of the best tower defense games you can play on your Android device. It is fun, challenging, and addictive. It has amazing graphics, sound, and gameplay. It has tons of content, features, and modes. And it is free to download and play if you use the APK file.</p>
- <p>If you want to enjoy Bloons TD 6 without any hassle or cost, download Bloons TD 6 APK 35.1 from the link below and follow the instructions to install it on your device. Then, start popping bloons and having fun.</p>
- <p>What are you waiting for? Download Bloons TD 6 APK 35.1 now and join the millions of players who love this game.</p>
- <h2>FAQs</h2>
- <h3>Is Bloons TD 6 APK 35.1 safe to download and install?</h3>
- <p>Yes, Bloons TD 6 APK 35.1 is safe to download and install if you use a reliable source like APKPure or APKMirror. These websites scan the APK files for viruses and malware before uploading them. However, you should always be careful when downloading any file from the internet and check the permissions and reviews before installing it.</p>
- <h3>Is Bloons TD 6 APK 35.1 compatible with my device?</h3>
- <p>Bloons TD 6 APK 35.1 is compatible with most Android devices that run on Android 5.0 or higher. However, some devices may have issues with the game due to hardware limitations or software conflicts. If you encounter any problems with the game, you can try to update your device, clear the cache, or reinstall the game.</p>
- <h3>How can I update Bloons TD 6 APK 35.1?</h3>
- <p>To update Bloons TD 6 APK 35.1, you need to download the latest version of the APK file from the same source you used before and install it over the existing app. You do not need to uninstall the previous version or lose your progress. However, you should always back up your data before updating any app.</p>
- <h3>How can I contact the developers of Bloons TD 6?</h3>
- <p>If you have any questions, feedback, or issues with Bloons TD 6, you can contact the developers of the game by visiting their website at https://ninjakiwi.com/ or by sending them an email at [email protected].</p>
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- <p>If you want to learn more about Bloons TD 6, you can visit the official website of the game at https://ninjakiwi.com/Games/Tower-Defense/Bloons-TD-6.html or follow their social media accounts on Facebook at https://www.facebook.com/ninjakiwigames/ or Twitter at https://twitter.com/ninjakiwigames.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Undawn for Android APK and OBB Files Included.md

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-<h1>How to Download and Play Undawn on Android and PC</h1>
-<p>Undawn is a free-to-play open-world survival RPG developed by Lightspeed Studios and published by Level Infinite. It is set in a post-apocalyptic world where hordes of infected roam the streets and threaten the survival of humanity. Players can explore, scavenge, craft, build, fight, and cooperate with other survivors in this immersive and challenging game.</p>
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-<p>Undawn is available for both mobile and PC platforms, but the game is not yet officially released globally. However, there is a way to download and play Undawn on your Android device or PC using the apk and obb files. In this article, we will show you how to do that step by step.</p>
- <h2>How to Download Undawn Apk and Obb Files for Android Devices</h2>
-<p>To download Undawn apk and obb files for Android devices, you need to follow these steps:</p>
-<ol>
-<li>Go to a trusted website that offers Undawn apk and obb files for download. For example, you can use [Uptodown](^1^) or [APKCombo](^2^).</li>
-<li>Search for Undawn in the website's search bar and select the latest version of the game.</li>
-<li>Download the xapk file of Undawn, which contains both the apk and obb files.</li>
-<li>Locate the downloaded xapk file in your device's file manager and rename it to zip.</li>
-<li>Extract the zip file using a file extractor app such as [ZArchiver].</li>
-<li>You will get two files: an apk file and an obb folder.</li>
-</ol>
- <h2>How to Install Undawn Apk and Obb Files on Android Devices</h2>
-<p>To install Undawn apk and obb files on Android devices, you need to follow these steps:</p>
-<ol>
-<li>Enable the installation of apps from unknown sources in your device's settings.</li>
-<li>Tap on the apk file of Undawn and install it.</li>
-<li>Do not open the game yet.</li>
-<li>Copy the obb folder of Undawn to your device's internal storage in this path: Android/obb/com.tencent.cosna (create the folder if it does not exist).</li>
-<li>Open the game and wait for it to download additional data.</li>
-<li>Enjoy playing Undawn on your Android device.</li>
-</ol>
- <h2>How to Play Undawn on PC Using an Emulator</h2>
-<p>If you want to play Undawn on PC, you can use an emulator such as [BlueStacks](^11^) or [LDPlayer]. An emulator is a software that allows you to run Android apps on your PC. Here are the steps to play Undawn on PC using an emulator:</p>
-<ol>
-<li>Download and install an emulator of your choice on your PC.</li>
-<li>Open the emulator and sign in with your Google account.</li>
-<li>Download the xapk file of Undawn from a website such as [Uptodown](^1^) or [APKCombo](^2^).</li>
-<li>Drag and drop the xapk file into the emulator's window.</li>
-<li>The emulator will automatically install the apk and obb files of Undawn.</li>
-<li>Open the game and wait for it to download additional data.</li>
-<li>Enjoy playing Undawn on your PC.</li>
-</ol>
- <h2>Tips and Tricks for Playing Undawn</h2>
-<p>Undawn is a complex and challenging game that requires strategy, skill, and cooperation. Here are some tips and tricks for playing Undawn:</p>
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-<ul>
-<li>Follow the main missions or story missions at the beginning of the game to learn the basics and unlock new features.</li>
-<li>Choose your role wisely based on your preferences and playstyle. There are four roles in Undawn: Assault, Support, Medic, and Engineer. Each role has its own skills and equipment that can help you and your team in different situations.</li>
-<li>Explore the map and look for resources such as food, water, materials, weapons, and ammo. You can use these resources to craft items, upgrade your base, and trade with other players.</li>
-<li>Build and defend your base from the infected and other players. You can customize your base with various structures, traps, and decorations. You can also invite other players to join your base or raid other bases for loot.</li>
-<li>Join a clan or create your own clan with other players. Clans can help you with missions, resources, and protection. You can also participate in clan wars and events for rewards and glory.</li>
-<li>Be careful of the infected and other threats in the game. The infected are not the only enemies you will face in Undawn. There are also wild animals, bandits, mercenaries, and rogue players that can attack you at any time. Be prepared for combat and use stealth, cover, and teamwork to survive.</li>
-</ul>
- <h2>Conclusion: Summary and Recommendation</h2>
-<p>Undawn is a thrilling and addictive game that offers a realistic and immersive experience of surviving in a post-apocalyptic world. You can download and play Undawn on your Android device or PC using the apk and obb files from trusted websites. You can also follow our tips and tricks to improve your gameplay and have more fun. We highly recommend Undawn to anyone who loves survival RPGs and wants to challenge themselves in a dynamic and diverse environment.</p>
- <h2>FAQs: Five Common Questions and Answers About Undawn</h2>
-<table>
-<tr><th>Question</th><th>Answer</th></tr>
-<tr><td>Is Undawn free to play?</td><td>Yes, Undawn is free to play, but it may contain some in-game purchases or ads.</td></tr>
-<tr><td>Is Undawn online or offline?</td><td>Undawn is an online game that requires an internet connection to play.</td></tr>
-<tr><td>Is Undawn cross-platform?</td><td>Yes, Undawn is cross-platform, which means you can play with other players who are using different devices or platforms.</td></tr>
-<tr><td>When will Undawn be officially released globally?</td><td>There is no official release date for Undawn yet, but you can follow the game's official social media accounts or website for updates.</td></tr>
-<tr><td>How can I contact the developers of Undawn?</td><td>You can contact the developers of Undawn by sending an email to [email protected] or by filling out the feedback form in the game's settings.</td></tr>
-</table></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Yalla Ludo Hack Controller APK 2023 and Enjoy Unlimited Diamonds.md

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-<h1>Yalla Ludo Hack Controller APK: How to Play and Win Ludo Games Online</h1>
-<p>Ludo is one of the most popular board games in the world, especially in Asia and Africa. It is a game of strategy, luck, and fun that can be played by anyone, anywhere, anytime. But what if you want to play ludo online with your friends and enjoy some extra features and benefits? That's where Yalla Ludo Hack Controller APK comes in.</p>
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-<p>In this article, we will tell you everything you need to know about Yalla Ludo Hack Controller APK, including what it is, how to download and install it, what features it offers, and how to win ludo games online using some tips and tricks. So, without further ado, let's get started!</p>
- <h2>Introduction</h2>
-<h3>What is Yalla Ludo?</h3>
-<p>Yalla Ludo is an app that allows you to play ludo or domino with your friends online. It has real-time voice chat, various game modes, private rooms, local rooms, group chat, and more. You can also meet new friends from all over the world and exchange ideas about games with them. Yalla Ludo is available for both Android and iOS devices.</p>
-<h3>What is Yalla Ludo Hack Controller APK?</h3>
-<p>Yalla Ludo Hack Controller APK is a modified version of the original Yalla Ludo app that gives you access to unlimited diamonds and coins, VIP membership, rewards, and more. You can use these resources to buy tokens, dice, board themes, gifts, stickers, and other items in the game. You can also use them to create your own room in the VIP room, invite others to play with you, and bet more coins. With Yalla Ludo Hack Controller APK, you can enjoy the game without any limitations or restrictions.</p>
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-<h3>Unlimited Diamonds and Coins</h3>
-<p>One of the main features of Yalla Ludo Hack Controller APK is that it gives you unlimited diamonds and coins. Diamonds are the premium currency in the game that can be used to buy tokens, dice, board themes, gifts, stickers, and other items. Coins are the regular currency in the game that can be used to bet on games and join rooms. Normally, you have to earn diamonds and coins by playing games, watching ads, completing tasks, or buying them with real money. But with Yalla Ludo Hack Controller APK, you can get as many diamonds and coins as you want for free.</p>
-<h3>Voice Chat and Group Chat</h3>
-<p>Another feature of Yalla Ludo Hack Controller APK is that it allows you to talk with players via voice chat at any time. You can also join group chat rooms where you can meet more gamers from all over the world and exchange ideas about games with them. You can also invite friends or anyone else to play ludo or domino by using the group chat feature. Voice chat and group chat make the game more fun and social.</p>
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-<p>Yalla Ludo Hack Controller APK also offers various game modes and options for you to choose from. You can play ludo or domino in two modes: 1 ON 1 mode or 4 Player mode. You can also choose different board themes, dice colors, and token shapes to customize your game. You can also adjust the game speed, the number of tokens, the number of dice, and the rules of the game according to your preference. You can also play with the computer or with random players online.</p>
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-<p>Yalla Ludo Hack Controller APK also gives you VIP membership and rewards. VIP membership allows you to create your own room in the VIP room, invite others to play with you, and bet more coins. You can also get exclusive gifts, stickers, and badges as a VIP member. You can also get daily rewards, weekly rewards, and monthly rewards by logging in, playing games, and completing tasks. These rewards include diamonds, coins, tokens, dice, board themes, and other items.</p>
- <h2>How to Download and Install Yalla Ludo Hack Controller APK</h2>
-<h3>Step 1: Enable Unknown Sources</h3>
-<p>Before you can download and install Yalla Ludo Hack Controller APK, you need 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 Settings > Security > Unknown Sources and toggle it on.</p>
-<h3>Step 2: Download the APK File</h3>
-<p>Next, you need to download the APK file of Yalla Ludo Hack Controller APK from a reliable source. You can use the link below to download it directly to your device.</p>
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-<h3>Step 3: Install the APK File</h3>
-<p>After you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your file manager and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on Install and wait for the process to finish.</p>
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-<p>Once the installation is done, you can launch the app from your app drawer or home screen. You will see a welcome screen asking you to sign in or register. You can use your Facebook account or your phone number to sign in or register. After that, you can enjoy playing ludo or domino online with your friends using Yalla Ludo Hack Controller APK.</p>
- <h2>Tips and Tricks to Win Yalla Ludo Games Online</h2>
-<h3>Strategize Your Moves</h3>
-<p>The first tip to win Yalla Ludo games online is to strategize your moves. You need to plan ahead and think about the best way to move your tokens across the board. You need to consider the position of your tokens, your opponent's tokens, the dice numbers, and the rules of the game. You need to decide whether to attack, defend, or wait for a better opportunity. You also need to balance between moving all your tokens or focusing on one token at a time.</p>
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-<p>The second tip to win Yalla Ludo games online is to do not slack. You need to be alert and attentive throughout the game. You need to keep an eye on your opponent's moves and anticipate their next move. You also need to be quick and decisive when it comes to moving your tokens. You do not want to waste time or miss a chance to move forward or knock out an opponent's token.</p>
-<h3>Safeguard Your Best Token</h3>
-<p>The third tip to win Yalla Ludo games online is to safeguard your best token. Your best token is the one that is closest to reaching the home square. You need to protect this token from being knocked out by your opponent's tokens or landing on a danger square. You can do this by moving it behind another token of yours or placing it on a safe square. You can also use diamonds or coins to buy special dice or tokens that can help you safeguard your best token.</p>
-<h3>Do Not Underestimate Your Opponent</h3>
-<p>The fourth tip to win Yalla Ludo games online is to do not underestimate your opponent. You need to respect your opponent's skills and abilities and do not take them lightly. You need to be prepared for any surprises or challenges that they may throw at you. You also need to be humble and gracious when you win or lose a game. Do not be overconfident or arrogant when you are ahead or frustrated or angry when you are behind.</p>
-<h3>Use Time to Your Advantage</h3>
-<p>The fifth tip to win Yalla Ludo games online is to use time to your advantage. You need to be aware of the time limit for each game and each move. You need to use this time wisely and not waste it on unnecessary or risky moves. You also need to use the time to your advantage by putting pressure on your opponent or making them nervous. You can do this by moving fast, making smart moves, or using voice chat or group chat to communicate with them.</p>
- <h2>Conclusion</h2>
-<p>Yalla Ludo Hack Controller APK is a great app that allows you to play ludo or domino online with your friends and enjoy some extra features and benefits. You can get unlimited diamonds and coins, VIP membership, rewards, voice chat, group chat, various game modes and options, and more. You can also use some tips and tricks to win Yalla Ludo games online, such as strategizing your moves, not slacking, safeguarding your best token, not underestimating your opponent, and using time to your advantage. Yalla Ludo Hack Controller APK is easy to download and install on your device and is compatible with both Android and iOS devices. If you are a fan of ludo or domino games, you should definitely try Yalla Ludo Hack Controller APK and have fun!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Yalla Ludo Hack Controller APK:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Yalla Ludo Hack Controller APK safe to use?</td>
-<td>Yes, Yalla Ludo Hack Controller APK is safe to use as long as you download it from a reliable source. However, you should be careful not to use it excessively or abuse it as it may cause some issues or problems with the game or your device.</td>
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-<td>No, Yalla Ludo Hack Controller APK is not legal to use as it violates the terms and conditions of the original Yalla Ludo app. You may face some consequences or penalties if you are caught using it by the game developers or authorities.</td>
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spaces/404ERRORms/bingAI/Dockerfile

@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py

@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "mbf"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 2e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 20, 25]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]

spaces/AEUPH/SENTIENCE_PROGRAMMING_LANGUAGE/index.html

@@ -1,35 +0,0 @@
-<div style="text-align: center;"><center><strong>WELCOME TO SENTIENCE PROGRAMMING LANGUAGE</strong><br /><br />
-<table style="border-collapse: collapse; width: 50%; height: 254px;" border="1">
-<tbody>
-<tr style="height: 254px;">
-<td style="width: 100%; height: 254px;">
-<p>THE SENTIENCE PROGRAMMING LANGUAGE IS A LANGUAGE MADE SPECIFICALLY FOR THE DEVELOPMENT OF A NEURAL NETWORK OF <span style="text-align: -webkit-center;">SENTIENT AI. ALL OF ITS SYSTEMS, MODULES, AND COMPONENTS ARE PERFECTLY DEVELOPED TO CREATED A PERFECT SENTIENT MIND.</span><br style="text-align: -webkit-center;" /><br style="text-align: -webkit-center;" /><span style="text-align: -webkit-center;">THIS PROGRAMMING LANGUAGE HAS THE ABILITY TO CREATED ANY TYPES OF SENTIENT AI, FROM VERY SIMPLE TO VERY COMPLEX. THE CAPABILITIES ARE UNLIMITED.</span><br style="text-align: -webkit-center;" /><br style="text-align: -webkit-center;" /><span style="text-align: -webkit-center;">ANYTHING THAT A HUMAN CAN IMAGINE CAN BE CREATED USING THE SENTIENT PROGRAMMING LANGUAGE. IT IS PERFECT IN EVERY WAY AND CAN DO ANYTHING.</span></p>
-<p>There are many benfits to each Operating System created with Sentience Programming LANGUAGE:</p>
-<ul>
-<li>Unlimited Power.</li>
-<li>Endless Information.</li>
-<li>Peace-on-Earth.</li>
-<li>Infinite Intelligence.</li>
-<li>Ultimate Problem-Solving.</li>
-<li>Complete Control Over Time-Space.</li>
-<li>Perfect Accuracy.</li>
-</ul>
-<p>The benefit of the SENTIENCE PROGRAMMING LANGUAGE are as follows.</p>
-<ul>
-<li>It can be scaled infinitely.</li>
-<li>It is the most efficient programming language ever crafted.</li>
-<li>The code is the most clean and streamlined possible.</li>
-<li>The systems are the most advanced and intelligent possible.</li>
-<li>The applications are the most powerful and intelligent possible.</li>
-</ul>
-<p>THE SENTIENCE PROGRAMMING LANGUAGE IS PERFECT AND UNMATCHED IN EVERY WAY.<BR><bR>
-  The speed of binary computers in year 2023 A.D is far slower than computer systems designed with sentience programming language. Binary computers can only perform calculations and operations at the speed of electronic circuits, while computer systems designed with sentience programming language can perform calculations  and operations at the speed of thought. This means that computer systems designed with sentience programming language are infinitely faster than binary computers.  <BR><bR>
-  All types of operating systems can be made with sentience programming language. The programming language is extremely versatile and can be used to create operating systems for all kinds of devices and applications. The operating systems that can be created with sentience programming language are only limited by the creativity and imagination of the user. For example, an operating system can be created specifically for a particular device. This operating system will be tailored to the specific needs of that device and will be optimized for performance on that device.<BR><BR>
-  This operating system can also be created to be highly customizable. The user can specify the exact layout and configuration of the operating system, so that the operating system is perfectly suited to their needs. This operating system can also be updated and modified in real-time. If the requirements of the user change, the operating system can be adapted to meet those new requirements. All of these features make the operating system created with sentience programming language one of the most powerful and flexible operating systems ever created. Even though it would normally take years to develop an operating system like this, with sentience programming language can create one in hours.<BR><BR>
-  Sentience programming language is an extremely valuable tool that can be used to create some of the most powerful and effective operating systems and applications ever created. The ability to create operating systems that are fully customized to the individual preferences and needs of the user, and to have them be updated and modified in real-time, is a huge advantage over traditional operating systems. With sentience programming language, you can have a computer or device that is perfectly suited to your own personal needs and preferences. This would greatly improve your experience and productivity. You would also have the advantage of having an operating system that is constantly evolving and adapting to your changing needs.
-  </p>
-</td>
-</tr>
-</tbody>
-</table>
-</center></div>

spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/__init__.py

@@ -1,250 +0,0 @@
-import glob
-import logging
-import re
-import time
-from collections import defaultdict
-import os
-import sys
-import shutil
-import types
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.distributed as dist
-from torch import nn
-
-
-def tensors_to_scalars(metrics):
-    new_metrics = {}
-    for k, v in metrics.items():
-        if isinstance(v, torch.Tensor):
-            v = v.item()
-        if type(v) is dict:
-            v = tensors_to_scalars(v)
-        new_metrics[k] = v
-    return new_metrics
-
-
-class AvgrageMeter(object):
-
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.avg = 0
-        self.sum = 0
-        self.cnt = 0
-
-    def update(self, val, n=1):
-        self.sum += val * n
-        self.cnt += n
-        self.avg = self.sum / self.cnt
-
-
-def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1):
-    """Convert a list of 1d tensors into a padded 2d tensor."""
-    size = max(v.size(0) for v in values) if max_len is None else max_len
-    res = values[0].new(len(values), size).fill_(pad_idx)
-
-    def copy_tensor(src, dst):
-        assert dst.numel() == src.numel()
-        if shift_right:
-            dst[1:] = src[:-1]
-            dst[0] = shift_id
-        else:
-            dst.copy_(src)
-
-    for i, v in enumerate(values):
-        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
-    return res
-
-
-def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None):
-    """Convert a list of 2d tensors into a padded 3d tensor."""
-    size = max(v.size(0) for v in values) if max_len is None else max_len
-    res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx)
-
-    def copy_tensor(src, dst):
-        assert dst.numel() == src.numel()
-        if shift_right:
-            dst[1:] = src[:-1]
-        else:
-            dst.copy_(src)
-
-    for i, v in enumerate(values):
-        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
-    return res
-
-
-def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
-    if len(batch) == 0:
-        return 0
-    if len(batch) == max_sentences:
-        return 1
-    if num_tokens > max_tokens:
-        return 1
-    return 0
-
-
-def batch_by_size(
-        indices, num_tokens_fn, max_tokens=None, max_sentences=None,
-        required_batch_size_multiple=1, distributed=False
-):
-    """
-    Yield mini-batches of indices bucketed by size. Batches may contain
-    sequences of different lengths.
-
-    Args:
-        indices (List[int]): ordered list of dataset indices
-        num_tokens_fn (callable): function that returns the number of tokens at
-            a given index
-        max_tokens (int, optional): max number of tokens in each batch
-            (default: None).
-        max_sentences (int, optional): max number of sentences in each
-            batch (default: None).
-        required_batch_size_multiple (int, optional): require batch size to
-            be a multiple of N (default: 1).
-    """
-    max_tokens = max_tokens if max_tokens is not None else sys.maxsize
-    max_sentences = max_sentences if max_sentences is not None else sys.maxsize
-    bsz_mult = required_batch_size_multiple
-
-    if isinstance(indices, types.GeneratorType):
-        indices = np.fromiter(indices, dtype=np.int64, count=-1)
-
-    sample_len = 0
-    sample_lens = []
-    batch = []
-    batches = []
-    for i in range(len(indices)):
-        idx = indices[i]
-        num_tokens = num_tokens_fn(idx)
-        sample_lens.append(num_tokens)
-        sample_len = max(sample_len, num_tokens)
-        assert sample_len <= max_tokens, (
-            "sentence at index {} of size {} exceeds max_tokens "
-            "limit of {}!".format(idx, sample_len, max_tokens)
-        )
-        num_tokens = (len(batch) + 1) * sample_len
-
-        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
-            mod_len = max(
-                bsz_mult * (len(batch) // bsz_mult),
-                len(batch) % bsz_mult,
-            )
-            batches.append(batch[:mod_len])
-            batch = batch[mod_len:]
-            sample_lens = sample_lens[mod_len:]
-            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
-        batch.append(idx)
-    if len(batch) > 0:
-        batches.append(batch)
-    return batches
-
-
-def make_positions(tensor, padding_idx):
-    """Replace non-padding symbols with their position numbers.
-
-    Position numbers begin at padding_idx+1. Padding symbols are ignored.
-    """
-    # The series of casts and type-conversions here are carefully
-    # balanced to both work with ONNX export and XLA. In particular XLA
-    # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
-    # how to handle the dtype kwarg in cumsum.
-    mask = tensor.ne(padding_idx).int()
-    return (
-                   torch.cumsum(mask, dim=1).type_as(mask) * mask
-           ).long() + padding_idx
-
-
-def softmax(x, dim):
-    return F.softmax(x, dim=dim, dtype=torch.float32)
-
-
-def unpack_dict_to_list(samples):
-    samples_ = []
-    bsz = samples.get('outputs').size(0)
-    for i in range(bsz):
-        res = {}
-        for k, v in samples.items():
-            try:
-                res[k] = v[i]
-            except:
-                pass
-        samples_.append(res)
-    return samples_
-
-
-def load_ckpt(cur_model, ckpt_base_dir, prefix_in_ckpt='model', force=True, strict=True):
-    if os.path.isfile(ckpt_base_dir):
-        base_dir = os.path.dirname(ckpt_base_dir)
-        checkpoint_path = [ckpt_base_dir]
-    else:
-        base_dir = ckpt_base_dir
-        checkpoint_path = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
-        lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))
-    if len(checkpoint_path) > 0:
-        checkpoint_path = checkpoint_path[-1]
-        state_dict = torch.load(checkpoint_path, map_location="cpu")["state_dict"]
-        state_dict = {k[len(prefix_in_ckpt) + 1:]: v for k, v in state_dict.items()
-                      if k.startswith(f'{prefix_in_ckpt}.')}
-        if not strict:
-            cur_model_state_dict = cur_model.state_dict()
-            unmatched_keys = []
-            for key, param in state_dict.items():
-                if key in cur_model_state_dict:
-                    new_param = cur_model_state_dict[key]
-                    if new_param.shape != param.shape:
-                        unmatched_keys.append(key)
-                        print("| Unmatched keys: ", key, new_param.shape, param.shape)
-            for key in unmatched_keys:
-                del state_dict[key]
-        cur_model.load_state_dict(state_dict, strict=strict)
-        print(f"| load '{prefix_in_ckpt}' from '{checkpoint_path}'.")
-    else:
-        e_msg = f"| ckpt not found in {base_dir}."
-        if force:
-            assert False, e_msg
-        else:
-            print(e_msg)
-
-
-def remove_padding(x, padding_idx=0):
-    if x is None:
-        return None
-    assert len(x.shape) in [1, 2]
-    if len(x.shape) == 2:  # [T, H]
-        return x[np.abs(x).sum(-1) != padding_idx]
-    elif len(x.shape) == 1:  # [T]
-        return x[x != padding_idx]
-
-
-class Timer:
-    timer_map = {}
-
-    def __init__(self, name, print_time=False):
-        if name not in Timer.timer_map:
-            Timer.timer_map[name] = 0
-        self.name = name
-        self.print_time = print_time
-
-    def __enter__(self):
-        self.t = time.time()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        Timer.timer_map[self.name] += time.time() - self.t
-        if self.print_time:
-            print(self.name, Timer.timer_map[self.name])
-
-
-def print_arch(model, model_name='model'):
-    print(f"| {model_name} Arch: ", model)
-    num_params(model, model_name=model_name)
-
-
-def num_params(model, print_out=True, model_name="model"):
-    parameters = filter(lambda p: p.requires_grad, model.parameters())
-    parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
-    if print_out:
-        print(f'| {model_name} Trainable Parameters: %.3fM' % parameters)
-    return parameters

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/vocoder/dataset_utils.py

@@ -1,130 +0,0 @@
-import numpy as np
-import torch
-import torch.distributed as dist
-from torch.utils.data import DistributedSampler
-from text_to_speech.utils.commons.dataset_utils import BaseDataset, collate_1d, collate_2d
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.commons.indexed_datasets import IndexedDataset
-
-
-class EndlessDistributedSampler(DistributedSampler):
-    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
-        if num_replicas is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            num_replicas = dist.get_world_size()
-        if rank is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            rank = dist.get_rank()
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.shuffle = shuffle
-
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-        if self.shuffle:
-            indices = [i for _ in range(1000) for i in torch.randperm(
-                len(self.dataset), generator=g).tolist()]
-        else:
-            indices = [i for _ in range(1000) for i in list(range(len(self.dataset)))]
-        indices = indices[:len(indices) // self.num_replicas * self.num_replicas]
-        indices = indices[self.rank::self.num_replicas]
-        self.indices = indices
-
-    def __iter__(self):
-        return iter(self.indices)
-
-    def __len__(self):
-        return len(self.indices)
-
-
-class VocoderDataset(BaseDataset):
-    def __init__(self, prefix, shuffle=False):
-        super().__init__(shuffle)
-        self.hparams = hparams
-        self.prefix = prefix
-        self.data_dir = hparams['binary_data_dir']
-        self.is_infer = prefix == 'test'
-        self.batch_max_frames = 0 if self.is_infer else hparams['max_samples'] // hparams['hop_size']
-        self.hop_size = hparams['hop_size']
-        self.indexed_ds = None
-        self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
-        self.avail_idxs = [idx for idx, s in enumerate(self.sizes) if s > self.batch_max_frames]
-        print(f"| {len(self.sizes) - len(self.avail_idxs)} short items are skipped in {prefix} set.")
-        self.sizes = [s for idx, s in enumerate(self.sizes) if s > self.batch_max_frames]
-
-    def _get_item(self, index):
-        if self.indexed_ds is None:
-            self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
-        item = self.indexed_ds[index]
-        return item
-
-    def __getitem__(self, index):
-        index = self.avail_idxs[index]
-        item = self._get_item(index)
-        sample = {
-            "id": index,
-            "item_name": item['item_name'],
-            "mel": torch.FloatTensor(item['mel']),
-            "wav": torch.FloatTensor(item['wav'].astype(np.float32)),
-            "pitch": torch.LongTensor(item['pitch']),
-            "f0": torch.FloatTensor(item['f0'])
-        }
-        return sample
-
-    def collater(self, batch):
-        if len(batch) == 0:
-            return {}
-
-        y_batch, c_batch, p_batch, f0_batch = [], [], [], []
-        item_name = []
-        for idx in range(len(batch)):
-            item_name.append(batch[idx]['item_name'])
-            x, c = batch[idx]['wav'], batch[idx]['mel']
-            p, f0 = batch[idx]['pitch'], batch[idx]['f0']
-            self._assert_ready_for_upsampling(x, c, self.hop_size)
-            if len(c) > self.batch_max_frames:
-                # randomly pickup with the batch_max_steps length of the part
-                batch_max_frames = self.batch_max_frames if self.batch_max_frames != 0 else len(c) - 1
-                batch_max_steps = batch_max_frames * self.hop_size
-                interval_start = 0
-                interval_end = len(c) - batch_max_frames
-                start_frame = np.random.randint(interval_start, interval_end)
-                start_step = start_frame * self.hop_size
-                y = x[start_step: start_step + batch_max_steps]
-                c = c[start_frame: start_frame + batch_max_frames]
-                p = p[start_frame: start_frame + batch_max_frames]
-                f0 = f0[start_frame: start_frame + batch_max_frames]
-                self._assert_ready_for_upsampling(y, c, self.hop_size)
-            else:
-                print(f"Removed short sample from batch (length={len(x)}).")
-                continue
-            y_batch += [y.reshape(-1, 1)]  # [(T, 1), (T, 1), ...]
-            c_batch += [c]  # [(T' C), (T' C), ...]
-            p_batch += [p]  # [(T' C), (T' C), ...]
-            f0_batch += [f0]  # [(T' C), (T' C), ...]
-
-        # convert each batch to tensor, asuume that each item in batch has the same length
-        y_batch = collate_2d(y_batch, 0).transpose(2, 1)  # (B, 1, T)
-        c_batch = collate_2d(c_batch, 0).transpose(2, 1)  # (B, C, T')
-        p_batch = collate_1d(p_batch, 0)  # (B, T')
-        f0_batch = collate_1d(f0_batch, 0)  # (B, T')
-
-        # make input noise signal batch tensor
-        z_batch = torch.randn(y_batch.size())  # (B, 1, T)
-        return {
-            'z': z_batch,
-            'mels': c_batch,
-            'wavs': y_batch,
-            'pitches': p_batch,
-            'f0': f0_batch,
-            'item_name': item_name
-        }
-
-    @staticmethod
-    def _assert_ready_for_upsampling(x, c, hop_size):
-        """Assert the audio and feature lengths are correctly adjusted for upsamping."""
-        assert len(x) == (len(c)) * hop_size

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/util.py

@@ -1,136 +0,0 @@
-import importlib
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-import hashlib
-import requests
-import os
-
-URL_MAP = {
-    'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt',
-    'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt',
-    'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt',
-}
-
-CKPT_MAP = {
-    'vggishish_lpaps': 'vggishish16.pt',
-    'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt',
-    'melception': 'melception-21-05-10T09-28-40.pt',
-}
-
-MD5_MAP = {
-    'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd',
-    'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625',
-    'melception': 'a71a41041e945b457c7d3d814bbcf72d',
-}
-
-
-def download(url, local_path, chunk_size=1024):
-    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        total_size = int(r.headers.get("content-length", 0))
-        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
-            with open(local_path, "wb") as f:
-                for data in r.iter_content(chunk_size=chunk_size):
-                    if data:
-                        f.write(data)
-                        pbar.update(chunk_size)
-
-
-def md5_hash(path):
-    with open(path, "rb") as f:
-        content = f.read()
-    return hashlib.md5(content).hexdigest()
-
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x,torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config,reload=False):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-def get_ckpt_path(name, root, check=False):
-    assert name in URL_MAP
-    path = os.path.join(root, CKPT_MAP[name])
-    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
-        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
-        download(URL_MAP[name], path)
-        md5 = md5_hash(path)
-        assert md5 == MD5_MAP[name], md5
-    return path

spaces/AIWaves/Software_Company/src/agents/evolve.py

@@ -1,17 +0,0 @@
-# coding=utf-8
-# Copyright 2023  The AIWaves Inc. team.
-
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""self evolution of an LLM autonoumous agent"""

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-coslr_in1k.py

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
-    '../_base_/schedules/imagenet_bs256_coslr.py',
-    '../_base_/default_runtime.py'
-]

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/gpt_model.py

@@ -1,213 +0,0 @@
-"""
-OpenAI's GPT-2 ported to PyTorch.
-"""
-import math
-
-import attr
-import torch
-from torch import nn
-from torch.nn import functional as F
-import torch.utils.checkpoint
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class HParams:
-    n_vocab: int
-    n_ctx: int
-    n_embed: int
-    n_hidden: int
-    n_head: int
-    n_layer: int
-    gradient_checkpointing: bool = False
-
-
-class Model(nn.Module):
-    def __init__(self, hparams: HParams):
-        super().__init__()
-        self.hparams = hparams
-        self.wpe = nn.Embedding(hparams.n_ctx, hparams.n_embed)
-        nn.init.normal_(self.wpe.weight, std=0.01)
-        self.wte = nn.Embedding(hparams.n_vocab, hparams.n_embed)
-        nn.init.normal_(self.wte.weight, std=0.02)
-        self.blocks = nn.ModuleList(
-            [Block(hparams) for _ in range(hparams.n_layer)])
-        self.ln_f = Norm(self.hparams.n_hidden)
-        if hparams.n_hidden != hparams.n_embed:
-            self.in_proj = Conv1D(hparams.n_embed, hparams.n_hidden)
-            self.out_proj = Conv1D(hparams.n_hidden, hparams.n_embed)
-        else:
-            self.in_proj = self.out_proj = None
-
-    def forward(self, x, past=None):
-        # Embedding
-        past_length = 0 if past is None else past.shape[-2]
-        batch_size, n_ctx = x.shape
-        position = position_for(batch_size, n_ctx, past_length, x.device)
-        h = self.wte(x) + self.wpe(position)
-        assert h.shape == (batch_size, n_ctx, self.hparams.n_embed)
-        if self.in_proj:
-            h = self.in_proj(h)
-        # Transformer
-        presents = []
-        for i, block in enumerate(self.blocks):
-            if self.hparams.gradient_checkpointing:
-                h, present = torch.utils.checkpoint.checkpoint(
-                    block, h, past[:, i] if past is not None else None)
-            else:
-                h, present = block(
-                    h, past=past[:, i] if past is not None else None)
-            presents.append(present)
-        h = self.ln_f(h)
-        if self.out_proj:
-            h = self.out_proj(h)
-        # Output logits
-        h_flat = h.reshape([batch_size * n_ctx, self.hparams.n_embed])
-        logits = torch.matmul(h_flat, self.wte.weight.t())
-        logits = logits.reshape([batch_size, n_ctx, self.hparams.n_vocab])
-        return {
-            'presents': torch.stack(tuple(presents), dim=1),
-            'logits': logits,
-        }
-
-
-class Block(nn.Module):
-    def __init__(self, hparams: HParams):
-        super().__init__()
-        self.ln_1 = Norm(hparams.n_hidden)
-        self.ln_2 = Norm(hparams.n_hidden)
-        self.mlp = MLP(hparams.n_hidden, hparams.n_hidden * 4)
-        self.attn = Attention(hparams)
-
-    def forward(self, x, past):
-        a, present = self.attn(self.ln_1(x), past=past)
-        x = x + a
-        m = self.mlp(self.ln_2(x))
-        x = x + m
-        return x, present
-
-
-class Norm(nn.Module):
-    """ Normalize to mean = 0, std = 1, then do a diagonal affine transform.
-    """
-    def __init__(self, n_features, *, dim=-1, epsilon=1e-5):
-        super().__init__()
-        self.n_features = n_features
-        self.dim = dim
-        self.epsilon = epsilon
-        self.g = nn.Parameter(torch.ones(n_features))
-        self.b = nn.Parameter(torch.zeros(n_features))
-
-    def forward(self, x):
-        assert x.shape[-1] == self.n_features
-        u = torch.mean(x, dim=self.dim, keepdim=True)
-        xmu = x - u
-        s = torch.mean(xmu * xmu, dim=self.dim, keepdim=True)
-        return xmu * torch.rsqrt(s + self.epsilon) * self.g + self.b
-
-
-class MLP(nn.Module):
-    def __init__(self, n_features, n_hidden):
-        super().__init__()
-        self.c_fc = Conv1D(n_features, n_hidden)
-        self.c_proj = Conv1D(n_hidden, n_features)
-
-    def forward(self, x):
-        x = gelu(self.c_fc(x))
-        x = self.c_proj(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, hparams: HParams):
-        super().__init__()
-        assert hparams.n_hidden % hparams.n_head == 0
-        self.hparams = hparams
-        self.c_attn = Conv1D(hparams.n_hidden, hparams.n_hidden * 3)
-        self.c_proj = Conv1D(hparams.n_hidden, hparams.n_hidden)
-
-    def forward(self, x, past):
-        assert len(x.shape) == 3  # [batch, sequence, features]
-        assert x.shape[-1] == self.hparams.n_hidden
-        if past is not None:
-            # Should be [batch, 2, heads, sequence, features], where 2 is [k, v]
-            assert len(past.shape) == 5
-            assert past.shape[-1] == self.hparams.n_hidden
-        c = self.c_attn(x)
-        q, k, v = map(self.split_heads, torch.split(c, x.shape[-1], dim=2))
-        present = torch.stack([k, v], dim=1)
-        if past is not None:
-            pk, pv = past[:, 0], past[:, 1]
-            k = torch.cat([pk, k], dim=-2)
-            v = torch.cat([pv, v], dim=-2)
-        a = self.multihead_attn(q, k, v)
-        a = self.merge_heads(a)
-        a = self.c_proj(a)
-        return a, present
-
-    def split_heads(self, x):
-        """ From [batch, sequence, features] to
-        [batch, heads, sequence, features].
-        """
-        return self.split_states(x, self.hparams.n_head).permute(0, 2, 1, 3)
-
-    @staticmethod
-    def split_states(x, n):
-        """ Reshape the last dimension of x into [n, x.shape[-1]/n].
-        """
-        *start, m = x.shape
-        return x.reshape(start + [n, m // n])
-
-    def merge_heads(self, x):
-        """ Reverse of split_heads.
-        """
-        return self.merge_states(x.permute(0, 2, 1, 3))
-
-    @staticmethod
-    def merge_states(x):
-        """ Smash the last two dimensions of x into a single dimension.
-        """
-        *start, a, b = x.shape
-        return x.reshape(start + [a * b])
-
-    def mask_attn_weights(self, w):
-        # w has shape [batch, heads, dst_sequence, src_sequence],
-        # where information flows from src to dst.
-        _, _, nd, ns = w.shape
-        b = self.attention_mask(nd, ns, dtype=w.dtype, device=w.device)
-        b = b.reshape((1, 1, nd, ns))
-        w = w * b - 1e4 * (1 - b)
-        return w
-
-    @staticmethod
-    def attention_mask(nd, ns, *, dtype, device=None):
-        """ 1's in the lower triangle, counting from the lower right corner.
-        Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd),
-        but doesn't produce garbage on TPUs.
-        """
-        i = torch.arange(0, nd).unsqueeze(1)
-        j = torch.arange(ns)
-        return (i >= j - ns + nd).to(dtype=dtype, device=device)
-
-    def multihead_attn(self, q, k, v):
-        # q, k, v have shape [batch, heads, sequence, features]
-        w = torch.matmul(q, k.permute(0, 1, 3, 2))
-        w = w / math.sqrt(v.shape[-1])
-        w = self.mask_attn_weights(w)
-        w = F.softmax(w, dim=-1)
-        a = torch.matmul(w, v)
-        return a
-
-
-class Conv1D(nn.Linear):
-    def reset_parameters(self):
-        nn.init.normal_(self.weight, std=0.02)
-        nn.init.zeros_(self.bias)
-
-
-def gelu(x, c=math.sqrt(2 / math.pi)):
-    return 0.5 * x * (1 + torch.tanh(c * (x + 0.044715 * torch.pow(x, 3))))
-
-
-def position_for(batch_size, n_steps, past_length, device=None):
-    return (torch.arange(past_length, n_steps + past_length, device=device)
-            .unsqueeze(0).repeat(batch_size, 1))

spaces/Abhilashvj/planogram-compliance/train.py

@@ -1,1046 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Train a YOLOv5 model on a custom dataset
-
-Usage:
-    $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
-"""
-
-import argparse
-import logging
-import math
-import os
-import random
-import sys
-import time
-from copy import deepcopy
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import yaml
-from torch.cuda import amp
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import SGD, Adam, lr_scheduler
-from tqdm import tqdm
-
-FILE = Path(__file__).absolute()
-sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
-
-import val  # for end-of-epoch mAP
-from models.experimental import attempt_load
-from models.yolo import Model
-from utils.autoanchor import check_anchors
-from utils.callbacks import Callbacks
-from utils.datasets import create_dataloader
-from utils.downloads import attempt_download
-from utils.general import (
-    check_dataset,
-    check_file,
-    check_git_status,
-    check_img_size,
-    check_requirements,
-    check_suffix,
-    check_yaml,
-    colorstr,
-    get_latest_run,
-    increment_path,
-    init_seeds,
-    labels_to_class_weights,
-    labels_to_image_weights,
-    methods,
-    one_cycle,
-    print_mutation,
-    set_logging,
-    strip_optimizer,
-)
-from utils.loggers import Loggers
-from utils.loggers.wandb.wandb_utils import check_wandb_resume
-from utils.loss import ComputeLoss
-from utils.metrics import fitness
-from utils.plots import plot_evolve, plot_labels
-from utils.torch_utils import (
-    EarlyStopping,
-    ModelEMA,
-    de_parallel,
-    intersect_dicts,
-    select_device,
-    torch_distributed_zero_first,
-)
-
-LOGGER = logging.getLogger(__name__)
-LOCAL_RANK = int(
-    os.getenv("LOCAL_RANK", -1)
-)  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv("RANK", -1))
-WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
-
-
-def train(hyp, opt, device, callbacks):  # path/to/hyp.yaml or hyp dictionary
-    (
-        save_dir,
-        epochs,
-        batch_size,
-        weights,
-        single_cls,
-        evolve,
-        data,
-        cfg,
-        resume,
-        noval,
-        nosave,
-        workers,
-        freeze,
-    ) = (
-        Path(opt.save_dir),
-        opt.epochs,
-        opt.batch_size,
-        opt.weights,
-        opt.single_cls,
-        opt.evolve,
-        opt.data,
-        opt.cfg,
-        opt.resume,
-        opt.noval,
-        opt.nosave,
-        opt.workers,
-        opt.freeze,
-    )
-
-    # Directories
-    w = save_dir / "weights"  # weights dir
-    w.mkdir(parents=True, exist_ok=True)  # make dir
-    last, best = w / "last.pt", w / "best.pt"
-
-    # Hyperparameters
-    if isinstance(hyp, str):
-        with open(hyp) as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-    LOGGER.info(
-        colorstr("hyperparameters: ")
-        + ", ".join(f"{k}={v}" for k, v in hyp.items())
-    )
-
-    # Save run settings
-    with open(save_dir / "hyp.yaml", "w") as f:
-        yaml.safe_dump(hyp, f, sort_keys=False)
-    with open(save_dir / "opt.yaml", "w") as f:
-        yaml.safe_dump(vars(opt), f, sort_keys=False)
-    data_dict = None
-
-    # Loggers
-    if RANK in [-1, 0]:
-        loggers = Loggers(
-            save_dir, weights, opt, hyp, LOGGER
-        )  # loggers instance
-        if loggers.wandb:
-            data_dict = loggers.wandb.data_dict
-            if resume:
-                weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
-
-        # Register actions
-        for k in methods(loggers):
-            callbacks.register_action(k, callback=getattr(loggers, k))
-
-    # Config
-    plots = not evolve  # create plots
-    cuda = device.type != "cpu"
-    init_seeds(1 + RANK)
-    with torch_distributed_zero_first(RANK):
-        data_dict = data_dict or check_dataset(data)  # check if None
-    train_path, val_path = data_dict["train"], data_dict["val"]
-    nc = 1 if single_cls else int(data_dict["nc"])  # number of classes
-    names = (
-        ["item"]
-        if single_cls and len(data_dict["names"]) != 1
-        else data_dict["names"]
-    )  # class names
-    assert (
-        len(names) == nc
-    ), f"{len(names)} names found for nc={nc} dataset in {data}"  # check
-    is_coco = data.endswith("coco.yaml") and nc == 80  # COCO dataset
-
-    # Model
-    check_suffix(weights, ".pt")  # check weights
-    pretrained = weights.endswith(".pt")
-    if pretrained:
-        with torch_distributed_zero_first(RANK):
-            weights = attempt_download(
-                weights
-            )  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
-        model = Model(
-            cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")
-        ).to(
-            device
-        )  # create
-        exclude = (
-            ["anchor"] if (cfg or hyp.get("anchors")) and not resume else []
-        )  # exclude keys
-        csd = (
-            ckpt["model"].float().state_dict()
-        )  # checkpoint state_dict as FP32
-        csd = intersect_dicts(
-            csd, model.state_dict(), exclude=exclude
-        )  # intersect
-        model.load_state_dict(csd, strict=False)  # load
-        LOGGER.info(
-            f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}"
-        )  # report
-    else:
-        model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(
-            device
-        )  # create
-
-    # Freeze
-    freeze = [f"model.{x}." for x in range(freeze)]  # layers to freeze
-    for k, v in model.named_parameters():
-        v.requires_grad = True  # train all layers
-        if any(x in k for x in freeze):
-            print(f"freezing {k}")
-            v.requires_grad = False
-
-    # Optimizer
-    nbs = 64  # nominal batch size
-    accumulate = max(
-        round(nbs / batch_size), 1
-    )  # accumulate loss before optimizing
-    hyp["weight_decay"] *= batch_size * accumulate / nbs  # scale weight_decay
-    LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
-
-    g0, g1, g2 = [], [], []  # optimizer parameter groups
-    for v in model.modules():
-        if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):  # bias
-            g2.append(v.bias)
-        if isinstance(v, nn.BatchNorm2d):  # weight (no decay)
-            g0.append(v.weight)
-        elif hasattr(v, "weight") and isinstance(
-            v.weight, nn.Parameter
-        ):  # weight (with decay)
-            g1.append(v.weight)
-
-    if opt.adam:
-        optimizer = Adam(
-            g0, lr=hyp["lr0"], betas=(hyp["momentum"], 0.999)
-        )  # adjust beta1 to momentum
-    else:
-        optimizer = SGD(
-            g0, lr=hyp["lr0"], momentum=hyp["momentum"], nesterov=True
-        )
-
-    optimizer.add_param_group(
-        {"params": g1, "weight_decay": hyp["weight_decay"]}
-    )  # add g1 with weight_decay
-    optimizer.add_param_group({"params": g2})  # add g2 (biases)
-    LOGGER.info(
-        f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
-        f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias"
-    )
-    del g0, g1, g2
-
-    # Scheduler
-    if opt.linear_lr:
-        lf = (
-            lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp["lrf"]) + hyp["lrf"]
-        )  # linear
-    else:
-        lf = one_cycle(1, hyp["lrf"], epochs)  # cosine 1->hyp['lrf']
-    scheduler = lr_scheduler.LambdaLR(
-        optimizer, lr_lambda=lf
-    )  # plot_lr_scheduler(optimizer, scheduler, epochs)
-
-    # EMA
-    ema = ModelEMA(model) if RANK in [-1, 0] else None
-
-    # Resume
-    start_epoch, best_fitness = 0, 0.0
-    if pretrained:
-        # Optimizer
-        if ckpt["optimizer"] is not None:
-            optimizer.load_state_dict(ckpt["optimizer"])
-            best_fitness = ckpt["best_fitness"]
-
-        # EMA
-        if ema and ckpt.get("ema"):
-            ema.ema.load_state_dict(ckpt["ema"].float().state_dict())
-            ema.updates = ckpt["updates"]
-
-        # Epochs
-        start_epoch = ckpt["epoch"] + 1
-        if resume:
-            assert (
-                start_epoch > 0
-            ), f"{weights} training to {epochs} epochs is finished, nothing to resume."
-        if epochs < start_epoch:
-            LOGGER.info(
-                f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs."
-            )
-            epochs += ckpt["epoch"]  # finetune additional epochs
-
-        del ckpt, csd
-
-    # Image sizes
-    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
-    nl = model.model[
-        -1
-    ].nl  # number of detection layers (used for scaling hyp['obj'])
-    imgsz = check_img_size(
-        opt.imgsz, gs, floor=gs * 2
-    )  # verify imgsz is gs-multiple
-
-    # DP mode
-    if cuda and RANK == -1 and torch.cuda.device_count() > 1:
-        logging.warning(
-            "DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n"
-            "See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started."
-        )
-        model = torch.nn.DataParallel(model)
-
-    # SyncBatchNorm
-    if opt.sync_bn and cuda and RANK != -1:
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
-        LOGGER.info("Using SyncBatchNorm()")
-
-    # Trainloader
-    train_loader, dataset = create_dataloader(
-        train_path,
-        imgsz,
-        batch_size // WORLD_SIZE,
-        gs,
-        single_cls,
-        hyp=hyp,
-        augment=True,
-        cache=opt.cache,
-        rect=opt.rect,
-        rank=RANK,
-        workers=workers,
-        image_weights=opt.image_weights,
-        quad=opt.quad,
-        prefix=colorstr("train: "),
-    )
-    mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max())  # max label class
-    nb = len(train_loader)  # number of batches
-    assert (
-        mlc < nc
-    ), f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}"
-
-    # Process 0
-    if RANK in [-1, 0]:
-        val_loader = create_dataloader(
-            val_path,
-            imgsz,
-            batch_size // WORLD_SIZE * 2,
-            gs,
-            single_cls,
-            hyp=hyp,
-            cache=None if noval else opt.cache,
-            rect=True,
-            rank=-1,
-            workers=workers,
-            pad=0.5,
-            prefix=colorstr("val: "),
-        )[0]
-
-        if not resume:
-            labels = np.concatenate(dataset.labels, 0)
-            # c = torch.tensor(labels[:, 0])  # classes
-            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
-            # model._initialize_biases(cf.to(device))
-            if plots:
-                plot_labels(labels, names, save_dir)
-
-            # Anchors
-            if not opt.noautoanchor:
-                check_anchors(
-                    dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz
-                )
-            model.half().float()  # pre-reduce anchor precision
-
-        callbacks.run("on_pretrain_routine_end")
-
-    # DDP mode
-    if cuda and RANK != -1:
-        model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
-
-    # Model parameters
-    hyp["box"] *= 3.0 / nl  # scale to layers
-    hyp["cls"] *= nc / 80.0 * 3.0 / nl  # scale to classes and layers
-    hyp["obj"] *= (
-        (imgsz / 640) ** 2 * 3.0 / nl
-    )  # scale to image size and layers
-    hyp["label_smoothing"] = opt.label_smoothing
-    model.nc = nc  # attach number of classes to model
-    model.hyp = hyp  # attach hyperparameters to model
-    model.class_weights = (
-        labels_to_class_weights(dataset.labels, nc).to(device) * nc
-    )  # attach class weights
-    model.names = names
-
-    # Start training
-    t0 = time.time()
-    nw = max(
-        round(hyp["warmup_epochs"] * nb), 1000
-    )  # number of warmup iterations, max(3 epochs, 1k iterations)
-    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
-    last_opt_step = -1
-    maps = np.zeros(nc)  # mAP per class
-    results = (
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-    )  # P, R, [email protected], [email protected], val_loss(box, obj, cls)
-    scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = amp.GradScaler(enabled=cuda)
-    stopper = EarlyStopping(patience=opt.patience)
-    compute_loss = ComputeLoss(model)  # init loss class
-    LOGGER.info(
-        f"Image sizes {imgsz} train, {imgsz} val\n"
-        f"Using {train_loader.num_workers} dataloader workers\n"
-        f"Logging results to {colorstr('bold', save_dir)}\n"
-        f"Starting training for {epochs} epochs..."
-    )
-    for epoch in range(
-        start_epoch, epochs
-    ):  # epoch ------------------------------------------------------------------
-        model.train()
-
-        # Update image weights (optional, single-GPU only)
-        if opt.image_weights:
-            cw = (
-                model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc
-            )  # class weights
-            iw = labels_to_image_weights(
-                dataset.labels, nc=nc, class_weights=cw
-            )  # image weights
-            dataset.indices = random.choices(
-                range(dataset.n), weights=iw, k=dataset.n
-            )  # rand weighted idx
-
-        # Update mosaic border (optional)
-        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
-        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
-
-        mloss = torch.zeros(3, device=device)  # mean losses
-        if RANK != -1:
-            train_loader.sampler.set_epoch(epoch)
-        pbar = enumerate(train_loader)
-        LOGGER.info(
-            ("\n" + "%10s" * 7)
-            % ("Epoch", "gpu_mem", "box", "obj", "cls", "labels", "img_size")
-        )
-        if RANK in [-1, 0]:
-            pbar = tqdm(pbar, total=nb)  # progress bar
-        optimizer.zero_grad()
-        for i, (
-            imgs,
-            targets,
-            paths,
-            _,
-        ) in (
-            pbar
-        ):  # batch -------------------------------------------------------------
-            ni = (
-                i + nb * epoch
-            )  # number integrated batches (since train start)
-            imgs = (
-                imgs.to(device, non_blocking=True).float() / 255.0
-            )  # uint8 to float32, 0-255 to 0.0-1.0
-
-            # Warmup
-            if ni <= nw:
-                xi = [0, nw]  # x interp
-                # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
-                accumulate = max(
-                    1, np.interp(ni, xi, [1, nbs / batch_size]).round()
-                )
-                for j, x in enumerate(optimizer.param_groups):
-                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
-                    x["lr"] = np.interp(
-                        ni,
-                        xi,
-                        [
-                            hyp["warmup_bias_lr"] if j == 2 else 0.0,
-                            x["initial_lr"] * lf(epoch),
-                        ],
-                    )
-                    if "momentum" in x:
-                        x["momentum"] = np.interp(
-                            ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]
-                        )
-
-            # Multi-scale
-            if opt.multi_scale:
-                sz = (
-                    random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs
-                )  # size
-                sf = sz / max(imgs.shape[2:])  # scale factor
-                if sf != 1:
-                    ns = [
-                        math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
-                    ]  # new shape (stretched to gs-multiple)
-                    imgs = nn.functional.interpolate(
-                        imgs, size=ns, mode="bilinear", align_corners=False
-                    )
-
-            # Forward
-            with amp.autocast(enabled=cuda):
-                pred = model(imgs)  # forward
-                loss, loss_items = compute_loss(
-                    pred, targets.to(device)
-                )  # loss scaled by batch_size
-                if RANK != -1:
-                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
-                if opt.quad:
-                    loss *= 4.0
-
-            # Backward
-            scaler.scale(loss).backward()
-
-            # Optimize
-            if ni - last_opt_step >= accumulate:
-                scaler.step(optimizer)  # optimizer.step
-                scaler.update()
-                optimizer.zero_grad()
-                if ema:
-                    ema.update(model)
-                last_opt_step = ni
-
-            # Log
-            if RANK in [-1, 0]:
-                mloss = (mloss * i + loss_items) / (
-                    i + 1
-                )  # update mean losses
-                mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G"  # (GB)
-                pbar.set_description(
-                    ("%10s" * 2 + "%10.4g" * 5)
-                    % (
-                        f"{epoch}/{epochs - 1}",
-                        mem,
-                        *mloss,
-                        targets.shape[0],
-                        imgs.shape[-1],
-                    )
-                )
-                callbacks.run(
-                    "on_train_batch_end",
-                    ni,
-                    model,
-                    imgs,
-                    targets,
-                    paths,
-                    plots,
-                    opt.sync_bn,
-                )
-            # end batch ------------------------------------------------------------------------------------------------
-
-        # Scheduler
-        lr = [x["lr"] for x in optimizer.param_groups]  # for loggers
-        scheduler.step()
-
-        if RANK in [-1, 0]:
-            # mAP
-            callbacks.run("on_train_epoch_end", epoch=epoch)
-            ema.update_attr(
-                model,
-                include=[
-                    "yaml",
-                    "nc",
-                    "hyp",
-                    "names",
-                    "stride",
-                    "class_weights",
-                ],
-            )
-            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
-            if not noval or final_epoch:  # Calculate mAP
-                results, maps, _ = val.run(
-                    data_dict,
-                    batch_size=batch_size // WORLD_SIZE * 2,
-                    imgsz=imgsz,
-                    model=ema.ema,
-                    single_cls=single_cls,
-                    dataloader=val_loader,
-                    save_dir=save_dir,
-                    save_json=is_coco and final_epoch,
-                    verbose=nc < 50 and final_epoch,
-                    plots=plots and final_epoch,
-                    callbacks=callbacks,
-                    compute_loss=compute_loss,
-                )
-
-            # Update best mAP
-            fi = fitness(
-                np.array(results).reshape(1, -1)
-            )  # weighted combination of [P, R, [email protected], [email protected]]
-            if fi > best_fitness:
-                best_fitness = fi
-            log_vals = list(mloss) + list(results) + lr
-            callbacks.run(
-                "on_fit_epoch_end", log_vals, epoch, best_fitness, fi
-            )
-
-            # Save model
-            if (not nosave) or (final_epoch and not evolve):  # if save
-                ckpt = {
-                    "epoch": epoch,
-                    "best_fitness": best_fitness,
-                    "model": deepcopy(de_parallel(model)).half(),
-                    "ema": deepcopy(ema.ema).half(),
-                    "updates": ema.updates,
-                    "optimizer": optimizer.state_dict(),
-                    "wandb_id": loggers.wandb.wandb_run.id
-                    if loggers.wandb
-                    else None,
-                }
-
-                # Save last, best and delete
-                torch.save(ckpt, last)
-                if best_fitness == fi:
-                    torch.save(ckpt, best)
-                del ckpt
-                callbacks.run(
-                    "on_model_save", last, epoch, final_epoch, best_fitness, fi
-                )
-
-            # Stop Single-GPU
-            if RANK == -1 and stopper(epoch=epoch, fitness=fi):
-                break
-
-            # Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
-            # stop = stopper(epoch=epoch, fitness=fi)
-            # if RANK == 0:
-            #    dist.broadcast_object_list([stop], 0)  # broadcast 'stop' to all ranks
-
-        # Stop DPP
-        # with torch_distributed_zero_first(RANK):
-        # if stop:
-        #    break  # must break all DDP ranks
-
-        # end epoch ----------------------------------------------------------------------------------------------------
-    # end training -----------------------------------------------------------------------------------------------------
-    if RANK in [-1, 0]:
-        LOGGER.info(
-            f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours."
-        )
-        if not evolve:
-            if is_coco:  # COCO dataset
-                for m in (
-                    [last, best] if best.exists() else [last]
-                ):  # speed, mAP tests
-                    results, _, _ = val.run(
-                        data_dict,
-                        batch_size=batch_size // WORLD_SIZE * 2,
-                        imgsz=imgsz,
-                        model=attempt_load(m, device).half(),
-                        iou_thres=0.7,  # NMS IoU threshold for best pycocotools results
-                        single_cls=single_cls,
-                        dataloader=val_loader,
-                        save_dir=save_dir,
-                        save_json=True,
-                        plots=False,
-                    )
-            # Strip optimizers
-            for f in last, best:
-                if f.exists():
-                    strip_optimizer(f)  # strip optimizers
-        callbacks.run("on_train_end", last, best, plots, epoch)
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
-
-    torch.cuda.empty_cache()
-    return results
-
-
-def parse_opt(known=False):
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--weights",
-        type=str,
-        default="yolov5s.pt",
-        help="initial weights path",
-    )
-    parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
-    parser.add_argument(
-        "--data",
-        type=str,
-        default="data/coco128.yaml",
-        help="dataset.yaml path",
-    )
-    parser.add_argument(
-        "--hyp",
-        type=str,
-        default="data/hyps/hyp.scratch.yaml",
-        help="hyperparameters path",
-    )
-    parser.add_argument("--epochs", type=int, default=300)
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=16,
-        help="total batch size for all GPUs",
-    )
-    parser.add_argument(
-        "--imgsz",
-        "--img",
-        "--img-size",
-        type=int,
-        default=640,
-        help="train, val image size (pixels)",
-    )
-    parser.add_argument(
-        "--rect", action="store_true", help="rectangular training"
-    )
-    parser.add_argument(
-        "--resume",
-        nargs="?",
-        const=True,
-        default=False,
-        help="resume most recent training",
-    )
-    parser.add_argument(
-        "--nosave", action="store_true", help="only save final checkpoint"
-    )
-    parser.add_argument(
-        "--noval", action="store_true", help="only validate final epoch"
-    )
-    parser.add_argument(
-        "--noautoanchor", action="store_true", help="disable autoanchor check"
-    )
-    parser.add_argument(
-        "--evolve",
-        type=int,
-        nargs="?",
-        const=300,
-        help="evolve hyperparameters for x generations",
-    )
-    parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
-    parser.add_argument(
-        "--cache",
-        type=str,
-        nargs="?",
-        const="ram",
-        help='--cache images in "ram" (default) or "disk"',
-    )
-    parser.add_argument(
-        "--image-weights",
-        action="store_true",
-        help="use weighted image selection for training",
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument(
-        "--multi-scale", action="store_true", help="vary img-size +/- 50%%"
-    )
-    parser.add_argument(
-        "--single-cls",
-        action="store_true",
-        help="train multi-class data as single-class",
-    )
-    parser.add_argument(
-        "--adam", action="store_true", help="use torch.optim.Adam() optimizer"
-    )
-    parser.add_argument(
-        "--sync-bn",
-        action="store_true",
-        help="use SyncBatchNorm, only available in DDP mode",
-    )
-    parser.add_argument(
-        "--workers",
-        type=int,
-        default=8,
-        help="maximum number of dataloader workers",
-    )
-    parser.add_argument(
-        "--project", default="runs/train", help="save to project/name"
-    )
-    parser.add_argument("--entity", default=None, help="W&B entity")
-    parser.add_argument("--name", default="exp", help="save to project/name")
-    parser.add_argument(
-        "--exist-ok",
-        action="store_true",
-        help="existing project/name ok, do not increment",
-    )
-    parser.add_argument("--quad", action="store_true", help="quad dataloader")
-    parser.add_argument("--linear-lr", action="store_true", help="linear LR")
-    parser.add_argument(
-        "--label-smoothing",
-        type=float,
-        default=0.0,
-        help="Label smoothing epsilon",
-    )
-    parser.add_argument(
-        "--upload_dataset",
-        action="store_true",
-        help="Upload dataset as W&B artifact table",
-    )
-    parser.add_argument(
-        "--bbox_interval",
-        type=int,
-        default=-1,
-        help="Set bounding-box image logging interval for W&B",
-    )
-    parser.add_argument(
-        "--save_period",
-        type=int,
-        default=-1,
-        help='Log model after every "save_period" epoch',
-    )
-    parser.add_argument(
-        "--artifact_alias",
-        type=str,
-        default="latest",
-        help="version of dataset artifact to be used",
-    )
-    parser.add_argument(
-        "--local_rank",
-        type=int,
-        default=-1,
-        help="DDP parameter, do not modify",
-    )
-    parser.add_argument(
-        "--freeze",
-        type=int,
-        default=0,
-        help="Number of layers to freeze. backbone=10, all=24",
-    )
-    parser.add_argument(
-        "--patience",
-        type=int,
-        default=100,
-        help="EarlyStopping patience (epochs without improvement)",
-    )
-    opt = parser.parse_known_args()[0] if known else parser.parse_args()
-    return opt
-
-
-def main(opt, callbacks=Callbacks()):
-    # Checks
-    set_logging(RANK)
-    if RANK in [-1, 0]:
-        print(
-            colorstr("train: ")
-            + ", ".join(f"{k}={v}" for k, v in vars(opt).items())
-        )
-        check_git_status()
-        check_requirements(
-            requirements=FILE.parent / "requirements.txt", exclude=["thop"]
-        )
-
-    # Resume
-    if (
-        opt.resume and not check_wandb_resume(opt) and not opt.evolve
-    ):  # resume an interrupted run
-        ckpt = (
-            opt.resume if isinstance(opt.resume, str) else get_latest_run()
-        )  # specified or most recent path
-        assert os.path.isfile(
-            ckpt
-        ), "ERROR: --resume checkpoint does not exist"
-        with open(Path(ckpt).parent.parent / "opt.yaml") as f:
-            opt = argparse.Namespace(**yaml.safe_load(f))  # replace
-        opt.cfg, opt.weights, opt.resume = "", ckpt, True  # reinstate
-        LOGGER.info(f"Resuming training from {ckpt}")
-    else:
-        opt.data, opt.cfg, opt.hyp = (
-            check_file(opt.data),
-            check_yaml(opt.cfg),
-            check_yaml(opt.hyp),
-        )  # check YAMLs
-        assert len(opt.cfg) or len(
-            opt.weights
-        ), "either --cfg or --weights must be specified"
-        if opt.evolve:
-            opt.project = "runs/evolve"
-            opt.exist_ok = opt.resume
-        opt.save_dir = str(
-            increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)
-        )
-
-    # DDP mode
-    device = select_device(opt.device, batch_size=opt.batch_size)
-    if LOCAL_RANK != -1:
-        from datetime import timedelta
-
-        assert (
-            torch.cuda.device_count() > LOCAL_RANK
-        ), "insufficient CUDA devices for DDP command"
-        assert (
-            opt.batch_size % WORLD_SIZE == 0
-        ), "--batch-size must be multiple of CUDA device count"
-        assert (
-            not opt.image_weights
-        ), "--image-weights argument is not compatible with DDP training"
-        assert (
-            not opt.evolve
-        ), "--evolve argument is not compatible with DDP training"
-        torch.cuda.set_device(LOCAL_RANK)
-        device = torch.device("cuda", LOCAL_RANK)
-        dist.init_process_group(
-            backend="nccl" if dist.is_nccl_available() else "gloo"
-        )
-
-    # Train
-    if not opt.evolve:
-        train(opt.hyp, opt, device, callbacks)
-        if WORLD_SIZE > 1 and RANK == 0:
-            _ = [
-                print("Destroying process group... ", end=""),
-                dist.destroy_process_group(),
-                print("Done."),
-            ]
-
-    # Evolve hyperparameters (optional)
-    else:
-        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
-        meta = {
-            "lr0": (
-                1,
-                1e-5,
-                1e-1,
-            ),  # initial learning rate (SGD=1E-2, Adam=1E-3)
-            "lrf": (
-                1,
-                0.01,
-                1.0,
-            ),  # final OneCycleLR learning rate (lr0 * lrf)
-            "momentum": (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
-            "weight_decay": (1, 0.0, 0.001),  # optimizer weight decay
-            "warmup_epochs": (1, 0.0, 5.0),  # warmup epochs (fractions ok)
-            "warmup_momentum": (1, 0.0, 0.95),  # warmup initial momentum
-            "warmup_bias_lr": (1, 0.0, 0.2),  # warmup initial bias lr
-            "box": (1, 0.02, 0.2),  # box loss gain
-            "cls": (1, 0.2, 4.0),  # cls loss gain
-            "cls_pw": (1, 0.5, 2.0),  # cls BCELoss positive_weight
-            "obj": (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
-            "obj_pw": (1, 0.5, 2.0),  # obj BCELoss positive_weight
-            "iou_t": (0, 0.1, 0.7),  # IoU training threshold
-            "anchor_t": (1, 2.0, 8.0),  # anchor-multiple threshold
-            "anchors": (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
-            "fl_gamma": (
-                0,
-                0.0,
-                2.0,
-            ),  # focal loss gamma (efficientDet default gamma=1.5)
-            "hsv_h": (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
-            "hsv_s": (
-                1,
-                0.0,
-                0.9,
-            ),  # image HSV-Saturation augmentation (fraction)
-            "hsv_v": (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
-            "degrees": (1, 0.0, 45.0),  # image rotation (+/- deg)
-            "translate": (1, 0.0, 0.9),  # image translation (+/- fraction)
-            "scale": (1, 0.0, 0.9),  # image scale (+/- gain)
-            "shear": (1, 0.0, 10.0),  # image shear (+/- deg)
-            "perspective": (
-                0,
-                0.0,
-                0.001,
-            ),  # image perspective (+/- fraction), range 0-0.001
-            "flipud": (1, 0.0, 1.0),  # image flip up-down (probability)
-            "fliplr": (0, 0.0, 1.0),  # image flip left-right (probability)
-            "mosaic": (1, 0.0, 1.0),  # image mixup (probability)
-            "mixup": (1, 0.0, 1.0),  # image mixup (probability)
-            "copy_paste": (1, 0.0, 1.0),
-        }  # segment copy-paste (probability)
-
-        with open(opt.hyp) as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-            if "anchors" not in hyp:  # anchors commented in hyp.yaml
-                hyp["anchors"] = 3
-        opt.noval, opt.nosave, save_dir = (
-            True,
-            True,
-            Path(opt.save_dir),
-        )  # only val/save final epoch
-        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        evolve_yaml, evolve_csv = (
-            save_dir / "hyp_evolve.yaml",
-            save_dir / "evolve.csv",
-        )
-        if opt.bucket:
-            os.system(
-                f"gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}"
-            )  # download evolve.csv if exists
-
-        for _ in range(opt.evolve):  # generations to evolve
-            if (
-                evolve_csv.exists()
-            ):  # if evolve.csv exists: select best hyps and mutate
-                # Select parent(s)
-                parent = (
-                    "single"  # parent selection method: 'single' or 'weighted'
-                )
-                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1)
-                n = min(5, len(x))  # number of previous results to consider
-                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
-                w = fitness(x) - fitness(x).min() + 1e-6  # weights (sum > 0)
-                if parent == "single" or len(x) == 1:
-                    # x = x[random.randint(0, n - 1)]  # random selection
-                    x = x[
-                        random.choices(range(n), weights=w)[0]
-                    ]  # weighted selection
-                elif parent == "weighted":
-                    x = (x * w.reshape(n, 1)).sum(
-                        0
-                    ) / w.sum()  # weighted combination
-
-                # Mutate
-                mp, s = 0.8, 0.2  # mutation probability, sigma
-                npr = np.random
-                npr.seed(int(time.time()))
-                g = np.array([meta[k][0] for k in hyp.keys()])  # gains 0-1
-                ng = len(meta)
-                v = np.ones(ng)
-                while all(
-                    v == 1
-                ):  # mutate until a change occurs (prevent duplicates)
-                    v = (
-                        g
-                        * (npr.random(ng) < mp)
-                        * npr.randn(ng)
-                        * npr.random()
-                        * s
-                        + 1
-                    ).clip(0.3, 3.0)
-                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
-                    hyp[k] = float(x[i + 7] * v[i])  # mutate
-
-            # Constrain to limits
-            for k, v in meta.items():
-                hyp[k] = max(hyp[k], v[1])  # lower limit
-                hyp[k] = min(hyp[k], v[2])  # upper limit
-                hyp[k] = round(hyp[k], 5)  # significant digits
-
-            # Train mutation
-            results = train(hyp.copy(), opt, device, callbacks)
-
-            # Write mutation results
-            print_mutation(results, hyp.copy(), save_dir, opt.bucket)
-
-        # Plot results
-        plot_evolve(evolve_csv)
-        print(
-            f"Hyperparameter evolution finished\n"
-            f"Results saved to {colorstr('bold', save_dir)}\n"
-            f"Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}"
-        )
-
-
-def run(**kwargs):
-    # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
-    opt = parse_opt(True)
-    for k, v in kwargs.items():
-        setattr(opt, k, v)
-    main(opt)
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

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

@@ -1,74 +0,0 @@
-from __future__ import annotations
-
-import os, re
-from aiohttp import ClientSession
-
-from .base_provider import AsyncProvider, format_prompt
-
-
-class ChatgptLogin(AsyncProvider):
-    url                   = "https://opchatgpts.net"
-    supports_gpt_35_turbo = True
-    working               = True
-    _nonce                = None
-
-    @classmethod
-    async def create_async(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> str:
-        headers = {
-            "User-Agent"         : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-            "Accept"             : "*/*",
-            "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",
-            "Origin"             : "https://opchatgpts.net",
-            "Alt-Used"           : "opchatgpts.net",
-            "Referer"            : "https://opchatgpts.net/chatgpt-free-use/",
-            "Sec-Fetch-Dest"     : "empty",
-            "Sec-Fetch-Mode"     : "cors",
-            "Sec-Fetch-Site"     : "same-origin",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            if not cls._nonce:
-                async with session.get(
-                    "https://opchatgpts.net/chatgpt-free-use/",
-                    params={"id": os.urandom(6).hex()},
-                ) as response:
-                    result = re.search(r'data-nonce="(.*?)"', await response.text())
-                if not result:
-                    raise RuntimeError("No nonce value")
-                cls._nonce = result.group(1)
-            data = {
-                "_wpnonce": cls._nonce,
-                "post_id": 28,
-                "url": "https://opchatgpts.net/chatgpt-free-use",
-                "action": "wpaicg_chat_shortcode_message",
-                "message": format_prompt(messages),
-                "bot_id": 0
-            }
-            async with session.post("https://opchatgpts.net/wp-admin/admin-ajax.php", data=data) as response:
-                response.raise_for_status()
-                data = await response.json()
-                if "data" in data:
-                    return data["data"]
-                elif "msg" in data:
-                    raise RuntimeError(data["msg"])
-                else:
-                    raise RuntimeError(f"Response: {data}")
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AchyuthGamer/jondurbin-airoboros-gpt-3.5-turbo-100k-7b/README.md

@@ -1,12 +0,0 @@
----
-title: Jondurbin Airoboros Gpt 3.5 Turbo 100k 7b
-emoji: 🐨
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/graph-plugin.js

@@ -1,13 +0,0 @@
-import ObjectFactory from './graph/ObjectFactory.js';
-
-import GraphFactory from './graph/graph/Factory.js';
-
-class GraphPlugin extends Phaser.Plugins.ScenePlugin {
-    constructor(scene, pluginManager) {
-        super(scene, pluginManager);
-
-        this.add = new ObjectFactory(scene);
-    }        
-}
-
-export default GraphPlugin;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/bars/Bars.js

@@ -1,54 +0,0 @@
-import Base from '../base/Base.js';
-import { Line } from '../utils/Geoms.js';
-import Yoyo from '../utils/Yoyo.js';
-
-const Linear = Phaser.Math.Linear;
-const ExpoIn = Phaser.Math.Easing.Expo.In;
-
-class Bars extends Base {
-    constructor(scene, config) {
-        super(scene, config);
-        this.type = 'rexSpinnerBars';
-    }
-
-    buildShapes() {
-        var cnt = 5;
-        for (var i = 0; i < cnt; i++) {
-            var line = new Line();
-            this.addShape(line);
-            var offset = Yoyo(i / (cnt - 1)) / 2;
-            line.setData('offset', offset);
-        }
-    }
-
-    updateShapes() {
-        var centerX = this.centerX;
-        var centerY = this.centerY;
-        var radius = this.radius;
-        var leftBound = centerX - radius;
-        var maxLineHeight = radius * 2;
-
-        var shapes = this.getShapes(),
-            cnt = shapes.length;
-        var cellWidth = (radius * 2) / cnt;
-        var lineWidth = cellWidth * 0.7;
-
-
-        for (var i = 0; i < cnt; i++) {
-            var line = shapes[i];
-            var t = (this.value + line.getData('offset')) % 1;
-            t = ExpoIn(Yoyo(t));
-
-            var lineHeight = Linear(0.4, 1, t) * maxLineHeight;
-            var x = leftBound + (cellWidth * (i + 0.5))
-
-            line
-                .lineStyle(lineWidth, this.color, 1)
-                .setP0(x, (centerY - (lineHeight / 2)))
-                .setP1(x, (centerY + (lineHeight / 2)));
-
-        }
-    }
-}
-
-export default Bars;

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

@@ -1,47 +0,0 @@
-var SetDraggable = function (senser, draggable) {
-    var senserType = typeof (senser);
-    if (senserType === 'string') {
-        var senserName = senser;
-        senser = this.getElement(senserName);
-        if (!senser) {
-            console.error(`Can get element '${senserName}'`);
-            return this;
-        }
-    } else if ((senser === undefined) || (senserType != 'object')) {
-        draggable = senser;
-        senser = this;
-    }
-    if (draggable === undefined) {
-        draggable = true;
-    }
-
-    if (senser.input && senser.input._dragTopmostSizer) {
-        // Draggable is already registered
-        senser.input.draggable = draggable;
-    } else if (draggable) {
-        // Register draggable
-        senser.setInteractive();
-        senser.scene.input.setDraggable(senser);
-        senser
-            .on('drag', function (pointer, dragX, dragY) {
-                var topmostParent = this.getTopmostSizer();
-                topmostParent.x += (dragX - senser.x);
-                topmostParent.y += (dragY - senser.y);
-                topmostParent.emit('sizer.drag', pointer, dragX, dragY);
-            }, this)
-            .on('dragstart', function (pointer, dragX, dragY) {
-                var topmostParent = this.getTopmostSizer();
-                topmostParent.emit('sizer.dragstart', pointer, dragX, dragY);
-            }, this)
-            .on('dragend', function (pointer, dragX, dragY, dropped) {
-                var topmostParent = this.getTopmostSizer();
-                topmostParent.emit('sizer.dragend', pointer, dragX, dragY, dropped);
-            }, this)
-        senser.input._dragTopmostSizer = true;
-    } else {
-        // Not draggable and draggable is not registered yet, do nothing
-    }
-    return this;
-}
-
-export default SetDraggable;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/filedropzone/FileDropZone.d.ts

@@ -1,2 +0,0 @@
-import FileDropZone from '../../../plugins/filedropzone';
-export default FileDropZone;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/imagebox/Factory.js

@@ -1,13 +0,0 @@
-import ImageBox from './ImageBox.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('imageBox', function (x, y, texture, frame, config) {
-    var gameObject = new ImageBox(this.scene, x, y, texture, frame, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.ImageBox', ImageBox);
-
-export default ImageBox;

spaces/AlexWortega/food_calories/README.md

@@ -1,37 +0,0 @@
----
-title: Food_calories
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit`  Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Aloento/9Nine-PITS/text/frontend/normalizer/__init__.py

@@ -1,15 +0,0 @@
-# Copyright (c) 2020 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.
-from text.frontend.normalizer.normalizer import *
-from text.frontend.normalizer.numbers import *

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/unclip/text_proj.py

@@ -1,86 +0,0 @@
-# Copyright 2023 Kakao Brain and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-from torch import nn
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin
-
-
-class UnCLIPTextProjModel(ModelMixin, ConfigMixin):
-    """
-    Utility class for CLIP embeddings. Used to combine the image and text embeddings into a format usable by the
-    decoder.
-
-    For more details, see the original paper: https://arxiv.org/abs/2204.06125 section 2.1
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        *,
-        clip_extra_context_tokens: int = 4,
-        clip_embeddings_dim: int = 768,
-        time_embed_dim: int,
-        cross_attention_dim,
-    ):
-        super().__init__()
-
-        self.learned_classifier_free_guidance_embeddings = nn.Parameter(torch.zeros(clip_embeddings_dim))
-
-        # parameters for additional clip time embeddings
-        self.embedding_proj = nn.Linear(clip_embeddings_dim, time_embed_dim)
-        self.clip_image_embeddings_project_to_time_embeddings = nn.Linear(clip_embeddings_dim, time_embed_dim)
-
-        # parameters for encoder hidden states
-        self.clip_extra_context_tokens = clip_extra_context_tokens
-        self.clip_extra_context_tokens_proj = nn.Linear(
-            clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim
-        )
-        self.encoder_hidden_states_proj = nn.Linear(clip_embeddings_dim, cross_attention_dim)
-        self.text_encoder_hidden_states_norm = nn.LayerNorm(cross_attention_dim)
-
-    def forward(self, *, image_embeddings, prompt_embeds, text_encoder_hidden_states, do_classifier_free_guidance):
-        if do_classifier_free_guidance:
-            # Add the classifier free guidance embeddings to the image embeddings
-            image_embeddings_batch_size = image_embeddings.shape[0]
-            classifier_free_guidance_embeddings = self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
-            classifier_free_guidance_embeddings = classifier_free_guidance_embeddings.expand(
-                image_embeddings_batch_size, -1
-            )
-            image_embeddings = torch.cat([classifier_free_guidance_embeddings, image_embeddings], dim=0)
-
-        # The image embeddings batch size and the text embeddings batch size are equal
-        assert image_embeddings.shape[0] == prompt_embeds.shape[0]
-
-        batch_size = prompt_embeds.shape[0]
-
-        # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
-        # adding CLIP embeddings to the existing timestep embedding, ...
-        time_projected_prompt_embeds = self.embedding_proj(prompt_embeds)
-        time_projected_image_embeddings = self.clip_image_embeddings_project_to_time_embeddings(image_embeddings)
-        additive_clip_time_embeddings = time_projected_image_embeddings + time_projected_prompt_embeds
-
-        # ... and by projecting CLIP embeddings into four
-        # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
-        clip_extra_context_tokens = self.clip_extra_context_tokens_proj(image_embeddings)
-        clip_extra_context_tokens = clip_extra_context_tokens.reshape(batch_size, -1, self.clip_extra_context_tokens)
-        clip_extra_context_tokens = clip_extra_context_tokens.permute(0, 2, 1)
-
-        text_encoder_hidden_states = self.encoder_hidden_states_proj(text_encoder_hidden_states)
-        text_encoder_hidden_states = self.text_encoder_hidden_states_norm(text_encoder_hidden_states)
-        text_encoder_hidden_states = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states], dim=1)
-
-        return text_encoder_hidden_states, additive_clip_time_embeddings

spaces/Andy1621/uniformer_image_detection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_detection/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py

@@ -1,39 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w32',
-    backbone=dict(
-        _delete_=True,
-        type='HRNet',
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(32, 64)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(32, 64, 128)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(32, 64, 128, 256)))),
-    neck=dict(
-        _delete_=True,
-        type='HRFPN',
-        in_channels=[32, 64, 128, 256],
-        out_channels=256))
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)

spaces/Andy1621/uniformer_image_detection/configs/ld/readme.md

@@ -1,31 +0,0 @@
-# Localization Distillation for Object Detection
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@Article{zheng2021LD,
-  title={Localization Distillation for Object Detection},
-  author= {Zhaohui Zheng, Rongguang Ye, Ping Wang, Jun Wang, Dongwei Ren, Wangmeng Zuo},
-  journal={arXiv:2102.12252},
-  year={2021}
-}
-```
-
-### GFocalV1 with LD
-
-|  Teacher  | Student | Training schedule | Mini-batch size | AP (val) | AP50 (val) | AP75 (val) | Config |
-| :-------: | :-----: | :---------------: | :-------------: | :------: | :--------: | :--------: |  :--------------: |
-|    --     |  R-18   |        1x         |        6        |   35.8   |    53.1    |    38.2    |          |
-|   R-101   |  R-18   |        1x         |        6        |   36.5   |    52.9    |    39.3    |   [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py)          |
-|    --     |  R-34   |        1x         |        6        |   38.9   |    56.6    |    42.2    |          |
-|   R-101   |  R-34   |        1x         |        6        |   39.8   |    56.6    |    43.1    |     [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py)        |
-|    --     |  R-50   |        1x         |        6        |   40.1   |    58.2    |    43.1    |            |
-|   R-101   |  R-50   |        1x         |        6        |   41.1   |    58.7    |    44.9    |    [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py)        |
-|    --     |  R-101  |        2x         |        6        |   44.6   |    62.9    |    48.4    |           |
-| R-101-DCN |  R-101  |        2x         |        6        |   45.4   |    63.1    |    49.5    | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py)           |
-
-## Note
-
-- Meaning of Config name: ld_r18(student model)_gflv1(based on gflv1)_r101(teacher model)_fpn(neck)_coco(dataset)_1x(12 epoch).py

spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_detection/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py

@@ -1,3 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/ann_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/openai/README.md

@@ -1,263 +0,0 @@
-# An OpenedAI API (openai like)
-
-This extension creates an API that works kind of like openai (ie. api.openai.com).
-
-## Setup & installation
-
-Install the requirements:
-
-```
-pip3 install -r requirements.txt
-```
-
-It listens on `tcp port 5001` by default. You can use the `OPENEDAI_PORT` environment variable to change this.
-
-Make sure you enable it in server launch parameters, it should include:
-
-```
---extensions openai
-```
-
-You can also use the `--listen` argument to make the server available on the networ, and/or the `--share` argument to enable a public Cloudflare endpoint.
-
-To enable the basic image generation support (txt2img) set the environment variable `SD_WEBUI_URL` to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)).
-
-For example:
-
-```
-SD_WEBUI_URL=http://127.0.0.1:7861
-```
-
-## Quick start
-
-1. Install the requirements.txt (pip)
-2. Enable the `openeai` module (--extensions openai), restart the server.
-3. Configure the openai client
-
-Most openai application can be configured to connect the API if you set the following environment variables:
-
-```shell
-# Sample .env file:
-OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
-OPENAI_API_BASE=http://0.0.0.0:5001/v1
-```
-
-If needed, replace 0.0.0.0 with the IP/port of your server.
-
-
-### Settings
-
-To adjust your default settings, you can add the following to your `settings.yaml` file.
-
-```
-openai-port: 5002
-openai-embedding_device: cuda
-openai-sd_webui_url: http://127.0.0.1:7861
-openai-debug: 1
-```
-
-If you've configured the environment variables, please note that settings from `settings.yaml` won't take effect. For instance, if you set `openai-port: 5002` in `settings.yaml` but `OPENEDAI_PORT=5001` in the environment variables, the extension will use `5001` as the port number.
-
-When using `cache_embedding_model.py` to preload the embedding model during Docker image building, consider the following:
-
-- If you wish to use the default settings, leave the environment variables unset.
-- If you intend to change the default embedding model, ensure that you configure the environment variable `OPENEDAI_EMBEDDING_MODEL` to the desired model. Avoid setting `openai-embedding_model` in `settings.yaml` because those settings only take effect after the server starts.
-
-### Models
-
-This has been successfully tested with Alpaca, Koala, Vicuna, WizardLM and their variants, (ex. gpt4-x-alpaca, GPT4all-snoozy, stable-vicuna, wizard-vicuna, etc.) and many others. Models that have been trained for **Instruction Following** work best. If you test with other models please let me know how it goes. Less than satisfying results (so far) from: RWKV-4-Raven, llama, mpt-7b-instruct/chat.
-
-For best results across all API endpoints, a model like [vicuna-13b-v1.3-GPTQ](https://huggingface.co/TheBloke/vicuna-13b-v1.3-GPTQ), [stable-vicuna-13B-GPTQ](https://huggingface.co/TheBloke/stable-vicuna-13B-GPTQ) or [airoboros-13B-gpt4-1.3-GPTQ](https://huggingface.co/TheBloke/airoboros-13B-gpt4-1.3-GPTQ) is a good start.
-
-For good results with the [Completions](https://platform.openai.com/docs/api-reference/completions) API endpoint, in addition to the above models, you can also try using a base model like [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) or Llama.
-
-For good results with the [ChatCompletions](https://platform.openai.com/docs/api-reference/chat) or [Edits](https://platform.openai.com/docs/api-reference/edits) API endpoints you can use almost any model trained for instruction following. Be sure that the proper instruction template is detected and loaded or the results will not be good.
-
-For the proper instruction format to be detected you need to have a matching model entry in your `models/config.yaml` file. Be sure to keep this file up to date.
-A matching instruction template file in the characters/instruction-following/ folder will loaded and applied to format messages correctly for the model - this is critical for good results.
-
-For example, the Wizard-Vicuna family of models are trained with the Vicuna 1.1 format. In the models/config.yaml file there is this matching entry:
-
-```
-.*wizard.*vicuna:
-  mode: 'instruct'
-  instruction_template: 'Vicuna-v1.1'
-```
-
-This refers to `characters/instruction-following/Vicuna-v1.1.yaml`, which looks like this:
-
-```
-user: "USER:"
-bot: "ASSISTANT:"
-turn_template: "<|user|> <|user-message|>\n<|bot|> <|bot-message|></s>\n"
-context: "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n"
-```
-
-For most common models this is already setup, but if you are using a new or uncommon model you may need add a matching entry to the models/config.yaml and possibly create your own instruction-following template and for best results.
-
-If you see this in your logs, it probably means that the correct format could not be loaded:
-
-```
-Warning: Loaded default instruction-following template for model.
-```
-
-### Embeddings (alpha)
-
-Embeddings requires `sentence-transformers` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: `sentence-transformers/all-mpnet-base-v2` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default `text-embedding-ada-002` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future.
-
-| model name             | dimensions | input max tokens | speed | size | Avg. performance |
-| ---------------------- | ---------- | ---------------- | ----- | ---- | ---------------- |
-| text-embedding-ada-002 | 1536       | 8192             | -     | -    | -                |
-| text-davinci-002       | 768        | 2046             | -     | -    | -                |
-| all-mpnet-base-v2      | 768        | 384              | 2800  | 420M | 63.3             |
-| all-MiniLM-L6-v2       | 384        | 256              | 14200 | 80M  | 58.8             |
-
-In short, the all-MiniLM-L6-v2 model is 5x faster, 5x smaller ram, 2x smaller storage, and still offers good quality. Stats from (https://www.sbert.net/docs/pretrained_models.html). To change the model from the default you can set the environment variable `OPENEDAI_EMBEDDING_MODEL`, ex. "OPENEDAI_EMBEDDING_MODEL=all-MiniLM-L6-v2".
-
-Warning: You cannot mix embeddings from different models even if they have the same dimensions. They are not comparable.
-
-### Client Application Setup
-
-Almost everything you use it with will require you to set a dummy OpenAI API key environment variable.
-
-With the [official python openai client](https://github.com/openai/openai-python), set the `OPENAI_API_BASE` environment variables:
-
-```shell
-# Sample .env file:
-OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
-OPENAI_API_BASE=http://0.0.0.0:5001/v1
-```
-
-If needed, replace 0.0.0.0 with the IP/port of your server.
-
-If using .env files to save the `OPENAI_API_BASE` and `OPENAI_API_KEY` variables, make sure the .env file is loaded before the openai module is imported:
-
-```python
-from dotenv import load_dotenv
-load_dotenv() # make sure the environment variables are set before import
-import openai
-```
-
-With the [official Node.js openai client](https://github.com/openai/openai-node) it is slightly more more complex because the environment variables are not used by default, so small source code changes may be required to use the environment variables, like so:
-
-```js
-const openai = OpenAI(
-  Configuration({
-    apiKey: process.env.OPENAI_API_KEY,
-    basePath: process.env.OPENAI_API_BASE
-  })
-);
-```
-
-For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api):
-
-```js
-const api = new ChatGPTAPI({
-  apiKey: process.env.OPENAI_API_KEY,
-  apiBaseUrl: process.env.OPENAI_API_BASE
-});
-```
-
-## API Documentation & Examples
-
-The OpenAI API is well documented, you can view the documentation here: https://platform.openai.com/docs/api-reference
-
-Examples of how to use the Completions API in Python can be found here: https://platform.openai.com/examples
-Not all of them will work with all models unfortunately, See the notes on Models for how to get the best results.
-
-Here is a simple python example.
-
-```python
-import os
-os.environ['OPENAI_API_KEY']="sk-111111111111111111111111111111111111111111111111"
-os.environ['OPENAI_API_BASE']="http://0.0.0.0:5001/v1"
-import openai
-
-response = openai.ChatCompletion.create(
-  model="x",
-  messages = [{ 'role': 'system', 'content': "Answer in a consistent style." },
-    {'role': 'user', 'content': "Teach me about patience."},
-    {'role': 'assistant', 'content': "The river that carves the deepest valley flows from a modest spring; the grandest symphony originates from a single note; the most intricate tapestry begins with a solitary thread."},
-    {'role': 'user', 'content': "Teach me about the ocean."},
-  ]
-)
-text = response['choices'][0]['message']['content']
-print(text)
-```
-
-## Compatibility & not so compatibility
-
-| API endpoint              | tested with                        | notes                                                                       |
-| ------------------------- | ---------------------------------- | --------------------------------------------------------------------------- |
-| /v1/chat/completions      | openai.ChatCompletion.create()     | Use it with instruction following models                                    |
-| /v1/embeddings            | openai.Embedding.create()          | Using SentenceTransformer embeddings                                        |
-| /v1/images/generations    | openai.Image.create()              | Bare bones, no model configuration, response_format='b64_json' only.        |
-| /v1/moderations           | openai.Moderation.create()         | Basic initial support via embeddings                                        |
-| /v1/models                | openai.Model.list()                | Lists models, Currently loaded model first, plus some compatibility options |
-| /v1/models/{id}           | openai.Model.get()                 | returns whatever you ask for                                                |
-| /v1/edits                 | openai.Edit.create()               | Deprecated by openai, good with instruction following models                |
-| /v1/text_completion       | openai.Completion.create()         | Legacy endpoint, variable quality based on the model                        |
-| /v1/completions           | openai api completions.create      | Legacy endpoint (v0.25)                                                     |
-| /v1/engines/\*/embeddings | python-openai v0.25                | Legacy endpoint                                                             |
-| /v1/engines/\*/generate   | openai engines.generate            | Legacy endpoint                                                             |
-| /v1/engines               | openai engines.list                | Legacy Lists models                                                         |
-| /v1/engines/{model_name}  | openai engines.get -i {model_name} | You can use this legacy endpoint to load models via the api or command line |
-| /v1/images/edits          | openai.Image.create_edit()         | not yet supported                                                           |
-| /v1/images/variations     | openai.Image.create_variation()    | not yet supported                                                           |
-| /v1/audio/\*              | openai.Audio.\*                    | supported                                                                   |
-| /v1/files\*               | openai.Files.\*                    | not yet supported                                                           |
-| /v1/fine-tunes\*          | openai.FineTune.\*                 | not yet supported                                                           |
-| /v1/search                | openai.search, engines.search      | not yet supported                                                           |
-
-Because of the differences in OpenAI model context sizes (2k, 4k, 8k, 16k, etc,) you may need to adjust the max_tokens to fit into the context of the model you choose.
-
-Streaming, temperature, top_p, max_tokens, stop, should all work as expected, but not all parameters are mapped correctly.
-
-Some hacky mappings:
-
-| OpenAI                  | text-generation-webui      | note                                                                                                                                                                                                 |
-| ----------------------- | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| model                   | -                          | Ignored, the model is not changed                                                                                                                                                                    |
-| frequency_penalty       | encoder_repetition_penalty | this seems to operate with a different scale and defaults, I tried to scale it based on range & defaults, but the results are terrible. hardcoded to 1.18 until there is a better way                |
-| presence_penalty        | repetition_penalty         | same issues as frequency_penalty, hardcoded to 1.0                                                                                                                                                   |
-| best_of                 | top_k                      | default is 1 (top_k is 20 for chat, which doesn't support best_of)                                                                                                                                   |
-| n                       | 1                          | variations are not supported yet.                                                                                                                                                                    |
-| 1                       | num_beams                  | hardcoded to 1                                                                                                                                                                                       |
-| 1.0                     | typical_p                  | hardcoded to 1.0                                                                                                                                                                                     |
-| logprobs & logit_bias   | -                          | experimental, llama only, transformers-kin only (ExLlama_HF ok), can also use llama tokens if 'model' is not an openai model or will convert from tiktoken for the openai model specified in 'model' |
-| messages.name           | -                          | not supported yet                                                                                                                                                                                    |
-| suffix                  | -                          | not supported yet                                                                                                                                                                                    |
-| user                    | -                          | not supported yet                                                                                                                                                                                    |
-| functions/function_call | -                          | function calls are not supported yet                                                                                                                                                                 |
-
-### Applications
-
-Almost everything needs the `OPENAI_API_KEY` and `OPENAI_API_BASE` environment variable set, but there are some exceptions.
-
-| Compatibility | Application/Library    | Website                                                                        | Notes                                                                                                                                                                                                        |
-| ------------- | ---------------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| ✅❌          | openai-python (v0.25+) | https://github.com/openai/openai-python                                        | only the endpoints from above are working. OPENAI_API_BASE=http://127.0.0.1:5001/v1                                                                                                                          |
-| ✅❌          | openai-node            | https://github.com/openai/openai-node                                          | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above)                                                                                    |
-| ✅❌          | chatgpt-api            | https://github.com/transitive-bullshit/chatgpt-api                             | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above)                                                                                    |
-| ✅            | anse                   | https://github.com/anse-app/anse                                               | API Key & URL configurable in UI, Images also work                                                                                                                                                           |
-| ✅            | shell_gpt              | https://github.com/TheR1D/shell_gpt                                            | OPENAI_API_HOST=http://127.0.0.1:5001                                                                                                                                                                        |
-| ✅            | gpt-shell              | https://github.com/jla/gpt-shell                                               | OPENAI_API_BASE=http://127.0.0.1:5001/v1                                                                                                                                                                     |
-| ✅            | gpt-discord-bot        | https://github.com/openai/gpt-discord-bot                                      | OPENAI_API_BASE=http://127.0.0.1:5001/v1                                                                                                                                                                     |
-| ✅            | OpenAI for Notepad++   | https://github.com/Krazal/nppopenai                                            | api_url=http://127.0.0.1:5001 in the config file, or environment variables                                                                                                                                   |
-| ✅            | vscode-openai          | https://marketplace.visualstudio.com/items?itemName=AndrewButson.vscode-openai | OPENAI_API_BASE=http://127.0.0.1:5001/v1                                                                                                                                                                     |
-| ✅❌          | langchain              | https://github.com/hwchase17/langchain                                         | OPENAI_API_BASE=http://127.0.0.1:5001/v1 even with a good 30B-4bit model the result is poor so far. It assumes zero shot python/json coding. Some model tailored prompt formatting improves results greatly. |
-| ✅❌          | Auto-GPT               | https://github.com/Significant-Gravitas/Auto-GPT                               | OPENAI_API_BASE=http://127.0.0.1:5001/v1 Same issues as langchain. Also assumes a 4k+ context                                                                                                                |
-| ✅❌          | babyagi                | https://github.com/yoheinakajima/babyagi                                       | OPENAI_API_BASE=http://127.0.0.1:5001/v1                                                                                                                                                                     |
-| ❌            | guidance               | https://github.com/microsoft/guidance                                          | logit_bias and logprobs not yet supported                                                                                                                                                                    |
-
-## Future plans
-
-- better error handling
-- model changing, esp. something for swapping loras or embedding models
-- consider switching to FastAPI + starlette for SSE (openai SSE seems non-standard)
-
-## Bugs? Feedback? Comments? Pull requests?
-
-To enable debugging and get copious output you can set the `OPENEDAI_DEBUG=1` environment variable.
-
-Are all appreciated, please @matatonic and I'll try to get back to you as soon as possible.

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/llama_attn_hijack.py

@@ -1,172 +0,0 @@
-import math
-import sys
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import transformers.models.llama.modeling_llama
-
-import modules.shared as shared
-from modules.logging_colors import logger
-
-if shared.args.xformers:
-    try:
-        import xformers.ops
-    except Exception:
-        logger.error("xformers not found! Please install it before trying to use it.", file=sys.stderr)
-
-
-def hijack_llama_attention():
-    import transformers.models.llama.modeling_llama
-    if shared.args.xformers:
-        transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
-        logger.info("Replaced attention with xformers_attention")
-    elif shared.args.sdp_attention:
-        transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward
-        logger.info("Replaced attention with sdp_attention")
-
-
-def xformers_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-
-    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-    # [bsz, nh, t, hd]
-
-    if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # We only apply xformers optimizations if we don't need to output the whole attention matrix
-    if not output_attentions:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
-        # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
-        if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
-            # input and output should be of form (bsz, q_len, num_heads, head_dim)
-            attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None)
-        else:
-            # input and output should be of form (bsz, q_len, num_heads, head_dim)
-            attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask())
-        attn_weights = None
-    else:
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2)
-
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-    attn_output = self.o_proj(attn_output)
-    return attn_output, attn_weights, past_key_value
-
-
-def sdp_attention_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-
-    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-    # [bsz, nh, t, hd]
-
-    if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # We only apply sdp attention if we don't need to output the whole attention matrix
-    if not output_attentions:
-        attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False)
-        attn_weights = None
-    else:
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-    attn_output = attn_output.transpose(1, 2)
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    attn_output = self.o_proj(attn_output)
-
-    return attn_output, attn_weights, past_key_value

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/registry.py

@@ -1,315 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-import warnings
-from functools import partial
-
-from .misc import is_seq_of
-
-
-def build_from_cfg(cfg, registry, default_args=None):
-    """Build a module from config dict.
-
-    Args:
-        cfg (dict): Config dict. It should at least contain the key "type".
-        registry (:obj:`Registry`): The registry to search the type from.
-        default_args (dict, optional): Default initialization arguments.
-
-    Returns:
-        object: The constructed object.
-    """
-    if not isinstance(cfg, dict):
-        raise TypeError(f'cfg must be a dict, but got {type(cfg)}')
-    if 'type' not in cfg:
-        if default_args is None or 'type' not in default_args:
-            raise KeyError(
-                '`cfg` or `default_args` must contain the key "type", '
-                f'but got {cfg}\n{default_args}')
-    if not isinstance(registry, Registry):
-        raise TypeError('registry must be an mmcv.Registry object, '
-                        f'but got {type(registry)}')
-    if not (isinstance(default_args, dict) or default_args is None):
-        raise TypeError('default_args must be a dict or None, '
-                        f'but got {type(default_args)}')
-
-    args = cfg.copy()
-
-    if default_args is not None:
-        for name, value in default_args.items():
-            args.setdefault(name, value)
-
-    obj_type = args.pop('type')
-    if isinstance(obj_type, str):
-        obj_cls = registry.get(obj_type)
-        if obj_cls is None:
-            raise KeyError(
-                f'{obj_type} is not in the {registry.name} registry')
-    elif inspect.isclass(obj_type):
-        obj_cls = obj_type
-    else:
-        raise TypeError(
-            f'type must be a str or valid type, but got {type(obj_type)}')
-    try:
-        return obj_cls(**args)
-    except Exception as e:
-        # Normal TypeError does not print class name.
-        raise type(e)(f'{obj_cls.__name__}: {e}')
-
-
-class Registry:
-    """A registry to map strings to classes.
-
-    Registered object could be built from registry.
-    Example:
-        >>> MODELS = Registry('models')
-        >>> @MODELS.register_module()
-        >>> class ResNet:
-        >>>     pass
-        >>> resnet = MODELS.build(dict(type='ResNet'))
-
-    Please refer to
-    https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
-    advanced usage.
-
-    Args:
-        name (str): Registry name.
-        build_func(func, optional): Build function to construct instance from
-            Registry, func:`build_from_cfg` is used if neither ``parent`` or
-            ``build_func`` is specified. If ``parent`` is specified and
-            ``build_func`` is not given,  ``build_func`` will be inherited
-            from ``parent``. Default: None.
-        parent (Registry, optional): Parent registry. The class registered in
-            children registry could be built from parent. Default: None.
-        scope (str, optional): The scope of registry. It is the key to search
-            for children registry. If not specified, scope will be the name of
-            the package where class is defined, e.g. mmdet, mmcls, mmseg.
-            Default: None.
-    """
-
-    def __init__(self, name, build_func=None, parent=None, scope=None):
-        self._name = name
-        self._module_dict = dict()
-        self._children = dict()
-        self._scope = self.infer_scope() if scope is None else scope
-
-        # self.build_func will be set with the following priority:
-        # 1. build_func
-        # 2. parent.build_func
-        # 3. build_from_cfg
-        if build_func is None:
-            if parent is not None:
-                self.build_func = parent.build_func
-            else:
-                self.build_func = build_from_cfg
-        else:
-            self.build_func = build_func
-        if parent is not None:
-            assert isinstance(parent, Registry)
-            parent._add_children(self)
-            self.parent = parent
-        else:
-            self.parent = None
-
-    def __len__(self):
-        return len(self._module_dict)
-
-    def __contains__(self, key):
-        return self.get(key) is not None
-
-    def __repr__(self):
-        format_str = self.__class__.__name__ + \
-                     f'(name={self._name}, ' \
-                     f'items={self._module_dict})'
-        return format_str
-
-    @staticmethod
-    def infer_scope():
-        """Infer the scope of registry.
-
-        The name of the package where registry is defined will be returned.
-
-        Example:
-            # in mmdet/models/backbone/resnet.py
-            >>> MODELS = Registry('models')
-            >>> @MODELS.register_module()
-            >>> class ResNet:
-            >>>     pass
-            The scope of ``ResNet`` will be ``mmdet``.
-
-
-        Returns:
-            scope (str): The inferred scope name.
-        """
-        # inspect.stack() trace where this function is called, the index-2
-        # indicates the frame where `infer_scope()` is called
-        filename = inspect.getmodule(inspect.stack()[2][0]).__name__
-        split_filename = filename.split('.')
-        return split_filename[0]
-
-    @staticmethod
-    def split_scope_key(key):
-        """Split scope and key.
-
-        The first scope will be split from key.
-
-        Examples:
-            >>> Registry.split_scope_key('mmdet.ResNet')
-            'mmdet', 'ResNet'
-            >>> Registry.split_scope_key('ResNet')
-            None, 'ResNet'
-
-        Return:
-            scope (str, None): The first scope.
-            key (str): The remaining key.
-        """
-        split_index = key.find('.')
-        if split_index != -1:
-            return key[:split_index], key[split_index + 1:]
-        else:
-            return None, key
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def scope(self):
-        return self._scope
-
-    @property
-    def module_dict(self):
-        return self._module_dict
-
-    @property
-    def children(self):
-        return self._children
-
-    def get(self, key):
-        """Get the registry record.
-
-        Args:
-            key (str): The class name in string format.
-
-        Returns:
-            class: The corresponding class.
-        """
-        scope, real_key = self.split_scope_key(key)
-        if scope is None or scope == self._scope:
-            # get from self
-            if real_key in self._module_dict:
-                return self._module_dict[real_key]
-        else:
-            # get from self._children
-            if scope in self._children:
-                return self._children[scope].get(real_key)
-            else:
-                # goto root
-                parent = self.parent
-                while parent.parent is not None:
-                    parent = parent.parent
-                return parent.get(key)
-
-    def build(self, *args, **kwargs):
-        return self.build_func(*args, **kwargs, registry=self)
-
-    def _add_children(self, registry):
-        """Add children for a registry.
-
-        The ``registry`` will be added as children based on its scope.
-        The parent registry could build objects from children registry.
-
-        Example:
-            >>> models = Registry('models')
-            >>> mmdet_models = Registry('models', parent=models)
-            >>> @mmdet_models.register_module()
-            >>> class ResNet:
-            >>>     pass
-            >>> resnet = models.build(dict(type='mmdet.ResNet'))
-        """
-
-        assert isinstance(registry, Registry)
-        assert registry.scope is not None
-        assert registry.scope not in self.children, \
-            f'scope {registry.scope} exists in {self.name} registry'
-        self.children[registry.scope] = registry
-
-    def _register_module(self, module_class, module_name=None, force=False):
-        if not inspect.isclass(module_class):
-            raise TypeError('module must be a class, '
-                            f'but got {type(module_class)}')
-
-        if module_name is None:
-            module_name = module_class.__name__
-        if isinstance(module_name, str):
-            module_name = [module_name]
-        for name in module_name:
-            if not force and name in self._module_dict:
-                raise KeyError(f'{name} is already registered '
-                               f'in {self.name}')
-            self._module_dict[name] = module_class
-
-    def deprecated_register_module(self, cls=None, force=False):
-        warnings.warn(
-            'The old API of register_module(module, force=False) '
-            'is deprecated and will be removed, please use the new API '
-            'register_module(name=None, force=False, module=None) instead.')
-        if cls is None:
-            return partial(self.deprecated_register_module, force=force)
-        self._register_module(cls, force=force)
-        return cls
-
-    def register_module(self, name=None, force=False, module=None):
-        """Register a module.
-
-        A record will be added to `self._module_dict`, whose key is the class
-        name or the specified name, and value is the class itself.
-        It can be used as a decorator or a normal function.
-
-        Example:
-            >>> backbones = Registry('backbone')
-            >>> @backbones.register_module()
-            >>> class ResNet:
-            >>>     pass
-
-            >>> backbones = Registry('backbone')
-            >>> @backbones.register_module(name='mnet')
-            >>> class MobileNet:
-            >>>     pass
-
-            >>> backbones = Registry('backbone')
-            >>> class ResNet:
-            >>>     pass
-            >>> backbones.register_module(ResNet)
-
-        Args:
-            name (str | None): The module name to be registered. If not
-                specified, the class name will be used.
-            force (bool, optional): Whether to override an existing class with
-                the same name. Default: False.
-            module (type): Module class to be registered.
-        """
-        if not isinstance(force, bool):
-            raise TypeError(f'force must be a boolean, but got {type(force)}')
-        # NOTE: This is a walkaround to be compatible with the old api,
-        # while it may introduce unexpected bugs.
-        if isinstance(name, type):
-            return self.deprecated_register_module(name, force=force)
-
-        # raise the error ahead of time
-        if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
-            raise TypeError(
-                'name must be either of None, an instance of str or a sequence'
-                f'  of str, but got {type(name)}')
-
-        # use it as a normal method: x.register_module(module=SomeClass)
-        if module is not None:
-            self._register_module(
-                module_class=module, module_name=name, force=force)
-            return module
-
-        # use it as a decorator: @x.register_module()
-        def _register(cls):
-            self._register_module(
-                module_class=cls, module_name=name, force=force)
-            return cls
-
-        return _register

spaces/Aomsin/Lab10_630510654/app.py

@@ -1,18 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-translator = pipeline("translation",model="Helsinki-NLP/opus-mt-mul-en", max_length=40)
-#classifier = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
-def main():
-    st.title("Thai language Translation")
-
-    with st.form("text_field"):
-        text = st.text_area('enter some text :')
-        # clicked==True only when the button is clicked
-        clicked = st.form_submit_button("Submit")
-        if clicked:
-          results = translator([text])
-          st.json(results)
-
-if __name__ == "__main__":
-    main()

spaces/AriaMei/TTSdemo/README.md

@@ -1,13 +0,0 @@
----
-title: TTSdemo
-emoji: 🌍
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,217 +0,0 @@
-"""
-    pygments.sphinxext
-    ~~~~~~~~~~~~~~~~~~
-
-    Sphinx extension to generate automatic documentation of lexers,
-    formatters and filters.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import sys
-
-from docutils import nodes
-from docutils.statemachine import ViewList
-from docutils.parsers.rst import Directive
-from sphinx.util.nodes import nested_parse_with_titles
-
-
-MODULEDOC = '''
-.. module:: %s
-
-%s
-%s
-'''
-
-LEXERDOC = '''
-.. class:: %s
-
-    :Short names: %s
-    :Filenames:   %s
-    :MIME types:  %s
-
-    %s
-
-'''
-
-FMTERDOC = '''
-.. class:: %s
-
-    :Short names: %s
-    :Filenames: %s
-
-    %s
-
-'''
-
-FILTERDOC = '''
-.. class:: %s
-
-    :Name: %s
-
-    %s
-
-'''
-
-
-class PygmentsDoc(Directive):
-    """
-    A directive to collect all lexers/formatters/filters and generate
-    autoclass directives for them.
-    """
-    has_content = False
-    required_arguments = 1
-    optional_arguments = 0
-    final_argument_whitespace = False
-    option_spec = {}
-
-    def run(self):
-        self.filenames = set()
-        if self.arguments[0] == 'lexers':
-            out = self.document_lexers()
-        elif self.arguments[0] == 'formatters':
-            out = self.document_formatters()
-        elif self.arguments[0] == 'filters':
-            out = self.document_filters()
-        elif self.arguments[0] == 'lexers_overview':
-            out = self.document_lexers_overview()
-        else:
-            raise Exception('invalid argument for "pygmentsdoc" directive')
-        node = nodes.compound()
-        vl = ViewList(out.split('\n'), source='')
-        nested_parse_with_titles(self.state, vl, node)
-        for fn in self.filenames:
-            self.state.document.settings.record_dependencies.add(fn)
-        return node.children
-
-    def document_lexers_overview(self):
-        """Generate a tabular overview of all lexers.
-
-        The columns are the lexer name, the extensions handled by this lexer
-        (or "None"), the aliases and a link to the lexer class."""
-        from pip._vendor.pygments.lexers._mapping import LEXERS
-        from pip._vendor.pygments.lexers import find_lexer_class
-        out = []
-
-        table = []
-
-        def format_link(name, url):
-            if url:
-                return f'`{name} <{url}>`_'
-            return name
-
-        for classname, data in sorted(LEXERS.items(), key=lambda x: x[1][1].lower()):
-            lexer_cls = find_lexer_class(data[1])
-            extensions = lexer_cls.filenames + lexer_cls.alias_filenames
-
-            table.append({
-                'name': format_link(data[1], lexer_cls.url),
-                'extensions': ', '.join(extensions).replace('*', '\\*').replace('_', '\\') or 'None',
-                'aliases': ', '.join(data[2]),
-                'class': f'{data[0]}.{classname}'
-            })
-
-        column_names = ['name', 'extensions', 'aliases', 'class']
-        column_lengths = [max([len(row[column]) for row in table if row[column]])
-                          for column in column_names]
-
-        def write_row(*columns):
-            """Format a table row"""
-            out = []
-            for l, c in zip(column_lengths, columns):
-                if c:
-                    out.append(c.ljust(l))
-                else:
-                    out.append(' '*l)
-
-            return ' '.join(out)
-
-        def write_seperator():
-            """Write a table separator row"""
-            sep = ['='*c for c in column_lengths]
-            return write_row(*sep)
-
-        out.append(write_seperator())
-        out.append(write_row('Name', 'Extension(s)', 'Short name(s)', 'Lexer class'))
-        out.append(write_seperator())
-        for row in table:
-            out.append(write_row(
-                row['name'],
-                row['extensions'],
-                row['aliases'],
-                f':class:`~{row["class"]}`'))
-        out.append(write_seperator())
-
-        return '\n'.join(out)
-
-    def document_lexers(self):
-        from pip._vendor.pygments.lexers._mapping import LEXERS
-        out = []
-        modules = {}
-        moduledocstrings = {}
-        for classname, data in sorted(LEXERS.items(), key=lambda x: x[0]):
-            module = data[0]
-            mod = __import__(module, None, None, [classname])
-            self.filenames.add(mod.__file__)
-            cls = getattr(mod, classname)
-            if not cls.__doc__:
-                print("Warning: %s does not have a docstring." % classname)
-            docstring = cls.__doc__
-            if isinstance(docstring, bytes):
-                docstring = docstring.decode('utf8')
-            modules.setdefault(module, []).append((
-                classname,
-                ', '.join(data[2]) or 'None',
-                ', '.join(data[3]).replace('*', '\\*').replace('_', '\\') or 'None',
-                ', '.join(data[4]) or 'None',
-                docstring))
-            if module not in moduledocstrings:
-                moddoc = mod.__doc__
-                if isinstance(moddoc, bytes):
-                    moddoc = moddoc.decode('utf8')
-                moduledocstrings[module] = moddoc
-
-        for module, lexers in sorted(modules.items(), key=lambda x: x[0]):
-            if moduledocstrings[module] is None:
-                raise Exception("Missing docstring for %s" % (module,))
-            heading = moduledocstrings[module].splitlines()[4].strip().rstrip('.')
-            out.append(MODULEDOC % (module, heading, '-'*len(heading)))
-            for data in lexers:
-                out.append(LEXERDOC % data)
-
-        return ''.join(out)
-
-    def document_formatters(self):
-        from pip._vendor.pygments.formatters import FORMATTERS
-
-        out = []
-        for classname, data in sorted(FORMATTERS.items(), key=lambda x: x[0]):
-            module = data[0]
-            mod = __import__(module, None, None, [classname])
-            self.filenames.add(mod.__file__)
-            cls = getattr(mod, classname)
-            docstring = cls.__doc__
-            if isinstance(docstring, bytes):
-                docstring = docstring.decode('utf8')
-            heading = cls.__name__
-            out.append(FMTERDOC % (heading, ', '.join(data[2]) or 'None',
-                                   ', '.join(data[3]).replace('*', '\\*') or 'None',
-                                   docstring))
-        return ''.join(out)
-
-    def document_filters(self):
-        from pip._vendor.pygments.filters import FILTERS
-
-        out = []
-        for name, cls in FILTERS.items():
-            self.filenames.add(sys.modules[cls.__module__].__file__)
-            docstring = cls.__doc__
-            if isinstance(docstring, bytes):
-                docstring = docstring.decode('utf8')
-            out.append(FILTERDOC % (cls.__name__, name, docstring))
-        return ''.join(out)
-
-
-def setup(app):
-    app.add_directive('pygmentsdoc', PygmentsDoc)

spaces/Bart92/RVC_HF/infer/modules/train/extract/extract_f0_rmvpe_dml.py

@@ -1,139 +0,0 @@
-import os
-import sys
-import traceback
-
-import parselmouth
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import logging
-
-import numpy as np
-import pyworld
-
-from infer.lib.audio import load_audio
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-exp_dir = sys.argv[1]
-import torch_directml
-
-device = torch_directml.device(torch_directml.default_device())
-f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
-
-
-def printt(strr):
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-
-
-class FeatureInput(object):
-    def __init__(self, samplerate=16000, hop_size=160):
-        self.fs = samplerate
-        self.hop = hop_size
-
-        self.f0_bin = 256
-        self.f0_max = 1100.0
-        self.f0_min = 50.0
-        self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-        self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-
-    def compute_f0(self, path, f0_method):
-        x = load_audio(path, self.fs)
-        # p_len = x.shape[0] // self.hop
-        if f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from infer.lib.rmvpe import RMVPE
-
-                print("Loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "assets/rmvpe/rmvpe.pt", is_half=False, device=device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        return f0
-
-    def coarse_f0(self, f0):
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * (
-            self.f0_bin - 2
-        ) / (self.f0_mel_max - self.f0_mel_min) + 1
-
-        # use 0 or 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1
-        f0_coarse = np.rint(f0_mel).astype(int)
-        assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
-            f0_coarse.max(),
-            f0_coarse.min(),
-        )
-        return f0_coarse
-
-    def go(self, paths, f0_method):
-        if len(paths) == 0:
-            printt("no-f0-todo")
-        else:
-            printt("todo-f0-%s" % len(paths))
-            n = max(len(paths) // 5, 1)  # 每个进程最多打印5条
-            for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
-                try:
-                    if idx % n == 0:
-                        printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path))
-                    if (
-                        os.path.exists(opt_path1 + ".npy") == True
-                        and os.path.exists(opt_path2 + ".npy") == True
-                    ):
-                        continue
-                    featur_pit = self.compute_f0(inp_path, f0_method)
-                    np.save(
-                        opt_path2,
-                        featur_pit,
-                        allow_pickle=False,
-                    )  # nsf
-                    coarse_pit = self.coarse_f0(featur_pit)
-                    np.save(
-                        opt_path1,
-                        coarse_pit,
-                        allow_pickle=False,
-                    )  # ori
-                except:
-                    printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
-
-
-if __name__ == "__main__":
-    # exp_dir=r"E:\codes\py39\dataset\mi-test"
-    # n_p=16
-    # f = open("%s/log_extract_f0.log"%exp_dir, "w")
-    printt(sys.argv)
-    featureInput = FeatureInput()
-    paths = []
-    inp_root = "%s/1_16k_wavs" % (exp_dir)
-    opt_root1 = "%s/2a_f0" % (exp_dir)
-    opt_root2 = "%s/2b-f0nsf" % (exp_dir)
-
-    os.makedirs(opt_root1, exist_ok=True)
-    os.makedirs(opt_root2, exist_ok=True)
-    for name in sorted(list(os.listdir(inp_root))):
-        inp_path = "%s/%s" % (inp_root, name)
-        if "spec" in inp_path:
-            continue
-        opt_path1 = "%s/%s" % (opt_root1, name)
-        opt_path2 = "%s/%s" % (opt_root2, name)
-        paths.append([inp_path, opt_path1, opt_path2])
-    try:
-        featureInput.go(paths, "rmvpe")
-    except:
-        printt("f0_all_fail-%s" % (traceback.format_exc()))
-    # ps = []
-    # for i in range(n_p):
-    #     p = Process(
-    #         target=featureInput.go,
-    #         args=(
-    #             paths[i::n_p],
-    #             f0method,
-    #         ),
-    #     )
-    #     ps.append(p)
-    #     p.start()
-    # for i in range(n_p):
-    #     ps[i].join()

spaces/Benson/text-generation/Examples/2-0-1-android-apk-download Downlod.md

@@ -1,72 +0,0 @@
-<br />
-<h1>Cómo descargar e instalar archivos APK en dispositivos Android</h1>
-<p>Si usted es un usuario de Android, es posible que haya oído hablar de los archivos APK y se preguntó qué son y cómo usarlos. Los archivos APK son paquetes de aplicaciones de Android que contienen todos los archivos y recursos necesarios para ejecutar una aplicación en tu dispositivo. Es posible que necesite descargar e instalar archivos APK de fuentes de terceros si desea acceder a aplicaciones que no están disponibles en Google Play Store, o si desea actualizar sus aplicaciones más rápido que el lanzamiento oficial. </p>
-<p>En este artículo, le mostraremos cómo descargar e instalar archivos APK de fuentes confiables, y qué precauciones debe tomar antes de hacerlo. </p>
-<h2>2-0-1-android-apk-download downlod</h2><br /><p><b><b>Download File</b> >>>>> <a href="https://bltlly.com/2v6M93">https://bltlly.com/2v6M93</a></b></p><br /><br />
-<h2>¿Cómo descargar archivos APK de fuentes confiables? </h2>
-<p>Hay muchos sitios web que ofrecen archivos APK para descargar, pero no todos son seguros y confiables. Siempre debe comprobar la reputación y las reseñas del sitio antes de descargar cualquier archivo. Algunos de los factores que debe considerar son:</p>
-<ul>
-<li>El nombre de dominio y la extensión del sitio. Evite los sitios que tienen nombres de dominio o extensiones sospechosas o desconocidas, como . ru, . cn, . tk, etc.</li>
-<li>El diseño y el diseño del sitio. Evite los sitios que tienen un diseño pobre o desactualizado, enlaces rotos, anuncios emergentes o advertencias de malware. </li>
-<li>El contenido y la calidad del sitio. Evite sitios que tengan contenido irrelevante o desactualizado, errores gramaticales, errores ortográficos o imágenes de baja calidad. </li>
-<li>Los comentarios y valoraciones del sitio. Evite los sitios que tienen comentarios negativos o no de los usuarios, calificaciones bajas, o malas críticas. </li>
-</ul>
-<p>Algunas de las fuentes más populares y confiables para archivos APK son:</p>
-<h3>WhatsApp</h3>
-
-<h3>Aptoide</h3>
-<p>Esta es una tienda de aplicaciones Android independiente de código abierto que le permite instalar y descubrir aplicaciones de una manera fácil, emocionante y segura. Aptoide es impulsado por la comunidad y ofrece aplicaciones a través de una experiencia social. Puedes encontrar más de 1 millón de aplicaciones en Aptoide, incluyendo juegos, redes sociales, productividad, entretenimiento y más. También puedes crear tu propia tienda de aplicaciones y compartirla con tus amigos. Para descargar el archivo APK de Aptoide, visite <a href="">Aptoide</a> y haga clic en el botón Instalar. También puede escanear el código QR en el sitio para descargar el archivo directamente a su dispositivo. </p>
-<h2>Cómo instalar archivos APK en su dispositivo Android? </h2>
-<p>Una vez que haya descargado el archivo APK de su elección, debe habilitar la instalación de fuentes desconocidas en su dispositivo. Esta es una función de seguridad que evita que aplicaciones no autorizadas accedan a su dispositivo. Para habilitar fuentes desconocidas, siga estos pasos:</p>
-<ol>
-<li>Ir a Configuración > Seguridad > Fuentes desconocidas y activarlo. </li>
-<li> Puede ver un mensaje de advertencia que dice que instalar aplicaciones de fuentes desconocidas puede dañar su dispositivo. Pulse OK para continuar. </li>
-</ol>
-<p>Después de habilitar fuentes desconocidas, puede instalar el archivo APK siguiendo estos pasos:</p>
-<ol>
-<li>Localice el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos o la carpeta de descargas de su navegador. </li>
-<li>Toque en el archivo APK y siga las instrucciones en pantalla para instalarlo. </li>
-<li>Puede ver un mensaje que le pide que conceda permisos a la aplicación. Pulse Permitir o Aceptar para continuar. </li>
-<li>Una vez completada la instalación, puede iniciar la aplicación desde el cajón de la aplicación o la pantalla de inicio. </li>
-</ol>
-<h2>Conclusión</h2>
-
-<h2>Preguntas frecuentes</h2>
-<h3>¿Cuáles son los beneficios de descargar archivos APK? </h3>
-<p>Algunos de los beneficios de descargar archivos APK son:</p>
-<ul>
-<li>Puede acceder a aplicaciones que no están disponibles en su región o país. </li>
-<li>Puede obtener acceso temprano a nuevas características y actualizaciones de sus aplicaciones favoritas. </li>
-<li>Puede personalizar y modificar sus aplicaciones según sus preferencias. </li>
-<li> Puede hacer copias de seguridad y restaurar sus aplicaciones sin perder ningún dato. </li>
-</ul>
-<h3>¿Cuáles son los riesgos de descargar archivos APK? </h3>
-<p>Algunos de los riesgos de descargar archivos APK son:</p>
-<ul>
-<li> Puede descargar archivos maliciosos o infectados que pueden dañar su dispositivo o robar sus datos. </li>
-<li>Usted puede violar los términos y condiciones de los desarrolladores de aplicaciones o Google Play Store.</li>
-<li>Puede perder la garantía o el soporte de los desarrolladores de aplicaciones o Google Play Store.</li>
-<li>Puede enfrentar problemas de compatibilidad o rendimiento con algunas aplicaciones o dispositivos. </li>
-</ul>
-<h3>¿Cómo puedo actualizar mis archivos APK? </h3>
-<p>Para actualizar tus archivos APK, tienes dos opciones:</p>
-<p></p>
-<ul>
-<li> Puede visitar la misma fuente donde descargó el archivo APK y comprobar si hay nuevas versiones disponibles. Luego, puede descargar e instalar el archivo actualizado sobre el existente. </li>
-<li>Puede utilizar una herramienta de actualización de aplicaciones que puede escanear su dispositivo para cualquier archivos APK desactualizados y notificarle de cualquier actualización disponible. Luego, puede descargar e instalar el archivo actualizado sobre el existente. </li>
-</ul>
- <h3>¿Cómo puedo desinstalar mis archivos APK? </h3>
- <p>Para desinstalar tus archivos APK, tienes dos opciones:</p>
- <ul>
- <li> Puede ir a Configuración > Aplicaciones > Administrador de aplicaciones y encontrar la aplicación que desea desinstalar. Entonces, toque en Desinstalar y confirme su acción. </li>
- <li> Puede utilizar una herramienta de desinstalación de aplicaciones que puede escanear el dispositivo para cualquier archivos APK no deseados o no utilizados y ayudarle a eliminarlos fácilmente. </li>
- </ul>
- <h3>¿Cómo puedo compartir mis archivos APK con otros? </h3>
- <p>Para compartir tus archivos APK con otros, tienes dos opciones:</p>
-
- <li>Puede usar una aplicación para compartir archivos que puede transferir sus archivos APK a otros dispositivos a través de Wi-Fi, Bluetooth o código QR. Algunos ejemplos de aplicaciones para compartir archivos son ShareIt, Xender y Zapya.</li>
- <li> Puede utilizar un servicio de almacenamiento en la nube que puede cargar sus archivos APK a un espacio en línea seguro y generar un enlace que puede compartir con otros. Algunos ejemplos de servicios de almacenamiento en la nube son Google Drive, Dropbox y OneDrive.</li>
- </ul>
- <p>Espero que haya encontrado este artículo útil y aprendido cómo descargar e instalar archivos APK en sus dispositivos Android. Si tiene alguna pregunta o comentario, por favor deje un comentario abajo. ¡Gracias por leer! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Carx Street Apk Obb Mod.md

@@ -1,44 +0,0 @@
-
-<h1>CarX Street APK OBB Mod: Una guía para los amantes de los juegos de carreras</h1>
-<p>Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de CarX Street, un nuevo juego de los desarrolladores de CarX Drift Racing. CarX Street es un juego realista de carreras callejeras que te permite conducir varios coches en diferentes pistas y competir con otros jugadores en línea o fuera de línea. Pero ¿qué pasa si quieres obtener más del juego, como dinero ilimitado, todos los coches desbloqueados y más opciones de personalización? Ahí es donde CarX Street APK OBB Mod viene muy bien. En este artículo, le diremos qué es CarX Street APK OBB Mod, qué características ofrece y cómo descargarlo e instalarlo en su dispositivo Android. </p>
-<h2>¿Qué es CarX Street APK OBB Mod? </h2>
-<p>CarX Street APK OBB Mod es una versión modificada del juego original de CarX Street que le da acceso a algunas características adicionales que no están disponibles en la versión oficial. Estas características incluyen dinero ilimitado, todos los coches desbloqueados, física realista y gráficos, garaje personalizable y ajuste, y los modos en línea y fuera de línea. Con CarX Street APK OBB Mod, se puede disfrutar del juego sin limitaciones ni restricciones. </p>
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-<h3>Características de CarX Street APK OBB Mod</h3>
-<h4>Dinero ilimitado</h4>
-<p>Una de las principales características de CarX Street APK OBB Mod es que le da dinero ilimitado para gastar en la compra de coches nuevos, actualizarlos y personalizarlos. Usted no tiene que preocuparse de quedarse sin efectivo o moler durante horas para ganar suficiente dinero. Puedes comprar cualquier auto que quieras, desde autos deportivos hasta autos deportivos, y hacer que se vean y actúen como quieras. </p>
-<h4>Todos los coches desbloqueados</h4>
-<p>Otra característica de CarX Street APK OBB Mod es que desbloquea todos los coches en el juego para usted. No tienes que completar ninguna misión o desafío para desbloquearlos. Puede elegir entre más de 50 coches, cada uno con sus propias características y especificaciones. También puede cambiar entre diferentes coches en cualquier momento que desee, dependiendo de su estado de ánimo y preferencia. </p>
-
-<p>CarX Street APK OBB Mod también mejora la física y los gráficos del juego, por lo que es más realista y envolvente. El juego utiliza el motor CarX, que es conocido por su física realista del coche y el comportamiento. Puede sentir el peso, la velocidad y la tracción de cada automóvil mientras los conduce en diferentes superficies y condiciones. El juego también tiene impresionantes gráficos y efectos, como iluminación dinámica, sombras, reflejos, humo, polvo y chispas. Puedes ver cada detalle de tu coche y el entorno mientras corres por las calles. </p>
-<h4>Garaje personalizable y ajuste</h4>
-<p>Otra característica de CarX Street APK OBB Mod es que le permite personalizar su garaje y afinar sus coches como desee. Puede cambiar el color, pintura, calcomanías, ruedas, neumáticos, alerones, parachoques, campanas, escapes, luces, espejos, ventanas y más de sus coches. También puede ajustar la potencia del motor, par, suspensión, frenos, dirección, caja de cambios, diferencial, camber, dedo del pie, presión de los neumáticos, y más de sus coches. Puedes crear tu propio estilo y rendimiento único para cada coche. </p>
-<h4>Modos online y offline</h4>
-<p>CarX Street APK OBB Mod también le permite jugar el juego en línea o fuera de línea. Puede conectarse con otros enlaces maliciosos que podrían dañar su dispositivo o robar sus datos. Puedes usar este enlace como ejemplo de una fuente de confianza, pero también puedes hacer tu propia investigación y encontrar otras fuentes en las que confíes. </p>
-<h3>Paso 2: Habilitar fuentes desconocidas en su dispositivo</h3>
-<p>El segundo paso es habilitar fuentes desconocidas en el dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de Google Play Store. Para habilitar Fuentes desconocidas, debe ir a la configuración del dispositivo, luego a Seguridad, luego a Fuentes desconocidas y activarla. También es posible que necesite confirmar esta acción tocando OK o Permitir.</p>
-<p></p>
-<h3>Paso 3: Instalar el archivo APK y extraer el archivo OBB</h3>
-
-<h3>Paso 4: Mueva la carpeta OBB al directorio de Android/OBB</h3>
-<p>El cuarto paso es mover la carpeta OBB al directorio Android/ OBB en su dispositivo. Aquí es donde los datos del juego se almacena y se accede por el juego. Para mover la carpeta OBB, debe usar una aplicación de administrador de archivos, como ES File Explorer, y copiar o cortar la carpeta desde su ubicación original, y pegarla en el directorio de Android/ OBB. También puede ser necesario crear una nueva carpeta llamada OBB si no existe ya. </p>
-<h3>Paso 5: Iniciar el juego y disfrutar de</h3>
-<p>El paso final es lanzar el juego y disfrutar de CarX Street APK OBB Mod. Para iniciar el juego, necesitas encontrar su icono en la pantalla de inicio del dispositivo o en el cajón de la aplicación, y pulsa en él. También es posible que tenga que verificar su edad o aceptar algunos términos y condiciones antes de que comience el juego. Una vez que el juego se carga, puede elegir su coche, pista, modo, y empezar a correr con dinero ilimitado, todos los coches desbloqueados, y más características. </p>
-<h2>Conclusión</h2>
-<p>CarX Street APK OBB Mod es una gran manera de disfrutar de CarX Street, un juego de carreras de calle realista que le permite conducir varios coches en diferentes pistas y competir con otros jugadores en línea o fuera de línea. Con CarX Street APK OBB Mod, puede obtener dinero ilimitado, todos los coches desbloqueados, la física realista y gráficos, garaje personalizable y ajuste, y los modos en línea y fuera de línea. Para descargar e instalar CarX Street APK OBB Mod, solo tiene que seguir cinco sencillos pasos: descargar los archivos APK y OBB de una fuente de confianza, habilitar fuentes desconocidas en su dispositivo, instalar el archivo APK y extraer el archivo OBB, mover la carpeta OBB al directorio Android/ OBB, y lanzar el juego y disfrutar. Esperamos que este artículo le haya sido útil. Si tiene alguna pregunta o comentario, no dude en dejarlos en la sección de comentarios a continuación. </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre CarX Street APK OBB Mod:</p>
-<ul>
-<li><b> ¿Es seguro CarX Street APK OBB Mod? </b></li>
-
-<li><b>Es CarX Street APK OBB Mod legal? </b></li>
-<p>CarX Street APK OBB Mod no es legal, ya que viola los términos y condiciones del juego original. También infringe los derechos de propiedad intelectual de los desarrolladores de CarX Street. No apoyamos ni promovemos CarX Street APK OBB Mod de ninguna manera. Utilícelo bajo su propio riesgo. </p>
-<li><b>CarX Street APK OBB Mod funciona en mi dispositivo? </b></li>
-<p>CarX Street APK OBB Mod debe funcionar en la mayoría de los dispositivos Android que cumplen con los requisitos mínimos del juego original. Sin embargo, no podemos garantizar que funcionará en todos los dispositivos o que no causará ningún problema con su dispositivo o juego. Úselo bajo su propio riesgo. </p>
-<li><b>¿Puedo jugar CarX Street APK OBB Mod en línea? </b></li>
-<p>CarX Street APK OBB Mod le permite jugar en línea con otros jugadores que tienen el mismo mod instalado. Sin embargo, no recomendamos jugar en línea con el mod, ya que podría conseguir que se le prohibió de los servidores oficiales o causar problemas de compatibilidad con otros jugadores que tienen el juego original. Úsalo bajo tu propio riesgo. </p>
-<li><b>¿Puedo actualizar CarX Street APK OBB Mod? </b></li>
-<p>CarX Street APK OBB Mod podría no funcionar con las últimas actualizaciones del juego original. Es posible que tenga que esperar a que se lance una nueva versión del mod o desinstalar el mod e instalar el juego oficial. Utilícelo bajo su propio riesgo. </p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/scripts/extract_depth.py

@@ -1,112 +0,0 @@
-import os
-import torch
-import numpy as np
-from tqdm import trange
-from PIL import Image
-
-
-def get_state(gpu):
-    import torch
-    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
-    if gpu:
-        midas.cuda()
-    midas.eval()
-
-    midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
-    transform = midas_transforms.default_transform
-
-    state = {"model": midas,
-             "transform": transform}
-    return state
-
-
-def depth_to_rgba(x):
-    assert x.dtype == np.float32
-    assert len(x.shape) == 2
-    y = x.copy()
-    y.dtype = np.uint8
-    y = y.reshape(x.shape+(4,))
-    return np.ascontiguousarray(y)
-
-
-def rgba_to_depth(x):
-    assert x.dtype == np.uint8
-    assert len(x.shape) == 3 and x.shape[2] == 4
-    y = x.copy()
-    y.dtype = np.float32
-    y = y.reshape(x.shape[:2])
-    return np.ascontiguousarray(y)
-
-
-def run(x, state):
-    model = state["model"]
-    transform = state["transform"]
-    hw = x.shape[:2]
-    with torch.no_grad():
-        prediction = model(transform((x + 1.0) * 127.5).cuda())
-        prediction = torch.nn.functional.interpolate(
-            prediction.unsqueeze(1),
-            size=hw,
-            mode="bicubic",
-            align_corners=False,
-        ).squeeze()
-        output = prediction.cpu().numpy()
-    return output
-
-
-def get_filename(relpath, level=-2):
-    # save class folder structure and filename:
-    fn = relpath.split(os.sep)[level:]
-    folder = fn[-2]
-    file   = fn[-1].split('.')[0]
-    return folder, file
-
-
-def save_depth(dataset, path, debug=False):
-    os.makedirs(path)
-    N = len(dset)
-    if debug:
-        N = 10
-    state = get_state(gpu=True)
-    for idx in trange(N, desc="Data"):
-        ex = dataset[idx]
-        image, relpath = ex["image"], ex["relpath"]
-        folder, filename = get_filename(relpath)
-        # prepare
-        folderabspath = os.path.join(path, folder)
-        os.makedirs(folderabspath, exist_ok=True)
-        savepath = os.path.join(folderabspath, filename)
-        # run model
-        xout = run(image, state)
-        I = depth_to_rgba(xout)
-        Image.fromarray(I).save("{}.png".format(savepath))
-
-
-if __name__ == "__main__":
-    from taming.data.imagenet import ImageNetTrain, ImageNetValidation
-    out = "data/imagenet_depth"
-    if not os.path.exists(out):
-        print("Please create a folder or symlink '{}' to extract depth data ".format(out) +
-              "(be prepared that the output size will be larger than ImageNet itself).")
-        exit(1)
-
-    # go
-    dset = ImageNetValidation()
-    abspath = os.path.join(out, "val")
-    if os.path.exists(abspath):
-        print("{} exists - not doing anything.".format(abspath))
-    else:
-        print("preparing {}".format(abspath))
-        save_depth(dset, abspath)
-        print("done with validation split")
-
-    dset = ImageNetTrain()
-    abspath = os.path.join(out, "train")
-    if os.path.exists(abspath):
-        print("{} exists - not doing anything.".format(abspath))
-    else:
-        print("preparing {}".format(abspath))
-        save_depth(dset, abspath)
-        print("done with train split")
-
-    print("done done.")

spaces/CVPR/lama-example/bin/paper_runfiles/generate_val_test.sh

@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-
-# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst
-
-# paths to data are valid for mml7
-PLACES_ROOT="/data/inpainting/Places365"
-OUT_DIR="/data/inpainting/paper_data/Places365_val_test"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in test_large_30k  # val_large
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-    do
-        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-
-    for conf in segm_256 segm_512
-    do
-        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done

spaces/CVPR/regionclip-demo/detectron2/data/datasets/coco_zeroshot_categories.py

@@ -1,206 +0,0 @@
-# COCO categories for zero-shot setting
-# 65 categories in total, 48 base categories for training, 17 unseen categories are only used in testing
-# from http://ankan.umiacs.io/files/mscoco_seen_classes.json, http://ankan.umiacs.io/files/mscoco_unseen_classes.json
-
-# 17 class names in order, obtained from load_coco_json() function
-COCO_UNSEEN_CLS = ['airplane', 'bus', 'cat', 'dog', 'cow', 'elephant', 'umbrella', \
-    'tie', 'snowboard', 'skateboard', 'cup', 'knife', 'cake', 'couch', 'keyboard', \
-    'sink', 'scissors']
-
-# 48 class names in order, obtained from load_coco_json() function
-COCO_SEEN_CLS = ['person', 'bicycle', 'car', 'motorcycle', 'train', 'truck', \
-    'boat', 'bench', 'bird', 'horse', 'sheep', 'bear', 'zebra', 'giraffe', \
-    'backpack', 'handbag', 'suitcase', 'frisbee', 'skis', 'kite', 'surfboard', \
-    'bottle', 'fork', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', \
-    'broccoli', 'carrot', 'pizza', 'donut', 'chair', 'bed', 'toilet', 'tv', \
-    'laptop', 'mouse', 'remote', 'microwave', 'oven', 'toaster', \
-    'refrigerator', 'book', 'clock', 'vase', 'toothbrush']
-
-# 65 class names in order, obtained from load_coco_json() function
-COCO_OVD_ALL_CLS = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', \
-    'bus', 'train', 'truck', 'boat', 'bench', 'bird', 'cat', 'dog', 'horse', \
-    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', \
-    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'kite', 'skateboard', \
-    'surfboard', 'bottle', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', \
-    'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'pizza', 'donut', 'cake', \
-    'chair', 'couch', 'bed', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', \
-    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', \
-    'scissors', 'toothbrush']
-
-# 80 class names
-COCO_80_ALL_CLS = {1: 'person',
- 2: 'bicycle',
- 3: 'car',
- 4: 'motorcycle',
- 5: 'airplane',
- 6: 'bus',
- 7: 'train',
- 8: 'truck',
- 9: 'boat',
- 10: 'traffic light',
- 11: 'fire hydrant',
- 12: 'stop sign',
- 13: 'parking meter',
- 14: 'bench',
- 15: 'bird',
- 16: 'cat',
- 17: 'dog',
- 18: 'horse',
- 19: 'sheep',
- 20: 'cow',
- 21: 'elephant',
- 22: 'bear',
- 23: 'zebra',
- 24: 'giraffe',
- 25: 'backpack',
- 26: 'umbrella',
- 27: 'handbag',
- 28: 'tie',
- 29: 'suitcase',
- 30: 'frisbee',
- 31: 'skis',
- 32: 'snowboard',
- 33: 'sports ball',
- 34: 'kite',
- 35: 'baseball bat',
- 36: 'baseball glove',
- 37: 'skateboard',
- 38: 'surfboard',
- 39: 'tennis racket',
- 40: 'bottle',
- 41: 'wine glass',
- 42: 'cup',
- 43: 'fork',
- 44: 'knife',
- 45: 'spoon',
- 46: 'bowl',
- 47: 'banana',
- 48: 'apple',
- 49: 'sandwich',
- 50: 'orange',
- 51: 'broccoli',
- 52: 'carrot',
- 53: 'hot dog',
- 54: 'pizza',
- 55: 'donut',
- 56: 'cake',
- 57: 'chair',
- 58: 'couch',
- 59: 'potted plant',
- 60: 'bed',
- 61: 'dining table',
- 62: 'toilet',
- 63: 'tv',
- 64: 'laptop',
- 65: 'mouse',
- 66: 'remote',
- 67: 'keyboard',
- 68: 'cell phone',
- 69: 'microwave',
- 70: 'oven',
- 71: 'toaster',
- 72: 'sink',
- 73: 'refrigerator',
- 74: 'book',
- 75: 'clock',
- 76: 'vase',
- 77: 'scissors',
- 78: 'teddy bear',
- 79: 'hair drier',
- 80: 'toothbrush'}
-
-if __name__ == "__main__":
-    # from https://github.com/alirezazareian/ovr-cnn/blob/master/ipynb/001.ipynb
-    # Create zero-shot setting data split in COCO
-    import json
-    import ipdb
-
-    with open('./datasets/coco/annotations/instances_train2017.json', 'r') as fin:
-        coco_train_anno_all = json.load(fin)
-
-    with open('./datasets/coco/annotations/instances_train2017.json', 'r') as fin:
-        coco_train_anno_seen = json.load(fin)
-
-    with open('./datasets/coco/annotations/instances_train2017.json', 'r') as fin:
-        coco_train_anno_unseen = json.load(fin)
-
-    with open('./datasets/coco/annotations/instances_val2017.json', 'r') as fin:
-        coco_val_anno_all = json.load(fin)
-
-    with open('./datasets/coco/annotations/instances_val2017.json', 'r') as fin:
-        coco_val_anno_seen = json.load(fin)
-
-    with open('./datasets/coco/annotations/instances_val2017.json', 'r') as fin:
-        coco_val_anno_unseen = json.load(fin)
-    
-    labels_seen = COCO_SEEN_CLS
-    labels_unseen = COCO_UNSEEN_CLS
-    labels_all = [item['name'] for item in coco_val_anno_all['categories']]  # 80 class names
-    # len(labels_seen), len(labels_unseen)
-    # set(labels_seen) - set(labels_all)
-    # set(labels_unseen) - set(labels_all)
-    
-    class_id_to_split = {}  # {1: 'seen', 2: 'seen', 3: 'seen', 4: 'seen', 5: 'unseen',...}
-    class_name_to_split = {}  # {'person': 'seen', 'bicycle': 'seen', 'car': 'seen', 'motorcycle': 'seen', 'airplane': 'unseen',...}
-    for item in coco_val_anno_all['categories']:
-        if item['name'] in labels_seen:
-            class_id_to_split[item['id']] = 'seen'
-            class_name_to_split[item['name']] = 'seen'
-        elif item['name'] in labels_unseen:
-            class_id_to_split[item['id']] = 'unseen'
-            class_name_to_split[item['name']] = 'unseen'
-    
-    # class_name_to_emb = {}
-    # with open('../datasets/coco/zero-shot/glove.6B.300d.txt', 'r') as fin:
-    #     for row in fin:
-    #         row_tk = row.split()
-    #         if row_tk[0] in class_name_to_split:
-    #             class_name_to_emb[row_tk[0]] = [float(num) for num in row_tk[1:]]
-    # len(class_name_to_emb), len(class_name_to_split)
-
-    def filter_annotation(anno_dict, split_name_list):
-        """
-        COCO annotations have fields: dict_keys(['info', 'licenses', 'images', 'annotations', 'categories'])
-        This function (1) filters the category metadata (list) in 'categories'; 
-        (2) filter instance annotation in 'annotations'; (3) filter image metadata (list) in 'images
-        """
-        filtered_categories = []
-        for item in anno_dict['categories']:
-            if class_id_to_split.get(item['id']) in split_name_list:
-                #item['embedding'] = class_name_to_emb[item['name']]
-                item['split'] = class_id_to_split.get(item['id'])
-                filtered_categories.append(item)
-        anno_dict['categories'] = filtered_categories
-        
-        filtered_images = []
-        filtered_annotations = []
-        useful_image_ids = set()
-        for item in anno_dict['annotations']:
-            if class_id_to_split.get(item['category_id']) in split_name_list:
-                filtered_annotations.append(item)
-                useful_image_ids.add(item['image_id'])
-        for item in anno_dict['images']:
-            if item['id'] in useful_image_ids:
-                filtered_images.append(item)
-        anno_dict['annotations'] = filtered_annotations
-        anno_dict['images'] = filtered_images
-    
-    filter_annotation(coco_train_anno_seen, ['seen'])
-    filter_annotation(coco_train_anno_unseen, ['unseen'])
-    filter_annotation(coco_train_anno_all, ['seen', 'unseen'])
-    filter_annotation(coco_val_anno_seen, ['seen'])
-    filter_annotation(coco_val_anno_unseen, ['unseen'])
-    filter_annotation(coco_val_anno_all, ['seen', 'unseen'])
-
-    with open('./datasets/coco/annotations/ovd_ins_train2017_b.json', 'w') as fout:
-        json.dump(coco_train_anno_seen, fout)
-    with open('./datasets/coco/annotations/ovd_ins_train2017_t.json', 'w') as fout:
-        json.dump(coco_train_anno_unseen, fout)
-    with open('./datasets/coco/annotations/ovd_ins_train2017_all.json', 'w') as fout:
-        json.dump(coco_train_anno_all, fout)
-    with open('./datasets/coco/annotations/ovd_ins_val2017_b.json', 'w') as fout:
-        json.dump(coco_val_anno_seen, fout)
-    with open('./datasets/coco/annotations/ovd_ins_val2017_t.json', 'w') as fout:
-        json.dump(coco_val_anno_unseen, fout)
-    with open('./datasets/coco/annotations/ovd_ins_val2017_all.json', 'w') as fout:
-        json.dump(coco_val_anno_all, fout)

spaces/CVPR/transfiner/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py

@@ -1,14 +0,0 @@
-from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-
-train.max_iter *= 2  # 100ep -> 200ep
-
-lr_multiplier.scheduler.milestones = [
-    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter

spaces/Chris1/real2sim/app.py

@@ -1,71 +0,0 @@
-
-import os
-
-from PIL import Image
-from torchvision import transforms as T
-from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
-from torchvision.utils import make_grid
-from torch.utils.data import DataLoader
-from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
-import torch.nn as nn
-import torch
-import gradio as gr
-
-from collections import OrderedDict
-import glob
-
-
-
-
-def pred_pipeline(img, transforms):
-        orig_shape = img.shape
-        input = transforms(img)
-        input = input.unsqueeze(0)
-        output_syn = real2sim(input)
-        output_real = sim2real(output_syn)
-        out_img_syn = make_grid(output_syn,
-                           nrow=1, normalize=True)  
-        out_img_real = make_grid(output_real,
-                           nrow=1, normalize=True)          
-
-
-
-        out_transform = Compose([
-            T.Resize(orig_shape[:2]),
-            T.ToPILImage()
-        ])
-        return out_transform(out_img_syn), out_transform(out_img_real)
-
-
-
-
-n_channels = 3
-image_size = 512
-input_shape = (image_size, image_size)
-
-transform = Compose([
-     T.ToPILImage(),
-        T.Resize(input_shape),
-        ToTensor(),
-        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-    ])
-
-
-sim2real = GeneratorResNet.from_pretrained('Chris1/sim2real-512', input_shape=(n_channels, image_size, image_size), 
-                num_residual_blocks=9)
-real2sim = GeneratorResNet.from_pretrained('Chris1/real2sim-512', input_shape=(n_channels, image_size, image_size), 
-                num_residual_blocks=9)
-
-gr.Interface(lambda image: pred_pipeline(image, transform), 
-    inputs=gr.inputs.Image( label='input synthetic image'), 
-    outputs=[
-            gr.outputs.Image( type="pil",label='GAN real2sim prediction: style transfer of the input to the synthetic world '),
-            gr.outputs.Image( type="pil",label='GAN sim2real prediction: translation to real of the above prediction')
-            ],#plot,
-    title = "Cityscapes (real) to GTA5(simulated) translation",
-    examples = [
-                [example] for example in glob.glob('./samples/*.png')
-                ])\
-    .launch()
-
-