Update parquet files (step 36 of 476)

spaces/0x90e/ESRGAN-MANGA/ESRGAN/block.py

@@ -1,261 +0,0 @@
-from collections import OrderedDict
-import torch
-import torch.nn as nn
-
-####################
-# Basic blocks
-####################
-
-
-def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1):
-    # helper selecting activation
-    # neg_slope: for leakyrelu and init of prelu
-    # n_prelu: for p_relu num_parameters
-    act_type = act_type.lower()
-    if act_type == 'relu':
-        layer = nn.ReLU(inplace)
-    elif act_type == 'leakyrelu':
-        layer = nn.LeakyReLU(neg_slope, inplace)
-    elif act_type == 'prelu':
-        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
-    else:
-        raise NotImplementedError('activation layer [%s] is not found' % act_type)
-    return layer
-
-
-def norm(norm_type, nc):
-    # helper selecting normalization layer
-    norm_type = norm_type.lower()
-    if norm_type == 'batch':
-        layer = nn.BatchNorm2d(nc, affine=True)
-    elif norm_type == 'instance':
-        layer = nn.InstanceNorm2d(nc, affine=False)
-    else:
-        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
-    return layer
-
-
-def pad(pad_type, padding):
-    # helper selecting padding layer
-    # if padding is 'zero', do by conv layers
-    pad_type = pad_type.lower()
-    if padding == 0:
-        return None
-    if pad_type == 'reflect':
-        layer = nn.ReflectionPad2d(padding)
-    elif pad_type == 'replicate':
-        layer = nn.ReplicationPad2d(padding)
-    else:
-        raise NotImplementedError('padding layer [%s] is not implemented' % pad_type)
-    return layer
-
-
-def get_valid_padding(kernel_size, dilation):
-    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
-    padding = (kernel_size - 1) // 2
-    return padding
-
-
-class ConcatBlock(nn.Module):
-    # Concat the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ConcatBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = torch.cat((x, self.sub(x)), dim=1)
-        return output
-
-    def __repr__(self):
-        tmpstr = 'Identity .. \n|'
-        modstr = self.sub.__repr__().replace('\n', '\n|')
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-class ShortcutBlock(nn.Module):
-    #Elementwise sum the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ShortcutBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = x + self.sub(x)
-        return output
-
-    def __repr__(self):
-        tmpstr = 'Identity + \n|'
-        modstr = self.sub.__repr__().replace('\n', '\n|')
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-def sequential(*args):
-    # Flatten Sequential. It unwraps nn.Sequential.
-    if len(args) == 1:
-        if isinstance(args[0], OrderedDict):
-            raise NotImplementedError('sequential does not support OrderedDict input.')
-        return args[0]  # No sequential is needed.
-    modules = []
-    for module in args:
-        if isinstance(module, nn.Sequential):
-            for submodule in module.children():
-                modules.append(submodule)
-        elif isinstance(module, nn.Module):
-            modules.append(module)
-    return nn.Sequential(*modules)
-
-
-def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
-               pad_type='zero', norm_type=None, act_type='relu', mode='CNA'):
-    """
-    Conv layer with padding, normalization, activation
-    mode: CNA --> Conv -> Norm -> Act
-        NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
-    """
-    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [%s]' % mode
-    padding = get_valid_padding(kernel_size, dilation)
-    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
-    padding = padding if pad_type == 'zero' else 0
-
-    c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, \
-            dilation=dilation, bias=bias, groups=groups)
-    a = act(act_type) if act_type else None
-    if 'CNA' in mode:
-        n = norm(norm_type, out_nc) if norm_type else None
-        return sequential(p, c, n, a)
-    elif mode == 'NAC':
-        if norm_type is None and act_type is not None:
-            a = act(act_type, inplace=False)
-            # Important!
-            # input----ReLU(inplace)----Conv--+----output
-            #        |________________________|
-            # inplace ReLU will modify the input, therefore wrong output
-        n = norm(norm_type, in_nc) if norm_type else None
-        return sequential(n, a, p, c)
-
-
-####################
-# Useful blocks
-####################
-
-
-class ResNetBlock(nn.Module):
-    """
-    ResNet Block, 3-3 style
-    with extra residual scaling used in EDSR
-    (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
-    """
-
-    def __init__(self, in_nc, mid_nc, out_nc, kernel_size=3, stride=1, dilation=1, groups=1, \
-            bias=True, pad_type='zero', norm_type=None, act_type='relu', mode='CNA', res_scale=1):
-        super(ResNetBlock, self).__init__()
-        conv0 = conv_block(in_nc, mid_nc, kernel_size, stride, dilation, groups, bias, pad_type, \
-            norm_type, act_type, mode)
-        if mode == 'CNA':
-            act_type = None
-        if mode == 'CNAC':  # Residual path: |-CNAC-|
-            act_type = None
-            norm_type = None
-        conv1 = conv_block(mid_nc, out_nc, kernel_size, stride, dilation, groups, bias, pad_type, \
-            norm_type, act_type, mode)
-        # if in_nc != out_nc:
-        #     self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \
-        #         None, None)
-        #     print('Need a projecter in ResNetBlock.')
-        # else:
-        #     self.project = lambda x:x
-        self.res = sequential(conv0, conv1)
-        self.res_scale = res_scale
-
-    def forward(self, x):
-        res = self.res(x).mul(self.res_scale)
-        return x + res
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    """
-    Residual Dense Block
-    style: 5 convs
-    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
-    """
-
-    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
-            norm_type=None, act_type='leakyrelu', mode='CNA'):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
-            norm_type=norm_type, act_type=act_type, mode=mode)
-        self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
-            norm_type=norm_type, act_type=act_type, mode=mode)
-        self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
-            norm_type=norm_type, act_type=act_type, mode=mode)
-        self.conv4 = conv_block(nc+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
-            norm_type=norm_type, act_type=act_type, mode=mode)
-        if mode == 'CNA':
-            last_act = None
-        else:
-            last_act = act_type
-        self.conv5 = conv_block(nc+4*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \
-            norm_type=norm_type, act_type=last_act, mode=mode)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv2(torch.cat((x, x1), 1))
-        x3 = self.conv3(torch.cat((x, x1, x2), 1))
-        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5.mul(0.2) + x
-
-
-class RRDB(nn.Module):
-    """
-    Residual in Residual Dense Block
-    """
-
-    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
-            norm_type=None, act_type='leakyrelu', mode='CNA'):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
-            norm_type, act_type, mode)
-        self.RDB2 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
-            norm_type, act_type, mode)
-        self.RDB3 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
-            norm_type, act_type, mode)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out.mul(0.2) + x
-
-
-####################
-# Upsampler
-####################
-
-
-def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
-                        pad_type='zero', norm_type=None, act_type='relu'):
-    """
-    Pixel shuffle layer
-    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
-    Neural Network, CVPR17)
-    """
-    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
-                        pad_type=pad_type, norm_type=None, act_type=None)
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
-    n = norm(norm_type, out_nc) if norm_type else None
-    a = act(act_type) if act_type else None
-    return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_blcok(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
-                pad_type='zero', norm_type=None, act_type='relu', mode='nearest'):
-    # Up conv
-    # described in https://distill.pub/2016/deconv-checkerboard/
-    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
-    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
-                        pad_type=pad_type, norm_type=norm_type, act_type=act_type)
-    return sequential(upsample, conv)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/account_creation.py

@@ -1,3 +0,0 @@
-import usesless
-
-usesless.Account.create(logging=True)

spaces/14-26AA/sovits_aishell3/app.py

@@ -1,39 +0,0 @@
-import gradio as gr
-from inference import infer
-import numpy as np
-
-spkdict=np.arange(175).tolist()
-spkdict=['speaker'+str(spk) for spk in spkdict]
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("Basic"):
-            gr.Markdown(value="""
-            本模型为基于soft-vc和vits的AI声线转换模型。\n
-            模型混合了aishell3（174说话人，共约80+h）和opencpop（1说话人，5+h）数据集训练，用A100单卡在60batchsize下训练了350左右epoch得到的。\n
-            模型对于通常的说话语音转换效果良好，唱歌的话需要在目标说话人音域范围内才能有较好效果。由于aishell3数据占比过大，训练epoch不足，opencpop说话人的高音部分质量不佳。\n
-            模型中各说话人的适宜中心音域分别为：\n
-            aishell3(0-173号) 女性说话：A3,男性说话：C3\n
-            opencpop(174号)   女性唱歌：C4-G4（超过C5基本失真）\n
-            如果转换通常说话音频，需要参考上面范围调key至目标说话人中心音域附近(如男性说话转为女性说话，key=8，反过来为-8(4-16这个区间基本都可以试试))\n
-            如果源音频为部分虚拟主播音频，通常音调会高于正常女性说话范围，可达到F4-A4左右，请适当降调\n
-            对于说话人的详细信息(如性别、年龄等)，可以在文件目录的spkdic_new.json中查看\n
-            \n
-            若合成效果不佳请首先考虑如下因素加以改善：\n
-            1.音域范围是否合适，可参考上文调整调key的参数，或更换说话人进行尝试\n
-            2.源音频是否存在杂音/bgm，请尽量使用干净的音源进行合成，录音时保持室内安静\n
-            3.源音频是否存在混响。较强的混响会显著干扰合成效果，导致ai念错字/音调识别错误等\n
-            4.再合成一次试试，每次合成会有部分随机性因素参与，微小的声调问题可能重新合成一次就不存在了。\n
-            5.部分对源音频的消伴奏/降噪处理会对合成效果有较大影响，虽然人听不出差别，但是对ai识别的频谱有影响。
-            """)
-            sid = gr.Dropdown(label="说话人",choices=spkdict, value='speaker0')
-            vc_audio = gr.Audio(label="上传音频,建议小于2分钟",type='filepath')
-            vc_record = gr.Audio(source="microphone", label="或者录制你的声音", type="filepath")
-            vc_transform = gr.Number(label="调key（按照十二平均律确定的半音，一整个8度就是12）",value=0)
-            vc_submit = gr.Button("转换", variant="primary")
-            vc_output1 = gr.Textbox(label="Output Message")
-            vc_output2 = gr.Audio(label="Output Audio")
-        vc_submit.click(infer, [vc_audio,vc_record,sid, vc_transform], [vc_output1, vc_output2])
-
-    app.launch()

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dont Panic! Heres Why Your PC Makes a Crackling Noise and How to Fix It.md

@@ -1,28 +0,0 @@
-<br />
-<h1>Why Does My PC Make a Crackling Noise and How to Fix It</h1>
-
-<p>If you hear a crackling or popping sound coming from your PC, you might be worried that something is wrong with your hardware. However, before you panic, you should know that there are many possible causes for this problem and some of them are easy to fix. In this article, we will explain why your PC makes a crackling noise and how to troubleshoot it.</p>
-
-<h2>What Causes Crackling or Popping Sound on a Windows PC?</h2>
-
-<p>Crackling or popping sound on a Windows PC can occur for a variety of reasons. Some of the most common ones are:</p>
-<h2>why does my pc make a crackling noise</h2><br /><p><b><b>Download File</b> ::: <a href="https://byltly.com/2uKzYR">https://byltly.com/2uKzYR</a></b></p><br /><br />
-
-<ul>
-<li><b>Audio device settings:</b> Your audio device settings, such as the audio format, sample rate, bit depth, and enhancements, might not be compatible with your speakers or headphones. This can result in distorted or noisy sound output.</li>
-<li><b>Sound driver:</b> Your sound driver might be missing, corrupted, outdated, or incompatible with your Windows version. This can affect the performance and quality of your audio device.</li>
-<li><b>Hardware interference:</b> Your audio device might be interfered by another hardware device, such as a microphone, webcam, USB device, or wireless adapter. This can cause electrical or radio frequency interference that manifests as crackling or popping sound.</li>
-<li><b>Fan bearing or cable rubbing:</b> Your PC fan might have a worn-out bearing or a loose cable that rubs against it. This can create a mechanical noise that sounds like crackling or popping.</li>
-</ul>
-
-<h2>How to Fix Crackling or Popping Sound on a Windows PC?</h2>
-
-<p>Depending on the cause of the problem, there are different ways to fix crackling or popping sound on a Windows PC. Here are some of the most effective solutions:</p>
-
-<ol>
-<li><b>Change your audio format:</b> Changing the audio quality on your output device can solve some sound problems. To do this, right-click the speaker icon in the notification area next to your clock and select <code>Playback Devices</code>. Double-click the default playback device, which has a green checkmark on its icon. Click the <code>Advanced</code> tab and use the <code>Default Format</code> box to select your sound quality level. Try setting your audio quality to <code>16 bit, 44100 Hz (CD Quality)</code>. Click <code>OK</code> afterwards and see if the crackling or other audio problems continue.</li>
-<li><b>Disable audio enhancements:</b> Some sound drivers use software enhancements in an attempt to improve your sound quality. If these aren't working properly or if your CPU is being taxed too heavily, these could result in sound problems. To disable sound enhancements, use the same Properties window. Click the <code>Enhancements</code> tab here—if you see one—and check the <code>Disable All Enhancements</code> checkbox. Click <code>OK</code> to save your changes and then test to see if the problems continue. Not all software drivers perform this function, so you won't always see the <code>Enhancements</code> tab on all systems.</li>
-<li><b>Disable exclusive mode:</b> Some sound drivers seem to have issue with the exclusive mode option that allows applications to take exclusive control of your sound card. This shouldn't normally be a problem: Blame bad sound drivers if it's causing issues on your system. You'll find this setting on the same window where the <code>Default Format</code> option is. Disable the <code>Allow applications to take exclusive control of this device</code> option under <code>Exclusive Mode</code>. Click <code>OK</code> and see if this solved your problem. This option normally isn't a problem, so you should probably re-enable it if disabling it doesn't solve the problem.</li>
-<li><b>Update your sound drivers:</b>A missing, corrupted, or outdated sound driver could be why you're experiencing crackling audio. Updating or reinstalling it can fix the issue in this regard. To update your sound driver manually, go to <code>Device Manager</code>, expand <</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/FL Studio 20 Keygen Reddit The Ultimate Guide to Unlocking All Features and Plugins.md

@@ -1,40 +0,0 @@
-
-<h1>FL Studio Keygen Reddit: How to Crack FL Studio 20 for Free</h1>
-<p>If you are looking for a way to crack FL Studio 20 for free, you might have come across some torrents or links that claim to offer a keygen or a patch for the popular music production software. But are they safe and reliable? And how do you use them?</p>
-<h2>fl studio keygen reddit</h2><br /><p><b><b>Download</b> > <a href="https://byltly.com/2uKwL4">https://byltly.com/2uKwL4</a></b></p><br /><br />
-<p>In this article, we will explain what a keygen is, how it works, and what are the risks and benefits of using one. We will also show you how to use a keygen from a reputable source, R2R, to unlock FL Studio 20 and enjoy its full features.</p>
-<h2>What is a Keygen?</h2>
-<p>A keygen, short for key generator, is a program that can generate valid serial numbers or license keys for a software application. A keygen can be used to activate a software without paying for it or going through the official registration process.</p>
-<p>A keygen usually works by exploiting a flaw or a weakness in the software's protection system, such as a weak encryption algorithm or a hardcoded key. A keygen can also emulate the server-side validation process and generate keys that match the expected format.</p>
-<h2>How to Use R2R Keygen for FL Studio 20?</h2>
-<p>R2R is a well-known group of crackers that release high-quality keygens and patches for various software applications, including FL Studio. R2R's keygen for FL Studio 20 can unlock all the features and plugins of the software, such as Edison, Gross Beat, Harmor, Sytrus, Maximus, and more.</p>
-<p>To use R2R's keygen for FL Studio 20, you need to follow these steps:</p>
-<ol>
-<li>Download the torrent file of FL Studio 20 from <a href="https://www.reddit.com/r/piratebay/comments/fmgn21/how_to_use_r2r_keygen_for_fl_studio_20/">this Reddit post</a>  by u/orbital_malice42. Make sure you download the one uploaded by Deepstatus, who is a verified uploader on Piratebay.</li>
-<li>Extract the .7z file using 7-Zip or WinRAR. You will get two folders: FL Studio 20 and Shared.</li>
-<li>Install FL Studio 20 by running the setup.exe file in the FL Studio 20 folder. Choose your preferred language and location. Do not run FL Studio after installation.</li>
-<li>Copy the Keygen.exe file from the Shared folder and paste it into the installation directory of FL Studio 20. The default location is C:\Program Files (x86)\Image-Line\FL Studio 20.</li>
-<li>Run the Keygen.exe file as administrator. You will see a window with a button that says Register. Click on it and wait for a few seconds. You will see a message that says "Successfully registered!"</li>
-<li>Open FL Studio 20 by running the fl.exe file in the installation directory. You should see that it is unlocked and activated. You can now use all the features and plugins of FL Studio 20 without any limitations.</li>
-</ol>
-<h2>What are the Risks and Benefits of Using a Keygen?</h2>
-<p>Using a keygen can have some advantages and disadvantages. Here are some of them:</p>
-<h3>Benefits</h3>
-<ul>
-<li>You can save money by not paying for the software license.</li>
-<li>You can access all the features and plugins of the software without any restrictions.</li>
-<li>You can use the software offline without needing an internet connection or an account.</li>
-</ul>
-<h3>Risks</h3>
-<ul>
-<li>You may violate the terms and conditions of the software developer and face legal consequences.</li>
-<li>You may expose your computer to malware or viruses that may be hidden in the keygen or the torrent file.</li>
-<li>You may not receive any updates or support from the software developer.</li>
-<li>You may experience some bugs or errors in the software that may affect its performance or functionality.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>FL Studio 20 is a powerful and versatile music production software that can help you create amazing beats and songs. However, it is also quite expensive and requires a license key to activate it.</p>
-<p>If you want to crack FL Studio</p>
-<p></p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/GSG HDRI Studio Pack 1.8 for Cinema 4D How to Achieve Realistic Reflections and Shadows.md

@@ -1,150 +0,0 @@
-
-<h1>GSG HDRI Studio Pack 1.8 for Cinema 4D: A Review</h1>
-<p>If you are looking for a way to create stunning lighting and reflections in Cinema 4D, you might have heard of GSG HDRI Studio Pack 1.8. This is a bundle of two plugins from Greyscalegorilla that allow you to browse and apply hundreds of high-quality HDRI (High Dynamic Range Images) in seconds. But what exactly is GSG HDRI Studio Pack 1.8, and why should you use it? In this article, we will review this product and show you how it can help you improve your 3D renders.</p>
- <h2>What is GSG HDRI Studio Pack 1.8?</h2>
-<p>GSG HDRI Studio Pack 1.8 is a collection of two plugins for Cinema 4D that make it easy to use HDRI lighting and reflections in your scenes. HDRI stands for High Dynamic Range Images, which are images that capture a wide range of brightness values, from very dark to very bright. By using HDRI as your light source, you can create realistic and natural lighting effects that mimic the real world.</p>
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-<h3>HDRI Studio Rig</h3>
-<p>HDRI Studio Rig is a plugin that lets you browse and apply HDRI from Greyscalegorilla's library or your own collection. You can then rotate, adjust, and place them in the perfect position for your scene. You can also create professional studio quality backdrops and seamless floors with this plugin. HDRI Studio Rig works with Cinema 4D's Standard and Physical renderers, and it is ideal for product shots, motion graphics, and animations.</p>
-<h3>HDRI Link</h3>
-<p>HDRI Link is a plugin that lets you connect any third-party render engine to Greyscalegorilla's library of HDRI or your own collection. You can instantly browse and apply HDRI with a simple drag-and-drop interface, without having to deal with complex settings or file paths. HDRI Link is compatible with popular render engines like Redshift, Octane, and Arnold, and it is ideal for photorealistic renders, architectural visualization, and VFX.</p>
- <h2>Why use GSG HDRI Studio Pack 1.8?</h2>
-<p>GSG HDRI Studio Pack 1.8 offers many benefits for Cinema 4D users who want to create better looking renders with less hassle.</p>
-<p>How to use GSG HDRI Studio Pack in Cinema 4D<br />
-GSG HDRI Studio Pack review and tutorial<br />
-Best HDRI lighting presets for Cinema 4D<br />
-GSG HDRI Studio Pack vs other HDRI plugins<br />
-Where to buy GSG HDRI Studio Pack for Cinema 4D<br />
-GSG HDRI Studio Pack features and benefits<br />
-How to create realistic renders with GSG HDRI Studio Pack<br />
-GSG HDRI Studio Pack compatibility and requirements<br />
-How to install and update GSG HDRI Studio Pack<br />
-GSG HDRI Studio Pack free download and trial<br />
-How to customize and save HDRI settings in GSG HDRI Studio Pack<br />
-GSG HDRI Studio Pack tips and tricks<br />
-How to optimize render speed with GSG HDRI Studio Pack<br />
-GSG HDRI Studio Pack customer testimonials and feedback<br />
-How to get support and help for GSG HDRI Studio Pack<br />
-How to use GSG HDRI Studio Pack with other Cinema 4D tools<br />
-GSG HDRI Studio Pack alternatives and competitors<br />
-How to add your own HDRIs to GSG HDRI Studio Pack<br />
-How to use GSG HDRI Studio Pack for animation and motion graphics<br />
-How to use GSG HDRI Studio Pack for product visualization and design<br />
-How to use GSG HDRI Studio Pack for architectural rendering and interior design<br />
-How to use GSG HDRI Studio Pack for character modeling and sculpting<br />
-How to use GSG HDRI Studio Pack for VFX and compositing<br />
-How to use GSG HDRI Studio Pack for game development and VR/AR<br />
-How to use GSG HDRI Studio Pack for photography and video editing<br />
-How to create stunning HDRIs with GSG HDRI Studio Pack<br />
-How to use GSG HDRI Studio Pack with Octane Render, Redshift, Arnold, etc.<br />
-How to use GSG HDRI Studio Pack with After Effects, Photoshop, Illustrator, etc.<br />
-How to use GSG HDRI Studio Pack with Blender, Maya, 3ds Max, etc.<br />
-How to use GSG HDRI Studio Pack with SketchUp, Revit, AutoCAD, etc.<br />
-How to use GSG HDRI Studio Pack with ZBrush, Substance Painter, Marvelous Designer, etc.<br />
-How to use GSG HDRI Studio Pack with Unity, Unreal Engine, Godot, etc.<br />
-How to use GSG HDRI Studio Pack with Premiere Pro, Final Cut Pro, DaVinci Resolve, etc.<br />
-How to use GSG HDRI Studio Pack with Lightroom, Capture One, Affinity Photo, etc.<br />
-What are the advantages of using HDRIs in Cinema 4D<br />
-What are the best practices for using HDRIs in Cinema 4D<br />
-What are the common mistakes and pitfalls when using HDRIs in Cinema 4D<br />
-What are the latest trends and developments in HDRIs and Cinema 4D<br />
-What are the best sources and resources for HDRIs and Cinema 4D<br />
-What are the best examples and inspirations of HDRIs and Cinema 4D projects</p>
-<h3>Benefits of HDRI lighting</h3>
-<p>HDRI lighting is one of the most realistic and natural ways to light your scenes in Cinema 4D. By using HDRI as your light source, you can achieve:</p>
-<ul>
-<li>Accurate color representation</li>
-<li>Soft shadows and reflections</li>
-<li>Global illumination effects</li>
-<li>Ambient occlusion effects</li>
-<li>Mood and atmosphere</li>
-</ul>
-<p>HDRI lighting can also save you time and resources by eliminating the need for multiple lights, complex setups, and long render times.</p>
-<h3>Features of GSG HDRI Studio Pack 1.8</h3>
-<p>GSG HDRI Studio Pack 1.8 offers many features that make it easy and fun to use HDRI lighting in Cinema 4D.</p>
-<ul>
-<li>Browse hundreds of HDRI in seconds with a simple interface</li>
-<li>Add beautiful reflections in seconds with a click</li>
-<li>Adjust brightness and reflections separately to find the perfect look</li>
-<li>Blur the HDRI to create better global illumination effects</li>
-<li>Use the C4D shadow catcher to adjust or remove shadows from your scene</li>
-<li>Preview your lighting before hitting render with rotation preview</li>
-<li>Create professional studio quality backdrops and seamless floors</li>
-<li>Switch between render engines without losing your settings</li>
-<li>Access exclusive Greyscalegorilla HDRI collections or use your own</li>
-</ul>
- <h2>How to use GSG HDRI Studio Pack 1.8?</h2>
-<p>GSG HDRI Studio Pack 1.8 is very easy to use in Cinema 4D.</p>
-<h3>Installation and compatibility</h3>
-<p>To install GSG HDRI Studio Pack 1.8, you need to have Cinema 4D R20 or higher installed on your computer. You also need to have a Greyscalegorilla Plus membership account, which gives you access to all of their products and training for one low price.</p>
- <p>To install the plugins, you need to download them from your Greyscalegorilla account page, unzip them, and copy them to your Cinema 4D plugins folder.</p>
- <p>To use the plugins, you need to activate them with your Greyscalegorilla Plus account credentials.</p>
- <p>GSG HDRI Studio Rig works with Cinema 4D's Standard and Physical renderers, while GSG HDRI Link works with third-party render engines like Redshift, Octane, and Arnold.</p>
- <h3>Browsing and applying HDRI</h3>
-<p>To browse and apply HDRI with GSG HDRI Studio Rig, you need to add an object to your scene (such as a sphere or a cube), then add an HDRi Studio Rig object from the plugins menu.</p>
- <p>This will open up the HDRi Browser window, where you can see all the available HDRi collections from Greyscalegorilla or your own folder.</p>
- <p>You can then drag any HDRi image onto the HDRi Preview window or double-click on it to apply it to your scene.</p>
- <p>You can also use the search bar or the filters to find the HDRi that suits your needs.</p>
- <p>You will see a small icon on the tag that indicates which render engine you are using. You can change it by clicking on it and selecting another one.</p>
- <p>This will open up the HDRI Browser window, where you can see all the available HDRI collections from Greyscalegorilla or your own folder.</p>
- <p>You can then drag any HDRI image onto the HDRI Link Tag or double-click on it to apply it to your scene.</p>
- <p>You can also use the search bar or the filters to find the HDRI that suits your needs.</p>
- <h3>Adjusting and customizing HDRI</h3>
-<p>To adjust and customize HDRI with GSG HDRI Studio Rig, you need to select the HDRi Studio Rig object and go to the attributes panel.</p>
- <p>There you will find several options to tweak your lighting and reflections, such as:</p>
-<ul>
-<li>Brightness: Adjusts the overall intensity of the HDRI</li>
-<li>Reflection: Adjusts the intensity of the reflections on your object</li>
-<li>Rotation: Rotates the HDRI around your scene</li>
-<li>Blur: Blurs the HDRI to create softer shadows and global illumination effects</li>
-<li>Fill Light: Adds a secondary light source to fill in the dark areas of your scene</li>
-<li>Color Correction: Applies hue, saturation, contrast, and gamma adjustments to the HDRI</li>
-<li>Floor: Enables or disables the seamless floor option and lets you change its color, height, and reflection</li>
-<li>Backdrop: Enables or disables the studio backdrop option and lets you change its color, height, and width</li>
-</ul>
- <p>To adjust and customize HDRI with GSG HDRI Link, you need to select the HDRi Link Tag and go to the attributes panel.</p>
- <p>There you will find a few options to tweak your lighting and reflections, such as:</p>
-<ul>
-<li>Brightness: Adjusts the overall intensity of the HDRI</li>
-<li>Reflection: Adjusts the intensity of the reflections on your object</li>
-<li>Rotation: Rotates the HDRI around your scene</li>
-<li>Blur: Blurs the HDRI to create softer shadows and global illumination effects</li>
-<li>Preview Mode: Enables or disables a low-resolution preview of the HDRI for faster feedback</li>
-</ul>
- <h2>Where to get GSG HDRI Studio Pack 1.8?</h2>
-<p>If you are interested in getting GSG HDRI Studio Pack 1.8, you have two options:</p>
-<h3>Pricing and plans</h3>
-<p>You can buy GSG HDRI Studio Pack 1.8 as a standalone product for $129. This will give you access to both plugins and 10 sample HDRI images. You can also buy additional HDRI collections from Greyscalegorilla's website, ranging from $49 to $99 each.</p>
- <p>You can also get GSG HDRI Studio Pack 1.8 as part of Greyscalegorilla Plus membership for $399 per year or $64 per month. This will give you access to all of Greyscalegorilla's products and training, including over 3,000 materials, HDRIs, and other 3D assets, all of their time-saving plugins for Cinema 4D, and 500+ hours of pro training.</p>
- <h3>Greyscalegorilla Plus membership</h3>
-<p>Greyscalegorilla Plus is a subscription service that gives you unlimited access to all of Greyscalegorilla's products and training for one low price. You can get over $13,000 worth of tools and training for only $399 per year or $64 per month.</p>
- <p>With Greyscalegorilla Plus, you can:</p>
-<ul>
-<li>Leverage over 3,000 materials, HDRIs, and other 3D assets to create stunning renders in Cinema 4D</li>
-<li>Use all of Greyscalegorilla's time-saving plugins for Cinema 4D, such as Signal, Transform, Light Kit Pro, GorillaCam, City Kit, Topcoat, Texture Kit Pro, and more</li>
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-<li>Enjoy a 60-day money-back guarantee if you are not satisfied with your membership</li>
-</ul>
- <h2>Conclusion</h2>
-<p>GSG HDRI Studio Pack 1.8 is a bundle of two plugins for Cinema 4D that let you browse and apply hundreds of high-quality HDRI in seconds. You can use them to create realistic and natural lighting and reflections in your scenes with ease. Whether you are using Cinema 4D's Standard and Physical renderers or third-party render engines like Redshift, Octane, or Arnold, GSG HDRI Studio Pack 1.8 can help you improve your renders.</p>
- <p>If you want to get GSG HDRI Studio Pack 1.8, you can buy it as a standalone product for $129 or as part of Greyscalegorilla Plus membership for $399 per year or $64 per month. Greyscalegorilla Plus gives you unlimited access to all of Greyscalegorilla's products and training for one low price.</p>
- <p>GSG HDRI Studio Pack 1.8 is a great product for Cinema 4D users who want to create better looking renders with less hassle. If you are interested in trying it out, you can visit Greyscalegorilla's website for more information.</p>
- <h2>FAQs</h2>
-<h3>What is HDRI?</h3>
-<p>HDRI stands for High Dynamic Range Images, which are images that capture a wide range of brightness values, from very dark to very bright. By using HDRI as your light source, you can create realistic and natural lighting effects that mimic the real world.</p>
- <h3>What is GSG HDRI Studio Pack 1.8?</h3>
-<p>GSG HDRI Studio Pack 1.8 is a collection of two plugins for Cinema 4D that make it easy to use HDRI lighting and reflections in your scenes. They are:</p>
-<ul>
-<li>HDRI Studio Rig: A plugin that works with Cinema 4D's Standard and Physical renderers</li>
-<li>HDRI Link: A plugin that works with third-party render engines like Redshift, Octane, and Arnold</li>
-</ul>
- <h3>How do I use GSG HDRI Studio Pack 1.8?</h3>
-<p>To use GSG HDRI Studio Pack 1.8, you need to add an object to your scene (such as a sphere or a cube), then add an HDRi Studio Rig object or an HDRi Link Tag from the plugins menu. This will open up the HDRi Browser window, where you can browse and apply any HDRi image from Greyscalegorilla's library or your own folder. You can then adjust and customize your lighting and reflections with various options in the attributes panel.</p>
- <h3>Where do I get GSG HDRI Studio Pack 1.8?</h3>
-<p>You can get GSG HDRI Studio Pack 1.8 from Greyscalegorilla's website. You can buy it as a standalone product for $129 or as part of Greyscalegorilla Plus membership for $399 per year or $64 per month.</p>
- <h3>What is Greyscalegorilla Plus?</h3>
-<p>Greyscalegorilla Plus is a subscription service that gives you unlimited access to all of Greyscalegorilla's products and training for one low price. You can get over $13,000 worth of tools and training for only $399 per year or $64 per month.</p>
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spaces/1line/AutoGPT/autogpt/commands/audio_text.py

@@ -1,36 +0,0 @@
-import json
-
-import requests
-
-from autogpt.config import Config
-from autogpt.workspace import path_in_workspace
-
-cfg = Config()
-
-
-def read_audio_from_file(audio_path):
-    audio_path = path_in_workspace(audio_path)
-    with open(audio_path, "rb") as audio_file:
-        audio = audio_file.read()
-    return read_audio(audio)
-
-
-def read_audio(audio):
-    model = cfg.huggingface_audio_to_text_model
-    api_url = f"https://api-inference.huggingface.co/models/{model}"
-    api_token = cfg.huggingface_api_token
-    headers = {"Authorization": f"Bearer {api_token}"}
-
-    if api_token is None:
-        raise ValueError(
-            "You need to set your Hugging Face API token in the config file."
-        )
-
-    response = requests.post(
-        api_url,
-        headers=headers,
-        data=audio,
-    )
-
-    text = json.loads(response.content.decode("utf-8"))["text"]
-    return "The audio says: " + text

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- <p>Do you like survival games? Do you enjoy dark humor and quirky characters? Do you want to experience a nuclear apocalypse without risking your life? If you answered yes to any of these questions, then you might want to try 60 Seconds! Reatomized, a game that lets you play as a suburban dad who has to save his family and himself from a nuclear blast. And the best part is, you can play it for free online without downloading anything. Here's how.</p>
- <h2>What is 60 Seconds! Reatomized?</h2>
- <p>60 Seconds! Reatomized is a game that combines two genres: survival simulator and dark comedy adventure. It was developed by Robot Gentleman and released in 2019 as a remastered version of the original 60 Seconds! game from 2015. Here are some of the features of the game:</p>
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- <h3>A post-apocalyptic survival simulator</h3>
- <p>In this game, you have to face the consequences of a nuclear war that has destroyed most of the world. You have to scavenge for supplies, ration food and water, deal with illnesses and injuries, and make tough decisions that will affect your survival. You also have to deal with random events and visitors that can either help or harm you. The game has four different modes: Atomic Drill, Apocalypse, Scavenge, and Survival. Each mode has its own rules and challenges.</p>
- <h3>A dark comedy adventure game</h3>
- <p>While the game has a serious theme, it also has a lot of humor and absurdity. The game is narrated by a sarcastic robot named Dolores, who comments on your actions and choices. The game also has a lot of references to pop culture, such as movies, books, games, and celebrities. The game also has a lot of funny scenarios and outcomes, such as turning into mutants, becoming cannibals, or joining cults. The game does not take itself too seriously and encourages you to have fun with it.</p>
- <h3>A remastered version of the original 60 Seconds!</h3>
- <p>60 Seconds! Reatomized is an improved version of the original game, with new features and content. Some of the improvements include:</p>
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-<li>New endings and achievements</li>
-<li>New characters and items</li>
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-<li>New game mode: Challenge</li>
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- <h3>Find a reliable website that offers the game</h3>
- <p>Go to one of the websites mentioned above or search for other websites that offer the game. Make sure that the website is safe and secure, and does not contain any viruses or malware. You can use an antivirus software or a browser extension to check the website's reputation and safety.</p>
- <h3>Choose your mode and difficulty level</h3>
- <p> <p>Once you have accessed the game on the website, you can choose the mode you want to play. The game has five modes: Atomic Drill, Apocalypse, Scavenge, Survival, and Challenge. Each mode has a different objective and gameplay. Here is a brief description of each mode:</p>
-<table>
-<tr>
-<th>Mode</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Atomic Drill</td>
-<td>This is the tutorial mode, where you can learn the basics of the game. You have to collect supplies and family members in 60 seconds and then survive in the bunker for a few days.</td>
-</tr>
-<tr>
-<td>Apocalypse</td>
-<td>This is the main mode, where you have to complete the full game. You have to collect supplies and family members in 60 seconds and then survive in the bunker as long as you can. You can choose from four difficulty levels: Little Boy, Fat Man, Tsar Bomba, and Scavenger.</td>
-</tr>
-<tr>
-<td>Scavenge</td>
-<td>This is a mode where you only have to collect supplies and family members in 60 seconds. You can choose from four difficulty levels: Easy, Normal, Hard, and Impossible.</td>
-</tr>
-<tr>
-<td>Survival</td>
-<td>This is a mode where you only have to survive in the bunker with the supplies and family members you have. You can choose from four difficulty levels: Easy, Normal, Hard, and Impossible.</td>
-</tr>
-<tr>
-<td>Challenge</td>
-<td>This is a mode where you have to complete a specific scenario with a set of rules and conditions. You can choose from 12 challenges, such as Cat Lady, Twins, or Soup Only.</td>
-</tr>
-</table>
- <h3>Collect supplies and family members in 60 seconds</h3>
- <p>After choosing your mode and difficulty level, you will start the game in your house. You will have 60 seconds to grab as many supplies and family members as you can and bring them to the fallout shelter in your backyard. You can use the arrow keys or the WASD keys to move around, and the spacebar or the left mouse button to pick up items or people. You can also use the E key or the right mouse button to drop items or people. You can only carry up to four items or people at a time, so you have to plan carefully what you need and what you can leave behind. Some of the items you can find are:</p>
-<ul>
-<li>Food: Canned soup and water bottles. You need these to feed yourself and your family.</li>
-<li>Medicine: First aid kit and medkit. You need these to heal yourself and your family from injuries or illnesses.</li>
-<li>Weapons: Axe, rifle, shotgun, pistol, ammo, padlock. You need these to defend yourself from raiders or other threats.</li>
-<li>Tools: Flashlight, radio, map, gas mask, bug spray, suitcase, Boy Scout book. You need these to communicate with others, explore outside, or fix things.</li>
-<li>Luxuries: Playing cards, chess board, harmonica, checkers board. You need these to entertain yourself and your family and prevent boredom or insanity.</li>
-<li>Pets: Cat or dog. You can choose to bring one of them with you for companionship.</li>
-</ul>
- <p>You also have to bring your family members with you. They are:</p>
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-<ul>
-<li>Ted: The protagonist and father of the family. He is strong and brave.</li>
-<li>Dolores: The wife of Ted and mother of the family. She is smart and resourceful.</li>
-<li>Mary Jane: The daughter of Ted and Dolores. She is adventurous and curious.</li>
-<li>Timmy: The son of Ted and Dolores. He is clever and optimistic.</li>
-</ul>
- <p>You have to decide who and what to bring with you before the time runs out. If you don't make it to the shelter in time, you will die in the blast. If you don't bring enough supplies or family members with you, you will have a harder time surviving in the bunker.</p>
- <h3>Survive in the bunker as long as you can</h3>
- <p>After collecting supplies and family members in 60 seconds, you will enter the bunker. This is where the survival part of the game begins. You will have to manage your resources, make decisions, and deal with events that will affect your survival. Here are some of the things you have to do:</p>
- - Feed yourself and your family every few days with soup and water. - Use medicine to treat injuries or illnesses that may occur. - Use weapons to fend off raiders or other enemies that may attack. - Use tools to communicate with other survivors or explore outside. - Use luxuries to - Use luxuries to keep yourself and your family happy and sane. - Follow the instructions or requests of Dolores, the robot narrator, who will guide you through the game. - Make choices that will affect your survival, such as who to send outside, who to trust, or what to trade. - Face random events and visitors that will have positive or negative consequences for you. - Try to find a way to escape the bunker or get rescued by the military or other survivors. <p>The game will end when you either die, escape, or get rescued. The game will also show you your stats, such as how many days you survived, how many items you used, and how many endings you unlocked. The game has over 100 endings, some of which are funny, sad, or bizarre.</p>
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- <p>There are many reasons why you might want to play 60 Seconds! Reatomized for free online without downloading anything. Here are some of them:</p>
- <h3>It's fun and challenging</h3>
- <p>The game is a mix of strategy, luck, and humor. You have to think fast and smart when collecting supplies and family members in 60 seconds. You also have to adapt to different situations and scenarios that will test your survival skills. The game is not easy, but it's rewarding when you manage to survive or achieve a good ending. The game also has a lot of humor and absurdity that will make you laugh or smile.</p>
- <h3>It's different every time you play</h3>
- <p>The game is randomly generated, which means that every time you play, you will have a different experience. The items and people you find in your house, the events and visitors you encounter in the bunker, and the endings you unlock will vary each time. The game also has different modes and difficulty levels that will change the gameplay and the challenge. The game has a lot of replay value and surprises.</p>
- <h3>It's compatible with any device and browser</h3>
- <p>The game is designed to run on any device and browser that supports HTML5. You don't need to download anything or install anything to play the game. You just need an internet connection and a web browser. You can play the game on your computer, laptop, tablet, or smartphone. You can also play the game on any operating system, such as Windows, Mac, Linux, Android, or iOS. The game is accessible and convenient for anyone.</p>
- <h2>Conclusion</h2>
- <p>60 Seconds! Reatomized is a game that lets you play as a suburban dad who has to save his family and himself from a nuclear blast. You can play it for free online without downloading anything on websites like [Gameroze.com] or [60secondsreatomizedgame.com]. The game is a combination of survival simulator and dark comedy adventure. It has four different modes: Atomic Drill, Apocalypse, Scavenge, Survival, and Challenge. It also has over 100 endings and new features and content. The game is fun and challenging, different every time you play, and compatible with any device and browser. If you are looking for a game that will test your survival skills and make you laugh at the same time, then you should try 60 Seconds! Reatomized.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about 60 Seconds! Reatomized:</p>
-<ul>
-<li><b>Q: How long does it take to finish the game?</b></li>
-<li>A: It depends on how well you play and what mode and difficulty level you choose. The game can last from a few minutes to several hours.</li>
-<li><b>Q: How can I save my progress in the game?</b></li>
-<li>A: The game has an auto-save feature that saves your progress every day in the bunker. You can also manually save your progress by clicking on the floppy disk icon in the top right corner of the screen.</li>
-<li><b>Q: How can I unlock more endings in the game?</b></li>
-<li>A: You can unlock more endings by playing different modes and difficulty levels, making different choices, using different items, interacting with different visitors, and exploring different locations.</li>
-<li><b>Q: How can I get more supplies in the game?</b></li>
-<li>A: You can get more supplies by scavenging outside the bunker with a gas mask and a map. You can also trade with other survivors or raiders who may visit your bunker.</li>
-<li><b>Q: How can I get rid of the cockroaches in the game?</b></li>
-<li>A: You can get rid of the cockroaches by using bug spray or fire. However, be careful not to burn your supplies or your family or yourself. You can also prevent the cockroaches from appearing by keeping your bunker clean and tidy.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/2ndelement/voicevox/test/test_kana_parser.py

@@ -1,688 +0,0 @@
-from typing import List
-from unittest import TestCase
-
-from voicevox_engine import kana_parser
-from voicevox_engine.kana_parser import create_kana
-from voicevox_engine.model import AccentPhrase, Mora, ParseKanaError, ParseKanaErrorCode
-
-
-def parse_kana(text: str) -> List[AccentPhrase]:
-    accent_phrases = kana_parser.parse_kana(text)
-    return accent_phrases
-
-
-class TestParseKana(TestCase):
-    def test_phrase_length(self):
-        self.assertEqual(len(parse_kana("ア'/ア'")), 2)
-        self.assertEqual(len(parse_kana("ア'、ア'")), 2)
-        self.assertEqual(len(parse_kana("ア'/ア'/ア'/ア'/ア'")), 5)
-        self.assertEqual(len(parse_kana("ス'")), 1)
-        self.assertEqual(len(parse_kana("_ス'")), 1)
-        self.assertEqual(len(parse_kana("ギェ'")), 1)
-        self.assertEqual(len(parse_kana("ギェ'、ギェ'/ギェ'")), 3)
-
-    def test_accent(self):
-        self.assertEqual(parse_kana("シャ'シシュシェショ")[0].accent, 1)
-        self.assertEqual(parse_kana("シャ'_シシュシェショ")[0].accent, 1)
-        self.assertEqual(parse_kana("シャシ'シュシェショ")[0].accent, 2)
-        self.assertEqual(parse_kana("シャ_シ'シュシェショ")[0].accent, 2)
-        self.assertEqual(parse_kana("シャシシュ'シェショ")[0].accent, 3)
-        self.assertEqual(parse_kana("シャ_シシュ'シェショ")[0].accent, 3)
-        self.assertEqual(parse_kana("シャシシュシェショ'")[0].accent, 5)
-        self.assertEqual(parse_kana("シャ_シシュシェショ'")[0].accent, 5)
-
-    def test_mora_length(self):
-        self.assertEqual(len(parse_kana("シャ'シシュシェショ")[0].moras), 5)
-        self.assertEqual(len(parse_kana("シャ'_シシュシェショ")[0].moras), 5)
-        self.assertEqual(len(parse_kana("シャシ'シュシェショ")[0].moras), 5)
-        self.assertEqual(len(parse_kana("シャ_シ'シュシェショ")[0].moras), 5)
-        self.assertEqual(len(parse_kana("シャシシュシェショ'")[0].moras), 5)
-        self.assertEqual(len(parse_kana("シャ_シシュシェショ'")[0].moras), 5)
-
-    def test_pause(self):
-        self.assertIsNone(parse_kana("ア'/ア'")[0].pause_mora)
-        self.assertIsNone(parse_kana("ア'/ア'")[1].pause_mora)
-        self.assertIsNotNone(parse_kana("ア'、ア'")[0].pause_mora)
-        self.assertIsNone(parse_kana("ア'、ア'")[1].pause_mora)
-
-    def test_unvoice(self):
-        self.assertEqual(parse_kana("ス'")[0].moras[0].vowel, "u")
-        self.assertEqual(parse_kana("_ス'")[0].moras[0].vowel, "U")
-
-    def test_roundtrip(self):
-        for text in ["コンニチワ'", "ワタシワ'/シャチョオデ'_ス", "トテモ'、エラ'インデス"]:
-            self.assertEqual(create_kana(parse_kana(text)), text)
-
-        for text in ["ヲ'", "ェ'"]:
-            self.assertEqual(create_kana(parse_kana(text)), text)
-
-    def _accent_phrase_marks_base(
-        self, text: str, expected_accent_phrases: List[AccentPhrase]
-    ) -> None:
-        accent_phrases = kana_parser.parse_kana(text)
-        self.assertEqual(expected_accent_phrases, accent_phrases)
-
-    def test_accent_phrase_marks(self):
-        def a_slash_a_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = a_slash_a_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ア'/ア'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def a_jp_comma_a_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=Mora(
-                        text="、",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="pau",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = a_jp_comma_a_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ア'、ア'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def a_slash_a_slash_a_slash_a_slash_a_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = a_slash_a_slash_a_slash_a_slash_a_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ア'/ア'/ア'/ア'/ア'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def su_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ス",
-                            consonant="s",
-                            consonant_length=0.0,
-                            vowel="u",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = su_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ス'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def under_score_su_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ス",
-                            consonant="s",
-                            consonant_length=0.0,
-                            vowel="U",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = under_score_su_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="_ス'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def gye_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = gye_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ギェ'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def gye_gye_gye_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=Mora(
-                        text="、",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="pau",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-            ]
-
-        expected_accent_phrases = gye_gye_gye_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ギェ'、ギェ'/ギェ'",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-    def test_interrogative_accent_phrase_marks(self):
-        def a_question_mark_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=True,
-                ),
-            ]
-
-        expected_accent_phrases = a_question_mark_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ア'？",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def gye_gye_gye_question_mark_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=Mora(
-                        text="、",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="pau",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ギェ",
-                            consonant="gy",
-                            consonant_length=0.0,
-                            vowel="e",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=True,
-                ),
-            ]
-
-        expected_accent_phrases = gye_gye_gye_question_mark_accent_phrases()
-        self._accent_phrase_marks_base(
-            text="ギェ'、ギェ'/ギェ'？",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-        def a_pause_a_question_pause_a_question_a_question_mark_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=Mora(
-                        text="、",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="pau",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=Mora(
-                        text="、",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="pau",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    is_interrogative=True,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=True,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=0.0,
-                            pitch=0.0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=True,
-                ),
-            ]
-
-        expected_accent_phrases = (
-            a_pause_a_question_pause_a_question_a_question_mark_accent_phrases()
-        )
-        self._accent_phrase_marks_base(
-            text="ア'、ア'？、ア'？/ア'？",
-            expected_accent_phrases=expected_accent_phrases,
-        )
-
-
-class TestParseKanaException(TestCase):
-    def _assert_error_code(self, kana: str, code: ParseKanaErrorCode):
-        with self.assertRaises(ParseKanaError) as err:
-            parse_kana(kana)
-        self.assertEqual(err.exception.errcode, code)
-
-    def test_exceptions(self):
-        self._assert_error_code("アクセント", ParseKanaErrorCode.ACCENT_NOTFOUND)
-        self._assert_error_code("'アクセント", ParseKanaErrorCode.ACCENT_TOP)
-        self._assert_error_code("ア'ク'セント", ParseKanaErrorCode.ACCENT_TWICE)
-        self._assert_error_code("ひ'らがな", ParseKanaErrorCode.UNKNOWN_TEXT)
-        self._assert_error_code("__ス'", ParseKanaErrorCode.UNKNOWN_TEXT)
-        self._assert_error_code("ア'/", ParseKanaErrorCode.EMPTY_PHRASE)
-        self._assert_error_code("/ア'", ParseKanaErrorCode.EMPTY_PHRASE)
-        self._assert_error_code("", ParseKanaErrorCode.EMPTY_PHRASE)
-
-        with self.assertRaises(ParseKanaError) as err:
-            parse_kana("ヒト'ツメ/フタツメ")
-        self.assertEqual(err.exception.errcode, ParseKanaErrorCode.ACCENT_NOTFOUND)
-        self.assertEqual(err.exception.kwargs, {"text": "フタツメ"})
-
-        with self.assertRaises(ParseKanaError) as err:
-            parse_kana("ア'/")
-        self.assertEqual(err.exception.errcode, ParseKanaErrorCode.EMPTY_PHRASE)
-        self.assertEqual(err.exception.kwargs, {"position": "2"})
-
-        with self.assertRaises(ParseKanaError) as err:
-            kana_parser.parse_kana("ア？ア'")
-        self.assertEqual(
-            err.exception.errcode, ParseKanaErrorCode.INTERROGATION_MARK_NOT_AT_END
-        )
-
-
-class TestCreateKana(TestCase):
-    def test_create_kana_interrogative(self):
-        def koreha_arimasuka_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="コ",
-                            consonant="k",
-                            consonant_length=2.5,
-                            vowel="o",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="レ",
-                            consonant="r",
-                            consonant_length=2.5,
-                            vowel="e",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="ワ",
-                            consonant="w",
-                            consonant_length=2.5,
-                            vowel="a",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                    ],
-                    accent=3,
-                    pause_mora=None,
-                    is_interrogative=False,
-                ),
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="ア",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="a",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="リ",
-                            consonant="r",
-                            consonant_length=2.5,
-                            vowel="i",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="マ",
-                            consonant="m",
-                            consonant_length=2.5,
-                            vowel="a",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="ス",
-                            consonant="s",
-                            consonant_length=2.5,
-                            vowel="U",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="カ",
-                            consonant="k",
-                            consonant_length=2.5,
-                            vowel="a",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                    ],
-                    accent=3,
-                    pause_mora=None,
-                    is_interrogative=False,
-                ),
-            ]
-
-        accent_phrases = koreha_arimasuka_accent_phrases()
-        self.assertEqual(create_kana(accent_phrases), "コレワ'/アリマ'_スカ")
-
-        accent_phrases = koreha_arimasuka_accent_phrases()
-        accent_phrases[-1].is_interrogative = True
-        self.assertEqual(create_kana(accent_phrases), "コレワ'/アリマ'_スカ？")
-
-        def kya_accent_phrases():
-            return [
-                AccentPhrase(
-                    moras=[
-                        Mora(
-                            text="キャ",
-                            consonant="ky",
-                            consonant_length=2.5,
-                            vowel="a",
-                            vowel_length=2.5,
-                            pitch=2.5,
-                        ),
-                        Mora(
-                            text="ッ",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="cl",
-                            vowel_length=0.1,
-                            pitch=0,
-                        ),
-                    ],
-                    accent=1,
-                    pause_mora=None,
-                    is_interrogative=False,
-                ),
-            ]
-
-        accent_phrases = kya_accent_phrases()
-        self.assertEqual(create_kana(accent_phrases), "キャ'ッ")
-
-        accent_phrases = kya_accent_phrases()
-        accent_phrases[-1].is_interrogative = True
-        self.assertEqual(create_kana(accent_phrases), "キャ'ッ？")

spaces/3laa2/Text2img/README.md

@@ -1,13 +0,0 @@
----
-title: Text2img
-emoji: 🔥
-colorFrom: yellow
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/42digital/DeepFashion_Classification/README.md

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

spaces/AI-ANK/PaLM-Kosmos-Vision/app.py

@@ -1,165 +0,0 @@
-import streamlit as st
-import extra_streamlit_components as stx
-import requests
-from PIL import Image
-from transformers import AutoProcessor, AutoModelForVision2Seq
-from io import BytesIO
-import replicate
-from llama_index.llms.palm import PaLM
-from llama_index import ServiceContext, VectorStoreIndex, Document
-from llama_index.memory import ChatMemoryBuffer
-import os
-import datetime
-
-# Set up the title of the application
-#st.title("PaLM-Kosmos-Vision")
-st.set_page_config(layout="wide")
-st.write("My version of ChatGPT vision. You can upload an image and start chatting with the LLM about the image")
-
-# Sidebar
-st.sidebar.markdown('## Created By')
-st.sidebar.markdown("""
-[Harshad Suryawanshi](https://www.linkedin.com/in/harshadsuryawanshi/)
-""")
-
-st.sidebar.markdown('## Other Projects')
-st.sidebar.markdown("""
-- [AI Equity Research Analyst](https://ai-eqty-rsrch-anlyst.streamlit.app/)
-- [Recasting "The Office" Scene](https://blackmirroroffice.streamlit.app/)
-- [Story Generator](https://appstorycombined-agaf9j4ceit.streamlit.app/)
-""")
-
-st.sidebar.markdown('## Disclaimer')
-st.sidebar.markdown("""
-This application is a conceptual prototype created to demonstrate the potential of Large Language Models (LLMs) in generating equity research reports. The contents generated by this application are purely illustrative and should not be construed as financial advice, endorsements, or recommendations. The author and the application do not provide any guarantee regarding the accuracy, completeness, or timeliness of the information provided.
-""")
-
-# Initialize the cookie manager
-cookie_manager = stx.CookieManager()
-
-# Function to get image caption via Kosmos2.
-@st.cache_data
-def get_image_caption(image_data):
-    input_data = {
-        "image": image_data,
-        "description_type": "Brief"
-    }
-    output = replicate.run(
-        "lucataco/kosmos-2:3e7b211c29c092f4bcc8853922cc986baa52efe255876b80cac2c2fbb4aff805",
-        input=input_data
-    )
-    # Split the output string on the newline character and take the first item
-    text_description = output.split('\n\n')[0]
-    return text_description
-
-# Function to create the chat engine.
-@st.cache_resource
-def create_chat_engine(img_desc, api_key):
-    llm = PaLM(api_key=api_key)
-    service_context = ServiceContext.from_defaults(llm=llm)
-    doc = Document(text=img_desc)
-    index = VectorStoreIndex.from_documents([doc], service_context=service_context)
-    chatmemory = ChatMemoryBuffer.from_defaults(token_limit=1500)
-    
-    chat_engine = index.as_chat_engine(
-        chat_mode="context",
-        system_prompt=(
-            f"You are a chatbot, able to have normal interactions, as well as talk. "
-            "You always answer in great detail and are polite. Your responses always descriptive. "
-            "Your job is to talk about an image the user has uploaded. Image description: {img_desc}."
-        ),
-        verbose=True,
-        memory=chatmemory
-    )
-    return chat_engine
-
-# Clear chat function
-def clear_chat():
-    if "messages" in st.session_state:
-        del st.session_state.messages
-    if "image_file" in st.session_state:
-        del st.session_state.image_file
-
-# Callback function to clear the chat when a new image is uploaded
-def on_image_upload():
-    clear_chat()        
-
-# Retrieve the message count from cookies
-message_count = cookie_manager.get(cookie='message_count')
-if message_count is None:
-    message_count = 0
-else:
-    message_count = int(message_count)
-
-# If the message limit has been reached, disable the inputs
-if message_count >= 20:
-    st.error("Notice: The maximum message limit for this demo version has been reached.")
-    # Disabling the uploader and input by not displaying them
-    image_uploader_placeholder = st.empty()  # Placeholder for the uploader
-    chat_input_placeholder = st.empty()      # Placeholder for the chat input
-else:
-    # Add a clear chat button
-    if st.button("Clear Chat"):
-        clear_chat()
-
-    # Image upload section.
-    image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"], key="uploaded_image", on_change=on_image_upload)
-    if image_file:
-        # Display the uploaded image at a standard width.
-        st.image(image_file, caption='Uploaded Image.', width=200)
-        # Process the uploaded image to get a caption.
-        image_data = BytesIO(image_file.getvalue())
-        img_desc = get_image_caption(image_data)
-        st.write("Image Uploaded Successfully. Ask me anything about it.")
-
-        # Initialize the chat engine with the image description.
-        chat_engine = create_chat_engine(img_desc, os.environ["GOOGLE_API_KEY"])
-
-    # Initialize session state for messages if it doesn't exist
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-
-    # Display previous messages
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-    # Handle new user input
-    user_input = st.chat_input("Ask me about the image:", key="chat_input")
-    if user_input:
-        # Append user message to the session state
-        st.session_state.messages.append({"role": "user", "content": user_input})
-
-        # Display user message immediately
-        with st.chat_message("user"):
-            st.markdown(user_input)
-
-        # Call the chat engine to get the response if an image has been uploaded
-        if image_file and user_input:
-            try:
-                with st.spinner('Waiting for the chat engine to respond...'):
-                    # Get the response from your chat engine
-                    response = chat_engine.chat(user_input)
-        
-                # Append assistant message to the session state
-                st.session_state.messages.append({"role": "assistant", "content": response})
-        
-                # Display the assistant message
-                with st.chat_message("assistant"):
-                    st.markdown(response)
-        
-            except Exception as e:
-                st.error(f'An error occurred: {e}')
-                # Optionally, you can choose to break the flow here if a critical error happens
-                # return
-        
-            # Increment the message count and update the cookie
-            message_count += 1
-            cookie_manager.set('message_count', str(message_count), expires_at=datetime.datetime.now() + datetime.timedelta(days=30))
-
-
-
-
-# Set Replicate and Google API keys
-os.environ['REPLICATE_API_TOKEN'] = st.secrets['REPLICATE_API_TOKEN']
-os.environ["GOOGLE_API_KEY"] = st.secrets['GOOGLE_API_KEY']

spaces/AIConsultant/MusicGen/audiocraft/models/unet.py

@@ -1,214 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Pytorch Unet Module used for diffusion.
-"""
-
-from dataclasses import dataclass
-import typing as tp
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from audiocraft.modules.transformer import StreamingTransformer, create_sin_embedding
-
-
-@dataclass
-class Output:
-    sample: torch.Tensor
-
-
-def get_model(cfg, channels: int, side: int, num_steps: int):
-    if cfg.model == 'unet':
-        return DiffusionUnet(
-            chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
-    else:
-        raise RuntimeError('Not Implemented')
-
-
-class ResBlock(nn.Module):
-    def __init__(self, channels: int, kernel: int = 3, norm_groups: int = 4,
-                 dilation: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
-                 dropout: float = 0.):
-        super().__init__()
-        stride = 1
-        padding = dilation * (kernel - stride) // 2
-        Conv = nn.Conv1d
-        Drop = nn.Dropout1d
-        self.norm1 = nn.GroupNorm(norm_groups, channels)
-        self.conv1 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
-        self.activation1 = activation()
-        self.dropout1 = Drop(dropout)
-
-        self.norm2 = nn.GroupNorm(norm_groups, channels)
-        self.conv2 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
-        self.activation2 = activation()
-        self.dropout2 = Drop(dropout)
-
-    def forward(self, x):
-        h = self.dropout1(self.conv1(self.activation1(self.norm1(x))))
-        h = self.dropout2(self.conv2(self.activation2(self.norm2(h))))
-        return x + h
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
-                 norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
-                 dropout: float = 0.):
-        super().__init__()
-        padding = (kernel - stride) // 2
-        self.res_blocks = nn.Sequential(
-            *[ResBlock(chin, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
-              for idx in range(res_blocks)])
-        self.norm = nn.GroupNorm(norm_groups, chin)
-        ConvTr = nn.ConvTranspose1d
-        self.convtr = ConvTr(chin, chout, kernel, stride, padding, bias=False)
-        self.activation = activation()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.res_blocks(x)
-        x = self.norm(x)
-        x = self.activation(x)
-        x = self.convtr(x)
-        return x
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
-                 norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
-                 dropout: float = 0.):
-        super().__init__()
-        padding = (kernel - stride) // 2
-        Conv = nn.Conv1d
-        self.conv = Conv(chin, chout, kernel, stride, padding, bias=False)
-        self.norm = nn.GroupNorm(norm_groups, chout)
-        self.activation = activation()
-        self.res_blocks = nn.Sequential(
-            *[ResBlock(chout, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
-              for idx in range(res_blocks)])
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        B, C, T = x.shape
-        stride, = self.conv.stride
-        pad = (stride - (T % stride)) % stride
-        x = F.pad(x, (0, pad))
-
-        x = self.conv(x)
-        x = self.norm(x)
-        x = self.activation(x)
-        x = self.res_blocks(x)
-        return x
-
-
-class BLSTM(nn.Module):
-    """BiLSTM with same hidden units as input dim.
-    """
-    def __init__(self, dim, layers=2):
-        super().__init__()
-        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
-        self.linear = nn.Linear(2 * dim, dim)
-
-    def forward(self, x):
-        x = x.permute(2, 0, 1)
-        x = self.lstm(x)[0]
-        x = self.linear(x)
-        x = x.permute(1, 2, 0)
-        return x
-
-
-class DiffusionUnet(nn.Module):
-    def __init__(self, chin: int = 3, hidden: int = 24, depth: int = 3, growth: float = 2.,
-                 max_channels: int = 10_000, num_steps: int = 1000, emb_all_layers=False, cross_attention: bool = False,
-                 bilstm: bool = False, transformer: bool = False,
-                 codec_dim: tp.Optional[int] = None, **kwargs):
-        super().__init__()
-        self.encoders = nn.ModuleList()
-        self.decoders = nn.ModuleList()
-        self.embeddings: tp.Optional[nn.ModuleList] = None
-        self.embedding = nn.Embedding(num_steps, hidden)
-        if emb_all_layers:
-            self.embeddings = nn.ModuleList()
-        self.condition_embedding: tp.Optional[nn.Module] = None
-        for d in range(depth):
-            encoder = EncoderLayer(chin, hidden, **kwargs)
-            decoder = DecoderLayer(hidden, chin, **kwargs)
-            self.encoders.append(encoder)
-            self.decoders.insert(0, decoder)
-            if emb_all_layers and d > 0:
-                assert self.embeddings is not None
-                self.embeddings.append(nn.Embedding(num_steps, hidden))
-            chin = hidden
-            hidden = min(int(chin * growth), max_channels)
-        self.bilstm: tp.Optional[nn.Module]
-        if bilstm:
-            self.bilstm = BLSTM(chin)
-        else:
-            self.bilstm = None
-        self.use_transformer = transformer
-        self.cross_attention = False
-        if transformer:
-            self.cross_attention = cross_attention
-            self.transformer = StreamingTransformer(chin, 8, 6, bias_ff=False, bias_attn=False,
-                                                    cross_attention=cross_attention)
-
-        self.use_codec = False
-        if codec_dim is not None:
-            self.conv_codec = nn.Conv1d(codec_dim, chin, 1)
-            self.use_codec = True
-
-    def forward(self, x: torch.Tensor, step: tp.Union[int, torch.Tensor], condition: tp.Optional[torch.Tensor] = None):
-        skips = []
-        bs = x.size(0)
-        z = x
-        view_args = [1]
-        if type(step) is torch.Tensor:
-            step_tensor = step
-        else:
-            step_tensor = torch.tensor([step], device=x.device, dtype=torch.long).expand(bs)
-
-        for idx, encoder in enumerate(self.encoders):
-            z = encoder(z)
-            if idx == 0:
-                z = z + self.embedding(step_tensor).view(bs, -1, *view_args).expand_as(z)
-            elif self.embeddings is not None:
-                z = z + self.embeddings[idx - 1](step_tensor).view(bs, -1, *view_args).expand_as(z)
-
-            skips.append(z)
-
-        if self.use_codec:  # insert condition in the bottleneck
-            assert condition is not None, "Model defined for conditionnal generation"
-            condition_emb = self.conv_codec(condition)  # reshape to the bottleneck dim
-            assert condition_emb.size(-1) <= 2 * z.size(-1), \
-                f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
-            if not self.cross_attention:
-
-                condition_emb = torch.nn.functional.interpolate(condition_emb, z.size(-1))
-                assert z.size() == condition_emb.size()
-                z += condition_emb
-                cross_attention_src = None
-            else:
-                cross_attention_src = condition_emb.permute(0, 2, 1)  # B, T, C
-                B, T, C = cross_attention_src.shape
-                positions = torch.arange(T, device=x.device).view(1, -1, 1)
-                pos_emb = create_sin_embedding(positions, C, max_period=10_000, dtype=cross_attention_src.dtype)
-                cross_attention_src = cross_attention_src + pos_emb
-        if self.use_transformer:
-            z = self.transformer(z.permute(0, 2, 1), cross_attention_src=cross_attention_src).permute(0, 2, 1)
-        else:
-            if self.bilstm is None:
-                z = torch.zeros_like(z)
-            else:
-                z = self.bilstm(z)
-
-        for decoder in self.decoders:
-            s = skips.pop(-1)
-            z = z[:, :, :s.shape[2]]
-            z = z + s
-            z = decoder(z)
-
-        z = z[:, :, :x.shape[2]]
-        return Output(z)

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/ema.py

@@ -1,76 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
-                             else torch.tensor(-1,dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                #remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.','')
-                self.m_name2s_name.update({name:s_name})
-                self.register_buffer(s_name,p.clone().detach().data)
-
-        self.collected_params = []
-
-    def forward(self,model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/test.py

@@ -1,31 +0,0 @@
-import argparse
-import random
-from tester import DiacritizationTester
-
-import numpy as np
-import torch
-
-
-SEED = 1234
-random.seed(SEED)
-np.random.seed(SEED)
-torch.manual_seed(SEED)
-torch.cuda.manual_seed(SEED)
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-
-
-def train_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model", dest="model_kind", type=str, required=True)
-    parser.add_argument("--config", dest="config", type=str, required=True)
-    parser.add_argument("--model_path", dest="model_path", type=str, required=False)
-    parser.add_argument("--test", dest="test", type=bool)
-    return parser
-
-
-parser = train_parser()
-args = parser.parse_args()
-
-tester = DiacritizationTester(args.config, args.model_kind)
-tester.run()

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

@@ -1,40 +0,0 @@
-from __future__ import annotations
-
-import json
-
-from ..requests import StreamSession
-from ..typing import AsyncGenerator, Messages
-from .base_provider import AsyncGeneratorProvider, format_prompt
-
-
-class You(AsyncGeneratorProvider):
-    url = "https://you.com"
-    working = True
-    supports_gpt_35_turbo = True
-
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        timeout: int = 120,
-        **kwargs,
-    ) -> AsyncGenerator:
-        async with StreamSession(proxies={"https": proxy}, impersonate="chrome107", timeout=timeout) as session:
-            headers = {
-                "Accept": "text/event-stream",
-                "Referer": f"{cls.url}/search?fromSearchBar=true&tbm=youchat",
-            }
-            data = {"q": format_prompt(messages), "domain": "youchat", "chat": ""}
-            async with session.get(
-                f"{cls.url}/api/streamingSearch",
-                params=data,
-                headers=headers
-            ) as response:
-                response.raise_for_status()
-                start = b'data: {"youChatToken": '
-                async for line in response.iter_lines():
-                    if line.startswith(start):
-                        yield json.loads(line[len(start):-1])

spaces/Adapter/T2I-Adapter/train_seg.py

@@ -1,372 +0,0 @@
-import cv2
-import torch
-import os
-from basicsr.utils import img2tensor, tensor2img, scandir, get_time_str, get_root_logger, get_env_info
-from ldm.data.dataset_coco import dataset_coco_mask_color
-import argparse
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.dpm_solver import DPMSolverSampler
-from omegaconf import OmegaConf
-from ldm.util import instantiate_from_config
-from ldm.modules.encoders.adapter import Adapter
-from PIL import Image
-import numpy as np
-import torch.nn as nn
-import matplotlib.pyplot as plt
-import time
-import os.path as osp
-from basicsr.utils.options import copy_opt_file, dict2str
-import logging
-from dist_util import init_dist, master_only, get_bare_model, get_dist_info
-
-def load_model_from_config(config, ckpt, verbose=False):
-    print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
-    model = instantiate_from_config(config.model)
-    m, u = model.load_state_dict(sd, strict=False)
-    if len(m) > 0 and verbose:
-        print("missing keys:")
-        print(m)
-    if len(u) > 0 and verbose:
-        print("unexpected keys:")
-        print(u)
-
-    model.cuda()
-    model.eval()
-    return model
-
-@master_only
-def mkdir_and_rename(path):
-    """mkdirs. If path exists, rename it with timestamp and create a new one.
-
-    Args:
-        path (str): Folder path.
-    """
-    if osp.exists(path):
-        new_name = path + '_archived_' + get_time_str()
-        print(f'Path already exists. Rename it to {new_name}', flush=True)
-        os.rename(path, new_name)
-    os.makedirs(path, exist_ok=True)
-    os.makedirs(osp.join(experiments_root, 'models'))
-    os.makedirs(osp.join(experiments_root, 'training_states'))
-    os.makedirs(osp.join(experiments_root, 'visualization'))
-
-def load_resume_state(opt):
-    resume_state_path = None
-    if opt.auto_resume:
-        state_path = osp.join('experiments', opt.name, 'training_states')
-        if osp.isdir(state_path):
-            states = list(scandir(state_path, suffix='state', recursive=False, full_path=False))
-            if len(states) != 0:
-                states = [float(v.split('.state')[0]) for v in states]
-                resume_state_path = osp.join(state_path, f'{max(states):.0f}.state')
-                opt.resume_state_path = resume_state_path
-    # else:
-    #     if opt['path'].get('resume_state'):
-    #         resume_state_path = opt['path']['resume_state']
-
-    if resume_state_path is None:
-        resume_state = None
-    else:
-        device_id = torch.cuda.current_device()
-        resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id))
-        # check_resume(opt, resume_state['iter'])
-    return resume_state
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--bsize",
-    type=int,
-    default=8,
-    help="the prompt to render"
-)
-parser.add_argument(
-    "--epochs",
-    type=int,
-    default=10000,
-    help="the prompt to render"
-)
-parser.add_argument(
-    "--num_workers",
-    type=int,
-    default=8,
-    help="the prompt to render"
-)
-parser.add_argument(
-    "--use_shuffle",
-    type=bool,
-    default=True,
-    help="the prompt to render"
-)
-parser.add_argument(
-        "--dpm_solver",
-        action='store_true',
-        help="use dpm_solver sampling",
-)
-parser.add_argument(
-        "--plms",
-        action='store_true',
-        help="use plms sampling",
-)
-parser.add_argument(
-        "--auto_resume",
-        action='store_true',
-        help="use plms sampling",
-)
-parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="ckp/sd-v1-4.ckpt",
-        help="path to checkpoint of model",
-)
-parser.add_argument(
-        "--config",
-        type=str,
-        default="configs/stable-diffusion/train_mask.yaml",
-        help="path to config which constructs model",
-)
-parser.add_argument(
-        "--print_fq",
-        type=int,
-        default=100,
-        help="path to config which constructs model",
-)
-parser.add_argument(
-        "--H",
-        type=int,
-        default=512,
-        help="image height, in pixel space",
-)
-parser.add_argument(
-    "--W",
-    type=int,
-    default=512,
-    help="image width, in pixel space",
-)
-parser.add_argument(
-    "--C",
-    type=int,
-    default=4,
-    help="latent channels",
-)
-parser.add_argument(
-    "--f",
-    type=int,
-    default=8,
-    help="downsampling factor",
-)
-parser.add_argument(
-        "--ddim_steps",
-        type=int,
-        default=50,
-        help="number of ddim sampling steps",
-)
-parser.add_argument(
-        "--n_samples",
-        type=int,
-        default=1,
-        help="how many samples to produce for each given prompt. A.k.a. batch size",
-)
-parser.add_argument(
-        "--ddim_eta",
-        type=float,
-        default=0.0,
-        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
-)
-parser.add_argument(
-        "--scale",
-        type=float,
-        default=7.5,
-        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
-)
-parser.add_argument(
-        "--gpus",
-        default=[0,1,2,3],
-        help="gpu idx",
-)
-parser.add_argument(
-        '--local_rank', 
-        default=0, 
-        type=int,
-        help='node rank for distributed training'
-)
-parser.add_argument(
-        '--launcher', 
-        default='pytorch', 
-        type=str,
-        help='node rank for distributed training'
-)
-opt = parser.parse_args()
-
-if __name__ == '__main__':
-    config = OmegaConf.load(f"{opt.config}")
-    opt.name = config['name']
-    
-    # distributed setting
-    init_dist(opt.launcher)
-    torch.backends.cudnn.benchmark = True
-    device='cuda'
-    torch.cuda.set_device(opt.local_rank)
-
-    # dataset
-    path_json_train = 'coco_stuff/mask/annotations/captions_train2017.json'
-    path_json_val = 'coco_stuff/mask/annotations/captions_val2017.json'
-    train_dataset = dataset_coco_mask_color(path_json_train, 
-    root_path_im='coco/train2017',
-    root_path_mask='coco_stuff/mask/train2017_color',
-    image_size=512
-    )
-    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
-    val_dataset = dataset_coco_mask_color(path_json_val, 
-    root_path_im='coco/val2017',
-    root_path_mask='coco_stuff/mask/val2017_color',
-    image_size=512
-    )
-    train_dataloader = torch.utils.data.DataLoader(
-            train_dataset,
-            batch_size=opt.bsize,
-            shuffle=(train_sampler is None),
-            num_workers=opt.num_workers,
-            pin_memory=True,
-            sampler=train_sampler)
-    val_dataloader = torch.utils.data.DataLoader(
-            val_dataset,
-            batch_size=1,
-            shuffle=False,
-            num_workers=1,
-            pin_memory=False)
-
-    # stable diffusion
-    model = load_model_from_config(config, f"{opt.ckpt}").to(device)
-    
-    # sketch encoder
-    model_ad = Adapter(cin=int(3*64), channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device)
-
-
-    # to gpus
-    model_ad = torch.nn.parallel.DistributedDataParallel(
-        model_ad,
-        device_ids=[opt.local_rank], 
-        output_device=opt.local_rank)
-    model = torch.nn.parallel.DistributedDataParallel(
-        model,
-        device_ids=[opt.local_rank], 
-        output_device=opt.local_rank)
-        # device_ids=[torch.cuda.current_device()])
-
-    # optimizer
-    params = list(model_ad.parameters())
-    optimizer = torch.optim.AdamW(params, lr=config['training']['lr'])
-
-    experiments_root = osp.join('experiments', opt.name)
-
-    # resume state
-    resume_state = load_resume_state(opt)
-    if resume_state is None:
-        mkdir_and_rename(experiments_root)
-        start_epoch = 0
-        current_iter = 0
-        # WARNING: should not use get_root_logger in the above codes, including the called functions
-        # Otherwise the logger will not be properly initialized
-        log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log")
-        logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
-        logger.info(get_env_info())
-        logger.info(dict2str(config))
-    else:
-        # WARNING: should not use get_root_logger in the above codes, including the called functions
-        # Otherwise the logger will not be properly initialized
-        log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log")
-        logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
-        logger.info(get_env_info())
-        logger.info(dict2str(config))
-        resume_optimizers = resume_state['optimizers']
-        optimizer.load_state_dict(resume_optimizers)
-        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
-        start_epoch = resume_state['epoch']
-        current_iter = resume_state['iter']
-
-    # copy the yml file to the experiment root
-    copy_opt_file(opt.config, experiments_root)
-
-    # training
-    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')
-    for epoch in range(start_epoch, opt.epochs):
-        train_dataloader.sampler.set_epoch(epoch)
-        # train
-        for _, data in enumerate(train_dataloader):
-            current_iter += 1
-            with torch.no_grad():
-                c = model.module.get_learned_conditioning(data['sentence'])
-                z = model.module.encode_first_stage((data['im']*2-1.).cuda(non_blocking=True))
-                z = model.module.get_first_stage_encoding(z)
-
-            mask = data['mask']
-            optimizer.zero_grad()
-            model.zero_grad()
-            features_adapter = model_ad(mask)
-            l_pixel, loss_dict = model(z, c=c, features_adapter = features_adapter)
-            l_pixel.backward()
-            optimizer.step()
-
-            if (current_iter+1)%opt.print_fq == 0:
-                logger.info(loss_dict)
-            
-            # save checkpoint
-            rank, _ = get_dist_info()
-            if (rank==0) and ((current_iter+1)%config['training']['save_freq'] == 0):
-                save_filename = f'model_ad_{current_iter+1}.pth'
-                save_path = os.path.join(experiments_root, 'models', save_filename)
-                save_dict = {}
-                model_ad_bare = get_bare_model(model_ad)
-                state_dict = model_ad_bare.state_dict()
-                for key, param in state_dict.items():
-                    if key.startswith('module.'):  # remove unnecessary 'module.'
-                        key = key[7:]
-                    save_dict[key] = param.cpu()
-                torch.save(save_dict, save_path)
-            # save state
-                state = {'epoch': epoch, 'iter': current_iter+1, 'optimizers': optimizer.state_dict()}
-                save_filename = f'{current_iter+1}.state'
-                save_path = os.path.join(experiments_root, 'training_states', save_filename)
-                torch.save(state, save_path)
-
-        # val
-        rank, _ = get_dist_info()
-        if rank==0:
-            for data in val_dataloader:
-                with torch.no_grad():
-                    if opt.dpm_solver:
-                        sampler = DPMSolverSampler(model.module)
-                    elif opt.plms:
-                        sampler = PLMSSampler(model.module)
-                    else:
-                        sampler = DDIMSampler(model.module)
-                    c = model.module.get_learned_conditioning(data['sentence'])
-                    mask = data['mask']
-                    im_mask = tensor2img(mask)
-                    cv2.imwrite(os.path.join(experiments_root, 'visualization', 'mask_%04d.png'%epoch), im_mask)
-                    features_adapter = model_ad(mask)
-                    shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
-                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
-                                                        conditioning=c,
-                                                        batch_size=opt.n_samples,
-                                                        shape=shape,
-                                                        verbose=False,
-                                                        unconditional_guidance_scale=opt.scale,
-                                                        unconditional_conditioning=model.module.get_learned_conditioning(opt.n_samples * [""]),
-                                                        eta=opt.ddim_eta,
-                                                        x_T=None,
-                                                        features_adapter=features_adapter)
-                    x_samples_ddim = model.module.decode_first_stage(samples_ddim)
-                    x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-                    x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
-                    for id_sample, x_sample in enumerate(x_samples_ddim):
-                        x_sample = 255.*x_sample
-                        img = x_sample.astype(np.uint8)
-                        img = cv2.putText(img.copy(), data['sentence'][0], (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
-                        cv2.imwrite(os.path.join(experiments_root, 'visualization', 'sample_e%04d_s%04d.png'%(epoch, id_sample)), img[:,:,::-1])
-                    break

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/RemoveChildMethods.js

@@ -1,55 +0,0 @@
-import Sizer from '../sizer/Sizer.js';
-import IsArray from '../../../plugins/utils/object/IsArray.js';
-
-const SizerRmove = Sizer.prototype.remove;
-const SizerClear = Sizer.prototype.clear;
-
-var Remove = function (gameObject, destroyChild) {
-    if (this.getParentSizer(gameObject) !== this) {
-        return this;
-    }
-
-    this.buttonGroup.remove(gameObject);
-    SizerRmove.call(this, gameObject, destroyChild);
-    return this;
-};
-
-export default {
-    remove(gameObject, destroyChild) {
-        // Remove gameObject no matter it is a button or not
-        if (IsArray(gameObject)) {
-            var gameObjects = gameObject;
-            for (var i = 0, cnt = gameObjects.length; i < cnt; i++) {
-                Remove.call(this, gameObjects[i], destroyChild);
-            }
-        } else {
-            Remove.call(this, gameObject, destroyChild);
-        }
-        return this;
-    },
-
-    clear(destroyChild) {
-        var buttons = this.buttonGroup.buttons;
-        buttons.length = 0;
-        SizerClear.call(this, destroyChild);
-        return this;
-    },
-
-    removeButton(gameObject, destroyChild) {
-        var gameObject = this.getButton(gameObject);
-        // Don't remove this gameObject, it is not a button
-        if (!gameObject) {
-            return this;
-        }
-        this.remove(gameObject, destroyChild);
-        return this;
-    },
-
-    clearButtons(destroyChild) {
-        var buttons = this.buttonGroup.buttons;
-        for (var i = buttons.length - 1; i >= 0; i--) {
-            Remove.call(this, buttons[i], destroyChild);
-        }
-        return this;
-    }
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.d.ts

@@ -1,5 +0,0 @@
-import CustomProgress from "./CustomProgress";
-
-export default function (
-    config?: CustomProgress.IConfig
-): CustomProgress;

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

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

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/space/Factory.d.ts

@@ -1,3 +0,0 @@
-import Space from './Space';
-
-export default function (): Space;

spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_detection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_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/mmdet/models/losses/gfocal_loss.py

@@ -1,188 +0,0 @@
-import mmcv
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def quality_focal_loss(pred, target, beta=2.0):
-    r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
-    Qualified and Distributed Bounding Boxes for Dense Object Detection
-    <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted joint representation of classification
-            and quality (IoU) estimation with shape (N, C), C is the number of
-            classes.
-        target (tuple([torch.Tensor])): Target category label with shape (N,)
-            and target quality label with shape (N,).
-        beta (float): The beta parameter for calculating the modulating factor.
-            Defaults to 2.0.
-
-    Returns:
-        torch.Tensor: Loss tensor with shape (N,).
-    """
-    assert len(target) == 2, """target for QFL must be a tuple of two elements,
-        including category label and quality label, respectively"""
-    # label denotes the category id, score denotes the quality score
-    label, score = target
-
-    # negatives are supervised by 0 quality score
-    pred_sigmoid = pred.sigmoid()
-    scale_factor = pred_sigmoid
-    zerolabel = scale_factor.new_zeros(pred.shape)
-    loss = F.binary_cross_entropy_with_logits(
-        pred, zerolabel, reduction='none') * scale_factor.pow(beta)
-
-    # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-    bg_class_ind = pred.size(1)
-    pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
-    pos_label = label[pos].long()
-    # positives are supervised by bbox quality (IoU) score
-    scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
-    loss[pos, pos_label] = F.binary_cross_entropy_with_logits(
-        pred[pos, pos_label], score[pos],
-        reduction='none') * scale_factor.abs().pow(beta)
-
-    loss = loss.sum(dim=1, keepdim=False)
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def distribution_focal_loss(pred, label):
-    r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning
-    Qualified and Distributed Bounding Boxes for Dense Object Detection
-    <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted general distribution of bounding boxes
-            (before softmax) with shape (N, n+1), n is the max value of the
-            integral set `{0, ..., n}` in paper.
-        label (torch.Tensor): Target distance label for bounding boxes with
-            shape (N,).
-
-    Returns:
-        torch.Tensor: Loss tensor with shape (N,).
-    """
-    dis_left = label.long()
-    dis_right = dis_left + 1
-    weight_left = dis_right.float() - label
-    weight_right = label - dis_left.float()
-    loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \
-        + F.cross_entropy(pred, dis_right, reduction='none') * weight_right
-    return loss
-
-
-@LOSSES.register_module()
-class QualityFocalLoss(nn.Module):
-    r"""Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:
-    Learning Qualified and Distributed Bounding Boxes for Dense Object
-    Detection <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        use_sigmoid (bool): Whether sigmoid operation is conducted in QFL.
-            Defaults to True.
-        beta (float): The beta parameter for calculating the modulating factor.
-            Defaults to 2.0.
-        reduction (str): Options are "none", "mean" and "sum".
-        loss_weight (float): Loss weight of current loss.
-    """
-
-    def __init__(self,
-                 use_sigmoid=True,
-                 beta=2.0,
-                 reduction='mean',
-                 loss_weight=1.0):
-        super(QualityFocalLoss, self).__init__()
-        assert use_sigmoid is True, 'Only sigmoid in QFL supported now.'
-        self.use_sigmoid = use_sigmoid
-        self.beta = beta
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): Predicted joint representation of
-                classification and quality (IoU) estimation with shape (N, C),
-                C is the number of classes.
-            target (tuple([torch.Tensor])): Target category label with shape
-                (N,) and target quality label with shape (N,).
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.use_sigmoid:
-            loss_cls = self.loss_weight * quality_focal_loss(
-                pred,
-                target,
-                weight,
-                beta=self.beta,
-                reduction=reduction,
-                avg_factor=avg_factor)
-        else:
-            raise NotImplementedError
-        return loss_cls
-
-
-@LOSSES.register_module()
-class DistributionFocalLoss(nn.Module):
-    r"""Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
-    Learning Qualified and Distributed Bounding Boxes for Dense Object
-    Detection <https://arxiv.org/abs/2006.04388>`_.
-
-    Args:
-        reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
-        loss_weight (float): Loss weight of current loss.
-    """
-
-    def __init__(self, reduction='mean', loss_weight=1.0):
-        super(DistributionFocalLoss, self).__init__()
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): Predicted general distribution of bounding
-                boxes (before softmax) with shape (N, n+1), n is the max value
-                of the integral set `{0, ..., n}` in paper.
-            target (torch.Tensor): Target distance label for bounding boxes
-                with shape (N,).
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_cls = self.loss_weight * distribution_focal_loss(
-            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
-        return loss_cls

spaces/AngoHF/ANGO-Leaderboard/components/top.py

@@ -1,13 +0,0 @@
-import gradio as gr
-
-from assets.content import TITLE, INTRODUCTION_TEXT
-from assets.path import SEASON
-
-
-def create_top():
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
-    with gr.Row():
-        season_dropdown = gr.Dropdown(choices=list(SEASON), value="latest", label="Season Select")
-        language_dropdown = gr.Dropdown(choices=['en', 'zh'], value='en', label='Language Select')
-    return {"season": season_dropdown, "language": language_dropdown}

spaces/AnimalEquality/chatbot/app.py

@@ -1,21 +0,0 @@
-from lv_recipe_chatbot.app import create_demo, ConversationBot
-from lv_recipe_chatbot.ingredient_vision import (
-    VeganIngredientFinder,
-    BlipImageCaptioning,
-)
-import os
-
-
-# for Hugging Face
-
-if __name__ == "__main__":
-    vegan_ingred_finder = VeganIngredientFinder()
-    img_cap = BlipImageCaptioning("cpu")
-    demo = create_demo(
-        ConversationBot(
-            vegan_ingred_finder=vegan_ingred_finder, img_cap=img_cap, verbose=True
-        )
-    )
-    demo.launch(
-        auth=(os.environ["GRADIO_DEMO_USERNAME"], os.environ["GRADIO_DEMO_PASSWORD"])
-    )

spaces/Anindya/Marketing_Campaign_LLM/README.md

@@ -1,13 +0,0 @@
----
-title: Marketing Campaign LLM
-emoji: 📚
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
----
-
-# Marketing_Campaign_LLM
-Simple Marketing Campaign app using LLM

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/main.py

@@ -1,382 +0,0 @@
-import io
-import logging
-import os
-import shutil
-import sys
-import tempfile
-from collections import OrderedDict
-from contextlib import contextmanager
-from typing import (IO, Dict, Iterable, Iterator, Mapping, Optional, Tuple,
-                    Union)
-
-from .parser import Binding, parse_stream
-from .variables import parse_variables
-
-# A type alias for a string path to be used for the paths in this file.
-# These paths may flow to `open()` and `shutil.move()`; `shutil.move()`
-# only accepts string paths, not byte paths or file descriptors. See
-# https://github.com/python/typeshed/pull/6832.
-StrPath = Union[str, 'os.PathLike[str]']
-
-logger = logging.getLogger(__name__)
-
-
-def with_warn_for_invalid_lines(mappings: Iterator[Binding]) -> Iterator[Binding]:
-    for mapping in mappings:
-        if mapping.error:
-            logger.warning(
-                "Python-dotenv could not parse statement starting at line %s",
-                mapping.original.line,
-            )
-        yield mapping
-
-
-class DotEnv:
-    def __init__(
-        self,
-        dotenv_path: Optional[StrPath],
-        stream: Optional[IO[str]] = None,
-        verbose: bool = False,
-        encoding: Optional[str] = None,
-        interpolate: bool = True,
-        override: bool = True,
-    ) -> None:
-        self.dotenv_path: Optional[StrPath] = dotenv_path
-        self.stream: Optional[IO[str]] = stream
-        self._dict: Optional[Dict[str, Optional[str]]] = None
-        self.verbose: bool = verbose
-        self.encoding: Optional[str] = encoding
-        self.interpolate: bool = interpolate
-        self.override: bool = override
-
-    @contextmanager
-    def _get_stream(self) -> Iterator[IO[str]]:
-        if self.dotenv_path and os.path.isfile(self.dotenv_path):
-            with open(self.dotenv_path, encoding=self.encoding) as stream:
-                yield stream
-        elif self.stream is not None:
-            yield self.stream
-        else:
-            if self.verbose:
-                logger.info(
-                    "Python-dotenv could not find configuration file %s.",
-                    self.dotenv_path or '.env',
-                )
-            yield io.StringIO('')
-
-    def dict(self) -> Dict[str, Optional[str]]:
-        """Return dotenv as dict"""
-        if self._dict:
-            return self._dict
-
-        raw_values = self.parse()
-
-        if self.interpolate:
-            self._dict = OrderedDict(resolve_variables(raw_values, override=self.override))
-        else:
-            self._dict = OrderedDict(raw_values)
-
-        return self._dict
-
-    def parse(self) -> Iterator[Tuple[str, Optional[str]]]:
-        with self._get_stream() as stream:
-            for mapping in with_warn_for_invalid_lines(parse_stream(stream)):
-                if mapping.key is not None:
-                    yield mapping.key, mapping.value
-
-    def set_as_environment_variables(self) -> bool:
-        """
-        Load the current dotenv as system environment variable.
-        """
-        if not self.dict():
-            return False
-
-        for k, v in self.dict().items():
-            if k in os.environ and not self.override:
-                continue
-            if v is not None:
-                os.environ[k] = v
-
-        return True
-
-    def get(self, key: str) -> Optional[str]:
-        """
-        """
-        data = self.dict()
-
-        if key in data:
-            return data[key]
-
-        if self.verbose:
-            logger.warning("Key %s not found in %s.", key, self.dotenv_path)
-
-        return None
-
-
-def get_key(
-    dotenv_path: StrPath,
-    key_to_get: str,
-    encoding: Optional[str] = "utf-8",
-) -> Optional[str]:
-    """
-    Get the value of a given key from the given .env.
-
-    Returns `None` if the key isn't found or doesn't have a value.
-    """
-    return DotEnv(dotenv_path, verbose=True, encoding=encoding).get(key_to_get)
-
-
-@contextmanager
-def rewrite(
-    path: StrPath,
-    encoding: Optional[str],
-) -> Iterator[Tuple[IO[str], IO[str]]]:
-    if not os.path.isfile(path):
-        with open(path, mode="w", encoding=encoding) as source:
-            source.write("")
-    with tempfile.NamedTemporaryFile(mode="w", encoding=encoding, delete=False) as dest:
-        try:
-            with open(path, encoding=encoding) as source:
-                yield (source, dest)
-        except BaseException:
-            os.unlink(dest.name)
-            raise
-    shutil.move(dest.name, path)
-
-
-def set_key(
-    dotenv_path: StrPath,
-    key_to_set: str,
-    value_to_set: str,
-    quote_mode: str = "always",
-    export: bool = False,
-    encoding: Optional[str] = "utf-8",
-) -> Tuple[Optional[bool], str, str]:
-    """
-    Adds or Updates a key/value to the given .env
-
-    If the .env path given doesn't exist, fails instead of risking creating
-    an orphan .env somewhere in the filesystem
-    """
-    if quote_mode not in ("always", "auto", "never"):
-        raise ValueError(f"Unknown quote_mode: {quote_mode}")
-
-    quote = (
-        quote_mode == "always"
-        or (quote_mode == "auto" and not value_to_set.isalnum())
-    )
-
-    if quote:
-        value_out = "'{}'".format(value_to_set.replace("'", "\\'"))
-    else:
-        value_out = value_to_set
-    if export:
-        line_out = f'export {key_to_set}={value_out}\n'
-    else:
-        line_out = f"{key_to_set}={value_out}\n"
-
-    with rewrite(dotenv_path, encoding=encoding) as (source, dest):
-        replaced = False
-        missing_newline = False
-        for mapping in with_warn_for_invalid_lines(parse_stream(source)):
-            if mapping.key == key_to_set:
-                dest.write(line_out)
-                replaced = True
-            else:
-                dest.write(mapping.original.string)
-                missing_newline = not mapping.original.string.endswith("\n")
-        if not replaced:
-            if missing_newline:
-                dest.write("\n")
-            dest.write(line_out)
-
-    return True, key_to_set, value_to_set
-
-
-def unset_key(
-    dotenv_path: StrPath,
-    key_to_unset: str,
-    quote_mode: str = "always",
-    encoding: Optional[str] = "utf-8",
-) -> Tuple[Optional[bool], str]:
-    """
-    Removes a given key from the given `.env` file.
-
-    If the .env path given doesn't exist, fails.
-    If the given key doesn't exist in the .env, fails.
-    """
-    if not os.path.exists(dotenv_path):
-        logger.warning("Can't delete from %s - it doesn't exist.", dotenv_path)
-        return None, key_to_unset
-
-    removed = False
-    with rewrite(dotenv_path, encoding=encoding) as (source, dest):
-        for mapping in with_warn_for_invalid_lines(parse_stream(source)):
-            if mapping.key == key_to_unset:
-                removed = True
-            else:
-                dest.write(mapping.original.string)
-
-    if not removed:
-        logger.warning("Key %s not removed from %s - key doesn't exist.", key_to_unset, dotenv_path)
-        return None, key_to_unset
-
-    return removed, key_to_unset
-
-
-def resolve_variables(
-    values: Iterable[Tuple[str, Optional[str]]],
-    override: bool,
-) -> Mapping[str, Optional[str]]:
-    new_values: Dict[str, Optional[str]] = {}
-
-    for (name, value) in values:
-        if value is None:
-            result = None
-        else:
-            atoms = parse_variables(value)
-            env: Dict[str, Optional[str]] = {}
-            if override:
-                env.update(os.environ)  # type: ignore
-                env.update(new_values)
-            else:
-                env.update(new_values)
-                env.update(os.environ)  # type: ignore
-            result = "".join(atom.resolve(env) for atom in atoms)
-
-        new_values[name] = result
-
-    return new_values
-
-
-def _walk_to_root(path: str) -> Iterator[str]:
-    """
-    Yield directories starting from the given directory up to the root
-    """
-    if not os.path.exists(path):
-        raise IOError('Starting path not found')
-
-    if os.path.isfile(path):
-        path = os.path.dirname(path)
-
-    last_dir = None
-    current_dir = os.path.abspath(path)
-    while last_dir != current_dir:
-        yield current_dir
-        parent_dir = os.path.abspath(os.path.join(current_dir, os.path.pardir))
-        last_dir, current_dir = current_dir, parent_dir
-
-
-def find_dotenv(
-    filename: str = '.env',
-    raise_error_if_not_found: bool = False,
-    usecwd: bool = False,
-) -> str:
-    """
-    Search in increasingly higher folders for the given file
-
-    Returns path to the file if found, or an empty string otherwise
-    """
-
-    def _is_interactive():
-        """ Decide whether this is running in a REPL or IPython notebook """
-        main = __import__('__main__', None, None, fromlist=['__file__'])
-        return not hasattr(main, '__file__')
-
-    if usecwd or _is_interactive() or getattr(sys, 'frozen', False):
-        # Should work without __file__, e.g. in REPL or IPython notebook.
-        path = os.getcwd()
-    else:
-        # will work for .py files
-        frame = sys._getframe()
-        current_file = __file__
-
-        while frame.f_code.co_filename == current_file:
-            assert frame.f_back is not None
-            frame = frame.f_back
-        frame_filename = frame.f_code.co_filename
-        path = os.path.dirname(os.path.abspath(frame_filename))
-
-    for dirname in _walk_to_root(path):
-        check_path = os.path.join(dirname, filename)
-        if os.path.isfile(check_path):
-            return check_path
-
-    if raise_error_if_not_found:
-        raise IOError('File not found')
-
-    return ''
-
-
-def load_dotenv(
-    dotenv_path: Optional[StrPath] = None,
-    stream: Optional[IO[str]] = None,
-    verbose: bool = False,
-    override: bool = False,
-    interpolate: bool = True,
-    encoding: Optional[str] = "utf-8",
-) -> bool:
-    """Parse a .env file and then load all the variables found as environment variables.
-
-    Parameters:
-        dotenv_path: Absolute or relative path to .env file.
-        stream: Text stream (such as `io.StringIO`) with .env content, used if
-            `dotenv_path` is `None`.
-        verbose: Whether to output a warning the .env file is missing.
-        override: Whether to override the system environment variables with the variables
-            from the `.env` file.
-        encoding: Encoding to be used to read the file.
-    Returns:
-        Bool: True if at least one environment variable is set else False
-
-    If both `dotenv_path` and `stream` are `None`, `find_dotenv()` is used to find the
-    .env file.
-    """
-    if dotenv_path is None and stream is None:
-        dotenv_path = find_dotenv()
-
-    dotenv = DotEnv(
-        dotenv_path=dotenv_path,
-        stream=stream,
-        verbose=verbose,
-        interpolate=interpolate,
-        override=override,
-        encoding=encoding,
-    )
-    return dotenv.set_as_environment_variables()
-
-
-def dotenv_values(
-    dotenv_path: Optional[StrPath] = None,
-    stream: Optional[IO[str]] = None,
-    verbose: bool = False,
-    interpolate: bool = True,
-    encoding: Optional[str] = "utf-8",
-) -> Dict[str, Optional[str]]:
-    """
-    Parse a .env file and return its content as a dict.
-
-    The returned dict will have `None` values for keys without values in the .env file.
-    For example, `foo=bar` results in `{"foo": "bar"}` whereas `foo` alone results in
-    `{"foo": None}`
-
-    Parameters:
-        dotenv_path: Absolute or relative path to the .env file.
-        stream: `StringIO` object with .env content, used if `dotenv_path` is `None`.
-        verbose: Whether to output a warning if the .env file is missing.
-        encoding: Encoding to be used to read the file.
-
-    If both `dotenv_path` and `stream` are `None`, `find_dotenv()` is used to find the
-    .env file.
-    """
-    if dotenv_path is None and stream is None:
-        dotenv_path = find_dotenv()
-
-    return DotEnv(
-        dotenv_path=dotenv_path,
-        stream=stream,
-        verbose=verbose,
-        interpolate=interpolate,
-        override=True,
-        encoding=encoding,
-    ).dict()

spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/core.py

@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1

spaces/Benson/text-generation/Examples/Colinas De Acero 2.md

@@ -1,92 +0,0 @@
-
-<h1>Hills of Steel 2: Un juego de tanques basado en la física con batallas por equipos 3vs3 en tiempo real</h1>
-<p>Si usted está buscando un divertido y adictivo juego de tanques que se puede jugar con sus amigos u otros jugadores en línea, entonces usted debe echa un vistazo a Hills of Steel 2. Este es un juego de tanques basado en la física que es una secuela del popular juego Hills of Steel. En este juego, puedes elegir entre 18 tanques diferentes, cada uno con sus propias habilidades y objetos únicos, y competir en batallas de equipo en tiempo real 3vs3 en varias colinas. También puedes unirte o crear un clan, participar en ocho eventos en línea, subir las tablas de clasificación, ganar recompensas gratis y chatear con otros jugadores en el servidor activo de Discord. En este artículo, te contaremos más sobre las características, la jugabilidad, los pros y los contras, y las preguntas frecuentes de Hills of Steel 2.</p>
-<h2>colinas de acero 2</h2><br /><p><b><b>Download File</b> &#10003; <a href="https://bltlly.com/2v6LXY">https://bltlly.com/2v6LXY</a></b></p><br /><br />
- <h2>Características de Hills of Steel 2</h2>
-<p>Hills of Steel 2 es un juego gratuito que ofrece muchas características para los amantes de los tanques. Estos son algunos de ellos:</p>
-<h3>Clanes</h3>
-<p>Puedes crear tu propio clan o unirte a uno existente y competir con otros clanes en las tablas de clasificación. También puedes chatear con los miembros de tu clan, invitarlos a jugar contigo y compartir tus mejores momentos. Los clanes son una gran manera de hacer nuevos amigos y divertirse más en el juego. </p>
-<h3>Eventos</h3>
-<p>Puedes participar en ocho eventos en línea que tienen diferentes modos y objetivos. Algunos de los eventos son:</p>
-<ul>
-<li>Equipo de supervivencia: El último equipo de pie gana. </li>
-<li>bunker bash: destruir el búnker enemigo antes de que destruyan el tuyo. </li>
-<li>Captura de estrellas: recoge tantas estrellas como sea posible evitando el fuego enemigo. </li>
-<li>Batalla del jefe: Equipo con otros jugadores para derrotar a un tanque poderoso jefe. </li>
-<li>Duelo raro: Lucha contra otro jugador usando un tanque raro. </li>
-<li>duelo épico: lucha contra otro jugador usando un tanque épico. </li>
-<li>Dominación: Captura y mantén tantas banderas como sea posible. </li>
-<li>Alboroto: Destruye tantos tanques enemigos como sea posible en un tiempo limitado. </li>
-</ul>
-
-<h3>Tanques</h3>
-<p>Puedes desbloquear y personalizar 18 tanques únicos con diferentes habilidades y objetos. Algunos de los tanques son:</p>
-<p></p>
-<ul>
-<li>Joker: Un pequeño tanque que tiene un gran golpe. </li>
-<li>Morty: Un tanque sanador de bombas. </li>
-<li>Stinger: Un tanque que causa estragos con salvos de cohetes. </li>
-<li>Buck: Un feroz combatiente de corto alcance. </li>
-<li>Titan: Un tanque grande y robusto que puede llamar en ataques aéreos. </li>
-<li>Wally: Un tanque que perfora a través de las líneas enemigas. </li>
-<li>Sparky: Un tanque de relámpago sobrealimentado. </li>
-<li>Ninja: Un tanque de sigilo con espada fatal. </li>
-<li>Gatlyn: Un tanque de disparo rápido con torretas desplegables. </li>
-<li>Phoenix Continuando el artículo: <li>Phoenix: Un tanque ardiente que puede revivir de las cenizas. </li>
-<li>Reaper: Un tanque mortal que puede cosechar almas. </li>
-<li>Arachno: Un tanque parecido a una araña que puede colocar minas y telarañas. </li>
-<li>Blaze: Un tanque lanzallamas que puede incendiar a los enemigos. </li>
-<li>Frosty: Un tanque helado que puede congelar enemigos y crear paredes de hielo. </li>
-<li>Thor: Un tanque atronador que puede convocar rayos y tormentas. </li>
-<li>Draco: Un tanque tipo dragón que puede respirar fuego y volar. </li>
-<li>Escorpio: Un tanque tipo escorpión que puede picar a los enemigos y excavar bajo tierra. </li>
-</ul>
-<p>Puedes actualizar tus tanques con monedas y gemas, y equiparlos con diferentes elementos como escudos, imanes, boosters y más. También puedes cambiar la apariencia de tus tanques con pieles y pegatinas. Los tanques son una gran manera de expresar tu personalidad y estilo en el juego. </p>
-<h3>Tablas de clasificación</h3>
-<p>Puedes subir de rango y convertirte en el mejor de tu país o del mundo ganando batallas y ganando trofeos. También puede comparar sus estadísticas y logros con otros jugadores y ver cómo se apilan. Las tablas de clasificación son una gran manera de desafiarte y mostrar tus habilidades en el juego. </p>
-<h3>Recompensas</h3>
-
-<h3>Comunidad</h3>
-<p>Puedes chatear con otros jugadores en el servidor activo de Discord donde puedes encontrar consejos, guías, noticias, actualizaciones, memes, fan art y más. También puedes unirte a la página oficial de Facebook y a la cuenta de Instagram donde puedes ver las últimas publicaciones de los desarrolladores y otros jugadores. Comunidad es una gran manera de conectarse con otros fans y mantenerse al día sobre el juego. </p>
- <h2>Cómo jugar Hills of Steel 2</h2>
-<p>Hills of Steel 2 es un juego de tanques basado en la física que es fácil de aprender pero difícil de dominar. Aquí hay algunas instrucciones básicas sobre cómo jugar:</p>
-<h3>Controles</h3>
-<p>Puedes controlar tu tanque usando dos botones en la pantalla: uno para avanzar o retroceder, y otro para apuntar y disparar. También puede tocar en su tanque para activar su habilidad especial o elemento. Puede ajustar la sensibilidad de los controles en el menú de configuración. </p>
-<h3>Estrategia</h3>
-<p>Puedes mejorar tus posibilidades de ganar siguiendo algunos consejos y trucos sencillos:</p>
-<ul>
-<li>Elija un tanque que se adapte a su estilo de juego y el modo de evento. Por ejemplo, si te gusta ser agresivo e infligir mucho daño, es posible que quieras usar Buck o Stinger. Si te gusta apoyar y sanar a tus compañeros de equipo, es posible que quieras usar Morty o Phoenix.</li>
-<li>Usa el terreno a tu favor. Por ejemplo, puedes esconderte detrás de colinas u obstáculos para evitar el fuego enemigo, o usar rampas o pendientes para ganar impulso o saltar sobre los enemigos. </li>
-<li>Trabaja junto con tus compañeros de equipo. Por ejemplo, puedes coordinar tus ataques, cubrirse las espaldas o compartir objetos o habilidades. </li>
-<li>Sea consciente de su entorno. Por ejemplo, puede estar atento a los movimientos enemigos, proyectiles, minas, banderas, estrellas u otros objetos en el mapa. </li>
-<li>Diviértete y experimenta. Por ejemplo, puedes probar diferentes combinaciones de tanques, objetos, pieles, pegatinas o estrategias para ver qué funciona mejor para ti. </li>
-</ul>
- <h2>Pros y contras de las colinas de acero 2</h2>
-
-<h3>Pros</h3>
-<ul>
-<li>El juego tiene gráficos coloridos y animaciones suaves que lo hacen visualmente atractivo. </li>
-<li>El juego tiene física realista y dinámica de juego que lo hacen desafiante y emocionante. </li>
-<li>El juego tiene una variedad de tanques, objetos, pieles, pegatinas, eventos, modos, mapas, Continuando el artículo: <li>El juego tiene un montón de características, recompensas y actualizaciones que lo hacen gratificante y atractivo. </li>
-<li>El juego tiene una comunidad amigable y activa que lo hace social y divertido. </li>
-</ul>
-<h3>Contras</h3>
-<ul>
-<li>El juego puede ser frustrante e injusto a veces debido a retrasos, problemas técnicos, hackers, o tanques o artículos desequilibrados. </li>
-<li> El juego puede ser repetitivo y aburrido después de un tiempo debido a la falta de variedad o innovación. </li>
-<li>El juego puede ser caro y pagar para ganar si desea desbloquear o actualizar todo más rápido o más fácil. </li>
-<li>El juego puede ser adictivo y poco saludable si lo juegas demasiado o descuidas otros aspectos de tu vida. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Hills of Steel 2 es un juego de tanques basado en la física que es una secuela del popular juego Hills of Steel. Es un juego gratuito que ofrece muchas características para los amantes de los tanques, como clanes, eventos, tanques, tablas de clasificación, recompensas y comunidad. Es un juego divertido y adictivo que se puede jugar con tus amigos u otros jugadores en línea en tiempo real 3vs3 batallas de equipo en varias colinas. Sin embargo, también tiene algunos inconvenientes, como retraso, problemas técnicos, hackers, tanques o artículos desequilibrados, repetición, aburrimiento, gastos y adicción. Por lo tanto, le recomendamos que lo pruebe por sí mismo y vea si le gusta o no. Puedes descargarlo desde la Google Play Store o la App Store gratis. </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes y sus respuestas sobre Hills of Steel 2:</p>
-<ol>
-
-<li>P: ¿Cómo puedo desbloquear más tanques en el juego? <br>A: Puedes desbloquear más tanques alcanzando ciertos niveles de trofeos, abriendo cofres, participando en eventos o comprándolos con gemas. </li>
-<li>P: ¿Cómo puedo actualizar mis tanques en el juego? <br>A: Puedes actualizar tus tanques gastando monedas y gemas en ellos. También puedes equiparlos con diferentes artículos que puedes comprar con monedas o gemas. </li>
-<li>P: ¿Cómo puedo cambiar la apariencia de mis tanques en el juego? <br>A: Puede cambiar la apariencia de sus tanques mediante la aplicación de pieles y pegatinas que se pueden desbloquear de cofres, carretera temporada, camino trofeo, eventos, o comprar con gemas. </li>
-<li>P: ¿Cómo puedo contactar a los desarrolladores o reportar un problema en el juego? <br>A: Puede contactar a los desarrolladores o reportar un problema enviando un correo electrónico a [email protected] o uniéndose a su servidor Discord en https://discord.gg/hillsofsteel2.</li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/taming/data/faceshq.py

@@ -1,134 +0,0 @@
-import os
-import numpy as np
-import albumentations
-from torch.utils.data import Dataset
-
-from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex
-
-
-class FacesBase(Dataset):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.data = None
-        self.keys = None
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        example = self.data[i]
-        ex = {}
-        if self.keys is not None:
-            for k in self.keys:
-                ex[k] = example[k]
-        else:
-            ex = example
-        return ex
-
-
-class CelebAHQTrain(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/celebahq"
-        with open("data/celebahqtrain.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = NumpyPaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class CelebAHQValidation(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/celebahq"
-        with open("data/celebahqvalidation.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = NumpyPaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FFHQTrain(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/ffhq"
-        with open("data/ffhqtrain.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FFHQValidation(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/ffhq"
-        with open("data/ffhqvalidation.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FacesHQTrain(Dataset):
-    # CelebAHQ [0] + FFHQ [1]
-    def __init__(self, size, keys=None, crop_size=None, coord=False):
-        d1 = CelebAHQTrain(size=size, keys=keys)
-        d2 = FFHQTrain(size=size, keys=keys)
-        self.data = ConcatDatasetWithIndex([d1, d2])
-        self.coord = coord
-        if crop_size is not None:
-            self.cropper = albumentations.RandomCrop(height=crop_size,width=crop_size)
-            if self.coord:
-                self.cropper = albumentations.Compose([self.cropper],
-                                                      additional_targets={"coord": "image"})
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        ex, y = self.data[i]
-        if hasattr(self, "cropper"):
-            if not self.coord:
-                out = self.cropper(image=ex["image"])
-                ex["image"] = out["image"]
-            else:
-                h,w,_ = ex["image"].shape
-                coord = np.arange(h*w).reshape(h,w,1)/(h*w)
-                out = self.cropper(image=ex["image"], coord=coord)
-                ex["image"] = out["image"]
-                ex["coord"] = out["coord"]
-        ex["class"] = y
-        return ex
-
-
-class FacesHQValidation(Dataset):
-    # CelebAHQ [0] + FFHQ [1]
-    def __init__(self, size, keys=None, crop_size=None, coord=False):
-        d1 = CelebAHQValidation(size=size, keys=keys)
-        d2 = FFHQValidation(size=size, keys=keys)
-        self.data = ConcatDatasetWithIndex([d1, d2])
-        self.coord = coord
-        if crop_size is not None:
-            self.cropper = albumentations.CenterCrop(height=crop_size,width=crop_size)
-            if self.coord:
-                self.cropper = albumentations.Compose([self.cropper],
-                                                      additional_targets={"coord": "image"})
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        ex, y = self.data[i]
-        if hasattr(self, "cropper"):
-            if not self.coord:
-                out = self.cropper(image=ex["image"])
-                ex["image"] = out["image"]
-            else:
-                h,w,_ = ex["image"].shape
-                coord = np.arange(h*w).reshape(h,w,1)/(h*w)
-                out = self.cropper(image=ex["image"], coord=coord)
-                ex["image"] = out["image"]
-                ex["coord"] = out["coord"]
-        ex["class"] = y
-        return ex

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

@@ -1,90 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from botocore.exceptions import ClientError
-from botocore.utils import get_service_module_name
-
-
-class BaseClientExceptions:
-    ClientError = ClientError
-
-    def __init__(self, code_to_exception):
-        """Base class for exceptions object on a client
-
-        :type code_to_exception: dict
-        :param code_to_exception: Mapping of error codes (strings) to exception
-            class that should be raised when encountering a particular
-            error code.
-        """
-        self._code_to_exception = code_to_exception
-
-    def from_code(self, error_code):
-        """Retrieves the error class based on the error code
-
-        This is helpful for identifying the exception class needing to be
-        caught based on the ClientError.parsed_reponse['Error']['Code'] value
-
-        :type error_code: string
-        :param error_code: The error code associated to a ClientError exception
-
-        :rtype: ClientError or a subclass of ClientError
-        :returns: The appropriate modeled exception class for that error
-            code. If the error code does not match any of the known
-            modeled exceptions then return a generic ClientError.
-        """
-        return self._code_to_exception.get(error_code, self.ClientError)
-
-    def __getattr__(self, name):
-        exception_cls_names = [
-            exception_cls.__name__
-            for exception_cls in self._code_to_exception.values()
-        ]
-        raise AttributeError(
-            fr"{self} object has no attribute {name}. "
-            fr"Valid exceptions are: {', '.join(exception_cls_names)}"
-        )
-
-
-class ClientExceptionsFactory:
-    def __init__(self):
-        self._client_exceptions_cache = {}
-
-    def create_client_exceptions(self, service_model):
-        """Creates a ClientExceptions object for the particular service client
-
-        :type service_model: botocore.model.ServiceModel
-        :param service_model: The service model for the client
-
-        :rtype: object that subclasses from BaseClientExceptions
-        :returns: The exceptions object of a client that can be used
-            to grab the various different modeled exceptions.
-        """
-        service_name = service_model.service_name
-        if service_name not in self._client_exceptions_cache:
-            client_exceptions = self._create_client_exceptions(service_model)
-            self._client_exceptions_cache[service_name] = client_exceptions
-        return self._client_exceptions_cache[service_name]
-
-    def _create_client_exceptions(self, service_model):
-        cls_props = {}
-        code_to_exception = {}
-        for error_shape in service_model.error_shapes:
-            exception_name = str(error_shape.name)
-            exception_cls = type(exception_name, (ClientError,), {})
-            cls_props[exception_name] = exception_cls
-            code = str(error_shape.error_code)
-            code_to_exception[code] = exception_cls
-        cls_name = str(get_service_module_name(service_model) + 'Exceptions')
-        client_exceptions_cls = type(
-            cls_name, (BaseClientExceptions,), cls_props
-        )
-        return client_exceptions_cls(code_to_exception)

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

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

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

@@ -1,80 +0,0 @@
-"""A simple log mechanism styled after PEP 282."""
-
-# The class here is styled after PEP 282 so that it could later be
-# replaced with a standard Python logging implementation.
-
-import sys
-
-DEBUG = 1
-INFO = 2
-WARN = 3
-ERROR = 4
-FATAL = 5
-
-
-class Log:
-    def __init__(self, threshold=WARN):
-        self.threshold = threshold
-
-    def _log(self, level, msg, args):
-        if level not in (DEBUG, INFO, WARN, ERROR, FATAL):
-            raise ValueError('%s wrong log level' % str(level))
-
-        if level >= self.threshold:
-            if args:
-                msg = msg % args
-            if level in (WARN, ERROR, FATAL):
-                stream = sys.stderr
-            else:
-                stream = sys.stdout
-            try:
-                stream.write('%s\n' % msg)
-            except UnicodeEncodeError:
-                # emulate backslashreplace error handler
-                encoding = stream.encoding
-                msg = msg.encode(encoding, "backslashreplace").decode(encoding)
-                stream.write('%s\n' % msg)
-            stream.flush()
-
-    def log(self, level, msg, *args):
-        self._log(level, msg, args)
-
-    def debug(self, msg, *args):
-        self._log(DEBUG, msg, args)
-
-    def info(self, msg, *args):
-        self._log(INFO, msg, args)
-
-    def warn(self, msg, *args):
-        self._log(WARN, msg, args)
-
-    def error(self, msg, *args):
-        self._log(ERROR, msg, args)
-
-    def fatal(self, msg, *args):
-        self._log(FATAL, msg, args)
-
-
-_global_log = Log()
-log = _global_log.log
-debug = _global_log.debug
-info = _global_log.info
-warn = _global_log.warn
-error = _global_log.error
-fatal = _global_log.fatal
-
-
-def set_threshold(level):
-    # return the old threshold for use from tests
-    old = _global_log.threshold
-    _global_log.threshold = level
-    return old
-
-
-def set_verbosity(v):
-    if v <= 0:
-        set_threshold(WARN)
-    elif v == 1:
-        set_threshold(INFO)
-    elif v >= 2:
-        set_threshold(DEBUG)

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_adapters.py

@@ -1,68 +0,0 @@
-import re
-import textwrap
-import email.message
-
-from ._text import FoldedCase
-
-
-class Message(email.message.Message):
-    multiple_use_keys = set(
-        map(
-            FoldedCase,
-            [
-                'Classifier',
-                'Obsoletes-Dist',
-                'Platform',
-                'Project-URL',
-                'Provides-Dist',
-                'Provides-Extra',
-                'Requires-Dist',
-                'Requires-External',
-                'Supported-Platform',
-                'Dynamic',
-            ],
-        )
-    )
-    """
-    Keys that may be indicated multiple times per PEP 566.
-    """
-
-    def __new__(cls, orig: email.message.Message):
-        res = super().__new__(cls)
-        vars(res).update(vars(orig))
-        return res
-
-    def __init__(self, *args, **kwargs):
-        self._headers = self._repair_headers()
-
-    # suppress spurious error from mypy
-    def __iter__(self):
-        return super().__iter__()
-
-    def _repair_headers(self):
-        def redent(value):
-            "Correct for RFC822 indentation"
-            if not value or '\n' not in value:
-                return value
-            return textwrap.dedent(' ' * 8 + value)
-
-        headers = [(key, redent(value)) for key, value in vars(self)['_headers']]
-        if self._payload:
-            headers.append(('Description', self.get_payload()))
-        return headers
-
-    @property
-    def json(self):
-        """
-        Convert PackageMetadata to a JSON-compatible format
-        per PEP 0566.
-        """
-
-        def transform(key):
-            value = self.get_all(key) if key in self.multiple_use_keys else self[key]
-            if key == 'Keywords':
-                value = re.split(r'\s+', value)
-            tk = key.lower().replace('-', '_')
-            return tk, value
-
-        return dict(map(transform, map(FoldedCase, self)))

spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/app.py

@@ -1,280 +0,0 @@
-import logging
-import time
-from pathlib import Path
-
-import gradio as gr
-import nltk
-from cleantext import clean
-from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
-from utils import load_example_filenames, truncate_word_count
-
-_here = Path(__file__).parent
-
-nltk.download("stopwords")
-
-logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
-)
-
-
-def proc_submission(
-    input_text: str,
-    model_size: str,
-    num_beams,
-    token_batch_length,
-    length_penalty,
-    max_input_length: int = 3060,
-):
-    """
-    proc_submission - a helper function for the gradio module to process submissions
-    Args:
-        input_text (str): the input text to summarize
-        model_size (str): the size of the model to use
-        num_beams (int): the number of beams to use
-        token_batch_length (int): the length of the token batches to use
-        length_penalty (float): the length penalty to use
-        repetition_penalty (float): the repetition penalty to use
-        no_repeat_ngram_size (int): the no repeat ngram size to use
-        max_input_length (int, optional): the maximum input length to use. Defaults to 768.
-    Returns:
-        str in HTML format, string of the summary, str of score
-    """
-
-    settings_det = {
-        "length_penalty": float(length_penalty),
-        "repetition_penalty": 3.5,
-        "no_repeat_ngram_size": 3,
-        "encoder_no_repeat_ngram_size": 4,
-        "num_beams": int(num_beams),
-        "min_length": 100,
-        "max_length": 512,#int(token_batch_length // 4),
-        "early_stopping": True,
-        "do_sample": False,
-    }
-    settings_tldr = {
-        "length_penalty": float(length_penalty),
-        "repetition_penalty": 3.5,
-        "no_repeat_ngram_size": 3,
-        "encoder_no_repeat_ngram_size": 4,
-        "num_beams": int(num_beams),
-        "min_length": 11,
-        "max_length": 62,
-        "early_stopping": True,
-        "do_sample": False,
-    }
-    
-    if model_size == "tldr":
-        settings = settings_tldr
-    else:
-        settings = settings_det
-    
-    st = time.perf_counter()
-    history = {}
-    clean_text = clean(input_text, extra_spaces=True, lowercase=True, reg="\b(?!(?:Although|Also)\b)(?:[A-Z][A-Za-z'`-]+)(?:,? (?:(?:and |& )?(?:[A-Z][A-Za-z'`-]+)|(?:et al.?)))*(?:, *(?:19|20)[0-9][0-9](?:, p\.? [0-9]+)?| *\((?:19|20)[0-9][0-9](?:, p\.? [0-9]+)?\))", reg_replace="")
-    #max_input_length = 2048 if model_size == "tldr" else max_input_length
-    processed = truncate_word_count(clean_text, max_input_length)
-
-    if processed["was_truncated"]:
-        tr_in = processed["truncated_text"]
-        msg = f"Input text was truncated to {max_input_length} words to fit within the computational constraints of the inference API"
-        logging.warning(msg)
-        history["WARNING"] = msg
-    else:
-        tr_in = input_text
-        msg = None
-
-    _summaries = summarize_via_tokenbatches(
-        tr_in,
-        model_sm if model_size == "tldr" else model,
-        tokenizer_sm if model_size == "tldr" else tokenizer,
-        batch_length=token_batch_length,
-        **settings,
-    )
-    sum_text = [f"Section {i}: " + s["summary"][0] for i, s in enumerate(_summaries)]
-    rates = [
-        f" - Section {i}: {round(s['compression_rate'],3)}"
-        for i, s in enumerate(_summaries)
-    ]
-
-    sum_text_out = "\n".join(sum_text)
-    history["Compression Rates"] = "<br><br>"
-    rates_out = "\n".join(rates)
-    rt = round((time.perf_counter() - st) / 60, 2)
-    print(f"Runtime: {rt} minutes")
-    html = ""
-    html += f"<p>Runtime: {rt} minutes on CPU</p>"
-    if msg is not None:
-        html += f"<h2>WARNING:</h2><hr><b>{msg}</b><br><br>"
-
-    html += ""
-
-    return html, sum_text_out, rates_out
-
-
-def load_single_example_text(
-    example_path: str or Path,
-):
-    """
-    load_single_example - a helper function for the gradio module to load examples
-    Returns:
-        list of str, the examples
-    """
-    global name_to_path
-    full_ex_path = name_to_path[example_path]
-    full_ex_path = Path(full_ex_path)
-    # load the examples into a list
-    with open(full_ex_path, "r", encoding="utf-8", errors="ignore") as f:
-        raw_text = f.read()
-        text = clean(raw_text, extra_spaces=True, lowercase=False) #see if it works
-    return text
-
-
-def load_uploaded_file(file_obj):
-    """
-    load_uploaded_file - process an uploaded file
-    Args:
-        file_obj (POTENTIALLY list): Gradio file object inside a list
-    Returns:
-        str, the uploaded file contents
-    """
-
-    # file_path = Path(file_obj[0].name)
-
-    # check if mysterious file object is a list
-    if isinstance(file_obj, list):
-        file_obj = file_obj[0]
-    file_path = Path(file_obj.name)
-    try:
-        with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
-            raw_text = f.read()
-        text = clean(raw_text, extra_spaces=True, lowercase=True, reg="\s(?=[\,.':;!?])",reg_replace="")
-        return text
-    except Exception as e:
-        logging.info(f"Trying to load file with path {file_path}, error: {e}")
-        return "Error: Could not read file. Ensure that it is a valid text file with encoding UTF-8."
-
-
-if __name__ == "__main__":
-
-    model, tokenizer = load_model_and_tokenizer("Blaise-g/longt5_tglobal_large_sumpubmed")
-    model_sm, tokenizer_sm = load_model_and_tokenizer("Blaise-g/longt5_tglobal_large_scitldr")
-
-    name_to_path = load_example_filenames(_here / "examples")
-    logging.info(f"Loaded {len(name_to_path)} examples")
-    demo = gr.Blocks()
-
-    with demo:
-
-        gr.Markdown("# Automatic summarization of biomedical research papers with neural abstractive methods into a long and comprehensive synopsis or extreme TLDR summary version")
-        gr.Markdown(
-            "A demo developed for my Master Thesis project using ad-hoc fine-tuned abstractive summarization models to summarize long biomedical articles into a detailed, explanatory synopsis or extreme TLDR summary."
-        )
-        with gr.Column():
-
-            gr.Markdown("### Select Summary type and text generation parameters then load input text")
-            gr.Markdown(
-                "Enter text below in the text area or alternatively load an example below or upload a file."
-            )
-            with gr.Row():
-                model_size = gr.Radio(
-                    choices=["tldr", "detailed"], label="Summary type", value="detailed"
-                )
-                num_beams = gr.Radio(
-                    choices=[2, 3, 4],
-                    label="Beam Search: Number of Beams",
-                    value=2,
-                )
-            gr.Markdown(
-                "_For optimal results use a GPU as the hosted CPU inference is lacking at times and hinders the output summary quality as well as forcing to divide the input text into batches._"
-            )
-            with gr.Row():
-                length_penalty = gr.inputs.Slider(
-                    minimum=0.5,
-                    maximum=1.0,
-                    label="length penalty",
-                    default=0.7,
-                    step=0.05,
-                )
-                token_batch_length = gr.Radio(
-                    choices=[1024, 2048, 3060],
-                    label="token batch length",
-                    value=2048,
-                )
-            with gr.Row():
-                example_name = gr.Dropdown(
-                    list(name_to_path.keys()),
-                    label="Choose an Example",
-                )
-                load_examples_button = gr.Button(
-                    "Load Example",
-                )
-            input_text = gr.Textbox(
-                lines=6,
-                label="Input Text (for summarization)",
-                placeholder="Enter any scientific text to be condensed into a detailed, explanatory synopsis or TLDR summary version. The input text is divided into batches of the selected token lengths to fit within the memory constraints, pre-processed and fed into the model of choice. The models were trained to handle long scientific papers but generalize reasonably well also to shorter text documents like scientific abstracts. Might take a while to produce long summaries :)",
-            )
-            gr.Markdown("Upload your own file:")
-            with gr.Row():
-                uploaded_file = gr.File(
-                    label="Upload a text file",
-                    file_count="single",
-                    type="file",
-                )
-                load_file_button = gr.Button("Load Uploaded File")
-
-            gr.Markdown("---")
-
-        with gr.Column():
-            gr.Markdown("## Generate Summary")
-            gr.Markdown(
-                "Summary generation should take approximately 2-3 minutes for most generation settings but can take significantly more time for very long documents with a high beam number."
-            )
-            summarize_button = gr.Button(
-                "Summarize!",
-                variant="primary",
-            )
-
-            output_text = gr.HTML("<p><em>Output will appear below:</em></p>")
-            gr.Markdown("### Summary Output")
-            summary_text = gr.Textbox(
-                label="Summary 📝", placeholder="The generated 📝 will appear here"
-            )
-            gr.Markdown(
-                "The compression rate 🗜 indicates the ratio between the machine-generated summary length and the input text (from 0% to 100%). The higher the 🗜 the more extreme the summary is."
-            )
-            compression_rate = gr.Textbox(
-                label="Compression rate 🗜", placeholder="The 🗜 will appear here"
-            )
-            gr.Markdown("---")
-
-        with gr.Column():
-            gr.Markdown("## About the Models")
-            gr.Markdown(
-                "- [Blaise-g/longt5_tglobal_large_sumpubmed](https://huggingface.co/Blaise-g/longt5_tglobal_large_sumpubmed) is a fine-tuned checkpoint of [Stancld/longt5-tglobal-large-16384-pubmed-3k_steps](https://huggingface.co/Stancld/longt5-tglobal-large-16384-pubmed-3k_steps) on the [SumPubMed dataset](https://aclanthology.org/2021.acl-srw.30/). [Blaise-g/longt5_tglobal_large_scitldr](https://huggingface.co/Blaise-g/longt5_tglobal_large_scitldr) is a fine-tuned checkpoint of [Blaise-g/longt5_tglobal_large_sumpubmed](https://huggingface.co/Blaise-g/longt5_tglobal_large_sumpubmed) on the [Scitldr dataset](https://arxiv.org/abs/2004.15011). The goal was to create two models capable of handling the complex information contained in long biomedical documents and subsequently producing scientific summaries according to one of the two possible levels of conciseness: 1) A long explanatory synopsis that retains the majority of domain-specific language used in the original source text. 2)A one sentence long, TLDR style summary."
-            )
-            gr.Markdown(
-                "- The two most important text generation parameters are the number of beams and length penalty : 1) Choosing a higher number of beams for the beam search algorithm results in generating a summary with higher probability (hence theoretically higher quality) at the cost of increasing computation times and memory usage. 2) The length penalty encourages the model to generate longer (with values closer to 1.0) or shorter (with values closer to 0.0) summary sequences by placing an exponential penalty on the beam score according to the current sequence length."
-            )
-            gr.Markdown("---")
-
-        load_examples_button.click(
-            fn=load_single_example_text, inputs=[example_name], outputs=[input_text]
-        )
-
-        load_file_button.click(
-            fn=load_uploaded_file, inputs=uploaded_file, outputs=[input_text]
-        )
-
-        summarize_button.click(
-            fn=proc_submission,
-            inputs=[
-                input_text,
-                model_size,
-                num_beams,
-                token_batch_length,
-                length_penalty,
-            ],
-            outputs=[output_text, summary_text, compression_rate],
-        )
-
-    demo.launch(enable_queue=True, share=False)

spaces/CVPR/LIVE/pybind11/tools/pybind11Tools.cmake

@@ -1,188 +0,0 @@
-# tools/pybind11Tools.cmake -- Build system for the pybind11 modules
-#
-# Copyright (c) 2015 Wenzel Jakob <[email protected]>
-#
-# All rights reserved. Use of this source code is governed by a
-# BSD-style license that can be found in the LICENSE file.
-
-# Built-in in CMake 3.5+
-include(CMakeParseArguments)
-
-if(pybind11_FIND_QUIETLY)
-  set(_pybind11_quiet QUIET)
-endif()
-
-# If this is the first run, PYTHON_VERSION can stand in for PYBIND11_PYTHON_VERSION
-if(NOT DEFINED PYBIND11_PYTHON_VERSION AND DEFINED PYTHON_VERSION)
-  message(WARNING "Set PYBIND11_PYTHON_VERSION to search for a specific version, not "
-                  "PYTHON_VERSION (which is an output). Assuming that is what you "
-                  "meant to do and continuing anyway.")
-  set(PYBIND11_PYTHON_VERSION
-      "${PYTHON_VERSION}"
-      CACHE STRING "Python version to use for compiling modules")
-  unset(PYTHON_VERSION)
-  unset(PYTHON_VERSION CACHE)
-else()
-  # If this is set as a normal variable, promote it, otherwise, make an empty cache variable.
-  set(PYBIND11_PYTHON_VERSION
-      "${PYBIND11_PYTHON_VERSION}"
-      CACHE STRING "Python version to use for compiling modules")
-endif()
-
-# A user can set versions manually too
-set(Python_ADDITIONAL_VERSIONS
-    "3.9;3.8;3.7;3.6;3.5;3.4"
-    CACHE INTERNAL "")
-
-list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}")
-find_package(PythonLibsNew ${PYBIND11_PYTHON_VERSION} MODULE REQUIRED ${_pybind11_quiet})
-list(REMOVE_AT CMAKE_MODULE_PATH -1)
-
-# Cache variables so pybind11_add_module can be used in parent projects
-set(PYTHON_INCLUDE_DIRS
-    ${PYTHON_INCLUDE_DIRS}
-    CACHE INTERNAL "")
-set(PYTHON_LIBRARIES
-    ${PYTHON_LIBRARIES}
-    CACHE INTERNAL "")
-set(PYTHON_MODULE_PREFIX
-    ${PYTHON_MODULE_PREFIX}
-    CACHE INTERNAL "")
-set(PYTHON_MODULE_EXTENSION
-    ${PYTHON_MODULE_EXTENSION}
-    CACHE INTERNAL "")
-set(PYTHON_VERSION_MAJOR
-    ${PYTHON_VERSION_MAJOR}
-    CACHE INTERNAL "")
-set(PYTHON_VERSION_MINOR
-    ${PYTHON_VERSION_MINOR}
-    CACHE INTERNAL "")
-set(PYTHON_VERSION
-    ${PYTHON_VERSION}
-    CACHE INTERNAL "")
-set(PYTHON_IS_DEBUG
-    "${PYTHON_IS_DEBUG}"
-    CACHE INTERNAL "")
-
-if(PYBIND11_MASTER_PROJECT)
-  if(PYTHON_MODULE_EXTENSION MATCHES "pypy")
-    if(NOT DEFINED PYPY_VERSION)
-      execute_process(
-        COMMAND ${PYTHON_EXECUTABLE} -c
-                [=[import sys; print(".".join(map(str, sys.pypy_version_info[:3])))]=]
-        OUTPUT_VARIABLE pypy_version)
-      set(PYPY_VERSION
-          ${pypy_version}
-          CACHE INTERNAL "")
-    endif()
-    message(STATUS "PYPY ${PYPY_VERSION} (Py ${PYTHON_VERSION})")
-  else()
-    message(STATUS "PYTHON ${PYTHON_VERSION}")
-  endif()
-endif()
-
-# Only add Python for build - must be added during the import for config since it has to be re-discovered.
-set_property(
-  TARGET pybind11::pybind11
-  APPEND
-  PROPERTY INTERFACE_INCLUDE_DIRECTORIES $<BUILD_INTERFACE:${PYTHON_INCLUDE_DIRS}>)
-
-# Python debug libraries expose slightly different objects before 3.8
-# https://docs.python.org/3.6/c-api/intro.html#debugging-builds
-# https://stackoverflow.com/questions/39161202/how-to-work-around-missing-pymodule-create2-in-amd64-win-python35-d-lib
-if(PYTHON_IS_DEBUG)
-  set_property(
-    TARGET pybind11::pybind11
-    APPEND
-    PROPERTY INTERFACE_COMPILE_DEFINITIONS Py_DEBUG)
-endif()
-
-set_property(
-  TARGET pybind11::module
-  APPEND
-  PROPERTY
-    INTERFACE_LINK_LIBRARIES pybind11::python_link_helper
-    "$<$<OR:$<PLATFORM_ID:Windows>,$<PLATFORM_ID:Cygwin>>:$<BUILD_INTERFACE:${PYTHON_LIBRARIES}>>")
-
-if(PYTHON_VERSION VERSION_LESS 3)
-  set_property(
-    TARGET pybind11::pybind11
-    APPEND
-    PROPERTY INTERFACE_LINK_LIBRARIES pybind11::python2_no_register)
-endif()
-
-set_property(
-  TARGET pybind11::embed
-  APPEND
-  PROPERTY INTERFACE_LINK_LIBRARIES pybind11::pybind11 $<BUILD_INTERFACE:${PYTHON_LIBRARIES}>)
-
-function(pybind11_extension name)
-  # The prefix and extension are provided by FindPythonLibsNew.cmake
-  set_target_properties(${name} PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
-                                           SUFFIX "${PYTHON_MODULE_EXTENSION}")
-endfunction()
-
-# Build a Python extension module:
-# pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
-#                     [NO_EXTRAS] [THIN_LTO] source1 [source2 ...])
-#
-function(pybind11_add_module target_name)
-  set(options MODULE SHARED EXCLUDE_FROM_ALL NO_EXTRAS SYSTEM THIN_LTO)
-  cmake_parse_arguments(ARG "${options}" "" "" ${ARGN})
-
-  if(ARG_MODULE AND ARG_SHARED)
-    message(FATAL_ERROR "Can't be both MODULE and SHARED")
-  elseif(ARG_SHARED)
-    set(lib_type SHARED)
-  else()
-    set(lib_type MODULE)
-  endif()
-
-  if(ARG_EXCLUDE_FROM_ALL)
-    set(exclude_from_all EXCLUDE_FROM_ALL)
-  else()
-    set(exclude_from_all "")
-  endif()
-
-  add_library(${target_name} ${lib_type} ${exclude_from_all} ${ARG_UNPARSED_ARGUMENTS})
-
-  target_link_libraries(${target_name} PRIVATE pybind11::module)
-
-  if(ARG_SYSTEM)
-    message(
-      STATUS
-        "Warning: this does not have an effect - use NO_SYSTEM_FROM_IMPORTED if using imported targets"
-    )
-  endif()
-
-  pybind11_extension(${target_name})
-
-  # -fvisibility=hidden is required to allow multiple modules compiled against
-  # different pybind versions to work properly, and for some features (e.g.
-  # py::module_local).  We force it on everything inside the `pybind11`
-  # namespace; also turning it on for a pybind module compilation here avoids
-  # potential warnings or issues from having mixed hidden/non-hidden types.
-  set_target_properties(${target_name} PROPERTIES CXX_VISIBILITY_PRESET "hidden"
-                                                  CUDA_VISIBILITY_PRESET "hidden")
-
-  if(ARG_NO_EXTRAS)
-    return()
-  endif()
-
-  if(NOT DEFINED CMAKE_INTERPROCEDURAL_OPTIMIZATION)
-    if(ARG_THIN_LTO)
-      target_link_libraries(${target_name} PRIVATE pybind11::thin_lto)
-    else()
-      target_link_libraries(${target_name} PRIVATE pybind11::lto)
-    endif()
-  endif()
-
-  if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
-    pybind11_strip(${target_name})
-  endif()
-
-  if(MSVC)
-    target_link_libraries(${target_name} PRIVATE pybind11::windows_extras)
-  endif()
-
-endfunction()

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

@@ -1,130 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/type_traits.h>
-#include <limits>
-
-//#include <stdint.h> // for intmax_t (not provided on MSVS 2005)
-
-namespace thrust
-{
-
-namespace detail
-{
-
-// XXX good enough for the platforms we care about
-typedef long long intmax_t;
-
-template<typename Number>
-  struct is_signed
-    : integral_constant<bool, std::numeric_limits<Number>::is_signed>
-{}; // end is_signed
-
-
-template<typename T>
-  struct num_digits
-    : eval_if<
-        std::numeric_limits<T>::is_specialized,
-        integral_constant<
-          int,
-          std::numeric_limits<T>::digits
-        >,
-        integral_constant<
-          int,
-          sizeof(T) * std::numeric_limits<unsigned char>::digits - (is_signed<T>::value ? 1 : 0)  
-        >
-      >::type
-{}; // end num_digits
-
-
-template<typename Integer>
-  struct integer_difference
-    //: eval_if<
-    //    sizeof(Integer) >= sizeof(intmax_t),
-    //    eval_if<
-    //      is_signed<Integer>::value,
-    //      identity_<Integer>,
-    //      identity_<intmax_t>
-    //    >,
-    //    eval_if<
-    //      sizeof(Integer) < sizeof(std::ptrdiff_t),
-    //      identity_<std::ptrdiff_t>,
-    //      identity_<intmax_t>
-    //    >
-    //  >
-{
-  private:
-    // XXX workaround a pedantic warning in old versions of g++
-    //     which complains about &&ing with a constant value
-    template<bool x, bool y>
-      struct and_
-    {
-      static const bool value = false;
-    };
-
-    template<bool y>
-      struct and_<true,y>
-    {
-      static const bool value = y;
-    };
-
-  public:
-    typedef typename
-      eval_if<
-        and_<
-          std::numeric_limits<Integer>::is_signed,
-          // digits is the number of no-sign bits
-          (!std::numeric_limits<Integer>::is_bounded || (int(std::numeric_limits<Integer>::digits) + 1 >= num_digits<intmax_t>::value))
-        >::value,
-        identity_<Integer>,
-        eval_if<
-          int(std::numeric_limits<Integer>::digits) + 1 < num_digits<signed int>::value,
-          identity_<signed int>,
-          eval_if<
-            int(std::numeric_limits<Integer>::digits) + 1 < num_digits<signed long>::value,
-            identity_<signed long>,
-            identity_<intmax_t>
-          >
-        >
-      >::type type;
-}; // end integer_difference
-
-
-template<typename Number>
-  struct numeric_difference
-    : eval_if<
-      is_integral<Number>::value,
-      integer_difference<Number>,
-      identity_<Number>
-    >
-{}; // end numeric_difference
-
-
-template<typename Number>
-__host__ __device__
-typename numeric_difference<Number>::type
-numeric_distance(Number x, Number y)
-{
-  typedef typename numeric_difference<Number>::type difference_type;
-  return difference_type(y) - difference_type(x);
-} // end numeric_distance
-
-} // end detail
-
-} // end thrust
-

spaces/CVPR/WALT/mmdet/models/backbones/ssd_vgg.py

@@ -1,169 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init
-from mmcv.runner import load_checkpoint
-
-from mmdet.utils import get_root_logger
-from ..builder import BACKBONES
-
-
-@BACKBONES.register_module()
-class SSDVGG(VGG):
-    """VGG Backbone network for single-shot-detection.
-
-    Args:
-        input_size (int): width and height of input, from {300, 512}.
-        depth (int): Depth of vgg, from {11, 13, 16, 19}.
-        out_indices (Sequence[int]): Output from which stages.
-
-    Example:
-        >>> self = SSDVGG(input_size=300, depth=11)
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 3, 300, 300)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        (1, 1024, 19, 19)
-        (1, 512, 10, 10)
-        (1, 256, 5, 5)
-        (1, 256, 3, 3)
-        (1, 256, 1, 1)
-    """
-    extra_setting = {
-        300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256),
-        512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128),
-    }
-
-    def __init__(self,
-                 input_size,
-                 depth,
-                 with_last_pool=False,
-                 ceil_mode=True,
-                 out_indices=(3, 4),
-                 out_feature_indices=(22, 34),
-                 l2_norm_scale=20.):
-        # TODO: in_channels for mmcv.VGG
-        super(SSDVGG, self).__init__(
-            depth,
-            with_last_pool=with_last_pool,
-            ceil_mode=ceil_mode,
-            out_indices=out_indices)
-        assert input_size in (300, 512)
-        self.input_size = input_size
-
-        self.features.add_module(
-            str(len(self.features)),
-            nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
-        self.features.add_module(
-            str(len(self.features)),
-            nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6))
-        self.features.add_module(
-            str(len(self.features)), nn.ReLU(inplace=True))
-        self.features.add_module(
-            str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1))
-        self.features.add_module(
-            str(len(self.features)), nn.ReLU(inplace=True))
-        self.out_feature_indices = out_feature_indices
-
-        self.inplanes = 1024
-        self.extra = self._make_extra_layers(self.extra_setting[input_size])
-        self.l2_norm = L2Norm(
-            self.features[out_feature_indices[0] - 1].out_channels,
-            l2_norm_scale)
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.features.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-                elif isinstance(m, nn.Linear):
-                    normal_init(m, std=0.01)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-        for m in self.extra.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-
-        constant_init(self.l2_norm, self.l2_norm.scale)
-
-    def forward(self, x):
-        """Forward function."""
-        outs = []
-        for i, layer in enumerate(self.features):
-            x = layer(x)
-            if i in self.out_feature_indices:
-                outs.append(x)
-        for i, layer in enumerate(self.extra):
-            x = F.relu(layer(x), inplace=True)
-            if i % 2 == 1:
-                outs.append(x)
-        outs[0] = self.l2_norm(outs[0])
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-    def _make_extra_layers(self, outplanes):
-        layers = []
-        kernel_sizes = (1, 3)
-        num_layers = 0
-        outplane = None
-        for i in range(len(outplanes)):
-            if self.inplanes == 'S':
-                self.inplanes = outplane
-                continue
-            k = kernel_sizes[num_layers % 2]
-            if outplanes[i] == 'S':
-                outplane = outplanes[i + 1]
-                conv = nn.Conv2d(
-                    self.inplanes, outplane, k, stride=2, padding=1)
-            else:
-                outplane = outplanes[i]
-                conv = nn.Conv2d(
-                    self.inplanes, outplane, k, stride=1, padding=0)
-            layers.append(conv)
-            self.inplanes = outplanes[i]
-            num_layers += 1
-        if self.input_size == 512:
-            layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1))
-
-        return nn.Sequential(*layers)
-
-
-class L2Norm(nn.Module):
-
-    def __init__(self, n_dims, scale=20., eps=1e-10):
-        """L2 normalization layer.
-
-        Args:
-            n_dims (int): Number of dimensions to be normalized
-            scale (float, optional): Defaults to 20..
-            eps (float, optional): Used to avoid division by zero.
-                Defaults to 1e-10.
-        """
-        super(L2Norm, self).__init__()
-        self.n_dims = n_dims
-        self.weight = nn.Parameter(torch.Tensor(self.n_dims))
-        self.eps = eps
-        self.scale = scale
-
-    def forward(self, x):
-        """Forward function."""
-        # normalization layer convert to FP32 in FP16 training
-        x_float = x.float()
-        norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps
-        return (self.weight[None, :, None, None].float().expand_as(x_float) *
-                x_float / norm).type_as(x)

spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py

@@ -1,172 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, normal_init, xavier_init
-
-from mmdet.models.backbones.resnet import Bottleneck
-from mmdet.models.builder import HEADS
-from .bbox_head import BBoxHead
-
-
-class BasicResBlock(nn.Module):
-    """Basic residual block.
-
-    This block is a little different from the block in the ResNet backbone.
-    The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        out_channels (int): Channels of the output feature map.
-        conv_cfg (dict): The config dict for convolution layers.
-        norm_cfg (dict): The config dict for normalization layers.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN')):
-        super(BasicResBlock, self).__init__()
-
-        # main path
-        self.conv1 = ConvModule(
-            in_channels,
-            in_channels,
-            kernel_size=3,
-            padding=1,
-            bias=False,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg)
-        self.conv2 = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-        # identity path
-        self.conv_identity = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size=1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        identity = x
-
-        x = self.conv1(x)
-        x = self.conv2(x)
-
-        identity = self.conv_identity(identity)
-        out = x + identity
-
-        out = self.relu(out)
-        return out
-
-
-@HEADS.register_module()
-class DoubleConvFCBBoxHead(BBoxHead):
-    r"""Bbox head used in Double-Head R-CNN
-
-    .. code-block:: none
-
-                                          /-> cls
-                      /-> shared convs ->
-                                          \-> reg
-        roi features
-                                          /-> cls
-                      \-> shared fc    ->
-                                          \-> reg
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_convs=0,
-                 num_fcs=0,
-                 conv_out_channels=1024,
-                 fc_out_channels=1024,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 **kwargs):
-        kwargs.setdefault('with_avg_pool', True)
-        super(DoubleConvFCBBoxHead, self).__init__(**kwargs)
-        assert self.with_avg_pool
-        assert num_convs > 0
-        assert num_fcs > 0
-        self.num_convs = num_convs
-        self.num_fcs = num_fcs
-        self.conv_out_channels = conv_out_channels
-        self.fc_out_channels = fc_out_channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-
-        # increase the channel of input features
-        self.res_block = BasicResBlock(self.in_channels,
-                                       self.conv_out_channels)
-
-        # add conv heads
-        self.conv_branch = self._add_conv_branch()
-        # add fc heads
-        self.fc_branch = self._add_fc_branch()
-
-        out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes
-        self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg)
-
-        self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1)
-        self.relu = nn.ReLU(inplace=True)
-
-    def _add_conv_branch(self):
-        """Add the fc branch which consists of a sequential of conv layers."""
-        branch_convs = nn.ModuleList()
-        for i in range(self.num_convs):
-            branch_convs.append(
-                Bottleneck(
-                    inplanes=self.conv_out_channels,
-                    planes=self.conv_out_channels // 4,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        return branch_convs
-
-    def _add_fc_branch(self):
-        """Add the fc branch which consists of a sequential of fc layers."""
-        branch_fcs = nn.ModuleList()
-        for i in range(self.num_fcs):
-            fc_in_channels = (
-                self.in_channels *
-                self.roi_feat_area if i == 0 else self.fc_out_channels)
-            branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels))
-        return branch_fcs
-
-    def init_weights(self):
-        # conv layers are already initialized by ConvModule
-        normal_init(self.fc_cls, std=0.01)
-        normal_init(self.fc_reg, std=0.001)
-
-        for m in self.fc_branch.modules():
-            if isinstance(m, nn.Linear):
-                xavier_init(m, distribution='uniform')
-
-    def forward(self, x_cls, x_reg):
-        # conv head
-        x_conv = self.res_block(x_reg)
-
-        for conv in self.conv_branch:
-            x_conv = conv(x_conv)
-
-        if self.with_avg_pool:
-            x_conv = self.avg_pool(x_conv)
-
-        x_conv = x_conv.view(x_conv.size(0), -1)
-        bbox_pred = self.fc_reg(x_conv)
-
-        # fc head
-        x_fc = x_cls.view(x_cls.size(0), -1)
-        for fc in self.fc_branch:
-            x_fc = self.relu(fc(x_fc))
-
-        cls_score = self.fc_cls(x_fc)
-
-        return cls_score, bbox_pred

spaces/CVPR/lama-example/predict.py

@@ -1,89 +0,0 @@
-#!/usr/bin/env python3
-
-# Example command:
-# ./bin/predict.py \
-#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \
-#       indir=<path to input data> \
-#       outdir=<where to store predicts>
-
-import logging
-import os
-import sys
-import traceback
-
-from saicinpainting.evaluation.utils import move_to_device
-
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint
-from saicinpainting.utils import register_debug_signal_handlers
-
-LOGGER = logging.getLogger(__name__)
-
-
-@hydra.main(config_path='configs/prediction', config_name='default.yaml')
-def main(predict_config: OmegaConf):
-    try:
-        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log
-
-        device = torch.device(predict_config.device)
-
-        train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
-        with open(train_config_path, 'r') as f:
-            train_config = OmegaConf.create(yaml.safe_load(f))
-        
-        train_config.training_model.predict_only = True
-
-        out_ext = predict_config.get('out_ext', '.png')
-
-        checkpoint_path = os.path.join(predict_config.model.path, 
-                                       'models', 
-                                       predict_config.model.checkpoint)
-        model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
-        model.freeze()
-        model.to(device)
-
-        if not predict_config.indir.endswith('/'):
-            predict_config.indir += '/'
-
-        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
-        with torch.no_grad():
-            for img_i in tqdm.trange(len(dataset)):
-                mask_fname = dataset.mask_filenames[img_i]
-                cur_out_fname = os.path.join(
-                    predict_config.outdir, 
-                    os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
-                )
-                os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-                batch = move_to_device(default_collate([dataset[img_i]]), device)
-                batch['mask'] = (batch['mask'] > 0) * 1
-                batch = model(batch)
-                cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
-
-                cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
-                cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
-                cv2.imwrite(cur_out_fname, cur_res)
-    except KeyboardInterrupt:
-        LOGGER.warning('Interrupted by user')
-    except Exception as ex:
-        LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
-        sys.exit(1)
-
-
-if __name__ == '__main__':
-    main()

spaces/CikeyQI/Yunzai/Yunzai/lib/bot.js

@@ -1,231 +0,0 @@
-import "./config/init.js"
-import cfg from "./config/config.js"
-import PluginsLoader from "./plugins/loader.js"
-import ListenerLoader from "./listener/loader.js"
-import { EventEmitter } from "events"
-import express from "express"
-import http from "http"
-import { WebSocketServer } from "ws"
-import _ from "lodash"
-
-export default class Yunzai extends EventEmitter {
-  constructor() {
-    super()
-    this.uin = []
-    this.adapter = []
-    this.express = express()
-    this.server = http.createServer(this.express)
-    this.server.on("upgrade", (req, socket, head) => {
-      this.wss.handleUpgrade(req, socket, head, conn => {
-        conn.id = `${req.connection.remoteAddress}-${req.headers["sec-websocket-key"]}`
-        this.makeLog("mark", `${logger.blue(`[${conn.id} <=> ${req.url}]`)} 建立连接：${JSON.stringify(req.headers)}`)
-        conn.on("error", logger.error)
-        conn.on("close", () => this.makeLog("mark", `${logger.blue(`[${conn.id} <≠> ${req.url}]`)} 断开连接`))
-        conn.on("message", msg => this.makeLog("debug", `${logger.blue(`[${conn.id} => ${req.url}]`)} 消息：${String(msg).trim()}`))
-        conn.sendMsg = msg => {
-          if (typeof msg == "object")
-            msg = JSON.stringify(msg)
-          this.makeLog("debug", `${logger.blue(`[${conn.id} <= ${req.url}]`)} 消息：${msg}`)
-          return conn.send(msg)
-        }
-        for (const i of this.wsf[req.url.split("/")[1]] || [])
-          i(conn, req, socket, head)
-      })
-    })
-    this.wss = new WebSocketServer({ noServer: true })
-    this.wsf = {}
-  }
-
-  makeLog(level, msg) {
-    logger[level](_.truncate(msg, { length: cfg.bot.logLength }))
-  }
-
-  em(name = "", data = {}) {
-    if (data.self_id)
-      Object.defineProperty(data, "bot", { value: Bot[data.self_id] })
-    while (true) {
-      this.emit(name, data)
-      const i = name.lastIndexOf(".")
-      if (i == -1) break
-      name = name.slice(0, i)
-    }
-  }
-
-  async run() {
-    await import("./plugins/stdin.js")
-    await PluginsLoader.load()
-    await ListenerLoader.load()
-    this.serverLoad()
-    this.emit("online", this)
-  }
-
-  serverLoad() {
-    this.express.use(req => {
-      logger.mark(`${logger.blue(`[${req.ip} => ${req.url}]`)} HTTP ${req.method} 请求：${JSON.stringify(req.headers)}`)
-      req.res.redirect("https://github.com/TimeRainStarSky/Yunzai")
-    })
-
-    this.server.listen(cfg.bot.port, () => {
-      const host = this.server.address().address
-      const port = this.server.address().port
-      logger.mark(`启动 HTTP 服务器：${logger.green(`http://[${host}]:${port}`)}`)
-      for (const i of Object.keys(this.wsf))
-        logger.info(`本机 ${i} 连接地址：${logger.blue(`ws://localhost:${port}/${i}`)}`)
-    })
-  }
-
-  getFriendArray() {
-    const array = []
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].fl || [])
-        array.push({ ...i, bot_id })
-    return array
-  }
-
-  getFriendList() {
-    const array = []
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].fl || [])
-        array.push(id)
-    return array
-  }
-
-  getFriendMap() {
-    const map = new Map
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].fl || [])
-        map.set(id, { ...i, bot_id })
-    return map
-  }
-  get fl() { return this.getFriendMap() }
-
-  getGroupArray() {
-    const array = []
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].gl || [])
-        array.push({ ...i, bot_id })
-    return array
-  }
-
-  getGroupList() {
-    const array = []
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].gl || [])
-        array.push(id)
-    return array
-  }
-
-  getGroupMap() {
-    const map = new Map
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].gl || [])
-        map.set(id, { ...i, bot_id })
-    return map
-  }
-  get gl() { return this.getGroupMap() }
-  get gml() {
-    const map = new Map
-    for (const bot_id of this.uin)
-      for (const [id, i] of this[bot_id].gml || [])
-        map.set(id, i)
-    return map
-  }
-
-  pickFriend(user_id) {
-    user_id = Number(user_id) || String(user_id)
-    const user = this.fl.get(user_id)
-    if (user) return this[user.bot_id].pickFriend(user_id)
-    logger.error(`获取用户对象失败：找不到用户 ${logger.red(user_id)}`)
-  }
-  get pickUser() { return this.pickFriend }
-
-  pickGroup(group_id) {
-    group_id = Number(group_id) || String(group_id)
-    const group = this.gl.get(group_id)
-    if (group) return this[group.bot_id].pickGroup(group_id)
-    logger.error(`获取群对象失败：找不到群 ${logger.red(group_id)}`)
-  }
-
-  pickMember(group_id, user_id) {
-    const group = this.pickGroup(group_id)
-    if (group) return group.pickMember(user_id)
-  }
-
-  sendFriendMsg(bot_id, user_id, msg) {
-    try {
-      if (!bot_id)
-        return this.pickFriend(user_id).sendMsg(msg)
-
-      if (this[bot_id])
-        return this[bot_id].pickFriend(user_id).sendMsg(msg)
-
-      return new Promise(resolve =>
-        this.once(`connect.${bot_id}`, data =>
-          resolve(data.bot.pickFriend(user_id).sendMsg(msg))))
-    } catch (err) {
-      logger.error(`${logger.blue(`[${bot_id}]`)} 发送好友消息失败：[$${user_id}] ${err}`)
-    }
-    return false
-  }
-
-  sendGroupMsg(bot_id, group_id, msg) {
-    try {
-      if (!bot_id)
-        return this.pickGroup(group_id).sendMsg(msg)
-
-      if (this[bot_id])
-        return this[bot_id].pickGroup(group_id).sendMsg(msg)
-
-      return new Promise(resolve =>
-        this.once(`connect.${bot_id}`, data =>
-          resolve(data.bot.pickGroup(group_id).sendMsg(msg))))
-    } catch (err) {
-      logger.error(`${logger.blue(`[${bot_id}]`)} 发送群消息失败：[$${group_id}] ${err}`)
-    }
-    return false
-  }
-
-  async getFriendMsg(fnc = () => true) {
-    if (typeof fnc != "function") {
-      const { self_id, user_id } = fnc
-      fnc = data => data.self_id == self_id && data.user_id == user_id
-    }
-
-    while (true) {
-      const msg = await new Promise(resolve => {
-        this.once("message", data => {
-          if (data.message && fnc(data)) {
-            let msg = ""
-            for (const i of data.message)
-              if (i.type = "text")
-                msg += i.text.trim()
-            resolve(msg)
-          } else {
-            resolve(false)
-          }
-        })
-      })
-      if (msg) return msg
-    }
-  }
-
-  getMasterMsg() {
-    return this.getFriendMsg(data =>
-      cfg.master[data.self_id]?.includes(String(data.user_id)))
-  }
-
-  sendMasterMsg(msg) {
-    for (const bot_id in cfg.master)
-      for (const user_id of cfg.master[bot_id])
-        this.sendFriendMsg(bot_id, user_id, msg)
-  }
-
-  makeForwardMsg(msg) { return { type: "node", data: msg } }
-
-  async sendForwardMsg(send, msg) {
-    const messages = []
-    for (const { message } of msg)
-      messages.push(await send(message))
-    return messages
-  }
-}

spaces/Codecooker/rvcapi/src/trainset_preprocess_pipeline_print.py

@@ -1,146 +0,0 @@
-import sys, os, multiprocessing
-from scipy import signal
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-inp_root = sys.argv[1]
-sr = int(sys.argv[2])
-n_p = int(sys.argv[3])
-exp_dir = sys.argv[4]
-noparallel = sys.argv[5] == "True"
-import numpy as np, os, traceback
-from slicer2 import Slicer
-import librosa, traceback
-from scipy.io import wavfile
-import multiprocessing
-from my_utils import load_audio
-import tqdm
-
-DoFormant = False
-Quefrency = 1.0
-Timbre = 1.0
-
-mutex = multiprocessing.Lock()
-f = open("%s/preprocess.log" % exp_dir, "a+")
-
-
-def println(strr):
-    mutex.acquire()
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-    mutex.release()
-
-
-class PreProcess:
-    def __init__(self, sr, exp_dir):
-        self.slicer = Slicer(
-            sr=sr,
-            threshold=-42,
-            min_length=1500,
-            min_interval=400,
-            hop_size=15,
-            max_sil_kept=500,
-        )
-        self.sr = sr
-        self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
-        self.per = 3.0
-        self.overlap = 0.3
-        self.tail = self.per + self.overlap
-        self.max = 0.9
-        self.alpha = 0.75
-        self.exp_dir = exp_dir
-        self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
-        self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
-        os.makedirs(self.exp_dir, exist_ok=True)
-        os.makedirs(self.gt_wavs_dir, exist_ok=True)
-        os.makedirs(self.wavs16k_dir, exist_ok=True)
-
-    def norm_write(self, tmp_audio, idx0, idx1):
-        tmp_max = np.abs(tmp_audio).max()
-        if tmp_max > 2.5:
-            print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
-            return
-        tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
-            1 - self.alpha
-        ) * tmp_audio
-        wavfile.write(
-            "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
-            self.sr,
-            tmp_audio.astype(np.float32),
-        )
-        tmp_audio = librosa.resample(
-            tmp_audio, orig_sr=self.sr, target_sr=16000
-        )  # , res_type="soxr_vhq"
-        wavfile.write(
-            "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
-            16000,
-            tmp_audio.astype(np.float32),
-        )
-
-    def pipeline(self, path, idx0):
-        try:
-            audio = load_audio(path, self.sr, DoFormant, Quefrency, Timbre)
-            # zero phased digital filter cause pre-ringing noise...
-            # audio = signal.filtfilt(self.bh, self.ah, audio)
-            audio = signal.lfilter(self.bh, self.ah, audio)
-
-            idx1 = 0
-            for audio in self.slicer.slice(audio):
-                i = 0
-                while 1:
-                    start = int(self.sr * (self.per - self.overlap) * i)
-                    i += 1
-                    if len(audio[start:]) > self.tail * self.sr:
-                        tmp_audio = audio[start : start + int(self.per * self.sr)]
-                        self.norm_write(tmp_audio, idx0, idx1)
-                        idx1 += 1
-                    else:
-                        tmp_audio = audio[start:]
-                        idx1 += 1
-                        break
-                self.norm_write(tmp_audio, idx0, idx1)
-            # println("%s->Suc." % path)
-        except:
-            println("%s->%s" % (path, traceback.format_exc()))
-
-    def pipeline_mp(self, infos, thread_n):
-        for path, idx0 in tqdm.tqdm(
-            infos, position=thread_n, leave=True, desc="thread:%s" % thread_n
-        ):
-            self.pipeline(path, idx0)
-
-    def pipeline_mp_inp_dir(self, inp_root, n_p):
-        try:
-            infos = [
-                ("%s/%s" % (inp_root, name), idx)
-                for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
-            ]
-            if noparallel:
-                for i in range(n_p):
-                    self.pipeline_mp(infos[i::n_p])
-            else:
-                ps = []
-                for i in range(n_p):
-                    p = multiprocessing.Process(
-                        target=self.pipeline_mp, args=(infos[i::n_p], i)
-                    )
-                    ps.append(p)
-                    p.start()
-                for i in range(n_p):
-                    ps[i].join()
-        except:
-            println("Fail. %s" % traceback.format_exc())
-
-
-def preprocess_trainset(inp_root, sr, n_p, exp_dir):
-    pp = PreProcess(sr, exp_dir)
-    println("start preprocess")
-    println(sys.argv)
-    pp.pipeline_mp_inp_dir(inp_root, n_p)
-    println("end preprocess")
-
-
-if __name__ == "__main__":
-    preprocess_trainset(inp_root, sr, n_p, exp_dir)

spaces/DCandE/rvc-models/config.py

@@ -1,88 +0,0 @@
-########################硬件参数########################
-
-# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
-device = "cuda:0"
-
-# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
-is_half = True
-
-# 默认0用上所有线程，写数字限制CPU资源使用
-n_cpu = 0
-
-########################硬件参数########################
-
-
-##################下为参数处理逻辑，勿动##################
-
-########################命令行参数########################
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--port", type=int, default=7865, help="Listen port")
-parser.add_argument("--pycmd", type=str, default="python", help="Python command")
-parser.add_argument("--colab", action="store_true", help="Launch in colab")
-parser.add_argument(
-    "--noparallel", action="store_true", help="Disable parallel processing"
-)
-parser.add_argument(
-    "--noautoopen", action="store_true", help="Do not open in browser automatically"
-)
-cmd_opts, unknown = parser.parse_known_args()
-
-python_cmd = cmd_opts.pycmd
-listen_port = cmd_opts.port
-iscolab = cmd_opts.colab
-noparallel = cmd_opts.noparallel
-noautoopen = cmd_opts.noautoopen
-########################命令行参数########################
-
-import sys
-import torch
-
-
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-# check `getattr` and try it for compatibility
-def has_mps() -> bool:
-    if sys.platform != "darwin":
-        return False
-    else:
-        if not getattr(torch, "has_mps", False):
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-if not torch.cuda.is_available():
-    if has_mps():
-        print("没有发现支持的N卡, 使用MPS进行推理")
-        device = "mps"
-    else:
-        print("没有发现支持的N卡, 使用CPU进行推理")
-        device = "cpu"
-        is_half = False
-
-if device not in ["cpu", "mps"]:
-    gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
-    if "16" in gpu_name or "MX" in gpu_name:
-        print("16系显卡/MX系显卡强制单精度")
-        is_half = False
-
-from multiprocessing import cpu_count
-
-if n_cpu == 0:
-    n_cpu = cpu_count()
-if is_half:
-    # 6G显存配置
-    x_pad = 3
-    x_query = 10
-    x_center = 60
-    x_max = 65
-else:
-    # 5G显存配置
-    x_pad = 1
-    x_query = 6
-    x_center = 38
-    x_max = 41

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7648fc8d.js

@@ -1,7 +0,0 @@
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s>=0&&(r=Math.max(r,this.positions[s]+this.children[s].length+this.from)),new E(e.types[this.type],this.children,this.positions,r-this.from).balance({makeTree:(i,o,a)=>new E(F.none,i,o,a,this.hashProp)})}}var f;(function(t){t[t.Document=1]="Document",t[t.CodeBlock=2]="CodeBlock",t[t.FencedCode=3]="FencedCode",t[t.Blockquote=4]="Blockquote",t[t.HorizontalRule=5]="HorizontalRule",t[t.BulletList=6]="BulletList",t[t.OrderedList=7]="OrderedList",t[t.ListItem=8]="ListItem",t[t.ATXHeading1=9]="ATXHeading1",t[t.ATXHeading2=10]="ATXHeading2",t[t.ATXHeading3=11]="ATXHeading3",t[t.ATXHeading4=12]="ATXHeading4",t[t.ATXHeading5=13]="ATXHeading5",t[t.ATXHeading6=14]="ATXHeading6",t[t.SetextHeading1=15]="SetextHeading1",t[t.SetextHeading2=16]="SetextHeading2",t[t.HTMLBlock=17]="HTMLBlock",t[t.LinkReference=18]="LinkReference",t[t.Paragraph=19]="Paragraph",t[t.CommentBlock=20]="CommentBlock",t[t.ProcessingInstructionBlock=21]="ProcessingInstructionBlock",t[t.Escape=22]="Escape",t[t.Entity=23]="Entity",t[t.HardBreak=24]="HardBreak",t[t.Emphasis=25]="Emphasis",t[t.StrongEmphasis=26]="StrongEmphasis",t[t.Link=27]="Link",t[t.Image=28]="Image",t[t.InlineCode=29]="InlineCode",t[t.HTMLTag=30]="HTMLTag",t[t.Comment=31]="Comment",t[t.ProcessingInstruction=32]="ProcessingInstruction",t[t.URL=33]="URL",t[t.HeaderMark=34]="HeaderMark",t[t.QuoteMark=35]="QuoteMark",t[t.ListMark=36]="ListMark",t[t.LinkMark=37]="LinkMark",t[t.EmphasisMark=38]="EmphasisMark",t[t.CodeMark=39]="CodeMark",t[t.CodeText=40]="CodeText",t[t.CodeInfo=41]="CodeInfo",t[t.LinkTitle=42]="LinkTitle",t[t.LinkLabel=43]="LinkLabel"})(f||(f={}));class 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-//# sourceMappingURL=index-7648fc8d.js.map

spaces/Dana19/animal_classifier/app.py

@@ -1,29 +0,0 @@
-import gradio as gr
-import fastai
-from fastai.vision.all import *
-
-learn = load_learner("export.pkl")
-
-labels = learn.dls.vocab
-
-def predict(img):
-  img = PILImage.create(img)
-  pred, pred_idx, probs = learn.predict(img)
-  return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = 'Animal Classifier'
-description = 'Animal classifier tried with downloaded data from internet. Created as a demo for Gradio and HuggingFace Spaces.'
-
-examples = ['c.jpeg', 'e.jpeg', 'e2.jpeg', 'e3.jpeg', 'g.jpeg', 'ec.jpeg']
-interpretation = 'default'
-enable_queue = True
-
-gr.Interface(fn = predict, 
-             inputs = gr.inputs.Image(shape=(224,224)),
-             outputs = gr.outputs.Label(num_top_classes = 4),
-             title = title, 
-             description = description, 
-             examples = examples,
-             interpretation = interpretation, 
-             enable_queue = enable_queue
-             ).launch(share = False)

spaces/DeathRoad/PornagraphyIsGreat/README.md

@@ -1,10 +0,0 @@
----
-title: PornagraphyIsGreat
-emoji: 🐠
-colorFrom: green
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h

@@ -1,22 +0,0 @@
-/*
- * File   : prroi_pooling_gpu.h
- * Author : Jiayuan Mao, Tete Xiao
- * Email  : [email protected], [email protected] 
- * Date   : 07/13/2018
- * 
- * Distributed under terms of the MIT license.
- * Copyright (c) 2017 Megvii Technology Limited.
- */
-
-int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale);
-
-int prroi_pooling_backward_cuda(
-    THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff,
-    int pooled_height, int pooled_width, float spatial_scale
-);
-
-int prroi_pooling_coor_backward_cuda(
-    THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff,
-    int pooled_height, int pooled_width, float spatial_scal
-);
-

spaces/DrGabrielLopez/fractal-generator/fractal_generator.py

@@ -1,82 +0,0 @@
-from enum import Enum
-
-import numpy as np
-import plotly.express as px
-
-
-class FractalType(Enum):
-    Julia = 1
-    Mandelbrot = 2
-
-
-class FractalGenerator:
-    """Creates a single fractal object and either returns it as as a numpy array, plot it or persists it as an pgn
-    image. The output of this class is used by FractalTrainingValidationSet to generate training/val sets
-    Args:
-        complex_function -- complex function to make a Julia fractal
-        n -- fractal size will ne n*n
-        xlim,ylim -- tuples with the plotting region on the complex plane
-        thr -- once a function grows larger that this number is considered to be divergent to infinity
-        max_iter -- number of compositions of the complex function with itself
-        type_ -- fractal type
-        fractal -- numpy array with the fractal
-    """
-
-    def __init__(self, n=256, xlim=(-2, 2), ylim=(-2, 2), thr=2, max_iter=10):
-        self.type_ = None
-        self.fractal = None
-        self.n = n
-        self.xlim = xlim
-        self.ylim = ylim
-        self.thr = thr
-        self.max_iter = max_iter
-
-    def create_julia(self, complex_function=lambda z: np.sin(z ** 4 + 1.41)):
-        """Creates a fractal of the Julia family, the fractal is stored inside self.fractal"""
-        fractal = np.zeros((self.n, self.n), dtype="complex")
-        x_space = np.linspace(self.xlim[0], self.xlim[1], self.n)
-        y_space = np.linspace(self.ylim[0], self.ylim[1], self.n)
-        for ix, x in enumerate(x_space):
-            for iy, y in enumerate(y_space):
-                for i in range(self.max_iter):
-                    if i == 0:
-                        z = complex(x, y)
-                    z = complex_function(z)
-                    if np.abs(z) >= self.thr:
-                        z = self.thr
-                        break
-                fractal[ix, iy] = z
-        self.fractal = np.abs(fractal)
-        self.type_ = FractalType.Julia
-        return self
-
-    def create_mandelbrot(self):
-        """Creates a fractal of the Mandelbrot family, the fractal is stored inside self.fractal"""
-        fractal = np.zeros((self.n, self.n), dtype="complex")
-        x_space = np.linspace(self.xlim[0], self.xlim[1], self.n)
-        y_space = np.linspace(self.ylim[0], self.ylim[1], self.n)
-        for ix, x in enumerate(x_space):
-            for iy, y in enumerate(y_space):
-                for i in range(self.max_iter):
-                    if i == 0:
-                        z = 0
-                    z = z ** 2 + complex(x, y)
-                    if np.abs(z) >= self.thr:
-                        z = self.thr
-                        break
-                fractal[ix, iy] = z
-        self.fractal = np.abs(fractal.transpose())
-        self.type_ = FractalType.Mandelbrot
-        return self
-
-    def plot(self, **kwargs):
-        if self.fractal is None:
-            print("Nothing to plot. Generate a fractal first.")
-            return None
-        random_colormap = np.random.choice(
-            ["orrd", "inferno_r", "hot_r", "jet_r", "purples", "agsunset_r"]
-        )
-        fig = px.imshow(
-            img=self.fractal, color_continuous_scale=random_colormap, **kwargs
-        )
-        return fig