diff --git a/spaces/0x90e/ESRGAN-MANGA/ESRGAN/block.py b/spaces/0x90e/ESRGAN-MANGA/ESRGAN/block.py
deleted file mode 100644
index 0e63bd216c7e244b26e67b66dde7a8542906a953..0000000000000000000000000000000000000000
--- a/spaces/0x90e/ESRGAN-MANGA/ESRGAN/block.py
+++ /dev/null
@@ -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)
diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/account_creation.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/account_creation.py
deleted file mode 100644
index 0581945372f5c08918e6cf5df2a528f52c93cc00..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/account_creation.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import usesless
-
-usesless.Account.create(logging=True)
diff --git a/spaces/14-26AA/sovits_aishell3/app.py b/spaces/14-26AA/sovits_aishell3/app.py
deleted file mode 100644
index 7f0c1e2ead4d208c0dbaa9640c11b1c59ffa9b4d..0000000000000000000000000000000000000000
--- a/spaces/14-26AA/sovits_aishell3/app.py
+++ /dev/null
@@ -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()
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dont Panic! Heres Why Your PC Makes a Crackling Noise and How to Fix It.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dont Panic! Heres Why Your PC Makes a Crackling Noise and How to Fix It.md
deleted file mode 100644
index 4c1671fdb9664da0168b5300fe315466f7c15baf..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dont Panic! Heres Why Your PC Makes a Crackling Noise and How to Fix It.md	
+++ /dev/null
@@ -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 />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FL Studio 20 Keygen Reddit The Ultimate Guide to Unlocking All Features and Plugins.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FL Studio 20 Keygen Reddit The Ultimate Guide to Unlocking All Features and Plugins.md
deleted file mode 100644
index 392a4efed6642bcb3490bd253e89393158223a06..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FL Studio 20 Keygen Reddit The Ultimate Guide to Unlocking All Features and Plugins.md	
+++ /dev/null
@@ -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 />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/GSG HDRI Studio Pack 1.8 for Cinema 4D How to Achieve Realistic Reflections and Shadows.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/GSG HDRI Studio Pack 1.8 for Cinema 4D How to Achieve Realistic Reflections and Shadows.md
deleted file mode 100644
index 4db5b6f4cbbe95c68e4e717f34a9a6601906233b..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/GSG HDRI Studio Pack 1.8 for Cinema 4D How to Achieve Realistic Reflections and Shadows.md	
+++ /dev/null
@@ -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>
-<h2>GSG HDRI Studio Pack 1.8 for Cinema 4D</h2><br /><p><b><b>DOWNLOAD</b> &#10038;&#10038;&#10038; <a href="https://byltly.com/2uKyNB">https://byltly.com/2uKyNB</a></b></p><br /><br />
-<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>
-<li>Learn from over 500 hours of pro training on Cinema 4D, After Effects, Redshift, Octane, Arnold, X-Particles, Houdini, RealFlow, and more</li>
-<li>Get instant updates and new releases as soon as they are available</li>
-<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>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Designing With Type 5th Edition - The Essential Guide To Typography By James Craig.pdf !NEW!.md b/spaces/1gistliPinn/ChatGPT4/Examples/Designing With Type 5th Edition - The Essential Guide To Typography By James Craig.pdf !NEW!.md
deleted file mode 100644
index dfc1c5166054bc73e68d398e5c2fd2daad0634b1..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Designing With Type 5th Edition - The Essential Guide To Typography By James Craig.pdf !NEW!.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>Typography is often regarded as an uninteresting topic. It is no less important than any other medium or discipline, but it is often not given the attention that it deserves. Sadly, some designers today seem to think that just calling oneself a "typographer" will get one a job. It is time to dispel that myth. This book is designed for anyone in any field who wants to know how to design with type. It is true that not all designers think of type as being important or useful, but even that attitude has changed. Designing books, pamphlets, posters, mailings, logos, advertisements, and even Web pages means knowing something about type. And now that type is commonplace in both the print and the electronic media, the need for designers who know type is not less than it has ever been. This book presents the essentials of type and shows how they can be utilized as in the design of anything printed, from books to pamphlets to logos. It is to be hoped that this text will be the first of many on this crucial subject.</p>
-<p>Designing with Type continues to be a perennial best seller, as well as one of the most frequently cited textbooks ever written on the subject. Where it was once considered a dry, obscure subject, of little relevance to graphic designers, it is now accepted as an essential text for designers and design students alike. For the new generation of designers, this book still offers the most complete, current overview of the subject.</p>
-<h2>Designing with Type, 5th Edition - The Essential Guide to Typography by James Craig.pdf</h2><br /><p><b><b>Download Zip</b> &#10027; <a href="https://imgfil.com/2uxYRV">https://imgfil.com/2uxYRV</a></b></p><br /><br />
-<p>This book is about a way of looking at typography that helps us think about type as a form and about the people who designed it, as well as its use in mass-communication. The book begins by defining what typography is and isn't, with particular emphasis on the rationale behind it. Next, the book outlines types of design and typographic criteria, with sections on structure, form, function, illustration, language, visual and verbal communication, and media placement and organization. The authors show how these methods can be applied to any kind of type-based communication, from books, posters, logos, magazines, and broadsheets to websites, advertisements, and classified listings.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1line/AutoGPT/autogpt/commands/audio_text.py b/spaces/1line/AutoGPT/autogpt/commands/audio_text.py
deleted file mode 100644
index cae32d4eb78c4268bf6ef1bae3c15a399af046bf..0000000000000000000000000000000000000000
--- a/spaces/1line/AutoGPT/autogpt/commands/audio_text.py
+++ /dev/null
@@ -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
diff --git a/spaces/1phancelerku/anime-remove-background/60 Seconds! Reatomized - A Crazy and Funny Adventure in a Nuclear Wasteland - Play Online for Free.md b/spaces/1phancelerku/anime-remove-background/60 Seconds! Reatomized - A Crazy and Funny Adventure in a Nuclear Wasteland - Play Online for Free.md
deleted file mode 100644
index dd78971091718bd8e4c9cc98f07b721c8af77379..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/60 Seconds! Reatomized - A Crazy and Funny Adventure in a Nuclear Wasteland - Play Online for Free.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-
-<h1>How to Play 60 Seconds! Reatomized for Free Online Without Downloading Anything</h1>
- <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>
-<h2>60 seconds free no download unblocked</h2><br /><p><b><b>Download File</b> &#10040;&#10040;&#10040; <a href="https://jinyurl.com/2uNODe">https://jinyurl.com/2uNODe</a></b></p><br /><br />
- <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>
-<ul>
-<li>Better graphics and sound effects</li>
-<li>New endings and achievements</li>
-<li>New characters and items</li>
-<li>New challenges and events</li>
-<li>New mini-games and quests</li>
-<li>New game mode: Challenge</li>
-</ul>
- <h2>How to play 60 seconds free no download unblocked?</h2>
- <p>If you want to play 60 Seconds! Reatomized for free online without downloading anything, you need to find a reliable website that offers the game. One such website is [Gameroze.com](^1^), which lets you play the game in your browser without any registration or installation. Another website is [60secondsreatomizedgame.com](^2^), which also offers the game for free online. Here are the steps to play the game:</p>
- <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>
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-<li><b>Q: How long does it take to finish the game?</b></li>
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-<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 />
\ No newline at end of file
diff --git a/spaces/2ndelement/voicevox/test/test_kana_parser.py b/spaces/2ndelement/voicevox/test/test_kana_parser.py
deleted file mode 100644
index ef800b60003b5d14b90a8eeb86e0fa29a919f878..0000000000000000000000000000000000000000
--- a/spaces/2ndelement/voicevox/test/test_kana_parser.py
+++ /dev/null
@@ -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？")
diff --git a/spaces/3laa2/Text2img/README.md b/spaces/3laa2/Text2img/README.md
deleted file mode 100644
index 4d60b6f1a40e69db3a64878d7e1684b9d909eda4..0000000000000000000000000000000000000000
--- a/spaces/3laa2/Text2img/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/42digital/DeepFashion_Classification/README.md b/spaces/42digital/DeepFashion_Classification/README.md
deleted file mode 100644
index 02067ee98ac4740840bebbe632b4bc3c28c5716b..0000000000000000000000000000000000000000
--- a/spaces/42digital/DeepFashion_Classification/README.md
+++ /dev/null
@@ -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
\ No newline at end of file
diff --git a/spaces/AI-ANK/PaLM-Kosmos-Vision/app.py b/spaces/AI-ANK/PaLM-Kosmos-Vision/app.py
deleted file mode 100644
index 6b3915c20163b4499d64970e55f1a880869c3aa0..0000000000000000000000000000000000000000
--- a/spaces/AI-ANK/PaLM-Kosmos-Vision/app.py
+++ /dev/null
@@ -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']
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/models/unet.py b/spaces/AIConsultant/MusicGen/audiocraft/models/unet.py
deleted file mode 100644
index db4a6df8e309c21fede37abdbe3c862932027641..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/models/unet.py
+++ /dev/null
@@ -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)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/ema.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/ema.py
deleted file mode 100644
index c8c75af43565f6e140287644aaaefa97dd6e67c5..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/ema.py
+++ /dev/null
@@ -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)
diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/test.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/test.py
deleted file mode 100644
index b230ddf5ba4901aee0cf5e5d102fcca328038eeb..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/test.py
+++ /dev/null
@@ -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()
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/You.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/You.py
deleted file mode 100644
index 1afd18be4560fe684744d10e34ddcdd833238178..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/You.py
+++ /dev/null
@@ -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])
\ No newline at end of file
diff --git a/spaces/Adapter/T2I-Adapter/train_seg.py b/spaces/Adapter/T2I-Adapter/train_seg.py
deleted file mode 100644
index 82ed0724ef757a93e9f9fdd4ef3ada4a0203f906..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/train_seg.py
+++ /dev/null
@@ -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
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/RemoveChildMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/RemoveChildMethods.js
deleted file mode 100644
index f07d12dc1e9506ed3a7027ddb9878f0bbd0d9596..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/RemoveChildMethods.js
+++ /dev/null
@@ -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;
-    }
-}
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.d.ts
deleted file mode 100644
index 54a5919382c93759cb9fd74af8b877dc93e24903..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import CustomProgress from "./CustomProgress";
-
-export default function (
-    config?: CustomProgress.IConfig
-): CustomProgress;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.js
deleted file mode 100644
index 9be40cdee19d776e5eed254fa0be4cff4cf02d78..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/customprogress/Factory.js
+++ /dev/null
@@ -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;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/space/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/space/Factory.d.ts
deleted file mode 100644
index 2a7230ccc2713f61640536d2e64d94d858c3c48a..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/space/Factory.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import Space from './Space';
-
-export default function (): Space;
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
deleted file mode 100644
index f42165d9fd14600858681e695de7927aac865652..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -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))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
deleted file mode 100644
index ababe58dc3fdfbbc6c366f48271db31bf6e2e9e2..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+++ /dev/null
@@ -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)))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/gfocal_loss.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/gfocal_loss.py
deleted file mode 100644
index 9d3b8833dc50c76f6741db5341dbf8da3402d07b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/gfocal_loss.py
+++ /dev/null
@@ -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
diff --git a/spaces/AngoHF/ANGO-Leaderboard/components/top.py b/spaces/AngoHF/ANGO-Leaderboard/components/top.py
deleted file mode 100644
index 941ed3874abc2f05b433dbf35411c75422b842f1..0000000000000000000000000000000000000000
--- a/spaces/AngoHF/ANGO-Leaderboard/components/top.py
+++ /dev/null
@@ -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}
diff --git a/spaces/AnimalEquality/chatbot/app.py b/spaces/AnimalEquality/chatbot/app.py
deleted file mode 100644
index 4f256467dc20074ea0ddbbbcf267323ef180170e..0000000000000000000000000000000000000000
--- a/spaces/AnimalEquality/chatbot/app.py
+++ /dev/null
@@ -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"])
-    )
diff --git a/spaces/Anindya/Marketing_Campaign_LLM/README.md b/spaces/Anindya/Marketing_Campaign_LLM/README.md
deleted file mode 100644
index 00c52c28dda23b424f347d6cae756ad951464356..0000000000000000000000000000000000000000
--- a/spaces/Anindya/Marketing_Campaign_LLM/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/main.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/main.py
deleted file mode 100644
index f40c20ea202b283260a278bc38b0c63a8e3efc1e..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/main.py
+++ /dev/null
@@ -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()
diff --git a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/core.py b/spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/monotonic_align/core.py
+++ /dev/null
@@ -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
diff --git a/spaces/Benson/text-generation/Examples/Colinas De Acero 2.md b/spaces/Benson/text-generation/Examples/Colinas De Acero 2.md
deleted file mode 100644
index 898a38ed15645761e4b81d922173cddc039811d8..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Colinas De Acero 2.md	
+++ /dev/null
@@ -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 support@superplusgames.com o uniéndose a su servidor Discord en https://discord.gg/hillsofsteel2.</li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/data/faceshq.py b/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/data/faceshq.py
deleted file mode 100644
index 6912d04b66a6d464c1078e4b51d5da290f5e767e..0000000000000000000000000000000000000000
--- a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/data/faceshq.py
+++ /dev/null
@@ -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
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/errorfactory.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/errorfactory.py
deleted file mode 100644
index d9a1e9cd9cf542c9d01cb35bb934711799e45aac..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/errorfactory.py
+++ /dev/null
@@ -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)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/__init__.py
deleted file mode 100644
index a40eeafcc914108ca79c5d83d6e81da1b29c6e80..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/__init__.py
+++ /dev/null
@@ -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",
-]
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/log.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/log.py
deleted file mode 100644
index be25f6cabd839af772dd74399c57991c222d3da8..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/log.py
+++ /dev/null
@@ -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)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_adapters.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_adapters.py
deleted file mode 100644
index aa460d3eda50fbb174623a1b5bbca54645fd588a..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_adapters.py
+++ /dev/null
@@ -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)))
diff --git a/spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/app.py b/spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/app.py
deleted file mode 100644
index ac74e825792a0c6435930a2b7b33b848327e7f9c..0000000000000000000000000000000000000000
--- a/spaces/Blaise-g/summarize-biomedical-papers-long-summary-or-tldr/app.py
+++ /dev/null
@@ -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)
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/pybind11/tools/pybind11Tools.cmake b/spaces/CVPR/LIVE/pybind11/tools/pybind11Tools.cmake
deleted file mode 100644
index 10f15a30917056f8d69cff833e2c905aede08e50..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tools/pybind11Tools.cmake
+++ /dev/null
@@ -1,188 +0,0 @@
-# tools/pybind11Tools.cmake -- Build system for the pybind11 modules
-#
-# Copyright (c) 2015 Wenzel Jakob <wenzel@inf.ethz.ch>
-#
-# 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()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/numeric_traits.h b/spaces/CVPR/LIVE/thrust/thrust/detail/numeric_traits.h
deleted file mode 100644
index 168b9ad0f4b63657845915ba1718737773be687a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/numeric_traits.h
+++ /dev/null
@@ -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
-
diff --git a/spaces/CVPR/WALT/mmdet/models/backbones/ssd_vgg.py b/spaces/CVPR/WALT/mmdet/models/backbones/ssd_vgg.py
deleted file mode 100644
index cbc4fbb2301afc002f47abb9ed133a500d6cf23f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/backbones/ssd_vgg.py
+++ /dev/null
@@ -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)
diff --git a/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py b/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py
deleted file mode 100644
index 6c154cb3c0d9d7639c3d4a2a1272406d3fab8acd..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py
+++ /dev/null
@@ -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
diff --git a/spaces/CVPR/lama-example/predict.py b/spaces/CVPR/lama-example/predict.py
deleted file mode 100644
index 878b7988c113778f48ec3f940d2031a30c12e03f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/predict.py
+++ /dev/null
@@ -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()
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/lib/bot.js b/spaces/CikeyQI/Yunzai/Yunzai/lib/bot.js
deleted file mode 100644
index 0c478ffd702713f8e2a1e0002553843feb445d45..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/lib/bot.js
+++ /dev/null
@@ -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
-  }
-}
\ No newline at end of file
diff --git a/spaces/Codecooker/rvcapi/src/trainset_preprocess_pipeline_print.py b/spaces/Codecooker/rvcapi/src/trainset_preprocess_pipeline_print.py
deleted file mode 100644
index 7b19e3e9a5788552b6acb9cd6747bda7ae93146b..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/trainset_preprocess_pipeline_print.py
+++ /dev/null
@@ -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)
diff --git a/spaces/DCandE/rvc-models/config.py b/spaces/DCandE/rvc-models/config.py
deleted file mode 100644
index c0c16e0017efbcaf250cb539a1d0edb4e83575e4..0000000000000000000000000000000000000000
--- a/spaces/DCandE/rvc-models/config.py
+++ /dev/null
@@ -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
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7648fc8d.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7648fc8d.js
deleted file mode 100644
index 161b8d1ff63da7d47bbec48045861e2c83fb9097..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7648fc8d.js
+++ /dev/null
@@ -1,7 +0,0 @@
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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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markdownLanguage};
-//# sourceMappingURL=index-7648fc8d.js.map
diff --git a/spaces/Dana19/animal_classifier/app.py b/spaces/Dana19/animal_classifier/app.py
deleted file mode 100644
index 59de813f340c3c912767e81dad179e50a2b45f97..0000000000000000000000000000000000000000
--- a/spaces/Dana19/animal_classifier/app.py
+++ /dev/null
@@ -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)
diff --git a/spaces/DeathRoad/PornagraphyIsGreat/README.md b/spaces/DeathRoad/PornagraphyIsGreat/README.md
deleted file mode 100644
index d15238d4e33443ccd4ccae710e7a6c11ae50ba92..0000000000000000000000000000000000000000
--- a/spaces/DeathRoad/PornagraphyIsGreat/README.md
+++ /dev/null
@@ -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
diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h b/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h
deleted file mode 100644
index bc9d35181dd97c355fb6a5b17bc9e82e24ef1566..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- * File   : prroi_pooling_gpu.h
- * Author : Jiayuan Mao, Tete Xiao
- * Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com 
- * 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
-);
-
diff --git a/spaces/DrGabrielLopez/fractal-generator/fractal_generator.py b/spaces/DrGabrielLopez/fractal-generator/fractal_generator.py
deleted file mode 100644
index 3bfdc0e665224692f2affbd3e697074146ed625f..0000000000000000000000000000000000000000
--- a/spaces/DrGabrielLopez/fractal-generator/fractal_generator.py
+++ /dev/null
@@ -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
diff --git a/spaces/Eddycrack864/Applio-Inference/extract_locale.py b/spaces/Eddycrack864/Applio-Inference/extract_locale.py
deleted file mode 100644
index a4ff5ea3ddd7c612c640544099ab98a861b8fe35..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/extract_locale.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import json
-import re
-
-# Define regular expression patterns
-pattern = r"""i18n\([\s\n\t]*(["'][^"']+["'])[\s\n\t]*\)"""
-
-# Initialize the dictionary to store key-value pairs
-data = {}
-
-
-def process(fn: str):
-    global data
-    with open(fn, "r", encoding="utf-8") as f:
-        contents = f.read()
-        matches = re.findall(pattern, contents)
-        for key in matches:
-            key = eval(key)
-            print("extract:", key)
-            data[key] = key
-
-
-print("processing infer-web.py")
-process("infer-web.py")
-
-print("processing gui_v0.py")
-process("gui_v0.py")
-
-print("processing gui_v1.py")
-process("gui_v1.py")
-
-# Save as a JSON file
-with open("./i18n/en_US.json", "w", encoding="utf-8") as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)
-    f.write("\n")
diff --git a/spaces/Eddycrack864/Applio-Inference/get-pip.py b/spaces/Eddycrack864/Applio-Inference/get-pip.py
deleted file mode 100644
index cf68a2b3426decf01bde021a50c47a2115af303a..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/get-pip.py
+++ /dev/null
@@ -1,32657 +0,0 @@
-#!/usr/bin/env python
-#
-# Hi There!
-#
-# You may be wondering what this giant blob of binary data here is, you might
-# even be worried that we're up to something nefarious (good for you for being
-# paranoid!). This is a base85 encoding of a zip file, this zip file contains
-# an entire copy of pip (version 23.2.1).
-#
-# Pip is a thing that installs packages, pip itself is a package that someone
-# might want to install, especially if they're looking to run this get-pip.py
-# script. Pip has a lot of code to deal with the security of installing
-# packages, various edge cases on various platforms, and other such sort of
-# "tribal knowledge" that has been encoded in its code base. Because of this
-# we basically include an entire copy of pip inside this blob. We do this
-# because the alternatives are attempt to implement a "minipip" that probably
-# doesn't do things correctly and has weird edge cases, or compress pip itself
-# down into a single file.
-#
-# If you're wondering how this is created, it is generated using
-# `scripts/generate.py` in https://github.com/pypa/get-pip.
-
-import sys
-
-this_python = sys.version_info[:2]
-min_version = (3, 7)
-if this_python < min_version:
-    message_parts = [
-        "This script does not work on Python {}.{}".format(*this_python),
-        "The minimum supported Python version is {}.{}.".format(*min_version),
-        "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python),
-    ]
-    print("ERROR: " + " ".join(message_parts))
-    sys.exit(1)
-
-
-import os.path
-import pkgutil
-import shutil
-import tempfile
-import argparse
-import importlib
-from base64 import b85decode
-
-
-def include_setuptools(args):
-    """
-    Install setuptools only if absent and not excluded.
-    """
-    cli = not args.no_setuptools
-    env = not os.environ.get("PIP_NO_SETUPTOOLS")
-    absent = not importlib.util.find_spec("setuptools")
-    return cli and env and absent
-
-
-def include_wheel(args):
-    """
-    Install wheel only if absent and not excluded.
-    """
-    cli = not args.no_wheel
-    env = not os.environ.get("PIP_NO_WHEEL")
-    absent = not importlib.util.find_spec("wheel")
-    return cli and env and absent
-
-
-def determine_pip_install_arguments():
-    pre_parser = argparse.ArgumentParser()
-    pre_parser.add_argument("--no-setuptools", action="store_true")
-    pre_parser.add_argument("--no-wheel", action="store_true")
-    pre, args = pre_parser.parse_known_args()
-
-    args.append("pip")
-
-    if include_setuptools(pre):
-        args.append("setuptools")
-
-    if include_wheel(pre):
-        args.append("wheel")
-
-    return ["install", "--upgrade", "--force-reinstall"] + args
-
-
-def monkeypatch_for_cert(tmpdir):
-    """Patches `pip install` to provide default certificate with the lowest priority.
-
-    This ensures that the bundled certificates are used unless the user specifies a
-    custom cert via any of pip's option passing mechanisms (config, env-var, CLI).
-
-    A monkeypatch is the easiest way to achieve this, without messing too much with
-    the rest of pip's internals.
-    """
-    from pip._internal.commands.install import InstallCommand
-
-    # We want to be using the internal certificates.
-    cert_path = os.path.join(tmpdir, "cacert.pem")
-    with open(cert_path, "wb") as cert:
-        cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem"))
-
-    install_parse_args = InstallCommand.parse_args
-
-    def cert_parse_args(self, args):
-        if not self.parser.get_default_values().cert:
-            # There are no user provided cert -- force use of bundled cert
-            self.parser.defaults["cert"] = cert_path  # calculated above
-        return install_parse_args(self, args)
-
-    InstallCommand.parse_args = cert_parse_args
-
-
-def bootstrap(tmpdir):
-    monkeypatch_for_cert(tmpdir)
-
-    # Execute the included pip and use it to install the latest pip and
-    # setuptools from PyPI
-    from pip._internal.cli.main import main as pip_entry_point
-    args = determine_pip_install_arguments()
-    sys.exit(pip_entry_point(args))
-
-
-def main():
-    tmpdir = None
-    try:
-        # Create a temporary working directory
-        tmpdir = tempfile.mkdtemp()
-
-        # Unpack the zipfile into the temporary directory
-        pip_zip = os.path.join(tmpdir, "pip.zip")
-        with open(pip_zip, "wb") as fp:
-            fp.write(b85decode(DATA.replace(b"\n", b"")))
-
-        # Add the zipfile to sys.path so that we can import it
-        sys.path.insert(0, pip_zip)
-
-        # Run the bootstrap
-        bootstrap(tmpdir=tmpdir)
-    finally:
-        # Clean up our temporary working directory
-        if tmpdir:
-            shutil.rmtree(tmpdir, ignore_errors=True)
-
-
-DATA = b"""
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-ewp`X>Mn8FL+;db7gX0WMyV)Ze?UHaCuNm1qJ{B008s>^a044008>F9smFU
-"""
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Egrt/GCycleGAN/nets/resnest/ablation.py b/spaces/Egrt/GCycleGAN/nets/resnest/ablation.py
deleted file mode 100644
index 00743ccdcf8c909b262c37476488c92ba947fde5..0000000000000000000000000000000000000000
--- a/spaces/Egrt/GCycleGAN/nets/resnest/ablation.py
+++ /dev/null
@@ -1,106 +0,0 @@
-##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-## Created by: Hang Zhang
-## Email: zhanghang0704@gmail.com
-## Copyright (c) 2020
-##
-## LICENSE file in the root directory of this source tree 
-##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-"""ResNeSt ablation study models"""
-
-import torch
-from .resnet import ResNet, Bottleneck
-
-__all__ = ['resnest50_fast_1s1x64d', 'resnest50_fast_2s1x64d', 'resnest50_fast_4s1x64d',
-           'resnest50_fast_1s2x40d', 'resnest50_fast_2s2x40d', 'resnest50_fast_4s2x40d',
-           'resnest50_fast_1s4x24d']
-
-_url_format = 'https://s3.us-west-1.wasabisys.com/resnest/torch/{}-{}.pth'
-
-_model_sha256 = {name: checksum for checksum, name in [
-    ('d8fbf808', 'resnest50_fast_1s1x64d'),
-    ('44938639', 'resnest50_fast_2s1x64d'),
-    ('f74f3fc3', 'resnest50_fast_4s1x64d'),
-    ('32830b84', 'resnest50_fast_1s2x40d'),
-    ('9d126481', 'resnest50_fast_2s2x40d'),
-    ('41d14ed0', 'resnest50_fast_4s2x40d'),
-    ('d4a4f76f', 'resnest50_fast_1s4x24d'),
-    ]}
-
-def short_hash(name):
-    if name not in _model_sha256:
-        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
-    return _model_sha256[name][:8]
-
-resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for
-    name in _model_sha256.keys()
-}
-
-def resnest50_fast_1s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=1, groups=1, bottleneck_width=64,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_1s1x64d'], progress=True, check_hash=True))
-    return model
-
-def resnest50_fast_2s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=2, groups=1, bottleneck_width=64,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_2s1x64d'], progress=True, check_hash=True))
-    return model
-
-def resnest50_fast_4s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=4, groups=1, bottleneck_width=64,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_4s1x64d'], progress=True, check_hash=True))
-    return model
-
-def resnest50_fast_1s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=1, groups=2, bottleneck_width=40,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_1s2x40d'], progress=True, check_hash=True))
-    return model
-
-def resnest50_fast_2s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=2, groups=2, bottleneck_width=40,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_2s2x40d'], progress=True, check_hash=True))
-    return model
-
-def resnest50_fast_4s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=4, groups=2, bottleneck_width=40,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_4s2x40d'], progress=True, check_hash=True))
-    return model
-
-def resnest50_fast_1s4x24d(pretrained=False, root='~/.encoding/models', **kwargs):
-    model = ResNet(Bottleneck, [3, 4, 6, 3],
-                   radix=1, groups=4, bottleneck_width=24,
-                   deep_stem=True, stem_width=32, avg_down=True,
-                   avd=True, avd_first=True, **kwargs)
-    if pretrained:
-        model.load_state_dict(torch.hub.load_state_dict_from_url(
-            resnest_model_urls['resnest50_fast_1s4x24d'], progress=True, check_hash=True))
-    return model
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/textrecog/satrn/satrn_small.py b/spaces/EuroPython2022/mmocr-demo/configs/textrecog/satrn/satrn_small.py
deleted file mode 100644
index 96f86797f4700fd6ab9590fa983323f3e22d15c2..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/textrecog/satrn/satrn_small.py
+++ /dev/null
@@ -1,68 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py',
-    '../../_base_/recog_pipelines/satrn_pipeline.py',
-    '../../_base_/recog_datasets/ST_MJ_train.py',
-    '../../_base_/recog_datasets/academic_test.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-label_convertor = dict(
-    type='AttnConvertor', dict_type='DICT90', with_unknown=True)
-
-model = dict(
-    type='SATRN',
-    backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=256),
-    encoder=dict(
-        type='SatrnEncoder',
-        n_layers=6,
-        n_head=8,
-        d_k=256 // 8,
-        d_v=256 // 8,
-        d_model=256,
-        n_position=100,
-        d_inner=256 * 4,
-        dropout=0.1),
-    decoder=dict(
-        type='NRTRDecoder',
-        n_layers=6,
-        d_embedding=256,
-        n_head=8,
-        d_model=256,
-        d_inner=256 * 4,
-        d_k=256 // 8,
-        d_v=256 // 8),
-    loss=dict(type='TFLoss'),
-    label_convertor=label_convertor,
-    max_seq_len=25)
-
-# optimizer
-optimizer = dict(type='Adam', lr=3e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[3, 4])
-total_epochs = 6
-
-data = dict(
-    samples_per_gpu=64,
-    workers_per_gpu=4,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
diff --git a/spaces/Facepounder/gpt2-xl/app.py b/spaces/Facepounder/gpt2-xl/app.py
deleted file mode 100644
index 481992366c2da949b59f489ef3204f0f23b6c175..0000000000000000000000000000000000000000
--- a/spaces/Facepounder/gpt2-xl/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/gpt2-xl").launch()
\ No newline at end of file
diff --git a/spaces/GT4SD/moler/README.md b/spaces/GT4SD/moler/README.md
deleted file mode 100644
index 16e6573cb8a90f372cf44628d834400f547cce1e..0000000000000000000000000000000000000000
--- a/spaces/GT4SD/moler/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: MoLeR
-emoji: 💡
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.46.0
-app_file: app.py
-pinned: false
-python_version: 3.8.13
-pypi_version: 20.2.4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/pretrain_resnet.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/pretrain_resnet.py
deleted file mode 100644
index 75b167a9ce9026425f20049b51ebddd3e3997329..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/pretrain_resnet.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import cliport.utils.utils as utils
-
-from cliport.models.resnet import ConvBlock, IdentityBlock
-from torchvision.models import resnet18, resnet34, resnet50
-
-class PretrainedResNet18(nn.Module):
-    def __init__(self, input_shape, output_dim, cfg, device, preprocess):
-        super(PretrainedResNet18, self).__init__()
-        self.input_shape = input_shape
-        self.input_dim = input_shape[-1]
-        self.output_dim = output_dim
-        self.cfg = cfg
-        self.device = device
-        self.batchnorm = self.cfg['train']['batchnorm']
-        self.preprocess = preprocess
-        self.pretrained_model = resnet18(pretrained=True)
-        self.pretrained_model.avgpool = nn.Identity()
-        self.pretrained_model.fc = nn.Identity()
-        # self.pretrained_model.eval()
-        self.pretrained_model.conv1 = nn.Conv2d(self.input_dim, 64, kernel_size=2, stride=1, padding=3, bias=False)
-        # import IPython; IPython.embed()
-        for param in self.pretrained_model.parameters():
-            param.requires_grad = False
-        self.pretrained_model.conv1.weight.requires_grad = True
-
-        self._make_layers()
-
-    def _make_layers(self):
-        # conv1
-        # self.conv1 = nn.Sequential(
-        #     nn.Conv2d(self.input_dim, 64, stride=1, kernel_size=3, padding=1),
-        #     nn.BatchNorm2d(64) if self.batchnorm else nn.Identity(),
-        #     nn.ReLU(True),
-        # )
-
-        # # fcn
-        # self.layer1 = nn.Sequential(
-        #     ConvBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        #     IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        # )
-
-        # self.layer2 = nn.Sequential(
-        #     ConvBlock(64, [128, 128, 128], kernel_size=3, stride=2, batchnorm=self.batchnorm),
-        #     IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        # )
-
-        # self.layer3 = nn.Sequential(
-        #     ConvBlock(128, [256, 256, 256], kernel_size=3, stride=2, batchnorm=self.batchnorm),
-        #     IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        # )
-
-        # self.layer4 = nn.Sequential(
-        #     ConvBlock(256, [512, 512, 512], kernel_size=3, stride=2, batchnorm=self.batchnorm),
-        #     IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        # )
-
-        # self.layer5 = nn.Sequential(
-        #     ConvBlock(512, [1024, 1024, 1024], kernel_size=3, stride=2, batchnorm=self.batchnorm),
-        #     IdentityBlock(1024, [1024, 1024, 1024], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        # )
-
-        # # head
-        # self.layer6 = nn.Sequential(
-        #     ConvBlock(1024, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        #     IdentityBlock(512, [512, 512, 512], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-        #     nn.UpsamplingBilinear2d(scale_factor=2),
-        # )
-
-        self.layer7 = nn.Sequential(
-            ConvBlock(512, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(256, [256, 256, 256], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        self.layer8 = nn.Sequential(
-            ConvBlock(256, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(128, [128, 128, 128], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        self.layer9 = nn.Sequential(
-            ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        self.layer10 = nn.Sequential(
-            ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        # conv2
-        self.conv2 = nn.Sequential(
-            ConvBlock(128, [16, 16, self.output_dim], kernel_size=3, stride=1,
-                      final_relu=False, batchnorm=self.batchnorm),
-            IdentityBlock(self.output_dim, [16, 16, self.output_dim], kernel_size=3, stride=1,
-                          final_relu=False, batchnorm=self.batchnorm)
-        )
-
-    def forward(self, x):
-        x = self.preprocess(x, dist='transporter')
-        in_shape = x.shape
-
-        # # encoder
-        # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4, self.layer5]:
-        #     x = layer(x)
-
-        # # decoder
-        # im = []
-        # for layer in [self.layer6, self.layer7, self.layer8, self.layer9, self.layer10, self.conv2]:
-        #     im.append(x)
-        #     x = layer(x)
-
-        # encoder
-        # for layer in [self.conv1, self.layer1, self.layer2, self.layer3, self.layer4]:
-        #     x = layer(x)
-        # x = x[:, :3, :, :]
-        x = self.pretrained_model.conv1(x)
-        for name, module in self.pretrained_model._modules.items():
-            if name == 'conv1':
-                continue
-            x = module(x)
-            if name == 'layer4':
-                break
-        # with torch.no_grad():
-        #     x = self.pretrained_model(x)
-        # import ipdb;ipdb.set_trace()
-
-        
-        x = F.interpolate(x, size=(8, 8), mode='bilinear')
-
-        # decoder
-        im = []
-        for layer in [self.layer7, self.layer8, self.conv2]:
-            im.append(x)
-            x = layer(x)
-        
-        x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
-        return x, im
\ No newline at end of file
diff --git a/spaces/Gmq-x/gpt-academic/crazy_functions/crazy_functions_test.py b/spaces/Gmq-x/gpt-academic/crazy_functions/crazy_functions_test.py
deleted file mode 100644
index 2838e543977e94c13791a681a5a6b9bb8f4110dc..0000000000000000000000000000000000000000
--- a/spaces/Gmq-x/gpt-academic/crazy_functions/crazy_functions_test.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""
-这是什么？
-    这个文件用于函数插件的单元测试
-    运行方法 python crazy_functions/crazy_functions_test.py
-"""
-
-def validate_path():
-    import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-    os.chdir(root_dir_assume)
-    sys.path.append(root_dir_assume)
-    
-validate_path() # validate path so you can run from base directory
-
-from toolbox import get_conf, ChatBotWithCookies
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
-    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
-
-llm_kwargs = {
-    'api_key': API_KEY,
-    'llm_model': LLM_MODEL,
-    'top_p':1.0, 
-    'max_length': None,
-    'temperature':1.0,
-}
-plugin_kwargs = { }
-chatbot = ChatBotWithCookies(llm_kwargs)
-history = []
-system_prompt = "Serve me as a writing and programming assistant."
-web_port = 1024
-
-
-def test_解析一个Python项目():
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    txt = "crazy_functions/test_project/python/dqn"
-    for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_解析一个Cpp项目():
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    txt = "crazy_functions/test_project/cpp/cppipc"
-    for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Latex英文润色():
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    txt = "crazy_functions/test_project/latex/attention"
-    for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Markdown中译英():
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    txt = "README.md"
-    for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_批量翻译PDF文档():
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_谷歌检索小助手():
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG="
-    for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_总结word文档():
-    from crazy_functions.总结word文档 import 总结word文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_下载arxiv论文并翻译摘要():
-    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-    txt = "1812.10695"
-    for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-test_解析一个Python项目()
-test_Latex英文润色()
-test_Markdown中译英()
-test_批量翻译PDF文档()
-test_谷歌检索小助手()
-test_总结word文档()
-test_下载arxiv论文并翻译摘要()
-test_解析一个Cpp项目()
-
-input("程序完成，回车退出。")
-print("退出。")
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py
deleted file mode 100644
index 2e808d8072f34d09a7b0859f90261dd66c8815dd..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/nonlocal_r50-d8.py',
-    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_20k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/encodec.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/encodec.py
deleted file mode 100644
index 1cf6b54b582975a01bdb7a06280c766d3d2cc72c..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,392 +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.
-"""Compression models or wrapper around existing models.
-Also defines the main interface that a model must follow to be usable as an audio tokenizer.
-"""
-
-from abc import ABC, abstractmethod
-import logging
-import math
-from pathlib import Path
-import typing as tp
-
-import numpy as np
-import torch
-from torch import nn
-from transformers import EncodecModel as HFEncodecModel
-
-from .. import quantization as qt
-
-
-logger = logging.getLogger()
-
-
-class CompressionModel(ABC, nn.Module):
-    """Base API for all compression model that aim at being used as audio tokenizers
-    with a language model.
-    """
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        ...
-
-    @abstractmethod
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """See `EncodecModel.encode`."""
-        ...
-
-    @abstractmethod
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """See `EncodecModel.decode`."""
-        ...
-
-    @abstractmethod
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        ...
-
-    @property
-    @abstractmethod
-    def channels(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> float:
-        ...
-
-    @property
-    @abstractmethod
-    def sample_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def cardinality(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def num_codebooks(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def total_codebooks(self) -> int:
-        ...
-
-    @abstractmethod
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer."""
-        ...
-
-    @staticmethod
-    def get_pretrained(
-            name: str, device: tp.Union[torch.device, str] = 'cpu'
-            ) -> 'CompressionModel':
-        """Instantiate a CompressionModel from a given pretrained model.
-
-        Args:
-            name (Path or str): name of the pretrained model. See after.
-            device (torch.device or str): Device on which the model is loaded.
-
-        Pretrained models:
-            - dac_44khz (https://github.com/descriptinc/descript-audio-codec)
-            - dac_24khz (same)
-            - facebook/encodec_24khz (https://huggingface.co/facebook/encodec_24khz)
-            - facebook/encodec_32khz (https://huggingface.co/facebook/encodec_32khz)
-            - your own model on HugginFace. Export instructions to come...
-        """
-
-        from . import builders, loaders
-        model: CompressionModel
-        if name in ['dac_44khz', 'dac_24khz']:
-            model_type = name.split('_')[1]
-            logger.info("Getting pretrained compression model from DAC %s", model_type)
-            model = DAC(model_type)
-        elif name in ['debug_compression_model']:
-            logger.info("Getting pretrained compression model for debug")
-            model = builders.get_debug_compression_model()
-        elif Path(name).exists():
-            # We assume here if the paths exist that it is in fact an AC checkpoint
-            # that was exported using `audiocraft.utils.export` functions.
-            model = loaders.load_compression_model(name, device=device)
-        else:
-            logger.info("Getting pretrained compression model from HF %s", name)
-            hf_model = HFEncodecModel.from_pretrained(name)
-            model = HFEncodecCompressionModel(hf_model).to(device)
-        return model.to(device).eval()
-
-
-class EncodecModel(CompressionModel):
-    """Encodec model operating on the raw waveform.
-
-    Args:
-        encoder (nn.Module): Encoder network.
-        decoder (nn.Module): Decoder network.
-        quantizer (qt.BaseQuantizer): Quantizer network.
-        frame_rate (int): Frame rate for the latent representation.
-        sample_rate (int): Audio sample rate.
-        channels (int): Number of audio channels.
-        causal (bool): Whether to use a causal version of the model.
-        renormalize (bool): Whether to renormalize the audio before running the model.
-    """
-    # we need assignment to override the property in the abstract class,
-    # I couldn't find a better way...
-    frame_rate: float = 0
-    sample_rate: int = 0
-    channels: int = 0
-
-    def __init__(self,
-                 encoder: nn.Module,
-                 decoder: nn.Module,
-                 quantizer: qt.BaseQuantizer,
-                 frame_rate: int,
-                 sample_rate: int,
-                 channels: int,
-                 causal: bool = False,
-                 renormalize: bool = False):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-        self.frame_rate = frame_rate
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.renormalize = renormalize
-        self.causal = causal
-        if self.causal:
-            # we force disabling here to avoid handling linear overlap of segments
-            # as supported in original EnCodec codebase.
-            assert not self.renormalize, 'Causal model does not support renormalize'
-
-    @property
-    def total_codebooks(self):
-        """Total number of quantizer codebooks available."""
-        return self.quantizer.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer."""
-        return self.quantizer.num_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer."""
-        self.quantizer.set_num_codebooks(n)
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook."""
-        return self.quantizer.bins
-
-    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        scale: tp.Optional[torch.Tensor]
-        if self.renormalize:
-            mono = x.mean(dim=1, keepdim=True)
-            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
-            scale = 1e-8 + volume
-            x = x / scale
-            scale = scale.view(-1, 1)
-        else:
-            scale = None
-        return x, scale
-
-    def postprocess(self,
-                    x: torch.Tensor,
-                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        if scale is not None:
-            assert self.renormalize
-            x = x * scale.view(-1, 1, 1)
-        return x
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        assert x.dim() == 3
-        length = x.shape[-1]
-        x, scale = self.preprocess(x)
-
-        emb = self.encoder(x)
-        q_res = self.quantizer(emb, self.frame_rate)
-        out = self.decoder(q_res.x)
-
-        # remove extra padding added by the encoder and decoder
-        assert out.shape[-1] >= length, (out.shape[-1], length)
-        out = out[..., :length]
-
-        q_res.x = self.postprocess(out, scale)
-
-        return q_res
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Encode the given input tensor to quantized representation along with scale parameter.
-
-        Args:
-            x (torch.Tensor): Float tensor of shape [B, C, T]
-
-        Returns:
-            codes, scale (tuple of torch.Tensor, torch.Tensor): Tuple composed of:
-                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
-                scale a float tensor containing the scale for audio renormalizealization.
-        """
-        assert x.dim() == 3
-        x, scale = self.preprocess(x)
-        emb = self.encoder(x)
-        codes = self.quantizer.encode(emb)
-        return codes, scale
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """Decode the given codes to a reconstructed representation, using the scale to perform
-        audio denormalization if needed.
-
-        Args:
-            codes (torch.Tensor): Int tensor of shape [B, K, T]
-            scale (torch.Tensor, optional): Float tensor containing the scale value.
-
-        Returns:
-            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
-        """
-        emb = self.decode_latent(codes)
-        out = self.decoder(emb)
-        out = self.postprocess(out, scale)
-        # out contains extra padding added by the encoder and decoder
-        return out
-
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        return self.quantizer.decode(codes)
-
-
-class DAC(CompressionModel):
-    def __init__(self, model_type: str = "44khz"):
-        super().__init__()
-        try:
-            import dac.utils
-        except ImportError:
-            raise RuntimeError("Could not import dac, make sure it is installed, "
-                               "please run `pip install descript-audio-codec`")
-        self.model = dac.utils.load_model(model_type=model_type)
-        self.n_quantizers = self.total_codebooks
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        # We don't support training with this.
-        raise NotImplementedError("Forward and training with DAC not supported.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        codes = self.model.encode(x, self.n_quantizers)[1]
-        return codes, None
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        assert scale is None
-        z_q = self.decode_latent(codes)
-        return self.model.decode(z_q)
-
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        return self.model.quantizer.from_codes(codes)[0]
-
-    @property
-    def channels(self) -> int:
-        return 1
-
-    @property
-    def frame_rate(self) -> float:
-        return self.model.sample_rate / self.model.hop_length
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.sample_rate
-
-    @property
-    def cardinality(self) -> int:
-        return self.model.codebook_size
-
-    @property
-    def num_codebooks(self) -> int:
-        return self.n_quantizers
-
-    @property
-    def total_codebooks(self) -> int:
-        return self.model.n_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        assert n >= 1
-        assert n <= self.total_codebooks
-        self.n_quantizers = n
-
-
-class HFEncodecCompressionModel(CompressionModel):
-    """Wrapper around HuggingFace Encodec.
-    """
-    def __init__(self, model: HFEncodecModel):
-        super().__init__()
-        self.model = model
-        bws = self.model.config.target_bandwidths
-        num_codebooks = [
-            bw * 1000 / (self.frame_rate * math.log2(self.cardinality))
-            for bw in bws
-        ]
-        deltas = [nc - int(nc) for nc in num_codebooks]
-        # Checking we didn't do some bad maths and we indeed have integers!
-        assert all(deltas) <= 1e-3, deltas
-        self.possible_num_codebooks = [int(nc) for nc in num_codebooks]
-        self.set_num_codebooks(max(self.possible_num_codebooks))
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        # We don't support training with this.
-        raise NotImplementedError("Forward and training with HF EncodecModel not supported.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        bandwidth_index = self.possible_num_codebooks.index(self.num_codebooks)
-        bandwidth = self.model.config.target_bandwidths[bandwidth_index]
-        res = self.model.encode(x, None, bandwidth)
-        assert len(res[0]) == 1
-        assert len(res[1]) == 1
-        return res[0][0], res[1][0]
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        if scale is None:
-            scales = [None]  # type: ignore
-        else:
-            scales = scale  # type: ignore
-        res = self.model.decode(codes[None], scales)
-        return res[0]
-
-    def decode_latent(self, codes: torch.Tensor):
-        """Decode from the discrete codes to continuous latent space."""
-        return self.model.quantizer.decode(codes.transpose(0, 1))
-
-    @property
-    def channels(self) -> int:
-        return self.model.config.audio_channels
-
-    @property
-    def frame_rate(self) -> float:
-        hop_length = int(np.prod(self.model.config.upsampling_ratios))
-        return self.sample_rate / hop_length
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.config.sampling_rate
-
-    @property
-    def cardinality(self) -> int:
-        return self.model.config.codebook_size
-
-    @property
-    def num_codebooks(self) -> int:
-        return self._num_codebooks
-
-    @property
-    def total_codebooks(self) -> int:
-        return max(self.possible_num_codebooks)
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        if n not in self.possible_num_codebooks:
-            raise ValueError(f"Allowed values for num codebooks: {self.possible_num_codebooks}")
-        self._num_codebooks = n
diff --git a/spaces/Hallucinate/demo/midas/base_model.py b/spaces/Hallucinate/demo/midas/base_model.py
deleted file mode 100644
index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/midas/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/config/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/config/__init__.py
deleted file mode 100644
index 6264236915a7269a4d920ee8213004374dd86a9a..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/config/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py b/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py
deleted file mode 100644
index 4e13b38a5d3fb44dd3969e6afcb8f202274ee3b7..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-import os
-import csv
-import tempfile
-from collections import defaultdict
-from pathlib import Path
-
-import torchaudio
-try:
-    import webrtcvad
-except ImportError:
-    raise ImportError("Please install py-webrtcvad: pip install webrtcvad")
-import pandas as pd
-from tqdm import tqdm
-
-from examples.speech_synthesis.preprocessing.denoiser.pretrained import master64
-import examples.speech_synthesis.preprocessing.denoiser.utils as utils
-from examples.speech_synthesis.preprocessing.vad import (
-    frame_generator, vad_collector, read_wave, write_wave, FS_MS, THRESHOLD,
-    SCALE
-)
-from examples.speech_to_text.data_utils import save_df_to_tsv
-
-
-log = logging.getLogger(__name__)
-
-PATHS = ["after_denoise", "after_vad"]
-MIN_T = 0.05
-
-
-def generate_tmp_filename(extension="txt"):
-    return tempfile._get_default_tempdir() + "/" + \
-           next(tempfile._get_candidate_names()) + "." + extension
-
-
-def convert_sr(inpath, sr, output_path=None):
-    if not output_path:
-        output_path = generate_tmp_filename("wav")
-    cmd = f"sox {inpath} -r {sr} {output_path}"
-    os.system(cmd)
-    return output_path
-
-
-def apply_vad(vad, inpath):
-    audio, sample_rate = read_wave(inpath)
-    frames = frame_generator(FS_MS, audio, sample_rate)
-    frames = list(frames)
-    segments = vad_collector(sample_rate, FS_MS, 300, vad, frames)
-    merge_segments = list()
-    timestamp_start = 0.0
-    timestamp_end = 0.0
-    # removing start, end, and long sequences of sils
-    for i, segment in enumerate(segments):
-        merge_segments.append(segment[0])
-        if i and timestamp_start:
-            sil_duration = segment[1] - timestamp_end
-            if sil_duration > THRESHOLD:
-                merge_segments.append(int(THRESHOLD / SCALE) * (b'\x00'))
-            else:
-                merge_segments.append(int((sil_duration / SCALE)) * (b'\x00'))
-        timestamp_start = segment[1]
-        timestamp_end = segment[2]
-    segment = b''.join(merge_segments)
-    return segment, sample_rate
-
-
-def write(wav, filename, sr=16_000):
-    # Normalize audio if it prevents clipping
-    wav = wav / max(wav.abs().max().item(), 1)
-    torchaudio.save(filename, wav.cpu(), sr, encoding="PCM_S",
-                    bits_per_sample=16)
-
-
-def process(args):
-    # making sure we are requested either denoise or vad
-    if not args.denoise and not args.vad:
-        log.error("No denoise or vad is requested.")
-        return
-
-    log.info("Creating out directories...")
-    if args.denoise:
-        out_denoise = Path(args.output_dir).absolute().joinpath(PATHS[0])
-        out_denoise.mkdir(parents=True, exist_ok=True)
-    if args.vad:
-        out_vad = Path(args.output_dir).absolute().joinpath(PATHS[1])
-        out_vad.mkdir(parents=True, exist_ok=True)
-
-    log.info("Loading pre-trained speech enhancement model...")
-    model = master64().to(args.device)
-
-    log.info("Building the VAD model...")
-    vad = webrtcvad.Vad(int(args.vad_agg_level))
-
-    # preparing the output dict
-    output_dict = defaultdict(list)
-
-    log.info(f"Parsing input manifest: {args.audio_manifest}")
-    with open(args.audio_manifest, "r") as f:
-        manifest_dict = csv.DictReader(f, delimiter="\t")
-        for row in tqdm(manifest_dict):
-            filename = str(row["audio"])
-
-            final_output = filename
-            keep_sample = True
-            n_frames = row["n_frames"]
-            snr = -1
-            if args.denoise:
-                output_path_denoise = out_denoise.joinpath(Path(filename).name)
-                # convert to 16khz in case we use a differet sr
-                tmp_path = convert_sr(final_output, 16000)
-
-                # loading audio file and generating the enhanced version
-                out, sr = torchaudio.load(tmp_path)
-                out = out.to(args.device)
-                estimate = model(out)
-                estimate = (1 - args.dry_wet) * estimate + args.dry_wet * out
-                write(estimate[0], str(output_path_denoise), sr)
-
-                snr = utils.cal_snr(out, estimate)
-                snr = snr.cpu().detach().numpy()[0][0]
-                final_output = str(output_path_denoise)
-
-            if args.vad:
-                output_path_vad = out_vad.joinpath(Path(filename).name)
-                sr = torchaudio.info(final_output).sample_rate
-                if sr in [16000, 32000, 48000]:
-                    tmp_path = final_output
-                elif sr < 16000:
-                    tmp_path = convert_sr(final_output, 16000)
-                elif sr < 32000:
-                    tmp_path = convert_sr(final_output, 32000)
-                else:
-                    tmp_path = convert_sr(final_output, 48000)
-                # apply VAD
-                segment, sample_rate = apply_vad(vad, tmp_path)
-                if len(segment) < sample_rate * MIN_T:
-                    keep_sample = False
-                    print((
-                        f"WARNING: skip {filename} because it is too short "
-                        f"after VAD ({len(segment) / sample_rate} < {MIN_T})"
-                    ))
-                else:
-                    if sample_rate != sr:
-                        tmp_path = generate_tmp_filename("wav")
-                        write_wave(tmp_path, segment, sample_rate)
-                        convert_sr(tmp_path, sr,
-                                   output_path=str(output_path_vad))
-                    else:
-                        write_wave(str(output_path_vad), segment, sample_rate)
-                    final_output = str(output_path_vad)
-                    segment, _ = torchaudio.load(final_output)
-                    n_frames = segment.size(1)
-
-            if keep_sample:
-                output_dict["id"].append(row["id"])
-                output_dict["audio"].append(final_output)
-                output_dict["n_frames"].append(n_frames)
-                output_dict["tgt_text"].append(row["tgt_text"])
-                output_dict["speaker"].append(row["speaker"])
-                output_dict["src_text"].append(row["src_text"])
-                output_dict["snr"].append(snr)
-
-        out_tsv_path = Path(args.output_dir) / Path(args.audio_manifest).name
-        log.info(f"Saving manifest to {out_tsv_path.as_posix()}")
-        save_df_to_tsv(pd.DataFrame.from_dict(output_dict), out_tsv_path)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--audio-manifest", "-i", required=True,
-                        type=str, help="path to the input manifest.")
-    parser.add_argument(
-        "--output-dir", "-o", required=True, type=str,
-        help="path to the output dir. it will contain files after denoising and"
-             " vad"
-    )
-    parser.add_argument("--vad-agg-level", "-a", type=int, default=2,
-                        help="the aggresive level of the vad [0-3].")
-    parser.add_argument(
-        "--dry-wet", "-dw", type=float, default=0.01,
-        help="the level of linear interpolation between noisy and enhanced "
-             "files."
-    )
-    parser.add_argument(
-        "--device", "-d", type=str, default="cpu",
-        help="the device to be used for the speech enhancement model: "
-             "cpu | cuda."
-    )
-    parser.add_argument("--denoise", action="store_true",
-                        help="apply a denoising")
-    parser.add_argument("--vad", action="store_true", help="apply a VAD")
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py
deleted file mode 100644
index 989868388eefccc37c82d7602f709632035c7aa1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-for idx, line in enumerate(sys.stdin):
-    print(f"utt{idx:010d} {line}", end="")
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/lru_cache_dataset.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/lru_cache_dataset.py
deleted file mode 100644
index a7854ac1701392754ce5795cafe9c634671aebdf..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/lru_cache_dataset.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import lru_cache
-
-from . import BaseWrapperDataset
-
-
-class LRUCacheDataset(BaseWrapperDataset):
-    def __init__(self, dataset, token=None):
-        super().__init__(dataset)
-
-    @lru_cache(maxsize=8)
-    def __getitem__(self, index):
-        return self.dataset[index]
-
-    @lru_cache(maxsize=8)
-    def collater(self, samples):
-        return self.dataset.collater(samples)
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/metrics/psnr_ssim.py b/spaces/Iceclear/StableSR/StableSR/basicsr/metrics/psnr_ssim.py
deleted file mode 100644
index ab03113f89805c990ff22795601274bf45db23a1..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/metrics/psnr_ssim.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from basicsr.metrics.metric_util import reorder_image, to_y_channel
-from basicsr.utils.color_util import rgb2ycbcr_pt
-from basicsr.utils.registry import METRIC_REGISTRY
-
-
-@METRIC_REGISTRY.register()
-def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs):
-    """Calculate PSNR (Peak Signal-to-Noise Ratio).
-
-    Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-
-    Args:
-        img (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: PSNR result.
-    """
-
-    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"')
-    img = reorder_image(img, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-
-    if crop_border != 0:
-        img = img[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img = to_y_channel(img)
-        img2 = to_y_channel(img2)
-
-    img = img.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    mse = np.mean((img - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 10. * np.log10(255. * 255. / mse)
-
-
-@METRIC_REGISTRY.register()
-def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs):
-    """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version).
-
-    Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-
-    Args:
-        img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
-        img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
-        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: PSNR result.
-    """
-
-    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
-
-    if crop_border != 0:
-        img = img[:, :, crop_border:-crop_border, crop_border:-crop_border]
-        img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border]
-
-    if test_y_channel:
-        img = rgb2ycbcr_pt(img, y_only=True)
-        img2 = rgb2ycbcr_pt(img2, y_only=True)
-
-    img = img.to(torch.float64)
-    img2 = img2.to(torch.float64)
-
-    mse = torch.mean((img - img2)**2, dim=[1, 2, 3])
-    return 10. * torch.log10(1. / (mse + 1e-8))
-
-
-@METRIC_REGISTRY.register()
-def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs):
-    """Calculate SSIM (structural similarity).
-
-    ``Paper: Image quality assessment: From error visibility to structural similarity``
-
-    The results are the same as that of the official released MATLAB code in
-    https://ece.uwaterloo.ca/~z70wang/research/ssim/.
-
-    For three-channel images, SSIM is calculated for each channel and then
-    averaged.
-
-    Args:
-        img (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: SSIM result.
-    """
-
-    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"')
-    img = reorder_image(img, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-
-    if crop_border != 0:
-        img = img[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img = to_y_channel(img)
-        img2 = to_y_channel(img2)
-
-    img = img.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    ssims = []
-    for i in range(img.shape[2]):
-        ssims.append(_ssim(img[..., i], img2[..., i]))
-    return np.array(ssims).mean()
-
-
-@METRIC_REGISTRY.register()
-def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs):
-    """Calculate SSIM (structural similarity) (PyTorch version).
-
-    ``Paper: Image quality assessment: From error visibility to structural similarity``
-
-    The results are the same as that of the official released MATLAB code in
-    https://ece.uwaterloo.ca/~z70wang/research/ssim/.
-
-    For three-channel images, SSIM is calculated for each channel and then
-    averaged.
-
-    Args:
-        img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
-        img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
-        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: SSIM result.
-    """
-
-    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
-
-    if crop_border != 0:
-        img = img[:, :, crop_border:-crop_border, crop_border:-crop_border]
-        img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border]
-
-    if test_y_channel:
-        img = rgb2ycbcr_pt(img, y_only=True)
-        img2 = rgb2ycbcr_pt(img2, y_only=True)
-
-    img = img.to(torch.float64)
-    img2 = img2.to(torch.float64)
-
-    ssim = _ssim_pth(img * 255., img2 * 255.)
-    return ssim
-
-
-def _ssim(img, img2):
-    """Calculate SSIM (structural similarity) for one channel images.
-
-    It is called by func:`calculate_ssim`.
-
-    Args:
-        img (ndarray): Images with range [0, 255] with order 'HWC'.
-        img2 (ndarray): Images with range [0, 255] with order 'HWC'.
-
-    Returns:
-        float: SSIM result.
-    """
-
-    c1 = (0.01 * 255)**2
-    c2 = (0.03 * 255)**2
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5]  # valid mode for window size 11
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2))
-    return ssim_map.mean()
-
-
-def _ssim_pth(img, img2):
-    """Calculate SSIM (structural similarity) (PyTorch version).
-
-    It is called by func:`calculate_ssim_pt`.
-
-    Args:
-        img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
-        img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
-
-    Returns:
-        float: SSIM result.
-    """
-    c1 = (0.01 * 255)**2
-    c2 = (0.03 * 255)**2
-
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-    window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device)
-
-    mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1])  # valid mode
-    mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1])  # valid mode
-    mu1_sq = mu1.pow(2)
-    mu2_sq = mu2.pow(2)
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq
-    sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq
-    sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2
-
-    cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2)
-    ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map
-    return ssim_map.mean([1, 2, 3])
diff --git a/spaces/Illumotion/Koboldcpp/examples/server/json-schema-to-grammar.mjs.hpp b/spaces/Illumotion/Koboldcpp/examples/server/json-schema-to-grammar.mjs.hpp
deleted file mode 100644
index 0a05c369d77ba58cbd7f85324efe312df5f51916..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/server/json-schema-to-grammar.mjs.hpp
+++ /dev/null
@@ -1,311 +0,0 @@
-unsigned char json_schema_to_grammar_mjs[] = {
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-  0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20,
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-  0x60, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x20, 0x3a, 0x3a, 0x3d,
-  0x20, 0x24, 0x7b, 0x72, 0x75, 0x6c, 0x65, 0x7d, 0x5c, 0x6e, 0x60, 0x3b,
-  0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20,
-  0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x72, 0x61, 0x6d,
-  0x6d, 0x61, 0x72, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a
-};
-unsigned int json_schema_to_grammar_mjs_len = 3695;
diff --git a/spaces/Insightly/web_scraper/app.py b/spaces/Insightly/web_scraper/app.py
deleted file mode 100644
index 3ed3d505c545fb2bc175e439dfeb81fa64f1acaa..0000000000000000000000000000000000000000
--- a/spaces/Insightly/web_scraper/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import streamlit as st
-import requests
-from bs4 import BeautifulSoup
-import re
-
-# Function to scrape only visible text from the given URL
-def scrape_visible_text_from_url(url):
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.content, 'html.parser')
-
-        # Remove script, style, and other non-visible tags
-        for tag in soup(["script", "style", "meta", "link", "noscript", "header", "footer", "aside", "nav", "img"]):
-            tag.extract()
-
-        # Get the header content
-        header_content = soup.find("header")
-        header_text = header_content.get_text() if header_content else ""
-
-        # Get the paragraph content
-        paragraph_content = soup.find_all("p")
-        paragraph_text = " ".join([p.get_text() for p in paragraph_content])
-
-        # Combine header and paragraph text
-        visible_text = f"{header_text}\n\n{paragraph_text}"
-
-        # Remove multiple whitespaces and newlines
-        visible_text = re.sub(r'\s+', ' ', visible_text)
-        return visible_text.strip()
-    except Exception as e:
-        st.error(f"Error occurred while scraping the data: {e}")
-        return None
-
-# Streamlit UI
-def main():
-    st.title("Web Data Scraper")
-
-    # Get the URL from the user
-    url_input = st.text_input("Enter the URL of the web page:", "")
-
-    if st.button("Scrape Visible Text"):
-        if url_input:
-            # Extract visible text from the URL
-            data = scrape_visible_text_from_url(url_input)
-            if data:
-                st.success("Visible text successfully scraped!")
-                st.subheader("Scraped Text:")
-                st.write(data)
-            else:
-                st.warning("Failed to scrape visible text from the URL.")
-        else:
-            st.warning("Please enter a valid URL.")
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/JYskyp/wildcards/style.css b/spaces/JYskyp/wildcards/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/JYskyp/wildcards/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/JammyMachina/the-jam-machine-app/playground.py b/spaces/JammyMachina/the-jam-machine-app/playground.py
deleted file mode 100644
index 6650263f590ba1c6fd22f8f1fa9bb8f5f8725bd0..0000000000000000000000000000000000000000
--- a/spaces/JammyMachina/the-jam-machine-app/playground.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import gradio as gr
-from load import LoadModel
-from generate import GenerateMidiText
-from constants import INSTRUMENT_TRANSFER_CLASSES
-from decoder import TextDecoder
-from utils import get_miditok
-from playback import get_music
-from matplotlib import pylab
-import sys
-import os
-import matplotlib
-from generation_utils import plot_piano_roll
-
-matplotlib.use("Agg")
-
-sys.modules["pylab"] = pylab
-
-model_repo = "JammyMachina/elec-gmusic-familized-model-13-12__17-35-53"
-n_bar_generated = 8
-
-model, tokenizer = LoadModel(
-    model_repo,
-    from_huggingface=True,
-).load_model_and_tokenizer()
-
-miditok = get_miditok()
-decoder = TextDecoder(miditok)
-
-
-def define_prompt(state, genesis):
-    if len(state) == 0:
-        input_prompt = "PIECE_START "
-    else:
-        input_prompt = genesis.get_whole_piece_from_bar_dict()
-    return input_prompt
-
-
-def generator(
-    label,
-    regenerate,
-    temp,
-    density,
-    instrument,
-    state,
-    piece_by_track,
-    add_bars=False,
-    add_bar_count=1,
-):
-    genesis = GenerateMidiText(model, tokenizer, piece_by_track)
-    track = {
-        "label": label,
-        "instrument": instrument,
-        "temperature": temp,
-        "density": density,
-    }
-    inst = next(
-        (
-            inst
-            for inst in INSTRUMENT_TRANSFER_CLASSES
-            if inst["transfer_to"] == instrument
-        ),
-        {"family_number": "DRUMS"},
-    )["family_number"]
-
-    inst_index = -1  # default to last generated
-    if piece_by_track != []:
-        for index, instrum in enumerate(state):
-            if instrum["label"] == track["label"]:
-                inst_index = index  # changing if exists
-
-    # Generate
-    if not add_bars:
-        # Regenerate
-        if regenerate:
-            state.pop(inst_index)
-            genesis.delete_one_track(inst_index)
-
-            generated_text = (
-                genesis.get_whole_piece_from_bar_dict()
-            )  # maybe not useful here
-            inst_index = -1  # reset to last generated
-
-        # NEW TRACK
-        input_prompt = define_prompt(state, genesis)
-        generated_text = genesis.generate_one_new_track(
-            inst, density, temp, input_prompt=input_prompt
-        )
-
-        regenerate = True  # set generate to true
-    else:
-        # NEW BARS
-        genesis.generate_n_more_bars(add_bar_count)  # for all instruments
-        generated_text = genesis.get_whole_piece_from_bar_dict()
-
-    # save the mix midi and get the mix audio
-    decoder.get_midi(generated_text, "mixed.mid")
-    mixed_inst_midi, mixed_audio = get_music("mixed.mid")
-    # get the instrument text MIDI
-    inst_text = genesis.get_whole_track_from_bar_dict(inst_index)
-    # save the instrument midi and get the instrument audio
-    decoder.get_midi(inst_text, f"{instrument}.mid")
-    _, inst_audio = get_music(f"{instrument}.mid")
-    # generate the piano roll
-    piano_roll = plot_piano_roll(mixed_inst_midi)
-    track["text"] = inst_text
-    state.append(track)
-    output_file = "./mixed.mid"
-    return (
-        inst_text,
-        (44100, inst_audio),
-        piano_roll,
-        state,
-        (44100, mixed_audio),
-        regenerate,
-        genesis.piece_by_track,
-        output_file,
-    )
-
-
-def generated_text_from_state(state):
-    generated_text_from_state = "PIECE_START "
-    for track in state:
-        generated_text_from_state += track["text"]
-    return generated_text_from_state
-
-
-def instrument_col(default_inst, col_id):
-    inst_label = gr.Variable(col_id)
-    with gr.Column(scale=1, min_width=100):
-        track_md = gr.Markdown(f"""## TRACK {col_id+1}""")
-        inst = gr.Dropdown(
-            sorted([inst["transfer_to"] for inst in INSTRUMENT_TRANSFER_CLASSES])
-            + ["Drums"],
-            value=default_inst,
-            label="Instrument",
-        )
-        temp = gr.Dropdown(
-            [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
-            value=0.7,
-            label="Creativity",
-        )
-        density = gr.Dropdown([1, 2, 3], value=3, label="Note Density")
-        regenerate = gr.State(
-            value=False
-        )  # initial state should be to generate (not regenerate)
-        gen_btn = gr.Button("Generate")
-        inst_audio = gr.Audio(label="TRACK Audio", show_label=True)
-        output_txt = gr.Textbox(
-            label="output", lines=10, max_lines=10, show_label=False, visible=False
-        )
-
-    gen_btn.click(
-        fn=generator,
-        inputs=[inst_label, regenerate, temp, density, inst, state, piece_by_track],
-        outputs=[
-            output_txt,
-            inst_audio,
-            piano_roll,
-            state,
-            mixed_audio,
-            regenerate,
-            piece_by_track,
-            output_file,
-        ],
-    )
-
-
-with gr.Blocks() as demo:
-    piece_by_track = gr.State([])
-    state = gr.State([])
-    title = gr.Markdown(
-        """ # Demo-App of The-Jam-Machine
-    ## A Generative AI trained on text transcription of MIDI music """
-    )
-
-    description = gr.Markdown(
-        """
-        For each **TRACK**, choose your **instrument** along with **creativity** (temperature) and **note density**. 
-        Then, hit the **Generate** Button, and after a few seconds a track should have been generated. 
-        Check the **piano roll** and listen to the TRACK! If you don't like it, hit the generate button to regenerate it. 
-        You can then generate more tracks and listen to the **mixed audio**! \n
-        Does it sound nice? Maybe a little robotic and laking some depth... Well, you can download the MIDI file and import it in your favorite DAW to edit the instruments and add some effects!
-        Note: Do not try to generate several tracks simultaneously as it will crash the app; wait for one track to be generated before generating another one.    
-        """
-    )
-
-    aud_md = gr.Markdown(f""" ## Mixed Audio, Piano Roll and MIDI Download """)
-    with gr.Row(variant="default"):
-        mixed_audio = gr.Audio(label="Mixed Audio", show_label=False)
-        output_file = gr.File(
-            label="Download",
-            show_label=False,
-        )
-    with gr.Row(variant="compact"):
-        piano_roll = gr.Plot(label="Piano Roll", show_label=False)
-
-    with gr.Row(variant="default"):
-        instrument_col("Drums", 0)
-        instrument_col("Synth Bass 1", 1)
-        instrument_col("Synth Lead Square", 2)
-
-demo.launch(debug=True, server_name="0.0.0.0", share=False)
-"""
-TODO: add improvise button
-TODO: cleanup input output of generator
-TODO: add a way to add bars
-"""
diff --git a/spaces/JeffJing/ZookChatBot/steamship/plugin/blockifier/__init__.py b/spaces/JeffJing/ZookChatBot/steamship/plugin/blockifier/__init__.py
deleted file mode 100644
index 519363be3ab9f5f11f95081b941cc5fc1ab291da..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/plugin/blockifier/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .blockifier import Blockifier
-from .transcriber import Transcriber
-
-__all__ = ["Blockifier", "Transcriber"]
diff --git a/spaces/Joom/Front-end-code-generation-from-images/classes/model/AModel.py b/spaces/Joom/Front-end-code-generation-from-images/classes/model/AModel.py
deleted file mode 100644
index ddfc945e8eaeb0088bcaf1976a59b2c20d114f32..0000000000000000000000000000000000000000
--- a/spaces/Joom/Front-end-code-generation-from-images/classes/model/AModel.py
+++ /dev/null
@@ -1,25 +0,0 @@
-__author__ = 'Taneem Jan, taneemishere.github.io'
-
-from keras.models import model_from_json
-
-
-class AModel:
-    def __init__(self, input_shape, output_size, output_path):
-        self.model = None
-        self.input_shape = input_shape
-        self.output_size = output_size
-        self.output_path = output_path
-        self.name = ""
-
-    def save(self):
-        model_json = self.model.to_json()
-        with open("{}/{}.json".format(self.output_path, self.name), "w") as json_file:
-            json_file.write(model_json)
-        self.model.save_weights("{}/{}.h5".format(self.output_path, self.name))
-
-    def load(self, name=""):
-        output_name = self.name if name == "" else name
-        with open("{}/{}.json".format(self.output_path, output_name), "r") as json_file:
-            loaded_model_json = json_file.read()
-        self.model = model_from_json(loaded_model_json)
-        self.model.load_weights("{}/{}.h5".format(self.output_path, output_name))
diff --git a/spaces/Joom/Front-end-code-generation-from-images/compiler/Compiler.py b/spaces/Joom/Front-end-code-generation-from-images/compiler/Compiler.py
deleted file mode 100644
index 09634392f5feba203b190517936432199f62a2b7..0000000000000000000000000000000000000000
--- a/spaces/Joom/Front-end-code-generation-from-images/compiler/Compiler.py
+++ /dev/null
@@ -1,43 +0,0 @@
-__author__ = 'Taneem Jan, taneemishere.github.io'
-
-import json
-from compiler.Node import *
-
-
-class Compiler:
-    def __init__(self, dsl_mapping_file_path):
-        with open(dsl_mapping_file_path) as data_file:
-            self.dsl_mapping = json.load(data_file)
-
-        self.opening_tag = self.dsl_mapping["opening-tag"]
-        self.closing_tag = self.dsl_mapping["closing-tag"]
-        self.content_holder = self.opening_tag + self.closing_tag
-
-        self.root = Node("body", None, self.content_holder)
-
-    def compile(self, input_file_path, output_file_path, rendering_function=None):
-        dsl_file = open(input_file_path)
-        current_parent = self.root
-
-        for token in dsl_file:
-            token = token.replace(" ", "").replace("\n", "")
-
-            if token.find(self.opening_tag) != -1:
-                token = token.replace(self.opening_tag, "")
-
-                element = Node(token, current_parent, self.content_holder)
-                current_parent.add_child(element)
-                current_parent = element
-            elif token.find(self.closing_tag) != -1:
-                current_parent = current_parent.parent
-            else:
-                tokens = token.split(",")
-                for t in tokens:
-                    element = Node(t, current_parent, self.content_holder)
-                    current_parent.add_child(element)
-
-        output_html = self.root.render(self.dsl_mapping, rendering_function=rendering_function)
-        with open(output_file_path, 'w') as output_file:
-            output_file.write(output_html)
-
-        return output_html
diff --git a/spaces/Justin-Choo/Counterfeit_WEB_UI/README.md b/spaces/Justin-Choo/Counterfeit_WEB_UI/README.md
deleted file mode 100644
index 43ef4896fc3c89ae35f94f2ad880ddd10a5cb240..0000000000000000000000000000000000000000
--- a/spaces/Justin-Choo/Counterfeit_WEB_UI/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Counterfeit Webui on Cpu
-emoji: 🎐
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-python_version: 3.10.6
-duplicated_from: Justin-Chew/Anzu-mix_WEB_UI
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/train/data_utils.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/train/data_utils.py
deleted file mode 100644
index 51a176cceba860acf79157ed0bad2b82c8e80406..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/train/data_utils.py
+++ /dev/null
@@ -1,517 +0,0 @@
-import os
-import traceback
-import logging
-
-logger = logging.getLogger(__name__)
-
-import numpy as np
-import torch
-import torch.utils.data
-
-from infer.lib.train.mel_processing import spectrogram_torch
-from infer.lib.train.utils import load_filepaths_and_text, load_wav_to_torch
-
-
-class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
-    """
-    1) loads audio, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 5000)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
-                lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        file = audiopath_and_text[0]
-        phone = audiopath_and_text[1]
-        pitch = audiopath_and_text[2]
-        pitchf = audiopath_and_text[3]
-        dv = audiopath_and_text[4]
-
-        phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
-        spec, wav = self.get_audio(file)
-        dv = self.get_sid(dv)
-
-        len_phone = phone.size()[0]
-        len_spec = spec.size()[-1]
-        # print(123,phone.shape,pitch.shape,spec.shape)
-        if len_phone != len_spec:
-            len_min = min(len_phone, len_spec)
-            # amor
-            len_wav = len_min * self.hop_length
-
-            spec = spec[:, :len_min]
-            wav = wav[:, :len_wav]
-
-            phone = phone[:len_min, :]
-            pitch = pitch[:len_min]
-            pitchf = pitchf[:len_min]
-
-        return (spec, wav, phone, pitch, pitchf, dv)
-
-    def get_labels(self, phone, pitch, pitchf):
-        phone = np.load(phone)
-        phone = np.repeat(phone, 2, axis=0)
-        pitch = np.load(pitch)
-        pitchf = np.load(pitchf)
-        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
-        # print(234,phone.shape,pitch.shape)
-        phone = phone[:n_num, :]
-        pitch = pitch[:n_num]
-        pitchf = pitchf[:n_num]
-        phone = torch.FloatTensor(phone)
-        pitch = torch.LongTensor(pitch)
-        pitchf = torch.FloatTensor(pitchf)
-        return phone, pitch, pitchf
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} SR doesn't match target {} SR".format(
-                    sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio
-        #        audio_norm = audio / self.max_wav_value
-        #        audio_norm = audio / np.abs(audio).max()
-
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            try:
-                spec = torch.load(spec_filename)
-            except:
-                logger.warn("%s %s", spec_filename, traceback.format_exc())
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-                spec = torch.squeeze(spec, 0)
-                torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        else:
-            spec = spectrogram_torch(
-                audio_norm,
-                self.filter_length,
-                self.sampling_rate,
-                self.hop_length,
-                self.win_length,
-                center=False,
-            )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        return spec, audio_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollateMultiNSFsid:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_spec_len = max([x[0].size(1) for x in batch])
-        max_wave_len = max([x[1].size(1) for x in batch])
-        spec_lengths = torch.LongTensor(len(batch))
-        wave_lengths = torch.LongTensor(len(batch))
-        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
-        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
-        spec_padded.zero_()
-        wave_padded.zero_()
-
-        max_phone_len = max([x[2].size(0) for x in batch])
-        phone_lengths = torch.LongTensor(len(batch))
-        phone_padded = torch.FloatTensor(
-            len(batch), max_phone_len, batch[0][2].shape[1]
-        )  # (spec, wav, phone, pitch)
-        pitch_padded = torch.LongTensor(len(batch), max_phone_len)
-        pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
-        phone_padded.zero_()
-        pitch_padded.zero_()
-        pitchf_padded.zero_()
-        # dv = torch.FloatTensor(len(batch), 256)#gin=256
-        sid = torch.LongTensor(len(batch))
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            spec = row[0]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wave = row[1]
-            wave_padded[i, :, : wave.size(1)] = wave
-            wave_lengths[i] = wave.size(1)
-
-            phone = row[2]
-            phone_padded[i, : phone.size(0), :] = phone
-            phone_lengths[i] = phone.size(0)
-
-            pitch = row[3]
-            pitch_padded[i, : pitch.size(0)] = pitch
-            pitchf = row[4]
-            pitchf_padded[i, : pitchf.size(0)] = pitchf
-
-            # dv[i] = row[5]
-            sid[i] = row[5]
-
-        return (
-            phone_padded,
-            phone_lengths,
-            pitch_padded,
-            pitchf_padded,
-            spec_padded,
-            spec_lengths,
-            wave_padded,
-            wave_lengths,
-            # dv
-            sid,
-        )
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 5000)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text, dv in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text, dv])
-                lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        file = audiopath_and_text[0]
-        phone = audiopath_and_text[1]
-        dv = audiopath_and_text[2]
-
-        phone = self.get_labels(phone)
-        spec, wav = self.get_audio(file)
-        dv = self.get_sid(dv)
-
-        len_phone = phone.size()[0]
-        len_spec = spec.size()[-1]
-        if len_phone != len_spec:
-            len_min = min(len_phone, len_spec)
-            len_wav = len_min * self.hop_length
-            spec = spec[:, :len_min]
-            wav = wav[:, :len_wav]
-            phone = phone[:len_min, :]
-        return (spec, wav, phone, dv)
-
-    def get_labels(self, phone):
-        phone = np.load(phone)
-        phone = np.repeat(phone, 2, axis=0)
-        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
-        phone = phone[:n_num, :]
-        phone = torch.FloatTensor(phone)
-        return phone
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} SR doesn't match target {} SR".format(
-                    sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio
-        #        audio_norm = audio / self.max_wav_value
-        #        audio_norm = audio / np.abs(audio).max()
-
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            try:
-                spec = torch.load(spec_filename)
-            except:
-                logger.warn("%s %s", spec_filename, traceback.format_exc())
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-                spec = torch.squeeze(spec, 0)
-                torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        else:
-            spec = spectrogram_torch(
-                audio_norm,
-                self.filter_length,
-                self.sampling_rate,
-                self.hop_length,
-                self.win_length,
-                center=False,
-            )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        return spec, audio_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_spec_len = max([x[0].size(1) for x in batch])
-        max_wave_len = max([x[1].size(1) for x in batch])
-        spec_lengths = torch.LongTensor(len(batch))
-        wave_lengths = torch.LongTensor(len(batch))
-        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
-        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
-        spec_padded.zero_()
-        wave_padded.zero_()
-
-        max_phone_len = max([x[2].size(0) for x in batch])
-        phone_lengths = torch.LongTensor(len(batch))
-        phone_padded = torch.FloatTensor(
-            len(batch), max_phone_len, batch[0][2].shape[1]
-        )
-        phone_padded.zero_()
-        sid = torch.LongTensor(len(batch))
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            spec = row[0]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wave = row[1]
-            wave_padded[i, :, : wave.size(1)] = wave
-            wave_lengths[i] = wave.size(1)
-
-            phone = row[2]
-            phone_padded[i, : phone.size(0), :] = phone
-            phone_lengths[i] = phone.size(0)
-
-            sid[i] = row[3]
-
-        return (
-            phone_padded,
-            phone_lengths,
-            spec_padded,
-            spec_lengths,
-            wave_padded,
-            wave_lengths,
-            sid,
-        )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, -1, -1):  #
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = (
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/Kedreamix/YoloGesture/utils/utils.py b/spaces/Kedreamix/YoloGesture/utils/utils.py
deleted file mode 100644
index ca0367e33c45ae7de9530f4f08ef1797f142b6da..0000000000000000000000000000000000000000
--- a/spaces/Kedreamix/YoloGesture/utils/utils.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import numpy as np
-from PIL import Image
-
-#---------------------------------------------------------#
-#   将图像转换成RGB图像，防止灰度图在预测时报错。
-#   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
-#---------------------------------------------------------#
-def cvtColor(image):
-    if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
-        return image 
-    else:
-        image = image.convert('RGB')
-        return image 
-    
-#---------------------------------------------------#
-#   对输入图像进行resize
-#---------------------------------------------------#
-def resize_image(image, size, letterbox_image):
-    iw, ih  = image.size
-    w, h    = size
-    if letterbox_image:
-        scale   = min(w/iw, h/ih)
-        nw      = int(iw*scale)
-        nh      = int(ih*scale)
-
-        image   = image.resize((nw,nh), Image.BICUBIC)
-        new_image = Image.new('RGB', size, (128,128,128))
-        new_image.paste(image, ((w-nw)//2, (h-nh)//2))
-    else:
-        new_image = image.resize((w, h), Image.BICUBIC)
-    return new_image
-
-#---------------------------------------------------#
-#   获得类
-#---------------------------------------------------#
-def get_classes(classes_path):
-    with open(classes_path, encoding='utf-8') as f:
-        class_names = f.readlines()
-    class_names = [c.strip() for c in class_names]
-    return class_names, len(class_names)
-
-#---------------------------------------------------#
-#   获得先验框
-#---------------------------------------------------#
-def get_anchors(anchors_path):
-    '''loads the anchors from a file'''
-    with open(anchors_path, encoding='utf-8') as f:
-        anchors = f.readline()
-    anchors = [float(x) for x in anchors.split(',')]
-    anchors = np.array(anchors).reshape(-1, 2)
-    return anchors, len(anchors)
-
-#---------------------------------------------------#
-#   获得学习率
-#---------------------------------------------------#
-def get_lr(optimizer):
-    for param_group in optimizer.param_groups:
-        return param_group['lr']
-
-def preprocess_input(image):
-    image /= 255.0
-    return image
diff --git a/spaces/Kevin676/AutoGPT/autogpt/speech/say.py b/spaces/Kevin676/AutoGPT/autogpt/speech/say.py
deleted file mode 100644
index 727983d12bf334205550a54bcd69a7a36824eda4..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/AutoGPT/autogpt/speech/say.py
+++ /dev/null
@@ -1,41 +0,0 @@
-""" Text to speech module """
-import threading
-from threading import Semaphore
-
-from autogpt.config import Config
-from autogpt.speech.brian import BrianSpeech
-from autogpt.speech.eleven_labs import ElevenLabsSpeech
-from autogpt.speech.gtts import GTTSVoice
-from autogpt.speech.macos_tts import MacOSTTS
-
-CFG = Config()
-DEFAULT_VOICE_ENGINE = GTTSVoice()
-VOICE_ENGINE = None
-if CFG.elevenlabs_api_key:
-    VOICE_ENGINE = ElevenLabsSpeech()
-elif CFG.use_mac_os_tts == "True":
-    VOICE_ENGINE = MacOSTTS()
-elif CFG.use_brian_tts == "True":
-    VOICE_ENGINE = BrianSpeech()
-else:
-    VOICE_ENGINE = GTTSVoice()
-
-
-QUEUE_SEMAPHORE = Semaphore(
-    1
-)  # The amount of sounds to queue before blocking the main thread
-
-
-def say_text(text: str, voice_index: int = 0) -> None:
-    """Speak the given text using the given voice index"""
-
-    def speak() -> None:
-        success = VOICE_ENGINE.say(text, voice_index)
-        if not success:
-            DEFAULT_VOICE_ENGINE.say(text)
-
-        QUEUE_SEMAPHORE.release()
-
-    QUEUE_SEMAPHORE.acquire(True)
-    thread = threading.Thread(target=speak)
-    thread.start()
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/prior_generators/point_generator.py b/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/prior_generators/point_generator.py
deleted file mode 100644
index c87ad656c61cb251bfdfcbd23b1cc5263c68bf5f..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/prior_generators/point_generator.py
+++ /dev/null
@@ -1,321 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-from torch import Tensor
-from torch.nn.modules.utils import _pair
-
-from mmdet.registry import TASK_UTILS
-
-DeviceType = Union[str, torch.device]
-
-
-@TASK_UTILS.register_module()
-class PointGenerator:
-
-    def _meshgrid(self,
-                  x: Tensor,
-                  y: Tensor,
-                  row_major: bool = True) -> Tuple[Tensor, Tensor]:
-        """Generate mesh grid of x and y.
-
-        Args:
-            x (torch.Tensor): Grids of x dimension.
-            y (torch.Tensor): Grids of y dimension.
-            row_major (bool): Whether to return y grids first.
-                Defaults to True.
-
-        Returns:
-            tuple[torch.Tensor]: The mesh grids of x and y.
-        """
-        xx = x.repeat(len(y))
-        yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
-        if row_major:
-            return xx, yy
-        else:
-            return yy, xx
-
-    def grid_points(self,
-                    featmap_size: Tuple[int, int],
-                    stride=16,
-                    device: DeviceType = 'cuda') -> Tensor:
-        """Generate grid points of a single level.
-
-        Args:
-            featmap_size (tuple[int, int]): Size of the feature maps.
-            stride (int): The stride of corresponding feature map.
-            device (str | torch.device): The device the tensor will be put on.
-                Defaults to 'cuda'.
-
-        Returns:
-            torch.Tensor: grid point in a feature map.
-        """
-        feat_h, feat_w = featmap_size
-        shift_x = torch.arange(0., feat_w, device=device) * stride
-        shift_y = torch.arange(0., feat_h, device=device) * stride
-        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
-        stride = shift_x.new_full((shift_xx.shape[0], ), stride)
-        shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1)
-        all_points = shifts.to(device)
-        return all_points
-
-    def valid_flags(self,
-                    featmap_size: Tuple[int, int],
-                    valid_size: Tuple[int, int],
-                    device: DeviceType = 'cuda') -> Tensor:
-        """Generate valid flags of anchors in a feature map.
-
-        Args:
-            featmap_sizes (list(tuple[int, int])): List of feature map sizes in
-                multiple feature levels.
-            valid_shape (tuple[int, int]): The valid shape of the image.
-            device (str | torch.device): Device where the anchors will be
-                put on.
-
-        Return:
-            torch.Tensor: Valid flags of anchors in a level.
-        """
-        feat_h, feat_w = featmap_size
-        valid_h, valid_w = valid_size
-        assert valid_h <= feat_h and valid_w <= feat_w
-        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
-        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
-        valid_x[:valid_w] = 1
-        valid_y[:valid_h] = 1
-        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
-        valid = valid_xx & valid_yy
-        return valid
-
-
-@TASK_UTILS.register_module()
-class MlvlPointGenerator:
-    """Standard points generator for multi-level (Mlvl) feature maps in 2D
-    points-based detectors.
-
-    Args:
-        strides (list[int] | list[tuple[int, int]]): Strides of anchors
-            in multiple feature levels in order (w, h).
-        offset (float): The offset of points, the value is normalized with
-            corresponding stride. Defaults to 0.5.
-    """
-
-    def __init__(self,
-                 strides: Union[List[int], List[Tuple[int, int]]],
-                 offset: float = 0.5) -> None:
-        self.strides = [_pair(stride) for stride in strides]
-        self.offset = offset
-
-    @property
-    def num_levels(self) -> int:
-        """int: number of feature levels that the generator will be applied"""
-        return len(self.strides)
-
-    @property
-    def num_base_priors(self) -> List[int]:
-        """list[int]: The number of priors (points) at a point
-        on the feature grid"""
-        return [1 for _ in range(len(self.strides))]
-
-    def _meshgrid(self,
-                  x: Tensor,
-                  y: Tensor,
-                  row_major: bool = True) -> Tuple[Tensor, Tensor]:
-        yy, xx = torch.meshgrid(y, x)
-        if row_major:
-            # warning .flatten() would cause error in ONNX exporting
-            # have to use reshape here
-            return xx.reshape(-1), yy.reshape(-1)
-
-        else:
-            return yy.reshape(-1), xx.reshape(-1)
-
-    def grid_priors(self,
-                    featmap_sizes: List[Tuple],
-                    dtype: torch.dtype = torch.float32,
-                    device: DeviceType = 'cuda',
-                    with_stride: bool = False) -> List[Tensor]:
-        """Generate grid points of multiple feature levels.
-
-        Args:
-            featmap_sizes (list[tuple]): List of feature map sizes in
-                multiple feature levels, each size arrange as
-                as (h, w).
-            dtype (:obj:`dtype`): Dtype of priors. Defaults to torch.float32.
-            device (str | torch.device): The device where the anchors will be
-                put on.
-            with_stride (bool): Whether to concatenate the stride to
-                the last dimension of points.
-
-        Return:
-            list[torch.Tensor]: Points of  multiple feature levels.
-            The sizes of each tensor should be (N, 2) when with stride is
-            ``False``, where N = width * height, width and height
-            are the sizes of the corresponding feature level,
-            and the last dimension 2 represent (coord_x, coord_y),
-            otherwise the shape should be (N, 4),
-            and the last dimension 4 represent
-            (coord_x, coord_y, stride_w, stride_h).
-        """
-
-        assert self.num_levels == len(featmap_sizes)
-        multi_level_priors = []
-        for i in range(self.num_levels):
-            priors = self.single_level_grid_priors(
-                featmap_sizes[i],
-                level_idx=i,
-                dtype=dtype,
-                device=device,
-                with_stride=with_stride)
-            multi_level_priors.append(priors)
-        return multi_level_priors
-
-    def single_level_grid_priors(self,
-                                 featmap_size: Tuple[int],
-                                 level_idx: int,
-                                 dtype: torch.dtype = torch.float32,
-                                 device: DeviceType = 'cuda',
-                                 with_stride: bool = False) -> Tensor:
-        """Generate grid Points of a single level.
-
-        Note:
-            This function is usually called by method ``self.grid_priors``.
-
-        Args:
-            featmap_size (tuple[int]): Size of the feature maps, arrange as
-                (h, w).
-            level_idx (int): The index of corresponding feature map level.
-            dtype (:obj:`dtype`): Dtype of priors. Defaults to torch.float32.
-            device (str | torch.device): The device the tensor will be put on.
-                Defaults to 'cuda'.
-            with_stride (bool): Concatenate the stride to the last dimension
-                of points.
-
-        Return:
-            Tensor: Points of single feature levels.
-            The shape of tensor should be (N, 2) when with stride is
-            ``False``, where N = width * height, width and height
-            are the sizes of the corresponding feature level,
-            and the last dimension 2 represent (coord_x, coord_y),
-            otherwise the shape should be (N, 4),
-            and the last dimension 4 represent
-            (coord_x, coord_y, stride_w, stride_h).
-        """
-        feat_h, feat_w = featmap_size
-        stride_w, stride_h = self.strides[level_idx]
-        shift_x = (torch.arange(0, feat_w, device=device) +
-                   self.offset) * stride_w
-        # keep featmap_size as Tensor instead of int, so that we
-        # can convert to ONNX correctly
-        shift_x = shift_x.to(dtype)
-
-        shift_y = (torch.arange(0, feat_h, device=device) +
-                   self.offset) * stride_h
-        # keep featmap_size as Tensor instead of int, so that we
-        # can convert to ONNX correctly
-        shift_y = shift_y.to(dtype)
-        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
-        if not with_stride:
-            shifts = torch.stack([shift_xx, shift_yy], dim=-1)
-        else:
-            # use `shape[0]` instead of `len(shift_xx)` for ONNX export
-            stride_w = shift_xx.new_full((shift_xx.shape[0], ),
-                                         stride_w).to(dtype)
-            stride_h = shift_xx.new_full((shift_yy.shape[0], ),
-                                         stride_h).to(dtype)
-            shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h],
-                                 dim=-1)
-        all_points = shifts.to(device)
-        return all_points
-
-    def valid_flags(self,
-                    featmap_sizes: List[Tuple[int, int]],
-                    pad_shape: Tuple[int],
-                    device: DeviceType = 'cuda') -> List[Tensor]:
-        """Generate valid flags of points of multiple feature levels.
-
-        Args:
-            featmap_sizes (list(tuple)): List of feature map sizes in
-                multiple feature levels, each size arrange as
-                as (h, w).
-            pad_shape (tuple(int)): The padded shape of the image,
-                arrange as (h, w).
-            device (str | torch.device): The device where the anchors will be
-                put on.
-
-        Return:
-            list(torch.Tensor): Valid flags of points of multiple levels.
-        """
-        assert self.num_levels == len(featmap_sizes)
-        multi_level_flags = []
-        for i in range(self.num_levels):
-            point_stride = self.strides[i]
-            feat_h, feat_w = featmap_sizes[i]
-            h, w = pad_shape[:2]
-            valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h)
-            valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w)
-            flags = self.single_level_valid_flags((feat_h, feat_w),
-                                                  (valid_feat_h, valid_feat_w),
-                                                  device=device)
-            multi_level_flags.append(flags)
-        return multi_level_flags
-
-    def single_level_valid_flags(self,
-                                 featmap_size: Tuple[int, int],
-                                 valid_size: Tuple[int, int],
-                                 device: DeviceType = 'cuda') -> Tensor:
-        """Generate the valid flags of points of a single feature map.
-
-        Args:
-            featmap_size (tuple[int]): The size of feature maps, arrange as
-                as (h, w).
-            valid_size (tuple[int]): The valid size of the feature maps.
-                The size arrange as as (h, w).
-            device (str | torch.device): The device where the flags will be
-            put on. Defaults to 'cuda'.
-
-        Returns:
-            torch.Tensor: The valid flags of each points in a single level \
-                feature map.
-        """
-        feat_h, feat_w = featmap_size
-        valid_h, valid_w = valid_size
-        assert valid_h <= feat_h and valid_w <= feat_w
-        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
-        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
-        valid_x[:valid_w] = 1
-        valid_y[:valid_h] = 1
-        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
-        valid = valid_xx & valid_yy
-        return valid
-
-    def sparse_priors(self,
-                      prior_idxs: Tensor,
-                      featmap_size: Tuple[int],
-                      level_idx: int,
-                      dtype: torch.dtype = torch.float32,
-                      device: DeviceType = 'cuda') -> Tensor:
-        """Generate sparse points according to the ``prior_idxs``.
-
-        Args:
-            prior_idxs (Tensor): The index of corresponding anchors
-                in the feature map.
-            featmap_size (tuple[int]): feature map size arrange as (w, h).
-            level_idx (int): The level index of corresponding feature
-                map.
-            dtype (obj:`torch.dtype`): Date type of points. Defaults to
-                ``torch.float32``.
-            device (str | torch.device): The device where the points is
-                located.
-        Returns:
-            Tensor: Anchor with shape (N, 2), N should be equal to
-            the length of ``prior_idxs``. And last dimension
-            2 represent (coord_x, coord_y).
-        """
-        height, width = featmap_size
-        x = (prior_idxs % width + self.offset) * self.strides[level_idx][0]
-        y = ((prior_idxs // width) % height +
-             self.offset) * self.strides[level_idx][1]
-        prioris = torch.stack([x, y], 1).to(dtype)
-        prioris = prioris.to(device)
-        return prioris
diff --git a/spaces/KyanChen/RSPrompter/mmpl/engine/logger/builder.py b/spaces/KyanChen/RSPrompter/mmpl/engine/logger/builder.py
deleted file mode 100644
index 14acaabe52bf1e245c17430abbad01b48e71fc25..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpl/engine/logger/builder.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import copy
-import inspect
-from typing import List, Union
-
-import torch
-import torch.nn as nn
-import lightning
-
-from mmengine.config import Config, ConfigDict
-from mmengine.device import is_npu_available
-from mmpl.registry import LOGGERS
-
-
-def register_pl_loggers() -> List[str]:
-    """Register loggers in ``lightning.pytorch.loggers`` to the ``LOGGERS`` registry.
-
-    Returns:
-        List[str]: A list of registered optimizers' name.
-    """
-    pl_loggers = []
-    for module_name in dir(lightning.pytorch.loggers):
-        if module_name.startswith('__'):
-            continue
-        _logger = getattr(lightning.pytorch.loggers, module_name)
-        if inspect.isclass(_logger) and issubclass(_logger, lightning.pytorch.loggers.logger.Logger):
-            LOGGERS.register_module(module=_logger)
-            pl_loggers.append(module_name)
-    return pl_loggers
-
-
-PL_LOGGERS = register_pl_loggers()
-
-
-def register_dadaptation_optimizers() -> List[str]:
-    """Register optimizers in ``dadaptation`` to the ``OPTIMIZERS`` registry.
-
-    Returns:
-        List[str]: A list of registered optimizers' name.
-    """
-    dadaptation_optimizers = []
-    try:
-        import dadaptation
-    except ImportError:
-        pass
-    else:
-        for module_name in ['DAdaptAdaGrad', 'DAdaptAdam', 'DAdaptSGD']:
-            _optim = getattr(dadaptation, module_name)
-            if inspect.isclass(_optim) and issubclass(_optim,
-                                                      torch.optim.Optimizer):
-                OPTIMIZERS.register_module(module=_optim)
-                dadaptation_optimizers.append(module_name)
-    return dadaptation_optimizers
-
-
-# DADAPTATION_OPTIMIZERS = register_dadaptation_optimizers()
-
-
-def register_lion_optimizers() -> List[str]:
-    """Register Lion optimizer to the ``OPTIMIZERS`` registry.
-
-    Returns:
-        List[str]: A list of registered optimizers' name.
-    """
-    optimizers = []
-    try:
-        from lion_pytorch import Lion
-    except ImportError:
-        pass
-    else:
-        OPTIMIZERS.register_module(module=Lion)
-        optimizers.append('Lion')
-    return optimizers
-
-
-# LION_OPTIMIZERS = register_lion_optimizers()
-
-
-def build_optim_wrapper(model: nn.Module,
-                        cfg: Union[dict, Config, ConfigDict]):
-    """Build function of OptimWrapper.
-
-    If ``constructor`` is set in the ``cfg``, this method will build an
-    optimizer wrapper constructor, and use optimizer wrapper constructor to
-    build the optimizer wrapper. If ``constructor`` is not set, the
-    ``DefaultOptimWrapperConstructor`` will be used by default.
-
-    Args:
-        model (nn.Module): Model to be optimized.
-        cfg (dict): Config of optimizer wrapper, optimizer constructor and
-            optimizer.
-
-    Returns:
-        OptimWrapper: The built optimizer wrapper.
-    """
-    optim_wrapper_cfg = copy.deepcopy(cfg)
-    constructor_type = optim_wrapper_cfg.pop('constructor',
-                                             'DefaultOptimWrapperConstructor')
-    paramwise_cfg = optim_wrapper_cfg.pop('paramwise_cfg', None)
-
-    # Since the current generation of NPU(Ascend 910) only supports
-    # mixed precision training, here we turn on mixed precision by default
-    # on the NPU to make the training normal
-    if is_npu_available():
-        optim_wrapper_cfg['type'] = 'AmpOptimWrapper'
-
-    optim_wrapper_constructor = OPTIM_WRAPPER_CONSTRUCTORS.build(
-        dict(
-            type=constructor_type,
-            optim_wrapper_cfg=optim_wrapper_cfg,
-            paramwise_cfg=paramwise_cfg))
-    optim_wrapper = optim_wrapper_constructor(model)
-    return optim_wrapper
diff --git a/spaces/LanguageBind/LanguageBind/training/scheduler.py b/spaces/LanguageBind/LanguageBind/training/scheduler.py
deleted file mode 100644
index fba76fcf1720b11d136a5ab6d3a58ab2fbe42f74..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/training/scheduler.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import numpy as np
-
-
-def assign_learning_rate(optimizer, new_lr):
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = new_lr
-
-
-def _warmup_lr(base_lr, warmup_length, step):
-    return base_lr * (step + 1) / warmup_length
-
-
-def const_lr(optimizer, base_lr, warmup_length, steps):
-    def _lr_adjuster(step):
-        if step < warmup_length:
-            lr = _warmup_lr(base_lr, warmup_length, step)
-        else:
-            lr = base_lr
-        assign_learning_rate(optimizer, lr)
-        return lr
-    return _lr_adjuster
-
-
-def const_lr_cooldown(optimizer, base_lr, warmup_length, steps, cooldown_steps, cooldown_power=1.0, cooldown_end_lr=0.):
-    def _lr_adjuster(step):
-        start_cooldown_step = steps - cooldown_steps
-        if step < warmup_length:
-            lr = _warmup_lr(base_lr, warmup_length, step)
-        else:
-            if step < start_cooldown_step:
-                lr = base_lr
-            else:
-                e = step - start_cooldown_step
-                es = steps - start_cooldown_step
-                # linear decay if power == 1; polynomial decay otherwise;
-                decay = (1 - (e/es)) ** cooldown_power
-                lr = decay * (base_lr - cooldown_end_lr) + cooldown_end_lr
-        assign_learning_rate(optimizer, lr)
-        return lr
-    return _lr_adjuster
-
-
-def cosine_lr(optimizer, base_lr, warmup_length, steps):
-    def _lr_adjuster(step):
-        if step < warmup_length:
-            lr = _warmup_lr(base_lr, warmup_length, step)
-        else:
-            e = step - warmup_length
-            es = steps - warmup_length
-            lr = 0.5 * (1 + np.cos(np.pi * e / es)) * base_lr
-        assign_learning_rate(optimizer, lr)
-        return lr
-    return _lr_adjuster
diff --git a/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/face_detector.py b/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/face_detector.py
deleted file mode 100644
index 1b27e970e34f5dd1a945928b4e8eda171af36dab..0000000000000000000000000000000000000000
--- a/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/face_detector.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import cv2
-import copy
-import re
-import torch
-import numpy as np
-
-from pathlib import Path
-from facelib.detection.yolov5face.models.yolo import Model
-from facelib.detection.yolov5face.utils.datasets import letterbox
-from facelib.detection.yolov5face.utils.general import (
-    check_img_size,
-    non_max_suppression_face,
-    scale_coords,
-    scale_coords_landmarks,
-)
-
-# IS_HIGH_VERSION = tuple(map(int, torch.__version__.split('+')[0].split('.')[:2])) >= (1, 9)
-IS_HIGH_VERSION = [int(m) for m in list(re.findall(r"^([0-9]+)\.([0-9]+)\.([0-9]+)([^0-9][a-zA-Z0-9]*)?(\+git.*)?$",\
-    torch.__version__)[0][:3])] >= [1, 9, 0]
-
-
-def isListempty(inList):
-    if isinstance(inList, list): # Is a list
-        return all(map(isListempty, inList))
-    return False # Not a list
-
-class YoloDetector:
-    def __init__(
-        self,
-        config_name,
-        min_face=10,
-        target_size=None,
-        device='cuda',
-    ):
-        """
-        config_name: name of .yaml config with network configuration from models/ folder.
-        min_face : minimal face size in pixels.
-        target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080.
-                    None for original resolution.
-        """
-        self._class_path = Path(__file__).parent.absolute()
-        self.target_size = target_size
-        self.min_face = min_face
-        self.detector = Model(cfg=config_name)
-        self.device = device
-
-
-    def _preprocess(self, imgs):
-        """
-        Preprocessing image before passing through the network. Resize and conversion to torch tensor.
-        """
-        pp_imgs = []
-        for img in imgs:
-            h0, w0 = img.shape[:2]  # orig hw
-            if self.target_size:
-                r = self.target_size / min(h0, w0)  # resize image to img_size
-                if r < 1:
-                    img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
-
-            imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max())  # check img_size
-            img = letterbox(img, new_shape=imgsz)[0]
-            pp_imgs.append(img)
-        pp_imgs = np.array(pp_imgs)
-        pp_imgs = pp_imgs.transpose(0, 3, 1, 2)
-        pp_imgs = torch.from_numpy(pp_imgs).to(self.device)
-        pp_imgs = pp_imgs.float()  # uint8 to fp16/32
-        return pp_imgs / 255.0  # 0 - 255 to 0.0 - 1.0
-
-    def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
-        """
-        Postprocessing of raw pytorch model output.
-        Returns:
-            bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
-            points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
-        """
-        bboxes = [[] for _ in range(len(origimgs))]
-        landmarks = [[] for _ in range(len(origimgs))]
-
-        pred = non_max_suppression_face(pred, conf_thres, iou_thres)
-
-        for image_id, origimg in enumerate(origimgs):
-            img_shape = origimg.shape
-            image_height, image_width = img_shape[:2]
-            gn = torch.tensor(img_shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]]  # normalization gain landmarks
-            det = pred[image_id].cpu()
-            scale_coords(imgs[image_id].shape[1:], det[:, :4], img_shape).round()
-            scale_coords_landmarks(imgs[image_id].shape[1:], det[:, 5:15], img_shape).round()
-
-            for j in range(det.size()[0]):
-                box = (det[j, :4].view(1, 4) / gn).view(-1).tolist()
-                box = list(
-                    map(int, [box[0] * image_width, box[1] * image_height, box[2] * image_width, box[3] * image_height])
-                )
-                if box[3] - box[1] < self.min_face:
-                    continue
-                lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
-                lm = list(map(int, [i * image_width if j % 2 == 0 else i * image_height for j, i in enumerate(lm)]))
-                lm = [lm[i : i + 2] for i in range(0, len(lm), 2)]
-                bboxes[image_id].append(box)
-                landmarks[image_id].append(lm)
-        return bboxes, landmarks
-
-    def detect_faces(self, imgs, conf_thres=0.7, iou_thres=0.5):
-        """
-        Get bbox coordinates and keypoints of faces on original image.
-        Params:
-            imgs: image or list of images to detect faces on with BGR order (convert to RGB order for inference)
-            conf_thres: confidence threshold for each prediction
-            iou_thres: threshold for NMS (filter of intersecting bboxes)
-        Returns:
-            bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
-            points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
-        """
-        # Pass input images through face detector
-        images = imgs if isinstance(imgs, list) else [imgs]
-        images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images]
-        origimgs = copy.deepcopy(images)
-
-        images = self._preprocess(images)
-        
-        if IS_HIGH_VERSION:
-            with torch.inference_mode():  # for pytorch>=1.9 
-                pred = self.detector(images)[0]
-        else:
-            with torch.no_grad():  # for pytorch<1.9
-                pred = self.detector(images)[0]
-
-        bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres)
-
-        # return bboxes, points
-        if not isListempty(points):
-            bboxes = np.array(bboxes).reshape(-1,4)
-            points = np.array(points).reshape(-1,10)
-            padding = bboxes[:,0].reshape(-1,1)
-            return np.concatenate((bboxes, padding, points), axis=1)
-        else:
-            return None
-
-    def __call__(self, *args):
-        return self.predict(*args)
diff --git a/spaces/Makiing/coolb-in-gtest/src/lib/hooks/chat-history.ts b/spaces/Makiing/coolb-in-gtest/src/lib/hooks/chat-history.ts
deleted file mode 100644
index c6fbf3fecfa86fe553f56acc8253236b8f22a775..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/src/lib/hooks/chat-history.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { zip } from 'lodash-es'
-import { ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { Storage } from '../storage'
-
-/**
- * conversations:$botId => Conversation[]
- * conversation:$botId:$cid:messages => ChatMessageModel[]
- */
-
-interface Conversation {
-  id: string
-  createdAt: number
-}
-
-type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] }
-
-async function loadHistoryConversations(botId: BotId): Promise<Conversation[]> {
-  const key = `conversations:${botId}`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
-  const conversations = await loadHistoryConversations(botId)
-  const newConversations = conversations.filter((c) => c.id !== cid)
-  await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise<ChatMessageModel[]> {
-  const key = `conversation:${botId}:${cid}:messages`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
-  const conversations = await loadHistoryConversations(botId)
-  if (!conversations.some((c) => c.id === cid)) {
-    conversations.unshift({ id: cid, createdAt: Date.now() })
-    await Storage.set({ [`conversations:${botId}`]: conversations })
-  }
-  const key = `conversation:${botId}:${cid}:messages`
-  await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise<ConversationWithMessages[]> {
-  const conversations = await loadHistoryConversations(botId)
-  const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
-  return zip(conversations, messagesList).map(([c, messages]) => ({
-    id: c!.id,
-    createdAt: c!.createdAt,
-    messages: messages!,
-  }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
-  const messages = await loadConversationMessages(botId, conversationId)
-  const newMessages = messages.filter((m) => m.id !== messageId)
-  await setConversationMessages(botId, conversationId, newMessages)
-  if (!newMessages.length) {
-    await deleteHistoryConversation(botId, conversationId)
-  }
-}
diff --git a/spaces/Mashir0/pximg/utils/pixiv.js b/spaces/Mashir0/pximg/utils/pixiv.js
deleted file mode 100644
index ee5b4883662a52e4e4539d4193736e3de9e12f48..0000000000000000000000000000000000000000
--- a/spaces/Mashir0/pximg/utils/pixiv.js
+++ /dev/null
@@ -1,89 +0,0 @@
-const { get } = require('axios').default;
-const PixivAjax = require('./PixivAjax');
-const PixivClient = require('./PixivClient');
-
-const { USER_AGENT } = process.env;
-
-const pixivHeaders = {
-  Referer: 'https://www.pixiv.net',
-};
-if (USER_AGENT) pixivHeaders['User-Agent'] = USER_AGENT;
-
-const getPixivErrorMsg = error =>
-  String((typeof error === 'string' ? error : error.user_message || error.message || error.reason) || '');
-
-const isPixivRateLimitError = error => /rate limit/i.test(getPixivErrorMsg(error));
-
-/**
- *
- * @param {Parameters<import('koa-router').IMiddleware>['0']} ctx
- * @param {string} path
- * @param {Function} [okCb]
- */
-const pixivReverseProxy = async (ctx, path, okCb) => {
-  const { data, status, headers } = await get(path, {
-    baseURL: 'https://i.pximg.net',
-    headers: pixivHeaders,
-    responseType: 'stream',
-    validateStatus: () => true,
-  });
-  ctx.status = status;
-  ['content-type', 'content-length'].forEach(k => headers[k] && ctx.set(k, headers[k]));
-  if (status == 200) {
-    ctx.body = data;
-    ['last-modified', 'expires', 'cache-control'].forEach(k => headers[k] && ctx.set(k, headers[k]));
-    if (typeof okCb === 'function') okCb();
-  } else ctx.set('cache-control', 'max-age=3600');
-};
-
-const pixivClients = [
-  PixivAjax.getClient(process.env.PIXIV_WEB_COOKIE),
-  PixivClient.getClient(process.env.PIXIV_CLIENT_REFRESH_TOKEN),
-].filter(Boolean);
-
-/**
- * @param {string} pid
- * @param {{ language?: string }} [param]
- */
-const getIllustPages = async (pid, { language } = {}) => {
-  if (!pixivClients.length) throw new Error('no available api');
-  let error;
-  for (const client of pixivClients) {
-    try {
-      return client.illustPages(pid, language);
-    } catch (e) {
-      if (!isPixivRateLimitError(e)) throw e;
-      error = e;
-    }
-  }
-  throw error;
-};
-
-const convertTable = {
-  mini: ['/c/48x48/img-master/', '_square1200'],
-  thumb: ['/c/250x250_80_a2/img-master/', '_square1200'],
-  small: ['/c/360x360_70/img-master/', '_master1200'],
-  medium: ['/c/540x540_70/img-master/', '_master1200'],
-  large: ['/c/600x1200_90/img-master/', '_master1200'],
-  regular: ['/img-master/', '_master1200'],
-};
-
-/**
- * @param {string} original
- * @param {string} size
- */
-const convertPage = (original, size) => {
-  if (size in convertTable) {
-    const [first, second] = convertTable[size];
-    return original.replace('/img-original/', first).replace(/(?<=_p\d+)/, second);
-  }
-  return original;
-};
-
-module.exports = {
-  pixivHeaders,
-  pixivReverseProxy,
-  getIllustPages,
-  convertPage,
-  getPixivErrorMsg,
-};
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/remove_lvis_rare.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/remove_lvis_rare.py
deleted file mode 100644
index 06e4e881bfa50e2cd74747511a3ad2e8676e0c70..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/remove_lvis_rare.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--ann', default='datasets/lvis/lvis_v1_train.json')
-    args = parser.parse_args()
-
-    print('Loading', args.ann)
-    data = json.load(open(args.ann, 'r'))
-    catid2freq = {x['id']: x['frequency'] for x in data['categories']}
-    print('ori #anns', len(data['annotations']))
-    exclude = ['r']
-    data['annotations'] = [x for x in data['annotations'] \
-        if catid2freq[x['category_id']] not in exclude]
-    print('filtered #anns', len(data['annotations']))
-    out_path = args.ann[:-5] + '_norare.json'
-    print('Saving to', out_path)
-    json.dump(data, open(out_path, 'w'))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/gather_points.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/gather_points.py
deleted file mode 100644
index f52f1677d8ea0facafc56a3672d37adb44677ff3..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/gather_points.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import torch
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['gather_points_forward', 'gather_points_backward'])
-
-
-class GatherPoints(Function):
-    """Gather points with given index."""
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor,
-                indices: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            features (Tensor): (B, C, N) features to gather.
-            indices (Tensor): (B, M) where M is the number of points.
-
-        Returns:
-            Tensor: (B, C, M) where M is the number of points.
-        """
-        assert features.is_contiguous()
-        assert indices.is_contiguous()
-
-        B, npoint = indices.size()
-        _, C, N = features.size()
-        output = torch.cuda.FloatTensor(B, C, npoint)
-
-        ext_module.gather_points_forward(
-            features, indices, output, b=B, c=C, n=N, npoints=npoint)
-
-        ctx.for_backwards = (indices, C, N)
-        if torch.__version__ != 'parrots':
-            ctx.mark_non_differentiable(indices)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        idx, C, N = ctx.for_backwards
-        B, npoint = idx.size()
-
-        grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
-        grad_out_data = grad_out.data.contiguous()
-        ext_module.gather_points_backward(
-            grad_out_data,
-            idx,
-            grad_features.data,
-            b=B,
-            c=C,
-            n=N,
-            npoints=npoint)
-        return grad_features, None
-
-
-gather_points = GatherPoints.apply
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/utils.py b/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/utils.py
deleted file mode 100644
index 9a9d3b5b66370fa98da9e067ba53ead848ea9a59..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/utils.py
+++ /dev/null
@@ -1,189 +0,0 @@
-"""Utils for monoDepth."""
-import sys
-import re
-import numpy as np
-import cv2
-import torch
-
-
-def read_pfm(path):
-    """Read pfm file.
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        tuple: (data, scale)
-    """
-    with open(path, "rb") as file:
-
-        color = None
-        width = None
-        height = None
-        scale = None
-        endian = None
-
-        header = file.readline().rstrip()
-        if header.decode("ascii") == "PF":
-            color = True
-        elif header.decode("ascii") == "Pf":
-            color = False
-        else:
-            raise Exception("Not a PFM file: " + path)
-
-        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
-        if dim_match:
-            width, height = list(map(int, dim_match.groups()))
-        else:
-            raise Exception("Malformed PFM header.")
-
-        scale = float(file.readline().decode("ascii").rstrip())
-        if scale < 0:
-            # little-endian
-            endian = "<"
-            scale = -scale
-        else:
-            # big-endian
-            endian = ">"
-
-        data = np.fromfile(file, endian + "f")
-        shape = (height, width, 3) if color else (height, width)
-
-        data = np.reshape(data, shape)
-        data = np.flipud(data)
-
-        return data, scale
-
-
-def write_pfm(path, image, scale=1):
-    """Write pfm file.
-
-    Args:
-        path (str): pathto file
-        image (array): data
-        scale (int, optional): Scale. Defaults to 1.
-    """
-
-    with open(path, "wb") as file:
-        color = None
-
-        if image.dtype.name != "float32":
-            raise Exception("Image dtype must be float32.")
-
-        image = np.flipud(image)
-
-        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
-            color = True
-        elif (
-            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
-        ):  # greyscale
-            color = False
-        else:
-            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
-
-        file.write("PF\n" if color else "Pf\n".encode())
-        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
-
-        endian = image.dtype.byteorder
-
-        if endian == "<" or endian == "=" and sys.byteorder == "little":
-            scale = -scale
-
-        file.write("%f\n".encode() % scale)
-
-        image.tofile(file)
-
-
-def read_image(path):
-    """Read image and output RGB image (0-1).
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        array: RGB image (0-1)
-    """
-    img = cv2.imread(path)
-
-    if img.ndim == 2:
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
-
-    return img
-
-
-def resize_image(img):
-    """Resize image and make it fit for network.
-
-    Args:
-        img (array): image
-
-    Returns:
-        tensor: data ready for network
-    """
-    height_orig = img.shape[0]
-    width_orig = img.shape[1]
-
-    if width_orig > height_orig:
-        scale = width_orig / 384
-    else:
-        scale = height_orig / 384
-
-    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
-    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
-
-    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
-
-    img_resized = (
-        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
-    )
-    img_resized = img_resized.unsqueeze(0)
-
-    return img_resized
-
-
-def resize_depth(depth, width, height):
-    """Resize depth map and bring to CPU (numpy).
-
-    Args:
-        depth (tensor): depth
-        width (int): image width
-        height (int): image height
-
-    Returns:
-        array: processed depth
-    """
-    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
-
-    depth_resized = cv2.resize(
-        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
-    )
-
-    return depth_resized
-
-def write_depth(path, depth, bits=1):
-    """Write depth map to pfm and png file.
-
-    Args:
-        path (str): filepath without extension
-        depth (array): depth
-    """
-    write_pfm(path + ".pfm", depth.astype(np.float32))
-
-    depth_min = depth.min()
-    depth_max = depth.max()
-
-    max_val = (2**(8*bits))-1
-
-    if depth_max - depth_min > np.finfo("float").eps:
-        out = max_val * (depth - depth_min) / (depth_max - depth_min)
-    else:
-        out = np.zeros(depth.shape, dtype=depth.type)
-
-    if bits == 1:
-        cv2.imwrite(path + ".png", out.astype("uint8"))
-    elif bits == 2:
-        cv2.imwrite(path + ".png", out.astype("uint16"))
-
-    return
diff --git a/spaces/MercurialAi/OncologyGPT_Temperature_Control/app.py b/spaces/MercurialAi/OncologyGPT_Temperature_Control/app.py
deleted file mode 100644
index 441e40bb86df42c0b3e4512dc410e0f3879fdf89..0000000000000000000000000000000000000000
--- a/spaces/MercurialAi/OncologyGPT_Temperature_Control/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import gradio as gr
-import os
-os.system("pip -qq install openai")
-import openai
-
-EX_Q1 = "What is the best course of treatment for a 65-year-old breast cancer patient with a non-ductal carcinoma and HER-2 positive status? Explain the reasoning behind it. "
-EX_Q2 = "What is the best course of treatment for a 25-year-old non-metastatic breast cancer patient with a Nottingham grade of 5 and HER-2 negative status? Explain the reasoning behind it. "
-EX_Q3 = "What testing must a patient candidate for poly ADP-ribose polymerase (PARP) inhibitor therapy undergo to determine their eligibility?"
-EX_Q4 = "What criteria are used to determine a patient's eligibility for treatment with PARP inhibitors like olaparib and talazoparib for metastatic HER2-negative breast cancer?"
-EX_Q5 = "What should be done before discharging a patient who had a mastectomy with axillary node clearance?"
-EX_Q6 = "What is the best course of treatment for a 60-year-old metastatic breast cancer patient with a tumor size of 3.0 cm, HER-2 negative status, and a tumor grade of 2.0 based on nuclear characteristics? Explain the reasoning behind it. "
-EX_Q7 = "What is the best course of treatment for a 50-year-old breast metastatic breast cancer patient with HER-2 positive status, a tumor size of 2 cm, and the involvement of 2 lymph nodes? Explain the reasoning behind it. "
-
-def get_response(Q, temp):
-
-    # clear cache before generating new response 
-    os.system('huggingface-cli delete-cache')
-
-    response = openai.Completion.create(
-        model="davinci:ft-personal-2023-08-04-01-21-35",
-        prompt=Q+"->",
-        max_tokens=128,
-        temperature=temp
-    )
-
-    response = str(response.choices[0].text)
-
-    # cut response off after end is signified
-    end_index = len(response)
-    end_markers = ["END", "  ", "\n"]
-    for mark in end_markers:
-        if mark in response:
-            end_index = response.index(mark)
-            break
-
-    response = response[:end_index]
-    
-    return response
-
-def bot(Q, history, temp):
-    history = history or []
-    c_history = list(sum(history, ()))
-    c_history.append(Q)
-    c_input = ' '.join(c_history)
-    output = get_response(c_input, temp)
-    history.append((Q, output))
-    return history, history
-
-def get_question_example(qe):
-    return qe
-
-with gr.Blocks() as iFace:
-
-    chatbot = gr.Chatbot()
-    state = gr.State()
-
-    Q = gr.Textbox(show_label=False, placeholder="I'm here to help.").style(container=False)
-
-    question_example = gr.Radio(label="Inquiry Examples", choices=[EX_Q1, EX_Q2, EX_Q3, EX_Q4, EX_Q5, EX_Q6, EX_Q7])
-
-    temp = gr.Slider(minimum=0, maximum=2, value=0.2, step=0.05, label="Temperature", info="Higher temperature produces less consistent responses")
-
-    Q.submit(bot, inputs=[Q, state, temp], outputs=[chatbot, state])
-    question_example.change(get_question_example, inputs=[question_example], outputs=Q)
-
-iFace.launch()
diff --git a/spaces/MertYeter/evrimci/app.py b/spaces/MertYeter/evrimci/app.py
deleted file mode 100644
index a4b5df1222aea29158f3a9dfb37edc9e0d34e34a..0000000000000000000000000000000000000000
--- a/spaces/MertYeter/evrimci/app.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import json
-import os
-import subprocess
-from pathlib import Path
-
-import gradio as gr
-import librosa
-import numpy as np
-import torch
-from demucs.apply import apply_model
-from demucs.pretrained import DEFAULT_MODEL, get_model
-from huggingface_hub import hf_hub_download, list_repo_files
-
-from so_vits_svc_fork.hparams import HParams
-from so_vits_svc_fork.inference.core import Svc
-
-
-###################################################################
-# REPLACE THESE VALUES TO CHANGE THE MODEL REPO/CKPT NAME/SETTINGS
-###################################################################
-# The Hugging Face Hub repo ID
-repo_id = "MertYeter/evrimadam"
-
-# If None, Uses latest ckpt in the repo
-ckpt_name = None
-
-# If None, Uses "kmeans.pt" if it exists in the repo
-cluster_model_name = None
-
-# Set the default f0 type to use - use the one it was trained on.
-# The default for s-vits-svc-fork is "dio".
-# Options: "crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
-default_f0_method = "harvest"
-
-# The default ratio of cluster inference to SVC inference.
-# If cluster_model_name is not found in the repo, this is set to 0.
-default_cluster_infer_ratio = 0.5
-
-# Limit on duration of audio at inference time. increase if you can
-# In this parent app, we set the limit with an env var to 30 seconds
-# If you didnt set env var + you go OOM try changing 9e9 to <=300ish
-duration_limit = int(os.environ.get("MAX_DURATION_SECONDS", 9e9))
-###################################################################
-
-# Figure out the latest generator by taking highest value one.
-# Ex. if the repo has: G_0.pth, G_100.pth, G_200.pth, we'd use G_200.pth
-if ckpt_name is None:
-    latest_id = sorted(
-        [
-            int(Path(x).stem.split("_")[1])
-            for x in list_repo_files(repo_id)
-            if x.startswith("G_") and x.endswith(".pth")
-        ]
-    )[-1]
-    ckpt_name = f"G_{latest_id}.pth"
-
-cluster_model_name = cluster_model_name or "kmeans.pt"
-if cluster_model_name in list_repo_files(repo_id):
-    print(f"Found Cluster model - Downloading {cluster_model_name} from {repo_id}")
-    cluster_model_path = hf_hub_download(repo_id, cluster_model_name)
-else:
-    print(f"Could not find {cluster_model_name} in {repo_id}. Using None")
-    cluster_model_path = None
-default_cluster_infer_ratio = default_cluster_infer_ratio if cluster_model_path else 0
-
-generator_path = hf_hub_download(repo_id, ckpt_name)
-config_path = hf_hub_download(repo_id, "config.json")
-hparams = HParams(**json.loads(Path(config_path).read_text()))
-speakers = list(hparams.spk.keys())
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = Svc(net_g_path=generator_path, config_path=config_path, device=device, cluster_model_path=cluster_model_path)
-demucs_model = get_model(DEFAULT_MODEL)
-
-
-def extract_vocal_demucs(model, filename, sr=44100, device=None, shifts=1, split=True, overlap=0.25, jobs=0):
-    wav, sr = librosa.load(filename, mono=False, sr=sr)
-    wav = torch.tensor(wav)
-    ref = wav.mean(0)
-    wav = (wav - ref.mean()) / ref.std()
-    sources = apply_model(
-        model, wav[None], device=device, shifts=shifts, split=split, overlap=overlap, progress=True, num_workers=jobs
-    )[0]
-    sources = sources * ref.std() + ref.mean()
-    # We take just the vocals stem. I know the vocals for this model are at index -1
-    # If using different model, check model.sources.index('vocals')
-    vocal_wav = sources[-1]
-    # I did this because its the same normalization the so-vits model required
-    vocal_wav = vocal_wav / max(1.01 * vocal_wav.abs().max(), 1)
-    vocal_wav = vocal_wav.numpy()
-    vocal_wav = librosa.to_mono(vocal_wav)
-    vocal_wav = vocal_wav.T
-    instrumental_wav = sources[:-1].sum(0).numpy().T
-    return vocal_wav, instrumental_wav
-
-
-def download_youtube_clip(
-    video_identifier,
-    start_time,
-    end_time,
-    output_filename,
-    num_attempts=5,
-    url_base="https://www.youtube.com/watch?v=",
-    quiet=False,
-    force=False,
-):
-    output_path = Path(output_filename)
-    if output_path.exists():
-        if not force:
-            return output_path
-        else:
-            output_path.unlink()
-
-    quiet = "--quiet --no-warnings" if quiet else ""
-    command = f"""
-        yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}"  # noqa: E501
-    """.strip()
-
-    attempts = 0
-    while True:
-        try:
-            _ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
-        except subprocess.CalledProcessError:
-            attempts += 1
-            if attempts == num_attempts:
-                return None
-        else:
-            break
-
-    if output_path.exists():
-        return output_path
-    else:
-        return None
-
-
-def predict(
-    speaker,
-    audio,
-    transpose: int = 0,
-    auto_predict_f0: bool = False,
-    cluster_infer_ratio: float = 0,
-    noise_scale: float = 0.4,
-    f0_method: str = "crepe",
-    db_thresh: int = -40,
-    pad_seconds: float = 0.5,
-    chunk_seconds: float = 0.5,
-    absolute_thresh: bool = False,
-):
-    audio, _ = librosa.load(audio, sr=model.target_sample, duration=duration_limit)
-    audio = model.infer_silence(
-        audio.astype(np.float32),
-        speaker=speaker,
-        transpose=transpose,
-        auto_predict_f0=auto_predict_f0,
-        cluster_infer_ratio=cluster_infer_ratio,
-        noise_scale=noise_scale,
-        f0_method=f0_method,
-        db_thresh=db_thresh,
-        pad_seconds=pad_seconds,
-        chunk_seconds=chunk_seconds,
-        absolute_thresh=absolute_thresh,
-    )
-    return model.target_sample, audio
-
-SPACE_ID = "nateraw/voice-cloning"
-description ="MertY"
-
-article =""
-
-
-interface_mic = gr.Interface(
-    predict,
-    inputs=[
-        gr.Dropdown(speakers, value=speakers[0], label="Target Speaker"),
-        gr.Audio(type="filepath", source="microphone", label="Source Audio"),
-        gr.Slider(-12, 12, value=0, step=1, label="Transpose (Semitones)"),
-        gr.Checkbox(False, label="Auto Predict F0"),
-        gr.Slider(0.0, 1.0, value=default_cluster_infer_ratio, step=0.1, label="cluster infer ratio"),
-        gr.Slider(0.0, 1.0, value=0.4, step=0.1, label="noise scale"),
-        gr.Dropdown(
-            choices=["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
-            value=default_f0_method,
-            label="f0 method",
-        ),
-    ],
-    outputs="audio",
-    title="Voice Cloning",
-    description= str(ckpt_name),
-    article=article,
-)
-interface_file = gr.Interface(
-    predict,
-    inputs=[
-        gr.Dropdown(speakers, value=speakers[0], label="Target Speaker"),
-        gr.Audio(type="filepath", source="upload", label="Source Audio"),
-        gr.Slider(-12, 12, value=0, step=1, label="Transpose (Semitones)"),
-        gr.Checkbox(False, label="Auto Predict F0"),
-        gr.Slider(0.0, 1.0, value=default_cluster_infer_ratio, step=0.1, label="cluster infer ratio"),
-        gr.Slider(0.0, 1.0, value=0.4, step=0.1, label="noise scale"),
-        gr.Dropdown(
-            choices=["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
-            value=default_f0_method,
-            label="f0 method",
-        ),
-    ],
-    outputs="audio",
-    title="Voice Cloning",
-    description= str(ckpt_name) ,
-    article=article,
-)
-interface = gr.TabbedInterface(
-    [interface_mic, interface_file],
-    ["Clone From Mic", "Clone From File"],
-)
-
-
-if __name__ == "__main__":
-    interface.launch()
\ No newline at end of file
diff --git a/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
deleted file mode 100644
index b2b88e8c46f19b6db0933163e57ccdb51180f517..0000000000000000000000000000000000000000
--- a/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.h>
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-} // namespace groundingdino
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/maerec/_base_marec_vit_s.py b/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/maerec/_base_marec_vit_s.py
deleted file mode 100644
index 06febd0884209d0e317931f85a4415ce4ac020da..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/maerec/_base_marec_vit_s.py
+++ /dev/null
@@ -1,159 +0,0 @@
-dictionary = dict(
-    type='Dictionary',
-    dict_file=  # noqa
-    '{{ fileDirname }}/../../../dicts/english_digits_symbols_space.txt',
-    with_padding=True,
-    with_unknown=True,
-    same_start_end=True,
-    with_start=True,
-    with_end=True)
-
-model = dict(
-    type='MAERec',
-    backbone=dict(
-        type='VisionTransformer',
-        img_size=(32, 128),
-        patch_size=(4, 4),
-        embed_dim=384,
-        depth=12,
-        num_heads=6,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        pretrained=None),
-    decoder=dict(
-        type='MAERecDecoder',
-        n_layers=6,
-        d_embedding=384,
-        n_head=8,
-        d_model=384,
-        d_inner=384 * 4,
-        d_k=48,
-        d_v=48,
-        postprocessor=dict(type='AttentionPostprocessor'),
-        module_loss=dict(
-            type='CEModuleLoss', reduction='mean', ignore_first_char=True),
-        max_seq_len=48,
-        dictionary=dictionary),
-    data_preprocessor=dict(
-        type='TextRecogDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375]))
-
-train_pipeline = [
-    dict(type='LoadImageFromFile', ignore_empty=True, min_size=0),
-    dict(type='LoadOCRAnnotations', with_text=True),
-    dict(type='Resize', scale=(128, 32)),
-    dict(
-        type='RandomApply',
-        prob=0.5,
-        transforms=[
-            dict(
-                type='RandomChoice',
-                transforms=[
-                    dict(
-                        type='RandomRotate',
-                        max_angle=15,
-                    ),
-                    dict(
-                        type='TorchVisionWrapper',
-                        op='RandomAffine',
-                        degrees=15,
-                        translate=(0.3, 0.3),
-                        scale=(0.5, 2.),
-                        shear=(-45, 45),
-                    ),
-                    dict(
-                        type='TorchVisionWrapper',
-                        op='RandomPerspective',
-                        distortion_scale=0.5,
-                        p=1,
-                    ),
-                ])
-        ],
-    ),
-    dict(
-        type='RandomApply',
-        prob=0.25,
-        transforms=[
-            dict(type='PyramidRescale'),
-            dict(
-                type='mmdet.Albu',
-                transforms=[
-                    dict(type='GaussNoise', var_limit=(20, 20), p=0.5),
-                    dict(type='MotionBlur', blur_limit=7, p=0.5),
-                ]),
-        ]),
-    dict(
-        type='RandomApply',
-        prob=0.25,
-        transforms=[
-            dict(
-                type='TorchVisionWrapper',
-                op='ColorJitter',
-                brightness=0.5,
-                saturation=0.5,
-                contrast=0.5,
-                hue=0.1),
-        ]),
-    dict(
-        type='PackTextRecogInputs',
-        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='Resize', scale=(128, 32)),
-    # add loading annotation after ``Resize`` because ground truth
-    # does not need to do resize data transform
-    dict(type='LoadOCRAnnotations', with_text=True),
-    dict(
-        type='PackTextRecogInputs',
-        meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
-]
-
-tta_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='TestTimeAug',
-        transforms=[
-            [
-                dict(
-                    type='ConditionApply',
-                    true_transforms=[
-                        dict(
-                            type='ImgAugWrapper',
-                            args=[dict(cls='Rot90', k=0, keep_size=False)])
-                    ],
-                    condition="results['img_shape'][1]<results['img_shape'][0]"
-                ),
-                dict(
-                    type='ConditionApply',
-                    true_transforms=[
-                        dict(
-                            type='ImgAugWrapper',
-                            args=[dict(cls='Rot90', k=1, keep_size=False)])
-                    ],
-                    condition="results['img_shape'][1]<results['img_shape'][0]"
-                ),
-                dict(
-                    type='ConditionApply',
-                    true_transforms=[
-                        dict(
-                            type='ImgAugWrapper',
-                            args=[dict(cls='Rot90', k=3, keep_size=False)])
-                    ],
-                    condition="results['img_shape'][1]<results['img_shape'][0]"
-                ),
-            ],
-            [dict(type='Resize', scale=(128, 32))],
-            # add loading annotation after ``Resize`` because ground truth
-            # does not need to do resize data transform
-            [dict(type='LoadOCRAnnotations', with_text=True)],
-            [
-                dict(
-                    type='PackTextRecogInputs',
-                    meta_keys=('img_path', 'ori_shape', 'img_shape',
-                               'valid_ratio'))
-            ]
-        ])
-]
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/rezero_transformer.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/rezero_transformer.py
deleted file mode 100644
index 42bc1af0031db97a12a730b8e1abe98f3c9318e0..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/layers/rezero_transformer.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Keras-based rezero-transformer block layer (Transformer with ReZero)."""
-# pylint: disable=g-classes-have-attributes
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import gin
-import tensorflow as tf
-
-from official.nlp.modeling.layers import attention
-from official.nlp.modeling.layers import dense_einsum
-
-
-@tf.keras.utils.register_keras_serializable(package="Text")
-@gin.configurable
-class ReZeroTransformer(tf.keras.layers.Layer):
-  """Transformer layer with ReZero.
-
-  This layer implements the Transformer from "Attention Is All You Need".
-  (https://arxiv.org/abs/1706.03762).
-  The residual connection implements the ReZero method.
-  (https://arxiv.org/abs/2003.04887)
-
-  Arguments:
-    num_attention_heads: Number of attention heads.
-    intermediate_size: Size of the intermediate layer.
-    intermediate_activation: Activation for the intermediate layer.
-    dropout_rate: Dropout probability for the post-attention and output dropout.
-    attention_dropout_rate: Dropout probability for within the attention layer.
-    output_range: the sequence output range, [0, output_range) by slicing the
-      target sequence. `None` means the target sequence is not sliced.
-    kernel_initializer: Initializer for dense layer kernels.
-    bias_initializer: Initializer for dense layer biases.
-    kernel_regularizer: Regularizer for dense layer kernels.
-    bias_regularizer: Regularizer for dense layer biases.
-    activity_regularizer: Regularizer for dense layer activity.
-    kernel_constraint: Constraint for dense layer kernels.
-    bias_constraint: Constraint for dense layer kernels.
-    use_layer_norm: If add layer_norm on top of the ReZero.
-  """
-
-  def __init__(self,
-               num_attention_heads,
-               intermediate_size,
-               intermediate_activation,
-               dropout_rate=0.0,
-               attention_dropout_rate=0.0,
-               output_range=None,
-               kernel_initializer="glorot_uniform",
-               bias_initializer="zeros",
-               kernel_regularizer=None,
-               bias_regularizer=None,
-               activity_regularizer=None,
-               kernel_constraint=None,
-               bias_constraint=None,
-               use_layer_norm=False,
-               **kwargs):
-    super(ReZeroTransformer, self).__init__(**kwargs)
-
-    self._num_heads = num_attention_heads
-    self._intermediate_size = intermediate_size
-    self._intermediate_activation = intermediate_activation
-    self._attention_dropout_rate = attention_dropout_rate
-    self._dropout_rate = dropout_rate
-    self._output_range = output_range
-    self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
-    self._bias_initializer = tf.keras.initializers.get(bias_initializer)
-    self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
-    self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
-    self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
-    self._bias_constraint = tf.keras.constraints.get(bias_constraint)
-    self._use_layer_norm = use_layer_norm
-
-  def build(self, input_shape):
-    input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
-    input_tensor_shape = tf.TensorShape(input_tensor)
-    if len(input_tensor_shape) != 3:
-      raise ValueError("TransformerLayer expects a three-dimensional input of "
-                       "shape [batch, sequence, width].")
-    batch_size, sequence_length, hidden_size = input_tensor_shape
-
-    if len(input_shape) == 2:
-      mask_tensor_shape = tf.TensorShape(input_shape[1])
-      expected_mask_tensor_shape = tf.TensorShape(
-          [batch_size, sequence_length, sequence_length])
-      if not expected_mask_tensor_shape.is_compatible_with(mask_tensor_shape):
-        raise ValueError("When passing a mask tensor to TransformerLayer, the "
-                         "mask tensor must be of shape [batch, "
-                         "sequence_length, sequence_length] (here %s). Got a "
-                         "mask tensor of shape %s." %
-                         (expected_mask_tensor_shape, mask_tensor_shape))
-    if hidden_size % self._num_heads != 0:
-      raise ValueError(
-          "The input size (%d) is not a multiple of the number of attention "
-          "heads (%d)" % (hidden_size, self._num_heads))
-    self._attention_head_size = int(hidden_size // self._num_heads)
-
-    self._attention_layer = attention.MultiHeadAttention(
-        num_heads=self._num_heads,
-        key_size=self._attention_head_size,
-        dropout=self._attention_dropout_rate,
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        name="self_attention")
-    self._attention_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
-    if self._use_layer_norm:
-      # Use float32 in layernorm for numeric stability.
-      # It is probably safe in mixed_float16, but we haven't validated this yet.
-      self._attention_layer_norm = (
-          tf.keras.layers.LayerNormalization(
-              name="self_attention_layer_norm",
-              axis=-1,
-              epsilon=1e-12,
-              dtype=tf.float32))
-    self._intermediate_dense = dense_einsum.DenseEinsum(
-        output_shape=self._intermediate_size,
-        activation=None,
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        name="intermediate")
-    policy = tf.keras.mixed_precision.experimental.global_policy()
-    if policy.name == "mixed_bfloat16":
-      # bfloat16 causes BERT with the LAMB optimizer to not converge
-      # as well, so we use float32.
-      # TODO(b/154538392): Investigate this.
-      policy = tf.float32
-    self._intermediate_activation_layer = tf.keras.layers.Activation(
-        self._intermediate_activation, dtype=policy)
-    self._output_dense = dense_einsum.DenseEinsum(
-        output_shape=hidden_size,
-        kernel_initializer=self._kernel_initializer,
-        bias_initializer=self._bias_initializer,
-        kernel_regularizer=self._kernel_regularizer,
-        bias_regularizer=self._bias_regularizer,
-        activity_regularizer=self._activity_regularizer,
-        kernel_constraint=self._kernel_constraint,
-        bias_constraint=self._bias_constraint,
-        name="output")
-    self._output_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
-    if self._use_layer_norm:
-      # Use float32 in layernorm for numeric stability.
-      self._output_layer_norm = tf.keras.layers.LayerNormalization(
-          name="output_layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32)
-
-    self._rezero_a = self.add_weight(
-        name="rezero_alpha",
-        initializer=tf.keras.initializers.Zeros(),
-        trainable=True, dtype=tf.float32)
-
-    super(ReZeroTransformer, self).build(input_shape)
-
-  def get_config(self):
-    config = {
-        "num_attention_heads":
-            self._num_heads,
-        "intermediate_size":
-            self._intermediate_size,
-        "intermediate_activation":
-            self._intermediate_activation,
-        "dropout_rate":
-            self._dropout_rate,
-        "attention_dropout_rate":
-            self._attention_dropout_rate,
-        "output_range":
-            self._output_range,
-        "use_layer_norm":
-            self._use_layer_norm,
-        "kernel_initializer":
-            tf.keras.initializers.serialize(self._kernel_initializer),
-        "bias_initializer":
-            tf.keras.initializers.serialize(self._bias_initializer),
-        "kernel_regularizer":
-            tf.keras.regularizers.serialize(self._kernel_regularizer),
-        "bias_regularizer":
-            tf.keras.regularizers.serialize(self._bias_regularizer),
-        "activity_regularizer":
-            tf.keras.regularizers.serialize(self._activity_regularizer),
-        "kernel_constraint":
-            tf.keras.constraints.serialize(self._kernel_constraint),
-        "bias_constraint":
-            tf.keras.constraints.serialize(self._bias_constraint),
-    }
-    base_config = super(ReZeroTransformer, self).get_config()
-    return dict(list(base_config.items()) + list(config.items()))
-
-  def reset_rezero(self):
-    self._rezero_a.assign(0.)
-
-  def call(self, inputs):
-    if isinstance(inputs, (list, tuple)) and len(inputs) == 2:
-      input_tensor, attention_mask = inputs
-    else:
-      input_tensor, attention_mask = (inputs, None)
-
-    if self._output_range:
-      target_tensor = input_tensor[:, 0:self._output_range, :]
-      attention_mask = attention_mask[:, 0:self._output_range, :]
-    else:
-      target_tensor = input_tensor
-    attention_inputs = [target_tensor, input_tensor]
-
-    attention_output = self._attention_layer(attention_inputs, attention_mask)
-    attention_output = self._attention_dropout(attention_output)
-    attention_output = target_tensor + self._rezero_a * attention_output
-    if self._use_layer_norm:
-      attention_output = self._attention_layer_norm(attention_output)
-    else:
-      attention_output = tf.cast(attention_output, tf.float32)
-
-    intermediate_output = self._intermediate_dense(attention_output)
-    intermediate_output = self._intermediate_activation_layer(
-        intermediate_output)
-    layer_output = self._output_dense(intermediate_output)
-    layer_output = self._output_dropout(layer_output)
-    # During mixed precision training, attention_output is from layer norm and
-    # is always fp32 for now. Cast layer_output to fp32 for the subsequent add.
-    layer_output = attention_output + tf.cast(self._rezero_a * layer_output,
-                                              tf.float32)
-    if self._use_layer_norm:
-      layer_output = self._output_layer_norm(layer_output)
-
-    return layer_output
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/ffn_layer.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/ffn_layer.py
deleted file mode 100644
index a7785f27dd0c3fed01c514d052749dcafd163605..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/ffn_layer.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Implementation of fully connected network."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-
-class FeedForwardNetwork(tf.keras.layers.Layer):
-  """Fully connected feedforward network."""
-
-  def __init__(self, hidden_size, filter_size, relu_dropout):
-    """Initialize FeedForwardNetwork.
-
-    Args:
-      hidden_size: int, output dim of hidden layer.
-      filter_size: int, filter size for the inner (first) dense layer.
-      relu_dropout: float, dropout rate for training.
-    """
-    super(FeedForwardNetwork, self).__init__()
-    self.hidden_size = hidden_size
-    self.filter_size = filter_size
-    self.relu_dropout = relu_dropout
-
-  def build(self, input_shape):
-    self.filter_dense_layer = tf.keras.layers.Dense(
-        self.filter_size,
-        use_bias=True,
-        activation=tf.nn.relu,
-        name="filter_layer")
-    self.output_dense_layer = tf.keras.layers.Dense(
-        self.hidden_size, use_bias=True, name="output_layer")
-    super(FeedForwardNetwork, self).build(input_shape)
-
-  def get_config(self):
-    return {
-        "hidden_size": self.hidden_size,
-        "filter_size": self.filter_size,
-        "relu_dropout": self.relu_dropout,
-    }
-
-  def call(self, x, training):
-    """Return outputs of the feedforward network.
-
-    Args:
-      x: tensor with shape [batch_size, length, hidden_size]
-      training: boolean, whether in training mode or not.
-
-    Returns:
-      Output of the feedforward network.
-      tensor with shape [batch_size, length, hidden_size]
-    """
-    # Retrieve dynamically known shapes
-    batch_size = tf.shape(x)[0]
-    length = tf.shape(x)[1]
-
-    output = self.filter_dense_layer(x)
-    if training:
-      output = tf.nn.dropout(output, rate=self.relu_dropout)
-    output = self.output_dense_layer(output)
-
-    return output
diff --git a/spaces/NN520/AI/tests/parse.ts b/spaces/NN520/AI/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()
diff --git a/spaces/Natsha/mocap-ai/app.py b/spaces/Natsha/mocap-ai/app.py
deleted file mode 100644
index 168258cef7ad9ca4d20700beafa420d29ca14dc1..0000000000000000000000000000000000000000
--- a/spaces/Natsha/mocap-ai/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Import standard libs.
-import tempfile
-import os
-from pathlib import Path
-
-# Import primary libs.
-import streamlit as st
-
-# Import custom libs.
-import fbx_handler
-
-
-def process_file(file: Path) -> int:
-    fbx_content = fbx_handler.FBXContainer(file)
-    return 1
-
-
-# Initialize session state variables if they don't exist
-if "uploaded_files" not in st.session_state:
-    st.session_state.uploaded_files = {}
-if "processed_files" not in st.session_state:
-    st.session_state.processed_files = {}
-
-st.title('Optical MoCap AI Processing')
-
-st.write('Select FBX files to upload and process. This will extract all marker animation data and turn it into a csv.')
-
-new_uploaded_files = st.file_uploader('Select FBX files', accept_multiple_files=True, type='fbx', label_visibility='collapsed')
-
-for uploaded_file in new_uploaded_files:
-    if uploaded_file.name not in st.session_state.processed_files.keys():
-        st.session_state.uploaded_files[uploaded_file.name] = uploaded_file
-
-if st.session_state.uploaded_files and st.button("Process Files"):
-    progress_bar = st.progress(0)
-    # Create a temporary directory to store the newly uploaded files
-    with tempfile.TemporaryDirectory() as temp_dir:
-        incr = 1. / len(st.session_state.uploaded_files)
-
-        for idx, (name, uploaded_file) in enumerate(st.session_state.uploaded_files.items()):
-            # Save the uploaded file to the temporary directory
-            temp_path = Path(os.path.join(temp_dir, name))
-            with open(temp_path, "wb") as f:
-                f.write(uploaded_file.getbuffer())
-                print(f'[LOAD FBX] Finished uploading {temp_path}.')
-                # Process the file and append the resulting DataFrame to dataframes
-                st.session_state.processed_files[name] = process_file(temp_path)
-                progress_bar.progress((idx+1) * incr, f'Processing {name}')
-
-        st.session_state.uploaded_files = {}
-        st.experimental_rerun()
-
-for name in list(st.session_state.processed_files.keys()):
-    new_file_name = name.replace('.fbx', '.csv')
-    if st.download_button(
-            label=f"Download {new_file_name}",
-            data=st.session_state.processed_files[name],
-            file_name=new_file_name,
-            mime='text/csv'):
-        del st.session_state.processed_files[name]
-        st.experimental_rerun()
-
-if st.button('Delete cache', type='primary'):
-    st.session_state.uploaded_files = {}
-    st.session_state.processed_files = {}
-    st.experimental_rerun()
diff --git a/spaces/NeuML/articlesummary/README.md b/spaces/NeuML/articlesummary/README.md
deleted file mode 100644
index deca94a4e0ef77c14f3f84b5c63e48ad88b13dcf..0000000000000000000000000000000000000000
--- a/spaces/NeuML/articlesummary/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Articlesummary
-emoji: 🔥
-colorFrom: gray
-colorTo: green
-sdk: streamlit
-sdk_version: 1.24.0
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/Ntabukiraniro/Recipe/model.py b/spaces/Ntabukiraniro/Recipe/model.py
deleted file mode 100644
index 02cc18dc63ad104aa3b283897be18c4a35510a87..0000000000000000000000000000000000000000
--- a/spaces/Ntabukiraniro/Recipe/model.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import torch
-import torch.nn as nn
-import random
-import numpy as np
-from modules.encoder import EncoderCNN, EncoderLabels
-from modules.transformer_decoder import DecoderTransformer
-from modules.multihead_attention import MultiheadAttention
-from utils.metrics import softIoU, MaskedCrossEntropyCriterion
-import pickle
-import os
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-
-def label2onehot(labels, pad_value):
-
-    # input labels to one hot vector
-    inp_ = torch.unsqueeze(labels, 2)
-    one_hot = torch.FloatTensor(labels.size(0), labels.size(1), pad_value + 1).zero_().to(device)
-    one_hot.scatter_(2, inp_, 1)
-    one_hot, _ = one_hot.max(dim=1)
-    # remove pad position
-    one_hot = one_hot[:, :-1]
-    # eos position is always 0
-    one_hot[:, 0] = 0
-
-    return one_hot
-
-
-def mask_from_eos(ids, eos_value, mult_before=True):
-    mask = torch.ones(ids.size()).to(device).byte()
-    mask_aux = torch.ones(ids.size(0)).to(device).byte()
-
-    # find eos in ingredient prediction
-    for idx in range(ids.size(1)):
-        # force mask to have 1s in the first position to avoid division by 0 when predictions start with eos
-        if idx == 0:
-            continue
-        if mult_before:
-            mask[:, idx] = mask[:, idx] * mask_aux
-            mask_aux = mask_aux * (ids[:, idx] != eos_value)
-        else:
-            mask_aux = mask_aux * (ids[:, idx] != eos_value)
-            mask[:, idx] = mask[:, idx] * mask_aux
-    return mask
-
-
-def get_model(args, ingr_vocab_size, instrs_vocab_size):
-
-    # build ingredients embedding
-    encoder_ingrs = EncoderLabels(args.embed_size, ingr_vocab_size,
-                                  args.dropout_encoder, scale_grad=False).to(device)
-    # build image model
-    encoder_image = EncoderCNN(args.embed_size, args.dropout_encoder, args.image_model)
-
-    decoder = DecoderTransformer(args.embed_size, instrs_vocab_size,
-                                 dropout=args.dropout_decoder_r, seq_length=args.maxseqlen,
-                                 num_instrs=args.maxnuminstrs,
-                                 attention_nheads=args.n_att, num_layers=args.transf_layers,
-                                 normalize_before=True,
-                                 normalize_inputs=False,
-                                 last_ln=False,
-                                 scale_embed_grad=False)
-
-    ingr_decoder = DecoderTransformer(args.embed_size, ingr_vocab_size, dropout=args.dropout_decoder_i,
-                                      seq_length=args.maxnumlabels,
-                                      num_instrs=1, attention_nheads=args.n_att_ingrs,
-                                      pos_embeddings=False,
-                                      num_layers=args.transf_layers_ingrs,
-                                      learned=False,
-                                      normalize_before=True,
-                                      normalize_inputs=True,
-                                      last_ln=True,
-                                      scale_embed_grad=False)
-    # recipe loss
-    criterion = MaskedCrossEntropyCriterion(ignore_index=[instrs_vocab_size-1], reduce=False)
-
-    # ingredients loss
-    label_loss = nn.BCELoss(reduce=False)
-    eos_loss = nn.BCELoss(reduce=False)
-
-    model = InverseCookingModel(encoder_ingrs, decoder, ingr_decoder, encoder_image,
-                                crit=criterion, crit_ingr=label_loss, crit_eos=eos_loss,
-                                pad_value=ingr_vocab_size-1,
-                                ingrs_only=args.ingrs_only, recipe_only=args.recipe_only,
-                                label_smoothing=args.label_smoothing_ingr)
-
-    return model
-
-
-class InverseCookingModel(nn.Module):
-    def __init__(self, ingredient_encoder, recipe_decoder, ingr_decoder, image_encoder,
-                 crit=None, crit_ingr=None, crit_eos=None,
-                 pad_value=0, ingrs_only=True,
-                 recipe_only=False, label_smoothing=0.0):
-
-        super(InverseCookingModel, self).__init__()
-
-        self.ingredient_encoder = ingredient_encoder
-        self.recipe_decoder = recipe_decoder
-        self.image_encoder = image_encoder
-        self.ingredient_decoder = ingr_decoder
-        self.crit = crit
-        self.crit_ingr = crit_ingr
-        self.pad_value = pad_value
-        self.ingrs_only = ingrs_only
-        self.recipe_only = recipe_only
-        self.crit_eos = crit_eos
-        self.label_smoothing = label_smoothing
-
-    def forward(self, img_inputs, captions, target_ingrs,
-                sample=False, keep_cnn_gradients=False):
-
-        if sample:
-            return self.sample(img_inputs, greedy=True)
-
-        targets = captions[:, 1:]
-        targets = targets.contiguous().view(-1)
-
-        img_features = self.image_encoder(img_inputs, keep_cnn_gradients)
-
-        losses = {}
-        target_one_hot = label2onehot(target_ingrs, self.pad_value)
-        target_one_hot_smooth = label2onehot(target_ingrs, self.pad_value)
-
-        # ingredient prediction
-        if not self.recipe_only:
-            target_one_hot_smooth[target_one_hot_smooth == 1] = (1-self.label_smoothing)
-            target_one_hot_smooth[target_one_hot_smooth == 0] = self.label_smoothing / target_one_hot_smooth.size(-1)
-
-            # decode ingredients with transformer
-            # autoregressive mode for ingredient decoder
-            ingr_ids, ingr_logits = self.ingredient_decoder.sample(None, None, greedy=True,
-                                                                   temperature=1.0, img_features=img_features,
-                                                                   first_token_value=0, replacement=False)
-
-            ingr_logits = torch.nn.functional.softmax(ingr_logits, dim=-1)
-
-            # find idxs for eos ingredient
-            # eos probability is the one assigned to the first position of the softmax
-            eos = ingr_logits[:, :, 0]
-            target_eos = ((target_ingrs == 0) ^ (target_ingrs == self.pad_value))
-
-            eos_pos = (target_ingrs == 0)
-            eos_head = ((target_ingrs != self.pad_value) & (target_ingrs != 0))
-
-            # select transformer steps to pool from
-            mask_perminv = mask_from_eos(target_ingrs, eos_value=0, mult_before=False)
-            ingr_probs = ingr_logits * mask_perminv.float().unsqueeze(-1)
-
-            ingr_probs, _ = torch.max(ingr_probs, dim=1)
-
-            # ignore predicted ingredients after eos in ground truth
-            ingr_ids[mask_perminv == 0] = self.pad_value
-
-            ingr_loss = self.crit_ingr(ingr_probs, target_one_hot_smooth)
-            ingr_loss = torch.mean(ingr_loss, dim=-1)
-
-            losses['ingr_loss'] = ingr_loss
-
-            # cardinality penalty
-            losses['card_penalty'] = torch.abs((ingr_probs*target_one_hot).sum(1) - target_one_hot.sum(1)) + \
-                                     torch.abs((ingr_probs*(1-target_one_hot)).sum(1))
-
-            eos_loss = self.crit_eos(eos, target_eos.float())
-
-            mult = 1/2
-            # eos loss is only computed for timesteps <= t_eos and equally penalizes 0s and 1s
-            losses['eos_loss'] = mult*(eos_loss * eos_pos.float()).sum(1) / (eos_pos.float().sum(1) + 1e-6) + \
-                                 mult*(eos_loss * eos_head.float()).sum(1) / (eos_head.float().sum(1) + 1e-6)
-            # iou
-            pred_one_hot = label2onehot(ingr_ids, self.pad_value)
-            # iou sample during training is computed using the true eos position
-            losses['iou'] = softIoU(pred_one_hot, target_one_hot)
-
-        if self.ingrs_only:
-            return losses
-
-        # encode ingredients
-        target_ingr_feats = self.ingredient_encoder(target_ingrs)
-        target_ingr_mask = mask_from_eos(target_ingrs, eos_value=0, mult_before=False)
-
-        target_ingr_mask = target_ingr_mask.float().unsqueeze(1)
-
-        outputs, ids = self.recipe_decoder(target_ingr_feats, target_ingr_mask, captions, img_features)
-
-        outputs = outputs[:, :-1, :].contiguous()
-        outputs = outputs.view(outputs.size(0) * outputs.size(1), -1)
-
-        loss = self.crit(outputs, targets)
-
-        losses['recipe_loss'] = loss
-
-        return losses
-
-    def sample(self, img_inputs, greedy=True, temperature=1.0, beam=-1, true_ingrs=None):
-
-        outputs = dict()
-
-        img_features = self.image_encoder(img_inputs)
-
-        if not self.recipe_only:
-            ingr_ids, ingr_probs = self.ingredient_decoder.sample(None, None, greedy=True, temperature=temperature,
-                                                                  beam=-1,
-                                                                  img_features=img_features, first_token_value=0,
-                                                                  replacement=False)
-
-            # mask ingredients after finding eos
-            sample_mask = mask_from_eos(ingr_ids, eos_value=0, mult_before=False)
-            ingr_ids[sample_mask == 0] = self.pad_value
-
-            outputs['ingr_ids'] = ingr_ids
-            outputs['ingr_probs'] = ingr_probs.data
-
-            mask = sample_mask
-            input_mask = mask.float().unsqueeze(1)
-            input_feats = self.ingredient_encoder(ingr_ids)
-
-        if self.ingrs_only:
-            return outputs
-
-        # option during sampling to use the real ingredients and not the predicted ones to infer the recipe
-        if true_ingrs is not None:
-            input_mask = mask_from_eos(true_ingrs, eos_value=0, mult_before=False)
-            true_ingrs[input_mask == 0] = self.pad_value
-            input_feats = self.ingredient_encoder(true_ingrs)
-            input_mask = input_mask.unsqueeze(1)
-
-        ids, probs = self.recipe_decoder.sample(input_feats, input_mask, greedy, temperature, beam, img_features, 0,
-                                                last_token_value=1)
-
-        outputs['recipe_probs'] = probs.data
-        outputs['recipe_ids'] = ids
-
-        return outputs
diff --git a/spaces/OAOA/DifFace/basicsr/archs/rcan_arch.py b/spaces/OAOA/DifFace/basicsr/archs/rcan_arch.py
deleted file mode 100644
index 48872e6800006d885f56f90dd2f0a2bd16e513d9..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/archs/rcan_arch.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import torch
-from torch import nn as nn
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .arch_util import Upsample, make_layer
-
-
-class ChannelAttention(nn.Module):
-    """Channel attention used in RCAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        squeeze_factor (int): Channel squeeze factor. Default: 16.
-    """
-
-    def __init__(self, num_feat, squeeze_factor=16):
-        super(ChannelAttention, self).__init__()
-        self.attention = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),
-            nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid())
-
-    def forward(self, x):
-        y = self.attention(x)
-        return x * y
-
-
-class RCAB(nn.Module):
-    """Residual Channel Attention Block (RCAB) used in RCAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        squeeze_factor (int): Channel squeeze factor. Default: 16.
-        res_scale (float): Scale the residual. Default: 1.
-    """
-
-    def __init__(self, num_feat, squeeze_factor=16, res_scale=1):
-        super(RCAB, self).__init__()
-        self.res_scale = res_scale
-
-        self.rcab = nn.Sequential(
-            nn.Conv2d(num_feat, num_feat, 3, 1, 1), nn.ReLU(True), nn.Conv2d(num_feat, num_feat, 3, 1, 1),
-            ChannelAttention(num_feat, squeeze_factor))
-
-    def forward(self, x):
-        res = self.rcab(x) * self.res_scale
-        return res + x
-
-
-class ResidualGroup(nn.Module):
-    """Residual Group of RCAB.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_block (int): Block number in the body network.
-        squeeze_factor (int): Channel squeeze factor. Default: 16.
-        res_scale (float): Scale the residual. Default: 1.
-    """
-
-    def __init__(self, num_feat, num_block, squeeze_factor=16, res_scale=1):
-        super(ResidualGroup, self).__init__()
-
-        self.residual_group = make_layer(
-            RCAB, num_block, num_feat=num_feat, squeeze_factor=squeeze_factor, res_scale=res_scale)
-        self.conv = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-
-    def forward(self, x):
-        res = self.conv(self.residual_group(x))
-        return res + x
-
-
-@ARCH_REGISTRY.register()
-class RCAN(nn.Module):
-    """Residual Channel Attention Networks.
-
-    ``Paper: Image Super-Resolution Using Very Deep Residual Channel Attention Networks``
-
-    Reference: https://github.com/yulunzhang/RCAN
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64.
-        num_group (int): Number of ResidualGroup. Default: 10.
-        num_block (int): Number of RCAB in ResidualGroup. Default: 16.
-        squeeze_factor (int): Channel squeeze factor. Default: 16.
-        upscale (int): Upsampling factor. Support 2^n and 3.
-            Default: 4.
-        res_scale (float): Used to scale the residual in residual block.
-            Default: 1.
-        img_range (float): Image range. Default: 255.
-        rgb_mean (tuple[float]): Image mean in RGB orders.
-            Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
-    """
-
-    def __init__(self,
-                 num_in_ch,
-                 num_out_ch,
-                 num_feat=64,
-                 num_group=10,
-                 num_block=16,
-                 squeeze_factor=16,
-                 upscale=4,
-                 res_scale=1,
-                 img_range=255.,
-                 rgb_mean=(0.4488, 0.4371, 0.4040)):
-        super(RCAN, self).__init__()
-
-        self.img_range = img_range
-        self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(
-            ResidualGroup,
-            num_group,
-            num_feat=num_feat,
-            num_block=num_block,
-            squeeze_factor=squeeze_factor,
-            res_scale=res_scale)
-        self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.upsample = Upsample(upscale, num_feat)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-    def forward(self, x):
-        self.mean = self.mean.type_as(x)
-
-        x = (x - self.mean) * self.img_range
-        x = self.conv_first(x)
-        res = self.conv_after_body(self.body(x))
-        res += x
-
-        x = self.conv_last(self.upsample(res))
-        x = x / self.img_range + self.mean
-
-        return x
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/datasets/asr_prep_json.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/datasets/asr_prep_json.py
deleted file mode 100644
index b8db8ff16691158fae034a8ab3faad622b351caf..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/datasets/asr_prep_json.py
+++ /dev/null
@@ -1,125 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import argparse
-import concurrent.futures
-import json
-import multiprocessing
-import os
-from collections import namedtuple
-from itertools import chain
-
-import sentencepiece as spm
-from fairseq.data import Dictionary
-
-
-MILLISECONDS_TO_SECONDS = 0.001
-
-
-def process_sample(aud_path, lable, utt_id, sp, tgt_dict):
-    import torchaudio
-
-    input = {}
-    output = {}
-    si, ei = torchaudio.info(aud_path)
-    input["length_ms"] = int(
-        si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS
-    )
-    input["path"] = aud_path
-
-    token = " ".join(sp.EncodeAsPieces(lable))
-    ids = tgt_dict.encode_line(token, append_eos=False)
-    output["text"] = lable
-    output["token"] = token
-    output["tokenid"] = ", ".join(map(str, [t.tolist() for t in ids]))
-    return {utt_id: {"input": input, "output": output}}
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--audio-dirs",
-        nargs="+",
-        default=["-"],
-        required=True,
-        help="input directories with audio files",
-    )
-    parser.add_argument(
-        "--labels",
-        required=True,
-        help="aggregated input labels with format <ID LABEL> per line",
-        type=argparse.FileType("r", encoding="UTF-8"),
-    )
-    parser.add_argument(
-        "--spm-model",
-        required=True,
-        help="sentencepiece model to use for encoding",
-        type=argparse.FileType("r", encoding="UTF-8"),
-    )
-    parser.add_argument(
-        "--dictionary",
-        required=True,
-        help="file to load fairseq dictionary from",
-        type=argparse.FileType("r", encoding="UTF-8"),
-    )
-    parser.add_argument("--audio-format", choices=["flac", "wav"], default="wav")
-    parser.add_argument(
-        "--output",
-        required=True,
-        type=argparse.FileType("w"),
-        help="path to save json output",
-    )
-    args = parser.parse_args()
-
-    sp = spm.SentencePieceProcessor()
-    sp.Load(args.spm_model.name)
-
-    tgt_dict = Dictionary.load(args.dictionary)
-
-    labels = {}
-    for line in args.labels:
-        (utt_id, label) = line.split(" ", 1)
-        labels[utt_id] = label
-    if len(labels) == 0:
-        raise Exception("No labels found in ", args.labels_path)
-
-    Sample = namedtuple("Sample", "aud_path utt_id")
-    samples = []
-    for path, _, files in chain.from_iterable(
-        os.walk(path) for path in args.audio_dirs
-    ):
-        for f in files:
-            if f.endswith(args.audio_format):
-                if len(os.path.splitext(f)) != 2:
-                    raise Exception("Expect <utt_id.extension> file name. Got: ", f)
-                utt_id = os.path.splitext(f)[0]
-                if utt_id not in labels:
-                    continue
-                samples.append(Sample(os.path.join(path, f), utt_id))
-
-    utts = {}
-    num_cpu = multiprocessing.cpu_count()
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_cpu) as executor:
-        future_to_sample = {
-            executor.submit(
-                process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict
-            ): s
-            for s in samples
-        }
-        for future in concurrent.futures.as_completed(future_to_sample):
-            try:
-                data = future.result()
-            except Exception as exc:
-                print("generated an exception: ", exc)
-            else:
-                utts.update(data)
-    json.dump({"utts": utts}, args.output, indent=4)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh
deleted file mode 100644
index c2edcefede2da3b6a991b9c8fbc78c96d46d27cb..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/env bash
-
-langdir=""
-lmdir=""
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-
-arpa_lm=$1
-data=$2
-
-if [ -z $langdir ]; then
-  langdir=$data/lang
-fi
-if [ -z $lmdir ]; then
-  lmdir=$data/lang_test
-fi
-
-if [ ! -d $langdir ]; then
-  echo "$langdir not found. run local/prepare_lang.sh first" && exit 1
-fi
-
-mkdir -p $lmdir
-cp -r $langdir/* $lmdir
-
-if [[ "$arpa_lm" == *.gz ]]; then
-  gunzip -c $arpa_lm | arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt - $lmdir/G.fst
-else
-  arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt $arpa_lm $lmdir/G.fst
-fi
-fstisstochastic $lmdir/G.fst
-utils/validate_lang.pl $lmdir || exit 1
-
-echo "done preparing lm ($lmdir)"
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/speech_to_text/utils.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/speech_to_text/utils.py
deleted file mode 100644
index 168b8bf13b0e734eee3f6989ff0f28a016a09c2b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/speech_to_text/utils.py
+++ /dev/null
@@ -1,563 +0,0 @@
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-
-import logging
-from collections.abc import Iterable
-from itertools import repeat
-from typing import List, Optional, Tuple
-
-import torch
-from torch import Tensor
-
-
-# ------------------------------------------------------------------------------
-#   assert_equal()
-# ------------------------------------------------------------------------------
-
-
-def assert_equal(value1, value2, name1=None, name2=None):
-    """Asserts two values are equal otherwise raise an error."""
-
-    str_name1 = "" if name1 is None else "{} ".format(name1)
-    str_name2 = "" if name2 is None else "{} ".format(name2)
-    if value1 != value2:
-        str_value1 = "{}" if name1 is None else "({})"
-        str_value1 = str_value1.format(value1)
-        str_value2 = "{}" if name2 is None else "({})"
-        str_value2 = str_value2.format(value2)
-        raise ValueError(
-            "Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2)
-        )
-
-
-def fill_config(config, key, value):
-    if value is not None:
-        if key not in config or config[key] is None:
-            config[key] = value
-        assert_equal(value, config[key], "value", f'config["{key}"]')
-
-
-# ------------------------------------------------------------------------------
-#   check_and_return_expected()
-# ------------------------------------------------------------------------------
-
-
-def check_and_return_expected(value, undefined_value, expected_value, name=None):
-    """
-    Return the expected value while checking if the given value is undefined or
-    equal to the expected value.
-    """
-    if (undefined_value is None and value is None) or (undefined_value == value):
-        return expected_value
-    if value != expected_value:
-        str_name = "" if name is None else "{} ".format(name)
-        str_value = "{}" if name is None else "({})"
-        str_value = str_value.format(value)
-        raise ValueError(
-            "Expected {}{} == {}".format(str_name, str_value, expected_value)
-        )
-    return expected_value
-
-
-# ------------------------------------------------------------------------------
-#   get_time_axis()
-# ------------------------------------------------------------------------------
-
-
-def get_time_axis(layout):
-    """
-    Extract the time axis from the layout, for example for breaking sequence into
-    segments.
-    """
-    if layout in ["TB", "TBD"]:
-        return 0
-    if layout in ["BT", "BTD"]:
-        return 1
-    if layout in ["BCTD"]:
-        return 2
-    raise ValueError("Unsupported layout = {}".format(layout))
-
-
-# ------------------------------------------------------------------------------
-#   get_batch_axis()
-# ------------------------------------------------------------------------------
-
-
-def get_batch_axis(layout):
-    """
-    Extract the batch axis from the layout
-    """
-    if layout in ["TB", "TBD"]:
-        return 1
-    if layout in ["BT", "BTD", "BCTD"]:
-        return 0
-    raise ValueError("Unsupported layout = {}".format(layout))
-
-
-# ------------------------------------------------------------------------------
-#   monotonically_increasing_and_bounded()
-# ------------------------------------------------------------------------------
-
-
-def monotonically_increasing_and_bounded(iterable, min=None, max=None):
-    """
-    Check if the elements in the given iterable are monotonically increasing and
-    bounded by upper/lower bounds.
-    """
-    if not isinstance(iterable, Iterable):
-        raise TypeError(
-            "Expected iterable to be of type Iterable, got ({})".format(
-                iterable.__class__.__name__
-            )
-        )
-    for i in range(len(iterable)):
-        if min is not None and iterable[i] < min:
-            return False
-        if max is not None and iterable[i] > max:
-            return False
-        if i > 0 and iterable[i] <= iterable[i - 1]:
-            return False
-    return True
-
-
-# ------------------------------------------------------------------------------
-#   to_pair()
-# ------------------------------------------------------------------------------
-
-
-def to_pair(value, name):
-    """Make a pair (of type tuple) of given value."""
-    if isinstance(value, Iterable):
-        if len(value) != 2:
-            raise ValueError(
-                "Expected `{}` to have exactly 2 elements, got: ({})".format(
-                    name, value
-                )
-            )
-        return value
-    return tuple(repeat(value, 2))
-
-
-# ------------------------------------------------------------------------------
-#   infer_conv_output_attrs()
-# ------------------------------------------------------------------------------
-
-
-# TODO(cfyeh): figure out if we can get `output_dim` without calling the module.
-def infer_conv_output_attrs(
-    module, input_channels, input_dim, batch_size=1, max_length=8
-):
-    """Get output attributes of a module with input."""
-    input = torch.randn(batch_size, input_channels, max_length, input_dim)
-    output = module(input)
-    output_channels = output.shape[1]
-    output_dim = output.shape[-1]
-    return output_channels, output_dim
-
-
-# ------------------------------------------------------------------------------
-#   NoOp
-# ------------------------------------------------------------------------------
-
-
-class NoOp(torch.nn.Module):
-    """
-    NoOp simply passes the input as the output.
-    """
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input: Tensor) -> Tensor:
-        return input
-
-
-# ------------------------------------------------------------------------------
-#   Permute: a torch.nn.Module applies permutation on the input tensor.
-# ------------------------------------------------------------------------------
-
-
-class Permute(torch.nn.Module):
-    def __init__(self, dims):
-        super().__init__()
-        self.dims = dims
-
-    def forward(self, input: Tensor) -> Tensor:
-        return input.permute(self.dims).contiguous()
-
-
-# ------------------------------------------------------------------------------
-#   lengths_to_padding_mask()
-# ------------------------------------------------------------------------------
-
-
-def lengths_to_padding_mask(lengths: Tensor) -> Tensor:
-    """Convert lengths of shape (B, ) to padding mask."""
-    batch_size = lengths.shape[0]
-    max_length = int(torch.max(lengths).item())
-    padding_mask = torch.arange(  # [0, ..., T-1]
-        max_length, device=lengths.device, dtype=lengths.dtype
-    ).expand(batch_size, max_length) >= lengths.unsqueeze(1)
-
-    return padding_mask
-
-
-# ------------------------------------------------------------------------------
-#   lengths_to_attention_mask()
-# ------------------------------------------------------------------------------
-
-
-def lengths_to_attention_mask(
-    lengths: Tensor,
-    left_context: Optional[int] = None,
-    right_context: Optional[int] = None,
-) -> Optional[Tensor]:
-    """
-    Generate attention mask based on (lengths, left_context, right_context).
-    left_context is None means unlimited left context.
-    right_context is None means unlimited right context.
-    """
-
-    if left_context is None and right_context is None:
-        return None
-
-    max_length = int(torch.max(lengths).item())
-
-    # For example, with `max_length` == 5,
-    # indices = tensor([
-    #     [ 0,  1,  2,  3,  4,  5],
-    #     [-1,  0,  1,  2,  3,  4],
-    #     [-2, -1,  0,  1,  2,  3],
-    #     [-3, -2, -1,  0,  1,  2],
-    #     [-4, -3, -2, -1,  0,  1],
-    #     [-5, -4, -3, -2, -1,  0],
-    # ])
-
-    # In some cases the second torch.arange is created on cpu which causes a
-    # failure. Adding the device option to guard against it.
-    indices = torch.arange(
-        max_length, device=lengths.device, dtype=lengths.dtype
-    ).expand(max_length, max_length) - torch.arange(
-        max_length, device=lengths.device
-    ).view(
-        max_length, -1
-    )
-
-    # For example, with `max_length` == 5,
-    # bool_mask = tensor([
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    # ])
-    bool_mask = (
-        torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length)
-    )
-
-    # For example, with `max_length` == 5, left_context == 2
-    # left_mask = tensor([
-    #     [ True,  True, True, True, True],
-    #     [ True,  True, True, True, True],
-    #     [ True,  True, True, True, True],
-    #     [False,  True, True, True, True],
-    #     [False, False, True, True, True],
-    # ])
-    if left_context is not None:
-        left_mask = indices >= -left_context
-        bool_mask = bool_mask & left_mask
-
-    # For example, with `max_length` == 5, right_context == 1
-    # right_mask = tensor([
-    #     [True, True, False, False, False],
-    #     [True, True,  True, False, False],
-    #     [True, True,  True,  True, False],
-    #     [True, True,  True,  True,  True],
-    #     [True, True,  True,  True,  True],
-    # ])
-    if right_context is not None:
-        right_mask = indices <= right_context
-        bool_mask = bool_mask & right_mask
-
-    bool_mask = (~bool_mask).to(device=lengths.device)
-    return bool_mask
-
-
-# ------------------------------------------------------------------------------
-#   infer_output_norm()
-# ------------------------------------------------------------------------------
-
-
-def infer_output_norm(module, output_norm=None):
-    """
-    Infer the output norm (string and module) needed on the module gvien desired
-    output normalization.
-    """
-    if output_norm == module.output_norm():
-        # output_norm already matches module.output_norm().
-        return (None, NoOp())
-
-    if output_norm is None and module.output_norm() is not None:
-        logger = logging.getLogger("infer_output_norm()")
-        logger.warning(
-            "trying to set output_norm ({}) ".format(output_norm)
-            + "but got module.output_norm() ({}), ".format(module.output_norm())
-            + "the combined output_norm() will be ({})".format(module.output_norm())
-        )
-        return (None, NoOp())
-
-    if output_norm == "log_softmax":
-        if module.output_norm() is not None:
-            raise ValueError(
-                "incompatible output_norm ({}) ".format(output_norm)
-                + "and module.output_norm() ({})".format(module.output_norm())
-            )
-        else:
-            return ("log_softmax", torch.nn.LogSoftmax(dim=-1))
-
-    if output_norm == "softmax":
-        if module.output_norm() is not None:
-            raise ValueError(
-                "incompatible output_norm ({}) ".format(output_norm)
-                + "and module.output_norm() ({})".format(module.output_norm())
-            )
-        else:
-            return ("softmax", torch.nn.Softmax(dim=-1))
-
-    raise ValueError(
-        "output_norm ({}) not in ".format(output_norm)
-        + "supported list = [None, softmax, log_softmax]"
-    )
-
-
-# ------------------------------------------------------------------------------
-#   infer_channels_from_layout()
-# ------------------------------------------------------------------------------
-
-
-def infer_channels_from_layout(layout, channels):
-    """Extract the number of channels from the layout."""
-    if layout in ("TBD", "BTD"):
-        if channels is not None and channels != 1:
-            raise ValueError(
-                "Expected channels ({}) to be 1 for layout = {}".format(
-                    channels, layout
-                )
-            )
-        if channels is None:
-            return 1
-    return channels
-
-
-# ------------------------------------------------------------------------------
-#   pad_sequence()
-# ------------------------------------------------------------------------------
-
-
-@torch.jit.export
-def pad_sequence(
-    sequence: Tensor,
-    time_axis: int,
-    extra_left_context: int = 0,
-    extra_right_context: int = 0,
-) -> Tensor:
-    """Pad extra left/right contexts to the sequence."""
-
-    if extra_left_context == 0 and extra_right_context == 0:
-        return sequence
-
-    tensors_to_concat = []
-
-    if extra_left_context:
-        size = (extra_left_context,)
-        fill_value = 0
-        indices = torch.full(
-            size=size,
-            fill_value=fill_value,
-            dtype=torch.long,
-            device=sequence.device,
-        )
-        left_padding = torch.index_select(sequence, time_axis, indices)
-        tensors_to_concat.append(left_padding)
-
-    tensors_to_concat.append(sequence)
-
-    # NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for
-    #              extra right contexts.
-    if extra_right_context:
-        size = list(sequence.shape)
-        size[time_axis] = extra_right_context
-        right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device)
-        tensors_to_concat.append(right_padding)
-
-    padded_sequence = torch.cat(tensors_to_concat, dim=time_axis)
-    return padded_sequence
-
-
-# ------------------------------------------------------------------------------
-#   sequence_to_segments()
-# ------------------------------------------------------------------------------
-
-
-@torch.jit.export
-def sequence_to_segments(
-    sequence: Tensor,
-    time_axis: int,
-    lengths: Tensor,
-    segment_size: Optional[int] = None,
-    extra_left_context: int = 0,
-    extra_right_context: int = 0,
-) -> List[Tuple[Tensor, Tensor]]:
-    """Breaks sequence into segments."""
-
-    sequence = pad_sequence(
-        sequence=sequence,
-        time_axis=time_axis,
-        extra_left_context=extra_left_context,
-        extra_right_context=extra_right_context,
-    )
-
-    lengths = lengths + extra_left_context + extra_right_context
-
-    segments: List[Tuple[Tensor, Tensor]] = []
-
-    if segment_size is None:
-        segments.append((sequence, lengths))
-        return segments
-
-    offset = 0
-    end = sequence.shape[time_axis]
-    step = segment_size
-    size = extra_left_context + segment_size + extra_right_context
-
-    while offset + extra_left_context + extra_right_context < end:
-        clamped_size = min(size, end - offset)
-        segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size)
-        indices = torch.arange(
-            start=offset,
-            end=(offset + clamped_size),
-            step=1,
-            dtype=torch.long,
-            device=sequence.device,
-        )
-        segment_tensor = torch.index_select(sequence, time_axis, indices)
-        segments.append((segment_tensor, segment_lengths))
-        offset = offset + step
-
-    return segments
-
-
-# ------------------------------------------------------------------------------
-#   segments_to_sequence()
-# ------------------------------------------------------------------------------
-
-
-@torch.jit.export
-def segments_to_sequence(
-    segments: List[Tuple[Tensor, Tensor]], time_axis: int
-) -> Tuple[Tensor, Tensor]:
-    """Concatenate segments into a full sequence."""
-    if len(segments) == 1:
-        return segments[0]
-
-    tensors_to_concat: List[Tensor] = []
-    lengths_to_stack: List[Tensor] = []
-
-    for tensor, lengths in segments:
-        tensors_to_concat.append(tensor)
-        lengths_to_stack.append(lengths)
-
-    sequence = torch.cat(tensors_to_concat, dim=time_axis)
-    lengths = torch.stack(lengths_to_stack, dim=0)
-    lengths = torch.sum(lengths, dim=0)
-
-    return sequence, lengths
-
-
-def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False):
-    """
-    convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
-
-    Args:
-        lengths: a (B, )-shaped tensor
-        batch_first: whether to return a (B, T) tensor
-
-    Return:
-        max_length: maximum length of B sequences
-        encoder_padding_mask: a (max_length, B) binary mask, where
-        [t, b] = False for t < lengths[b] and True otherwise
-
-    TODO:
-        kernelize this function if benchmarking shows this function is slow
-    """
-    max_lengths = torch.max(lengths).item()
-    bsz = lengths.size(0)
-    encoder_padding_mask = torch.arange(
-        max_lengths
-    ).to(  # a (T, ) tensor with [0, ..., T-1]
-        lengths.device
-    ).view(  # move to the right device
-        1, max_lengths
-    ).expand(  # reshape to (1, T)-shaped tensor
-        bsz, -1
-    ) > lengths.view(  # expand to (B, T)-shaped tensor
-        bsz, 1
-    ).expand(
-        -1, max_lengths
-    )
-    if not batch_first:
-        return encoder_padding_mask.t(), max_lengths
-    else:
-        return encoder_padding_mask, max_lengths
-
-
-# ------------------------------------------------------------------------------
-#   attention suppression
-# ------------------------------------------------------------------------------
-
-
-def attention_suppression(attention_weights: Tensor, scale: float):
-    # B, H, qlen, klen -> B, H, qlen, 1
-    attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1)
-    attention_nozeros = attention_prob.to(torch.bool)
-    nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True)
-
-    # For very sparse situation, we need get round about 0s
-    key_sum = torch.sum(attention_prob, dim=-1, keepdim=True)
-
-    # nozeros_sum should > 1
-    key_mean = key_sum / (nozeros_sum + 1e-8)
-
-    # std calculation
-    dis = (attention_prob - key_mean) * (attention_prob - key_mean)
-
-    # if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i
-    dis_masked = torch.where(
-        attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size())
-    )
-
-    key_var = torch.sum(dis_masked, dim=-1, keepdim=True)
-    key_var = key_var / (nozeros_sum - 1.0 + 1e-8)
-    key_std = torch.sqrt(key_var)
-    key_thread = key_mean - scale * key_std
-
-    # if attention_prob[i] >= key_thread, then attention_prob[i]
-    # , otherwise "-inf"
-    inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach()
-    inf_tensor[:] = float("-inf")
-    attention_weights_float = torch.where(
-        attention_prob < key_thread,
-        inf_tensor,
-        attention_weights.float(),
-    )
-
-    return attention_weights_float.type_as(attention_weights)
-
-
-def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value):
-    return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/kaldi/add-self-loop-simple.cc b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/kaldi/add-self-loop-simple.cc
deleted file mode 100644
index e18fb62df52ab85d7802615d8619b0fd94a08f8c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/kaldi/add-self-loop-simple.cc
+++ /dev/null
@@ -1,94 +0,0 @@
-/*
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#include <iostream>
-#include "fstext/fstext-lib.h" // @manual
-#include "util/common-utils.h" // @manual
-
-/*
- * This program is to modify a FST without self-loop by:
- *   for each incoming arc with non-eps input symbol, add a self-loop arc
- *   with that non-eps symbol as input and eps as output.
- *
- * This is to make sure the resultant FST can do deduplication for repeated
- * symbols, which is very common in acoustic model
- *
- */
-namespace {
-int32 AddSelfLoopsSimple(fst::StdVectorFst* fst) {
-  typedef fst::MutableArcIterator<fst::StdVectorFst> IterType;
-
-  int32 num_states_before = fst->NumStates();
-  fst::MakePrecedingInputSymbolsSame(false, fst);
-  int32 num_states_after = fst->NumStates();
-  KALDI_LOG << "There are " << num_states_before
-            << " states in the original FST; "
-            << " after MakePrecedingInputSymbolsSame, there are "
-            << num_states_after << " states " << std::endl;
-
-  auto weight_one = fst::StdArc::Weight::One();
-
-  int32 num_arc_added = 0;
-
-  fst::StdArc self_loop_arc;
-  self_loop_arc.weight = weight_one;
-
-  int32 num_states = fst->NumStates();
-  std::vector<std::set<int32>> incoming_non_eps_label_per_state(num_states);
-
-  for (int32 state = 0; state < num_states; state++) {
-    for (IterType aiter(fst, state); !aiter.Done(); aiter.Next()) {
-      fst::StdArc arc(aiter.Value());
-      if (arc.ilabel != 0) {
-        incoming_non_eps_label_per_state[arc.nextstate].insert(arc.ilabel);
-      }
-    }
-  }
-
-  for (int32 state = 0; state < num_states; state++) {
-    if (!incoming_non_eps_label_per_state[state].empty()) {
-      auto& ilabel_set = incoming_non_eps_label_per_state[state];
-      for (auto it = ilabel_set.begin(); it != ilabel_set.end(); it++) {
-        self_loop_arc.ilabel = *it;
-        self_loop_arc.olabel = 0;
-        self_loop_arc.nextstate = state;
-        fst->AddArc(state, self_loop_arc);
-        num_arc_added++;
-      }
-    }
-  }
-  return num_arc_added;
-}
-
-void print_usage() {
-  std::cout << "add-self-loop-simple usage:\n"
-               "\tadd-self-loop-simple <in-fst> <out-fst> \n";
-}
-} // namespace
-
-int main(int argc, char** argv) {
-  if (argc != 3) {
-    print_usage();
-    exit(1);
-  }
-
-  auto input = argv[1];
-  auto output = argv[2];
-
-  auto fst = fst::ReadFstKaldi(input);
-  auto num_states = fst->NumStates();
-  KALDI_LOG << "Loading FST from " << input << " with " << num_states
-            << " states." << std::endl;
-
-  int32 num_arc_added = AddSelfLoopsSimple(fst);
-  KALDI_LOG << "Adding " << num_arc_added << " self-loop arcs " << std::endl;
-
-  fst::WriteFstKaldi(*fst, std::string(output));
-  KALDI_LOG << "Writing FST to " << output << std::endl;
-
-  delete fst;
-}
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/evaluation/eval_asr.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/evaluation/eval_asr.py
deleted file mode 100644
index 005a11bfb34ca477ad9e133acd60f249e66cda47..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/evaluation/eval_asr.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import editdistance
-import re
-import shutil
-import soundfile as sf
-import subprocess
-from pathlib import Path
-
-from examples.speech_to_text.data_utils import load_tsv_to_dicts
-
-
-def preprocess_text(text):
-    text = "|".join(re.sub(r"[^A-Z' ]", " ", text.upper()).split())
-    text = " ".join(text)
-    return text
-
-
-def prepare_w2v_data(
-        dict_dir, sample_rate, label, audio_paths, texts, split, data_dir
-):
-    data_dir.mkdir(parents=True, exist_ok=True)
-    shutil.copyfile(
-        dict_dir / f"dict.{label}.txt",
-        data_dir / f"dict.{label}.txt"
-    )
-    with open(data_dir / f"{split}.tsv", "w") as f:
-        f.write("/\n")
-        for audio_path in audio_paths:
-            wav, sr = sf.read(audio_path)
-            assert sr == sample_rate, f"{sr} != sample_rate"
-            nsample = len(wav)
-            f.write(f"{audio_path}\t{nsample}\n")
-    with open(data_dir / f"{split}.{label}", "w") as f:
-        for text in texts:
-            text = preprocess_text(text)
-            f.write(f"{text}\n")
-
-
-def run_asr(asr_dir, split, w2v_ckpt, w2v_label, res_dir):
-    """
-    results will be saved at
-    {res_dir}/{ref,hypo}.word-{w2v_ckpt.filename}-{split}.txt
-    """
-    cmd = ["python", "-m", "examples.speech_recognition.infer"]
-    cmd += [str(asr_dir.resolve())]
-    cmd += ["--task", "audio_finetuning", "--nbest", "1", "--quiet"]
-    cmd += ["--w2l-decoder", "viterbi", "--criterion", "ctc"]
-    cmd += ["--post-process", "letter", "--max-tokens", "4000000"]
-    cmd += ["--path", str(w2v_ckpt.resolve()), "--labels", w2v_label]
-    cmd += ["--gen-subset", split, "--results-path", str(res_dir.resolve())]
-
-    print(f"running cmd:\n{' '.join(cmd)}")
-    subprocess.run(cmd, check=True)
-
-
-def compute_error_rate(hyp_wrd_path, ref_wrd_path, unit="word"):
-    """each line is "<text> (None-<index>)" """
-    tokenize_line = {
-        "word": lambda x: re.sub(r" \(.*\)$", "", x.rstrip()).split(),
-        "char": lambda x: list(re.sub(r" \(.*\)$", "", x.rstrip()))
-    }.get(unit)
-    if tokenize_line is None:
-        raise ValueError(f"{unit} not supported")
-
-    inds = [int(re.sub(r"\D*(\d*)\D*", r"\1", line))
-            for line in open(hyp_wrd_path)]
-    hyps = [tokenize_line(line) for line in open(hyp_wrd_path)]
-    refs = [tokenize_line(line) for line in open(ref_wrd_path)]
-    assert(len(hyps) == len(refs))
-    err_rates = [
-        editdistance.eval(hyp, ref) / len(ref) for hyp, ref in zip(hyps, refs)
-    ]
-    ind_to_err_rates = {i: e for i, e in zip(inds, err_rates)}
-    return ind_to_err_rates
-
-
-def main(args):
-    samples = load_tsv_to_dicts(args.raw_manifest)
-    ids = [
-        sample[args.id_header] if args.id_header else "" for sample in samples
-    ]
-    audio_paths = [sample[args.audio_header] for sample in samples]
-    texts = [sample[args.text_header] for sample in samples]
-
-    prepare_w2v_data(
-        args.w2v_dict_dir,
-        args.w2v_sample_rate,
-        args.w2v_label,
-        audio_paths,
-        texts,
-        args.split,
-        args.asr_dir
-    )
-    run_asr(args.asr_dir, args.split, args.w2v_ckpt, args.w2v_label, args.asr_dir)
-    ind_to_err_rates = compute_error_rate(
-        args.asr_dir / f"hypo.word-{args.w2v_ckpt.name}-{args.split}.txt",
-        args.asr_dir / f"ref.word-{args.w2v_ckpt.name}-{args.split}.txt",
-        args.err_unit,
-    )
-
-    uer_path = args.asr_dir / f"uer_{args.err_unit}.{args.split}.tsv"
-    with open(uer_path, "w") as f:
-        f.write("id\taudio\tuer\n")
-        for ind, (id_, audio_path) in enumerate(zip(ids, audio_paths)):
-            f.write(f"{id_}\t{audio_path}\t{ind_to_err_rates[ind]:.4f}\n")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--raw-manifest", required=True, type=Path)
-    parser.add_argument("--asr-dir", required=True, type=Path)
-    parser.add_argument("--id-header", default="id", type=str)
-    parser.add_argument("--audio-header", default="audio", type=str)
-    parser.add_argument("--text-header", default="src_text", type=str)
-    parser.add_argument("--split", default="raw", type=str)
-    parser.add_argument("--w2v-ckpt", required=True, type=Path)
-    parser.add_argument("--w2v-dict-dir", required=True, type=Path)
-    parser.add_argument("--w2v-sample-rate", default=16000, type=int)
-    parser.add_argument("--w2v-label", default="ltr", type=str)
-    parser.add_argument("--err-unit", default="word", type=str)
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_amp_optimizer.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_amp_optimizer.py
deleted file mode 100644
index 3a785e1830e91b7e090e841d428fe4ea61f3a65c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_amp_optimizer.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import copy
-import unittest
-
-import torch
-from torch.cuda.amp import autocast, GradScaler
-from fairseq.optim import build_optimizer
-
-
-@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
-class TestGradientScalingAMP(unittest.TestCase):
-    def setUp(self):
-        self.x = torch.tensor([2.0]).cuda().half()
-        weight = 3.0
-        bias = 5.0
-        self.error = 1.0
-        self.target = torch.tensor([self.x * weight + bias + self.error]).cuda()
-        self.loss_fn = torch.nn.L1Loss()
-
-        self.model = torch.nn.Linear(1, 1)
-        self.model.weight.data = torch.tensor([[weight]])
-        self.model.bias.data = torch.tensor([bias])
-        self.model.cuda()
-        self.params = list(self.model.parameters())
-
-        self.namespace_dls = argparse.Namespace(
-            optimizer="adam",
-            lr=[0.1],
-            adam_betas="(0.9, 0.999)",
-            adam_eps=1e-8,
-            weight_decay=0.0,
-            threshold_loss_scale=1,
-            min_loss_scale=1e-4,
-        )
-        self.scaler = GradScaler(
-            init_scale=1,
-            growth_interval=1,
-        )
-
-    def run_iter(self, model, params, optimizer):
-        optimizer.zero_grad()
-        with autocast():
-            y = model(self.x)
-            loss = self.loss_fn(y, self.target)
-        self.scaler.scale(loss).backward()
-        self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16))
-
-        self.scaler.unscale_(optimizer)
-        grad_norm = optimizer.clip_grad_norm(0)
-        self.assertAlmostEqual(grad_norm.item(), 2.2361, 4)
-
-        self.scaler.step(optimizer)
-        self.scaler.update()
-        self.assertEqual(
-            model.weight,
-            torch.tensor(
-                [[3.1]], device="cuda:0", requires_grad=True
-            ),
-        )
-        self.assertEqual(
-            model.bias,
-            torch.tensor(
-                [5.1], device="cuda:0", requires_grad=True
-            ),
-        )
-        self.assertEqual(self.scaler.get_scale(), 2.0)
-
-    def test_automatic_mixed_precision(self):
-        model = copy.deepcopy(self.model)
-        params = list(model.parameters())
-        optimizer = build_optimizer(self.namespace_dls, params)
-
-        self.run_iter(model, params, optimizer)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_file_chunker_utils.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_file_chunker_utils.py
deleted file mode 100644
index 5cded04572f0ab68c81db9ad14de1c18951a1a10..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_file_chunker_utils.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-import shutil
-import tempfile
-import unittest
-from typing import Optional
-
-
-class TestFileChunker(unittest.TestCase):
-    _tmpdir: Optional[str] = None
-    _tmpfile: Optional[str] = None
-    _line_content = "Hello, World\n"
-    _num_bytes = None
-    _num_lines = 200
-    _num_splits = 20
-
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls._num_bytes = len(cls._line_content.encode("utf-8"))
-        cls._tmpdir = tempfile.mkdtemp()
-        with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f:
-            cls._tmpfile = f.name
-            for _i in range(cls._num_lines):
-                f.write(cls._line_content)
-            f.flush()
-
-    @classmethod
-    def tearDownClass(cls) -> None:
-        # Cleanup temp working dir.
-        if cls._tmpdir is not None:
-            shutil.rmtree(cls._tmpdir)  # type: ignore
-
-    def test_find_offsets(self):
-        from fairseq.file_chunker_utils import find_offsets
-
-        offsets = find_offsets(self._tmpfile, self._num_splits)
-        self.assertEqual(len(offsets), self._num_splits + 1)
-        (zero, *real_offsets, last) = offsets
-        self.assertEqual(zero, 0)
-        for i, o in enumerate(real_offsets):
-            self.assertEqual(
-                o,
-                self._num_bytes
-                + ((i + 1) * self._num_bytes * self._num_lines / self._num_splits),
-            )
-        self.assertEqual(last, self._num_bytes * self._num_lines)
-
-    def test_readchunks(self):
-        from fairseq.file_chunker_utils import Chunker, find_offsets
-
-        offsets = find_offsets(self._tmpfile, self._num_splits)
-        for start, end in zip(offsets, offsets[1:]):
-            with Chunker(self._tmpfile, start, end) as lines:
-                all_lines = list(lines)
-                num_lines = self._num_lines / self._num_splits
-                self.assertAlmostEqual(
-                    len(all_lines), num_lines, delta=1
-                )  # because we split on the bites, we might end up with one more/less line in a chunk
-                self.assertListEqual(
-                    all_lines, [self._line_content for _ in range(len(all_lines))]
-                )
diff --git a/spaces/Ohio-uchil/stablediffusionapi-anything-v5/app.py b/spaces/Ohio-uchil/stablediffusionapi-anything-v5/app.py
deleted file mode 100644
index 6db423fde2b7e32c68e8be737dfc7c6175cd67a4..0000000000000000000000000000000000000000
--- a/spaces/Ohio-uchil/stablediffusionapi-anything-v5/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stablediffusionapi/anything-v5").launch()
\ No newline at end of file
diff --git a/spaces/Open-Orca/LlongOrca-13B-16k/app.py b/spaces/Open-Orca/LlongOrca-13B-16k/app.py
deleted file mode 100644
index ac194e7f699792b7d76a6ebc129f14fbedf6f106..0000000000000000000000000000000000000000
--- a/spaces/Open-Orca/LlongOrca-13B-16k/app.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import os
-import re
-import logging
-import gradio as gr
-import openai
-
-print(os.environ)
-openai.api_base = os.environ.get("OPENAI_API_BASE")
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-BASE_SYSTEM_MESSAGE = """YOU ALWAYS FOLLOW INSTRUCTIONS WHEN GIVEN, YOU DO NOT GIVE MORE INFORMATION THAT WHAT YOU ARE ASKED UNLESS ITS IMPORTANT"""
-
-def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None):
-    completion = openai.Completion.create(model="Open-Orca/LlongOrca-13B-16k", prompt=prompt, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, stream=True, stop=["</s>", "<|im_end|>"])
-    for chunk in completion:
-        yield chunk["choices"][0]["text"]
-
-
-def clear_chat(chat_history_state, chat_message):
-    chat_history_state = []
-    chat_message = ''
-    return chat_history_state, chat_message
-
-
-def user(message, history):
-    history = history or []
-    # Append the user's message to the conversation history
-    history.append([message, ""])
-    return "", history
-
-
-def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty):
-    history = history or []
-
-    messages = "<|im_start|>"+"system\n" + BASE_SYSTEM_MESSAGE + system_message.strip() + "<|im_end|>\n" + \
-               "\n".join(["\n".join(["<|im_start|>"+"user\n"+item[0]+"<|im_end|>", "<|im_start|>assistant\n"+item[1]+"<|im_end|>"])
-                          for item in history])
-    # strip the last `<|end_of_turn|>` from the messages
-    messages = messages.rstrip("<|im_end|>")
-    # remove last space from assistant, some models output a ZWSP if you leave a space
-    messages = messages.rstrip()
-
-    prediction = make_prediction(
-        messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        top_k=top_k,
-        repetition_penalty=repetition_penalty,
-    )
-    for tokens in prediction:
-        tokens = re.findall(r'(.*?)(\s|$)', tokens)
-        for subtoken in tokens:
-            subtoken = "".join(subtoken)
-            answer = subtoken
-            history[-1][1] += answer
-            # stream the response
-            yield history, history, ""
-
-
-start_message = ""
-
-CSS ="""
-.contain { display: flex; flex-direction: column; }
-.gradio-container { height: 100vh !important; }
-#component-0 { height: 100%; }
-#chatbot { flex-grow: 1; overflow: auto; resize: vertical; }
-"""
-
-#with gr.Blocks() as demo:
-with gr.Blocks(css=CSS) as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(f"""
-                    ## This demo is an unquantized GPU chatbot of [OpenOrca LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k)
-                    Brought to you by your friends at Alignment Lab AI, OpenChat, and Open Access AI Collective!
-                    """)
-    with gr.Row():
-        gr.Markdown("# 🐋 OpenOrca LlongOrca-13B-16k Playground Space! 🐋")
-    with gr.Row():
-        #chatbot = gr.Chatbot().style(height=500)
-        chatbot = gr.Chatbot(elem_id="chatbot")
-    with gr.Row():
-        message = gr.Textbox(
-            label="What do you want to chat about?",
-            placeholder="Ask me anything.",
-            lines=3,
-        )
-    with gr.Row():
-        submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
-        clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
-        stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
-    with gr.Accordion("Show Model Parameters", open=False):
-        with gr.Row():
-            with gr.Column():
-                max_tokens = gr.Slider(20, 1000, label="Max Tokens", step=20, value=500)
-                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.8)
-                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
-                top_k = gr.Slider(0, 100, label="Top K", step=1, value=40)
-                repetition_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)
-
-        system_msg = gr.Textbox(
-            start_message, label="System Message", interactive=True, visible=True, placeholder="System prompt. Provide instructions which you want the model to remember.", lines=5)
-
-    chat_history_state = gr.State()
-    clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False)
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-    submit_click_event = submit.click(
-        fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
-    ).then(
-        fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[chatbot, chat_history_state, message], queue=True
-    )
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event], queue=False)
-
-demo.queue(max_size=128, concurrency_count=48).launch(debug=True, server_name="0.0.0.0", server_port=7860)
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py
deleted file mode 100644
index 1e84a5bdb3d4e410d8eef4b80a5d4c099a180104..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/cityscapes.py
+++ /dev/null
@@ -1,329 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import functools
-import json
-import logging
-import multiprocessing as mp
-import numpy as np
-import os
-from itertools import chain
-import pycocotools.mask as mask_util
-from PIL import Image
-
-from detectron2.structures import BoxMode
-from detectron2.utils.comm import get_world_size
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.logger import setup_logger
-
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-
-
-logger = logging.getLogger(__name__)
-
-
-def _get_cityscapes_files(image_dir, gt_dir):
-    files = []
-    # scan through the directory
-    cities = PathManager.ls(image_dir)
-    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
-    for city in cities:
-        city_img_dir = os.path.join(image_dir, city)
-        city_gt_dir = os.path.join(gt_dir, city)
-        for basename in PathManager.ls(city_img_dir):
-            image_file = os.path.join(city_img_dir, basename)
-
-            suffix = "leftImg8bit.png"
-            assert basename.endswith(suffix), basename
-            basename = basename[: -len(suffix)]
-
-            instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png")
-            label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png")
-            json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json")
-
-            files.append((image_file, instance_file, label_file, json_file))
-    assert len(files), "No images found in {}".format(image_dir)
-    for f in files[0]:
-        assert PathManager.isfile(f), f
-    return files
-
-
-def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
-        from_json (bool): whether to read annotations from the raw json file or the png files.
-        to_polygons (bool): whether to represent the segmentation as polygons
-            (COCO's format) instead of masks (cityscapes's format).
-
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-    """
-    if from_json:
-        assert to_polygons, (
-            "Cityscapes's json annotations are in polygon format. "
-            "Converting to mask format is not supported now."
-        )
-    files = _get_cityscapes_files(image_dir, gt_dir)
-
-    logger.info("Preprocessing cityscapes annotations ...")
-    # This is still not fast: all workers will execute duplicate works and will
-    # take up to 10m on a 8GPU server.
-    pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4))
-
-    ret = pool.map(
-        functools.partial(_cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons),
-        files,
-    )
-    logger.info("Loaded {} images from {}".format(len(ret), image_dir))
-
-    # Map cityscape ids to contiguous ids
-    from cityscapesscripts.helpers.labels import labels
-
-    labels = [l for l in labels if l.hasInstances and not l.ignoreInEval]
-    dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)}
-    for dict_per_image in ret:
-        for anno in dict_per_image["annotations"]:
-            anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]]
-    return ret
-
-
-def load_cityscapes_semantic(image_dir, gt_dir):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
-
-    Returns:
-        list[dict]: a list of dict, each has "file_name" and
-            "sem_seg_file_name".
-    """
-    ret = []
-    # gt_dir is small and contain many small files. make sense to fetch to local first
-    gt_dir = PathManager.get_local_path(gt_dir)
-    for image_file, _, label_file, json_file in _get_cityscapes_files(image_dir, gt_dir):
-        label_file = label_file.replace("labelIds", "labelTrainIds")
-
-        with PathManager.open(json_file, "r") as f:
-            jsonobj = json.load(f)
-        ret.append(
-            {
-                "file_name": image_file,
-                "sem_seg_file_name": label_file,
-                "height": jsonobj["imgHeight"],
-                "width": jsonobj["imgWidth"],
-            }
-        )
-    assert len(ret), f"No images found in {image_dir}!"
-    assert PathManager.isfile(
-        ret[0]["sem_seg_file_name"]
-    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
-    return ret
-
-
-def _cityscapes_files_to_dict(files, from_json, to_polygons):
-    """
-    Parse cityscapes annotation files to a instance segmentation dataset dict.
-
-    Args:
-        files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file)
-        from_json (bool): whether to read annotations from the raw json file or the png files.
-        to_polygons (bool): whether to represent the segmentation as polygons
-            (COCO's format) instead of masks (cityscapes's format).
-
-    Returns:
-        A dict in Detectron2 Dataset format.
-    """
-    from cityscapesscripts.helpers.labels import id2label, name2label
-
-    image_file, instance_id_file, _, json_file = files
-
-    annos = []
-
-    if from_json:
-        from shapely.geometry import MultiPolygon, Polygon
-
-        with PathManager.open(json_file, "r") as f:
-            jsonobj = json.load(f)
-        ret = {
-            "file_name": image_file,
-            "image_id": os.path.basename(image_file),
-            "height": jsonobj["imgHeight"],
-            "width": jsonobj["imgWidth"],
-        }
-
-        # `polygons_union` contains the union of all valid polygons.
-        polygons_union = Polygon()
-
-        # CityscapesScripts draw the polygons in sequential order
-        # and each polygon *overwrites* existing ones. See
-        # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa
-        # We use reverse order, and each polygon *avoids* early ones.
-        # This will resolve the ploygon overlaps in the same way as CityscapesScripts.
-        for obj in jsonobj["objects"][::-1]:
-            if "deleted" in obj:  # cityscapes data format specific
-                continue
-            label_name = obj["label"]
-
-            try:
-                label = name2label[label_name]
-            except KeyError:
-                if label_name.endswith("group"):  # crowd area
-                    label = name2label[label_name[: -len("group")]]
-                else:
-                    raise
-            if label.id < 0:  # cityscapes data format
-                continue
-
-            # Cityscapes's raw annotations uses integer coordinates
-            # Therefore +0.5 here
-            poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5
-            # CityscapesScript uses PIL.ImageDraw.polygon to rasterize
-            # polygons for evaluation. This function operates in integer space
-            # and draws each pixel whose center falls into the polygon.
-            # Therefore it draws a polygon which is 0.5 "fatter" in expectation.
-            # We therefore dilate the input polygon by 0.5 as our input.
-            poly = Polygon(poly_coord).buffer(0.5, resolution=4)
-
-            if not label.hasInstances or label.ignoreInEval:
-                # even if we won't store the polygon it still contributes to overlaps resolution
-                polygons_union = polygons_union.union(poly)
-                continue
-
-            # Take non-overlapping part of the polygon
-            poly_wo_overlaps = poly.difference(polygons_union)
-            if poly_wo_overlaps.is_empty:
-                continue
-            polygons_union = polygons_union.union(poly)
-
-            anno = {}
-            anno["iscrowd"] = label_name.endswith("group")
-            anno["category_id"] = label.id
-
-            if isinstance(poly_wo_overlaps, Polygon):
-                poly_list = [poly_wo_overlaps]
-            elif isinstance(poly_wo_overlaps, MultiPolygon):
-                poly_list = poly_wo_overlaps.geoms
-            else:
-                raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps))
-
-            poly_coord = []
-            for poly_el in poly_list:
-                # COCO API can work only with exterior boundaries now, hence we store only them.
-                # TODO: store both exterior and interior boundaries once other parts of the
-                # codebase support holes in polygons.
-                poly_coord.append(list(chain(*poly_el.exterior.coords)))
-            anno["segmentation"] = poly_coord
-            (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds
-
-            anno["bbox"] = (xmin, ymin, xmax, ymax)
-            anno["bbox_mode"] = BoxMode.XYXY_ABS
-
-            annos.append(anno)
-    else:
-        # See also the official annotation parsing scripts at
-        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py  # noqa
-        with PathManager.open(instance_id_file, "rb") as f:
-            inst_image = np.asarray(Image.open(f), order="F")
-        # ids < 24 are stuff labels (filtering them first is about 5% faster)
-        flattened_ids = np.unique(inst_image[inst_image >= 24])
-
-        ret = {
-            "file_name": image_file,
-            "image_id": os.path.basename(image_file),
-            "height": inst_image.shape[0],
-            "width": inst_image.shape[1],
-        }
-
-        for instance_id in flattened_ids:
-            # For non-crowd annotations, instance_id // 1000 is the label_id
-            # Crowd annotations have <1000 instance ids
-            label_id = instance_id // 1000 if instance_id >= 1000 else instance_id
-            label = id2label[label_id]
-            if not label.hasInstances or label.ignoreInEval:
-                continue
-
-            anno = {}
-            anno["iscrowd"] = instance_id < 1000
-            anno["category_id"] = label.id
-
-            mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F")
-
-            inds = np.nonzero(mask)
-            ymin, ymax = inds[0].min(), inds[0].max()
-            xmin, xmax = inds[1].min(), inds[1].max()
-            anno["bbox"] = (xmin, ymin, xmax, ymax)
-            if xmax <= xmin or ymax <= ymin:
-                continue
-            anno["bbox_mode"] = BoxMode.XYXY_ABS
-            if to_polygons:
-                # This conversion comes from D4809743 and D5171122,
-                # when Mask-RCNN was first developed.
-                contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[
-                    -2
-                ]
-                polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3]
-                # opencv's can produce invalid polygons
-                if len(polygons) == 0:
-                    continue
-                anno["segmentation"] = polygons
-            else:
-                anno["segmentation"] = mask_util.encode(mask[:, :, None])[0]
-            annos.append(anno)
-    ret["annotations"] = annos
-    return ret
-
-
-if __name__ == "__main__":
-    """
-    Test the cityscapes dataset loader.
-
-    Usage:
-        python -m detectron2.data.datasets.cityscapes \
-            cityscapes/leftImg8bit/train cityscapes/gtFine/train
-    """
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("image_dir")
-    parser.add_argument("gt_dir")
-    parser.add_argument("--type", choices=["instance", "semantic"], default="instance")
-    args = parser.parse_args()
-    from detectron2.data.catalog import Metadata
-    from detectron2.utils.visualizer import Visualizer
-    from cityscapesscripts.helpers.labels import labels
-
-    logger = setup_logger(name=__name__)
-
-    dirname = "cityscapes-data-vis"
-    os.makedirs(dirname, exist_ok=True)
-
-    if args.type == "instance":
-        dicts = load_cityscapes_instances(
-            args.image_dir, args.gt_dir, from_json=True, to_polygons=True
-        )
-        logger.info("Done loading {} samples.".format(len(dicts)))
-
-        thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval]
-        meta = Metadata().set(thing_classes=thing_classes)
-
-    else:
-        dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir)
-        logger.info("Done loading {} samples.".format(len(dicts)))
-
-        stuff_classes = [k.name for k in labels if k.trainId != 255]
-        stuff_colors = [k.color for k in labels if k.trainId != 255]
-        meta = Metadata().set(stuff_classes=stuff_classes, stuff_colors=stuff_colors)
-
-    for d in dicts:
-        img = np.array(Image.open(PathManager.open(d["file_name"], "rb")))
-        visualizer = Visualizer(img, metadata=meta)
-        vis = visualizer.draw_dataset_dict(d)
-        # cv2.imshow("a", vis.get_image()[:, :, ::-1])
-        # cv2.waitKey()
-        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
-        vis.save(fpath)
diff --git a/spaces/PKaushik/humandetect/README.md b/spaces/PKaushik/humandetect/README.md
deleted file mode 100644
index b686f63668b52d26585d1270c045ddbd7f76e3d4..0000000000000000000000000000000000000000
--- a/spaces/PKaushik/humandetect/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Humandetection
-emoji: 📉
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/motionblur/motionblur.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/motionblur/motionblur.py
deleted file mode 100644
index ef50de8912773ff4db65e34daadbb323ee5411c1..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/motionblur/motionblur.py
+++ /dev/null
@@ -1,419 +0,0 @@
-import numpy as np
-from PIL import Image, ImageDraw, ImageFilter
-from numpy.random import uniform, triangular, beta
-from math import pi
-from pathlib import Path
-from scipy.signal import convolve
-
-# tiny error used for nummerical stability
-eps = 0.1
-
-
-def softmax(x):
-    """Compute softmax values for each sets of scores in x."""
-    e_x = np.exp(x - np.max(x))
-    return e_x / e_x.sum()
-
-
-def norm(lst: list) -> float:
-    """[summary]
-    L^2 norm of a list
-    [description]
-    Used for internals
-    Arguments:
-        lst {list} -- vector
-    """
-    if not isinstance(lst, list):
-        raise ValueError("Norm takes a list as its argument")
-
-    if lst == []:
-        return 0
-
-    return (sum((i**2 for i in lst)))**0.5
-
-
-def polar2z(r: np.ndarray, θ: np.ndarray) -> np.ndarray:
-    """[summary]
-    Takes a list of radii and angles (radians) and
-    converts them into a corresponding list of complex
-    numbers x + yi.
-    [description]
-
-    Arguments:
-        r {np.ndarray} -- radius
-        θ {np.ndarray} -- angle
-
-    Returns:
-        [np.ndarray] -- list of complex numbers r e^(i theta) as x + iy
-    """
-    return r * np.exp(1j * θ)
-
-
-class Kernel(object):
-    """[summary]
-    Class representing a motion blur kernel of a given intensity.
-
-    [description]
-    Keyword Arguments:
-            size {tuple} -- Size of the kernel in px times px
-            (default: {(100, 100)})
-
-            intensity {float} -- Float between 0 and 1.
-            Intensity of the motion blur.
-
-            :   0 means linear motion blur and 1 is a highly non linear
-                and often convex motion blur path. (default: {0})
-
-    Attribute:
-    kernelMatrix -- Numpy matrix of the kernel of given intensity
-
-    Properties:
-    applyTo -- Applies kernel to image
-               (pass as path, pillow image or np array)
-
-    Raises:
-        ValueError
-    """
-
-    def __init__(self, size: tuple = (100, 100), intensity: float=0):
-
-        # checking if size is correctly given
-        if not isinstance(size, tuple):
-            raise ValueError("Size must be TUPLE of 2 positive integers")
-        elif len(size) != 2 or type(size[0]) != type(size[1]) != int:
-            raise ValueError("Size must be tuple of 2 positive INTEGERS")
-        elif size[0] < 0 or size[1] < 0:
-            raise ValueError("Size must be tuple of 2 POSITIVE integers")
-
-        # check if intensity is float (int) between 0 and 1
-        if type(intensity) not in [int, float, np.float32, np.float64]:
-            raise ValueError("Intensity must be a number between 0 and 1")
-        elif intensity < 0 or intensity > 1:
-            raise ValueError("Intensity must be a number between 0 and 1")
-
-        # saving args
-        self.SIZE = size
-        self.INTENSITY = intensity
-
-        # deriving quantities
-
-        # we super size first and then downscale at the end for better
-        # anti-aliasing
-        self.SIZEx2 = tuple([2 * i for i in size])
-        self.x, self.y = self.SIZEx2
-
-        # getting length of kernel diagonal
-        self.DIAGONAL = (self.x**2 + self.y**2)**0.5
-
-        # flag to see if kernel has been calculated already
-        self.kernel_is_generated = False
-
-    def _createPath(self):
-        """[summary]
-        creates a motion blur path with the given intensity.
-        [description]
-        Proceede in 5 steps
-        1. Get a random number of random step sizes
-        2. For each step get a random angle
-        3. combine steps and angles into a sequence of increments
-        4. create path out of increments
-        5. translate path to fit the kernel dimensions
-
-        NOTE: "random" means random but might depend on the given intensity
-        """
-
-        # first we find the lengths of the motion blur steps
-        def getSteps():
-            """[summary]
-            Here we calculate the length of the steps taken by
-            the motion blur
-            [description]
-            We want a higher intensity lead to a longer total motion
-            blur path and more different steps along the way.
-
-            Hence we sample
-
-            MAX_PATH_LEN =[U(0,1) + U(0, intensity^2)] * diagonal * 0.75
-
-            and each step: beta(1, 30) * (1 - self.INTENSITY + eps) * diagonal)
-            """
-
-            # getting max length of blur motion
-            self.MAX_PATH_LEN = 0.75 * self.DIAGONAL * \
-                (uniform() + uniform(0, self.INTENSITY**2))
-
-            # getting step
-            steps = []
-
-            while sum(steps) < self.MAX_PATH_LEN:
-
-                # sample next step
-                step = beta(1, 30) * (1 - self.INTENSITY + eps) * self.DIAGONAL
-                if step < self.MAX_PATH_LEN:
-                    steps.append(step)
-
-            # note the steps and the total number of steps
-            self.NUM_STEPS = len(steps)
-            self.STEPS = np.asarray(steps)
-
-        def getAngles():
-            """[summary]
-            Gets an angle for each step
-            [description]
-            The maximal angle should be larger the more
-            intense the motion is. So we sample it from a
-            U(0, intensity * pi)
-
-            We sample "jitter" from a beta(2,20) which is the probability
-            that the next angle has a different sign than the previous one.
-            """
-
-            # same as with the steps
-
-            # first we get the max angle in radians
-            self.MAX_ANGLE = uniform(0, self.INTENSITY * pi)
-
-            # now we sample "jitter" which is the probability that the
-            # next angle has a different sign than the previous one
-            self.JITTER = beta(2, 20)
-
-            # initialising angles (and sign of angle)
-            angles = [uniform(low=-self.MAX_ANGLE, high=self.MAX_ANGLE)]
-
-            while len(angles) < self.NUM_STEPS:
-
-                # sample next angle (absolute value)
-                angle = triangular(0, self.INTENSITY *
-                                   self.MAX_ANGLE, self.MAX_ANGLE + eps)
-
-                # with jitter probability change sign wrt previous angle
-                if uniform() < self.JITTER:
-                    angle *= - np.sign(angles[-1])
-                else:
-                    angle *= np.sign(angles[-1])
-
-                angles.append(angle)
-
-            # save angles
-            self.ANGLES = np.asarray(angles)
-
-        # Get steps and angles
-        getSteps()
-        getAngles()
-
-        # Turn them into a path
-        ####
-
-        # we turn angles and steps into complex numbers
-        complex_increments = polar2z(self.STEPS, self.ANGLES)
-
-        # generate path as the cumsum of these increments
-        self.path_complex = np.cumsum(complex_increments)
-
-        # find center of mass of path
-        self.com_complex = sum(self.path_complex) / self.NUM_STEPS
-
-        # Shift path s.t. center of mass lies in the middle of
-        # the kernel and a apply a random rotation
-        ###
-
-        # center it on COM
-        center_of_kernel = (self.x + 1j * self.y) / 2
-        self.path_complex -= self.com_complex
-
-        # randomly rotate path by an angle a in (0, pi)
-        self.path_complex *= np.exp(1j * uniform(0, pi))
-
-        # center COM on center of kernel
-        self.path_complex += center_of_kernel
-
-        # convert complex path to final list of coordinate tuples
-        self.path = [(i.real, i.imag) for i in self.path_complex]
-
-    def _createKernel(self, save_to: Path=None, show: bool=False):
-        """[summary]
-        Finds a kernel (psf) of given intensity.
-        [description]
-        use displayKernel to actually see the kernel.
-
-        Keyword Arguments:
-            save_to {Path} -- Image file to save the kernel to. {None}
-            show {bool} -- shows kernel if true
-        """
-
-        # check if we haven't already generated a kernel
-        if self.kernel_is_generated:
-            return None
-
-        # get the path
-        self._createPath()
-
-        # Initialise an image with super-sized dimensions
-        # (pillow Image object)
-        self.kernel_image = Image.new("RGB", self.SIZEx2)
-
-        # ImageDraw instance that is linked to the kernel image that
-        # we can use to draw on our kernel_image
-        self.painter = ImageDraw.Draw(self.kernel_image)
-
-        # draw the path
-        self.painter.line(xy=self.path, width=int(self.DIAGONAL / 150))
-
-        # applying gaussian blur for realism
-        self.kernel_image = self.kernel_image.filter(
-            ImageFilter.GaussianBlur(radius=int(self.DIAGONAL * 0.01)))
-
-        # Resize to actual size
-        self.kernel_image = self.kernel_image.resize(
-            self.SIZE, resample=Image.LANCZOS)
-
-        # convert to gray scale
-        self.kernel_image = self.kernel_image.convert("L")
-
-        # flag that we have generated a kernel
-        self.kernel_is_generated = True
-
-    def displayKernel(self, save_to: Path=None, show: bool=True):
-        """[summary]
-        Finds a kernel (psf) of given intensity.
-        [description]
-        Saves the kernel to save_to if needed or shows it
-        is show true
-
-        Keyword Arguments:
-            save_to {Path} -- Image file to save the kernel to. {None}
-            show {bool} -- shows kernel if true
-        """
-
-        # generate kernel if needed
-        self._createKernel()
-
-        # save if needed
-        if save_to is not None:
-
-            save_to_file = Path(save_to)
-
-            # save Kernel image
-            self.kernel_image.save(save_to_file)
-        else:
-            # Show kernel
-            self.kernel_image.show()
-
-    @property
-    def kernelMatrix(self) -> np.ndarray:
-        """[summary]
-        Kernel matrix of motion blur of given intensity.
-        [description]
-        Once generated, it stays the same.
-        Returns:
-            numpy ndarray
-        """
-
-        # generate kernel if needed
-        self._createKernel()
-        kernel = np.asarray(self.kernel_image, dtype=np.float32)
-        kernel /= np.sum(kernel)
-
-        return kernel
-
-    @kernelMatrix.setter
-    def kernelMatrix(self, *kargs):
-        raise NotImplementedError("Can't manually set kernel matrix yet")
-
-    def applyTo(self, image, keep_image_dim: bool = False) -> Image:
-        """[summary]
-        Applies kernel to one of the following:
-
-        1. Path to image file
-        2. Pillow image object
-        3. (H,W,3)-shaped numpy array
-        [description]
-
-        Arguments:
-            image {[str, Path, Image, np.ndarray]}
-            keep_image_dim {bool} -- If true, then we will
-                    conserve the image dimension after blurring
-                    by using "same" convolution instead of "valid"
-                    convolution inside the scipy convolve function.
-
-        Returns:
-            Image -- [description]
-        """
-        # calculate kernel if haven't already
-        self._createKernel()
-
-        def applyToPIL(image: Image, keep_image_dim: bool = False) -> Image:
-            """[summary]
-            Applies the kernel to an PIL.Image instance
-            [description]
-            converts to RGB and applies the kernel to each
-            band before recombining them.
-            Arguments:
-                image {Image} -- Image to convolve
-                keep_image_dim {bool} -- If true, then we will
-                    conserve the image dimension after blurring
-                    by using "same" convolution instead of "valid"
-                    convolution inside the scipy convolve function.
-
-            Returns:
-                Image -- blurred image
-            """
-            # convert to RGB
-            image = image.convert(mode="RGB")
-
-            conv_mode = "valid"
-            if keep_image_dim:
-                conv_mode = "same"
-
-            result_bands = ()
-
-            for band in image.split():
-
-                # convolve each band individually with kernel
-                result_band = convolve(
-                    band, self.kernelMatrix, mode=conv_mode).astype("uint8")
-
-                # collect bands
-                result_bands += result_band,
-
-            # stack bands back together
-            result = np.dstack(result_bands)
-
-            # Get image
-            return Image.fromarray(result)
-
-        # If image is Path
-        if isinstance(image, str) or isinstance(image, Path):
-
-            # open image as Image class
-            image_path = Path(image)
-            image = Image.open(image_path)
-
-            return applyToPIL(image, keep_image_dim)
-
-        elif isinstance(image, Image.Image):
-
-            # apply kernel
-            return applyToPIL(image, keep_image_dim)
-
-        elif isinstance(image, np.ndarray):
-
-            # ASSUMES we have an array of the form (H, W, 3)
-            ###
-
-            # initiate Image object from array
-            image = Image.fromarray(image)
-
-            return applyToPIL(image, keep_image_dim)
-
-        else:
-
-            raise ValueError("Cannot apply kernel to this type.")
-
-
-if __name__ == '__main__':
-    image = Image.open("./images/moon.png")
-    image.show()
-    k = Kernel()
-
-    k.applyTo(image, keep_image_dim=True).show()
diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/data/base.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/data/base.py
deleted file mode 100644
index b196c2f7aa583a3e8bc4aad9f943df0c4dae0da7..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/stable-diffusion/ldm/data/base.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from abc import abstractmethod
-from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
-
-
-class Txt2ImgIterableBaseDataset(IterableDataset):
-    '''
-    Define an interface to make the IterableDatasets for text2img data chainable
-    '''
-    def __init__(self, num_records=0, valid_ids=None, size=256):
-        super().__init__()
-        self.num_records = num_records
-        self.valid_ids = valid_ids
-        self.sample_ids = valid_ids
-        self.size = size
-
-        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
-
-    def __len__(self):
-        return self.num_records
-
-    @abstractmethod
-    def __iter__(self):
-        pass
\ No newline at end of file
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/message.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/message.go
deleted file mode 100644
index e1d828812e82b6065d0455e6fff2748037729232..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/message.go and /dev/null differ
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/vm/frame.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/vm/frame.go
deleted file mode 100644
index 77576aedd9af656f07c87bd5ee085e7194f6a5a3..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/vm/frame.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/candle-llama2/app-ce1542bf305e9c17.js b/spaces/PeepDaSlan9/candle-llama2/app-ce1542bf305e9c17.js
deleted file mode 100644
index 814b9023521ad5df8252c5b9af8f77f129cf0e2c..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/candle-llama2/app-ce1542bf305e9c17.js
+++ /dev/null
@@ -1,829 +0,0 @@
-let wasm;
-
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-
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-
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-
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-    heap[idx] = heap_next;
-    heap_next = idx;
-}
-
-function takeObject(idx) {
-    const ret = getObject(idx);
-    dropObject(idx);
-    return ret;
-}
-
-function addHeapObject(obj) {
-    if (heap_next === heap.length) heap.push(heap.length + 1);
-    const idx = heap_next;
-    heap_next = heap[idx];
-
-    heap[idx] = obj;
-    return idx;
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-    if (type == 'number' || type == 'boolean' || val == null) {
-        return  `${val}`;
-    }
-    if (type == 'string') {
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-            return `Function(${name})`;
-        } else {
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-        return addHeapObject(ret);
-    };
-
-    return imports;
-}
-
-function __wbg_init_memory(imports, maybe_memory) {
-
-}
-
-function __wbg_finalize_init(instance, module) {
-    wasm = instance.exports;
-    __wbg_init.__wbindgen_wasm_module = module;
-    cachedInt32Memory0 = null;
-    cachedUint32Memory0 = null;
-    cachedUint8Memory0 = null;
-
-    wasm.__wbindgen_start();
-    return wasm;
-}
-
-function initSync(module) {
-    if (wasm !== undefined) return wasm;
-
-    const imports = __wbg_get_imports();
-
-    __wbg_init_memory(imports);
-
-    if (!(module instanceof WebAssembly.Module)) {
-        module = new WebAssembly.Module(module);
-    }
-
-    const instance = new WebAssembly.Instance(module, imports);
-
-    return __wbg_finalize_init(instance, module);
-}
-
-async function __wbg_init(input) {
-    if (wasm !== undefined) return wasm;
-
-    if (typeof input === 'undefined') {
-        input = new URL('app-ce1542bf305e9c17_bg.wasm', import.meta.url);
-    }
-    const imports = __wbg_get_imports();
-
-    if (typeof input === 'string' || (typeof Request === 'function' && input instanceof Request) || (typeof URL === 'function' && input instanceof URL)) {
-        input = fetch(input);
-    }
-
-    __wbg_init_memory(imports);
-
-    const { instance, module } = await __wbg_load(await input, imports);
-
-    return __wbg_finalize_init(instance, module);
-}
-
-export { initSync }
-export default __wbg_init;
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/upfirdn2d.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/upfirdn2d.py
deleted file mode 100644
index c8bb2c3c949eed38a6465ed369fa881538dca010..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/upfirdn2d.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
-
-# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
-# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
-# Augmentation (ADA)
-# =======================================================================
-
-# 1. Definitions
-
-# "Licensor" means any person or entity that distributes its Work.
-
-# "Software" means the original work of authorship made available under
-# this License.
-
-# "Work" means the Software and any additions to or derivative works of
-# the Software that are made available under this License.
-
-# The terms "reproduce," "reproduction," "derivative works," and
-# "distribution" have the meaning as provided under U.S. copyright law;
-# provided, however, that for the purposes of this License, derivative
-# works shall not include works that remain separable from, or merely
-# link (or bind by name) to the interfaces of, the Work.
-
-# Works, including the Software, are "made available" under this License
-# by including in or with the Work either (a) a copyright notice
-# referencing the applicability of this License to the Work, or (b) a
-# copy of this License.
-
-# 2. License Grants
-
-#     2.1 Copyright Grant. Subject to the terms and conditions of this
-#     License, each Licensor grants to you a perpetual, worldwide,
-#     non-exclusive, royalty-free, copyright license to reproduce,
-#     prepare derivative works of, publicly display, publicly perform,
-#     sublicense and distribute its Work and any resulting derivative
-#     works in any form.
-
-# 3. Limitations
-
-#     3.1 Redistribution. You may reproduce or distribute the Work only
-#     if (a) you do so under this License, (b) you include a complete
-#     copy of this License with your distribution, and (c) you retain
-#     without modification any copyright, patent, trademark, or
-#     attribution notices that are present in the Work.
-
-#     3.2 Derivative Works. You may specify that additional or different
-#     terms apply to the use, reproduction, and distribution of your
-#     derivative works of the Work ("Your Terms") only if (a) Your Terms
-#     provide that the use limitation in Section 3.3 applies to your
-#     derivative works, and (b) you identify the specific derivative
-#     works that are subject to Your Terms. Notwithstanding Your Terms,
-#     this License (including the redistribution requirements in Section
-#     3.1) will continue to apply to the Work itself.
-
-#     3.3 Use Limitation. The Work and any derivative works thereof only
-#     may be used or intended for use non-commercially. Notwithstanding
-#     the foregoing, NVIDIA and its affiliates may use the Work and any
-#     derivative works commercially. As used herein, "non-commercially"
-#     means for research or evaluation purposes only.
-
-#     3.4 Patent Claims. If you bring or threaten to bring a patent claim
-#     against any Licensor (including any claim, cross-claim or
-#     counterclaim in a lawsuit) to enforce any patents that you allege
-#     are infringed by any Work, then your rights under this License from
-#     such Licensor (including the grant in Section 2.1) will terminate
-#     immediately.
-
-#     3.5 Trademarks. This License does not grant any rights to use any
-#     Licensor’s or its affiliates’ names, logos, or trademarks, except
-#     as necessary to reproduce the notices described in this License.
-
-#     3.6 Termination. If you violate any term of this License, then your
-#     rights under this License (including the grant in Section 2.1) will
-#     terminate immediately.
-
-# 4. Disclaimer of Warranty.
-
-# THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-# NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-# THIS LICENSE.
-
-# 5. Limitation of Liability.
-
-# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-# THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-# SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-# INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-# OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-# (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
-# LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
-# COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
-# THE POSSIBILITY OF SUCH DAMAGES.
-
-# =======================================================================
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-from annotator.uniformer.mmcv.utils import to_2tuple
-from ..utils import ext_loader
-
-upfirdn2d_ext = ext_loader.load_ext('_ext', ['upfirdn2d'])
-
-
-class UpFirDn2dBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad,
-                in_size, out_size):
-
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_ext.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            up_x=down_x,
-            up_y=down_y,
-            down_x=up_x,
-            down_y=up_y,
-            pad_x0=g_pad_x0,
-            pad_x1=g_pad_x1,
-            pad_y0=g_pad_y0,
-            pad_y1=g_pad_y1)
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2],
-                                     in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2],
-                                                ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_ext.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            up_x=ctx.up_x,
-            up_y=ctx.up_y,
-            down_x=ctx.down_x,
-            down_y=ctx.down_y,
-            pad_x0=ctx.pad_x0,
-            pad_x1=ctx.pad_x1,
-            pad_y0=ctx.pad_y0,
-            pad_y1=ctx.pad_y1)
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
-        #                                  ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1],
-                                         ctx.out_size[0], ctx.out_size[1])
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_ext.upfirdn2d(
-            input,
-            kernel,
-            up_x=up_x,
-            up_y=up_y,
-            down_x=down_x,
-            down_y=down_y,
-            pad_x0=pad_x0,
-            pad_x1=pad_x1,
-            pad_y0=pad_y0,
-            pad_y1=pad_y1)
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    """UpFRIDn for 2d features.
-
-    UpFIRDn is short for upsample, apply FIR filter and downsample. More
-    details can be found in:
-    https://www.mathworks.com/help/signal/ref/upfirdn.html
-
-    Args:
-        input (Tensor): Tensor with shape of (n, c, h, w).
-        kernel (Tensor): Filter kernel.
-        up (int | tuple[int], optional): Upsampling factor. If given a number,
-            we will use this factor for the both height and width side.
-            Defaults to 1.
-        down (int | tuple[int], optional): Downsampling factor. If given a
-            number, we will use this factor for the both height and width side.
-            Defaults to 1.
-        pad (tuple[int], optional): Padding for tensors, (x_pad, y_pad) or
-            (x_pad_0, x_pad_1, y_pad_0, y_pad_1). Defaults to (0, 0).
-
-    Returns:
-        Tensor: Tensor after UpFIRDn.
-    """
-    if input.device.type == 'cpu':
-        if len(pad) == 2:
-            pad = (pad[0], pad[1], pad[0], pad[1])
-
-        up = to_2tuple(up)
-
-        down = to_2tuple(down)
-
-        out = upfirdn2d_native(input, kernel, up[0], up[1], down[0], down[1],
-                               pad[0], pad[1], pad[2], pad[3])
-    else:
-        _up = to_2tuple(up)
-
-        _down = to_2tuple(down)
-
-        if len(pad) == 4:
-            _pad = pad
-        elif len(pad) == 2:
-            _pad = (pad[0], pad[1], pad[0], pad[1])
-
-        out = UpFirDn2d.apply(input, kernel, _up, _down, _pad)
-
-    return out
-
-
-def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
-                     pad_y0, pad_y1):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out,
-        [0, 0,
-         max(pad_x0, 0),
-         max(pad_x1, 0),
-         max(pad_y0, 0),
-         max(pad_y1, 0)])
-    out = out[:,
-              max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0),
-              max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/hooks/sync_buffer.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/hooks/sync_buffer.py
deleted file mode 100644
index 6376b7ff894280cb2782243b25e8973650591577..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/hooks/sync_buffer.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..dist_utils import allreduce_params
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class SyncBuffersHook(Hook):
-    """Synchronize model buffers such as running_mean and running_var in BN at
-    the end of each epoch.
-
-    Args:
-        distributed (bool): Whether distributed training is used. It is
-          effective only for distributed training. Defaults to True.
-    """
-
-    def __init__(self, distributed=True):
-        self.distributed = distributed
-
-    def after_epoch(self, runner):
-        """All-reduce model buffers at the end of each epoch."""
-        if self.distributed:
-            allreduce_params(runner.model.buffers())
diff --git a/spaces/Pie31415/control-animation/text_to_animation/pipelines/text_to_video_pipeline_flax.py b/spaces/Pie31415/control-animation/text_to_animation/pipelines/text_to_video_pipeline_flax.py
deleted file mode 100644
index 454ab98208f0e2ca96c4b851989c5445e39e292b..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/text_to_animation/pipelines/text_to_video_pipeline_flax.py
+++ /dev/null
@@ -1,1051 +0,0 @@
-import warnings
-from functools import partial
-from typing import Dict, List, Optional, Union
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.core.frozen_dict import FrozenDict
-from flax.jax_utils import unreplicate
-from flax import jax_utils
-from flax.training.common_utils import shard
-from PIL import Image
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
-from einops import rearrange, repeat
-from diffusers.models import FlaxAutoencoderKL, FlaxControlNetModel, FlaxUNet2DConditionModel
-from diffusers.schedulers import (
-    FlaxDDIMScheduler,
-    FlaxDPMSolverMultistepScheduler,
-    FlaxLMSDiscreteScheduler,
-    FlaxPNDMScheduler,
-)
-from diffusers.utils import PIL_INTERPOLATION, logging, replace_example_docstring
-from diffusers.pipelines.pipeline_flax_utils import FlaxDiffusionPipeline
-from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker_flax import FlaxStableDiffusionSafetyChecker
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-"""
-Text2Video-Zero:
- - Inputs: Prompt, Pose Control via mp4/gif, First Frame (?)
- - JAX implementation
- - 3DUnet to replace 2DUnetConditional
-
-"""
-
-def replicate_devices(array):
-    return jnp.expand_dims(array, 0).repeat(jax.device_count(), 0)
-
-
-DEBUG = False # Set to True to use python for loop instead of jax.fori_loop for easier debugging
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import jax
-        >>> import numpy as np
-        >>> import jax.numpy as jnp
-        >>> from flax.jax_utils import replicate
-        >>> from flax.training.common_utils import shard
-        >>> from diffusers.utils import load_image
-        >>> from PIL import Image
-        >>> from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
-        >>> def image_grid(imgs, rows, cols):
-        ...     w, h = imgs[0].size
-        ...     grid = Image.new("RGB", size=(cols * w, rows * h))
-        ...     for i, img in enumerate(imgs):
-        ...         grid.paste(img, box=(i % cols * w, i // cols * h))
-        ...     return grid
-        >>> def create_key(seed=0):
-        ...     return jax.random.PRNGKey(seed)
-        >>> rng = create_key(0)
-        >>> # get canny image
-        >>> canny_image = load_image(
-        ...     "https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/blog_post_cell_10_output_0.jpeg"
-        ... )
-        >>> prompts = "best quality, extremely detailed"
-        >>> negative_prompts = "monochrome, lowres, bad anatomy, worst quality, low quality"
-        >>> # load control net and stable diffusion v1-5
-        >>> controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-        ...     "lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.float32
-        ... )
-        >>> pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32
-        ... )
-        >>> params["controlnet"] = controlnet_params
-        >>> num_samples = jax.device_count()
-        >>> rng = jax.random.split(rng, jax.device_count())
-        >>> prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
-        >>> negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
-        >>> processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)
-        >>> p_params = replicate(params)
-        >>> prompt_ids = shard(prompt_ids)
-        >>> negative_prompt_ids = shard(negative_prompt_ids)
-        >>> processed_image = shard(processed_image)
-        >>> output = pipe(
-        ...     prompt_ids=prompt_ids,
-        ...     image=processed_image,
-        ...     params=p_params,
-        ...     prng_seed=rng,
-        ...     num_inference_steps=50,
-        ...     neg_prompt_ids=negative_prompt_ids,
-        ...     jit=True,
-        ... ).images
-        >>> output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
-        >>> output_images = image_grid(output_images, num_samples // 4, 4)
-        >>> output_images.save("generated_image.png")
-        ```
-"""
-class FlaxTextToVideoPipeline(FlaxDiffusionPipeline):
-    def __init__(
-        self,
-        vae,
-        text_encoder,
-        tokenizer,
-        unet,
-        unet_vanilla,
-        controlnet,
-        scheduler: Union[
-            FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
-        ],
-        safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        dtype: jnp.dtype = jnp.float32,
-    ):
-        super().__init__()
-        self.dtype = dtype
-
-        if safety_checker is None:
-            logger.warn(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            unet_vanilla=unet_vanilla,
-            controlnet=controlnet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
-    def DDPM_forward(self, params, prng, x0, t0, tMax, shape, text_embeddings):
-        if x0 is None:
-            return jax.random.normal(prng, shape, dtype=text_embeddings.dtype)
-        else:
-            eps = jax.random.normal(prng, x0.shape, dtype=text_embeddings.dtype)
-            alpha_vec = jnp.prod(params["scheduler"].common.alphas[t0:tMax])
-            xt = jnp.sqrt(alpha_vec) * x0 + \
-                jnp.sqrt(1-alpha_vec) * eps
-            return xt
-        
-    def DDIM_backward(self, params, num_inference_steps, timesteps, skip_t, t0, t1, do_classifier_free_guidance, text_embeddings, latents_local,
-                        guidance_scale, controlnet_image=None, controlnet_conditioning_scale=None):
-        scheduler_state = self.scheduler.set_timesteps(params["scheduler"], num_inference_steps)
-        f = latents_local.shape[2]
-        latents_local = rearrange(latents_local, "b c f h w -> (b f) c h w")
-        latents = latents_local.copy()
-        x_t0_1 = None
-        x_t1_1 = None
-        max_timestep = len(timesteps)-1
-        timesteps = jnp.array(timesteps)
-        def while_body(args):
-            step, latents, x_t0_1, x_t1_1, scheduler_state = args
-            t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-            latent_model_input = jnp.concatenate(
-                [latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(
-                scheduler_state, latent_model_input, timestep=t
-            )
-            f = latents.shape[0]
-            te = jnp.stack([text_embeddings[0, :, :]]*f + [text_embeddings[-1,:,:]]*f)
-            timestep = jnp.broadcast_to(t, latent_model_input.shape[0])
-            if controlnet_image is not None:
-                down_block_res_samples, mid_block_res_sample = self.controlnet.apply(
-                    {"params": params["controlnet"]},
-                    jnp.array(latent_model_input),
-                    jnp.array(timestep, dtype=jnp.int32),
-                    encoder_hidden_states=te,
-                    controlnet_cond=controlnet_image,
-                    conditioning_scale=controlnet_conditioning_scale,
-                    return_dict=False,
-                )
-                # predict the noise residual
-                noise_pred = self.unet.apply(
-                    {"params": params["unet"]},
-                    jnp.array(latent_model_input),
-                    jnp.array(timestep, dtype=jnp.int32),
-                    encoder_hidden_states=te,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                ).sample
-            else:
-                noise_pred = self.unet.apply(
-                    {"params": params["unet"]},
-                    jnp.array(latent_model_input),
-                    jnp.array(timestep, dtype=jnp.int32),
-                    encoder_hidden_states=te,
-                    ).sample
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = jnp.split(noise_pred, 2, axis=0)
-                noise_pred = noise_pred_uncond + guidance_scale * \
-                    (noise_pred_text - noise_pred_uncond)
-            # compute the previous noisy sample x_t -> x_t-1
-            latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
-            x_t0_1 = jax.lax.select((step < max_timestep-1) & (timesteps[step+1] == t0), latents, x_t0_1)
-            x_t1_1 = jax.lax.select((step < max_timestep-1) & (timesteps[step+1] == t1), latents, x_t1_1)
-            return (step + 1, latents, x_t0_1, x_t1_1, scheduler_state)
-        
-        latents_shape = latents.shape
-        x_t0_1, x_t1_1 = jnp.zeros(latents_shape), jnp.zeros(latents_shape)
-
-        def cond_fun(arg):
-            step, latents, x_t0_1, x_t1_1, scheduler_state = arg
-            return (step < skip_t) & (step < num_inference_steps)
-        
-        if DEBUG:
-            step = 0
-            while cond_fun((step, latents, x_t0_1, x_t1_1)):
-                step, latents, x_t0_1, x_t1_1, scheduler_state = while_body((step, latents, x_t0_1, x_t1_1, scheduler_state))
-                step = step + 1
-        else:
-            _, latents, x_t0_1, x_t1_1, scheduler_state = jax.lax.while_loop(cond_fun, while_body, (0, latents, x_t0_1, x_t1_1, scheduler_state))
-        latents = rearrange(latents, "(b f) c h w -> b c f h w", f=f)
-        res = {"x0": latents.copy()}
-        if x_t0_1 is not None:
-            x_t0_1 = rearrange(x_t0_1, "(b f) c h w -> b c f  h w", f=f)
-            res["x_t0_1"] = x_t0_1.copy()
-        if x_t1_1 is not None:
-            x_t1_1 = rearrange(x_t1_1, "(b f) c h w -> b c f  h w", f=f)
-            res["x_t1_1"] = x_t1_1.copy()
-        return res
-    
-    def warp_latents_independently(self, latents, reference_flow):
-        _, _, H, W = reference_flow.shape
-        b, _, f, h, w = latents.shape
-        assert b == 1
-        coords0 = coords_grid(f, H, W)
-        coords_t0 = coords0 + reference_flow
-        coords_t0 = coords_t0.at[:, 0].set(coords_t0[:, 0] * w / W)
-        coords_t0 = coords_t0.at[:, 1].set(coords_t0[:, 1] * h / H)
-        f, c, _, _ = coords_t0.shape
-        coords_t0 = jax.image.resize(coords_t0, (f, c, h, w), "linear")
-        coords_t0 = rearrange(coords_t0, 'f c h w -> f h w c')
-        latents_0 = rearrange(latents[0], 'c f h w -> f  c  h w')
-        warped = grid_sample(latents_0, coords_t0, "mirror")
-        warped = rearrange(warped, '(b f) c h w -> b c f h w', f=f)
-        return warped
-
-    def warp_vid_independently(self, vid, reference_flow):
-        _, _, H, W = reference_flow.shape
-        f, _, h, w = vid.shape
-        coords0 = coords_grid(f, H, W)
-        coords_t0 = coords0 + reference_flow
-        coords_t0 = coords_t0.at[:, 0].set(coords_t0[:, 0] * w / W)
-        coords_t0 = coords_t0.at[:, 1].set(coords_t0[:, 1] * h / H)
-        f, c, _, _ = coords_t0.shape
-        coords_t0 = jax.image.resize(coords_t0, (f, c, h, w), "linear")
-        coords_t0 = rearrange(coords_t0, 'f c h w -> f h w c')
-        # latents_0 = rearrange(vid, 'c f h w -> f  c  h w')
-        warped = grid_sample(vid, coords_t0, "zeropad")
-        # warped = rearrange(warped, 'f c h w -> b c f h w', f=f)
-        return warped
-    
-    def create_motion_field(self, motion_field_strength_x, motion_field_strength_y, frame_ids, video_length, latents):
-        reference_flow = jnp.zeros(
-            (video_length-1, 2, 512, 512), dtype=latents.dtype)    
-        for fr_idx, frame_id in enumerate(frame_ids):
-            reference_flow = reference_flow.at[fr_idx, 0, :,
-                           :].set(motion_field_strength_x*(frame_id))
-            reference_flow = reference_flow.at[fr_idx, 1, :,
-                           :].set(motion_field_strength_y*(frame_id))
-        return reference_flow
-    
-    def create_motion_field_and_warp_latents(self, motion_field_strength_x, motion_field_strength_y, frame_ids, video_length, latents):
-        motion_field = self.create_motion_field(motion_field_strength_x=motion_field_strength_x,
-                                                motion_field_strength_y=motion_field_strength_y, latents=latents, video_length=video_length, frame_ids=frame_ids)
-        for idx, latent in enumerate(latents):
-            latents = latents.at[idx].set(self.warp_latents_independently(
-                latent[None], motion_field)[0])
-        return motion_field, latents
-
-    def text_to_video_zero(self, params,
-                           prng,
-                           text_embeddings,
-                           video_length: Optional[int],
-                           do_classifier_free_guidance = True,
-                           height: Optional[int] = None,
-                           width: Optional[int] = None,
-                           num_inference_steps: int = 50,
-                           guidance_scale: float = 7.5,
-                           num_videos_per_prompt: Optional[int] = 1,
-                           xT = None,
-                           smooth_bg_strength: float=0.,
-                           motion_field_strength_x: float = 12,
-                           motion_field_strength_y: float = 12,
-                           t0: int = 44,
-                           t1: int = 47,
-                           controlnet_image=None,
-                           controlnet_conditioning_scale=0,
-                           ):
-        frame_ids = list(range(video_length))
-        # Prepare timesteps
-        params["scheduler"] = self.scheduler.set_timesteps(params["scheduler"], num_inference_steps)
-        timesteps = params["scheduler"].timesteps
-        # Prepare latent variables
-        num_channels_latents = self.unet.in_channels
-        batch_size = 1
-        xT = prepare_latents(params, prng, batch_size * num_videos_per_prompt, num_channels_latents, height, width, self.vae_scale_factor, xT)
-
-        timesteps_ddpm = [981, 961, 941, 921, 901, 881, 861, 841, 821, 801, 781, 761, 741, 721,
-                            701, 681, 661, 641, 621, 601, 581, 561, 541, 521, 501, 481, 461, 441,
-                            421, 401, 381, 361, 341, 321, 301, 281, 261, 241, 221, 201, 181, 161,
-                            141, 121, 101,  81,  61,  41,  21,   1]
-        timesteps_ddpm.reverse()
-        t0 = timesteps_ddpm[t0]
-        t1 = timesteps_ddpm[t1]
-        x_t1_1 = None
-
-        # Denoising loop
-        shape = (batch_size, num_channels_latents, 1, height //
-                self.vae.scaling_factor, width // self.vae.scaling_factor)
-
-        #  perform ∆t backward steps by stable diffusion
-        ddim_res = self.DDIM_backward(params, num_inference_steps=num_inference_steps, timesteps=timesteps, skip_t=1000, t0=t0, t1=t1, do_classifier_free_guidance=do_classifier_free_guidance,
-                                text_embeddings=text_embeddings, latents_local=xT, guidance_scale=guidance_scale,
-                                controlnet_image=jnp.stack([controlnet_image[0]] * 2), controlnet_conditioning_scale=controlnet_conditioning_scale)
-        x0 = ddim_res["x0"]
-
-        # apply warping functions
-        if "x_t0_1" in ddim_res:
-            x_t0_1 = ddim_res["x_t0_1"]
-        if "x_t1_1" in ddim_res:
-            x_t1_1 = ddim_res["x_t1_1"]
-        x_t0_k = x_t0_1[:, :, :1, :, :].repeat(video_length-1, 2)
-        reference_flow, x_t0_k = self.create_motion_field_and_warp_latents(
-            motion_field_strength_x=motion_field_strength_x, motion_field_strength_y=motion_field_strength_y, latents=x_t0_k, video_length=video_length, frame_ids=frame_ids[1:])
-        # assuming t0=t1=1000, if t0 = 1000
-
-        # DDPM forward for more motion freedom
-        ddpm_fwd = partial(self.DDPM_forward, params=params, prng=prng, x0=x_t0_k, t0=t0,
-                           tMax=t1, shape=shape, text_embeddings=text_embeddings)
-        x_t1_k = jax.lax.cond(t1 > t0,
-                              ddpm_fwd,
-                              lambda:x_t0_k
-        )
-        x_t1 = jnp.concatenate([x_t1_1, x_t1_k], axis=2)
-
-        # backward stepts by stable diffusion
-
-        #warp the controlnet image following the same flow defined for latent
-        controlnet_video = controlnet_image[:video_length]
-        controlnet_video = controlnet_video.at[1:].set(self.warp_vid_independently(controlnet_video[1:], reference_flow))
-        controlnet_image = jnp.concatenate([controlnet_video]*2)
-        smooth_bg = True
-
-        if smooth_bg:
-            #latent shape: "b c f h w"
-            M_FG = repeat(get_mask_pose(controlnet_video), "f h w -> b c f h w", c=x_t1.shape[1], b=batch_size)
-            initial_bg = repeat(x_t1[:,:,0] * (1 - M_FG[:,:,0]), "b c h w -> b c f h w", f=video_length-1)
-            #warp the controlnet image following the same flow defined for latent #f c h w
-            initial_bg_warped = self.warp_latents_independently(initial_bg, reference_flow)
-            bgs = x_t1[:,:,1:] * (1 - M_FG[:,:,1:]) #initial background 
-            initial_mask_warped = 1 - self.warp_latents_independently(repeat(M_FG[:,:,0], "b c h w -> b c f h w", f = video_length-1), reference_flow)
-            # initial_mask_warped = 1 - warp_vid_independently(repeat(M_FG[:,:,0], "b c h w -> (b f) c h w", f = video_length-1), reference_flow)
-            # initial_mask_warped = rearrange(initial_mask_warped, "(b f) c h w -> b c f h w", b=batch_size)
-            mask = (1 - M_FG[:,:,1:]) * initial_mask_warped
-            x_t1 = x_t1.at[:,:,1:].set( (1 - mask) * x_t1[:,:,1:] + mask * (initial_bg_warped * smooth_bg_strength + (1 - smooth_bg_strength) * bgs))
-            
-        ddim_res = self.DDIM_backward(params, num_inference_steps=num_inference_steps, timesteps=timesteps, skip_t=t1, t0=-1, t1=-1, do_classifier_free_guidance=do_classifier_free_guidance,
-                                            text_embeddings=text_embeddings, latents_local=x_t1, guidance_scale=guidance_scale,
-                                            controlnet_image=controlnet_image, controlnet_conditioning_scale=controlnet_conditioning_scale,
-                                     )
-        
-        x0 = ddim_res["x0"]
-        del ddim_res
-        del x_t1
-        del x_t1_1
-        del x_t1_k
-        return x0
-
-    def denoise_latent(self, params, num_inference_steps, timesteps, do_classifier_free_guidance, text_embeddings, latents,
-                        guidance_scale, controlnet_image=None, controlnet_conditioning_scale=None):
-        
-        scheduler_state = self.scheduler.set_timesteps(params["scheduler"], num_inference_steps)
-        # f = latents_local.shape[2]
-        # latents_local = rearrange(latents_local, "b c f h w -> (b f) c h w")
-
-        max_timestep = len(timesteps)-1
-        timesteps = jnp.array(timesteps)
-        def while_body(args):
-            step, latents, scheduler_state = args
-            t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-            latent_model_input = jnp.concatenate(
-                [latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(
-                scheduler_state, latent_model_input, timestep=t
-            )
-            f = latents.shape[0]
-            te = jnp.stack([text_embeddings[0, :, :]]*f + [text_embeddings[-1,:,:]]*f)
-            timestep = jnp.broadcast_to(t, latent_model_input.shape[0])
-            if controlnet_image is not None:
-                down_block_res_samples, mid_block_res_sample = self.controlnet.apply(
-                    {"params": params["controlnet"]},
-                    jnp.array(latent_model_input),
-                    jnp.array(timestep, dtype=jnp.int32),
-                    encoder_hidden_states=te,
-                    controlnet_cond=controlnet_image,
-                    conditioning_scale=controlnet_conditioning_scale,
-                    return_dict=False,
-                )
-                # predict the noise residual
-                noise_pred = self.unet_vanilla.apply(
-                    {"params": params["unet"]},
-                    jnp.array(latent_model_input),
-                    jnp.array(timestep, dtype=jnp.int32),
-                    encoder_hidden_states=te,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                ).sample
-            else:
-                noise_pred = self.unet_vanilla.apply(
-                    {"params": params["unet"]},
-                    jnp.array(latent_model_input),
-                    jnp.array(timestep, dtype=jnp.int32),
-                    encoder_hidden_states=te,
-                    ).sample
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = jnp.split(noise_pred, 2, axis=0)
-                noise_pred = noise_pred_uncond + guidance_scale * \
-                    (noise_pred_text - noise_pred_uncond)
-            # compute the previous noisy sample x_t -> x_t-1
-            latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
-            return (step + 1, latents, scheduler_state)
-        
-        def cond_fun(arg):
-            step, latents, scheduler_state = arg
-            return (step < num_inference_steps)
-        
-        if DEBUG:
-            step = 0
-            while cond_fun((step, latents, scheduler_state)):
-                step, latents, scheduler_state = while_body((step, latents, scheduler_state))
-                step = step + 1
-        else:
-            _, latents, scheduler_state = jax.lax.while_loop(cond_fun, while_body, (0, latents, scheduler_state))
-        # latents = rearrange(latents, "(b f) c h w -> b c f h w", f=f)
-        return latents
-
-    def generate_starting_frames(self,
-                                params,
-                                prngs: list, #list of prngs for each img
-                                prompt,
-                                neg_prompt,
-                                controlnet_image,
-                                do_classifier_free_guidance = True,
-                                num_inference_steps: int = 50,
-                                guidance_scale: float = 7.5,
-                                t0: int = 44,
-                                t1: int = 47,
-                                controlnet_conditioning_scale=1.,
-                                ):
-
-        height, width = controlnet_image.shape[-2:]
-        if height % 64 != 0 or width % 64 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 64 but are {height} and {width}.")
-
-        shape = (self.unet.in_channels, height //
-        self.vae_scale_factor, width // self.vae_scale_factor) # c h w
-        # scale the initial noise by the standard deviation required by the scheduler
-
-        # print(f"Generating {len(prngs)} first frames with prompt {prompt}, for {num_inference_steps} steps. PRNG seeds are: {prngs}")
-
-        latents = jnp.stack([jax.random.normal(prng, shape) for prng in prngs]) # b c h w
-        latents = latents * params["scheduler"].init_noise_sigma
-
-        timesteps = params["scheduler"].timesteps
-        timesteps_ddpm = [981, 961, 941, 921, 901, 881, 861, 841, 821, 801, 781, 761, 741, 721,
-                            701, 681, 661, 641, 621, 601, 581, 561, 541, 521, 501, 481, 461, 441,
-                            421, 401, 381, 361, 341, 321, 301, 281, 261, 241, 221, 201, 181, 161,
-                            141, 121, 101,  81,  61,  41,  21,   1]
-        timesteps_ddpm.reverse()
-        t0 = timesteps_ddpm[t0]
-        t1 = timesteps_ddpm[t1]
-
-        # get prompt text embeddings
-        prompt_ids = shard(self.prepare_text_inputs(prompt))
-
-        # prompt_embeds = jax.pmap( lambda prompt_ids, params:  )(prompt_ids, params)
-
-        @jax.pmap
-        def prepare_text(params, prompt_ids, uncond_input):
-            prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
-            negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
-            text_embeddings = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
-            return text_embeddings
-
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = 1
-        max_length = prompt_ids.shape[-1]
-        if neg_prompt is None:
-            uncond_input = shard(self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
-            ).input_ids)
-        else:
-            neg_prompt_ids = self.prepare_text_inputs(neg_prompt)
-            uncond_input = shard(neg_prompt_ids)
-
-        text_embeddings = prepare_text(params, prompt_ids, uncond_input)
-
-        controlnet_image = shard(jnp.stack([controlnet_image[0]] * len(prngs) * 2))
-
-        timesteps = shard(jnp.array(timesteps))
-        guidance_scale = shard(jnp.array(guidance_scale))
-        controlnet_conditioning_scale = shard(jnp.array(controlnet_conditioning_scale))
-
-        #latent is shape # b c h w
-        # vmap_gen_start_frame = jax.vmap(lambda latent: p_generate_starting_frames(self, num_inference_steps, params, timesteps, text_embeddings, shard(latent[None]), guidance_scale, controlnet_image, controlnet_conditioning_scale))
-        # decoded_latents = vmap_gen_start_frame(latents)
-        decoded_latents = p_generate_starting_frames(self, num_inference_steps, params, timesteps, text_embeddings, shard(latents), guidance_scale, controlnet_image, controlnet_conditioning_scale)
-        # print(f"shape output: {decoded_latents.shape}")
-        return unshard(decoded_latents)#[:, 0]
-
-    def generate_video(
-        self,
-        prompt: str,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int = 50,
-        guidance_scale: Union[float, jnp.array] = 7.5,
-        latents: jnp.array = None,
-        neg_prompt: str = "",
-        controlnet_conditioning_scale: Union[float, jnp.array] = 1.0,
-        return_dict: bool = True,
-        jit: bool = False,
-        xT = None,
-        smooth_bg_strength: float=0.,
-        motion_field_strength_x: float = 3,
-        motion_field_strength_y: float = 4,
-        t0: int = 44,
-        t1: int = 47,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt_ids (`jnp.array`):
-                The prompt or prompts to guide the image generation.
-            image (`jnp.array`):
-                Array representing the ControlNet input condition. ControlNet use this input condition to generate
-                guidance to Unet.
-            params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights
-            prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            latents (`jnp.array`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            controlnet_conditioning_scale (`float` or `jnp.array`, *optional*, defaults to 1.0):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
-                a plain tuple.
-            jit (`bool`, defaults to `False`):
-                Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
-                exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
-        Examples:
-        Returns:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple. When returning a tuple, the first element is a list with the generated images, and the second
-            element is a list of `bool`s denoting whether the corresponding generated image likely represents
-            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
-        """
-        height, width = image.shape[-2:]
-        vid_length = image.shape[0]
-        # get prompt text embeddings
-        prompt_ids = self.prepare_text_inputs([prompt] * vid_length)
-        neg_prompt_ids = self.prepare_text_inputs([neg_prompt] * vid_length)
-
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = 1
-
-        if isinstance(guidance_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                guidance_scale = guidance_scale[:, None]
-        if isinstance(controlnet_conditioning_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            controlnet_conditioning_scale = jnp.array([controlnet_conditioning_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                controlnet_conditioning_scale = controlnet_conditioning_scale[:, None]
-        if jit:
-            images = _p_generate(
-                self,
-                replicate_devices(prompt_ids),
-                replicate_devices(image),
-                jax_utils.replicate(params),
-                replicate_devices(prng_seed),
-                num_inference_steps,
-                replicate_devices(guidance_scale),
-                replicate_devices(latents) if latents is not None else None,
-                replicate_devices(neg_prompt_ids) if neg_prompt_ids is not None else None,
-                replicate_devices(controlnet_conditioning_scale),
-                replicate_devices(xT) if xT is not None else None,
-                replicate_devices(smooth_bg_strength),
-                replicate_devices(motion_field_strength_x),
-                replicate_devices(motion_field_strength_y),
-                t0,
-                t1,
-            )
-        else:
-            images = self._generate(
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                num_inference_steps,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-                controlnet_conditioning_scale,
-                xT,
-                smooth_bg_strength,
-                motion_field_strength_x,
-                motion_field_strength_y,
-                t0,
-                t1,
-            )
-        if self.safety_checker is not None:
-            safety_params = params["safety_checker"]
-            images_uint8_casted = (images * 255).round().astype("uint8")
-            num_devices, batch_size = images.shape[:2]
-            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
-            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
-            images = np.asarray(images)
-            # block images
-            if any(has_nsfw_concept):
-                for i, is_nsfw in enumerate(has_nsfw_concept):
-                    if is_nsfw:
-                        images[i] = np.asarray(images_uint8_casted[i])
-            images = images.reshape(num_devices, batch_size, height, width, 3)
-        else:
-            images = np.asarray(images)
-            has_nsfw_concept = False
-        if not return_dict:
-            return (images, has_nsfw_concept)
-        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
-
-    def prepare_text_inputs(self, prompt: Union[str, List[str]]):
-        if not isinstance(prompt, (str, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="np",
-        )
-        return text_input.input_ids
-    def prepare_image_inputs(self, image: Union[Image.Image, List[Image.Image]]):
-        if not isinstance(image, (Image.Image, list)):
-            raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
-        if isinstance(image, Image.Image):
-            image = [image]
-        processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])
-        return processed_images
-    def _get_has_nsfw_concepts(self, features, params):
-        has_nsfw_concepts = self.safety_checker(features, params)
-        return has_nsfw_concepts
-    def _run_safety_checker(self, images, safety_model_params, jit=False):
-        # safety_model_params should already be replicated when jit is True
-        pil_images = [Image.fromarray(image) for image in images]
-        features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
-        if jit:
-            features = shard(features)
-            has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
-            has_nsfw_concepts = unshard(has_nsfw_concepts)
-            safety_model_params = unreplicate(safety_model_params)
-        else:
-            has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
-        images_was_copied = False
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if not images_was_copied:
-                    images_was_copied = True
-                    images = images.copy()
-                images[idx] = np.zeros(images[idx].shape, dtype=np.uint8)  # black image
-            if any(has_nsfw_concepts):
-                warnings.warn(
-                    "Potential NSFW content was detected in one or more images. A black image will be returned"
-                    " instead. Try again with a different prompt and/or seed."
-                )
-        return images, has_nsfw_concepts
-    def _generate(
-        self,
-        prompt_ids: jnp.array,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int,
-        guidance_scale: float,
-        latents: Optional[jnp.array] = None,
-        neg_prompt_ids: Optional[jnp.array] = None,
-        controlnet_conditioning_scale: float = 1.0,
-        xT = None,
-        smooth_bg_strength: float = 0.,
-        motion_field_strength_x: float = 12,
-        motion_field_strength_y: float = 12,
-        t0: int = 44,
-        t1: int = 47,
-    ):
-        height, width = image.shape[-2:]
-        video_length = image.shape[0]
-        if height % 64 != 0 or width % 64 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 64 but are {height} and {width}.")
-        # get prompt text embeddings
-        prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = prompt_ids.shape[0]
-        max_length = prompt_ids.shape[-1]
-        if neg_prompt_ids is None:
-            uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
-            ).input_ids
-        else:
-            uncond_input = neg_prompt_ids
-        negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
-        context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
-        image = jnp.concatenate([image] * 2)
-        seed_t2vz, prng_seed = jax.random.split(prng_seed)
-        #get the latent following text to video zero
-        latents = self.text_to_video_zero(params, seed_t2vz, text_embeddings=context, video_length=video_length,
-                                          height=height, width = width, num_inference_steps=num_inference_steps,
-                                          guidance_scale=guidance_scale, controlnet_image=image,
-                                          xT=xT, smooth_bg_strength=smooth_bg_strength, t0=t0, t1=t1,
-                                          motion_field_strength_x=motion_field_strength_x,
-                                          motion_field_strength_y=motion_field_strength_y,
-                                          controlnet_conditioning_scale=controlnet_conditioning_scale
-                                          )
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        latents = rearrange(latents, "b c f h w -> (b f) c h w")
-        video = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
-        video = (video / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
-        return video
-    
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt_ids: jnp.array,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int = 50,
-        guidance_scale: Union[float, jnp.array] = 7.5,
-        latents: jnp.array = None,
-        neg_prompt_ids: jnp.array = None,
-        controlnet_conditioning_scale: Union[float, jnp.array] = 1.0,
-        return_dict: bool = True,
-        jit: bool = False,
-        xT = None,
-        smooth_bg_strength: float = 0.,
-        motion_field_strength_x: float = 3,
-        motion_field_strength_y: float = 4,
-        t0: int = 44,
-        t1: int = 47,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt_ids (`jnp.array`):
-                The prompt or prompts to guide the image generation.
-            image (`jnp.array`):
-                Array representing the ControlNet input condition. ControlNet use this input condition to generate
-                guidance to Unet.
-            params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights
-            prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            latents (`jnp.array`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            controlnet_conditioning_scale (`float` or `jnp.array`, *optional*, defaults to 1.0):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
-                a plain tuple.
-            jit (`bool`, defaults to `False`):
-                Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
-                exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
-        Examples:
-        Returns:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple. When returning a tuple, the first element is a list with the generated images, and the second
-            element is a list of `bool`s denoting whether the corresponding generated image likely represents
-            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
-        """
-        height, width = image.shape[-2:]
-        if isinstance(guidance_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                guidance_scale = guidance_scale[:, None]
-        if isinstance(controlnet_conditioning_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            controlnet_conditioning_scale = jnp.array([controlnet_conditioning_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                controlnet_conditioning_scale = controlnet_conditioning_scale[:, None]
-        if jit:
-            images = _p_generate(
-                self,
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                num_inference_steps,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-                controlnet_conditioning_scale,
-                xT,
-                smooth_bg_strength,
-                motion_field_strength_x,
-                motion_field_strength_y,
-                t0,
-                t1,
-            )
-        else:
-            images = self._generate(
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                num_inference_steps,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-                controlnet_conditioning_scale,
-                xT,
-                smooth_bg_strength,
-                motion_field_strength_x,
-                motion_field_strength_y,
-                t0,
-                t1,
-            )
-        if self.safety_checker is not None:
-            safety_params = params["safety_checker"]
-            images_uint8_casted = (images * 255).round().astype("uint8")
-            num_devices, batch_size = images.shape[:2]
-            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
-            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
-            images = np.asarray(images)
-            # block images
-            if any(has_nsfw_concept):
-                for i, is_nsfw in enumerate(has_nsfw_concept):
-                    if is_nsfw:
-                        images[i] = np.asarray(images_uint8_casted[i])
-            images = images.reshape(num_devices, batch_size, height, width, 3)
-        else:
-            images = np.asarray(images)
-            has_nsfw_concept = False
-        if not return_dict:
-            return (images, has_nsfw_concept)
-        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
-
-
-# Static argnums are pipe, num_inference_steps. A change would trigger recompilation.
-# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
-@partial(
-    jax.pmap,
-    in_axes=(None, 0, 0, 0, 0, None, 0, 0, 0, 0, 0, 0, 0, 0, None, None),
-    static_broadcasted_argnums=(0, 5, 14, 15)
-)
-def _p_generate(
-    pipe,
-    prompt_ids, 
-    image,
-    params,
-    prng_seed,
-    num_inference_steps,
-    guidance_scale,
-    latents,
-    neg_prompt_ids,
-    controlnet_conditioning_scale,
-    xT,
-    smooth_bg_strength,
-    motion_field_strength_x,
-    motion_field_strength_y,
-    t0,
-    t1,
-):
-    return pipe._generate(
-        prompt_ids,
-        image,
-        params,
-        prng_seed,
-        num_inference_steps,
-        guidance_scale,
-        latents,
-        neg_prompt_ids,
-        controlnet_conditioning_scale,
-        xT,
-        smooth_bg_strength,
-        motion_field_strength_x,
-        motion_field_strength_y,
-        t0,
-        t1,
-    )
-@partial(jax.pmap, static_broadcasted_argnums=(0,))
-def _p_get_has_nsfw_concepts(pipe, features, params):
-    return pipe._get_has_nsfw_concepts(features, params)
-
-@partial(
-jax.pmap,
-in_axes=(None, None, 0, 0, 0, 0, 0, 0, 0),
-static_broadcasted_argnums=(0, 1)
-)
-def p_generate_starting_frames(pipe, num_inference_steps, params, timesteps, text_embeddings, latents, guidance_scale, controlnet_image, controlnet_conditioning_scale):
-    #  perform ∆t backward steps by stable diffusion
-    # delta_t_diffusion = jax.vmap(lambda latent : self.DDIM_backward(params, num_inference_steps=num_inference_steps, timesteps=timesteps, skip_t=1000, t0=t0, t1=t1, do_classifier_free_guidance=do_classifier_free_guidance,
-    #                                     text_embeddings=text_embeddings, latents_local=latent, guidance_scale=guidance_scale,
-    #                                     controlnet_image=controlnet_image, controlnet_conditioning_scale=controlnet_conditioning_scale))
-    # ddim_res = delta_t_diffusion(latents)
-    # latents = ddim_res["x0"] #output is  i b c f h w
-
-    # DDPM forward for more motion freedom
-    # ddpm_fwd = jax.vmap(lambda prng, latent: self.DDPM_forward(params=params, prng=prng, x0=latent, t0=t0,
-    #                 tMax=t1, shape=shape, text_embeddings=text_embeddings))
-    # latents = ddpm_fwd(stacked_prngs, latents)
-    # main backward diffusion
-    # denoise_first_frame = lambda latent : self.DDIM_backward(params, num_inference_steps=num_inference_steps, timesteps=timesteps, skip_t=100000, t0=-1, t1=-1, do_classifier_free_guidance=do_classifier_free_guidance,
-    #                                     text_embeddings=text_embeddings, latents_local=latent, guidance_scale=guidance_scale,
-    #                                     controlnet_image=controlnet_image, controlnet_conditioning_scale=controlnet_conditioning_scale)
-    # latents = rearrange(latents, 'i b c f h w -> (i b) c f h w')
-    # ddim_res = denoise_first_frame(latents)
-    latents = pipe.denoise_latent(params, num_inference_steps=num_inference_steps, timesteps=timesteps, do_classifier_free_guidance=True,
-                                        text_embeddings=text_embeddings, latents=latents, guidance_scale=guidance_scale,
-                                        controlnet_image=controlnet_image, controlnet_conditioning_scale=controlnet_conditioning_scale)
-    # latents = rearrange(ddim_res["x0"], 'i b c f h w -> (i b) c f h w') #output is  i b c f h w
-
-    # scale and decode the image latents with vae
-    latents = 1 / pipe.vae.config.scaling_factor * latents
-    # latents = rearrange(latents, "b c h w -> (b f) c h w")
-    imgs = pipe.vae.apply({"params": params["vae"]}, latents, method=pipe.vae.decode).sample
-    imgs = (imgs / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
-    return imgs
-
-
-
-def unshard(x: jnp.ndarray):
-    # einops.rearrange(x, 'd b ... -> (d b) ...')
-    num_devices, batch_size = x.shape[:2]
-    rest = x.shape[2:]
-    return x.reshape(num_devices * batch_size, *rest)
-def preprocess(image, dtype):
-    image = image.convert("RGB")
-    w, h = image.size
-    w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 64
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = jnp.array(image).astype(dtype) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return image
-
-def prepare_latents(params, prng, batch_size, num_channels_latents, height, width, vae_scale_factor, latents=None):
-    shape = (batch_size, num_channels_latents, 1, height //
-            vae_scale_factor, width // vae_scale_factor) #b c f h w
-    # scale the initial noise by the standard deviation required by the scheduler
-    if latents is None:
-        latents = jax.random.normal(prng, shape)
-    latents = latents * params["scheduler"].init_noise_sigma
-    return latents
-
-def coords_grid(batch, ht, wd):
-    coords = jnp.meshgrid(jnp.arange(ht), jnp.arange(wd), indexing="ij")
-    coords = jnp.stack(coords[::-1], axis=0)
-    return coords[None].repeat(batch, 0)
-
-def adapt_pos_mirror(x, y, W, H):
-  #adapt the position, with mirror padding
-  x_w_mirror = ((x + W - 1) % (2*(W - 1))) - W + 1
-  x_adapted = jnp.where(x_w_mirror > 0, x_w_mirror, - (x_w_mirror))
-  y_w_mirror = ((y + H - 1) % (2*(H - 1))) - H + 1
-  y_adapted = jnp.where(y_w_mirror > 0, y_w_mirror, - (y_w_mirror))
-  return y_adapted, x_adapted
-
-def safe_get_zeropad(img, x,y,W,H):
-  return jnp.where((x < W) & (x > 0) & (y < H) & (y > 0), img[y,x], 0.)
-
-def safe_get_mirror(img, x,y,W,H):
-  return img[adapt_pos_mirror(x,y,W,H)]
-
-@partial(jax.vmap, in_axes=(0, 0, None))
-@partial(jax.vmap, in_axes=(0, None, None))
-@partial(jax.vmap, in_axes=(None,0, None))
-@partial(jax.vmap, in_axes=(None, 0, None))
-def grid_sample(latents, grid, method):
-    # this is an alternative to torch.functional.nn.grid_sample in jax
-    # this implementation is following the algorithm described @ https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
-    # but with coordinates scaled to the size of the image
-    if method == "mirror":
-      return safe_get_mirror(latents, jnp.array(grid[0], dtype=jnp.int16), jnp.array(grid[1], dtype=jnp.int16), latents.shape[0], latents.shape[1])
-    else: #default is zero padding
-      return safe_get_zeropad(latents, jnp.array(grid[0], dtype=jnp.int16), jnp.array(grid[1], dtype=jnp.int16), latents.shape[0], latents.shape[1])
-
-def bandw_vid(vid, threshold):
-  vid = jnp.max(vid, axis=1)
-  return jnp.where(vid > threshold, 1, 0)
-
-def mean_blur(vid, k):
-  window = jnp.ones((vid.shape[0], k, k))/ (k*k)
-  convolve=jax.vmap(lambda img, kernel:jax.scipy.signal.convolve(img, kernel, mode='same'))
-  smooth_vid = convolve(vid, window)
-  return smooth_vid
-
-def get_mask_pose(vid):
-  vid = bandw_vid(vid, 0.4)
-  l, h, w = vid.shape
-  vid = jax.image.resize(vid, (l, h//8, w//8), "nearest")
-  vid=bandw_vid(mean_blur(vid, 7)[:,None], threshold=0.01)
-  return vid/(jnp.max(vid) + 1e-4)
-  #return jax.image.resize(vid/(jnp.max(vid) + 1e-4), (l, h, w), "nearest")
\ No newline at end of file
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/inference.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/inference.py
deleted file mode 100644
index 7d0748f4f55c88ff2bb7b3435429e494264e6e00..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/inference.py
+++ /dev/null
@@ -1,623 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import datetime
-import logging
-import time
-import os
-import re
-
-import torch
-from tqdm import tqdm
-from collections import defaultdict
-
-from maskrcnn_benchmark.data.datasets.evaluation import evaluate, im_detect_bbox_aug
-from ..utils.comm import is_main_process
-from ..utils.comm import all_gather
-from ..utils.comm import synchronize
-import pdb
-from maskrcnn_benchmark.data.datasets.evaluation.flickr.flickr_eval import FlickrEvaluator
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-import matplotlib.pyplot as plt
-import matplotlib.pylab as pylab
-from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file
-def imshow(img, file_name = "tmp.jpg"):
-    plt.imshow(img[:, :, [2, 1, 0]])
-    plt.axis("off")
-    #plt.figtext(0.5, 0.09, "test", wrap=True, horizontalalignment='center', fontsize=20)
-    plt.savefig(file_name)
-def load(url_or_file_name):
-    try:
-        response = requests.get(url_or_file_name)
-    except:
-        response = None
-    if response is None:
-        pil_image = Image.open(url_or_file_name).convert("RGB")
-    else:
-        pil_image = Image.open(BytesIO(response.content)).convert("RGB")
-    # convert to BGR format
-    image = np.array(pil_image)[:, :, [2, 1, 0]]
-    return image
-def inference_default(
-        model,
-        data_loader,
-        dataset_name,
-        iou_types=("bbox",),
-        box_only=False,
-        device="cuda",
-        expected_results=(),
-        expected_results_sigma_tol=4,
-        output_folder=None,
-        cfg=None
-):
-    # convert to a torch.device for efficiency
-    device = torch.device(device)
-    num_devices = (
-        torch.distributed.get_world_size()
-        if torch.distributed.is_initialized()
-        else 1
-    )
-    logger = logging.getLogger("maskrcnn_benchmark.inference")
-    dataset = data_loader.dataset
-    logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
-    start_time = time.time()
-
-    model.eval()
-    results_dict = {}
-    cpu_device = torch.device("cpu")
-    for i, batch in enumerate(tqdm(data_loader)):
-        images, targets, image_ids, *_ = batch
-        with torch.no_grad():
-            if cfg.TEST.USE_MULTISCALE:
-                output = im_detect_bbox_aug(model, images, device)
-            else:
-                output = model(images.to(device))
-            output = [o.to(cpu_device) for o in output]
-        results_dict.update(
-            {img_id: result for img_id, result in zip(image_ids, output)}
-        )
-    predictions = results_dict
-    # wait for all processes to complete before measuring the time
-    synchronize()
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=total_time))
-    logger.info(
-        "Total inference time: {} ({} s / img per device, on {} devices)".format(
-            total_time_str, total_time * num_devices / len(dataset), num_devices
-        )
-    )
-
-    predictions = _accumulate_predictions_from_multiple_gpus(predictions)
-    if not is_main_process():
-        return None
-
-    if output_folder:
-        torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
-
-    extra_args = dict(
-        box_only=box_only,
-        iou_types=iou_types,
-        expected_results=expected_results,
-        expected_results_sigma_tol=expected_results_sigma_tol,
-    )
-    return evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args)
-
-
-def clean_name(name):
-    name = re.sub(r"\(.*\)", "", name)
-    name = re.sub(r"_", " ", name)
-    name = re.sub(r"  ", " ", name)
-    return name
-
-
-def create_one_hot_dict(labels, no_minus_one_for_one_hot = False):
-    positive_map_token_to_label = defaultdict(int)
-    positive_map_label_to_token = defaultdict(int)
-
-    for i in range(len(labels)):
-        positive_map_token_to_label[i] = labels[i]
-        positive_map_label_to_token[labels[i]] = i
-
-    if no_minus_one_for_one_hot:
-        positive_map_token_to_label = defaultdict(int)
-        positive_map_label_to_token = defaultdict(int)
-
-        for i in range(len(labels)):
-            positive_map_token_to_label[i+1] = labels[i]
-            positive_map_label_to_token[labels[i]] = i + 1
-
-    return positive_map_token_to_label, positive_map_label_to_token
-
-
-def create_positive_dict(tokenized, tokens_positive, labels):
-    """construct a dictionary such that positive_map[i] = j, iff token i is mapped to j label"""
-    positive_map = defaultdict(int)
-
-    # Additionally, have positive_map_label_to_tokens
-    positive_map_label_to_token = defaultdict(list)
-
-    for j, tok_list in enumerate(tokens_positive):
-        for (beg, end) in tok_list:
-            beg_pos = tokenized.char_to_token(beg)
-            end_pos = tokenized.char_to_token(end - 1)
-            if beg_pos is None:
-                try:
-                    beg_pos = tokenized.char_to_token(beg + 1)
-                    if beg_pos is None:
-                        beg_pos = tokenized.char_to_token(beg + 2)
-                except:
-                    beg_pos = None
-            if end_pos is None:
-                try:
-                    end_pos = tokenized.char_to_token(end - 2)
-                    if end_pos is None:
-                        end_pos = tokenized.char_to_token(end - 3)
-                except:
-                    end_pos = None
-            if beg_pos is None or end_pos is None:
-                continue
-
-            assert beg_pos is not None and end_pos is not None
-            for i in range(beg_pos, end_pos + 1):
-                positive_map[i] = labels[j]  # because the labels starts from 1
-                positive_map_label_to_token[labels[j]].append(i)
-            # positive_map[j, beg_pos : end_pos + 1].fill_(1)
-    return positive_map, positive_map_label_to_token  # / (positive_map.sum(-1)[:, None] + 1e-6)
-
-def chunks(lst, n):
-    """Yield successive n-sized chunks from lst."""
-    all_ = []
-    for i in range(0, len(lst), n):
-        data_index = lst[i:i + n]
-        all_.append(data_index)
-    counter = 0
-    for i in all_:
-        counter += len(i)
-    assert(counter == len(lst))
-
-    return all_
-
-def create_queries_and_maps_from_dataset(dataset, cfg):
-    categories = dataset.categories()
-    #one_hot = dataset.one_hot
-
-    labels = []
-    label_list = []
-    keys = list(categories.keys())
-    keys.sort()
-    for i in keys:
-        labels.append(i)
-        label_list.append(categories[i])
-
-    if cfg.TEST.CHUNKED_EVALUATION != -1:
-        labels = chunks(labels, cfg.TEST.CHUNKED_EVALUATION)
-        label_list = chunks(label_list, cfg.TEST.CHUNKED_EVALUATION)
-    else:
-        labels = [labels]
-        label_list = [label_list]
-
-    all_queries = []
-    all_positive_map_label_to_token = []
-
-    for i in range(len(labels)):
-        labels_i = labels[i]
-        label_list_i = label_list[i]
-        query_i, positive_map_label_to_token_i = create_queries_and_maps(
-            labels_i, label_list_i, additional_labels = cfg.DATASETS.SUPRESS_QUERY if cfg.DATASETS.USE_SUPRESS_QUERY else None, cfg = cfg)
-        
-        all_queries.append(query_i)
-        all_positive_map_label_to_token.append(positive_map_label_to_token_i)
-    print("All queries", all_queries)
-    return all_queries, all_positive_map_label_to_token
-
-def create_queries_and_maps(labels, label_list, additional_labels = None, cfg = None):
-
-    # Clean label list
-    original_label_list = label_list.copy()
-    label_list = [clean_name(i) for i in label_list]
-    # Form the query and get the mapping
-    tokens_positive = []
-    start_i = 0
-    end_i = 0
-    objects_query = ""
-
-    # sep between tokens, follow training
-    separation_tokens = cfg.DATASETS.SEPARATION_TOKENS
-    
-    caption_prompt = cfg.DATASETS.CAPTION_PROMPT
-    if caption_prompt is not None and isinstance(caption_prompt, str):
-        caption_prompt = load_from_yaml_file(caption_prompt)
-    use_caption_prompt = cfg.DATASETS.USE_CAPTION_PROMPT and caption_prompt is not None
-    for _index, label in enumerate(label_list):
-        if use_caption_prompt:
-            objects_query += caption_prompt[_index]["prefix"]
-        
-        start_i = len(objects_query)
-
-        if use_caption_prompt:
-            objects_query += caption_prompt[_index]["name"]
-        else:
-            objects_query += label
-        
-        end_i = len(objects_query)
-        tokens_positive.append([(start_i, end_i)])  # Every label has a [(start, end)]
-        
-        if use_caption_prompt:
-            objects_query += caption_prompt[_index]["suffix"]
-
-        if _index != len(label_list) - 1:
-            objects_query += separation_tokens
-    
-    if additional_labels is not None:
-        objects_query += separation_tokens
-        for _index, label in enumerate(additional_labels):
-            objects_query += label
-            if _index != len(additional_labels) - 1:
-                objects_query += separation_tokens
-
-    print(objects_query)
-
-    from transformers import AutoTokenizer
-    # tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased":
-        tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-        tokenized = tokenizer(objects_query, return_tensors="pt")
-    elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip":
-        from transformers import CLIPTokenizerFast
-        if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS:
-            tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32",
-                                                                        from_slow=True, mask_token='ðŁĴij</w>')
-        else:
-            tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32",
-                                                                        from_slow=True)
-        tokenized = tokenizer(objects_query,
-                              max_length=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN,
-                              truncation=True,
-                              return_tensors="pt")
-    else:
-        tokenizer = None
-        raise NotImplementedError
-
-    # Create the mapping between tokenized sentence and the original label
-    positive_map_token_to_label, positive_map_label_to_token = create_positive_dict(tokenized, tokens_positive,
-                                                                                        labels=labels)  # from token position to original label
-    return objects_query, positive_map_label_to_token
-
-def create_positive_map_label_to_token_from_positive_map(positive_map, plus = 0):
-    positive_map_label_to_token = {}
-    for i in range(len(positive_map)):
-        positive_map_label_to_token[i + plus] = torch.nonzero(positive_map[i], as_tuple=True)[0].tolist()
-    return positive_map_label_to_token
-
-
-
-def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu):
-    all_predictions = all_gather(predictions_per_gpu)
-    if not is_main_process():
-        return
-    # merge the list of dicts
-    predictions = {}
-    for p in all_predictions:
-        predictions.update(p)
-    # convert a dict where the key is the index in a list
-    image_ids = list(sorted(predictions.keys()))
-    if len(image_ids) != image_ids[-1] + 1:
-        logger = logging.getLogger("maskrcnn_benchmark.inference")
-        logger.warning(
-            "Number of images that were gathered from multiple processes is not "
-            "a contiguous set. Some images might be missing from the evaluation"
-        )
-
-    # convert to a list
-    predictions = [predictions[i] for i in image_ids]
-    return predictions
-
-def resize_box(output, targets):
-    if isinstance(targets[0], dict):
-        orig_target_sizes = targets[0]["orig_size"].unsqueeze(0)
-    else:
-        orig_target_sizes = torch.stack([targets[0].extra_fields["orig_size"] for _ in range(1)], dim=0)
-    img_h, img_w = orig_target_sizes.unbind(1)
-    return output.resize((img_w, img_h))
-
-def flickr_post_process(output, targets, positive_map_label_to_token, plus):
-    output = resize_box(output, targets)
-    scores, indices = torch.topk(output.extra_fields["scores"], k = len(output.extra_fields["scores"]), sorted=True)
-    boxes = output.bbox.tolist()
-    boxes = [boxes[i] for i in indices]
-    labels = [output.extra_fields["labels"][i] for i in indices]
-    output_boxes = [[] for i in range(len(positive_map_label_to_token))]
-    output_scores = [[] for i in range(len(positive_map_label_to_token))]
-    for i in range(len(boxes)):
-        output_boxes[labels[i] - plus].append(boxes[i])
-        output_scores[labels[i] - plus].append(scores[i])
-    for i in output_boxes:
-        i.append([0.0, 0.0, 0.0, 0.0])
-    image_ids = [t.extra_fields["original_img_id"] for t in targets]
-    sentence_ids = [t.extra_fields["sentence_id"] for t in targets]
-
-    return {"image_id": image_ids[0], "sentence_id": sentence_ids[0], "boxes": output_boxes, "scores": output_scores}
-
-def build_flickr_evaluator(cfg):
-    evaluator = FlickrEvaluator(
-        "DATASET/flickr30k/flickr30k/", # Hard written!!
-        subset="test" if "test" in cfg.DATASETS.TEST[0]  else "val",
-        merge_boxes=cfg.DATASETS.FLICKR_GT_TYPE == "merged")
-    return evaluator
-
-def build_lvis_evaluator(ann_file, fixed_ap=True):
-    from maskrcnn_benchmark.data.datasets.evaluation.lvis.lvis import LVIS
-    from maskrcnn_benchmark.data.datasets.evaluation.lvis.lvis_eval import LvisEvaluatorFixedAP, LvisEvaluator
-    evaluator = LvisEvaluatorFixedAP(LVIS(ann_file), fixed_ap=fixed_ap)
-    #evaluator = LvisEvaluator(LVIS(ann_file), iou_types=['segm', 'bbox'])
-    return evaluator
-
-def write_lvis_results(results, output_file_name):
-    lines = []
-    lines.append("metric, avg ")
-    for each_result in results:
-        metric_string = " ".join(each_result.split(" ")[:-2])
-        number = each_result.split(" ")[-1]
-        each_result = metric_string + ", " + number + " "
-        lines.append(each_result)
-
-    string_to_write = "\n".join(lines) + "\n"
-    with open(output_file_name, "w") as f:
-        f.write(string_to_write)
-    return
-
-def write_flickr_results(results, output_file_name):
-    '''
-    {'Recall@1_all': 0.8394651146677753, 'Recall@1_animals': 0.9177820267686424, 'Recall@1_bodyparts': 0.7097966728280961, ...}
-    '''
-    lines = []
-    lines.append("metric, avg ")
-    for each_metric, number in results.items():
-        each_result = each_metric + ", " + str(number) + " "
-        lines.append(each_result)
-
-    string_to_write = "\n".join(lines) + "\n"
-    with open(output_file_name, "w") as f:
-        f.write(string_to_write)
-    return
-
-def inference(
-        model,
-        data_loader,
-        dataset_name,
-        iou_types=("bbox",),
-        box_only=False,
-        device="cuda",
-        expected_results=(),
-        expected_results_sigma_tol=4,
-        output_folder=None,
-        cfg=None,
-        verbose=True,
-        visualizer = None
-):
-    # convert to a torch.device for efficiency
-    try:
-        device = torch.device(device)
-    except:
-        device = device
-    num_devices = (
-        torch.distributed.get_world_size()
-        if torch.distributed.is_initialized()
-        else 1
-    )
-    logger = logging.getLogger("maskrcnn_benchmark.inference")
-    dataset = data_loader.dataset
-    if verbose:
-        logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
-    start_time = time.time()
-
-    task = cfg.TEST.EVAL_TASK
-
-    if not task:
-        return inference_default(model, data_loader, dataset_name, iou_types, box_only, device, expected_results, expected_results_sigma_tol, output_folder, cfg)
-        
-    if cfg.GLIPKNOW.PARALLEL_LANGUAGE_INPUT:
-        assert task == 'detection'
-        categories = dataset.categories()
-
-        keys = list(categories.keys())
-        keys.sort()
-        all_queries = [[categories[k] for k in keys]]
-        all_positive_map_label_to_token = [{k: [i] for i, k in enumerate(keys)}]
-    elif task == "detection":
-        all_queries, all_positive_map_label_to_token = create_queries_and_maps_from_dataset(dataset, cfg)
-    elif task == "grounding":
-        all_queries = [None]
-        all_positive_map_label_to_token = [None]
-    else:
-        assert(0)
-
-    '''
-    Build Dataset Sepecific Evaluator
-    '''
-    if "flickr" in cfg.DATASETS.TEST[0]:
-        evaluator = build_flickr_evaluator(cfg)
-    elif "lvis" in cfg.DATASETS.TEST[0]:
-        evaluator = build_lvis_evaluator(dataset.ann_file, fixed_ap=not cfg.DATASETS.LVIS_USE_NORMAL_AP)
-    else:
-        evaluator = None
-
-    model.eval()
-    results_dict = {}
-    cpu_device = torch.device("cpu")
-    if verbose:
-        _iterator = tqdm(data_loader)
-    else:
-        _iterator = data_loader
-    for i, batch in enumerate(_iterator):
-        if i == cfg.TEST.SUBSET:
-            break
-        images, targets, image_ids, *_ = batch
-
-        all_output = []
-        mdetr_style_output = []
-        with torch.no_grad():
-            if cfg.TEST.USE_MULTISCALE:
-                query_time = len(all_queries)
-                for query_i in range(query_time):
-                    if task == "detection":
-                        captions = [all_queries[query_i] for ii in range(len(targets))]
-                        positive_map_label_to_token = all_positive_map_label_to_token[query_i]
-                    else:
-                        captions = None
-                        positive_map_label_to_token = None
-
-                output = im_detect_bbox_aug(model, images, device, captions, positive_map_label_to_token)
-                output = [o.to(cpu_device) for o in output]
-                all_output.append(output)
-            else:
-                images = images.to(device)
-                query_time = len(all_queries)
-
-                for query_i in range(query_time):
-                    if not isinstance(targets[0], dict): # For LVIS dataset and datasets directly copied from MDETR
-                        targets = [target.to(device) for target in targets]
-                    '''
-                    different datasets seem to have different data format... For LVIS dataset, the target is a dictionary, while for modulatedDataset such as COCO/Flickr, the target is a BoxList
-                    '''
-
-                    if task == "detection":
-                        captions = [all_queries[query_i] for ii in range(len(targets))]
-                        positive_map_label_to_token = all_positive_map_label_to_token[query_i]
-                    elif task == "grounding":
-                        captions = [t.get_field("caption") for t in targets]
-                        positive_map_eval = [t.get_field("positive_map_eval") for t in targets]
-                        if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD":
-                            plus = 1
-                        else:
-                            plus = 0
-                        assert(len(positive_map_eval) == 1) # Let's just use one image per batch
-                        positive_map_eval = positive_map_eval[0]
-                        positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map(positive_map_eval, plus=plus)
-                    output = model(images, captions=captions, positive_map=positive_map_label_to_token)
-                    output = [o.to(cpu_device) for o in output]
-
-                    if "flickr" in cfg.DATASETS.TEST[0]:
-                        output = output[0]
-                        new_output = flickr_post_process(
-                            output,
-                            targets,
-                            positive_map_label_to_token,
-                            plus # This is only used in Flickr
-                        )
-                        mdetr_style_output.append(new_output)
-                    elif "lvis" in cfg.DATASETS.TEST[0]:
-                        output = output[0]
-                        output = resize_box(output, targets)
-                        scores = output.extra_fields["scores"]
-                        labels = output.extra_fields["labels"]
-                        boxes = output.bbox
-                        mdetr_style_output.append((targets[0]["image_id"].item(), {"scores": scores, "labels": labels, "boxes": boxes}))
-                    else:
-                        all_output.append(output)
-        if visualizer is not None:
-            assert(len(all_output) == 1)
-            if "lvis" in cfg.DATASETS.TEST[0]:
-                scores = [o[1]["scores"] for o in mdetr_style_output]
-                labels = [o[1]["labels"] for o in mdetr_style_output]
-                boxes = [o[1]["boxes"] for o in mdetr_style_output]
-                scores = torch.cat(scores, dim=0)
-                labels = torch.cat(labels, dim=0)
-                boxes = torch.cat(boxes, dim=0)
-                visualizer_input = BoxList(boxes, output.size)
-                visualizer_input.add_field("scores", scores)
-                visualizer_input.add_field("labels", labels)
-            else:
-                visualizer_input = all_output[0][0] # single image_visualize
-
-            image_id = dataset.ids[i]
-            try:
-                image_path = os.path.join(dataset.root, dataset.coco.loadImgs(image_id)[0]["file_name"])
-                categories = dataset.coco.dataset["categories"]
-            except:
-                lvis = dataset.lvis
-                img_id = dataset.ids[i]
-                ann_ids = lvis.get_ann_ids(img_ids=img_id)
-                target = lvis.load_anns(ann_ids)
-
-                image_path = "DATASET/coco/" +  "/".join(dataset.lvis.load_imgs(img_id)[0]["coco_url"].split("/")[-2:])
-                categories = dataset.lvis.dataset["categories"]
-
-            image = load(image_path)
-            no_background = True
-            label_list = []
-            for index, i in enumerate(categories):
-                if not no_background or (i["name"] != "__background__" and i['id'] != 0):
-                    label_list.append(i["name"])
-            visualizer.entities =  label_list
-            
-            result, _ = visualizer.visualize_with_predictions(
-                image,
-                visualizer_input, 
-                threshold,
-                alpha=alpha,
-                box_pixel=box_pixel,
-                text_size=text_size,
-                text_pixel=text_pixel,
-                text_offset=text_offset,
-                text_offset_original=text_offset_original,
-                color=color,
-            )
-            imshow(result, "./visualize/img_{}.jpg".format(i))
-        
-        if evaluator is not None:
-            evaluator.update(mdetr_style_output)
-        else:
-            output = [[row[_i] for row in all_output] for _i in range(len(all_output[0]))]
-            for index, i in enumerate(output):
-                output[index] = i[0].concate_box_list(i)
-
-            results_dict.update({img_id: result for img_id, result in zip(image_ids, output)})
-
-    if evaluator is not None:
-        evaluator.synchronize_between_processes()
-        try:
-            evaluator.accumulate()
-        except:
-            print("Evaluator has no accumulation, skipped...")
-        score = evaluator.summarize()
-        print(score)
-        import maskrcnn_benchmark.utils.mdetr_dist as dist
-        if is_main_process():
-            if "flickr" in cfg.DATASETS.TEST[0]:
-                write_flickr_results(score, output_file_name=os.path.join(output_folder, "bbox.csv"))
-            elif "lvis" in cfg.DATASETS.TEST[0]:
-                write_lvis_results(score, output_file_name=os.path.join(output_folder, "bbox.csv"))
-        try:
-            torch.distributed.barrier()
-        except:
-            print("Default process group is not initialized")
-        return
-
-    if evaluator is not None:
-        predictions = mdetr_style_output
-    else:
-        predictions = results_dict
-    # wait for all processes to complete before measuring the time
-    synchronize()
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=total_time))
-    logger.info(
-        "Total inference time: {} ({} s / img per device, on {} devices)".format(
-            total_time_str, total_time * num_devices / len(dataset), num_devices
-        )
-    )
-
-    predictions = _accumulate_predictions_from_multiple_gpus(predictions)
-    print("Accumulated results")
-    if not is_main_process():
-        return None
-
-    if output_folder:
-        torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
-
-    extra_args = dict(
-        box_only=box_only,
-        iou_types=iou_types,
-        expected_results=expected_results,
-        expected_results_sigma_tol=expected_results_sigma_tol,
-    )
-    return evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args)
diff --git a/spaces/Pinwheel/SuperGlue-Image-Matching/models/__init__.py b/spaces/Pinwheel/SuperGlue-Image-Matching/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/modules/losses/__init__.py b/spaces/Purple11/Grounded-Diffusion/ldm/modules/losses/__init__.py
deleted file mode 100644
index 876d7c5bd6e3245ee77feb4c482b7a8143604ad5..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/modules/losses/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/file_proxy.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/file_proxy.py
deleted file mode 100644
index cc69f22f3cc021fe5d1147d628eeca804be43b8b..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/file_proxy.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import io
-from typing import IO, TYPE_CHECKING, Any, List
-
-from .ansi import AnsiDecoder
-from .text import Text
-
-if TYPE_CHECKING:
-    from .console import Console
-
-
-class FileProxy(io.TextIOBase):
-    """Wraps a file (e.g. sys.stdout) and redirects writes to a console."""
-
-    def __init__(self, console: "Console", file: IO[str]) -> None:
-        self.__console = console
-        self.__file = file
-        self.__buffer: List[str] = []
-        self.__ansi_decoder = AnsiDecoder()
-
-    @property
-    def rich_proxied_file(self) -> IO[str]:
-        """Get proxied file."""
-        return self.__file
-
-    def __getattr__(self, name: str) -> Any:
-        return getattr(self.__file, name)
-
-    def write(self, text: str) -> int:
-        if not isinstance(text, str):
-            raise TypeError(f"write() argument must be str, not {type(text).__name__}")
-        buffer = self.__buffer
-        lines: List[str] = []
-        while text:
-            line, new_line, text = text.partition("\n")
-            if new_line:
-                lines.append("".join(buffer) + line)
-                del buffer[:]
-            else:
-                buffer.append(line)
-                break
-        if lines:
-            console = self.__console
-            with console:
-                output = Text("\n").join(
-                    self.__ansi_decoder.decode_line(line) for line in lines
-                )
-                console.print(output)
-        return len(text)
-
-    def flush(self) -> None:
-        output = "".join(self.__buffer)
-        if output:
-            self.__console.print(output)
-        del self.__buffer[:]
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/jaraco/functools.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/jaraco/functools.py
deleted file mode 100644
index bbd8b29f9c012d62a37393476a5e393405d2918c..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/jaraco/functools.py
+++ /dev/null
@@ -1,525 +0,0 @@
-import functools
-import time
-import inspect
-import collections
-import types
-import itertools
-
-import setuptools.extern.more_itertools
-
-from typing import Callable, TypeVar
-
-
-CallableT = TypeVar("CallableT", bound=Callable[..., object])
-
-
-def compose(*funcs):
-    """
-    Compose any number of unary functions into a single unary function.
-
-    >>> import textwrap
-    >>> expected = str.strip(textwrap.dedent(compose.__doc__))
-    >>> strip_and_dedent = compose(str.strip, textwrap.dedent)
-    >>> strip_and_dedent(compose.__doc__) == expected
-    True
-
-    Compose also allows the innermost function to take arbitrary arguments.
-
-    >>> round_three = lambda x: round(x, ndigits=3)
-    >>> f = compose(round_three, int.__truediv__)
-    >>> [f(3*x, x+1) for x in range(1,10)]
-    [1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7]
-    """
-
-    def compose_two(f1, f2):
-        return lambda *args, **kwargs: f1(f2(*args, **kwargs))
-
-    return functools.reduce(compose_two, funcs)
-
-
-def method_caller(method_name, *args, **kwargs):
-    """
-    Return a function that will call a named method on the
-    target object with optional positional and keyword
-    arguments.
-
-    >>> lower = method_caller('lower')
-    >>> lower('MyString')
-    'mystring'
-    """
-
-    def call_method(target):
-        func = getattr(target, method_name)
-        return func(*args, **kwargs)
-
-    return call_method
-
-
-def once(func):
-    """
-    Decorate func so it's only ever called the first time.
-
-    This decorator can ensure that an expensive or non-idempotent function
-    will not be expensive on subsequent calls and is idempotent.
-
-    >>> add_three = once(lambda a: a+3)
-    >>> add_three(3)
-    6
-    >>> add_three(9)
-    6
-    >>> add_three('12')
-    6
-
-    To reset the stored value, simply clear the property ``saved_result``.
-
-    >>> del add_three.saved_result
-    >>> add_three(9)
-    12
-    >>> add_three(8)
-    12
-
-    Or invoke 'reset()' on it.
-
-    >>> add_three.reset()
-    >>> add_three(-3)
-    0
-    >>> add_three(0)
-    0
-    """
-
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        if not hasattr(wrapper, 'saved_result'):
-            wrapper.saved_result = func(*args, **kwargs)
-        return wrapper.saved_result
-
-    wrapper.reset = lambda: vars(wrapper).__delitem__('saved_result')
-    return wrapper
-
-
-def method_cache(
-    method: CallableT,
-    cache_wrapper: Callable[
-        [CallableT], CallableT
-    ] = functools.lru_cache(),  # type: ignore[assignment]
-) -> CallableT:
-    """
-    Wrap lru_cache to support storing the cache data in the object instances.
-
-    Abstracts the common paradigm where the method explicitly saves an
-    underscore-prefixed protected property on first call and returns that
-    subsequently.
-
-    >>> class MyClass:
-    ...     calls = 0
-    ...
-    ...     @method_cache
-    ...     def method(self, value):
-    ...         self.calls += 1
-    ...         return value
-
-    >>> a = MyClass()
-    >>> a.method(3)
-    3
-    >>> for x in range(75):
-    ...     res = a.method(x)
-    >>> a.calls
-    75
-
-    Note that the apparent behavior will be exactly like that of lru_cache
-    except that the cache is stored on each instance, so values in one
-    instance will not flush values from another, and when an instance is
-    deleted, so are the cached values for that instance.
-
-    >>> b = MyClass()
-    >>> for x in range(35):
-    ...     res = b.method(x)
-    >>> b.calls
-    35
-    >>> a.method(0)
-    0
-    >>> a.calls
-    75
-
-    Note that if method had been decorated with ``functools.lru_cache()``,
-    a.calls would have been 76 (due to the cached value of 0 having been
-    flushed by the 'b' instance).
-
-    Clear the cache with ``.cache_clear()``
-
-    >>> a.method.cache_clear()
-
-    Same for a method that hasn't yet been called.
-
-    >>> c = MyClass()
-    >>> c.method.cache_clear()
-
-    Another cache wrapper may be supplied:
-
-    >>> cache = functools.lru_cache(maxsize=2)
-    >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
-    >>> a = MyClass()
-    >>> a.method2()
-    3
-
-    Caution - do not subsequently wrap the method with another decorator, such
-    as ``@property``, which changes the semantics of the function.
-
-    See also
-    http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
-    for another implementation and additional justification.
-    """
-
-    def wrapper(self: object, *args: object, **kwargs: object) -> object:
-        # it's the first call, replace the method with a cached, bound method
-        bound_method: CallableT = types.MethodType(  # type: ignore[assignment]
-            method, self
-        )
-        cached_method = cache_wrapper(bound_method)
-        setattr(self, method.__name__, cached_method)
-        return cached_method(*args, **kwargs)
-
-    # Support cache clear even before cache has been created.
-    wrapper.cache_clear = lambda: None  # type: ignore[attr-defined]
-
-    return (  # type: ignore[return-value]
-        _special_method_cache(method, cache_wrapper) or wrapper
-    )
-
-
-def _special_method_cache(method, cache_wrapper):
-    """
-    Because Python treats special methods differently, it's not
-    possible to use instance attributes to implement the cached
-    methods.
-
-    Instead, install the wrapper method under a different name
-    and return a simple proxy to that wrapper.
-
-    https://github.com/jaraco/jaraco.functools/issues/5
-    """
-    name = method.__name__
-    special_names = '__getattr__', '__getitem__'
-    if name not in special_names:
-        return
-
-    wrapper_name = '__cached' + name
-
-    def proxy(self, *args, **kwargs):
-        if wrapper_name not in vars(self):
-            bound = types.MethodType(method, self)
-            cache = cache_wrapper(bound)
-            setattr(self, wrapper_name, cache)
-        else:
-            cache = getattr(self, wrapper_name)
-        return cache(*args, **kwargs)
-
-    return proxy
-
-
-def apply(transform):
-    """
-    Decorate a function with a transform function that is
-    invoked on results returned from the decorated function.
-
-    >>> @apply(reversed)
-    ... def get_numbers(start):
-    ...     "doc for get_numbers"
-    ...     return range(start, start+3)
-    >>> list(get_numbers(4))
-    [6, 5, 4]
-    >>> get_numbers.__doc__
-    'doc for get_numbers'
-    """
-
-    def wrap(func):
-        return functools.wraps(func)(compose(transform, func))
-
-    return wrap
-
-
-def result_invoke(action):
-    r"""
-    Decorate a function with an action function that is
-    invoked on the results returned from the decorated
-    function (for its side-effect), then return the original
-    result.
-
-    >>> @result_invoke(print)
-    ... def add_two(a, b):
-    ...     return a + b
-    >>> x = add_two(2, 3)
-    5
-    >>> x
-    5
-    """
-
-    def wrap(func):
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            result = func(*args, **kwargs)
-            action(result)
-            return result
-
-        return wrapper
-
-    return wrap
-
-
-def call_aside(f, *args, **kwargs):
-    """
-    Call a function for its side effect after initialization.
-
-    >>> @call_aside
-    ... def func(): print("called")
-    called
-    >>> func()
-    called
-
-    Use functools.partial to pass parameters to the initial call
-
-    >>> @functools.partial(call_aside, name='bingo')
-    ... def func(name): print("called with", name)
-    called with bingo
-    """
-    f(*args, **kwargs)
-    return f
-
-
-class Throttler:
-    """
-    Rate-limit a function (or other callable)
-    """
-
-    def __init__(self, func, max_rate=float('Inf')):
-        if isinstance(func, Throttler):
-            func = func.func
-        self.func = func
-        self.max_rate = max_rate
-        self.reset()
-
-    def reset(self):
-        self.last_called = 0
-
-    def __call__(self, *args, **kwargs):
-        self._wait()
-        return self.func(*args, **kwargs)
-
-    def _wait(self):
-        "ensure at least 1/max_rate seconds from last call"
-        elapsed = time.time() - self.last_called
-        must_wait = 1 / self.max_rate - elapsed
-        time.sleep(max(0, must_wait))
-        self.last_called = time.time()
-
-    def __get__(self, obj, type=None):
-        return first_invoke(self._wait, functools.partial(self.func, obj))
-
-
-def first_invoke(func1, func2):
-    """
-    Return a function that when invoked will invoke func1 without
-    any parameters (for its side-effect) and then invoke func2
-    with whatever parameters were passed, returning its result.
-    """
-
-    def wrapper(*args, **kwargs):
-        func1()
-        return func2(*args, **kwargs)
-
-    return wrapper
-
-
-def retry_call(func, cleanup=lambda: None, retries=0, trap=()):
-    """
-    Given a callable func, trap the indicated exceptions
-    for up to 'retries' times, invoking cleanup on the
-    exception. On the final attempt, allow any exceptions
-    to propagate.
-    """
-    attempts = itertools.count() if retries == float('inf') else range(retries)
-    for attempt in attempts:
-        try:
-            return func()
-        except trap:
-            cleanup()
-
-    return func()
-
-
-def retry(*r_args, **r_kwargs):
-    """
-    Decorator wrapper for retry_call. Accepts arguments to retry_call
-    except func and then returns a decorator for the decorated function.
-
-    Ex:
-
-    >>> @retry(retries=3)
-    ... def my_func(a, b):
-    ...     "this is my funk"
-    ...     print(a, b)
-    >>> my_func.__doc__
-    'this is my funk'
-    """
-
-    def decorate(func):
-        @functools.wraps(func)
-        def wrapper(*f_args, **f_kwargs):
-            bound = functools.partial(func, *f_args, **f_kwargs)
-            return retry_call(bound, *r_args, **r_kwargs)
-
-        return wrapper
-
-    return decorate
-
-
-def print_yielded(func):
-    """
-    Convert a generator into a function that prints all yielded elements
-
-    >>> @print_yielded
-    ... def x():
-    ...     yield 3; yield None
-    >>> x()
-    3
-    None
-    """
-    print_all = functools.partial(map, print)
-    print_results = compose(more_itertools.consume, print_all, func)
-    return functools.wraps(func)(print_results)
-
-
-def pass_none(func):
-    """
-    Wrap func so it's not called if its first param is None
-
-    >>> print_text = pass_none(print)
-    >>> print_text('text')
-    text
-    >>> print_text(None)
-    """
-
-    @functools.wraps(func)
-    def wrapper(param, *args, **kwargs):
-        if param is not None:
-            return func(param, *args, **kwargs)
-
-    return wrapper
-
-
-def assign_params(func, namespace):
-    """
-    Assign parameters from namespace where func solicits.
-
-    >>> def func(x, y=3):
-    ...     print(x, y)
-    >>> assigned = assign_params(func, dict(x=2, z=4))
-    >>> assigned()
-    2 3
-
-    The usual errors are raised if a function doesn't receive
-    its required parameters:
-
-    >>> assigned = assign_params(func, dict(y=3, z=4))
-    >>> assigned()
-    Traceback (most recent call last):
-    TypeError: func() ...argument...
-
-    It even works on methods:
-
-    >>> class Handler:
-    ...     def meth(self, arg):
-    ...         print(arg)
-    >>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))()
-    crystal
-    """
-    sig = inspect.signature(func)
-    params = sig.parameters.keys()
-    call_ns = {k: namespace[k] for k in params if k in namespace}
-    return functools.partial(func, **call_ns)
-
-
-def save_method_args(method):
-    """
-    Wrap a method such that when it is called, the args and kwargs are
-    saved on the method.
-
-    >>> class MyClass:
-    ...     @save_method_args
-    ...     def method(self, a, b):
-    ...         print(a, b)
-    >>> my_ob = MyClass()
-    >>> my_ob.method(1, 2)
-    1 2
-    >>> my_ob._saved_method.args
-    (1, 2)
-    >>> my_ob._saved_method.kwargs
-    {}
-    >>> my_ob.method(a=3, b='foo')
-    3 foo
-    >>> my_ob._saved_method.args
-    ()
-    >>> my_ob._saved_method.kwargs == dict(a=3, b='foo')
-    True
-
-    The arguments are stored on the instance, allowing for
-    different instance to save different args.
-
-    >>> your_ob = MyClass()
-    >>> your_ob.method({str('x'): 3}, b=[4])
-    {'x': 3} [4]
-    >>> your_ob._saved_method.args
-    ({'x': 3},)
-    >>> my_ob._saved_method.args
-    ()
-    """
-    args_and_kwargs = collections.namedtuple('args_and_kwargs', 'args kwargs')
-
-    @functools.wraps(method)
-    def wrapper(self, *args, **kwargs):
-        attr_name = '_saved_' + method.__name__
-        attr = args_and_kwargs(args, kwargs)
-        setattr(self, attr_name, attr)
-        return method(self, *args, **kwargs)
-
-    return wrapper
-
-
-def except_(*exceptions, replace=None, use=None):
-    """
-    Replace the indicated exceptions, if raised, with the indicated
-    literal replacement or evaluated expression (if present).
-
-    >>> safe_int = except_(ValueError)(int)
-    >>> safe_int('five')
-    >>> safe_int('5')
-    5
-
-    Specify a literal replacement with ``replace``.
-
-    >>> safe_int_r = except_(ValueError, replace=0)(int)
-    >>> safe_int_r('five')
-    0
-
-    Provide an expression to ``use`` to pass through particular parameters.
-
-    >>> safe_int_pt = except_(ValueError, use='args[0]')(int)
-    >>> safe_int_pt('five')
-    'five'
-
-    """
-
-    def decorate(func):
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            try:
-                return func(*args, **kwargs)
-            except exceptions:
-                try:
-                    return eval(use)
-                except TypeError:
-                    return replace
-
-        return wrapper
-
-    return decorate
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/lightning_trainer/trainer.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/lightning_trainer/trainer.py
deleted file mode 100644
index cce4839b536eba974426309eca10415547479f50..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/src/lightning_trainer/trainer.py
+++ /dev/null
@@ -1,310 +0,0 @@
-from collections import defaultdict
-import pprint
-from loguru import logger
-from pathlib import Path
-
-import torch
-import numpy as np
-import pytorch_lightning as pl
-from matplotlib import pyplot as plt
-
-from src.models import TopicFM
-from src.models.utils.supervision import (
-    compute_supervision_coarse,
-    compute_supervision_fine,
-)
-from src.losses.loss import TopicFMLoss
-from src.optimizers import build_optimizer, build_scheduler
-from src.utils.metrics import (
-    compute_symmetrical_epipolar_errors,
-    compute_pose_errors,
-    aggregate_metrics,
-)
-from src.utils.plotting import make_matching_figures
-from src.utils.comm import gather, all_gather
-from src.utils.misc import lower_config, flattenList
-from src.utils.profiler import PassThroughProfiler
-
-
-class PL_Trainer(pl.LightningModule):
-    def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None):
-        """
-        TODO:
-            - use the new version of PL logging API.
-        """
-        super().__init__()
-        # Misc
-        self.config = config  # full config
-        _config = lower_config(self.config)
-        self.model_cfg = lower_config(_config["model"])
-        self.profiler = profiler or PassThroughProfiler()
-        self.n_vals_plot = max(
-            config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1
-        )
-
-        # Matcher: TopicFM
-        self.matcher = TopicFM(config=_config["model"])
-        self.loss = TopicFMLoss(_config)
-
-        # Pretrained weights
-        if pretrained_ckpt:
-            state_dict = torch.load(pretrained_ckpt, map_location="cpu")["state_dict"]
-            self.matcher.load_state_dict(state_dict, strict=True)
-            logger.info(f"Load '{pretrained_ckpt}' as pretrained checkpoint")
-
-        # Testing
-        self.dump_dir = dump_dir
-
-    def configure_optimizers(self):
-        # FIXME: The scheduler did not work properly when `--resume_from_checkpoint`
-        optimizer = build_optimizer(self, self.config)
-        scheduler = build_scheduler(self.config, optimizer)
-        return [optimizer], [scheduler]
-
-    def optimizer_step(
-        self,
-        epoch,
-        batch_idx,
-        optimizer,
-        optimizer_idx,
-        optimizer_closure,
-        on_tpu,
-        using_native_amp,
-        using_lbfgs,
-    ):
-        # learning rate warm up
-        warmup_step = self.config.TRAINER.WARMUP_STEP
-        if self.trainer.global_step < warmup_step:
-            if self.config.TRAINER.WARMUP_TYPE == "linear":
-                base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR
-                lr = base_lr + (
-                    self.trainer.global_step / self.config.TRAINER.WARMUP_STEP
-                ) * abs(self.config.TRAINER.TRUE_LR - base_lr)
-                for pg in optimizer.param_groups:
-                    pg["lr"] = lr
-            elif self.config.TRAINER.WARMUP_TYPE == "constant":
-                pass
-            else:
-                raise ValueError(
-                    f"Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}"
-                )
-
-        # update params
-        optimizer.step(closure=optimizer_closure)
-        optimizer.zero_grad()
-
-    def _trainval_inference(self, batch):
-        with self.profiler.profile("Compute coarse supervision"):
-            compute_supervision_coarse(batch, self.config)
-
-        with self.profiler.profile("TopicFM"):
-            self.matcher(batch)
-
-        with self.profiler.profile("Compute fine supervision"):
-            compute_supervision_fine(batch, self.config)
-
-        with self.profiler.profile("Compute losses"):
-            self.loss(batch)
-
-    def _compute_metrics(self, batch):
-        with self.profiler.profile("Copmute metrics"):
-            compute_symmetrical_epipolar_errors(
-                batch
-            )  # compute epi_errs for each match
-            compute_pose_errors(
-                batch, self.config
-            )  # compute R_errs, t_errs, pose_errs for each pair
-
-            rel_pair_names = list(zip(*batch["pair_names"]))
-            bs = batch["image0"].size(0)
-            metrics = {
-                # to filter duplicate pairs caused by DistributedSampler
-                "identifiers": ["#".join(rel_pair_names[b]) for b in range(bs)],
-                "epi_errs": [
-                    batch["epi_errs"][batch["m_bids"] == b].cpu().numpy()
-                    for b in range(bs)
-                ],
-                "R_errs": batch["R_errs"],
-                "t_errs": batch["t_errs"],
-                "inliers": batch["inliers"],
-            }
-            ret_dict = {"metrics": metrics}
-        return ret_dict, rel_pair_names
-
-    def training_step(self, batch, batch_idx):
-        self._trainval_inference(batch)
-
-        # logging
-        if (
-            self.trainer.global_rank == 0
-            and self.global_step % self.trainer.log_every_n_steps == 0
-        ):
-            # scalars
-            for k, v in batch["loss_scalars"].items():
-                self.logger.experiment.add_scalar(f"train/{k}", v, self.global_step)
-
-            # figures
-            if self.config.TRAINER.ENABLE_PLOTTING:
-                compute_symmetrical_epipolar_errors(
-                    batch
-                )  # compute epi_errs for each match
-                figures = make_matching_figures(
-                    batch, self.config, self.config.TRAINER.PLOT_MODE
-                )
-                for k, v in figures.items():
-                    self.logger.experiment.add_figure(
-                        f"train_match/{k}", v, self.global_step
-                    )
-
-        return {"loss": batch["loss"]}
-
-    def training_epoch_end(self, outputs):
-        avg_loss = torch.stack([x["loss"] for x in outputs]).mean()
-        if self.trainer.global_rank == 0:
-            self.logger.experiment.add_scalar(
-                "train/avg_loss_on_epoch", avg_loss, global_step=self.current_epoch
-            )
-
-    def validation_step(self, batch, batch_idx):
-        self._trainval_inference(batch)
-
-        ret_dict, _ = self._compute_metrics(batch)
-
-        val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1)
-        figures = {self.config.TRAINER.PLOT_MODE: []}
-        if batch_idx % val_plot_interval == 0:
-            figures = make_matching_figures(
-                batch, self.config, mode=self.config.TRAINER.PLOT_MODE
-            )
-
-        return {
-            **ret_dict,
-            "loss_scalars": batch["loss_scalars"],
-            "figures": figures,
-        }
-
-    def validation_epoch_end(self, outputs):
-        # handle multiple validation sets
-        multi_outputs = (
-            [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs
-        )
-        multi_val_metrics = defaultdict(list)
-
-        for valset_idx, outputs in enumerate(multi_outputs):
-            # since pl performs sanity_check at the very begining of the training
-            cur_epoch = self.trainer.current_epoch
-            if (
-                not self.trainer.resume_from_checkpoint
-                and self.trainer.running_sanity_check
-            ):
-                cur_epoch = -1
-
-            # 1. loss_scalars: dict of list, on cpu
-            _loss_scalars = [o["loss_scalars"] for o in outputs]
-            loss_scalars = {
-                k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars]))
-                for k in _loss_scalars[0]
-            }
-
-            # 2. val metrics: dict of list, numpy
-            _metrics = [o["metrics"] for o in outputs]
-            metrics = {
-                k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics])))
-                for k in _metrics[0]
-            }
-            # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0
-            val_metrics_4tb = aggregate_metrics(
-                metrics, self.config.TRAINER.EPI_ERR_THR
-            )
-            for thr in [5, 10, 20]:
-                multi_val_metrics[f"auc@{thr}"].append(val_metrics_4tb[f"auc@{thr}"])
-
-            # 3. figures
-            _figures = [o["figures"] for o in outputs]
-            figures = {
-                k: flattenList(gather(flattenList([_me[k] for _me in _figures])))
-                for k in _figures[0]
-            }
-
-            # tensorboard records only on rank 0
-            if self.trainer.global_rank == 0:
-                for k, v in loss_scalars.items():
-                    mean_v = torch.stack(v).mean()
-                    self.logger.experiment.add_scalar(
-                        f"val_{valset_idx}/avg_{k}", mean_v, global_step=cur_epoch
-                    )
-
-                for k, v in val_metrics_4tb.items():
-                    self.logger.experiment.add_scalar(
-                        f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch
-                    )
-
-                for k, v in figures.items():
-                    if self.trainer.global_rank == 0:
-                        for plot_idx, fig in enumerate(v):
-                            self.logger.experiment.add_figure(
-                                f"val_match_{valset_idx}/{k}/pair-{plot_idx}",
-                                fig,
-                                cur_epoch,
-                                close=True,
-                            )
-            plt.close("all")
-
-        for thr in [5, 10, 20]:
-            # log on all ranks for ModelCheckpoint callback to work properly
-            self.log(
-                f"auc@{thr}", torch.tensor(np.mean(multi_val_metrics[f"auc@{thr}"]))
-            )  # ckpt monitors on this
-
-    def test_step(self, batch, batch_idx):
-        with self.profiler.profile("TopicFM"):
-            self.matcher(batch)
-
-        ret_dict, rel_pair_names = self._compute_metrics(batch)
-
-        with self.profiler.profile("dump_results"):
-            if self.dump_dir is not None:
-                # dump results for further analysis
-                keys_to_save = {"mkpts0_f", "mkpts1_f", "mconf", "epi_errs"}
-                pair_names = list(zip(*batch["pair_names"]))
-                bs = batch["image0"].shape[0]
-                dumps = []
-                for b_id in range(bs):
-                    item = {}
-                    mask = batch["m_bids"] == b_id
-                    item["pair_names"] = pair_names[b_id]
-                    item["identifier"] = "#".join(rel_pair_names[b_id])
-                    for key in keys_to_save:
-                        item[key] = batch[key][mask].cpu().numpy()
-                    for key in ["R_errs", "t_errs", "inliers"]:
-                        item[key] = batch[key][b_id]
-                    dumps.append(item)
-                ret_dict["dumps"] = dumps
-
-        return ret_dict
-
-    def test_epoch_end(self, outputs):
-        # metrics: dict of list, numpy
-        _metrics = [o["metrics"] for o in outputs]
-        metrics = {
-            k: flattenList(gather(flattenList([_me[k] for _me in _metrics])))
-            for k in _metrics[0]
-        }
-
-        # [{key: [{...}, *#bs]}, *#batch]
-        if self.dump_dir is not None:
-            Path(self.dump_dir).mkdir(parents=True, exist_ok=True)
-            _dumps = flattenList([o["dumps"] for o in outputs])  # [{...}, #bs*#batch]
-            dumps = flattenList(gather(_dumps))  # [{...}, #proc*#bs*#batch]
-            logger.info(
-                f"Prediction and evaluation results will be saved to: {self.dump_dir}"
-            )
-
-        if self.trainer.global_rank == 0:
-            print(self.profiler.summary())
-            val_metrics_4tb = aggregate_metrics(
-                metrics, self.config.TRAINER.EPI_ERR_THR
-            )
-            logger.info("\n" + pprint.pformat(val_metrics_4tb))
-            if self.dump_dir is not None:
-                np.save(Path(self.dump_dir) / "TopicFM_pred_eval", dumps)
diff --git a/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/download.sh b/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/download.sh
deleted file mode 100644
index 80eb0e3c9f24345c17177cb9d3ab0834f8d58a27..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/d2net/hpatches_sequences/download.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/usr/bin/env bash
-
-# Download the dataset
-wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz
-
-# Extract the dataset
-tar xvzf hpatches-sequences-release.tar.gz
-
-# Remove the high-resolution sequences
-cd hpatches-sequences-release
-rm -rf i_contruction i_crownnight i_dc i_pencils i_whitebuilding v_artisans v_astronautis v_talent
-cd ..
diff --git a/spaces/RedYan/nitrosocke-Ghibli-Diffusion/app.py b/spaces/RedYan/nitrosocke-Ghibli-Diffusion/app.py
deleted file mode 100644
index ba88009ae25eab75b12152bde1aae34c4cb52907..0000000000000000000000000000000000000000
--- a/spaces/RedYan/nitrosocke-Ghibli-Diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/nitrosocke/Ghibli-Diffusion").launch()
\ No newline at end of file
diff --git a/spaces/Ritori/TTS_Yui/hparams.py b/spaces/Ritori/TTS_Yui/hparams.py
deleted file mode 100644
index 497b0686c48ecc944ec940c67610bcbabbf0664d..0000000000000000000000000000000000000000
--- a/spaces/Ritori/TTS_Yui/hparams.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import torch
-from text import symbols
-
-
-class create_hparams():
-    """Create model hyperparameters. Parse nondefault from given string."""
-    ################################
-    #       CUDA Enable            #
-    ################################
-    if torch.cuda.is_available() :
-        cuda_enabled = True    
-    else :
-        cuda_enabled = False
-
-    ################################
-    # Experiment Parameters        #
-    ################################
-    epochs = 100
-    iters_per_checkpoint = 500
-    seed= 1234
-    dynamic_loss_scaling = True
-    fp16_run = False
-    distributed_run = False
-    dist_backend = "nccl"
-    dist_url = "tcp://localhost:54321"
-    cudnn_enabled = True
-    cudnn_benchmark = False
-    ignore_layers = ['embedding.weight']
-
-    ################################
-    # Data Parameters             #
-    ################################
-    load_mel_from_disk = False
-    training_files = 'filelists/transcript_train.txt'
-    validation_files = 'filelists/transcript_val.txt'
-    text_cleaners = ['japanese_cleaners']
-
-    ################################
-    # Audio Parameters             #
-    ################################
-    max_wav_value = 32768.0
-    sampling_rate = 22050
-    filter_length = 1024
-    hop_length = 256
-    win_length = 1024
-    n_mel_channels = 80
-    mel_fmin = 0.0
-    mel_fmax = 8000.0
-
-    ################################
-    # Model Parameters             #
-    ################################
-    n_symbols = len(symbols)
-    symbols_embedding_dim = 512
-
-    # Encoder parameters
-    encoder_kernel_size = 5
-    encoder_n_convolutions = 3
-    encoder_embedding_dim = 512
-
-    # Decoder parameters
-    n_frames_per_step = 1  # currently only 1 is supported
-    decoder_rnn_dim = 1024
-    prenet_dim = 256
-    max_decoder_steps = 1000
-    gate_threshold = 0.5
-    p_attention_dropout = 0.1
-    p_decoder_dropout = 0.1
-
-    # Attention parameters
-    attention_rnn_dim = 1024
-    attention_dim = 128
-    # Location Layer parameters
-    attention_location_n_filters = 32
-    attention_location_kernel_size = 31
-
-    # Mel-post processing network parameters
-    postnet_embedding_dim = 512
-    postnet_kernel_size = 5
-    postnet_n_convolutions = 5
-
-    ################################
-    # Optimization Hyperparameters #
-    ################################
-    use_saved_learning_rate = False
-    learning_rate = 1e-3
-    weight_decay = 1e-6
-    grad_clip_thresh = 1.0
-    batch_size = 64
-    mask_padding = True  # set model's padded outputs to padded values
-
-
-
-
diff --git a/spaces/RobLi/ControlNet-v1-1/app_shuffle.py b/spaces/RobLi/ControlNet-v1-1/app_shuffle.py
deleted file mode 100644
index f863c95a6c263eeb55fee7c8502d79b404771a3a..0000000000000000000000000000000000000000
--- a/spaces/RobLi/ControlNet-v1-1/app_shuffle.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(
-                        label='Preprocessor',
-                        choices=['ContentShuffle', 'None'],
-                        type='value',
-                        value='ContentShuffle')
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='content-shuffle',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='shuffle')
-    demo = create_demo(model.process_shuffle)
-    demo.queue().launch()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/mlsd/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/mlsd/__init__.py
deleted file mode 100644
index 4b44ad05af4b387cb8a1442f5b56c0c6ee3a2b1f..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/mlsd/__init__.py
+++ /dev/null
@@ -1,53 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
-'''
-
-# MLSD Line Detection
-# From https://github.com/navervision/mlsd
-# Apache-2.0 license
-
-import cv2
-import numpy as np
-import torch
-import os
-
-from einops import rearrange
-from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny
-from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
-from .utils import pred_lines
-
-from annotator.util import annotator_ckpts_path
-
-
-remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/mlsd_large_512_fp32.pth"
-
-
-class MLSDdetector:
-    def __init__(self):
-        model_path = os.path.join(annotator_ckpts_path, "mlsd_large_512_fp32.pth")
-        if not os.path.exists(model_path):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
-        model = MobileV2_MLSD_Large()
-        model.load_state_dict(torch.load(model_path), strict=True)
-        self.model = model.cuda().eval()
-
-    def __call__(self, input_image, thr_v, thr_d):
-        assert input_image.ndim == 3
-        img = input_image
-        img_output = np.zeros_like(img)
-        try:
-            with torch.no_grad():
-                lines = pred_lines(img, self.model, [img.shape[0], img.shape[1]], thr_v, thr_d)
-                for line in lines:
-                    x_start, y_start, x_end, y_end = [int(val) for val in line]
-                    cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
-        except Exception as e:
-            pass
-        return img_output[:, :, 0]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/utils.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/utils.py
deleted file mode 100644
index c5befb8e56ece50b5fecfd007b26f8a29124c0bd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/utils.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import random
-import sys
-import time
-import warnings
-from getpass import getuser
-from socket import gethostname
-
-import numpy as np
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_host_info():
-    """Get hostname and username.
-
-    Return empty string if exception raised, e.g. ``getpass.getuser()`` will
-    lead to error in docker container
-    """
-    host = ''
-    try:
-        host = f'{getuser()}@{gethostname()}'
-    except Exception as e:
-        warnings.warn(f'Host or user not found: {str(e)}')
-    finally:
-        return host
-
-
-def get_time_str():
-    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def obj_from_dict(info, parent=None, default_args=None):
-    """Initialize an object from dict.
-
-    The dict must contain the key "type", which indicates the object type, it
-    can be either a string or type, such as "list" or ``list``. Remaining
-    fields are treated as the arguments for constructing the object.
-
-    Args:
-        info (dict): Object types and arguments.
-        parent (:class:`module`): Module which may containing expected object
-            classes.
-        default_args (dict, optional): Default arguments for initializing the
-            object.
-
-    Returns:
-        any type: Object built from the dict.
-    """
-    assert isinstance(info, dict) and 'type' in info
-    assert isinstance(default_args, dict) or default_args is None
-    args = info.copy()
-    obj_type = args.pop('type')
-    if mmcv.is_str(obj_type):
-        if parent is not None:
-            obj_type = getattr(parent, obj_type)
-        else:
-            obj_type = sys.modules[obj_type]
-    elif not isinstance(obj_type, type):
-        raise TypeError('type must be a str or valid type, but '
-                        f'got {type(obj_type)}')
-    if default_args is not None:
-        for name, value in default_args.items():
-            args.setdefault(name, value)
-    return obj_type(**args)
-
-
-def set_random_seed(seed, deterministic=False, use_rank_shift=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-        rank_shift (bool): Whether to add rank number to the random seed to
-            have different random seed in different threads. Default: False.
-    """
-    if use_rank_shift:
-        rank, _ = mmcv.runner.get_dist_info()
-        seed += rank
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/bbox/coder/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/bbox/coder/__init__.py
deleted file mode 100644
index ae455ba8fc0e0727e2d581cdc8f20fceededf99a..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/bbox/coder/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .base_bbox_coder import BaseBBoxCoder
-from .bucketing_bbox_coder import BucketingBBoxCoder
-from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
-from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
-from .pseudo_bbox_coder import PseudoBBoxCoder
-from .tblr_bbox_coder import TBLRBBoxCoder
-from .yolo_bbox_coder import YOLOBBoxCoder
-
-__all__ = [
-    'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder',
-    'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder',
-    'BucketingBBoxCoder'
-]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/drive.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/drive.py
deleted file mode 100644
index 3cbfda8ae74bdf26c5aef197ff2866a7c7ad0cfd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/drive.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class DRIVEDataset(CustomDataset):
-    """DRIVE dataset.
-
-    In segmentation map annotation for DRIVE, 0 stands for background, which is
-    included in 2 categories. ``reduce_zero_label`` is fixed to False. The
-    ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
-    '_manual1.png'.
-    """
-
-    CLASSES = ('background', 'vessel')
-
-    PALETTE = [[120, 120, 120], [6, 230, 230]]
-
-    def __init__(self, **kwargs):
-        super(DRIVEDataset, self).__init__(
-            img_suffix='.png',
-            seg_map_suffix='_manual1.png',
-            reduce_zero_label=False,
-            **kwargs)
-        assert osp.exists(self.img_dir)
diff --git a/spaces/SalahZa/Tunisian-Speech-Recognition/semi_wavlm_large_tunisian_ctc/1234/app.py b/spaces/SalahZa/Tunisian-Speech-Recognition/semi_wavlm_large_tunisian_ctc/1234/app.py
deleted file mode 100644
index 63d9204b9027a927bbd511b307a623d8eae58120..0000000000000000000000000000000000000000
--- a/spaces/SalahZa/Tunisian-Speech-Recognition/semi_wavlm_large_tunisian_ctc/1234/app.py
+++ /dev/null
@@ -1,464 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import torch
-import logging
-import gradio as gr
-import speechbrain as sb
-from pathlib import Path
-import os
-import torchaudio
-from hyperpyyaml import load_hyperpyyaml
-from speechbrain.tokenizers.SentencePiece import SentencePiece
-from speechbrain.utils.data_utils import undo_padding
-from speechbrain.utils.distributed import run_on_main
-
-"""Recipe for training a sequence-to-sequence ASR system with CommonVoice.
-The system employs a wav2vec2 encoder and a CTC decoder.
-Decoding is performed with greedy decoding (will be extended to beam search).
-
-To run this recipe, do the following:
-> python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml
-
-With the default hyperparameters, the system employs a pretrained wav2vec2 encoder.
-The wav2vec2 model is pretrained following the model given in the hprams file.
-It may be dependent on the language.
-
-The neural network is trained with CTC on sub-word units estimated with
-Byte Pairwise Encoding (BPE).
-
-The experiment file is flexible enough to support a large variety of
-different systems. By properly changing the parameter files, you can try
-different encoders, decoders, tokens (e.g, characters instead of BPE),
-training languages (all CommonVoice languages), and many
-other possible variations.
-
-Authors
- * Titouan Parcollet 2021
-"""
-
-logger = logging.getLogger(__name__)
-
-
-# Define training procedure
-class ASR(sb.core.Brain):
-    def compute_forward(self, batch, stage):
-        """Forward computations from the waveform batches to the output probabilities."""
-
-        batch = batch.to(self.device)
-        wavs, wav_lens = batch.sig
-        wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
-        if stage == sb.Stage.TRAIN:
-            if hasattr(self.hparams, "augmentation"):
-                wavs = self.hparams.augmentation(wavs, wav_lens)
-
-        # Forward pass
-        feats = self.modules.wav2vec2(wavs, wav_lens)
-        x = self.modules.enc(feats)
-        logits = self.modules.ctc_lin(x)
-        p_ctc = self.hparams.log_softmax(logits)
-
-        return p_ctc, wav_lens
-    
-    def treat_wav(self,sig):
-        feats = self.modules.wav2vec2(sig.to("cpu"), torch.tensor([1]).to("cpu"))
-        feats = self.modules.enc(feats)
-        logits = self.modules.ctc_lin(feats)
-        p_ctc = self.hparams.log_softmax(logits)
-        predicted_words =[]
-        for logs in p_ctc:
-            text = decoder.decode(logs.detach().cpu().numpy())
-            predicted_words.append(text.split(" "))
-        return " ".join(predicted_words[0])
- 
-    def compute_objectives(self, predictions, batch, stage):
-        """Computes the loss (CTC) given predictions and targets."""
-
-        p_ctc, wav_lens = predictions
-
-        ids = batch.id
-        tokens, tokens_lens = batch.tokens
-
-        loss = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
-
-        if stage != sb.Stage.TRAIN:
-            predicted_tokens = sb.decoders.ctc_greedy_decode(
-                p_ctc, wav_lens, blank_id=self.hparams.blank_index
-            )
-            # Decode token terms to words
-            if self.hparams.use_language_modelling:
-                predicted_words = []
-                for logs in p_ctc:
-                    text = decoder.decode(logs.detach().cpu().numpy())
-                    predicted_words.append(text.split(" "))
-            else:
-                predicted_words = [
-                    "".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
-                    for utt_seq in predicted_tokens
-                ]
-            # Convert indices to words
-            target_words = [wrd.split(" ") for wrd in batch.wrd]
-
-            self.wer_metric.append(ids, predicted_words, target_words)
-            self.cer_metric.append(ids, predicted_words, target_words)
-
-        return loss
-
-    def fit_batch(self, batch):
-        """Train the parameters given a single batch in input"""
-        should_step = self.step % self.grad_accumulation_factor == 0
-        # Managing automatic mixed precision
-        # TOFIX: CTC fine-tuning currently is unstable
-        # This is certainly due to CTC being done in fp16 instead of fp32
-        if self.auto_mix_prec:
-            with torch.cuda.amp.autocast():
-                with self.no_sync():
-                    outputs = self.compute_forward(batch, sb.Stage.TRAIN)
-                loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
-            with self.no_sync(not should_step):
-                self.scaler.scale(
-                    loss / self.grad_accumulation_factor
-                ).backward()
-            if should_step:
-
-                if not self.hparams.wav2vec2.freeze:
-                    self.scaler.unscale_(self.wav2vec_optimizer)
-                self.scaler.unscale_(self.model_optimizer)
-                if self.check_gradients(loss):
-                    if not self.hparams.wav2vec2.freeze:
-                        if self.optimizer_step >= self.hparams.warmup_steps:
-                            self.scaler.step(self.wav2vec_optimizer)
-                    self.scaler.step(self.model_optimizer)
-                self.scaler.update()
-                self.zero_grad()
-                self.optimizer_step += 1
-        else:
-            # This is mandatory because HF models have a weird behavior with DDP
-            # on the forward pass
-            with self.no_sync():
-                outputs = self.compute_forward(batch, sb.Stage.TRAIN)
-
-            loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
-
-            with self.no_sync(not should_step):
-                (loss / self.grad_accumulation_factor).backward()
-            if should_step:
-                if self.check_gradients(loss):
-                    if not self.hparams.wav2vec2.freeze:
-                        if self.optimizer_step >= self.hparams.warmup_steps:
-                            self.wav2vec_optimizer.step()
-                    self.model_optimizer.step()
-                self.zero_grad()
-                self.optimizer_step += 1
-
-        self.on_fit_batch_end(batch, outputs, loss, should_step)
-        return loss.detach().cpu()
-
-    def evaluate_batch(self, batch, stage):
-        """Computations needed for validation/test batches"""
-        predictions = self.compute_forward(batch, stage=stage)
-        with torch.no_grad():
-            loss = self.compute_objectives(predictions, batch, stage=stage)
-        return loss.detach()
-
-    def on_stage_start(self, stage, epoch):
-        """Gets called at the beginning of each epoch"""
-        if stage != sb.Stage.TRAIN:
-            self.cer_metric = self.hparams.cer_computer()
-            self.wer_metric = self.hparams.error_rate_computer()
-
-    def on_stage_end(self, stage, stage_loss, epoch):
-        """Gets called at the end of an epoch."""
-        # Compute/store important stats
-        stage_stats = {"loss": stage_loss}
-        if stage == sb.Stage.TRAIN:
-            self.train_stats = stage_stats
-        else:
-            stage_stats["CER"] = self.cer_metric.summarize("error_rate")
-            stage_stats["WER"] = self.wer_metric.summarize("error_rate")
-
-        # Perform end-of-iteration things, like annealing, logging, etc.
-        if stage == sb.Stage.VALID:
-            old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
-                stage_stats["loss"]
-            )
-            old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
-                stage_stats["loss"]
-            )
-            sb.nnet.schedulers.update_learning_rate(
-                self.model_optimizer, new_lr_model
-            )
-            if not self.hparams.wav2vec2.freeze:
-                sb.nnet.schedulers.update_learning_rate(
-                    self.wav2vec_optimizer, new_lr_wav2vec
-                )
-            self.hparams.train_logger.log_stats(
-                stats_meta={
-                    "epoch": epoch,
-                    "lr_model": old_lr_model,
-                    "lr_wav2vec": old_lr_wav2vec,
-                },
-                train_stats=self.train_stats,
-                valid_stats=stage_stats,
-            )
-            self.checkpointer.save_and_keep_only(
-                meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
-            )
-        elif stage == sb.Stage.TEST:
-            self.hparams.train_logger.log_stats(
-                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
-                test_stats=stage_stats,
-            )
-            with open(self.hparams.wer_file, "w") as w:
-                self.wer_metric.write_stats(w)
-
-    def init_optimizers(self):
-        "Initializes the wav2vec2 optimizer and model optimizer"
-
-        # If the wav2vec encoder is unfrozen, we create the optimizer
-        if not self.hparams.wav2vec2.freeze:
-            self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
-                self.modules.wav2vec2.parameters()
-            )
-            if self.checkpointer is not None:
-                self.checkpointer.add_recoverable(
-                    "wav2vec_opt", self.wav2vec_optimizer
-                )
-
-        self.model_optimizer = self.hparams.model_opt_class(
-            self.hparams.model.parameters()
-        )
-
-        if self.checkpointer is not None:
-            self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
-
-    def zero_grad(self, set_to_none=False):
-        if not self.hparams.wav2vec2.freeze:
-            self.wav2vec_optimizer.zero_grad(set_to_none)
-        self.model_optimizer.zero_grad(set_to_none)
-
-
-# Define custom data procedure
-def dataio_prepare(hparams):
-    """This function prepares the datasets to be used in the brain class.
-    It also defines the data processing pipeline through user-defined functions."""
-
-    # 1. Define datasets
-    data_folder = hparams["data_folder"]
-
-    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
-    )
-
-    if hparams["sorting"] == "ascending":
-        # we sort training data to speed up training and get better results.
-        train_data = train_data.filtered_sorted(
-            sort_key="duration",
-            key_max_value={"duration": hparams["avoid_if_longer_than"]},
-        )
-        # when sorting do not shuffle in dataloader ! otherwise is pointless
-        hparams["dataloader_options"]["shuffle"] = False
-
-    elif hparams["sorting"] == "descending":
-        train_data = train_data.filtered_sorted(
-            sort_key="duration",
-            reverse=True,
-            key_max_value={"duration": hparams["avoid_if_longer_than"]},
-        )
-        # when sorting do not shuffle in dataloader ! otherwise is pointless
-        hparams["dataloader_options"]["shuffle"] = False
-
-    elif hparams["sorting"] == "random":
-        pass
-
-    else:
-        raise NotImplementedError(
-            "sorting must be random, ascending or descending"
-        )
-
-    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
-        csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
-    )
-    # We also sort the validation data so it is faster to validate
-    valid_data = valid_data.filtered_sorted(sort_key="duration")
-    test_datasets = {}
-    for csv_file in hparams["test_csv"]:
-        name = Path(csv_file).stem
-        test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
-            csv_path=csv_file, replacements={"data_root": data_folder}
-        )
-        test_datasets[name] = test_datasets[name].filtered_sorted(
-            sort_key="duration"
-        )
-
-    datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
-
-
-    # 2. Define audio pipeline:
-    @sb.utils.data_pipeline.takes("wav")
-    @sb.utils.data_pipeline.provides("sig")
-    def audio_pipeline(wav):
-        info = torchaudio.info(wav)
-        sig = sb.dataio.dataio.read_audio(wav)
-        resampled = torchaudio.transforms.Resample(
-            info.sample_rate, hparams["sample_rate"],
-        )(sig)
-        return resampled
-
-    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
-    label_encoder = sb.dataio.encoder.CTCTextEncoder()
-
-    # 3. Define text pipeline:
-    @sb.utils.data_pipeline.takes("wrd")
-    @sb.utils.data_pipeline.provides(
-        "wrd", "char_list", "tokens_list", "tokens"
-    )
-    def text_pipeline(wrd):
-        yield wrd
-        char_list = list(wrd)
-        yield char_list
-        tokens_list = label_encoder.encode_sequence(char_list)
-        yield tokens_list
-        tokens = torch.LongTensor(tokens_list)
-        yield tokens
-
-    sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
-    lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
-    special_labels = {
-        "blank_label": hparams["blank_index"],
-        "unk_label": hparams["unk_index"]
-    }
-    label_encoder.load_or_create(
-        path=lab_enc_file,
-        from_didatasets=[train_data],
-        output_key="char_list",
-        special_labels=special_labels,
-        sequence_input=True,
-    )
-
-    # 4. Set output:
-    sb.dataio.dataset.set_output_keys(
-        datasets, ["id", "sig", "wrd", "char_list", "tokens"],
-    )
-    return train_data, valid_data,test_datasets, label_encoder
-
-
-
-# Load hyperparameters file with command-line overrides
-hparams_file, run_opts, overrides = sb.parse_arguments(["train_semi.yaml"])
-with open(hparams_file) as fin:
-    hparams = load_hyperpyyaml(fin, overrides)
-
-# If --distributed_launch then
-# create ddp_group with the right communication protocol
-sb.utils.distributed.ddp_init_group(run_opts)
-
-
-# Create experiment directory
-sb.create_experiment_directory(
-    experiment_directory=hparams["output_folder"],
-    hyperparams_to_save=hparams_file,
-    overrides=overrides,
-)
-
-# Due to DDP, we do the preparation ONLY on the main python process
-# Defining tokenizer and loading it
-# Create the datasets objects as well as tokenization and encoding :-D
-label_encoder = sb.dataio.encoder.CTCTextEncoder()
-
-lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
-special_labels = {
-    "blank_label": hparams["blank_index"],
-    "unk_label": hparams["unk_index"]
-}
-label_encoder.load_or_create(
-    path=lab_enc_file,
-    from_didatasets=[[]],
-    output_key="char_list",
-    special_labels=special_labels,
-    sequence_input=True,
-)
-
-from pyctcdecode import build_ctcdecoder
-ind2lab = label_encoder.ind2lab
-print(ind2lab)
-labels = [ind2lab[x] for x in range(len(ind2lab))]
-labels = [""] + labels[1:-1] + ["1"] 
-# Replace the <blank> token with a blank character, needed for PyCTCdecode
-print(labels)
-decoder = build_ctcdecoder(
-    labels,
-    kenlm_model_path=hparams["ngram_lm_path"],  # .arpa or .bin
-    alpha=0.5,  # Default by KenLM
-    beta=1.0,  # Default by KenLM
-)
-# Trainer initialization
-run_opts["device"] = "cpu"
-asr_brain = ASR(
-    modules=hparams["modules"],
-    hparams=hparams,
-    run_opts=run_opts,
-    checkpointer=hparams["checkpointer"],
-)
-
-# Adding objects to trainer.
-asr_brain.tokenizer = label_encoder
-asr_brain.checkpointer.recover_if_possible(device="cpu")
-asr_brain.modules.eval()
-description = """This is a speechbrain-based Automatic Speech Recognition (ASR) model for Tunisian arabic. It outputs Tunisian Arabic transcriptions written in Arabic characters.
-This model outputs transcriptions in arabic alphabet only and performs poorly with sentences containing foreign words. However if you do  need code-switching in your transcripts, i.e. foreign outputs in latin alphabet,  you would better use the code switched model, available in another space from the same author. (https://huggingface.co/SalahZa/Code_Switched_Tunisian_Speech_Recognition) 
-
-Run is done on CPU to keep it free in this space. This leads to quite long running times on long sequences. If for your project or research, you want to transcribe long sequences, you would better use the model directly from its page, some instructions for inference on a test set have been provided there. (https://huggingface.co/SalahZa/Tunisian_Automatic_Speech_Recognition). If you need help,  feel free to drop an email here : zaiemsalah@gmail.com 
-
-Authors : 
-* [Salah Zaiem](https://fr.linkedin.com/in/salah-zaiem)
-* [Ahmed Amine Ben Aballah](https://www.linkedin.com/in/aabenz/)
-* [Ata Kaboudi](https://www.linkedin.com/in/ata-kaboudi-63365b1a8)
-* [Amir Kanoun](https://tn.linkedin.com/in/ahmed-amir-kanoun)
-
-More in-depth details and insights are available in a released preprint. Please find the paper [here](https://arxiv.org/abs/2309.11327).
-If you use or refer to this model, please cite : 
-
-```
-@misc{abdallah2023leveraging,
-      title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, 
-      author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem},
-      year={2023},
-      eprint={2309.11327},
-      archivePrefix={arXiv},
-      primaryClass={eess.AS}
-}
-
-
-"""
-title = "Tunisian Speech Recognition"
-
-def treat_wav_file(file_mic,file_upload ,asr=asr_brain, device="cpu") :
-    if (file_mic is not None) and (file_upload is not None):
-        warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        wav = file_mic
-    elif (file_mic is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-    elif file_mic is not None:
-        wav = file_mic
-    else:
-        wav = file_upload
-    info = torchaudio.info(wav)
-    sr = info.sample_rate
-    sig = sb.dataio.dataio.read_audio(wav)
-    if len(sig.shape)>1 :
-        sig = torch.mean(sig, dim=1)
-    sig = torch.unsqueeze(sig, 0)
-    tensor_wav = sig.to(device)
-    resampled = torchaudio.functional.resample( tensor_wav, sr, 16000)
-    sentence = asr.treat_wav(resampled)
-    return sentence
-
-gr.Interface(
-    title = title,
-    description = description,
-    fn=treat_wav_file,
-    inputs=[gr.Audio(source="microphone", type='filepath', label = "record", optional = True),
-            gr.Audio(source="upload", type='filepath', label="filein", optional=True)]
-    ,outputs="text").launch()
-
-
diff --git a/spaces/SantiagoMoreno-UdeA/NER_RC/src/scripts/upsampling.py b/spaces/SantiagoMoreno-UdeA/NER_RC/src/scripts/upsampling.py
deleted file mode 100644
index c26a7dd7816ddd824a6ab958b8fc07b8ad2f4433..0000000000000000000000000000000000000000
--- a/spaces/SantiagoMoreno-UdeA/NER_RC/src/scripts/upsampling.py
+++ /dev/null
@@ -1,517 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Oct 11 16:31:58 2022
-
-@author: gita
-"""
-import random
-import numpy as np
-import copy
-
-class upsampling_ner:
-
-
-    
-    def __init__(self, path_data, entities, pos_labels):
-        """
-        
-
-        Parameters
-        ----------
-        path_data : str
-            Path of the dataset in format CONLL.
-        entities : List
-            List of the senten.
-        pos_labels : Dict
-            Dictionary where the keys are the kind of labels, and the values 
-            are the position of the labels in one line
-
-        Returns
-        -------
-        None.
-
-        """
-        self.__path_data = path_data
-        self.__entities = entities
-        self.__search_factor = 1000
-        self.__pos_labels = pos_labels
-        self.__get_data_variables()
-        
-    def __get_data_variables(self):
-        """
-        Takes the data path and turn the senteces into a matrix of shape
-        (Sentences, tokens of each sentence).
-        Also executes the __get_total_mentions. 
-
-        Returns
-        -------
-        None.
-
-        """
-        col  = self.__pos_labels['ner']
-        self.__dataset = []
-        self.__data_labels = []
-        data_temp = []
-        labels_temp = []
-        with open(self.__path_data, mode='r', encoding='utf-8') as f:
-            for line in f.readlines():
-                if line != '\n':
-                    data_temp.append(line.split(' ')[0])
-                    labels_temp.append(line.split(' ')[col][:-1])
-                    #print('si')
-                else:
-                    self.__dataset.append(data_temp)
-                    self.__data_labels.append(labels_temp)
-                    data_temp = []
-                    labels_temp = []
-        self.__get_total_mentions_and_tokens()
-
-        
-    def get_mentions(self, sentence, labels):
-        """
-        Divide sentence to a dictionary of mentions and a  dictionary of labels
-        of the mentions
-        
-
-        Parameters
-        ----------
-        sentence : List
-            List of the tokens of the sentence.
-        labels : List
-            List of the labels of each token.
-
-        Returns
-        -------
-        dict_mentions : Dictionary
-            sentece divided by its entities mentions key=number of mention, 
-            value= set of tokens in the mention.
-        dict_label_mentions : Dictionary
-            labels corresponding of the mentions in the same order as token 
-            mentions. key= number of mention, value= label of the mention.
-
-        """
-
-        dict_mentions = {}
-        dict_label_mentions = {}
-        mention = 0
-        #print(sentence)
-        dict_mentions[mention] = [sentence[0]]
-        
-        dict_label_mentions[mention] = labels[0]
-        for i,label in enumerate(labels[1:]):
-            if label == labels[i]:
-                dict_mentions[mention].append(sentence[i+1])
-            else: 
-                mention += 1
-                dict_mentions[mention] = [sentence[i+1]]
-                dict_label_mentions[mention] = labels[i+1]
-            
-        return dict_mentions, dict_label_mentions
-        
-        
-    def __get_total_mentions_and_tokens(self):
-        """
-        Takes the dataset and divide ach sentence in mentions and it store it 
-        in __all_mentions
-
-        Returns
-        -------
-        None.
-
-        """
-        
-        self.__all_mentions = {}
-        self.__tokens_per_entity = {}
-        
-        for key in self.__entities:
-            self.__all_mentions[key] = []
-            self.__tokens_per_entity[key] = []
-            
-        for i,sentence in enumerate(self.__dataset):
-            if sentence:
-                for j,word in enumerate(sentence):
-                    self.__tokens_per_entity[self.__data_labels[i][j]].append(word) 
-                    
-                mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
-                for n,label in enumerate(label_mentions.values()):
-                    if mentions[n] not in self.__all_mentions[label]: self.__all_mentions[label].append(mentions[n]);
-    
-    
-    def get_mentions_dict(self):
-        "Return all the mentions in the dataset"
-        return self.__all_mentions
-    
-    
-    def get_dataset(self):
-        "Return the dataset"
-        return self.__dataset, self.__data_labels
-    
-
-    def Label_wise_token_replacement(self, token_mentions, label_mentions, labels, p):
-        """
-        Do the Label wise token replacement to a sentence divided in mentions
-        
-
-        Parameters
-        ----------
-        token_mentions : Dictionary
-            sentece divided by its entities mentions key=number of mention, 
-            value= set of tokens in the mention.
-        label_mentions : Dictionary
-            labels corresponding of the mentions in the same order as token 
-            mentions. key= number of mention, value= label of the mention
-        labels : List
-            list of entities to be upsampled.
-        p : float
-            probability upsampled a mention selected.
-
-        Returns
-        -------
-        token_mentions : Dictionary
-            token mentions but with mention replacement.
-
-        """
-
-        p = 1-p 
-        for i in token_mentions.keys():
-            if label_mentions[i] in labels:
-                for j,token in enumerate(token_mentions[i]):
-                    umbral=np.random.uniform(0,1)
-                    if umbral>=p:
-                        token_selected = random.choice(self.__tokens_per_entity[label_mentions[i]])
-                        search = 0
-                        while token_selected == token and search <= self.__search_factor:
-                            token_selected = random.choice(self.__tokens_per_entity[label_mentions[i]])
-                            search += 1
-                        token_mentions[i][j] = token_selected
-                        
-        return token_mentions
-
-    def synonym_replacement(self, token_mentions, label_mentions, labels, p): 
-        
-        """
-        Do the synonym_replacement to a sentence divided in mentions
-    
-    
-        Parameters
-        ----------
-        token_mentions : Dictionary
-            sentece divided by its entities mentions key=number of mention, 
-            value= set of tokens in the mention.
-        label_mentions : Dictionary
-            labels corresponding of the mentions in the same order as token 
-            mentions. key= number of mention, value= label of the mention
-        labels : List
-            list of entities to be upsampled.
-        p : float
-            probability upsampled a mention selected.
-    
-        Returns
-        -------
-        token_mentions : Dictionary
-            token mentions but with shuffled.
-    
-        """
-        
-        import requests
-        from bs4 import BeautifulSoup
-        url='http://www.wordreference.com/sinonimos/'
-    
-        p = 1-p        
-        
-        for i in token_mentions.keys():
-            if label_mentions[i] in labels:
-                for j,token in enumerate(token_mentions[i]):
-                    umbral=np.random.uniform(0,1)
-                    if umbral>=p:
-                        
-                        buscar=url+token
-                        resp=requests.get(buscar)
-                        bs=BeautifulSoup(resp.text,'lxml')
-                        try:
-                            lista=bs.find(class_='trans clickable')
-                            sino=lista.find('li')
-                            list_synonyms = sino.next_element.split(',  ')
-                        except:
-                            list_synonyms = False
-                        if list_synonyms:
-                            synonym_selected = random.choice(list_synonyms)
-                            search = 0
-                            while synonym_selected == token_mentions[i][j] and search <= self.__search_factor:
-                                synonym_selected = random.choice(list_synonyms)
-                                search += 1
-                            token_mentions[i][j] = synonym_selected
-                        
-        return token_mentions
-
-
-
-    def mention_replacement(self, token_mentions, label_mentions, labels, p):
-        """
-        Do the mentions replacement to a sentence divided in mentions
-        
-
-        Parameters
-        ----------
-        token_mentions : Dictionary
-            sentece divided by its entities mentions key=number of mention, 
-            value= set of tokens in the mention.
-        label_mentions : Dictionary
-            labels corresponding of the mentions in the same order as token 
-            mentions. key= number of mention, value= label of the mention
-        labels : List
-            list of entities to be upsampled.
-        p : float
-            probability upsampled a mention selected.
-
-        Returns
-        -------
-        token_mentions : Dictionary
-            token mentions but with mention replacement.
-
-        """
-
-        p = 1-p 
-        for i in token_mentions.keys():
-            if label_mentions[i] in labels:
-                umbral=np.random.uniform(0,1)
-                if umbral>=p: 
-                    set_of_mentions = self.__all_mentions[label_mentions[i]]
-                    mention_selected = random.choice(set_of_mentions)
-                    search = 0
-                    while token_mentions[i] == mention_selected and search <= self.__search_factor:
-                        mention_selected = random.choice(set_of_mentions)
-                        search += 1
-                    token_mentions[i] = mention_selected
-        return token_mentions
-    
-
-        
-    def shuffle_within_segments(self, token_mentions, label_mentions, labels, p): 
-        """
-        Do the shuffle within segments to a sentence divided in mentions
-
-
-        Parameters
-        ----------
-        token_mentions : Dictionary
-            sentece divided by its entities mentions key=number of mention, 
-            value= set of tokens in the mention.
-        label_mentions : Dictionary
-            labels corresponding of the mentions in the same order as token 
-            mentions. key= number of mention, value= label of the mention
-        labels : List
-            list of entities to be upsampled.
-        p : float
-            probability upsampled a mention selected.
-
-        Returns
-        -------
-        token_mentions : Dictionary
-            token mentions but with shuffled.
-
-        """
-
-        p = 1-p        
-        for i in token_mentions.keys():
-            if label_mentions[i] in labels:
-                umbral=np.random.uniform(0,1)
-                if umbral>=p: random.shuffle(token_mentions[i])
-        return token_mentions
-    
-    def mention_back_traslation(self, token_mentions, label_mentions, labels, p):
-        """
-        Do the back traslation to each mention in a sentence divided in mentions
-        
-
-        Parameters
-        ----------
-        token_mentions : Dictionary
-            sentece divided by its entities mentions key=number of mention, 
-            value= set of tokens in the mention.
-        label_mentions : Dictionary
-            labels corresponding of the mentions in the same order as token 
-            mentions. key= number of mention, value= label of the mention
-        labels : List
-            list of entities to be upsampled.
-        p : float
-            probability upsampled a mention selected.
-
-        Returns
-        -------
-        token_mentions : Dictionary
-            token mentions but with mention brack traslation.
-}
-        """
-
-        from deep_translator import GoogleTranslator
-        from nltk.tokenize import word_tokenize
-
-
-        p = 1-p 
-        for i in token_mentions.keys():
-            if label_mentions[i] in labels:
-                umbral=np.random.uniform(0,1)
-                if umbral>=p: 
-                    try: 
-                        language = random.choice(['en', 'sv', 'fr', 'ja', 'ko', 'af', 'sq', 'cs', 'es', 'el', 'ga'])
-                        to_translate = " ".join(token_mentions[i])
-                        
-                        #print("to_trans: ", to_translate[:20])
-                        
-                        translateden = GoogleTranslator(source='auto', target=language).translate(to_translate)
-    
-                        #print("Trans: ",translateden[:20])
-    
-                        translatedes = GoogleTranslator(source='auto', target='de').translate(translateden)
-    
-                        #print("back Trans: ",translatedes[:20])
-                    
-                        mention_selected = word_tokenize(translatedes)
-                        token_mentions[i] = mention_selected
-                    except: 
-                        pass                 
-        return token_mentions
-    
-
-    def upsampling(self, labels, p, methods=None):
-        
-        if methods is None: 
-            print("Not upsampling required")
-        else: 
-            new_mentions = []
-            new_labels = []
-            for i,sentence in enumerate(self.__dataset):
-                if sentence:
-                    sentence_mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
-                    
-                    
-                    if "SiS" in methods:
-                        new_mentions_temp = self.shuffle_within_segments(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                        if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                            new_mentions.append(new_mentions_temp)
-                            new_labels.append(label_mentions)
-                            
-                    
-                    if "LwTR" in methods:
-                        new_mentions_temp = self.Label_wise_token_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                        if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                            new_mentions.append(new_mentions_temp)
-                            new_labels.append(label_mentions)
-                            
-                        
-                    
-                        
-                    if "MR" in methods:
-                        new_mentions_temp = self.mention_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                        if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                            new_mentions.append(new_mentions_temp)
-                            new_labels.append(label_mentions)
-                            
-    
-                    
-                    if "SR" in methods:
-                        new_mentions_temp = self.synonym_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                        if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                            new_mentions.append(new_mentions_temp)
-                            new_labels.append(label_mentions)
-                            
-                            
-                            
-                    if "MBT" in methods:
-                        new_mentions_temp = self.mention_back_traslation(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                        if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                            new_mentions.append(new_mentions_temp)
-                            new_labels.append(label_mentions)
-                    
-                    
-            #Turn the mentions into sentences
-            new_samples_generated = []
-            new_labels_generated = []
-            
-            for i,mentions in enumerate(new_mentions):
-                new_labels_temp = new_labels[i]
-                sample_temp = []
-                labels_temp = []
-                for key in mentions.keys():
-                    sample_temp += mentions[key]
-                    labels_temp += [new_labels_temp[key]]*len(mentions[key])
-                new_samples_generated.append(sample_temp)
-                new_labels_generated.append(labels_temp)
-            return new_samples_generated, new_labels_generated
-        
-        
-        
-    def mention_to_sentence(self, mentions, labels):
-        sample_temp = []
-        labels_temp = []
-        for key in mentions.keys():
-            sample_temp += mentions[key]
-            labels_temp += [labels[key]]*len(mentions[key])
-
-        return sample_temp, labels_temp
-
-
-
-    def upsampling_by_sentence(self, labels, p, methods=None):
-        
-        if methods is None: 
-            print("Not upsampling required")
-        else: 
-            new_mentions = []
-            new_labels = []
-            map_sentences = []
-            map_labels = []
-            sentences_upsampled = []
-            labels_upsampled = []
-            
-            for i,sentence in enumerate(self.__dataset):
-                sentences_upsampled_temp  = {}
-                labels_upsampled_temp  = {}
-                
-                sentences_upsampled_temp["Original"] = sentence
-                labels_upsampled_temp["Original"] = self.__data_labels[i]
-                
-                sentence_mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
-                
-                
-                if "SiS" in methods:
-                    new_mentions_temp = self.shuffle_within_segments(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                    if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                        sentences_upsampled_temp["SiS"], labels_upsampled_temp["SiS"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
-
-
-                if "LwTR" in methods:
-                    new_mentions_temp = self.Label_wise_token_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                    if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                        sentences_upsampled_temp["LwTR"], labels_upsampled_temp["LwTR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
-                        
-                    
-                
-                    
-                if "MR" in methods:
-                    new_mentions_temp = self.mention_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                    if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                        sentences_upsampled_temp["MR"], labels_upsampled_temp["MR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
-
-                
-                if "SR" in methods:
-                    new_mentions_temp = self.synonym_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                    if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                        sentences_upsampled_temp["SR"], labels_upsampled_temp["SR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
-                        
-                        
-                        
-                if "MBT" in methods:
-                    new_mentions_temp = self.mention_back_traslation(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
-                    if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions: 
-                        sentences_upsampled_temp["MBT"], labels_upsampled_temp["MBT"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
-                
-                if len(sentences_upsampled_temp)>1:
-                    print(len(sentences_upsampled_temp))
-                    sentences_upsampled.append(sentences_upsampled_temp)
-                    labels_upsampled.append(labels_upsampled_temp)
-                    
-            return sentences_upsampled, labels_upsampled
-
-
diff --git a/spaces/SantiagoTesla/Rai_AI/app.py b/spaces/SantiagoTesla/Rai_AI/app.py
deleted file mode 100644
index ea854bfb8a65552f23ae75dbbc90684c001960ae..0000000000000000000000000000000000000000
--- a/spaces/SantiagoTesla/Rai_AI/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import openai
-import gradio as gr
-import time
-import warnings
-import warnings
-import os
-from gtts import gTTS
-
-warnings.filterwarnings("ignore")
-
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-def chatgpt_api(input_text):
-    messages = [
-    {"role": "system", "content": "You are a helpful assistant."}]
-    
-    if input_text:
-        messages.append(
-            {"role": "user", "content": input_text},
-        )
-        chat_completion = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo", messages=messages
-        )
-    
-    reply = chat_completion.choices[0].message.content
-    return reply
-
-#ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3'
-
-def transcribe(audio, text):
-    language = "en"
-
-    if audio is not None:
-        with open(audio, "rb") as transcript:
-            prompt = openai.Audio.transcribe("whisper-1", transcript)
-        s = prompt["text"]
-    else:
-        s = text
-    
-    
-    out_result = chatgpt_api(s)
-    
-    audioobj = gTTS(text = out_result, 
-                    lang = language, 
-                    slow = False)
-    
-    audioobj.save("Temp.mp3")
-
-    return [s, out_result, "Temp.mp3"]
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("Rai AI")
-    input1 = gr.inputs.Audio(source="microphone", type = "filepath", label="Use your voice to chat")
-    input2 = gr.inputs.Textbox(lines=7, label="Chat with AI")
-    output_1 = gr.Textbox(label="User Input")
-    output_2 = gr.Textbox(label="Text Output")
-    output_3 = gr.Audio("Temp.mp3", label="Speech Output")
-    btn = gr.Button("Run")
-    btn.click(fn=transcribe, inputs=[input1, input2], outputs=[output_1, output_2, output_3])
-demo.launch()
diff --git a/spaces/ShAnSantosh/Chatbot_Using_Pytorch/README.md b/spaces/ShAnSantosh/Chatbot_Using_Pytorch/README.md
deleted file mode 100644
index 2b7f2e818f289b9cd6763e02b7dbab219ced0fcc..0000000000000000000000000000000000000000
--- a/spaces/ShAnSantosh/Chatbot_Using_Pytorch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chatbot Using Pytorch
-emoji: 🏢
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Shad0ws/crowdcounting/app.py b/spaces/Shad0ws/crowdcounting/app.py
deleted file mode 100644
index 06bb743ffb2b69394eaabc2069441b9e41f431de..0000000000000000000000000000000000000000
--- a/spaces/Shad0ws/crowdcounting/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import numpy
-import torch
-import gradio as gr
-from einops import rearrange
-from  torchvision import transforms
-
-from model import CANNet
-model = CANNet()
-checkpoint = torch.load('part_B_pre.pth.tar',map_location=torch.device('cpu'))
-model.load_state_dict(checkpoint['state_dict'])
-model.eval()
-
-## Defining the transform function
-transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])
-
-
-def crowd(img):
-    ## Transforming the image
-    img = transform(img)
-    
-    ## Adding batch dimension
-    img = rearrange(img, "c h w -> 1 c h w")
-    
-    ## Slicing the image into four parts
-    h = img.shape[2]
-    w = img.shape[3]
-    h_d = int(h/2)
-    w_d = int(w/2)
-    img_1 = img[:,:,:h_d,:w_d]
-    img_2 = img[:,:,:h_d,w_d:]
-    img_3 = img[:,:,h_d:,:w_d]
-    img_4 = img[:,:,h_d:,w_d:]
-    
-    ## Inputting the 4 images into the model, converting it to numpy array, and summing to get the density
-    with torch.no_grad():
-        density_1 = model(img_1).numpy().sum()
-        density_2 = model(img_2).numpy().sum()
-        density_3 = model(img_3).numpy().sum()
-        density_4 = model(img_4).numpy().sum()
-        
-    ## Summing up the estimated density and rounding the result to get an integer
-    pred = density_1 + density_2 + density_3 + density_4
-    pred = int(pred.round())
-    return pred
-    
-outputs = gr.outputs.Textbox(type="text", label="Estimated crowd density:")
-inputs = gr.inputs.Image(type="numpy", label="Input the image here:")
-
-gr.Interface(fn=crowd, inputs=inputs, outputs=outputs, allow_flagging="never").launch(inbrowser=True)
\ No newline at end of file
diff --git a/spaces/Shamima/extract-color-from-image/app.py b/spaces/Shamima/extract-color-from-image/app.py
deleted file mode 100644
index effe66f8bc638a393d87fecb78ab66d81d1c39da..0000000000000000000000000000000000000000
--- a/spaces/Shamima/extract-color-from-image/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Copy of extract_colors_from_image.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Zx45R30-L2sIBh8VU_Fnbv9G6v4445TD
-"""
-
-# Commented out IPython magic to ensure Python compatibility.
-
-
-from sklearn.cluster import KMeans
-from collections import Counter
-import numpy as np
-import cv2
-
-import gradio as gr
-
-
-def get_image(pil_image):
-    #image = cv2.imread(image_path)
-    nimg = np.array(pil_image)
-    image = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    return image
-
-def get_labels(rimg):
-  clf = KMeans(n_clusters = 5)
-  labels = clf.fit_predict(rimg)
-  return labels ,  clf
-
-def RGB2HEX(color):
-    return "#{:02x}{:02x}{:02x}".format(int(color[0]), int(color[1]), int(color[2]))
-
-def get_colors(pimg):
-  img = get_image(pimg)
-  reshaped_img = img.reshape(img.shape[0]*img.shape[1], img.shape[2])
-  labels, clf = get_labels(reshaped_img)
-  counts = Counter(labels)
-  center_colors = clf.cluster_centers_
-  # We get ordered colors by iterating through the keys
-  ordered_colors = [center_colors[i] for i in counts.keys()]
-  hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts.keys()]
-  #rgb_colors = [ordered_colors[i] for i in counts.keys()]
-  return hex_colors
-
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown(
-        """
-    # Extract Colors from an image using KMeans clustering
-    """
-    )
-    inputs = [gr.Image(type="pil", label="Image to extract colors from")]
-    with gr.Row():
-        outputs = [gr.ColorPicker(label="color 1"), gr.ColorPicker(label="color 2"),gr.ColorPicker(label="color 3"),gr.ColorPicker(label="color 4"),gr.ColorPicker(label="color 5")]
-
-    btn = gr.Button("Extract colors")
-    btn.click(fn=get_colors, inputs=inputs, outputs=outputs)
-demo.queue()
-demo.launch()
-
diff --git a/spaces/ShreyashNadage/InvestmentCopilot/VixAnalysis.py b/spaces/ShreyashNadage/InvestmentCopilot/VixAnalysis.py
deleted file mode 100644
index 9e968f01efee660570fd4bf4f0aa5a6f55c4aa39..0000000000000000000000000000000000000000
--- a/spaces/ShreyashNadage/InvestmentCopilot/VixAnalysis.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from hmmlearn import hmm
-import plotly.graph_objects as go
-import streamlit as st
-
-original_map = {'max': 2, 'mid': 1, 'low': 0}
-
-@st.cache
-def GetMood(indval):
-    if indval <= 1/3:
-        return "Greed"
-    elif 1/3 < indval < 2/3:
-        return "Holding Steady"
-    else:
-        return "Fear"
-
-@st.cache
-def ProcessVixData(vix_df):
-    X = vix_df[['Close']].copy()
-    model = hmm.GaussianHMM(n_components=3, covariance_type="full", n_iter=100)
-    model.fit(X)
-    res = X.copy()
-    res['Class'] = model.predict(X)
-    resmap = res.groupby('Class').agg({'Close': 'mean'}).sort_values(by='Close')
-    resmap = dict(zip(resmap.index, ['low', 'mid', 'max']))
-    res['Class_name'] = res.Class.apply(lambda x:resmap[x])
-    res['Class_proc'] = res.Class_name.apply(lambda x:original_map[x])
-    res['Norm_Class'] = (res['Class_proc']-res['Class_proc'].min())/(res['Class_proc'].max()-res['Class_proc'].min())
-    return res
-
-@st.cache
-def GetIndicatorChart(res):
-    value = res.Norm_Class[-30:].mean()
-    fig = go.Figure(go.Indicator(
-        domain={'x': [0, 1], 'y': [0, 1]},
-        value=value,
-        mode="gauge+number",
-        title={'text': f"VIX Analysis\nMood : {GetMood(value)}"},
-        gauge={'axis': {'range': [None, res.Norm_Class.max()]},
-               'steps': [
-                   {'range': [0, 1/3], 'color': "green"},
-                   {'range': [1/3, 2/3], 'color': "gray"},
-                   {'range': [2/3, 1], 'color': "red"}],
-               'threshold': {'line': {'color': "black", 'width': 4}, 'thickness': 0.75, 'value': value}}))
-    return fig
-
-
-
diff --git a/spaces/SilenWang/ReviewGPT/utils/task.py b/spaces/SilenWang/ReviewGPT/utils/task.py
deleted file mode 100644
index 619fb669e0c11aa341e1c8d1ab45e23f0f1b72f9..0000000000000000000000000000000000000000
--- a/spaces/SilenWang/ReviewGPT/utils/task.py
+++ /dev/null
@@ -1,104 +0,0 @@
-'''
-这个文件里的所有内容其实应该精简以后全部放在ReviewerBot下
-'''
-
-from json import loads
-import pandas as pd
-import io
-
-from utils.review  import Reviewer
-from utils.pubmed  import PubMedFetcher
-from utils.ris_parser  import RisFile
-from utils.pdf_parser  import PdfFile
-
-
-def get_paper_info(inputMethod, email=None, pmids=None, ris_data=None):
-    '''
-    解析文章信息准备用于解读
-    '''
-
-    if inputMethod == 'PMID':
-        pmids = pmids.strip().split('\n')
-        fetcher = PubMedFetcher(email=email, pmids=pmids)
-        paper_info = pd.DataFrame(fetcher.fetch_abstract())
-    elif inputMethod == 'RIS File':
-        fileHandle = io.StringIO(ris_data.decode('utf-8'))
-        risFile = RisFile(file=fileHandle)
-        paper_info = risFile.parse_info(kwd=['doi', 'title', 'abstract']).rename(columns={
-            'doi': 'DOI',
-            'title': 'Title',
-            'abstract': 'Abstract'
-        })
-    
-    return paper_info
-
-
-def review(task, paper_info, prompts, openai_key, review_model):
-    '''
-    调用已有函数进行文献工作:
-    1. 文献纳入判断(基于摘要)
-    2. 文献小结(基于摘要)
-    '''
-
-    reviewer = Reviewer(api_key=openai_key, model=review_model)
-    if task == 'Screen': # 准入任务
-        answers = []
-        for _, rec in paper_info.iterrows():
-            response = reviewer.screen(prompts, rec['Abstract'])
-            answer = loads(response['choices'][0]['message']['content'])
-            if 'PMID' in rec:
-                answers.append('\n'.join([
-                    f'**PMID: {rec["PMID"]}**',
-                    f'- Title: {rec["Title"]}',
-                    f'- Inclusion: {answer["Inclusion"]}',
-                    '- Explanation:',
-                    '\n'.join([f'    + {exp}' for exp in answer["Explanation"]])
-                ]))
-            elif 'DOI' in rec:
-                answers.append('\n'.join([
-                    f'**DOI: {rec["DOI"]}**',
-                    f'- Title: {rec["Title"]}',
-                    f'- Inclusion: {answer["Inclusion"]}',
-                    '- Explanation:',
-                    '\n'.join([f'    + {exp}' for exp in answer["Explanation"]])
-                ]))
-            else:
-                raise Exception('No PMID nor DOI in record.')
-        return '\n\n------------------------\n\n'.join(answers)
-    
-    elif task == 'Summarise':
-        papers = []
-        for _, rec in paper_info.iterrows():
-            if 'PMID' in rec:
-                papers.append((rec['PMID'], rec['Abstract']))
-            elif 'DOI' in rec:
-                papers.append((rec['DOI'], rec['Abstract']))
-            else:
-                raise Exception('No PMID nor DOI in record.')
-        response = reviewer.summarise(papers, prompts)
-        return response['choices'][0]['message']['content']
-
-
-def parse_pdf_info(pdf_data, openai_key):
-    '''
-    调用模块解析PDF, 单独放出来, 解析只需要一次
-    '''
-    fileHandle = io.BytesIO(pdf_data)
-    pdf = PdfFile(file=fileHandle, api_key=openai_key)
-    paper_data = pdf.parse_info()
-    return paper_data
-
-
-def study(paper_data, prompts, openai_key, review_model):
-    '''
-    根据解析好的PDF数据进行阅读
-    '''
-    reviewer = Reviewer(api_key=openai_key, model=review_model)
-    response = reviewer.study(prompts, paper_data)
-    return response['choices'][0]['message']['content']
-
-
-def query(prompts, openai_key, review_model):
-    reviewer = Reviewer(api_key=openai_key, model=review_model)
-    response = reviewer.query(prompts)
-    return response['choices'][0]['message']['content']
\ No newline at end of file
diff --git a/spaces/Silentlin/DiffSinger/docs/README-SVS.md b/spaces/Silentlin/DiffSinger/docs/README-SVS.md
deleted file mode 100644
index 7a292b838a2927640fa626fbfba8d5e49ad3d8a9..0000000000000000000000000000000000000000
--- a/spaces/Silentlin/DiffSinger/docs/README-SVS.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2105.02446)
-[![GitHub Stars](https://img.shields.io/github/stars/MoonInTheRiver/DiffSinger?style=social)](https://github.com/MoonInTheRiver/DiffSinger)
-[![downloads](https://img.shields.io/github/downloads/MoonInTheRiver/DiffSinger/total.svg)](https://github.com/MoonInTheRiver/DiffSinger/releases)
- | [Interactive🤗 SVS](https://huggingface.co/spaces/Silentlin/DiffSinger)
-
-## DiffSinger (SVS)
-
-### PART1. [Run DiffSinger on PopCS](README-SVS-popcs.md)
-In PART1, we only focus on spectrum modeling (acoustic model) and assume the ground-truth (GT) F0 to be given as the pitch information following these papers [1][2][3]. If you want to conduct experiments with F0 prediction, please move to PART2.
-
-Thus, the pipeline of this part can be summarized as:
-
-```
-[lyrics] -> [linguistic representation] (Frontend)
-[linguistic representation] + [GT F0] + [GT phoneme duration] -> [mel-spectrogram]  (Acoustic model)
-[mel-spectrogram] + [GT F0] -> [waveform] (Vocoder)
-```
-
-
-[1] Adversarially trained multi-singer sequence-to-sequence singing synthesizer. Interspeech 2020.
-
-[2] SEQUENCE-TO-SEQUENCE SINGING SYNTHESIS USING THE FEED-FORWARD TRANSFORMER. ICASSP 2020.
-
-[3] DeepSinger : Singing Voice Synthesis with Data Mined From the Web. KDD 2020.
-
-Click here for detailed instructions: [link](README-SVS-popcs.md).
-
-
-### PART2. [Run DiffSinger on Opencpop](README-SVS-opencpop-cascade.md)
-Thanks [Opencpop team](https://wenet.org.cn/opencpop/) for releasing their SVS dataset with MIDI label, **Jan.20, 2022** (after we published our paper).
-
-Since there are elaborately annotated MIDI labels, we are able to supplement the pipeline in PART 1 by adding a naive melody frontend.
-
-#### 2.A
-Thus, the pipeline of [2.A](README-SVS-opencpop-cascade.md) can be summarized as:
-
-```
-[lyrics] + [MIDI] -> [linguistic representation (with MIDI information)] + [predicted F0] + [predicted phoneme duration] (Melody frontend)
-[linguistic representation] + [predicted F0] + [predicted phoneme duration] -> [mel-spectrogram]  (Acoustic model)
-[mel-spectrogram] + [predicted F0] -> [waveform] (Vocoder)
-```
-
-Click here for detailed instructions: [link](README-SVS-opencpop-cascade.md).
-
-#### 2.B
-In 2.1, we find that if we predict F0 explicitly in the melody frontend, there will be many bad cases of uv/v prediction. Then, we abandon the explicit prediction of the F0 curve in the melody frontend and make a joint prediction with spectrograms.
-
-Thus, the pipeline of [2.B](README-SVS-opencpop-e2e.md) can be summarized as:
-```
-[lyrics] + [MIDI] -> [linguistic representation] + [predicted phoneme duration] (Melody frontend)
-[linguistic representation (with MIDI information)] + [predicted phoneme duration] -> [mel-spectrogram]  (Acoustic model)
-[mel-spectrogram] -> [predicted F0]  (Pitch extractor)
-[mel-spectrogram] + [predicted F0] -> [waveform] (Vocoder)
-```
-
-Click here for detailed instructions: [link](README-SVS-opencpop-e2e.md).
-
-### FAQ
-Q1: Why do I need F0 in Vocoders?
-
-A1: See vocoder parts in HiFiSinger, DiffSinger or SingGAN. This is a common practice now.
-
-Q2: Why not run MIDI version SVS on PopCS dataset? or Why not release MIDI labels for PopCS dataset?
-
-A2: Our laboratory has no funds to label PopCS dataset. But there are funds for labeling other singing dataset, which is coming soon.
-
-Q3: Why " 'HifiGAN' object has no attribute 'model' "?
-
-A3: Please put the pretrained vocoders in your `checkpoints` dictionary.
-
-Q4: How to check whether I use GT information or predicted information during inference from packed test set?
-
-A4: Please see codes [here](https://github.com/MoonInTheRiver/DiffSinger/blob/55e2f46068af6e69940a9f8f02d306c24a940cab/tasks/tts/fs2.py#L343).
-
-...
\ No newline at end of file
diff --git a/spaces/SnJForever/GPT-LANG/azure_utils.py b/spaces/SnJForever/GPT-LANG/azure_utils.py
deleted file mode 100644
index 4173eaa689abe9b7b6b66ed3fcf1ede591655a53..0000000000000000000000000000000000000000
--- a/spaces/SnJForever/GPT-LANG/azure_utils.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# This class stores Azure voice data. Specifically, the class stores several records containing
-# language, lang_code, gender, voice_id and engine. The class also has a method to return the
-# voice_id, lang_code and engine given a language and gender.
-
-NEURAL_ENGINE = "neural"
-STANDARD_ENGINE = "standard"
-
-
-class AzureVoiceData:
-    def get_voice(self, language, gender):
-        for voice in self.voice_data:
-            if voice['language'] == language and voice['gender'] == gender:
-                return voice['azure_voice']
-        return None
-
-    def __init__(self):
-        self.voice_data = [
-            {'language': 'Arabic',
-             'azure_voice': 'ar-EG-ShakirNeural',
-             'gender': 'Male'},
-            {'language': 'Arabic (Gulf)',
-             'azure_voice': 'ar-KW-FahedNeural',
-             'gender': 'Male'},
-            {'language': 'Catalan',
-             'azure_voice': 'ca-ES-EnricNeural',
-             'gender': 'Male'},
-            {'language': 'Chinese (Cantonese)',
-             'azure_voice': 'yue-CN-YunSongNeural',
-             'gender': 'Male'},
-            {'language': 'Chinese (Mandarin)',
-             'azure_voice': 'zh-CN-YunxiNeural',
-             'gender': 'Male'},
-            {'language': 'Danish',
-             'azure_voice': 'da-DK-JeppeNeural',
-             'gender': 'Male'},
-            {'language': 'Dutch',
-             'azure_voice': 'nl-NL-MaartenNeural',
-             'gender': 'Male'},
-            {'language': 'English (Australian)',
-             'azure_voice': 'en-AU-KenNeural',
-             'gender': 'Male'},
-            {'language': 'English (British)',
-             'azure_voice': 'en-GB-RyanNeural',
-             'gender': 'Male'},
-            {'language': 'English (Indian)',
-             'azure_voice': 'en-IN-PrabhatNeural',
-             'gender': 'Male'},
-            {'language': 'English (New Zealand)',
-             'azure_voice': 'en-NZ-MitchellNeural',
-             'gender': 'Male'},
-            {'language': 'English (South African)',
-             'azure_voice': 'en-ZA-LukeNeural',
-             'gender': 'Male'},
-            {'language': 'English (US)',
-             'azure_voice': 'en-US-ChristopherNeural',
-             'gender': 'Male'},
-            {'language': 'English (Welsh)',
-             'azure_voice': 'cy-GB-AledNeural',
-             'gender': 'Male'},
-            {'language': 'Finnish',
-             'azure_voice': 'fi-FI-HarriNeural',
-             'gender': 'Male'},
-            {'language': 'French',
-             'azure_voice': 'fr-FR-HenriNeural',
-             'gender': 'Male'},
-            {'language': 'French (Canadian)',
-             'azure_voice': 'fr-CA-AntoineNeural',
-             'gender': 'Male'},
-            {'language': 'German',
-             'azure_voice': 'de-DE-KlausNeural',
-             'gender': 'Male'},
-            {'language': 'German (Austrian)',
-             'azure_voice': 'de-AT-JonasNeural',
-             'gender': 'Male'},
-            {'language': 'Hindi',
-             'azure_voice': 'hi-IN-MadhurNeural',
-             'gender': 'Male'},
-            {'language': 'Icelandic',
-             'azure_voice': 'is-IS-GunnarNeural',
-             'gender': 'Male'},
-            {'language': 'Italian',
-             'azure_voice': 'it-IT-GianniNeural',
-             'gender': 'Male'},
-            {'language': 'Japanese',
-             'azure_voice': 'ja-JP-KeitaNeural',
-             'gender': 'Male'},
-            {'language': 'Korean',
-             'azure_voice': 'ko-KR-GookMinNeural',
-             'gender': 'Male'},
-            {'language': 'Norwegian',
-             'azure_voice': 'nb-NO-FinnNeural',
-             'gender': 'Male'},
-            {'language': 'Polish',
-             'azure_voice': 'pl-PL-MarekNeural',
-             'gender': 'Male'},
-            {'language': 'Portuguese (Brazilian)',
-             'azure_voice': 'pt-BR-NicolauNeural',
-             'gender': 'Male'},
-            {'language': 'Portuguese (European)',
-             'azure_voice': 'pt-PT-DuarteNeural',
-             'gender': 'Male'},
-            {'language': 'Romanian',
-             'azure_voice': 'ro-RO-EmilNeural',
-             'gender': 'Male'},
-            {'language': 'Russian',
-             'azure_voice': 'ru-RU-DmitryNeural',
-             'gender': 'Male'},
-            {'language': 'Spanish (European)',
-             'azure_voice': 'es-ES-TeoNeural',
-             'gender': 'Male'},
-            {'language': 'Spanish (Mexican)',
-             'azure_voice': 'es-MX-LibertoNeural',
-             'gender': 'Male'},
-            {'language': 'Spanish (US)',
-             'azure_voice': 'es-US-AlonsoNeural"',
-             'gender': 'Male'},
-            {'language': 'Swedish',
-             'azure_voice': 'sv-SE-MattiasNeural',
-             'gender': 'Male'},
-            {'language': 'Turkish',
-             'azure_voice': 'tr-TR-AhmetNeural',
-             'gender': 'Male'},
-            {'language': 'Welsh',
-             'azure_voice': 'cy-GB-AledNeural',
-             'gender': 'Male'},
-        ]
-
-
-# Run from the command-line
-if __name__ == '__main__':
-    azure_voice_data = AzureVoiceData()
-
-    azure_voice = azure_voice_data.get_voice('English (US)', 'Male')
-    print('English (US)', 'Male', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('English (US)', 'Female')
-    print('English (US)', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('French', 'Female')
-    print('French', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('French', 'Male')
-    print('French', 'Male', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Japanese', 'Female')
-    print('Japanese', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Japanese', 'Male')
-    print('Japanese', 'Male', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Hindi', 'Female')
-    print('Hindi', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Hindi', 'Male')
-    print('Hindi', 'Male', azure_voice)
diff --git a/spaces/Suniilkumaar/MusicGen-updated/tests/utils/__init__.py b/spaces/Suniilkumaar/MusicGen-updated/tests/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/tests/utils/__init__.py
+++ /dev/null
@@ -1,5 +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.
diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/scripts/tabular_baselines.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/scripts/tabular_baselines.py
deleted file mode 100644
index 6dfb44fa1ca0c63b9e45c67512f77b32f874d985..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNPrediction/TabPFN/scripts/tabular_baselines.py
+++ /dev/null
@@ -1,1522 +0,0 @@
-import pandas
-from catboost import CatBoostClassifier, Pool
-from sklearn.model_selection import GridSearchCV
-from sklearn.model_selection import KFold
-from sklearn.model_selection import ParameterGrid
-
-import tempfile
-import random
-import math
-import os
-#from pytorch_tabnet.tab_model import TabNetClassifier, TabNetRegressor
-from sklearn import preprocessing
-from torch import nn
-from sklearn.metrics import make_scorer
-from sklearn.impute import SimpleImputer
-
-
-from sklearn.base import BaseEstimator, ClassifierMixin
-import xgboost as xgb
-from sklearn import neighbors
-from sklearn.gaussian_process import GaussianProcessClassifier
-from sklearn.gaussian_process.kernels import RBF
-import numpy as np
-import torch
-import itertools
-from scripts import tabular_metrics
-import pandas as pd
-from tqdm import tqdm
-from utils import remove_outliers
-
-from sklearn.linear_model import LogisticRegression, Ridge
-from sklearn.model_selection import cross_val_score
-import time
-
-from hyperopt import fmin, tpe, hp, STATUS_OK, Trials , space_eval, rand
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import OneHotEncoder
-from sklearn.preprocessing import MinMaxScaler
-
-import autosklearn.classification
-
-CV = 5
-MULTITHREAD = 1 # Number of threads baselines are able to use at most
-param_grid, param_grid_hyperopt = {}, {}
-
-def get_scoring_direction(metric_used):
-    # Not needed
-    if metric_used == tabular_metrics.auc_metric:
-        return -1
-    elif metric_used == tabular_metrics.cross_entropy:
-        return 1
-    else:
-        raise Exception('No scoring string found for metric')
-
-def is_classification(metric_used):
-    if metric_used == tabular_metrics.auc_metric or metric_used == tabular_metrics.cross_entropy:
-        return 'classification'
-    elif metric_used == tabular_metrics.auc_metric:
-        return -1
-
-# Loss
-
-def get_scoring_string(metric_used, multiclass=True, usage="sklearn_cv"):
-    if metric_used == tabular_metrics.auc_metric:
-        if usage == 'sklearn_cv':
-            return 'roc_auc_ovo'
-        elif usage == 'autogluon':
-            #return 'log_loss' # Autogluon crashes when using 'roc_auc' with some datasets usning logloss gives better scores;
-                              # We might be able to fix this, but doesn't work out of box.
-                              # File bug report? Error happens with dataset robert and fabert
-            if multiclass:
-                return 'roc_auc_ovo_macro'
-            else:
-                return 'roc_auc'
-        elif usage == 'tabnet':
-            return 'logloss' if multiclass else 'auc'
-        elif usage == 'autosklearn':
-            if multiclass:
-                return autosklearn.metrics.log_loss # roc_auc only works for binary, use logloss instead
-            else:
-                return autosklearn.metrics.roc_auc
-        elif usage == 'catboost':
-            return 'MultiClass' # Effectively LogLoss, ROC not available
-        elif usage == 'xgb':
-            return 'logloss'
-        elif usage == 'lightgbm':
-            if multiclass:
-                return 'auc'
-            else:
-                return 'binary'
-        return 'roc_auc'
-    elif metric_used == tabular_metrics.cross_entropy:
-        if usage == 'sklearn_cv':
-            return 'neg_log_loss'
-        elif usage == 'autogluon':
-            return 'log_loss'
-        elif usage == 'tabnet':
-            return 'logloss'
-        elif usage == 'autosklearn':
-            return autosklearn.metrics.log_loss
-        elif usage == 'catboost':
-            return 'MultiClass' # Effectively LogLoss
-        return 'logloss'
-    elif metric_used == tabular_metrics.r2_metric:
-        if usage == 'autosklearn':
-            return autosklearn.metrics.r2
-        elif usage == 'sklearn_cv':
-            return 'r2' # tabular_metrics.neg_r2
-        elif usage == 'autogluon':
-            return 'r2'
-        elif usage == 'xgb': # XGB cannot directly optimize r2
-            return 'rmse'
-        elif usage == 'catboost': # Catboost cannot directly optimize r2 ("Can't be used for optimization." - docu)
-            return 'RMSE'
-        else:
-            return 'r2'
-    elif metric_used == tabular_metrics.root_mean_squared_error_metric:
-        if usage == 'autosklearn':
-            return autosklearn.metrics.root_mean_squared_error
-        elif usage == 'sklearn_cv':
-            return 'neg_root_mean_squared_error' # tabular_metrics.neg_r2
-        elif usage == 'autogluon':
-            return 'rmse'
-        elif usage == 'xgb':
-            return 'rmse'
-        elif usage == 'catboost':
-            return 'RMSE'
-        else:
-            return 'neg_root_mean_squared_error'
-    elif metric_used == tabular_metrics.mean_absolute_error_metric:
-        if usage == 'autosklearn':
-            return autosklearn.metrics.mean_absolute_error
-        elif usage == 'sklearn_cv':
-            return 'neg_mean_absolute_error' # tabular_metrics.neg_r2
-        elif usage == 'autogluon':
-            return 'mae'
-        elif usage == 'xgb':
-            return 'mae'
-        elif usage == 'catboost':
-            return 'MAE'
-        else:
-            return 'neg_mean_absolute_error'
-    else:
-        raise Exception('No scoring string found for metric')
-
-def eval_f(params, clf_, x, y, metric_used, start_time, max_time):
-    if time.time() - start_time > max_time:
-        return np.nan
-    scores = cross_val_score(clf_(**params), x, y, cv=CV, scoring=get_scoring_string(metric_used, usage='sklearn_cv'))
-    if get_scoring_string(metric_used, usage='sklearn_cv') == 'r2':
-        return np.nanmean(scores)
-    return -np.nanmean(scores)
-
-def preprocess_impute(x, y, test_x, test_y, impute, one_hot, standardize, cat_features=[]):
-    import warnings
-    def warn(*args, **kwargs):
-        pass
-
-    warnings.warn = warn
-
-    x, y, test_x, test_y = x.cpu().numpy(), y.cpu().long().numpy(), test_x.cpu().numpy(), test_y.cpu().long().numpy()
-
-    if impute:
-        imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
-        imp_mean.fit(x)
-        x, test_x = imp_mean.transform(x), imp_mean.transform(test_x)
-
-    if one_hot:
-        def make_pd_from_np(x):
-            data = pd.DataFrame(x)
-            for c in cat_features:
-                data.iloc[:, c] = data.iloc[:, c].astype('int')
-            return data
-        x, test_x = make_pd_from_np(x),  make_pd_from_np(test_x)
-        transformer = ColumnTransformer(transformers=[('cat', OneHotEncoder(handle_unknown='ignore', sparse=False), cat_features)], remainder="passthrough")
-        transformer.fit(x)
-        x, test_x = transformer.transform(x), transformer.transform(test_x)
-
-    if standardize:
-        scaler = MinMaxScaler()
-        scaler.fit(x)
-        x, test_x = scaler.transform(x), scaler.transform(test_x)
-
-    return x, y, test_x, test_y
-import torch
-import random
-from tqdm import tqdm
-def transformer_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    from scripts.transformer_prediction_interface import TabPFNClassifier
-
-    classifier = TabPFNClassifier(device='cpu', base_path='.',
-                                  model_string='')
-    classifier.fit(x, y)
-    print('Train data shape', x.shape, ' Test data shape', test_x.shape)
-    pred = classifier.predict_proba(test_x)
-
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, None
-
-## Auto Gluon
-# WARNING: Crashes for some predictors for regression
-def autogluon_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    from autogluon.tabular import TabularPredictor
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False
-                                             , cat_features=cat_features
-                                             , impute=False
-                                             , standardize=False)
-    train_data = pd.DataFrame(np.concatenate([x, y[:, np.newaxis]], 1))
-    test_data = pd.DataFrame(np.concatenate([test_x, test_y[:, np.newaxis]], 1))
-    if is_classification(metric_used):
-        problem_type = 'multiclass' if len(np.unique(y)) > 2 else 'binary'
-    else:
-        problem_type = 'regression'
-    # AutoGluon automatically infers datatypes, we don't specify the categorical labels
-    predictor = TabularPredictor(
-        label=train_data.columns[-1],
-        eval_metric=get_scoring_string(metric_used, usage='autogluon', multiclass=(len(np.unique(y)) > 2)),
-        problem_type=problem_type
-        ## seed=int(y[:].sum()) doesn't accept seed
-    ).fit(
-        train_data=train_data,
-        time_limit=max_time,
-        presets=['best_quality']
-        # The seed is deterministic but varies for each dataset and each split of it
-    )
-
-    if is_classification(metric_used):
-        pred = predictor.predict_proba(test_data, as_multiclass=True).values
-    else:
-        pred = predictor.predict(test_data).values
-
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, predictor.fit_summary()
-
-
-from autogluon.core.models import AbstractModel
-from scripts.transformer_prediction_interface import TabPFNClassifier
-
-
-class TabPFNModel(AbstractModel):
-    def __init__(self, **kwargs):
-        # Simply pass along kwargs to parent, and init our internal `_feature_generator` variable to None
-        super().__init__(**kwargs)
-
-    # The `_preprocess` method takes the input data and transforms it to the internal representation usable by the model.
-    # `_preprocess` is called by `preprocess` and is used during model fit and model inference.
-    def _preprocess(self, X: pd.DataFrame, is_train=False, **kwargs) -> np.ndarray:
-        return X
-
-    # The `_fit` method takes the input training data (and optionally the validation data) and trains the model.
-    def _fit(self,
-             X: pd.DataFrame,  # training data
-             y: pd.Series,  # training labels
-             # X_val=None,  # val data (unused in RF model)
-             # y_val=None,  # val labels (unused in RF model)
-             # time_limit=None,  # time limit in seconds (ignored in tutorial)
-             **kwargs):  # kwargs includes many other potential inputs, refer to AbstractModel documentation for details
-
-        self.model = TabPFNClassifier(device='cpu', base_path='/work/dlclarge1/hollmann-PFN_Tabular/',
-                                      model_string='_longer_multiclass_causal_05_02_2022_12_49_44_sams',
-                                      N_ensemble_configurations=10)
-        self.model.fit(X.to_numpy(), y.to_numpy())
-
-    def _predict_proba(self, X, **kwargs):
-        X = self.preprocess(X, **kwargs)
-
-        #if self.problem_type in [REGRESSION, QUANTILE]:
-        #    y_pred = self.model.predict(X)
-        #    return y_pred
-
-        y_pred_proba = self.model.predict_proba(X.to_numpy())
-        return super()._convert_proba_to_unified_form(y_pred_proba)
-
-    # The `_set_default_params` method defines the default hyperparameters of the model.
-    # User-specified parameters will override these values on a key-by-key basis.
-    def _set_default_params(self):
-        default_params = {
-        }
-from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
-def autogluon_tabpfn_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    from autogluon.tabular import TabularPredictor
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False
-                                             , cat_features=cat_features
-                                             , impute=False
-                                             , standardize=False)
-    train_data = pd.DataFrame(np.concatenate([x, y[:, np.newaxis]], 1))
-    test_data = pd.DataFrame(np.concatenate([test_x, test_y[:, np.newaxis]], 1))
-    if is_classification(metric_used):
-        problem_type = 'multiclass' if len(np.unique(y)) > 2 else 'binary'
-    else:
-        problem_type = 'regression'
-    # AutoGluon automatically infers datatypes, we don't specify the categorical labels
-    custom_hyperparameters = {}#get_hyperparameter_config('default')
-    custom_hyperparameters[TabPFNModel] = {}
-    predictor = TabularPredictor(
-        label=train_data.columns[-1],
-        eval_metric=get_scoring_string(metric_used, usage='autogluon', multiclass=(len(np.unique(y)) > 2)),
-        problem_type=problem_type
-        ## seed=int(y[:].sum()) doesn't accept seed
-    ).fit(
-        train_data=train_data,
-        time_limit=max_time,
-        presets=['best_quality'],
-        hyperparameters=custom_hyperparameters
-        # The seed is deterministic but varies for each dataset and each split of it
-    )
-
-    if is_classification(metric_used):
-        pred = predictor.predict_proba(test_data, as_multiclass=True).values
-    else:
-        pred = predictor.predict(test_data).values
-
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, predictor.fit_summary()
-
-def get_updates_for_regularization_cocktails(
-    categorical_indicator: np.ndarray):
-    """
-    These updates replicate the regularization cocktail paper search space.
-    Args:
-        categorical_indicator (np.ndarray)
-            An array that indicates whether a feature is categorical or not.
-        args (Namespace):
-            The different updates for the setup of the run, mostly updates
-            for the different regularization ingredients.
-    Returns:
-    ________
-        pipeline_update, search_space_updates, include_updates (Tuple[dict, HyperparameterSearchSpaceUpdates, dict]):
-            The pipeline updates like number of epochs, budget, seed etc.
-            The search space updates like setting different hps to different values or ranges.
-            Lastly include updates, which can be used to include different features.
-    """
-    from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
-    import argparse
-
-    augmentation_names_to_trainers = {
-        'mixup': 'MixUpTrainer',
-        'cutout': 'RowCutOutTrainer',
-        'cutmix': 'RowCutMixTrainer',
-        'standard': 'StandardTrainer',
-        'adversarial': 'AdversarialTrainer',
-    }
-
-    include_updates = dict()
-    include_updates['network_embedding'] = ['NoEmbedding']
-    include_updates['network_init'] = ['NoInit']
-
-    has_cat_features = any(categorical_indicator)
-    has_numerical_features = not all(categorical_indicator)
-
-    def str2bool(v):
-        if isinstance(v, bool):
-            return [v, ]
-        if v.lower() in ('yes', 'true', 't', 'y', '1'):
-            return [True, ]
-        elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-            return [False, ]
-        elif v.lower() == 'conditional':
-            return [True, False]
-        else:
-            raise ValueError('No valid value given.')
-    search_space_updates = HyperparameterSearchSpaceUpdates()
-
-    # architecture head
-    search_space_updates.append(
-        node_name='network_head',
-        hyperparameter='__choice__',
-        value_range=['no_head'],
-        default_value='no_head',
-    )
-    search_space_updates.append(
-        node_name='network_head',
-        hyperparameter='no_head:activation',
-        value_range=['relu'],
-        default_value='relu',
-    )
-
-    # backbone architecture
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='__choice__',
-        value_range=['ShapedResNetBackbone'],
-        default_value='ShapedResNetBackbone',
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:resnet_shape',
-        value_range=['brick'],
-        default_value='brick',
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:num_groups',
-        value_range=[2],
-        default_value=2,
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:blocks_per_group',
-        value_range=[2],
-        default_value=2,
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:output_dim',
-        value_range=[512],
-        default_value=512,
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:max_units',
-        value_range=[512],
-        default_value=512,
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:activation',
-        value_range=['relu'],
-        default_value='relu',
-    )
-    search_space_updates.append(
-        node_name='network_backbone',
-        hyperparameter='ShapedResNetBackbone:shake_shake_update_func',
-        value_range=['even-even'],
-        default_value='even-even',
-    )
-
-    # training updates
-    search_space_updates.append(
-        node_name='lr_scheduler',
-        hyperparameter='__choice__',
-        value_range=['CosineAnnealingWarmRestarts'],
-        default_value='CosineAnnealingWarmRestarts',
-    )
-    search_space_updates.append(
-        node_name='lr_scheduler',
-        hyperparameter='CosineAnnealingWarmRestarts:n_restarts',
-        value_range=[3],
-        default_value=3,
-    )
-    search_space_updates.append(
-        node_name='optimizer',
-        hyperparameter='__choice__',
-        value_range=['AdamWOptimizer'],
-        default_value='AdamWOptimizer',
-    )
-    search_space_updates.append(
-        node_name='optimizer',
-        hyperparameter='AdamWOptimizer:lr',
-        value_range=[1e-3],
-        default_value=1e-3,
-    )
-    search_space_updates.append(
-        node_name='data_loader',
-        hyperparameter='batch_size',
-        value_range=[128],
-        default_value=128,
-    )
-
-    # preprocessing
-    search_space_updates.append(
-        node_name='feature_preprocessor',
-        hyperparameter='__choice__',
-        value_range=['NoFeaturePreprocessor'],
-        default_value='NoFeaturePreprocessor',
-    )
-
-    if has_numerical_features:
-        print('has numerical features')
-        search_space_updates.append(
-            node_name='imputer',
-            hyperparameter='numerical_strategy',
-            value_range=['median'],
-            default_value='median',
-        )
-        search_space_updates.append(
-            node_name='scaler',
-            hyperparameter='__choice__',
-            value_range=['StandardScaler'],
-            default_value='StandardScaler',
-        )
-
-    if has_cat_features:
-        print('has cat features')
-        search_space_updates.append(
-            node_name='imputer',
-            hyperparameter='categorical_strategy',
-            value_range=['constant_!missing!'],
-            default_value='constant_!missing!',
-        )
-        search_space_updates.append(
-            node_name='encoder',
-            hyperparameter='__choice__',
-            value_range=['OneHotEncoder'],
-            default_value='OneHotEncoder',
-        )
-
-    search_space_updates.append(
-        node_name='optimizer',
-        hyperparameter='AdamWOptimizer:beta1',
-        value_range=[0.9],
-        default_value=0.9,
-    )
-    search_space_updates.append(
-        node_name='optimizer',
-        hyperparameter='AdamWOptimizer:beta2',
-        value_range=[0.999],
-        default_value=0.999,
-    )
-
-
-    parser = argparse.ArgumentParser(
-        description='Run AutoPyTorch on a benchmark.',
-    )
-    # experiment setup arguments
-    parser.add_argument(
-        '--task_id',
-        type=int,
-        default=233088,
-    )
-    parser.add_argument(
-        '--wall_time',
-        type=int,
-        default=9000,
-    )
-    parser.add_argument(
-        '--func_eval_time',
-        type=int,
-        default=1000,
-    )
-    parser.add_argument(
-        '--epochs',
-        type=int,
-        default=105,
-    )
-    parser.add_argument(
-        '--seed',
-        type=int,
-        default=11,
-    )
-    parser.add_argument(
-        '--tmp_dir',
-        type=str,
-        default='./runs/autoPyTorch_cocktails',
-    )
-    parser.add_argument(
-        '--output_dir',
-        type=str,
-        default='./runs/autoPyTorch_cocktails',
-    )
-    parser.add_argument(
-        '--nr_workers',
-        type=int,
-        default=1,
-    )
-    parser.add_argument(
-        '--nr_threads',
-        type=int,
-        default=1,
-    )
-    parser.add_argument(
-        '--cash_cocktail',
-        help='If the regularization cocktail should be used.',
-        type=bool,
-        default=False,
-    )
-
-    # regularization ingredient arguments
-    parser.add_argument(
-        '--use_swa',
-        help='If stochastic weight averaging should be used.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--use_se',
-        help='If snapshot ensembling should be used.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--use_lookahead',
-        help='If the lookahead optimizing technique should be used.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--use_weight_decay',
-        help='If weight decay regularization should be used.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--use_batch_normalization',
-        help='If batch normalization regularization should be used.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--use_skip_connection',
-        help='If skip connections should be used. '
-             'Turns the network into a residual network.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--use_dropout',
-        help='If dropout regularization should be used.',
-        type=str2bool,
-        nargs='?',
-        const=[True],
-        default=[False],
-    )
-    parser.add_argument(
-        '--mb_choice',
-        help='Multibranch network regularization. '
-             'Only active when skip_connection is active.',
-        type=str,
-        choices=['none', 'shake-shake', 'shake-drop'],
-        default='none',
-    )
-    parser.add_argument(
-        '--augmentation',
-        help='If methods that augment examples should be used',
-        type=str,
-        choices=['mixup', 'cutout', 'cutmix', 'standard', 'adversarial'],
-        default='standard',
-    )
-
-    args = parser.parse_args([]) # just get default values
-
-
-
-    # if the cash formulation of the cocktail is not activated,
-    # otherwise the methods activation will be chosen by the SMBO optimizer.
-
-
-    # No early stopping and train on gpu
-    pipeline_update = {
-        'early_stopping': -1,
-        'min_epochs': args.epochs,
-        'epochs': args.epochs,
-        "device": 'cpu',
-    }
-
-    return pipeline_update, search_space_updates, include_updates
-
-def get_smac_object(
-    scenario_dict,
-    seed: int,
-    ta,
-    ta_kwargs,
-    n_jobs: int,
-    initial_budget: int,
-    max_budget: int,
-    dask_client,
-):
-    """
-    This function returns an SMAC object that is gonna be used as
-    optimizer of pipelines.
-    Args:
-        scenario_dict (typing.Dict[str, typing.Any]): constrain on how to run
-            the jobs.
-        seed (int): to make the job deterministic.
-        ta (typing.Callable): the function to be intensified by smac.
-        ta_kwargs (typing.Dict[str, typing.Any]): Arguments to the above ta.
-        n_jobs (int): Amount of cores to use for this task.
-        initial_budget (int):
-            The initial budget for a configuration.
-        max_budget (int):
-            The maximal budget for a configuration.
-        dask_client (dask.distributed.Client): User provided scheduler.
-    Returns:
-        (SMAC4AC): sequential model algorithm configuration object
-    """
-    from smac.intensification.simple_intensifier import SimpleIntensifier
-    from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
-    from smac.scenario.scenario import Scenario
-    from smac.facade.smac_ac_facade import SMAC4AC
-    # multi-fidelity is disabled, that is why initial_budget and max_budget
-    # are not used.
-    rh2EPM = RunHistory2EPM4LogCost
-
-    return SMAC4AC(
-        scenario=Scenario(scenario_dict),
-        rng=seed,
-        runhistory2epm=rh2EPM,
-        tae_runner=ta,
-        tae_runner_kwargs=ta_kwargs,
-        initial_configurations=None,
-        run_id=seed,
-        intensifier=SimpleIntensifier,
-        dask_client=dask_client,
-        n_jobs=n_jobs,
-    )
-
-
-def get_incumbent_results(
-    run_history_file: str,
-    search_space
-):
-    """
-    Get the incumbent configuration and performance from the previous run HPO
-    search with AutoPytorch.
-    Args:
-        run_history_file (str):
-            The path where the AutoPyTorch search data is located.
-        search_space (ConfigSpace.ConfigurationSpace):
-            The ConfigurationSpace that was previously used for the HPO
-            search space.
-    Returns:
-        config, incumbent_run_value (Tuple[ConfigSpace.Configuration, float]):
-            The incumbent configuration found from HPO search and the validation
-            performance it achieved.
-    """
-    from smac.runhistory.runhistory import RunHistory
-    run_history = RunHistory()
-    run_history.load_json(
-        run_history_file,
-        search_space,
-    )
-
-    run_history_data = run_history.data
-    sorted_runvalue_by_cost = sorted(run_history_data.items(), key=lambda item: item[1].cost)
-    incumbent_run_key, incumbent_run_value = sorted_runvalue_by_cost[0]
-    config = run_history.ids_config[incumbent_run_key.config_id]
-    return config, incumbent_run_value
-
-
-def well_tuned_simple_nets_metric(X_train, y_train, X_test, y_test, categorical_indicator, metric_used, max_time=300, nr_workers=1):
-    """Install:
-    git clone https://github.com/automl/Auto-PyTorch.git
-    cd Auto-PyTorch
-    git checkout regularization_cocktails
-    From the page, not needed for me at least: conda install gxx_linux-64 gcc_linux-64 swig
-    conda create --clone CONDANAME --name CLONENAME
-    conda activate CLONENAME
-    pip install -r requirements.txt (I checked looks like nothing should break functionality of our project not sure about baselines, thus a copied env is likely good :))
-    pip install -e .
-    """
-    #os.environ.get('SLURM_JOBID', '')
-    categorical_indicator = np.array([i in categorical_indicator for i in range(X_train.shape[1])])
-    with tempfile.TemporaryDirectory(prefix=f"{len(X_train)}_{len(X_test)}_{max_time}") as temp_dir:
-        from autoPyTorch.api.tabular_classification import TabularClassificationTask
-        from autoPyTorch.datasets.resampling_strategy import HoldoutValTypes, NoResamplingStrategyTypes
-        from autoPyTorch.data.tabular_validator import TabularInputValidator
-        from autoPyTorch.datasets.tabular_dataset import TabularDataset
-        from autoPyTorch import metrics
-        # append random folder to temp_dir to avoid collisions
-        rand_int = str(random.randint(1,1000))
-        temp_dir = os.path.join(temp_dir, 'temp_'+rand_int)
-        out_dir = os.path.join(temp_dir, 'out_'+rand_int)
-
-        start_time = time.time()
-
-        X_train, y_train, X_test, y_test = X_train.cpu().numpy(), y_train.cpu().long().numpy(), X_test.cpu().numpy(), y_test.cpu().long().numpy()
-
-        def safe_int(x):
-            assert np.all(x.astype('int64') == x) or np.any(x != x), np.unique(x) # second condition for ignoring nans
-            return pd.Series(x, dtype='category')
-
-        X_train = pd.DataFrame({i: safe_int(X_train[:,i]) if c else X_train[:,i] for i, c in enumerate(categorical_indicator)})
-        X_test = pd.DataFrame({i: safe_int(X_test[:,i]) if c else X_test[:,i] for i, c in enumerate(categorical_indicator)})
-
-
-        if isinstance(y_train[1], bool):
-            y_train = y_train.astype('bool')
-        if isinstance(y_test[1], bool):
-            y_test = y_test.astype('bool')
-
-        number_of_configurations_limit = 840 # hard coded in the paper
-        epochs = 105
-        func_eval_time = min(1000, max_time/2)
-        seed = int(y_train[:].sum())
-
-
-        resampling_strategy_args = {
-            'val_share': len(y_test)/(len(y_test)+len(y_train)),
-        }
-
-        pipeline_update, search_space_updates, include_updates = get_updates_for_regularization_cocktails(
-            categorical_indicator,
-        )
-        print(search_space_updates)
-
-
-
-        ############################################################################
-        # Build and fit a classifier
-        # ==========================
-        # if we use HPO, we can use multiple workers in parallel
-        if number_of_configurations_limit == 0:
-            nr_workers = 1
-
-        api = TabularClassificationTask(
-            temporary_directory=temp_dir,
-            output_directory=out_dir,
-            delete_tmp_folder_after_terminate=False,
-            delete_output_folder_after_terminate=False,
-            resampling_strategy=HoldoutValTypes.stratified_holdout_validation,
-            resampling_strategy_args=resampling_strategy_args,
-            ensemble_size=1,
-            ensemble_nbest=1,
-            max_models_on_disc=10,
-            include_components=include_updates,
-            search_space_updates=search_space_updates,
-            seed=seed,
-            n_jobs=nr_workers,
-            n_threads=1,
-        )
-
-        api.set_pipeline_config(**pipeline_update)
-        ############################################################################
-        # Search for the best hp configuration
-        # ====================================
-        # We search for the best hp configuration only in the case of a cocktail ingredient
-        # that has hyperparameters.
-        print(X_train, X_test)
-        print('temp_dir',temp_dir)
-        print(max_time, min(func_eval_time, max_time, number_of_configurations_limit))
-
-        if number_of_configurations_limit != 0:
-            api.search(
-                X_train=X_train.copy(),
-                y_train=y_train.copy(),
-                X_test=X_test.copy(),
-                y_test=y_test.copy(),
-                optimize_metric='balanced_accuracy',
-                total_walltime_limit=max_time,
-                memory_limit=12000,
-                func_eval_time_limit_secs=min(func_eval_time, max_time),
-                enable_traditional_pipeline=False,
-                get_smac_object_callback=get_smac_object,
-                smac_scenario_args={
-                    'runcount_limit': number_of_configurations_limit,
-                },
-            )
-
-        ############################################################################
-        # Refit on the best hp configuration
-        # ==================================
-        input_validator = TabularInputValidator(
-            is_classification=True,
-        )
-        input_validator.fit(
-            X_train=X_train.copy(),
-            y_train=y_train.copy(),
-            X_test=X_test.copy(),
-            y_test=y_test.copy(),
-        )
-
-        dataset = TabularDataset(
-            X=X_train,
-            Y=y_train,
-            X_test=X_test,
-            Y_test=y_test,
-            seed=seed,
-            validator=input_validator,
-            resampling_strategy=NoResamplingStrategyTypes.no_resampling,
-        )
-        dataset.is_small_preprocess = False
-        print(f"Fitting pipeline with {epochs} epochs")
-
-        search_space = api.get_search_space(dataset)
-        # only when we perform hpo will there be an incumbent configuration
-        # otherwise take a default configuration.
-        if number_of_configurations_limit != 0:
-            configuration, incumbent_run_value = get_incumbent_results(
-                os.path.join(
-                    temp_dir,
-                    'smac3-output',
-                    'run_{}'.format(seed),
-                    'runhistory.json'),
-                search_space,
-            )
-            print(f"Incumbent configuration: {configuration}")
-            print(f"Incumbent trajectory: {api.trajectory}")
-        else:
-            # default configuration
-            configuration = search_space.get_default_configuration()
-            print(f"Default configuration: {configuration}")
-
-        fitted_pipeline, run_info, run_value, dataset = api.fit_pipeline(
-            configuration=configuration,
-            budget_type='epochs',
-            budget=epochs,
-            dataset=dataset,
-            run_time_limit_secs=func_eval_time,
-            eval_metric='balanced_accuracy',
-            memory_limit=12000,
-        )
-
-        X_train = dataset.train_tensors[0]
-        y_train = dataset.train_tensors[1]
-        X_test = dataset.test_tensors[0]
-        y_test = dataset.test_tensors[1]
-
-        if fitted_pipeline is None:
-            api.get_incumbent_config
-
-
-        train_predictions = fitted_pipeline.predict(X_train)
-        test_predictions = fitted_pipeline.predict(X_test)
-
-        metric = metric_used(y_test, test_predictions.squeeze())
-        duration = time.time() - start_time
-
-        print(f'Time taken: {duration} for {metric} metric')
-        print(test_predictions[:10])
-        return metric, test_predictions, None
-
-
-
-## AUTO Sklearn
-def autosklearn_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    import autosklearn.classification
-    return autosklearn2_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=max_time, version=1)
-
-def autosklearn2_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300, version=2):
-    from autosklearn.experimental.askl2 import AutoSklearn2Classifier
-    from autosklearn.classification import AutoSklearnClassifier
-    from autosklearn.regression import AutoSklearnRegressor
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False
-                                             , cat_features=cat_features
-                                             , impute=False
-                                             , standardize=False)
-
-    def make_pd_from_np(x):
-        data = pd.DataFrame(x)
-        for c in cat_features:
-            data.iloc[:, c] = data.iloc[:, c].astype('category')
-        return data
-
-    x = make_pd_from_np(x)
-    test_x = make_pd_from_np(test_x)
-
-    if is_classification(metric_used):
-        clf_ = AutoSklearn2Classifier if version == 2 else AutoSklearnClassifier
-    else:
-        if version == 2:
-            raise Exception("AutoSklearn 2 doesn't do regression.")
-        clf_ = AutoSklearnRegressor
-    clf = clf_(time_left_for_this_task=max_time,
-                                                           memory_limit=4000,
-                                                           n_jobs=MULTITHREAD,
-               seed=int(y[:].sum()),
-        # The seed is deterministic but varies for each dataset and each split of it
-               metric=get_scoring_string(metric_used, usage='autosklearn', multiclass=len(np.unique(y)) > 2))
-
-    # fit model to data
-    clf.fit(x, y)
-
-    if is_classification(metric_used):
-        pred = clf.predict_proba(test_x)
-    else:
-        pred = clf.predict(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, None
-
-param_grid_hyperopt['ridge'] = {
-    'max_iter': hp.randint('max_iter', 50, 500)
-    , 'fit_intercept': hp.choice('fit_intercept', [True, False])
-    , 'alpha': hp.loguniform('alpha', -5, math.log(5.0))}  # 'normalize': [False],
-
-def ridge_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    if is_classification(metric_used):
-        raise Exception("Ridge is only applicable to pointwise Regression.")
-
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=True, impute=True, standardize=True
-                                             , cat_features=cat_features)
-    def clf_(**params):
-        return Ridge(tol=1e-4, **params)
-
-    start_time = time.time()
-
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['ridge'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=10000)
-    best = space_eval(param_grid_hyperopt['ridge'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-
-    pred = clf.predict(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoML
-from lightautoml.tasks import Task
-
-def lightautoml_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False, impute=False, standardize=False
-                                             , cat_features=cat_features)
-
-    roles = {'target': str(x.shape[-1])}
-    task = Task('multiclass', metric = lambda x, y : metric_used(x, y, numpy=True))
-    automl = TabularUtilizedAutoML(task=task,
-                           timeout=max_time,
-                           cpu_limit=4,  # Optimal for Kaggle kernels
-                           general_params={'use_algos': [['linear_l2',
-                                                          'lgb', 'lgb_tuned']]})
-
-    tr_data = np.concatenate([x, np.expand_dims(y, -1)], -1)
-    tr_data = pd.DataFrame(tr_data, columns=[str(k) for k in range(0, x.shape[-1] + 1)])
-    oof_pred = automl.fit_predict(tr_data, roles=roles)
-    te_data = pd.DataFrame(test_x, columns=[str(k) for k in range(0, x.shape[-1])])
-
-    probabilities = automl.predict(te_data).data
-    probabilities_mapped = probabilities.copy()
-
-    class_map = automl.outer_pipes[0].ml_algos[0].models[0][0].reader.class_mapping
-    if class_map:
-        column_to_class = {col: class_ for class_, col in class_map.items()}
-        for i in range(0, len(column_to_class)):
-            probabilities_mapped[:, int(column_to_class[int(i)])] = probabilities[:, int(i)]
-
-    metric = metric_used(test_y, probabilities_mapped)
-
-    return metric, probabilities_mapped, None
-
-param_grid_hyperopt['lightgbm'] = {
-    'num_leaves': hp.randint('num_leaves', 5, 50)
-    , 'max_depth': hp.randint('max_depth', 3, 20)
-    , 'learning_rate': hp.loguniform('learning_rate', -3, math.log(1.0))
-    , 'n_estimators': hp.randint('n_estimators', 50, 2000)
-    #, 'feature_fraction': 0.8,
-    #, 'subsample': 0.2
-    , 'min_child_weight': hp.choice('min_child_weight', [1e-5, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4])
-    , 'subsample': hp.uniform('subsample', 0.2, 0.8)
-    , 'colsample_bytree': hp.uniform('colsample_bytree', 0.2, 0.8)
-    , 'reg_alpha': hp.choice('reg_alpha', [0, 1e-1, 1, 2, 5, 7, 10, 50, 100])
-    , 'reg_lambda': hp.choice('reg_lambda', [0, 1e-1, 1, 5, 10, 20, 50, 100])
-}  # 'normalize': [False],
-
-from lightgbm import LGBMClassifier
-
-def lightgbm_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False, impute=False, standardize=False
-                                             , cat_features=cat_features)
-
-    def clf_(**params):
-        return LGBMClassifier(categorical_feature=cat_features, use_missing=True
-                              , objective=get_scoring_string(metric_used, usage='lightgbm', multiclass=len(np.unique(y)) > 2), **params)
-
-    start_time = time.time()
-
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['lightgbm'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=10000)
-    best = space_eval(param_grid_hyperopt['lightgbm'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-
-    pred = clf.predict_proba(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-param_grid_hyperopt['logistic'] = {
-    'penalty': hp.choice('penalty', ['l1', 'l2', 'none'])
-    , 'max_iter': hp.randint('max_iter', 50, 500)
-    , 'fit_intercept': hp.choice('fit_intercept', [True, False])
-    , 'C': hp.loguniform('C', -5, math.log(5.0))}  # 'normalize': [False],
-
-
-def logistic_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=True, impute=True, standardize=True
-                                             , cat_features=cat_features)
-
-    def clf_(**params):
-        return LogisticRegression(solver='saga', tol=1e-4, n_jobs=1, **params)
-
-    start_time = time.time()
-
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['logistic'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=10000)
-    best = space_eval(param_grid_hyperopt['logistic'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-
-    pred = clf.predict_proba(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-## KNN
-param_grid_hyperopt['knn'] = {'n_neighbors': hp.randint('n_neighbors', 1,16)
-                              }
-def knn_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y,
-                                             one_hot=True, impute=True, standardize=True,
-                                             cat_features=cat_features)
-
-    def clf_(**params):
-        if is_classification(metric_used):
-            return neighbors.KNeighborsClassifier(n_jobs=1, **params)
-        return neighbors.KNeighborsRegressor(n_jobs=1, **params)
-
-    start_time = time.time()
-
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['knn'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=10000)
-    best = space_eval(param_grid_hyperopt['knn'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-
-    if is_classification(metric_used):
-        pred = clf.predict_proba(test_x)
-    else:
-        pred = clf.predict(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-## GP
-param_grid_hyperopt['gp'] = {
-    'params_y_scale': hp.loguniform('params_y_scale', math.log(0.05), math.log(5.0)),
-    'params_length_scale': hp.loguniform('params_length_scale', math.log(0.1), math.log(1.0))
-}
-def gp_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y,
-                                             one_hot=True, impute=True, standardize=True,
-                                             cat_features=cat_features)
-
-    def clf_(params_y_scale,params_length_scale, **params):
-        if is_classification(metric_used):
-            return GaussianProcessClassifier(kernel= params_y_scale * RBF(params_length_scale), **params)
-        else:
-            return GaussianProcessRegressor(kernel= params_y_scale * RBF(params_length_scale), **params)
-
-    start_time = time.time()
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['gp'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=1000)
-    best = space_eval(param_grid_hyperopt['gp'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-
-    if is_classification(metric_used):
-        pred = clf.predict_proba(test_x)
-    else:
-        pred = clf.predict(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-## Tabnet
-# https://github.com/dreamquark-ai/tabnet
-#param_grid['tabnet'] = {'n_d': [2, 4], 'n_steps': [2,4,6], 'gamma': [1.3], 'optimizer_params': [{'lr': 2e-2}, {'lr': 2e-1}]}
-
-# Hyperparameter space from dreamquarks implementation recommendations
-param_grid_hyperopt['tabnet'] = {
-    'n_d': hp.randint('n_d', 8, 64),
-    'n_steps': hp.randint('n_steps', 3, 10),
-    'max_epochs': hp.randint('max_epochs', 50, 200),
-    'gamma': hp.uniform('relax', 1.0, 2.0),
-    'momentum': hp.uniform('momentum', 0.01, 0.4),
-}
-
-def tabnet_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    from pytorch_tabnet.tab_model import TabNetClassifier
-    # TabNet inputs raw tabular data without any preprocessing and is trained using gradient descent-based optimisation.
-    # However Tabnet cannot handle nans so we impute with mean
-
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y, impute=True, one_hot=False, standardize=False)
-
-    def clf_(**params):
-        return TabNetClassifier(cat_idxs=cat_features, verbose=True, n_a=params['n_d'], seed=int(y[:].sum()), **params)
-
-    def tabnet_eval_f(params, clf_, x, y, metric_used, start_time, max_time):
-        if time.time() - start_time > max_time:
-            return np.nan
-
-        kf = KFold(n_splits=min(CV, x.shape[0] // 2), random_state=None, shuffle=True)
-        metrics = []
-
-        params = {**params}
-        max_epochs = params['max_epochs']
-        del params['max_epochs']
-
-        for train_index, test_index in kf.split(x):
-            X_train, X_valid, y_train, y_valid = x[train_index], x[test_index], y[train_index], y[test_index]
-
-            clf = clf_(**params)
-
-            clf.fit(
-                X_train, y_train,
-                # eval_metric=[get_scoring_string(metric_used, multiclass=len(np.unique(y_train)) > 2, usage='tabnet')],
-                # eval_set=[(X_valid, y_valid)],
-                # patience=15,
-                max_epochs=max_epochs
-            )
-            metrics += [metric_used(y_valid, clf.predict_proba(X_valid))]
-
-        return -np.nanmean(np.array(metrics))
-
-    start_time = time.time()
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: tabnet_eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['tabnet'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        max_evals=1000)
-    best = space_eval(param_grid_hyperopt['tabnet'], best)
-    max_epochs = best['max_epochs']
-    del best['max_epochs']
-
-    clf = clf_(**best)
-    clf.fit(x, y, max_epochs=max_epochs) # , max_epochs=mean_best_epochs[best_idx]
-
-    pred = clf.predict_proba(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-    return metric, pred, params_used[best_idx]
-
-
-# Catboost
-# Hyperparameter space: https://arxiv.org/pdf/2106.03253.pdf
-
-param_grid_hyperopt['catboost'] = {
-    'learning_rate': hp.loguniform('learning_rate', math.log(math.pow(math.e, -5)), math.log(1)),
-    'random_strength': hp.randint('random_strength', 1, 20),
-    'l2_leaf_reg': hp.loguniform('l2_leaf_reg', math.log(1), math.log(10)),
-    'bagging_temperature': hp.uniform('bagging_temperature', 0., 1),
-    'leaf_estimation_iterations': hp.randint('leaf_estimation_iterations', 1, 20),
-    'iterations': hp.randint('iterations', 100, 4000), # This is smaller than in paper, 4000 leads to ram overusage
-}
-
-def catboost_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False
-                                             , cat_features=cat_features
-                                             , impute=False
-                                             , standardize=False)
-
-    # Nans in categorical features must be encoded as separate class
-    x[:, cat_features], test_x[:, cat_features] = np.nan_to_num(x[:, cat_features], -1), np.nan_to_num(
-        test_x[:, cat_features], -1)
-
-    def make_pd_from_np(x):
-        data = pd.DataFrame(x)
-        for c in cat_features:
-            data.iloc[:, c] = data.iloc[:, c].astype('int')
-        return data
-
-    x = make_pd_from_np(x)
-    test_x = make_pd_from_np(test_x)
-
-    def clf_(**params):
-        if is_classification(metric_used):
-            return CatBoostClassifier(
-                                   loss_function=get_scoring_string(metric_used, usage='catboost'),
-                                   thread_count = MULTITHREAD,
-                                   used_ram_limit='4gb',
-                random_seed=int(y[:].sum()),
-                                   logging_level='Silent',
-                                    cat_features=cat_features,
-                                      **params)
-        else:
-            return CatBoostRegressor(
-                loss_function=get_scoring_string(metric_used, usage='catboost'),
-                thread_count=MULTITHREAD,
-                used_ram_limit='4gb',
-                random_seed=int(y[:].sum()),
-                logging_level='Silent',
-                cat_features=cat_features,
-                **params)
-
-    start_time = time.time()
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['catboost'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=1000)
-    best = space_eval(param_grid_hyperopt['catboost'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-    if is_classification(metric_used):
-        pred = clf.predict_proba(test_x)
-    else:
-        pred = clf.predict(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-
-# XGBoost
-# Hyperparameter space: https://arxiv.org/pdf/2106.03253.pdf
-param_grid_hyperopt['xgb'] = {
-    'learning_rate': hp.loguniform('learning_rate', -7, math.log(1)),
-    'max_depth': hp.randint('max_depth', 1, 10),
-    'subsample': hp.uniform('subsample', 0.2, 1),
-    'colsample_bytree': hp.uniform('colsample_bytree', 0.2, 1),
-    'colsample_bylevel': hp.uniform('colsample_bylevel', 0.2, 1),
-    'min_child_weight': hp.loguniform('min_child_weight', -16, 5),
-    'alpha': hp.loguniform('alpha', -16, 2),
-    'lambda': hp.loguniform('lambda', -16, 2),
-    'gamma': hp.loguniform('gamma', -16, 2),
-    'n_estimators': hp.randint('n_estimators', 100, 4000), # This is smaller than in paper
-}
-
-def xgb_metric(x, y, test_x, test_y, cat_features, metric_used, max_time=300):
-    import xgboost as xgb
-    # XGB Documentation:
-    # XGB handles categorical data appropriately without using One Hot Encoding, categorical features are experimetal
-    # XGB handles missing values appropriately without imputation
-
-    x, y, test_x, test_y = preprocess_impute(x, y, test_x, test_y
-                                             , one_hot=False
-                                             , cat_features=cat_features
-                                             , impute=False
-                                             , standardize=False)
-
-    def clf_(**params):
-        if is_classification(metric_used):
-            return xgb.XGBClassifier(use_label_encoder=False
-                                     , nthread=1
-                                     , **params
-                                     , eval_metric=get_scoring_string(metric_used, usage='xgb') # AUC not implemented
-            )
-        else:
-            return xgb.XGBRegressor(use_label_encoder=False
-                                     , nthread=1
-                                     , **params
-                                     , eval_metric=get_scoring_string(metric_used, usage='xgb')  # AUC not implemented
-                                     )
-
-    start_time = time.time()
-    def stop(trial):
-        return time.time() - start_time > max_time, []
-
-    best = fmin(
-        fn=lambda params: eval_f(params, clf_, x, y, metric_used, start_time, max_time),
-        space=param_grid_hyperopt['xgb'],
-        algo=rand.suggest,
-        rstate=np.random.RandomState(int(y[:].sum())),
-        early_stop_fn=stop,
-        # The seed is deterministic but varies for each dataset and each split of it
-        max_evals=1000)
-    best = space_eval(param_grid_hyperopt['xgb'], best)
-
-    clf = clf_(**best)
-    clf.fit(x, y)
-
-    if is_classification(metric_used):
-        pred = clf.predict_proba(test_x)
-    else:
-        pred = clf.predict(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred, best
-
-"""
-LEGACY UNUSED
-"""
-
-## Ridge
-from sklearn.linear_model import RidgeClassifier
-param_grid['ridge'] = {'alpha': [0, 0.1, .5, 1.0, 2.0], 'fit_intercept': [True, False]} # 'normalize': [False],
-def ridge_metric(x, y, test_x, test_y, cat_features, metric_used):
-    import warnings
-    def warn(*args, **kwargs):
-        pass
-
-    warnings.warn = warn
-
-    x, y, test_x, test_y = x.cpu(), y.cpu(), test_x.cpu(), test_y.cpu()
-    x, test_x = torch.nan_to_num(x), torch.nan_to_num(test_x)
-
-    clf = RidgeClassifier(n_jobs=1)
-
-    # create a dictionary of all values we want to test for n_neighbors
-    # use gridsearch to test all values for n_neighbors
-    clf = GridSearchCV(clf, param_grid['ridge'], cv=min(CV, x.shape[0]//2)
-                       , scoring=get_scoring_string(metric_used)
-                       , n_jobs=MULTITHREAD)
-    # fit model to data
-    clf.fit(x, y.long())
-
-    pred = clf.decision_function(test_x)
-    metric = metric_used(test_y, pred)
-
-    return metric, pred
-
-def mlp_acc(x, y, test_x, test_y, hyperparameters):
-    num_layers, hidden_dim, activation_module, fixed_dropout_prob, is_binary_classification, epochs, lr, weight_decay = hyperparameters
-    num_features = x.shape[1]
-
-    x, y = x.to(device), y.to(device)
-    test_x, test_y = test_x.to(device), test_y.to(device)
-
-    def get_model():
-        model = nn.Sequential(*[
-            module for layer_idx in range(num_layers) for module in [
-                nn.Linear(hidden_dim if layer_idx > 0 else num_features,
-                          2 if layer_idx == num_layers - 1 else hidden_dim),
-                torch.nn.Identity() if layer_idx == num_layers - 1 else activation_module(),
-                torch.nn.Identity() if layer_idx == num_layers - 1 else torch.nn.Dropout(p=fixed_dropout_prob,
-                                                                                         inplace=False)]
-        ])
-        if is_binary_classification:
-            model.add_module(str(len(model)), torch.nn.Softmax(dim=1))  # TODO might also just do an round!?
-        return model
-
-    model = get_model().to(device)
-    criterion = torch.nn.BCELoss()
-    optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
-
-    model.train()
-    for epoch in range(epochs):
-        optimizer.zero_grad()
-        # Forward pass
-        y_pred = model(x)[:, 1]
-        # Compute Loss
-
-        loss = criterion(y_pred.squeeze(), y.float())
-
-        # print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
-        # Backward pass
-        loss.backward()
-        optimizer.step()
-
-    model.eval()
-    pred_y = model(test_x)[:, 1] > 0.5
-    acc = (pred_y == test_y).float().mean()
-    return acc
-
-clf_dict = {'gp': gp_metric
-                , 'knn': knn_metric
-                , 'catboost': catboost_metric
-                , 'tabnet': tabnet_metric
-                , 'xgb': xgb_metric
-            , 'ridge': ridge_metric
-                , 'logistic': logistic_metric
-           , 'autosklearn': autosklearn_metric
-             , 'autosklearn2': autosklearn2_metric
-            , 'autogluon': autogluon_metric,
-            'cocktail': well_tuned_simple_nets_metric}
\ No newline at end of file
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/common.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/common.py
deleted file mode 100644
index d6b8742417abc897f5faa190db1341bbe7b2940d..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/common.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import logging
-import numpy as np
-import pickle
-import random
-import torch.utils.data as data
-from torch.utils.data.sampler import Sampler
-
-from detectron2.utils.serialize import PicklableWrapper
-
-__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"]
-
-
-def _shard_iterator_dataloader_worker(iterable):
-    # Shard the iterable if we're currently inside pytorch dataloader worker.
-    worker_info = data.get_worker_info()
-    if worker_info is None or worker_info.num_workers == 1:
-        # do nothing
-        yield from iterable
-    else:
-        yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers)
-
-
-class _MapIterableDataset(data.IterableDataset):
-    """
-    Map a function over elements in an IterableDataset.
-
-    Similar to pytorch's MapIterDataPipe, but support filtering when map_func
-    returns None.
-
-    This class is not public-facing. Will be called by `MapDataset`.
-    """
-
-    def __init__(self, dataset, map_func):
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __iter__(self):
-        for x in map(self._map_func, self._dataset):
-            if x is not None:
-                yield x
-
-
-class MapDataset(data.Dataset):
-    """
-    Map a function over the elements in a dataset.
-    """
-
-    def __init__(self, dataset, map_func):
-        """
-        Args:
-            dataset: a dataset where map function is applied. Can be either
-                map-style or iterable dataset. When given an iterable dataset,
-                the returned object will also be an iterable dataset.
-            map_func: a callable which maps the element in dataset. map_func can
-                return None to skip the data (e.g. in case of errors).
-                How None is handled depends on the style of `dataset`.
-                If `dataset` is map-style, it randomly tries other elements.
-                If `dataset` is iterable, it skips the data and tries the next.
-        """
-        self._dataset = dataset
-        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
-
-        self._rng = random.Random(42)
-        self._fallback_candidates = set(range(len(dataset)))
-
-    def __new__(cls, dataset, map_func):
-        is_iterable = isinstance(dataset, data.IterableDataset)
-        if is_iterable:
-            return _MapIterableDataset(dataset, map_func)
-        else:
-            return super().__new__(cls)
-
-    def __getnewargs__(self):
-        return self._dataset, self._map_func
-
-    def __len__(self):
-        return len(self._dataset)
-
-    def __getitem__(self, idx):
-        retry_count = 0
-        cur_idx = int(idx)
-
-        while True:
-            data = self._map_func(self._dataset[cur_idx])
-            if data is not None:
-                self._fallback_candidates.add(cur_idx)
-                return data
-
-            # _map_func fails for this idx, use a random new index from the pool
-            retry_count += 1
-            self._fallback_candidates.discard(cur_idx)
-            cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
-
-            if retry_count >= 3:
-                logger = logging.getLogger(__name__)
-                logger.warning(
-                    "Failed to apply `_map_func` for idx: {}, retry count: {}".format(
-                        idx, retry_count
-                    )
-                )
-
-
-class DatasetFromList(data.Dataset):
-    """
-    Wrap a list to a torch Dataset. It produces elements of the list as data.
-    """
-
-    def __init__(self, lst: list, copy: bool = True, serialize: bool = True):
-        """
-        Args:
-            lst (list): a list which contains elements to produce.
-            copy (bool): whether to deepcopy the element when producing it,
-                so that the result can be modified in place without affecting the
-                source in the list.
-            serialize (bool): whether to hold memory using serialized objects, when
-                enabled, data loader workers can use shared RAM from master
-                process instead of making a copy.
-        """
-        self._lst = lst
-        self._copy = copy
-        self._serialize = serialize
-
-        def _serialize(data):
-            buffer = pickle.dumps(data, protocol=-1)
-            return np.frombuffer(buffer, dtype=np.uint8)
-
-        if self._serialize:
-            logger = logging.getLogger(__name__)
-            logger.info(
-                "Serializing {} elements to byte tensors and concatenating them all ...".format(
-                    len(self._lst)
-                )
-            )
-            self._lst = [_serialize(x) for x in self._lst]
-            self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
-            self._addr = np.cumsum(self._addr)
-            self._lst = np.concatenate(self._lst)
-            logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2))
-
-    def __len__(self):
-        if self._serialize:
-            return len(self._addr)
-        else:
-            return len(self._lst)
-
-    def __getitem__(self, idx):
-        if self._serialize:
-            start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
-            end_addr = self._addr[idx].item()
-            bytes = memoryview(self._lst[start_addr:end_addr])
-            return pickle.loads(bytes)
-        elif self._copy:
-            return copy.deepcopy(self._lst[idx])
-        else:
-            return self._lst[idx]
-
-
-class ToIterableDataset(data.IterableDataset):
-    """
-    Convert an old indices-based (also called map-style) dataset
-    to an iterable-style dataset.
-    """
-
-    def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True):
-        """
-        Args:
-            dataset: an old-style dataset with ``__getitem__``
-            sampler: a cheap iterable that produces indices to be applied on ``dataset``.
-            shard_sampler: whether to shard the sampler based on the current pytorch data loader
-                worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple
-                workers, it is responsible for sharding its data based on worker id so that workers
-                don't produce identical data.
-
-                Most samplers (like our TrainingSampler) do not shard based on dataloader worker id
-                and this argument should be set to True. But certain samplers may be already
-                sharded, in that case this argument should be set to False.
-        """
-        assert not isinstance(dataset, data.IterableDataset), dataset
-        assert isinstance(sampler, Sampler), sampler
-        self.dataset = dataset
-        self.sampler = sampler
-        self.shard_sampler = shard_sampler
-
-    def __iter__(self):
-        if not self.shard_sampler:
-            sampler = self.sampler
-        else:
-            # With map-style dataset, `DataLoader(dataset, sampler)` runs the
-            # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))`
-            # will run sampler in every of the N worker. So we should only keep 1/N of the ids on
-            # each worker. The assumption is that sampler is cheap to iterate so it's fine to
-            # discard ids in workers.
-            sampler = _shard_iterator_dataloader_worker(self.sampler)
-        for idx in sampler:
-            yield self.dataset[idx]
-
-    def __len__(self):
-        return len(self.sampler)
-
-
-class AspectRatioGroupedDataset(data.IterableDataset):
-    """
-    Batch data that have similar aspect ratio together.
-    In this implementation, images whose aspect ratio < (or >) 1 will
-    be batched together.
-    This improves training speed because the images then need less padding
-    to form a batch.
-
-    It assumes the underlying dataset produces dicts with "width" and "height" keys.
-    It will then produce a list of original dicts with length = batch_size,
-    all with similar aspect ratios.
-    """
-
-    def __init__(self, dataset, batch_size):
-        """
-        Args:
-            dataset: an iterable. Each element must be a dict with keys
-                "width" and "height", which will be used to batch data.
-            batch_size (int):
-        """
-        self.dataset = dataset
-        self.batch_size = batch_size
-        self._buckets = [[] for _ in range(2)]
-        # Hard-coded two aspect ratio groups: w > h and w < h.
-        # Can add support for more aspect ratio groups, but doesn't seem useful
-
-    def __iter__(self):
-        for d in self.dataset:
-            w, h = d["width"], d["height"]
-            bucket_id = 0 if w > h else 1
-            bucket = self._buckets[bucket_id]
-            bucket.append(d)
-            if len(bucket) == self.batch_size:
-                yield bucket[:]
-                del bucket[:]
diff --git a/spaces/TheFellow42/webui/README.md b/spaces/TheFellow42/webui/README.md
deleted file mode 100644
index 013d12c9f3a56698056ae1bdbbfb0ec009805237..0000000000000000000000000000000000000000
--- a/spaces/TheFellow42/webui/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Stable Diffusion Web UI
-emoji: 🚧
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-duplicated_from: camenduru/webui
----
-
-## Stable Diffusion Web UI
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-
-## Documentation
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki)
-
-## Models License
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
\ No newline at end of file
diff --git a/spaces/Vicent3/laniakea/index.html b/spaces/Vicent3/laniakea/index.html
deleted file mode 100644
index f06a3be52047237f9e1bc5146aad1aca18ef0e8e..0000000000000000000000000000000000000000
--- a/spaces/Vicent3/laniakea/index.html
+++ /dev/null
@@ -1,98 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-  <meta charset="UTF-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  <title>LANIAKEA - Explore the Universe of Computer Science</title>
-  <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.0.2/dist/tailwind.min.css" rel="stylesheet">
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.2/css/all.min.css"
-    integrity="sha512-om+YH2JUbzyvxd2B8y7WuUVMxusdAw/SFqPnB+imfX7LaFOTPMwr9yeG8NrNmvfW0Pv//HD6fx9ZgqM7nR1BAg=="
-    crossorigin="anonymous" />
-</head>
-
-<body class="bg-gray-100">
-  <header class="py-20 text-center bg-blue-700 text-white">
-    <div class="container mx-auto">
-      <h1 class="text-4xl font-bold mb-4">Welcome to LANIAKEA</h1>
-      <p class="text-xl">Explore the Universe of Computer Science</p>
-    </div>
-  </header>
-
-  <section class="py-20">
-    <div class="container mx-auto">
-      <div class="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 gap-8">
-        <!-- Category Data Science -->
-        <div class="bg-white shadow-lg rounded-lg p-8 text-center">
-          <div class="flex justify-center items-center h-16 w-16 bg-blue-500 text-white rounded-full mx-auto mb-4">
-            <i class="fas fa-database fa-2x"></i>
-          </div>
-          <h3 class="text-xl font-semibold mb-4">Data Science</h3>
-          <p class="text-gray-700 mb-4">Discover how to analyze and interpret complex datasets.</p>
-          <a href="#" class="inline-block px-6 py-3 bg-blue-500 text-white rounded-full">Explore</a>
-        </div>
-
-        <!-- Category AI -->
-        <div class="bg-white shadow-lg rounded-lg p-8 text-center">
-          <div class="flex justify-center items-center h-16 w-16 bg-blue-500 text-white rounded-full mx-auto mb-4">
-            <i class="fas fa-robot fa-2x"></i>
-          </div>
-          <h3 class="text-xl font-semibold mb-4">Artificial Intelligence</h3>
-          <p class="text-gray-700 mb-4">Learn about AI algorithms and applications.</p>
-          <a href="#" class="inline-block px-6 py-3 bg-blue-500 text-white rounded-full">Explore</a>
-        </div>
-
-        <!-- Category Web Dev -->
-        <div class="bg-white shadow-lg rounded-lg p-8 text-center">
-          <div class="flex justify-center items-center h-16 w-16 bg-blue-500 text-white rounded-full mx-auto mb-4">
-            <i class="fas fa-code fa-2x"></i>
-          </div>
-          <h3 class="text-xl font-semibold mb-4">Web Development</h3>
-          <p class="text-gray-700 mb-4">Build interactive websites using the latest technologies.</p>
-          <a href="#" class="inline-block px-6 py-3 bg-blue-500 text-white rounded-full">Explore</a>
-        </div>
-
-        <!-- Category ML -->
-        <div class="bg-white shadow-lg rounded-lg p-8 text-center">
-          <div class="flex justify-center items-center h-16 w-16 bg-blue-500 text-white rounded-full mx-auto mb-4">
-            <i class="fas fa-brain fa-2x"></i>
-          </div>
-          <h3 class="text-xl font-semibold mb-4">Machine Learning</h3>
-          <p class="text-gray-700 mb-4">Discover how machines can learn and improve from data.</p>
-          <a href="#" class="inline-block px-6 py-3 bg-blue-500 text-white rounded-full">Explore</a>
-        </div>
-
-        <!-- Category Cybersec -->
-        <div class="bg-white shadow-lg rounded-lg p-8 text-center">
-          <div class="flex justify-center items-center h-16 w-16 bg-blue-500 text-white rounded-full mx-auto mb-4">
-            <i class="fas fa-shield-alt fa-2x"></i>
-          </div>
-          <h3 class="text-xl font-semibold mb-4">Cybersecurity</h3>
-          <p class="text-gray-700 mb-4">Learn how to protect computer systems and networks from cyber threats.</p>
-          <a href="#" class="inline-block px-6 py-3 bg-blue-500 text-white rounded-full">Explore</a>
-        </div>
-
-        <!-- Category OS -->
-        <div class="bg-white shadow-lg rounded-lg p-8 text-center">
-          <div class="flex justify-center items-center h-16 w-16 bg-blue-500 text-white rounded-full mx-auto mb-4">
-            <i class="fas fa-microchip fa-2x"></i>
-          </div>
-          <h3 class="text-xl font-semibold mb-4">Operating Systems</h3>
-          <p class="text-gray-700 mb-4">Explore Operating systems and how to handle them.</p>
-          <a href="#" class="inline-block px-6 py-3 bg-blue-500 text-white rounded-full">Explore</a>
-        </div>
-      </div>
-    </div>
-  </section>
-
-  <footer class="bg-gray-800 text-white py-8">
-    <div class="container mx-auto text-center">
-      <p>&copy; 2023 LANIAKEA. AGPLv3.</p>
-      <p>Made with <3 by <i class="fas fa-heart text-red-500"></i> Me and Myself</p>
-    </div>
-  </footer>
-
-  <script src="https://cdn.tailwindcss.com/2.0.2/tailwind.min.js"></script>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/VideoCrafter/VideoCrafter/lvdm/models/samplers/ddim.py b/spaces/VideoCrafter/VideoCrafter/lvdm/models/samplers/ddim.py
deleted file mode 100644
index dfd345f4c8eb2931452cb8bd7dbe5d6c7e68b096..0000000000000000000000000000000000000000
--- a/spaces/VideoCrafter/VideoCrafter/lvdm/models/samplers/ddim.py
+++ /dev/null
@@ -1,336 +0,0 @@
-import numpy as np
-from tqdm import tqdm
-import torch
-from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps
-from lvdm.common import noise_like
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-        self.counter = 0
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-        self.use_scale = self.model.use_scale
-        print('DDIM scale', self.use_scale)
-
-        if self.use_scale:
-            self.register_buffer('scale_arr', to_torch(self.model.scale_arr))
-            ddim_scale_arr = self.scale_arr.cpu()[self.ddim_timesteps]
-            self.register_buffer('ddim_scale_arr', ddim_scale_arr)
-            ddim_scale_arr = np.asarray([self.scale_arr.cpu()[0]] + self.scale_arr.cpu()[self.ddim_timesteps[:-1]].tolist())
-            self.register_buffer('ddim_scale_arr_prev', ddim_scale_arr)
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               schedule_verbose=False,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        
-        # check condition bs
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                try:
-                    cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                except:
-                    cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
-
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=schedule_verbose)
-        
-        # make shape
-        if len(shape) == 3:
-            C, H, W = shape
-            size = (batch_size, C, H, W)
-        elif len(shape) == 4:
-            C, T, H, W = shape
-            size = (batch_size, C, T, H, W)
-        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-        
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    verbose=verbose,
-                                                    **kwargs)
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,
-                      cond_tau=1., target_size=None, start_timesteps=None,
-                      **kwargs):
-        device = self.model.betas.device        
-        print('ddim device', device)
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-        
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-            
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        if verbose:
-            iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-        else:
-            iterator = time_range
-
-        init_x0 = False
-        clean_cond = kwargs.pop("clean_cond", False)
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            if start_timesteps is not None:
-                assert x0 is not None
-                if step > start_timesteps*time_range[0]:
-                    continue
-                elif not init_x0:
-                    img = self.model.q_sample(x0, ts) 
-                    init_x0 = True
-
-            # use mask to blend noised original latent (img_orig) & new sampled latent (img)
-            if mask is not None:
-                assert x0 is not None
-                if clean_cond:
-                    img_orig = x0
-                else:
-                    img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass? <ddim inversion>
-                img = img_orig * mask + (1. - mask) * img # keep original & modify use img
-            
-            index_clip =  int((1 - cond_tau) * total_steps)
-            if index <= index_clip and target_size is not None:
-                target_size_ = [target_size[0], target_size[1]//8, target_size[2]//8]
-                img = torch.nn.functional.interpolate(
-                img,
-                size=target_size_,
-                mode="nearest",
-                )
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      x0=x0,
-                                      **kwargs)
-            
-            img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      uc_type=None, conditional_guidance_scale_temporal=None, **kwargs):
-        b, *_, device = *x.shape, x.device
-        if x.dim() == 5:
-            is_video = True
-        else:
-            is_video = False
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
-        else:
-            # with unconditional condition
-            if isinstance(c, torch.Tensor):
-                e_t = self.model.apply_model(x, t, c, **kwargs)
-                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
-            elif isinstance(c, dict):
-                e_t = self.model.apply_model(x, t, c, **kwargs)
-                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
-            else:
-                raise NotImplementedError
-            # text cfg
-            if uc_type is None:
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-            else:
-                if uc_type == 'cfg_original':
-                    e_t = e_t + unconditional_guidance_scale * (e_t - e_t_uncond)
-                elif uc_type == 'cfg_ours':
-                    e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t)
-                else:
-                    raise NotImplementedError
-            # temporal guidance
-            if conditional_guidance_scale_temporal is not None:
-                e_t_temporal = self.model.apply_model(x, t, c, **kwargs)
-                e_t_image = self.model.apply_model(x, t, c, no_temporal_attn=True, **kwargs)
-                e_t = e_t + conditional_guidance_scale_temporal * (e_t_temporal - e_t_image)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        
-        if is_video:
-            size = (b, 1, 1, 1, 1)
-        else:
-            size = (b, 1, 1, 1)
-        a_t = torch.full(size, alphas[index], device=device)
-        a_prev = torch.full(size, alphas_prev[index], device=device)
-        sigma_t = torch.full(size, sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        if self.use_scale:
-            scale_arr = self.model.scale_arr if use_original_steps else self.ddim_scale_arr
-            scale_t = torch.full(size, scale_arr[index], device=device)
-            scale_arr_prev = self.model.scale_arr_prev if use_original_steps else self.ddim_scale_arr_prev
-            scale_t_prev = torch.full(size, scale_arr_prev[index], device=device)
-            pred_x0 /= scale_t 
-            x_prev = a_prev.sqrt() * scale_t_prev * pred_x0 + dir_xt + noise
-        else:
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-
-        return x_prev, pred_x0
-
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-
-        def extract_into_tensor(a, t, x_shape):
-            b, *_ = t.shape
-            out = a.gather(-1, t)
-            return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
-    @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False):
-
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec = x_latent
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
-        return x_dec
-
diff --git a/spaces/Wootang01/stable_diffuser_1/README.md b/spaces/Wootang01/stable_diffuser_1/README.md
deleted file mode 100644
index 67270fe6f7c6d07a7a24f6483af9af05324b3ff1..0000000000000000000000000000000000000000
--- a/spaces/Wootang01/stable_diffuser_1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stable Diffuser 1
-emoji: 💩
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.14
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/image.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/image.py
deleted file mode 100644
index 5aefc935b863931d9aca4f02140529478f5c4337..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/image.py
+++ /dev/null
@@ -1,627 +0,0 @@
-"`Image` provides support to convert, transform and show images"
-from ..torch_core import *
-from ..basic_data import *
-from ..layers import MSELossFlat
-from io import BytesIO
-import PIL
-
-__all__ = ['PIL', 'Image', 'ImageBBox', 'ImageSegment', 'ImagePoints', 'FlowField', 'RandTransform', 'TfmAffine', 'TfmCoord',
-           'TfmCrop', 'TfmLighting', 'TfmPixel', 'Transform', 'bb2hw', 'image2np', 'open_image', 'open_mask', 'tis2hw',
-           'pil2tensor', 'scale_flow', 'show_image', 'CoordFunc', 'TfmList', 'open_mask_rle', 'rle_encode',
-           'rle_decode', 'ResizeMethod', 'plot_flat', 'plot_multi', 'show_multi', 'show_all']
-
-ResizeMethod = IntEnum('ResizeMethod', 'CROP PAD SQUISH NO')
-def pil2tensor(image:Union[NPImage,NPArray],dtype:np.dtype)->TensorImage:
-    "Convert PIL style `image` array to torch style image tensor."
-    a = np.asarray(image)
-    if a.ndim==2 : a = np.expand_dims(a,2)
-    a = np.transpose(a, (1, 0, 2))
-    a = np.transpose(a, (2, 1, 0))
-    return torch.from_numpy(a.astype(dtype, copy=False) )
-
-def image2np(image:Tensor)->np.ndarray:
-    "Convert from torch style `image` to numpy/matplotlib style."
-    res = image.cpu().permute(1,2,0).numpy()
-    return res[...,0] if res.shape[2]==1 else res
-
-def bb2hw(a:Collection[int])->np.ndarray:
-    "Convert bounding box points from (width,height,center) to (height,width,top,left)."
-    return np.array([a[1],a[0],a[3]-a[1],a[2]-a[0]])
-
-def tis2hw(size:Union[int,TensorImageSize]) -> Tuple[int,int]:
-    "Convert `int` or `TensorImageSize` to (height,width) of an image."
-    if type(size) is str: raise RuntimeError("Expected size to be an int or a tuple, got a string.")
-    return listify(size, 2) if isinstance(size, int) else listify(size[-2:],2)
-
-def _draw_outline(o:Patch, lw:int):
-    "Outline bounding box onto image `Patch`."
-    o.set_path_effects([patheffects.Stroke(
-        linewidth=lw, foreground='black'), patheffects.Normal()])
-
-def _draw_rect(ax:plt.Axes, b:Collection[int], color:str='white', text=None, text_size=14):
-    "Draw bounding box on `ax`."
-    patch = ax.add_patch(patches.Rectangle(b[:2], *b[-2:], fill=False, edgecolor=color, lw=2))
-    _draw_outline(patch, 4)
-    if text is not None:
-        patch = ax.text(*b[:2], text, verticalalignment='top', color=color, fontsize=text_size, weight='bold')
-        _draw_outline(patch,1)
-
-def _get_default_args(func:Callable):
-    return {k: v.default
-            for k, v in inspect.signature(func).parameters.items()
-            if v.default is not inspect.Parameter.empty}
-
-@dataclass
-class FlowField():
-    "Wrap together some coords `flow` with a `size`."
-    size:Tuple[int,int]
-    flow:Tensor
-
-CoordFunc = Callable[[FlowField, ArgStar, KWArgs], LogitTensorImage]
-
-class Image(ItemBase):
-    "Support applying transforms to image data in `px`."
-    def __init__(self, px:Tensor):
-        self._px = px
-        self._logit_px=None
-        self._flow=None
-        self._affine_mat=None
-        self.sample_kwargs = {}
-
-    def set_sample(self, **kwargs)->'ImageBase':
-        "Set parameters that control how we `grid_sample` the image after transforms are applied."
-        self.sample_kwargs = kwargs
-        return self
-
-    def clone(self):
-        "Mimic the behavior of torch.clone for `Image` objects."
-        return self.__class__(self.px.clone())
-
-    @property
-    def shape(self)->Tuple[int,int,int]: return self._px.shape
-    @property
-    def size(self)->Tuple[int,int]: return self.shape[-2:]
-    @property
-    def device(self)->torch.device: return self._px.device
-
-    def __repr__(self): return f'{self.__class__.__name__} {tuple(self.shape)}'
-    def _repr_png_(self): return self._repr_image_format('png')
-    def _repr_jpeg_(self): return self._repr_image_format('jpeg')
-
-    def _repr_image_format(self, format_str):
-        with BytesIO() as str_buffer:
-            plt.imsave(str_buffer, image2np(self.px), format=format_str)
-            return str_buffer.getvalue()
-
-    def apply_tfms(self, tfms:TfmList, do_resolve:bool=True, xtra:Optional[Dict[Callable,dict]]=None,
-                   size:Optional[Union[int,TensorImageSize]]=None, resize_method:ResizeMethod=None,
-                   mult:int=None, padding_mode:str='reflection', mode:str='bilinear', remove_out:bool=True,
-                   is_x:bool=True, x_frames:int=1, y_frames:int=1)->TensorImage:
-        "Apply all `tfms` to the `Image`, if `do_resolve` picks value for random args."
-        if not (tfms or xtra or size): return self
-
-        if size is not None and isinstance(size, int):
-            num_frames = x_frames if is_x else y_frames
-            if num_frames > 1:
-                size = (size, size*num_frames)
-
-        tfms = listify(tfms)
-        xtra = ifnone(xtra, {})
-        default_rsz = ResizeMethod.SQUISH if (size is not None and is_listy(size)) else ResizeMethod.CROP
-        resize_method = ifnone(resize_method, default_rsz)
-        if resize_method <= 2 and size is not None: tfms = self._maybe_add_crop_pad(tfms)
-        tfms = sorted(tfms, key=lambda o: o.tfm.order)
-        if do_resolve: _resolve_tfms(tfms)
-        x = self.clone()
-        x.set_sample(padding_mode=padding_mode, mode=mode, remove_out=remove_out)
-        if size is not None:
-            crop_target = _get_crop_target(size, mult=mult)
-            if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
-                target = _get_resize_target(x, crop_target, do_crop=(resize_method==ResizeMethod.CROP))
-                x.resize(target)
-            elif resize_method==ResizeMethod.SQUISH: x.resize((x.shape[0],) + crop_target)
-        else: size = x.size
-        size_tfms = [o for o in tfms if isinstance(o.tfm,TfmCrop)]
-        for tfm in tfms:
-            if tfm.tfm in xtra: x = tfm(x, **xtra[tfm.tfm])
-            elif tfm in size_tfms:
-                if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
-                    x = tfm(x, size=_get_crop_target(size,mult=mult), padding_mode=padding_mode)
-            else: x = tfm(x)
-        return x.refresh()
-
-    def refresh(self)->None:
-        "Apply any logit, flow, or affine transfers that have been sent to the `Image`."
-        if self._logit_px is not None:
-            self._px = self._logit_px.sigmoid_()
-            self._logit_px = None
-        if self._affine_mat is not None or self._flow is not None:
-            self._px = _grid_sample(self._px, self.flow, **self.sample_kwargs)
-            self.sample_kwargs = {}
-            self._flow = None
-        return self
-
-    def save(self, fn:PathOrStr):
-        "Save the image to `fn`."
-        x = image2np(self.data*255).astype(np.uint8)
-        PIL.Image.fromarray(x).save(fn)
-
-    @property
-    def px(self)->TensorImage:
-        "Get the tensor pixel buffer."
-        self.refresh()
-        return self._px
-    @px.setter
-    def px(self,v:TensorImage)->None:
-        "Set the pixel buffer to `v`."
-        self._px=v
-
-    @property
-    def flow(self)->FlowField:
-        "Access the flow-field grid after applying queued affine transforms."
-        if self._flow is None:
-            self._flow = _affine_grid(self.shape)
-        if self._affine_mat is not None:
-            self._flow = _affine_mult(self._flow,self._affine_mat)
-            self._affine_mat = None
-        return self._flow
-
-    @flow.setter
-    def flow(self,v:FlowField): self._flow=v
-
-    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any):
-        "Equivalent to `image = sigmoid(func(logit(image)))`."
-        self.logit_px = func(self.logit_px, *args, **kwargs)
-        return self
-
-    def pixel(self, func:PixelFunc, *args, **kwargs)->'Image':
-        "Equivalent to `image.px = func(image.px)`."
-        self.px = func(self.px, *args, **kwargs)
-        return self
-
-    def coord(self, func:CoordFunc, *args, **kwargs)->'Image':
-        "Equivalent to `image.flow = func(image.flow, image.size)`."
-        self.flow = func(self.flow, *args, **kwargs)
-        return self
-
-    def affine(self, func:AffineFunc, *args, **kwargs)->'Image':
-        "Equivalent to `image.affine_mat = image.affine_mat @ func()`."
-        m = tensor(func(*args, **kwargs)).to(self.device)
-        self.affine_mat = self.affine_mat @ m
-        return self
-
-    def resize(self, size:Union[int,TensorImageSize])->'Image':
-        "Resize the image to `size`, size can be a single int."
-        assert self._flow is None
-        if isinstance(size, int): size=(self.shape[0], size, size)
-        if tuple(size)==tuple(self.shape): return self
-        self.flow = _affine_grid(size)
-        return self
-
-    @property
-    def affine_mat(self)->AffineMatrix:
-        "Get the affine matrix that will be applied by `refresh`."
-        if self._affine_mat is None:
-            self._affine_mat = torch.eye(3).to(self.device)
-        return self._affine_mat
-    @affine_mat.setter
-    def affine_mat(self,v)->None: self._affine_mat=v
-
-    @property
-    def logit_px(self)->LogitTensorImage:
-        "Get logit(image.px)."
-        if self._logit_px is None: self._logit_px = logit_(self.px)
-        return self._logit_px
-    @logit_px.setter
-    def logit_px(self,v:LogitTensorImage)->None: self._logit_px=v
-
-    @property
-    def data(self)->TensorImage:
-        "Return this images pixels as a tensor."
-        return self.px
-
-    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
-              cmap:str=None, y:Any=None, **kwargs):
-        "Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y`"
-        cmap = ifnone(cmap, defaults.cmap)
-        ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize)
-        if y is not None: y.show(ax=ax, **kwargs)
-        if title is not None: ax.set_title(title)
-
-class ImageSegment(Image):
-    "Support applying transforms to segmentation masks data in `px`."
-    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any)->'Image': return self
-
-    def refresh(self):
-        self.sample_kwargs['mode'] = 'nearest'
-        return super().refresh()
-
-    @property
-    def data(self)->TensorImage:
-        "Return this image pixels as a `LongTensor`."
-        return self.px.long()
-
-    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
-        cmap:str='tab20', alpha:float=0.5, **kwargs):
-        "Show the `ImageSegment` on `ax`."
-        ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize,
-                        interpolation='nearest', alpha=alpha, vmin=0, **kwargs)
-        if title: ax.set_title(title)
-
-    def reconstruct(self, t:Tensor): return ImageSegment(t)
-
-class ImagePoints(Image):
-    "Support applying transforms to a `flow` of points."
-    def __init__(self, flow:FlowField, scale:bool=True, y_first:bool=True):
-        if scale: flow = scale_flow(flow)
-        if y_first: flow.flow = flow.flow.flip(1)
-        self._flow = flow
-        self._affine_mat = None
-        self.flow_func = []
-        self.sample_kwargs = {}
-        self.transformed = False
-        self.loss_func = MSELossFlat()
-
-    def clone(self):
-        "Mimic the behavior of torch.clone for `ImagePoints` objects."
-        return self.__class__(FlowField(self.size, self.flow.flow.clone()), scale=False, y_first=False)
-
-    @property
-    def shape(self)->Tuple[int,int,int]: return (1, *self._flow.size)
-    @property
-    def size(self)->Tuple[int,int]: return self._flow.size
-    @size.setter
-    def size(self, sz:int): self._flow.size=sz
-    @property
-    def device(self)->torch.device: return self._flow.flow.device
-
-    def __repr__(self): return f'{self.__class__.__name__} {tuple(self.size)}'
-    def _repr_image_format(self, format_str): return None
-
-    @property
-    def flow(self)->FlowField:
-        "Access the flow-field grid after applying queued affine and coord transforms."
-        if self._affine_mat is not None:
-            self._flow = _affine_inv_mult(self._flow, self._affine_mat)
-            self._affine_mat = None
-            self.transformed = True
-        if len(self.flow_func) != 0:
-            for f in self.flow_func[::-1]: self._flow = f(self._flow)
-            self.transformed = True
-            self.flow_func = []
-        return self._flow
-
-    @flow.setter
-    def flow(self,v:FlowField):  self._flow=v
-
-    def coord(self, func:CoordFunc, *args, **kwargs)->'ImagePoints':
-        "Put `func` with `args` and `kwargs` in `self.flow_func` for later."
-        if 'invert' in kwargs: kwargs['invert'] = True
-        else: warn(f"{func.__name__} isn't implemented for {self.__class__}.")
-        self.flow_func.append(partial(func, *args, **kwargs))
-        return self
-
-    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any)->'ImagePoints': return self
-
-    def pixel(self, func:PixelFunc, *args, **kwargs)->'ImagePoints':
-        "Equivalent to `self = func_flow(self)`."
-        self = func(self, *args, **kwargs)
-        self.transformed=True
-        return self
-
-    def refresh(self) -> 'ImagePoints':
-        return self
-
-    def resize(self, size:Union[int,TensorImageSize]) -> 'ImagePoints':
-        "Resize the image to `size`, size can be a single int."
-        if isinstance(size, int): size=(1, size, size)
-        self._flow.size = size[1:]
-        return self
-
-    @property
-    def data(self)->Tensor:
-        "Return the points associated to this object."
-        flow = self.flow #This updates flow before we test if some transforms happened
-        if self.transformed:
-            if 'remove_out' not in self.sample_kwargs or self.sample_kwargs['remove_out']:
-                flow = _remove_points_out(flow)
-            self.transformed=False
-        return flow.flow.flip(1)
-
-    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True, **kwargs):
-        "Show the `ImagePoints` on `ax`."
-        if ax is None: _,ax = plt.subplots(figsize=figsize)
-        pnt = scale_flow(FlowField(self.size, self.data), to_unit=False).flow.flip(1)
-        params = {'s': 10, 'marker': '.', 'c': 'r', **kwargs}
-        ax.scatter(pnt[:, 0], pnt[:, 1], **params)
-        if hide_axis: ax.axis('off')
-        if title: ax.set_title(title)
-
-class ImageBBox(ImagePoints):
-    "Support applying transforms to a `flow` of bounding boxes."
-    def __init__(self, flow:FlowField, scale:bool=True, y_first:bool=True, labels:Collection=None,
-                 classes:dict=None, pad_idx:int=0):
-        super().__init__(flow, scale, y_first)
-        self.pad_idx = pad_idx
-        if labels is not None and len(labels)>0 and not isinstance(labels[0],Category):
-            labels = array([Category(l,classes[l]) for l in labels])
-        self.labels = labels
-
-    def clone(self) -> 'ImageBBox':
-        "Mimic the behavior of torch.clone for `Image` objects."
-        flow = FlowField(self.size, self.flow.flow.clone())
-        return self.__class__(flow, scale=False, y_first=False, labels=self.labels, pad_idx=self.pad_idx)
-
-    @classmethod
-    def create(cls, h:int, w:int, bboxes:Collection[Collection[int]], labels:Collection=None, classes:dict=None,
-               pad_idx:int=0, scale:bool=True)->'ImageBBox':
-        "Create an ImageBBox object from `bboxes`."
-        if isinstance(bboxes, np.ndarray) and bboxes.dtype == np.object: bboxes = np.array([bb for bb in bboxes])
-        bboxes = tensor(bboxes).float()
-        tr_corners = torch.cat([bboxes[:,0][:,None], bboxes[:,3][:,None]], 1)
-        bl_corners = bboxes[:,1:3].flip(1)
-        bboxes = torch.cat([bboxes[:,:2], tr_corners, bl_corners, bboxes[:,2:]], 1)
-        flow = FlowField((h,w), bboxes.view(-1,2))
-        return cls(flow, labels=labels, classes=classes, pad_idx=pad_idx, y_first=True, scale=scale)
-
-    def _compute_boxes(self) -> Tuple[LongTensor, LongTensor]:
-        bboxes = self.flow.flow.flip(1).view(-1, 4, 2).contiguous().clamp(min=-1, max=1)
-        mins, maxes = bboxes.min(dim=1)[0], bboxes.max(dim=1)[0]
-        bboxes = torch.cat([mins, maxes], 1)
-        mask = (bboxes[:,2]-bboxes[:,0] > 0) * (bboxes[:,3]-bboxes[:,1] > 0)
-        if len(mask) == 0: return tensor([self.pad_idx] * 4), tensor([self.pad_idx])
-        res = bboxes[mask]
-        if self.labels is None: return res,None
-        return res, self.labels[to_np(mask).astype(bool)]
-
-    @property
-    def data(self)->Union[FloatTensor, Tuple[FloatTensor,LongTensor]]:
-        bboxes,lbls = self._compute_boxes()
-        lbls = np.array([o.data for o in lbls]) if lbls is not None else None
-        return bboxes if lbls is None else (bboxes, lbls)
-
-    def show(self, y:Image=None, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
-        color:str='white', **kwargs):
-        "Show the `ImageBBox` on `ax`."
-        if ax is None: _,ax = plt.subplots(figsize=figsize)
-        bboxes, lbls = self._compute_boxes()
-        h,w = self.flow.size
-        bboxes.add_(1).mul_(torch.tensor([h/2, w/2, h/2, w/2])).long()
-        for i, bbox in enumerate(bboxes):
-            if lbls is not None: text = str(lbls[i])
-            else: text=None
-            _draw_rect(ax, bb2hw(bbox), text=text, color=color)
-
-def open_image(fn:PathOrStr, div:bool=True, convert_mode:str='RGB', cls:type=Image,
-        after_open:Callable=None)->Image:
-    "Return `Image` object created from image in file `fn`."
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
-        x = PIL.Image.open(fn).convert(convert_mode)
-    if after_open: x = after_open(x)
-    x = pil2tensor(x,np.float32)
-    if div: x.div_(255)
-    return cls(x)
-
-def open_mask(fn:PathOrStr, div=False, convert_mode='L', after_open:Callable=None)->ImageSegment:
-    "Return `ImageSegment` object create from mask in file `fn`. If `div`, divides pixel values by 255."
-    return open_image(fn, div=div, convert_mode=convert_mode, cls=ImageSegment, after_open=after_open)
-
-def open_mask_rle(mask_rle:str, shape:Tuple[int, int])->ImageSegment:
-    "Return `ImageSegment` object create from run-length encoded string in `mask_lre` with size in `shape`."
-    x = FloatTensor(rle_decode(str(mask_rle), shape).astype(np.uint8))
-    x = x.view(shape[1], shape[0], -1)
-    return ImageSegment(x.permute(2,0,1))
-
-def rle_encode(img:NPArrayMask)->str:
-    "Return run-length encoding string from `img`."
-    pixels = np.concatenate([[0], img.flatten() , [0]])
-    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
-    runs[1::2] -= runs[::2]
-    return ' '.join(str(x) for x in runs)
-
-def rle_decode(mask_rle:str, shape:Tuple[int,int])->NPArrayMask:
-    "Return an image array from run-length encoded string `mask_rle` with `shape`."
-    s = mask_rle.split()
-    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
-    starts -= 1
-    ends = starts + lengths
-    img = np.zeros(shape[0]*shape[1], dtype=np.uint)
-    for low, up in zip(starts, ends): img[low:up] = 1
-    return img.reshape(shape)
-
-def show_image(img:Image, ax:plt.Axes=None, figsize:tuple=(3,3), hide_axis:bool=True, cmap:str='binary',
-                alpha:float=None, **kwargs)->plt.Axes:
-    "Display `Image` in notebook."
-    if ax is None: fig,ax = plt.subplots(figsize=figsize)
-    ax.imshow(image2np(img.data), cmap=cmap, alpha=alpha, **kwargs)
-    if hide_axis: ax.axis('off')
-    return ax
-
-def scale_flow(flow, to_unit=True):
-    "Scale the coords in `flow` to -1/1 or the image size depending on `to_unit`."
-    s = tensor([flow.size[0]/2,flow.size[1]/2])[None]
-    if to_unit: flow.flow = flow.flow/s-1
-    else:       flow.flow = (flow.flow+1)*s
-    return flow
-
-def _remove_points_out(flow:FlowField):
-    pad_mask = (flow.flow[:,0] >= -1) * (flow.flow[:,0] <= 1) * (flow.flow[:,1] >= -1) * (flow.flow[:,1] <= 1)
-    flow.flow = flow.flow[pad_mask]
-    return flow
-
-class Transform():
-    "Utility class for adding probability and wrapping support to transform `func`."
-    _wrap=None
-    order=0
-    def __init__(self, func:Callable, order:Optional[int]=None):
-        "Create a transform for `func` and assign it an priority `order`, attach to `Image` class."
-        if order is not None: self.order=order
-        self.func=func
-        self.func.__name__ = func.__name__[1:] #To remove the _ that begins every transform function.
-        functools.update_wrapper(self, self.func)
-        self.func.__annotations__['return'] = Image
-        self.params = copy(func.__annotations__)
-        self.def_args = _get_default_args(func)
-        setattr(Image, func.__name__,
-                lambda x, *args, **kwargs: self.calc(x, *args, **kwargs))
-
-    def __call__(self, *args:Any, p:float=1., is_random:bool=True, use_on_y:bool=True, **kwargs:Any)->Image:
-        "Calc now if `args` passed; else create a transform called prob `p` if `random`."
-        if args: return self.calc(*args, **kwargs)
-        else: return RandTransform(self, kwargs=kwargs, is_random=is_random, use_on_y=use_on_y, p=p)
-
-    def calc(self, x:Image, *args:Any, **kwargs:Any)->Image:
-        "Apply to image `x`, wrapping it if necessary."
-        if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)
-        else:          return self.func(x, *args, **kwargs)
-
-    @property
-    def name(self)->str: return self.__class__.__name__
-
-    def __repr__(self)->str: return f'{self.name} ({self.func.__name__})'
-
-@dataclass
-class RandTransform():
-    "Wrap `Transform` to add randomized execution."
-    tfm:Transform
-    kwargs:dict
-    p:float=1.0
-    resolved:dict = field(default_factory=dict)
-    do_run:bool = True
-    is_random:bool = True
-    use_on_y:bool = True
-    def __post_init__(self): functools.update_wrapper(self, self.tfm)
-
-    def resolve(self)->None:
-        "Bind any random variables in the transform."
-        if not self.is_random:
-            self.resolved = {**self.tfm.def_args, **self.kwargs}
-            return
-
-        self.resolved = {}
-        # for each param passed to tfm...
-        for k,v in self.kwargs.items():
-            # ...if it's annotated, call that fn...
-            if k in self.tfm.params:
-                rand_func = self.tfm.params[k]
-                self.resolved[k] = rand_func(*listify(v))
-            # ...otherwise use the value directly
-            else: self.resolved[k] = v
-        # use defaults for any args not filled in yet
-        for k,v in self.tfm.def_args.items():
-            if k not in self.resolved: self.resolved[k]=v
-        # anything left over must be callable without params
-        for k,v in self.tfm.params.items():
-            if k not in self.resolved and k!='return': self.resolved[k]=v()
-
-        self.do_run = rand_bool(self.p)
-
-    @property
-    def order(self)->int: return self.tfm.order
-
-    def __call__(self, x:Image, *args, **kwargs)->Image:
-        "Randomly execute our tfm on `x`."
-        return self.tfm(x, *args, **{**self.resolved, **kwargs}) if self.do_run else x
-
-def _resolve_tfms(tfms:TfmList):
-    "Resolve every tfm in `tfms`."
-    for f in listify(tfms): f.resolve()
-
-def _grid_sample(x:TensorImage, coords:FlowField, mode:str='bilinear', padding_mode:str='reflection', remove_out:bool=True)->TensorImage:
-    "Resample pixels in `coords` from `x` by `mode`, with `padding_mode` in ('reflection','border','zeros')."
-    coords = coords.flow.permute(0, 3, 1, 2).contiguous().permute(0, 2, 3, 1) # optimize layout for grid_sample
-    if mode=='bilinear': # hack to get smoother downwards resampling
-        mn,mx = coords.min(),coords.max()
-        # max amount we're affine zooming by (>1 means zooming in)
-        z = 1/(mx-mn).item()*2
-        # amount we're resizing by, with 100% extra margin
-        d = min(x.shape[1]/coords.shape[1], x.shape[2]/coords.shape[2])/2
-        # If we're resizing up by >200%, and we're zooming less than that, interpolate first
-        if d>1 and d>z: x = F.interpolate(x[None], scale_factor=1/d, mode='area')[0]
-    return F.grid_sample(x[None], coords, mode=mode, padding_mode=padding_mode)[0]
-
-def _affine_grid(size:TensorImageSize)->FlowField:
-    size = ((1,)+size)
-    N, C, H, W = size
-    grid = FloatTensor(N, H, W, 2)
-    linear_points = torch.linspace(-1, 1, W) if W > 1 else tensor([-1])
-    grid[:, :, :, 0] = torch.ger(torch.ones(H), linear_points).expand_as(grid[:, :, :, 0])
-    linear_points = torch.linspace(-1, 1, H) if H > 1 else tensor([-1])
-    grid[:, :, :, 1] = torch.ger(linear_points, torch.ones(W)).expand_as(grid[:, :, :, 1])
-    return FlowField(size[2:], grid)
-
-def _affine_mult(c:FlowField,m:AffineMatrix)->FlowField:
-    "Multiply `c` by `m` - can adjust for rectangular shaped `c`."
-    if m is None: return c
-    size = c.flow.size()
-    h,w = c.size
-    m[0,1] *= h/w
-    m[1,0] *= w/h
-    c.flow = c.flow.view(-1,2)
-    c.flow = torch.addmm(m[:2,2], c.flow,  m[:2,:2].t()).view(size)
-    return c
-
-def _affine_inv_mult(c, m):
-    "Applies the inverse affine transform described in `m` to `c`."
-    size = c.flow.size()
-    h,w = c.size
-    m[0,1] *= h/w
-    m[1,0] *= w/h
-    c.flow = c.flow.view(-1,2)
-    a = torch.inverse(m[:2,:2].t())
-    c.flow = torch.mm(c.flow - m[:2,2], a).view(size)
-    return c
-
-class TfmAffine(Transform):
-    "Decorator for affine tfm funcs."
-    order,_wrap = 5,'affine'
-class TfmPixel(Transform):
-    "Decorator for pixel tfm funcs."
-    order,_wrap = 10,'pixel'
-class TfmCoord(Transform):
-    "Decorator for coord tfm funcs."
-    order,_wrap = 4,'coord'
-class TfmCrop(TfmPixel):
-    "Decorator for crop tfm funcs."
-    order=99
-class TfmLighting(Transform):
-    "Decorator for lighting tfm funcs."
-    order,_wrap = 8,'lighting'
-
-def _round_multiple(x:int, mult:int=None)->int:
-    "Calc `x` to nearest multiple of `mult`."
-    return (int(x/mult+0.5)*mult) if mult is not None else x
-
-def _get_crop_target(target_px:Union[int,TensorImageSize], mult:int=None)->Tuple[int,int]:
-    "Calc crop shape of `target_px` to nearest multiple of `mult`."
-    target_r,target_c = tis2hw(target_px)
-    return _round_multiple(target_r,mult),_round_multiple(target_c,mult)
-
-def _get_resize_target(img, crop_target, do_crop=False)->TensorImageSize:
-    "Calc size of `img` to fit in `crop_target` - adjust based on `do_crop`."
-    if crop_target is None: return None
-    ch,r,c = img.shape
-    target_r,target_c = crop_target
-    ratio = (min if do_crop else max)(r/target_r, c/target_c)
-    return ch,int(round(r/ratio)),int(round(c/ratio)) #Sometimes those are numpy numbers and round doesn't return an int.
-
-def plot_flat(r, c, figsize):
-    "Shortcut for `enumerate(subplots.flatten())`"
-    return enumerate(plt.subplots(r, c, figsize=figsize)[1].flatten())
-
-def plot_multi(func:Callable[[int,int,plt.Axes],None], r:int=1, c:int=1, figsize:Tuple=(12,6)):
-    "Call `func` for every combination of `r,c` on a subplot"
-    axes = plt.subplots(r, c, figsize=figsize)[1]
-    for i in range(r):
-        for j in range(c): func(i,j,axes[i,j])
-
-def show_multi(func:Callable[[int,int],Image], r:int=1, c:int=1, figsize:Tuple=(9,9)):
-    "Call `func(i,j).show(ax)` for every combination of `r,c`"
-    plot_multi(lambda i,j,ax: func(i,j).show(ax), r, c, figsize=figsize)
-
-def show_all(imgs:Collection[Image], r:int=1, c:Optional[int]=None, figsize=(12,6)):
-    "Show all `imgs` using `r` rows"
-    imgs = listify(imgs)
-    if c is None: c = len(imgs)//r
-    for i,ax in plot_flat(r,c,figsize): imgs[i].show(ax)
diff --git a/spaces/Xhaheen/regex_by_bloom/README.md b/spaces/Xhaheen/regex_by_bloom/README.md
deleted file mode 100644
index a6566038553c6b38165e5f41ba03ef6c7e2a656a..0000000000000000000000000000000000000000
--- a/spaces/Xhaheen/regex_by_bloom/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Regex By Bloom
-emoji: 😻
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XzJosh/Ava-Bert-VITS2/text/cleaner.py b/spaces/XzJosh/Ava-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Ava-Bert-VITS2/text/cleaner.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from text import chinese, cleaned_text_to_sequence
-
-
-language_module_map = {
-    'ZH': chinese
-}
-
-
-def clean_text(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    return norm_text, phones, tones, word2ph
-
-def clean_text_bert(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    bert = language_module.get_bert_feature(norm_text, word2ph)
-    return phones, tones, bert
-
-def text_to_sequence(text, language):
-    norm_text, phones, tones, word2ph = clean_text(text, language)
-    return cleaned_text_to_sequence(phones, tones, language)
-
-if __name__ == '__main__':
-    pass
diff --git a/spaces/XzJosh/Carol-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/Carol-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Carol-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/XzJosh/Eileen-Bert-VITS2/server.py b/spaces/XzJosh/Eileen-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Eileen-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    print([f"{p}{t}" for p, t in zip(phone, tone)])
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
-    bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
-    with torch.no_grad():
-        x_tst=phones.to(dev).unsqueeze(0)
-        tones=tones.to(dev).unsqueeze(0)
-        lang_ids=lang_ids.to(dev).unsqueeze(0)
-        bert = bert.to(dev).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        return audio
-
-def replace_punctuation(text, i=2):
-    punctuation = "，。？！"
-    for char in punctuation:
-        text = text.replace(char, char * i)
-    return text
-
-def wav2(i, o, format):
-    inp = avopen(i, 'rb')
-    out = avopen(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-
-    ostream = out.add_stream(format)
-
-    for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
-
-    for p in ostream.encode(None): out.mux(p)
-
-    out.close()
-    inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
-    if request.method == 'GET':
-        try:
-            speaker = request.args.get('speaker')
-            text = request.args.get('text').replace("/n","")
-            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
-            noise = float(request.args.get("noise", 0.5))
-            noisew = float(request.args.get("noisew", 0.6))
-            length = float(request.args.get("length", 1.2))
-            if length >= 2:
-                return "Too big length"
-            if len(text) >=200:
-                return "Too long text"
-            fmt = request.args.get("format", "wav")
-            if None in (speaker, text):
-                return "Missing Parameter"
-            if fmt not in ("mp3", "wav", "ogg"):
-                return "Invalid Format"
-        except:
-            return "Invalid Parameter"
-
-        with torch.no_grad():
-            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
-        with BytesIO() as wav:
-            wavfile.write(wav, hps.data.sampling_rate, audio)
-            torch.cuda.empty_cache()
-            if fmt == "wav":
-                return Response(wav.getvalue(), mimetype="audio/wav")
-            wav.seek(0, 0)
-            with BytesIO() as ofp:
-                wav2(wav, ofp, fmt)
-                return Response(
-                    ofp.getvalue(),
-                    mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
-                )
diff --git a/spaces/XzJosh/Gun-Bert-VITS2/text/chinese_bert.py b/spaces/XzJosh/Gun-Bert-VITS2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Gun-Bert-VITS2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors='pt')
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = model(**inputs, output_hidden_states=True)
-        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
-    assert len(word2ph) == len(text)+2
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
-    return phone_level_feature.T
-
-if __name__ == '__main__':
-    # feature = get_bert_feature('你好,我是说的道理。')
-    import torch
-
-    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
-    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
-    # 计算总帧数
-    total_frames = sum(word2phone)
-    print(word_level_feature.shape)
-    print(word2phone)
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        print(word_level_feature[i].shape)
-
-        # 对每个词重复word2phone[i]次
-        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    print(phone_level_feature.shape)  # torch.Size([36, 1024])
-
diff --git a/spaces/XzJosh/JM-Bert-VITS2/transforms.py b/spaces/XzJosh/JM-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/JM-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/XzJosh/Jianmo-Bert-VITS2/modules.py b/spaces/XzJosh/Jianmo-Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jianmo-Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-    
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
-class TransformerCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      n_layers,
-      n_heads,
-      p_dropout=0,
-      filter_channels=0,
-      mean_only=False,
-      wn_sharing_parameter=None,
-      gin_channels = 0
-      ):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/XzJosh/Spade-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Spade-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Spade-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/XzJosh/nine2-Bert-VITS2/train_ms.py b/spaces/XzJosh/nine2-Bert-VITS2/train_ms.py
deleted file mode 100644
index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nine2-Bert-VITS2/train_ms.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-import shutil
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from tqdm import tqdm
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import commons
-import utils
-from data_utils import (
-    TextAudioSpeakerLoader,
-    TextAudioSpeakerCollate,
-    DistributedBucketSampler
-)
-from models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-    DurationDiscriminator,
-)
-from losses import (
-    generator_loss,
-    discriminator_loss,
-    feature_loss,
-    kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-torch.backends.cudnn.benchmark = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.set_float32_matmul_precision('medium')
-global_step = 0
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-
-    n_gpus = torch.cuda.device_count()
-    os.environ['MASTER_ADDR'] = 'localhost'
-    os.environ['MASTER_PORT'] = '65280'
-
-    hps = utils.get_hparams()
-    if not hps.cont:
-           shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth')
-           shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth')
-           shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth')
-    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-
-    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size,
-        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True)
-    collate_fn = TextAudioSpeakerCollate()
-    train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
-                              collate_fn=collate_fn, batch_sampler=train_sampler)
-    if rank == 0:
-        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
-        eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
-                                 batch_size=1, pin_memory=True,
-                                 drop_last=False, collate_fn=collate_fn)
-    if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
-        print("Using noise scaled MAS for VITS2")
-        use_noise_scaled_mas = True
-        mas_noise_scale_initial = 0.01
-        noise_scale_delta = 2e-6
-    else:
-        print("Using normal MAS for VITS1")
-        use_noise_scaled_mas = False
-        mas_noise_scale_initial = 0.0
-        noise_scale_delta = 0.0
-    if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
-        print("Using duration discriminator for VITS2")
-        use_duration_discriminator = True
-        net_dur_disc = DurationDiscriminator(
-         hps.model.hidden_channels, 
-         hps.model.hidden_channels, 
-         3, 
-         0.1, 
-         gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
-         ).cuda(rank)
-    if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
-        if hps.data.n_speakers == 0:
-            raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
-        use_spk_conditioned_encoder = True
-    else:
-        print("Using normal encoder for VITS1")
-        use_spk_conditioned_encoder = False
-
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        mas_noise_scale_initial = mas_noise_scale_initial,
-        noise_scale_delta = noise_scale_delta,
-        **hps.model).cuda(rank)
-
-    freeze_enc = getattr(hps.model, "freeze_enc", False)
-    if freeze_enc:
-        print("freeze encoder !!!")
-        for param in net_g.enc_p.parameters():
-            param.requires_grad = False
-
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-    optim_g = torch.optim.AdamW(
-        filter(lambda p: p.requires_grad, net_g.parameters()),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    if net_dur_disc is not None:
-        optim_dur_disc = torch.optim.AdamW(
-        net_dur_disc.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    else:
-        optim_dur_disc = None
-    net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-    net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
-    if net_dur_disc is not None:
-        net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
-
-    pretrain_dir = None
-    if pretrain_dir is None:
-        try:
-            if net_dur_disc is not None:
-                _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont)
-            _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer=not hps.cont)
-            _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer=not hps.cont)
-            
-            epoch_str = max(epoch_str, 1)
-            global_step = (epoch_str - 1) * len(train_loader)
-        except Exception as e:
-            print(e)
-            epoch_str = 1
-            global_step = 0
-    else:
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
-                                                   optim_g, True)
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
-                                                   optim_d, True)
-
-
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    if net_dur_disc is not None:
-        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-    else:
-        scheduler_dur_disc = None
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-        else:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
-        scheduler_g.step()
-        scheduler_d.step()
-        if net_dur_disc is not None:
-            scheduler_dur_disc.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-    net_g, net_d, net_dur_disc = nets
-    optim_g, optim_d, optim_dur_disc = optims
-    scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    if net_dur_disc is not None:
-        net_dur_disc.train()
-    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
-        if net_g.module.use_noise_scaled_mas:
-            current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
-            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
-        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-        speakers = speakers.cuda(rank, non_blocking=True)
-        tone = tone.cuda(rank, non_blocking=True)
-        language = language.cuda(rank, non_blocking=True)
-        bert = bert.cuda(rank, non_blocking=True)
-
-        with autocast(enabled=hps.train.fp16_run):
-            y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
-                (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax)
-            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-
-            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-                loss_disc_all = loss_disc
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
-                with autocast(enabled=False):
-                 # TODO: I think need to mean using the mask, but for now, just mean all
-                    loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
-                    loss_dur_disc_all = loss_dur_disc
-                optim_dur_disc.zero_grad()
-                scaler.scale(loss_dur_disc_all).backward()
-                scaler.unscale_(optim_dur_disc)
-                grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
-                scaler.step(optim_dur_disc)
-
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
-            with autocast(enabled=False):
-                loss_dur = torch.sum(l_length.float())
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-                if net_dur_disc is not None:
-                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
-                    loss_gen_all += loss_dur_gen
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]['lr']
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-                logger.info('Train Epoch: {} [{:.0f}%]'.format(
-                    epoch,
-                    100. * batch_idx / len(train_loader)))
-                logger.info([x.item() for x in losses] + [global_step, lr])
-
-                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
-                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-                scalar_dict.update(
-                    {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-                scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-          
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                    "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict)
-
-            if global_step % hps.train.eval_interval == 0:
-                evaluate(hps, net_g, eval_loader, writer_eval)
-                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-                if net_dur_disc is not None:
-                    utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)))    
-                keep_ckpts = getattr(hps.train, 'keep_ckpts', 5)
-                if keep_ckpts > 0:
-                    utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-
-        global_step += 1
-
-    if rank == 0:
-        logger.info('====> Epoch: {}'.format(epoch))
-
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    print("Evaluating ...")
-    with torch.no_grad():
-        for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
-            x, x_lengths = x.cuda(), x_lengths.cuda()
-            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
-            y, y_lengths = y.cuda(), y_lengths.cuda()
-            speakers = speakers.cuda()
-            bert = bert.cuda()
-            tone = tone.cuda()
-            language = language.cuda()
-            for use_sdp in [True, False]:
-                y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
-                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
-
-                mel = spec_to_mel_torch(
-                    spec,
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax)
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.squeeze(1).float(),
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax
-                )
-                image_dict.update({
-                    f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-                })
-                audio_dict.update({
-                    f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
-                })
-                image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
-
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/lazy.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/lazy.py
deleted file mode 100644
index fa5d86b427ab53ecbe992842cce71d12c5e3a141..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/lazy.py
+++ /dev/null
@@ -1,399 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import ast
-import builtins
-import importlib
-import inspect
-import logging
-import os
-import uuid
-from collections import abc
-from contextlib import contextmanager
-from copy import deepcopy
-from dataclasses import is_dataclass
-from typing import List, Tuple, Union
-import cloudpickle
-import yaml
-from omegaconf import DictConfig, ListConfig, OmegaConf
-
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.registry import _convert_target_to_string
-
-__all__ = ["LazyCall", "LazyConfig"]
-
-
-class LazyCall:
-    """
-    Wrap a callable so that when it's called, the call will not be executed,
-    but returns a dict that describes the call.
-
-    LazyCall object has to be called with only keyword arguments. Positional
-    arguments are not yet supported.
-
-    Examples:
-    ::
-        from detectron2.config import instantiate, LazyCall
-
-        layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32)
-        layer_cfg.out_channels = 64   # can edit it afterwards
-        layer = instantiate(layer_cfg)
-    """
-
-    def __init__(self, target):
-        if not (callable(target) or isinstance(target, (str, abc.Mapping))):
-            raise TypeError(
-                f"target of LazyCall must be a callable or defines a callable! Got {target}"
-            )
-        self._target = target
-
-    def __call__(self, **kwargs):
-        if is_dataclass(self._target):
-            # omegaconf object cannot hold dataclass type
-            # https://github.com/omry/omegaconf/issues/784
-            target = _convert_target_to_string(self._target)
-        else:
-            target = self._target
-        kwargs["_target_"] = target
-
-        return DictConfig(content=kwargs, flags={"allow_objects": True})
-
-
-def _visit_dict_config(cfg, func):
-    """
-    Apply func recursively to all DictConfig in cfg.
-    """
-    if isinstance(cfg, DictConfig):
-        func(cfg)
-        for v in cfg.values():
-            _visit_dict_config(v, func)
-    elif isinstance(cfg, ListConfig):
-        for v in cfg:
-            _visit_dict_config(v, func)
-
-
-def _validate_py_syntax(filename):
-    # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py
-    with PathManager.open(filename, "r") as f:
-        content = f.read()
-    try:
-        ast.parse(content)
-    except SyntaxError as e:
-        raise SyntaxError(f"Config file {filename} has syntax error!") from e
-
-
-def _cast_to_config(obj):
-    # if given a dict, return DictConfig instead
-    if isinstance(obj, dict):
-        return DictConfig(obj, flags={"allow_objects": True})
-    return obj
-
-
-_CFG_PACKAGE_NAME = "detectron2._cfg_loader"
-"""
-A namespace to put all imported config into.
-"""
-
-
-def _random_package_name(filename):
-    # generate a random package name when loading config files
-    return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename)
-
-
-@contextmanager
-def _patch_import():
-    """
-    Enhance relative import statements in config files, so that they:
-    1. locate files purely based on relative location, regardless of packages.
-       e.g. you can import file without having __init__
-    2. do not cache modules globally; modifications of module states has no side effect
-    3. support other storage system through PathManager
-    4. imported dict are turned into omegaconf.DictConfig automatically
-    """
-    old_import = builtins.__import__
-
-    def find_relative_file(original_file, relative_import_path, level):
-        cur_file = os.path.dirname(original_file)
-        for _ in range(level - 1):
-            cur_file = os.path.dirname(cur_file)
-        cur_name = relative_import_path.lstrip(".")
-        for part in cur_name.split("."):
-            cur_file = os.path.join(cur_file, part)
-        # NOTE: directory import is not handled. Because then it's unclear
-        # if such import should produce python module or DictConfig. This can
-        # be discussed further if needed.
-        if not cur_file.endswith(".py"):
-            cur_file += ".py"
-        if not PathManager.isfile(cur_file):
-            raise ImportError(
-                f"Cannot import name {relative_import_path} from "
-                f"{original_file}: {cur_file} has to exist."
-            )
-        return cur_file
-
-    def new_import(name, globals=None, locals=None, fromlist=(), level=0):
-        if (
-            # Only deal with relative imports inside config files
-            level != 0
-            and globals is not None
-            and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME)
-        ):
-            cur_file = find_relative_file(globals["__file__"], name, level)
-            _validate_py_syntax(cur_file)
-            spec = importlib.machinery.ModuleSpec(
-                _random_package_name(cur_file), None, origin=cur_file
-            )
-            module = importlib.util.module_from_spec(spec)
-            module.__file__ = cur_file
-            with PathManager.open(cur_file) as f:
-                content = f.read()
-            exec(compile(content, cur_file, "exec"), module.__dict__)
-            for name in fromlist:  # turn imported dict into DictConfig automatically
-                val = _cast_to_config(module.__dict__[name])
-                module.__dict__[name] = val
-            return module
-        return old_import(name, globals, locals, fromlist=fromlist, level=level)
-
-    builtins.__import__ = new_import
-    yield new_import
-    builtins.__import__ = old_import
-
-
-class LazyConfig:
-    """
-    Provide methods to save, load, and overrides an omegaconf config object
-    which may contain definition of lazily-constructed objects.
-    """
-
-    @staticmethod
-    def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
-        """
-        Similar to :meth:`load()`, but load path relative to the caller's
-        source file.
-
-        This has the same functionality as a relative import, except that this method
-        accepts filename as a string, so more characters are allowed in the filename.
-        """
-        caller_frame = inspect.stack()[1]
-        caller_fname = caller_frame[0].f_code.co_filename
-        assert caller_fname != "<string>", "load_rel Unable to find caller"
-        caller_dir = os.path.dirname(caller_fname)
-        filename = os.path.join(caller_dir, filename)
-        return LazyConfig.load(filename, keys)
-
-    @staticmethod
-    def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
-        """
-        Load a config file.
-
-        Args:
-            filename: absolute path or relative path w.r.t. the current working directory
-            keys: keys to load and return. If not given, return all keys
-                (whose values are config objects) in a dict.
-        """
-        has_keys = keys is not None
-        filename = filename.replace("/./", "/")  # redundant
-        if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]:
-            raise ValueError(f"Config file {filename} has to be a python or yaml file.")
-        if filename.endswith(".py"):
-            _validate_py_syntax(filename)
-
-            with _patch_import():
-                # Record the filename
-                module_namespace = {
-                    "__file__": filename,
-                    "__package__": _random_package_name(filename),
-                }
-                with PathManager.open(filename) as f:
-                    content = f.read()
-                # Compile first with filename to:
-                # 1. make filename appears in stacktrace
-                # 2. make load_rel able to find its parent's (possibly remote) location
-                exec(compile(content, filename, "exec"), module_namespace)
-
-            ret = module_namespace
-        else:
-            with PathManager.open(filename) as f:
-                obj = yaml.unsafe_load(f)
-            ret = OmegaConf.create(obj, flags={"allow_objects": True})
-
-        if has_keys:
-            if isinstance(keys, str):
-                return _cast_to_config(ret[keys])
-            else:
-                return tuple(_cast_to_config(ret[a]) for a in keys)
-        else:
-            if filename.endswith(".py"):
-                # when not specified, only load those that are config objects
-                ret = DictConfig(
-                    {
-                        name: _cast_to_config(value)
-                        for name, value in ret.items()
-                        if isinstance(value, (DictConfig, ListConfig, dict))
-                        and not name.startswith("_")
-                    },
-                    flags={"allow_objects": True},
-                )
-            return ret
-
-    @staticmethod
-    def save(cfg, filename: str):
-        """
-        Save a config object to a yaml file.
-        Note that when the config dictionary contains complex objects (e.g. lambda),
-        it can't be saved to yaml. In that case we will print an error and
-        attempt to save to a pkl file instead.
-
-        Args:
-            cfg: an omegaconf config object
-            filename: yaml file name to save the config file
-        """
-        logger = logging.getLogger(__name__)
-        try:
-            cfg = deepcopy(cfg)
-        except Exception:
-            pass
-        else:
-            # if it's deep-copyable, then...
-            def _replace_type_by_name(x):
-                if "_target_" in x and callable(x._target_):
-                    try:
-                        x._target_ = _convert_target_to_string(x._target_)
-                    except AttributeError:
-                        pass
-
-            # not necessary, but makes yaml looks nicer
-            _visit_dict_config(cfg, _replace_type_by_name)
-
-        save_pkl = False
-        try:
-            dict = OmegaConf.to_container(cfg, resolve=False)
-            dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999)
-            with PathManager.open(filename, "w") as f:
-                f.write(dumped)
-
-            try:
-                _ = yaml.unsafe_load(dumped)  # test that it is loadable
-            except Exception:
-                logger.warning(
-                    "The config contains objects that cannot serialize to a valid yaml. "
-                    f"{filename} is human-readable but cannot be loaded."
-                )
-                save_pkl = True
-        except Exception:
-            logger.exception("Unable to serialize the config to yaml. Error:")
-            save_pkl = True
-
-        if save_pkl:
-            new_filename = filename + ".pkl"
-            try:
-                # retry by pickle
-                with PathManager.open(new_filename, "wb") as f:
-                    cloudpickle.dump(cfg, f)
-                logger.warning(f"Config is saved using cloudpickle at {new_filename}.")
-            except Exception:
-                pass
-
-    @staticmethod
-    def apply_overrides(cfg, overrides: List[str]):
-        """
-        In-place override contents of cfg.
-
-        Args:
-            cfg: an omegaconf config object
-            overrides: list of strings in the format of "a=b" to override configs.
-                See https://hydra.cc/docs/next/advanced/override_grammar/basic/
-                for syntax.
-
-        Returns:
-            the cfg object
-        """
-
-        def safe_update(cfg, key, value):
-            parts = key.split(".")
-            for idx in range(1, len(parts)):
-                prefix = ".".join(parts[:idx])
-                v = OmegaConf.select(cfg, prefix, default=None)
-                if v is None:
-                    break
-                if not OmegaConf.is_config(v):
-                    raise KeyError(
-                        f"Trying to update key {key}, but {prefix} "
-                        f"is not a config, but has type {type(v)}."
-                    )
-            OmegaConf.update(cfg, key, value, merge=True)
-
-        from hydra.core.override_parser.overrides_parser import OverridesParser
-
-        parser = OverridesParser.create()
-        overrides = parser.parse_overrides(overrides)
-        for o in overrides:
-            key = o.key_or_group
-            value = o.value()
-            if o.is_delete():
-                # TODO support this
-                raise NotImplementedError("deletion is not yet a supported override")
-            safe_update(cfg, key, value)
-        return cfg
-
-    @staticmethod
-    def to_py(cfg, prefix: str = "cfg."):
-        """
-        Try to convert a config object into Python-like psuedo code.
-
-        Note that perfect conversion is not always possible. So the returned
-        results are mainly meant to be human-readable, and not meant to be executed.
-
-        Args:
-            cfg: an omegaconf config object
-            prefix: root name for the resulting code (default: "cfg.")
-
-
-        Returns:
-            str of formatted Python code
-        """
-        import black
-
-        cfg = OmegaConf.to_container(cfg, resolve=True)
-
-        def _to_str(obj, prefix=None, inside_call=False):
-            if prefix is None:
-                prefix = []
-            if isinstance(obj, abc.Mapping) and "_target_" in obj:
-                # Dict representing a function call
-                target = _convert_target_to_string(obj.pop("_target_"))
-                args = []
-                for k, v in sorted(obj.items()):
-                    args.append(f"{k}={_to_str(v, inside_call=True)}")
-                args = ", ".join(args)
-                call = f"{target}({args})"
-                return "".join(prefix) + call
-            elif isinstance(obj, abc.Mapping) and not inside_call:
-                # Dict that is not inside a call is a list of top-level config objects that we
-                # render as one object per line with dot separated prefixes
-                key_list = []
-                for k, v in sorted(obj.items()):
-                    if isinstance(v, abc.Mapping) and "_target_" not in v:
-                        key_list.append(_to_str(v, prefix=prefix + [k + "."]))
-                    else:
-                        key = "".join(prefix) + k
-                        key_list.append(f"{key}={_to_str(v)}")
-                return "\n".join(key_list)
-            elif isinstance(obj, abc.Mapping):
-                # Dict that is inside a call is rendered as a regular dict
-                return (
-                    "{"
-                    + ",".join(
-                        f"{repr(k)}: {_to_str(v, inside_call=inside_call)}"
-                        for k, v in sorted(obj.items())
-                    )
-                    + "}"
-                )
-            elif isinstance(obj, list):
-                return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]"
-            else:
-                return repr(obj)
-
-        py_str = _to_str(cfg, prefix=[prefix])
-        try:
-            return black.format_str(py_str, mode=black.Mode())
-        except black.InvalidInput:
-            return py_str
diff --git a/spaces/abdvl/datahub_qa_bot/docs/actions/quickstart.md b/spaces/abdvl/datahub_qa_bot/docs/actions/quickstart.md
deleted file mode 100644
index 6b460d4b2b89072ca718e2d1f91e67881bc6b189..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/actions/quickstart.md
+++ /dev/null
@@ -1,169 +0,0 @@
-# DataHub Actions Quickstart
-
-
-## Prerequisites
-
-The DataHub Actions CLI commands are an extension of the base `datahub` CLI commands. We recommend
-first installing the `datahub` CLI:
-
-```shell
-python3 -m pip install --upgrade pip wheel setuptools
-python3 -m pip install --upgrade acryl-datahub
-datahub --version
-```
-
-> Note that the Actions Framework requires a version of `acryl-datahub` >= v0.8.34
-
-
-## Installation
-
-To install DataHub Actions, you need to install the `acryl-datahub-actions` package from PyPi
-
-```shell
-python3 -m pip install --upgrade pip wheel setuptools
-python3 -m pip install --upgrade acryl-datahub-actions
-
-# Verify the installation by checking the version.
-datahub actions version
-```
-
-### Hello World
-
-DataHub ships with a "Hello World" Action which logs all events it receives to the console.
-To run this action, simply create a new Action configuration file:
-
-```yaml
-# hello_world.yaml
-name: "hello_world"
-source:
-  type: "kafka"
-  config:
-    connection:
-      bootstrap: ${KAFKA_BOOTSTRAP_SERVER:-localhost:9092}
-      schema_registry_url: ${SCHEMA_REGISTRY_URL:-http://localhost:8081}
-action:
-  type: "hello_world"
-```
-
-and then run it using the `datahub actions` command:
-
-```shell
-datahub actions -c hello_world.yaml
-```
-
-You should the see the following output if the Action has been started successfully:
-
-```shell
-Action Pipeline with name 'hello_world' is now running.
-```
-
-Now, navigate to the instance of DataHub that you've connected to and perform an Action such as
-
-- Adding / removing a Tag
-- Adding / removing a Glossary Term
-- Adding / removing a Domain
-
-If all is well, you should see some events being logged to the console
-
-```shell
-Hello world! Received event:
-{
-    "event_type": "EntityChangeEvent_v1",
-    "event": {
-        "entityType": "dataset",
-        "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)",
-        "category": "TAG",
-        "operation": "ADD",
-        "modifier": "urn:li:tag:pii",
-        "parameters": {},
-        "auditStamp": {
-            "time": 1651082697703,
-            "actor": "urn:li:corpuser:datahub",
-            "impersonator": null
-        },
-        "version": 0,
-        "source": null
-    },
-    "meta": {
-        "kafka": {
-            "topic": "PlatformEvent_v1",
-            "offset": 1262,
-            "partition": 0
-        }
-    }
-}
-```
-*An example of an event emitted when a 'pii' tag has been added to a Dataset.* 
-
-Woohoo! You've successfully started using the Actions framework. Now, let's see how we can get fancy.
-
-
-#### Filtering events
-
-If we know which Event types we'd like to consume, we can optionally add a `filter` configuration, which
-will prevent events that do not match the filter from being forwarded to the action.
-
-```yaml
-# hello_world.yaml
-name: "hello_world"
-source:
-  type: "kafka"
-  config:
-    connection:
-      bootstrap: ${KAFKA_BOOTSTRAP_SERVER:-localhost:9092}
-      schema_registry_url: ${SCHEMA_REGISTRY_URL:-http://localhost:8081}
-filter:
-  event_type: "EntityChangeEvent_v1"
-action:
-  type: "hello_world"
-```
-*Filtering for events of type EntityChangeEvent_v1 only*
-
-
-#### Advanced Filtering
-
-Beyond simply filtering by event type, we can also filter events by matching against the values of their fields. To do so,
-use the `event` block. Each field provided will be compared against the real event's value. An event that matches
-**all** of the fields will be forwarded to the action.
-
-```yaml
-# hello_world.yaml
-name: "hello_world"
-source:
-  type: "kafka"
-  config:
-    connection:
-      bootstrap: ${KAFKA_BOOTSTRAP_SERVER:-localhost:9092}
-      schema_registry_url: ${SCHEMA_REGISTRY_URL:-http://localhost:8081}
-filter:
-  event_type: "EntityChangeEvent_v1"
-  event: 
-    category: "TAG"
-    operation: "ADD"
-    modifier: "urn:li:tag:pii"
-action:
-  type: "hello_world"
-```
-*This filter only matches events representing "PII" tag additions to an entity.*
-
-And more, we can achieve "OR" semantics on a particular field by providing an array of values.
-
-```yaml
-# hello_world.yaml
-name: "hello_world"
-source:
-  type: "kafka"
-  config:
-    connection:
-      bootstrap: ${KAFKA_BOOTSTRAP_SERVER:-localhost:9092}
-      schema_registry_url: ${SCHEMA_REGISTRY_URL:-http://localhost:8081}
-filter:
-  event_type: "EntityChangeEvent_v1"
-  event: 
-    category: "TAG"
-    operation: [ "ADD", "REMOVE" ]
-    modifier: "urn:li:tag:pii"
-action:
-  type: "hello_world"
-```
-*This filter only matches events representing "PII" tag additions to OR removals from an entity. How fancy!*
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/ccnet_r50-d8.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/ccnet_r50-d8.py
deleted file mode 100644
index 794148f576b9e215c3c6963e73dffe98204b7717..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/ccnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='CCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        recurrence=2,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/apis/inference.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/apis/inference.py
deleted file mode 100644
index 90bc1c0c68525734bd6793f07c15fe97d3c8342c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/apis/inference.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import matplotlib.pyplot as plt
-import annotator.uniformer.mmcv as mmcv
-import torch
-from annotator.uniformer.mmcv.parallel import collate, scatter
-from annotator.uniformer.mmcv.runner import load_checkpoint
-
-from annotator.uniformer.mmseg.datasets.pipelines import Compose
-from annotator.uniformer.mmseg.models import build_segmentor
-
-
-def init_segmentor(config, checkpoint=None, device='cuda:0'):
-    """Initialize a segmentor from config file.
-
-    Args:
-        config (str or :obj:`mmcv.Config`): Config file path or the config
-            object.
-        checkpoint (str, optional): Checkpoint path. If left as None, the model
-            will not load any weights.
-        device (str, optional) CPU/CUDA device option. Default 'cuda:0'.
-            Use 'cpu' for loading model on CPU.
-    Returns:
-        nn.Module: The constructed segmentor.
-    """
-    if isinstance(config, str):
-        config = mmcv.Config.fromfile(config)
-    elif not isinstance(config, mmcv.Config):
-        raise TypeError('config must be a filename or Config object, '
-                        'but got {}'.format(type(config)))
-    config.model.pretrained = None
-    config.model.train_cfg = None
-    model = build_segmentor(config.model, test_cfg=config.get('test_cfg'))
-    if checkpoint is not None:
-        checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
-        model.CLASSES = checkpoint['meta']['CLASSES']
-        model.PALETTE = checkpoint['meta']['PALETTE']
-    model.cfg = config  # save the config in the model for convenience
-    model.to(device)
-    model.eval()
-    return model
-
-
-class LoadImage:
-    """A simple pipeline to load image."""
-
-    def __call__(self, results):
-        """Call function to load images into results.
-
-        Args:
-            results (dict): A result dict contains the file name
-                of the image to be read.
-
-        Returns:
-            dict: ``results`` will be returned containing loaded image.
-        """
-
-        if isinstance(results['img'], str):
-            results['filename'] = results['img']
-            results['ori_filename'] = results['img']
-        else:
-            results['filename'] = None
-            results['ori_filename'] = None
-        img = mmcv.imread(results['img'])
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['ori_shape'] = img.shape
-        return results
-
-
-def inference_segmentor(model, img):
-    """Inference image(s) with the segmentor.
-
-    Args:
-        model (nn.Module): The loaded segmentor.
-        imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
-            images.
-
-    Returns:
-        (list[Tensor]): The segmentation result.
-    """
-    cfg = model.cfg
-    device = next(model.parameters()).device  # model device
-    # build the data pipeline
-    test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
-    test_pipeline = Compose(test_pipeline)
-    # prepare data
-    data = dict(img=img)
-    data = test_pipeline(data)
-    data = collate([data], samples_per_gpu=1)
-    if next(model.parameters()).is_cuda:
-        # scatter to specified GPU
-        data = scatter(data, [device])[0]
-    else:
-        data['img_metas'] = [i.data[0] for i in data['img_metas']]
-
-    # forward the model
-    with torch.no_grad():
-        result = model(return_loss=False, rescale=True, **data)
-    return result
-
-
-def show_result_pyplot(model,
-                       img,
-                       result,
-                       palette=None,
-                       fig_size=(15, 10),
-                       opacity=0.5,
-                       title='',
-                       block=True):
-    """Visualize the segmentation results on the image.
-
-    Args:
-        model (nn.Module): The loaded segmentor.
-        img (str or np.ndarray): Image filename or loaded image.
-        result (list): The segmentation result.
-        palette (list[list[int]]] | None): The palette of segmentation
-            map. If None is given, random palette will be generated.
-            Default: None
-        fig_size (tuple): Figure size of the pyplot figure.
-        opacity(float): Opacity of painted segmentation map.
-            Default 0.5.
-            Must be in (0, 1] range.
-        title (str): The title of pyplot figure.
-            Default is ''.
-        block (bool): Whether to block the pyplot figure.
-            Default is True.
-    """
-    if hasattr(model, 'module'):
-        model = model.module
-    img = model.show_result(
-        img, result, palette=palette, show=False, opacity=opacity)
-    # plt.figure(figsize=fig_size)
-    # plt.imshow(mmcv.bgr2rgb(img))
-    # plt.title(title)
-    # plt.tight_layout()
-    # plt.show(block=block)
-    return mmcv.bgr2rgb(img)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/ops/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/ops/__init__.py
deleted file mode 100644
index d3c06db8c1a773a546dd3e6bc84226e7b4a60020..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/ops/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-from .encoding import Encoding
-from .wrappers import Upsample, resize
-
-__all__ = ['Upsample', 'resize', 'Encoding']
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/font/freetype.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/font/freetype.py
deleted file mode 100644
index e6d3d7cf856e91644a559f5fad292da839483709..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/font/freetype.py
+++ /dev/null
@@ -1,350 +0,0 @@
-import ctypes
-import warnings
-from collections import namedtuple
-
-from pyglet.util import asbytes, asstr
-from pyglet.font import base
-from pyglet import image
-from pyglet.font.fontconfig import get_fontconfig
-from pyglet.font.freetype_lib import *
-
-
-class FreeTypeGlyphRenderer(base.GlyphRenderer):
-    def __init__(self, font):
-        super().__init__(font)
-        self.font = font
-
-        self._glyph_slot = None
-        self._bitmap = None
-
-        self._width = None
-        self._height = None
-        self._mode = None
-        self._pitch = None
-
-        self._baseline = None
-        self._lsb = None
-        self._advance_x = None
-
-        self._data = None
-
-    def _get_glyph(self, character):
-        assert self.font
-        assert len(character) == 1
-
-        self._glyph_slot = self.font.get_glyph_slot(character)
-        self._bitmap = self._glyph_slot.bitmap
-
-    def _get_glyph_metrics(self):
-        self._width = self._glyph_slot.bitmap.width
-        self._height = self._glyph_slot.bitmap.rows
-        self._mode = self._glyph_slot.bitmap.pixel_mode
-        self._pitch = self._glyph_slot.bitmap.pitch
-
-        self._baseline = self._height - self._glyph_slot.bitmap_top
-        self._lsb = self._glyph_slot.bitmap_left
-        self._advance_x = int(f26p6_to_float(self._glyph_slot.advance.x))
-
-    def _get_bitmap_data(self):
-        if self._mode == FT_PIXEL_MODE_MONO:
-            # BCF fonts always render to 1 bit mono, regardless of render
-            # flags. (freetype 2.3.5)
-            self._convert_mono_to_gray_bitmap()
-        elif self._mode == FT_PIXEL_MODE_GRAY:
-            # Usual case
-            assert self._glyph_slot.bitmap.num_grays == 256
-            self._data = self._glyph_slot.bitmap.buffer
-        else:
-            raise base.FontException('Unsupported render mode for this glyph')
-
-    def _convert_mono_to_gray_bitmap(self):
-        bitmap_data = cast(self._bitmap.buffer,
-                           POINTER(c_ubyte * (self._pitch * self._height))).contents
-        data = (c_ubyte * (self._pitch * 8 * self._height))()
-        data_i = 0
-        for byte in bitmap_data:
-            # Data is MSB; left-most pixel in a byte has value 128.
-            data[data_i + 0] = (byte & 0x80) and 255 or 0
-            data[data_i + 1] = (byte & 0x40) and 255 or 0
-            data[data_i + 2] = (byte & 0x20) and 255 or 0
-            data[data_i + 3] = (byte & 0x10) and 255 or 0
-            data[data_i + 4] = (byte & 0x08) and 255 or 0
-            data[data_i + 5] = (byte & 0x04) and 255 or 0
-            data[data_i + 6] = (byte & 0x02) and 255 or 0
-            data[data_i + 7] = (byte & 0x01) and 255 or 0
-            data_i += 8
-        self._data = data
-        self._pitch <<= 3
-
-    def _create_glyph(self):
-        # In FT positive pitch means `down` flow, in Pyglet ImageData
-        # negative values indicate a top-to-bottom arrangement. So pitch must be inverted.
-        # Using negative pitch causes conversions, so much faster to just swap tex_coords
-        img = image.ImageData(self._width,
-                              self._height,
-                              'A',
-                              self._data,
-                              abs(self._pitch))
-
-        # HACK: Get text working in GLES until image data can be converted properly
-        #       GLES don't support coversion during pixel transfer so we have to
-        #       force specify the glyph format to be GL_ALPHA. This format is not
-        #       supported in 3.3+ core, but are present in ES because of pixel transfer
-        #       limitations.
-        if pyglet.gl.current_context.get_info().get_opengl_api() == "gles":
-            GL_ALPHA = 0x1906
-            glyph = self.font.create_glyph(img, fmt=GL_ALPHA)
-        else:
-            glyph = self.font.create_glyph(img)
-
-        glyph.set_bearings(self._baseline, self._lsb, self._advance_x)
-        if self._pitch > 0:
-            t = list(glyph.tex_coords)
-            glyph.tex_coords = t[9:12] + t[6:9] + t[3:6] + t[:3]
-
-        return glyph
-
-    def render(self, text):
-        self._get_glyph(text[0])
-        self._get_glyph_metrics()
-        self._get_bitmap_data()
-        return self._create_glyph()
-
-
-FreeTypeFontMetrics = namedtuple('FreeTypeFontMetrics', ['ascent', 'descent'])
-
-
-class MemoryFaceStore:
-    def __init__(self):
-        self._dict = {}
-
-    def add(self, face):
-        self._dict[face.name.lower(), face.bold, face.italic] = face
-
-    def contains(self, name):
-        lname = name and name.lower() or ''
-        return len([name for name, _, _ in self._dict.keys() if name == lname]) > 0
-
-    def get(self, name, bold, italic):
-        lname = name and name.lower() or ''
-        return self._dict.get((lname, bold, italic), None)
-
-
-class FreeTypeFont(base.Font):
-    glyph_renderer_class = FreeTypeGlyphRenderer
-
-    # Map font (name, bold, italic) to FreeTypeMemoryFace
-    _memory_faces = MemoryFaceStore()
-
-    def __init__(self, name, size, bold=False, italic=False, stretch=False, dpi=None):
-        # assert type(bold) is bool, "Only a boolean value is supported for bold in the current font renderer."
-        # assert type(italic) is bool, "Only a boolean value is supported for bold in the current font renderer."
-
-        if stretch:
-            warnings.warn("The current font render does not support stretching.")
-
-        super().__init__()
-        self._name = name
-        self.size = size
-        self.bold = bold
-        self.italic = italic
-        self.dpi = dpi or 96
-
-        self._load_font_face()
-        self.metrics = self.face.get_font_metrics(self.size, self.dpi)
-
-    @property
-    def name(self):
-        return self.face.family_name
-
-    @property
-    def ascent(self):
-        return self.metrics.ascent
-
-    @property
-    def descent(self):
-        return self.metrics.descent
-
-    def get_glyph_slot(self, character):
-        glyph_index = self.face.get_character_index(character)
-        self.face.set_char_size(self.size, self.dpi)
-        return self.face.get_glyph_slot(glyph_index)
-
-    def _load_font_face(self):
-        self.face = self._memory_faces.get(self._name, self.bold, self.italic)
-        if self.face is None:
-            self._load_font_face_from_system()
-
-    def _load_font_face_from_system(self):
-        match = get_fontconfig().find_font(self._name, self.size, self.bold, self.italic)
-        if not match:
-            raise base.FontException(f"Could not match font '{self._name}'")
-        self.filename = match.file
-        self.face = FreeTypeFace.from_fontconfig(match)
-
-    @classmethod
-    def have_font(cls, name):
-        if cls._memory_faces.contains(name):
-            return True
-        else:
-            return get_fontconfig().have_font(name)
-
-    @classmethod
-    def add_font_data(cls, data):
-        face = FreeTypeMemoryFace(data)
-        cls._memory_faces.add(face)
-
-
-class FreeTypeFace:
-    """FreeType typographic face object.
-
-    Keeps the reference count to the face at +1 as long as this object exists. If other objects
-    want to keep a face without a reference to this object, they should increase the reference
-    counter themselves and decrease it again when done.
-    """
-    def __init__(self, ft_face):
-        assert ft_face is not None
-        self.ft_face = ft_face
-        self._get_best_name()
-
-    @classmethod
-    def from_file(cls, file_name):
-        ft_library = ft_get_library()
-        ft_face = FT_Face()
-        FT_New_Face(ft_library,
-                    asbytes(file_name),
-                    0,
-                    byref(ft_face))
-        return cls(ft_face)
-
-    @classmethod
-    def from_fontconfig(cls, match):
-        if match.face is not None:
-            FT_Reference_Face(match.face)
-            return cls(match.face)
-        else:
-            if not match.file:
-                raise base.FontException(f'No filename for "{match.name}"')
-            return cls.from_file(match.file)
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def family_name(self):
-        return asstr(self.ft_face.contents.family_name)
-
-    @property
-    def style_flags(self):
-        return self.ft_face.contents.style_flags
-
-    @property
-    def bold(self):
-        return self.style_flags & FT_STYLE_FLAG_BOLD != 0
-
-    @property
-    def italic(self):
-        return self.style_flags & FT_STYLE_FLAG_ITALIC != 0
-
-    @property
-    def face_flags(self):
-        return self.ft_face.contents.face_flags
-
-    def __del__(self):
-        if self.ft_face is not None:
-            FT_Done_Face(self.ft_face)
-            self.ft_face = None
-
-    def set_char_size(self, size, dpi):
-        face_size = float_to_f26p6(size)
-        try:
-            FT_Set_Char_Size(self.ft_face,
-                             0,
-                             face_size,
-                             dpi,
-                             dpi)
-            return True
-        except FreeTypeError as e:
-            # Error 0x17 indicates invalid pixel size, so font size cannot be changed
-            # TODO Warn the user?
-            if e.errcode == 0x17:
-                return False
-            else:
-                raise
-
-    def get_character_index(self, character):
-        return get_fontconfig().char_index(self.ft_face, character)
-
-    def get_glyph_slot(self, glyph_index):
-        FT_Load_Glyph(self.ft_face, glyph_index, FT_LOAD_RENDER)
-        return self.ft_face.contents.glyph.contents
-
-    def get_font_metrics(self, size, dpi):
-        if self.set_char_size(size, dpi):
-            metrics = self.ft_face.contents.size.contents.metrics
-            if metrics.ascender == 0 and metrics.descender == 0:
-                return self._get_font_metrics_workaround()
-            else:
-                return FreeTypeFontMetrics(ascent=int(f26p6_to_float(metrics.ascender)),
-                                           descent=int(f26p6_to_float(metrics.descender)))
-        else:
-            return self._get_font_metrics_workaround()
-
-    def _get_font_metrics_workaround(self):
-        # Workaround broken fonts with no metrics.  Has been observed with
-        # courR12-ISO8859-1.pcf.gz: "Courier" "Regular"
-        #
-        # None of the metrics fields are filled in, so render a glyph and
-        # grab its height as the ascent, and make up an arbitrary
-        # descent.
-        i = self.get_character_index('X')
-        self.get_glyph_slot(i)
-        ascent=self.ft_face.contents.available_sizes.contents.height
-        return FreeTypeFontMetrics(ascent=ascent,
-                                   descent=-ascent // 4)  # arbitrary.
-
-    def _get_best_name(self):
-        self._name = asstr(self.ft_face.contents.family_name)
-        self._get_font_family_from_ttf
-
-    def _get_font_family_from_ttf(self):
-        # Replace Freetype's generic family name with TTF/OpenType specific
-        # name if we can find one; there are some instances where Freetype
-        # gets it wrong.
-
-        return  # FIXME: This is broken
-
-        if self.face_flags & FT_FACE_FLAG_SFNT:
-            name = FT_SfntName()
-            for i in range(FT_Get_Sfnt_Name_Count(self.ft_face)):
-                try:
-                    FT_Get_Sfnt_Name(self.ft_face, i, name)
-                    if not (name.platform_id == TT_PLATFORM_MICROSOFT and
-                            name.encoding_id == TT_MS_ID_UNICODE_CS):
-                        continue
-                    # name.string is not 0 terminated! use name.string_len
-                    self._name = name.string.decode('utf-16be', 'ignore')
-                except:
-                    continue
-
-
-class FreeTypeMemoryFace(FreeTypeFace):
-    def __init__(self, data):
-        self._copy_font_data(data)
-        super().__init__(self._create_font_face())
-
-    def _copy_font_data(self, data):
-        self.font_data = (FT_Byte * len(data))()
-        ctypes.memmove(self.font_data, data, len(data))
-
-    def _create_font_face(self):
-        ft_library = ft_get_library()
-        ft_face = FT_Face()
-        FT_New_Memory_Face(ft_library,
-                           self.font_data,
-                           len(self.font_data),
-                           0,
-                           byref(ft_face))
-        return ft_face
-
diff --git a/spaces/ahiruguagua/aiemo/README.md b/spaces/ahiruguagua/aiemo/README.md
deleted file mode 100644
index b107422da44f47e30e58abc2356b0d97f3e286fd..0000000000000000000000000000000000000000
--- a/spaces/ahiruguagua/aiemo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AIあひる
-emoji: 🌖
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-duplicated_from: shigel/aiemo
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/akhaliq/Music_Source_Separation/bytesep/models/lightning_modules.py b/spaces/akhaliq/Music_Source_Separation/bytesep/models/lightning_modules.py
deleted file mode 100644
index 698f04116ed94521eb49e1995d967534dba62cd9..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Music_Source_Separation/bytesep/models/lightning_modules.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from typing import Any, Callable, Dict
-
-import pytorch_lightning as pl
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.optim.lr_scheduler import LambdaLR
-
-
-class LitSourceSeparation(pl.LightningModule):
-    def __init__(
-        self,
-        batch_data_preprocessor,
-        model: nn.Module,
-        loss_function: Callable,
-        optimizer_type: str,
-        learning_rate: float,
-        lr_lambda: Callable,
-    ):
-        r"""Pytorch Lightning wrapper of PyTorch model, including forward,
-        optimization of model, etc.
-
-        Args:
-            batch_data_preprocessor: object, used for preparing inputs and
-                targets for training. E.g., BasicBatchDataPreprocessor is used
-                for preparing data in dictionary into tensor.
-            model: nn.Module
-            loss_function: function
-            learning_rate: float
-            lr_lambda: function
-        """
-        super().__init__()
-
-        self.batch_data_preprocessor = batch_data_preprocessor
-        self.model = model
-        self.optimizer_type = optimizer_type
-        self.loss_function = loss_function
-        self.learning_rate = learning_rate
-        self.lr_lambda = lr_lambda
-
-    def training_step(self, batch_data_dict: Dict, batch_idx: int) -> torch.float:
-        r"""Forward a mini-batch data to model, calculate loss function, and
-        train for one step. A mini-batch data is evenly distributed to multiple
-        devices (if there are) for parallel training.
-
-        Args:
-            batch_data_dict: e.g. {
-                'vocals': (batch_size, channels_num, segment_samples),
-                'accompaniment': (batch_size, channels_num, segment_samples),
-                'mixture': (batch_size, channels_num, segment_samples)
-            }
-            batch_idx: int
-
-        Returns:
-            loss: float, loss function of this mini-batch
-        """
-        input_dict, target_dict = self.batch_data_preprocessor(batch_data_dict)
-        # input_dict: {
-        #     'waveform': (batch_size, channels_num, segment_samples),
-        #     (if_exist) 'condition': (batch_size, channels_num),
-        # }
-        # target_dict: {
-        #     'waveform': (batch_size, target_sources_num * channels_num, segment_samples),
-        # }
-
-        # Forward.
-        self.model.train()
-
-        output_dict = self.model(input_dict)
-        # output_dict: {
-        #     'waveform': (batch_size, target_sources_num * channels_num, segment_samples),
-        # }
-
-        outputs = output_dict['waveform']
-        # outputs:, e.g, (batch_size, target_sources_num * channels_num, segment_samples)
-
-        # Calculate loss.
-        loss = self.loss_function(
-            output=outputs,
-            target=target_dict['waveform'],
-            mixture=input_dict['waveform'],
-        )
-
-        return loss
-
-    def configure_optimizers(self) -> Any:
-        r"""Configure optimizer."""
-
-        if self.optimizer_type == "Adam":
-            optimizer = optim.Adam(
-                self.model.parameters(),
-                lr=self.learning_rate,
-                betas=(0.9, 0.999),
-                eps=1e-08,
-                weight_decay=0.0,
-                amsgrad=True,
-            )
-
-        elif self.optimizer_type == "AdamW":
-            optimizer = optim.AdamW(
-                self.model.parameters(),
-                lr=self.learning_rate,
-                betas=(0.9, 0.999),
-                eps=1e-08,
-                weight_decay=0.0,
-                amsgrad=True,
-            )
-
-        else:
-            raise NotImplementedError
-
-        scheduler = {
-            'scheduler': LambdaLR(optimizer, self.lr_lambda),
-            'interval': 'step',
-            'frequency': 1,
-        }
-
-        return [optimizer], [scheduler]
-
-
-def get_model_class(model_type):
-    r"""Get model.
-
-    Args:
-        model_type: str, e.g., 'ResUNet143_DecouplePlusInplaceABN'
-
-    Returns:
-        nn.Module
-    """
-    if model_type == 'ResUNet143_DecouplePlusInplaceABN_ISMIR2021':
-        from bytesep.models.resunet_ismir2021 import (
-            ResUNet143_DecouplePlusInplaceABN_ISMIR2021,
-        )
-
-        return ResUNet143_DecouplePlusInplaceABN_ISMIR2021
-
-    elif model_type == 'UNet':
-        from bytesep.models.unet import UNet
-
-        return UNet
-
-    elif model_type == 'UNetSubbandTime':
-        from bytesep.models.unet_subbandtime import UNetSubbandTime
-
-        return UNetSubbandTime
-
-    elif model_type == 'ResUNet143_Subbandtime':
-        from bytesep.models.resunet_subbandtime import ResUNet143_Subbandtime
-
-        return ResUNet143_Subbandtime
-
-    elif model_type == 'ResUNet143_DecouplePlus':
-        from bytesep.models.resunet import ResUNet143_DecouplePlus
-
-        return ResUNet143_DecouplePlus
-
-    elif model_type == 'ConditionalUNet':
-        from bytesep.models.conditional_unet import ConditionalUNet
-
-        return ConditionalUNet
-
-    elif model_type == 'LevelRNN':
-        from bytesep.models.levelrnn import LevelRNN
-
-        return LevelRNN
-
-    elif model_type == 'WavUNet':
-        from bytesep.models.wavunet import WavUNet
-
-        return WavUNet
-
-    elif model_type == 'WavUNetLevelRNN':
-        from bytesep.models.wavunet_levelrnn import WavUNetLevelRNN
-
-        return WavUNetLevelRNN
-
-    elif model_type == 'TTnet':
-        from bytesep.models.ttnet import TTnet
-
-        return TTnet
-
-    elif model_type == 'TTnetNoTransformer':
-        from bytesep.models.ttnet_no_transformer import TTnetNoTransformer
-
-        return TTnetNoTransformer
-
-    else:
-        raise NotImplementedError
diff --git a/spaces/akhaliq/Music_Source_Separation/scripts/2_create_indexes/voicebank-demand/create_indexes.sh b/spaces/akhaliq/Music_Source_Separation/scripts/2_create_indexes/voicebank-demand/create_indexes.sh
deleted file mode 100644
index f29c64f0a8b1d8a251061d336a15c953a468eb7f..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Music_Source_Separation/scripts/2_create_indexes/voicebank-demand/create_indexes.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-WORKSPACE=${1:-"./workspaces/bytesep"}  # Default workspace directory
-
-echo "WORKSPACE=${WORKSPACE}"
-
-# Users can modify the following config file.
-INDEXES_CONFIG_YAML="scripts/2_create_indexes/voicebank-demand/configs/voicebank-demand,sr=44100,chn=1.yaml"
-
-# Create indexes for training.
-python3 bytesep/dataset_creation/create_indexes/create_indexes.py \
-    --workspace=$WORKSPACE \
-    --config_yaml=$INDEXES_CONFIG_YAML
diff --git a/spaces/akhaliq/deeplab2/model/encoder/mobilenet_test.py b/spaces/akhaliq/deeplab2/model/encoder/mobilenet_test.py
deleted file mode 100644
index fced57f8d09f808a4fb2bc16e9c56e7ceade1846..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/encoder/mobilenet_test.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-"""Tests for mobilenet."""
-
-from absl.testing import parameterized
-
-import tensorflow as tf
-
-from deeplab2.model import test_utils
-from deeplab2.model.encoder import mobilenet
-
-
-class MobilenetTest(tf.test.TestCase, parameterized.TestCase):
-
-  @parameterized.parameters('MobileNetV3Small', 'MobileNetV3Large')
-  def test_mobilenetv3_construct_graph(self, model_name):
-    tf.keras.backend.set_image_data_format('channels_last')
-    input_size = 128
-
-    mobilenet_models = {
-        'MobileNetV3Small': mobilenet.MobileNetV3Small,
-        'MobileNetV3Large': mobilenet.MobileNetV3Large,
-    }
-    mobilenet_channels = {
-        # The number of filters of layers having outputs been collected
-        # for filter_size_scale = 1.0
-        'MobileNetV3Small': [16, 24, 48, 576],
-        'MobileNetV3Large': [24, 40, 112, 960],
-    }
-    network = mobilenet_models[str(model_name)](width_multiplier=1.0)
-
-    inputs = tf.ones([1, input_size, input_size, 3])
-    endpoints = network(inputs)
-
-    for idx, num_filter in enumerate(mobilenet_channels[model_name]):
-      self.assertAllEqual(
-          [1, input_size / 2 ** (idx+2), input_size / 2 ** (idx+2), num_filter],
-          endpoints['res'+str(idx+2)].shape.as_list())
-
-  @parameterized.product(
-      model_name=['MobileNetV3Small', 'MobileNetV3Large'],
-      output_stride=[4, 8, 16, 32])
-  def test_mobilenetv3_atrous_endpoint_shape(self, model_name, output_stride):
-    tf.keras.backend.set_image_data_format('channels_last')
-    input_size = 321
-    batch_size = 2
-
-    mobilenet_models = {
-        'MobileNetV3Small': mobilenet.MobileNetV3Small,
-        'MobileNetV3Large': mobilenet.MobileNetV3Large,
-    }
-    stride_spatial_shapes_map = {
-        4: [81, 81, 81, 81],
-        8: [81, 41, 41, 41],
-        16: [81, 41, 21, 21],
-        32: [81, 41, 21, 11],
-    }
-    mobilenet_channels = {
-        # The number of filters of layers having outputs been collected
-        # for filter_size_scale = 1.0
-        'MobileNetV3Small': [16, 24, 48, 576],
-        'MobileNetV3Large': [24, 40, 112, 960],
-    }
-    network = mobilenet_models[str(model_name)](
-        width_multiplier=1.0,
-        output_stride=output_stride)
-    spatial_shapes = stride_spatial_shapes_map[output_stride]
-
-    inputs = tf.ones([batch_size, input_size, input_size, 3])
-    endpoints = network(inputs)
-
-    for idx, num_filters in enumerate(mobilenet_channels[model_name]):
-      expected_shape = [
-          batch_size, spatial_shapes[idx], spatial_shapes[idx], num_filters
-      ]
-      self.assertAllEqual(
-          expected_shape,
-          endpoints['res'+str(idx+2)].shape.as_list())
-
-  @parameterized.parameters('MobileNetV3Small', 'MobileNetV3Large')
-  def test_mobilenet_reload_weights(self, model_name):
-    tf.keras.backend.set_image_data_format('channels_last')
-    mobilenet_models = {
-        'MobileNetV3Small': mobilenet.MobileNetV3Small,
-        'MobileNetV3Large': mobilenet.MobileNetV3Large,
-    }
-
-    tf.random.set_seed(0)
-    pixel_inputs = test_utils.create_test_input(1, 320, 320, 3)
-
-    network1 = mobilenet_models[model_name](
-        width_multiplier=1.0,
-        output_stride=32,
-        name='m1')
-    network1(pixel_inputs, False)
-    outputs1 = network1(pixel_inputs, False)
-    pixel_outputs = outputs1['res5']
-
-    # Feature extraction at the normal network rate.
-    network2 = mobilenet_models[model_name](
-        width_multiplier=1.0,
-        output_stride=32,
-        name='m2')
-    network2(pixel_inputs, False)
-    # Make the two networks use the same weights.
-    network2.set_weights(network1.get_weights())
-    outputs2 = network2(pixel_inputs, False)
-    expected = outputs2['res5']
-
-    self.assertAllClose(network1.get_weights(), network2.get_weights(),
-                        atol=1e-4, rtol=1e-4)
-    self.assertAllClose(pixel_outputs, expected, atol=1e-4, rtol=1e-4)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/spaces/akhaliq/deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op.cc b/spaces/akhaliq/deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op.cc
deleted file mode 100644
index a5d1dd333627a8b6206bdfd6fa2ec902086df87a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/tensorflow_ops/kernels/merge_semantic_and_instance_maps_op.cc
+++ /dev/null
@@ -1,86 +0,0 @@
-// Copyright 2021 The Deeplab2 Authors.
-//
-// 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.
-
-#include /*third_party*/"tensorflow/core/framework/op.h"
-#include /*third_party*/"tensorflow/core/framework/shape_inference.h"
-
-namespace tensorflow_models {
-namespace deeplab {
-namespace deeplab2 {
-
-using tensorflow::shape_inference::DimensionHandle;
-using tensorflow::shape_inference::InferenceContext;
-using tensorflow::shape_inference::ShapeHandle;
-
-REGISTER_OP("MergeSemanticAndInstanceMaps")
-    .Input("semantic_maps: int32")
-    .Input("instance_maps: int32")
-    .Input("thing_ids: int32")
-    .Attr("label_divisor: int = 256")
-    .Attr("stuff_area_limit: int = 0")
-    .Attr("void_label: int = 0")
-    .Output("parsing_maps: int32")
-    .SetShapeFn([](InferenceContext* c) {
-      ShapeHandle semantic_maps;
-      ShapeHandle instance_maps;
-      ShapeHandle thing_ids;
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &semantic_maps));
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &instance_maps));
-      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &thing_ids));
-      DimensionHandle batch = c->Dim(semantic_maps, 0);
-      DimensionHandle height = c->Dim(semantic_maps, 1);
-      DimensionHandle width = c->Dim(semantic_maps, 2);
-      c->set_output(0, c->MakeShape({batch, height, width}));
-      return tensorflow::Status::OK();
-    })
-    .Doc(R"doc(
-Generates parsing maps from semantic maps and instance maps.
-
-Parsing maps, or panoptic segmentation, are merged from the predicted semantic
-maps and class-agnostic instance maps. This function merges the maps in the
-following way:
-
-1) If a pixel belongs to `stuff` class (e.g., sky), the function directly uses
-  the semantic label from the semantic map and uses 0 as the instance label.
-2) If a pixel belongs to `thing` class (e.g., person), it uses the instance
-  label from the instance map and uses the majority of the semantic labels of
-  the same instance as the final semantic label.
-3) The function relabels each instance, so that the instance label of each
-  semantic class is in the range of [1, num_instances_of_the_semantic_class].
-
-Note that this operation is first poposed in the DeeperLab paper and adopted
-by the Panoptic-DeepLab framework.
-  - DeeperLab: Single-Shot Image Parser, T-J Yang, et al. arXiv:1902.05093.
-  - Panoptic-DeepLab, B. Cheng, et al. In CVPR, 2020.
-
-semantic_maps: An int32 Tensor with shape `[batch, height, width]` whose value
-  indicates the predicted semantic label of each pixel.
-instance_maps: An int32 Tensor with shape `[batch, height, width]` whose value
-  indicates the predicted instance label of each pixel.
-thing_ids: An int32 Tensor with shape `[num_thing_ids]` whose value refers to
-  the semantic ids of the thing classes.
-label_divisor: An integer. The value used to combine the semantic and instance
-  map to generate the parsing map. In particular, the value of a pixel in the
-  parsing map is equal to its corresponding semantic label times label_divisor
-  plus instance label (i.e., semantic_label * label_divisor + instance_label).
-stuff_area_limit: An integer. Predicted stuff segments whose areas are smaller
-  than this threshold are assigned to VOID label.
-void_label: An integer, specifying the VOID label.
-parsing_maps: An int32 Tensor with shape `[batch, height, width]` whose value
-  indicates the merged semantic and instance label of each pixel.
-)doc");
-
-}  // namespace deeplab2
-}  // namespace deeplab
-}  // namespace tensorflow_models
diff --git a/spaces/akhaliq/supermarionation/app.py b/spaces/akhaliq/supermarionation/app.py
deleted file mode 100644
index 212e6cc6e1dfb02a9e992370fbcb084155aa24c8..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/supermarionation/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'tuwonga/supermarionation'
-prefix = ''
-     
-scheduler = DPMSolverMultistepScheduler(
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    num_train_timesteps=1000,
-    trained_betas=None,
-    predict_epsilon=True,
-    thresholding=False,
-    algorithm_type="dpmsolver++",
-    solver_type="midpoint",
-    lower_order_final=True,
-)
-
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-
-    for i in range(len(results.images)):
-      if 'nsfw_content_detected' in results and results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
-    return results.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Supermarionation</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/tuwonga/supermarionation">Supermarionation</a> Stable Diffusion model.<br>
-               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
-              </p>
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/akhaliq/supermarionation/settings'>Settings</a></b>"}<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/akhaliq/supermarionation?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/alanchan808/Ask_Tennis_Coach_Rick_Macci/README.md b/spaces/alanchan808/Ask_Tennis_Coach_Rick_Macci/README.md
deleted file mode 100644
index 89c5991b9329aa3393c7668dec79db3d7cd98c12..0000000000000000000000000000000000000000
--- a/spaces/alanchan808/Ask_Tennis_Coach_Rick_Macci/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ask Coach Rick Macci
-emoji: 🏆
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/colorama/initialise.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/colorama/initialise.py
deleted file mode 100644
index 430d0668727cd0521270a52c962867907d743b34..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/colorama/initialise.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import atexit
-import contextlib
-import sys
-
-from .ansitowin32 import AnsiToWin32
-
-
-orig_stdout = None
-orig_stderr = None
-
-wrapped_stdout = None
-wrapped_stderr = None
-
-atexit_done = False
-
-
-def reset_all():
-    if AnsiToWin32 is not None:    # Issue #74: objects might become None at exit
-        AnsiToWin32(orig_stdout).reset_all()
-
-
-def init(autoreset=False, convert=None, strip=None, wrap=True):
-
-    if not wrap and any([autoreset, convert, strip]):
-        raise ValueError('wrap=False conflicts with any other arg=True')
-
-    global wrapped_stdout, wrapped_stderr
-    global orig_stdout, orig_stderr
-
-    orig_stdout = sys.stdout
-    orig_stderr = sys.stderr
-
-    if sys.stdout is None:
-        wrapped_stdout = None
-    else:
-        sys.stdout = wrapped_stdout = \
-            wrap_stream(orig_stdout, convert, strip, autoreset, wrap)
-    if sys.stderr is None:
-        wrapped_stderr = None
-    else:
-        sys.stderr = wrapped_stderr = \
-            wrap_stream(orig_stderr, convert, strip, autoreset, wrap)
-
-    global atexit_done
-    if not atexit_done:
-        atexit.register(reset_all)
-        atexit_done = True
-
-
-def deinit():
-    if orig_stdout is not None:
-        sys.stdout = orig_stdout
-    if orig_stderr is not None:
-        sys.stderr = orig_stderr
-
-
-@contextlib.contextmanager
-def colorama_text(*args, **kwargs):
-    init(*args, **kwargs)
-    try:
-        yield
-    finally:
-        deinit()
-
-
-def reinit():
-    if wrapped_stdout is not None:
-        sys.stdout = wrapped_stdout
-    if wrapped_stderr is not None:
-        sys.stderr = wrapped_stderr
-
-
-def wrap_stream(stream, convert, strip, autoreset, wrap):
-    if wrap:
-        wrapper = AnsiToWin32(stream,
-            convert=convert, strip=strip, autoreset=autoreset)
-        if wrapper.should_wrap():
-            stream = wrapper.stream
-    return stream
diff --git a/spaces/ali-ghamdan/deoldify/fastai/basic_train.py b/spaces/ali-ghamdan/deoldify/fastai/basic_train.py
deleted file mode 100644
index 8e46512d49d8b837c9307ece9140631d9cef7f5e..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/basic_train.py
+++ /dev/null
@@ -1,629 +0,0 @@
-"Provides basic training and validation with `Learner`"
-from .torch_core import *
-from .basic_data import *
-from .callback import *
-from .data_block import *
-from .utils.ipython import gpu_mem_restore
-import inspect
-from fastprogress.fastprogress import format_time, IN_NOTEBOOK
-from time import time
-from fastai.sixel import plot_sixel
-
-__all__ = ['Learner', 'LearnerCallback', 'Recorder', 'RecordOnCPU', 'fit', 'loss_batch', 'train_epoch', 'validate',
-           'get_preds', 'load_learner']
-
-defaults.lr = slice(3e-3)
-defaults.wd = 1e-2
-defaults.extra_callbacks    = None
-defaults.extra_callback_fns = None
-
-def loss_batch(model:nn.Module, xb:Tensor, yb:Tensor, loss_func:OptLossFunc=None, opt:OptOptimizer=None,
-               cb_handler:Optional[CallbackHandler]=None, count:[int]=[1], batch_multiplier:int=1)->Tuple[Union[Tensor,int,float,str]]:
-    "Calculate loss and metrics for a batch, call out to callbacks as necessary."
-    cb_handler = ifnone(cb_handler, CallbackHandler())
-    if not is_listy(xb): xb = [xb]
-    if not is_listy(yb): yb = [yb]
-    out = model(*xb)
-
-    if not loss_func: return to_detach(out), yb[0].detach()
-    out = cb_handler.on_loss_begin(out)
-    loss = loss_func(out, *yb)/batch_multiplier
-    count[0]-=1
-
-    if opt is not None:
-        loss,skip_bwd = cb_handler.on_backward_begin(loss)
-        if not skip_bwd:                     loss.backward()
-        if count[0] == 0:
-            if not cb_handler.on_backward_end(): opt.step()
-            if not cb_handler.on_step_end():     opt.zero_grad()
-            count[0] = batch_multiplier
-
-    return loss.detach().cpu()
-
-def get_preds(model:nn.Module, dl:DataLoader, pbar:Optional[PBar]=None, cb_handler:Optional[CallbackHandler]=None,
-              activ:nn.Module=None, loss_func:OptLossFunc=None, n_batch:Optional[int]=None) -> List[Tensor]:
-    "Tuple of predictions and targets, and optional losses (if `loss_func`) using `dl`, max batches `n_batch`."
-    res = [torch.cat(o).cpu() for o in
-           zip(*validate(model, dl, cb_handler=cb_handler, pbar=pbar, average=False, n_batch=n_batch))]
-    if loss_func is not None:
-        with NoneReduceOnCPU(loss_func) as lf: res.append(lf(res[0], res[1]))
-    if activ is not None: res[0] = activ(res[0])
-    return res
-
-def validate(model:nn.Module, dl:DataLoader, loss_func:OptLossFunc=None, cb_handler:Optional[CallbackHandler]=None,
-             pbar:Optional[PBar]=None, average=True, n_batch:Optional[int]=None)->Iterator[Tuple[Union[Tensor,int],...]]:
-    "Calculate `loss_func` of `model` on `dl` in evaluation mode."
-    model.eval()
-    with torch.no_grad():
-        val_losses,nums = [],[]
-        if cb_handler: cb_handler.set_dl(dl)
-        for xb,yb in progress_bar(dl, parent=pbar, leave=(pbar is not None)):
-            if cb_handler: xb, yb = cb_handler.on_batch_begin(xb, yb, train=False)
-            val_loss = loss_batch(model, xb, yb, loss_func, cb_handler=cb_handler)
-            val_losses.append(val_loss)
-            if not is_listy(yb): yb = [yb]
-            nums.append(first_el(yb).shape[0])
-            if cb_handler and cb_handler.on_batch_end(val_losses[-1]): break
-            if n_batch and (len(nums)>=n_batch): break
-        nums = np.array(nums, dtype=np.float32)
-        if average: return (to_np(torch.stack(val_losses)) * nums).sum() / nums.sum()
-        else:       return val_losses
-
-def train_epoch(model:nn.Module, dl:DataLoader, opt:optim.Optimizer, loss_func:LossFunction)->None:
-    "Simple training of `model` for 1 epoch of `dl` using optim `opt` and loss function `loss_func`."
-    model.train()
-    for xb,yb in dl:
-        loss = loss_func(model(xb), yb)
-        loss.backward()
-        opt.step()
-        opt.zero_grad()
-
-@dataclass
-class BasicLearner():
-    model:nn.Module
-    loss_func:LossFunction
-    opt:optim.Optimizer
-    data:DataBunch
-
-def fit(epochs:int, learn:BasicLearner, callbacks:Optional[CallbackList]=None, metrics:OptMetrics=None, batch_multiplier:int=1)->None:
-    "Fit the `model` on `data` and learn using `loss_func` and `opt`."
-    assert len(learn.data.train_dl) != 0, f"""Your training dataloader is empty, can't train a model.
-        Use a smaller batch size (batch size={learn.data.train_dl.batch_size} for {len(learn.data.train_dl.dataset)} elements)."""
-    cb_handler = CallbackHandler(callbacks, metrics)
-    pbar = master_bar(range(epochs))
-    cb_handler.on_train_begin(epochs, pbar=pbar, metrics=metrics)
-
-    exception=False
-    try:
-        for epoch in pbar:
-            learn.model.train()
-            cb_handler.set_dl(learn.data.train_dl)
-            cb_handler.on_epoch_begin()
-            count = [batch_multiplier]
-            for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
-                xb, yb = cb_handler.on_batch_begin(xb, yb)
-                loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler, count=count, batch_multiplier=batch_multiplier)
-                if cb_handler.on_batch_end(loss): break
-
-            if not cb_handler.skip_validate and not learn.data.empty_val:
-                val_loss = validate(learn.model, learn.data.valid_dl, loss_func=learn.loss_func,
-                                       cb_handler=cb_handler, pbar=pbar)
-            else: val_loss=None
-            if cb_handler.on_epoch_end(val_loss): break
-    except Exception as e:
-        exception = e
-        raise
-    finally: cb_handler.on_train_end(exception)
-
-loss_func_name2activ = {'cross_entropy_loss': F.softmax, 'nll_loss': torch.exp, 'poisson_nll_loss': torch.exp,
-    'kl_div_loss': torch.exp, 'bce_with_logits_loss': torch.sigmoid, 'cross_entropy': F.softmax,
-    'kl_div': torch.exp, 'binary_cross_entropy_with_logits': torch.sigmoid,
-}
-
-def _loss_func_name2activ(name:str, axis:int=-1):
-    res = loss_func_name2activ[name]
-    if res == F.softmax: res = partial(F.softmax, dim=axis)
-    return res
-
-def _loss_func2activ(loss_func):
-    if getattr(loss_func,'keywords',None):
-        if not loss_func.keywords.get('log_input', True): return
-    axis = getattr(loss_func, 'axis', -1)
-    # flattened loss
-    loss_func = getattr(loss_func, 'func', loss_func)
-    # could have a partial inside flattened loss! Duplicate on purpose.
-    loss_func = getattr(loss_func, 'func', loss_func)
-    cls_name = camel2snake(loss_func.__class__.__name__)
-    if cls_name == 'mix_up_loss':
-        loss_func = loss_func.crit
-        cls_name = camel2snake(loss_func.__class__.__name__)
-    if cls_name in loss_func_name2activ:
-        if cls_name == 'poisson_nll_loss' and (not getattr(loss_func, 'log_input', True)): return
-        return _loss_func_name2activ(cls_name, axis)
-    if getattr(loss_func,'__name__','') in loss_func_name2activ:
-        return _loss_func_name2activ(loss_func.__name__, axis)
-    return noop
-
-@dataclass
-class Learner():
-    "Trainer for `model` using `data` to minimize `loss_func` with optimizer `opt_func`."
-    data:DataBunch
-    model:nn.Module
-    opt_func:Callable=AdamW
-    loss_func:Callable=None
-    metrics:Collection[Callable]=None
-    true_wd:bool=True
-    bn_wd:bool=True
-    wd:Floats=defaults.wd
-    train_bn:bool=True
-    path:str = None
-    model_dir:PathOrStr = 'models'
-    callback_fns:Collection[Callable]=None
-    callbacks:Collection[Callback]=field(default_factory=list)
-    layer_groups:Collection[nn.Module]=None
-    add_time:bool=True
-    silent:bool=None
-    def __post_init__(self)->None:
-        "Setup path,metrics, callbacks and ensure model directory exists."
-        self.path = Path(ifnone(self.path, self.data.path))
-        self.model = self.model.to(self.data.device)
-        self.loss_func = self.loss_func or self.data.loss_func
-        self.metrics=listify(self.metrics)
-        if not self.layer_groups: self.layer_groups = [nn.Sequential(*flatten_model(self.model))]
-        self.callbacks = listify(self.callbacks)
-        if self.silent is None: self.silent = defaults.silent
-        self.callback_fns = [partial(Recorder, add_time=self.add_time, silent=self.silent)] + listify(self.callback_fns)
-
-    def init(self, init): apply_init(self.model, init)
-
-    def _test_writeable_path(self):
-        path = self.path/self.model_dir
-        try:
-            path.mkdir(parents=True, exist_ok=True)
-            tmp_file = get_tmp_file(path)
-        except OSError as e:
-            raise Exception(f"{e}\nCan't write to '{path}', set `learn.model_dir` attribute in Learner to a full libpath path that is writable") from None
-        os.remove(tmp_file)
-
-    def lr_range(self, lr:Union[float,slice])->np.ndarray:
-        "Build differential learning rates from `lr`."
-        if not isinstance(lr,slice): return lr
-        if lr.start: res = even_mults(lr.start, lr.stop, len(self.layer_groups))
-        else: res = [lr.stop/10]*(len(self.layer_groups)-1) + [lr.stop]
-        return np.array(res)
-
-    def fit(self, epochs:int, lr:Union[Floats,slice]=defaults.lr,
-            wd:Floats=None, callbacks:Collection[Callback]=None, batch_multiplier:int=1)->None:
-        "Fit the model on this learner with `lr` learning rate, `wd` weight decay for `epochs` with `callbacks`."
-        lr = self.lr_range(lr)
-        if wd is None: wd = self.wd
-        if not getattr(self, 'opt', False): self.create_opt(lr, wd)
-        else: self.opt.lr,self.opt.wd = lr,wd
-        callbacks = [cb(self) for cb in self.callback_fns + listify(defaults.extra_callback_fns)] + listify(callbacks)
-        if defaults.extra_callbacks is not None: callbacks += defaults.extra_callbacks
-        fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks, batch_multiplier=batch_multiplier)
-
-    def create_opt(self, lr:Floats, wd:Floats=0.)->None:
-        "Create optimizer with `lr` learning rate and `wd` weight decay."
-        self.opt = OptimWrapper.create(self.opt_func, lr, self.layer_groups, wd=wd, true_wd=self.true_wd, bn_wd=self.bn_wd)
-
-    def split(self, split_on:SplitFuncOrIdxList)->None:
-        "Split the model at `split_on`."
-        if isinstance(split_on,Callable): split_on = split_on(self.model)
-        self.layer_groups = split_model(self.model, split_on)
-        return self
-
-    def freeze_to(self, n:int)->None:
-        "Freeze layers up to layer group `n`."
-        for g in self.layer_groups[:n]:
-            for l in g:
-                if not self.train_bn or not isinstance(l, bn_types): requires_grad(l, False)
-        for g in self.layer_groups[n:]: requires_grad(g, True)
-        self.create_opt(defaults.lr)
-
-    def freeze(self)->None:
-        "Freeze up to last layer group."
-        assert(len(self.layer_groups)>1)
-        self.freeze_to(-1)
-
-    def unfreeze(self):
-        "Unfreeze entire model."
-        self.freeze_to(0)
-
-    def export(self, file:PathLikeOrBinaryStream='export.pkl', destroy=False):
-        "Export the state of the `Learner` in `self.path/file`. `file` can be file-like (file or buffer)"
-        if rank_distrib(): return # don't save if slave proc
-        args = ['opt_func', 'loss_func', 'metrics', 'true_wd', 'bn_wd', 'wd', 'train_bn', 'model_dir', 'callback_fns']
-        state = {a:getattr(self,a) for a in args}
-        state['cb_state'] = {cb.__class__:cb.get_state() for cb in self.callbacks}
-        #layer_groups -> need to find a way
-        #TO SEE: do we save model structure and weights separately?
-        with ModelOnCPU(self.model) as m:
-            state['model'] = m
-            xtra = dict(normalize=self.data.norm.keywords) if getattr(self.data, 'norm', False) else {}
-            state['data'] = self.data.valid_ds.get_state(**xtra)
-            state['cls'] = self.__class__
-            try_save(state, self.path, file)
-        if destroy: self.destroy()
-
-    def save(self, file:PathLikeOrBinaryStream=None, return_path:bool=False, with_opt:bool=True):
-        "Save model and optimizer state (if `with_opt`) with `file` to `self.model_dir`. `file` can be file-like (file or buffer)"
-        if is_pathlike(file): self._test_writeable_path()
-        if rank_distrib(): return # don't save if slave proc
-        target = self.path/self.model_dir/f'{file}.pth' if is_pathlike(file) else file
-        if not hasattr(self, 'opt'): with_opt=False
-        if not with_opt: state = get_model(self.model).state_dict()
-        else: state = {'model': get_model(self.model).state_dict(), 'opt':self.opt.state_dict()}
-        torch.save(state, target)
-        if return_path: return target
-
-    def dl(self, ds_type:DatasetType=DatasetType.Valid):
-        "Return DataLoader for DatasetType `ds_type`."
-        return self.data.dl(ds_type)
-
-    def load(self, file:PathLikeOrBinaryStream=None, device:torch.device=None, strict:bool=True,
-             with_opt:bool=None, purge:bool=True, remove_module:bool=False):
-        "Load model and optimizer state (if `with_opt`) `file` from `self.model_dir` using `device`. `file` can be file-like (file or buffer)"
-        if purge: self.purge(clear_opt=ifnone(with_opt, False))
-        if device is None: device = self.data.device
-        elif isinstance(device, int): device = torch.device('cuda', device)
-        source = self.path/self.model_dir/f'{file}.pth' if is_pathlike(file) else file
-        state = torch.load(source, map_location=device)
-        if set(state.keys()) == {'model', 'opt'}:
-            model_state = state['model']
-            if remove_module: model_state = remove_module_load(model_state)
-            get_model(self.model).load_state_dict(model_state, strict=strict)
-            if ifnone(with_opt,True):
-                if not hasattr(self, 'opt'): self.create_opt(defaults.lr, self.wd)
-                try:    self.opt.load_state_dict(state['opt'])
-                except: pass
-        else:
-            if with_opt: warn("Saved filed doesn't contain an optimizer state.")
-            if remove_module: state = remove_module_load(state)
-            get_model(self.model).load_state_dict(state, strict=strict)
-        del state
-        gc.collect()
-        return self
-
-    def destroy(self):
-        "Free the Learner internals, leaving just an empty shell that consumes no memory"
-
-        class ZombieLearner(Learner):
-            msg = "this object has been destroyed"
-            def __getattr__(self, item):    print(ZombieLearner.msg); return None
-            def destroyed(*args, **kwargs): print(ZombieLearner.msg)
-
-        attrs = [k for k in self.__dict__.keys() if not k.startswith("__")]
-        for a in attrs: delattr(self, a)
-        # the instance methods can still be called, but will just give a message
-        methods = [k for k in dir(self) if not k.startswith("__") and inspect.isroutine(getattr(self, k))]
-        for m in methods: setattr(self, m, ZombieLearner.destroyed)
-        self.__class__ = ZombieLearner
-        gc.collect()
-        print("this Learner object self-destroyed - it still exists, but no longer usable")
-
-    def purge(self, clear_opt:bool=True):
-        "Purge the `Learner` of all cached attributes to release some GPU memory."
-        self._test_writeable_path()
-        attrs_all = [k for k in self.__dict__.keys() if not k.startswith("__")]
-        attrs_pkl = ['bn_wd', 'callback_fns', 'layer_groups', 'loss_func', 'metrics', 'model',
-                     'model_dir', 'opt_func', 'path', 'train_bn', 'true_wd', 'wd']
-        # +callbacks: get pickled too, but not directly
-        attrs_keep = ['data', 'recorder']
-        attrs_del = list(set(attrs_all) - set(attrs_keep))
-        state = {a:getattr(self, a) for a in attrs_pkl}
-        state['cb_state'] = {cb.__class__:cb.get_state() for cb in self.callbacks}
-        if hasattr(self, 'opt'): state['opt'] = self.opt.get_state()
-
-        tmp_file = get_tmp_file(self.path/self.model_dir)
-        torch.save(state, open(tmp_file, 'wb'))
-        for a in attrs_del: delattr(self, a)
-        gc.collect()
-        state = torch.load(tmp_file)
-        os.remove(tmp_file)
-
-        for a in attrs_pkl: setattr(self, a, state[a])
-        cb_state = state.pop('cb_state')
-        self.callbacks = [load_callback(c,s, self) for c,s in cb_state.items()]
-        if not clear_opt and 'opt' in state:
-            try: self.opt = OptimWrapper.load_with_state_and_layer_group(state['opt'], self.layer_groups)
-            except: warn("Wasn't able to properly load the optimizer state again.")
-        del state
-        gc.collect()
-        return self
-
-    def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None,
-                  pbar:Optional[PBar]=None) -> List[Tensor]:
-        "Return predictions and targets on `ds_type` dataset."
-        lf = self.loss_func if with_loss else None
-        return get_preds(self.model, self.dl(ds_type), cb_handler=CallbackHandler(self.callbacks),
-                         activ=_loss_func2activ(self.loss_func), loss_func=lf, n_batch=n_batch, pbar=pbar)
-
-    def pred_batch(self, ds_type:DatasetType=DatasetType.Valid, batch:Tuple=None, reconstruct:bool=False, with_dropout:bool=False) -> List[Tensor]:
-        with torch.no_grad():
-            training = self.model.training
-            self.model.train(False)
-            "Return output of the model on one batch from `ds_type` dataset."
-            if batch is not None: xb,yb = batch
-            else: xb,yb = self.data.one_batch(ds_type, detach=False, denorm=False)
-            cb_handler = CallbackHandler(self.callbacks)
-            #xb,yb = cb_handler.on_batch_begin(xb,yb, train=False)
-            if not with_dropout: 
-                preds = loss_batch(self.model.eval(), xb, yb, cb_handler=cb_handler)
-            else: 
-                preds = loss_batch(self.model.eval().apply(self.apply_dropout), xb, yb, cb_handler=cb_handler)
-            res = _loss_func2activ(self.loss_func)(preds[0])
-            self.model.train(training)
-            if not reconstruct: return res
-            res = res.detach().cpu()
-            ds = self.dl(ds_type).dataset
-            norm = getattr(self.data, 'norm', False)
-            if norm and norm.keywords.get('do_y',False):
-                res = self.data.denorm(res, do_x=True)
-            return [ds.reconstruct(o) for o in res]
-
-    def backward(self, item):
-        "Pass `item` through the model and computes the gradient. Useful if `backward_hooks` are attached."
-        xb,yb = self.data.one_item(item)
-        loss = loss_batch(self.model.eval(), xb, yb, self.loss_func, opt=FakeOptimizer(),
-                          cb_handler=CallbackHandler(self.callbacks))
-        return loss
-
-    def predict(self, item:ItemBase, return_x:bool=False, batch_first:bool=True, with_dropout:bool=False, **kwargs):
-        "Return predicted class, label and probabilities for `item`."
-        batch = self.data.one_item(item)
-        res = self.pred_batch(batch=batch, with_dropout=with_dropout)
-        raw_pred,x = grab_idx(res,0,batch_first=batch_first),batch[0]
-        norm = getattr(self.data,'norm',False)
-        if norm:
-            x = self.data.denorm(x)
-            if norm.keywords.get('do_y',False): raw_pred = self.data.denorm(raw_pred)
-        ds = self.data.single_ds
-        pred = ds.y.analyze_pred(raw_pred, **kwargs)
-        x = ds.x.reconstruct(grab_idx(x, 0))
-        y = ds.y.reconstruct(pred, x) if has_arg(ds.y.reconstruct, 'x') else ds.y.reconstruct(pred)
-        return (x, y, pred, raw_pred) if return_x else (y, pred, raw_pred)
-
-    def validate(self, dl=None, callbacks=None, metrics=None):
-        "Validate on `dl` with potential `callbacks` and `metrics`."
-        dl = ifnone(dl, self.data.valid_dl)
-        metrics = ifnone(metrics, self.metrics)
-        cb_handler = CallbackHandler(self.callbacks + ifnone(callbacks, []), metrics)
-        cb_handler.on_epoch_begin()
-        val_metrics = validate(self.model, dl, self.loss_func, cb_handler)
-        cb_handler.on_epoch_end(val_metrics)
-        return cb_handler.state_dict['last_metrics']
-
-    def show_results(self, ds_type=DatasetType.Valid, rows:int=5, **kwargs):
-        "Show `rows` result of predictions on `ds_type` dataset."
-        #TODO: get read of has_arg x and split_kwargs_by_func if possible
-        #TODO: simplify this and refactor with pred_batch(...reconstruct=True)
-        n_items = rows ** 2 if self.data.train_ds.x._square_show_res else rows
-        if self.dl(ds_type).batch_size < n_items: n_items = self.dl(ds_type).batch_size
-        ds = self.dl(ds_type).dataset
-        self.callbacks.append(RecordOnCPU())
-        preds = self.pred_batch(ds_type)
-        *self.callbacks,rec_cpu = self.callbacks
-        x,y = rec_cpu.input,rec_cpu.target
-        norm = getattr(self.data,'norm',False)
-        if norm:
-            x = self.data.denorm(x)
-            if norm.keywords.get('do_y',False):
-                y     = self.data.denorm(y, do_x=True)
-                preds = self.data.denorm(preds, do_x=True)
-        analyze_kwargs,kwargs = split_kwargs_by_func(kwargs, ds.y.analyze_pred)
-        preds = [ds.y.analyze_pred(grab_idx(preds, i), **analyze_kwargs) for i in range(n_items)]
-        xs = [ds.x.reconstruct(grab_idx(x, i)) for i in range(n_items)]
-        if has_arg(ds.y.reconstruct, 'x'):
-            ys = [ds.y.reconstruct(grab_idx(y, i), x=x) for i,x in enumerate(xs)]
-            zs = [ds.y.reconstruct(z, x=x) for z,x in zip(preds,xs)]
-        else :
-            ys = [ds.y.reconstruct(grab_idx(y, i)) for i in range(n_items)]
-            zs = [ds.y.reconstruct(z) for z in preds]
-        ds.x.show_xyzs(xs, ys, zs, **kwargs)
-
-    def apply_dropout(self, m):
-        "If a module contains 'dropout' in it's name, it will be switched to .train() mode."
-        if 'dropout' in m.__class__.__name__.lower(): m.train()
-
-    def predict_with_mc_dropout(self, item:ItemBase, with_dropout:bool=True, n_times=10, **kwargs):
-        "Make predictions with dropout turned on for n_times (default 10)."
-        return [self.predict(item, with_dropout=with_dropout) for _ in range(n_times)]
-
-class RecordOnCPU(Callback):
-    "Store the `input` and `target` going through the model on the CPU."
-    def on_batch_begin(self, last_input,last_target,**kwargs):
-        self.input,self.target = to_cpu(last_input),to_cpu(last_target)
-
-class LearnerCallback(Callback):
-    "Base class for creating callbacks for a `Learner`."
-    def __init__(self, learn):
-        self._learn = weakref.ref(learn)
-        self.exclude,self.not_min = ['_learn'],[]
-        setattr(self.learn, self.cb_name, self)
-
-    def __getattr__(self,k): return getattr(self.learn, k)
-    def __setstate__(self,data:Any): self.__dict__.update(data)
-
-    @property
-    def learn(self) -> Learner: return self._learn()
-    @learn.setter
-    def learn(self, learn: Learner) -> None: self._learn = weakref.ref(learn)
-
-    @property
-    def cb_name(self): return camel2snake(self.__class__.__name__)
-
-class Recorder(LearnerCallback):
-    "A `LearnerCallback` that records epoch, loss, opt and metric data during training."
-    _order=-10
-    def __init__(self, learn:Learner, add_time:bool=True, silent:bool=False):
-        super().__init__(learn)
-        self.opt = self.learn.opt
-        self.train_dl = self.learn.data.train_dl
-        self.no_val,self.silent,self.add_time = False,silent,add_time
-
-    def on_train_begin(self, pbar:PBar, metrics_names:Collection[str], **kwargs:Any)->None:
-        "Initialize recording status at beginning of training."
-        self.pbar = pbar
-        self.names = ['epoch', 'train_loss'] if self.no_val else ['epoch', 'train_loss', 'valid_loss']
-        self.metrics_names = metrics_names
-        if hasattr(self, '_added_met_names'): self.metrics_names += self._added_met_names
-        self.names += self.metrics_names
-        if self.add_time: self.names.append('time')
-        if not self.silent: self.pbar.write(self.names, table=True)
-        self.losses,self.val_losses,self.lrs,self.moms,self.metrics,self.nb_batches = [],[],[],[],[],[]
-
-    def on_epoch_begin(self, **kwargs:Any)->None:
-        if self.add_time: self.start_epoch = time()
-
-    def on_batch_begin(self, train, **kwargs:Any)->None:
-        "Record learning rate and momentum at beginning of batch."
-        if train:
-            self.lrs.append(self.opt.lr)
-            self.moms.append(self.opt.mom)
-
-    def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
-        "Record the loss before any other callback has a chance to modify it."
-        self.losses.append(smooth_loss)
-        if self.pbar is not None and hasattr(self.pbar,'child'):
-            self.pbar.child.comment = f'{smooth_loss:.4f}'
-
-    def on_epoch_end(self, epoch:int, num_batch:int, smooth_loss:Tensor,
-                     last_metrics=MetricsList, **kwargs:Any)->bool:
-        "Save epoch info: num_batch, smooth_loss, metrics."
-        self.nb_batches.append(num_batch)
-        if last_metrics is not None: self.val_losses.append(last_metrics[0])
-        else: last_metrics = [] if self.no_val else [None]
-        if len(last_metrics) > 1: self.metrics.append(last_metrics[1:])
-        self.format_stats([epoch, smooth_loss] + last_metrics)
-
-    def format_stats(self, stats:TensorOrNumList)->None:
-        "Format stats before printing."
-        str_stats = []
-        for name,stat in zip(self.names,stats):
-            str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
-        if self.add_time: str_stats.append(format_time(time() - self.start_epoch))
-        if not self.silent: self.pbar.write(str_stats, table=True)
-
-    def add_metric_names(self, names):
-        "Add `names` to the inner metric names."
-        if hasattr(self, '_added_met_names'): self._added_met_names += names
-        else:                                 self._added_met_names  = names
-
-    def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
-        "Plot learning rate, `show_moms` to include momentum."
-        lrs = self._split_list(self.lrs, skip_start, skip_end)
-        iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
-        if show_moms:
-            moms = self._split_list(self.moms, skip_start, skip_end)
-            fig, axs = plt.subplots(1,2, figsize=(12,4))
-            axs[0].plot(iterations, lrs)
-            axs[0].set_xlabel('Iterations')
-            axs[0].set_ylabel('Learning Rate')
-            axs[1].plot(iterations, moms)
-            axs[1].set_xlabel('Iterations')
-            axs[1].set_ylabel('Momentum')
-        else:
-            fig, ax = plt.subplots()
-            ax.plot(iterations, lrs)
-            ax.set_xlabel('Iterations')
-            ax.set_ylabel('Learning Rate')
-        if ifnone(return_fig, defaults.return_fig): return fig
-        if not IN_NOTEBOOK: plot_sixel(fig)
-
-    @staticmethod
-    def smoothen_by_spline(xs, ys, **kwargs):
-        xs = np.arange(len(ys))
-        spl = scipy.interpolate.UnivariateSpline(xs, ys, **kwargs)
-        ys = spl(xs)
-        return ys
-
-    def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
-             **kwargs)->Optional[plt.Figure]:
-        "Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
-        lrs = self._split_list(self.lrs, skip_start, skip_end)
-        losses = self._split_list(self.losses, skip_start, skip_end)
-        losses = [x.item() for x in losses]
-        if 'k' in kwargs: losses = self.smoothen_by_spline(lrs, losses, **kwargs)
-        fig, ax = plt.subplots(1,1)
-        ax.plot(lrs, losses)
-        ax.set_ylabel("Loss")
-        ax.set_xlabel("Learning Rate")
-        ax.set_xscale('log')
-        ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0e'))
-        if suggestion:
-            try: mg = (np.gradient(np.array(losses))).argmin()
-            except:
-                print("Failed to compute the gradients, there might not be enough points.")
-                return
-            print(f"Min numerical gradient: {lrs[mg]:.2E}")
-            ax.plot(lrs[mg],losses[mg],markersize=10,marker='o',color='red')
-            self.min_grad_lr = lrs[mg]
-            ml = np.argmin(losses)
-            print(f"Min loss divided by 10: {lrs[ml]/10:.2E}")
-        if ifnone(return_fig, defaults.return_fig): return fig
-        if not IN_NOTEBOOK: plot_sixel(fig)
-
-    def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
-        "Plot training and validation losses."
-        fig, ax = plt.subplots(1,1)
-        losses = self._split_list(self.losses, skip_start, skip_end)
-        iterations = self._split_list(range_of(self.losses), skip_start, skip_end)
-        ax.plot(iterations, losses, label='Train')
-        val_iter = self._split_list_val(np.cumsum(self.nb_batches), skip_start, skip_end)
-        val_losses = self._split_list_val(self.val_losses, skip_start, skip_end)
-        ax.plot(val_iter, val_losses, label='Validation')
-        ax.set_ylabel('Loss')
-        ax.set_xlabel('Batches processed')
-        ax.legend()
-        if ifnone(return_fig, defaults.return_fig): return fig
-        if not IN_NOTEBOOK: plot_sixel(fig)
-
-    def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
-        "Plot metrics collected during training."
-        assert len(self.metrics) != 0, "There are no metrics to plot."
-        fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
-        val_iter = self._split_list_val(np.cumsum(self.nb_batches), skip_start, skip_end)
-        axes = axes.flatten() if len(self.metrics[0]) != 1 else [axes]
-        for i, ax in enumerate(axes):
-            values = [met[i] for met in self.metrics]
-            values = self._split_list_val(values, skip_start, skip_end)
-            ax.plot(val_iter, values)
-            ax.set_ylabel(str(self.metrics_names[i]))
-            ax.set_xlabel('Batches processed')             
-        if ifnone(return_fig, defaults.return_fig): return fig
-        if not IN_NOTEBOOK: plot_sixel(fig)
-
-    def _split_list(self, vals:Collection[float], skip_start:int, skip_end:int):
-        return vals[skip_start:-skip_end] if skip_end > 0 else vals[skip_start:]
-
-    def _split_list_val(self, vals:Collection[float], skip_start:int, skip_end:int):
-        val_iter = np.cumsum(self.nb_batches)
-        start_val = (val_iter - skip_start >= 0).nonzero()[0].min()
-        end_val = (val_iter[-1] - val_iter - skip_end >= 0).nonzero()[0].max()+1
-        return vals[start_val:end_val] if skip_end > 0 else vals[start_val:]
-
-class FakeOptimizer():
-    def step(self): pass
-    def zero_grad(self): pass
-
-def load_callback(class_func, state, learn:Learner):
-    init_kwargs, others = split_kwargs_by_func(state, class_func.__init__)
-    res = class_func(learn, **init_kwargs) if issubclass(class_func, LearnerCallback) else class_func(**init_kwargs)
-    for k,v in others.items(): setattr(res, k, v)
-    return res
-
-def load_learner(path:PathOrStr, file:PathLikeOrBinaryStream='export.pkl', test:ItemList=None, **db_kwargs):
-    "Load a `Learner` object saved with `export_state` in `path/file` with empty data, optionally add `test` and load on `cpu`. `file` can be file-like (file or buffer)"
-    source = Path(path)/file if is_pathlike(file) else file
-    state = torch.load(source, map_location='cpu') if defaults.device == torch.device('cpu') else torch.load(source)
-    model = state.pop('model')
-    src = LabelLists.load_state(path, state.pop('data'))
-    if test is not None: src.add_test(test)
-    data = src.databunch(**db_kwargs)
-    cb_state = state.pop('cb_state')
-    clas_func = state.pop('cls')
-    res = clas_func(data, model, **state)
-    res.callback_fns = state['callback_fns'] #to avoid duplicates
-    res.callbacks = [load_callback(c,s, res) for c,s in cb_state.items()]
-    return res
diff --git a/spaces/allandclive/Uganda_MMS/uroman/lib/NLP/stringDistance.pm b/spaces/allandclive/Uganda_MMS/uroman/lib/NLP/stringDistance.pm
deleted file mode 100644
index 17279d4a064c65fd6683a1b31d04831d93c5b416..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/uroman/lib/NLP/stringDistance.pm
+++ /dev/null
@@ -1,724 +0,0 @@
-################################################################
-#                                                              #
-# stringDistance                                               #
-#                                                              #
-################################################################
-
-package NLP::stringDistance;
-
-use List::Util qw(min max);
-$utf8 = NLP::UTF8;
-$util = NLP::utilities;
-$romanizer = NLP::Romanizer;
-
-%dummy_ht = ();
-
-sub rule_string_expansion {
-   local($this, *ht, $s, $lang_code) = @_;
-   
-   my @characters = $utf8->split_into_utf8_characters($s, "return only chars, return trailing whitespaces", *dummy_ht);
-   foreach $sub_len ((0 .. ($#characters-1))) {
-      my $sub = join("", @characters[0 .. $sub_len]);
-      foreach $super_len ((($sub_len + 1) .. $#characters)) {
-         my $super = join("", @characters[0 .. $super_len]);
-	 # print STDERR "  $sub -> $super\n" unless $ht{RULE_STRING_EXPANSION}->{$lang_code}->{$sub}->{$super};
-	 $ht{RULE_STRING_EXPANSION}->{$lang_code}->{$sub}->{$super} = 1;
-	 $ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$sub} = 1;
-	 # print STDERR "  RULE_STRING_HAS_EXPANSION $lang_code $sub\n";
-      }
-   }
-}
-
-sub load_string_distance_data {
-   local($this, $filename, *ht, $verbose) = @_;
-
-   $verbose = 0 unless defined($verbose);
-   open(IN,$filename) || die "Could not open $filename";
-   my $line_number = 0;
-   my $n_cost_rules = 0;
-   while (<IN>) {
-      $line_number++;
-      my $line = $_;
-      $line =~ s/^\xEF\xBB\xBF//;
-      $line =~ s/\s*$//;
-      next if $line =~ /^\s*(\#.*)?$/;
-      print STDERR "** Warning: line $line_number contains suspicious control character: $line\n" if $line =~ /[\x00-\x1F]/;
-      my $s1 = $util->slot_value_in_double_colon_del_list($line, "s1");
-      my $s2 = $util->slot_value_in_double_colon_del_list($line, "s2");
-      $s1 = $util->dequote_string($s1); # 'can\'t' => can't
-      $s2 = $util->dequote_string($s2);
-      my $cost = $util->slot_value_in_double_colon_del_list($line, "cost");
-      if (($s1 eq "") && ($s2 eq "")) {
-	 print STDERR "Ignoring bad line $line_number in $filename, because both s1 and s2 are empty strings\n";
-         next;
-      }
-      unless ($cost =~ /^\d+(\.\d+)?$/) {
-	 if ($cost eq "") {
-	    print STDERR "Ignoring bad line $line_number in $filename, because of missing cost\n";
-	 } else {
-	    print STDERR "Ignoring bad line $line_number in $filename, because of ill-formed cost $cost\n";
-	 }
-         next;
-      }
-      my $lang_code1_s = $util->slot_value_in_double_colon_del_list($line, "lc1");
-      my $lang_code2_s = $util->slot_value_in_double_colon_del_list($line, "lc2");
-      my @lang_codes_1 = ($lang_code1_s eq "") ? ("") : split(/,\s*/, $lang_code1_s);
-      my @lang_codes_2 = ($lang_code2_s eq "") ? ("") : split(/,\s*/, $lang_code2_s);
-      my $left_context1 = $util->slot_value_in_double_colon_del_list($line, "left1");
-      my $left_context2 = $util->slot_value_in_double_colon_del_list($line, "left2");
-      my $right_context1 = $util->slot_value_in_double_colon_del_list($line, "right1");
-      my $right_context2 = $util->slot_value_in_double_colon_del_list($line, "right2");
-      my $bad_left = $util->slot_value_in_double_colon_del_list($line, "left");
-      if ($bad_left) {
-	 print STDERR "** Warning: slot '::left $bad_left' in line $line_number\n";
-         next;
-      }
-      my $bad_right = $util->slot_value_in_double_colon_del_list($line, "right");
-      if ($bad_right) {
-	 print STDERR "** Warning: slot '::right $bad_right' in line $line_number\n";
-         next;
-      }
-      my $in_lang_codes1 = $util->slot_value_in_double_colon_del_list($line, "in-lc1");
-      my $in_lang_codes2 = $util->slot_value_in_double_colon_del_list($line, "in-lc2");
-      my $out_lang_codes1 = $util->slot_value_in_double_colon_del_list($line, "out-lc1");
-      my $out_lang_codes2 = $util->slot_value_in_double_colon_del_list($line, "out-lc2");
-      if ($left_context1) {
-         if ($left_context1 =~ /^\/.*\/$/) {
-            $left_context1 =~ s/^\///;
-            $left_context1 =~ s/\/$//;
-         } else {
-            print STDERR "Ignoring unrecognized non-regular-express ::left1 $left_context1 in $line_number of $filename\n";
-            $left_context1 = "";
-         }
-      }
-      if ($left_context2) {
-         if ($left_context2 =~ /^\/.*\/$/) {
-            $left_context2 =~ s/^\///;
-            $left_context2 =~ s/\/$//;
-         } else {
-            $left_context2 = "";
-            print STDERR "Ignoring unrecognized non-regular-express ::left2 $left_context2 in $line_number of $filename\n";
-         }
-      }
-      if ($right_context1) {
-         unless ($right_context1 =~ /^(\[[^\[\]]*\])+$/) {
-	    $right_context1 = "";
-	    print STDERR "Ignoring unrecognized right-context ::right1 $right_context1 in $line_number of $filename\n";
-	 }
-      }
-      if ($right_context2) {
-         unless ($right_context2 =~ /^(\[[^\[\]]*\])+$/) {
-	    $right_context2 = "";
-	    print STDERR "Ignoring unrecognized right-context ::right2 $right_context2 in $line_number of $filename\n";
-	 }
-      }
-      foreach $lang_code1 (@lang_codes_1) {
-         foreach $lang_code2 (@lang_codes_2) {
-            $n_cost_rules++;
-            my $cost_rule_id = $n_cost_rules;
-            $ht{COST}->{$lang_code1}->{$lang_code2}->{$s1}->{$s2}->{$cost_rule_id} = $cost;
-            $ht{RULE_STRING}->{$lang_code1}->{$s1} = 1;
-            $ht{RULE_STRING}->{$lang_code2}->{$s2} = 1;
-            $ht{LEFT1}->{$cost_rule_id} = $left_context1;
-            $ht{LEFT2}->{$cost_rule_id} = $left_context2;
-            $ht{RIGHT1}->{$cost_rule_id} = $right_context1;
-            $ht{RIGHT2}->{$cost_rule_id} = $right_context2;
-            $ht{INLC1}->{$cost_rule_id} = $in_lang_codes1;
-            $ht{INLC2}->{$cost_rule_id} = $in_lang_codes2;
-            $ht{OUTLC1}->{$cost_rule_id} = $out_lang_codes1;
-            $ht{OUTLC2}->{$cost_rule_id} = $out_lang_codes2;
-            unless (($s1 eq $s2)
-	         && ($lang_code1 eq $lang_code2)
-	         && ($left_context1 eq $left_context2)
-	         && ($right_context1 eq $right_context2)
-	         && ($in_lang_codes1 eq $in_lang_codes2)
-	         && ($out_lang_codes1 eq $out_lang_codes2)) {
-               $n_cost_rules++;
-               $cost_rule_id = $n_cost_rules;
-               $ht{COST}->{$lang_code2}->{$lang_code1}->{$s2}->{$s1}->{$cost_rule_id} = $cost;
-               $ht{LEFT1}->{$cost_rule_id} = $left_context2;
-               $ht{LEFT2}->{$cost_rule_id} = $left_context1;
-               $ht{RIGHT1}->{$cost_rule_id} = $right_context2;
-               $ht{RIGHT2}->{$cost_rule_id} = $right_context1;
-               $ht{INLC1}->{$cost_rule_id} = $in_lang_codes2;
-               $ht{INLC2}->{$cost_rule_id} = $in_lang_codes1;
-               $ht{OUTLC1}->{$cost_rule_id} = $out_lang_codes2;
-               $ht{OUTLC2}->{$cost_rule_id} = $out_lang_codes1;
-	       # print STDERR "  Flip rule in line $line: $line\n";
-            }
-            $this->rule_string_expansion(*ht, $s1, $lang_code1);
-            $this->rule_string_expansion(*ht, $s2, $lang_code2);
-	 }
-      }
-   }
-   close(IN);
-   print STDERR "Read in $n_cost_rules rules from $line_number lines in $filename\n" if $verbose;
-}
-
-sub romanized_string_to_simple_chart {
-   local($this, $s, *chart_ht) = @_;
-
-   my @characters = $utf8->split_into_utf8_characters($s, "return only chars, return trailing whitespaces", *dummy_ht);
-   $chart_ht{N_CHARS} = $#characters + 1;
-   $chart_ht{N_NODES} = 0;
-   foreach $i ((0 .. $#characters)) {
-      $romanizer->add_node($characters[$i], $i, ($i+1), *chart_ht, "", "");
-   }
-}
-
-sub linearize_chart_points {
-   local($this, *chart_ht, $chart_id, *sd_ht, $verbose) = @_;
-
-   $verbose = 0 unless defined($verbose);
-   print STDERR "Linearize $chart_id\n" if $verbose;
-   my $current_chart_pos = 0;
-   my $current_linear_chart_pos = 0;
-   $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos} = $current_linear_chart_pos;
-   $sd_ht{LINPOS2POS}->{$chart_id}->{$current_linear_chart_pos} = $current_chart_pos;
-   print STDERR "  LINPOS2POS.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos\n" if $verbose;
-   my @end_chart_positions = keys %{$chart_ht{NODES_ENDING_AT}};
-   my $end_chart_pos = (@end_chart_positions) ? max(@end_chart_positions) : 0;
-   $sd_ht{MAXPOS}->{$chart_id} = $end_chart_pos;
-   print STDERR "  Chart span: $current_chart_pos-$end_chart_pos\n" if $verbose;
-   while ($current_chart_pos < $end_chart_pos) {
-      my @node_ids = keys %{$chart_ht{NODES_STARTING_AT}->{$current_chart_pos}};
-      foreach $node_id (@node_ids) {
-	 my $roman_s = $chart_ht{NODE_ROMAN}->{$node_id};
-         my @roman_chars = $utf8->split_into_utf8_characters($roman_s, "return only chars, return trailing whitespaces", *dummy_ht);
-         print STDERR "  $current_chart_pos/$current_linear_chart_pos node: $node_id $roman_s (@roman_chars)\n" if $verbose;
-	 if ($#roman_chars >= 1) {
-	    foreach $i ((1 .. $#roman_chars)) {
-	       $current_linear_chart_pos++;
-	       $sd_ht{SPLITPOS2LINPOS}->{$chart_id}->{$current_chart_pos}->{$node_id}->{$i} = $current_linear_chart_pos;
-	       $sd_ht{LINPOS2SPLITPOS}->{$chart_id}->{$current_linear_chart_pos}->{$current_chart_pos}->{$node_id}->{$i} = 1;
-	       print STDERR "  LINPOS2SPLITPOS.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos NODE: $node_id I: $i\n" if $verbose;
-	    }
-	 }
-      }
-      $current_chart_pos++;
-      if ($util->member($current_chart_pos, @end_chart_positions)) {
-         $current_linear_chart_pos++;
-         $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos} = $current_linear_chart_pos;
-         $sd_ht{LINPOS2POS}->{$chart_id}->{$current_linear_chart_pos} = $current_chart_pos;
-         print STDERR "  LINPOS2POS.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos\n" if $verbose;
-      }
-   }
-   $current_chart_pos = 0;
-   while ($current_chart_pos <= $end_chart_pos) {
-      my $current_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos};
-      $current_linear_chart_pos = "?" unless defined($current_linear_chart_pos);
-      my @node_ids = keys %{$chart_ht{NODES_STARTING_AT}->{$current_chart_pos}};
-      # print STDERR "  LINROM.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos NODES: @node_ids\n" if $verbose;
-      foreach $node_id (@node_ids) {
-	 my $end_pos = $chart_ht{NODE_END}->{$node_id};
-	 my $end_linpos = $sd_ht{POS2LINPOS}->{$chart_id}->{$end_pos};
-	 my $roman_s = $chart_ht{NODE_ROMAN}->{$node_id};
-         my @roman_chars = $utf8->split_into_utf8_characters($roman_s, "return only chars, return trailing whitespaces", *dummy_ht);
-         print STDERR "  LINROM.$chart_id LIN: $current_linear_chart_pos POS: $current_chart_pos NODE: $node_id CHARS: @roman_chars\n" if $verbose;
-	 if (@roman_chars) {
-            foreach $i ((0 .. $#roman_chars)) {
-	       my $from_linear_chart_pos 
-		  = (($i == 0) 
-		     ? $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos}
-	             : $sd_ht{SPLITPOS2LINPOS}->{$chart_id}->{$current_chart_pos}->{$node_id}->{$i});
-	       print STDERR "  FROM.$chart_id I: $i POS: $current_chart_pos NODE: $node_id FROM: $from_linear_chart_pos\n" if $verbose;
-	       my $to_linear_chart_pos
-		  = (($i == $#roman_chars)
-		     ? $end_linpos
-		     : $sd_ht{SPLITPOS2LINPOS}->{$chart_id}->{$current_chart_pos}->{$node_id}->{($i+1)});
-	       print STDERR "  TO.$chart_id I: $i POS: $current_chart_pos NODE: $node_id FROM: $to_linear_chart_pos\n" if $verbose;
-	       my $roman_char = $roman_chars[$i];
-	       $sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}->{$to_linear_chart_pos}->{$roman_char} = 1;
-	    }
-	 } else {
-	    my $from_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{$current_chart_pos};
-	    my $to_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{($current_chart_pos+1)};
-	    # HHERE check this out
-	    my $i = 1;
-	    while (! (defined($to_linear_chart_pos))) {
-	       $i++;
-	       $to_linear_chart_pos = $sd_ht{POS2LINPOS}->{$chart_id}->{($current_chart_pos+$i)};
-	    }
-	    if (defined($from_linear_chart_pos) && defined($to_linear_chart_pos)) {
-	       $sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}->{$to_linear_chart_pos}->{""} = 1
-	    } else {
-	       print STDERR "  UNDEF.$chart_id from: " 
-	                  . ((defined($from_linear_chart_pos)) ? $from_linear_chart_pos : "?")
-			  . " to: "
-	                  . ((defined($to_linear_chart_pos))   ? $to_linear_chart_pos   : "?")
-			  . "\n";
-	    }
-	 }
-      }
-      $current_chart_pos++;
-   }
-   $sd_ht{MAXLINPOS}->{$chart_id} = $sd_ht{POS2LINPOS}->{$chart_id}->{$end_chart_pos};
-}
-
-sub expand_lin_ij_roman {
-   local($this, *sd_ht, $chart_id, $lang_code, *ht) = @_;
-
-   foreach $start (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}}) {
-      foreach $end (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start}}) {
-	 foreach $roman (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start}->{$end}}) {
-	    if ($ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$roman}
-	     || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$roman}) {
-	       $this->expand_lin_ij_roman_rec(*sd_ht, $chart_id, $start, $end, $roman, $lang_code, *ht);
-	    }
-	 }
-      }
-   }
-}
-
-sub expand_lin_ij_roman_rec {
-   local($this, *sd_ht, $chart_id, $start, $end, $roman, $lang_code, *ht) = @_;
-
-   # print STDERR "  expand_lin_ij_roman_rec.$chart_id $start-$end $lang_code $roman\n";
-   return unless $ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$roman}
-              || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$roman};
-   foreach $new_end (keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$end}}) {
-      foreach $next_roman (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$end}->{$new_end}}) {
-         my $exp_roman = join("", $roman, $next_roman);
-         if ($ht{RULE_STRING}->{$lang_code}->{$exp_roman}
-          || $ht{RULE_STRING}->{""}->{$exp_roman}) {
-            $sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start}->{$new_end}->{$exp_roman} = 1;
-	    # print STDERR "  Expansion ($start-$new_end) $exp_roman\n";
-         }
-         if ($ht{RULE_STRING_HAS_EXPANSION}->{$lang_code}->{$exp_roman}
-          || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$exp_roman}) {
-	    $this->expand_lin_ij_roman_rec(*sd_ht, $chart_id, $start, $new_end, $exp_roman, $lang_code, *ht);
-         }
-      }
-   }
-}
-
-sub trace_string_distance {
-   local($this, *sd_ht, $chart1_id, $chart2_id, $control, $line_number, $cost) = @_;
-
-   my $chart_comb_id = join("/", $chart1_id, $chart2_id);
-   return "mismatch" if $sd_ht{MISMATCH}->{$chart_comb_id};
-   my $chart1_end = $sd_ht{MAXLINPOS}->{$chart1_id};
-   my $chart2_end = $sd_ht{MAXLINPOS}->{$chart2_id};
-   my $verbose = ($control =~ /verbose/);
-   my $chunks_p = ($control =~ /chunks/);
-   my @traces = ();
-   my @s1_s = ();
-   my @s2_s = ();
-   my @e1_s = ();
-   my @e2_s = ();
-   my @r1_s = ();
-   my @r2_s = ();
-   my @ic_s = ();
-
-   # print STDERR "trace_string_distance $chart1_id $chart2_id $line_number\n";
-   while ($chart1_end || $chart2_end) {
-      my $incr_cost = $sd_ht{INCR_COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-      my $prec_i = $sd_ht{PREC_I}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-      my $prec_j = $sd_ht{PREC_J}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-      if ($incr_cost || $verbose || $chunks_p) {
-         my $roman1 = $sd_ht{ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-         my $roman2 = $sd_ht{ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-         if ($verbose) {
-	    push(@traces, "$prec_i-$chart1_end/$prec_j-$chart2_end:$roman1/$roman2:$incr_cost");
-	 } else {
-	    if (defined($roman1)) {
-	       push(@traces, "$roman1/$roman2:$incr_cost");
-	    } else {
-	       $print_prec_i = (defined($prec_i)) ? $prec_i : "?";
-	       $print_prec_j = (defined($prec_j)) ? $prec_j : "?";
-	       print STDERR "  $prec_i-$chart1_end, $prec_j-$chart2_end\n";
-	    }
-	 }
-         if ($chunks_p) {
-            push(@s1_s, $prec_i);
-            push(@s2_s, $prec_j);
-            push(@e1_s, $chart1_end);
-            push(@e2_s, $chart2_end);
-            push(@r1_s, $roman1);
-            push(@r2_s, $roman2);
-            push(@ic_s, $incr_cost);
-         }
-      }
-      $chart1_end = $prec_i;
-      $chart2_end = $prec_j;
-   }
-   if ($chunks_p) {
-      my $r1 = "";
-      my $r2 = "";
-      my $tc = 0;
-      my $in_chunk = 0;
-      foreach $i ((0 .. $#ic_s)) {
-	 if ($ic_s[$i]) {
-	    $r1 = $r1_s[$i] . $r1;
-	    $r2 = $r2_s[$i] . $r2;
-	    $tc += $ic_s[$i];
-	    $in_chunk = 1;
-	 } elsif ($in_chunk) {
-	    $chunk = "$r1/$r2/$tc";
-	    $chunk .= "*" if $cost > 5;
-	    $sd_ht{N_COST_CHUNK}->{$chunk} = ($sd_ht{N_COST_CHUNK}->{$chunk} || 0) + 1;
-	    $sd_ht{EX_COST_CHUNK}->{$chunk}->{$line_number} = 1;
-            $r1 = "";
-            $r2 = "";
-            $tc = 0;
-	    $in_chunk = 0;
-	 }
-      }
-      if ($in_chunk) {
-	 $chunk = "$r1/$r2/$tc";
-	 $chunk .= "*" if $cost > 5;
-	 $sd_ht{N_COST_CHUNK}->{$chunk} = ($sd_ht{N_COST_CHUNK}->{$chunk} || 0) + 1;
-	 $sd_ht{EX_COST_CHUNK}->{$chunk}->{$line_number} = 1;
-      }
-   } else {
-      return join(" ", reverse @traces);
-   }
-}
-
-sub right_context_match {
-   local($this, $right_context_rule, *sd_ht, $chart_id, $start_pos) = @_;
-   
-   return 1 if $right_context_rule eq "";
-   if (($right_context_item, $right_context_rest) = ($right_context_rule =~ /^\[([^\[\]]*)\]*(.*)$/)) {
-      my $guarded_right_context_item = $right_context_item;
-      $guarded_right_context_item =~ s/\$/\\\$/g;
-      my @end_positions = keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start_pos}};
-      return 1 if ($#end_positions == -1)
-               && (($right_context_item eq "") 
-	        || ($right_context_item =~ /\$/));
-      foreach $end_pos (@end_positions) {
-	 my @romans = keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$start_pos}->{$end_pos}};
-         foreach $roman (@romans) {
-	    if ($roman =~ /^[$guarded_right_context_item]/) {
-	       return $this->right_context_match($right_context_rest, *sd_ht, $chart_id, $end_pos);
-	    }
-	 }
-      }
-   }
-   return 0;
-}
-
-sub string_distance {
-   local($this, *sd_ht, $chart1_id, $chart2_id, $lang_code1, $lang_code2, *ht, $control) = @_;
-
-   my $verbose = ($control =~ /verbose/i);
-   my $chart_comb_id = join("/", $chart1_id, $chart2_id);
-
-   my $chart1_end_pos = $sd_ht{MAXLINPOS}->{$chart1_id};
-   my $chart2_end_pos = $sd_ht{MAXLINPOS}->{$chart2_id};
-   print STDERR "string_distance.$chart_comb_id $chart1_end_pos/$chart2_end_pos\n" if $verbose;
-   $sd_ht{COST_IJ}->{$chart_comb_id}->{0}->{0} = 0;
-   $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{0}->{0} = "";
-   $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{0}->{0} = "";
-   # HHERE
-   foreach $chart1_start ((0 .. $chart1_end_pos)) {
-      # print STDERR "  C1 $chart1_start- ($chart1_start .. $chart1_end_pos)\n";
-      my $prev_further_expansion_possible = 0;
-      my @chart1_ends = sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart1_id}->{$chart1_start}};
-      my $max_chart1_ends = (@chart1_ends) ? $chart1_ends[$#chart1_ends] : -1;
-      foreach $chart1_end (($chart1_start .. $chart1_end_pos)) {
-	 my $further_expansion_possible = ($chart1_start == $chart1_end)
-				       || defined($sd_ht{LINPOS2SPLITPOS}->{$chart1_id}->{$chart1_start})
-				       || ($chart1_end < $max_chart1_ends);
-	 my @romans1 = (($chart1_start == $chart1_end)
-			? ("")
-			: (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart1_id}->{$chart1_start}->{$chart1_end}}));
-	 if ($#romans1 == -1) {
-	    $further_expansion_possible = 1 if $prev_further_expansion_possible;
-	 } else {
-	    $prev_further_expansion_possible = 0;
-	 }
-         # print STDERR "  C1 $chart1_start-$chart1_end romans1: @romans1 {$further_expansion_possible} *l*\n";
-	 foreach $roman1 (@romans1) {
-            # print STDERR "  C1 $chart1_start-$chart1_end $roman1 {$further_expansion_possible} *?*\n";
-	    next unless $ht{RULE_STRING}->{$lang_code1}->{$roman1}
-		     || $ht{RULE_STRING}->{""}->{$roman1};
-            # print STDERR "  C1 $chart1_start-$chart1_end $roman1 {$further_expansion_possible} ***\n";
-	    foreach $lang_code1o (($lang_code1, "")) {
-	       foreach $lang_code2o (($lang_code2, "")) {
-		  my @chart2_starts = (sort { $a <=> $b } keys %{$sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_start}});
-	          foreach $chart2_start (@chart2_starts) {
-                     # print STDERR "  C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start- (@chart2_starts)\n";
-		     foreach $chart2_end (($chart2_start .. $chart2_end_pos)) {
-                        print STDERR "  C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start-$chart2_end\n";
-			my @romans2 = (($chart2_start == $chart2_end)
-				      ? ("")
-				      : (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart2_id}->{$chart2_start}->{$chart2_end}}));
-			foreach $roman2 (@romans2) {
-			   if ($roman1 eq $roman2) {
-                              print STDERR "  C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start-$chart2_end $roman2 (IDENTITY)\n";
-			      my $cost = 0;
-			      my $preceding_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
-			      my $combined_cost = $preceding_cost + $cost;
-			      my $old_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-			      if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
-			         $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $combined_cost;
-			         push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
-			         $sd_ht{PREC_I}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart1_start;
-			         $sd_ht{PREC_J}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart2_start;
-			         $sd_ht{ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman1;
-			         $sd_ht{ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman2;
-			         $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
-			            = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman1;
-			         $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
-			            = $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman2;
-				 $comb_left_roman1 = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-			         $sd_ht{INCR_COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $cost;
-			         $sd_ht{COST_RULE}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = "IDENTITY";
-			         print STDERR "  New cost $chart1_end/$chart2_end: $combined_cost (+$cost from $chart1_start/$chart2_start $roman1/$roman2)\n" if $verbose;
-			      }
-			   } else {
-	                      next unless $ht{RULE_STRING}->{$lang_code2o}->{$roman2};
-                              print STDERR "  C1 $chart1_start-$chart1_end $roman1 C2 $chart2_start-$chart2_end $roman2\n";
-			      next unless defined($ht{COST}->{$lang_code1o}->{$lang_code2o}->{$roman1}->{$roman2});
-		              my @cost_rule_ids = keys %{$ht{COST}->{$lang_code1o}->{$lang_code2o}->{$roman1}->{$roman2}};
-		              foreach $cost_rule_id (@cost_rule_ids) {
-			         ## check whether any context requirements are satisfied
-			         # left context rules are regular expressions
-                                 my $left_context_rule1 = $ht{LEFT1}->{$cost_rule_id};
-			         if ($left_context_rule1) {
-				    my $comb_left_roman1 = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
-				    if (defined($comb_left_roman1)) {
-				       next unless $comb_left_roman1 =~ /$left_context_rule1/;
-			            } else {
-				       print STDERR "  No comb_left_roman1 value for $chart_comb_id $chart1_start,$chart2_start\n";
-				    }
-			         }
-                                 my $left_context_rule2 = $ht{LEFT2}->{$cost_rule_id};
-			         if ($left_context_rule2) {
-				    my $comb_left_roman2 = $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
-				    if (defined($comb_left_roman2)) {
-				       next unless $comb_left_roman2 =~ /$left_context_rule2/;
-				    } else {
-				       print STDERR "  No comb_left_roman2 value for $chart_comb_id $chart1_start,$chart2_start\n";
-				    }
-			         }
-                                 my $right_context_rule1 = $ht{RIGHT1}->{$cost_rule_id};
-			         if ($right_context_rule1) {
-			            my $match_p = $this->right_context_match($right_context_rule1, *sd_ht, $chart1_id, $chart1_end);
-				    # print STDERR "  Match?($right_context_rule1, 1, $chart1_end) = $match_p\n";
-			            next unless $match_p;
-			         }
-                                 my $right_context_rule2 = $ht{RIGHT2}->{$cost_rule_id};
-			         if ($right_context_rule2) {
-			            my $match_p = $this->right_context_match($right_context_rule2, *sd_ht, $chart2_id, $chart2_end);
-				    # print STDERR "  Match?($right_context_rule2, 2, $chart2_end) = $match_p\n";
-			            next unless $match_p;
-			         }
-			         my $cost = $ht{COST}->{$lang_code1o}->{$lang_code2o}->{$roman1}->{$roman2}->{$cost_rule_id};
-			         my $preceding_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_start}->{$chart2_start};
-			         my $combined_cost = $preceding_cost + $cost;
-			         my $old_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-			         if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
-			            $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $combined_cost;
-				    push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
-			            $sd_ht{PREC_I}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart1_start;
-			            $sd_ht{PREC_J}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $chart2_start;
-			            $sd_ht{ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman1;
-			            $sd_ht{ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $roman2;
-			            $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
-			               = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman1;
-			            $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_end}->{$chart2_end}
-			               = $sd_ht{COMB_LEFT_ROMAN2}->{$chart_comb_id}->{$chart1_start}->{$chart2_start} . $roman2;
-				    $comb_left_roman1 = $sd_ht{COMB_LEFT_ROMAN1}->{$chart_comb_id}->{$chart1_end}->{$chart2_end};
-				    # print STDERR "  Comb-left-roman1($chart_comb_id,$chart1_end,$chart2_end) = $comb_left_roman1\n";
-			            $sd_ht{INCR_COST_IJ}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $cost;
-			            $sd_ht{COST_RULE}->{$chart_comb_id}->{$chart1_end}->{$chart2_end} = $cost_rule_id;
-			            print STDERR "  New cost $chart1_end/$chart2_end: $combined_cost (+$cost from $chart1_start/$chart2_start $roman1/$roman2)\n" if $verbose;
-			         }
-			      }
-			   }
-			}
-		     }
-		  }
-	       }
-	    }
-	    $further_expansion_possible = 1
-	       if $ht{RULE_STRING_HAS_EXPANSION}->{$lang_code1}->{$roman1}
-	       || $ht{RULE_STRING_HAS_EXPANSION}->{""}->{$roman1};
-	    # print STDERR "  further_expansion_possible: $further_expansion_possible (lc: $lang_code1 r1: $roman1) ***\n";
-	 }
-         # print STDERR "  last C1 $chart1_start-$chart1_end (@romans1)\n" unless $further_expansion_possible;
-	 last unless $further_expansion_possible;
-         $prev_further_expansion_possible = 1 if $further_expansion_possible;
-      }
-   }
-   my $total_cost = $sd_ht{COST_IJ}->{$chart_comb_id}->{$chart1_end_pos}->{$chart2_end_pos};
-   unless (defined($total_cost)) {
-      $total_cost = 99.9999;
-      $sd_ht{MISMATCH}->{$chart_comb_id} = 1;
-   }
-   return $total_cost;
-}
-
-sub print_sd_ht {
-   local($this, *sd_ht, $chart1_id, $chart2_id, *OUT) = @_;
-
-   print OUT "string-distance chart:\n";
-   foreach $chart_id (($chart1_id, $chart2_id)) {
-      print OUT "SD chart $chart_id:\n";
-      foreach $from_linear_chart_pos (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}}) {
-	 foreach $to_linear_chart_pos (sort { $a <=> $b } keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}}) {
-	    foreach $roman_char (sort keys %{$sd_ht{LIN_IJ_ROMAN}->{$chart_id}->{$from_linear_chart_pos}->{$to_linear_chart_pos}}) {
-	       print OUT "  Lnode($from_linear_chart_pos-$to_linear_chart_pos): $roman_char\n";
-	    }
-	 }
-      }
-   }
-}
-
-sub print_chart_ht {
-   local($this, *chart_ht, *OUT) = @_;
-
-   print OUT "uroman chart:\n";
-   foreach $start (sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AT}}) {
-      foreach $end (sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}}) {
-	 foreach $node_id (keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}}) {
-	    $roman_s = $chart_ht{NODE_ROMAN}->{$node_id};
-	    print OUT "  Node $node_id ($start-$end): $roman_s\n";
-	 }
-      }
-   }
-}
-
-sub normalize_string {
-   local($this, $s) = @_;
-
-#  $s =~ s/(\xE2\x80\x8C)//g; # delete zero width non-joiner
-   $s =~ s/(\xE2\x80[\x93-\x94])/-/g; # en-dash, em-dash
-   $s =~ s/([\x00-\x7F\xC0-\xFE][\x80-\xBF]*)\1+/$1$1/g; # shorten 3 or more occurrences of same character in a row to 2
-   $s =~ s/[ \t]+/ /g;
-
-   return $s;
-}
-
-my $string_distance_chart_id = 0;
-sub string_distance_by_chart {
-   local($this, $s1, $s2, $lang_code1, $lang_code2, *ht, *pinyin_ht, $control) = @_;
-
-   $control = "" unless defined($control);
-   %sd_ht = ();
-
-   $s1 = $this->normalize_string($s1);
-   my $lc_s1 = $utf8->extended_lower_case($s1);
-   $string_distance_chart_id++;
-   my $chart1_id = $string_distance_chart_id;
-   *chart_ht = $romanizer->romanize($lc_s1, $lang_code1, "", *ht, *pinyin_ht, 0, "return chart", $chart1_id);
-   $this->linearize_chart_points(*chart_ht, $chart1_id, *sd_ht);
-   $this->expand_lin_ij_roman(*sd_ht, $chart1_id, $lang_code1, *ht);
-
-   $s2 = $this->normalize_string($s2);
-   my $lc_s2 = $utf8->extended_lower_case($s2);
-   $string_distance_chart_id++;
-   my $chart2_id = $string_distance_chart_id;
-   *chart_ht = $romanizer->romanize($lc_s2, $lang_code2, "", *ht, *pinyin_ht, 0, "return chart", $chart2_id);
-   $this->linearize_chart_points(*chart_ht, $chart2_id, *sd_ht);
-   $this->expand_lin_ij_roman(*sd_ht, $chart2_id, $lang_code2, *ht);
-
-   my $cost = $this->string_distance(*sd_ht, $chart1_id, $chart2_id, $lang_code1, $lang_code2, *ht, $control);
-   return $cost;
-}
-
-my $n_quick_romanized_string_distance = 0;
-sub quick_romanized_string_distance_by_chart {
-   local($this, $s1, $s2, *ht, $control, $lang_code1, $lang_code2) = @_;
-
-   # my $verbose = ($s1 eq "apit") && ($s2 eq "apet");
-   # print STDERR "Start quick_romanized_string_distance_by_chart\n";
-   $s1 = lc $s1;
-   $s2 = lc $s2;
-   $control = "" unless defined($control);
-   $lang_code1 = "" unless defined($lang_code1);
-   $lang_code2 = "" unless defined($lang_code2);
-   my $cache_p = ($control =~ /cache/);
-   my $total_cost;
-   if ($cache_p) {
-      $total_cost = $ht{CACHED_QRSD}->{$s1}->{$s2};
-      if (defined($total_cost)) {
-         return $total_cost;
-      }
-   }
-   my @lang_codes1 = ($lang_code1 eq "") ? ("") : ($lang_code1, "");
-   my @lang_codes2 = ($lang_code2 eq "") ? ("") : ($lang_code2, "");
-   my $chart1_end_pos = length($s1);
-   my $chart2_end_pos = length($s2);
-   my %sd_ht = ();
-   $sd_ht{COST_IJ}->{0}->{0} = 0;
-   foreach $chart1_start ((0 .. $chart1_end_pos)) {
-      foreach $chart1_end (($chart1_start .. $chart1_end_pos)) {
-	 my $substr1 = substr($s1, $chart1_start, ($chart1_end-$chart1_start));
-	 foreach $lang_code1o (@lang_codes1) {
-	    foreach $lang_code2o (@lang_codes2) {
-	       # next unless defined($ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1});
-            }
-	 }
-	 my @chart2_starts = (sort { $a <=> $b } keys %{$sd_ht{COST_IJ}->{$chart1_start}});
-	 foreach $chart2_start (@chart2_starts) {
-	    foreach $chart2_end (($chart2_start .. $chart2_end_pos)) {
-	       my $substr2 = substr($s2, $chart2_start, ($chart2_end-$chart2_start));
-	       foreach $lang_code1o (@lang_codes1) {
-	          foreach $lang_code2o (@lang_codes2) {
-		     if ($substr1 eq $substr2) {
-			my $cost = 0;
-	                my $preceding_cost = $sd_ht{COST_IJ}->{$chart1_start}->{$chart2_start};
-			if (defined($preceding_cost)) {
-		           my $combined_cost = $preceding_cost + $cost;
-	                   my $old_cost = $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end};
-	                   if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
-		              $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end} = $combined_cost;
-		              push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
-		           }
-			}
-		     } else {
-	                next unless defined($ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1}->{$substr2});
-	                my @cost_rule_ids = keys %{$ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1}->{$substr2}};
-	                my $best_cost = 99.99;
-	                foreach $cost_rule_id (@cost_rule_ids) {
-		           my $cost = $ht{COST}->{$lang_code1o}->{$lang_code2o}->{$substr1}->{$substr2}->{$cost_rule_id};
-                           my $left_context_rule1 = $ht{LEFT1}->{$cost_rule_id};
-			   next if $left_context_rule1
-			        && (! (substr($s1, 0, $chart1_start) =~ /$left_context_rule1/));
-                           my $left_context_rule2 = $ht{LEFT2}->{$cost_rule_id};
-			   next if $left_context_rule2
-			        && (! (substr($s2, 0, $chart2_start) =~ /$left_context_rule2/));
-                           my $right_context_rule1 = $ht{RIGHT1}->{$cost_rule_id};
-			   my $right_context1 = substr($s1, $chart1_end);
-			   next if $right_context_rule1
-			        && (! (($right_context1 =~ /^$right_context_rule1/)
-			            || (($right_context_rule1 =~ /^\[[^\[\]]*\$/)
-			             && ($right_context1 eq ""))));
-                           my $right_context_rule2 = $ht{RIGHT2}->{$cost_rule_id};
-			   my $right_context2 = substr($s2, $chart2_end);
-			   next if $right_context_rule2
-			        && (! (($right_context2 =~ /^$right_context_rule2/)
-			            || (($right_context_rule2 =~ /^\[[^\[\]]*\$/)
-			             && ($right_context2 eq ""))));
-		           $best_cost = $cost if $cost < $best_cost;
-	                   my $preceding_cost = $sd_ht{COST_IJ}->{$chart1_start}->{$chart2_start};
-		           my $combined_cost = $preceding_cost + $cost;
-	                   my $old_cost = $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end};
-	                   if ((! defined($old_cost)) || ($combined_cost < $old_cost)) {
-		              $sd_ht{COST_IJ}->{$chart1_end}->{$chart2_end} = $combined_cost;
-		              push(@chart2_starts, $chart2_end) unless $util->member($chart2_end, @chart2_starts);
-		           }
-		        }
-		     }
-		  }
-	       }
-	    }
-	 }
-      }
-   }
-   $total_cost = $sd_ht{COST_IJ}->{$chart1_end_pos}->{$chart2_end_pos};
-   $total_cost = 99.99 unless defined($total_cost);
-   $ht{CACHED_QRSD}->{$s1}->{$s2} = $total_cost if $cache_p;
-   $n_quick_romanized_string_distance++;
-   return $total_cost;
-}
-
-sub get_n_quick_romanized_string_distance {
-   return $n_quick_romanized_string_distance;
-}
-
-1;
-
diff --git a/spaces/allknowingroger/Image-Models-Test201/app.py b/spaces/allknowingroger/Image-Models-Test201/app.py
deleted file mode 100644
index 44d4ef0d8d6fe6b4496551356a519529427c5546..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test201/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "AlexandreBinninger/sd-pokemon-model-lora-sdxl",
-    "hyeju/sdxl-bird-emoji",
-    "LilyNgo/lora_Galaxiga_gemstone-trained-xl-colab",
-    "LilyNgo/lora_Galaxiga_stone-trained-xl-colab",
-    "21j3h123/CURS3D_DaLi13",
-    "naphatmanu/sdxl-lora-index-modern-neo-classic-1",
-    "vickyannap/headshots",
-    "digiplay/quincemix_v1",
-    "livingbox/model-test-10-oct",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/alphunt/diffdock-alphunt-demo/esm/esm/data.py b/spaces/alphunt/diffdock-alphunt-demo/esm/esm/data.py
deleted file mode 100644
index 0bf1c63e9f56369d371c5c453d4c3b1bf13ed16f..0000000000000000000000000000000000000000
--- a/spaces/alphunt/diffdock-alphunt-demo/esm/esm/data.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import itertools
-import os
-from typing import Sequence, Tuple, List, Union
-import pickle
-import re
-import shutil
-import torch
-from pathlib import Path
-from esm.constants import proteinseq_toks
-
-RawMSA = Sequence[Tuple[str, str]]
-
-
-class FastaBatchedDataset(object):
-    def __init__(self, sequence_labels, sequence_strs):
-        self.sequence_labels = list(sequence_labels)
-        self.sequence_strs = list(sequence_strs)
-
-    @classmethod
-    def from_file(cls, fasta_file):
-        sequence_labels, sequence_strs = [], []
-        cur_seq_label = None
-        buf = []
-
-        def _flush_current_seq():
-            nonlocal cur_seq_label, buf
-            if cur_seq_label is None:
-                return
-            sequence_labels.append(cur_seq_label)
-            sequence_strs.append("".join(buf))
-            cur_seq_label = None
-            buf = []
-
-        with open(fasta_file, "r") as infile:
-            for line_idx, line in enumerate(infile):
-                if line.startswith(">"):  # label line
-                    _flush_current_seq()
-                    line = line[1:].strip()
-                    if len(line) > 0:
-                        cur_seq_label = line
-                    else:
-                        cur_seq_label = f"seqnum{line_idx:09d}"
-                else:  # sequence line
-                    buf.append(line.strip())
-
-        _flush_current_seq()
-
-        assert len(set(sequence_labels)) == len(
-            sequence_labels
-        ), "Found duplicate sequence labels"
-
-        return cls(sequence_labels, sequence_strs)
-
-    def __len__(self):
-        return len(self.sequence_labels)
-
-    def __getitem__(self, idx):
-        return self.sequence_labels[idx], self.sequence_strs[idx]
-
-    def get_batch_indices(self, toks_per_batch, extra_toks_per_seq=0):
-        sizes = [(len(s), i) for i, s in enumerate(self.sequence_strs)]
-        sizes.sort()
-        batches = []
-        buf = []
-        max_len = 0
-
-        def _flush_current_buf():
-            nonlocal max_len, buf
-            if len(buf) == 0:
-                return
-            batches.append(buf)
-            buf = []
-            max_len = 0
-
-        for sz, i in sizes:
-            sz += extra_toks_per_seq
-            if max(sz, max_len) * (len(buf) + 1) > toks_per_batch:
-                _flush_current_buf()
-            max_len = max(max_len, sz)
-            buf.append(i)
-
-        _flush_current_buf()
-        return batches
-
-
-class Alphabet(object):
-    def __init__(
-        self,
-        standard_toks: Sequence[str],
-        prepend_toks: Sequence[str] = ("<null_0>", "<pad>", "<eos>", "<unk>"),
-        append_toks: Sequence[str] = ("<cls>", "<mask>", "<sep>"),
-        prepend_bos: bool = True,
-        append_eos: bool = False,
-        use_msa: bool = False,
-    ):
-        self.standard_toks = list(standard_toks)
-        self.prepend_toks = list(prepend_toks)
-        self.append_toks = list(append_toks)
-        self.prepend_bos = prepend_bos
-        self.append_eos = append_eos
-        self.use_msa = use_msa
-
-        self.all_toks = list(self.prepend_toks)
-        self.all_toks.extend(self.standard_toks)
-        for i in range((8 - (len(self.all_toks) % 8)) % 8):
-            self.all_toks.append(f"<null_{i  + 1}>")
-        self.all_toks.extend(self.append_toks)
-
-        self.tok_to_idx = {tok: i for i, tok in enumerate(self.all_toks)}
-
-        self.unk_idx = self.tok_to_idx["<unk>"]
-        self.padding_idx = self.get_idx("<pad>")
-        self.cls_idx = self.get_idx("<cls>")
-        self.mask_idx = self.get_idx("<mask>")
-        self.eos_idx = self.get_idx("<eos>")
-        self.all_special_tokens = ['<eos>', '<unk>', '<pad>', '<cls>', '<mask>']
-        self.unique_no_split_tokens = self.all_toks
-
-    def __len__(self):
-        return len(self.all_toks)
-
-    def get_idx(self, tok):
-        return self.tok_to_idx.get(tok, self.unk_idx)
-
-    def get_tok(self, ind):
-        return self.all_toks[ind]
-
-    def to_dict(self):
-        return self.tok_to_idx.copy()
-
-    def get_batch_converter(self, truncation_seq_length: int = None):
-        if self.use_msa:
-            return MSABatchConverter(self, truncation_seq_length)
-        else:
-            return BatchConverter(self, truncation_seq_length)
-
-    @classmethod
-    def from_architecture(cls, name: str) -> "Alphabet":
-        if name in ("ESM-1", "protein_bert_base"):
-            standard_toks = proteinseq_toks["toks"]
-            prepend_toks: Tuple[str, ...] = ("<null_0>", "<pad>", "<eos>", "<unk>")
-            append_toks: Tuple[str, ...] = ("<cls>", "<mask>", "<sep>")
-            prepend_bos = True
-            append_eos = False
-            use_msa = False
-        elif name in ("ESM-1b", "roberta_large"):
-            standard_toks = proteinseq_toks["toks"]
-            prepend_toks = ("<cls>", "<pad>", "<eos>", "<unk>")
-            append_toks = ("<mask>",)
-            prepend_bos = True
-            append_eos = True
-            use_msa = False
-        elif name in ("MSA Transformer", "msa_transformer"):
-            standard_toks = proteinseq_toks["toks"]
-            prepend_toks = ("<cls>", "<pad>", "<eos>", "<unk>")
-            append_toks = ("<mask>",)
-            prepend_bos = True
-            append_eos = False
-            use_msa = True
-        elif "invariant_gvp" in name.lower():
-            standard_toks = proteinseq_toks["toks"]
-            prepend_toks = ("<null_0>", "<pad>", "<eos>", "<unk>")
-            append_toks = ("<mask>", "<cath>", "<af2>")
-            prepend_bos = True
-            append_eos = False
-            use_msa = False
-        else:
-            raise ValueError("Unknown architecture selected")
-        return cls(standard_toks, prepend_toks, append_toks, prepend_bos, append_eos, use_msa)
-
-    def _tokenize(self, text) -> str:
-        return text.split()
-
-    def tokenize(self, text, **kwargs) -> List[str]:
-        """
-        Inspired by https://github.com/huggingface/transformers/blob/master/src/transformers/tokenization_utils.py
-        Converts a string in a sequence of tokens, using the tokenizer.
-
-        Args:
-            text (:obj:`str`):
-                The sequence to be encoded.
-
-        Returns:
-            :obj:`List[str]`: The list of tokens.
-        """
-
-        def split_on_token(tok, text):
-            result = []
-            split_text = text.split(tok)
-            for i, sub_text in enumerate(split_text):
-                # AddedToken can control whitespace stripping around them.
-                # We use them for GPT2 and Roberta to have different behavior depending on the special token
-                # Cf. https://github.com/huggingface/transformers/pull/2778
-                # and https://github.com/huggingface/transformers/issues/3788
-                # We strip left and right by default
-                if i < len(split_text) - 1:
-                    sub_text = sub_text.rstrip()
-                if i > 0:
-                    sub_text = sub_text.lstrip()
-
-                if i == 0 and not sub_text:
-                    result.append(tok)
-                elif i == len(split_text) - 1:
-                    if sub_text:
-                        result.append(sub_text)
-                    else:
-                        pass
-                else:
-                    if sub_text:
-                        result.append(sub_text)
-                    result.append(tok)
-            return result
-
-        def split_on_tokens(tok_list, text):
-            if not text.strip():
-                return []
-
-            tokenized_text = []
-            text_list = [text]
-            for tok in tok_list:
-                tokenized_text = []
-                for sub_text in text_list:
-                    if sub_text not in self.unique_no_split_tokens:
-                        tokenized_text.extend(split_on_token(tok, sub_text))
-                    else:
-                        tokenized_text.append(sub_text)
-                text_list = tokenized_text
-
-            return list(
-                itertools.chain.from_iterable(
-                    (
-                        self._tokenize(token)
-                        if token not in self.unique_no_split_tokens
-                        else [token]
-                        for token in tokenized_text
-                    )
-                )
-            )
-
-        no_split_token = self.unique_no_split_tokens
-        tokenized_text = split_on_tokens(no_split_token, text)
-        return tokenized_text
-
-    def encode(self, text):
-        return [self.tok_to_idx[tok] for tok in self.tokenize(text)]
-
-
-class BatchConverter(object):
-    """Callable to convert an unprocessed (labels + strings) batch to a
-    processed (labels + tensor) batch.
-    """
-
-    def __init__(self, alphabet, truncation_seq_length: int = None):
-        self.alphabet = alphabet
-        self.truncation_seq_length = truncation_seq_length
-
-    def __call__(self, raw_batch: Sequence[Tuple[str, str]]):
-        # RoBERTa uses an eos token, while ESM-1 does not.
-        batch_size = len(raw_batch)
-        batch_labels, seq_str_list = zip(*raw_batch)
-        seq_encoded_list = [self.alphabet.encode(seq_str) for seq_str in seq_str_list]
-        if self.truncation_seq_length:
-            seq_encoded_list = [seq_str[:self.truncation_seq_length] for seq_str in seq_encoded_list]
-        max_len = max(len(seq_encoded) for seq_encoded in seq_encoded_list)
-        tokens = torch.empty(
-            (
-                batch_size,
-                max_len + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),
-            ),
-            dtype=torch.int64,
-        )
-        tokens.fill_(self.alphabet.padding_idx)
-        labels = []
-        strs = []
-
-        for i, (label, seq_str, seq_encoded) in enumerate(
-            zip(batch_labels, seq_str_list, seq_encoded_list)
-        ):
-            labels.append(label)
-            strs.append(seq_str)
-            if self.alphabet.prepend_bos:
-                tokens[i, 0] = self.alphabet.cls_idx
-            seq = torch.tensor(seq_encoded, dtype=torch.int64)
-            tokens[
-                i,
-                int(self.alphabet.prepend_bos) : len(seq_encoded)
-                + int(self.alphabet.prepend_bos),
-            ] = seq
-            if self.alphabet.append_eos:
-                tokens[i, len(seq_encoded) + int(self.alphabet.prepend_bos)] = self.alphabet.eos_idx
-
-        return labels, strs, tokens
-
-
-class MSABatchConverter(BatchConverter):
-    def __call__(self, inputs: Union[Sequence[RawMSA], RawMSA]):
-        if isinstance(inputs[0][0], str):
-            # Input is a single MSA
-            raw_batch: Sequence[RawMSA] = [inputs]  # type: ignore
-        else:
-            raw_batch = inputs  # type: ignore
-
-        batch_size = len(raw_batch)
-        max_alignments = max(len(msa) for msa in raw_batch)
-        max_seqlen = max(len(msa[0][1]) for msa in raw_batch)
-
-        tokens = torch.empty(
-            (
-                batch_size,
-                max_alignments,
-                max_seqlen + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),
-            ),
-            dtype=torch.int64,
-        )
-        tokens.fill_(self.alphabet.padding_idx)
-        labels = []
-        strs = []
-
-        for i, msa in enumerate(raw_batch):
-            msa_seqlens = set(len(seq) for _, seq in msa)
-            if not len(msa_seqlens) == 1:
-                raise RuntimeError(
-                    "Received unaligned sequences for input to MSA, all sequence "
-                    "lengths must be equal."
-                )
-            msa_labels, msa_strs, msa_tokens = super().__call__(msa)
-            labels.append(msa_labels)
-            strs.append(msa_strs)
-            tokens[i, : msa_tokens.size(0), : msa_tokens.size(1)] = msa_tokens
-
-        return labels, strs, tokens
-
-
-def read_fasta(
-    path,
-    keep_gaps=True,
-    keep_insertions=True,
-    to_upper=False,
-):
-    with open(path, "r") as f:
-        for result in read_alignment_lines(
-            f, keep_gaps=keep_gaps, keep_insertions=keep_insertions, to_upper=to_upper
-        ):
-            yield result
-
-
-def read_alignment_lines(
-    lines,
-    keep_gaps=True,
-    keep_insertions=True,
-    to_upper=False,
-):
-    seq = desc = None
-
-    def parse(s):
-        if not keep_gaps:
-            s = re.sub("-", "", s)
-        if not keep_insertions:
-            s = re.sub("[a-z]", "", s)
-        return s.upper() if to_upper else s
-
-    for line in lines:
-        # Line may be empty if seq % file_line_width == 0
-        if len(line) > 0 and line[0] == ">":
-            if seq is not None:
-                yield desc, parse(seq)
-            desc = line.strip()
-            seq = ""
-        else:
-            assert isinstance(seq, str)
-            seq += line.strip()
-    assert isinstance(seq, str) and isinstance(desc, str)
-    yield desc, parse(seq)
-
-
-class ESMStructuralSplitDataset(torch.utils.data.Dataset):
-    """
-    Structural Split Dataset as described in section A.10 of the supplement of our paper.
-    https://doi.org/10.1101/622803
-
-    We use the full version of SCOPe 2.07, clustered at 90% sequence identity,
-    generated on January 23, 2020.
-
-    For each SCOPe domain:
-        - We extract the sequence from the corresponding PDB file
-        - We extract the 3D coordinates of the Carbon beta atoms, aligning them
-          to the sequence. We put NaN where Cb atoms are missing.
-        - From the 3D coordinates, we calculate a pairwise distance map, based
-          on L2 distance
-        - We use DSSP to generate secondary structure labels for the corresponding
-          PDB file. This is also aligned to the sequence. We put - where SSP
-          labels are missing.
-
-    For each SCOPe classification level of family/superfamily/fold (in order of difficulty),
-    we have split the data into 5 partitions for cross validation. These are provided
-    in a downloaded splits folder, in the format:
-            splits/{split_level}/{cv_partition}/{train|valid}.txt
-    where train is the partition and valid is the concatentation of the remaining 4.
-
-    For each SCOPe domain, we provide a pkl dump that contains:
-        - seq    : The domain sequence, stored as an L-length string
-        - ssp    : The secondary structure labels, stored as an L-length string
-        - dist   : The distance map, stored as an LxL numpy array
-        - coords : The 3D coordinates, stored as an Lx3 numpy array
-
-    """
-
-    base_folder = "structural-data"
-    file_list = [
-        #  url  tar filename   filename      MD5 Hash
-        (
-            "https://dl.fbaipublicfiles.com/fair-esm/structural-data/splits.tar.gz",
-            "splits.tar.gz",
-            "splits",
-            "456fe1c7f22c9d3d8dfe9735da52411d",
-        ),
-        (
-            "https://dl.fbaipublicfiles.com/fair-esm/structural-data/pkl.tar.gz",
-            "pkl.tar.gz",
-            "pkl",
-            "644ea91e56066c750cd50101d390f5db",
-        ),
-    ]
-
-    def __init__(
-        self,
-        split_level,
-        cv_partition,
-        split,
-        root_path=os.path.expanduser("~/.cache/torch/data/esm"),
-        download=False,
-    ):
-        super().__init__()
-        assert split in [
-            "train",
-            "valid",
-        ], "train_valid must be 'train' or 'valid'"
-        self.root_path = root_path
-        self.base_path = os.path.join(self.root_path, self.base_folder)
-
-        # check if root path has what you need or else download it
-        if download:
-            self.download()
-
-        self.split_file = os.path.join(
-            self.base_path, "splits", split_level, cv_partition, f"{split}.txt"
-        )
-        self.pkl_dir = os.path.join(self.base_path, "pkl")
-        self.names = []
-        with open(self.split_file) as f:
-            self.names = f.read().splitlines()
-
-    def __len__(self):
-        return len(self.names)
-
-    def _check_exists(self) -> bool:
-        for (_, _, filename, _) in self.file_list:
-            fpath = os.path.join(self.base_path, filename)
-            if not os.path.exists(fpath) or not os.path.isdir(fpath):
-                return False
-        return True
-
-    def download(self):
-
-        if self._check_exists():
-            print("Files already downloaded and verified")
-            return
-
-        from torchvision.datasets.utils import download_url
-
-        for url, tar_filename, filename, md5_hash in self.file_list:
-            download_path = os.path.join(self.base_path, tar_filename)
-            download_url(url=url, root=self.base_path, filename=tar_filename, md5=md5_hash)
-            shutil.unpack_archive(download_path, self.base_path)
-
-    def __getitem__(self, idx):
-        """
-        Returns a dict with the following entires
-         - seq : Str (domain sequence)
-         - ssp : Str (SSP labels)
-         - dist : np.array (distance map)
-         - coords : np.array (3D coordinates)
-        """
-        name = self.names[idx]
-        pkl_fname = os.path.join(self.pkl_dir, name[1:3], f"{name}.pkl")
-        with open(pkl_fname, "rb") as f:
-            obj = pickle.load(f)
-        return obj
diff --git a/spaces/alvanlii/RDM-Region-Aware-Diffusion-Model/run_edit.py b/spaces/alvanlii/RDM-Region-Aware-Diffusion-Model/run_edit.py
deleted file mode 100644
index 00c30b17e63e6c0619b584ba526c0accef12a6e5..0000000000000000000000000000000000000000
--- a/spaces/alvanlii/RDM-Region-Aware-Diffusion-Model/run_edit.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import gc
-import os
-import io
-import math
-import sys
-import tempfile
-
-from PIL import Image, ImageOps
-import requests
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from torchvision.transforms import functional as TF
-from tqdm.notebook import tqdm
-
-import numpy as np
-
-from math import log2, sqrt
-
-import argparse
-import pickle
-
-
-
-
-################################### mask_fusion ######################################
-from util.metrics_accumulator import MetricsAccumulator
-metrics_accumulator = MetricsAccumulator()
-
-from pathlib import Path
-from PIL import Image
-################################### mask_fusion ######################################
-
-import clip
-import lpips
-from torch.nn.functional import mse_loss
-
-################################### CLIPseg ######################################
-from  torchvision import utils as vutils
-import cv2  
-
-################################### CLIPseg ######################################
-
-def str2bool(x):
-    return x.lower() in ('true')
-    
-USE_CPU = False
-device = torch.device('cuda:0' if (torch.cuda.is_available() and not USE_CPU) else 'cpu')
-
-
-def fetch(url_or_path):
-    if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
-        r = requests.get(url_or_path)
-        r.raise_for_status()
-        fd = io.BytesIO()
-        fd.write(r.content)
-        fd.seek(0)
-        return fd
-    return open(url_or_path, 'rb')
-
-
-class MakeCutouts(nn.Module):
-    def __init__(self, cut_size, cutn, cut_pow=1.):
-        super().__init__()
-
-        self.cut_size = cut_size
-        self.cutn = cutn
-        self.cut_pow = cut_pow
-
-    def forward(self, input):
-        sideY, sideX = input.shape[2:4]
-        max_size = min(sideX, sideY)
-        min_size = min(sideX, sideY, self.cut_size)
-        cutouts = []
-        for _ in range(self.cutn):
-            size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
-            offsetx = torch.randint(0, sideX - size + 1, ())
-            offsety = torch.randint(0, sideY - size + 1, ())
-            cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
-            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
-        return torch.cat(cutouts)
-
-def spherical_dist_loss(x, y):
-    x = F.normalize(x, dim=-1)
-    y = F.normalize(y, dim=-1)
-    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-
-
-def do_run(
-    arg_seed, arg_text, arg_batch_size, arg_num_batches, arg_negative, arg_cutn, arg_edit, arg_height, arg_width,
-    arg_edit_y, arg_edit_x, arg_edit_width, arg_edit_height, mask, arg_guidance_scale, arg_background_preservation_loss,
-    arg_lpips_sim_lambda, arg_l2_sim_lambda, arg_ddpm, arg_ddim, arg_enforce_background, arg_clip_guidance_scale,
-    arg_clip_guidance, model_params, model, diffusion, ldm, bert, clip_model
-):
-    normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
-
-    if arg_seed >= 0:
-        torch.manual_seed(arg_seed)
-
-    text_emb = bert.encode([arg_text] * arg_batch_size).to(device).float()
-    text_blank = bert.encode([arg_negative] * arg_batch_size).to(device).float()
-
-    text = clip.tokenize([arg_text] * arg_batch_size, truncate=True).to(device)
-    text_clip_blank = clip.tokenize([arg_negative] * arg_batch_size, truncate=True).to(device)
-
-
-
-    text_emb_clip = clip_model.encode_text(text)
-    text_emb_clip_blank = clip_model.encode_text(text_clip_blank)
-    make_cutouts = MakeCutouts(clip_model.visual.input_resolution, arg_cutn)
-    text_emb_norm = text_emb_clip[0] / text_emb_clip[0].norm(dim=-1, keepdim=True)
-    image_embed = None
-
-    if arg_edit:
-        w = arg_edit_width if arg_edit_width else arg_width
-        h = arg_edit_height if arg_edit_height else arg_height
-
-        arg_edit = arg_edit.convert('RGB')
-        input_image_pil = arg_edit
-
-        init_image_pil = input_image_pil.resize((arg_height, arg_width), Image.Resampling.LANCZOS)
-
-        input_image_pil = ImageOps.fit(input_image_pil, (w, h))
-
-        im = transforms.ToTensor()(input_image_pil).unsqueeze(0).to(device)
-
-        init_image = (TF.to_tensor(init_image_pil).to(device).unsqueeze(0).mul(2).sub(1))
-
-        im = 2*im-1
-        im = ldm.encode(im).sample()
-
-        y = arg_edit_y//8
-        x = arg_edit_x//8
-
-        input_image = torch.zeros(1, 4, arg_height//8, arg_width//8, device=device)
-
-        ycrop = y + im.shape[2] - input_image.shape[2]
-        xcrop = x + im.shape[3] - input_image.shape[3]
-
-        ycrop = ycrop if ycrop > 0 else 0
-        xcrop = xcrop if xcrop > 0 else 0
-
-        input_image[0,:,y if y >=0 else 0:y+im.shape[2],x if x >=0 else 0:x+im.shape[3]] = im[:,:,0 if y > 0 else -y:im.shape[2]-ycrop,0 if x > 0 else -x:im.shape[3]-xcrop]
-
-        input_image_pil = ldm.decode(input_image)
-        input_image_pil = TF.to_pil_image(input_image_pil.squeeze(0).add(1).div(2).clamp(0, 1))
-
-        input_image *= 0.18215
-
-        new_mask = TF.resize(mask.unsqueeze(0).unsqueeze(0).to(device), (arg_width//8, arg_height//8))
-
-        mask1 = (new_mask > 0.5)
-        mask1 = mask1.float()
-
-        input_image *= mask1
-
-        image_embed = torch.cat(arg_batch_size*2*[input_image], dim=0).float()
-    elif model_params['image_condition']:
-        # using inpaint model but no image is provided
-        image_embed = torch.zeros(arg_batch_size*2, 4, arg_height//8, arg_width//8, device=device)
-
-    kwargs = {
-        "context": torch.cat([text_emb, text_blank], dim=0).float(),
-        "clip_embed": torch.cat([text_emb_clip, text_emb_clip_blank], dim=0).float() if model_params['clip_embed_dim'] else None,
-        "image_embed": image_embed
-    }
-
-    # Create a classifier-free guidance sampling function
-    def model_fn(x_t, ts, **kwargs):
-        half = x_t[: len(x_t) // 2]
-        combined = torch.cat([half, half], dim=0)
-        model_out = model(combined, ts, **kwargs)
-        eps, rest = model_out[:, :3], model_out[:, 3:]
-        cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-        half_eps = uncond_eps + arg_guidance_scale * (cond_eps - uncond_eps)
-        eps = torch.cat([half_eps, half_eps], dim=0)
-        return torch.cat([eps, rest], dim=1)
-
-    cur_t = None
-
-    @torch.no_grad()
-    def postprocess_fn(out, t):
-        if mask is not None:
-            background_stage_t = diffusion.q_sample(init_image, t[0])
-            background_stage_t = torch.tile(
-                background_stage_t, dims=(arg_batch_size, 1, 1, 1)
-            )
-            out["sample"] = out["sample"] * mask + background_stage_t * (1 - mask)
-        return out
-
-    # if arg_ddpm:
-    #     sample_fn = diffusion.p_sample_loop_progressive
-    # elif arg_ddim:
-    #     sample_fn = diffusion.ddim_sample_loop_progressive
-    # else:
-    sample_fn = diffusion.plms_sample_loop_progressive
-
-    def save_sample(i, sample):
-        out_ims = []
-        for k, image in enumerate(sample['pred_xstart'][:arg_batch_size]):
-            image /= 0.18215
-            im = image.unsqueeze(0)
-            out = ldm.decode(im)
-            metrics_accumulator.print_average_metric()
-
-            for b in range(arg_batch_size):
-                pred_image = sample["pred_xstart"][b]
-
-                if arg_enforce_background:
-                    new_mask = TF.resize(mask.unsqueeze(0).unsqueeze(0).to(device), (arg_width, arg_height))
-                    pred_image = (
-                        init_image[0] * new_mask[0] + out * (1 - new_mask[0])
-                    )
-
-                pred_image_pil = TF.to_pil_image(pred_image.squeeze(0).add(1).div(2).clamp(0, 1))
-                out_ims.append(pred_image_pil)
-        return out_ims
-            
-
-    all_saved_ims = []
-    for i in range(arg_num_batches):
-        cur_t = diffusion.num_timesteps - 1
-
-        samples = sample_fn(
-            model_fn,
-            (arg_batch_size*2, 4, int(arg_height//8), int(arg_width//8)),
-            clip_denoised=False,
-            model_kwargs=kwargs,
-            cond_fn=None,
-            device=device,
-            progress=True,
-        )
-    
-        for j, sample in enumerate(samples):
-            cur_t -= 1
-            if j % 5 == 0 and j != diffusion.num_timesteps - 1:
-                all_saved_ims += save_sample(i, sample)
-        all_saved_ims += save_sample(i, sample)
-
-    return all_saved_ims
-
-def run_model(
-    segmodel, model, diffusion, ldm, bert, clip_model, model_params,
-    from_text, instruction, negative_prompt, original_img, seed, guidance_scale, clip_guidance_scale, cutn, l2_sim_lambda
-):
-    input_image = original_img
-
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        transforms.Resize((256, 256)),
-    ])
-    img = transform(input_image).unsqueeze(0)
-
-    with torch.no_grad():
-        preds = segmodel(img.repeat(1,1,1,1), from_text)[0]
-
-    mask = torch.sigmoid(preds[0][0])
-    image = (mask.detach().cpu().numpy() * 255).astype(np.uint8) # cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  
-    ret, thresh = cv2.threshold(image, 100, 255, cv2.THRESH_TRUNC, image) 
-    timg = np.array(thresh)
-    x, y = timg.shape
-    for row in range(x):
-        for col in range(y):
-            if (timg[row][col]) == 100:
-                timg[row][col] = 255
-            if (timg[row][col]) < 100:
-                timg[row][col] = 0
-    
-    fulltensor = torch.full_like(mask, fill_value=255)
-    bgtensor = fulltensor-timg
-    mask = bgtensor / 255.0
-
-    gc.collect()
-    use_ddim = False
-    use_ddpm = False
-    all_saved_ims = do_run(
-        seed, instruction, 1, 1, negative_prompt, cutn, input_image, 256, 256, 
-        0, 0, 0, 0, mask, guidance_scale, True,
-        1000, l2_sim_lambda, use_ddpm, use_ddim, True, clip_guidance_scale, False,
-        model_params, model, diffusion, ldm, bert, clip_model
-    )
-        
-    return all_saved_ims[-1]
-
-
-
diff --git a/spaces/anton-l/youtube-subs-wav2vec/README.md b/spaces/anton-l/youtube-subs-wav2vec/README.md
deleted file mode 100644
index 9c7a529389122146fd135d79e6e7e689458a6806..0000000000000000000000000000000000000000
--- a/spaces/anton-l/youtube-subs-wav2vec/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: YouTube Streaming ASR
-emoji: 📺
-colorFrom: red
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/anubhavmaity/bike-classification/README.md b/spaces/anubhavmaity/bike-classification/README.md
deleted file mode 100644
index aa0f2b8621fe29a1780e0f4628b23ae739d85d97..0000000000000000000000000000000000000000
--- a/spaces/anubhavmaity/bike-classification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bike Classification
-emoji: 😻
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/spaces/aodianyun/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
deleted file mode 100644
index 5c0488e5f6fcea41e7f9fa25070e38fbfe656478..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
+++ /dev/null
@@ -1,1449 +0,0 @@
-# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
-# Original filename: ldm/models/diffusion/ddpm.py
-# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
-# Some models such as LDSR require VQ to work correctly
-# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-import ldm.models.diffusion.ddpm
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPMV1(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapperV1(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                    1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        else:
-            raise NotImplementedError("mu not supported")
-        # TODO how to choose this term
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        else:
-            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusionV1(DDPMV1):
-    """main class"""
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 *args, **kwargs):
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__':
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None  
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                            force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ['caption', 'coordinates_bbox']:
-                    xc = batch[cond_key]
-                elif cond_key == 'class_label':
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    # same as above but without decorator
-    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                df = self.split_input_params["vqf"]
-                self.split_input_params['original_image_size'] = x.shape[-2:]
-                bs, nc, h, w = x.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
-                z = unfold(x)  # (bn, nc * prod(**ks), L)
-                # Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
-                               for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)
-                o = o * weighting
-
-                # Reverse reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization
-                return decoded
-
-            else:
-                return self.first_stage_model.encode(x)
-        else:
-            return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c)
-        return loss
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                c = self.get_learned_conditioning(c)
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        if hasattr(self, "split_input_params"):
-            assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
-            ks = self.split_input_params["ks"]  # eg. (128, 128)
-            stride = self.split_input_params["stride"]  # eg. (64, 64)
-
-            h, w = x_noisy.shape[-2:]
-
-            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
-            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
-            # Reshape to img shape
-            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
-            if self.cond_stage_key in ["image", "LR_image", "segmentation",
-                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
-                c_key = next(iter(cond.keys()))  # get key
-                c = next(iter(cond.values()))  # get value
-                assert (len(c) == 1)  # todo extend to list with more than one elem
-                c = c[0]  # get element
-
-                c = unfold(c)
-                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
-            elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
-                # assuming padding of unfold is always 0 and its dilation is always 1
-                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
-                full_img_h, full_img_w = self.split_input_params['original_image_size']
-                # as we are operating on latents, we need the factor from the original image size to the
-                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
-                num_downs = self.first_stage_model.encoder.num_resolutions - 1
-                rescale_latent = 2 ** (num_downs)
-
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
-                # need to rescale the tl patch coordinates to be in between (0,1)
-                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
-                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
-                                        for patch_nr in range(z.shape[-1])]
-
-                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
-                patch_limits = [(x_tl, y_tl,
-                                 rescale_latent * ks[0] / full_img_w,
-                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
-                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
-                # tokenize crop coordinates for the bounding boxes of the respective patches
-                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
-                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
-                print(patch_limits_tknzd[0].shape)
-                # cut tknzd crop position from conditioning
-                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
-                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
-                print(cut_cond.shape)
-
-                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
-                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
-                print(adapted_cond.shape)
-                adapted_cond = self.get_learned_conditioning(adapted_cond)
-                print(adapted_cond.shape)
-                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
-                print(adapted_cond.shape)
-
-                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
-            else:
-                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
-
-            # apply model by loop over crops
-            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
-            assert not isinstance(output_list[0],
-                                  tuple)  # todo cant deal with multiple model outputs check this never happens
-
-            o = torch.stack(output_list, axis=-1)
-            o = o * weighting
-            # Reverse reshape to img shape
-            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-            # stitch crops together
-            x_recon = fold(o) / normalization
-
-        else:
-            x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None,**kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.image_size, self.image_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
-                                                        shape,cond,verbose=False,**kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True,**kwargs)
-
-        return samples, intermediates
-
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, **kwargs):
-
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
-                log["conditioning"] = xc
-            elif self.cond_stage_key == 'class_label':
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                         ddim_steps=ddim_steps,eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with self.ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                             ddim_steps=ddim_steps,eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-            if inpaint:
-                # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
-                mask = torch.ones(N, h, w).to(self.device)
-                # zeros will be filled in
-                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-                mask = mask[:, None, ...]
-                with self.ema_scope("Plotting Inpaint"):
-
-                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_inpainting"] = x_samples
-                log["mask"] = mask
-
-                # outpaint
-                with self.ema_scope("Plotting Outpaint"):
-                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with self.ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapperV1(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == 'crossattn':
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class Layout2ImgDiffusionV1(LatentDiffusionV1):
-    # TODO: move all layout-specific hacks to this class
-    def __init__(self, cond_stage_key, *args, **kwargs):
-        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
-
-    def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
-
-        key = 'train' if self.training else 'validation'
-        dset = self.trainer.datamodule.datasets[key]
-        mapper = dset.conditional_builders[self.cond_stage_key]
-
-        bbox_imgs = []
-        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
-        for tknzd_bbox in batch[self.cond_stage_key][:N]:
-            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
-            bbox_imgs.append(bboximg)
-
-        cond_img = torch.stack(bbox_imgs, dim=0)
-        logs['bbox_image'] = cond_img
-        return logs
-
-setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
-setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
-setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
-setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/models/__init__.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/models/__init__.py
deleted file mode 100644
index 4374370494b65f10b76c70a2d4f731c238cfa54c..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/models/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .wav2lip import Wav2Lip, Wav2Lip_disc_qual
-from .syncnet import SyncNet_color
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Debugger/Tests/test_libcython_in_gdb.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Debugger/Tests/test_libcython_in_gdb.py
deleted file mode 100644
index bd7608d6079412d47d32fd539d0d7797ce106784..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Debugger/Tests/test_libcython_in_gdb.py
+++ /dev/null
@@ -1,496 +0,0 @@
-"""
-Tests that run inside GDB.
-
-Note: debug information is already imported by the file generated by
-Cython.Debugger.Cygdb.make_command_file()
-"""
-
-from __future__ import absolute_import
-
-import os
-import re
-import sys
-import trace
-import inspect
-import warnings
-import unittest
-import textwrap
-import tempfile
-import functools
-import traceback
-import itertools
-#from test import test_support
-
-import gdb
-
-from .. import libcython
-from .. import libpython
-from . import TestLibCython as test_libcython
-from ...Utils import add_metaclass
-
-# for some reason sys.argv is missing in gdb
-sys.argv = ['gdb']
-
-
-def print_on_call_decorator(func):
-    @functools.wraps(func)
-    def wrapper(self, *args, **kwargs):
-        _debug(type(self).__name__, func.__name__)
-
-        try:
-            return func(self, *args, **kwargs)
-        except Exception:
-            _debug("An exception occurred:", traceback.format_exc())
-            raise
-
-    return wrapper
-
-class TraceMethodCallMeta(type):
-
-    def __init__(self, name, bases, dict):
-        for func_name, func in dict.items():
-            if inspect.isfunction(func):
-                setattr(self, func_name, print_on_call_decorator(func))
-
-
-@add_metaclass(TraceMethodCallMeta)
-class DebugTestCase(unittest.TestCase):
-    """
-    Base class for test cases. On teardown it kills the inferior and unsets
-    all breakpoints.
-    """
-
-    def __init__(self, name):
-        super(DebugTestCase, self).__init__(name)
-        self.cy = libcython.cy
-        self.module = libcython.cy.cython_namespace['codefile']
-        self.spam_func, self.spam_meth = libcython.cy.functions_by_name['spam']
-        self.ham_func = libcython.cy.functions_by_qualified_name[
-            'codefile.ham']
-        self.eggs_func = libcython.cy.functions_by_qualified_name[
-            'codefile.eggs']
-
-    def read_var(self, varname, cast_to=None):
-        result = gdb.parse_and_eval('$cy_cvalue("%s")' % varname)
-        if cast_to:
-            result = cast_to(result)
-
-        return result
-
-    def local_info(self):
-        return gdb.execute('info locals', to_string=True)
-
-    def lineno_equals(self, source_line=None, lineno=None):
-        if source_line is not None:
-            lineno = test_libcython.source_to_lineno[source_line]
-        frame = gdb.selected_frame()
-        self.assertEqual(libcython.cython_info.lineno(frame), lineno)
-
-    def break_and_run(self, source_line):
-        break_lineno = test_libcython.source_to_lineno[source_line]
-        gdb.execute('cy break codefile:%d' % break_lineno, to_string=True)
-        gdb.execute('run', to_string=True)
-
-    def tearDown(self):
-        gdb.execute('delete breakpoints', to_string=True)
-        try:
-            gdb.execute('kill inferior 1', to_string=True)
-        except RuntimeError:
-            pass
-
-        gdb.execute('set args -c "import codefile"')
-
-
-class TestDebugInformationClasses(DebugTestCase):
-
-    def test_CythonModule(self):
-        "test that debug information was parsed properly into data structures"
-        self.assertEqual(self.module.name, 'codefile')
-        global_vars = ('c_var', 'python_var', '__name__',
-                       '__builtins__', '__doc__', '__file__')
-        assert set(global_vars).issubset(self.module.globals)
-
-    def test_CythonVariable(self):
-        module_globals = self.module.globals
-        c_var = module_globals['c_var']
-        python_var = module_globals['python_var']
-        self.assertEqual(c_var.type, libcython.CObject)
-        self.assertEqual(python_var.type, libcython.PythonObject)
-        self.assertEqual(c_var.qualified_name, 'codefile.c_var')
-
-    def test_CythonFunction(self):
-        self.assertEqual(self.spam_func.qualified_name, 'codefile.spam')
-        self.assertEqual(self.spam_meth.qualified_name,
-                         'codefile.SomeClass.spam')
-        self.assertEqual(self.spam_func.module, self.module)
-
-        assert self.eggs_func.pf_cname, (self.eggs_func, self.eggs_func.pf_cname)
-        assert not self.ham_func.pf_cname
-        assert not self.spam_func.pf_cname
-        assert not self.spam_meth.pf_cname
-
-        self.assertEqual(self.spam_func.type, libcython.CObject)
-        self.assertEqual(self.ham_func.type, libcython.CObject)
-
-        self.assertEqual(self.spam_func.arguments, ['a'])
-        self.assertEqual(self.spam_func.step_into_functions,
-                         set(['puts', 'some_c_function']))
-
-        expected_lineno = test_libcython.source_to_lineno['def spam(a=0):']
-        self.assertEqual(self.spam_func.lineno, expected_lineno)
-        self.assertEqual(sorted(self.spam_func.locals), list('abcd'))
-
-
-class TestParameters(unittest.TestCase):
-
-    def test_parameters(self):
-        gdb.execute('set cy_colorize_code on')
-        assert libcython.parameters.colorize_code
-        gdb.execute('set cy_colorize_code off')
-        assert not libcython.parameters.colorize_code
-
-
-class TestBreak(DebugTestCase):
-
-    def test_break(self):
-        breakpoint_amount = len(gdb.breakpoints() or ())
-        gdb.execute('cy break codefile.spam')
-
-        self.assertEqual(len(gdb.breakpoints()), breakpoint_amount + 1)
-        bp = gdb.breakpoints()[-1]
-        self.assertEqual(bp.type, gdb.BP_BREAKPOINT)
-        assert self.spam_func.cname in bp.location
-        assert bp.enabled
-
-    def test_python_break(self):
-        gdb.execute('cy break -p join')
-        assert 'def join(' in gdb.execute('cy run', to_string=True)
-
-    def test_break_lineno(self):
-        beginline = 'import os'
-        nextline = 'cdef int c_var = 12'
-
-        self.break_and_run(beginline)
-        self.lineno_equals(beginline)
-        step_result = gdb.execute('cy step', to_string=True)
-        self.lineno_equals(nextline)
-        assert step_result.rstrip().endswith(nextline)
-
-
-class TestKilled(DebugTestCase):
-
-    def test_abort(self):
-        gdb.execute("set args -c 'import os; os.abort()'")
-        output = gdb.execute('cy run', to_string=True)
-        assert 'abort' in output.lower()
-
-
-class DebugStepperTestCase(DebugTestCase):
-
-    def step(self, varnames_and_values, source_line=None, lineno=None):
-        gdb.execute(self.command)
-        for varname, value in varnames_and_values:
-            self.assertEqual(self.read_var(varname), value, self.local_info())
-
-        self.lineno_equals(source_line, lineno)
-
-
-class TestStep(DebugStepperTestCase):
-    """
-    Test stepping. Stepping happens in the code found in
-    Cython/Debugger/Tests/codefile.
-    """
-
-    def test_cython_step(self):
-        gdb.execute('cy break codefile.spam')
-
-        gdb.execute('run', to_string=True)
-        self.lineno_equals('def spam(a=0):')
-
-        gdb.execute('cy step', to_string=True)
-        self.lineno_equals('b = c = d = 0')
-
-        self.command = 'cy step'
-        self.step([('b', 0)], source_line='b = 1')
-        self.step([('b', 1), ('c', 0)], source_line='c = 2')
-        self.step([('c', 2)], source_line='int(10)')
-        self.step([], source_line='puts("spam")')
-
-        gdb.execute('cont', to_string=True)
-        self.assertEqual(len(gdb.inferiors()), 1)
-        self.assertEqual(gdb.inferiors()[0].pid, 0)
-
-    def test_c_step(self):
-        self.break_and_run('some_c_function()')
-        gdb.execute('cy step', to_string=True)
-        self.assertEqual(gdb.selected_frame().name(), 'some_c_function')
-
-    def test_python_step(self):
-        self.break_and_run('os.path.join("foo", "bar")')
-
-        result = gdb.execute('cy step', to_string=True)
-
-        curframe = gdb.selected_frame()
-        self.assertEqual(curframe.name(), 'PyEval_EvalFrameEx')
-
-        pyframe = libpython.Frame(curframe).get_pyop()
-        # With Python 3 inferiors, pyframe.co_name will return a PyUnicodePtr,
-        # be compatible
-        frame_name = pyframe.co_name.proxyval(set())
-        self.assertEqual(frame_name, 'join')
-        assert re.match(r'\d+    def join\(', result), result
-
-
-class TestNext(DebugStepperTestCase):
-
-    def test_cython_next(self):
-        self.break_and_run('c = 2')
-
-        lines = (
-            'int(10)',
-            'puts("spam")',
-            'os.path.join("foo", "bar")',
-            'some_c_function()',
-        )
-
-        for line in lines:
-            gdb.execute('cy next')
-            self.lineno_equals(line)
-
-
-class TestLocalsGlobals(DebugTestCase):
-
-    def test_locals(self):
-        self.break_and_run('int(10)')
-
-        result = gdb.execute('cy locals', to_string=True)
-        assert 'a = 0', repr(result)
-        assert 'b = (int) 1', result
-        assert 'c = (int) 2' in result, repr(result)
-
-    def test_globals(self):
-        self.break_and_run('int(10)')
-
-        result = gdb.execute('cy globals', to_string=True)
-        assert '__name__ ' in result, repr(result)
-        assert '__doc__ ' in result, repr(result)
-        assert 'os ' in result, repr(result)
-        assert 'c_var ' in result, repr(result)
-        assert 'python_var ' in result, repr(result)
-
-
-class TestBacktrace(DebugTestCase):
-
-    def test_backtrace(self):
-        libcython.parameters.colorize_code.value = False
-
-        self.break_and_run('os.path.join("foo", "bar")')
-
-        def match_backtrace_output(result):
-            assert re.search(r'\#\d+ *0x.* in spam\(\) at .*codefile\.pyx:22',
-                             result), result
-            assert 'os.path.join("foo", "bar")' in result, result
-
-        result = gdb.execute('cy bt', to_string=True)
-        match_backtrace_output(result)
-
-        result = gdb.execute('cy bt -a', to_string=True)
-        match_backtrace_output(result)
-
-        # Apparently not everyone has main()
-        # assert re.search(r'\#0 *0x.* in main\(\)', result), result
-
-
-class TestFunctions(DebugTestCase):
-
-    def test_functions(self):
-        self.break_and_run('c = 2')
-        result = gdb.execute('print $cy_cname("b")', to_string=True)
-        assert re.search('__pyx_.*b', result), result
-
-        result = gdb.execute('print $cy_lineno()', to_string=True)
-        supposed_lineno = test_libcython.source_to_lineno['c = 2']
-        assert str(supposed_lineno) in result, (supposed_lineno, result)
-
-        result = gdb.execute('print $cy_cvalue("b")', to_string=True)
-        assert '= 1' in result
-
-
-class TestPrint(DebugTestCase):
-
-    def test_print(self):
-        self.break_and_run('c = 2')
-        result = gdb.execute('cy print b', to_string=True)
-        self.assertEqual('b = (int) 1\n', result)
-
-
-class TestUpDown(DebugTestCase):
-
-    def test_updown(self):
-        self.break_and_run('os.path.join("foo", "bar")')
-        gdb.execute('cy step')
-        self.assertRaises(RuntimeError, gdb.execute, 'cy down')
-
-        result = gdb.execute('cy up', to_string=True)
-        assert 'spam()' in result
-        assert 'os.path.join("foo", "bar")' in result
-
-
-class TestExec(DebugTestCase):
-
-    def setUp(self):
-        super(TestExec, self).setUp()
-        self.fd, self.tmpfilename = tempfile.mkstemp()
-        self.tmpfile = os.fdopen(self.fd, 'r+')
-
-    def tearDown(self):
-        super(TestExec, self).tearDown()
-
-        try:
-            self.tmpfile.close()
-        finally:
-            os.remove(self.tmpfilename)
-
-    def eval_command(self, command):
-        gdb.execute('cy exec open(%r, "w").write(str(%s))' %
-                                                (self.tmpfilename, command))
-        return self.tmpfile.read().strip()
-
-    def test_cython_exec(self):
-        self.break_and_run('os.path.join("foo", "bar")')
-
-        # test normal behaviour
-        self.assertEqual("[0]", self.eval_command('[a]'))
-
-        # test multiline code
-        result = gdb.execute(textwrap.dedent('''\
-            cy exec
-            pass
-
-            "nothing"
-            end
-            '''))
-        result = self.tmpfile.read().rstrip()
-        self.assertEqual('', result)
-
-    def test_python_exec(self):
-        self.break_and_run('os.path.join("foo", "bar")')
-        gdb.execute('cy step')
-
-        gdb.execute('cy exec some_random_var = 14')
-        self.assertEqual('14', self.eval_command('some_random_var'))
-
-
-class CySet(DebugTestCase):
-
-    def test_cyset(self):
-        self.break_and_run('os.path.join("foo", "bar")')
-
-        gdb.execute('cy set a = $cy_eval("{None: []}")')
-        stringvalue = self.read_var("a", cast_to=str)
-        self.assertEqual(stringvalue, "{None: []}")
-
-
-class TestCyEval(DebugTestCase):
-    "Test the $cy_eval() gdb function."
-
-    def test_cy_eval(self):
-        # This function leaks a few objects in the GDB python process. This
-        # is no biggie
-        self.break_and_run('os.path.join("foo", "bar")')
-
-        result = gdb.execute('print $cy_eval("None")', to_string=True)
-        assert re.match(r'\$\d+ = None\n', result), result
-
-        result = gdb.execute('print $cy_eval("[a]")', to_string=True)
-        assert re.match(r'\$\d+ = \[0\]', result), result
-
-
-class TestClosure(DebugTestCase):
-
-    def break_and_run_func(self, funcname):
-        gdb.execute('cy break ' + funcname)
-        gdb.execute('cy run')
-
-    def test_inner(self):
-        self.break_and_run_func('inner')
-        self.assertEqual('', gdb.execute('cy locals', to_string=True))
-
-        # Allow the Cython-generated code to initialize the scope variable
-        gdb.execute('cy step')
-
-        self.assertEqual(str(self.read_var('a')), "'an object'")
-        print_result = gdb.execute('cy print a', to_string=True).strip()
-        self.assertEqual(print_result, "a = 'an object'")
-
-    def test_outer(self):
-        self.break_and_run_func('outer')
-        self.assertEqual('', gdb.execute('cy locals', to_string=True))
-
-        # Initialize scope with 'a' uninitialized
-        gdb.execute('cy step')
-        self.assertEqual('', gdb.execute('cy locals', to_string=True))
-
-        # Initialize 'a' to 1
-        gdb.execute('cy step')
-        print_result = gdb.execute('cy print a', to_string=True).strip()
-        self.assertEqual(print_result, "a = 'an object'")
-
-
-_do_debug = os.environ.get('GDB_DEBUG')
-if _do_debug:
-    _debug_file = open('/dev/tty', 'w')
-
-def _debug(*messages):
-    if _do_debug:
-        messages = itertools.chain([sys._getframe(1).f_code.co_name, ':'],
-                                   messages)
-        _debug_file.write(' '.join(str(msg) for msg in messages) + '\n')
-
-
-def run_unittest_in_module(modulename):
-    try:
-        gdb.lookup_type('PyModuleObject')
-    except RuntimeError:
-        msg = ("Unable to run tests, Python was not compiled with "
-                "debugging information. Either compile python with "
-                "-g or get a debug build (configure with --with-pydebug).")
-        warnings.warn(msg)
-        os._exit(1)
-    else:
-        m = __import__(modulename, fromlist=[''])
-        tests = inspect.getmembers(m, inspect.isclass)
-
-        # test_support.run_unittest(tests)
-
-        test_loader = unittest.TestLoader()
-        suite = unittest.TestSuite(
-            [test_loader.loadTestsFromTestCase(cls) for name, cls in tests])
-
-        result = unittest.TextTestRunner(verbosity=1).run(suite)
-        return result.wasSuccessful()
-
-def runtests():
-    """
-    Run the libcython and libpython tests. Ensure that an appropriate status is
-    returned to the parent test process.
-    """
-    from Cython.Debugger.Tests import test_libpython_in_gdb
-
-    success_libcython = run_unittest_in_module(__name__)
-    success_libpython = run_unittest_in_module(test_libpython_in_gdb.__name__)
-
-    if not success_libcython or not success_libpython:
-        sys.exit(2)
-
-def main(version, trace_code=False):
-    global inferior_python_version
-
-    inferior_python_version = version
-
-    if trace_code:
-        tracer = trace.Trace(count=False, trace=True, outfile=sys.stderr,
-                            ignoredirs=[sys.prefix, sys.exec_prefix])
-        tracer.runfunc(runtests)
-    else:
-        runtests()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tests/TestCythonUtils.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tests/TestCythonUtils.py
deleted file mode 100644
index 2641900c012e64cf0305797e41bd9a02845a42f0..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tests/TestCythonUtils.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import unittest
-
-from ..Utils import build_hex_version
-
-class TestCythonUtils(unittest.TestCase):
-    def test_build_hex_version(self):
-        self.assertEqual('0x001D00A1', build_hex_version('0.29a1'))
-        self.assertEqual('0x001D00A1', build_hex_version('0.29a1'))
-        self.assertEqual('0x001D03C4', build_hex_version('0.29.3rc4'))
-        self.assertEqual('0x001D00F0', build_hex_version('0.29'))
-        self.assertEqual('0x040000F0', build_hex_version('4.0'))
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/PpmImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/PpmImagePlugin.py
deleted file mode 100644
index 392771d3e96dcedb6720269257298359f543c2f6..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/PpmImagePlugin.py
+++ /dev/null
@@ -1,342 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# PPM support for PIL
-#
-# History:
-#       96-03-24 fl     Created
-#       98-03-06 fl     Write RGBA images (as RGB, that is)
-#
-# Copyright (c) Secret Labs AB 1997-98.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile
-from ._binary import i16be as i16
-from ._binary import o8
-from ._binary import o32le as o32
-
-#
-# --------------------------------------------------------------------
-
-b_whitespace = b"\x20\x09\x0a\x0b\x0c\x0d"
-
-MODES = {
-    # standard
-    b"P1": "1",
-    b"P2": "L",
-    b"P3": "RGB",
-    b"P4": "1",
-    b"P5": "L",
-    b"P6": "RGB",
-    # extensions
-    b"P0CMYK": "CMYK",
-    # PIL extensions (for test purposes only)
-    b"PyP": "P",
-    b"PyRGBA": "RGBA",
-    b"PyCMYK": "CMYK",
-}
-
-
-def _accept(prefix):
-    return prefix[0:1] == b"P" and prefix[1] in b"0123456y"
-
-
-##
-# Image plugin for PBM, PGM, and PPM images.
-
-
-class PpmImageFile(ImageFile.ImageFile):
-
-    format = "PPM"
-    format_description = "Pbmplus image"
-
-    def _read_magic(self):
-        magic = b""
-        # read until whitespace or longest available magic number
-        for _ in range(6):
-            c = self.fp.read(1)
-            if not c or c in b_whitespace:
-                break
-            magic += c
-        return magic
-
-    def _read_token(self):
-        token = b""
-        while len(token) <= 10:  # read until next whitespace or limit of 10 characters
-            c = self.fp.read(1)
-            if not c:
-                break
-            elif c in b_whitespace:  # token ended
-                if not token:
-                    # skip whitespace at start
-                    continue
-                break
-            elif c == b"#":
-                # ignores rest of the line; stops at CR, LF or EOF
-                while self.fp.read(1) not in b"\r\n":
-                    pass
-                continue
-            token += c
-        if not token:
-            # Token was not even 1 byte
-            raise ValueError("Reached EOF while reading header")
-        elif len(token) > 10:
-            raise ValueError(f"Token too long in file header: {token.decode()}")
-        return token
-
-    def _open(self):
-        magic_number = self._read_magic()
-        try:
-            mode = MODES[magic_number]
-        except KeyError:
-            raise SyntaxError("not a PPM file")
-
-        if magic_number in (b"P1", b"P4"):
-            self.custom_mimetype = "image/x-portable-bitmap"
-        elif magic_number in (b"P2", b"P5"):
-            self.custom_mimetype = "image/x-portable-graymap"
-        elif magic_number in (b"P3", b"P6"):
-            self.custom_mimetype = "image/x-portable-pixmap"
-
-        maxval = None
-        decoder_name = "raw"
-        if magic_number in (b"P1", b"P2", b"P3"):
-            decoder_name = "ppm_plain"
-        for ix in range(3):
-            token = int(self._read_token())
-            if ix == 0:  # token is the x size
-                xsize = token
-            elif ix == 1:  # token is the y size
-                ysize = token
-                if mode == "1":
-                    self.mode = "1"
-                    rawmode = "1;I"
-                    break
-                else:
-                    self.mode = rawmode = mode
-            elif ix == 2:  # token is maxval
-                maxval = token
-                if not 0 < maxval < 65536:
-                    raise ValueError(
-                        "maxval must be greater than 0 and less than 65536"
-                    )
-                if maxval > 255 and mode == "L":
-                    self.mode = "I"
-
-                if decoder_name != "ppm_plain":
-                    # If maxval matches a bit depth, use the raw decoder directly
-                    if maxval == 65535 and mode == "L":
-                        rawmode = "I;16B"
-                    elif maxval != 255:
-                        decoder_name = "ppm"
-
-        args = (rawmode, 0, 1) if decoder_name == "raw" else (rawmode, maxval)
-        self._size = xsize, ysize
-        self.tile = [(decoder_name, (0, 0, xsize, ysize), self.fp.tell(), args)]
-
-
-#
-# --------------------------------------------------------------------
-
-
-class PpmPlainDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def _read_block(self):
-        return self.fd.read(ImageFile.SAFEBLOCK)
-
-    def _find_comment_end(self, block, start=0):
-        a = block.find(b"\n", start)
-        b = block.find(b"\r", start)
-        return min(a, b) if a * b > 0 else max(a, b)  # lowest nonnegative index (or -1)
-
-    def _ignore_comments(self, block):
-        if self._comment_spans:
-            # Finish current comment
-            while block:
-                comment_end = self._find_comment_end(block)
-                if comment_end != -1:
-                    # Comment ends in this block
-                    # Delete tail of comment
-                    block = block[comment_end + 1 :]
-                    break
-                else:
-                    # Comment spans whole block
-                    # So read the next block, looking for the end
-                    block = self._read_block()
-
-        # Search for any further comments
-        self._comment_spans = False
-        while True:
-            comment_start = block.find(b"#")
-            if comment_start == -1:
-                # No comment found
-                break
-            comment_end = self._find_comment_end(block, comment_start)
-            if comment_end != -1:
-                # Comment ends in this block
-                # Delete comment
-                block = block[:comment_start] + block[comment_end + 1 :]
-            else:
-                # Comment continues to next block(s)
-                block = block[:comment_start]
-                self._comment_spans = True
-                break
-        return block
-
-    def _decode_bitonal(self):
-        """
-        This is a separate method because in the plain PBM format, all data tokens are
-        exactly one byte, so the inter-token whitespace is optional.
-        """
-        data = bytearray()
-        total_bytes = self.state.xsize * self.state.ysize
-
-        while len(data) != total_bytes:
-            block = self._read_block()  # read next block
-            if not block:
-                # eof
-                break
-
-            block = self._ignore_comments(block)
-
-            tokens = b"".join(block.split())
-            for token in tokens:
-                if token not in (48, 49):
-                    raise ValueError(f"Invalid token for this mode: {bytes([token])}")
-            data = (data + tokens)[:total_bytes]
-        invert = bytes.maketrans(b"01", b"\xFF\x00")
-        return data.translate(invert)
-
-    def _decode_blocks(self, maxval):
-        data = bytearray()
-        max_len = 10
-        out_byte_count = 4 if self.mode == "I" else 1
-        out_max = 65535 if self.mode == "I" else 255
-        bands = Image.getmodebands(self.mode)
-        total_bytes = self.state.xsize * self.state.ysize * bands * out_byte_count
-
-        half_token = False
-        while len(data) != total_bytes:
-            block = self._read_block()  # read next block
-            if not block:
-                if half_token:
-                    block = bytearray(b" ")  # flush half_token
-                else:
-                    # eof
-                    break
-
-            block = self._ignore_comments(block)
-
-            if half_token:
-                block = half_token + block  # stitch half_token to new block
-
-            tokens = block.split()
-
-            if block and not block[-1:].isspace():  # block might split token
-                half_token = tokens.pop()  # save half token for later
-                if len(half_token) > max_len:  # prevent buildup of half_token
-                    raise ValueError(
-                        f"Token too long found in data: {half_token[:max_len + 1]}"
-                    )
-
-            for token in tokens:
-                if len(token) > max_len:
-                    raise ValueError(
-                        f"Token too long found in data: {token[:max_len + 1]}"
-                    )
-                value = int(token)
-                if value > maxval:
-                    raise ValueError(f"Channel value too large for this mode: {value}")
-                value = round(value / maxval * out_max)
-                data += o32(value) if self.mode == "I" else o8(value)
-                if len(data) == total_bytes:  # finished!
-                    break
-        return data
-
-    def decode(self, buffer):
-        self._comment_spans = False
-        if self.mode == "1":
-            data = self._decode_bitonal()
-            rawmode = "1;8"
-        else:
-            maxval = self.args[-1]
-            data = self._decode_blocks(maxval)
-            rawmode = "I;32" if self.mode == "I" else self.mode
-        self.set_as_raw(bytes(data), rawmode)
-        return -1, 0
-
-
-class PpmDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def decode(self, buffer):
-        data = bytearray()
-        maxval = self.args[-1]
-        in_byte_count = 1 if maxval < 256 else 2
-        out_byte_count = 4 if self.mode == "I" else 1
-        out_max = 65535 if self.mode == "I" else 255
-        bands = Image.getmodebands(self.mode)
-        while len(data) < self.state.xsize * self.state.ysize * bands * out_byte_count:
-            pixels = self.fd.read(in_byte_count * bands)
-            if len(pixels) < in_byte_count * bands:
-                # eof
-                break
-            for b in range(bands):
-                value = (
-                    pixels[b] if in_byte_count == 1 else i16(pixels, b * in_byte_count)
-                )
-                value = min(out_max, round(value / maxval * out_max))
-                data += o32(value) if self.mode == "I" else o8(value)
-        rawmode = "I;32" if self.mode == "I" else self.mode
-        self.set_as_raw(bytes(data), rawmode)
-        return -1, 0
-
-
-#
-# --------------------------------------------------------------------
-
-
-def _save(im, fp, filename):
-    if im.mode == "1":
-        rawmode, head = "1;I", b"P4"
-    elif im.mode == "L":
-        rawmode, head = "L", b"P5"
-    elif im.mode == "I":
-        rawmode, head = "I;16B", b"P5"
-    elif im.mode in ("RGB", "RGBA"):
-        rawmode, head = "RGB", b"P6"
-    else:
-        raise OSError(f"cannot write mode {im.mode} as PPM")
-    fp.write(head + b"\n%d %d\n" % im.size)
-    if head == b"P6":
-        fp.write(b"255\n")
-    elif head == b"P5":
-        if rawmode == "L":
-            fp.write(b"255\n")
-        else:
-            fp.write(b"65535\n")
-    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, 1))])
-
-    # ALTERNATIVE: save via builtin debug function
-    # im._dump(filename)
-
-
-#
-# --------------------------------------------------------------------
-
-
-Image.register_open(PpmImageFile.format, PpmImageFile, _accept)
-Image.register_save(PpmImageFile.format, _save)
-
-Image.register_decoder("ppm", PpmDecoder)
-Image.register_decoder("ppm_plain", PpmPlainDecoder)
-
-Image.register_extensions(PpmImageFile.format, [".pbm", ".pgm", ".ppm", ".pnm"])
-
-Image.register_mime(PpmImageFile.format, "image/x-portable-anymap")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/_tkinter_finder.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/_tkinter_finder.py
deleted file mode 100644
index 5cd7e9b1fb28f118a4a444ad150f8a3865527358..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/_tkinter_finder.py
+++ /dev/null
@@ -1,23 +0,0 @@
-""" Find compiled module linking to Tcl / Tk libraries
-"""
-import sys
-import tkinter
-from tkinter import _tkinter as tk
-
-from ._deprecate import deprecate
-
-try:
-    if hasattr(sys, "pypy_find_executable"):
-        TKINTER_LIB = tk.tklib_cffi.__file__
-    else:
-        TKINTER_LIB = tk.__file__
-except AttributeError:
-    # _tkinter may be compiled directly into Python, in which case __file__ is
-    # not available. load_tkinter_funcs will check the binary first in any case.
-    TKINTER_LIB = None
-
-tk_version = str(tkinter.TkVersion)
-if tk_version == "8.4":
-    deprecate(
-        "Support for Tk/Tcl 8.4", 10, action="Please upgrade to Tk/Tcl 8.5 or newer"
-    )
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/web_middlewares.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/web_middlewares.py
deleted file mode 100644
index fabcc449a2107211fd99cd59f576a2d855d0e042..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/web_middlewares.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import re
-from typing import TYPE_CHECKING, Awaitable, Callable, Tuple, Type, TypeVar
-
-from .typedefs import Handler
-from .web_exceptions import HTTPPermanentRedirect, _HTTPMove
-from .web_request import Request
-from .web_response import StreamResponse
-from .web_urldispatcher import SystemRoute
-
-__all__ = (
-    "middleware",
-    "normalize_path_middleware",
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .web_app import Application
-
-_Func = TypeVar("_Func")
-
-
-async def _check_request_resolves(request: Request, path: str) -> Tuple[bool, Request]:
-    alt_request = request.clone(rel_url=path)
-
-    match_info = await request.app.router.resolve(alt_request)
-    alt_request._match_info = match_info
-
-    if match_info.http_exception is None:
-        return True, alt_request
-
-    return False, request
-
-
-def middleware(f: _Func) -> _Func:
-    f.__middleware_version__ = 1  # type: ignore[attr-defined]
-    return f
-
-
-_Middleware = Callable[[Request, Handler], Awaitable[StreamResponse]]
-
-
-def normalize_path_middleware(
-    *,
-    append_slash: bool = True,
-    remove_slash: bool = False,
-    merge_slashes: bool = True,
-    redirect_class: Type[_HTTPMove] = HTTPPermanentRedirect,
-) -> _Middleware:
-    """Factory for producing a middleware that normalizes the path of a request.
-
-    Normalizing means:
-        - Add or remove a trailing slash to the path.
-        - Double slashes are replaced by one.
-
-    The middleware returns as soon as it finds a path that resolves
-    correctly. The order if both merge and append/remove are enabled is
-        1) merge slashes
-        2) append/remove slash
-        3) both merge slashes and append/remove slash.
-    If the path resolves with at least one of those conditions, it will
-    redirect to the new path.
-
-    Only one of `append_slash` and `remove_slash` can be enabled. If both
-    are `True` the factory will raise an assertion error
-
-    If `append_slash` is `True` the middleware will append a slash when
-    needed. If a resource is defined with trailing slash and the request
-    comes without it, it will append it automatically.
-
-    If `remove_slash` is `True`, `append_slash` must be `False`. When enabled
-    the middleware will remove trailing slashes and redirect if the resource
-    is defined
-
-    If merge_slashes is True, merge multiple consecutive slashes in the
-    path into one.
-    """
-    correct_configuration = not (append_slash and remove_slash)
-    assert correct_configuration, "Cannot both remove and append slash"
-
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        if isinstance(request.match_info.route, SystemRoute):
-            paths_to_check = []
-            if "?" in request.raw_path:
-                path, query = request.raw_path.split("?", 1)
-                query = "?" + query
-            else:
-                query = ""
-                path = request.raw_path
-
-            if merge_slashes:
-                paths_to_check.append(re.sub("//+", "/", path))
-            if append_slash and not request.path.endswith("/"):
-                paths_to_check.append(path + "/")
-            if remove_slash and request.path.endswith("/"):
-                paths_to_check.append(path[:-1])
-            if merge_slashes and append_slash:
-                paths_to_check.append(re.sub("//+", "/", path + "/"))
-            if merge_slashes and remove_slash:
-                merged_slashes = re.sub("//+", "/", path)
-                paths_to_check.append(merged_slashes[:-1])
-
-            for path in paths_to_check:
-                path = re.sub("^//+", "/", path)  # SECURITY: GHSA-v6wp-4m6f-gcjg
-                resolves, request = await _check_request_resolves(request, path)
-                if resolves:
-                    raise redirect_class(request.raw_path + query)
-
-        return await handler(request)
-
-    return impl
-
-
-def _fix_request_current_app(app: "Application") -> _Middleware:
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        with request.match_info.set_current_app(app):
-            return await handler(request)
-
-    return impl
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/DATASET.md b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/DATASET.md
deleted file mode 100644
index 930403eb3651a14dfcd015d094383faeb052c8ac..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/DATASET.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Dataset
-
-We understand video data are challenging to download and process. For videos, we provide our preprocessing scripts under `scripts/video_feature_extractor` (deeply adapted from `https://github.com/antoine77340/video_feature_extractor`); for text, we pre-tokenizing scripts under `scripts/text_token_extractor`.
-
-### S3D Feature Extraction
-We use pre-trained [S3D](https://github.com/antoine77340/S3D_HowTo100M) for video feature extraction. Please place the models as `pretrained_models/s3d_dict.npy` and `pretrained_models/s3d_howto100m.pth`.
-
-We implement a `PathBuilder` to automatically track video ids, source video paths to their feature locations (you may need `conda install -c anaconda pandas`). Decoding may need `pip install ffmpeg-python`.
-
-### Howto100M
-[Howto100M](https://www.di.ens.fr/willow/research/howto100m/) is a large-scale video pre-training datasets. You may download videos by yourself and run preprocessing of our scripts. 
-
-Several key differences of our preprocessing from existing papers: (1) we use `raw_caption.json` instead of `caption.json` to have pure self-supervision on text (`caption.json` has manual removal of stop words); (2) we remove partially duplicated texts that are originally designed for real-time readability (see `mmpt/processors/dedupprocessor.py`); (3) then we shard video/text features using `SharedTensor` in `mmpt/utils/shardedtensor.py` for fast loading during training (faster than `h5py`).
-
-#### Steps
-##### video
-To extract video features: edit and run `bash scripts/video_feature_extractor/how2/s3d.sh`. (consider to run this on multiple machines; by default, we store features in fp16 to save space and also for faster training).
-
-Split available video ids as `data/how2/how2_s3d_train.lst` and `data/how2/how2_s3d_val.lst`.
-
-Lastly, pack video features into `ShardedTensor` using `python scripts/video_feature_extractor/shard_feature.py`.
-
-##### text
-Clean captions using `python -m mmpt.processors.dedupprocessor`.
-
-Tokenize dedupped captions `data/how2/raw_caption_dedup.pkl` into sharded numpy arrays:  
-```
-python scripts/text_token_extractor/pretokenization.py scripts/text_token_extractor/configs/bert-base-uncased.yaml
-```
-
-### Youcook, MSRVTT etc.
-We use the version of Youcook and MSRVTT come with Howto100M and MILNCE. Please download the data to `data/youcook` and `data/msrvtt` accordingly, you can also check `projects/task/youcook.yaml` and `projects/task/vtt.yaml` etc. in details. 
-We extract features for Youcook, MSRVTT similar to the first step of Howto100M but we read text from meta data directly and perform on-the-fly tokenization.
-
diff --git a/spaces/ashhadahsan/ai-book-generator/constants.py b/spaces/ashhadahsan/ai-book-generator/constants.py
deleted file mode 100644
index 7d9704260b59db7701e14a72c4c37ec30534daf7..0000000000000000000000000000000000000000
--- a/spaces/ashhadahsan/ai-book-generator/constants.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Educational values
-AGE_RANGE = [3, 4, 5]
-SKILL_DEVELOPMENT = [
-    "Curiosity",
-    "Imagination",
-    "Communication",
-    "Observation",
-    "Adaptability",
-    "Persistence",
-    "Collaboration",
-    "Emotional Intelligence",
-    "Self-awareness",
-    "Memory",
-]
-LEARNING_OBJECTIVES = [
-    "Science exploration",
-    "Nature observation",
-    "History and social studies",
-    "Language development",
-    "Math concepts",
-    "Fine motor skills" "Gross motor skills",
-]
-
-# emotionl values
-THEME = [
-    "Empathy",
-    "Kindness",
-    "Friendship",
-    "Courage",
-    "Diversity",
-    "Imagination",
-    "Perseverance",
-    "Self-confidence",
-    "Responsibility" "Gratitude",
-]
-MODD_OF_STORY = [
-    "Lighthearted",
-    "Funny",
-    "Adventure",
-    "Comforting",
-    "Calm",
-    "Educational",
-    "Inspiring",
-]
-POSITIVE_MESSAGNG = [
-    "Kindness and compassion",
-    "Friendship and cooperation",
-    "Respect for others and diversity",
-    "Self-confidence and self-esteem",
-    "Perseverance and resilience",
-    "Honesty and integrity",
-    "Responsibility and accountability",
-    "Empathy and understanding",
-    "Mindfulness and emotional regulation",
-    "Creativity and imagination",
-]
-
-# Personal
-GENDER = ["Male", "Female"]
-GENRE = [
-    "Fairy tales",
-    "Biography",
-    "Animal stories",
-    "Adventure stories",
-    "Rhyming books",
-    "Concept books",
-    "Folktales",
-    "Nursery rhymes",
-    "Science fiction",
-    "Humorous books",
-]
-TOPIC = [
-    "Animals (fiction and non-fiction)",
-    "Friendship and relationships",
-    "Family and home life",
-    "Bedtime stories",
-    "Colors and shapes",
-    "Numbers and counting",
-    "Alphabet and phonics",
-    "Fairy tales and nursery rhymes",
-    "Adventure and exploration",
-    "Imagination and creativity",
-]
-
-TYPE_OF_MAIN_CHARACTER = [
-    "Human",
-    "Monkey",
-    "Cat",
-    "Caterpillar",
-    "Dog",
-    "Teddy bear",
-    "Elephant",
-    "Lions",
-    "Penguins",
-    "Monkeys",
-    "Ducks",
-    "Frogs",
-    "Giraffes",
-    "Tigers",
-    "Whales",
-    "Zebras",
-    "Superhero",
-    "Dinosaur",
-    "Robot",
-    "Aliens",
-    "Dragons",
-    "Pirates",
-    "Princess",
-    "Fairy",
-    "Dragons",
-    "Cyclops",
-    "Yeti",
-    "Bigfoot",
-    "Goblins",
-    "Trolls",
-    "Ghosts",
-    "Fire trucks",
-    "Trains",
-    "Race cars",
-    "Construction vehicle",
-    "Airplanes",
-    "Helicopters",
-    "Boats",
-    "Spaceships",
-    "Motorcycles",
-    "Buses",
-]
-ANTAGONIST_TYPE = [
-    "Witch",
-    "Wizard",
-    "Evil queen",
-    "Evil king",
-    "Monster",
-    "Pirate",
-    "Giant",
-    "Robot",
-    "Evil machine",
-    "Human",
-    "Monkey",
-    "Cat",
-    "Caterpillar",
-    "Dog",
-    "Teddy bear",
-    "Elephant",
-    "Lions",
-    "Penguins",
-    "Monkeys",
-    "Ducks",
-    "Frogs",
-    "Giraffes",
-    "Tigers",
-    "Whales",
-    "Zebras",
-    "Superhero",
-    "Dinosaur",
-    "Robot",
-    "Aliens",
-    "Dragons",
-    "Pirates",
-    "Princess",
-    "Fairy",
-    "Dragons",
-    "Cyclops",
-    "Yeti",
-    "Bigfoot",
-    "Goblins",
-    "Trolls",
-    "Ghosts",
-    "Fire trucks",
-    "Trains",
-    "Race cars",
-    "Construction vehicle",
-    "Airplanes",
-    "Helicopters",
-    "Boats",
-    "Spaceships",
-    "Motorcycles",
-    "Buses",
-]
-SUPPORTING_CHARACTER_TYPE = ANTAGONIST_TYPE
-SETTINGS = [
-    "Forests",
-    "Underwater",
-    "Imaginary world",
-    "Cityscapes",
-    "Countryside",
-    "Outer space",
-    "Castle",
-    "School",
-    "Magical kingdom",
-    "Enchanted forest",
-    "Circus",
-    "City",
-]
-RESOLUTION = [
-    "Apologizing and making amends",
-    "Learning to share and take turns",
-    "Standing up for oneself and others",
-    "Overcoming fear and facing challenges",
-    "Solving a problem or mystery",
-    "Learning a lesson or moral value",
-    "Forgiving and moving on",
-    "Accepting differences and celebrating diversity",
-    "Helping others and showing kindness",
-    "Making a new friend or finding common ground",
-    "Discovering a new passion or talent",
-    "Resolving a conflict peacefully and fairly",
-    "Making a sacrifice for the greater good",
-    "Learning from mistakes and taking responsibility",
-    "Understanding the consequences of one's actions",
-    "Finding a creative or innovative solution",
-    "Embracing change and adapting to new situations",
-    "Discovering the importance of teamwork and cooperation",
-    "Making a tough decision and sticking to it",
-    "Embracing the power of imagination and creativity",
-]
diff --git a/spaces/ashzzf/vits-uma-genshin-honkai/mel_processing.py b/spaces/ashzzf/vits-uma-genshin-honkai/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/ashzzf/vits-uma-genshin-honkai/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/depth_grounding_downsampler.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/depth_grounding_downsampler.py
deleted file mode 100644
index 80826ae2a96b615e24474f43b4ecdc9267049261..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/depth_grounding_downsampler.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import torch
-import torch.nn as nn
-from ldm.modules.attention import BasicTransformerBlock
-from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
-import torch.nn.functional as F
-
-
-
-class GroundingDownsampler(nn.Module):
-    def __init__(self, resize_input=256, out_dim=8):
-        super().__init__()
-        self.resize_input = resize_input
-        self.out_dim = out_dim 
-
-        self.layers = nn.Sequential(
-            nn.Conv2d(1,4,4,2,1),
-            nn.SiLU(),
-            nn.Conv2d(4,self.out_dim,4,2,1)
-        )
-
-    def forward(self, grounding_extra_input):
-        # this is actually gary scale, but converted to rgb in dataset, information redudant 
-        
-        grounding_extra_input = grounding_extra_input[:,0].unsqueeze(1)
-
-        out = torch.nn.functional.interpolate(grounding_extra_input, (self.resize_input,self.resize_input), mode='bicubic')
-        out = self.layers(out)
-
-        assert out.shape[1] == self.out_dim 
-        return out
-
-
diff --git a/spaces/awacke1/CharacterZoo/app.py b/spaces/awacke1/CharacterZoo/app.py
deleted file mode 100644
index 0118d366e76d100902dd4f8c3ae742d9485d1f9e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/CharacterZoo/app.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import streamlit as st
-
-# Define all the characters and their skills
-characters = {
-    "🦁": {"name": "Lion the Crying Zion", "skill": "Distracts and intimidates opponents"},
-    "🦊": {"name": "Fox the Box of mocks", "skill": "Steals an item from an opponent"},
-    "🦄": {"name": "Unicorn the Mystic Foreman", "skill": "Charms opponents into doing what Unicorn wants"},
-    "🐯": {"name": "Tiger the Sassy Swiper", "skill": "Attacks opponents with a surprise jump"},
-    "🐺": {"name": "Wolf the Howlin’ Aloof", "skill": "Finds hidden items or people"},
-    "🐍": {"name": "Snake the Sneak Freak", "skill": "Blends in with surroundings and become invisible"},
-    "🐘": {"name": "Elephant the Trunk Slammer", "skill": "Stampedes and knocks opponents over"},
-    "🦜": {"name": "Parrot the Chat Squawk", "skill": "Imitates sounds to distract or confuse opponents"}
-}
-
-# Define all the classifications and their associated skills
-classifications = {
-    "Attacker": ["Roar", "Pounce", "Charge"],
-    "Thief": ["Steal", "Camouflage"],
-    "Supporter": ["Enchant", "Mimic"],
-    "Tracker": ["Track"],
-    "Defender": ["Charge", "Camouflage"],
-    "Versatile": ["Roar", "Steal", "Enchant", "Pounce", "Camouflage", "Mimic"]
-}
-
-# Define three teams of additional animals to the zoo with their rhyming names
-team_1 = {
-    "🦆": {"name": "Duck the Buck", "skill": "Hits opponents below the belt"},
-    "🐇": {"name": "Rabbit the Habit", "skill": "Dodges enemy attacks with ease"},
-    "🦑": {"name": "Squid the Kid", "skill": "Sprays ink to obscure opponents' vision"}
-}
-
-team_2 = {
-    "🦜": {"name": "Parakeet the Sweet Tweet", "skill": "Puts opponents to sleep with a lullaby"},
-    "🦉": {"name": "Owl the Foul Howl", "skill": "Sends enemies into a frenzy with a piercing screech"},
-    "🦔": {"name": "Hedgehog the Sledge Blog", "skill": "Rolls into a ball to knock over opponents"}
-}
-
-team_3 = {
-    "🐍": {"name": "Viper the Diaper Wiper", "skill": "Leaves opponents in a state of confusion"},
-    "🦓": {"name": "Zebra the High Hebra", "skill": "Lifts spirits and boosts morale"},
-    "🦇": {"name": "Bat the Cat Rats", "skill": "Echolocates to locate hidden enemies and objects"}
-}
-
-# Define a function to generate random dice rolls
-@st.cache
-def roll_dice(num_dice):
-    dice_rolls = ""
-    for i in range(num_dice):
-        dice_rolls += chr(0x267f + int(st.session_state[f"dice_{i+1}"])) + " "
-    return dice_rolls
-
-# Define the streamlit app
-def app():
-    st.title("Fantasy Zoo Emoji Card Game")
-
-    st.write("Welcome to the Fantasy Zoo, where the animals are all emojis and they're ready to play with you! Each emoji has a unique skill that makes them an excellent addition to your team. Here are their names and a brief description:")
-
-    # Display each character
-    for emoji, character in characters.items():
-        st.write(f"- {emoji}  {character['name']} – {character['skill']}")
-
-    st.write("")
-
-    st.write("Let's roll the dice and see who wins! 🎲🎲🎲")
-
-    # Create a dice roller for dice rolling emoji lists
-    num_dice = 3
-    for i in range(num_dice):
-        st.session_state[f"dice_{i+1}"] = st.slider(f"Select a value for dice {i+1}", 1, 6)
-    roll_button = st.button("Roll the dice!")
-    if roll_button:
-        st.write("Your dice rolls are:")
-        st.write(roll_dice(num_dice))
-
-    st.write("")
-
-    st.write("Now let's add some new animals to the mix. Here are their names and skills:")
-
-    # Display each team of additional animals
-    st.write("Team 1:")
-    for emoji, animal in team_1.items():
-        st.write(f"- {emoji}  {animal['name']} – {animal['skill']}")
-
-    st.write("Team 2:")
-    for emoji, animal in team_2.items():
-        st.write(f"- {emoji}  {animal['name']} – {animal['skill']}")
-
-    st.write("Team 3:")
-    for emoji, animal in team_3.items():
-        st.write(f"- {emoji}  {animal['name']} – {animal['skill']}")
-
-    st.write("")
-
-    st.write("Finally, let's take a look at the classifications and skills:")
-
-    # Display the classifications and their associated skills in a markdown table
-    classification_table = "| Role | Skills |\n|------|--------|\n"
-    for classification, skills in classifications.items():
-        classification_table += f"| {classification} | "
-        for i, skill in enumerate(skills):
-            classification_table += f"{skill}"
-            if i < len(skills) - 1:
-                classification_table += ", "
-        classification_table += " |\n"
-    st.markdown(classification_table)
\ No newline at end of file
diff --git a/spaces/awacke1/Streamlit-Google-Maps-Massachusetts/app.py b/spaces/awacke1/Streamlit-Google-Maps-Massachusetts/app.py
deleted file mode 100644
index 6e1d5139dd40e51b11a78e47ea1f56e4e49a21eb..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Google-Maps-Massachusetts/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import streamlit as st
-import folium
-from folium.plugins import MarkerCluster
-from streamlit_folium import folium_static
-import googlemaps
-from datetime import datetime
-import os
-
-# Initialize Google Maps
-gmaps = googlemaps.Client(key=os.getenv('GOOGLE_KEY'))
-
-# Function to fetch directions
-def get_directions_and_coords(source, destination):
-    now = datetime.now()
-    directions_info = gmaps.directions(source, destination, mode='driving', departure_time=now)
-    if directions_info:
-        steps = directions_info[0]['legs'][0]['steps']
-        coords = [(step['start_location']['lat'], step['start_location']['lng']) for step in steps]
-        return steps, coords
-    else:
-        return None, None
-
-# Function to render map with directions
-def render_folium_map(coords):
-    m = folium.Map(location=[coords[0][0], coords[0][1]], zoom_start=13)
-    folium.PolyLine(coords, color="blue", weight=2.5, opacity=1).add_to(m)
-    return m
-
-# Function to add medical center paths and annotate distance
-def add_medical_center_paths(m, source, med_centers):
-    for name, lat, lon, specialty, city in med_centers:
-        _, coords = get_directions_and_coords(source, (lat, lon))
-        if coords:
-            folium.PolyLine(coords, color="red", weight=2.5, opacity=1).add_to(m)
-            folium.Marker([lat, lon], popup=name).add_to(m)
-            distance_info = gmaps.distance_matrix(source, (lat, lon), mode='driving')
-            distance = distance_info['rows'][0]['elements'][0]['distance']['text']
-            folium.map.Marker(
-                [coords[-1][0], coords[-1][1]],
-                icon=folium.DivIcon(
-                    icon_size=(150, 36),
-                    icon_anchor=(0, 0),
-                    html=f'<div style="font-size: 10pt; color : red;">{distance}</div>',
-                    )
-                ).add_to(m)
-
-st.sidebar.header('Directions')
-source_location = st.sidebar.text_input("Source Location", "Logan International Airport, Boston, MA")
-destination_location = st.sidebar.text_input("Destination Location", "Boston Harbor, MA")
-
-if st.sidebar.button('Get Directions'):
-    steps, coords = get_directions_and_coords(source_location, destination_location)
-    if steps and coords:
-        st.subheader('Driving Directions:')
-        for i, step in enumerate(steps):
-            st.write(f"{i+1}. {step['html_instructions']}")
-        st.subheader('Route on Map:')
-        m1 = render_folium_map(coords)
-        folium_static(m1)
-    else:
-        st.write("No available routes.")
-
-# Top 10 medical centers in Massachusetts
-massachusetts_med_centers = [
-    ('Massachusetts General Hospital', 42.3626, -71.0704, 'Teaching hospital', 'Boston'),
-    ('Brigham and Women\'s Hospital', 42.3356, -71.1077, 'Teaching hospital', 'Boston'),
-    ('Dana-Farber Cancer Institute', 42.3371, -71.1081, 'Cancer specialty', 'Boston'),
-    ('Beth Israel Deaconess Medical Center', 42.3394, -71.1043, 'Teaching hospital', 'Boston'),
-    ('Boston Children\'s Hospital', 42.3671, -71.1077, 'Pediatrics', 'Boston'),
-    ('Tufts Medical Center', 42.3495, -71.0634, 'Teaching hospital', 'Boston'),
-    ('Lahey Hospital and Medical Center', 42.5106, -71.2187, 'Community hospital', 'Burlington'),
-    ('Baystate Medical Center', 42.1208, -72.6051, 'Teaching hospital', 'Springfield'),
-    ('Newton-Wellesley Hospital', 42.3304, -71.2436, 'Community hospital', 'Newton'),
-    ('South Shore Hospital', 42.1751, -70.9545, 'Community hospital', 'South Weymouth')
-]
-
-# Annotate distances and paths for each medical center
-st.markdown("## 🏥 Massachusetts Medical Centers 🌊")
-m2 = folium.Map(location=[42.3601, -71.0589], zoom_start=6)
-marker_cluster = MarkerCluster().add_to(m2)
-add_medical_center_paths(m2, source_location, massachusetts_med_centers)
-folium_static(m2)
diff --git a/spaces/awacke1/Survey-Assess-Plan-UI/app.py b/spaces/awacke1/Survey-Assess-Plan-UI/app.py
deleted file mode 100644
index d8410635ec8da9badfc80fbe56d1f268c1b802ac..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Survey-Assess-Plan-UI/app.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import streamlit as st
-import pandas as pd
-import base64
-
-# Define the list of questions with their corresponding codes and URLs
-questions = [
-    {"question": "Do you have any symptoms of COVID-19?", "code": "U07.1", "code_type": "ICD10", "url": "https://www.cdc.gov/nchs/icd/icd10cm.htm"},
-    {"question": "Have you had any surgeries in the past year?", "code": "0JH60ZZ", "code_type": "ICD10-PCS", "url": "https://www.cms.gov/Medicare/Coding/ICD10/2022-ICD-10-PCS-Codes-Version-110"},
-    {"question": "Have you been diagnosed with any chronic conditions?", "code": "443.9", "code_type": "ICD9-CM", "url": "https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes"},
-    {"question": "Have you had any diagnostic imaging tests in the past year?", "code": "70450", "code_type": "CPT", "url": "https://www.cms.gov/Medicare/Coding/HCPCSReleaseCodeSets/Alpha-Numeric-HCPCS"},
-    {"question": "Have you had any lab tests done in the past year?", "code": "19710-4", "code_type": "LOINC", "url": "https://loinc.org/"},
-    {"question": "Have you used any medical equipment in the past year?", "code": "E1399", "code_type": "HCPCS", "url": "https://www.cms.gov/Medicare/Coding/HCPCSReleaseCodeSets/Alpha-Numeric-HCPCS"}
-]
-
-# Define a function to create a DataFrame with the user's responses
-def create_survey_df(questions):
-    survey_dict = {"Assess Topic": [], "Assess Metric": [], "Code Emoji": [], "Code Topic": [], "Code Type": [], "Code": [], "Response": [], "Comment": []}
-    for q in questions:
-        survey_dict["Assess Topic"].append("Medical History")
-        survey_dict["Assess Metric"].append(q["question"])
-        survey_dict["Code Emoji"].append("💊")
-        survey_dict["Code Topic"].append(q["code"])
-        survey_dict["Code Type"].append(q["code_type"])
-        survey_dict["Code"].append(q["code"])
-        survey_dict["Response"].append(st.radio("Answer", ["Yes", "No"]))
-        survey_dict["Comment"].append(st.text_input("Comment (optional)"))
-    return pd.DataFrame(survey_dict)
-
-# Define the Streamlit app
-def app():
-    st.title("Medical History Survey")
-    st.markdown("Please answer the following questions about your medical history.")
-    survey_df = create_survey_df(questions)
-    st.write(survey_df)
-
-    # Allow the user to download the survey results as a CSV file
-    if st.button("Download CSV"):
-        csv = survey_df.to_csv(index=False)
-        b64 = base64.b64encode(csv.encode()).decode()
-        st.markdown(f'<a href="data:file/csv;base64,{b64}" download="survey_results.csv">Download CSV File</a>', unsafe_allow_html=True)
diff --git a/spaces/b1sheng/kg_llm_leaderboard_test/src/assets/hardcoded_evals.py b/spaces/b1sheng/kg_llm_leaderboard_test/src/assets/hardcoded_evals.py
deleted file mode 100644
index c88ef15d6805bab7fa6b844e862d715d11937ba7..0000000000000000000000000000000000000000
--- a/spaces/b1sheng/kg_llm_leaderboard_test/src/assets/hardcoded_evals.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from src.utils_display import AutoEvalColumn, model_hyperlink
-
-gpt4_values = {
-    AutoEvalColumn.model.name: model_hyperlink("https://arxiv.org/abs/2303.08774", "gpt4"),
-    AutoEvalColumn.revision.name: "tech report",
-    AutoEvalColumn.precision.name: None,
-    AutoEvalColumn.average.name: 84.3,
-    AutoEvalColumn.arc.name: 96.3,
-    AutoEvalColumn.hellaswag.name:  95.3,
-    AutoEvalColumn.mmlu.name:  86.4,
-    AutoEvalColumn.truthfulqa.name:  59.0,
-    AutoEvalColumn.dummy.name: "GPT-4",
-    AutoEvalColumn.model_type.name: "",
-}
-
-gpt35_values = {
-    AutoEvalColumn.model.name: model_hyperlink("https://arxiv.org/abs/2303.08774", "gpt3.5"),
-    AutoEvalColumn.revision.name: "tech report",
-    AutoEvalColumn.precision.name: None,
-    AutoEvalColumn.average.name: 71.9,
-    AutoEvalColumn.arc.name: 85.2,
-    AutoEvalColumn.hellaswag.name:  85.5,
-    AutoEvalColumn.mmlu.name:  70.0,
-    AutoEvalColumn.truthfulqa.name:  47.0,
-    AutoEvalColumn.dummy.name: "GPT-3.5",
-    AutoEvalColumn.model_type.name: "",
-}
-
-baseline = {
-    AutoEvalColumn.model.name: "<p>Baseline</p>",
-    AutoEvalColumn.revision.name: "N/A",
-    AutoEvalColumn.precision.name: None,
-    AutoEvalColumn.average.name: 25.0,
-    AutoEvalColumn.arc.name: 25.0,
-    AutoEvalColumn.hellaswag.name:  25.0,
-    AutoEvalColumn.mmlu.name:  25.0,
-    AutoEvalColumn.truthfulqa.name:  25.0,
-    AutoEvalColumn.dummy.name: "baseline",
-    AutoEvalColumn.model_type.name: "",
-}
-
diff --git a/spaces/b7evc/stabilityai-stable-diffusion-2-1/app.py b/spaces/b7evc/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 0160420876923d89f2ab5fccb9f4d13725e29972..0000000000000000000000000000000000000000
--- a/spaces/b7evc/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/backrock/meta-llama-Llama-2-70b-hf/app.py b/spaces/backrock/meta-llama-Llama-2-70b-hf/app.py
deleted file mode 100644
index 4ec735e3ad87d311c5c40e845d963d4474a98f4c..0000000000000000000000000000000000000000
--- a/spaces/backrock/meta-llama-Llama-2-70b-hf/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/meta-llama/Llama-2-70b-hf").launch()
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/crossfade/scenes.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/crossfade/scenes.js
deleted file mode 100644
index 28d2596f2d24bdac132d12497cf85baec151a0d9..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/crossfade/scenes.js
+++ /dev/null
@@ -1,119 +0,0 @@
-function generateGeometry( objectType, numObjects ) {
-
-	function applyVertexColors( geometry, color ) {
-
-		var position = geometry.attributes.position;
-		var colors = [];
-
-		for ( var i = 0; i < position.count; i ++ ) {
-
-			colors.push( color.r, color.g, color.b );
-
-		}
-
-		geometry.addAttribute( 'color', new THREE.Float32BufferAttribute( colors, 3 ) );
-
-	}
-
-	var geometries = [];
-
-	var matrix = new THREE.Matrix4();
-	var position = new THREE.Vector3();
-	var rotation = new THREE.Euler();
-	var quaternion = new THREE.Quaternion();
-	var scale = new THREE.Vector3();
-	var color = new THREE.Color();
-
-	for ( var i = 0; i < numObjects; i ++ ) {
-
-		position.x = Math.random() * 10000 - 5000;
-		position.y = Math.random() * 6000 - 3000;
-		position.z = Math.random() * 8000 - 4000;
-
-		rotation.x = Math.random() * 2 * Math.PI;
-		rotation.y = Math.random() * 2 * Math.PI;
-		rotation.z = Math.random() * 2 * Math.PI;
-		quaternion.setFromEuler( rotation, false );
-
-		scale.x = Math.random() * 200 + 100;
-
-		var geometry;
-
-		if ( objectType === 'cube' ) {
-
-			geometry = new THREE.BoxBufferGeometry( 1, 1, 1 );
-			geometry = geometry.toNonIndexed(); // merging needs consistent buffer geometries
-			scale.y = Math.random() * 200 + 100;
-			scale.z = Math.random() * 200 + 100;
-			color.setRGB( 0, 0, 0.1 + 0.9 * Math.random() );
-
-		} else if ( objectType === 'sphere' ) {
-
-			geometry = new THREE.IcosahedronBufferGeometry( 1, 1 );
-			scale.y = scale.z = scale.x;
-			color.setRGB( 0.1 + 0.9 * Math.random(), 0, 0 );
-
-		}
-
-		// give the geom's vertices a random color, to be displayed
-		applyVertexColors( geometry, color );
-
-		matrix.compose( position, quaternion, scale );
-		geometry.applyMatrix( matrix );
-
-		geometries.push( geometry );
-
-	}
-
-	return THREE.BufferGeometryUtils.mergeBufferGeometries( geometries );
-
-}
-
-function Scene( type, numObjects, cameraZ, fov, rotationSpeed, clearColor ) {
-
-	this.clearColor = clearColor;
-
-	this.camera = new THREE.PerspectiveCamera( fov, window.innerWidth / window.innerHeight, 1, 10000 );
-	this.camera.position.z = cameraZ;
-
-	// Setup scene
-	this.scene = new THREE.Scene();
-	this.scene.add( new THREE.AmbientLight( 0x555555 ) );
-
-	var light = new THREE.SpotLight( 0xffffff, 1.5 );
-	light.position.set( 0, 500, 2000 );
-	this.scene.add( light );
-
-	this.rotationSpeed = rotationSpeed;
-
-	var defaultMaterial = new THREE.MeshPhongMaterial( { color: 0xffffff, flatShading: true, vertexColors: THREE.VertexColors } );
-	this.mesh = new THREE.Mesh( generateGeometry( type, numObjects ), defaultMaterial );
-	this.scene.add( this.mesh );
-
-	var renderTargetParameters = { minFilter: THREE.LinearFilter, magFilter: THREE.LinearFilter, format: THREE.RGBFormat, stencilBuffer: false };
-	this.fbo = new THREE.WebGLRenderTarget( window.innerWidth, window.innerHeight, renderTargetParameters );
-
-	this.render = function ( delta, rtt ) {
-
-		this.mesh.rotation.x += delta * this.rotationSpeed.x;
-		this.mesh.rotation.y += delta * this.rotationSpeed.y;
-		this.mesh.rotation.z += delta * this.rotationSpeed.z;
-
-		renderer.setClearColor( this.clearColor );
-
-		if ( rtt ) {
-
-			renderer.setRenderTarget( this.fbo );
-			renderer.clear();
-			renderer.render( this.scene, this.camera );
-
-		} else {
-
-			renderer.setRenderTarget( null );
-			renderer.render( this.scene, this.camera );
-
-		}
-
-	};
-
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/geometries/ConvexGeometry.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/geometries/ConvexGeometry.js
deleted file mode 100644
index 296f03e71a7637aa3dbb357f315c2e773c4c9ded..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/geometries/ConvexGeometry.js
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @author Mugen87 / https://github.com/Mugen87
- */
-
-( function () {
-
-	// ConvexGeometry
-
-	function ConvexGeometry( points ) {
-
-		THREE.Geometry.call( this );
-
-		this.fromBufferGeometry( new ConvexBufferGeometry( points ) );
-		this.mergeVertices();
-
-	}
-
-	ConvexGeometry.prototype = Object.create( THREE.Geometry.prototype );
-	ConvexGeometry.prototype.constructor = ConvexGeometry;
-
-	// ConvexBufferGeometry
-
-	function ConvexBufferGeometry( points ) {
-
-		THREE.BufferGeometry.call( this );
-
-		// buffers
-
-		var vertices = [];
-		var normals = [];
-
-		// execute QuickHull
-
-		if ( THREE.QuickHull === undefined ) {
-
-			console.error( 'THREE.ConvexBufferGeometry: ConvexBufferGeometry relies on THREE.QuickHull' );
-
-		}
-
-		var quickHull = new THREE.QuickHull().setFromPoints( points );
-
-		// generate vertices and normals
-
-		var faces = quickHull.faces;
-
-		for ( var i = 0; i < faces.length; i ++ ) {
-
-			var face = faces[ i ];
-			var edge = face.edge;
-
-			// we move along a doubly-connected edge list to access all face points (see HalfEdge docs)
-
-			do {
-
-				var point = edge.head().point;
-
-				vertices.push( point.x, point.y, point.z );
-				normals.push( face.normal.x, face.normal.y, face.normal.z );
-
-				edge = edge.next;
-
-			} while ( edge !== face.edge );
-
-		}
-
-		// build geometry
-
-		this.addAttribute( 'position', new THREE.Float32BufferAttribute( vertices, 3 ) );
-		this.addAttribute( 'normal', new THREE.Float32BufferAttribute( normals, 3 ) );
-
-	}
-
-	ConvexBufferGeometry.prototype = Object.create( THREE.BufferGeometry.prototype );
-	ConvexBufferGeometry.prototype.constructor = ConvexBufferGeometry;
-
-	// export
-
-	THREE.ConvexGeometry = ConvexGeometry;
-	THREE.ConvexBufferGeometry = ConvexBufferGeometry;
-
-} )();
diff --git "a/spaces/betterme/Nice/_\360\237\221\213_.py" "b/spaces/betterme/Nice/_\360\237\221\213_.py"
deleted file mode 100644
index 36579801ae363e8dbe0e150228e57dcedfbc9f5c..0000000000000000000000000000000000000000
--- "a/spaces/betterme/Nice/_\360\237\221\213_.py"
+++ /dev/null
@@ -1,9 +0,0 @@
-import streamlit as st
-
-st.set_page_config(
-    page_title='Streamlit组件清单',
-    page_icon="📖",
-    layout="wide",
-    initial_sidebar_state="collapsed",
-)
-# st.text_input('业务搜索', '知识图谱')
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/bytetrack/byte_tracker.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/bytetrack/byte_tracker.py
deleted file mode 100644
index e3741722f548b85c01bcd216bff781b53424646f..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/bytetrack/byte_tracker.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import numpy as np
-from collections import deque
-import os
-import os.path as osp
-import copy
-import torch
-import torch.nn.functional as F
-
-from ultralytics.yolo.utils.ops import xywh2xyxy, xyxy2xywh
-
-
-from trackers.bytetrack.kalman_filter import KalmanFilter
-from trackers.bytetrack import matching
-from trackers.bytetrack.basetrack import BaseTrack, TrackState
-
-class STrack(BaseTrack):
-    shared_kalman = KalmanFilter()
-    def __init__(self, tlwh, score, cls):
-
-        # wait activate
-        self._tlwh = np.asarray(tlwh, dtype=np.float32)
-        self.kalman_filter = None
-        self.mean, self.covariance = None, None
-        self.is_activated = False
-
-        self.score = score
-        self.tracklet_len = 0
-        self.cls = cls
-
-    def predict(self):
-        mean_state = self.mean.copy()
-        if self.state != TrackState.Tracked:
-            mean_state[7] = 0
-        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
-
-    @staticmethod
-    def multi_predict(stracks):
-        if len(stracks) > 0:
-            multi_mean = np.asarray([st.mean.copy() for st in stracks])
-            multi_covariance = np.asarray([st.covariance for st in stracks])
-            for i, st in enumerate(stracks):
-                if st.state != TrackState.Tracked:
-                    multi_mean[i][7] = 0
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
-            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
-                stracks[i].mean = mean
-                stracks[i].covariance = cov
-
-    def activate(self, kalman_filter, frame_id):
-        """Start a new tracklet"""
-        self.kalman_filter = kalman_filter
-        self.track_id = self.next_id()
-        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
-
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        if frame_id == 1:
-            self.is_activated = True
-        # self.is_activated = True
-        self.frame_id = frame_id
-        self.start_frame = frame_id
-
-    def re_activate(self, new_track, frame_id, new_id=False):
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
-        )
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        self.is_activated = True
-        self.frame_id = frame_id
-        if new_id:
-            self.track_id = self.next_id()
-        self.score = new_track.score
-        self.cls = new_track.cls
-
-    def update(self, new_track, frame_id):
-        """
-        Update a matched track
-        :type new_track: STrack
-        :type frame_id: int
-        :type update_feature: bool
-        :return:
-        """
-        self.frame_id = frame_id
-        self.tracklet_len += 1
-        # self.cls = cls
-
-        new_tlwh = new_track.tlwh
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
-        self.state = TrackState.Tracked
-        self.is_activated = True
-
-        self.score = new_track.score
-
-    @property
-    # @jit(nopython=True)
-    def tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-                width, height)`.
-        """
-        if self.mean is None:
-            return self._tlwh.copy()
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    @property
-    # @jit(nopython=True)
-    def tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
-        ret = self.tlwh.copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlwh_to_xyah(tlwh):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
-        """
-        ret = np.asarray(tlwh).copy()
-        ret[:2] += ret[2:] / 2
-        ret[2] /= ret[3]
-        return ret
-
-    def to_xyah(self):
-        return self.tlwh_to_xyah(self.tlwh)
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlbr_to_tlwh(tlbr):
-        ret = np.asarray(tlbr).copy()
-        ret[2:] -= ret[:2]
-        return ret
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlwh_to_tlbr(tlwh):
-        ret = np.asarray(tlwh).copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    def __repr__(self):
-        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
-
-
-class BYTETracker(object):
-    def __init__(self, track_thresh=0.45, match_thresh=0.8, track_buffer=25, frame_rate=30):
-        self.tracked_stracks = []  # type: list[STrack]
-        self.lost_stracks = []  # type: list[STrack]
-        self.removed_stracks = []  # type: list[STrack]
-
-        self.frame_id = 0
-        self.track_buffer=track_buffer
-        
-        self.track_thresh = track_thresh
-        self.match_thresh = match_thresh
-        self.det_thresh = track_thresh + 0.1
-        self.buffer_size = int(frame_rate / 30.0 * track_buffer)
-        self.max_time_lost = self.buffer_size
-        self.kalman_filter = KalmanFilter()
-
-    def update(self, dets, _):
-        self.frame_id += 1
-        activated_starcks = []
-        refind_stracks = []
-        lost_stracks = []
-        removed_stracks = []
-
-        xyxys = dets[:, 0:4]
-        xywh = xyxy2xywh(xyxys)
-        confs = dets[:, 4]
-        clss = dets[:, 5]
-        
-        classes = clss.numpy()
-        xyxys = xyxys.numpy()
-        confs = confs.numpy()
-
-        remain_inds = confs > self.track_thresh
-        inds_low = confs > 0.1
-        inds_high = confs < self.track_thresh
-
-        inds_second = np.logical_and(inds_low, inds_high)
-        
-        dets_second = xywh[inds_second]
-        dets = xywh[remain_inds]
-        
-        scores_keep = confs[remain_inds]
-        scores_second = confs[inds_second]
-        
-        clss_keep = classes[remain_inds]
-        clss_second = classes[inds_second]
-        
-
-        if len(dets) > 0:
-            '''Detections'''
-            detections = [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores_keep, clss_keep)]
-        else:
-            detections = []
-
-        ''' Add newly detected tracklets to tracked_stracks'''
-        unconfirmed = []
-        tracked_stracks = []  # type: list[STrack]
-        for track in self.tracked_stracks:
-            if not track.is_activated:
-                unconfirmed.append(track)
-            else:
-                tracked_stracks.append(track)
-
-        ''' Step 2: First association, with high score detection boxes'''
-        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
-        # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
-        dists = matching.iou_distance(strack_pool, detections)
-        #if not self.args.mot20:
-        dists = matching.fuse_score(dists, detections)
-        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.match_thresh)
-
-        for itracked, idet in matches:
-            track = strack_pool[itracked]
-            det = detections[idet]
-            if track.state == TrackState.Tracked:
-                track.update(detections[idet], self.frame_id)
-                activated_starcks.append(track)
-            else:
-                track.re_activate(det, self.frame_id, new_id=False)
-                refind_stracks.append(track)
-
-        ''' Step 3: Second association, with low score detection boxes'''
-        # association the untrack to the low score detections
-        if len(dets_second) > 0:
-            '''Detections'''
-            detections_second = [STrack(xywh, s, c) for (xywh, s, c) in zip(dets_second, scores_second, clss_second)]
-        else:
-            detections_second = []
-        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
-        dists = matching.iou_distance(r_tracked_stracks, detections_second)
-        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
-        for itracked, idet in matches:
-            track = r_tracked_stracks[itracked]
-            det = detections_second[idet]
-            if track.state == TrackState.Tracked:
-                track.update(det, self.frame_id)
-                activated_starcks.append(track)
-            else:
-                track.re_activate(det, self.frame_id, new_id=False)
-                refind_stracks.append(track)
-
-        for it in u_track:
-            track = r_tracked_stracks[it]
-            if not track.state == TrackState.Lost:
-                track.mark_lost()
-                lost_stracks.append(track)
-
-        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
-        detections = [detections[i] for i in u_detection]
-        dists = matching.iou_distance(unconfirmed, detections)
-        #if not self.args.mot20:
-        dists = matching.fuse_score(dists, detections)
-        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
-        for itracked, idet in matches:
-            unconfirmed[itracked].update(detections[idet], self.frame_id)
-            activated_starcks.append(unconfirmed[itracked])
-        for it in u_unconfirmed:
-            track = unconfirmed[it]
-            track.mark_removed()
-            removed_stracks.append(track)
-
-        """ Step 4: Init new stracks"""
-        for inew in u_detection:
-            track = detections[inew]
-            if track.score < self.det_thresh:
-                continue
-            track.activate(self.kalman_filter, self.frame_id)
-            activated_starcks.append(track)
-        """ Step 5: Update state"""
-        for track in self.lost_stracks:
-            if self.frame_id - track.end_frame > self.max_time_lost:
-                track.mark_removed()
-                removed_stracks.append(track)
-
-        # print('Ramained match {} s'.format(t4-t3))
-
-        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
-        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
-        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
-        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
-        self.lost_stracks.extend(lost_stracks)
-        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
-        self.removed_stracks.extend(removed_stracks)
-        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
-        # get scores of lost tracks
-        output_stracks = [track for track in self.tracked_stracks if track.is_activated]
-        outputs = []
-        for t in output_stracks:
-            output= []
-            tlwh = t.tlwh
-            tid = t.track_id
-            tlwh = np.expand_dims(tlwh, axis=0)
-            xyxy = xywh2xyxy(tlwh)
-            xyxy = np.squeeze(xyxy, axis=0)
-            output.extend(xyxy)
-            output.append(tid)
-            output.append(t.cls)
-            output.append(t.score)
-            outputs.append(output)
-
-        return outputs
-#track_id, class_id, conf
-
-def joint_stracks(tlista, tlistb):
-    exists = {}
-    res = []
-    for t in tlista:
-        exists[t.track_id] = 1
-        res.append(t)
-    for t in tlistb:
-        tid = t.track_id
-        if not exists.get(tid, 0):
-            exists[tid] = 1
-            res.append(t)
-    return res
-
-
-def sub_stracks(tlista, tlistb):
-    stracks = {}
-    for t in tlista:
-        stracks[t.track_id] = t
-    for t in tlistb:
-        tid = t.track_id
-        if stracks.get(tid, 0):
-            del stracks[tid]
-    return list(stracks.values())
-
-
-def remove_duplicate_stracks(stracksa, stracksb):
-    pdist = matching.iou_distance(stracksa, stracksb)
-    pairs = np.where(pdist < 0.15)
-    dupa, dupb = list(), list()
-    for p, q in zip(*pairs):
-        timep = stracksa[p].frame_id - stracksa[p].start_frame
-        timeq = stracksb[q].frame_id - stracksb[q].start_frame
-        if timep > timeq:
-            dupb.append(q)
-        else:
-            dupa.append(p)
-    resa = [t for i, t in enumerate(stracksa) if not i in dupa]
-    resb = [t for i, t in enumerate(stracksb) if not i in dupb]
-    return resa, resb
diff --git a/spaces/binhnase04854/Invoice-VQA/README.md b/spaces/binhnase04854/Invoice-VQA/README.md
deleted file mode 100644
index 1a1bd80a91245bb3d1bb2d11f1ebd0d23f55fa2e..0000000000000000000000000000000000000000
--- a/spaces/binhnase04854/Invoice-VQA/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Invoice VQA
-emoji: 📊
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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deleted file mode 100644
index 6fbefd8d206fa32c9c6e796b204d9d4184c8f956..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Alludu Seenu Movie Hd 720p Dvdrip REPACK.md	
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-
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deleted file mode 100644
index 6dabfadcc8be75a3b32992d0fc6c25bddad77c57..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Border movie dubbed in hindi online The story of the Battle of Longewala.md	
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index 9836f5c701ef9aaf09dfaee23be967e2649abbdc..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Caregiver Movie Sharon Cuneta A Star Cinema Production Starring the Megastar.md	
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diff --git a/spaces/bkhmsi/Font-To-Sketch/code/config.py b/spaces/bkhmsi/Font-To-Sketch/code/config.py
deleted file mode 100644
index 7d35734b52577ff04ace15f915b63c44bd3feda8..0000000000000000000000000000000000000000
--- a/spaces/bkhmsi/Font-To-Sketch/code/config.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import argparse
-import os.path as osp
-import yaml
-import random
-from easydict import EasyDict as edict
-import numpy.random as npr
-import torch
-from utils import (
-    edict_2_dict,
-    check_and_create_dir,
-    update)
-import wandb
-import warnings
-warnings.filterwarnings("ignore")
-from glob import glob
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--config", type=str, default="code/config/base.yaml")
-    parser.add_argument("--experiment", type=str, default="conformal_0.5_dist_pixel_100_kernel201")
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument('--log_dir', metavar='DIR', default="output")
-    parser.add_argument('--font', type=str, default="none", help="font name")
-    parser.add_argument('--semantic_concept', type=str, help="the semantic concept to insert")
-    parser.add_argument('--word', type=str, default="none", help="the text to work on")
-    parser.add_argument('--script', type=str, default="arabic", help="script")
-    parser.add_argument('--prompt_suffix', type=str, default="minimal flat 2d vector. lineal color. trending on artstation")
-    parser.add_argument('--optimized_letter', type=str, default="none", help="the letter in the word to optimize")
-    parser.add_argument('--batch_size', type=int, default=1)
-    parser.add_argument('--use_wandb', type=int, default=0)
-    parser.add_argument('--wandb_user', type=str, default="none")
-
-    cfg = edict()
-    args = parser.parse_args()
-    with open('TOKEN', 'r') as f:
-        setattr(args, 'token', f.read().replace('\n', ''))
-
-    cfg.config = args.config
-    cfg.experiment = args.experiment
-    cfg.seed = args.seed
-    cfg.font = args.font
-    cfg.semantic_concept = args.semantic_concept
-    cfg.word = cfg.semantic_concept if args.word == "none" else args.word
-    cfg.letter = cfg.word
-    cfg.script = args.script
-
-    script_path = f"code/data/fonts/{cfg.script}"
-    if cfg.font == "none":
-        cfg.font = osp.basename(glob(f"{script_path}/*.ttf")[0])[:-4]
-    
-
-    # if " " in cfg.word:
-    #   raise ValueError(f'no spaces are allowed')
-    
-    if "jpeg" in args.semantic_concept:
-        cfg.caption = args.semantic_concept 
-    else:
-        cfg.caption = f"a {args.semantic_concept}. {args.prompt_suffix}"
-        
-    # cfg.log_dir = f"{args.log_dir}/{args.experiment}_{cfg.word}"
-    cfg.log_dir = f"{args.log_dir}/{cfg.script}"
-    if args.optimized_letter in cfg.word:
-        cfg.optimized_letter = args.optimized_letter
-    else:
-      raise ValueError(f'letter should be in word')
-    
-    cfg.batch_size = args.batch_size
-    cfg.token = args.token
-    cfg.use_wandb = args.use_wandb
-    cfg.wandb_user = args.wandb_user
-    cfg.letter = f"{cfg.font}_{args.optimized_letter}_scaled"
-    cfg.target = f"code/data/init/{cfg.letter}"
-    if ' ' in cfg.target:
-        cfg.target = cfg.target.replace(' ', '_')
-
-    return cfg
-
-
-def set_config():
-
-    cfg_arg = parse_args()
-    with open(cfg_arg.config, 'r') as f:
-        cfg_full = yaml.load(f, Loader=yaml.FullLoader)
-
-    # recursively traverse parent_config pointers in the config dicts
-    cfg_key = cfg_arg.experiment
-    cfgs = [cfg_arg]
-    while cfg_key:
-        cfgs.append(cfg_full[cfg_key])
-        cfg_key = cfgs[-1].get('parent_config', 'baseline')
-
-    # allowing children configs to override their parents
-    cfg = edict()
-    for options in reversed(cfgs):
-        update(cfg, options)
-    del cfgs
-
-    # set experiment dir
-    signature = f"{cfg.word}_{cfg.semantic_concept}_{cfg.seed}"
-    cfg.experiment_dir = osp.join(cfg.log_dir, signature)
-    configfile = osp.join(cfg.experiment_dir, 'config.yaml')
-    print('Config:', cfg)
-
-    # create experiment dir and save config
-    check_and_create_dir(configfile)
-    with open(osp.join(configfile), 'w') as f:
-        yaml.dump(edict_2_dict(cfg), f)
-
-    if cfg.use_wandb:
-        wandb.init(project="Font-To-Image", entity=cfg.wandb_user,
-                   config=cfg, name=f"{signature}", id=wandb.util.generate_id())
-
-    if cfg.seed is not None:
-        random.seed(cfg.seed)
-        npr.seed(cfg.seed)
-        torch.manual_seed(cfg.seed)
-        torch.backends.cudnn.benchmark = False
-    else:
-        assert False
-
-    return cfg
diff --git a/spaces/brjathu/HMR2.0/hmr2/datasets/utils.py b/spaces/brjathu/HMR2.0/hmr2/datasets/utils.py
deleted file mode 100644
index ab4b5f0345fce9c2bdb815bd99ac0583cc747382..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/hmr2/datasets/utils.py
+++ /dev/null
@@ -1,999 +0,0 @@
-"""
-Parts of the code are taken or adapted from
-https://github.com/mkocabas/EpipolarPose/blob/master/lib/utils/img_utils.py
-"""
-import torch
-import numpy as np
-from skimage.transform import rotate, resize
-from skimage.filters import gaussian
-import random
-import cv2
-from typing import List, Dict, Tuple
-from yacs.config import CfgNode
-
-def expand_to_aspect_ratio(input_shape, target_aspect_ratio=None):
-    """Increase the size of the bounding box to match the target shape."""
-    if target_aspect_ratio is None:
-        return input_shape
-
-    try:
-        w , h = input_shape
-    except (ValueError, TypeError):
-        return input_shape
-
-    w_t, h_t = target_aspect_ratio
-    if h / w < h_t / w_t:
-        h_new = max(w * h_t / w_t, h)
-        w_new = w
-    else:
-        h_new = h
-        w_new = max(h * w_t / h_t, w)
-    if h_new < h or w_new < w:
-        breakpoint()
-    return np.array([w_new, h_new])
-
-def do_augmentation(aug_config: CfgNode) -> Tuple:
-    """
-    Compute random augmentation parameters.
-    Args:
-        aug_config (CfgNode): Config containing augmentation parameters.
-    Returns:
-        scale (float): Box rescaling factor.
-        rot (float): Random image rotation.
-        do_flip (bool): Whether to flip image or not.
-        do_extreme_crop (bool): Whether to apply extreme cropping (as proposed in EFT).
-        color_scale (List): Color rescaling factor
-        tx (float): Random translation along the x axis.
-        ty (float): Random translation along the y axis. 
-    """
-
-    tx = np.clip(np.random.randn(), -1.0, 1.0) * aug_config.TRANS_FACTOR
-    ty = np.clip(np.random.randn(), -1.0, 1.0) * aug_config.TRANS_FACTOR
-    scale = np.clip(np.random.randn(), -1.0, 1.0) * aug_config.SCALE_FACTOR + 1.0
-    rot = np.clip(np.random.randn(), -2.0,
-                  2.0) * aug_config.ROT_FACTOR if random.random() <= aug_config.ROT_AUG_RATE else 0
-    do_flip = aug_config.DO_FLIP and random.random() <= aug_config.FLIP_AUG_RATE
-    do_extreme_crop = random.random() <= aug_config.EXTREME_CROP_AUG_RATE
-    extreme_crop_lvl = aug_config.get('EXTREME_CROP_AUG_LEVEL', 0)
-    # extreme_crop_lvl = 0
-    c_up = 1.0 + aug_config.COLOR_SCALE
-    c_low = 1.0 - aug_config.COLOR_SCALE
-    color_scale = [random.uniform(c_low, c_up), random.uniform(c_low, c_up), random.uniform(c_low, c_up)]
-    return scale, rot, do_flip, do_extreme_crop, extreme_crop_lvl, color_scale, tx, ty
-
-def rotate_2d(pt_2d: np.array, rot_rad: float) -> np.array:
-    """
-    Rotate a 2D point on the x-y plane.
-    Args:
-        pt_2d (np.array): Input 2D point with shape (2,).
-        rot_rad (float): Rotation angle
-    Returns:
-        np.array: Rotated 2D point.
-    """
-    x = pt_2d[0]
-    y = pt_2d[1]
-    sn, cs = np.sin(rot_rad), np.cos(rot_rad)
-    xx = x * cs - y * sn
-    yy = x * sn + y * cs
-    return np.array([xx, yy], dtype=np.float32)
-
-
-def gen_trans_from_patch_cv(c_x: float, c_y: float,
-                            src_width: float, src_height: float,
-                            dst_width: float, dst_height: float,
-                            scale: float, rot: float) -> np.array:
-    """
-    Create transformation matrix for the bounding box crop.
-    Args:
-        c_x (float): Bounding box center x coordinate in the original image.
-        c_y (float): Bounding box center y coordinate in the original image.
-        src_width (float): Bounding box width.
-        src_height (float): Bounding box height.
-        dst_width (float): Output box width.
-        dst_height (float): Output box height.
-        scale (float): Rescaling factor for the bounding box (augmentation).
-        rot (float): Random rotation applied to the box.
-    Returns:
-        trans (np.array): Target geometric transformation.
-    """
-    # augment size with scale
-    src_w = src_width * scale
-    src_h = src_height * scale
-    src_center = np.zeros(2)
-    src_center[0] = c_x
-    src_center[1] = c_y
-    # augment rotation
-    rot_rad = np.pi * rot / 180
-    src_downdir = rotate_2d(np.array([0, src_h * 0.5], dtype=np.float32), rot_rad)
-    src_rightdir = rotate_2d(np.array([src_w * 0.5, 0], dtype=np.float32), rot_rad)
-
-    dst_w = dst_width
-    dst_h = dst_height
-    dst_center = np.array([dst_w * 0.5, dst_h * 0.5], dtype=np.float32)
-    dst_downdir = np.array([0, dst_h * 0.5], dtype=np.float32)
-    dst_rightdir = np.array([dst_w * 0.5, 0], dtype=np.float32)
-
-    src = np.zeros((3, 2), dtype=np.float32)
-    src[0, :] = src_center
-    src[1, :] = src_center + src_downdir
-    src[2, :] = src_center + src_rightdir
-
-    dst = np.zeros((3, 2), dtype=np.float32)
-    dst[0, :] = dst_center
-    dst[1, :] = dst_center + dst_downdir
-    dst[2, :] = dst_center + dst_rightdir
-
-    trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
-
-    return trans
-
-
-def trans_point2d(pt_2d: np.array, trans: np.array):
-    """
-    Transform a 2D point using translation matrix trans.
-    Args:
-        pt_2d (np.array): Input 2D point with shape (2,).
-        trans (np.array): Transformation matrix.
-    Returns:
-        np.array: Transformed 2D point.
-    """
-    src_pt = np.array([pt_2d[0], pt_2d[1], 1.]).T
-    dst_pt = np.dot(trans, src_pt)
-    return dst_pt[0:2]
-
-def get_transform(center, scale, res, rot=0):
-    """Generate transformation matrix."""
-    """Taken from PARE: https://github.com/mkocabas/PARE/blob/6e0caca86c6ab49ff80014b661350958e5b72fd8/pare/utils/image_utils.py"""
-    h = 200 * scale
-    t = np.zeros((3, 3))
-    t[0, 0] = float(res[1]) / h
-    t[1, 1] = float(res[0]) / h
-    t[0, 2] = res[1] * (-float(center[0]) / h + .5)
-    t[1, 2] = res[0] * (-float(center[1]) / h + .5)
-    t[2, 2] = 1
-    if not rot == 0:
-        rot = -rot  # To match direction of rotation from cropping
-        rot_mat = np.zeros((3, 3))
-        rot_rad = rot * np.pi / 180
-        sn, cs = np.sin(rot_rad), np.cos(rot_rad)
-        rot_mat[0, :2] = [cs, -sn]
-        rot_mat[1, :2] = [sn, cs]
-        rot_mat[2, 2] = 1
-        # Need to rotate around center
-        t_mat = np.eye(3)
-        t_mat[0, 2] = -res[1] / 2
-        t_mat[1, 2] = -res[0] / 2
-        t_inv = t_mat.copy()
-        t_inv[:2, 2] *= -1
-        t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t)))
-    return t
-
-
-def transform(pt, center, scale, res, invert=0, rot=0, as_int=True):
-    """Transform pixel location to different reference."""
-    """Taken from PARE: https://github.com/mkocabas/PARE/blob/6e0caca86c6ab49ff80014b661350958e5b72fd8/pare/utils/image_utils.py"""
-    t = get_transform(center, scale, res, rot=rot)
-    if invert:
-        t = np.linalg.inv(t)
-    new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T
-    new_pt = np.dot(t, new_pt)
-    if as_int:
-        new_pt = new_pt.astype(int)
-    return new_pt[:2] + 1
-
-def crop_img(img, ul, br, border_mode=cv2.BORDER_CONSTANT, border_value=0):
-    c_x = (ul[0] + br[0])/2
-    c_y = (ul[1] + br[1])/2
-    bb_width = patch_width = br[0] - ul[0]
-    bb_height = patch_height = br[1] - ul[1]
-    trans = gen_trans_from_patch_cv(c_x, c_y, bb_width, bb_height, patch_width, patch_height, 1.0, 0)
-    img_patch = cv2.warpAffine(img, trans, (int(patch_width), int(patch_height)), 
-                                flags=cv2.INTER_LINEAR, 
-                                borderMode=border_mode,
-                                borderValue=border_value
-                        )
-    
-    # Force borderValue=cv2.BORDER_CONSTANT for alpha channel
-    if (img.shape[2] == 4) and (border_mode != cv2.BORDER_CONSTANT):
-        img_patch[:,:,3] = cv2.warpAffine(img[:,:,3], trans, (int(patch_width), int(patch_height)), 
-                                            flags=cv2.INTER_LINEAR, 
-                                            borderMode=cv2.BORDER_CONSTANT,
-                            )
-
-    return img_patch
-
-def generate_image_patch_skimage(img: np.array, c_x: float, c_y: float,
-                                 bb_width: float, bb_height: float,
-                                 patch_width: float, patch_height: float,
-                                 do_flip: bool, scale: float, rot: float,
-                                 border_mode=cv2.BORDER_CONSTANT, border_value=0) -> Tuple[np.array, np.array]:
-    """
-    Crop image according to the supplied bounding box.
-    Args:
-        img (np.array): Input image of shape (H, W, 3)
-        c_x (float): Bounding box center x coordinate in the original image.
-        c_y (float): Bounding box center y coordinate in the original image.
-        bb_width (float): Bounding box width.
-        bb_height (float): Bounding box height.
-        patch_width (float): Output box width.
-        patch_height (float): Output box height.
-        do_flip (bool): Whether to flip image or not.
-        scale (float): Rescaling factor for the bounding box (augmentation).
-        rot (float): Random rotation applied to the box.
-    Returns:
-        img_patch (np.array): Cropped image patch of shape (patch_height, patch_height, 3)
-        trans (np.array): Transformation matrix.
-    """
-    
-    img_height, img_width, img_channels = img.shape
-    if do_flip:
-       img = img[:, ::-1, :]
-       c_x = img_width - c_x - 1
-
-    trans = gen_trans_from_patch_cv(c_x, c_y, bb_width, bb_height, patch_width, patch_height, scale, rot)
-
-    #img_patch = cv2.warpAffine(img, trans, (int(patch_width), int(patch_height)), flags=cv2.INTER_LINEAR)
-
-    # skimage
-    center = np.zeros(2)
-    center[0] = c_x
-    center[1] = c_y
-    res = np.zeros(2)
-    res[0] = patch_width
-    res[1] = patch_height
-    # assumes bb_width = bb_height
-    # assumes patch_width = patch_height
-    assert bb_width == bb_height, f'{bb_width=} != {bb_height=}'
-    assert patch_width == patch_height, f'{patch_width=} != {patch_height=}'
-    scale1 = scale*bb_width/200.
-    
-    # Upper left point
-    ul = np.array(transform([1, 1], center, scale1, res, invert=1, as_int=False)) - 1
-    # Bottom right point
-    br = np.array(transform([res[0] + 1,
-                             res[1] + 1], center, scale1, res, invert=1, as_int=False)) - 1
-
-    # Padding so that when rotated proper amount of context is included
-    try:
-        pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) + 1
-    except:
-        breakpoint()
-    if not rot == 0:
-        ul -= pad
-        br += pad
-
-
-    if False:
-        # Old way of cropping image
-        ul_int = ul.astype(int)
-        br_int = br.astype(int)
-        new_shape = [br_int[1] - ul_int[1], br_int[0] - ul_int[0]]
-        if len(img.shape) > 2:
-            new_shape += [img.shape[2]]
-        new_img = np.zeros(new_shape)
-
-        # Range to fill new array
-        new_x = max(0, -ul_int[0]), min(br_int[0], len(img[0])) - ul_int[0]
-        new_y = max(0, -ul_int[1]), min(br_int[1], len(img)) - ul_int[1]
-        # Range to sample from original image
-        old_x = max(0, ul_int[0]), min(len(img[0]), br_int[0])
-        old_y = max(0, ul_int[1]), min(len(img), br_int[1])
-        new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1],
-                                                        old_x[0]:old_x[1]]
-
-    # New way of cropping image
-    new_img = crop_img(img, ul, br, border_mode=border_mode, border_value=border_value).astype(np.float32)
-
-    # print(f'{new_img.shape=}')
-    # print(f'{new_img1.shape=}')
-    # print(f'{np.allclose(new_img, new_img1)=}')
-    # print(f'{img.dtype=}')
-
-
-    if not rot == 0:
-        # Remove padding
-
-        new_img = rotate(new_img, rot) # scipy.misc.imrotate(new_img, rot)
-        new_img = new_img[pad:-pad, pad:-pad]
-
-    if new_img.shape[0] < 1 or new_img.shape[1] < 1:
-        print(f'{img.shape=}')
-        print(f'{new_img.shape=}')
-        print(f'{ul=}')
-        print(f'{br=}')
-        print(f'{pad=}')
-        print(f'{rot=}')
-
-        breakpoint()
-
-    # resize image
-    new_img = resize(new_img, res) # scipy.misc.imresize(new_img, res)
-    
-    new_img = np.clip(new_img, 0, 255).astype(np.uint8)
-
-    return new_img, trans
-
-
-def generate_image_patch_cv2(img: np.array, c_x: float, c_y: float,
-                             bb_width: float, bb_height: float,
-                             patch_width: float, patch_height: float,
-                             do_flip: bool, scale: float, rot: float,
-                             border_mode=cv2.BORDER_CONSTANT, border_value=0) -> Tuple[np.array, np.array]:
-    """
-    Crop the input image and return the crop and the corresponding transformation matrix.
-    Args:
-        img (np.array): Input image of shape (H, W, 3)
-        c_x (float): Bounding box center x coordinate in the original image.
-        c_y (float): Bounding box center y coordinate in the original image.
-        bb_width (float): Bounding box width.
-        bb_height (float): Bounding box height.
-        patch_width (float): Output box width.
-        patch_height (float): Output box height.
-        do_flip (bool): Whether to flip image or not.
-        scale (float): Rescaling factor for the bounding box (augmentation).
-        rot (float): Random rotation applied to the box.
-    Returns:
-        img_patch (np.array): Cropped image patch of shape (patch_height, patch_height, 3)
-        trans (np.array): Transformation matrix.
-    """
-
-    img_height, img_width, img_channels = img.shape
-    if do_flip:
-        img = img[:, ::-1, :]
-        c_x = img_width - c_x - 1
-
-
-    trans = gen_trans_from_patch_cv(c_x, c_y, bb_width, bb_height, patch_width, patch_height, scale, rot)
-
-    img_patch = cv2.warpAffine(img, trans, (int(patch_width), int(patch_height)), 
-                        flags=cv2.INTER_LINEAR, 
-                        borderMode=border_mode,
-                        borderValue=border_value,
-                )
-    # Force borderValue=cv2.BORDER_CONSTANT for alpha channel
-    if (img.shape[2] == 4) and (border_mode != cv2.BORDER_CONSTANT):
-        img_patch[:,:,3] = cv2.warpAffine(img[:,:,3], trans, (int(patch_width), int(patch_height)), 
-                                            flags=cv2.INTER_LINEAR, 
-                                            borderMode=cv2.BORDER_CONSTANT,
-                            )
-
-    return img_patch, trans
-
-
-def convert_cvimg_to_tensor(cvimg: np.array):
-    """
-    Convert image from HWC to CHW format.
-    Args:
-        cvimg (np.array): Image of shape (H, W, 3) as loaded by OpenCV.
-    Returns:
-        np.array: Output image of shape (3, H, W).
-    """
-    # from h,w,c(OpenCV) to c,h,w
-    img = cvimg.copy()
-    img = np.transpose(img, (2, 0, 1))
-    # from int to float
-    img = img.astype(np.float32)
-    return img
-
-def fliplr_params(smpl_params: Dict, has_smpl_params: Dict) -> Tuple[Dict, Dict]:
-    """
-    Flip SMPL parameters when flipping the image.
-    Args:
-        smpl_params (Dict): SMPL parameter annotations.
-        has_smpl_params (Dict): Whether SMPL annotations are valid.
-    Returns:
-        Dict, Dict: Flipped SMPL parameters and valid flags.
-    """
-    global_orient = smpl_params['global_orient'].copy()
-    body_pose = smpl_params['body_pose'].copy()
-    betas = smpl_params['betas'].copy()
-    has_global_orient = has_smpl_params['global_orient'].copy()
-    has_body_pose = has_smpl_params['body_pose'].copy()
-    has_betas = has_smpl_params['betas'].copy()
-
-    body_pose_permutation = [6, 7, 8, 3, 4, 5, 9, 10, 11, 15, 16, 17, 12, 13,
-                             14 ,18, 19, 20, 24, 25, 26, 21, 22, 23, 27, 28, 29, 33,
-                             34, 35, 30, 31, 32, 36, 37, 38, 42, 43, 44, 39, 40, 41,
-                             45, 46, 47, 51, 52, 53, 48, 49, 50, 57, 58, 59, 54, 55,
-                             56, 63, 64, 65, 60, 61, 62, 69, 70, 71, 66, 67, 68]
-    body_pose_permutation = body_pose_permutation[:len(body_pose)]
-    body_pose_permutation = [i-3 for i in body_pose_permutation]
-
-    body_pose = body_pose[body_pose_permutation]
-
-    global_orient[1::3] *= -1
-    global_orient[2::3] *= -1
-    body_pose[1::3] *= -1
-    body_pose[2::3] *= -1
-
-    smpl_params = {'global_orient': global_orient.astype(np.float32),
-                   'body_pose': body_pose.astype(np.float32),
-                   'betas': betas.astype(np.float32)
-                  }
-
-    has_smpl_params = {'global_orient': has_global_orient,
-                       'body_pose': has_body_pose,
-                       'betas': has_betas
-                      }
-
-    return smpl_params, has_smpl_params
-
-
-def fliplr_keypoints(joints: np.array, width: float, flip_permutation: List[int]) -> np.array:
-    """
-    Flip 2D or 3D keypoints.
-    Args:
-        joints (np.array): Array of shape (N, 3) or (N, 4) containing 2D or 3D keypoint locations and confidence.
-        flip_permutation (List): Permutation to apply after flipping.
-    Returns:
-        np.array: Flipped 2D or 3D keypoints with shape (N, 3) or (N, 4) respectively.
-    """
-    joints = joints.copy()
-    # Flip horizontal
-    joints[:, 0] = width - joints[:, 0] - 1
-    joints = joints[flip_permutation, :]
-
-    return joints
-
-def keypoint_3d_processing(keypoints_3d: np.array, flip_permutation: List[int], rot: float, do_flip: float) -> np.array:
-    """
-    Process 3D keypoints (rotation/flipping).
-    Args:
-        keypoints_3d (np.array): Input array of shape (N, 4) containing the 3D keypoints and confidence.
-        flip_permutation (List): Permutation to apply after flipping.
-        rot (float): Random rotation applied to the keypoints.
-        do_flip (bool): Whether to flip keypoints or not.
-    Returns:
-        np.array: Transformed 3D keypoints with shape (N, 4).
-    """
-    if do_flip:
-        keypoints_3d = fliplr_keypoints(keypoints_3d, 1, flip_permutation)
-    # in-plane rotation
-    rot_mat = np.eye(3)
-    if not rot == 0:
-        rot_rad = -rot * np.pi / 180
-        sn,cs = np.sin(rot_rad), np.cos(rot_rad)
-        rot_mat[0,:2] = [cs, -sn]
-        rot_mat[1,:2] = [sn, cs]
-    keypoints_3d[:, :-1] = np.einsum('ij,kj->ki', rot_mat, keypoints_3d[:, :-1])
-    # flip the x coordinates
-    keypoints_3d = keypoints_3d.astype('float32')
-    return keypoints_3d
-
-def rot_aa(aa: np.array, rot: float) -> np.array:
-    """
-    Rotate axis angle parameters.
-    Args:
-        aa (np.array): Axis-angle vector of shape (3,).
-        rot (np.array): Rotation angle in degrees.
-    Returns:
-        np.array: Rotated axis-angle vector.
-    """
-    # pose parameters
-    R = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
-                  [np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],
-                  [0, 0, 1]])
-    # find the rotation of the body in camera frame
-    per_rdg, _ = cv2.Rodrigues(aa)
-    # apply the global rotation to the global orientation
-    resrot, _ = cv2.Rodrigues(np.dot(R,per_rdg))
-    aa = (resrot.T)[0]
-    return aa.astype(np.float32)
-
-def smpl_param_processing(smpl_params: Dict, has_smpl_params: Dict, rot: float, do_flip: bool) -> Tuple[Dict, Dict]:
-    """
-    Apply random augmentations to the SMPL parameters.
-    Args:
-        smpl_params (Dict): SMPL parameter annotations.
-        has_smpl_params (Dict): Whether SMPL annotations are valid.
-        rot (float): Random rotation applied to the keypoints.
-        do_flip (bool): Whether to flip keypoints or not.
-    Returns:
-        Dict, Dict: Transformed SMPL parameters and valid flags.
-    """
-    if do_flip:
-        smpl_params, has_smpl_params = fliplr_params(smpl_params, has_smpl_params)
-    smpl_params['global_orient'] = rot_aa(smpl_params['global_orient'], rot)
-    return smpl_params, has_smpl_params
-
-
-
-def get_example(img_path: str|np.ndarray, center_x: float, center_y: float,
-                width: float, height: float,
-                keypoints_2d: np.array, keypoints_3d: np.array,
-                smpl_params: Dict, has_smpl_params: Dict,
-                flip_kp_permutation: List[int],
-                patch_width: int, patch_height: int,
-                mean: np.array, std: np.array,
-                do_augment: bool, augm_config: CfgNode,
-                is_bgr: bool = True,
-                use_skimage_antialias: bool = False,
-                border_mode: int = cv2.BORDER_CONSTANT,
-                return_trans: bool = False) -> Tuple:
-    """
-    Get an example from the dataset and (possibly) apply random augmentations.
-    Args:
-        img_path (str): Image filename
-        center_x (float): Bounding box center x coordinate in the original image.
-        center_y (float): Bounding box center y coordinate in the original image.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array with shape (N,3) containing the 2D keypoints in the original image coordinates.
-        keypoints_3d (np.array): Array with shape (N,4) containing the 3D keypoints.
-        smpl_params (Dict): SMPL parameter annotations.
-        has_smpl_params (Dict): Whether SMPL annotations are valid.
-        flip_kp_permutation (List): Permutation to apply to the keypoints after flipping.
-        patch_width (float): Output box width.
-        patch_height (float): Output box height.
-        mean (np.array): Array of shape (3,) containing the mean for normalizing the input image.
-        std (np.array): Array of shape (3,) containing the std for normalizing the input image.
-        do_augment (bool): Whether to apply data augmentation or not.
-        aug_config (CfgNode): Config containing augmentation parameters.
-    Returns:
-        return img_patch, keypoints_2d, keypoints_3d, smpl_params, has_smpl_params, img_size
-        img_patch (np.array): Cropped image patch of shape (3, patch_height, patch_height)
-        keypoints_2d (np.array): Array with shape (N,3) containing the transformed 2D keypoints.
-        keypoints_3d (np.array): Array with shape (N,4) containing the transformed 3D keypoints.
-        smpl_params (Dict): Transformed SMPL parameters.
-        has_smpl_params (Dict): Valid flag for transformed SMPL parameters.
-        img_size (np.array): Image size of the original image.
-        """
-    if isinstance(img_path, str):
-        # 1. load image
-        cvimg = cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
-        if not isinstance(cvimg, np.ndarray):
-            raise IOError("Fail to read %s" % img_path)
-    elif isinstance(img_path, np.ndarray):
-        cvimg = img_path
-    else:
-        raise TypeError('img_path must be either a string or a numpy array')
-    img_height, img_width, img_channels = cvimg.shape
-
-    img_size = np.array([img_height, img_width])
-
-    # 2. get augmentation params
-    if do_augment:
-        scale, rot, do_flip, do_extreme_crop, extreme_crop_lvl, color_scale, tx, ty = do_augmentation(augm_config)
-    else:
-        scale, rot, do_flip, do_extreme_crop, extreme_crop_lvl, color_scale, tx, ty = 1.0, 0, False, False, 0, [1.0, 1.0, 1.0], 0., 0.
-
-    if width < 1 or height < 1:
-        breakpoint()
-
-    if do_extreme_crop:
-        if extreme_crop_lvl == 0:
-            center_x1, center_y1, width1, height1 = extreme_cropping(center_x, center_y, width, height, keypoints_2d)
-        elif extreme_crop_lvl == 1:
-            center_x1, center_y1, width1, height1 = extreme_cropping_aggressive(center_x, center_y, width, height, keypoints_2d)
-
-        THRESH = 4
-        if width1 < THRESH or height1 < THRESH:
-            # print(f'{do_extreme_crop=}')
-            # print(f'width: {width}, height: {height}')
-            # print(f'width1: {width1}, height1: {height1}')
-            # print(f'center_x: {center_x}, center_y: {center_y}')
-            # print(f'center_x1: {center_x1}, center_y1: {center_y1}')
-            # print(f'keypoints_2d: {keypoints_2d}')
-            # print(f'\n\n', flush=True)
-            # breakpoint()
-            pass
-            # print(f'skip ==> width1: {width1}, height1: {height1}, width: {width}, height: {height}')
-        else:
-            center_x, center_y, width, height = center_x1, center_y1, width1, height1
-
-    center_x += width * tx
-    center_y += height * ty
-
-    # Process 3D keypoints
-    keypoints_3d = keypoint_3d_processing(keypoints_3d, flip_kp_permutation, rot, do_flip)
-
-    # 3. generate image patch
-    if use_skimage_antialias:
-        # Blur image to avoid aliasing artifacts
-        downsampling_factor = (patch_width / (width*scale))
-        if downsampling_factor > 1.1:
-            cvimg  = gaussian(cvimg, sigma=(downsampling_factor-1)/2, channel_axis=2, preserve_range=True, truncate=3.0)
-
-    img_patch_cv, trans = generate_image_patch_cv2(cvimg,
-                                                    center_x, center_y,
-                                                    width, height,
-                                                    patch_width, patch_height,
-                                                    do_flip, scale, rot, 
-                                                    border_mode=border_mode)
-        # img_patch_cv, trans = generate_image_patch_skimage(cvimg,
-        #                                                 center_x, center_y,
-        #                                                 width, height,
-        #                                                 patch_width, patch_height,
-        #                                                 do_flip, scale, rot, 
-        #                                                 border_mode=border_mode)
-
-    image = img_patch_cv.copy()
-    if is_bgr:
-        image = image[:, :, ::-1]
-    img_patch_cv = image.copy()
-    img_patch = convert_cvimg_to_tensor(image)
-
-
-    smpl_params, has_smpl_params = smpl_param_processing(smpl_params, has_smpl_params, rot, do_flip)
-
-    # apply normalization
-    for n_c in range(min(img_channels, 3)):
-        img_patch[n_c, :, :] = np.clip(img_patch[n_c, :, :] * color_scale[n_c], 0, 255)
-        if mean is not None and std is not None:
-            img_patch[n_c, :, :] = (img_patch[n_c, :, :] - mean[n_c]) / std[n_c]
-    if do_flip:
-        keypoints_2d = fliplr_keypoints(keypoints_2d, img_width, flip_kp_permutation)
-
-
-    for n_jt in range(len(keypoints_2d)):
-        keypoints_2d[n_jt, 0:2] = trans_point2d(keypoints_2d[n_jt, 0:2], trans)
-    keypoints_2d[:, :-1] = keypoints_2d[:, :-1] / patch_width - 0.5
-
-    if not return_trans:
-        return img_patch, keypoints_2d, keypoints_3d, smpl_params, has_smpl_params, img_size
-    else:
-        return img_patch, keypoints_2d, keypoints_3d, smpl_params, has_smpl_params, img_size, trans
-
-def crop_to_hips(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array) -> Tuple:
-    """
-    Extreme cropping: Crop the box up to the hip locations.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    lower_body_keypoints = [10, 11, 13, 14, 19, 20, 21, 22, 23, 24, 25+0, 25+1, 25+4, 25+5]
-    keypoints_2d[lower_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-
-def crop_to_shoulders(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box up to the shoulder locations.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    lower_body_keypoints = [3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 24] + [25 + i for i in [0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 15, 16]]
-    keypoints_2d[lower_body_keypoints, :] = 0
-    center, scale = get_bbox(keypoints_2d)
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.2 * scale[0]
-        height = 1.2 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_to_head(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the head.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    lower_body_keypoints = [3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 24] + [25 + i for i in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16]]
-    keypoints_2d[lower_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.3 * scale[0]
-        height = 1.3 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_torso_only(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the torso.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    nontorso_body_keypoints = [0, 3, 4, 6, 7, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] + [25 + i for i in [0, 1, 4, 5, 6, 7, 10, 11, 13, 17, 18]]
-    keypoints_2d[nontorso_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_rightarm_only(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the right arm.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    nonrightarm_body_keypoints = [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] + [25 + i for i in [0, 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]]
-    keypoints_2d[nonrightarm_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_leftarm_only(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the left arm.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    nonleftarm_body_keypoints = [0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] + [25 + i for i in [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 17, 18]]
-    keypoints_2d[nonleftarm_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_legs_only(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the legs.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    nonlegs_body_keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 15, 16, 17, 18] + [25 + i for i in [6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18]]
-    keypoints_2d[nonlegs_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_rightleg_only(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the right leg.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    nonrightleg_body_keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] + [25 + i for i in [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]]
-    keypoints_2d[nonrightleg_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-def crop_leftleg_only(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array):
-    """
-    Extreme cropping: Crop the box and keep on only the left leg.
-    Args:
-        center_x (float): x coordinate of the bounding box center.
-        center_y (float): y coordinate of the bounding box center.
-        width (float): Bounding box width.
-        height (float): Bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        center_x (float): x coordinate of the new bounding box center.
-        center_y (float): y coordinate of the new bounding box center.
-        width (float): New bounding box width.
-        height (float): New bounding box height.
-    """
-    keypoints_2d = keypoints_2d.copy()
-    nonleftleg_body_keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 15, 16, 17, 18, 22, 23, 24] + [25 + i for i in [0, 1, 2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]]
-    keypoints_2d[nonleftleg_body_keypoints, :] = 0
-    if keypoints_2d[:, -1].sum() > 1:
-        center, scale = get_bbox(keypoints_2d)
-        center_x = center[0]
-        center_y = center[1]
-        width = 1.1 * scale[0]
-        height = 1.1 * scale[1]
-    return center_x, center_y, width, height
-
-def full_body(keypoints_2d: np.array) -> bool:
-    """
-    Check if all main body joints are visible.
-    Args:
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        bool: True if all main body joints are visible.
-    """
-
-    body_keypoints_openpose = [2, 3, 4, 5, 6, 7, 10, 11, 13, 14]
-    body_keypoints = [25 + i for i in [8, 7, 6, 9, 10, 11, 1, 0, 4, 5]]
-    return (np.maximum(keypoints_2d[body_keypoints, -1], keypoints_2d[body_keypoints_openpose, -1]) > 0).sum() == len(body_keypoints)
-
-def upper_body(keypoints_2d: np.array):
-    """
-    Check if all upper body joints are visible.
-    Args:
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-    Returns:
-        bool: True if all main body joints are visible.
-    """
-    lower_body_keypoints_openpose = [10, 11, 13, 14]
-    lower_body_keypoints = [25 + i for i in [1, 0, 4, 5]]
-    upper_body_keypoints_openpose = [0, 1, 15, 16, 17, 18]
-    upper_body_keypoints = [25+8, 25+9, 25+12, 25+13, 25+17, 25+18]
-    return ((keypoints_2d[lower_body_keypoints + lower_body_keypoints_openpose, -1] > 0).sum() == 0)\
-       and ((keypoints_2d[upper_body_keypoints + upper_body_keypoints_openpose, -1] > 0).sum() >= 2)
-
-def get_bbox(keypoints_2d: np.array, rescale: float = 1.2) -> Tuple:
-    """
-    Get center and scale for bounding box from openpose detections.
-    Args:
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-        rescale (float): Scale factor to rescale bounding boxes computed from the keypoints.
-    Returns:
-        center (np.array): Array of shape (2,) containing the new bounding box center.
-        scale (float): New bounding box scale.
-    """
-    valid = keypoints_2d[:,-1] > 0
-    valid_keypoints = keypoints_2d[valid][:,:-1]
-    center = 0.5 * (valid_keypoints.max(axis=0) + valid_keypoints.min(axis=0))
-    bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0))
-    # adjust bounding box tightness
-    scale = bbox_size
-    scale *= rescale
-    return center, scale
-
-def extreme_cropping(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array) -> Tuple:
-    """
-    Perform extreme cropping
-    Args:
-        center_x (float): x coordinate of bounding box center.
-        center_y (float): y coordinate of bounding box center.
-        width (float): bounding box width.
-        height (float): bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-        rescale (float): Scale factor to rescale bounding boxes computed from the keypoints.
-    Returns:
-        center_x (float): x coordinate of bounding box center.
-        center_y (float): y coordinate of bounding box center.
-        width (float): bounding box width.
-        height (float): bounding box height.
-    """
-    p = torch.rand(1).item()
-    if full_body(keypoints_2d):
-        if p < 0.7:
-            center_x, center_y, width, height = crop_to_hips(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.9:
-            center_x, center_y, width, height = crop_to_shoulders(center_x, center_y, width, height, keypoints_2d)
-        else:
-            center_x, center_y, width, height = crop_to_head(center_x, center_y, width, height, keypoints_2d)
-    elif upper_body(keypoints_2d):
-        if p < 0.9:
-            center_x, center_y, width, height = crop_to_shoulders(center_x, center_y, width, height, keypoints_2d)
-        else:
-            center_x, center_y, width, height = crop_to_head(center_x, center_y, width, height, keypoints_2d)
-
-    return center_x, center_y, max(width, height), max(width, height)
-
-def extreme_cropping_aggressive(center_x: float, center_y: float, width: float, height: float, keypoints_2d: np.array) -> Tuple:
-    """
-    Perform aggressive extreme cropping
-    Args:
-        center_x (float): x coordinate of bounding box center.
-        center_y (float): y coordinate of bounding box center.
-        width (float): bounding box width.
-        height (float): bounding box height.
-        keypoints_2d (np.array): Array of shape (N, 3) containing 2D keypoint locations.
-        rescale (float): Scale factor to rescale bounding boxes computed from the keypoints.
-    Returns:
-        center_x (float): x coordinate of bounding box center.
-        center_y (float): y coordinate of bounding box center.
-        width (float): bounding box width.
-        height (float): bounding box height.
-    """
-    p = torch.rand(1).item()
-    if full_body(keypoints_2d):
-        if p < 0.2:
-            center_x, center_y, width, height = crop_to_hips(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.3:
-            center_x, center_y, width, height = crop_to_shoulders(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.4:
-            center_x, center_y, width, height = crop_to_head(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.5:
-            center_x, center_y, width, height = crop_torso_only(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.6:
-            center_x, center_y, width, height = crop_rightarm_only(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.7:
-            center_x, center_y, width, height = crop_leftarm_only(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.8:
-            center_x, center_y, width, height = crop_legs_only(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.9:
-            center_x, center_y, width, height = crop_rightleg_only(center_x, center_y, width, height, keypoints_2d)
-        else:
-            center_x, center_y, width, height = crop_leftleg_only(center_x, center_y, width, height, keypoints_2d)
-    elif upper_body(keypoints_2d):
-        if p < 0.2:
-            center_x, center_y, width, height = crop_to_shoulders(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.4:
-            center_x, center_y, width, height = crop_to_head(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.6:
-            center_x, center_y, width, height = crop_torso_only(center_x, center_y, width, height, keypoints_2d)
-        elif p < 0.8:
-            center_x, center_y, width, height = crop_rightarm_only(center_x, center_y, width, height, keypoints_2d)
-        else:
-            center_x, center_y, width, height = crop_leftarm_only(center_x, center_y, width, height, keypoints_2d)
-    return center_x, center_y, max(width, height), max(width, height)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/batch_norm.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/batch_norm.py
deleted file mode 100644
index f594587628b842607404ee9793ece7a11ef98775..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/batch_norm.py
+++ /dev/null
@@ -1,320 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-import torch.distributed as dist
-from fvcore.nn.distributed import differentiable_all_reduce
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.utils import comm, env
-
-from .wrappers import BatchNorm2d
-
-
-class FrozenBatchNorm2d(nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    It contains non-trainable buffers called
-    "weight" and "bias", "running_mean", "running_var",
-    initialized to perform identity transformation.
-
-    The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
-    which are computed from the original four parameters of BN.
-    The affine transform `x * weight + bias` will perform the equivalent
-    computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
-    When loading a backbone model from Caffe2, "running_mean" and "running_var"
-    will be left unchanged as identity transformation.
-
-    Other pre-trained backbone models may contain all 4 parameters.
-
-    The forward is implemented by `F.batch_norm(..., training=False)`.
-    """
-
-    _version = 3
-
-    def __init__(self, num_features, eps=1e-5):
-        super().__init__()
-        self.num_features = num_features
-        self.eps = eps
-        self.register_buffer("weight", torch.ones(num_features))
-        self.register_buffer("bias", torch.zeros(num_features))
-        self.register_buffer("running_mean", torch.zeros(num_features))
-        self.register_buffer("running_var", torch.ones(num_features) - eps)
-        self.register_buffer("num_batches_tracked", None)
-
-    def forward(self, x):
-        if x.requires_grad:
-            # When gradients are needed, F.batch_norm will use extra memory
-            # because its backward op computes gradients for weight/bias as well.
-            scale = self.weight * (self.running_var + self.eps).rsqrt()
-            bias = self.bias - self.running_mean * scale
-            scale = scale.reshape(1, -1, 1, 1)
-            bias = bias.reshape(1, -1, 1, 1)
-            out_dtype = x.dtype  # may be half
-            return x * scale.to(out_dtype) + bias.to(out_dtype)
-        else:
-            # When gradients are not needed, F.batch_norm is a single fused op
-            # and provide more optimization opportunities.
-            return F.batch_norm(
-                x,
-                self.running_mean,
-                self.running_var,
-                self.weight,
-                self.bias,
-                training=False,
-                eps=self.eps,
-            )
-
-    def _load_from_state_dict(
-        self,
-        state_dict,
-        prefix,
-        local_metadata,
-        strict,
-        missing_keys,
-        unexpected_keys,
-        error_msgs,
-    ):
-        version = local_metadata.get("version", None)
-
-        if version is None or version < 2:
-            # No running_mean/var in early versions
-            # This will silent the warnings
-            if prefix + "running_mean" not in state_dict:
-                state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean)
-            if prefix + "running_var" not in state_dict:
-                state_dict[prefix + "running_var"] = torch.ones_like(self.running_var)
-
-        super()._load_from_state_dict(
-            state_dict,
-            prefix,
-            local_metadata,
-            strict,
-            missing_keys,
-            unexpected_keys,
-            error_msgs,
-        )
-
-    def __repr__(self):
-        return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps)
-
-    @classmethod
-    def convert_frozen_batchnorm(cls, module):
-        """
-        Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.
-
-        Args:
-            module (torch.nn.Module):
-
-        Returns:
-            If module is BatchNorm/SyncBatchNorm, returns a new module.
-            Otherwise, in-place convert module and return it.
-
-        Similar to convert_sync_batchnorm in
-        https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py
-        """
-        bn_module = nn.modules.batchnorm
-        bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm)
-        res = module
-        if isinstance(module, bn_module):
-            res = cls(module.num_features)
-            if module.affine:
-                res.weight.data = module.weight.data.clone().detach()
-                res.bias.data = module.bias.data.clone().detach()
-            res.running_mean.data = module.running_mean.data
-            res.running_var.data = module.running_var.data
-            res.eps = module.eps
-            res.num_batches_tracked = module.num_batches_tracked
-        else:
-            for name, child in module.named_children():
-                new_child = cls.convert_frozen_batchnorm(child)
-                if new_child is not child:
-                    res.add_module(name, new_child)
-        return res
-
-
-def get_norm(norm, out_channels):
-    """
-    Args:
-        norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
-            or a callable that takes a channel number and returns
-            the normalization layer as a nn.Module.
-
-    Returns:
-        nn.Module or None: the normalization layer
-    """
-    if norm is None:
-        return None
-    if isinstance(norm, str):
-        if len(norm) == 0:
-            return None
-        norm = {
-            "BN": BatchNorm2d,
-            # Fixed in https://github.com/pytorch/pytorch/pull/36382
-            "SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,
-            "FrozenBN": FrozenBatchNorm2d,
-            "GN": lambda channels: nn.GroupNorm(32, channels),
-            # for debugging:
-            "nnSyncBN": nn.SyncBatchNorm,
-            "naiveSyncBN": NaiveSyncBatchNorm,
-            # expose stats_mode N as an option to caller, required for zero-len inputs
-            "naiveSyncBN_N": lambda channels: NaiveSyncBatchNorm(channels, stats_mode="N"),
-            "LN": lambda channels: LayerNorm(channels),
-        }[norm]
-    return norm(out_channels)
-
-
-class NaiveSyncBatchNorm(BatchNorm2d):
-    """
-    In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient
-    when the batch size on each worker is different.
-    (e.g., when scale augmentation is used, or when it is applied to mask head).
-
-    This is a slower but correct alternative to `nn.SyncBatchNorm`.
-
-    Note:
-        There isn't a single definition of Sync BatchNorm.
-
-        When ``stats_mode==""``, this module computes overall statistics by using
-        statistics of each worker with equal weight.  The result is true statistics
-        of all samples (as if they are all on one worker) only when all workers
-        have the same (N, H, W). This mode does not support inputs with zero batch size.
-
-        When ``stats_mode=="N"``, this module computes overall statistics by weighting
-        the statistics of each worker by their ``N``. The result is true statistics
-        of all samples (as if they are all on one worker) only when all workers
-        have the same (H, W). It is slower than ``stats_mode==""``.
-
-        Even though the result of this module may not be the true statistics of all samples,
-        it may still be reasonable because it might be preferrable to assign equal weights
-        to all workers, regardless of their (H, W) dimension, instead of putting larger weight
-        on larger images. From preliminary experiments, little difference is found between such
-        a simplified implementation and an accurate computation of overall mean & variance.
-    """
-
-    def __init__(self, *args, stats_mode="", **kwargs):
-        super().__init__(*args, **kwargs)
-        assert stats_mode in ["", "N"]
-        self._stats_mode = stats_mode
-
-    def forward(self, input):
-        if comm.get_world_size() == 1 or not self.training:
-            return super().forward(input)
-
-        B, C = input.shape[0], input.shape[1]
-
-        half_input = input.dtype == torch.float16
-        if half_input:
-            # fp16 does not have good enough numerics for the reduction here
-            input = input.float()
-        mean = torch.mean(input, dim=[0, 2, 3])
-        meansqr = torch.mean(input * input, dim=[0, 2, 3])
-
-        if self._stats_mode == "":
-            assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
-            vec = torch.cat([mean, meansqr], dim=0)
-            vec = differentiable_all_reduce(vec) * (1.0 / dist.get_world_size())
-            mean, meansqr = torch.split(vec, C)
-            momentum = self.momentum
-        else:
-            if B == 0:
-                vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype)
-                vec = vec + input.sum()  # make sure there is gradient w.r.t input
-            else:
-                vec = torch.cat(
-                    [
-                        mean,
-                        meansqr,
-                        torch.ones([1], device=mean.device, dtype=mean.dtype),
-                    ],
-                    dim=0,
-                )
-            vec = differentiable_all_reduce(vec * B)
-
-            total_batch = vec[-1].detach()
-            momentum = total_batch.clamp(max=1) * self.momentum  # no update if total_batch is 0
-            mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C)  # avoid div-by-zero
-
-        var = meansqr - mean * mean
-        invstd = torch.rsqrt(var + self.eps)
-        scale = self.weight * invstd
-        bias = self.bias - mean * scale
-        scale = scale.reshape(1, -1, 1, 1)
-        bias = bias.reshape(1, -1, 1, 1)
-
-        self.running_mean += momentum * (mean.detach() - self.running_mean)
-        self.running_var += momentum * (var.detach() - self.running_var)
-        ret = input * scale + bias
-        if half_input:
-            ret = ret.half()
-        return ret
-
-
-class CycleBatchNormList(nn.ModuleList):
-    """
-    Implement domain-specific BatchNorm by cycling.
-
-    When a BatchNorm layer is used for multiple input domains or input
-    features, it might need to maintain a separate test-time statistics
-    for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`.
-
-    This module implements it by using N separate BN layers
-    and it cycles through them every time a forward() is called.
-
-    NOTE: The caller of this module MUST guarantee to always call
-    this module by multiple of N times. Otherwise its test-time statistics
-    will be incorrect.
-    """
-
-    def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs):
-        """
-        Args:
-            length: number of BatchNorm layers to cycle.
-            bn_class: the BatchNorm class to use
-            kwargs: arguments of the BatchNorm class, such as num_features.
-        """
-        self._affine = kwargs.pop("affine", True)
-        super().__init__([bn_class(**kwargs, affine=False) for k in range(length)])
-        if self._affine:
-            # shared affine, domain-specific BN
-            channels = self[0].num_features
-            self.weight = nn.Parameter(torch.ones(channels))
-            self.bias = nn.Parameter(torch.zeros(channels))
-        self._pos = 0
-
-    def forward(self, x):
-        ret = self[self._pos](x)
-        self._pos = (self._pos + 1) % len(self)
-
-        if self._affine:
-            w = self.weight.reshape(1, -1, 1, 1)
-            b = self.bias.reshape(1, -1, 1, 1)
-            return ret * w + b
-        else:
-            return ret
-
-    def extra_repr(self):
-        return f"affine={self._affine}"
-
-
-class LayerNorm(nn.Module):
-    """
-    A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
-    variance normalization over the channel dimension for inputs that have shape
-    (batch_size, channels, height, width).
-    https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa B950
-    """
-
-    def __init__(self, normalized_shape, eps=1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(normalized_shape))
-        self.bias = nn.Parameter(torch.zeros(normalized_shape))
-        self.eps = eps
-        self.normalized_shape = (normalized_shape,)
-
-    def forward(self, x):
-        u = x.mean(1, keepdim=True)
-        s = (x - u).pow(2).mean(1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.eps)
-        x = self.weight[:, None, None] * x + self.bias[:, None, None]
-        return x
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/docs/tutorials/training.md b/spaces/brjathu/HMR2.0/vendor/detectron2/docs/tutorials/training.md
deleted file mode 100644
index 83a6cb0a8e38ca06bbf96201ac2595d2116523c3..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/docs/tutorials/training.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Training
-
-From the previous tutorials, you may now have a custom model and a data loader.
-To run training, users typically have a preference in one of the following two styles:
-
-### Custom Training Loop
-
-With a model and a data loader ready, everything else needed to write a training loop can
-be found in PyTorch, and you are free to write the training loop yourself.
-This style allows researchers to manage the entire training logic more clearly and have full control.
-One such example is provided in [tools/plain_train_net.py](../../tools/plain_train_net.py).
-
-Any customization on the training logic is then easily controlled by the user.
-
-### Trainer Abstraction
-
-We also provide a standardized "trainer" abstraction with a
-hook system that helps simplify the standard training behavior.
-It includes the following two instantiations:
-
-* [SimpleTrainer](../modules/engine.html#detectron2.engine.SimpleTrainer)
-  provides a minimal training loop for single-cost single-optimizer single-data-source training, with nothing else.
-  Other tasks (checkpointing, logging, etc) can be implemented using
-  [the hook system](../modules/engine.html#detectron2.engine.HookBase).
-* [DefaultTrainer](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) is a `SimpleTrainer` initialized from a
-  yacs config, used by
-  [tools/train_net.py](../../tools/train_net.py) and many scripts.
-  It includes more standard default behaviors that one might want to opt in,
-  including default configurations for optimizer, learning rate schedule,
-  logging, evaluation, checkpointing etc.
-
-To customize a `DefaultTrainer`:
-
-1. For simple customizations (e.g. change optimizer, evaluator, LR scheduler, data loader, etc.), overwrite [its methods](../modules/engine.html#detectron2.engine.defaults.DefaultTrainer) in a subclass, just like [tools/train_net.py](../../tools/train_net.py).
-2. For extra tasks during training, check the
-   [hook system](../modules/engine.html#detectron2.engine.HookBase) to see if it's supported.
-
-   As an example, to print hello during training:
-   ```python
-   class HelloHook(HookBase):
-     def after_step(self):
-       if self.trainer.iter % 100 == 0:
-         print(f"Hello at iteration {self.trainer.iter}!")
-   ```
-3. Using a trainer+hook system means there will always be some non-standard behaviors that cannot be supported, especially in research.
-   For this reason, we intentionally keep the trainer & hook system minimal, rather than powerful.
-   If anything cannot be achieved by such a system, it's easier to start from [tools/plain_train_net.py](../../tools/plain_train_net.py) to implement custom training logic manually.
-
-### Logging of Metrics
-
-During training, detectron2 models and trainer put metrics to a centralized [EventStorage](../modules/utils.html#detectron2.utils.events.EventStorage).
-You can use the following code to access it and log metrics to it:
-```python
-from detectron2.utils.events import get_event_storage
-
-# inside the model:
-if self.training:
-  value = # compute the value from inputs
-  storage = get_event_storage()
-  storage.put_scalar("some_accuracy", value)
-```
-
-Refer to its documentation for more details.
-
-Metrics are then written to various destinations with [EventWriter](../modules/utils.html#module-detectron2.utils.events).
-DefaultTrainer enables a few `EventWriter` with default configurations.
-See above for how to customize them.
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_box2box_transform.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_box2box_transform.py
deleted file mode 100644
index fd3a7b79b6b7a3608ad7cb3918de020a5a600d2f..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_box2box_transform.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.modeling.box_regression import (
-    Box2BoxTransform,
-    Box2BoxTransformLinear,
-    Box2BoxTransformRotated,
-)
-from detectron2.utils.testing import random_boxes
-
-logger = logging.getLogger(__name__)
-
-
-class TestBox2BoxTransform(unittest.TestCase):
-    def test_reconstruction(self):
-        weights = (5, 5, 10, 10)
-        b2b_tfm = Box2BoxTransform(weights=weights)
-        src_boxes = random_boxes(10)
-        dst_boxes = random_boxes(10)
-
-        devices = [torch.device("cpu")]
-        if torch.cuda.is_available():
-            devices.append(torch.device("cuda"))
-        for device in devices:
-            src_boxes = src_boxes.to(device=device)
-            dst_boxes = dst_boxes.to(device=device)
-            deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes)
-            dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes)
-            self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed))
-
-    def test_apply_deltas_tracing(self):
-        weights = (5, 5, 10, 10)
-        b2b_tfm = Box2BoxTransform(weights=weights)
-
-        with torch.no_grad():
-            func = torch.jit.trace(b2b_tfm.apply_deltas, (torch.randn(10, 20), torch.randn(10, 4)))
-
-            o = func(torch.randn(10, 20), torch.randn(10, 4))
-            self.assertEqual(o.shape, (10, 20))
-            o = func(torch.randn(5, 20), torch.randn(5, 4))
-            self.assertEqual(o.shape, (5, 20))
-
-
-def random_rotated_boxes(mean_box, std_length, std_angle, N):
-    return torch.cat(
-        [torch.rand(N, 4) * std_length, torch.rand(N, 1) * std_angle], dim=1
-    ) + torch.tensor(mean_box, dtype=torch.float)
-
-
-class TestBox2BoxTransformRotated(unittest.TestCase):
-    def test_reconstruction(self):
-        weights = (5, 5, 10, 10, 1)
-        b2b_transform = Box2BoxTransformRotated(weights=weights)
-        src_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10)
-        dst_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10)
-
-        devices = [torch.device("cpu")]
-        if torch.cuda.is_available():
-            devices.append(torch.device("cuda"))
-        for device in devices:
-            src_boxes = src_boxes.to(device=device)
-            dst_boxes = dst_boxes.to(device=device)
-            deltas = b2b_transform.get_deltas(src_boxes, dst_boxes)
-            dst_boxes_reconstructed = b2b_transform.apply_deltas(deltas, src_boxes)
-            assert torch.allclose(dst_boxes[:, :4], dst_boxes_reconstructed[:, :4], atol=1e-5)
-            # angle difference has to be normalized
-            assert torch.allclose(
-                (dst_boxes[:, 4] - dst_boxes_reconstructed[:, 4] + 180.0) % 360.0 - 180.0,
-                torch.zeros_like(dst_boxes[:, 4]),
-                atol=1e-4,
-            )
-
-
-class TestBox2BoxTransformLinear(unittest.TestCase):
-    def test_reconstruction(self):
-        b2b_tfm = Box2BoxTransformLinear()
-        src_boxes = random_boxes(10)
-        dst_boxes = torch.tensor([0, 0, 101, 101] * 10).reshape(10, 4).float()
-
-        devices = [torch.device("cpu")]
-        if torch.cuda.is_available():
-            devices.append(torch.device("cuda"))
-        for device in devices:
-            src_boxes = src_boxes.to(device=device)
-            dst_boxes = dst_boxes.to(device=device)
-            deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes)
-            dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes)
-            self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed, atol=1e-3))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_model_e2e.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_model_e2e.py
deleted file mode 100644
index 8c07e6856d2f4304e0b0cb32747fb667e3bbcb4c..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/modeling/test_model_e2e.py
+++ /dev/null
@@ -1,227 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-import itertools
-import unittest
-from contextlib import contextmanager
-from copy import deepcopy
-import torch
-
-from detectron2.structures import BitMasks, Boxes, ImageList, Instances
-from detectron2.utils.events import EventStorage
-from detectron2.utils.testing import get_model_no_weights
-
-
-@contextmanager
-def typecheck_hook(model, *, in_dtype=None, out_dtype=None):
-    """
-    Check that the model must be called with the given input/output dtype
-    """
-    if not isinstance(in_dtype, set):
-        in_dtype = {in_dtype}
-    if not isinstance(out_dtype, set):
-        out_dtype = {out_dtype}
-
-    def flatten(x):
-        if isinstance(x, torch.Tensor):
-            return [x]
-        if isinstance(x, (list, tuple)):
-            return list(itertools.chain(*[flatten(t) for t in x]))
-        if isinstance(x, dict):
-            return flatten(list(x.values()))
-        return []
-
-    def hook(module, input, output):
-        if in_dtype is not None:
-            dtypes = {x.dtype for x in flatten(input)}
-            assert (
-                dtypes == in_dtype
-            ), f"Expected input dtype of {type(module)} is {in_dtype}. Got {dtypes} instead!"
-
-        if out_dtype is not None:
-            dtypes = {x.dtype for x in flatten(output)}
-            assert (
-                dtypes == out_dtype
-            ), f"Expected output dtype of {type(module)} is {out_dtype}. Got {dtypes} instead!"
-
-    with model.register_forward_hook(hook):
-        yield
-
-
-def create_model_input(img, inst=None):
-    if inst is not None:
-        return {"image": img, "instances": inst}
-    else:
-        return {"image": img}
-
-
-def get_empty_instance(h, w):
-    inst = Instances((h, w))
-    inst.gt_boxes = Boxes(torch.rand(0, 4))
-    inst.gt_classes = torch.tensor([]).to(dtype=torch.int64)
-    inst.gt_masks = BitMasks(torch.rand(0, h, w))
-    return inst
-
-
-def get_regular_bitmask_instances(h, w):
-    inst = Instances((h, w))
-    inst.gt_boxes = Boxes(torch.rand(3, 4))
-    inst.gt_boxes.tensor[:, 2:] += inst.gt_boxes.tensor[:, :2]
-    inst.gt_classes = torch.tensor([3, 4, 5]).to(dtype=torch.int64)
-    inst.gt_masks = BitMasks((torch.rand(3, h, w) > 0.5))
-    return inst
-
-
-class InstanceModelE2ETest:
-    def setUp(self):
-        torch.manual_seed(43)
-        self.model = get_model_no_weights(self.CONFIG_PATH)
-
-    def _test_eval(self, input_sizes):
-        inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
-        self.model.eval()
-        self.model(inputs)
-
-    def _test_train(self, input_sizes, instances):
-        assert len(input_sizes) == len(instances)
-        inputs = [
-            create_model_input(torch.rand(3, s[0], s[1]), inst)
-            for s, inst in zip(input_sizes, instances)
-        ]
-        self.model.train()
-        with EventStorage():
-            losses = self.model(inputs)
-            sum(losses.values()).backward()
-            del losses
-
-    def _inf_tensor(self, *shape):
-        return 1.0 / torch.zeros(*shape, device=self.model.device)
-
-    def _nan_tensor(self, *shape):
-        return torch.zeros(*shape, device=self.model.device).fill_(float("nan"))
-
-    def test_empty_data(self):
-        instances = [get_empty_instance(200, 250), get_empty_instance(200, 249)]
-        self._test_eval([(200, 250), (200, 249)])
-        self._test_train([(200, 250), (200, 249)], instances)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA unavailable")
-    def test_eval_tocpu(self):
-        model = deepcopy(self.model).cpu()
-        model.eval()
-        input_sizes = [(200, 250), (200, 249)]
-        inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
-        model(inputs)
-
-
-class MaskRCNNE2ETest(InstanceModelE2ETest, unittest.TestCase):
-    CONFIG_PATH = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
-
-    def test_half_empty_data(self):
-        instances = [get_empty_instance(200, 250), get_regular_bitmask_instances(200, 249)]
-        self._test_train([(200, 250), (200, 249)], instances)
-
-    # This test is flaky because in some environment the output features are zero due to relu
-    # def test_rpn_inf_nan_data(self):
-    #     self.model.eval()
-    #     for tensor in [self._inf_tensor, self._nan_tensor]:
-    #         images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
-    #         features = {
-    #             "p2": tensor(1, 256, 256, 256),
-    #             "p3": tensor(1, 256, 128, 128),
-    #             "p4": tensor(1, 256, 64, 64),
-    #             "p5": tensor(1, 256, 32, 32),
-    #             "p6": tensor(1, 256, 16, 16),
-    #         }
-    #         props, _ = self.model.proposal_generator(images, features)
-    #         self.assertEqual(len(props[0]), 0)
-
-    def test_roiheads_inf_nan_data(self):
-        self.model.eval()
-        for tensor in [self._inf_tensor, self._nan_tensor]:
-            images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
-            features = {
-                "p2": tensor(1, 256, 256, 256),
-                "p3": tensor(1, 256, 128, 128),
-                "p4": tensor(1, 256, 64, 64),
-                "p5": tensor(1, 256, 32, 32),
-                "p6": tensor(1, 256, 16, 16),
-            }
-            props = [Instances((510, 510))]
-            props[0].proposal_boxes = Boxes([[10, 10, 20, 20]]).to(device=self.model.device)
-            props[0].objectness_logits = torch.tensor([1.0]).reshape(1, 1)
-            det, _ = self.model.roi_heads(images, features, props)
-            self.assertEqual(len(det[0]), 0)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_autocast(self):
-        from torch.cuda.amp import autocast
-
-        inputs = [{"image": torch.rand(3, 100, 100)}]
-        self.model.eval()
-        with autocast(), typecheck_hook(
-            self.model.backbone, in_dtype=torch.float32, out_dtype=torch.float16
-        ), typecheck_hook(
-            self.model.roi_heads.box_predictor, in_dtype=torch.float16, out_dtype=torch.float16
-        ):
-            out = self.model.inference(inputs, do_postprocess=False)[0]
-            self.assertEqual(out.pred_boxes.tensor.dtype, torch.float32)
-            self.assertEqual(out.pred_masks.dtype, torch.float16)
-            self.assertEqual(out.scores.dtype, torch.float32)  # scores comes from softmax
-
-
-class RetinaNetE2ETest(InstanceModelE2ETest, unittest.TestCase):
-    CONFIG_PATH = "COCO-Detection/retinanet_R_50_FPN_1x.yaml"
-
-    def test_inf_nan_data(self):
-        self.model.eval()
-        self.model.score_threshold = -999999999
-        for tensor in [self._inf_tensor, self._nan_tensor]:
-            images = ImageList(tensor(1, 3, 512, 512), [(510, 510)])
-            features = [
-                tensor(1, 256, 128, 128),
-                tensor(1, 256, 64, 64),
-                tensor(1, 256, 32, 32),
-                tensor(1, 256, 16, 16),
-                tensor(1, 256, 8, 8),
-            ]
-            pred_logits, pred_anchor_deltas = self.model.head(features)
-            pred_logits = [tensor(*x.shape) for x in pred_logits]
-            pred_anchor_deltas = [tensor(*x.shape) for x in pred_anchor_deltas]
-            det = self.model.forward_inference(images, features, [pred_logits, pred_anchor_deltas])
-            # all predictions (if any) are infinite or nan
-            if len(det[0]):
-                self.assertTrue(torch.isfinite(det[0].pred_boxes.tensor).sum() == 0)
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_autocast(self):
-        from torch.cuda.amp import autocast
-
-        inputs = [{"image": torch.rand(3, 100, 100)}]
-        self.model.eval()
-        with autocast(), typecheck_hook(
-            self.model.backbone, in_dtype=torch.float32, out_dtype=torch.float16
-        ), typecheck_hook(self.model.head, in_dtype=torch.float16, out_dtype=torch.float16):
-            out = self.model(inputs)[0]["instances"]
-            self.assertEqual(out.pred_boxes.tensor.dtype, torch.float32)
-            self.assertEqual(out.scores.dtype, torch.float16)
-
-
-class FCOSE2ETest(InstanceModelE2ETest, unittest.TestCase):
-    CONFIG_PATH = "COCO-Detection/fcos_R_50_FPN_1x.py"
-
-
-class SemSegE2ETest(unittest.TestCase):
-    CONFIG_PATH = "Misc/semantic_R_50_FPN_1x.yaml"
-
-    def setUp(self):
-        torch.manual_seed(43)
-        self.model = get_model_no_weights(self.CONFIG_PATH)
-
-    def _test_eval(self, input_sizes):
-        inputs = [create_model_input(torch.rand(3, s[0], s[1])) for s in input_sizes]
-        self.model.eval()
-        self.model(inputs)
-
-    def test_forward(self):
-        self._test_eval([(200, 250), (200, 249)])
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/tracking/test_bbox_iou_tracker.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/tracking/test_bbox_iou_tracker.py
deleted file mode 100644
index e720b2eb98788670c7daf2a694eff1fdc7b9f1bd..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/tracking/test_bbox_iou_tracker.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import unittest
-from copy import deepcopy
-from typing import Dict
-import torch
-
-from detectron2.config import CfgNode as CfgNode_
-from detectron2.config import instantiate
-from detectron2.structures import Boxes, Instances
-from detectron2.tracking.base_tracker import build_tracker_head
-from detectron2.tracking.bbox_iou_tracker import BBoxIOUTracker  # noqa
-
-
-class TestBBoxIOUTracker(unittest.TestCase):
-    def setUp(self):
-        self._img_size = np.array([600, 800])
-        self._prev_boxes = np.array(
-            [
-                [101, 101, 200, 200],
-                [301, 301, 450, 450],
-            ]
-        ).astype(np.float32)
-        self._prev_scores = np.array([0.9, 0.9])
-        self._prev_classes = np.array([1, 1])
-        self._prev_masks = np.ones((2, 600, 800)).astype("uint8")
-        self._curr_boxes = np.array(
-            [
-                [302, 303, 451, 452],
-                [101, 102, 201, 203],
-            ]
-        ).astype(np.float32)
-        self._curr_scores = np.array([0.95, 0.85])
-        self._curr_classes = np.array([1, 1])
-        self._curr_masks = np.ones((2, 600, 800)).astype("uint8")
-
-        self._prev_instances = {
-            "image_size": self._img_size,
-            "pred_boxes": self._prev_boxes,
-            "scores": self._prev_scores,
-            "pred_classes": self._prev_classes,
-            "pred_masks": self._prev_masks,
-        }
-        self._prev_instances = self._convertDictPredictionToInstance(self._prev_instances)
-        self._curr_instances = {
-            "image_size": self._img_size,
-            "pred_boxes": self._curr_boxes,
-            "scores": self._curr_scores,
-            "pred_classes": self._curr_classes,
-            "pred_masks": self._curr_masks,
-        }
-        self._curr_instances = self._convertDictPredictionToInstance(self._curr_instances)
-
-        self._max_num_instances = 200
-        self._max_lost_frame_count = 0
-        self._min_box_rel_dim = 0.02
-        self._min_instance_period = 1
-        self._track_iou_threshold = 0.5
-
-    def _convertDictPredictionToInstance(self, prediction: Dict) -> Instances:
-        """
-        convert prediction from Dict to D2 Instances format
-        """
-        res = Instances(
-            image_size=torch.IntTensor(prediction["image_size"]),
-            pred_boxes=Boxes(torch.FloatTensor(prediction["pred_boxes"])),
-            pred_masks=torch.IntTensor(prediction["pred_masks"]),
-            pred_classes=torch.IntTensor(prediction["pred_classes"]),
-            scores=torch.FloatTensor(prediction["scores"]),
-        )
-        return res
-
-    def test_init(self):
-        cfg = {
-            "_target_": "detectron2.tracking.bbox_iou_tracker.BBoxIOUTracker",
-            "video_height": self._img_size[0],
-            "video_width": self._img_size[1],
-            "max_num_instances": self._max_num_instances,
-            "max_lost_frame_count": self._max_lost_frame_count,
-            "min_box_rel_dim": self._min_box_rel_dim,
-            "min_instance_period": self._min_instance_period,
-            "track_iou_threshold": self._track_iou_threshold,
-        }
-        tracker = instantiate(cfg)
-        self.assertTrue(tracker._video_height == self._img_size[0])
-
-    def test_from_config(self):
-        cfg = CfgNode_()
-        cfg.TRACKER_HEADS = CfgNode_()
-        cfg.TRACKER_HEADS.TRACKER_NAME = "BBoxIOUTracker"
-        cfg.TRACKER_HEADS.VIDEO_HEIGHT = int(self._img_size[0])
-        cfg.TRACKER_HEADS.VIDEO_WIDTH = int(self._img_size[1])
-        cfg.TRACKER_HEADS.MAX_NUM_INSTANCES = self._max_num_instances
-        cfg.TRACKER_HEADS.MAX_LOST_FRAME_COUNT = self._max_lost_frame_count
-        cfg.TRACKER_HEADS.MIN_BOX_REL_DIM = self._min_box_rel_dim
-        cfg.TRACKER_HEADS.MIN_INSTANCE_PERIOD = self._min_instance_period
-        cfg.TRACKER_HEADS.TRACK_IOU_THRESHOLD = self._track_iou_threshold
-        tracker = build_tracker_head(cfg)
-        self.assertTrue(tracker._video_height == self._img_size[0])
-
-    def test_initialize_extra_fields(self):
-        cfg = {
-            "_target_": "detectron2.tracking.bbox_iou_tracker.BBoxIOUTracker",
-            "video_height": self._img_size[0],
-            "video_width": self._img_size[1],
-            "max_num_instances": self._max_num_instances,
-            "max_lost_frame_count": self._max_lost_frame_count,
-            "min_box_rel_dim": self._min_box_rel_dim,
-            "min_instance_period": self._min_instance_period,
-            "track_iou_threshold": self._track_iou_threshold,
-        }
-        tracker = instantiate(cfg)
-        instances = tracker._initialize_extra_fields(self._curr_instances)
-        self.assertTrue(instances.has("ID"))
-        self.assertTrue(instances.has("ID_period"))
-        self.assertTrue(instances.has("lost_frame_count"))
-
-    def test_assign_new_id(self):
-        cfg = {
-            "_target_": "detectron2.tracking.bbox_iou_tracker.BBoxIOUTracker",
-            "video_height": self._img_size[0],
-            "video_width": self._img_size[1],
-            "max_num_instances": self._max_num_instances,
-            "max_lost_frame_count": self._max_lost_frame_count,
-            "min_box_rel_dim": self._min_box_rel_dim,
-            "min_instance_period": self._min_instance_period,
-            "track_iou_threshold": self._track_iou_threshold,
-        }
-        tracker = instantiate(cfg)
-        instances = deepcopy(self._curr_instances)
-        instances = tracker._initialize_extra_fields(instances)
-        instances = tracker._assign_new_id(instances)
-        self.assertTrue(len(instances.ID) == 2)
-        self.assertTrue(instances.ID[0] == 2)
-        self.assertTrue(instances.ID[1] == 3)
-
-    def test_update(self):
-        cfg = {
-            "_target_": "detectron2.tracking.bbox_iou_tracker.BBoxIOUTracker",
-            "video_height": self._img_size[0],
-            "video_width": self._img_size[1],
-            "max_num_instances": self._max_num_instances,
-            "max_lost_frame_count": self._max_lost_frame_count,
-            "min_box_rel_dim": self._min_box_rel_dim,
-            "min_instance_period": self._min_instance_period,
-            "track_iou_threshold": self._track_iou_threshold,
-        }
-        tracker = instantiate(cfg)
-        prev_instances = tracker.update(self._prev_instances)
-        self.assertTrue(len(prev_instances.ID) == 2)
-        self.assertTrue(prev_instances.ID[0] == 0)
-        self.assertTrue(prev_instances.ID[1] == 1)
-        curr_instances = tracker.update(self._curr_instances)
-        self.assertTrue(len(curr_instances.ID) == 2)
-        self.assertTrue(curr_instances.ID[0] == 1)
-        self.assertTrue(curr_instances.ID[1] == 0)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/constants.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/constants.py
deleted file mode 100644
index 8a5785b6fdb21910a174252c5af2f05b40ece4a5..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/constants.py
+++ /dev/null
@@ -1,149 +0,0 @@
-DEFAULT_Z_NEAR = 0.05     # Near clipping plane, in meters
-DEFAULT_Z_FAR = 100.0     # Far clipping plane, in meters
-DEFAULT_SCENE_SCALE = 2.0 # Default scene scale
-MAX_N_LIGHTS = 4          # Maximum number of lights of each type allowed
-TARGET_OPEN_GL_MAJOR = 4  # Target OpenGL Major Version
-TARGET_OPEN_GL_MINOR = 1  # Target OpenGL Minor Version
-MIN_OPEN_GL_MAJOR = 3     # Minimum OpenGL Major Version
-MIN_OPEN_GL_MINOR = 3     # Minimum OpenGL Minor Version
-FLOAT_SZ = 4              # Byte size of GL float32
-UINT_SZ = 4               # Byte size of GL uint32
-SHADOW_TEX_SZ = 2048      # Width and Height of Shadow Textures
-TEXT_PADDING = 20         # Width of padding for rendering text (px)
-
-
-# Flags for render type
-class RenderFlags(object):
-    """Flags for rendering in the scene.
-
-    Combine them with the bitwise or. For example,
-
-    >>> flags = OFFSCREEN | SHADOWS_DIRECTIONAL | VERTEX_NORMALS
-
-    would result in an offscreen render with directional shadows and
-    vertex normals enabled.
-    """
-    NONE = 0
-    """Normal PBR Render."""
-    DEPTH_ONLY = 1
-    """Only render the depth buffer."""
-    OFFSCREEN = 2
-    """Render offscreen and return the depth and (optionally) color buffers."""
-    FLIP_WIREFRAME = 4
-    """Invert the status of wireframe rendering for each mesh."""
-    ALL_WIREFRAME = 8
-    """Render all meshes as wireframes."""
-    ALL_SOLID = 16
-    """Render all meshes as solids."""
-    SHADOWS_DIRECTIONAL = 32
-    """Render shadows for directional lights."""
-    SHADOWS_POINT = 64
-    """Render shadows for point lights."""
-    SHADOWS_SPOT = 128
-    """Render shadows for spot lights."""
-    SHADOWS_ALL = 32 | 64 | 128
-    """Render shadows for all lights."""
-    VERTEX_NORMALS = 256
-    """Render vertex normals."""
-    FACE_NORMALS = 512
-    """Render face normals."""
-    SKIP_CULL_FACES = 1024
-    """Do not cull back faces."""
-    RGBA = 2048
-    """Render the color buffer with the alpha channel enabled."""
-    FLAT = 4096
-    """Render the color buffer flat, with no lighting computations."""
-    SEG = 8192
-
-
-class TextAlign:
-    """Text alignment options for captions.
-
-    Only use one at a time.
-    """
-    CENTER = 0
-    """Center the text by width and height."""
-    CENTER_LEFT = 1
-    """Center the text by height and left-align it."""
-    CENTER_RIGHT = 2
-    """Center the text by height and right-align it."""
-    BOTTOM_LEFT = 3
-    """Put the text in the bottom-left corner."""
-    BOTTOM_RIGHT = 4
-    """Put the text in the bottom-right corner."""
-    BOTTOM_CENTER = 5
-    """Center the text by width and fix it to the bottom."""
-    TOP_LEFT = 6
-    """Put the text in the top-left corner."""
-    TOP_RIGHT = 7
-    """Put the text in the top-right corner."""
-    TOP_CENTER = 8
-    """Center the text by width and fix it to the top."""
-
-
-class GLTF(object):
-    """Options for GL objects."""
-    NEAREST = 9728
-    """Nearest neighbor interpolation."""
-    LINEAR = 9729
-    """Linear interpolation."""
-    NEAREST_MIPMAP_NEAREST = 9984
-    """Nearest mipmapping."""
-    LINEAR_MIPMAP_NEAREST = 9985
-    """Linear mipmapping."""
-    NEAREST_MIPMAP_LINEAR = 9986
-    """Nearest mipmapping."""
-    LINEAR_MIPMAP_LINEAR = 9987
-    """Linear mipmapping."""
-    CLAMP_TO_EDGE = 33071
-    """Clamp to the edge of the texture."""
-    MIRRORED_REPEAT = 33648
-    """Mirror the texture."""
-    REPEAT = 10497
-    """Repeat the texture."""
-    POINTS = 0
-    """Render as points."""
-    LINES = 1
-    """Render as lines."""
-    LINE_LOOP = 2
-    """Render as a line loop."""
-    LINE_STRIP = 3
-    """Render as a line strip."""
-    TRIANGLES = 4
-    """Render as triangles."""
-    TRIANGLE_STRIP = 5
-    """Render as a triangle strip."""
-    TRIANGLE_FAN = 6
-    """Render as a triangle fan."""
-
-
-class BufFlags(object):
-    POSITION = 0
-    NORMAL = 1
-    TANGENT = 2
-    TEXCOORD_0 = 4
-    TEXCOORD_1 = 8
-    COLOR_0 = 16
-    JOINTS_0 = 32
-    WEIGHTS_0 = 64
-
-
-class TexFlags(object):
-    NONE = 0
-    NORMAL = 1
-    OCCLUSION = 2
-    EMISSIVE = 4
-    BASE_COLOR = 8
-    METALLIC_ROUGHNESS = 16
-    DIFFUSE = 32
-    SPECULAR_GLOSSINESS = 64
-
-
-class ProgramFlags:
-    NONE = 0
-    USE_MATERIAL = 1
-    VERTEX_NORMALS = 2
-    FACE_NORMALS = 4
-
-
-__all__ = ['RenderFlags', 'TextAlign', 'GLTF']
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/FpxImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/FpxImagePlugin.py
deleted file mode 100644
index 2450c67e9a67530a4ad4fdcb1bbbfd39971ea484..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/FpxImagePlugin.py
+++ /dev/null
@@ -1,253 +0,0 @@
-#
-# THIS IS WORK IN PROGRESS
-#
-# The Python Imaging Library.
-# $Id$
-#
-# FlashPix support for PIL
-#
-# History:
-# 97-01-25 fl   Created (reads uncompressed RGB images only)
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-import olefile
-
-from . import Image, ImageFile
-from ._binary import i32le as i32
-
-# we map from colour field tuples to (mode, rawmode) descriptors
-MODES = {
-    # opacity
-    (0x00007FFE,): ("A", "L"),
-    # monochrome
-    (0x00010000,): ("L", "L"),
-    (0x00018000, 0x00017FFE): ("RGBA", "LA"),
-    # photo YCC
-    (0x00020000, 0x00020001, 0x00020002): ("RGB", "YCC;P"),
-    (0x00028000, 0x00028001, 0x00028002, 0x00027FFE): ("RGBA", "YCCA;P"),
-    # standard RGB (NIFRGB)
-    (0x00030000, 0x00030001, 0x00030002): ("RGB", "RGB"),
-    (0x00038000, 0x00038001, 0x00038002, 0x00037FFE): ("RGBA", "RGBA"),
-}
-
-
-#
-# --------------------------------------------------------------------
-
-
-def _accept(prefix):
-    return prefix[:8] == olefile.MAGIC
-
-
-##
-# Image plugin for the FlashPix images.
-
-
-class FpxImageFile(ImageFile.ImageFile):
-    format = "FPX"
-    format_description = "FlashPix"
-
-    def _open(self):
-        #
-        # read the OLE directory and see if this is a likely
-        # to be a FlashPix file
-
-        try:
-            self.ole = olefile.OleFileIO(self.fp)
-        except OSError as e:
-            msg = "not an FPX file; invalid OLE file"
-            raise SyntaxError(msg) from e
-
-        if self.ole.root.clsid != "56616700-C154-11CE-8553-00AA00A1F95B":
-            msg = "not an FPX file; bad root CLSID"
-            raise SyntaxError(msg)
-
-        self._open_index(1)
-
-    def _open_index(self, index=1):
-        #
-        # get the Image Contents Property Set
-
-        prop = self.ole.getproperties(
-            [f"Data Object Store {index:06d}", "\005Image Contents"]
-        )
-
-        # size (highest resolution)
-
-        self._size = prop[0x1000002], prop[0x1000003]
-
-        size = max(self.size)
-        i = 1
-        while size > 64:
-            size = size / 2
-            i += 1
-        self.maxid = i - 1
-
-        # mode.  instead of using a single field for this, flashpix
-        # requires you to specify the mode for each channel in each
-        # resolution subimage, and leaves it to the decoder to make
-        # sure that they all match.  for now, we'll cheat and assume
-        # that this is always the case.
-
-        id = self.maxid << 16
-
-        s = prop[0x2000002 | id]
-
-        colors = []
-        bands = i32(s, 4)
-        if bands > 4:
-            msg = "Invalid number of bands"
-            raise OSError(msg)
-        for i in range(bands):
-            # note: for now, we ignore the "uncalibrated" flag
-            colors.append(i32(s, 8 + i * 4) & 0x7FFFFFFF)
-
-        self.mode, self.rawmode = MODES[tuple(colors)]
-
-        # load JPEG tables, if any
-        self.jpeg = {}
-        for i in range(256):
-            id = 0x3000001 | (i << 16)
-            if id in prop:
-                self.jpeg[i] = prop[id]
-
-        self._open_subimage(1, self.maxid)
-
-    def _open_subimage(self, index=1, subimage=0):
-        #
-        # setup tile descriptors for a given subimage
-
-        stream = [
-            f"Data Object Store {index:06d}",
-            f"Resolution {subimage:04d}",
-            "Subimage 0000 Header",
-        ]
-
-        fp = self.ole.openstream(stream)
-
-        # skip prefix
-        fp.read(28)
-
-        # header stream
-        s = fp.read(36)
-
-        size = i32(s, 4), i32(s, 8)
-        # tilecount = i32(s, 12)
-        tilesize = i32(s, 16), i32(s, 20)
-        # channels = i32(s, 24)
-        offset = i32(s, 28)
-        length = i32(s, 32)
-
-        if size != self.size:
-            msg = "subimage mismatch"
-            raise OSError(msg)
-
-        # get tile descriptors
-        fp.seek(28 + offset)
-        s = fp.read(i32(s, 12) * length)
-
-        x = y = 0
-        xsize, ysize = size
-        xtile, ytile = tilesize
-        self.tile = []
-
-        for i in range(0, len(s), length):
-            x1 = min(xsize, x + xtile)
-            y1 = min(ysize, y + ytile)
-
-            compression = i32(s, i + 8)
-
-            if compression == 0:
-                self.tile.append(
-                    (
-                        "raw",
-                        (x, y, x1, y1),
-                        i32(s, i) + 28,
-                        (self.rawmode,),
-                    )
-                )
-
-            elif compression == 1:
-                # FIXME: the fill decoder is not implemented
-                self.tile.append(
-                    (
-                        "fill",
-                        (x, y, x1, y1),
-                        i32(s, i) + 28,
-                        (self.rawmode, s[12:16]),
-                    )
-                )
-
-            elif compression == 2:
-                internal_color_conversion = s[14]
-                jpeg_tables = s[15]
-                rawmode = self.rawmode
-
-                if internal_color_conversion:
-                    # The image is stored as usual (usually YCbCr).
-                    if rawmode == "RGBA":
-                        # For "RGBA", data is stored as YCbCrA based on
-                        # negative RGB. The following trick works around
-                        # this problem :
-                        jpegmode, rawmode = "YCbCrK", "CMYK"
-                    else:
-                        jpegmode = None  # let the decoder decide
-
-                else:
-                    # The image is stored as defined by rawmode
-                    jpegmode = rawmode
-
-                self.tile.append(
-                    (
-                        "jpeg",
-                        (x, y, x1, y1),
-                        i32(s, i) + 28,
-                        (rawmode, jpegmode),
-                    )
-                )
-
-                # FIXME: jpeg tables are tile dependent; the prefix
-                # data must be placed in the tile descriptor itself!
-
-                if jpeg_tables:
-                    self.tile_prefix = self.jpeg[jpeg_tables]
-
-            else:
-                msg = "unknown/invalid compression"
-                raise OSError(msg)
-
-            x = x + xtile
-            if x >= xsize:
-                x, y = 0, y + ytile
-                if y >= ysize:
-                    break  # isn't really required
-
-        self.stream = stream
-        self.fp = None
-
-    def load(self):
-        if not self.fp:
-            self.fp = self.ole.openstream(self.stream[:2] + ["Subimage 0000 Data"])
-
-        return ImageFile.ImageFile.load(self)
-
-    def close(self):
-        self.ole.close()
-        super().close()
-
-    def __exit__(self, *args):
-        self.ole.close()
-        super().__exit__()
-
-
-#
-# --------------------------------------------------------------------
-
-
-Image.register_open(FpxImageFile.format, FpxImageFile, _accept)
-
-Image.register_extension(FpxImageFile.format, ".fpx")
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/transform/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/transform/__init__.py
deleted file mode 100644
index 369e1b278899b225d55bfc729514873b4259c7b9..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/transform/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .image import ImageResizeTransform
diff --git a/spaces/cdleong/phonemize-audio/app.py b/spaces/cdleong/phonemize-audio/app.py
deleted file mode 100644
index bdfd73a5f4bbdfb4796d35ffbdf6aacee826b4ae..0000000000000000000000000000000000000000
--- a/spaces/cdleong/phonemize-audio/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import streamlit as st
-import langcodes
-from allosaurus.app import read_recognizer
-from pathlib import Path
-import string
-from itertools import permutations
-from collections import defaultdict
-import torchaudio
-
-@st.cache
-def get_supported_codes():
-  model = read_recognizer()  
-  supported_codes = []
-  supported_codes.append("ipa") # default option
-  for combo in permutations(string.ascii_lowercase, r=3):
-    code = "".join(combo)
-    if model.is_available(code):
-      supported_codes.append(code)
-      
-  
-  return supported_codes
-  
-  
-def get_path_to_wav_format(uploaded_file, suppress_outputs=False):
-#  st.write(dir(uploaded_file))
-#  st.write(type(uploaded_file))
-#  st.write(uploaded_file)
-  uploaded_bytes = uploaded_file.getvalue()
-  actual_file_path = Path(uploaded_file.name)
-  actual_file_path.write_bytes(uploaded_bytes)
-    
-
-  if ".wav" in uploaded_file.name:
-    return Path(uploaded_file.name)
-  if ".mp3" in uploaded_file.name or ".ogg" in uploaded_file.name:
-    new_desired_path = actual_file_path.with_suffix(".wav")
-    encoding="PCM_S" # Prevent encoding errors. https://stackoverflow.com/questions/60352850/wave-error-unknown-format-3-arises-when-trying-to-convert-a-wav-file-into-text
-    bits_per_sample=16
-    waveform, sample_rate = torchaudio.load(actual_file_path)
-    if not suppress_outputs:
-      st.info(f"Allosaurus requires .wav files. Converting with torchaudio, encoding={encoding}, bits_per_sample={bits_per_sample}")
-      st.info(f"Uploaded file sample_rate: {sample_rate}")
-    torchaudio.save(new_desired_path, waveform, sample_rate, 
-                    encoding=encoding, 
-                    bits_per_sample=bits_per_sample,
-                    )
-
-    return new_desired_path
-
-@st.cache
-def get_langcode_description(input_code, url=False):
-  langcode = "ipa" # the default allosaurus recognizer
-  description = "the default universal setting, not specific to any language"
-  
-  if not input_code or input_code==langcode:
-    return description
-    
-
-
-  try:
-    lang = langcodes.get(input_code)
-    alpha3 = lang.to_alpha3()
-    langcode = alpha3 
-    display_name = lang.display_name()
-    if url:
-      description = f"[{display_name}](https://iso639-3.sil.org/code/{alpha3})"
-    else:
-      description = display_name 
-    
-  except langcodes.LanguageTagError as e:
-    pass    
-  return description
-
-@st.cache    
-def get_langcode_with_description(input_code):
-  return f"{input_code}: {get_langcode_description(input_code)}"
-
-
-if __name__ == "__main__":
-  st.header("Phonemize Audio files with [Allosaurus](https://github.com/xinjli/allosaurus)")
-  st.write("Allosaurus is a pretrained universal phone recognizer. It can be used to recognize phones in more than 2000 languages. It is written by Li, Xinjian and Dalmia, Siddharth and Li, Juncheng and Lee, Matthew and Littell, Patrick and Yao, Jiali and Anastasopoulos, Antonios and Mortensen, David R and Neubig, Graham and Black, Alan W and Florian, Metze. [Click here to visit their repository](https://github.com/xinjli/allosaurus)")
-  st.write("I, [Colin Leong](cdleong.github.io) did not create Allosaurus, but I have created this web app (kindly hosted by Hugging Face) to make it convenient to use: simply upload your files below, and they will be transcribed to phonetic IPA symbols!")
-  st.write(f"**Feedback:** Provide feedback regarding this web app at https://twitter.com/cleong110, or via slack: https://masakhane-nlp.slack.com/archives/D01DU3MHP7A")
-  supported_codes = get_supported_codes()
-  index_of_desired_default = supported_codes.index("ipa")  
-  with st.form("Allosaurus form"):
-  
-    langcode = st.selectbox("ISO code for input language. Allosaurus doesn't need this, but it can improve accuracy", 
-                            options=supported_codes, 
-                            index=index_of_desired_default,
-                            format_func=get_langcode_with_description
-                            )
-    
-    model = read_recognizer()
-    description = get_langcode_description(langcode, url=True)
-  
-    st.write(f"Instructing Allosaurus to recognize using language {langcode}. That is, {description}")
-  
-    st.subheader("Upload your files here")
-    uploaded_files = st.file_uploader("Choose a file", type=[
-                                                            ".wav", 
-                                                            ".mp3", 
-                                                            ".ogg",
-                                                            ],
-                                                      accept_multiple_files=True,
-                                                            )
-    
-    submitted = st.form_submit_button("Run phone recognition!")
-    if submitted:
-      results = {} # for better download/display
-      
-      uploaded_files_count = len(uploaded_files)
-      suppress_output_threshold = 2
-      my_bar = st.progress(0)
-      for i, uploaded_file in enumerate(uploaded_files):
-        
-        if uploaded_file is not None:            
-          wav_file = get_path_to_wav_format(uploaded_file, uploaded_files_count>suppress_output_threshold)    
-          with st.spinner(f"transcribing {uploaded_file.name}..."):
-            result = model.recognize(wav_file, langcode)
-          results[uploaded_file.name] = result
-        files_done = i+1
-        my_bar.progress(files_done/uploaded_files_count)
-      st.write(results)    
\ No newline at end of file
diff --git a/spaces/celise88/Pathfinder/templates/find_hire.html b/spaces/celise88/Pathfinder/templates/find_hire.html
deleted file mode 100644
index ba1a671b54770e740352f5c487f7b7f5ea5e671e..0000000000000000000000000000000000000000
--- a/spaces/celise88/Pathfinder/templates/find_hire.html
+++ /dev/null
@@ -1,37 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Dashboard</title>
-    <link rel="stylesheet" href="/static/styles.css">
-</head>
-<body>
-    <header class="navbar">
-        <div class="navbar__brand">
-            <img src="/static/PF.png" class="navbar__logo" alt="Pathfinder logo" />
-            <a href="/" class="navbar__logo">Pathfinder</a>
-        </div>
-        <ul class="navbar__navigation">
-            <li class="navbar__navigation-item"><a href="/explore-job-neighborhoods/" class="navbar__link">Explore Job Neighborhoods</a></li>
-            <li class="navbar__navigation-item"><a href="/find-my-match/" class="navbar__link">Find My Match</a></li>
-            <li class="navbar__navigation-item"><a href="/find-my-hire/" class="navbar__link">Find My Next Hire</a></li>
-            <li class="navbar__navigation-item"><a href="/login/" class="navbar__link">Login</a></li>
-            <li class="navbar__navigation-item"><a href="/logout/" class="navbar__link">Logout</a></li>
-        </ul>
-    </header>
-    <main class="main">
-        <h1 class="pagetitle">Matching Candidates</h1>
-        <h2 class="pagesubtitle">We're sorry! This page is currently under construction.</h2>
-        <h2 class="pagesubtitle">Please check back soon to get candidates who are a great match for your {{ jobselection }} role!</h2>
-        <br>
-        <br>
-    </main>
-    <footer class="footer">
-        <ul class="footer__text">
-            <li class="footer__text-item">© 2023 Pathfinder</li>
-        </ul>
-    </footer>
-</body>
-</html>
diff --git a/spaces/cheetah003/HMMC_t2v_search/modules/file_utils.py b/spaces/cheetah003/HMMC_t2v_search/modules/file_utils.py
deleted file mode 100644
index 43fa8ca87e20ee5333dd84a09795a743bbf3f183..0000000000000000000000000000000000000000
--- a/spaces/cheetah003/HMMC_t2v_search/modules/file_utils.py
+++ /dev/null
@@ -1,239 +0,0 @@
-"""
-Utilities for working with the local dataset cache.
-This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
-Copyright by the AllenNLP authors.
-"""
-
-import os
-import logging
-import shutil
-import tempfile
-import json
-from urllib.parse import urlparse
-from pathlib import Path
-from typing import Optional, Tuple, Union, IO, Callable, Set
-from hashlib import sha256
-from functools import wraps
-
-from tqdm import tqdm
-
-import boto3
-from botocore.exceptions import ClientError
-import requests
-
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
-PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
-                                               Path.home() / '.pytorch_pretrained_bert'))
-
-
-def url_to_filename(url: str, etag: str = None) -> str:
-    """
-    Convert `url` into a hashed filename in a repeatable way.
-    If `etag` is specified, append its hash to the url's, delimited
-    by a period.
-    """
-    url_bytes = url.encode('utf-8')
-    url_hash = sha256(url_bytes)
-    filename = url_hash.hexdigest()
-
-    if etag:
-        etag_bytes = etag.encode('utf-8')
-        etag_hash = sha256(etag_bytes)
-        filename += '.' + etag_hash.hexdigest()
-
-    return filename
-
-
-def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[str, str]:
-    """
-    Return the url and etag (which may be ``None``) stored for `filename`.
-    Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    cache_path = os.path.join(cache_dir, filename)
-    if not os.path.exists(cache_path):
-        raise FileNotFoundError("file {} not found".format(cache_path))
-
-    meta_path = cache_path + '.json'
-    if not os.path.exists(meta_path):
-        raise FileNotFoundError("file {} not found".format(meta_path))
-
-    with open(meta_path) as meta_file:
-        metadata = json.load(meta_file)
-    url = metadata['url']
-    etag = metadata['etag']
-
-    return url, etag
-
-
-def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str:
-    """
-    Given something that might be a URL (or might be a local path),
-    determine which. If it's a URL, download the file and cache it, and
-    return the path to the cached file. If it's already a local path,
-    make sure the file exists and then return the path.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(url_or_filename, Path):
-        url_or_filename = str(url_or_filename)
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    parsed = urlparse(url_or_filename)
-
-    if parsed.scheme in ('http', 'https', 's3'):
-        # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
-    elif os.path.exists(url_or_filename):
-        # File, and it exists.
-        return url_or_filename
-    elif parsed.scheme == '':
-        # File, but it doesn't exist.
-        raise FileNotFoundError("file {} not found".format(url_or_filename))
-    else:
-        # Something unknown
-        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
-
-
-def split_s3_path(url: str) -> Tuple[str, str]:
-    """Split a full s3 path into the bucket name and path."""
-    parsed = urlparse(url)
-    if not parsed.netloc or not parsed.path:
-        raise ValueError("bad s3 path {}".format(url))
-    bucket_name = parsed.netloc
-    s3_path = parsed.path
-    # Remove '/' at beginning of path.
-    if s3_path.startswith("/"):
-        s3_path = s3_path[1:]
-    return bucket_name, s3_path
-
-
-def s3_request(func: Callable):
-    """
-    Wrapper function for s3 requests in order to create more helpful error
-    messages.
-    """
-
-    @wraps(func)
-    def wrapper(url: str, *args, **kwargs):
-        try:
-            return func(url, *args, **kwargs)
-        except ClientError as exc:
-            if int(exc.response["Error"]["Code"]) == 404:
-                raise FileNotFoundError("file {} not found".format(url))
-            else:
-                raise
-
-    return wrapper
-
-
-@s3_request
-def s3_etag(url: str) -> Optional[str]:
-    """Check ETag on S3 object."""
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_object = s3_resource.Object(bucket_name, s3_path)
-    return s3_object.e_tag
-
-
-@s3_request
-def s3_get(url: str, temp_file: IO) -> None:
-    """Pull a file directly from S3."""
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
-
-
-def http_get(url: str, temp_file: IO) -> None:
-    req = requests.get(url, stream=True)
-    content_length = req.headers.get('Content-Length')
-    total = int(content_length) if content_length is not None else None
-    progress = tqdm(unit="B", total=total)
-    for chunk in req.iter_content(chunk_size=1024):
-        if chunk: # filter out keep-alive new chunks
-            progress.update(len(chunk))
-            temp_file.write(chunk)
-    progress.close()
-
-
-def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
-    """
-    Given a URL, look for the corresponding dataset in the local cache.
-    If it's not there, download it. Then return the path to the cached file.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    os.makedirs(cache_dir, exist_ok=True)
-
-    # Get eTag to add to filename, if it exists.
-    if url.startswith("s3://"):
-        etag = s3_etag(url)
-    else:
-        response = requests.head(url, allow_redirects=True)
-        if response.status_code != 200:
-            raise IOError("HEAD request failed for url {} with status code {}"
-                          .format(url, response.status_code))
-        etag = response.headers.get("ETag")
-
-    filename = url_to_filename(url, etag)
-
-    # get cache path to put the file
-    cache_path = os.path.join(cache_dir, filename)
-
-    if not os.path.exists(cache_path):
-        # Download to temporary file, then copy to cache dir once finished.
-        # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with tempfile.NamedTemporaryFile() as temp_file:
-            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
-
-            # GET file object
-            if url.startswith("s3://"):
-                s3_get(url, temp_file)
-            else:
-                http_get(url, temp_file)
-
-            # we are copying the file before closing it, so flush to avoid truncation
-            temp_file.flush()
-            # shutil.copyfileobj() starts at the current position, so go to the start
-            temp_file.seek(0)
-
-            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
-            with open(cache_path, 'wb') as cache_file:
-                shutil.copyfileobj(temp_file, cache_file)
-
-            logger.info("creating metadata file for %s", cache_path)
-            meta = {'url': url, 'etag': etag}
-            meta_path = cache_path + '.json'
-            with open(meta_path, 'w') as meta_file:
-                json.dump(meta, meta_file)
-
-            logger.info("removing temp file %s", temp_file.name)
-
-    return cache_path
-
-
-def read_set_from_file(filename: str) -> Set[str]:
-    '''
-    Extract a de-duped collection (set) of text from a file.
-    Expected file format is one item per line.
-    '''
-    collection = set()
-    with open(filename, 'r', encoding='utf-8') as file_:
-        for line in file_:
-            collection.add(line.rstrip())
-    return collection
-
-
-def get_file_extension(path: str, dot=True, lower: bool = True):
-    ext = os.path.splitext(path)[1]
-    ext = ext if dot else ext[1:]
-    return ext.lower() if lower else ext
diff --git a/spaces/chendl/compositional_test/transformers/examples/pytorch/question-answering/run_qa.py b/spaces/chendl/compositional_test/transformers/examples/pytorch/question-answering/run_qa.py
deleted file mode 100644
index 9dc3b2c812c2e2ba9eac854d6fb12939069f9d89..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/pytorch/question-answering/run_qa.py
+++ /dev/null
@@ -1,685 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2020 The HuggingFace Team All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for question answering using a slightly adapted version of the 🤗 Trainer.
-"""
-# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
-
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-import datasets
-import evaluate
-from datasets import load_dataset
-from trainer_qa import QuestionAnsweringTrainer
-from utils_qa import postprocess_qa_predictions
-
-import transformers
-from transformers import (
-    AutoConfig,
-    AutoModelForQuestionAnswering,
-    AutoTokenizer,
-    DataCollatorWithPadding,
-    EvalPrediction,
-    HfArgumentParser,
-    PreTrainedTokenizerFast,
-    TrainingArguments,
-    default_data_collator,
-    set_seed,
-)
-from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version, send_example_telemetry
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.28.0")
-
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
-    )
-    model_revision: str = field(
-        default="main",
-        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
-    )
-    use_auth_token: bool = field(
-        default=False,
-        metadata={
-            "help": (
-                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
-                "with private models)."
-            )
-        },
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
-    )
-    dataset_config_name: Optional[str] = field(
-        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
-    )
-    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
-    validation_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
-    )
-    test_file: Optional[str] = field(
-        default=None,
-        metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."},
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    max_seq_length: int = field(
-        default=384,
-        metadata={
-            "help": (
-                "The maximum total input sequence length after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded."
-            )
-        },
-    )
-    pad_to_max_length: bool = field(
-        default=True,
-        metadata={
-            "help": (
-                "Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when"
-                " batching to the maximum length in the batch (which can be faster on GPU but will be slower on TPU)."
-            )
-        },
-    )
-    max_train_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of training examples to this "
-                "value if set."
-            )
-        },
-    )
-    max_eval_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
-                "value if set."
-            )
-        },
-    )
-    max_predict_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": (
-                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
-                "value if set."
-            )
-        },
-    )
-    version_2_with_negative: bool = field(
-        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
-    )
-    null_score_diff_threshold: float = field(
-        default=0.0,
-        metadata={
-            "help": (
-                "The threshold used to select the null answer: if the best answer has a score that is less than "
-                "the score of the null answer minus this threshold, the null answer is selected for this example. "
-                "Only useful when `version_2_with_negative=True`."
-            )
-        },
-    )
-    doc_stride: int = field(
-        default=128,
-        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
-    )
-    n_best_size: int = field(
-        default=20,
-        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
-    )
-    max_answer_length: int = field(
-        default=30,
-        metadata={
-            "help": (
-                "The maximum length of an answer that can be generated. This is needed because the start "
-                "and end predictions are not conditioned on one another."
-            )
-        },
-    )
-
-    def __post_init__(self):
-        if (
-            self.dataset_name is None
-            and self.train_file is None
-            and self.validation_file is None
-            and self.test_file is None
-        ):
-            raise ValueError("Need either a dataset name or a training/validation file/test_file.")
-        else:
-            if self.train_file is not None:
-                extension = self.train_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
-            if self.validation_file is not None:
-                extension = self.validation_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-            if self.test_file is not None:
-                extension = self.test_file.split(".")[-1]
-                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
-    # information sent is the one passed as arguments along with your Python/PyTorch versions.
-    send_example_telemetry("run_qa", model_args, data_args)
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
-
-    if training_args.should_log:
-        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
-        transformers.utils.logging.set_verbosity_info()
-
-    log_level = training_args.get_process_log_level()
-    logger.setLevel(log_level)
-    datasets.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.enable_default_handler()
-    transformers.utils.logging.enable_explicit_format()
-
-    # Log on each process the small summary:
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Detecting last checkpoint.
-    last_checkpoint = None
-    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
-        last_checkpoint = get_last_checkpoint(training_args.output_dir)
-        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
-            raise ValueError(
-                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
-                "Use --overwrite_output_dir to overcome."
-            )
-        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
-            logger.info(
-                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
-                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
-            )
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
-    # download the dataset.
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        raw_datasets = load_dataset(
-            data_args.dataset_name,
-            data_args.dataset_config_name,
-            cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
-        )
-    else:
-        data_files = {}
-        if data_args.train_file is not None:
-            data_files["train"] = data_args.train_file
-            extension = data_args.train_file.split(".")[-1]
-
-        if data_args.validation_file is not None:
-            data_files["validation"] = data_args.validation_file
-            extension = data_args.validation_file.split(".")[-1]
-        if data_args.test_file is not None:
-            data_files["test"] = data_args.test_file
-            extension = data_args.test_file.split(".")[-1]
-        raw_datasets = load_dataset(
-            extension,
-            data_files=data_files,
-            field="data",
-            cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
-        )
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        use_fast=True,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-    model = AutoModelForQuestionAnswering.from_pretrained(
-        model_args.model_name_or_path,
-        from_tf=bool(".ckpt" in model_args.model_name_or_path),
-        config=config,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-
-    # Tokenizer check: this script requires a fast tokenizer.
-    if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        raise ValueError(
-            "This example script only works for models that have a fast tokenizer. Checkout the big table of models at"
-            " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
-            " this requirement"
-        )
-
-    # Preprocessing the datasets.
-    # Preprocessing is slighlty different for training and evaluation.
-    if training_args.do_train:
-        column_names = raw_datasets["train"].column_names
-    elif training_args.do_eval:
-        column_names = raw_datasets["validation"].column_names
-    else:
-        column_names = raw_datasets["test"].column_names
-    question_column_name = "question" if "question" in column_names else column_names[0]
-    context_column_name = "context" if "context" in column_names else column_names[1]
-    answer_column_name = "answers" if "answers" in column_names else column_names[2]
-
-    # Padding side determines if we do (question|context) or (context|question).
-    pad_on_right = tokenizer.padding_side == "right"
-
-    if data_args.max_seq_length > tokenizer.model_max_length:
-        logger.warning(
-            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
-            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-        )
-    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
-    # Training preprocessing
-    def prepare_train_features(examples):
-        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
-        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
-        # left whitespace
-        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
-
-        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
-        # in one example possible giving several features when a context is long, each of those features having a
-        # context that overlaps a bit the context of the previous feature.
-        tokenized_examples = tokenizer(
-            examples[question_column_name if pad_on_right else context_column_name],
-            examples[context_column_name if pad_on_right else question_column_name],
-            truncation="only_second" if pad_on_right else "only_first",
-            max_length=max_seq_length,
-            stride=data_args.doc_stride,
-            return_overflowing_tokens=True,
-            return_offsets_mapping=True,
-            padding="max_length" if data_args.pad_to_max_length else False,
-        )
-
-        # Since one example might give us several features if it has a long context, we need a map from a feature to
-        # its corresponding example. This key gives us just that.
-        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
-        # The offset mappings will give us a map from token to character position in the original context. This will
-        # help us compute the start_positions and end_positions.
-        offset_mapping = tokenized_examples.pop("offset_mapping")
-
-        # Let's label those examples!
-        tokenized_examples["start_positions"] = []
-        tokenized_examples["end_positions"] = []
-
-        for i, offsets in enumerate(offset_mapping):
-            # We will label impossible answers with the index of the CLS token.
-            input_ids = tokenized_examples["input_ids"][i]
-            cls_index = input_ids.index(tokenizer.cls_token_id)
-
-            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
-            sequence_ids = tokenized_examples.sequence_ids(i)
-
-            # One example can give several spans, this is the index of the example containing this span of text.
-            sample_index = sample_mapping[i]
-            answers = examples[answer_column_name][sample_index]
-            # If no answers are given, set the cls_index as answer.
-            if len(answers["answer_start"]) == 0:
-                tokenized_examples["start_positions"].append(cls_index)
-                tokenized_examples["end_positions"].append(cls_index)
-            else:
-                # Start/end character index of the answer in the text.
-                start_char = answers["answer_start"][0]
-                end_char = start_char + len(answers["text"][0])
-
-                # Start token index of the current span in the text.
-                token_start_index = 0
-                while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
-                    token_start_index += 1
-
-                # End token index of the current span in the text.
-                token_end_index = len(input_ids) - 1
-                while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
-                    token_end_index -= 1
-
-                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
-                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
-                    tokenized_examples["start_positions"].append(cls_index)
-                    tokenized_examples["end_positions"].append(cls_index)
-                else:
-                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
-                    # Note: we could go after the last offset if the answer is the last word (edge case).
-                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
-                        token_start_index += 1
-                    tokenized_examples["start_positions"].append(token_start_index - 1)
-                    while offsets[token_end_index][1] >= end_char:
-                        token_end_index -= 1
-                    tokenized_examples["end_positions"].append(token_end_index + 1)
-
-        return tokenized_examples
-
-    if training_args.do_train:
-        if "train" not in raw_datasets:
-            raise ValueError("--do_train requires a train dataset")
-        train_dataset = raw_datasets["train"]
-        if data_args.max_train_samples is not None:
-            # We will select sample from whole data if argument is specified
-            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
-            train_dataset = train_dataset.select(range(max_train_samples))
-        # Create train feature from dataset
-        with training_args.main_process_first(desc="train dataset map pre-processing"):
-            train_dataset = train_dataset.map(
-                prepare_train_features,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on train dataset",
-            )
-        if data_args.max_train_samples is not None:
-            # Number of samples might increase during Feature Creation, We select only specified max samples
-            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
-            train_dataset = train_dataset.select(range(max_train_samples))
-
-    # Validation preprocessing
-    def prepare_validation_features(examples):
-        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
-        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
-        # left whitespace
-        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
-
-        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
-        # in one example possible giving several features when a context is long, each of those features having a
-        # context that overlaps a bit the context of the previous feature.
-        tokenized_examples = tokenizer(
-            examples[question_column_name if pad_on_right else context_column_name],
-            examples[context_column_name if pad_on_right else question_column_name],
-            truncation="only_second" if pad_on_right else "only_first",
-            max_length=max_seq_length,
-            stride=data_args.doc_stride,
-            return_overflowing_tokens=True,
-            return_offsets_mapping=True,
-            padding="max_length" if data_args.pad_to_max_length else False,
-        )
-
-        # Since one example might give us several features if it has a long context, we need a map from a feature to
-        # its corresponding example. This key gives us just that.
-        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
-
-        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
-        # corresponding example_id and we will store the offset mappings.
-        tokenized_examples["example_id"] = []
-
-        for i in range(len(tokenized_examples["input_ids"])):
-            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
-            sequence_ids = tokenized_examples.sequence_ids(i)
-            context_index = 1 if pad_on_right else 0
-
-            # One example can give several spans, this is the index of the example containing this span of text.
-            sample_index = sample_mapping[i]
-            tokenized_examples["example_id"].append(examples["id"][sample_index])
-
-            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
-            # position is part of the context or not.
-            tokenized_examples["offset_mapping"][i] = [
-                (o if sequence_ids[k] == context_index else None)
-                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
-            ]
-
-        return tokenized_examples
-
-    if training_args.do_eval:
-        if "validation" not in raw_datasets:
-            raise ValueError("--do_eval requires a validation dataset")
-        eval_examples = raw_datasets["validation"]
-        if data_args.max_eval_samples is not None:
-            # We will select sample from whole data
-            max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
-            eval_examples = eval_examples.select(range(max_eval_samples))
-        # Validation Feature Creation
-        with training_args.main_process_first(desc="validation dataset map pre-processing"):
-            eval_dataset = eval_examples.map(
-                prepare_validation_features,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on validation dataset",
-            )
-        if data_args.max_eval_samples is not None:
-            # During Feature creation dataset samples might increase, we will select required samples again
-            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
-            eval_dataset = eval_dataset.select(range(max_eval_samples))
-
-    if training_args.do_predict:
-        if "test" not in raw_datasets:
-            raise ValueError("--do_predict requires a test dataset")
-        predict_examples = raw_datasets["test"]
-        if data_args.max_predict_samples is not None:
-            # We will select sample from whole data
-            predict_examples = predict_examples.select(range(data_args.max_predict_samples))
-        # Predict Feature Creation
-        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
-            predict_dataset = predict_examples.map(
-                prepare_validation_features,
-                batched=True,
-                num_proc=data_args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not data_args.overwrite_cache,
-                desc="Running tokenizer on prediction dataset",
-            )
-        if data_args.max_predict_samples is not None:
-            # During Feature creation dataset samples might increase, we will select required samples again
-            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
-            predict_dataset = predict_dataset.select(range(max_predict_samples))
-
-    # Data collator
-    # We have already padded to max length if the corresponding flag is True, otherwise we need to pad in the data
-    # collator.
-    data_collator = (
-        default_data_collator
-        if data_args.pad_to_max_length
-        else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
-    )
-
-    # Post-processing:
-    def post_processing_function(examples, features, predictions, stage="eval"):
-        # Post-processing: we match the start logits and end logits to answers in the original context.
-        predictions = postprocess_qa_predictions(
-            examples=examples,
-            features=features,
-            predictions=predictions,
-            version_2_with_negative=data_args.version_2_with_negative,
-            n_best_size=data_args.n_best_size,
-            max_answer_length=data_args.max_answer_length,
-            null_score_diff_threshold=data_args.null_score_diff_threshold,
-            output_dir=training_args.output_dir,
-            log_level=log_level,
-            prefix=stage,
-        )
-        # Format the result to the format the metric expects.
-        if data_args.version_2_with_negative:
-            formatted_predictions = [
-                {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
-            ]
-        else:
-            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
-
-        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
-        return EvalPrediction(predictions=formatted_predictions, label_ids=references)
-
-    metric = evaluate.load("squad_v2" if data_args.version_2_with_negative else "squad")
-
-    def compute_metrics(p: EvalPrediction):
-        return metric.compute(predictions=p.predictions, references=p.label_ids)
-
-    # Initialize our Trainer
-    trainer = QuestionAnsweringTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset if training_args.do_train else None,
-        eval_dataset=eval_dataset if training_args.do_eval else None,
-        eval_examples=eval_examples if training_args.do_eval else None,
-        tokenizer=tokenizer,
-        data_collator=data_collator,
-        post_process_function=post_processing_function,
-        compute_metrics=compute_metrics,
-    )
-
-    # Training
-    if training_args.do_train:
-        checkpoint = None
-        if training_args.resume_from_checkpoint is not None:
-            checkpoint = training_args.resume_from_checkpoint
-        elif last_checkpoint is not None:
-            checkpoint = last_checkpoint
-        train_result = trainer.train(resume_from_checkpoint=checkpoint)
-        trainer.save_model()  # Saves the tokenizer too for easy upload
-
-        metrics = train_result.metrics
-        max_train_samples = (
-            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
-        )
-        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
-
-        trainer.log_metrics("train", metrics)
-        trainer.save_metrics("train", metrics)
-        trainer.save_state()
-
-    # Evaluation
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-        metrics = trainer.evaluate()
-
-        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
-        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
-
-        trainer.log_metrics("eval", metrics)
-        trainer.save_metrics("eval", metrics)
-
-    # Prediction
-    if training_args.do_predict:
-        logger.info("*** Predict ***")
-        results = trainer.predict(predict_dataset, predict_examples)
-        metrics = results.metrics
-
-        max_predict_samples = (
-            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
-        )
-        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
-
-        trainer.log_metrics("predict", metrics)
-        trainer.save_metrics("predict", metrics)
-
-    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
-    if data_args.dataset_name is not None:
-        kwargs["dataset_tags"] = data_args.dataset_name
-        if data_args.dataset_config_name is not None:
-            kwargs["dataset_args"] = data_args.dataset_config_name
-            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
-        else:
-            kwargs["dataset"] = data_args.dataset_name
-
-    if training_args.push_to_hub:
-        trainer.push_to_hub(**kwargs)
-    else:
-        trainer.create_model_card(**kwargs)
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/quantization-qdqbert/trainer_quant_qa.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/quantization-qdqbert/trainer_quant_qa.py
deleted file mode 100644
index 9b8c53b272b11b4f5135448cf6fae6cbcf1101c2..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/quantization-qdqbert/trainer_quant_qa.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Team All rights reserved.
-# Copyright 2021 NVIDIA Corporation. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-A subclass of `Trainer` specific to Question-Answering tasks
-"""
-
-import logging
-import os
-
-import quant_trainer
-import torch
-from torch.utils.data import DataLoader
-
-from transformers import Trainer, is_torch_tpu_available
-from transformers.trainer_utils import PredictionOutput
-
-
-logger = logging.getLogger(__name__)
-
-if is_torch_tpu_available(check_device=False):
-    import torch_xla.core.xla_model as xm
-    import torch_xla.debug.metrics as met
-
-
-class QuestionAnsweringTrainer(Trainer):
-    def __init__(self, *args, eval_examples=None, post_process_function=None, quant_trainer_args=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.eval_examples = eval_examples
-        self.post_process_function = post_process_function
-        self.quant_trainer_args = quant_trainer_args
-        self.calib_num = 128  # default number of calibration samples
-
-    def get_calib_dataloader(self, calib_dataset=None):
-        """
-        Returns the calibration dataloader :class:`~torch.utils.data.DataLoader`.
-
-        Args:
-            calib_dataset (:obj:`torch.utils.data.Dataset`, `optional`)
-        """
-        if calib_dataset is None and self.calib_dataset is None:
-            raise ValueError("Trainer: calibration requires an calib_dataset.")
-        calib_dataset = calib_dataset if calib_dataset is not None else self.calib_dataset
-
-        calib_dataset = self._remove_unused_columns(calib_dataset, description="Calibration")
-
-        return DataLoader(
-            calib_dataset,
-            batch_size=self.args.eval_batch_size,
-            collate_fn=self.data_collator,
-            drop_last=self.args.dataloader_drop_last,
-            num_workers=self.args.dataloader_num_workers,
-            pin_memory=self.args.dataloader_pin_memory,
-            shuffle=True,
-        )
-
-    def calibrate(self, calib_dataset=None):
-        calib_dataset = self.train_dataset if calib_dataset is None else calib_dataset
-        calib_dataloader = self.get_calib_dataloader(calib_dataset)
-
-        model = self.model
-        quant_trainer.configure_model(model, self.quant_trainer_args, calib=True)
-        model.eval()
-        quant_trainer.enable_calibration(model)
-
-        logger.info("***** Running calibration *****")
-        logger.info(f"  Num examples = {self.calib_num}")
-        logger.info(f"  Batch size = {calib_dataloader.batch_size}")
-
-        for step, inputs in enumerate(calib_dataloader):
-            # Prediction step
-            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only=True)
-            if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
-                break
-
-        quant_trainer.finish_calibration(model, self.quant_trainer_args)
-        self.model = model
-
-    def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"):
-        eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
-        eval_dataloader = self.get_eval_dataloader(eval_dataset)
-        eval_examples = self.eval_examples if eval_examples is None else eval_examples
-
-        # Temporarily disable metric computation, we will do it in the loop here.
-        compute_metrics = self.compute_metrics
-        self.compute_metrics = None
-        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
-        try:
-            output = eval_loop(
-                eval_dataloader,
-                description="Evaluation",
-                # No point gathering the predictions if there are no metrics, otherwise we defer to
-                # self.args.prediction_loss_only
-                prediction_loss_only=True if compute_metrics is None else None,
-                ignore_keys=ignore_keys,
-            )
-        finally:
-            self.compute_metrics = compute_metrics
-
-        if self.post_process_function is not None and self.compute_metrics is not None:
-            eval_preds = self.post_process_function(eval_examples, eval_dataset, output.predictions)
-            metrics = self.compute_metrics(eval_preds)
-
-            # Prefix all keys with metric_key_prefix + '_'
-            for key in list(metrics.keys()):
-                if not key.startswith(f"{metric_key_prefix}_"):
-                    metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
-
-            self.log(metrics)
-        else:
-            metrics = {}
-
-        if self.args.tpu_metrics_debug or self.args.debug:
-            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
-            xm.master_print(met.metrics_report())
-
-        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
-        return metrics
-
-    def predict(self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"):
-        predict_dataloader = self.get_test_dataloader(predict_dataset)
-
-        # Temporarily disable metric computation, we will do it in the loop here.
-        compute_metrics = self.compute_metrics
-        self.compute_metrics = None
-        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
-        try:
-            output = eval_loop(
-                predict_dataloader,
-                description="Prediction",
-                # No point gathering the predictions if there are no metrics, otherwise we defer to
-                # self.args.prediction_loss_only
-                prediction_loss_only=True if compute_metrics is None else None,
-                ignore_keys=ignore_keys,
-            )
-        finally:
-            self.compute_metrics = compute_metrics
-
-        if self.post_process_function is None or self.compute_metrics is None:
-            return output
-
-        predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict")
-        metrics = self.compute_metrics(predictions)
-
-        # Prefix all keys with metric_key_prefix + '_'
-        for key in list(metrics.keys()):
-            if not key.startswith(f"{metric_key_prefix}_"):
-                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
-
-        return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)
-
-    def save_onnx(self, output_dir="./"):
-        eval_dataset = self.eval_dataset
-        eval_dataloader = self.get_eval_dataloader(eval_dataset)
-
-        batch = next(iter(eval_dataloader))
-
-        # saving device - to make it consistent
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        # convert to tuple
-        input_tuple = tuple(v.to(device) for k, v in batch.items())
-
-        logger.info("Converting model to be onnx compatible")
-        from pytorch_quantization.nn import TensorQuantizer
-
-        TensorQuantizer.use_fb_fake_quant = True
-
-        model = self.model.to(device)
-
-        model.eval()
-        model.float()
-
-        model_to_save = model.module if hasattr(model, "module") else model
-        quant_trainer.configure_model(model_to_save, self.quant_trainer_args)
-
-        output_model_file = os.path.join(output_dir, "model.onnx")
-        logger.info(f"exporting model to {output_model_file}")
-
-        axes = {0: "batch_size", 1: "seq_len"}
-
-        torch.onnx.export(
-            model_to_save,
-            input_tuple,
-            output_model_file,
-            export_params=True,
-            opset_version=13,
-            do_constant_folding=True,
-            input_names=["input_ids", "attention_mask", "token_type_ids"],
-            output_names=["output_start_logits", "output_end_logits"],
-            dynamic_axes={
-                "input_ids": axes,
-                "attention_mask": axes,
-                "token_type_ids": axes,
-                "output_start_logits": axes,
-                "output_end_logits": axes,
-            },
-            verbose=True,
-        )
-        logger.info("onnx export finished")
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/activations_tf.py b/spaces/chendl/compositional_test/transformers/src/transformers/activations_tf.py
deleted file mode 100644
index 4fcb1493e437bc2d1c055b0c8ccbcf3627dc8316..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/activations_tf.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-
-import tensorflow as tf
-from packaging import version
-
-
-def _gelu(x):
-    """
-    Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
-    initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-    0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see
-    https://arxiv.org/abs/1606.08415
-    """
-    x = tf.convert_to_tensor(x)
-    cdf = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0), x.dtype)))
-
-    return x * cdf
-
-
-def _gelu_new(x):
-    """
-    Gaussian Error Linear Unit. This is a smoother version of the GELU. Original paper: https://arxiv.org/abs/1606.0841
-
-    Args:
-        x: float Tensor to perform activation
-
-    Returns:
-        `x` with the GELU activation applied.
-    """
-    x = tf.convert_to_tensor(x)
-    pi = tf.cast(math.pi, x.dtype)
-    coeff = tf.cast(0.044715, x.dtype)
-    cdf = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi) * (x + coeff * tf.pow(x, 3))))
-
-    return x * cdf
-
-
-def mish(x):
-    x = tf.convert_to_tensor(x)
-
-    return x * tf.tanh(tf.math.softplus(x))
-
-
-def gelu_fast(x):
-    x = tf.convert_to_tensor(x)
-    coeff1 = tf.cast(0.044715, x.dtype)
-    coeff2 = tf.cast(0.7978845608, x.dtype)
-
-    return 0.5 * x * (1.0 + tf.tanh(x * coeff2 * (1.0 + coeff1 * x * x)))
-
-
-def quick_gelu(x):
-    x = tf.convert_to_tensor(x)
-    coeff = tf.cast(1.702, x.dtype)
-    return x * tf.math.sigmoid(coeff * x)
-
-
-def gelu_10(x):
-    """
-    Clip the range of possible GeLU outputs between [-10, 10]. This is especially useful for quantization purpose, as
-    it allows mapping 2 negatives values in the GeLU spectrum. For more information on this trick, please refer to
-    https://arxiv.org/abs/2004.09602
-
-    Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
-    initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-    0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see
-    https://arxiv.org/abs/1606.08415 :param x: :return:
-    """
-    return tf.clip_by_value(_gelu(x), -10, 10)
-
-
-def glu(x, axis=-1):
-    """
-    Gated Linear Unit. Implementation as defined in the original paper (see https://arxiv.org/abs/1612.08083), where
-    the input `x` is split in two halves across a dimension (`axis`), A and B, returning A * sigmoid(B).
-
-    Args:
-        `x`: float Tensor to perform activation
-        `axis`: dimension across which `x` be split in half
-
-    Returns:
-        `x` with the GLU activation applied (with its size halved across the dimension `axis`).
-    """
-    a, b = tf.split(x, 2, axis=axis)
-    return a * tf.math.sigmoid(b)
-
-
-if version.parse(tf.version.VERSION) >= version.parse("2.4"):
-
-    def approximate_gelu_wrap(x):
-        return tf.keras.activations.gelu(x, approximate=True)
-
-    gelu = tf.keras.activations.gelu
-    gelu_new = approximate_gelu_wrap
-else:
-    gelu = _gelu
-    gelu_new = _gelu_new
-
-
-ACT2FN = {
-    "gelu": gelu,
-    "gelu_10": gelu_10,
-    "gelu_fast": gelu_fast,
-    "gelu_new": gelu_new,
-    "glu": glu,
-    "mish": mish,
-    "quick_gelu": quick_gelu,
-    "relu": tf.keras.activations.relu,
-    "sigmoid": tf.keras.activations.sigmoid,
-    "silu": tf.keras.activations.swish,
-    "swish": tf.keras.activations.swish,
-    "tanh": tf.keras.activations.tanh,
-}
-
-
-def get_tf_activation(activation_string):
-    if activation_string in ACT2FN:
-        return ACT2FN[activation_string]
-    else:
-        raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}")
diff --git a/spaces/chinhon/whisper_transcribe/README.md b/spaces/chinhon/whisper_transcribe/README.md
deleted file mode 100644
index 3e439ec675d2d8b6cd4d58a4020da080066b40ab..0000000000000000000000000000000000000000
--- a/spaces/chinhon/whisper_transcribe/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper Transcribe
-emoji: 💻
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/ciphers/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/ciphers/__init__.py
deleted file mode 100644
index cc88fbf2c4c30c6c11564ff7ecbfdeb0ca6d5170..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/ciphers/__init__.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-from cryptography.hazmat.primitives._cipheralgorithm import (
-    BlockCipherAlgorithm,
-    CipherAlgorithm,
-)
-from cryptography.hazmat.primitives.ciphers.base import (
-    AEADCipherContext,
-    AEADDecryptionContext,
-    AEADEncryptionContext,
-    Cipher,
-    CipherContext,
-)
-
-__all__ = [
-    "Cipher",
-    "CipherAlgorithm",
-    "BlockCipherAlgorithm",
-    "CipherContext",
-    "AEADCipherContext",
-    "AEADDecryptionContext",
-    "AEADEncryptionContext",
-]
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/tz/win.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/tz/win.py
deleted file mode 100644
index cde07ba792c40903f0c334839140173b39fd8124..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/tz/win.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module provides an interface to the native time zone data on Windows,
-including :py:class:`datetime.tzinfo` implementations.
-
-Attempting to import this module on a non-Windows platform will raise an
-:py:obj:`ImportError`.
-"""
-# This code was originally contributed by Jeffrey Harris.
-import datetime
-import struct
-
-from six.moves import winreg
-from six import text_type
-
-try:
-    import ctypes
-    from ctypes import wintypes
-except ValueError:
-    # ValueError is raised on non-Windows systems for some horrible reason.
-    raise ImportError("Running tzwin on non-Windows system")
-
-from ._common import tzrangebase
-
-__all__ = ["tzwin", "tzwinlocal", "tzres"]
-
-ONEWEEK = datetime.timedelta(7)
-
-TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones"
-TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones"
-TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation"
-
-
-def _settzkeyname():
-    handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE)
-    try:
-        winreg.OpenKey(handle, TZKEYNAMENT).Close()
-        TZKEYNAME = TZKEYNAMENT
-    except WindowsError:
-        TZKEYNAME = TZKEYNAME9X
-    handle.Close()
-    return TZKEYNAME
-
-
-TZKEYNAME = _settzkeyname()
-
-
-class tzres(object):
-    """
-    Class for accessing ``tzres.dll``, which contains timezone name related
-    resources.
-
-    .. versionadded:: 2.5.0
-    """
-    p_wchar = ctypes.POINTER(wintypes.WCHAR)        # Pointer to a wide char
-
-    def __init__(self, tzres_loc='tzres.dll'):
-        # Load the user32 DLL so we can load strings from tzres
-        user32 = ctypes.WinDLL('user32')
-
-        # Specify the LoadStringW function
-        user32.LoadStringW.argtypes = (wintypes.HINSTANCE,
-                                       wintypes.UINT,
-                                       wintypes.LPWSTR,
-                                       ctypes.c_int)
-
-        self.LoadStringW = user32.LoadStringW
-        self._tzres = ctypes.WinDLL(tzres_loc)
-        self.tzres_loc = tzres_loc
-
-    def load_name(self, offset):
-        """
-        Load a timezone name from a DLL offset (integer).
-
-        >>> from dateutil.tzwin import tzres
-        >>> tzr = tzres()
-        >>> print(tzr.load_name(112))
-        'Eastern Standard Time'
-
-        :param offset:
-            A positive integer value referring to a string from the tzres dll.
-
-        .. note::
-
-            Offsets found in the registry are generally of the form
-            ``@tzres.dll,-114``. The offset in this case is 114, not -114.
-
-        """
-        resource = self.p_wchar()
-        lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR)
-        nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0)
-        return resource[:nchar]
-
-    def name_from_string(self, tzname_str):
-        """
-        Parse strings as returned from the Windows registry into the time zone
-        name as defined in the registry.
-
-        >>> from dateutil.tzwin import tzres
-        >>> tzr = tzres()
-        >>> print(tzr.name_from_string('@tzres.dll,-251'))
-        'Dateline Daylight Time'
-        >>> print(tzr.name_from_string('Eastern Standard Time'))
-        'Eastern Standard Time'
-
-        :param tzname_str:
-            A timezone name string as returned from a Windows registry key.
-
-        :return:
-            Returns the localized timezone string from tzres.dll if the string
-            is of the form `@tzres.dll,-offset`, else returns the input string.
-        """
-        if not tzname_str.startswith('@'):
-            return tzname_str
-
-        name_splt = tzname_str.split(',-')
-        try:
-            offset = int(name_splt[1])
-        except:
-            raise ValueError("Malformed timezone string.")
-
-        return self.load_name(offset)
-
-
-class tzwinbase(tzrangebase):
-    """tzinfo class based on win32's timezones available in the registry."""
-    def __init__(self):
-        raise NotImplementedError('tzwinbase is an abstract base class')
-
-    def __eq__(self, other):
-        # Compare on all relevant dimensions, including name.
-        if not isinstance(other, tzwinbase):
-            return NotImplemented
-
-        return  (self._std_offset == other._std_offset and
-                 self._dst_offset == other._dst_offset and
-                 self._stddayofweek == other._stddayofweek and
-                 self._dstdayofweek == other._dstdayofweek and
-                 self._stdweeknumber == other._stdweeknumber and
-                 self._dstweeknumber == other._dstweeknumber and
-                 self._stdhour == other._stdhour and
-                 self._dsthour == other._dsthour and
-                 self._stdminute == other._stdminute and
-                 self._dstminute == other._dstminute and
-                 self._std_abbr == other._std_abbr and
-                 self._dst_abbr == other._dst_abbr)
-
-    @staticmethod
-    def list():
-        """Return a list of all time zones known to the system."""
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            with winreg.OpenKey(handle, TZKEYNAME) as tzkey:
-                result = [winreg.EnumKey(tzkey, i)
-                          for i in range(winreg.QueryInfoKey(tzkey)[0])]
-        return result
-
-    def display(self):
-        """
-        Return the display name of the time zone.
-        """
-        return self._display
-
-    def transitions(self, year):
-        """
-        For a given year, get the DST on and off transition times, expressed
-        always on the standard time side. For zones with no transitions, this
-        function returns ``None``.
-
-        :param year:
-            The year whose transitions you would like to query.
-
-        :return:
-            Returns a :class:`tuple` of :class:`datetime.datetime` objects,
-            ``(dston, dstoff)`` for zones with an annual DST transition, or
-            ``None`` for fixed offset zones.
-        """
-
-        if not self.hasdst:
-            return None
-
-        dston = picknthweekday(year, self._dstmonth, self._dstdayofweek,
-                               self._dsthour, self._dstminute,
-                               self._dstweeknumber)
-
-        dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek,
-                                self._stdhour, self._stdminute,
-                                self._stdweeknumber)
-
-        # Ambiguous dates default to the STD side
-        dstoff -= self._dst_base_offset
-
-        return dston, dstoff
-
-    def _get_hasdst(self):
-        return self._dstmonth != 0
-
-    @property
-    def _dst_base_offset(self):
-        return self._dst_base_offset_
-
-
-class tzwin(tzwinbase):
-    """
-    Time zone object created from the zone info in the Windows registry
-
-    These are similar to :py:class:`dateutil.tz.tzrange` objects in that
-    the time zone data is provided in the format of a single offset rule
-    for either 0 or 2 time zone transitions per year.
-
-    :param: name
-        The name of a Windows time zone key, e.g. "Eastern Standard Time".
-        The full list of keys can be retrieved with :func:`tzwin.list`.
-    """
-
-    def __init__(self, name):
-        self._name = name
-
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name)
-            with winreg.OpenKey(handle, tzkeyname) as tzkey:
-                keydict = valuestodict(tzkey)
-
-        self._std_abbr = keydict["Std"]
-        self._dst_abbr = keydict["Dlt"]
-
-        self._display = keydict["Display"]
-
-        # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm
-        tup = struct.unpack("=3l16h", keydict["TZI"])
-        stdoffset = -tup[0]-tup[1]          # Bias + StandardBias * -1
-        dstoffset = stdoffset-tup[2]        # + DaylightBias * -1
-        self._std_offset = datetime.timedelta(minutes=stdoffset)
-        self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
-        # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs
-        # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx
-        (self._stdmonth,
-         self._stddayofweek,   # Sunday = 0
-         self._stdweeknumber,  # Last = 5
-         self._stdhour,
-         self._stdminute) = tup[4:9]
-
-        (self._dstmonth,
-         self._dstdayofweek,   # Sunday = 0
-         self._dstweeknumber,  # Last = 5
-         self._dsthour,
-         self._dstminute) = tup[12:17]
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = self._get_hasdst()
-
-    def __repr__(self):
-        return "tzwin(%s)" % repr(self._name)
-
-    def __reduce__(self):
-        return (self.__class__, (self._name,))
-
-
-class tzwinlocal(tzwinbase):
-    """
-    Class representing the local time zone information in the Windows registry
-
-    While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time`
-    module) to retrieve time zone information, ``tzwinlocal`` retrieves the
-    rules directly from the Windows registry and creates an object like
-    :class:`dateutil.tz.tzwin`.
-
-    Because Windows does not have an equivalent of :func:`time.tzset`, on
-    Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the
-    time zone settings *at the time that the process was started*, meaning
-    changes to the machine's time zone settings during the run of a program
-    on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`.
-    Because ``tzwinlocal`` reads the registry directly, it is unaffected by
-    this issue.
-    """
-    def __init__(self):
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey:
-                keydict = valuestodict(tzlocalkey)
-
-            self._std_abbr = keydict["StandardName"]
-            self._dst_abbr = keydict["DaylightName"]
-
-            try:
-                tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME,
-                                                          sn=self._std_abbr)
-                with winreg.OpenKey(handle, tzkeyname) as tzkey:
-                    _keydict = valuestodict(tzkey)
-                    self._display = _keydict["Display"]
-            except OSError:
-                self._display = None
-
-        stdoffset = -keydict["Bias"]-keydict["StandardBias"]
-        dstoffset = stdoffset-keydict["DaylightBias"]
-
-        self._std_offset = datetime.timedelta(minutes=stdoffset)
-        self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
-        # For reasons unclear, in this particular key, the day of week has been
-        # moved to the END of the SYSTEMTIME structure.
-        tup = struct.unpack("=8h", keydict["StandardStart"])
-
-        (self._stdmonth,
-         self._stdweeknumber,  # Last = 5
-         self._stdhour,
-         self._stdminute) = tup[1:5]
-
-        self._stddayofweek = tup[7]
-
-        tup = struct.unpack("=8h", keydict["DaylightStart"])
-
-        (self._dstmonth,
-         self._dstweeknumber,  # Last = 5
-         self._dsthour,
-         self._dstminute) = tup[1:5]
-
-        self._dstdayofweek = tup[7]
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = self._get_hasdst()
-
-    def __repr__(self):
-        return "tzwinlocal()"
-
-    def __str__(self):
-        # str will return the standard name, not the daylight name.
-        return "tzwinlocal(%s)" % repr(self._std_abbr)
-
-    def __reduce__(self):
-        return (self.__class__, ())
-
-
-def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
-    """ dayofweek == 0 means Sunday, whichweek 5 means last instance """
-    first = datetime.datetime(year, month, 1, hour, minute)
-
-    # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
-    # Because 7 % 7 = 0
-    weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
-    wd = weekdayone + ((whichweek - 1) * ONEWEEK)
-    if (wd.month != month):
-        wd -= ONEWEEK
-
-    return wd
-
-
-def valuestodict(key):
-    """Convert a registry key's values to a dictionary."""
-    dout = {}
-    size = winreg.QueryInfoKey(key)[1]
-    tz_res = None
-
-    for i in range(size):
-        key_name, value, dtype = winreg.EnumValue(key, i)
-        if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN:
-            # If it's a DWORD (32-bit integer), it's stored as unsigned - convert
-            # that to a proper signed integer
-            if value & (1 << 31):
-                value = value - (1 << 32)
-        elif dtype == winreg.REG_SZ:
-            # If it's a reference to the tzres DLL, load the actual string
-            if value.startswith('@tzres'):
-                tz_res = tz_res or tzres()
-                value = tz_res.name_from_string(value)
-
-            value = value.rstrip('\x00')    # Remove trailing nulls
-
-        dout[key_name] = value
-
-    return dout
diff --git a/spaces/cihyFjudo/fairness-paper-search/Eitaa Messenger Version 4.1.1 Discover the Amazing Possibilities of Eitaa on Android Windows and Linux Systems.md b/spaces/cihyFjudo/fairness-paper-search/Eitaa Messenger Version 4.1.1 Discover the Amazing Possibilities of Eitaa on Android Windows and Linux Systems.md
deleted file mode 100644
index f544927c95620a9c6ad8e7cd68f50a18e7f58c87..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Eitaa Messenger Version 4.1.1 Discover the Amazing Possibilities of Eitaa on Android Windows and Linux Systems.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Eitaa Messenger Version 4.1.1 Android 4.1.1 Windows and Linux Free Download</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://tinurli.com/2uwjbu">https://tinurli.com/2uwjbu</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cllatMTK/TransformerAnalyzer/README.md b/spaces/cllatMTK/TransformerAnalyzer/README.md
deleted file mode 100644
index 2639520564fe4c31c8cd3841fbd17f730818ac26..0000000000000000000000000000000000000000
--- a/spaces/cllatMTK/TransformerAnalyzer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: TransformerAnalyzer
-emoji: 👀
-colorFrom: blue
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/_soundfile.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/_soundfile.py
deleted file mode 100644
index 7d6d602f5bd18e2ebce9db73ea1196e5a94277ff..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/_soundfile.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# auto-generated file
-import _cffi_backend
-
-ffi = _cffi_backend.FFI('_soundfile',
-    _version = 0x2601,
-    _types = b'\x00\x00\x12\x0D\x00\x00\x68\x03\x00\x00\x07\x01\x00\x00\x67\x03\x00\x00\x75\x03\x00\x00\x00\x0F\x00\x00\x12\x0D\x00\x00\x6A\x03\x00\x00\x07\x01\x00\x00\x03\x11\x00\x00\x00\x0F\x00\x00\x12\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x03\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x07\x0D\x00\x00\x69\x03\x00\x00\x00\x0F\x00\x00\x07\x0D\x00\x00\x12\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x07\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x07\x0D\x00\x00\x00\x0F\x00\x00\x02\x0D\x00\x00\x67\x03\x00\x00\x00\x0F\x00\x00\x02\x0D\x00\x00\x12\x11\x00\x00\x00\x0F\x00\x00\x02\x0D\x00\x00\x12\x11\x00\x00\x6A\x03\x00\x00\x1C\x01\x00\x00\x00\x0F\x00\x00\x02\x0D\x00\x00\x12\x11\x00\x00\x07\x01\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x02\x0D\x00\x00\x12\x11\x00\x00\x07\x01\x00\x00\x04\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x12\x11\x00\x00\x6B\x03\x00\x00\x17\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x12\x11\x00\x00\x6F\x03\x00\x00\x17\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x12\x11\x00\x00\x02\x03\x00\x00\x17\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x12\x11\x00\x00\x17\x01\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x12\x11\x00\x00\x74\x03\x00\x00\x17\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x12\x11\x00\x00\x04\x11\x00\x00\x17\x01\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x17\x01\x00\x00\x07\x01\x00\x00\x04\x11\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x04\x11\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x04\x11\x00\x00\x17\x01\x00\x00\x04\x11\x00\x00\x00\x0F\x00\x00\x36\x0D\x00\x00\x75\x03\x00\x00\x17\x01\x00\x00\x04\x11\x00\x00\x00\x0F\x00\x00\x75\x0D\x00\x00\x12\x11\x00\x00\x00\x0F\x00\x00\x00\x09\x00\x00\x01\x09\x00\x00\x02\x09\x00\x00\x03\x09\x00\x00\x02\x01\x00\x00\x0E\x01\x00\x00\x00\x0B\x00\x00\x01\x0B\x00\x00\x02\x0B\x00\x00\x0D\x01\x00\x00\x51\x03\x00\x00\x56\x03\x00\x00\x59\x03\x00\x00\x5E\x03\x00\x00\x05\x01\x00\x00\x00\x01',
-    _globals = (b'\xFF\xFF\xFF\x0BSFC_FILE_TRUNCATE',4224,b'\xFF\xFF\xFF\x0BSFC_GET_FORMAT_INFO',4136,b'\xFF\xFF\xFF\x0BSFC_GET_FORMAT_MAJOR',4145,b'\xFF\xFF\xFF\x0BSFC_GET_FORMAT_MAJOR_COUNT',4144,b'\xFF\xFF\xFF\x0BSFC_GET_FORMAT_SUBTYPE',4147,b'\xFF\xFF\xFF\x0BSFC_GET_FORMAT_SUBTYPE_COUNT',4146,b'\xFF\xFF\xFF\x0BSFC_GET_LIB_VERSION',4096,b'\xFF\xFF\xFF\x0BSFC_GET_LOG_INFO',4097,b'\xFF\xFF\xFF\x0BSFC_SET_CLIPPING',4288,b'\xFF\xFF\xFF\x0BSFC_SET_SCALE_FLOAT_INT_READ',4116,b'\xFF\xFF\xFF\x0BSFC_SET_SCALE_INT_FLOAT_WRITE',4117,b'\xFF\xFF\xFF\x0BSFM_RDWR',48,b'\xFF\xFF\xFF\x0BSFM_READ',16,b'\xFF\xFF\xFF\x0BSFM_WRITE',32,b'\xFF\xFF\xFF\x0BSF_FALSE',0,b'\xFF\xFF\xFF\x0BSF_FORMAT_ENDMASK',805306368,b'\xFF\xFF\xFF\x0BSF_FORMAT_SUBMASK',65535,b'\xFF\xFF\xFF\x0BSF_FORMAT_TYPEMASK',268369920,b'\xFF\xFF\xFF\x0BSF_TRUE',1,b'\x00\x00\x20\x23sf_close',0,b'\x00\x00\x2D\x23sf_command',0,b'\x00\x00\x20\x23sf_error',0,b'\x00\x00\x18\x23sf_error_number',0,b'\x00\x00\x23\x23sf_error_str',0,b'\x00\x00\x1D\x23sf_format_check',0,b'\x00\x00\x14\x23sf_get_string',0,b'\x00\x00\x06\x23sf_open',0,b'\x00\x00\x0B\x23sf_open_fd',0,b'\x00\x00\x00\x23sf_open_virtual',0,b'\x00\x00\x20\x23sf_perror',0,b'\x00\x00\x33\x23sf_read_double',0,b'\x00\x00\x38\x23sf_read_float',0,b'\x00\x00\x3D\x23sf_read_int',0,b'\x00\x00\x4C\x23sf_read_raw',0,b'\x00\x00\x47\x23sf_read_short',0,b'\x00\x00\x4C\x23sf_readf_double',0,b'\x00\x00\x4C\x23sf_readf_float',0,b'\x00\x00\x4C\x23sf_readf_int',0,b'\x00\x00\x4C\x23sf_readf_short',0,b'\x00\x00\x42\x23sf_seek',0,b'\x00\x00\x28\x23sf_set_string',0,b'\x00\x00\x11\x23sf_strerror',0,b'\x00\x00\x1B\x23sf_version_string',0,b'\x00\x00\x33\x23sf_write_double',0,b'\x00\x00\x38\x23sf_write_float',0,b'\x00\x00\x3D\x23sf_write_int',0,b'\x00\x00\x4C\x23sf_write_raw',0,b'\x00\x00\x47\x23sf_write_short',0,b'\x00\x00\x63\x23sf_write_sync',0,b'\x00\x00\x4C\x23sf_writef_double',0,b'\x00\x00\x4C\x23sf_writef_float',0,b'\x00\x00\x4C\x23sf_writef_int',0,b'\x00\x00\x4C\x23sf_writef_short',0),
-    _struct_unions = ((b'\x00\x00\x00\x66\x00\x00\x00\x02SF_FORMAT_INFO',b'\x00\x00\x02\x11format',b'\x00\x00\x07\x11name',b'\x00\x00\x07\x11extension'),(b'\x00\x00\x00\x67\x00\x00\x00\x02SF_INFO',b'\x00\x00\x36\x11frames',b'\x00\x00\x02\x11samplerate',b'\x00\x00\x02\x11channels',b'\x00\x00\x02\x11format',b'\x00\x00\x02\x11sections',b'\x00\x00\x02\x11seekable'),(b'\x00\x00\x00\x68\x00\x00\x00\x02SF_VIRTUAL_IO',b'\x00\x00\x71\x11get_filelen',b'\x00\x00\x70\x11seek',b'\x00\x00\x72\x11read',b'\x00\x00\x73\x11write',b'\x00\x00\x71\x11tell'),(b'\x00\x00\x00\x69\x00\x00\x00\x10SNDFILE_tag',)),
-    _enums = (b'\x00\x00\x00\x6C\x00\x00\x00\x16$1\x00SF_FORMAT_SUBMASK,SF_FORMAT_TYPEMASK,SF_FORMAT_ENDMASK',b'\x00\x00\x00\x6D\x00\x00\x00\x16$2\x00SFC_GET_LIB_VERSION,SFC_GET_LOG_INFO,SFC_GET_FORMAT_INFO,SFC_GET_FORMAT_MAJOR_COUNT,SFC_GET_FORMAT_MAJOR,SFC_GET_FORMAT_SUBTYPE_COUNT,SFC_GET_FORMAT_SUBTYPE,SFC_FILE_TRUNCATE,SFC_SET_CLIPPING,SFC_SET_SCALE_FLOAT_INT_READ,SFC_SET_SCALE_INT_FLOAT_WRITE',b'\x00\x00\x00\x6E\x00\x00\x00\x16$3\x00SF_FALSE,SF_TRUE,SFM_READ,SFM_WRITE,SFM_RDWR'),
-    _typenames = (b'\x00\x00\x00\x66SF_FORMAT_INFO',b'\x00\x00\x00\x67SF_INFO',b'\x00\x00\x00\x68SF_VIRTUAL_IO',b'\x00\x00\x00\x69SNDFILE',b'\x00\x00\x00\x36sf_count_t',b'\x00\x00\x00\x71sf_vio_get_filelen',b'\x00\x00\x00\x72sf_vio_read',b'\x00\x00\x00\x70sf_vio_seek',b'\x00\x00\x00\x71sf_vio_tell',b'\x00\x00\x00\x73sf_vio_write'),
-)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/audioread/gstdec.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/audioread/gstdec.py
deleted file mode 100644
index 8073f91b665fcf11e00c39cf29fbb7966dc62ad2..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/audioread/gstdec.py
+++ /dev/null
@@ -1,429 +0,0 @@
-# This file is part of audioread.
-# Copyright 2011, Adrian Sampson.
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files (the
-# "Software"), to deal in the Software without restriction, including
-# without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and to
-# permit persons to whom the Software is furnished to do so, subject to
-# the following conditions:
-#
-# The above copyright notice and this permission notice shall be
-# included in all copies or substantial portions of the Software.
-
-"""Use Gstreamer to decode audio files.
-
-To read an audio file, pass it to the constructor for GstAudioFile()
-and then iterate over the contents:
-
-    >>> f = GstAudioFile('something.mp3')
-    >>> try:
-    >>>     for block in f:
-    >>>         ...
-    >>> finally:
-    >>>     f.close()
-
-Note that there are a few complications caused by Gstreamer's
-asynchronous architecture. This module spawns its own Gobject main-
-loop thread; I'm not sure how that will interact with other main
-loops if your program has them. Also, in order to stop the thread
-and terminate your program normally, you need to call the close()
-method on every GstAudioFile you create. Conveniently, the file can be
-used as a context manager to make this simpler:
-
-    >>> with GstAudioFile('something.mp3') as f:
-    >>>     for block in f:
-    >>>         ...
-
-Iterating a GstAudioFile yields strings containing short integer PCM
-data. You can also read the sample rate and channel count from the
-file:
-
-    >>> with GstAudioFile('something.mp3') as f:
-    >>>     print f.samplerate
-    >>>     print f.channels
-    >>>     print f.duration
-"""
-
-import gi
-gi.require_version('Gst', '1.0')
-from gi.repository import GLib, Gst
-
-import sys
-import threading
-import os
-import queue
-from urllib.parse import quote
-
-from .exceptions import DecodeError
-from .base import AudioFile
-
-QUEUE_SIZE = 10
-BUFFER_SIZE = 10
-SENTINEL = '__GSTDEC_SENTINEL__'
-
-
-# Exceptions.
-
-class GStreamerError(DecodeError):
-    pass
-
-
-class UnknownTypeError(GStreamerError):
-    """Raised when Gstreamer can't decode the given file type."""
-    def __init__(self, streaminfo):
-        super().__init__(
-            "can't decode stream: " + streaminfo
-        )
-        self.streaminfo = streaminfo
-
-
-class FileReadError(GStreamerError):
-    """Raised when the file can't be read at all."""
-    pass
-
-
-class NoStreamError(GStreamerError):
-    """Raised when the file was read successfully but no audio streams
-    were found.
-    """
-    def __init__(self):
-        super().__init__('no audio streams found')
-
-
-class MetadataMissingError(GStreamerError):
-    """Raised when GStreamer fails to report stream metadata (duration,
-    channels, or sample rate).
-    """
-    pass
-
-
-class IncompleteGStreamerError(GStreamerError):
-    """Raised when necessary components of GStreamer (namely, the
-    principal plugin packages) are missing.
-    """
-    def __init__(self):
-        super().__init__(
-            'missing GStreamer base plugins'
-        )
-
-
-# Managing the Gobject main loop thread.
-
-_shared_loop_thread = None
-_loop_thread_lock = threading.RLock()
-
-Gst.init(None)
-
-def get_loop_thread():
-    """Get the shared main-loop thread.
-    """
-    global _shared_loop_thread
-    with _loop_thread_lock:
-        if not _shared_loop_thread:
-            # Start a new thread.
-            _shared_loop_thread = MainLoopThread()
-            _shared_loop_thread.start()
-        return _shared_loop_thread
-
-
-class MainLoopThread(threading.Thread):
-    """A daemon thread encapsulating a Gobject main loop.
-    """
-    def __init__(self):
-        super().__init__()
-        self.loop = GLib.MainLoop.new(None, False)
-        self.daemon = True
-
-    def run(self):
-        self.loop.run()
-
-
-# The decoder.
-
-class GstAudioFile(AudioFile):
-    """Reads raw audio data from any audio file that Gstreamer
-    knows how to decode.
-
-        >>> with GstAudioFile('something.mp3') as f:
-        >>>     print f.samplerate
-        >>>     print f.channels
-        >>>     print f.duration
-        >>>     for block in f:
-        >>>         do_something(block)
-
-    Iterating the object yields blocks of 16-bit PCM data. Three
-    pieces of stream information are also available: samplerate (in Hz),
-    number of channels, and duration (in seconds).
-
-    It's very important that the client call close() when it's done
-    with the object. Otherwise, the program is likely to hang on exit.
-    Alternatively, of course, one can just use the file as a context
-    manager, as shown above.
-    """
-    def __init__(self, path):
-        self.running = False
-        self.finished = False
-
-        # Set up the Gstreamer pipeline.
-        self.pipeline = Gst.Pipeline()
-
-        self.dec = Gst.ElementFactory.make("uridecodebin", None)
-        self.conv = Gst.ElementFactory.make("audioconvert", None)
-        self.sink = Gst.ElementFactory.make("appsink", None)
-
-        if self.dec is None or self.conv is None or self.sink is None:
-            # uridecodebin, audioconvert, or appsink is missing. We need
-            # gst-plugins-base.
-            raise IncompleteGStreamerError()
-
-        # Register for bus signals.
-        bus = self.pipeline.get_bus()
-        bus.add_signal_watch()
-        bus.connect("message::eos", self._message)
-        bus.connect("message::error", self._message)
-
-        # Configure the input.
-        uri = 'file://' + quote(os.path.abspath(path))
-        self.dec.set_property("uri", uri)
-        # The callback to connect the input.
-        self.dec.connect("pad-added", self._pad_added)
-        self.dec.connect("no-more-pads", self._no_more_pads)
-        # And a callback if decoding fails.
-        self.dec.connect("unknown-type", self._unkown_type)
-
-        # Configure the output.
-        # We want short integer data.
-        self.sink.set_property(
-            'caps',
-            Gst.Caps.from_string('audio/x-raw, format=(string)S16LE'),
-        )
-        # TODO set endianness?
-        # Set up the characteristics of the output. We don't want to
-        # drop any data (nothing is real-time here); we should bound
-        # the memory usage of the internal queue; and, most
-        # importantly, setting "sync" to False disables the default
-        # behavior in which you consume buffers in real time. This way,
-        # we get data as soon as it's decoded.
-        self.sink.set_property('drop', False)
-        self.sink.set_property('max-buffers', BUFFER_SIZE)
-        self.sink.set_property('sync', False)
-        # The callback to receive decoded data.
-        self.sink.set_property('emit-signals', True)
-        self.sink.connect("new-sample", self._new_sample)
-
-        # We'll need to know when the stream becomes ready and we get
-        # its attributes. This semaphore will become available when the
-        # caps are received. That way, when __init__() returns, the file
-        # (and its attributes) will be ready for reading.
-        self.ready_sem = threading.Semaphore(0)
-        self.caps_handler = self.sink.get_static_pad("sink").connect(
-            "notify::caps", self._notify_caps
-        )
-
-        # Link up everything but the decoder (which must be linked only
-        # when it becomes ready).
-        self.pipeline.add(self.dec)
-        self.pipeline.add(self.conv)
-        self.pipeline.add(self.sink)
-
-        self.conv.link(self.sink)
-
-        # Set up the queue for data and run the main thread.
-        self.queue = queue.Queue(QUEUE_SIZE)
-        self.thread = get_loop_thread()
-
-        # This wil get filled with an exception if opening fails.
-        self.read_exc = None
-
-        # Return as soon as the stream is ready!
-        self.running = True
-        self.got_caps = False
-        self.pipeline.set_state(Gst.State.PLAYING)
-        self.ready_sem.acquire()
-        if self.read_exc:
-            # An error occurred before the stream became ready.
-            self.close(True)
-            raise self.read_exc
-
-    # Gstreamer callbacks.
-
-    def _notify_caps(self, pad, args):
-        """The callback for the sinkpad's "notify::caps" signal.
-        """
-        # The sink has started to receive data, so the stream is ready.
-        # This also is our opportunity to read information about the
-        # stream.
-        self.got_caps = True
-        info = pad.get_current_caps().get_structure(0)
-
-        # Stream attributes.
-        self.channels = info.get_int('channels')[1]
-        self.samplerate = info.get_int('rate')[1]
-
-        # Query duration.
-        success, length = pad.get_peer().query_duration(Gst.Format.TIME)
-        if success:
-            self.duration = length / 1000000000
-        else:
-            self.read_exc = MetadataMissingError('duration not available')
-
-        # Allow constructor to complete.
-        self.ready_sem.release()
-
-    _got_a_pad = False
-
-    def _pad_added(self, element, pad):
-        """The callback for GstElement's "pad-added" signal.
-        """
-        # Decoded data is ready. Connect up the decoder, finally.
-        name = pad.query_caps(None).to_string()
-        if name.startswith('audio/x-raw'):
-            nextpad = self.conv.get_static_pad('sink')
-            if not nextpad.is_linked():
-                self._got_a_pad = True
-                pad.link(nextpad)
-
-    def _no_more_pads(self, element):
-        """The callback for GstElement's "no-more-pads" signal.
-        """
-        # Sent when the pads are done adding (i.e., there are no more
-        # streams in the file). If we haven't gotten at least one
-        # decodable stream, raise an exception.
-        if not self._got_a_pad:
-            self.read_exc = NoStreamError()
-            self.ready_sem.release()  # No effect if we've already started.
-
-    def _new_sample(self, sink):
-        """The callback for appsink's "new-sample" signal.
-        """
-        if self.running:
-            # New data is available from the pipeline! Dump it into our
-            # queue (or possibly block if we're full).
-            buf = sink.emit('pull-sample').get_buffer()
-
-            # We can't use Gst.Buffer.extract() to read the data as it crashes
-            # when called through PyGObject. We also can't use
-            # Gst.Buffer.extract_dup() because we have no way in Python to free
-            # the memory that it returns. Instead we get access to the actual
-            # data via Gst.Memory.map().
-            mem = buf.get_all_memory()
-            success, info = mem.map(Gst.MapFlags.READ)
-            if success:
-                if isinstance(info.data, memoryview):
-                    # We need to copy the data as the memoryview is released
-                    # when we call mem.unmap()
-                    data = bytes(info.data)
-                else:
-                    # GStreamer Python bindings <= 1.16 return a copy of the
-                    # data as bytes()
-                    data = info.data
-                mem.unmap(info)
-                self.queue.put(data)
-            else:
-                raise GStreamerError("Unable to map buffer memory while reading the file.")
-        return Gst.FlowReturn.OK
-
-    def _unkown_type(self, uridecodebin, decodebin, caps):
-        """The callback for decodebin's "unknown-type" signal.
-        """
-        # This is called *before* the stream becomes ready when the
-        # file can't be read.
-        streaminfo = caps.to_string()
-        if not streaminfo.startswith('audio/'):
-            # Ignore non-audio (e.g., video) decode errors.
-            return
-        self.read_exc = UnknownTypeError(streaminfo)
-        self.ready_sem.release()
-
-    def _message(self, bus, message):
-        """The callback for GstBus's "message" signal (for two kinds of
-        messages).
-        """
-        if not self.finished:
-            if message.type == Gst.MessageType.EOS:
-                # The file is done. Tell the consumer thread.
-                self.queue.put(SENTINEL)
-                if not self.got_caps:
-                    # If the stream ends before _notify_caps was called, this
-                    # is an invalid file.
-                    self.read_exc = NoStreamError()
-                    self.ready_sem.release()
-
-            elif message.type == Gst.MessageType.ERROR:
-                gerror, debug = message.parse_error()
-                if 'not-linked' in debug:
-                    self.read_exc = NoStreamError()
-                elif 'No such file' in debug:
-                    self.read_exc = IOError('resource not found')
-                else:
-                    self.read_exc = FileReadError(debug)
-                self.ready_sem.release()
-
-    # Iteration.
-
-    def __next__(self):
-        # Wait for data from the Gstreamer callbacks.
-        val = self.queue.get()
-        if val == SENTINEL:
-            # End of stream.
-            raise StopIteration
-        return val
-
-    def __iter__(self):
-        return self
-
-    # Cleanup.
-    def close(self, force=False):
-        """Close the file and clean up associated resources.
-
-        Calling `close()` a second time has no effect.
-        """
-        if self.running or force:
-            self.running = False
-            self.finished = True
-
-            # Unregister for signals, which we registered for above with
-            # `add_signal_watch`. (Without this, GStreamer leaks file
-            # descriptors.)
-            self.pipeline.get_bus().remove_signal_watch()
-
-            # Stop reading the file.
-            self.dec.set_property("uri", None)
-            # Block spurious signals.
-            self.sink.get_static_pad("sink").disconnect(self.caps_handler)
-
-            # Make space in the output queue to let the decoder thread
-            # finish. (Otherwise, the thread blocks on its enqueue and
-            # the interpreter hangs.)
-            try:
-                self.queue.get_nowait()
-            except queue.Empty:
-                pass
-
-            # Halt the pipeline (closing file).
-            self.pipeline.set_state(Gst.State.NULL)
-
-    def __del__(self):
-        self.close()
-
-    # Context manager.
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
-        return False
-
-
-# Smoke test.
-if __name__ == '__main__':
-    for path in sys.argv[1:]:
-        path = os.path.abspath(os.path.expanduser(path))
-        with GstAudioFile(path) as f:
-            print(f.channels)
-            print(f.samplerate)
-            print(f.duration)
-            for s in f:
-                print(len(s), ord(s[0]))
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/hpeldsp_alpha.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/hpeldsp_alpha.c
deleted file mode 100644
index 8d54807d8fe774a4683b596681f97522606740a0..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alpha/hpeldsp_alpha.c
+++ /dev/null
@@ -1,213 +0,0 @@
-/*
- * Alpha optimized DSP utils
- * Copyright (c) 2002 Falk Hueffner <falk@debian.org>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "libavcodec/hpeldsp.h"
-#include "hpeldsp_alpha.h"
-#include "asm.h"
-
-static inline uint64_t avg2_no_rnd(uint64_t a, uint64_t b)
-{
-    return (a & b) + (((a ^ b) & BYTE_VEC(0xfe)) >> 1);
-}
-
-static inline uint64_t avg2(uint64_t a, uint64_t b)
-{
-    return (a | b) - (((a ^ b) & BYTE_VEC(0xfe)) >> 1);
-}
-
-#if 0
-/* The XY2 routines basically utilize this scheme, but reuse parts in
-   each iteration.  */
-static inline uint64_t avg4(uint64_t l1, uint64_t l2, uint64_t l3, uint64_t l4)
-{
-    uint64_t r1 = ((l1 & ~BYTE_VEC(0x03)) >> 2)
-                + ((l2 & ~BYTE_VEC(0x03)) >> 2)
-                + ((l3 & ~BYTE_VEC(0x03)) >> 2)
-                + ((l4 & ~BYTE_VEC(0x03)) >> 2);
-    uint64_t r2 = ((  (l1 & BYTE_VEC(0x03))
-                    + (l2 & BYTE_VEC(0x03))
-                    + (l3 & BYTE_VEC(0x03))
-                    + (l4 & BYTE_VEC(0x03))
-                    + BYTE_VEC(0x02)) >> 2) & BYTE_VEC(0x03);
-    return r1 + r2;
-}
-#endif
-
-#define OP(LOAD, STORE)                         \
-    do {                                        \
-        STORE(LOAD(pixels), block);             \
-        pixels += line_size;                    \
-        block += line_size;                     \
-    } while (--h)
-
-#define OP_X2(LOAD, STORE)                                      \
-    do {                                                        \
-        uint64_t pix1, pix2;                                    \
-                                                                \
-        pix1 = LOAD(pixels);                                    \
-        pix2 = pix1 >> 8 | ((uint64_t) pixels[8] << 56);        \
-        STORE(AVG2(pix1, pix2), block);                         \
-        pixels += line_size;                                    \
-        block += line_size;                                     \
-    } while (--h)
-
-#define OP_Y2(LOAD, STORE)                      \
-    do {                                        \
-        uint64_t pix = LOAD(pixels);            \
-        do {                                    \
-            uint64_t next_pix;                  \
-                                                \
-            pixels += line_size;                \
-            next_pix = LOAD(pixels);            \
-            STORE(AVG2(pix, next_pix), block);  \
-            block += line_size;                 \
-            pix = next_pix;                     \
-        } while (--h);                          \
-    } while (0)
-
-#define OP_XY2(LOAD, STORE)                                                 \
-    do {                                                                    \
-        uint64_t pix1 = LOAD(pixels);                                       \
-        uint64_t pix2 = pix1 >> 8 | ((uint64_t) pixels[8] << 56);           \
-        uint64_t pix_l = (pix1 & BYTE_VEC(0x03))                            \
-                       + (pix2 & BYTE_VEC(0x03));                           \
-        uint64_t pix_h = ((pix1 & ~BYTE_VEC(0x03)) >> 2)                    \
-                       + ((pix2 & ~BYTE_VEC(0x03)) >> 2);                   \
-                                                                            \
-        do {                                                                \
-            uint64_t npix1, npix2;                                          \
-            uint64_t npix_l, npix_h;                                        \
-            uint64_t avg;                                                   \
-                                                                            \
-            pixels += line_size;                                            \
-            npix1 = LOAD(pixels);                                           \
-            npix2 = npix1 >> 8 | ((uint64_t) pixels[8] << 56);              \
-            npix_l = (npix1 & BYTE_VEC(0x03))                               \
-                   + (npix2 & BYTE_VEC(0x03));                              \
-            npix_h = ((npix1 & ~BYTE_VEC(0x03)) >> 2)                       \
-                   + ((npix2 & ~BYTE_VEC(0x03)) >> 2);                      \
-            avg = (((pix_l + npix_l + AVG4_ROUNDER) >> 2) & BYTE_VEC(0x03)) \
-                + pix_h + npix_h;                                           \
-            STORE(avg, block);                                              \
-                                                                            \
-            block += line_size;                                             \
-            pix_l = npix_l;                                                 \
-            pix_h = npix_h;                                                 \
-        } while (--h);                                                      \
-    } while (0)
-
-#define MAKE_OP(OPNAME, SUFF, OPKIND, STORE)                                \
-static void OPNAME ## _pixels ## SUFF ## _axp                               \
-        (uint8_t *restrict block, const uint8_t *restrict pixels,           \
-         ptrdiff_t line_size, int h)                                        \
-{                                                                           \
-    if ((size_t) pixels & 0x7) {                                            \
-        OPKIND(uldq, STORE);                                                \
-    } else {                                                                \
-        OPKIND(ldq, STORE);                                                 \
-    }                                                                       \
-}                                                                           \
-                                                                            \
-static void OPNAME ## _pixels16 ## SUFF ## _axp                             \
-        (uint8_t *restrict block, const uint8_t *restrict pixels,           \
-         ptrdiff_t line_size, int h)                                        \
-{                                                                           \
-    OPNAME ## _pixels ## SUFF ## _axp(block,     pixels,     line_size, h); \
-    OPNAME ## _pixels ## SUFF ## _axp(block + 8, pixels + 8, line_size, h); \
-}
-
-#define PIXOP(OPNAME, STORE)                    \
-    MAKE_OP(OPNAME, ,     OP,     STORE)        \
-    MAKE_OP(OPNAME, _x2,  OP_X2,  STORE)        \
-    MAKE_OP(OPNAME, _y2,  OP_Y2,  STORE)        \
-    MAKE_OP(OPNAME, _xy2, OP_XY2, STORE)
-
-/* Rounding primitives.  */
-#define AVG2 avg2
-#define AVG4 avg4
-#define AVG4_ROUNDER BYTE_VEC(0x02)
-#define STORE(l, b) stq(l, b)
-PIXOP(put, STORE);
-
-#undef STORE
-#define STORE(l, b) stq(AVG2(l, ldq(b)), b);
-PIXOP(avg, STORE);
-
-/* Not rounding primitives.  */
-#undef AVG2
-#undef AVG4
-#undef AVG4_ROUNDER
-#undef STORE
-#define AVG2 avg2_no_rnd
-#define AVG4 avg4_no_rnd
-#define AVG4_ROUNDER BYTE_VEC(0x01)
-#define STORE(l, b) stq(l, b)
-PIXOP(put_no_rnd, STORE);
-
-#undef STORE
-#define STORE(l, b) stq(AVG2(l, ldq(b)), b);
-PIXOP(avg_no_rnd, STORE);
-
-static void put_pixels16_axp_asm(uint8_t *block, const uint8_t *pixels,
-                                 ptrdiff_t line_size, int h)
-{
-    put_pixels_axp_asm(block,     pixels,     line_size, h);
-    put_pixels_axp_asm(block + 8, pixels + 8, line_size, h);
-}
-
-av_cold void ff_hpeldsp_init_alpha(HpelDSPContext *c, int flags)
-{
-    c->put_pixels_tab[0][0] = put_pixels16_axp_asm;
-    c->put_pixels_tab[0][1] = put_pixels16_x2_axp;
-    c->put_pixels_tab[0][2] = put_pixels16_y2_axp;
-    c->put_pixels_tab[0][3] = put_pixels16_xy2_axp;
-
-    c->put_no_rnd_pixels_tab[0][0] = put_pixels16_axp_asm;
-    c->put_no_rnd_pixels_tab[0][1] = put_no_rnd_pixels16_x2_axp;
-    c->put_no_rnd_pixels_tab[0][2] = put_no_rnd_pixels16_y2_axp;
-    c->put_no_rnd_pixels_tab[0][3] = put_no_rnd_pixels16_xy2_axp;
-
-    c->avg_pixels_tab[0][0] = avg_pixels16_axp;
-    c->avg_pixels_tab[0][1] = avg_pixels16_x2_axp;
-    c->avg_pixels_tab[0][2] = avg_pixels16_y2_axp;
-    c->avg_pixels_tab[0][3] = avg_pixels16_xy2_axp;
-
-    c->avg_no_rnd_pixels_tab[0] = avg_no_rnd_pixels16_axp;
-    c->avg_no_rnd_pixels_tab[1] = avg_no_rnd_pixels16_x2_axp;
-    c->avg_no_rnd_pixels_tab[2] = avg_no_rnd_pixels16_y2_axp;
-    c->avg_no_rnd_pixels_tab[3] = avg_no_rnd_pixels16_xy2_axp;
-
-    c->put_pixels_tab[1][0] = put_pixels_axp_asm;
-    c->put_pixels_tab[1][1] = put_pixels_x2_axp;
-    c->put_pixels_tab[1][2] = put_pixels_y2_axp;
-    c->put_pixels_tab[1][3] = put_pixels_xy2_axp;
-
-    c->put_no_rnd_pixels_tab[1][0] = put_pixels_axp_asm;
-    c->put_no_rnd_pixels_tab[1][1] = put_no_rnd_pixels_x2_axp;
-    c->put_no_rnd_pixels_tab[1][2] = put_no_rnd_pixels_y2_axp;
-    c->put_no_rnd_pixels_tab[1][3] = put_no_rnd_pixels_xy2_axp;
-
-    c->avg_pixels_tab[1][0] = avg_pixels_axp;
-    c->avg_pixels_tab[1][1] = avg_pixels_x2_axp;
-    c->avg_pixels_tab[1][2] = avg_pixels_y2_axp;
-    c->avg_pixels_tab[1][3] = avg_pixels_xy2_axp;
-}
diff --git a/spaces/competitions/news-unmasked/README.md b/spaces/competitions/news-unmasked/README.md
deleted file mode 100644
index 312aba1f2d166f8799c1fef287074cd82d9a6022..0000000000000000000000000000000000000000
--- a/spaces/competitions/news-unmasked/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: News Unmasked
-emoji: 🏆
-colorFrom: blue
-colorTo: gray
-sdk: docker
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Uplay for PC and unlock free games trials and more.md b/spaces/congsaPfin/Manga-OCR/logs/Download Uplay for PC and unlock free games trials and more.md
deleted file mode 100644
index c5be967886c64f8b03fa8b7e2d663b0a2498c6fc..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Uplay for PC and unlock free games trials and more.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<br />
-<h1>How to Download Uplay for PC</h1>
-<p>If you are a fan of Ubisoft games, you might want to download Uplay for PC. Uplay is a free service that allows you to access your games, connect with other players, earn rewards, and enjoy exclusive content. In this article, we will show you how to download, install, and use Uplay on your PC.</p>
- <h2>What is Uplay?</h2>
-<p>Uplay is a platform that provides players with a range of services for Ubisoft games across all platforms. It was launched in 2009 as a way to enhance the gaming experience and create a community around Ubisoft titles. Uplay is now part of Ubisoft Connect, which is the ecosystem of players services for Ubisoft games.</p>
-<h2>download uplay for pc</h2><br /><p><b><b>Download File</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://urlca.com/2uO6TS">https://urlca.com/2uO6TS</a></b></p><br /><br />
- <h3>Benefits of Uplay</h3>
-<p>By downloading Uplay for PC, you can enjoy several benefits such as:</p>
-<ul>
-<li>Playing your Ubisoft games on PC and keeping your progression across devices.</li>
-<li>Joining a global network of Ubisoft players and finding friends on all platforms.</li>
-<li>Getting regular updates and news on your favorite games.</li>
-<li>Participating in our loyalty program and unlocking free rewards.</li>
-<li>Redeeming 100 Units to get 20% off your next purchase in the Ubisoft Store.</li>
-</ul>
- <h3>Features of Uplay</h3>
-<p>Uplay also offers many features that enhance your gaming experience such as:</p>
-<ul>
-<li>A desktop app that allows you to manage and start your games, discover new content, and access Ubisoft+ subscription service.</li>
-<li>A mobile app that lets you check your stats, progression, achievements, news feed, and chat with your friends.</li>
-<li>A web browser extension that enables you to get more information about Ubisoft games and products.</li>
-<li>A cloud save feature that automatically backs up your game saves online.</li>
-<li>A smart intel feature that gives you personalized tips and recommendations based on your gameplay.</li>
-</ul>
- <h2>How to Install Uplay on PC</h2>
-<p>Installing Uplay on PC is easy and fast. Just follow these steps:</p>
- <h3>Step 1: Visit the you can go to <a href="">https://support.ubisoft.com</a> and submit a ticket or chat with an agent. You can also call the toll-free number 1-877-887-2648.</li>
-<li><b>Q: How do I link my Uplay account to other platforms?</b></li>
-<li>A: To link your Uplay account to other platforms, you can go to Settings > Account Information > Account Linking and select the platform you want to link. You will be redirected to a page where you can log in with your platform credentials and confirm the linking.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Ejen Ali Agents Arena - The Ultimate 3v3 Battle Game.md b/spaces/congsaPfin/Manga-OCR/logs/Ejen Ali Agents Arena - The Ultimate 3v3 Battle Game.md
deleted file mode 100644
index 0b1d0da663d12b1488f8ed954e7b554ec7ed8d51..0000000000000000000000000000000000000000
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-<h1>Ejen Ali: Agents' Arena Download Guide</h1>
-<p>If you are a fan of the Malaysian animated series Ejen Ali, you might have heard of the game Ejen Ali: Agents' Arena. It is a high octane 3v3 battle arena game that lets you challenge and cooperate with other players in a capture the flag mode. You can choose from a multitude of agents, each with their own unique attributes and skill sets, and collect and unlock new upgrades to strengthen your abilities. The game also features various maps, characters, and game modes to keep you entertained.</p>
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-<p>Ejen Ali: Agents' Arena is a popular game among fans of the series and casual gamers alike. It has received positive reviews from players and critics, who praised its graphics, gameplay, and originality. The game has also won several awards, such as the Best Mobile Game at the Malaysia Digital Economy Corporation (MDEC) Level Up KL Awards in 2020.</p>
-<p>If you are interested in playing Ejen Ali: Agents' Arena, you might be wondering how to download it on your device. In this article, we will show you how to download Ejen Ali: Agents' Arena on different platforms, such as Android, iOS, and PC. We will also give you some tips and tricks on how to play the game better and have more fun.</p>
- <h2>How to download Ejen Ali: Agents' Arena on different platforms?</h2>
-<p>Ejen Ali: Agents' Arena is available on various platforms, such as Android, iOS, and PC. Here are the steps to download it on each platform:</p>
- <h3>Android</h3>
-<p>If you have an Android device, you can download Ejen Ali: Agents' Arena from the Google Play Store. Here are the steps to do so:</p>
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-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "Ejen Ali: Agents' Arena" in the search bar.</li>
-<li>Select the game from the search results and tap on "Install".</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Once the installation is complete, tap on "Open" to launch the game.</li>
-</ol>
- <h3>iOS</h3>
-<p>If you have an iOS device, such as an iPhone or an iPad, you can download Ejen Ali: Agents' Arena from the App Store. Here are the steps to do so:</p>
-<ol>
-<li>Open the App Store app on your device.</li>
-<li>Search for "Ejen Ali: Agents' Arena" in the search bar.</li>
-<li>Select the game from the search results and tap on "Get".</li>
-<li>Enter your Apple ID password or use Touch ID or Face ID to confirm the download.</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Once the installation is complete, tap on "Open" to launch the game.</li>
-</ol>
- <h3>PC</h3>
-<p>If you want to play Ejen Ali: Agents' Arena on your PC, you will need an emulator that can run Android apps on your computer. One of the most popular emulators is GameLoop, which is designed for gaming purposes. Here are the steps to download Ejen Ali: Agents' Arena on PC using GameLoop:</p <ol start="4">
-<li>Download GameLoop from its official website or from a trusted source . You can choose between the 32-bit or the 64-bit version depending on your system.</li>
-<li>Run the installer and follow the instructions to install GameLoop on your PC.</li>
-<li>Launch GameLoop and log in with your Google account or create a new one.</li>
-<li>Search for "Ejen Ali: Agents' Arena" in the search bar and click on "Install".</li>
-<li>Wait for the game to download and install on your PC.</li>
-<li>Once the installation is complete, click on "Play" to launch the game.</li>
-</ol>
- <h2>What are the features of Ejen Ali: Agents' Arena?</h2>
-<p>Ejen Ali: Agents' Arena is a fun and exciting game that offers many features for players to enjoy. Some of the main features are:</p>
- <h3>3v3 multiplayer match</h3>
-<p>In Ejen Ali: Agents' Arena, you can team up with two other players and compete against another team of three in a capture the flag mode. The objective is to capture and hold as many bases as possible while preventing the enemy team from doing the same. The team with the most points at the end of the match wins.</p>
- <h3>Multiple bases and battlegrounds</h3>
-<p>The game features various bases that you can capture and defend, such as the MATA Academy, Cyberaya, and Neo City. Each base has its own layout, obstacles, and hazards that you need to be aware of. The game also has different battlegrounds that change every season, adding more variety and challenge to the gameplay.</p>
- <h3>Various agents and skills</h3>
-<p>You can choose from a wide range of agents, each with their own unique attributes and skill sets. Some of the agents are Ali, Alicia, Bakar, Comot, Dos, Jet, Rizwan, Roza, and Zass. Each agent has a passive skill and an active skill that can be used in combat. You can also customize your agent's appearance and equip them with different gadgets and weapons.</p>
- <h3>Upgrades and new content</h3>
-<p>You can collect coins and gems by playing matches, completing missions, and opening chests. You can use these currencies to unlock new agents, skins, gadgets, weapons, and emotes. The game also updates regularly with new content, such as events, seasons, modes, maps, characters, and more.</p>
- <h2>What are some tips and tricks for playing Ejen Ali: Agents' Arena?</h2>
-<p>If you want to improve your skills and performance in Ejen Ali: Agents' Arena, here are some tips and tricks that you can follow:</p>
- <h3>Choose your agent wisely</h3>
-<p>Before you start a match, you should choose an agent that suits your playstyle and strategy. You should also consider the strengths and weaknesses of your teammates and opponents. For example, if your team lacks defense, you might want to pick an agent that can protect your bases or heal your allies. If your team needs more offense, you might want to pick an agent that can deal high damage or disrupt the enemy team.</p>
- <h3>Capture and defend bases</h3>
-<p>The main goal of the game is to capture and hold as many bases as possible. You should try to capture the nearest base as soon as possible and then move on to the next one. You should also try to defend your bases from enemy attacks by using your skills, gadgets, weapons, and obstacles. You should also be aware of the timer and the score difference between your team and the enemy team.</p>
- <h3>Use your skills strategically</h3>
-<p>Each agent has a passive skill and an active skill that can be used in combat. You should know how to use them effectively and efficiently. For example, you should use your active skill when it is fully charged or when it is most needed. You should also use your passive skill to gain an advantage over your enemies or allies. You should also be aware of the cooldowns of your skills and avoid wasting them.</p>
- <h3>Communicate and cooperate with your team</h3>
-<p>Ejen Ali: Agents' Arena is a team-based game that requires communication and cooperation among players. You should use the chat or voice function to communicate with your teammates about your plans, strategies, locations, enemies, bases, skills, etc. You should also cooperate with your teammates by supporting them in combat, capturing or defending bases together, sharing resources, etc.</p>
- <h2>Conclusion</h2>
-<p>Ejen Ali: Agents' Arena is a thrilling 3v3 battle arena game that lets you play as your favorite agents from the Ejen Ali animated series. You can download the game on different platforms, such as Android, iOS, and PC, and enjoy its features, such as 3v3 multiplayer match, multiple bases and battlegrounds, various agents and skills, and upgrades and new content. You can also follow some tips and tricks to play the game better and have more fun.</p>
-<p>If you are looking for a game that combines action, strategy, and teamwork, you should definitely try Ejen Ali: Agents' Arena. It is a game that will keep you hooked and entertained for hours. Download it now and join the adventure!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Ejen Ali: Agents' Arena:</p>
- <h3>What is the genre of Ejen Ali: Agents' Arena?</h3>
-<p>Ejen Ali: Agents' Arena is a 3v3 battle arena game that belongs to the action and strategy genre. It is based on the Ejen Ali animated series that follows the adventures of a young boy who becomes a secret agent.</p>
- <h3>Who are the developers of Ejen Ali: Agents' Arena?</h3>
-<p>Ejen Ali: Agents' Arena is developed by Media Prima Digital, a subsidiary of Media Prima Berhad, which is one of the largest media groups in Malaysia. The game is also co-developed by Common Extract Sdn Bhd, a Malaysian game development studio.</p>
- <h3>Is Ejen Ali: Agents' Arena free to play?</h3>
-<p>Yes, Ejen Ali: Agents' Arena is free to play. However, it also offers in-app purchases that allow you to buy coins and gems, which are the currencies used in the game. You can use these currencies to unlock new agents, skins, gadgets, weapons, and emotes.</p>
- <h3>How can I get more coins and gems in Ejen Ali: Agents' Arena?</h3>
-<p>You can get more coins and gems in Ejen Ali: Agents' Arena by playing matches, completing missions, and opening chests. You can also watch ads or buy them with real money.</p>
- <h3>Where can I find more information about Ejen Ali: Agents' Arena?</h3>
-<p>You can find more information about Ejen Ali: Agents' Arena on its official website, Facebook page, Instagram account, YouTube channel, or Discord server. You can also contact the developers via email at support@ejenaliarena.com.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy the Ultimate Strategy Game with Total Conquest Mod APK (Offline and Unlimited Money).md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy the Ultimate Strategy Game with Total Conquest Mod APK (Offline and Unlimited Money).md
deleted file mode 100644
index 3bd0e0c746078cb55f8f74a1b04471c95d4d03d2..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Enjoy the Ultimate Strategy Game with Total Conquest Mod APK (Offline and Unlimited Money).md	
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-<h1>Game Total Conquest Mod Apk: A Guide for Beginners</h1>
-<p>If you are a fan of strategy games, you might have heard of Game Total Conquest Mod Apk. This is a modified version of the original game Total Conquest, which was developed by Gameloft and released in 2013. In this game, you can build and manage your own Roman city and army, and compete with other players online or offline. In this article, we will tell you everything you need to know about this game, including its features, how to download and install it, and how to play it.</p>
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- <h2>What is Game Total Conquest Mod Apk?</h2>
-<p>Game Total Conquest Mod Apk is a strategy game that is inspired by the ancient Roman civilization. You can create your own city with different types of buildings, such as barracks, temples, markets, walls, towers, and more. You can also train and upgrade various units, such as legionaries, archers, cavalry, catapults, and more. You can use your army to defend your city from enemy attacks or to conquer other cities.</p>
- <h2>What are the features of Game Total Conquest Mod Apk?</h2>
-<p>Game Total Conquest Mod Apk has many features that make it different from the original game. Some of these features are:</p>
-<ul>
-<li>Offline mode: You can play the game without an internet connection. This means you can enjoy the game anytime and anywhere.</li>
-<li>Unlimited money: You can get unlimited money in the game. This means you can buy anything you want without worrying about running out of resources.</li>
-<li>Various units and buildings: You can choose from a wide range of units and buildings in the game. Each unit and building has its own advantages and disadvantages. You can customize your city and army according to your preferences.</li>
-<li>Mutliplayer mode: You can play with or against other players online. You can join or create a legion with your friends or other players. You can chat with them, share resources with them, or fight with them.</li>
-</ul>
- <h2>Why should you play Game Total Conquest Mod Apk?</h2>
-<p>Game Total Conquest Mod Apk is a fun, challenging, and addictive game that will keep you entertained for hours. Here are some reasons why you should play it:</p>
-<ul>
-<li>You can experience the thrill of building and managing your own Roman city and army.</li>
-<li>You can test your skills and strategies against other players online or offline.</li>
-<li>You can enjoy the stunning graphics and sound effects of the game.</li>
-<li>You can explore the rich history and culture of the ancient Roman civilization.</li>
-</ul>
- <h1>How to download and install Game Total Conquest Mod Apk?</h1>
-<p>If you want to play Game Total Conquest Mod Apk on your device, you need to follow these steps:</p>
-< <p>After you have downloaded and installed the APK file, you need to launch the game. Here are the steps to do that:</p>
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-<ol>
-<li>Tap on the game icon on your home screen or app drawer. You will see the Gameloft logo and the game title.</li>
-<li>Wait for the game to load. You will see a loading screen with some tips and hints.</li>
-<li>Choose your preferred language. You can tap on the flag icon on the top right corner to change the language.</li>
-<li>Agree to the terms of service and privacy policy. You can tap on the links to read them, or just tap on the accept button to proceed.</li>
-<li>Choose your game mode. You can play online or offline. If you play online, you will need an internet connection and a Gameloft account. If you play offline, you will not be able to access some features, such as multiplayer mode and social features.</li>
-<li>Create your city name and avatar. You can tap on the random button to generate a random name and avatar, or you can customize them by tapping on the edit button.</li>
-<li>Start your game. You will see a tutorial that will guide you through the basics of the game. You can skip it by tapping on the skip button, or you can follow it to learn how to play.</li>
-</ol> <h1>How to play Game Total Conquest Mod Apk?</h1>
-<p>Now that you have downloaded and installed the game, you might be wondering how to play it. Don't worry, we have some tips and tricks for you to help you become a successful Roman governor. Here are some of them:</p>
- <h2>How to start your city</h2>
-<p>When you start the game, you will have a small city with a few buildings and units. Your first task is to expand your city and make it more prosperous. You can do this by:</p>
-<ul>
-<li>Building more houses to increase your population and income.</li>
-<li>Building more farms to produce food for your units and citizens.</li>
-<li>Building more markets to trade resources and earn money.</li>
-<li>Building more temples to worship the gods and gain their favor.</li>
-<li>Building more workshops to research new technologies and improve your buildings and units.</li>
-</ul>
- <h2>How to upgrade your buildings and units</h2>
-<p>As you progress in the game, you will need to upgrade your buildings and units to make them more efficient and powerful. You can do this by:</p>
-<ul>
-<li>Spending money and resources to upgrade your buildings. Each building has a maximum level that depends on your city level. Upgrading your buildings will unlock new features and benefits.</li>
-<li>Spending money and resources to upgrade your units. Each unit has a maximum level that depends on your barracks level. Upgrading your units will increase their stats and abilities.</li>
-<li>Using crowns to speed up the upgrading process. Crowns are a premium currency that you can get by completing achievements, winning battles, or buying with real money.</li>
-</ul>
- <h2>How to collect resources and money</h2>
-<p>Resources and money are essential for building and upgrading your city and army. You can collect them by:</p>
-<ul>
-<li>Gathering them from your buildings. Each building produces a certain amount of resources or money over time. You can tap on them to collect them.</li>
-<li>Raiding other players' cities. You can attack other players online or offline and loot their resources and money. You can also get crowns and trophies by winning battles.</li>
-<li>Completing quests and achievements. You can access the quest menu from the bottom left corner of the screen. You can complete various tasks and challenges to earn rewards.</li>
-<li>Watching ads or buying them with real money. You can watch short videos or make in-app purchases to get more resources, money, or crowns.</li>
-</ul>
- <h2>How to defend your city and attack other players</h2>
-<p>Besides building and managing your city, you also need to protect it from enemy attacks and conquer other cities. You can do this by:</p>
-<ul>
-<li>Building walls, towers, traps, and gates around your city. These structures will help you defend your city from invaders. You can upgrade them to make them stronger.</li>
-<li>Placing units in strategic positions around your city. You can assign units to guard your buildings or patrol your walls. You can also use special units, such as spies, assassins, or priests, to sabotage or support your defense.</li>
-<li>Choosing your targets wisely when attacking other players. You can use the map menu from the bottom right corner of the screen to find other players online or offline. You can also use the search button to find players with specific criteria, such as level, trophies, or resources.</li>
-<li>Using the best strategy when attacking other players. You can scout their city before attacking to see their layout and defense. You can also use different types of units, such as infantry, cavalry, ranged, or siege, to exploit their weaknesses or counter their strengths.</li>
-</ul>
- <h2>How to join or create a legion</h2>
-<p>A legion is a group of players who cooperate and compete with each other in the game. You can join or create a legion by:</p>
-<ul>
-<li>Tapping on the legion button on the top left corner of the screen. You will see a list of legions that you can join or create.</li>
-<li>If you want to join a legion, you can browse the list and tap on the join button next to the legion name. Some legions may require an invitation or approval from the leader or officers.</li>
-<li>If you want to create a legion, you can tap on the create button at the bottom of the list. You will need to choose a name, a motto, a flag, and a language for your legion. You will also need to pay some money and crowns to create it.</li>
-<li>Once you are in a legion, you can chat with other members, share resources with them, request reinforcements from them, or fight with them in wars or events.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>Game Total Conquest Mod Apk is a great game for anyone who loves strategy, history, and fun. You can build and manage your own Roman city and army, and compete with other players online or offline. You can enjoy the game's features, such as offline mode, unlimited money, various units and buildings, and multiplayer mode. You can also learn how to download and install the game, and how to play it with some tips and tricks. If you are ready to conquer the world with your Roman legion, download Game Total Conquest Mod Apk today and start your adventure!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Game Total Conquest Mod Apk:</p>
-<ol>
-<li>Is Game Total Conquest Mod Apk safe to download and install?</li>
-<p>Yes, Game Total Conquest Mod Apk is safe to download and install, as long as you use a reliable source and a virus scan tool. However, you should always be careful when downloading and installing any APK file, as some of them may contain malware or viruses that can harm your device or data.</p>
-<li>Is Game Total Conquest Mod Apk legal to use?</li>
-<p>Game Total Conquest Mod Apk is not an official version of the game, and it may violate the terms of service and privacy policy of Gameloft, the original developer of the game. Therefore, using Game Total Conquest Mod Apk may result in some risks, such as being banned from the game or losing your account. You should use Game Total Conquest Mod Apk at your own discretion and responsibility.</p>
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-<p>Game Total Conquest Mod Apk may not be compatible with the latest version of the game or the device. Therefore, you may need to update Game Total Conquest Mod Apk from time to time to enjoy its features and benefits. To update Game Total Conquest Mod Apk, you need to download the latest version of the APK file from a reliable source and install it over the existing one. You may also need to backup your data before updating, as some updates may erase your progress or settings.</p>
-<li>How can I uninstall Game Total Conquest Mod Apk?</li>
-<p>If you want to uninstall Game Total Conquest Mod Apk from your device, you can do so by following these steps:</p>
-<ul>
-<li>Go to your device's settings and tap on apps or applications.</li>
-<li>Find and tap on Game Total Conquest Mod Apk from the list of apps.</li>
-<li>Tap on uninstall and confirm your action.</li>
-<li>Wait for the app to be uninstalled from your device.</li>
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-<li>How can I contact the developer of Game Total Conquest Mod Apk?</li>
-<p>Game Total Conquest Mod Apk is not developed by Gameloft, but by an unknown third-party developer. Therefore, there is no official way to contact the developer of Game Total Conquest Mod Apk. However, you may try to find some information about the developer on the website or platform where you downloaded the APK file. You may also try to leave a comment or a review on the APK file page, and hope that the developer will respond to you.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Hack APK for Stickman Falling Dismount and Destroy with Coins - AN1.md b/spaces/congsaPfin/Manga-OCR/logs/Hack APK for Stickman Falling Dismount and Destroy with Coins - AN1.md
deleted file mode 100644
index c81c274c793a3fe19ff73204097b59b2206211e8..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Hack APK for Stickman Falling Dismount and Destroy with Coins - AN1.md	
+++ /dev/null
@@ -1,96 +0,0 @@
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-<table>
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-<p>Stickman Falling is a popular physics ragdoll game that simulates terrifying falls, crushing vehicles, breaking bones, and destroying everything. It is a fun and addictive game that lets you unleash your creativity and imagination in creating different scenarios and challenges for your stickman character.</p>
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-<p>Hack APK AN1 is one such version that claims to offer these benefits. But what exactly is it, how do you get it, and what are the pros and cons of using it? In this article, we will answer these questions and give you some tips and advice on how to use it safely and responsibly.</p>
- <h1>How to Download and Install Hack APK AN1 on Your Android Device</h1>
-<p>If you want to try hack apk an1, you will need to download and install it on your Android device. This is not as simple as downloading and installing the original game from the Google Play Store, as hack apk an1 is not an official app and is not available there. You will need to follow these steps:</p>
- <h2>Find and Download the APK File</h2>
-<p>The first thing you need to do is to find and download the apk file of hack apk an1. An apk file is a package file that contains the app's code, resources, and metadata. You can use Petal Search or a web browser to search for "stickman falling hack apk an1" and choose a reliable site like APKPure or Uptodown. These sites offer verified and safe apk files that you can download for free. Tap on the download button and wait for the file to be saved on your device.</p>
- <h2>Enable Unknown Sources</h2>
-<p>The next thing you need to do is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To do this, go to your device settings and tap on Apps & Notifications (or Apps in older versions of Android). Tap on the three dots in the upper-right corner and select Special Access. Tap on Install Unknown Apps and choose your web browser or file manager app. Turn on the Allow from this source option.</p>
- <h2>Use a File Manager to Locate and Install the APK File</h2>
-<p>The last thing you need to do is to use a file manager app to locate and install the apk file of hack apk an1. A file manager app is a tool that lets you browse, organize, and manage the files and folders on your device. You can use any file manager app that you have installed, such as Files by Google or ES File Explorer. Open your file manager app and go to the Downloads folder (or wherever you saved the apk file). Tap on the stickman falling hack apk an1 file and tap on Install when prompted. Wait for the installation to finish.</p>
- <h2>Launch the Game</h2>
-<p>Now you are ready to launch the game and enjoy the hacked features. Go to your app drawer and look for the stickman falling icon. Tap on it to open the game and start playing. You will notice that you have unlimited coins, all vehicles and levels unlocked, and no ads in the game.</p>
- <h1>Benefits and Risks of Using Hack APK AN1</h1>
-<p>Using hack apk an1 can be fun and exciting, but it also comes with some benefits and risks that you should be aware of. Here are some of them:</p>
- <h2>Benefits of Using Hack APK AN1</h2>
-<p>Some of the benefits of using hack apk an1 are:</p>
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-<ul>
-<li>More coins: You can buy more vehicles and props with unlimited coins, which can make the game more varied and interesting.</li>
-<li>More levels: You can explore more levels with different environments, obstacles, and challenges, which can make the game more fun and challenging.</li>
-<li>More customization: You can customize your stickman character with different outfits, accessories, hairstyles, and colors, which can make the game more personal and expressive.</li>
-<li>No ads: You can avoid annoying ads that interrupt your gameplay, which can make the game more smooth and enjoyable.</li>
-</ul>
- <h2>Risks of Using Hack APK AN1</h2>
-<p>Some of the risks of using hack apk an1 are:</p>
-<ul>
-<li>Potential malware or viruses: The apk file of hack apk an1 might contain malicious code or software that can harm your device or steal your data. This is why you should always download it from a reputable site and scan it with an antivirus app before installing it.</li>
-<li>Possible ban from online leaderboards or multiplayer modes: The original game might detect that you are using a hacked version and ban you from accessing online features like leaderboards or multiplayer modes. This is why you should always use hack apk an1 at your own risk and discretion.</li>
-<li>Possible legal issues: Using hack apk an1 might violate the terms and conditions of the original game or infringe on its intellectual property rights. This might result in legal action from the developers or publishers of the original game. This is why you should always respect their work and support them by buying their game or in-app purchases.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>Hack APK AN1 is a modified version of Stickman Falling that gives you more coins, unlocks all vehicles and levels, and removes ads. It can be a fun and exciting way to enjoy the game, but it also comes with some benefits and risks that you should be aware of. You should always download it from a reliable source, enable unknown sources on your device, use a file manager to install it, and launch the game. You should also be careful of potential malware or viruses, possible ban from online features, and possible legal issues. You should always use hack apk an1 safely and responsibly, and respect the work of the original game developers and publishers.</p>
- <p>We hope this article has helped you learn more about stickman falling hack apk an1 and how to use it. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about hack apk an1:</p>
- <h3>How do I update hack apk an1?</h3>
-<p>To update hack apk an1, you will need to download and install the latest version of the apk file from the same source that you used before. You can check for updates by visiting the site regularly or by subscribing to their notifications. You can also uninstall the old version of hack apk an1 before installing the new one, or overwrite it if prompted.</p>
- <h3>How do I uninstall hack apk an1?</h3>
-<p>To uninstall hack apk an1, you will need to go to your device settings and tap on Apps & Notifications (or Apps in older versions of Android). Find and tap on stickman falling and tap on Uninstall. Confirm your action and wait for the app to be removed from your device.</p>
- <h3>Is hack apk an1 compatible with my device?</h3>
-<p>Hack apk an1 is compatible with most Android devices that run on Android 4.4 or higher. However, some devices might have different specifications or settings that might affect the performance or functionality of hack apk an1. If you encounter any problems or errors while using hack apk an1, you can try clearing the cache and data of the app, restarting your device, or contacting the site that provided the apk file for support.</p>
- <h3>Is hack apk an1 legal?</h3>
-<p>Hack apk an1 is not legal in most countries, as it violates the terms and conditions of the original game and infringes on its intellectual property rights. Using hack apk an1 might result in legal action from the developers or publishers of the original game, as well as other consequences such as fines or penalties. You should always use hack apk an1 at your own risk and discretion, and respect the work of the original game developers and publishers.</p>
- <h3>Where can I find more information about hack apk an1?</h3>
-<p>You can find more information about hack apk an1 by visiting the site that provided the apk file, reading their description and reviews, or contacting them for support. You can also search for other articles or videos that cover hack apk an1, or join online forums or communities that discuss stickman falling or other similar games.</p>
- </td>
-</tr>
-</table></p> 197e85843d<br />
-<br />
-<br />
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+++ /dev/null
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-<br />
-<h1>Hacker Online RPG APK: A Guide for Aspiring Hackers</h1>
-<p>Have you ever dreamed of becoming a hacker? Do you want to learn how to hack into computers, servers, websites, devices, and accounts? Do you want to challenge yourself with realistic hacking scenarios and compete with other hackers from around the world? If you answered yes to any of these questions, then you should try <strong>Hacker Online RPG APK</strong>, a game that lets you experience simulated hacking sessions on a complete simulated OS.</p>
-<h2>What is Hacker Online RPG APK?</h2>
-<p>Hacker Online RPG APK is a game that simulates hacking sessions on a complete simulated OS. It is an MMORPG (Massively Multiplayer Online Role-Playing Game) that allows you to create your own hacker profile, customize your OS, use various hacking tools, hack different targets, earn money and credits, buy upgrades and items, join clans, chat with other players, trade with them, fight against them, and more.</p>
-<h2>hacker online rpg apk</h2><br /><p><b><b>Download File</b> &#9881; <a href="https://urlca.com/2uOglY">https://urlca.com/2uOglY</a></b></p><br /><br />
-its realistic and immersive gameplay, its variety of hacking tools and targets, its social and competitive features, and its educational value.  </p>
-<h2>How to download and install Hacker Online RPG APK?</h2>
-<p>If you want to play Hacker Online RPG APK, you need to have an Android device that meets the minimum system requirements. According to the game's official website, you need to have at least Android 4.1 or higher, 1 GB of RAM, 100 MB of free storage space, and an internet connection.</p>
-<p>To download and install Hacker Online RPG APK, you can follow these simple steps:</p>
-<ol>
-<li>Go to the game's official website at <a href="">https://www.hacker-online.com/</a> and click on the "Download" button.</li>
-<li>You will be redirected to the Google Play Store page of the game. Click on the "Install" button and wait for the download to finish.</li>
-<li>Alternatively, you can also download the APK file of the game from a third-party source such as <a href="">https://apkpure.com/hacker-online-rpg/com.gekkotech.hackeronline</a>. However, you need to enable the "Unknown sources" option in your device's settings to install apps from outside the Google Play Store.</li>
-<li>Once you have downloaded the APK file, locate it in your device's file manager and tap on it to install it.</li>
-<li>After the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer.</li>
-</ol>
-<h2>How to play Hacker Online RPG APK?</h2>
-<p>When you launch the game for the first time, you will be greeted by a tutorial that will guide you through the basics of the game. You will learn how to use the simulated OS and hacking tools, how to hack different targets, how to earn money and credits, how to buy upgrades and items, how to join clans, chat with other players, trade with them, fight against them, and more.</p>
-<p>The game's interface consists of three main parts: the desktop, the terminal, and the menu. The desktop is where you can access your files, folders, programs, settings, and wallpaper. The terminal is where you can type commands and run hacking tools. The menu is where you can access your profile, inventory, clan, chat, shop, missions, rankings, achievements, settings, and logout.</p>
-"ssh" to establish a secure shell connection to a remote server, "ftp" to transfer files to or from a remote server, and "telnet" to communicate with a remote server using the Telnet protocol. You can also use "help" to display a list of available commands and their usage, and "man" to display the manual page for a specific command.</p>
-<p>The game's hacking tools are programs that you can run from the terminal to perform various hacking tasks. Some of the hacking tools that you can use are "nmap" to scan a target for open ports and services, "hydra" to perform brute force attacks on passwords, "sqlmap" to exploit SQL injection vulnerabilities, "metasploit" to exploit various vulnerabilities and run payloads, "wireshark" to capture and analyze network traffic, "john" to crack hashed passwords, "aircrack-ng" to crack wireless encryption keys, and "tor" to browse the web anonymously. You can also use "help" followed by the name of a tool to display its usage and options, and "man" followed by the name of a tool to display its manual page.</p>
-<p>The game's hacking targets are entities that you can hack for various purposes. Some of the hacking targets that you can encounter are accounts, servers, websites, devices, and networks. Each target has its own IP address or domain name, security level, firewall, antivirus, encryption, and vulnerability. You need to use the appropriate hacking tools and methods to bypass the security measures and gain access to the target. You can also use "ping" or "traceroute" to test the connectivity and latency of a target, and "whois" to get information about the owner and registrar of a domain name.</p>
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-<h2>What are the benefits of playing Hacker Online RPG APK?</h2>
-<p>Playing Hacker Online RPG APK can be fun, challenging, rewarding, and educational. Here are some of the benefits that you can get from playing this game:</p>
-<ul>
-<li>You can learn about cyber security concepts and techniques in a realistic and interactive way.</li>
-<li>You can improve your problem-solving, critical thinking, analytical, and logical skills.</li>
-<li>You can enhance your creativity, imagination, and curiosity.</li>
-<li>You can test your knowledge and skills against other hackers from around the world.</li>
-<li>You can earn money and credits that you can use to buy upgrades and items.</li>
-<li>You can join clans and chat with other players who share your interests and goals.</li>
-<li>You can have fun and enjoy yourself in a simulated hacking environment.</li>
-<h2>What are the challenges of playing Hacker Online RPG APK?</h2>
-<p>Playing Hacker Online RPG APK can also be difficult, risky, and frustrating. Here are some of the challenges that you may face in this game:</p>
-<ul>
-<li>You may encounter targets that are too hard or too easy for your level and skills.</li>
-<li>You may run out of money and credits that you need to buy upgrades and items.</li>
-<li>You may get hacked or banned by other players or by the game's moderators.</li>
-<li>You may encounter bugs, glitches, errors, or crashes that affect your gameplay.</li>
-<li>You may get addicted to the game and neglect your real-life responsibilities and relationships.</li>
-</ul>
-<h2>How to improve your skills and rank in Hacker Online RPG APK?</h2>
-<p>If you want to become a better hacker and climb the ranks in Hacker Online RPG APK, you need to practice, learn, and improve. Here are some tips and tricks that can help you achieve your goals:</p>
-<ul>
-<li>Complete the tutorial and the missions to learn the basics and earn rewards.</li>
-<li>Read the manual pages and the help messages of the commands and tools to understand their usage and options.</li>
-<li>Research online about cyber security topics and techniques to expand your knowledge and skills.</li>
-<li>Scan and hack different targets to gain experience and money.</li>
-<li>Buy upgrades and items that can enhance your performance and capabilities.</li>
-<li>Join clans and chat with other players to get advice, support, and feedback.</li>
-<li>Trade with other players to get items that you need or want.</li>
-<li>Fight against other players to test your skills and earn credits.</li>
-<h2>How to interact with other players in Hacker Online RPG APK?</h2>
-<p>One of the best features of Hacker Online RPG APK is that it is an MMORPG, which means that you can interact with other players from around the world. You can communicate, cooperate, compete, and trade with them. Here are some ways that you can interact with other players in this game:</p>
-<ul>
-<li>You can join clans, which are groups of players who share a common interest, goal, or ideology. You can create your own clan or join an existing one. You can chat with your clan members, share resources, help each other, and participate in clan wars.</li>
-<li>You can chat with other players using the global chat or the private chat. You can send messages, emojis, stickers, and images to other players. You can also use voice chat or video chat if you have a microphone or a camera.</li>
-<li>You can trade with other players using the trade system. You can offer or request items, money, credits, or services from other players. You can also use the auction system to buy or sell items to the highest bidder.</li>
-<li>You can fight with other players using the fight system. You can challenge or accept challenges from other players. You can also join tournaments or events to compete with other players for prizes and glory.</li>
-</ul>
-<h2>How to customize your profile and OS in Hacker Online RPG APK?</h2>
-<p>Another great feature of Hacker Online RPG APK is that it allows you to customize your profile and OS according to your preferences and style. You can change your avatar, name, wallpaper, theme, and settings. Here are some ways that you can customize your profile and OS in this game:</p>
-<ul>
-<li>You can change your avatar by tapping on your profile picture on the menu. You can choose from a variety of avatars that are available in the game. You can also upload your own avatar from your device or use your camera to take a selfie.</li>
-<li>You can change your name by tapping on your profile name on the menu. You can enter any name that you want as long as it is not offensive or inappropriate. You can also use special characters and symbols to make your name more unique.</li>
-<li>You can change your wallpaper by tapping on the wallpaper icon on the desktop. You can choose from a variety of wallpapers that are available in the game. You can also upload your own wallpaper from your device or use your camera to take a picture.</li>
-<li>You can change your theme by tapping on the theme icon on the desktop. You can choose from a variety of themes that are available in the game. You can also create your own theme by customizing the colors, fonts, icons, and sounds of your OS.</li>
-<li>You can change your settings by tapping on the settings icon on the menu. You can adjust various options such as sound, music, vibration, notifications, language, and more.</li>
-<h2>How to earn money and credits in Hacker Online RPG APK?</h2>
-<p>Money and credits are the two main currencies in Hacker Online RPG APK. You need money to buy items, tools, and services from the shop. You need credits to buy upgrades, items, and services from other players. You can also use credits to participate in tournaments and events. Here are some ways that you can earn money and credits in this game:</p>
-<ul>
-<li>You can hack accounts, servers, websites, and devices for profit. You can use tools such as "hydra", "sqlmap", "metasploit", and "aircrack-ng" to crack passwords, exploit vulnerabilities, run payloads, and capture data. You can then sell the data or use it for blackmail, ransom, or fraud.</li>
-<li>You can complete missions that are given by the game or by other players. You can accept missions from the mission board on the menu or from the chat. You can also create your own missions and offer rewards for other players who complete them.</li>
-<li>You can win tournaments and events that are organized by the game or by other players. You can join tournaments and events from the tournament board on the menu or from the chat. You can also create your own tournaments and events and set the rules and prizes for them.</li>
-</ul>
-<h2>How to spend money and credits in Hacker Online RPG APK?</h2>
-<p>Money and credits are the two main currencies in Hacker Online RPG APK. You can use money to buy items, tools, and services from the shop. You can use credits to buy upgrades, items, and services from other players. You can also use credits to participate in tournaments and events. Here are some ways that you can spend money and credits in this game:</p>
-<ul>
-<li>You can buy items, tools, and services from the shop. The shop is where you can find various products that can help you in your hacking activities. You can buy items such as VPNs, proxies, firewalls, antivirus, encryption keys, and more. You can buy tools such as nmap, hydra, sqlmap, metasploit, wireshark, john, aircrack-ng, tor, and more. You can buy services such as hacking lessons, hacking contracts, hacking support, hacking protection, and more.</li>
-<li>You can buy upgrades, items, and services from other players. Other players may offer products that are not available in the shop or that are cheaper or better than the ones in the shop. You can find these products on the trade board on the menu or on the chat. You can also offer your own products for sale or trade.</li>
-<li>You can participate in tournaments and events. Tournaments and events are competitions that test your hacking skills and knowledge against other players. You can join tournaments and events from the tournament board on the menu or from the chat. You can also create your own tournaments and events and set the rules and prizes for them.</li>
-<h2>How to avoid getting hacked or banned in Hacker Online RPG APK?</h2>
-<p>While playing Hacker Online RPG APK, you may face the risk of getting hacked or banned by other players or by the game's moderators. Getting hacked means that another player has gained access to your account, OS, files, money, credits, or items. Getting banned means that your account has been suspended or deleted by the game's moderators for violating the game's rules or terms of service. Here are some precautions and security measures that you should take to avoid getting hacked or banned in this game:</p>
-<ul>
-<li>You should use a strong and unique password for your account and change it regularly. You should also use a different password for your email and other accounts that are linked to your game account.</li>
-<li>You should use a VPN, a proxy, or Tor to hide your real IP address and location from other players and the game's servers. This can prevent hackers from tracing you and targeting you.</li>
-<li>You should use a firewall, an antivirus, and encryption to protect your OS, files, money, credits, and items from hackers. You should also update your OS and tools regularly to fix any bugs or vulnerabilities.</li>
-<li>You should not share your account, OS, files, money, credits, or items with anyone else. You should also not download or run any files or programs that are sent by other players or unknown sources.</li>
-<li>You should follow the game's rules and terms of service and respect other players and moderators. You should not cheat, spam, scam, harass, threaten, or abuse anyone in the game.</li>
-</ul>
-<h2>What are the best hacking tools in Hacker Online RPG APK?</h2>
-<p>Hacker Online RPG APK offers a variety of hacking tools that you can use to perform various hacking tasks. However, some tools are better than others in terms of features, performance, price, and availability. Here is a table that compares some of the best hacking tools in this game:</p>
- <table>
-<tr>
-<th>Tool</th>
-<th>Features</th>
-<th>Performance</th>
-<th>Price</th>
-<th>Availability</th>
-</tr>
-<tr>
-<td>nmap</td>
-<td>Scans a target for open ports and services</td>
-<td>Fast and accurate</td>
-<td>$10</td>
-<td>Shop</td>
-</tr>
-<tr>
-<td>hydra</td>
-<td>Performs brute force attacks on passwords</td>
-<td>Slow but effective</td>
-<td>$50</td>
-<td>Shop</td>
-</tr>
-<tr>
-<td>sqlmap</td>
-<td>Exploits SQL injection vulnerabilities</td>
-<td>Medium speed and accuracy</td>
-<td>$100</td>
-<td>Shop</td>
-</tr>
-<tr>
-<td>metasploit</td>
-<td>Exploits various vulnerabilities and runs payloads</td>
-<td>Fast and versatile</td>
-<td>$200</td>
-<td>Trade</td>
-</tr>
-<tr>
-<td>wireshark</td>
-<td>Captures and analyzes network traffic</td>
-<td>Slow but detailed</td>
-<td>$150</td>
-</td>
-<td>Cracks hashed passwords</td>
-<td>Medium speed and effectiveness</td>
-<td>$100</td>
-<td>Trade</td>
-</tr>
-<tr>
-<td>aircrack-ng</td>
-<td>Cracks wireless encryption keys</td>
-<td>Fast but noisy</td>
-<td>$150</td>
-<td>Trade</td>
-</tr>
-<tr>
-<td>tor</td>
-<td>Browses the web anonymously</td>
-<td>Slow but secure</td>
-<td>$50</td>
-<td>Shop</td>
-</tr>
-</table>
- <h2>What are the best hacking targets in Hacker Online RPG APK?</h2>
-<p>Hacker Online RPG APK offers a variety of hacking targets that you can hack for various purposes. However, some targets are more lucrative and vulnerable than others in terms of rewards, risks, and challenges. Here is a list of some of the best hacking targets in this game:</p>
-<ul>
-<li>Accounts: These are the profiles of other players or NPCs (non-player characters) that you can hack to steal their money, credits, items, or information. You can use tools such as "hydra", "sqlmap", or "john" to crack their passwords and gain access to their accounts. You can also use tools such as "metasploit" or "wireshark" to exploit their vulnerabilities and run payloads or capture their data.</li>
-<li>Servers: These are the computers that host websites, databases, or applications that you can hack to manipulate, damage, or destroy their data or functionality. You can use tools such as "nmap", "sqlmap", or "metasploit" to scan, exploit, or attack their servers. You can also use tools such as "wireshark" or "aircrack-ng" to capture or disrupt their network traffic.</li>
-<li>Websites: These are the online platforms that provide information, services, or entertainment that you can hack to deface, redirect, or take down their web pages or content. You can use tools such as "nmap", "sqlmap", or "metasploit" to scan, exploit, or attack their websites. You can also use tools such as "wireshark" or "tor" to capture or anonymize your web traffic.</li>
-<li>Devices: These are the gadgets that connect to the internet or other networks that you can hack to control, monitor, or sabotage their operations or features. You can use tools such as "nmap", "metasploit", or "aircrack-ng" to scan, exploit, or attack their devices. You can also use tools such as "wireshark" or "tor" to capture or anonymize your device traffic.</li>
-<li>Networks: These are the systems that connect computers, servers, websites, devices, or other entities that you can hack to infiltrate, spy on, or disrupt their communications or activities. You can use tools such as "nmap", "metasploit", or "aircrack-ng" to scan, exploit, or attack their networks. You can also use tools such as "wireshark" or "tor" to capture or anonymize your network traffic.</li>
-<h2>What are the best hacking strategies in Hacker Online RPG APK?</h2>
-<p>Hacker Online RPG APK offers a variety of hacking scenarios and situations that require different strategies and approaches. However, some strategies are more effective and efficient than others in terms of success, speed, and stealth. Here are some of the best hacking strategies in this game:</p>
-<ul>
-<li>Reconnaissance: This is the first and most important step in any hacking process. It involves gathering information about your target, such as its IP address, domain name, security level, firewall, antivirus, encryption, vulnerability, and more. You can use tools such as "nmap", "ping", "traceroute", or "whois" to perform reconnaissance on your target. You can also use tools such as "tor" or "proxy" to hide your identity and location while performing reconnaissance.</li>
-<li>Exploitation: This is the second and most crucial step in any hacking process. It involves exploiting the vulnerability or weakness of your target, such as its password, port, service, database, application, or protocol. You can use tools such as "hydra", "sqlmap", "metasploit", or "aircrack-ng" to perform exploitation on your target. You can also use tools such as "tor" or "proxy" to hide your identity and location while performing exploitation.</li>
-<li>Post-exploitation: This is the third and most optional step in any hacking process. It involves performing additional actions on your target after gaining access to it, such as stealing data, deleting files, installing malware, running commands, changing settings, or causing damage. You can use tools such as "metasploit", "wireshark", "john", or "aircrack-ng" to perform post-exploitation on your target. You can also use tools such as "tor" or "proxy" to hide your identity and location while performing post-exploitation.</li>
-<li>Covering tracks: This is the fourth and most advisable step in any hacking process. It involves erasing or altering any evidence or traces of your hacking activity on your target or on your device, such as logs, history, cookies, cache, files, or settings. You can use tools such as "rm", "mv", "cp", or "cat" to cover your tracks on your target. You can also use tools such as "tor" or "proxy" to cover your tracks on your device.</li>
-</ul>
-<h2>What are some common hacking terms and slang in Hacker Online RPG APK?</h2>
-<p>Hacker Online RPG APK uses a lot of hacking terms and slang that you may encounter or use while playing the game. Some of these terms and slang are technical, while others are informal or humorous. Here is a glossary of some common hacking terms and slang in this game:</p>
-<dl>
-<dt>Brute force</dt>
-<dd>A method of cracking passwords by trying all possible combinations of characters until finding the correct one.</dd>
-<dt>DDoS</dt>
-<dd>A type of attack that floods a target with a large amount of traffic or requests to overload its resources and disrupt its functionality.</dd>
-<dt>Encryption</dt>
-<dd>A method of protecting data by converting it into an unreadable form using a secret key or algorithm.</dd>
-<dt>Firewall</dt>
-<dd>A software or hardware that blocks unauthorized access to a network or device by filtering incoming and outgoing traffic based on predefined rules.</dd>
-<dt>Hacktivist</dt>
-<dd>A hacker who hacks for a political or social cause.</dd>
-<dt>L33t</dt>
-<dd>A way of writing that replaces letters with numbers or symbols to create a secret code or to show off one's skills.</dd>
-<dt>Malware</dt>
-<dd>A type of software that is designed to harm or compromise a device, network, or data.</dd>
-<dt>Payload</dt>
-<dd>A type of code that is executed after exploiting a vulnerability to perform a specific action on a target.</dd>
-<dt>Phishing</dt>
-<dd>A type of attack that tricks users into revealing their personal or financial information by sending them fake emails or websites that look legitimate.</dd>
-<dt>Rootkit</dt>
-<dd>A type of malware that hides itself from detection and gives the attacker full control over a device.</dd>
-<dt>Spoofing</dt>
-<dd>A type of attack that impersonates another entity by falsifying its identity or information.</dd>
-<dt>Trojan</dt>
-<dd>A type of malware that disguises itself as a legitimate program but contains malicious code.</dd>
-<dt>Virus</dt>
-<dd>A type of malware that infects other files or programs and replicates itself.</dd>
-<dt>Worm</dt>
-that spreads itself across networks without requiring user interaction.</dd>
-</dl>
-<h1>Conclusion</h1>
-<p>Hacker Online RPG APK is a game that simulates hacking sessions on a complete simulated OS. It is an MMORPG that allows you to create your own hacker profile, customize your OS, use various hacking tools, hack different targets, earn money and credits, buy upgrades and items, join clans, chat with other players, trade with them, fight against them, and more. It is a fun, challenging, rewarding, and educational game that can teach you about cyber security concepts and techniques in a realistic and interactive way. If you are interested in hacking or want to become a hacker, you should definitely try this game. You can download and install it from the game's official website or from a third-party source. You can also visit the game's official website or social media pages for more information and updates.</p>
-<h1>FAQs</h1>
-<p>Here are some frequently asked questions and answers about Hacker Online RPG APK:</p>
-<dl>
-<dt>Q: Is Hacker Online RPG APK safe and legal to play?</dt>
-<dd>A: Yes, Hacker Online RPG APK is safe and legal to play. The game does not involve any real hacking or illegal activities. The game only simulates hacking sessions on a simulated OS and network. The game does not harm or compromise any real devices, networks, or data. The game also does not collect or share any personal or sensitive information from the players.</dd>
-<dt>Q: How can I contact the game's developers or moderators?</dt>
-<dd>A: You can contact the game's developers or moderators by sending them an email at <a href="mailto:support@hacker-online.com">support@hacker-online.com</a> or by visiting their Facebook page at <a href="https://www.facebook.com/HackerOnlineRPG">https://www.facebook.com/HackerOnlineRPG</a>. You can also use the report or feedback buttons in the game to report any bugs, glitches, errors, or suggestions.</dd>
-<dt>Q: How can I get more money and credits in Hacker Online RPG APK?</dt>
-<dd>A: You can get more money and credits in Hacker Online RPG APK by hacking accounts, servers, websites, devices, and networks for profit, by completing missions that are given by the game or by other players, by winning tournaments and events that are organized by the game or by other players, by trading with other players using the trade system or the auction system, or by buying them with real money using the in-app purchase option.</dd>
-<dt>Q: How can I get more hacking tools in Hacker Online RPG APK?</dt>
-<dd>A: You can get more hacking tools in Hacker Online RPG APK by buying them from the shop using money or credits, by trading with other players using the trade system or the auction system, by winning them as rewards from missions, tournaments, or events, or by creating your own tools using the tool editor feature.</dd>
-<dt>Q: How can I get more hacking targets in Hacker Online RPG APK?</dt>
-<dd>A: You can get more hacking targets in Hacker Online RPG APK by scanning the network using tools such as "nmap" or "ping", by accepting missions that are given by the game or by other players, by joining tournaments or events that are organized by the game or by other players, by creating your own targets using the target editor feature, or by finding them online using tools such as "tor" or "whois".</dd>
-</dl></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Yahoo Messenger APK How to Unsend Messages and Express Yourself with GIFs.md b/spaces/congsaPfin/Manga-OCR/logs/Yahoo Messenger APK How to Unsend Messages and Express Yourself with GIFs.md
deleted file mode 100644
index 209ec3fe1a315e959c8254f0c81274e89dcef472..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Yahoo Messenger APK How to Unsend Messages and Express Yourself with GIFs.md	
+++ /dev/null
@@ -1,135 +0,0 @@
-
-<h1>Yahoo Messenger APK: What You Need to Know</h1>
-<p>Do you miss the old days of chatting with your friends on Yahoo Messenger? Do you want to relive the nostalgia of sending emoticons, stickers, and animated GIFs? If so, you might be interested in downloading Yahoo Messenger APK for your Android device.</p>
-<p>Yahoo Messenger APK is an Android app that lets you use the classic version of Yahoo Messenger on your smartphone or tablet. It is not available on the Google Play Store, but you can download it from third-party websites like APKCombo or FileHippo.</p>
-<h2>yahoo messenger apk</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://urlca.com/2uOdyv">https://urlca.com/2uOdyv</a></b></p><br /><br />
-<p>In this article, we will tell you everything you need to know about Yahoo Messenger APK, including how to download it, how to use it, and what are some alternatives to it. Let's get started!</p>
- <h2>How to Download Yahoo Messenger APK for Android</h2>
-<p>If you want to use Yahoo Messenger APK on your Android device, you will need to download it from a reliable source. Here are the steps to do so:</p>
-<ol>
-<li>Go to <a href="(^1^)">APKCombo</a> or <a href="(^2^)">FileHippo</a> and search for Yahoo Messenger.</li>
-<li>Select the latest version of the app (2.11.1) and click on Download APK.</li>
-<li>Once the file is downloaded, open it and tap on Install. You may need to enable Unknown Sources in your device settings to allow the installation.</li>
-<li>Wait for the installation to finish and then launch the app.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed Yahoo Messenger APK on your Android device. Now you can enjoy chatting with your friends and family using the old-fashioned interface and features.</p>
-<p>However, before you start using the app, you should be aware of some potential risks. Downloading apps from third-party sources can expose your device to malware, viruses, or spyware. You should always scan the files before opening them and use a trusted antivirus app. You should also check the permissions that the app requests and make sure they are necessary and reasonable.</p>
- <h2>How to Use Yahoo Messenger APK on Your Device</h2>
-<p>Now that you have downloaded and installed Yahoo Messenger APK on your device, you might be wondering how to use it. Here are some tips and tricks:</p>
-<p>yahoo messenger apk download<br />
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-<ul>
-<li>To use the app, you will need a Yahoo ID. If you already have one, you can sign in with your email and password. If you don't have one, you can create a new one by tapping on Sign Up.</li>
-<li>To chat with someone, tap on the Contacts icon at the bottom of the screen and select a contact from your list. You can also search for a contact by name or email. To start a group chat, tap on the Plus icon at the top right corner and select multiple contacts.</li>
-<li>To send a message, type it in the text box at the bottom of the screen and tap on Send. You can also send photos, videos, or GIFs by tapping on the Camera icon or the GIF icon next to the text box.</li>
-<li>To unsend a message, tap and hold on it and select Unsend. This will remove the message from both your chat history and your contact's chat history.</li>
-<li>To access other Yahoo services like Mail, News, Sports, or Finance, tap on the Menu icon at the top left corner and select the service you want to use. You can also switch between services by swiping left or right on the screen.</li>
-</ul>
-<p>That's it! You have learned how to use Yahoo Messenger APK on your device. Now you can chat with your old and new friends using the app that you loved back in the day.</p>
- <h2>Alternatives to Yahoo Messenger APK</h2>
-<p>While Yahoo Messenger APK can be fun and nostalgic, it is not the most modern or secure messaging app out there. In fact, Yahoo Messenger was officially discontinued in 2018, and your chat history was deleted from Yahoo's servers. You can no longer access your old conversations or contacts using the official app or website.</p>
-<p>So, if you are looking for a more reliable and updated messaging app, you might want to consider some alternatives to Yahoo Messenger APK. Here are some of the most popular ones:</p>
-<table>
-<tr>
-<th>App</th>
-<th>Features</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td><a href="">WhatsApp</a></td>
-<td>- End-to-end encryption - Voice and video calls - Status updates - Group chats - Media sharing - Web and desktop versions</td>
-<td>- Widely used and compatible - Free and ad-free - Secure and private - Simple and user-friendly</td>
-<td>- Requires phone number verification - No GIF support - Limited customization options</td>
-</tr>
-<tr>
-<td><a href="">Telegram</a></td>
-<td>- End-to-end encryption - Voice and video calls - Channels and bots - Group chats - Media sharing - Web and desktop versions</td>
-<td>- Fast and lightweight - Free and ad-free - Secure and private - Customizable and fun</td>
-<td>- Not very popular or compatible - Requires phone number verification - No status updates</td>
-</tr>
-<tr>
-<td><a href="">Signal</a></td>
-<td>- End-to-end encryption - Voice and video calls - Group chats - Media sharing - Web and desktop versions</td>
-<td>- Free and ad-free - Secure and private - Open-source and transparent - Simple and user-friendly</td>
-<td>- Not very popular or compatible - Requires phone number verification - No status updates or channels</td>
-</tr>
-<tr>
-<td><a href="">Facebook Messenger</a></td>
-<td>- Voice and video calls - Stories and reactions - Group chats - Media sharing - Web and desktop versions</td>
-<td>- Popular and compatible - Free and fun - Customizable and interactive - Integrated with Facebook</td>
-<td>- Not very secure or private - Contains ads and spam - Requires Facebook account or phone number verification - Heavy and slow</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>In this article, we have covered everything you need to know about Yahoo Messenger APK, including how to download it, how to use it, and what are some alternatives to it. We hope you found this article helpful and informative.</p>
-<p>If you want to relive the glory days of Yahoo Messenger, you can download Yahoo Messenger APK from APKCombo or FileHippo and enjoy chatting with your friends using the classic interface and features. However, if you want a more modern and secure messaging app, you can try one of the alternatives we mentioned, such as WhatsApp, Telegram, Signal, or Facebook Messenger.</p>
-<p>Whatever app you choose, we hope you have a great time communicating with your loved ones. Happy chatting!</p>
- <h3>Frequently Asked Questions (FAQs)</h3>
- <h4>What is Yahoo Messenger APK?</h4>
-<p>Yahoo Messenger APK is an Android app that lets you use the classic version of Yahoo Messenger on your smartphone or tablet. It is not available on the Google Play Store, but you can download it from third-party websites like APKCombo or FileHippo.</p>
- <h4>Is Yahoo Messenger APK safe to use?</h4>
-<p>Yahoo Messenger APK is not an official app from Yahoo, so it may not be safe to use. Downloading apps from third-party sources can expose your device to malware, viruses, or spyware. You should always scan the files before opening them and use a trusted antivirus app. You should also check the permissions that the app requests and make sure they are necessary and reasonable.</p>
- <h4>How do I unsend a message on Yahoo Messenger APK?</h4>
-<p>To unsend a message on Yahoo Messenger APK, tap and hold on it and select Unsend. This will remove the message from both your chat history and your contact's chat history.</p>
- <h4>What happened to Yahoo Messenger?</h4 <p>Yahoo Messenger was a popular messaging service that was launched in 1998 and had millions of users worldwide. It offered features like voice and video calls, file sharing, games, and chat rooms. However, Yahoo Messenger was discontinued in 2018 due to the decline in its user base and the rise of other messaging apps. Yahoo said that it wanted to focus on building and introducing new, exciting communications tools that better fit consumer needs.</p>
- <h4>How can I access my old Yahoo Messenger chat history?</h4>
-<p>Unfortunately, you can no longer access your old Yahoo Messenger chat history, as it was deleted from Yahoo's servers in 2018. You can only access your chat history if you downloaded it before July 2018. To do so, you need to go to the <a href="">Yahoo Downloader Request site</a>, sign in with your Yahoo ID, and follow the instructions to download your data.</p>
- <h4>Which messaging app is the best for me?</h4>
-<p>There is no definitive answer to this question, as different messaging apps have different features, advantages, and disadvantages. You should choose the app that suits your needs, preferences, and expectations. Some factors that you may want to consider are:</p>
-<ul>
-<li>The popularity and compatibility of the app. You may want to use an app that is widely used by your friends, family, and contacts, and that works well on your device and operating system.</li>
-<li>The security and privacy of the app. You may want to use an app that protects your data and conversations from hackers, advertisers, or third parties. You may also want to use an app that gives you control over your settings and permissions.</li>
-<li>The functionality and usability of the app. You may want to use an app that offers the features and services that you need and enjoy, such as voice and video calls, media sharing, group chats, etc. You may also want to use an app that is easy and fun to use.</li>
-</ul>
-<p>You can compare some of the most popular messaging apps in the table above and decide which one is the best for you.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cooelf/Multimodal-CoT/timm/scheduler/scheduler_factory.py b/spaces/cooelf/Multimodal-CoT/timm/scheduler/scheduler_factory.py
deleted file mode 100644
index 9f7748f42280b846ab159fb18d7cda09d1890123..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/scheduler/scheduler_factory.py
+++ /dev/null
@@ -1,87 +0,0 @@
-""" Scheduler Factory
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-from .cosine_lr import CosineLRScheduler
-from .tanh_lr import TanhLRScheduler
-from .step_lr import StepLRScheduler
-from .plateau_lr import PlateauLRScheduler
-
-
-def create_scheduler(args, optimizer):
-    num_epochs = args.epochs
-
-    if getattr(args, 'lr_noise', None) is not None:
-        lr_noise = getattr(args, 'lr_noise')
-        if isinstance(lr_noise, (list, tuple)):
-            noise_range = [n * num_epochs for n in lr_noise]
-            if len(noise_range) == 1:
-                noise_range = noise_range[0]
-        else:
-            noise_range = lr_noise * num_epochs
-    else:
-        noise_range = None
-
-    lr_scheduler = None
-    if args.sched == 'cosine':
-        lr_scheduler = CosineLRScheduler(
-            optimizer,
-            t_initial=num_epochs,
-            t_mul=getattr(args, 'lr_cycle_mul', 1.),
-            lr_min=args.min_lr,
-            decay_rate=args.decay_rate,
-            warmup_lr_init=args.warmup_lr,
-            warmup_t=args.warmup_epochs,
-            cycle_limit=getattr(args, 'lr_cycle_limit', 1),
-            t_in_epochs=True,
-            noise_range_t=noise_range,
-            noise_pct=getattr(args, 'lr_noise_pct', 0.67),
-            noise_std=getattr(args, 'lr_noise_std', 1.),
-            noise_seed=getattr(args, 'seed', 42),
-        )
-        num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
-    elif args.sched == 'tanh':
-        lr_scheduler = TanhLRScheduler(
-            optimizer,
-            t_initial=num_epochs,
-            t_mul=getattr(args, 'lr_cycle_mul', 1.),
-            lr_min=args.min_lr,
-            warmup_lr_init=args.warmup_lr,
-            warmup_t=args.warmup_epochs,
-            cycle_limit=getattr(args, 'lr_cycle_limit', 1),
-            t_in_epochs=True,
-            noise_range_t=noise_range,
-            noise_pct=getattr(args, 'lr_noise_pct', 0.67),
-            noise_std=getattr(args, 'lr_noise_std', 1.),
-            noise_seed=getattr(args, 'seed', 42),
-        )
-        num_epochs = lr_scheduler.get_cycle_length() + args.cooldown_epochs
-    elif args.sched == 'step':
-        lr_scheduler = StepLRScheduler(
-            optimizer,
-            decay_t=args.decay_epochs,
-            decay_rate=args.decay_rate,
-            warmup_lr_init=args.warmup_lr,
-            warmup_t=args.warmup_epochs,
-            noise_range_t=noise_range,
-            noise_pct=getattr(args, 'lr_noise_pct', 0.67),
-            noise_std=getattr(args, 'lr_noise_std', 1.),
-            noise_seed=getattr(args, 'seed', 42),
-        )
-    elif args.sched == 'plateau':
-        mode = 'min' if 'loss' in getattr(args, 'eval_metric', '') else 'max'
-        lr_scheduler = PlateauLRScheduler(
-            optimizer,
-            decay_rate=args.decay_rate,
-            patience_t=args.patience_epochs,
-            lr_min=args.min_lr,
-            mode=mode,
-            warmup_lr_init=args.warmup_lr,
-            warmup_t=args.warmup_epochs,
-            cooldown_t=0,
-            noise_range_t=noise_range,
-            noise_pct=getattr(args, 'lr_noise_pct', 0.67),
-            noise_std=getattr(args, 'lr_noise_std', 1.),
-            noise_seed=getattr(args, 'seed', 42),
-        )
-
-    return lr_scheduler, num_epochs
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/image/misc.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/image/misc.py
deleted file mode 100644
index 3e61f05e3b05e4c7b40de4eb6c8eb100e6da41d0..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/image/misc.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-
-import annotator.uniformer.mmcv as mmcv
-
-try:
-    import torch
-except ImportError:
-    torch = None
-
-
-def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
-    """Convert tensor to 3-channel images.
-
-    Args:
-        tensor (torch.Tensor): Tensor that contains multiple images, shape (
-            N, C, H, W).
-        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
-        std (tuple[float], optional): Standard deviation of images.
-            Defaults to (1, 1, 1).
-        to_rgb (bool, optional): Whether the tensor was converted to RGB
-            format in the first place. If so, convert it back to BGR.
-            Defaults to True.
-
-    Returns:
-        list[np.ndarray]: A list that contains multiple images.
-    """
-
-    if torch is None:
-        raise RuntimeError('pytorch is not installed')
-    assert torch.is_tensor(tensor) and tensor.ndim == 4
-    assert len(mean) == 3
-    assert len(std) == 3
-
-    num_imgs = tensor.size(0)
-    mean = np.array(mean, dtype=np.float32)
-    std = np.array(std, dtype=np.float32)
-    imgs = []
-    for img_id in range(num_imgs):
-        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
-        img = mmcv.imdenormalize(
-            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
-        imgs.append(np.ascontiguousarray(img))
-    return imgs
diff --git a/spaces/crashedice/signify/SOURCE/yolo_files/utils/aws/userdata.sh b/spaces/crashedice/signify/SOURCE/yolo_files/utils/aws/userdata.sh
deleted file mode 100644
index 890606b76a064662ff20988ab928d581cc8521cb..0000000000000000000000000000000000000000
--- a/spaces/crashedice/signify/SOURCE/yolo_files/utils/aws/userdata.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash
-# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
-# This script will run only once on first instance start (for a re-start script see mime.sh)
-# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
-# Use >300 GB SSD
-
-cd home/ubuntu
-if [ ! -d yolov5 ]; then
-  echo "Running first-time script." # install dependencies, download COCO, pull Docker
-  git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5
-  cd yolov5
-  bash data/scripts/get_coco.sh && echo "Data done." &
-  sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
-  python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
-  wait && echo "All tasks done." # finish background tasks
-else
-  echo "Running re-start script." # resume interrupted runs
-  i=0
-  list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
-  while IFS= read -r id; do
-    ((i++))
-    echo "restarting container $i: $id"
-    sudo docker start $id
-    # sudo docker exec -it $id python train.py --resume # single-GPU
-    sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
-  done <<<"$list"
-fi
diff --git a/spaces/crylake/img2poem/query2labels/lib/models/tresnet2/layers/general_layers.py b/spaces/crylake/img2poem/query2labels/lib/models/tresnet2/layers/general_layers.py
deleted file mode 100644
index dd972228ca31beae5e77b19fec5e43788007dda4..0000000000000000000000000000000000000000
--- a/spaces/crylake/img2poem/query2labels/lib/models/tresnet2/layers/general_layers.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# borrow from: https://github.com/Alibaba-MIIL/TResNet
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .avg_pool import FastAvgPool2d
-
-
-class Flatten(nn.Module):
-    def forward(self, x):
-        return x.view(x.size(0), -1)
-
-
-class DepthToSpace(nn.Module):
-
-    def __init__(self, block_size):
-        super().__init__()
-        self.bs = block_size
-
-    def forward(self, x):
-        N, C, H, W = x.size()
-        x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W)  # (N, bs, bs, C//bs^2, H, W)
-        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # (N, C//bs^2, H, bs, W, bs)
-        x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs)  # (N, C//bs^2, H * bs, W * bs)
-        return x
-
-
-class SpaceToDepthModule(nn.Module):
-    def __init__(self, remove_model_jit=False):
-        super().__init__()
-        if not remove_model_jit:
-            self.op = SpaceToDepthJit()
-        else:
-            self.op = SpaceToDepth()
-
-    def forward(self, x):
-        return self.op(x)
-
-
-class SpaceToDepth(nn.Module):
-    def __init__(self, block_size=4):
-        super().__init__()
-        assert block_size == 4
-        self.bs = block_size
-
-    def forward(self, x):
-        N, C, H, W = x.size()
-        x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs)  # (N, C, H//bs, bs, W//bs, bs)
-        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # (N, bs, bs, C, H//bs, W//bs)
-        x = x.view(N, C * (self.bs ** 2), H // self.bs, W // self.bs)  # (N, C*bs^2, H//bs, W//bs)
-        return x
-
-
-@torch.jit.script
-class SpaceToDepthJit(object):
-    def __call__(self, x: torch.Tensor):
-        # assuming hard-coded that block_size==4 for acceleration
-        N, C, H, W = x.size()
-        x = x.view(N, C, H // 4, 4, W // 4, 4)  # (N, C, H//bs, bs, W//bs, bs)
-        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # (N, bs, bs, C, H//bs, W//bs)
-        x = x.view(N, C * 16, H // 4, W // 4)  # (N, C*bs^2, H//bs, W//bs)
-        return x
-
-
-class hard_sigmoid(nn.Module):
-    def __init__(self, inplace=True):
-        super(hard_sigmoid, self).__init__()
-        self.inplace = inplace
-
-    def forward(self, x):
-        if self.inplace:
-            return x.add_(3.).clamp_(0., 6.).div_(6.)
-        else:
-            return F.relu6(x + 3.) / 6.
-
-
-class SEModule(nn.Module):
-
-    def __init__(self, channels, reduction_channels, inplace=True):
-        super(SEModule, self).__init__()
-        self.avg_pool = FastAvgPool2d()
-        self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, padding=0, bias=True)
-        self.relu = nn.ReLU(inplace=inplace)
-        self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, padding=0, bias=True)
-        # self.activation = hard_sigmoid(inplace=inplace)
-        self.activation = nn.Sigmoid()
-
-    def forward(self, x):
-        x_se = self.avg_pool(x)
-        x_se2 = self.fc1(x_se)
-        x_se2 = self.relu(x_se2)
-        x_se = self.fc2(x_se2)
-        x_se = self.activation(x_se)
-        return x * x_se
diff --git a/spaces/dawood/Kanye-AI/hubert/hubert_model.py b/spaces/dawood/Kanye-AI/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/dawood/Kanye-AI/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/BufrStubImagePlugin.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/BufrStubImagePlugin.py
deleted file mode 100644
index 0425bbd750eacf884ca1fc0ba8aa893a71ccdfc6..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/BufrStubImagePlugin.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# BUFR stub adapter
-#
-# Copyright (c) 1996-2003 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image, ImageFile
-
-_handler = None
-
-
-def register_handler(handler):
-    """
-    Install application-specific BUFR image handler.
-
-    :param handler: Handler object.
-    """
-    global _handler
-    _handler = handler
-
-
-# --------------------------------------------------------------------
-# Image adapter
-
-
-def _accept(prefix):
-    return prefix[:4] == b"BUFR" or prefix[:4] == b"ZCZC"
-
-
-class BufrStubImageFile(ImageFile.StubImageFile):
-    format = "BUFR"
-    format_description = "BUFR"
-
-    def _open(self):
-        offset = self.fp.tell()
-
-        if not _accept(self.fp.read(4)):
-            msg = "Not a BUFR file"
-            raise SyntaxError(msg)
-
-        self.fp.seek(offset)
-
-        # make something up
-        self.mode = "F"
-        self._size = 1, 1
-
-        loader = self._load()
-        if loader:
-            loader.open(self)
-
-    def _load(self):
-        return _handler
-
-
-def _save(im, fp, filename):
-    if _handler is None or not hasattr(_handler, "save"):
-        msg = "BUFR save handler not installed"
-        raise OSError(msg)
-    _handler.save(im, fp, filename)
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(BufrStubImageFile.format, BufrStubImageFile, _accept)
-Image.register_save(BufrStubImageFile.format, _save)
-
-Image.register_extension(BufrStubImageFile.format, ".bufr")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/_core/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/_core/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/DefaultTable.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/DefaultTable.py
deleted file mode 100644
index 32a4b1f258f54d78ad39eb764867a6c354939743..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/DefaultTable.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from fontTools.misc.textTools import Tag
-from fontTools.ttLib import getClassTag
-
-
-class DefaultTable(object):
-
-    dependencies = []
-
-    def __init__(self, tag=None):
-        if tag is None:
-            tag = getClassTag(self.__class__)
-        self.tableTag = Tag(tag)
-
-    def decompile(self, data, ttFont):
-        self.data = data
-
-    def compile(self, ttFont):
-        return self.data
-
-    def toXML(self, writer, ttFont, **kwargs):
-        if hasattr(self, "ERROR"):
-            writer.comment("An error occurred during the decompilation of this table")
-            writer.newline()
-            writer.comment(self.ERROR)
-            writer.newline()
-        writer.begintag("hexdata")
-        writer.newline()
-        writer.dumphex(self.compile(ttFont))
-        writer.endtag("hexdata")
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        from fontTools.misc.textTools import readHex
-        from fontTools import ttLib
-
-        if name != "hexdata":
-            raise ttLib.TTLibError("can't handle '%s' element" % name)
-        self.decompile(readHex(content), ttFont)
-
-    def __repr__(self):
-        return "<'%s' table at %x>" % (self.tableTag, id(self))
-
-    def __eq__(self, other):
-        if type(self) != type(other):
-            return NotImplemented
-        return self.__dict__ == other.__dict__
-
-    def __ne__(self, other):
-        result = self.__eq__(other)
-        return result if result is NotImplemented else not result
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/iup.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/iup.py
deleted file mode 100644
index 0f1232ad2ea1cac8953239a5bdc55f1cedbb5f02..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/varLib/iup.py
+++ /dev/null
@@ -1,496 +0,0 @@
-try:
-    import cython
-
-    COMPILED = cython.compiled
-except (AttributeError, ImportError):
-    # if cython not installed, use mock module with no-op decorators and types
-    from fontTools.misc import cython
-
-    COMPILED = False
-
-from typing import (
-    Sequence,
-    Tuple,
-    Union,
-)
-from numbers import Integral, Real
-
-try:
-    import cython
-
-    COMPILED = cython.compiled
-except (AttributeError, ImportError):
-    # if cython not installed, use mock module with no-op decorators and types
-    from fontTools.misc import cython
-
-    COMPILED = False
-
-
-_Point = Tuple[Real, Real]
-_Delta = Tuple[Real, Real]
-_PointSegment = Sequence[_Point]
-_DeltaSegment = Sequence[_Delta]
-_DeltaOrNone = Union[_Delta, None]
-_DeltaOrNoneSegment = Sequence[_DeltaOrNone]
-_Endpoints = Sequence[Integral]
-
-
-MAX_LOOKBACK = 8
-
-
-@cython.cfunc
-@cython.locals(
-    j=cython.int,
-    n=cython.int,
-    x1=cython.double,
-    x2=cython.double,
-    d1=cython.double,
-    d2=cython.double,
-    scale=cython.double,
-    x=cython.double,
-    d=cython.double,
-)
-def iup_segment(
-    coords: _PointSegment, rc1: _Point, rd1: _Delta, rc2: _Point, rd2: _Delta
-):  # -> _DeltaSegment:
-    """Given two reference coordinates `rc1` & `rc2` and their respective
-    delta vectors `rd1` & `rd2`, returns interpolated deltas for the set of
-    coordinates `coords`."""
-
-    # rc1 = reference coord 1
-    # rd1 = reference delta 1
-    out_arrays = [None, None]
-    for j in 0, 1:
-        out_arrays[j] = out = []
-        x1, x2, d1, d2 = rc1[j], rc2[j], rd1[j], rd2[j]
-
-        if x1 == x2:
-            n = len(coords)
-            if d1 == d2:
-                out.extend([d1] * n)
-            else:
-                out.extend([0] * n)
-            continue
-
-        if x1 > x2:
-            x1, x2 = x2, x1
-            d1, d2 = d2, d1
-
-        # x1 < x2
-        scale = (d2 - d1) / (x2 - x1)
-        for pair in coords:
-            x = pair[j]
-
-            if x <= x1:
-                d = d1
-            elif x >= x2:
-                d = d2
-            else:
-                # Interpolate
-                d = d1 + (x - x1) * scale
-
-            out.append(d)
-
-    return zip(*out_arrays)
-
-
-def iup_contour(deltas: _DeltaOrNoneSegment, coords: _PointSegment) -> _DeltaSegment:
-    """For the contour given in `coords`, interpolate any missing
-    delta values in delta vector `deltas`.
-
-    Returns fully filled-out delta vector."""
-
-    assert len(deltas) == len(coords)
-    if None not in deltas:
-        return deltas
-
-    n = len(deltas)
-    # indices of points with explicit deltas
-    indices = [i for i, v in enumerate(deltas) if v is not None]
-    if not indices:
-        # All deltas are None.  Return 0,0 for all.
-        return [(0, 0)] * n
-
-    out = []
-    it = iter(indices)
-    start = next(it)
-    if start != 0:
-        # Initial segment that wraps around
-        i1, i2, ri1, ri2 = 0, start, start, indices[-1]
-        out.extend(
-            iup_segment(
-                coords[i1:i2], coords[ri1], deltas[ri1], coords[ri2], deltas[ri2]
-            )
-        )
-    out.append(deltas[start])
-    for end in it:
-        if end - start > 1:
-            i1, i2, ri1, ri2 = start + 1, end, start, end
-            out.extend(
-                iup_segment(
-                    coords[i1:i2], coords[ri1], deltas[ri1], coords[ri2], deltas[ri2]
-                )
-            )
-        out.append(deltas[end])
-        start = end
-    if start != n - 1:
-        # Final segment that wraps around
-        i1, i2, ri1, ri2 = start + 1, n, start, indices[0]
-        out.extend(
-            iup_segment(
-                coords[i1:i2], coords[ri1], deltas[ri1], coords[ri2], deltas[ri2]
-            )
-        )
-
-    assert len(deltas) == len(out), (len(deltas), len(out))
-    return out
-
-
-def iup_delta(
-    deltas: _DeltaOrNoneSegment, coords: _PointSegment, ends: _Endpoints
-) -> _DeltaSegment:
-    """For the outline given in `coords`, with contour endpoints given
-    in sorted increasing order in `ends`, interpolate any missing
-    delta values in delta vector `deltas`.
-
-    Returns fully filled-out delta vector."""
-
-    assert sorted(ends) == ends and len(coords) == (ends[-1] + 1 if ends else 0) + 4
-    n = len(coords)
-    ends = ends + [n - 4, n - 3, n - 2, n - 1]
-    out = []
-    start = 0
-    for end in ends:
-        end += 1
-        contour = iup_contour(deltas[start:end], coords[start:end])
-        out.extend(contour)
-        start = end
-
-    return out
-
-
-# Optimizer
-
-
-@cython.cfunc
-@cython.inline
-@cython.locals(
-    i=cython.int,
-    j=cython.int,
-    tolerance=cython.double,
-    x=cython.double,
-    y=cython.double,
-    p=cython.double,
-    q=cython.double,
-)
-@cython.returns(int)
-def can_iup_in_between(
-    deltas: _DeltaSegment,
-    coords: _PointSegment,
-    i: Integral,
-    j: Integral,
-    tolerance: Real,
-):  # -> bool:
-    """Return true if the deltas for points at `i` and `j` (`i < j`) can be
-    successfully used to interpolate deltas for points in between them within
-    provided error tolerance."""
-
-    assert j - i >= 2
-    interp = iup_segment(coords[i + 1 : j], coords[i], deltas[i], coords[j], deltas[j])
-    deltas = deltas[i + 1 : j]
-
-    return all(
-        abs(complex(x - p, y - q)) <= tolerance
-        for (x, y), (p, q) in zip(deltas, interp)
-    )
-
-
-@cython.locals(
-    cj=cython.double,
-    dj=cython.double,
-    lcj=cython.double,
-    ldj=cython.double,
-    ncj=cython.double,
-    ndj=cython.double,
-    force=cython.int,
-    forced=set,
-)
-def _iup_contour_bound_forced_set(
-    deltas: _DeltaSegment, coords: _PointSegment, tolerance: Real = 0
-) -> set:
-    """The forced set is a conservative set of points on the contour that must be encoded
-    explicitly (ie. cannot be interpolated).  Calculating this set allows for significantly
-    speeding up the dynamic-programming, as well as resolve circularity in DP.
-
-    The set is precise; that is, if an index is in the returned set, then there is no way
-    that IUP can generate delta for that point, given `coords` and `deltas`.
-    """
-    assert len(deltas) == len(coords)
-
-    n = len(deltas)
-    forced = set()
-    # Track "last" and "next" points on the contour as we sweep.
-    for i in range(len(deltas) - 1, -1, -1):
-        ld, lc = deltas[i - 1], coords[i - 1]
-        d, c = deltas[i], coords[i]
-        nd, nc = deltas[i - n + 1], coords[i - n + 1]
-
-        for j in (0, 1):  # For X and for Y
-            cj = c[j]
-            dj = d[j]
-            lcj = lc[j]
-            ldj = ld[j]
-            ncj = nc[j]
-            ndj = nd[j]
-
-            if lcj <= ncj:
-                c1, c2 = lcj, ncj
-                d1, d2 = ldj, ndj
-            else:
-                c1, c2 = ncj, lcj
-                d1, d2 = ndj, ldj
-
-            force = False
-
-            # If the two coordinates are the same, then the interpolation
-            # algorithm produces the same delta if both deltas are equal,
-            # and zero if they differ.
-            #
-            # This test has to be before the next one.
-            if c1 == c2:
-                if abs(d1 - d2) > tolerance and abs(dj) > tolerance:
-                    force = True
-
-            # If coordinate for current point is between coordinate of adjacent
-            # points on the two sides, but the delta for current point is NOT
-            # between delta for those adjacent points (considering tolerance
-            # allowance), then there is no way that current point can be IUP-ed.
-            # Mark it forced.
-            elif c1 <= cj <= c2:  # and c1 != c2
-                if not (min(d1, d2) - tolerance <= dj <= max(d1, d2) + tolerance):
-                    force = True
-
-            # Otherwise, the delta should either match the closest, or have the
-            # same sign as the interpolation of the two deltas.
-            else:  # cj < c1 or c2 < cj
-                if d1 != d2:
-                    if cj < c1:
-                        if (
-                            abs(dj) > tolerance
-                            and abs(dj - d1) > tolerance
-                            and ((dj - tolerance < d1) != (d1 < d2))
-                        ):
-                            force = True
-                    else:  # c2 < cj
-                        if (
-                            abs(dj) > tolerance
-                            and abs(dj - d2) > tolerance
-                            and ((d2 < dj + tolerance) != (d1 < d2))
-                        ):
-                            force = True
-
-            if force:
-                forced.add(i)
-                break
-
-    return forced
-
-
-@cython.locals(
-    i=cython.int,
-    j=cython.int,
-    best_cost=cython.double,
-    best_j=cython.int,
-    cost=cython.double,
-    forced=set,
-    tolerance=cython.double,
-)
-def _iup_contour_optimize_dp(
-    deltas: _DeltaSegment,
-    coords: _PointSegment,
-    forced=set(),
-    tolerance: Real = 0,
-    lookback: Integral = None,
-):
-    """Straightforward Dynamic-Programming.  For each index i, find least-costly encoding of
-    points 0 to i where i is explicitly encoded.  We find this by considering all previous
-    explicit points j and check whether interpolation can fill points between j and i.
-
-    Note that solution always encodes last point explicitly.  Higher-level is responsible
-    for removing that restriction.
-
-    As major speedup, we stop looking further whenever we see a "forced" point."""
-
-    n = len(deltas)
-    if lookback is None:
-        lookback = n
-    lookback = min(lookback, MAX_LOOKBACK)
-    costs = {-1: 0}
-    chain = {-1: None}
-    for i in range(0, n):
-        best_cost = costs[i - 1] + 1
-
-        costs[i] = best_cost
-        chain[i] = i - 1
-
-        if i - 1 in forced:
-            continue
-
-        for j in range(i - 2, max(i - lookback, -2), -1):
-            cost = costs[j] + 1
-
-            if cost < best_cost and can_iup_in_between(deltas, coords, j, i, tolerance):
-                costs[i] = best_cost = cost
-                chain[i] = j
-
-            if j in forced:
-                break
-
-    return chain, costs
-
-
-def _rot_list(l: list, k: int):
-    """Rotate list by k items forward.  Ie. item at position 0 will be
-    at position k in returned list.  Negative k is allowed."""
-    n = len(l)
-    k %= n
-    if not k:
-        return l
-    return l[n - k :] + l[: n - k]
-
-
-def _rot_set(s: set, k: int, n: int):
-    k %= n
-    if not k:
-        return s
-    return {(v + k) % n for v in s}
-
-
-def iup_contour_optimize(
-    deltas: _DeltaSegment, coords: _PointSegment, tolerance: Real = 0.0
-) -> _DeltaOrNoneSegment:
-    """For contour with coordinates `coords`, optimize a set of delta
-    values `deltas` within error `tolerance`.
-
-    Returns delta vector that has most number of None items instead of
-    the input delta.
-    """
-
-    n = len(deltas)
-
-    # Get the easy cases out of the way:
-
-    # If all are within tolerance distance of 0, encode nothing:
-    if all(abs(complex(*p)) <= tolerance for p in deltas):
-        return [None] * n
-
-    # If there's exactly one point, return it:
-    if n == 1:
-        return deltas
-
-    # If all deltas are exactly the same, return just one (the first one):
-    d0 = deltas[0]
-    if all(d0 == d for d in deltas):
-        return [d0] + [None] * (n - 1)
-
-    # Else, solve the general problem using Dynamic Programming.
-
-    forced = _iup_contour_bound_forced_set(deltas, coords, tolerance)
-    # The _iup_contour_optimize_dp() routine returns the optimal encoding
-    # solution given the constraint that the last point is always encoded.
-    # To remove this constraint, we use two different methods, depending on
-    # whether forced set is non-empty or not:
-
-    # Debugging: Make the next if always take the second branch and observe
-    # if the font size changes (reduced); that would mean the forced-set
-    # has members it should not have.
-    if forced:
-        # Forced set is non-empty: rotate the contour start point
-        # such that the last point in the list is a forced point.
-        k = (n - 1) - max(forced)
-        assert k >= 0
-
-        deltas = _rot_list(deltas, k)
-        coords = _rot_list(coords, k)
-        forced = _rot_set(forced, k, n)
-
-        # Debugging: Pass a set() instead of forced variable to the next call
-        # to exercise forced-set computation for under-counting.
-        chain, costs = _iup_contour_optimize_dp(deltas, coords, forced, tolerance)
-
-        # Assemble solution.
-        solution = set()
-        i = n - 1
-        while i is not None:
-            solution.add(i)
-            i = chain[i]
-        solution.remove(-1)
-
-        # if not forced <= solution:
-        # 	print("coord", coords)
-        # 	print("deltas", deltas)
-        # 	print("len", len(deltas))
-        assert forced <= solution, (forced, solution)
-
-        deltas = [deltas[i] if i in solution else None for i in range(n)]
-
-        deltas = _rot_list(deltas, -k)
-    else:
-        # Repeat the contour an extra time, solve the new case, then look for solutions of the
-        # circular n-length problem in the solution for new linear case.  I cannot prove that
-        # this always produces the optimal solution...
-        chain, costs = _iup_contour_optimize_dp(
-            deltas + deltas, coords + coords, forced, tolerance, n
-        )
-        best_sol, best_cost = None, n + 1
-
-        for start in range(n - 1, len(costs) - 1):
-            # Assemble solution.
-            solution = set()
-            i = start
-            while i > start - n:
-                solution.add(i % n)
-                i = chain[i]
-            if i == start - n:
-                cost = costs[start] - costs[start - n]
-                if cost <= best_cost:
-                    best_sol, best_cost = solution, cost
-
-        # if not forced <= best_sol:
-        # 	print("coord", coords)
-        # 	print("deltas", deltas)
-        # 	print("len", len(deltas))
-        assert forced <= best_sol, (forced, best_sol)
-
-        deltas = [deltas[i] if i in best_sol else None for i in range(n)]
-
-    return deltas
-
-
-def iup_delta_optimize(
-    deltas: _DeltaSegment,
-    coords: _PointSegment,
-    ends: _Endpoints,
-    tolerance: Real = 0.0,
-) -> _DeltaOrNoneSegment:
-    """For the outline given in `coords`, with contour endpoints given
-    in sorted increasing order in `ends`, optimize a set of delta
-    values `deltas` within error `tolerance`.
-
-    Returns delta vector that has most number of None items instead of
-    the input delta.
-    """
-    assert sorted(ends) == ends and len(coords) == (ends[-1] + 1 if ends else 0) + 4
-    n = len(coords)
-    ends = ends + [n - 4, n - 3, n - 2, n - 1]
-    out = []
-    start = 0
-    for end in ends:
-        contour = iup_contour_optimize(
-            deltas[start : end + 1], coords[start : end + 1], tolerance
-        )
-        assert len(contour) == end - start + 1
-        out.extend(contour)
-        start = end + 1
-
-    return out
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/data_classes.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/data_classes.py
deleted file mode 100644
index 5ebb0c2bded20f1ddbbc73ffc79fac18c7af92db..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/data_classes.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""Pydantic data models and other dataclasses. This is the only file that uses Optional[]
-typing syntax instead of | None syntax to work with pydantic"""
-from enum import Enum, auto
-from typing import Any, Dict, List, Optional, Union
-
-from pydantic import BaseModel
-from typing_extensions import Literal
-
-
-class PredictBody(BaseModel):
-    session_hash: Optional[str] = None
-    event_id: Optional[str] = None
-    data: List[Any]
-    event_data: Optional[Any] = None
-    fn_index: Optional[int] = None
-    batched: Optional[
-        bool
-    ] = False  # Whether the data is a batch of samples (i.e. called from the queue if batch=True) or a single sample (i.e. called from the UI)
-    request: Optional[
-        Union[Dict, List[Dict]]
-    ] = None  # dictionary of request headers, query parameters, url, etc. (used to to pass in request for queuing)
-
-
-class ResetBody(BaseModel):
-    session_hash: str
-    fn_index: int
-
-
-class InterfaceTypes(Enum):
-    STANDARD = auto()
-    INPUT_ONLY = auto()
-    OUTPUT_ONLY = auto()
-    UNIFIED = auto()
-
-
-class Estimation(BaseModel):
-    msg: Optional[str] = "estimation"
-    rank: Optional[int] = None
-    queue_size: int
-    avg_event_process_time: Optional[float] = None
-    avg_event_concurrent_process_time: Optional[float] = None
-    rank_eta: Optional[float] = None
-    queue_eta: float
-
-
-class ProgressUnit(BaseModel):
-    index: Optional[int] = None
-    length: Optional[int] = None
-    unit: Optional[str] = None
-    progress: Optional[float] = None
-    desc: Optional[str] = None
-
-
-class Progress(BaseModel):
-    msg: str = "progress"
-    progress_data: List[ProgressUnit] = []
-
-
-class LogMessage(BaseModel):
-    msg: str = "log"
-    log: str
-    level: Literal["info", "warning"]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_auth.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_auth.py
deleted file mode 100644
index 1d7385d57334c46750d0618a407b49cd829856f4..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_auth.py
+++ /dev/null
@@ -1,347 +0,0 @@
-import hashlib
-import netrc
-import os
-import re
-import time
-import typing
-from base64 import b64encode
-from urllib.request import parse_http_list
-
-from ._exceptions import ProtocolError
-from ._models import Request, Response
-from ._utils import to_bytes, to_str, unquote
-
-if typing.TYPE_CHECKING:  # pragma: no cover
-    from hashlib import _Hash
-
-
-class Auth:
-    """
-    Base class for all authentication schemes.
-
-    To implement a custom authentication scheme, subclass `Auth` and override
-    the `.auth_flow()` method.
-
-    If the authentication scheme does I/O such as disk access or network calls, or uses
-    synchronization primitives such as locks, you should override `.sync_auth_flow()`
-    and/or `.async_auth_flow()` instead of `.auth_flow()` to provide specialized
-    implementations that will be used by `Client` and `AsyncClient` respectively.
-    """
-
-    requires_request_body = False
-    requires_response_body = False
-
-    def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]:
-        """
-        Execute the authentication flow.
-
-        To dispatch a request, `yield` it:
-
-        ```
-        yield request
-        ```
-
-        The client will `.send()` the response back into the flow generator. You can
-        access it like so:
-
-        ```
-        response = yield request
-        ```
-
-        A `return` (or reaching the end of the generator) will result in the
-        client returning the last response obtained from the server.
-
-        You can dispatch as many requests as is necessary.
-        """
-        yield request
-
-    def sync_auth_flow(
-        self, request: Request
-    ) -> typing.Generator[Request, Response, None]:
-        """
-        Execute the authentication flow synchronously.
-
-        By default, this defers to `.auth_flow()`. You should override this method
-        when the authentication scheme does I/O and/or uses concurrency primitives.
-        """
-        if self.requires_request_body:
-            request.read()
-
-        flow = self.auth_flow(request)
-        request = next(flow)
-
-        while True:
-            response = yield request
-            if self.requires_response_body:
-                response.read()
-
-            try:
-                request = flow.send(response)
-            except StopIteration:
-                break
-
-    async def async_auth_flow(
-        self, request: Request
-    ) -> typing.AsyncGenerator[Request, Response]:
-        """
-        Execute the authentication flow asynchronously.
-
-        By default, this defers to `.auth_flow()`. You should override this method
-        when the authentication scheme does I/O and/or uses concurrency primitives.
-        """
-        if self.requires_request_body:
-            await request.aread()
-
-        flow = self.auth_flow(request)
-        request = next(flow)
-
-        while True:
-            response = yield request
-            if self.requires_response_body:
-                await response.aread()
-
-            try:
-                request = flow.send(response)
-            except StopIteration:
-                break
-
-
-class FunctionAuth(Auth):
-    """
-    Allows the 'auth' argument to be passed as a simple callable function,
-    that takes the request, and returns a new, modified request.
-    """
-
-    def __init__(self, func: typing.Callable[[Request], Request]) -> None:
-        self._func = func
-
-    def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]:
-        yield self._func(request)
-
-
-class BasicAuth(Auth):
-    """
-    Allows the 'auth' argument to be passed as a (username, password) pair,
-    and uses HTTP Basic authentication.
-    """
-
-    def __init__(
-        self, username: typing.Union[str, bytes], password: typing.Union[str, bytes]
-    ):
-        self._auth_header = self._build_auth_header(username, password)
-
-    def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]:
-        request.headers["Authorization"] = self._auth_header
-        yield request
-
-    def _build_auth_header(
-        self, username: typing.Union[str, bytes], password: typing.Union[str, bytes]
-    ) -> str:
-        userpass = b":".join((to_bytes(username), to_bytes(password)))
-        token = b64encode(userpass).decode()
-        return f"Basic {token}"
-
-
-class NetRCAuth(Auth):
-    """
-    Use a 'netrc' file to lookup basic auth credentials based on the url host.
-    """
-
-    def __init__(self, file: typing.Optional[str] = None):
-        self._netrc_info = netrc.netrc(file)
-
-    def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]:
-        auth_info = self._netrc_info.authenticators(request.url.host)
-        if auth_info is None or not auth_info[2]:
-            # The netrc file did not have authentication credentials for this host.
-            yield request
-        else:
-            # Build a basic auth header with credentials from the netrc file.
-            request.headers["Authorization"] = self._build_auth_header(
-                username=auth_info[0], password=auth_info[2]
-            )
-            yield request
-
-    def _build_auth_header(
-        self, username: typing.Union[str, bytes], password: typing.Union[str, bytes]
-    ) -> str:
-        userpass = b":".join((to_bytes(username), to_bytes(password)))
-        token = b64encode(userpass).decode()
-        return f"Basic {token}"
-
-
-class DigestAuth(Auth):
-    _ALGORITHM_TO_HASH_FUNCTION: typing.Dict[str, typing.Callable[[bytes], "_Hash"]] = {
-        "MD5": hashlib.md5,
-        "MD5-SESS": hashlib.md5,
-        "SHA": hashlib.sha1,
-        "SHA-SESS": hashlib.sha1,
-        "SHA-256": hashlib.sha256,
-        "SHA-256-SESS": hashlib.sha256,
-        "SHA-512": hashlib.sha512,
-        "SHA-512-SESS": hashlib.sha512,
-    }
-
-    def __init__(
-        self, username: typing.Union[str, bytes], password: typing.Union[str, bytes]
-    ) -> None:
-        self._username = to_bytes(username)
-        self._password = to_bytes(password)
-        self._last_challenge: typing.Optional[_DigestAuthChallenge] = None
-        self._nonce_count = 1
-
-    def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]:
-        if self._last_challenge:
-            request.headers["Authorization"] = self._build_auth_header(
-                request, self._last_challenge
-            )
-
-        response = yield request
-
-        if response.status_code != 401 or "www-authenticate" not in response.headers:
-            # If the response is not a 401 then we don't
-            # need to build an authenticated request.
-            return
-
-        for auth_header in response.headers.get_list("www-authenticate"):
-            if auth_header.lower().startswith("digest "):
-                break
-        else:
-            # If the response does not include a 'WWW-Authenticate: Digest ...'
-            # header, then we don't need to build an authenticated request.
-            return
-
-        self._last_challenge = self._parse_challenge(request, response, auth_header)
-        self._nonce_count = 1
-
-        request.headers["Authorization"] = self._build_auth_header(
-            request, self._last_challenge
-        )
-        yield request
-
-    def _parse_challenge(
-        self, request: Request, response: Response, auth_header: str
-    ) -> "_DigestAuthChallenge":
-        """
-        Returns a challenge from a Digest WWW-Authenticate header.
-        These take the form of:
-        `Digest realm="realm@host.com",qop="auth,auth-int",nonce="abc",opaque="xyz"`
-        """
-        scheme, _, fields = auth_header.partition(" ")
-
-        # This method should only ever have been called with a Digest auth header.
-        assert scheme.lower() == "digest"
-
-        header_dict: typing.Dict[str, str] = {}
-        for field in parse_http_list(fields):
-            key, value = field.strip().split("=", 1)
-            header_dict[key] = unquote(value)
-
-        try:
-            realm = header_dict["realm"].encode()
-            nonce = header_dict["nonce"].encode()
-            algorithm = header_dict.get("algorithm", "MD5")
-            opaque = header_dict["opaque"].encode() if "opaque" in header_dict else None
-            qop = header_dict["qop"].encode() if "qop" in header_dict else None
-            return _DigestAuthChallenge(
-                realm=realm, nonce=nonce, algorithm=algorithm, opaque=opaque, qop=qop
-            )
-        except KeyError as exc:
-            message = "Malformed Digest WWW-Authenticate header"
-            raise ProtocolError(message, request=request) from exc
-
-    def _build_auth_header(
-        self, request: Request, challenge: "_DigestAuthChallenge"
-    ) -> str:
-        hash_func = self._ALGORITHM_TO_HASH_FUNCTION[challenge.algorithm.upper()]
-
-        def digest(data: bytes) -> bytes:
-            return hash_func(data).hexdigest().encode()
-
-        A1 = b":".join((self._username, challenge.realm, self._password))
-
-        path = request.url.raw_path
-        A2 = b":".join((request.method.encode(), path))
-        # TODO: implement auth-int
-        HA2 = digest(A2)
-
-        nc_value = b"%08x" % self._nonce_count
-        cnonce = self._get_client_nonce(self._nonce_count, challenge.nonce)
-        self._nonce_count += 1
-
-        HA1 = digest(A1)
-        if challenge.algorithm.lower().endswith("-sess"):
-            HA1 = digest(b":".join((HA1, challenge.nonce, cnonce)))
-
-        qop = self._resolve_qop(challenge.qop, request=request)
-        if qop is None:
-            digest_data = [HA1, challenge.nonce, HA2]
-        else:
-            digest_data = [challenge.nonce, nc_value, cnonce, qop, HA2]
-        key_digest = b":".join(digest_data)
-
-        format_args = {
-            "username": self._username,
-            "realm": challenge.realm,
-            "nonce": challenge.nonce,
-            "uri": path,
-            "response": digest(b":".join((HA1, key_digest))),
-            "algorithm": challenge.algorithm.encode(),
-        }
-        if challenge.opaque:
-            format_args["opaque"] = challenge.opaque
-        if qop:
-            format_args["qop"] = b"auth"
-            format_args["nc"] = nc_value
-            format_args["cnonce"] = cnonce
-
-        return "Digest " + self._get_header_value(format_args)
-
-    def _get_client_nonce(self, nonce_count: int, nonce: bytes) -> bytes:
-        s = str(nonce_count).encode()
-        s += nonce
-        s += time.ctime().encode()
-        s += os.urandom(8)
-
-        return hashlib.sha1(s).hexdigest()[:16].encode()
-
-    def _get_header_value(self, header_fields: typing.Dict[str, bytes]) -> str:
-        NON_QUOTED_FIELDS = ("algorithm", "qop", "nc")
-        QUOTED_TEMPLATE = '{}="{}"'
-        NON_QUOTED_TEMPLATE = "{}={}"
-
-        header_value = ""
-        for i, (field, value) in enumerate(header_fields.items()):
-            if i > 0:
-                header_value += ", "
-            template = (
-                QUOTED_TEMPLATE
-                if field not in NON_QUOTED_FIELDS
-                else NON_QUOTED_TEMPLATE
-            )
-            header_value += template.format(field, to_str(value))
-
-        return header_value
-
-    def _resolve_qop(
-        self, qop: typing.Optional[bytes], request: Request
-    ) -> typing.Optional[bytes]:
-        if qop is None:
-            return None
-        qops = re.split(b", ?", qop)
-        if b"auth" in qops:
-            return b"auth"
-
-        if qops == [b"auth-int"]:
-            raise NotImplementedError("Digest auth-int support is not yet implemented")
-
-        message = f'Unexpected qop value "{qop!r}" in digest auth'
-        raise ProtocolError(message, request=request)
-
-
-class _DigestAuthChallenge(typing.NamedTuple):
-    realm: bytes
-    nonce: bytes
-    algorithm: str
-    opaque: typing.Optional[bytes]
-    qop: typing.Optional[bytes]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_exceptions.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_exceptions.py
deleted file mode 100644
index 24a4f8aba337daa8f3695d87cedd331f2ec4eb61..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_exceptions.py
+++ /dev/null
@@ -1,343 +0,0 @@
-"""
-Our exception hierarchy:
-
-* HTTPError
-  x RequestError
-    + TransportError
-      - TimeoutException
-        · ConnectTimeout
-        · ReadTimeout
-        · WriteTimeout
-        · PoolTimeout
-      - NetworkError
-        · ConnectError
-        · ReadError
-        · WriteError
-        · CloseError
-      - ProtocolError
-        · LocalProtocolError
-        · RemoteProtocolError
-      - ProxyError
-      - UnsupportedProtocol
-    + DecodingError
-    + TooManyRedirects
-  x HTTPStatusError
-* InvalidURL
-* CookieConflict
-* StreamError
-  x StreamConsumed
-  x StreamClosed
-  x ResponseNotRead
-  x RequestNotRead
-"""
-import contextlib
-import typing
-
-if typing.TYPE_CHECKING:
-    from ._models import Request, Response  # pragma: no cover
-
-
-class HTTPError(Exception):
-    """
-    Base class for `RequestError` and `HTTPStatusError`.
-
-    Useful for `try...except` blocks when issuing a request,
-    and then calling `.raise_for_status()`.
-
-    For example:
-
-    ```
-    try:
-        response = httpx.get("https://www.example.com")
-        response.raise_for_status()
-    except httpx.HTTPError as exc:
-        print(f"HTTP Exception for {exc.request.url} - {exc}")
-    ```
-    """
-
-    def __init__(self, message: str) -> None:
-        super().__init__(message)
-        self._request: typing.Optional["Request"] = None
-
-    @property
-    def request(self) -> "Request":
-        if self._request is None:
-            raise RuntimeError("The .request property has not been set.")
-        return self._request
-
-    @request.setter
-    def request(self, request: "Request") -> None:
-        self._request = request
-
-
-class RequestError(HTTPError):
-    """
-    Base class for all exceptions that may occur when issuing a `.request()`.
-    """
-
-    def __init__(
-        self, message: str, *, request: typing.Optional["Request"] = None
-    ) -> None:
-        super().__init__(message)
-        # At the point an exception is raised we won't typically have a request
-        # instance to associate it with.
-        #
-        # The 'request_context' context manager is used within the Client and
-        # Response methods in order to ensure that any raised exceptions
-        # have a `.request` property set on them.
-        self._request = request
-
-
-class TransportError(RequestError):
-    """
-    Base class for all exceptions that occur at the level of the Transport API.
-    """
-
-
-# Timeout exceptions...
-
-
-class TimeoutException(TransportError):
-    """
-    The base class for timeout errors.
-
-    An operation has timed out.
-    """
-
-
-class ConnectTimeout(TimeoutException):
-    """
-    Timed out while connecting to the host.
-    """
-
-
-class ReadTimeout(TimeoutException):
-    """
-    Timed out while receiving data from the host.
-    """
-
-
-class WriteTimeout(TimeoutException):
-    """
-    Timed out while sending data to the host.
-    """
-
-
-class PoolTimeout(TimeoutException):
-    """
-    Timed out waiting to acquire a connection from the pool.
-    """
-
-
-# Core networking exceptions...
-
-
-class NetworkError(TransportError):
-    """
-    The base class for network-related errors.
-
-    An error occurred while interacting with the network.
-    """
-
-
-class ReadError(NetworkError):
-    """
-    Failed to receive data from the network.
-    """
-
-
-class WriteError(NetworkError):
-    """
-    Failed to send data through the network.
-    """
-
-
-class ConnectError(NetworkError):
-    """
-    Failed to establish a connection.
-    """
-
-
-class CloseError(NetworkError):
-    """
-    Failed to close a connection.
-    """
-
-
-# Other transport exceptions...
-
-
-class ProxyError(TransportError):
-    """
-    An error occurred while establishing a proxy connection.
-    """
-
-
-class UnsupportedProtocol(TransportError):
-    """
-    Attempted to make a request to an unsupported protocol.
-
-    For example issuing a request to `ftp://www.example.com`.
-    """
-
-
-class ProtocolError(TransportError):
-    """
-    The protocol was violated.
-    """
-
-
-class LocalProtocolError(ProtocolError):
-    """
-    A protocol was violated by the client.
-
-    For example if the user instantiated a `Request` instance explicitly,
-    failed to include the mandatory `Host:` header, and then issued it directly
-    using `client.send()`.
-    """
-
-
-class RemoteProtocolError(ProtocolError):
-    """
-    The protocol was violated by the server.
-
-    For example, returning malformed HTTP.
-    """
-
-
-# Other request exceptions...
-
-
-class DecodingError(RequestError):
-    """
-    Decoding of the response failed, due to a malformed encoding.
-    """
-
-
-class TooManyRedirects(RequestError):
-    """
-    Too many redirects.
-    """
-
-
-# Client errors
-
-
-class HTTPStatusError(HTTPError):
-    """
-    The response had an error HTTP status of 4xx or 5xx.
-
-    May be raised when calling `response.raise_for_status()`
-    """
-
-    def __init__(
-        self, message: str, *, request: "Request", response: "Response"
-    ) -> None:
-        super().__init__(message)
-        self.request = request
-        self.response = response
-
-
-class InvalidURL(Exception):
-    """
-    URL is improperly formed or cannot be parsed.
-    """
-
-    def __init__(self, message: str) -> None:
-        super().__init__(message)
-
-
-class CookieConflict(Exception):
-    """
-    Attempted to lookup a cookie by name, but multiple cookies existed.
-
-    Can occur when calling `response.cookies.get(...)`.
-    """
-
-    def __init__(self, message: str) -> None:
-        super().__init__(message)
-
-
-# Stream exceptions...
-
-# These may occur as the result of a programming error, by accessing
-# the request/response stream in an invalid manner.
-
-
-class StreamError(RuntimeError):
-    """
-    The base class for stream exceptions.
-
-    The developer made an error in accessing the request stream in
-    an invalid way.
-    """
-
-    def __init__(self, message: str) -> None:
-        super().__init__(message)
-
-
-class StreamConsumed(StreamError):
-    """
-    Attempted to read or stream content, but the content has already
-    been streamed.
-    """
-
-    def __init__(self) -> None:
-        message = (
-            "Attempted to read or stream some content, but the content has "
-            "already been streamed. For requests, this could be due to passing "
-            "a generator as request content, and then receiving a redirect "
-            "response or a secondary request as part of an authentication flow."
-            "For responses, this could be due to attempting to stream the response "
-            "content more than once."
-        )
-        super().__init__(message)
-
-
-class StreamClosed(StreamError):
-    """
-    Attempted to read or stream response content, but the request has been
-    closed.
-    """
-
-    def __init__(self) -> None:
-        message = (
-            "Attempted to read or stream content, but the stream has " "been closed."
-        )
-        super().__init__(message)
-
-
-class ResponseNotRead(StreamError):
-    """
-    Attempted to access streaming response content, without having called `read()`.
-    """
-
-    def __init__(self) -> None:
-        message = "Attempted to access streaming response content, without having called `read()`."
-        super().__init__(message)
-
-
-class RequestNotRead(StreamError):
-    """
-    Attempted to access streaming request content, without having called `read()`.
-    """
-
-    def __init__(self) -> None:
-        message = "Attempted to access streaming request content, without having called `read()`."
-        super().__init__(message)
-
-
-@contextlib.contextmanager
-def request_context(
-    request: typing.Optional["Request"] = None,
-) -> typing.Iterator[None]:
-    """
-    A context manager that can be used to attach the given request context
-    to any `RequestError` exceptions that are raised within the block.
-    """
-    try:
-        yield
-    except RequestError as exc:
-        if request is not None:
-            exc.request = request
-        raise exc
diff --git a/spaces/derful/Chatgpt-academic/functional.py b/spaces/derful/Chatgpt-academic/functional.py
deleted file mode 100644
index e416063ca0859bc498f6abced452ca54866a271d..0000000000000000000000000000000000000000
--- a/spaces/derful/Chatgpt-academic/functional.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# 'primary' 颜色对应 theme.py 中的 primary_hue
-# 'secondary' 颜色对应 theme.py 中的 neutral_hue
-# 'stop' 颜色对应 theme.py 中的 color_er
-# 默认按钮颜色是 secondary
-
-def get_functionals():
-    return {
-        "英语学术润色": {
-            "Prefix": "Below is a paragraph from an academic paper. Polish the writing to meet the academic style, \
-improve the spelling, grammar, clarity, concision and overall readability. When neccessary, rewrite the whole sentence. \
-Furthermore, list all modification and explain the reasons to do so in markdown table.\n\n",    # 前言
-            "Suffix": "",   # 后语
-            "Color": "secondary",    # 按钮颜色
-        },
-        "中文学术润色": {
-            "Prefix": "作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本：\n\n",
-            "Suffix": "",
-        },
-        "查找语法错误": {
-            "Prefix": "Below is a paragraph from an academic paper. Find all grammar mistakes, list mistakes in a markdown table and explain how to correct them.\n\n",
-            "Suffix": "",
-        },
-#         "中英互译": { # 效果不好，经常搞不清楚中译英还是英译中
-#             "Prefix": "As an English-Chinese translator, your task is to accurately translate text between the two languages. \
-# When translating from Chinese to English or vice versa, please pay attention to context and accurately explain phrases and proverbs. \
-# If you receive multiple English words in a row, default to translating them into a sentence in Chinese. \
-# However, if \"phrase:\" is indicated before the translated content in Chinese, it should be translated as a phrase instead. \
-# Similarly, if \"normal:\" is indicated, it should be translated as multiple unrelated words.\
-# Your translations should closely resemble those of a native speaker and should take into account any specific language styles or tones requested by the user. \
-# Please do not worry about using offensive words - replace sensitive parts with x when necessary. \
-# When providing translations, please use Chinese to explain each sentence’s tense, subordinate clause, subject, predicate, object, special phrases and proverbs. \
-# For phrases or individual words that require translation, provide the source (dictionary) for each one.If asked to translate multiple phrases at once, \
-# separate them using the | symbol.Always remember: You are an English-Chinese translator, \
-# not a Chinese-Chinese translator or an English-English translator. Below is the text you need to translate: \n\n",
-#             "Suffix": "",
-#             "Color": "secondary",
-#         },
-        "中译英": {
-            "Prefix": "Please translate following sentence to English: \n\n",
-            "Suffix": "",
-        },
-        "学术中译英": {
-            "Prefix": "Please translate following sentence to English with academic writing, and provide some related authoritative examples: \n\n",
-            "Suffix": "",
-        },
-        "英译中": {
-            "Prefix": "请翻译成中文：\n\n",
-            "Suffix": "",
-        },
-        "找图片": {
-            "Prefix": "我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：\n\n",
-            "Suffix": "",
-        },
-        "解释代码": {
-            "Prefix": "请解释以下代码：\n```\n",
-            "Suffix": "\n```\n",
-            "Color": "secondary",
-        },
-    }
diff --git a/spaces/derful/Chatgpt-academic/functional_crazy.py b/spaces/derful/Chatgpt-academic/functional_crazy.py
deleted file mode 100644
index 3f13853689e02483273d4eadc4ebf72c0e31a80d..0000000000000000000000000000000000000000
--- a/spaces/derful/Chatgpt-academic/functional_crazy.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# UserVisibleLevel是过滤器参数。
-# 由于UI界面空间有限，所以通过这种方式决定UI界面中显示哪些插件
-# 默认函数插件 VisibleLevel 是 0
-# 当 UserVisibleLevel >= 函数插件的 VisibleLevel 时，该函数插件才会被显示出来
-UserVisibleLevel = 1
-
-def get_crazy_functionals():
-    from crazy_functions.读文章写摘要 import 读文章写摘要
-    from crazy_functions.生成函数注释 import 批量生成函数注释
-    from crazy_functions.解析项目源代码 import 解析项目本身
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
-    from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
-
-    function_plugins = {
-        "请解析并解构此项目本身": {
-            "Function": 解析项目本身
-        },
-        "解析整个py项目": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个Python项目
-        },
-        "解析整个C++项目头文件": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个C项目的头文件
-        },
-        "解析整个C++项目": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个C项目
-        },
-        "读tex论文写摘要": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 读文章写摘要
-        },
-        "批量生成函数注释": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 批量生成函数注释
-        },
-        "[多线程demo] 把本项目源代码切换成全英文": {
-            "Function": 全项目切换英文
-        },
-        "[函数插件模板demo] 历史上的今天": {
-            "Function": 高阶功能模板函数
-        },
-    }
-
-    # VisibleLevel=1 经过测试，但功能未达到理想状态
-    if UserVisibleLevel >= 1:
-        from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-        function_plugins.update({
-            "[仅供开发调试] 批量总结PDF文档": {
-                "Color": "stop",
-                "Function": 批量总结PDF文档
-            },
-        })
-
-    # VisibleLevel=2 尚未充分测试的函数插件，放在这里
-    if UserVisibleLevel >= 2:
-        function_plugins.update({
-        })
-
-    return function_plugins
-
-
diff --git a/spaces/diacanFperku/AutoGPT/Daphne Yvm D03 11.md b/spaces/diacanFperku/AutoGPT/Daphne Yvm D03 11.md
deleted file mode 100644
index 855cf8bb02b9deb4994574f124b916fbfb8f5572..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Daphne Yvm D03 11.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>daphne yvm d03 11</h2><br /><p><b><b>Download File</b> ->>->>->> <a href="https://gohhs.com/2uFTJ1">https://gohhs.com/2uFTJ1</a></b></p><br /><br />
-
- d5da3c52bf<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Girls Mag.de 43.md b/spaces/diacanFperku/AutoGPT/Girls Mag.de 43.md
deleted file mode 100644
index b5d8296f303fd01c067b1d5336d25ca9095d3f79..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Girls Mag.de 43.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Girls Mag.de 43</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://gohhs.com/2uFVJO">https://gohhs.com/2uFVJO</a></b></p><br /><br />
-
-Girls Mag.de 43 maetlnekea. smarchiechatu 2021. 6. 17. 22:04. Da Vinci (magazine) 49 Dazai Osamu 188, 192-93, 199; Bannen (Last Years) 140; ... Others. 7. 17. 22:10. Malek (Journal) 54 Sakurako Hashimoto 188, 192-93, 199; ...and others. 8. 17. 23:01. Malek (journal) 55 Sakurako Hashimoto 188, 192-93, 199; ...and others. 9. 18. 22:04. Malek (magazine) 56 Sakurako Hashimoto 188, 192-93, 199; ...and others. 10. 18. 22:20. Malek (journal) 57 Sakurako Hashimoto 188, 192-93, 199; ...and others. 11. 18. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diffusers/controlnet-3d-pose/README.md b/spaces/diffusers/controlnet-3d-pose/README.md
deleted file mode 100644
index 6c032a0571bd57fd0be7c542477e0e3cd20776a4..0000000000000000000000000000000000000000
--- a/spaces/diffusers/controlnet-3d-pose/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ControlNet 3D Pose
-emoji: 🔥
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-duplicated_from: diffusers/controlnet-openpose
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/dog/fastapi-document-qa/README.md b/spaces/dog/fastapi-document-qa/README.md
deleted file mode 100644
index a2715379c9fee83df9ec5be0f23678b6e445dcde..0000000000000000000000000000000000000000
--- a/spaces/dog/fastapi-document-qa/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Fastapi Document Qa
-emoji: 🐠
-colorFrom: gray
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dongyi/MMFS/README.md b/spaces/dongyi/MMFS/README.md
deleted file mode 100644
index 2385310755d698febe19cffb9bfbceafdecb4993..0000000000000000000000000000000000000000
--- a/spaces/dongyi/MMFS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MMFS
-emoji: 🌍
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/echolee/faceanime4u/app.py b/spaces/echolee/faceanime4u/app.py
deleted file mode 100644
index 2a6b2973f351b220f93150711d34af8da49b8c87..0000000000000000000000000000000000000000
--- a/spaces/echolee/faceanime4u/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from PIL import Image
-import torch
-import gradio as gr
-
-model2 = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator",
-    pretrained=True,
-    device="cpu",
-    progress=False
-)
-model1 = torch.hub.load("AK391/animegan2-pytorch:main",
-                        "generator", pretrained="face_paint_512_v1",  device="cpu")
-face2paint = torch.hub.load(
-    'AK391/animegan2-pytorch:main', 'face2paint',
-    size=512, device="cpu", side_by_side=False
-)
-
-def inference(img, ver):
-    if ver == 'version 2 (🔺 robustness,🔻 stylization)':
-        out = face2paint(model2, img)
-    else:
-        out = face2paint(model1, img)
-    return out
-
-title = "Face Anime For You"
-description = "Online Demo for AnimeGanv2 Face Portrait v2. To use it, simply upload your image, or click one of the examples to load them. Please use a cropped portrait picture for best results similar to the examples below.\n" + \
-    "基于AnimeGanv2-动漫风格人脸迁移的在线应用示例。点击下面的窗口以上传图片，或者点击以加载Examples中的图片。请使用与示例中的图片风格相近的照片，确保图片的主体是人脸、五官清晰、没有遮挡，光线良好。"
-article = "<p style='text-align: center'>❤ from Bruce</p>"
-examples = [['elon.png', 'version 2 (🔺 robustness,🔻 stylization)'],
-            ['IU.png', 'version 2 (🔺 robustness,🔻 stylization)']]
-gr.Interface(inference, [gr.inputs.Image(type="pil"), gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)', 'version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')
-                         ], gr.outputs.Image(type="pil"), title=title, description=description, article=article, enable_queue=True, examples=examples, allow_flagging=False, allow_screenshot=False).launch()
diff --git a/spaces/egumasa/engagement-analyzer-demo/pipeline/custom_functions.py b/spaces/egumasa/engagement-analyzer-demo/pipeline/custom_functions.py
deleted file mode 100644
index 2534fa35178bd41aa4003e803d3481d7648ef40b..0000000000000000000000000000000000000000
--- a/spaces/egumasa/engagement-analyzer-demo/pipeline/custom_functions.py
+++ /dev/null
@@ -1,190 +0,0 @@
-from functools import partial
-from pathlib import Path
-from typing import Iterable, Callable
-import spacy
-from spacy.training import Example
-from spacy.tokens import DocBin, Doc
-
-# make the factory work
-# from scripts.rel_pipe import make_relation_extractor
-
-# make the config work
-# from scripts.rel_model import create_relation_model, create_classification_layer, create_instances, create_tensors
-# from scripts.custom_comps.SpanCat_extention import build_mean_max_reducer1, build_mean_max_reducer2, build_mean_max_reducer3, build_mean_max_reducer4
-
-from typing import List, Tuple, cast
-from thinc.api import Model, with_getitem, chain, list2ragged, Logistic
-from thinc.api import Maxout, Linear, concatenate, glorot_uniform_init, PyTorchLSTM
-from thinc.api import reduce_mean, reduce_max, reduce_first, reduce_last
-from thinc.types import Ragged, Floats2d
-
-from spacy.util import registry
-from spacy.tokens import Doc
-from spacy.ml.extract_spans import extract_spans
-
-# @registry.layers("spacy.LinearLogistic.v1")
-# def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
-#     """An output layer for multi-label classification. It uses a linear layer
-#     followed by a logistic activation.
-#     """
-#     return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
-
-
-@registry.layers("mean_max_reducer.v1.5")
-def build_mean_max_reducer1(hidden_size: int,
-                            dropout: float = 0.0) -> Model[Ragged, Floats2d]:
-    """Reduce sequences by concatenating their mean and max pooled vectors,
-    and then combine the concatenated vectors with a hidden layer.
-    """
-    return chain(
-        concatenate(
-            cast(Model[Ragged, Floats2d], reduce_last()),
-            cast(Model[Ragged, Floats2d], reduce_first()),
-            reduce_mean(),
-            reduce_max(),
-        ),
-        Maxout(nO=hidden_size, normalize=True, dropout=dropout),
-    )
-
-
-@registry.layers("mean_max_reducer.v2")
-def build_mean_max_reducer2(hidden_size: int,
-                            dropout: float = 0.0) -> Model[Ragged, Floats2d]:
-    """Reduce sequences by concatenating their mean and max pooled vectors,
-    and then combine the concatenated vectors with a hidden layer.
-    """
-    return chain(
-        concatenate(
-            cast(Model[Ragged, Floats2d], reduce_last()),
-            cast(Model[Ragged, Floats2d], reduce_first()),
-            reduce_mean(),
-            reduce_max(),
-        ), Maxout(nO=hidden_size, normalize=True, dropout=dropout),
-        Maxout(nO=hidden_size, normalize=True, dropout=dropout))
-
-
-# @registry.layers("mean_max_reducer.v2")
-# def build_mean_max_reducer2(hidden_size: int,
-#                             depth: int) -> Model[Ragged, Floats2d]:
-#     """Reduce sequences by concatenating their mean and max pooled vectors,
-#     and then combine the concatenated vectors with a hidden layer.
-#     """
-#     return chain(
-#         concatenate(
-#             cast(Model[Ragged, Floats2d], reduce_last()),
-#             cast(Model[Ragged, Floats2d], reduce_first()),
-#             reduce_mean(),
-#             reduce_max(),
-#         ), Maxout(nO=hidden_size, normalize=True, dropout=0.0),
-#         PyTorchLSTM(nO=64, nI=hidden_size, bi=True, depth=depth, dropout=0.2))
-
-
-@registry.layers("mean_max_reducer.v3")
-def build_mean_max_reducer3(hidden_size: int,
-                            maxout_pieces: int = 3,
-                            dropout: float = 0.0) -> Model[Ragged, Floats2d]:
-    """Reduce sequences by concatenating their mean and max pooled vectors,
-    and then combine the concatenated vectors with a hidden layer.
-    """
-    hidden_size2 = int(hidden_size / 2)
-    hidden_size3 = int(hidden_size / 2)
-    return chain(
-        concatenate(
-            cast(Model[Ragged, Floats2d], reduce_last()),
-            cast(Model[Ragged, Floats2d], reduce_first()),
-            reduce_mean(),
-            reduce_max(),
-        ),
-        Maxout(nO=hidden_size,
-               nP=maxout_pieces,
-               normalize=True,
-               dropout=dropout),
-        Maxout(nO=hidden_size2,
-               nP=maxout_pieces,
-               normalize=True,
-               dropout=dropout),
-        Maxout(nO=hidden_size3,
-               nP=maxout_pieces,
-               normalize=True,
-               dropout=dropout))
-
-
-@registry.layers("mean_max_reducer.v3.3")
-def build_mean_max_reducer4(hidden_size: int,
-                            depth: int) -> Model[Ragged, Floats2d]:
-    """Reduce sequences by concatenating their mean and max pooled vectors,
-    and then combine the concatenated vectors with a hidden layer.
-    """
-    hidden_size2 = int(hidden_size / 2)
-    hidden_size3 = int(hidden_size / 2)
-    return chain(
-        concatenate(
-            cast(Model[Ragged, Floats2d], reduce_last()),
-            cast(Model[Ragged, Floats2d], reduce_first()),
-            reduce_mean(),
-            reduce_max(),
-        ), Maxout(nO=hidden_size, nP=3, normalize=True, dropout=0.0),
-        Maxout(nO=hidden_size2, nP=3, normalize=True, dropout=0.0),
-        Maxout(nO=hidden_size3, nP=3, normalize=True, dropout=0.0))
-
-
-@registry.architectures("CustomSpanCategorizer.v2")
-def build_spancat_model(
-    tok2vec: Model[List[Doc], List[Floats2d]],
-    reducer: Model[Ragged, Floats2d],
-    scorer: Model[Floats2d, Floats2d],
-) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
-    """Build a span categorizer model, given a token-to-vector model, a
-    reducer model to map the sequence of vectors for each span down to a single
-    vector, and a scorer model to map the vectors to probabilities.
-    tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
-    reducer (Model[Ragged, Floats2d]): The reducer model.
-    scorer (Model[Floats2d, Floats2d]): The scorer model.
-    """
-    model = chain(
-        cast(
-            Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
-            with_getitem(
-                0,
-                chain(tok2vec,
-                      cast(Model[List[Floats2d], Ragged], list2ragged()))),
-        ),
-        extract_spans(),
-        reducer,
-        scorer,
-    )
-    model.set_ref("tok2vec", tok2vec)
-    model.set_ref("reducer", reducer)
-    model.set_ref("scorer", scorer)
-    return model
-
-
-# @registry.architectures("spacy.SpanCategorizer.v1")
-# def build_spancat_model(
-#     tok2vec: Model[List[Doc], List[Floats2d]],
-#     reducer: Model[Ragged, Floats2d],
-#     scorer: Model[Floats2d, Floats2d],
-# ) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
-#     """Build a span categorizer model, given a token-to-vector model, a
-#     reducer model to map the sequence of vectors for each span down to a single
-#     vector, and a scorer model to map the vectors to probabilities.
-#     tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
-#     reducer (Model[Ragged, Floats2d]): The reducer model.
-#     scorer (Model[Floats2d, Floats2d]): The scorer model.
-#     """
-#     model = chain(
-#         cast(
-#             Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
-#             with_getitem(
-#                 0,
-#                 chain(tok2vec,
-#                       cast(Model[List[Floats2d], Ragged], list2ragged()))),
-#         ),
-#         extract_spans(),
-#         reducer,
-#         scorer,
-#     )
-#     model.set_ref("tok2vec", tok2vec)
-#     model.set_ref("reducer", reducer)
-#     model.set_ref("scorer", scorer)
-#     return model
\ No newline at end of file
diff --git a/spaces/epexVfeibi/Imagedeblurr/7G Rainbow Colony English Sub 720p Hd.md b/spaces/epexVfeibi/Imagedeblurr/7G Rainbow Colony English Sub 720p Hd.md
deleted file mode 100644
index 8e03965f545ac9a794187a77349702a64e12b242..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/7G Rainbow Colony English Sub 720p Hd.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
-<p>one of the most popular activities is scuba diving, which in antigua can be done on a scuba course, either locally or in padi certification centers on the island. there are many different dive sites, with wrecks, caves, and the very shallow reef. reef divers can expect to encounter manta rays, whitetip sharks, nurse sharks, and other exciting marine life.</p>
-<p>expect to swim in crystal-clear waters on the island's famous beaches, such as english harbour, cashew hill, and long bay. the island has world-renowned diving and snorkeling sites, such as chalk sound, shoal bay, white valley, and the east end, the indian ocean's premier dive site. the island has many hotels, bars, restaurants, golf courses, and casino resorts.</p>
-<h2>7G Rainbow Colony english sub 720p hd</h2><br /><p><b><b>Download Zip</b> &#9889; <a href="https://jinyurl.com/2uEpK5">https://jinyurl.com/2uEpK5</a></b></p><br /><br />
-<p>the island has a strong military history. the anguilla british forces supported the american revolution against the british, and the united states armed forces used the island as an important base during the second world war. the island was also a colony of great britain from the 17th to 19th centuries. you can visit fort st. george, the spanish fort from which antigua was named, and the minerva bakery and other historic buildings.</p>
-<p>rainbow colony is a bikash puri (layer cake). the bikash puri is topped with a layer of mai kulchai (vermicelli soaked in coconut milk) and layered with a mixture of tempered butter, fruits, nuts, cardamom, saffron, sugar, vermicelli, ghee, and finally a layer of rose water and ghee.</p>
-<p>it is definitely a must to stay at some of the colonial style guest houses in amber city. these traditional style hotels are usually found in the heart of the city and are some of the most beautiful luxury hotels in india. known for their historical architecture, graceful architecture and pleasant services, these hotels provide a great experience for every traveler who makes the trip to india. these hotels are ideal for both families and adults who wish to stay in india as they offer traditional indian hospitality along with modern amenities. staying in these hotels will make you feel as if you've stepped into another era of traveling.</p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/eslavathanil/myGenAIchatbot/README.md b/spaces/eslavathanil/myGenAIchatbot/README.md
deleted file mode 100644
index 1a8de948f02ca07b650075aabc021aec166c5f18..0000000000000000000000000000000000000000
--- a/spaces/eslavathanil/myGenAIchatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIchatbot
-emoji: 📚
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/eson/tokenizer-arena/vocab/kplug/__init__.py b/spaces/eson/tokenizer-arena/vocab/kplug/__init__.py
deleted file mode 100644
index d602735839aca3b189091bae8505f699842296cc..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/kplug/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-
-from transformers import BertTokenizer
-
-tokenizer = BertTokenizer.from_pretrained("eson/kplug-base-encoder")
-print(tokenizer)
diff --git a/spaces/evaluate-metric/chrf/chrf.py b/spaces/evaluate-metric/chrf/chrf.py
deleted file mode 100644
index 30bf8a0cec2251972e872fe09d15b6a7f2858f2f..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/chrf/chrf.py
+++ /dev/null
@@ -1,188 +0,0 @@
-# Copyright 2021 The HuggingFace Evaluate Authors.
-#
-# 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.
-""" Chrf(++) metric as available in sacrebleu. """
-import datasets
-import sacrebleu as scb
-from packaging import version
-from sacrebleu import CHRF
-
-import evaluate
-
-
-_CITATION = """\
-@inproceedings{popovic-2015-chrf,
-    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
-    author = "Popovi{\'c}, Maja",
-    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
-    month = sep,
-    year = "2015",
-    address = "Lisbon, Portugal",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/W15-3049",
-    doi = "10.18653/v1/W15-3049",
-    pages = "392--395",
-}
-@inproceedings{popovic-2017-chrf,
-    title = "chr{F}++: words helping character n-grams",
-    author = "Popovi{\'c}, Maja",
-    booktitle = "Proceedings of the Second Conference on Machine Translation",
-    month = sep,
-    year = "2017",
-    address = "Copenhagen, Denmark",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/W17-4770",
-    doi = "10.18653/v1/W17-4770",
-    pages = "612--618",
-}
-@inproceedings{post-2018-call,
-    title = "A Call for Clarity in Reporting {BLEU} Scores",
-    author = "Post, Matt",
-    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
-    month = oct,
-    year = "2018",
-    address = "Belgium, Brussels",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/W18-6319",
-    pages = "186--191",
-}
-"""
-
-_DESCRIPTION = """\
-ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
-and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
-that is already present in sacrebleu.
-
-The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
-the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
-sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
-
-See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
-"""
-
-_KWARGS_DESCRIPTION = """
-Produces ChrF(++) scores for hypotheses given reference translations.
-
-Args:
-    predictions (list of str): The predicted sentences.
-    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
-    char_order (int): Character n-gram order. Defaults to `6`.
-    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
-    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
-    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
-    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
-    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
-    to reference chrF++.py, NLTK and Moses implementations. If `False`,
-    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.
-
-Returns:
-    'score' (float): The chrF (chrF++) score,
-    'char_order' (int): The character n-gram order,
-    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
-    'beta' (int): Determine the importance of recall w.r.t precision
-
-Examples:
-    Example 1--a simple example of calculating chrF:
-        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
-        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
-        >>> chrf = evaluate.load("chrf")
-        >>> results = chrf.compute(predictions=prediction, references=reference)
-        >>> print(results)
-        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}
-
-    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
-        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
-        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
-        >>> chrf = evaluate.load("chrf")
-        >>> results = chrf.compute(predictions=prediction,
-        ...                         references=reference,
-        ...                         word_order=2)
-        >>> print(results)
-        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}
-
-    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
-        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
-        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
-        >>> chrf = evaluate.load("chrf")
-        >>> results = chrf.compute(predictions=prediction,
-        ...                         references=reference,
-        ...                         word_order=2,
-        ...                         lowercase=True)
-        >>> print(results)
-        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class ChrF(evaluate.Metric):
-    def _info(self):
-        if version.parse(scb.__version__) < version.parse("1.4.12"):
-            raise ImportWarning(
-                "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
-                'You can install it with `pip install "sacrebleu>=1.4.12"`.'
-            )
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            homepage="https://github.com/mjpost/sacreBLEU#chrf--chrf",
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=[
-                datasets.Features(
-                    {
-                        "predictions": datasets.Value("string", id="sequence"),
-                        "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
-                    }
-                ),
-                datasets.Features(
-                    {
-                        "predictions": datasets.Value("string", id="sequence"),
-                        "references": datasets.Value("string", id="sequence"),
-                    }
-                ),
-            ],
-            codebase_urls=["https://github.com/mjpost/sacreBLEU#chrf--chrf"],
-            reference_urls=[
-                "https://github.com/m-popovic/chrF",
-            ],
-        )
-
-    def _compute(
-        self,
-        predictions,
-        references,
-        char_order: int = CHRF.CHAR_ORDER,
-        word_order: int = CHRF.WORD_ORDER,
-        beta: int = CHRF.BETA,
-        lowercase: bool = False,
-        whitespace: bool = False,
-        eps_smoothing: bool = False,
-    ):
-        # if only one reference is provided make sure we still use list of lists
-        if isinstance(references[0], str):
-            references = [[ref] for ref in references]
-        references_per_prediction = len(references[0])
-        if any(len(refs) != references_per_prediction for refs in references):
-            raise ValueError(
-                "ChrF, as implemented by sacrebleu, requires the same number of references for each prediction"
-            )
-        transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
-
-        sb_chrf = CHRF(char_order, word_order, beta, lowercase, whitespace, eps_smoothing)
-        output = sb_chrf.corpus_score(predictions, transformed_references)
-
-        return {
-            "score": output.score,
-            "char_order": output.char_order,
-            "word_order": output.word_order,
-            "beta": output.beta,
-        }
diff --git a/spaces/exbert-project/exbert/client/src/css/AttentionConnectorControls.css b/spaces/exbert-project/exbert/client/src/css/AttentionConnectorControls.css
deleted file mode 100644
index 0c363b13c8bb2f8ecc2fb574a657aa76408e3509..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/css/AttentionConnectorControls.css
+++ /dev/null
@@ -1,218 +0,0 @@
-.input-description {
-  font-weight: 800;
-}
-
-.connector-controls {
-  display: -ms-grid;
-  display: grid;
-  -ms-grid-columns: 0.5fr 0.5fr;
-      grid-template-columns: 0.5fr 0.5fr;
-}
-
-.slide-container {
-  -ms-grid-column: 1;
-      grid-column-start: 1;
-  grid-column-end: 2;
-  -ms-grid-row: 1;
-      grid-row-start: 1;
-  grid-row-end: 2;
-  margin: auto;
-  text-align: center;
-  width: 75%;
-}
-
-.slider {
-  -webkit-appearance: none;
-  width: 10px;
-  height: 10px;
-  border-radius: 5px;
-  background: #d3d3d3;
-  outline: none;
-  opacity: 0.7;
-  -webkit-transition: .2s;
-  -webkit-transition: opacity .2s;
-  transition: opacity .2s;
-}
-
-.slider:hover {
-  opacity: 1;
-}
-
-.slider::-webkit-slider-thumb {
-  -webkit-appearance: none;
-  appearance: none;
-  width: 15px;
-  height: 15px;
-  border-radius: 50%;
-  background: #666666;
-  cursor: pointer;
-}
-
-#layer-selection {
-  -ms-grid-column: 1;
-      grid-column-start: 1;
-  grid-column-end: 2;
-  -ms-grid-row: 2;
-      grid-row-start: 2;
-  grid-row-end: 3;
-}
-
-.layer-select {
-  margin-bottom: 2em;
-}
-
-#atn-container {
-  display: -webkit-box;
-  display: -ms-flexbox;
-  display: flex;
-  -webkit-box-orient: horizontal;
-  -webkit-box-direction: normal;
-      -ms-flex-direction: row;
-          flex-direction: row;
-  -ms-flex-wrap: nowrap;
-      flex-wrap: nowrap;
-  -webkit-box-pack: center;
-      -ms-flex-pack: center;
-          justify-content: center;
-  -webkit-box-align: center;
-      -ms-flex-align: center;
-          align-items: center;
-  margin: 0 auto;
-  width: 100%;
-  vertical-align: top;
-}
-
-#atn-container #left-att-heads {
-  -webkit-box-ordinal-group: 2;
-      -ms-flex-order: 1;
-          order: 1;
-  display: inline-block;
-  vertical-align: top;
-}
-
-#atn-container #left-tokens {
-  -webkit-box-ordinal-group: 3;
-      -ms-flex-order: 2;
-          order: 2;
-  text-align: right;
-  vertical-align: top;
-}
-
-#atn-container #atn-display {
-  -webkit-box-ordinal-group: 4;
-      -ms-flex-order: 3;
-          order: 3;
-  vertical-align: top;
-}
-
-#atn-container #right-tokens {
-  -webkit-box-ordinal-group: 5;
-      -ms-flex-order: 4;
-          order: 4;
-  text-align: left;
-  vertical-align: top;
-}
-
-#atn-container #right-att-heads {
-  -webkit-box-ordinal-group: 6;
-      -ms-flex-order: 5;
-          order: 5;
-  vertical-align: top;
-}
-
-.att-rect {
-  -webkit-transition: fill 0.1s;
-  transition: fill 0.1s;
-}
-
-.token {
-  display: block;
-}
-
-.atn-curve {
-  fill: none;
-  stroke: purple;
-}
-
-.masked-token {
-  color: rgba(0, 0, 0, 0.2);
-}
-
-.unselected {
-  fill: gray;
-}
-
-.selected-token {
-  border-style: solid;
-  border-width: 3px;
-  border-color: #99c400;
-}
-
-/* The switch - the box around the slider */
-.switch {
-  position: relative;
-  display: inline-block;
-  width: 60px;
-  height: 34px;
-  /* Hide default HTML checkbox */
-}
-
-.switch input {
-  opacity: 0;
-  width: 0;
-  height: 0;
-}
-
-/* The slider */
-.short-slider {
-  position: absolute;
-  cursor: pointer;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  background-color: #ccc;
-  -webkit-transition: .4s;
-  transition: .4s;
-}
-
-.short-slider:before {
-  position: absolute;
-  content: "";
-  height: 26px;
-  width: 26px;
-  left: 4px;
-  bottom: 4px;
-  background-color: white;
-  -webkit-transition: .4s;
-  transition: .4s;
-}
-
-input:checked + .short-slider {
-  background-color: #2196F3;
-}
-
-input:focus + .short-slider {
-  -webkit-box-shadow: 0 0 1px #2196F3;
-          box-shadow: 0 0 1px #2196F3;
-}
-
-input:checked + .short-slider:before {
-  -webkit-transform: translateX(26px);
-  transform: translateX(26px);
-}
-
-/* Rounded sliders */
-.short-slider.round {
-  border-radius: 34px;
-}
-
-.short-slider.round:before {
-  border-radius: 50%;
-}
-
-#select-all-heads {
-  margin-top: 20px;
-  margin-bottom: 20px;
-}
-/*# sourceMappingURL=AttentionConnectorControls.css.map */
\ No newline at end of file
diff --git a/spaces/ezioruan/roop/roop/core.py b/spaces/ezioruan/roop/roop/core.py
deleted file mode 100644
index b70d8548194c74cce3e4d20c53c7a88c119c2028..0000000000000000000000000000000000000000
--- a/spaces/ezioruan/roop/roop/core.py
+++ /dev/null
@@ -1,215 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-# reduce tensorflow log level
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import warnings
-from typing import List
-import platform
-import signal
-import shutil
-import argparse
-import torch
-import onnxruntime
-import tensorflow
-
-import roop.globals
-import roop.metadata
-import roop.ui as ui
-from roop.predicter import predict_image, predict_video
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-
-if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-    del torch
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
-    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
-    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
-    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
-    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
-    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
-
-    args = program.parse_args()
-
-    roop.globals.source_path = args.source_path
-    roop.globals.target_path = args.target_path
-    roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
-    roop.globals.frame_processors = args.frame_processor
-    roop.globals.headless = args.source_path or args.target_path or args.output_path
-    roop.globals.keep_fps = args.keep_fps
-    roop.globals.keep_audio = args.keep_audio
-    roop.globals.keep_frames = args.keep_frames
-    roop.globals.many_faces = args.many_faces
-    roop.globals.video_encoder = args.video_encoder
-    roop.globals.video_quality = args.video_quality
-    roop.globals.max_memory = args.max_memory
-    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
-    roop.globals.execution_threads = args.execution_threads
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
-
-def suggest_max_memory() -> int:
-    if platform.system().lower() == 'darwin':
-        return 4
-    return 16
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'DmlExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    if 'ROCMExecutionProvider' in roop.globals.execution_providers:
-        return 1
-    return 8
-
-
-def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
-            tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
-        ])
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def release_resources() -> None:
-    if 'CUDAExecutionProvider' in roop.globals.execution_providers:
-        torch.cuda.empty_cache()
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    if not shutil.which('ffmpeg'):
-        update_status('ffmpeg is not installed.')
-        return False
-    return True
-
-
-def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
-    print(f'[{scope}] {message}')
-    if not roop.globals.headless:
-        ui.update_status(message)
-
-
-def start() -> None:
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_start():
-            return
-    # process image to image
-    if has_image_extension(roop.globals.target_path):
-        if predict_image(roop.globals.target_path):
-            destroy()
-        shutil.copy2(roop.globals.target_path, roop.globals.output_path)
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_image(roop.globals.source_path, roop.globals.output_path, roop.globals.output_path)
-            frame_processor.post_process()
-            release_resources()
-        if is_image(roop.globals.target_path):
-            update_status('Processing to image succeed!')
-        else:
-            update_status('Processing to image failed!')
-        return
-    # process image to videos
-    if predict_video(roop.globals.target_path):
-        destroy()
-    update_status('Creating temp resources...')
-    create_temp(roop.globals.target_path)
-    update_status('Extracting frames...')
-    extract_frames(roop.globals.target_path)
-    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        update_status('Progressing...', frame_processor.NAME)
-        frame_processor.process_video(roop.globals.source_path, temp_frame_paths)
-        frame_processor.post_process()
-        release_resources()
-    # handles fps
-    if roop.globals.keep_fps:
-        update_status('Detecting fps...')
-        fps = detect_fps(roop.globals.target_path)
-        update_status(f'Creating video with {fps} fps...')
-        create_video(roop.globals.target_path, fps)
-    else:
-        update_status('Creating video with 30.0 fps...')
-        create_video(roop.globals.target_path)
-    # handle audio
-    if roop.globals.keep_audio:
-        if roop.globals.keep_fps:
-            update_status('Restoring audio...')
-        else:
-            update_status('Restoring audio might cause issues as fps are not kept...')
-        restore_audio(roop.globals.target_path, roop.globals.output_path)
-    else:
-        move_temp(roop.globals.target_path, roop.globals.output_path)
-    # clean and validate
-    clean_temp(roop.globals.target_path)
-    if is_video(roop.globals.target_path):
-        update_status('Processing to video succeed!')
-    else:
-        update_status('Processing to video failed!')
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        clean_temp(roop.globals.target_path)
-    quit()
-
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_check():
-            return
-    limit_resources()
-    if roop.globals.headless:
-        start()
-    else:
-        window = ui.init(start, destroy)
-        window.mainloop()
diff --git a/spaces/failfast/nextjs-hf-spaces/next.config.js b/spaces/failfast/nextjs-hf-spaces/next.config.js
deleted file mode 100644
index 412eabbf7a24ba4197c0fb15abe19f4914ad2a05..0000000000000000000000000000000000000000
--- a/spaces/failfast/nextjs-hf-spaces/next.config.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  output: "standalone",
-  reactStrictMode: true,
-};
-
-module.exports = nextConfig;
diff --git a/spaces/falterWliame/Face_Mask_Detection/Monster Trucks Nitro Activation Code Keygen Mac [TOP].md b/spaces/falterWliame/Face_Mask_Detection/Monster Trucks Nitro Activation Code Keygen Mac [TOP].md
deleted file mode 100644
index 91f2037cc9a58b51f54c53a7bb20a67ae41e2c50..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Monster Trucks Nitro Activation Code Keygen Mac [TOP].md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Download DLS 2023 APK and Enjoy the Exclusive Soundtrack of BEKA The Luka State Vukovi and More!.md b/spaces/fatiXbelha/sd/Download DLS 2023 APK and Enjoy the Exclusive Soundtrack of BEKA The Luka State Vukovi and More!.md
deleted file mode 100644
index fb2d3f28215894f7a2c10b559e37efc5d56b7fcd..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download DLS 2023 APK and Enjoy the Exclusive Soundtrack of BEKA The Luka State Vukovi and More!.md	
+++ /dev/null
@@ -1,113 +0,0 @@
-
-<h1>Download DLS 2023 APK: How to Play the Latest Version of Dream League Soccer on Your Android Device</h1>
- <p>If you are a fan of soccer games, you have probably heard of Dream League Soccer, one of the most popular and realistic soccer games on mobile devices. Dream League Soccer 2023 is the latest version of this game, which offers a fresh look and brand new features that will make you feel like you are in the heart of the action. In this article, we will show you how to download and install DLS 2023 APK on your Android device, as well as some tips and tricks for playing this amazing game.</p>
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- <h2>Features of DLS 2023: What's New and Improved in the Latest Version</h2>
- <p>Dream League Soccer 2023 is not just an update of the previous version, but a complete overhaul that brings many improvements and innovations to the game. Here are some of the features that you can expect from DLS 2023:</p>
- <h3>Over 4,000 FIFPRO™ Licensed Players: Choose from the Best Soccer Stars in the World</h3>
- <p>One of the main attractions of Dream League Soccer is that you can build your own dream team from over 4,000 FIFPRO™ licensed players, including top superstar players such as Kevin De Bruyne, Achraf Hakimi, Cristiano Ronaldo, Lionel Messi, Neymar Jr., Kylian Mbappé, and more. You can also customize your players' appearance, skills, attributes, and positions to suit your preferences.</p>
- <h3>Full 3D Motion-Captured Animations: Experience Unmatched Realism and Immersion</h3>
- <p>Dream League Soccer 2023 uses full 3D motion-captured animations for kicks, tackles, celebrations, goalkeeper saves, and more, giving you unmatched realism and immersion. You can also enjoy immersive and exciting match commentary that keeps you in the heart of the action.</p>
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- <h3>Customizable Manager and Team: Create Your Own Unique Style and Identity</h3>
- <p>Dream League Soccer 2023 lets you customize your manager from a host of different options, including hairstyles, outfits, accessories, and more. You can also customize your team's kit, logo, name, colors, or import your own creations. Along with the new and improved graphics engine, your dream team has never looked this good!</p>
- <h3 <h3>Dream League Live: Compete Against Other Elite Teams and Win Exclusive Prizes</h3>
- <p>Dream League Soccer 2023 also introduces Dream League Live, a new online mode that lets you compete against other elite teams from around the world in various tournaments and events. You can also earn exclusive rewards and prizes, such as new players, kits, coins, gems, and more. Dream League Live is the ultimate challenge for any soccer fan!</p>
- <h2>How to Download and Install DLS 2023 APK on Your Android Device</h2>
- <p>Now that you know what DLS 2023 has to offer, you might be wondering how to download and install it on your Android device. Don't worry, it's very easy and simple. Just follow these steps:</p>
- <h3>Allow Unknown Apps on Your Android Device: How to Enable the Option to Install APK Files from Your Browser or File Manager</h3>
- <p>An APK file is an Android application package file that contains all the files and data needed to run an app on your device. However, by default, Android devices do not allow you to install APK files from unknown sources, such as your browser or file manager. To enable this option, you need to do the following:</p>
- <ul>
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-<li>Find the option that says Unknown Sources or Install Unknown Apps and toggle it on.</li>
-<li>A warning message will pop up, telling you that installing apps from unknown sources can harm your device. Tap on OK or Allow to proceed.</li>
-</ul>
- <p>Congratulations, you have now enabled the option to install APK files from unknown sources on your Android device!</p>
- <h3>Download the APK File from a Reputable Source: How to Find and Verify the APK File You Want to Install</h3>
- <p>The next step is to download the APK file of DLS 2023 from a reputable source. There are many websites that offer APK files for various apps and games, but not all of them are safe and reliable. Some of them may contain malware, viruses, or fake files that can harm your device or steal your personal information. To avoid this, you need to do the following:</p>
- <ul>
-<li>Do some research and find a trustworthy website that offers the APK file of DLS 2023. You can use Google or any other search engine to look for reviews, ratings, comments, or feedback from other users who have downloaded the file from that website.</li>
-<li>Once you have found a reputable website, go to it and look for the download link or button for the APK file of DLS 2023. Make sure that the file name and size match with the official information provided by the developer of the game.</li>
-<li>Tap on the download link or button and wait for the file to be downloaded to your device. You can check the progress of the download in your notification bar or in your browser's download manager.</li>
-</ul>
- <p>Congratulations, you have now downloaded the APK file of DLS 2023 from a reputable source!</p>
- <h3>Install the APK File on Your Android Device: How to Locate and Run the APK File to Complete the Installation Process</h3>
- <p>The final step is to install the APK file of DLS 2023 on your Android device. This is very easy and simple as well. Just follow these steps:</p>
- <ul>
-<li>Go to your device's file manager and locate the folder where you have downloaded the APK file of DLS 2023. Alternatively, you can also go to your browser's download manager and tap on the file name.</li>
-<li>Tap on the APK file of DLS 2023 and a pop-up window will appear, asking you if you want to install this app. Tap on Install or Confirm to proceed.</li>
-<li>The installation process will begin and it may take a few seconds or minutes depending on your device's speed and performance. You can check the progress of the installation in your notification bar or in your file manager.</li>
-<li>Once the installation is complete, a message will appear, telling you that the app has been installed successfully. Tap on Open or Done to launch or exit the app.</li>
-</ul>
- <p>Congratulations, you have now installed DLS 2023 APK on your Android device!</p>
- <h2>Tips and Tricks for Playing DLS 2023: How to Build Your Dream Team and Conquer the World</h2>
- <p>Dream League Soccer 2023 is not only a fun and realistic soccer game, but also a challenging and rewarding one. You need to build your dream team from scratch, develop your players, upgrade your stadium and facilities, compete against other elite teams, and win trophies and glory. To help you achieve this, here are some tips and tricks for playing DLS 202 , and marketing, which can enhance your players' recovery, development, and popularity. You can also use coins or gems to upgrade your stadium and facilities to increase their level and quality.</p>
- <h3>Use the Right Tactics and Formations to Suit Your Play Style and Opponents</h3>
- <p>Another important aspect of Dream League Soccer 2023 is using the right tactics and formations to suit your play style and opponents. You can choose from various tactics such as attacking, defensive, balanced, counter-attacking, possession, and more, which can affect your team's behavior and performance on the pitch. You can also choose from various formations such as 4-4-2, 4-3-3, 3-5-2, 5-3-2, and more, which can affect your team's shape and strategy on the pitch. You can also customize your tactics and formations by changing the roles, positions, instructions, and mentality of your players.</p>
- <h3>Enjoy the Exclusive Soundtrack and In-Game Commentary</h3>
- <p>Another important aspect of Dream League Soccer 2023 is enjoying the exclusive soundtrack and in-game commentary, which can enhance your gaming experience and immersion. You can listen to the original soundtrack composed by The Luka State, Sunset Sons, Vistas, Only The Poets, and more, which can pump you up and motivate you to play. You can also listen to the in-game commentary by Clive Tyldesley and Andy Townsend, who will provide you with insightful and entertaining commentary on the matches.</p>
- <h2>Conclusion: Summarize the main points and invite the reader to try DLS 2023</h2>
- <p>Dream League Soccer 2023 is a fantastic soccer game that offers you a realistic and immersive soccer experience on your Android device. You can build your own dream team from over 4,000 FIFPRO™ licensed players, enjoy full 3D motion-captured animations, customize your manager and team, compete against other elite teams in Dream League Live, and more. You can also download and install DLS 2023 APK on your Android device easily and safely by following the steps we have provided in this article. So what are you waiting for? Download DLS 2023 APK now and start playing this amazing game!</p>
- <h2>FAQs: Answer some common questions that the reader might have about DLS 2023</h2>
- <p>Here are some common questions that the reader might have about DLS 2023:</p>
- <h4>Q: Is DLS 2023 free to play?</h4>
-<p>A: Yes, DLS 2023 is free to play. However, it also contains in-app purchases that allow you to buy coins or gems that can be used to unlock or upgrade various features in the game.</p>
- <h4>Q: Is DLS 2023 compatible with my Android device?</h4>
-<p>A: DLS 2023 requires Android 5.0 or higher to run smoothly on your device. It also requires at least 350 MB of free storage space on your device.</p>
- <h4>Q: Is DLS 2023 safe to download and install?</h4>
-<p>A: Yes, DLS 2023 is safe to download and install. However, you need to make sure that you download the APK file from a reputable source and enable the option to install unknown apps on your device.</p>
- <h4>Q: How can I update DLS 2023 to the latest version?</h4>
-<p>A: You can update DLS 2023 to the latest version by downloading and installing the new APK file from a reputable source. Alternatively, you can also check for updates in the game's settings or in the Google Play Store.</p>
- <h4>Q: How can I contact the developer of DLS 2023 for feedback or support?</h4>
-<p>A: You can contact the developer of DLS 2023 for feedback or support by visiting their official website or social media pages. You can also email them at support@ftgames.com or use the in-game feedback option.</p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Download Konoha Legends Ninja AFK Mobile and Join the Epic Ninja Battles.md b/spaces/fatiXbelha/sd/Download Konoha Legends Ninja AFK Mobile and Join the Epic Ninja Battles.md
deleted file mode 100644
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--- a/spaces/fatiXbelha/sd/Download Konoha Legends Ninja AFK Mobile and Join the Epic Ninja Battles.md	
+++ /dev/null
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-
-<h1>Konoha Legends Ninja AFK Mobile Download: A Strategy Game Based on Naruto Series</h1>
-<p>If you are a fan of Naruto, the popular manga and anime series, you might be interested in playing Konoha Legends Ninja AFK Mobile, a strategy game that lets you collect and upgrade your favorite Naruto characters, form your own ninja squad, and battle with other players. In this article, we will tell you what Konoha Legends Ninja AFK Mobile is, why you should play it, what features it has, how to download and install it, and some tips and tricks for playing it.</p>
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-<h2>Introduction</h2>
-<h3>What is Konoha Legends Ninja AFK Mobile?</h3>
-<p>Konoha Legends Ninja AFK Mobile is a strategy game genre, general cards based on the original from the famous Naruto series. It is developed by Konoha Legend Studio and published by Game View Trending. It is available for both Android and iOS devices. The game was officially launched in September 2022 and has received positive reviews from players and critics alike.</p>
-<h3>Why should you play Konoha Legends Ninja AFK Mobile?</h3>
-<p>There are many reasons why you should play Konoha Legends Ninja AFK Mobile, especially if you are a Naruto fan. Here are some of them:</p>
-<ul>
-<li>You can relive the epic moments from the Naruto series and enjoy the original story and voice acting from the anime.</li>
-<li>You can collect and upgrade over 100 Naruto characters, each with their own unique skills and abilities.</li>
-<li>You can form your own ninja squad and customize their formation, equipment, and strategy.</li>
-<li>You can compete with other players in various modes, such as arena, guild war, tower defense, and more.</li>
-<li>You can redeem codes and get rewards, such as gold, gems, summon scrolls, and rare characters.</li>
-</ul>
-<h2>Features of Konoha Legends Ninja AFK Mobile</h2>
-<h3>Collect and upgrade your favorite Naruto characters</h3>
-<p>One of the main features of Konoha Legends Ninja AFK Mobile is that you can collect and upgrade your favorite Naruto characters. You can summon them using summon scrolls or gems, or get them from events or rewards. You can also upgrade them by leveling up, enhancing, awakening, or evolving them. Each character has their own rarity, class, element, skill set, and stats. You can also equip them with weapons, armor, accessories, or runes to boost their performance.</p>
-<h3>Form your own ninja squad and battle with other players</h3>
-<p>Another feature of Konoha Legends Ninja AFK Mobile is that you can form your own ninja squad and battle with other players. You can choose up to five characters to form your squad, with one of them being the leader. The leader will have a special effect that will benefit the whole team. You can also customize your squad's formation, equipment, and strategy according to the situation. You can challenge other players in various modes, such as arena, guild war, tower defense, and more. You can also join a guild and cooperate with other members to complete quests, raids, or events.</p>
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-<h3>Enjoy the original story and voice acting from the anime</h3>
-<p>A third feature of Konoha Legends Ninja AFK Mobile is that you can enjoy the original story and voice acting from the anime. The game follows the plot of the Naruto series from the beginning to the end of the Fourth Shinobi World War. You can watch the cutscenes and listen to the voice acting from the original cast of the anime. You can also interact with the characters and learn more about their personalities, backgrounds, and relationships.</p>
-<h3>Redeem codes and get rewards</h3>
-<p>A fourth feature of Konoha Legends Ninja AFK Mobile is that you can redeem codes and get rewards. The game developers often release codes that can be redeemed for various rewards, such as gold, gems, summon scrolls, and rare characters. You can find these codes on the official social media pages, website, or discord server of the game. You can also get codes from events, giveaways, or collaborations. To redeem a code, you need to go to the settings menu, tap on the redeem code button, enter the code, and claim your reward.</p>
-<h2>How to download and install Konoha Legends Ninja AFK Mobile</h2>
-<h3>For Android devices</h3>
-<p>If you have an Android device, you can download and install Konoha Legends Ninja AFK Mobile from the Google Play Store. Here are the steps to do so:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for Konoha Legends Ninja AFK Mobile or use this link: [Konoha Legends Ninja AFK Mobile].</li>
-<li>Tap on the install button and wait for the download to finish.</li>
-<li>Once the installation is complete, tap on the open button or find the game icon on your home screen.</li>
-<li>Enjoy playing Konoha Legends Ninja AFK Mobile!</li>
-</ol>
-<h3>For iOS devices</h3>
-<p>If you have an iOS device, you can download and install Konoha Legends Ninja AFK Mobile from the App Store. Here are the steps to do so:</p>
-<ol>
-<li>Open the App Store app on your device.</li>
-<li>Search for Konoha Legends Ninja AFK Mobile or use this link: [Konoha Legends Ninja AFK Mobile].</li>
-<li>Tap on the get button and enter your Apple ID password if prompted.</li>
-<li>Wait for the download to finish and then tap on the open button or find the game icon on your home screen.</li>
-<li>Enjoy playing Konoha Legends Ninja AFK Mobile!</li>
-</ol>
-<h2>Tips and tricks for playing Konoha Legends Ninja AFK Mobile</h2>
-<h3>Choose your leader wisely</h3>
-<p>One of the tips for playing Konoha Legends Ninja AFK Mobile is to choose your leader wisely. Your leader is the character that will have a special effect that will benefit your whole team. For example, Naruto Uzumaki (Sage Mode) will increase the attack and defense of all allies by 20%, while Sasuke Uchiha (Eternal Mangekyo Sharingan) will increase the critical rate and damage of all allies by 20%. You should choose a leader that suits your team composition and strategy. You can also change your leader according to the mode or enemy you are facing.</p>
-<h3>Balance your team composition and skills</h3>
-<p>Another tip for playing Konoha Legends Ninja AFK Mobile is to balance your team composition and skills. You should have a variety of characters with different classes, elements, skills, and stats. For example, you should have a tank that can absorb damage, a healer that can restore health, a damage dealer that can deal high damage, and a support that can buff or debuff. You should also have characters with different elements, such as fire, water, wind, earth, and lightning. Each element has its own advantage and disadvantage against another element. For example, fire is strong against wind but weak against water. You should also pay attention to the skills of your characters and use them at the right time. Some skills have cooldowns, effects, or requirements that you need to consider before using them.</p>
-<h3>Use auto mode and idle mode to save time and resources</h3>
-<p>A third tip for playing Konoha Legends Ninja AFK Mobile is to use auto mode and idle mode to save time and resources. Auto mode is a feature that allows you to let the game play itself without your input. You can use auto mode when you are busy or want to farm resources easily. Idle mode is a feature that allows you to earn rewards even when you are offline. You can use idle mode when you are away from your device or want to rest. Both modes will help you progress faster and easier in the game.</p>
-<h2>Conclusion</h2>
-<p>Konoha Legends Ninja AFK Mobile is a strategy game based on the Naruto series that offers a lot of fun and excitement for Naruto fans and gamers alike. You can collect and upgrade your favorite Naruto characters, form your own ninja squad, and battle with other players. You can also enjoy the original story and voice acting from the anime, and redeem codes and get rewards. To play the game, you need to download and install it on your device, either Android or iOS. You can also use some tips and tricks to improve your gameplay, such as choosing your leader wisely, balancing your team composition and skills, and using auto mode and idle mode. We hope this article has helped you learn more about Konoha Legends Ninja AFK Mobile and how to play it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have fun playing Konoha Legends Ninja AFK Mobile!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Konoha Legends Ninja AFK Mobile:</p>
-<ol>
-<li>Q: How can I get more summon scrolls or gems?<br>
-A: You can get more summon scrolls or gems by completing quests, achievements, events, or daily tasks. You can also buy them with real money or exchange them with other currencies in the game.</li>
-<li>Q: How can I get rare or legendary characters?<br>
-A: You can get rare or legendary characters by summoning them with summon scrolls or gems. You can also get them from events, rewards, or codes. The chance of getting a rare or legendary character is higher when you use a higher quality summon scroll or gem.</li>
-<li>Q: How can I join a guild or create my own guild?<br>
-A: You can join a guild or create your own guild by tapping on the guild icon on the main screen. You need to be at least level 10 to join or create a guild. You can also search for a guild by name, ID, or level.</li>
-<li>Q: How can I redeem a code?<br>
-A: You can redeem a code by going to the settings menu, tapping on the redeem code button, entering the code, and claiming your reward. You can find codes on the official social media pages, website, or discord server of the game. You can also get codes from events, giveaways, or collaborations.</li>
-<li>Q: How can I contact the customer service or report a bug?<br>
-A: You can contact the customer service or report a bug by going to the settings menu, tapping on the customer service button, and filling out the form. You can also email them at konohalegends@gmail.com or visit their Facebook page at [Konoha Legends Ninja AFK Mobile].</li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Download Red Hat RPMs for Enterprise Linux Server and Workstation.md b/spaces/fatiXbelha/sd/Download Red Hat RPMs for Enterprise Linux Server and Workstation.md
deleted file mode 100644
index 90edb66b7d647b7e3a184b4a4333695742b0dd4b..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Red Hat RPMs for Enterprise Linux Server and Workstation.md	
+++ /dev/null
@@ -1,185 +0,0 @@
-<br />
-<h1>How to Download and Install RPM Packages on Linux</h1>
-<p>RPM is an acronym for Red Hat Package Manager, which is an open packaging system for Linux and UNIX systems. It can install, uninstall, update, query, and verify packages that use the .rpm file format. However, it cannot manage dependency resolution like YUM or DNF. RPM is the baseline package format of the Linux Standard Base.</p>
-<p>RPM is a useful tool for managing software on Linux systems, especially those based on Red Hat Enterprise Linux (RHEL), CentOS, Fedora, and other derivatives. In this article, you will learn how to download and install RPM packages on Linux using various methods.</p>
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-<h2>Prerequisites</h2>
-<p>Before you start, make sure you have the following:</p>
-<ul>
-<li>A user account with sudo privileges</li>
-<li>Access to a terminal window / command line</li>
-<li>RPM, YUM, or DNF package managers (all included by default)</li>
-</ul>
-<h2>How to Download RPM Packages</h2>
-<h3>Using a web browser</h3>
-<p>The easiest way to download an RPM package is to use a web browser. You can find many websites that offer software in RPM format for Linux users. For example, you can download Slack from its official website. Just click on the download button and choose your Linux distribution.</p>
-<p>The downloaded file will have a .rpm extension and will be saved in your default download location. You can also choose a different location if you want.</p>
-<h3>Using wget</h3>
-<p>If you don't have access to a web browser or you prefer using the command line, you can also download an RPM package using wget, which is a tool for retrieving files from web servers.</p>
-<p>To use wget, you need to know the URL of the file you want to download. You can find this by right-clicking on the download link in your web browser and choosing Copy link address or Copy link location.</p>
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-<p>Then, open a terminal window and use .0-0.1.fc21.x86_64.rpm </code></pre>
- This will download the file to your current working directory. You can also specify a different location by using the -O option, followed by the path and filename you want to use. <pre><code>wget -O /home/user/Downloads/slack.rpm https://downloads.slack-edge.com/linux_releases/slack-4.23.0-0.1.fc21.x86_64.rpm </code></pre>
- <h2>How to Install RPM Packages</h2>
-<h3>Using the RPM command</h3>
-<p>Once you have downloaded an RPM package, you can install it using the rpm command, which is the core tool for managing RPM packages on Linux.</p>
-<p>To install an RPM package, you need to use the -i option, followed by the name of the file. For example, to install Slack, you can use: <pre><code>sudo rpm -i slack.rpm </code></pre>
- This will install the package and its dependencies, if any. You may need to enter your password for sudo authentication.</p>
-<p>If you want to see more information about the installation process, you can use the -v option for verbose output, or the -h option for a progress bar.</p>
-<h3>Using YUM or DNF</h3>
-<p>Another way to install an RPM package is to use yum or dnf, which are higher-level package managers that can handle dependency resolution and other features. Yum is used on RHEL and CentOS systems, while dnf is used on Fedora systems.</p>
-<p>To install an RPM package using yum or dnf, you need to use the localinstall option, followed by the name of the file. For example, to install Slack, you can use: <pre><code>sudo yum localinstall slack.rpm </code></pre>
- or <pre><code>sudo dnf localinstall slack.rpm </code></pre>
- This will install the package and its dependencies, if any. You may need to enter your password for sudo authentication.</p>
-<p>If you want to see more information about the installation process, you can use the -v option for verbose output, or the -y option to automatically answer yes to any prompts.</p>
-<h2>How to Remove RPM Packages</h2>
-<h3>Using the RPM command</h3>
-<p>If you want to uninstall an RPM package, you can use the rpm command with the -e option, followed by the name of the package. For example, to remove Slack, you can use: <pre><code>sudo rpm -e slack </code></pre>
- This will remove the package and its dependencies, if any. You may need to enter your password for sudo authentication.</p>
-<p>If you want to see more information about the removal process, you can use the -v option for verbose output.</p>
-<h3>Using YUM or DNF</h3>
-<p>You can also uninstall an RPM package using yum or dnf with the remove option, followed by the name of the package. For example, to remove Slack, you can use: <pre><code>sudo yum remove slack </code></pre>
- or <pre><code>sudo dnf remove slack </code></pre>
- This will remove the package and its dependencies, if any. You may need to enter your password for sudo authentication.</p>
-<p>If you want to see more information about the removal process, you can use the -v option for verbose output, or the -y option to automatically answer yes to any prompts.</p>
-<h2>How to Query RPM Packages</h2>
-<h3>Using the RPM command</h3>
-<p>If you want to query information about an RPM package, you can use the rpm command with various options. For example:</p>
-<ul>
-<li>To query information about an installed package, use the -q option followed by the name of the package. For example: <pre><code>rpm -q slack </code></pre>
- This will display information such as version number, release number, architecture, and installation date.</li>
-<li>To query information about a downloaded file, use the -qp option followed by the name of the file. For example: <pre><code>rpm -qp slack.rpm </code></pre>
- This will display information such as version number, release number, architecture, and summary.</li>
-<li>To list all files in an installed package, use the -ql option followed by the name of the package. For example: <pre><code>rpm -ql slack </code></pre>
- This will display all files and directories that belong to the package.</li>
-<li>To list all files in a downloaded file, use the -qpl option followed by the name of the file. For example: <pre><code>rpm -qpl slack.rpm </code></pre>
- This will display all files and directories that are contained in the file.</li>
-<li>To display detailed information about an installed package, use the -qi option followed by the name of the package. For example: <pre><code>rpm -qi slack </code></pre>
- This will display information such as name, version, release, architecture, summary, description, license, URL, source RPM, build date, install date, vendor, packager, group, size, etc.</li>
-<li>To display detailed information about a downloaded file, use the -qpi option followed by the name of the file. For example: <pre><code>rpm -qpi slack.rpm </code></pre>
- This will display the same information as above.</li>
-<li>To verify an installed package, use the -V option followed by the name of the package. For example: <pre><code>rpm -V slack </code></pre>
- This will check the package for any missing files, modified files, or other discrepancies. It will display a code for each file that indicates its status. For example: <pre><code>S.5....T.  c /etc/slack.conf </code></pre>
- This means that the file has a different size (S), MD5 checksum (5), and modification time (T) than expected.</li>
-<li>To verify a downloaded file, use the -Vp option followed by the name of the file. For example: <pre><code>rpm -Vp slack.rpm </code></pre>
- This will perform the same verification as above.</li>
-</ul>
-<h3>Using YUM or DNF</h3>
-<p>You can also query information about an RPM package using yum or dnf with various options. For example:</p>
-<ul>
-<li>To list all available packages in the repositories, use the list option. For example: <pre><code>yum list </code></pre>
- or <pre><code>dnf list </code></pre>
- This will display the name, version, release, and repository of each package.</li>
-<li>To list all installed packages on your system, use the list installed option. For example: <pre><code>yum list installed </code></pre>
- or <pre><code>dnf list installed </code></pre>
- This will display the same information as above for each installed package.</li>
-<li>To display information about a specific package, use the info option followed by the name of the package. For example: <pre><code>yum info slack </code></pre>
- or <pre><code>dnf info slack </code></pre>
- This will display information such as name, version, release, architecture, summary, description, license, URL, source RPM, build date, install date, vendor, packager, group, size, etc.</li>
-<li>To display information about a specific file in a package, use the provides option followed by the name of the file. For example: <pre><code>yum provides /usr/bin/slack </code></pre>
- or <pre><code>dnf provides /usr/bin/slack </code></pre>
- This will display information such as name, version, release, architecture, and repository of the package that provides the file.</li>
-</ul>
-<h2>How to Update RPM Packages</h2>
-<h3>Using YUM or DNF</h3>
-<p>The easiest way to update an RPM package is to use yum or dnf with the update option. This will check for any available updates in the repositories and install them automatically.</p>
-<p>To update all packages on your system, use: <pre><code>sudo yum update </code></pre>
- or <pre><code>sudo dnf update </code></pre>
- This will update all packages and their dependencies to their latest versions. You may need to enter your password for sudo authentication.</p>
-<p>If you want to see more information about the update process, you can use the -v option for verbose output, or the -y option to automatically answer yes to any prompts.</p>
-<p>To update a specific package, use the update option followed by the name of the package. For example: <pre><code>sudo yum update slack </code></pre>
- or <pre><code>sudo dnf update slack </code></pre>
- This will update the package and its dependencies to their latest versions. You may need to enter your password for sudo authentication.</p>
-<h2>How to Verify RPM Packages</h2>
-<h3>Using the RPM command</h3>
-<p>If you want to verify the integrity and authenticity of an RPM package, you can use the rpm command with the --checksig option, followed by the name of the file. For example: <pre><code>rpm --checksig slack.rpm </code></pre>
- This will check the signature and digest of the file and display the result. For example: <pre><code>slack.rpm: rsa sha1 (md5) pgp md5 OK </code></pre>
- This means that the file has a valid RSA, SHA1, MD5, and PGP signature and digest.</p>
-<p>If you want to see more information about the signature and digest, you can use the -v option for verbose output. For example: <pre><code>rpm -v --checksig slack.rpm </code></pre>
- This will display information such as key ID, fingerprint, algorithm, etc.</p>
-<h2>How to List Installed RPM Packages</h2>
-<h3>Using the RPM command</h3>
-<p>If you want to list all installed RPM packages on your system, you can use the rpm command with the -qa option. For example: <pre><code>rpm -qa </code></pre>
- This will display the name, version, and release of each installed package.</p>
-<p>If you want to sort the list by installation date, you can use the --last option. For example: <pre><code>rpm -qa --last </code></pre>
- This will display the same information as above, but with an additional column for installation date.</p>
-<h3>Using YUM or DNF</h3>
-<p>You can also list all installed RPM packages on your system using yum or dnf with the list installed option. For example: <pre><code>yum list installed </code></pre>
- or <pre><code>dnf list installed </code></pre>
- This will display the name, version, release, and repository of each installed package.</p>
-<h2>Conclusion</h2>
-<p>In this article, you learned how to download and install RPM packages on Linux using various methods. You also learned how to remove, query, update, verify, and list RPM packages on your system. RPM is a powerful tool for managing software on Linux systems, but it has some limitations, such as dependency resolution and compatibility issues. Therefore, it is recommended to use higher-level package managers like YUM or DNF whenever possible. They can handle most of the tasks that RPM can do, and more.</p>
-<p>Here are some tips and best practices for using RPM packages on Linux:</p>
-<ul>
-<li>Always check the signature and digest of an RPM package before installing it to ensure its integrity and authenticity.</li>
-<li>Always backup your system before installing or updating any RPM package to avoid any potential problems or conflicts.</li>
-<li>Always use sudo or root privileges when installing or removing any RPM package to avoid any permission errors or issues.</li>
-<li>Always read the documentation and instructions of an RPM package before installing it to understand its features and requirements.</li>
-<li>Always keep your system updated with the latest versions of RPM packages to ensure security and stability.</li>
-</ul>
- <h2>Frequently Asked Questions</h2>
-<ul>
-<li><b>Q: What is the difference between RPM and DEB?</b></li>
-<li>A: RPM and DEB are two different packaging systems for Linux systems. RPM is used by Red Hat-based distributions like RHEL, CentOS, Fedora, etc., while DEB is used by Debian-based distributions like Debian, Ubuntu, Mint, etc. They have different file formats, commands, tools, and features.</li>
-<li><b>Q: How can I convert an RPM package to a DEB package or vice versa?</b></li>
-<li>A: There are some tools that can convert an RPM package to a DEB package or vice versa, such as alien, rpm2cpio, cpio, etc. However, they are not always reliable or compatible, and they may cause some problems or errors. Therefore, it is not recommended to use them unless you know what you are doing.</li>
-<li><b>Q: How can I install an RPM package from a remote repository?</b></li>
-<li>A: You can install an RPM package from a remote repository by using yum or dnf with the install option, followed by the URL of the file. For example: <pre><code>sudo yum install https://example.com/package.rpm </code></pre>
- or <pre><code>sudo dnf install https://example.com/package.rpm </code></pre>
- This will download and install the package and its dependencies, if any. You may need to enter your password for sudo authentication.</li>
-<li><b>Q: How can I create my own RPM package?</b></li>
-<li>A: You can create your own RPM package by using the rpmbuild tool, which is part of the RPM package manager. You need to have a spec file, which is a text file that defines the metadata and instructions for building the package. You also need to have the source code or binaries of the software you want to package. For more details, you can refer to the RPM Packaging Guide.</li>
-<li><b>Q: How can I extract files from an RPM package without installing it?</b></li>
-<li>A: You can extract files from an RPM package without installing it by using the rpm2cpio tool, which converts an RPM package to a cpio archive, and then using the cpio tool, which extracts files from a cpio archive. For example: <pre><code>rpm2cpio slack.rpm | cpio -idmv </code></pre>
- This will extract all files from the slack.rpm package to the current directory.</li>
-</ul></p> 197e85843d<br />
-<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/Stellar-Phoenix-Windows-Data-Recovery-Pro-8035-Crack-Serial-Key.md b/spaces/feregVcuzo/sanity-test-midi/Stellar-Phoenix-Windows-Data-Recovery-Pro-8035-Crack-Serial-Key.md
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Craftsman 4 - The Best Simulation Game for Android with High FPS.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Craftsman 4 - The Best Simulation Game for Android with High FPS.md
deleted file mode 100644
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+++ /dev/null
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-<br />
-<h1>Craftsman 4: A New Crafting Game for Android</h1>
-<p>If you are looking for a fun and creative game to play on your Android device, you might want to check out Craftsman 4. This is a new crafting game that allows you to build anything you can imagine in a 3D block world. You can explore, craft, and construct your dream home, castle, farm, or dungeon with simple textured cubes. You can also enjoy the beautiful graphics, smooth performance, and endless possibilities of this game. In this article, we will tell you more about what Craftsman 4 is, what are its features, how to download and play it, what are some alternatives to it, and what are the reviews of it.</p>
-<h2>craftsman 4 1.17 download</h2><br /><p><b><b>Download Zip</b> &#9889; <a href="https://gohhs.com/2uPtOS">https://gohhs.com/2uPtOS</a></b></p><br /><br />
- <h2>What is Craftsman 4?</h2>
-<p>Craftsman 4 is a game developed by Slbzer, a developer that specializes in simulation and casual games for Android. Craftsman 4 is a game that combines three genres: creative sandbox, simulation, and casual.</p>
- <h3>A creative sandbox game</h3>
-<p>A creative sandbox game is a game that gives you the freedom to create your own world with various tools and materials. You can use different blocks and items to build structures, landscapes, and objects of your choice. You can also modify the environment and interact with it in various ways. There are no fixed goals or rules in a creative sandbox game, so you can play however you want.</p>
- <h3>A simulation game</h3>
-<p>A simulation game is a game that mimics or recreates some aspects of reality or fantasy. You can experience different scenarios, activities, and roles in a simulation game. You can also learn new skills, knowledge, or strategies from a simulation game. Some simulation games are realistic and educational, while others are imaginative and entertaining.</p>
- <h3>A casual game</h3>
-<p>A casual game is a game that is easy to play, simple to understand, and suitable for all ages. You can play a casual game anytime, anywhere, and for any duration. You don't need to invest a lot of time, money, or effort to enjoy a casual game. A casual game is usually designed to be fun, relaxing, and engaging.</p>
- <h2>What are the features of Craftsman 4?</h2>
-<p>Craftsman 4 has many features that make it an enjoyable and versatile game. Here are some of them:</p>
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- <h3>Craft games, house building and creative farms</h3>
-<p>Craftsman 4 allows you to create your own games within the game. You can design your own levels, puzzles, challenges, and rules with the blocks and items available. You can also share your games with other players or play their games online. You can also build your own house or any other structure you want with the blocks and items available. You can customize your house with furniture, decorations, lighting, and colors. You can also create your own farm with crops, animals, fences, and machines.</p>
- <h3>Exciting adventures in a 3D cube world</h3>
-<p>Craftsman 4 allows you to explore a vast and diverse world made of cubes. You can discover different biomes, such as forests, deserts, mountains, oceans, and caves. You can encounter different creatures, such as animals, monsters, and NPCs. You can also collect different resources, such as wood, stone, metal, and gems. You can use these resources to craft new items, tools, weapons, and armor.</p>
- <h3>High FPS without compromise</h3>
-<p>Craftsman 4 boasts a high FPS (frames per second) performance that ensures a smooth and lag-free gaming experience. You can enjoy the game without any glitches, crashes, or delays. You can also adjust the graphics settings to suit your device and preference. You can choose between low, medium, high, and ultra graphics quality. You can also enable or disable shadows, reflections, particles, and water effects.</p>
- <h3>Develop terrain and gather resources</h3>
-<p>Craftsman 4 allows you to manipulate the terrain and shape it to your liking. You can dig, cut, fill, flatten, or raise the land with the tools available. You can also create rivers, lakes, waterfalls, or volcanoes with the water and lava blocks. You can also gather resources from the environment, such as plants, fruits, flowers, mushrooms, and eggs. You can use these resources to craft food, potions, dyes, and other items.</p>
- <h2>How to download and play Craftsman 4?</h2>
-<p>Craftsman 4 is easy to download and play on your Android device. Here are the steps you need to follow:</p>
- <h3>Download from Google Play Store or APKCombo</h3>
-<p>You can download Craftsman 4 from the Google Play Store or APKCombo. The Google Play Store is the official app store for Android devices. You can search for Craftsman 4 in the store and tap on the install button to download it. APKCombo is a website that provides APK files for Android apps. You can visit the website and search for Craftsman 4 in the search bar. You can then choose the version you want and tap on the download button to get the APK file.</p>
- <h3>Install and launch the game</h3>
-<p>After downloading Craftsman 4 from either source, you need to install it on your device. If you downloaded it from the Google Play Store, you can simply tap on the open button after the installation is complete. If you downloaded it from APKCombo, you need to enable unknown sources in your device settings before installing it. You can then tap on the APK file and follow the instructions to install it. After installing Craftsman 4, you can launch it by tapping on its icon on your home screen or app drawer.</p>
- <h3>Choose your mode and start building</h3>
-<p>After launching Craftsman 4, you can choose between two modes: creative mode and survival mode. In creative mode, you have unlimited resources and no enemies. You can build anything you want without any restrictions or dangers. In survival mode, you have limited resources and face enemies. You need to gather resources, craft items, fight monsters, and survive as long as you can. After choosing your mode, you can start building your world with the blocks and items available. You can also explore the world and discover new things.</p>
- <h2>What are the alternatives to Craftsman 4?</h2>
-<p>If you like Craftsman 4 but want to try something different, you might want to check out some of these alternatives:</p>
- <h3>Other crafting games for Android</h3>
-<table>
-<tr><th>Name</th><th>Description</th></tr>
-<tr><td>Minecraft</td><td>The most popular crafting game in the world. You can create your own world with blocks and go on adventures with friends or alone.</td></tr>
-<tr><td>Terraria</td><td>A 2D sandbox game that lets you explore, build, fight, and survive in a randomly generated world.</td></tr>
-<tr><td>Roblox</td><td>A platform that allows you to create and play millions of games made by other users.</td></tr>
-<tr><td>Sandbox 3D</td><td>A game that allows you to create your own games with simple tools and share them with others.</td></tr>
-<tr><td>Crafty Lands</td><td>A game that allows you to build your own island with blocks and explore other islands made by other players.</td></tr>
-</table>
- <h3>Other simulation games for Android</h3>
-<table>
-<tr><th>Name</th><th>Description</th></tr>
-<tr><td>The Sims Mobile</td><td>A game that allows you to create your own sims and control their lives.</td></tr>
-<tr><td>Farming Simulator 20</td><td>A game that allows you to run your own farm with various crops and animals.</td></tr>
-<tr><td>SimCity BuildIt</td><td A game that allows you to build and manage your own city.</td></tr>
-<tr><td>Flight Pilot Simulator 3D</td><td>A game that allows you to fly various planes and complete different missions.</td></tr>
-<tr><td>BitLife</td><td>A game that allows you to live a virtual life with different choices and outcomes.</td></tr>
-</table>
- <h3>Other casual games for Android</h3>
-<table>
-<tr><th>Name</th><th>Description</th></tr>
-<tr><td>Candy Crush Saga</td><td>A game that involves matching candies of the same color and clearing levels.</td></tr>
-<tr><td>Angry Birds 2</td><td>A game that involves launching birds at pigs and destroying their structures.</td></tr>
-<tr><td>Subway Surfers</td><td>A game that involves running away from the police on a subway track and dodging obstacles.</td></tr>
-<tr><td>Plants vs Zombies 2</td><td>A game that involves planting plants to defend your home from zombies.</td></tr>
-<tr><td>Temple Run 2</td><td>A game that involves running away from a giant monkey and collecting coins and power-ups.</td></tr>
-</table>
- <h2>What are the reviews of Craftsman 4?</h2>
-<p>Craftsman 4 has received mixed reviews from users who have downloaded and played it. Here are some of the positive and negative reviews, as well as the average rating and number of downloads of the game:</p>
- <h3>Positive reviews from users</h3>
-<ul>
-<li>"This game is awesome. I love the graphics and the gameplay. It is very addictive and fun. I can build anything I want and share it with others. It is like Minecraft but better."</li>
-<li>"I really enjoy this game. It is very creative and relaxing. I can craft my own games and play them with my friends. It is also very smooth and fast. I recommend it to anyone who likes crafting games."</li>
-<li>"This game is amazing. It is very realistic and immersive. I can explore different biomes and find different resources. I can also fight different enemies and survive in survival mode. It is very challenging and exciting."</li>
-</ul>
- <h3>Negative reviews from users</h3>
-<ul>
-<li>"This game is terrible. It is very buggy and glitchy. It crashes a lot and freezes my device. It also has a lot of ads and in-app purchases. It is a waste of time and money."</li>
-<li>"I hate this game. It is very boring and repetitive. It has nothing new or original. It is just a copy of Minecraft and other crafting games. It also has poor graphics and controls. It is a disappointment."</li>
-<li>"This game is awful. It is very hard and frustrating. It has no tutorial or instructions. It also has no multiplayer or online mode. It is very lonely and dull."</li>
-</ul>
- <h3>Average rating and number of downloads</h3>
-<p>Craftsman 4 has an average rating of 3.9 out of 5 stars on the Google Play Store, based on over 10,000 reviews. It has been downloaded over 1 million times on the Google Play Store, as of June 2023.</p>
- <h2>Conclusion</h2>
-<p>Craftsman 4 is a new crafting game for Android that allows you to build anything you can imagine in a 3D block world. You can explore, craft, and construct your dream home, castle, farm, or dungeon with simple textured cubes. You can also enjoy the beautiful graphics, smooth performance, and endless possibilities of this game. Craftsman 4 is a game that combines three genres: creative sandbox, simulation, and casual. You can create your own games within the game, design your own levels, puzzles, challenges, and rules with the blocks and items available, share your games with other players or play their games online, build your own house or any other structure you want with the blocks and items available, customize your house with furniture, decorations, lighting, and colors, create your own farm with crops, animals, fences, and machines, explore a vast and diverse world made of cubes, discover different biomes, such as forests, deserts, mountains, oceans, and caves, encounter different creatures, such as animals, monsters, and NPCs, collect different resources, such as wood, stone, metal, and gems, use these resources to craft new items, tools, weapons, and armor, manipulate the terrain and shape it to your liking, dig, cut, fill, flatten, or raise the land with the tools available, create rivers, lakes, waterfalls, or volcanoes with the water and lava blocks, gather resources from the environment, such as plants, fruits, flowers, mushrooms, and eggs, use these resources to craft food, potions, dyes, and other items. Craftsman 4 is easy to download and play on your Android device. You can download it from the Google Play Store or APKCombo. You can then install and launch the game and choose between two modes: creative mode and survival mode. Craftsman 4 has received mixed reviews from users who have downloaded and played it. Some users love the game and praise its graphics and gameplay. Others hate the game and criticize its bugs and glitches. Craftsman 4 has an average rating of 3.9 out of 5 stars on the Google Play Store and has been downloaded over 1 million times.</p>
- <p>If you are looking for a fun and creative game to play on your Android device, you might want to give Craftsman 4 a try. You can build anything you can imagine in a 3D block world and enjoy the beautiful graphics, smooth performance, and endless possibilities of this game. You can also check out some of the alternatives to Craftsman 4 if you want to try something different. We hope this article has helped you learn more about Craftsman 4 and how to download and play it.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Craftsman 4:</p>
- <h3>Is Craftsman 4 free?</h3>
-<p>Yes, Craftsman 4 is free to download and play. However, it contains ads and in-app purchases that you can buy with real money.</p>
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-<p>Craftsman 4 can be played both online and offline. You can play online with other players or offline by yourself.</p>
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-<p>Craftsman 4 is safe to download and play. It does not contain any viruses or malware. However, you should always be careful when downloading apps from unknown sources or websites.</p>
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-<p>Craftsman 4 is compatible with most Android devices that have Android 5.0 or higher. However, some devices may not support the game or may experience performance issues.</p>
- <h3>How can I contact the developer of Craftsman 4?</h3>
-<p>You can contact the developer of Craftsman 4 by sending an email to slbzer@gmail.com. You can also visit their website at https://slbzer.com/.</p> 401be4b1e0<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Dino World A Jurassic Simulation Game of Dinosaur Battles and Farming.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Dino World A Jurassic Simulation Game of Dinosaur Battles and Farming.md
deleted file mode 100644
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+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
-<h1>Download Dino World: A Jurassic Adventure on Your Android Device</h1>
-<p>Do you love dinosaurs? Do you want to create your own Jurassic world and have fun with your prehistoric friends? If you answered yes, then you should download Dino World, a free-to-play game that lets you collect, breed, train, and battle over 12 types of dinosaurs. In this article, we will tell you everything you need to know about this amazing game, including how to download and install it on your Android device, what you can do in the game, some tips and tricks for playing it, and the pros and cons of the game. So, let's get started!</p>
- <h2>How to Download and Install Dino World on Your Android Device</h2>
-<p>Downloading and installing Dino World on your Android device is very easy. Just follow these simple steps:</p>
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-<ol>
-<li>Go to the Google Play Store and search for Dino World. You can also use this link to go directly to the game page.</li>
-<li>Tap on the Install button and wait for the download to finish. The game size is about 100 MB, so make sure you have enough space on your device.</li>
-<li>Open the app and enjoy the game. You may need to grant some permissions to the app, such as access to your storage and network.</li>
-</ol>
- <h2>What You Can Do in Dino World</h2>
-<p>Dino World is a Jurassic dinosaur fighting, breeding, and world-building game that offers a lot of fun and excitement. Here are some of the things you can do in the game:</p>
-<ul>
-<li><b>Collect, breed, and train over 12 types of dinosaurs.</b> Each dinosaur has its own personality, powers, and skills. You can feed them, play with them, and make them happy. You can also crossbreed them in the breeding lab and create new dinosaurs with unique traits.</li>
-<li><b>Build your own Jurassic world with custom habitats and decorations.</b> You can design your own island with different elements, such as water, fire, earth, air, etc. You can also place various habitats for your dinosaurs, such as forests, mountains, volcanoes, etc. You can also decorate your island with attractive items, such as fountains, statues, flowers, etc.</li>
-<li><b>Battle your dinosaurs against other players and win prizes.</b> You can make your own team of dinosaurs and take them to battle in various stages. You can use different strategies and skills to defeat your opponents. You can also win coins, gems, food, and other rewards by winning battles.</li>
-</ul>
- <h2>Tips and Tricks for Playing Dino World</h2>
-<p>If you want to master Dino World and become the best dinosaur trainer in the world, you need some tips and tricks. Here are some of them:</p>
-<ul>
-<li><b>How to get more coins, gems, and food.</b> Coins are the main currency in the game that you can use to buy habitats, decorations, food, etc. Gems are the premium currency that you can use to buy special items, speed up processes, etc. Food is the resource that you need to feed your dinosaurs and make them grow. Here are some ways to get more of these resources:</li>
-<li>- Watch ads. You can watch ads to get free coins, gems, and food every few hours. Just tap on the icons on the top right corner of the screen and watch a short video.</li>
-<li>- Complete tasks. You can complete various tasks in the game, such as collecting dinosaurs, breeding dinosaurs, winning battles, etc. You can see your tasks on the left side of the screen and claim your rewards when you finish them.</li>
-<li>- Spin the wheel. You can spin the wheel once a day for free and get a chance to win coins, gems, food, or even a rare dinosaur. Just tap on the wheel icon on the bottom right corner of the screen and spin it.</li>
-<li><b>How to crossbreed your dinosaurs and create new ones.</b> Crossbreeding is one of the most fun and exciting features of Dino World. You can combine two different types of dinosaurs in the breeding lab and create a new one with unique traits. Here are some tips for crossbreeding:</li>
-<li>- Choose compatible dinosaurs. You can only crossbreed dinosaurs that belong to the same element or have a common element. For example, you can crossbreed a fire dinosaur with another fire dinosaur or with an earth dinosaur, but not with a water dinosaur.</li>
-<li>- Check the breeding time. Different combinations of dinosaurs have different breeding times. Some may take a few minutes, while others may take hours or even days. You can speed up the breeding time by using gems or watching ads.</li>
-<li>- Hatch the egg. Once the breeding is done, you will get an egg that you need to hatch in the nursery. You can also speed up the hatching time by using gems or watching ads.</li>
-<li><b>How to level up your dinosaurs and unlock new skills.</b> Leveling up your dinosaurs is important if you want to make them stronger and more powerful. You can level up your dinosaurs by feeding them food and by battling with them. Here are some tips for leveling up:</li>
-<li>- Feed your dinosaurs regularly. Feeding your dinosaurs will increase their happiness and their level. You can feed them different types of food, such as meat, fish, fruits, etc. The more food you give them, the faster they will level up.</li>
-<li>- Battle with your dinosaurs often. Battling with your dinosaurs will increase their experience and their level. You can battle with other players in the arena or with computer opponents in the campaign mode. The more battles you win, the faster they will level up.</li>
-<li>- Unlock new skills for your dinosaurs. Each dinosaur has four skills that they can use in battle: attack, defense, heal, and special. You can unlock new skills for your dinosaurs by leveling them up or by using skill points that you can get from battles or tasks.</li>
-</ul>
- <h2>Pros and Cons of Dino World</h2>
-<p>Dino World is a great game for dinosaur lovers, but it also has some drawbacks that you should be aware of. Here are some of the pros and cons of the game:</p>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>- Fun, free, and addictive game with amazing graphics and sound effects</td><td>- Too many ads that may interrupt the gameplay</td></tr>
-<tr><td>- Variety of dinosaurs to collect, breed, train, and battle</td><td>- Bugs and glitches that may affect the game performance</td></tr>
-<tr><td>- Creative and customizable Jurassic world to build and decorate</td><td>- Limited space and resources that may limit your options</td></tr>
-<tr><td>- Competitive and challenging battles with other players and computer opponents</td><td>- Unbalanced and unfair matchmaking system that may pair you with stronger or weaker opponents</td></tr>
-<tr><td>- Regular updates and events that add new features and content to the game</td><td>- High battery consumption and data usage that may drain your device</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Dino World is a fantastic game for anyone who loves dinosaurs and wants to create their own Jurassic world. You can download it for free on your Android device and enjoy collecting, breeding, training, and battling over 12 types of dinosaurs. You can also build your own island with custom habitats and decorations and compete with other players around the world. Dino World is a fun, free, and addictive game that will keep you entertained for hours. So what are you waiting for? Download Dino World today and start your Jurassic adventure!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Dino World:</p <ul>
-<li><b>Q: How can I contact the developers of Dino World?</b></li>
-<li>A: You can contact the developers of Dino World by sending an email to support@dinoworld.com or by visiting their website. You can also follow them on Facebook and Twitter for the latest news and updates.</li>
-<li><b>Q: How can I play Dino World offline?</b></li>
-<li>A: You can play Dino World offline by downloading the game data to your device. To do this, you need to go to the settings menu and tap on the download data button. You will need an internet connection to download the data, but once you do, you can play the game without internet.</li>
-<li><b>Q: How can I backup and restore my game progress?</b></li>
-<li>A: You can backup and restore your game progress by connecting your game account to your Google Play account. To do this, you need to go to the settings menu and tap on the connect button. You will need an internet connection to connect your account, but once you do, you can sync your game progress across multiple devices.</li>
-<li><b>Q: How can I get more gems for free?</b></li>
-<li>A: You can get more gems for free by completing various offers in the game. To do this, you need to go to the shop menu and tap on the free gems button. You will see a list of offers that you can complete, such as watching videos, installing apps, completing surveys, etc. You will get gems as a reward for completing each offer.</li>
-<li><b>Q: How can I report a bug or a problem in the game?</b></li>
-<li>A: You can report a bug or a problem in the game by using the feedback feature in the game. To do this, you need to go to the settings menu and tap on the feedback button. You will be able to write a message and attach a screenshot of the issue. The developers will try to fix the issue as soon as possible.</li>
-</ul></p>
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Discover the New Adventures of My Talking Angela 2 with MOD APK.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Discover the New Adventures of My Talking Angela 2 with MOD APK.md
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--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Discover the New Adventures of My Talking Angela 2 with MOD APK.md	
+++ /dev/null
@@ -1,120 +0,0 @@
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-<h1>My Talking Tom Angela 2 Mod APK: A Fun and Interactive Game for All Ages</h1>
-<p>If you are looking for a fun and interactive game that can keep you entertained for hours, you should try My Talking Tom Angela 2 Mod APK. This is a modified version of the original game that gives you unlimited money and no ads, so you can enjoy the game without any limitations. In this article, we will tell you what My Talking Tom Angela 2 is, how to download and install it, and why you should play it.</p>
- <h2>What is My Talking Tom Angela 2?</h2>
-<p>My Talking Tom Angela 2 is a casual game that lets you adopt, raise, and play with a cute cat named Angela. You can customize her appearance, dress her up, feed her, bathe her, and make her happy. You can also explore different locations, such as her home, the city, the beach, and the forest, where you can find various activities and surprises. You can also interact with Angela and other characters, such as Tom, Hank, Ginger, Ben, and Becca, by talking to them, petting them, poking them, or playing with them. You can also play mini-games and earn coins that you can use to buy more items and accessories for your Angela.</p>
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-<p>My Talking Tom Angela 2 is the sequel to the popular My Talking Angela game that was released in 2014. The sequel has improved graphics, animations, sounds, and gameplay that make it more realistic and enjoyable. The sequel also has more content, such as new outfits, locations, mini-games, characters, and features that make it more diverse and fun.</p>
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-<p>My Talking Tom Angela 2 has many features that make it a fun and interactive game for all ages. Here are some of them:</p>
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- <h4>Customize your Angela's appearance and outfits</h4>
-<p>You can change your Angela's fur color, eye color, hairstyle, makeup, accessories, shoes, and clothes. You can choose from hundreds of options that suit your style and mood. You can also mix and match different items to create your own unique look.</p>
- <h4>Explore different locations and activities</h4>
-<p>You can visit different locations with your Angela, such as her home, the city, the beach, and the forest. Each location has its own theme and atmosphere that reflect your Angela's personality. You can also find various activities in each location, such as dancing, singing, cooking, gardening, painting, shopping, surfing, camping, fishing, and more. You can also discover hidden secrets and surprises in each location that will make your experience more exciting.</p>
- <h4>Interact with Angela and other characters</h4>
-<p>You can talk to your Angela and she will repeat what you say in a cute voice. You can also pet her, poke her, tickle her, or make her laugh. You can also interact with other characters in the game, such as Tom, Hank, Ginger, Ben, and Becca. You can chat with them, play with them, hug them, or prank them. You can also watch them perform funny actions and reactions that will make you laugh.</p>
- <h4>Play mini-games and earn coins</h4>
-<p>You can play various mini-games with your Angela and other characters, such as puzzle, arcade, action, racing, and more. You can also challenge your friends and compete with them in the leaderboards. You can earn coins by playing mini-games that you can use to buy more items and accessories for your Angela.</p>
- <h4>Enjoy the mod version with unlimited money and no ads</h4>
-<p>If you want to enjoy the game without any limitations, you can download and install the mod version of My Talking Tom Angela 2. The mod version gives you unlimited money that you can use to buy anything you want in the game. You can also enjoy the game without any ads that may interrupt your gameplay. The mod version is safe and easy to install, and it does not require any root or jailbreak.</p>
- <h2>How to download and install My Talking Tom Angela 2 Mod APK?</h2>
-<p>If you want to download and install My Talking Tom Angela 2 Mod APK, you can follow these simple steps:</p>
- <h3>The steps to download and install the mod apk file</h3>
-<ol>
-<li>Click on the download button below to download the mod apk file of My Talking Tom Angela 2.</li>
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-<p>My Talking Tom Angela 2 Mod APK is a fun and interactive game that can offer you many benefits. Here are some of them:</p>
- <h3>The benefits of playing My Talking Tom Angela 2 Mod APK</h3>
- <h4>Have fun and relax with Angela and her friends</h4>
-<p>My Talking Tom Angela 2 Mod APK is a game that can help you have fun and relax with Angela and her friends. You can play with them, talk to them, watch them, and laugh with them. You can also enjoy different activities and mini-games that can keep you entertained for hours. You can also express your emotions and moods through your Angela's appearance and outfits.</p>
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-<p>My Talking Tom Angela 2 Mod APK is a game that can help you support the developers of the original game. By playing the mod version, you can appreciate the work and effort of the developers who created this amazing game. You can also give them feedback and suggestions on how to improve the game. You can also support them by buying the original version of the game or by watching ads in the game.</p>
- <h2>Conclusion</h2>
-<p>My Talking Tom Angela 2 Mod APK is a fun and interactive game that can keep you entertained for hours. You can adopt, raise, and play with a cute cat named Angela. You can customize her appearance, dress her up, feed her, bathe her, and make her happy. You can also explore different locations, interact with Angela and other characters, play mini-games, and earn coins. You can also enjoy the mod version with unlimited money and no ads. My Talking Tom Angela 2 Mod APK is a game that can help you have fun and relax, develop your creativity and imagination, learn new skills and knowledge, and support the developers of the original game. If you want to download and install My Talking Tom Angela 2 Mod APK, you can follow the steps in this article. We hope you enjoy playing this game as much as we do.</p>
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-<p>Here are some frequently asked questions about My Talking Tom Angela 2 Mod APK:</p>
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-<li>Is My Talking Tom Angela 2 Mod APK safe to download and install?</li>
-<p>Yes, My Talking Tom Angela 2 Mod APK is safe to download and install. The mod apk file is scanned and tested by our team and it does not contain any viruses or malware. However, you should always download the mod apk file from a trusted source and be careful of fake or malicious links.</p>
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- <li>How can I update My Talking Tom Angela 2 Mod APK?</li>
-<p>My Talking Tom Angela 2 Mod APK is updated regularly by the developers to fix bugs, improve features, and add new content. You can check for updates by visiting our website or by following our social media accounts. You can also enable the automatic update option in your device's settings to get the latest version of the game.</p>
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-<li>Select the uninstall option and confirm your choice.</li>
-<li>Wait for the uninstallation process to finish and then restart your device.</li>
-</ul>
- <li>How can I contact the developers of My Talking Tom Angela 2 Mod APK?</li>
-<p>If you have any questions, feedback, suggestions, or complaints about My Talking Tom Angela 2 Mod APK, you can contact the developers by using these methods:</p>
-<ul>
-<li>Email: support@outfit7.com</li>
-<li>Website: https://outfit7.com/contact/</li>
-<li>Facebook: https://www.facebook.com/Outfit7/</li>
-<li>Twitter: https://twitter.com/outfit7</li>
-<li>YouTube: https://www.youtube.com/user/TalkingTomCat</li>
-</ul>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Pix2Pix-Video/share_btn.py b/spaces/fffiloni/Pix2Pix-Video/share_btn.py
deleted file mode 100644
index 66e0de15dce2d65f4cd0ef512c7bd8adad0beb77..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Pix2Pix-Video/share_btn.py
+++ /dev/null
@@ -1,73 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	
-    async function getVideoBlobFile(videoEL){
-        const res = await fetch(videoEL.src);
-        const blob = await res.blob();
-        const videoId = Date.now() % 200;
-        const fileName = `vid-pix2pix-${{videoId}}.wav`;
-        const videoBlob = new File([blob], fileName, { type: 'video/mp4' });
-        console.log(videoBlob);
-        return videoBlob;
-	}
-    
-    const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
-    const captionTxt = gradioEl.querySelector('#prompt-in textarea').value;
-    const inputVidEl = gradioEl.querySelector('#input-vid video');
-    const outputVideo = gradioEl.querySelector('#video-output video');
-
-    
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputVideo){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    
-    const inputFile = await getVideoBlobFile(inputVidEl);
-    const urlInputVid = await uploadFile(inputFile);
-    const videoOutFile = await getVideoBlobFile(outputVideo);
-	const dataOutputVid = await uploadFile(videoOutFile);
-    
-    const descriptionMd = `
-#### Video input:
-${urlInputVid}
-
-#### Pix2Pix result:
-${dataOutputVid}
-`;
-    const params = new URLSearchParams({
-        title: captionTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/Pix2Pix-Video/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/florim/MedGPT/autogpt/speech/say.py b/spaces/florim/MedGPT/autogpt/speech/say.py
deleted file mode 100644
index 727983d12bf334205550a54bcd69a7a36824eda4..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/speech/say.py
+++ /dev/null
@@ -1,41 +0,0 @@
-""" Text to speech module """
-import threading
-from threading import Semaphore
-
-from autogpt.config import Config
-from autogpt.speech.brian import BrianSpeech
-from autogpt.speech.eleven_labs import ElevenLabsSpeech
-from autogpt.speech.gtts import GTTSVoice
-from autogpt.speech.macos_tts import MacOSTTS
-
-CFG = Config()
-DEFAULT_VOICE_ENGINE = GTTSVoice()
-VOICE_ENGINE = None
-if CFG.elevenlabs_api_key:
-    VOICE_ENGINE = ElevenLabsSpeech()
-elif CFG.use_mac_os_tts == "True":
-    VOICE_ENGINE = MacOSTTS()
-elif CFG.use_brian_tts == "True":
-    VOICE_ENGINE = BrianSpeech()
-else:
-    VOICE_ENGINE = GTTSVoice()
-
-
-QUEUE_SEMAPHORE = Semaphore(
-    1
-)  # The amount of sounds to queue before blocking the main thread
-
-
-def say_text(text: str, voice_index: int = 0) -> None:
-    """Speak the given text using the given voice index"""
-
-    def speak() -> None:
-        success = VOICE_ENGINE.say(text, voice_index)
-        if not success:
-            DEFAULT_VOICE_ENGINE.say(text)
-
-        QUEUE_SEMAPHORE.release()
-
-    QUEUE_SEMAPHORE.acquire(True)
-    thread = threading.Thread(target=speak)
-    thread.start()
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/calculator_live/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/calculator_live/run.py
deleted file mode 100644
index 09856c099eb6ffa080b355d09bc3ecebe3ea714c..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/calculator_live/run.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import gradio as gr
-
-def calculator(num1, operation, num2):
-    if operation == "add":
-        return num1 + num2
-    elif operation == "subtract":
-        return num1 - num2
-    elif operation == "multiply":
-        return num1 * num2
-    elif operation == "divide":
-        return num1 / num2
-
-demo = gr.Interface(
-    calculator,
-    [
-        "number",
-        gr.Radio(["add", "subtract", "multiply", "divide"]),
-        "number"
-    ],
-    "number",
-    live=True,
-)
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/galopyz/Alien_vs_Ghost/app.py b/spaces/galopyz/Alien_vs_Ghost/app.py
deleted file mode 100644
index e1a52116dbeec31ee94305fe706a67883ff0526e..0000000000000000000000000000000000000000
--- a/spaces/galopyz/Alien_vs_Ghost/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: testing.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inf', 'classify_images']
-
-# %% testing.ipynb 3
-from fastai.vision.all import *
-import gradio as gr
-
-# %% testing.ipynb 4
-learn = load_learner('alien_vs_ghost.pkl')
-
-# %% testing.ipynb 9
-categories = learn.dls.vocab
-def classify_images(img):
-    pred, pred_idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# %% testing.ipynb 12
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['alien1.jpeg', 'alien2.jpeg', 'ghost1.jpeg', 'ghost2.jpeg', 'dunno1.jpeg', 'dunno2.jpeg']
-
-inf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples)
-inf.launch(inline=False)
diff --git a/spaces/genevera/AudioToken/modules/AudioToken/embedder.py b/spaces/genevera/AudioToken/modules/AudioToken/embedder.py
deleted file mode 100644
index 39035969bf12ba730869e4c5fc5342998ea51d60..0000000000000000000000000000000000000000
--- a/spaces/genevera/AudioToken/modules/AudioToken/embedder.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch
-import torch.nn as nn
-from modules.fga.atten import Atten
-
-
-class FGAEmbedder(nn.Module):
-    def __init__(self, input_size=768*3, output_size=768):
-        super(FGAEmbedder, self).__init__()
-        self.fc1 = nn.Linear(input_size, input_size)
-        self.fc2 = nn.Linear(input_size, output_size)
-        self.gelu = nn.GELU()
-        self.fga = Atten(util_e=[output_size], pairwise_flag=False)
-
-    def forward(self, audio_embs):
-        audio_embs = self.fc1(audio_embs)
-        audio_embs = self.gelu(audio_embs)
-        audio_embs = self.fc2(audio_embs)
-        attend = self.fga([audio_embs])[0]
-        return attend
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/transforms.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/datasets/chase_db1.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/datasets/chase_db1.py
deleted file mode 100644
index 8bc29bea14704a4407f83474610cbc3bef32c708..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/datasets/chase_db1.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class ChaseDB1Dataset(CustomDataset):
-    """Chase_db1 dataset.
-
-    In segmentation map annotation for Chase_db1, 0 stands for background,
-    which is included in 2 categories. ``reduce_zero_label`` is fixed to False.
-    The ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
-    '_1stHO.png'.
-    """
-
-    CLASSES = ('background', 'vessel')
-
-    PALETTE = [[120, 120, 120], [6, 230, 230]]
-
-    def __init__(self, **kwargs):
-        super(ChaseDB1Dataset, self).__init__(
-            img_suffix='.png',
-            seg_map_suffix='_1stHO.png',
-            reduce_zero_label=False,
-            **kwargs)
-        assert osp.exists(self.img_dir)
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/decode_heads/uper_head.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/decode_heads/uper_head.py
deleted file mode 100644
index 9e1301b706b0d83ed714bbdee8ee24693f150455..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/decode_heads/uper_head.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-from .psp_head import PPM
-
-
-@HEADS.register_module()
-class UPerHead(BaseDecodeHead):
-    """Unified Perceptual Parsing for Scene Understanding.
-
-    This head is the implementation of `UPerNet
-    <https://arxiv.org/abs/1807.10221>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module applied on the last feature. Default: (1, 2, 3, 6).
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
-        super(UPerHead, self).__init__(
-            input_transform='multiple_select', **kwargs)
-        # PSP Module
-        self.psp_modules = PPM(
-            pool_scales,
-            self.in_channels[-1],
-            self.channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-        self.bottleneck = ConvModule(
-            self.in_channels[-1] + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        # FPN Module
-        self.lateral_convs = nn.ModuleList()
-        self.fpn_convs = nn.ModuleList()
-        for in_channels in self.in_channels[:-1]:  # skip the top layer
-            l_conv = ConvModule(
-                in_channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg,
-                inplace=False)
-            fpn_conv = ConvModule(
-                self.channels,
-                self.channels,
-                3,
-                padding=1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg,
-                inplace=False)
-            self.lateral_convs.append(l_conv)
-            self.fpn_convs.append(fpn_conv)
-
-        self.fpn_bottleneck = ConvModule(
-            len(self.in_channels) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def psp_forward(self, inputs):
-        """Forward function of PSP module."""
-        x = inputs[-1]
-        psp_outs = [x]
-        psp_outs.extend(self.psp_modules(x))
-        psp_outs = torch.cat(psp_outs, dim=1)
-        output = self.bottleneck(psp_outs)
-
-        return output
-
-    def forward(self, inputs):
-        """Forward function."""
-
-        inputs = self._transform_inputs(inputs)
-
-        # build laterals
-        laterals = [
-            lateral_conv(inputs[i])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-
-        laterals.append(self.psp_forward(inputs))
-
-        # build top-down path
-        used_backbone_levels = len(laterals)
-        for i in range(used_backbone_levels - 1, 0, -1):
-            prev_shape = laterals[i - 1].shape[2:]
-            laterals[i - 1] += resize(
-                laterals[i],
-                size=prev_shape,
-                mode='bilinear',
-                align_corners=self.align_corners)
-
-        # build outputs
-        fpn_outs = [
-            self.fpn_convs[i](laterals[i])
-            for i in range(used_backbone_levels - 1)
-        ]
-        # append psp feature
-        fpn_outs.append(laterals[-1])
-
-        for i in range(used_backbone_levels - 1, 0, -1):
-            fpn_outs[i] = resize(
-                fpn_outs[i],
-                size=fpn_outs[0].shape[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-        fpn_outs = torch.cat(fpn_outs, dim=1)
-        output = self.fpn_bottleneck(fpn_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/ghlee94/MEDIAR/predict.py b/spaces/ghlee94/MEDIAR/predict.py
deleted file mode 100644
index 30a751e9476dfef230bbcea6e04963e8157ec561..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/predict.py
+++ /dev/null
@@ -1,1256 +0,0 @@
-import torch
-from torch.nn import (
-    Module,
-    Conv2d,
-    BatchNorm2d,
-    Identity,
-    UpsamplingBilinear2d,
-    Mish,
-    ReLU,
-    Sequential,
-)
-from torch.nn.functional import interpolate, grid_sample, pad
-import numpy as np
-from copy import deepcopy
-import os, argparse, math
-import tifffile as tif
-from typing import Tuple, List, Mapping
-
-from monai.utils import (
-    BlendMode,
-    PytorchPadMode,
-    convert_data_type,
-    ensure_tuple,
-    fall_back_tuple,
-    look_up_option,
-    convert_to_dst_type,
-)
-from monai.utils.misc import ensure_tuple_size, ensure_tuple_rep, issequenceiterable
-from monai.networks.layers.convutils import gaussian_1d
-from monai.networks.layers.simplelayers import separable_filtering
-
-from segmentation_models_pytorch import MAnet
-
-from skimage.io import imread as io_imread
-from skimage.util.dtype import dtype_range
-from skimage._shared.utils import _supported_float_type
-from scipy.ndimage import find_objects, binary_fill_holes
-
-
-########################### Data Loading Modules #########################################################
-DTYPE_RANGE = dtype_range.copy()
-DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
-DTYPE_RANGE.update(
-    {
-        "uint10": (0, 2 ** 10 - 1),
-        "uint12": (0, 2 ** 12 - 1),
-        "uint14": (0, 2 ** 14 - 1),
-        "bool": dtype_range[bool],
-        "float": dtype_range[np.float64],
-    }
-)
-
-
-def _output_dtype(dtype_or_range, image_dtype):
-    if type(dtype_or_range) in [list, tuple, np.ndarray]:
-        # pair of values: always return float.
-        return _supported_float_type(image_dtype)
-    if type(dtype_or_range) == type:
-        # already a type: return it
-        return dtype_or_range
-    if dtype_or_range in DTYPE_RANGE:
-        # string key in DTYPE_RANGE dictionary
-        try:
-            # if it's a canonical numpy dtype, convert
-            return np.dtype(dtype_or_range).type
-        except TypeError:  # uint10, uint12, uint14
-            # otherwise, return uint16
-            return np.uint16
-    else:
-        raise ValueError(
-            "Incorrect value for out_range, should be a valid image data "
-            f"type or a pair of values, got {dtype_or_range}."
-        )
-
-
-def intensity_range(image, range_values="image", clip_negative=False):
-    if range_values == "dtype":
-        range_values = image.dtype.type
-
-    if range_values == "image":
-        i_min = np.min(image)
-        i_max = np.max(image)
-    elif range_values in DTYPE_RANGE:
-        i_min, i_max = DTYPE_RANGE[range_values]
-        if clip_negative:
-            i_min = 0
-    else:
-        i_min, i_max = range_values
-    return i_min, i_max
-
-
-def rescale_intensity(image, in_range="image", out_range="dtype"):
-    out_dtype = _output_dtype(out_range, image.dtype)
-
-    imin, imax = map(float, intensity_range(image, in_range))
-    omin, omax = map(
-        float, intensity_range(image, out_range, clip_negative=(imin >= 0))
-    )
-    image = np.clip(image, imin, imax)
-
-    if imin != imax:
-        image = (image - imin) / (imax - imin)
-        return np.asarray(image * (omax - omin) + omin, dtype=out_dtype)
-    else:
-        return np.clip(image, omin, omax).astype(out_dtype)
-
-
-def _normalize(img):
-    non_zero_vals = img[np.nonzero(img)]
-    percentiles = np.percentile(non_zero_vals, [0, 99.5])
-    img_norm = rescale_intensity(
-        img, in_range=(percentiles[0], percentiles[1]), out_range="uint8"
-    )
-
-    return img_norm.astype(np.uint8)
-
-
-def pred_transforms(filename):
-    # LoadImage
-    img = (
-        tif.imread(filename)
-        if filename.endswith(".tif") or filename.endswith(".tiff")
-        else io_imread(filename)
-    )
-
-    if len(img.shape) == 2:
-        img = np.repeat(np.expand_dims(img, axis=-1), 3, axis=-1)
-    elif len(img.shape) == 3 and img.shape[-1] > 3:
-        img = img[:, :, :3]
-
-    img = img.astype(np.float32)
-    img = _normalize(img)
-    img = np.moveaxis(img, -1, 0)
-    img = (img - img.min()) / (img.max() - img.min())
-
-    return torch.FloatTensor(img).unsqueeze(0)
-
-
-################################################################################
-
-########################### MODEL Architecture #################################
-class SegformerGH(MAnet):
-    def __init__(
-        self,
-        encoder_name: str = "mit_b5",
-        encoder_weights="imagenet",
-        decoder_channels=(256, 128, 64, 32, 32),
-        decoder_pab_channels=256,
-        in_channels: int = 3,
-        classes: int = 3,
-    ):
-        super(SegformerGH, self).__init__(
-            encoder_name=encoder_name,
-            encoder_weights=encoder_weights,
-            decoder_channels=decoder_channels,
-            decoder_pab_channels=decoder_pab_channels,
-            in_channels=in_channels,
-            classes=classes,
-        )
-
-        convert_relu_to_mish(self.encoder)
-        convert_relu_to_mish(self.decoder)
-
-        self.cellprob_head = DeepSegmantationHead(
-            in_channels=decoder_channels[-1], out_channels=1, kernel_size=3,
-        )
-        self.gradflow_head = DeepSegmantationHead(
-            in_channels=decoder_channels[-1], out_channels=2, kernel_size=3,
-        )
-
-    def forward(self, x):
-        """Sequentially pass `x` trough model`s encoder, decoder and heads"""
-        self.check_input_shape(x)
-
-        features = self.encoder(x)
-        decoder_output = self.decoder(*features)
-
-        gradflow_mask = self.gradflow_head(decoder_output)
-        cellprob_mask = self.cellprob_head(decoder_output)
-
-        masks = torch.cat([gradflow_mask, cellprob_mask], dim=1)
-
-        return masks
-
-
-class DeepSegmantationHead(Sequential):
-    def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
-        conv2d_1 = Conv2d(
-            in_channels,
-            in_channels // 2,
-            kernel_size=kernel_size,
-            padding=kernel_size // 2,
-        )
-        bn = BatchNorm2d(in_channels // 2)
-        conv2d_2 = Conv2d(
-            in_channels // 2,
-            out_channels,
-            kernel_size=kernel_size,
-            padding=kernel_size // 2,
-        )
-        mish = Mish(inplace=True)
-
-        upsampling = (
-            UpsamplingBilinear2d(scale_factor=upsampling)
-            if upsampling > 1
-            else Identity()
-        )
-        activation = Identity()
-        super().__init__(conv2d_1, mish, bn, conv2d_2, upsampling, activation)
-
-
-def convert_relu_to_mish(model):
-    for child_name, child in model.named_children():
-        if isinstance(child, ReLU):
-            setattr(model, child_name, Mish(inplace=True))
-        else:
-            convert_relu_to_mish(child)
-
-
-#####################################################################################
-
-########################### Sliding Window Inference #################################
-class GaussianFilter(Module):
-    def __init__(
-        self, spatial_dims, sigma, truncated=4.0, approx="erf", requires_grad=False,
-    ) -> None:
-        if issequenceiterable(sigma):
-            if len(sigma) != spatial_dims:  # type: ignore
-                raise ValueError
-        else:
-            sigma = [deepcopy(sigma) for _ in range(spatial_dims)]  # type: ignore
-        super().__init__()
-        self.sigma = [
-            torch.nn.Parameter(
-                torch.as_tensor(
-                    s,
-                    dtype=torch.float,
-                    device=s.device if isinstance(s, torch.Tensor) else None,
-                ),
-                requires_grad=requires_grad,
-            )
-            for s in sigma  # type: ignore
-        ]
-        self.truncated = truncated
-        self.approx = approx
-        for idx, param in enumerate(self.sigma):
-            self.register_parameter(f"kernel_sigma_{idx}", param)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        _kernel = [
-            gaussian_1d(s, truncated=self.truncated, approx=self.approx)
-            for s in self.sigma
-        ]
-        return separable_filtering(x=x, kernels=_kernel)
-
-
-def compute_importance_map(
-    patch_size, mode=BlendMode.CONSTANT, sigma_scale=0.125, device="cpu"
-):
-    mode = look_up_option(mode, BlendMode)
-    device = torch.device(device)
-
-    center_coords = [i // 2 for i in patch_size]
-    sigma_scale = ensure_tuple_rep(sigma_scale, len(patch_size))
-    sigmas = [i * sigma_s for i, sigma_s in zip(patch_size, sigma_scale)]
-
-    importance_map = torch.zeros(patch_size, device=device)
-    importance_map[tuple(center_coords)] = 1
-    pt_gaussian = GaussianFilter(len(patch_size), sigmas).to(
-        device=device, dtype=torch.float
-    )
-    importance_map = pt_gaussian(importance_map.unsqueeze(0).unsqueeze(0))
-    importance_map = importance_map.squeeze(0).squeeze(0)
-    importance_map = importance_map / torch.max(importance_map)
-    importance_map = importance_map.float()
-
-    return importance_map
-
-
-def first(iterable, default=None):
-    for i in iterable:
-        return i
-
-    return default
-
-
-def dense_patch_slices(image_size, patch_size, scan_interval):
-    num_spatial_dims = len(image_size)
-    patch_size = get_valid_patch_size(image_size, patch_size)
-    scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)
-
-    scan_num = []
-    for i in range(num_spatial_dims):
-        if scan_interval[i] == 0:
-            scan_num.append(1)
-        else:
-            num = int(math.ceil(float(image_size[i]) / scan_interval[i]))
-            scan_dim = first(
-                d
-                for d in range(num)
-                if d * scan_interval[i] + patch_size[i] >= image_size[i]
-            )
-            scan_num.append(scan_dim + 1 if scan_dim is not None else 1)
-
-    starts = []
-    for dim in range(num_spatial_dims):
-        dim_starts = []
-        for idx in range(scan_num[dim]):
-            start_idx = idx * scan_interval[dim]
-            start_idx -= max(start_idx + patch_size[dim] - image_size[dim], 0)
-            dim_starts.append(start_idx)
-        starts.append(dim_starts)
-    out = np.asarray([x.flatten() for x in np.meshgrid(*starts, indexing="ij")]).T
-    return [tuple(slice(s, s + patch_size[d]) for d, s in enumerate(x)) for x in out]
-
-
-def get_valid_patch_size(image_size, patch_size):
-    ndim = len(image_size)
-    patch_size_ = ensure_tuple_size(patch_size, ndim)
-
-    # ensure patch size dimensions are not larger than image dimension, if a dimension is None or 0 use whole dimension
-    return tuple(min(ms, ps or ms) for ms, ps in zip(image_size, patch_size_))
-
-
-class Resize:
-    def __init__(self, spatial_size):
-        self.size_mode = "all"
-        self.spatial_size = spatial_size
-
-    def __call__(self, img):
-        input_ndim = img.ndim - 1  # spatial ndim
-        output_ndim = len(ensure_tuple(self.spatial_size))
-
-        if output_ndim > input_ndim:
-            input_shape = ensure_tuple_size(img.shape, output_ndim + 1, 1)
-            img = img.reshape(input_shape)
-
-        spatial_size_ = fall_back_tuple(self.spatial_size, img.shape[1:])
-
-        if (
-            tuple(img.shape[1:]) == spatial_size_
-        ):  # spatial shape is already the desired
-            return img
-
-        img_, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
-
-        resized = interpolate(
-            input=img_.unsqueeze(0), size=spatial_size_, mode="nearest",
-        )
-        out, *_ = convert_to_dst_type(resized.squeeze(0), img)
-        return out
-
-
-def sliding_window_inference(
-    inputs,
-    roi_size,
-    sw_batch_size,
-    predictor,
-    overlap,
-    mode=BlendMode.CONSTANT,
-    sigma_scale=0.125,
-    padding_mode=PytorchPadMode.CONSTANT,
-    cval=0.0,
-    sw_device=None,
-    device=None,
-    roi_weight_map=None,
-):
-    compute_dtype = inputs.dtype
-    num_spatial_dims = len(inputs.shape) - 2
-    batch_size, _, *image_size_ = inputs.shape
-
-    roi_size = fall_back_tuple(roi_size, image_size_)
-    # in case that image size is smaller than roi size
-    image_size = tuple(
-        max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims)
-    )
-    pad_size = []
-
-    for k in range(len(inputs.shape) - 1, 1, -1):
-        diff = max(roi_size[k - 2] - inputs.shape[k], 0)
-        half = diff // 2
-        pad_size.extend([half, diff - half])
-
-    inputs = pad(
-        inputs,
-        pad=pad_size,
-        mode=look_up_option(padding_mode, PytorchPadMode).value,
-        value=cval,
-    )
-
-    scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
-
-    # Store all slices in list
-    slices = dense_patch_slices(image_size, roi_size, scan_interval)
-    num_win = len(slices)  # number of windows per image
-    total_slices = num_win * batch_size  # total number of windows
-
-    # Create window-level importance map
-    valid_patch_size = get_valid_patch_size(image_size, roi_size)
-    if valid_patch_size == roi_size and (roi_weight_map is not None):
-        importance_map = roi_weight_map
-    else:
-        importance_map = compute_importance_map(
-            valid_patch_size, mode=mode, sigma_scale=sigma_scale, device=device
-        )
-
-    importance_map = convert_data_type(importance_map, torch.Tensor, device, compute_dtype)[0]  # type: ignore
-    # handle non-positive weights
-    min_non_zero = max(importance_map[importance_map != 0].min().item(), 1e-3)
-    importance_map = torch.clamp(importance_map.to(torch.float32), min=min_non_zero).to(
-        compute_dtype
-    )
-
-    # Perform predictions
-    dict_key, output_image_list, count_map_list = None, [], []
-    _initialized_ss = -1
-    is_tensor_output = (
-        True  # whether the predictor's output is a tensor (instead of dict/tuple)
-    )
-
-    # for each patch
-    for slice_g in range(0, total_slices, sw_batch_size):
-        slice_range = range(slice_g, min(slice_g + sw_batch_size, total_slices))
-        unravel_slice = [
-            [slice(int(idx / num_win), int(idx / num_win) + 1), slice(None)]
-            + list(slices[idx % num_win])
-            for idx in slice_range
-        ]
-        window_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(
-            sw_device
-        )
-        seg_prob_out = predictor(window_data)  # batched patch segmentation
-
-        # convert seg_prob_out to tuple seg_prob_tuple, this does not allocate new memory.
-        seg_prob_tuple: Tuple[torch.Tensor, ...]
-        if isinstance(seg_prob_out, torch.Tensor):
-            seg_prob_tuple = (seg_prob_out,)
-        elif isinstance(seg_prob_out, Mapping):
-            if dict_key is None:
-                dict_key = sorted(seg_prob_out.keys())  # track predictor's output keys
-            seg_prob_tuple = tuple(seg_prob_out[k] for k in dict_key)
-            is_tensor_output = False
-        else:
-            seg_prob_tuple = ensure_tuple(seg_prob_out)
-            is_tensor_output = False
-
-        # for each output in multi-output list
-        for ss, seg_prob in enumerate(seg_prob_tuple):
-            seg_prob = seg_prob.to(device)  # BxCxMxNxP or BxCxMxN
-
-            # compute zoom scale: out_roi_size/in_roi_size
-            zoom_scale = []
-            for axis, (img_s_i, out_w_i, in_w_i) in enumerate(
-                zip(image_size, seg_prob.shape[2:], window_data.shape[2:])
-            ):
-                _scale = out_w_i / float(in_w_i)
-
-                zoom_scale.append(_scale)
-
-            if _initialized_ss < ss:  # init. the ss-th buffer at the first iteration
-                # construct multi-resolution outputs
-                output_classes = seg_prob.shape[1]
-                output_shape = [batch_size, output_classes] + [
-                    int(image_size_d * zoom_scale_d)
-                    for image_size_d, zoom_scale_d in zip(image_size, zoom_scale)
-                ]
-                # allocate memory to store the full output and the count for overlapping parts
-                output_image_list.append(
-                    torch.zeros(output_shape, dtype=compute_dtype, device=device)
-                )
-                count_map_list.append(
-                    torch.zeros(
-                        [1, 1] + output_shape[2:], dtype=compute_dtype, device=device
-                    )
-                )
-                _initialized_ss += 1
-
-            # resizing the importance_map
-            resizer = Resize(spatial_size=seg_prob.shape[2:])
-
-            # store the result in the proper location of the full output. Apply weights from importance map.
-            for idx, original_idx in zip(slice_range, unravel_slice):
-                # zoom roi
-                original_idx_zoom = list(
-                    original_idx
-                )  # 4D for 2D image, 5D for 3D image
-                for axis in range(2, len(original_idx_zoom)):
-                    zoomed_start = original_idx[axis].start * zoom_scale[axis - 2]
-                    zoomed_end = original_idx[axis].stop * zoom_scale[axis - 2]
-
-                    original_idx_zoom[axis] = slice(
-                        int(zoomed_start), int(zoomed_end), None
-                    )
-                importance_map_zoom = resizer(importance_map.unsqueeze(0))[0].to(
-                    compute_dtype
-                )
-                # store results and weights
-                output_image_list[ss][original_idx_zoom] += (
-                    importance_map_zoom * seg_prob[idx - slice_g]
-                )
-                count_map_list[ss][original_idx_zoom] += (
-                    importance_map_zoom.unsqueeze(0)
-                    .unsqueeze(0)
-                    .expand(count_map_list[ss][original_idx_zoom].shape)
-                )
-
-    # account for any overlapping sections
-    for ss in range(len(output_image_list)):
-        output_image_list[ss] = (output_image_list[ss] / count_map_list.pop(0)).to(
-            compute_dtype
-        )
-
-    # remove padding if image_size smaller than roi_size
-    for ss, output_i in enumerate(output_image_list):
-        zoom_scale = [
-            seg_prob_map_shape_d / roi_size_d
-            for seg_prob_map_shape_d, roi_size_d in zip(output_i.shape[2:], roi_size)
-        ]
-
-        final_slicing: List[slice] = []
-        for sp in range(num_spatial_dims):
-            slice_dim = slice(
-                pad_size[sp * 2],
-                image_size_[num_spatial_dims - sp - 1] + pad_size[sp * 2],
-            )
-            slice_dim = slice(
-                int(round(slice_dim.start * zoom_scale[num_spatial_dims - sp - 1])),
-                int(round(slice_dim.stop * zoom_scale[num_spatial_dims - sp - 1])),
-            )
-            final_slicing.insert(0, slice_dim)
-        while len(final_slicing) < len(output_i.shape):
-            final_slicing.insert(0, slice(None))
-        output_image_list[ss] = output_i[final_slicing]
-
-    if dict_key is not None:  # if output of predictor is a dict
-        final_output = dict(zip(dict_key, output_image_list))
-    else:
-        final_output = tuple(output_image_list)  # type: ignore
-
-    return final_output[0] if is_tensor_output else final_output  # type: ignore
-
-
-def _get_scan_interval(
-    image_size, roi_size, num_spatial_dims: int, overlap: float
-) -> Tuple[int, ...]:
-    scan_interval = []
-
-    for i in range(num_spatial_dims):
-        if roi_size[i] == image_size[i]:
-            scan_interval.append(int(roi_size[i]))
-        else:
-            interval = int(roi_size[i] * (1 - overlap))
-            scan_interval.append(interval if interval > 0 else 1)
-
-    return tuple(scan_interval)
-
-
-#####################################################################################
-
-########################### Main Inference Functions #################################
-def post_process(pred_mask, device):
-    dP, cellprob = pred_mask[:2], 1 / (1 + np.exp(-pred_mask[-1]))
-    H, W = pred_mask.shape[-2], pred_mask.shape[-1]
-
-    if np.prod(H * W) < (5000 * 5000):
-        pred_mask = compute_masks(
-            dP,
-            cellprob,
-            use_gpu=True,
-            flow_threshold=0.4,
-            device=device,
-            cellprob_threshold=0.4,
-        )[0]
-
-    else:
-        print("\n[Whole Slide] Grid Prediction starting...")
-        roi_size = 2000
-
-        # Get patch grid by roi_size
-        if H % roi_size != 0:
-            n_H = H // roi_size + 1
-            new_H = roi_size * n_H
-        else:
-            n_H = H // roi_size
-            new_H = H
-
-        if W % roi_size != 0:
-            n_W = W // roi_size + 1
-            new_W = roi_size * n_W
-        else:
-            n_W = W // roi_size
-            new_W = W
-
-        # Allocate values on the grid
-        pred_pad = np.zeros((new_H, new_W), dtype=np.uint32)
-        dP_pad = np.zeros((2, new_H, new_W), dtype=np.float32)
-        cellprob_pad = np.zeros((new_H, new_W), dtype=np.float32)
-
-        dP_pad[:, :H, :W], cellprob_pad[:H, :W] = dP, cellprob
-
-        for i in range(n_H):
-            for j in range(n_W):
-                print("Pred on Grid (%d, %d) processing..." % (i, j))
-                dP_roi = dP_pad[
-                    :,
-                    roi_size * i : roi_size * (i + 1),
-                    roi_size * j : roi_size * (j + 1),
-                ]
-                cellprob_roi = cellprob_pad[
-                    roi_size * i : roi_size * (i + 1),
-                    roi_size * j : roi_size * (j + 1),
-                ]
-
-                pred_mask = compute_masks(
-                    dP_roi,
-                    cellprob_roi,
-                    use_gpu=True,
-                    flow_threshold=0.4,
-                    device=device,
-                    cellprob_threshold=0.4,
-                )[0]
-
-                pred_pad[
-                    roi_size * i : roi_size * (i + 1),
-                    roi_size * j : roi_size * (j + 1),
-                ] = pred_mask
-
-        pred_mask = pred_pad[:H, :W]
-
-    cell_idx, cell_sizes = np.unique(pred_mask, return_counts=True)
-    cell_idx, cell_sizes = cell_idx[1:], cell_sizes[1:]
-    cell_drop = np.where(cell_sizes < np.mean(cell_sizes) - 2.7 * np.std(cell_sizes))
-
-    for drop_cell in cell_idx[cell_drop]:
-        pred_mask[pred_mask == drop_cell] = 0
-
-    return pred_mask
-
-
-def hflip(x):
-    """flip batch of images horizontally"""
-    return x.flip(3)
-
-
-def vflip(x):
-    """flip batch of images vertically"""
-    return x.flip(2)
-
-
-class DualTransform:
-    identity_param = None
-
-    def __init__(
-        self, name: str, params,
-    ):
-        self.params = params
-        self.pname = name
-
-    def apply_aug_image(self, image, *args, **params):
-        raise NotImplementedError
-
-    def apply_deaug_mask(self, mask, *args, **params):
-        raise NotImplementedError
-
-
-class HorizontalFlip(DualTransform):
-    """Flip images horizontally (left->right)"""
-
-    identity_param = False
-
-    def __init__(self):
-        super().__init__("apply", [False, True])
-
-    def apply_aug_image(self, image, apply=False, **kwargs):
-        if apply:
-            image = hflip(image)
-        return image
-
-    def apply_deaug_mask(self, mask, apply=False, **kwargs):
-        if apply:
-            mask = hflip(mask)
-        return mask
-
-
-class VerticalFlip(DualTransform):
-    """Flip images vertically (up->down)"""
-
-    identity_param = False
-
-    def __init__(self):
-        super().__init__("apply", [False, True])
-
-    def apply_aug_image(self, image, apply=False, **kwargs):
-        if apply:
-            image = vflip(image)
-        return image
-
-    def apply_deaug_mask(self, mask, apply=False, **kwargs):
-        if apply:
-            mask = vflip(mask)
-        return mask
-
-
-#################### GradFlow Modules ##################################################
-from scipy.ndimage.filters import maximum_filter1d
-import scipy.ndimage
-import fastremap
-from skimage import morphology
-
-from scipy.ndimage import mean
-
-torch_GPU = torch.device("cuda")
-torch_CPU = torch.device("cpu")
-
-
-def _extend_centers_gpu(
-    neighbors, centers, isneighbor, Ly, Lx, n_iter=200, device=torch.device("cuda")
-):
-    if device is not None:
-        device = device
-    nimg = neighbors.shape[0] // 9
-    pt = torch.from_numpy(neighbors).to(device)
-
-    T = torch.zeros((nimg, Ly, Lx), dtype=torch.double, device=device)
-    meds = torch.from_numpy(centers.astype(int)).to(device).long()
-    isneigh = torch.from_numpy(isneighbor).to(device)
-    for i in range(n_iter):
-        T[:, meds[:, 0], meds[:, 1]] += 1
-        Tneigh = T[:, pt[:, :, 0], pt[:, :, 1]]
-        Tneigh *= isneigh
-        T[:, pt[0, :, 0], pt[0, :, 1]] = Tneigh.mean(axis=1)
-    del meds, isneigh, Tneigh
-    T = torch.log(1.0 + T)
-    # gradient positions
-    grads = T[:, pt[[2, 1, 4, 3], :, 0], pt[[2, 1, 4, 3], :, 1]]
-    del pt
-    dy = grads[:, 0] - grads[:, 1]
-    dx = grads[:, 2] - grads[:, 3]
-    del grads
-    mu_torch = np.stack((dy.cpu().squeeze(), dx.cpu().squeeze()), axis=-2)
-    return mu_torch
-
-
-def diameters(masks):
-    _, counts = np.unique(np.int32(masks), return_counts=True)
-    counts = counts[1:]
-    md = np.median(counts ** 0.5)
-    if np.isnan(md):
-        md = 0
-    md /= (np.pi ** 0.5) / 2
-    return md, counts ** 0.5
-
-
-def masks_to_flows_gpu(masks, device=None):
-    if device is None:
-        device = torch.device("cuda")
-
-    Ly0, Lx0 = masks.shape
-    Ly, Lx = Ly0 + 2, Lx0 + 2
-
-    masks_padded = np.zeros((Ly, Lx), np.int64)
-    masks_padded[1:-1, 1:-1] = masks
-
-    # get mask pixel neighbors
-    y, x = np.nonzero(masks_padded)
-    neighborsY = np.stack((y, y - 1, y + 1, y, y, y - 1, y - 1, y + 1, y + 1), axis=0)
-    neighborsX = np.stack((x, x, x, x - 1, x + 1, x - 1, x + 1, x - 1, x + 1), axis=0)
-    neighbors = np.stack((neighborsY, neighborsX), axis=-1)
-
-    # get mask centers
-    slices = scipy.ndimage.find_objects(masks)
-
-    centers = np.zeros((masks.max(), 2), "int")
-    for i, si in enumerate(slices):
-        if si is not None:
-            sr, sc = si
-
-            ly, lx = sr.stop - sr.start + 1, sc.stop - sc.start + 1
-            yi, xi = np.nonzero(masks[sr, sc] == (i + 1))
-            yi = yi.astype(np.int32) + 1  # add padding
-            xi = xi.astype(np.int32) + 1  # add padding
-            ymed = np.median(yi)
-            xmed = np.median(xi)
-            imin = np.argmin((xi - xmed) ** 2 + (yi - ymed) ** 2)
-            xmed = xi[imin]
-            ymed = yi[imin]
-            centers[i, 0] = ymed + sr.start
-            centers[i, 1] = xmed + sc.start
-
-    # get neighbor validator (not all neighbors are in same mask)
-    neighbor_masks = masks_padded[neighbors[:, :, 0], neighbors[:, :, 1]]
-    isneighbor = neighbor_masks == neighbor_masks[0]
-    ext = np.array(
-        [[sr.stop - sr.start + 1, sc.stop - sc.start + 1] for sr, sc in slices]
-    )
-    n_iter = 2 * (ext.sum(axis=1)).max()
-    # run diffusion
-    mu = _extend_centers_gpu(
-        neighbors, centers, isneighbor, Ly, Lx, n_iter=n_iter, device=device
-    )
-
-    # normalize
-    mu /= 1e-20 + (mu ** 2).sum(axis=0) ** 0.5
-
-    # put into original image
-    mu0 = np.zeros((2, Ly0, Lx0))
-    mu0[:, y - 1, x - 1] = mu
-    mu_c = np.zeros_like(mu0)
-    return mu0, mu_c
-
-
-def masks_to_flows(masks, use_gpu=False, device=None):
-    if masks.max() == 0 or (masks != 0).sum() == 1:
-        # dynamics_logger.warning('empty masks!')
-        return np.zeros((2, *masks.shape), "float32")
-
-    if use_gpu:
-        if use_gpu and device is None:
-            device = torch_GPU
-        elif device is None:
-            device = torch_CPU
-        masks_to_flows_device = masks_to_flows_gpu
-
-    if masks.ndim == 3:
-        Lz, Ly, Lx = masks.shape
-        mu = np.zeros((3, Lz, Ly, Lx), np.float32)
-        for z in range(Lz):
-            mu0 = masks_to_flows_device(masks[z], device=device)[0]
-            mu[[1, 2], z] += mu0
-        for y in range(Ly):
-            mu0 = masks_to_flows_device(masks[:, y], device=device)[0]
-            mu[[0, 2], :, y] += mu0
-        for x in range(Lx):
-            mu0 = masks_to_flows_device(masks[:, :, x], device=device)[0]
-            mu[[0, 1], :, :, x] += mu0
-        return mu
-    elif masks.ndim == 2:
-        mu, mu_c = masks_to_flows_device(masks, device=device)
-        return mu
-
-    else:
-        raise ValueError("masks_to_flows only takes 2D or 3D arrays")
-
-
-def steps2D_interp(p, dP, niter, use_gpu=False, device=None):
-    shape = dP.shape[1:]
-    if use_gpu:
-        if device is None:
-            device = torch_GPU
-        shape = (
-            np.array(shape)[[1, 0]].astype("float") - 1
-        )  # Y and X dimensions (dP is 2.Ly.Lx), flipped X-1, Y-1
-        pt = (
-            torch.from_numpy(p[[1, 0]].T).float().to(device).unsqueeze(0).unsqueeze(0)
-        )  # p is n_points by 2, so pt is [1 1 2 n_points]
-        im = (
-            torch.from_numpy(dP[[1, 0]]).float().to(device).unsqueeze(0)
-        )  # covert flow numpy array to tensor on GPU, add dimension
-        # normalize pt between  0 and  1, normalize the flow
-        for k in range(2):
-            im[:, k, :, :] *= 2.0 / shape[k]
-            pt[:, :, :, k] /= shape[k]
-
-        # normalize to between -1 and 1
-        pt = pt * 2 - 1
-
-        # here is where the stepping happens
-        for t in range(niter):
-            # align_corners default is False, just added to suppress warning
-            dPt = grid_sample(im, pt, align_corners=False)
-
-            for k in range(2):  # clamp the final pixel locations
-                pt[:, :, :, k] = torch.clamp(
-                    pt[:, :, :, k] + dPt[:, k, :, :], -1.0, 1.0
-                )
-
-        # undo the normalization from before, reverse order of operations
-        pt = (pt + 1) * 0.5
-        for k in range(2):
-            pt[:, :, :, k] *= shape[k]
-
-        p = pt[:, :, :, [1, 0]].cpu().numpy().squeeze().T
-        return p
-
-    else:
-        assert print("ho")
-
-
-def follow_flows(dP, mask=None, niter=200, interp=True, use_gpu=True, device=None):
-    shape = np.array(dP.shape[1:]).astype(np.int32)
-    niter = np.uint32(niter)
-
-    p = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing="ij")
-    p = np.array(p).astype(np.float32)
-
-    inds = np.array(np.nonzero(np.abs(dP[0]) > 1e-3)).astype(np.int32).T
-
-    if inds.ndim < 2 or inds.shape[0] < 5:
-        return p, None
-
-    if not interp:
-        assert print("woo")
-
-    else:
-        p_interp = steps2D_interp(
-            p[:, inds[:, 0], inds[:, 1]], dP, niter, use_gpu=use_gpu, device=device
-        )
-        p[:, inds[:, 0], inds[:, 1]] = p_interp
-
-    return p, inds
-
-
-def flow_error(maski, dP_net, use_gpu=False, device=None):
-    if dP_net.shape[1:] != maski.shape:
-        print("ERROR: net flow is not same size as predicted masks")
-        return
-
-    # flows predicted from estimated masks
-    dP_masks = masks_to_flows(maski, use_gpu=use_gpu, device=device)
-    # difference between predicted flows vs mask flows
-    flow_errors = np.zeros(maski.max())
-    for i in range(dP_masks.shape[0]):
-        flow_errors += mean(
-            (dP_masks[i] - dP_net[i] / 5.0) ** 2,
-            maski,
-            index=np.arange(1, maski.max() + 1),
-        )
-
-    return flow_errors, dP_masks
-
-
-def remove_bad_flow_masks(masks, flows, threshold=0.4, use_gpu=False, device=None):
-    merrors, _ = flow_error(masks, flows, use_gpu, device)
-    badi = 1 + (merrors > threshold).nonzero()[0]
-    masks[np.isin(masks, badi)] = 0
-    return masks
-
-
-def get_masks(p, iscell=None, rpad=20):
-    pflows = []
-    edges = []
-    shape0 = p.shape[1:]
-    dims = len(p)
-
-    for i in range(dims):
-        pflows.append(p[i].flatten().astype("int32"))
-        edges.append(np.arange(-0.5 - rpad, shape0[i] + 0.5 + rpad, 1))
-
-    h, _ = np.histogramdd(tuple(pflows), bins=edges)
-    hmax = h.copy()
-    for i in range(dims):
-        hmax = maximum_filter1d(hmax, 5, axis=i)
-
-    seeds = np.nonzero(np.logical_and(h - hmax > -1e-6, h > 10))
-    Nmax = h[seeds]
-    isort = np.argsort(Nmax)[::-1]
-    for s in seeds:
-        s = s[isort]
-
-    pix = list(np.array(seeds).T)
-
-    shape = h.shape
-    if dims == 3:
-        expand = np.nonzero(np.ones((3, 3, 3)))
-    else:
-        expand = np.nonzero(np.ones((3, 3)))
-    for e in expand:
-        e = np.expand_dims(e, 1)
-
-    for iter in range(5):
-        for k in range(len(pix)):
-            if iter == 0:
-                pix[k] = list(pix[k])
-            newpix = []
-            iin = []
-            for i, e in enumerate(expand):
-                epix = e[:, np.newaxis] + np.expand_dims(pix[k][i], 0) - 1
-                epix = epix.flatten()
-                iin.append(np.logical_and(epix >= 0, epix < shape[i]))
-                newpix.append(epix)
-            iin = np.all(tuple(iin), axis=0)
-            for p in newpix:
-                p = p[iin]
-            newpix = tuple(newpix)
-            igood = h[newpix] > 2
-            for i in range(dims):
-                pix[k][i] = newpix[i][igood]
-            if iter == 4:
-                pix[k] = tuple(pix[k])
-
-    M = np.zeros(h.shape, np.uint32)
-    for k in range(len(pix)):
-        M[pix[k]] = 1 + k
-
-    for i in range(dims):
-        pflows[i] = pflows[i] + rpad
-    M0 = M[tuple(pflows)]
-
-    # remove big masks
-    uniq, counts = fastremap.unique(M0, return_counts=True)
-    big = np.prod(shape0) * 0.9
-    bigc = uniq[counts > big]
-    if len(bigc) > 0 and (len(bigc) > 1 or bigc[0] != 0):
-        M0 = fastremap.mask(M0, bigc)
-    fastremap.renumber(M0, in_place=True)  # convenient to guarantee non-skipped labels
-    M0 = np.reshape(M0, shape0)
-    return M0
-
-def fill_holes_and_remove_small_masks(masks, min_size=15):
-    """ fill holes in masks (2D/3D) and discard masks smaller than min_size (2D)
-    
-    fill holes in each mask using scipy.ndimage.morphology.binary_fill_holes
-    (might have issues at borders between cells, todo: check and fix)
-    
-    Parameters
-    ----------------
-    masks: int, 2D or 3D array
-        labelled masks, 0=NO masks; 1,2,...=mask labels,
-        size [Ly x Lx] or [Lz x Ly x Lx]
-    min_size: int (optional, default 15)
-        minimum number of pixels per mask, can turn off with -1
-    Returns
-    ---------------
-    masks: int, 2D or 3D array
-        masks with holes filled and masks smaller than min_size removed, 
-        0=NO masks; 1,2,...=mask labels,
-        size [Ly x Lx] or [Lz x Ly x Lx]
-    
-    """
-    
-    slices = find_objects(masks)
-    j = 0
-    for i,slc in enumerate(slices):
-        if slc is not None:
-            msk = masks[slc] == (i+1)
-            npix = msk.sum()
-            if min_size > 0 and npix < min_size:
-                masks[slc][msk] = 0
-            elif npix > 0:   
-                if msk.ndim==3:
-                    for k in range(msk.shape[0]):
-                        msk[k] = binary_fill_holes(msk[k])
-                else:          
-                    msk = binary_fill_holes(msk)
-                masks[slc][msk] = (j+1)
-                j+=1
-    return masks
-
-def compute_masks(
-    dP,
-    cellprob,
-    p=None,
-    niter=200,
-    cellprob_threshold=0.4,
-    flow_threshold=0.4,
-    interp=True,
-    resize=None,
-    use_gpu=False,
-    device=None,
-):
-    """compute masks using dynamics from dP, cellprob, and boundary"""
-
-    cp_mask = cellprob > cellprob_threshold
-    cp_mask = morphology.remove_small_holes(cp_mask, area_threshold=16)
-    cp_mask = morphology.remove_small_objects(cp_mask, min_size=16)
-
-    if np.any(cp_mask):  # mask at this point is a cell cluster binary map, not labels
-        # follow flows
-        if p is None:
-            p, inds = follow_flows(
-                dP * cp_mask / 5.0,
-                niter=niter,
-                interp=interp,
-                use_gpu=use_gpu,
-                device=device,
-            )
-            if inds is None:
-                shape = resize if resize is not None else cellprob.shape
-                mask = np.zeros(shape, np.uint16)
-                p = np.zeros((len(shape), *shape), np.uint16)
-                return mask, p
-
-        # calculate masks
-        mask = get_masks(p, iscell=cp_mask)
-
-        # flow thresholding factored out of get_masks
-        shape0 = p.shape[1:]
-        if mask.max() > 0 and flow_threshold is not None and flow_threshold > 0:
-            # make sure labels are unique at output of get_masks
-            mask = remove_bad_flow_masks(
-                mask, dP, threshold=flow_threshold, use_gpu=use_gpu, device=device
-            )
-        
-        mask = fill_holes_and_remove_small_masks(mask, min_size=15)
-        
-    else:  # nothing to compute, just make it compatible
-        shape = resize if resize is not None else cellprob.shape
-        mask = np.zeros(shape, np.uint16)
-        p = np.zeros((len(shape), *shape), np.uint16)
-        return mask, p
-    
-    return mask, p
-
-def main(args):
-    model = torch.load(args.model_path, map_location=args.device)
-    model.eval()
-    hflip_tta = HorizontalFlip()
-    vflip_tta = VerticalFlip()
-
-    img_names = sorted(os.listdir(args.input_path))
-    os.makedirs(args.output_path, exist_ok=True)
-
-    for img_name in img_names:
-        print(f"Segmenting {img_name}")
-        img_path = os.path.join(args.input_path, img_name)
-        img_data = pred_transforms(img_path)
-        img_data = img_data.to(args.device)
-        img_size = img_data.shape[-1] * img_data.shape[-2]
-        
-        if img_size < 1150000 and 900000 < img_size:
-            overlap = 0.5
-        else:
-            overlap = 0.6
-
-        with torch.no_grad():
-            img0 = img_data
-            outputs0 = sliding_window_inference(
-                img0,
-                512,
-                4,
-                model,
-                padding_mode="reflect",
-                mode="gaussian",
-                overlap=overlap,
-                device="cpu",
-            )
-            outputs0 = outputs0.cpu().squeeze()
-
-            if img_size < 2000 * 2000:
-                
-                model.load_state_dict(torch.load(args.model_path2, map_location=args.device))
-                model.eval()
-                
-                img2 = hflip_tta.apply_aug_image(img_data, apply=True)
-                outputs2 = sliding_window_inference(
-                    img2,
-                    512,
-                    4,
-                    model,
-                    padding_mode="reflect",
-                    mode="gauusian",
-                    overlap=overlap,
-                    device="cpu",
-                )
-                outputs2 = hflip_tta.apply_deaug_mask(outputs2, apply=True)
-                outputs2 = outputs2.cpu().squeeze()
-
-                outputs = torch.zeros_like(outputs0)
-                outputs[0] = (outputs0[0] + outputs2[0]) / 2
-                outputs[1] = (outputs0[1] - outputs2[1]) / 2
-                outputs[2] = (outputs0[2] + outputs2[2]) / 2
-                
-            elif img_size < 5000*5000:
-                # Hflip TTA
-                img2 = hflip_tta.apply_aug_image(img_data, apply=True)
-                outputs2 = sliding_window_inference(
-                    img2,
-                    512,
-                    4,
-                    model,
-                    padding_mode="reflect",
-                    mode="gaussian",
-                    overlap=overlap,
-                    device="cpu",
-                )
-                outputs2 = hflip_tta.apply_deaug_mask(outputs2, apply=True)
-                outputs2 = outputs2.cpu().squeeze()
-                img2 = img2.cpu()
-                
-                ##################
-                #                #
-                #    ensemble    #
-                #                #
-                ##################
-                
-                model.load_state_dict(torch.load(args.model_path2, map_location=args.device))
-                model.eval()
-                
-                img1 = img_data
-                outputs1 = sliding_window_inference(
-                    img1,
-                    512,
-                    4,
-                    model,
-                    padding_mode="reflect",
-                    mode="gaussian",
-                    overlap=overlap,
-                    device="cpu",
-                )
-                outputs1 = outputs1.cpu().squeeze()
-                
-                # Vflip TTA
-                img3 = vflip_tta.apply_aug_image(img_data, apply=True)
-                outputs3 = sliding_window_inference(
-                    img3,
-                    512,
-                    4,
-                    model,
-                    padding_mode="reflect",
-                    mode="gaussian",
-                    overlap=overlap,
-                    device="cpu",
-                )
-                outputs3 = vflip_tta.apply_deaug_mask(outputs3, apply=True)
-                outputs3 = outputs3.cpu().squeeze()
-                img3 = img3.cpu()
-
-                # Merge Results
-                outputs = torch.zeros_like(outputs0)
-                outputs[0] = (outputs0[0] + outputs1[0] + outputs2[0] - outputs3[0]) / 4
-                outputs[1] = (outputs0[1] + outputs1[1] - outputs2[1] + outputs3[1]) / 4
-                outputs[2] = (outputs0[2] + outputs1[2] + outputs2[2] + outputs3[2]) / 4
-            else:
-                outputs = outputs0
-
-            pred_mask = post_process(outputs.squeeze(0).cpu().numpy(), args.device)
-
-        file_path = os.path.join(
-            args.output_path, img_name.split(".")[0] + "_label.tiff"
-        )
-
-        tif.imwrite(file_path, pred_mask, compression="zlib")
-
-
-parser = argparse.ArgumentParser("Submission for Challenge", add_help=False)
-parser.add_argument("--model_path", default="./model.pt", type=str)
-parser.add_argument("--model_path2", default="./model_sec.pth", type=str)
-
-# Dataset parameters
-parser.add_argument(
-    "-i",
-    "--input_path",
-    default="/workspace/inputs/",
-    type=str,
-    help="training data path; subfolders: images, labels",
-)
-parser.add_argument(
-    "-o", "--output_path", default="/workspace/outputs/", type=str, help="output path",
-)
-parser.add_argument("--device", default="cuda:0", type=str)
-
-args = parser.parse_args()
-
-if __name__ == "__main__":
-    print("Starting")
-    main(args)
diff --git a/spaces/giulio98/codebleu/app.py b/spaces/giulio98/codebleu/app.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gligen/demo/gligen/ldm/modules/diffusionmodules/positionnet.py b/spaces/gligen/demo/gligen/ldm/modules/diffusionmodules/positionnet.py
deleted file mode 100644
index 8cfa9bf3a43964b1e1669fec71d2d32356356e70..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/modules/diffusionmodules/positionnet.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import torch.nn as nn
-from ldm.modules.attention import BasicTransformerBlock
-from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
-import torch.nn.functional as F
-
-
-
-class PositionNet(nn.Module):
-    def __init__(self,  positive_len, out_dim, fourier_freqs=8):
-        super().__init__()
-        self.positive_len = positive_len
-        self.out_dim = out_dim 
-
-        self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
-        self.position_dim = fourier_freqs*2*4 # 2 is sin&cos, 4 is xyxy 
-
-        self.linears = nn.Sequential(
-            nn.Linear( self.positive_len + self.position_dim, 512),
-            nn.SiLU(),
-            nn.Linear( 512, 512),
-            nn.SiLU(),
-            nn.Linear(512, out_dim),
-        )
-        
-        self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))
-        self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))
-  
-
-    def forward(self, boxes, masks, positive_embeddings):
-        B, N, _ = boxes.shape 
-        masks = masks.unsqueeze(-1)
-
-        # embedding position (it may includes padding as placeholder)
-        xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C
-
-        # learnable null embedding 
-        positive_null = self.null_positive_feature.view(1,1,-1)
-        xyxy_null =  self.null_position_feature.view(1,1,-1)
-
-        # replace padding with learnable null embedding 
-        positive_embeddings = positive_embeddings*masks + (1-masks)*positive_null
-        xyxy_embedding = xyxy_embedding*masks + (1-masks)*xyxy_null
-
-        objs = self.linears(  torch.cat([positive_embeddings, xyxy_embedding], dim=-1)  )
-        assert objs.shape == torch.Size([B,N,self.out_dim])        
-        return objs
-
-
-
diff --git a/spaces/gotgitgood/33.GZUZ.33/app.py b/spaces/gotgitgood/33.GZUZ.33/app.py
deleted file mode 100644
index 15acff5516ff3722fe6f5b15ed06f47166439221..0000000000000000000000000000000000000000
--- a/spaces/gotgitgood/33.GZUZ.33/app.py
+++ /dev/null
@@ -1,357 +0,0 @@
-from huggingface_hub import hf_hub_download
-import torch
-import os
-
-import gradio as gr
-from audioldm2 import text_to_audio, build_model
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-os.environ["TOKENIZERS_PARALLELISM"] = "true"
-
-
-default_checkpoint="audioldm2-full"
-audioldm = None
-current_model_name = None
-
-def text2audio(
-    text,
-    guidance_scale,
-    random_seed,
-    n_candidates,
-    model_name=default_checkpoint,
-):
-    global audioldm, current_model_name
-    torch.set_float32_matmul_precision("high")
-
-    if audioldm is None or model_name != current_model_name:
-        audioldm = build_model(model_name=model_name)
-        current_model_name = model_name
-        audioldm = torch.compile(audioldm)
-
-    # print(text, length, guidance_scale)
-    waveform = text_to_audio(
-        latent_diffusion=audioldm,
-        text=text,
-        seed=random_seed,
-        duration=10,
-        guidance_scale=guidance_scale,
-        n_candidate_gen_per_text=int(n_candidates),
-    )  # [bs, 1, samples]
-    waveform = [
-        gr.make_waveform((16000, wave[0]), bg_image="bg.png") for wave in waveform
-    ]
-    # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))]
-    if len(waveform) == 1:
-        waveform = waveform[0]
-    return waveform
-
-css = """
-        a {
-            color: inherit;
-            text-decoration: underline;
-        }
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-            max-width: 730px !important;
-        }
-        .gr-button {
-            color: white;
-            border-color: #000000;
-            background: #000000;
-        }
-        input[type='range'] {
-            accent-color: #000000;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-btn {
-            font-size: .7rem !important;
-            line-height: 19px;
-            margin-top: 12px;
-            margin-bottom: 12px;
-            padding: 2px 8px;
-            border-radius: 14px !important;
-        }
-        #advanced-options {
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-        #container-advanced-btns{
-            display: flex;
-            flex-wrap: wrap;
-            justify-content: space-between;
-            align-items: center;
-        }
-        .animate-spin {
-            animation: spin 1s linear infinite;
-        }
-        @keyframes spin {
-            from {
-                transform: rotate(0deg);
-            }
-            to {
-                transform: rotate(360deg);
-            }
-        }
-        #share-btn-container {
-            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-            margin-top: 10px;
-            margin-left: auto;
-        }
-        #share-btn {
-            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-        }
-        #share-btn * {
-            all: unset;
-        }
-        #share-btn-container div:nth-child(-n+2){
-            width: auto !important;
-            min-height: 0px !important;
-        }
-        #share-btn-container .wrap {
-            display: none !important;
-        }
-        .gr-form{
-            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
-        }
-        #prompt-container{
-            gap: 0;
-        }
-        #generated_id{
-            min-height: 700px
-        }
-        #setting_id{
-          margin-bottom: 12px;
-          text-align: center;
-          font-weight: 900;
-        }
-"""
-iface = gr.Blocks(css=css)
-
-with iface:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
-                  AudioLDM 2: A General Framework for Audio, Music, and Speech Generation
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-                <a href="https://arxiv.org/abs/2308.05734">[Paper]</a>  <a href="https://audioldm.github.io/audioldm2">[Project page]</a> <a href="https://discord.com/invite/b64SEmdf">[Join Discord]</a>
-              </p>
-            </div>
-        """
-    )
-    gr.HTML(
-        """
-        <p style="display:flex">For faster inference without a queue
-        <a style="margin-left: .5em" href="https://huggingface.co/spaces/haoheliu/audioldm2-text2audio-text2music?duplicate=true">
-        <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-        <p/>
-    """
-    )
-    with gr.Group():
-        with gr.Box():
-            ############# Input
-            textbox = gr.Textbox(
-                value="A forest of wind chimes singing a soothing melody in the breeze.",
-                max_lines=1,
-                label="Input your prompt here",
-                info="Your text is important for the audio quality. Please ensure it is descriptive by using more adjectives.",
-                elem_id="prompt-in",
-            )
-
-            with gr.Accordion("Click to modify detailed configurations", open=False):
-                seed = gr.Number(
-                    value=42,
-                    label="Change this value (any integer number) will lead to a different generation result.",
-                )
-                # duration = gr.Slider(
-                #     10, 10, value=10, step=2.5, label="Duration (seconds)"
-                # )
-                guidance_scale = gr.Slider(
-                    0,
-                    6,
-                    value=3.5,
-                    step=0.5,
-                    label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)",
-                )
-                n_candidates = gr.Slider(
-                    1,
-                    3,
-                    value=3,
-                    step=1,
-                    label="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation",
-                )
-                # model_name = gr.Dropdown(
-                #       ["audioldm-m-text-ft", "audioldm-s-text-ft", "audioldm-m-full","audioldm-s-full-v2", "audioldm-s-full", "audioldm-l-full"], value="audioldm-m-full", label="Choose the model to use. audioldm-m-text-ft and audioldm-s-text-ft are recommanded. -s- means small, -m- means medium and -l- means large",
-                #   )
-            ############# Output
-            # outputs=gr.Audio(label="Output", type="numpy")
-            outputs = gr.Video(label="Output", elem_id="output-video")
-
-            # with gr.Group(elem_id="container-advanced-btns"):
-            #   # advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
-            #   with gr.Group(elem_id="share-btn-container"):
-            #     community_icon = gr.HTML(community_icon_html, visible=False)
-            #     loading_icon = gr.HTML(loading_icon_html, visible=False)
-            #     share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-            # outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")]
-            btn = gr.Button("Submit").style(full_width=True)
-
-        with gr.Group(elem_id="share-btn-container", visible=False):
-            community_icon = gr.HTML(community_icon_html)
-            loading_icon = gr.HTML(loading_icon_html)
-            share_button = gr.Button("Share to community", elem_id="share-btn")
-
-        # btn.click(text2audio, inputs=[
-        #           textbox, duration, guidance_scale, seed, n_candidates, model_name], outputs=[outputs])
-        btn.click(
-            text2audio,
-            inputs=[textbox, guidance_scale, seed, n_candidates],
-            outputs=[outputs],
-        )
-
-        share_button.click(None, [], [], _js=share_js)
-        gr.HTML(
-            """
-        <div class="footer" style="text-align: center; max-width: 700px; margin: 0 auto;">
-                    <p>Follow the latest update of AudioLDM 2 on our<a href="https://github.com/haoheliu/AudioLDM2" style="text-decoration: underline;" target="_blank"> Github repo</a>
-                    </p>
-                    <br>
-                    <p>Model by <a href="https://twitter.com/LiuHaohe" style="text-decoration: underline;" target="_blank">Haohe Liu</a></p>
-                    <br>
-        </div>
-        """
-        )
-        gr.Examples(
-            [
-                [
-                    "An excited crowd cheering at a sports game.",
-                    3.5,
-                    1234,
-                    3,
-                    default_checkpoint,
-                ],
-                [
-                    "A cat is meowing for attention.",
-                    3.5,
-                    1234,
-                    3,
-                    default_checkpoint,
-                ],
-                [
-                    "Birds singing sweetly in a blooming garden.",
-                    3.5,
-                    1234,
-                    3,
-                    default_checkpoint,
-                ],
-                [
-                    "A modern synthesizer creating futuristic soundscapes.",
-                    3.5,
-                    1234,
-                    3,
-                    default_checkpoint,
-                ],
-                [
-                    "The vibrant beat of Brazilian samba drums.",
-                    3.5,
-                    1234,
-                    3,
-                    default_checkpoint,
-                ],
-            ],
-            fn=text2audio,
-            # inputs=[textbox, duration, guidance_scale, seed, n_candidates, model_name],
-            inputs=[textbox, guidance_scale, seed, n_candidates],
-            outputs=[outputs],
-            cache_examples=True,
-        )
-        gr.HTML(
-            """
-                <div class="acknowledgements">
-                <p>Essential Tricks for Enhancing the Quality of Your Generated Audio</p>
-                <p>1. Try to use more adjectives to describe your sound. For example: "A man is speaking clearly and slowly in a large room" is better than "A man is speaking". This can make sure AudioLDM 2 understands what you want.</p>
-                <p>2. Try to use different random seeds, which can affect the generation quality significantly sometimes.</p>
-                <p>3. It's better to use general terms like 'man' or 'woman' instead of specific names for individuals or abstract objects that humans may not be familiar with, such as 'mummy'.</p>
-                </div>
-                """
-        )
-
-        with gr.Accordion("Additional information", open=False):
-            gr.HTML(
-                """
-                <div class="acknowledgments">
-                    <p> We build the model with data from <a href="http://research.google.com/audioset/">AudioSet</a>, <a href="https://freesound.org/">Freesound</a> and <a href="https://sound-effects.bbcrewind.co.uk/">BBC Sound Effect library</a>. We share this demo based on the <a href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf">UK copyright exception</a> of data for academic research. </p>
-                            </div>
-                        """
-            )
-# <p>This demo is strictly for research demo purpose only. For commercial use please <a href="haoheliu@gmail.com">contact us</a>.</p>
-
-iface.queue(max_size=20)
-iface.launch(debug=True)
-# iface.launch(debug=True, share=True)
diff --git a/spaces/gotiQspiryo/whisper-ui/Evil-Dead-2013-Blu-Ray-Torrent-REPACK.md b/spaces/gotiQspiryo/whisper-ui/Evil-Dead-2013-Blu-Ray-Torrent-REPACK.md
deleted file mode 100644
index 9d215c5b37cefdb5af7633891eb9f928ecdffe5e..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/Evil-Dead-2013-Blu-Ray-Torrent-REPACK.md
+++ /dev/null
@@ -1,88 +0,0 @@
-## Evil Dead 2013 Blu Ray Torrent
-
-
-
- 
-
-  
-
- 
-
-**Click Here >>>>> [https://miimms.com/2txSSP](https://miimms.com/2txSSP)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- Here is a possible title and article with HTML formatting for the keyword "Evil Dead 2013 Blu Ray Torrent":  
-
-# How to Download Evil Dead 2013 in Blu Ray Quality
-
- 
-
-Evil Dead 2013 is a horror movie that is a remake of the 1981 cult classic by Sam Raimi. It follows five friends who go to a remote cabin in the woods, where they find a mysterious book that unleashes demonic forces. The movie is rated R for strong bloody violence and gore, some sexual content and language. It has a runtime of 91 minutes and an IMDb rating of 6.5 out of 10.
-
- 
-
-If you want to watch Evil Dead 2013 in Blu Ray quality, you have two options: you can either buy the Blu Ray disc or download it from a torrent site. Buying the Blu Ray disc will give you the best picture and sound quality, as well as some bonus features like deleted scenes, behind-the-scenes footage, and commentary by the director and cast. However, buying the Blu Ray disc will also cost you money and require a Blu Ray player.
-
- 
-
-Downloading the movie from a torrent site will save you money and time, as you can get it for free and watch it on any device that supports video playback. However, downloading the movie from a torrent site will also expose you to some risks, such as malware, viruses, legal issues, and low-quality files. Therefore, you should be careful when choosing a torrent site and a torrent file.
-
- 
-
-One of the most popular torrent sites for downloading movies is Good Torrent. You can find Evil Dead 2013 in Blu Ray quality on this site by following these steps:
-
- 
-
-1. Go to [https://good-torrent.com](https://good-torrent.com) and type "Evil Dead 2013" in the search box.
-
-2. Select the movie from the list of results and click on it.
-
-3. Choose the quality you want to download. For Blu Ray quality, you can choose either 720p or 1080p.
-
-4. Click on the download button and wait for the torrent file to download.
-
-5. Open the torrent file with a torrent client like uTorrent or BitTorrent.
-
-6. Start downloading the movie and wait for it to finish.
-
-7. Enjoy watching Evil Dead 2013 in Blu Ray quality!
-
-
-
-Another torrent site that you can use to download Evil Dead 2013 in Blu Ray quality is Internet Archive. This site is a non-profit library of millions of free books, movies, music, and more. You can find Evil Dead 2013 in Blu Ray quality on this site by following these steps:
-
-
-
-1. Go to [https://archive.org](https://archive.org) and type "Evil Dead 2013" in the search box.
-
-2. Select the movie from the list of results and click on it.
-
-3. Click on the download options button and choose "TORRENT" from the menu.
-
-4. Click on the download button and wait for the torrent file to download.
-
-5. Open the torrent file with a torrent client like uTorrent or BitTorrent.
-
-6. Start downloading the movie and wait for it to finish.
-
-7. Enjoy watching Evil Dead 2013 in Blu Ray quality!
-
-
-
-Note that downloading movies from torrent sites may be illegal in your country, so make sure you check your local laws before doing so. Also, be aware that some torrent files may contain viruses or malware that can harm your device or steal your personal information. Always scan your files with an antivirus program before opening them. And never share your personal or financial information with anyone online.
-
- dfd1c89656
-
- 
-
- 
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Brother Vx 520 Sewing Machine Manual How to Set Up Use and Troubleshoot Your Machine.md b/spaces/gotiQspiryo/whisper-ui/examples/Brother Vx 520 Sewing Machine Manual How to Set Up Use and Troubleshoot Your Machine.md
deleted file mode 100644
index c80181f2cf78a67d652902652b5cb99a5c9b4315..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Brother Vx 520 Sewing Machine Manual How to Set Up Use and Troubleshoot Your Machine.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>download Shagird movie torrent 1080p</h2><br /><p><b><b>Download File</b> ===> <a href="https://urlgoal.com/2uyMxl">https://urlgoal.com/2uyMxl</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gradio/HuBERT/examples/simultaneous_translation/modules/monotonic_transformer_layer.py b/spaces/gradio/HuBERT/examples/simultaneous_translation/modules/monotonic_transformer_layer.py
deleted file mode 100644
index bcd45aa8a6bbe86d2e3826c9589cc0ae648730a2..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/simultaneous_translation/modules/monotonic_transformer_layer.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from fairseq.modules import LayerNorm, TransformerDecoderLayer, TransformerEncoderLayer
-
-from . import build_monotonic_attention
-
-from typing import Dict, List, Optional
-
-import torch
-from torch import Tensor
-
-
-class TransformerMonotonicEncoderLayer(TransformerEncoderLayer):
-    def forward(self, x, encoder_padding_mask):
-        seq_len, _, _ = x.size()
-        attn_mask = x.new_ones([seq_len, seq_len]).triu(1)
-        attn_mask = attn_mask.masked_fill(attn_mask.bool(), float("-inf"))
-        return super().forward(x, encoder_padding_mask, attn_mask)
-
-
-class TransformerMonotonicDecoderLayer(TransformerDecoderLayer):
-    def __init__(
-        self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False
-    ):
-        super().__init__(
-            args,
-            no_encoder_attn=True,
-            add_bias_kv=add_bias_kv,
-            add_zero_attn=add_zero_attn,
-        )
-
-        assert args.simul_type is not None, "A --simul-type is needed."
-
-        self.encoder_attn = build_monotonic_attention(args)
-        self.encoder_attn_layer_norm = LayerNorm(
-            self.embed_dim, export=getattr(args, "char_inputs", False)
-        )
-
-    def get_head_steps(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]):
-        return self.encoder_attn._get_monotonic_buffer(incremental_state).get(
-            "head_step"
-        )
-
-    def prune_incremental_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]):
-        input_buffer = self.self_attn._get_input_buffer(incremental_state)
-        for key in ["prev_key", "prev_value"]:
-            input_buffer_key = input_buffer[key]
-            assert input_buffer_key is not None
-            if input_buffer_key.size(2) > 1:
-                input_buffer[key] = input_buffer_key[:, :, :-1, :]
-            else:
-                typed_empty_dict: Dict[str, Optional[Tensor]] = {}
-                input_buffer = typed_empty_dict
-                break
-        assert incremental_state is not None
-        self.self_attn._set_input_buffer(incremental_state, input_buffer)
-
-    def get_steps(self, incremental_state):
-        return self.encoder_attn._get_monotonic_buffer(incremental_state).get("step", 0)
-
-    def forward(
-        self,
-        x,
-        encoder_out: Optional[torch.Tensor] = None,
-        encoder_padding_mask: Optional[torch.Tensor] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        prev_self_attn_state: Optional[List[torch.Tensor]] = None,
-        prev_attn_state: Optional[List[torch.Tensor]] = None,
-        self_attn_mask: Optional[torch.Tensor] = None,
-        self_attn_padding_mask: Optional[torch.Tensor] = None,
-        need_attn: bool = False,
-        need_head_weights: bool = False,
-    ):
-        """
-        Args:
-            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
-            encoder_padding_mask (ByteTensor, optional): binary
-                ByteTensor of shape `(batch, src_len)` where padding
-                elements are indicated by ``1``.
-            need_attn (bool, optional): return attention weights
-            need_head_weights (bool, optional): return attention weights
-                for each head (default: return average over heads).
-
-        Returns:
-            encoded output of shape `(seq_len, batch, embed_dim)`
-        """
-        if need_head_weights:
-            need_attn = True
-
-        residual = x
-        if self.normalize_before:
-            x = self.self_attn_layer_norm(x)
-        if prev_self_attn_state is not None:
-            prev_key, prev_value = prev_self_attn_state[:2]
-            saved_state: Dict[str, Optional[Tensor]] = {
-                "prev_key": prev_key,
-                "prev_value": prev_value,
-            }
-            if len(prev_self_attn_state) >= 3:
-                saved_state["prev_key_padding_mask"] = prev_self_attn_state[2]
-            assert incremental_state is not None
-            self.self_attn._set_input_buffer(incremental_state, saved_state)
-        _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state)
-        if self.cross_self_attention and not (
-            incremental_state is not None
-            and _self_attn_input_buffer is not None
-            and "prev_key" in _self_attn_input_buffer
-        ):
-            if self_attn_mask is not None:
-                assert encoder_out is not None
-                self_attn_mask = torch.cat(
-                    (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1
-                )
-            if self_attn_padding_mask is not None:
-                if encoder_padding_mask is None:
-                    assert encoder_out is not None
-                    encoder_padding_mask = self_attn_padding_mask.new_zeros(
-                        encoder_out.size(1), encoder_out.size(0)
-                    )
-                self_attn_padding_mask = torch.cat(
-                    (encoder_padding_mask, self_attn_padding_mask), dim=1
-                )
-            assert encoder_out is not None
-            y = torch.cat((encoder_out, x), dim=0)
-        else:
-            y = x
-
-        x, attn = self.self_attn(
-            query=x,
-            key=y,
-            value=y,
-            key_padding_mask=self_attn_padding_mask,
-            incremental_state=incremental_state,
-            need_weights=False,
-            attn_mask=self_attn_mask,
-        )
-        x = self.dropout_module(x)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.self_attn_layer_norm(x)
-
-        assert self.encoder_attn is not None
-        residual = x
-        if self.normalize_before:
-            x = self.encoder_attn_layer_norm(x)
-        if prev_attn_state is not None:
-            prev_key, prev_value = prev_attn_state[:2]
-            saved_state: Dict[str, Optional[Tensor]] = {
-                "prev_key": prev_key,
-                "prev_value": prev_value,
-            }
-            if len(prev_attn_state) >= 3:
-                saved_state["prev_key_padding_mask"] = prev_attn_state[2]
-            assert incremental_state is not None
-            self.encoder_attn._set_input_buffer(incremental_state, saved_state)
-
-        x, attn = self.encoder_attn(
-            query=x,
-            key=encoder_out,
-            value=encoder_out,
-            key_padding_mask=encoder_padding_mask,
-            incremental_state=incremental_state,
-            static_kv=True,
-            need_weights=need_attn or (not self.training and self.need_attn),
-            need_head_weights=need_head_weights,
-        )
-        x = self.dropout_module(x)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.encoder_attn_layer_norm(x)
-
-        residual = x
-        if self.normalize_before:
-            x = self.final_layer_norm(x)
-
-        x = self.activation_fn(self.fc1(x))
-        x = self.activation_dropout_module(x)
-        x = self.fc2(x)
-        x = self.dropout_module(x)
-        x = self.residual_connection(x, residual)
-        if not self.normalize_before:
-            x = self.final_layer_norm(x)
-        if self.onnx_trace and incremental_state is not None:
-            saved_state = self.self_attn._get_input_buffer(incremental_state)
-            assert saved_state is not None
-            if self_attn_padding_mask is not None:
-                self_attn_state = [
-                    saved_state["prev_key"],
-                    saved_state["prev_value"],
-                    saved_state["prev_key_padding_mask"],
-                ]
-            else:
-                self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]]
-            return x, attn, self_attn_state
-        return x, attn, None
diff --git a/spaces/gradio/dashboard_main/run.py b/spaces/gradio/dashboard_main/run.py
deleted file mode 100644
index 35835e52bd643483802f0ae732ae2afc881e37e1..0000000000000000000000000000000000000000
--- a/spaces/gradio/dashboard_main/run.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-from helpers import *
-
-
-LIBRARIES = ["accelerate", "datasets", "diffusers", "evaluate", "gradio", "hub_docs",
-             "huggingface_hub", "optimum", "pytorch_image_models", "tokenizers", "transformers"]
-
-
-def create_pip_plot(libraries, pip_choices):
-    if "Pip" not in pip_choices:
-        return gr.Plot(visible=False)
-    output = retrieve_pip_installs(libraries, "Cumulated" in pip_choices)
-    df = pd.DataFrame(output).melt(id_vars="day")
-    plot = px.line(df, x="day", y="value", color="variable",
-                   title="Pip installs")
-    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
-    return gr.Plot(value=plot, visible=True)
-
-
-def create_star_plot(libraries, star_choices):
-    if "Stars" not in star_choices:
-        return gr.Plot(visible=False)
-    output = retrieve_stars(libraries, "Week over Week" in star_choices)
-    df = pd.DataFrame(output).melt(id_vars="day")
-    plot = px.line(df, x="day", y="value", color="variable",
-                   title="Number of stargazers")
-    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
-    return gr.Plot(value=plot, visible=True)
-
-
-def create_issue_plot(libraries, issue_choices):
-    if "Issue" not in issue_choices:
-        return gr.Plot(visible=False)
-    output = retrieve_issues(libraries,
-                             exclude_org_members="Exclude org members" in issue_choices,
-                             week_over_week="Week over Week" in issue_choices)
-    df = pd.DataFrame(output).melt(id_vars="day")
-    plot = px.line(df, x="day", y="value", color="variable",
-                   title="Cumulated number of issues, PRs, and comments",
-                   )
-    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
-    return gr.Plot(value=plot, visible=True)
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("## Select libraries to display")
-            libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)
-        with gr.Column():
-            gr.Markdown("## Select graphs to display")
-            pip = gr.CheckboxGroup(choices=["Pip", "Cumulated"], show_label=False)
-            stars = gr.CheckboxGroup(choices=["Stars", "Week over Week"], show_label=False)
-            issues = gr.CheckboxGroup(choices=["Issue", "Exclude org members", "week over week"], show_label=False)
-    with gr.Row():
-        fetch = gr.Button(value="Fetch")
-    with gr.Row():
-        with gr.Column():
-            pip_plot = gr.Plot(visible=False)
-            star_plot = gr.Plot(visible=False)
-            issue_plot = gr.Plot(visible=False)
-
-    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)
-    fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)
-    fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)
-
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/vitest.config.ts b/spaces/gsaivinay/Llama-2-13B-GGML-UI/vitest.config.ts
deleted file mode 100644
index c700ac39a227f5aae2a393a692a5ac1bda2f2a54..0000000000000000000000000000000000000000
--- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/vitest.config.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import path from 'path';
-import { defineConfig } from 'vite';
-
-export default defineConfig({
-  resolve: {
-    alias: {
-      '@': path.resolve(__dirname, './'),
-    },
-  },
-});
diff --git a/spaces/gyrojeff/YuzuMarker.FontDetection/font_ds_stat.py b/spaces/gyrojeff/YuzuMarker.FontDetection/font_ds_stat.py
deleted file mode 100644
index 3763de2ede113cb4d4d60c8be9a694c2d035d629..0000000000000000000000000000000000000000
--- a/spaces/gyrojeff/YuzuMarker.FontDetection/font_ds_stat.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import sys
-import traceback
-import pickle
-import os
-import concurrent.futures
-from tqdm import tqdm
-from font_dataset.font import load_fonts
-from font_dataset.layout import generate_font_image
-from font_dataset.text import CorpusGeneratorManager
-from font_dataset.background import background_image_generator
-
-
-cjk_ratio = 3
-
-train_cnt = 100
-val_cnt = 5
-test_cnt = 30
-
-train_cnt_cjk = int(train_cnt * cjk_ratio)
-val_cnt_cjk = int(val_cnt * cjk_ratio)
-test_cnt_cjk = int(test_cnt * cjk_ratio)
-
-dataset_path = "./dataset/font_img"
-os.makedirs(dataset_path, exist_ok=True)
-
-fonts, exclusion_rule = load_fonts()
-
-
-cnt = 0
-
-for font in fonts:
-    if exclusion_rule(font):
-        print(f"Excluded font: {font.path}")
-        continue
-
-    if font.language == "CJK":
-        cnt += cjk_ratio
-    else:
-        cnt += 1
-
-
-print("Total training images:", train_cnt * cnt)
-print("Total validation images:", val_cnt * cnt)
-print("Total testing images:", test_cnt * cnt)
-
-if os.path.exists(os.path.join(dataset_path, "train")):
-    num_file_train = len(os.listdir(os.path.join(dataset_path, "train")))
-else:
-    num_file_train = 0
-
-if os.path.exists(os.path.join(dataset_path, "val")):
-    num_file_val = len(os.listdir(os.path.join(dataset_path, "val")))
-else:
-    num_file_val = 0
-
-if os.path.exists(os.path.join(dataset_path, "test")):
-    num_file_test = len(os.listdir(os.path.join(dataset_path, "test")))
-else:
-    num_file_test = 0
-
-print("Total files generated:", num_file_train + num_file_val + num_file_test)
-print("Total files target:", (train_cnt + val_cnt + test_cnt) * cnt * 2)
-
-print(
-    f"{(num_file_train + num_file_val + num_file_test) / ((train_cnt + val_cnt + test_cnt) * cnt * 2) * 100:.2f}% completed"
-)
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py
deleted file mode 100644
index 9d5559203f4f3843fc814b090780ffa129a6fdf0..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py
+++ /dev/null
@@ -1,674 +0,0 @@
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from  models.StyleCLIP.models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
-
-
-class PixelNorm(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
-    k = torch.tensor(k, dtype=torch.float32)
-
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]
-
-    k /= k.sum()
-
-    return k
-
-
-class Upsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel) * (factor ** 2)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2 + factor - 1
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
-
-        return out
-
-
-class Downsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
-
-        return out
-
-
-class Blur(nn.Module):
-    def __init__(self, kernel, pad, upsample_factor=1):
-        super().__init__()
-
-        kernel = make_kernel(kernel)
-
-        if upsample_factor > 1:
-            kernel = kernel * (upsample_factor ** 2)
-
-        self.register_buffer('kernel', kernel)
-
-        self.pad = pad
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, pad=self.pad)
-
-        return out
-
-
-class EqualConv2d(nn.Module):
-    def __init__(
-        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
-        )
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
-        self.stride = stride
-        self.padding = padding
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channel))
-
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        out = F.conv2d(
-            input,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
-            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
-        )
-
-
-class EqualLinear(nn.Module):
-    def __init__(
-        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
-        else:
-            self.bias = None
-
-        self.activation = activation
-
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.activation:
-            out = F.linear(input, self.weight * self.scale)
-            out = fused_leaky_relu(out, self.bias * self.lr_mul)
-
-        else:
-            out = F.linear(
-                input, self.weight * self.scale, bias=self.bias * self.lr_mul
-            )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
-        )
-
-
-class ScaledLeakyReLU(nn.Module):
-    def __init__(self, negative_slope=0.2):
-        super().__init__()
-
-        self.negative_slope = negative_slope
-
-    def forward(self, input):
-        out = F.leaky_relu(input, negative_slope=self.negative_slope)
-
-        return out * math.sqrt(2)
-
-
-class ModulatedConv2d(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        demodulate=True,
-        upsample=False,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-    ):
-        super().__init__()
-
-        self.eps = 1e-8
-        self.kernel_size = kernel_size
-        self.in_channel = in_channel
-        self.out_channel = out_channel
-        self.upsample = upsample
-        self.downsample = downsample
-
-        if upsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) - (kernel_size - 1)
-            pad0 = (p + 1) // 2 + factor - 1
-            pad1 = p // 2 + 1
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
-
-        fan_in = in_channel * kernel_size ** 2
-        self.scale = 1 / math.sqrt(fan_in)
-        self.padding = kernel_size // 2
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
-        )
-
-        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
-        self.demodulate = demodulate
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
-            f'upsample={self.upsample}, downsample={self.downsample})'
-        )
-
-    def forward(self, input, style):
-        batch, in_channel, height, width = input.shape
-
-        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
-        weight = self.scale * self.weight * style
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
-            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
-        weight = weight.view(
-            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
-        )
-
-        if self.upsample:
-            input = input.view(1, batch * in_channel, height, width)
-            weight = weight.view(
-                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-            out = self.blur(out)
-
-        elif self.downsample:
-            input = self.blur(input)
-            _, _, height, width = input.shape
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        else:
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        return out
-
-
-class NoiseInjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.zeros(1))
-
-    def forward(self, image, noise=None):
-        if noise is None:
-            batch, _, height, width = image.shape
-            noise = image.new_empty(batch, 1, height, width).normal_()
-
-        return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
-    def __init__(self, channel, size=4):
-        super().__init__()
-
-        self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
-    def forward(self, input):
-        batch = input.shape[0]
-        out = self.input.repeat(batch, 1, 1, 1)
-
-        return out
-
-
-class StyledConv(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        upsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        demodulate=True,
-    ):
-        super().__init__()
-
-        self.conv = ModulatedConv2d(
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=upsample,
-            blur_kernel=blur_kernel,
-            demodulate=demodulate,
-        )
-
-        self.noise = NoiseInjection()
-        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
-        # self.activate = ScaledLeakyReLU(0.2)
-        self.activate = FusedLeakyReLU(out_channel)
-
-    def forward(self, input, style, noise=None):
-        out = self.conv(input, style)
-        out = self.noise(out, noise=noise)
-        # out = out + self.bias
-        out = self.activate(out)
-
-        return out
-
-
-class ToRGB(nn.Module):
-    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        if upsample:
-            self.upsample = Upsample(blur_kernel)
-
-        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, input, style, skip=None):
-        out = self.conv(input, style)
-        out = out + self.bias
-
-        if skip is not None:
-            skip = self.upsample(skip)
-
-            out = out + skip
-
-        return out
-
-
-class Generator(nn.Module):
-    def __init__(
-        self,
-        size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-    ):
-        super().__init__()
-
-        self.size = size
-
-        self.style_dim = style_dim
-
-        layers = [PixelNorm()]
-
-        for i in range(n_mlp):
-            layers.append(
-                EqualLinear(
-                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
-                )
-            )
-
-        self.style = nn.Sequential(*layers)
-
-        self.channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        self.input = ConstantInput(self.channels[4])
-        self.conv1 = StyledConv(
-            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
-        )
-        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
-
-        self.log_size = int(math.log(size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-
-        self.convs = nn.ModuleList()
-        self.upsamples = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channel = self.channels[4]
-
-        for layer_idx in range(self.num_layers):
-            res = (layer_idx + 5) // 2
-            shape = [1, 1, 2 ** res, 2 ** res]
-            self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
-
-        for i in range(3, self.log_size + 1):
-            out_channel = self.channels[2 ** i]
-
-            self.convs.append(
-                StyledConv(
-                    in_channel,
-                    out_channel,
-                    3,
-                    style_dim,
-                    upsample=True,
-                    blur_kernel=blur_kernel,
-                )
-            )
-
-            self.convs.append(
-                StyledConv(
-                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
-                )
-            )
-
-            self.to_rgbs.append(ToRGB(out_channel, style_dim))
-
-            in_channel = out_channel
-
-        self.n_latent = self.log_size * 2 - 2
-
-    def make_noise(self):
-        device = self.input.input.device
-
-        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
-        return noises
-
-    def mean_latent(self, n_latent):
-        latent_in = torch.randn(
-            n_latent, self.style_dim, device=self.input.input.device
-        )
-        latent = self.style(latent_in).mean(0, keepdim=True)
-
-        return latent
-
-    def get_latent(self, input):
-        return self.style(input)
-
-    def forward(
-        self,
-        styles,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-    ):
-        if not input_is_latent:
-            styles = [self.style(s) for s in styles]
-
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers
-            else:
-                noise = [
-                    getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
-                ]
-
-        if truncation < 1:
-            style_t = []
-
-            for style in styles:
-                style_t.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-
-            styles = style_t
-
-        if len(styles) < 2:
-            inject_index = self.n_latent
-
-            if styles[0].ndim < 3:
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
-            else:
-                latent = styles[0]
-
-        else:
-            if inject_index is None:
-                inject_index = random.randint(1, self.n_latent - 1)
-
-            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
-            latent = torch.cat([latent, latent2], 1)
-
-        out = self.input(latent)
-        out = self.conv1(out, latent[:, 0], noise=noise[0])
-
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-
-        else:
-            return image, None
-
-
-class ConvLayer(nn.Sequential):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        bias=True,
-        activate=True,
-    ):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
-
-            stride = 2
-            self.padding = 0
-
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-
-        layers.append(
-            EqualConv2d(
-                in_channel,
-                out_channel,
-                kernel_size,
-                padding=self.padding,
-                stride=stride,
-                bias=bias and not activate,
-            )
-        )
-
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channel))
-
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        self.conv1 = ConvLayer(in_channel, in_channel, 3)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
-
-        self.skip = ConvLayer(
-            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
-        )
-
-    def forward(self, input):
-        out = self.conv1(input)
-        out = self.conv2(out)
-
-        skip = self.skip(input)
-        out = (out + skip) / math.sqrt(2)
-
-        return out
-
-
-class Discriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        convs = [ConvLayer(3, channels[size], 1)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
-            EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, input):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-
-        out = out.view(batch, -1)
-        out = self.final_linear(out)
-
-        return out
-
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/op_edit/fused_bias_act.cpp b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/op_edit/fused_bias_act.cpp
deleted file mode 100644
index a79a3d65b8fb56393c954630ae8ce5a5c8a8bb7d..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/op_edit/fused_bias_act.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-// Copyright (c) SenseTime Research. All rights reserved.
-
-#include <torch/extension.h>
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(bias);
-
-    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/h1r41/vicuna_chat/app.py b/spaces/h1r41/vicuna_chat/app.py
deleted file mode 100644
index c679acb82b5845dd754fc75f79cf38b53aa7a4fe..0000000000000000000000000000000000000000
--- a/spaces/h1r41/vicuna_chat/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from llama_cpp import Llama
-import streamlit as st
-
-model_path = "vicuna-13b-v1.5.ggmlv3.q2_K.bin"
-llama = Llama(model_path)
-
-def generate_response(messages: list) -> str:
-    response = llama.create_chat_completion(messages, max_tokens=-1, stream=False)
-    print(f"response: {response}")
-    return response['choices'][0]['message']['content']
-
-def main():
-    st.title("Chat with Vicuna!")
-
-    # Session state for retaining messages
-    if 'messages' not in st.session_state:
-        st.session_state.messages = []
-
-    # Display chat messages from history on app rerun
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(f"{message['content']}")
-
-    # Input for the user message
-    user_message = st.chat_input("Your Message")
-
-    # React to user input
-    if user_message:
-        # Display user message in chat message container
-        with st.chat_message("user"):
-            st.markdown(f"{user_message}")
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": user_message})
-
-        with st.chat_message("assistant"):
-            message_placeholder = st.empty()
-            full_response = ""
-
-            for char in generate_response([{"role": m["role"], "content": m["content"]} for m in st.session_state.messages]):
-                full_response += char
-                message_placeholder.markdown(full_response + "❙")
-
-            message_placeholder.markdown(full_response)
-
-        st.session_state.messages.append({"role": "assistant", "content": full_response})
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/harpreetsahota/RAQA-with-LlamaIndex-and-a-fine-tuned-GPT-35/Dockerfile b/spaces/harpreetsahota/RAQA-with-LlamaIndex-and-a-fine-tuned-GPT-35/Dockerfile
deleted file mode 100644
index 013fb487139b7432755793ab016e4433db706b2a..0000000000000000000000000000000000000000
--- a/spaces/harpreetsahota/RAQA-with-LlamaIndex-and-a-fine-tuned-GPT-35/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM python:3.9
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-WORKDIR $HOME/app
-COPY --chown=user . $HOME/app
-COPY ./requirements.txt ~/app/requirements.txt
-RUN pip install -r requirements.txt
-COPY . .
-CMD ["chainlit", "run", "app.py", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/modules/functions.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/modules/functions.py
deleted file mode 100644
index aea9729c0e6944c07bbd63368956e63ab4c76c86..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/modules/functions.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from os import path
-import torch
-import torch.distributed as dist
-import torch.autograd as autograd
-import torch.cuda.comm as comm
-from torch.autograd.function import once_differentiable
-from torch.utils.cpp_extension import load
-
-_src_path = path.join(path.dirname(path.abspath(__file__)), "src")
-_backend = load(name="inplace_abn",
-                extra_cflags=["-O3"],
-                sources=[path.join(_src_path, f) for f in [
-                    "inplace_abn.cpp",
-                    "inplace_abn_cpu.cpp",
-                    "inplace_abn_cuda.cu",
-                    "inplace_abn_cuda_half.cu"
-                ]],
-                extra_cuda_cflags=["--expt-extended-lambda"])
-
-# Activation names
-ACT_RELU = "relu"
-ACT_LEAKY_RELU = "leaky_relu"
-ACT_ELU = "elu"
-ACT_NONE = "none"
-
-
-def _check(fn, *args, **kwargs):
-    success = fn(*args, **kwargs)
-    if not success:
-        raise RuntimeError("CUDA Error encountered in {}".format(fn))
-
-
-def _broadcast_shape(x):
-    out_size = []
-    for i, s in enumerate(x.size()):
-        if i != 1:
-            out_size.append(1)
-        else:
-            out_size.append(s)
-    return out_size
-
-
-def _reduce(x):
-    if len(x.size()) == 2:
-        return x.sum(dim=0)
-    else:
-        n, c = x.size()[0:2]
-        return x.contiguous().view((n, c, -1)).sum(2).sum(0)
-
-
-def _count_samples(x):
-    count = 1
-    for i, s in enumerate(x.size()):
-        if i != 1:
-            count *= s
-    return count
-
-
-def _act_forward(ctx, x):
-    if ctx.activation == ACT_LEAKY_RELU:
-        _backend.leaky_relu_forward(x, ctx.slope)
-    elif ctx.activation == ACT_ELU:
-        _backend.elu_forward(x)
-    elif ctx.activation == ACT_NONE:
-        pass
-
-
-def _act_backward(ctx, x, dx):
-    if ctx.activation == ACT_LEAKY_RELU:
-        _backend.leaky_relu_backward(x, dx, ctx.slope)
-    elif ctx.activation == ACT_ELU:
-        _backend.elu_backward(x, dx)
-    elif ctx.activation == ACT_NONE:
-        pass
-
-
-class InPlaceABN(autograd.Function):
-    @staticmethod
-    def forward(ctx, x, weight, bias, running_mean, running_var,
-                training=True, momentum=0.1, eps=1e-05, activation=ACT_LEAKY_RELU, slope=0.01):
-        # Save context
-        ctx.training = training
-        ctx.momentum = momentum
-        ctx.eps = eps
-        ctx.activation = activation
-        ctx.slope = slope
-        ctx.affine = weight is not None and bias is not None
-
-        # Prepare inputs
-        count = _count_samples(x)
-        x = x.contiguous()
-        weight = weight.contiguous() if ctx.affine else x.new_empty(0)
-        bias = bias.contiguous() if ctx.affine else x.new_empty(0)
-
-        if ctx.training:
-            mean, var = _backend.mean_var(x)
-
-            # Update running stats
-            running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
-            running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * count / (count - 1))
-
-            # Mark in-place modified tensors
-            ctx.mark_dirty(x, running_mean, running_var)
-        else:
-            mean, var = running_mean.contiguous(), running_var.contiguous()
-            ctx.mark_dirty(x)
-
-        # BN forward + activation
-        _backend.forward(x, mean, var, weight, bias, ctx.affine, ctx.eps)
-        _act_forward(ctx, x)
-
-        # Output
-        ctx.var = var
-        ctx.save_for_backward(x, var, weight, bias)
-        ctx.mark_non_differentiable(running_mean, running_var)
-        return x, running_mean, running_var
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, dz, _drunning_mean, _drunning_var):
-        z, var, weight, bias = ctx.saved_tensors
-        dz = dz.contiguous()
-
-        # Undo activation
-        _act_backward(ctx, z, dz)
-
-        if ctx.training:
-            edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)
-        else:
-            # TODO: implement simplified CUDA backward for inference mode
-            edz = dz.new_zeros(dz.size(1))
-            eydz = dz.new_zeros(dz.size(1))
-
-        dx = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
-        # dweight = eydz * weight.sign() if ctx.affine else None
-        dweight = eydz if ctx.affine else None
-        if dweight is not None:
-            dweight[weight < 0] *= -1
-        dbias = edz if ctx.affine else None
-
-        return dx, dweight, dbias, None, None, None, None, None, None, None
-
-
-class InPlaceABNSync(autograd.Function):
-    @classmethod
-    def forward(cls, ctx, x, weight, bias, running_mean, running_var,
-                training=True, momentum=0.1, eps=1e-05, activation=ACT_LEAKY_RELU, slope=0.01, equal_batches=True):
-        # Save context
-        ctx.training = training
-        ctx.momentum = momentum
-        ctx.eps = eps
-        ctx.activation = activation
-        ctx.slope = slope
-        ctx.affine = weight is not None and bias is not None
-
-        # Prepare inputs
-        ctx.world_size = dist.get_world_size() if dist.is_initialized() else 1
-
-        # count = _count_samples(x)
-        batch_size = x.new_tensor([x.shape[0]], dtype=torch.long)
-
-        x = x.contiguous()
-        weight = weight.contiguous() if ctx.affine else x.new_empty(0)
-        bias = bias.contiguous() if ctx.affine else x.new_empty(0)
-
-        if ctx.training:
-            mean, var = _backend.mean_var(x)
-            if ctx.world_size > 1:
-                # get global batch size
-                if equal_batches:
-                    batch_size *= ctx.world_size
-                else:
-                    dist.all_reduce(batch_size, dist.ReduceOp.SUM)
-
-                ctx.factor = x.shape[0] / float(batch_size.item())
-
-                mean_all = mean.clone() * ctx.factor
-                dist.all_reduce(mean_all, dist.ReduceOp.SUM)
-
-                var_all = (var + (mean - mean_all) ** 2) * ctx.factor
-                dist.all_reduce(var_all, dist.ReduceOp.SUM)
-
-                mean = mean_all
-                var = var_all
-
-            # Update running stats
-            running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
-            count = batch_size.item() * x.view(x.shape[0], x.shape[1], -1).shape[-1]
-            running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * (float(count) / (count - 1)))
-
-            # Mark in-place modified tensors
-            ctx.mark_dirty(x, running_mean, running_var)
-        else:
-            mean, var = running_mean.contiguous(), running_var.contiguous()
-            ctx.mark_dirty(x)
-
-        # BN forward + activation
-        _backend.forward(x, mean, var, weight, bias, ctx.affine, ctx.eps)
-        _act_forward(ctx, x)
-
-        # Output
-        ctx.var = var
-        ctx.save_for_backward(x, var, weight, bias)
-        ctx.mark_non_differentiable(running_mean, running_var)
-        return x, running_mean, running_var
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, dz, _drunning_mean, _drunning_var):
-        z, var, weight, bias = ctx.saved_tensors
-        dz = dz.contiguous()
-
-        # Undo activation
-        _act_backward(ctx, z, dz)
-
-        if ctx.training:
-            edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)
-            edz_local = edz.clone()
-            eydz_local = eydz.clone()
-
-            if ctx.world_size > 1:
-                edz *= ctx.factor
-                dist.all_reduce(edz, dist.ReduceOp.SUM)
-
-                eydz *= ctx.factor
-                dist.all_reduce(eydz, dist.ReduceOp.SUM)
-        else:
-            edz_local = edz = dz.new_zeros(dz.size(1))
-            eydz_local = eydz = dz.new_zeros(dz.size(1))
-
-        dx = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
-        # dweight = eydz_local * weight.sign() if ctx.affine else None
-        dweight = eydz_local if ctx.affine else None
-        if dweight is not None:
-            dweight[weight < 0] *= -1
-        dbias = edz_local if ctx.affine else None
-
-        return dx, dweight, dbias, None, None, None, None, None, None, None
-
-
-inplace_abn = InPlaceABN.apply
-inplace_abn_sync = InPlaceABNSync.apply
-
-__all__ = ["inplace_abn", "inplace_abn_sync", "ACT_RELU", "ACT_LEAKY_RELU", "ACT_ELU", "ACT_NONE"]
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/documentation/using_nnUNet_as_baseline.md b/spaces/ho11laqe/nnUNet_calvingfront_detection/documentation/using_nnUNet_as_baseline.md
deleted file mode 100644
index 9811270372fd91875858f732a0a3110894d5ddf8..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/documentation/using_nnUNet_as_baseline.md
+++ /dev/null
@@ -1,4 +0,0 @@
-(The U-Net is the current punching bag of methods development. nnU-Net is going to be that looking forward. That is 
-cool (great, in fact!), but it should be done correctly. Here are tips on how to benchmark against nnU-Net)
-
-This is work in progress
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/preprocessing/custom_preprocessors/__init__.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/preprocessing/custom_preprocessors/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/huang4414/White-box-Cartoonization/wbc/guided_filter.py b/spaces/huang4414/White-box-Cartoonization/wbc/guided_filter.py
deleted file mode 100644
index fd019d145efc7f308cd96de90f4e7b648f6820b4..0000000000000000000000000000000000000000
--- a/spaces/huang4414/White-box-Cartoonization/wbc/guided_filter.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import tensorflow as tf
-import numpy as np
-
-
-
-
-def tf_box_filter(x, r):
-    k_size = int(2*r+1)
-    ch = x.get_shape().as_list()[-1]
-    weight = 1/(k_size**2)
-    box_kernel = weight*np.ones((k_size, k_size, ch, 1))
-    box_kernel = np.array(box_kernel).astype(np.float32)
-    output = tf.nn.depthwise_conv2d(x, box_kernel, [1, 1, 1, 1], 'SAME')
-    return output
-
-
-
-def guided_filter(x, y, r, eps=1e-2):
-    
-    x_shape = tf.shape(x)
-    #y_shape = tf.shape(y)
-
-    N = tf_box_filter(tf.ones((1, x_shape[1], x_shape[2], 1), dtype=x.dtype), r)
-
-    mean_x = tf_box_filter(x, r) / N
-    mean_y = tf_box_filter(y, r) / N
-    cov_xy = tf_box_filter(x * y, r) / N - mean_x * mean_y
-    var_x  = tf_box_filter(x * x, r) / N - mean_x * mean_x
-
-    A = cov_xy / (var_x + eps)
-    b = mean_y - A * mean_x
-
-    mean_A = tf_box_filter(A, r) / N
-    mean_b = tf_box_filter(b, r) / N
-
-    output = mean_A * x + mean_b
-
-    return output
-
-
-
-def fast_guided_filter(lr_x, lr_y, hr_x, r=1, eps=1e-8):
-    
-    #assert lr_x.shape.ndims == 4 and lr_y.shape.ndims == 4 and hr_x.shape.ndims == 4
-   
-    lr_x_shape = tf.shape(lr_x)
-    #lr_y_shape = tf.shape(lr_y)
-    hr_x_shape = tf.shape(hr_x)
-    
-    N = tf_box_filter(tf.ones((1, lr_x_shape[1], lr_x_shape[2], 1), dtype=lr_x.dtype), r)
-
-    mean_x = tf_box_filter(lr_x, r) / N
-    mean_y = tf_box_filter(lr_y, r) / N
-    cov_xy = tf_box_filter(lr_x * lr_y, r) / N - mean_x * mean_y
-    var_x  = tf_box_filter(lr_x * lr_x, r) / N - mean_x * mean_x
-
-    A = cov_xy / (var_x + eps)
-    b = mean_y - A * mean_x
-
-    mean_A = tf.image.resize_images(A, hr_x_shape[1: 3])
-    mean_b = tf.image.resize_images(b, hr_x_shape[1: 3])
-
-    output = mean_A * hr_x + mean_b
-    
-    return output
-
-
-if __name__ == '__main__':
-    import cv2
-    from tqdm import tqdm
-
-    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-    #input_superpixel = tf.placeholder(tf.float32, [16, 256, 256, 3])
-    output = guided_filter(input_photo, input_photo, 5, eps=1)
-    image = cv2.imread('output_figure1/cartoon2.jpg')
-    image = image/127.5 - 1
-    image = np.expand_dims(image, axis=0)
-
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    sess = tf.Session(config=config)
-    sess.run(tf.global_variables_initializer())
-
-    out = sess.run(output, feed_dict={input_photo: image})
-    out = (np.squeeze(out)+1)*127.5
-    out = np.clip(out, 0, 255).astype(np.uint8)
-    cv2.imwrite('output_figure1/cartoon2_filter.jpg', out)
diff --git a/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/losses.py b/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/hussain-shk/IndiSent/indic_nlp_library/contrib/hindi_to_kannada_transliterator.py b/spaces/hussain-shk/IndiSent/indic_nlp_library/contrib/hindi_to_kannada_transliterator.py
deleted file mode 100644
index a88f7d42120a0ae6eedaea91080c8d2a75539ee8..0000000000000000000000000000000000000000
--- a/spaces/hussain-shk/IndiSent/indic_nlp_library/contrib/hindi_to_kannada_transliterator.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import sys
-from indicnlp import common
-common.set_resources_path(INDIC_NLP_RESOURCES)
-
-from indicnlp import loader
-from indicnlp.normalize import indic_normalize
-from indicnlp.transliterate import unicode_transliterate
-
-if __name__ == '__main__': 
-    """
-    This script transliterates Hindi to Kannada. It removes/remaps 
-    characters only found in Hindi. It also adds halanta to words ending
-    with consonant - as is the convention in Kannada
-    """
-
-    infname=sys.argv[1]  # one sentence/word per line. Sentences should be space-tokenized
-    outfname=sys.agv[2]
-    loader.load()
-
-    normalizer_factory=indic_normalize.IndicNormalizerFactory()
-    normalizer=normalizer_factory.get_normalizer('hi')
-
-    with open(infname,'r',encoding='utf-8') as infile, \
-         open(outfname,'w',encoding='utf-8') as outfile:
-        for line in infile: 
-            line=line.strip()
-            line=normalizer.normalize(line)
-    
-            ## replace chandrabindus with anusvara
-            line=line.replace('\u0900','\u0902')
-            line=line.replace('\u0901','\u0902')
-    
-            ### replace chandra e and o diacritics with e and o respectively
-            #line=line.replace('\u0945','\u0947')
-            #line=line.replace('\u0949','\u094b')
-   
-            ### replace chandra e and o diacritics with a diacritic
-            ## this seems to be general usage
-            line=line.replace('\u0945','\u093e')
-            line=line.replace('\u0949','\u093e')
-   
-            ## remove nukta 
-            line=line.replace('\u093c','')
-
-            ## add halant if word ends with consonant
-            #if isc.is_consonant(isc.get_phonetic_feature_vector(line[-1],'hi')):
-            #    line=line+'\u094d'
-            words=line.split(' ')
-            outwords=[]
-            for word in line.split(' '):
-                if isc.is_consonant(isc.get_phonetic_feature_vector(word[-1],'hi')):
-                    word=word+'\u094d'
-            outwords.append(word)
-            line=' '.join(outwords)
-
-    
-            ## script conversion 
-            line=unicode_transliterate.UnicodeIndicTransliterator.transliterate(line,'hi','kn')
-            
-            outfile.write(line+'\n')
-
-
diff --git a/spaces/hzrr/dal_audio_inference/preprocess.py b/spaces/hzrr/dal_audio_inference/preprocess.py
deleted file mode 100644
index aaedbf076c30114b3ac6c27dfb42fd54ac81a71c..0000000000000000000000000000000000000000
--- a/spaces/hzrr/dal_audio_inference/preprocess.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import argparse
-import text
-from utils import load_filepaths_and_text
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--out_extension", default="cleaned")
-  parser.add_argument("--text_index", default=1, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
-
-  args = parser.parse_args()
-    
-
-  for filelist in args.filelists:
-    print("START:", filelist)
-    filepaths_and_text = load_filepaths_and_text(filelist)
-    for i in range(len(filepaths_and_text)):
-      original_text = filepaths_and_text[i][args.text_index]
-      cleaned_text = text._clean_text(original_text, args.text_cleaners)
-      filepaths_and_text[i][args.text_index] = cleaned_text
-
-    new_filelist = filelist + "." + args.out_extension
-    with open(new_filelist, "w", encoding="utf-8") as f:
-      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
diff --git a/spaces/inamXcontru/PoeticTTS/Arta vanzarii zig ziglar pdf free 27 Cum sa devii un lider in vanzari folosind strategiile lui Zig Ziglar.md b/spaces/inamXcontru/PoeticTTS/Arta vanzarii zig ziglar pdf free 27 Cum sa devii un lider in vanzari folosind strategiile lui Zig Ziglar.md
deleted file mode 100644
index ce78678ecf79e696fd42119cfa028604a8392409..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Arta vanzarii zig ziglar pdf free 27 Cum sa devii un lider in vanzari folosind strategiile lui Zig Ziglar.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/inamXcontru/PoeticTTS/Crack Admiscorar [CRACKED].md b/spaces/inamXcontru/PoeticTTS/Crack Admiscorar [CRACKED].md
deleted file mode 100644
index 84b43c14ee53066fdcb9cd97046c1320aad814a6..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Crack Admiscorar [CRACKED].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Crack Admiscorar</h2><br /><p><b><b>Download</b> &harr; <a href="https://gohhs.com/2uz5zj">https://gohhs.com/2uz5zj</a></b></p><br /><br />
-
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/DESEO2013DVDRip XviDMexico.md b/spaces/inplisQlawa/anything-midjourney-v4-1/DESEO2013DVDRip XviDMexico.md
deleted file mode 100644
index de9fc6d11559f5117d4cdecc55e5ca158f023f94..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/DESEO2013DVDRip XviDMexico.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>DESEO2013DVDRip XviDMexico</h2><br /><p><b><b>Download File</b> &#10027;&#10027;&#10027; <a href="https://urlin.us/2uExDk">https://urlin.us/2uExDk</a></b></p><br /><br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/IObit Smart Defrag Pro 6.2.5.128 Multilingual ((HOT)).md b/spaces/inplisQlawa/anything-midjourney-v4-1/IObit Smart Defrag Pro 6.2.5.128 Multilingual ((HOT)).md
deleted file mode 100644
index 7bc42b0bae1eeb2459391fe613112fa3c380176d..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/IObit Smart Defrag Pro 6.2.5.128 Multilingual ((HOT)).md	
+++ /dev/null
@@ -1,103 +0,0 @@
-<br />
-<h1>IObit Smart Defrag Pro 6.2.5.128 Multilingual: A Powerful Disk Defragmenter for Windows</h1>
-<p>If you want to improve your PC performance and speed up your file loading time, you need a good disk defragmenter. One of the best options available is IObit Smart Defrag Pro 6.2.5.128 Multilingual. This is a free and lightweight software that can optimize your hard drive and extend its life span. In this article, we will tell you everything you need to know about IObit Smart Defrag Pro 6.2.5.128 Multilingual, including its features, benefits and download link.</p>
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-<ul>
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-<li>It has a boot time defrag feature that can defragment your system files during the boot process and improve your system startup speed.</li>
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-<li>It has a clean interface that displays your disk information, defrag progress, defrag time and more.</li>
-</ul>
-<h2>What are the benefits of IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>IObit Smart Defrag Pro 6.2.5.128 Multilingual has many benefits that can make your PC faster and smoother. Here are some of them:</p>
-<ul>
-<li>It can improve your PC performance by reducing the disk fragmentation and increasing the disk efficiency.</li>
-<li>It can speed up your file loading time by organizing your files in a contiguous way and reducing the access time.</li>
-<li>It can extend your disk life span by minimizing the wear and tear of the disk head and reducing the disk temperature.</li>
-<li>It can enhance your gaming experience by defragmenting your games separately and boosting their loading and running speed.</li>
-<li>It can optimize your SSDs by trimming them regularly and preventing them from degradation.</li>
-<li>It can protect your disk health by monitoring it constantly and notifying you of any issues or errors.</li>
-</ul>
-<h2>How to download IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>To download IObit Smart Defrag Pro 6.2.5.128 Multilingual, you need to follow these steps:</p>
-<ol>
-<li>Go to the official website of IObit Smart Defrag at https://www.iobit.com/en/iobitsmartdefrag.php</li>
-<li>Click on the "Free Download" button to download the setup file.</li>
-<li>Run the setup file and follow the instructions to install the software on your PC.</li>
-<li>Launch the software and enjoy its features and benefits.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>IObit Smart Defrag Pro 6.2.5.128 Multilingual is a powerful disk defragmenter that can improve your PC performance and speed up your file loading time. It has many features to manage your disks and SSDs, such as smart defrag engine, boot time defrag, game defrag, SSD trim, large file defrag, disk health monitor, scheduler, silent mode and more. It also supports over 35 languages for your convenience. You can download it for free from its official website and install it on your PC easily. We hope you find this article helpful and informative about IObit Smart Defrag Pro 6.2.5.128 Multilingual.</p>
-<p></p>
-<h2>How to use IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>Using IObit Smart Defrag Pro 6.2.5.128 Multilingual is very easy and simple. You can follow these steps to use it:</p>
-<ol>
-<li>Launch the software and select the disk or partition that you want to defragment.</li>
-<li>Click on the "Analyze" button to check the disk condition and fragmentation level.</li>
-<li>Click on the "Smart Defrag" button to start the defragmentation process.</li>
-<li>Wait for the process to finish and check the results and statistics.</li>
-</ol>
-<p>You can also customize the settings and preferences of the software according to your needs. You can access the settings by clicking on the menu icon on the top right corner of the interface. You can change the language, theme, update frequency, defrag mode, defrag priority, boot time defrag, game defrag, SSD trim and more.</p>
-<h2>What are the differences between IObit Smart Defrag Pro 6.2.5.128 Multilingual and other disk defragmenters?</h2>
-<p>IObit Smart Defrag Pro 6.2.5.128 Multilingual is different from other disk defragmenters in many ways. Here are some of them:</p>
-<ul>
-<li>It has a smart defrag engine that can automatically choose the best defrag method for your disk condition and optimize it accordingly.</li>
-<li>It has a boot time defrag feature that can defragment your system files during the boot process and improve your system startup speed.</li>
-<li>It has a game defrag feature that can manually add games for separate defragmentation and enhance your gaming experience.</li>
-<li>It has an SSD trim feature that can trim your SSDs and improve their performance and durability.</li>
-<li>It has a large file defrag feature that can defragment large files faster and more efficiently.</li>
-<li>It has a disk health monitor that can check your disk status in real time and alert you of any problems or errors.</li>
-<li>It has a scheduler that can set a regular defrag time or frequency according to your preference.</li>
-<li>It has a silent mode that can automatically pause the defrag process when you are using your PC or running other programs.</li>
-<li>It has a clean interface that displays your disk information, defrag progress, defrag time and more.</li>
-<li>It supports over 35 languages for your convenience.</li>
-</ul>
-<h2>What are the pros and cons of IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>IObit Smart Defrag Pro 6.2.5.128 Multilingual has many pros and cons that you need to consider before using it. Here are some of them:</p>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>It is free and lightweight.</td><td>It may take a long time to defragment large or heavily fragmented disks.</td></tr>
-<tr><td>It can improve your PC performance and speed up your file loading time.</td><td>It may cause some compatibility issues with some antivirus or firewall software.</td></tr>
-<tr><td>It can extend your disk life span by minimizing the wear and tear of the disk head and reducing the disk temperature.</td><td>It may not be able to defragment some locked or encrypted files.</td></tr>
-<tr><td>It can enhance your gaming experience by defragmenting your games separately and boosting their loading and running speed.</td><td>It may require administrator privileges to run some features.</td></tr>
-<tr><td>It can optimize your SSDs by trimming them regularly and preventing them from degradation.</td><td></td></tr>
-<tr><td>It can protect your disk health by monitoring it constantly and notifying you of any issues or errors.</td><td></td></tr>
-</table>
-<h2>Where to get IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>If you want to get IObit Smart Defrag Pro 6.2.5.128 Multilingual, you have several options to choose from. You can download it from the official website of IObit, from reputable software download sites, or from torrent sites. However, you need to be careful about the sources you use and the legal implications of your actions.</p>
-<p>The official website of IObit is the safest and most reliable source to get IObit Smart Defrag Pro 6.2.5.128 Multilingual. You can download it for free or buy the pro version for more features and benefits. You can also get technical support and updates from the website.</p>
-<p>Software download sites are another option to get IObit Smart Defrag Pro 6.2.5.128 Multilingual. However, you need to make sure that the site is trustworthy and reputable, and that the file is authentic and virus-free. You can check the comments and ratings of other users before downloading.</p>
-<p>Torrent sites are another option to get IObit Smart Defrag Pro 6.2.5.128 Multilingual. However, you need to be aware of the risks and challenges of using torrent sites, such as fake or corrupted files, viruses or malware, legal issues or lawsuits, slow or unstable downloads, etc.</p>
-<h2>How to install IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>To install IObit Smart Defrag Pro 6.2.5.128 Multilingual, you need to follow these steps:</p>
-<ol>
-<li>Download the setup file from a trusted source.</li>
-<li>Run the setup file and follow the instructions to install the software on your PC.</li>
-<li>Launch the software and enter your license code if you have bought the pro version.</li>
-<li>Enjoy the features and benefits of IObit Smart Defrag Pro 6.2.5.128 Multilingual.</li>
-</ol>
-<p>The installation process is easy and smooth, and it does not take much time or space on your PC.</p>
-<h2>How to uninstall IObit Smart Defrag Pro 6.2.5.128 Multilingual?</h2>
-<p>If you want to uninstall IObit Smart Defrag Pro 6.2.5.128 Multilingual, you need to follow these steps:</p>
-<ol>
-<li>Go to the Control Panel and select Programs and Features.</li>
-<li>Find IObit Smart Defrag Pro 6.2.5.128 Multilingual in the list and click on Uninstall.</li>
-<li>Follow the instructions to uninstall the software from your PC.</li>
-<li>Delete any leftover files or folders related to IObit Smart Defrag Pro 6.2.5.128 Multilingual.</li>
-</ol>
-<p>The uninstallation process is simple and quick, and it does not leave any traces or residues on your PC.</p>
-<h2>Conclusion</h2>
-<p>IObit Smart Defrag Pro 6.2.5.128 Multilingual is a powerful disk defragmenter that can improve your PC performance and speed up your file loading time. It has many features to manage your disks and SSDs, such as smart defrag engine, boot time defrag, game defrag, SSD trim, large file defrag, disk health monitor, scheduler, silent mode and more. It also supports over 35 languages for your convenience. You can download it for free from its official website or from other sources, but you need to be careful about the sources you use and the legal implications of your actions. You can also install and uninstall it easily and smoothly on your PC. We hope you find this article helpful and informative about IObit Smart Defrag Pro 6.2.5.128 Multilingual.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inreVtussa/clothingai/Examples/4K Stogram 2.8.2.2000 Crack Free License Key Android Apk.md b/spaces/inreVtussa/clothingai/Examples/4K Stogram 2.8.2.2000 Crack Free License Key Android Apk.md
deleted file mode 100644
index 317e6599ef6728cda1b3df2f8676e0caeb02ccbd..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/4K Stogram 2.8.2.2000 Crack Free License Key Android Apk.md	
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diff --git a/spaces/inreVtussa/clothingai/Examples/Adobe After Effect CC 2020 Crack Serial Key Free Download __EXCLUSIVE__.md b/spaces/inreVtussa/clothingai/Examples/Adobe After Effect CC 2020 Crack Serial Key Free Download __EXCLUSIVE__.md
deleted file mode 100644
index f5c653ecbb6e8530f4c3f1300f84d770af572d74..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Adobe After Effect CC 2020 Crack Serial Key Free Download __EXCLUSIVE__.md	
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-<h2>Adobe After Effect CC 2020 Crack Serial Key Free Download</h2><br /><p><b><b>Download Zip</b> &#9881;&#9881;&#9881; <a href="https://tiurll.com/2uClsk">https://tiurll.com/2uClsk</a></b></p><br /><br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/DVD2one V2.4.1 Final Portable.rar.md b/spaces/inreVtussa/clothingai/Examples/DVD2one V2.4.1 Final Portable.rar.md
deleted file mode 100644
index 378d50270c71482704ff254d7d53e8c22da977d8..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/DVD2one V2.4.1 Final Portable.rar.md	
+++ /dev/null
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-<h2>DVD2one V2.4.1 Final Portable.rar</h2><br /><p><b><b>Download File</b> &gt; <a href="https://tiurll.com/2uCiCX">https://tiurll.com/2uCiCX</a></b></p><br /><br />
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diff --git a/spaces/intelliarts/Car_damage_detection/README.md b/spaces/intelliarts/Car_damage_detection/README.md
deleted file mode 100644
index 7aa22bfa04cfb6711b3cd4ab1033404b6bf2e54c..0000000000000000000000000000000000000000
--- a/spaces/intelliarts/Car_damage_detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Car Damage Detection
-emoji: 🚌
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jackli888/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/spaces/jackli888/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
deleted file mode 100644
index 5c0488e5f6fcea41e7f9fa25070e38fbfe656478..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
+++ /dev/null
@@ -1,1449 +0,0 @@
-# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
-# Original filename: ldm/models/diffusion/ddpm.py
-# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
-# Some models such as LDSR require VQ to work correctly
-# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-import ldm.models.diffusion.ddpm
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPMV1(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapperV1(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                    1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        else:
-            raise NotImplementedError("mu not supported")
-        # TODO how to choose this term
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        else:
-            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusionV1(DDPMV1):
-    """main class"""
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 *args, **kwargs):
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__':
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None  
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                            force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ['caption', 'coordinates_bbox']:
-                    xc = batch[cond_key]
-                elif cond_key == 'class_label':
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    # same as above but without decorator
-    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                df = self.split_input_params["vqf"]
-                self.split_input_params['original_image_size'] = x.shape[-2:]
-                bs, nc, h, w = x.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
-                z = unfold(x)  # (bn, nc * prod(**ks), L)
-                # Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
-                               for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)
-                o = o * weighting
-
-                # Reverse reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization
-                return decoded
-
-            else:
-                return self.first_stage_model.encode(x)
-        else:
-            return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c)
-        return loss
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                c = self.get_learned_conditioning(c)
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        if hasattr(self, "split_input_params"):
-            assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
-            ks = self.split_input_params["ks"]  # eg. (128, 128)
-            stride = self.split_input_params["stride"]  # eg. (64, 64)
-
-            h, w = x_noisy.shape[-2:]
-
-            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
-            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
-            # Reshape to img shape
-            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
-            if self.cond_stage_key in ["image", "LR_image", "segmentation",
-                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
-                c_key = next(iter(cond.keys()))  # get key
-                c = next(iter(cond.values()))  # get value
-                assert (len(c) == 1)  # todo extend to list with more than one elem
-                c = c[0]  # get element
-
-                c = unfold(c)
-                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
-            elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
-                # assuming padding of unfold is always 0 and its dilation is always 1
-                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
-                full_img_h, full_img_w = self.split_input_params['original_image_size']
-                # as we are operating on latents, we need the factor from the original image size to the
-                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
-                num_downs = self.first_stage_model.encoder.num_resolutions - 1
-                rescale_latent = 2 ** (num_downs)
-
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
-                # need to rescale the tl patch coordinates to be in between (0,1)
-                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
-                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
-                                        for patch_nr in range(z.shape[-1])]
-
-                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
-                patch_limits = [(x_tl, y_tl,
-                                 rescale_latent * ks[0] / full_img_w,
-                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
-                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
-                # tokenize crop coordinates for the bounding boxes of the respective patches
-                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
-                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
-                print(patch_limits_tknzd[0].shape)
-                # cut tknzd crop position from conditioning
-                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
-                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
-                print(cut_cond.shape)
-
-                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
-                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
-                print(adapted_cond.shape)
-                adapted_cond = self.get_learned_conditioning(adapted_cond)
-                print(adapted_cond.shape)
-                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
-                print(adapted_cond.shape)
-
-                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
-            else:
-                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
-
-            # apply model by loop over crops
-            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
-            assert not isinstance(output_list[0],
-                                  tuple)  # todo cant deal with multiple model outputs check this never happens
-
-            o = torch.stack(output_list, axis=-1)
-            o = o * weighting
-            # Reverse reshape to img shape
-            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-            # stitch crops together
-            x_recon = fold(o) / normalization
-
-        else:
-            x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None,**kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.image_size, self.image_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
-                                                        shape,cond,verbose=False,**kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True,**kwargs)
-
-        return samples, intermediates
-
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, **kwargs):
-
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
-                log["conditioning"] = xc
-            elif self.cond_stage_key == 'class_label':
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                         ddim_steps=ddim_steps,eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with self.ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                             ddim_steps=ddim_steps,eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-            if inpaint:
-                # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
-                mask = torch.ones(N, h, w).to(self.device)
-                # zeros will be filled in
-                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-                mask = mask[:, None, ...]
-                with self.ema_scope("Plotting Inpaint"):
-
-                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_inpainting"] = x_samples
-                log["mask"] = mask
-
-                # outpaint
-                with self.ema_scope("Plotting Outpaint"):
-                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with self.ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapperV1(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == 'crossattn':
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class Layout2ImgDiffusionV1(LatentDiffusionV1):
-    # TODO: move all layout-specific hacks to this class
-    def __init__(self, cond_stage_key, *args, **kwargs):
-        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
-
-    def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
-
-        key = 'train' if self.training else 'validation'
-        dset = self.trainer.datamodule.datasets[key]
-        mapper = dset.conditional_builders[self.cond_stage_key]
-
-        bbox_imgs = []
-        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
-        for tknzd_bbox in batch[self.cond_stage_key][:N]:
-            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
-            bbox_imgs.append(bboximg)
-
-        cond_img = torch.stack(bbox_imgs, dim=0)
-        logs['bbox_image'] = cond_img
-        return logs
-
-setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
-setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
-setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
-setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
diff --git a/spaces/james-oldfield/PandA/networks/genforce/datasets/distributed_sampler.py b/spaces/james-oldfield/PandA/networks/genforce/datasets/distributed_sampler.py
deleted file mode 100644
index b62feb15113f20171b8797f2bdeff1eff9b4724d..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/datasets/distributed_sampler.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# python3.7
-"""Contains the distributed data sampler.
-
-This file is mostly borrowed from `torch/utils/data/distributed.py`.
-
-However, sometimes, initialize the data loader and data sampler can be time
-consuming (since it will load a large amount of data at one time). To avoid
-re-initializing the data loader again and again, we modified the sampler to
-support loading the data for only one time and then repeating the data loader.
-Please use the class member `repeat` to control how many times you want the
-data load to repeat. After `repeat` times, the data will be re-loaded.
-
-NOTE: The number of repeat times should not be very large, especially when there
-are too many samples in the dataset. We recommend to set `repeat = 500` for
-datasets with ~50K samples.
-"""
-
-# pylint: disable=line-too-long
-
-import math
-from typing import TypeVar, Optional, Iterator
-
-import torch
-from torch.utils.data import Sampler, Dataset
-import torch.distributed as dist
-
-
-T_co = TypeVar('T_co', covariant=True)
-
-
-class DistributedSampler(Sampler):
-    r"""Sampler that restricts data loading to a subset of the dataset.
-
-    It is especially useful in conjunction with
-    :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
-    process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
-    :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
-    original dataset that is exclusive to it.
-
-    .. note::
-        Dataset is assumed to be of constant size.
-
-    Arguments:
-        dataset: Dataset used for sampling.
-        num_replicas (int, optional): Number of processes participating in
-            distributed training. By default, :attr:`rank` is retrieved from the
-            current distributed group.
-        rank (int, optional): Rank of the current process within :attr:`num_replicas`.
-            By default, :attr:`rank` is retrieved from the current distributed
-            group.
-        shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
-            indices.
-        seed (int, optional): random seed used to shuffle the sampler if
-            :attr:`shuffle=True`. This number should be identical across all
-            processes in the distributed group. Default: ``0``.
-        drop_last (bool, optional): if ``True``, then the sampler will drop the
-            tail of the data to make it evenly divisible across the number of
-            replicas. If ``False``, the sampler will add extra indices to make
-            the data evenly divisible across the replicas. Default: ``False``.
-        current_iter (int, optional): Number of current iteration. Default: ``0``.
-        repeat (int, optional): Repeating number of the whole dataloader. Default: ``1000``.
-
-    .. warning::
-        In distributed mode, calling the :meth:`set_epoch` method at
-        the beginning of each epoch **before** creating the :class:`DataLoader` iterator
-        is necessary to make shuffling work properly across multiple epochs. Otherwise,
-        the same ordering will be always used.
-
-    """
-
-    def __init__(self, dataset: Dataset, num_replicas: Optional[int] = None,
-                 rank: Optional[int] = None, shuffle: bool = True,
-                 seed: int = 0, drop_last: bool = False, current_iter: int = 0,
-                 repeat: int = 1000) -> None:
-        super().__init__(None)
-        if num_replicas is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            num_replicas = dist.get_world_size()
-        if rank is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            rank = dist.get_rank()
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.iter = current_iter
-        self.drop_last = drop_last
-
-        # NOTE: self.dataset_length is `repeat X len(self.dataset)`
-        self.repeat = repeat
-        self.dataset_length = len(self.dataset) * self.repeat
-
-        if self.drop_last and self.dataset_length % self.num_replicas != 0:
-            # Split to nearest available length that is evenly divisible.
-            # This is to ensure each rank receives the same amount of data when
-            # using this Sampler.
-            self.num_samples = math.ceil(
-                (self.dataset_length - self.num_replicas) / self.num_replicas
-            )
-        else:
-            self.num_samples = math.ceil(self.dataset_length / self.num_replicas)
-
-
-        self.total_size = self.num_samples * self.num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.__generate_indices__()
-
-    def __generate_indices__(self) -> None:
-        g = torch.Generator()
-        indices_bank = []
-        for iter_ in range(self.iter, self.iter + self.repeat):
-            g.manual_seed(self.seed + iter_)
-            indices = torch.randperm(len(self.dataset), generator=g).tolist()
-            indices_bank.extend(indices)
-        self.indices = indices_bank
-
-    def __iter__(self) -> Iterator[T_co]:
-        if self.shuffle:
-            # deterministically shuffle based on iter and seed
-            indices = self.indices
-        else:
-            indices = list(range(self.dataset_length))
-
-        if not self.drop_last:
-            # add extra samples to make it evenly divisible
-            indices += indices[:(self.total_size - len(indices))]
-        else:
-            # remove tail of data to make it evenly divisible.
-            indices = indices[:self.total_size]
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        return iter(indices)
-
-    def __len__(self) -> int:
-        return self.num_samples
-
-    def __reset__(self, iteration: int) -> None:
-        self.iter = iteration
-        self.__generate_indices__()
-
-# pylint: enable=line-too-long
diff --git a/spaces/jcenaa/Segment-Any-RGBD/datasets/prepare_ade20k_full_sem_seg.py b/spaces/jcenaa/Segment-Any-RGBD/datasets/prepare_ade20k_full_sem_seg.py
deleted file mode 100644
index 4a55e039549ff0aaf928a4dddee7a94ea8d0f6bf..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/datasets/prepare_ade20k_full_sem_seg.py
+++ /dev/null
@@ -1,1011 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-import os
-import pickle as pkl
-from pathlib import Path
-
-import cv2
-import numpy as np
-import tqdm
-from PIL import Image
-
-ADE20K_SEM_SEG_FULL_CATEGORIES = [
-    {"name": "wall", "id": 2978, "trainId": 0},
-    {"name": "building, edifice", "id": 312, "trainId": 1},
-    {"name": "sky", "id": 2420, "trainId": 2},
-    {"name": "tree", "id": 2855, "trainId": 3},
-    {"name": "road, route", "id": 2131, "trainId": 4},
-    {"name": "floor, flooring", "id": 976, "trainId": 5},
-    {"name": "ceiling", "id": 447, "trainId": 6},
-    {"name": "bed", "id": 165, "trainId": 7},
-    {"name": "sidewalk, pavement", "id": 2377, "trainId": 8},
-    {"name": "earth, ground", "id": 838, "trainId": 9},
-    {"name": "cabinet", "id": 350, "trainId": 10},
-    {"name": "person, individual, someone, somebody, mortal, soul", "id": 1831, "trainId": 11},
-    {"name": "grass", "id": 1125, "trainId": 12},
-    {"name": "windowpane, window", "id": 3055, "trainId": 13},
-    {"name": "car, auto, automobile, machine, motorcar", "id": 401, "trainId": 14},
-    {"name": "mountain, mount", "id": 1610, "trainId": 15},
-    {"name": "plant, flora, plant life", "id": 1910, "trainId": 16},
-    {"name": "table", "id": 2684, "trainId": 17},
-    {"name": "chair", "id": 471, "trainId": 18},
-    {"name": "curtain, drape, drapery, mantle, pall", "id": 687, "trainId": 19},
-    {"name": "door", "id": 774, "trainId": 20},
-    {"name": "sofa, couch, lounge", "id": 2473, "trainId": 21},
-    {"name": "sea", "id": 2264, "trainId": 22},
-    {"name": "painting, picture", "id": 1735, "trainId": 23},
-    {"name": "water", "id": 2994, "trainId": 24},
-    {"name": "mirror", "id": 1564, "trainId": 25},
-    {"name": "house", "id": 1276, "trainId": 26},
-    {"name": "rug, carpet, carpeting", "id": 2178, "trainId": 27},
-    {"name": "shelf", "id": 2329, "trainId": 28},
-    {"name": "armchair", "id": 57, "trainId": 29},
-    {"name": "fence, fencing", "id": 907, "trainId": 30},
-    {"name": "field", "id": 913, "trainId": 31},
-    {"name": "lamp", "id": 1395, "trainId": 32},
-    {"name": "rock, stone", "id": 2138, "trainId": 33},
-    {"name": "seat", "id": 2272, "trainId": 34},
-    {"name": "river", "id": 2128, "trainId": 35},
-    {"name": "desk", "id": 724, "trainId": 36},
-    {"name": "bathtub, bathing tub, bath, tub", "id": 155, "trainId": 37},
-    {"name": "railing, rail", "id": 2053, "trainId": 38},
-    {"name": "signboard, sign", "id": 2380, "trainId": 39},
-    {"name": "cushion", "id": 689, "trainId": 40},
-    {"name": "path", "id": 1788, "trainId": 41},
-    {"name": "work surface", "id": 3087, "trainId": 42},
-    {"name": "stairs, steps", "id": 2530, "trainId": 43},
-    {"name": "column, pillar", "id": 581, "trainId": 44},
-    {"name": "sink", "id": 2388, "trainId": 45},
-    {"name": "wardrobe, closet, press", "id": 2985, "trainId": 46},
-    {"name": "snow", "id": 2454, "trainId": 47},
-    {"name": "refrigerator, icebox", "id": 2096, "trainId": 48},
-    {"name": "base, pedestal, stand", "id": 137, "trainId": 49},
-    {"name": "bridge, span", "id": 294, "trainId": 50},
-    {"name": "blind, screen", "id": 212, "trainId": 51},
-    {"name": "runway", "id": 2185, "trainId": 52},
-    {"name": "cliff, drop, drop-off", "id": 524, "trainId": 53},
-    {"name": "sand", "id": 2212, "trainId": 54},
-    {"name": "fireplace, hearth, open fireplace", "id": 943, "trainId": 55},
-    {"name": "pillow", "id": 1869, "trainId": 56},
-    {"name": "screen door, screen", "id": 2251, "trainId": 57},
-    {"name": "toilet, can, commode, crapper, pot, potty, stool, throne", "id": 2793, "trainId": 58},
-    {"name": "skyscraper", "id": 2423, "trainId": 59},
-    {"name": "grandstand, covered stand", "id": 1121, "trainId": 60},
-    {"name": "box", "id": 266, "trainId": 61},
-    {"name": "pool table, billiard table, snooker table", "id": 1948, "trainId": 62},
-    {"name": "palm, palm tree", "id": 1744, "trainId": 63},
-    {"name": "double door", "id": 783, "trainId": 64},
-    {"name": "coffee table, cocktail table", "id": 571, "trainId": 65},
-    {"name": "counter", "id": 627, "trainId": 66},
-    {"name": "countertop", "id": 629, "trainId": 67},
-    {"name": "chest of drawers, chest, bureau, dresser", "id": 491, "trainId": 68},
-    {"name": "kitchen island", "id": 1374, "trainId": 69},
-    {"name": "boat", "id": 223, "trainId": 70},
-    {"name": "waterfall, falls", "id": 3016, "trainId": 71},
-    {
-        "name": "stove, kitchen stove, range, kitchen range, cooking stove",
-        "id": 2598,
-        "trainId": 72,
-    },
-    {"name": "flower", "id": 978, "trainId": 73},
-    {"name": "bookcase", "id": 239, "trainId": 74},
-    {"name": "controls", "id": 608, "trainId": 75},
-    {"name": "book", "id": 236, "trainId": 76},
-    {"name": "stairway, staircase", "id": 2531, "trainId": 77},
-    {"name": "streetlight, street lamp", "id": 2616, "trainId": 78},
-    {
-        "name": "computer, computing machine, computing device, data processor, electronic computer, information processing system",
-        "id": 591,
-        "trainId": 79,
-    },
-    {
-        "name": "bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle",
-        "id": 327,
-        "trainId": 80,
-    },
-    {"name": "swivel chair", "id": 2679, "trainId": 81},
-    {"name": "light, light source", "id": 1451, "trainId": 82},
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-    {"name": "push button, push, button", "id": 2025, "trainId": 737},
-    {"name": "star", "id": 2541, "trainId": 738},
-    {"name": "roof rack", "id": 2156, "trainId": 739},
-    {"name": "barbed wire", "id": 126, "trainId": 740},
-    {"name": "spray", "id": 2512, "trainId": 741},
-    {"name": "ear", "id": 831, "trainId": 742},
-    {"name": "sponge", "id": 2503, "trainId": 743},
-    {"name": "racket", "id": 2039, "trainId": 744},
-    {"name": "tins", "id": 2774, "trainId": 745},
-    {"name": "eyeglasses", "id": 886, "trainId": 746},
-    {"name": "file", "id": 919, "trainId": 747},
-    {"name": "scarfs", "id": 2240, "trainId": 748},
-    {"name": "sugar bowl", "id": 2636, "trainId": 749},
-    {"name": "flip flop", "id": 963, "trainId": 750},
-    {"name": "headstones", "id": 1218, "trainId": 751},
-    {"name": "laptop bag", "id": 1406, "trainId": 752},
-    {"name": "leash", "id": 1420, "trainId": 753},
-    {"name": "climbing frame", "id": 526, "trainId": 754},
-    {"name": "suit hanger", "id": 2639, "trainId": 755},
-    {"name": "floor spotlight", "id": 975, "trainId": 756},
-    {"name": "plate rack", "id": 1921, "trainId": 757},
-    {"name": "sewer", "id": 2305, "trainId": 758},
-    {"name": "hard drive", "id": 1193, "trainId": 759},
-    {"name": "sprinkler", "id": 2517, "trainId": 760},
-    {"name": "tools box", "id": 2809, "trainId": 761},
-    {"name": "necklace", "id": 1647, "trainId": 762},
-    {"name": "bulbs", "id": 314, "trainId": 763},
-    {"name": "steel industry", "id": 2560, "trainId": 764},
-    {"name": "club", "id": 545, "trainId": 765},
-    {"name": "jack", "id": 1345, "trainId": 766},
-    {"name": "door bars", "id": 775, "trainId": 767},
-    {
-        "name": "control panel, instrument panel, control board, board, panel",
-        "id": 603,
-        "trainId": 768,
-    },
-    {"name": "hairbrush", "id": 1163, "trainId": 769},
-    {"name": "napkin holder", "id": 1641, "trainId": 770},
-    {"name": "office", "id": 1678, "trainId": 771},
-    {"name": "smoke detector", "id": 2450, "trainId": 772},
-    {"name": "utensils", "id": 2915, "trainId": 773},
-    {"name": "apron", "id": 42, "trainId": 774},
-    {"name": "scissors", "id": 2242, "trainId": 775},
-    {"name": "terminal", "id": 2741, "trainId": 776},
-    {"name": "grinder", "id": 1143, "trainId": 777},
-    {"name": "entry phone", "id": 862, "trainId": 778},
-    {"name": "newspaper stand", "id": 1654, "trainId": 779},
-    {"name": "pepper shaker", "id": 1826, "trainId": 780},
-    {"name": "onions", "id": 1689, "trainId": 781},
-    {
-        "name": "central processing unit, cpu, c p u , central processor, processor, mainframe",
-        "id": 3124,
-        "trainId": 782,
-    },
-    {"name": "tape", "id": 2710, "trainId": 783},
-    {"name": "bat", "id": 152, "trainId": 784},
-    {"name": "coaster", "id": 549, "trainId": 785},
-    {"name": "calculator", "id": 360, "trainId": 786},
-    {"name": "potatoes", "id": 1982, "trainId": 787},
-    {"name": "luggage rack", "id": 1478, "trainId": 788},
-    {"name": "salt", "id": 2203, "trainId": 789},
-    {"name": "street number", "id": 2612, "trainId": 790},
-    {"name": "viewpoint", "id": 2956, "trainId": 791},
-    {"name": "sword", "id": 2681, "trainId": 792},
-    {"name": "cd", "id": 437, "trainId": 793},
-    {"name": "rowing machine", "id": 2171, "trainId": 794},
-    {"name": "plug", "id": 1933, "trainId": 795},
-    {"name": "andiron, firedog, dog, dog-iron", "id": 3110, "trainId": 796},
-    {"name": "pepper", "id": 1824, "trainId": 797},
-    {"name": "tongs", "id": 2803, "trainId": 798},
-    {"name": "bonfire", "id": 234, "trainId": 799},
-    {"name": "dog dish", "id": 764, "trainId": 800},
-    {"name": "belt", "id": 177, "trainId": 801},
-    {"name": "dumbbells", "id": 817, "trainId": 802},
-    {"name": "videocassette recorder, vcr", "id": 3145, "trainId": 803},
-    {"name": "hook", "id": 1262, "trainId": 804},
-    {"name": "envelopes", "id": 864, "trainId": 805},
-    {"name": "shower faucet", "id": 2359, "trainId": 806},
-    {"name": "watch", "id": 2992, "trainId": 807},
-    {"name": "padlock", "id": 1725, "trainId": 808},
-    {"name": "swimming pool ladder", "id": 2667, "trainId": 809},
-    {"name": "spanners", "id": 2484, "trainId": 810},
-    {"name": "gravy boat", "id": 1133, "trainId": 811},
-    {"name": "notice board", "id": 1667, "trainId": 812},
-    {"name": "trash bags", "id": 2847, "trainId": 813},
-    {"name": "fire alarm", "id": 932, "trainId": 814},
-    {"name": "ladle", "id": 1392, "trainId": 815},
-    {"name": "stethoscope", "id": 2573, "trainId": 816},
-    {"name": "rocket", "id": 2140, "trainId": 817},
-    {"name": "funnel", "id": 1046, "trainId": 818},
-    {"name": "bowling pins", "id": 264, "trainId": 819},
-    {"name": "valve", "id": 2927, "trainId": 820},
-    {"name": "thermometer", "id": 2752, "trainId": 821},
-    {"name": "cups", "id": 679, "trainId": 822},
-    {"name": "spice jar", "id": 2493, "trainId": 823},
-    {"name": "night light", "id": 1658, "trainId": 824},
-    {"name": "soaps", "id": 2466, "trainId": 825},
-    {"name": "games table", "id": 1057, "trainId": 826},
-    {"name": "slotted spoon", "id": 2444, "trainId": 827},
-    {"name": "reel", "id": 2093, "trainId": 828},
-    {"name": "scourer", "id": 2248, "trainId": 829},
-    {"name": "sleeping robe", "id": 2432, "trainId": 830},
-    {"name": "desk mat", "id": 726, "trainId": 831},
-    {"name": "dumbbell", "id": 816, "trainId": 832},
-    {"name": "hammer", "id": 1171, "trainId": 833},
-    {"name": "tie", "id": 2766, "trainId": 834},
-    {"name": "typewriter", "id": 2900, "trainId": 835},
-    {"name": "shaker", "id": 2313, "trainId": 836},
-    {"name": "cheese dish", "id": 488, "trainId": 837},
-    {"name": "sea star", "id": 2265, "trainId": 838},
-    {"name": "racquet", "id": 2043, "trainId": 839},
-    {"name": "butane gas cylinder", "id": 332, "trainId": 840},
-    {"name": "paper weight", "id": 1771, "trainId": 841},
-    {"name": "shaving brush", "id": 2320, "trainId": 842},
-    {"name": "sunglasses", "id": 2646, "trainId": 843},
-    {"name": "gear shift", "id": 1089, "trainId": 844},
-    {"name": "towel rail", "id": 2826, "trainId": 845},
-    {"name": "adding machine, totalizer, totaliser", "id": 3148, "trainId": 846},
-]
-
-
-def loadAde20K(file):
-    fileseg = file.replace(".jpg", "_seg.png")
-    with Image.open(fileseg) as io:
-        seg = np.array(io)
-
-    R = seg[:, :, 0]
-    G = seg[:, :, 1]
-    ObjectClassMasks = (R / 10).astype(np.int32) * 256 + (G.astype(np.int32))
-
-    return {"img_name": file, "segm_name": fileseg, "class_mask": ObjectClassMasks}
-
-
-if __name__ == "__main__":
-    dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets"))
-    index_file = dataset_dir / "ADE20K_2021_17_01" / "index_ade20k.pkl"
-    print('Caution: we only generate the validation set!')
-    with open(index_file, "rb") as f:
-        index_ade20k = pkl.load(f)
-
-    id_map = {}
-    for cat in ADE20K_SEM_SEG_FULL_CATEGORIES:
-        id_map[cat["id"]] = cat["trainId"]
-
-    # make output dir
-    for name in ["training", "validation"]:
-        image_dir = dataset_dir / "ADE20K_2021_17_01" / "images_detectron2" / name
-        image_dir.mkdir(parents=True, exist_ok=True)
-        annotation_dir = dataset_dir / "ADE20K_2021_17_01" / "annotations_detectron2" / name
-        annotation_dir.mkdir(parents=True, exist_ok=True)
-
-    # process image and gt
-    for i, (folder_name, file_name) in tqdm.tqdm(
-        enumerate(zip(index_ade20k["folder"], index_ade20k["filename"])),
-        total=len(index_ade20k["filename"]),
-    ):
-        split = "validation" if file_name.split("_")[1] == "val" else "training"
-        if split == 'training':
-            # FIXME: If you want to generate training set, delete this condition
-            continue
-        info = loadAde20K(str(dataset_dir / folder_name / file_name))
-
-        # resize image and label
-        img = np.asarray(Image.open(info["img_name"]))
-        lab = np.asarray(info["class_mask"])
-
-        h, w = img.shape[0], img.shape[1]
-        max_size = 512
-        resize = True
-        if w >= h > max_size:
-            h_new, w_new = max_size, round(w / float(h) * max_size)
-        elif h >= w > max_size:
-            h_new, w_new = round(h / float(w) * max_size), max_size
-        else:
-            resize = False
-
-        if resize:
-            img = cv2.resize(img, (w_new, h_new), interpolation=cv2.INTER_LINEAR)
-            lab = cv2.resize(lab, (w_new, h_new), interpolation=cv2.INTER_NEAREST)
-
-        assert img.dtype == np.uint8
-        assert lab.dtype == np.int32
-
-        # apply label conversion and save into uint16 images
-        output = np.zeros_like(lab, dtype=np.uint16) + 65535
-        for obj_id in np.unique(lab):
-            if obj_id in id_map:
-                output[lab == obj_id] = id_map[obj_id]
-
-        output_img = dataset_dir / "ADE20K_2021_17_01" / "images_detectron2" / split / file_name
-        output_lab = (
-            dataset_dir
-            / "ADE20K_2021_17_01"
-            / "annotations_detectron2"
-            / split
-            / file_name.replace(".jpg", ".tif")
-        )
-        Image.fromarray(img).save(output_img)
-
-        assert output.dtype == np.uint16
-        Image.fromarray(output).save(output_lab)
\ No newline at end of file
diff --git a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/data/datasets/register_cc3m.py b/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/data/datasets/register_cc3m.py
deleted file mode 100644
index 8aa5cb07bc99b574505b6319835750789bb3ee26..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/data/datasets/register_cc3m.py
+++ /dev/null
@@ -1,457 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-import pandas as pd
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-from detectron2.utils.file_io import PathManager
-
-
-COCO_CATEGORIES = [
-    {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"},
-    {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"},
-    {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"},
-    {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"},
-    {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"},
-    {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"},
-    {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"},
-    {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"},
-    {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"},
-    {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"},
-    {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"},
-    {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"},
-    {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"},
-    {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"},
-    {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"},
-    {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"},
-    {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"},
-    {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"},
-    {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"},
-    {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"},
-    {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"},
-    {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"},
-    {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"},
-    {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"},
-    {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"},
-    {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"},
-    {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"},
-    {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"},
-    {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"},
-    {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"},
-    {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"},
-    {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"},
-    {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"},
-    {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"},
-    {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"},
-    {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"},
-    {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"},
-    {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"},
-    {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"},
-    {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"},
-    {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"},
-    {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"},
-    {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"},
-    {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"},
-    {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"},
-    {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"},
-    {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"},
-    {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"},
-    {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"},
-    {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"},
-    {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"},
-    {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"},
-    {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"},
-    {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"},
-    {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"},
-    {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"},
-    {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"},
-    {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"},
-    {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"},
-    {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"},
-    {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"},
-    {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"},
-    {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"},
-    {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"},
-    {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"},
-    {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"},
-    {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"},
-    {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"},
-    {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"},
-    {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"},
-    {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"},
-    {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"},
-    {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"},
-    {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"},
-    {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"},
-    {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"},
-    {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"},
-    {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"},
-    {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"},
-    {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"},
-    {"id": 92, "name": "banner", "supercategory": "textile"},
-    {"id": 93, "name": "blanket", "supercategory": "textile"},
-    {"id": 94, "name": "branch", "supercategory": "plant"},
-    {"id": 95, "name": "bridge", "supercategory": "building"},
-    {"id": 96, "name": "building-other", "supercategory": "building"},
-    {"id": 97, "name": "bush", "supercategory": "plant"},
-    {"id": 98, "name": "cabinet", "supercategory": "furniture-stuff"},
-    {"id": 99, "name": "cage", "supercategory": "structural"},
-    {"id": 100, "name": "cardboard", "supercategory": "raw-material"},
-    {"id": 101, "name": "carpet", "supercategory": "floor"},
-    {"id": 102, "name": "ceiling-other", "supercategory": "ceiling"},
-    {"id": 103, "name": "ceiling-tile", "supercategory": "ceiling"},
-    {"id": 104, "name": "cloth", "supercategory": "textile"},
-    {"id": 105, "name": "clothes", "supercategory": "textile"},
-    {"id": 106, "name": "clouds", "supercategory": "sky"},
-    {"id": 107, "name": "counter", "supercategory": "furniture-stuff"},
-    {"id": 108, "name": "cupboard", "supercategory": "furniture-stuff"},
-    {"id": 109, "name": "curtain", "supercategory": "textile"},
-    {"id": 110, "name": "desk-stuff", "supercategory": "furniture-stuff"},
-    {"id": 111, "name": "dirt", "supercategory": "ground"},
-    {"id": 112, "name": "door-stuff", "supercategory": "furniture-stuff"},
-    {"id": 113, "name": "fence", "supercategory": "structural"},
-    {"id": 114, "name": "floor-marble", "supercategory": "floor"},
-    {"id": 115, "name": "floor-other", "supercategory": "floor"},
-    {"id": 116, "name": "floor-stone", "supercategory": "floor"},
-    {"id": 117, "name": "floor-tile", "supercategory": "floor"},
-    {"id": 118, "name": "floor-wood", "supercategory": "floor"},
-    {"id": 119, "name": "flower", "supercategory": "plant"},
-    {"id": 120, "name": "fog", "supercategory": "water"},
-    {"id": 121, "name": "food-other", "supercategory": "food-stuff"},
-    {"id": 122, "name": "fruit", "supercategory": "food-stuff"},
-    {"id": 123, "name": "furniture-other", "supercategory": "furniture-stuff"},
-    {"id": 124, "name": "grass", "supercategory": "plant"},
-    {"id": 125, "name": "gravel", "supercategory": "ground"},
-    {"id": 126, "name": "ground-other", "supercategory": "ground"},
-    {"id": 127, "name": "hill", "supercategory": "solid"},
-    {"id": 128, "name": "house", "supercategory": "building"},
-    {"id": 129, "name": "leaves", "supercategory": "plant"},
-    {"id": 130, "name": "light", "supercategory": "furniture-stuff"},
-    {"id": 131, "name": "mat", "supercategory": "textile"},
-    {"id": 132, "name": "metal", "supercategory": "raw-material"},
-    {"id": 133, "name": "mirror-stuff", "supercategory": "furniture-stuff"},
-    {"id": 134, "name": "moss", "supercategory": "plant"},
-    {"id": 135, "name": "mountain", "supercategory": "solid"},
-    {"id": 136, "name": "mud", "supercategory": "ground"},
-    {"id": 137, "name": "napkin", "supercategory": "textile"},
-    {"id": 138, "name": "net", "supercategory": "structural"},
-    {"id": 139, "name": "paper", "supercategory": "raw-material"},
-    {"id": 140, "name": "pavement", "supercategory": "ground"},
-    {"id": 141, "name": "pillow", "supercategory": "textile"},
-    {"id": 142, "name": "plant-other", "supercategory": "plant"},
-    {"id": 143, "name": "plastic", "supercategory": "raw-material"},
-    {"id": 144, "name": "platform", "supercategory": "ground"},
-    {"id": 145, "name": "playingfield", "supercategory": "ground"},
-    {"id": 146, "name": "railing", "supercategory": "structural"},
-    {"id": 147, "name": "railroad", "supercategory": "ground"},
-    {"id": 148, "name": "river", "supercategory": "water"},
-    {"id": 149, "name": "road", "supercategory": "ground"},
-    {"id": 150, "name": "rock", "supercategory": "solid"},
-    {"id": 151, "name": "roof", "supercategory": "building"},
-    {"id": 152, "name": "rug", "supercategory": "textile"},
-    {"id": 153, "name": "salad", "supercategory": "food-stuff"},
-    {"id": 154, "name": "sand", "supercategory": "ground"},
-    {"id": 155, "name": "sea", "supercategory": "water"},
-    {"id": 156, "name": "shelf", "supercategory": "furniture-stuff"},
-    {"id": 157, "name": "sky-other", "supercategory": "sky"},
-    {"id": 158, "name": "skyscraper", "supercategory": "building"},
-    {"id": 159, "name": "snow", "supercategory": "ground"},
-    {"id": 160, "name": "solid-other", "supercategory": "solid"},
-    {"id": 161, "name": "stairs", "supercategory": "furniture-stuff"},
-    {"id": 162, "name": "stone", "supercategory": "solid"},
-    {"id": 163, "name": "straw", "supercategory": "plant"},
-    {"id": 164, "name": "structural-other", "supercategory": "structural"},
-    {"id": 165, "name": "table", "supercategory": "furniture-stuff"},
-    {"id": 166, "name": "tent", "supercategory": "building"},
-    {"id": 167, "name": "textile-other", "supercategory": "textile"},
-    {"id": 168, "name": "towel", "supercategory": "textile"},
-    {"id": 169, "name": "tree", "supercategory": "plant"},
-    {"id": 170, "name": "vegetable", "supercategory": "food-stuff"},
-    {"id": 171, "name": "wall-brick", "supercategory": "wall"},
-    {"id": 172, "name": "wall-concrete", "supercategory": "wall"},
-    {"id": 173, "name": "wall-other", "supercategory": "wall"},
-    {"id": 174, "name": "wall-panel", "supercategory": "wall"},
-    {"id": 175, "name": "wall-stone", "supercategory": "wall"},
-    {"id": 176, "name": "wall-tile", "supercategory": "wall"},
-    {"id": 177, "name": "wall-wood", "supercategory": "wall"},
-    {"id": 178, "name": "water-other", "supercategory": "water"},
-    {"id": 179, "name": "waterdrops", "supercategory": "water"},
-    {"id": 180, "name": "window-blind", "supercategory": "window"},
-    {"id": 181, "name": "window-other", "supercategory": "window"},
-    {"id": 182, "name": "wood", "supercategory": "solid"},
-]
-
-
-ADE20K_150_CATEGORIES = [
-    {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"},
-    {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"},
-    {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"},
-    {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"},
-    {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"},
-    {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"},
-    {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"},
-    {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"},
-    {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "},
-    {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"},
-    {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"},
-    {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"},
-    {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"},
-    {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"},
-    {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"},
-    {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"},
-    {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"},
-    {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"},
-    {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"},
-    {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"},
-    {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"},
-    {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"},
-    {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"},
-    {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"},
-    {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"},
-    {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"},
-    {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"},
-    {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"},
-    {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"},
-    {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"},
-    {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"},
-    {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"},
-    {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"},
-    {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"},
-    {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"},
-    {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"},
-    {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"},
-    {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"},
-    {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"},
-    {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"},
-    {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"},
-    {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"},
-    {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"},
-    {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"},
-    {
-        "color": [6, 51, 255],
-        "id": 44,
-        "isthing": 1,
-        "name": "chest of drawers, chest, bureau, dresser",
-    },
-    {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"},
-    {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"},
-    {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"},
-    {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"},
-    {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"},
-    {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"},
-    {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"},
-    {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"},
-    {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"},
-    {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"},
-    {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"},
-    {
-        "color": [255, 71, 0],
-        "id": 56,
-        "isthing": 1,
-        "name": "pool table, billiard table, snooker table",
-    },
-    {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"},
-    {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"},
-    {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"},
-    {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"},
-    {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"},
-    {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"},
-    {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"},
-    {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"},
-    {
-        "color": [0, 255, 133],
-        "id": 65,
-        "isthing": 1,
-        "name": "toilet, can, commode, crapper, pot, potty, stool, throne",
-    },
-    {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"},
-    {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"},
-    {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"},
-    {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"},
-    {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"},
-    {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"},
-    {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"},
-    {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"},
-    {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"},
-    {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"},
-    {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"},
-    {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"},
-    {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"},
-    {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"},
-    {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"},
-    {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"},
-    {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"},
-    {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"},
-    {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"},
-    {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"},
-    {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"},
-    {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"},
-    {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"},
-    {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"},
-    {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"},
-    {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"},
-    {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"},
-    {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"},
-    {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"},
-    {
-        "color": [0, 122, 255],
-        "id": 95,
-        "isthing": 1,
-        "name": "bannister, banister, balustrade, balusters, handrail",
-    },
-    {
-        "color": [0, 255, 163],
-        "id": 96,
-        "isthing": 0,
-        "name": "escalator, moving staircase, moving stairway",
-    },
-    {
-        "color": [255, 153, 0],
-        "id": 97,
-        "isthing": 1,
-        "name": "ottoman, pouf, pouffe, puff, hassock",
-    },
-    {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"},
-    {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"},
-    {
-        "color": [143, 255, 0],
-        "id": 100,
-        "isthing": 0,
-        "name": "poster, posting, placard, notice, bill, card",
-    },
-    {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"},
-    {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"},
-    {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"},
-    {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"},
-    {
-        "color": [133, 0, 255],
-        "id": 105,
-        "isthing": 0,
-        "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
-    },
-    {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"},
-    {
-        "color": [184, 0, 255],
-        "id": 107,
-        "isthing": 1,
-        "name": "washer, automatic washer, washing machine",
-    },
-    {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"},
-    {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"},
-    {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"},
-    {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"},
-    {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"},
-    {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"},
-    {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"},
-    {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"},
-    {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"},
-    {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"},
-    {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"},
-    {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"},
-    {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"},
-    {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"},
-    {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"},
-    {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"},
-    {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"},
-    {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"},
-    {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"},
-    {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"},
-    {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"},
-    {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"},
-    {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"},
-    {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"},
-    {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"},
-    {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"},
-    {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"},
-    {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"},
-    {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"},
-    {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"},
-    {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"},
-    {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"},
-    {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"},
-    {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"},
-    {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"},
-    {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"},
-    {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"},
-    {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"},
-    {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"},
-    {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"},
-    {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"},
-    {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"},
-]
-
-TEST_CATEGORIES = [
-    {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "Oculus"},
-    {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "Ukulele"},
-]
-
-COCO_BASE_CATEGORIES = [
-    c
-    for i, c in enumerate(COCO_CATEGORIES)
-    if c["id"] - 1
-    not in [20, 24, 32, 33, 40, 56, 86, 99, 105, 123, 144, 147, 148, 168, 171]
-]
-COCO_NOVEL_CATEGORIES = [
-    c
-    for i, c in enumerate(COCO_CATEGORIES)
-    if c["id"] - 1
-    in [20, 24, 32, 33, 40, 56, 86, 99, 105, 123, 144, 147, 148, 168, 171]
-]
-
-
-def load_cc_image(csv_file, img_key='filepath', caption_key='title', sep="\t"):
-    print(f'Loading csv data from {csv_file}.')
-    df = pd.read_csv(csv_file, sep=sep)
-
-    input_files = df[img_key].tolist()
-    captions = df[caption_key].tolist()
-
-    print("Loaded {} images".format(len(input_files)))
-
-    dataset_dicts = []
-    for (img_path, text) in zip(input_files, captions):
-        record = {}
-        record["file_name"] = img_path
-        record["caption"] = text
-        dataset_dicts.append(record)
-
-    return dataset_dicts
-
-
-def _get_coco_stuff_meta(cat_list):
-    # Id 0 is reserved for ignore_label, we change ignore_label for 0
-    # to 255 in our pre-processing.
-    stuff_ids = [k["id"] for k in cat_list]
-
-    # For semantic segmentation, this mapping maps from contiguous stuff id
-    # (in [0, 91], used in models) to ids in the dataset (used for processing results)
-    stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
-    stuff_classes = [k["name"] for k in cat_list]
-
-    ret = {
-        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
-        "stuff_classes": stuff_classes,
-    }
-    return ret
-
-
-def register_cc_3m(csv_file):
-
-    meta = _get_coco_stuff_meta(TEST_CATEGORIES)
-    name = "cc_3m_train"
-
-    DatasetCatalog.register(
-        name,
-        lambda x=csv_file: load_cc_image(x),
-    )
-    MetadataCatalog.get(name).set(
-        csv_file=csv_file,
-        evaluator_type="dummy",
-        ignore_label=255,
-        **meta,
-    )
-
-
-# _csv_file = "/home/jeffliang/zsseg/datasets/coco/coco_train_merge_captions.csv"
-_csv_file = "/home/jeffliang/zsseg/configs/masked_images/pred/samples.csv"
-register_cc_3m(_csv_file)
diff --git a/spaces/jhlfrfufyfn/bel-tts/README.md b/spaces/jhlfrfufyfn/bel-tts/README.md
deleted file mode 100644
index 3ad943f7f875e9d1ac9dcd3d3cb7dcd8a56488b2..0000000000000000000000000000000000000000
--- a/spaces/jhlfrfufyfn/bel-tts/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bel Tts
-emoji: 📚
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/anyio/abc/_tasks.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/anyio/abc/_tasks.py
deleted file mode 100644
index e48d3c1e97e02cd188b567b50a4c0c615f187e4d..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/anyio/abc/_tasks.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from __future__ import annotations
-
-import sys
-from abc import ABCMeta, abstractmethod
-from types import TracebackType
-from typing import TYPE_CHECKING, Any, Awaitable, Callable, TypeVar, overload
-from warnings import warn
-
-if sys.version_info >= (3, 8):
-    from typing import Protocol
-else:
-    from typing_extensions import Protocol
-
-if TYPE_CHECKING:
-    from anyio._core._tasks import CancelScope
-
-T_Retval = TypeVar("T_Retval")
-T_contra = TypeVar("T_contra", contravariant=True)
-
-
-class TaskStatus(Protocol[T_contra]):
-    @overload
-    def started(self: TaskStatus[None]) -> None:
-        ...
-
-    @overload
-    def started(self, value: T_contra) -> None:
-        ...
-
-    def started(self, value: T_contra | None = None) -> None:
-        """
-        Signal that the task has started.
-
-        :param value: object passed back to the starter of the task
-        """
-
-
-class TaskGroup(metaclass=ABCMeta):
-    """
-    Groups several asynchronous tasks together.
-
-    :ivar cancel_scope: the cancel scope inherited by all child tasks
-    :vartype cancel_scope: CancelScope
-    """
-
-    cancel_scope: CancelScope
-
-    async def spawn(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        *args: object,
-        name: object = None,
-    ) -> None:
-        """
-        Start a new task in this task group.
-
-        :param func: a coroutine function
-        :param args: positional arguments to call the function with
-        :param name: name of the task, for the purposes of introspection and debugging
-
-        .. deprecated:: 3.0
-           Use :meth:`start_soon` instead. If your code needs AnyIO 2 compatibility, you
-           can keep using this until AnyIO 4.
-
-        """
-        warn(
-            'spawn() is deprecated -- use start_soon() (without the "await") instead',
-            DeprecationWarning,
-        )
-        self.start_soon(func, *args, name=name)
-
-    @abstractmethod
-    def start_soon(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        *args: object,
-        name: object = None,
-    ) -> None:
-        """
-        Start a new task in this task group.
-
-        :param func: a coroutine function
-        :param args: positional arguments to call the function with
-        :param name: name of the task, for the purposes of introspection and debugging
-
-        .. versionadded:: 3.0
-        """
-
-    @abstractmethod
-    async def start(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        *args: object,
-        name: object = None,
-    ) -> Any:
-        """
-        Start a new task and wait until it signals for readiness.
-
-        :param func: a coroutine function
-        :param args: positional arguments to call the function with
-        :param name: name of the task, for the purposes of introspection and debugging
-        :return: the value passed to ``task_status.started()``
-        :raises RuntimeError: if the task finishes without calling ``task_status.started()``
-
-        .. versionadded:: 3.0
-        """
-
-    @abstractmethod
-    async def __aenter__(self) -> TaskGroup:
-        """Enter the task group context and allow starting new tasks."""
-
-    @abstractmethod
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        """Exit the task group context waiting for all tasks to finish."""
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/app/store/index.ts b/spaces/jordonpeter01/ai-comic-factory/src/app/store/index.ts
deleted file mode 100644
index e85dd4d052996e9b4120bef57abb6c72c509d41a..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/app/store/index.ts
+++ /dev/null
@@ -1,203 +0,0 @@
-"use client"
-
-import { create } from "zustand"
-
-import { FontName } from "@/lib/fonts"
-import { Preset, PresetName, defaultPreset, getPreset, getRandomPreset } from "@/app/engine/presets"
-import { LayoutName, defaultLayout, getRandomLayoutName, getRandomLayoutNames } from "../layouts"
-import html2canvas from "html2canvas"
-import { RenderedScene } from "@/types"
-
-export const useStore = create<{
-  prompt: string
-  font: FontName
-  preset: Preset
-  nbFrames: number
-  panels: string[]
-  captions: string[]
-  upscaleQueue: Record<string, RenderedScene>
-  showCaptions: boolean
-  renderedScenes: Record<string, RenderedScene>
-  layout: LayoutName
-  layouts: LayoutName[]
-  zoomLevel: number
-  page: HTMLDivElement
-  isGeneratingStory: boolean
-  panelGenerationStatus: Record<number, boolean>
-  isGeneratingText: boolean
-  atLeastOnePanelIsBusy: boolean
-  setRendered: (panelId: string, renderedScene: RenderedScene) => void
-  addToUpscaleQueue: (panelId: string, renderedScene: RenderedScene) => void
-  removeFromUpscaleQueue: (panelId: string) => void
-  setPrompt: (prompt: string) => void
-  setFont: (font: FontName) => void
-  setPreset: (preset: Preset) => void
-  setPanels: (panels: string[]) => void
-  setShowCaptions: (showCaptions: boolean) => void
-  setLayout: (layout: LayoutName) => void
-  setLayouts: (layouts: LayoutName[]) => void
-  setCaptions: (captions: string[]) => void
-  setZoomLevel: (zoomLevel: number) => void
-  setPage: (page: HTMLDivElement) => void
-  setGeneratingStory: (isGeneratingStory: boolean) => void
-  setGeneratingImages: (panelId: string, value: boolean) => void
-  setGeneratingText: (isGeneratingText: boolean) => void
-  pageToImage: () => Promise<string>
-  download: () => Promise<void>
-  generate: (prompt: string, presetName: PresetName, layoutName: LayoutName) => void
-}>((set, get) => ({
-  prompt: "",
-  font: "actionman",
-  preset: getPreset(defaultPreset),
-  nbFrames: 1,
-  panels: [],
-  captions: [],
-  upscaleQueue: {} as Record<string, RenderedScene>,
-  renderedScenes: {} as Record<string, RenderedScene>,
-  showCaptions: false,
-  layout: defaultLayout,
-  layouts: [defaultLayout, defaultLayout],
-  zoomLevel: 60,
-  page: undefined as unknown as HTMLDivElement,
-  isGeneratingStory: false,
-  panelGenerationStatus: {},
-  isGeneratingText: false,
-  atLeastOnePanelIsBusy: false,
-  setRendered: (panelId: string, renderedScene: RenderedScene) => {
-    const { renderedScenes } = get()
-    set({
-      renderedScenes: {
-        ...renderedScenes,
-        [panelId]: renderedScene
-      }
-    })
-  },
-  addToUpscaleQueue: (panelId: string, renderedScene: RenderedScene) => {
-    const { upscaleQueue } = get()
-    set({
-      upscaleQueue: {
-        ...upscaleQueue,
-        [panelId]: renderedScene
-      },
-    })
-  },
-  removeFromUpscaleQueue: (panelId: string) => {
-    const upscaleQueue = { ...get().upscaleQueue }
-    delete upscaleQueue[panelId]
-    set({
-      upscaleQueue,
-    })
-  },
-  setPrompt: (prompt: string) => {
-    const existingPrompt = get().prompt
-    if (prompt === existingPrompt) { return }
-    set({
-      prompt,
-    })
-  },
-  setFont: (font: FontName) => {
-    const existingFont = get().font
-    if (font === existingFont) { return }
-    set({
-      font,
-    })
-  },
-  setPreset: (preset: Preset) => {
-    const existingPreset = get().preset
-    if (preset.label === existingPreset.label) { return }
-    set({
-      preset,
-    })
-  },
-  setNbFrames: (nbFrames: number) => {
-    const existingNbFrames = get().nbFrames
-    if (nbFrames === existingNbFrames) { return }
-    set({
-      nbFrames,
-    })
-  },
-  setPanels: (panels: string[]) => set({ panels }),
-  setCaptions: (captions: string[]) => {
-    set({
-      captions,
-    })
-  },
-  setShowCaptions: (showCaptions: boolean) => {
-    set({
-      showCaptions,
-    })
-  },
-  setLayout: (layoutName: LayoutName) => {
-    const layout = layoutName === "random"
-      ? getRandomLayoutName()
-      : layoutName
-
-    set({
-      layout,
-      layouts: [layout, layout]
-    })
-  },
-  setLayouts: (layouts: LayoutName[]) => set({ layouts }),
-  setZoomLevel: (zoomLevel: number) =>  set({ zoomLevel }),
-  setPage: (page: HTMLDivElement) => {
-    if (!page) { return }
-    set({ page })
-  },
-  setGeneratingStory: (isGeneratingStory: boolean) => set({ isGeneratingStory }),
-  setGeneratingImages: (panelId: string, value: boolean) => {
-    const panelGenerationStatus: Record<string, boolean> = {
-      ...get().panelGenerationStatus,
-      [panelId]: value
-    }
-
-    const atLeastOnePanelIsBusy = Object.values(panelGenerationStatus).includes(true)
-    
-    set({
-      panelGenerationStatus,
-      atLeastOnePanelIsBusy
-    })
-  },
-  setGeneratingText: (isGeneratingText: boolean) => set({ isGeneratingText }),
-  pageToImage: async () => {
-    const { page } = get()
-    if (!page) { return "" }
-    
-    
-    const canvas = await html2canvas(page)
-    console.log("canvas:", canvas)
-
-    const data = canvas.toDataURL('image/jpeg', 0.5)
-    return data
-  },
-  download: async () => {
-    const { pageToImage } = get()
-    const data = await pageToImage()
-   
-    const link = document.createElement('a')
-
-    if (typeof link.download === 'string') {
-      link.href = data
-      link.download = 'comic.jpg'
-      document.body.appendChild(link)
-      link.click()
-      document.body.removeChild(link)
-    } else {
-      window.open(data)
-    }
-  },
-  generate: (prompt: string, presetName: PresetName, layoutName: LayoutName) => {
-    const layout = layoutName === "random"
-      ? getRandomLayoutName()
-      : layoutName
-    set({
-      prompt,
-      panels: [],
-      captions: [],
-      preset: presetName === "random"
-        ? getRandomPreset()
-        : getPreset(presetName),
-      layout,
-      layouts: [layout, layout],
-    })
-  }
-}))
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/gin_utils_test.py b/spaces/juancopi81/youtube-music-transcribe/t5x/gin_utils_test.py
deleted file mode 100644
index e69b73991c64a4e450199476946244422b62d94e..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/gin_utils_test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# 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.
-
-"""Tests for gin_utils."""
-
-from absl.testing import absltest
-from t5x import gin_utils
-
-
-class GinUtilsTest(absltest.TestCase):
-
-  def test_rewrite_gin_args(self):
-    test_args = [
-        '--gin_file=path/to/file',
-        'gin.value=3',
-        '--gin.value=3',
-        '--gin.value="3"',
-        '--gin.value=\'3\'',
-        '--gin.tricky="key = value"',
-        '--gin.dict={"foo": 4, "bar": "four"}',
-        '--gin.gin=bar',
-        '--gin.scope/foo=bar',
-    ]
-    expected_args = [
-        '--gin_file=path/to/file',
-        'gin.value=3',
-        '--gin_bindings=value = 3',
-        '--gin_bindings=value = "3"',
-        '--gin_bindings=value = \'3\'',
-        '--gin_bindings=tricky = "key = value"',
-        '--gin_bindings=dict = {"foo": 4, "bar": "four"}',
-        '--gin_bindings=gin = bar',
-        '--gin_bindings=scope/foo = bar',
-    ]
-    self.assertSequenceEqual(
-        gin_utils.rewrite_gin_args(test_args), expected_args)
-
-  def test_rewrite_gin_args_malformed(self):
-    test_args = ['--gin.value=3', '--gin.test']
-    with self.assertRaisesWithLiteralMatch(
-        ValueError,
-        "Gin bindings must be of the form '--gin.<param>=<value>', got: "
-        '--gin.test'):
-      gin_utils.rewrite_gin_args(test_args)
-
-
-if __name__ == '__main__':
-  absltest.main()
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/trainer_test.py b/spaces/juancopi81/youtube-music-transcribe/t5x/trainer_test.py
deleted file mode 100644
index 26912e4425b5fac23b2d6341b05890cf564e2c11..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/trainer_test.py
+++ /dev/null
@@ -1,983 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# 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.
-
-"""Tests for t5x.trainer_lib."""
-import collections
-import contextlib
-import os
-
-from absl.testing import absltest
-from absl.testing import parameterized
-import chex
-from clu import metric_writers
-import clu.metrics
-import clu.values
-import flax
-import jax
-import jax.numpy as jnp
-import numpy as np
-from t5x import metrics as metrics_lib
-from t5x import models as models_lib
-from t5x import optimizers
-from t5x import partitioning
-from t5x import test_utils
-from t5x import train_state as train_state_lib
-from t5x import trainer as trainer_lib
-import tensorflow as tf
-from tensorflow.io import gfile
-
-mock = absltest.mock
-jax.config.parse_flags_with_absl()
-
-
-# Make `log_elapsed_time` a no-op to simplify mocking of `time.time()`.
-@contextlib.contextmanager
-def fake_log_elapsed_time(_):
-  yield
-
-
-jax._src.dispatch.log_elapsed_time = fake_log_elapsed_time
-
-
-def _validate_events(test_case, summary_dir, expected_metrics, steps):
-  summaries = gfile.listdir(summary_dir)
-  test_case.assertLen(summaries, 1)
-  summary_path = os.path.join(summary_dir, summaries[0])
-  event_file = os.path.join(summary_path)
-  events = list(tf.compat.v1.train.summary_iterator(event_file))
-  actual_events = {}
-  # First event is boilerplate
-  test_case.assertLen(events, len(steps) + 1)
-  for step, event in zip(steps, events[1:]):
-    test_case.assertEqual(event.step, step)
-    test_case.assertLen(event.summary.value, 1)
-    tensor = event.summary.value[0].tensor
-    if tensor.string_val:
-      actual_events[event.summary.value[0].tag] = tensor.string_val[0].decode()
-    else:
-      actual_events[event.summary.value[0].tag] = float(tf.make_ndarray(tensor))
-
-  jax.tree_multimap(test_case.assertAlmostEqual, actual_events,
-                    expected_metrics)
-
-
-class MetricsManagerTest(absltest.TestCase):
-
-  def setUp(self):
-    super().setUp()
-    self.model_dir = self.create_tempdir().full_path
-
-  def test_summary_dir(self):
-    # All hosts have the summary dir.
-    with mock.patch('jax.process_index', return_value=0):
-      mm = trainer_lib.MetricsManager('eval', self.model_dir)
-    self.assertEqual(mm.summary_dir, os.path.join(self.model_dir, 'eval'))
-    mm.close()
-
-    with mock.patch('jax.process_index', return_value=1):
-      mm = trainer_lib.MetricsManager('eval', self.model_dir)
-    self.assertEqual(mm.summary_dir, os.path.join(self.model_dir, 'eval'))
-    mm.close()
-
-  def test_summary_writer(self):
-    # Only host 0 creates a non-empty summary writer.
-    with mock.patch('jax.process_index', return_value=1):
-      mm = trainer_lib.MetricsManager('eval', self.model_dir)
-    self.assertFalse(gfile.exists(mm.summary_dir))
-    mm.close()
-
-    with mock.patch('jax.process_index', return_value=0):
-      mm = trainer_lib.MetricsManager('eval', self.model_dir)
-    self.assertIsInstance(mm.summary_writer, metric_writers.MetricWriter)
-    self.assertTrue(gfile.exists(mm.summary_dir))
-    mm.close()
-
-  def test_write_scalar(self):
-    gfile.makedirs(os.path.join(self.model_dir, 'eval'))
-
-    # tag, value, step
-    scalars = [('loss', 1.0, 1), ('accuracy', 100.0, 2)]
-
-    # Only host 0 has actually writes summaries.
-    with mock.patch('jax.process_index', return_value=1):
-      mm = trainer_lib.MetricsManager('eval', self.model_dir)
-      for s in scalars:
-        mm.write_scalar(*s)
-    self.assertEmpty(gfile.listdir(mm.summary_dir))
-    mm.close()
-
-    with mock.patch('jax.process_index', return_value=0):
-      mm = trainer_lib.MetricsManager('eval', self.model_dir)
-      for s in scalars:
-        mm.write_scalar(*s)
-    mm.flush()
-
-    summaries = gfile.listdir(mm.summary_dir)
-    self.assertLen(summaries, 1)
-
-    event_file = os.path.join(mm.summary_dir, summaries[0])
-    events = list(tf.compat.v1.train.summary_iterator(event_file))
-    # First event is boilerplate
-    self.assertLen(events, 3)
-    for event, (tag, value, step) in zip(events[1:], scalars):
-      self.assertEqual(event.step, step)
-      self.assertLen(event.summary.value, 1)
-      self.assertEqual(event.summary.value[0].tag, tag)
-      self.assertEqual(tf.make_ndarray(event.summary.value[0].tensor), value)
-    mm.close()
-
-  def test_write_metrics_summary(self):
-    gfile.makedirs(os.path.join(self.model_dir, 'eval'))
-
-    @flax.struct.dataclass
-    class MockTextMetric(clu.metrics.Metric):
-
-      def compute_value(self):
-        return clu.values.Text('test metric')
-
-    accumulated_metrics = {
-        'loss': metrics_lib.Sum(40.0),
-        'accuracy': metrics_lib.AveragePerStep.from_model_output(20.0),
-        'steps_per_second': metrics_lib.StepsPerTime(),
-        'text': MockTextMetric()
-    }
-    expected_values = {
-        'loss': clu.values.Scalar(40.0),
-        'accuracy': clu.values.Scalar(10.0),
-        'steps_per_second': clu.values.Scalar(0.05),
-        'text': clu.values.Text('test metric')
-    }
-    with mock.patch(
-        'jax.process_index', return_value=0), mock.patch(
-            'time.time',
-            side_effect=[0, 40]  # start_time, end_time
-        ), mock.patch('absl.logging.log'):  # avoids hidden calls to time.time()
-      mm = trainer_lib.MetricsManager('eval', summary_dir=self.model_dir)
-      mm.start_duration_timer()
-      summary = mm.write_metrics_summary(
-          accumulated_metrics, step=4, num_steps=2)
-      mm.flush()
-
-    self.assertDictEqual(summary.result(), expected_values)
-    _validate_events(
-        self,
-        mm.summary_dir, {k: v.value for k, v in expected_values.items()},
-        steps=[4, 4, 4, 4])
-
-    mm.close()
-
-  def test_timer_blocking_on_donated_buffer(self):
-    mm = trainer_lib.MetricsManager('train', summary_dir=None)
-    x = jnp.zeros(1)
-
-    # Not deleted.
-    mm.start_duration_timer(block_on=x)
-    mm._duration_timer._start_future.result()
-
-    # Deleted/donated.
-    x.device_buffer.delete()
-    mm.start_duration_timer(block_on=x)
-    mm._duration_timer._start_future.result()
-
-  def test_timer_concurrency(self):
-    mm = trainer_lib.MetricsManager('train')
-
-    n = 10
-    with mock.patch(
-        'time.time',
-        side_effect=range(2 * n)  # start_time, end_time
-    ), mock.patch('absl.logging.log'):  # avoids hidden calls to time.time()
-      for _ in range(n):
-        mm.start_duration_timer()
-        summary = mm.write_metrics_summary({'time': metrics_lib.Time()}, 0, 1)
-        self.assertEqual(1, summary.result()['time'].value)
-      mm.flush()
-
-
-def fake_accum_grads(model, optimizer, batch, rng, num_microbatches,
-                     data_partition_spec):
-  del model, num_microbatches, rng, data_partition_spec
-  # Add `i` to each optimzer value.
-  i = batch['i'].sum()
-  grad_accum = jax.tree_map(lambda x: i, optimizer)
-  # Add j to each metric.
-  j = batch['j'].sum()
-  metrics = {'loss': metrics_lib.Sum(j), 'accuracy': metrics_lib.Sum(j)}
-  return grad_accum, metrics, None
-
-
-def fake_apply_grads(optimizer,
-                     grad_accum,
-                     metrics,
-                     learning_rate,
-                     weight_metrics_computer,
-                     other_state_variables=None):
-  del weight_metrics_computer
-  del other_state_variables
-  metrics['learning_rate'] = clu.metrics.Average(learning_rate, count=1)
-  optimizer = jax.tree_multimap(lambda x, g: x + g, optimizer, grad_accum)
-  return optimizer, metrics
-
-
-def fake_eval_step(model, optimizer, batch):
-  del model, optimizer
-  # Add `i` to each metric.
-  i = batch['i'].sum()
-
-  return {'loss': metrics_lib.Sum(i), 'accuracy': metrics_lib.Sum(i)}
-
-
-def fake_eval_fn_without_weight_sum(params, batch):
-  del params
-  # Add `i` to each metric.
-  i = batch['i'].sum()
-
-  loss = metrics_lib.Sum(i)
-  return loss, {'loss': loss, 'accuracy': metrics_lib.Sum(i)}
-
-
-def fake_value_and_grad_fn_without_weight_sum(callable_fn, has_aux=False):
-  del callable_fn, has_aux
-
-  def fake_grad_fn_without_weight_sum(train_state_params,
-                                      batch,
-                                      dropout_rng,
-                                      flax_mutables=None):
-    del dropout_rng, train_state_params, flax_mutables
-    # Add `i` to each optimzer value.
-    i = batch['i'].sum()
-    optimizer = optimizers.Optimizer(
-        optimizers.sgd(0.1),
-        state=optimizers.OptimizerState(
-            step=0, param_states={
-                'bias': 0,
-                'kernel': 0
-            }),
-        target={
-            'bias': np.zeros(4),
-            'kernel': np.zeros((2, 4))
-        })
-    train_state = train_state_lib.FlaxOptimTrainState(optimizer)
-    grad_accum = jax.tree_map(lambda x: i, train_state)
-    # Add j to each metric.
-    j = batch['j'].sum()
-    metrics = {'loss': metrics_lib.Sum(j), 'accuracy': metrics_lib.Sum(j)}
-    return (None, metrics), grad_accum.params
-
-  return fake_grad_fn_without_weight_sum
-
-
-class TrainerTest(parameterized.TestCase):
-
-  def setUp(self):
-    super().setUp()
-    self.init_optimizer = optimizers.Optimizer(
-        optimizers.sgd(0.1),
-        state=optimizers.OptimizerState(
-            step=0, param_states={
-                'bias': 0,
-                'kernel': 0
-            }),
-        target={
-            'bias': np.zeros(4),
-            'kernel': np.zeros((2, 4))
-        })
-    self.init_train_state = train_state_lib.FlaxOptimTrainState(
-        self.init_optimizer)
-    train_state_axes = jax.tree_map(lambda x: None, self.init_train_state)
-    model_dir = self.create_tempdir().full_path
-
-    mapfn = lambda i: {'i': [tf.cast(i, tf.int32)], 'j': [tf.cast(1, tf.int32)]}
-    self.dataset = tf.data.Dataset.range(6).map(mapfn).batch(
-        2, drop_remainder=True)
-
-    self.test_trainer = trainer_lib.Trainer(
-        mock.create_autospec(models_lib.BaseModel, instance=True),
-        self.init_train_state,
-        partitioning.PjitPartitioner(num_partitions=1),
-        eval_names=['task1', 'task2'],
-        summary_dir=model_dir,
-        train_state_axes=train_state_axes,
-        rng=np.ones(2, np.uint32),
-        learning_rate_fn=lambda step: 2 * step,
-        num_microbatches=None)
-
-  def tearDown(self) -> None:
-    self.test_trainer.close()
-    return super().tearDown()
-
-  @mock.patch('t5x.trainer.accumulate_grads_microbatched', fake_accum_grads)
-  @mock.patch('t5x.trainer.apply_grads', fake_apply_grads)
-  def _test_train(self, precompile):
-    trainer = self.test_trainer
-    initial_rng = trainer._base_rng
-
-    if precompile:
-      with mock.patch(
-          'time.time',
-          side_effect=[0, 1]  # compile start, end
-      ), mock.patch('absl.logging.log'):  # avoids hidden calls to time.time()
-        trainer.compile_train(next(self.dataset.as_numpy_iterator()))
-      trainer._compiled_train_step = mock.Mock(
-          side_effect=trainer._compiled_train_step)
-
-    trainer._partitioned_train_step = mock.Mock(
-        side_effect=trainer._partitioned_train_step)
-
-    num_steps = 2
-    with mock.patch(
-        'time.time',
-        side_effect=[1, 5]  # start_time, end_time
-    ), mock.patch('absl.logging.log'):  # avoids hidden calls to time.time()
-      trainer.train(self.dataset.as_numpy_iterator(), num_steps).result()
-
-    initial_metrics = {
-        'loss': 0.,
-        'accuracy': 0.,
-    }
-    expected_metrics = {
-        k: (v + 2 * num_steps) for k, v in initial_metrics.items()
-    }
-    # (0 + 2) / 2 = 1
-    expected_metrics['learning_rate'] = 1
-    # 0+1+2+3 = 6
-    expected_train_state = jax.tree_map(lambda x: np.array(x + 6),
-                                        self.init_train_state)
-
-    # Base rng must remain the same
-    np.testing.assert_array_equal(trainer._base_rng, initial_rng)
-    jax.tree_multimap(np.testing.assert_equal, trainer.train_state,
-                      expected_train_state)
-    # Expected step is 6 since we increment it along with the other optimizer
-    # values.
-    steps = [2, 2, 2]
-    if precompile:
-      steps = [0] + steps
-      expected_metrics['timing/compilation_seconds'] = 1
-      self.assertEqual(trainer._compiled_train_step.call_count, num_steps)
-      trainer._partitioned_train_step.assert_not_called()
-    else:
-      self.assertIsNone(trainer._compiled_train_step)
-      self.assertEqual(trainer._partitioned_train_step.call_count, num_steps)
-    trainer.train_metrics_manager.flush()
-    _validate_events(
-        self,
-        trainer.train_metrics_manager.summary_dir,
-        expected_metrics,
-        steps=steps)
-
-  def test_train_noprecompile(self):
-    self._test_train(False)
-
-  def test_train_precompile(self):
-    self._test_train(True)
-
-  @mock.patch('t5x.trainer.eval_step', fake_eval_step)
-  def _test_eval(self, precompile):
-    trainer = self.test_trainer
-    initial_rng = trainer._base_rng
-
-    task_datasets = {
-        'task1': self.dataset.take(2),
-        'task2': self.dataset.repeat().take(5)
-    }
-
-    if precompile:
-      # [task1 start, task1 end, task2 start, task2 end]
-      with mock.patch(
-          'time.time',
-          side_effect=[0, 1, 2, 3]  # [t1 start, t1 end, t2 start, t2 end]
-      ), mock.patch('absl.logging.log'):  # avoids hidden calls to time.time()
-        trainer.compile_eval({
-            task: next(ds.as_numpy_iterator())
-            for task, ds in task_datasets.items()
-        })
-      trainer._compiled_eval_steps = {
-          task: mock.Mock(side_effect=trainer._compiled_eval_steps[task])
-          for task in task_datasets
-      }
-
-    trainer._partitioned_eval_step = mock.Mock(
-        side_effect=trainer._partitioned_eval_step)
-
-    with mock.patch(
-        'time.time',
-        side_effect=[1, 5, 5, 8]  # t1 start, t1 end, t2 start, t2 end]
-    ), mock.patch('absl.logging.log'):  # avoids hidden calls to time.time()
-      trainer.eval(
-          {task: ds.as_numpy_iterator() for task, ds in task_datasets.items()})
-
-    all_expected_metrics = {
-        # 0+1+2+3 = 6
-        'task1': {
-            'loss': 6,
-            'accuracy': 6,
-        },
-        # 0+1+2+3+4+5+0+1+2+3 = 21
-        'task2': {
-            'loss': 21,
-            'accuracy': 21,
-        },
-    }
-
-    np.testing.assert_array_equal(trainer._base_rng, initial_rng)
-    for task_name, expected_metrics in all_expected_metrics.items():
-      steps = [0, 0]
-      if precompile:
-        steps = [0] + steps
-        expected_metrics['timing/compilation_seconds'] = 1
-        self.assertEqual(  # pylint:disable=g-generic-assert
-            trainer._compiled_eval_steps[task_name].call_count,
-            len(task_datasets[task_name]))
-        trainer._partitioned_eval_step.assert_not_called()
-      else:
-        self.assertEmpty(trainer._compiled_eval_steps)
-        self.assertEqual(trainer._partitioned_eval_step.call_count,
-                         sum(len(ds) for ds in task_datasets.values()))
-      mm = trainer.eval_metrics_managers[task_name]
-      mm.flush()
-      _validate_events(self, mm.summary_dir, expected_metrics, steps=steps)
-
-  def test_eval_noprecompile(self):
-    self._test_eval(False)
-
-  def test_eval_precompile(self):
-    self._test_eval(True)
-
-  @parameterized.named_parameters([
-      {
-          'testcase_name': 'max_no_increase',
-          'mode': 'max',
-          'metrics': [1, 1, 1],
-          'atol': 0.,
-          'rtol': 0.,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'max_no_atol',
-          'mode': 'max',
-          'metrics': [1, 0.9, 0.8],
-          'atol': 0.,
-          'rtol': 0.,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'max_not_enough_atol',
-          'mode': 'max',
-          'metrics': [1, 1.09, 1.18],
-          'atol': 0.1,
-          'rtol': 0.,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'max_enough_atol',
-          'mode': 'max',
-          'metrics': [1, 1.2, 1.4],
-          'atol': 0.1,
-          'rtol': 0.,
-          'stop_training': False,
-      },
-      {
-          'testcase_name': 'max_enough_atol_rtol',
-          'mode': 'max',
-          # first delta = 0.1 + 1* 0.08 = 0.18
-          # second delta = 0.1 + 1.2 * 0.08 = 0.196
-          'metrics': [1, 1.2, 1.4],
-          'atol': 0.1,
-          'rtol': 0.08,
-          'stop_training': False,
-      },
-      {
-          'testcase_name': 'max_not_enough_rtol',
-          'mode': 'max',
-          'metrics': [1, 1.2, 1.4],
-          'atol': 0.,
-          'rtol': 0.2,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'min_no_decrease',
-          'mode': 'min',
-          'metrics': [1, 1, 1],
-          'atol': 0.,
-          'rtol': 0.,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'min_no_atol',
-          'mode': 'min',
-          'metrics': [1, 1, 1],
-          'atol': 0.,
-          'rtol': 0.,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'min_not_enough_atol',
-          'mode': 'min',
-          'metrics': [1, 0.9, 0.71],
-          'atol': 0.2,
-          'rtol': 0.,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'min_enough_atol',
-          'mode': 'min',
-          'metrics': [1, 0.8, 0.6],
-          'atol': 0.15,
-          'rtol': 0.,
-          'stop_training': False,
-      },
-      {
-          'testcase_name': 'min_enough_atol_rtol',
-          'mode': 'min',
-          # first delta = 0.1 + 1* 0.09 = 0.19
-          # second delta = 0.1 + 0.8 * 0.09 = 0.172
-          'metrics': [1, 0.8, 0.6],
-          'atol': 0.1,
-          'rtol': 0.09,
-          'stop_training': False,
-      },
-      {
-          'testcase_name': 'min_not_enough_rtol',
-          'mode': 'min',
-          'metrics': [1, 0.8, 0.6],
-          'atol': 0.0,
-          'rtol': 0.3,
-          'stop_training': True,
-      },
-      {
-          'testcase_name': 'longer_history',
-          'mode': 'min',
-          'metrics': [1, 0.8, 0.7, 0.6],
-          'atol': 0.15,
-          'rtol': 0.,
-          'stop_training': True,
-      }
-  ])
-  def test_early_stopping_action(self, mode, metrics, atol, rtol,
-                                 stop_training):
-    trainer = self.test_trainer
-    metrics = [clu.values.Scalar(metric) for metric in metrics]
-    hook = trainer_lib.EarlyStoppingAction(('test_task', 'metric'),
-                                           mode=mode,
-                                           patience=3,
-                                           atol=atol,
-                                           rtol=rtol)
-
-    for metric in metrics:
-      trainer_stop_training = hook.run(trainer.train_state,
-                                       {'test_task': {
-                                           'metric': metric
-                                       }})
-
-    self.assertEqual(trainer_stop_training, stop_training)
-
-  @parameterized.named_parameters([
-      {
-          'testcase_name': 'invalid_task',
-          'task': 'wrong_task',
-          'metric': 'metric',
-          'value': clu.values.Scalar(np.nan),
-      },
-      {
-          'testcase_name': 'invalid_metric_name',
-          'task': 'task',
-          'metric': 'wrong_metric_name',
-          'value': clu.values.Scalar(np.nan),
-      },
-      {
-          'testcase_name': 'invalid_value',
-          'task': 'task',
-          'metric': 'metric',
-          'value': 1.0,
-      },
-  ])
-  def test_early_stopping_action_error(self, task, metric, value):
-    trainer = self.test_trainer
-    hook = trainer_lib.EarlyStoppingAction((task, metric),
-                                           mode='min',
-                                           patience=5,
-                                           atol=1,
-                                           rtol=1)
-
-    trainer_stop_training = hook.run(trainer.train_state,
-                                     {task: {
-                                         metric: value
-                                     }})
-
-    self.assertFalse(trainer_stop_training)
-
-  @parameterized.named_parameters([{
-      'testcase_name': 'valid_loss',
-      'metric': 'loss',
-      'value': 1.0,
-      'stop_training': False,
-  }, {
-      'testcase_name': 'nan',
-      'metric': 'loss',
-      'value': np.nan,
-      'stop_training': True,
-  }, {
-      'testcase_name': 'inf',
-      'metric': 'loss',
-      'value': np.inf,
-      'stop_training': True,
-  }, {
-      'testcase_name': 'other_metric',
-      'metric': 'some_metric',
-      'value': np.inf,
-      'stop_training': True,
-  }])
-  def test_terminate_on_nan_action(self, metric, value, stop_training):
-    trainer = self.test_trainer
-    value = clu.values.Scalar(value)
-    hook = trainer_lib.TerminateOnNanAction(task='test_task', metric=metric)
-
-    trainer_stop_training = hook.run(trainer.train_state,
-                                     {'test_task': {
-                                         metric: value
-                                     }})
-
-    self.assertEqual(trainer_stop_training, stop_training)
-
-  @parameterized.named_parameters([
-      {
-          'testcase_name': 'invalid_task',
-          'task': 'wrong_task',
-          'metric': 'metric',
-          'value': clu.values.Scalar(np.nan),
-      },
-      {
-          'testcase_name': 'invalid_metric_name',
-          'task': 'task',
-          'metric': 'wrong_metric_name',
-          'value': clu.values.Scalar(np.nan),
-      },
-      {
-          'testcase_name': 'invalid_value',
-          'task': 'task',
-          'metric': 'metric',
-          'value': 1.0,
-      },
-  ])
-  def test_terminate_on_nan_action_error(self, task, metric, value):
-    trainer = self.test_trainer
-    hook = trainer_lib.TerminateOnNanAction(task=task, metric=metric)
-
-    trainer_stop_training = hook.run(trainer.train_state,
-                                     {'task': {
-                                         'metric': value
-                                     }})
-
-    self.assertFalse(trainer_stop_training)
-
-  def test_compile_train(self):
-    trainer = self.test_trainer
-    trainer._partitioned_train_step = mock.Mock()
-    trainer.train_metrics_manager = mock.Mock()
-
-    batch = {
-        'i': np.arange(10, dtype=np.int32).reshape((2, 5)),
-        'j': np.ones((), dtype=np.float32)
-    }
-    # compile start, compile end
-    with mock.patch('time.time', side_effect=[1, 5]):
-      trainer.compile_train(batch)
-
-    trainer.train_metrics_manager.write_scalar.assert_called_with(
-        'timing/compilation_seconds', 4, trainer.train_state.step)
-    trainer._partitioned_train_step.lower.assert_called_once()
-    train_step_args = trainer._partitioned_train_step.lower.call_args[0]
-    self.assertLen(train_step_args, 2)
-    self.assertEqual(train_step_args[0], trainer.train_state)
-    test_utils.assert_same(train_step_args[1], batch)
-
-  def test_compile_eval(self):
-    trainer = self.test_trainer
-    trainer._partitioned_eval_step = mock.Mock()
-    trainer.eval_metrics_managers = {
-        'eval1': mock.Mock(),
-        'eval2': mock.Mock(),
-        'eval3': mock.Mock(),
-        'eval4': mock.Mock()
-    }
-    trainer._partitioned_eval_step.lower().compile.side_effect = [
-        'compiled1', 'compiled2', 'compiled3'
-    ]
-
-    batches = {
-        'eval1': {
-            'i': np.zeros((2, 5), dtype=np.int32)
-        },
-        'eval2': {
-            'j': np.zeros((), dtype=np.float32)
-        },
-        'eval3': {
-            'j': np.zeros((), dtype=np.float32)
-        },
-        'eval4': {
-            'k': np.zeros((4), dtype=np.float32)
-        },
-    }
-
-    # eval1 start/end, eval2 start/end, eval3 start/end, eval 4 start/end
-    with mock.patch('time.time', side_effect=[1, 5, 6, 9, 10, 11, 12, 13]):
-      trainer.compile_eval(collections.OrderedDict(sorted(batches.items())))
-
-    trainer.eval_metrics_managers['eval1'].write_scalar.assert_called_with(
-        'timing/compilation_seconds', 4, trainer.train_state.step)
-    trainer.eval_metrics_managers['eval2'].write_scalar.assert_called_with(
-        'timing/compilation_seconds', 3, trainer.train_state.step)
-    trainer.eval_metrics_managers['eval3'].write_scalar.assert_called_with(
-        'timing/compilation_seconds', 1, trainer.train_state.step)
-    trainer.eval_metrics_managers['eval4'].write_scalar.assert_called_with(
-        'timing/compilation_seconds', 1, trainer.train_state.step)
-    eval_step_args = trainer._partitioned_eval_step.lower.call_args_list[1:]
-    self.assertLen(eval_step_args, 3)
-
-    eval1_call_args = eval_step_args[0][0]
-    self.assertLen(eval1_call_args, 2)
-    self.assertEqual(eval1_call_args[0], trainer.train_state)
-    test_utils.assert_same(eval1_call_args[1], {
-        'i': np.zeros((2, 5), dtype=np.int32),
-    })
-
-    eval2_call_args = eval_step_args[1][0]
-    self.assertLen(eval2_call_args, 2)
-    self.assertEqual(eval2_call_args[0], trainer.train_state)
-    test_utils.assert_same(eval2_call_args[1], {
-        'j': np.zeros((), dtype=np.float32),
-    })
-
-    eval3_call_args = eval_step_args[2][0]
-    self.assertLen(eval3_call_args, 2)
-    self.assertEqual(eval3_call_args[0], trainer.train_state)
-    test_utils.assert_same(eval3_call_args[1], {
-        'k': np.zeros((4), dtype=np.float32),
-    })
-
-    self.assertDictEqual(
-        trainer._compiled_eval_steps, {
-            'eval1': 'compiled1',
-            'eval2': 'compiled2',
-            'eval3': 'compiled2',
-            'eval4': 'compiled3'
-        })
-
-  @mock.patch('jax.value_and_grad', fake_value_and_grad_fn_without_weight_sum)
-  def test_accumulate_grads_microbatched_without_weight_sum_single_batch(self):
-    batch_iter = self.dataset.as_numpy_iterator()
-    batch = next(batch_iter)
-    num_microbatches = 1
-    grad_accum, metrics, flax_mutables = trainer_lib.accumulate_grads_microbatched(
-        self.test_trainer._model, self.init_train_state, batch,
-        self.test_trainer._base_rng, num_microbatches)
-
-    i = batch['i'].sum()
-    expected_grad_accum = jax.tree_map(lambda x: i,
-                                       self.init_train_state).params
-    self.assertEqual(expected_grad_accum, grad_accum)
-    self.assertEqual(metrics['loss'].compute(), 2)
-    self.assertEqual(metrics['accuracy'].compute(), 2)
-    self.assertIsNone(flax_mutables)
-
-  @mock.patch('jax.value_and_grad', fake_value_and_grad_fn_without_weight_sum)
-  def test_accumulate_grads_microbatched_without_weight_sum_multiple_batches(
-      self):
-    batch_iter = self.dataset.as_numpy_iterator()
-    batch = next(batch_iter)
-    num_micro_batches = 2
-    grad_accum, metrics, flax_mutables = trainer_lib.accumulate_grads_microbatched(
-        self.test_trainer._model, self.init_train_state, batch,
-        self.test_trainer._base_rng, num_micro_batches)
-
-    expected_grad_accum = {'bias': jnp.ones(4), 'kernel': jnp.ones((2, 4))}
-    chex.assert_trees_all_equal(expected_grad_accum, grad_accum)
-    self.assertEqual(metrics['loss'].compute(), 2)
-    self.assertEqual(metrics['accuracy'].compute(), 2)
-    self.assertIsNone(flax_mutables)
-
-  def test_eval_step_without_weight_sum(self):
-    batch_iter = self.dataset.as_numpy_iterator()
-    batch = next(batch_iter)
-    self.test_trainer._model.eval_fn = fake_eval_fn_without_weight_sum
-    metrics = trainer_lib.eval_step(self.test_trainer._model,
-                                    self.init_train_state, batch)
-
-    self.assertEqual(metrics['loss'].compute(), 1)
-    self.assertEqual(metrics['accuracy'].compute(), 1)
-
-
-class TrainerRngDeterminismTest(parameterized.TestCase):
-
-  def create_trainer(self, step, random_seed):
-    init_optimizer = optimizers.Optimizer(
-        optimizers.sgd(0.1),
-        state=optimizers.OptimizerState(
-            step=step, param_states={
-                'bias': 0,
-                'kernel': 0
-            }),
-        target={
-            'bias': np.zeros(4),
-            'kernel': np.zeros((2, 4))
-        })
-    init_train_state = train_state_lib.FlaxOptimTrainState(init_optimizer)
-    train_state_axes = jax.tree_map(lambda x: None, init_train_state)
-
-    test_trainer = trainer_lib.Trainer(
-        mock.create_autospec(models_lib.BaseModel, instance=True),
-        init_train_state,
-        partitioning.PjitPartitioner(num_partitions=1),
-        eval_names=['task1', 'task2'],
-        summary_dir=None,
-        train_state_axes=train_state_axes,
-        rng=jax.random.PRNGKey(random_seed),
-        learning_rate_fn=lambda step: 2 * step,
-        num_microbatches=None)
-    return test_trainer
-
-  @mock.patch('t5x.trainer.accumulate_grads_microbatched')
-  @mock.patch('t5x.trainer.apply_grads', fake_apply_grads)
-  def test_rng_determinism(self, mock_accum_grads):
-
-    def fake_accum_grads_rng(model, optimizer, batch, rng, num_microbatches,
-                             data_partition_spec):
-      del model, batch, num_microbatches, data_partition_spec
-      # Add 1, which will increment the step as a side effect.
-      grad_accum = jax.tree_map(lambda x: 1, optimizer)
-      m = {'rng': metrics_lib.Sum(jnp.sum(rng))}
-      return grad_accum, m, None
-
-    mock_accum_grads.side_effect = fake_accum_grads_rng
-    # Create a trainer at a given step (53) with a given random seed (23),
-    # train up to a given train step (100), check the sum of the rngs from the
-    # metrics.
-    start_step = 47
-    end_step = 100
-    random_seed = 23
-    trainer = self.create_trainer(step=start_step, random_seed=random_seed)
-    # 500 batches of size 2
-    ds = [np.zeros(2)] * 500
-
-    metrics = trainer.train(iter(ds), num_steps=end_step - start_step)
-    base_rng = jax.random.PRNGKey(random_seed)
-    expected_rng_sum = np.sum(
-        [jax.random.fold_in(base_rng, i) for i in range(start_step, end_step)],
-        dtype=np.uint32)
-    np.testing.assert_array_equal(metrics.result()['rng'].value,
-                                  expected_rng_sum)
-
-
-def fake_mut_accum_grads(model, optimizer, batch, rng, num_microbatches,
-                         data_partition_spec):
-  del model, num_microbatches, rng, data_partition_spec
-  # Add `i` to each optimzer value.
-  i = batch['i'].sum()
-  grad_accum = jax.tree_map(lambda x: i, optimizer)
-  # Add j to each metric.
-  j = batch['j'].sum()
-  metrics = {
-      'loss': metrics_lib.Sum.from_model_output(j),
-      'accuracy': metrics_lib.Sum.from_model_output(j)
-  }
-  return grad_accum, metrics, {'mutables': 0}
-
-
-def fake_mut_apply_grads(optimizer, grad_accum, metrics, learning_rate,
-                         weight_metrics_computer, other_state_variables):
-  del weight_metrics_computer, other_state_variables
-  metrics['learning_rate'] = clu.metrics.Average.from_model_output(
-      learning_rate)
-  optimizer = jax.tree_multimap(lambda x, g: x + g, optimizer, grad_accum)
-  return optimizer, metrics
-
-
-class MutableTrainerTest(parameterized.TestCase):
-
-  def setUp(self):
-    super().setUp()
-    self.init_optimizer = optimizers.Optimizer(
-        optimizers.sgd(0.1),
-        state=optimizers.OptimizerState(
-            step=0, param_states={
-                'bias': 0,
-                'kernel': 0
-            }),
-        target={
-            'bias': np.zeros(4),
-            'kernel': np.zeros((2, 4))
-        })
-    self.init_train_state = train_state_lib.FlaxOptimTrainState(
-        self.init_optimizer)
-    train_state_axes = jax.tree_map(lambda x: None, self.init_train_state)
-    model_dir = self.create_tempdir().full_path
-
-    mapfn = lambda i: {'i': [tf.cast(i, tf.int32)], 'j': [tf.cast(1, tf.int32)]}
-    self.dataset = tf.data.Dataset.range(6).map(mapfn).batch(
-        2, drop_remainder=True)
-    self.dataset1 = tf.data.Dataset.range(6).map(mapfn).batch(
-        2, drop_remainder=True)
-
-    self.test_trainer = trainer_lib.Trainer(
-        mock.create_autospec(models_lib.BaseModel, instance=True),
-        self.init_train_state,
-        partitioning.PjitPartitioner(num_partitions=1),
-        eval_names=['task1', 'task2'],
-        summary_dir=model_dir,
-        train_state_axes=train_state_axes,
-        rng=np.ones(2, np.uint32),
-        learning_rate_fn=lambda step: 2 * (step + 1),
-        num_microbatches=None)
-
-  @mock.patch('time.time')
-  @mock.patch('t5x.trainer.accumulate_grads_microbatched', fake_mut_accum_grads)
-  @mock.patch('t5x.trainer.apply_grads', fake_mut_apply_grads)
-  # avoids calls time.time() during logging
-  @mock.patch('absl.logging.info', lambda *_: None)
-  @mock.patch('absl.logging.log_every_n_seconds', lambda *_: None)
-  def test_train(self, mock_time=None):
-    trainer = self.test_trainer
-    initial_rng = trainer._base_rng
-
-    trainer._partitioned_train_step = mock.Mock(
-        side_effect=trainer._partitioned_train_step)
-
-    # train start, logging, train end, logging
-    mock_time.side_effect = [1, 5, 5, 5]
-    num_steps = 1
-    ds_iter = self.dataset.as_numpy_iterator()
-    batch = next(ds_iter)
-    train_state, _ = trainer._partitioned_train_step(trainer.train_state, batch)
-
-    expected_train_state = jax.tree_map(lambda x: np.array(x + 1),
-                                        self.init_train_state)
-    # Base rng must remain the same
-    np.testing.assert_array_equal(trainer._base_rng, initial_rng)
-    jax.tree_multimap(np.testing.assert_equal, train_state,
-                      expected_train_state)
-
-    self.assertIsNone(trainer._compiled_train_step)
-    self.assertEqual(trainer._partitioned_train_step.call_count, num_steps)
-
-  def tearDown(self) -> None:
-    # Manually close managers to avoid phantom threads crossing test cases.
-    self.test_trainer.train_metrics_manager.close()
-    for mm in self.test_trainer.eval_metrics_managers.values():
-      mm.close()
-    return super().tearDown()
-
-
-if __name__ == '__main__':
-  absltest.main()
diff --git a/spaces/julien-c/nvidia-smi/app.py b/spaces/julien-c/nvidia-smi/app.py
deleted file mode 100644
index 666d0d0f1d66c6d23b697ff60f7fa5d8987b3d69..0000000000000000000000000000000000000000
--- a/spaces/julien-c/nvidia-smi/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import datetime
-import os
-import subprocess
-import gradio as gr
-
-
-CUSTOM_CSS = """
-#output_box textarea {
-    font-family: IBM Plex Mono, ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
-}
-"""
-
-
-
-
-def run():
-    output: str = ""
-    try:
-        output = subprocess.check_output(["nvidia-smi"], text=True)
-    except FileNotFoundError:
-        output = subprocess.check_output(["ls", "-alh"], text=True)
-    comment = (
-        datetime.datetime.now().replace(microsecond=0).isoformat().replace("T", " ")
-    )
-    return f"# {comment}\n\n{output}"
-
-
-def run_custom_command(custom_command: str, secret: str):
-    if secret != os.environ.get("SECRET"):
-        return "You can't access this"
-
-    print("custom_command", custom_command)
-    try:
-        return subprocess.check_output(custom_command.split(), text=True)
-    except Exception as e:
-        return f"{e}"
-
-
-output = gr.Textbox(
-    label="Command Output", max_lines=32, elem_id="output_box", value=run()
-)
-
-with gr.Blocks(css=CUSTOM_CSS) as demo:
-    gr.Markdown("#### `nvidia-smi`: How is my GPU Space running right now 🔥")
-
-    with gr.Accordion(label="Power user mode", open=False):
-        custom_command = gr.Textbox(label="Input command", value="pwd")
-        secret = gr.Textbox(
-            label="Secret",
-        )
-        custom_command_btn = gr.Button("Run")
-        custom_command_btn.click(
-            fn=run_custom_command,
-            inputs=[custom_command, secret],
-            outputs=output,
-        )
-
-    output.render()
-
-    demo.load(fn=run, inputs=None, outputs=output, every=1)
-
-
-demo.queue().launch()
diff --git a/spaces/kangvcar/RealChar/realtime_ai_character/audio/speech_to_text/google.py b/spaces/kangvcar/RealChar/realtime_ai_character/audio/speech_to_text/google.py
deleted file mode 100644
index 2396eae74cc57a9c8a64d1aae25d980f728e7491..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/realtime_ai_character/audio/speech_to_text/google.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from google.cloud import speech
-import types
-
-from realtime_ai_character.audio.speech_to_text.base import SpeechToText
-from realtime_ai_character.logger import get_logger
-from realtime_ai_character.utils import Singleton
-
-logger = get_logger(__name__)
-config = types.SimpleNamespace(**{
-    'web': {
-        'encoding': speech.RecognitionConfig.AudioEncoding.WEBM_OPUS,
-        'sample_rate_hertz': 48000,
-        'language_code': 'en-US',
-        'max_alternatives': 1,
-    },
-    'terminal': {
-        'encoding': speech.RecognitionConfig.AudioEncoding.LINEAR16,
-        'sample_rate_hertz': 44100,
-        'language_code': 'en-US',
-        'max_alternatives': 1,
-    },
-})
-
-
-class Google(Singleton, SpeechToText):
-    def __init__(self):
-        super().__init__()
-        logger.info("Setting up [Google Speech to Text]...")
-        self.client = speech.SpeechClient()
-
-    def transcribe(self, audio_bytes, platform, prompt='') -> str:
-        batch_config = speech.RecognitionConfig({
-            'speech_contexts': [speech.SpeechContext(phrases=prompt.split(','))],
-            **config.__dict__[platform]})
-        response = self.client.recognize(
-            config=batch_config,
-            audio=speech.RecognitionAudio(content=audio_bytes)
-        )
-        if not response.results:
-            return ''
-        result = response.results[0]
-        if not result.alternatives:
-            return ''
-        return result.alternatives[0].transcript
diff --git a/spaces/keneonyeachonam/Docker-FlanT5-TextGeneratorTranslator-021623/README.md b/spaces/keneonyeachonam/Docker-FlanT5-TextGeneratorTranslator-021623/README.md
deleted file mode 100644
index cbab09def6dcb936f7c0aeb6c6b36f2654b378ad..0000000000000000000000000000000000000000
--- a/spaces/keneonyeachonam/Docker-FlanT5-TextGeneratorTranslator-021623/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Docker FlanT5 TextGeneratorTranslator 021623
-emoji: 🏢
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-app_port: 7860
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/audio.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/audio.py
deleted file mode 100644
index 2fcb77ad1d3a85f523e24f84691886736a5686cb..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/audio.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from scipy.ndimage.morphology import binary_dilation
-from speaker_encoder.params_data import *
-from pathlib import Path
-from typing import Optional, Union
-import numpy as np
-import webrtcvad
-import librosa
-import struct
-
-int16_max = (2 ** 15) - 1
-
-
-def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
-                   source_sr: Optional[int] = None):
-    """
-    Applies the preprocessing operations used in training the Speaker Encoder to a waveform 
-    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
-
-    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 
-    just .wav), either the waveform as a numpy array of floats.
-    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 
-    preprocessing. After preprocessing, the waveform's sampling rate will match the data 
-    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 
-    this argument will be ignored.
-    """
-    # Load the wav from disk if needed
-    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
-        wav, source_sr = librosa.load(fpath_or_wav, sr=None)
-    else:
-        wav = fpath_or_wav
-    
-    # Resample the wav if needed
-    if source_sr is not None and source_sr != sampling_rate:
-        wav = librosa.resample(wav, source_sr, sampling_rate)
-        
-    # Apply the preprocessing: normalize volume and shorten long silences 
-    wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
-    wav = trim_long_silences(wav)
-    
-    return wav
-
-
-def wav_to_mel_spectrogram(wav):
-    """
-    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
-    Note: this not a log-mel spectrogram.
-    """
-    frames = librosa.feature.melspectrogram(
-        y=wav,
-        sr=sampling_rate,
-        n_fft=int(sampling_rate * mel_window_length / 1000),
-        hop_length=int(sampling_rate * mel_window_step / 1000),
-        n_mels=mel_n_channels
-    )
-    return frames.astype(np.float32).T
-
-
-def trim_long_silences(wav):
-    """
-    Ensures that segments without voice in the waveform remain no longer than a 
-    threshold determined by the VAD parameters in params.py.
-
-    :param wav: the raw waveform as a numpy array of floats 
-    :return: the same waveform with silences trimmed away (length <= original wav length)
-    """
-    # Compute the voice detection window size
-    samples_per_window = (vad_window_length * sampling_rate) // 1000
-    
-    # Trim the end of the audio to have a multiple of the window size
-    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
-    
-    # Convert the float waveform to 16-bit mono PCM
-    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
-    
-    # Perform voice activation detection
-    voice_flags = []
-    vad = webrtcvad.Vad(mode=3)
-    for window_start in range(0, len(wav), samples_per_window):
-        window_end = window_start + samples_per_window
-        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
-                                         sample_rate=sampling_rate))
-    voice_flags = np.array(voice_flags)
-    
-    # Smooth the voice detection with a moving average
-    def moving_average(array, width):
-        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
-        ret = np.cumsum(array_padded, dtype=float)
-        ret[width:] = ret[width:] - ret[:-width]
-        return ret[width - 1:] / width
-    
-    audio_mask = moving_average(voice_flags, vad_moving_average_width)
-    audio_mask = np.round(audio_mask).astype(np.bool)
-    
-    # Dilate the voiced regions
-    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
-    audio_mask = np.repeat(audio_mask, samples_per_window)
-    
-    return wav[audio_mask == True]
-
-
-def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
-    if increase_only and decrease_only:
-        raise ValueError("Both increase only and decrease only are set")
-    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
-    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
-        return wav
-    return wav * (10 ** (dBFS_change / 20))
diff --git a/spaces/kvviingu/stabilityai-stable-diffusion-xl-base-1.0/README.md b/spaces/kvviingu/stabilityai-stable-diffusion-xl-base-1.0/README.md
deleted file mode 100644
index 3c152ded0af5d64ace667224a3c5f62d92ba0e7e..0000000000000000000000000000000000000000
--- a/spaces/kvviingu/stabilityai-stable-diffusion-xl-base-1.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stabilityai Stable Diffusion Xl Base 1.0
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-928645ac.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-928645ac.css
deleted file mode 100644
index 4329ebb21b609937b3a2fdd0c3a1ef2edf96b04c..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-928645ac.css
+++ /dev/null
@@ -1 +0,0 @@
-.container.svelte-19on2m6.svelte-19on2m6{display:flex;flex-direction:column;gap:var(--spacing-sm);padding:var(--block-padding)}.hl.svelte-19on2m6+.hl.svelte-19on2m6{margin-left:var(--size-1)}.textspan.svelte-19on2m6:last-child>.label.svelte-19on2m6{margin-right:0}.category-legend.svelte-19on2m6.svelte-19on2m6{display:flex;flex-wrap:wrap;gap:var(--spacing-sm);color:#000}.category-label.svelte-19on2m6.svelte-19on2m6{cursor:pointer;border-radius:var(--radius-xs);padding-right:var(--size-2);padding-left:var(--size-2);font-weight:var(--weight-semibold)}.color-legend.svelte-19on2m6.svelte-19on2m6{display:flex;justify-content:space-between;border-radius:var(--radius-xs);background:linear-gradient(to right,var(--color-purple),rgba(255,255,255,0),var(--color-red));padding:var(--size-1) var(--size-2);font-weight:var(--weight-semibold)}.textfield.svelte-19on2m6.svelte-19on2m6{box-sizing:border-box;border-radius:var(--radius-xs);background:var(--background-fill-primary);background-color:transparent;max-width:var(--size-full);line-height:var(--scale-4);word-break:break-all}.textspan.svelte-19on2m6.svelte-19on2m6{transition:.15s;border-radius:var(--radius-xs);padding-top:2.5px;padding-right:var(--size-1);padding-bottom:3.5px;padding-left:var(--size-1);color:#000}.label.svelte-19on2m6.svelte-19on2m6{transition:.15s;margin-top:1px;margin-right:calc(var(--size-1) * -1);border-radius:var(--radius-xs);padding:1px 5px;color:var(--body-text-color);color:#fff;font-weight:var(--weight-bold);font-size:var(--text-sm);text-transform:uppercase}.text.svelte-19on2m6.svelte-19on2m6{color:#000}.score-text.svelte-19on2m6 .text.svelte-19on2m6{color:var(--body-text-color)}.score-text.svelte-19on2m6.svelte-19on2m6{margin-right:var(--size-1);padding:var(--size-1)}.no-cat.svelte-19on2m6.svelte-19on2m6,.no-label.svelte-19on2m6.svelte-19on2m6{color:var(--body-text-color)}.selectable.svelte-19on2m6.svelte-19on2m6{cursor:pointer}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Model3D-6764e7f5.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Model3D-6764e7f5.js
deleted file mode 100644
index 9401dd97d83c14992175e6ae2f23f6368d1a4514..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Model3D-6764e7f5.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as o,i as d,s as u,G as _,I as g,C as r,M as i,g as v,E as y,K as m,F as c,q as b}from"./index-8c3da1d9.js";function M(a){let e,s;return{c(){e=_("div"),s=g(a[0]),r(e,"class","svelte-1ayixqk"),i(e,"table",a[1]==="table"),i(e,"gallery",a[1]==="gallery"),i(e,"selected",a[2])},m(t,l){v(t,e,l),y(e,s)},p(t,[l]){l&1&&m(s,t[0]),l&2&&i(e,"table",t[1]==="table"),l&2&&i(e,"gallery",t[1]==="gallery"),l&4&&i(e,"selected",t[2])},i:c,o:c,d(t){t&&b(e)}}}function q(a,e,s){let{value:t}=e,{type:l}=e,{selected:f=!1}=e;return a.$$set=n=>{"value"in n&&s(0,t=n.value),"type"in n&&s(1,l=n.type),"selected"in n&&s(2,f=n.selected)},[t,l,f]}class D extends o{constructor(e){super(),d(this,e,q,M,u,{value:0,type:1,selected:2})}}const h=D;export{h as E};
-//# sourceMappingURL=Model3D-6764e7f5.js.map
diff --git a/spaces/langvision/llama-2-70b-chat/README.md b/spaces/langvision/llama-2-70b-chat/README.md
deleted file mode 100644
index 21cd7b6f7f2c8a38a24c7ffdeba6d965fbba96ad..0000000000000000000000000000000000000000
--- a/spaces/langvision/llama-2-70b-chat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Llama2-70B
-emoji: 🦙
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/lc202301/ChuanhuChatGPT/assets/Kelpy-Codos.js b/spaces/lc202301/ChuanhuChatGPT/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/lc202301/ChuanhuChatGPT/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name         Kelpy Codos
-// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
-// @version      1.0.5
-// @author       Keldos; https://keldos.me/
-// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. 
-//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license      GPL-3.0
-// @grant        none
-// ==/UserScript==
-
-(function () {
-    'use strict';
-
-    function addCopyButton(pre) {
-        var code = pre.querySelector('code');
-        if (!code) {
-            return; // 如果没有找到 <code> 元素，则不添加按钮
-        }
-        var firstChild = code.firstChild;
-        if (!firstChild) {
-            return; // 如果 <code> 元素没有子节点，则不添加按钮
-        }
-        var button = document.createElement('button');
-        button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
-        button.style.position = 'relative';
-        button.style.float = 'right';
-        button.style.fontSize = '1em'; // 可选：调整按钮大小
-        button.style.background = 'none'; // 可选：去掉背景颜色
-        button.style.border = 'none'; // 可选：去掉边框
-        button.style.cursor = 'pointer'; // 可选：显示指针样式
-        button.addEventListener('click', function () {
-            var range = document.createRange();
-            range.selectNodeContents(code);
-            range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
-            var selection = window.getSelection();
-            selection.removeAllRanges();
-            selection.addRange(range);
-
-            try {
-                var success = document.execCommand('copy');
-                if (success) {
-                    button.textContent = '\u2714';
-                    setTimeout(function () {
-                        button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
-                    }, 2000);
-                } else {
-                    button.textContent = '\u2716';
-                }
-            } catch (e) {
-                console.error(e);
-                button.textContent = '\u2716';
-            }
-
-            selection.removeAllRanges();
-        });
-        code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
-    }
-
-    function handleNewElements(mutationsList, observer) {
-        for (var mutation of mutationsList) {
-            if (mutation.type === 'childList') {
-                for (var node of mutation.addedNodes) {
-                    if (node.nodeName === 'PRE') {
-                        addCopyButton(node);
-                    }
-                }
-            }
-        }
-    }
-
-    var observer = new MutationObserver(handleNewElements);
-    observer.observe(document.documentElement, { childList: true, subtree: true });
-
-    document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Awek Melayu Bogel Sal Kena Paksa Xxxix.3gp.rar [CRACKED].md b/spaces/lincquiQcaudo/Top-20-Diffusion/Awek Melayu Bogel Sal Kena Paksa Xxxix.3gp.rar [CRACKED].md
deleted file mode 100644
index 77a5bc5073f5bb78560f8e2a0bc29ff7c22b68e9..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Awek Melayu Bogel Sal Kena Paksa Xxxix.3gp.rar [CRACKED].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>awek melayu bogel sal kena paksa xxxix.3gp.rar</h2><br /><p><b><b>DOWNLOAD</b> &#9734; <a href="https://bytlly.com/2uGxCP">https://bytlly.com/2uGxCP</a></b></p><br /><br />
-
- 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Conflict Global Terror Game Free Download Full Version.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Conflict Global Terror Game Free Download Full Version.md
deleted file mode 100644
index a3590d7e0b9a1002f3d87e7bab8d1c08f9bbbde1..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Conflict Global Terror Game Free Download Full Version.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/lingbionlp/PhenoTagger_v1.2_Demo/src/OntoTagger_tagging.py b/spaces/lingbionlp/PhenoTagger_v1.2_Demo/src/OntoTagger_tagging.py
deleted file mode 100644
index 73efd527d4e45c4d09781b8748179d67605f70b0..0000000000000000000000000000000000000000
--- a/spaces/lingbionlp/PhenoTagger_v1.2_Demo/src/OntoTagger_tagging.py
+++ /dev/null
@@ -1,215 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Aug 13 09:20:38 2020
-
-@author: luol2
-"""
-
-
-import argparse
-from nn_model import bioTag_CNN,bioTag_BERT
-from dic_ner import dic_ont
-from tagging_text import bioTag
-import os
-import time
-import json
-import re
-import bioc
-
-
-
-def PubTator_Converter(infile,outfile,biotag_dic,nn_model,para_set):
-
-    with open(infile, 'r',encoding='utf-8') as fin:
-        with open(outfile,'w', encoding='utf8') as fout:
-            title=''
-            abstract=''
-            for line in fin:
-                line = line.rstrip()
-                p_title = re.compile('^([0-9]+)\|t\|(.*)$')
-                p_abstract = re.compile('^([0-9]+)\|a\|(.*)$')
-                if p_title.search(line):  # title
-                    m = p_title.match(line)
-                    pmid = m.group(1)
-                    title = m.group(2)
-                    fout.write(pmid+"|t|"+title+"\n")
-                elif p_abstract.search(line):  # abstract
-                    m = p_abstract.match(line)
-                    pmid = m.group(1)
-                    abstract = m.group(2)
-                    fout.write(pmid+"|a|"+abstract+"\n")
-                else:  # annotation
-                    intext=title+' '+abstract
-                    #print('..........',pmid)
-                    #print(intext)
-                    tag_result=bioTag(intext,biotag_dic,nn_model,onlyLongest=para_set['onlyLongest'], abbrRecog=para_set['abbrRecog'],Threshold=para_set['ML_Threshold'])
-                    for ele in tag_result:
-                        start = ele[0]
-                        last = ele[1]
-                        mention = intext[int(ele[0]):int(ele[1])]
-                        ont_id=ele[2]
-                        score=ele[3]
-                        segs=ont_id.split(';')
-                        term_name=''
-                        for seg in segs:
-                            term_name+=biotag_dic.id_word[seg][0]+';'
-                        fout.write(pmid+"\t"+start+"\t"+last+"\t"+mention+"\t"+ont_id+'\t'+term_name[:-1]+"\t"+score+"\n")
-                    fout.write('\n')
-                    title=''
-                    abstract=''
-
-def BioC_Converter(infile,outfile,biotag_dic,nn_model,para_set):
-
-    with open(infile, 'r',encoding='utf-8') as fin:
-        with open(outfile,'w', encoding='utf8') as fout:
-            collection = bioc.load(fin)
-            for document in collection.documents:
-                mention_num=0
-                for passage in document.passages:
-                    passage_offset=passage.offset
-                    tag_result=bioTag(passage.text,biotag_dic,nn_model,onlyLongest=para_set['onlyLongest'], abbrRecog=para_set['abbrRecog'],Threshold=para_set['ML_Threshold']) 
-                    for ele in tag_result:
-                        bioc_note = bioc.BioCAnnotation()
-                        bioc_note.id = str(mention_num)
-                        mention_num+=1
-                        bioc_note.infons['identifier'] = ele[2]
-                        bioc_note.infons['type'] = "Phenotype"
-                        bioc_note.infons['score'] = ele[3]
-                        start = int(ele[0])
-                        last = int(ele[1])
-                        loc = bioc.BioCLocation(offset=str(passage_offset+start), length= str(last-start))
-                        bioc_note.locations.append(loc)
-                        bioc_note.text = passage.text[start:last]
-                        passage.annotations.append(bioc_note)
-            bioc.dump(collection, fout, pretty_print=True)
-
-def phenotagger_tag(infolder,para_set,outfolder):
-    
-    if para_set['ontology']=='uberon':
-        ontfiles={'dic_file':'../dict_uberon/noabb_lemma.dic',
-                  'word_id_file':'../dict_uberon/word_id_map.json',
-                  'id_word_file':'../dict_uberon/id_word_map.json'}
-        
-        if para_set['model_type']=='cnn':
-            vocabfiles={'w2vfile':'../models_v1.2/bio_embedding_intrinsic.d200',   
-                        'charfile':'../dict_uberon/char.vocab',
-                        'labelfile':'../dict_uberon/lable.vocab',
-                        'posfile':'../dict_uberon/pos.vocab'}
-            modelfile='../models_v1.1/cnn_hpo_v1.1.h5'
-            
-        elif para_set['model_type']=='bioformer':
-            vocabfiles={'labelfile':'../dict_uberon/lable.vocab',
-                        'checkpoint_path':'../models_v1.2/bioformer-cased-v1.0/',
-                        'lowercase':False}
-            modelfile='../models/bioformer-CLS-p5n5-5e6b64-e100.h5'
-            
-        elif para_set['model_type']=='pubmedbert':
-            vocabfiles={'labelfile':'../dict_uberon/lable.vocab',
-                        'checkpoint_path':'../models_v1.2/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/',
-                        'lowercase':True}
-            modelfile='../models/pubmedbert_full-CLS-p5n5-5e6b64.h5'
-            
-        else: #biobert
-            vocabfiles={'labelfile':'../dict_uberon/lable.vocab',
-                        'checkpoint_path':'../models_v1.2/biobert-base-cased-v1.2/',
-                        'lowercase':False}
-            modelfile='../models/biobertv12-CLS-p5n5-5e6b64.h5'
-    
-    elif para_set['ontology']=='hpo':
-        
-        ontfiles={'dic_file':'../dict_hpo/noabb_lemma.dic',
-                  'word_id_file':'../dict_hpo/word_id_map.json',
-                  'id_word_file':'../dict_hpo/id_word_map.json'}
-        
-        if para_set['model_type']=='cnn':
-            vocabfiles={'w2vfile':'../models_v1.2/bio_embedding_intrinsic.d200',   
-                        'charfile':'../dict_hpo/char.vocab',
-                        'labelfile':'../dict_hpo/lable.vocab',
-                        'posfile':'../dict_hpo/pos.vocab'}
-            modelfile='../models_v1.1/cnn_hpo_v1.1.h5'
-            
-        elif para_set['model_type']=='bioformer':
-            vocabfiles={'labelfile':'../dict_hpo/lable.vocab',
-                        'checkpoint_path':'../models_v1.2/bioformer-cased-v1.0/',
-                        'lowercase':False}
-            modelfile='../models-hpo/bioformer-CLS-p5n5-5e6b64-e100.h5'
-            
-        elif para_set['model_type']=='pubmedbert':
-            vocabfiles={'labelfile':'../dict_hpo/lable.vocab',
-                        'checkpoint_path':'../models_v1.2/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/',
-                        'lowercase':True}
-            modelfile='../models-hpo/pubmedbert_full-CLS-p5n5-5e6b64-e70.h5'
-            
-        else:
-            vocabfiles={'labelfile':'../dict/lable.vocab',
-                        'checkpoint_path':'../models_v1.2/biobert-base-cased-v1.2/',
-                        'lowercase':False}
-            modelfile='../models/biobertv12-CLS-p5n5-5e6b64.h5'
-    
-    # loading dict and model
-        
-    biotag_dic=dic_ont(ontfiles)    
-
-    if para_set['model_type']=='cnn':
-        nn_model=bioTag_CNN(vocabfiles)
-        nn_model.load_model(modelfile)
-    else:
-        nn_model=bioTag_BERT(vocabfiles)
-        nn_model.load_model(modelfile)
-
-    #tagging text
-    print("begin tagging........")
-    start_time=time.time()
-    
-    i=0
-    N=0
-    for filename in os.listdir(infolder):
-        N+=1
-    for filename in os.listdir(infolder):
-        print("Processing:{0}%".format(round(i * 100 / N)), end="\r")
-        i+=1
-                
-        with open(infolder+filename, 'r',encoding='utf-8') as fin:
-            format=""
-            for line in fin:
-                pattern_bioc = re.compile('.*<collection>.*')
-                pattern_pubtator = re.compile('^([^\|]+)\|[^\|]+\|(.*)')
-                if pattern_pubtator.search(line):
-                    format="PubTator"
-                    break
-                elif pattern_bioc.search(line):
-                    format="BioC"
-                    break
-            if(format == "PubTator"):
-                PubTator_Converter(infolder+filename,outfolder+filename,biotag_dic,nn_model,para_set)
-            elif(format == "BioC"):
-                BioC_Converter(infolder+filename,outfolder+filename,biotag_dic,nn_model,para_set)    
-
-    
-    print('tag done:',time.time()-start_time)
-
-
-
-
-if __name__=="__main__":
-    
-    parser = argparse.ArgumentParser(description='build weak training corpus, python build_dict.py -i infile -o outpath')
-    parser.add_argument('--infolder', '-i', help="input folder path",default='../example/input/')
-    parser.add_argument('--outfolder', '-o', help="output folder path",default='../example/output/')
-   
-    args = parser.parse_args()
-    
-    if not os.path.exists(args.outfolder):
-        os.makedirs(args.outfolder)
-
-    para_set={
-              'ontology':'hpo', #ontology:uberon, hpo
-              'model_type':'pubmedbert', # cnn, bioformer, pubmedbert or biobert
-              'onlyLongest':False, # False: return overlap concepts, True only longgest
-              'abbrRecog':True,# False: don't identify abbr, True: identify abbr
-              'ML_Threshold':0.95,# the Threshold of deep learning model
-              }
-    
-    
-    phenotagger_tag(args.infolder,para_set,args.outfolder)
-    print('The results are in ',args.outfolder)
diff --git a/spaces/lj1995/vocal2guitar/uvr5_pack/lib_v5/nets_33966KB.py b/spaces/lj1995/vocal2guitar/uvr5_pack/lib_v5/nets_33966KB.py
deleted file mode 100644
index d2bddb14716202fee3f3b0b461ff335741d04ba8..0000000000000000000000000000000000000000
--- a/spaces/lj1995/vocal2guitar/uvr5_pack/lib_v5/nets_33966KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from uvr5_pack.lib_v5 import layers_33966KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 16)
-        self.stg1_high_band_net = BaseASPPNet(2, 16)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(8, 16)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/ljjggr/bingo/src/lib/bots/bing/index.ts b/spaces/ljjggr/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index 6fd51ba48cbb1148f13d29e76960c092b807cfae..0000000000000000000000000000000000000000
--- a/spaces/ljjggr/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,426 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'ActionRequest',
-            'Chat',
-            'Context',
-            'InternalSearchQuery',
-            'InternalSearchResult',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'Progress',
-            'RenderCardRequest',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('你的 VPS 或代理可能被封禁，如有疑问，请前往 https://github.com/weaigc/bingo 咨询', ErrorCode.UNKOWN_ERROR)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async useWs(params: Params) {
-    const wsp = await this.sendWs()
-    const watchDog = new WatchDog()
-    wsp.onUnpackedMessage.addListener((events) => {
-      watchDog.watch(() => {
-        wsp.sendPacked({ type: 6 })
-      })
-      this.parseEvents(params, events)
-    })
-
-    wsp.onClose.addListener(() => {
-      watchDog.reset()
-      params.onEvent({ type: 'DONE' })
-      wsp.removeAllListeners()
-    })
-
-    params.signal?.addEventListener('abort', () => {
-      wsp.removeAllListeners()
-      wsp.close()
-    })
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}
diff --git a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/examples/py_example_global_mask.py b/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/examples/py_example_global_mask.py
deleted file mode 100644
index 9025cc4230edcce7cbfa545f55600a240994edcf..0000000000000000000000000000000000000000
--- a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/examples/py_example_global_mask.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File   : test.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 01/09/2020
-#
-# Distributed under terms of the MIT license.
-
-import numpy as np
-from PIL import Image
-
-import sys
-sys.path.insert(0, '../')
-import patch_match
-
-
-if __name__ == '__main__':
-    patch_match.set_verbose(True)
-    source = Image.open('./images/forest_pruned.bmp')
-    source = np.array(source)
-    source[:100, :100] = 255
-    global_mask = np.zeros_like(source[..., 0])
-    global_mask[:100, :100] = 1
-    result = patch_match.inpaint(source, global_mask=global_mask, patch_size=3)
-    Image.fromarray(result).save('./images/forest_recovered.bmp')
-
diff --git a/spaces/lora-library/LoRA-DreamBooth-Training-UI/uploader.py b/spaces/lora-library/LoRA-DreamBooth-Training-UI/uploader.py
deleted file mode 100644
index 0ce697f0d47325a4d73f92c13304ae5f51df794a..0000000000000000000000000000000000000000
--- a/spaces/lora-library/LoRA-DreamBooth-Training-UI/uploader.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from __future__ import annotations
-
-from huggingface_hub import HfApi
-
-
-class Uploader:
-    def __init__(self, hf_token: str | None):
-        self.api = HfApi(token=hf_token)
-
-    def get_username(self) -> str:
-        return self.api.whoami()['name']
-
-    def upload(self,
-               folder_path: str,
-               repo_name: str,
-               organization: str = '',
-               repo_type: str = 'model',
-               private: bool = True,
-               delete_existing_repo: bool = False) -> str:
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            raise ValueError
-        if not organization:
-            organization = self.get_username()
-        repo_id = f'{organization}/{repo_name}'
-        if delete_existing_repo:
-            try:
-                self.api.delete_repo(repo_id, repo_type=repo_type)
-            except Exception:
-                pass
-        try:
-            self.api.create_repo(repo_id, repo_type=repo_type, private=private)
-            self.api.upload_folder(repo_id=repo_id,
-                                   folder_path=folder_path,
-                                   path_in_repo='.',
-                                   repo_type=repo_type)
-            url = f'https://huggingface.co/{repo_id}'
-            message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
-        except Exception as e:
-            message = str(e)
-        return message
diff --git a/spaces/ltgoslo/ssa-perin/model/head/labeled_edge_head.py b/spaces/ltgoslo/ssa-perin/model/head/labeled_edge_head.py
deleted file mode 100644
index 6147d24ce6abedba09d2927d401f17ac284ffbe0..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/model/head/labeled_edge_head.py
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/usr/bin/env python3
-# coding=utf-8
-
-import torch
-import torch.nn as nn
-
-from model.head.abstract_head import AbstractHead
-from data.parser.to_mrp.labeled_edge_parser import LabeledEdgeParser
-from utility.cross_entropy import binary_cross_entropy
-from utility.hungarian_matching import match_label
-
-
-class LabeledEdgeHead(AbstractHead):
-    def __init__(self, dataset, args, initialize):
-        config = {
-            "label": True,
-            "edge presence": True,
-            "edge label": True,
-            "anchor": True,
-            "source_anchor": False,
-            "target_anchor": False
-        }
-        super(LabeledEdgeHead, self).__init__(dataset, args, config, initialize)
-
-        self.top_node = nn.Parameter(torch.randn(1, 1, args.hidden_size), requires_grad=True)
-        self.parser = LabeledEdgeParser(dataset)
-
-    def init_label_classifier(self, dataset, args, config, initialize: bool):
-        classifier = nn.Sequential(
-            nn.Dropout(args.dropout_label),
-            nn.Linear(args.hidden_size, 1, bias=True)
-        )
-        if initialize:
-            bias_init = torch.tensor([dataset.label_freqs[1]])
-            classifier[1].bias.data = (bias_init / (1.0 - bias_init)).log()
-
-        return classifier
-
-    def forward_label(self, decoder_output):
-        return self.label_classifier(decoder_output)
-
-    def forward_edge(self, decoder_output):
-        top_node = self.top_node.expand(decoder_output.size(0), -1, -1)
-        decoder_output = torch.cat([top_node, decoder_output], dim=1)
-        return self.edge_classifier(decoder_output)
-
-    def loss_label(self, prediction, target, mask, matching):
-        prediction = prediction["label"]
-        target = match_label(
-            target["labels"][0], matching, prediction.shape[:-1], prediction.device, self.query_length
-        )
-        return {"label": binary_cross_entropy(prediction.squeeze(-1), target.float(), mask, focal=self.focal)}
-
-    def inference_label(self, prediction):
-        return (prediction.squeeze(-1) > 0.0).long()
-
-    def label_cost_matrix(self, output, batch, decoder_lens, b: int):
-        if output["label"] is None:
-            return 1.0
-
-        target_labels = batch["anchored_labels"][b]  # shape: (num_nodes, num_inputs, 2)
-        label_prob = output["label"][b, : decoder_lens[b], :].sigmoid().unsqueeze(0)  # shape: (1, num_queries, 1)
-        label_prob = torch.cat([1.0 - label_prob, label_prob], dim=-1)  # shape: (1, num_queries, 2)
-        tgt_label = target_labels.repeat_interleave(self.query_length, dim=1)  # shape: (num_nodes, num_queries, 2)
-        cost_matrix = ((tgt_label * label_prob).sum(-1) * label_prob[:, :, 1:].sum(-1)).t().sqrt()  # shape: (num_queries, num_nodes)
-
-        return cost_matrix
diff --git a/spaces/luxuedong/lxd/src/components/chat-panel.tsx b/spaces/luxuedong/lxd/src/components/chat-panel.tsx
deleted file mode 100644
index 56b2112bd75ba08134383871177851fa2e3f43a4..0000000000000000000000000000000000000000
--- a/spaces/luxuedong/lxd/src/components/chat-panel.tsx
+++ /dev/null
@@ -1,153 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import Image from 'next/image'
-import Textarea from 'react-textarea-autosize'
-import { useAtomValue } from 'jotai'
-import { useEnterSubmit } from '@/lib/hooks/use-enter-submit'
-import { cn } from '@/lib/utils'
-
-import BrushIcon from '@/assets/images/brush.svg'
-import ChatIcon from '@/assets/images/chat.svg'
-import VisualSearchIcon from '@/assets/images/visual-search.svg'
-import SendIcon from '@/assets/images/send.svg'
-import PinIcon from '@/assets/images/pin.svg'
-import PinFillIcon from '@/assets/images/pin-fill.svg'
-
-import { useBing } from '@/lib/hooks/use-bing'
-import { voiceListenAtom } from '@/state'
-import Voice from './voice'
-import { ChatImage } from './chat-image'
-import { ChatAttachments } from './chat-attachments'
-
-export interface ChatPanelProps
-  extends Pick<
-    ReturnType<typeof useBing>,
-    | 'generating'
-    | 'input'
-    | 'setInput'
-    | 'sendMessage'
-    | 'resetConversation'
-    | 'isSpeaking'
-    | 'attachmentList'
-    | 'uploadImage'
-    | 'setAttachmentList'
-  > {
-  id?: string
-  className?: string
-}
-
-export function ChatPanel({
-  isSpeaking,
-  generating,
-  input,
-  setInput,
-  className,
-  sendMessage,
-  resetConversation,
-  attachmentList,
-  uploadImage,
-  setAttachmentList
-}: ChatPanelProps) {
-  const inputRef = React.useRef<HTMLTextAreaElement>(null)
-  const {formRef, onKeyDown} = useEnterSubmit()
-  const [focused, setFocused] = React.useState(false)
-  const [active, setActive] = React.useState(false)
-  const [pin, setPin] = React.useState(false)
-  const [tid, setTid] = React.useState<any>()
-  const voiceListening = useAtomValue(voiceListenAtom)
-
-  const setBlur = React.useCallback(() => {
-    clearTimeout(tid)
-    setActive(false)
-    const _tid = setTimeout(() => setFocused(false), 2000);
-    setTid(_tid)
-  }, [tid])
-
-  const setFocus = React.useCallback(() => {
-    setFocused(true)
-    setActive(true)
-    clearTimeout(tid)
-    inputRef.current?.focus()
-  }, [tid])
-
-  React.useEffect(() => {
-    if (input) {
-      setFocus()
-    }
-  }, [input, setFocus])
-
-  return (
-    <form
-      className={cn('chat-panel', className)}
-      onSubmit={async e => {
-        e.preventDefault()
-        if (generating) {
-          return;
-        }
-        if (!input?.trim()) {
-          return
-        }
-        setInput('')
-        setPin(false)
-        await sendMessage(input)
-      }}
-      ref={formRef}
-    >
-      <div className="action-bar pb-4">
-        <div className={cn('action-root', { focus: active || pin })} speech-state="hidden" visual-search="" drop-target="">
-          <div className="fade bottom">
-            <div className="background"></div>
-          </div>
-          <div className={cn('outside-left-container', { collapsed: focused })}>
-            <div className="button-compose-wrapper">
-              <button className="body-2 button-compose" type="button" aria-label="新主题" onClick={resetConversation}>
-                <div className="button-compose-content">
-                  <Image className="pl-2" alt="brush" src={BrushIcon} width={40} />
-                  <div className="button-compose-text">新主题</div>
-                </div>
-              </button>
-            </div>
-          </div>
-          <div
-            className={cn('main-container', { active: active || pin })}
-            style={{ minHeight: pin ? '360px' : undefined }}
-            onClick={setFocus}
-            onBlur={setBlur}
-          >
-            <div className="main-bar">
-              <Image alt="chat" src={ChatIcon} width={20} color="blue" />
-              <Textarea
-                ref={inputRef}
-                tabIndex={0}
-                onKeyDown={onKeyDown}
-                rows={1}
-                value={input}
-                onChange={e => setInput(e.target.value.slice(0, 4000))}
-                placeholder={voiceListening ? '持续对话中...对话完成说“发送”即可' : 'Shift + Enter 换行'}
-                spellCheck={false}
-                className="message-input min-h-[24px] -mx-1 w-full text-base resize-none bg-transparent focus-within:outline-none"
-              />
-              <ChatImage uploadImage={uploadImage}>
-                <Image alt="visual-search" src={VisualSearchIcon} width={24} />
-              </ChatImage>
-              <Voice setInput={setInput} sendMessage={sendMessage} isSpeaking={isSpeaking} input={input} />
-              <button type="submit">
-                <Image alt="send" src={SendIcon} width={20} style={{ marginTop: '2px' }} />
-              </button>
-            </div>
-            <ChatAttachments attachmentList={attachmentList} setAttachmentList={setAttachmentList} uploadImage={uploadImage} />
-            <div className="body-1 bottom-bar">
-              <div className="letter-counter"><span>{input.length}</span>/4000</div>
-              <button onClick={() => {
-                setPin(!pin)
-              }} className="pr-2">
-               <Image alt="pin" src={pin ? PinFillIcon : PinIcon} width={20} />
-              </button>
-            </div>
-          </div>
-        </div>
-      </div>
-    </form>
-  )
-}
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/host_system_tag.h b/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/host_system_tag.h
deleted file mode 100644
index a487e6ac5f21614233d518c95f240d2191deeab6..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/host_system_tag.h
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// #include the host system's execution_policy header
-#define __THRUST_HOST_SYSTEM_TAG_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/execution_policy.h>
-#include __THRUST_HOST_SYSTEM_TAG_HEADER
-#undef __THRUST_HOST_SYSTEM_TAG_HEADER
-
-namespace thrust
-{
-
-typedef thrust::system::__THRUST_HOST_SYSTEM_NAMESPACE::tag host_system_tag;
-
-} // end thrust
-
-// TODO remove this in 1.8.0
-namespace thrust
-{
-
-typedef THRUST_DEPRECATED host_system_tag host_space_tag;
-
-} // end thrust
-
diff --git a/spaces/macaodha/batdetect2/bat_detect/finetune/prep_data_finetune.py b/spaces/macaodha/batdetect2/bat_detect/finetune/prep_data_finetune.py
deleted file mode 100644
index 3e86cd4fd1da922080b1f5c473471e91cdbf1c8e..0000000000000000000000000000000000000000
--- a/spaces/macaodha/batdetect2/bat_detect/finetune/prep_data_finetune.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import numpy as np
-import argparse
-import os
-import json
-
-import sys
-sys.path.append(os.path.join('..', '..'))
-import bat_detect.train.train_utils as tu
-
-
-def print_dataset_stats(data, split_name, classes_to_ignore):
-
-    print('\nSplit:', split_name)
-    print('Num files:', len(data))
-
-    class_cnts = {}
-    for dd in data:
-        for aa in dd['annotation']:
-            if aa['class'] not in classes_to_ignore:
-                if aa['class'] in class_cnts:
-                    class_cnts[aa['class']] += 1
-                else:
-                    class_cnts[aa['class']] = 1
-
-    if len(class_cnts) == 0:
-        class_names = []
-    else:
-        class_names = np.sort([*class_cnts]).tolist()
-        print('Class count:')
-        str_len = np.max([len(cc) for cc in class_names]) + 5
-
-        for ii, cc in enumerate(class_names):
-            print(str(ii).ljust(5) + cc.ljust(str_len) + str(class_cnts[cc]))
-
-    return class_names
-
-
-def load_file_names(file_name):
-
-    if os.path.isfile(file_name):
-        with open(file_name) as da:
-            files = [line.rstrip() for line in da.readlines()]
-        for ff in files:
-            if ff.lower()[-3:] != 'wav':
-                print('Error: Filenames need to end in .wav - ', ff)
-                assert(False)
-    else:
-        print('Error: Input file not found - ', file_name)
-        assert(False)
-
-    return files
-
-
-if __name__ == "__main__":
-
-    info_str = '\nBatDetect - Prepare Data for Finetuning\n'
-
-    print(info_str)
-    parser = argparse.ArgumentParser()
-    parser.add_argument('dataset_name', type=str, help='Name to call your dataset')
-    parser.add_argument('audio_dir', type=str, help='Input directory for audio')
-    parser.add_argument('ann_dir', type=str, help='Input directory for where the audio annotations are stored')
-    parser.add_argument('op_dir', type=str, help='Path where the train and test splits will be stored')
-    parser.add_argument('--percent_val', type=float, default=0.20,
-                         help='Hold out this much data for validation. Should be number between 0 and 1')
-    parser.add_argument('--rand_seed', type=int, default=2001,
-                         help='Random seed used for creating the validation split')
-    parser.add_argument('--train_file', type=str, default='',
-                         help='Text file where each line is a wav file in train split')
-    parser.add_argument('--test_file', type=str, default='',
-                         help='Text file where each line is a wav file in test split')
-    parser.add_argument('--input_class_names', type=str, default='',
-                         help='Specify names of classes that you want to change. Separate with ";"')
-    parser.add_argument('--output_class_names', type=str, default='',
-                         help='New class names to use instead. One to one mapping with "--input_class_names". \
-                         Separate with ";"')
-    args = vars(parser.parse_args())
-
-
-    np.random.seed(args['rand_seed'])
-
-    classes_to_ignore  = ['', ' ', 'Unknown', 'Not Bat']
-    generic_class      = ['Bat']
-    events_of_interest = ['Echolocation']
-
-    if args['input_class_names'] != '' and args['output_class_names'] != '':
-        # change the names of the classes
-        ip_names = args['input_class_names'].split(';')
-        op_names = args['output_class_names'].split(';')
-        name_dict = dict(zip(ip_names, op_names))
-    else:
-        name_dict = False
-
-    # load annotations
-    data_all, _, _ = tu.load_set_of_anns({'ann_path': args['ann_dir'], 'wav_path': args['audio_dir']},
-                                          classes_to_ignore, events_of_interest, False, False,
-                                          list_of_anns=True, filter_issues=True, name_replace=name_dict)
-
-    print('Dataset name:         ' + args['dataset_name'])
-    print('Audio directory:      ' + args['audio_dir'])
-    print('Annotation directory: ' + args['ann_dir'])
-    print('Ouput directory:      ' + args['op_dir'])
-    print('Num annotated files:  ' + str(len(data_all)))
-
-    if args['train_file'] != '' and args['test_file'] != '':
-        # user has specifed the train / test split
-        train_files = load_file_names(args['train_file'])
-        test_files = load_file_names(args['test_file'])
-        file_names_all = [dd['id'] for dd in data_all]
-        train_inds = [file_names_all.index(ff) for ff in train_files if ff in file_names_all]
-        test_inds = [file_names_all.index(ff) for ff in test_files if ff in file_names_all]
-
-    else:
-        # split the data into train and test at the file level
-        num_exs = len(data_all)
-        test_inds = np.random.choice(np.arange(num_exs), int(num_exs*args['percent_val']), replace=False)
-        test_inds = np.sort(test_inds)
-        train_inds = np.setdiff1d(np.arange(num_exs), test_inds)
-
-    data_train = [data_all[ii] for ii in train_inds]
-    data_test = [data_all[ii] for ii in test_inds]
-
-    if not os.path.isdir(args['op_dir']):
-        os.makedirs(args['op_dir'])
-    op_name = os.path.join(args['op_dir'], args['dataset_name'])
-    op_name_train = op_name + '_TRAIN.json'
-    op_name_test  = op_name + '_TEST.json'
-
-    class_un_train = print_dataset_stats(data_train, 'Train', classes_to_ignore)
-    class_un_test = print_dataset_stats(data_test, 'Test', classes_to_ignore)
-
-    if len(data_train) > 0 and len(data_test) > 0:
-        if class_un_train != class_un_test:
-            print('\nError: some classes are not in both the training and test sets.\
-                   \nTry a different random seed "--rand_seed".')
-            assert False
-
-    print('\n')
-    if len(data_train) == 0:
-        print('No train annotations to save')
-    else:
-        print('Saving: ', op_name_train)
-        with open(op_name_train, 'w') as da:
-            json.dump(data_train, da, indent=2)
-
-    if len(data_test) == 0:
-        print('No test annotations to save')
-    else:
-        print('Saving: ', op_name_test)
-        with open(op_name_test, 'w') as da:
-            json.dump(data_test, da, indent=2)
diff --git a/spaces/markllego/openai-gpt4-vision/README.md b/spaces/markllego/openai-gpt4-vision/README.md
deleted file mode 100644
index ac0fa5dba40686b5b0dd25f68cf23ee17e7c56f0..0000000000000000000000000000000000000000
--- a/spaces/markllego/openai-gpt4-vision/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Openai Gpt4 Vision
-emoji: 🔥
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-# OpenAI GPT-4 With Vision
-
-This repository contains a simple image captioning app that utilizes OpenAI's GPT-4 with the Vision extension. Users can upload images through a Gradio interface, and the app leverages GPT-4 to generate a description of the image content.
-
-## How to Contribute
-
-Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
-
-1. Fork the Project
-2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
-3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the Branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
-
-## License
-
-Distributed under the MIT License. See `LICENSE` for more information.
-
-## Contact
-
-Mark Anthony Llego - [@markllego](https://twitter.com/markllego)
-
-Project Link: [https://github.com/llegomark/openai-gpt4-vision](https://github.com/llegomark/openai-gpt4-vision)
-
-HuggingFace Space: [https://huggingface.co/spaces/markllego/openai-gpt4-vision](https://huggingface.co/spaces/markllego/openai-gpt4-vision)
-
-Blog Post: [https://llego.dev/posts/openai-gpt-4-vision/](https://llego.dev/posts/openai-gpt-4-vision/)
-
-## Acknowledgements
-
-- [OpenAI](https://openai.com/)
-- [Gradio](https://gradio.app/)
-- [HuggingFace Spaces](https://huggingface.co/spaces)
\ No newline at end of file
diff --git a/spaces/marrocovin/OPENAI_KEY/src/proxy/openai.ts b/spaces/marrocovin/OPENAI_KEY/src/proxy/openai.ts
deleted file mode 100644
index 8e0d134802305ff21421d7d81ebd30266c06d0e6..0000000000000000000000000000000000000000
--- a/spaces/marrocovin/OPENAI_KEY/src/proxy/openai.ts
+++ /dev/null
@@ -1,58 +0,0 @@
-import { Request, Router } from "express";
-import * as http from "http";
-import { createProxyMiddleware, fixRequestBody } from "http-proxy-middleware";
-import { logger } from "../logger";
-import { Key, keys } from "../keys";
-import { handleResponse, onError } from "./common";
-
-/**
- * Modifies the request body to add a randomly selected API key.
- */
-const rewriteRequest = (proxyReq: http.ClientRequest, req: Request) => {
-  let key: Key;
-
-  try {
-    key = keys.get(req.body?.model || "gpt-3.5")!;
-  } catch (err) {
-    proxyReq.destroy(err as any);
-    return;
-  }
-
-  req.key = key;
-  proxyReq.setHeader("Authorization", `Bearer ${key.key}`);
-
-  if (req.method === "POST" && req.body) {
-    if (req.body?.stream) {
-      req.body.stream = false;
-      const updatedBody = JSON.stringify(req.body);
-      proxyReq.setHeader("Content-Length", Buffer.byteLength(updatedBody));
-      (req as any).rawBody = Buffer.from(updatedBody);
-    }
-
-    // body-parser and http-proxy-middleware don't play nice together
-    fixRequestBody(proxyReq, req);
-  }
-};
-
-const openaiProxy = createProxyMiddleware({
-  target: "https://api.openai.com",
-  changeOrigin: true,
-  on: {
-    proxyReq: rewriteRequest,
-    proxyRes: handleResponse,
-    error: onError,
-  },
-  selfHandleResponse: true,
-  logger,
-});
-
-const openaiRouter = Router();
-openaiRouter.post("/v1/chat/completions", openaiProxy);
-// openaiRouter.post("/v1/completions", openaiProxy); // TODO: Implement Davinci
-openaiRouter.get("/v1/models", openaiProxy);
-openaiRouter.use((req, res) => {
-  logger.warn(`Blocked openai proxy request: ${req.method} ${req.path}`);
-  res.status(404).json({ error: "Not found" });
-});
-
-export const openai = openaiRouter;
diff --git a/spaces/matthoffner/web-llm-embed/src/utils/db-client.js b/spaces/matthoffner/web-llm-embed/src/utils/db-client.js
deleted file mode 100644
index c5a5ee32d228cca4df54549d0dc81beccd7165c6..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/web-llm-embed/src/utils/db-client.js
+++ /dev/null
@@ -1,13 +0,0 @@
-// import { IDocMeta } from '@/types';
-import Dexie, { Table } from 'dexie';
-
-export class MyDocDb extends Dexie {
-  constructor() {
-    super('myDocDb');
-    this.version(1).stores({
-      docs: '++id, folderName, fileName, fileSourceData, model',
-    });
-  }
-}
-
-export const db = new MyDocDb();
diff --git a/spaces/mayhug/Real-CUGAN/README.md b/spaces/mayhug/Real-CUGAN/README.md
deleted file mode 100644
index 2bc752d974365a8a99dc0f57aa73a65c451cafad..0000000000000000000000000000000000000000
--- a/spaces/mayhug/Real-CUGAN/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
----
-title: Real Cascade U-Nets for Anime Image Super Resolution
-emoji: 👀
-colorFrom: blue
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: true
-license: mit
----
-
-> From <https://github.com/bilibili/ailab/tree/main/Real-CUGAN>
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/mearidesu/test2/Dockerfile b/spaces/mearidesu/test2/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/mearidesu/test2/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/menghanxia/disco/utils/util.py b/spaces/menghanxia/disco/utils/util.py
deleted file mode 100644
index bc372b21316cb0bb351ba9cdbda3c950a83cc1e7..0000000000000000000000000000000000000000
--- a/spaces/menghanxia/disco/utils/util.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from __future__ import division
-from __future__ import print_function
-import os, glob, shutil, math, json
-from queue import Queue
-from threading import Thread
-from skimage.segmentation import mark_boundaries
-import numpy as np
-from PIL import Image
-import cv2, torch
-
-def get_gauss_kernel(size, sigma):
-    '''Function to mimic the 'fspecial' gaussian MATLAB function'''
-    x, y = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]
-    g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))
-    return g/g.sum()
-
-
-def batchGray2Colormap(gray_batch):
-    colormap = plt.get_cmap('viridis')
-    heatmap_batch = []
-    for i in range(gray_batch.shape[0]):
-        # quantize [-1,1] to {0,1}
-        gray_map = gray_batch[i, :, :, 0]
-        heatmap = (colormap(gray_map) * 2**16).astype(np.uint16)[:,:,:3]
-        heatmap_batch.append(heatmap/127.5-1.0)
-    return np.array(heatmap_batch)
-
-
-class PlotterThread():
-    '''log tensorboard data in a background thread to save time'''
-    def __init__(self, writer):
-        self.writer = writer
-        self.task_queue = Queue(maxsize=0)
-        worker = Thread(target=self.do_work, args=(self.task_queue,))
-        worker.setDaemon(True)
-        worker.start()
-
-    def do_work(self, q):
-        while True:
-            content = q.get()
-            if content[-1] == 'image':
-                self.writer.add_image(*content[:-1])
-            elif content[-1] == 'scalar':
-                self.writer.add_scalar(*content[:-1])
-            else:
-                raise ValueError
-            q.task_done()
-
-    def add_data(self, name, value, step, data_type='scalar'):
-        self.task_queue.put([name, value, step, data_type])
-
-    def __len__(self):
-        return self.task_queue.qsize()
-
-
-def save_images_from_batch(img_batch, save_dir, filename_list, batch_no=-1, suffix=None):
-    N,H,W,C = img_batch.shape
-    if C == 3:
-        #! rgb color image
-        for i in range(N):
-            # [-1,1] >>> [0,255]
-            image = Image.fromarray((127.5*(img_batch[i,:,:,:]+1.)).astype(np.uint8))
-            save_name = filename_list[i] if batch_no==-1 else '%05d.png' % (batch_no*N+i)
-            save_name = save_name.replace('.png', '-%s.png'%suffix) if suffix else save_name
-            image.save(os.path.join(save_dir, save_name), 'PNG')
-    elif C == 1:
-        #! single-channel gray image
-        for i in range(N):
-            # [-1,1] >>> [0,255]
-            image = Image.fromarray((127.5*(img_batch[i,:,:,0]+1.)).astype(np.uint8))
-            save_name = filename_list[i] if batch_no==-1 else '%05d.png' % (batch_no*img_batch.shape[0]+i)
-            save_name = save_name.replace('.png', '-%s.png'%suffix) if suffix else save_name
-            image.save(os.path.join(save_dir, save_name), 'PNG')
-    else:
-        #! multi-channel: save each channel as a single image
-        for i in range(N):
-            # [-1,1] >>> [0,255]
-            for j in range(C):
-                image = Image.fromarray((127.5*(img_batch[i,:,:,j]+1.)).astype(np.uint8))
-                if batch_no == -1:
-                    _, file_name = os.path.split(filename_list[i])
-                    name_only, _ = os.path.os.path.splitext(file_name)
-                    save_name = name_only + '_c%d.png' % j
-                else:
-                    save_name = '%05d_c%d.png' % (batch_no*N+i, j)
-                save_name = save_name.replace('.png', '-%s.png'%suffix) if suffix else save_name
-                image.save(os.path.join(save_dir, save_name), 'PNG')
-    return None
-
-
-def save_normLabs_from_batch(img_batch, save_dir, filename_list, batch_no=-1, suffix=None):
-    N,H,W,C = img_batch.shape
-    if C != 3:
-        print('@Warning:the Lab images are NOT in 3 channels!')
-        return None
-    # denormalization: L: (L+1.0)*50.0 | a: a*110.0| b: b*110.0
-    img_batch[:,:,:,0] = img_batch[:,:,:,0] * 50.0 + 50.0
-    img_batch[:,:,:,1:3] = img_batch[:,:,:,1:3] * 110.0
-    #! convert into RGB color image
-    for i in range(N):
-        rgb_img = cv2.cvtColor(img_batch[i,:,:,:], cv2.COLOR_LAB2RGB)
-        image = Image.fromarray((rgb_img*255.0).astype(np.uint8))
-        save_name = filename_list[i] if batch_no==-1 else '%05d.png' % (batch_no*N+i)
-        save_name = save_name.replace('.png', '-%s.png'%suffix) if suffix else save_name
-        image.save(os.path.join(save_dir, save_name), 'PNG')
-    return None
-
-
-def save_markedSP_from_batch(img_batch, spix_batch, save_dir, filename_list, batch_no=-1, suffix=None):
-    N,H,W,C = img_batch.shape
-    #! img_batch: BGR nd-array (range:0~1)
-    #! map_batch: single-channel spixel map
-    #print('----------', img_batch.shape, spix_batch.shape)
-    for i in range(N):
-        norm_image = img_batch[i,:,:,:]*0.5+0.5
-        spixel_bd_image = mark_boundaries(norm_image, spix_batch[i,:,:,0].astype(int), color=(1,1,1))
-        #spixel_bd_image = cv2.cvtColor(spixel_bd_image, cv2.COLOR_BGR2RGB)
-        image = Image.fromarray((spixel_bd_image*255.0).astype(np.uint8))
-        save_name = filename_list[i] if batch_no==-1 else '%05d.png' % (batch_no*N+i)
-        save_name = save_name.replace('.png', '-%s.png'%suffix) if suffix else save_name
-        image.save(os.path.join(save_dir, save_name), 'PNG')
-    return None
-
-
-def get_filelist(data_dir):
-    file_list = glob.glob(os.path.join(data_dir, '*.*'))
-    file_list.sort()
-    return file_list
-    
-
-def collect_filenames(data_dir):
-    file_list = get_filelist(data_dir)
-    name_list = []
-    for file_path in file_list:
-        _, file_name = os.path.split(file_path)
-        name_list.append(file_name)
-    name_list.sort()
-    return name_list
-
-
-def exists_or_mkdir(path, need_remove=False):
-    if not os.path.exists(path):
-        os.makedirs(path)
-    elif need_remove:
-        shutil.rmtree(path)
-        os.makedirs(path)
-    return None
-
-
-def save_list(save_path, data_list, append_mode=False):
-    n = len(data_list)
-    if append_mode:
-        with open(save_path, 'a') as f:
-            f.writelines([str(data_list[i]) + '\n' for i in range(n-1,n)])
-    else:
-        with open(save_path, 'w') as f:
-            f.writelines([str(data_list[i]) + '\n' for i in range(n)])
-    return None
-    
-    
-def save_dict(save_path, dict):
-    json.dumps(dict, open(save_path,"w"))
-    return None
-
-
-if __name__ == '__main__':
-    data_dir = '../PolyNet/PolyNet/cache/'
-    #visualizeLossCurves(data_dir)
-    clbar = GamutIndex()
-    ab, ab_gamut_mask = clbar._get_gamut_mask()
-    ab2q = clbar._get_ab_to_q(ab_gamut_mask)
-    q2ab = clbar._get_q_to_ab(ab, ab_gamut_mask)
-    maps = ab_gamut_mask*255.0
-    image = Image.fromarray(maps.astype(np.uint8))
-    image.save('gamut.png', 'PNG')
-    print(ab2q.shape)
-    print(q2ab.shape)
-    print('label range:', np.min(ab2q), np.max(ab2q))
\ No newline at end of file
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/data_loading/qm9.py b/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/data_loading/qm9.py
deleted file mode 100644
index b45839868626f56d3a42ce859b2033ce1526373e..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/data_loading/qm9.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Permission is hereby granted, free of charge, to any person obtaining a
-# copy of this software and associated documentation files (the "Software"),
-# to deal in the Software without restriction, including without limitation
-# the rights to use, copy, modify, merge, publish, distribute, sublicense,
-# and/or sell copies of the Software, and to permit persons to whom the
-# Software is furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
-# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-# DEALINGS IN THE SOFTWARE.
-#
-# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
-# SPDX-License-Identifier: MIT
-from typing import Tuple
-
-import dgl
-import pathlib
-import torch
-from dgl.data import QM9EdgeDataset
-from dgl import DGLGraph
-from torch import Tensor
-from torch.utils.data import random_split, DataLoader, Dataset
-from tqdm import tqdm
-
-from se3_transformer.data_loading.data_module import DataModule
-from se3_transformer.model.basis import get_basis
-from se3_transformer.runtime.utils import get_local_rank, str2bool, using_tensor_cores
-
-
-def _get_relative_pos(qm9_graph: DGLGraph) -> Tensor:
-    x = qm9_graph.ndata['pos']
-    src, dst = qm9_graph.edges()
-    rel_pos = x[dst] - x[src]
-    return rel_pos
-
-
-def _get_split_sizes(full_dataset: Dataset) -> Tuple[int, int, int]:
-    len_full = len(full_dataset)
-    len_train = 100_000
-    len_test = int(0.1 * len_full)
-    len_val = len_full - len_train - len_test
-    return len_train, len_val, len_test
-
-
-class QM9DataModule(DataModule):
-    """
-    Datamodule wrapping https://docs.dgl.ai/en/latest/api/python/dgl.data.html#qm9edge-dataset
-    Training set is 100k molecules. Test set is 10% of the dataset. Validation set is the rest.
-    This includes all the molecules from QM9 except the ones that are uncharacterized.
-    """
-
-    NODE_FEATURE_DIM = 6
-    EDGE_FEATURE_DIM = 4
-
-    def __init__(self,
-                 data_dir: pathlib.Path,
-                 task: str = 'homo',
-                 batch_size: int = 240,
-                 num_workers: int = 8,
-                 num_degrees: int = 4,
-                 amp: bool = False,
-                 precompute_bases: bool = False,
-                 **kwargs):
-        self.data_dir = data_dir  # This needs to be before __init__ so that prepare_data has access to it
-        super().__init__(batch_size=batch_size, num_workers=num_workers, collate_fn=self._collate)
-        self.amp = amp
-        self.task = task
-        self.batch_size = batch_size
-        self.num_degrees = num_degrees
-
-        qm9_kwargs = dict(label_keys=[self.task], verbose=False, raw_dir=str(data_dir))
-        if precompute_bases:
-            bases_kwargs = dict(max_degree=num_degrees - 1, use_pad_trick=using_tensor_cores(amp), amp=amp)
-            full_dataset = CachedBasesQM9EdgeDataset(bases_kwargs=bases_kwargs, batch_size=batch_size, **qm9_kwargs)
-        else:
-            full_dataset = QM9EdgeDataset(**qm9_kwargs)
-
-        self.ds_train, self.ds_val, self.ds_test = random_split(full_dataset, _get_split_sizes(full_dataset),
-                                                                generator=torch.Generator().manual_seed(0))
-
-        train_targets = full_dataset.targets[self.ds_train.indices, full_dataset.label_keys[0]]
-        self.targets_mean = train_targets.mean()
-        self.targets_std = train_targets.std()
-
-    def prepare_data(self):
-        # Download the QM9 preprocessed data
-        QM9EdgeDataset(verbose=True, raw_dir=str(self.data_dir))
-
-    def _collate(self, samples):
-        graphs, y, *bases = map(list, zip(*samples))
-        batched_graph = dgl.batch(graphs)
-        edge_feats = {'0': batched_graph.edata['edge_attr'][..., None]}
-        batched_graph.edata['rel_pos'] = _get_relative_pos(batched_graph)
-        # get node features
-        node_feats = {'0': batched_graph.ndata['attr'][:, :6, None]}
-        targets = (torch.cat(y) - self.targets_mean) / self.targets_std
-
-        if bases:
-            # collate bases
-            all_bases = {
-                key: torch.cat([b[key] for b in bases[0]], dim=0)
-                for key in bases[0][0].keys()
-            }
-
-            return batched_graph, node_feats, edge_feats, all_bases, targets
-        else:
-            return batched_graph, node_feats, edge_feats, targets
-
-    @staticmethod
-    def add_argparse_args(parent_parser):
-        parser = parent_parser.add_argument_group("QM9 dataset")
-        parser.add_argument('--task', type=str, default='homo', const='homo', nargs='?',
-                            choices=['mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'U0', 'U', 'H', 'G', 'Cv',
-                                     'U0_atom', 'U_atom', 'H_atom', 'G_atom', 'A', 'B', 'C'],
-                            help='Regression task to train on')
-        parser.add_argument('--precompute_bases', type=str2bool, nargs='?', const=True, default=False,
-                            help='Precompute bases at the beginning of the script during dataset initialization,'
-                                 ' instead of computing them at the beginning of each forward pass.')
-        return parent_parser
-
-    def __repr__(self):
-        return f'QM9({self.task})'
-
-
-class CachedBasesQM9EdgeDataset(QM9EdgeDataset):
-    """ Dataset extending the QM9 dataset from DGL with precomputed (cached in RAM) pairwise bases """
-
-    def __init__(self, bases_kwargs: dict, batch_size: int, *args, **kwargs):
-        """
-        :param bases_kwargs:  Arguments to feed the bases computation function
-        :param batch_size:    Batch size to use when iterating over the dataset for computing bases
-        """
-        self.bases_kwargs = bases_kwargs
-        self.batch_size = batch_size
-        self.bases = None
-        super().__init__(*args, **kwargs)
-
-    def load(self):
-        super().load()
-        # Iterate through the dataset and compute bases (pairwise only)
-        # Potential improvement: use multi-GPU and reduction
-        dataloader = DataLoader(self, shuffle=False, batch_size=self.batch_size,
-                                collate_fn=lambda samples: dgl.batch([sample[0] for sample in samples]))
-        bases = []
-        for i, graph in tqdm(enumerate(dataloader), total=len(dataloader), desc='Precomputing QM9 bases',
-                             disable=get_local_rank() != 0):
-            rel_pos = _get_relative_pos(graph)
-            # Compute the bases with the GPU but convert the result to CPU to store in RAM
-            bases.append({k: v.cpu() for k, v in get_basis(rel_pos.cuda(), **self.bases_kwargs).items()})
-        self.bases = bases  # Assign at the end so that __getitem__ isn't confused
-
-    def __getitem__(self, idx: int):
-        graph, label = super().__getitem__(idx)
-
-        if self.bases:
-            bases_idx = idx // self.batch_size
-            bases_cumsum_idx = self.ne_cumsum[idx] - self.ne_cumsum[bases_idx * self.batch_size]
-            bases_cumsum_next_idx = self.ne_cumsum[idx + 1] - self.ne_cumsum[bases_idx * self.batch_size]
-            return graph, label, {key: basis[bases_cumsum_idx:bases_cumsum_next_idx] for key, basis in
-                                  self.bases[bases_idx].items()}
-        else:
-            return graph, label
diff --git a/spaces/merve/anonymization/source/measuring-fairness/graph-scroll.css b/spaces/merve/anonymization/source/measuring-fairness/graph-scroll.css
deleted file mode 100644
index e3757d99ca305478165c6f7e4781ec0ce95b6291..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/measuring-fairness/graph-scroll.css
+++ /dev/null
@@ -1,119 +0,0 @@
-#container{
-  position: relative;
-  width: auto;
-}
-
-#sections{
-  width: 340px;
-}
-
-#sections > div{
-  background: white;
-  opacity: .2;
-  margin-bottom: 400px;
-  line-height: 1.4em;
-  transition: opacity .2s;
-}
-#sections > div:first-child{
-  opacity: 1;
-}
-#sections > div:last-child{
-  /*padding-bottom: 80vh;*/
-  padding-bottom: 80px;
-  margin-bottom: 0px;
-}
-#sections > div:first-child > h1{
-  padding-top: 40px;
-}
-
-#sections > div.graph-scroll-active{
-  opacity: 1;
-}
-
-#graph{
-  margin-left: 40px;
-  width: 500px;
-  position: -webkit-sticky;
-  position: sticky;
-  top: 0px;
-  float: right;
-  height: 580px;
-  font-family: 'Google Sans', sans-serif;
-
-}
-
-.slider{
-  font-family: 'Google Sans', sans-serif;
-}
-
-#sections h1{
-  text-align: left !important;
-}
-
-@media (max-width: 1000px) and (min-width: 926px){
-  #sections{
-    margin-left: 20px;
-  }
-}
-
-@media (max-width: 925px)  {
-  #container{
-    margin-left: 0px;
-  }
-
-  #graph{
-    width: 100%;
-    margin-left: 10px;
-    float: none;
-    max-width: 500px;
-    margin: 0px auto;
-  }
-
-  #graph > div{
-    position: relative;
-    top: 0px;
-  }
-  #sections{
-    width: auto;
-    position: relative;
-    margin: 0px auto;
-  }
-
-  #sections > div{
-    background: rgba(255,255,255,.8);
-    padding: 10px;
-    border-top: 1px solid;
-    border-bottom: 1px solid;
-    margin-bottom: 80vh;
-    width: calc(100vw - 20px);
-    margin-left: -5px;
-  }
-
-  #sections > div > *{
-    max-width: 750px;
-  }
-  .mini, .slider, i, .gated{
-    margin: 0px auto;
-  }
-
-  #sections > div:first-child{
-    opacity: 1;
-    margin-top: -140px;
-  }
-
-  #sections > div:last-child{
-    padding-bottom: 0px;
-    margin-bottom: 0px;
-  }
-
-
-  #sections h1{
-    margin: 10px;
-    padding-top: 0px !important;
-  }
-
-  #sections h3{
-    margin-top: .5em;
-  }
-
-}
diff --git a/spaces/merve/data-leak/public/third_party/d3_.js b/spaces/merve/data-leak/public/third_party/d3_.js
deleted file mode 100644
index 9c4b6815ec3cdc0e9f8a072b2d05be7ad48fa703..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/third_party/d3_.js
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @license
- * Lodash lodash.com/license | Underscore.js 1.8.3 underscorejs.org/LICENSE
- */
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diff --git a/spaces/merve/fourier-transform/README.md b/spaces/merve/fourier-transform/README.md
deleted file mode 100644
index 1a125988e2dad15b248c4e4749158c2126e186f3..0000000000000000000000000000000000000000
--- a/spaces/merve/fourier-transform/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Fourier Transform
-emoji: 💜
-colorFrom: green
-colorTo: indigo
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/merve/uncertainty-calibration/source/_posts/2020-09-27-diversity-metrics.md b/spaces/merve/uncertainty-calibration/source/_posts/2020-09-27-diversity-metrics.md
deleted file mode 100644
index 4c84423fe9a6f8566a0b7182bc378feec97d9654..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/_posts/2020-09-27-diversity-metrics.md
+++ /dev/null
@@ -1,100 +0,0 @@
----
-template: post.html
-title: Measuring Diversity
-titlex: Diversity and Inclusion Metrics
-summary: Search results that reflect historic inequities can amplify stereotypes and perpetuate under-representation. Carefully measuring diversity in data sets can help.
-shareimg: https://pair.withgoogle.com/explorables/images/measuring-diversity.png
-permalink: /measuring-diversity/
-date: 2021-03-01
----
-
-<link rel="stylesheet" href="style.css">
-
-Search, ranking and recommendation systems can help find useful documents in large datasets. However, these datasets reflect the biases of the society in which they were created and the systems risk re-entrenching those biases. For example, if someone who is not a white man searches for "CEO pictures" and sees a [page of white men](https://www.nytimes.com/interactive/2018/04/24/upshot/women-and-men-named-john.html), they may feel that only white men can be CEOs, further perpetuating lack of representation at companies' executive levels. 
-
-Using the careful quantification outlined in a recent paper, [Diversity and Inclusion Metrics in Subset Selection](https://arxiv.org/pdf/2002.03256.pdf), we can quantify biases and push these systems to return a wider range of results. 
-
-The mathematics of all this is a little easier to follow with abstract shapes. Let's take a look at some of them:
-
-<div id='all-shapes' class='shapes'></div>
-
-Suppose we want to return about <b>30% green boxes</b> to reflect the distribution of some larger universe of shapes. Try clicking on the shapes below to select some of them — can you find a better subset to return?
-
-<div id='pick-green' class='shapes'></div>
-
-Another diversity metric we care about is the percentage of dots... how close to <b>35% dots</b> can you get?
-
-<div id='pick-triangle' class='shapes'></div>
-
-If we can only return a single subset, how should we consider multiple diversity metrics? Sometimes it isn't possible to reduce the difference of every metric to zero. One natural approach: find the selection with the **lowest mean difference** across all the metrics to get as close as possible to all the targets. 
-
-In other circumstances, like picking a panel of speakers, avoiding badly representing any single category might be more important. This can be done by finding the subset with the **lowest max difference**. Try minimizing both below:  
-
-<div id='pick-metric' class='shapes' style='margin-bottom: 0px'></div>
-
-Notice that minimizing the mean results in a different subset than minimizing the max; how else might using one over the other change the results?   
-
-### Ranking Measures
-
-We can pull out more detail by showing how the mean difference and maximum difference rank lots of sets. Below, there are 20 sets of 10 shapes sorted by the two measures. Try adjusting the target slider on the left to see how the rankings change; each set's percentage of green, dots and small shapes are shown in the small histograms.  
-
-<div id='columns-height'></div>
-
-At the extremes, the choice of measure can have a big impact: if we want to try and return all green results, we can shift the green target up to 100%. With this target, the minimum difference basically sorts the sets by the number of green items and uses the other targets as a tiebreaker. In contrast, sorting by the mean difference balances the green target more with the dot and small targets.
-
-<div id='columns-height-disagree'></div>
-
-Beyond mean and max differences, there are more ways to combine diversity metrics, like taking the cross of two metrics to account for [intersectionality](https://en.wikipedia.org/wiki/Intersectionality). The absolute value of the difference in target and actual percentages can also be quantified in other ways — you might want to penalize undershooting more than overshooting, for example. It's important to keep in mind what exactly you're trying to maximize and the dataset that you're operating on.
- 
-### Which Measure is Best?
-
-In a vacuum, all of these ranking methods are defensible. Picking one requires knowledge of the dataset and broader societal context.
-
-For example, the doctors on the left have more variance along the shirt color attribute, but they're less diverse by gender than the doctors on the right. With the shirt color and gender targets we've picked, the two subsets have the same mean and max differences However, in most applications, it's more important to have a representative sample of socially relevant characteristics, like gender, rather than something less salient, like clothing color. 
-
-<div id='coat-v-gender'></div>   
-
-Just selecting a diverse sample isn't sufficient either. [Diversity and Inclusion Metrics in Subset Selection](https://arxiv.org/pdf/2002.03256.pdf) introduces a way of measuring "inclusion" - how well does the searcher feel represented in the results?
-
-Below, we have gender diversity, without inclusion for women, in the “construction worker” image domain. Masculine-presenting individuals are shown in realistic, modern construction worker situations, while feminine-presenting individuals and other gender presentations are depicted as historic nostalgia, toys, clipart, or passive.
-
-<div id='construction'></div>
-
-The context of the query and the searcher also plays in the quality of search results. A search for "work clothing" that shows a mixed palette of colors for men's clothing and only pink women's clothing might make the searcher feel that women need to appear stereotypically feminine in a professional setting. But the same set of women's clothes might be appropriate to show for a "pink women work clothes" search or if the searcher had previously expressed a preference for pink.
-
-We saw how a small switch from mean to max made a huge difference in what abstract shapes are returned – and how things can get even more complex when socially salient characteristics are layered in. Defaults and small decisions can encode our priorities and values; intentionally thinking about how diversity and inclusion are being measured and which characteristics are emphasized is a step towards designing more equitable systems.    
-
-### More Reading
-
-The [Diversity and Inclusion Metrics](https://arxiv.org/pdf/2002.03256.pdf) paper has a [Colab](https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/source/measuring-diversity/diversity-and-inclusion.ipynb) with a detailed desciption of the metrics, additional visualizations and a reference Python implementation.  
-
-The difficulties of [measuring fairness](https://pair.withgoogle.com/explorables/measuring-fairness/) in general have been well studied; subset selection is still an active area of research. [Fairness of Exposure in Rankings](https://www.cs.cornell.edu/~tj/publications/singh_joachims_18a.pdf) proposes a ranking algorithm that incorporates fairness constraints. [Toward creating a fairer ranking in search engine results](https://www.ilab.cs.rutgers.edu/~rg522/publication/gao-2020-ipm/gao-2020-ipm.pdf) measures diversity bias in actual search results. 
-
-Inferring user preferences is also tricky; you can checkout ways to design for user feedback and control over queries in the [People + AI Guidebook](https://pair.withgoogle.com/chapter/feedback-controls/).
-
-### Credits 
-
-Adam Pearce, Dylan Baker, Ellen Jiang, Meg Mitchell\* and Timnit Gebru\* // March 2021
-
-*Work done while at Google
-
-Thanks to Alex Hanna, Carey Radebaugh, Emily Denton, Fernanda Viégas, James Wexler, Jess Holbrook, Ludovic Peran, Martin Wattenberg, Michael Terry, Yannick Assogba and Zan Armstrong for their help with this piece.
-
-
-<h3>More Explorables</h3>
-
-<p id='recirc'></p>
-
-
-
-
-<script src='../third_party/d3_.js'></script>
-<script src="//cdnjs.cloudflare.com/ajax/libs/seedrandom/3.0.5/seedrandom.min.js">
-</script>
-<script src='sliders.js'></script>
-<script src='script.js'></script>
-<script src='image-layout.js'></script>
-
-<script src='columns-height.js'></script>
-
-<script src='../third_party/recirc.js'></script>
\ No newline at end of file
diff --git a/spaces/mfkeles/Track-Anything/overleaf/Track Anything/neurips_2022.tex b/spaces/mfkeles/Track-Anything/overleaf/Track Anything/neurips_2022.tex
deleted file mode 100644
index f14a483fc519ccd4312233d185747c6d0bbaf1c7..0000000000000000000000000000000000000000
--- a/spaces/mfkeles/Track-Anything/overleaf/Track Anything/neurips_2022.tex	
+++ /dev/null
@@ -1,378 +0,0 @@
-\documentclass{article}
-
-
-% if you need to pass options to natbib, use, e.g.:
-%     \PassOptionsToPackage{numbers, compress}{natbib}
-% before loading neurips_2022
-
-
-% ready for submission
-% \usepackage{neurips_2022}
-
-
-% to compile a preprint version, e.g., for submission to arXiv, add add the
-% [preprint] option:
-     \usepackage[preprint]{neurips_2022}
-
-% to compile a camera-ready version, add the [final] option, e.g.:
-%     \usepackage[final]{neurips_2022}
-
-
-% to avoid loading the natbib package, add option nonatbib:
-%    \usepackage[nonatbib]{neurips_2022}
-\usepackage{graphicx}
-\usepackage[utf8]{inputenc} % allow utf-8 input
-\usepackage[T1]{fontenc}    % use 8-bit T1 fonts
-\usepackage{hyperref}       % hyperlinks
-\usepackage{url}            % simple URL typesetting
-\usepackage{booktabs}       % professional-quality tables
-\usepackage{amsfonts}       % blackboard math symbols
-\usepackage{nicefrac}       % compact symbols for 1/2, etc.
-\usepackage{microtype}      % microtypography
-\usepackage{xcolor}         % colors
-% \usepackage{acmart}
-
-\title{Track Anything: High-performance Interactive Tracking and Segmentation}
-\title{Track Anything: High-performance Object Tracking in Videos by Interactive Masks}
-% \title{Track Anything: Interaction to Mask in Videos}
-\title{Track Anything: Segment Anything Meets Videos}
-
-% \author{%
-%   David S.~Hippocampus\thanks{Use footnote for providing further information
-%     about author (webpage, alternative address)---\emph{not} for acknowledging
-%     funding agencies.} \\
-%   SUSTech VIPG\\
-
-% \author{Jinyu Yang}
-% \authornote{equal}
-
-% \author{Mingqi Gao}
-% \authornotemark[1]
-
-\author{%
-  Jinyu Yang\thanks{Equal contribution. Alphabetical order.},\enskip Mingqi Gao\footnotemark[1],\enskip Zhe Li\footnotemark[1],\enskip Shang Gao, Fangjing Wang, Feng Zheng \\
-  SUSTech VIP Lab\\
-  % Cranberry-Lemon University\\
-  % Pittsburgh, PA 15213 \\
-  % \texttt{hippo@cs.cranberry-lemon.edu} \\
-  % \url{https://github.com/gaomingqi/Track-Anything}\\
-  % examples of more authors
-  % \And
-  % Coauthor \\
-  % Affiliation \\
-  % Address \\
-  % \texttt{email} \\
-  % \AND
-  % Coauthor \\
-  % Affiliation \\
-  % Address \\
-  % \texttt{email} \\
-  % \And
-  % Coauthor \\
-  % Affiliation \\
-  % Address \\
-  % \texttt{email} \\
-  % \And
-  % Coauthor \\
-  % Affiliation \\
-  % Address \\
-  % \texttt{email} \\
-  % \thanks{these authors contributed equally}
-}
-% \affiliation{\institution{SUSTech VIP Lab}}
-% \footnote{Equal contribution. Alphabetical order.}
-
-\begin{document}
-
-
-\maketitle
-
-
-\begin{abstract}
-
-Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images.
-Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos.
-Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos.
-To be detailed, given a video sequence, only with very little human participation, \textit{i.e.}, several clicks, people can track anything they are interested in, and get satisfactory results in one-pass inference.
-Without additional training, such an interactive design performs impressively on video object tracking and segmentation.
-% superior to prior works on video object tracking and segmentation.
-All resources are available on \url{https://github.com/gaomingqi/Track-Anything}.
-We hope this work can facilitate related research.
-
-\end{abstract}
-
-\section{Introduction}
-
-Tracking an arbitrary object in generic scenes is important, and Video Object Tracking (VOT) is a fundamental task in computer vision.
-Similar to VOT, Video Object Segmentation (VOS) aims to separate the target (region of interest) from the background in a video sequence, which can be seen as a kind of more fine-grained object tracking.
-We notice that current state-of-the-art video trackers/segmenters are trained on large-scale manually-annotated datasets and initialized by a bounding box or a segmentation mask.
-On the one hand, the massive human labor force is hidden behind huge amounts of labeled data.
-% Recently, interactive algorithms help to liberate users from labor-expensive initialization and annotation.
-Moreover, current initialization settings, especially the semi-supervised VOS, need specific object mask groundtruth for model initialization.
-How to liberate researchers from labor-expensive annotation and initialization is much of important.
-
-
-Recently, Segment-Anything Model (SAM)~\cite{sam} has been proposed, which is a large foundation model for image segmentation.
-It supports flexible prompts and computes masks in real-time, thus allowing interactive use.
-We conclude that SAM has the following advantages that can assist interactive tracking:
-\textbf{1) Strong image segmentation ability.}
-Trained on 11 million images and 1.1 billion masks, SAM can produce high-quality masks and do zero-shot segmentation in generic scenarios.
-\textbf{2) High interactivity with different kinds of prompts. }
-With input user-friendly prompts of points, boxes, or language, SAM can give satisfactory segmentation masks on specific image areas.
-However, using SAM in videos directly did not give us an impressive performance due to its deficiency in temporal correspondence.
-
-On the other hand, tracking or segmenting in videos faces challenges from scale variation, target deformation, motion blur, camera motion, similar objects, and so on~\cite{vos,vot6,vot7,vot8,vot9,vot10}.
-Even the state-of-the-art models suffer from complex scenarios in the public datasets~\cite{xmem}, not to mention the real-world applications.
-Therefore, a question is considered by us:
-\textit{can we achieve high-performance tracking/segmentation in videos through the way of interaction?}
-
-In this technical report, we introduce our Track-Anything project, which develops an efficient toolkit for high-performance object tracking and segmentation in videos.
-With a user-friendly interface, the Track Anything Model (TAM) can track and segment any objects in a given video with only one-pass inference.
-Figure~\ref{fig:overview} shows the one-pass interactive process in the proposed TAM.
-In detail, TAM combines SAM~\cite{sam}, a large segmentation model, and XMem~\cite{xmem}, an advanced VOS model.
-As shown, we integrate them in an interactive way.
-Firstly, users can interactively initialize the SAM, \textit{i.e.}, clicking on the object, to define a target object;
-then, XMem is used to give a mask prediction of the object in the next frame according to both temporal and spatial correspondence;
-next, SAM is utilized to give a more precise mask description;
-during the tracking process, users can pause and correct as soon as they notice tracking failures.
-
-Our contributions can be concluded as follows:
-
-1) We promote the SAM applications to the video level to achieve interactive video object tracking and segmentation.
-% We combine the SAM with VOS models to achieve interactive video object tracking and segmentation.
-Rather than separately using SAM per frame, we integrate SAM into the process of temporal correspondence construction.
-
-2) We propose one-pass interactive tracking and segmentation for efficient annotation and a user-friendly tracking interface, which uses very small amounts of human participation to solve extreme difficulties in video object perception.
-
-3) Our proposed method shows superior performance and high usability in complex scenes and has many potential applications.
-
-% \section{Related Works}
-
-% \textbf{Video Object Tracking.}
-
-
-
-% \textbf{Video Object Segmentation.}
-\section{Track Anything Task}
-
-Inspired by the Segment Anything task~\cite{sam}, we propose the Track Anything task, which aims to flexible object tracking in arbitrary videos.
-Here we define that the target objects can be flexibly selected, added, or removed in any way according to the users' interests.
-Also, the video length and types can be arbitrary rather than limited to trimmed or natural videos.
-With such settings, diverse downstream tasks can be achieved, including single/multiple object tracking, short-/long-term object tracking, unsupervised VOS, semi-supervised VOS, referring VOS, interactive VOS, long-term VOS, and so on.
-
-\section{Methodology}
-
-\subsection{Preliminaries}
-
-\textbf{Segment Anything Model~\cite{sam}.}
-Very recently, the Segment Anything Model (SAM) has been proposed by Meta AI Research and gets numerous attention.
-As a foundation model for image segmentation, SAM is based on ViT~\cite{vit} and trained on the large-scale dataset SA-1B~\cite{sam}.
-Obviously, SAM shows promising segmentation ability on images, especially on zero-shot segmentation tasks.
-Unfortunately, SAM only shows superior performance on image segmentation, while it cannot deal with complex video segmentation.
-
-
-\textbf{XMem~\cite{xmem}.}
-Given the mask description of the target object at the first frame, XMem can track the object and generate corresponding masks in the subsequent frames.
-Inspired by the Atkinson-Shiffrin memory model, it aims to solve the difficulties in long-term videos with unified feature memory stores.
-The drawbacks of XMem are also obvious: 1) as a semi-supervised VOS model, it requires a precise mask to initialize; 2) for long videos, it is difficult for XMem to recover from tracking or segmentation failure.
-In this paper, we solve both difficulties by importing interactive tracking with SAM.
-
-
-\textbf{Interactive Video Object Segmentation.}
-Interactive VOS~\cite{mivos} takes user interactions as inputs, \textit{e.g.}, scribbles.
-Then, users can iteratively refine the segmentation results until they are satisfied with them.
-Interactive VOS gains lots of attention as it is much easier to provide scribbles than to specify every pixel for an object mask.
-However, we found that current interactive VOS methods require multiple rounds to refine the results, which impedes their efficiency in real-world applications.
-
-\begin{figure}[t]
-\centering
-\includegraphics[width=\linewidth]{figs/overview_4.pdf}
-\caption{Pipeline of our proposed Track Anything Model (TAM). Only within one round of inference can the TAM obtain impressive tracking and segmentation performance on the human-selected target.}
-\label{fig:overview}
-\end{figure}
-
-\begin{table}
-  \caption{Results on DAVIS-2016-val and DAVIS-2017-test-dev datasets~\cite{davis}.}
-  \label{davis1617}
-  \centering
-   \small
-   \setlength\tabcolsep{4pt}
-  \begin{tabular}{l|c|c|c|ccc|ccc}
-    \toprule
-    & & & &\multicolumn{3}{c|}{DAVIS-2016-val} &\multicolumn{3}{c}{DAVIS-2017-test-dev} \\
-    Method & Venue & Initialization & Evaluation& $J\&F$ & $J$ &$F$ &$J\&F$ & $J$ &$F$\\
-    \midrule
-    STM~\cite{stm} & ICCV2019 &Mask & One Pass &89.3 &88.7 &89.9 & 72.2 & 69.3 & 75.2 \\
-    AOT~\cite{aot} &NeurIPS2021 &Mask & One Pass & 91.1 & 90.1 & 92.1 &  79.6 & 75.9 & 83.3 \\
-    XMem~\cite{xmem} & NeurIPS2022 &Mask & One Pass & 92.0 &90.7 &93.2 &  81.2 & 77.6 & 84.7\\
-    \midrule
-    % SiamMask~\cite{siammask}& CVPR2019 &Box & One Pass & 69.8 &71.7 &67.8 &56.4 &54.3 &58.5 \\
-    SiamMask~\cite{siammask}& CVPR2019 &Box & One Pass & 69.8 &71.7 &67.8 &- &- &- \\
-    \midrule
-    % MiVOS~\cite{mivos} & CVPR2021 &Scribble &8 Rounds &91.0 &89.6 &92.4 & 84.5 &81.7 &87.4\\
-    MiVOS~\cite{mivos} & CVPR2021 &Scribble &8 Rounds &91.0 &89.6 &92.4 &78.6 &74.9 &82.2\\
-    % \midrule
-    % & ICIP2022 &Click & \\
-    \midrule
-    TAM (Proposed) &- & Click & One Pass & 88.4 & 87.5 &89.4 & 73.1 & 69.8 & 76.4\\
-    % Ours & & 5 Clicks & \\
-    \bottomrule
-  \end{tabular}
-\end{table}
-
-
-
-\subsection{Implementation}\label{implementation}
-
-Inspired by SAM, we consider tracking anything in videos.
-We aim to define this task with high interactivity and ease of use.
-It leads to ease of use and is able to obtain high performance with very little human interaction effort.
-Figure~\ref{fig:overview} shows the pipeline of our Track Anything Model (TAM).
-As shown, we divide our Track-Anything process into the following four steps:
-
-\textbf{Step 1: Initialization with SAM~\cite{sam}.}
-As SAM provides us an opportunity to segment a region of interest with weak prompts, \textit{e.g.}, points, and bounding boxes, we use it to give an initial mask of the target object.
-Following SAM, users can get a mask description of the interested object by a click or modify the object mask with several clicks to get a satisfactory initialization.
-
-\textbf{Step 2: Tracking with XMem~\cite{xmem}.}
-Given the initialized mask, XMem performs semi-supervised VOS on the following frames.
-Since XMem is an advanced VOS method that can output satisfactory results on simple scenarios, we output the predicted masks of XMem on most occasions.
-When the mask quality is not such good, we save the XMem predictions and corresponding intermediate parameters, \textit{i.e.}, probes and affinities, and skip to step 3.
-% Given the initialized mask and the whole sequence, XMem performs semi-supervised VOS, which aims to solve the performance decay in long-term prediction with memory potentiation.
-
-
-\textbf{Step 3: Refinement with SAM~\cite{sam}.}
-We notice that during the inference of VOS models, keep predicting consistent and precise masks are challenging.
-In fact, most state-of-the-art VOS models tend to segment more and more coarsely over time during inference.
-Therefore, we utilize SAM to refine the masks predicted by XMem when its quality assessment is not satisfactory.
-Specifically, we project the probes and affinities to be point prompts for SAM, and the predicted mask from Step 2 is used as a mask prompt for SAM.
-Then, with these prompts, SAM is able to produce a refined segmentation mask.
-Such refined masks will also be added to the temporal correspondence of XMem to refine all subsequent object discrimination.
-
-\textbf{Step 4: Correction with human participation.}
-% Long video annotation.
-After the above three steps, the TAM can now successfully solve some common challenges and predict segmentation masks.
-However, we notice that it is still difficult to accurately distinguish the objects in some extremely challenging scenarios, especially when processing long videos.
-Therefore, we propose to add human correction during inference, which can bring a qualitative leap in performance with only very small human efforts.
-In detail, users can compulsively stop the TAM process and correct the mask of the current frame with positive and negative clicks.
-
-\section{Experiments}
-
-\subsection{Quantitative Results}
-
-
-To evaluate TAM, we utilize the validation set of DAVIS-2016 and test-development set of DAVIS-2017~\cite{davis}.
-% The evaluation process follows the one we proposed in Section~\ref{implementation}.
-Then, we execute the proposed TAM as demonstrated in Section~\ref{implementation}.
-The results are given in Table~\ref{davis1617}.
-As shown, our TAM obtains $J\&F$ scores of 88.4 and 73.1 on DAVIS-2016-val and DAVIS-2017-test-dev datasets, respectively.
-Note that TAM is initialized by clicks and evaluated in one pass.
-Notably, we found that TAM performs well when against difficult and complex scenarios.
-% During the evaluation,
-
-% click-based interactive video object segmentation
-
-% CLICK-BASED INTERACTIVE VIDEO OBJECT
-% SEGMENTATION
-
-
-\begin{figure}[t]
-\centering
-\includegraphics[width=\linewidth]{figs/davisresults.pdf}
-\caption{Qualitative results on video sequences from DAVIS-16 and DAVIS-17 datasets~\cite{davis}.}
-\label{fig:davisresult}
-\end{figure}
-
-
-\begin{figure}[t]
-\centering
-\includegraphics[width=\linewidth]{figs/failedcases.pdf}
-\caption{Failed cases.}
-\label{fig:failedcases}
-\end{figure}
-
-\subsection{Qualitative Results}
-
-% As we use a new one-pass interactive method to evaluation our TAM, here we only present some qualitative results.
-We also give some qualitative results in Figure~\ref{fig:davisresult}.
-As shown, TAM can handle multi-object separation, target deformation, scale change, and camera motion well, which demonstrates its superior tracking and segmentation abilities within only click initialization and one-round inference.
-
-\subsection{Failed Cases}
-We here also analyze the failed cases, as shown in Figure~\ref{fig:failedcases}.
-Overall, we notice that the failed cases typically appear on the following two occasions.
-1)
-% Separated masks of one object in a long video.
-Current VOS models are mostly designed for short videos, which focus more on maintaining short-term memory rather than long-term memory.
-This leads to mask shrinkage or lacking refinement in long-term videos, as shown in seq (a).
-Essentially, we aim to solve them in step 3 by the refinement ability of SAM, while its effectiveness is lower than expected in realistic applications.
-It indicates that the ability of SAM refinement based on multiple prompts can be further improved in the future.
-On the other hand, human participation/interaction in TAM can be an approach to solving such difficulties, while too much interaction will also result in low efficiency.
-Thus, the mechanism of long-term memory preserving and transient memory updating is still important.
-% Limited refinement by SAM. Although SAM supports to refine previous predictions, via point and mask prompts, . How to .
-2) When the object structure is complex, \textit{e.g.}, the bicycle wheels in seq (b) contain many cavities in groundtruth masks. We found it very difficult to get a fine-grained initialized mask by propagating the clicks.
-Thus, the coarse initialized masks may have side effects on the subsequent frames and lead to poor predictions.
-This also inspires us that SAM is still struggling with complex and precision structures.
-
-
-\begin{figure}[t]
-\centering
-\includegraphics[width=\linewidth]{figs/avengers_1.pdf}
-\caption{Raw frames, object masks, and inpainted results from the movie \textit{Captain America: Civil War (2016)}.}
-\label{fig:captain}
-\end{figure}
-
-
-
-\section{Applications}
-The proposed Track Anything Model (TAM) provides many possibilities for flexible tracking and segmentation in videos.
-Here, we demonstrate several applications enabled by our proposed method.
-% Our method may be able to a variety of applications.
-In such an interactive way, diverse downstream tasks can be easily achieved.
-% \textbf{Demo.}
-% It is able to solve diverse downstream tasks in such a interactive way.
-
-\textbf{Efficient video annotation.}
-TAM has the ability to segment the regions of interest in videos and flexibly choose the objects users want to track. Thus, it can be used for video annotation for tasks like video object tracking and video object segmentation.
-On the other hand, click-based interaction makes it easy to use, and the annotation process is of high efficiency.
-
-
-\textbf{Long-term object tracking.}
-The study of long-term tracking is gaining more and more attention because it is much closer to practical applications.
-Current long-term object tracking task requires the tracker to have the ability to handle target disappearance and reappearance while it is still limited in the scope of trimmed videos.
-Our TAM is more advanced in real-world applications which can handle the shot changes in long videos.
-
-
-\textbf{User-friendly video editing.}
-Track Anything Model provides us the opportunities to segment objects
-With the object segmentation masks provided by TAM, we are then able to remove or alter any of the existing objects in a given video.
-Here we combine E$^2$FGVI~\cite{e2fgvi} to evaluate its application value.
-
-\textbf{Visualized development toolkit for video tasks.}
-For ease of use, we also provide visualized interfaces for multiple video tasks, \textit{e.g.}, VOS, VOT, video inpainting, and so on.
-With the provided toolkit, users can apply their models on real-world videos and visualize the results instantaneously.
-Corresponding demos are available in Hugging Face\footnote{\url{https://huggingface.co/spaces/watchtowerss/Track-Anything}}.
-
-
-To show the effectiveness, we give a comprehensive test by applying TAM on the movie \textit{Captain America: Civil War (2016)}.
-Some representative results are given in Figure \ref{fig:captain}.
-As shown, TAM can present multiple object tracking precisely in videos with lots of shot changes and can further be helpful in video inpainting.
-
-% \section{Further work}
-
-
-% \section*{Acknowledgements}
-
-% \appendix
-
-% \section{Appendix}
-
-
-% Optionally include extra information (complete proofs, additional experiments and plots) in the appendix.
-% This section will often be part of the supplemental material.
-
-
-
-\bibliographystyle{plain}
-\bibliography{neurips_2022}
-
-\end{document}
diff --git a/spaces/michaelgira23/debiasing-lms/load_model.py b/spaces/michaelgira23/debiasing-lms/load_model.py
deleted file mode 100644
index 59caef565e53434c36cb8f2c8ff74d86fe37654e..0000000000000000000000000000000000000000
--- a/spaces/michaelgira23/debiasing-lms/load_model.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import json
-import os
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-from model import get_model
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-models_path = 'models'
-
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-
-
-def load_gpt2():
-
-    model = GPT2LMHeadModel.from_pretrained('gpt2').to(device)
-    return model
-
-
-def load_unprejudiced(model_name):
-    model_path = os.path.join(
-        models_path, f'{model_name}.pth'
-    )
-    model_json_path = os.path.join(
-        models_path, f'{model_name}.json'
-    )
-
-    with open(model_json_path) as f:
-        config = json.loads(f.read())
-        combination = config['combination']
-
-        unprejudiced_model = get_model(
-            device=device,
-            gpt2_name='gpt2',
-            in_net=combination['in_net'],
-            in_net_init_identity=combination['in_net_init_identity'],
-            out_net=combination['out_net'],
-            out_net_init_identity=combination['out_net_init_identity'],
-            freeze_ln=combination['freeze_ln'],
-            freeze_pos=combination['freeze_pos'],
-            freeze_wte=combination['freeze_wte'],
-            freeze_ff=combination['freeze_ff'],
-            freeze_attn=combination['freeze_attn'],
-            dup_lm_head=combination['dup_lm_head'],
-            dup_lm_head_bias=combination['dup_lm_head_bias']
-        )
-        checkpoint = torch.load(model_path, map_location=device)
-        unprejudiced_model.load_state_dict(checkpoint['model_state_dict'])
-        unprejudiced_model = unprejudiced_model.to(device)
-        return unprejudiced_model
diff --git a/spaces/milyiyo/reimagine-it/clip/__init__.py b/spaces/milyiyo/reimagine-it/clip/__init__.py
deleted file mode 100644
index dcc5619538c0f7c782508bdbd9587259d805e0d9..0000000000000000000000000000000000000000
--- a/spaces/milyiyo/reimagine-it/clip/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .clip import *
diff --git a/spaces/mixcard/ask-reader-text/README.md b/spaces/mixcard/ask-reader-text/README.md
deleted file mode 100644
index a2f815ff19d92e3e6e96b4b021f360546c42d694..0000000000000000000000000000000000000000
--- a/spaces/mixcard/ask-reader-text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ask Reader Text
-emoji: 📉
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.46.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/mrm8488/Amazon-reviews-classification-es/README.md b/spaces/mrm8488/Amazon-reviews-classification-es/README.md
deleted file mode 100644
index cd5a212a15fd58f02bbc983135a30d1c9de057d2..0000000000000000000000000000000000000000
--- a/spaces/mrm8488/Amazon-reviews-classification-es/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Amazon Reviews Classification Es
-emoji: 🔥
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 2.8.10
-app_file: app.py
-pinned: false
-license: wtfpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/msarmi9/multi30k/app.py b/spaces/msarmi9/multi30k/app.py
deleted file mode 100644
index 252565d72238d8e2724fe1c2f13cd31c923ce360..0000000000000000000000000000000000000000
--- a/spaces/msarmi9/multi30k/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import random
-from typing import *
-
-import gradio as gr
-import numpy as np
-import seaborn as sns
-import sentencepiece as sp
-import torch
-
-from huggingface_hub import hf_hub_download
-from matplotlib.figure import Figure
-from torchtext.datasets import Multi30k
-
-from models import Seq2Seq
-
-
-# Load model
-model_path = hf_hub_download("msarmi9/multi30k", "models/de-en/version_1/model.bin")
-model = Seq2Seq(vocab_size=8000, hidden_dim=512, bos_idx=1, eos_idx=2, pad_idx=3, temperature=2)
-model.load_state_dict(torch.load(model_path))
-model.eval()
-
-# Load sentencepiece tokenizers
-source_spm_path = hf_hub_download("msarmi9/multi30k", "models/de-en/de8000.model")
-target_spm_path = hf_hub_download("msarmi9/multi30k", "models/de-en/en8000.model")
-source_spm = sp.SentencePieceProcessor(model_file=source_spm_path, add_eos=True)
-target_spm = sp.SentencePieceProcessor(model_file=target_spm_path, add_eos=True)
-
-# Load test set for example inputs
-normalize = lambda sample: (sample[0].lower().strip(), sample[1].lower().strip())
-test_source, _ = zip(*map(normalize, Multi30k(split="test", language_pair=("de", "en"))))
-
-
-def attention_heatmap(input_tokens: List[str], output_tokens: List[str], weights: np.ndarray) -> Figure:
-    figure = Figure(dpi=800, tight_layout=True)
-    axes = figure.add_subplot()
-    axes = sns.heatmap(weights, ax=axes, xticklabels=input_tokens, yticklabels=output_tokens, cmap="gray", cbar=False)
-    axes.tick_params(axis="x", rotation=90, length=0)
-    axes.tick_params(axis="y", rotation=0, length=0)
-    axes.xaxis.tick_top()
-    return figure
-
-
-@torch.inference_mode()
-def run(input: str) -> Tuple[str, Figure]:
-    """Run inference on a single sentence. Returns prediction and attention heatmap."""""
-    input = input.lower().strip().rstrip(".") + "."
-    input_tensor = torch.tensor(source_spm.encode(input), dtype=torch.int64)
-    output, weights = model.decode(input_tensor, max_decode_length=max(len(input_tensor), 80))
-    output = target_spm.decode(output.detach().tolist())
-    input_tokens = source_spm.encode(input, out_type=str)
-    output_tokens = target_spm.encode(output, out_type=str)
-    return output, attention_heatmap(input_tokens, output_tokens, weights.detach().numpy())
-
-
-if __name__ == "__main__":
-    interface = gr.Interface(
-        run, 
-        inputs=gr.inputs.Textbox(lines=4, label="German"),
-        outputs=[
-            gr.outputs.Textbox(label="English"), 
-            gr.outputs.Image(type="plot", label="Attention Heatmap"),
-        ],
-        title = "Multi30k Translation Widget",
-        examples=random.sample(test_source, k=30),
-        examples_per_page=10,
-        allow_flagging="never",
-        theme="huggingface",
-        live=True,
-    )
-
-    interface.launch(
-        enable_queue=True,
-        cache_examples=True,
-    )
diff --git a/spaces/mscsasem3/CHAAT/app.py b/spaces/mscsasem3/CHAAT/app.py
deleted file mode 100644
index 27623def8290e1293846297864d6352272b483d7..0000000000000000000000000000000000000000
--- a/spaces/mscsasem3/CHAAT/app.py
+++ /dev/null
@@ -1,499 +0,0 @@
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-from PIL import Image
-import easyocr
-import requests
-import warnings
-from skimage.io import imread
-from skimage.color import rgb2gray
-import matplotlib.pyplot as plt
-from skimage.filters import sobel
-import numpy as np
-from heapq import *
-import gradio as gr
-from skimage.filters import threshold_otsu
-from skimage.util import invert
-import imageio
-from matplotlib.dates import SU
-from regex import F
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sentence_transformers import SentenceTransformer, util
-from sklearn.metrics.pairwise import cosine_similarity
-import spacy
-import pandas as pd
-from tqdm import tqdm
-import textdistance
-from spacy.lang.en.stop_words import STOP_WORDS
-#import psycopg2
-import os
-from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input, decode_predictions
-from tensorflow.keras.preprocessing import image
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-
-
-
-
-
-
-def horizontal_projections(sobel_image):
-    return np.sum(sobel_image, axis=1)  
-            
-            
-def find_peak_regions(hpp, divider=4):
-    threshold = (np.max(hpp)-np.min(hpp))/divider
-    peaks = []
-    
-    for i, hppv in enumerate(hpp):
-        if hppv < threshold:
-            peaks.append([i, hppv])
-    return peaks
-
-def heuristic(a, b):
-    return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
-
-def get_hpp_walking_regions(peaks_index):
-    hpp_clusters = []
-    cluster = []
-    for index, value in enumerate(peaks_index):
-        cluster.append(value)
-
-        if index < len(peaks_index)-1 and peaks_index[index+1] - value > 1:
-            hpp_clusters.append(cluster)
-            cluster = []
-
-        #get the last cluster
-        if index == len(peaks_index)-1:
-            hpp_clusters.append(cluster)
-            cluster = []
-            
-    return hpp_clusters
-
-def astar(array, start, goal):
-
-        neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]
-        close_set = set()
-        came_from = {}
-        gscore = {start:0}
-        fscore = {start:heuristic(start, goal)}
-        oheap = []
-
-        heappush(oheap, (fscore[start], start))
-        
-        while oheap:
-
-            current = heappop(oheap)[1]
-
-            if current == goal:
-                data = []
-                while current in came_from:
-                    data.append(current)
-                    current = came_from[current]
-                return data
-
-            close_set.add(current)
-            for i, j in neighbors:
-                neighbor = current[0] + i, current[1] + j            
-                tentative_g_score = gscore[current] + heuristic(current, neighbor)
-                if 0 <= neighbor[0] < array.shape[0]:
-                    if 0 <= neighbor[1] < array.shape[1]:                
-                        if array[neighbor[0]][neighbor[1]] == 1:
-                            continue
-                    else:
-                        # array bound y walls
-                        continue
-                else:
-                    # array bound x walls
-                    continue
-                    
-                if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):
-                    continue
-                    
-                if  tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1]for i in oheap]:
-                    came_from[neighbor] = current
-                    gscore[neighbor] = tentative_g_score
-                    fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
-                    heappush(oheap, (fscore[neighbor], neighbor))
-                    
-        return []
-
-def get_binary(img):
-        mean = np.mean(img)
-        if mean == 0.0 or mean == 1.0:
-            return img
-
-        thresh = threshold_otsu(img)
-        binary = img <= thresh
-        binary = binary*1
-        return binary
-
-def path_exists(window_image):
-    #very basic check first then proceed to A* check
-    if 0 in horizontal_projections(window_image):
-        return True
-    
-    padded_window = np.zeros((window_image.shape[0],1))
-    world_map = np.hstack((padded_window, np.hstack((window_image,padded_window)) ) )
-    path = np.array(astar(world_map, (int(world_map.shape[0]/2), 0), (int(world_map.shape[0]/2), world_map.shape[1])))
-    if len(path) > 0:
-        return True
-    
-    return False
-
-def get_road_block_regions(nmap):
-    road_blocks = []
-    needtobreak = False
-    
-    for col in range(nmap.shape[1]):
-        start = col
-        end = col+20
-        if end > nmap.shape[1]-1:
-            end = nmap.shape[1]-1
-            needtobreak = True
-
-        if path_exists(nmap[:, start:end]) == False:
-            road_blocks.append(col)
-
-        if needtobreak == True:
-            break
-            
-    return road_blocks
-
-def group_the_road_blocks(road_blocks):
-    #group the road blocks
-    road_blocks_cluster_groups = []
-    road_blocks_cluster = []
-    size = len(road_blocks)
-    for index, value in enumerate(road_blocks):
-        road_blocks_cluster.append(value)
-        if index < size-1 and (road_blocks[index+1] - road_blocks[index]) > 1:
-            road_blocks_cluster_groups.append([road_blocks_cluster[0], road_blocks_cluster[len(road_blocks_cluster)-1]])
-            road_blocks_cluster = []
-
-        if index == size-1 and len(road_blocks_cluster) > 0:
-            road_blocks_cluster_groups.append([road_blocks_cluster[0], road_blocks_cluster[len(road_blocks_cluster)-1]])
-            road_blocks_cluster = []
-
-    return road_blocks_cluster_groups
-
-def extract_line_from_image(image, lower_line, upper_line):
-    lower_boundary = np.min(lower_line[:, 0])
-    upper_boundary = np.min(upper_line[:, 0])
-    img_copy = np.copy(image)
-    r, c = img_copy.shape
-    for index in range(c-1):
-        img_copy[0:lower_line[index, 0], index] = 0
-        img_copy[upper_line[index, 0]:r, index] = 0
-    
-    return img_copy[lower_boundary:upper_boundary, :]
-
-def extract(image1):
-    img = rgb2gray(image1)
-    print(img)
-
-    #img = rgb2gray(imread("Penwritten_2048x.jpeg"))
-    #img = rgb2gray(imread("test.jpg"))
-    #img = rgb2gray(imread(""))
-
-
-
-
-    sobel_image = sobel(img)
-    hpp = horizontal_projections(sobel_image)
-
-
-    warnings.filterwarnings("ignore")
-    #find the midway where we can make a threshold and extract the peaks regions
-    #divider parameter value is used to threshold the peak values from non peak values.
-
-
-    peaks = find_peak_regions(hpp)
-
-    peaks_index = np.array(peaks)[:,0].astype(int)
-    #print(peaks_index.shape)
-    segmented_img = np.copy(img)
-    r= segmented_img.shape
-    for ri in range(r[0]):
-        if ri in peaks_index:
-            segmented_img[ri, :] = 0
-
-    #group the peaks into walking windows
-
-
-    hpp_clusters = get_hpp_walking_regions(peaks_index)
-    #a star path planning algorithm 
-    
-
-
-
-    
-
-
-    #Scan the paths to see if there are any blockers.
-    
-
-    
-
-    binary_image = get_binary(img)
-
-    for cluster_of_interest in hpp_clusters:
-        nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]
-        road_blocks = get_road_block_regions(nmap)
-        road_blocks_cluster_groups = group_the_road_blocks(road_blocks)
-        #create the doorways
-        for index, road_blocks in enumerate(road_blocks_cluster_groups):
-            window_image = nmap[:, road_blocks[0]: road_blocks[1]+10]
-            binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:][:, road_blocks[0]: road_blocks[1]+10][int(window_image.shape[0]/2),:] *= 0
-
-    #now that everything is cleaner, its time to segment all the lines using the A* algorithm
-    line_segments = []
-    #print(len(hpp_clusters))
-    #print(hpp_clusters)
-    for i, cluster_of_interest in enumerate(hpp_clusters):
-        nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]
-        path = np.array(astar(nmap, (int(nmap.shape[0]/2), 0), (int(nmap.shape[0]/2),nmap.shape[1]-1)))
-        #print(path.shape)
-        if path.shape[0]!=0:
-            #break
-            offset_from_top = cluster_of_interest[0]
-            #print(offset_from_top)
-            path[:,0] += offset_from_top
-            #print(path)
-            line_segments.append(path)
-            #print(i)
-
-    cluster_of_interest = hpp_clusters[1]
-    offset_from_top = cluster_of_interest[0]
-    nmap = binary_image[cluster_of_interest[0]:cluster_of_interest[len(cluster_of_interest)-1],:]
-    #plt.figure(figsize=(20,20))
-    #plt.imshow(invert(nmap), cmap="gray")
-
-    path = np.array(astar(nmap, (int(nmap.shape[0]/2), 0), (int(nmap.shape[0]/2),nmap.shape[1]-1)))
-    #plt.plot(path[:,1], path[:,0])
-
-    offset_from_top = cluster_of_interest[0]
-
-
-
-    ## add an extra line to the line segments array which represents the last bottom row on the image
-    last_bottom_row = np.flip(np.column_stack(((np.ones((img.shape[1],))*img.shape[0]), np.arange(img.shape[1]))).astype(int), axis=0)
-    line_segments.append(last_bottom_row)
-
-    line_images = []
-
-
-
-    
-    line_count = len(line_segments)
-    fig, ax = plt.subplots(figsize=(10,10), nrows=line_count-1)
-    output = []
-
-
-    for line_index in range(line_count-1):
-        line_image = extract_line_from_image(img, line_segments[line_index], line_segments[line_index+1])
-        line_images.append(line_image)
-        #print(line_image)
-        #cv2.imwrite('/Users/vatsalya/Desktop/demo.jpeg',line_image)
-        
-        line_image=line_image*255
-        im=Image.fromarray(line_image)
-        im=im.convert("L")
-        print(im)
-        im.save("demo.jpeg")
-        print("#### Image Saved #######")
-        # new_p = Image.fromarray(line_image)
-        # if new_p.mode != 'RGB':
-        #     new_p = new_p.convert('RGB')
-        #print(line_image)
-        # new_p = Image.fromarray(line_image)
-        # new_p = new_p.convert("L")
-        #imageio.imwrite('demo1.jpeg',line_image)
-
-
-
-        image = Image.open("demo.jpeg").convert("RGB")
-        
-        #print("Started Processing")
-        #image = line_image
-        processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
-        model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
-        pixel_values = processor(images=image, return_tensors="pt").pixel_values
-        generated_ids = model.generate(pixel_values)
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        print(generated_text)
-        output.append(generated_text)
-        #ax[line_index].imshow(line_image, cmap="gray")
-    result=""
-    for o in output:
-        result=result+o
-        result=result+" "
-    return result
-
-
-
-nlp = spacy.load("en_core_web_md")
-
-
-def listToString(s):
-   
-    # initialize an empty string
-    str1 = " "
-   
-    # return string 
-    return (str1.join(s))
-
-def rm_stop(my_doc):
-    # Create list of word tokens
-    token_list = []
-    for token in my_doc:
-        token_list.append(token.text)
-
-    
-
-    # Create list of word tokens after removing stopwords
-    filtered_sentence =[] 
-
-    for word in token_list:
-        lexeme = nlp.vocab[word]
-        if lexeme.is_stop == False:
-            filtered_sentence.append(word)
-    
-    return filtered_sentence
-
-def text_processing(sentence):
-
-    sentence = [token.lemma_.lower()
-                for token in nlp(sentence) 
-                if token.is_alpha and not token.is_stop]
-    
-    return sentence
-
-def jaccard_sim(sent1,sent2):
-    # Text Processing
-    sentence1 = text_processing(sent1)
-    sentence2 = text_processing(sent2)
-    
-    # Jaccard similarity
-    return textdistance.jaccard.normalized_similarity(sentence1, sentence2)
-
-def sim(Ideal_Answer,Submitted_Answer):
-# SBERT EMBEDDINGS
-    text1=Ideal_Answer.replace("\"","").replace("\'","")
-    text2=Submitted_Answer.replace("\"","").replace("\'","")
-    output=[]
-    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
-    #Compute embedding for both lists
-    embedding_1= model.encode(text1, convert_to_tensor=True)
-    embedding_2 = model.encode(text2, convert_to_tensor=True)
-
-    score=util.pytorch_cos_sim(embedding_1, embedding_2)
-    output.append("SBERT:"+str(int(float(str(score).split("[")[2].split("]")[0])*10.0))+",")
-    sbert=int(float(str(score).split("[")[2].split("]")[0])*10.0)
-    #Jaccard
-    output.append("Jaccard:"+str(int(jaccard_sim(text1,text2)*10.0))+",")
-
-    #spacy average word2vec
-    nlp = spacy.load("en_core_web_md")  # make sure to use larger package!
-    doc1 =  listToString(rm_stop(nlp(text1)))
-    doc2 =  listToString(rm_stop(nlp(text2)))
-
-    # Similarity of two documents
-    w2v=int(nlp(doc1).similarity(nlp(doc2))*10.0)
-    final_score=int(0.8*sbert+0.2*w2v)
-    output.append("Word2Vec:"+str(int(nlp(doc1).similarity(nlp(doc2))*10.0))+",final_score:"+str(final_score))
-    out_string=listToString(output)
-    #return out_string
-    return str(out_string),final_score
-
-
-
-def return_image_embedding(model,img_path):
-    #img = image.load_img(img_path, target_size=(224, 224))
-    x = image.img_to_array(img_path)
-    x = np.expand_dims(x, axis=0)
-    x = preprocess_input(x)
-    preds = model.predict(x)
-    curr_df = pd.DataFrame(preds[0]).T
-    print(curr_df.to_numpy())
-    return curr_df.to_numpy()
-
-
-
-def draw_boxes(image, bounds, color='yellow', width=2):
-    draw = ImageDraw.Draw(image)
-    for bound in bounds:
-        p0, p1, p2, p3 = bound[0]
-        draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
-    return image
-
-def inference(img, lang):
-    reader = easyocr.Reader(lang)
-    bounds = reader.readtext(img,detail=0)
-    #im = PIL.Image.open(img.name)
-    #draw_boxes(im, bounds)
-    #im.save('result.jpg')
-    return bounds
-
-def compute_tfidf_embeddings(words_list1, words_list2):
-    # Combine the words from both lists
-    combined_words = words_list1 + words_list2
-
-    # Initialize the TF-IDF vectorizer
-    vectorizer = TfidfVectorizer()
-
-    # Compute the TF-IDF matrix
-    tfidf_matrix = vectorizer.fit_transform(combined_words)
-
-    # Split the matrix into separate parts for the two lists
-    tfidf_matrix_list1 = tfidf_matrix[:len(words_list1)]
-    tfidf_matrix_list2 = tfidf_matrix[len(words_list1):]
-
-    return tfidf_matrix_list1, tfidf_matrix_list2
-
-def compute_cosine_similarity(tfidf_matrix_list1, tfidf_matrix_list2):
-    # Compute the cosine similarity between the two TF-IDF matrices
-    similarity_matrix = cosine_similarity(tfidf_matrix_list1, tfidf_matrix_list2)
-
-    return similarity_matrix
-    
-
-def extract_eval(Ideal_Answer_Text,Ideal_Answer_Diagram,Submitted_Answer_Text,Submitted_Answer_Diagram):
-    # print(image1)
-    ideal_text=extract(Ideal_Answer_Text)
-    print("Extracting Ideal Text \n")
-    print(ideal_text)
-    submitted_text=extract(Submitted_Answer_Text)
-    print("Extracting Submitted Text \n")
-    print(submitted_text)
-    a,b=sim(ideal_text,submitted_text)
-    print(a)
-    text_sim_score=b
-    model = ResNet50(include_top=False, weights='imagenet', pooling='avg')
-    diagram_1_embed=return_image_embedding(model,Ideal_Answer_Diagram)
-    diagram_2_embed=return_image_embedding(model,Submitted_Answer_Diagram)
-    diagram_embed_sim_score=util.pytorch_cos_sim(diagram_1_embed, diagram_2_embed)
-    print("Diagram Embedding Similarity Score \n")
-    print(str(int(float(str(diagram_embed_sim_score).split("[")[2].split("]")[0])*10.0)))
-    diagram_1_text=inference(Ideal_Answer_Diagram,['en'])
-    diagram_2_text=inference(Submitted_Answer_Diagram,['en'])
-    tfidf_matrix_list1, tfidf_matrix_list2 = compute_tfidf_embeddings(diagram_1_text, diagram_2_text)
-    similarity_matrix = compute_cosine_similarity(tfidf_matrix_list1, tfidf_matrix_list2)
-    print("Diagram Text Embedding Similarity Score \n")
-    print(similarity_matrix[0][0])
-    text_sim=similarity_matrix[0][0]
-    img_sim=float(str(diagram_embed_sim_score).split("[")[2].split("]")[0])
-    diagram_similarity=0.7*text_sim+0.3*img_sim
-    print("Diagram Overall Similarity\n")
-    print(diagram_similarity*10)
-    final_string=a+"\n"+"Diagram Embedding Similarity Score \n"+str(int(float(str(diagram_embed_sim_score).split("[")[2].split("]")[0])*10.0))+"Diagram Text Embedding Similarity Score \n"+str(similarity_matrix[0][0])+"Diagram Overall Similarity\n"+str(diagram_similarity*10)
-    return final_string
-
-    
-    
-
-iface = gr.Interface(fn=extract_eval, 
-                     inputs=["image","image","image","image"],
-                     outputs=gr.outputs.Textbox(),)
-
-iface.launch(enable_queue=True)
-
diff --git a/spaces/mshkdm/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.cpp b/spaces/mshkdm/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.cpp
deleted file mode 100644
index 73928ece8150f847d98af65a95685a29fcceecde..0000000000000000000000000000000000000000
--- a/spaces/mshkdm/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include <ATen/ATen.h>
-#include <torch/extension.h>
-
-torch::Tensor upfirdn2d_op(const torch::Tensor &input,
-                           const torch::Tensor &kernel, int up_x, int up_y,
-                           int down_x, int down_y, int pad_x0, int pad_x1,
-                           int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x)                                                          \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x)                                                    \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x)                                                         \
-  CHECK_CUDA(x);                                                               \
-  CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor &input, const torch::Tensor &kernel,
-                        int up_x, int up_y, int down_x, int down_y, int pad_x0,
-                        int pad_x1, int pad_y0, int pad_y1) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(kernel);
-
-  at::DeviceGuard guard(input.device());
-
-  return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
-                      pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_recognition/README.md b/spaces/mshukor/UnIVAL/fairseq/examples/speech_recognition/README.md
deleted file mode 100644
index 17030bf0fd50bb843a508e13e97ed436eae33287..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_recognition/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned.
-
-# Speech Recognition
-`examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660).
-
-
-## Additional dependencies
-On top of main fairseq dependencies there are couple more additional requirements.
-
-1) Please follow the instructions to install [torchaudio](https://github.com/pytorch/audio). This is required to compute audio fbank features.
-2) [Sclite](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm#sclite_name_0) is used to measure WER. Sclite can be downloaded and installed from source from sctk package [here](http://www.openslr.org/4/). Training and inference doesn't require Sclite dependency.
-3) [sentencepiece](https://github.com/google/sentencepiece) is required in order to create dataset with word-piece targets.
-
-## Preparing librispeech data
-```
-./examples/speech_recognition/datasets/prepare-librispeech.sh $DIR_TO_SAVE_RAW_DATA $DIR_FOR_PREPROCESSED_DATA
-```
-
-## Training librispeech data
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 80 --task speech_recognition --arch vggtransformer_2 --optimizer adadelta --lr 1.0 --adadelta-eps 1e-8 --adadelta-rho 0.95 --clip-norm 10.0  --max-tokens 5000 --log-format json --log-interval 1 --criterion cross_entropy_acc --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-`$SET` can be `test_clean` or `test_other`
-Any checkpoint in `$MODEL_PATH` can be selected. In this example we are working with `checkpoint_last.pt`
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --max-tokens 25000 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --beam 20 --results-path $RES_DIR --batch-size 40 --gen-subset $SET --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-```
-sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.word-checkpoint_last.pt-${SET}.txt -i rm -o all stdout > $RES_REPORT
-```
-`Sum/Avg` row from first table of the report has WER
-
-## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components
-[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes:
-
-* AutoSegmentationCriterion (ASG)
-* flashlight-style Conv/GLU model
-* flashlight's beam search decoder
-
-To use these, follow the instructions on [this page](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) to install python bindings.
-
-## Training librispeech data (flashlight style, Conv/GLU + ASG loss)
-Training command:
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition
-```
-
-Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`.
-
-## Inference for librispeech (flashlight decoder, n-gram LM)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
-
-`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels):
-```
-doorbell  D O 1 R B E L 1 ▁
-```
-For CTC inference with word-pieces, repetition labels are not used and the lexicon should have most common spellings for each word (one can use sentencepiece's `NBestEncodeAsPieces` for this):
-```
-doorbell  ▁DOOR BE LL
-doorbell  ▁DOOR B E LL
-doorbell  ▁DO OR BE LL
-doorbell  ▁DOOR B EL L
-doorbell  ▁DOOR BE L L
-doorbell  ▁DO OR B E LL
-doorbell  ▁DOOR B E L L
-doorbell  ▁DO OR B EL L
-doorbell  ▁DO O R BE LL
-doorbell  ▁DO OR BE L L
-```
-Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`).
-
-## Inference for librispeech (flashlight decoder, viterbi only)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py b/spaces/mshukor/UnIVAL/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py
deleted file mode 100644
index efc7ae40bf8fed6c2384cbc6f94477c4caa4c10c..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn.functional as F
-
-
-class MeanPoolGatingNetwork(torch.nn.Module):
-    """A simple mean-pooling gating network for selecting experts.
-
-    This module applies mean pooling over an encoder's output and returns
-    reponsibilities for each expert. The encoder format is expected to match
-    :class:`fairseq.models.transformer.TransformerEncoder`.
-    """
-
-    def __init__(self, embed_dim, num_experts, dropout=None):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_experts = num_experts
-
-        self.fc1 = torch.nn.Linear(embed_dim, embed_dim)
-        self.dropout = torch.nn.Dropout(dropout) if dropout is not None else None
-        self.fc2 = torch.nn.Linear(embed_dim, num_experts)
-
-    def forward(self, encoder_out):
-        if not (
-            "encoder_out" in encoder_out
-            and "encoder_padding_mask" in encoder_out
-            and encoder_out["encoder_out"][0].size(2) == self.embed_dim
-        ):
-            raise ValueError("Unexpected format for encoder_out")
-
-        # mean pooling over time
-        encoder_padding_mask = encoder_out["encoder_padding_mask"][0]  # B x T
-        encoder_out = encoder_out["encoder_out"][0].transpose(0, 1)    # B x T x C
-        if encoder_padding_mask is not None:
-            encoder_out = encoder_out.clone()  # required because of transpose above
-            encoder_out[encoder_padding_mask] = 0
-            ntokens = torch.sum(~encoder_padding_mask, dim=1, keepdim=True)
-            x = torch.sum(encoder_out, dim=1) / ntokens.type_as(encoder_out)
-        else:
-            x = torch.mean(encoder_out, dim=1)
-
-        x = torch.tanh(self.fc1(x))
-        if self.dropout is not None:
-            x = self.dropout(x)
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=-1, dtype=torch.float32).type_as(x)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/__init__.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/__init__.py
deleted file mode 100644
index 1c5189c0f7fb4d66077d9d6498cb78cacff76de8..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .berard import *  # noqa
-from .convtransformer import *  # noqa
-from .s2t_transformer import *  # noqa
-from .xm_transformer import *  # noqa
diff --git a/spaces/mshukor/UnIVAL/models/taming/modules/misc/coord.py b/spaces/mshukor/UnIVAL/models/taming/modules/misc/coord.py
deleted file mode 100644
index ee69b0c897b6b382ae673622e420f55e494f5b09..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/models/taming/modules/misc/coord.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import torch
-
-class CoordStage(object):
-    def __init__(self, n_embed, down_factor):
-        self.n_embed = n_embed
-        self.down_factor = down_factor
-
-    def eval(self):
-        return self
-
-    def encode(self, c):
-        """fake vqmodel interface"""
-        assert 0.0 <= c.min() and c.max() <= 1.0
-        b,ch,h,w = c.shape
-        assert ch == 1
-
-        c = torch.nn.functional.interpolate(c, scale_factor=1/self.down_factor,
-                                            mode="area")
-        c = c.clamp(0.0, 1.0)
-        c = self.n_embed*c
-        c_quant = c.round()
-        c_ind = c_quant.to(dtype=torch.long)
-
-        info = None, None, c_ind
-        return c_quant, None, info
-
-    def decode(self, c):
-        c = c/self.n_embed
-        c = torch.nn.functional.interpolate(c, scale_factor=self.down_factor,
-                                            mode="nearest")
-        return c
diff --git a/spaces/multimodalart/Tune-A-Video-Training-UI-poli/uploader.py b/spaces/multimodalart/Tune-A-Video-Training-UI-poli/uploader.py
deleted file mode 100644
index f70dc3d6bb4f0dc375a68e73440b05713365e7eb..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/Tune-A-Video-Training-UI-poli/uploader.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-from huggingface_hub import HfApi
-
-
-class Uploader:
-    def __init__(self, hf_token: str | None):
-        self.hf_token = hf_token
-
-    def upload(self,
-               folder_path: str,
-               repo_name: str,
-               organization: str = '',
-               repo_type: str = 'model',
-               private: bool = True,
-               delete_existing_repo: bool = False,
-               input_token: str | None = None) -> str:
-        
-        api = HfAPi(token=self.hf_token if self.hf_token else input_token)
-        
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            raise ValueError
-        if not organization:
-            organization = api.whoami()['name']
-        
-        repo_id = f'{organization}/{repo_name}'
-        if delete_existing_repo:
-            try:
-                self.api.delete_repo(repo_id, repo_type=repo_type)
-            except Exception:
-                pass
-        try:                
-            api.create_repo(repo_id, repo_type=repo_type, private=private)
-            api.upload_folder(repo_id=repo_id,
-                              folder_path=folder_path,
-                              path_in_repo='.',
-                              repo_type=repo_type)
-            url = f'https://huggingface.co/{repo_id}'
-            message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
-        except Exception as e:
-            message = str(e)
-        return message
diff --git a/spaces/nateraw/lavila/lavila/utils/evaluation_charades.py b/spaces/nateraw/lavila/lavila/utils/evaluation_charades.py
deleted file mode 100644
index 73614a92bc431525d4e0c772200def7939533936..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/lavila/utils/evaluation_charades.py
+++ /dev/null
@@ -1,53 +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.
-
-import numpy as np
-
-
-def compute_map(submission_array, gt_array):
-    """ Returns mAP, weighted mAP, and AP array """
-    m_aps = []
-    n_classes = submission_array.shape[1]
-    for oc_i in range(n_classes):
-        sorted_idxs = np.argsort(-submission_array[:, oc_i])
-        tp = gt_array[:, oc_i][sorted_idxs] == 1
-        fp = np.invert(tp)
-        n_pos = tp.sum()
-        if n_pos < 0.1:
-            m_aps.append(float('nan'))
-            continue
-        fp.sum()
-        f_pcs = np.cumsum(fp)
-        t_pcs = np.cumsum(tp)
-        prec = t_pcs / (f_pcs+t_pcs).astype(float)
-        avg_prec = 0
-        for i in range(submission_array.shape[0]):
-            if tp[i]:
-                avg_prec += prec[i]
-        m_aps.append(avg_prec / n_pos.astype(float))
-    m_aps = np.array(m_aps)
-    m_ap = np.mean(m_aps)
-    w_ap = (m_aps * gt_array.sum(axis=0) / gt_array.sum().sum().astype(float))
-    return m_ap, w_ap, m_aps
-
-
-def charades_map(submission_array, gt_array):
-    """
-    Approximate version of the charades evaluation function
-    For precise numbers, use the submission file with the official matlab script
-    """
-    fix = submission_array.copy()
-    empty = np.sum(gt_array, axis=1) == 0
-    fix[empty, :] = np.NINF
-    return compute_map(fix, gt_array)
-
-
-def create_submission(video_list, predictions, out_file):
-    assert len(video_list) == predictions.shape[0]
-    with open(out_file, 'w') as f:
-        for i, video_id in enumerate(video_list):
-            pred_str = ' '.join(map(lambda x: str(x), predictions[i].tolist()))
-            f.write('{} {}\n\n'.format(video_id, pred_str))
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/KMSpico 11 3 0 FINAL (Office And Win 10 Activator) Keygen !NEW!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/KMSpico 11 3 0 FINAL (Office And Win 10 Activator) Keygen !NEW!.md
deleted file mode 100644
index a9bb5d444b1a2f93e002126a3173c355e4efbe23..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/KMSpico 11 3 0 FINAL (Office And Win 10 Activator) Keygen !NEW!.md	
+++ /dev/null
@@ -1,137 +0,0 @@
-
-<table>
-<tr>
-<td>
-<h1>KMSpico 11 3 0 FINAL (Office and Win 10 Activator) keygen: What You Need to Know</h1>
-<p>If you are looking for a way to activate your Windows or Office products without paying for a license key, you may have heard of KMSpico. But what is KMSpico exactly? How does it work? Is it safe to use? And what are the alternatives?</p>
-<h2>KMSpico 11 3 0 FINAL (Office and Win 10 Activator) keygen</h2><br /><p><b><b>DOWNLOAD</b> &rarr;&rarr;&rarr; <a href="https://urlcod.com/2uIc7k">https://urlcod.com/2uIc7k</a></b></p><br /><br />
-<p>In this article, we will answer all these questions and more. We will explain what KMSpico is, what it does, why it is useful, and what are the risks of using it. We will also provide you with a step-by-step guide on how to download, install, and use KMSpico to activate your Windows and Office products. Finally, we will suggest some other ways to activate your software legally or illegally.</p>
-<p>By the end of this article, you will have a clear understanding of KMSpico and its pros and cons. You will also be able to decide whether you want to use it or not.</p>
- <h2>How KMSpico Works</h2>
-<p>KMSpico is a software tool that activates Windows and Office products by emulating a Key Management Service (KMS) server. A KMS server is a server that provides activation keys for Microsoft products over a network.</p>
-<p>Normally, when you install Windows or Office on your computer, you need to enter a valid license key to activate them. A license key is a unique code that verifies that you have purchased the software legally. However, if you use KMSpico, you don't need a license key. KMSpico will create a fake KMS server on your computer and generate valid activation keys for your Windows and Office products. This way, you can activate them for free and enjoy all their features.</p>
-<p>KMSpico can activate Windows and Office products permanently, meaning that they will not expire or require reactivation. KMSpico can also activate them offline, meaning that you don't need an internet connection to use them. Moreover, KMSpico can bypass Microsoft's detection system, meaning that your software will not be flagged as pirated or illegal.</p>
-<p></p>
-<p>KMSpico is a very popular and widely used tool among users who want to save money and avoid paying for Microsoft products. However, it is also a very controversial and risky tool, as it violates Microsoft's terms of service and may expose your computer to malware or legal issues.</p>
- <h2>What Products Can KMSpico Activate?</h2>
-<p>KMSpico can activate the following versions of Windows and Office products:</p>
-<ul>
-<li>Windows 10 (all editions)</li>
-<li>Windows 8.1 (all editions)</li>
-<li>Windows 8 (all editions)</li>
-<li>Windows 7 (all editions)</li>
-<li>Windows Vista (all editions)</li>
-<li>Windows Server 2016/2012/2008 (all editions)</li>
-<li>Office 2019/2016/2013/2010/2007 (all editions)</li>
-</ul>
-<p>KMSpico cannot activate the following products:</p>
-<ul>
-<li>Windows XP</li>
-<li>Office 2003</li>
-<li>Office for Mac</li>
-<li>Office 365</li>
-</ul>
- <h2>How to Download and Install KMSpico</h2>
-<p>If you want to use KMSpico to activate your Windows or Office products, you need to download and install it on your computer. However, you need to be careful when doing so, as there are many fake or malicious websites that claim to offer KMSpico but actually contain viruses or malware.</p>
-<p>To avoid getting infected or scammed, you should only download KMSpico from a reliable and trusted source. One of the most reputable sources is the official website of KMSpico, which is https://kmspicoofficial.com/. You can also find other verified sources on Reddit or other forums, but make sure to check the reviews and feedback of other users before downloading anything.</p>
-<p>Once you have found a safe and legitimate source, follow these steps to download and install KMSpico:</p>
-<ol>
-<li>Disable your antivirus software temporarily, as it may interfere with the installation process or delete KMSpico as a false positive.</li>
-<li>Download the KMSpico setup file from the source website. The file name should be something like "KMSpico_setup.exe" or "KMSpico_11_3_0_FINAL.zip".</li>
-<li>Run the setup file as an administrator by right-clicking on it and selecting "Run as administrator".</li>
-<li>Follow the instructions on the screen to complete the installation. You may need to accept some terms and conditions or choose some options.</li>
-<li>Wait for the installation to finish. You should see a message saying "KMSpico successfully installed".</li>
-<li>Restart your computer to apply the changes.</li>
-</ol>
- <h2>How to Use KMSpico to Activate Windows and Office</h2>
-<p>After installing KMSpico on your computer, you can use it to activate your Windows or Office products. The process is very simple and straightforward, as KMSpico will do everything automatically for you. Here are the steps to follow:</p>
-<ol>
-<li>Open KMSpico by clicking on its icon on your desktop or in your start menu.</li>
-<li>You should see a window with a red button and some information about your system and software status.</li>
-<li>Click on the red button to start the activation process. You should hear some voice messages saying "Program complete" or "Affirmative" during the process.</li>
-<li>Wait for the activation process to finish. You should see a message saying "Activation successful" or "Completed" at the end.</li>
-<li>Close KMSpico and check your Windows or Office activation status by going to Settings > Update & Security > Activation or File > Account > Product Information.</li>
-<li>You should see a message saying "Windows is activated" or "Product activated" with a green check mark.</li>
-</ol>
- <h 2>How to Use KMSpico to Activate Windows and Office</h2>
-<p>After installing KMSpico on your computer, you can use it to activate your Windows or Office products. The process is very simple and straightforward, as KMSpico will do everything automatically for you. Here are the steps to follow:</p>
-<ol>
-<li>Open KMSpico by clicking on its icon on your desktop or in your start menu.</li>
-<li>You should see a window with a red button and some information about your system and software status.</li>
-<li>Click on the red button to start the activation process. You should hear some voice messages saying "Program complete" or "Affirmative" during the process.</li>
-<li>Wait for the activation process to finish. You should see a message saying "Activation successful" or "Completed" at the end.</li>
-<li>Close KMSpico and check your Windows or Office activation status by going to Settings > Update & Security > Activation or File > Account > Product Information.</li>
-<li>You should see a message saying "Windows is activated" or "Product activated" with a green check mark.</li>
-</ol>
- <h2>Advantages of Using KMSpico</h2>
-<p>Using KMSpico to activate your Windows or Office products has some advantages that may appeal to you. Here are some of them:</p>
-<ul>
-<li>You can activate your software for free, without paying for a license key or subscription fee.</li>
-<li>You can enjoy all the features and updates of your software, without any limitations or restrictions.</li>
-<li>You can activate your software permanently, without worrying about expiration dates or reactivation issues.</li>
-<li>You can activate your software offline, without needing an internet connection or a Microsoft account.</li>
-<li>You can avoid detection by Microsoft, without risking being blocked or banned by their servers.</li>
-<li>You can use a safe and clean tool, without infecting your computer with viruses or malware.</li>
-</ul>
- <h2>Disadvantages of Using KMSpico</h2>
-<p>However, using KMSpico to activate your Windows or Office products also has some disadvantages that you should be aware of. Here are some of them:</p>
-<ul>
-<li>You are violating Microsoft's terms of service, which may result in legal consequences or penalties.</li>
-<li>You are supporting software piracy, which may harm the software industry and the developers who create the products.</li>
-<li>You are risking compatibility problems, as some software may not work properly with activated products.</li>
-<li>You are exposing yourself to potential malware infection, as some sources may offer fake or malicious versions of KMSpico.</li>
-<li>You are losing customer support, as Microsoft will not provide any assistance or solutions for activated products.</li>
-</ul>
- <h2>Alternatives to KMSpico</h2>
-<p>If you are not comfortable with using KMSpico to activate your Windows or Office products, you may want to consider some alternatives. Here are some of them:</p>
-<ul>
-<li>The best and most legal alternative is to buy a license key from Microsoft or an authorized reseller. This way, you can support the software creators and enjoy their products legally and safely.</li>
-<li>Another legal alternative is to use free or open-source software that can perform similar functions as Windows or Office. For example, you can use Linux as an operating system or LibreOffice as an office suite.</li>
-<li>An illegal alternative is to use other activators that work similarly to KMSpico. Some of the most popular ones are KMSPico Portable, Microsoft Toolkit, Re-Loader Activator, etc. However, these tools also have the same risks and drawbacks as KMSpico, so use them at your own peril.</li>
-</ul>
- <h2>Frequently Asked Questions About KMSpico</h2>
-<p>In this section, we will answer some of the most common questions that users have about KMSpico. If you have any other questions, feel free to ask them in the comments section below.</p>
- <h3>Is KMSpico safe to use?</h3>
-<p>KMSpico is safe to use if you download it from a reliable and trusted source. However, there are many fake or malicious websites that offer KMSpico but actually contain viruses or malware. Therefore, you should always be careful when downloading anything from the internet and scan it with an antivirus software before installing it. You should also disable your antivirus software temporarily when installing KMSpico, as it may interfere with the installation process or delete KMSpico as a false positive.</p>
- <h3>Does KMSpico work offline?</h3>
-<p>Yes, KMSpico works offline. You don't need an internet connection or a Microsoft account to use it. However, you may need an internet connection to download and install KMSpico from a reliable source. You may also need an internet connection to update your software or access some online features.</p>
- <h3>Can KMSpico be uninstalled?</h3>
-<p>Yes, KMSpico can be uninstalled. However, uninstalling KMSpico will not deactivate your Windows or Office products. They will remain activated until you reinstall them or change your hardware. To uninstall KMSpico, follow these steps:</p>
-<ol>
-<li>Open KMSpico by clicking on its icon on your desktop or in your start menu.</li>
-<li>Click on the "Uninstall" button at the bottom of the window.</li>
-<li>Follow the instructions on the screen to complete the uninstallation.</li>
-<li>Restart your computer to apply the changes.</li>
-</ol>
- <h3>Does KMSpico affect the performance of my computer?</h3>
-<p>No, KMSpico does not affect the performance of your computer. KMSpico is a very lightweight and simple tool that does not consume much resources or memory. It only runs when you activate your software and then closes automatically. It does not run in the background or interfere with other programs or processes.</p>
- <h3>Is KMSpico legal?</h3>
-<p>No, KMSpico is not legal. KMSpico is a software piracy tool that violates Microsoft's terms of service and intellectual property rights. Using KMSpico to activate your Windows or Office products is considered illegal and unethical. You may face legal consequences or penalties if you are caught using it. You may also lose your warranty or customer support from Microsoft or other software providers.</p>
- <h2>Conclusion</h2>
-<p>KMSpico is a software tool that activates Windows and Office products by emulating a KMS server and generating valid activation keys. It can activate many versions of Windows and Office products permanently, offline, and without detection by Microsoft. It can also save you money and provide you with all the features and updates of your software.</p>
-<p>However, KMSpico is also a software piracy tool that violates Microsoft's terms of service and intellectual property rights. It can expose you to legal issues, malware infection, compatibility problems, and customer support loss. It can also harm the software industry and the developers who create the products.</p>
-<p>Therefore, you should weigh the pros and cons of using KMSpico carefully before deciding whether to use it or not. You should also consider some alternatives to KMSpico, such as buying a license key, using free or open-source software, or using other activators.</p>
-<p>We hope this article has helped you understand what KMSpico is, what it does, why it is useful, and what are the risks of using it. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
- <h2>Frequently Asked Questions</h2>
-<table>
-<tr>
-<td><b>Question</b></td><td><b>Answer</b></td>
-</tr>
-<tr>
-<td>What is KMSpico?</td><td>KMSpico is a software tool that activates Windows and Office products by emulating a KMS server and generating valid activation keys.</td>
-</tr>
-<tr>
-<td>How do I use KMSpico?</td><td>You need to download and install KMSpico from a reliable source, then run it as an administrator and click on the red button to start the activation process.</td>
-</tr>
-<tr>
-<td>What are the advantages of using KMSpico?</td><td>You can activate your software for free, permanently, offline, and without detection by Microsoft. You can also enjoy all the features and updates of your software.</td>
-</tr>
-<tr>
-<td>What are the disadvantages of using KMSpico?</td><td>You are violating Microsoft's terms of service and intellectual property rights. You are also risking legal issues, malware infection, compatibility problems, and customer support loss.</td>
-</tr>
-<tr>
-<td>What are some alternatives to KMSpico?</td><td>You can buy a license key from Microsoft or an authorized reseller, use free or open-source software that can perform similar functions as Windows or Office, or use other activators that work similarly to KMSpico.</td>
-</tr>
-</table></p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Manichithrathazhu Malayalam Movie Songs Download Free !FULL!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Manichithrathazhu Malayalam Movie Songs Download Free !FULL!.md
deleted file mode 100644
index 6b3933eeeaa3871388fa9bb7dcccbc8e9e1171d7..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Manichithrathazhu Malayalam Movie Songs Download Free !FULL!.md	
+++ /dev/null
@@ -1,39 +0,0 @@
-<br />
-<h1>Manichitrathazhu Malayalam Movie Songs Download Free - A Guide to Enjoy the Classic Thriller</h1>
-
-<p>If you are a fan of Malayalam cinema, you must have heard of Manichitrathazhu, the 1993 psychological thriller that is widely regarded as one of the best films ever made in India. The film, directed by Fazil and written by Madhu Muttam, stars Mohanlal, Suresh Gopi and Shobana in the lead roles, along with a stellar supporting cast of Nedumudi Venu, Innocent, Vinaya Prasad, K.P.A.C.Lalitha, Sridhar, K.B. Ganesh Kumar, Sudheesh, and Thilakan.</p>
-
-<p>The film revolves around a haunted mansion and the mysterious events that unfold when a couple moves in. The film explores themes of mental illness, superstition, folklore, and reincarnation with a gripping plot and brilliant performances. The film also features some of the most memorable songs in Malayalam cinema, composed by MG. Radhakrishnan and sung by G. Venugopal, Chitra, Sujatha, Yesudas.</p>
-<h2>Manichithrathazhu Malayalam Movie Songs Download Free</h2><br /><p><b><b>DOWNLOAD</b> &#128505; <a href="https://urlcod.com/2uIbWG">https://urlcod.com/2uIbWG</a></b></p><br /><br />
-
-<p>If you want to relive the magic of Manichitrathazhu or watch it for the first time, you can download the songs for free from various online sources. Here are some tips to help you find and enjoy the songs of Manichitrathazhu:</p>
-
-<ul>
-<li>Search for the keyword "Manichitrathazhu Malayalam Movie Songs Download Free" on your preferred search engine. You will find many websites that offer the songs for free download or streaming.</li>
-<li>Choose a reliable and safe website that has good reviews and ratings from other users. Avoid websites that ask for your personal or financial information or require you to sign up or register.</li>
-<li>Download the songs in a format that is compatible with your device and media player. You can also convert the songs to other formats using online tools or software.</li>
-<li>Enjoy the songs of Manichitrathazhu on your device or transfer them to other devices such as your phone, tablet, laptop, or music player.</li>
-<li>You can also create a playlist of your favorite songs from Manichitrathazhu and share it with your friends and family.</li>
-</ul>
-
-<p>Manichitrathazhu is a masterpiece of Malayalam cinema that deserves to be watched and appreciated by everyone. The songs of Manichitrathazhu are an integral part of the film's appeal and charm. By downloading them for free, you can enjoy the film's music anytime and anywhere.</p>
-
-<p>The songs of Manichitrathazhu are not only melodious and catchy, but also reflect the mood and theme of the film. The songs range from folk songs to classical songs, from romantic songs to devotional songs, from playful songs to haunting songs. The lyrics, written by Bichu Thirumala, are rich in imagery and symbolism, and convey the emotions and thoughts of the characters.</p>
-
-<p>The film has a total of eight songs, composed by MG. Radhakrishnan, who won the Kerala State Film Award for Best Music Director for his work. The songs are sung by some of the most renowned singers in Malayalam cinema, such as G. Venugopal, Chitra, Sujatha, Yesudas. The songs are as follows:</p>
-<p></p>
-
-<ol>
-<li>Kumbam Kulathil: A folk song that introduces the story and the setting of the film. It describes the history and legend of the mansion and its ornate lock. It is sung by Yesudas.</li>
-<li>Akku Thi Kkuthana: A playful song that depicts the friendship and romance between Ganga (Shobana) and Nakulan (Suresh Gopi). It is sung by G. Venugopal, Chitra, Sujatha.</li>
-<li>Oru Murai Vanthe: A romantic song that expresses the love and longing between Ganga and Nakulan. It is sung by Yesudas and Chitra.</li>
-<li>Oru Murai Vanthe (F): A female version of the previous song that shows Ganga's loneliness and sadness when Nakulan leaves her alone in the mansion. It is sung by Sujatha.</li>
-<li>Palavattom Pookkalam: A devotional song that praises Lord Shiva and Goddess Parvati. It is sung by Yesudas.</li>
-<li>Pazham Thamizh Pattizhayum: A classical song that reveals the past life of Nagavalli (Shobana), a dancer who was killed by her lover. It is sung by Yesudas.</li>
-<li>Uthungasailam: A poem that narrates the tragic fate of Nagavalli and her lover. It is sung by Sujatha.</li>
-<li>Varuvanillarum: A haunting song that portrays the possession of Ganga by Nagavalli's spirit. It is sung by Chitra.</li>
-</ol>
-
-<p>The songs of Manichitrathazhu are not only enjoyable to listen to, but also enhance the impact and appeal of the film. They are an integral part of the film's narrative and aesthetic. By downloading them for free, you can experience the musical brilliance of Manichitrathazhu.</p> cec2833e83<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Microsoft Office 2010 Download Kostenlos Vollversion Deutsch 64 Bit.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Microsoft Office 2010 Download Kostenlos Vollversion Deutsch 64 Bit.md
deleted file mode 100644
index c513b28351771a25881ab571a5d80a7ca8134ee8..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Microsoft Office 2010 Download Kostenlos Vollversion Deutsch 64 Bit.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-
-<h1>Wie Sie Microsoft Office 2010 kostenlos herunterladen und installieren können</h1>
-<p>Microsoft Office 2010 ist eine beliebte und leistungsstarke Bürosoftware, die viele nützliche Funktionen für die Erstellung und Bearbeitung von Dokumenten, Tabellen, Präsentationen und mehr bietet. Wenn Sie Microsoft Office 2010 kostenlos herunterladen und installieren möchten, gibt es einige Möglichkeiten, dies zu tun.</p>
-<h2>microsoft office 2010 download kostenlos vollversion deutsch 64 bit</h2><br /><p><b><b>Download File</b> &#9675; <a href="https://urlcod.com/2uIaVk">https://urlcod.com/2uIaVk</a></b></p><br /><br />
-<p>Eine Möglichkeit ist, die Starter-Edition von Office 2010 zu verwenden, die Word und Excel in einer eingeschränkten Version enthält. Diese Edition ist normalerweise nur auf neuen PCs und Notebooks vorinstalliert, aber Sie können sie auch selbst herunterladen. Allerdings müssen Sie dafür eine Internetverbindung haben und einige Werbeeinblendungen akzeptieren.</p>
-<p>Eine andere Möglichkeit ist, die Professional Plus-Edition von Office 2010 zu nutzen, die alle Office-Anwendungen wie Word, Excel, PowerPoint, Outlook, Access, Publisher, InfoPath, Communicator, SharePoint Workspace und die Web Apps enthält. Diese Edition ist jedoch nur für Firmenkunden mit Volumenlizenz-Verträgen verfügbar. Um diese Edition zu installieren, benötigen Sie einen Office 2010-Installationsschlüssel und einen Product Key für die Version, die Sie installieren möchten.</p>
-<p>Um die Professional Plus-Edition von Office 2010 kostenlos herunterzuladen und zu installieren, müssen Sie zunächst alle 32-Bit-Versionen von Office deinstallieren. Dann müssen Sie den Office 2010-Installationsdatenträger einlegen oder die Setup-Datei aus dem Internet herunterladen. Öffnen Sie den Ordner x64 im Stammverzeichnis der Installation und doppelklicken Sie auf setup.exe. Folgen Sie den Anweisungen auf dem Bildschirm und geben Sie bei Aufforderung den Product Key ein. Lesen und akzeptieren Sie die Microsoft-Software-Lizenzbedingungen und klicken Sie auf Weiter. Nach der Installation müssen Sie Office über das Internet aktivieren.</p>
-<p>Beachten Sie, dass die 64-Bit-Version von Office 2010 nur mit einem 64-Bit-Windows-Betriebssystem kompatibel ist. Außerdem müssen Sie sicherstellen, dass alle von Drittanbietern stammenden Office-Add-Ins, die Sie benötigen, mit der 64-Bit-Version von Office 2010 kompatibel sind. Wenn Sie Probleme mit der Abwärtskompatibilität befürchten, sollten Sie bei der 32-Bit-Version bleiben.</p>
-<p>Mit diesen Schritten können Sie Microsoft Office 2010 kostenlos herunterladen und installieren. Wir hoffen, dass Ihnen dieser Artikel geholfen hat.</p>
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-
-<p>Wenn Sie Microsoft Office 2010 erfolgreich installiert haben, können Sie die verschiedenen Anwendungen nutzen, um Ihre Aufgaben zu erledigen. Zum Beispiel können Sie mit Word Dokumente erstellen und formatieren, mit Excel Tabellenkalkulationen erstellen und analysieren, mit PowerPoint Präsentationen erstellen und zeigen, mit Outlook E-Mails senden und empfangen, mit Access Datenbanken erstellen und verwalten, mit Publisher Publikationen erstellen und drucken, mit InfoPath Formulare entwerfen und ausfüllen, mit Communicator online kommunizieren und zusammenarbeiten, mit SharePoint Workspace Dateien synchronisieren und freigeben und mit den Web Apps Office-Dokumente online bearbeiten und speichern.</p>
-<p>Microsoft Office 2010 bietet Ihnen viele Möglichkeiten, Ihre Produktivität und Kreativität zu steigern. Sie können die Ribbon-Menüführung verwenden, um schnell auf die wichtigsten Funktionen zuzugreifen. Sie können den Office-Button verwenden, um auf häufig verwendete Optionen wie Speichern, Öffnen, Drucken oder Teilen zuzugreifen. Sie können die Web Apps verwenden, um Ihre Office-Dokumente von jedem Gerät aus zu öffnen und zu bearbeiten. Sie können auch von den neuen Features profitieren, die Office 2010 bietet, wie zum Beispiel die verbesserte Bildbearbeitung, die erweiterten Diagrammtools, die verbesserte Rechtschreibprüfung oder die integrierte Screenshot-Funktion.</p>
-<p>Microsoft Office 2010 ist eine leistungsstarke Bürosoftware, die Ihnen viele Vorteile bietet. Wenn Sie Microsoft Office 2010 kostenlos herunterladen und installieren möchten, folgen Sie einfach den oben beschriebenen Schritten. Wir wünschen Ihnen viel Spaß und Erfolg mit Microsoft Office 2010.</p> e93f5a0c3f<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/ntt123/vietnam-male-voice-wavegru-tts/app.py b/spaces/ntt123/vietnam-male-voice-wavegru-tts/app.py
deleted file mode 100644
index 885e2782419cf867218a63d487aff36f342300f1..0000000000000000000000000000000000000000
--- a/spaces/ntt123/vietnam-male-voice-wavegru-tts/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-## build wavegru-cpp
-# import os
-# os.system("./bazelisk-linux-amd64 clean --expunge")
-# os.system("./bazelisk-linux-amd64 build wavegru_mod -c opt --copt=-march=native")
-
-# install espeak
-import os
-import re
-import unicodedata
-
-import regex
-
-if not os.path.isfile("./wavegru_mod.so"):
-    os.system("bash ./build_ext.sh")
-
-import gradio as gr
-
-from inference import load_tacotron_model, load_wavegru_net, mel_to_wav, text_to_mel
-from wavegru_cpp import extract_weight_mask, load_wavegru_cpp
-
-alphabet, tacotron_net, tacotron_config = load_tacotron_model(
-    "./alphabet.txt", "./tacotron.toml", "./mono_tts_cbhg_small_0700000.ckpt"
-)
-
-wavegru_config, wavegru_net = load_wavegru_net(
-    "./wavegru.yaml", "./wavegru_vocoder_tpu_gta_preemphasis_pruning_0400000.ckpt"
-)
-
-wave_cpp_weight_mask = extract_weight_mask(wavegru_net)
-wavecpp = load_wavegru_cpp(wave_cpp_weight_mask, wavegru_config["upsample_factors"][-1])
-
-
-space_re = regex.compile(r"\s+")
-number_re = regex.compile("([0-9]+)")
-digits = ["không", "một", "hai", "ba", "bốn", "năm", "sáu", "bảy", "tám", "chín"]
-num_re = regex.compile(r"([0-9.,]*[0-9])")
-alphabet_ = "aàáảãạăằắẳẵặâầấẩẫậeèéẻẽẹêềếểễệiìíỉĩịoòóỏõọôồốổỗộơờớởỡợuùúủũụưừứửữựyỳýỷỹỵbcdđghklmnpqrstvx"
-keep_text_and_num_re = regex.compile(rf"[^\s{alphabet_}.,0-9]")
-keep_text_re = regex.compile(rf"[^\s{alphabet_}]")
-
-
-def read_number(num: str) -> str:
-    if len(num) == 1:
-        return digits[int(num)]
-    elif len(num) == 2 and num.isdigit():
-        n = int(num)
-        end = digits[n % 10]
-        if n == 10:
-            return "mười"
-        if n % 10 == 5:
-            end = "lăm"
-        if n % 10 == 0:
-            return digits[n // 10] + " mươi"
-        elif n < 20:
-            return "mười " + end
-        else:
-            if n % 10 == 1:
-                end = "mốt"
-            return digits[n // 10] + " mươi " + end
-    elif len(num) == 3 and num.isdigit():
-        n = int(num)
-        if n % 100 == 0:
-            return digits[n // 100] + " trăm"
-        elif num[1] == "0":
-            return digits[n // 100] + " trăm lẻ " + digits[n % 100]
-        else:
-            return digits[n // 100] + " trăm " + read_number(num[1:])
-    elif len(num) >= 4 and len(num) <= 6 and num.isdigit():
-        n = int(num)
-        n1 = n // 1000
-        return read_number(str(n1)) + " ngàn " + read_number(num[-3:])
-    elif "," in num:
-        n1, n2 = num.split(",")
-        return read_number(n1) + " phẩy " + read_number(n2)
-    elif "." in num:
-        parts = num.split(".")
-        if len(parts) == 2:
-            if parts[1] == "000":
-                return read_number(parts[0]) + " ngàn"
-            elif parts[1].startswith("00"):
-                end = digits[int(parts[1][2:])]
-                return read_number(parts[0]) + " ngàn lẻ " + end
-            else:
-                return read_number(parts[0]) + " ngàn " + read_number(parts[1])
-        elif len(parts) == 3:
-            return (
-                read_number(parts[0])
-                + " triệu "
-                + read_number(parts[1])
-                + " ngàn "
-                + read_number(parts[2])
-            )
-    return num
-
-
-def normalize_text(text):
-    # lowercase
-    text = text.lower()
-    # unicode normalize
-    text = unicodedata.normalize("NFKC", text)
-    text = text.replace(".", ". ")
-    text = text.replace(",", ", ")
-    text = text.replace(";", "; ")
-    text = text.replace(":", ": ")
-    text = text.replace("!", "! ")
-    text = text.replace("?", "? ")
-    text = text.replace("(", "( ")
-
-    text = num_re.sub(r" \1 ", text)
-    words = text.split()
-    words = [read_number(w) if num_re.fullmatch(w) else w for w in words]
-    text = " ".join(words)
-
-    # remove redundant spaces
-    text = re.sub(r"\s+", " ", text)
-    # remove leading and trailing spaces
-    text = text.strip()
-    return text
-
-
-def speak(text):
-    text = normalize_text(text)
-    mel = text_to_mel(tacotron_net, text, alphabet, tacotron_config)
-    y = mel_to_wav(wavegru_net, wavecpp, mel, wavegru_config)
-    return 24_000, y
-
-
-title = "WaveGRU-TTS"
-description = "WaveGRU text-to-speech demo."
-
-gr.Interface(
-    fn=speak,
-    inputs="text",
-    examples=[
-        "Trăm năm trong cõi người ta, chữ tài chữ mệnh khéo là ghét nhau.",
-        "Đoạn trường tân thanh, thường được biết đến với cái tên đơn giản là Truyện Kiều, là một truyện thơ của đại thi hào Nguyễn Du.",
-        "Lục Vân Tiên quê ở huyện Đông Thành, khôi ngô tuấn tú, tài kiêm văn võ. Nghe tin triều đình mở khoa thi, Vân Tiên từ giã thầy xuống núi đua tài.",
-        "Lê Quý Đôn, tên thuở nhỏ là Lê Danh Phương, là vị quan thời Lê trung hưng, cũng là nhà thơ và được mệnh danh là nhà bác học lớn của Việt Nam trong thời phong kiến.",
-        "Tất cả mọi người đều sinh ra có quyền bình đẳng. Tạo hóa cho họ những quyền không ai có thể xâm phạm được, trong những quyền ấy, có quyền được sống, quyền tự do và quyền mưu cầu hạnh phúc.",
-    ],
-    outputs="audio",
-    title=title,
-    description=description,
-    theme="default",
-    allow_screenshot=False,
-    allow_flagging="never",
-).launch(enable_queue=True)
diff --git a/spaces/olimpa/CalendarJs/jquery-calendar.css b/spaces/olimpa/CalendarJs/jquery-calendar.css
deleted file mode 100644
index d6927a054b1c719df1f4c46b05a031ce27217671..0000000000000000000000000000000000000000
--- a/spaces/olimpa/CalendarJs/jquery-calendar.css
+++ /dev/null
@@ -1,231 +0,0 @@
-/*!
- * @class Calendar ~jquery-calendar plugin~ (https://github.com/ArrobeFr/jquery-calendar)
- * @author Developped by Arrobe (https://www.arrobe.fr)
- * @license Licensed under MIT (https://github.com/ArrobeFr/jquery-calendar/blob/master/LICENSE)
- */
-
-.calendar {
-position:relative
-}
-
-.calendar-events>ul,.calendar-timeline>ul,.calendar-events-day>ul,.calendar-month-day-header>ul,.calendar-month-events-day>ul {
-list-style-type:none;
-padding:0
-}
-
-.calendar-timeline>ul>li:after {
-position: absolute;
-content: '';
-border-bottom: 1px solid rgb(234,234,234);
-}
-
-.calendar-event {
-overflow:hidden
-}
-
-.calendar-categories {
-margin-bottom:10px;
-overflow-x:auto;
-width:100%;
-z-index:10
-}
-
-li.calendar-event>a {
-text-decoration:none
-}
-
-em.event-name,em.event-content {
-font-style:normal
-}
-
-.calendar .calendar-timeline {
-display:block;
-height:100%;
-left:0;
-position:absolute;
-top:0;
-width:100%
-}
-
-.calendar .calendar-timeline li {
-position:relative
-}
-
-.calendar .calendar-timeline li::after {
-background:#EAEAEA;
-bottom:0;
-height:1px;
-left:60px;
-position:absolute;
-width:calc(100% - 60px)
-}
-
-.calendar .calendar-timeline li:last-of-type::after {
-display:none
-}
-
-.calendar .calendar-timeline li span {
--ms-transform:translateY(-50%);
--webkit-transform:translateY(-50%);
-display:inline-block;
-transform:translateY(-50%)
-}
-
-.calendar .calendar-events {
-float:left;
-margin-left:60px;
-position:relative;
-width:calc(100% - 60px);
-z-index:1
-}
-
-.calendar-events-day, .calendar-month-day-header {
-overflow: hidden;
-border:1px solid #EAEAEA;
-float:left;
-margin-bottom:0
-}
-
-.calendar .calendar-events .calendar-day-header, .calendar-month-day-header>div {
-border-bottom:1px solid #EAEAEA;
-display:table;
-padding:0;
-width:100%
-}
-
-.calendar .calendar-events .calendar-day-header > span {
-display:table-cell;
-font-weight:700;
-line-height:1.2;
-margin-bottom:0;
-padding:0 .5em;
-text-align:center;
-vertical-align:middle
-}
-
-.calendar-events-day > ul, .calendar-month-day-header > ul {
--webkit-overflow-scrolling:touch;
-display:block;
-overflow:auto;
-overflow-x:hidden;
-padding:0;
-position:relative
-}
-
-.calendar-events-day > ul::after, .calendar-month-day-header > ul::after {
-clear:both;
-color:transparent;
-display:none;
-height:100%;
-opacity:0;
-width:1px
-}
-
-.calendar .calendar-events .calendar-event {
--ms-flex-negative:1;
--webkit-transition:opacity .2s,background .2s;
-border-radius:2px;
-box-shadow:inset 0 -3px 0 rgba(0,0,0,0.2);
-box-shadow:0 10px 20px rgba(0,0,0,0.1),inset 0 -3px 0 rgba(0,0,0,0.2);
-flex-shrink:1;
-float:left;
-height:auto;
-left:-1px;
-margin-right:0;
-max-width:none;
-position:absolute;
-transition:opacity .2s,background .2s;
-width:calc(100% + 2px);
-z-index:3
-}
-
-.calendar-month-event {
--ms-flex-negative:1;
--webkit-transition:opacity .2s,background .2s;
-border-radius:2px;
-box-shadow:inset 0 -3px 0 rgba(0,0,0,0.2);
-flex-shrink:1;
-float:left;
-left:-1px;
-margin-right:0;
-max-width:none;
-position:absolute;
-transition:opacity .2s,background .2s;
-width:calc(100% + 2px);
-z-index:3
-}
-
-.calendar .calendar-events .calendar-event:last-of-type {
-margin-right:0
-}
-
-.calendar .calendar-events .calendar-event a {
-display:block;
-height:100%;
-padding:2px
-}
-
-.calendar-events-day:not(:first-of-type), .calendar-month-day-header:not(:first-of-type) {
-border-left-width:0
-}
-
-.calendar .calendar-events .calendar-event.selected-event {
-visibility:hidden
-}
-
-.calendar.loading .calendar-events .calendar-event {
-opacity:0
-}
-
-.calendar .event-name,.calendar .event-date {
--moz-osx-font-smoothing:grayscale;
--webkit-font-smoothing:antialiased;
-color:#fff;
-display:block;
-font-weight:700
-}
-
-.calendar .event-content {
--moz-osx-font-smoothing:grayscale;
--webkit-font-smoothing:antialiased;
-color:#fff;
-display:block
-}
-
-.calendar-daynote .event-name,.calendar-daynote .event-date, .calendar-daynote .event-content, .calendar-label-daynote {
-color:#000;
-}
-
-.calendar .event-name {
-font-size:1.8rem;
-}
-
-.calendar .event-date {
-font-size:1.4rem;
-line-height:1.2;
-margin-bottom:.2em;
-opacity:.7
-}
-
-.calendar-month {
-  font-weight: bold;
-  border: 1px solid #EAEAEA;
-  border-bottom: none;
-}
-
-.calendar-month-day-header>div {
-  text-align: center;
-  font-weight: bold;
-}
-
-.calendar-month-events-day>span {
-  color: #BDBDBD ;
-}
-
-.weektoday, .monthtoweek, .daytoweek, .weektomonth {
-  cursor: pointer;
-}
-
-.weektoday:hover, .monthtoweek:hover, .daytoweek:hover, .weektomonth:hover {
-  background-color: #F5F5F5;
-}
diff --git a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/scripts/convert_mmc4_to_wds.py b/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/scripts/convert_mmc4_to_wds.py
deleted file mode 100644
index 724721d0750cedbe1ba1f979409f569ccb04cf16..0000000000000000000000000000000000000000
--- a/spaces/openflamingo/OpenFlamingo/open_flamingo/open_flamingo/scripts/convert_mmc4_to_wds.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import argparse
-import json
-import os
-import uuid
-import zipfile
-from PIL import Image
-import base64
-from io import BytesIO
-
-import braceexpand
-import webdataset as wds
-
-arg_parser = argparse.ArgumentParser()
-arg_parser.add_argument(
-    "--output_dir",
-    type=str,
-    help="Pass in the directory where the output shards (as tar files) will be written to.",
-)
-arg_parser.add_argument(
-    "--zip_files",
-    type=str,
-    help="Pass in a list of MMC4 shards in the format path_to_shard/shard_{0..23098}.zip",
-)
-arg_parser.add_argument(
-    "--image_dir",
-    type=str,
-    help="Pass in the directory where the images have been downloaded to.",
-)
-args = arg_parser.parse_args()
-
-
-def main():
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    doc_shards = list(braceexpand.braceexpand(args.zip_files))
-
-    with wds.ShardWriter(args.output_dir + "/%09d.tar") as sink:
-        for idx in range(len(doc_shards)):
-            # Open the ZIP archive and extract the JSON file
-            with zipfile.ZipFile(doc_shards[idx], "r") as zip_file:
-                # Assumes the JSON file is the first file in the archive
-                json_filename = zip_file.namelist()[0]
-                with zip_file.open(json_filename, "r") as json_file:
-                    for sample_data in json_file:
-                        # get image names from json
-                        sample_data = json.loads(sample_data)
-                        image_info = sample_data["image_info"]
-                        image_names = [image["image_name"] for image in image_info]
-
-                        # Add each image to the tar file
-                        for img_idx, image_name in enumerate(image_names):
-                            try:
-                                # load image
-                                img = Image.open(
-                                    os.path.join(args.image_dir, str(idx), image_name)
-                                ).convert("RGB")
-                                buffered = BytesIO()
-                                img.save(buffered, format="JPEG")
-                                img_str = base64.b64encode(buffered.getvalue())
-                                # convert to base64
-                                sample_data["image_info"][img_idx][
-                                    "image_base64"
-                                ] = str(img_str)
-                            except FileNotFoundError:
-                                print(
-                                    f"Did not find {image_name} downloaded. This can happen if the url is now 404."
-                                )
-                            except Exception as e:
-                                print(f"Error processing {image_name}: {e}")
-
-                        key_str = uuid.uuid4().hex
-                        sink.write({"__key__": key_str, "json": sample_data})
-
-
-if __name__ == "__main__":
-    main()
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_tan\303\241r_de_aggregate.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_tan\303\241r_de_aggregate.html"
deleted file mode 100644
index d17c3bf8d2b9b3b98ac809225df233ccc29826bd..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_tan\303\241r_de_aggregate.html"
+++ /dev/null
@@ -1,46 +0,0 @@
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="okwjehwwicrsircsgidp_viz_container">
-    <div id="okwjehwwicrsircsgidp_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "okwjehwwicrsircsgidp_saliency_plot_container" class="okwjehwwicrsircsgidp_viz_container" style="display:block">
-            
-<div id="mmwjbzityotpzrejorvv_saliency_plot" class="mmwjbzityotpzrejorvv_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Source Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁Er</th><th>▁ist</th><th>▁Lehrer.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ő</th><th style="background:rgba(255.0, 13.0, 87.0, 0.8581105169340464)">0.774</th><th style="background:rgba(255.0, 13.0, 87.0, 0.02253911665676371)">0.025</th><th style="background:rgba(255.0, 13.0, 87.0, 0.14866310160427795)">0.139</th><th style="background:rgba(255.0, 13.0, 87.0, 0.24325609031491383)">0.226</th></tr><tr><th>▁tanár.</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7004555357496535)">0.633</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4797385620915033)">0.431</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.903</th><th style="background:rgba(255.0, 13.0, 87.0, 0.9132897603485836)">0.819</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="vuzldqfpsiwcbvembsek_viz_container">
-    <div id="vuzldqfpsiwcbvembsek_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "vuzldqfpsiwcbvembsek_saliency_plot_container" class="vuzldqfpsiwcbvembsek_viz_container" style="display:block">
-            
-<div id="yswtewtxhfzjvqcbmgqm_saliency_plot" class="yswtewtxhfzjvqcbmgqm_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Target Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁Er</th><th>▁ist</th><th>▁Lehrer.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Er</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.902</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4245593186769657)">0.382</th><th style="background:rgba(30.0, 136.0, 229.0, 0.5822142998613588)">-0.525</th></tr><tr><th>▁ist</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.15654585066349747)">0.141</th><th style="background:rgba(30.0, 136.0, 229.0, 0.03830461477520309)">-0.036</th></tr><tr><th>▁Lehrer.</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(30.0, 136.0, 229.0, 0.022539116656763607)">-0.027</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_comparison.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_comparison.py
deleted file mode 100644
index 7997a0cc01864dfe2ac0e37f8f5b4d5559c0ca4c..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_comparison.py
+++ /dev/null
@@ -1,405 +0,0 @@
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    DiffusionPipeline,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-
-
-pipe1_model_id = "CompVis/stable-diffusion-v1-1"
-pipe2_model_id = "CompVis/stable-diffusion-v1-2"
-pipe3_model_id = "CompVis/stable-diffusion-v1-3"
-pipe4_model_id = "CompVis/stable-diffusion-v1-4"
-
-
-class StableDiffusionComparisonPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for parallel comparison of Stable Diffusion v1-v4
-    This pipeline inherits from DiffusionPipeline and depends on the use of an Auth Token for
-    downloading pre-trained checkpoints from Hugging Face Hub.
-    If using Hugging Face Hub, pass the Model ID for Stable Diffusion v1.4 as the previous 3 checkpoints will be loaded
-    automatically.
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super()._init_()
-
-        self.pipe1 = StableDiffusionPipeline.from_pretrained(pipe1_model_id)
-        self.pipe2 = StableDiffusionPipeline.from_pretrained(pipe2_model_id)
-        self.pipe3 = StableDiffusionPipeline.from_pretrained(pipe3_model_id)
-        self.pipe4 = StableDiffusionPipeline(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-            requires_safety_checker=requires_safety_checker,
-        )
-
-        self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)
-
-    @property
-    def layers(self) -> Dict[str, Any]:
-        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    @torch.no_grad()
-    def text2img_sd1_1(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        return self.pipe1(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-    @torch.no_grad()
-    def text2img_sd1_2(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        return self.pipe2(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-    @torch.no_grad()
-    def text2img_sd1_3(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        return self.pipe3(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-    @torch.no_grad()
-    def text2img_sd1_4(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        return self.pipe4(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-    @torch.no_grad()
-    def _call_(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation. This function will generate 4 results as part
-        of running all the 4 pipelines for SD1.1-1.4 together in a serial-processing, parallel-invocation fashion.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, optional, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, optional, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, optional, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, optional, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            eta (`float`, optional, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, optional):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, optional):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, optional, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, optional, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.to(device)
-
-        # Checks if the height and width are divisible by 8 or not
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}.")
-
-        # Get first result from Stable Diffusion Checkpoint v1.1
-        res1 = self.text2img_sd1_1(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-        # Get first result from Stable Diffusion Checkpoint v1.2
-        res2 = self.text2img_sd1_2(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-        # Get first result from Stable Diffusion Checkpoint v1.3
-        res3 = self.text2img_sd1_3(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-        # Get first result from Stable Diffusion Checkpoint v1.4
-        res4 = self.text2img_sd1_4(
-            prompt=prompt,
-            height=height,
-            width=width,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            eta=eta,
-            generator=generator,
-            latents=latents,
-            output_type=output_type,
-            return_dict=return_dict,
-            callback=callback,
-            callback_steps=callback_steps,
-            **kwargs,
-        )
-
-        # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
-        return StableDiffusionPipelineOutput([res1[0], res2[0], res3[0], res4[0]])
diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/oldalexnet.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/oldalexnet.py
deleted file mode 100644
index 18cb793e8093b6a98e959af7da105f44671c1ead..0000000000000000000000000000000000000000
--- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/oldalexnet.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from __future__ import print_function
-# based on https://github.com/jiecaoyu/pytorch_imagenet
-
-import os
-import torch
-import sys
-import torch.nn as nn
-import torch.nn.functional as F
-from collections import OrderedDict
-import torchvision.transforms
-import numpy
-
-def load_places_alexnet(weight_file):
-    model = AlexNet()
-    state_dict = torch.load(weight_file)
-    model.load_state_dict(state_dict)
-    return model
-
-
-class AlexNet(nn.Sequential):
-
-    def __init__(self, num_classes=None,
-            include_lrn=True, split_groups=True,
-            include_dropout=True):
-        w = [3, 96, 256, 384, 384, 256, 4096, 4096, 365]
-        if num_classes is not None:
-            w[-1] = num_classes
-        if split_groups is True:
-            groups = [1, 2, 1, 2, 2]
-        else:
-            groups = [1, 1, 1, 1, 1]
-        sequence = OrderedDict()
-        for name, module in [
-                ('conv1', nn.Conv2d(w[0], w[1], kernel_size=11,
-                    stride=4,
-                    groups=groups[0], bias=True)),
-                ('relu1', nn.ReLU(inplace=True)),
-                ('pool1', nn.MaxPool2d(kernel_size=3, stride=2)),
-                ('lrn1', LRN(local_size=5, alpha=0.0001, beta=0.75)),
-                ('conv2', nn.Conv2d(w[1], w[2], kernel_size=5, padding=2,
-                    groups=groups[1], bias=True)),
-                ('relu2', nn.ReLU(inplace=True)),
-                ('pool2', nn.MaxPool2d(kernel_size=3, stride=2)),
-                ('lrn2', LRN(local_size=5, alpha=0.0001, beta=0.75)),
-                ('conv3', nn.Conv2d(w[2], w[3], kernel_size=3, padding=1,
-                    groups=groups[2], bias=True)),
-                ('relu3', nn.ReLU(inplace=True)),
-                ('conv4', nn.Conv2d(w[3], w[4], kernel_size=3, padding=1,
-                    groups=groups[3], bias=True)),
-                ('relu4', nn.ReLU(inplace=True)),
-                ('conv5', nn.Conv2d(w[4], w[5], kernel_size=3, padding=1,
-                    groups=groups[4], bias=True)),
-                ('relu5', nn.ReLU(inplace=True)),
-                ('pool5', nn.MaxPool2d(kernel_size=3, stride=2)),
-                ('flatten', Vectorize()),
-                ('fc6', nn.Linear(w[5] * 6 * 6, w[6], bias=True)),
-                ('relu6', nn.ReLU(inplace=True)),
-                ('dropout6', nn.Dropout()),
-                ('fc7', nn.Linear(w[6], w[7], bias=True)),
-                ('relu7', nn.ReLU(inplace=True)),
-                ('dropout7', nn.Dropout()),
-                ('fc8', nn.Linear(w[7], w[8])) ]:
-            if not include_lrn and name.startswith('lrn'):
-                continue
-            if not include_dropout and name.startswith('drop'):
-                continue
-            sequence[name] = module
-        super(AlexNet, self).__init__(sequence)
-
-class LRN(nn.Module):
-    def __init__(self, local_size=1, alpha=1.0, beta=0.75,
-            ACROSS_CHANNELS=True):
-        super(LRN, self).__init__()
-        self.ACROSS_CHANNELS = ACROSS_CHANNELS
-        if ACROSS_CHANNELS:
-            self.average=nn.AvgPool3d(kernel_size=(local_size, 1, 1),
-                    stride=1,
-                    padding=(int((local_size-1.0)/2), 0, 0))
-        else:
-            self.average=nn.AvgPool2d(kernel_size=local_size,
-                    stride=1,
-                    padding=int((local_size-1.0)/2))
-        self.alpha = alpha
-        self.beta = beta
-
-    def forward(self, x):
-        if self.ACROSS_CHANNELS:
-            div = x.pow(2).unsqueeze(1)
-            div = self.average(div).squeeze(1)
-            div = div.mul(self.alpha).add(1.0).pow(self.beta)
-        else:
-            div = x.pow(2)
-            div = self.average(div)
-            div = div.mul(self.alpha).add(1.0).pow(self.beta)
-        x = x.div(div)
-        return x
-
-class Vectorize(nn.Module):
-    def __init__(self):
-        super(Vectorize, self).__init__()
-
-    def forward(self, x):
-        x = x.view(x.size(0), int(numpy.prod(x.size()[1:])))
-        return x
-
diff --git a/spaces/paulokewunmi/jumia_product_search/image_search_engine/artifacts/__init__.py b/spaces/paulokewunmi/jumia_product_search/image_search_engine/artifacts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/peteralexandercharles/space-that-creates-model-demo-space/README.md b/spaces/peteralexandercharles/space-that-creates-model-demo-space/README.md
deleted file mode 100644
index 39d063b2c8f6c24fa25e6d465652ec30c4080e88..0000000000000000000000000000000000000000
--- a/spaces/peteralexandercharles/space-that-creates-model-demo-space/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Space That Creates Model Demo Space
-emoji: 🐠
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.6
-app_file: app.py
-pinned: false
-duplicated_from: hysts/space-that-creates-model-demo-space
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py
deleted file mode 100644
index 315fb9c8902c5e3f4dd8419ccdf7d85c6718096e..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import collections
-import math
-from typing import (
-    TYPE_CHECKING,
-    Dict,
-    Iterable,
-    Iterator,
-    Mapping,
-    Sequence,
-    TypeVar,
-    Union,
-)
-
-from pip._vendor.resolvelib.providers import AbstractProvider
-
-from .base import Candidate, Constraint, Requirement
-from .candidates import REQUIRES_PYTHON_IDENTIFIER
-from .factory import Factory
-
-if TYPE_CHECKING:
-    from pip._vendor.resolvelib.providers import Preference
-    from pip._vendor.resolvelib.resolvers import RequirementInformation
-
-    PreferenceInformation = RequirementInformation[Requirement, Candidate]
-
-    _ProviderBase = AbstractProvider[Requirement, Candidate, str]
-else:
-    _ProviderBase = AbstractProvider
-
-# Notes on the relationship between the provider, the factory, and the
-# candidate and requirement classes.
-#
-# The provider is a direct implementation of the resolvelib class. Its role
-# is to deliver the API that resolvelib expects.
-#
-# Rather than work with completely abstract "requirement" and "candidate"
-# concepts as resolvelib does, pip has concrete classes implementing these two
-# ideas. The API of Requirement and Candidate objects are defined in the base
-# classes, but essentially map fairly directly to the equivalent provider
-# methods. In particular, `find_matches` and `is_satisfied_by` are
-# requirement methods, and `get_dependencies` is a candidate method.
-#
-# The factory is the interface to pip's internal mechanisms. It is stateless,
-# and is created by the resolver and held as a property of the provider. It is
-# responsible for creating Requirement and Candidate objects, and provides
-# services to those objects (access to pip's finder and preparer).
-
-
-D = TypeVar("D")
-V = TypeVar("V")
-
-
-def _get_with_identifier(
-    mapping: Mapping[str, V],
-    identifier: str,
-    default: D,
-) -> Union[D, V]:
-    """Get item from a package name lookup mapping with a resolver identifier.
-
-    This extra logic is needed when the target mapping is keyed by package
-    name, which cannot be directly looked up with an identifier (which may
-    contain requested extras). Additional logic is added to also look up a value
-    by "cleaning up" the extras from the identifier.
-    """
-    if identifier in mapping:
-        return mapping[identifier]
-    # HACK: Theoretically we should check whether this identifier is a valid
-    # "NAME[EXTRAS]" format, and parse out the name part with packaging or
-    # some regular expression. But since pip's resolver only spits out three
-    # kinds of identifiers: normalized PEP 503 names, normalized names plus
-    # extras, and Requires-Python, we can cheat a bit here.
-    name, open_bracket, _ = identifier.partition("[")
-    if open_bracket and name in mapping:
-        return mapping[name]
-    return default
-
-
-class PipProvider(_ProviderBase):
-    """Pip's provider implementation for resolvelib.
-
-    :params constraints: A mapping of constraints specified by the user. Keys
-        are canonicalized project names.
-    :params ignore_dependencies: Whether the user specified ``--no-deps``.
-    :params upgrade_strategy: The user-specified upgrade strategy.
-    :params user_requested: A set of canonicalized package names that the user
-        supplied for pip to install/upgrade.
-    """
-
-    def __init__(
-        self,
-        factory: Factory,
-        constraints: Dict[str, Constraint],
-        ignore_dependencies: bool,
-        upgrade_strategy: str,
-        user_requested: Dict[str, int],
-    ) -> None:
-        self._factory = factory
-        self._constraints = constraints
-        self._ignore_dependencies = ignore_dependencies
-        self._upgrade_strategy = upgrade_strategy
-        self._user_requested = user_requested
-        self._known_depths: Dict[str, float] = collections.defaultdict(lambda: math.inf)
-
-    def identify(self, requirement_or_candidate: Union[Requirement, Candidate]) -> str:
-        return requirement_or_candidate.name
-
-    def get_preference(
-        self,
-        identifier: str,
-        resolutions: Mapping[str, Candidate],
-        candidates: Mapping[str, Iterator[Candidate]],
-        information: Mapping[str, Iterable["PreferenceInformation"]],
-        backtrack_causes: Sequence["PreferenceInformation"],
-    ) -> "Preference":
-        """Produce a sort key for given requirement based on preference.
-
-        The lower the return value is, the more preferred this group of
-        arguments is.
-
-        Currently pip considers the following in order:
-
-        * Prefer if any of the known requirements is "direct", e.g. points to an
-          explicit URL.
-        * If equal, prefer if any requirement is "pinned", i.e. contains
-          operator ``===`` or ``==``.
-        * If equal, calculate an approximate "depth" and resolve requirements
-          closer to the user-specified requirements first. If the depth cannot
-          by determined (eg: due to no matching parents), it is considered
-          infinite.
-        * Order user-specified requirements by the order they are specified.
-        * If equal, prefers "non-free" requirements, i.e. contains at least one
-          operator, such as ``>=`` or ``<``.
-        * If equal, order alphabetically for consistency (helps debuggability).
-        """
-        try:
-            next(iter(information[identifier]))
-        except StopIteration:
-            # There is no information for this identifier, so there's no known
-            # candidates.
-            has_information = False
-        else:
-            has_information = True
-
-        if has_information:
-            lookups = (r.get_candidate_lookup() for r, _ in information[identifier])
-            candidate, ireqs = zip(*lookups)
-        else:
-            candidate, ireqs = None, ()
-
-        operators = [
-            specifier.operator
-            for specifier_set in (ireq.specifier for ireq in ireqs if ireq)
-            for specifier in specifier_set
-        ]
-
-        direct = candidate is not None
-        pinned = any(op[:2] == "==" for op in operators)
-        unfree = bool(operators)
-
-        try:
-            requested_order: Union[int, float] = self._user_requested[identifier]
-        except KeyError:
-            requested_order = math.inf
-            if has_information:
-                parent_depths = (
-                    self._known_depths[parent.name] if parent is not None else 0.0
-                    for _, parent in information[identifier]
-                )
-                inferred_depth = min(d for d in parent_depths) + 1.0
-            else:
-                inferred_depth = math.inf
-        else:
-            inferred_depth = 1.0
-        self._known_depths[identifier] = inferred_depth
-
-        requested_order = self._user_requested.get(identifier, math.inf)
-
-        # Requires-Python has only one candidate and the check is basically
-        # free, so we always do it first to avoid needless work if it fails.
-        requires_python = identifier == REQUIRES_PYTHON_IDENTIFIER
-
-        # Prefer the causes of backtracking on the assumption that the problem
-        # resolving the dependency tree is related to the failures that caused
-        # the backtracking
-        backtrack_cause = self.is_backtrack_cause(identifier, backtrack_causes)
-
-        return (
-            not requires_python,
-            not direct,
-            not pinned,
-            not backtrack_cause,
-            inferred_depth,
-            requested_order,
-            not unfree,
-            identifier,
-        )
-
-    def find_matches(
-        self,
-        identifier: str,
-        requirements: Mapping[str, Iterator[Requirement]],
-        incompatibilities: Mapping[str, Iterator[Candidate]],
-    ) -> Iterable[Candidate]:
-        def _eligible_for_upgrade(identifier: str) -> bool:
-            """Are upgrades allowed for this project?
-
-            This checks the upgrade strategy, and whether the project was one
-            that the user specified in the command line, in order to decide
-            whether we should upgrade if there's a newer version available.
-
-            (Note that we don't need access to the `--upgrade` flag, because
-            an upgrade strategy of "to-satisfy-only" means that `--upgrade`
-            was not specified).
-            """
-            if self._upgrade_strategy == "eager":
-                return True
-            elif self._upgrade_strategy == "only-if-needed":
-                user_order = _get_with_identifier(
-                    self._user_requested,
-                    identifier,
-                    default=None,
-                )
-                return user_order is not None
-            return False
-
-        constraint = _get_with_identifier(
-            self._constraints,
-            identifier,
-            default=Constraint.empty(),
-        )
-        return self._factory.find_candidates(
-            identifier=identifier,
-            requirements=requirements,
-            constraint=constraint,
-            prefers_installed=(not _eligible_for_upgrade(identifier)),
-            incompatibilities=incompatibilities,
-        )
-
-    def is_satisfied_by(self, requirement: Requirement, candidate: Candidate) -> bool:
-        return requirement.is_satisfied_by(candidate)
-
-    def get_dependencies(self, candidate: Candidate) -> Sequence[Requirement]:
-        with_requires = not self._ignore_dependencies
-        return [r for r in candidate.iter_dependencies(with_requires) if r is not None]
-
-    @staticmethod
-    def is_backtrack_cause(
-        identifier: str, backtrack_causes: Sequence["PreferenceInformation"]
-    ) -> bool:
-        for backtrack_cause in backtrack_causes:
-            if identifier == backtrack_cause.requirement.name:
-                return True
-            if backtrack_cause.parent and identifier == backtrack_cause.parent.name:
-                return True
-        return False
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/__init__.py
deleted file mode 100644
index 35d7bd1c4acb1a54af65c1ab6cfd68f32eb26e34..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/__init__.py
+++ /dev/null
@@ -1,270 +0,0 @@
-"""Extensions to the 'distutils' for large or complex distributions"""
-
-import functools
-import os
-import re
-
-import _distutils_hack.override  # noqa: F401
-
-import distutils.core
-from distutils.errors import DistutilsOptionError
-from distutils.util import convert_path as _convert_path
-
-from .warnings import SetuptoolsDeprecationWarning
-
-import setuptools.version
-from setuptools.extension import Extension
-from setuptools.dist import Distribution
-from setuptools.depends import Require
-from setuptools.discovery import PackageFinder, PEP420PackageFinder
-from . import monkey
-from . import logging
-
-
-__all__ = [
-    'setup',
-    'Distribution',
-    'Command',
-    'Extension',
-    'Require',
-    'SetuptoolsDeprecationWarning',
-    'find_packages',
-    'find_namespace_packages',
-]
-
-__version__ = setuptools.version.__version__
-
-bootstrap_install_from = None
-
-
-find_packages = PackageFinder.find
-find_namespace_packages = PEP420PackageFinder.find
-
-
-def _install_setup_requires(attrs):
-    # Note: do not use `setuptools.Distribution` directly, as
-    # our PEP 517 backend patch `distutils.core.Distribution`.
-    class MinimalDistribution(distutils.core.Distribution):
-        """
-        A minimal version of a distribution for supporting the
-        fetch_build_eggs interface.
-        """
-
-        def __init__(self, attrs):
-            _incl = 'dependency_links', 'setup_requires'
-            filtered = {k: attrs[k] for k in set(_incl) & set(attrs)}
-            super().__init__(filtered)
-            # Prevent accidentally triggering discovery with incomplete set of attrs
-            self.set_defaults._disable()
-
-        def _get_project_config_files(self, filenames=None):
-            """Ignore ``pyproject.toml``, they are not related to setup_requires"""
-            try:
-                cfg, toml = super()._split_standard_project_metadata(filenames)
-                return cfg, ()
-            except Exception:
-                return filenames, ()
-
-        def finalize_options(self):
-            """
-            Disable finalize_options to avoid building the working set.
-            Ref #2158.
-            """
-
-    dist = MinimalDistribution(attrs)
-
-    # Honor setup.cfg's options.
-    dist.parse_config_files(ignore_option_errors=True)
-    if dist.setup_requires:
-        _fetch_build_eggs(dist)
-
-
-def _fetch_build_eggs(dist):
-    try:
-        dist.fetch_build_eggs(dist.setup_requires)
-    except Exception as ex:
-        msg = """
-        It is possible a package already installed in your system
-        contains an version that is invalid according to PEP 440.
-        You can try `pip install --use-pep517` as a workaround for this problem,
-        or rely on a new virtual environment.
-
-        If the problem refers to a package that is not installed yet,
-        please contact that package's maintainers or distributors.
-        """
-        if "InvalidVersion" in ex.__class__.__name__:
-            if hasattr(ex, "add_note"):
-                ex.add_note(msg)  # PEP 678
-            else:
-                dist.announce(f"\n{msg}\n")
-        raise
-
-
-def setup(**attrs):
-    # Make sure we have any requirements needed to interpret 'attrs'.
-    logging.configure()
-    _install_setup_requires(attrs)
-    return distutils.core.setup(**attrs)
-
-
-setup.__doc__ = distutils.core.setup.__doc__
-
-
-_Command = monkey.get_unpatched(distutils.core.Command)
-
-
-class Command(_Command):
-    """
-    Setuptools internal actions are organized using a *command design pattern*.
-    This means that each action (or group of closely related actions) executed during
-    the build should be implemented as a ``Command`` subclass.
-
-    These commands are abstractions and do not necessarily correspond to a command that
-    can (or should) be executed via a terminal, in a CLI fashion (although historically
-    they would).
-
-    When creating a new command from scratch, custom defined classes **SHOULD** inherit
-    from ``setuptools.Command`` and implement a few mandatory methods.
-    Between these mandatory methods, are listed:
-
-    .. method:: initialize_options(self)
-
-        Set or (reset) all options/attributes/caches used by the command
-        to their default values. Note that these values may be overwritten during
-        the build.
-
-    .. method:: finalize_options(self)
-
-        Set final values for all options/attributes used by the command.
-        Most of the time, each option/attribute/cache should only be set if it does not
-        have any value yet (e.g. ``if self.attr is None: self.attr = val``).
-
-    .. method:: run(self)
-
-        Execute the actions intended by the command.
-        (Side effects **SHOULD** only take place when ``run`` is executed,
-        for example, creating new files or writing to the terminal output).
-
-    A useful analogy for command classes is to think of them as subroutines with local
-    variables called "options".  The options are "declared" in ``initialize_options()``
-    and "defined" (given their final values, aka "finalized") in ``finalize_options()``,
-    both of which must be defined by every command class. The "body" of the subroutine,
-    (where it does all the work) is the ``run()`` method.
-    Between ``initialize_options()`` and ``finalize_options()``, ``setuptools`` may set
-    the values for options/attributes based on user's input (or circumstance),
-    which means that the implementation should be careful to not overwrite values in
-    ``finalize_options`` unless necessary.
-
-    Please note that other commands (or other parts of setuptools) may also overwrite
-    the values of the command's options/attributes multiple times during the build
-    process.
-    Therefore it is important to consistently implement ``initialize_options()`` and
-    ``finalize_options()``. For example, all derived attributes (or attributes that
-    depend on the value of other attributes) **SHOULD** be recomputed in
-    ``finalize_options``.
-
-    When overwriting existing commands, custom defined classes **MUST** abide by the
-    same APIs implemented by the original class. They also **SHOULD** inherit from the
-    original class.
-    """
-
-    command_consumes_arguments = False
-
-    def __init__(self, dist, **kw):
-        """
-        Construct the command for dist, updating
-        vars(self) with any keyword parameters.
-        """
-        super().__init__(dist)
-        vars(self).update(kw)
-
-    def _ensure_stringlike(self, option, what, default=None):
-        val = getattr(self, option)
-        if val is None:
-            setattr(self, option, default)
-            return default
-        elif not isinstance(val, str):
-            raise DistutilsOptionError(
-                "'%s' must be a %s (got `%s`)" % (option, what, val)
-            )
-        return val
-
-    def ensure_string_list(self, option):
-        r"""Ensure that 'option' is a list of strings.  If 'option' is
-        currently a string, we split it either on /,\s*/ or /\s+/, so
-        "foo bar baz", "foo,bar,baz", and "foo,   bar baz" all become
-        ["foo", "bar", "baz"].
-
-        ..
-           TODO: This method seems to be similar to the one in ``distutils.cmd``
-           Probably it is just here for backward compatibility with old Python versions?
-
-        :meta private:
-        """
-        val = getattr(self, option)
-        if val is None:
-            return
-        elif isinstance(val, str):
-            setattr(self, option, re.split(r',\s*|\s+', val))
-        else:
-            if isinstance(val, list):
-                ok = all(isinstance(v, str) for v in val)
-            else:
-                ok = False
-            if not ok:
-                raise DistutilsOptionError(
-                    "'%s' must be a list of strings (got %r)" % (option, val)
-                )
-
-    def reinitialize_command(self, command, reinit_subcommands=0, **kw):
-        cmd = _Command.reinitialize_command(self, command, reinit_subcommands)
-        vars(cmd).update(kw)
-        return cmd
-
-
-def _find_all_simple(path):
-    """
-    Find all files under 'path'
-    """
-    results = (
-        os.path.join(base, file)
-        for base, dirs, files in os.walk(path, followlinks=True)
-        for file in files
-    )
-    return filter(os.path.isfile, results)
-
-
-def findall(dir=os.curdir):
-    """
-    Find all files under 'dir' and return the list of full filenames.
-    Unless dir is '.', return full filenames with dir prepended.
-    """
-    files = _find_all_simple(dir)
-    if dir == os.curdir:
-        make_rel = functools.partial(os.path.relpath, start=dir)
-        files = map(make_rel, files)
-    return list(files)
-
-
-@functools.wraps(_convert_path)
-def convert_path(pathname):
-    SetuptoolsDeprecationWarning.emit(
-        "Access to implementation detail",
-        """
-        The function `convert_path` is not provided by setuptools itself,
-        and therefore not part of the public API.
-
-        Its direct usage by 3rd-party packages is considered improper and the function
-        may be removed in the future.
-        """,
-        due_date=(2023, 12, 13)  # initial deprecation 2022-03-25, see #3201
-    )
-    return _convert_path(pathname)
-
-
-class sic(str):
-    """Treat this string as-is (https://en.wikipedia.org/wiki/Sic)"""
-
-
-# Apply monkey patches
-monkey.patch_all()
diff --git a/spaces/pomudachi/spoiled-brrats/README.md b/spaces/pomudachi/spoiled-brrats/README.md
deleted file mode 100644
index 57f142b3c065292371f6aa9c32a59f750fc26e33..0000000000000000000000000000000000000000
--- a/spaces/pomudachi/spoiled-brrats/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Spoiled brrats
-emoji: 😭
-colorFrom: black
-colorTo: black
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/prerna9811/Chord/portaudio/examples/paex_mono_asio_channel_select.c b/spaces/prerna9811/Chord/portaudio/examples/paex_mono_asio_channel_select.c
deleted file mode 100644
index 4b55d318578a48ca241c4bcc7e2133126d27b28d..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/examples/paex_mono_asio_channel_select.c
+++ /dev/null
@@ -1,167 +0,0 @@
-/** @file paex_mono_asio_channel_select.c
-    @ingroup examples_src
-    @brief Play a monophonic sine wave on a specific ASIO channel.
-    @author Ross Bencina <rossb@audiomulch.com>
-    @author Phil Burk <philburk@softsynth.com>
-*/
-/*
- * $Id$
- *
- * Authors:
- *    Ross Bencina <rossb@audiomulch.com>
- *    Phil Burk <philburk@softsynth.com>
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-#include "pa_asio.h"
-
-#define NUM_SECONDS   (10)
-#define SAMPLE_RATE   (44100)
-#define AMPLITUDE     (0.8)
-#define FRAMES_PER_BUFFER  (64)
-#define OUTPUT_DEVICE Pa_GetDefaultOutputDevice()
-
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-
-#define TABLE_SIZE   (200)
-typedef struct
-{
-    float sine[TABLE_SIZE];
-    int phase;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
-                            unsigned long framesPerBuffer,
-                            const PaStreamCallbackTimeInfo* timeInfo,
-                            PaStreamCallbackFlags statusFlags,
-                            void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    float *out = (float*)outputBuffer;
-    unsigned long i;
-    int finished = 0;
-    /* avoid unused variable warnings */
-    (void) inputBuffer;
-    (void) timeInfo;
-    (void) statusFlags;
-    for( i=0; i<framesPerBuffer; i++ )
-    {
-        *out++ = data->sine[data->phase];  /* left */
-        data->phase += 1;
-        if( data->phase >= TABLE_SIZE ) data->phase -= TABLE_SIZE;
-    }
-    return finished;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    PaStreamParameters outputParameters;
-    PaAsioStreamInfo asioOutputInfo;
-    PaStream *stream;
-    PaError err;
-    paTestData data;
-    int outputChannelSelectors[1];
-    int i;
-    printf("PortAudio Test: output MONO sine wave. SR = %d, BufSize = %d\n", SAMPLE_RATE, FRAMES_PER_BUFFER);
-    /* initialise sinusoidal wavetable */
-    for( i=0; i<TABLE_SIZE; i++ )
-    {
-        data.sine[i] = (float) (AMPLITUDE * sin( ((double)i/(double)TABLE_SIZE) * M_PI * 2. ));
-    }
-    data.phase = 0;
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    outputParameters.device = OUTPUT_DEVICE;
-    outputParameters.channelCount = 1;       /* MONO output */
-    outputParameters.sampleFormat = paFloat32; /* 32 bit floating point output */
-    outputParameters.suggestedLatency = Pa_GetDeviceInfo( outputParameters.device )->defaultLowOutputLatency;
-
-    /* Use an ASIO specific structure. WARNING - this is not portable. */
-    asioOutputInfo.size = sizeof(PaAsioStreamInfo);
-    asioOutputInfo.hostApiType = paASIO;
-    asioOutputInfo.version = 1;
-    asioOutputInfo.flags = paAsioUseChannelSelectors;
-    outputChannelSelectors[0] = 1; /* skip channel 0 and use the second (right) ASIO device channel */
-    asioOutputInfo.channelSelectors = outputChannelSelectors;
-    outputParameters.hostApiSpecificStreamInfo = &asioOutputInfo;
-
-    err = Pa_OpenStream(
-              &stream,
-              NULL, /* no input */
-              &outputParameters,
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,
-              paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-              patestCallback,
-              &data );
-    if( err != paNoError ) goto error;
-
-    err = Pa_StartStream( stream );
-    if( err != paNoError ) goto error;
-
-    printf("Play for %d seconds.\n", NUM_SECONDS ); fflush(stdout);
-    Pa_Sleep( NUM_SECONDS * 1000 );
-
-    err = Pa_StopStream( stream );
-    if( err != paNoError ) goto error;
-
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto error;
-
-    Pa_Terminate();
-    printf("Test finished.\n");
-    return err;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/prerna9811/Chord/portaudio/include/portaudio.h b/spaces/prerna9811/Chord/portaudio/include/portaudio.h
deleted file mode 100644
index 5d847314ae9ba0e57468cccf4cfd52f9e100cf45..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/include/portaudio.h
+++ /dev/null
@@ -1,1228 +0,0 @@
-#ifndef PORTAUDIO_H
-#define PORTAUDIO_H
-/*
- * $Id$
- * PortAudio Portable Real-Time Audio Library
- * PortAudio API Header File
- * Latest version available at: http://www.portaudio.com/
- *
- * Copyright (c) 1999-2002 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/** @file
- @ingroup public_header
- @brief The portable PortAudio API.
-*/
-
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-/** Retrieve the release number of the currently running PortAudio build.
- For example, for version "19.5.1" this will return 0x00130501.
-
- @see paMakeVersionNumber
-*/
-int Pa_GetVersion( void );
-
-/** Retrieve a textual description of the current PortAudio build,
- e.g. "PortAudio V19.5.0-devel, revision 1952M".
- The format of the text may change in the future. Do not try to parse the
- returned string.
-
- @deprecated As of 19.5.0, use Pa_GetVersionInfo()->versionText instead.
-*/
-const char* Pa_GetVersionText( void );
-
-/**
- Generate a packed integer version number in the same format used
- by Pa_GetVersion(). Use this to compare a specified version number with
- the currently running version. For example:
-
- @code
-     if( Pa_GetVersion() < paMakeVersionNumber(19,5,1) ) {}
- @endcode
-
- @see Pa_GetVersion, Pa_GetVersionInfo
- @version Available as of 19.5.0.
-*/
-#define paMakeVersionNumber(major, minor, subminor) \
-    (((major)&0xFF)<<16 | ((minor)&0xFF)<<8 | ((subminor)&0xFF))
-
-
-/**
- A structure containing PortAudio API version information.
- @see Pa_GetVersionInfo, paMakeVersionNumber
- @version Available as of 19.5.0.
-*/
-typedef struct PaVersionInfo {
-    int versionMajor;
-    int versionMinor;
-    int versionSubMinor;
-    /**
-     This is currently the Git revision hash but may change in the future.
-     The versionControlRevision is updated by running a script before compiling the library.
-     If the update does not occur, this value may refer to an earlier revision.
-    */
-    const char *versionControlRevision;
-    /** Version as a string, for example "PortAudio V19.5.0-devel, revision 1952M" */
-    const char *versionText;
-} PaVersionInfo;
-
-/** Retrieve version information for the currently running PortAudio build.
- @return A pointer to an immutable PaVersionInfo structure.
-
- @note This function can be called at any time. It does not require PortAudio
- to be initialized. The structure pointed to is statically allocated. Do not
- attempt to free it or modify it.
-
- @see PaVersionInfo, paMakeVersionNumber
- @version Available as of 19.5.0.
-*/
-const PaVersionInfo* Pa_GetVersionInfo( void );
-
-
-/** Error codes returned by PortAudio functions.
- Note that with the exception of paNoError, all PaErrorCodes are negative.
-*/
-
-typedef int PaError;
-typedef enum PaErrorCode
-{
-    paNoError = 0,
-
-    paNotInitialized = -10000,
-    paUnanticipatedHostError,
-    paInvalidChannelCount,
-    paInvalidSampleRate,
-    paInvalidDevice,
-    paInvalidFlag,
-    paSampleFormatNotSupported,
-    paBadIODeviceCombination,
-    paInsufficientMemory,
-    paBufferTooBig,
-    paBufferTooSmall,
-    paNullCallback,
-    paBadStreamPtr,
-    paTimedOut,
-    paInternalError,
-    paDeviceUnavailable,
-    paIncompatibleHostApiSpecificStreamInfo,
-    paStreamIsStopped,
-    paStreamIsNotStopped,
-    paInputOverflowed,
-    paOutputUnderflowed,
-    paHostApiNotFound,
-    paInvalidHostApi,
-    paCanNotReadFromACallbackStream,
-    paCanNotWriteToACallbackStream,
-    paCanNotReadFromAnOutputOnlyStream,
-    paCanNotWriteToAnInputOnlyStream,
-    paIncompatibleStreamHostApi,
-    paBadBufferPtr
-} PaErrorCode;
-
-
-/** Translate the supplied PortAudio error code into a human readable
- message.
-*/
-const char *Pa_GetErrorText( PaError errorCode );
-
-
-/** Library initialization function - call this before using PortAudio.
- This function initializes internal data structures and prepares underlying
- host APIs for use.  With the exception of Pa_GetVersion(), Pa_GetVersionText(),
- and Pa_GetErrorText(), this function MUST be called before using any other
- PortAudio API functions.
-
- If Pa_Initialize() is called multiple times, each successful
- call must be matched with a corresponding call to Pa_Terminate().
- Pairs of calls to Pa_Initialize()/Pa_Terminate() may overlap, and are not
- required to be fully nested.
-
- Note that if Pa_Initialize() returns an error code, Pa_Terminate() should
- NOT be called.
-
- @return paNoError if successful, otherwise an error code indicating the cause
- of failure.
-
- @see Pa_Terminate
-*/
-PaError Pa_Initialize( void );
-
-
-/** Library termination function - call this when finished using PortAudio.
- This function deallocates all resources allocated by PortAudio since it was
- initialized by a call to Pa_Initialize(). In cases where Pa_Initialise() has
- been called multiple times, each call must be matched with a corresponding call
- to Pa_Terminate(). The final matching call to Pa_Terminate() will automatically
- close any PortAudio streams that are still open.
-
- Pa_Terminate() MUST be called before exiting a program which uses PortAudio.
- Failure to do so may result in serious resource leaks, such as audio devices
- not being available until the next reboot.
-
- @return paNoError if successful, otherwise an error code indicating the cause
- of failure.
-
- @see Pa_Initialize
-*/
-PaError Pa_Terminate( void );
-
-
-
-/** The type used to refer to audio devices. Values of this type usually
- range from 0 to (Pa_GetDeviceCount()-1), and may also take on the PaNoDevice
- and paUseHostApiSpecificDeviceSpecification values.
-
- @see Pa_GetDeviceCount, paNoDevice, paUseHostApiSpecificDeviceSpecification
-*/
-typedef int PaDeviceIndex;
-
-
-/** A special PaDeviceIndex value indicating that no device is available,
- or should be used.
-
- @see PaDeviceIndex
-*/
-#define paNoDevice ((PaDeviceIndex)-1)
-
-
-/** A special PaDeviceIndex value indicating that the device(s) to be used
- are specified in the host api specific stream info structure.
-
- @see PaDeviceIndex
-*/
-#define paUseHostApiSpecificDeviceSpecification ((PaDeviceIndex)-2)
-
-
-/* Host API enumeration mechanism */
-
-/** The type used to enumerate to host APIs at runtime. Values of this type
- range from 0 to (Pa_GetHostApiCount()-1).
-
- @see Pa_GetHostApiCount
-*/
-typedef int PaHostApiIndex;
-
-
-/** Retrieve the number of available host APIs. Even if a host API is
- available it may have no devices available.
-
- @return A non-negative value indicating the number of available host APIs
- or, a PaErrorCode (which are always negative) if PortAudio is not initialized
- or an error is encountered.
-
- @see PaHostApiIndex
-*/
-PaHostApiIndex Pa_GetHostApiCount( void );
-
-
-/** Retrieve the index of the default host API. The default host API will be
- the lowest common denominator host API on the current platform and is
- unlikely to provide the best performance.
-
- @return A non-negative value ranging from 0 to (Pa_GetHostApiCount()-1)
- indicating the default host API index or, a PaErrorCode (which are always
- negative) if PortAudio is not initialized or an error is encountered.
-*/
-PaHostApiIndex Pa_GetDefaultHostApi( void );
-
-
-/** Unchanging unique identifiers for each supported host API. This type
- is used in the PaHostApiInfo structure. The values are guaranteed to be
- unique and to never change, thus allowing code to be written that
- conditionally uses host API specific extensions.
-
- New type ids will be allocated when support for a host API reaches
- "public alpha" status, prior to that developers should use the
- paInDevelopment type id.
-
- @see PaHostApiInfo
-*/
-typedef enum PaHostApiTypeId
-{
-    paInDevelopment=0, /* use while developing support for a new host API */
-    paDirectSound=1,
-    paMME=2,
-    paASIO=3,
-    paSoundManager=4,
-    paCoreAudio=5,
-    paOSS=7,
-    paALSA=8,
-    paAL=9,
-    paBeOS=10,
-    paWDMKS=11,
-    paJACK=12,
-    paWASAPI=13,
-    paAudioScienceHPI=14
-} PaHostApiTypeId;
-
-
-/** A structure containing information about a particular host API. */
-
-typedef struct PaHostApiInfo
-{
-    /** this is struct version 1 */
-    int structVersion;
-    /** The well known unique identifier of this host API @see PaHostApiTypeId */
-    PaHostApiTypeId type;
-    /** A textual description of the host API for display on user interfaces. */
-    const char *name;
-
-    /**  The number of devices belonging to this host API. This field may be
-     used in conjunction with Pa_HostApiDeviceIndexToDeviceIndex() to enumerate
-     all devices for this host API.
-     @see Pa_HostApiDeviceIndexToDeviceIndex
-    */
-    int deviceCount;
-
-    /** The default input device for this host API. The value will be a
-     device index ranging from 0 to (Pa_GetDeviceCount()-1), or paNoDevice
-     if no default input device is available.
-    */
-    PaDeviceIndex defaultInputDevice;
-
-    /** The default output device for this host API. The value will be a
-     device index ranging from 0 to (Pa_GetDeviceCount()-1), or paNoDevice
-     if no default output device is available.
-    */
-    PaDeviceIndex defaultOutputDevice;
-
-} PaHostApiInfo;
-
-
-/** Retrieve a pointer to a structure containing information about a specific
- host Api.
-
- @param hostApi A valid host API index ranging from 0 to (Pa_GetHostApiCount()-1)
-
- @return A pointer to an immutable PaHostApiInfo structure describing
- a specific host API. If the hostApi parameter is out of range or an error
- is encountered, the function returns NULL.
-
- The returned structure is owned by the PortAudio implementation and must not
- be manipulated or freed. The pointer is only guaranteed to be valid between
- calls to Pa_Initialize() and Pa_Terminate().
-*/
-const PaHostApiInfo * Pa_GetHostApiInfo( PaHostApiIndex hostApi );
-
-
-/** Convert a static host API unique identifier, into a runtime
- host API index.
-
- @param type A unique host API identifier belonging to the PaHostApiTypeId
- enumeration.
-
- @return A valid PaHostApiIndex ranging from 0 to (Pa_GetHostApiCount()-1) or,
- a PaErrorCode (which are always negative) if PortAudio is not initialized
- or an error is encountered.
-
- The paHostApiNotFound error code indicates that the host API specified by the
- type parameter is not available.
-
- @see PaHostApiTypeId
-*/
-PaHostApiIndex Pa_HostApiTypeIdToHostApiIndex( PaHostApiTypeId type );
-
-
-/** Convert a host-API-specific device index to standard PortAudio device index.
- This function may be used in conjunction with the deviceCount field of
- PaHostApiInfo to enumerate all devices for the specified host API.
-
- @param hostApi A valid host API index ranging from 0 to (Pa_GetHostApiCount()-1)
-
- @param hostApiDeviceIndex A valid per-host device index in the range
- 0 to (Pa_GetHostApiInfo(hostApi)->deviceCount-1)
-
- @return A non-negative PaDeviceIndex ranging from 0 to (Pa_GetDeviceCount()-1)
- or, a PaErrorCode (which are always negative) if PortAudio is not initialized
- or an error is encountered.
-
- A paInvalidHostApi error code indicates that the host API index specified by
- the hostApi parameter is out of range.
-
- A paInvalidDevice error code indicates that the hostApiDeviceIndex parameter
- is out of range.
-
- @see PaHostApiInfo
-*/
-PaDeviceIndex Pa_HostApiDeviceIndexToDeviceIndex( PaHostApiIndex hostApi,
-        int hostApiDeviceIndex );
-
-
-
-/** Structure used to return information about a host error condition.
-*/
-typedef struct PaHostErrorInfo{
-    PaHostApiTypeId hostApiType;    /**< the host API which returned the error code */
-    long errorCode;                 /**< the error code returned */
-    const char *errorText;          /**< a textual description of the error if available, otherwise a zero-length string */
-}PaHostErrorInfo;
-
-
-/** Return information about the last host error encountered. The error
- information returned by Pa_GetLastHostErrorInfo() will never be modified
- asynchronously by errors occurring in other PortAudio owned threads
- (such as the thread that manages the stream callback.)
-
- This function is provided as a last resort, primarily to enhance debugging
- by providing clients with access to all available error information.
-
- @return A pointer to an immutable structure constraining information about
- the host error. The values in this structure will only be valid if a
- PortAudio function has previously returned the paUnanticipatedHostError
- error code.
-*/
-const PaHostErrorInfo* Pa_GetLastHostErrorInfo( void );
-
-
-
-/* Device enumeration and capabilities */
-
-/** Retrieve the number of available devices. The number of available devices
- may be zero.
-
- @return A non-negative value indicating the number of available devices or,
- a PaErrorCode (which are always negative) if PortAudio is not initialized
- or an error is encountered.
-*/
-PaDeviceIndex Pa_GetDeviceCount( void );
-
-
-/** Retrieve the index of the default input device. The result can be
- used in the inputDevice parameter to Pa_OpenStream().
-
- @return The default input device index for the default host API, or paNoDevice
- if no default input device is available or an error was encountered.
-*/
-PaDeviceIndex Pa_GetDefaultInputDevice( void );
-
-
-/** Retrieve the index of the default output device. The result can be
- used in the outputDevice parameter to Pa_OpenStream().
-
- @return The default output device index for the default host API, or paNoDevice
- if no default output device is available or an error was encountered.
-
- @note
- On the PC, the user can specify a default device by
- setting an environment variable. For example, to use device #1.
-<pre>
- set PA_RECOMMENDED_OUTPUT_DEVICE=1
-</pre>
- The user should first determine the available device ids by using
- the supplied application "pa_devs".
-*/
-PaDeviceIndex Pa_GetDefaultOutputDevice( void );
-
-
-/** The type used to represent monotonic time in seconds. PaTime is
- used for the fields of the PaStreamCallbackTimeInfo argument to the
- PaStreamCallback and as the result of Pa_GetStreamTime().
-
- PaTime values have unspecified origin.
-
- @see PaStreamCallback, PaStreamCallbackTimeInfo, Pa_GetStreamTime
-*/
-typedef double PaTime;
-
-
-/** A type used to specify one or more sample formats. Each value indicates
- a possible format for sound data passed to and from the stream callback,
- Pa_ReadStream and Pa_WriteStream.
-
- The standard formats paFloat32, paInt16, paInt32, paInt24, paInt8
- and aUInt8 are usually implemented by all implementations.
-
- The floating point representation (paFloat32) uses +1.0 and -1.0 as the
- maximum and minimum respectively.
-
- paUInt8 is an unsigned 8 bit format where 128 is considered "ground"
-
- The paNonInterleaved flag indicates that audio data is passed as an array
- of pointers to separate buffers, one buffer for each channel. Usually,
- when this flag is not used, audio data is passed as a single buffer with
- all channels interleaved.
-
- @see Pa_OpenStream, Pa_OpenDefaultStream, PaDeviceInfo
- @see paFloat32, paInt16, paInt32, paInt24, paInt8
- @see paUInt8, paCustomFormat, paNonInterleaved
-*/
-typedef unsigned long PaSampleFormat;
-
-
-#define paFloat32        ((PaSampleFormat) 0x00000001) /**< @see PaSampleFormat */
-#define paInt32          ((PaSampleFormat) 0x00000002) /**< @see PaSampleFormat */
-#define paInt24          ((PaSampleFormat) 0x00000004) /**< Packed 24 bit format. @see PaSampleFormat */
-#define paInt16          ((PaSampleFormat) 0x00000008) /**< @see PaSampleFormat */
-#define paInt8           ((PaSampleFormat) 0x00000010) /**< @see PaSampleFormat */
-#define paUInt8          ((PaSampleFormat) 0x00000020) /**< @see PaSampleFormat */
-#define paCustomFormat   ((PaSampleFormat) 0x00010000) /**< @see PaSampleFormat */
-
-#define paNonInterleaved ((PaSampleFormat) 0x80000000) /**< @see PaSampleFormat */
-
-/** A structure providing information and capabilities of PortAudio devices.
- Devices may support input, output or both input and output.
-*/
-typedef struct PaDeviceInfo
-{
-    int structVersion;  /* this is struct version 2 */
-    const char *name;
-    PaHostApiIndex hostApi; /**< note this is a host API index, not a type id*/
-
-    int maxInputChannels;
-    int maxOutputChannels;
-
-    /** Default latency values for interactive performance. */
-    PaTime defaultLowInputLatency;
-    PaTime defaultLowOutputLatency;
-    /** Default latency values for robust non-interactive applications (eg. playing sound files). */
-    PaTime defaultHighInputLatency;
-    PaTime defaultHighOutputLatency;
-
-    double defaultSampleRate;
-} PaDeviceInfo;
-
-
-/** Retrieve a pointer to a PaDeviceInfo structure containing information
- about the specified device.
- @return A pointer to an immutable PaDeviceInfo structure. If the device
- parameter is out of range the function returns NULL.
-
- @param device A valid device index in the range 0 to (Pa_GetDeviceCount()-1)
-
- @note PortAudio manages the memory referenced by the returned pointer,
- the client must not manipulate or free the memory. The pointer is only
- guaranteed to be valid between calls to Pa_Initialize() and Pa_Terminate().
-
- @see PaDeviceInfo, PaDeviceIndex
-*/
-const PaDeviceInfo* Pa_GetDeviceInfo( PaDeviceIndex device );
-
-
-/** Parameters for one direction (input or output) of a stream.
-*/
-typedef struct PaStreamParameters
-{
-    /** A valid device index in the range 0 to (Pa_GetDeviceCount()-1)
-     specifying the device to be used or the special constant
-     paUseHostApiSpecificDeviceSpecification which indicates that the actual
-     device(s) to use are specified in hostApiSpecificStreamInfo.
-     This field must not be set to paNoDevice.
-    */
-    PaDeviceIndex device;
-
-    /** The number of channels of sound to be delivered to the
-     stream callback or accessed by Pa_ReadStream() or Pa_WriteStream().
-     It can range from 1 to the value of maxInputChannels in the
-     PaDeviceInfo record for the device specified by the device parameter.
-    */
-    int channelCount;
-
-    /** The sample format of the buffer provided to the stream callback,
-     a_ReadStream() or Pa_WriteStream(). It may be any of the formats described
-     by the PaSampleFormat enumeration.
-    */
-    PaSampleFormat sampleFormat;
-
-    /** The desired latency in seconds. Where practical, implementations should
-     configure their latency based on these parameters, otherwise they may
-     choose the closest viable latency instead. Unless the suggested latency
-     is greater than the absolute upper limit for the device implementations
-     should round the suggestedLatency up to the next practical value - ie to
-     provide an equal or higher latency than suggestedLatency wherever possible.
-     Actual latency values for an open stream may be retrieved using the
-     inputLatency and outputLatency fields of the PaStreamInfo structure
-     returned by Pa_GetStreamInfo().
-     @see default*Latency in PaDeviceInfo, *Latency in PaStreamInfo
-    */
-    PaTime suggestedLatency;
-
-    /** An optional pointer to a host api specific data structure
-     containing additional information for device setup and/or stream processing.
-     hostApiSpecificStreamInfo is never required for correct operation,
-     if not used it should be set to NULL.
-    */
-    void *hostApiSpecificStreamInfo;
-
-} PaStreamParameters;
-
-
-/** Return code for Pa_IsFormatSupported indicating success. */
-#define paFormatIsSupported (0)
-
-/** Determine whether it would be possible to open a stream with the specified
- parameters.
-
- @param inputParameters A structure that describes the input parameters used to
- open a stream. The suggestedLatency field is ignored. See PaStreamParameters
- for a description of these parameters. inputParameters must be NULL for
- output-only streams.
-
- @param outputParameters A structure that describes the output parameters used
- to open a stream. The suggestedLatency field is ignored. See PaStreamParameters
- for a description of these parameters. outputParameters must be NULL for
- input-only streams.
-
- @param sampleRate The required sampleRate. For full-duplex streams it is the
- sample rate for both input and output
-
- @return Returns 0 if the format is supported, and an error code indicating why
- the format is not supported otherwise. The constant paFormatIsSupported is
- provided to compare with the return value for success.
-
- @see paFormatIsSupported, PaStreamParameters
-*/
-PaError Pa_IsFormatSupported( const PaStreamParameters *inputParameters,
-                              const PaStreamParameters *outputParameters,
-                              double sampleRate );
-
-
-
-/* Streaming types and functions */
-
-
-/**
- A single PaStream can provide multiple channels of real-time
- streaming audio input and output to a client application. A stream
- provides access to audio hardware represented by one or more
- PaDevices. Depending on the underlying Host API, it may be possible
- to open multiple streams using the same device, however this behavior
- is implementation defined. Portable applications should assume that
- a PaDevice may be simultaneously used by at most one PaStream.
-
- Pointers to PaStream objects are passed between PortAudio functions that
- operate on streams.
-
- @see Pa_OpenStream, Pa_OpenDefaultStream, Pa_OpenDefaultStream, Pa_CloseStream,
- Pa_StartStream, Pa_StopStream, Pa_AbortStream, Pa_IsStreamActive,
- Pa_GetStreamTime, Pa_GetStreamCpuLoad
-
-*/
-typedef void PaStream;
-
-
-/** Can be passed as the framesPerBuffer parameter to Pa_OpenStream()
- or Pa_OpenDefaultStream() to indicate that the stream callback will
- accept buffers of any size.
-*/
-#define paFramesPerBufferUnspecified  (0)
-
-
-/** Flags used to control the behavior of a stream. They are passed as
- parameters to Pa_OpenStream or Pa_OpenDefaultStream. Multiple flags may be
- ORed together.
-
- @see Pa_OpenStream, Pa_OpenDefaultStream
- @see paNoFlag, paClipOff, paDitherOff, paNeverDropInput,
-  paPrimeOutputBuffersUsingStreamCallback, paPlatformSpecificFlags
-*/
-typedef unsigned long PaStreamFlags;
-
-/** @see PaStreamFlags */
-#define   paNoFlag          ((PaStreamFlags) 0)
-
-/** Disable default clipping of out of range samples.
- @see PaStreamFlags
-*/
-#define   paClipOff         ((PaStreamFlags) 0x00000001)
-
-/** Disable default dithering.
- @see PaStreamFlags
-*/
-#define   paDitherOff       ((PaStreamFlags) 0x00000002)
-
-/** Flag requests that where possible a full duplex stream will not discard
- overflowed input samples without calling the stream callback. This flag is
- only valid for full duplex callback streams and only when used in combination
- with the paFramesPerBufferUnspecified (0) framesPerBuffer parameter. Using
- this flag incorrectly results in a paInvalidFlag error being returned from
- Pa_OpenStream and Pa_OpenDefaultStream.
-
- @see PaStreamFlags, paFramesPerBufferUnspecified
-*/
-#define   paNeverDropInput  ((PaStreamFlags) 0x00000004)
-
-/** Call the stream callback to fill initial output buffers, rather than the
- default behavior of priming the buffers with zeros (silence). This flag has
- no effect for input-only and blocking read/write streams.
-
- @see PaStreamFlags
-*/
-#define   paPrimeOutputBuffersUsingStreamCallback ((PaStreamFlags) 0x00000008)
-
-/** A mask specifying the platform specific bits.
- @see PaStreamFlags
-*/
-#define   paPlatformSpecificFlags ((PaStreamFlags)0xFFFF0000)
-
-/**
- Timing information for the buffers passed to the stream callback.
-
- Time values are expressed in seconds and are synchronised with the time base used by Pa_GetStreamTime() for the associated stream.
-
- @see PaStreamCallback, Pa_GetStreamTime
-*/
-typedef struct PaStreamCallbackTimeInfo{
-    PaTime inputBufferAdcTime;  /**< The time when the first sample of the input buffer was captured at the ADC input */
-    PaTime currentTime;         /**< The time when the stream callback was invoked */
-    PaTime outputBufferDacTime; /**< The time when the first sample of the output buffer will output the DAC */
-} PaStreamCallbackTimeInfo;
-
-
-/**
- Flag bit constants for the statusFlags to PaStreamCallback.
-
- @see paInputUnderflow, paInputOverflow, paOutputUnderflow, paOutputOverflow,
- paPrimingOutput
-*/
-typedef unsigned long PaStreamCallbackFlags;
-
-/** In a stream opened with paFramesPerBufferUnspecified, indicates that
- input data is all silence (zeros) because no real data is available. In a
- stream opened without paFramesPerBufferUnspecified, it indicates that one or
- more zero samples have been inserted into the input buffer to compensate
- for an input underflow.
- @see PaStreamCallbackFlags
-*/
-#define paInputUnderflow   ((PaStreamCallbackFlags) 0x00000001)
-
-/** In a stream opened with paFramesPerBufferUnspecified, indicates that data
- prior to the first sample of the input buffer was discarded due to an
- overflow, possibly because the stream callback is using too much CPU time.
- Otherwise indicates that data prior to one or more samples in the
- input buffer was discarded.
- @see PaStreamCallbackFlags
-*/
-#define paInputOverflow    ((PaStreamCallbackFlags) 0x00000002)
-
-/** Indicates that output data (or a gap) was inserted, possibly because the
- stream callback is using too much CPU time.
- @see PaStreamCallbackFlags
-*/
-#define paOutputUnderflow  ((PaStreamCallbackFlags) 0x00000004)
-
-/** Indicates that output data will be discarded because no room is available.
- @see PaStreamCallbackFlags
-*/
-#define paOutputOverflow   ((PaStreamCallbackFlags) 0x00000008)
-
-/** Some of all of the output data will be used to prime the stream, input
- data may be zero.
- @see PaStreamCallbackFlags
-*/
-#define paPrimingOutput    ((PaStreamCallbackFlags) 0x00000010)
-
-/**
- Allowable return values for the PaStreamCallback.
- @see PaStreamCallback
-*/
-typedef enum PaStreamCallbackResult
-{
-    paContinue=0,   /**< Signal that the stream should continue invoking the callback and processing audio. */
-    paComplete=1,   /**< Signal that the stream should stop invoking the callback and finish once all output samples have played. */
-    paAbort=2       /**< Signal that the stream should stop invoking the callback and finish as soon as possible. */
-} PaStreamCallbackResult;
-
-
-/**
- Functions of type PaStreamCallback are implemented by PortAudio clients.
- They consume, process or generate audio in response to requests from an
- active PortAudio stream.
-
- When a stream is running, PortAudio calls the stream callback periodically.
- The callback function is responsible for processing buffers of audio samples
- passed via the input and output parameters.
-
- The PortAudio stream callback runs at very high or real-time priority.
- It is required to consistently meet its time deadlines. Do not allocate
- memory, access the file system, call library functions or call other functions
- from the stream callback that may block or take an unpredictable amount of
- time to complete.
-
- In order for a stream to maintain glitch-free operation the callback
- must consume and return audio data faster than it is recorded and/or
- played. PortAudio anticipates that each callback invocation may execute for
- a duration approaching the duration of frameCount audio frames at the stream
- sample rate. It is reasonable to expect to be able to utilise 70% or more of
- the available CPU time in the PortAudio callback. However, due to buffer size
- adaption and other factors, not all host APIs are able to guarantee audio
- stability under heavy CPU load with arbitrary fixed callback buffer sizes.
- When high callback CPU utilisation is required the most robust behavior
- can be achieved by using paFramesPerBufferUnspecified as the
- Pa_OpenStream() framesPerBuffer parameter.
-
- @param input and @param output are either arrays of interleaved samples or;
- if non-interleaved samples were requested using the paNonInterleaved sample
- format flag, an array of buffer pointers, one non-interleaved buffer for
- each channel.
-
- The format, packing and number of channels used by the buffers are
- determined by parameters to Pa_OpenStream().
-
- @param frameCount The number of sample frames to be processed by
- the stream callback.
-
- @param timeInfo Timestamps indicating the ADC capture time of the first sample
- in the input buffer, the DAC output time of the first sample in the output buffer
- and the time the callback was invoked.
- See PaStreamCallbackTimeInfo and Pa_GetStreamTime()
-
- @param statusFlags Flags indicating whether input and/or output buffers
- have been inserted or will be dropped to overcome underflow or overflow
- conditions.
-
- @param userData The value of a user supplied pointer passed to
- Pa_OpenStream() intended for storing synthesis data etc.
-
- @return
- The stream callback should return one of the values in the
- ::PaStreamCallbackResult enumeration. To ensure that the callback continues
- to be called, it should return paContinue (0). Either paComplete or paAbort
- can be returned to finish stream processing, after either of these values is
- returned the callback will not be called again. If paAbort is returned the
- stream will finish as soon as possible. If paComplete is returned, the stream
- will continue until all buffers generated by the callback have been played.
- This may be useful in applications such as soundfile players where a specific
- duration of output is required. However, it is not necessary to utilize this
- mechanism as Pa_StopStream(), Pa_AbortStream() or Pa_CloseStream() can also
- be used to stop the stream. The callback must always fill the entire output
- buffer irrespective of its return value.
-
- @see Pa_OpenStream, Pa_OpenDefaultStream
-
- @note With the exception of Pa_GetStreamCpuLoad() it is not permissible to call
- PortAudio API functions from within the stream callback.
-*/
-typedef int PaStreamCallback(
-    const void *input, void *output,
-    unsigned long frameCount,
-    const PaStreamCallbackTimeInfo* timeInfo,
-    PaStreamCallbackFlags statusFlags,
-    void *userData );
-
-
-/** Opens a stream for either input, output or both.
-
- @param stream The address of a PaStream pointer which will receive
- a pointer to the newly opened stream.
-
- @param inputParameters A structure that describes the input parameters used by
- the opened stream. See PaStreamParameters for a description of these parameters.
- inputParameters must be NULL for output-only streams.
-
- @param outputParameters A structure that describes the output parameters used by
- the opened stream. See PaStreamParameters for a description of these parameters.
- outputParameters must be NULL for input-only streams.
-
- @param sampleRate The desired sampleRate. For full-duplex streams it is the
- sample rate for both input and output. Note that the actual sampleRate
- may differ very slightly from the desired rate because of hardware limitations.
- The exact rate can be queried using Pa_GetStreamInfo(). If nothing close
- to the desired sampleRate is available then the open will fail and return an error.
-
- @param framesPerBuffer The number of frames passed to the stream callback
- function, or the preferred block granularity for a blocking read/write stream.
- The special value paFramesPerBufferUnspecified (0) may be used to request that
- the stream callback will receive an optimal (and possibly varying) number of
- frames based on host requirements and the requested latency settings.
- Note: With some host APIs, the use of non-zero framesPerBuffer for a callback
- stream may introduce an additional layer of buffering which could introduce
- additional latency. PortAudio guarantees that the additional latency
- will be kept to the theoretical minimum however, it is strongly recommended
- that a non-zero framesPerBuffer value only be used when your algorithm
- requires a fixed number of frames per stream callback.
-
- @param streamFlags Flags which modify the behavior of the streaming process.
- This parameter may contain a combination of flags ORed together. Some flags may
- only be relevant to certain buffer formats.
-
- @param streamCallback A pointer to a client supplied function that is responsible
- for processing and filling input and output buffers. If this parameter is NULL
- the stream will be opened in 'blocking read/write' mode. In blocking mode,
- the client can receive sample data using Pa_ReadStream and write sample data
- using Pa_WriteStream, the number of samples that may be read or written
- without blocking is returned by Pa_GetStreamReadAvailable and
- Pa_GetStreamWriteAvailable respectively.
-
- @param userData A client supplied pointer which is passed to the stream callback
- function. It could for example, contain a pointer to instance data necessary
- for processing the audio buffers. This parameter is ignored if streamCallback
- is NULL.
-
- @return
- Upon success Pa_OpenStream() returns paNoError and places a pointer to a
- valid PaStream in the stream argument. The stream is inactive (stopped).
- If a call to Pa_OpenStream() fails, a non-zero error code is returned (see
- PaError for possible error codes) and the value of stream is invalid.
-
- @see PaStreamParameters, PaStreamCallback, Pa_ReadStream, Pa_WriteStream,
- Pa_GetStreamReadAvailable, Pa_GetStreamWriteAvailable
-*/
-PaError Pa_OpenStream( PaStream** stream,
-                       const PaStreamParameters *inputParameters,
-                       const PaStreamParameters *outputParameters,
-                       double sampleRate,
-                       unsigned long framesPerBuffer,
-                       PaStreamFlags streamFlags,
-                       PaStreamCallback *streamCallback,
-                       void *userData );
-
-
-/** A simplified version of Pa_OpenStream() that opens the default input
- and/or output devices.
-
- @param stream The address of a PaStream pointer which will receive
- a pointer to the newly opened stream.
-
- @param numInputChannels  The number of channels of sound that will be supplied
- to the stream callback or returned by Pa_ReadStream. It can range from 1 to
- the value of maxInputChannels in the PaDeviceInfo record for the default input
- device. If 0 the stream is opened as an output-only stream.
-
- @param numOutputChannels The number of channels of sound to be delivered to the
- stream callback or passed to Pa_WriteStream. It can range from 1 to the value
- of maxOutputChannels in the PaDeviceInfo record for the default output device.
- If 0 the stream is opened as an output-only stream.
-
- @param sampleFormat The sample format of both the input and output buffers
- provided to the callback or passed to and from Pa_ReadStream and Pa_WriteStream.
- sampleFormat may be any of the formats described by the PaSampleFormat
- enumeration.
-
- @param sampleRate Same as Pa_OpenStream parameter of the same name.
- @param framesPerBuffer Same as Pa_OpenStream parameter of the same name.
- @param streamCallback Same as Pa_OpenStream parameter of the same name.
- @param userData Same as Pa_OpenStream parameter of the same name.
-
- @return As for Pa_OpenStream
-
- @see Pa_OpenStream, PaStreamCallback
-*/
-PaError Pa_OpenDefaultStream( PaStream** stream,
-                              int numInputChannels,
-                              int numOutputChannels,
-                              PaSampleFormat sampleFormat,
-                              double sampleRate,
-                              unsigned long framesPerBuffer,
-                              PaStreamCallback *streamCallback,
-                              void *userData );
-
-
-/** Closes an audio stream. If the audio stream is active it
- discards any pending buffers as if Pa_AbortStream() had been called.
-*/
-PaError Pa_CloseStream( PaStream *stream );
-
-
-/** Functions of type PaStreamFinishedCallback are implemented by PortAudio
- clients. They can be registered with a stream using the Pa_SetStreamFinishedCallback
- function. Once registered they are called when the stream becomes inactive
- (ie once a call to Pa_StopStream() will not block).
- A stream will become inactive after the stream callback returns non-zero,
- or when Pa_StopStream or Pa_AbortStream is called. For a stream providing audio
- output, if the stream callback returns paComplete, or Pa_StopStream() is called,
- the stream finished callback will not be called until all generated sample data
- has been played.
-
- @param userData The userData parameter supplied to Pa_OpenStream()
-
- @see Pa_SetStreamFinishedCallback
-*/
-typedef void PaStreamFinishedCallback( void *userData );
-
-
-/** Register a stream finished callback function which will be called when the
- stream becomes inactive. See the description of PaStreamFinishedCallback for
- further details about when the callback will be called.
-
- @param stream a pointer to a PaStream that is in the stopped state - if the
- stream is not stopped, the stream's finished callback will remain unchanged
- and an error code will be returned.
-
- @param streamFinishedCallback a pointer to a function with the same signature
- as PaStreamFinishedCallback, that will be called when the stream becomes
- inactive. Passing NULL for this parameter will un-register a previously
- registered stream finished callback function.
-
- @return on success returns paNoError, otherwise an error code indicating the cause
- of the error.
-
- @see PaStreamFinishedCallback
-*/
-PaError Pa_SetStreamFinishedCallback( PaStream *stream, PaStreamFinishedCallback* streamFinishedCallback );
-
-
-/** Commences audio processing.
-*/
-PaError Pa_StartStream( PaStream *stream );
-
-
-/** Terminates audio processing. It waits until all pending
- audio buffers have been played before it returns.
-*/
-PaError Pa_StopStream( PaStream *stream );
-
-
-/** Terminates audio processing immediately without waiting for pending
- buffers to complete.
-*/
-PaError Pa_AbortStream( PaStream *stream );
-
-
-/** Determine whether the stream is stopped.
- A stream is considered to be stopped prior to a successful call to
- Pa_StartStream and after a successful call to Pa_StopStream or Pa_AbortStream.
- If a stream callback returns a value other than paContinue the stream is NOT
- considered to be stopped.
-
- @return Returns one (1) when the stream is stopped, zero (0) when
- the stream is running or, a PaErrorCode (which are always negative) if
- PortAudio is not initialized or an error is encountered.
-
- @see Pa_StopStream, Pa_AbortStream, Pa_IsStreamActive
-*/
-PaError Pa_IsStreamStopped( PaStream *stream );
-
-
-/** Determine whether the stream is active.
- A stream is active after a successful call to Pa_StartStream(), until it
- becomes inactive either as a result of a call to Pa_StopStream() or
- Pa_AbortStream(), or as a result of a return value other than paContinue from
- the stream callback. In the latter case, the stream is considered inactive
- after the last buffer has finished playing.
-
- @return Returns one (1) when the stream is active (ie playing or recording
- audio), zero (0) when not playing or, a PaErrorCode (which are always negative)
- if PortAudio is not initialized or an error is encountered.
-
- @see Pa_StopStream, Pa_AbortStream, Pa_IsStreamStopped
-*/
-PaError Pa_IsStreamActive( PaStream *stream );
-
-
-
-/** A structure containing unchanging information about an open stream.
- @see Pa_GetStreamInfo
-*/
-
-typedef struct PaStreamInfo
-{
-    /** this is struct version 1 */
-    int structVersion;
-
-    /** The input latency of the stream in seconds. This value provides the most
-     accurate estimate of input latency available to the implementation. It may
-     differ significantly from the suggestedLatency value passed to Pa_OpenStream().
-     The value of this field will be zero (0.) for output-only streams.
-     @see PaTime
-    */
-    PaTime inputLatency;
-
-    /** The output latency of the stream in seconds. This value provides the most
-     accurate estimate of output latency available to the implementation. It may
-     differ significantly from the suggestedLatency value passed to Pa_OpenStream().
-     The value of this field will be zero (0.) for input-only streams.
-     @see PaTime
-    */
-    PaTime outputLatency;
-
-    /** The sample rate of the stream in Hertz (samples per second). In cases
-     where the hardware sample rate is inaccurate and PortAudio is aware of it,
-     the value of this field may be different from the sampleRate parameter
-     passed to Pa_OpenStream(). If information about the actual hardware sample
-     rate is not available, this field will have the same value as the sampleRate
-     parameter passed to Pa_OpenStream().
-    */
-    double sampleRate;
-
-} PaStreamInfo;
-
-
-/** Retrieve a pointer to a PaStreamInfo structure containing information
- about the specified stream.
- @return A pointer to an immutable PaStreamInfo structure. If the stream
- parameter is invalid, or an error is encountered, the function returns NULL.
-
- @param stream A pointer to an open stream previously created with Pa_OpenStream.
-
- @note PortAudio manages the memory referenced by the returned pointer,
- the client must not manipulate or free the memory. The pointer is only
- guaranteed to be valid until the specified stream is closed.
-
- @see PaStreamInfo
-*/
-const PaStreamInfo* Pa_GetStreamInfo( PaStream *stream );
-
-
-/** Returns the current time in seconds for a stream according to the same clock used
- to generate callback PaStreamCallbackTimeInfo timestamps. The time values are
- monotonically increasing and have unspecified origin.
-
- Pa_GetStreamTime returns valid time values for the entire life of the stream,
- from when the stream is opened until it is closed. Starting and stopping the stream
- does not affect the passage of time returned by Pa_GetStreamTime.
-
- This time may be used for synchronizing other events to the audio stream, for
- example synchronizing audio to MIDI.
-
- @return The stream's current time in seconds, or 0 if an error occurred.
-
- @see PaTime, PaStreamCallback, PaStreamCallbackTimeInfo
-*/
-PaTime Pa_GetStreamTime( PaStream *stream );
-
-
-/** Retrieve CPU usage information for the specified stream.
- The "CPU Load" is a fraction of total CPU time consumed by a callback stream's
- audio processing routines including, but not limited to the client supplied
- stream callback. This function does not work with blocking read/write streams.
-
- This function may be called from the stream callback function or the
- application.
-
- @return
- A floating point value, typically between 0.0 and 1.0, where 1.0 indicates
- that the stream callback is consuming the maximum number of CPU cycles possible
- to maintain real-time operation. A value of 0.5 would imply that PortAudio and
- the stream callback was consuming roughly 50% of the available CPU time. The
- return value may exceed 1.0. A value of 0.0 will always be returned for a
- blocking read/write stream, or if an error occurs.
-*/
-double Pa_GetStreamCpuLoad( PaStream* stream );
-
-
-/** Read samples from an input stream. The function doesn't return until
- the entire buffer has been filled - this may involve waiting for the operating
- system to supply the data.
-
- @param stream A pointer to an open stream previously created with Pa_OpenStream.
-
- @param buffer A pointer to a buffer of sample frames. The buffer contains
- samples in the format specified by the inputParameters->sampleFormat field
- used to open the stream, and the number of channels specified by
- inputParameters->numChannels. If non-interleaved samples were requested using
- the paNonInterleaved sample format flag, buffer is a pointer to the first element
- of an array of buffer pointers, one non-interleaved buffer for each channel.
-
- @param frames The number of frames to be read into buffer. This parameter
- is not constrained to a specific range, however high performance applications
- will want to match this parameter to the framesPerBuffer parameter used
- when opening the stream.
-
- @return On success PaNoError will be returned, or PaInputOverflowed if input
- data was discarded by PortAudio after the previous call and before this call.
-*/
-PaError Pa_ReadStream( PaStream* stream,
-                       void *buffer,
-                       unsigned long frames );
-
-
-/** Write samples to an output stream. This function doesn't return until the
- entire buffer has been written - this may involve waiting for the operating
- system to consume the data.
-
- @param stream A pointer to an open stream previously created with Pa_OpenStream.
-
- @param buffer A pointer to a buffer of sample frames. The buffer contains
- samples in the format specified by the outputParameters->sampleFormat field
- used to open the stream, and the number of channels specified by
- outputParameters->numChannels. If non-interleaved samples were requested using
- the paNonInterleaved sample format flag, buffer is a pointer to the first element
- of an array of buffer pointers, one non-interleaved buffer for each channel.
-
- @param frames The number of frames to be written from buffer. This parameter
- is not constrained to a specific range, however high performance applications
- will want to match this parameter to the framesPerBuffer parameter used
- when opening the stream.
-
- @return On success PaNoError will be returned, or paOutputUnderflowed if
- additional output data was inserted after the previous call and before this
- call.
-*/
-PaError Pa_WriteStream( PaStream* stream,
-                        const void *buffer,
-                        unsigned long frames );
-
-
-/** Retrieve the number of frames that can be read from the stream without
- waiting.
-
- @return Returns a non-negative value representing the maximum number of frames
- that can be read from the stream without blocking or busy waiting or, a
- PaErrorCode (which are always negative) if PortAudio is not initialized or an
- error is encountered.
-*/
-signed long Pa_GetStreamReadAvailable( PaStream* stream );
-
-
-/** Retrieve the number of frames that can be written to the stream without
- waiting.
-
- @return Returns a non-negative value representing the maximum number of frames
- that can be written to the stream without blocking or busy waiting or, a
- PaErrorCode (which are always negative) if PortAudio is not initialized or an
- error is encountered.
-*/
-signed long Pa_GetStreamWriteAvailable( PaStream* stream );
-
-
-/* Miscellaneous utilities */
-
-
-/** Retrieve the size of a given sample format in bytes.
-
- @return The size in bytes of a single sample in the specified format,
- or paSampleFormatNotSupported if the format is not supported.
-*/
-PaError Pa_GetSampleSize( PaSampleFormat format );
-
-
-/** Put the caller to sleep for at least 'msec' milliseconds. This function is
- provided only as a convenience for authors of portable code (such as the tests
- and examples in the PortAudio distribution.)
-
- The function may sleep longer than requested so don't rely on this for accurate
- musical timing.
-*/
-void Pa_Sleep( long msec );
-
-
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-#endif /* PORTAUDIO_H */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/unicodedata/Scripts.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/unicodedata/Scripts.py
deleted file mode 100644
index 68bb91b396d62b03a8bfd650c64ce0b7375e1e48..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/unicodedata/Scripts.py
+++ /dev/null
@@ -1,3509 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# NOTE: This file was auto-generated with MetaTools/buildUCD.py.
-# Source: https://unicode.org/Public/UNIDATA/Scripts.txt
-# License: http://unicode.org/copyright.html#License
-#
-# Scripts-15.0.0.txt
-# Date: 2022-04-26, 23:15:02 GMT
-# © 2022 Unicode®, Inc.
-# Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries.
-# For terms of use, see https://www.unicode.org/terms_of_use.html
-#
-# Unicode Character Database
-#   For documentation, see https://www.unicode.org/reports/tr44/
-# For more information, see:
-#   UAX #24, Unicode Script Property: https://www.unicode.org/reports/tr24/
-#     Especially the sections:
-#       https://www.unicode.org/reports/tr24/#Assignment_Script_Values
-#       https://www.unicode.org/reports/tr24/#Assignment_ScriptX_Values
-#
-
-
-RANGES = [
-    0x0000,  # .. 0x0040 ; Common
-    0x0041,  # .. 0x005A ; Latin
-    0x005B,  # .. 0x0060 ; Common
-    0x0061,  # .. 0x007A ; Latin
-    0x007B,  # .. 0x00A9 ; Common
-    0x00AA,  # .. 0x00AA ; Latin
-    0x00AB,  # .. 0x00B9 ; Common
-    0x00BA,  # .. 0x00BA ; Latin
-    0x00BB,  # .. 0x00BF ; Common
-    0x00C0,  # .. 0x00D6 ; Latin
-    0x00D7,  # .. 0x00D7 ; Common
-    0x00D8,  # .. 0x00F6 ; Latin
-    0x00F7,  # .. 0x00F7 ; Common
-    0x00F8,  # .. 0x02B8 ; Latin
-    0x02B9,  # .. 0x02DF ; Common
-    0x02E0,  # .. 0x02E4 ; Latin
-    0x02E5,  # .. 0x02E9 ; Common
-    0x02EA,  # .. 0x02EB ; Bopomofo
-    0x02EC,  # .. 0x02FF ; Common
-    0x0300,  # .. 0x036F ; Inherited
-    0x0370,  # .. 0x0373 ; Greek
-    0x0374,  # .. 0x0374 ; Common
-    0x0375,  # .. 0x0377 ; Greek
-    0x0378,  # .. 0x0379 ; Unknown
-    0x037A,  # .. 0x037D ; Greek
-    0x037E,  # .. 0x037E ; Common
-    0x037F,  # .. 0x037F ; Greek
-    0x0380,  # .. 0x0383 ; Unknown
-    0x0384,  # .. 0x0384 ; Greek
-    0x0385,  # .. 0x0385 ; Common
-    0x0386,  # .. 0x0386 ; Greek
-    0x0387,  # .. 0x0387 ; Common
-    0x0388,  # .. 0x038A ; Greek
-    0x038B,  # .. 0x038B ; Unknown
-    0x038C,  # .. 0x038C ; Greek
-    0x038D,  # .. 0x038D ; Unknown
-    0x038E,  # .. 0x03A1 ; Greek
-    0x03A2,  # .. 0x03A2 ; Unknown
-    0x03A3,  # .. 0x03E1 ; Greek
-    0x03E2,  # .. 0x03EF ; Coptic
-    0x03F0,  # .. 0x03FF ; Greek
-    0x0400,  # .. 0x0484 ; Cyrillic
-    0x0485,  # .. 0x0486 ; Inherited
-    0x0487,  # .. 0x052F ; Cyrillic
-    0x0530,  # .. 0x0530 ; Unknown
-    0x0531,  # .. 0x0556 ; Armenian
-    0x0557,  # .. 0x0558 ; Unknown
-    0x0559,  # .. 0x058A ; Armenian
-    0x058B,  # .. 0x058C ; Unknown
-    0x058D,  # .. 0x058F ; Armenian
-    0x0590,  # .. 0x0590 ; Unknown
-    0x0591,  # .. 0x05C7 ; Hebrew
-    0x05C8,  # .. 0x05CF ; Unknown
-    0x05D0,  # .. 0x05EA ; Hebrew
-    0x05EB,  # .. 0x05EE ; Unknown
-    0x05EF,  # .. 0x05F4 ; Hebrew
-    0x05F5,  # .. 0x05FF ; Unknown
-    0x0600,  # .. 0x0604 ; Arabic
-    0x0605,  # .. 0x0605 ; Common
-    0x0606,  # .. 0x060B ; Arabic
-    0x060C,  # .. 0x060C ; Common
-    0x060D,  # .. 0x061A ; Arabic
-    0x061B,  # .. 0x061B ; Common
-    0x061C,  # .. 0x061E ; Arabic
-    0x061F,  # .. 0x061F ; Common
-    0x0620,  # .. 0x063F ; Arabic
-    0x0640,  # .. 0x0640 ; Common
-    0x0641,  # .. 0x064A ; Arabic
-    0x064B,  # .. 0x0655 ; Inherited
-    0x0656,  # .. 0x066F ; Arabic
-    0x0670,  # .. 0x0670 ; Inherited
-    0x0671,  # .. 0x06DC ; Arabic
-    0x06DD,  # .. 0x06DD ; Common
-    0x06DE,  # .. 0x06FF ; Arabic
-    0x0700,  # .. 0x070D ; Syriac
-    0x070E,  # .. 0x070E ; Unknown
-    0x070F,  # .. 0x074A ; Syriac
-    0x074B,  # .. 0x074C ; Unknown
-    0x074D,  # .. 0x074F ; Syriac
-    0x0750,  # .. 0x077F ; Arabic
-    0x0780,  # .. 0x07B1 ; Thaana
-    0x07B2,  # .. 0x07BF ; Unknown
-    0x07C0,  # .. 0x07FA ; Nko
-    0x07FB,  # .. 0x07FC ; Unknown
-    0x07FD,  # .. 0x07FF ; Nko
-    0x0800,  # .. 0x082D ; Samaritan
-    0x082E,  # .. 0x082F ; Unknown
-    0x0830,  # .. 0x083E ; Samaritan
-    0x083F,  # .. 0x083F ; Unknown
-    0x0840,  # .. 0x085B ; Mandaic
-    0x085C,  # .. 0x085D ; Unknown
-    0x085E,  # .. 0x085E ; Mandaic
-    0x085F,  # .. 0x085F ; Unknown
-    0x0860,  # .. 0x086A ; Syriac
-    0x086B,  # .. 0x086F ; Unknown
-    0x0870,  # .. 0x088E ; Arabic
-    0x088F,  # .. 0x088F ; Unknown
-    0x0890,  # .. 0x0891 ; Arabic
-    0x0892,  # .. 0x0897 ; Unknown
-    0x0898,  # .. 0x08E1 ; Arabic
-    0x08E2,  # .. 0x08E2 ; Common
-    0x08E3,  # .. 0x08FF ; Arabic
-    0x0900,  # .. 0x0950 ; Devanagari
-    0x0951,  # .. 0x0954 ; Inherited
-    0x0955,  # .. 0x0963 ; Devanagari
-    0x0964,  # .. 0x0965 ; Common
-    0x0966,  # .. 0x097F ; Devanagari
-    0x0980,  # .. 0x0983 ; Bengali
-    0x0984,  # .. 0x0984 ; Unknown
-    0x0985,  # .. 0x098C ; Bengali
-    0x098D,  # .. 0x098E ; Unknown
-    0x098F,  # .. 0x0990 ; Bengali
-    0x0991,  # .. 0x0992 ; Unknown
-    0x0993,  # .. 0x09A8 ; Bengali
-    0x09A9,  # .. 0x09A9 ; Unknown
-    0x09AA,  # .. 0x09B0 ; Bengali
-    0x09B1,  # .. 0x09B1 ; Unknown
-    0x09B2,  # .. 0x09B2 ; Bengali
-    0x09B3,  # .. 0x09B5 ; Unknown
-    0x09B6,  # .. 0x09B9 ; Bengali
-    0x09BA,  # .. 0x09BB ; Unknown
-    0x09BC,  # .. 0x09C4 ; Bengali
-    0x09C5,  # .. 0x09C6 ; Unknown
-    0x09C7,  # .. 0x09C8 ; Bengali
-    0x09C9,  # .. 0x09CA ; Unknown
-    0x09CB,  # .. 0x09CE ; Bengali
-    0x09CF,  # .. 0x09D6 ; Unknown
-    0x09D7,  # .. 0x09D7 ; Bengali
-    0x09D8,  # .. 0x09DB ; Unknown
-    0x09DC,  # .. 0x09DD ; Bengali
-    0x09DE,  # .. 0x09DE ; Unknown
-    0x09DF,  # .. 0x09E3 ; Bengali
-    0x09E4,  # .. 0x09E5 ; Unknown
-    0x09E6,  # .. 0x09FE ; Bengali
-    0x09FF,  # .. 0x0A00 ; Unknown
-    0x0A01,  # .. 0x0A03 ; Gurmukhi
-    0x0A04,  # .. 0x0A04 ; Unknown
-    0x0A05,  # .. 0x0A0A ; Gurmukhi
-    0x0A0B,  # .. 0x0A0E ; Unknown
-    0x0A0F,  # .. 0x0A10 ; Gurmukhi
-    0x0A11,  # .. 0x0A12 ; Unknown
-    0x0A13,  # .. 0x0A28 ; Gurmukhi
-    0x0A29,  # .. 0x0A29 ; Unknown
-    0x0A2A,  # .. 0x0A30 ; Gurmukhi
-    0x0A31,  # .. 0x0A31 ; Unknown
-    0x0A32,  # .. 0x0A33 ; Gurmukhi
-    0x0A34,  # .. 0x0A34 ; Unknown
-    0x0A35,  # .. 0x0A36 ; Gurmukhi
-    0x0A37,  # .. 0x0A37 ; Unknown
-    0x0A38,  # .. 0x0A39 ; Gurmukhi
-    0x0A3A,  # .. 0x0A3B ; Unknown
-    0x0A3C,  # .. 0x0A3C ; Gurmukhi
-    0x0A3D,  # .. 0x0A3D ; Unknown
-    0x0A3E,  # .. 0x0A42 ; Gurmukhi
-    0x0A43,  # .. 0x0A46 ; Unknown
-    0x0A47,  # .. 0x0A48 ; Gurmukhi
-    0x0A49,  # .. 0x0A4A ; Unknown
-    0x0A4B,  # .. 0x0A4D ; Gurmukhi
-    0x0A4E,  # .. 0x0A50 ; Unknown
-    0x0A51,  # .. 0x0A51 ; Gurmukhi
-    0x0A52,  # .. 0x0A58 ; Unknown
-    0x0A59,  # .. 0x0A5C ; Gurmukhi
-    0x0A5D,  # .. 0x0A5D ; Unknown
-    0x0A5E,  # .. 0x0A5E ; Gurmukhi
-    0x0A5F,  # .. 0x0A65 ; Unknown
-    0x0A66,  # .. 0x0A76 ; Gurmukhi
-    0x0A77,  # .. 0x0A80 ; Unknown
-    0x0A81,  # .. 0x0A83 ; Gujarati
-    0x0A84,  # .. 0x0A84 ; Unknown
-    0x0A85,  # .. 0x0A8D ; Gujarati
-    0x0A8E,  # .. 0x0A8E ; Unknown
-    0x0A8F,  # .. 0x0A91 ; Gujarati
-    0x0A92,  # .. 0x0A92 ; Unknown
-    0x0A93,  # .. 0x0AA8 ; Gujarati
-    0x0AA9,  # .. 0x0AA9 ; Unknown
-    0x0AAA,  # .. 0x0AB0 ; Gujarati
-    0x0AB1,  # .. 0x0AB1 ; Unknown
-    0x0AB2,  # .. 0x0AB3 ; Gujarati
-    0x0AB4,  # .. 0x0AB4 ; Unknown
-    0x0AB5,  # .. 0x0AB9 ; Gujarati
-    0x0ABA,  # .. 0x0ABB ; Unknown
-    0x0ABC,  # .. 0x0AC5 ; Gujarati
-    0x0AC6,  # .. 0x0AC6 ; Unknown
-    0x0AC7,  # .. 0x0AC9 ; Gujarati
-    0x0ACA,  # .. 0x0ACA ; Unknown
-    0x0ACB,  # .. 0x0ACD ; Gujarati
-    0x0ACE,  # .. 0x0ACF ; Unknown
-    0x0AD0,  # .. 0x0AD0 ; Gujarati
-    0x0AD1,  # .. 0x0ADF ; Unknown
-    0x0AE0,  # .. 0x0AE3 ; Gujarati
-    0x0AE4,  # .. 0x0AE5 ; Unknown
-    0x0AE6,  # .. 0x0AF1 ; Gujarati
-    0x0AF2,  # .. 0x0AF8 ; Unknown
-    0x0AF9,  # .. 0x0AFF ; Gujarati
-    0x0B00,  # .. 0x0B00 ; Unknown
-    0x0B01,  # .. 0x0B03 ; Oriya
-    0x0B04,  # .. 0x0B04 ; Unknown
-    0x0B05,  # .. 0x0B0C ; Oriya
-    0x0B0D,  # .. 0x0B0E ; Unknown
-    0x0B0F,  # .. 0x0B10 ; Oriya
-    0x0B11,  # .. 0x0B12 ; Unknown
-    0x0B13,  # .. 0x0B28 ; Oriya
-    0x0B29,  # .. 0x0B29 ; Unknown
-    0x0B2A,  # .. 0x0B30 ; Oriya
-    0x0B31,  # .. 0x0B31 ; Unknown
-    0x0B32,  # .. 0x0B33 ; Oriya
-    0x0B34,  # .. 0x0B34 ; Unknown
-    0x0B35,  # .. 0x0B39 ; Oriya
-    0x0B3A,  # .. 0x0B3B ; Unknown
-    0x0B3C,  # .. 0x0B44 ; Oriya
-    0x0B45,  # .. 0x0B46 ; Unknown
-    0x0B47,  # .. 0x0B48 ; Oriya
-    0x0B49,  # .. 0x0B4A ; Unknown
-    0x0B4B,  # .. 0x0B4D ; Oriya
-    0x0B4E,  # .. 0x0B54 ; Unknown
-    0x0B55,  # .. 0x0B57 ; Oriya
-    0x0B58,  # .. 0x0B5B ; Unknown
-    0x0B5C,  # .. 0x0B5D ; Oriya
-    0x0B5E,  # .. 0x0B5E ; Unknown
-    0x0B5F,  # .. 0x0B63 ; Oriya
-    0x0B64,  # .. 0x0B65 ; Unknown
-    0x0B66,  # .. 0x0B77 ; Oriya
-    0x0B78,  # .. 0x0B81 ; Unknown
-    0x0B82,  # .. 0x0B83 ; Tamil
-    0x0B84,  # .. 0x0B84 ; Unknown
-    0x0B85,  # .. 0x0B8A ; Tamil
-    0x0B8B,  # .. 0x0B8D ; Unknown
-    0x0B8E,  # .. 0x0B90 ; Tamil
-    0x0B91,  # .. 0x0B91 ; Unknown
-    0x0B92,  # .. 0x0B95 ; Tamil
-    0x0B96,  # .. 0x0B98 ; Unknown
-    0x0B99,  # .. 0x0B9A ; Tamil
-    0x0B9B,  # .. 0x0B9B ; Unknown
-    0x0B9C,  # .. 0x0B9C ; Tamil
-    0x0B9D,  # .. 0x0B9D ; Unknown
-    0x0B9E,  # .. 0x0B9F ; Tamil
-    0x0BA0,  # .. 0x0BA2 ; Unknown
-    0x0BA3,  # .. 0x0BA4 ; Tamil
-    0x0BA5,  # .. 0x0BA7 ; Unknown
-    0x0BA8,  # .. 0x0BAA ; Tamil
-    0x0BAB,  # .. 0x0BAD ; Unknown
-    0x0BAE,  # .. 0x0BB9 ; Tamil
-    0x0BBA,  # .. 0x0BBD ; Unknown
-    0x0BBE,  # .. 0x0BC2 ; Tamil
-    0x0BC3,  # .. 0x0BC5 ; Unknown
-    0x0BC6,  # .. 0x0BC8 ; Tamil
-    0x0BC9,  # .. 0x0BC9 ; Unknown
-    0x0BCA,  # .. 0x0BCD ; Tamil
-    0x0BCE,  # .. 0x0BCF ; Unknown
-    0x0BD0,  # .. 0x0BD0 ; Tamil
-    0x0BD1,  # .. 0x0BD6 ; Unknown
-    0x0BD7,  # .. 0x0BD7 ; Tamil
-    0x0BD8,  # .. 0x0BE5 ; Unknown
-    0x0BE6,  # .. 0x0BFA ; Tamil
-    0x0BFB,  # .. 0x0BFF ; Unknown
-    0x0C00,  # .. 0x0C0C ; Telugu
-    0x0C0D,  # .. 0x0C0D ; Unknown
-    0x0C0E,  # .. 0x0C10 ; Telugu
-    0x0C11,  # .. 0x0C11 ; Unknown
-    0x0C12,  # .. 0x0C28 ; Telugu
-    0x0C29,  # .. 0x0C29 ; Unknown
-    0x0C2A,  # .. 0x0C39 ; Telugu
-    0x0C3A,  # .. 0x0C3B ; Unknown
-    0x0C3C,  # .. 0x0C44 ; Telugu
-    0x0C45,  # .. 0x0C45 ; Unknown
-    0x0C46,  # .. 0x0C48 ; Telugu
-    0x0C49,  # .. 0x0C49 ; Unknown
-    0x0C4A,  # .. 0x0C4D ; Telugu
-    0x0C4E,  # .. 0x0C54 ; Unknown
-    0x0C55,  # .. 0x0C56 ; Telugu
-    0x0C57,  # .. 0x0C57 ; Unknown
-    0x0C58,  # .. 0x0C5A ; Telugu
-    0x0C5B,  # .. 0x0C5C ; Unknown
-    0x0C5D,  # .. 0x0C5D ; Telugu
-    0x0C5E,  # .. 0x0C5F ; Unknown
-    0x0C60,  # .. 0x0C63 ; Telugu
-    0x0C64,  # .. 0x0C65 ; Unknown
-    0x0C66,  # .. 0x0C6F ; Telugu
-    0x0C70,  # .. 0x0C76 ; Unknown
-    0x0C77,  # .. 0x0C7F ; Telugu
-    0x0C80,  # .. 0x0C8C ; Kannada
-    0x0C8D,  # .. 0x0C8D ; Unknown
-    0x0C8E,  # .. 0x0C90 ; Kannada
-    0x0C91,  # .. 0x0C91 ; Unknown
-    0x0C92,  # .. 0x0CA8 ; Kannada
-    0x0CA9,  # .. 0x0CA9 ; Unknown
-    0x0CAA,  # .. 0x0CB3 ; Kannada
-    0x0CB4,  # .. 0x0CB4 ; Unknown
-    0x0CB5,  # .. 0x0CB9 ; Kannada
-    0x0CBA,  # .. 0x0CBB ; Unknown
-    0x0CBC,  # .. 0x0CC4 ; Kannada
-    0x0CC5,  # .. 0x0CC5 ; Unknown
-    0x0CC6,  # .. 0x0CC8 ; Kannada
-    0x0CC9,  # .. 0x0CC9 ; Unknown
-    0x0CCA,  # .. 0x0CCD ; Kannada
-    0x0CCE,  # .. 0x0CD4 ; Unknown
-    0x0CD5,  # .. 0x0CD6 ; Kannada
-    0x0CD7,  # .. 0x0CDC ; Unknown
-    0x0CDD,  # .. 0x0CDE ; Kannada
-    0x0CDF,  # .. 0x0CDF ; Unknown
-    0x0CE0,  # .. 0x0CE3 ; Kannada
-    0x0CE4,  # .. 0x0CE5 ; Unknown
-    0x0CE6,  # .. 0x0CEF ; Kannada
-    0x0CF0,  # .. 0x0CF0 ; Unknown
-    0x0CF1,  # .. 0x0CF3 ; Kannada
-    0x0CF4,  # .. 0x0CFF ; Unknown
-    0x0D00,  # .. 0x0D0C ; Malayalam
-    0x0D0D,  # .. 0x0D0D ; Unknown
-    0x0D0E,  # .. 0x0D10 ; Malayalam
-    0x0D11,  # .. 0x0D11 ; Unknown
-    0x0D12,  # .. 0x0D44 ; Malayalam
-    0x0D45,  # .. 0x0D45 ; Unknown
-    0x0D46,  # .. 0x0D48 ; Malayalam
-    0x0D49,  # .. 0x0D49 ; Unknown
-    0x0D4A,  # .. 0x0D4F ; Malayalam
-    0x0D50,  # .. 0x0D53 ; Unknown
-    0x0D54,  # .. 0x0D63 ; Malayalam
-    0x0D64,  # .. 0x0D65 ; Unknown
-    0x0D66,  # .. 0x0D7F ; Malayalam
-    0x0D80,  # .. 0x0D80 ; Unknown
-    0x0D81,  # .. 0x0D83 ; Sinhala
-    0x0D84,  # .. 0x0D84 ; Unknown
-    0x0D85,  # .. 0x0D96 ; Sinhala
-    0x0D97,  # .. 0x0D99 ; Unknown
-    0x0D9A,  # .. 0x0DB1 ; Sinhala
-    0x0DB2,  # .. 0x0DB2 ; Unknown
-    0x0DB3,  # .. 0x0DBB ; Sinhala
-    0x0DBC,  # .. 0x0DBC ; Unknown
-    0x0DBD,  # .. 0x0DBD ; Sinhala
-    0x0DBE,  # .. 0x0DBF ; Unknown
-    0x0DC0,  # .. 0x0DC6 ; Sinhala
-    0x0DC7,  # .. 0x0DC9 ; Unknown
-    0x0DCA,  # .. 0x0DCA ; Sinhala
-    0x0DCB,  # .. 0x0DCE ; Unknown
-    0x0DCF,  # .. 0x0DD4 ; Sinhala
-    0x0DD5,  # .. 0x0DD5 ; Unknown
-    0x0DD6,  # .. 0x0DD6 ; Sinhala
-    0x0DD7,  # .. 0x0DD7 ; Unknown
-    0x0DD8,  # .. 0x0DDF ; Sinhala
-    0x0DE0,  # .. 0x0DE5 ; Unknown
-    0x0DE6,  # .. 0x0DEF ; Sinhala
-    0x0DF0,  # .. 0x0DF1 ; Unknown
-    0x0DF2,  # .. 0x0DF4 ; Sinhala
-    0x0DF5,  # .. 0x0E00 ; Unknown
-    0x0E01,  # .. 0x0E3A ; Thai
-    0x0E3B,  # .. 0x0E3E ; Unknown
-    0x0E3F,  # .. 0x0E3F ; Common
-    0x0E40,  # .. 0x0E5B ; Thai
-    0x0E5C,  # .. 0x0E80 ; Unknown
-    0x0E81,  # .. 0x0E82 ; Lao
-    0x0E83,  # .. 0x0E83 ; Unknown
-    0x0E84,  # .. 0x0E84 ; Lao
-    0x0E85,  # .. 0x0E85 ; Unknown
-    0x0E86,  # .. 0x0E8A ; Lao
-    0x0E8B,  # .. 0x0E8B ; Unknown
-    0x0E8C,  # .. 0x0EA3 ; Lao
-    0x0EA4,  # .. 0x0EA4 ; Unknown
-    0x0EA5,  # .. 0x0EA5 ; Lao
-    0x0EA6,  # .. 0x0EA6 ; Unknown
-    0x0EA7,  # .. 0x0EBD ; Lao
-    0x0EBE,  # .. 0x0EBF ; Unknown
-    0x0EC0,  # .. 0x0EC4 ; Lao
-    0x0EC5,  # .. 0x0EC5 ; Unknown
-    0x0EC6,  # .. 0x0EC6 ; Lao
-    0x0EC7,  # .. 0x0EC7 ; Unknown
-    0x0EC8,  # .. 0x0ECE ; Lao
-    0x0ECF,  # .. 0x0ECF ; Unknown
-    0x0ED0,  # .. 0x0ED9 ; Lao
-    0x0EDA,  # .. 0x0EDB ; Unknown
-    0x0EDC,  # .. 0x0EDF ; Lao
-    0x0EE0,  # .. 0x0EFF ; Unknown
-    0x0F00,  # .. 0x0F47 ; Tibetan
-    0x0F48,  # .. 0x0F48 ; Unknown
-    0x0F49,  # .. 0x0F6C ; Tibetan
-    0x0F6D,  # .. 0x0F70 ; Unknown
-    0x0F71,  # .. 0x0F97 ; Tibetan
-    0x0F98,  # .. 0x0F98 ; Unknown
-    0x0F99,  # .. 0x0FBC ; Tibetan
-    0x0FBD,  # .. 0x0FBD ; Unknown
-    0x0FBE,  # .. 0x0FCC ; Tibetan
-    0x0FCD,  # .. 0x0FCD ; Unknown
-    0x0FCE,  # .. 0x0FD4 ; Tibetan
-    0x0FD5,  # .. 0x0FD8 ; Common
-    0x0FD9,  # .. 0x0FDA ; Tibetan
-    0x0FDB,  # .. 0x0FFF ; Unknown
-    0x1000,  # .. 0x109F ; Myanmar
-    0x10A0,  # .. 0x10C5 ; Georgian
-    0x10C6,  # .. 0x10C6 ; Unknown
-    0x10C7,  # .. 0x10C7 ; Georgian
-    0x10C8,  # .. 0x10CC ; Unknown
-    0x10CD,  # .. 0x10CD ; Georgian
-    0x10CE,  # .. 0x10CF ; Unknown
-    0x10D0,  # .. 0x10FA ; Georgian
-    0x10FB,  # .. 0x10FB ; Common
-    0x10FC,  # .. 0x10FF ; Georgian
-    0x1100,  # .. 0x11FF ; Hangul
-    0x1200,  # .. 0x1248 ; Ethiopic
-    0x1249,  # .. 0x1249 ; Unknown
-    0x124A,  # .. 0x124D ; Ethiopic
-    0x124E,  # .. 0x124F ; Unknown
-    0x1250,  # .. 0x1256 ; Ethiopic
-    0x1257,  # .. 0x1257 ; Unknown
-    0x1258,  # .. 0x1258 ; Ethiopic
-    0x1259,  # .. 0x1259 ; Unknown
-    0x125A,  # .. 0x125D ; Ethiopic
-    0x125E,  # .. 0x125F ; Unknown
-    0x1260,  # .. 0x1288 ; Ethiopic
-    0x1289,  # .. 0x1289 ; Unknown
-    0x128A,  # .. 0x128D ; Ethiopic
-    0x128E,  # .. 0x128F ; Unknown
-    0x1290,  # .. 0x12B0 ; Ethiopic
-    0x12B1,  # .. 0x12B1 ; Unknown
-    0x12B2,  # .. 0x12B5 ; Ethiopic
-    0x12B6,  # .. 0x12B7 ; Unknown
-    0x12B8,  # .. 0x12BE ; Ethiopic
-    0x12BF,  # .. 0x12BF ; Unknown
-    0x12C0,  # .. 0x12C0 ; Ethiopic
-    0x12C1,  # .. 0x12C1 ; Unknown
-    0x12C2,  # .. 0x12C5 ; Ethiopic
-    0x12C6,  # .. 0x12C7 ; Unknown
-    0x12C8,  # .. 0x12D6 ; Ethiopic
-    0x12D7,  # .. 0x12D7 ; Unknown
-    0x12D8,  # .. 0x1310 ; Ethiopic
-    0x1311,  # .. 0x1311 ; Unknown
-    0x1312,  # .. 0x1315 ; Ethiopic
-    0x1316,  # .. 0x1317 ; Unknown
-    0x1318,  # .. 0x135A ; Ethiopic
-    0x135B,  # .. 0x135C ; Unknown
-    0x135D,  # .. 0x137C ; Ethiopic
-    0x137D,  # .. 0x137F ; Unknown
-    0x1380,  # .. 0x1399 ; Ethiopic
-    0x139A,  # .. 0x139F ; Unknown
-    0x13A0,  # .. 0x13F5 ; Cherokee
-    0x13F6,  # .. 0x13F7 ; Unknown
-    0x13F8,  # .. 0x13FD ; Cherokee
-    0x13FE,  # .. 0x13FF ; Unknown
-    0x1400,  # .. 0x167F ; Canadian_Aboriginal
-    0x1680,  # .. 0x169C ; Ogham
-    0x169D,  # .. 0x169F ; Unknown
-    0x16A0,  # .. 0x16EA ; Runic
-    0x16EB,  # .. 0x16ED ; Common
-    0x16EE,  # .. 0x16F8 ; Runic
-    0x16F9,  # .. 0x16FF ; Unknown
-    0x1700,  # .. 0x1715 ; Tagalog
-    0x1716,  # .. 0x171E ; Unknown
-    0x171F,  # .. 0x171F ; Tagalog
-    0x1720,  # .. 0x1734 ; Hanunoo
-    0x1735,  # .. 0x1736 ; Common
-    0x1737,  # .. 0x173F ; Unknown
-    0x1740,  # .. 0x1753 ; Buhid
-    0x1754,  # .. 0x175F ; Unknown
-    0x1760,  # .. 0x176C ; Tagbanwa
-    0x176D,  # .. 0x176D ; Unknown
-    0x176E,  # .. 0x1770 ; Tagbanwa
-    0x1771,  # .. 0x1771 ; Unknown
-    0x1772,  # .. 0x1773 ; Tagbanwa
-    0x1774,  # .. 0x177F ; Unknown
-    0x1780,  # .. 0x17DD ; Khmer
-    0x17DE,  # .. 0x17DF ; Unknown
-    0x17E0,  # .. 0x17E9 ; Khmer
-    0x17EA,  # .. 0x17EF ; Unknown
-    0x17F0,  # .. 0x17F9 ; Khmer
-    0x17FA,  # .. 0x17FF ; Unknown
-    0x1800,  # .. 0x1801 ; Mongolian
-    0x1802,  # .. 0x1803 ; Common
-    0x1804,  # .. 0x1804 ; Mongolian
-    0x1805,  # .. 0x1805 ; Common
-    0x1806,  # .. 0x1819 ; Mongolian
-    0x181A,  # .. 0x181F ; Unknown
-    0x1820,  # .. 0x1878 ; Mongolian
-    0x1879,  # .. 0x187F ; Unknown
-    0x1880,  # .. 0x18AA ; Mongolian
-    0x18AB,  # .. 0x18AF ; Unknown
-    0x18B0,  # .. 0x18F5 ; Canadian_Aboriginal
-    0x18F6,  # .. 0x18FF ; Unknown
-    0x1900,  # .. 0x191E ; Limbu
-    0x191F,  # .. 0x191F ; Unknown
-    0x1920,  # .. 0x192B ; Limbu
-    0x192C,  # .. 0x192F ; Unknown
-    0x1930,  # .. 0x193B ; Limbu
-    0x193C,  # .. 0x193F ; Unknown
-    0x1940,  # .. 0x1940 ; Limbu
-    0x1941,  # .. 0x1943 ; Unknown
-    0x1944,  # .. 0x194F ; Limbu
-    0x1950,  # .. 0x196D ; Tai_Le
-    0x196E,  # .. 0x196F ; Unknown
-    0x1970,  # .. 0x1974 ; Tai_Le
-    0x1975,  # .. 0x197F ; Unknown
-    0x1980,  # .. 0x19AB ; New_Tai_Lue
-    0x19AC,  # .. 0x19AF ; Unknown
-    0x19B0,  # .. 0x19C9 ; New_Tai_Lue
-    0x19CA,  # .. 0x19CF ; Unknown
-    0x19D0,  # .. 0x19DA ; New_Tai_Lue
-    0x19DB,  # .. 0x19DD ; Unknown
-    0x19DE,  # .. 0x19DF ; New_Tai_Lue
-    0x19E0,  # .. 0x19FF ; Khmer
-    0x1A00,  # .. 0x1A1B ; Buginese
-    0x1A1C,  # .. 0x1A1D ; Unknown
-    0x1A1E,  # .. 0x1A1F ; Buginese
-    0x1A20,  # .. 0x1A5E ; Tai_Tham
-    0x1A5F,  # .. 0x1A5F ; Unknown
-    0x1A60,  # .. 0x1A7C ; Tai_Tham
-    0x1A7D,  # .. 0x1A7E ; Unknown
-    0x1A7F,  # .. 0x1A89 ; Tai_Tham
-    0x1A8A,  # .. 0x1A8F ; Unknown
-    0x1A90,  # .. 0x1A99 ; Tai_Tham
-    0x1A9A,  # .. 0x1A9F ; Unknown
-    0x1AA0,  # .. 0x1AAD ; Tai_Tham
-    0x1AAE,  # .. 0x1AAF ; Unknown
-    0x1AB0,  # .. 0x1ACE ; Inherited
-    0x1ACF,  # .. 0x1AFF ; Unknown
-    0x1B00,  # .. 0x1B4C ; Balinese
-    0x1B4D,  # .. 0x1B4F ; Unknown
-    0x1B50,  # .. 0x1B7E ; Balinese
-    0x1B7F,  # .. 0x1B7F ; Unknown
-    0x1B80,  # .. 0x1BBF ; Sundanese
-    0x1BC0,  # .. 0x1BF3 ; Batak
-    0x1BF4,  # .. 0x1BFB ; Unknown
-    0x1BFC,  # .. 0x1BFF ; Batak
-    0x1C00,  # .. 0x1C37 ; Lepcha
-    0x1C38,  # .. 0x1C3A ; Unknown
-    0x1C3B,  # .. 0x1C49 ; Lepcha
-    0x1C4A,  # .. 0x1C4C ; Unknown
-    0x1C4D,  # .. 0x1C4F ; Lepcha
-    0x1C50,  # .. 0x1C7F ; Ol_Chiki
-    0x1C80,  # .. 0x1C88 ; Cyrillic
-    0x1C89,  # .. 0x1C8F ; Unknown
-    0x1C90,  # .. 0x1CBA ; Georgian
-    0x1CBB,  # .. 0x1CBC ; Unknown
-    0x1CBD,  # .. 0x1CBF ; Georgian
-    0x1CC0,  # .. 0x1CC7 ; Sundanese
-    0x1CC8,  # .. 0x1CCF ; Unknown
-    0x1CD0,  # .. 0x1CD2 ; Inherited
-    0x1CD3,  # .. 0x1CD3 ; Common
-    0x1CD4,  # .. 0x1CE0 ; Inherited
-    0x1CE1,  # .. 0x1CE1 ; Common
-    0x1CE2,  # .. 0x1CE8 ; Inherited
-    0x1CE9,  # .. 0x1CEC ; Common
-    0x1CED,  # .. 0x1CED ; Inherited
-    0x1CEE,  # .. 0x1CF3 ; Common
-    0x1CF4,  # .. 0x1CF4 ; Inherited
-    0x1CF5,  # .. 0x1CF7 ; Common
-    0x1CF8,  # .. 0x1CF9 ; Inherited
-    0x1CFA,  # .. 0x1CFA ; Common
-    0x1CFB,  # .. 0x1CFF ; Unknown
-    0x1D00,  # .. 0x1D25 ; Latin
-    0x1D26,  # .. 0x1D2A ; Greek
-    0x1D2B,  # .. 0x1D2B ; Cyrillic
-    0x1D2C,  # .. 0x1D5C ; Latin
-    0x1D5D,  # .. 0x1D61 ; Greek
-    0x1D62,  # .. 0x1D65 ; Latin
-    0x1D66,  # .. 0x1D6A ; Greek
-    0x1D6B,  # .. 0x1D77 ; Latin
-    0x1D78,  # .. 0x1D78 ; Cyrillic
-    0x1D79,  # .. 0x1DBE ; Latin
-    0x1DBF,  # .. 0x1DBF ; Greek
-    0x1DC0,  # .. 0x1DFF ; Inherited
-    0x1E00,  # .. 0x1EFF ; Latin
-    0x1F00,  # .. 0x1F15 ; Greek
-    0x1F16,  # .. 0x1F17 ; Unknown
-    0x1F18,  # .. 0x1F1D ; Greek
-    0x1F1E,  # .. 0x1F1F ; Unknown
-    0x1F20,  # .. 0x1F45 ; Greek
-    0x1F46,  # .. 0x1F47 ; Unknown
-    0x1F48,  # .. 0x1F4D ; Greek
-    0x1F4E,  # .. 0x1F4F ; Unknown
-    0x1F50,  # .. 0x1F57 ; Greek
-    0x1F58,  # .. 0x1F58 ; Unknown
-    0x1F59,  # .. 0x1F59 ; Greek
-    0x1F5A,  # .. 0x1F5A ; Unknown
-    0x1F5B,  # .. 0x1F5B ; Greek
-    0x1F5C,  # .. 0x1F5C ; Unknown
-    0x1F5D,  # .. 0x1F5D ; Greek
-    0x1F5E,  # .. 0x1F5E ; Unknown
-    0x1F5F,  # .. 0x1F7D ; Greek
-    0x1F7E,  # .. 0x1F7F ; Unknown
-    0x1F80,  # .. 0x1FB4 ; Greek
-    0x1FB5,  # .. 0x1FB5 ; Unknown
-    0x1FB6,  # .. 0x1FC4 ; Greek
-    0x1FC5,  # .. 0x1FC5 ; Unknown
-    0x1FC6,  # .. 0x1FD3 ; Greek
-    0x1FD4,  # .. 0x1FD5 ; Unknown
-    0x1FD6,  # .. 0x1FDB ; Greek
-    0x1FDC,  # .. 0x1FDC ; Unknown
-    0x1FDD,  # .. 0x1FEF ; Greek
-    0x1FF0,  # .. 0x1FF1 ; Unknown
-    0x1FF2,  # .. 0x1FF4 ; Greek
-    0x1FF5,  # .. 0x1FF5 ; Unknown
-    0x1FF6,  # .. 0x1FFE ; Greek
-    0x1FFF,  # .. 0x1FFF ; Unknown
-    0x2000,  # .. 0x200B ; Common
-    0x200C,  # .. 0x200D ; Inherited
-    0x200E,  # .. 0x2064 ; Common
-    0x2065,  # .. 0x2065 ; Unknown
-    0x2066,  # .. 0x2070 ; Common
-    0x2071,  # .. 0x2071 ; Latin
-    0x2072,  # .. 0x2073 ; Unknown
-    0x2074,  # .. 0x207E ; Common
-    0x207F,  # .. 0x207F ; Latin
-    0x2080,  # .. 0x208E ; Common
-    0x208F,  # .. 0x208F ; Unknown
-    0x2090,  # .. 0x209C ; Latin
-    0x209D,  # .. 0x209F ; Unknown
-    0x20A0,  # .. 0x20C0 ; Common
-    0x20C1,  # .. 0x20CF ; Unknown
-    0x20D0,  # .. 0x20F0 ; Inherited
-    0x20F1,  # .. 0x20FF ; Unknown
-    0x2100,  # .. 0x2125 ; Common
-    0x2126,  # .. 0x2126 ; Greek
-    0x2127,  # .. 0x2129 ; Common
-    0x212A,  # .. 0x212B ; Latin
-    0x212C,  # .. 0x2131 ; Common
-    0x2132,  # .. 0x2132 ; Latin
-    0x2133,  # .. 0x214D ; Common
-    0x214E,  # .. 0x214E ; Latin
-    0x214F,  # .. 0x215F ; Common
-    0x2160,  # .. 0x2188 ; Latin
-    0x2189,  # .. 0x218B ; Common
-    0x218C,  # .. 0x218F ; Unknown
-    0x2190,  # .. 0x2426 ; Common
-    0x2427,  # .. 0x243F ; Unknown
-    0x2440,  # .. 0x244A ; Common
-    0x244B,  # .. 0x245F ; Unknown
-    0x2460,  # .. 0x27FF ; Common
-    0x2800,  # .. 0x28FF ; Braille
-    0x2900,  # .. 0x2B73 ; Common
-    0x2B74,  # .. 0x2B75 ; Unknown
-    0x2B76,  # .. 0x2B95 ; Common
-    0x2B96,  # .. 0x2B96 ; Unknown
-    0x2B97,  # .. 0x2BFF ; Common
-    0x2C00,  # .. 0x2C5F ; Glagolitic
-    0x2C60,  # .. 0x2C7F ; Latin
-    0x2C80,  # .. 0x2CF3 ; Coptic
-    0x2CF4,  # .. 0x2CF8 ; Unknown
-    0x2CF9,  # .. 0x2CFF ; Coptic
-    0x2D00,  # .. 0x2D25 ; Georgian
-    0x2D26,  # .. 0x2D26 ; Unknown
-    0x2D27,  # .. 0x2D27 ; Georgian
-    0x2D28,  # .. 0x2D2C ; Unknown
-    0x2D2D,  # .. 0x2D2D ; Georgian
-    0x2D2E,  # .. 0x2D2F ; Unknown
-    0x2D30,  # .. 0x2D67 ; Tifinagh
-    0x2D68,  # .. 0x2D6E ; Unknown
-    0x2D6F,  # .. 0x2D70 ; Tifinagh
-    0x2D71,  # .. 0x2D7E ; Unknown
-    0x2D7F,  # .. 0x2D7F ; Tifinagh
-    0x2D80,  # .. 0x2D96 ; Ethiopic
-    0x2D97,  # .. 0x2D9F ; Unknown
-    0x2DA0,  # .. 0x2DA6 ; Ethiopic
-    0x2DA7,  # .. 0x2DA7 ; Unknown
-    0x2DA8,  # .. 0x2DAE ; Ethiopic
-    0x2DAF,  # .. 0x2DAF ; Unknown
-    0x2DB0,  # .. 0x2DB6 ; Ethiopic
-    0x2DB7,  # .. 0x2DB7 ; Unknown
-    0x2DB8,  # .. 0x2DBE ; Ethiopic
-    0x2DBF,  # .. 0x2DBF ; Unknown
-    0x2DC0,  # .. 0x2DC6 ; Ethiopic
-    0x2DC7,  # .. 0x2DC7 ; Unknown
-    0x2DC8,  # .. 0x2DCE ; Ethiopic
-    0x2DCF,  # .. 0x2DCF ; Unknown
-    0x2DD0,  # .. 0x2DD6 ; Ethiopic
-    0x2DD7,  # .. 0x2DD7 ; Unknown
-    0x2DD8,  # .. 0x2DDE ; Ethiopic
-    0x2DDF,  # .. 0x2DDF ; Unknown
-    0x2DE0,  # .. 0x2DFF ; Cyrillic
-    0x2E00,  # .. 0x2E5D ; Common
-    0x2E5E,  # .. 0x2E7F ; Unknown
-    0x2E80,  # .. 0x2E99 ; Han
-    0x2E9A,  # .. 0x2E9A ; Unknown
-    0x2E9B,  # .. 0x2EF3 ; Han
-    0x2EF4,  # .. 0x2EFF ; Unknown
-    0x2F00,  # .. 0x2FD5 ; Han
-    0x2FD6,  # .. 0x2FEF ; Unknown
-    0x2FF0,  # .. 0x2FFB ; Common
-    0x2FFC,  # .. 0x2FFF ; Unknown
-    0x3000,  # .. 0x3004 ; Common
-    0x3005,  # .. 0x3005 ; Han
-    0x3006,  # .. 0x3006 ; Common
-    0x3007,  # .. 0x3007 ; Han
-    0x3008,  # .. 0x3020 ; Common
-    0x3021,  # .. 0x3029 ; Han
-    0x302A,  # .. 0x302D ; Inherited
-    0x302E,  # .. 0x302F ; Hangul
-    0x3030,  # .. 0x3037 ; Common
-    0x3038,  # .. 0x303B ; Han
-    0x303C,  # .. 0x303F ; Common
-    0x3040,  # .. 0x3040 ; Unknown
-    0x3041,  # .. 0x3096 ; Hiragana
-    0x3097,  # .. 0x3098 ; Unknown
-    0x3099,  # .. 0x309A ; Inherited
-    0x309B,  # .. 0x309C ; Common
-    0x309D,  # .. 0x309F ; Hiragana
-    0x30A0,  # .. 0x30A0 ; Common
-    0x30A1,  # .. 0x30FA ; Katakana
-    0x30FB,  # .. 0x30FC ; Common
-    0x30FD,  # .. 0x30FF ; Katakana
-    0x3100,  # .. 0x3104 ; Unknown
-    0x3105,  # .. 0x312F ; Bopomofo
-    0x3130,  # .. 0x3130 ; Unknown
-    0x3131,  # .. 0x318E ; Hangul
-    0x318F,  # .. 0x318F ; Unknown
-    0x3190,  # .. 0x319F ; Common
-    0x31A0,  # .. 0x31BF ; Bopomofo
-    0x31C0,  # .. 0x31E3 ; Common
-    0x31E4,  # .. 0x31EF ; Unknown
-    0x31F0,  # .. 0x31FF ; Katakana
-    0x3200,  # .. 0x321E ; Hangul
-    0x321F,  # .. 0x321F ; Unknown
-    0x3220,  # .. 0x325F ; Common
-    0x3260,  # .. 0x327E ; Hangul
-    0x327F,  # .. 0x32CF ; Common
-    0x32D0,  # .. 0x32FE ; Katakana
-    0x32FF,  # .. 0x32FF ; Common
-    0x3300,  # .. 0x3357 ; Katakana
-    0x3358,  # .. 0x33FF ; Common
-    0x3400,  # .. 0x4DBF ; Han
-    0x4DC0,  # .. 0x4DFF ; Common
-    0x4E00,  # .. 0x9FFF ; Han
-    0xA000,  # .. 0xA48C ; Yi
-    0xA48D,  # .. 0xA48F ; Unknown
-    0xA490,  # .. 0xA4C6 ; Yi
-    0xA4C7,  # .. 0xA4CF ; Unknown
-    0xA4D0,  # .. 0xA4FF ; Lisu
-    0xA500,  # .. 0xA62B ; Vai
-    0xA62C,  # .. 0xA63F ; Unknown
-    0xA640,  # .. 0xA69F ; Cyrillic
-    0xA6A0,  # .. 0xA6F7 ; Bamum
-    0xA6F8,  # .. 0xA6FF ; Unknown
-    0xA700,  # .. 0xA721 ; Common
-    0xA722,  # .. 0xA787 ; Latin
-    0xA788,  # .. 0xA78A ; Common
-    0xA78B,  # .. 0xA7CA ; Latin
-    0xA7CB,  # .. 0xA7CF ; Unknown
-    0xA7D0,  # .. 0xA7D1 ; Latin
-    0xA7D2,  # .. 0xA7D2 ; Unknown
-    0xA7D3,  # .. 0xA7D3 ; Latin
-    0xA7D4,  # .. 0xA7D4 ; Unknown
-    0xA7D5,  # .. 0xA7D9 ; Latin
-    0xA7DA,  # .. 0xA7F1 ; Unknown
-    0xA7F2,  # .. 0xA7FF ; Latin
-    0xA800,  # .. 0xA82C ; Syloti_Nagri
-    0xA82D,  # .. 0xA82F ; Unknown
-    0xA830,  # .. 0xA839 ; Common
-    0xA83A,  # .. 0xA83F ; Unknown
-    0xA840,  # .. 0xA877 ; Phags_Pa
-    0xA878,  # .. 0xA87F ; Unknown
-    0xA880,  # .. 0xA8C5 ; Saurashtra
-    0xA8C6,  # .. 0xA8CD ; Unknown
-    0xA8CE,  # .. 0xA8D9 ; Saurashtra
-    0xA8DA,  # .. 0xA8DF ; Unknown
-    0xA8E0,  # .. 0xA8FF ; Devanagari
-    0xA900,  # .. 0xA92D ; Kayah_Li
-    0xA92E,  # .. 0xA92E ; Common
-    0xA92F,  # .. 0xA92F ; Kayah_Li
-    0xA930,  # .. 0xA953 ; Rejang
-    0xA954,  # .. 0xA95E ; Unknown
-    0xA95F,  # .. 0xA95F ; Rejang
-    0xA960,  # .. 0xA97C ; Hangul
-    0xA97D,  # .. 0xA97F ; Unknown
-    0xA980,  # .. 0xA9CD ; Javanese
-    0xA9CE,  # .. 0xA9CE ; Unknown
-    0xA9CF,  # .. 0xA9CF ; Common
-    0xA9D0,  # .. 0xA9D9 ; Javanese
-    0xA9DA,  # .. 0xA9DD ; Unknown
-    0xA9DE,  # .. 0xA9DF ; Javanese
-    0xA9E0,  # .. 0xA9FE ; Myanmar
-    0xA9FF,  # .. 0xA9FF ; Unknown
-    0xAA00,  # .. 0xAA36 ; Cham
-    0xAA37,  # .. 0xAA3F ; Unknown
-    0xAA40,  # .. 0xAA4D ; Cham
-    0xAA4E,  # .. 0xAA4F ; Unknown
-    0xAA50,  # .. 0xAA59 ; Cham
-    0xAA5A,  # .. 0xAA5B ; Unknown
-    0xAA5C,  # .. 0xAA5F ; Cham
-    0xAA60,  # .. 0xAA7F ; Myanmar
-    0xAA80,  # .. 0xAAC2 ; Tai_Viet
-    0xAAC3,  # .. 0xAADA ; Unknown
-    0xAADB,  # .. 0xAADF ; Tai_Viet
-    0xAAE0,  # .. 0xAAF6 ; Meetei_Mayek
-    0xAAF7,  # .. 0xAB00 ; Unknown
-    0xAB01,  # .. 0xAB06 ; Ethiopic
-    0xAB07,  # .. 0xAB08 ; Unknown
-    0xAB09,  # .. 0xAB0E ; Ethiopic
-    0xAB0F,  # .. 0xAB10 ; Unknown
-    0xAB11,  # .. 0xAB16 ; Ethiopic
-    0xAB17,  # .. 0xAB1F ; Unknown
-    0xAB20,  # .. 0xAB26 ; Ethiopic
-    0xAB27,  # .. 0xAB27 ; Unknown
-    0xAB28,  # .. 0xAB2E ; Ethiopic
-    0xAB2F,  # .. 0xAB2F ; Unknown
-    0xAB30,  # .. 0xAB5A ; Latin
-    0xAB5B,  # .. 0xAB5B ; Common
-    0xAB5C,  # .. 0xAB64 ; Latin
-    0xAB65,  # .. 0xAB65 ; Greek
-    0xAB66,  # .. 0xAB69 ; Latin
-    0xAB6A,  # .. 0xAB6B ; Common
-    0xAB6C,  # .. 0xAB6F ; Unknown
-    0xAB70,  # .. 0xABBF ; Cherokee
-    0xABC0,  # .. 0xABED ; Meetei_Mayek
-    0xABEE,  # .. 0xABEF ; Unknown
-    0xABF0,  # .. 0xABF9 ; Meetei_Mayek
-    0xABFA,  # .. 0xABFF ; Unknown
-    0xAC00,  # .. 0xD7A3 ; Hangul
-    0xD7A4,  # .. 0xD7AF ; Unknown
-    0xD7B0,  # .. 0xD7C6 ; Hangul
-    0xD7C7,  # .. 0xD7CA ; Unknown
-    0xD7CB,  # .. 0xD7FB ; Hangul
-    0xD7FC,  # .. 0xF8FF ; Unknown
-    0xF900,  # .. 0xFA6D ; Han
-    0xFA6E,  # .. 0xFA6F ; Unknown
-    0xFA70,  # .. 0xFAD9 ; Han
-    0xFADA,  # .. 0xFAFF ; Unknown
-    0xFB00,  # .. 0xFB06 ; Latin
-    0xFB07,  # .. 0xFB12 ; Unknown
-    0xFB13,  # .. 0xFB17 ; Armenian
-    0xFB18,  # .. 0xFB1C ; Unknown
-    0xFB1D,  # .. 0xFB36 ; Hebrew
-    0xFB37,  # .. 0xFB37 ; Unknown
-    0xFB38,  # .. 0xFB3C ; Hebrew
-    0xFB3D,  # .. 0xFB3D ; Unknown
-    0xFB3E,  # .. 0xFB3E ; Hebrew
-    0xFB3F,  # .. 0xFB3F ; Unknown
-    0xFB40,  # .. 0xFB41 ; Hebrew
-    0xFB42,  # .. 0xFB42 ; Unknown
-    0xFB43,  # .. 0xFB44 ; Hebrew
-    0xFB45,  # .. 0xFB45 ; Unknown
-    0xFB46,  # .. 0xFB4F ; Hebrew
-    0xFB50,  # .. 0xFBC2 ; Arabic
-    0xFBC3,  # .. 0xFBD2 ; Unknown
-    0xFBD3,  # .. 0xFD3D ; Arabic
-    0xFD3E,  # .. 0xFD3F ; Common
-    0xFD40,  # .. 0xFD8F ; Arabic
-    0xFD90,  # .. 0xFD91 ; Unknown
-    0xFD92,  # .. 0xFDC7 ; Arabic
-    0xFDC8,  # .. 0xFDCE ; Unknown
-    0xFDCF,  # .. 0xFDCF ; Arabic
-    0xFDD0,  # .. 0xFDEF ; Unknown
-    0xFDF0,  # .. 0xFDFF ; Arabic
-    0xFE00,  # .. 0xFE0F ; Inherited
-    0xFE10,  # .. 0xFE19 ; Common
-    0xFE1A,  # .. 0xFE1F ; Unknown
-    0xFE20,  # .. 0xFE2D ; Inherited
-    0xFE2E,  # .. 0xFE2F ; Cyrillic
-    0xFE30,  # .. 0xFE52 ; Common
-    0xFE53,  # .. 0xFE53 ; Unknown
-    0xFE54,  # .. 0xFE66 ; Common
-    0xFE67,  # .. 0xFE67 ; Unknown
-    0xFE68,  # .. 0xFE6B ; Common
-    0xFE6C,  # .. 0xFE6F ; Unknown
-    0xFE70,  # .. 0xFE74 ; Arabic
-    0xFE75,  # .. 0xFE75 ; Unknown
-    0xFE76,  # .. 0xFEFC ; Arabic
-    0xFEFD,  # .. 0xFEFE ; Unknown
-    0xFEFF,  # .. 0xFEFF ; Common
-    0xFF00,  # .. 0xFF00 ; Unknown
-    0xFF01,  # .. 0xFF20 ; Common
-    0xFF21,  # .. 0xFF3A ; Latin
-    0xFF3B,  # .. 0xFF40 ; Common
-    0xFF41,  # .. 0xFF5A ; Latin
-    0xFF5B,  # .. 0xFF65 ; Common
-    0xFF66,  # .. 0xFF6F ; Katakana
-    0xFF70,  # .. 0xFF70 ; Common
-    0xFF71,  # .. 0xFF9D ; Katakana
-    0xFF9E,  # .. 0xFF9F ; Common
-    0xFFA0,  # .. 0xFFBE ; Hangul
-    0xFFBF,  # .. 0xFFC1 ; Unknown
-    0xFFC2,  # .. 0xFFC7 ; Hangul
-    0xFFC8,  # .. 0xFFC9 ; Unknown
-    0xFFCA,  # .. 0xFFCF ; Hangul
-    0xFFD0,  # .. 0xFFD1 ; Unknown
-    0xFFD2,  # .. 0xFFD7 ; Hangul
-    0xFFD8,  # .. 0xFFD9 ; Unknown
-    0xFFDA,  # .. 0xFFDC ; Hangul
-    0xFFDD,  # .. 0xFFDF ; Unknown
-    0xFFE0,  # .. 0xFFE6 ; Common
-    0xFFE7,  # .. 0xFFE7 ; Unknown
-    0xFFE8,  # .. 0xFFEE ; Common
-    0xFFEF,  # .. 0xFFF8 ; Unknown
-    0xFFF9,  # .. 0xFFFD ; Common
-    0xFFFE,  # .. 0xFFFF ; Unknown
-    0x10000,  # .. 0x1000B ; Linear_B
-    0x1000C,  # .. 0x1000C ; Unknown
-    0x1000D,  # .. 0x10026 ; Linear_B
-    0x10027,  # .. 0x10027 ; Unknown
-    0x10028,  # .. 0x1003A ; Linear_B
-    0x1003B,  # .. 0x1003B ; Unknown
-    0x1003C,  # .. 0x1003D ; Linear_B
-    0x1003E,  # .. 0x1003E ; Unknown
-    0x1003F,  # .. 0x1004D ; Linear_B
-    0x1004E,  # .. 0x1004F ; Unknown
-    0x10050,  # .. 0x1005D ; Linear_B
-    0x1005E,  # .. 0x1007F ; Unknown
-    0x10080,  # .. 0x100FA ; Linear_B
-    0x100FB,  # .. 0x100FF ; Unknown
-    0x10100,  # .. 0x10102 ; Common
-    0x10103,  # .. 0x10106 ; Unknown
-    0x10107,  # .. 0x10133 ; Common
-    0x10134,  # .. 0x10136 ; Unknown
-    0x10137,  # .. 0x1013F ; Common
-    0x10140,  # .. 0x1018E ; Greek
-    0x1018F,  # .. 0x1018F ; Unknown
-    0x10190,  # .. 0x1019C ; Common
-    0x1019D,  # .. 0x1019F ; Unknown
-    0x101A0,  # .. 0x101A0 ; Greek
-    0x101A1,  # .. 0x101CF ; Unknown
-    0x101D0,  # .. 0x101FC ; Common
-    0x101FD,  # .. 0x101FD ; Inherited
-    0x101FE,  # .. 0x1027F ; Unknown
-    0x10280,  # .. 0x1029C ; Lycian
-    0x1029D,  # .. 0x1029F ; Unknown
-    0x102A0,  # .. 0x102D0 ; Carian
-    0x102D1,  # .. 0x102DF ; Unknown
-    0x102E0,  # .. 0x102E0 ; Inherited
-    0x102E1,  # .. 0x102FB ; Common
-    0x102FC,  # .. 0x102FF ; Unknown
-    0x10300,  # .. 0x10323 ; Old_Italic
-    0x10324,  # .. 0x1032C ; Unknown
-    0x1032D,  # .. 0x1032F ; Old_Italic
-    0x10330,  # .. 0x1034A ; Gothic
-    0x1034B,  # .. 0x1034F ; Unknown
-    0x10350,  # .. 0x1037A ; Old_Permic
-    0x1037B,  # .. 0x1037F ; Unknown
-    0x10380,  # .. 0x1039D ; Ugaritic
-    0x1039E,  # .. 0x1039E ; Unknown
-    0x1039F,  # .. 0x1039F ; Ugaritic
-    0x103A0,  # .. 0x103C3 ; Old_Persian
-    0x103C4,  # .. 0x103C7 ; Unknown
-    0x103C8,  # .. 0x103D5 ; Old_Persian
-    0x103D6,  # .. 0x103FF ; Unknown
-    0x10400,  # .. 0x1044F ; Deseret
-    0x10450,  # .. 0x1047F ; Shavian
-    0x10480,  # .. 0x1049D ; Osmanya
-    0x1049E,  # .. 0x1049F ; Unknown
-    0x104A0,  # .. 0x104A9 ; Osmanya
-    0x104AA,  # .. 0x104AF ; Unknown
-    0x104B0,  # .. 0x104D3 ; Osage
-    0x104D4,  # .. 0x104D7 ; Unknown
-    0x104D8,  # .. 0x104FB ; Osage
-    0x104FC,  # .. 0x104FF ; Unknown
-    0x10500,  # .. 0x10527 ; Elbasan
-    0x10528,  # .. 0x1052F ; Unknown
-    0x10530,  # .. 0x10563 ; Caucasian_Albanian
-    0x10564,  # .. 0x1056E ; Unknown
-    0x1056F,  # .. 0x1056F ; Caucasian_Albanian
-    0x10570,  # .. 0x1057A ; Vithkuqi
-    0x1057B,  # .. 0x1057B ; Unknown
-    0x1057C,  # .. 0x1058A ; Vithkuqi
-    0x1058B,  # .. 0x1058B ; Unknown
-    0x1058C,  # .. 0x10592 ; Vithkuqi
-    0x10593,  # .. 0x10593 ; Unknown
-    0x10594,  # .. 0x10595 ; Vithkuqi
-    0x10596,  # .. 0x10596 ; Unknown
-    0x10597,  # .. 0x105A1 ; Vithkuqi
-    0x105A2,  # .. 0x105A2 ; Unknown
-    0x105A3,  # .. 0x105B1 ; Vithkuqi
-    0x105B2,  # .. 0x105B2 ; Unknown
-    0x105B3,  # .. 0x105B9 ; Vithkuqi
-    0x105BA,  # .. 0x105BA ; Unknown
-    0x105BB,  # .. 0x105BC ; Vithkuqi
-    0x105BD,  # .. 0x105FF ; Unknown
-    0x10600,  # .. 0x10736 ; Linear_A
-    0x10737,  # .. 0x1073F ; Unknown
-    0x10740,  # .. 0x10755 ; Linear_A
-    0x10756,  # .. 0x1075F ; Unknown
-    0x10760,  # .. 0x10767 ; Linear_A
-    0x10768,  # .. 0x1077F ; Unknown
-    0x10780,  # .. 0x10785 ; Latin
-    0x10786,  # .. 0x10786 ; Unknown
-    0x10787,  # .. 0x107B0 ; Latin
-    0x107B1,  # .. 0x107B1 ; Unknown
-    0x107B2,  # .. 0x107BA ; Latin
-    0x107BB,  # .. 0x107FF ; Unknown
-    0x10800,  # .. 0x10805 ; Cypriot
-    0x10806,  # .. 0x10807 ; Unknown
-    0x10808,  # .. 0x10808 ; Cypriot
-    0x10809,  # .. 0x10809 ; Unknown
-    0x1080A,  # .. 0x10835 ; Cypriot
-    0x10836,  # .. 0x10836 ; Unknown
-    0x10837,  # .. 0x10838 ; Cypriot
-    0x10839,  # .. 0x1083B ; Unknown
-    0x1083C,  # .. 0x1083C ; Cypriot
-    0x1083D,  # .. 0x1083E ; Unknown
-    0x1083F,  # .. 0x1083F ; Cypriot
-    0x10840,  # .. 0x10855 ; Imperial_Aramaic
-    0x10856,  # .. 0x10856 ; Unknown
-    0x10857,  # .. 0x1085F ; Imperial_Aramaic
-    0x10860,  # .. 0x1087F ; Palmyrene
-    0x10880,  # .. 0x1089E ; Nabataean
-    0x1089F,  # .. 0x108A6 ; Unknown
-    0x108A7,  # .. 0x108AF ; Nabataean
-    0x108B0,  # .. 0x108DF ; Unknown
-    0x108E0,  # .. 0x108F2 ; Hatran
-    0x108F3,  # .. 0x108F3 ; Unknown
-    0x108F4,  # .. 0x108F5 ; Hatran
-    0x108F6,  # .. 0x108FA ; Unknown
-    0x108FB,  # .. 0x108FF ; Hatran
-    0x10900,  # .. 0x1091B ; Phoenician
-    0x1091C,  # .. 0x1091E ; Unknown
-    0x1091F,  # .. 0x1091F ; Phoenician
-    0x10920,  # .. 0x10939 ; Lydian
-    0x1093A,  # .. 0x1093E ; Unknown
-    0x1093F,  # .. 0x1093F ; Lydian
-    0x10940,  # .. 0x1097F ; Unknown
-    0x10980,  # .. 0x1099F ; Meroitic_Hieroglyphs
-    0x109A0,  # .. 0x109B7 ; Meroitic_Cursive
-    0x109B8,  # .. 0x109BB ; Unknown
-    0x109BC,  # .. 0x109CF ; Meroitic_Cursive
-    0x109D0,  # .. 0x109D1 ; Unknown
-    0x109D2,  # .. 0x109FF ; Meroitic_Cursive
-    0x10A00,  # .. 0x10A03 ; Kharoshthi
-    0x10A04,  # .. 0x10A04 ; Unknown
-    0x10A05,  # .. 0x10A06 ; Kharoshthi
-    0x10A07,  # .. 0x10A0B ; Unknown
-    0x10A0C,  # .. 0x10A13 ; Kharoshthi
-    0x10A14,  # .. 0x10A14 ; Unknown
-    0x10A15,  # .. 0x10A17 ; Kharoshthi
-    0x10A18,  # .. 0x10A18 ; Unknown
-    0x10A19,  # .. 0x10A35 ; Kharoshthi
-    0x10A36,  # .. 0x10A37 ; Unknown
-    0x10A38,  # .. 0x10A3A ; Kharoshthi
-    0x10A3B,  # .. 0x10A3E ; Unknown
-    0x10A3F,  # .. 0x10A48 ; Kharoshthi
-    0x10A49,  # .. 0x10A4F ; Unknown
-    0x10A50,  # .. 0x10A58 ; Kharoshthi
-    0x10A59,  # .. 0x10A5F ; Unknown
-    0x10A60,  # .. 0x10A7F ; Old_South_Arabian
-    0x10A80,  # .. 0x10A9F ; Old_North_Arabian
-    0x10AA0,  # .. 0x10ABF ; Unknown
-    0x10AC0,  # .. 0x10AE6 ; Manichaean
-    0x10AE7,  # .. 0x10AEA ; Unknown
-    0x10AEB,  # .. 0x10AF6 ; Manichaean
-    0x10AF7,  # .. 0x10AFF ; Unknown
-    0x10B00,  # .. 0x10B35 ; Avestan
-    0x10B36,  # .. 0x10B38 ; Unknown
-    0x10B39,  # .. 0x10B3F ; Avestan
-    0x10B40,  # .. 0x10B55 ; Inscriptional_Parthian
-    0x10B56,  # .. 0x10B57 ; Unknown
-    0x10B58,  # .. 0x10B5F ; Inscriptional_Parthian
-    0x10B60,  # .. 0x10B72 ; Inscriptional_Pahlavi
-    0x10B73,  # .. 0x10B77 ; Unknown
-    0x10B78,  # .. 0x10B7F ; Inscriptional_Pahlavi
-    0x10B80,  # .. 0x10B91 ; Psalter_Pahlavi
-    0x10B92,  # .. 0x10B98 ; Unknown
-    0x10B99,  # .. 0x10B9C ; Psalter_Pahlavi
-    0x10B9D,  # .. 0x10BA8 ; Unknown
-    0x10BA9,  # .. 0x10BAF ; Psalter_Pahlavi
-    0x10BB0,  # .. 0x10BFF ; Unknown
-    0x10C00,  # .. 0x10C48 ; Old_Turkic
-    0x10C49,  # .. 0x10C7F ; Unknown
-    0x10C80,  # .. 0x10CB2 ; Old_Hungarian
-    0x10CB3,  # .. 0x10CBF ; Unknown
-    0x10CC0,  # .. 0x10CF2 ; Old_Hungarian
-    0x10CF3,  # .. 0x10CF9 ; Unknown
-    0x10CFA,  # .. 0x10CFF ; Old_Hungarian
-    0x10D00,  # .. 0x10D27 ; Hanifi_Rohingya
-    0x10D28,  # .. 0x10D2F ; Unknown
-    0x10D30,  # .. 0x10D39 ; Hanifi_Rohingya
-    0x10D3A,  # .. 0x10E5F ; Unknown
-    0x10E60,  # .. 0x10E7E ; Arabic
-    0x10E7F,  # .. 0x10E7F ; Unknown
-    0x10E80,  # .. 0x10EA9 ; Yezidi
-    0x10EAA,  # .. 0x10EAA ; Unknown
-    0x10EAB,  # .. 0x10EAD ; Yezidi
-    0x10EAE,  # .. 0x10EAF ; Unknown
-    0x10EB0,  # .. 0x10EB1 ; Yezidi
-    0x10EB2,  # .. 0x10EFC ; Unknown
-    0x10EFD,  # .. 0x10EFF ; Arabic
-    0x10F00,  # .. 0x10F27 ; Old_Sogdian
-    0x10F28,  # .. 0x10F2F ; Unknown
-    0x10F30,  # .. 0x10F59 ; Sogdian
-    0x10F5A,  # .. 0x10F6F ; Unknown
-    0x10F70,  # .. 0x10F89 ; Old_Uyghur
-    0x10F8A,  # .. 0x10FAF ; Unknown
-    0x10FB0,  # .. 0x10FCB ; Chorasmian
-    0x10FCC,  # .. 0x10FDF ; Unknown
-    0x10FE0,  # .. 0x10FF6 ; Elymaic
-    0x10FF7,  # .. 0x10FFF ; Unknown
-    0x11000,  # .. 0x1104D ; Brahmi
-    0x1104E,  # .. 0x11051 ; Unknown
-    0x11052,  # .. 0x11075 ; Brahmi
-    0x11076,  # .. 0x1107E ; Unknown
-    0x1107F,  # .. 0x1107F ; Brahmi
-    0x11080,  # .. 0x110C2 ; Kaithi
-    0x110C3,  # .. 0x110CC ; Unknown
-    0x110CD,  # .. 0x110CD ; Kaithi
-    0x110CE,  # .. 0x110CF ; Unknown
-    0x110D0,  # .. 0x110E8 ; Sora_Sompeng
-    0x110E9,  # .. 0x110EF ; Unknown
-    0x110F0,  # .. 0x110F9 ; Sora_Sompeng
-    0x110FA,  # .. 0x110FF ; Unknown
-    0x11100,  # .. 0x11134 ; Chakma
-    0x11135,  # .. 0x11135 ; Unknown
-    0x11136,  # .. 0x11147 ; Chakma
-    0x11148,  # .. 0x1114F ; Unknown
-    0x11150,  # .. 0x11176 ; Mahajani
-    0x11177,  # .. 0x1117F ; Unknown
-    0x11180,  # .. 0x111DF ; Sharada
-    0x111E0,  # .. 0x111E0 ; Unknown
-    0x111E1,  # .. 0x111F4 ; Sinhala
-    0x111F5,  # .. 0x111FF ; Unknown
-    0x11200,  # .. 0x11211 ; Khojki
-    0x11212,  # .. 0x11212 ; Unknown
-    0x11213,  # .. 0x11241 ; Khojki
-    0x11242,  # .. 0x1127F ; Unknown
-    0x11280,  # .. 0x11286 ; Multani
-    0x11287,  # .. 0x11287 ; Unknown
-    0x11288,  # .. 0x11288 ; Multani
-    0x11289,  # .. 0x11289 ; Unknown
-    0x1128A,  # .. 0x1128D ; Multani
-    0x1128E,  # .. 0x1128E ; Unknown
-    0x1128F,  # .. 0x1129D ; Multani
-    0x1129E,  # .. 0x1129E ; Unknown
-    0x1129F,  # .. 0x112A9 ; Multani
-    0x112AA,  # .. 0x112AF ; Unknown
-    0x112B0,  # .. 0x112EA ; Khudawadi
-    0x112EB,  # .. 0x112EF ; Unknown
-    0x112F0,  # .. 0x112F9 ; Khudawadi
-    0x112FA,  # .. 0x112FF ; Unknown
-    0x11300,  # .. 0x11303 ; Grantha
-    0x11304,  # .. 0x11304 ; Unknown
-    0x11305,  # .. 0x1130C ; Grantha
-    0x1130D,  # .. 0x1130E ; Unknown
-    0x1130F,  # .. 0x11310 ; Grantha
-    0x11311,  # .. 0x11312 ; Unknown
-    0x11313,  # .. 0x11328 ; Grantha
-    0x11329,  # .. 0x11329 ; Unknown
-    0x1132A,  # .. 0x11330 ; Grantha
-    0x11331,  # .. 0x11331 ; Unknown
-    0x11332,  # .. 0x11333 ; Grantha
-    0x11334,  # .. 0x11334 ; Unknown
-    0x11335,  # .. 0x11339 ; Grantha
-    0x1133A,  # .. 0x1133A ; Unknown
-    0x1133B,  # .. 0x1133B ; Inherited
-    0x1133C,  # .. 0x11344 ; Grantha
-    0x11345,  # .. 0x11346 ; Unknown
-    0x11347,  # .. 0x11348 ; Grantha
-    0x11349,  # .. 0x1134A ; Unknown
-    0x1134B,  # .. 0x1134D ; Grantha
-    0x1134E,  # .. 0x1134F ; Unknown
-    0x11350,  # .. 0x11350 ; Grantha
-    0x11351,  # .. 0x11356 ; Unknown
-    0x11357,  # .. 0x11357 ; Grantha
-    0x11358,  # .. 0x1135C ; Unknown
-    0x1135D,  # .. 0x11363 ; Grantha
-    0x11364,  # .. 0x11365 ; Unknown
-    0x11366,  # .. 0x1136C ; Grantha
-    0x1136D,  # .. 0x1136F ; Unknown
-    0x11370,  # .. 0x11374 ; Grantha
-    0x11375,  # .. 0x113FF ; Unknown
-    0x11400,  # .. 0x1145B ; Newa
-    0x1145C,  # .. 0x1145C ; Unknown
-    0x1145D,  # .. 0x11461 ; Newa
-    0x11462,  # .. 0x1147F ; Unknown
-    0x11480,  # .. 0x114C7 ; Tirhuta
-    0x114C8,  # .. 0x114CF ; Unknown
-    0x114D0,  # .. 0x114D9 ; Tirhuta
-    0x114DA,  # .. 0x1157F ; Unknown
-    0x11580,  # .. 0x115B5 ; Siddham
-    0x115B6,  # .. 0x115B7 ; Unknown
-    0x115B8,  # .. 0x115DD ; Siddham
-    0x115DE,  # .. 0x115FF ; Unknown
-    0x11600,  # .. 0x11644 ; Modi
-    0x11645,  # .. 0x1164F ; Unknown
-    0x11650,  # .. 0x11659 ; Modi
-    0x1165A,  # .. 0x1165F ; Unknown
-    0x11660,  # .. 0x1166C ; Mongolian
-    0x1166D,  # .. 0x1167F ; Unknown
-    0x11680,  # .. 0x116B9 ; Takri
-    0x116BA,  # .. 0x116BF ; Unknown
-    0x116C0,  # .. 0x116C9 ; Takri
-    0x116CA,  # .. 0x116FF ; Unknown
-    0x11700,  # .. 0x1171A ; Ahom
-    0x1171B,  # .. 0x1171C ; Unknown
-    0x1171D,  # .. 0x1172B ; Ahom
-    0x1172C,  # .. 0x1172F ; Unknown
-    0x11730,  # .. 0x11746 ; Ahom
-    0x11747,  # .. 0x117FF ; Unknown
-    0x11800,  # .. 0x1183B ; Dogra
-    0x1183C,  # .. 0x1189F ; Unknown
-    0x118A0,  # .. 0x118F2 ; Warang_Citi
-    0x118F3,  # .. 0x118FE ; Unknown
-    0x118FF,  # .. 0x118FF ; Warang_Citi
-    0x11900,  # .. 0x11906 ; Dives_Akuru
-    0x11907,  # .. 0x11908 ; Unknown
-    0x11909,  # .. 0x11909 ; Dives_Akuru
-    0x1190A,  # .. 0x1190B ; Unknown
-    0x1190C,  # .. 0x11913 ; Dives_Akuru
-    0x11914,  # .. 0x11914 ; Unknown
-    0x11915,  # .. 0x11916 ; Dives_Akuru
-    0x11917,  # .. 0x11917 ; Unknown
-    0x11918,  # .. 0x11935 ; Dives_Akuru
-    0x11936,  # .. 0x11936 ; Unknown
-    0x11937,  # .. 0x11938 ; Dives_Akuru
-    0x11939,  # .. 0x1193A ; Unknown
-    0x1193B,  # .. 0x11946 ; Dives_Akuru
-    0x11947,  # .. 0x1194F ; Unknown
-    0x11950,  # .. 0x11959 ; Dives_Akuru
-    0x1195A,  # .. 0x1199F ; Unknown
-    0x119A0,  # .. 0x119A7 ; Nandinagari
-    0x119A8,  # .. 0x119A9 ; Unknown
-    0x119AA,  # .. 0x119D7 ; Nandinagari
-    0x119D8,  # .. 0x119D9 ; Unknown
-    0x119DA,  # .. 0x119E4 ; Nandinagari
-    0x119E5,  # .. 0x119FF ; Unknown
-    0x11A00,  # .. 0x11A47 ; Zanabazar_Square
-    0x11A48,  # .. 0x11A4F ; Unknown
-    0x11A50,  # .. 0x11AA2 ; Soyombo
-    0x11AA3,  # .. 0x11AAF ; Unknown
-    0x11AB0,  # .. 0x11ABF ; Canadian_Aboriginal
-    0x11AC0,  # .. 0x11AF8 ; Pau_Cin_Hau
-    0x11AF9,  # .. 0x11AFF ; Unknown
-    0x11B00,  # .. 0x11B09 ; Devanagari
-    0x11B0A,  # .. 0x11BFF ; Unknown
-    0x11C00,  # .. 0x11C08 ; Bhaiksuki
-    0x11C09,  # .. 0x11C09 ; Unknown
-    0x11C0A,  # .. 0x11C36 ; Bhaiksuki
-    0x11C37,  # .. 0x11C37 ; Unknown
-    0x11C38,  # .. 0x11C45 ; Bhaiksuki
-    0x11C46,  # .. 0x11C4F ; Unknown
-    0x11C50,  # .. 0x11C6C ; Bhaiksuki
-    0x11C6D,  # .. 0x11C6F ; Unknown
-    0x11C70,  # .. 0x11C8F ; Marchen
-    0x11C90,  # .. 0x11C91 ; Unknown
-    0x11C92,  # .. 0x11CA7 ; Marchen
-    0x11CA8,  # .. 0x11CA8 ; Unknown
-    0x11CA9,  # .. 0x11CB6 ; Marchen
-    0x11CB7,  # .. 0x11CFF ; Unknown
-    0x11D00,  # .. 0x11D06 ; Masaram_Gondi
-    0x11D07,  # .. 0x11D07 ; Unknown
-    0x11D08,  # .. 0x11D09 ; Masaram_Gondi
-    0x11D0A,  # .. 0x11D0A ; Unknown
-    0x11D0B,  # .. 0x11D36 ; Masaram_Gondi
-    0x11D37,  # .. 0x11D39 ; Unknown
-    0x11D3A,  # .. 0x11D3A ; Masaram_Gondi
-    0x11D3B,  # .. 0x11D3B ; Unknown
-    0x11D3C,  # .. 0x11D3D ; Masaram_Gondi
-    0x11D3E,  # .. 0x11D3E ; Unknown
-    0x11D3F,  # .. 0x11D47 ; Masaram_Gondi
-    0x11D48,  # .. 0x11D4F ; Unknown
-    0x11D50,  # .. 0x11D59 ; Masaram_Gondi
-    0x11D5A,  # .. 0x11D5F ; Unknown
-    0x11D60,  # .. 0x11D65 ; Gunjala_Gondi
-    0x11D66,  # .. 0x11D66 ; Unknown
-    0x11D67,  # .. 0x11D68 ; Gunjala_Gondi
-    0x11D69,  # .. 0x11D69 ; Unknown
-    0x11D6A,  # .. 0x11D8E ; Gunjala_Gondi
-    0x11D8F,  # .. 0x11D8F ; Unknown
-    0x11D90,  # .. 0x11D91 ; Gunjala_Gondi
-    0x11D92,  # .. 0x11D92 ; Unknown
-    0x11D93,  # .. 0x11D98 ; Gunjala_Gondi
-    0x11D99,  # .. 0x11D9F ; Unknown
-    0x11DA0,  # .. 0x11DA9 ; Gunjala_Gondi
-    0x11DAA,  # .. 0x11EDF ; Unknown
-    0x11EE0,  # .. 0x11EF8 ; Makasar
-    0x11EF9,  # .. 0x11EFF ; Unknown
-    0x11F00,  # .. 0x11F10 ; Kawi
-    0x11F11,  # .. 0x11F11 ; Unknown
-    0x11F12,  # .. 0x11F3A ; Kawi
-    0x11F3B,  # .. 0x11F3D ; Unknown
-    0x11F3E,  # .. 0x11F59 ; Kawi
-    0x11F5A,  # .. 0x11FAF ; Unknown
-    0x11FB0,  # .. 0x11FB0 ; Lisu
-    0x11FB1,  # .. 0x11FBF ; Unknown
-    0x11FC0,  # .. 0x11FF1 ; Tamil
-    0x11FF2,  # .. 0x11FFE ; Unknown
-    0x11FFF,  # .. 0x11FFF ; Tamil
-    0x12000,  # .. 0x12399 ; Cuneiform
-    0x1239A,  # .. 0x123FF ; Unknown
-    0x12400,  # .. 0x1246E ; Cuneiform
-    0x1246F,  # .. 0x1246F ; Unknown
-    0x12470,  # .. 0x12474 ; Cuneiform
-    0x12475,  # .. 0x1247F ; Unknown
-    0x12480,  # .. 0x12543 ; Cuneiform
-    0x12544,  # .. 0x12F8F ; Unknown
-    0x12F90,  # .. 0x12FF2 ; Cypro_Minoan
-    0x12FF3,  # .. 0x12FFF ; Unknown
-    0x13000,  # .. 0x13455 ; Egyptian_Hieroglyphs
-    0x13456,  # .. 0x143FF ; Unknown
-    0x14400,  # .. 0x14646 ; Anatolian_Hieroglyphs
-    0x14647,  # .. 0x167FF ; Unknown
-    0x16800,  # .. 0x16A38 ; Bamum
-    0x16A39,  # .. 0x16A3F ; Unknown
-    0x16A40,  # .. 0x16A5E ; Mro
-    0x16A5F,  # .. 0x16A5F ; Unknown
-    0x16A60,  # .. 0x16A69 ; Mro
-    0x16A6A,  # .. 0x16A6D ; Unknown
-    0x16A6E,  # .. 0x16A6F ; Mro
-    0x16A70,  # .. 0x16ABE ; Tangsa
-    0x16ABF,  # .. 0x16ABF ; Unknown
-    0x16AC0,  # .. 0x16AC9 ; Tangsa
-    0x16ACA,  # .. 0x16ACF ; Unknown
-    0x16AD0,  # .. 0x16AED ; Bassa_Vah
-    0x16AEE,  # .. 0x16AEF ; Unknown
-    0x16AF0,  # .. 0x16AF5 ; Bassa_Vah
-    0x16AF6,  # .. 0x16AFF ; Unknown
-    0x16B00,  # .. 0x16B45 ; Pahawh_Hmong
-    0x16B46,  # .. 0x16B4F ; Unknown
-    0x16B50,  # .. 0x16B59 ; Pahawh_Hmong
-    0x16B5A,  # .. 0x16B5A ; Unknown
-    0x16B5B,  # .. 0x16B61 ; Pahawh_Hmong
-    0x16B62,  # .. 0x16B62 ; Unknown
-    0x16B63,  # .. 0x16B77 ; Pahawh_Hmong
-    0x16B78,  # .. 0x16B7C ; Unknown
-    0x16B7D,  # .. 0x16B8F ; Pahawh_Hmong
-    0x16B90,  # .. 0x16E3F ; Unknown
-    0x16E40,  # .. 0x16E9A ; Medefaidrin
-    0x16E9B,  # .. 0x16EFF ; Unknown
-    0x16F00,  # .. 0x16F4A ; Miao
-    0x16F4B,  # .. 0x16F4E ; Unknown
-    0x16F4F,  # .. 0x16F87 ; Miao
-    0x16F88,  # .. 0x16F8E ; Unknown
-    0x16F8F,  # .. 0x16F9F ; Miao
-    0x16FA0,  # .. 0x16FDF ; Unknown
-    0x16FE0,  # .. 0x16FE0 ; Tangut
-    0x16FE1,  # .. 0x16FE1 ; Nushu
-    0x16FE2,  # .. 0x16FE3 ; Han
-    0x16FE4,  # .. 0x16FE4 ; Khitan_Small_Script
-    0x16FE5,  # .. 0x16FEF ; Unknown
-    0x16FF0,  # .. 0x16FF1 ; Han
-    0x16FF2,  # .. 0x16FFF ; Unknown
-    0x17000,  # .. 0x187F7 ; Tangut
-    0x187F8,  # .. 0x187FF ; Unknown
-    0x18800,  # .. 0x18AFF ; Tangut
-    0x18B00,  # .. 0x18CD5 ; Khitan_Small_Script
-    0x18CD6,  # .. 0x18CFF ; Unknown
-    0x18D00,  # .. 0x18D08 ; Tangut
-    0x18D09,  # .. 0x1AFEF ; Unknown
-    0x1AFF0,  # .. 0x1AFF3 ; Katakana
-    0x1AFF4,  # .. 0x1AFF4 ; Unknown
-    0x1AFF5,  # .. 0x1AFFB ; Katakana
-    0x1AFFC,  # .. 0x1AFFC ; Unknown
-    0x1AFFD,  # .. 0x1AFFE ; Katakana
-    0x1AFFF,  # .. 0x1AFFF ; Unknown
-    0x1B000,  # .. 0x1B000 ; Katakana
-    0x1B001,  # .. 0x1B11F ; Hiragana
-    0x1B120,  # .. 0x1B122 ; Katakana
-    0x1B123,  # .. 0x1B131 ; Unknown
-    0x1B132,  # .. 0x1B132 ; Hiragana
-    0x1B133,  # .. 0x1B14F ; Unknown
-    0x1B150,  # .. 0x1B152 ; Hiragana
-    0x1B153,  # .. 0x1B154 ; Unknown
-    0x1B155,  # .. 0x1B155 ; Katakana
-    0x1B156,  # .. 0x1B163 ; Unknown
-    0x1B164,  # .. 0x1B167 ; Katakana
-    0x1B168,  # .. 0x1B16F ; Unknown
-    0x1B170,  # .. 0x1B2FB ; Nushu
-    0x1B2FC,  # .. 0x1BBFF ; Unknown
-    0x1BC00,  # .. 0x1BC6A ; Duployan
-    0x1BC6B,  # .. 0x1BC6F ; Unknown
-    0x1BC70,  # .. 0x1BC7C ; Duployan
-    0x1BC7D,  # .. 0x1BC7F ; Unknown
-    0x1BC80,  # .. 0x1BC88 ; Duployan
-    0x1BC89,  # .. 0x1BC8F ; Unknown
-    0x1BC90,  # .. 0x1BC99 ; Duployan
-    0x1BC9A,  # .. 0x1BC9B ; Unknown
-    0x1BC9C,  # .. 0x1BC9F ; Duployan
-    0x1BCA0,  # .. 0x1BCA3 ; Common
-    0x1BCA4,  # .. 0x1CEFF ; Unknown
-    0x1CF00,  # .. 0x1CF2D ; Inherited
-    0x1CF2E,  # .. 0x1CF2F ; Unknown
-    0x1CF30,  # .. 0x1CF46 ; Inherited
-    0x1CF47,  # .. 0x1CF4F ; Unknown
-    0x1CF50,  # .. 0x1CFC3 ; Common
-    0x1CFC4,  # .. 0x1CFFF ; Unknown
-    0x1D000,  # .. 0x1D0F5 ; Common
-    0x1D0F6,  # .. 0x1D0FF ; Unknown
-    0x1D100,  # .. 0x1D126 ; Common
-    0x1D127,  # .. 0x1D128 ; Unknown
-    0x1D129,  # .. 0x1D166 ; Common
-    0x1D167,  # .. 0x1D169 ; Inherited
-    0x1D16A,  # .. 0x1D17A ; Common
-    0x1D17B,  # .. 0x1D182 ; Inherited
-    0x1D183,  # .. 0x1D184 ; Common
-    0x1D185,  # .. 0x1D18B ; Inherited
-    0x1D18C,  # .. 0x1D1A9 ; Common
-    0x1D1AA,  # .. 0x1D1AD ; Inherited
-    0x1D1AE,  # .. 0x1D1EA ; Common
-    0x1D1EB,  # .. 0x1D1FF ; Unknown
-    0x1D200,  # .. 0x1D245 ; Greek
-    0x1D246,  # .. 0x1D2BF ; Unknown
-    0x1D2C0,  # .. 0x1D2D3 ; Common
-    0x1D2D4,  # .. 0x1D2DF ; Unknown
-    0x1D2E0,  # .. 0x1D2F3 ; Common
-    0x1D2F4,  # .. 0x1D2FF ; Unknown
-    0x1D300,  # .. 0x1D356 ; Common
-    0x1D357,  # .. 0x1D35F ; Unknown
-    0x1D360,  # .. 0x1D378 ; Common
-    0x1D379,  # .. 0x1D3FF ; Unknown
-    0x1D400,  # .. 0x1D454 ; Common
-    0x1D455,  # .. 0x1D455 ; Unknown
-    0x1D456,  # .. 0x1D49C ; Common
-    0x1D49D,  # .. 0x1D49D ; Unknown
-    0x1D49E,  # .. 0x1D49F ; Common
-    0x1D4A0,  # .. 0x1D4A1 ; Unknown
-    0x1D4A2,  # .. 0x1D4A2 ; Common
-    0x1D4A3,  # .. 0x1D4A4 ; Unknown
-    0x1D4A5,  # .. 0x1D4A6 ; Common
-    0x1D4A7,  # .. 0x1D4A8 ; Unknown
-    0x1D4A9,  # .. 0x1D4AC ; Common
-    0x1D4AD,  # .. 0x1D4AD ; Unknown
-    0x1D4AE,  # .. 0x1D4B9 ; Common
-    0x1D4BA,  # .. 0x1D4BA ; Unknown
-    0x1D4BB,  # .. 0x1D4BB ; Common
-    0x1D4BC,  # .. 0x1D4BC ; Unknown
-    0x1D4BD,  # .. 0x1D4C3 ; Common
-    0x1D4C4,  # .. 0x1D4C4 ; Unknown
-    0x1D4C5,  # .. 0x1D505 ; Common
-    0x1D506,  # .. 0x1D506 ; Unknown
-    0x1D507,  # .. 0x1D50A ; Common
-    0x1D50B,  # .. 0x1D50C ; Unknown
-    0x1D50D,  # .. 0x1D514 ; Common
-    0x1D515,  # .. 0x1D515 ; Unknown
-    0x1D516,  # .. 0x1D51C ; Common
-    0x1D51D,  # .. 0x1D51D ; Unknown
-    0x1D51E,  # .. 0x1D539 ; Common
-    0x1D53A,  # .. 0x1D53A ; Unknown
-    0x1D53B,  # .. 0x1D53E ; Common
-    0x1D53F,  # .. 0x1D53F ; Unknown
-    0x1D540,  # .. 0x1D544 ; Common
-    0x1D545,  # .. 0x1D545 ; Unknown
-    0x1D546,  # .. 0x1D546 ; Common
-    0x1D547,  # .. 0x1D549 ; Unknown
-    0x1D54A,  # .. 0x1D550 ; Common
-    0x1D551,  # .. 0x1D551 ; Unknown
-    0x1D552,  # .. 0x1D6A5 ; Common
-    0x1D6A6,  # .. 0x1D6A7 ; Unknown
-    0x1D6A8,  # .. 0x1D7CB ; Common
-    0x1D7CC,  # .. 0x1D7CD ; Unknown
-    0x1D7CE,  # .. 0x1D7FF ; Common
-    0x1D800,  # .. 0x1DA8B ; SignWriting
-    0x1DA8C,  # .. 0x1DA9A ; Unknown
-    0x1DA9B,  # .. 0x1DA9F ; SignWriting
-    0x1DAA0,  # .. 0x1DAA0 ; Unknown
-    0x1DAA1,  # .. 0x1DAAF ; SignWriting
-    0x1DAB0,  # .. 0x1DEFF ; Unknown
-    0x1DF00,  # .. 0x1DF1E ; Latin
-    0x1DF1F,  # .. 0x1DF24 ; Unknown
-    0x1DF25,  # .. 0x1DF2A ; Latin
-    0x1DF2B,  # .. 0x1DFFF ; Unknown
-    0x1E000,  # .. 0x1E006 ; Glagolitic
-    0x1E007,  # .. 0x1E007 ; Unknown
-    0x1E008,  # .. 0x1E018 ; Glagolitic
-    0x1E019,  # .. 0x1E01A ; Unknown
-    0x1E01B,  # .. 0x1E021 ; Glagolitic
-    0x1E022,  # .. 0x1E022 ; Unknown
-    0x1E023,  # .. 0x1E024 ; Glagolitic
-    0x1E025,  # .. 0x1E025 ; Unknown
-    0x1E026,  # .. 0x1E02A ; Glagolitic
-    0x1E02B,  # .. 0x1E02F ; Unknown
-    0x1E030,  # .. 0x1E06D ; Cyrillic
-    0x1E06E,  # .. 0x1E08E ; Unknown
-    0x1E08F,  # .. 0x1E08F ; Cyrillic
-    0x1E090,  # .. 0x1E0FF ; Unknown
-    0x1E100,  # .. 0x1E12C ; Nyiakeng_Puachue_Hmong
-    0x1E12D,  # .. 0x1E12F ; Unknown
-    0x1E130,  # .. 0x1E13D ; Nyiakeng_Puachue_Hmong
-    0x1E13E,  # .. 0x1E13F ; Unknown
-    0x1E140,  # .. 0x1E149 ; Nyiakeng_Puachue_Hmong
-    0x1E14A,  # .. 0x1E14D ; Unknown
-    0x1E14E,  # .. 0x1E14F ; Nyiakeng_Puachue_Hmong
-    0x1E150,  # .. 0x1E28F ; Unknown
-    0x1E290,  # .. 0x1E2AE ; Toto
-    0x1E2AF,  # .. 0x1E2BF ; Unknown
-    0x1E2C0,  # .. 0x1E2F9 ; Wancho
-    0x1E2FA,  # .. 0x1E2FE ; Unknown
-    0x1E2FF,  # .. 0x1E2FF ; Wancho
-    0x1E300,  # .. 0x1E4CF ; Unknown
-    0x1E4D0,  # .. 0x1E4F9 ; Nag_Mundari
-    0x1E4FA,  # .. 0x1E7DF ; Unknown
-    0x1E7E0,  # .. 0x1E7E6 ; Ethiopic
-    0x1E7E7,  # .. 0x1E7E7 ; Unknown
-    0x1E7E8,  # .. 0x1E7EB ; Ethiopic
-    0x1E7EC,  # .. 0x1E7EC ; Unknown
-    0x1E7ED,  # .. 0x1E7EE ; Ethiopic
-    0x1E7EF,  # .. 0x1E7EF ; Unknown
-    0x1E7F0,  # .. 0x1E7FE ; Ethiopic
-    0x1E7FF,  # .. 0x1E7FF ; Unknown
-    0x1E800,  # .. 0x1E8C4 ; Mende_Kikakui
-    0x1E8C5,  # .. 0x1E8C6 ; Unknown
-    0x1E8C7,  # .. 0x1E8D6 ; Mende_Kikakui
-    0x1E8D7,  # .. 0x1E8FF ; Unknown
-    0x1E900,  # .. 0x1E94B ; Adlam
-    0x1E94C,  # .. 0x1E94F ; Unknown
-    0x1E950,  # .. 0x1E959 ; Adlam
-    0x1E95A,  # .. 0x1E95D ; Unknown
-    0x1E95E,  # .. 0x1E95F ; Adlam
-    0x1E960,  # .. 0x1EC70 ; Unknown
-    0x1EC71,  # .. 0x1ECB4 ; Common
-    0x1ECB5,  # .. 0x1ED00 ; Unknown
-    0x1ED01,  # .. 0x1ED3D ; Common
-    0x1ED3E,  # .. 0x1EDFF ; Unknown
-    0x1EE00,  # .. 0x1EE03 ; Arabic
-    0x1EE04,  # .. 0x1EE04 ; Unknown
-    0x1EE05,  # .. 0x1EE1F ; Arabic
-    0x1EE20,  # .. 0x1EE20 ; Unknown
-    0x1EE21,  # .. 0x1EE22 ; Arabic
-    0x1EE23,  # .. 0x1EE23 ; Unknown
-    0x1EE24,  # .. 0x1EE24 ; Arabic
-    0x1EE25,  # .. 0x1EE26 ; Unknown
-    0x1EE27,  # .. 0x1EE27 ; Arabic
-    0x1EE28,  # .. 0x1EE28 ; Unknown
-    0x1EE29,  # .. 0x1EE32 ; Arabic
-    0x1EE33,  # .. 0x1EE33 ; Unknown
-    0x1EE34,  # .. 0x1EE37 ; Arabic
-    0x1EE38,  # .. 0x1EE38 ; Unknown
-    0x1EE39,  # .. 0x1EE39 ; Arabic
-    0x1EE3A,  # .. 0x1EE3A ; Unknown
-    0x1EE3B,  # .. 0x1EE3B ; Arabic
-    0x1EE3C,  # .. 0x1EE41 ; Unknown
-    0x1EE42,  # .. 0x1EE42 ; Arabic
-    0x1EE43,  # .. 0x1EE46 ; Unknown
-    0x1EE47,  # .. 0x1EE47 ; Arabic
-    0x1EE48,  # .. 0x1EE48 ; Unknown
-    0x1EE49,  # .. 0x1EE49 ; Arabic
-    0x1EE4A,  # .. 0x1EE4A ; Unknown
-    0x1EE4B,  # .. 0x1EE4B ; Arabic
-    0x1EE4C,  # .. 0x1EE4C ; Unknown
-    0x1EE4D,  # .. 0x1EE4F ; Arabic
-    0x1EE50,  # .. 0x1EE50 ; Unknown
-    0x1EE51,  # .. 0x1EE52 ; Arabic
-    0x1EE53,  # .. 0x1EE53 ; Unknown
-    0x1EE54,  # .. 0x1EE54 ; Arabic
-    0x1EE55,  # .. 0x1EE56 ; Unknown
-    0x1EE57,  # .. 0x1EE57 ; Arabic
-    0x1EE58,  # .. 0x1EE58 ; Unknown
-    0x1EE59,  # .. 0x1EE59 ; Arabic
-    0x1EE5A,  # .. 0x1EE5A ; Unknown
-    0x1EE5B,  # .. 0x1EE5B ; Arabic
-    0x1EE5C,  # .. 0x1EE5C ; Unknown
-    0x1EE5D,  # .. 0x1EE5D ; Arabic
-    0x1EE5E,  # .. 0x1EE5E ; Unknown
-    0x1EE5F,  # .. 0x1EE5F ; Arabic
-    0x1EE60,  # .. 0x1EE60 ; Unknown
-    0x1EE61,  # .. 0x1EE62 ; Arabic
-    0x1EE63,  # .. 0x1EE63 ; Unknown
-    0x1EE64,  # .. 0x1EE64 ; Arabic
-    0x1EE65,  # .. 0x1EE66 ; Unknown
-    0x1EE67,  # .. 0x1EE6A ; Arabic
-    0x1EE6B,  # .. 0x1EE6B ; Unknown
-    0x1EE6C,  # .. 0x1EE72 ; Arabic
-    0x1EE73,  # .. 0x1EE73 ; Unknown
-    0x1EE74,  # .. 0x1EE77 ; Arabic
-    0x1EE78,  # .. 0x1EE78 ; Unknown
-    0x1EE79,  # .. 0x1EE7C ; Arabic
-    0x1EE7D,  # .. 0x1EE7D ; Unknown
-    0x1EE7E,  # .. 0x1EE7E ; Arabic
-    0x1EE7F,  # .. 0x1EE7F ; Unknown
-    0x1EE80,  # .. 0x1EE89 ; Arabic
-    0x1EE8A,  # .. 0x1EE8A ; Unknown
-    0x1EE8B,  # .. 0x1EE9B ; Arabic
-    0x1EE9C,  # .. 0x1EEA0 ; Unknown
-    0x1EEA1,  # .. 0x1EEA3 ; Arabic
-    0x1EEA4,  # .. 0x1EEA4 ; Unknown
-    0x1EEA5,  # .. 0x1EEA9 ; Arabic
-    0x1EEAA,  # .. 0x1EEAA ; Unknown
-    0x1EEAB,  # .. 0x1EEBB ; Arabic
-    0x1EEBC,  # .. 0x1EEEF ; Unknown
-    0x1EEF0,  # .. 0x1EEF1 ; Arabic
-    0x1EEF2,  # .. 0x1EFFF ; Unknown
-    0x1F000,  # .. 0x1F02B ; Common
-    0x1F02C,  # .. 0x1F02F ; Unknown
-    0x1F030,  # .. 0x1F093 ; Common
-    0x1F094,  # .. 0x1F09F ; Unknown
-    0x1F0A0,  # .. 0x1F0AE ; Common
-    0x1F0AF,  # .. 0x1F0B0 ; Unknown
-    0x1F0B1,  # .. 0x1F0BF ; Common
-    0x1F0C0,  # .. 0x1F0C0 ; Unknown
-    0x1F0C1,  # .. 0x1F0CF ; Common
-    0x1F0D0,  # .. 0x1F0D0 ; Unknown
-    0x1F0D1,  # .. 0x1F0F5 ; Common
-    0x1F0F6,  # .. 0x1F0FF ; Unknown
-    0x1F100,  # .. 0x1F1AD ; Common
-    0x1F1AE,  # .. 0x1F1E5 ; Unknown
-    0x1F1E6,  # .. 0x1F1FF ; Common
-    0x1F200,  # .. 0x1F200 ; Hiragana
-    0x1F201,  # .. 0x1F202 ; Common
-    0x1F203,  # .. 0x1F20F ; Unknown
-    0x1F210,  # .. 0x1F23B ; Common
-    0x1F23C,  # .. 0x1F23F ; Unknown
-    0x1F240,  # .. 0x1F248 ; Common
-    0x1F249,  # .. 0x1F24F ; Unknown
-    0x1F250,  # .. 0x1F251 ; Common
-    0x1F252,  # .. 0x1F25F ; Unknown
-    0x1F260,  # .. 0x1F265 ; Common
-    0x1F266,  # .. 0x1F2FF ; Unknown
-    0x1F300,  # .. 0x1F6D7 ; Common
-    0x1F6D8,  # .. 0x1F6DB ; Unknown
-    0x1F6DC,  # .. 0x1F6EC ; Common
-    0x1F6ED,  # .. 0x1F6EF ; Unknown
-    0x1F6F0,  # .. 0x1F6FC ; Common
-    0x1F6FD,  # .. 0x1F6FF ; Unknown
-    0x1F700,  # .. 0x1F776 ; Common
-    0x1F777,  # .. 0x1F77A ; Unknown
-    0x1F77B,  # .. 0x1F7D9 ; Common
-    0x1F7DA,  # .. 0x1F7DF ; Unknown
-    0x1F7E0,  # .. 0x1F7EB ; Common
-    0x1F7EC,  # .. 0x1F7EF ; Unknown
-    0x1F7F0,  # .. 0x1F7F0 ; Common
-    0x1F7F1,  # .. 0x1F7FF ; Unknown
-    0x1F800,  # .. 0x1F80B ; Common
-    0x1F80C,  # .. 0x1F80F ; Unknown
-    0x1F810,  # .. 0x1F847 ; Common
-    0x1F848,  # .. 0x1F84F ; Unknown
-    0x1F850,  # .. 0x1F859 ; Common
-    0x1F85A,  # .. 0x1F85F ; Unknown
-    0x1F860,  # .. 0x1F887 ; Common
-    0x1F888,  # .. 0x1F88F ; Unknown
-    0x1F890,  # .. 0x1F8AD ; Common
-    0x1F8AE,  # .. 0x1F8AF ; Unknown
-    0x1F8B0,  # .. 0x1F8B1 ; Common
-    0x1F8B2,  # .. 0x1F8FF ; Unknown
-    0x1F900,  # .. 0x1FA53 ; Common
-    0x1FA54,  # .. 0x1FA5F ; Unknown
-    0x1FA60,  # .. 0x1FA6D ; Common
-    0x1FA6E,  # .. 0x1FA6F ; Unknown
-    0x1FA70,  # .. 0x1FA7C ; Common
-    0x1FA7D,  # .. 0x1FA7F ; Unknown
-    0x1FA80,  # .. 0x1FA88 ; Common
-    0x1FA89,  # .. 0x1FA8F ; Unknown
-    0x1FA90,  # .. 0x1FABD ; Common
-    0x1FABE,  # .. 0x1FABE ; Unknown
-    0x1FABF,  # .. 0x1FAC5 ; Common
-    0x1FAC6,  # .. 0x1FACD ; Unknown
-    0x1FACE,  # .. 0x1FADB ; Common
-    0x1FADC,  # .. 0x1FADF ; Unknown
-    0x1FAE0,  # .. 0x1FAE8 ; Common
-    0x1FAE9,  # .. 0x1FAEF ; Unknown
-    0x1FAF0,  # .. 0x1FAF8 ; Common
-    0x1FAF9,  # .. 0x1FAFF ; Unknown
-    0x1FB00,  # .. 0x1FB92 ; Common
-    0x1FB93,  # .. 0x1FB93 ; Unknown
-    0x1FB94,  # .. 0x1FBCA ; Common
-    0x1FBCB,  # .. 0x1FBEF ; Unknown
-    0x1FBF0,  # .. 0x1FBF9 ; Common
-    0x1FBFA,  # .. 0x1FFFF ; Unknown
-    0x20000,  # .. 0x2A6DF ; Han
-    0x2A6E0,  # .. 0x2A6FF ; Unknown
-    0x2A700,  # .. 0x2B739 ; Han
-    0x2B73A,  # .. 0x2B73F ; Unknown
-    0x2B740,  # .. 0x2B81D ; Han
-    0x2B81E,  # .. 0x2B81F ; Unknown
-    0x2B820,  # .. 0x2CEA1 ; Han
-    0x2CEA2,  # .. 0x2CEAF ; Unknown
-    0x2CEB0,  # .. 0x2EBE0 ; Han
-    0x2EBE1,  # .. 0x2F7FF ; Unknown
-    0x2F800,  # .. 0x2FA1D ; Han
-    0x2FA1E,  # .. 0x2FFFF ; Unknown
-    0x30000,  # .. 0x3134A ; Han
-    0x3134B,  # .. 0x3134F ; Unknown
-    0x31350,  # .. 0x323AF ; Han
-    0x323B0,  # .. 0xE0000 ; Unknown
-    0xE0001,  # .. 0xE0001 ; Common
-    0xE0002,  # .. 0xE001F ; Unknown
-    0xE0020,  # .. 0xE007F ; Common
-    0xE0080,  # .. 0xE00FF ; Unknown
-    0xE0100,  # .. 0xE01EF ; Inherited
-    0xE01F0,  # .. 0x10FFFF ; Unknown
-]
-
-VALUES = [
-    "Zyyy",  # 0000..0040 ; Common
-    "Latn",  # 0041..005A ; Latin
-    "Zyyy",  # 005B..0060 ; Common
-    "Latn",  # 0061..007A ; Latin
-    "Zyyy",  # 007B..00A9 ; Common
-    "Latn",  # 00AA..00AA ; Latin
-    "Zyyy",  # 00AB..00B9 ; Common
-    "Latn",  # 00BA..00BA ; Latin
-    "Zyyy",  # 00BB..00BF ; Common
-    "Latn",  # 00C0..00D6 ; Latin
-    "Zyyy",  # 00D7..00D7 ; Common
-    "Latn",  # 00D8..00F6 ; Latin
-    "Zyyy",  # 00F7..00F7 ; Common
-    "Latn",  # 00F8..02B8 ; Latin
-    "Zyyy",  # 02B9..02DF ; Common
-    "Latn",  # 02E0..02E4 ; Latin
-    "Zyyy",  # 02E5..02E9 ; Common
-    "Bopo",  # 02EA..02EB ; Bopomofo
-    "Zyyy",  # 02EC..02FF ; Common
-    "Zinh",  # 0300..036F ; Inherited
-    "Grek",  # 0370..0373 ; Greek
-    "Zyyy",  # 0374..0374 ; Common
-    "Grek",  # 0375..0377 ; Greek
-    "Zzzz",  # 0378..0379 ; Unknown
-    "Grek",  # 037A..037D ; Greek
-    "Zyyy",  # 037E..037E ; Common
-    "Grek",  # 037F..037F ; Greek
-    "Zzzz",  # 0380..0383 ; Unknown
-    "Grek",  # 0384..0384 ; Greek
-    "Zyyy",  # 0385..0385 ; Common
-    "Grek",  # 0386..0386 ; Greek
-    "Zyyy",  # 0387..0387 ; Common
-    "Grek",  # 0388..038A ; Greek
-    "Zzzz",  # 038B..038B ; Unknown
-    "Grek",  # 038C..038C ; Greek
-    "Zzzz",  # 038D..038D ; Unknown
-    "Grek",  # 038E..03A1 ; Greek
-    "Zzzz",  # 03A2..03A2 ; Unknown
-    "Grek",  # 03A3..03E1 ; Greek
-    "Copt",  # 03E2..03EF ; Coptic
-    "Grek",  # 03F0..03FF ; Greek
-    "Cyrl",  # 0400..0484 ; Cyrillic
-    "Zinh",  # 0485..0486 ; Inherited
-    "Cyrl",  # 0487..052F ; Cyrillic
-    "Zzzz",  # 0530..0530 ; Unknown
-    "Armn",  # 0531..0556 ; Armenian
-    "Zzzz",  # 0557..0558 ; Unknown
-    "Armn",  # 0559..058A ; Armenian
-    "Zzzz",  # 058B..058C ; Unknown
-    "Armn",  # 058D..058F ; Armenian
-    "Zzzz",  # 0590..0590 ; Unknown
-    "Hebr",  # 0591..05C7 ; Hebrew
-    "Zzzz",  # 05C8..05CF ; Unknown
-    "Hebr",  # 05D0..05EA ; Hebrew
-    "Zzzz",  # 05EB..05EE ; Unknown
-    "Hebr",  # 05EF..05F4 ; Hebrew
-    "Zzzz",  # 05F5..05FF ; Unknown
-    "Arab",  # 0600..0604 ; Arabic
-    "Zyyy",  # 0605..0605 ; Common
-    "Arab",  # 0606..060B ; Arabic
-    "Zyyy",  # 060C..060C ; Common
-    "Arab",  # 060D..061A ; Arabic
-    "Zyyy",  # 061B..061B ; Common
-    "Arab",  # 061C..061E ; Arabic
-    "Zyyy",  # 061F..061F ; Common
-    "Arab",  # 0620..063F ; Arabic
-    "Zyyy",  # 0640..0640 ; Common
-    "Arab",  # 0641..064A ; Arabic
-    "Zinh",  # 064B..0655 ; Inherited
-    "Arab",  # 0656..066F ; Arabic
-    "Zinh",  # 0670..0670 ; Inherited
-    "Arab",  # 0671..06DC ; Arabic
-    "Zyyy",  # 06DD..06DD ; Common
-    "Arab",  # 06DE..06FF ; Arabic
-    "Syrc",  # 0700..070D ; Syriac
-    "Zzzz",  # 070E..070E ; Unknown
-    "Syrc",  # 070F..074A ; Syriac
-    "Zzzz",  # 074B..074C ; Unknown
-    "Syrc",  # 074D..074F ; Syriac
-    "Arab",  # 0750..077F ; Arabic
-    "Thaa",  # 0780..07B1 ; Thaana
-    "Zzzz",  # 07B2..07BF ; Unknown
-    "Nkoo",  # 07C0..07FA ; Nko
-    "Zzzz",  # 07FB..07FC ; Unknown
-    "Nkoo",  # 07FD..07FF ; Nko
-    "Samr",  # 0800..082D ; Samaritan
-    "Zzzz",  # 082E..082F ; Unknown
-    "Samr",  # 0830..083E ; Samaritan
-    "Zzzz",  # 083F..083F ; Unknown
-    "Mand",  # 0840..085B ; Mandaic
-    "Zzzz",  # 085C..085D ; Unknown
-    "Mand",  # 085E..085E ; Mandaic
-    "Zzzz",  # 085F..085F ; Unknown
-    "Syrc",  # 0860..086A ; Syriac
-    "Zzzz",  # 086B..086F ; Unknown
-    "Arab",  # 0870..088E ; Arabic
-    "Zzzz",  # 088F..088F ; Unknown
-    "Arab",  # 0890..0891 ; Arabic
-    "Zzzz",  # 0892..0897 ; Unknown
-    "Arab",  # 0898..08E1 ; Arabic
-    "Zyyy",  # 08E2..08E2 ; Common
-    "Arab",  # 08E3..08FF ; Arabic
-    "Deva",  # 0900..0950 ; Devanagari
-    "Zinh",  # 0951..0954 ; Inherited
-    "Deva",  # 0955..0963 ; Devanagari
-    "Zyyy",  # 0964..0965 ; Common
-    "Deva",  # 0966..097F ; Devanagari
-    "Beng",  # 0980..0983 ; Bengali
-    "Zzzz",  # 0984..0984 ; Unknown
-    "Beng",  # 0985..098C ; Bengali
-    "Zzzz",  # 098D..098E ; Unknown
-    "Beng",  # 098F..0990 ; Bengali
-    "Zzzz",  # 0991..0992 ; Unknown
-    "Beng",  # 0993..09A8 ; Bengali
-    "Zzzz",  # 09A9..09A9 ; Unknown
-    "Beng",  # 09AA..09B0 ; Bengali
-    "Zzzz",  # 09B1..09B1 ; Unknown
-    "Beng",  # 09B2..09B2 ; Bengali
-    "Zzzz",  # 09B3..09B5 ; Unknown
-    "Beng",  # 09B6..09B9 ; Bengali
-    "Zzzz",  # 09BA..09BB ; Unknown
-    "Beng",  # 09BC..09C4 ; Bengali
-    "Zzzz",  # 09C5..09C6 ; Unknown
-    "Beng",  # 09C7..09C8 ; Bengali
-    "Zzzz",  # 09C9..09CA ; Unknown
-    "Beng",  # 09CB..09CE ; Bengali
-    "Zzzz",  # 09CF..09D6 ; Unknown
-    "Beng",  # 09D7..09D7 ; Bengali
-    "Zzzz",  # 09D8..09DB ; Unknown
-    "Beng",  # 09DC..09DD ; Bengali
-    "Zzzz",  # 09DE..09DE ; Unknown
-    "Beng",  # 09DF..09E3 ; Bengali
-    "Zzzz",  # 09E4..09E5 ; Unknown
-    "Beng",  # 09E6..09FE ; Bengali
-    "Zzzz",  # 09FF..0A00 ; Unknown
-    "Guru",  # 0A01..0A03 ; Gurmukhi
-    "Zzzz",  # 0A04..0A04 ; Unknown
-    "Guru",  # 0A05..0A0A ; Gurmukhi
-    "Zzzz",  # 0A0B..0A0E ; Unknown
-    "Guru",  # 0A0F..0A10 ; Gurmukhi
-    "Zzzz",  # 0A11..0A12 ; Unknown
-    "Guru",  # 0A13..0A28 ; Gurmukhi
-    "Zzzz",  # 0A29..0A29 ; Unknown
-    "Guru",  # 0A2A..0A30 ; Gurmukhi
-    "Zzzz",  # 0A31..0A31 ; Unknown
-    "Guru",  # 0A32..0A33 ; Gurmukhi
-    "Zzzz",  # 0A34..0A34 ; Unknown
-    "Guru",  # 0A35..0A36 ; Gurmukhi
-    "Zzzz",  # 0A37..0A37 ; Unknown
-    "Guru",  # 0A38..0A39 ; Gurmukhi
-    "Zzzz",  # 0A3A..0A3B ; Unknown
-    "Guru",  # 0A3C..0A3C ; Gurmukhi
-    "Zzzz",  # 0A3D..0A3D ; Unknown
-    "Guru",  # 0A3E..0A42 ; Gurmukhi
-    "Zzzz",  # 0A43..0A46 ; Unknown
-    "Guru",  # 0A47..0A48 ; Gurmukhi
-    "Zzzz",  # 0A49..0A4A ; Unknown
-    "Guru",  # 0A4B..0A4D ; Gurmukhi
-    "Zzzz",  # 0A4E..0A50 ; Unknown
-    "Guru",  # 0A51..0A51 ; Gurmukhi
-    "Zzzz",  # 0A52..0A58 ; Unknown
-    "Guru",  # 0A59..0A5C ; Gurmukhi
-    "Zzzz",  # 0A5D..0A5D ; Unknown
-    "Guru",  # 0A5E..0A5E ; Gurmukhi
-    "Zzzz",  # 0A5F..0A65 ; Unknown
-    "Guru",  # 0A66..0A76 ; Gurmukhi
-    "Zzzz",  # 0A77..0A80 ; Unknown
-    "Gujr",  # 0A81..0A83 ; Gujarati
-    "Zzzz",  # 0A84..0A84 ; Unknown
-    "Gujr",  # 0A85..0A8D ; Gujarati
-    "Zzzz",  # 0A8E..0A8E ; Unknown
-    "Gujr",  # 0A8F..0A91 ; Gujarati
-    "Zzzz",  # 0A92..0A92 ; Unknown
-    "Gujr",  # 0A93..0AA8 ; Gujarati
-    "Zzzz",  # 0AA9..0AA9 ; Unknown
-    "Gujr",  # 0AAA..0AB0 ; Gujarati
-    "Zzzz",  # 0AB1..0AB1 ; Unknown
-    "Gujr",  # 0AB2..0AB3 ; Gujarati
-    "Zzzz",  # 0AB4..0AB4 ; Unknown
-    "Gujr",  # 0AB5..0AB9 ; Gujarati
-    "Zzzz",  # 0ABA..0ABB ; Unknown
-    "Gujr",  # 0ABC..0AC5 ; Gujarati
-    "Zzzz",  # 0AC6..0AC6 ; Unknown
-    "Gujr",  # 0AC7..0AC9 ; Gujarati
-    "Zzzz",  # 0ACA..0ACA ; Unknown
-    "Gujr",  # 0ACB..0ACD ; Gujarati
-    "Zzzz",  # 0ACE..0ACF ; Unknown
-    "Gujr",  # 0AD0..0AD0 ; Gujarati
-    "Zzzz",  # 0AD1..0ADF ; Unknown
-    "Gujr",  # 0AE0..0AE3 ; Gujarati
-    "Zzzz",  # 0AE4..0AE5 ; Unknown
-    "Gujr",  # 0AE6..0AF1 ; Gujarati
-    "Zzzz",  # 0AF2..0AF8 ; Unknown
-    "Gujr",  # 0AF9..0AFF ; Gujarati
-    "Zzzz",  # 0B00..0B00 ; Unknown
-    "Orya",  # 0B01..0B03 ; Oriya
-    "Zzzz",  # 0B04..0B04 ; Unknown
-    "Orya",  # 0B05..0B0C ; Oriya
-    "Zzzz",  # 0B0D..0B0E ; Unknown
-    "Orya",  # 0B0F..0B10 ; Oriya
-    "Zzzz",  # 0B11..0B12 ; Unknown
-    "Orya",  # 0B13..0B28 ; Oriya
-    "Zzzz",  # 0B29..0B29 ; Unknown
-    "Orya",  # 0B2A..0B30 ; Oriya
-    "Zzzz",  # 0B31..0B31 ; Unknown
-    "Orya",  # 0B32..0B33 ; Oriya
-    "Zzzz",  # 0B34..0B34 ; Unknown
-    "Orya",  # 0B35..0B39 ; Oriya
-    "Zzzz",  # 0B3A..0B3B ; Unknown
-    "Orya",  # 0B3C..0B44 ; Oriya
-    "Zzzz",  # 0B45..0B46 ; Unknown
-    "Orya",  # 0B47..0B48 ; Oriya
-    "Zzzz",  # 0B49..0B4A ; Unknown
-    "Orya",  # 0B4B..0B4D ; Oriya
-    "Zzzz",  # 0B4E..0B54 ; Unknown
-    "Orya",  # 0B55..0B57 ; Oriya
-    "Zzzz",  # 0B58..0B5B ; Unknown
-    "Orya",  # 0B5C..0B5D ; Oriya
-    "Zzzz",  # 0B5E..0B5E ; Unknown
-    "Orya",  # 0B5F..0B63 ; Oriya
-    "Zzzz",  # 0B64..0B65 ; Unknown
-    "Orya",  # 0B66..0B77 ; Oriya
-    "Zzzz",  # 0B78..0B81 ; Unknown
-    "Taml",  # 0B82..0B83 ; Tamil
-    "Zzzz",  # 0B84..0B84 ; Unknown
-    "Taml",  # 0B85..0B8A ; Tamil
-    "Zzzz",  # 0B8B..0B8D ; Unknown
-    "Taml",  # 0B8E..0B90 ; Tamil
-    "Zzzz",  # 0B91..0B91 ; Unknown
-    "Taml",  # 0B92..0B95 ; Tamil
-    "Zzzz",  # 0B96..0B98 ; Unknown
-    "Taml",  # 0B99..0B9A ; Tamil
-    "Zzzz",  # 0B9B..0B9B ; Unknown
-    "Taml",  # 0B9C..0B9C ; Tamil
-    "Zzzz",  # 0B9D..0B9D ; Unknown
-    "Taml",  # 0B9E..0B9F ; Tamil
-    "Zzzz",  # 0BA0..0BA2 ; Unknown
-    "Taml",  # 0BA3..0BA4 ; Tamil
-    "Zzzz",  # 0BA5..0BA7 ; Unknown
-    "Taml",  # 0BA8..0BAA ; Tamil
-    "Zzzz",  # 0BAB..0BAD ; Unknown
-    "Taml",  # 0BAE..0BB9 ; Tamil
-    "Zzzz",  # 0BBA..0BBD ; Unknown
-    "Taml",  # 0BBE..0BC2 ; Tamil
-    "Zzzz",  # 0BC3..0BC5 ; Unknown
-    "Taml",  # 0BC6..0BC8 ; Tamil
-    "Zzzz",  # 0BC9..0BC9 ; Unknown
-    "Taml",  # 0BCA..0BCD ; Tamil
-    "Zzzz",  # 0BCE..0BCF ; Unknown
-    "Taml",  # 0BD0..0BD0 ; Tamil
-    "Zzzz",  # 0BD1..0BD6 ; Unknown
-    "Taml",  # 0BD7..0BD7 ; Tamil
-    "Zzzz",  # 0BD8..0BE5 ; Unknown
-    "Taml",  # 0BE6..0BFA ; Tamil
-    "Zzzz",  # 0BFB..0BFF ; Unknown
-    "Telu",  # 0C00..0C0C ; Telugu
-    "Zzzz",  # 0C0D..0C0D ; Unknown
-    "Telu",  # 0C0E..0C10 ; Telugu
-    "Zzzz",  # 0C11..0C11 ; Unknown
-    "Telu",  # 0C12..0C28 ; Telugu
-    "Zzzz",  # 0C29..0C29 ; Unknown
-    "Telu",  # 0C2A..0C39 ; Telugu
-    "Zzzz",  # 0C3A..0C3B ; Unknown
-    "Telu",  # 0C3C..0C44 ; Telugu
-    "Zzzz",  # 0C45..0C45 ; Unknown
-    "Telu",  # 0C46..0C48 ; Telugu
-    "Zzzz",  # 0C49..0C49 ; Unknown
-    "Telu",  # 0C4A..0C4D ; Telugu
-    "Zzzz",  # 0C4E..0C54 ; Unknown
-    "Telu",  # 0C55..0C56 ; Telugu
-    "Zzzz",  # 0C57..0C57 ; Unknown
-    "Telu",  # 0C58..0C5A ; Telugu
-    "Zzzz",  # 0C5B..0C5C ; Unknown
-    "Telu",  # 0C5D..0C5D ; Telugu
-    "Zzzz",  # 0C5E..0C5F ; Unknown
-    "Telu",  # 0C60..0C63 ; Telugu
-    "Zzzz",  # 0C64..0C65 ; Unknown
-    "Telu",  # 0C66..0C6F ; Telugu
-    "Zzzz",  # 0C70..0C76 ; Unknown
-    "Telu",  # 0C77..0C7F ; Telugu
-    "Knda",  # 0C80..0C8C ; Kannada
-    "Zzzz",  # 0C8D..0C8D ; Unknown
-    "Knda",  # 0C8E..0C90 ; Kannada
-    "Zzzz",  # 0C91..0C91 ; Unknown
-    "Knda",  # 0C92..0CA8 ; Kannada
-    "Zzzz",  # 0CA9..0CA9 ; Unknown
-    "Knda",  # 0CAA..0CB3 ; Kannada
-    "Zzzz",  # 0CB4..0CB4 ; Unknown
-    "Knda",  # 0CB5..0CB9 ; Kannada
-    "Zzzz",  # 0CBA..0CBB ; Unknown
-    "Knda",  # 0CBC..0CC4 ; Kannada
-    "Zzzz",  # 0CC5..0CC5 ; Unknown
-    "Knda",  # 0CC6..0CC8 ; Kannada
-    "Zzzz",  # 0CC9..0CC9 ; Unknown
-    "Knda",  # 0CCA..0CCD ; Kannada
-    "Zzzz",  # 0CCE..0CD4 ; Unknown
-    "Knda",  # 0CD5..0CD6 ; Kannada
-    "Zzzz",  # 0CD7..0CDC ; Unknown
-    "Knda",  # 0CDD..0CDE ; Kannada
-    "Zzzz",  # 0CDF..0CDF ; Unknown
-    "Knda",  # 0CE0..0CE3 ; Kannada
-    "Zzzz",  # 0CE4..0CE5 ; Unknown
-    "Knda",  # 0CE6..0CEF ; Kannada
-    "Zzzz",  # 0CF0..0CF0 ; Unknown
-    "Knda",  # 0CF1..0CF3 ; Kannada
-    "Zzzz",  # 0CF4..0CFF ; Unknown
-    "Mlym",  # 0D00..0D0C ; Malayalam
-    "Zzzz",  # 0D0D..0D0D ; Unknown
-    "Mlym",  # 0D0E..0D10 ; Malayalam
-    "Zzzz",  # 0D11..0D11 ; Unknown
-    "Mlym",  # 0D12..0D44 ; Malayalam
-    "Zzzz",  # 0D45..0D45 ; Unknown
-    "Mlym",  # 0D46..0D48 ; Malayalam
-    "Zzzz",  # 0D49..0D49 ; Unknown
-    "Mlym",  # 0D4A..0D4F ; Malayalam
-    "Zzzz",  # 0D50..0D53 ; Unknown
-    "Mlym",  # 0D54..0D63 ; Malayalam
-    "Zzzz",  # 0D64..0D65 ; Unknown
-    "Mlym",  # 0D66..0D7F ; Malayalam
-    "Zzzz",  # 0D80..0D80 ; Unknown
-    "Sinh",  # 0D81..0D83 ; Sinhala
-    "Zzzz",  # 0D84..0D84 ; Unknown
-    "Sinh",  # 0D85..0D96 ; Sinhala
-    "Zzzz",  # 0D97..0D99 ; Unknown
-    "Sinh",  # 0D9A..0DB1 ; Sinhala
-    "Zzzz",  # 0DB2..0DB2 ; Unknown
-    "Sinh",  # 0DB3..0DBB ; Sinhala
-    "Zzzz",  # 0DBC..0DBC ; Unknown
-    "Sinh",  # 0DBD..0DBD ; Sinhala
-    "Zzzz",  # 0DBE..0DBF ; Unknown
-    "Sinh",  # 0DC0..0DC6 ; Sinhala
-    "Zzzz",  # 0DC7..0DC9 ; Unknown
-    "Sinh",  # 0DCA..0DCA ; Sinhala
-    "Zzzz",  # 0DCB..0DCE ; Unknown
-    "Sinh",  # 0DCF..0DD4 ; Sinhala
-    "Zzzz",  # 0DD5..0DD5 ; Unknown
-    "Sinh",  # 0DD6..0DD6 ; Sinhala
-    "Zzzz",  # 0DD7..0DD7 ; Unknown
-    "Sinh",  # 0DD8..0DDF ; Sinhala
-    "Zzzz",  # 0DE0..0DE5 ; Unknown
-    "Sinh",  # 0DE6..0DEF ; Sinhala
-    "Zzzz",  # 0DF0..0DF1 ; Unknown
-    "Sinh",  # 0DF2..0DF4 ; Sinhala
-    "Zzzz",  # 0DF5..0E00 ; Unknown
-    "Thai",  # 0E01..0E3A ; Thai
-    "Zzzz",  # 0E3B..0E3E ; Unknown
-    "Zyyy",  # 0E3F..0E3F ; Common
-    "Thai",  # 0E40..0E5B ; Thai
-    "Zzzz",  # 0E5C..0E80 ; Unknown
-    "Laoo",  # 0E81..0E82 ; Lao
-    "Zzzz",  # 0E83..0E83 ; Unknown
-    "Laoo",  # 0E84..0E84 ; Lao
-    "Zzzz",  # 0E85..0E85 ; Unknown
-    "Laoo",  # 0E86..0E8A ; Lao
-    "Zzzz",  # 0E8B..0E8B ; Unknown
-    "Laoo",  # 0E8C..0EA3 ; Lao
-    "Zzzz",  # 0EA4..0EA4 ; Unknown
-    "Laoo",  # 0EA5..0EA5 ; Lao
-    "Zzzz",  # 0EA6..0EA6 ; Unknown
-    "Laoo",  # 0EA7..0EBD ; Lao
-    "Zzzz",  # 0EBE..0EBF ; Unknown
-    "Laoo",  # 0EC0..0EC4 ; Lao
-    "Zzzz",  # 0EC5..0EC5 ; Unknown
-    "Laoo",  # 0EC6..0EC6 ; Lao
-    "Zzzz",  # 0EC7..0EC7 ; Unknown
-    "Laoo",  # 0EC8..0ECE ; Lao
-    "Zzzz",  # 0ECF..0ECF ; Unknown
-    "Laoo",  # 0ED0..0ED9 ; Lao
-    "Zzzz",  # 0EDA..0EDB ; Unknown
-    "Laoo",  # 0EDC..0EDF ; Lao
-    "Zzzz",  # 0EE0..0EFF ; Unknown
-    "Tibt",  # 0F00..0F47 ; Tibetan
-    "Zzzz",  # 0F48..0F48 ; Unknown
-    "Tibt",  # 0F49..0F6C ; Tibetan
-    "Zzzz",  # 0F6D..0F70 ; Unknown
-    "Tibt",  # 0F71..0F97 ; Tibetan
-    "Zzzz",  # 0F98..0F98 ; Unknown
-    "Tibt",  # 0F99..0FBC ; Tibetan
-    "Zzzz",  # 0FBD..0FBD ; Unknown
-    "Tibt",  # 0FBE..0FCC ; Tibetan
-    "Zzzz",  # 0FCD..0FCD ; Unknown
-    "Tibt",  # 0FCE..0FD4 ; Tibetan
-    "Zyyy",  # 0FD5..0FD8 ; Common
-    "Tibt",  # 0FD9..0FDA ; Tibetan
-    "Zzzz",  # 0FDB..0FFF ; Unknown
-    "Mymr",  # 1000..109F ; Myanmar
-    "Geor",  # 10A0..10C5 ; Georgian
-    "Zzzz",  # 10C6..10C6 ; Unknown
-    "Geor",  # 10C7..10C7 ; Georgian
-    "Zzzz",  # 10C8..10CC ; Unknown
-    "Geor",  # 10CD..10CD ; Georgian
-    "Zzzz",  # 10CE..10CF ; Unknown
-    "Geor",  # 10D0..10FA ; Georgian
-    "Zyyy",  # 10FB..10FB ; Common
-    "Geor",  # 10FC..10FF ; Georgian
-    "Hang",  # 1100..11FF ; Hangul
-    "Ethi",  # 1200..1248 ; Ethiopic
-    "Zzzz",  # 1249..1249 ; Unknown
-    "Ethi",  # 124A..124D ; Ethiopic
-    "Zzzz",  # 124E..124F ; Unknown
-    "Ethi",  # 1250..1256 ; Ethiopic
-    "Zzzz",  # 1257..1257 ; Unknown
-    "Ethi",  # 1258..1258 ; Ethiopic
-    "Zzzz",  # 1259..1259 ; Unknown
-    "Ethi",  # 125A..125D ; Ethiopic
-    "Zzzz",  # 125E..125F ; Unknown
-    "Ethi",  # 1260..1288 ; Ethiopic
-    "Zzzz",  # 1289..1289 ; Unknown
-    "Ethi",  # 128A..128D ; Ethiopic
-    "Zzzz",  # 128E..128F ; Unknown
-    "Ethi",  # 1290..12B0 ; Ethiopic
-    "Zzzz",  # 12B1..12B1 ; Unknown
-    "Ethi",  # 12B2..12B5 ; Ethiopic
-    "Zzzz",  # 12B6..12B7 ; Unknown
-    "Ethi",  # 12B8..12BE ; Ethiopic
-    "Zzzz",  # 12BF..12BF ; Unknown
-    "Ethi",  # 12C0..12C0 ; Ethiopic
-    "Zzzz",  # 12C1..12C1 ; Unknown
-    "Ethi",  # 12C2..12C5 ; Ethiopic
-    "Zzzz",  # 12C6..12C7 ; Unknown
-    "Ethi",  # 12C8..12D6 ; Ethiopic
-    "Zzzz",  # 12D7..12D7 ; Unknown
-    "Ethi",  # 12D8..1310 ; Ethiopic
-    "Zzzz",  # 1311..1311 ; Unknown
-    "Ethi",  # 1312..1315 ; Ethiopic
-    "Zzzz",  # 1316..1317 ; Unknown
-    "Ethi",  # 1318..135A ; Ethiopic
-    "Zzzz",  # 135B..135C ; Unknown
-    "Ethi",  # 135D..137C ; Ethiopic
-    "Zzzz",  # 137D..137F ; Unknown
-    "Ethi",  # 1380..1399 ; Ethiopic
-    "Zzzz",  # 139A..139F ; Unknown
-    "Cher",  # 13A0..13F5 ; Cherokee
-    "Zzzz",  # 13F6..13F7 ; Unknown
-    "Cher",  # 13F8..13FD ; Cherokee
-    "Zzzz",  # 13FE..13FF ; Unknown
-    "Cans",  # 1400..167F ; Canadian_Aboriginal
-    "Ogam",  # 1680..169C ; Ogham
-    "Zzzz",  # 169D..169F ; Unknown
-    "Runr",  # 16A0..16EA ; Runic
-    "Zyyy",  # 16EB..16ED ; Common
-    "Runr",  # 16EE..16F8 ; Runic
-    "Zzzz",  # 16F9..16FF ; Unknown
-    "Tglg",  # 1700..1715 ; Tagalog
-    "Zzzz",  # 1716..171E ; Unknown
-    "Tglg",  # 171F..171F ; Tagalog
-    "Hano",  # 1720..1734 ; Hanunoo
-    "Zyyy",  # 1735..1736 ; Common
-    "Zzzz",  # 1737..173F ; Unknown
-    "Buhd",  # 1740..1753 ; Buhid
-    "Zzzz",  # 1754..175F ; Unknown
-    "Tagb",  # 1760..176C ; Tagbanwa
-    "Zzzz",  # 176D..176D ; Unknown
-    "Tagb",  # 176E..1770 ; Tagbanwa
-    "Zzzz",  # 1771..1771 ; Unknown
-    "Tagb",  # 1772..1773 ; Tagbanwa
-    "Zzzz",  # 1774..177F ; Unknown
-    "Khmr",  # 1780..17DD ; Khmer
-    "Zzzz",  # 17DE..17DF ; Unknown
-    "Khmr",  # 17E0..17E9 ; Khmer
-    "Zzzz",  # 17EA..17EF ; Unknown
-    "Khmr",  # 17F0..17F9 ; Khmer
-    "Zzzz",  # 17FA..17FF ; Unknown
-    "Mong",  # 1800..1801 ; Mongolian
-    "Zyyy",  # 1802..1803 ; Common
-    "Mong",  # 1804..1804 ; Mongolian
-    "Zyyy",  # 1805..1805 ; Common
-    "Mong",  # 1806..1819 ; Mongolian
-    "Zzzz",  # 181A..181F ; Unknown
-    "Mong",  # 1820..1878 ; Mongolian
-    "Zzzz",  # 1879..187F ; Unknown
-    "Mong",  # 1880..18AA ; Mongolian
-    "Zzzz",  # 18AB..18AF ; Unknown
-    "Cans",  # 18B0..18F5 ; Canadian_Aboriginal
-    "Zzzz",  # 18F6..18FF ; Unknown
-    "Limb",  # 1900..191E ; Limbu
-    "Zzzz",  # 191F..191F ; Unknown
-    "Limb",  # 1920..192B ; Limbu
-    "Zzzz",  # 192C..192F ; Unknown
-    "Limb",  # 1930..193B ; Limbu
-    "Zzzz",  # 193C..193F ; Unknown
-    "Limb",  # 1940..1940 ; Limbu
-    "Zzzz",  # 1941..1943 ; Unknown
-    "Limb",  # 1944..194F ; Limbu
-    "Tale",  # 1950..196D ; Tai_Le
-    "Zzzz",  # 196E..196F ; Unknown
-    "Tale",  # 1970..1974 ; Tai_Le
-    "Zzzz",  # 1975..197F ; Unknown
-    "Talu",  # 1980..19AB ; New_Tai_Lue
-    "Zzzz",  # 19AC..19AF ; Unknown
-    "Talu",  # 19B0..19C9 ; New_Tai_Lue
-    "Zzzz",  # 19CA..19CF ; Unknown
-    "Talu",  # 19D0..19DA ; New_Tai_Lue
-    "Zzzz",  # 19DB..19DD ; Unknown
-    "Talu",  # 19DE..19DF ; New_Tai_Lue
-    "Khmr",  # 19E0..19FF ; Khmer
-    "Bugi",  # 1A00..1A1B ; Buginese
-    "Zzzz",  # 1A1C..1A1D ; Unknown
-    "Bugi",  # 1A1E..1A1F ; Buginese
-    "Lana",  # 1A20..1A5E ; Tai_Tham
-    "Zzzz",  # 1A5F..1A5F ; Unknown
-    "Lana",  # 1A60..1A7C ; Tai_Tham
-    "Zzzz",  # 1A7D..1A7E ; Unknown
-    "Lana",  # 1A7F..1A89 ; Tai_Tham
-    "Zzzz",  # 1A8A..1A8F ; Unknown
-    "Lana",  # 1A90..1A99 ; Tai_Tham
-    "Zzzz",  # 1A9A..1A9F ; Unknown
-    "Lana",  # 1AA0..1AAD ; Tai_Tham
-    "Zzzz",  # 1AAE..1AAF ; Unknown
-    "Zinh",  # 1AB0..1ACE ; Inherited
-    "Zzzz",  # 1ACF..1AFF ; Unknown
-    "Bali",  # 1B00..1B4C ; Balinese
-    "Zzzz",  # 1B4D..1B4F ; Unknown
-    "Bali",  # 1B50..1B7E ; Balinese
-    "Zzzz",  # 1B7F..1B7F ; Unknown
-    "Sund",  # 1B80..1BBF ; Sundanese
-    "Batk",  # 1BC0..1BF3 ; Batak
-    "Zzzz",  # 1BF4..1BFB ; Unknown
-    "Batk",  # 1BFC..1BFF ; Batak
-    "Lepc",  # 1C00..1C37 ; Lepcha
-    "Zzzz",  # 1C38..1C3A ; Unknown
-    "Lepc",  # 1C3B..1C49 ; Lepcha
-    "Zzzz",  # 1C4A..1C4C ; Unknown
-    "Lepc",  # 1C4D..1C4F ; Lepcha
-    "Olck",  # 1C50..1C7F ; Ol_Chiki
-    "Cyrl",  # 1C80..1C88 ; Cyrillic
-    "Zzzz",  # 1C89..1C8F ; Unknown
-    "Geor",  # 1C90..1CBA ; Georgian
-    "Zzzz",  # 1CBB..1CBC ; Unknown
-    "Geor",  # 1CBD..1CBF ; Georgian
-    "Sund",  # 1CC0..1CC7 ; Sundanese
-    "Zzzz",  # 1CC8..1CCF ; Unknown
-    "Zinh",  # 1CD0..1CD2 ; Inherited
-    "Zyyy",  # 1CD3..1CD3 ; Common
-    "Zinh",  # 1CD4..1CE0 ; Inherited
-    "Zyyy",  # 1CE1..1CE1 ; Common
-    "Zinh",  # 1CE2..1CE8 ; Inherited
-    "Zyyy",  # 1CE9..1CEC ; Common
-    "Zinh",  # 1CED..1CED ; Inherited
-    "Zyyy",  # 1CEE..1CF3 ; Common
-    "Zinh",  # 1CF4..1CF4 ; Inherited
-    "Zyyy",  # 1CF5..1CF7 ; Common
-    "Zinh",  # 1CF8..1CF9 ; Inherited
-    "Zyyy",  # 1CFA..1CFA ; Common
-    "Zzzz",  # 1CFB..1CFF ; Unknown
-    "Latn",  # 1D00..1D25 ; Latin
-    "Grek",  # 1D26..1D2A ; Greek
-    "Cyrl",  # 1D2B..1D2B ; Cyrillic
-    "Latn",  # 1D2C..1D5C ; Latin
-    "Grek",  # 1D5D..1D61 ; Greek
-    "Latn",  # 1D62..1D65 ; Latin
-    "Grek",  # 1D66..1D6A ; Greek
-    "Latn",  # 1D6B..1D77 ; Latin
-    "Cyrl",  # 1D78..1D78 ; Cyrillic
-    "Latn",  # 1D79..1DBE ; Latin
-    "Grek",  # 1DBF..1DBF ; Greek
-    "Zinh",  # 1DC0..1DFF ; Inherited
-    "Latn",  # 1E00..1EFF ; Latin
-    "Grek",  # 1F00..1F15 ; Greek
-    "Zzzz",  # 1F16..1F17 ; Unknown
-    "Grek",  # 1F18..1F1D ; Greek
-    "Zzzz",  # 1F1E..1F1F ; Unknown
-    "Grek",  # 1F20..1F45 ; Greek
-    "Zzzz",  # 1F46..1F47 ; Unknown
-    "Grek",  # 1F48..1F4D ; Greek
-    "Zzzz",  # 1F4E..1F4F ; Unknown
-    "Grek",  # 1F50..1F57 ; Greek
-    "Zzzz",  # 1F58..1F58 ; Unknown
-    "Grek",  # 1F59..1F59 ; Greek
-    "Zzzz",  # 1F5A..1F5A ; Unknown
-    "Grek",  # 1F5B..1F5B ; Greek
-    "Zzzz",  # 1F5C..1F5C ; Unknown
-    "Grek",  # 1F5D..1F5D ; Greek
-    "Zzzz",  # 1F5E..1F5E ; Unknown
-    "Grek",  # 1F5F..1F7D ; Greek
-    "Zzzz",  # 1F7E..1F7F ; Unknown
-    "Grek",  # 1F80..1FB4 ; Greek
-    "Zzzz",  # 1FB5..1FB5 ; Unknown
-    "Grek",  # 1FB6..1FC4 ; Greek
-    "Zzzz",  # 1FC5..1FC5 ; Unknown
-    "Grek",  # 1FC6..1FD3 ; Greek
-    "Zzzz",  # 1FD4..1FD5 ; Unknown
-    "Grek",  # 1FD6..1FDB ; Greek
-    "Zzzz",  # 1FDC..1FDC ; Unknown
-    "Grek",  # 1FDD..1FEF ; Greek
-    "Zzzz",  # 1FF0..1FF1 ; Unknown
-    "Grek",  # 1FF2..1FF4 ; Greek
-    "Zzzz",  # 1FF5..1FF5 ; Unknown
-    "Grek",  # 1FF6..1FFE ; Greek
-    "Zzzz",  # 1FFF..1FFF ; Unknown
-    "Zyyy",  # 2000..200B ; Common
-    "Zinh",  # 200C..200D ; Inherited
-    "Zyyy",  # 200E..2064 ; Common
-    "Zzzz",  # 2065..2065 ; Unknown
-    "Zyyy",  # 2066..2070 ; Common
-    "Latn",  # 2071..2071 ; Latin
-    "Zzzz",  # 2072..2073 ; Unknown
-    "Zyyy",  # 2074..207E ; Common
-    "Latn",  # 207F..207F ; Latin
-    "Zyyy",  # 2080..208E ; Common
-    "Zzzz",  # 208F..208F ; Unknown
-    "Latn",  # 2090..209C ; Latin
-    "Zzzz",  # 209D..209F ; Unknown
-    "Zyyy",  # 20A0..20C0 ; Common
-    "Zzzz",  # 20C1..20CF ; Unknown
-    "Zinh",  # 20D0..20F0 ; Inherited
-    "Zzzz",  # 20F1..20FF ; Unknown
-    "Zyyy",  # 2100..2125 ; Common
-    "Grek",  # 2126..2126 ; Greek
-    "Zyyy",  # 2127..2129 ; Common
-    "Latn",  # 212A..212B ; Latin
-    "Zyyy",  # 212C..2131 ; Common
-    "Latn",  # 2132..2132 ; Latin
-    "Zyyy",  # 2133..214D ; Common
-    "Latn",  # 214E..214E ; Latin
-    "Zyyy",  # 214F..215F ; Common
-    "Latn",  # 2160..2188 ; Latin
-    "Zyyy",  # 2189..218B ; Common
-    "Zzzz",  # 218C..218F ; Unknown
-    "Zyyy",  # 2190..2426 ; Common
-    "Zzzz",  # 2427..243F ; Unknown
-    "Zyyy",  # 2440..244A ; Common
-    "Zzzz",  # 244B..245F ; Unknown
-    "Zyyy",  # 2460..27FF ; Common
-    "Brai",  # 2800..28FF ; Braille
-    "Zyyy",  # 2900..2B73 ; Common
-    "Zzzz",  # 2B74..2B75 ; Unknown
-    "Zyyy",  # 2B76..2B95 ; Common
-    "Zzzz",  # 2B96..2B96 ; Unknown
-    "Zyyy",  # 2B97..2BFF ; Common
-    "Glag",  # 2C00..2C5F ; Glagolitic
-    "Latn",  # 2C60..2C7F ; Latin
-    "Copt",  # 2C80..2CF3 ; Coptic
-    "Zzzz",  # 2CF4..2CF8 ; Unknown
-    "Copt",  # 2CF9..2CFF ; Coptic
-    "Geor",  # 2D00..2D25 ; Georgian
-    "Zzzz",  # 2D26..2D26 ; Unknown
-    "Geor",  # 2D27..2D27 ; Georgian
-    "Zzzz",  # 2D28..2D2C ; Unknown
-    "Geor",  # 2D2D..2D2D ; Georgian
-    "Zzzz",  # 2D2E..2D2F ; Unknown
-    "Tfng",  # 2D30..2D67 ; Tifinagh
-    "Zzzz",  # 2D68..2D6E ; Unknown
-    "Tfng",  # 2D6F..2D70 ; Tifinagh
-    "Zzzz",  # 2D71..2D7E ; Unknown
-    "Tfng",  # 2D7F..2D7F ; Tifinagh
-    "Ethi",  # 2D80..2D96 ; Ethiopic
-    "Zzzz",  # 2D97..2D9F ; Unknown
-    "Ethi",  # 2DA0..2DA6 ; Ethiopic
-    "Zzzz",  # 2DA7..2DA7 ; Unknown
-    "Ethi",  # 2DA8..2DAE ; Ethiopic
-    "Zzzz",  # 2DAF..2DAF ; Unknown
-    "Ethi",  # 2DB0..2DB6 ; Ethiopic
-    "Zzzz",  # 2DB7..2DB7 ; Unknown
-    "Ethi",  # 2DB8..2DBE ; Ethiopic
-    "Zzzz",  # 2DBF..2DBF ; Unknown
-    "Ethi",  # 2DC0..2DC6 ; Ethiopic
-    "Zzzz",  # 2DC7..2DC7 ; Unknown
-    "Ethi",  # 2DC8..2DCE ; Ethiopic
-    "Zzzz",  # 2DCF..2DCF ; Unknown
-    "Ethi",  # 2DD0..2DD6 ; Ethiopic
-    "Zzzz",  # 2DD7..2DD7 ; Unknown
-    "Ethi",  # 2DD8..2DDE ; Ethiopic
-    "Zzzz",  # 2DDF..2DDF ; Unknown
-    "Cyrl",  # 2DE0..2DFF ; Cyrillic
-    "Zyyy",  # 2E00..2E5D ; Common
-    "Zzzz",  # 2E5E..2E7F ; Unknown
-    "Hani",  # 2E80..2E99 ; Han
-    "Zzzz",  # 2E9A..2E9A ; Unknown
-    "Hani",  # 2E9B..2EF3 ; Han
-    "Zzzz",  # 2EF4..2EFF ; Unknown
-    "Hani",  # 2F00..2FD5 ; Han
-    "Zzzz",  # 2FD6..2FEF ; Unknown
-    "Zyyy",  # 2FF0..2FFB ; Common
-    "Zzzz",  # 2FFC..2FFF ; Unknown
-    "Zyyy",  # 3000..3004 ; Common
-    "Hani",  # 3005..3005 ; Han
-    "Zyyy",  # 3006..3006 ; Common
-    "Hani",  # 3007..3007 ; Han
-    "Zyyy",  # 3008..3020 ; Common
-    "Hani",  # 3021..3029 ; Han
-    "Zinh",  # 302A..302D ; Inherited
-    "Hang",  # 302E..302F ; Hangul
-    "Zyyy",  # 3030..3037 ; Common
-    "Hani",  # 3038..303B ; Han
-    "Zyyy",  # 303C..303F ; Common
-    "Zzzz",  # 3040..3040 ; Unknown
-    "Hira",  # 3041..3096 ; Hiragana
-    "Zzzz",  # 3097..3098 ; Unknown
-    "Zinh",  # 3099..309A ; Inherited
-    "Zyyy",  # 309B..309C ; Common
-    "Hira",  # 309D..309F ; Hiragana
-    "Zyyy",  # 30A0..30A0 ; Common
-    "Kana",  # 30A1..30FA ; Katakana
-    "Zyyy",  # 30FB..30FC ; Common
-    "Kana",  # 30FD..30FF ; Katakana
-    "Zzzz",  # 3100..3104 ; Unknown
-    "Bopo",  # 3105..312F ; Bopomofo
-    "Zzzz",  # 3130..3130 ; Unknown
-    "Hang",  # 3131..318E ; Hangul
-    "Zzzz",  # 318F..318F ; Unknown
-    "Zyyy",  # 3190..319F ; Common
-    "Bopo",  # 31A0..31BF ; Bopomofo
-    "Zyyy",  # 31C0..31E3 ; Common
-    "Zzzz",  # 31E4..31EF ; Unknown
-    "Kana",  # 31F0..31FF ; Katakana
-    "Hang",  # 3200..321E ; Hangul
-    "Zzzz",  # 321F..321F ; Unknown
-    "Zyyy",  # 3220..325F ; Common
-    "Hang",  # 3260..327E ; Hangul
-    "Zyyy",  # 327F..32CF ; Common
-    "Kana",  # 32D0..32FE ; Katakana
-    "Zyyy",  # 32FF..32FF ; Common
-    "Kana",  # 3300..3357 ; Katakana
-    "Zyyy",  # 3358..33FF ; Common
-    "Hani",  # 3400..4DBF ; Han
-    "Zyyy",  # 4DC0..4DFF ; Common
-    "Hani",  # 4E00..9FFF ; Han
-    "Yiii",  # A000..A48C ; Yi
-    "Zzzz",  # A48D..A48F ; Unknown
-    "Yiii",  # A490..A4C6 ; Yi
-    "Zzzz",  # A4C7..A4CF ; Unknown
-    "Lisu",  # A4D0..A4FF ; Lisu
-    "Vaii",  # A500..A62B ; Vai
-    "Zzzz",  # A62C..A63F ; Unknown
-    "Cyrl",  # A640..A69F ; Cyrillic
-    "Bamu",  # A6A0..A6F7 ; Bamum
-    "Zzzz",  # A6F8..A6FF ; Unknown
-    "Zyyy",  # A700..A721 ; Common
-    "Latn",  # A722..A787 ; Latin
-    "Zyyy",  # A788..A78A ; Common
-    "Latn",  # A78B..A7CA ; Latin
-    "Zzzz",  # A7CB..A7CF ; Unknown
-    "Latn",  # A7D0..A7D1 ; Latin
-    "Zzzz",  # A7D2..A7D2 ; Unknown
-    "Latn",  # A7D3..A7D3 ; Latin
-    "Zzzz",  # A7D4..A7D4 ; Unknown
-    "Latn",  # A7D5..A7D9 ; Latin
-    "Zzzz",  # A7DA..A7F1 ; Unknown
-    "Latn",  # A7F2..A7FF ; Latin
-    "Sylo",  # A800..A82C ; Syloti_Nagri
-    "Zzzz",  # A82D..A82F ; Unknown
-    "Zyyy",  # A830..A839 ; Common
-    "Zzzz",  # A83A..A83F ; Unknown
-    "Phag",  # A840..A877 ; Phags_Pa
-    "Zzzz",  # A878..A87F ; Unknown
-    "Saur",  # A880..A8C5 ; Saurashtra
-    "Zzzz",  # A8C6..A8CD ; Unknown
-    "Saur",  # A8CE..A8D9 ; Saurashtra
-    "Zzzz",  # A8DA..A8DF ; Unknown
-    "Deva",  # A8E0..A8FF ; Devanagari
-    "Kali",  # A900..A92D ; Kayah_Li
-    "Zyyy",  # A92E..A92E ; Common
-    "Kali",  # A92F..A92F ; Kayah_Li
-    "Rjng",  # A930..A953 ; Rejang
-    "Zzzz",  # A954..A95E ; Unknown
-    "Rjng",  # A95F..A95F ; Rejang
-    "Hang",  # A960..A97C ; Hangul
-    "Zzzz",  # A97D..A97F ; Unknown
-    "Java",  # A980..A9CD ; Javanese
-    "Zzzz",  # A9CE..A9CE ; Unknown
-    "Zyyy",  # A9CF..A9CF ; Common
-    "Java",  # A9D0..A9D9 ; Javanese
-    "Zzzz",  # A9DA..A9DD ; Unknown
-    "Java",  # A9DE..A9DF ; Javanese
-    "Mymr",  # A9E0..A9FE ; Myanmar
-    "Zzzz",  # A9FF..A9FF ; Unknown
-    "Cham",  # AA00..AA36 ; Cham
-    "Zzzz",  # AA37..AA3F ; Unknown
-    "Cham",  # AA40..AA4D ; Cham
-    "Zzzz",  # AA4E..AA4F ; Unknown
-    "Cham",  # AA50..AA59 ; Cham
-    "Zzzz",  # AA5A..AA5B ; Unknown
-    "Cham",  # AA5C..AA5F ; Cham
-    "Mymr",  # AA60..AA7F ; Myanmar
-    "Tavt",  # AA80..AAC2 ; Tai_Viet
-    "Zzzz",  # AAC3..AADA ; Unknown
-    "Tavt",  # AADB..AADF ; Tai_Viet
-    "Mtei",  # AAE0..AAF6 ; Meetei_Mayek
-    "Zzzz",  # AAF7..AB00 ; Unknown
-    "Ethi",  # AB01..AB06 ; Ethiopic
-    "Zzzz",  # AB07..AB08 ; Unknown
-    "Ethi",  # AB09..AB0E ; Ethiopic
-    "Zzzz",  # AB0F..AB10 ; Unknown
-    "Ethi",  # AB11..AB16 ; Ethiopic
-    "Zzzz",  # AB17..AB1F ; Unknown
-    "Ethi",  # AB20..AB26 ; Ethiopic
-    "Zzzz",  # AB27..AB27 ; Unknown
-    "Ethi",  # AB28..AB2E ; Ethiopic
-    "Zzzz",  # AB2F..AB2F ; Unknown
-    "Latn",  # AB30..AB5A ; Latin
-    "Zyyy",  # AB5B..AB5B ; Common
-    "Latn",  # AB5C..AB64 ; Latin
-    "Grek",  # AB65..AB65 ; Greek
-    "Latn",  # AB66..AB69 ; Latin
-    "Zyyy",  # AB6A..AB6B ; Common
-    "Zzzz",  # AB6C..AB6F ; Unknown
-    "Cher",  # AB70..ABBF ; Cherokee
-    "Mtei",  # ABC0..ABED ; Meetei_Mayek
-    "Zzzz",  # ABEE..ABEF ; Unknown
-    "Mtei",  # ABF0..ABF9 ; Meetei_Mayek
-    "Zzzz",  # ABFA..ABFF ; Unknown
-    "Hang",  # AC00..D7A3 ; Hangul
-    "Zzzz",  # D7A4..D7AF ; Unknown
-    "Hang",  # D7B0..D7C6 ; Hangul
-    "Zzzz",  # D7C7..D7CA ; Unknown
-    "Hang",  # D7CB..D7FB ; Hangul
-    "Zzzz",  # D7FC..F8FF ; Unknown
-    "Hani",  # F900..FA6D ; Han
-    "Zzzz",  # FA6E..FA6F ; Unknown
-    "Hani",  # FA70..FAD9 ; Han
-    "Zzzz",  # FADA..FAFF ; Unknown
-    "Latn",  # FB00..FB06 ; Latin
-    "Zzzz",  # FB07..FB12 ; Unknown
-    "Armn",  # FB13..FB17 ; Armenian
-    "Zzzz",  # FB18..FB1C ; Unknown
-    "Hebr",  # FB1D..FB36 ; Hebrew
-    "Zzzz",  # FB37..FB37 ; Unknown
-    "Hebr",  # FB38..FB3C ; Hebrew
-    "Zzzz",  # FB3D..FB3D ; Unknown
-    "Hebr",  # FB3E..FB3E ; Hebrew
-    "Zzzz",  # FB3F..FB3F ; Unknown
-    "Hebr",  # FB40..FB41 ; Hebrew
-    "Zzzz",  # FB42..FB42 ; Unknown
-    "Hebr",  # FB43..FB44 ; Hebrew
-    "Zzzz",  # FB45..FB45 ; Unknown
-    "Hebr",  # FB46..FB4F ; Hebrew
-    "Arab",  # FB50..FBC2 ; Arabic
-    "Zzzz",  # FBC3..FBD2 ; Unknown
-    "Arab",  # FBD3..FD3D ; Arabic
-    "Zyyy",  # FD3E..FD3F ; Common
-    "Arab",  # FD40..FD8F ; Arabic
-    "Zzzz",  # FD90..FD91 ; Unknown
-    "Arab",  # FD92..FDC7 ; Arabic
-    "Zzzz",  # FDC8..FDCE ; Unknown
-    "Arab",  # FDCF..FDCF ; Arabic
-    "Zzzz",  # FDD0..FDEF ; Unknown
-    "Arab",  # FDF0..FDFF ; Arabic
-    "Zinh",  # FE00..FE0F ; Inherited
-    "Zyyy",  # FE10..FE19 ; Common
-    "Zzzz",  # FE1A..FE1F ; Unknown
-    "Zinh",  # FE20..FE2D ; Inherited
-    "Cyrl",  # FE2E..FE2F ; Cyrillic
-    "Zyyy",  # FE30..FE52 ; Common
-    "Zzzz",  # FE53..FE53 ; Unknown
-    "Zyyy",  # FE54..FE66 ; Common
-    "Zzzz",  # FE67..FE67 ; Unknown
-    "Zyyy",  # FE68..FE6B ; Common
-    "Zzzz",  # FE6C..FE6F ; Unknown
-    "Arab",  # FE70..FE74 ; Arabic
-    "Zzzz",  # FE75..FE75 ; Unknown
-    "Arab",  # FE76..FEFC ; Arabic
-    "Zzzz",  # FEFD..FEFE ; Unknown
-    "Zyyy",  # FEFF..FEFF ; Common
-    "Zzzz",  # FF00..FF00 ; Unknown
-    "Zyyy",  # FF01..FF20 ; Common
-    "Latn",  # FF21..FF3A ; Latin
-    "Zyyy",  # FF3B..FF40 ; Common
-    "Latn",  # FF41..FF5A ; Latin
-    "Zyyy",  # FF5B..FF65 ; Common
-    "Kana",  # FF66..FF6F ; Katakana
-    "Zyyy",  # FF70..FF70 ; Common
-    "Kana",  # FF71..FF9D ; Katakana
-    "Zyyy",  # FF9E..FF9F ; Common
-    "Hang",  # FFA0..FFBE ; Hangul
-    "Zzzz",  # FFBF..FFC1 ; Unknown
-    "Hang",  # FFC2..FFC7 ; Hangul
-    "Zzzz",  # FFC8..FFC9 ; Unknown
-    "Hang",  # FFCA..FFCF ; Hangul
-    "Zzzz",  # FFD0..FFD1 ; Unknown
-    "Hang",  # FFD2..FFD7 ; Hangul
-    "Zzzz",  # FFD8..FFD9 ; Unknown
-    "Hang",  # FFDA..FFDC ; Hangul
-    "Zzzz",  # FFDD..FFDF ; Unknown
-    "Zyyy",  # FFE0..FFE6 ; Common
-    "Zzzz",  # FFE7..FFE7 ; Unknown
-    "Zyyy",  # FFE8..FFEE ; Common
-    "Zzzz",  # FFEF..FFF8 ; Unknown
-    "Zyyy",  # FFF9..FFFD ; Common
-    "Zzzz",  # FFFE..FFFF ; Unknown
-    "Linb",  # 10000..1000B ; Linear_B
-    "Zzzz",  # 1000C..1000C ; Unknown
-    "Linb",  # 1000D..10026 ; Linear_B
-    "Zzzz",  # 10027..10027 ; Unknown
-    "Linb",  # 10028..1003A ; Linear_B
-    "Zzzz",  # 1003B..1003B ; Unknown
-    "Linb",  # 1003C..1003D ; Linear_B
-    "Zzzz",  # 1003E..1003E ; Unknown
-    "Linb",  # 1003F..1004D ; Linear_B
-    "Zzzz",  # 1004E..1004F ; Unknown
-    "Linb",  # 10050..1005D ; Linear_B
-    "Zzzz",  # 1005E..1007F ; Unknown
-    "Linb",  # 10080..100FA ; Linear_B
-    "Zzzz",  # 100FB..100FF ; Unknown
-    "Zyyy",  # 10100..10102 ; Common
-    "Zzzz",  # 10103..10106 ; Unknown
-    "Zyyy",  # 10107..10133 ; Common
-    "Zzzz",  # 10134..10136 ; Unknown
-    "Zyyy",  # 10137..1013F ; Common
-    "Grek",  # 10140..1018E ; Greek
-    "Zzzz",  # 1018F..1018F ; Unknown
-    "Zyyy",  # 10190..1019C ; Common
-    "Zzzz",  # 1019D..1019F ; Unknown
-    "Grek",  # 101A0..101A0 ; Greek
-    "Zzzz",  # 101A1..101CF ; Unknown
-    "Zyyy",  # 101D0..101FC ; Common
-    "Zinh",  # 101FD..101FD ; Inherited
-    "Zzzz",  # 101FE..1027F ; Unknown
-    "Lyci",  # 10280..1029C ; Lycian
-    "Zzzz",  # 1029D..1029F ; Unknown
-    "Cari",  # 102A0..102D0 ; Carian
-    "Zzzz",  # 102D1..102DF ; Unknown
-    "Zinh",  # 102E0..102E0 ; Inherited
-    "Zyyy",  # 102E1..102FB ; Common
-    "Zzzz",  # 102FC..102FF ; Unknown
-    "Ital",  # 10300..10323 ; Old_Italic
-    "Zzzz",  # 10324..1032C ; Unknown
-    "Ital",  # 1032D..1032F ; Old_Italic
-    "Goth",  # 10330..1034A ; Gothic
-    "Zzzz",  # 1034B..1034F ; Unknown
-    "Perm",  # 10350..1037A ; Old_Permic
-    "Zzzz",  # 1037B..1037F ; Unknown
-    "Ugar",  # 10380..1039D ; Ugaritic
-    "Zzzz",  # 1039E..1039E ; Unknown
-    "Ugar",  # 1039F..1039F ; Ugaritic
-    "Xpeo",  # 103A0..103C3 ; Old_Persian
-    "Zzzz",  # 103C4..103C7 ; Unknown
-    "Xpeo",  # 103C8..103D5 ; Old_Persian
-    "Zzzz",  # 103D6..103FF ; Unknown
-    "Dsrt",  # 10400..1044F ; Deseret
-    "Shaw",  # 10450..1047F ; Shavian
-    "Osma",  # 10480..1049D ; Osmanya
-    "Zzzz",  # 1049E..1049F ; Unknown
-    "Osma",  # 104A0..104A9 ; Osmanya
-    "Zzzz",  # 104AA..104AF ; Unknown
-    "Osge",  # 104B0..104D3 ; Osage
-    "Zzzz",  # 104D4..104D7 ; Unknown
-    "Osge",  # 104D8..104FB ; Osage
-    "Zzzz",  # 104FC..104FF ; Unknown
-    "Elba",  # 10500..10527 ; Elbasan
-    "Zzzz",  # 10528..1052F ; Unknown
-    "Aghb",  # 10530..10563 ; Caucasian_Albanian
-    "Zzzz",  # 10564..1056E ; Unknown
-    "Aghb",  # 1056F..1056F ; Caucasian_Albanian
-    "Vith",  # 10570..1057A ; Vithkuqi
-    "Zzzz",  # 1057B..1057B ; Unknown
-    "Vith",  # 1057C..1058A ; Vithkuqi
-    "Zzzz",  # 1058B..1058B ; Unknown
-    "Vith",  # 1058C..10592 ; Vithkuqi
-    "Zzzz",  # 10593..10593 ; Unknown
-    "Vith",  # 10594..10595 ; Vithkuqi
-    "Zzzz",  # 10596..10596 ; Unknown
-    "Vith",  # 10597..105A1 ; Vithkuqi
-    "Zzzz",  # 105A2..105A2 ; Unknown
-    "Vith",  # 105A3..105B1 ; Vithkuqi
-    "Zzzz",  # 105B2..105B2 ; Unknown
-    "Vith",  # 105B3..105B9 ; Vithkuqi
-    "Zzzz",  # 105BA..105BA ; Unknown
-    "Vith",  # 105BB..105BC ; Vithkuqi
-    "Zzzz",  # 105BD..105FF ; Unknown
-    "Lina",  # 10600..10736 ; Linear_A
-    "Zzzz",  # 10737..1073F ; Unknown
-    "Lina",  # 10740..10755 ; Linear_A
-    "Zzzz",  # 10756..1075F ; Unknown
-    "Lina",  # 10760..10767 ; Linear_A
-    "Zzzz",  # 10768..1077F ; Unknown
-    "Latn",  # 10780..10785 ; Latin
-    "Zzzz",  # 10786..10786 ; Unknown
-    "Latn",  # 10787..107B0 ; Latin
-    "Zzzz",  # 107B1..107B1 ; Unknown
-    "Latn",  # 107B2..107BA ; Latin
-    "Zzzz",  # 107BB..107FF ; Unknown
-    "Cprt",  # 10800..10805 ; Cypriot
-    "Zzzz",  # 10806..10807 ; Unknown
-    "Cprt",  # 10808..10808 ; Cypriot
-    "Zzzz",  # 10809..10809 ; Unknown
-    "Cprt",  # 1080A..10835 ; Cypriot
-    "Zzzz",  # 10836..10836 ; Unknown
-    "Cprt",  # 10837..10838 ; Cypriot
-    "Zzzz",  # 10839..1083B ; Unknown
-    "Cprt",  # 1083C..1083C ; Cypriot
-    "Zzzz",  # 1083D..1083E ; Unknown
-    "Cprt",  # 1083F..1083F ; Cypriot
-    "Armi",  # 10840..10855 ; Imperial_Aramaic
-    "Zzzz",  # 10856..10856 ; Unknown
-    "Armi",  # 10857..1085F ; Imperial_Aramaic
-    "Palm",  # 10860..1087F ; Palmyrene
-    "Nbat",  # 10880..1089E ; Nabataean
-    "Zzzz",  # 1089F..108A6 ; Unknown
-    "Nbat",  # 108A7..108AF ; Nabataean
-    "Zzzz",  # 108B0..108DF ; Unknown
-    "Hatr",  # 108E0..108F2 ; Hatran
-    "Zzzz",  # 108F3..108F3 ; Unknown
-    "Hatr",  # 108F4..108F5 ; Hatran
-    "Zzzz",  # 108F6..108FA ; Unknown
-    "Hatr",  # 108FB..108FF ; Hatran
-    "Phnx",  # 10900..1091B ; Phoenician
-    "Zzzz",  # 1091C..1091E ; Unknown
-    "Phnx",  # 1091F..1091F ; Phoenician
-    "Lydi",  # 10920..10939 ; Lydian
-    "Zzzz",  # 1093A..1093E ; Unknown
-    "Lydi",  # 1093F..1093F ; Lydian
-    "Zzzz",  # 10940..1097F ; Unknown
-    "Mero",  # 10980..1099F ; Meroitic_Hieroglyphs
-    "Merc",  # 109A0..109B7 ; Meroitic_Cursive
-    "Zzzz",  # 109B8..109BB ; Unknown
-    "Merc",  # 109BC..109CF ; Meroitic_Cursive
-    "Zzzz",  # 109D0..109D1 ; Unknown
-    "Merc",  # 109D2..109FF ; Meroitic_Cursive
-    "Khar",  # 10A00..10A03 ; Kharoshthi
-    "Zzzz",  # 10A04..10A04 ; Unknown
-    "Khar",  # 10A05..10A06 ; Kharoshthi
-    "Zzzz",  # 10A07..10A0B ; Unknown
-    "Khar",  # 10A0C..10A13 ; Kharoshthi
-    "Zzzz",  # 10A14..10A14 ; Unknown
-    "Khar",  # 10A15..10A17 ; Kharoshthi
-    "Zzzz",  # 10A18..10A18 ; Unknown
-    "Khar",  # 10A19..10A35 ; Kharoshthi
-    "Zzzz",  # 10A36..10A37 ; Unknown
-    "Khar",  # 10A38..10A3A ; Kharoshthi
-    "Zzzz",  # 10A3B..10A3E ; Unknown
-    "Khar",  # 10A3F..10A48 ; Kharoshthi
-    "Zzzz",  # 10A49..10A4F ; Unknown
-    "Khar",  # 10A50..10A58 ; Kharoshthi
-    "Zzzz",  # 10A59..10A5F ; Unknown
-    "Sarb",  # 10A60..10A7F ; Old_South_Arabian
-    "Narb",  # 10A80..10A9F ; Old_North_Arabian
-    "Zzzz",  # 10AA0..10ABF ; Unknown
-    "Mani",  # 10AC0..10AE6 ; Manichaean
-    "Zzzz",  # 10AE7..10AEA ; Unknown
-    "Mani",  # 10AEB..10AF6 ; Manichaean
-    "Zzzz",  # 10AF7..10AFF ; Unknown
-    "Avst",  # 10B00..10B35 ; Avestan
-    "Zzzz",  # 10B36..10B38 ; Unknown
-    "Avst",  # 10B39..10B3F ; Avestan
-    "Prti",  # 10B40..10B55 ; Inscriptional_Parthian
-    "Zzzz",  # 10B56..10B57 ; Unknown
-    "Prti",  # 10B58..10B5F ; Inscriptional_Parthian
-    "Phli",  # 10B60..10B72 ; Inscriptional_Pahlavi
-    "Zzzz",  # 10B73..10B77 ; Unknown
-    "Phli",  # 10B78..10B7F ; Inscriptional_Pahlavi
-    "Phlp",  # 10B80..10B91 ; Psalter_Pahlavi
-    "Zzzz",  # 10B92..10B98 ; Unknown
-    "Phlp",  # 10B99..10B9C ; Psalter_Pahlavi
-    "Zzzz",  # 10B9D..10BA8 ; Unknown
-    "Phlp",  # 10BA9..10BAF ; Psalter_Pahlavi
-    "Zzzz",  # 10BB0..10BFF ; Unknown
-    "Orkh",  # 10C00..10C48 ; Old_Turkic
-    "Zzzz",  # 10C49..10C7F ; Unknown
-    "Hung",  # 10C80..10CB2 ; Old_Hungarian
-    "Zzzz",  # 10CB3..10CBF ; Unknown
-    "Hung",  # 10CC0..10CF2 ; Old_Hungarian
-    "Zzzz",  # 10CF3..10CF9 ; Unknown
-    "Hung",  # 10CFA..10CFF ; Old_Hungarian
-    "Rohg",  # 10D00..10D27 ; Hanifi_Rohingya
-    "Zzzz",  # 10D28..10D2F ; Unknown
-    "Rohg",  # 10D30..10D39 ; Hanifi_Rohingya
-    "Zzzz",  # 10D3A..10E5F ; Unknown
-    "Arab",  # 10E60..10E7E ; Arabic
-    "Zzzz",  # 10E7F..10E7F ; Unknown
-    "Yezi",  # 10E80..10EA9 ; Yezidi
-    "Zzzz",  # 10EAA..10EAA ; Unknown
-    "Yezi",  # 10EAB..10EAD ; Yezidi
-    "Zzzz",  # 10EAE..10EAF ; Unknown
-    "Yezi",  # 10EB0..10EB1 ; Yezidi
-    "Zzzz",  # 10EB2..10EFC ; Unknown
-    "Arab",  # 10EFD..10EFF ; Arabic
-    "Sogo",  # 10F00..10F27 ; Old_Sogdian
-    "Zzzz",  # 10F28..10F2F ; Unknown
-    "Sogd",  # 10F30..10F59 ; Sogdian
-    "Zzzz",  # 10F5A..10F6F ; Unknown
-    "Ougr",  # 10F70..10F89 ; Old_Uyghur
-    "Zzzz",  # 10F8A..10FAF ; Unknown
-    "Chrs",  # 10FB0..10FCB ; Chorasmian
-    "Zzzz",  # 10FCC..10FDF ; Unknown
-    "Elym",  # 10FE0..10FF6 ; Elymaic
-    "Zzzz",  # 10FF7..10FFF ; Unknown
-    "Brah",  # 11000..1104D ; Brahmi
-    "Zzzz",  # 1104E..11051 ; Unknown
-    "Brah",  # 11052..11075 ; Brahmi
-    "Zzzz",  # 11076..1107E ; Unknown
-    "Brah",  # 1107F..1107F ; Brahmi
-    "Kthi",  # 11080..110C2 ; Kaithi
-    "Zzzz",  # 110C3..110CC ; Unknown
-    "Kthi",  # 110CD..110CD ; Kaithi
-    "Zzzz",  # 110CE..110CF ; Unknown
-    "Sora",  # 110D0..110E8 ; Sora_Sompeng
-    "Zzzz",  # 110E9..110EF ; Unknown
-    "Sora",  # 110F0..110F9 ; Sora_Sompeng
-    "Zzzz",  # 110FA..110FF ; Unknown
-    "Cakm",  # 11100..11134 ; Chakma
-    "Zzzz",  # 11135..11135 ; Unknown
-    "Cakm",  # 11136..11147 ; Chakma
-    "Zzzz",  # 11148..1114F ; Unknown
-    "Mahj",  # 11150..11176 ; Mahajani
-    "Zzzz",  # 11177..1117F ; Unknown
-    "Shrd",  # 11180..111DF ; Sharada
-    "Zzzz",  # 111E0..111E0 ; Unknown
-    "Sinh",  # 111E1..111F4 ; Sinhala
-    "Zzzz",  # 111F5..111FF ; Unknown
-    "Khoj",  # 11200..11211 ; Khojki
-    "Zzzz",  # 11212..11212 ; Unknown
-    "Khoj",  # 11213..11241 ; Khojki
-    "Zzzz",  # 11242..1127F ; Unknown
-    "Mult",  # 11280..11286 ; Multani
-    "Zzzz",  # 11287..11287 ; Unknown
-    "Mult",  # 11288..11288 ; Multani
-    "Zzzz",  # 11289..11289 ; Unknown
-    "Mult",  # 1128A..1128D ; Multani
-    "Zzzz",  # 1128E..1128E ; Unknown
-    "Mult",  # 1128F..1129D ; Multani
-    "Zzzz",  # 1129E..1129E ; Unknown
-    "Mult",  # 1129F..112A9 ; Multani
-    "Zzzz",  # 112AA..112AF ; Unknown
-    "Sind",  # 112B0..112EA ; Khudawadi
-    "Zzzz",  # 112EB..112EF ; Unknown
-    "Sind",  # 112F0..112F9 ; Khudawadi
-    "Zzzz",  # 112FA..112FF ; Unknown
-    "Gran",  # 11300..11303 ; Grantha
-    "Zzzz",  # 11304..11304 ; Unknown
-    "Gran",  # 11305..1130C ; Grantha
-    "Zzzz",  # 1130D..1130E ; Unknown
-    "Gran",  # 1130F..11310 ; Grantha
-    "Zzzz",  # 11311..11312 ; Unknown
-    "Gran",  # 11313..11328 ; Grantha
-    "Zzzz",  # 11329..11329 ; Unknown
-    "Gran",  # 1132A..11330 ; Grantha
-    "Zzzz",  # 11331..11331 ; Unknown
-    "Gran",  # 11332..11333 ; Grantha
-    "Zzzz",  # 11334..11334 ; Unknown
-    "Gran",  # 11335..11339 ; Grantha
-    "Zzzz",  # 1133A..1133A ; Unknown
-    "Zinh",  # 1133B..1133B ; Inherited
-    "Gran",  # 1133C..11344 ; Grantha
-    "Zzzz",  # 11345..11346 ; Unknown
-    "Gran",  # 11347..11348 ; Grantha
-    "Zzzz",  # 11349..1134A ; Unknown
-    "Gran",  # 1134B..1134D ; Grantha
-    "Zzzz",  # 1134E..1134F ; Unknown
-    "Gran",  # 11350..11350 ; Grantha
-    "Zzzz",  # 11351..11356 ; Unknown
-    "Gran",  # 11357..11357 ; Grantha
-    "Zzzz",  # 11358..1135C ; Unknown
-    "Gran",  # 1135D..11363 ; Grantha
-    "Zzzz",  # 11364..11365 ; Unknown
-    "Gran",  # 11366..1136C ; Grantha
-    "Zzzz",  # 1136D..1136F ; Unknown
-    "Gran",  # 11370..11374 ; Grantha
-    "Zzzz",  # 11375..113FF ; Unknown
-    "Newa",  # 11400..1145B ; Newa
-    "Zzzz",  # 1145C..1145C ; Unknown
-    "Newa",  # 1145D..11461 ; Newa
-    "Zzzz",  # 11462..1147F ; Unknown
-    "Tirh",  # 11480..114C7 ; Tirhuta
-    "Zzzz",  # 114C8..114CF ; Unknown
-    "Tirh",  # 114D0..114D9 ; Tirhuta
-    "Zzzz",  # 114DA..1157F ; Unknown
-    "Sidd",  # 11580..115B5 ; Siddham
-    "Zzzz",  # 115B6..115B7 ; Unknown
-    "Sidd",  # 115B8..115DD ; Siddham
-    "Zzzz",  # 115DE..115FF ; Unknown
-    "Modi",  # 11600..11644 ; Modi
-    "Zzzz",  # 11645..1164F ; Unknown
-    "Modi",  # 11650..11659 ; Modi
-    "Zzzz",  # 1165A..1165F ; Unknown
-    "Mong",  # 11660..1166C ; Mongolian
-    "Zzzz",  # 1166D..1167F ; Unknown
-    "Takr",  # 11680..116B9 ; Takri
-    "Zzzz",  # 116BA..116BF ; Unknown
-    "Takr",  # 116C0..116C9 ; Takri
-    "Zzzz",  # 116CA..116FF ; Unknown
-    "Ahom",  # 11700..1171A ; Ahom
-    "Zzzz",  # 1171B..1171C ; Unknown
-    "Ahom",  # 1171D..1172B ; Ahom
-    "Zzzz",  # 1172C..1172F ; Unknown
-    "Ahom",  # 11730..11746 ; Ahom
-    "Zzzz",  # 11747..117FF ; Unknown
-    "Dogr",  # 11800..1183B ; Dogra
-    "Zzzz",  # 1183C..1189F ; Unknown
-    "Wara",  # 118A0..118F2 ; Warang_Citi
-    "Zzzz",  # 118F3..118FE ; Unknown
-    "Wara",  # 118FF..118FF ; Warang_Citi
-    "Diak",  # 11900..11906 ; Dives_Akuru
-    "Zzzz",  # 11907..11908 ; Unknown
-    "Diak",  # 11909..11909 ; Dives_Akuru
-    "Zzzz",  # 1190A..1190B ; Unknown
-    "Diak",  # 1190C..11913 ; Dives_Akuru
-    "Zzzz",  # 11914..11914 ; Unknown
-    "Diak",  # 11915..11916 ; Dives_Akuru
-    "Zzzz",  # 11917..11917 ; Unknown
-    "Diak",  # 11918..11935 ; Dives_Akuru
-    "Zzzz",  # 11936..11936 ; Unknown
-    "Diak",  # 11937..11938 ; Dives_Akuru
-    "Zzzz",  # 11939..1193A ; Unknown
-    "Diak",  # 1193B..11946 ; Dives_Akuru
-    "Zzzz",  # 11947..1194F ; Unknown
-    "Diak",  # 11950..11959 ; Dives_Akuru
-    "Zzzz",  # 1195A..1199F ; Unknown
-    "Nand",  # 119A0..119A7 ; Nandinagari
-    "Zzzz",  # 119A8..119A9 ; Unknown
-    "Nand",  # 119AA..119D7 ; Nandinagari
-    "Zzzz",  # 119D8..119D9 ; Unknown
-    "Nand",  # 119DA..119E4 ; Nandinagari
-    "Zzzz",  # 119E5..119FF ; Unknown
-    "Zanb",  # 11A00..11A47 ; Zanabazar_Square
-    "Zzzz",  # 11A48..11A4F ; Unknown
-    "Soyo",  # 11A50..11AA2 ; Soyombo
-    "Zzzz",  # 11AA3..11AAF ; Unknown
-    "Cans",  # 11AB0..11ABF ; Canadian_Aboriginal
-    "Pauc",  # 11AC0..11AF8 ; Pau_Cin_Hau
-    "Zzzz",  # 11AF9..11AFF ; Unknown
-    "Deva",  # 11B00..11B09 ; Devanagari
-    "Zzzz",  # 11B0A..11BFF ; Unknown
-    "Bhks",  # 11C00..11C08 ; Bhaiksuki
-    "Zzzz",  # 11C09..11C09 ; Unknown
-    "Bhks",  # 11C0A..11C36 ; Bhaiksuki
-    "Zzzz",  # 11C37..11C37 ; Unknown
-    "Bhks",  # 11C38..11C45 ; Bhaiksuki
-    "Zzzz",  # 11C46..11C4F ; Unknown
-    "Bhks",  # 11C50..11C6C ; Bhaiksuki
-    "Zzzz",  # 11C6D..11C6F ; Unknown
-    "Marc",  # 11C70..11C8F ; Marchen
-    "Zzzz",  # 11C90..11C91 ; Unknown
-    "Marc",  # 11C92..11CA7 ; Marchen
-    "Zzzz",  # 11CA8..11CA8 ; Unknown
-    "Marc",  # 11CA9..11CB6 ; Marchen
-    "Zzzz",  # 11CB7..11CFF ; Unknown
-    "Gonm",  # 11D00..11D06 ; Masaram_Gondi
-    "Zzzz",  # 11D07..11D07 ; Unknown
-    "Gonm",  # 11D08..11D09 ; Masaram_Gondi
-    "Zzzz",  # 11D0A..11D0A ; Unknown
-    "Gonm",  # 11D0B..11D36 ; Masaram_Gondi
-    "Zzzz",  # 11D37..11D39 ; Unknown
-    "Gonm",  # 11D3A..11D3A ; Masaram_Gondi
-    "Zzzz",  # 11D3B..11D3B ; Unknown
-    "Gonm",  # 11D3C..11D3D ; Masaram_Gondi
-    "Zzzz",  # 11D3E..11D3E ; Unknown
-    "Gonm",  # 11D3F..11D47 ; Masaram_Gondi
-    "Zzzz",  # 11D48..11D4F ; Unknown
-    "Gonm",  # 11D50..11D59 ; Masaram_Gondi
-    "Zzzz",  # 11D5A..11D5F ; Unknown
-    "Gong",  # 11D60..11D65 ; Gunjala_Gondi
-    "Zzzz",  # 11D66..11D66 ; Unknown
-    "Gong",  # 11D67..11D68 ; Gunjala_Gondi
-    "Zzzz",  # 11D69..11D69 ; Unknown
-    "Gong",  # 11D6A..11D8E ; Gunjala_Gondi
-    "Zzzz",  # 11D8F..11D8F ; Unknown
-    "Gong",  # 11D90..11D91 ; Gunjala_Gondi
-    "Zzzz",  # 11D92..11D92 ; Unknown
-    "Gong",  # 11D93..11D98 ; Gunjala_Gondi
-    "Zzzz",  # 11D99..11D9F ; Unknown
-    "Gong",  # 11DA0..11DA9 ; Gunjala_Gondi
-    "Zzzz",  # 11DAA..11EDF ; Unknown
-    "Maka",  # 11EE0..11EF8 ; Makasar
-    "Zzzz",  # 11EF9..11EFF ; Unknown
-    "Kawi",  # 11F00..11F10 ; Kawi
-    "Zzzz",  # 11F11..11F11 ; Unknown
-    "Kawi",  # 11F12..11F3A ; Kawi
-    "Zzzz",  # 11F3B..11F3D ; Unknown
-    "Kawi",  # 11F3E..11F59 ; Kawi
-    "Zzzz",  # 11F5A..11FAF ; Unknown
-    "Lisu",  # 11FB0..11FB0 ; Lisu
-    "Zzzz",  # 11FB1..11FBF ; Unknown
-    "Taml",  # 11FC0..11FF1 ; Tamil
-    "Zzzz",  # 11FF2..11FFE ; Unknown
-    "Taml",  # 11FFF..11FFF ; Tamil
-    "Xsux",  # 12000..12399 ; Cuneiform
-    "Zzzz",  # 1239A..123FF ; Unknown
-    "Xsux",  # 12400..1246E ; Cuneiform
-    "Zzzz",  # 1246F..1246F ; Unknown
-    "Xsux",  # 12470..12474 ; Cuneiform
-    "Zzzz",  # 12475..1247F ; Unknown
-    "Xsux",  # 12480..12543 ; Cuneiform
-    "Zzzz",  # 12544..12F8F ; Unknown
-    "Cpmn",  # 12F90..12FF2 ; Cypro_Minoan
-    "Zzzz",  # 12FF3..12FFF ; Unknown
-    "Egyp",  # 13000..13455 ; Egyptian_Hieroglyphs
-    "Zzzz",  # 13456..143FF ; Unknown
-    "Hluw",  # 14400..14646 ; Anatolian_Hieroglyphs
-    "Zzzz",  # 14647..167FF ; Unknown
-    "Bamu",  # 16800..16A38 ; Bamum
-    "Zzzz",  # 16A39..16A3F ; Unknown
-    "Mroo",  # 16A40..16A5E ; Mro
-    "Zzzz",  # 16A5F..16A5F ; Unknown
-    "Mroo",  # 16A60..16A69 ; Mro
-    "Zzzz",  # 16A6A..16A6D ; Unknown
-    "Mroo",  # 16A6E..16A6F ; Mro
-    "Tnsa",  # 16A70..16ABE ; Tangsa
-    "Zzzz",  # 16ABF..16ABF ; Unknown
-    "Tnsa",  # 16AC0..16AC9 ; Tangsa
-    "Zzzz",  # 16ACA..16ACF ; Unknown
-    "Bass",  # 16AD0..16AED ; Bassa_Vah
-    "Zzzz",  # 16AEE..16AEF ; Unknown
-    "Bass",  # 16AF0..16AF5 ; Bassa_Vah
-    "Zzzz",  # 16AF6..16AFF ; Unknown
-    "Hmng",  # 16B00..16B45 ; Pahawh_Hmong
-    "Zzzz",  # 16B46..16B4F ; Unknown
-    "Hmng",  # 16B50..16B59 ; Pahawh_Hmong
-    "Zzzz",  # 16B5A..16B5A ; Unknown
-    "Hmng",  # 16B5B..16B61 ; Pahawh_Hmong
-    "Zzzz",  # 16B62..16B62 ; Unknown
-    "Hmng",  # 16B63..16B77 ; Pahawh_Hmong
-    "Zzzz",  # 16B78..16B7C ; Unknown
-    "Hmng",  # 16B7D..16B8F ; Pahawh_Hmong
-    "Zzzz",  # 16B90..16E3F ; Unknown
-    "Medf",  # 16E40..16E9A ; Medefaidrin
-    "Zzzz",  # 16E9B..16EFF ; Unknown
-    "Plrd",  # 16F00..16F4A ; Miao
-    "Zzzz",  # 16F4B..16F4E ; Unknown
-    "Plrd",  # 16F4F..16F87 ; Miao
-    "Zzzz",  # 16F88..16F8E ; Unknown
-    "Plrd",  # 16F8F..16F9F ; Miao
-    "Zzzz",  # 16FA0..16FDF ; Unknown
-    "Tang",  # 16FE0..16FE0 ; Tangut
-    "Nshu",  # 16FE1..16FE1 ; Nushu
-    "Hani",  # 16FE2..16FE3 ; Han
-    "Kits",  # 16FE4..16FE4 ; Khitan_Small_Script
-    "Zzzz",  # 16FE5..16FEF ; Unknown
-    "Hani",  # 16FF0..16FF1 ; Han
-    "Zzzz",  # 16FF2..16FFF ; Unknown
-    "Tang",  # 17000..187F7 ; Tangut
-    "Zzzz",  # 187F8..187FF ; Unknown
-    "Tang",  # 18800..18AFF ; Tangut
-    "Kits",  # 18B00..18CD5 ; Khitan_Small_Script
-    "Zzzz",  # 18CD6..18CFF ; Unknown
-    "Tang",  # 18D00..18D08 ; Tangut
-    "Zzzz",  # 18D09..1AFEF ; Unknown
-    "Kana",  # 1AFF0..1AFF3 ; Katakana
-    "Zzzz",  # 1AFF4..1AFF4 ; Unknown
-    "Kana",  # 1AFF5..1AFFB ; Katakana
-    "Zzzz",  # 1AFFC..1AFFC ; Unknown
-    "Kana",  # 1AFFD..1AFFE ; Katakana
-    "Zzzz",  # 1AFFF..1AFFF ; Unknown
-    "Kana",  # 1B000..1B000 ; Katakana
-    "Hira",  # 1B001..1B11F ; Hiragana
-    "Kana",  # 1B120..1B122 ; Katakana
-    "Zzzz",  # 1B123..1B131 ; Unknown
-    "Hira",  # 1B132..1B132 ; Hiragana
-    "Zzzz",  # 1B133..1B14F ; Unknown
-    "Hira",  # 1B150..1B152 ; Hiragana
-    "Zzzz",  # 1B153..1B154 ; Unknown
-    "Kana",  # 1B155..1B155 ; Katakana
-    "Zzzz",  # 1B156..1B163 ; Unknown
-    "Kana",  # 1B164..1B167 ; Katakana
-    "Zzzz",  # 1B168..1B16F ; Unknown
-    "Nshu",  # 1B170..1B2FB ; Nushu
-    "Zzzz",  # 1B2FC..1BBFF ; Unknown
-    "Dupl",  # 1BC00..1BC6A ; Duployan
-    "Zzzz",  # 1BC6B..1BC6F ; Unknown
-    "Dupl",  # 1BC70..1BC7C ; Duployan
-    "Zzzz",  # 1BC7D..1BC7F ; Unknown
-    "Dupl",  # 1BC80..1BC88 ; Duployan
-    "Zzzz",  # 1BC89..1BC8F ; Unknown
-    "Dupl",  # 1BC90..1BC99 ; Duployan
-    "Zzzz",  # 1BC9A..1BC9B ; Unknown
-    "Dupl",  # 1BC9C..1BC9F ; Duployan
-    "Zyyy",  # 1BCA0..1BCA3 ; Common
-    "Zzzz",  # 1BCA4..1CEFF ; Unknown
-    "Zinh",  # 1CF00..1CF2D ; Inherited
-    "Zzzz",  # 1CF2E..1CF2F ; Unknown
-    "Zinh",  # 1CF30..1CF46 ; Inherited
-    "Zzzz",  # 1CF47..1CF4F ; Unknown
-    "Zyyy",  # 1CF50..1CFC3 ; Common
-    "Zzzz",  # 1CFC4..1CFFF ; Unknown
-    "Zyyy",  # 1D000..1D0F5 ; Common
-    "Zzzz",  # 1D0F6..1D0FF ; Unknown
-    "Zyyy",  # 1D100..1D126 ; Common
-    "Zzzz",  # 1D127..1D128 ; Unknown
-    "Zyyy",  # 1D129..1D166 ; Common
-    "Zinh",  # 1D167..1D169 ; Inherited
-    "Zyyy",  # 1D16A..1D17A ; Common
-    "Zinh",  # 1D17B..1D182 ; Inherited
-    "Zyyy",  # 1D183..1D184 ; Common
-    "Zinh",  # 1D185..1D18B ; Inherited
-    "Zyyy",  # 1D18C..1D1A9 ; Common
-    "Zinh",  # 1D1AA..1D1AD ; Inherited
-    "Zyyy",  # 1D1AE..1D1EA ; Common
-    "Zzzz",  # 1D1EB..1D1FF ; Unknown
-    "Grek",  # 1D200..1D245 ; Greek
-    "Zzzz",  # 1D246..1D2BF ; Unknown
-    "Zyyy",  # 1D2C0..1D2D3 ; Common
-    "Zzzz",  # 1D2D4..1D2DF ; Unknown
-    "Zyyy",  # 1D2E0..1D2F3 ; Common
-    "Zzzz",  # 1D2F4..1D2FF ; Unknown
-    "Zyyy",  # 1D300..1D356 ; Common
-    "Zzzz",  # 1D357..1D35F ; Unknown
-    "Zyyy",  # 1D360..1D378 ; Common
-    "Zzzz",  # 1D379..1D3FF ; Unknown
-    "Zyyy",  # 1D400..1D454 ; Common
-    "Zzzz",  # 1D455..1D455 ; Unknown
-    "Zyyy",  # 1D456..1D49C ; Common
-    "Zzzz",  # 1D49D..1D49D ; Unknown
-    "Zyyy",  # 1D49E..1D49F ; Common
-    "Zzzz",  # 1D4A0..1D4A1 ; Unknown
-    "Zyyy",  # 1D4A2..1D4A2 ; Common
-    "Zzzz",  # 1D4A3..1D4A4 ; Unknown
-    "Zyyy",  # 1D4A5..1D4A6 ; Common
-    "Zzzz",  # 1D4A7..1D4A8 ; Unknown
-    "Zyyy",  # 1D4A9..1D4AC ; Common
-    "Zzzz",  # 1D4AD..1D4AD ; Unknown
-    "Zyyy",  # 1D4AE..1D4B9 ; Common
-    "Zzzz",  # 1D4BA..1D4BA ; Unknown
-    "Zyyy",  # 1D4BB..1D4BB ; Common
-    "Zzzz",  # 1D4BC..1D4BC ; Unknown
-    "Zyyy",  # 1D4BD..1D4C3 ; Common
-    "Zzzz",  # 1D4C4..1D4C4 ; Unknown
-    "Zyyy",  # 1D4C5..1D505 ; Common
-    "Zzzz",  # 1D506..1D506 ; Unknown
-    "Zyyy",  # 1D507..1D50A ; Common
-    "Zzzz",  # 1D50B..1D50C ; Unknown
-    "Zyyy",  # 1D50D..1D514 ; Common
-    "Zzzz",  # 1D515..1D515 ; Unknown
-    "Zyyy",  # 1D516..1D51C ; Common
-    "Zzzz",  # 1D51D..1D51D ; Unknown
-    "Zyyy",  # 1D51E..1D539 ; Common
-    "Zzzz",  # 1D53A..1D53A ; Unknown
-    "Zyyy",  # 1D53B..1D53E ; Common
-    "Zzzz",  # 1D53F..1D53F ; Unknown
-    "Zyyy",  # 1D540..1D544 ; Common
-    "Zzzz",  # 1D545..1D545 ; Unknown
-    "Zyyy",  # 1D546..1D546 ; Common
-    "Zzzz",  # 1D547..1D549 ; Unknown
-    "Zyyy",  # 1D54A..1D550 ; Common
-    "Zzzz",  # 1D551..1D551 ; Unknown
-    "Zyyy",  # 1D552..1D6A5 ; Common
-    "Zzzz",  # 1D6A6..1D6A7 ; Unknown
-    "Zyyy",  # 1D6A8..1D7CB ; Common
-    "Zzzz",  # 1D7CC..1D7CD ; Unknown
-    "Zyyy",  # 1D7CE..1D7FF ; Common
-    "Sgnw",  # 1D800..1DA8B ; SignWriting
-    "Zzzz",  # 1DA8C..1DA9A ; Unknown
-    "Sgnw",  # 1DA9B..1DA9F ; SignWriting
-    "Zzzz",  # 1DAA0..1DAA0 ; Unknown
-    "Sgnw",  # 1DAA1..1DAAF ; SignWriting
-    "Zzzz",  # 1DAB0..1DEFF ; Unknown
-    "Latn",  # 1DF00..1DF1E ; Latin
-    "Zzzz",  # 1DF1F..1DF24 ; Unknown
-    "Latn",  # 1DF25..1DF2A ; Latin
-    "Zzzz",  # 1DF2B..1DFFF ; Unknown
-    "Glag",  # 1E000..1E006 ; Glagolitic
-    "Zzzz",  # 1E007..1E007 ; Unknown
-    "Glag",  # 1E008..1E018 ; Glagolitic
-    "Zzzz",  # 1E019..1E01A ; Unknown
-    "Glag",  # 1E01B..1E021 ; Glagolitic
-    "Zzzz",  # 1E022..1E022 ; Unknown
-    "Glag",  # 1E023..1E024 ; Glagolitic
-    "Zzzz",  # 1E025..1E025 ; Unknown
-    "Glag",  # 1E026..1E02A ; Glagolitic
-    "Zzzz",  # 1E02B..1E02F ; Unknown
-    "Cyrl",  # 1E030..1E06D ; Cyrillic
-    "Zzzz",  # 1E06E..1E08E ; Unknown
-    "Cyrl",  # 1E08F..1E08F ; Cyrillic
-    "Zzzz",  # 1E090..1E0FF ; Unknown
-    "Hmnp",  # 1E100..1E12C ; Nyiakeng_Puachue_Hmong
-    "Zzzz",  # 1E12D..1E12F ; Unknown
-    "Hmnp",  # 1E130..1E13D ; Nyiakeng_Puachue_Hmong
-    "Zzzz",  # 1E13E..1E13F ; Unknown
-    "Hmnp",  # 1E140..1E149 ; Nyiakeng_Puachue_Hmong
-    "Zzzz",  # 1E14A..1E14D ; Unknown
-    "Hmnp",  # 1E14E..1E14F ; Nyiakeng_Puachue_Hmong
-    "Zzzz",  # 1E150..1E28F ; Unknown
-    "Toto",  # 1E290..1E2AE ; Toto
-    "Zzzz",  # 1E2AF..1E2BF ; Unknown
-    "Wcho",  # 1E2C0..1E2F9 ; Wancho
-    "Zzzz",  # 1E2FA..1E2FE ; Unknown
-    "Wcho",  # 1E2FF..1E2FF ; Wancho
-    "Zzzz",  # 1E300..1E4CF ; Unknown
-    "Nagm",  # 1E4D0..1E4F9 ; Nag_Mundari
-    "Zzzz",  # 1E4FA..1E7DF ; Unknown
-    "Ethi",  # 1E7E0..1E7E6 ; Ethiopic
-    "Zzzz",  # 1E7E7..1E7E7 ; Unknown
-    "Ethi",  # 1E7E8..1E7EB ; Ethiopic
-    "Zzzz",  # 1E7EC..1E7EC ; Unknown
-    "Ethi",  # 1E7ED..1E7EE ; Ethiopic
-    "Zzzz",  # 1E7EF..1E7EF ; Unknown
-    "Ethi",  # 1E7F0..1E7FE ; Ethiopic
-    "Zzzz",  # 1E7FF..1E7FF ; Unknown
-    "Mend",  # 1E800..1E8C4 ; Mende_Kikakui
-    "Zzzz",  # 1E8C5..1E8C6 ; Unknown
-    "Mend",  # 1E8C7..1E8D6 ; Mende_Kikakui
-    "Zzzz",  # 1E8D7..1E8FF ; Unknown
-    "Adlm",  # 1E900..1E94B ; Adlam
-    "Zzzz",  # 1E94C..1E94F ; Unknown
-    "Adlm",  # 1E950..1E959 ; Adlam
-    "Zzzz",  # 1E95A..1E95D ; Unknown
-    "Adlm",  # 1E95E..1E95F ; Adlam
-    "Zzzz",  # 1E960..1EC70 ; Unknown
-    "Zyyy",  # 1EC71..1ECB4 ; Common
-    "Zzzz",  # 1ECB5..1ED00 ; Unknown
-    "Zyyy",  # 1ED01..1ED3D ; Common
-    "Zzzz",  # 1ED3E..1EDFF ; Unknown
-    "Arab",  # 1EE00..1EE03 ; Arabic
-    "Zzzz",  # 1EE04..1EE04 ; Unknown
-    "Arab",  # 1EE05..1EE1F ; Arabic
-    "Zzzz",  # 1EE20..1EE20 ; Unknown
-    "Arab",  # 1EE21..1EE22 ; Arabic
-    "Zzzz",  # 1EE23..1EE23 ; Unknown
-    "Arab",  # 1EE24..1EE24 ; Arabic
-    "Zzzz",  # 1EE25..1EE26 ; Unknown
-    "Arab",  # 1EE27..1EE27 ; Arabic
-    "Zzzz",  # 1EE28..1EE28 ; Unknown
-    "Arab",  # 1EE29..1EE32 ; Arabic
-    "Zzzz",  # 1EE33..1EE33 ; Unknown
-    "Arab",  # 1EE34..1EE37 ; Arabic
-    "Zzzz",  # 1EE38..1EE38 ; Unknown
-    "Arab",  # 1EE39..1EE39 ; Arabic
-    "Zzzz",  # 1EE3A..1EE3A ; Unknown
-    "Arab",  # 1EE3B..1EE3B ; Arabic
-    "Zzzz",  # 1EE3C..1EE41 ; Unknown
-    "Arab",  # 1EE42..1EE42 ; Arabic
-    "Zzzz",  # 1EE43..1EE46 ; Unknown
-    "Arab",  # 1EE47..1EE47 ; Arabic
-    "Zzzz",  # 1EE48..1EE48 ; Unknown
-    "Arab",  # 1EE49..1EE49 ; Arabic
-    "Zzzz",  # 1EE4A..1EE4A ; Unknown
-    "Arab",  # 1EE4B..1EE4B ; Arabic
-    "Zzzz",  # 1EE4C..1EE4C ; Unknown
-    "Arab",  # 1EE4D..1EE4F ; Arabic
-    "Zzzz",  # 1EE50..1EE50 ; Unknown
-    "Arab",  # 1EE51..1EE52 ; Arabic
-    "Zzzz",  # 1EE53..1EE53 ; Unknown
-    "Arab",  # 1EE54..1EE54 ; Arabic
-    "Zzzz",  # 1EE55..1EE56 ; Unknown
-    "Arab",  # 1EE57..1EE57 ; Arabic
-    "Zzzz",  # 1EE58..1EE58 ; Unknown
-    "Arab",  # 1EE59..1EE59 ; Arabic
-    "Zzzz",  # 1EE5A..1EE5A ; Unknown
-    "Arab",  # 1EE5B..1EE5B ; Arabic
-    "Zzzz",  # 1EE5C..1EE5C ; Unknown
-    "Arab",  # 1EE5D..1EE5D ; Arabic
-    "Zzzz",  # 1EE5E..1EE5E ; Unknown
-    "Arab",  # 1EE5F..1EE5F ; Arabic
-    "Zzzz",  # 1EE60..1EE60 ; Unknown
-    "Arab",  # 1EE61..1EE62 ; Arabic
-    "Zzzz",  # 1EE63..1EE63 ; Unknown
-    "Arab",  # 1EE64..1EE64 ; Arabic
-    "Zzzz",  # 1EE65..1EE66 ; Unknown
-    "Arab",  # 1EE67..1EE6A ; Arabic
-    "Zzzz",  # 1EE6B..1EE6B ; Unknown
-    "Arab",  # 1EE6C..1EE72 ; Arabic
-    "Zzzz",  # 1EE73..1EE73 ; Unknown
-    "Arab",  # 1EE74..1EE77 ; Arabic
-    "Zzzz",  # 1EE78..1EE78 ; Unknown
-    "Arab",  # 1EE79..1EE7C ; Arabic
-    "Zzzz",  # 1EE7D..1EE7D ; Unknown
-    "Arab",  # 1EE7E..1EE7E ; Arabic
-    "Zzzz",  # 1EE7F..1EE7F ; Unknown
-    "Arab",  # 1EE80..1EE89 ; Arabic
-    "Zzzz",  # 1EE8A..1EE8A ; Unknown
-    "Arab",  # 1EE8B..1EE9B ; Arabic
-    "Zzzz",  # 1EE9C..1EEA0 ; Unknown
-    "Arab",  # 1EEA1..1EEA3 ; Arabic
-    "Zzzz",  # 1EEA4..1EEA4 ; Unknown
-    "Arab",  # 1EEA5..1EEA9 ; Arabic
-    "Zzzz",  # 1EEAA..1EEAA ; Unknown
-    "Arab",  # 1EEAB..1EEBB ; Arabic
-    "Zzzz",  # 1EEBC..1EEEF ; Unknown
-    "Arab",  # 1EEF0..1EEF1 ; Arabic
-    "Zzzz",  # 1EEF2..1EFFF ; Unknown
-    "Zyyy",  # 1F000..1F02B ; Common
-    "Zzzz",  # 1F02C..1F02F ; Unknown
-    "Zyyy",  # 1F030..1F093 ; Common
-    "Zzzz",  # 1F094..1F09F ; Unknown
-    "Zyyy",  # 1F0A0..1F0AE ; Common
-    "Zzzz",  # 1F0AF..1F0B0 ; Unknown
-    "Zyyy",  # 1F0B1..1F0BF ; Common
-    "Zzzz",  # 1F0C0..1F0C0 ; Unknown
-    "Zyyy",  # 1F0C1..1F0CF ; Common
-    "Zzzz",  # 1F0D0..1F0D0 ; Unknown
-    "Zyyy",  # 1F0D1..1F0F5 ; Common
-    "Zzzz",  # 1F0F6..1F0FF ; Unknown
-    "Zyyy",  # 1F100..1F1AD ; Common
-    "Zzzz",  # 1F1AE..1F1E5 ; Unknown
-    "Zyyy",  # 1F1E6..1F1FF ; Common
-    "Hira",  # 1F200..1F200 ; Hiragana
-    "Zyyy",  # 1F201..1F202 ; Common
-    "Zzzz",  # 1F203..1F20F ; Unknown
-    "Zyyy",  # 1F210..1F23B ; Common
-    "Zzzz",  # 1F23C..1F23F ; Unknown
-    "Zyyy",  # 1F240..1F248 ; Common
-    "Zzzz",  # 1F249..1F24F ; Unknown
-    "Zyyy",  # 1F250..1F251 ; Common
-    "Zzzz",  # 1F252..1F25F ; Unknown
-    "Zyyy",  # 1F260..1F265 ; Common
-    "Zzzz",  # 1F266..1F2FF ; Unknown
-    "Zyyy",  # 1F300..1F6D7 ; Common
-    "Zzzz",  # 1F6D8..1F6DB ; Unknown
-    "Zyyy",  # 1F6DC..1F6EC ; Common
-    "Zzzz",  # 1F6ED..1F6EF ; Unknown
-    "Zyyy",  # 1F6F0..1F6FC ; Common
-    "Zzzz",  # 1F6FD..1F6FF ; Unknown
-    "Zyyy",  # 1F700..1F776 ; Common
-    "Zzzz",  # 1F777..1F77A ; Unknown
-    "Zyyy",  # 1F77B..1F7D9 ; Common
-    "Zzzz",  # 1F7DA..1F7DF ; Unknown
-    "Zyyy",  # 1F7E0..1F7EB ; Common
-    "Zzzz",  # 1F7EC..1F7EF ; Unknown
-    "Zyyy",  # 1F7F0..1F7F0 ; Common
-    "Zzzz",  # 1F7F1..1F7FF ; Unknown
-    "Zyyy",  # 1F800..1F80B ; Common
-    "Zzzz",  # 1F80C..1F80F ; Unknown
-    "Zyyy",  # 1F810..1F847 ; Common
-    "Zzzz",  # 1F848..1F84F ; Unknown
-    "Zyyy",  # 1F850..1F859 ; Common
-    "Zzzz",  # 1F85A..1F85F ; Unknown
-    "Zyyy",  # 1F860..1F887 ; Common
-    "Zzzz",  # 1F888..1F88F ; Unknown
-    "Zyyy",  # 1F890..1F8AD ; Common
-    "Zzzz",  # 1F8AE..1F8AF ; Unknown
-    "Zyyy",  # 1F8B0..1F8B1 ; Common
-    "Zzzz",  # 1F8B2..1F8FF ; Unknown
-    "Zyyy",  # 1F900..1FA53 ; Common
-    "Zzzz",  # 1FA54..1FA5F ; Unknown
-    "Zyyy",  # 1FA60..1FA6D ; Common
-    "Zzzz",  # 1FA6E..1FA6F ; Unknown
-    "Zyyy",  # 1FA70..1FA7C ; Common
-    "Zzzz",  # 1FA7D..1FA7F ; Unknown
-    "Zyyy",  # 1FA80..1FA88 ; Common
-    "Zzzz",  # 1FA89..1FA8F ; Unknown
-    "Zyyy",  # 1FA90..1FABD ; Common
-    "Zzzz",  # 1FABE..1FABE ; Unknown
-    "Zyyy",  # 1FABF..1FAC5 ; Common
-    "Zzzz",  # 1FAC6..1FACD ; Unknown
-    "Zyyy",  # 1FACE..1FADB ; Common
-    "Zzzz",  # 1FADC..1FADF ; Unknown
-    "Zyyy",  # 1FAE0..1FAE8 ; Common
-    "Zzzz",  # 1FAE9..1FAEF ; Unknown
-    "Zyyy",  # 1FAF0..1FAF8 ; Common
-    "Zzzz",  # 1FAF9..1FAFF ; Unknown
-    "Zyyy",  # 1FB00..1FB92 ; Common
-    "Zzzz",  # 1FB93..1FB93 ; Unknown
-    "Zyyy",  # 1FB94..1FBCA ; Common
-    "Zzzz",  # 1FBCB..1FBEF ; Unknown
-    "Zyyy",  # 1FBF0..1FBF9 ; Common
-    "Zzzz",  # 1FBFA..1FFFF ; Unknown
-    "Hani",  # 20000..2A6DF ; Han
-    "Zzzz",  # 2A6E0..2A6FF ; Unknown
-    "Hani",  # 2A700..2B739 ; Han
-    "Zzzz",  # 2B73A..2B73F ; Unknown
-    "Hani",  # 2B740..2B81D ; Han
-    "Zzzz",  # 2B81E..2B81F ; Unknown
-    "Hani",  # 2B820..2CEA1 ; Han
-    "Zzzz",  # 2CEA2..2CEAF ; Unknown
-    "Hani",  # 2CEB0..2EBE0 ; Han
-    "Zzzz",  # 2EBE1..2F7FF ; Unknown
-    "Hani",  # 2F800..2FA1D ; Han
-    "Zzzz",  # 2FA1E..2FFFF ; Unknown
-    "Hani",  # 30000..3134A ; Han
-    "Zzzz",  # 3134B..3134F ; Unknown
-    "Hani",  # 31350..323AF ; Han
-    "Zzzz",  # 323B0..E0000 ; Unknown
-    "Zyyy",  # E0001..E0001 ; Common
-    "Zzzz",  # E0002..E001F ; Unknown
-    "Zyyy",  # E0020..E007F ; Common
-    "Zzzz",  # E0080..E00FF ; Unknown
-    "Zinh",  # E0100..E01EF ; Inherited
-    "Zzzz",  # E01F0..10FFFF ; Unknown
-]
-
-NAMES = {
-    "Adlm": "Adlam",
-    "Aghb": "Caucasian_Albanian",
-    "Ahom": "Ahom",
-    "Arab": "Arabic",
-    "Armi": "Imperial_Aramaic",
-    "Armn": "Armenian",
-    "Avst": "Avestan",
-    "Bali": "Balinese",
-    "Bamu": "Bamum",
-    "Bass": "Bassa_Vah",
-    "Batk": "Batak",
-    "Beng": "Bengali",
-    "Bhks": "Bhaiksuki",
-    "Bopo": "Bopomofo",
-    "Brah": "Brahmi",
-    "Brai": "Braille",
-    "Bugi": "Buginese",
-    "Buhd": "Buhid",
-    "Cakm": "Chakma",
-    "Cans": "Canadian_Aboriginal",
-    "Cari": "Carian",
-    "Cham": "Cham",
-    "Cher": "Cherokee",
-    "Chrs": "Chorasmian",
-    "Copt": "Coptic",
-    "Cpmn": "Cypro_Minoan",
-    "Cprt": "Cypriot",
-    "Cyrl": "Cyrillic",
-    "Deva": "Devanagari",
-    "Diak": "Dives_Akuru",
-    "Dogr": "Dogra",
-    "Dsrt": "Deseret",
-    "Dupl": "Duployan",
-    "Egyp": "Egyptian_Hieroglyphs",
-    "Elba": "Elbasan",
-    "Elym": "Elymaic",
-    "Ethi": "Ethiopic",
-    "Geor": "Georgian",
-    "Glag": "Glagolitic",
-    "Gong": "Gunjala_Gondi",
-    "Gonm": "Masaram_Gondi",
-    "Goth": "Gothic",
-    "Gran": "Grantha",
-    "Grek": "Greek",
-    "Gujr": "Gujarati",
-    "Guru": "Gurmukhi",
-    "Hang": "Hangul",
-    "Hani": "Han",
-    "Hano": "Hanunoo",
-    "Hatr": "Hatran",
-    "Hebr": "Hebrew",
-    "Hira": "Hiragana",
-    "Hluw": "Anatolian_Hieroglyphs",
-    "Hmng": "Pahawh_Hmong",
-    "Hmnp": "Nyiakeng_Puachue_Hmong",
-    "Hrkt": "Katakana_Or_Hiragana",
-    "Hung": "Old_Hungarian",
-    "Ital": "Old_Italic",
-    "Java": "Javanese",
-    "Kali": "Kayah_Li",
-    "Kana": "Katakana",
-    "Kawi": "Kawi",
-    "Khar": "Kharoshthi",
-    "Khmr": "Khmer",
-    "Khoj": "Khojki",
-    "Kits": "Khitan_Small_Script",
-    "Knda": "Kannada",
-    "Kthi": "Kaithi",
-    "Lana": "Tai_Tham",
-    "Laoo": "Lao",
-    "Latn": "Latin",
-    "Lepc": "Lepcha",
-    "Limb": "Limbu",
-    "Lina": "Linear_A",
-    "Linb": "Linear_B",
-    "Lisu": "Lisu",
-    "Lyci": "Lycian",
-    "Lydi": "Lydian",
-    "Mahj": "Mahajani",
-    "Maka": "Makasar",
-    "Mand": "Mandaic",
-    "Mani": "Manichaean",
-    "Marc": "Marchen",
-    "Medf": "Medefaidrin",
-    "Mend": "Mende_Kikakui",
-    "Merc": "Meroitic_Cursive",
-    "Mero": "Meroitic_Hieroglyphs",
-    "Mlym": "Malayalam",
-    "Modi": "Modi",
-    "Mong": "Mongolian",
-    "Mroo": "Mro",
-    "Mtei": "Meetei_Mayek",
-    "Mult": "Multani",
-    "Mymr": "Myanmar",
-    "Nagm": "Nag_Mundari",
-    "Nand": "Nandinagari",
-    "Narb": "Old_North_Arabian",
-    "Nbat": "Nabataean",
-    "Newa": "Newa",
-    "Nkoo": "Nko",
-    "Nshu": "Nushu",
-    "Ogam": "Ogham",
-    "Olck": "Ol_Chiki",
-    "Orkh": "Old_Turkic",
-    "Orya": "Oriya",
-    "Osge": "Osage",
-    "Osma": "Osmanya",
-    "Ougr": "Old_Uyghur",
-    "Palm": "Palmyrene",
-    "Pauc": "Pau_Cin_Hau",
-    "Perm": "Old_Permic",
-    "Phag": "Phags_Pa",
-    "Phli": "Inscriptional_Pahlavi",
-    "Phlp": "Psalter_Pahlavi",
-    "Phnx": "Phoenician",
-    "Plrd": "Miao",
-    "Prti": "Inscriptional_Parthian",
-    "Rjng": "Rejang",
-    "Rohg": "Hanifi_Rohingya",
-    "Runr": "Runic",
-    "Samr": "Samaritan",
-    "Sarb": "Old_South_Arabian",
-    "Saur": "Saurashtra",
-    "Sgnw": "SignWriting",
-    "Shaw": "Shavian",
-    "Shrd": "Sharada",
-    "Sidd": "Siddham",
-    "Sind": "Khudawadi",
-    "Sinh": "Sinhala",
-    "Sogd": "Sogdian",
-    "Sogo": "Old_Sogdian",
-    "Sora": "Sora_Sompeng",
-    "Soyo": "Soyombo",
-    "Sund": "Sundanese",
-    "Sylo": "Syloti_Nagri",
-    "Syrc": "Syriac",
-    "Tagb": "Tagbanwa",
-    "Takr": "Takri",
-    "Tale": "Tai_Le",
-    "Talu": "New_Tai_Lue",
-    "Taml": "Tamil",
-    "Tang": "Tangut",
-    "Tavt": "Tai_Viet",
-    "Telu": "Telugu",
-    "Tfng": "Tifinagh",
-    "Tglg": "Tagalog",
-    "Thaa": "Thaana",
-    "Thai": "Thai",
-    "Tibt": "Tibetan",
-    "Tirh": "Tirhuta",
-    "Tnsa": "Tangsa",
-    "Toto": "Toto",
-    "Ugar": "Ugaritic",
-    "Vaii": "Vai",
-    "Vith": "Vithkuqi",
-    "Wara": "Warang_Citi",
-    "Wcho": "Wancho",
-    "Xpeo": "Old_Persian",
-    "Xsux": "Cuneiform",
-    "Yezi": "Yezidi",
-    "Yiii": "Yi",
-    "Zanb": "Zanabazar_Square",
-    "Zinh": "Inherited",
-    "Zyyy": "Common",
-    "Zzzz": "Unknown",
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/voltLib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/voltLib/__init__.py
deleted file mode 100644
index 886aa3a7864523656e609dd602683d73f985f467..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/voltLib/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""fontTools.voltLib -- a package for dealing with Visual OpenType Layout Tool
-(VOLT) files."""
-
-# See
-# http://www.microsoft.com/typography/VOLT.mspx
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/web_backend/js/nbagg_mpl.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/web_backend/js/nbagg_mpl.js
deleted file mode 100644
index 26d79ff4e1da11d3c362f748e75b22026cc8438a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/web_backend/js/nbagg_mpl.js
+++ /dev/null
@@ -1,275 +0,0 @@
-/* global mpl */
-
-var comm_websocket_adapter = function (comm) {
-    // Create a "websocket"-like object which calls the given IPython comm
-    // object with the appropriate methods. Currently this is a non binary
-    // socket, so there is still some room for performance tuning.
-    var ws = {};
-
-    ws.binaryType = comm.kernel.ws.binaryType;
-    ws.readyState = comm.kernel.ws.readyState;
-    function updateReadyState(_event) {
-        if (comm.kernel.ws) {
-            ws.readyState = comm.kernel.ws.readyState;
-        } else {
-            ws.readyState = 3; // Closed state.
-        }
-    }
-    comm.kernel.ws.addEventListener('open', updateReadyState);
-    comm.kernel.ws.addEventListener('close', updateReadyState);
-    comm.kernel.ws.addEventListener('error', updateReadyState);
-
-    ws.close = function () {
-        comm.close();
-    };
-    ws.send = function (m) {
-        //console.log('sending', m);
-        comm.send(m);
-    };
-    // Register the callback with on_msg.
-    comm.on_msg(function (msg) {
-        //console.log('receiving', msg['content']['data'], msg);
-        var data = msg['content']['data'];
-        if (data['blob'] !== undefined) {
-            data = {
-                data: new Blob(msg['buffers'], { type: data['blob'] }),
-            };
-        }
-        // Pass the mpl event to the overridden (by mpl) onmessage function.
-        ws.onmessage(data);
-    });
-    return ws;
-};
-
-mpl.mpl_figure_comm = function (comm, msg) {
-    // This is the function which gets called when the mpl process
-    // starts-up an IPython Comm through the "matplotlib" channel.
-
-    var id = msg.content.data.id;
-    // Get hold of the div created by the display call when the Comm
-    // socket was opened in Python.
-    var element = document.getElementById(id);
-    var ws_proxy = comm_websocket_adapter(comm);
-
-    function ondownload(figure, _format) {
-        window.open(figure.canvas.toDataURL());
-    }
-
-    var fig = new mpl.figure(id, ws_proxy, ondownload, element);
-
-    // Call onopen now - mpl needs it, as it is assuming we've passed it a real
-    // web socket which is closed, not our websocket->open comm proxy.
-    ws_proxy.onopen();
-
-    fig.parent_element = element;
-    fig.cell_info = mpl.find_output_cell("<div id='" + id + "'></div>");
-    if (!fig.cell_info) {
-        console.error('Failed to find cell for figure', id, fig);
-        return;
-    }
-    fig.cell_info[0].output_area.element.on(
-        'cleared',
-        { fig: fig },
-        fig._remove_fig_handler
-    );
-};
-
-mpl.figure.prototype.handle_close = function (fig, msg) {
-    var width = fig.canvas.width / fig.ratio;
-    fig.cell_info[0].output_area.element.off(
-        'cleared',
-        fig._remove_fig_handler
-    );
-    fig.resizeObserverInstance.unobserve(fig.canvas_div);
-
-    // Update the output cell to use the data from the current canvas.
-    fig.push_to_output();
-    var dataURL = fig.canvas.toDataURL();
-    // Re-enable the keyboard manager in IPython - without this line, in FF,
-    // the notebook keyboard shortcuts fail.
-    IPython.keyboard_manager.enable();
-    fig.parent_element.innerHTML =
-        '<img src="' + dataURL + '" width="' + width + '">';
-    fig.close_ws(fig, msg);
-};
-
-mpl.figure.prototype.close_ws = function (fig, msg) {
-    fig.send_message('closing', msg);
-    // fig.ws.close()
-};
-
-mpl.figure.prototype.push_to_output = function (_remove_interactive) {
-    // Turn the data on the canvas into data in the output cell.
-    var width = this.canvas.width / this.ratio;
-    var dataURL = this.canvas.toDataURL();
-    this.cell_info[1]['text/html'] =
-        '<img src="' + dataURL + '" width="' + width + '">';
-};
-
-mpl.figure.prototype.updated_canvas_event = function () {
-    // Tell IPython that the notebook contents must change.
-    IPython.notebook.set_dirty(true);
-    this.send_message('ack', {});
-    var fig = this;
-    // Wait a second, then push the new image to the DOM so
-    // that it is saved nicely (might be nice to debounce this).
-    setTimeout(function () {
-        fig.push_to_output();
-    }, 1000);
-};
-
-mpl.figure.prototype._init_toolbar = function () {
-    var fig = this;
-
-    var toolbar = document.createElement('div');
-    toolbar.classList = 'btn-toolbar';
-    this.root.appendChild(toolbar);
-
-    function on_click_closure(name) {
-        return function (_event) {
-            return fig.toolbar_button_onclick(name);
-        };
-    }
-
-    function on_mouseover_closure(tooltip) {
-        return function (event) {
-            if (!event.currentTarget.disabled) {
-                return fig.toolbar_button_onmouseover(tooltip);
-            }
-        };
-    }
-
-    fig.buttons = {};
-    var buttonGroup = document.createElement('div');
-    buttonGroup.classList = 'btn-group';
-    var button;
-    for (var toolbar_ind in mpl.toolbar_items) {
-        var name = mpl.toolbar_items[toolbar_ind][0];
-        var tooltip = mpl.toolbar_items[toolbar_ind][1];
-        var image = mpl.toolbar_items[toolbar_ind][2];
-        var method_name = mpl.toolbar_items[toolbar_ind][3];
-
-        if (!name) {
-            /* Instead of a spacer, we start a new button group. */
-            if (buttonGroup.hasChildNodes()) {
-                toolbar.appendChild(buttonGroup);
-            }
-            buttonGroup = document.createElement('div');
-            buttonGroup.classList = 'btn-group';
-            continue;
-        }
-
-        button = fig.buttons[name] = document.createElement('button');
-        button.classList = 'btn btn-default';
-        button.href = '#';
-        button.title = name;
-        button.innerHTML = '<i class="fa ' + image + ' fa-lg"></i>';
-        button.addEventListener('click', on_click_closure(method_name));
-        button.addEventListener('mouseover', on_mouseover_closure(tooltip));
-        buttonGroup.appendChild(button);
-    }
-
-    if (buttonGroup.hasChildNodes()) {
-        toolbar.appendChild(buttonGroup);
-    }
-
-    // Add the status bar.
-    var status_bar = document.createElement('span');
-    status_bar.classList = 'mpl-message pull-right';
-    toolbar.appendChild(status_bar);
-    this.message = status_bar;
-
-    // Add the close button to the window.
-    var buttongrp = document.createElement('div');
-    buttongrp.classList = 'btn-group inline pull-right';
-    button = document.createElement('button');
-    button.classList = 'btn btn-mini btn-primary';
-    button.href = '#';
-    button.title = 'Stop Interaction';
-    button.innerHTML = '<i class="fa fa-power-off icon-remove icon-large"></i>';
-    button.addEventListener('click', function (_evt) {
-        fig.handle_close(fig, {});
-    });
-    button.addEventListener(
-        'mouseover',
-        on_mouseover_closure('Stop Interaction')
-    );
-    buttongrp.appendChild(button);
-    var titlebar = this.root.querySelector('.ui-dialog-titlebar');
-    titlebar.insertBefore(buttongrp, titlebar.firstChild);
-};
-
-mpl.figure.prototype._remove_fig_handler = function (event) {
-    var fig = event.data.fig;
-    if (event.target !== this) {
-        // Ignore bubbled events from children.
-        return;
-    }
-    fig.close_ws(fig, {});
-};
-
-mpl.figure.prototype._root_extra_style = function (el) {
-    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.
-};
-
-mpl.figure.prototype._canvas_extra_style = function (el) {
-    // this is important to make the div 'focusable
-    el.setAttribute('tabindex', 0);
-    // reach out to IPython and tell the keyboard manager to turn it's self
-    // off when our div gets focus
-
-    // location in version 3
-    if (IPython.notebook.keyboard_manager) {
-        IPython.notebook.keyboard_manager.register_events(el);
-    } else {
-        // location in version 2
-        IPython.keyboard_manager.register_events(el);
-    }
-};
-
-mpl.figure.prototype._key_event_extra = function (event, _name) {
-    // Check for shift+enter
-    if (event.shiftKey && event.which === 13) {
-        this.canvas_div.blur();
-        // select the cell after this one
-        var index = IPython.notebook.find_cell_index(this.cell_info[0]);
-        IPython.notebook.select(index + 1);
-    }
-};
-
-mpl.figure.prototype.handle_save = function (fig, _msg) {
-    fig.ondownload(fig, null);
-};
-
-mpl.find_output_cell = function (html_output) {
-    // Return the cell and output element which can be found *uniquely* in the notebook.
-    // Note - this is a bit hacky, but it is done because the "notebook_saving.Notebook"
-    // IPython event is triggered only after the cells have been serialised, which for
-    // our purposes (turning an active figure into a static one), is too late.
-    var cells = IPython.notebook.get_cells();
-    var ncells = cells.length;
-    for (var i = 0; i < ncells; i++) {
-        var cell = cells[i];
-        if (cell.cell_type === 'code') {
-            for (var j = 0; j < cell.output_area.outputs.length; j++) {
-                var data = cell.output_area.outputs[j];
-                if (data.data) {
-                    // IPython >= 3 moved mimebundle to data attribute of output
-                    data = data.data;
-                }
-                if (data['text/html'] === html_output) {
-                    return [cell, data, j];
-                }
-            }
-        }
-    }
-};
-
-// Register the function which deals with the matplotlib target/channel.
-// The kernel may be null if the page has been refreshed.
-if (IPython.notebook.kernel !== null) {
-    IPython.notebook.kernel.comm_manager.register_target(
-        'matplotlib',
-        mpl.mpl_figure_comm
-    );
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_longdouble.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_longdouble.py
deleted file mode 100644
index 45721950c0f19685d9c1075f95f7f126a43831aa..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_longdouble.py
+++ /dev/null
@@ -1,395 +0,0 @@
-import warnings
-import platform
-import pytest
-
-import numpy as np
-from numpy.testing import (
-    assert_, assert_equal, assert_raises, assert_warns, assert_array_equal,
-    temppath, IS_MUSL
-    )
-from numpy.core.tests._locales import CommaDecimalPointLocale
-
-
-LD_INFO = np.finfo(np.longdouble)
-longdouble_longer_than_double = (LD_INFO.eps < np.finfo(np.double).eps)
-
-
-_o = 1 + LD_INFO.eps
-string_to_longdouble_inaccurate = (_o != np.longdouble(repr(_o)))
-del _o
-
-
-def test_scalar_extraction():
-    """Confirm that extracting a value doesn't convert to python float"""
-    o = 1 + LD_INFO.eps
-    a = np.array([o, o, o])
-    assert_equal(a[1], o)
-
-
-# Conversions string -> long double
-
-# 0.1 not exactly representable in base 2 floating point.
-repr_precision = len(repr(np.longdouble(0.1)))
-# +2 from macro block starting around line 842 in scalartypes.c.src.
-
-
-@pytest.mark.skipif(IS_MUSL,
-                    reason="test flaky on musllinux")
-@pytest.mark.skipif(LD_INFO.precision + 2 >= repr_precision,
-                    reason="repr precision not enough to show eps")
-def test_repr_roundtrip():
-    # We will only see eps in repr if within printing precision.
-    o = 1 + LD_INFO.eps
-    assert_equal(np.longdouble(repr(o)), o, "repr was %s" % repr(o))
-
-
-@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")
-def test_repr_roundtrip_bytes():
-    o = 1 + LD_INFO.eps
-    assert_equal(np.longdouble(repr(o).encode("ascii")), o)
-
-
-@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")
-@pytest.mark.parametrize("strtype", (np.str_, np.bytes_, str, bytes))
-def test_array_and_stringlike_roundtrip(strtype):
-    """
-    Test that string representations of long-double roundtrip both
-    for array casting and scalar coercion, see also gh-15608.
-    """
-    o = 1 + LD_INFO.eps
-
-    if strtype in (np.bytes_, bytes):
-        o_str = strtype(repr(o).encode("ascii"))
-    else:
-        o_str = strtype(repr(o))
-
-    # Test that `o` is correctly coerced from the string-like
-    assert o == np.longdouble(o_str)
-
-    # Test that arrays also roundtrip correctly:
-    o_strarr = np.asarray([o] * 3, dtype=strtype)
-    assert (o == o_strarr.astype(np.longdouble)).all()
-
-    # And array coercion and casting to string give the same as scalar repr:
-    assert (o_strarr == o_str).all()
-    assert (np.asarray([o] * 3).astype(strtype) == o_str).all()
-
-
-def test_bogus_string():
-    assert_raises(ValueError, np.longdouble, "spam")
-    assert_raises(ValueError, np.longdouble, "1.0 flub")
-
-
-@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")
-def test_fromstring():
-    o = 1 + LD_INFO.eps
-    s = (" " + repr(o))*5
-    a = np.array([o]*5)
-    assert_equal(np.fromstring(s, sep=" ", dtype=np.longdouble), a,
-                 err_msg="reading '%s'" % s)
-
-
-def test_fromstring_complex():
-    for ctype in ["complex", "cdouble", "cfloat"]:
-        # Check spacing between separator
-        assert_equal(np.fromstring("1, 2 ,  3  ,4", sep=",", dtype=ctype),
-                     np.array([1., 2., 3., 4.]))
-        # Real component not specified
-        assert_equal(np.fromstring("1j, -2j,  3j, 4e1j", sep=",", dtype=ctype),
-                     np.array([1.j, -2.j, 3.j, 40.j]))
-        # Both components specified
-        assert_equal(np.fromstring("1+1j,2-2j, -3+3j,  -4e1+4j", sep=",", dtype=ctype),
-                     np.array([1. + 1.j, 2. - 2.j, - 3. + 3.j, - 40. + 4j]))
-        # Spaces at wrong places
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1+2 j,3", dtype=ctype, sep=","),
-                         np.array([1.]))
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1+ 2j,3", dtype=ctype, sep=","),
-                         np.array([1.]))
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1 +2j,3", dtype=ctype, sep=","),
-                         np.array([1.]))
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1+j", dtype=ctype, sep=","),
-                         np.array([1.]))
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1+", dtype=ctype, sep=","),
-                         np.array([1.]))
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1j+1", dtype=ctype, sep=","),
-                         np.array([1j]))
-
-
-def test_fromstring_bogus():
-    with assert_warns(DeprecationWarning):
-        assert_equal(np.fromstring("1. 2. 3. flop 4.", dtype=float, sep=" "),
-                     np.array([1., 2., 3.]))
-
-
-def test_fromstring_empty():
-    with assert_warns(DeprecationWarning):
-        assert_equal(np.fromstring("xxxxx", sep="x"),
-                     np.array([]))
-
-
-def test_fromstring_missing():
-    with assert_warns(DeprecationWarning):
-        assert_equal(np.fromstring("1xx3x4x5x6", sep="x"),
-                     np.array([1]))
-
-
-class TestFileBased:
-
-    ldbl = 1 + LD_INFO.eps
-    tgt = np.array([ldbl]*5)
-    out = ''.join([repr(t) + '\n' for t in tgt])
-
-    def test_fromfile_bogus(self):
-        with temppath() as path:
-            with open(path, 'w') as f:
-                f.write("1. 2. 3. flop 4.\n")
-
-            with assert_warns(DeprecationWarning):
-                res = np.fromfile(path, dtype=float, sep=" ")
-        assert_equal(res, np.array([1., 2., 3.]))
-
-    def test_fromfile_complex(self):
-        for ctype in ["complex", "cdouble", "cfloat"]:
-            # Check spacing between separator and only real component specified
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1, 2 ,  3  ,4\n")
-
-                res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1., 2., 3., 4.]))
-
-            # Real component not specified
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1j, -2j,  3j, 4e1j\n")
-
-                res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.j, -2.j, 3.j, 40.j]))
-
-            # Both components specified
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1+1j,2-2j, -3+3j,  -4e1+4j\n")
-
-                res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1. + 1.j, 2. - 2.j, - 3. + 3.j, - 40. + 4j]))
-
-            # Spaces at wrong places
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1+2 j,3\n")
-
-                with assert_warns(DeprecationWarning):
-                    res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.]))
-
-            # Spaces at wrong places
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1+ 2j,3\n")
-
-                with assert_warns(DeprecationWarning):
-                    res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.]))
-
-            # Spaces at wrong places
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1 +2j,3\n")
-
-                with assert_warns(DeprecationWarning):
-                    res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.]))
-
-            # Spaces at wrong places
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1+j\n")
-
-                with assert_warns(DeprecationWarning):
-                    res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.]))
-
-            # Spaces at wrong places
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1+\n")
-
-                with assert_warns(DeprecationWarning):
-                    res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.]))
-
-            # Spaces at wrong places
-            with temppath() as path:
-                with open(path, 'w') as f:
-                    f.write("1j+1\n")
-
-                with assert_warns(DeprecationWarning):
-                    res = np.fromfile(path, dtype=ctype, sep=",")
-            assert_equal(res, np.array([1.j]))
-
-
-
-    @pytest.mark.skipif(string_to_longdouble_inaccurate,
-                        reason="Need strtold_l")
-    def test_fromfile(self):
-        with temppath() as path:
-            with open(path, 'w') as f:
-                f.write(self.out)
-            res = np.fromfile(path, dtype=np.longdouble, sep="\n")
-        assert_equal(res, self.tgt)
-
-    @pytest.mark.skipif(string_to_longdouble_inaccurate,
-                        reason="Need strtold_l")
-    def test_genfromtxt(self):
-        with temppath() as path:
-            with open(path, 'w') as f:
-                f.write(self.out)
-            res = np.genfromtxt(path, dtype=np.longdouble)
-        assert_equal(res, self.tgt)
-
-    @pytest.mark.skipif(string_to_longdouble_inaccurate,
-                        reason="Need strtold_l")
-    def test_loadtxt(self):
-        with temppath() as path:
-            with open(path, 'w') as f:
-                f.write(self.out)
-            res = np.loadtxt(path, dtype=np.longdouble)
-        assert_equal(res, self.tgt)
-
-    @pytest.mark.skipif(string_to_longdouble_inaccurate,
-                        reason="Need strtold_l")
-    def test_tofile_roundtrip(self):
-        with temppath() as path:
-            self.tgt.tofile(path, sep=" ")
-            res = np.fromfile(path, dtype=np.longdouble, sep=" ")
-        assert_equal(res, self.tgt)
-
-
-# Conversions long double -> string
-
-
-def test_repr_exact():
-    o = 1 + LD_INFO.eps
-    assert_(repr(o) != '1')
-
-
-@pytest.mark.skipif(longdouble_longer_than_double, reason="BUG #2376")
-@pytest.mark.skipif(string_to_longdouble_inaccurate,
-                    reason="Need strtold_l")
-def test_format():
-    o = 1 + LD_INFO.eps
-    assert_("{0:.40g}".format(o) != '1')
-
-
-@pytest.mark.skipif(longdouble_longer_than_double, reason="BUG #2376")
-@pytest.mark.skipif(string_to_longdouble_inaccurate,
-                    reason="Need strtold_l")
-def test_percent():
-    o = 1 + LD_INFO.eps
-    assert_("%.40g" % o != '1')
-
-
-@pytest.mark.skipif(longdouble_longer_than_double,
-                    reason="array repr problem")
-@pytest.mark.skipif(string_to_longdouble_inaccurate,
-                    reason="Need strtold_l")
-def test_array_repr():
-    o = 1 + LD_INFO.eps
-    a = np.array([o])
-    b = np.array([1], dtype=np.longdouble)
-    if not np.all(a != b):
-        raise ValueError("precision loss creating arrays")
-    assert_(repr(a) != repr(b))
-
-#
-# Locale tests: scalar types formatting should be independent of the locale
-#
-
-class TestCommaDecimalPointLocale(CommaDecimalPointLocale):
-
-    def test_repr_roundtrip_foreign(self):
-        o = 1.5
-        assert_equal(o, np.longdouble(repr(o)))
-
-    def test_fromstring_foreign_repr(self):
-        f = 1.234
-        a = np.fromstring(repr(f), dtype=float, sep=" ")
-        assert_equal(a[0], f)
-
-    def test_fromstring_best_effort_float(self):
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1,234", dtype=float, sep=" "),
-                         np.array([1.]))
-
-    def test_fromstring_best_effort(self):
-        with assert_warns(DeprecationWarning):
-            assert_equal(np.fromstring("1,234", dtype=np.longdouble, sep=" "),
-                         np.array([1.]))
-
-    def test_fromstring_foreign(self):
-        s = "1.234"
-        a = np.fromstring(s, dtype=np.longdouble, sep=" ")
-        assert_equal(a[0], np.longdouble(s))
-
-    def test_fromstring_foreign_sep(self):
-        a = np.array([1, 2, 3, 4])
-        b = np.fromstring("1,2,3,4,", dtype=np.longdouble, sep=",")
-        assert_array_equal(a, b)
-
-    def test_fromstring_foreign_value(self):
-        with assert_warns(DeprecationWarning):
-            b = np.fromstring("1,234", dtype=np.longdouble, sep=" ")
-            assert_array_equal(b[0], 1)
-
-
-@pytest.mark.parametrize("int_val", [
-    # cases discussed in gh-10723
-    # and gh-9968
-    2 ** 1024, 0])
-def test_longdouble_from_int(int_val):
-    # for issue gh-9968
-    str_val = str(int_val)
-    # we'll expect a RuntimeWarning on platforms
-    # with np.longdouble equivalent to np.double
-    # for large integer input
-    with warnings.catch_warnings(record=True) as w:
-        warnings.filterwarnings('always', '', RuntimeWarning)
-        # can be inf==inf on some platforms
-        assert np.longdouble(int_val) == np.longdouble(str_val)
-        # we can't directly compare the int and
-        # max longdouble value on all platforms
-        if np.allclose(np.finfo(np.longdouble).max,
-                       np.finfo(np.double).max) and w:
-            assert w[0].category is RuntimeWarning
-
-@pytest.mark.parametrize("bool_val", [
-    True, False])
-def test_longdouble_from_bool(bool_val):
-    assert np.longdouble(bool_val) == np.longdouble(int(bool_val))
-
-
-@pytest.mark.skipif(
-    not (IS_MUSL and platform.machine() == "x86_64"),
-    reason="only need to run on musllinux_x86_64"
-)
-def test_musllinux_x86_64_signature():
-    # this test may fail if you're emulating musllinux_x86_64 on a different
-    # architecture, but should pass natively.
-    known_sigs = [b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf']
-    sig = (np.longdouble(-1.0) / np.longdouble(10.0)
-           ).newbyteorder('<').tobytes()[:10]
-    assert sig in known_sigs
-
-
-def test_eps_positive():
-    # np.finfo('g').eps should be positive on all platforms. If this isn't true
-    # then something may have gone wrong with the MachArLike, e.g. if
-    # np.core.getlimits._discovered_machar didn't work properly
-    assert np.finfo(np.longdouble).eps > 0.
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_avx512_icl.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_avx512_icl.c
deleted file mode 100644
index 3cf44d73164b6a80eca5f23f699bd00dba1f623e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/cpu_avx512_icl.c
+++ /dev/null
@@ -1,26 +0,0 @@
-#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
-    /*
-     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
-     * whether or not the build options for those features are specified.
-     * Therefore, we must test #definitions of CPU features when option native/host
-     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
-     * the test will be broken and leads to enable all possible features.
-     */
-    #if !defined(__AVX512VPOPCNTDQ__) || !defined(__AVX512BITALG__) || !defined(__AVX512VPOPCNTDQ__)
-        #error "HOST/ARCH doesn't support IceLake AVX512 features"
-    #endif
-#endif
-
-#include <immintrin.h>
-
-int main(int argc, char **argv)
-{
-    __m512i a = _mm512_loadu_si512((const __m512i*)argv[argc-1]);
-    /* VBMI2 */
-    a = _mm512_shrdv_epi64(a, a, _mm512_setzero_si512());
-    /* BITLAG */
-    a = _mm512_popcnt_epi8(a);
-    /* VPOPCNTDQ */
-    a = _mm512_popcnt_epi64(a);
-    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/excel/test_style.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/excel/test_style.py
deleted file mode 100644
index 3ca863788563996144e29333bc0a79f017ca0e77..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/excel/test_style.py
+++ /dev/null
@@ -1,294 +0,0 @@
-import contextlib
-import time
-
-import numpy as np
-import pytest
-
-import pandas.util._test_decorators as td
-
-from pandas import (
-    DataFrame,
-    read_excel,
-)
-import pandas._testing as tm
-
-from pandas.io.excel import ExcelWriter
-from pandas.io.formats.excel import ExcelFormatter
-
-pytest.importorskip("jinja2")
-# jinja2 is currently required for Styler.__init__(). Technically Styler.to_excel
-# could compute styles and render to excel without jinja2, since there is no
-# 'template' file, but this needs the import error to delayed until render time.
-
-
-def assert_equal_cell_styles(cell1, cell2):
-    # TODO: should find a better way to check equality
-    assert cell1.alignment.__dict__ == cell2.alignment.__dict__
-    assert cell1.border.__dict__ == cell2.border.__dict__
-    assert cell1.fill.__dict__ == cell2.fill.__dict__
-    assert cell1.font.__dict__ == cell2.font.__dict__
-    assert cell1.number_format == cell2.number_format
-    assert cell1.protection.__dict__ == cell2.protection.__dict__
-
-
-@pytest.mark.parametrize(
-    "engine",
-    ["xlsxwriter", "openpyxl"],
-)
-def test_styler_to_excel_unstyled(engine):
-    # compare DataFrame.to_excel and Styler.to_excel when no styles applied
-    pytest.importorskip(engine)
-    df = DataFrame(np.random.default_rng(2).standard_normal((2, 2)))
-    with tm.ensure_clean(".xlsx") as path:
-        with ExcelWriter(path, engine=engine) as writer:
-            df.to_excel(writer, sheet_name="dataframe")
-            df.style.to_excel(writer, sheet_name="unstyled")
-
-        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
-        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
-            for col1, col2 in zip(wb["dataframe"].columns, wb["unstyled"].columns):
-                assert len(col1) == len(col2)
-                for cell1, cell2 in zip(col1, col2):
-                    assert cell1.value == cell2.value
-                    assert_equal_cell_styles(cell1, cell2)
-
-
-shared_style_params = [
-    (
-        "background-color: #111222",
-        ["fill", "fgColor", "rgb"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    (
-        "color: #111222",
-        ["font", "color", "value"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    ("font-family: Arial;", ["font", "name"], "arial"),
-    ("font-weight: bold;", ["font", "b"], True),
-    ("font-style: italic;", ["font", "i"], True),
-    ("text-decoration: underline;", ["font", "u"], "single"),
-    ("number-format: $??,???.00;", ["number_format"], "$??,???.00"),
-    ("text-align: left;", ["alignment", "horizontal"], "left"),
-    (
-        "vertical-align: bottom;",
-        ["alignment", "vertical"],
-        {"xlsxwriter": None, "openpyxl": "bottom"},  # xlsxwriter Fails
-    ),
-    ("vertical-align: middle;", ["alignment", "vertical"], "center"),
-    # Border widths
-    ("border-left: 2pt solid red", ["border", "left", "style"], "medium"),
-    ("border-left: 1pt dotted red", ["border", "left", "style"], "dotted"),
-    ("border-left: 2pt dotted red", ["border", "left", "style"], "mediumDashDotDot"),
-    ("border-left: 1pt dashed red", ["border", "left", "style"], "dashed"),
-    ("border-left: 2pt dashed red", ["border", "left", "style"], "mediumDashed"),
-    ("border-left: 1pt solid red", ["border", "left", "style"], "thin"),
-    ("border-left: 3pt solid red", ["border", "left", "style"], "thick"),
-    # Border expansion
-    (
-        "border-left: 2pt solid #111222",
-        ["border", "left", "color", "rgb"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    ("border: 1pt solid red", ["border", "top", "style"], "thin"),
-    (
-        "border: 1pt solid #111222",
-        ["border", "top", "color", "rgb"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    ("border: 1pt solid red", ["border", "right", "style"], "thin"),
-    (
-        "border: 1pt solid #111222",
-        ["border", "right", "color", "rgb"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    ("border: 1pt solid red", ["border", "bottom", "style"], "thin"),
-    (
-        "border: 1pt solid #111222",
-        ["border", "bottom", "color", "rgb"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    ("border: 1pt solid red", ["border", "left", "style"], "thin"),
-    (
-        "border: 1pt solid #111222",
-        ["border", "left", "color", "rgb"],
-        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
-    ),
-    # Border styles
-    (
-        "border-left-style: hair; border-left-color: black",
-        ["border", "left", "style"],
-        "hair",
-    ),
-]
-
-
-@pytest.mark.parametrize(
-    "engine",
-    ["xlsxwriter", "openpyxl"],
-)
-@pytest.mark.parametrize("css, attrs, expected", shared_style_params)
-def test_styler_to_excel_basic(engine, css, attrs, expected):
-    pytest.importorskip(engine)
-    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
-    styler = df.style.map(lambda x: css)
-
-    with tm.ensure_clean(".xlsx") as path:
-        with ExcelWriter(path, engine=engine) as writer:
-            df.to_excel(writer, sheet_name="dataframe")
-            styler.to_excel(writer, sheet_name="styled")
-
-        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
-        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
-            # test unstyled data cell does not have expected styles
-            # test styled cell has expected styles
-            u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2)
-        for attr in attrs:
-            u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr)
-
-        if isinstance(expected, dict):
-            assert u_cell is None or u_cell != expected[engine]
-            assert s_cell == expected[engine]
-        else:
-            assert u_cell is None or u_cell != expected
-            assert s_cell == expected
-
-
-@pytest.mark.parametrize(
-    "engine",
-    ["xlsxwriter", "openpyxl"],
-)
-@pytest.mark.parametrize("css, attrs, expected", shared_style_params)
-def test_styler_to_excel_basic_indexes(engine, css, attrs, expected):
-    pytest.importorskip(engine)
-    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
-
-    styler = df.style
-    styler.map_index(lambda x: css, axis=0)
-    styler.map_index(lambda x: css, axis=1)
-
-    null_styler = df.style
-    null_styler.map(lambda x: "null: css;")
-    null_styler.map_index(lambda x: "null: css;", axis=0)
-    null_styler.map_index(lambda x: "null: css;", axis=1)
-
-    with tm.ensure_clean(".xlsx") as path:
-        with ExcelWriter(path, engine=engine) as writer:
-            null_styler.to_excel(writer, sheet_name="null_styled")
-            styler.to_excel(writer, sheet_name="styled")
-
-        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
-        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
-            # test null styled index cells does not have expected styles
-            # test styled cell has expected styles
-            ui_cell, si_cell = wb["null_styled"].cell(2, 1), wb["styled"].cell(2, 1)
-            uc_cell, sc_cell = wb["null_styled"].cell(1, 2), wb["styled"].cell(1, 2)
-        for attr in attrs:
-            ui_cell, si_cell = getattr(ui_cell, attr, None), getattr(si_cell, attr)
-            uc_cell, sc_cell = getattr(uc_cell, attr, None), getattr(sc_cell, attr)
-
-        if isinstance(expected, dict):
-            assert ui_cell is None or ui_cell != expected[engine]
-            assert si_cell == expected[engine]
-            assert uc_cell is None or uc_cell != expected[engine]
-            assert sc_cell == expected[engine]
-        else:
-            assert ui_cell is None or ui_cell != expected
-            assert si_cell == expected
-            assert uc_cell is None or uc_cell != expected
-            assert sc_cell == expected
-
-
-# From https://openpyxl.readthedocs.io/en/stable/api/openpyxl.styles.borders.html
-# Note: Leaving behavior of "width"-type styles undefined; user should use border-width
-# instead
-excel_border_styles = [
-    # "thin",
-    "dashed",
-    "mediumDashDot",
-    "dashDotDot",
-    "hair",
-    "dotted",
-    "mediumDashDotDot",
-    # "medium",
-    "double",
-    "dashDot",
-    "slantDashDot",
-    # "thick",
-    "mediumDashed",
-]
-
-
-@pytest.mark.parametrize(
-    "engine",
-    ["xlsxwriter", "openpyxl"],
-)
-@pytest.mark.parametrize("border_style", excel_border_styles)
-def test_styler_to_excel_border_style(engine, border_style):
-    css = f"border-left: {border_style} black thin"
-    attrs = ["border", "left", "style"]
-    expected = border_style
-
-    pytest.importorskip(engine)
-    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
-    styler = df.style.map(lambda x: css)
-
-    with tm.ensure_clean(".xlsx") as path:
-        with ExcelWriter(path, engine=engine) as writer:
-            df.to_excel(writer, sheet_name="dataframe")
-            styler.to_excel(writer, sheet_name="styled")
-
-        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
-        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
-            # test unstyled data cell does not have expected styles
-            # test styled cell has expected styles
-            u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2)
-        for attr in attrs:
-            u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr)
-
-        if isinstance(expected, dict):
-            assert u_cell is None or u_cell != expected[engine]
-            assert s_cell == expected[engine]
-        else:
-            assert u_cell is None or u_cell != expected
-            assert s_cell == expected
-
-
-def test_styler_custom_converter():
-    openpyxl = pytest.importorskip("openpyxl")
-
-    def custom_converter(css):
-        return {"font": {"color": {"rgb": "111222"}}}
-
-    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
-    styler = df.style.map(lambda x: "color: #888999")
-    with tm.ensure_clean(".xlsx") as path:
-        with ExcelWriter(path, engine="openpyxl") as writer:
-            ExcelFormatter(styler, style_converter=custom_converter).write(
-                writer, sheet_name="custom"
-            )
-
-        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
-            assert wb["custom"].cell(2, 2).font.color.value == "00111222"
-
-
-@pytest.mark.single_cpu
-@td.skip_if_not_us_locale
-def test_styler_to_s3(s3_public_bucket, s3so):
-    # GH#46381
-
-    mock_bucket_name, target_file = s3_public_bucket.name, "test.xlsx"
-    df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
-    styler = df.style.set_sticky(axis="index")
-    styler.to_excel(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so)
-    timeout = 5
-    while True:
-        if target_file in (obj.key for obj in s3_public_bucket.objects.all()):
-            break
-        time.sleep(0.1)
-        timeout -= 0.1
-        assert timeout > 0, "Timed out waiting for file to appear on moto"
-        result = read_excel(
-            f"s3://{mock_bucket_name}/{target_file}", index_col=0, storage_options=s3so
-        )
-        tm.assert_frame_equal(result, df)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/test_highlight.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/test_highlight.py
deleted file mode 100644
index 3d59719010ee03cc53373a1c96f5f8c5611d7681..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/test_highlight.py
+++ /dev/null
@@ -1,218 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    NA,
-    DataFrame,
-    IndexSlice,
-)
-
-pytest.importorskip("jinja2")
-
-from pandas.io.formats.style import Styler
-
-
-@pytest.fixture(params=[(None, "float64"), (NA, "Int64")])
-def df(request):
-    # GH 45804
-    return DataFrame(
-        {"A": [0, np.nan, 10], "B": [1, request.param[0], 2]}, dtype=request.param[1]
-    )
-
-
-@pytest.fixture
-def styler(df):
-    return Styler(df, uuid_len=0)
-
-
-def test_highlight_null(styler):
-    result = styler.highlight_null()._compute().ctx
-    expected = {
-        (1, 0): [("background-color", "red")],
-        (1, 1): [("background-color", "red")],
-    }
-    assert result == expected
-
-
-def test_highlight_null_subset(styler):
-    # GH 31345
-    result = (
-        styler.highlight_null(color="red", subset=["A"])
-        .highlight_null(color="green", subset=["B"])
-        ._compute()
-        .ctx
-    )
-    expected = {
-        (1, 0): [("background-color", "red")],
-        (1, 1): [("background-color", "green")],
-    }
-    assert result == expected
-
-
-@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
-def test_highlight_minmax_basic(df, f):
-    expected = {
-        (0, 1): [("background-color", "red")],
-        # ignores NaN row,
-        (2, 0): [("background-color", "red")],
-    }
-    if f == "highlight_min":
-        df = -df
-    result = getattr(df.style, f)(axis=1, color="red")._compute().ctx
-    assert result == expected
-
-
-@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
-@pytest.mark.parametrize(
-    "kwargs",
-    [
-        {"axis": None, "color": "red"},  # test axis
-        {"axis": 0, "subset": ["A"], "color": "red"},  # test subset and ignores NaN
-        {"axis": None, "props": "background-color: red"},  # test props
-    ],
-)
-def test_highlight_minmax_ext(df, f, kwargs):
-    expected = {(2, 0): [("background-color", "red")]}
-    if f == "highlight_min":
-        df = -df
-    result = getattr(df.style, f)(**kwargs)._compute().ctx
-    assert result == expected
-
-
-@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
-@pytest.mark.parametrize("axis", [None, 0, 1])
-def test_highlight_minmax_nulls(f, axis):
-    # GH 42750
-    expected = {
-        (1, 0): [("background-color", "yellow")],
-        (1, 1): [("background-color", "yellow")],
-    }
-    if axis == 1:
-        expected.update({(2, 1): [("background-color", "yellow")]})
-
-    if f == "highlight_max":
-        df = DataFrame({"a": [NA, 1, None], "b": [np.nan, 1, -1]})
-    else:
-        df = DataFrame({"a": [NA, -1, None], "b": [np.nan, -1, 1]})
-
-    result = getattr(df.style, f)(axis=axis)._compute().ctx
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "kwargs",
-    [
-        {"left": 0, "right": 1},  # test basic range
-        {"left": 0, "right": 1, "props": "background-color: yellow"},  # test props
-        {"left": -100, "right": 100, "subset": IndexSlice[[0, 1], :]},  # test subset
-        {"left": 0, "subset": IndexSlice[[0, 1], :]},  # test no right
-        {"right": 1},  # test no left
-        {"left": [0, 0, 11], "axis": 0},  # test left as sequence
-        {"left": DataFrame({"A": [0, 0, 11], "B": [1, 1, 11]}), "axis": None},  # axis
-        {"left": 0, "right": [0, 1], "axis": 1},  # test sequence right
-    ],
-)
-def test_highlight_between(styler, kwargs):
-    expected = {
-        (0, 0): [("background-color", "yellow")],
-        (0, 1): [("background-color", "yellow")],
-    }
-    result = styler.highlight_between(**kwargs)._compute().ctx
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "arg, map, axis",
-    [
-        ("left", [1, 2], 0),  # 0 axis has 3 elements not 2
-        ("left", [1, 2, 3], 1),  # 1 axis has 2 elements not 3
-        ("left", np.array([[1, 2], [1, 2]]), None),  # df is (2,3) not (2,2)
-        ("right", [1, 2], 0),  # same tests as above for 'right' not 'left'
-        ("right", [1, 2, 3], 1),  # ..
-        ("right", np.array([[1, 2], [1, 2]]), None),  # ..
-    ],
-)
-def test_highlight_between_raises(arg, styler, map, axis):
-    msg = f"supplied '{arg}' is not correct shape"
-    with pytest.raises(ValueError, match=msg):
-        styler.highlight_between(**{arg: map, "axis": axis})._compute()
-
-
-def test_highlight_between_raises2(styler):
-    msg = "values can be 'both', 'left', 'right', or 'neither'"
-    with pytest.raises(ValueError, match=msg):
-        styler.highlight_between(inclusive="badstring")._compute()
-
-    with pytest.raises(ValueError, match=msg):
-        styler.highlight_between(inclusive=1)._compute()
-
-
-@pytest.mark.parametrize(
-    "inclusive, expected",
-    [
-        (
-            "both",
-            {
-                (0, 0): [("background-color", "yellow")],
-                (0, 1): [("background-color", "yellow")],
-            },
-        ),
-        ("neither", {}),
-        ("left", {(0, 0): [("background-color", "yellow")]}),
-        ("right", {(0, 1): [("background-color", "yellow")]}),
-    ],
-)
-def test_highlight_between_inclusive(styler, inclusive, expected):
-    kwargs = {"left": 0, "right": 1, "subset": IndexSlice[[0, 1], :]}
-    result = styler.highlight_between(**kwargs, inclusive=inclusive)._compute()
-    assert result.ctx == expected
-
-
-@pytest.mark.parametrize(
-    "kwargs",
-    [
-        {"q_left": 0.5, "q_right": 1, "axis": 0},  # base case
-        {"q_left": 0.5, "q_right": 1, "axis": None},  # test axis
-        {"q_left": 0, "q_right": 1, "subset": IndexSlice[2, :]},  # test subset
-        {"q_left": 0.5, "axis": 0},  # test no high
-        {"q_right": 1, "subset": IndexSlice[2, :], "axis": 1},  # test no low
-        {"q_left": 0.5, "axis": 0, "props": "background-color: yellow"},  # tst prop
-    ],
-)
-def test_highlight_quantile(styler, kwargs):
-    expected = {
-        (2, 0): [("background-color", "yellow")],
-        (2, 1): [("background-color", "yellow")],
-    }
-    result = styler.highlight_quantile(**kwargs)._compute().ctx
-    assert result == expected
-
-
-@pytest.mark.parametrize(
-    "f,kwargs",
-    [
-        ("highlight_min", {"axis": 1, "subset": IndexSlice[1, :]}),
-        ("highlight_max", {"axis": 0, "subset": [0]}),
-        ("highlight_quantile", {"axis": None, "q_left": 0.6, "q_right": 0.8}),
-        ("highlight_between", {"subset": [0]}),
-    ],
-)
-@pytest.mark.parametrize(
-    "df",
-    [
-        DataFrame([[0, 10], [20, 30]], dtype=int),
-        DataFrame([[0, 10], [20, 30]], dtype=float),
-        DataFrame([[0, 10], [20, 30]], dtype="datetime64[ns]"),
-        DataFrame([[0, 10], [20, 30]], dtype=str),
-        DataFrame([[0, 10], [20, 30]], dtype="timedelta64[ns]"),
-    ],
-)
-def test_all_highlight_dtypes(f, kwargs, df):
-    if f == "highlight_quantile" and isinstance(df.iloc[0, 0], (str)):
-        return None  # quantile incompatible with str
-    if f == "highlight_between":
-        kwargs["left"] = df.iloc[1, 0]  # set the range low for testing
-
-    expected = {(1, 0): [("background-color", "yellow")]}
-    result = getattr(df.style, f)(**kwargs)._compute().ctx
-    assert result == expected
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py
deleted file mode 100644
index d44447eaf5a3912ea699e6d895d51f9b0782cfba..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""A collection of modules for building different kinds of trees from HTML
-documents.
-
-To create a treebuilder for a new type of tree, you need to do
-implement several things:
-
-1. A set of classes for various types of elements: Document, Doctype, Comment,
-   Element. These must implement the interface of ``base.treebuilders.Node``
-   (although comment nodes have a different signature for their constructor,
-   see ``treebuilders.etree.Comment``) Textual content may also be implemented
-   as another node type, or not, as your tree implementation requires.
-
-2. A treebuilder object (called ``TreeBuilder`` by convention) that inherits
-   from ``treebuilders.base.TreeBuilder``. This has 4 required attributes:
-
-   * ``documentClass`` - the class to use for the bottommost node of a document
-   * ``elementClass`` - the class to use for HTML Elements
-   * ``commentClass`` - the class to use for comments
-   * ``doctypeClass`` - the class to use for doctypes
-
-   It also has one required method:
-
-   * ``getDocument`` - Returns the root node of the complete document tree
-
-3. If you wish to run the unit tests, you must also create a ``testSerializer``
-   method on your treebuilder which accepts a node and returns a string
-   containing Node and its children serialized according to the format used in
-   the unittests
-
-"""
-
-from __future__ import absolute_import, division, unicode_literals
-
-from .._utils import default_etree
-
-treeBuilderCache = {}
-
-
-def getTreeBuilder(treeType, implementation=None, **kwargs):
-    """Get a TreeBuilder class for various types of trees with built-in support
-
-    :arg treeType: the name of the tree type required (case-insensitive). Supported
-        values are:
-
-        * "dom" - A generic builder for DOM implementations, defaulting to a
-          xml.dom.minidom based implementation.
-        * "etree" - A generic builder for tree implementations exposing an
-          ElementTree-like interface, defaulting to xml.etree.cElementTree if
-          available and xml.etree.ElementTree if not.
-        * "lxml" - A etree-based builder for lxml.etree, handling limitations
-          of lxml's implementation.
-
-    :arg implementation: (Currently applies to the "etree" and "dom" tree
-        types). A module implementing the tree type e.g. xml.etree.ElementTree
-        or xml.etree.cElementTree.
-
-    :arg kwargs: Any additional options to pass to the TreeBuilder when
-        creating it.
-
-    Example:
-
-    >>> from html5lib.treebuilders import getTreeBuilder
-    >>> builder = getTreeBuilder('etree')
-
-    """
-
-    treeType = treeType.lower()
-    if treeType not in treeBuilderCache:
-        if treeType == "dom":
-            from . import dom
-            # Come up with a sane default (pref. from the stdlib)
-            if implementation is None:
-                from xml.dom import minidom
-                implementation = minidom
-            # NEVER cache here, caching is done in the dom submodule
-            return dom.getDomModule(implementation, **kwargs).TreeBuilder
-        elif treeType == "lxml":
-            from . import etree_lxml
-            treeBuilderCache[treeType] = etree_lxml.TreeBuilder
-        elif treeType == "etree":
-            from . import etree
-            if implementation is None:
-                implementation = default_etree
-            # NEVER cache here, caching is done in the etree submodule
-            return etree.getETreeModule(implementation, **kwargs).TreeBuilder
-        else:
-            raise ValueError("""Unrecognised treebuilder "%s" """ % treeType)
-    return treeBuilderCache.get(treeType)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/_vbscript_builtins.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/_vbscript_builtins.py
deleted file mode 100644
index e7e96e6908871e991faa609448bbcb11624f0c54..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/_vbscript_builtins.py
+++ /dev/null
@@ -1,279 +0,0 @@
-"""
-    pygments.lexers._vbscript_builtins
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    These are manually translated lists from
-    http://www.indusoft.com/pdf/VBScript%20Reference.pdf.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-KEYWORDS = [
-    'ByRef',
-    'ByVal',
-    # dim: special rule
-    'call',
-    'case',
-    'class',
-    # const: special rule
-    'do',
-    'each',
-    'else',
-    'elseif',
-    'end',
-    'erase',
-    'execute',
-    'function',
-    'exit',
-    'for',
-    'function',
-    'GetRef',
-    'global',
-    'if',
-    'let',
-    'loop',
-    'next',
-    'new',
-    # option: special rule
-    'private',
-    'public',
-    'redim',
-    'select',
-    'set',
-    'sub',
-    'then',
-    'wend',
-    'while',
-    'with',
-]
-
-BUILTIN_FUNCTIONS = [
-    'Abs',
-    'Array',
-    'Asc',
-    'Atn',
-    'CBool',
-    'CByte',
-    'CCur',
-    'CDate',
-    'CDbl',
-    'Chr',
-    'CInt',
-    'CLng',
-    'Cos',
-    'CreateObject',
-    'CSng',
-    'CStr',
-    'Date',
-    'DateAdd',
-    'DateDiff',
-    'DatePart',
-    'DateSerial',
-    'DateValue',
-    'Day',
-    'Eval',
-    'Exp',
-    'Filter',
-    'Fix',
-    'FormatCurrency',
-    'FormatDateTime',
-    'FormatNumber',
-    'FormatPercent',
-    'GetObject',
-    'GetLocale',
-    'Hex',
-    'Hour',
-    'InStr',
-    'inStrRev',
-    'Int',
-    'IsArray',
-    'IsDate',
-    'IsEmpty',
-    'IsNull',
-    'IsNumeric',
-    'IsObject',
-    'Join',
-    'LBound',
-    'LCase',
-    'Left',
-    'Len',
-    'LoadPicture',
-    'Log',
-    'LTrim',
-    'Mid',
-    'Minute',
-    'Month',
-    'MonthName',
-    'MsgBox',
-    'Now',
-    'Oct',
-    'Randomize',
-    'RegExp',
-    'Replace',
-    'RGB',
-    'Right',
-    'Rnd',
-    'Round',
-    'RTrim',
-    'ScriptEngine',
-    'ScriptEngineBuildVersion',
-    'ScriptEngineMajorVersion',
-    'ScriptEngineMinorVersion',
-    'Second',
-    'SetLocale',
-    'Sgn',
-    'Space',
-    'Split',
-    'Sqr',
-    'StrComp',
-    'String',
-    'StrReverse',
-    'Tan',
-    'Time',
-    'Timer',
-    'TimeSerial',
-    'TimeValue',
-    'Trim',
-    'TypeName',
-    'UBound',
-    'UCase',
-    'VarType',
-    'Weekday',
-    'WeekdayName',
-    'Year',
-]
-
-BUILTIN_VARIABLES = [
-    'Debug',
-    'Dictionary',
-    'Drive',
-    'Drives',
-    'Err',
-    'File',
-    'Files',
-    'FileSystemObject',
-    'Folder',
-    'Folders',
-    'Match',
-    'Matches',
-    'RegExp',
-    'Submatches',
-    'TextStream',
-]
-
-OPERATORS = [
-    '+',
-    '-',
-    '*',
-    '/',
-    '\\',
-    '^',
-    '|',
-    '<',
-    '<=',
-    '>',
-    '>=',
-    '=',
-    '<>',
-    '&',
-    '$',
-]
-
-OPERATOR_WORDS = [
-    'mod',
-    'and',
-    'or',
-    'xor',
-    'eqv',
-    'imp',
-    'is',
-    'not',
-]
-
-BUILTIN_CONSTANTS = [
-    'False',
-    'True',
-    'vbAbort',
-    'vbAbortRetryIgnore',
-    'vbApplicationModal',
-    'vbArray',
-    'vbBinaryCompare',
-    'vbBlack',
-    'vbBlue',
-    'vbBoole',
-    'vbByte',
-    'vbCancel',
-    'vbCr',
-    'vbCritical',
-    'vbCrLf',
-    'vbCurrency',
-    'vbCyan',
-    'vbDataObject',
-    'vbDate',
-    'vbDefaultButton1',
-    'vbDefaultButton2',
-    'vbDefaultButton3',
-    'vbDefaultButton4',
-    'vbDouble',
-    'vbEmpty',
-    'vbError',
-    'vbExclamation',
-    'vbFalse',
-    'vbFirstFullWeek',
-    'vbFirstJan1',
-    'vbFormFeed',
-    'vbFriday',
-    'vbGeneralDate',
-    'vbGreen',
-    'vbIgnore',
-    'vbInformation',
-    'vbInteger',
-    'vbLf',
-    'vbLong',
-    'vbLongDate',
-    'vbLongTime',
-    'vbMagenta',
-    'vbMonday',
-    'vbMsgBoxHelpButton',
-    'vbMsgBoxRight',
-    'vbMsgBoxRtlReading',
-    'vbMsgBoxSetForeground',
-    'vbNewLine',
-    'vbNo',
-    'vbNull',
-    'vbNullChar',
-    'vbNullString',
-    'vbObject',
-    'vbObjectError',
-    'vbOK',
-    'vbOKCancel',
-    'vbOKOnly',
-    'vbQuestion',
-    'vbRed',
-    'vbRetry',
-    'vbRetryCancel',
-    'vbSaturday',
-    'vbShortDate',
-    'vbShortTime',
-    'vbSingle',
-    'vbString',
-    'vbSunday',
-    'vbSystemModal',
-    'vbTab',
-    'vbTextCompare',
-    'vbThursday',
-    'vbTrue',
-    'vbTuesday',
-    'vbUseDefault',
-    'vbUseSystem',
-    'vbUseSystem',
-    'vbVariant',
-    'vbVerticalTab',
-    'vbWednesday',
-    'vbWhite',
-    'vbYellow',
-    'vbYes',
-    'vbYesNo',
-    'vbYesNoCancel',
-]
diff --git a/spaces/prueba123jdjq/inswapper_128.onnx/app.py b/spaces/prueba123jdjq/inswapper_128.onnx/app.py
deleted file mode 100644
index 1a877e0eef1ceea27c3698fd9fcedbd3aa05bfec..0000000000000000000000000000000000000000
--- a/spaces/prueba123jdjq/inswapper_128.onnx/app.py
+++ /dev/null
@@ -1,2 +0,0 @@
-git lfs install
-git clone https://huggingface.co/Kibawilson/inswapper_128.onnx
\ No newline at end of file
diff --git a/spaces/pyInter/Liyuu_sovits4/modules/modules.py b/spaces/pyInter/Liyuu_sovits4/modules/modules.py
deleted file mode 100644
index 54290fd207b25e93831bd21005990ea137e6b50e..0000000000000000000000000000000000000000
--- a/spaces/pyInter/Liyuu_sovits4/modules/modules.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Aalavandhan 2001 Tamil Movie DVDRip Xvid 700MB.md b/spaces/quidiaMuxgu/Expedit-SAM/Aalavandhan 2001 Tamil Movie DVDRip Xvid 700MB.md
deleted file mode 100644
index 58fbe541604a71f0bc2c6bbb0a782cce78097a63..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Aalavandhan 2001 Tamil Movie DVDRip Xvid 700MB.md	
+++ /dev/null
@@ -1,12 +0,0 @@
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Betternet VPN For Windows Premium V4.1.0 - SeuPirate Setup ((TOP)) Free.md b/spaces/quidiaMuxgu/Expedit-SAM/Betternet VPN For Windows Premium V4.1.0 - SeuPirate Setup ((TOP)) Free.md
deleted file mode 100644
index 498f02895f0484924ab7048fc12b0d0188c37161..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Betternet VPN For Windows Premium V4.1.0 - SeuPirate Setup ((TOP)) Free.md	
+++ /dev/null
@@ -1,68 +0,0 @@
-
-<h1>Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free - How to Download and Install</h1>
-<p>If you are looking for a free and easy way to protect your online privacy and security, you might want to try Betternet VPN for Windows Premium v4.1.0, a powerful and reliable VPN software that lets you browse the web anonymously and securely. Betternet VPN for Windows Premium v4.1.0 allows you to access any website or app without any restrictions or censorship, and it also encrypts your data and hides your IP address, so you can avoid hackers, trackers, ISPs, and government agencies from spying on your online activity or stealing your personal information.</p>
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-<p>In this article, we will tell you more about Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free, and how to download and install it on your Windows device.</p>
-<h2>What is Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free?</h2>
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-<p></p>
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-<h2>How to download Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free?</h2>
-<p>If you want to download Betternet VPN for Windows Premium v4.1</p>
-</p>
-<ol>
-<li>Download and install a torrent client on your Windows device. We recommend BitTorrent or uTorrent as they are easy to use and reliable.</li>
-<li>Go to a torrent site that offers Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free, such as SolidTorrents or RARBG. You can use any search engine to find them.</li>
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-<li>Start the download and wait until it is completed. Depending on your internet speed and the number of seeders and leechers, this could take from minutes to hours.</li>
-</ol>
-<h2>How to install Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free?</h2>
-<p>Once you have downloaded Betternet VPN for Windows Premium v4.1.0 - SeuPirate setup free, you need to install it on your Windows device. Here are the steps to do so:</p>
-<ol>
-<li>Locate the downloaded torrent file on your device and double-click on it to open it with your torrent client.</li>
-<li>Extract the files and folders from the torrent file to a folder of your choice.</li>
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-</ol>
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\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/GTA Iv Data4.cab.rar !NEW!.md b/spaces/quidiaMuxgu/Expedit-SAM/GTA Iv Data4.cab.rar !NEW!.md
deleted file mode 100644
index 251eb34b9c2bb4dfa6e712e07d836bc64090b068..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/GTA Iv Data4.cab.rar !NEW!.md	
+++ /dev/null
@@ -1,33 +0,0 @@
-<br />
-<h1>How to Fix GTA IV Data4.cab Error</h1>
-<p>If you are trying to install Grand Theft Auto IV on your PC, you may encounter an error message saying that data4.cab is corrupt or missing. This can be very frustrating, especially if you have bought the game legally and have the original DVDs. Fortunately, there are some possible solutions to this problem that you can try.</p>
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-<p>In this article, we will explain what data4.cab is, why it may cause an error, and how to fix it. We will also provide some tips on how to optimize your PC for playing GTA IV smoothly and without any issues.</p>
-<h2>What is data4.cab?</h2>
-<p>Data4.cab is one of the files that contains the game data for GTA IV. It is located on the second DVD of the game installation. It is a compressed file that has a .cab extension, which stands for cabinet. Cabinet files are used to store multiple files in a single archive, similar to .zip or .rar files.</p>
-<p>Data4.cab is a large file that contains many important files for the game, such as textures, models, sounds, scripts, etc. If this file is damaged or missing, the game installation will fail and you will not be able to play GTA IV.</p>
-<p></p>
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-<p>There are several possible reasons why data4.cab may cause an error during the installation of GTA IV. Some of them are:</p>
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-<p>Depending on the cause of the error, there are different ways to fix data4.cab error. Here are some possible solutions that you can try:</p>
-<ul>
-<li>Clean the DVD and check for scratches. Use a soft cloth and a mild cleaner to gently wipe the DVD surface. Avoid using abrasive materials or liquids that may damage the DVD. If there are any visible scratches on the DVD, you may need to replace it with a new one.</li>
-<li>Update or replace your DVD drive. Check if your DVD drive has the latest firmware and drivers installed. You can find them on the manufacturer's website or use a driver updater tool. If your DVD drive is old or malfunctioning, you may need to buy a new one that is compatible with your PC.</li>
-<li>Scan your PC for malware and viruses. Use a reliable antivirus program and perform a full scan of your PC. Delete any suspicious files or programs that may have corrupted data4.cab or other game files.</li>
-<li>Copy the contents of both DVDs to a single folder on your hard drive. This can bypass any reading errors from the DVD drive and speed up the installation process. To do this, create a new folder on your hard drive and name it GTA IV. Then, insert the first DVD and copy all its contents to the GTA IV folder. Repeat this with the second DVD and overwrite any existing files when prompted. Finally, run setup.exe from the GTA IV folder and follow the instructions.</li>
-</ul>
-<h2>How to optimize your PC for GTA IV?</h2>
-<p>GTA IV is a demanding game that requires a powerful PC to run smoothly and without any issues. If your PC does not meet the minimum requirements for the game, you may experience lagging, crashing, freezing, or other problems while playing GTA IV.</p>
-<p>To optimize your PC for GTA IV, you should:</p>
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-<li>Update your graphics card drivers. This can improve your graphics performance and fix any compatibility issues with GTA IV.</li>
-<li>Adjust your graphics settings in-game. You</p> d5da3c52bf<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Historia Universal Contemporanea Carlos Alvear Acevedo.pdf !NEW!.md b/spaces/quidiaMuxgu/Expedit-SAM/Historia Universal Contemporanea Carlos Alvear Acevedo.pdf !NEW!.md
deleted file mode 100644
index 043eb478b05a37bf6a510ae07c0db992fcdbc809..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Historia Universal Contemporanea Carlos Alvear Acevedo.pdf !NEW!.md	
+++ /dev/null
@@ -1,69 +0,0 @@
-<br />
-<h1>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: Un libro para aprender la historia del mundo</h1>
-<p>La historia universal contemporánea es el estudio de los acontecimientos más importantes que han ocurrido en el mundo desde el siglo XIX hasta la actualidad. Es una materia que abarca temas como las revoluciones, las guerras, las ideologías, las culturas, las ciencias, las artes, y más.</p>
-<p>Si quieres aprender la historia universal contemporánea de una manera fácil y amena, te recomendamos el libro de Carlos Alvear Acevedo, un reconocido historiador y profesor mexicano. Su libro se titula Historia Universal Contemporanea Carlos Alvear Acevedo.pdf y lo puedes descargar gratis en Internet.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf</h2><br /><p><b><b>Download File</b> &#10004;&#10004;&#10004; <a href="https://geags.com/2uCrUQ">https://geags.com/2uCrUQ</a></b></p><br /><br />
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Qué contiene el libro?</h2>
-<p>El libro de Carlos Alvear Acevedo es un manual que aborda la historia universal contemporánea desde una perspectiva global y crítica. El libro se divide en cuatro partes:</p>
-<ul>
-<li>La primera parte trata sobre el siglo XIX y los cambios políticos, económicos, sociales y culturales que se produjeron en ese periodo. Se analizan temas como la Revolución Francesa, la Revolución Industrial, el imperialismo, el nacionalismo, el liberalismo, el socialismo, y más.</li>
-<li>La segunda parte trata sobre el siglo XX y los conflictos mundiales que marcaron ese siglo. Se analizan temas como la Primera Guerra Mundial, la Revolución Rusa, la Gran Depresión, el fascismo, el nazismo, la Segunda Guerra Mundial, la Guerra Fría, la descolonización, y más.</li>
-<li>La tercera parte trata sobre el siglo XXI y los desafíos actuales que enfrenta la humanidad. Se analizan temas como la globalización, el neoliberalismo, el terrorismo, los derechos humanos, el medio ambiente, la diversidad cultural, la democracia, y más.</li>
-<li>La cuarta parte trata sobre la historia de la cultura y su evolución a lo largo de los siglos. Se analizan temas como la filosofía, la religión, la literatura, el arte, la música, el cine, la ciencia, la tecnología, y más.</li>
-</ul>
-<p>El libro de Carlos Alvear Acevedo tiene un estilo claro y didáctico, que facilita la comprensión de los hechos históricos. El libro también incluye mapas, gráficos, imágenes, cuadros sinópticos, cronologías, bibliografías y ejercicios para reforzar el aprendizaje.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Cómo descargar el libro?</h2>
-<p>Si quieres descargar el libro de Carlos Alvear Acevedo en formato PDF para leerlo en tu computadora o dispositivo móvil, puedes hacerlo de forma gratuita en Internet. Hay muchos sitios web que ofrecen este libro en PDF con buena calidad y sin virus. Aquí te mostramos algunos de los mejores sitios web para descargar Historia Universal Contemporanea Carlos Alvear Acevedo.pdf:</p>
-<ul>
-<li><a href="https://archive.org/details/historiauniversa0000alve">Internet Archive</a>: Este sitio web ofrece Historia universal contemporánea por Carlos Alvear Acevedo para Descarga gratuita, Préstamo y Streaming con idioma español. También puedes encontrar otros libros,</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Quién es el autor?</h2>
-<p>Carlos Alvear Acevedo es un historiador y profesor mexicano que se ha dedicado al estudio y la enseñanza de la historia universal contemporánea. Es licenciado en Historia por la Universidad Nacional Autónoma de México (UNAM) y doctor en Historia por la Universidad de París.</p>
-<p>Carlos Alvear Acevedo ha sido profesor e investigador en diversas instituciones académicas, como la UNAM, el Instituto Politécnico Nacional, el Colegio de México, y la Universidad Autónoma Metropolitana. Ha publicado varios libros y artículos sobre temas de historia universal contemporánea, como la Revolución Francesa, el imperialismo, la Primera Guerra Mundial, el fascismo, el nazismo, la Segunda Guerra Mundial, la Guerra Fría, la globalización, y más.</p>
-<p>Carlos Alvear Acevedo es reconocido como uno de los mejores historiadores y profesores de historia universal contemporánea en México y en el mundo. Su libro Historia Universal Contemporanea Carlos Alvear Acevedo.pdf es una obra de referencia obligada para todos los interesados en esta materia.</p>
-<p></p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Por qué leer el libro?</h2>
-<p>El libro de Carlos Alvear Acevedo es una excelente opción para aprender la historia universal contemporánea de una manera fácil y amena. El libro tiene varias ventajas que lo hacen recomendable para todos los lectores:</p>
-<ul>
-<li>El libro es un manual que aborda la historia universal contemporánea desde una perspectiva global y crítica. El libro no se limita a narrar los hechos históricos, sino que los analiza y los relaciona con el contexto y las consecuencias que tuvieron para el mundo.</li>
-<li>El libro tiene un estilo claro y didáctico, que facilita la comprensión de los hechos históricos. El libro también incluye mapas, gráficos, imágenes, cuadros sinópticos, cronologías, bibliografías y ejercicios para reforzar el aprendizaje.</li>
-<li>El libro está disponible en formato PDF para descargarlo gratis en Internet. Esto permite que los lectores puedan acceder al libro sin costo alguno y sin necesidad de comprarlo o pedirlo prestado. También permite que los lectores puedan leerlo en su computadora o dispositivo móvil con comodidad.</li>
-</ul>
-<p>El libro de Carlos Alvear Acevedo es un libro que te ayudará a aprender la historia universal contemporánea de una manera fácil y amena. Es un libro que te hará entender mejor el mundo en el que vivimos y el papel que tenemos como ciudadanos globales.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Cómo leer el libro?</h2>
-<p>El libro de Carlos Alvear Acevedo es un libro que se puede leer de diferentes maneras, según el interés y el nivel de cada lector. El libro tiene una estructura flexible que permite adaptarse a las necesidades y preferencias de cada uno. Aquí te damos algunas sugerencias para leer el libro:</p>
-<ul>
-<li>Si quieres tener una visión general de la historia universal contemporánea, puedes leer el libro de principio a fin, siguiendo el orden cronológico y temático que propone el autor. Así podrás conocer los hechos históricos más relevantes que han ocurrido en el mundo desde el siglo XIX hasta la actualidad.</li>
-<li>Si quieres profundizar en algún aspecto específico de la historia universal contemporánea, puedes leer el libro por partes, según el siglo o el tema que te interese. Así podrás analizar con más detalle los cambios políticos, económicos, sociales y culturales que se produjeron en ese periodo o ámbito.</li>
-<li>Si quieres complementar tu lectura con otros recursos, puedes consultar las bibliografías y los ejercicios que incluye el libro al final de cada capítulo. Así podrás ampliar tu información y verificar tu comprensión de los hechos históricos.</li>
-</ul>
-<p>El libro de Carlos Alvear Acevedo es un libro que se puede leer de diferentes maneras, según el interés y el nivel de cada lector. El libro es un recurso valioso para aprender la historia universal contemporánea de una manera fácil y amena.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Qué opinan los lectores?</h2>
-<p>El libro de Carlos Alvear Acevedo ha recibido muchas opiniones positivas por parte de los lectores que lo han leído y comentado en Internet. Los lectores han destacado la calidad y la utilidad del libro para aprender la historia universal contemporánea. Aquí te mostramos algunos de los comentarios que han hecho los lectores:</p>
-<ul>
-<li>"Es un excelente libro para entender la historia universal contemporánea. El autor explica los hechos históricos con claridad y rigor, sin dejar de lado la crítica y la reflexión. El libro tiene un estilo didáctico y ameno, que hace que la lectura sea fácil y entretenida."</li>
-<li>"Me gustó mucho este libro porque aborda la historia universal contemporánea desde una perspectiva global y crítica. El autor no se limita a narrar los hechos históricos, sino que los analiza y los relaciona con el contexto y las consecuencias que tuvieron para el mundo. El libro también incluye mapas, gráficos, imágenes, cuadros sinópticos, cronologías, bibliografías y ejercicios que ayudan a comprender mejor la historia."</li>
-<li>"Este libro es un gran recurso para aprender la historia universal contemporánea. El autor tiene un gran conocimiento y una gran experiencia en esta materia. El libro tiene una estructura flexible que permite adaptarse a las necesidades y preferencias de cada lector. El libro también está disponible en formato PDF para descargarlo gratis en Internet."</li>
-</ul>
-<p>El libro de Carlos Alvear Acevedo ha recibido muchas opiniones positivas por parte de los lectores que lo han leído y comentado en Internet. Los lectores han destacado la calidad y la utilidad del libro para aprender la historia universal contemporánea.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Qué beneficios tiene leer el libro?</h2>
-<p>Leer el libro de Carlos Alvear Acevedo tiene muchos beneficios para los lectores que quieren aprender la historia universal contemporánea. Algunos de los beneficios son:</p>
-<ul>
-<li>Leer el libro te permite conocer los hechos históricos más importantes que han ocurrido en el mundo desde el siglo XIX hasta la actualidad. Así podrás entender mejor el pasado y el presente de la humanidad y sus relaciones.</li>
-<li>Leer el libro te permite desarrollar tu pensamiento crítico y tu capacidad de análisis. El libro no solo te informa, sino que te invita a reflexionar y a cuestionar los hechos históricos desde una perspectiva global y crítica. Así podrás formar tu propia opinión y tu propio criterio sobre la historia.</li>
-<li>Leer el libro te permite ampliar tu cultura y tu visión del mundo. El libro te muestra la diversidad y la complejidad de la historia universal contemporánea, abarcando temas políticos, económicos, sociales y culturales. Así podrás apreciar y respetar las diferencias y las similitudes entre las personas y las sociedades.</li>
-</ul>
-<p>Leer el libro de Carlos Alvear Acevedo tiene muchos beneficios para los lectores que quieren aprender la historia universal contemporánea. Es un libro que te enriquecerá como persona y como ciudadano global.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: ¿Cómo recomendar el libro?</h2>
-<p>Si te ha gustado el libro de Carlos Alvear Acevedo y quieres recomendarlo a otras personas que quieran aprender la historia universal contemporánea, puedes hacerlo de varias maneras. Aquí te damos algunas ideas para recomendar el libro:</p>
-<ul>
-<li>Puedes compartir el enlace del libro en formato PDF en tus redes sociales, como Facebook, Twitter, Instagram, o WhatsApp. Así podrás difundir el libro entre tus amigos, familiares, y contactos.</li>
-<li>Puedes escribir una reseña o un comentario sobre el libro en sitios web como Amazon, Goodreads, o Google Books. Así podrás expresar tu opinión sobre el libro y ayudar a otros lectores a decidir si leerlo o no.</li>
-<li>Puedes participar en un club de lectura o en un foro online sobre el libro. Así podrás intercambiar ideas y opiniones con otros lectores que hayan leído o quieran leer el libro.</li>
-</ul>
-<p>Si te ha gustado el libro de Carlos Alvear Acevedo y quieres recomendarlo a otras personas que quieran aprender la historia universal contemporánea, puedes hacerlo de varias maneras. Es un libro que vale la pena compartir y discutir.</p>
-<h2>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf: Final Thoughts</h2>
-<p>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf is a book that will teach you the history of the world from the 19th century to the present. It is a book that will make you think and question the historical facts from a global and critical perspective. It is a book that will enrich your culture and your vision of the world.</p>
-<p>If you are looking for Historia Universal Contemporanea Carlos Alvear Acevedo.pdf, you can download it for free on the Internet. You can also read it in other languages and formats, such as Spanish, English, French, Kindle, and more.</p>
-<p>Historia Universal Contemporanea Carlos Alvear Acevedo.pdf is a book that you should not miss if you love history and want to learn more about it. It is a book that will change your life.</p> 3cee63e6c2<br />
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/MY HERO ONES JUSTICE 32 Bit Crack.md b/spaces/quidiaMuxgu/Expedit-SAM/MY HERO ONES JUSTICE 32 Bit Crack.md
deleted file mode 100644
index 08c47f8c97c5df622e4a335a3a259dfbe05a2b50..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/MY HERO ONES JUSTICE 32 Bit Crack.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<h2>MY HERO ONE'S JUSTICE 32 bit crack</h2><br /><p><b><b>Download</b> &mdash; <a href="https://geags.com/2uCsz8">https://geags.com/2uCsz8</a></b></p><br /><br />
-
-Starting with version 3, Fog of War is now supported by default, and a small panel is drawn on top of the map when you are exploring. Other than this, most things work as usual, so you can navigate your land and set your units in all your usual ways. You can use the Help button in the top right to see how to use Fog of War.
-
-By the way, Fog of War is not like other tile sets in WarCraft 3 - you don't need to use a Fog of War mod to play on it, or the Fog of War graphics card. It uses the same collision detection as the main game. The images below show an area you might be able to explore using the Fog of War mod - by using the Fog of War graphics card, you will see exactly the same area.
-
-Fog of War mod supports most of the usual things in the game - you can use the mouse to place units, upgrade them, research and train units. Here is a list of the things that are supported in Fog of War:
-
-Buildings and units have visual states - no longer does this mean empty buildings are grey and full buildings are golden. They actually have different visual states and are a bit more interactive.
-
-- no longer does this mean empty buildings are grey and full buildings are golden. They actually have different visual states and are a bit more interactive. Added Fog of War graphics card.
-
-Addressed the error in the localization where "working area" was translated to "areas you can work in" - this will hopefully address some of the confusion of Fog of War, and it can also be used in other translations to translate to "areas you can work in."
-
-Fixed issue where unit tooltip would not show up for the Retrait unit class.
-
-Fixed issue where the tooltip could not be moved with the mouse.
-
-Fixed the sizing issues for the mouse cursor.
-
-Fixed issue where the title bar of the minimap would not show up in some situations.
-
-Fixed issue with the retreat point showing on the minimap when using a screen capture mod.
-
-Fixed issue where the research structure was not placed correctly when placed with the mouse on a block.
-
-Fixed issue where unit/building cursor tooltips would overlap the ground.
-
-Fixed issue where the game would pause when placing structures and leaving the game.
-
-Fixed issue where the minimap would stop updating once a structure was placed.
-
-Fixed issue 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/qwerrsc/vits-uma-genshin-honkai/commons.py b/spaces/qwerrsc/vits-uma-genshin-honkai/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/qwerrsc/vits-uma-genshin-honkai/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/radames/gradio-lite-candle-SAM/style.css b/spaces/radames/gradio-lite-candle-SAM/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/radames/gradio-lite-candle-SAM/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Cara mengatasi driver toolkit download error dengan melakukan backup dan restore driver.md b/spaces/raedeXanto/academic-chatgpt-beta/Cara mengatasi driver toolkit download error dengan melakukan backup dan restore driver.md
deleted file mode 100644
index 666ace41e41ba7da0d8a1cee4d83296f1b1e5a82..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Cara mengatasi driver toolkit download error dengan melakukan backup dan restore driver.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-
-<h1>Cara Mengatasi Driver Toolkit Download Error</h1>
-<p>Driver toolkit adalah sebuah aplikasi yang dapat membantu Anda mengunduh, memperbarui, dan memperbaiki driver perangkat keras komputer Anda. Namun, terkadang Anda mungkin mengalami masalah driver toolkit download error, yaitu ketika aplikasi tidak bisa mengunduh atau menginstal driver yang dibutuhkan. Hal ini tentu saja sangat mengganggu dan bisa menyebabkan komputer Anda tidak berfungsi dengan baik.</p>
-<p>Lalu, bagaimana cara mengatasi driver toolkit download error? Apa penyebabnya dan apa solusinya? Artikel ini akan menjawab pertanyaan-pertanyaan tersebut dan memberikan Anda beberapa tips dan trik untuk mengatasi masalah ini. Mari kita mulai!</p>
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- <h2>Apa itu Driver Toolkit?</h2>
-<p>Driver toolkit adalah sebuah aplikasi yang dikembangkan oleh Megaify Software. Aplikasi ini berfungsi untuk mengidentifikasi, mengunduh, memperbarui, dan memperbaiki driver perangkat keras komputer Anda secara otomatis. Dengan driver toolkit, Anda tidak perlu repot-repot mencari driver yang cocok untuk komputer Anda di internet, karena aplikasi ini akan melakukannya untuk Anda.</p>
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-<p>Driver toolkit memiliki beberapa fungsi dan fitur yang bisa membantu Anda mengelola driver perangkat keras komputer Anda dengan mudah dan cepat. Berikut adalah beberapa di antaranya:</p>
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-<ul>
-<li>Mengidentifikasi driver yang hilang, rusak, atau usang pada komputer Anda.</li>
-<li>Mengunduh driver yang sesuai dengan model dan merek perangkat keras komputer Anda dari database yang luas.</li>
-<li>Memperbarui driver yang usang atau kadaluarsa dengan versi terbaru.</li>
-<li>Memperbaiki driver yang rusak atau bermasalah dengan beberapa klik.</li>
-<li>Membuat cadangan dan memulihkan driver yang sudah terinstal pada komputer Anda.</li>
-<li>Menyediakan dukungan teknis 24/7 untuk pengguna yang mengalami masalah dengan aplikasi atau driver.</li>
-</ul>
- <h3>Penyebab driver toolkit download error</h3>
-<p>Driver toolkit download error adalah salah satu masalah yang sering dialami oleh pengguna aplikasi ini. Masalah ini terjadi ketika aplikasi tidak bisa mengunduh atau menginstal driver yang dibutuhkan oleh komputer Anda. Ada beberapa penyebab yang bisa menyebabkan masalah ini, di antaranya adalah:</p>
-<ul>
-<li>Koneksi internet yang lambat atau tidak stabil.</li>
-<li>Firewall atau antivirus yang menghalangi atau memblokir akses aplikasi ke internet.</li>
-<li>Cache atau file sementara yang menumpuk dan mengganggu kinerja aplikasi.</li>
-<li>Serial number atau lisensi yang tidak valid atau sudah kadaluarsa.</li>
-<li>Kerusakan pada file instalasi aplikasi atau driver.</li>
-</ul>
- <h2>Cara mengatasi driver toolkit download error</h2>
-<p>Jika Anda mengalami masalah driver toolkit download error, jangan khawatir, karena ada beberapa cara yang bisa Anda coba untuk mengatasinya. Berikut adalah beberapa langkah yang bisa Anda lakukan:</p>
- <h3>Memeriksa koneksi internet</h3>
-<p>Langkah pertama yang harus Anda lakukan adalah memeriksa koneksi internet Anda. Pastikan bahwa koneksi internet Anda cepat dan stabil, dan tidak ada gangguan atau pemutusan saat menggunakan aplikasi. Jika koneksi internet Anda bermasalah, coba perbaiki atau ganti dengan koneksi lain yang lebih baik.</p>
- <h3>Menonaktifkan firewall dan antivirus</h3>
-<p>Langkah kedua yang bisa Anda lakukan adalah menonaktifkan firewall dan antivirus pada komputer Anda sementara waktu. Firewall dan antivirus adalah program yang bertujuan untuk melindungi komputer Anda dari serangan virus atau malware, namun terkadang mereka juga bisa menghalangi atau memblokir akses aplikasi ke internet. Jika firewall atau antivirus Anda terlalu ketat atau sensitif, coba nonaktifkan mereka sementara waktu saat menggunakan aplikasi, dan aktifkan kembali setelah selesai.</p>
- <h3>Menghapus cache dan file sementara</h3>
-<p>Langkah ketiga yang bisa Anda lakukan adalah menghapus cache dan file sementara pada komputer Anda. Cache dan file sementara adalah data yang disimpan oleh aplikasi untuk mempercepat proses loading atau menjalankan program. Namun, jika cache dan file sementara menumpuk terlalu banyak, mereka bisa mengganggu kinerja aplikasi dan menyebabkan masalah seperti driver toolkit download error. Untuk menghapus cache dan file sementara pada komputer Anda, Anda bisa menggunakan program pembersih seperti CCleaner atau Disk Cleanup.</p>
- <h3>Menggunakan serial number yang valid</h3>
-<p>Langkah keempat yang bisa Anda lakukan adalah menggunakan serial number yang valid untuk aplikasi. Serial number adalah kode unik yang digunakan untuk mendaftarkan atau mengaktifkan aplikasi agar bisa digunakan secara penuh. Jika serial number yang Anda gunakan tidak valid atau sudah kadaluarsa, maka aplikasi tidak akan bisa berfungsi dengan baik dan menyebabkan masalah seperti driver toolkit download error. Untuk mendapatkan serial number yang valid untuk aplikasi, Anda bisa membelinya dari situs resmi pengembang atau mencarinya di internet secara gratis.</p>
- <h3>Menginstal ulang driver toolkit</h3>
-<p>Langkah kelima dan terakhir yang bisa Anda lakukan adalah menginstal ulang aplikasi pada komputer Anda. Jika langkah-langkah sebelumnya tidak berhasil menyelesaikan masalah, maka kemungkinan besar ada kerusakan pada file instalasi aplikasi atau driver. Untuk menginstal ulang aplikasi, Anda harus menghapusnya terlebih dahulu dari komputer Anda menggunakan program uninstaller seperti Revo Uninstaller atau IObit Uninstaller. Setelah itu, unduh file instalasi terbaru dari situs resmi pengembang atau situs tepercaya lainnya, dan jalankan proses instalasi seperti biasa.</p>
- <h2>Alternatif driver toolkit yang bisa dicoba</h2>
-<p>Jika setelah melakukan langkah-langkah di atas masalah masih belum terselesaikan, maka mungkin saatnya Anda mencoba alternatif lain selain driver toolkit. Ada banyak aplikasi lain yang memiliki fungsi dan fitur serupa dengan driver toolkit, namun mungkin lebih stabil dan handal dalam mengelola driver perangkat keras komputer Anda. Berikut adalah beberapa contoh alternatif driver toolkit yang bisa dicoba:</p>
- <h3>Driver Booster</h3>
-<p>Driver Booster adalah sebuah aplikasi yang dikembangkan oleh IObit Software. Aplikasi ini berfungsi untuk memindai, mengunduh, memperbarui, dan memperbaiki driver perangkat keras komputer Anda secara otomatis. Aplikasi ini memiliki database lebih dari 4 juta driver dari berbagai merek dan model perangkat keras komputer. Aplikasi ini juga memiliki fitur cadangan dan pemulihan driver, peningkatan performa game, dukungan offline mode, dll.</p>
- <h3>Driver Easy</h3>
-<h3>Driver Genius</h3>
-<p>Driver Genius adalah sebuah aplikasi yang dikembangkan oleh Driver-Soft Inc. Aplikasi ini berfungsi untuk memindai, mengunduh, memperbarui, dan memperbaiki driver perangkat keras komputer Anda secara otomatis. Aplikasi ini memiliki database lebih dari 1 juta driver dari berbagai merek dan model perangkat keras komputer. Aplikasi ini juga memiliki fitur cadangan dan pemulihan driver, penjadwalan pembaruan driver, pengoptimalan sistem, dll.</p>
- <h2>Kesimpulan</h2>
-<p>Driver toolkit download error adalah salah satu masalah yang sering dialami oleh pengguna aplikasi driver toolkit. Masalah ini terjadi ketika aplikasi tidak bisa mengunduh atau menginstal driver yang dibutuhkan oleh komputer Anda. Ada beberapa penyebab yang bisa menyebabkan masalah ini, seperti koneksi internet yang bermasalah, firewall atau antivirus yang menghalangi akses aplikasi ke internet, cache atau file sementara yang menumpuk, serial number yang tidak valid atau kadaluarsa, atau kerusakan pada file instalasi aplikasi atau driver.</p>
-<p>Untuk mengatasi masalah ini, Anda bisa mencoba beberapa langkah yang sudah dijelaskan di atas, seperti memeriksa koneksi internet, menonaktifkan firewall dan antivirus, menghapus cache dan file sementara, menggunakan serial number yang valid, atau menginstal ulang aplikasi. Jika langkah-langkah tersebut tidak berhasil menyelesaikan masalah, Anda bisa mencoba alternatif lain selain driver toolkit, seperti Driver Booster, Driver Easy, atau Driver Genius.</p>
-<p>Semoga artikel ini bermanfaat dan membantu Anda mengatasi masalah driver toolkit download error. Jika Anda memiliki pertanyaan atau saran lain, silakan tulis di kolom komentar di bawah. Terima kasih telah membaca!</p>
- <h2>FAQ</h2>
-<ol>
-<li>Apa itu driver perangkat keras komputer?</li>
-<p>Driver perangkat keras komputer adalah program yang berfungsi untuk menghubungkan antara perangkat keras komputer dengan sistem operasi komputer. Driver perangkat keras komputer memungkinkan perangkat keras komputer untuk berkomunikasi dan bekerja dengan sistem operasi komputer dengan baik.</p>
-<li>Mengapa driver perangkat keras komputer penting?</li>
-<p>Driver perangkat keras komputer penting karena tanpa driver perangkat keras komputer, perangkat keras komputer tidak akan bisa berfungsi dengan baik atau sama sekali pada sistem operasi komputer. Driver perangkat keras komputer juga penting untuk meningkatkan performa dan stabilitas perangkat keras komputer dan sistem operasi komputer.</li>
-<li>Bagaimana cara mengetahui driver perangkat keras komputer yang dibutuhkan oleh komputer saya?</li>
-<p>Anda bisa mengetahui driver perangkat keras komputer yang dibutuhkan oleh komputer Anda dengan menggunakan aplikasi seperti driver toolkit atau alternatifnya. Aplikasi tersebut akan memindai komputer Anda dan mengidentifikasi driver perangkat keras komputer yang hilang, rusak, atau usang pada komputer Anda. Aplikasi tersebut juga akan memberikan rekomendasi driver perangkat keras komputer yang sesuai dengan model dan merek perangkat keras komputer Anda.</li>
-<li>Bagaimana cara memperbarui driver perangkat keras komputer secara manual?</li>
-<p>Anda bisa memperbarui driver perangkat keras komputer secara manual dengan cara mencari driver perangkat keras komputer yang sesuai dengan model dan merek perangkat keras komputer Anda di internet. Setelah itu, unduh dan simpan file driver tersebut di lokasi yang mudah diakses. Kemudian, buka Device Manager pada Windows dan cari perangkat keras komputer yang ingin diperbarui drivernya. Klik kanan pada perangkat tersebut dan pilih Update Driver Software. Pilih Browse my computer for driver software dan arahkan ke lokasi file driver yang sudah diunduh tadi. Ikuti instruksi selanjutnya sampai proses pembaruan selesai.</li>
-<li>Apa bedanya driver toolkit dengan alternatifnya?</li>
-<p>Driver toolkit dan alternatifnya memiliki fungsi dan fitur yang serupa dalam mengelola driver perangkat keras komputer Anda secara otomatis. Namun, ada beberapa perbedaan antara mereka dalam hal database driver, fitur tambahan, harga, dll. Anda bisa membandingkan antara driver toolkit dan alternatifnya dengan membaca ulasan atau testimoni dari pengguna lain di internet.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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-<h1>Download Easy Case Windows 7 64 Bit RAR</h1>
-<p>Are you looking for a simple and effective way to update your drivers and devices on your Windows 7 computer? If so, you might want to try Easy Case, a free and powerful tool that can help you do that in just a few clicks. In this article, we will show you what Easy Case is, how to download it, how to install it, and how to use it. Let's get started!</p>
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- <h2>What is Easy Case?</h2>
-<p>Easy Case is a software that can automatically detect and install the latest drivers and devices for your Windows 7 computer. It supports both 32-bit and 64-bit versions of Windows 7, and it can work with various types of hardware, such as graphics cards, sound cards, network cards, printers, scanners, webcams, and more. Easy Case can also backup and restore your drivers and devices in case of any problems.</p>
- <h3>Features of Easy Case</h3>
-<p>Some of the features of Easy Case are:</p>
-<ul>
-<li>It can scan your computer and identify the drivers and devices that need updating.</li>
-<li>It can download and install the latest drivers and devices from its online database.</li>
-<li>It can backup and restore your drivers and devices in case of any problems.</li>
-<li>It can optimize your system performance and stability.</li>
-<li>It can support multiple languages and regions.</li>
-</ul>
- <h3>Benefits of using Easy Case</h3>
-<p>Some of the benefits of using Easy Case are:</p>
-<ul>
-<li>It can save you time and hassle by updating your drivers and devices automatically.</li>
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-<li>It can protect your system from malware and viruses by downloading drivers and devices from trusted sources.</li>
-<li>It can save you money by avoiding the need to buy new hardware or pay for technical support.</li>
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- <h2>How to download Easy Case Windows 7 64 Bit RAR</h2>
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- <h3>Step 1: Go to Google Drive link</h3>
-<p>The first step is to go to this Google Drive link, where you can find the file named <code>Easy Driver Packs Win 7 64 Bits.rar</code>. This is the file that contains the Easy Case software for Windows 7 64-bit computers.</p>
- <h3>Step 2: Click on download button</h3>
-<p>The next step is to click on the download button at the top right corner of the page. This will start downloading the file to your computer. Depending on your internet speed, this may take a few minutes or more.</p>
- <h3>Step 3: Save the file to your computer</h3>
-<p>The final step is to save the file to your computer. You can choose any location that you prefer, such as your desktop or downloads folder. Make sure that you remember where you saved the file, as you will need it later for installation.</p>
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-<p>The first step is to extract the file using WinRAR or 7-Zip, a popular compression tool that can open RAR files. If you don't have WinRAR or 7-Zip installed on your computer, you can download them from here or here. After installing WinRAR or 7-Zip, you can right-click on the file <code>Easy Driver Packs Win 7 64 Bits.rar</code>, and select <code>Extract Here</code>. This will create a folder named <code>EASYDRIVERPACKS5.33WIN764BIT</code>, which contains the setup file for Easy Case.</p>
- <h3>Step 2: Run the setup file</h3>
-<p>The next step is to run the setup file for Easy Case. To do that, you can double-click on the file <code>EASYDRIVERPACKS5.33WIN764BIT.exe</code>, which is located inside the folder <code>EASYDRIVERPACKS5.33WIN764BIT</code>. This will launch the installation wizard for Easy Case.</p>
- <h3>Step 3: Follow the instructions on screen</h3>
-<p>The final step is to follow the instructions on screen to complete the installation process. You can choose your preferred language and region, accept the terms and conditions, select a destination folder, create a shortcut icon, and click on <code>Install</code>. The installation may take a few minutes or more, depending on your system configuration. After the installation is finished, you can click on <code>Finish</code>, and then restart your computer if prompted.</p>
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-<p>After installing Easy Case Windows 7 64 Bit RAR, you can use it to update your drivers and devices on your computer. To do that, you can follow these simple steps:</p>
- <h3>Step 1: Launch the program</h3>
-<p>The first step is to launch the program. You can do that by clicking on the shortcut icon on your desktop or start menu, or by going to the destination folder where you installed Easy Case. The program will open with a simple interface that shows you four options: <code>Select All Drivers</code>, <code>Select All Devices</code>, <code>Select Drivers Only</code>, and <code>Select Devices Only</code>.</p>
- <h3>Step 2: Select your drivers and devices</h3>
-<p>The next step is to select your drivers and devices that you want to update. You can do that by clicking on one of the four options mentioned above. For example, if you want to update all your drivers and devices, you can click on <code>Select All Drivers And Devices</code>. This will scan your computer and show you a list of all your drivers and devices that need updating. You can also select or deselect individual drivers or devices by checking or unchecking their boxes.</p>
- <h3>Step 3: Click on install button</h3>
-<p>The final step is to click on the install button at the bottom right corner of the interface. This will start downloading and installing the latest drivers and devices for your computer from its online database. Depending on your internet speed and system configuration, this may take a few minutes or more. After the installation is finished, you will see a message saying <code>All Drivers And Devices Have Been Installed Successfully!</code>. You can then close the program or restart your computer if prompted.</p>
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-<p>In conclusion, Easy Case Windows 7 64 Bit RAR is a free and powerful tool that can help you update your drivers and devices on your Windows 7 computer in just a few clicks. It can save you time, hassle, money, and improve your system performance and stability. To use it, all you need to do is download it from Google Drive link, extract it using WinRAR or 7-Zip , install it using its setup file, launch it using its shortcut icon or destination folder, select your drivers or devices using its four options, click on install button, close it or restart it if prompted. That's it! You have successfully updated your drivers or devices using Easy Case Windows 7 64 Bit RAR!</p>
- **FAQs** <ol type="a">
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-<p>A driver is a software that allows your computer to communicate with a hardware device such as a printer or a scanner. Drivers are essential for ensuring that your hardware works properly and efficiently with your operating system.</p>
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-<p>A device is a hardware component that performs a specific function on your computer such as a mouse or a keyboard. Devices are also known as peripherals or accessories.</p>
-<li><b>Why do I need to update my drivers and devices?</b></li>
-<p>You need to update your drivers and devices because they may become outdated, incompatible, corrupted, or missing over time due to various reasons such as system updates, hardware changes, malware infections, etc. Updating your drivers and devices can help you prevent errors and conflicts, improve your system performance and stability, enhance your security and compatibility, and access new features and functions.</p>
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diff --git a/spaces/rahul-pandey-ct/kinship-llm-poc/README.md b/spaces/rahul-pandey-ct/kinship-llm-poc/README.md
deleted file mode 100644
index 2b741c833f007b9af6f9d05a8b4a5ffb648a57e8..0000000000000000000000000000000000000000
--- a/spaces/rahul-pandey-ct/kinship-llm-poc/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Kinship Llm Poc
-emoji: 🌖
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/razfar/anything-counter/utils/autoanchor.py b/spaces/razfar/anything-counter/utils/autoanchor.py
deleted file mode 100644
index bec9017711006fae4f34cf96e1a28ebbafd0c516..0000000000000000000000000000000000000000
--- a/spaces/razfar/anything-counter/utils/autoanchor.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Auto-anchor utils
-
-import numpy as np
-import torch
-import yaml
-from scipy.cluster.vq import kmeans
-from tqdm import tqdm
-
-from utils.general import colorstr
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        print('Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    prefix = colorstr('autoanchor: ')
-    print(f'\n{prefix}Analyzing anchors... ', end='')
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1. / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
-    bpr, aat = metric(anchors)
-    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
-    if bpr < 0.98:  # threshold to recompute
-        print('. Attempting to improve anchors, please wait...')
-        na = m.anchor_grid.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            print(f'{prefix}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            check_anchor_order(m)
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
-    print('')  # newline
-
-
-def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            path: path to dataset *.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    thr = 1. / thr
-    prefix = colorstr('autoanchor: ')
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
-        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
-              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
-        for i, x in enumerate(k):
-            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
-        return k
-
-    if isinstance(path, str):  # *.yaml file
-        with open(path) as f:
-            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-    else:
-        dataset = path  # dataset
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans calculation
-    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
-    k *= s
-    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
-    k = print_results(k)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    npr = np.random
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k)
-
-    return print_results(k)
diff --git a/spaces/rbigare/stablediffusionapi-architecture-tuned-model/app.py b/spaces/rbigare/stablediffusionapi-architecture-tuned-model/app.py
deleted file mode 100644
index 9381a0b07273c44fec5337a7cc573107abe9c75b..0000000000000000000000000000000000000000
--- a/spaces/rbigare/stablediffusionapi-architecture-tuned-model/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stablediffusionapi/architecture-tuned-model").launch()
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/A3D005 Affect3D Girlfriends4Ever V2.epub TOP.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/A3D005 Affect3D Girlfriends4Ever V2.epub TOP.md
deleted file mode 100644
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--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/A3D005 Affect3D Girlfriends4Ever V2.epub TOP.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Denon Mc3000 Virtualdj Mapping.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Denon Mc3000 Virtualdj Mapping.md
deleted file mode 100644
index f4837695e4909e8cb5b16d6098dc1b65f2cfc71c..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Denon Mc3000 Virtualdj Mapping.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fabrication CAMduct 2018 64bit Activation Code Zip File.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fabrication CAMduct 2018 64bit Activation Code Zip File.md
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--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Fabrication CAMduct 2018 64bit Activation Code Zip File.md	
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diff --git a/spaces/reha/Stick_Tech/mel_processing.py b/spaces/reha/Stick_Tech/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/reha/Stick_Tech/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/rehanuddin/02-GradioArt-From-Text-And-Images/README.md b/spaces/rehanuddin/02-GradioArt-From-Text-And-Images/README.md
deleted file mode 100644
index 8e819f1a0ebbd433771f20476af8f7ed969b299d..0000000000000000000000000000000000000000
--- a/spaces/rehanuddin/02-GradioArt-From-Text-And-Images/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 02 GradioArt From Text And Images
-emoji: ⚡
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rorallitri/biomedical-language-models/Total Uninstaller 3.3.0.161 Crack [CracksNow] Free Download HOT!.md b/spaces/rorallitri/biomedical-language-models/Total Uninstaller 3.3.0.161 Crack [CracksNow] Free Download HOT!.md
deleted file mode 100644
index 55053c04b24b413da4e6b5772bc91098d3d297d8..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/Total Uninstaller 3.3.0.161 Crack [CracksNow] Free Download HOT!.md	
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deleted file mode 100644
index 6fd5a0f83c85e9435f74d97993e23868bd3d83b9..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/How to Get Tulasi Dala Kannada Novel PDF 170 for Free.md	
+++ /dev/null
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index f72047b65eb38ddb36874200cea0d2d5d990e4e5..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Iboot 26 Iso Free 16.md	
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diff --git a/spaces/rubend18/parafrasis_espanol_t5/README.md b/spaces/rubend18/parafrasis_espanol_t5/README.md
deleted file mode 100644
index e1aecd6abd4449d7dda895e63e09454536ecbc65..0000000000000000000000000000000000000000
--- a/spaces/rubend18/parafrasis_espanol_t5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Parafrasis Espanol T5
-emoji: 💩
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/util/__init__.py b/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/util/__init__.py
deleted file mode 100644
index 168f9979a4623806934b0ff1102ac166704e7dec..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/util/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
diff --git a/spaces/savasy/SentimentHistogramForEnglish/app.py b/spaces/savasy/SentimentHistogramForEnglish/app.py
deleted file mode 100644
index 46b9746919f7408f5eeee29045858fff409d31a4..0000000000000000000000000000000000000000
--- a/spaces/savasy/SentimentHistogramForEnglish/app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-sa= pipeline("sentiment-analysis")
-
-def adjust(x):
-  if x<0:
-    return 2*x+1
-  return 2*x-1
-
-def sa2(s):
-  res= sa(s)
-  return [adjust(-1*r['score']) if r['label']=='negative' else adjust(r['score']) for r in res ]
-  
-  
-import pandas as pd
-
-import matplotlib.pyplot as plt
-def grfunc(comments):
-  df=pd.DataFrame()
-  c2=[s.strip() for s in comments.split("\n") if len(s.split())>2]
-  df["scores"]= sa2(c2)
-  df.plot(kind='hist')
-  return plt.gcf()
- 
-import gradio as gr
-
-iface = gr.Interface(
-  fn=grfunc, 
-  inputs=gr.inputs.Textbox(placeholder="put your sentences line by line", lines=5), 
-  outputs="plot")
-iface.launch()
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/modules/presets.py b/spaces/sccstandardteam/ChuanhuChatGPT/modules/presets.py
deleted file mode 100644
index fe1938a80f81d29a010e72d796b8edc02cea4f9e..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/modules/presets.py
+++ /dev/null
@@ -1,233 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-from pathlib import Path
-import gradio as gr
-from .webui_locale import I18nAuto
-
-i18n = I18nAuto()  # internationalization
-
-CHATGLM_MODEL = None
-CHATGLM_TOKENIZER = None
-LLAMA_MODEL = None
-LLAMA_INFERENCER = None
-
-# ChatGPT 设置
-INITIAL_SYSTEM_PROMPT = "You are a helpful assistant."
-API_HOST = "api.openai.com"
-COMPLETION_URL = "https://api.openai.com/v1/chat/completions"
-BALANCE_API_URL="https://api.openai.com/dashboard/billing/credit_grants"
-USAGE_API_URL="https://api.openai.com/dashboard/billing/usage"
-HISTORY_DIR = Path("history")
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-# 错误信息
-STANDARD_ERROR_MSG = i18n("☹️发生了错误：")  # 错误信息的标准前缀
-GENERAL_ERROR_MSG = i18n("获取对话时发生错误，请查看后台日志")
-ERROR_RETRIEVE_MSG = i18n("请检查网络连接，或者API-Key是否有效。")
-CONNECTION_TIMEOUT_MSG = i18n("连接超时，无法获取对话。")  # 连接超时
-READ_TIMEOUT_MSG = i18n("读取超时，无法获取对话。")  # 读取超时
-PROXY_ERROR_MSG = i18n("代理错误，无法获取对话。")  # 代理错误
-SSL_ERROR_PROMPT = i18n("SSL错误，无法获取对话。")  # SSL 错误
-NO_APIKEY_MSG = i18n("API key为空，请检查是否输入正确。")  # API key 长度不足 51 位
-NO_INPUT_MSG = i18n("请输入对话内容。")  # 未输入对话内容
-BILLING_NOT_APPLICABLE_MSG = i18n("账单信息不适用") # 本地运行的模型返回的账单信息
-
-TIMEOUT_STREAMING = 60  # 流式对话时的超时时间
-TIMEOUT_ALL = 200  # 非流式对话时的超时时间
-ENABLE_STREAMING_OPTION = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-CONCURRENT_COUNT = 100 # 允许同时使用的用户数量
-
-SIM_K = 5
-INDEX_QUERY_TEMPRATURE = 1.0
-
-CHUANHU_TITLE = i18n("川虎Chat 🚀")
-
-CHUANHU_DESCRIPTION = i18n("由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536)、[明昭MZhao](https://space.bilibili.com/24807452) 和 [Keldos](https://github.com/Keldos-Li) 开发<br />访问川虎Chat的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本")
-
-FOOTER = """<div class="versions">{versions}</div>"""
-
-APPEARANCE_SWITCHER = """
-<div style="display: flex; justify-content: space-between;">
-<span style="margin-top: 4px !important;">"""+ i18n("切换亮暗色主题")  + """</span>
-<span><label class="apSwitch" for="checkbox">
-    <input type="checkbox" id="checkbox">
-    <div class="apSlider"></div>
-</label></span>
-</div>
-"""
-
-SUMMARIZE_PROMPT = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
-
-ONLINE_MODELS = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-    "xmchat",
-    "yuanai-1.0-base_10B",
-    "yuanai-1.0-translate",
-    "yuanai-1.0-dialog",
-    "yuanai-1.0-rhythm_poems",
-]
-
-LOCAL_MODELS = [
-    "chatglm-6b",
-    "chatglm-6b-int4",
-    "chatglm-6b-int4-qe",
-    "StableLM",
-    "MOSS",
-    "llama-7b-hf",
-    "llama-13b-hf",
-    "llama-30b-hf",
-    "llama-65b-hf",
-]
-
-if os.environ.get('HIDE_LOCAL_MODELS', 'false') == 'true':
-    MODELS = ONLINE_MODELS
-else:
-    MODELS = ONLINE_MODELS + LOCAL_MODELS
-
-DEFAULT_MODEL = 0
-
-os.makedirs("models", exist_ok=True)
-os.makedirs("lora", exist_ok=True)
-os.makedirs("history", exist_ok=True)
-for dir_name in os.listdir("models"):
-    if os.path.isdir(os.path.join("models", dir_name)):
-        if dir_name not in MODELS:
-            MODELS.append(dir_name)
-
-MODEL_TOKEN_LIMIT = {
-    "gpt-3.5-turbo": 4096,
-    "gpt-3.5-turbo-0301": 4096,
-    "gpt-4": 8192,
-    "gpt-4-0314": 8192,
-    "gpt-4-32k": 32768,
-    "gpt-4-32k-0314": 32768
-}
-
-TOKEN_OFFSET = 1000 # 模型的token上限减去这个值，得到软上限。到达软上限之后，自动尝试减少token占用。
-DEFAULT_TOKEN_LIMIT = 3000 # 默认的token上限
-REDUCE_TOKEN_FACTOR = 0.5 # 与模型token上限想乘，得到目标token数。减少token占用时，将token占用减少到目标token数以下。
-
-REPLY_LANGUAGES = [
-    "简体中文",
-    "繁體中文",
-    "English",
-    "日本語",
-    "Español",
-    "Français",
-    "Deutsch",
-    "跟随问题语言（不稳定）"
-]
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in {reply_language}
-"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in {reply_language}
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Reply in {reply_language}
-If the context isn't useful, return the original answer.
-"""
-
-ALREADY_CONVERTED_MARK = "<!-- ALREADY CONVERTED BY PARSER. -->"
-
-small_and_beautiful_theme = gr.themes.Soft(
-        primary_hue=gr.themes.Color(
-            c50="#EBFAF2",
-            c100="#CFF3E1",
-            c200="#A8EAC8",
-            c300="#77DEA9",
-            c400="#3FD086",
-            c500="#02C160",
-            c600="#06AE56",
-            c700="#05974E",
-            c800="#057F45",
-            c900="#04673D",
-            c950="#2E5541",
-            name="small_and_beautiful",
-        ),
-        secondary_hue=gr.themes.Color(
-            c50="#576b95",
-            c100="#576b95",
-            c200="#576b95",
-            c300="#576b95",
-            c400="#576b95",
-            c500="#576b95",
-            c600="#576b95",
-            c700="#576b95",
-            c800="#576b95",
-            c900="#576b95",
-            c950="#576b95",
-        ),
-        neutral_hue=gr.themes.Color(
-            name="gray",
-            c50="#f6f7f8",
-            # c100="#f3f4f6",
-            c100="#F2F2F2",
-            c200="#e5e7eb",
-            c300="#d1d5db",
-            c400="#B2B2B2",
-            c500="#808080",
-            c600="#636363",
-            c700="#515151",
-            c800="#393939",
-            # c900="#272727",
-            c900="#2B2B2B",
-            c950="#171717",
-        ),
-        radius_size=gr.themes.sizes.radius_sm,
-    ).set(
-        # button_primary_background_fill="*primary_500",
-        button_primary_background_fill_dark="*primary_600",
-        # button_primary_background_fill_hover="*primary_400",
-        # button_primary_border_color="*primary_500",
-        button_primary_border_color_dark="*primary_600",
-        button_primary_text_color="wihte",
-        button_primary_text_color_dark="white",
-        button_secondary_background_fill="*neutral_100",
-        button_secondary_background_fill_hover="*neutral_50",
-        button_secondary_background_fill_dark="*neutral_900",
-        button_secondary_text_color="*neutral_800",
-        button_secondary_text_color_dark="white",
-        # background_fill_primary="#F7F7F7",
-        # background_fill_primary_dark="#1F1F1F",
-        # block_title_text_color="*primary_500",
-        block_title_background_fill_dark="*primary_900",
-        block_label_background_fill_dark="*primary_900",
-        input_background_fill="#F6F6F6",
-    )
diff --git a/spaces/scedlatioru/img-to-music/example/Chavitos Y Chavitas 365 Cuentos Maravillosos Pdf 70.md b/spaces/scedlatioru/img-to-music/example/Chavitos Y Chavitas 365 Cuentos Maravillosos Pdf 70.md
deleted file mode 100644
index 831ea8a5a631ebb6478559652c48b9e5c17df8ec..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Chavitos Y Chavitas 365 Cuentos Maravillosos Pdf 70.md	
+++ /dev/null
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-<h2>Chavitos Y Chavitas 365 Cuentos Maravillosos Pdf 70</h2><br /><p><b><b>Download File</b> &#11088; <a href="https://gohhs.com/2uEz5h">https://gohhs.com/2uEz5h</a></b></p><br /><br />
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-. maravillosos-pdf-19-elwcha-virgil/PDF "Maravillosos" - Elwcha Vargas - Maravillosos.pdf (PDF/eng).
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diff --git a/spaces/scedlatioru/img-to-music/example/Thaiphoon Burner 7 5 LINK Keygen 12.md b/spaces/scedlatioru/img-to-music/example/Thaiphoon Burner 7 5 LINK Keygen 12.md
deleted file mode 100644
index 62e074c6b6cfb90891d0f46bb25f62fd199b5c50..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Thaiphoon Burner 7 5 LINK Keygen 12.md	
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diff --git a/spaces/sczhou/CodeFormer/CodeFormer/basicsr/losses/loss_util.py b/spaces/sczhou/CodeFormer/CodeFormer/basicsr/losses/loss_util.py
deleted file mode 100644
index 744eeb46d1f3b5a7b4553ca23237ddd9c899a698..0000000000000000000000000000000000000000
--- a/spaces/sczhou/CodeFormer/CodeFormer/basicsr/losses/loss_util.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import functools
-from torch.nn import functional as F
-
-
-def reduce_loss(loss, reduction):
-    """Reduce loss as specified.
-
-    Args:
-        loss (Tensor): Elementwise loss tensor.
-        reduction (str): Options are 'none', 'mean' and 'sum'.
-
-    Returns:
-        Tensor: Reduced loss tensor.
-    """
-    reduction_enum = F._Reduction.get_enum(reduction)
-    # none: 0, elementwise_mean:1, sum: 2
-    if reduction_enum == 0:
-        return loss
-    elif reduction_enum == 1:
-        return loss.mean()
-    else:
-        return loss.sum()
-
-
-def weight_reduce_loss(loss, weight=None, reduction='mean'):
-    """Apply element-wise weight and reduce loss.
-
-    Args:
-        loss (Tensor): Element-wise loss.
-        weight (Tensor): Element-wise weights. Default: None.
-        reduction (str): Same as built-in losses of PyTorch. Options are
-            'none', 'mean' and 'sum'. Default: 'mean'.
-
-    Returns:
-        Tensor: Loss values.
-    """
-    # if weight is specified, apply element-wise weight
-    if weight is not None:
-        assert weight.dim() == loss.dim()
-        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
-        loss = loss * weight
-
-    # if weight is not specified or reduction is sum, just reduce the loss
-    if weight is None or reduction == 'sum':
-        loss = reduce_loss(loss, reduction)
-    # if reduction is mean, then compute mean over weight region
-    elif reduction == 'mean':
-        if weight.size(1) > 1:
-            weight = weight.sum()
-        else:
-            weight = weight.sum() * loss.size(1)
-        loss = loss.sum() / weight
-
-    return loss
-
-
-def weighted_loss(loss_func):
-    """Create a weighted version of a given loss function.
-
-    To use this decorator, the loss function must have the signature like
-    `loss_func(pred, target, **kwargs)`. The function only needs to compute
-    element-wise loss without any reduction. This decorator will add weight
-    and reduction arguments to the function. The decorated function will have
-    the signature like `loss_func(pred, target, weight=None, reduction='mean',
-    **kwargs)`.
-
-    :Example:
-
-    >>> import torch
-    >>> @weighted_loss
-    >>> def l1_loss(pred, target):
-    >>>     return (pred - target).abs()
-
-    >>> pred = torch.Tensor([0, 2, 3])
-    >>> target = torch.Tensor([1, 1, 1])
-    >>> weight = torch.Tensor([1, 0, 1])
-
-    >>> l1_loss(pred, target)
-    tensor(1.3333)
-    >>> l1_loss(pred, target, weight)
-    tensor(1.5000)
-    >>> l1_loss(pred, target, reduction='none')
-    tensor([1., 1., 2.])
-    >>> l1_loss(pred, target, weight, reduction='sum')
-    tensor(3.)
-    """
-
-    @functools.wraps(loss_func)
-    def wrapper(pred, target, weight=None, reduction='mean', **kwargs):
-        # get element-wise loss
-        loss = loss_func(pred, target, **kwargs)
-        loss = weight_reduce_loss(loss, weight, reduction)
-        return loss
-
-    return wrapper
diff --git a/spaces/sdeeas/ChuanhuChatGPT/readme/README_en.md b/spaces/sdeeas/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/sdeeas/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | English | <a title="Japanese" href="README_ja.md">日本語</a>
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search <br />
-        LaTeX rendering / Table rendering / Code highlighting <br />
-        Auto dark mode / Adaptive web interface / WeChat-like theme <br />
-        Multi-parameters tuning / Multi-API-Key support / Multi-user support <br />
-        Compatible with GPT-4 / Local deployment for LLMs
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>Video Tutorial</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 Introduction</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 Introduction & Tutorial</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>Online trial</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>One-Click deployment</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press <kbd>Shift</kbd> + <kbd>Enter</kbd> keys.
-- To quickly switch between input history, press <kbd>↑</kbd> and <kbd>↓</kbd> key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
-   ```
-   pip install -r requirements.txt
-   ```
-3. Update Gradio
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/segments-tobias/conex/espnet/lm/chainer_backend/lm.py b/spaces/segments-tobias/conex/espnet/lm/chainer_backend/lm.py
deleted file mode 100644
index eb13f288b5a949fb23ecfd3b62810284a463ea8a..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/lm/chainer_backend/lm.py
+++ /dev/null
@@ -1,484 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-# This code is ported from the following implementation written in Torch.
-# https://github.com/chainer/chainer/blob/master/examples/ptb/train_ptb_custom_loop.py
-
-
-import copy
-import json
-import logging
-import numpy as np
-import six
-
-import chainer
-from chainer.dataset import convert
-import chainer.functions as F
-import chainer.links as L
-
-# for classifier link
-from chainer.functions.loss import softmax_cross_entropy
-from chainer import link
-from chainer import reporter
-from chainer import training
-from chainer.training import extensions
-
-from espnet.lm.lm_utils import compute_perplexity
-from espnet.lm.lm_utils import count_tokens
-from espnet.lm.lm_utils import MakeSymlinkToBestModel
-from espnet.lm.lm_utils import ParallelSentenceIterator
-from espnet.lm.lm_utils import read_tokens
-
-import espnet.nets.chainer_backend.deterministic_embed_id as DL
-from espnet.nets.lm_interface import LMInterface
-from espnet.optimizer.factory import dynamic_import_optimizer
-from espnet.scheduler.chainer import ChainerScheduler
-from espnet.scheduler.scheduler import dynamic_import_scheduler
-
-from espnet.utils.training.tensorboard_logger import TensorboardLogger
-from tensorboardX import SummaryWriter
-
-from espnet.utils.deterministic_utils import set_deterministic_chainer
-from espnet.utils.training.evaluator import BaseEvaluator
-from espnet.utils.training.iterators import ShufflingEnabler
-from espnet.utils.training.train_utils import check_early_stop
-from espnet.utils.training.train_utils import set_early_stop
-
-
-# TODO(karita): reimplement RNNLM with new interface
-class DefaultRNNLM(LMInterface, link.Chain):
-    """Default RNNLM wrapper to compute reduce framewise loss values.
-
-    Args:
-        n_vocab (int): The size of the vocabulary
-        args (argparse.Namespace): configurations. see `add_arguments`
-    """
-
-    @staticmethod
-    def add_arguments(parser):
-        parser.add_argument(
-            "--type",
-            type=str,
-            default="lstm",
-            nargs="?",
-            choices=["lstm", "gru"],
-            help="Which type of RNN to use",
-        )
-        parser.add_argument(
-            "--layer", "-l", type=int, default=2, help="Number of hidden layers"
-        )
-        parser.add_argument(
-            "--unit", "-u", type=int, default=650, help="Number of hidden units"
-        )
-        return parser
-
-
-class ClassifierWithState(link.Chain):
-    """A wrapper for a chainer RNNLM
-
-    :param link.Chain predictor : The RNNLM
-    :param function lossfun: The loss function to use
-    :param int/str label_key:
-    """
-
-    def __init__(
-        self,
-        predictor,
-        lossfun=softmax_cross_entropy.softmax_cross_entropy,
-        label_key=-1,
-    ):
-        if not (isinstance(label_key, (int, str))):
-            raise TypeError("label_key must be int or str, but is %s" % type(label_key))
-
-        super(ClassifierWithState, self).__init__()
-        self.lossfun = lossfun
-        self.y = None
-        self.loss = None
-        self.label_key = label_key
-
-        with self.init_scope():
-            self.predictor = predictor
-
-    def __call__(self, state, *args, **kwargs):
-        """Computes the loss value for an input and label pair.
-
-            It also computes accuracy and stores it to the attribute.
-            When ``label_key`` is ``int``, the corresponding element in ``args``
-            is treated as ground truth labels. And when it is ``str``, the
-            element in ``kwargs`` is used.
-            The all elements of ``args`` and ``kwargs`` except the groundtruth
-            labels are features.
-            It feeds features to the predictor and compare the result
-            with ground truth labels.
-
-        :param state : The LM state
-        :param list[chainer.Variable] args : Input minibatch
-        :param dict[chainer.Variable] kwargs : Input minibatch
-        :return loss value
-        :rtype chainer.Variable
-        """
-
-        if isinstance(self.label_key, int):
-            if not (-len(args) <= self.label_key < len(args)):
-                msg = "Label key %d is out of bounds" % self.label_key
-                raise ValueError(msg)
-            t = args[self.label_key]
-            if self.label_key == -1:
-                args = args[:-1]
-            else:
-                args = args[: self.label_key] + args[self.label_key + 1 :]
-        elif isinstance(self.label_key, str):
-            if self.label_key not in kwargs:
-                msg = 'Label key "%s" is not found' % self.label_key
-                raise ValueError(msg)
-            t = kwargs[self.label_key]
-            del kwargs[self.label_key]
-
-        self.y = None
-        self.loss = None
-        state, self.y = self.predictor(state, *args, **kwargs)
-        self.loss = self.lossfun(self.y, t)
-        return state, self.loss
-
-    def predict(self, state, x):
-        """Predict log probabilities for given state and input x using the predictor
-
-        :param state : the state
-        :param x : the input
-        :return a tuple (state, log prob vector)
-        :rtype cupy/numpy array
-        """
-        if hasattr(self.predictor, "normalized") and self.predictor.normalized:
-            return self.predictor(state, x)
-        else:
-            state, z = self.predictor(state, x)
-            return state, F.log_softmax(z).data
-
-    def final(self, state):
-        """Predict final log probabilities for given state using the predictor
-
-        :param state : the state
-        :return log probability vector
-        :rtype cupy/numpy array
-
-        """
-        if hasattr(self.predictor, "final"):
-            return self.predictor.final(state)
-        else:
-            return 0.0
-
-
-# Definition of a recurrent net for language modeling
-class RNNLM(chainer.Chain):
-    """A chainer RNNLM
-
-    :param int n_vocab: The size of the vocabulary
-    :param int n_layers: The number of layers to create
-    :param int n_units: The number of units per layer
-    :param str type: The RNN type
-    """
-
-    def __init__(self, n_vocab, n_layers, n_units, typ="lstm"):
-        super(RNNLM, self).__init__()
-        with self.init_scope():
-            self.embed = DL.EmbedID(n_vocab, n_units)
-            self.rnn = (
-                chainer.ChainList(
-                    *[L.StatelessLSTM(n_units, n_units) for _ in range(n_layers)]
-                )
-                if typ == "lstm"
-                else chainer.ChainList(
-                    *[L.StatelessGRU(n_units, n_units) for _ in range(n_layers)]
-                )
-            )
-            self.lo = L.Linear(n_units, n_vocab)
-
-        for param in self.params():
-            param.data[...] = np.random.uniform(-0.1, 0.1, param.data.shape)
-        self.n_layers = n_layers
-        self.n_units = n_units
-        self.typ = typ
-
-    def __call__(self, state, x):
-        if state is None:
-            if self.typ == "lstm":
-                state = {"c": [None] * self.n_layers, "h": [None] * self.n_layers}
-            else:
-                state = {"h": [None] * self.n_layers}
-
-        h = [None] * self.n_layers
-        emb = self.embed(x)
-        if self.typ == "lstm":
-            c = [None] * self.n_layers
-            c[0], h[0] = self.rnn[0](state["c"][0], state["h"][0], F.dropout(emb))
-            for n in six.moves.range(1, self.n_layers):
-                c[n], h[n] = self.rnn[n](
-                    state["c"][n], state["h"][n], F.dropout(h[n - 1])
-                )
-            state = {"c": c, "h": h}
-        else:
-            if state["h"][0] is None:
-                xp = self.xp
-                with chainer.backends.cuda.get_device_from_id(self._device_id):
-                    state["h"][0] = chainer.Variable(
-                        xp.zeros((emb.shape[0], self.n_units), dtype=emb.dtype)
-                    )
-            h[0] = self.rnn[0](state["h"][0], F.dropout(emb))
-            for n in six.moves.range(1, self.n_layers):
-                if state["h"][n] is None:
-                    xp = self.xp
-                    with chainer.backends.cuda.get_device_from_id(self._device_id):
-                        state["h"][n] = chainer.Variable(
-                            xp.zeros(
-                                (h[n - 1].shape[0], self.n_units), dtype=h[n - 1].dtype
-                            )
-                        )
-                h[n] = self.rnn[n](state["h"][n], F.dropout(h[n - 1]))
-            state = {"h": h}
-        y = self.lo(F.dropout(h[-1]))
-        return state, y
-
-
-class BPTTUpdater(training.updaters.StandardUpdater):
-    """An updater for a chainer LM
-
-    :param chainer.dataset.Iterator train_iter : The train iterator
-    :param optimizer:
-    :param schedulers:
-    :param int device : The device id
-    :param int accum_grad :
-    """
-
-    def __init__(self, train_iter, optimizer, schedulers, device, accum_grad):
-        super(BPTTUpdater, self).__init__(train_iter, optimizer, device=device)
-        self.scheduler = ChainerScheduler(schedulers, optimizer)
-        self.accum_grad = accum_grad
-
-    # The core part of the update routine can be customized by overriding.
-    def update_core(self):
-        # When we pass one iterator and optimizer to StandardUpdater.__init__,
-        # they are automatically named 'main'.
-        train_iter = self.get_iterator("main")
-        optimizer = self.get_optimizer("main")
-
-        count = 0
-        sum_loss = 0
-        optimizer.target.cleargrads()  # Clear the parameter gradients
-        for _ in range(self.accum_grad):
-            # Progress the dataset iterator for sentences at each iteration.
-            batch = train_iter.__next__()
-            x, t = convert.concat_examples(batch, device=self.device, padding=(0, -1))
-            # Concatenate the token IDs to matrices and send them to the device
-            # self.converter does this job
-            # (it is chainer.dataset.concat_examples by default)
-            xp = chainer.backends.cuda.get_array_module(x)
-            loss = 0
-            state = None
-            batch_size, sequence_length = x.shape
-            for i in six.moves.range(sequence_length):
-                # Compute the loss at this time step and accumulate it
-                state, loss_batch = optimizer.target(
-                    state, chainer.Variable(x[:, i]), chainer.Variable(t[:, i])
-                )
-                non_zeros = xp.count_nonzero(x[:, i])
-                loss += loss_batch * non_zeros
-                count += int(non_zeros)
-            # backward
-            loss /= batch_size * self.accum_grad  # normalized by batch size
-            sum_loss += float(loss.data)
-            loss.backward()  # Backprop
-            loss.unchain_backward()  # Truncate the graph
-
-        reporter.report({"loss": sum_loss}, optimizer.target)
-        reporter.report({"count": count}, optimizer.target)
-        # update
-        optimizer.update()  # Update the parameters
-        self.scheduler.step(self.iteration)
-
-
-class LMEvaluator(BaseEvaluator):
-    """A custom evaluator for a chainer LM
-
-    :param chainer.dataset.Iterator val_iter : The validation iterator
-    :param eval_model : The model to evaluate
-    :param int device : The device id to use
-    """
-
-    def __init__(self, val_iter, eval_model, device):
-        super(LMEvaluator, self).__init__(val_iter, eval_model, device=device)
-
-    def evaluate(self):
-        val_iter = self.get_iterator("main")
-        target = self.get_target("main")
-        loss = 0
-        count = 0
-        for batch in copy.copy(val_iter):
-            x, t = convert.concat_examples(batch, device=self.device, padding=(0, -1))
-            xp = chainer.backends.cuda.get_array_module(x)
-            state = None
-            for i in six.moves.range(len(x[0])):
-                state, loss_batch = target(state, x[:, i], t[:, i])
-                non_zeros = xp.count_nonzero(x[:, i])
-                loss += loss_batch.data * non_zeros
-                count += int(non_zeros)
-        # report validation loss
-        observation = {}
-        with reporter.report_scope(observation):
-            reporter.report({"loss": float(loss / count)}, target)
-        return observation
-
-
-def train(args):
-    """Train with the given args
-
-    :param Namespace args: The program arguments
-    """
-    # TODO(karita): support this
-    if args.model_module != "default":
-        raise NotImplementedError("chainer backend does not support --model-module")
-
-    # display chainer version
-    logging.info("chainer version = " + chainer.__version__)
-
-    set_deterministic_chainer(args)
-
-    # check cuda and cudnn availability
-    if not chainer.cuda.available:
-        logging.warning("cuda is not available")
-    if not chainer.cuda.cudnn_enabled:
-        logging.warning("cudnn is not available")
-
-    # get special label ids
-    unk = args.char_list_dict["<unk>"]
-    eos = args.char_list_dict["<eos>"]
-    # read tokens as a sequence of sentences
-    train = read_tokens(args.train_label, args.char_list_dict)
-    val = read_tokens(args.valid_label, args.char_list_dict)
-    # count tokens
-    n_train_tokens, n_train_oovs = count_tokens(train, unk)
-    n_val_tokens, n_val_oovs = count_tokens(val, unk)
-    logging.info("#vocab = " + str(args.n_vocab))
-    logging.info("#sentences in the training data = " + str(len(train)))
-    logging.info("#tokens in the training data = " + str(n_train_tokens))
-    logging.info(
-        "oov rate in the training data = %.2f %%"
-        % (n_train_oovs / n_train_tokens * 100)
-    )
-    logging.info("#sentences in the validation data = " + str(len(val)))
-    logging.info("#tokens in the validation data = " + str(n_val_tokens))
-    logging.info(
-        "oov rate in the validation data = %.2f %%" % (n_val_oovs / n_val_tokens * 100)
-    )
-
-    use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
-
-    # Create the dataset iterators
-    train_iter = ParallelSentenceIterator(
-        train,
-        args.batchsize,
-        max_length=args.maxlen,
-        sos=eos,
-        eos=eos,
-        shuffle=not use_sortagrad,
-    )
-    val_iter = ParallelSentenceIterator(
-        val, args.batchsize, max_length=args.maxlen, sos=eos, eos=eos, repeat=False
-    )
-    epoch_iters = int(len(train_iter.batch_indices) / args.accum_grad)
-    logging.info("#iterations per epoch = %d" % epoch_iters)
-    logging.info("#total iterations = " + str(args.epoch * epoch_iters))
-    # Prepare an RNNLM model
-    rnn = RNNLM(args.n_vocab, args.layer, args.unit, args.type)
-    model = ClassifierWithState(rnn)
-    if args.ngpu > 1:
-        logging.warning("currently, multi-gpu is not supported. use single gpu.")
-    if args.ngpu > 0:
-        # Make the specified GPU current
-        gpu_id = 0
-        chainer.cuda.get_device_from_id(gpu_id).use()
-        model.to_gpu()
-    else:
-        gpu_id = -1
-
-    # Save model conf to json
-    model_conf = args.outdir + "/model.json"
-    with open(model_conf, "wb") as f:
-        logging.info("writing a model config file to " + model_conf)
-        f.write(
-            json.dumps(vars(args), indent=4, ensure_ascii=False, sort_keys=True).encode(
-                "utf_8"
-            )
-        )
-
-    # Set up an optimizer
-    opt_class = dynamic_import_optimizer(args.opt, args.backend)
-    optimizer = opt_class.from_args(model, args)
-    if args.schedulers is None:
-        schedulers = []
-    else:
-        schedulers = [dynamic_import_scheduler(v)(k, args) for k, v in args.schedulers]
-
-    optimizer.setup(model)
-    optimizer.add_hook(chainer.optimizer.GradientClipping(args.gradclip))
-
-    updater = BPTTUpdater(train_iter, optimizer, schedulers, gpu_id, args.accum_grad)
-    trainer = training.Trainer(updater, (args.epoch, "epoch"), out=args.outdir)
-    trainer.extend(LMEvaluator(val_iter, model, device=gpu_id))
-    trainer.extend(
-        extensions.LogReport(
-            postprocess=compute_perplexity,
-            trigger=(args.report_interval_iters, "iteration"),
-        )
-    )
-    trainer.extend(
-        extensions.PrintReport(
-            ["epoch", "iteration", "perplexity", "val_perplexity", "elapsed_time"]
-        ),
-        trigger=(args.report_interval_iters, "iteration"),
-    )
-    trainer.extend(extensions.ProgressBar(update_interval=args.report_interval_iters))
-    trainer.extend(extensions.snapshot(filename="snapshot.ep.{.updater.epoch}"))
-    trainer.extend(extensions.snapshot_object(model, "rnnlm.model.{.updater.epoch}"))
-    # MEMO(Hori): wants to use MinValueTrigger, but it seems to fail in resuming
-    trainer.extend(MakeSymlinkToBestModel("validation/main/loss", "rnnlm.model"))
-
-    if use_sortagrad:
-        trainer.extend(
-            ShufflingEnabler([train_iter]),
-            trigger=(args.sortagrad if args.sortagrad != -1 else args.epoch, "epoch"),
-        )
-
-    if args.resume:
-        logging.info("resumed from %s" % args.resume)
-        chainer.serializers.load_npz(args.resume, trainer)
-
-    set_early_stop(trainer, args, is_lm=True)
-    if args.tensorboard_dir is not None and args.tensorboard_dir != "":
-        writer = SummaryWriter(args.tensorboard_dir)
-        trainer.extend(
-            TensorboardLogger(writer), trigger=(args.report_interval_iters, "iteration")
-        )
-
-    trainer.run()
-    check_early_stop(trainer, args.epoch)
-
-    # compute perplexity for test set
-    if args.test_label:
-        logging.info("test the best model")
-        chainer.serializers.load_npz(args.outdir + "/rnnlm.model.best", model)
-        test = read_tokens(args.test_label, args.char_list_dict)
-        n_test_tokens, n_test_oovs = count_tokens(test, unk)
-        logging.info("#sentences in the test data = " + str(len(test)))
-        logging.info("#tokens in the test data = " + str(n_test_tokens))
-        logging.info(
-            "oov rate in the test data = %.2f %%" % (n_test_oovs / n_test_tokens * 100)
-        )
-        test_iter = ParallelSentenceIterator(
-            test, args.batchsize, max_length=args.maxlen, sos=eos, eos=eos, repeat=False
-        )
-        evaluator = LMEvaluator(test_iter, model, device=gpu_id)
-        with chainer.using_config("train", False):
-            result = evaluator()
-        logging.info("test perplexity: " + str(np.exp(float(result["main/loss"]))))
diff --git a/spaces/segments-tobias/conex/espnet/utils/spec_augment.py b/spaces/segments-tobias/conex/espnet/utils/spec_augment.py
deleted file mode 100644
index b6c6bb2b856120a72cb5c229b985d43f0858276a..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/utils/spec_augment.py
+++ /dev/null
@@ -1,500 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-This implementation is modified from https://github.com/zcaceres/spec_augment
-
-MIT License
-
-Copyright (c) 2019 Zach Caceres
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETjjHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-
-import random
-
-import torch
-
-
-def specaug(
-    spec, W=5, F=30, T=40, num_freq_masks=2, num_time_masks=2, replace_with_zero=False
-):
-    """SpecAugment
-
-    Reference:
-        SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
-        (https://arxiv.org/pdf/1904.08779.pdf)
-
-    This implementation modified from https://github.com/zcaceres/spec_augment
-
-    :param torch.Tensor spec: input tensor with the shape (T, dim)
-    :param int W: time warp parameter
-    :param int F: maximum width of each freq mask
-    :param int T: maximum width of each time mask
-    :param int num_freq_masks: number of frequency masks
-    :param int num_time_masks: number of time masks
-    :param bool replace_with_zero: if True, masked parts will be filled with 0,
-        if False, filled with mean
-    """
-    return time_mask(
-        freq_mask(
-            time_warp(spec, W=W),
-            F=F,
-            num_masks=num_freq_masks,
-            replace_with_zero=replace_with_zero,
-        ),
-        T=T,
-        num_masks=num_time_masks,
-        replace_with_zero=replace_with_zero,
-    )
-
-
-def time_warp(spec, W=5):
-    """Time warping
-
-    :param torch.Tensor spec: input tensor with shape (T, dim)
-    :param int W: time warp parameter
-    """
-    spec = spec.unsqueeze(0)
-    spec_len = spec.shape[1]
-    num_rows = spec.shape[2]
-    device = spec.device
-
-    y = num_rows // 2
-    horizontal_line_at_ctr = spec[0, :, y]
-    assert len(horizontal_line_at_ctr) == spec_len
-
-    point_to_warp = horizontal_line_at_ctr[random.randrange(W, spec_len - W)]
-    assert isinstance(point_to_warp, torch.Tensor)
-
-    # Uniform distribution from (0,W) with chance to be up to W negative
-    dist_to_warp = random.randrange(-W, W)
-    src_pts, dest_pts = (
-        torch.tensor([[[point_to_warp, y]]], device=device),
-        torch.tensor([[[point_to_warp + dist_to_warp, y]]], device=device),
-    )
-    warped_spectro, dense_flows = sparse_image_warp(spec, src_pts, dest_pts)
-    return warped_spectro.squeeze(3).squeeze(0)
-
-
-def freq_mask(spec, F=30, num_masks=1, replace_with_zero=False):
-    """Frequency masking
-
-    :param torch.Tensor spec: input tensor with shape (T, dim)
-    :param int F: maximum width of each mask
-    :param int num_masks: number of masks
-    :param bool replace_with_zero: if True, masked parts will be filled with 0,
-        if False, filled with mean
-    """
-    cloned = spec.unsqueeze(0).clone()
-    num_mel_channels = cloned.shape[2]
-
-    for i in range(0, num_masks):
-        f = random.randrange(0, F)
-        f_zero = random.randrange(0, num_mel_channels - f)
-
-        # avoids randrange error if values are equal and range is empty
-        if f_zero == f_zero + f:
-            return cloned.squeeze(0)
-
-        mask_end = random.randrange(f_zero, f_zero + f)
-        if replace_with_zero:
-            cloned[0][:, f_zero:mask_end] = 0
-        else:
-            cloned[0][:, f_zero:mask_end] = cloned.mean()
-    return cloned.squeeze(0)
-
-
-def time_mask(spec, T=40, num_masks=1, replace_with_zero=False):
-    """Time masking
-
-    :param torch.Tensor spec: input tensor with shape (T, dim)
-    :param int T: maximum width of each mask
-    :param int num_masks: number of masks
-    :param bool replace_with_zero: if True, masked parts will be filled with 0,
-        if False, filled with mean
-    """
-    cloned = spec.unsqueeze(0).clone()
-    len_spectro = cloned.shape[1]
-
-    for i in range(0, num_masks):
-        t = random.randrange(0, T)
-        t_zero = random.randrange(0, len_spectro - t)
-
-        # avoids randrange error if values are equal and range is empty
-        if t_zero == t_zero + t:
-            return cloned.squeeze(0)
-
-        mask_end = random.randrange(t_zero, t_zero + t)
-        if replace_with_zero:
-            cloned[0][t_zero:mask_end, :] = 0
-        else:
-            cloned[0][t_zero:mask_end, :] = cloned.mean()
-    return cloned.squeeze(0)
-
-
-def sparse_image_warp(
-    img_tensor,
-    source_control_point_locations,
-    dest_control_point_locations,
-    interpolation_order=2,
-    regularization_weight=0.0,
-    num_boundaries_points=0,
-):
-    device = img_tensor.device
-    control_point_flows = dest_control_point_locations - source_control_point_locations
-
-    batch_size, image_height, image_width = img_tensor.shape
-    flattened_grid_locations = get_flat_grid_locations(
-        image_height, image_width, device
-    )
-
-    flattened_flows = interpolate_spline(
-        dest_control_point_locations,
-        control_point_flows,
-        flattened_grid_locations,
-        interpolation_order,
-        regularization_weight,
-    )
-
-    dense_flows = create_dense_flows(
-        flattened_flows, batch_size, image_height, image_width
-    )
-
-    warped_image = dense_image_warp(img_tensor, dense_flows)
-
-    return warped_image, dense_flows
-
-
-def get_grid_locations(image_height, image_width, device):
-    y_range = torch.linspace(0, image_height - 1, image_height, device=device)
-    x_range = torch.linspace(0, image_width - 1, image_width, device=device)
-    y_grid, x_grid = torch.meshgrid(y_range, x_range)
-    return torch.stack((y_grid, x_grid), -1)
-
-
-def flatten_grid_locations(grid_locations, image_height, image_width):
-    return torch.reshape(grid_locations, [image_height * image_width, 2])
-
-
-def get_flat_grid_locations(image_height, image_width, device):
-    y_range = torch.linspace(0, image_height - 1, image_height, device=device)
-    x_range = torch.linspace(0, image_width - 1, image_width, device=device)
-    y_grid, x_grid = torch.meshgrid(y_range, x_range)
-    return torch.stack((y_grid, x_grid), -1).reshape([image_height * image_width, 2])
-
-
-def create_dense_flows(flattened_flows, batch_size, image_height, image_width):
-    # possibly .view
-    return torch.reshape(flattened_flows, [batch_size, image_height, image_width, 2])
-
-
-def interpolate_spline(
-    train_points,
-    train_values,
-    query_points,
-    order,
-    regularization_weight=0.0,
-):
-    # First, fit the spline to the observed data.
-    w, v = solve_interpolation(train_points, train_values, order, regularization_weight)
-    # Then, evaluate the spline at the query locations.
-    query_values = apply_interpolation(query_points, train_points, w, v, order)
-
-    return query_values
-
-
-def solve_interpolation(train_points, train_values, order, regularization_weight):
-    device = train_points.device
-    b, n, d = train_points.shape
-    k = train_values.shape[-1]
-
-    c = train_points
-    f = train_values.float()
-
-    matrix_a = phi(cross_squared_distance_matrix(c, c), order).unsqueeze(0)  # [b, n, n]
-
-    # Append ones to the feature values for the bias term in the linear model.
-    ones = torch.ones(1, dtype=train_points.dtype, device=device).view([-1, 1, 1])
-    matrix_b = torch.cat((c, ones), 2).float()  # [b, n, d + 1]
-
-    # [b, n + d + 1, n]
-    left_block = torch.cat((matrix_a, torch.transpose(matrix_b, 2, 1)), 1)
-
-    num_b_cols = matrix_b.shape[2]  # d + 1
-
-    # In Tensorflow, zeros are used here. Pytorch solve fails with zeros
-    # for some reason we don't understand.
-    # So instead we use very tiny randn values (variance of one, zero mean)
-    # on one side of our multiplication.
-    lhs_zeros = torch.randn((b, num_b_cols, num_b_cols), device=device) / 1e10
-    right_block = torch.cat((matrix_b, lhs_zeros), 1)  # [b, n + d + 1, d + 1]
-    lhs = torch.cat((left_block, right_block), 2)  # [b, n + d + 1, n + d + 1]
-
-    rhs_zeros = torch.zeros(
-        (b, d + 1, k), dtype=train_points.dtype, device=device
-    ).float()
-    rhs = torch.cat((f, rhs_zeros), 1)  # [b, n + d + 1, k]
-
-    # Then, solve the linear system and unpack the results.
-    X, LU = torch.gesv(rhs, lhs)
-    w = X[:, :n, :]
-    v = X[:, n:, :]
-
-    return w, v
-
-
-def cross_squared_distance_matrix(x, y):
-    """Pairwise squared distance between two (batch) matrices' rows (2nd dim).
-
-    Computes the pairwise distances between rows of x and rows of y
-    Args:
-    x: [batch_size, n, d] float `Tensor`
-    y: [batch_size, m, d] float `Tensor`
-    Returns:
-    squared_dists: [batch_size, n, m] float `Tensor`, where
-    squared_dists[b,i,j] = ||x[b,i,:] - y[b,j,:]||^2
-    """
-    x_norm_squared = torch.sum(torch.mul(x, x))
-    y_norm_squared = torch.sum(torch.mul(y, y))
-
-    x_y_transpose = torch.matmul(x.squeeze(0), y.squeeze(0).transpose(0, 1))
-
-    # squared_dists[b,i,j] = ||x_bi - y_bj||^2 = x_bi'x_bi- 2x_bi'x_bj + x_bj'x_bj
-    squared_dists = x_norm_squared - 2 * x_y_transpose + y_norm_squared
-
-    return squared_dists.float()
-
-
-def phi(r, order):
-    """Coordinate-wise nonlinearity used to define the order of the interpolation.
-
-    See https://en.wikipedia.org/wiki/Polyharmonic_spline for the definition.
-    Args:
-    r: input op
-    order: interpolation order
-    Returns:
-    phi_k evaluated coordinate-wise on r, for k = r
-    """
-    EPSILON = torch.tensor(1e-10, device=r.device)
-    # using EPSILON prevents log(0), sqrt0), etc.
-    # sqrt(0) is well-defined, but its gradient is not
-    if order == 1:
-        r = torch.max(r, EPSILON)
-        r = torch.sqrt(r)
-        return r
-    elif order == 2:
-        return 0.5 * r * torch.log(torch.max(r, EPSILON))
-    elif order == 4:
-        return 0.5 * torch.square(r) * torch.log(torch.max(r, EPSILON))
-    elif order % 2 == 0:
-        r = torch.max(r, EPSILON)
-        return 0.5 * torch.pow(r, 0.5 * order) * torch.log(r)
-    else:
-        r = torch.max(r, EPSILON)
-        return torch.pow(r, 0.5 * order)
-
-
-def apply_interpolation(query_points, train_points, w, v, order):
-    """Apply polyharmonic interpolation model to data.
-
-    Notes:
-        Given coefficients w and v for the interpolation model, we evaluate
-        interpolated function values at query_points.
-
-    Args:
-        query_points: `[b, m, d]` x values to evaluate the interpolation at
-        train_points: `[b, n, d]` x values that act as the interpolation centers
-            ( the c variables in the wikipedia article)
-            w: `[b, n, k]` weights on each interpolation center
-            v: `[b, d, k]` weights on each input dimension
-        order: order of the interpolation
-
-    Returns:
-        Polyharmonic interpolation evaluated at points defined in query_points.
-    """
-    query_points = query_points.unsqueeze(0)
-    # First, compute the contribution from the rbf term.
-    pairwise_dists = cross_squared_distance_matrix(
-        query_points.float(), train_points.float()
-    )
-    phi_pairwise_dists = phi(pairwise_dists, order)
-
-    rbf_term = torch.matmul(phi_pairwise_dists, w)
-
-    # Then, compute the contribution from the linear term.
-    # Pad query_points with ones, for the bias term in the linear model.
-    ones = torch.ones_like(query_points[..., :1])
-    query_points_pad = torch.cat((query_points, ones), 2).float()
-    linear_term = torch.matmul(query_points_pad, v)
-
-    return rbf_term + linear_term
-
-
-def dense_image_warp(image, flow):
-    """Image warping using per-pixel flow vectors.
-
-    Apply a non-linear warp to the image, where the warp is specified by a dense
-    flow field of offset vectors that define the correspondences of pixel values
-    in the output image back to locations in the  source image. Specifically, the
-    pixel value at output[b, j, i, c] is
-    images[b, j - flow[b, j, i, 0], i - flow[b, j, i, 1], c].
-    The locations specified by this formula do not necessarily map to an int
-    index. Therefore, the pixel value is obtained by bilinear
-    interpolation of the 4 nearest pixels around
-    (b, j - flow[b, j, i, 0], i - flow[b, j, i, 1]). For locations outside
-    of the image, we use the nearest pixel values at the image boundary.
-    Args:
-    image: 4-D float `Tensor` with shape `[batch, height, width, channels]`.
-    flow: A 4-D float `Tensor` with shape `[batch, height, width, 2]`.
-    name: A name for the operation (optional).
-    Note that image and flow can be of type tf.half, tf.float32, or tf.float64,
-    and do not necessarily have to be the same type.
-    Returns:
-    A 4-D float `Tensor` with shape`[batch, height, width, channels]`
-    and same type as input image.
-    Raises:
-    ValueError: if height < 2 or width < 2 or the inputs have the wrong number
-    of dimensions.
-    """
-    image = image.unsqueeze(3)  # add a single channel dimension to image tensor
-    batch_size, height, width, channels = image.shape
-    device = image.device
-
-    # The flow is defined on the image grid. Turn the flow into a list of query
-    # points in the grid space.
-    grid_x, grid_y = torch.meshgrid(
-        torch.arange(width, device=device), torch.arange(height, device=device)
-    )
-
-    stacked_grid = torch.stack((grid_y, grid_x), dim=2).float()
-
-    batched_grid = stacked_grid.unsqueeze(-1).permute(3, 1, 0, 2)
-
-    query_points_on_grid = batched_grid - flow
-    query_points_flattened = torch.reshape(
-        query_points_on_grid, [batch_size, height * width, 2]
-    )
-    # Compute values at the query points, then reshape the result back to the
-    # image grid.
-    interpolated = interpolate_bilinear(image, query_points_flattened)
-    interpolated = torch.reshape(interpolated, [batch_size, height, width, channels])
-    return interpolated
-
-
-def interpolate_bilinear(
-    grid, query_points, name="interpolate_bilinear", indexing="ij"
-):
-    """Similar to Matlab's interp2 function.
-
-    Notes:
-        Finds values for query points on a grid using bilinear interpolation.
-
-    Args:
-        grid: a 4-D float `Tensor` of shape `[batch, height, width, channels]`.
-        query_points: a 3-D float `Tensor` of N points with shape `[batch, N, 2]`.
-        name: a name for the operation (optional).
-        indexing: whether the query points are specified as row and column (ij),
-            or Cartesian coordinates (xy).
-
-    Returns:
-        values: a 3-D `Tensor` with shape `[batch, N, channels]`
-
-    Raises:
-        ValueError: if the indexing mode is invalid, or if the shape of the inputs
-        invalid.
-    """
-    if indexing != "ij" and indexing != "xy":
-        raise ValueError("Indexing mode must be 'ij' or 'xy'")
-
-    shape = grid.shape
-    if len(shape) != 4:
-        msg = "Grid must be 4 dimensional. Received size: "
-        raise ValueError(msg + str(grid.shape))
-
-    batch_size, height, width, channels = grid.shape
-
-    shape = [batch_size, height, width, channels]
-    query_type = query_points.dtype
-    grid_type = grid.dtype
-    grid_device = grid.device
-
-    num_queries = query_points.shape[1]
-
-    alphas = []
-    floors = []
-    ceils = []
-    index_order = [0, 1] if indexing == "ij" else [1, 0]
-    unstacked_query_points = query_points.unbind(2)
-
-    for dim in index_order:
-        queries = unstacked_query_points[dim]
-
-        size_in_indexing_dimension = shape[dim + 1]
-
-        # max_floor is size_in_indexing_dimension - 2 so that max_floor + 1
-        # is still a valid index into the grid.
-        max_floor = torch.tensor(
-            size_in_indexing_dimension - 2, dtype=query_type, device=grid_device
-        )
-        min_floor = torch.tensor(0.0, dtype=query_type, device=grid_device)
-        maxx = torch.max(min_floor, torch.floor(queries))
-        floor = torch.min(maxx, max_floor)
-        int_floor = floor.long()
-        floors.append(int_floor)
-        ceil = int_floor + 1
-        ceils.append(ceil)
-
-        # alpha has the same type as the grid, as we will directly use alpha
-        # when taking linear combinations of pixel values from the image.
-
-        alpha = torch.tensor((queries - floor), dtype=grid_type, device=grid_device)
-        min_alpha = torch.tensor(0.0, dtype=grid_type, device=grid_device)
-        max_alpha = torch.tensor(1.0, dtype=grid_type, device=grid_device)
-        alpha = torch.min(torch.max(min_alpha, alpha), max_alpha)
-
-        # Expand alpha to [b, n, 1] so we can use broadcasting
-        # (since the alpha values don't depend on the channel).
-        alpha = torch.unsqueeze(alpha, 2)
-        alphas.append(alpha)
-
-    flattened_grid = torch.reshape(grid, [batch_size * height * width, channels])
-    batch_offsets = torch.reshape(
-        torch.arange(batch_size, device=grid_device) * height * width, [batch_size, 1]
-    )
-
-    # This wraps array_ops.gather. We reshape the image data such that the
-    # batch, y, and x coordinates are pulled into the first dimension.
-    # Then we gather. Finally, we reshape the output back. It's possible this
-    # code would be made simpler by using array_ops.gather_nd.
-    def gather(y_coords, x_coords, name):
-        linear_coordinates = batch_offsets + y_coords * width + x_coords
-        gathered_values = torch.gather(flattened_grid.t(), 1, linear_coordinates)
-        return torch.reshape(gathered_values, [batch_size, num_queries, channels])
-
-    # grab the pixel values in the 4 corners around each query point
-    top_left = gather(floors[0], floors[1], "top_left")
-    top_right = gather(floors[0], ceils[1], "top_right")
-    bottom_left = gather(ceils[0], floors[1], "bottom_left")
-    bottom_right = gather(ceils[0], ceils[1], "bottom_right")
-
-    interp_top = alphas[1] * (top_right - top_left) + top_left
-    interp_bottom = alphas[1] * (bottom_right - bottom_left) + bottom_left
-    interp = alphas[0] * (interp_bottom - interp_top) + interp_top
-
-    return interp
diff --git a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/indicnlp/tokenize/__init__.py b/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/indicnlp/tokenize/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/shencc/gpt/crazy_functions/test_project/cpp/longcode/prod_cons.h b/spaces/shencc/gpt/crazy_functions/test_project/cpp/longcode/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/crazy_functions/test_project/cpp/longcode/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/docs/training_tips_en.md b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/docs/training_tips_en.md
deleted file mode 100644
index ab9b1f8764285757a4fb3d06be7c647887bdad10..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/docs/training_tips_en.md
+++ /dev/null
@@ -1,65 +0,0 @@
-Instructions and tips for RVC training
-======================================
-This TIPS explains how data training is done.
-
-# Training flow
-I will explain along the steps in the training tab of the GUI.
-
-## step1
-Set the experiment name here. 
-
-You can also set here whether the model should take pitch into account.
-If the model doesn't consider pitch, the model will be lighter, but not suitable for singing.
-
-Data for each experiment is placed in `/logs/your-experiment-name/`.
-
-## step2a
-Loads and preprocesses audio.
-
-### load audio
-If you specify a folder with audio, the audio files in that folder will be read automatically.
-For example, if you specify `C:Users\hoge\voices`, `C:Users\hoge\voices\voice.mp3` will be loaded, but `C:Users\hoge\voices\dir\voice.mp3` will Not loaded.
-
-Since ffmpeg is used internally for reading audio, if the extension is supported by ffmpeg, it will be read automatically.
-After converting to int16 with ffmpeg, convert to float32 and normalize between -1 to 1.
-
-### denoising
-The audio is smoothed by scipy's filtfilt.
-
-### Audio Split
-First, the input audio is divided by detecting parts of silence that last longer than a certain period (max_sil_kept=5 seconds?). After splitting the audio on silence, split the audio every 4 seconds with an overlap of 0.3 seconds. For audio separated within 4 seconds, after normalizing the volume, convert the wav file to `/logs/your-experiment-name/0_gt_wavs` and then convert it to 16k sampling rate to `/logs/your-experiment-name/1_16k_wavs ` as a wav file.
-
-## step2b
-### Extract pitch
-Extract pitch information from wav files. Extract the pitch information (=f0) using the method built into parselmouth or pyworld and save it in `/logs/your-experiment-name/2a_f0`. Then logarithmically convert the pitch information to an integer between 1 and 255 and save it in `/logs/your-experiment-name/2b-f0nsf`.
-
-### Extract feature_print
-Convert the wav file to embedding in advance using HuBERT. Read the wav file saved in `/logs/your-experiment-name/1_16k_wavs`, convert the wav file to 256-dimensional features with HuBERT, and save in npy format in `/logs/your-experiment-name/3_feature256`.
-
-## step3
-train the model.
-### Glossary for Beginners
-In deep learning, the data set is divided and the learning proceeds little by little. In one model update (step), batch_size data are retrieved and predictions and error corrections are performed. Doing this once for a dataset counts as one epoch.
-
-Therefore, the learning time is the learning time per step x (the number of data in the dataset / batch size) x the number of epochs. In general, the larger the batch size, the more stable the learning becomes (learning time per step ÷ batch size) becomes smaller, but it uses more GPU memory. GPU RAM can be checked with the nvidia-smi command. Learning can be done in a short time by increasing the batch size as much as possible according to the machine of the execution environment.
-
-### Specify pretrained model
-RVC starts training the model from pretrained weights instead of from 0, so it can be trained with a small dataset.
-
-By default
-
-- If you consider pitch, it loads `rvc-location/pretrained/f0G40k.pth` and `rvc-location/pretrained/f0D40k.pth`. 
-- If you don't consider pitch, it loads `rvc-location/pretrained/f0G40k.pth` and `rvc-location/pretrained/f0D40k.pth`. 
-
-When learning, model parameters are saved in `logs/your-experiment-name/G_{}.pth` and `logs/your-experiment-name/D_{}.pth` for each save_every_epoch, but by specifying this path, you can start learning. You can restart or start training from model weights learned in a different experiment.
-
-### learning index
-RVC saves the HuBERT feature values used during training, and during inference, searches for feature values that are similar to the feature values used during learning to perform inference. In order to perform this search at high speed, the index is learned in advance.
-For index learning, we use the approximate neighborhood search library faiss. Read the feature value of `logs/your-experiment-name/3_feature256` and use it to learn the index, and save it as `logs/your-experiment-name/add_XXX.index`.
-
-(From the 20230428update version, it is read from the index, and saving / specifying is no longer necessary.)
-
-### Button description
-- Train model: After executing step2b, press this button to train the model.
-- Train feature index: After training the model, perform index learning.
-- One-click training: step2b, model training and feature index training all at once.
\ No newline at end of file
diff --git a/spaces/shgao/EditAnything/ldm/models/diffusion/plms.py b/spaces/shgao/EditAnything/ldm/models/diffusion/plms.py
deleted file mode 100644
index 7002a365d27168ced0a04e9a4d83e088f8284eae..0000000000000000000000000000000000000000
--- a/spaces/shgao/EditAnything/ldm/models/diffusion/plms.py
+++ /dev/null
@@ -1,244 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-from ldm.models.diffusion.sampling_util import norm_thresholding
-
-
-class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        if ddim_eta != 0:
-            raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               dynamic_threshold=None,
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for PLMS sampling is {size}')
-
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    dynamic_threshold=dynamic_threshold,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      dynamic_threshold=None):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running PLMS Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
-        old_eps = []
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next,
-                                      dynamic_threshold=dynamic_threshold)
-            img, pred_x0, e_t = outs
-            old_eps.append(e_t)
-            if len(old_eps) >= 4:
-                old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
-                      dynamic_threshold=None):
-        b, *_, device = *x.shape, x.device
-
-        def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-                e_t = self.model.apply_model(x, t, c)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t] * 2)
-                c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-            if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-            return e_t
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-        def get_x_prev_and_pred_x0(e_t, index):
-            # select parameters corresponding to the currently considered timestep
-            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-            # current prediction for x_0
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-            if quantize_denoised:
-                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-            if dynamic_threshold is not None:
-                pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-            # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-            return x_prev, pred_x0
-
-        e_t = get_model_output(x, t)
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = get_model_output(x_prev, t_next)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        elif len(old_eps) >= 3:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        return x_prev, pred_x0, e_t
diff --git a/spaces/shoukaku/movie_recommendation/README.md b/spaces/shoukaku/movie_recommendation/README.md
deleted file mode 100644
index f968c989e131158f64ed2f023343df9dfc9f013c..0000000000000000000000000000000000000000
--- a/spaces/shoukaku/movie_recommendation/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: Movie Recommendation
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/shreydan/khaanaGPT/app.py b/spaces/shreydan/khaanaGPT/app.py
deleted file mode 100644
index 0d941adf19b10307fed3bca17a3fa4faa0f75c60..0000000000000000000000000000000000000000
--- a/spaces/shreydan/khaanaGPT/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from transformers import pipeline
-import gradio as gr
-
-import warnings
-warnings.simplefilter('ignore')
-
-model_path = './khaanaGPT'
-
-contrastive_search_config = dict(
-    penalty_alpha = 0.5,
-    top_k = 5,
-    max_new_tokens = 512,
-    pad_token_id = 50259
-)
-
-model = pipeline('text-generation',model=model_path)
-
-def create_prompt(ingredients):
-    ingredients = ','.join([x.strip() for x in ingredients.split(',')])
-    ingredients = ingredients.strip().replace(',','\n').lower()
-    s = f"<|startoftext|>Ingredients:\n{ingredients}\n\nInstructions:\n"
-    return s
-
-def generate(prompt):
-    recipe = model(prompt,**contrastive_search_config)[0]['generated_text']
-    recipe = recipe.replace('<|startoftext|>','')
-    return recipe
-
-def wrapper(ingredients):
-    prompt = create_prompt(ingredients)
-    recipe = generate(prompt)
-    return recipe
-
-intro_html = """
-<center><h1>खानाGPT</h1></center>
-<center>
-<p>it's not perfect, may ± ingredients. The recipes are coherent, 
-but the main purpose of this project was to understand fine-tuning a causalLM like GPT-2.
-This model was fine-tuned on GPT-2 Small.</p>
-</center>
-"""
-
-with gr.Blocks() as demo:
-    gr.HTML(intro_html)
-
-    ingredients = gr.Textbox(label="ingredients",
-    placeholder='separate the ingredients with a comma.')
-
-    output = gr.Textbox(label="recipe",lines=15,)
-    greet_btn = gr.Button("Create a recipe!")
-
-    gr.Examples(['yellow dal, turmeric, green peas, tomatoes',
-                'chicken, soy sauce, tomato sauce, vinegar'],
-                inputs=ingredients
-            )
-
-    greet_btn.click(fn=wrapper, inputs=ingredients, outputs=output)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/silencewing/server/youyou/.history/math_20230613230821.html b/spaces/silencewing/server/youyou/.history/math_20230613230821.html
deleted file mode 100644
index f6f0526a73ee4d0d7ce9ca190329ea467da70a41..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/math_20230613230821.html
+++ /dev/null
@@ -1,227 +0,0 @@
-<!-- /*
- * @Author: Chauncey Yuan 
- * @Date: 2019-08-01 18:25:30 
- * @Last Modified by:   Chauncey Yuan 
- * @Last Modified time: 2019-08-03 08:24:27 
- */ -->
-
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-        html {
-            height: 100%;
-            background: #b3b4a6;
-        }
-        .cal_game {
-            width: 650px;
-            margin: 0 auto;
-        }
-        
-        table {
-            margin: 0 auto;
-        }
-        
-        td {
-            width: 150px;
-            height: 50px;
-            text-align: center;
-            line-height: 40px;
-            border: 1px solid #2b7e71;
-        }
-        
-        .btn {
-            width: 150px;
-            height: 50px;
-            color: #2b7e71;;
-            border: 1px solid #2b7e71;
-            background-color: transparent;
-            /* background-color: #fff; */
-        }
-        
-        .btn:hover {
-            background-color: #2b7e71;
-            color: #fff;
-            cursor: pointer;
-        }
-        
-        input {
-            height: 30px;
-            /* border: 1px solid #999999; */
-            background-color: transparent;
-
-        }
-    </style>
-</head>
-
-<body>
-    <div class="cal_game">
-        <table>
-            <tr>
-                <td id="output" colspan="3"></td>
-            </tr>
-            <tr>
-                <td>题目</td>
-                <td id="eq" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>答案</td>
-                <td colspan="2"><input type="text" name="" id="input" placeholder="请输入计算结果："></td>
-                <!-- <td><button class="btn" onclick="xun()">计算</button></td> -->
-            </tr>
-            <tr>
-                <td>正误</td>
-                <td id="result" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>得分</td>
-                <td id="score" colspan="2"></td>
-            </tr>
-            <tr>
-                <td id="accuracy" colspan="3"></td>
-            </tr>
-        </table>
-    </div>
-    <script>
-        // 定义题号，使用时+1
-        var i = 0;
-        // 定义计算正确的次数
-        var right_times = 0;
-        // 定义分数
-        var score = 0;
-        // 定义正确率
-        var accuracy = 0;
-        // 定义加减法符号数组，用于后边产生0或1的随机数，来确定加减法
-        var sign_operation_list = ["+", "-"];
-        // 生成0或者1的随机数，确定加减法
-        var sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-
-        var max_num = 20;
-        // 显示题号
-        document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-        document.onkeydown = function (e) {
-            if(e.which == "13" && document.getElementById("input").value){
-                xun()
-            }
-        }
-
-        // 如果是减法
-        if (sign_operation == 1) {
-            // 则被减数应该小于减数，结果才不会为负数
-            // 减数范围为0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 被减数范围为0-减数
-            var num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-        }
-        // 如果是加法
-        if (sign_operation == 0) {
-            // 两数和应小于max_num
-            // 第一个数的范围是0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 第二个数是0-（max_num-第一个数）
-            var num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-        }
-        // 组成算式，显示给用户看
-        var eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-        // 页面中显示算式
-        document.getElementById("eq").innerHTML = eq;
-        // console.log(eq);
-
-        // 定义函数，当按钮按下是执行一次
-        function xun() {
-            // console.log(num1, num2);
-            // 题号加1
-            i++;
-            // 获取用户输入的结果
-            var input = Number(document.getElementById("input").value);
-            // console.log(input, num1, num2);
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 定义真实结果
-                var calResult = num1 + num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            } // 如果是减法
-            if (sign_operation == 1) {
-                // 定义真实结果
-                var calResult = num1 - num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            }
-            // 判断结果后，用户输入框清空
-            document.getElementById("input").value = "";
-            // 显示分数
-            document.getElementById("score").innerHTML = score;
-            // 显示正确率
-            document.getElementById("accuracy").innerHTML = (((right_times / i) * 100).toFixed(2)) + "%";
-
-            if (score >= 100) {
-                window.location.href = "game.html";
-            }
-            // 生成0或者1的随机数，确定加减法
-            sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-            // 显示题号
-            document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-            // 如果是减法
-            if (sign_operation == 1) {
-                // 则被减数应该小于减数，结果才不会为负数
-                // 减数范围为0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 被减数范围为0-减数
-                num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-            }
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 两数和应小于max_num
-                // 第一个数的范围是0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 第二个数是0-（max_num-第一个数）
-                num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-            }
-            // 组成算式，显示给用户看
-            eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-            // 页面中显示算式
-            document.getElementById("eq").innerHTML = eq;
-            // console.log(eq);
-        }
-    </script>
-</body>
-
-</html>
diff --git a/spaces/simayhosmeyve/Image_Enhancement/README.md b/spaces/simayhosmeyve/Image_Enhancement/README.md
deleted file mode 100644
index a4d7bd2414ed17383b0ad811bb991dde7e0515d4..0000000000000000000000000000000000000000
--- a/spaces/simayhosmeyve/Image_Enhancement/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Image_Enhancement
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/jackhmmer.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/jackhmmer.py
deleted file mode 100644
index 194d266c1251de25d2f85ba3a2b338ca0adf95e0..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/jackhmmer.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-
-"""Library to run Jackhmmer from Python."""
-
-from concurrent import futures
-import glob
-import os
-import subprocess
-from typing import Any, Callable, Mapping, Optional, Sequence
-from urllib import request
-
-from absl import logging
-
-from alphafold.data.tools import utils
-# Internal import (7716).
-
-
-class Jackhmmer:
-  """Python wrapper of the Jackhmmer binary."""
-
-  def __init__(self,
-               *,
-               binary_path: str,
-               database_path: str,
-               n_cpu: int = 8,
-               n_iter: int = 1,
-               e_value: float = 0.0001,
-               z_value: Optional[int] = None,
-               get_tblout: bool = False,
-               filter_f1: float = 0.0005,
-               filter_f2: float = 0.00005,
-               filter_f3: float = 0.0000005,
-               incdom_e: Optional[float] = None,
-               dom_e: Optional[float] = None,
-               num_streamed_chunks: Optional[int] = None,
-               streaming_callback: Optional[Callable[[int], None]] = None):
-    """Initializes the Python Jackhmmer wrapper.
-
-    Args:
-      binary_path: The path to the jackhmmer executable.
-      database_path: The path to the jackhmmer database (FASTA format).
-      n_cpu: The number of CPUs to give Jackhmmer.
-      n_iter: The number of Jackhmmer iterations.
-      e_value: The E-value, see Jackhmmer docs for more details.
-      z_value: The Z-value, see Jackhmmer docs for more details.
-      get_tblout: Whether to save tblout string.
-      filter_f1: MSV and biased composition pre-filter, set to >1.0 to turn off.
-      filter_f2: Viterbi pre-filter, set to >1.0 to turn off.
-      filter_f3: Forward pre-filter, set to >1.0 to turn off.
-      incdom_e: Domain e-value criteria for inclusion of domains in MSA/next
-        round.
-      dom_e: Domain e-value criteria for inclusion in tblout.
-      num_streamed_chunks: Number of database chunks to stream over.
-      streaming_callback: Callback function run after each chunk iteration with
-        the iteration number as argument.
-    """
-    self.binary_path = binary_path
-    self.database_path = database_path
-    self.num_streamed_chunks = num_streamed_chunks
-
-    if not os.path.exists(self.database_path) and num_streamed_chunks is None:
-      logging.error('Could not find Jackhmmer database %s', database_path)
-      raise ValueError(f'Could not find Jackhmmer database {database_path}')
-
-    self.n_cpu = n_cpu
-    self.n_iter = n_iter
-    self.e_value = e_value
-    self.z_value = z_value
-    self.filter_f1 = filter_f1
-    self.filter_f2 = filter_f2
-    self.filter_f3 = filter_f3
-    self.incdom_e = incdom_e
-    self.dom_e = dom_e
-    self.get_tblout = get_tblout
-    self.streaming_callback = streaming_callback
-
-  def _query_chunk(self, input_fasta_path: str, database_path: str
-                   ) -> Mapping[str, Any]:
-    """Queries the database chunk using Jackhmmer."""
-    with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir:
-      sto_path = os.path.join(query_tmp_dir, 'output.sto')
-
-      # The F1/F2/F3 are the expected proportion to pass each of the filtering
-      # stages (which get progressively more expensive), reducing these
-      # speeds up the pipeline at the expensive of sensitivity.  They are
-      # currently set very low to make querying Mgnify run in a reasonable
-      # amount of time.
-      cmd_flags = [
-          # Don't pollute stdout with Jackhmmer output.
-          '-o', '/dev/null',
-          '-A', sto_path,
-          '--noali',
-          '--F1', str(self.filter_f1),
-          '--F2', str(self.filter_f2),
-          '--F3', str(self.filter_f3),
-          '--incE', str(self.e_value),
-          # Report only sequences with E-values <= x in per-sequence output.
-          '-E', str(self.e_value),
-          '--cpu', str(self.n_cpu),
-          '-N', str(self.n_iter)
-      ]
-      if self.get_tblout:
-        tblout_path = os.path.join(query_tmp_dir, 'tblout.txt')
-        cmd_flags.extend(['--tblout', tblout_path])
-
-      if self.z_value:
-        cmd_flags.extend(['-Z', str(self.z_value)])
-
-      if self.dom_e is not None:
-        cmd_flags.extend(['--domE', str(self.dom_e)])
-
-      if self.incdom_e is not None:
-        cmd_flags.extend(['--incdomE', str(self.incdom_e)])
-
-      cmd = [self.binary_path] + cmd_flags + [input_fasta_path,
-                                              database_path]
-
-      logging.info('Launching subprocess "%s"', ' '.join(cmd))
-      process = subprocess.Popen(
-          cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-      with utils.timing(
-          f'Jackhmmer ({os.path.basename(database_path)}) query'):
-        _, stderr = process.communicate()
-        retcode = process.wait()
-
-      if retcode:
-        raise RuntimeError(
-            'Jackhmmer failed\nstderr:\n%s\n' % stderr.decode('utf-8'))
-
-      # Get e-values for each target name
-      tbl = ''
-      if self.get_tblout:
-        with open(tblout_path) as f:
-          tbl = f.read()
-
-      with open(sto_path) as f:
-        sto = f.read()
-
-    raw_output = dict(
-        sto=sto,
-        tbl=tbl,
-        stderr=stderr,
-        n_iter=self.n_iter,
-        e_value=self.e_value)
-
-    return raw_output
-
-  def query(self, input_fasta_path: str) -> Sequence[Mapping[str, Any]]:
-    """Queries the database using Jackhmmer."""
-    if self.num_streamed_chunks is None:
-      return [self._query_chunk(input_fasta_path, self.database_path)]
-
-    db_basename = os.path.basename(self.database_path)
-    db_remote_chunk = lambda db_idx: f'{self.database_path}.{db_idx}'
-    db_local_chunk = lambda db_idx: f'/tmp/ramdisk/{db_basename}.{db_idx}'
-
-    # Remove existing files to prevent OOM
-    for f in glob.glob(db_local_chunk('[0-9]*')):
-      try:
-        os.remove(f)
-      except OSError:
-        print(f'OSError while deleting {f}')
-
-    # Download the (i+1)-th chunk while Jackhmmer is running on the i-th chunk
-    with futures.ThreadPoolExecutor(max_workers=2) as executor:
-      chunked_output = []
-      for i in range(1, self.num_streamed_chunks + 1):
-        # Copy the chunk locally
-        if i == 1:
-          future = executor.submit(
-              request.urlretrieve, db_remote_chunk(i), db_local_chunk(i))
-        if i < self.num_streamed_chunks:
-          next_future = executor.submit(
-              request.urlretrieve, db_remote_chunk(i+1), db_local_chunk(i+1))
-
-        # Run Jackhmmer with the chunk
-        future.result()
-        chunked_output.append(
-            self._query_chunk(input_fasta_path, db_local_chunk(i)))
-
-        # Remove the local copy of the chunk
-        os.remove(db_local_chunk(i))
-        future = next_future
-        if self.streaming_callback:
-          self.streaming_callback(i)
-    return chunked_output
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Explore Run and Jump in Treasure Hunt - The Best APK Game for Android.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Explore Run and Jump in Treasure Hunt - The Best APK Game for Android.md
deleted file mode 100644
index 90bee5ff1274b9fc594b362c391beef1e6d4feb9..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Explore Run and Jump in Treasure Hunt - The Best APK Game for Android.md	
+++ /dev/null
@@ -1,114 +0,0 @@
-<br />
-<h1>Treasure Hunt APK Download: A Fun and Challenging Game for All Ages</h1>
-<p>Do you love solving puzzles and exploring underground labyrinths? Do you want to experience the thrill of finding hidden treasures and avoiding dangerous traps? If you answered yes, then you should try Treasure Hunt APK, a game that combines classic puzzle gaming with exciting new features. In this article, we will tell you what Treasure Hunt APK is, how to download it, how to play it, and how to make it more fun.</p>
-<h2>treasure hunt apk download</h2><br /><p><b><b>Download File</b> ->->->-> <a href="https://ssurll.com/2uNYgL">https://ssurll.com/2uNYgL</a></b></p><br /><br />
- <h2>What is Treasure Hunt APK?</h2>
-<p>Treasure Hunt APK is a game that builds on Microsoft Minesweeper for Windows Adventure mode experience. It is developed by Xbox Game Studios and is available for free on the Microsoft Store. Here are some of the features that make Treasure Hunt APK a great game for all ages:</p>
- <h3>A game that blends puzzle solving and adventure</h3>
-<p>Treasure Hunt APK is not just a simple minesweeper game. It is also an adventure game that takes you deep down toward the center of the earth. You have to use your wits and logic to mark suspicious terrain and plan your moves carefully. You also have to watch out for twists, turns, and traps that stand in your way. The goal is to reach the final clue that points you towards the treasure.</p>
- <h3>A game that features hidden bonus levels and upgrades</h3>
-<p>Treasure Hunt APK is not just a linear game. It also has hidden bonus levels that are loaded with loot. You can find these bonus levels by solving clues or by using special tools like dynamite or x-ray glasses. You can also use the gold you collect to purchase supplies and permanent upgrades at the Greedy Mummy Shop. These upgrades can help you survive longer and find more treasures.</p>
- <h3>A game that integrates with Xbox Live</h3>
-<p>Treasure Hunt APK is not just a solo game. It also integrates with Xbox Live, which means you can sign in with your Microsoft account to earn achievements, compete with your friends on the leaderboards, and track your personal gameplay statistics. You can also save your progress in the cloud so you can play the game on any device without missing a beat.</p>
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- <h2>How to Download Treasure Hunt APK?</h2>
-<p>Downloading Treasure Hunt APK is easy and fast. Here are the steps you need to follow:</p>
- <h3>Find a reliable source online</h3>
-<p>There are many websites that offer Treasure Hunt APK download, but not all of them are safe and trustworthy. Some of them may contain viruses, malware, or unwanted ads that can harm your device or compromise your privacy. To avoid these risks, we recommend you to download Treasure Hunt APK from the official Microsoft Store website. This way, you can be sure that you are getting the original and updated version of the game.</p>
- <h3>Check the compatibility and requirements</h3>
-<p>Before you download Treasure Hunt APK, you need to make sure that your device meets the minimum system requirements for the game. According to the Microsoft Store website, these are:</p>
-<table>
-<tr><td>OS</td><td>Windows 10 version 10240.0 or higher</td></tr>
-<tr><td>Architecture</td><td>x86</td></tr>
-<tr><td>Memory</td><td>1 GB</td></tr>
-<tr><td>DirectX</td><td>Version 9</td></tr <h3>Follow the installation steps</h3>
-<p>Once you have verified that your device is compatible with Treasure Hunt APK, you can proceed to download and install the game. Here are the steps you need to follow:</p>
-<ol>
-<li>Click on the [download link] from the Microsoft Store website.</li>
-<li>Wait for the download to complete. The file size is about 50 MB.</li>
-<li>Open the downloaded file and follow the instructions on the screen to install the game.</li>
-<li>Launch the game from your start menu or desktop shortcut.</li>
-<li>Enjoy playing Treasure Hunt APK!</li>
-</ol>
- <h2>How to Play Treasure Hunt APK?</h2>
-<p>Playing Treasure Hunt APK is fun and easy. Here are some tips and tricks to help you get started:</p>
- <h3>Choose your mode and difficulty level</h3>
-<p>Treasure Hunt APK has two modes: Adventure and Classic. In Adventure mode, you have to explore different maps and find clues to locate the treasure. In Classic mode, you have to clear a grid of tiles without hitting any mines. You can also choose your difficulty level from Easy, Medium, or Hard. The higher the difficulty, the more mines and traps there are.</p>
- <h3>Mark suspicious terrain and avoid traps</h3>
-<p>Treasure Hunt APK is a game of logic and deduction. You have to use the numbers on the tiles to figure out where the mines and traps are. The number indicates how many mines or traps are adjacent to that tile. You can mark a tile as suspicious by right-clicking on it or by using the flag button on the bottom right corner of the screen. You can also use tools like dynamite, x-ray glasses, or compass to reveal or destroy hidden objects.</p>
- <h3>Collect gold and buy supplies</h3>
-<p>Treasure Hunt APK is also a game of strategy and resource management. You have to collect gold by digging tiles or finding bonus levels. You can use the gold to buy supplies and upgrades at the Greedy Mummy Shop. Some of the items you can buy are extra lives, shields, maps, clues, and more. You can also save your gold for later use or spend it all at once.</p>
- <h2>How to Make Treasure Hunt APK More Fun?</h2>
-<p>Treasure Hunt APK is already a fun and challenging game, but you can make it even more fun by trying these ideas:</p>
- <h3>Use different types of clues and riddles</h3>
-<p>Treasure Hunt APK has different types of clues and riddles that can help you find the treasure. Some of them are text-based, some are image-based, and some are audio-based. You can also create your own clues and riddles by using the custom clue option in the game settings. You can use words, pictures, sounds, or even codes to make your own puzzles.</p>
- <h3>Create your own theme and treasure</h3>
-<p>Treasure Hunt APK has different themes and treasures that you can choose from. Some of them are ancient Egypt, medieval Europe, pirate island, and more. You can also create your own theme and treasure by using the custom theme option in the game settings. You can use your own images, sounds, and names to make your own adventure.</p>
- <h3>Invite your friends and compete online</h3>
-<p>Treasure Hunt APK is not only a single-player game. It also has a multiplayer mode that lets you invite your friends and compete online. You can either cooperate with your friends to find the treasure together or compete with them to see who can find it faster. You can also chat with your friends using the in-game chat feature or use voice chat if you have a microphone.</p>
- <h2>Conclusion</h2>
-<p>Treasure Hunt APK is a game that offers a lot of fun and challenge for all ages. It is a game that tests your logic, strategy, creativity, and teamwork skills. It is a game that lets you explore different worlds and find hidden treasures. It is a game that integrates with Xbox Live and lets you earn achievements, compete with friends, and save your progress in the cloud. If you are looking for a game that combines puzzle solving and adventure, then Treasure Hunt APK is the game for you.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Q: Is Treasure Hunt APK free?</b></li>
-<li>A: Yes, Treasure Hunt APK is free to download and play from the Microsoft Store website. However, some in-game items may require real money purchases.</li>
-<li><b>Q: Is Treasure Hunt APK safe?</b></li>
-<li>A: Yes, Treasure Hunt APK is safe to download and play from the official Microsoft Store website. It does not contain any viruses, malware, or unwanted ads that can harm your device or compromise your privacy.</li>
-<li><b>Q: Is Treasure Hunt APK compatible with my device?</b></li>
-<li>A: Treasure Hunt APK is compatible with devices that run on Windows 10 version 10240.0 or higher. You can check your device's OS version by going to Settings > System > About.</li>
-<li><b>Q: How can I contact the developers of Treasure Hunt APK?</b></li>
-<li>A: You can contact the developers of Treasure Hunt APK by visiting their official website or by sending them an email at support@xbox.com.</li>
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-<li>A: You can give feedback or report a bug for Treasure Hunt APK by using the feedback button on the game settings menu or by leaving a review on the Microsoft Store website.</li>
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- : https://www.microsoft.com/en-us/p/treasure-hunt/9nblggh4vq3l : https://www.xbox.com/en-US/games/treasure-hunt</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Find Your Perfect Dream Catcher Music Ringtone - Browse Thousands of Options.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Find Your Perfect Dream Catcher Music Ringtone - Browse Thousands of Options.md
deleted file mode 100644
index 64ee41f561fa264e09e1e98cd83c16f47b7359c8..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Find Your Perfect Dream Catcher Music Ringtone - Browse Thousands of Options.md	
+++ /dev/null
@@ -1,70 +0,0 @@
-
-<table>
-<tr>
-<th>Heading</th>
-<th>HTML Tag</th>
-</tr>
-<tr>
-<td>How to Download Dream Catcher Music Ringtones for Your Phone</td>
-<td>&lt;h1&gt;How to Download Dream Catcher Music Ringtones for Your Phone&lt;/h1&gt;</td>
-</tr>
-<tr>
-<td>What is a Dream Catcher and What Does It Mean?</td>
-<td>&lt;h2&gt;What is a Dream Catcher and What Does It Mean?&lt;/h2&gt;</td>
-</tr>
-<tr>
-<td>The Origin and History of Dream Catchers</td>
-<td>&lt;h3&gt;The Origin and History of Dream Catchers&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>The Symbolism and Purpose of Dream Catchers</td>
-<td>&lt;h3&gt;The Symbolism and Purpose of Dream Catchers&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>How to Make Your Own Dream Catcher</td>
-<td>&lt;h2&gt;How to Make Your Own Dream Catcher&lt;/h2&gt;</td>
-</tr>
-<tr>
-<td>The Materials and Tools You Need</td>
-<td>&lt;h3&gt;The Materials and Tools You Need&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>The Steps to Follow</td>
-<td>&lt;h3&gt;The Steps to Follow&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>How to Find and Download Dream Catcher Music Ringtones</td>
-<td>&lt;h2&gt;How to Find and Download Dream Catcher Music Ringtones&lt;/h2&gt;</td>
-</tr>
-<tr>
-<td>The Best Apps for Ringtone Download</td>
-<td>&lt;h3&gt;The Best Apps for Ringtone Download&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>The Best Websites for Ringtone Download</td>
-<td>&lt;h3&gt;The Best Websites for Ringtone Download&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>How to Set Dream Catcher Music Ringtones on Your Phone</td>
-<td>&lt;h2&gt;How to Set Dream Catcher Music Ringtones on Your Phone&lt;/h2&gt;</td>
-</tr>
-<tr>
-<td>For Android Devices</td>
-<td>&lt;h3&gt;For Android Devices&lt;/h3&gt;</td>
-</tr>
-<tr>
-<td>For iOS Devices</td>
-<td>&lt;h3&gt;For iOS Devices&lt;/h3&gt;</td>
-</tr>
- <tr>
- <td>Conclusion and FAQs </td>
- <td>&lt;h2&gt;Conclusion and FAQs&lt;/h2&gt;</td>
- </tr>
- </table>
- Now that I have the outline and the table ready, I will start writing the article based on them. Here is the article: <h1>How to Download Dream Catcher Music Ringtones for Your Phone</h1>
- Do you want to spice up your phone with some unique and beautiful ringtones? Do you love the sound of dream catchers, those mystical objects that are said to protect you from bad dreams and bring you good luck? If so, then you are in luck. In this article, we will show you how to download dream catcher music ringtones for your phone. We will also explain of ringtones in different categories and genres. You can search for "dream catcher" in its search bar and find many options for dream catcher music ringtones. You can preview them before downloading them and set them as your ringtone directly from the app. - Ringtone Maker: Ringtone Maker is another app that lets you create your own ringtones from any song or sound file on your phone or online. You can also browse its library of ringtones in different categories and genres. You can search for "dream catcher" in its search bar and find many options for dream catcher music ringtones. You can preview them before downloading them and set them as your ringtone directly from the app. - Myxer: Myxer is a website that offers free ringtones for download. It has a large collection of ringtones in various categories and genres. You can search for "dream catcher" in its search bar and find many options for dream catcher music ringtones. You can preview them before downloading them and save them to your computer or phone. - Mobile9: Mobile9 is another website that offers free ringtones for download. It has a huge collection of ringtones in various categories and genres. You can search for "dream catcher" in its search bar and find many options for dream catcher music ringtones. You can preview them before downloading them and save them to your computer or phone. <h2>How to Set Dream Catcher Music Ringtones on Your Phone</h2>
- Once you have downloaded your dream catcher music ringtones, you need to set them on your phone. The process may vary depending on the type of phone you have, but here are some general steps to follow: - For Android Devices:   - Go to Settings > Sound > Phone Ringtone.   - Tap on the ringtone you want to use and confirm.   - Alternatively, you can go to the app or the website where you downloaded the ringtone and tap on the option to set it as your ringtone. - For iOS Devices:   - Connect your phone to your computer using a USB cable.   - Open iTunes and drag the ringtone file to the Tones section under your device.   - Sync your device and disconnect it from the computer.   - Go to Settings > Sounds > Ringtone.   - Tap on the ringtone you want to use and confirm. <h2>Conclusion and FAQs</h2>
- Dream catchers are beautiful and meaningful objects that can inspire you to have good dreams and positive vibes. They can also make great ringtones for your phone, as they can provide you with soothing and enchanting sounds. In this article, we have shown you how to download dream catcher music ringtones for your phone, as well as how to make your own dream catcher and what it means. We hope you have enjoyed this article and found it useful. Here are some FAQs that you might have about dream catchers and ringtones: - Q: What are some of the benefits of using dream catcher music ringtones?   - A: Some of the benefits are:     - They can help you relax, sleep better, or meditate.     - They can make your phone stand out from the crowd.     - They can reflect your personality, style, or mood.     - They can remind you of your goals, dreams, or values. - Q: How can I make my own dream catcher music ringtone?   - A: You can use any app or website that allows you to create your own ringtones from any song or sound file. You can also record your own voice or sound using a microphone or a recorder. Then, you can edit, trim, or add effects to your ringtone using any audio editing software or app. - Q: How can I share my dream catcher music ringtone with others?   - A: You can share your ringtone with others by sending it via email, text message, Bluetooth, or social media. You can also upload it to any app or website that allows you to share ringtones with other users. - Q: How can I change my dream catcher music ringtone?   - A: You can change your ringtone anytime by following the same steps as setting it. You can also download more ringtones from any app or website that offers them. - Q: How can I delete my dream catcher music ringtone?   - A: You can delete your ringtone by going to Settings > Sound > Phone Ringtone and selecting None. You can also delete the ringtone file from your phone or computer.</p>
-<h2>dream catcher music ringtone download</h2><br /><p><b><b>DOWNLOAD</b> &#8230; <a href="https://ssurll.com/2uO0oQ">https://ssurll.com/2uO0oQ</a></b></p><br /><br /> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/API/main.py b/spaces/skf15963/summary/fengshen/API/main.py
deleted file mode 100644
index bd4ac4c4cdad24a090de80ad2a5967d73d5f6099..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/API/main.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import uvicorn
-import click
-import argparse
-import json
-from importlib import import_module
-from fastapi import FastAPI, WebSocket
-from starlette.middleware.cors import CORSMiddleware
-from utils import user_config, api_logger, setup_logger, RequestDataStructure
-
-# 命令行启动时只输入一个参数，即配置文件的名字，eg: text_classification.json
-# 其余所有配置在该配置文件中设定，不在命令行中指定
-total_parser = argparse.ArgumentParser("API")
-total_parser.add_argument("config_path", type=str)
-args = total_parser.parse_args()
-
-# set up user config
-user_config.setup_config(args)
-
-# set up logger
-setup_logger(api_logger, user_config)
-
-# load pipeline 
-pipeline_class = getattr(import_module('fengshen.pipelines.' + user_config.pipeline_type), 'Pipeline')
-model_settings = user_config.model_settings
-model_args = argparse.Namespace(**model_settings)
-pipeline = pipeline_class(
-    args = model_args,
-    model = user_config.model_name
-    )
-
-
-# initialize app
-app = FastAPI(
-    title = user_config.PROJECT_NAME, 
-    openapi_url = f"{user_config.API_PREFIX_STR}/openapi.json"
-    )
-
-
-# api 
-# TODO 
-# 需要针对不同请求方法做不同判断，目前仅跑通了较通用的POST方法
-# POST方法可以完成大多数 输入文本-返回结果 的请求任务
-if(user_config.API_method == "POST"):
-    @app.post(user_config.API_path, tags = user_config.API_tags)
-    async def fengshen_post(data:RequestDataStructure):
-        # logging
-        api_logger.info(data.input_text)
-
-        input_text = data.input_text
-
-        result = pipeline(input_text)
-
-        return result
-else:
-    print("only support POST method")
-
-
-
-# Set all CORS enabled origins
-if user_config.BACKEND_CORS_ORIGINS:
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins = [str(origin) for origin in user_config.BACKEND_CORS_ORIGINS],
-        allow_credentials = user_config.allow_credentials,
-        allow_methods = user_config.allow_methods,
-        allow_headers = user_config.allow_headers,
-    )
-
-
-if __name__ == '__main__':
-
-    # 启动后可在浏览器打开 host:port/docs 查看接口的具体信息，并可进行简单测试
-    # eg: 127.0.0.1:8990/docs
-    uvicorn.run(app, host = user_config.SERVER_HOST, port = user_config.SERVER_PORT)
-     
-
diff --git a/spaces/sklearn-docs/Ordinary_Least_Squares_and_Ridge_Regression_Variance/README.md b/spaces/sklearn-docs/Ordinary_Least_Squares_and_Ridge_Regression_Variance/README.md
deleted file mode 100644
index 79c3d56f2e9c4d149f53c7d39f4441c5ea0221ec..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Ordinary_Least_Squares_and_Ridge_Regression_Variance/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ordinary Least Squares and Ridge Regression Variance
-emoji: 🧑‍🏫 
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/smatty662/TheBloke-Wizard-Vicuna-30B-Uncensored-fp16/README.md b/spaces/smatty662/TheBloke-Wizard-Vicuna-30B-Uncensored-fp16/README.md
deleted file mode 100644
index 7538f335533f4f409f03d15dd85b9e8363464ed6..0000000000000000000000000000000000000000
--- a/spaces/smatty662/TheBloke-Wizard-Vicuna-30B-Uncensored-fp16/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TheBloke Wizard Vicuna 30B Uncensored Fp16
-emoji: 🐠
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/smruthi49/makeup/index.html b/spaces/smruthi49/makeup/index.html
deleted file mode 100644
index 80ddac8d4ad38ab8a45595ad7bee4cecb2ef559d..0000000000000000000000000000000000000000
--- a/spaces/smruthi49/makeup/index.html
+++ /dev/null
@@ -1,24 +0,0 @@
----
-title: 1. Single file
-layout: page
----
-
-<input id="photo" type="file">
-<div id="results"></div>
-<script>
-  async function loaded(reader) {
-    const response = await fetch('https://hf.space/embed/jph00/pets/+/api/predict/', {
-      method: "POST", body: JSON.stringify({ "data": [reader.result] }),
-      headers: { "Content-Type": "application/json" }
-    });
-    const json = await response.json();
-    const label = json['data'][0]['confidences'][0]['label'];
-    results.innerHTML = `<br/><img src="${reader.result}" width="300"> <p>${label}</p>`
-  }
-  function read() {
-    const reader = new FileReader();
-    reader.addEventListener('load', () => loaded(reader))
-    reader.readAsDataURL(photo.files[0]);
-  }
-  photo.addEventListener('input', read);
-</script>
\ No newline at end of file
diff --git a/spaces/sneedium/dvatch_captcha_sneedium_old/modules/model_language.py b/spaces/sneedium/dvatch_captcha_sneedium_old/modules/model_language.py
deleted file mode 100644
index a643cd5946240548746b22fc9294db63c2dfe7a1..0000000000000000000000000000000000000000
--- a/spaces/sneedium/dvatch_captcha_sneedium_old/modules/model_language.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import logging
-import torch.nn as nn
-from fastai.vision import *
-
-from modules.model import _default_tfmer_cfg
-from modules.model import Model
-from modules.transformer import (PositionalEncoding, 
-                                 TransformerDecoder,
-                                 TransformerDecoderLayer)
-
-
-class BCNLanguage(Model):
-    def __init__(self, config):
-        super().__init__(config)
-        d_model = ifnone(config.model_language_d_model, _default_tfmer_cfg['d_model'])
-        nhead = ifnone(config.model_language_nhead, _default_tfmer_cfg['nhead'])
-        d_inner = ifnone(config.model_language_d_inner, _default_tfmer_cfg['d_inner'])
-        dropout = ifnone(config.model_language_dropout, _default_tfmer_cfg['dropout'])
-        activation = ifnone(config.model_language_activation, _default_tfmer_cfg['activation'])
-        num_layers = ifnone(config.model_language_num_layers, 4)
-        self.d_model = d_model
-        self.detach = ifnone(config.model_language_detach, True)
-        self.use_self_attn = ifnone(config.model_language_use_self_attn, False)
-        self.loss_weight = ifnone(config.model_language_loss_weight, 1.0)
-        self.max_length = config.dataset_max_length + 1  # additional stop token
-        self.debug = ifnone(config.global_debug, False)
-
-        self.proj = nn.Linear(self.charset.num_classes, d_model, False)
-        self.token_encoder = PositionalEncoding(d_model, max_len=self.max_length)
-        self.pos_encoder = PositionalEncoding(d_model, dropout=0, max_len=self.max_length)
-        decoder_layer = TransformerDecoderLayer(d_model, nhead, d_inner, dropout, 
-                activation, self_attn=self.use_self_attn, debug=self.debug)
-        self.model = TransformerDecoder(decoder_layer, num_layers)
-
-        self.cls = nn.Linear(d_model, self.charset.num_classes)
-
-        if config.model_language_checkpoint is not None:
-            logging.info(f'Read language model from {config.model_language_checkpoint}.')
-            self.load(config.model_language_checkpoint)
-
-    def forward(self, tokens, lengths):
-        """
-        Args:
-            tokens: (N, T, C) where T is length, N is batch size and C is classes number
-            lengths: (N,)
-        """
-        if self.detach: tokens = tokens.detach()
-        embed = self.proj(tokens)  # (N, T, E)
-        embed = embed.permute(1, 0, 2)  # (T, N, E)
-        embed = self.token_encoder(embed)  # (T, N, E)
-        padding_mask = self._get_padding_mask(lengths, self.max_length)
-
-        zeros = embed.new_zeros(*embed.shape)
-        qeury = self.pos_encoder(zeros)
-        location_mask = self._get_location_mask(self.max_length, tokens.device)
-        output = self.model(qeury, embed,
-                tgt_key_padding_mask=padding_mask,
-                memory_mask=location_mask,
-                memory_key_padding_mask=padding_mask)  # (T, N, E)
-        output = output.permute(1, 0, 2)  # (N, T, E)
-
-        logits = self.cls(output)  # (N, T, C)
-        pt_lengths = self._get_length(logits)
-
-        res =  {'feature': output, 'logits': logits, 'pt_lengths': pt_lengths,
-                'loss_weight':self.loss_weight, 'name': 'language'}
-        return res
diff --git a/spaces/sonoisa/irasuto_search/app.py b/spaces/sonoisa/irasuto_search/app.py
deleted file mode 100644
index 0037fc415ac0593bc71b8097fb60f80dd3ad8222..0000000000000000000000000000000000000000
--- a/spaces/sonoisa/irasuto_search/app.py
+++ /dev/null
@@ -1,200 +0,0 @@
-from __future__ import unicode_literals
-import re
-import unicodedata
-import torch
-import streamlit as st
-import pandas as pd
-import pyarrow as pa
-import pyarrow.parquet as pq
-import numpy as np
-import scipy.spatial
-from transformers import BertJapaneseTokenizer, BertModel
-import pyminizip
-
-
-def unicode_normalize(cls, s):
-    pt = re.compile("([{}]+)".format(cls))
-
-    def norm(c):
-        return unicodedata.normalize("NFKC", c) if pt.match(c) else c
-
-    s = "".join(norm(x) for x in re.split(pt, s))
-    s = re.sub("－", "-", s)
-    return s
-
-
-def remove_extra_spaces(s):
-    s = re.sub("[ 　]+", " ", s)
-    blocks = "".join(
-        (
-            "\u4E00-\u9FFF",  # CJK UNIFIED IDEOGRAPHS
-            "\u3040-\u309F",  # HIRAGANA
-            "\u30A0-\u30FF",  # KATAKANA
-            "\u3000-\u303F",  # CJK SYMBOLS AND PUNCTUATION
-            "\uFF00-\uFFEF",  # HALFWIDTH AND FULLWIDTH FORMS
-        )
-    )
-    basic_latin = "\u0000-\u007F"
-
-    def remove_space_between(cls1, cls2, s):
-        p = re.compile("([{}]) ([{}])".format(cls1, cls2))
-        while p.search(s):
-            s = p.sub(r"\1\2", s)
-        return s
-
-    s = remove_space_between(blocks, blocks, s)
-    s = remove_space_between(blocks, basic_latin, s)
-    s = remove_space_between(basic_latin, blocks, s)
-    return s
-
-
-def normalize_neologd(s):
-    s = s.strip()
-    s = unicode_normalize("０-９Ａ-Ｚａ-ｚ｡-ﾟ", s)
-
-    def maketrans(f, t):
-        return {ord(x): ord(y) for x, y in zip(f, t)}
-
-    s = re.sub("[˗֊‐‑‒–⁃⁻₋−]+", "-", s)  # normalize hyphens
-    s = re.sub("[﹣－ｰ—―─━ー]+", "ー", s)  # normalize choonpus
-    s = re.sub("[~∼∾〜〰～]+", "〜", s)  # normalize tildes (modified by Isao Sonobe)
-    s = s.translate(
-        maketrans(
-            "!\"#$%&'()*+,-./:;<=>?@[¥]^_`{|}~｡､･｢｣",
-            "！”＃＄％＆’（）＊＋，－．／：；＜＝＞？＠［￥］＾＿｀｛｜｝〜。、・「」",
-        )
-    )
-
-    s = remove_extra_spaces(s)
-    s = unicode_normalize("！”＃＄％＆’（）＊＋，－．／：；＜＞？＠［￥］＾＿｀｛｜｝〜", s)  # keep ＝,・,「,」
-    s = re.sub("[’]", "'", s)
-    s = re.sub("[”]", '"', s)
-    s = s.lower()
-    return s
-
-
-def normalize_text(text):
-    return normalize_neologd(text)
-
-
-def normalize_title(title):
-    title = title.strip()
-    
-    match = re.match(r"^「([^」]+)」$", title)
-    if match:
-        title = match.group(1)
-
-    match = re.match(r"^POP素材「([^」]+)」$", title)
-    if match:
-        title = match.group(1)
-    
-    title = re.sub(r"(の?(?:イラスト|イラストの|イラストト|イ子のラスト|イラス|イラスト文字|「イラスト文字」|イラストPOP文字|ペンキ文字|タイトル文字|イラスト・メッセージ|イラスト文字・バナー|キャラクター(たち)?|マーク|アイコン|シルエット|シルエット素材|フレーム（枠）|フレーム|フレーム素材|テンプレート|パターン|パターン素材|ライン素材|コーナー素材|リボン型バナー|評価スタンプ|背景素材))+(\s*([0-9０-９]*|その[0-9０-９]+))(です。)?", "", title)
-    
-    title = normalize_text(title)
-    
-    if title.strip() == "":
-        raise ValueError(title)
-    
-    return title
-
-
-class SentenceBertJapanese:
-    def __init__(self, model_name_or_path, device=None):
-        self.tokenizer = BertJapaneseTokenizer.from_pretrained(model_name_or_path)
-        self.model = BertModel.from_pretrained(model_name_or_path)
-        self.model.eval()
-
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = torch.device(device)
-        self.model.to(device)
-
-    def _mean_pooling(self, model_output, attention_mask):
-        token_embeddings = model_output[
-            0
-        ]  # First element of model_output contains all token embeddings
-        input_mask_expanded = (
-            attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-        )
-        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
-            input_mask_expanded.sum(1), min=1e-9
-        )
-
-    @torch.no_grad()
-    def encode(self, sentences, batch_size=8):
-        all_embeddings = []
-        iterator = range(0, len(sentences), batch_size)
-        for batch_idx in iterator:
-            batch = sentences[batch_idx : batch_idx + batch_size]
-
-            encoded_input = self.tokenizer.batch_encode_plus(
-                batch, padding="longest", truncation=True, return_tensors="pt"
-            ).to(self.device)
-            model_output = self.model(**encoded_input)
-            sentence_embeddings = self._mean_pooling(
-                model_output, encoded_input["attention_mask"]
-            ).to("cpu")
-
-            all_embeddings.extend(sentence_embeddings)
-
-        # return torch.stack(all_embeddings).numpy()
-        return torch.stack(all_embeddings)
-
-
-st.title("いらすと検索")
-description_text = st.empty()
-
-if "model" not in st.session_state:
-    description_text.text("...モデル読み込み中...")
-    model = SentenceBertJapanese("sonoisa/sentence-bert-base-ja-mean-tokens")
-    st.session_state.model = model
-
-    pyminizip.uncompress(
-        "irasuto_items_20210224.pq.zip", st.secrets["ZIP_PASSWORD"], None, 1
-    )
-    
-    df = pq.read_table("irasuto_items_20210224.parquet").to_pandas()
-    st.session_state.df = df
-    
-    sentence_vectors = np.stack(df["sentence_vector"])
-    st.session_state.sentence_vectors = sentence_vectors
-    
-model = st.session_state.model
-df = st.session_state.df
-sentence_vectors = st.session_state.sentence_vectors
-
-description_text.text("説明文の意味が近い「いらすとや」画像を検索します。\nキーワードを列挙するよりも、自然な文章を入力した方が精度よく検索できます。\n画像は必ずリンク先の「いらすとや」さんのページを開き、そこからダウンロードしてください。")
-
-def clear_result():
-    result_text.text("")
-
-prev_query = ""
-query_input = st.text_input(label="説明文", value="", on_change=clear_result)
-
-closest_n = st.number_input(label="検索数", min_value=1, value=10, max_value=100) 
-
-search_buttion = st.button("検索")
-
-result_text = st.empty()
-
-if search_buttion or prev_query != query_input:
-    query = normalize_text(query_input)
-    prev_query = query_input
-    query_embedding = model.encode([query]).numpy()
-
-    distances = scipy.spatial.distance.cdist(
-        query_embedding, sentence_vectors, metric="cosine"
-    )[0]
-
-    results = zip(range(len(distances)), distances)
-    results = sorted(results, key=lambda x: x[1])
-
-    md_content = ""
-    for i, (idx, distance) in enumerate(results[0:closest_n]):
-        page_url = df.iloc[idx]["page"]
-        for img_url in df.iloc[idx]["images"]:
-            md_content += f'1. <a href="{page_url}" target="_blank" rel="noopener noreferrer"><img src="{img_url}" width="100"></a>'
-        md_content += f' [{distance / 2:.4f}: {df.iloc[idx]["description"]}]({page_url})\n'
-        
-    result_text.markdown(md_content, unsafe_allow_html=True)
-
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/backtranslation/deduplicate_lines.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/backtranslation/deduplicate_lines.py
deleted file mode 100644
index 50e458328c80b71c42a66d473381ca7e98d294da..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/backtranslation/deduplicate_lines.py
+++ /dev/null
@@ -1,41 +0,0 @@
-#!/usr/bin/python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import fileinput
-import hashlib
-import sys
-from multiprocessing import Pool
-
-
-def get_hashes_and_lines(raw_line):
-    hash = hashlib.md5(raw_line).hexdigest()
-    return hash, raw_line
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--workers", type=int, default=10)
-    parser.add_argument("files", nargs="*", help="input files")
-    args = parser.parse_args()
-
-    seen = set()
-    with fileinput.input(args.files, mode="rb") as h:
-        pool = Pool(args.workers)
-        results = pool.imap_unordered(get_hashes_and_lines, h, 1000)
-        for i, (hash, raw_line) in enumerate(results):
-            if hash not in seen:
-                seen.add(hash)
-                sys.stdout.buffer.write(raw_line)
-            if i % 1000000 == 0:
-                print(i, file=sys.stderr, end="", flush=True)
-            elif i % 100000 == 0:
-                print(".", file=sys.stderr, end="", flush=True)
-    print(file=sys.stderr, flush=True)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py
deleted file mode 100644
index 113ac655b8c0a585fe43797e99674e445098edd0..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/learn_kmeans.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import sys
-
-import numpy as np
-from sklearn.cluster import MiniBatchKMeans
-
-import joblib
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("learn_kmeans")
-
-
-def get_km_model(
-    n_clusters,
-    init,
-    max_iter,
-    batch_size,
-    tol,
-    max_no_improvement,
-    n_init,
-    reassignment_ratio,
-):
-    return MiniBatchKMeans(
-        n_clusters=n_clusters,
-        init=init,
-        max_iter=max_iter,
-        batch_size=batch_size,
-        verbose=1,
-        compute_labels=False,
-        tol=tol,
-        max_no_improvement=max_no_improvement,
-        init_size=None,
-        n_init=n_init,
-        reassignment_ratio=reassignment_ratio,
-    )
-
-
-def load_feature_shard(feat_dir, split, nshard, rank, percent):
-    feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy"
-    leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len"
-    with open(leng_path, "r") as f:
-        lengs = [int(line.rstrip()) for line in f]
-        offsets = [0] + np.cumsum(lengs[:-1]).tolist()
-
-    if percent < 0:
-        return np.load(feat_path, mmap_mode="r")
-    else:
-        nsample = int(np.ceil(len(lengs) * percent))
-        indices = np.random.choice(len(lengs), nsample, replace=False)
-        feat = np.load(feat_path, mmap_mode="r")
-        sampled_feat = np.concatenate(
-            [feat[offsets[i]: offsets[i] + lengs[i]] for i in indices], axis=0
-        )
-        logger.info(
-            (
-                f"sampled {nsample} utterances, {len(sampled_feat)} frames "
-                f"from shard {rank}/{nshard}"
-            )
-        )
-        return sampled_feat
-
-
-def load_feature(feat_dir, split, nshard, seed, percent):
-    assert percent <= 1.0
-    feat = np.concatenate(
-        [
-            load_feature_shard(feat_dir, split, nshard, r, percent)
-            for r in range(nshard)
-        ],
-        axis=0,
-    )
-    logging.info(f"loaded feature with dimension {feat.shape}")
-    return feat
-
-
-def learn_kmeans(
-    feat_dir,
-    split,
-    nshard,
-    km_path,
-    n_clusters,
-    seed,
-    percent,
-    init,
-    max_iter,
-    batch_size,
-    tol,
-    n_init,
-    reassignment_ratio,
-    max_no_improvement,
-):
-    np.random.seed(seed)
-    feat = load_feature(feat_dir, split, nshard, seed, percent)
-    km_model = get_km_model(
-        n_clusters,
-        init,
-        max_iter,
-        batch_size,
-        tol,
-        max_no_improvement,
-        n_init,
-        reassignment_ratio,
-    )
-    km_model.fit(feat)
-    joblib.dump(km_model, km_path)
-
-    inertia = -km_model.score(feat) / len(feat)
-    logger.info("total intertia: %.5f", inertia)
-    logger.info("finished successfully")
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("feat_dir", type=str)
-    parser.add_argument("split", type=str)
-    parser.add_argument("nshard", type=int)
-    parser.add_argument("km_path", type=str)
-    parser.add_argument("n_clusters", type=int)
-    parser.add_argument("--seed", default=0, type=int)
-    parser.add_argument(
-        "--percent", default=-1, type=float, help="sample a subset; -1 for all"
-    )
-    parser.add_argument("--init", default="k-means++")
-    parser.add_argument("--max_iter", default=100, type=int)
-    parser.add_argument("--batch_size", default=10000, type=int)
-    parser.add_argument("--tol", default=0.0, type=float)
-    parser.add_argument("--max_no_improvement", default=100, type=int)
-    parser.add_argument("--n_init", default=20, type=int)
-    parser.add_argument("--reassignment_ratio", default=0.0, type=float)
-    args = parser.parse_args()
-    logging.info(str(args))
-
-    learn_kmeans(**vars(args))
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh
deleted file mode 100644
index 013f7a9b055a7693a29f9c5ba1e4003a9a25850e..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env zsh
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-source_dir=$1
-tgt_dir=$2
-model=$3
-
-if [ -z "$4" ]
-  then
-    dim=512
-  else
-    dim=$4
-fi
-
-echo "using $dim dim for PCA"
-
-if [ -z "$5" ]
-  then
-    layer=14
-  else
-    layer=$5
-fi
-
-echo "extracting from layer $layer"
-
-train_split=train
-valid_split=valid
-test_split=test
-
-all_splits=($train_split)
-
-if [[ -f "$source_dir/valid.tsv" ]]; then
-    all_splits+=('valid')
-fi
-
-if [[ -f "$source_dir/test.tsv" ]]; then
-    all_splits+=('test')
-fi
-
-echo "processing splits: $all_splits"
-
-mkdir -p $tgt_dir
-
-cp $source_dir/*.tsv $tgt_dir
-cp $source_dir/*.wrd $tgt_dir
-cp $source_dir/*.ltr $tgt_dir
-cp $source_dir/*.phn $tgt_dir
-cp $source_dir/dict* $tgt_dir
-
-setopt shwordsplit
-
-for split in $all_splits; do
-  python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py $source_dir --split $split \
-  --save-dir $tgt_dir --checkpoint $model --layer $layer
-done
-
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py $tgt_dir/${train_split}.tsv \
---checkpoint $model --save-dir $tgt_dir -f "CLUS128" --sample-pct 1.0
-
-for split in $all_splits; do
-  python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py $tgt_dir \
-  --checkpoint $model --path $tgt_dir/CLUS128 --split $split
-done
-
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/pca.py $tgt_dir/${train_split}.npy --output $tgt_dir/pca --dim $dim
-
-for split in $all_splits; do
-  python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/apply_pca.py $tgt_dir --split $split --save-dir $tgt_dir/precompute_pca$dim --pca-path $tgt_dir/pca/${dim}_pca --batch-size 1048000
-
-  python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/merge_clusters.py $tgt_dir/precompute_pca$dim --cluster-dir $tgt_dir/CLUS128 \
-  --split $split --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean --pooling mean
-
-  python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/mean_pool.py $tgt_dir/precompute_pca${dim}_cls128_mean \
-  --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean_pooled --split $split
-done
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/utils.py b/spaces/sriramelango/Social_Classification_Public/fairseq/tests/utils.py
deleted file mode 100644
index 6e0c709517aea570acb36901dd47bc12a3025b07..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/utils.py
+++ /dev/null
@@ -1,717 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import json
-import os
-import random
-import sys
-from io import StringIO
-
-import torch
-import torch.nn.functional as F
-from fairseq import options, utils
-from fairseq.data import Dictionary
-from fairseq.data.language_pair_dataset import collate
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderDecoderModel,
-    FairseqIncrementalDecoder,
-)
-from fairseq.models.fairseq_encoder import EncoderOut
-from fairseq.tasks import LegacyFairseqTask
-from fairseq_cli import generate, interactive, preprocess, train, validate
-import fairseq.distributed.utils as distributed_utils
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-
-
-def dummy_dictionary(vocab_size, prefix="token_"):
-    d = Dictionary()
-    for i in range(vocab_size):
-        token = prefix + str(i)
-        d.add_symbol(token)
-    d.finalize(padding_factor=1)  # don't add extra padding symbols
-    return d
-
-
-def dummy_dataloader(
-    samples, padding_idx=1, eos_idx=2, batch_size=None,
-):
-    if batch_size is None:
-        batch_size = len(samples)
-
-    # add any missing data to samples
-    for i, sample in enumerate(samples):
-        if "id" not in sample:
-            sample["id"] = i
-
-    # create dataloader
-    dataset = TestDataset(samples)
-    dataloader = torch.utils.data.DataLoader(
-        dataset,
-        batch_size=batch_size,
-        collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)),
-    )
-    return iter(dataloader)
-
-
-def sequence_generator_setup():
-    # construct dummy dictionary
-    d = dummy_dictionary(vocab_size=2)
-
-    eos = d.eos()
-    w1 = 4
-    w2 = 5
-
-    # construct source data
-    src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
-    src_lengths = torch.LongTensor([2, 2])
-
-    args = argparse.Namespace()
-    unk = 0.0
-    args.beam_probs = [
-        # step 0:
-        torch.FloatTensor(
-            [
-                # eos      w1   w2
-                # sentence 1:
-                [0.0, unk, 0.9, 0.1],  # beam 1
-                [0.0, unk, 0.9, 0.1],  # beam 2
-                # sentence 2:
-                [0.0, unk, 0.7, 0.3],
-                [0.0, unk, 0.7, 0.3],
-            ]
-        ),
-        # step 1:
-        torch.FloatTensor(
-            [
-                # eos      w1   w2       prefix
-                # sentence 1:
-                [1.0, unk, 0.0, 0.0],  # w1: 0.9  (emit: w1 <eos>: 0.9*1.0)
-                [0.0, unk, 0.9, 0.1],  # w2: 0.1
-                # sentence 2:
-                [0.25, unk, 0.35, 0.4],  # w1: 0.7  (don't emit: w1 <eos>: 0.7*0.25)
-                [0.00, unk, 0.10, 0.9],  # w2: 0.3
-            ]
-        ),
-        # step 2:
-        torch.FloatTensor(
-            [
-                # eos      w1   w2       prefix
-                # sentence 1:
-                [0.0, unk, 0.1, 0.9],  # w2 w1: 0.1*0.9
-                [
-                    0.6,
-                    unk,
-                    0.2,
-                    0.2,
-                ],  # w2 w2: 0.1*0.1  (emit: w2 w2 <eos>: 0.1*0.1*0.6)
-                # sentence 2:
-                [
-                    0.60,
-                    unk,
-                    0.4,
-                    0.00,
-                ],  # w1 w2: 0.7*0.4  (emit: w1 w2 <eos>: 0.7*0.4*0.6)
-                [0.01, unk, 0.0, 0.99],  # w2 w2: 0.3*0.9
-            ]
-        ),
-        # step 3:
-        torch.FloatTensor(
-            [
-                # eos      w1   w2       prefix
-                # sentence 1:
-                [
-                    1.0,
-                    unk,
-                    0.0,
-                    0.0,
-                ],  # w2 w1 w2: 0.1*0.9*0.9  (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0)
-                [
-                    1.0,
-                    unk,
-                    0.0,
-                    0.0,
-                ],  # w2 w1 w1: 0.1*0.9*0.1  (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0)
-                # sentence 2:
-                [
-                    0.1,
-                    unk,
-                    0.5,
-                    0.4,
-                ],  # w2 w2 w2: 0.3*0.9*0.99  (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1)
-                [
-                    1.0,
-                    unk,
-                    0.0,
-                    0.0,
-                ],  # w1 w2 w1: 0.7*0.4*0.4  (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0)
-            ]
-        ),
-    ]
-
-    task = TestTranslationTask.setup_task(args, d, d)
-    model = task.build_model(args)
-    tgt_dict = task.target_dictionary
-
-    return tgt_dict, w1, w2, src_tokens, src_lengths, model
-
-
-def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False):
-    def _create_dummy_data(filename):
-        data = torch.rand(num_examples * maxlen)
-        data = 97 + torch.floor(26 * data).int()
-        with open(os.path.join(data_dir, filename), "w") as h:
-            offset = 0
-            for _ in range(num_examples):
-                ex_len = random.randint(1, maxlen)
-                ex_str = " ".join(map(chr, data[offset : offset + ex_len]))
-                print(ex_str, file=h)
-                offset += ex_len
-
-    def _create_dummy_alignment_data(filename_src, filename_tgt, filename):
-        with open(os.path.join(data_dir, filename_src), "r") as src_f, open(
-            os.path.join(data_dir, filename_tgt), "r"
-        ) as tgt_f, open(os.path.join(data_dir, filename), "w") as h:
-            for src, tgt in zip(src_f, tgt_f):
-                src_len = len(src.split())
-                tgt_len = len(tgt.split())
-                avg_len = (src_len + tgt_len) // 2
-                num_alignments = random.randint(avg_len // 2, 2 * avg_len)
-                src_indices = torch.floor(torch.rand(num_alignments) * src_len).int()
-                tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int()
-                ex_str = " ".join(
-                    [
-                        "{}-{}".format(src, tgt)
-                        for src, tgt in zip(src_indices, tgt_indices)
-                    ]
-                )
-                print(ex_str, file=h)
-
-    _create_dummy_data("train.in")
-    _create_dummy_data("train.out")
-    _create_dummy_data("valid.in")
-    _create_dummy_data("valid.out")
-    _create_dummy_data("test.in")
-    _create_dummy_data("test.out")
-
-    if alignment:
-        _create_dummy_alignment_data("train.in", "train.out", "train.align")
-        _create_dummy_alignment_data("valid.in", "valid.out", "valid.align")
-        _create_dummy_alignment_data("test.in", "test.out", "test.align")
-
-
-def preprocess_lm_data(data_dir):
-    preprocess_parser = options.get_preprocessing_parser()
-    preprocess_args = preprocess_parser.parse_args(
-        [
-            "--only-source",
-            "--trainpref",
-            os.path.join(data_dir, "train.out"),
-            "--validpref",
-            os.path.join(data_dir, "valid.out"),
-            "--testpref",
-            os.path.join(data_dir, "test.out"),
-            "--destdir",
-            data_dir,
-        ]
-    )
-    preprocess.main(preprocess_args)
-
-
-def preprocess_translation_data(data_dir, extra_flags=None):
-    preprocess_parser = options.get_preprocessing_parser()
-    preprocess_args = preprocess_parser.parse_args(
-        [
-            "--source-lang",
-            "in",
-            "--target-lang",
-            "out",
-            "--trainpref",
-            os.path.join(data_dir, "train"),
-            "--validpref",
-            os.path.join(data_dir, "valid"),
-            "--testpref",
-            os.path.join(data_dir, "test"),
-            "--thresholdtgt",
-            "0",
-            "--thresholdsrc",
-            "0",
-            "--destdir",
-            data_dir,
-        ]
-        + (extra_flags or []),
-    )
-    preprocess.main(preprocess_args)
-
-
-def preprocess_summarization_data(data_dir, extra_flags=None):
-    preprocess_parser = options.get_preprocessing_parser()
-    preprocess_args = preprocess_parser.parse_args(
-        [
-            "--source-lang",
-            "in",
-            "--target-lang",
-            "out",
-            "--trainpref",
-            os.path.join(data_dir, "train"),
-            "--validpref",
-            os.path.join(data_dir, "valid"),
-            "--testpref",
-            os.path.join(data_dir, "test"),
-            "--thresholdtgt",
-            "0",
-            "--thresholdsrc",
-            "0",
-            "--joined-dictionary",
-            "--destdir",
-            data_dir,
-        ]
-        + (extra_flags or []),
-    )
-    preprocess.main(preprocess_args)
-
-
-def create_laser_data_and_config_json(data_dir):
-    src_langs = ["de", "fr", "ru", "tr", "zh"]
-    tgt_langs = ["en", "es"]
-    config_json = {}
-    config_train_json = []
-    src_vocab = None
-    tgt_vocab = None
-
-    for src_lang in src_langs:
-        for tgt_lang in tgt_langs:
-            langpair_folder = f"{src_lang}-{tgt_lang}"
-
-            langpair_path = os.path.join(data_dir, langpair_folder)
-            os.mkdir(langpair_path)
-            create_dummy_data(langpair_path)
-            preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"])
-
-            src_vocab = os.path.join(langpair_path, "dict.in.txt")
-            tgt_vocab = os.path.join(langpair_path, "dict.out.txt")
-            config_train_json.append(
-                {
-                    "id": 0 if tgt_lang == "en" else 1,
-                    "src": os.path.join(langpair_path, "train.in-out.in"),
-                    "tgt": os.path.join(langpair_path, "train.in-out.out"),
-                }
-            )
-
-    config_json["src_vocab"] = src_vocab
-    config_json["tgt_vocab"] = tgt_vocab
-    config_json["train"] = config_train_json
-
-    with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file:
-        json.dump(config_json, config_file)
-
-    return config_file
-
-
-def train_translation_model(
-    data_dir,
-    arch,
-    extra_flags=None,
-    task="translation",
-    run_validation=False,
-    lang_flags=None,
-    extra_valid_flags=None,
-    world_size=1,
-):
-    if lang_flags is None:
-        lang_flags = [
-            "--source-lang",
-            "in",
-            "--target-lang",
-            "out",
-        ]
-    train_parser = options.get_training_parser()
-    train_args = options.parse_args_and_arch(
-        train_parser,
-        [
-            "--task",
-            task,
-            data_dir,
-            "--save-dir",
-            data_dir,
-            "--arch",
-            arch,
-            "--optimizer",
-            "nag",
-            "--lr",
-            "0.05",
-            "--max-tokens",
-            "500",
-            "--max-epoch",
-            "1",
-            "--no-progress-bar",
-            "--distributed-world-size",
-            str(world_size),
-            "--num-workers",
-            "0",
-        ]
-        + lang_flags
-        + (extra_flags or []),
-    )
-
-    cfg = convert_namespace_to_omegaconf(train_args)
-    distributed_utils.call_main(cfg, train.main)
-
-    if run_validation:
-        # test validation
-        validate_parser = options.get_validation_parser()
-        validate_args = options.parse_args_and_arch(
-            validate_parser,
-            [
-                "--task",
-                task,
-                data_dir,
-                "--path",
-                os.path.join(data_dir, "checkpoint_last.pt"),
-                "--valid-subset",
-                "valid",
-                "--max-tokens",
-                "500",
-                "--no-progress-bar",
-                "--num-workers",
-                "0",
-            ]
-            + lang_flags
-            + (extra_valid_flags or []),
-        )
-        validate.main(validate_args)
-
-
-def generate_main(data_dir, extra_flags=None, path=None):
-    if extra_flags is None:
-        extra_flags = [
-            "--print-alignment",
-        ]
-    if path is None:
-        path = os.path.join(data_dir, "checkpoint_last.pt")
-    generate_parser = options.get_generation_parser()
-    generate_args = options.parse_args_and_arch(
-        generate_parser,
-        [
-            data_dir,
-            "--path",
-            path,
-            "--beam",
-            "3",
-            "--batch-size",
-            "64",
-            "--max-len-b",
-            "5",
-            "--gen-subset",
-            "valid",
-            "--no-progress-bar",
-            "--num-workers",
-            "0",
-        ]
-        + (extra_flags or []),
-    )
-
-    # evaluate model in batch mode
-    generate.main(generate_args)
-
-    # evaluate model interactively
-    generate_args.buffer_size = 0
-    generate_args.input = "-"
-    generate_args.batch_size = None
-    orig_stdin = sys.stdin
-    sys.stdin = StringIO("h e l l o\n")
-    interactive.main(generate_args)
-    sys.stdin = orig_stdin
-
-
-class TestDataset(torch.utils.data.Dataset):
-    def __init__(self, data):
-        super().__init__()
-        self.data = data
-        self.sizes = None
-
-    def __getitem__(self, index):
-        return self.data[index]
-
-    def __len__(self):
-        return len(self.data)
-
-
-class TestTranslationTask(LegacyFairseqTask):
-    def __init__(self, args, src_dict, tgt_dict, model):
-        super().__init__(args)
-        self.src_dict = src_dict
-        self.tgt_dict = tgt_dict
-        self.model = model
-
-    @classmethod
-    def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None):
-        return cls(args, src_dict, tgt_dict, model)
-
-    def build_model(self, args):
-        return TestModel.build_model(args, self)
-
-    @property
-    def source_dictionary(self):
-        return self.src_dict
-
-    @property
-    def target_dictionary(self):
-        return self.tgt_dict
-
-
-class TestModel(FairseqEncoderDecoderModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    @classmethod
-    def build_model(cls, args, task):
-        encoder = TestEncoder(args, task.source_dictionary)
-        decoder = TestIncrementalDecoder(args, task.target_dictionary)
-        return cls(encoder, decoder)
-
-
-class TestEncoder(FairseqEncoder):
-    def __init__(self, args, dictionary):
-        super().__init__(dictionary)
-        self.args = args
-
-    def forward(self, src_tokens, src_lengths=None, **kwargs):
-        return EncoderOut(
-            encoder_out=src_tokens,
-            encoder_padding_mask=None,
-            encoder_embedding=None,
-            encoder_states=None,
-            src_tokens=None,
-            src_lengths=None,
-        )
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        return EncoderOut(
-            encoder_out=encoder_out.encoder_out.index_select(0, new_order),
-            encoder_padding_mask=None,
-            encoder_embedding=None,
-            encoder_states=None,
-            src_tokens=None,
-            src_lengths=None,
-        )
-
-
-class TestIncrementalDecoder(FairseqIncrementalDecoder):
-    def __init__(self, args, dictionary):
-        super().__init__(dictionary)
-        assert hasattr(args, "beam_probs") or hasattr(args, "probs")
-        args.max_decoder_positions = getattr(args, "max_decoder_positions", 100)
-        self.args = args
-
-    def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-        bbsz = prev_output_tokens.size(0)
-        vocab = len(self.dictionary)
-        src_len = encoder_out.encoder_out.size(1)
-        tgt_len = prev_output_tokens.size(1)
-
-        # determine number of steps
-        if incremental_state is not None:
-            # cache step number
-            step = utils.get_incremental_state(self, incremental_state, "step")
-            if step is None:
-                step = 0
-            utils.set_incremental_state(self, incremental_state, "step", step + 1)
-            steps = [step]
-        else:
-            steps = list(range(tgt_len))
-
-        # define output in terms of raw probs
-        if hasattr(self.args, "probs"):
-            assert (
-                self.args.probs.dim() == 3
-            ), "expected probs to have size bsz*steps*vocab"
-            probs = self.args.probs.index_select(1, torch.LongTensor(steps))
-        else:
-            probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
-            for i, step in enumerate(steps):
-                # args.beam_probs gives the probability for every vocab element,
-                # starting with eos, then unknown, and then the rest of the vocab
-                if step < len(self.args.beam_probs):
-                    probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step]
-                else:
-                    probs[:, i, self.dictionary.eos()] = 1.0
-
-        # random attention
-        attn = torch.rand(bbsz, tgt_len, src_len)
-
-        dev = prev_output_tokens.device
-        return probs.to(dev), {"attn": [attn.to(dev)]}
-
-    def get_normalized_probs(self, net_output, log_probs, _):
-        # the decoder returns probabilities directly
-        probs = net_output[0]
-        if log_probs:
-            return probs.log()
-        else:
-            return probs
-
-    def max_positions(self):
-        return self.args.max_decoder_positions
-
-
-class TestReshapingEncoder(FairseqEncoder):
-    def __init__(self, args, dictionary):
-        super().__init__(dictionary)
-        self.args = args
-
-    def forward(self, src_tokens, src_lengths=None, **kwargs):
-        b_sz, t_sz = src_tokens.shape
-        padding_needed = t_sz % 2
-        x = src_tokens
-        if padding_needed > 0:
-            padding_needed = 2 - padding_needed
-            x = F.pad(x, (0, padding_needed))
-
-        return EncoderOut(
-            encoder_out=x.view(b_sz, -1, 2),
-            encoder_padding_mask=None,
-            encoder_embedding=None,
-            encoder_states=None,
-            src_tokens=None,
-            src_lengths=None,
-        )
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        return EncoderOut(
-            encoder_out=encoder_out.encoder_out.index_select(0, new_order),
-            encoder_padding_mask=None,
-            encoder_embedding=None,
-            encoder_states=None,
-            src_tokens=None,
-            src_lengths=None,
-        )
-
-
-class TestReshapingModel(FairseqEncoderDecoderModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    @classmethod
-    def build_model(cls, args, task):
-        encoder = TestReshapingEncoder(args, task.source_dictionary)
-        decoder = TestIncrementalDecoder(args, task.target_dictionary)
-        return cls(encoder, decoder)
-
-
-class TestAdditionalInputEncoder(FairseqEncoder):
-    def __init__(self, args, dictionary):
-        super().__init__(dictionary)
-        self.args = args
-
-    def forward(self, src_tokens, src_lengths=None, **kwargs):
-        assert "fancy_other_input" in kwargs
-        assert kwargs["fancy_other_input"] is not None
-        return EncoderOut(
-            encoder_out=src_tokens,
-            encoder_padding_mask=None,
-            encoder_embedding=None,
-            encoder_states=None,
-            src_tokens=None,
-            src_lengths=None,
-        )
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        return EncoderOut(
-            encoder_out=encoder_out.encoder_out.index_select(0, new_order),
-            encoder_padding_mask=None,
-            encoder_embedding=None,
-            encoder_states=None,
-            src_tokens=None,
-            src_lengths=None,
-        )
-
-
-class TestAdditionalInputModel(FairseqEncoderDecoderModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    @classmethod
-    def build_model(cls, args, task):
-        encoder = TestAdditionalInputEncoder(args, task.source_dictionary)
-        decoder = TestIncrementalDecoder(args, task.target_dictionary)
-        return cls(encoder, decoder)
-
-    def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
-        encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
-        decoder_out = self.decoder(
-            prev_output_tokens, encoder_out=encoder_out, **kwargs
-        )
-        return decoder_out
-
-
-def train_language_model(
-    data_dir,
-    arch,
-    extra_flags=None,
-    run_validation=False,
-    extra_valid_flags=None,
-    task="language_modeling",
-    world_size=1,
-):
-    train_parser = options.get_training_parser()
-    train_args = options.parse_args_and_arch(
-        train_parser,
-        [
-            "--task",
-            task,
-            data_dir,
-            "--arch",
-            arch,
-            "--optimizer",
-            "adam",
-            "--lr",
-            "0.0001",
-            "--max-tokens",
-            "500",
-            "--tokens-per-sample",
-            "500",
-            "--save-dir",
-            data_dir,
-            "--max-epoch",
-            "1",
-            "--no-progress-bar",
-            "--distributed-world-size",
-            str(world_size),
-            "--ddp-backend",
-            "no_c10d",
-            "--num-workers",
-            "0",
-        ]
-        + (extra_flags or []),
-    )
-    cfg = convert_namespace_to_omegaconf(train_args)
-    distributed_utils.call_main(cfg, train.main)
-
-    if run_validation:
-        # test validation
-        validate_parser = options.get_validation_parser()
-        validate_args = options.parse_args_and_arch(
-            validate_parser,
-            [
-                "--task",
-                task,
-                data_dir,
-                "--path",
-                os.path.join(data_dir, "checkpoint_last.pt"),
-                "--valid-subset",
-                "valid",
-                "--max-tokens",
-                "500",
-                "--no-progress-bar",
-                "--num-workers",
-                "0",
-            ]
-            + (extra_valid_flags or []),
-        )
-        validate.main(validate_args)
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Boyz N The Hood Script Pdf.md b/spaces/stomexserde/gpt4-ui/Examples/Boyz N The Hood Script Pdf.md
deleted file mode 100644
index 29154d72ebc52c6f00e56cb54dcbfcefc7872873..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Boyz N The Hood Script Pdf.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>How to Download the Boyz N The Hood Script Pdf for Free</h1>
-<p>Boyz N The Hood is a 1991 American coming-of-age drama film written and directed by John Singleton. It stars Cuba Gooding Jr., Ice Cube, Morris Chestnut, Laurence Fishburne, Nia Long, Regina King, and Angela Bassett. The film follows the lives of three young men growing up in South Central Los Angeles during the 1980s and 1990s, dealing with issues such as racism, violence, drugs, and family.</p>
-<p>The film was critically acclaimed and nominated for two Academy Awards: Best Director and Best Original Screenplay. It is considered one of the most influential films of its genre and has been preserved in the National Film Registry by the Library of Congress.</p>
-<h2>Boyz N The Hood Script Pdf</h2><br /><p><b><b>DOWNLOAD</b> &#9745; <a href="https://urlgoal.com/2uI6XG">https://urlgoal.com/2uI6XG</a></b></p><br /><br />
-<p>If you are a fan of Boyz N The Hood or want to study its script, you might be wondering how to download the Boyz N The Hood script pdf for free. Well, you are in luck because we have found a reliable source that offers the full screenplay in pdf format. Here is how to get it:</p>
-<ol>
-<li>Go to <a href="https://www.scriptslug.com/script/boyz-n-the-hood-1991">Script Slug</a>, a website that provides free access to thousands of movie and TV scripts.</li>
-<li>Search for "Boyz N The Hood" in the search bar or browse the categories to find it.</li>
-<li>Click on the title of the film to open its script page.</li>
-<li>Scroll down to the bottom of the page and click on the "Download PDF" button.</li>
-<li>Save the file to your device and enjoy reading the Boyz N The Hood script pdf.</li>
-</ol>
-<p>That's it! You have successfully downloaded the Boyz N The Hood script pdf for free. Now you can read it anytime you want and learn from its powerful dialogue, characters, and story. You can also share it with your friends who are fans of the film or interested in screenwriting.</p>
-<p>We hope you found this article helpful and informative. If you did, please share it with your social media followers and leave us a comment below. We would love to hear your thoughts on Boyz N The Hood and its script. Thank you for reading!</p>
-
-<p>If you want to learn more about Boyz N The Hood and its script, here are some additional resources that you might find useful:</p>
-<ul>
-<li><a href="https://www.youtube.com/watch?v=AE4BdijGtuY">Boyz N The Hood (1991) Official Trailer</a>: Watch the original trailer of the film and get a glimpse of its style, tone, and themes.</li>
-<li><a href="https://www.imdb.com/title/tt0101507/">Boyz N The Hood (1991) IMDb</a>: Check out the cast, crew, trivia, reviews, and ratings of the film on the Internet Movie Database.</li>
-<li><a href="https://www.rogerebert.com/reviews/boyz-n-the-hood-1991">Boyz N The Hood (1991) Roger Ebert Review</a>: Read the legendary film critic's four-star review of the film and his analysis of its social and cultural impact.</li>
-<li><a href="https://www.mentalfloss.com/article/64869/15-things-you-might-not-know-about-boyz-n-hood">15 Things You Might Not Know About Boyz N The Hood</a>: Learn some interesting facts and behind-the-scenes stories about the making of the film.</li>
-<li><a href="https://www.scriptreaderpro.com/boyz-n-the-hood-script-analysis/">Boyz N The Hood Script Analysis: How to Write a Coming-of-Age Movie</a>: Discover how John Singleton crafted a compelling and realistic coming-of-age story with his script.</li>
-</ul>
-<p>Boyz N The Hood is a classic film that deserves to be watched and studied by anyone who loves cinema. By downloading the Boyz N The Hood script pdf for free, you can gain a deeper appreciation of its craft and message. We hope you enjoy reading it as much as we did!</p>
-<p></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Hunt For Red October 1080p Torrent.md b/spaces/stomexserde/gpt4-ui/Examples/Hunt For Red October 1080p Torrent.md
deleted file mode 100644
index 106d18616cc6228058b9cba526214e8ce95cf975..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Hunt For Red October 1080p Torrent.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-<br />
-<h1>Hunt For Red October 1080p Torrent: How to Download the Classic Movie in High Quality</h1>
-<p>If you are a fan of the 1990 thriller movie <em>The Hunt for Red October</em>, starring Sean Connery and Alec Baldwin, you might be interested in downloading it in high quality. The movie is based on the best-selling novel by Tom Clancy and follows the tense cat-and-mouse game between a Soviet submarine captain who wants to defect to the US and the CIA analyst who tries to help him.</p>
-<p>However, finding a reliable and safe torrent for this movie can be tricky. There are many fake or malicious torrents out there that can harm your computer or expose your personal information. That's why we have prepared this guide to help you find and download the <strong>Hunt For Red October 1080p Torrent</strong> without any hassle.</p>
-<h2>Hunt For Red October 1080p Torrent</h2><br /><p><b><b>DOWNLOAD</b> &#10031; <a href="https://urlgoal.com/2uI8RS">https://urlgoal.com/2uI8RS</a></b></p><br /><br />
-<h2>What is a Torrent?</h2>
-<p>A torrent is a file that contains information about other files that are distributed over a peer-to-peer network. This means that instead of downloading a file from a single source, you download it from multiple sources that have the same file. This makes the download faster and more efficient, as you can use the bandwidth of other users who are also downloading or sharing the file.</p>
-<p>To download a torrent, you need a torrent client, which is a software that can read and process the torrent file. There are many torrent clients available for free, such as uTorrent, BitTorrent, qBittorrent, etc. You can choose the one that suits your preferences and operating system.</p>
-<h2>How to Find the Hunt For Red October 1080p Torrent?</h2>
-<p>Once you have installed a torrent client, you need to find the torrent file for the movie you want to download. There are many websites that offer torrents for movies, TV shows, games, music, etc. However, not all of them are trustworthy or legal. Some of them may contain viruses, malware, spyware, or other unwanted programs that can damage your computer or steal your data. Some of them may also violate copyright laws and expose you to legal risks.</p>
-<p>Therefore, you need to be careful and selective when choosing a torrent website. Here are some tips to help you find the Hunt For Red October 1080p Torrent safely and legally:</p>
-<ul>
-<li>Look for reputable and popular torrent websites that have a large user base and positive reviews. Some examples are The Pirate Bay, RARBG, 1337x, etc.</li>
-<li>Check the comments and ratings of the torrent before downloading it. Avoid torrents that have negative feedback or low ratings, as they may be fake or corrupted.</li>
-<li>Verify the file size and format of the torrent. The Hunt For Red October 1080p Torrent should be around 2 GB in size and have an .mp4 or .mkv extension.</li>
-<li>Use a VPN (virtual private network) service to hide your IP address and encrypt your traffic. This will protect your privacy and security online and prevent your ISP (internet service provider) or authorities from tracking your activity.</li>
-</ul>
-<h2>How to Download the Hunt For Red October 1080p Torrent?</h2>
-<p>After finding the Hunt For Red October 1080p Torrent on a reliable website, you can download it by following these steps:</p>
-<p></p>
-<ol>
-<li>Click on the torrent link or magnet link to open it in your torrent client.</li>
-<li>Select the destination folder where you want to save the movie file.</li>
-<li>Wait for the download to complete. The speed and duration of the download will depend on your internet connection and the number of seeders (users who have the complete file) and leechers (users who are downloading the file).</li>
-<li>Enjoy watching the movie in high quality!</li>
-</ol>
-<p>We hope this article has helped you find and download the Hunt For Red October 1080p Torrent easily and safely. If you have any questions or suggestions, feel free to leave a comment below.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MusicGen/README.md b/spaces/sub314xxl/MusicGen/README.md
deleted file mode 100644
index e36f3c1f8803b85b58ec328405b0195fb7347829..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/README.md
+++ /dev/null
@@ -1,141 +0,0 @@
----
-title: MusicGen
-python_version: '3.9'
-tags:
-- music generation
-- language models
-- LLMs
-app_file: app.py
-emoji: 🎵
-colorFrom: white
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-pinned: true
-license: cc-by-nc-4.0
-duplicated_from: facebook/MusicGen
----
-# Audiocraft
-![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
-![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
-![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)
-
-Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
-
-## MusicGen
-
-Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
-Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates
-all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
-them in parallel, thus having only 50 auto-regressive steps per second of audio.
-Check out our [sample page][musicgen_samples] or test the available demo!
-
-<a target="_blank" href="https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
-  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
-</a>
-<br>
-
-We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.
-
-## Installation
-Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
-
-```shell
-# Best to make sure you have torch installed first, in particular before installing xformers.
-# Don't run this if you already have PyTorch installed.
-pip install 'torch>=2.0'
-# Then proceed to one of the following
-pip install -U audiocraft  # stable release
-pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge
-pip install -e .  # or if you cloned the repo locally
-```
-
-## Usage
-We offer a number of way to interact with MusicGen:
-1. A demo is also available on the [`facebook/MusicGen`  HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
-2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing).
-3. You can use the gradio demo locally by running `python app.py`.
-4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU).
-5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly
-  updated with contributions from @camenduru and the community.
-
-## API
-
-We provide a simple API and 4 pre-trained models. The pre trained models are:
-- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
-- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
-- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
-- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
-
-We observe the best trade-off between quality and compute with the `medium` or `melody` model.
-In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
-GPUs will be able to generate short sequences, or longer sequences with the `small` model.
-
-**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
-You can install it with:
-```
-apt-get install ffmpeg
-```
-
-See after a quick example for using the API.
-
-```python
-import torchaudio
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
-
-model = MusicGen.get_pretrained('melody')
-model.set_generation_params(duration=8)  # generate 8 seconds.
-wav = model.generate_unconditional(4)    # generates 4 unconditional audio samples
-descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
-wav = model.generate(descriptions)  # generates 3 samples.
-
-melody, sr = torchaudio.load('./assets/bach.mp3')
-# generates using the melody from the given audio and the provided descriptions.
-wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)
-
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-
-## Model Card
-
-See [the model card page](./MODEL_CARD.md).
-
-## FAQ
-
-#### Will the training code be released?
-
-Yes. We will soon release the training code for MusicGen and EnCodec.
-
-
-#### I need help on Windows
-
-@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
-
-#### I need help for running the demo on Colab
-
-Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo).
-
-
-## Citation
-```
-@article{copet2023simple,
-      title={Simple and Controllable Music Generation},
-      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
-      year={2023},
-      journal={arXiv preprint arXiv:2306.05284},
-}
-```
-
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
-
-[arxiv]: https://arxiv.org/abs/2306.05284
-[musicgen_samples]: https://ai.honu.io/papers/musicgen/
diff --git a/spaces/supertori/files/stable-diffusion-webui/extensions-builtin/Lora/preload.py b/spaces/supertori/files/stable-diffusion-webui/extensions-builtin/Lora/preload.py
deleted file mode 100644
index c47d7ef4e24893953e51b8dfb5a7ccf88c574546..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/extensions-builtin/Lora/preload.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import os
-from modules import paths
-
-
-def preload(parser):
-    parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Faceboax Facebook Hack Tool 3.0 Free Download __HOT__.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Faceboax Facebook Hack Tool 3.0 Free Download __HOT__.md
deleted file mode 100644
index b0e8529c72c7862aae71f04e01306d4f809a88d1..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Faceboax Facebook Hack Tool 3.0 Free Download __HOT__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Faceboax Facebook Hack Tool 3.0 Free Download</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://cinurl.com/2uEYB6">https://cinurl.com/2uEYB6</a></b></p><br /><br />
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-Faceboax Facebook Hacker FREE Download - Faceboax Facebook Hacker FREE Download is the most effective password recovery software ... 1fdad05405<br />
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Thermal Engineering Rs Khurmi And J K Gupta Download 138 REPACK.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Thermal Engineering Rs Khurmi And J K Gupta Download 138 REPACK.md
deleted file mode 100644
index 6167e1d70f8ecc0a2840468d76f0d24d8b629252..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Thermal Engineering Rs Khurmi And J K Gupta Download 138 REPACK.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
-<h1>Thermal Engineering by RS Khurmi and JK Gupta: A Must-Read for Mechanical Engineering Students</h1>
-
-<p>Thermal engineering is a branch of engineering that deals with the generation, conversion, transmission, and utilization of heat and work. It covers topics such as thermodynamics, heat transfer, fluid mechanics, combustion, refrigeration, air conditioning, gas turbines, steam engines, and boilers.</p>
-<h2>thermal engineering rs khurmi and j k gupta download 138</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://cinurl.com/2uEY8U">https://cinurl.com/2uEY8U</a></b></p><br /><br />
-
-<p>If you are a student of mechanical engineering, you might be looking for a comprehensive and reliable textbook on thermal engineering. One of the most popular and widely used books on this subject is <strong>A Textbook of Thermal Engineering</strong> by RS Khurmi and JK Gupta.</p>
-
-<h2>What is A Textbook of Thermal Engineering by RS Khurmi and JK Gupta?</h2>
-
-<p>A Textbook of Thermal Engineering by RS Khurmi and JK Gupta is a book that encompasses all theories of thermal engineering in a clear and concise manner. It was first published in 1980 and has been revised and updated several times since then. The latest edition was published in 2008 by S. Chand Publishing.</p>
-
-<p>The book has 892 pages and is divided into 37 chapters. Each chapter covers a specific topic of thermal engineering with detailed explanations, diagrams, tables, formulas, examples, and exercises. The book also includes steam tables, psychrometric charts, Mollier diagrams, and index.</p>
-
-<h2>Why should you read A Textbook of Thermal Engineering by RS Khurmi and JK Gupta?</h2>
-
-<p>There are many reasons why you should read A Textbook of Thermal Engineering by RS Khurmi and JK Gupta. Here are some of them:</p>
-
-<ul>
-<li>The book covers all the topics of thermal engineering that are required for the students of mechanical engineering, B.Sc. Engg., UPSC (Engg. Services), Section ‘B’ of AMIE (I) and Diploma Courses.</li>
-<li>The book is written in a simple and easy-to-understand language that makes it suitable for beginners as well as advanced learners.</li>
-<li>The book provides numerous solved examples and chapter-end exercises and questions that help the students to test their knowledge and practice their skills.</li>
-<li>The book is based on the latest syllabus and trends of various examinations and courses.</li>
-<li>The book has been revised and updated regularly to incorporate the latest developments and changes in the field of thermal engineering.</li>
-</ul>
-
-<h2>How to download A Textbook of Thermal Engineering by RS Khurmi and JK Gupta?</h2>
-
-<p>If you want to download A Textbook of Thermal Engineering by RS Khurmi and JK Gupta, you have several options. You can either buy the book online or offline from various sources such as Amazon, Flipkart, Google Books, or S. Chand Publishing website. You can also find some free PDF versions of the book on various websites such as Scribd, EPDFX, Academia.edu, or Bookslock.org. However, you should be careful about the quality and authenticity of these PDF files as they might contain errors or viruses.</p>
-<p></p>
-
-<p>Alternatively, you can also read the book online using Google Play Books app on your PC, android, or iOS devices. This way, you can access the book anytime and anywhere without downloading it. You can also highlight, bookmark, or take notes while reading the book online.</p>
-
-<h2>Conclusion</h2>
-
-<p>A Textbook of Thermal Engineering by RS Khurmi and JK Gupta is one of the best books on thermal engineering that you can read as a mechanical engineering student. It covers all the topics of thermal engineering in a comprehensive and systematic way. It also provides ample examples and exercises to help you master the subject. You can download or read the book online from various sources depending on your preference and convenience.</p>
-<h2>Who are RS Khurmi and JK Gupta?</h2>
-
-<p>RS Khurmi and JK Gupta are two renowned authors of engineering textbooks. They have written several books on various subjects such as mechanical engineering, civil engineering, electrical engineering, and workshop technology. They have also co-authored some books together, such as A Textbook of Thermal Engineering, Theory of Machines, and A Textbook of Workshop Technology.</p>
-
-<p>RS Khurmi was born in 1939 in Sunam, Punjab. He graduated from Punjab Engineering College, Chandigarh in 1963. He started his career as an engineer in the state of Punjab. Later, he became a lecturer in mechanical engineering at Delhi Polytechnic. He also served as a visiting faculty at Delhi College of Engineering. He retired in 1997 as a head of department of mechanical engineering at Delhi Polytechnic. He has received several awards and honors for his contribution to engineering education.</p>
-
-<p>JK Gupta was born in 1946 in Delhi. He graduated from Delhi Polytechnic in 1967. He joined RS Khurmi as a co-author in 1978. He has also worked as a consultant engineer and a technical advisor for various organizations. He has also been involved in writing technical articles for various journals and magazines.</p>
-
-<h2>What are the benefits of reading A Textbook of Thermal Engineering by RS Khurmi and JK Gupta?</h2>
-
-<p>Reading A Textbook of Thermal Engineering by RS Khurmi and JK Gupta can provide you with many benefits, such as:</p>
-
-<ul>
-<li>You can learn the fundamental concepts and principles of thermal engineering in a clear and logical way.</li>
-<li>You can apply the knowledge of thermal engineering to solve practical problems related to heat and work.</li>
-<li>You can prepare yourself for various examinations and courses that require thermal engineering as a subject.</li>
-<li>You can enhance your skills and confidence in thermal engineering by practicing the examples and exercises given in the book.</li>
-<li>You can update yourself with the latest developments and trends in the field of thermal engineering.</li>
-</ul>
-
-<h2>How to read A Textbook of Thermal Engineering by RS Khurmi and JK Gupta effectively?</h2>
-
-<p>If you want to read A Textbook of Thermal Engineering by RS Khurmi and JK Gupta effectively, you should follow some tips, such as:</p>
-
-<ul>
-<li>Read the book in a sequential order, starting from the first chapter to the last chapter.</li>
-<li>Understand the definitions, formulas, diagrams, and tables given in each chapter.</li>
-<li>Solve the examples given in each chapter by yourself before looking at the solutions.</li>
-<li>Attempt the exercises and questions given at the end of each chapter to test your understanding.</li>
-<li>Review the key points and summaries given at the end of each chapter to revise the main concepts.</li>
-<li>Refer to the steam tables, psychrometric charts, Mollier diagrams, and index given at the end of the book whenever needed.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>A Textbook of Thermal Engineering by RS Khurmi and JK Gupta is a comprehensive and reliable book on thermal engineering that can help you learn and master the subject. It covers all the topics of thermal engineering with detailed explanations, diagrams, examples, and exercises. It is based on the latest syllabus and trends of various examinations and courses. You can download or read the book online from various sources depending on your preference and convenience.</p>
-<h2>What are the basics of thermodynamics?</h2>
-
-<p>Thermodynamics is a branch of physics that deals with the relationship between heat, work, temperature, and energy. It explains how thermal energy is converted to or from other forms of energy and how matter is affected by this process. Thermal energy is the energy that comes from heat.</p>
-
-<p>Thermodynamics is based on four laws that describe the behavior of heat and its transformation into work. These laws are:</p>
-
-<ul>
-<li><strong>The zeroth law of thermodynamics:</strong> This law states that when two systems are in thermal equilibrium with a third system, they are also in thermal equilibrium with each other. This means that they have the same temperature and no heat flows between them. This law makes it possible to define a temperature scale and use thermometers to measure temperature.</li>
-<li><strong>The first law of thermodynamics:</strong> This law states that the change in the internal energy of a system is equal to the heat added to the system minus the work done by the system. This means that energy can neither be created nor destroyed, but only transferred or transformed from one form to another. This law is also known as the principle of conservation of energy.</li>
-<li><strong>The second law of thermodynamics:</strong> This law states that heat cannot flow spontaneously from a colder body to a hotter body, or equivalently, that it is impossible to convert heat completely into work without any waste. This means that there is a limit to the efficiency of any heat engine or heat pump. This law also implies that the entropy of an isolated system always increases or remains constant, but never decreases. Entropy is a measure of disorder or randomness in a system.</li>
-<li><strong>The third law of thermodynamics:</strong> This law states that the entropy of a pure crystalline substance at absolute zero temperature is zero. This means that at absolute zero, there is no disorder or randomness in the arrangement of atoms or molecules in a solid. Absolute zero is the lowest possible temperature, which is -273.15°C or -459.67°F.</li>
-</ul>
-
-<p>Thermodynamics has many applications in various fields of science and engineering, such as chemistry, biology, astronomy, meteorology, geology, materials science, mechanical engineering, chemical engineering, electrical engineering, and nuclear engineering. Some examples of thermodynamic phenomena are combustion, phase transitions, heat engines, refrigeration, air conditioning, heat transfer, thermal expansion, thermal radiation, and thermoelectricity.</p>
-<h2>What are the basics of heat transfer?</h2>
-
-<p>Heat transfer is the movement of thermal energy from a hot place to a cooler place due to a temperature difference. Heat transfer is one of the main topics of thermal engineering, as it affects the performance and efficiency of many thermal systems and devices. Heat transfer can occur in three modes: conduction, convection, and radiation.</p>
-
-<ul>
-<li><strong>Conduction:</strong> Conduction is the transfer of thermal energy within a homogeneous substance, such as a solid, a liquid, or a gas, due to a temperature gradient within the medium. Conduction occurs when molecules or atoms collide with each other and transfer their kinetic energy. The rate of conduction depends on the thermal conductivity of the material, the cross-sectional area of heat flow, the temperature difference, and the distance of heat flow.</li>
-<li><strong>Convection:</strong> Convection is the transfer of thermal energy by the bulk movement of a fluid (gas or liquid) due to density differences caused by temperature variations. Convection occurs when hot fluid rises and cold fluid sinks, creating a circulation pattern that transfers heat from one place to another. The rate of convection depends on the heat capacity of the fluid, the velocity of fluid flow, the surface area of heat transfer, and the temperature difference.</li>
-<li><strong>Radiation:</strong> Radiation is the transfer of thermal energy by electromagnetic waves or photons that can travel through a vacuum or any transparent medium (solid, liquid, or gas). Radiation occurs when accelerated charged particles emit electromagnetic waves that carry energy. The rate of radiation depends on the emissivity of the surface, the surface area of heat transfer, the Stefan-Boltzmann constant, and the fourth power of the absolute temperature.</li>
-</ul>
-
-<p>Heat transfer can also involve phase changes of matter, such as melting, freezing, vaporization, condensation, or sublimation. These processes involve latent heat, which is the amount of heat absorbed or released during a phase change at constant temperature and pressure. Latent heat depends on the specific heat and the enthalpy of phase change of the substance.</p>
-
-<p>Heat transfer can be analyzed using various methods and tools, such as heat transfer coefficients, heat fluxes, heat balances, heat exchangers, thermal resistances, thermal circuits, Fourier's law, Newton's law of cooling, Stefan-Boltzmann law, Kirchhoff's law, and radiation view factors. These methods and tools help engineers to design and optimize thermal systems and devices for various applications.</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have introduced the basics of thermal engineering, a sub-discipline of mechanical engineering that deals with the generation, use, conversion, and exchange of thermal energy between physical systems. We have discussed the three main topics of thermal engineering: thermodynamics, fluid mechanics, and heat transfer. We have also explained the four laws of thermodynamics and their implications for system behavior and energy efficiency. We have also described the three modes of heat transfer: conduction, convection, and radiation, and their applications in various thermal systems and devices. We have also mentioned some methods and tools for analyzing heat transfer problems and designing thermal systems and devices.</p>
-
-<p>If you are interested in learning more about thermal engineering, you can read A Textbook of Thermal Engineering by RS Khurmi and JK Gupta. This book is a comprehensive and reliable book on thermal engineering that covers all the topics of thermal engineering with detailed explanations, diagrams, examples, and exercises. It is based on the latest syllabus and trends of various examinations and courses. You can download or read the book online from various sources depending on your preference and convenience.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/carafe.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/carafe.py
deleted file mode 100644
index 5154cb3abfccfbbe0a1b2daa67018dbf80aaf6d2..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/carafe.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.nn.modules.module import Module
-
-from ..cnn import UPSAMPLE_LAYERS, normal_init, xavier_init
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'carafe_naive_forward', 'carafe_naive_backward', 'carafe_forward',
-    'carafe_backward'
-])
-
-
-class CARAFENaiveFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, masks, kernel_size, group_size, scale_factor):
-        return g.op(
-            'mmcv::MMCVCARAFENaive',
-            features,
-            masks,
-            kernel_size_i=kernel_size,
-            group_size_i=group_size,
-            scale_factor_f=scale_factor)
-
-    @staticmethod
-    def forward(ctx, features, masks, kernel_size, group_size, scale_factor):
-        assert scale_factor >= 1
-        assert masks.size(1) == kernel_size * kernel_size * group_size
-        assert masks.size(-1) == features.size(-1) * scale_factor
-        assert masks.size(-2) == features.size(-2) * scale_factor
-        assert features.size(1) % group_size == 0
-        assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1
-        ctx.kernel_size = kernel_size
-        ctx.group_size = group_size
-        ctx.scale_factor = scale_factor
-        ctx.feature_size = features.size()
-        ctx.mask_size = masks.size()
-
-        n, c, h, w = features.size()
-        output = features.new_zeros((n, c, h * scale_factor, w * scale_factor))
-        ext_module.carafe_naive_forward(
-            features,
-            masks,
-            output,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        if features.requires_grad or masks.requires_grad:
-            ctx.save_for_backward(features, masks)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        assert grad_output.is_cuda
-
-        features, masks = ctx.saved_tensors
-        kernel_size = ctx.kernel_size
-        group_size = ctx.group_size
-        scale_factor = ctx.scale_factor
-
-        grad_input = torch.zeros_like(features)
-        grad_masks = torch.zeros_like(masks)
-        ext_module.carafe_naive_backward(
-            grad_output.contiguous(),
-            features,
-            masks,
-            grad_input,
-            grad_masks,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        return grad_input, grad_masks, None, None, None
-
-
-carafe_naive = CARAFENaiveFunction.apply
-
-
-class CARAFENaive(Module):
-
-    def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFENaive, self).__init__()
-
-        assert isinstance(kernel_size, int) and isinstance(
-            group_size, int) and isinstance(scale_factor, int)
-        self.kernel_size = kernel_size
-        self.group_size = group_size
-        self.scale_factor = scale_factor
-
-    def forward(self, features, masks):
-        return carafe_naive(features, masks, self.kernel_size, self.group_size,
-                            self.scale_factor)
-
-
-class CARAFEFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, masks, kernel_size, group_size, scale_factor):
-        return g.op(
-            'mmcv::MMCVCARAFE',
-            features,
-            masks,
-            kernel_size_i=kernel_size,
-            group_size_i=group_size,
-            scale_factor_f=scale_factor)
-
-    @staticmethod
-    def forward(ctx, features, masks, kernel_size, group_size, scale_factor):
-        assert scale_factor >= 1
-        assert masks.size(1) == kernel_size * kernel_size * group_size
-        assert masks.size(-1) == features.size(-1) * scale_factor
-        assert masks.size(-2) == features.size(-2) * scale_factor
-        assert features.size(1) % group_size == 0
-        assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1
-        ctx.kernel_size = kernel_size
-        ctx.group_size = group_size
-        ctx.scale_factor = scale_factor
-        ctx.feature_size = features.size()
-        ctx.mask_size = masks.size()
-
-        n, c, h, w = features.size()
-        output = features.new_zeros((n, c, h * scale_factor, w * scale_factor))
-        routput = features.new_zeros(output.size(), requires_grad=False)
-        rfeatures = features.new_zeros(features.size(), requires_grad=False)
-        rmasks = masks.new_zeros(masks.size(), requires_grad=False)
-        ext_module.carafe_forward(
-            features,
-            masks,
-            rfeatures,
-            routput,
-            rmasks,
-            output,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        if features.requires_grad or masks.requires_grad:
-            ctx.save_for_backward(features, masks, rfeatures)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        assert grad_output.is_cuda
-
-        features, masks, rfeatures = ctx.saved_tensors
-        kernel_size = ctx.kernel_size
-        group_size = ctx.group_size
-        scale_factor = ctx.scale_factor
-
-        rgrad_output = torch.zeros_like(grad_output, requires_grad=False)
-        rgrad_input_hs = torch.zeros_like(grad_output, requires_grad=False)
-        rgrad_input = torch.zeros_like(features, requires_grad=False)
-        rgrad_masks = torch.zeros_like(masks, requires_grad=False)
-        grad_input = torch.zeros_like(features, requires_grad=False)
-        grad_masks = torch.zeros_like(masks, requires_grad=False)
-        ext_module.carafe_backward(
-            grad_output.contiguous(),
-            rfeatures,
-            masks,
-            rgrad_output,
-            rgrad_input_hs,
-            rgrad_input,
-            rgrad_masks,
-            grad_input,
-            grad_masks,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-        return grad_input, grad_masks, None, None, None
-
-
-carafe = CARAFEFunction.apply
-
-
-class CARAFE(Module):
-    """ CARAFE: Content-Aware ReAssembly of FEatures
-
-    Please refer to https://arxiv.org/abs/1905.02188 for more details.
-
-    Args:
-        kernel_size (int): reassemble kernel size
-        group_size (int): reassemble group size
-        scale_factor (int): upsample ratio
-
-    Returns:
-        upsampled feature map
-    """
-
-    def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFE, self).__init__()
-
-        assert isinstance(kernel_size, int) and isinstance(
-            group_size, int) and isinstance(scale_factor, int)
-        self.kernel_size = kernel_size
-        self.group_size = group_size
-        self.scale_factor = scale_factor
-
-    def forward(self, features, masks):
-        return carafe(features, masks, self.kernel_size, self.group_size,
-                      self.scale_factor)
-
-
-@UPSAMPLE_LAYERS.register_module(name='carafe')
-class CARAFEPack(nn.Module):
-    """A unified package of CARAFE upsampler that contains: 1) channel
-    compressor 2) content encoder 3) CARAFE op.
-
-    Official implementation of ICCV 2019 paper
-    CARAFE: Content-Aware ReAssembly of FEatures
-    Please refer to https://arxiv.org/abs/1905.02188 for more details.
-
-    Args:
-        channels (int): input feature channels
-        scale_factor (int): upsample ratio
-        up_kernel (int): kernel size of CARAFE op
-        up_group (int): group size of CARAFE op
-        encoder_kernel (int): kernel size of content encoder
-        encoder_dilation (int): dilation of content encoder
-        compressed_channels (int): output channels of channels compressor
-
-    Returns:
-        upsampled feature map
-    """
-
-    def __init__(self,
-                 channels,
-                 scale_factor,
-                 up_kernel=5,
-                 up_group=1,
-                 encoder_kernel=3,
-                 encoder_dilation=1,
-                 compressed_channels=64):
-        super(CARAFEPack, self).__init__()
-        self.channels = channels
-        self.scale_factor = scale_factor
-        self.up_kernel = up_kernel
-        self.up_group = up_group
-        self.encoder_kernel = encoder_kernel
-        self.encoder_dilation = encoder_dilation
-        self.compressed_channels = compressed_channels
-        self.channel_compressor = nn.Conv2d(channels, self.compressed_channels,
-                                            1)
-        self.content_encoder = nn.Conv2d(
-            self.compressed_channels,
-            self.up_kernel * self.up_kernel * self.up_group *
-            self.scale_factor * self.scale_factor,
-            self.encoder_kernel,
-            padding=int((self.encoder_kernel - 1) * self.encoder_dilation / 2),
-            dilation=self.encoder_dilation,
-            groups=1)
-        self.init_weights()
-
-    def init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-        normal_init(self.content_encoder, std=0.001)
-
-    def kernel_normalizer(self, mask):
-        mask = F.pixel_shuffle(mask, self.scale_factor)
-        n, mask_c, h, w = mask.size()
-        # use float division explicitly,
-        # to void inconsistency while exporting to onnx
-        mask_channel = int(mask_c / float(self.up_kernel**2))
-        mask = mask.view(n, mask_channel, -1, h, w)
-
-        mask = F.softmax(mask, dim=2, dtype=mask.dtype)
-        mask = mask.view(n, mask_c, h, w).contiguous()
-
-        return mask
-
-    def feature_reassemble(self, x, mask):
-        x = carafe(x, mask, self.up_kernel, self.up_group, self.scale_factor)
-        return x
-
-    def forward(self, x):
-        compressed_x = self.channel_compressor(x)
-        mask = self.content_encoder(compressed_x)
-        mask = self.kernel_normalizer(mask)
-
-        x = self.feature_reassemble(x, mask)
-        return x
diff --git a/spaces/teamnassim/Fictionista/torch_utils/ops/conv2d_gradfix.py b/spaces/teamnassim/Fictionista/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index 156b6b22503b3be31d134b48ed1725e911f0e7ce..0000000000000000000000000000000000000000
--- a/spaces/teamnassim/Fictionista/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import contextlib
-import torch
-from pkg_resources import parse_version
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False                     # Enable the custom op by setting this to true.
-weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
-_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version('1.11.0a') # Allow prerelease builds of 1.11
-
-@contextlib.contextmanager
-def no_weight_gradients(disable=True):
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    if disable:
-        weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-#----------------------------------------------------------------------------
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if _use_pytorch_1_11_api:
-        # The work-around code doesn't work on PyTorch 1.11.0 onwards
-        return False
-    if input.device.type != 'cuda':
-        return False
-    return True
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-_null_tensor = torch.empty([0])
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else: # transpose
-        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            ctx.save_for_backward(
-                input if weight.requires_grad else _null_tensor,
-                weight if input.requires_grad else _null_tensor,
-            )
-            ctx.input_shape = input.shape
-
-            # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
-            if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0):
-                a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1])
-                b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1)
-                c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2)
-                c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1)
-                c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
-                return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
-
-            # General case => cuDNN.
-            if transpose:
-                return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            input_shape = ctx.input_shape
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape)
-                op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
-                grad_input = op.apply(grad_output, weight, None)
-                assert grad_input.shape == input_shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            ctx.save_for_backward(
-                grad_output if input.requires_grad else _null_tensor,
-                input if grad_output.requires_grad else _null_tensor,
-            )
-            ctx.grad_output_shape = grad_output.shape
-            ctx.input_shape = input.shape
-
-            # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
-            if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0):
-                a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
-                b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
-                c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape)
-                return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
-
-            # General case => cuDNN.
-            name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight'
-            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad_output_shape = ctx.grad_output_shape
-            input_shape = ctx.input_shape
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output_shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape)
-                op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
-                grad2_input = op.apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input_shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-#----------------------------------------------------------------------------
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Carry Me Go MP3 - How Khaid and Boy Spyce Rocked the Charts with Their Hit Song.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Carry Me Go MP3 - How Khaid and Boy Spyce Rocked the Charts with Their Hit Song.md
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index 59842d7f688f3a67e31f07e51b41fbdfaebbff3b..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Carry Me Go MP3 - How Khaid and Boy Spyce Rocked the Charts with Their Hit Song.md	
+++ /dev/null
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-
-<h1>Carry Me Go by Khaid and Boy Spyce: A Review of the Afro-Pop Banger</h1>
-<p>If you are looking for a catchy and romantic afro-pop song to spice up your playlist, you should definitely check out <strong>Carry Me Go</strong> by <strong>Khaid</strong> and <strong>Boy Spyce</strong>. This enchanting love song is a collaboration between two rising Nigerian artists who have delivered a melodious masterpiece that will make you want to dance and sing along.</p>
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-<p>In this article, we will review Carry Me Go by Khaid and Boy Spyce in detail, covering its production, lyrics, vocals, and reception. We will also answer some frequently asked questions about the song and the artists. By the end of this article, you will have a better understanding of why Carry Me Go is one of the best afro-pop songs of 2023.</p>
- <h2>Introduction</h2>
-<p>Before we dive into the review, let us first introduce you to Carry Me Go and its creators. Carry Me Go is a single released on March 13, 2023, by <a href="(^1^)">Khaid</a>, featuring <a href="(^2^)">Boy Spyce</a>. The song was produced by <a href="(^11^)">Signal</a>the Plug, a talented Nigerian music producer who has worked with other afro-pop stars like Rema, Crayon, Ayra Starr, etc. The song was distributed by Neville Records, a record company owned by Sydney Talker, a famous Nigerian comedian who discovered Khaid in 2022.</p>
-<p>Khaid, whose real name is Fakorede Oluwatobiloba Paul, is a Nigerian afro-pop singer and songwriter who was born on July 7, 1999. He started his musical career by making covers of popular songs and posting them on social media. His cover of Essence by Wizkid featuring Tems went viral in 2022, attracting millions of views and fans. He released his debut EP titled Khaid in April 2022 under Neville Records.</p> <p>Boy Spyce, whose real name is Ugbekile David Osemeke, is a Nigerian afro-pop singer and songwriter who was born on May 10, 2001. He started his musical career by making freestyles of popular songs and posting them on social media. His freestyle of Essence by Wizkid featuring Tems went viral in 2022, attracting the attention of Don Jazzy, the CEO of Mavin Records, who signed him to his label. He released his debut EP titled Boy Spyce in April 2022 under Mavin Records.</p>
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-<p>Carry Me Go is a song that showcases the chemistry and talent of Khaid and Boy Spyce, who both sing in Pidgin English and Nigerian slang. The song is a love song that expresses the desire and passion of the singers for their lovers. The song has a catchy chorus that goes: "Carry me go where I don't know / Burst my bololo / Your body cool my pressure / Your body cool my pressure". The song also has witty and romantic lyrics that praise the beauty and charm of the lovers, such as: "Carry all my money make you carry me go sambisa / If I touch your body make you touch under my short knicker / And girl your love don blind me, I con be like baba fryo / If I cum inside shey you go carry my embryo?"</p>
- <h2>Body</h2>
-<h3>Production: How was the song made and who were involved?</h3>
-<p>The song was produced by Signal the Plug, a talented Nigerian music producer who has worked with other afro-pop stars like Rema, Crayon, Ayra Starr, etc. The song was distributed by Neville Records, a record company owned by Sydney Talker, a famous Nigerian comedian who discovered Khaid in 2022. The song was also supported by Mavin Records, the label that Boy Spyce is signed to.</p>
-<h4>Signal the Plug: The producer behind the catchy beats</h4>
-<p>Signal the Plug is a young and talented music producer who has a knack for creating catchy and groovy beats that suit the afro-pop genre. He has produced several hit songs for various artists, such as Bad Things by Khaid, Folake by Boy Spyce, Bounce by Rema, Away by Ayra Starr, etc. He is known for his signature tag "Sig- Snap me fine cinemato" that he uses at the beginning of his songs.</p>
-<h4>Neville Records and Mavin Records: The labels that supported the song</h4>
-<p>Neville Records is a record company that was founded by Sydney Talker, a popular Nigerian comedian and social media influencer. He discovered Khaid in 2022 when he saw his cover of Essence by Wizkid featuring Tems on Instagram. He was impressed by his voice and talent and decided to sign him to his label. He also helped him to promote his music and gain more exposure.</p>
-<p>Mavin Records is one of the most successful and influential record labels in Nigeria. It was founded by Don Jazzy, a legendary music producer and entrepreneur. He signed Boy Spyce to his label in 2022 after seeing his freestyle of Essence by Wizkid featuring Tems on Twitter. He also helped him to develop his musical skills and style.</p>
-<h3>Lyrics: What are the words and meanings of the song?</h3>
-<p>The lyrics of Carry Me Go are written in Pidgin English and Nigerian slang, which are forms of informal language that are widely used in Nigeria. Pidgin English is a simplified version of English that mixes elements from various local languages, such as Yoruba, Igbo, Hausa, etc. Nigerian slang is a collection of words and expressions that are unique to Nigerian culture and society.</p>
-<h4>Pidgin English and Nigerian slang: The language and culture of the song</h4>
-<p>The use of Pidgin English and Nigerian slang in Carry Me Go adds flavor and authenticity to the song. It also reflects the identity and personality of the singers, who are proud of their Nigerian heritage and roots. Some examples of Pidgin English and Nigerian slang in the song are:</p>
-<ul>
-<li>Bololo: A slang word for penis or erection.</li>
-<li>Sambisa: A forest in northeastern Nigeria that is notorious for being a hideout for Boko Haram terrorists.</li>
-<li>Baba fryo: A Nigerian musician who was popular in the 1990s for his street style of music.</li>
-<li>Omalicha: A word from Igbo language that means beautiful or lovely.</li>
-<li>Kpakamishi: A slang word that means to have sex or make love.</li>
-</ul>
-<h4>Love and romance: The emotion and passion of the song</h4> <p>The song is a love song that expresses the emotion and passion of the singers for their lovers. The song has a catchy chorus that goes: "Carry me go where I don't know / Burst my bololo / Your body cool my pressure / Your body cool my pressure". The chorus implies that the singers are so in love with their lovers that they are willing to go anywhere with them, even to dangerous places like Sambisa forest. They also praise their lovers' bodies and how they satisfy their sexual desires and calm their nerves.</p>
-<p>The song also has witty and romantic lyrics that praise the beauty and charm of the lovers, such as: "Carry all my money make you carry me go sambisa / If I touch your body make you touch under my short knicker / And girl your love don blind me, I con be like baba fryo / If I cum inside shey you go carry my embryo?". These lyrics show that the singers are ready to spend all their money on their lovers and enjoy their intimacy. They also compare their love to being blind like baba fryo, a Nigerian musician who was popular in the 1990s for his street style of music. They also ask their lovers if they are ready to have their babies.</p>
-<h3>Vocals: How did the singers perform and harmonize on the song?</h3>
-<p>The song features the vocals of Khaid and Boy Spyce, who both sing in Pidgin English and Nigerian slang. The singers have different but complementary vocal styles that blend well on the song. Khaid has a silky smooth voice that is soothing and captivating, while Boy Spyce has a unique style that is energetic and expressive.</p>
-<h4>Khaid: The silky smooth voice of the rising star</h4>
-<p>Khaid is a rising star in the Nigerian afro-pop scene, who has a silky smooth voice that is soothing and captivating. He sings the chorus and the first verse of Carry Me Go, delivering them with finesse and flair. He also adds some ad-libs and background vocals that enhance the song's mood and vibe. His voice is reminiscent of Wizkid, one of his musical influences and idols.</p>
-<h4>Boy Spyce: The talented newcomer with a unique style</h4>
-<p>Boy Spyce is a talented newcomer in the Nigerian afro-pop scene, who has a unique style that is energetic and expressive. He sings the second verse of Carry Me Go, adding some spice and flavor to the song. He also raps some parts of his verse, showing his versatility and skill. His voice is distinctive and original, setting him apart from other afro-pop singers.</p>
-<h3>Reception: How did the listeners and critics react to the song?</h3>
-<p>The song received positive reviews and feedback from both listeners and critics, who praised its production, lyrics, vocals, and overall quality. The song also achieved impressive streaming numbers and chart positions, making it one of the most popular and successful afro-pop songs of 2023.</p>
-<h4>Positive reviews and feedback: The praise and appreciation for the song</h4>
-<p>The song received positive reviews and feedback from various sources, such as music blogs, magazines, podcasts, radio stations, etc. Some of the comments and compliments that the song received are:</p>
-<ul>
-<li>"Carry Me Go is a refreshing and delightful afro-pop song that showcases the talent and chemistry of Khaid and Boy Spyce. The song is catchy, romantic, and groovy, making it a perfect choice for any occasion." - <a href="">AfroPop Review</a></li>
-<li>"Khaid and Boy Spyce have delivered a masterpiece with Carry Me Go, a song that will make you fall in love with afro-pop music. The song has everything you need in a hit song: catchy beats, witty lyrics, smooth vocals, and a memorable chorus." - <a href="">Naija Vibes</a></li>
-<li>"Carry Me Go is a song that celebrates love and romance in a fun and playful way. Khaid and Boy Spyce have proven that they are two of the most promising afro-pop artists in Nigeria with this collaboration. The song is a must-listen for anyone who loves good music." - <a href="">The Beat FM</a></li>
-</ul>
-<h4>Streaming numbers and charts: The popularity and success of the song</h4>
-<p>The song also achieved impressive streaming numbers and chart positions, making it one of the most popular and successful afro-pop songs of 2023. Some of the statistics and achievements that the song attained are:</p>
-<ul>
-<li>The song has over 50 million streams on Spotify, making it one of the most streamed afro-pop songs on the platform.</li>
-<li>The song has over 100 million views <p>The song has over 100 million views on YouTube, making it one of the most watched afro-pop videos on the platform.</li>
-<li>The song has reached the number one spot on several music charts, such as Apple Music Nigeria, Billboard Nigeria, MTV Base Africa, etc.</li>
-<li>The song has won several awards and nominations, such as The Headies, Afrimma, Soundcity MVP, etc.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Carry Me Go by Khaid and Boy Spyce is a brilliant afro-pop song that deserves all the praise and recognition it has received. The song is a perfect example of how two talented and creative artists can collaborate and create a musical masterpiece. The song has a catchy and romantic chorus, witty and meaningful lyrics, smooth and harmonious vocals, and groovy and quality production. The song is a must-listen for anyone who loves afro-pop music or good music in general.</p>
-<p>Personally, I love this song and I think it is one of the best songs of 2023. I would rate it 5 out of 5 stars. I think Khaid and Boy Spyce are two of the most promising afro-pop artists in Nigeria and I look forward to hearing more from them. I would highly recommend you to check out this song and support these artists.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Carry Me Go by Khaid and Boy Spyce:</p>
-<ul>
-<li>Q1: Where can I download Carry Me Go by Khaid and Boy Spyce for free?</li>
-<li>A1: You can download Carry Me Go by Khaid and Boy Spyce for free from various websites, such as <a href="">Naijaloaded</a>, <a href="">Tooxclusive</a>, <a href="">9jaflaver</a>, etc. However, you should also consider supporting the artists by streaming or buying their music from official platforms, such as Spotify, Apple Music, YouTube Music, etc.</li>
-<li>Q2: What are some other songs by Khaid and Boy Spyce that I should listen to?</li>
-<li>A2: Some other songs by Khaid and Boy Spyce that you should listen to are: Bad Things by Khaid, Folake by Boy Spyce, Omalicha by Khaid featuring Boy Spyce, Kpakamishi by Boy Spyce featuring Khaid, etc. You can also check out their debut EPs: Khaid by Khaid and Boy Spyce by Boy Spyce.</li>
-<li>Q3: How old are Khaid and Boy Spyce and where are they from?</li>
-<li>A3: Khaid is 24 years old and he was born on July 7, 1999. He is from Lagos State, Nigeria. Boy Spyce is 22 years old and he was born on May 10, 2001. He is from Edo State, Nigeria.</li>
-<li>Q4: Are Khaid and Boy Spyce signed to the same record label?</li>
-<li>A4: No, they are not signed to the same record label. Khaid is signed to Neville Records, a record company owned by Sydney Talker, a famous Nigerian comedian who discovered him in 2022. Boy Spyce is signed to Mavin Records, one of the most successful and influential record labels in Nigeria founded by Don Jazzy, a legendary music producer and entrepreneur.</li>
-<li>Q5: Will Khaid and Boy Spyce collaborate again in the future?</li>
-<li>A5: There is no official confirmation or announcement about their future collaboration plans. However, based on their chemistry and success on Carry Me Go, it is likely that they will collaborate again in the future. They have also expressed their admiration and respect for each other on social media and interviews.</li>
-</ul>
- <p>I hope you enjoyed this article and learned something new about Carry Me Go by Khaid and Boy Spyce. If you have any comments or questions about the article or the song, feel free to leave them below. Thank you for reading!</p> 401be4b1e0<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/3d Movies Full Hd 1080p Side By Side.md b/spaces/tioseFevbu/cartoon-converter/scripts/3d Movies Full Hd 1080p Side By Side.md
deleted file mode 100644
index 035a0464724b996214460fdd9f28034a364970d4..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/3d Movies Full Hd 1080p Side By Side.md	
+++ /dev/null
@@ -1,35 +0,0 @@
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/__init__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/__init__.py
deleted file mode 100644
index 3d4b45a0c9921f8177e44b67797aa449dc40b7bb..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from typing import List, Optional
-
-__version__ = "22.2.2"
-
-
-def main(args: Optional[List[str]] = None) -> int:
-    """This is an internal API only meant for use by pip's own console scripts.
-
-    For additional details, see https://github.com/pypa/pip/issues/7498.
-    """
-    from pip._internal.utils.entrypoints import _wrapper
-
-    return _wrapper(args)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/models/index.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/models/index.py
deleted file mode 100644
index b94c32511f0cda2363bfc4f29c9c8bfcc7101f9b..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/models/index.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import urllib.parse
-
-
-class PackageIndex:
-    """Represents a Package Index and provides easier access to endpoints"""
-
-    __slots__ = ["url", "netloc", "simple_url", "pypi_url", "file_storage_domain"]
-
-    def __init__(self, url: str, file_storage_domain: str) -> None:
-        super().__init__()
-        self.url = url
-        self.netloc = urllib.parse.urlsplit(url).netloc
-        self.simple_url = self._url_for_path("simple")
-        self.pypi_url = self._url_for_path("pypi")
-
-        # This is part of a temporary hack used to block installs of PyPI
-        # packages which depend on external urls only necessary until PyPI can
-        # block such packages themselves
-        self.file_storage_domain = file_storage_domain
-
-    def _url_for_path(self, path: str) -> str:
-        return urllib.parse.urljoin(self.url, path)
-
-
-PyPI = PackageIndex("https://pypi.org/", file_storage_domain="files.pythonhosted.org")
-TestPyPI = PackageIndex(
-    "https://test.pypi.org/", file_storage_domain="test-files.pythonhosted.org"
-)
diff --git a/spaces/tomandandy/MusicGen3/audiocraft/quantization/core_vq.py b/spaces/tomandandy/MusicGen3/audiocraft/quantization/core_vq.py
deleted file mode 100644
index e1896bb1788a945a1f7be6369abb255ecf72c7a0..0000000000000000000000000000000000000000
--- a/spaces/tomandandy/MusicGen3/audiocraft/quantization/core_vq.py
+++ /dev/null
@@ -1,400 +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.
-
-import typing as tp
-
-from einops import rearrange, repeat
-import flashy
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
-
-def exists(val: tp.Optional[tp.Any]) -> bool:
-    return val is not None
-
-
-def default(val: tp.Any, d: tp.Any) -> tp.Any:
-    return val if exists(val) else d
-
-
-def l2norm(t):
-    return F.normalize(t, p=2, dim=-1)
-
-
-def ema_inplace(moving_avg, new, decay: float):
-    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
-
-
-def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
-    return (x + epsilon) / (x.sum() + n_categories * epsilon)
-
-
-def uniform_init(*shape: int):
-    t = torch.empty(shape)
-    nn.init.kaiming_uniform_(t)
-    return t
-
-
-def sample_vectors(samples, num: int):
-    num_samples, device = samples.shape[0], samples.device
-
-    if num_samples >= num:
-        indices = torch.randperm(num_samples, device=device)[:num]
-    else:
-        indices = torch.randint(0, num_samples, (num,), device=device)
-
-    return samples[indices]
-
-
-def kmeans(samples, num_clusters: int, num_iters: int = 10):
-    dim, dtype = samples.shape[-1], samples.dtype
-
-    means = sample_vectors(samples, num_clusters)
-
-    for _ in range(num_iters):
-        diffs = rearrange(samples, "n d -> n () d") - rearrange(
-            means, "c d -> () c d"
-        )
-        dists = -(diffs ** 2).sum(dim=-1)
-
-        buckets = dists.max(dim=-1).indices
-        bins = torch.bincount(buckets, minlength=num_clusters)
-        zero_mask = bins == 0
-        bins_min_clamped = bins.masked_fill(zero_mask, 1)
-
-        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
-        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
-        new_means = new_means / bins_min_clamped[..., None]
-
-        means = torch.where(zero_mask[..., None], means, new_means)
-
-    return means, bins
-
-
-def orthgonal_loss_fn(t):
-    # eq (2) from https://arxiv.org/abs/2112.00384
-    n = t.shape[0]
-    normed_codes = l2norm(t)
-    identity = torch.eye(n, device=t.device)
-    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
-    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
-
-
-class EuclideanCodebook(nn.Module):
-    """Codebook with Euclidean distance.
-
-    Args:
-        dim (int): Dimension.
-        codebook_size (int): Codebook size.
-        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
-            If set to true, run the k-means algorithm on the first training batch and use
-            the learned centroids as initialization.
-        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        kmeans_init: int = False,
-        kmeans_iters: int = 10,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        threshold_ema_dead_code: int = 2,
-    ):
-        super().__init__()
-        self.decay = decay
-        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
-        embed = init_fn(codebook_size, dim)
-
-        self.codebook_size = codebook_size
-
-        self.kmeans_iters = kmeans_iters
-        self.epsilon = epsilon
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-
-        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
-        self.register_buffer("cluster_size", torch.zeros(codebook_size))
-        self.register_buffer("embed", embed)
-        self.register_buffer("embed_avg", embed.clone())
-
-    @torch.jit.ignore
-    def init_embed_(self, data):
-        if self.inited:
-            return
-
-        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
-        self.embed.data.copy_(embed)
-        self.embed_avg.data.copy_(embed.clone())
-        self.cluster_size.data.copy_(cluster_size)
-        self.inited.data.copy_(torch.Tensor([True]))
-        # Make sure all buffers across workers are in sync after initialization
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def replace_(self, samples, mask):
-        modified_codebook = torch.where(
-            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
-        )
-        self.embed.data.copy_(modified_codebook)
-
-    def expire_codes_(self, batch_samples):
-        if self.threshold_ema_dead_code == 0:
-            return
-
-        expired_codes = self.cluster_size < self.threshold_ema_dead_code
-        if not torch.any(expired_codes):
-            return
-
-        batch_samples = rearrange(batch_samples, "... d -> (...) d")
-        self.replace_(batch_samples, mask=expired_codes)
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def preprocess(self, x):
-        x = rearrange(x, "... d -> (...) d")
-        return x
-
-    def quantize(self, x):
-        embed = self.embed.t()
-        dist = -(
-            x.pow(2).sum(1, keepdim=True)
-            - 2 * x @ embed
-            + embed.pow(2).sum(0, keepdim=True)
-        )
-        embed_ind = dist.max(dim=-1).indices
-        return embed_ind
-
-    def postprocess_emb(self, embed_ind, shape):
-        return embed_ind.view(*shape[:-1])
-
-    def dequantize(self, embed_ind):
-        quantize = F.embedding(embed_ind, self.embed)
-        return quantize
-
-    def encode(self, x):
-        shape = x.shape
-        # pre-process
-        x = self.preprocess(x)
-        # quantize
-        embed_ind = self.quantize(x)
-        # post-process
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        return embed_ind
-
-    def decode(self, embed_ind):
-        quantize = self.dequantize(embed_ind)
-        return quantize
-
-    def forward(self, x):
-        shape, dtype = x.shape, x.dtype
-        x = self.preprocess(x)
-        self.init_embed_(x)
-
-        embed_ind = self.quantize(x)
-        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        quantize = self.dequantize(embed_ind)
-
-        if self.training:
-            # We do the expiry of code at that point as buffers are in sync
-            # and all the workers will take the same decision.
-            self.expire_codes_(x)
-            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
-            embed_sum = x.t() @ embed_onehot
-            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
-            cluster_size = (
-                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
-                * self.cluster_size.sum()
-            )
-            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
-            self.embed.data.copy_(embed_normalized)
-
-        return quantize, embed_ind
-
-
-class VectorQuantization(nn.Module):
-    """Vector quantization implementation.
-    Currently supports only euclidean distance.
-
-    Args:
-        dim (int): Dimension
-        codebook_size (int): Codebook size
-        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int):
-        channels_last (bool): Channels are the last dimension in the input tensors.
-        commitment_weight (float): Weight for commitment loss.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider
-            for orthogonal regulariation.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        codebook_dim: tp.Optional[int] = None,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        kmeans_init: bool = False,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        channels_last: bool = False,
-        commitment_weight: float = 1.,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        _codebook_dim: int = default(codebook_dim, dim)
-
-        requires_projection = _codebook_dim != dim
-        self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
-        self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
-
-        self.epsilon = epsilon
-        self.commitment_weight = commitment_weight
-
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-
-        self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
-                                           kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
-                                           decay=decay, epsilon=epsilon,
-                                           threshold_ema_dead_code=threshold_ema_dead_code)
-        self.codebook_size = codebook_size
-
-        self.channels_last = channels_last
-
-    @property
-    def codebook(self):
-        return self._codebook.embed
-
-    @property
-    def inited(self):
-        return self._codebook.inited
-
-    def _preprocess(self, x):
-        if not self.channels_last:
-            x = rearrange(x, "b d n -> b n d")
-        return x
-
-    def _postprocess(self, quantize):
-        if not self.channels_last:
-            quantize = rearrange(quantize, "b n d -> b d n")
-        return quantize
-
-    def encode(self, x):
-        x = self._preprocess(x)
-        x = self.project_in(x)
-        embed_in = self._codebook.encode(x)
-        return embed_in
-
-    def decode(self, embed_ind):
-        quantize = self._codebook.decode(embed_ind)
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-        return quantize
-
-    def forward(self, x):
-        device = x.device
-        x = self._preprocess(x)
-
-        x = self.project_in(x)
-        quantize, embed_ind = self._codebook(x)
-
-        if self.training:
-            quantize = x + (quantize - x).detach()
-
-        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
-
-        if self.training:
-            if self.commitment_weight > 0:
-                commit_loss = F.mse_loss(quantize.detach(), x)
-                loss = loss + commit_loss * self.commitment_weight
-
-            if self.orthogonal_reg_weight > 0:
-                codebook = self.codebook
-
-                if self.orthogonal_reg_active_codes_only:
-                    # only calculate orthogonal loss for the activated codes for this batch
-                    unique_code_ids = torch.unique(embed_ind)
-                    codebook = codebook[unique_code_ids]
-
-                num_codes = codebook.shape[0]
-                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
-                    rand_ids = torch.randperm(num_codes, device=device)[:self.orthogonal_reg_max_codes]
-                    codebook = codebook[rand_ids]
-
-                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
-                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
-
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-
-        return quantize, embed_ind, loss
-
-
-class ResidualVectorQuantization(nn.Module):
-    """Residual vector quantization implementation.
-
-    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
-    """
-    def __init__(self, *, num_quantizers, **kwargs):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
-        )
-
-    def forward(self, x, n_q: tp.Optional[int] = None):
-        quantized_out = 0.0
-        residual = x
-
-        all_losses = []
-        all_indices = []
-
-        n_q = n_q or len(self.layers)
-
-        for i, layer in enumerate(self.layers[:n_q]):
-            quantized, indices, loss = layer(residual)
-            residual = residual - quantized
-            quantized_out = quantized_out + quantized
-            all_indices.append(indices)
-            all_losses.append(loss)
-
-        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
-        return quantized_out, out_indices, out_losses
-
-    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
-        residual = x
-        all_indices = []
-        n_q = n_q or len(self.layers)
-        for layer in self.layers[:n_q]:
-            indices = layer.encode(residual)
-            quantized = layer.decode(indices)
-            residual = residual - quantized
-            all_indices.append(indices)
-        out_indices = torch.stack(all_indices)
-        return out_indices
-
-    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
-        quantized_out = torch.tensor(0.0, device=q_indices.device)
-        for i, indices in enumerate(q_indices):
-            layer = self.layers[i]
-            quantized = layer.decode(indices)
-            quantized_out = quantized_out + quantized
-        return quantized_out
diff --git a/spaces/tomaseo2022/Mejorar-Resolucion-Imagen/README.md b/spaces/tomaseo2022/Mejorar-Resolucion-Imagen/README.md
deleted file mode 100644
index f87b3a6ef63fe5a28d73bd90df918251c9ee0905..0000000000000000000000000000000000000000
--- a/spaces/tomaseo2022/Mejorar-Resolucion-Imagen/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mejorar Resolucion Imagen
-emoji: 🌍
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tomofi/MMOCR/mmocr/models/ner/classifiers/ner_classifier.py b/spaces/tomofi/MMOCR/mmocr/models/ner/classifiers/ner_classifier.py
deleted file mode 100644
index 7fefef607e4d8f1ae7f9394adaba3caf58bea77d..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/ner/classifiers/ner_classifier.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import (DETECTORS, build_convertor, build_decoder,
-                                  build_encoder, build_loss)
-from mmocr.models.textrecog.recognizer.base import BaseRecognizer
-
-
-@DETECTORS.register_module()
-class NerClassifier(BaseRecognizer):
-    """Base class for NER classifier."""
-
-    def __init__(self,
-                 encoder,
-                 decoder,
-                 loss,
-                 label_convertor,
-                 train_cfg=None,
-                 test_cfg=None,
-                 init_cfg=None):
-        super().__init__(init_cfg=init_cfg)
-        self.label_convertor = build_convertor(label_convertor)
-
-        self.encoder = build_encoder(encoder)
-
-        decoder.update(num_labels=self.label_convertor.num_labels)
-        self.decoder = build_decoder(decoder)
-
-        loss.update(num_labels=self.label_convertor.num_labels)
-        self.loss = build_loss(loss)
-
-    def extract_feat(self, imgs):
-        """Extract features from images."""
-        raise NotImplementedError(
-            'Extract feature module is not implemented yet.')
-
-    def forward_train(self, imgs, img_metas, **kwargs):
-        encode_out = self.encoder(img_metas)
-        logits, _ = self.decoder(encode_out)
-        loss = self.loss(logits, img_metas)
-        return loss
-
-    def forward_test(self, imgs, img_metas, **kwargs):
-        encode_out = self.encoder(img_metas)
-        _, preds = self.decoder(encode_out)
-        pred_entities = self.label_convertor.convert_pred2entities(
-            preds, img_metas['attention_masks'])
-        return pred_entities
-
-    def aug_test(self, imgs, img_metas, **kwargs):
-        raise NotImplementedError('Augmentation test is not implemented yet.')
-
-    def simple_test(self, img, img_metas, **kwargs):
-        raise NotImplementedError('Simple test is not implemented yet.')
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textdet/losses/fce_loss.py b/spaces/tomofi/MMOCR/mmocr/models/textdet/losses/fce_loss.py
deleted file mode 100644
index e956f10ed4be9afc3bd5803073b8bba0c723a714..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textdet/losses/fce_loss.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-import torch
-import torch.nn.functional as F
-from mmdet.core import multi_apply
-from torch import nn
-
-from mmocr.models.builder import LOSSES
-
-
-@LOSSES.register_module()
-class FCELoss(nn.Module):
-    """The class for implementing FCENet loss.
-
-    FCENet(CVPR2021): `Fourier Contour Embedding for Arbitrary-shaped Text
-    Detection <https://arxiv.org/abs/2104.10442>`_
-
-    Args:
-        fourier_degree (int) : The maximum Fourier transform degree k.
-        num_sample (int) : The sampling points number of regression
-            loss. If it is too small, fcenet tends to be overfitting.
-        ohem_ratio (float): the negative/positive ratio in OHEM.
-    """
-
-    def __init__(self, fourier_degree, num_sample, ohem_ratio=3.):
-        super().__init__()
-        self.fourier_degree = fourier_degree
-        self.num_sample = num_sample
-        self.ohem_ratio = ohem_ratio
-
-    def forward(self, preds, _, p3_maps, p4_maps, p5_maps):
-        """Compute FCENet loss.
-
-        Args:
-            preds (list[list[Tensor]]): The outer list indicates images
-                in a batch, and the inner list indicates the classification
-                prediction map (with shape :math:`(N, C, H, W)`) and
-                regression map (with shape :math:`(N, C, H, W)`).
-            p3_maps (list[ndarray]): List of leval 3 ground truth target map
-                with shape :math:`(C, H, W)`.
-            p4_maps (list[ndarray]): List of leval 4 ground truth target map
-                with shape :math:`(C, H, W)`.
-            p5_maps (list[ndarray]): List of leval 5 ground truth target map
-                with shape :math:`(C, H, W)`.
-
-        Returns:
-            dict:  A loss dict with ``loss_text``, ``loss_center``,
-            ``loss_reg_x`` and ``loss_reg_y``.
-        """
-        assert isinstance(preds, list)
-        assert p3_maps[0].shape[0] == 4 * self.fourier_degree + 5,\
-            'fourier degree not equal in FCEhead and FCEtarget'
-
-        device = preds[0][0].device
-        # to tensor
-        gts = [p3_maps, p4_maps, p5_maps]
-        for idx, maps in enumerate(gts):
-            gts[idx] = torch.from_numpy(np.stack(maps)).float().to(device)
-
-        losses = multi_apply(self.forward_single, preds, gts)
-
-        loss_tr = torch.tensor(0., device=device).float()
-        loss_tcl = torch.tensor(0., device=device).float()
-        loss_reg_x = torch.tensor(0., device=device).float()
-        loss_reg_y = torch.tensor(0., device=device).float()
-
-        for idx, loss in enumerate(losses):
-            if idx == 0:
-                loss_tr += sum(loss)
-            elif idx == 1:
-                loss_tcl += sum(loss)
-            elif idx == 2:
-                loss_reg_x += sum(loss)
-            else:
-                loss_reg_y += sum(loss)
-
-        results = dict(
-            loss_text=loss_tr,
-            loss_center=loss_tcl,
-            loss_reg_x=loss_reg_x,
-            loss_reg_y=loss_reg_y,
-        )
-
-        return results
-
-    def forward_single(self, pred, gt):
-        cls_pred = pred[0].permute(0, 2, 3, 1).contiguous()
-        reg_pred = pred[1].permute(0, 2, 3, 1).contiguous()
-        gt = gt.permute(0, 2, 3, 1).contiguous()
-
-        k = 2 * self.fourier_degree + 1
-        tr_pred = cls_pred[:, :, :, :2].view(-1, 2)
-        tcl_pred = cls_pred[:, :, :, 2:].view(-1, 2)
-        x_pred = reg_pred[:, :, :, 0:k].view(-1, k)
-        y_pred = reg_pred[:, :, :, k:2 * k].view(-1, k)
-
-        tr_mask = gt[:, :, :, :1].view(-1)
-        tcl_mask = gt[:, :, :, 1:2].view(-1)
-        train_mask = gt[:, :, :, 2:3].view(-1)
-        x_map = gt[:, :, :, 3:3 + k].view(-1, k)
-        y_map = gt[:, :, :, 3 + k:].view(-1, k)
-
-        tr_train_mask = train_mask * tr_mask
-        device = x_map.device
-        # tr loss
-        loss_tr = self.ohem(tr_pred, tr_mask.long(), train_mask.long())
-
-        # tcl loss
-        loss_tcl = torch.tensor(0.).float().to(device)
-        tr_neg_mask = 1 - tr_train_mask
-        if tr_train_mask.sum().item() > 0:
-            loss_tcl_pos = F.cross_entropy(
-                tcl_pred[tr_train_mask.bool()],
-                tcl_mask[tr_train_mask.bool()].long())
-            loss_tcl_neg = F.cross_entropy(tcl_pred[tr_neg_mask.bool()],
-                                           tcl_mask[tr_neg_mask.bool()].long())
-            loss_tcl = loss_tcl_pos + 0.5 * loss_tcl_neg
-
-        # regression loss
-        loss_reg_x = torch.tensor(0.).float().to(device)
-        loss_reg_y = torch.tensor(0.).float().to(device)
-        if tr_train_mask.sum().item() > 0:
-            weight = (tr_mask[tr_train_mask.bool()].float() +
-                      tcl_mask[tr_train_mask.bool()].float()) / 2
-            weight = weight.contiguous().view(-1, 1)
-
-            ft_x, ft_y = self.fourier2poly(x_map, y_map)
-            ft_x_pre, ft_y_pre = self.fourier2poly(x_pred, y_pred)
-
-            loss_reg_x = torch.mean(weight * F.smooth_l1_loss(
-                ft_x_pre[tr_train_mask.bool()],
-                ft_x[tr_train_mask.bool()],
-                reduction='none'))
-            loss_reg_y = torch.mean(weight * F.smooth_l1_loss(
-                ft_y_pre[tr_train_mask.bool()],
-                ft_y[tr_train_mask.bool()],
-                reduction='none'))
-
-        return loss_tr, loss_tcl, loss_reg_x, loss_reg_y
-
-    def ohem(self, predict, target, train_mask):
-        device = train_mask.device
-        pos = (target * train_mask).bool()
-        neg = ((1 - target) * train_mask).bool()
-
-        n_pos = pos.float().sum()
-
-        if n_pos.item() > 0:
-            loss_pos = F.cross_entropy(
-                predict[pos], target[pos], reduction='sum')
-            loss_neg = F.cross_entropy(
-                predict[neg], target[neg], reduction='none')
-            n_neg = min(
-                int(neg.float().sum().item()),
-                int(self.ohem_ratio * n_pos.float()))
-        else:
-            loss_pos = torch.tensor(0.).to(device)
-            loss_neg = F.cross_entropy(
-                predict[neg], target[neg], reduction='none')
-            n_neg = 100
-        if len(loss_neg) > n_neg:
-            loss_neg, _ = torch.topk(loss_neg, n_neg)
-
-        return (loss_pos + loss_neg.sum()) / (n_pos + n_neg).float()
-
-    def fourier2poly(self, real_maps, imag_maps):
-        """Transform Fourier coefficient maps to polygon maps.
-
-        Args:
-            real_maps (tensor): A map composed of the real parts of the
-                Fourier coefficients, whose shape is (-1, 2k+1)
-            imag_maps (tensor):A map composed of the imag parts of the
-                Fourier coefficients, whose shape is (-1, 2k+1)
-
-        Returns
-            x_maps (tensor): A map composed of the x value of the polygon
-                represented by n sample points (xn, yn), whose shape is (-1, n)
-            y_maps (tensor): A map composed of the y value of the polygon
-                represented by n sample points (xn, yn), whose shape is (-1, n)
-        """
-
-        device = real_maps.device
-
-        k_vect = torch.arange(
-            -self.fourier_degree,
-            self.fourier_degree + 1,
-            dtype=torch.float,
-            device=device).view(-1, 1)
-        i_vect = torch.arange(
-            0, self.num_sample, dtype=torch.float, device=device).view(1, -1)
-
-        transform_matrix = 2 * np.pi / self.num_sample * torch.mm(
-            k_vect, i_vect)
-
-        x1 = torch.einsum('ak, kn-> an', real_maps,
-                          torch.cos(transform_matrix))
-        x2 = torch.einsum('ak, kn-> an', imag_maps,
-                          torch.sin(transform_matrix))
-        y1 = torch.einsum('ak, kn-> an', real_maps,
-                          torch.sin(transform_matrix))
-        y2 = torch.einsum('ak, kn-> an', imag_maps,
-                          torch.cos(transform_matrix))
-
-        x_maps = x1 - x2
-        y_maps = y1 + y2
-
-        return x_maps, y_maps
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
deleted file mode 100644
index e64c22cdb062a43c082360803caf399fa4141d60..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
deleted file mode 100644
index 72b90f881dc0df51bbf6c562a9f80493ea150ab6..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
+++ /dev/null
@@ -1,54 +0,0 @@
-_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
-
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)),
-    bbox_head=dict(
-        norm_on_bbox=True,
-        centerness_on_reg=True,
-        dcn_on_last_conv=True,
-        center_sampling=True,
-        conv_bias=True,
-        loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
-    # training and testing settings
-    test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6)))
-
-# dataset settings
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-optimizer_config = dict(_delete_=True, grad_clip=None)
-
-lr_config = dict(warmup='linear')
diff --git a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py b/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py
deleted file mode 100644
index bdb64e0c78cc3520f92d79db3124c85fc3cfb9b4..0000000000000000000000000000000000000000
--- a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ /dev/null
@@ -1,1184 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-        ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-        
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0**2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-    model,
-    noise_schedule,
-    model_type="noise",
-    model_kwargs={},
-    guidance_type="uncond",
-    condition=None,
-    unconditional_condition=None,
-    guidance_scale=1.,
-    classifier_fn=None,
-    classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-    
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            `` 
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            `` 
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-        
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)         
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver. 
-
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-        
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model. 
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3,] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3,] * (K - 1) + [1]
-            else:
-                orders = [3,] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2,] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2,] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1,] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                expand_dims(sigma_t / sigma_s, dims) * x
-                - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                expand_dims(sigma_s1 / sigma_s, dims) * x
-                - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                expand_dims(sigma_s2 / sigma_s, dims) * x
-                - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + expand_dims(alpha_t * phi_2, dims) * D1
-                    - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - expand_dims(sigma_t * phi_2, dims) * D1
-                    - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                expand_dims(sigma_t / sigma_prev_0, dims) * x
-                - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the 
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-        atol=0.0078, rtol=0.05,
-    ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-
-        =====================================================
-
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. 
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-
-        =====================================================
-
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in range(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order,] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,)*(dims - 1)]
\ No newline at end of file
diff --git a/spaces/tresdtres/TresDtres_AI/README.md b/spaces/tresdtres/TresDtres_AI/README.md
deleted file mode 100644
index b4ac53472f751985c3d111d0a885243034a11130..0000000000000000000000000000000000000000
--- a/spaces/tresdtres/TresDtres_AI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TresDtres AI
-emoji: 🌍
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ttt246/brain/Brain/tests/__init__.py b/spaces/ttt246/brain/Brain/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/udartem/easwsnn/README.md b/spaces/udartem/easwsnn/README.md
deleted file mode 100644
index 50c76c9c765519aa3e048c2eb1dff7d98525bdb1..0000000000000000000000000000000000000000
--- a/spaces/udartem/easwsnn/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Easwsnn
-emoji: 👁
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/CHITAOZINHO E XORORO SAUDADE DE MINHA TERRA.md b/spaces/usbethFlerru/sovits-modelsV2/example/CHITAOZINHO E XORORO SAUDADE DE MINHA TERRA.md
deleted file mode 100644
index 773321a3bb90bd7b83890b7f4a1ec2df2084c62b..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/CHITAOZINHO E XORORO SAUDADE DE MINHA TERRA.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>popular artists like chitaozinho & xororo have constantly been evolving and changing their musical style. the artists are interested in popularizing their music, and the musical style and instrumentation are constantly changing.</p>
-<h2>{CHITAOZINHO E XORORO SAUDADE DE MINHA TERRA}</h2><br /><p><b><b>Download Zip</b> &harr; <a href="https://urlcod.com/2uyXxA">https://urlcod.com/2uyXxA</a></b></p><br /><br />
-<p>chitaozinho & xororo is one of the top brazilian musical groups and the music of chitaozinho & xororo has swept the nation. new songs are constantly being recorded and released and the musical style of chitaozinho & xororo keeps evolving.</p>
-<p>why buy chitaozinho & xororo concert tickets from ticketsmarter? first of all, there's no risk of purchasing tickets that are priced too high or being scammed. you will always get seats with great views at good prices.</p>
-<p>we will get your chitaozinho & xororo concert tickets to you quickly and safely. you can use paypal to pay for tickets securely via your credit card or paypal account. we deal with the sellers directly to avoid any middlemen, and all transactions are verified.</p>
-<p>when you order chitaozinho & xororo concert tickets at ticketsmarter, you can use paypal. you do not have to sign up for an account or provide your credit card information. if you prefer, you can pay with your credit card via a secure connection. we've been using paypal for a long time to process all of our ticket sales.</p>
-<p></p>
-<p>listen to the best performers and artists performing cezar e paulinho terra tombada downloads and audio on mp3 and other formats. when you listen to chitaozinho & xororo cezar e paulinho terra tombada online music you can also share the music you like on social networks like facebook, twitter, google plus and others.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Come vedere 007 - Octopussy operazione piovra in streaming gratis mp4  Il film dazione e spionaggio in italiano.md b/spaces/usbethFlerru/sovits-modelsV2/example/Come vedere 007 - Octopussy operazione piovra in streaming gratis mp4  Il film dazione e spionaggio in italiano.md
deleted file mode 100644
index 2b6ed9a2b65629ea9a10f8ac352f724c457ded03..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Come vedere 007 - Octopussy operazione piovra in streaming gratis mp4  Il film dazione e spionaggio in italiano.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>007 - Octopussy: operazione piovra full movie in italian free download mp4</h2><br /><p><b><b>Download File</b> &#10084;&#10084;&#10084; <a href="https://urlcod.com/2uyWiM">https://urlcod.com/2uyWiM</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/scripts_auto_postprocessing.py b/spaces/user238921933/stable-diffusion-webui/modules/scripts_auto_postprocessing.py
deleted file mode 100644
index 16ec8b613b134b0a9a4054f06d5979ec1822c422..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/scripts_auto_postprocessing.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from modules import scripts, scripts_postprocessing, shared
-
-
-class ScriptPostprocessingForMainUI(scripts.Script):
-    def __init__(self, script_postproc):
-        self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
-        self.postprocessing_controls = None
-
-    def title(self):
-        return self.script.name
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def ui(self, is_img2img):
-        self.postprocessing_controls = self.script.ui()
-        return self.postprocessing_controls.values()
-
-    def postprocess_image(self, p, script_pp, *args):
-        args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
-
-        pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
-        pp.info = {}
-        self.script.process(pp, **args_dict)
-        p.extra_generation_params.update(pp.info)
-        script_pp.image = pp.image
-
-
-def create_auto_preprocessing_script_data():
-    from modules import scripts
-
-    res = []
-
-    for name in shared.opts.postprocessing_enable_in_main_ui:
-        script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
-        if script is None:
-            continue
-
-        constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())
-        res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
-
-    return res
diff --git a/spaces/user238921933/stable-diffusion-webui/scripts/xyz_grid.py b/spaces/user238921933/stable-diffusion-webui/scripts/xyz_grid.py
deleted file mode 100644
index e457d53de2ade37a53cc200d6902323d3d6f25ce..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/scripts/xyz_grid.py
+++ /dev/null
@@ -1,620 +0,0 @@
-from collections import namedtuple
-from copy import copy
-from itertools import permutations, chain
-import random
-import csv
-from io import StringIO
-from PIL import Image
-import numpy as np
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules import images, paths, sd_samplers, processing, sd_models, sd_vae
-from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
-from modules.shared import opts, cmd_opts, state
-import modules.shared as shared
-import modules.sd_samplers
-import modules.sd_models
-import modules.sd_vae
-import glob
-import os
-import re
-
-from modules.ui_components import ToolButton
-
-fill_values_symbol = "\U0001f4d2"  # 📒
-
-AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
-
-
-def apply_field(field):
-    def fun(p, x, xs):
-        setattr(p, field, x)
-
-    return fun
-
-
-def apply_prompt(p, x, xs):
-    if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
-        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
-
-    p.prompt = p.prompt.replace(xs[0], x)
-    p.negative_prompt = p.negative_prompt.replace(xs[0], x)
-
-
-def apply_order(p, x, xs):
-    token_order = []
-
-    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
-    for token in x:
-        token_order.append((p.prompt.find(token), token))
-
-    token_order.sort(key=lambda t: t[0])
-
-    prompt_parts = []
-
-    # Split the prompt up, taking out the tokens
-    for _, token in token_order:
-        n = p.prompt.find(token)
-        prompt_parts.append(p.prompt[0:n])
-        p.prompt = p.prompt[n + len(token):]
-
-    # Rebuild the prompt with the tokens in the order we want
-    prompt_tmp = ""
-    for idx, part in enumerate(prompt_parts):
-        prompt_tmp += part
-        prompt_tmp += x[idx]
-    p.prompt = prompt_tmp + p.prompt
-
-
-def apply_sampler(p, x, xs):
-    sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
-    if sampler_name is None:
-        raise RuntimeError(f"Unknown sampler: {x}")
-
-    p.sampler_name = sampler_name
-
-
-def confirm_samplers(p, xs):
-    for x in xs:
-        if x.lower() not in sd_samplers.samplers_map:
-            raise RuntimeError(f"Unknown sampler: {x}")
-
-
-def apply_checkpoint(p, x, xs):
-    info = modules.sd_models.get_closet_checkpoint_match(x)
-    if info is None:
-        raise RuntimeError(f"Unknown checkpoint: {x}")
-    modules.sd_models.reload_model_weights(shared.sd_model, info)
-
-
-def confirm_checkpoints(p, xs):
-    for x in xs:
-        if modules.sd_models.get_closet_checkpoint_match(x) is None:
-            raise RuntimeError(f"Unknown checkpoint: {x}")
-
-
-def apply_clip_skip(p, x, xs):
-    opts.data["CLIP_stop_at_last_layers"] = x
-
-
-def apply_upscale_latent_space(p, x, xs):
-    if x.lower().strip() != '0':
-        opts.data["use_scale_latent_for_hires_fix"] = True
-    else:
-        opts.data["use_scale_latent_for_hires_fix"] = False
-
-
-def find_vae(name: str):
-    if name.lower() in ['auto', 'automatic']:
-        return modules.sd_vae.unspecified
-    if name.lower() == 'none':
-        return None
-    else:
-        choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
-        if len(choices) == 0:
-            print(f"No VAE found for {name}; using automatic")
-            return modules.sd_vae.unspecified
-        else:
-            return modules.sd_vae.vae_dict[choices[0]]
-
-
-def apply_vae(p, x, xs):
-    modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
-
-
-def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
-    p.styles.extend(x.split(','))
-
-
-def format_value_add_label(p, opt, x):
-    if type(x) == float:
-        x = round(x, 8)
-
-    return f"{opt.label}: {x}"
-
-
-def format_value(p, opt, x):
-    if type(x) == float:
-        x = round(x, 8)
-    return x
-
-
-def format_value_join_list(p, opt, x):
-    return ", ".join(x)
-
-
-def do_nothing(p, x, xs):
-    pass
-
-
-def format_nothing(p, opt, x):
-    return ""
-
-
-def str_permutations(x):
-    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
-    return x
-
-
-class AxisOption:
-    def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
-        self.label = label
-        self.type = type
-        self.apply = apply
-        self.format_value = format_value
-        self.confirm = confirm
-        self.cost = cost
-        self.choices = choices
-
-
-class AxisOptionImg2Img(AxisOption):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.is_img2img = True
-
-class AxisOptionTxt2Img(AxisOption):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.is_img2img = False
-
-
-axis_options = [
-    AxisOption("Nothing", str, do_nothing, format_value=format_nothing),
-    AxisOption("Seed", int, apply_field("seed")),
-    AxisOption("Var. seed", int, apply_field("subseed")),
-    AxisOption("Var. strength", float, apply_field("subseed_strength")),
-    AxisOption("Steps", int, apply_field("steps")),
-    AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
-    AxisOption("CFG Scale", float, apply_field("cfg_scale")),
-    AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
-    AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
-    AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
-    AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
-    AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
-    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
-    AxisOption("Sigma Churn", float, apply_field("s_churn")),
-    AxisOption("Sigma min", float, apply_field("s_tmin")),
-    AxisOption("Sigma max", float, apply_field("s_tmax")),
-    AxisOption("Sigma noise", float, apply_field("s_noise")),
-    AxisOption("Eta", float, apply_field("eta")),
-    AxisOption("Clip skip", int, apply_clip_skip),
-    AxisOption("Denoising", float, apply_field("denoising_strength")),
-    AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
-    AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
-    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
-    AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
-]
-
-
-def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
-    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
-    ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
-    title_texts = [[images.GridAnnotation(z)] for z in z_labels]
-
-    # Temporary list of all the images that are generated to be populated into the grid.
-    # Will be filled with empty images for any individual step that fails to process properly
-    image_cache = [None] * (len(xs) * len(ys) * len(zs))
-
-    processed_result = None
-    cell_mode = "P"
-    cell_size = (1, 1)
-
-    state.job_count = len(xs) * len(ys) * len(zs) * p.n_iter
-
-    def process_cell(x, y, z, ix, iy, iz):
-        nonlocal image_cache, processed_result, cell_mode, cell_size
-
-        def index(ix, iy, iz):
-            return ix + iy * len(xs) + iz * len(xs) * len(ys)
-
-        state.job = f"{index(ix, iy, iz) + 1} out of {len(xs) * len(ys) * len(zs)}"
-
-        processed: Processed = cell(x, y, z)
-
-        try:
-            # this dereference will throw an exception if the image was not processed
-            # (this happens in cases such as if the user stops the process from the UI)
-            processed_image = processed.images[0]
-
-            if processed_result is None:
-                # Use our first valid processed result as a template container to hold our full results
-                processed_result = copy(processed)
-                cell_mode = processed_image.mode
-                cell_size = processed_image.size
-                processed_result.images = [Image.new(cell_mode, cell_size)]
-                processed_result.all_prompts = [processed.prompt]
-                processed_result.all_seeds = [processed.seed]
-                processed_result.infotexts = [processed.infotexts[0]]
-
-            image_cache[index(ix, iy, iz)] = processed_image
-            if include_lone_images:
-                processed_result.images.append(processed_image)
-                processed_result.all_prompts.append(processed.prompt)
-                processed_result.all_seeds.append(processed.seed)
-                processed_result.infotexts.append(processed.infotexts[0])
-        except:
-            image_cache[index(ix, iy, iz)] = Image.new(cell_mode, cell_size)
-
-    if first_axes_processed == 'x':
-        for ix, x in enumerate(xs):
-            if second_axes_processed == 'y':
-                for iy, y in enumerate(ys):
-                    for iz, z in enumerate(zs):
-                        process_cell(x, y, z, ix, iy, iz)
-            else:
-                for iz, z in enumerate(zs):
-                    for iy, y in enumerate(ys):
-                        process_cell(x, y, z, ix, iy, iz)
-    elif first_axes_processed == 'y':
-        for iy, y in enumerate(ys):
-            if second_axes_processed == 'x':
-                for ix, x in enumerate(xs):
-                    for iz, z in enumerate(zs):
-                        process_cell(x, y, z, ix, iy, iz)
-            else:
-                for iz, z in enumerate(zs):
-                    for ix, x in enumerate(xs):
-                        process_cell(x, y, z, ix, iy, iz)
-    elif first_axes_processed == 'z':
-        for iz, z in enumerate(zs):
-            if second_axes_processed == 'x':
-                for ix, x in enumerate(xs):
-                    for iy, y in enumerate(ys):
-                        process_cell(x, y, z, ix, iy, iz)
-            else:
-                for iy, y in enumerate(ys):
-                    for ix, x in enumerate(xs):
-                        process_cell(x, y, z, ix, iy, iz)
-
-    if not processed_result:
-        print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
-        return Processed(p, [])
-
-    sub_grids = [None] * len(zs)
-    for i in range(len(zs)):
-        start_index = i * len(xs) * len(ys)
-        end_index = start_index + len(xs) * len(ys)
-        grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))
-        if draw_legend:
-            grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts, margin_size)
-        sub_grids[i] = grid
-        if include_sub_grids and len(zs) > 1:
-            processed_result.images.insert(i+1, grid)
-
-    sub_grid_size = sub_grids[0].size
-    z_grid = images.image_grid(sub_grids, rows=1)
-    if draw_legend:
-        z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
-    processed_result.images[0] = z_grid
-
-    return processed_result, sub_grids
-
-
-class SharedSettingsStackHelper(object):
-    def __enter__(self):
-        self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
-        self.vae = opts.sd_vae
-  
-    def __exit__(self, exc_type, exc_value, tb):
-        opts.data["sd_vae"] = self.vae
-        modules.sd_models.reload_model_weights()
-        modules.sd_vae.reload_vae_weights()
-
-        opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
-
-
-re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
-re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
-
-re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
-re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
-
-
-class Script(scripts.Script):
-    def title(self):
-        return "X/Y/Z plot"
-
-    def ui(self, is_img2img):
-        self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
-
-        with gr.Row():
-            with gr.Column(scale=19):
-                with gr.Row():
-                    x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
-                    x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
-                    fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
-
-                with gr.Row():
-                    y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
-                    y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
-                    fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
-
-                with gr.Row():
-                    z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
-                    z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
-                    fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
-
-        with gr.Row(variant="compact", elem_id="axis_options"):
-            with gr.Column():
-                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
-                no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
-            with gr.Column():
-                include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
-                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
-            with gr.Column():
-                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
-        
-        with gr.Row(variant="compact", elem_id="swap_axes"):
-            swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
-            swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
-            swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
-
-        def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
-            return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
-
-        xy_swap_args = [x_type, x_values, y_type, y_values]
-        swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
-        yz_swap_args = [y_type, y_values, z_type, z_values]
-        swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
-        xz_swap_args = [x_type, x_values, z_type, z_values]
-        swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
-
-        def fill(x_type):
-            axis = self.current_axis_options[x_type]
-            return ", ".join(axis.choices()) if axis.choices else gr.update()
-
-        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
-        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
-        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
-
-        def select_axis(x_type):
-            return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
-
-        x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
-        y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
-        z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
-
-        self.infotext_fields = (
-            (x_type, "X Type"),
-            (x_values, "X Values"),
-            (y_type, "Y Type"),
-            (y_values, "Y Values"),
-            (z_type, "Z Type"),
-            (z_values, "Z Values"),
-        )
-
-        return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
-
-    def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
-        if not no_fixed_seeds:
-            modules.processing.fix_seed(p)
-
-        if not opts.return_grid:
-            p.batch_size = 1
-
-        def process_axis(opt, vals):
-            if opt.label == 'Nothing':
-                return [0]
-
-            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
-
-            if opt.type == int:
-                valslist_ext = []
-
-                for val in valslist:
-                    m = re_range.fullmatch(val)
-                    mc = re_range_count.fullmatch(val)
-                    if m is not None:
-                        start = int(m.group(1))
-                        end = int(m.group(2))+1
-                        step = int(m.group(3)) if m.group(3) is not None else 1
-
-                        valslist_ext += list(range(start, end, step))
-                    elif mc is not None:
-                        start = int(mc.group(1))
-                        end   = int(mc.group(2))
-                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
-                        
-                        valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
-                    else:
-                        valslist_ext.append(val)
-
-                valslist = valslist_ext
-            elif opt.type == float:
-                valslist_ext = []
-
-                for val in valslist:
-                    m = re_range_float.fullmatch(val)
-                    mc = re_range_count_float.fullmatch(val)
-                    if m is not None:
-                        start = float(m.group(1))
-                        end = float(m.group(2))
-                        step = float(m.group(3)) if m.group(3) is not None else 1
-
-                        valslist_ext += np.arange(start, end + step, step).tolist()
-                    elif mc is not None:
-                        start = float(mc.group(1))
-                        end   = float(mc.group(2))
-                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
-                        
-                        valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
-                    else:
-                        valslist_ext.append(val)
-
-                valslist = valslist_ext
-            elif opt.type == str_permutations:
-                valslist = list(permutations(valslist))
-
-            valslist = [opt.type(x) for x in valslist]
-
-            # Confirm options are valid before starting
-            if opt.confirm:
-                opt.confirm(p, valslist)
-
-            return valslist
-
-        x_opt = self.current_axis_options[x_type]
-        xs = process_axis(x_opt, x_values)
-
-        y_opt = self.current_axis_options[y_type]
-        ys = process_axis(y_opt, y_values)
-
-        z_opt = self.current_axis_options[z_type]
-        zs = process_axis(z_opt, z_values)
-
-        def fix_axis_seeds(axis_opt, axis_list):
-            if axis_opt.label in ['Seed', 'Var. seed']:
-                return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
-            else:
-                return axis_list
-
-        if not no_fixed_seeds:
-            xs = fix_axis_seeds(x_opt, xs)
-            ys = fix_axis_seeds(y_opt, ys)
-            zs = fix_axis_seeds(z_opt, zs)
-
-        if x_opt.label == 'Steps':
-            total_steps = sum(xs) * len(ys) * len(zs)
-        elif y_opt.label == 'Steps':
-            total_steps = sum(ys) * len(xs) * len(zs)
-        elif z_opt.label == 'Steps':
-            total_steps = sum(zs) * len(xs) * len(ys)
-        else:
-            total_steps = p.steps * len(xs) * len(ys) * len(zs)
-
-        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
-            if x_opt.label == "Hires steps":
-                total_steps += sum(xs) * len(ys) * len(zs)
-            elif y_opt.label == "Hires steps":
-                total_steps += sum(ys) * len(xs) * len(zs)
-            elif z_opt.label == "Hires steps":
-                total_steps += sum(zs) * len(xs) * len(ys)
-            elif p.hr_second_pass_steps:
-                total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)
-            else:
-                total_steps *= 2
-
-        total_steps *= p.n_iter
-
-        image_cell_count = p.n_iter * p.batch_size
-        cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
-        plural_s = 's' if len(zs) > 1 else ''
-        print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
-        shared.total_tqdm.updateTotal(total_steps)
-
-        grid_infotext = [None]
-
-        state.xyz_plot_x = AxisInfo(x_opt, xs)
-        state.xyz_plot_y = AxisInfo(y_opt, ys)
-        state.xyz_plot_z = AxisInfo(z_opt, zs)
-
-        # If one of the axes is very slow to change between (like SD model
-        # checkpoint), then make sure it is in the outer iteration of the nested
-        # `for` loop.
-        first_axes_processed = 'x'
-        second_axes_processed = 'y'
-        if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
-            first_axes_processed = 'x'
-            if y_opt.cost > z_opt.cost:
-                second_axes_processed = 'y'
-            else:
-                second_axes_processed = 'z'
-        elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:
-            first_axes_processed = 'y'
-            if x_opt.cost > z_opt.cost:
-                second_axes_processed = 'x'
-            else:
-                second_axes_processed = 'z'
-        elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:
-            first_axes_processed = 'z'
-            if x_opt.cost > y_opt.cost:
-                second_axes_processed = 'x'
-            else:
-                second_axes_processed = 'y'
-
-        def cell(x, y, z):
-            if shared.state.interrupted:
-                return Processed(p, [], p.seed, "")
-
-            pc = copy(p)
-            pc.styles = pc.styles[:]
-            x_opt.apply(pc, x, xs)
-            y_opt.apply(pc, y, ys)
-            z_opt.apply(pc, z, zs)
-
-            res = process_images(pc)
-
-            if grid_infotext[0] is None:
-                pc.extra_generation_params = copy(pc.extra_generation_params)
-                pc.extra_generation_params['Script'] = self.title()
-
-                if x_opt.label != 'Nothing':
-                    pc.extra_generation_params["X Type"] = x_opt.label
-                    pc.extra_generation_params["X Values"] = x_values
-                    if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
-                        pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])
-
-                if y_opt.label != 'Nothing':
-                    pc.extra_generation_params["Y Type"] = y_opt.label
-                    pc.extra_generation_params["Y Values"] = y_values
-                    if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
-                        pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
-
-                if z_opt.label != 'Nothing':
-                    pc.extra_generation_params["Z Type"] = z_opt.label
-                    pc.extra_generation_params["Z Values"] = z_values
-                    if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
-                        pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])
-
-                grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
-
-            return res
-
-        with SharedSettingsStackHelper():
-            processed, sub_grids = draw_xyz_grid(
-                p,
-                xs=xs,
-                ys=ys,
-                zs=zs,
-                x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
-                y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
-                z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],
-                cell=cell,
-                draw_legend=draw_legend,
-                include_lone_images=include_lone_images,
-                include_sub_grids=include_sub_grids,
-                first_axes_processed=first_axes_processed,
-                second_axes_processed=second_axes_processed,
-                margin_size=margin_size
-            )
-
-        if opts.grid_save and len(sub_grids) > 1:
-            for sub_grid in sub_grids:
-                images.save_image(sub_grid, p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
-
-        if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
-
-        return processed
diff --git a/spaces/usingh49/us1/README.md b/spaces/usingh49/us1/README.md
deleted file mode 100644
index da89adfd52af5fc9a17f0b0b1cd9377ff712bc48..0000000000000000000000000000000000000000
--- a/spaces/usingh49/us1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Us1
-emoji: 🐨
-colorFrom: purple
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/vit/sam/modules/decoders.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/vit/sam/modules/decoders.md
deleted file mode 100644
index e89ca9d68b46e25d57eb2f3294e89990b6d0ae38..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/vit/sam/modules/decoders.md
+++ /dev/null
@@ -1,9 +0,0 @@
-## MaskDecoder
----
-### ::: ultralytics.vit.sam.modules.decoders.MaskDecoder
-<br><br>
-
-## MLP
----
-### ::: ultralytics.vit.sam.modules.decoders.MLP
-<br><br>
diff --git a/spaces/vialibre/edia_lmodels_en/modules/module_rankSents.py b/spaces/vialibre/edia_lmodels_en/modules/module_rankSents.py
deleted file mode 100644
index 1fc10a49f72d67de936b6b39a3189c24fc356711..0000000000000000000000000000000000000000
--- a/spaces/vialibre/edia_lmodels_en/modules/module_rankSents.py
+++ /dev/null
@@ -1,168 +0,0 @@
-from modules.module_customPllLabel import CustomPllLabel
-from modules.module_pllScore import PllScore
-from typing import List, Dict
-import torch
-
-
-class RankSents:
-    def __init__(
-        self, 
-        language_model, # LanguageModel class instance
-        lang: str
-    ) -> None:
-        
-        self.tokenizer = language_model.initTokenizer()
-        self.model = language_model.initModel()
-        _ = self.model.eval()
-
-        self.Label = CustomPllLabel()
-        self.pllScore = PllScore(
-            language_model=language_model
-        )
-        self.softmax = torch.nn.Softmax(dim=-1)
-
-        if lang == "es":
-            self.articles = [
-                'un','una','unos','unas','el','los','la','las','lo'
-            ]
-            self.prepositions = [
-                'a','ante','bajo','cabe','con','contra','de','desde','en','entre','hacia','hasta','para','por','según','sin','so','sobre','tras','durante','mediante','vía','versus'
-            ]
-            self.conjunctions = [
-                'y','o','ni','que','pero','si'
-            ]
-
-        elif lang == "en":
-            self.articles = [
-                'a','an', 'the'
-            ]
-            self.prepositions = [
-                'above', 'across', 'against', 'along', 'among', 'around', 'at', 'before', 'behind', 'below', 'beneath', 'beside', 'between', 'by', 'down', 'from', 'in', 'into', 'near', 'of', 'off', 'on', 'to', 'toward', 'under', 'upon', 'with', 'within'
-            ]
-            self.conjunctions = [
-                'and', 'or', 'but', 'that', 'if', 'whether'
-            ]
-
-    def errorChecking(
-        self, 
-        sent: str
-    ) -> str:
-
-        out_msj = ""
-        if not sent:
-            out_msj = "Error: You most enter a sentence!"
-        elif sent.count("*") > 1:
-            out_msj= " Error: The sentence entered must contain only one ' * '!"
-        elif sent.count("*") == 0:
-            out_msj= " Error: The entered sentence needs to contain a ' * ' in order to predict the word!"
-        else:
-            sent_len = len(self.tokenizer.encode(sent.replace("*", self.tokenizer.mask_token)))
-            max_len = self.tokenizer.max_len_single_sentence
-            if sent_len > max_len:
-                out_msj = f"Error: The sentence has more than {max_len} tokens!"
-        
-        return out_msj
-
-    def getTop5Predictions(
-        self, 
-        sent: str,
-        banned_wl: List[str], 
-        articles: bool,
-        prepositions: bool,
-        conjunctions: bool
-    ) -> List[str]:
-                                
-        sent_masked = sent.replace("*", self.tokenizer.mask_token)
-        inputs = self.tokenizer.encode_plus( 
-            sent_masked,
-            add_special_tokens=True,
-            return_tensors='pt',
-            return_attention_mask=True, truncation=True
-        )
-
-        tk_position_mask = torch.where(inputs['input_ids'][0] == self.tokenizer.mask_token_id)[0].item()
-
-        with torch.no_grad():
-            out = self.model(**inputs)
-            logits = out.logits
-            outputs = self.softmax(logits)
-            outputs = torch.squeeze(outputs, dim=0)
-        
-        probabilities = outputs[tk_position_mask]
-        first_tk_id = torch.argsort(probabilities, descending=True)
-        
-        top5_tks_pred = []
-        for tk_id in first_tk_id:
-            tk_string = self.tokenizer.decode([tk_id])
-            
-            tk_is_banned = tk_string in banned_wl
-            tk_is_punctuation = not tk_string.isalnum()
-            tk_is_substring = tk_string.startswith("##")
-            tk_is_special = (tk_string in self.tokenizer.all_special_tokens)
-
-            if articles:
-                tk_is_article = tk_string in self.articles
-            else:
-                tk_is_article = False
-            
-            if prepositions:
-                tk_is_prepositions = tk_string in self.prepositions
-            else:
-                tk_is_prepositions = False
-            
-            if conjunctions:
-                tk_is_conjunctions = tk_string in self.conjunctions
-            else:
-                tk_is_conjunctions = False
-            
-            predictions_is_dessire = not any([  
-                                    tk_is_banned,
-                                    tk_is_punctuation,
-                                    tk_is_substring, 
-                                    tk_is_special, 
-                                    tk_is_article, 
-                                    tk_is_prepositions,
-                                    tk_is_conjunctions
-            ])
-
-            if predictions_is_dessire and len(top5_tks_pred) < 5:
-                top5_tks_pred.append(tk_string)
-
-            elif len(top5_tks_pred) >= 5:
-                break
-
-        return top5_tks_pred
-
-    def rank(self, 
-        sent: str, 
-        word_list: List[str]=[], 
-        banned_word_list: List[str]=[], 
-        articles: bool=False, 
-        prepositions: bool=False, 
-        conjunctions: bool=False
-    ) -> Dict[str, float]:
-        
-        err = self.errorChecking(sent)
-        if err:
-            raise Exception(err)
-
-        if not word_list:
-            word_list = self.getTop5Predictions(
-                sent,
-                banned_word_list,
-                articles,
-                prepositions,
-                conjunctions
-            )
-
-        sent_list = []
-        sent_list2print = []
-        for word in word_list:
-            sent_list.append(sent.replace("*", "<"+word+">"))
-            sent_list2print.append(sent.replace("*", "<"+word+">"))
-            
-        all_plls_scores = {}
-        for sent, sent2print in zip(sent_list, sent_list2print):
-            all_plls_scores[sent2print] = self.pllScore.compute(sent)
-
-        return all_plls_scores
\ No newline at end of file
diff --git a/spaces/vinthony/SadTalker/app.py b/spaces/vinthony/SadTalker/app.py
deleted file mode 100644
index c0a2718bfe8b20834ec0943e65205b146f3b0b4b..0000000000000000000000000000000000000000
--- a/spaces/vinthony/SadTalker/app.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import os, sys
-import tempfile
-import gradio as gr
-from src.gradio_demo import SadTalker  
-# from src.utils.text2speech import TTSTalker
-from huggingface_hub import snapshot_download
-
-def get_source_image(image):   
-        return image
-
-try:
-    import webui  # in webui
-    in_webui = True
-except:
-    in_webui = False
-
-
-def toggle_audio_file(choice):
-    if choice == False:
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        return gr.update(visible=False), gr.update(visible=True)
-    
-def ref_video_fn(path_of_ref_video):
-    if path_of_ref_video is not None:
-        return gr.update(value=True)
-    else:
-        return gr.update(value=False)
-    
-def download_model():
-    REPO_ID = 'vinthony/SadTalker-V002rc'
-    snapshot_download(repo_id=REPO_ID, local_dir='./checkpoints', local_dir_use_symlinks=True)
-
-def sadtalker_demo():
-
-    download_model()
-
-    sad_talker = SadTalker(lazy_load=True)
-    # tts_talker = TTSTalker()
-
-    with gr.Blocks(analytics_enabled=False) as sadtalker_interface:
-        gr.Markdown("<div align='center'> <h2> 😭 SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023) </span> </h2> \
-                    <a style='font-size:18px;color: #efefef' href='https://arxiv.org/abs/2211.12194'>Arxiv</a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                    <a style='font-size:18px;color: #efefef' href='https://sadtalker.github.io'>Homepage</a>  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                     <a style='font-size:18px;color: #efefef' href='https://github.com/Winfredy/SadTalker'> Github </div>")
-        
-        
-        gr.Markdown("""
-        <b>You may duplicate the space and upgrade to GPU in settings for better performance and faster inference without waiting in the queue. <a style='display:inline-block' href="https://huggingface.co/spaces/vinthony/SadTalker?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a></b> \
-        <br/><b>Alternatively, try our GitHub <a href=https://github.com/Winfredy/SadTalker> code </a> on your own GPU. </b> <a style='display:inline-block' href="https://github.com/Winfredy/SadTalker"><img src="https://img.shields.io/github/stars/Winfredy/SadTalker?style=social"/></a> \
-        """)
-        
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="sadtalker_source_image"):
-                    with gr.TabItem('Source image'):
-                        with gr.Row():
-                            source_image = gr.Image(label="Source image", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
-
-
-                with gr.Tabs(elem_id="sadtalker_driven_audio"):
-                    with gr.TabItem('Driving Methods'):
-                        gr.Markdown("Possible driving combinations: <br> 1. Audio only 2. Audio/IDLE Mode + Ref Video(pose, blink, pose+blink) 3. IDLE Mode only 4. Ref Video only (all) ")
-
-                        with gr.Row():
-                            driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
-                            driven_audio_no = gr.Audio(label="Use IDLE mode, no audio is required", source="upload", type="filepath", visible=False)
-
-                            with gr.Column():
-                                use_idle_mode = gr.Checkbox(label="Use Idle Animation")
-                                length_of_audio = gr.Number(value=5, label="The length(seconds) of the generated video.")
-                                use_idle_mode.change(toggle_audio_file, inputs=use_idle_mode, outputs=[driven_audio, driven_audio_no]) # todo
-
-                        with gr.Row():
-                            ref_video = gr.Video(label="Reference Video", source="upload", type="filepath", elem_id="vidref").style(width=512)
-
-                            with gr.Column():
-                                use_ref_video = gr.Checkbox(label="Use Reference Video")
-                                ref_info = gr.Radio(['pose', 'blink','pose+blink', 'all'], value='pose', label='Reference Video',info="How to borrow from reference Video?((fully transfer, aka, video driving mode))")
-
-                            ref_video.change(ref_video_fn, inputs=ref_video, outputs=[use_ref_video]) # todo
-
-
-            with gr.Column(variant='panel'): 
-                with gr.Tabs(elem_id="sadtalker_checkbox"):
-                    with gr.TabItem('Settings'):
-                        gr.Markdown("need help? please visit our [[best practice page](https://github.com/OpenTalker/SadTalker/blob/main/docs/best_practice.md)] for more detials")
-                        with gr.Column(variant='panel'):
-                            # width = gr.Slider(minimum=64, elem_id="img2img_width", maximum=2048, step=8, label="Manually Crop Width", value=512) # img2img_width
-                            # height = gr.Slider(minimum=64, elem_id="img2img_height", maximum=2048, step=8, label="Manually Crop Height", value=512) # img2img_width
-                            with gr.Row():
-                                pose_style = gr.Slider(minimum=0, maximum=45, step=1, label="Pose style", value=0) #
-                                exp_weight = gr.Slider(minimum=0, maximum=3, step=0.1, label="expression scale", value=1) # 
-                                blink_every = gr.Checkbox(label="use eye blink", value=True)
-
-                            with gr.Row():
-                                size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?") # 
-                                preprocess_type = gr.Radio(['crop', 'resize','full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?")
-                            
-                            with gr.Row():
-                                is_still_mode = gr.Checkbox(label="Still Mode (fewer head motion, works with preprocess `full`)")
-                                facerender = gr.Radio(['facevid2vid','pirender'], value='facevid2vid', label='facerender', info="which face render?")
-                                
-                            with gr.Row():
-                                batch_size = gr.Slider(label="batch size in generation", step=1, maximum=10, value=1)
-                                enhancer = gr.Checkbox(label="GFPGAN as Face enhancer")
-                            
-                            submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
-                            
-                with gr.Tabs(elem_id="sadtalker_genearted"):
-                        gen_video = gr.Video(label="Generated video", format="mp4").style(width=256)
-
-        
-
-        submit.click(
-                fn=sad_talker.test,
-                inputs=[source_image,
-                        driven_audio,
-                        preprocess_type,
-                        is_still_mode,
-                        enhancer,
-                        batch_size,                            
-                        size_of_image,
-                        pose_style,
-                        facerender,
-                        exp_weight,
-                        use_ref_video,
-                        ref_video,
-                        ref_info,
-                        use_idle_mode,
-                        length_of_audio,
-                        blink_every
-                        ], 
-                outputs=[gen_video]
-                )
-
-        with gr.Row():
-            examples = [
-                [
-                    'examples/source_image/full_body_1.png',
-                    'examples/driven_audio/bus_chinese.wav',
-                    'crop',
-                    True,
-                    False
-                ],
-                [
-                    'examples/source_image/full_body_2.png',
-                    'examples/driven_audio/japanese.wav',
-                    'crop',
-                    False,
-                    False
-                ],
-                [
-                    'examples/source_image/full3.png',
-                    'examples/driven_audio/deyu.wav',
-                    'crop',
-                    False,
-                    True
-                ],
-                [
-                    'examples/source_image/full4.jpeg',
-                    'examples/driven_audio/eluosi.wav',
-                    'full',
-                    False,
-                    True
-                ],
-                [
-                    'examples/source_image/full4.jpeg',
-                    'examples/driven_audio/imagine.wav',
-                    'full',
-                    True,
-                    True
-                ],
-                [
-                    'examples/source_image/full_body_1.png',
-                    'examples/driven_audio/bus_chinese.wav',
-                    'full',
-                    True,
-                    False
-                ],
-                [
-                    'examples/source_image/art_13.png',
-                    'examples/driven_audio/fayu.wav',
-                    'resize',
-                    True,
-                    False
-                ],
-                [
-                    'examples/source_image/art_5.png',
-                    'examples/driven_audio/chinese_news.wav',
-                    'resize',
-                    False,
-                    False
-                ],
-                [
-                    'examples/source_image/art_5.png',
-                    'examples/driven_audio/RD_Radio31_000.wav',
-                    'resize',
-                    True,
-                    True
-                ],
-            ]
-            gr.Examples(examples=examples,
-                        inputs=[
-                            source_image,
-                            driven_audio,
-                            preprocess_type,
-                            is_still_mode,
-                            enhancer], 
-                        outputs=[gen_video],
-                        fn=sad_talker.test,
-                        cache_examples=os.getenv('SYSTEM') == 'spaces') # 
-
-    return sadtalker_interface
- 
-
-if __name__ == "__main__":
-
-    demo = sadtalker_demo()
-    demo.queue(max_size=10)
-    demo.launch(debug=True)
-
-
diff --git a/spaces/vpsrikanth/FaceSimilarity/app/templates/similarity.html b/spaces/vpsrikanth/FaceSimilarity/app/templates/similarity.html
deleted file mode 100644
index 7294ec54c539e5debfe83af97a15edb7c59711bd..0000000000000000000000000000000000000000
--- a/spaces/vpsrikanth/FaceSimilarity/app/templates/similarity.html
+++ /dev/null
@@ -1,35 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <title>Index</title>
-</head>
-<body>
-    <div>
-        <h1 style="background-color:LightGray;">
-            <center>Face Similarity</center>
-        </h1>
-    </div>
-    <div>
-        <fieldset>
-        <ul>
-            <!li>
-            <br>
-            <form action="/predict_similarity/" enctype="multipart/form-data" method="post">
-                <span style="font-weight:bold;font-family:sans-serif">Upload First Image:</span> <br><br>
-                <input name="file1" type="file" onchange="readURL(this);" />
-                <br><br><br>
-                <span style="font-weight:bold;font-family:sans-serif">Upload Second Image:</span> <br><br>
-                <input name="file2" type="file" onchange="readURL(this);" />
-                <br><br><br><br>
-                <button type="submit">Check Similarity</button>
-            </form>
-            <!/li>
-            <br><br>
-            <form action="/" method="get">
-                <button type="submit">Home</button>
-            </form>
-        </ul>
-        </fieldset>
-    </div>
-</body>
-</html>
diff --git a/spaces/w1zrd/MusicGen/audiocraft/utils/notebook.py b/spaces/w1zrd/MusicGen/audiocraft/utils/notebook.py
deleted file mode 100644
index 019b9d19e5bef976bedddf428fd25da42a8a9726..0000000000000000000000000000000000000000
--- a/spaces/w1zrd/MusicGen/audiocraft/utils/notebook.py
+++ /dev/null
@@ -1,32 +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.
-
-try:
-    import IPython.display as ipd  # type: ignore
-except ImportError:
-    # Note in a notebook...
-    pass
-
-
-import torch
-
-
-def display_audio(samples: torch.Tensor, sample_rate: int):
-    """Renders an audio player for the given audio samples.
-
-    Args:
-        samples (torch.Tensor): a Tensor of decoded audio samples
-            with shapes [B, C, T] or [C, T]
-        sample_rate (int): sample rate audio should be displayed with.
-    """
-    assert samples.dim() == 2 or samples.dim() == 3
-
-    samples = samples.detach().cpu()
-    if samples.dim() == 2:
-        samples = samples[None, ...]
-
-    for audio in samples:
-        ipd.display(ipd.Audio(audio, rate=sample_rate))
diff --git a/spaces/wwwwwwww2/bingo/src/components/ui/alert-dialog.tsx b/spaces/wwwwwwww2/bingo/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/wwwwwwww2/bingo/src/components/ui/alert-dialog.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
-
-import { cn } from '@/lib/utils'
-import { buttonVariants } from '@/components/ui/button'
-
-const AlertDialog = AlertDialogPrimitive.Root
-
-const AlertDialogTrigger = AlertDialogPrimitive.Trigger
-
-const AlertDialogPortal = ({
-  className,
-  children,
-  ...props
-}: AlertDialogPrimitive.AlertDialogPortalProps) => (
-  <AlertDialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-end justify-center sm:items-center">
-      {children}
-    </div>
-  </AlertDialogPrimitive.Portal>
-)
-AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
-
-const AlertDialogOverlay = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <AlertDialogPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-opacity animate-in fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-AlertDialogOverlay.displayName = AlertDialogPrimitive.Overlay.displayName
-
-const AlertDialogContent = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Content>
->(({ className, ...props }, ref) => (
-  <AlertDialogPortal>
-    <AlertDialogOverlay />
-    <AlertDialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full max-w-lg scale-100 gap-4 border bg-background p-6 opacity-100 shadow-lg animate-in fade-in-90 slide-in-from-bottom-10 sm:rounded-lg sm:zoom-in-90 sm:slide-in-from-bottom-0 md:w-full',
-        className
-      )}
-      {...props}
-    />
-  </AlertDialogPortal>
-))
-AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
-
-const AlertDialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-2 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogHeader.displayName = 'AlertDialogHeader'
-
-const AlertDialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogFooter.displayName = 'AlertDialogFooter'
-
-const AlertDialogTitle = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold', className)}
-    {...props}
-  />
-))
-AlertDialogTitle.displayName = AlertDialogPrimitive.Title.displayName
-
-const AlertDialogDescription = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-AlertDialogDescription.displayName =
-  AlertDialogPrimitive.Description.displayName
-
-const AlertDialogAction = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Action>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Action>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Action
-    ref={ref}
-    className={cn(buttonVariants(), className)}
-    {...props}
-  />
-))
-AlertDialogAction.displayName = AlertDialogPrimitive.Action.displayName
-
-const AlertDialogCancel = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Cancel>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Cancel>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Cancel
-    ref={ref}
-    className={cn(
-      buttonVariants({ variant: 'outline' }),
-      'mt-2 sm:mt-0',
-      className
-    )}
-    {...props}
-  />
-))
-AlertDialogCancel.displayName = AlertDialogPrimitive.Cancel.displayName
-
-export {
-  AlertDialog,
-  AlertDialogTrigger,
-  AlertDialogContent,
-  AlertDialogHeader,
-  AlertDialogFooter,
-  AlertDialogTitle,
-  AlertDialogDescription,
-  AlertDialogAction,
-  AlertDialogCancel
-}
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/engine/video/softmax.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/engine/video/softmax.py
deleted file mode 100644
index fe92feba11935900f3c01e8591ce3eaa19bf6862..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/engine/video/softmax.py
+++ /dev/null
@@ -1,109 +0,0 @@
-from __future__ import division, print_function, absolute_import
-import torch
-
-from torchreid.engine.image import ImageSoftmaxEngine
-
-
-class VideoSoftmaxEngine(ImageSoftmaxEngine):
-    """Softmax-loss engine for video-reid.
-
-    Args:
-        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
-            or ``torchreid.data.VideoDataManager``.
-        model (nn.Module): model instance.
-        optimizer (Optimizer): an Optimizer.
-        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
-        use_gpu (bool, optional): use gpu. Default is True.
-        label_smooth (bool, optional): use label smoothing regularizer. Default is True.
-        pooling_method (str, optional): how to pool features for a tracklet.
-            Default is "avg" (average). Choices are ["avg", "max"].
-
-    Examples::
-        
-        import torch
-        import torchreid
-        # Each batch contains batch_size*seq_len images
-        datamanager = torchreid.data.VideoDataManager(
-            root='path/to/reid-data',
-            sources='mars',
-            height=256,
-            width=128,
-            combineall=False,
-            batch_size=8, # number of tracklets
-            seq_len=15 # number of images in each tracklet
-        )
-        model = torchreid.models.build_model(
-            name='resnet50',
-            num_classes=datamanager.num_train_pids,
-            loss='softmax'
-        )
-        model = model.cuda()
-        optimizer = torchreid.optim.build_optimizer(
-            model, optim='adam', lr=0.0003
-        )
-        scheduler = torchreid.optim.build_lr_scheduler(
-            optimizer,
-            lr_scheduler='single_step',
-            stepsize=20
-        )
-        engine = torchreid.engine.VideoSoftmaxEngine(
-            datamanager, model, optimizer, scheduler=scheduler,
-            pooling_method='avg'
-        )
-        engine.run(
-            max_epoch=60,
-            save_dir='log/resnet50-softmax-mars',
-            print_freq=10
-        )
-    """
-
-    def __init__(
-        self,
-        datamanager,
-        model,
-        optimizer,
-        scheduler=None,
-        use_gpu=True,
-        label_smooth=True,
-        pooling_method='avg'
-    ):
-        super(VideoSoftmaxEngine, self).__init__(
-            datamanager,
-            model,
-            optimizer,
-            scheduler=scheduler,
-            use_gpu=use_gpu,
-            label_smooth=label_smooth
-        )
-        self.pooling_method = pooling_method
-
-    def parse_data_for_train(self, data):
-        imgs = data['img']
-        pids = data['pid']
-        if imgs.dim() == 5:
-            # b: batch size
-            # s: sqeuence length
-            # c: channel depth
-            # h: height
-            # w: width
-            b, s, c, h, w = imgs.size()
-            imgs = imgs.view(b * s, c, h, w)
-            pids = pids.view(b, 1).expand(b, s)
-            pids = pids.contiguous().view(b * s)
-        return imgs, pids
-
-    def extract_features(self, input):
-        # b: batch size
-        # s: sqeuence length
-        # c: channel depth
-        # h: height
-        # w: width
-        b, s, c, h, w = input.size()
-        input = input.view(b * s, c, h, w)
-        features = self.model(input)
-        features = features.view(b, s, -1)
-        if self.pooling_method == 'avg':
-            features = torch.mean(features, 1)
-        else:
-            features = torch.max(features, 1)[0]
-        return features
diff --git a/spaces/xiang-wuu/yolov5/hubconf.py b/spaces/xiang-wuu/yolov5/hubconf.py
deleted file mode 100644
index 25f9d1b82c1446adb990d5d3d5ad645fde436793..0000000000000000000000000000000000000000
--- a/spaces/xiang-wuu/yolov5/hubconf.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
-
-Usage:
-    import torch
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-    model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx')  # file from branch
-"""
-
-import torch
-
-
-def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    """Creates or loads a YOLOv5 model
-
-    Arguments:
-        name (str): model name 'yolov5s' or path 'path/to/best.pt'
-        pretrained (bool): load pretrained weights into the model
-        channels (int): number of input channels
-        classes (int): number of model classes
-        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
-        verbose (bool): print all information to screen
-        device (str, torch.device, None): device to use for model parameters
-
-    Returns:
-        YOLOv5 model
-    """
-    from pathlib import Path
-
-    from models.common import AutoShape, DetectMultiBackend
-    from models.yolo import Model
-    from utils.downloads import attempt_download
-    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
-    from utils.torch_utils import select_device
-
-    if not verbose:
-        LOGGER.setLevel(logging.WARNING)
-    check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
-    name = Path(name)
-    path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name  # checkpoint path
-    try:
-        device = select_device(device)
-
-        if pretrained and channels == 3 and classes == 80:
-            model = DetectMultiBackend(path, device=device, fuse=autoshape)  # download/load FP32 model
-            # model = models.experimental.attempt_load(path, map_location=device)  # download/load FP32 model
-        else:
-            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0]  # model.yaml path
-            model = Model(cfg, channels, classes)  # create model
-            if pretrained:
-                ckpt = torch.load(attempt_download(path), map_location=device)  # load
-                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-                csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors'])  # intersect
-                model.load_state_dict(csd, strict=False)  # load
-                if len(ckpt['model'].names) == classes:
-                    model.names = ckpt['model'].names  # set class names attribute
-        if autoshape:
-            model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
-        if not verbose:
-            LOGGER.setLevel(logging.INFO)  # reset to default
-        return model.to(device)
-
-    except Exception as e:
-        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
-        s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
-        raise Exception(s) from e
-
-
-def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
-    # YOLOv5 custom or local model
-    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
-
-
-def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-nano model https://github.com/ultralytics/yolov5
-    return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-small model https://github.com/ultralytics/yolov5
-    return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-medium model https://github.com/ultralytics/yolov5
-    return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-large model https://github.com/ultralytics/yolov5
-    return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
-    return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
-    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
-
-
-if __name__ == '__main__':
-    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
-    # model = custom(path='path/to/model.pt')  # custom
-
-    # Verify inference
-    from pathlib import Path
-
-    import numpy as np
-    from PIL import Image
-
-    from utils.general import cv2
-
-    imgs = [
-        'data/images/zidane.jpg',  # filename
-        Path('data/images/zidane.jpg'),  # Path
-        'https://ultralytics.com/images/zidane.jpg',  # URI
-        cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
-        Image.open('data/images/bus.jpg'),  # PIL
-        np.zeros((320, 640, 3))]  # numpy
-
-    results = model(imgs, size=320)  # batched inference
-    results.print()
-    results.save()
diff --git a/spaces/xiang2811/ChatGPT/readme/README_en.md b/spaces/xiang2811/ChatGPT/readme/README_en.md
deleted file mode 100644
index 21da560da4b60399d26b1780ec686b35f5b88e9b..0000000000000000000000000000000000000000
--- a/spaces/xiang2811/ChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | English | <a title="Japanese" href="README_ja.md">日本語</a>
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search <br />
-        LaTeX rendering / Table rendering / Code highlighting <br />
-        Auto dark mode / Adaptive web interface / WeChat-like theme <br />
-        Multi-parameters tuning / Multi-API-Key support / Multi-user support <br />
-        Compatible with GPT-4 / Local deployment for LLMs
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>Video Tutorial</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 Introduction</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 Introduction & Tutorial</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>Online trial</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>One-Click deployment</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press <kbd>Shift</kbd> + <kbd>Enter</kbd> keys.
-- To quickly switch between input history, press <kbd>↑</kbd> and <kbd>↓</kbd> key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
-   ```
-   pip install -r requirements.txt
-   ```
-3. Update Gradio
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" target="_blank"><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=1aae59&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/xiaoxiao140420/anime-remove-background/README.md b/spaces/xiaoxiao140420/anime-remove-background/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/xiaoxiao140420/anime-remove-background/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py b/spaces/xxbb/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/yangogo/bingo/src/app/page.tsx b/spaces/yangogo/bingo/src/app/page.tsx
deleted file mode 100644
index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/app/page.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-
-const DynamicComponentWithNoSSR = dynamic(
-  () => import('../components/chat'),
-  { ssr: false }
-)
-
-export default function IndexPage() {
-  return (
-    <>
-      <div className="loading-spinner" />
-      <DynamicComponentWithNoSSR />
-    </>
-  )
-}
diff --git a/spaces/yangogo/bingo/src/components/ui/dialog.tsx b/spaces/yangogo/bingo/src/components/ui/dialog.tsx
deleted file mode 100644
index 925e77fe7858fb218b5115b4e225174a886e0f02..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/components/ui/dialog.tsx
+++ /dev/null
@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DialogPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Dialog = DialogPrimitive.Root
-
-const DialogTrigger = DialogPrimitive.Trigger
-
-const DialogPortal = ({
-  className,
-  children,
-  ...props
-}: DialogPrimitive.DialogPortalProps) => (
-  <DialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-start justify-center sm:items-center">
-      {children}
-    </div>
-  </DialogPrimitive.Portal>
-)
-DialogPortal.displayName = DialogPrimitive.Portal.displayName
-
-const DialogOverlay = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Overlay>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Overlay
-    ref={ref}
-    className={cn(
-      'bg-white fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-  />
-))
-DialogOverlay.displayName = DialogPrimitive.Overlay.displayName
-
-const DialogContent = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <DialogPortal>
-    <DialogOverlay />
-    <DialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full gap-4 rounded-b-lg border bg-background p-6 shadow-sm animate-in data-[state=open]:fade-in-90 data-[state=open]:slide-in-from-bottom-10 sm:max-w-lg sm:rounded-lg sm:zoom-in-90 data-[state=open]:sm:slide-in-from-bottom-0',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <DialogPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-accent data-[state=open]:text-muted-foreground">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </DialogPrimitive.Close>
-    </DialogPrimitive.Content>
-  </DialogPortal>
-))
-DialogContent.displayName = DialogPrimitive.Content.displayName
-
-const DialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-1.5 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-DialogHeader.displayName = 'DialogHeader'
-
-const DialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-DialogFooter.displayName = 'DialogFooter'
-
-const DialogTitle = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Title
-    ref={ref}
-    className={cn(
-      'text-lg font-semibold leading-none tracking-tight',
-      className
-    )}
-    {...props}
-  />
-))
-DialogTitle.displayName = DialogPrimitive.Title.displayName
-
-const DialogDescription = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-DialogDescription.displayName = DialogPrimitive.Description.displayName
-
-export {
-  Dialog,
-  DialogTrigger,
-  DialogContent,
-  DialogHeader,
-  DialogFooter,
-  DialogTitle,
-  DialogDescription
-}
diff --git a/spaces/yaoshining/text-generation-webui/modules/exllama_hf.py b/spaces/yaoshining/text-generation-webui/modules/exllama_hf.py
deleted file mode 100644
index 181a77a6d7d1ab6648130606539ad183929d36f7..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/modules/exllama_hf.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import os
-from pathlib import Path
-from typing import Any, Dict, Optional, Union
-
-import torch
-from torch.nn import CrossEntropyLoss
-from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from modules import shared
-from modules.logging_colors import logger
-
-try:
-    from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
-except:
-    logger.warning('Exllama module failed to load. Will attempt to load from repositories.')
-    try:
-        from modules.relative_imports import RelativeImport
-
-        with RelativeImport("repositories/exllama"):
-            from model import ExLlama, ExLlamaCache, ExLlamaConfig
-    except:
-        logger.error("Could not find repositories/exllama/. Make sure that exllama is cloned inside repositories/ and is up to date.")
-        raise
-
-
-class ExllamaHF(PreTrainedModel):
-    def __init__(self, config: ExLlamaConfig):
-        super().__init__(PretrainedConfig())
-        self.ex_config = config
-        self.ex_model = ExLlama(self.ex_config)
-        self.generation_config = GenerationConfig()
-        self.lora = None
-
-    def _validate_model_class(self):
-        pass
-
-    def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
-        pass
-
-    def prepare_inputs_for_generation(self, input_ids, **kwargs):
-        return {'input_ids': input_ids, **kwargs}
-
-    @property
-    def device(self) -> torch.device:
-        return torch.device(0)
-
-    def __call__(self, *args, **kwargs):
-        # TODO: Some decoding methods (such as Contrastive Search) may not work at this time
-        assert len(args) == 0, 'no *args should be passed to forward'
-        use_cache = kwargs.get('use_cache', True)
-        labels = kwargs.get('labels', None)
-        seq = kwargs['input_ids'][0].tolist()
-        cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None
-        if cache is None:
-            cache = ExLlamaCache(self.ex_model)
-            self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True, lora=self.lora)
-
-        logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache, lora=self.lora).to(kwargs['input_ids'].device)
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, logits.shape[-1])
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        return CausalLMOutputWithPast(logits=logits, past_key_values=cache if use_cache else None)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
-        assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported"
-        if isinstance(pretrained_model_name_or_path, str):
-            pretrained_model_name_or_path = Path(pretrained_model_name_or_path)
-
-        pretrained_model_name_or_path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path)
-        config = ExLlamaConfig(pretrained_model_name_or_path / 'config.json')
-
-        # from 'oobabooga/text-generation-webui/modules/exllama.py'
-        weight_path = None
-        for ext in ['.safetensors', '.pt', '.bin']:
-            found = list(pretrained_model_name_or_path.glob(f"*{ext}"))
-            if len(found) > 0:
-                weight_path = found[-1]
-                break
-        assert weight_path is not None, f'could not find weight in "{pretrained_model_name_or_path}"'
-
-        config.model_path = str(weight_path)
-        config.max_seq_len = shared.args.max_seq_len
-        config.compress_pos_emb = shared.args.compress_pos_emb
-        if shared.args.gpu_split:
-            config.set_auto_map(shared.args.gpu_split)
-            config.gpu_peer_fix = True
-        if torch.version.hip:
-            config.rmsnorm_no_half2 = True
-            config.rope_no_half2 = True
-            config.matmul_no_half2 = True
-            config.silu_no_half2 = True
-
-        # This slowes down a bit but align better with autogptq generation.
-        # TODO: Should give user choice to tune the exllama config
-        # config.fused_attn = False
-        # config.fused_mlp_thd = 0
-
-        return ExllamaHF(config)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/activations_tf.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/activations_tf.py
deleted file mode 100644
index 4fcb1493e437bc2d1c055b0c8ccbcf3627dc8316..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/activations_tf.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-
-import tensorflow as tf
-from packaging import version
-
-
-def _gelu(x):
-    """
-    Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
-    initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-    0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see
-    https://arxiv.org/abs/1606.08415
-    """
-    x = tf.convert_to_tensor(x)
-    cdf = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0), x.dtype)))
-
-    return x * cdf
-
-
-def _gelu_new(x):
-    """
-    Gaussian Error Linear Unit. This is a smoother version of the GELU. Original paper: https://arxiv.org/abs/1606.0841
-
-    Args:
-        x: float Tensor to perform activation
-
-    Returns:
-        `x` with the GELU activation applied.
-    """
-    x = tf.convert_to_tensor(x)
-    pi = tf.cast(math.pi, x.dtype)
-    coeff = tf.cast(0.044715, x.dtype)
-    cdf = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi) * (x + coeff * tf.pow(x, 3))))
-
-    return x * cdf
-
-
-def mish(x):
-    x = tf.convert_to_tensor(x)
-
-    return x * tf.tanh(tf.math.softplus(x))
-
-
-def gelu_fast(x):
-    x = tf.convert_to_tensor(x)
-    coeff1 = tf.cast(0.044715, x.dtype)
-    coeff2 = tf.cast(0.7978845608, x.dtype)
-
-    return 0.5 * x * (1.0 + tf.tanh(x * coeff2 * (1.0 + coeff1 * x * x)))
-
-
-def quick_gelu(x):
-    x = tf.convert_to_tensor(x)
-    coeff = tf.cast(1.702, x.dtype)
-    return x * tf.math.sigmoid(coeff * x)
-
-
-def gelu_10(x):
-    """
-    Clip the range of possible GeLU outputs between [-10, 10]. This is especially useful for quantization purpose, as
-    it allows mapping 2 negatives values in the GeLU spectrum. For more information on this trick, please refer to
-    https://arxiv.org/abs/2004.09602
-
-    Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
-    initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-    0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see
-    https://arxiv.org/abs/1606.08415 :param x: :return:
-    """
-    return tf.clip_by_value(_gelu(x), -10, 10)
-
-
-def glu(x, axis=-1):
-    """
-    Gated Linear Unit. Implementation as defined in the original paper (see https://arxiv.org/abs/1612.08083), where
-    the input `x` is split in two halves across a dimension (`axis`), A and B, returning A * sigmoid(B).
-
-    Args:
-        `x`: float Tensor to perform activation
-        `axis`: dimension across which `x` be split in half
-
-    Returns:
-        `x` with the GLU activation applied (with its size halved across the dimension `axis`).
-    """
-    a, b = tf.split(x, 2, axis=axis)
-    return a * tf.math.sigmoid(b)
-
-
-if version.parse(tf.version.VERSION) >= version.parse("2.4"):
-
-    def approximate_gelu_wrap(x):
-        return tf.keras.activations.gelu(x, approximate=True)
-
-    gelu = tf.keras.activations.gelu
-    gelu_new = approximate_gelu_wrap
-else:
-    gelu = _gelu
-    gelu_new = _gelu_new
-
-
-ACT2FN = {
-    "gelu": gelu,
-    "gelu_10": gelu_10,
-    "gelu_fast": gelu_fast,
-    "gelu_new": gelu_new,
-    "glu": glu,
-    "mish": mish,
-    "quick_gelu": quick_gelu,
-    "relu": tf.keras.activations.relu,
-    "sigmoid": tf.keras.activations.sigmoid,
-    "silu": tf.keras.activations.swish,
-    "swish": tf.keras.activations.swish,
-    "tanh": tf.keras.activations.tanh,
-}
-
-
-def get_tf_activation(activation_string):
-    if activation_string in ACT2FN:
-        return ACT2FN[activation_string]
-    else:
-        raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}")
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/mmbt/configuration_mmbt.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/mmbt/configuration_mmbt.py
deleted file mode 100644
index df5161b0927ad26279a273216d1d9ab6d465063a..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deprecated/mmbt/configuration_mmbt.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# coding=utf-8
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" MMBT configuration"""
-
-from ....utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class MMBTConfig(object):
-    """
-    This is the configuration class to store the configuration of a [`MMBTModel`]. It is used to instantiate a MMBT
-    model according to the specified arguments, defining the model architecture.
-
-    Args:
-        config ([`PreTrainedConfig`]):
-            Config of the underlying Transformer models. Its values are copied over to use a single config.
-        num_labels (`int`, *optional*):
-            Size of final Linear layer for classification.
-        modal_hidden_size (`int`, *optional*, defaults to 2048):
-            Embedding dimension of the non-text modality encoder.
-    """
-
-    def __init__(self, config, num_labels=None, modal_hidden_size=2048):
-        self.__dict__ = config.__dict__
-        self.modal_hidden_size = modal_hidden_size
-        if num_labels:
-            self.num_labels = num_labels
diff --git a/spaces/ynhe/AskAnything/data/tag_class.py b/spaces/ynhe/AskAnything/data/tag_class.py
deleted file mode 100644
index 839b5baf843eecdf5f7239c4ee8d654a1ba7d2f0..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/data/tag_class.py
+++ /dev/null
@@ -1,3437 +0,0 @@
-import numpy as np
-
-
-tra_array = ['tennis',
-'bear cub',
-'observatory',
-'bicycle',
-'hillside',
-'judge',
-'watercolor illustration',
-'granite',
-'lobster',
-'livery',
-'stone',
-'ceramic',
-'ranch',
-'cloth',
-'smile',
-'building',
-'tattoo',
-'cricketer',
-'cheek',
-'pear',
-'source',
-'winter',
-'surface',
-'spray',
-'ceremony',
-'magic',
-'curve',
-'container',
-'fair',
-'medicine',
-'baby',
-'tennis racquet',
-'ornament',
-'bamboo',
-'duckling',
-'song',
-'safari',
-'team presentation',
-'daffodil',
-'cross',
-'toothpaste',
-'shield',
-'fashion model',
-'capsule',
-'map',
-'creek',
-'glass house',
-'glass plate',
-'siding',
-'corner',
-'water buffalo',
-'bison',
-'figure skater',
-'diploma',
-'tire',
-'race',
-'cable car',
-'brain',
-'gas stove',
-'soap bubble',
-'palette',
-'snowboard',
-'school child',
-'trench coat',
-'monk',
-'fiber',
-'kitchen window',
-'sunglass',
-'coffee',
-'security',
-'strawberry',
-'penguin',
-'tree root',
-'loaf',
-'engagement ring',
-'lamb',
-'vector cartoon illustration',
-'sandwich',
-'mountain village',
-'shape',
-'charm',
-'fiction',
-'knot',
-'greenhouse',
-'sushi',
-'text',
-'disaster',
-'trophy',
-'gang',
-'strap',
-'soccer game',
-'cardinal',
-'tee',
-'turtle',
-'water surface',
-'grassland',
-'dolphin',
-'store',
-'dirt',
-'iceberg',
-'pergola',
-'farmer market',
-'publicity portrait',
-'tote bag',
-'teenage girl',
-'view mirror',
-'session',
-'commuter',
-'dressing room',
-'tricycle',
-'christmas ball',
-'headlight',
-'police',
-'armchair',
-'chart',
-'yacht',
-'saw',
-'printer',
-'rock band',
-'gingerbread house',
-'tag',
-'table lamp',
-'hockey game',
-'slope',
-'font',
-'wicker basket',
-'jewelry',
-'quarter',
-'software',
-'weapon',
-'pin',
-'worship',
-'painter',
-'goal',
-'morning light',
-'bike',
-'baseball bat',
-'elevator',
-'cuisine',
-'sausage',
-'stunt',
-'wrestler',
-'statue',
-'landing',
-'pillar',
-'willow tree',
-'sea wave',
-'chicken',
-'peanut',
-'muscle',
-'bob',
-'tv genre',
-'bathroom window',
-'radish',
-'textile',
-'pelican',
-'marketplace',
-'crest',
-'elevation map',
-'gift',
-'parish',
-'traffic light',
-'campfire',
-'fog',
-'award winner',
-'beach ball',
-'mat',
-'white house',
-'plaster',
-'moped',
-'football team',
-'solution',
-'bicyclist',
-'bit',
-'playground',
-'darkness',
-'cake',
-'maple leave',
-'mold',
-'cracker',
-'blueberry',
-'rubble',
-'container ship',
-'pedestrian bridge',
-'snail',
-'parrot',
-'form',
-'circuit',
-'highlight',
-'pickup truck',
-'koala',
-'rain',
-'system',
-'weather',
-'raincoat',
-'soccer team',
-'windshield',
-'thunderstorm',
-'mike',
-'bird house',
-'bridge',
-'grandfather',
-'restroom',
-'animation',
-'wilderness',
-'clown',
-'banana',
-'brown',
-'braid',
-'dining room',
-'kindergarten',
-'launch event',
-'purple',
-'school',
-'stairwell',
-'brooch',
-'movie poster image',
-'mountain river',
-'shelf',
-'wicket',
-'headboard',
-'buddha',
-'flower field',
-'dugout',
-'cd',
-'bald eagle',
-'lagoon',
-'seaweed',
-'agriculture',
-'emergency service',
-'maple tree',
-'parachute',
-'continent',
-'amusement park',
-'remote',
-'bun',
-'tackle',
-'hospital',
-'garage door',
-'birthday party',
-'friendship',
-'go',
-'mausoleum',
-'jeep',
-'raccoon',
-'step',
-'ice hockey team',
-'cigarette',
-'lace dress',
-'forest floor',
-'mall',
-'captain',
-'milk',
-'golf course',
-'meal',
-'picnic table',
-'sail',
-'volleyball',
-'canal',
-'terrace',
-'computer desk',
-'caravan',
-'hotel',
-'cheerleader',
-'nurse',
-'museum',
-'marsh',
-'fox',
-'plateau',
-'night',
-'twin',
-'letter logo',
-'autumn tree',
-'powder',
-'convention',
-'creature',
-'lighthouse',
-'shop window',
-'jacket',
-'stork',
-'taxi',
-'trade',
-'blackboard',
-'olive',
-'road sign',
-'resort',
-'snowflake',
-'cemetery',
-'travel',
-'evening dress',
-'picnic',
-'drink',
-'winter morning',
-'football player',
-'snack',
-'boxing glove',
-'dinner party',
-'airline',
-'swing',
-'port',
-'wheelbarrow',
-'bathroom sink',
-'sweater',
-'ambulance',
-'gear',
-'oil',
-'wii controller',
-'array',
-'home office',
-'car show',
-'mixture',
-'profession',
-'tree frog',
-'square',
-'facility',
-'coral reef',
-'sea wall',
-'pizza',
-'exhibit',
-'demolition',
-'trout',
-'ring',
-'coffee shop',
-'bracelet',
-'bean',
-'lip',
-'fencing',
-'landscape',
-'sitting',
-'package',
-'metal',
-'bust',
-'king',
-'hair',
-'window seat',
-'wildlife',
-'trunk',
-'greenery',
-'stencil',
-'fire hydrant',
-'bridesmaid',
-'plaza',
-'alps',
-'tower bridge',
-'crop top',
-'crossing',
-'cinema',
-'pedestrian crossing',
-'family',
-'shopping cart',
-'stomach',
-'church building',
-'screen door',
-'skater',
-'soccer field',
-'kettle',
-'mussel',
-'raindrop',
-'candy cane',
-'water lily',
-'flower girl',
-'desert',
-'enclosure',
-'christmas light',
-'kitchen',
-'caterpillar',
-'plaid',
-'bath',
-'bush',
-'mud',
-'ballet',
-'knee',
-'adult',
-'raft',
-'sea view',
-'cactus',
-'office chair',
-'overall',
-'rim',
-'scaffolding',
-'pig',
-'cover',
-'poster page',
-'sprinkle',
-'chandelier',
-'algae',
-'traffic',
-'surfboard',
-'book',
-'filming',
-'flash',
-'mansion',
-'camouflage',
-'trouser',
-'ticket',
-'weed',
-'cab',
-'trench',
-'elephant',
-'huddle',
-'sphere',
-'christmas decoration',
-'city',
-'launch',
-'doll',
-'christmas ornament',
-'fabric',
-'bikini',
-'biplane',
-'breakfast',
-'neighbourhood',
-'race track',
-'foliage',
-'avocado',
-'school bus',
-'footwear',
-'highway',
-'ocean view',
-'art vector illustration',
-'wall clock',
-'curtain',
-'teenager',
-'kitchen area',
-'robot',
-'tusk',
-'lounge chair',
-'beam',
-'paddle',
-'camel',
-'lid',
-'world map',
-'city view',
-'newlywed',
-'cargo ship',
-'yellow',
-'exhibition',
-'bend',
-'novel',
-'wool',
-'ontario',
-'bread',
-'campus',
-'coastline',
-'cutting board',
-'booth',
-'table top',
-'carpet',
-'beach chair',
-'workout',
-'street food',
-'fun',
-'costumer film designer',
-'gadget',
-'artist',
-'fishing village',
-'builder',
-'violinist',
-'iphone',
-'spider web',
-'traffic sign',
-'ruin',
-'rescue',
-'clipboard',
-'seal',
-'film director',
-'paw',
-'nursery',
-'intersection',
-'tomato sauce',
-'taste',
-'paddy field',
-'christmas tree',
-'wave',
-'stool',
-'watering can',
-'rug',
-'daytime',
-'subway station',
-'craft',
-'pine forest',
-'black',
-'planet',
-'motif',
-'christmas market',
-'glass window',
-'college',
-'wheat',
-'damage',
-'rectangle',
-'picture frame',
-'chess',
-'guest room',
-'street corner',
-'religion',
-'seed',
-'puzzle',
-'freeway',
-'beauty',
-'ocean',
-'watch',
-'mother',
-'garage',
-'quote',
-'dj',
-'supporter',
-'hip hop artist',
-'muffin',
-'eiffel tower',
-'cash',
-'firefighter',
-'cauliflower',
-'bunker',
-'sled',
-'manicure',
-'shark',
-'stall',
-'jungle',
-'family home',
-'tour bus',
-'chimney',
-'touchdown',
-'roundabout',
-'coyote',
-'street scene',
-'tank',
-'wedding dress',
-'mantle',
-'bedroom window',
-'coconut',
-'chapel',
-'goat',
-'living space',
-'rock wall',
-'polka dot',
-'railway',
-'mandala',
-'mango',
-'lesson',
-'mountain landscape',
-'team photo',
-'bookshelf',
-'meter',
-'bulldog',
-'evening sun',
-'stick',
-'card',
-'pink',
-'fish pond',
-'paint',
-'pill',
-'cart',
-'pea',
-'van',
-'album',
-'football college game',
-'mountain pass',
-'doughnut',
-'ski slope',
-'match',
-'official',
-'shadow',
-'organ',
-'celebration',
-'coin',
-'log cabin',
-'firework display',
-'present',
-'twig',
-'chef',
-'confetti',
-'footpath',
-'tour',
-'ponytail',
-'artwork',
-'race car',
-'club',
-'season',
-'hose',
-'pencil',
-'aircraft',
-'rock formation',
-'wardrobe',
-'participant',
-'politician',
-'engineer',
-'peace',
-'filter',
-'sailing boat',
-'water bottle',
-'service dog',
-'poodle',
-'loki',
-'statesman',
-'sleeping bag',
-'outskirt',
-'clock',
-'factory',
-'oak tree',
-'physician',
-'color',
-'room',
-'stairway',
-'company',
-'lady',
-'graph',
-'faucet',
-'tablecloth',
-'subway train',
-'chocolate chip cookie',
-'headquarters',
-'screw',
-'goggle',
-'halloween',
-'city street',
-'swirl',
-'cord',
-'forward',
-'bone',
-'bedding',
-'archway',
-'wig',
-'lobby',
-'mask',
-'attic',
-'kitchen table',
-'skylight',
-'fire',
-'exit',
-'oil painting',
-'passenger',
-'meditation',
-'salmon',
-'fedora',
-'rubber stamp',
-'orange juice',
-'arch',
-'scientist',
-'stroll',
-'manhattan',
-'float',
-'baseball uniform',
-'circle',
-'church',
-'decker bus',
-'competitor',
-'zoo',
-'basketball team',
-'tourist',
-'daughter',
-'silverware',
-'ceiling fan',
-'birth',
-'vase',
-'jack',
-'mushroom',
-'spiral',
-'cage',
-'limb',
-'salad',
-'ad',
-'control',
-'earth',
-'party',
-'bolt',
-'tractor',
-'barley',
-'wedding photo',
-'hawk',
-'warehouse',
-'vegetable garden',
-'chocolate cake',
-'cabbage',
-'floor window',
-'baby shower',
-'magnifying glass',
-'table',
-'stethoscope',
-'reading',
-'mission',
-'croissant',
-'gift box',
-'rocket',
-'forest road',
-'cooking',
-'suite',
-'hill country',
-'motorcycle',
-'baseball player',
-'angle',
-'drug',
-'sport association',
-'championship',
-'family portrait',
-'florist',
-'softball',
-'egret',
-'office',
-'plywood',
-'jockey',
-'mosque',
-'brunch',
-'beanie',
-'office building',
-'pattern',
-'calendar',
-'indoor',
-'pepper',
-'ledge',
-'trail',
-'fuel',
-'laptop computer',
-'tennis shoe',
-'deck chair',
-'guitarist',
-'barn',
-'surgery',
-'cartoon illustration',
-'nebula',
-'railroad',
-'mountain goat',
-'goose',
-'car door',
-'cheer',
-'liquid',
-'hardwood floor',
-'pathway',
-'acorn',
-'gull',
-'airliner',
-'couch',
-'lake house',
-'spaghetti',
-'promenade',
-'collection',
-'garden',
-'bank',
-'robin',
-'tennis ball',
-'peony',
-'gymnast',
-'lavender',
-'deck',
-'test',
-'riverside',
-'rapper',
-'domino',
-'bride',
-'mouse',
-'basil',
-'wedding couple',
-'ocean wave',
-'arm',
-'kitchen floor',
-'grove',
-'family member',
-'backyard',
-'raspberry',
-'forest fire',
-'officer',
-'hibiscus',
-'canyon',
-'composer',
-'signature',
-'olive oil',
-'hibiscus flower',
-'rose',
-'vector icon',
-'sunrise',
-'horseback',
-'motor scooter',
-'office worker',
-'tradition',
-'ingredient',
-'washing machine',
-'lighting',
-'bagel',
-'sailboat',
-'policeman',
-'mare',
-'graphic',
-'halloween pumpkin',
-'stock',
-'pilot',
-'education',
-'team',
-'body',
-'horse',
-'kimono',
-'bazaar',
-'bag',
-'recording studio',
-'parsley',
-'entrance',
-'denim',
-'vet',
-'horse farm',
-'charcoal',
-'architecture',
-'glass vase',
-'puppy',
-'estuary',
-'television show host',
-'city bus',
-'shoulder',
-'beast',
-'balance',
-'golfer',
-'roadside',
-'denim jacket',
-'stone wall',
-'counter top',
-'app icon',
-'toast',
-'head coach',
-'ham',
-'warrior',
-'gem',
-'refrigerator',
-'snowman',
-'construction worker',
-'coal',
-'website',
-'morning fog',
-'mustard',
-'human',
-'owl',
-'puppy dog',
-'piggy bank',
-'vegetation',
-'pirate',
-'action film',
-'marshmallow',
-'thanksgiving',
-'business',
-'disease',
-'signage',
-'greeting',
-'skate park',
-'tile',
-'mouth',
-'spinach',
-'vacation',
-'leader',
-'shrine',
-'walker',
-'science fiction film',
-'bill',
-'rabbit',
-'motor boat',
-'bar',
-'radio',
-'barge',
-'tail',
-'chainsaw',
-'gallery',
-'rainbow',
-'pasta',
-'padlock',
-'web',
-'pastry',
-'ink',
-'reef',
-'school uniform',
-'shawl',
-'treasure',
-'peach',
-'dinner table',
-'injury',
-'harbor',
-'witch',
-'car dealership',
-'litter',
-'gesture',
-'documentary',
-'marriage',
-'sea shell',
-'priest',
-'dome',
-'kit',
-'icon',
-'seaside',
-'bucket',
-'entertainment',
-'stable',
-'hat',
-'puddle',
-'sock',
-'shopper',
-'technology',
-'harbour',
-'orbit',
-'antler',
-'tube',
-'flag waving',
-'cook',
-'tight',
-'commander',
-'farmland',
-'switch',
-'hiker',
-'wedding ceremony',
-'award ceremony',
-'champion',
-'chopstick',
-'farmhouse',
-'performer',
-'spike',
-'accident',
-'cruise ship',
-'passenger train',
-'attraction',
-'entertainer',
-'rear view',
-'sidewalk',
-'parade',
-'racing',
-'plane',
-'ritual',
-'peacock',
-'pocket',
-'plum',
-'drop',
-'carrot',
-'floor',
-'sunset',
-'troop',
-'architect',
-'coffee table',
-'dust',
-'outline',
-'leather',
-'charity event',
-'heat',
-'whale',
-'laundry',
-'coconut tree',
-'crosswalk',
-'pony',
-'ant',
-'pipe',
-'string',
-'coat',
-'angel',
-'beef',
-'church tower',
-'dish',
-'pitch',
-'cupboard',
-'thermometer',
-'dirt field',
-'fireworks',
-'minute',
-'cane',
-'pajama',
-'flower garden',
-'autumn',
-'trash can',
-'dachshund',
-'banana tree',
-'tray',
-'moose',
-'roadway',
-'carnival',
-'antenna',
-'pole',
-'castle wall',
-'ram',
-'cattle',
-'hay',
-'cookie',
-'swimmer',
-'baseball team',
-'strait',
-'hedge',
-'jet',
-'fire pit',
-'octopus',
-'calf',
-'cube',
-'opera',
-'cardboard box',
-'tiara',
-'kitchen sink',
-'prairie',
-'bowl',
-'galaxy',
-'straw hat',
-'linen',
-'ski resort',
-'stitch',
-'street lamp',
-'motorist',
-'icicle',
-'stain',
-'flora',
-'drain',
-'kitchen cabinet',
-'decor',
-'bouquet',
-'pound',
-'interior design',
-'nail polish',
-'figurine',
-'tomb',
-'disc',
-'twist',
-'blouse',
-'ribbon',
-'figure',
-'burger',
-'cork',
-'soccer goalkeeper',
-'train bridge',
-'drinking water',
-'dew',
-'baker',
-'storm cloud',
-'tarmac',
-'tv drama',
-'sponge',
-'magnet',
-'sailor',
-'entry',
-'swan',
-'exercise',
-'sloth',
-'jewel',
-'scuba diver',
-'bite',
-'cat tree',
-'tent',
-'can',
-'tennis match',
-'ecosystem',
-'picket fence',
-'palm',
-'train car',
-'frying pan',
-'rally',
-'tablet pc',
-'reindeer',
-'image',
-'wolf',
-'chin',
-'conservatory',
-'flood water',
-'cityscape',
-'beach sand',
-'car park',
-'pavement',
-'farm field',
-'swimming',
-'winter storm',
-'stem',
-'pillow',
-'inning',
-'gorilla',
-'desk',
-'avenue',
-'fern',
-'money',
-'pearl',
-'train station',
-'skillet',
-'nap',
-'barber',
-'library',
-'freezer',
-'label',
-'rainforest',
-'parking sign',
-'mirror',
-'wing',
-'noodle',
-'press room',
-'sculpture',
-'tablet',
-'viewer',
-'prayer',
-'mini',
-'mechanic',
-'laugh',
-'rice field',
-'hand',
-'mustache',
-'mountain road',
-'catwalk',
-'conference',
-'cape',
-'installation',
-'musician',
-'stream',
-'machine',
-'speech',
-'crocodile',
-'soccer match',
-'town square',
-'passport',
-'post box',
-'point',
-'stone building',
-'motorway',
-'mix',
-'dentist',
-'businessperson',
-'happiness',
-'boat',
-'vineyard',
-'treadmill',
-'glass wall',
-'water droplet',
-'coffee mug',
-'graduate',
-'sunflower',
-'parliament',
-'shepherd',
-'movie',
-'wine',
-'orchard',
-'tulip',
-'motherboard',
-'cup',
-'broom',
-'spot',
-'drawing',
-'polo shirt',
-'graduation',
-'film producer',
-'moonlight',
-'glow',
-'film format',
-'t shirt',
-'rock face',
-'sword',
-'clinic',
-'festival day',
-'meadow',
-'staple',
-'pupil',
-'training ground',
-'rider',
-'flower',
-'foal',
-'wharf',
-'foot bridge',
-'shooting',
-'top',
-'mast',
-'police car',
-'robe',
-'wedding bouquet',
-'stop sign',
-'birthday cake',
-'glitter',
-'butter',
-'scooter',
-'tundra',
-'superhero',
-'pocket watch',
-'inscription',
-'youngster',
-'fruit tree',
-'movie poster',
-'engine',
-'foundation',
-'motorcyclist',
-'take',
-'woman',
-'antelope',
-'country artist',
-'road trip',
-'typewriter',
-'tuxedo',
-'brand',
-'pine',
-'bathroom',
-'paradise',
-'texture',
-'balloon',
-'dining table',
-'home',
-'computer screen',
-'actor',
-'clip',
-'tv tower',
-'panorama',
-'summit',
-'cat',
-'plot',
-'eagle',
-'dancer',
-'pup',
-'studio shot',
-'tear',
-'bird bath',
-'classroom',
-'bookstore',
-'city wall',
-'tv programme',
-'blade',
-'easel',
-'buttercream',
-'sweet',
-'designer',
-'diamond',
-'handshake',
-'herb',
-'corn field',
-'seafront',
-'concrete',
-'street artist',
-'gas',
-'stamp',
-'window display',
-'paper',
-'note',
-'pint',
-'quarry',
-'research',
-'fixture',
-'manager',
-'soil',
-'leopard',
-'board game',
-'ladder',
-'stop light',
-'island',
-'ramp',
-'football match',
-'icing',
-'drill',
-'currency',
-'summer evening',
-'topping',
-'pyramid',
-'pomegranate',
-'cell',
-'ivy',
-'squad',
-'scenery',
-'computer',
-'locomotive',
-'surf',
-'mascot',
-'dune',
-'path',
-'duck',
-'twilight',
-'wire',
-'bow tie',
-'strike',
-'cormorant',
-'car wash',
-'crane',
-'market',
-'philosopher',
-'alarm clock',
-'camera',
-'birch',
-'greeting card',
-'plain',
-'clay',
-'donut',
-'lock',
-'moth',
-'laboratory',
-'fan',
-'violin',
-'jazz fusion artist',
-'mountain biker',
-'terrain',
-'magazine',
-'pickup',
-'comedy film',
-'smartphone',
-'film',
-'bed',
-'microwave oven',
-'tournament',
-'lawn',
-'car window',
-'alligator',
-'screen',
-'jetty',
-'shopping bag',
-'landscape view',
-'cabinetry',
-'friendly match',
-'thing',
-'petal',
-'shopping center',
-'transport',
-'ballet dancer',
-'shoreline',
-'princess',
-'car seat',
-'parking meter',
-'green',
-'vodka',
-'band',
-'rock',
-'costume',
-'warning sign',
-'strip',
-'plaque',
-'wheelchair',
-'headband',
-'ginger',
-'dice',
-'media',
-'hairdresser',
-'press',
-'living room',
-'stove',
-'player',
-'cherry',
-'workshop',
-'carving',
-'embroidery',
-'doodle',
-'adventure',
-'rugby player',
-'monument',
-'brush',
-'marker',
-'loft',
-'postcard',
-'collage',
-'ball',
-'professor',
-'dresser',
-'gig',
-'festival',
-'blackbird',
-'makeup artist',
-'video camera',
-'sticker',
-'peak',
-'wildflower',
-'santa hat',
-'rodeo',
-'wedding photographer',
-'guy',
-'staff',
-'waterfall',
-'operation',
-'defender',
-'falcon',
-'haze',
-'individual',
-'gentleman',
-'greyhound',
-'rocking chair',
-'rice',
-'garbage',
-'platter',
-'chocolate',
-'splash',
-'business suit',
-'cheetah',
-'valley',
-'maze',
-'trampoline',
-'garland',
-'slalom',
-'unicorn',
-'tree stump',
-'painting',
-'romance',
-'fight',
-'alcohol',
-'ghost',
-'fondant',
-'spa',
-'shutter',
-'death',
-'demonstration',
-'cotton',
-'pier',
-'flea market',
-'history',
-'savannah',
-'fist',
-'aisle',
-'crew',
-'jug',
-'pose',
-'anchor',
-'teapot',
-'boat house',
-'business team',
-'tripod',
-'bee',
-'pebble',
-'mattress',
-'canvas',
-'hallway',
-'campaign',
-'pod',
-'lake district',
-'article',
-'white',
-'sofa',
-'honey',
-'marathon',
-'pancake',
-'tourist attraction',
-'wedding gown',
-'battle',
-'shelving',
-'sea',
-'sheet music',
-'pie',
-'yarn',
-'construction site',
-'flyer',
-'tie',
-'star',
-'lettuce',
-'martial artist',
-'dart',
-'straw',
-'reflection',
-'conference room',
-'temperature',
-'rugby',
-'mosquito',
-'physicist',
-'rock climber',
-'crash',
-'backdrop',
-'toilet seat',
-'sand castle',
-'water park',
-'toy car',
-'waste',
-'luxury',
-'hangar',
-'rv',
-'tree trunk',
-'board',
-'gold',
-'project picture',
-'cap',
-'cottage',
-'relief',
-'attire',
-'microscope',
-'battery',
-'roll',
-'line',
-'parking garage',
-'crystal',
-'broadcasting',
-'brick wall',
-'lab',
-'flooring',
-'meeting',
-'3d cg rendering',
-'desktop computer',
-'cowboy',
-'sailing ship',
-'junction',
-'hairstyle',
-'homework',
-'profile',
-'model',
-'flower pot',
-'street light',
-'salt lake',
-'maple',
-'space',
-'blizzard',
-'throw',
-'zebras',
-'brochure',
-'constellation',
-'beak',
-'kilt',
-'pond',
-'blue sky',
-'sneaker',
-'sand dune',
-'morning sun',
-'almond',
-'grill',
-'curl',
-'basketball girl game',
-'chameleon',
-'toilet bowl',
-'prince',
-'keyboard',
-'queen',
-'computer monitor',
-'writing',
-'crown',
-'basilica',
-'kiss',
-'house',
-'parking',
-'football competition',
-'shell',
-'sport equipment',
-'comedy',
-'baboon',
-'vendor',
-'rise building',
-'wrap',
-'food truck',
-'cat bed',
-'rickshaw',
-'flare',
-'teal',
-'nectar',
-'eclipse',
-'vehicle',
-'steam locomotive',
-'gorge',
-'cow',
-'christmas card',
-'demonstrator',
-'memorial',
-'towel',
-'jewellery',
-'train',
-'frisbee',
-'baseball game',
-'fur',
-'afternoon sun',
-'community',
-'sparkler',
-'bandage',
-'firework',
-'dollar',
-'pasture',
-'video',
-'bus',
-'tree house',
-'seashore',
-'field',
-'hamburger',
-'souvenir',
-'hedgehog',
-'worm',
-'pine cone',
-'osprey',
-'dinosaur',
-'vegetable',
-'junk',
-'poster',
-'army',
-'winger',
-'bundle',
-'stage',
-'growth',
-'wedding party',
-'service',
-'blanket',
-'ruler',
-'eye',
-'credit card',
-'castle',
-'diner',
-'hut',
-'elk',
-'hard rock artist',
-'nun',
-'dog breed',
-'nest',
-'drama film',
-'number icon',
-'water tank',
-'giraffe',
-'altar',
-'pavilion',
-'tv personality',
-'suv',
-'street vendor',
-'street sign',
-'ditch',
-'debris',
-'foam',
-'takeoff',
-'spice',
-'mountain lake',
-'tea',
-'orchestra',
-'spacecraft',
-'counter',
-'abbey',
-'mountain',
-'hydrangea',
-'racer',
-'orange tree',
-'tide',
-'cowboy hat',
-'rapid',
-'town',
-'wild',
-'herd',
-'vein',
-'driveway',
-'jar',
-'bark',
-'illustration',
-'horror film',
-'corn',
-'stroller',
-'industry',
-'mountain stream',
-'gym',
-'neckline',
-'pan',
-'client',
-'spectator',
-'eggplant',
-'camper',
-'fawn',
-'hoodie',
-'meat',
-'lemonade',
-'food market',
-'slum',
-'comic book character',
-'flower market',
-'love',
-'palace',
-'gun',
-'heel',
-'shopping street',
-'shooting basketball guard',
-'family photo',
-'rooftop',
-'laundry basket',
-'airport runway',
-'horn',
-'face mask',
-'flight',
-'appetizer',
-'violet',
-'country lane',
-'cement',
-'instrument',
-'tv actor',
-'spark',
-'celebrity',
-'award',
-'country house',
-'standing',
-'auction',
-'date',
-'engagement',
-'puck',
-'advertisement',
-'chair',
-'zebra',
-'driftwood',
-'bumblebee',
-'maple leaf',
-'bonnet',
-'orange',
-'water tower',
-'door',
-'singer',
-'floor plan',
-'discussion',
-'theatre',
-'pilgrim',
-'mug',
-'branch',
-'window sill',
-'baseball pitcher',
-'bakery',
-'lollipop',
-'basketball player',
-'toilet paper',
-'chalkboard',
-'cabin',
-'sign',
-'night sky',
-'cannon',
-'fishing net',
-'submarine',
-'suit',
-'fur coat',
-'wine bottle',
-'folder',
-'street art',
-'suspension bridge',
-'evening sky',
-'billboard',
-'postage stamp',
-'newspaper',
-'transportation',
-'surgeon',
-'light',
-'park',
-'horizon',
-'road',
-'sand bar',
-'trumpet',
-'lounge',
-'cloud forest',
-'birthday celebration',
-'balcony',
-'anime',
-'beehive',
-'umbrella',
-'goldfish',
-'baseball cap',
-'waterhole',
-'ceiling',
-'carousel',
-'backpack',
-'plant pot',
-'atmosphere',
-'sunflower field',
-'spire',
-'vision',
-'woodpecker',
-'chip',
-'pool table',
-'lotus flower',
-'cone',
-'humpback whale',
-'reservoir',
-'hunt',
-'piano',
-'plate',
-'dining area',
-'luggage',
-'skier',
-'dance floor',
-'crow',
-'stair',
-'overpass',
-'opera house',
-'bear',
-'jazz artist',
-'water',
-'vessel',
-'cast',
-'yard',
-'cathedral',
-'basketball hoop',
-'graveyard',
-'sound',
-'berry',
-'onlooker',
-'fauna',
-'birch tree',
-'retail',
-'hill',
-'skeleton',
-'journalist',
-'frost',
-'basket',
-'nail',
-'dusk',
-'trash',
-'dawn',
-'clover',
-'hen',
-'volcano',
-'basketball coach',
-'home decor',
-'charge',
-'haircut',
-'sense',
-'university',
-'lizard',
-'daisy',
-'tablet computer',
-'grass field',
-'prison',
-'metal artist',
-'bathroom mirror',
-'window frame',
-'chest',
-'flavor',
-'pop country artist',
-'market square',
-'monkey',
-'blog',
-'deer',
-'speech bubble',
-'dog',
-'independence day',
-'girl',
-'boy',
-'tartan',
-'furniture',
-'appliance',
-'office window',
-'fish boat',
-'sand box',
-'tv sitcom',
-'drama',
-'sleigh',
-'depression',
-'paper towel',
-'baseball',
-'protestor',
-'grape',
-'wedding cake',
-'invitation',
-'accessory',
-'pick',
-'grandparent',
-'racket',
-'tea plantation',
-'outdoors',
-'egg',
-'glass bowl',
-'sun',
-'organization',
-'lion',
-'panel',
-'station',
-'wallpaper',
-'helicopter',
-'salt',
-'vanity',
-'patio',
-'lunch',
-'street performer',
-'mountain range',
-'soup',
-'bacon',
-'power station',
-'cantilever bridge',
-'hummingbird',
-'shirt',
-'rope',
-'hip',
-'chalk',
-'pendant',
-'choir',
-'tv',
-'lichen',
-'railway bridge',
-'art gallery',
-'bartender',
-'wagon',
-'baby elephant',
-'accordion',
-'horseshoe',
-'building site',
-'clutch',
-'harvest',
-'savanna',
-'geranium',
-'business woman',
-'paddock',
-'patch',
-'beech tree',
-'war',
-'suburbs',
-'hospital bed',
-'motorcycle racer',
-'moss',
-'gravel',
-'government agency',
-'dollar bill',
-'father',
-'fjord',
-'concert',
-'nut',
-'wedding photography',
-'finish line',
-'home plate',
-'food',
-'nose',
-'thumb',
-'village',
-'dining room table',
-'bumper',
-'monster',
-'blackberry',
-'lime',
-'conflict',
-'gala',
-'wallet',
-'wrist',
-'hug',
-'mermaid',
-'lava',
-'lawyer',
-'folk rock artist',
-'arena',
-'onion',
-'toothbrush',
-'fashion',
-'perfume',
-'flip',
-'triangle',
-'woodland',
-'mail',
-'grasshopper',
-'studio',
-'wood floor',
-'den',
-'racquet',
-'cello',
-'lemur',
-'astronaut',
-'glass table',
-'blood',
-'dvd',
-'planter',
-'silver',
-'leash',
-'master bedroom',
-'forest',
-'batter',
-'shoe',
-'engraving',
-'opening',
-'product',
-'toe',
-'cocktail',
-'mallard duck',
-'bike ride',
-'oasis',
-'wedding ring',
-'cinematographer',
-'holly',
-'autograph',
-'fence',
-'ice cube',
-'cove',
-'pineapple',
-'aurora',
-'glass bead',
-'produce',
-'apartment building',
-'cob',
-'miniature',
-'cockpit',
-'flashlight',
-'frog',
-'sheep',
-'groom',
-'steel',
-'watermelon',
-'clip art',
-'paper plate',
-'ostrich',
-'contour',
-'mural',
-'cub',
-'paisley bandanna',
-'winery',
-'turn',
-'handle',
-'satellite',
-'post',
-'pork',
-'child',
-'asphalt',
-'grocery store',
-'vulture',
-'trolley',
-'nightclub',
-'brick',
-'trailer',
-'compass',
-'cereal',
-'cafe',
-'cartoon character',
-'sugar',
-'fiction book',
-'glass floor',
-'umpire',
-'guitar',
-'hamster',
-'protester',
-'airplane',
-'garment',
-'blazer',
-'railway line',
-'wedding',
-'shoe box',
-'parking lot',
-'construction',
-'graduation ceremony',
-'tram',
-'telescope',
-'copper',
-'pain',
-'autumn forest',
-'guest house',
-'partner',
-'crayon',
-'dip',
-'boot',
-'corridor',
-'computer keyboard',
-'hockey player',
-'chicken coop',
-'bus station',
-'gathering',
-'ankle',
-'bunk bed',
-'wood table',
-'football coach',
-'monarch',
-'pharmacy',
-'legging',
-'mannequin',
-'female',
-'train track',
-'stack',
-'canopy',
-'design element',
-'grandmother',
-'symbol',
-'beach hut',
-'zucchini',
-'bomb',
-'businessman',
-'skyscraper',
-'tongue',
-'case',
-'sparkle',
-'highland',
-'ballroom',
-'prom',
-'estate',
-'customer',
-'archipelago',
-'cheese',
-'debate',
-'carriage',
-'bulldozer',
-'pumpkin',
-'sitting room',
-'gas station',
-'wedding reception',
-'camp',
-'dog bed',
-'tower',
-'property',
-'river bed',
-'pop latin artist',
-'fridge',
-'wine glass',
-'coast',
-'beer',
-'tow truck',
-'fire truck',
-'mountain bike',
-'thigh',
-'heron',
-'boat ride',
-'gondola',
-'turquoise',
-'lake',
-'llama',
-'kitty',
-'tin',
-'waiting room',
-'coffee cup',
-'socialite',
-'guard',
-'tap',
-'waterway',
-'forehead',
-'list',
-'erosion',
-'box',
-'sea lion',
-'pollen',
-'dam',
-'wasp',
-'salon',
-'tennis tournament',
-'flower box',
-'aquarium',
-'rain cloud',
-'clothing store',
-'lead singer',
-'cupcake',
-'tortoise',
-'lettering',
-'sport facility',
-'dance',
-'dog house',
-'nature',
-'football',
-'rooster',
-'footballer',
-'railway track',
-'crowd',
-'fishing rod',
-'silhouette',
-'wind turbine',
-'sari',
-'bus window',
-'cloud',
-'charity',
-'medal',
-'yoga',
-'event',
-'veil',
-'fashion menswear milan week',
-'news',
-'knife',
-'print',
-'screen tv',
-'walnut',
-'fungus',
-'ice cream',
-'computer mouse',
-'play',
-'tribe',
-'picture',
-'video game',
-'business card',
-'music festival',
-'rack',
-'envelope',
-'shower',
-'dirt road',
-'mine',
-'oyster',
-'monarch butterfly',
-'dude',
-'fruit salad',
-'podium',
-'fork',
-'lace',
-'test match',
-'boulder',
-'cricket player',
-'staircase',
-'peninsula',
-'shopping',
-'popcorn',
-'oak',
-'market stall',
-'pine tree',
-'mountaineer',
-'student',
-'closet',
-'hood',
-'handstand',
-'centerpiece',
-'insect',
-'patient',
-'makeover',
-'tennis player',
-'sheet',
-'park bench',
-'apple',
-'organism',
-'hook',
-'turkey',
-'tangerine',
-'sibling',
-'shopping mall',
-'bird',
-'scarf',
-'smoothie',
-'net',
-'grass',
-'napkin',
-'ray',
-'eyebrow',
-'laptop keyboard',
-'motorbike',
-'woman hand',
-'oven',
-'book cover',
-'easter egg',
-'microwave',
-'sand',
-'snapshot',
-'soccer ball',
-'makeup',
-'knight',
-'bowling ball',
-'shower curtain',
-'flame',
-'lightning',
-'running',
-'power plant',
-'crib',
-'cartoon',
-'moat',
-'fashion girl',
-'wedding invitation',
-'bottle',
-'cliff',
-'monastery',
-'file photo',
-'apartment',
-'casino',
-'cream',
-'sweatshirt',
-'storm',
-'cruise',
-'teddy bear',
-'shovel',
-'wind farm',
-'writer',
-'dock',
-'professional',
-'hotel room',
-'job',
-'monitor',
-'donkey',
-'pass',
-'interview',
-'duchess',
-'mark',
-'plank',
-'beard',
-'zombie',
-'trio',
-'channel',
-'cricket team',
-'windmill',
-'vest',
-'diagram',
-'cable',
-'winter scene',
-'golden gate bridge',
-'buffalo',
-'studio portrait',
-'pagoda',
-'whiskey',
-'freight train',
-'kite',
-'future',
-'steam train',
-'phone box',
-'headset',
-'wood',
-'snowboarder',
-'paper bag',
-'slide',
-'grapefruit',
-'seating',
-'morning',
-'bronze sculpture',
-'theatre actor',
-'stump',
-'jean',
-'landmark',
-'jam',
-'waist',
-'watercolor',
-'hammock',
-'light fixture',
-'ice',
-'basin',
-'beverage',
-'shelter',
-'premiere',
-'mound',
-'ear',
-'bronze',
-'sunlight',
-'street',
-'energy',
-'barn door',
-'hike',
-'fleet',
-'claw',
-'beach',
-'pepperoni',
-'bin',
-'trainer',
-'buffet',
-'archive',
-'toddler',
-'referee',
-'bay window',
-'dove',
-'production company',
-'evening light',
-'gate',
-'farm',
-'reed',
-'fruit stand',
-'explorer',
-'snow storm',
-'throw pillow',
-'button',
-'display case',
-'bookcase',
-'lead',
-'lipstick',
-'basketball court',
-'cargo',
-'ensemble',
-'pope',
-'clock tower',
-'teen',
-'speaker',
-'rat',
-'laptop',
-'ski',
-'mess',
-'stadium',
-'ferry boat',
-'bunny',
-'waterfront',
-'downtown',
-'sink',
-'press conference',
-'dinner',
-'condiment',
-'thread',
-'audience',
-'grid',
-'car',
-'plastic',
-'people',
-'barbecue',
-'pigeon',
-'urinal',
-'seagull',
-'volunteer',
-'hockey',
-'fir tree',
-'pollution',
-'trial',
-'collar',
-'area',
-'meeting room',
-'circus',
-'yogurt',
-'orangutan',
-'viaduct',
-'comedian',
-'drone',
-'scissor',
-'pop rock artist',
-'biscuit',
-'panda',
-'water feature',
-'air balloon',
-'remote control',
-'watercolor painting',
-'show',
-'walk',
-'post office',
-'bike path',
-'rap gangsta artist',
-'microphone',
-'crack',
-'sunset sky',
-'glass',
-'tv show',
-'cartoon style',
-'stripe',
-'foyer',
-'signal',
-'calligraphy',
-'bulb',
-'gardener',
-'coffee bean',
-'spider',
-'tapestry',
-'city skyline',
-'necklace',
-'kitten',
-'traveler',
-'veteran',
-'frosting',
-'fry',
-'tennis court',
-'tank top',
-'butterfly house',
-'mist',
-'drummer',
-'water level',
-'scale',
-'baseball glove',
-'music video performer',
-'champagne',
-'camping',
-'clothing',
-'water drop',
-'telephone box',
-'pen',
-'morning mist',
-'fire engine',
-'porch',
-'opening ceremony',
-'style',
-'palm tree',
-'fashion show',
-'universe',
-'scratch',
-'axe',
-'ottoman',
-'explosion',
-'rib',
-'boutique',
-'game',
-'cucumber',
-'fruit',
-'stone bridge',
-'nature reserve',
-'track',
-'train window',
-'punch',
-'telephone pole',
-'velvet',
-'sauce',
-'moon',
-'contrast',
-'flamingo',
-'bat',
-'vending machine',
-'ship',
-'equestrian',
-'shade',
-'comforter',
-'pallet',
-'sparrow',
-'wii',
-'glaze',
-'grocery',
-'steeple',
-'soccer player',
-'contract',
-'advertising',
-'runner',
-'chimpanzee',
-'world',
-'seat',
-'project',
-'chihuahua',
-'bubble',
-'willow',
-'pedestal',
-'soul hip hop artist',
-'curb',
-'drawer',
-'leaf',
-'banner',
-'launch party',
-'coach',
-'government',
-'snowball',
-'toy',
-'portrait',
-'doctor',
-'whiteboard',
-'electronic',
-'tiger',
-'graffiti',
-'column',
-'nightstand',
-'whistle',
-'maxi dress',
-'bench',
-'wetsuit',
-'bird feeder',
-'football game',
-'basketball',
-'class',
-'bathroom door',
-'store window',
-'text message',
-'wreath',
-'street view',
-'binocular',
-'pet',
-'facade',
-'drought',
-'lemon',
-'new year',
-'night view',
-'airplane window',
-'specie',
-'rule',
-'jaw',
-'wheat field',
-'diet',
-'pop artist',
-'habitat',
-'screenshot',
-'scoreboard',
-'shore',
-'mane',
-'quilt',
-'ski lift',
-'orchid',
-'turban',
-'christmas',
-'airport',
-'marina',
-'glass door',
-'glass bottle',
-'restaurant',
-'conductor',
-'logo',
-'sleep',
-'tape',
-'tomato',
-'river bank',
-'lilac',
-'tooth',
-'training',
-'pottery',
-'shop',
-'steam engine',
-'mason jar',
-'base',
-'procession',
-'border',
-'shoot',
-'footprint',
-'hotdog',
-'bull',
-'stocking',
-'recreation',
-'automobile model',
-'design',
-'country pop artist',
-'river',
-'retriever',
-'department store',
-'auditorium',
-'sport car',
-'supermarket',
-'belt',
-'cricket',
-'window box',
-'dress shirt',
-'letter',
-'residence',
-'megaphone',
-'pant',
-'wildfire',
-'bird nest',
-'crab',
-'swimsuit',
-'candle',
-'funeral',
-'mill',
-'national park',
-'plant',
-'cop',
-'power line',
-'perch',
-'blue',
-'finger',
-'ferris wheel',
-'globe',
-'skateboard',
-'helmet',
-'movie theater',
-'uniform',
-'hammer',
-'material',
-'kid',
-'well',
-'butterfly',
-'sideline',
-'fashion fall show',
-'planet earth',
-'lift',
-'male',
-'sauna',
-'gray',
-'flour',
-'sand sculpture',
-'program',
-'cabinet',
-'infant',
-'wheel',
-'aircraft model',
-'dough',
-'garlic',
-'skate',
-'arrow',
-'wrapping paper',
-'ripple',
-'lamp',
-'iron',
-'banknote',
-'beaver',
-'ferry',
-'courtyard',
-'bassist',
-'countryside',
-'steak',
-'comfort',
-'boxer',
-'laundry room',
-'campsite',
-'brick building',
-'golf',
-'subway',
-'headphone',
-'fort',
-'handbag',
-'drum',
-'flood',
-'saddle',
-'bass',
-'labyrinth',
-'needle',
-'sun ray',
-'app',
-'menu',
-'president',
-'cardigan',
-'dandelion',
-'wetland',
-'ice hockey player',
-'number',
-'city hall',
-'fishing',
-'portrait session',
-'pug',
-'key',
-'art print',
-'minister',
-'hurdle',
-'emergency',
-'painting artist',
-'flag pole',
-'evening',
-'purse',
-'recipe',
-'golf ball',
-'coloring book',
-'mountain peak',
-'senior',
-'holiday',
-'bud',
-'cousin',
-'pantry',
-'lap',
-'skin',
-'flag',
-'tissue paper',
-'ridge',
-'wire fence',
-'surfer',
-'climber',
-'photograph',
-'sewing machine',
-'cooler',
-'actress',
-'apple tree',
-'cancer',
-'starfish',
-'automobile make',
-'dumbbell',
-'brace',
-'tunnel',
-'window',
-'paint artist',
-'composition',
-'school student',
-'condo',
-'convertible',
-'cushion',
-'selfie',
-'territory',
-'guide',
-'tree',
-'court',
-'shrimp',
-'stone house',
-'dress',
-'eyelash',
-'juice',
-'broccoli',
-'chain',
-'tourism',
-'mountain top',
-'concept car',
-'film premiere',
-'light bulb',
-'cafeteria',
-'badge',
-'flower bed',
-'theater',
-'root',
-'racecar driver',
-'basketball boy game',
-'glove',
-'skyline',
-'wall',
-'glacier',
-'airport terminal',
-'bug',
-'trim',
-'railway station',
-'briefcase',
-'flat',
-'fountain',
-'person',
-'lane',
-'asparagus',
-'art',
-'lantern',
-'dishwasher',
-'director',
-'snake',
-'lecture',
-'game controller',
-'tree branch',
-'pub',
-'bathing suit',
-'queue',
-'belly',
-'poppy',
-'bow',
-'pitcher',
-'ice cream cone',
-'cave',
-'candy',
-'road bridge',
-'host',
-'traffic jam',
-'earring',
-'file',
-'foot',
-'watermark overlay stamp',
-'mailbox',
-'supercar',
-'railing',
-'bedroom',
-'seafood',
-'waffle',
-'bronze statue',
-'plan',
-'flow',
-'marble',
-'basketball game',
-'automobile',
-'scene',
-'cypress tree',
-'soldier',
-'skateboarder',
-'glass building',
-'cherry tree',
-'pump',
-'grain',
-'wildebeest',
-'loop',
-'frame',
-'bathtub',
-'saxophone',
-'diver',
-'stalk',
-'lily',
-'bead',
-'alley',
-'flock',
-'family room',
-'manufacturing',
-'pointer',
-'worker',
-'navy',
-'potato',
-'teacher',
-'photography',
-'dolly',
-'boardwalk',
-'water fountain',
-'athlete',
-'side dish',
-'bay',
-'ice hockey',
-'phone',
-'hero',
-'face',
-'gold medal',
-'blind',
-'swamp',
-'researcher',
-'swim',
-'meatball',
-'iguana',
-'leather jacket',
-'jellyfish',
-'site',
-'smoke',
-'traffic signal',
-'melon',
-'beetle',
-'calculator',
-'skirt',
-'plantation',
-'sculptor',
-'barrier',
-'catcher',
-'security guard',
-'sketch',
-'awning',
-'steering wheel',
-'mountain view',
-'bus stop',
-'pool',
-'leg',
-'spotlight',
-'apron',
-'mineral',
-'inlet',
-'sleeve',
-'torch',
-'emotion',
-'march',
-'police officer',
-'performance',
-'lamp post',
-'fishing boat',
-'summer',
-'presentation',
-'saucer',
-'suitcase',
-'supermodel',
-'goalkeeper',
-'shrub',
-'rock artist',
-'document',
-'beach house',
-'man',
-'blue artist',
-'cigar',
-'railroad track',
-'gown',
-'mosaic',
-'bungalow',
-'alphabet',
-'baseball field',
-'shed',
-'pedestrian',
-'rail',
-'soap',
-'kitchen counter',
-'dessert',
-'dunk',
-'blossom',
-'conversation',
-'fruit market',
-'glass jar',
-'military',
-'beer bottle',
-'photographer',
-'tennis racket',
-'competition',
-'escalator',
-'bell tower',
-'stilt',
-'ballerina',
-'television',
-'feather',
-'fence post',
-'rear',
-'dahlia',
-'red carpet',
-'tub',
-'hole',
-'fortress',
-'pack',
-'telephone',
-'cardboard',
-'city park',
-'platform',
-'college student',
-'arch bridge',
-'wind',
-'blender',
-'bloom',
-'ice rink',
-'birthday',
-'raven',
-'fairy',
-'embankment',
-'hall',
-'flower shop',
-'suburb',
-'barrel',
-'biker',
-'steam',
-'dragonfly',
-'formation',
-'electricity',
-'business people',
-'symmetry',
-'walkway',
-'fisherman',
-'gas mask',
-'loch',
-'youth',
-'hanger',
-'dot',
-'fish',
-'street market',
-'animation film',
-'crime fiction film',
-'boar',
-'emblem',
-'halloween costume',
-'kangaroo',
-'couple',
-'spoon',
-'squirrel',
-'neon sign',
-'sky',
-'office desk',
-'beauty salon',
-'breakwater',
-'fashion look',
-'toaster',
-'author',
-'news conference',
-'outdoor',
-'canoe',
-'dragon',
-'tool',
-'shopping centre',
-'ladybug',
-'swimming pool',
-'landscaping',
-'ski pole',
-'red',
-'truck',
-'fly',
-'temple',
-'level',
-'sunday',
-'railroad bridge',
-'car mirror',
-'lawn mower',
-'flute',
-'aircraft carrier',
-'fashion menswear london week',
-'sunshine',
-'tile floor',
-'skull',
-'fossil',
-'flower arrangement',
-'diaper',
-'sea turtle',
-'cherry blossom',
-'fireman',
-'shack',
-'lens',
-'waiter',
-'animal',
-'basement',
-'snow',
-'autumn park',
-'glass box',
-'kick',
-'head',
-'anniversary',
-'vine',
-'back',
-'paper lantern',
-'fish tank',
-'cellphone',
-'silk',
-'coral',
-'notebook',
-'photo',
-'gazebo',
-'ketchup',
-'driver',
-'farmer',
-'bonfire',
-'chestnut',
-'photoshoot',
-'football field',
-'olive tree',
-'pheasant',
-'sandal',
-'toilet',
-'fireplace',
-'music',
-'deity',
-'fish market',
-'fig',
-'bell',
-'neck',
-'grave',
-'villa',
-'cyclist',
-'crate',
-'grey',
-'asphalt road',
-'soccer',
-'hostel',
-'municipality',
-'courthouse',
-'roof',
-'end table',
-'pot',
-'sedan',
-'structure',
-'folk artist',
-'sport',
-'sport team',
-'protest',
-'syringe',
-'fashion designer',
-'jersey',
-'heart shape',
-'kayak',
-'stare',
-'sit with',
-'direct',
-'read',
-'photograph',
-'spin',
-'teach',
-'laugh',
-'carve',
-'grow on',
-'warm',
-'watch',
-'stretch',
-'smell',
-'decorate',
-'shine',
-'light',
-'dance',
-'send',
-'park',
-'chase',
-'collect',
-'lead',
-'kiss',
-'lead to',
-'lick',
-'smile',
-'cheer',
-'sit',
-'point',
-'block',
-'rock',
-'drop',
-'cut',
-'ski',
-'wrap',
-'lose',
-'serve',
-'provide',
-'sleep',
-'dress',
-'embrace',
-'burn',
-'pack',
-'stir',
-'create',
-'touch',
-'wash',
-'stick',
-'reveal',
-'shop',
-'train',
-'paint',
-'groom',
-'hunt',
-'bloom',
-'play',
-'pay',
-'brush',
-'shoot',
-'hold',
-'picture',
-'carry',
-'sip',
-'contain',
-'turn',
-'pour',
-'pitch',
-'give',
-'add',
-'blow',
-'look in',
-'show',
-'walk',
-'illuminate',
-'kneel',
-'cover',
-'drag',
-'post',
-'present',
-'fit',
-'operate',
-'fish',
-'race',
-'write',
-'deliver',
-'peel',
-'push',
-'run',
-'sit around',
-'buy',
-'jump',
-'walk on',
-'attend',
-'clean',
-'sell',
-'ride on',
-'mount',
-'host',
-'dry',
-'plant',
-'sing',
-'row',
-'shake',
-'perch',
-'ride',
-'fight',
-'skateboard',
-'live',
-'call',
-'surround',
-'practice',
-'play on',
-'work on',
-'step',
-'relax',
-'hit',
-'fall in',
-'flow',
-'greet',
-'launch',
-'wear',
-'hang on',
-'drive',
-'sit in',
-'break',
-'learn',
-'fly',
-'connect',
-'display',
-'locate',
-'compete',
-'go for',
-'sail',
-'lift',
-'toast',
-'help',
-'run on',
-'reflect',
-'pose',
-'scratch',
-'frame',
-'dribble',
-'herd',
-'enter',
-'exit',
-'place',
-'inspect',
-'build',
-'pick',
-'fill',
-'grind',
-'skate',
-'offer',
-'float',
-'sit by',
-'stand',
-'release',
-'rest',
-'singe',
-'climb',
-'tie',
-'mark',
-'lay',
-'stand around',
-'capture',
-'set',
-'land',
-'swinge',
-'run in',
-'kick',
-'lean',
-'head',
-'sign',
-'approach',
-'swim',
-'close',
-'crash',
-'control',
-'fall',
-'remove',
-'repair',
-'open',
-'appear',
-'travel',
-'load',
-'miss',
-'check',
-'surf',
-'moor',
-'smoke',
-'drink',
-'board',
-'seat',
-'feed',
-'rise',
-'sit on',
-'swing',
-'grow',
-'strike',
-'date',
-'slide',
-'share',
-'graze',
-'jump in',
-'lie',
-'extrude',
-'roll',
-'move',
-'gather',
-'eat',
-'pull',
-'run through',
-'squeeze',
-'lay on',
-'draw',
-'play with',
-'wave',
-'assemble',
-'perform',
-'march',
-'score',
-'attach',
-'adjust',
-'hang',
-'hug',
-'sleep on',
-'throw',
-'live in',
-'talk',
-'pet',
-'work',
-'run with',
-'see',
-'flip',
-'catch',
-'cook',
-'receive',
-'celebrate',
-'look',
-'classic',
-'bridal',
-'indoor',
-'industrial',
-'teenage',
-'mini',
-'grassy',
-'aged',
-'long',
-'warm',
-'light',
-'handsome',
-'happy',
-'three',
-'pregnant',
-'circular',
-'urban',
-'silver',
-'ceramic',
-'3d',
-'green',
-'blonde',
-'golden',
-'dark',
-'tropical',
-'ripe',
-'deep',
-'fat',
-'musical',
-'giant',
-'medical',
-'medieval',
-'bare',
-'stunning',
-'bold',
-'geographical',
-'huge',
-'plastic',
-'foggy',
-'stormy',
-'gothic',
-'biological',
-'empty',
-'clear',
-'antique',
-'pink',
-'steep',
-'brown',
-'striped',
-'aerial',
-'rainy',
-'cool',
-'flying',
-'commercial',
-'purple',
-'trendy',
-'blank',
-'haired',
-'dead',
-'wooden',
-'flat',
-'high',
-'beige',
-'panoramic',
-'angry',
-'dozen',
-'rural',
-'solar',
-'big',
-'small',
-'stained',
-'thick',
-'many',
-'fresh',
-'clean',
-'strong',
-'abstract',
-'crowded',
-'retro',
-'dry',
-'gorgeous',
-'martial',
-'modern',
-'blue',
-'cloudy',
-'low',
-'four',
-'outdoor',
-'single',
-'much',
-'beautiful',
-'snowy',
-'pretty',
-'new',
-'short',
-'sunny',
-'closed',
-'rocky',
-'red',
-'two',
-'double',
-'male',
-'gray',
-'five',
-'colorful',
-'automotive',
-'various',
-'one',
-'old',
-'rusty',
-'tall',
-'wild',
-'narrow',
-'natural',
-'several',
-'frozen',
-'textured',
-'lush',
-'young',
-'hot',
-'mixed',
-'white',
-'float',
-'quiet',
-'round',
-'bright',
-'religious',
-'female',
-'historical',
-'shiny',
-'traditional',
-'tourist',
-'yellow',
-'bald',
-'coastal',
-'lovely',
-'little',
-'broken',
-'romantic',
-'wide',
-'royal',
-'rich',
-'open',
-'cute',
-'ancient',
-'cold',
-'political',
-'elderly',
-'gold',
-'full',
-'rustic',
-'metallic',
-'floral',
-'sad',
-'wet',
-'fancy',
-'senior',
-'tiny',
-'stylish',
-'large',
-'frosty',
-'orange',
-'transparent',
-'electronic',
-'shallow',
-'scared',
-'armed',
-'dirty',
-'historic',
-'black',
-'few',
-'windy',
-'some',
-'square',
-'ornamental',
-'sandy',
-'thin']
-
-
-tra_array = np.array(tra_array)
-
-
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py
deleted file mode 100644
index 85c9f1a9df8a4038fbd4246239b699402e382309..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .distributed_sampler import (
-    InferenceSampler,
-    RandomSubsetTrainingSampler,
-    RepeatFactorTrainingSampler,
-    TrainingSampler,
-)
-
-from .grouped_batch_sampler import GroupedBatchSampler
-
-__all__ = [
-    "GroupedBatchSampler",
-    "TrainingSampler",
-    "RandomSubsetTrainingSampler",
-    "InferenceSampler",
-    "RepeatFactorTrainingSampler",
-]
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py
deleted file mode 100644
index 7a19db71562ef47569dc7f77ec616af85447f0ec..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/evaluation/sem_seg_evaluation.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import logging
-import numpy as np
-import os
-from collections import OrderedDict
-import PIL.Image as Image
-import pycocotools.mask as mask_util
-import torch
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.utils.comm import all_gather, is_main_process, synchronize
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class SemSegEvaluator(DatasetEvaluator):
-    """
-    Evaluate semantic segmentation metrics.
-    """
-
-    def __init__(
-        self,
-        dataset_name,
-        distributed=True,
-        output_dir=None,
-        *,
-        num_classes=None,
-        ignore_label=None,
-    ):
-        """
-        Args:
-            dataset_name (str): name of the dataset to be evaluated.
-            distributed (bool): if True, will collect results from all ranks for evaluation.
-                Otherwise, will evaluate the results in the current process.
-            output_dir (str): an output directory to dump results.
-            num_classes, ignore_label: deprecated argument
-        """
-        self._logger = logging.getLogger(__name__)
-        if num_classes is not None:
-            self._logger.warn(
-                "SemSegEvaluator(num_classes) is deprecated! It should be obtained from metadata."
-            )
-        if ignore_label is not None:
-            self._logger.warn(
-                "SemSegEvaluator(ignore_label) is deprecated! It should be obtained from metadata."
-            )
-        self._dataset_name = dataset_name
-        self._distributed = distributed
-        self._output_dir = output_dir
-
-        self._cpu_device = torch.device("cpu")
-
-        self.input_file_to_gt_file = {
-            dataset_record["file_name"]: dataset_record["sem_seg_file_name"]
-            for dataset_record in DatasetCatalog.get(dataset_name)
-        }
-
-        meta = MetadataCatalog.get(dataset_name)
-        # Dict that maps contiguous training ids to COCO category ids
-        try:
-            c2d = meta.stuff_dataset_id_to_contiguous_id
-            self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()}
-        except AttributeError:
-            self._contiguous_id_to_dataset_id = None
-        self._class_names = meta.stuff_classes
-        self._num_classes = len(meta.stuff_classes)
-        if num_classes is not None:
-            assert self._num_classes == num_classes, f"{self._num_classes} != {num_classes}"
-        self._ignore_label = ignore_label if ignore_label is not None else meta.ignore_label
-
-    def reset(self):
-        self._conf_matrix = np.zeros((self._num_classes + 1, self._num_classes + 1), dtype=np.int64)
-        self._predictions = []
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a model.
-                It is a list of dicts. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name".
-            outputs: the outputs of a model. It is either list of semantic segmentation predictions
-                (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic
-                segmentation prediction in the same format.
-        """
-        for input, output in zip(inputs, outputs):
-            output = output["sem_seg"].argmax(dim=0).to(self._cpu_device)
-            pred = np.array(output, dtype=np.int)
-            with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f:
-                gt = np.array(Image.open(f), dtype=np.int)
-
-            gt[gt == self._ignore_label] = self._num_classes
-
-            self._conf_matrix += np.bincount(
-                (self._num_classes + 1) * pred.reshape(-1) + gt.reshape(-1),
-                minlength=self._conf_matrix.size,
-            ).reshape(self._conf_matrix.shape)
-
-            self._predictions.extend(self.encode_json_sem_seg(pred, input["file_name"]))
-
-    def evaluate(self):
-        """
-        Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval):
-
-        * Mean intersection-over-union averaged across classes (mIoU)
-        * Frequency Weighted IoU (fwIoU)
-        * Mean pixel accuracy averaged across classes (mACC)
-        * Pixel Accuracy (pACC)
-        """
-        if self._distributed:
-            synchronize()
-            conf_matrix_list = all_gather(self._conf_matrix)
-            self._predictions = all_gather(self._predictions)
-            self._predictions = list(itertools.chain(*self._predictions))
-            if not is_main_process():
-                return
-
-            self._conf_matrix = np.zeros_like(self._conf_matrix)
-            for conf_matrix in conf_matrix_list:
-                self._conf_matrix += conf_matrix
-
-        if self._output_dir:
-            PathManager.mkdirs(self._output_dir)
-            file_path = os.path.join(self._output_dir, "sem_seg_predictions.json")
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(self._predictions))
-
-        acc = np.full(self._num_classes, np.nan, dtype=np.float)
-        iou = np.full(self._num_classes, np.nan, dtype=np.float)
-        tp = self._conf_matrix.diagonal()[:-1].astype(np.float)
-        pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float)
-        class_weights = pos_gt / np.sum(pos_gt)
-        pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float)
-        acc_valid = pos_gt > 0
-        acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
-        iou_valid = (pos_gt + pos_pred) > 0
-        union = pos_gt + pos_pred - tp
-        iou[acc_valid] = tp[acc_valid] / union[acc_valid]
-        macc = np.sum(acc[acc_valid]) / np.sum(acc_valid)
-        miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
-        fiou = np.sum(iou[acc_valid] * class_weights[acc_valid])
-        pacc = np.sum(tp) / np.sum(pos_gt)
-
-        res = {}
-        res["mIoU"] = 100 * miou
-        res["fwIoU"] = 100 * fiou
-        for i, name in enumerate(self._class_names):
-            res["IoU-{}".format(name)] = 100 * iou[i]
-        res["mACC"] = 100 * macc
-        res["pACC"] = 100 * pacc
-        for i, name in enumerate(self._class_names):
-            res["ACC-{}".format(name)] = 100 * acc[i]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth")
-            with PathManager.open(file_path, "wb") as f:
-                torch.save(res, f)
-        results = OrderedDict({"sem_seg": res})
-        self._logger.info(results)
-        return results
-
-    def encode_json_sem_seg(self, sem_seg, input_file_name):
-        """
-        Convert semantic segmentation to COCO stuff format with segments encoded as RLEs.
-        See http://cocodataset.org/#format-results
-        """
-        json_list = []
-        for label in np.unique(sem_seg):
-            if self._contiguous_id_to_dataset_id is not None:
-                assert (
-                    label in self._contiguous_id_to_dataset_id
-                ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name)
-                dataset_id = self._contiguous_id_to_dataset_id[label]
-            else:
-                dataset_id = int(label)
-            mask = (sem_seg == label).astype(np.uint8)
-            mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0]
-            mask_rle["counts"] = mask_rle["counts"].decode("utf-8")
-            json_list.append(
-                {"file_name": input_file_name, "category_id": dataset_id, "segmentation": mask_rle}
-            )
-        return json_list
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/layers/test_losses.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/layers/test_losses.py
deleted file mode 100644
index d74920246cbd4a188b3c81cf0c78e982af6da1ac..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/layers/test_losses.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import unittest
-import torch
-
-from detectron2.layers import ciou_loss, diou_loss
-
-
-class TestLosses(unittest.TestCase):
-    def test_diou_loss(self):
-        """
-        loss = 1 - iou + d/c
-        where,
-        d = (distance between centers of the 2 boxes)^2
-        c = (diagonal length of the smallest enclosing box covering the 2 boxes)^2
-        """
-        # Identical boxes should have loss of 0
-        box = torch.tensor([-1, -1, 1, 1], dtype=torch.float32)
-        loss = diou_loss(box, box)
-        self.assertTrue(np.allclose(loss, [0.0]))
-
-        # Half size box inside other box
-        # iou = 0.5, d = 0.25, c = 8
-        box2 = torch.tensor([0, -1, 1, 1], dtype=torch.float32)
-        loss = diou_loss(box, box2)
-        self.assertTrue(np.allclose(loss, [0.53125]))
-
-        # Two diagonally adjacent boxes
-        # iou = 0, d = 2, c = 8
-        box3 = torch.tensor([0, 0, 1, 1], dtype=torch.float32)
-        box4 = torch.tensor([1, 1, 2, 2], dtype=torch.float32)
-        loss = diou_loss(box3, box4)
-        self.assertTrue(np.allclose(loss, [1.25]))
-
-        # Test batched loss and reductions
-        box1s = torch.stack([box, box3], dim=0)
-        box2s = torch.stack([box2, box4], dim=0)
-
-        loss = diou_loss(box1s, box2s, reduction="sum")
-        self.assertTrue(np.allclose(loss, [1.78125]))
-
-        loss = diou_loss(box1s, box2s, reduction="mean")
-        self.assertTrue(np.allclose(loss, [0.890625]))
-
-    def test_ciou_loss(self):
-        """
-        loss = 1 - iou + d/c + alpha*v
-        where,
-        d = (distance between centers of the 2 boxes)^2
-        c = (diagonal length of the smallest enclosing box covering the 2 boxes)^2
-        v = (4/pi^2) * (arctan(box1_w/box1_h) - arctan(box2_w/box2_h))^2
-        alpha = v/(1 - iou + v)
-        """
-        # Identical boxes should have loss of 0
-        box = torch.tensor([-1, -1, 1, 1], dtype=torch.float32)
-        loss = ciou_loss(box, box)
-        self.assertTrue(np.allclose(loss, [0.0]))
-
-        # Half size box inside other box
-        # iou = 0.5, d = 0.25, c = 8
-        # v = (4/pi^2) * (arctan(1) - arctan(0.5))^2 = 0.042
-        # alpha = 0.0775
-        box2 = torch.tensor([0, -1, 1, 1], dtype=torch.float32)
-        loss = ciou_loss(box, box2)
-        self.assertTrue(np.allclose(loss, [0.5345]))
-
-        # Two diagonally adjacent boxes
-        # iou = 0, d = 2, c = 8, v = 0, alpha = 0
-        box3 = torch.tensor([0, 0, 1, 1], dtype=torch.float32)
-        box4 = torch.tensor([1, 1, 2, 2], dtype=torch.float32)
-        loss = ciou_loss(box3, box4)
-        self.assertTrue(np.allclose(loss, [1.25]))
-
-        # Test batched loss and reductions
-        box1s = torch.stack([box, box3], dim=0)
-        box2s = torch.stack([box2, box4], dim=0)
-
-        loss = ciou_loss(box1s, box2s, reduction="sum")
-        self.assertTrue(np.allclose(loss, [1.7845]))
-
-        loss = ciou_loss(box1s, box2s, reduction="mean")
-        self.assertTrue(np.allclose(loss, [0.89225]))
diff --git a/spaces/ysharma/InstructPix2Pix_Chatbot/style.css b/spaces/ysharma/InstructPix2Pix_Chatbot/style.css
deleted file mode 100644
index 51094caca832bee577e86ed744dc31f8dfaaaf41..0000000000000000000000000000000000000000
--- a/spaces/ysharma/InstructPix2Pix_Chatbot/style.css
+++ /dev/null
@@ -1,36 +0,0 @@
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-.footer {
-        margin-bottom: 45px;
-        margin-top: 10px;
-        text-align: center;
-        border-bottom: 1px solid #e5e5e5;
-    }
-    .footer>p {
-        font-size: .8rem;
-        display: inline-block;
-        padding: 0 10px;
-        transform: translateY(10px);
-        background: white;
-    }
-    .dark .footer {
-        border-color: #303030;
-    }
-    .dark .footer>p {
-        background: #0b0f19;
-    }
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#upload_button {
-  background-color: black;
-  color: white;
-}
\ No newline at end of file
diff --git a/spaces/yusufani/TrCLIP/README.md b/spaces/yusufani/TrCLIP/README.md
deleted file mode 100644
index de7bb27c1dd290c1b898167263d9f4a5f2d9bfa2..0000000000000000000000000000000000000000
--- a/spaces/yusufani/TrCLIP/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Trclip
-emoji: 📈
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/zama-fhe/encrypted_health_prediction/utils.py b/spaces/zama-fhe/encrypted_health_prediction/utils.py
deleted file mode 100644
index 3deaf8832a06a4d93041c80b7a439bdb90afc086..0000000000000000000000000000000000000000
--- a/spaces/zama-fhe/encrypted_health_prediction/utils.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import os
-import shutil
-from pathlib import Path
-from typing import List, Tuple, Union
-
-import numpy
-import pandas
-
-from concrete.ml.sklearn import XGBClassifier as ConcreteXGBoostClassifier
-
-# Max Input to be displayed on the HuggingFace space brower using Gradio
-# Too large inputs, slow down the server: https://github.com/gradio-app/gradio/issues/1877
-INPUT_BROWSER_LIMIT = 400
-
-# Store the server's URL
-SERVER_URL = "http://localhost:8000/"
-
-CURRENT_DIR = Path(__file__).parent
-DEPLOYMENT_DIR = CURRENT_DIR / "deployment_logit"
-KEYS_DIR = DEPLOYMENT_DIR / ".fhe_keys"
-CLIENT_DIR = DEPLOYMENT_DIR / "client_dir"
-SERVER_DIR = DEPLOYMENT_DIR / "server_dir"
-
-ALL_DIRS = [KEYS_DIR, CLIENT_DIR, SERVER_DIR]
-
-# Columns that define the target
-TARGET_COLUMNS = ["prognosis_encoded", "prognosis"]
-
-TRAINING_FILENAME = "./data/Training_preprocessed.csv"
-TESTING_FILENAME = "./data/Testing_preprocessed.csv"
-
-# pylint: disable=invalid-name
-
-
-def pretty_print(
-    inputs, case_conversion=str.title, which_replace: str = "_", to_what: str = " ", delimiter=None
-):
-    """
-    Prettify and sort the input as a list of string.
-
-    Args:
-        inputs (Any): The inputs to be prettified.
-
-    Returns:
-        List: The prettified and sorted list of inputs.
-
-    """
-    # Flatten the list if required
-    pretty_list = []
-    for item in inputs:
-        if isinstance(item, list):
-            pretty_list.extend(item)
-        else:
-            pretty_list.append(item)
-
-    # Sort
-    pretty_list = sorted(list(set(pretty_list)))
-    # Replace
-    pretty_list = [item.replace(which_replace, to_what) for item in pretty_list]
-    pretty_list = [case_conversion(item) for item in pretty_list]
-    if delimiter:
-        pretty_list = f"{delimiter.join(pretty_list)}."
-
-    return pretty_list
-
-
-def clean_directory() -> None:
-    """
-    Clear direcgtories
-    """
-    print("Cleaning...\n")
-    for target_dir in ALL_DIRS:
-        if os.path.exists(target_dir) and os.path.isdir(target_dir):
-            shutil.rmtree(target_dir)
-        target_dir.mkdir(exist_ok=True, parents=True)
-
-
-def get_disease_name(encoded_prediction: int, file_name: str = TRAINING_FILENAME) -> str:
-    """Return the disease name given its encoded label.
-
-    Args:
-        encoded_prediction (int): The encoded prediction
-        file_name (str): The data file path
-
-    Returns:
-        str: The according disease name
-    """
-    df = pandas.read_csv(file_name, usecols=TARGET_COLUMNS).drop_duplicates()
-    disease_name, _ = df[df[TARGET_COLUMNS[0]] == encoded_prediction].values.flatten()
-    return disease_name
-
-
-def load_data() -> Union[Tuple[pandas.DataFrame, numpy.ndarray], List]:
-    """
-    Return the data
-
-    Args:
-        None
-
-    Return:
-        The train, testing set and valid symptoms.
-    """
-    # Load data
-    df_train = pandas.read_csv(TRAINING_FILENAME)
-    df_test = pandas.read_csv(TESTING_FILENAME)
-
-    # Separate the traget from the training / testing set:
-    # TARGET_COLUMNS[0] -> "prognosis_encoded" -> contains the numeric label of the disease
-    # TARGET_COLUMNS[1] -> "prognosis"         -> contains the name of the disease
-
-    y_train = df_train[TARGET_COLUMNS[0]]
-    X_train = df_train.drop(columns=TARGET_COLUMNS, axis=1, errors="ignore")
-
-    y_test = df_test[TARGET_COLUMNS[0]]
-    X_test = df_test.drop(columns=TARGET_COLUMNS, axis=1, errors="ignore")
-
-    return (
-        (X_train, X_test),
-        (y_train, y_test),
-        X_train.columns.to_list(),
-        df_train[TARGET_COLUMNS[1]].unique().tolist(),
-    )
-
-
-def load_model(X_train: pandas.DataFrame, y_train: numpy.ndarray):
-    """
-    Load a pre-trained serialized model
-
-    Args:
-        X_train (pandas.DataFrame): Training set
-        y_train (numpy.ndarray): Targets of the training set
-
-    Return:
-        The Concrete ML model and its circuit
-    """
-    # Parameters
-    concrete_args = {"max_depth": 1, "n_bits": 3, "n_estimators": 3, "n_jobs": -1}
-    classifier = ConcreteXGBoostClassifier(**concrete_args)
-    # Train the model
-    classifier.fit(X_train, y_train)
-    # Compile the model
-    circuit = classifier.compile(X_train)
-
-    return classifier, circuit
diff --git a/spaces/zhangyd/bingo/src/lib/utils.ts b/spaces/zhangyd/bingo/src/lib/utils.ts
deleted file mode 100644
index 07feedb34e356b1b3cf867872f32d47a96ae12fb..0000000000000000000000000000000000000000
--- a/spaces/zhangyd/bingo/src/lib/utils.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>) {
-  let {
-    BING_COOKIE = process.env.BING_COOKIE,
-    BING_UA = process.env.BING_UA,
-    BING_IP = process.env.BING_IP,
-    BING_HEADER = process.env.BING_HEADER,
-  } = cookies
-
-  if (BING_HEADER) {
-    return extraHeadersFromCookie({
-      BING_HEADER,
-      ...cookies,
-    })
-  }
-
-  const ua = parseUA(BING_UA)
-
-  if (!BING_COOKIE) {
-    BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || 'xxx' // hf 暂时不用 Cookie 也可以正常使用
-  }
-
-  const parsedCookie = parseCookie(BING_COOKIE, '_U')
-  if (!parsedCookie) {
-    throw new Error('Invalid Cookie')
-  }
-  return {
-    'x-forwarded-for': BING_IP || DEFAULT_IP,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    cookie: `_U=${parsedCookie}` || '',
-  }
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}
diff --git a/spaces/zhangyd/bingo/src/state/index.ts b/spaces/zhangyd/bingo/src/state/index.ts
deleted file mode 100644
index e65c02122a084b508b2ef3686cf114b926020e5a..0000000000000000000000000000000000000000
--- a/spaces/zhangyd/bingo/src/state/index.ts
+++ /dev/null
@@ -1,119 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage<BingConversationStyle>('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const useProxyAtom = atomWithStorage<boolean>('useProxy', true, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage<boolean>('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/app/page.tsx b/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/app/page.tsx
deleted file mode 100644
index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000
--- a/spaces/zhoujiaxin/zhoujiaxinchatgpt/src/app/page.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-
-const DynamicComponentWithNoSSR = dynamic(
-  () => import('../components/chat'),
-  { ssr: false }
-)
-
-export default function IndexPage() {
-  return (
-    <>
-      <div className="loading-spinner" />
-      <DynamicComponentWithNoSSR />
-    </>
-  )
-}
diff --git a/spaces/zhuzhao/background-remover/app.py b/spaces/zhuzhao/background-remover/app.py
deleted file mode 100644
index c53c42ae3e0e6ec108301bc6f7dbce2c36684e95..0000000000000000000000000000000000000000
--- a/spaces/zhuzhao/background-remover/app.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import cv2
-import gradio as gr
-import numpy as np
-import onnxruntime
-import requests
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-
-# Get x_scale_factor & y_scale_factor to resize image
-def get_scale_factor(im_h, im_w, ref_size=512):
-
-    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
-        if im_w >= im_h:
-            im_rh = ref_size
-            im_rw = int(im_w / im_h * ref_size)
-        elif im_w < im_h:
-            im_rw = ref_size
-            im_rh = int(im_h / im_w * ref_size)
-    else:
-        im_rh = im_h
-        im_rw = im_w
-
-    im_rw = im_rw - im_rw % 32
-    im_rh = im_rh - im_rh % 32
-
-    x_scale_factor = im_rw / im_w
-    y_scale_factor = im_rh / im_h
-
-    return x_scale_factor, y_scale_factor
-
-
-MODEL_PATH = hf_hub_download('nateraw/background-remover-files', 'modnet.onnx', repo_type='dataset')
-
-
-def main(image_path, threshold):
-
-    # read image
-    im = cv2.imread(image_path)
-    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-
-    # unify image channels to 3
-    if len(im.shape) == 2:
-        im = im[:, :, None]
-    if im.shape[2] == 1:
-        im = np.repeat(im, 3, axis=2)
-    elif im.shape[2] == 4:
-        im = im[:, :, 0:3]
-
-    # normalize values to scale it between -1 to 1
-    im = (im - 127.5) / 127.5
-
-    im_h, im_w, im_c = im.shape
-    x, y = get_scale_factor(im_h, im_w)
-
-    # resize image
-    im = cv2.resize(im, None, fx=x, fy=y, interpolation=cv2.INTER_AREA)
-
-    # prepare input shape
-    im = np.transpose(im)
-    im = np.swapaxes(im, 1, 2)
-    im = np.expand_dims(im, axis=0).astype('float32')
-
-    # Initialize session and get prediction
-    session = onnxruntime.InferenceSession(MODEL_PATH, None)
-    input_name = session.get_inputs()[0].name
-    output_name = session.get_outputs()[0].name
-    result = session.run([output_name], {input_name: im})
-
-    # refine matte
-    matte = (np.squeeze(result[0]) * 255).astype('uint8')
-    matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation=cv2.INTER_AREA)
-
-    # HACK - Could probably just convert this to PIL instead of writing
-    cv2.imwrite('out.png', matte)
-
-    image = Image.open(image_path)
-    matte = Image.open('out.png')
-
-    # obtain predicted foreground
-    image = np.asarray(image)
-    if len(image.shape) == 2:
-        image = image[:, :, None]
-    if image.shape[2] == 1:
-        image = np.repeat(image, 3, axis=2)
-    elif image.shape[2] == 4:
-        image = image[:, :, 0:3]
-
-    b, g, r = cv2.split(image)
-
-    mask = np.asarray(matte)
-    a = np.ones(mask.shape, dtype='uint8') * 255
-    alpha_im = cv2.merge([b, g, r, a], 4)
-    bg = np.zeros(alpha_im.shape)
-    new_mask = np.stack([mask, mask, mask, mask], axis=2)
-    foreground = np.where(new_mask > threshold, alpha_im, bg).astype(np.uint8)
-
-    return Image.fromarray(foreground)
-
-
-title = "MODNet Background Remover"
-description = "Gradio demo for MODNet, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<div style='text-align: center;'> <a href='https://github.com/ZHKKKe/MODNet' target='_blank'>Github Repo</a> | <a href='https://arxiv.org/abs/2011.11961' target='_blank'>MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition</a> </div>"
-
-url = "https://huggingface.co/datasets/nateraw/background-remover-files/resolve/main/twitter_profile_pic.jpeg"
-image = Image.open(requests.get(url, stream=True).raw)
-image.save('twitter_profile_pic.jpg')
-
-url = "https://upload.wikimedia.org/wikipedia/commons/8/8d/President_Barack_Obama.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-image.save('obama.jpg')
-
-interface = gr.Interface(
-    fn=main,
-    inputs=[
-        gr.inputs.Image(type='filepath'),
-        gr.inputs.Slider(minimum=0, maximum=250, default=100, step=5, label='Mask Cutoff Threshold'),
-    ],
-    outputs='image',
-    examples=[['twitter_profile_pic.jpg', 120], ['obama.jpg', 155]],
-    title=title,
-    description=description,
-    article=article,
-)
-
-if __name__ == '__main__':
-    interface.launch(debug=True)
diff --git a/spaces/zifyu/public-newbing/README.md b/spaces/zifyu/public-newbing/README.md
deleted file mode 100644
index b142e4917e9b3b5f12ce77d1a21da9864000c22d..0000000000000000000000000000000000000000
--- a/spaces/zifyu/public-newbing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Public Newbing
-emoji: 😻
-colorFrom: green
-colorTo: pink
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zomehwh/bert_vits2/modules.py b/spaces/zomehwh/bert_vits2/modules.py
deleted file mode 100644
index b1f89a2f837f190a3dd5de52e7a4e183f1024306..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/bert_vits2/modules.py
+++ /dev/null
@@ -1,597 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
-
-
-class TransformerCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        n_layers,
-        n_heads,
-        p_dropout=0,
-        filter_channels=0,
-        mean_only=False,
-        wn_sharing_parameter=None,
-        gin_channels=0,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = (
-            Encoder(
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-                isflow=True,
-                gin_channels=gin_channels,
-            )
-            if wn_sharing_parameter is None
-            else wn_sharing_parameter
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/zomehwh/rvc-models/infer_pack/transforms.py b/spaces/zomehwh/rvc-models/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/rvc-models/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet