diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (download Gangs Of Wasseypur movie ut) Anurag Kashyaps Magnum Opus.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (download Gangs Of Wasseypur movie ut) Anurag Kashyaps Magnum Opus.md
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-<p>Using HD online player for Gangs of Wasseypur movie also has some drawbacks that make it a risky option for some viewers. Here are some of them:</p>
-<ul>
-<li>You may encounter some legal issues or risks if you download or watch  the film from unauthorized sources. You may violate  the copyright laws  or face  the wrath  of  the producers  or distributors  of  the film.</li>
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-<li>You may miss some features or extras that are available on other platforms or media. You may not get  the subtitles, audio tracks, commentaries, behind-the-scenes, or bonus materials  that are included  in  the DVD  or Here are some FAQs at the end of the article: <h2>FAQs</h2>
-<ul>
-<li><strong>Is Gangs of Wasseypur based on a true story?</strong></li>
-<li>Yes, the movie is loosely based on the real-life gang wars, criminal activity, and political corruption that occurred in the region of Dhanbad, Jharkhand, India during the 1990s and 2000s.</li>
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-<li>The plot of Gangs of Wasseypur follows the violent rivalry between three crime families: the Khans, the Singhs, and the Qureshis, over the control of the coal-mining town of Wasseypur. The film spans 68 years from 1941 to 2009 and depicts the rise and fall of various characters involved in the blood feud.</li>
-<li><strong>Where were Gangs of Wasseypur filmed?</strong></li>
-<li>Gangs of Wasseypur were filmed in various locations in India, such as Varanasi, Bihar, Jharkhand, Delhi, Mumbai, and Goa. The film also used some archival footage and photographs to recreate the historical events and settings.</li>
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-<li>Anurag Kashyap hates Gangs of Wasseypur because he feels that everybody expects him to keep making the same kind of films which he is never going to do again in his life. He wants to make different kinds of films and move forward with his cinema. </li>
-</ul>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Dish-Network-Acquiring-Signal-535-Stuck.md b/spaces/1gistliPinn/ChatGPT4/Dish-Network-Acquiring-Signal-535-Stuck.md
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Mortal Kombat vs Street Fighter The Ultimate MUGEN Game.md b/spaces/1phancelerku/anime-remove-background/Download Mortal Kombat vs Street Fighter The Ultimate MUGEN Game.md
deleted file mode 100644
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-<ul>
-<li>Read and follow the game's installation guide and user agreement. The installation guide is a document that explains how to install and run the game on your PC. The user agreement is a contract that defines your rights and responsibilities as a user of the game. You should read and follow these documents carefully before installing and playing the game.</li>
-<li>Respect the game's intellectual property rights and avoid piracy. The game's intellectual property rights are the legal rights that the game's developer and publisher have over the game's content, such as characters, graphics, music, story, etc. Piracy is the illegal copying, distribution, or use of the game without permission or payment. You should respect the game's intellectual property rights and avoid piracy by purchasing the game from verified platforms, not sharing the game with others without authorization, not modifying the game without permission, and not using the game for commercial purposes.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Mortal Kombat vs Street Fighter PC from verified platforms, how to check your PC specs, how to avoid unverified sources, and how to follow the game's instructions and terms of service. By following these steps, you will be able to download this awesome fighting game on your PC safely and legally.</p>
-<p>Mortal Kombat vs Street Fighter PC is a fan-made crossover game that combines two of the most iconic franchises in the fighting genre. It features over 40 characters from both Mortal Kombat and Street Fighter universes, and various modes, stages , and moves. It is a great game for fans of both series, as well as for anyone who loves fighting games in general. It has high-quality graphics, sound, and gameplay, and it is compatible with most PC systems.</p>
-<p>Here are some tips and tricks for enjoying the game:</p>
-<ul>
-<li>Experiment with different characters and combinations. Each character has their own strengths, weaknesses, and special moves. You can mix and match characters from both universes to create your own team and strategy.</li>
-<li>Practice your skills and learn new moves. The game has a training mode where you can practice your combos, timing, and execution. You can also learn new moves by watching the move list or the demonstration videos.</li>
-<li>Challenge yourself and others. The game has various difficulty levels and modes, such as arcade, survival, versus, and team battle. You can also play online with other players from around the world, or locally with your friends.</li>
-</ul>
-<p>We hope you found this article helpful and informative. If you have any feedback or questions, please feel free to leave a comment below. We would love to hear from you. Happy gaming!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Mortal Kombat vs Street Fighter PC:</p>
-<h3>What are some other verified platforms for downloading PC games?</h3>
-<p>Some other verified platforms for downloading PC games are GOG.com, Origin, Uplay, Humble Bundle, itch.io, and Microsoft Store. These platforms also offer a variety of games for different genres and preferences.</p>
-<h3>What are some other popular fighting games for PC?</h3>
-<p>Some other popular fighting games for PC are Tekken 7, Street Fighter V, Mortal Kombat 11, Dragon Ball FighterZ, Injustice 2, Soulcalibur VI, Guilty Gear Strive, and BlazBlue: Cross Tag Battle. These games also feature different characters, modes, and mechanics.</p>
-<h3>What are some of the features and modes of Mortal Kombat vs Street Fighter PC?</h3>
-<p>Some of the features and modes of Mortal Kombat vs Street Fighter PC are:</p>
-<ul>
-<li>A roster of over 40 characters from both Mortal Kombat and Street Fighter universes, each with their own unique moves and abilities.</li>
-<li>A variety of stages from both series, each with their own background music and interactive elements.</li>
-<li>A selection of modes, such as arcade, survival, versus, team battle, training, online multiplayer, and options.</li>
-<li>A customization system that allows you to change your character's appearance, voice, color, costume, intro, outro, victory pose, taunt, and icon.</li>
-<li>A ranking system that tracks your progress and performance in the game.</li>
-</ul>
-<h3>How can I play Mortal Kombat vs Street Fighter PC online with other players?</h3 <p>You can play Mortal Kombat vs Street Fighter PC online with other players by using the online multiplayer mode. This mode allows you to join or create a lobby, where you can chat, invite, and challenge other players. You can also choose the region, rules, and settings of the lobby. You can also join or create a ranked match, where you can compete with other players for points and ranks. You can also view your stats, leaderboards, and replays in the online mode.</p>
-<h3>How can I customize my characters and settings in Mortal Kombat vs Street Fighter PC?</h3>
-<p>You can customize your characters and settings in Mortal Kombat vs Street Fighter PC by using the options mode. This mode allows you to change various aspects of the game, such as:</p>
-<ul>
-<li>Your character's appearance, voice, color, costume, intro, outro, victory pose, taunt, and icon. You can unlock more options by playing the game and earning coins.</li>
-<li>Your game's language, graphics, sound, controls, and display. You can adjust these options according to your preferences and PC specs.</li>
-<li>Your game's difficulty, time limit, rounds, damage, and blood. You can modify these options to make the game easier or harder.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/2023Liu2023/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/2023Liu2023/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/2023Liu2023/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
-  const isAtBottom = useAtBottom()
-
-  return (
-    <Button
-      variant="outline"
-      size="icon"
-      className={cn(
-        'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
-        isAtBottom ? 'opacity-0' : 'opacity-100',
-        className
-      )}
-      onClick={() =>
-        window.scrollTo({
-          top: document.body.offsetHeight,
-          behavior: 'smooth'
-        })
-      }
-      {...props}
-    >
-      <IconArrowDown />
-      <span className="sr-only">Scroll to bottom</span>
-    </Button>
-  )
-}
diff --git a/spaces/232labs/VToonify/vtoonify/model/bisenet/model.py b/spaces/232labs/VToonify/vtoonify/model/bisenet/model.py
deleted file mode 100644
index e61c0eb20aaa63065cc17bbcfe27b245f1f0dbf5..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/bisenet/model.py
+++ /dev/null
@@ -1,283 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from model.bisenet.resnet import Resnet18
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-
-class ConvBNReLU(nn.Module):
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan,
-                out_chan,
-                kernel_size = ks,
-                stride = stride,
-                padding = padding,
-                bias = False)
-        self.bn = nn.BatchNorm2d(out_chan)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-class BiSeNetOutput(nn.Module):
-    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.conv_out(x)
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class AttentionRefinementModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-
-class ContextPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(ContextPath, self).__init__()
-        self.resnet = Resnet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-        self.init_weight()
-
-    def forward(self, x):
-        H0, W0 = x.size()[2:]
-        feat8, feat16, feat32 = self.resnet(x)
-        H8, W8 = feat8.size()[2:]
-        H16, W16 = feat16.size()[2:]
-        H32, W32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-### This is not used, since I replace this with the resnet feature with the same size
-class SpatialPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(SpatialPath, self).__init__()
-        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
-        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv1(x)
-        feat = self.conv2(feat)
-        feat = self.conv3(feat)
-        feat = self.conv_out(feat)
-        return feat
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class FeatureFusionModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan,
-                out_chan//4,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.conv2 = nn.Conv2d(out_chan//4,
-                out_chan,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class BiSeNet(nn.Module):
-    def __init__(self, n_classes, *args, **kwargs):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        ## here self.sp is deleted
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, n_classes)
-        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
-        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
-        self.init_weight()
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
-        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        feat_out = self.conv_out(feat_fuse)
-        feat_out16 = self.conv_out16(feat_cp8)
-        feat_out32 = self.conv_out32(feat_cp16)
-
-        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
-        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
-        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
-        return feat_out, feat_out16, feat_out32
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
-        for name, child in self.named_children():
-            child_wd_params, child_nowd_params = child.get_params()
-            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
-                lr_mul_wd_params += child_wd_params
-                lr_mul_nowd_params += child_nowd_params
-            else:
-                wd_params += child_wd_params
-                nowd_params += child_nowd_params
-        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
-
-
-if __name__ == "__main__":
-    net = BiSeNet(19)
-    net.cuda()
-    net.eval()
-    in_ten = torch.randn(16, 3, 640, 480).cuda()
-    out, out16, out32 = net(in_ten)
-    print(out.shape)
-
-    net.get_params()
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/utils/cache.py b/spaces/AIConsultant/MusicGen/audiocraft/utils/cache.py
deleted file mode 100644
index 2fccc0acda4027b0bd36756a29b2d5cee318294d..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/utils/cache.py
+++ /dev/null
@@ -1,323 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from concurrent.futures import ThreadPoolExecutor
-from collections import deque
-from functools import partial
-from hashlib import sha1
-import logging
-from pathlib import Path
-import sys
-import typing as tp
-import zipfile
-
-import flashy
-import torch
-
-
-logger = logging.getLogger(__name__)
-
-
-def get_full_embed(full_embed: torch.Tensor, x: tp.Any, idx: int, device: tp.Union[str, torch.device]) -> torch.Tensor:
-    """Utility function for the EmbeddingCache, returning the full embedding without any chunking.
-    This method can be used in case there is no need in extracting a chunk of the full embedding
-    read from the cache.
-
-    Args:
-        full_embed (torch.Tensor): The full embedding.
-        x (any): Batch object from which the full embedding is derived.
-        idx (torch.Tensor): Index of object to consider in the batch object.
-    Returns:
-        full_embed (torch.Tensor): The full embedding
-    """
-    return full_embed.to(device)
-
-
-class EmbeddingCache:
-    """Cache around embeddings computation for faster execution.
-    The EmbeddingCache is storing pre-computed embeddings on disk and provides a simple API
-    to retrieve the pre-computed embeddings on full inputs and extract only a given chunk
-    using a user-provided function. When the cache is warm (all embeddings are pre-computed),
-    the EmbeddingCache allows for faster training as it removes the need of computing the embeddings.
-    Additionally, it provides in-memory cache around the loaded embeddings to limit IO footprint
-    and synchronization points in the forward calls.
-
-    Args:
-        cache_path (Path): Path to folder where all pre-computed embeddings are saved on disk.
-        device (str or torch.device): Device on which the embedding is returned.
-        compute_embed_fn (callable[[Path, any, int], torch.Tensor], optional): Function to compute
-            the embedding from a given object and path. This user provided function can compute the
-            embedding from the provided object or using the provided path as entry point. The last parameter
-            specify the index corresponding to the current embedding in the object that can represent batch metadata.
-        extract_embed_fn (callable[[torch.Tensor, any, int], torch.Tensor], optional): Function to extract
-            the desired embedding chunk from the full embedding loaded from the cache. The last parameter
-            specify the index corresponding to the current embedding in the object that can represent batch metadata.
-            If not specified, will return the full embedding unmodified.
-    """
-    def __init__(self, cache_path: tp.Union[Path], device: tp.Union[str, torch.device],
-                 compute_embed_fn: tp.Callable[[Path, tp.Any, int], torch.Tensor],
-                 extract_embed_fn: tp.Optional[tp.Callable[[torch.Tensor, tp.Any, int], torch.Tensor]] = None):
-        self.cache_path = Path(cache_path)
-        self.device = device
-        self._compute_embed_fn = compute_embed_fn
-        self._extract_embed_fn: tp.Callable[[torch.Tensor, tp.Any, int], torch.Tensor]
-        if extract_embed_fn is not None:
-            self._extract_embed_fn = extract_embed_fn
-        else:
-            self._extract_embed_fn = partial(get_full_embed, device=device)
-        if self.cache_path is not None:
-            self.cache_path.mkdir(exist_ok=True, parents=True)
-            logger.info(f"Cache instantiated at: {self.cache_path}")
-            self.pool = ThreadPoolExecutor(8)
-            self.pool.__enter__()
-        self._current_batch_cache: dict = {}
-        self._memory_cache: dict = {}
-
-    def _get_cache_path(self, path: tp.Union[Path, str]):
-        """Get cache path for the given file path."""
-        sig = sha1(str(path).encode()).hexdigest()
-        return self.cache_path / sig
-
-    @staticmethod
-    def _get_full_embed_from_cache(cache: Path):
-        """Loads full pre-computed embedding from the cache."""
-        try:
-            embed = torch.load(cache, 'cpu')
-        except Exception as exc:
-            logger.error("Error loading %s: %r", cache, exc)
-            embed = None
-        return embed
-
-    def get_embed_from_cache(self, paths: tp.List[Path], x: tp.Any) -> torch.Tensor:
-        """Get embedding from cache, computing and storing it to cache if not already cached.
-        The EmbeddingCache first tries to load the embedding from the in-memory cache
-        containing the pre-computed chunks populated through `populate_embed_cache`.
-        If not found, the full embedding is computed and stored on disk to be later accessed
-        to populate the in-memory cache, and the desired embedding chunk is extracted and returned.
-
-        Args:
-            paths (list[Path or str]): List of paths from where the embeddings can be loaded.
-            x (any): Object from which the embedding is extracted.
-        """
-        embeds = []
-        for idx, path in enumerate(paths):
-            cache = self._get_cache_path(path)
-            if cache in self._current_batch_cache:
-                embed = self._current_batch_cache[cache]
-            else:
-                full_embed = self._compute_embed_fn(path, x, idx)
-                try:
-                    with flashy.utils.write_and_rename(cache, pid=True) as f:
-                        torch.save(full_embed.cpu(), f)
-                except Exception as exc:
-                    logger.error('Error saving embed %s (%s): %r', cache, full_embed.shape, exc)
-                else:
-                    logger.info('New embed cache saved: %s (%s)', cache, full_embed.shape)
-                    embed = self._extract_embed_fn(full_embed, x, idx)
-            embeds.append(embed)
-        embed = torch.stack(embeds, dim=0)
-        return embed
-
-    def populate_embed_cache(self, paths: tp.List[Path], x: tp.Any) -> None:
-        """Populate in-memory caches for embeddings reading from the embeddings stored on disk.
-        The in-memory caches consist in a cache for the full embedding and another cache for the
-        final embedding chunk. Such caches are used to limit the IO access when computing the actual embeddings
-        and reduce the IO footprint and synchronization points during forward passes.
-
-        Args:
-            paths (list[Path]): List of paths from where the embeddings can be loaded.
-            x (any): Object from which the embedding is extracted.
-        """
-        self._current_batch_cache.clear()
-        if self.cache_path is not None:
-            futures: list = []
-            for path in paths:
-                assert path is not None, "Path is required for computation from cache"
-                cache = self._get_cache_path(path)
-                if cache in self._memory_cache or not cache.exists():
-                    futures.append(None)
-                else:
-                    futures.append(self.pool.submit(EmbeddingCache._get_full_embed_from_cache, cache))
-            for idx, (path, future) in enumerate(zip(paths, futures)):
-                assert path is not None
-                cache = self._get_cache_path(path)
-                full_embed = None
-                if future is None:
-                    if cache in self._memory_cache:
-                        full_embed = self._memory_cache[cache]
-                else:
-                    full_embed = future.result()
-                    if full_embed is not None:
-                        self._memory_cache[cache] = full_embed
-                        full_embed = full_embed.to(self.device)
-                if full_embed is not None:
-                    embed = self._extract_embed_fn(full_embed, x, idx)
-                    self._current_batch_cache[cache] = embed
-
-
-class CachedBatchWriter:
-    """Write pre computed caches for mini batches. This can
-    make loading a lot more efficient depending on your filesystem.
-
-    Args:
-        cache_folder (Path): folder in which the cached minibatches
-            will be stored.
-
-    Inside cache folder, the structure is the following:
-    `epoch_number / update_number.zip`
-    And the zip file contains one entry per batch item.
-
-    It is possible to use the cache with a batch size smaller than
-    created with but obviously not larger. Make sure to call the
-    `start_epoch(epoch)` method for indicating changes of epochs.
-
-    See the grid `audiocraft/grids/musicgen/musicgen_warmup_cache.py`
-    for an example of how to warmup the cache.
-    """
-    def __init__(self, cache_folder: Path):
-        self.cache_folder = cache_folder
-        self._current_epoch: tp.Optional[int] = None
-        self._current_index = 0
-
-    def start_epoch(self, epoch: int):
-        """Call at the beginning of each epoch.
-        """
-        self._current_epoch = epoch
-        self._current_index = 0
-        self._zip_path.parent.mkdir(exist_ok=True, parents=True)
-
-    @staticmethod
-    def _get_zip_path(cache_folder: Path, epoch: int, index: int):
-        return cache_folder / f"{epoch:05d}" / f"{index:06d}.zip"
-
-    @property
-    def _zip_path(self):
-        assert self._current_epoch is not None
-        return CachedBatchWriter._get_zip_path(self.cache_folder, self._current_epoch, self._current_index)
-
-    def save(self, *content):
-        """Save one mini batch. This function is distributed-aware
-        and will automatically merge all the items from the different
-        workers.
-        """
-        all_contents = []
-        for rank in range(flashy.distrib.world_size()):
-            their_content = flashy.distrib.broadcast_object(content, src=rank)
-            all_contents.append(their_content)
-
-        if flashy.distrib.is_rank_zero():
-            idx = 0
-            with flashy.utils.write_and_rename(self._zip_path) as tmp:
-                with zipfile.ZipFile(tmp, 'w') as zf:
-                    for content in all_contents:
-                        for vals in zip(*content):
-                            with zf.open(f'{idx}', 'w') as f:  # type: ignore
-                                torch.save(vals, f)
-                            idx += 1
-        flashy.distrib.barrier()
-        self._current_index += 1
-
-
-class CachedBatchLoader:
-    """Loader for cached mini-batches dumped with `CachedBatchWriter`.
-
-    Args:
-        cache_folder (Path): folder in which the cached minibatches are stored.
-        batch_size (int): batch size (per GPU) expected.
-        num_workers (int): number of workers to use for loading.
-        min_length (int): minimum expected length for each epoch. If some
-            mini-batches are missing, and error is raised.
-
-    This is iterable just like a regular DataLoader.
-    """
-
-    def __init__(self, cache_folder: Path, batch_size: int,
-                 num_workers: int = 10, min_length: int = 1):
-        self.cache_folder = cache_folder
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-        self.min_length = min_length
-        self._current_epoch: tp.Optional[int] = None
-        self.sampler = None  # for compatibility with the regular DataLoader
-
-    def __len__(self):
-        path = CachedBatchWriter._get_zip_path(self.cache_folder, self._current_epoch or 0, 0).parent
-        return len([p for p in path.iterdir() if p.suffix == ".zip"])
-
-    def start_epoch(self, epoch: int):
-        """Call at the beginning of each epoch.
-        """
-        self._current_epoch = epoch
-
-    def _zip_path(self, index: int):
-        assert self._current_epoch is not None
-        return CachedBatchWriter._get_zip_path(self.cache_folder, self._current_epoch, index)
-
-    def _load_one(self, index: int):
-        zip_path = self._zip_path(index)
-        if not zip_path.exists():
-            if index < self.min_length:
-                raise RuntimeError(f"Cache should have at least {self.min_length} batches, but {index} doesn't exist")
-
-            return None
-        mode = "rb" if sys.version_info >= (3, 9) else "r"
-        try:
-            with zipfile.ZipFile(zip_path, 'r') as zf:
-                rank = flashy.distrib.rank()
-                world_size = flashy.distrib.world_size()
-                root = zipfile.Path(zf)
-                items = list(root.iterdir())
-                total_batch_size = self.batch_size * world_size
-                if len(items) < total_batch_size:
-                    raise RuntimeError(
-                        f"The cache can handle a max batch size of {len(items)}, "
-                        f"but {total_batch_size} is needed.")
-                start = rank * self.batch_size
-                items = items[start: start + self.batch_size]
-                assert len(items) == self.batch_size
-                entries = []
-                entries = [torch.load(item.open(mode), 'cpu') for item in items]  # type: ignore
-                transposed = zip(*entries)
-                out = []
-                for part in transposed:
-                    assert len(part) > 0
-                    if isinstance(part[0], torch.Tensor):
-                        out.append(torch.stack(part))
-                    else:
-                        out.append(part)
-                return out
-        except Exception:
-            logger.error("Error when reading zip path %s", zip_path)
-            raise
-
-    def __iter__(self):
-        """This will yields tuples, exactly as provided to the
-        `CachedBatchWriter.save` method.
-        """
-        pool = ThreadPoolExecutor(self.num_workers)
-        next_index = 0
-        queue = deque()
-
-        def _get_next():
-            nonlocal next_index
-            r = queue.popleft().result()
-            if r is None:
-                return None
-            else:
-                queue.append(pool.submit(self._load_one, next_index))
-                next_index += 1
-            return r
-
-        with pool:
-            # fill the buffer of fetching jobs.
-            for _ in range(2 * self.num_workers):
-                queue.append(pool.submit(self._load_one, next_index))
-                next_index += 1
-            while True:
-                batch = _get_next()
-                if batch is None:
-                    return
-                yield batch
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/util.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/util.py
deleted file mode 100644
index 85a90a3af06d6f593d596366a5b85b62b4ddf498..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/util.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import importlib
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-import hashlib
-import requests
-import os
-
-URL_MAP = {
-    'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt',
-    'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt',
-    'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt',
-}
-
-CKPT_MAP = {
-    'vggishish_lpaps': 'vggishish16.pt',
-    'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt',
-    'melception': 'melception-21-05-10T09-28-40.pt',
-}
-
-MD5_MAP = {
-    'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd',
-    'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625',
-    'melception': 'a71a41041e945b457c7d3d814bbcf72d',
-}
-
-
-def download(url, local_path, chunk_size=1024):
-    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        total_size = int(r.headers.get("content-length", 0))
-        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
-            with open(local_path, "wb") as f:
-                for data in r.iter_content(chunk_size=chunk_size):
-                    if data:
-                        f.write(data)
-                        pbar.update(chunk_size)
-
-
-def md5_hash(path):
-    with open(path, "rb") as f:
-        content = f.read()
-    return hashlib.md5(content).hexdigest()
-
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x,torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config,reload=False):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-def get_ckpt_path(name, root, check=False):
-    assert name in URL_MAP
-    path = os.path.join(root, CKPT_MAP[name])
-    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
-        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
-        download(URL_MAP[name], path)
-        md5 = md5_hash(path)
-        assert md5 == MD5_MAP[name], md5
-    return path
diff --git a/spaces/AIxPha/QSign/Dockerfile b/spaces/AIxPha/QSign/Dockerfile
deleted file mode 100644
index 197343d759e41dd113b8ae94765f408a3f789f0c..0000000000000000000000000000000000000000
--- a/spaces/AIxPha/QSign/Dockerfile
+++ /dev/null
@@ -1,15 +0,0 @@
-#程序开源地址 https://github.com/fuqiuluo/unidbg-fetch-qsign
-
-FROM openjdk:11.0-jdk
-
-ENV TZ Asia/Shanghai
-
-WORKDIR /app
-
-COPY unidbg-fetch-qsign /app
-
-CMD bash bin/unidbg-fetch-qsign --host=0.0.0.0 --port=7860  --count=5 --library=txlib --android_id=
-
-EXPOSE 7860
-
-#抱脸推荐项目 https://github.com/CikeyQi/QQsign_docs
\ No newline at end of file
diff --git a/spaces/Abdllh/Arabic_Poems_Generator/app.py b/spaces/Abdllh/Arabic_Poems_Generator/app.py
deleted file mode 100644
index 811474966d90bca03ab0435a063511445a197293..0000000000000000000000000000000000000000
--- a/spaces/Abdllh/Arabic_Poems_Generator/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-import gradio as gr
-
-def generate_text(model,temperature, start_string):
-  char2idx={'\t': 0, '\n': 1, ' ': 2, 'ء': 3, 'آ': 4, 'أ': 5, 'ؤ': 6, 'إ': 7, 'ئ': 8, 'ا': 9, 'ب': 10, 'ة': 11, 'ت': 12, 'ث': 13, 'ج': 14, 'ح': 15, 'خ': 16, 'د': 17, 'ذ': 18, 'ر': 19, 'ز': 20, 'س': 21, 'ش': 22, 'ص': 23, 'ض': 24, 'ط': 25, 'ظ': 26, 'ع': 27, 'غ': 28, 'ف': 29, 'ق': 30, 'ك': 31, 'ل': 32, 'م': 33, 'ن': 34, 'ه': 35, 'و': 36, 'ى': 37, 'ي': 38}
-  idx2char=['\t', '\n', ' ', 'ء', 'آ', 'أ', 'ؤ', 'إ', 'ئ', 'ا', 'ب', 'ة', 'ت',
-       'ث', 'ج', 'ح', 'خ', 'د', 'ذ', 'ر', 'ز', 'س', 'ش', 'ص', 'ض', 'ط',
-       'ظ', 'ع', 'غ', 'ف', 'ق', 'ك', 'ل', 'م', 'ن', 'ه', 'و', 'ى', 'ي']
-  # Evaluation step (generating text using the learned model)
-
-  # Number of characters to generate
-  num_generate = 1000
-
-  # Converting our start string to numbers (vectorizing)
-  input_eval = [char2idx[s] for s in start_string]
-  input_eval = tf.expand_dims(input_eval, 0)
-
-  # Empty string to store our results
-  text_generated = []
-
-  # Low temperatures results in more predictable text.
-  # Higher temperatures results in more surprising text.
-  # Experiment to find the best setting.
-
-  # Here batch size == 1
-  model.reset_states()
-  for i in range(num_generate):
-      predictions = model(input_eval)
-      # remove the batch dimension
-      predictions = tf.squeeze(predictions, 0)
-
-      # using a random.categorical distribution to predict the word returned by the model
-      predictions = predictions / temperature
-      predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
-      
-      input_eval = tf.expand_dims([predicted_id], 0)
-
-      text_generated.append(idx2char[predicted_id])
-
-  return (start_string + ''.join(text_generated))
-
-reconstructed_model = keras.models.load_model("poems_generation_GRU (1).h5")
-
-def generate_poem(start,temperature):
-    return generate_text(reconstructed_model,temperature, start_string=u""+start )
-
-iface = gr.Interface(fn=generate_poem, inputs=["text",gr.Slider(0, 1, value=1)], outputs="text")
-iface.launch()
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatForAi.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatForAi.py
deleted file mode 100644
index 86b296396bcb2bdbb0cca4ba864b18ffdfdf2313..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatForAi.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from __future__ import annotations
-
-from ..typing import AsyncGenerator
-from ..requests import StreamSession
-from .base_provider import AsyncGeneratorProvider
-
-
-class ChatForAi(AsyncGeneratorProvider):
-    url                   = "https://chatforai.com"
-    supports_gpt_35_turbo = True
-    working               = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        timeout: int = 30,
-        **kwargs
-    ) -> AsyncGenerator:
-        async with StreamSession(impersonate="chrome107", timeout=timeout) as session:
-            prompt = messages[-1]["content"]
-            data = {
-                "conversationId": "temp",
-                "conversationType": "chat_continuous",
-                "botId": "chat_continuous",
-                "globalSettings":{
-                    "baseUrl": "https://api.openai.com",
-                    "model": model if model else "gpt-3.5-turbo",
-                    "messageHistorySize": 5,
-                    "temperature": 0.7,
-                    "top_p": 1,
-                    **kwargs
-                },
-                "botSettings": {},
-                "prompt": prompt,
-                "messages": messages,
-            }
-            async with session.post(f"{cls.url}/api/handle/provider-openai", json=data) as response:
-                response.raise_for_status()
-                async for chunk in response.iter_content():
-                    yield chunk.decode()
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/DeepAi.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/DeepAi.py
deleted file mode 100644
index bac3e3fe64b34d9ce2e5465ff99e84c7b2cf5411..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/DeepAi.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from __future__ import annotations
-
-import json
-import js2py
-import random
-import hashlib
-from aiohttp import ClientSession
-
-from ..typing       import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-
-
-class DeepAi(AsyncGeneratorProvider):
-    url: str              = "https://deepai.org"
-    working               = True
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    async def create_async_generator(
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        
-        token_js = """
-var agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36'
-var a, b, c, d, e, h, f, l, g, k, m, n, r, x, C, E, N, F, T, O, P, w, D, G, Q, R, W, I, aa, fa, na, oa, ha, ba, X, ia, ja, ka, J, la, K, L, ca, S, U, M, ma, B, da, V, Y;
-h = Math.round(1E11 * Math.random()) + "";
-f = function () {
-    for (var p = [], q = 0; 64 > q;) p[q] = 0 | 4294967296 * Math.sin(++q % Math.PI);
-    
-    return function (t) {
-        var v, y, H, ea = [v = 1732584193, y = 4023233417, ~v, ~y],
-            Z = [],
-            A = unescape(encodeURI(t)) + "\u0080",
-            z = A.length;
-        t = --z / 4 + 2 | 15;
-        for (Z[--t] = 8 * z; ~z;) Z[z >> 2] |= A.charCodeAt(z) << 8 * z--;
-        for (q = A = 0; q < t; q += 16) {
-            for (z = ea; 64 > A; z = [H = z[3], v + ((H = z[0] + [v & y | ~v & H, H & v | ~H & y, v ^ y ^ H, y ^ (v | ~H)][z = A >> 4] + p[A] + ~~Z[q | [A, 5 * A + 1, 3 * A + 5, 7 * A][z] & 15]) << (z = [7, 12, 17, 22, 5, 9, 14, 20, 4, 11, 16, 23, 6, 10, 15, 21][4 * z + A++ % 4]) | H >>> -z), v, y]) v = z[1] | 0, y = z[2];
-            for (A = 4; A;) ea[--A] += z[A]
-        }
-        for (t = ""; 32 > A;) t += (ea[A >> 3] >> 4 * (1 ^ A++) & 15).toString(16);
-        return t.split("").reverse().join("")
-    }
-}();
-
-"tryit-" + h + "-" + f(agent + f(agent + f(agent + h + "x")));
-"""
-
-        payload = {"chat_style": "chat", "chatHistory": json.dumps(messages)}
-        api_key = js2py.eval_js(token_js)
-        headers = {
-            "api-key": api_key,
-            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36",
-            **kwargs.get("headers", {})
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            fill = "ing_is"
-            fill = f"ack{fill}_a_crim"
-            async with session.post(f"https://api.deepai.org/h{fill}e", proxy=proxy, data=payload) as response:
-                response.raise_for_status()
-                async for stream in response.content.iter_any():
-                    if stream:
-                        yield stream.decode()
-
-
-def get_api_key(user_agent: str):
-    e = str(round(1E11 * random.random()))
-
-    def hash(data: str):    
-        return hashlib.md5(data.encode()).hexdigest()[::-1]
-
-    return f"tryit-{e}-" + hash(user_agent + hash(user_agent + hash(user_agent + e + "x")))
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/mapper/__init__.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/mapper/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/configuration.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/configuration.md
deleted file mode 100644
index a10e348acdefedafd67e670f05413fe845b78c20..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/configuration.md
+++ /dev/null
@@ -1,30 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Configuration
-
-Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
-
-<Tip>
-
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
-
-</Tip>
-
-## ConfigMixin
-
-[[autodoc]] ConfigMixin
-	- load_config
-	- from_config
-	- save_config
-	- to_json_file
-	- to_json_string
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img.py
deleted file mode 100644
index ec4598906a6fc3365afa803b0eabd2e3e298603b..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import random
-import unittest
-
-import torch
-
-from diffusers import IFImg2ImgPipeline
-from diffusers.utils import floats_tensor
-from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import skip_mps, torch_device
-
-from ..pipeline_params import (
-    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
-    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
-)
-from ..test_pipelines_common import PipelineTesterMixin
-from . import IFPipelineTesterMixin
-
-
-@skip_mps
-class IFImg2ImgPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):
-    pipeline_class = IFImg2ImgPipeline
-    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
-    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
-    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
-
-    def get_dummy_components(self):
-        return self._get_dummy_components()
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "image": image,
-            "generator": generator,
-            "num_inference_steps": 2,
-            "output_type": "numpy",
-        }
-
-        return inputs
-
-    def test_save_load_optional_components(self):
-        self._test_save_load_optional_components()
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)
-
-    @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
-    def test_save_load_float16(self):
-        # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
-        super().test_save_load_float16(expected_max_diff=1e-1)
-
-    @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
-    def test_float16_inference(self):
-        super().test_float16_inference(expected_max_diff=1e-1)
-
-    def test_attention_slicing_forward_pass(self):
-        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)
-
-    def test_save_load_local(self):
-        self._test_save_load_local()
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(
-            expected_max_diff=1e-2,
-        )
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
deleted file mode 100644
index d0fd9fa0284f17272c0785701f2ae81860bc04b6..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
+++ /dev/null
@@ -1,10 +0,0 @@
-_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w40',
-    backbone=dict(
-        type='HRNet',
-        extra=dict(
-            stage2=dict(num_channels=(40, 80)),
-            stage3=dict(num_channels=(40, 80, 160)),
-            stage4=dict(num_channels=(40, 80, 160, 320)))),
-    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/apis/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/apis/__init__.py
deleted file mode 100644
index 1d8035b74877fdeccaa41cbc10a9f1f9924eac85..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/apis/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from .inference import (async_inference_detector, inference_detector,
-                        init_detector, show_result_pyplot)
-from .test import multi_gpu_test, single_gpu_test
-from .train import get_root_logger, set_random_seed, train_detector
-
-__all__ = [
-    'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector',
-    'async_inference_detector', 'inference_detector', 'show_result_pyplot',
-    'multi_gpu_test', 'single_gpu_test'
-]
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/DeepSpeed.md b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/DeepSpeed.md
deleted file mode 100644
index 6170f6819ca072ff50fd1146b64d73f74ab00473..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/DeepSpeed.md
+++ /dev/null
@@ -1,24 +0,0 @@
-An alternative way of reducing the GPU memory usage of models is to use the `DeepSpeed ZeRO-3` optimization.
-
-With this, I have been able to load a 6b model (GPT-J 6B) with less than 6GB of VRAM. The speed of text generation is very decent and much better than what would be accomplished with `--auto-devices --gpu-memory 6`.
-
-As far as I know, DeepSpeed is only available for Linux at the moment.
-
-### How to use it
-
-1. Install DeepSpeed: 
-
-```
-conda install -c conda-forge mpi4py mpich
-pip install -U deepspeed
-```
-
-2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example:
-
-```
-deepspeed --num_gpus=1 server.py --deepspeed --chat --model gpt-j-6B
-```
-
-### Learn more
-
-For more information, check out [this comment](https://github.com/oobabooga/text-generation-webui/issues/40#issuecomment-1412038622) by 81300, who came up with the DeepSpeed support in this web UI.
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/builder.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/builder.py
deleted file mode 100644
index 77c96ba0b2f30ead9da23f293c5dc84dd3e4a74f..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/builder.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-
-from ..utils import Registry
-
-RUNNERS = Registry('runner')
-RUNNER_BUILDERS = Registry('runner builder')
-
-
-def build_runner_constructor(cfg):
-    return RUNNER_BUILDERS.build(cfg)
-
-
-def build_runner(cfg, default_args=None):
-    runner_cfg = copy.deepcopy(cfg)
-    constructor_type = runner_cfg.pop('constructor',
-                                      'DefaultRunnerConstructor')
-    runner_constructor = build_runner_constructor(
-        dict(
-            type=constructor_type,
-            runner_cfg=runner_cfg,
-            default_args=default_args))
-    runner = runner_constructor()
-    return runner
diff --git a/spaces/Anthony7906/MengHuiMXD_GPT/custom.css b/spaces/Anthony7906/MengHuiMXD_GPT/custom.css
deleted file mode 100644
index 5143eb138ea2469d8c457c71cb210fd3fb7cbe15..0000000000000000000000000000000000000000
--- a/spaces/Anthony7906/MengHuiMXD_GPT/custom.css
+++ /dev/null
@@ -1,162 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: #FFFFFF !important;
-}
-[data-testid = "user"] {
-    background-color: #95EC69 !important;
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/transforms.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/transforms.py
deleted file mode 100644
index 91cf9269e4b31008a3ddca34a19b038a9b399991..0000000000000000000000000000000000000000
--- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/transforms.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Transforms and data augmentation for both image + bbox.
-"""
-import os
-import random
-
-import PIL
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
-
-from groundingdino.util.box_ops import box_xyxy_to_cxcywh
-from groundingdino.util.misc import interpolate
-
-
-def crop(image, target, region):
-    cropped_image = F.crop(image, *region)
-
-    target = target.copy()
-    i, j, h, w = region
-
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor([h, w])
-
-    fields = ["labels", "area", "iscrowd", "positive_map"]
-
-    if "boxes" in target:
-        boxes = target["boxes"]
-        max_size = torch.as_tensor([w, h], dtype=torch.float32)
-        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
-        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
-        cropped_boxes = cropped_boxes.clamp(min=0)
-        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
-        target["boxes"] = cropped_boxes.reshape(-1, 4)
-        target["area"] = area
-        fields.append("boxes")
-
-    if "masks" in target:
-        # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i : i + h, j : j + w]
-        fields.append("masks")
-
-    # remove elements for which the boxes or masks that have zero area
-    if "boxes" in target or "masks" in target:
-        # favor boxes selection when defining which elements to keep
-        # this is compatible with previous implementation
-        if "boxes" in target:
-            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
-            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
-        else:
-            keep = target["masks"].flatten(1).any(1)
-
-        for field in fields:
-            if field in target:
-                target[field] = target[field][keep]
-
-    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
-        # for debug and visualization only.
-        if "strings_positive" in target:
-            target["strings_positive"] = [
-                _i for _i, _j in zip(target["strings_positive"], keep) if _j
-            ]
-
-    return cropped_image, target
-
-
-def hflip(image, target):
-    flipped_image = F.hflip(image)
-
-    w, h = image.size
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
-            [w, 0, w, 0]
-        )
-        target["boxes"] = boxes
-
-    if "masks" in target:
-        target["masks"] = target["masks"].flip(-1)
-
-    return flipped_image, target
-
-
-def resize(image, target, size, max_size=None):
-    # size can be min_size (scalar) or (w, h) tuple
-
-    def get_size_with_aspect_ratio(image_size, size, max_size=None):
-        w, h = image_size
-        if max_size is not None:
-            min_original_size = float(min((w, h)))
-            max_original_size = float(max((w, h)))
-            if max_original_size / min_original_size * size > max_size:
-                size = int(round(max_size * min_original_size / max_original_size))
-
-        if (w <= h and w == size) or (h <= w and h == size):
-            return (h, w)
-
-        if w < h:
-            ow = size
-            oh = int(size * h / w)
-        else:
-            oh = size
-            ow = int(size * w / h)
-
-        return (oh, ow)
-
-    def get_size(image_size, size, max_size=None):
-        if isinstance(size, (list, tuple)):
-            return size[::-1]
-        else:
-            return get_size_with_aspect_ratio(image_size, size, max_size)
-
-    size = get_size(image.size, size, max_size)
-    rescaled_image = F.resize(image, size)
-
-    if target is None:
-        return rescaled_image, None
-
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
-    ratio_width, ratio_height = ratios
-
-    target = target.copy()
-    if "boxes" in target:
-        boxes = target["boxes"]
-        scaled_boxes = boxes * torch.as_tensor(
-            [ratio_width, ratio_height, ratio_width, ratio_height]
-        )
-        target["boxes"] = scaled_boxes
-
-    if "area" in target:
-        area = target["area"]
-        scaled_area = area * (ratio_width * ratio_height)
-        target["area"] = scaled_area
-
-    h, w = size
-    target["size"] = torch.tensor([h, w])
-
-    if "masks" in target:
-        target["masks"] = (
-            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
-        )
-
-    return rescaled_image, target
-
-
-def pad(image, target, padding):
-    # assumes that we only pad on the bottom right corners
-    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
-    if target is None:
-        return padded_image, None
-    target = target.copy()
-    # should we do something wrt the original size?
-    target["size"] = torch.tensor(padded_image.size[::-1])
-    if "masks" in target:
-        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
-    return padded_image, target
-
-
-class ResizeDebug(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        return resize(img, target, self.size)
-
-
-class RandomCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        region = T.RandomCrop.get_params(img, self.size)
-        return crop(img, target, region)
-
-
-class RandomSizeCrop(object):
-    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
-        # respect_boxes:    True to keep all boxes
-        #                   False to tolerence box filter
-        self.min_size = min_size
-        self.max_size = max_size
-        self.respect_boxes = respect_boxes
-
-    def __call__(self, img: PIL.Image.Image, target: dict):
-        init_boxes = len(target["boxes"])
-        max_patience = 10
-        for i in range(max_patience):
-            w = random.randint(self.min_size, min(img.width, self.max_size))
-            h = random.randint(self.min_size, min(img.height, self.max_size))
-            region = T.RandomCrop.get_params(img, [h, w])
-            result_img, result_target = crop(img, target, region)
-            if (
-                not self.respect_boxes
-                or len(result_target["boxes"]) == init_boxes
-                or i == max_patience - 1
-            ):
-                return result_img, result_target
-        return result_img, result_target
-
-
-class CenterCrop(object):
-    def __init__(self, size):
-        self.size = size
-
-    def __call__(self, img, target):
-        image_width, image_height = img.size
-        crop_height, crop_width = self.size
-        crop_top = int(round((image_height - crop_height) / 2.0))
-        crop_left = int(round((image_width - crop_width) / 2.0))
-        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
-
-
-class RandomHorizontalFlip(object):
-    def __init__(self, p=0.5):
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return hflip(img, target)
-        return img, target
-
-
-class RandomResize(object):
-    def __init__(self, sizes, max_size=None):
-        assert isinstance(sizes, (list, tuple))
-        self.sizes = sizes
-        self.max_size = max_size
-
-    def __call__(self, img, target=None):
-        size = random.choice(self.sizes)
-        return resize(img, target, size, self.max_size)
-
-
-class RandomPad(object):
-    def __init__(self, max_pad):
-        self.max_pad = max_pad
-
-    def __call__(self, img, target):
-        pad_x = random.randint(0, self.max_pad)
-        pad_y = random.randint(0, self.max_pad)
-        return pad(img, target, (pad_x, pad_y))
-
-
-class RandomSelect(object):
-    """
-    Randomly selects between transforms1 and transforms2,
-    with probability p for transforms1 and (1 - p) for transforms2
-    """
-
-    def __init__(self, transforms1, transforms2, p=0.5):
-        self.transforms1 = transforms1
-        self.transforms2 = transforms2
-        self.p = p
-
-    def __call__(self, img, target):
-        if random.random() < self.p:
-            return self.transforms1(img, target)
-        return self.transforms2(img, target)
-
-
-class ToTensor(object):
-    def __call__(self, img, target):
-        return F.to_tensor(img), target
-
-
-class RandomErasing(object):
-    def __init__(self, *args, **kwargs):
-        self.eraser = T.RandomErasing(*args, **kwargs)
-
-    def __call__(self, img, target):
-        return self.eraser(img), target
-
-
-class Normalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, image, target=None):
-        image = F.normalize(image, mean=self.mean, std=self.std)
-        if target is None:
-            return image, None
-        target = target.copy()
-        h, w = image.shape[-2:]
-        if "boxes" in target:
-            boxes = target["boxes"]
-            boxes = box_xyxy_to_cxcywh(boxes)
-            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
-            target["boxes"] = boxes
-        return image, target
-
-
-class Compose(object):
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, image, target):
-        for t in self.transforms:
-            image, target = t(image, target)
-        return image, target
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + "("
-        for t in self.transforms:
-            format_string += "\n"
-            format_string += "    {0}".format(t)
-        format_string += "\n)"
-        return format_string
diff --git a/spaces/Ashrafb/translate/README.md b/spaces/Ashrafb/translate/README.md
deleted file mode 100644
index ba4d140246a3bfcad817475290793a8054854c2c..0000000000000000000000000000000000000000
--- a/spaces/Ashrafb/translate/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: translate
-emoji: 🌍
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: NooneImportant/translate
----
-
-# [$hyoo_lingua](https://lingua.hyoo.ru/)
\ No newline at end of file
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/wheel_builder.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/wheel_builder.py
deleted file mode 100644
index 60d75dd18effb6e35b216cdfa3e30b8cc5bd620b..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/wheel_builder.py
+++ /dev/null
@@ -1,355 +0,0 @@
-"""Orchestrator for building wheels from InstallRequirements.
-"""
-
-import logging
-import os.path
-import re
-import shutil
-from typing import Iterable, List, Optional, Tuple
-
-from pip._vendor.packaging.utils import canonicalize_name, canonicalize_version
-from pip._vendor.packaging.version import InvalidVersion, Version
-
-from pip._internal.cache import WheelCache
-from pip._internal.exceptions import InvalidWheelFilename, UnsupportedWheel
-from pip._internal.metadata import FilesystemWheel, get_wheel_distribution
-from pip._internal.models.link import Link
-from pip._internal.models.wheel import Wheel
-from pip._internal.operations.build.wheel import build_wheel_pep517
-from pip._internal.operations.build.wheel_editable import build_wheel_editable
-from pip._internal.operations.build.wheel_legacy import build_wheel_legacy
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import ensure_dir, hash_file
-from pip._internal.utils.setuptools_build import make_setuptools_clean_args
-from pip._internal.utils.subprocess import call_subprocess
-from pip._internal.utils.temp_dir import TempDirectory
-from pip._internal.utils.urls import path_to_url
-from pip._internal.vcs import vcs
-
-logger = logging.getLogger(__name__)
-
-_egg_info_re = re.compile(r"([a-z0-9_.]+)-([a-z0-9_.!+-]+)", re.IGNORECASE)
-
-BuildResult = Tuple[List[InstallRequirement], List[InstallRequirement]]
-
-
-def _contains_egg_info(s: str) -> bool:
-    """Determine whether the string looks like an egg_info.
-
-    :param s: The string to parse. E.g. foo-2.1
-    """
-    return bool(_egg_info_re.search(s))
-
-
-def _should_build(
-    req: InstallRequirement,
-    need_wheel: bool,
-) -> bool:
-    """Return whether an InstallRequirement should be built into a wheel."""
-    if req.constraint:
-        # never build requirements that are merely constraints
-        return False
-    if req.is_wheel:
-        if need_wheel:
-            logger.info(
-                "Skipping %s, due to already being wheel.",
-                req.name,
-            )
-        return False
-
-    if need_wheel:
-        # i.e. pip wheel, not pip install
-        return True
-
-    # From this point, this concerns the pip install command only
-    # (need_wheel=False).
-
-    if not req.source_dir:
-        return False
-
-    if req.editable:
-        # we only build PEP 660 editable requirements
-        return req.supports_pyproject_editable()
-
-    return True
-
-
-def should_build_for_wheel_command(
-    req: InstallRequirement,
-) -> bool:
-    return _should_build(req, need_wheel=True)
-
-
-def should_build_for_install_command(
-    req: InstallRequirement,
-) -> bool:
-    return _should_build(req, need_wheel=False)
-
-
-def _should_cache(
-    req: InstallRequirement,
-) -> Optional[bool]:
-    """
-    Return whether a built InstallRequirement can be stored in the persistent
-    wheel cache, assuming the wheel cache is available, and _should_build()
-    has determined a wheel needs to be built.
-    """
-    if req.editable or not req.source_dir:
-        # never cache editable requirements
-        return False
-
-    if req.link and req.link.is_vcs:
-        # VCS checkout. Do not cache
-        # unless it points to an immutable commit hash.
-        assert not req.editable
-        assert req.source_dir
-        vcs_backend = vcs.get_backend_for_scheme(req.link.scheme)
-        assert vcs_backend
-        if vcs_backend.is_immutable_rev_checkout(req.link.url, req.source_dir):
-            return True
-        return False
-
-    assert req.link
-    base, ext = req.link.splitext()
-    if _contains_egg_info(base):
-        return True
-
-    # Otherwise, do not cache.
-    return False
-
-
-def _get_cache_dir(
-    req: InstallRequirement,
-    wheel_cache: WheelCache,
-) -> str:
-    """Return the persistent or temporary cache directory where the built
-    wheel need to be stored.
-    """
-    cache_available = bool(wheel_cache.cache_dir)
-    assert req.link
-    if cache_available and _should_cache(req):
-        cache_dir = wheel_cache.get_path_for_link(req.link)
-    else:
-        cache_dir = wheel_cache.get_ephem_path_for_link(req.link)
-    return cache_dir
-
-
-def _verify_one(req: InstallRequirement, wheel_path: str) -> None:
-    canonical_name = canonicalize_name(req.name or "")
-    w = Wheel(os.path.basename(wheel_path))
-    if canonicalize_name(w.name) != canonical_name:
-        raise InvalidWheelFilename(
-            "Wheel has unexpected file name: expected {!r}, "
-            "got {!r}".format(canonical_name, w.name),
-        )
-    dist = get_wheel_distribution(FilesystemWheel(wheel_path), canonical_name)
-    dist_verstr = str(dist.version)
-    if canonicalize_version(dist_verstr) != canonicalize_version(w.version):
-        raise InvalidWheelFilename(
-            "Wheel has unexpected file name: expected {!r}, "
-            "got {!r}".format(dist_verstr, w.version),
-        )
-    metadata_version_value = dist.metadata_version
-    if metadata_version_value is None:
-        raise UnsupportedWheel("Missing Metadata-Version")
-    try:
-        metadata_version = Version(metadata_version_value)
-    except InvalidVersion:
-        msg = f"Invalid Metadata-Version: {metadata_version_value}"
-        raise UnsupportedWheel(msg)
-    if metadata_version >= Version("1.2") and not isinstance(dist.version, Version):
-        raise UnsupportedWheel(
-            "Metadata 1.2 mandates PEP 440 version, "
-            "but {!r} is not".format(dist_verstr)
-        )
-
-
-def _build_one(
-    req: InstallRequirement,
-    output_dir: str,
-    verify: bool,
-    build_options: List[str],
-    global_options: List[str],
-    editable: bool,
-) -> Optional[str]:
-    """Build one wheel.
-
-    :return: The filename of the built wheel, or None if the build failed.
-    """
-    artifact = "editable" if editable else "wheel"
-    try:
-        ensure_dir(output_dir)
-    except OSError as e:
-        logger.warning(
-            "Building %s for %s failed: %s",
-            artifact,
-            req.name,
-            e,
-        )
-        return None
-
-    # Install build deps into temporary directory (PEP 518)
-    with req.build_env:
-        wheel_path = _build_one_inside_env(
-            req, output_dir, build_options, global_options, editable
-        )
-    if wheel_path and verify:
-        try:
-            _verify_one(req, wheel_path)
-        except (InvalidWheelFilename, UnsupportedWheel) as e:
-            logger.warning("Built %s for %s is invalid: %s", artifact, req.name, e)
-            return None
-    return wheel_path
-
-
-def _build_one_inside_env(
-    req: InstallRequirement,
-    output_dir: str,
-    build_options: List[str],
-    global_options: List[str],
-    editable: bool,
-) -> Optional[str]:
-    with TempDirectory(kind="wheel") as temp_dir:
-        assert req.name
-        if req.use_pep517:
-            assert req.metadata_directory
-            assert req.pep517_backend
-            if global_options:
-                logger.warning(
-                    "Ignoring --global-option when building %s using PEP 517", req.name
-                )
-            if build_options:
-                logger.warning(
-                    "Ignoring --build-option when building %s using PEP 517", req.name
-                )
-            if editable:
-                wheel_path = build_wheel_editable(
-                    name=req.name,
-                    backend=req.pep517_backend,
-                    metadata_directory=req.metadata_directory,
-                    tempd=temp_dir.path,
-                )
-            else:
-                wheel_path = build_wheel_pep517(
-                    name=req.name,
-                    backend=req.pep517_backend,
-                    metadata_directory=req.metadata_directory,
-                    tempd=temp_dir.path,
-                )
-        else:
-            wheel_path = build_wheel_legacy(
-                name=req.name,
-                setup_py_path=req.setup_py_path,
-                source_dir=req.unpacked_source_directory,
-                global_options=global_options,
-                build_options=build_options,
-                tempd=temp_dir.path,
-            )
-
-        if wheel_path is not None:
-            wheel_name = os.path.basename(wheel_path)
-            dest_path = os.path.join(output_dir, wheel_name)
-            try:
-                wheel_hash, length = hash_file(wheel_path)
-                shutil.move(wheel_path, dest_path)
-                logger.info(
-                    "Created wheel for %s: filename=%s size=%d sha256=%s",
-                    req.name,
-                    wheel_name,
-                    length,
-                    wheel_hash.hexdigest(),
-                )
-                logger.info("Stored in directory: %s", output_dir)
-                return dest_path
-            except Exception as e:
-                logger.warning(
-                    "Building wheel for %s failed: %s",
-                    req.name,
-                    e,
-                )
-        # Ignore return, we can't do anything else useful.
-        if not req.use_pep517:
-            _clean_one_legacy(req, global_options)
-        return None
-
-
-def _clean_one_legacy(req: InstallRequirement, global_options: List[str]) -> bool:
-    clean_args = make_setuptools_clean_args(
-        req.setup_py_path,
-        global_options=global_options,
-    )
-
-    logger.info("Running setup.py clean for %s", req.name)
-    try:
-        call_subprocess(
-            clean_args, command_desc="python setup.py clean", cwd=req.source_dir
-        )
-        return True
-    except Exception:
-        logger.error("Failed cleaning build dir for %s", req.name)
-        return False
-
-
-def build(
-    requirements: Iterable[InstallRequirement],
-    wheel_cache: WheelCache,
-    verify: bool,
-    build_options: List[str],
-    global_options: List[str],
-) -> BuildResult:
-    """Build wheels.
-
-    :return: The list of InstallRequirement that succeeded to build and
-        the list of InstallRequirement that failed to build.
-    """
-    if not requirements:
-        return [], []
-
-    # Build the wheels.
-    logger.info(
-        "Building wheels for collected packages: %s",
-        ", ".join(req.name for req in requirements),  # type: ignore
-    )
-
-    with indent_log():
-        build_successes, build_failures = [], []
-        for req in requirements:
-            assert req.name
-            cache_dir = _get_cache_dir(req, wheel_cache)
-            wheel_file = _build_one(
-                req,
-                cache_dir,
-                verify,
-                build_options,
-                global_options,
-                req.editable and req.permit_editable_wheels,
-            )
-            if wheel_file:
-                # Record the download origin in the cache
-                if req.download_info is not None:
-                    # download_info is guaranteed to be set because when we build an
-                    # InstallRequirement it has been through the preparer before, but
-                    # let's be cautious.
-                    wheel_cache.record_download_origin(cache_dir, req.download_info)
-                # Update the link for this.
-                req.link = Link(path_to_url(wheel_file))
-                req.local_file_path = req.link.file_path
-                assert req.link.is_wheel
-                build_successes.append(req)
-            else:
-                build_failures.append(req)
-
-    # notify success/failure
-    if build_successes:
-        logger.info(
-            "Successfully built %s",
-            " ".join([req.name for req in build_successes]),  # type: ignore
-        )
-    if build_failures:
-        logger.info(
-            "Failed to build %s",
-            " ".join([req.name for req in build_failures]),  # type: ignore
-        )
-    # Return a list of requirements that failed to build
-    return build_successes, build_failures
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/__init__.py
deleted file mode 100644
index ddfcf7f72f31658d75c8128de0732fbbf0e12b15..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Wrappers to call pyproject.toml-based build backend hooks.
-"""
-
-from ._impl import (
-    BackendInvalid,
-    BackendUnavailable,
-    BuildBackendHookCaller,
-    HookMissing,
-    UnsupportedOperation,
-    default_subprocess_runner,
-    quiet_subprocess_runner,
-)
-
-__version__ = '1.0.0'
-__all__ = [
-    'BackendUnavailable',
-    'BackendInvalid',
-    'HookMissing',
-    'UnsupportedOperation',
-    'default_subprocess_runner',
-    'quiet_subprocess_runner',
-    'BuildBackendHookCaller',
-]
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/readers.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/readers.py
deleted file mode 100644
index f1190ca452a1ce22ee9a1b304991d475281df8ca..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/readers.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import collections
-import pathlib
-import operator
-
-from . import abc
-
-from ._itertools import unique_everseen
-from ._compat import ZipPath
-
-
-def remove_duplicates(items):
-    return iter(collections.OrderedDict.fromkeys(items))
-
-
-class FileReader(abc.TraversableResources):
-    def __init__(self, loader):
-        self.path = pathlib.Path(loader.path).parent
-
-    def resource_path(self, resource):
-        """
-        Return the file system path to prevent
-        `resources.path()` from creating a temporary
-        copy.
-        """
-        return str(self.path.joinpath(resource))
-
-    def files(self):
-        return self.path
-
-
-class ZipReader(abc.TraversableResources):
-    def __init__(self, loader, module):
-        _, _, name = module.rpartition('.')
-        self.prefix = loader.prefix.replace('\\', '/') + name + '/'
-        self.archive = loader.archive
-
-    def open_resource(self, resource):
-        try:
-            return super().open_resource(resource)
-        except KeyError as exc:
-            raise FileNotFoundError(exc.args[0])
-
-    def is_resource(self, path):
-        # workaround for `zipfile.Path.is_file` returning true
-        # for non-existent paths.
-        target = self.files().joinpath(path)
-        return target.is_file() and target.exists()
-
-    def files(self):
-        return ZipPath(self.archive, self.prefix)
-
-
-class MultiplexedPath(abc.Traversable):
-    """
-    Given a series of Traversable objects, implement a merged
-    version of the interface across all objects. Useful for
-    namespace packages which may be multihomed at a single
-    name.
-    """
-
-    def __init__(self, *paths):
-        self._paths = list(map(pathlib.Path, remove_duplicates(paths)))
-        if not self._paths:
-            message = 'MultiplexedPath must contain at least one path'
-            raise FileNotFoundError(message)
-        if not all(path.is_dir() for path in self._paths):
-            raise NotADirectoryError('MultiplexedPath only supports directories')
-
-    def iterdir(self):
-        files = (file for path in self._paths for file in path.iterdir())
-        return unique_everseen(files, key=operator.attrgetter('name'))
-
-    def read_bytes(self):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    def read_text(self, *args, **kwargs):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    def is_dir(self):
-        return True
-
-    def is_file(self):
-        return False
-
-    def joinpath(self, child):
-        # first try to find child in current paths
-        for file in self.iterdir():
-            if file.name == child:
-                return file
-        # if it does not exist, construct it with the first path
-        return self._paths[0] / child
-
-    __truediv__ = joinpath
-
-    def open(self, *args, **kwargs):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    @property
-    def name(self):
-        return self._paths[0].name
-
-    def __repr__(self):
-        paths = ', '.join(f"'{path}'" for path in self._paths)
-        return f'MultiplexedPath({paths})'
-
-
-class NamespaceReader(abc.TraversableResources):
-    def __init__(self, namespace_path):
-        if 'NamespacePath' not in str(namespace_path):
-            raise ValueError('Invalid path')
-        self.path = MultiplexedPath(*list(namespace_path))
-
-    def resource_path(self, resource):
-        """
-        Return the file system path to prevent
-        `resources.path()` from creating a temporary
-        copy.
-        """
-        return str(self.path.joinpath(resource))
-
-    def files(self):
-        return self.path
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py
deleted file mode 100644
index 731320e74ebed4d8ceec58c07cb906542b8b021b..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-
-train.max_iter *= 2  # 100ep -> 200ep
-
-lr_multiplier.scheduler.milestones = [
-    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md
deleted file mode 100644
index 4e6c82ef3f75a73c7006f33d7c850a0d4781a58f..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-These are quick configs for performance or accuracy regression tracking purposes.
-
-* `*instance_test.yaml`: can train on 2 GPUs. They are used to test whether the training can
-  successfully finish. They are not expected to produce reasonable training results.
-* `*inference_acc_test.yaml`: They should be run using `--eval-only`. They run inference using pre-trained models and verify
-  the results are as expected.
-* `*training_acc_test.yaml`: They should be trained on 8 GPUs. They finish in about an hour and verify the training accuracy
-  is within the normal range.
diff --git a/spaces/BAAI/SegGPT/app.py b/spaces/BAAI/SegGPT/app.py
deleted file mode 100644
index 429e5d38fcbf8f44c09ab6d17a21fa26e915295d..0000000000000000000000000000000000000000
--- a/spaces/BAAI/SegGPT/app.py
+++ /dev/null
@@ -1,211 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import sys
-import io
-import requests
-import json
-import base64
-from PIL import Image
-import numpy as np
-import gradio as gr
-
-
-def inference_mask1_sam(prompt,
-              img,
-              img_):
-
-    files = {
-        "useSam" : 1,
-        "pimage" : resizeImg(prompt["image"]),
-        "pmask" : resizeImg(prompt["mask"]),
-        "img" : resizeImg(img),
-        "img_" : resizeImg(img_)
-    }
-    r = requests.post("http://120.92.79.209/painter/run", json = files)
-    a = json.loads(r.text)
-
-    res = []
-
-    for i in range(len(a)):
-        #out = Image.open(io.BytesIO(base64.b64decode(a[i])))
-        #out = out.resize((224, 224))
-        #res.append(np.uint8(np.array(out)))
-        res.append(np.uint8(np.array(Image.open(io.BytesIO(base64.b64decode(a[i]))))))
-    return res[1:] # remove prompt image
-
-def inference_mask1(prompt,
-              img,
-              img_):
-    files = {
-        "pimage" : resizeImg(prompt["image"]),
-        "pmask" : resizeImg(prompt["mask"]),
-        "img" : resizeImg(img),
-        "img_" : resizeImg(img_)
-    }
-    #r = requests.post("https://flagstudio.baai.ac.cn/painter/run", json = files)
-    r = requests.post("http://120.92.79.209/painter/run", json = files)
-    a = json.loads(r.text)
-    res = []
-    for i in range(len(a)):
-        #out = Image.open(io.BytesIO(base64.b64decode(a[i])))
-        #out = out.resize((224, 224))
-        #res.append(np.uint8(np.array(out)))
-        res.append(np.uint8(np.array(Image.open(io.BytesIO(base64.b64decode(a[i]))))))
-    return res
-
-
-
-def inference_mask_video(
-              prompt,
-              vid,
-              request: gr.Request,
-              ):
-
-
-    files = {
-        "pimage" : resizeImgIo(prompt["image"]),
-        "pmask" : resizeImgIo(prompt["mask"]),
-        "video" : open(vid, 'rb'),
-    }
-    r = requests.post("http://120.92.79.209/painter/runVideo", files = files)
-    '''
-    path = str(uuid.uuid4()) + "." + str(time.time())
-    fName = 'out.mp4'
-    file_out = "video/" + path + "." + fName
-    with open(file_out,"wb") as f:
-        f.write(r.content)
-    '''
-    a = json.loads(r.text)
-    return [np.uint8(np.array(Image.open(io.BytesIO(base64.b64decode(a["mask"]))))), a["url"]]
-
-
-def resizeImg(img):
-    res, hres = 448, 448
-    img = Image.fromarray(img).convert("RGB")
-    img = img.resize((res, hres))
-    temp = io.BytesIO()
-    img.save(temp, format="WEBP")
-    return base64.b64encode(temp.getvalue()).decode('ascii')
-
-def resizeImgIo(img):
-    res, hres = 448, 448
-    img = Image.fromarray(img).convert("RGB")
-    img = img.resize((res, hres))
-    temp = io.BytesIO()
-    img.save(temp, format="WEBP")
-    return io.BytesIO(temp.getvalue())
-
-
-# define app features and run
-
-examples = [
-            ['./images/hmbb_1.jpg', './images/hmbb_2.jpg', './images/hmbb_3.jpg'],
-            ['./images/rainbow_1.jpg', './images/rainbow_2.jpg', './images/rainbow_3.jpg'],
-            ['./images/earth_1.jpg', './images/earth_2.jpg', './images/earth_3.jpg'],
-            ['./images/obj_1.jpg', './images/obj_2.jpg', './images/obj_3.jpg'],
-            ['./images/ydt_2.jpg', './images/ydt_1.jpg', './images/ydt_3.jpg'],
-           ]
-
-examples_sam = [
-            ['./images/nc_1.jpg', './images/nc_2.jpg', './images/nc_3.jpg'],
-            ['./images/street_1.jpg', './images/street_2.jpg', './images/street_3.jpg'],
-            ['./images/hmbb_1.jpg', './images/hmbb_2.jpg', './images/hmbb_3.jpg'],
-            ['./images/earth_1.jpg', './images/earth_2.jpg', './images/earth_3.jpg'],
-            ['./images/ydt_2.jpg', './images/ydt_1.jpg', './images/ydt_3.jpg'],
-           ]
-
-examples_video = [
-            ['./videos/horse-running.jpg', './videos/horse-running.mp4'],
-            ['./videos/a_man_is_surfing_3_30.jpg', './videos/a_man_is_surfing_3_30.mp4'],
-    ['./videos/a_car_is_moving_on_the_road_40.jpg', './videos/a_car_is_moving_on_the_road_40.mp4'],
-['./videos/jeep-moving.jpg', './videos/jeep-moving.mp4'],
-['./videos/child-riding_lego.jpg', './videos/child-riding_lego.mp4'],
-]
-
-
-
-demo_mask = gr.Interface(fn=inference_mask1, 
-                   inputs=[gr.ImageMask(brush_radius=8, label="prompt (提示图)"), gr.Image(label="img1 (测试图1)"), gr.Image(label="img2 (测试图2)")], 
-                    #outputs=[gr.Image(shape=(448, 448), label="output1 (输出图1)"), gr.Image(shape=(448, 448), label="output2 (输出图2)")],
-                    outputs=[gr.Image(label="output1 (输出图1)").style(height=256, width=256), gr.Image(label="output2 (输出图2)").style(height=256, width=256)],
-                    #outputs=gr.Gallery(label="outputs (输出图)"),
-                    examples=examples,
-                    #title="SegGPT for Any Segmentation<br>(Painter Inside)",
-                    description="<p> \
-                    Choose an example below &#128293; &#128293;  &#128293; <br>\
-                    Or, upload by yourself: <br>\
-                    1. Upload images to be tested to 'img1' and/or 'img2'. <br>2. Upload a prompt image to 'prompt' and draw a mask.  <br>\
-                            <br> \
-                            💎 The more accurate you annotate, the more accurate the model predicts. <br>\
-                            💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\
-                            💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. \
-</p>",
-                   cache_examples=False,
-                   allow_flagging="never",
-                   )
-
-
-
-demo_mask_sam = gr.Interface(fn=inference_mask1_sam,
-                   inputs=[gr.ImageMask(brush_radius=4, label="prompt (提示图)"), gr.Image(label="img1 (测试图1)"), gr.Image(label="img2 (测试图2)")],
-                    #outputs=[gr.Image(shape=(448, 448), label="output1 (输出图1)"), gr.Image(shape=(448, 448), label="output2 (输出图2)")],
-                    # outputs=[gr.Image(label="output1 (输出图1)").style(height=256, width=256), gr.Image(label="output2 (输出图2)").style(height=256, width=256)],
-                    #outputs=gr.Gallery(label="outputs (输出图)"),
-                    outputs=[gr.Image(label="SAM output (mask)").style(height=256, width=256),gr.Image(label="output1 (输出图1)").style(height=256, width=256), gr.Image(label="output2 (输出图2)").style(height=256, width=256)],
-                    # outputs=[gr.Image(label="output3 (输出图1)").style(height=256, width=256), gr.Image(label="output4 (输出图2)").style(height=256, width=256)],
-                    examples=examples_sam,
-                    #title="SegGPT for Any Segmentation<br>(Painter Inside)",
-                    description="<p> \
-                    <strong>SAM+SegGPT: One touch for segmentation in all images or videos.</strong> <br>\
-                    Choose an example below &#128293; &#128293;  &#128293; <br>\
-                    Or, upload by yourself: <br>\
-                    1. Upload images to be tested to 'img1' and 'img2'. <br>2. Upload a prompt image to 'prompt' and draw <strong>a point or line on the target</strong>.  <br>\
-                            <br> \
-                            💎 SAM segments the target with any point or scribble, then SegGPT segments all other images. <br>\
-                            💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\
-                            💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. \
-</p>",
-                   cache_examples=False,
-                   allow_flagging="never",
-                   )
-
-demo_mask_video = gr.Interface(fn=inference_mask_video,
-                   inputs=[gr.ImageMask(label="prompt (提示图)"), gr.Video(label="video (测试视频)").style(height=448, width=448)],
-                    outputs=[gr.Image(label="SAM output (mask)").style(height=256, width=256), gr.Video().style(height=448, width=448)],
-                    examples=examples_video,
-                    description="<p> \
-                    <strong>SegGPT+SAM: One touch for any segmentation in a video.</strong> <br>\
-                    Choose an example below &#128293; &#128293;  &#128293; <br>\
-                    Or, upload by yourself: <br>\
-                    1. Upload a video to be tested to 'video'. If failed, please check the codec, we recommend h.264 by default. <br>2. Upload a prompt image to 'prompt' and draw <strong>a point or line on the target</strong>.  <br>\
-<br> \
-💎 SAM segments the target with any point or scribble, then SegGPT segments the whole video. <br>\
-💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\
-💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. <br> \
-                Note: we only take the first 16 frames for the demo.    \
-</p>",
-                   cache_examples=False,
-                   allow_flagging="never",
-                   )
-
-
-
-
-title = "SegGPT: Segmenting Everything In Context<br> \
-<div align='center'> \
-<h2><a href='https://arxiv.org/abs/2304.03284' target='_blank' rel='noopener'>[paper]</a> \
-<a href='https://github.com/baaivision/Painter' target='_blank' rel='noopener'>[code]</a></h2> \
-<br> \
-<image src='file/rainbow2.gif' width='720px' /> \
-<h2>SegGPT performs arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text, with only one single model.</h2> \
-</div> \
-"
-
-demo = gr.TabbedInterface([demo_mask_sam, demo_mask_video, demo_mask], ['SAM+SegGPT (一触百通)', '🎬Anything in a Video', 'General 1-shot'], title=title)
-
-#demo.launch(share=True, auth=("baai", "vision"))
-demo.launch(enable_queue=False)
-#demo.launch(server_name="0.0.0.0", server_port=34311)
-# -
-
-
diff --git a/spaces/Benson/text-generation/Examples/Asfalto 9 Leyendas Descargar Sin Tienda Microsoft.md b/spaces/Benson/text-generation/Examples/Asfalto 9 Leyendas Descargar Sin Tienda Microsoft.md
deleted file mode 100644
index aea18274f6ea6fcba7aa8650c8249d4086a38561..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto 9 Leyendas Descargar Sin Tienda Microsoft.md	
+++ /dev/null
@@ -1,47 +0,0 @@
-
-<h1>Cómo descargar OBB ARK Survival Evolved</h1>
-<p>ARK Survival Evolved es uno de los juegos de supervivencia de acción y aventura más populares e inmersivos del mercado. Te permite explorar, crear, domesticar y luchar en un vasto mundo abierto lleno de dinosaurios y otras criaturas. Sin embargo, si quieres disfrutar de la experiencia completa de este juego en tu dispositivo Android, tendrás que descargar e instalar los archivos OBB junto con el archivo APK. En este artículo, explicaremos qué son los archivos OBB, por qué los necesita y cómo instalarlos en su dispositivo Android. </p>
- <h2>¿Qué es ARK Survival Evolved? </h2>
-<h3>Características y jugabilidad</h3>
-<p>ARK Survival Evolved es un juego que combina conceptos prehistóricos y modernos para que los jugadores puedan sobrevivir y explorar una tierra sin fin. Puedes elegir jugar solo o con otros jugadores en línea, y usar tu astucia para matar o domar a las criaturas primitivas que vagan por la tierra. También puedes construir refugios, crear objetos, cultivar cultivos, investigar tecnologías y personalizar tu personaje. El juego cuenta con un dinámico ciclo día-noche, sistema meteorológico y física realista. También puedes experimentar diferentes mapas y modos, como Ragnarok, Valguero, Genesis y más. </p>
-<h2>asfalto 9 leyendas descargar sin tienda microsoft</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://bltlly.com/2v6Jal">https://bltlly.com/2v6Jal</a></b></p><br /><br />
- <h3>Descargar opciones y requisitos</h3>
-<p>ARK Survival Evolved está disponible para su descarga en varias plataformas, como PC, Xbox One, PlayStation 4, Nintendo Switch, iOS y Android. Sin embargo, el juego no es gratis y tendrás que comprarlo en la tienda oficial o en el sitio web de tu plataforma. Para dispositivos Android, puedes comprar el juego en Google Play Store por $19.99. El juego requiere Android 7.0 o superior, y al menos 2 GB de RAM y 2 GB de espacio de almacenamiento. También necesitará 2 GB adicionales de espacio de almacenamiento para los archivos OBB. </p>
- <h2>¿Qué son los archivos OBB y por qué los necesita? </h2>
-<h3>La diferencia entre archivos APK y OBB</h3>
-
- <h3>Los beneficios de los archivos OBB para ARK Survival Evolved</h3>
-<p>Como puedes imaginar, ARK Survival Evolved es un juego que tiene muchos datos que no se pueden comprimir en un solo archivo APK. El juego tiene gráficos de alta calidad, efectos de sonido, música, animaciones y más que mejoran la experiencia de juego. Por lo tanto, tendrá que descargar los archivos OBB junto con el archivo APK para disfrutar de todas las características del juego. Los archivos OBB también le permitirá actualizar el juego sin tener que descargar todo el archivo APK de nuevo. </p>
- <h2>Cómo instalar archivos OBB en dispositivos Android</h2>
-<h3>Paso 1: Permitir fuentes desconocidas</h3>
-<p>Antes de que pueda instalar cualquier archivo APK u OBB en su dispositivo Android, debe habilitar la opción para permitir fuentes desconocidas. Esta opción le permite instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. También es posible que necesite conceder permiso a aplicaciones específicas que utiliza para descargar o instalar archivos APK u OBB. </p>
- <h3>Paso <h3>Paso 2: Descargue los archivos OBB de una fuente confiable</h3>
-<p>Una vez que haya habilitado la opción de fuentes desconocidas, puede proceder a descargar los archivos OBB para ARK Survival Evolved. Puede encontrar muchos sitios web que ofrecen los archivos OBB de forma gratuita, pero debe tener cuidado y elegir una fuente confiable y segura. Algunos sitios web pueden contener malware, virus o archivos falsos que pueden dañar su dispositivo o comprometer sus datos. Por lo tanto, le recomendamos que utilice un sitio web confiable y verificado, como [APKPure] o [APKMody]. Estos sitios web proporcionan los archivos OBB más recientes y originales para ARK Survival Evolved, así como otros juegos y aplicaciones populares. </p>
- <h3>Paso 3: Extraiga y copie la carpeta OBB a la ruta de destino</h3>
-
-<ul>
-<li>Abra la aplicación de administrador de archivos y busque los archivos OBB descargados. </li>
-<li>Seleccione los archivos OBB y toque en Extraer.</li>
-<li>Elija una carpeta de destino donde desea extraer los archivos. Puede crear una nueva carpeta o usar una existente. </li>
-<li>Espere a que termine el proceso de extracción. </li>
-<li>Después de la extracción, verá una carpeta llamada com.studiowildcard.wardrumstudios.ark. Esta es la carpeta OBB para ARK Survival Evolved.</li>
-<li>Copie esta carpeta y péguela a la siguiente ruta: Almacenamiento interno > Android > obb. Si no ve la carpeta obb, puede crear una. </li>
-</ul>
- <h3>Paso 4: Iniciar el juego y disfrutar de</h3>
-<p>Ahora que ha instalado los archivos OBB, puede iniciar el juego y disfrutarlo en su dispositivo Android. Para ello, simplemente toca el icono de ARK Survival Evolved en la pantalla de inicio o en el cajón de aplicaciones. El juego verificará los archivos OBB y cargará los datos. Es posible que necesites conceder algunos permisos al juego, como acceso al almacenamiento, acceso a la ubicación, etc. Una vez que el juego esté cargado, puedes comenzar a jugar y explorar el mundo de ARK Survival Evolved.</p>
- <h2>Conclusión</h2>
-<p>En este artículo, le hemos mostrado cómo descargar e instalar los archivos OBB para ARK Survival Evolved en su dispositivo Android. Siguiendo estos sencillos pasos, podrás disfrutar de todas las características y gráficos de este increíble juego. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
-<h4> ¿Cuál es el tamaño de los archivos OBB para ARK Survival Evolved? </h4>
-<p>El tamaño de los archivos OBB para ARK Survival Evolved puede variar dependiendo de la versión y actualización del juego. Sin embargo, a partir de junio de 2023, la última versión del juego (v2.0.25) tiene un tamaño de archivo OBB de aproximadamente 2 GB.</p>
- <h4>¿Puedo jugar ARK Survival Evolved sin conexión? </h4>
-
- <h4>¿Puedo transferir mi progreso de un dispositivo a otro? </h4>
-<p>Sí, puede transferir su progreso de un dispositivo a otro mediante el uso de su cuenta de Google Play. Para ello, debe iniciar sesión con su cuenta de Google Play en ambos dispositivos y habilitar el almacenamiento en la nube en la configuración del juego. Luego, puedes sincronizar tu progreso entre dispositivos. </p>
-<p></p>
- <h4>¿Cómo puedo actualizar ARK Survival Evolved? </h4>
-<p>Para actualizar ARK Survival Evolved, necesita descargar e instalar los últimos archivos APK y OBB de una fuente confiable. También puedes buscar actualizaciones desde la configuración del juego o desde la Google Play Store.</p>
- <h4>¿Cómo puedo obtener más recursos y objetos en ARK Survival Evolved? </h4>
-<p>Puede obtener más recursos y artículos en ARK Survival Evolved explorando, cosechando, elaborando, comerciando o comprándolos con dinero real. También puedes usar trucos o mods para obtener recursos y objetos ilimitados, pero esto puede afectar tu experiencia de juego o causar errores. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Xender 2019.md b/spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Xender 2019.md
deleted file mode 100644
index a6b65c7610a76776dbcca1683b31f257eb6cf346..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Xender 2019.md	
+++ /dev/null
@@ -1,181 +0,0 @@
-<br />
-<h1>SharePoint Download 2016: Cómo comenzar con la plataforma de colaboración de Microsoft</h1>
- <p>Si está buscando una manera de organizar, almacenar, compartir y acceder a la información en su organización, es posible que desee considerar SharePoint. SharePoint es una plataforma basada en la web que se integra con Microsoft Office y le permite crear sitios web, bibliotecas de documentos, listas, flujos de trabajo y más. En este artículo, explicaremos qué es SharePoint, por qué podría necesitarlo, cómo descargar SharePoint Server 2016 y cómo usarlo de manera efectiva. </p>
- <h2>¿Qué es SharePoint y por qué lo necesita? </h2>
- <p>SharePoint es un producto de software que le permite crear y administrar sitios web para diversos fines, como intranets, extranets, sitios de equipos, sitios de proyectos, sistemas de gestión de documentos, bases de conocimiento, etc. SharePoint también proporciona herramientas para la colaboración, comunicación, búsqueda, inteligencia empresarial, redes sociales y automatización del flujo de trabajo. Con SharePoint, puede:</p>
-<h2>descarga de la aplicación xender 2019</h2><br /><p><b><b>Download</b> &#10001; &#10001; &#10001; <a href="https://bltlly.com/2v6JwA">https://bltlly.com/2v6JwA</a></b></p><br /><br />
- <ul>
-<li>Almacenar y organizar documentos, archivos, imágenes, vídeos y otros tipos de contenido en ubicaciones centralizadas. </li>
-<li>Acceder y editar contenido desde cualquier dispositivo, en cualquier lugar, en cualquier momento. </li>
-<li>Compartir contenido con usuarios internos y externos, como colegas, clientes, socios, proveedores, etc.</li>
-<li>Trabajar juntos en documentos y proyectos en tiempo real o asíncronamente. </li>
-<li>Administrar permisos y configuraciones de seguridad para contenido y usuarios. </li>
-<li>Encuentra información rápida y fácilmente usando motores de búsqueda y filtros. </li>
-<li>Analizar datos y generar informes utilizando tablas, gráficos, paneles, etc.</li>
-<li>Automatice los procesos de negocio y los flujos de trabajo utilizando formularios, alertas, notificaciones, etc.</li>
-<li>Personaliza la apariencia de tus sitios web usando temas, plantillas, partes web, etc.</li>
-</ul>
- <h3>Características y beneficios de SharePoint</h3>
- <p>Algunas de las características y beneficios clave de SharePoint son:</p>
- <ul>
-
-<li><strong>Flexibilidad:</strong> SharePoint se puede personalizar y configurar para adaptarse a diferentes necesidades y preferencias. </li>
-<li><strong>Integración:</strong> SharePoint puede integrarse con otros productos de Microsoft, como Office 365, Outlook, OneDrive, Teams, Power BI, etc., así como aplicaciones y servicios de terceros. </li>
-<li><strong>Fiabilidad:</strong> SharePoint puede proporcionar alta disponibilidad y rendimiento con opciones de copia de seguridad y recuperación. </li>
-<li><strong>Seguridad:</strong> SharePoint puede proteger sus datos y usuarios con cifrado, autenticación, autorización, auditoría, etc.</li>
-</ul>
- <h3>ediciones y versiones de SharePoint</h3>
- <p>Hay dos ediciones principales de SharePoint: SharePoint Online y SharePoint Server. SharePoint Online es un servicio basado en la nube que forma parte de la suite de Office 365. Puede suscribirse a SharePoint Online como un servicio independiente o como parte de un paquete con otras aplicaciones de Office 365. SharePoint Online ofrece actualizaciones automáticas, mantenimiento y soporte de Microsoft. Sin embargo, también tiene algunas limitaciones en términos de personalización y control. </p>
- <p>SharePoint Server es una solución local que puede instalar en sus propios servidores o en un entorno alojado. Puede comprar SharePoint Server como un producto independiente o como parte de un paquete con otros productos de Microsoft. SharePoint Server ofrece más flexibilidad y control sobre su entorno. Sin embargo, también requiere más recursos y experiencia para instalar, configurar y mantener. <p>Además de las ediciones, también hay diferentes versiones de SharePoint. La última versión es SharePoint Server 2019, que se lanzó en octubre de 2018. Sin embargo, si no está listo para actualizar a la última versión, todavía puede usar las versiones anteriores, como SharePoint Server 2016, que se lanzó en marzo de 2016. SharePoint Server 2016 es el foco de este artículo, y le mostraremos cómo descargarlo y usarlo en las siguientes secciones. </p>
-
- <p>Si desea descargar SharePoint Server 2016, deberá seguir estos pasos:</p>
- <h3>Requisitos y requisitos previos del sistema</h3>
- <p>Antes de descargar SharePoint Server 2016, tendrá que asegurarse de que su sistema cumple con los requisitos mínimos y que ha instalado los requisitos previos necesarios. Los requisitos del sistema para SharePoint Server 2016 son:</p>
- <ul>
-<li><strong>Sistema operativo:</strong> Windows Server 2012 R2 o Windows Server 2016</li>
-<li><strong>Procesador:</strong> 64 bits, 4 núcleos mínimo</li>
-<li><strong>Memoria:</strong> 16 GB mínimo</li>
-<li><strong>Espacio en el disco duro:</strong> 80 GB mínimo</li>
-<li><strong>Servidor de base de datos:</strong> Microsoft SQL Server 2014 Service Pack 1 o Microsoft SQL Server 2016</li>
-</ul>
- <p>Los requisitos previos para SharePoint Server 2016 son:</p>
-<p></p>
- <ul>
-<li><strong>. NET Framework 4.5.2 o superior</strong></li>
-<li><strong>Marco de administración de Windows 4.0 o superior</strong></li>
-<li><strong>Fundación de identidad de Windows (WIF) 1.0</strong></li>
-<li><strong>Microsoft Sync Framework Tiempo de ejecución v1.0 SP1 (x64)</strong></li>
-<li><strong>Windows Server AppFabric 1.1</strong></li>
-<li><strong>Paquete de actualización acumulativo 7 para Microsoft AppFabric 1.1 para Windows Server (KB3092423)</strong></li>
-<li><strong>Cliente de servicios de calidad de datos (DQSInstaller.exe)</strong></li>
-<li><strong>Microsoft ODBC Driver 11 para SQL Server</strong></li>
-<li><strong>Servicios de datos de Microsoft WCF 5.6</strong></li>
-<li><strong>Microsoft Information Protection and Control Client (MSIPC)</strong></li>
-<li><strong>Cliente nativo de Microsoft SQL Server 2012 (SQLNCI.msi)</strong></li>
-<li><strong>Herramientas del cliente de minería de datos de Microsoft SQL Server 2012 (SQLSERVER2012_ASADOMD10.msi)</strong></li>
-<li><strong>Servicios de análisis de Microsoft SQL Server 2012 ADOMD.NET (SQLSERVER2012_ADOMD10.msi)</strong></li>
-
-</ul>
- <p>Puede descargar e instalar los prerrequisitos utilizando la herramienta Pre-requisito del instalador que viene con el medio de instalación de SharePoint Server. Alternativamente, puede descargarlos e instalarlos manualmente desde el Centro de descargas de Microsoft.</p>
- <h3>Descargar opciones y claves del producto</h3>
- <p>Para descargar SharePoint Server 2016, tiene dos opciones: puede descargar una versión de prueba o una versión con licencia. La versión de prueba es gratuita y válida durante 180 días. La versión con licencia requiere una compra y una clave de producto. </p>
- <p>Puede descargar la versión de prueba de SharePoint Server 2016 desde el Microsoft Evaluation Center. Deberá iniciar sesión con su cuenta de Microsoft y proporcionar información básica, como su nombre, dirección de correo electrónico, país, etc. También deberá elegir el idioma y el formato de archivo (ISO o IMG) de la descarga. El tamaño del archivo es de aproximadamente 3 GB.</p>
- <p>Puede descargar la versión con licencia de SharePoint Server 2016 desde el Microsoft Volume Licensing Service Center. Deberá iniciar sesión con su cuenta de trabajo y tener un acuerdo de licencia válido con Microsoft. También tendrá que introducir su clave de producto para activar el software. La clave del producto es un código de 25 caracteres que se ve así: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX.</p>
- <h3>Pasos y consejos de instalación</h3>
- <p>Para instalar SharePoint Server 2016, deberá seguir estos pasos:</p>
- <ol>
-<li><strong>Grabar el archivo ISO o IMG en un DVD o montarlo como una unidad virtual. </strong></li>
-<li><strong>Ejecute el archivo setup.exe desde el medio de instalación. </strong></li>
-<li><strong>En la página Introduzca su clave de producto, ingrese su clave de producto si tiene una o déjela en blanco si está usando la versión de prueba. </strong></li>
-<li><strong>En la página Lea los términos de licencia de Microsoft Software, lea los términos y seleccione la casilla Acepto los términos de este acuerdo. </strong></li>
-
-<li><strong>En la pestaña Tipo de servidor, seleccione el tipo de servidor que desea instalar: Completo o Independiente. Complete significa que usará un servidor de base de datos independiente para SharePoint. Independiente significa que usará el servidor de base de datos incorporado en la misma máquina que SharePoint.</strong></li>
-<li><strong>En la pestaña Instalar requisitos previos de software, haga clic en Instalar requisitos previos para instalar los componentes necesarios para SharePoint Server 2016. Es posible que necesite reiniciar el equipo después de la instalación. </strong></li>
-<li><strong>En la pestaña Instalar servidor SharePoint, haga clic en Instalar servidor SharePoint para iniciar el proceso de instalación. Es posible que tenga que volver a introducir su clave de producto si está utilizando la versión de prueba. </strong></li>
-<li><strong>En la página Ejecutar asistente de configuración, seleccione la casilla Ejecutar el asistente de configuración de productos de SharePoint ahora y haga clic en Cerrar.</strong></li>
-<li><strong>En la página Productos de bienvenida a SharePoint, haga clic en Siguiente.</strong></li>
-<li><strong>En la página Conectar a una granja de servidores, seleccione si desea crear una nueva granja de servidores o unirse a una ya existente. Si está creando una nueva granja de servidores, deberá especificar el nombre del servidor de la base de datos, el nombre de la base de datos, la cuenta de acceso a la base de datos y la frase de contraseña. Si se une a una granja de servidores existente, necesitará especificar el nombre del servidor de base de datos, el nombre de la base de datos y la frase de contraseña. </strong></li>
-<li><strong>En la página Especificar rol de servidor, seleccione el rol de su servidor en la granja de servidores: Granja de un solo servidor, Interfaz web, Aplicación, Caché distribuida, Búsqueda, Personalizado o Carga especial. El rol determina los servicios y características que se habilitarán en el servidor. </strong></li>
-<li><strong>En la página Especificar configuración de la base de datos, introduzca el número de puerto y el proveedor de autenticación para su base de datos de configuración. Puede usar la configuración predeterminada o cambiarla según sus preferencias. </strong></li>
-
-<li><strong>En la página Configure SharePoint Central Administration Web Application, especifique si desea crear una nueva aplicación web para la Administración Central o usar una existente. Si está creando una nueva aplicación web, deberá especificar el número de puerto y el proveedor de autenticación para ella. También puede elegir si desea usar Secure Sockets Layer (SSL) para el cifrado. </strong></li>
-<li><strong>En la página Cómo completar el asistente de configuración de productos de SharePoint, revise sus ajustes y haga clic en Siguiente para aplicarlos. El proceso de configuración puede tardar varios minutos. </strong></li>
-<li><strong>En la página Configuración exitosa, haga clic en Finalizar para completar la instalación y configuración de SharePoint Server 2016. </strong></li>
-</ol>
- <p>Algunos consejos para instalar SharePoint Server 2016 son:</p>
- <ul>
-<li><strong>Asegúrese de que tiene suficiente espacio en disco y memoria para la instalación y operación de SharePoint Server 2016. </strong></li>
-<li><strong>Utilice una cuenta de dominio con privilegios de administrador local para la instalación y configuración de SharePoint Server 2016. </strong></li>
-<li><strong>Desactivar el software antivirus y la configuración del firewall que pueden interferir con la instalación y configuración de SharePoint Server 2016. </strong></li>
-<li><strong>Siga las mejores prácticas y recomendaciones de Microsoft para instalar y configurar SharePoint Server 2016. </strong></li>
-</ul>
- <h2>Cómo usar SharePoint Server 2016</h2>
- <p>Después de haber instalado y configurado SharePoint Server 2016, puede comenzar a usarlo para crear y administrar sitios web y contenido. Estas son algunas de las tareas básicas que puede realizar con SharePoint Server 2016:</p>
- <h3>Crear y gestionar sitios y subsitios</h3>
-
- <ol>
-<li><strong>Vaya al sitio donde desea crear un nuevo sitio o subsitio. </strong></li>
-<li><strong>Haga clic en Configuración > Contenido del sitio > Nuevo > Subsite.</strong></li>
-<li><strong>En la página Nuevo sitio SharePoint, ingrese un título, descripción, URL e idioma para su sitio o subsitio. </strong></li>
-<li><strong>Seleccione una plantilla para su sitio o subsitio de las opciones disponibles. Puedes elegir entre diferentes categorías, como Team Site, Project Site, Blog, Wiki, etc.</strong></li>
-<li><strong>Especifique los permisos para su sitio o subsitio. Puede optar por heredar los permisos del sitio padre o usar permisos únicos. También puede agregar o quitar usuarios y grupos y asignarles diferentes roles, como Propietarios, Miembros, Visitantes, etc.</strong></li>
-<li><strong>Especifique las opciones de navegación para su sitio o subsitio. Puede elegir mostrar el sitio o subsitio en la barra de enlaces superior y el inicio rápido del sitio padre o no. También puede cambiar el orden y la apariencia de los enlaces. </strong></li>
-<li><strong>Haga clic en Crear para crear su sitio o subsitio. </strong></li>
-</ol>
- <p>Para administrar un sitio o un subsitio, deberá seguir estos pasos:</p>
- <ol>
-<li><strong>Navegue hasta el sitio o subsitio que desea administrar. </strong></li>
-<li><strong>Haga clic en Configuración > Configuración del sitio.</strong></li>
-<li><strong>En la página Configuración del sitio, puede acceder a varias opciones para administrar su sitio o subsitio, como:</strong></li>
-<ul>
-<li><strong>Mira y siente:</strong> Puedes cambiar el título, la descripción, el logotipo, el tema, la página maestra, la navegación, etc. de tu sitio o subsitio. </li>
-<li><strong>Usuarios y Permisos:</strong> Puede administrar los usuarios y grupos que tienen acceso a su sitio o subsitio y sus roles y permisos. </li>
-<li><strong>Galerías de diseñadores web:</strong> Puede administrar las partes web, columnas de sitios, tipos de contenido, etc. que están disponibles para su sitio o subsitio. </li>
-
-<li><strong>Administración de colecciones de sitios:</strong> Puede administrar las funciones de recopilación de sitios, papelera de reciclaje, informes de registros de auditoría, etc. de su colección de sitios. </li>
-</ul>
-</ol>
- <h3>Trabajar con listas, bibliotecas y documentos</h3>
- <p>Una lista es una colección de datos que puede compartir con otros. Una biblioteca es un tipo de lista que almacena archivos, como documentos, imágenes, vídeos, etc. Puede crear listas y bibliotecas utilizando plantillas que proporcionan columnas y vistas predefinidas. También puede trabajar con listas y bibliotecas mediante acciones que le permiten agregar, editar, eliminar, ordenar, filtrar y buscar elementos en sus listas y bibliotecas. También puede administrar listas y bibliotecas usando configuraciones que controlan su apariencia, comportamiento y permisos. </p>
- <p>Para crear una lista o una biblioteca, deberá seguir estos pasos:</p>
- <ol>
-<li><strong>Vaya al sitio o al subsitio donde desea crear una nueva lista o biblioteca. </strong></li>
-<li><strong>Haga clic en Configuración > Contenido del sitio > Nuevo > Lista o Biblioteca.</strong></li>
-<li><strong>En la página Crear, seleccione una plantilla para su lista o biblioteca de las opciones disponibles. Puede elegir entre diferentes categorías, como Lista personalizada, Tareas, Calendario, Biblioteca de documentos, Biblioteca de imágenes, etc.</strong></li>
-<li><strong>Introduzca un nombre y una descripción para su lista o biblioteca y haga clic en Crear.</strong></li>
-</ol>
- <p>Para trabajar con una lista o una biblioteca, deberá seguir estos pasos:</p>
- <ol>
-<li><strong>Vaya a la lista o biblioteca con la que desea trabajar. </strong></li>
-<li><strong>En la página de lista o biblioteca, puede realizar varias acciones en los elementos de su lista o biblioteca, como:</strong></li>
-<ul>
-<li><strong>Agregar:</strong> Puede agregar nuevos elementos o archivos a su lista o biblioteca haciendo clic en Nuevo o Subir. También puede arrastrar y soltar archivos desde su computadora a su biblioteca. </li>
-
-<li><strong>Eliminar:</strong> Puede eliminar elementos o archivos de su lista o biblioteca seleccionándolos y haciendo clic en Eliminar. También puede moverlos a la papelera de reciclaje para su posterior recuperación. </li>
-<li><strong>Ordenar:</strong> Puede ordenar elementos o archivos en su lista o biblioteca haciendo clic en los encabezados de las columnas. También puede cambiar el orden de las columnas arrastrándolas y soltándolas. </li>
-<li><strong>Filtro:</strong> Puede filtrar elementos o archivos en su lista o biblioteca haciendo clic en el icono de filtro junto a los encabezados de las columnas. También puede usar filtros y vistas avanzadas para refinar sus resultados. </li>
-<li><strong>Buscar:</strong> Puede buscar artículos o archivos en su lista o biblioteca introduciendo palabras clave en el cuadro de búsqueda. También puede utilizar operadores de búsqueda y ámbitos para reducir su búsqueda. </li>
-</ul>
-</ol>
- <p>Para administrar una lista o una biblioteca, deberá seguir estos pasos:</p>
- <ol>
-<li><strong>Vaya a la lista o biblioteca que desea administrar. </strong></li>
-<li><strong>Haga clic en Configuración > Configuración de lista o Configuración de biblioteca.</strong></li>
-<li><strong>En la página Configuración, puede acceder a varias opciones para administrar su lista o biblioteca, como:</strong></li>
-<ul>
-<li><strong>Configuración general:</strong> Puede cambiar el nombre, la descripción, el control de versiones, la aprobación del contenido, etc. de su lista o biblioteca. </li>
-<li><strong>Columnas:</strong> Puede agregar, editar, eliminar, reordenar e indexar columnas en su lista o biblioteca. Las columnas son campos que almacenan información sobre sus elementos o archivos. </li>
-<li><strong>Vistas:</strong> Puede crear, editar, eliminar y administrar vistas en su lista o biblioteca. Las vistas son formas de mostrar y organizar tus elementos o archivos según ciertos criterios. </li>
-<li><strong>Permisos y administración:</strong> Puede administrar los permisos y la configuración de seguridad de su lista o biblioteca y sus elementos o archivos. También puede eliminar, guardar como plantilla, auditoría, etc. su lista o biblioteca. </li>
-</ul>
-</ol>
-
- <p>Uno de los principales beneficios de SharePoint es que le permite colaborar con otros usuarios en su contenido y proyectos. Puede compartir sus sitios, listas, bibliotecas y documentos con usuarios internos, como sus colegas, gerentes y empleados, o usuarios externos, como sus clientes, socios, proveedores, etc. También puede trabajar juntos en su contenido y proyectos en tiempo real o de forma asincrónica. Estas son algunas de las formas en que puede colaborar con otros usuarios usando SharePoint:</p>
- <ul>
-<li><strong>Compartir sitios, listas, bibliotecas y documentos:</strong> Puede compartir sus sitios, listas, bibliotecas y documentos con otros usuarios haciendo clic en Compartir en la esquina superior derecha de la página. A continuación, puede introducir los nombres o direcciones de correo electrónico de los usuarios con los que desea compartir y asignarles diferentes niveles de permisos, como Editar, Ver, Control completo, etc. También puede agregar un mensaje para explicar por qué comparte el contenido. También puede dejar de compartir el contenido en cualquier momento haciendo clic en Compartido con y eliminando a los usuarios de la lista. </li>
-<li><strong>Invitar a usuarios externos:</strong> Puede invitar a usuarios externos a acceder a sus sitios, listas, bibliotecas y documentos haciendo clic en Compartir e ingresando sus direcciones de correo electrónico. A continuación, puede elegir si desea exigirles que inicien sesión con una cuenta de Microsoft o una cuenta de trabajo o escuela, o permitirles acceder al contenido sin iniciar sesión. También puede elegir si desea enviarles una invitación por correo electrónico o copiar un enlace al contenido. También puede administrar los usuarios externos que tienen acceso a su contenido haciendo clic en Configuración del sitio > Permisos del sitio > Configuración de solicitud de acceso.</li>
-
-<li><strong>Usa el historial de versiones y echa un vistazo:</strong> Puedes usar el historial de versiones y las funciones para administrar los cambios y actualizaciones realizados en tus documentos por ti mismo o por otros usuarios. El historial de versiones le permite ver las versiones anteriores de un documento y restaurarlas si es necesario. La verificación le permite bloquear un documento para editarlo y evitar que otros hagan cambios hasta que lo vuelva a revisar. Para utilizar el historial de versiones, solo tiene que hacer clic en los puntos suspensivos (...) junto a un documento y seleccione Historial de versiones. Para utilizar check out, solo tiene que hacer clic en los puntos suspensivos (...) junto a un documento y seleccione Check Out.</li>
-</ul>
- <h3>Personalizar y configurar la configuración de SharePoint</h3>
- <p>Además de crear y administrar contenido y colaborar con otros usuarios, también puede personalizar y configurar la configuración de SharePoint para adaptarse a sus necesidades y preferencias. Puede cambiar la apariencia, el comportamiento y la funcionalidad de SharePoint utilizando varias opciones y herramientas. Estas son algunas de las cosas que puede personalizar y configurar en SharePoint:</p>
- <ul>
-<li><strong>Temas:</strong> Puede cambiar la combinación de colores y el estilo de fuente de sus sitios usando temas. Los temas son conjuntos predefinidos de colores y fuentes que puedes aplicar a tus sitios. Para cambiar el tema de tu sitio, solo tienes que hacer clic Configuración > Cambiar la apariencia > Tema y seleccionar un tema de las opciones disponibles. </li>
-<li><strong>Plantillas:</strong> Puede cambiar el diseño y las características de sus sitios utilizando plantillas. Las plantillas son conjuntos predefinidos de páginas web, listas, bibliotecas, partes web, etc. que puede aplicar a sus sitios. Para cambiar la plantilla de su sitio, solo tiene que hacer clic Configuración > Configuración del sitio > Acciones del sitio > Guardar sitio como plantilla y guardar su sitio como plantilla. Luego puede crear un nuevo sitio usando esa plantilla o aplicarla a un sitio existente. </li>
-
-<li><strong>Aplicaciones:</strong> Puede ampliar la funcionalidad y las características de SharePoint utilizando aplicaciones. Las aplicaciones son complementos que proporcionan funciones o servicios adicionales, como calendarios, encuestas, mapas, etc. Para agregar una aplicación a su sitio, solo tiene que hacer clic en Configuración > Contenido del sitio > Nuevo > Aplicación y seleccionar una aplicación de la SharePoint Store o el catálogo de aplicaciones de su organización. </li>
-<li><strong>Configuración del sitio:</strong> Puede cambiar la configuración que afecta a su sitio y su contenido utilizando la página Configuración del sitio. La página Configuración del sitio proporciona varias opciones para administrar su sitio, como título, descripción, logotipo, idioma, configuración regional, configuración de búsqueda, características del sitio, características de colección del sitio, etc. Para acceder a la página Configuración del sitio, solo tiene que hacer clic Configuración > Configuración del sitio.</li>
-</ul>
- <h2>Conclusión</h2>
- <p>SharePoint es una plataforma potente y versátil que puede ayudarlo a organizar, almacenar, compartir y acceder a la información en toda su organización. SharePoint Server 2016 es la última versión local de SharePoint que ofrece más flexibilidad y control sobre su entorno. En este artículo, hemos explicado qué es SharePoint, por qué podría necesitarlo, cómo descargar SharePoint Server 2016 y cómo usarlo de manera efectiva. Esperamos que este artículo le haya ayudado a comenzar con SharePoint y que disfrute usándolo para sus necesidades de contenido y colaboración. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre SharePoint Server 2016:</p>
- <h4>Q: ¿Cuánto cuesta SharePoint Server 2016? </h4>
-
- <h4>Q: ¿Cómo puedo actualizar desde una versión anterior de SharePoint a SharePoint Server 2016? </h4>
-<p>A: El proceso de actualización de una versión anterior de SharePoint a SharePoint Server 2016 implica dos pasos: actualización de la base de datos y actualización de la colección del sitio. La actualización de adjuntos de base de datos significa que debe separar las bases de datos de contenido de su granja de SharePoint anterior y adjuntarlas a su nueva granja de SharePoint Server 2016. La actualización de la colección de sitios significa que actualiza las colecciones de sitios en sus bases de datos de contenido a la nueva versión de SharePoint. Puede realizar la actualización de adjuntos de base de datos utilizando los comandos de PowerShell o Administración central. Puede realizar la actualización de la colección de sitios utilizando los comandos de PowerShell o la página Actualización de la colección de sitios. </p>
- <h4>Q: ¿Cómo puedo hacer copias de seguridad y restaurar mis datos de SharePoint Server 2016? </h4>
-
-<p>A: Puede solucionar y resolver problemas en SharePoint Server 2016 usando varias herramientas y recursos, como: - Visor de eventos: Puede usar Visor de eventos para ver los eventos y errores que ocurren en su entorno de SharePoint Server 2016. También puede filtrar, ordenar, exportar y archivar los eventos y errores para un análisis posterior. - ULS Viewer: Puede usar ULS Viewer para ver los registros del Unified Logging Service (ULS) generados por SharePoint Server 2016. Los registros de ULS contienen información detallada sobre las operaciones y actividades que ocurren en su entorno de SharePoint Server 2016. También puede filtrar, buscar, resaltar y exportar los registros de ULS para un análisis adicional. - Health Analyzer: Puede utilizar Health Analyzer para supervisar el estado y el rendimiento de su entorno de SharePoint Server 2016. Health Analyzer ejecuta varias reglas que verifican posibles problemas o problemas en su entorno. También puede ver los resultados de las reglas y seguir las recomendaciones para solucionar los problemas o problemas. - Panel de control para desarrolladores: Puede usar el panel de control para desarrolladores para medir el rendimiento y el uso de recursos de sus páginas y componentes de SharePoint Server 2016. Developer Dashboard muestra varias métricas, como el tiempo de ejecución, consultas SQL, llamadas a servicios web, etc. También puede habilitar o deshabilitar Developer Dashboard para diferentes ámbitos y usuarios. - Microsoft Support: Puede utilizar Microsoft Support para acceder a varios recursos y servicios que pueden ayudarle a solucionar y resolver problemas en SharePoint Server 2016. Algunos ejemplos de recursos y servicios de Microsoft Support son artículos de la Base de Conocimiento, foros, blogs, vídeos, seminarios web, etc. <h4>P: ¿Cómo puedo obtener más información sobre SharePoint Server 2016? </h4> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BetterAPI/BetterChat/src/lib/utils/analytics.ts b/spaces/BetterAPI/BetterChat/src/lib/utils/analytics.ts
deleted file mode 100644
index 72fd5d70df54c0436a8aa3f5fca4dfbcb5f64ff5..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat/src/lib/utils/analytics.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-export interface GAEvent {
-	hitType: "event";
-	eventCategory: string;
-	eventAction: string;
-	eventLabel?: string;
-	eventValue?: number;
-}
-
-// Send a Google Analytics event
-export function sendAnalyticsEvent({
-	eventCategory,
-	eventAction,
-	eventLabel,
-	eventValue,
-}: Omit<GAEvent, "hitType">): void {
-	// Mandatory fields
-	const event: GAEvent = {
-		hitType: "event",
-		eventCategory,
-		eventAction,
-	};
-	// Optional fields
-	if (eventLabel) {
-		event.eventLabel = eventLabel;
-	}
-	if (eventValue) {
-		event.eventValue = eventValue;
-	}
-
-	// @ts-expect-error typescript doesn't know gtag is on the window object
-	if (!!window?.gtag && typeof window?.gtag === "function") {
-		// @ts-expect-error typescript doesn't know gtag is on the window object
-		window?.gtag("event", eventAction, {
-			event_category: event.eventCategory,
-			event_label: event.eventLabel,
-			value: event.eventValue,
-		});
-	}
-}
diff --git a/spaces/BetterAPI/BetterChat_new/src/lib/utils/randomUuid.ts b/spaces/BetterAPI/BetterChat_new/src/lib/utils/randomUuid.ts
deleted file mode 100644
index 9d536365c57659305ad28d6fc06b89d77ab337ab..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat_new/src/lib/utils/randomUuid.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-type UUID = ReturnType<typeof crypto.randomUUID>;
-
-export function randomUUID(): UUID {
-	// Only on old safari / ios
-	if (!("randomUUID" in crypto)) {
-		return "10000000-1000-4000-8000-100000000000".replace(/[018]/g, (c) =>
-			(
-				Number(c) ^
-				(crypto.getRandomValues(new Uint8Array(1))[0] & (15 >> (Number(c) / 4)))
-			).toString(16)
-		) as UUID;
-	}
-	return crypto.randomUUID();
-}
diff --git a/spaces/BilalSardar/Lyrics-Text_to_music/app.py b/spaces/BilalSardar/Lyrics-Text_to_music/app.py
deleted file mode 100644
index 84aad1f50555da67b12cc4dff433c1c412b93aad..0000000000000000000000000000000000000000
--- a/spaces/BilalSardar/Lyrics-Text_to_music/app.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import sys
-import os
-from pathlib import Path
-import json
-import secrets
-import copy
-import gradio as gr
-import TMIDI
-import urllib
-from fuzzywuzzy import fuzz
-from fuzzywuzzy import process
-from itertools import islice, accumulate
-
-from pprint import pprint
-
-import tqdm.auto
-from tqdm import auto
-from midi2audio import FluidSynth
-from IPython.display import display, Javascript, HTML, Audio
-
-# only for plotting pianoroll
-import pretty_midi
-import librosa.display
-import matplotlib.pyplot as plt
-
-
-import mido
-from midi2audio import FluidSynth
-
-
-
-
-#midtoaud=gr.Interface.load(name="spaces/kboaten/MIDI-Audio-Extension")
-print('Loading the Karaoke model. Please wait...')
-data = TMIDI.Tegridy_Any_Pickle_File_Loader('Karaoke-English-Full')
-
-print('Done!')
-print('Prepping data...')
-
-kar_ev_f = data[2]
-
-kar = []
-karaoke = []
-
-for k in auto.tqdm(kar_ev_f):
-  k.sort(reverse=False, key=lambda x: x[1])
-  for kk in k:
-    
-    if kk[0] == 'note' or kk[0] == 'text_event':
-      kar.append(kk)
-
-kar_words = []
-for o in auto.tqdm(kar):
-  if o[0] != 'note':
-    kar_words.append(str(o[2]).lower())
-
-print('Done! Enjoy! :)')
-
-def TextToMusic(lyrics,notes,rand):
-  text = list(lyrics.split("."))
-  randomize_words_matching = rand
-  song = []
-
-  words_lst = ''
-
-  print('=' * 100)
-
-  print('Deep-Muse Text to Music Generator')
-  print('Starting up...')
-
-  print('=' * 100)
-
-  for t in auto.tqdm(text):
-    txt = t.lower().split(' ')
-    
-    kar_words_split = list(TMIDI.Tegridy_List_Slicer(kar_words, len(txt)))
-    
-    ratings = []
-
-    for k in kar_words_split:
-      ratings.append(fuzz.ratio(txt, k))
-    
-    if randomize_words_matching:
-      
-      try:
-        ind = ratings.index(secrets.choice([max(ratings)-5, max(ratings)-4, max(ratings)-3, max(ratings)-2, max(ratings)-1, max(ratings)]))
-      except:
-        ind = ratings.index(max(ratings))
-    
-    else:
-      ind = ratings.index(max(ratings))
-
-    words_list = kar_words_split[ind]
-    pos = ind * len(txt)
-    
-
-    print(words_list)
-
-    words_lst += ' '.join(words_list) + chr(10)
-
-    c = 0
-    for i in range(len(kar)):
-      if kar[i][0] != 'note':
-        if c == pos:
-          idx = i
-          break
-
-      if kar[i][0] != 'note':
-        c += 1
-  
-    c = 0
-    for i in range(idx, len(kar)):
-      if kar[i][0] != 'note':
-        if c == len(txt):
-          break
-
-      if kar[i][0] == 'note':
-        song.append(kar[i])
-
-      if kar[i][0] != 'note':
-        c += 1
-        song.append(kar[i])
-
-  so = [y for y in song if len(y) > 3]
-  if so != []: sigs = TMIDI.Tegridy_MIDI_Signature(so, so)
-
-  print('=' * 100)
-
-  print(sigs[0])
-
-  print('=' * 100)
-
-  song1 = []
-  p = song[0]
-  p[1] = 0
-  time = 0
-
-  song.sort(reverse=False, key=lambda x: x[1])
-
-  for i in range(len(song)-1):
-
-      ss = copy.deepcopy(song[i])
-      if song[i][1] != p[1]:
-        
-        if abs(song[i][1] - p[1]) > 1000:
-          time += 300
-        else:
-          time += abs(song[i][1] - p[1])
-
-        ss[1] = time 
-        song1.append(ss)
-        
-        p = copy.deepcopy(song[i])
-      else:
-        
-        ss[1] = time
-        song1.append(ss)
-        
-        p = copy.deepcopy(song[i])
-
-  pprint(words_lst, compact=True)
-  print('=' * 100)
-  TMIDI.Tegridy_SONG_to_MIDI_Converter(song1, output_file_name='deep-muse-Output-MIDI')
-  fname = 'deep-muse-Output-MIDI'
-
-  fn = os.path.basename(fname + '.mid')
-  fn1 = fn.split('.')[0]
-  print('Playing and plotting composition...')
-
-  pm = pretty_midi.PrettyMIDI(fname + '.mid')
-
-  # Retrieve piano roll of the MIDI file
-  piano_roll = pm.get_piano_roll()
-
-  plt.figure(figsize=(14, 5))
-  librosa.display.specshow(piano_roll, x_axis='time', y_axis='cqt_note', fmin=1, hop_length=160, sr=16000, cmap=plt.cm.hot)
-  plt.title('Composition: ' + fn1)
-  plt.savefig('my_plot.png')
-  print('Synthesizing the last output MIDI. Please stand-by... ')
-  
-  # url=midtoaud("deep-muse-Output-MIDI.mid",int(notes),fn_index=0)
-  # save_as = "file.wav"
-    
-  # data1 = urllib.request.urlopen(url)
-    
-  # f = open(save_as,'wb')
-  # f.write(data1.read())
-  # f.close()
-
-  mid = mido.MidiFile("deep-muse-Output-MIDI.mid")
-
-  fs = FluidSynth()
-  fs.midi_to_audio("deep-muse-Output-MIDI.mid", 'file.wav') 
-  return 'file.wav','my_plot.png'
-
-demo = gr.Interface(
-    fn=TextToMusic, 
-    inputs=[gr.inputs.Textbox(label='Enter Prompt'),gr.inputs.Number(label='Enter Number of Notes'),gr.inputs.Checkbox(label="randomize_words_matching")],
-    outputs=["audio","image"],
-    examples=[["I love you very very much.I can not live without you.You always present on my mind.I often think about you.I am all out of love I am so lost without you.",100,True]],
-    title="Lyrics Text To Music",
-    )
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/CVPR/Text2Human/Text2Human/models/losses/__init__.py b/spaces/CVPR/Text2Human/Text2Human/models/losses/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/__init__.py b/spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/__init__.py
deleted file mode 100644
index bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : __init__.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/replicate.py b/spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/replicate.py
deleted file mode 100644
index b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/replicate.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : replicate.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import functools
-
-from torch.nn.parallel.data_parallel import DataParallel
-
-__all__ = [
-    'CallbackContext',
-    'execute_replication_callbacks',
-    'DataParallelWithCallback',
-    'patch_replication_callback'
-]
-
-
-class CallbackContext(object):
-    pass
-
-
-def execute_replication_callbacks(modules):
-    """
-    Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
-
-    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
-    Note that, as all modules are isomorphism, we assign each sub-module with a context
-    (shared among multiple copies of this module on different devices).
-    Through this context, different copies can share some information.
-
-    We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
-    of any slave copies.
-    """
-    master_copy = modules[0]
-    nr_modules = len(list(master_copy.modules()))
-    ctxs = [CallbackContext() for _ in range(nr_modules)]
-
-    for i, module in enumerate(modules):
-        for j, m in enumerate(module.modules()):
-            if hasattr(m, '__data_parallel_replicate__'):
-                m.__data_parallel_replicate__(ctxs[j], i)
-
-
-class DataParallelWithCallback(DataParallel):
-    """
-    Data Parallel with a replication callback.
-
-    An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
-    original `replicate` function.
-    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
-
-    Examples:
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-        # sync_bn.__data_parallel_replicate__ will be invoked.
-    """
-
-    def replicate(self, module, device_ids):
-        modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
-        execute_replication_callbacks(modules)
-        return modules
-
-
-def patch_replication_callback(data_parallel):
-    """
-    Monkey-patch an existing `DataParallel` object. Add the replication callback.
-    Useful when you have customized `DataParallel` implementation.
-
-    Examples:
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
-        > patch_replication_callback(sync_bn)
-        # this is equivalent to
-        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-    """
-
-    assert isinstance(data_parallel, DataParallel)
-
-    old_replicate = data_parallel.replicate
-
-    @functools.wraps(old_replicate)
-    def new_replicate(module, device_ids):
-        modules = old_replicate(module, device_ids)
-        execute_replication_callbacks(modules)
-        return modules
-
-    data_parallel.replicate = new_replicate
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_scribble.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_scribble.py
deleted file mode 100644
index 17a8565cb741e12a65b46e3e7a66b20e7efb301c..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_scribble.py
+++ /dev/null
@@ -1,105 +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=['HED', 'PidiNet', 'None'],
-                        type='value',
-                        value='HED')
-                    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)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    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,
-            preprocess_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='scribble',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='scribble')
-    demo = create_demo(model.process_scribble)
-    demo.queue().launch()
diff --git a/spaces/CoPoBio/skin_cancer_risk_prediction/README.md b/spaces/CoPoBio/skin_cancer_risk_prediction/README.md
deleted file mode 100644
index 8b46d5f691342f64b13328831cbf52ec24bc8a85..0000000000000000000000000000000000000000
--- a/spaces/CoPoBio/skin_cancer_risk_prediction/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Skin Cancer Risk Prediction
-emoji: 🚀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.48.0
-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/CohereForAI/pokemon-cards-explorer/src/upsert.py b/spaces/CohereForAI/pokemon-cards-explorer/src/upsert.py
deleted file mode 100644
index d08efa73e7ccf55d383c081066505b7577394bf0..0000000000000000000000000000000000000000
--- a/spaces/CohereForAI/pokemon-cards-explorer/src/upsert.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os
-import openai
-import pinecone 
-from datasets import load_dataset
-
-from torch.utils.data import DataLoader
-
-OPENAI_API_KEY = os.environ['OPENAI_API_KEY']
-PINECONE_API_KEY = os.environ['PINECONE_API_KEY']
-
-openai.api_key = OPENAI_API_KEY
-
-pinecone.init(      
-	api_key=PINECONE_API_KEY,      
-	environment='us-west1-gcp'      
-)      
-index = pinecone.Index('pokemon-cards-v2')
-
-
-
-pcds = load_dataset("bhavnicksm/PokemonCardsPlus", split='train')
-
-def get_emb_text(example):
-    text = ""
-    text += f"Name: {example['name']}\n"
-    text += f"Set Name: {example['set_name']}\n"
-    text += f"Pokemon Caption: {example['blip_caption']}\n"
-    text += f"Card Description: {example['caption']}\n"
-    text += f"Pokemon Description: {example['pokemon_intro']}\n"
-    text += f"Pokedex Entry: {example['pokedex_text']}"
-    return text
-    
-pcds = pcds.map(lambda example: {"text" : get_emb_text(example)})
-pcds = pcds.map(lambda example : {"pokemon_image" : example['pokemon_image'] if example['pokemon_image'] != None else ''})
-pcds = pcds.map(lambda example : {"pokemon_intro" : example['pokemon_intro'] if example['pokemon_intro'] != None else ''})
-pcds = pcds.map(lambda example : {"pokedex_text" : example['pokedex_text'] if example['pokedex_text'] != None else ''})
-pcds = pcds.map(lambda example : {"blip_caption" : example['blip_caption'] if example['blip_caption'] != None else ''})
-
-dl = DataLoader(pcds, batch_size=64)
-
-pinecone_obj = {'id': None, 'values': None, 'metadata' : None}
-
-for batch in dl:
-
-    upsert_list = []
-    texts = batch['text']
-
-    response = openai.Embedding.create(
-    model="text-embedding-ada-002",
-    input=texts
-    )
-    embs = [x['embedding'] for x in response['data']]
-
-    for i in range(len(batch['id'])):
-        pcd = pinecone_obj.copy()
-        pcd['id'] = batch['id'][i]
-        pcd['values'] = embs[i]
-        pcd['metadata'] = {"img_url": batch['card_image'][i],
-                           "pokimg_url" : batch['pokemon_image'][i] if batch['pokemon_image'][i] != None else '', 
-                           "name" : batch['name'][i], 
-                           "description": batch['caption'][i],
-                           "pokemon_intro": batch['pokemon_intro'][i] if batch['pokemon_intro'][i] != None else '', 
-                           "pokedex_entry": batch['pokedex_text'][i] if batch['pokedex_text'][i] != None else '', 
-                           "blip_caption" : batch['blip_caption'][i] if batch['blip_caption'][i] != None else '',  
-                           "hp": int(batch['hp'][i])}
-        upsert_list.append(pcd)
-
-    upsert_resp = index.upsert(vectors=upsert_list)
-    
-print(index.describe_index_stats())
\ No newline at end of file
diff --git a/spaces/CorvaeOboro/gen_ability_icon/torch_utils/ops/upfirdn2d.h b/spaces/CorvaeOboro/gen_ability_icon/torch_utils/ops/upfirdn2d.h
deleted file mode 100644
index c9e2032bcac9d2abde7a75eea4d812da348afadd..0000000000000000000000000000000000000000
--- a/spaces/CorvaeOboro/gen_ability_icon/torch_utils/ops/upfirdn2d.h
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/DAOGEN/README/README.md b/spaces/DAOGEN/README/README.md
deleted file mode 100644
index f134e47fe319d9cc41b969e0ef4b3a222fbb36b0..0000000000000000000000000000000000000000
--- a/spaces/DAOGEN/README/README.md
+++ /dev/null
@@ -1,52 +0,0 @@
----
-title: README
-emoji: 🦄
-colorFrom: purple
-colorTo: yellow
-sdk: static
-pinned: false
----
-
-# 🎉 Welcome to the DAOGEN.ai Universe! 🌟
-
-👋 Hey there, fellow adventurers! Get ready to dive into the immersive world of DAOGEN, where AI meets blockchain and interactive storytelling! 🚀
-
-## About DAOGEN 🌍
-
-DAOGEN is not your ordinary project. We're on a mission to revolutionize storytelling and bring it to new frontiers! With the power of AI, blockchain, and the collective imagination of our community, we're building an unparalleled universe of captivating narratives and exciting adventures. 📚✨
-
-## Main Characters 🎭
-
-### 1. Luna Vega 🌙🎧💃🎨
-
-Luna Vega, our fearless Latina heroine, combines her skills as a boxer, hip-hop dancer, and graffiti artist to bring her unique flair to the digital realm. She's ready to inspire and empower others to unlock their creative potential. Get ready to dance to the rhythm of Luna's beats! 💥💃
-
-### 2. DJ Squircle 🎵🪐🎚️
-
-DJ Squircle, the master of sound and cosmic beats, will take you on an intergalactic journey like no other. Armed with the power of the Force hidden within otherworldly melodies, DJ Squircle is on a mission to share the magic of music with the world. Prepare for an epic sonic adventure! 🎵🌌✨
-
-### 3. Orion Galileo 🔬🚀🔭
-
-Orion Galileo, the brilliant scientist and inventor, is the genius behind the mind-bending technologies that drive the DAOGEN universe forward. With their unquenchable curiosity and groundbreaking innovations, they open doors to new realms of exploration and wonder. Prepare to be amazed by Orion's mind-blowing creations! 🧪🌠🔬
-
-### 4. The Sentinels 🗿🔐🔍
-
-The Sentinels, ancient guardians of wisdom and secrets, hold the key to unlocking the mysteries of DAOGEN. With their enigmatic presence and immense power, they guide adventurers through the vast universe, revealing hidden knowledge and challenging them to discover their true potential. Seek their guidance and unravel the secrets they protect! 🕵️‍♀️🔐🗺️
-
-## Getting Started 🚀
-
-To embark on your DAOGEN adventure, simply sign up for the next Chapter on our website and let the journey begin! 🌟💻
-
-## Contributing 🤝
-
-We thrive on the creativity and passion of our community! If you're excited to enhance our AI models, weave more interactivity into our narratives, or bring new characters to life, we welcome your contributions. Check out our website to sign up for the next Chapter we will be starting soon and join the adventure and become a part of the DAOGEN legacy! 🎉📝
-
-## Contact 📞✉️
-
-Questions, ideas, or just want to chat about all things DAOGEN? Reach out to us! You can find our contact information on our website. Let's connect and shape the future of immersive storytelling together! 🌈✨
-
-Get ready to unleash your creative force and embark on an adventure like no other! The DAOGEN universe awaits you! 🚀🌌
-
-Happy storytelling,
-
-The DAOGEN Team
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GribStubImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GribStubImagePlugin.py
deleted file mode 100644
index 8a799f19caac706a880218af257f40e9a386b489..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GribStubImagePlugin.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# GRIB 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 GRIB image handler.
-
-    :param handler: Handler object.
-    """
-    global _handler
-    _handler = handler
-
-
-# --------------------------------------------------------------------
-# Image adapter
-
-
-def _accept(prefix):
-    return prefix[:4] == b"GRIB" and prefix[7] == 1
-
-
-class GribStubImageFile(ImageFile.StubImageFile):
-    format = "GRIB"
-    format_description = "GRIB"
-
-    def _open(self):
-        offset = self.fp.tell()
-
-        if not _accept(self.fp.read(8)):
-            msg = "Not a GRIB 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 = "GRIB save handler not installed"
-        raise OSError(msg)
-    _handler.save(im, fp, filename)
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(GribStubImageFile.format, GribStubImageFile, _accept)
-Image.register_save(GribStubImageFile.format, _save)
-
-Image.register_extension(GribStubImageFile.format, ".grib")
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/PcdImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/PcdImagePlugin.py
deleted file mode 100644
index e390f3fe51dcb1ef4a490b55d18ac827e170aa37..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/PcdImagePlugin.py
+++ /dev/null
@@ -1,62 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# PCD file handling
-#
-# History:
-#       96-05-10 fl     Created
-#       96-05-27 fl     Added draft mode (128x192, 256x384)
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile
-
-##
-# Image plugin for PhotoCD images.  This plugin only reads the 768x512
-# image from the file; higher resolutions are encoded in a proprietary
-# encoding.
-
-
-class PcdImageFile(ImageFile.ImageFile):
-    format = "PCD"
-    format_description = "Kodak PhotoCD"
-
-    def _open(self):
-        # rough
-        self.fp.seek(2048)
-        s = self.fp.read(2048)
-
-        if s[:4] != b"PCD_":
-            msg = "not a PCD file"
-            raise SyntaxError(msg)
-
-        orientation = s[1538] & 3
-        self.tile_post_rotate = None
-        if orientation == 1:
-            self.tile_post_rotate = 90
-        elif orientation == 3:
-            self.tile_post_rotate = -90
-
-        self.mode = "RGB"
-        self._size = 768, 512  # FIXME: not correct for rotated images!
-        self.tile = [("pcd", (0, 0) + self.size, 96 * 2048, None)]
-
-    def load_end(self):
-        if self.tile_post_rotate:
-            # Handle rotated PCDs
-            self.im = self.im.rotate(self.tile_post_rotate)
-            self._size = self.im.size
-
-
-#
-# registry
-
-Image.register_open(PcdImageFile.format, PcdImageFile)
-
-Image.register_extension(PcdImageFile.format, ".pcd")
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py
deleted file mode 100644
index 8ef3c5ade2b1c2d52a084bd34f82598bb46f774f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py
+++ /dev/null
@@ -1,20 +0,0 @@
-""" TSI{0,1,2,3,5} are private tables used by Microsoft Visual TrueType (VTT)
-tool to store its hinting source data.
-
-TSI3 contains the text of the glyph programs in the form of 'VTTTalk' code.
-"""
-from fontTools import ttLib
-
-superclass = ttLib.getTableClass("TSI1")
-
-
-class table_T_S_I__3(superclass):
-
-    extras = {
-        0xFFFA: "reserved0",
-        0xFFFB: "reserved1",
-        0xFFFC: "reserved2",
-        0xFFFD: "reserved3",
-    }
-
-    indextable = "TSI2"
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3cb0bda2.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3cb0bda2.js
deleted file mode 100644
index a0cdde62ea8285eca85ee043154a1f7b429c8ec5..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3cb0bda2.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as r,e as h,s as v,k as g,o as w,z,v as k,x as B,a4 as C,P as R,p as S,R as q,A,F}from"./index-1d65707a.js";import{a as P}from"./Button-f155035a.js";import{X}from"./Blocks-c9e1499d.js";function j(t){let i=t[9](t[3])+"",a;return{c(){a=R(i)},m(e,s){S(e,a,s)},p(e,s){s&520&&i!==(i=e[9](e[3])+"")&&q(a,i)},d(e){e&&A(a)}}}function D(t){let i,a;return i=new P({props:{variant:t[4],elem_id:t[0],elem_classes:t[1],size:t[6],scale:t[7],min_width:t[8],visible:t[2],disabled:t[5]==="static",$$slots:{default:[j]},$$scope:{ctx:t}}}),i.$on("click",t[10]),{c(){g(i.$$.fragment)},m(e,s){w(i,e,s),a=!0},p(e,[s]){const l={};s&16&&(l.variant=e[4]),s&1&&(l.elem_id=e[0]),s&2&&(l.elem_classes=e[1]),s&64&&(l.size=e[6]),s&128&&(l.scale=e[7]),s&256&&(l.min_width=e[8]),s&4&&(l.visible=e[2]),s&32&&(l.disabled=e[5]==="static"),s&2568&&(l.$$scope={dirty:s,ctx:e}),i.$set(l)},i(e){a||(z(i.$$.fragment,e),a=!0)},o(e){k(i.$$.fragment,e),a=!1},d(e){B(i,e)}}}function E(t,i,a){let e;C(t,X,n=>a(9,e=n));let{elem_id:s=""}=i,{elem_classes:l=[]}=i,{visible:m=!0}=i,{value:u}=i,{variant:_="secondary"}=i,{mode:f="dynamic"}=i,{size:o="lg"}=i,{scale:c=null}=i,{min_width:d=void 0}=i;function b(n){F.call(this,t,n)}return t.$$set=n=>{"elem_id"in n&&a(0,s=n.elem_id),"elem_classes"in n&&a(1,l=n.elem_classes),"visible"in n&&a(2,m=n.visible),"value"in n&&a(3,u=n.value),"variant"in n&&a(4,_=n.variant),"mode"in n&&a(5,f=n.mode),"size"in n&&a(6,o=n.size),"scale"in n&&a(7,c=n.scale),"min_width"in n&&a(8,d=n.min_width)},[s,l,m,u,_,f,o,c,d,e,b]}class G extends r{constructor(i){super(),h(this,i,E,D,v,{elem_id:0,elem_classes:1,visible:2,value:3,variant:4,mode:5,size:6,scale:7,min_width:8})}}const K=G,L=["static","dynamic"],M=t=>({type:{payload:"string"},description:{payload:"button label"},example_data:t.value||"Run"});export{K as Component,M as document,L as modes};
-//# sourceMappingURL=index-3cb0bda2.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Button-89624748.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Button-89624748.js
deleted file mode 100644
index 4dc2efe70c3fdddb2c44c9c287e4b7e7f6fe1512..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Button-89624748.js
+++ /dev/null
@@ -1,4 +0,0 @@
-import{i as Q,a8 as C,S as q,e as A,s as E,z as k,v as B,a9 as I,E as T,N as L,aa as U,U as c,L as o,p as V,ab as D,ac as F,ad as K,q as G,A as N,K as y,Q as H,F as J}from"./index-3370be2a.js";function M(t){const e=t-1;return e*e*e+1}function x(t,{delay:e=0,duration:i=400,easing:n=Q}={}){const u=+getComputedStyle(t).opacity;return{delay:e,duration:i,easing:n,css:d=>`opacity: ${d*u}`}}function p(t,{delay:e=0,duration:i=400,easing:n=M,x:u=0,y:d=0,opacity:m=0}={}){const f=getComputedStyle(t),l=+f.opacity,s=f.transform==="none"?"":f.transform,h=l*(1-m),[r,b]=C(u),[w,_]=C(d);return{delay:e,duration:i,easing:n,css:(g,v)=>`
-			transform: ${s} translate(${(1-g)*r}${b}, ${(1-g)*w}${_});
-			opacity: ${l-h*v}`}}function P(t){let e,i,n;const u=t[17].default,d=I(u,t,t[16],null);let m=[{"data-testid":t[7]},{id:t[2]},{class:i="block "+t[3].join(" ")+" svelte-90oupt"}],f={};for(let l=0;l<m.length;l+=1)f=T(f,m[l]);return{c(){e=L(t[14]),d&&d.c(),U(t[14])(e,f),c(e,"hidden",t[10]===!1),c(e,"padded",t[6]),c(e,"border_focus",t[5]==="focus"),c(e,"hide-container",!t[8]&&!t[9]),o(e,"height",typeof t[0]=="number"?t[0]+"px":void 0),o(e,"width",typeof t[1]=="number"?`calc(min(${t[1]}px, 100%))`:void 0),o(e,"border-style",t[4]),o(e,"overflow",t[11]?"visible":"hidden"),o(e,"flex-grow",t[12]),o(e,"min-width",`calc(min(${t[13]}px, 100%))`),o(e,"border-width","var(--block-border-width)")},m(l,s){V(l,e,s),d&&d.m(e,null),n=!0},p(l,s){d&&d.p&&(!n||s&65536)&&D(d,u,l,l[16],n?K(u,l[16],s,null):F(l[16]),null),U(l[14])(e,f=G(m,[(!n||s&128)&&{"data-testid":l[7]},(!n||s&4)&&{id:l[2]},(!n||s&8&&i!==(i="block "+l[3].join(" ")+" svelte-90oupt"))&&{class:i}])),c(e,"hidden",l[10]===!1),c(e,"padded",l[6]),c(e,"border_focus",l[5]==="focus"),c(e,"hide-container",!l[8]&&!l[9]),s&1&&o(e,"height",typeof l[0]=="number"?l[0]+"px":void 0),s&2&&o(e,"width",typeof l[1]=="number"?`calc(min(${l[1]}px, 100%))`:void 0),s&16&&o(e,"border-style",l[4]),s&2048&&o(e,"overflow",l[11]?"visible":"hidden"),s&4096&&o(e,"flex-grow",l[12]),s&8192&&o(e,"min-width",`calc(min(${l[13]}px, 100%))`)},i(l){n||(k(d,l),n=!0)},o(l){B(d,l),n=!1},d(l){l&&N(e),d&&d.d(l)}}}function R(t){let e,i=t[14]&&P(t);return{c(){i&&i.c()},m(n,u){i&&i.m(n,u),e=!0},p(n,[u]){n[14]&&i.p(n,u)},i(n){e||(k(i,n),e=!0)},o(n){B(i,n),e=!1},d(n){i&&i.d(n)}}}function W(t,e,i){let{$$slots:n={},$$scope:u}=e,{height:d=void 0}=e,{width:m=void 0}=e,{elem_id:f=""}=e,{elem_classes:l=[]}=e,{variant:s="solid"}=e,{border_mode:h="base"}=e,{padding:r=!0}=e,{type:b="normal"}=e,{test_id:w=void 0}=e,{explicit_call:_=!1}=e,{container:g=!0}=e,{visible:v=!0}=e,{allow_overflow:z=!0}=e,{scale:j=null}=e,{min_width:S=0}=e,O=b==="fieldset"?"fieldset":"div";return t.$$set=a=>{"height"in a&&i(0,d=a.height),"width"in a&&i(1,m=a.width),"elem_id"in a&&i(2,f=a.elem_id),"elem_classes"in a&&i(3,l=a.elem_classes),"variant"in a&&i(4,s=a.variant),"border_mode"in a&&i(5,h=a.border_mode),"padding"in a&&i(6,r=a.padding),"type"in a&&i(15,b=a.type),"test_id"in a&&i(7,w=a.test_id),"explicit_call"in a&&i(8,_=a.explicit_call),"container"in a&&i(9,g=a.container),"visible"in a&&i(10,v=a.visible),"allow_overflow"in a&&i(11,z=a.allow_overflow),"scale"in a&&i(12,j=a.scale),"min_width"in a&&i(13,S=a.min_width),"$$scope"in a&&i(16,u=a.$$scope)},[d,m,f,l,s,h,r,w,_,g,v,z,j,S,O,b,u,n]}class $ extends q{constructor(e){super(),A(this,e,W,R,E,{height:0,width:1,elem_id:2,elem_classes:3,variant:4,border_mode:5,padding:6,type:15,test_id:7,explicit_call:8,container:9,visible:10,allow_overflow:11,scale:12,min_width:13})}}function X(t){let e,i,n,u,d;const m=t[9].default,f=I(m,t,t[8],null);return{c(){e=L("button"),f&&f.c(),y(e,"class",i=t[4]+" "+t[3]+" "+t[1].join(" ")+" svelte-1e89no8"),y(e,"id",t[0]),e.disabled=t[5],c(e,"hidden",!t[2]),o(e,"flex-grow",t[6]),o(e,"width",t[6]===0?"fit-content":null),o(e,"min-width",typeof t[7]=="number"?`calc(min(${t[7]}px, 100%))`:null)},m(l,s){V(l,e,s),f&&f.m(e,null),n=!0,u||(d=H(e,"click",t[10]),u=!0)},p(l,[s]){f&&f.p&&(!n||s&256)&&D(f,m,l,l[8],n?K(m,l[8],s,null):F(l[8]),null),(!n||s&26&&i!==(i=l[4]+" "+l[3]+" "+l[1].join(" ")+" svelte-1e89no8"))&&y(e,"class",i),(!n||s&1)&&y(e,"id",l[0]),(!n||s&32)&&(e.disabled=l[5]),(!n||s&30)&&c(e,"hidden",!l[2]),s&64&&o(e,"flex-grow",l[6]),s&64&&o(e,"width",l[6]===0?"fit-content":null),s&128&&o(e,"min-width",typeof l[7]=="number"?`calc(min(${l[7]}px, 100%))`:null)},i(l){n||(k(f,l),n=!0)},o(l){B(f,l),n=!1},d(l){l&&N(e),f&&f.d(l),u=!1,d()}}}function Y(t,e,i){let{$$slots:n={},$$scope:u}=e,{elem_id:d=""}=e,{elem_classes:m=[]}=e,{visible:f=!0}=e,{variant:l="secondary"}=e,{size:s="lg"}=e,{disabled:h=!1}=e,{scale:r=null}=e,{min_width:b=void 0}=e;function w(_){J.call(this,t,_)}return t.$$set=_=>{"elem_id"in _&&i(0,d=_.elem_id),"elem_classes"in _&&i(1,m=_.elem_classes),"visible"in _&&i(2,f=_.visible),"variant"in _&&i(3,l=_.variant),"size"in _&&i(4,s=_.size),"disabled"in _&&i(5,h=_.disabled),"scale"in _&&i(6,r=_.scale),"min_width"in _&&i(7,b=_.min_width),"$$scope"in _&&i(8,u=_.$$scope)},[d,m,f,l,s,h,r,b,u,n,w]}class ee extends q{constructor(e){super(),A(this,e,Y,X,E,{elem_id:0,elem_classes:1,visible:2,variant:3,size:4,disabled:5,scale:6,min_width:7})}}export{$ as B,ee as a,p as b,M as c,x as f};
-//# sourceMappingURL=Button-89624748.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_receivebuffer.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_receivebuffer.py
deleted file mode 100644
index 21a3870b6be9a6a365037326c2085ff2715022cc..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_receivebuffer.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import re
-from typing import Tuple
-
-import pytest
-
-from .._receivebuffer import ReceiveBuffer
-
-
-def test_receivebuffer() -> None:
-    b = ReceiveBuffer()
-    assert not b
-    assert len(b) == 0
-    assert bytes(b) == b""
-
-    b += b"123"
-    assert b
-    assert len(b) == 3
-    assert bytes(b) == b"123"
-
-    assert bytes(b) == b"123"
-
-    assert b.maybe_extract_at_most(2) == b"12"
-    assert b
-    assert len(b) == 1
-    assert bytes(b) == b"3"
-
-    assert bytes(b) == b"3"
-
-    assert b.maybe_extract_at_most(10) == b"3"
-    assert bytes(b) == b""
-
-    assert b.maybe_extract_at_most(10) is None
-    assert not b
-
-    ################################################################
-    # maybe_extract_until_next
-    ################################################################
-
-    b += b"123\n456\r\n789\r\n"
-
-    assert b.maybe_extract_next_line() == b"123\n456\r\n"
-    assert bytes(b) == b"789\r\n"
-
-    assert b.maybe_extract_next_line() == b"789\r\n"
-    assert bytes(b) == b""
-
-    b += b"12\r"
-    assert b.maybe_extract_next_line() is None
-    assert bytes(b) == b"12\r"
-
-    b += b"345\n\r"
-    assert b.maybe_extract_next_line() is None
-    assert bytes(b) == b"12\r345\n\r"
-
-    # here we stopped at the middle of b"\r\n" delimiter
-
-    b += b"\n6789aaa123\r\n"
-    assert b.maybe_extract_next_line() == b"12\r345\n\r\n"
-    assert b.maybe_extract_next_line() == b"6789aaa123\r\n"
-    assert b.maybe_extract_next_line() is None
-    assert bytes(b) == b""
-
-    ################################################################
-    # maybe_extract_lines
-    ################################################################
-
-    b += b"123\r\na: b\r\nfoo:bar\r\n\r\ntrailing"
-    lines = b.maybe_extract_lines()
-    assert lines == [b"123", b"a: b", b"foo:bar"]
-    assert bytes(b) == b"trailing"
-
-    assert b.maybe_extract_lines() is None
-
-    b += b"\r\n\r"
-    assert b.maybe_extract_lines() is None
-
-    assert b.maybe_extract_at_most(100) == b"trailing\r\n\r"
-    assert not b
-
-    # Empty body case (as happens at the end of chunked encoding if there are
-    # no trailing headers, e.g.)
-    b += b"\r\ntrailing"
-    assert b.maybe_extract_lines() == []
-    assert bytes(b) == b"trailing"
-
-
-@pytest.mark.parametrize(
-    "data",
-    [
-        pytest.param(
-            (
-                b"HTTP/1.1 200 OK\r\n",
-                b"Content-type: text/plain\r\n",
-                b"Connection: close\r\n",
-                b"\r\n",
-                b"Some body",
-            ),
-            id="with_crlf_delimiter",
-        ),
-        pytest.param(
-            (
-                b"HTTP/1.1 200 OK\n",
-                b"Content-type: text/plain\n",
-                b"Connection: close\n",
-                b"\n",
-                b"Some body",
-            ),
-            id="with_lf_only_delimiter",
-        ),
-        pytest.param(
-            (
-                b"HTTP/1.1 200 OK\n",
-                b"Content-type: text/plain\r\n",
-                b"Connection: close\n",
-                b"\n",
-                b"Some body",
-            ),
-            id="with_mixed_crlf_and_lf",
-        ),
-    ],
-)
-def test_receivebuffer_for_invalid_delimiter(data: Tuple[bytes]) -> None:
-    b = ReceiveBuffer()
-
-    for line in data:
-        b += line
-
-    lines = b.maybe_extract_lines()
-
-    assert lines == [
-        b"HTTP/1.1 200 OK",
-        b"Content-type: text/plain",
-        b"Connection: close",
-    ]
-    assert bytes(b) == b"Some body"
diff --git a/spaces/DaleChen/AutoGPT/autogpt/setup.py b/spaces/DaleChen/AutoGPT/autogpt/setup.py
deleted file mode 100644
index bfa68201b62bf67230a61fb1ecb00d1ab0ef0631..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/setup.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Set up the AI and its goals"""
-from colorama import Fore, Style
-
-from autogpt import utils
-from autogpt.config.ai_config import AIConfig
-from autogpt.logs import logger
-
-
-def prompt_user() -> AIConfig:
-    """Prompt the user for input
-
-    Returns:
-        AIConfig: The AIConfig object containing the user's input
-    """
-    ai_name = ""
-    # Construct the prompt
-    logger.typewriter_log(
-        "Welcome to Auto-GPT! ",
-        Fore.GREEN,
-        "run with '--help' for more information.",
-        speak_text=True,
-    )
-
-    logger.typewriter_log(
-        "Create an AI-Assistant:",
-        Fore.GREEN,
-        "Enter the name of your AI and its role below. Entering nothing will load"
-        " defaults.",
-        speak_text=True,
-    )
-
-    # Get AI Name from User
-    logger.typewriter_log(
-        "Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
-    )
-    ai_name = utils.clean_input("AI Name: ")
-    if ai_name == "":
-        ai_name = "Entrepreneur-GPT"
-
-    logger.typewriter_log(
-        f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
-    )
-
-    # Get AI Role from User
-    logger.typewriter_log(
-        "Describe your AI's role: ",
-        Fore.GREEN,
-        "For example, 'an AI designed to autonomously develop and run businesses with"
-        " the sole goal of increasing your net worth.'",
-    )
-    ai_role = utils.clean_input(f"{ai_name} is: ")
-    if ai_role == "":
-        ai_role = "an AI designed to autonomously develop and run businesses with the"
-        " sole goal of increasing your net worth."
-
-    # Enter up to 5 goals for the AI
-    logger.typewriter_log(
-        "Enter up to 5 goals for your AI: ",
-        Fore.GREEN,
-        "For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
-        " multiple businesses autonomously'",
-    )
-    print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
-    ai_goals = []
-    for i in range(5):
-        ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
-        if ai_goal == "":
-            break
-        ai_goals.append(ai_goal)
-    if not ai_goals:
-        ai_goals = [
-            "Increase net worth",
-            "Grow Twitter Account",
-            "Develop and manage multiple businesses autonomously",
-        ]
-
-    return AIConfig(ai_name, ai_role, ai_goals)
diff --git a/spaces/Docfile/open_llm_leaderboard/src/display_models/utils.py b/spaces/Docfile/open_llm_leaderboard/src/display_models/utils.py
deleted file mode 100644
index 09d6fe89ccbd9f0bc8b368c3c0d9ae28ad4f25d9..0000000000000000000000000000000000000000
--- a/spaces/Docfile/open_llm_leaderboard/src/display_models/utils.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import os
-from dataclasses import dataclass
-
-from huggingface_hub import HfApi
-
-API = HfApi()
-
-
-# These classes are for user facing column names, to avoid having to change them
-# all around the code when a modif is needed
-@dataclass
-class ColumnContent:
-    name: str
-    type: str
-    displayed_by_default: bool
-    hidden: bool = False
-
-
-def fields(raw_class):
-    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
-
-
-@dataclass(frozen=True)
-class AutoEvalColumn:  # Auto evals column
-    model_type_symbol = ColumnContent("T", "str", True)
-    model = ColumnContent("Model", "markdown", True)
-    average = ColumnContent("Average ⬆️", "number", True)
-    arc = ColumnContent("ARC", "number", True)
-    hellaswag = ColumnContent("HellaSwag", "number", True)
-    mmlu = ColumnContent("MMLU", "number", True)
-    truthfulqa = ColumnContent("TruthfulQA", "number", True)
-    model_type = ColumnContent("Type", "str", False)
-    precision = ColumnContent("Precision", "str", False)  # , True)
-    license = ColumnContent("Hub License", "str", False)
-    params = ColumnContent("#Params (B)", "number", False)
-    likes = ColumnContent("Hub ❤️", "number", False)
-    still_on_hub = ColumnContent("Available on the hub", "bool", False)
-    revision = ColumnContent("Model sha", "str", False, False)
-    dummy = ColumnContent(
-        "model_name_for_query", "str", True
-    )  # dummy col to implement search bar (hidden by custom CSS)
-
-
-@dataclass(frozen=True)
-class EloEvalColumn:  # Elo evals column
-    model = ColumnContent("Model", "markdown", True)
-    gpt4 = ColumnContent("GPT-4 (all)", "number", True)
-    human_all = ColumnContent("Human (all)", "number", True)
-    human_instruct = ColumnContent("Human (instruct)", "number", True)
-    human_code_instruct = ColumnContent("Human (code-instruct)", "number", True)
-
-
-@dataclass(frozen=True)
-class EvalQueueColumn:  # Queue column
-    model = ColumnContent("model", "markdown", True)
-    revision = ColumnContent("revision", "str", True)
-    private = ColumnContent("private", "bool", True)
-    precision = ColumnContent("precision", "str", True)
-    weight_type = ColumnContent("weight_type", "str", "Original")
-    status = ColumnContent("status", "str", True)
-
-
-LLAMAS = [
-    "huggingface/llama-7b",
-    "huggingface/llama-13b",
-    "huggingface/llama-30b",
-    "huggingface/llama-65b",
-]
-
-
-KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
-VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
-OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
-DOLLY_LINK = "https://huggingface.co/databricks/dolly-v2-12b"
-MODEL_PAGE = "https://huggingface.co/models"
-LLAMA_LINK = "https://ai.facebook.com/blog/large-language-model-llama-meta-ai/"
-VICUNA_LINK = "https://huggingface.co/CarperAI/stable-vicuna-13b-delta"
-ALPACA_LINK = "https://crfm.stanford.edu/2023/03/13/alpaca.html"
-
-
-def model_hyperlink(link, model_name):
-    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-
-
-def make_clickable_model(model_name):
-    link = f"https://huggingface.co/{model_name}"
-
-    if model_name in LLAMAS:
-        link = LLAMA_LINK
-        model_name = model_name.split("/")[1]
-    elif model_name == "HuggingFaceH4/stable-vicuna-13b-2904":
-        link = VICUNA_LINK
-        model_name = "stable-vicuna-13b"
-    elif model_name == "HuggingFaceH4/llama-7b-ift-alpaca":
-        link = ALPACA_LINK
-        model_name = "alpaca-13b"
-    if model_name == "dolly-12b":
-        link = DOLLY_LINK
-    elif model_name == "vicuna-13b":
-        link = VICUNA_LINK
-    elif model_name == "koala-13b":
-        link = KOALA_LINK
-    elif model_name == "oasst-12b":
-        link = OASST_LINK
-
-    details_model_name = model_name.replace("/", "__")
-    details_link = f"https://huggingface.co/datasets/open-llm-leaderboard/details_{details_model_name}"
-
-    if not bool(os.getenv("DEBUG", "False")):
-        # We only add these checks when not debugging, as they are extremely slow
-        print(f"details_link: {details_link}")
-        try:
-            check_path = list(
-                API.list_files_info(
-                    repo_id=f"open-llm-leaderboard/details_{details_model_name}",
-                    paths="README.md",
-                    repo_type="dataset",
-                )
-            )
-            print(f"check_path: {check_path}")
-        except Exception as err:
-            # No details repo for this model
-            print(f"No details repo for this model: {err}")
-            return model_hyperlink(link, model_name)
-
-    return model_hyperlink(link, model_name) + "  " + model_hyperlink(details_link, "📑")
-
-
-def styled_error(error):
-    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
-
-
-def styled_warning(warn):
-    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
-
-
-def styled_message(message):
-    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
-
-
-def has_no_nan_values(df, columns):
-    return df[columns].notna().all(axis=1)
-
-
-def has_nan_values(df, columns):
-    return df[columns].isna().any(axis=1)
diff --git a/spaces/Dorado607/ChuanhuChatGPT/README.md b/spaces/Dorado607/ChuanhuChatGPT/README.md
deleted file mode 100644
index f5284915a6398f495f5d522857481f286fe0f0fb..0000000000000000000000000000000000000000
--- a/spaces/Dorado607/ChuanhuChatGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.36.1
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Dorado607/ChuanhuChatGPT/modules/models/Google_PaLM.py b/spaces/Dorado607/ChuanhuChatGPT/modules/models/Google_PaLM.py
deleted file mode 100644
index 79ca042e228b25546600e4258a0b75790e25bb52..0000000000000000000000000000000000000000
--- a/spaces/Dorado607/ChuanhuChatGPT/modules/models/Google_PaLM.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from .base_model import BaseLLMModel
-import google.generativeai as palm
-
-class Google_PaLM_Client(BaseLLMModel):
-    def __init__(self, model_name, api_key, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        self.api_key = api_key
-
-    def _get_palm_style_input(self):
-        new_history = []
-        for item in self.history:
-            if item["role"] == "user":
-                new_history.append({'author': '1', 'content': item["content"]})
-            else:
-                new_history.append({'author': '0', 'content': item["content"]})
-        return new_history
-
-    def get_answer_at_once(self):
-        palm.configure(api_key=self.api_key)
-        messages = self._get_palm_style_input()
-        response = palm.chat(context=self.system_prompt, messages=messages, temperature=self.temperature, top_p=self.top_p)
-        if response.last is not None:
-            return response.last, len(response.last)
-        else:
-            reasons = '\n\n'.join(reason['reason'].name for reason in response.filters)
-            return "由于下面的原因，Google 拒绝返回 PaLM 的回答：\n\n" + reasons, 0
\ No newline at end of file
diff --git a/spaces/EMS-TU-Ilmenau/deepest-demo/README.md b/spaces/EMS-TU-Ilmenau/deepest-demo/README.md
deleted file mode 100644
index d236f7bd108fa171631a28f222e226e4af6d1761..0000000000000000000000000000000000000000
--- a/spaces/EMS-TU-Ilmenau/deepest-demo/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Deepest
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: "3.9"
-python_version: "3.10"
-app_file: app.py
-pinned: false
-model: St0nedB/deepest-public
----
-
-# Demo of `deepest` parameter estimator
-This repository is a demo of the `deepest` parameter estimator introduced in [arxiv](https://arxiv.org/abs/2211.04846).
-
-## Usage
-Start by installing the requirements from the `requirements.txt`.
-```bash
-python -m pip install -r requirements.txt
-```
-
-The repository uses `secrets` to avoid leaking non-public information (i.e. the code, the trained model, git urls and access tokens) which can occur when users clone the repository.
-If you are in possesion of those secrets, set them as the following environement variables in your shell:
-```bash
-export MODEL_TOKEN=<Access Token for the Huggingface Model Git>
-export GIT_TOKEN=<Access Token for the `deepest` repository>
-export GIT_URL=<Url of the `deepest` repository>
-export GIT_COMMIT=<The commit shasum for the `deepest` version>
-```
-
-Then run the `app.py` via
-```bash
-python app.py
-```
-
-## Paper
-You can find the current version of the paper on [arxiv](https://arxiv.org/abs/2211.04846).
\ No newline at end of file
diff --git a/spaces/Egrt/MaskGAN/model_data/README.md b/spaces/Egrt/MaskGAN/model_data/README.md
deleted file mode 100644
index 0b21b69e4a1a670db6ba50eb67edd45a04ddb7d9..0000000000000000000000000000000000000000
--- a/spaces/Egrt/MaskGAN/model_data/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-<!--
- * @Author: Egrt
- * @Date: 2022-04-11 20:53:42
- * @LastEditors: Egrt
- * @LastEditTime: 2022-04-11 20:53:42
- * @FilePath: \MaskGAN\model_data\README.md
--->
-存放模型权重
\ No newline at end of file
diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/tests/test_dataset.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/tests/test_dataset.py
deleted file mode 100644
index 715b4082645c131d43d728ae8f65bcc2430aa8c9..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/tests/test_dataset.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import pytest
-import yaml
-
-from realesrgan.data.realesrgan_dataset import RealESRGANDataset
-from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
-
-
-def test_realesrgan_dataset():
-
-    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = RealESRGANDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-    assert dataset.kernel_list == [
-        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
-    ]  # correct initialization the degradation configurations
-    assert dataset.betag_range2 == [0.5, 4]
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'tests/data/gt/baboon.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-
-    dataset = RealESRGANDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset.paths) == 2  # whether to read correct meta info
-    assert dataset.kernel_list == [
-        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
-    ]  # correct initialization the degradation configurations
-    assert dataset.betag_range2 == [0.5, 4]
-
-    # test __getitem__
-    result = dataset.__getitem__(1)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'comic'
-
-    # ------------------ test with sinc_prob = 0 -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-    opt['sinc_prob'] = 0
-    opt['sinc_prob2'] = 0
-    opt['final_sinc_prob'] = 0
-    dataset = RealESRGANDataset(opt)
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'baboon'
-
-    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
-    with pytest.raises(ValueError):
-        opt['dataroot_gt'] = 'tests/data/gt'
-        opt['io_backend']['type'] = 'lmdb'
-        dataset = RealESRGANDataset(opt)
-
-
-def test_realesrgan_paired_dataset():
-
-    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-    assert result['gt_path'] == 'tests/data/gt/baboon.png'
-    assert result['lq_path'] == 'tests/data/lq/baboon.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(1)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-    assert result['gt_path'] == 'comic'
-    assert result['lq_path'] == 'comic'
-
-    # ------------------ test paired_paths_from_folder -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt'
-    opt['dataroot_lq'] = 'tests/data/lq'
-    opt['io_backend'] = dict(type='disk')
-    opt['meta_info'] = None
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-
-    # ------------------ test normalization -------------------- #
-    dataset.mean = [0.5, 0.5, 0.5]
-    dataset.std = [0.5, 0.5, 0.5]
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
diff --git a/spaces/EuroPython2022/Fin-Eng-ASR-autosubtitles/app.py b/spaces/EuroPython2022/Fin-Eng-ASR-autosubtitles/app.py
deleted file mode 100644
index d978990d164274157179c3da2f0d3cd91db2e46d..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Fin-Eng-ASR-autosubtitles/app.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import gradio as gr
-import json
-from difflib import Differ
-import ffmpeg
-import os
-from pathlib import Path
-import time
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from transformers import MarianMTModel, MarianTokenizer
-import pandas as pd
-import re
-import time
-import os
-from fuzzywuzzy import fuzz
-from fastT5 import export_and_get_onnx_model
-import torch
-from transformers import pipeline
-
-MODEL  = "Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned"
-marian_nmt_model = "Helsinki-NLP/opus-mt-tc-big-fi-en"
-tokenizer_marian = MarianTokenizer.from_pretrained(marian_nmt_model)
-model = MarianMTModel.from_pretrained(marian_nmt_model)
-
-cuda = torch.device(
-    'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-sr_pipeline_device = 0 if torch.cuda.is_available() else -1
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-speech_recognizer = pipeline(
-    task="automatic-speech-recognition",
-    model=f'{MODEL}',
-    tokenizer=f'{MODEL}',
-    framework="pt",
-    device=sr_pipeline_device,
-)    
-
-model_checkpoint = 'Finnish-NLP/t5-small-nl24-casing-punctuation-correction'    
-tokenizer_t5 = AutoTokenizer.from_pretrained(model_checkpoint)
-model_t5 = export_and_get_onnx_model(model_checkpoint)
-#model_t5 = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, from_flax=False, torch_dtype=torch.float32).to(device)
-
-    
-    
-
-videos_out_path = Path("./videos_out")
-videos_out_path.mkdir(parents=True, exist_ok=True)
-
-samples_data = sorted(Path('examples').glob('*.json'))
-SAMPLES = []
-for file in samples_data:
-    with open(file) as f:
-        sample = json.load(f)
-    SAMPLES.append(sample)
-VIDEOS = list(map(lambda x: [x['video']], SAMPLES))
-
-total_inferences_since_reboot = 0
-total_cuts_since_reboot = 0
-
-
-
-
-async def speech_to_text(video_file_path):
-    """
-    Takes a video path to convert to audio, transcribe audio channel to text  timestamps
-
-    Using https://huggingface.co/tasks/automatic-speech-recognition pipeline
-    """
-    global total_inferences_since_reboot
-    if(video_file_path == None):
-        raise ValueError("Error no video input")
-
-    video_path = Path(video_file_path)
-    
-    try:
-        # convert video to audio 16k using PIPE to audio_memory
-        audio_memory, _ = ffmpeg.input(video_path).output(
-            '-', format="wav", ac=1, ar='16k').overwrite_output().global_args('-loglevel', 'quiet').run(capture_stdout=True)
-    except Exception as e:
-        raise RuntimeError("Error converting video to audio")
-
-    last_time = time.time()
-
-    try:
-        
-        output = speech_recognizer(
-            audio_memory, return_timestamps="word",  chunk_length_s=10, stride_length_s=(4, 2))
-        
-        transcription = output["text"].lower()
-        
-        timestamps = [[chunk["text"].lower(), chunk["timestamp"][0], chunk["timestamp"][1]]
-                        for chunk in output['chunks']]
-        input_ids = tokenizer_t5(transcription, return_tensors="pt").input_ids.to(device)
-        outputs = model_t5.generate(input_ids, max_length=128)
-        case_corrected_text = tokenizer_t5.decode(outputs[0], skip_special_tokens=True)
-        translated = model.generate(**tokenizer_marian([case_corrected_text], return_tensors="pt", padding=True))
-        translated_plain = "".join([tokenizer_marian.decode(t, skip_special_tokens=True) for t in translated])
-
-        for timestamp in timestamps:
-            total_inferences_since_reboot += 1
-        
-    
-        df = pd.DataFrame(timestamps, columns = ['word', 'start','stop'])
-    
-        df['start'] = df['start'].astype('float16')
-        df['stop'] = df['stop'].astype('float16')
-        
-
-        print("\n\ntotal_inferences_since_reboot: ",
-                total_inferences_since_reboot, "\n\n")
-        return (transcription, transcription, timestamps,df, case_corrected_text, translated_plain)
-    except Exception as e:
-        raise RuntimeError("Error Running inference with local model", e)
-
-
-def create_srt(text_out_t5, df):
-    
-    df.columns = ['word', 'start', 'stop']
-    
-    df_sentences = pd.DataFrame(columns=['sentence','start','stop','translated'])
-    found_match_value = 0
-    found_match_word = ""
-    
-    t5_sentences = re.split('[.]|[?]|[!]', text_out_t5)
-    t5_sentences = [sentence.replace('.','').replace('?','').replace('!','') for sentence in t5_sentences if sentence]
-    
-    for i, sentence in enumerate(t5_sentences):
-        sentence = sentence.lower().split(" ")
-        if i == 0:
-            df_subset = df[df['stop'] <10]
-            start = df.iloc[0]['start']
-        
-            for j, word in enumerate(df_subset['word']):
-              temp_value = fuzz.partial_ratio((word), sentence[-1])
-              if temp_value > found_match_value:
-                found_match_value = temp_value
-                found_match_word = word
-        
-            stop = df_subset[df_subset['word'] == found_match_word]
-            
-            translated = model.generate(**tokenizer_marian(t5_sentences[i], return_tensors="pt", padding=True))
-            translated_plain = [tokenizer_marian.decode(t, skip_special_tokens=True) for t in translated]
-            
-            dict_to_add = {
-                'sentence': t5_sentences[i],
-                'start': start,
-                'stop': stop.iloc[0]['stop'],
-                'translated': translated_plain[0]
-            }
-            
-            df_sentences = df_sentences.append(dict_to_add, ignore_index=True)
-            new_start = df.iloc[stop.index.values[0]+1]['start']
-            new_stop = new_start + 10
-        else:
-            found_match_value = 0
-            found_match_word = ""
-            
-            df_subset = df[(df['start'] >= new_start) & (df['stop'] <= new_stop)]
-            start = df_subset.iloc[0]['start']
-            
-            for j, word in enumerate(df_subset['word']):
-                temp_value = fuzz.partial_ratio((word), sentence[-1])
-                if temp_value > found_match_value:
-                    found_match_value = temp_value
-                    found_match_word = word
-            stop = df_subset[df_subset['word'] == found_match_word]
-            
-                
-            translated = model.generate(**tokenizer_marian(t5_sentences[i], return_tensors="pt", padding=True))
-            translated_plain = [tokenizer_marian.decode(t, skip_special_tokens=True) for t in translated]
-            
-            
-            dict_to_add = {
-                'sentence': t5_sentences[i],
-                'start': start,
-                'stop': stop.iloc[0]['stop'],
-                'translated': translated_plain[0]
-            }
-            df_sentences = df_sentences.append(dict_to_add, ignore_index=True)
-            try:
-                new_start = df.iloc[stop.index.values[0]+1]['start']
-                new_stop = new_start + 10
-            except Exception as e:
-                df_sentences = df_sentences.iloc[0:i+1]
-    
-    return df_sentences
-
-def create_srt_and_burn(video_in, srt_sentences):
-    srt_sentences.columns = ['sentence', 'start', 'stop','translated']
-    srt_sentences.dropna(inplace=True)
-    srt_sentences['start'] = srt_sentences['start'].astype('float')
-    srt_sentences['stop'] = srt_sentences['stop'].astype('float')
-    
-    
-    with open('testi.srt','w') as file:
-        for i in range(len(srt_sentences)):
-            file.write(str(i+1))
-            file.write('\n')
-            start = (time.strftime('%H:%M:%S', time.gmtime(srt_sentences.iloc[i]['start'])))
-            if "." in str(srt_sentences.iloc[i]['start']):
-                if len(str(srt_sentences.iloc[i]['start']).split('.')[1]) > 3:
-                    start = start + '.' + str(srt_sentences.iloc[i]['start']).split('.')[1][:3]
-                else:
-                    start = start + '.' + str(srt_sentences.iloc[i]['start']).split('.')[1]
-            file.write(start)
-            stop = (time.strftime('%H:%M:%S', time.gmtime(srt_sentences.iloc[i]['stop'])))
-            if len(str(srt_sentences.iloc[i]['stop']).split('.')[1]) > 3:
-                stop = stop + '.' + str(srt_sentences.iloc[i]['stop']).split('.')[1][:3]    
-            else:
-                stop = stop + '.' + str(srt_sentences.iloc[i]['stop']).split('.')[1]
-            file.write(' --> ')
-            file.write(stop)
-            file.write('\n')
-            file.writelines(srt_sentences.iloc[i]['translated'])
-            if int(i) != len(srt_sentences)-1:
-                file.write('\n\n')
-    try:
-        file1 = open('./testi.srt', 'r')
-        Lines = file1.readlines()
-        
-        count = 0
-        # Strips the newline character
-        for line in Lines:
-            count += 1
-                
-        
-        
-        video_out = str(Path(video_in)).replace('.mp4', '_out.mp4')
-        command = "ffmpeg -i {} -y -vf subtitles=./testi.srt {}".format(Path(video_in), Path(video_out))
-        os.system(command)
-        return video_out
-    except Exception as e:
-        print(e)
-        return video_out
-
-
-# ---- Gradio Layout -----
-video_in = gr.Video(label="Video file", interactive=True)
-text_in = gr.Textbox(label="Transcription", lines=10, interactive=True)
-text_out_t5 = gr.Textbox(label="Transcription T5", lines=10, interactive=True)
-translation_out = gr.Textbox(label="Translation", lines=10, interactive=True)
-text_out_timestamps = gr.Textbox(label="Word level timestamps", lines=10, interactive=True)
-srt_sentences = gr.DataFrame(label="Srt lines", row_count=(0, "dynamic"))
-video_out = gr.Video(label="Video Out")
-diff_out = gr.HighlightedText(label="Cuts Diffs", combine_adjacent=True)
-examples = gr.components.Dataset(
-    components=[video_in], samples=VIDEOS, type="index")
-
-demo = gr.Blocks(enable_queue=True, css='''
-#cut_btn, #reset_btn { align-self:stretch; }
-#\\31 3 { max-width: 540px; }
-.output-markdown {max-width: 65ch !important;}
-''')
-demo.encrypt = False
-with demo:
-    transcription_var = gr.Variable()
-    timestamps_var = gr.Variable()
-    timestamps_df = gr.Dataframe(visible=False, row_count=(0, "dynamic"))
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            # Create videos with English subtitles from videos spoken in Finnish
-            This project is a quick proof of concept of a simple video editor where you can add English subtitles to Finnish videos.
-            This space currently only works for short videos (Up to 128 tokens) but will be improved in next versions.
-            Space uses our finetuned Finnish ASR models, Our pretrained + finetuned Finnish T5 model for casing+punctuation correction and Opus-MT models from Helsinki University for Finnish --> English translation.
-            This space was inspired by https://huggingface.co/spaces/radames/edit-video-by-editing-text
-            ''')
-
-    with gr.Row():
-
-        examples.render()
-
-        def load_example(id):
-            video = SAMPLES[id]['video']
-            transcription = ''
-            timestamps = SAMPLES[id]['timestamps']
-
-            return (video, transcription, transcription, timestamps)
-
-        examples.click(
-            load_example,
-            inputs=[examples],
-            outputs=[video_in, text_in, transcription_var, timestamps_var],
-            queue=False)
-    with gr.Row():
-        with gr.Column():
-            video_in.render()
-            transcribe_btn = gr.Button("1. Press here to transcribe Audio")
-            transcribe_btn.click(speech_to_text, [video_in], [
-                text_in, transcription_var, text_out_timestamps,timestamps_df, text_out_t5, translation_out])
-
-    with gr.Row():
-        gr.Markdown('''
-        ### Here you will get varying outputs from different parts of the processing
-        ASR model output, T5 model output which corrects casing + hyphenation, sentence level translations and word level timestamps''')
-
-    with gr.Row():
-        with gr.Column():
-            text_in.render()
-        with gr.Column():
-            text_out_t5.render()
-        with gr.Column():
-            translation_out.render()
-        with gr.Column():
-            text_out_timestamps.render()
-    with gr.Row():
-        with gr.Column():
-            translate_and_make_srt_btn = gr.Button("2. Press here to create rows for subtitles")
-            translate_and_make_srt_btn.click(create_srt, [text_out_t5, timestamps_df], [
-                srt_sentences])
-    with gr.Row():
-        with gr.Column():
-            srt_sentences.render()
-    with gr.Row():
-        with gr.Column():
-            translate_and_make_srt_btn = gr.Button("3. Press here to create subtitle file and insert translations to video")
-            translate_and_make_srt_btn.click(create_srt_and_burn, [video_in, srt_sentences], [
-                video_out])
-            video_out.render()
-                
-if __name__ == "__main__":
-    demo.launch(debug=True)
diff --git a/spaces/Fernando22/freegpt-webui/server/website.py b/spaces/Fernando22/freegpt-webui/server/website.py
deleted file mode 100644
index 01b35dee1621b5b5bea49de330466ebb62817f20..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/server/website.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from flask import render_template, redirect, url_for, request, session
-from flask_babel import refresh
-from time import time
-from os import urandom
-from server.babel import get_locale, get_languages
-
-
-class Website:
-    def __init__(self, bp, url_prefix) -> None:
-        self.bp = bp
-        self.url_prefix = url_prefix
-        self.routes = {
-            '/': {
-                'function': lambda: redirect(url_for('._index')),
-                'methods': ['GET', 'POST']
-            },
-            '/chat/': {
-                'function': self._index,
-                'methods': ['GET', 'POST']
-            },
-            '/chat/<conversation_id>': {
-                'function': self._chat,
-                'methods': ['GET', 'POST']
-            },
-            '/change-language': {
-                'function': self.change_language,
-                'methods': ['POST']
-            },
-            '/get-locale': {
-                'function': self.get_locale,
-                'methods': ['GET']
-            },
-            '/get-languages': {
-                'function': self.get_languages,
-                'methods': ['GET']
-            }
-        }
-
-    def _chat(self, conversation_id):
-        if '-' not in conversation_id:
-            return redirect(url_for('._index'))
-
-        return render_template('index.html', chat_id=conversation_id, url_prefix=self.url_prefix)
-
-    def _index(self):
-        return render_template('index.html', chat_id=f'{urandom(4).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{hex(int(time() * 1000))[2:]}', url_prefix=self.url_prefix)
-
-    def change_language(self):
-        data = request.get_json()
-        session['language'] = data.get('language')
-        refresh()
-        return '', 204
-
-    def get_locale(self):
-        return get_locale()
-    
-    def get_languages(self):  
-        return get_languages()
diff --git a/spaces/Forbu14/LoiLibreQA/README.md b/spaces/Forbu14/LoiLibreQA/README.md
deleted file mode 100644
index 26c964cdb7cea1fba3ac0de15776573b25fb0d66..0000000000000000000000000000000000000000
--- a/spaces/Forbu14/LoiLibreQA/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LoiLibreQA
-emoji: 🌖
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.29.0
-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/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L12_Onnx.py b/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L12_Onnx.py
deleted file mode 100644
index 8dde0f173ed60169282128cc51eb1c200c5d82c5..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L12_Onnx.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import onnxruntime
-import torch
-
-class ContentVec768L12_Onnx(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/vec-768-layer-12.onnx",device=None):
-        print("load model(s) from {}".format(vec_path))
-        self.hidden_dim = 768
-        if device is None:
-            self.dev = torch.device("cpu")
-        else:
-            self.dev = torch.device(device)
-        if device == 'cpu' or device == torch.device("cpu") or device is None:
-            providers = ['CPUExecutionProvider']
-        elif device == 'cuda' or device == torch.device("cuda"):
-            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        feats = feats.unsqueeze(0).cpu().detach().numpy()
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)
-        return torch.tensor(logits[0]).transpose(1, 2).to(self.dev)
\ No newline at end of file
diff --git a/spaces/GIZ/embedding_visualisation/apps/intro.py b/spaces/GIZ/embedding_visualisation/apps/intro.py
deleted file mode 100644
index 85ae84e8d07403cc15bbac9e2eb7bbd6b6100e98..0000000000000000000000000000000000000000
--- a/spaces/GIZ/embedding_visualisation/apps/intro.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import plotly.express as px
-import streamlit as st
-from sentence_transformers import SentenceTransformer
-from huggingface_hub import hf_hub_url, cached_download
-import umap.umap_ as umap
-import pandas as pd
-import os
-import joblib
-
-def init_models():
-    model_name = 'sentence-transformers/all-MiniLM-L6-v2'
-    model = SentenceTransformer(model_name)
-    REPO_ID = "peter2000/umap_embed_3d_all-MiniLM-L6-v2"
-    FILENAME = "umap_embed_3d_all-MiniLM-L6-v2.sav"
-    umap_model= joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))
-    return model, umap_model
-
-def app():
-
-    with st.container():
-        st.markdown("<h1 style='text-align: center;  \
-                      color: black;'> Text Embedder</h1>", 
-                      unsafe_allow_html=True)
-        st.write(' ')
-        st.write(' ')
-
-    with st.expander("ℹ️ - About this app", expanded=True):
-
-        st.write(
-            """     
-            Information cartography - Get your word/phrase/sentence/paragraph embedded and visualized.
-            The (English) sentence-transformers model "all-MiniLM-L6-v2" maps sentences & paragraphs to a 384-dimensional dense vector space This is normally used for tasks like clustering or semantic search, but in this case, we use it to place your text to a 3D map. Before plotting, the dimension needs to be reduced to three so we can actually plot it, but preserve as much information as possible. For this, we use a technology called umap. The sentence transformer is context-sensitive and works best with whole sentences, to account for that we extend your text with "The book is about <text>".
-            
-            Simply put in your text and press EMBED, your examples will add up. You can use the category for different coloring.
-            """)
-
-        st.markdown("")
-        
-    word_to_embed_list =  st.session_state['embed_list']
-    cat_list =  st.session_state['cat_list']
-    
-    with st.container():
-        col1, col2 = st.columns(2)
-        with col1:
-            word_to_embed= st.text_input("Please enter your text here and we will embed it for you.", value="",)
-        with col2:                                 
-            cat= st.selectbox('Category',  ('1', '2', '3', '4', '5'))
-
-        
-        if st.button("Embed"):
-            with st.spinner("👑 Embedding your input"):
-                
-                model, umap_model = init_models()
-                
-              
-                word_to_embed_list.append(word_to_embed)
-                
-                st.session_state['embed_list'] = word_to_embed_list
-                cat_list .append(cat)
-                st.session_state['cat_list '] = cat_list 
-                
-                phrase_to_embed = ["The book is about "+ wte for wte in word_to_embed_list]
-                examples_embeddings = model.encode(phrase_to_embed)
- 
-                examples_umap = umap_model.transform(examples_embeddings)
-
-                #st.write(len(examples_umap))
-                
-                with st.spinner("👑 create visualisation"):  
-                      fig = px.scatter_3d(
-                          examples_umap[1:] , x=0, y=1, z=2,
-                           color=cat_list[1:] ,
-                          opacity = .7,    hover_data=[word_to_embed_list[1:]])
-                      fig.update_scenes(xaxis_visible=False, yaxis_visible=False,zaxis_visible=False )
-                      fig.update_traces(marker_size=4)
-                      st.plotly_chart(fig)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/protein_features.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/protein_features.py
deleted file mode 100644
index c78cfa5ea50baa63f9fabcb3bc7c7b66d10a1fa0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/tf/protein_features.py
+++ /dev/null
@@ -1,129 +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.
-
-"""Contains descriptions of various protein features."""
-import enum
-from typing import Dict, Optional, Sequence, Tuple, Union
-from alphafold.common import residue_constants
-import tensorflow.compat.v1 as tf
-
-# Type aliases.
-FeaturesMetadata = Dict[str, Tuple[tf.dtypes.DType, Sequence[Union[str, int]]]]
-
-
-class FeatureType(enum.Enum):
-  ZERO_DIM = 0  # Shape [x]
-  ONE_DIM = 1  # Shape [num_res, x]
-  TWO_DIM = 2  # Shape [num_res, num_res, x]
-  MSA = 3  # Shape [msa_length, num_res, x]
-
-
-# Placeholder values that will be replaced with their true value at runtime.
-NUM_RES = "num residues placeholder"
-NUM_SEQ = "length msa placeholder"
-NUM_TEMPLATES = "num templates placeholder"
-# Sizes of the protein features, NUM_RES and NUM_SEQ are allowed as placeholders
-# to be replaced with the number of residues and the number of sequences in the
-# multiple sequence alignment, respectively.
-
-
-FEATURES = {
-    #### Static features of a protein sequence ####
-    "aatype": (tf.float32, [NUM_RES, 21]),
-    "between_segment_residues": (tf.int64, [NUM_RES, 1]),
-    "deletion_matrix": (tf.float32, [NUM_SEQ, NUM_RES, 1]),
-    "domain_name": (tf.string, [1]),
-    "msa": (tf.int64, [NUM_SEQ, NUM_RES, 1]),
-    "num_alignments": (tf.int64, [NUM_RES, 1]),
-    "residue_index": (tf.int64, [NUM_RES, 1]),
-    "seq_length": (tf.int64, [NUM_RES, 1]),
-    "sequence": (tf.string, [1]),
-    "all_atom_positions": (tf.float32,
-                           [NUM_RES, residue_constants.atom_type_num, 3]),
-    "all_atom_mask": (tf.int64, [NUM_RES, residue_constants.atom_type_num]),
-    "resolution": (tf.float32, [1]),
-    "template_domain_names": (tf.string, [NUM_TEMPLATES]),
-    "template_sum_probs": (tf.float32, [NUM_TEMPLATES, 1]),
-    "template_aatype": (tf.float32, [NUM_TEMPLATES, NUM_RES, 22]),
-    "template_all_atom_positions": (tf.float32, [
-        NUM_TEMPLATES, NUM_RES, residue_constants.atom_type_num, 3
-    ]),
-    "template_all_atom_masks": (tf.float32, [
-        NUM_TEMPLATES, NUM_RES, residue_constants.atom_type_num, 1
-    ]),
-}
-
-FEATURE_TYPES = {k: v[0] for k, v in FEATURES.items()}
-FEATURE_SIZES = {k: v[1] for k, v in FEATURES.items()}
-
-
-def register_feature(name: str,
-                     type_: tf.dtypes.DType,
-                     shape_: Tuple[Union[str, int]]):
-  """Register extra features used in custom datasets."""
-  FEATURES[name] = (type_, shape_)
-  FEATURE_TYPES[name] = type_
-  FEATURE_SIZES[name] = shape_
-
-
-def shape(feature_name: str,
-          num_residues: int,
-          msa_length: int,
-          num_templates: Optional[int] = None,
-          features: Optional[FeaturesMetadata] = None):
-  """Get the shape for the given feature name.
-
-  This is near identical to _get_tf_shape_no_placeholders() but with 2
-  differences:
-  * This method does not calculate a single placeholder from the total number of
-    elements (eg given <NUM_RES, 3> and size := 12, this won't deduce NUM_RES
-    must be 4)
-  * This method will work with tensors
-
-  Args:
-    feature_name: String identifier for the feature. If the feature name ends
-      with "_unnormalized", this suffix is stripped off.
-    num_residues: The number of residues in the current domain - some elements
-      of the shape can be dynamic and will be replaced by this value.
-    msa_length: The number of sequences in the multiple sequence alignment, some
-      elements of the shape can be dynamic and will be replaced by this value.
-      If the number of alignments is unknown / not read, please pass None for
-      msa_length.
-    num_templates (optional): The number of templates in this tfexample.
-    features: A feature_name to (tf_dtype, shape) lookup; defaults to FEATURES.
-
-  Returns:
-    List of ints representation the tensor size.
-
-  Raises:
-    ValueError: If a feature is requested but no concrete placeholder value is
-        given.
-  """
-  features = features or FEATURES
-  if feature_name.endswith("_unnormalized"):
-    feature_name = feature_name[:-13]
-
-  unused_dtype, raw_sizes = features[feature_name]
-  replacements = {NUM_RES: num_residues,
-                  NUM_SEQ: msa_length}
-
-  if num_templates is not None:
-    replacements[NUM_TEMPLATES] = num_templates
-
-  sizes = [replacements.get(dimension, dimension) for dimension in raw_sizes]
-  for dimension in sizes:
-    if isinstance(dimension, str):
-      raise ValueError("Could not parse %s (shape: %s) with values: %s" % (
-          feature_name, raw_sizes, replacements))
-  return sizes
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
deleted file mode 100644
index 8e4b4ab23513a97adf4471ab3b33ca8abdb6dbe5..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
+++ /dev/null
@@ -1,45 +0,0 @@
-_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
-# model settings
-model = dict(
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            aggregation='sum',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2)),
-        grid_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2))))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 5a1d29e480cb46a763cb17d2105b3f040153d417..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnet18_v1c',
-    backbone=dict(depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 946bf4fc84236942a4462c2daa7637cace4e90cf..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
deleted file mode 100644
index 7b5421ad6877e4b35b0a6ae6e516e577404547ce..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/drive.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
-evaluation = dict(metric='mDice')
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/builders.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/builders.py
deleted file mode 100644
index 304d8f08d33a70e8be9388c855b2ae43bdf2683b..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/builders.py
+++ /dev/null
@@ -1,363 +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.
-
-"""
-All the functions to build the relevant solvers and used objects
-from the Hydra config.
-"""
-
-from enum import Enum
-import logging
-import typing as tp
-
-import dora
-import flashy
-import omegaconf
-import torch
-from torch import nn
-from torch.optim import Optimizer
-# LRScheduler was renamed in some torch versions
-try:
-    from torch.optim.lr_scheduler import LRScheduler  # type: ignore
-except ImportError:
-    from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
-
-from .base import StandardSolver
-from .. import adversarial, data, losses, metrics, optim
-from ..utils.utils import dict_from_config, get_loader
-
-
-logger = logging.getLogger(__name__)
-
-
-class DatasetType(Enum):
-    AUDIO = "audio"
-    MUSIC = "music"
-    SOUND = "sound"
-
-
-def get_solver(cfg: omegaconf.DictConfig) -> StandardSolver:
-    """Instantiate solver from config."""
-    from .audiogen import AudioGenSolver
-    from .compression import CompressionSolver
-    from .musicgen import MusicGenSolver
-    from .diffusion import DiffusionSolver
-    klass = {
-        'compression': CompressionSolver,
-        'musicgen': MusicGenSolver,
-        'audiogen': AudioGenSolver,
-        'lm': MusicGenSolver,  # backward compatibility
-        'diffusion': DiffusionSolver,
-        'sound_lm': AudioGenSolver,  # backward compatibility
-    }[cfg.solver]
-    return klass(cfg)  # type: ignore
-
-
-def get_optim_parameter_groups(model: nn.Module):
-    """Create parameter groups for the model using the appropriate method
-    if defined for each modules, to create the different groups.
-
-    Args:
-        model (nn.Module): torch model
-    Returns:
-        List of parameter groups
-    """
-    seen_params: tp.Set[nn.parameter.Parameter] = set()
-    other_params = []
-    groups = []
-    for name, module in model.named_modules():
-        if hasattr(module, 'make_optim_group'):
-            group = module.make_optim_group()
-            params = set(group['params'])
-            assert params.isdisjoint(seen_params)
-            seen_params |= set(params)
-            groups.append(group)
-    for param in model.parameters():
-        if param not in seen_params:
-            other_params.append(param)
-    groups.insert(0, {'params': other_params})
-    parameters = groups
-    return parameters
-
-
-def get_optimizer(params: tp.Union[nn.Module, tp.Iterable[torch.Tensor]], cfg: omegaconf.DictConfig) -> Optimizer:
-    """Build torch optimizer from config and set of parameters.
-    Supported optimizers: Adam, AdamW
-
-    Args:
-        params (nn.Module or iterable of torch.Tensor): Parameters to optimize.
-        cfg (DictConfig): Optimization-related configuration.
-    Returns:
-        torch.optim.Optimizer.
-    """
-    if 'optimizer' not in cfg:
-        if getattr(cfg, 'optim', None) is not None:
-            raise KeyError("Optimizer not found in config. Try instantiating optimizer from cfg.optim?")
-        else:
-            raise KeyError("Optimizer not found in config.")
-
-    parameters = get_optim_parameter_groups(params) if isinstance(params, nn.Module) else params
-    optimizer: torch.optim.Optimizer
-    if cfg.optimizer == 'adam':
-        optimizer = torch.optim.Adam(parameters, lr=cfg.lr, **cfg.adam)
-    elif cfg.optimizer == 'adamw':
-        optimizer = torch.optim.AdamW(parameters, lr=cfg.lr, **cfg.adam)
-    elif cfg.optimizer == 'dadam':
-        optimizer = optim.DAdaptAdam(parameters, lr=cfg.lr, **cfg.adam)
-    else:
-        raise ValueError(f"Unsupported LR Scheduler: {cfg.lr_scheduler}")
-    return optimizer
-
-
-def get_lr_scheduler(optimizer: torch.optim.Optimizer,
-                     cfg: omegaconf.DictConfig,
-                     total_updates: int) -> tp.Optional[LRScheduler]:
-    """Build torch learning rate scheduler from config and associated optimizer.
-    Supported learning rate schedulers: ExponentialLRScheduler, PlateauLRScheduler
-
-    Args:
-        optimizer (torch.optim.Optimizer): Optimizer.
-        cfg (DictConfig): Schedule-related configuration.
-        total_updates (int): Total number of updates.
-    Returns:
-        torch.optim.Optimizer.
-    """
-    if 'lr_scheduler' not in cfg:
-        raise KeyError("LR Scheduler not found in config")
-
-    lr_sched: tp.Optional[LRScheduler] = None
-    if cfg.lr_scheduler == 'step':
-        lr_sched = torch.optim.lr_scheduler.StepLR(optimizer, **cfg.step)
-    elif cfg.lr_scheduler == 'exponential':
-        lr_sched = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=cfg.exponential)
-    elif cfg.lr_scheduler == 'cosine':
-        kwargs = dict_from_config(cfg.cosine)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.CosineLRScheduler(
-            optimizer, warmup_steps=warmup_steps, total_steps=total_updates, **kwargs)
-    elif cfg.lr_scheduler == 'polynomial_decay':
-        kwargs = dict_from_config(cfg.polynomial_decay)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.PolynomialDecayLRScheduler(
-            optimizer, warmup_steps=warmup_steps, total_steps=total_updates, **kwargs)
-    elif cfg.lr_scheduler == 'inverse_sqrt':
-        kwargs = dict_from_config(cfg.inverse_sqrt)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.InverseSquareRootLRScheduler(optimizer, warmup_steps=warmup_steps, **kwargs)
-    elif cfg.lr_scheduler == 'linear_warmup':
-        kwargs = dict_from_config(cfg.linear_warmup)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.LinearWarmupLRScheduler(optimizer, warmup_steps=warmup_steps, **kwargs)
-    elif cfg.lr_scheduler is not None:
-        raise ValueError(f"Unsupported LR Scheduler: {cfg.lr_scheduler}")
-    return lr_sched
-
-
-def get_ema(module_dict: nn.ModuleDict, cfg: omegaconf.DictConfig) -> tp.Optional[optim.ModuleDictEMA]:
-    """Initialize Exponential Moving Average.
-
-    Args:
-        module_dict (nn.ModuleDict): ModuleDict for which to compute the EMA.
-        cfg (omegaconf.DictConfig): Optim EMA configuration.
-    Returns:
-        optim.ModuleDictEMA: EMA version of the ModuleDict.
-    """
-    kw: tp.Dict[str, tp.Any] = dict(cfg)
-    use = kw.pop('use', False)
-    decay = kw.pop('decay', None)
-    device = kw.pop('device', None)
-    if not use:
-        return None
-    if len(module_dict) == 0:
-        raise ValueError("Trying to build EMA but an empty module_dict source is provided!")
-    ema_module = optim.ModuleDictEMA(module_dict, decay=decay, device=device)
-    return ema_module
-
-
-def get_loss(loss_name: str, cfg: omegaconf.DictConfig):
-    """Instantiate loss from configuration."""
-    klass = {
-        'l1': torch.nn.L1Loss,
-        'l2': torch.nn.MSELoss,
-        'mel': losses.MelSpectrogramL1Loss,
-        'mrstft': losses.MRSTFTLoss,
-        'msspec': losses.MultiScaleMelSpectrogramLoss,
-        'sisnr': losses.SISNR,
-    }[loss_name]
-    kwargs = dict(getattr(cfg, loss_name))
-    return klass(**kwargs)
-
-
-def get_balancer(loss_weights: tp.Dict[str, float], cfg: omegaconf.DictConfig) -> losses.Balancer:
-    """Instantiate loss balancer from configuration for the provided weights."""
-    kwargs: tp.Dict[str, tp.Any] = dict_from_config(cfg)
-    return losses.Balancer(loss_weights, **kwargs)
-
-
-def get_adversary(name: str, cfg: omegaconf.DictConfig) -> nn.Module:
-    """Initialize adversary from config."""
-    klass = {
-        'msd': adversarial.MultiScaleDiscriminator,
-        'mpd': adversarial.MultiPeriodDiscriminator,
-        'msstftd': adversarial.MultiScaleSTFTDiscriminator,
-    }[name]
-    adv_cfg: tp.Dict[str, tp.Any] = dict(getattr(cfg, name))
-    return klass(**adv_cfg)
-
-
-def get_adversarial_losses(cfg) -> nn.ModuleDict:
-    """Initialize dict of adversarial losses from config."""
-    device = cfg.device
-    adv_cfg = getattr(cfg, 'adversarial')
-    adversaries = adv_cfg.get('adversaries', [])
-    adv_loss_name = adv_cfg['adv_loss']
-    feat_loss_name = adv_cfg.get('feat_loss')
-    normalize = adv_cfg.get('normalize', True)
-    feat_loss: tp.Optional[adversarial.FeatureMatchingLoss] = None
-    if feat_loss_name:
-        assert feat_loss_name in ['l1', 'l2'], f"Feature loss only support L1 or L2 but {feat_loss_name} found."
-        loss = get_loss(feat_loss_name, cfg)
-        feat_loss = adversarial.FeatureMatchingLoss(loss, normalize)
-    loss = adversarial.get_adv_criterion(adv_loss_name)
-    loss_real = adversarial.get_real_criterion(adv_loss_name)
-    loss_fake = adversarial.get_fake_criterion(adv_loss_name)
-    adv_losses = nn.ModuleDict()
-    for adv_name in adversaries:
-        adversary = get_adversary(adv_name, cfg).to(device)
-        optimizer = get_optimizer(adversary.parameters(), cfg.optim)
-        adv_loss = adversarial.AdversarialLoss(
-            adversary,
-            optimizer,
-            loss=loss,
-            loss_real=loss_real,
-            loss_fake=loss_fake,
-            loss_feat=feat_loss,
-            normalize=normalize
-        )
-        adv_losses[adv_name] = adv_loss
-    return adv_losses
-
-
-def get_visqol(cfg: omegaconf.DictConfig) -> metrics.ViSQOL:
-    """Instantiate ViSQOL metric from config."""
-    kwargs = dict_from_config(cfg)
-    return metrics.ViSQOL(**kwargs)
-
-
-def get_fad(cfg: omegaconf.DictConfig) -> metrics.FrechetAudioDistanceMetric:
-    """Instantiate Frechet Audio Distance metric from config."""
-    kwargs = dict_from_config(cfg.tf)
-    xp = dora.get_xp()
-    kwargs['log_folder'] = xp.folder
-    return metrics.FrechetAudioDistanceMetric(**kwargs)
-
-
-def get_kldiv(cfg: omegaconf.DictConfig) -> metrics.KLDivergenceMetric:
-    """Instantiate KL-Divergence metric from config."""
-    kld_metrics = {
-        'passt': metrics.PasstKLDivergenceMetric,
-    }
-    klass = kld_metrics[cfg.model]
-    kwargs = dict_from_config(cfg.get(cfg.model))
-    return klass(**kwargs)
-
-
-def get_text_consistency(cfg: omegaconf.DictConfig) -> metrics.TextConsistencyMetric:
-    """Instantiate Text Consistency metric from config."""
-    text_consistency_metrics = {
-        'clap': metrics.CLAPTextConsistencyMetric
-    }
-    klass = text_consistency_metrics[cfg.model]
-    kwargs = dict_from_config(cfg.get(cfg.model))
-    return klass(**kwargs)
-
-
-def get_chroma_cosine_similarity(cfg: omegaconf.DictConfig) -> metrics.ChromaCosineSimilarityMetric:
-    """Instantiate Chroma Cosine Similarity metric from config."""
-    assert cfg.model == 'chroma_base', "Only support 'chroma_base' method for chroma cosine similarity metric"
-    kwargs = dict_from_config(cfg.get(cfg.model))
-    return metrics.ChromaCosineSimilarityMetric(**kwargs)
-
-
-def get_audio_datasets(cfg: omegaconf.DictConfig,
-                       dataset_type: DatasetType = DatasetType.AUDIO) -> tp.Dict[str, torch.utils.data.DataLoader]:
-    """Build AudioDataset from configuration.
-
-    Args:
-        cfg (omegaconf.DictConfig): Configuration.
-        dataset_type: The type of dataset to create.
-    Returns:
-        dict[str, torch.utils.data.DataLoader]: Map of dataloader for each data split.
-    """
-    dataloaders: dict = {}
-
-    sample_rate = cfg.sample_rate
-    channels = cfg.channels
-    seed = cfg.seed
-    max_sample_rate = cfg.datasource.max_sample_rate
-    max_channels = cfg.datasource.max_channels
-
-    assert cfg.dataset is not None, "Could not find dataset definition in config"
-
-    dataset_cfg = dict_from_config(cfg.dataset)
-    splits_cfg: dict = {}
-    splits_cfg['train'] = dataset_cfg.pop('train')
-    splits_cfg['valid'] = dataset_cfg.pop('valid')
-    splits_cfg['evaluate'] = dataset_cfg.pop('evaluate')
-    splits_cfg['generate'] = dataset_cfg.pop('generate')
-    execute_only_stage = cfg.get('execute_only', None)
-
-    for split, path in cfg.datasource.items():
-        if not isinstance(path, str):
-            continue  # skipping this as not a path
-        if execute_only_stage is not None and split != execute_only_stage:
-            continue
-        logger.info(f"Loading audio data split {split}: {str(path)}")
-        assert (
-            cfg.sample_rate <= max_sample_rate
-        ), f"Expecting a max sample rate of {max_sample_rate} for datasource but {sample_rate} found."
-        assert (
-            cfg.channels <= max_channels
-        ), f"Expecting a max number of channels of {max_channels} for datasource but {channels} found."
-
-        split_cfg = splits_cfg[split]
-        split_kwargs = {k: v for k, v in split_cfg.items()}
-        kwargs = {**dataset_cfg, **split_kwargs}  # split kwargs overrides default dataset_cfg
-        kwargs['sample_rate'] = sample_rate
-        kwargs['channels'] = channels
-
-        if kwargs.get('permutation_on_files') and cfg.optim.updates_per_epoch:
-            kwargs['num_samples'] = (
-                flashy.distrib.world_size() * cfg.dataset.batch_size * cfg.optim.updates_per_epoch)
-
-        num_samples = kwargs['num_samples']
-        shuffle = kwargs['shuffle']
-
-        return_info = kwargs.pop('return_info')
-        batch_size = kwargs.pop('batch_size', None)
-        num_workers = kwargs.pop('num_workers')
-
-        if dataset_type == DatasetType.MUSIC:
-            dataset = data.music_dataset.MusicDataset.from_meta(path, **kwargs)
-        elif dataset_type == DatasetType.SOUND:
-            dataset = data.sound_dataset.SoundDataset.from_meta(path, **kwargs)
-        elif dataset_type == DatasetType.AUDIO:
-            dataset = data.info_audio_dataset.InfoAudioDataset.from_meta(path, return_info=return_info, **kwargs)
-        else:
-            raise ValueError(f"Dataset type is unsupported: {dataset_type}")
-
-        loader = get_loader(
-            dataset,
-            num_samples,
-            batch_size=batch_size,
-            num_workers=num_workers,
-            seed=seed,
-            collate_fn=dataset.collater if return_info else None,
-            shuffle=shuffle,
-        )
-        dataloaders[split] = loader
-
-    return dataloaders
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/fid.py b/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/fid.py
deleted file mode 100644
index c05eeda26b3a5ae5be060c158fc7a74d4ccbfb5f..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/models/stylegan2/stylegan2-pytorch/fid.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import argparse
-import pickle
-
-import torch
-from torch import nn
-import numpy as np
-from scipy import linalg
-from tqdm import tqdm
-
-from model import Generator
-from calc_inception import load_patched_inception_v3
-
-
-@torch.no_grad()
-def extract_feature_from_samples(
-    generator, inception, truncation, truncation_latent, batch_size, n_sample, device
-):
-    n_batch = n_sample // batch_size
-    resid = n_sample - (n_batch * batch_size)
-    batch_sizes = [batch_size] * n_batch + [resid]
-    features = []
-
-    for batch in tqdm(batch_sizes):
-        latent = torch.randn(batch, 512, device=device)
-        img, _ = g([latent], truncation=truncation, truncation_latent=truncation_latent)
-        feat = inception(img)[0].view(img.shape[0], -1)
-        features.append(feat.to('cpu'))
-
-    features = torch.cat(features, 0)
-
-    return features
-
-
-def calc_fid(sample_mean, sample_cov, real_mean, real_cov, eps=1e-6):
-    cov_sqrt, _ = linalg.sqrtm(sample_cov @ real_cov, disp=False)
-
-    if not np.isfinite(cov_sqrt).all():
-        print('product of cov matrices is singular')
-        offset = np.eye(sample_cov.shape[0]) * eps
-        cov_sqrt = linalg.sqrtm((sample_cov + offset) @ (real_cov + offset))
-
-    if np.iscomplexobj(cov_sqrt):
-        if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3):
-            m = np.max(np.abs(cov_sqrt.imag))
-
-            raise ValueError(f'Imaginary component {m}')
-
-        cov_sqrt = cov_sqrt.real
-
-    mean_diff = sample_mean - real_mean
-    mean_norm = mean_diff @ mean_diff
-
-    trace = np.trace(sample_cov) + np.trace(real_cov) - 2 * np.trace(cov_sqrt)
-
-    fid = mean_norm + trace
-
-    return fid
-
-
-if __name__ == '__main__':
-    device = 'cuda'
-
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--truncation', type=float, default=1)
-    parser.add_argument('--truncation_mean', type=int, default=4096)
-    parser.add_argument('--batch', type=int, default=64)
-    parser.add_argument('--n_sample', type=int, default=50000)
-    parser.add_argument('--size', type=int, default=256)
-    parser.add_argument('--inception', type=str, default=None, required=True)
-    parser.add_argument('ckpt', metavar='CHECKPOINT')
-
-    args = parser.parse_args()
-
-    ckpt = torch.load(args.ckpt)
-
-    g = Generator(args.size, 512, 8).to(device)
-    g.load_state_dict(ckpt['g_ema'])
-    g = nn.DataParallel(g)
-    g.eval()
-
-    if args.truncation < 1:
-        with torch.no_grad():
-            mean_latent = g.mean_latent(args.truncation_mean)
-
-    else:
-        mean_latent = None
-
-    inception = nn.DataParallel(load_patched_inception_v3()).to(device)
-    inception.eval()
-
-    features = extract_feature_from_samples(
-        g, inception, args.truncation, mean_latent, args.batch, args.n_sample, device
-    ).numpy()
-    print(f'extracted {features.shape[0]} features')
-
-    sample_mean = np.mean(features, 0)
-    sample_cov = np.cov(features, rowvar=False)
-
-    with open(args.inception, 'rb') as f:
-        embeds = pickle.load(f)
-        real_mean = embeds['mean']
-        real_cov = embeds['cov']
-
-    fid = calc_fid(sample_mean, sample_cov, real_mean, real_cov)
-
-    print('fid:', fid)
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/parallelfolder.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/parallelfolder.py
deleted file mode 100644
index a741691569a7c85e96d3b3d9be12b40d508f0044..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/parallelfolder.py
+++ /dev/null
@@ -1,118 +0,0 @@
-'''
-Variants of pytorch's ImageFolder for loading image datasets with more
-information, such as parallel feature channels in separate files,
-cached files with lists of filenames, etc.
-'''
-
-import os, torch, re
-import torch.utils.data as data
-from torchvision.datasets.folder import default_loader
-from PIL import Image
-from collections import OrderedDict
-from .progress import default_progress
-
-def grayscale_loader(path):
-    with open(path, 'rb') as f:
-        return Image.open(f).convert('L')
-
-class ParallelImageFolders(data.Dataset):
-    """
-    A data loader that looks for parallel image filenames, for example
-
-    photo1/park/004234.jpg
-    photo1/park/004236.jpg
-    photo1/park/004237.jpg
-
-    photo2/park/004234.png
-    photo2/park/004236.png
-    photo2/park/004237.png
-    """
-    def __init__(self, image_roots,
-            transform=None,
-            loader=default_loader,
-            stacker=None,
-            intersection=False,
-            verbose=None,
-            size=None):
-        self.image_roots = image_roots
-        self.images = make_parallel_dataset(image_roots,
-                intersection=intersection, verbose=verbose)
-        if len(self.images) == 0:
-            raise RuntimeError("Found 0 images within: %s" % image_roots)
-        if size is not None:
-            self.image = self.images[:size]
-        if transform is not None and not hasattr(transform, '__iter__'):
-            transform = [transform for _ in image_roots]
-        self.transforms = transform
-        self.stacker = stacker
-        self.loader = loader
-
-    def __getitem__(self, index):
-        paths = self.images[index]
-        sources = [self.loader(path) for path in paths]
-        # Add a common shared state dict to allow random crops/flips to be
-        # coordinated.
-        shared_state = {}
-        for s in sources:
-            s.shared_state = shared_state
-        if self.transforms is not None:
-            sources = [transform(source)
-                    for source, transform in zip(sources, self.transforms)]
-        if self.stacker is not None:
-            sources = self.stacker(sources)
-        else:
-            sources = tuple(sources)
-        return sources
-
-    def __len__(self):
-        return len(self.images)
-
-def is_npy_file(path):
-    return path.endswith('.npy') or path.endswith('.NPY')
-
-def is_image_file(path):
-    return None != re.search(r'\.(jpe?g|png)$', path, re.IGNORECASE)
-
-def walk_image_files(rootdir, verbose=None):
-    progress = default_progress(verbose)
-    indexfile = '%s.txt' % rootdir
-    if os.path.isfile(indexfile):
-        basedir = os.path.dirname(rootdir)
-        with open(indexfile) as f:
-            result = sorted([os.path.join(basedir, line.strip())
-                for line in progress(f.readlines(),
-                    desc='Reading %s' % os.path.basename(indexfile))])
-            return result
-    result = []
-    for dirname, _, fnames in sorted(progress(os.walk(rootdir),
-            desc='Walking %s' % os.path.basename(rootdir))):
-        for fname in sorted(fnames):
-            if is_image_file(fname) or is_npy_file(fname):
-                result.append(os.path.join(dirname, fname))
-    return result
-
-def make_parallel_dataset(image_roots, intersection=False, verbose=None):
-    """
-    Returns [(img1, img2), (img1, img2)..]
-    """
-    image_roots = [os.path.expanduser(d) for d in image_roots]
-    image_sets = OrderedDict()
-    for j, root in enumerate(image_roots):
-        for path in walk_image_files(root, verbose=verbose):
-            key = os.path.splitext(os.path.relpath(path, root))[0]
-            if key not in image_sets:
-                image_sets[key] = []
-            if not intersection and len(image_sets[key]) != j:
-                raise RuntimeError(
-                    'Images not parallel: %s missing from one dir' % (key))
-            image_sets[key].append(path)
-    tuples = []
-    for key, value in image_sets.items():
-        if len(value) != len(image_roots):
-            if intersection:
-                continue
-            else:
-                raise RuntimeError(
-                    'Images not parallel: %s missing from one dir' % (key))
-        tuples.append(tuple(value))
-    return tuples
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/data/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/data/__init__.py
deleted file mode 100644
index 47bb6e24ddf25aa4fd5bf0fe9672f89099efb9b4..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/data/__init__.py
+++ /dev/null
@@ -1,11 +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 .asr_dataset import AsrDataset
-
-
-__all__ = [
-    "AsrDataset",
-]
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/translation/prepare-wmt14en2fr.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/translation/prepare-wmt14en2fr.sh
deleted file mode 100644
index 2ac97a5b76fab255449493488ed8bd67350a7bac..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/translation/prepare-wmt14en2fr.sh
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/bin/bash
-# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-git clone https://github.com/rsennrich/subword-nmt.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-BPEROOT=subword-nmt/subword_nmt
-BPE_TOKENS=40000
-
-URLS=(
-    "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
-    "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
-    "http://statmt.org/wmt13/training-parallel-un.tgz"
-    "http://statmt.org/wmt14/training-parallel-nc-v9.tgz"
-    "http://statmt.org/wmt10/training-giga-fren.tar"
-    "http://statmt.org/wmt14/test-full.tgz"
-)
-FILES=(
-    "training-parallel-europarl-v7.tgz"
-    "training-parallel-commoncrawl.tgz"
-    "training-parallel-un.tgz"
-    "training-parallel-nc-v9.tgz"
-    "training-giga-fren.tar"
-    "test-full.tgz"
-)
-CORPORA=(
-    "training/europarl-v7.fr-en"
-    "commoncrawl.fr-en"
-    "un/undoc.2000.fr-en"
-    "training/news-commentary-v9.fr-en"
-    "giga-fren.release2.fixed"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
-    echo "Please set SCRIPTS variable correctly to point to Moses scripts."
-    exit
-fi
-
-src=en
-tgt=fr
-lang=en-fr
-prep=wmt14_en_fr
-tmp=$prep/tmp
-orig=orig
-
-mkdir -p $orig $tmp $prep
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
-    file=${FILES[i]}
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        url=${URLS[i]}
-        wget "$url"
-        if [ -f $file ]; then
-            echo "$url successfully downloaded."
-        else
-            echo "$url not successfully downloaded."
-            exit -1
-        fi
-        if [ ${file: -4} == ".tgz" ]; then
-            tar zxvf $file
-        elif [ ${file: -4} == ".tar" ]; then
-            tar xvf $file
-        fi
-    fi
-done
-
-gunzip giga-fren.release2.fixed.*.gz
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
-    rm $tmp/train.tags.$lang.tok.$l
-    for f in "${CORPORA[@]}"; do
-        cat $orig/$f.$l | \
-            perl $NORM_PUNC $l | \
-            perl $REM_NON_PRINT_CHAR | \
-            perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l
-    done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
-    if [ "$l" == "$src" ]; then
-        t="src"
-    else
-        t="ref"
-    fi
-    grep '<seg id' $orig/test-full/newstest2014-fren-$t.$l.sgm | \
-        sed -e 's/<seg id="[0-9]*">\s*//g' | \
-        sed -e 's/\s*<\/seg>\s*//g' | \
-        sed -e "s/\’/\'/g" | \
-    perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l
-    echo ""
-done
-
-echo "splitting train and valid..."
-for l in $src $tgt; do
-    awk '{if (NR%1333 == 0)  print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l
-    awk '{if (NR%1333 != 0)  print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l
-done
-
-TRAIN=$tmp/train.fr-en
-BPE_CODE=$prep/code
-rm -f $TRAIN
-for l in $src $tgt; do
-    cat $tmp/train.$l >> $TRAIN
-done
-
-echo "learn_bpe.py on ${TRAIN}..."
-python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE
-
-for L in $src $tgt; do
-    for f in train.$L valid.$L test.$L; do
-        echo "apply_bpe.py to ${f}..."
-        python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f
-    done
-done
-
-perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250
-perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250
-
-for L in $src $tgt; do
-    cp $tmp/bpe.test.$L $prep/test.$L
-done
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/add_target_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/add_target_dataset.py
deleted file mode 100644
index d8a08e746dedb8a5d9d9e4b9ad149e0da469d644..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/add_target_dataset.py
+++ /dev/null
@@ -1,79 +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
-
-from . import BaseWrapperDataset, data_utils
-from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
-
-
-class AddTargetDataset(BaseWrapperDataset):
-    def __init__(
-        self,
-        dataset,
-        labels,
-        pad,
-        eos,
-        batch_targets,
-        process_label=None,
-        label_len_fn=None,
-        add_to_input=False,
-        text_compression_level=TextCompressionLevel.none
-    ):
-        super().__init__(dataset)
-        self.labels = labels
-        self.batch_targets = batch_targets
-        self.pad = pad
-        self.eos = eos
-        self.process_label = process_label
-        self.label_len_fn = label_len_fn
-        self.add_to_input = add_to_input
-        self.text_compressor = TextCompressor(level=text_compression_level)
-
-    def get_label(self, index, process_fn=None):
-        lbl = self.labels[index]
-        lbl = self.text_compressor.decompress(lbl)
-        return lbl if process_fn is None else process_fn(lbl)
-
-    def __getitem__(self, index):
-        item = self.dataset[index]
-        item["label"] = self.get_label(index, process_fn=self.process_label)
-        return item
-
-    def size(self, index):
-        sz = self.dataset.size(index)
-        own_sz = self.label_len_fn(self.get_label(index))
-        return sz, own_sz
-
-    def collater(self, samples):
-        collated = self.dataset.collater(samples)
-        if len(collated) == 0:
-            return collated
-        indices = set(collated["id"].tolist())
-        target = [s["label"] for s in samples if s["id"] in indices]
-
-        if self.batch_targets:
-            collated["target_lengths"] = torch.LongTensor([len(t) for t in target])
-            target = data_utils.collate_tokens(target, pad_idx=self.pad, left_pad=False)
-            collated["ntokens"] = collated["target_lengths"].sum().item()
-        else:
-            collated["ntokens"] = sum([len(t) for t in target])
-
-        collated["target"] = target
-
-        if self.add_to_input:
-            eos = target.new_full((target.size(0), 1), self.eos)
-            collated["target"] = torch.cat([target, eos], dim=-1).long()
-            collated["net_input"]["prev_output_tokens"] = torch.cat(
-                [eos, target], dim=-1
-            ).long()
-            collated["ntokens"] += target.size(0)
-        return collated
-
-    def filter_indices_by_size(self, indices, max_sizes):
-        indices, ignored = data_utils._filter_by_size_dynamic(
-            indices, self.size, max_sizes
-        )
-        return indices, ignored
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/vggblock.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/vggblock.py
deleted file mode 100644
index ee5ee19a34816c7350c21fba7c4907fec8ca7a61..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/vggblock.py
+++ /dev/null
@@ -1,116 +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 __future__ import absolute_import, division, print_function, unicode_literals
-
-from collections.abc import Iterable
-from itertools import repeat
-
-import torch
-import torch.nn as nn
-
-
-def _pair(v):
-    if isinstance(v, Iterable):
-        assert len(v) == 2, "len(v) != 2"
-        return v
-    return tuple(repeat(v, 2))
-
-
-def infer_conv_output_dim(conv_op, input_dim, sample_inchannel):
-    sample_seq_len = 200
-    sample_bsz = 10
-    x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim)
-    # N x C x H x W
-    # N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim
-    x = conv_op(x)
-    # N x C x H x W
-    x = x.transpose(1, 2)
-    # N x H x C x W
-    bsz, seq = x.size()[:2]
-    per_channel_dim = x.size()[3]
-    # bsz: N, seq: H, CxW the rest
-    return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim
-
-
-class VGGBlock(torch.nn.Module):
-    """
-    VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf
-
-    Args:
-        in_channels: (int) number of input channels (typically 1)
-        out_channels: (int) number of output channels
-        conv_kernel_size: convolution channels
-        pooling_kernel_size: the size of the pooling window to take a max over
-        num_conv_layers: (int) number of convolution layers
-        input_dim: (int) input dimension
-        conv_stride: the stride of the convolving kernel.
-            Can be a single number or a tuple (sH, sW)  Default: 1
-        padding: implicit paddings on both sides of the input.
-            Can be a single number or a tuple (padH, padW). Default: None
-        layer_norm: (bool) if layer norm is going to be applied. Default: False
-
-    Shape:
-        Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
-        Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        conv_kernel_size,
-        pooling_kernel_size,
-        num_conv_layers,
-        input_dim,
-        conv_stride=1,
-        padding=None,
-        layer_norm=False,
-    ):
-        assert (
-            input_dim is not None
-        ), "Need input_dim for LayerNorm and infer_conv_output_dim"
-        super(VGGBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.conv_kernel_size = _pair(conv_kernel_size)
-        self.pooling_kernel_size = _pair(pooling_kernel_size)
-        self.num_conv_layers = num_conv_layers
-        self.padding = (
-            tuple(e // 2 for e in self.conv_kernel_size)
-            if padding is None
-            else _pair(padding)
-        )
-        self.conv_stride = _pair(conv_stride)
-
-        self.layers = nn.ModuleList()
-        for layer in range(num_conv_layers):
-            conv_op = nn.Conv2d(
-                in_channels if layer == 0 else out_channels,
-                out_channels,
-                self.conv_kernel_size,
-                stride=self.conv_stride,
-                padding=self.padding,
-            )
-            self.layers.append(conv_op)
-            if layer_norm:
-                conv_output_dim, per_channel_dim = infer_conv_output_dim(
-                    conv_op, input_dim, in_channels if layer == 0 else out_channels
-                )
-                self.layers.append(nn.LayerNorm(per_channel_dim))
-                input_dim = per_channel_dim
-            self.layers.append(nn.ReLU())
-
-        if self.pooling_kernel_size is not None:
-            pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True)
-            self.layers.append(pool_op)
-            self.total_output_dim, self.output_dim = infer_conv_output_dim(
-                pool_op, input_dim, out_channels
-            )
-
-    def forward(self, x):
-        for i, _ in enumerate(self.layers):
-            x = self.layers[i](x)
-        return x
diff --git a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/processtext/cleaners.py b/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/processtext/cleaners.py
deleted file mode 100644
index 996d6299f6e520600671a08e5897117200bd17ca..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Hindi_TTS/vakyansh_tts/src/glow_tts/processtext/cleaners.py
+++ /dev/null
@@ -1,99 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-"""
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-"""
-
-import re
-from unidecode import unidecode
-from .numbers import normalize_numbers
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r"\s+")
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [
-    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
-    for x in [
-        ("mrs", "misess"),
-        ("mr", "mister"),
-        ("dr", "doctor"),
-        ("st", "saint"),
-        ("co", "company"),
-        ("jr", "junior"),
-        ("maj", "major"),
-        ("gen", "general"),
-        ("drs", "doctors"),
-        ("rev", "reverend"),
-        ("lt", "lieutenant"),
-        ("hon", "honorable"),
-        ("sgt", "sergeant"),
-        ("capt", "captain"),
-        ("esq", "esquire"),
-        ("ltd", "limited"),
-        ("col", "colonel"),
-        ("ft", "fort"),
-    ]
-]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def expand_numbers(text):
-    return normalize_numbers(text)
-
-
-def lowercase(text):
-    return text.lower()
-
-
-def collapse_whitespace(text):
-    return re.sub(_whitespace_re, " ", text)
-
-
-def convert_to_ascii(text):
-    return unidecode(text)
-
-
-def basic_cleaners(text):
-    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def transliteration_cleaners(text):
-    """Pipeline for non-English text that transliterates to ASCII."""
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def english_cleaners(text):
-    """Pipeline for English text, including number and abbreviation expansion."""
-    text = convert_to_ascii(text)
-    text = lowercase(text)
-    text = expand_numbers(text)
-    text = expand_abbreviations(text)
-    text = collapse_whitespace(text)
-    return text
-
-
-def basic_indic_cleaners(text):
-    """Basic pipeline that collapses whitespace without transliteration."""
-    text = collapse_whitespace(text)
-    return text
diff --git a/spaces/HighCWu/GPEN/retinaface/layers/modules/multibox_loss.py b/spaces/HighCWu/GPEN/retinaface/layers/modules/multibox_loss.py
deleted file mode 100644
index cb8d6bb591c802c55dd2675cc1231b056691d112..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/GPEN/retinaface/layers/modules/multibox_loss.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Variable
-from utils.box_utils import match, log_sum_exp
-from data import cfg_mnet
-GPU = cfg_mnet['gpu_train']
-
-class MultiBoxLoss(nn.Module):
-    """SSD Weighted Loss Function
-    Compute Targets:
-        1) Produce Confidence Target Indices by matching  ground truth boxes
-           with (default) 'priorboxes' that have jaccard index > threshold parameter
-           (default threshold: 0.5).
-        2) Produce localization target by 'encoding' variance into offsets of ground
-           truth boxes and their matched  'priorboxes'.
-        3) Hard negative mining to filter the excessive number of negative examples
-           that comes with using a large number of default bounding boxes.
-           (default negative:positive ratio 3:1)
-    Objective Loss:
-        L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
-        Where, Lconf==the CrossEntropy Loss and Lloc==the SmoothL1 Loss
-        weighted by α which==set to 1 by cross val.
-        Args:
-            c: class confidences,
-            l: predicted boxes,
-            g: ground truth boxes
-            N: number of matched default boxes
-        See: https://arxiv.org/pdf/1512.02325.pdf for more details.
-    """
-
-    def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target):
-        super(MultiBoxLoss, self).__init__()
-        self.num_classes = num_classes
-        self.threshold = overlap_thresh
-        self.background_label = bkg_label
-        self.encode_target = encode_target
-        self.use_prior_for_matching = prior_for_matching
-        self.do_neg_mining = neg_mining
-        self.negpos_ratio = neg_pos
-        self.neg_overlap = neg_overlap
-        self.variance = [0.1, 0.2]
-
-    def forward(self, predictions, priors, targets):
-        """Multibox Loss
-        Args:
-            predictions (tuple): A tuple containing loc preds, conf preds,
-            and prior boxes from SSD net.
-                conf shape: torch.size(batch_size,num_priors,num_classes)
-                loc shape: torch.size(batch_size,num_priors,4)
-                priors shape: torch.size(num_priors,4)
-
-            ground_truth (tensor): Ground truth boxes and labels for a batch,
-                shape: [batch_size,num_objs,5] (last idx==the label).
-        """
-
-        loc_data, conf_data, landm_data = predictions
-        priors = priors
-        num = loc_data.size(0)
-        num_priors = (priors.size(0))
-
-        # match priors (default boxes) and ground truth boxes
-        loc_t = torch.Tensor(num, num_priors, 4)
-        landm_t = torch.Tensor(num, num_priors, 10)
-        conf_t = torch.LongTensor(num, num_priors)
-        for idx in range(num):
-            truths = targets[idx][:, :4].data
-            labels = targets[idx][:, -1].data
-            landms = targets[idx][:, 4:14].data
-            defaults = priors.data
-            match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx)
-        if GPU:
-            loc_t = loc_t.cuda()
-            conf_t = conf_t.cuda()
-            landm_t = landm_t.cuda()
-
-        zeros = torch.tensor(0).cuda()
-        # landm Loss (Smooth L1)
-        # Shape: [batch,num_priors,10]
-        pos1 = conf_t > zeros
-        num_pos_landm = pos1.long().sum(1, keepdim=True)
-        N1 = max(num_pos_landm.data.sum().float(), 1)
-        pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data)
-        landm_p = landm_data[pos_idx1].view(-1, 10)
-        landm_t = landm_t[pos_idx1].view(-1, 10)
-        loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum')
-
-
-        pos = conf_t != zeros
-        conf_t[pos] = 1
-
-        # Localization Loss (Smooth L1)
-        # Shape: [batch,num_priors,4]
-        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
-        loc_p = loc_data[pos_idx].view(-1, 4)
-        loc_t = loc_t[pos_idx].view(-1, 4)
-        loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
-
-        # Compute max conf across batch for hard negative mining
-        batch_conf = conf_data.view(-1, self.num_classes)
-        loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))
-
-        # Hard Negative Mining
-        loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
-        loss_c = loss_c.view(num, -1)
-        _, loss_idx = loss_c.sort(1, descending=True)
-        _, idx_rank = loss_idx.sort(1)
-        num_pos = pos.long().sum(1, keepdim=True)
-        num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
-        neg = idx_rank < num_neg.expand_as(idx_rank)
-
-        # Confidence Loss Including Positive and Negative Examples
-        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
-        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
-        conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes)
-        targets_weighted = conf_t[(pos+neg).gt(0)]
-        loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
-
-        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
-        N = max(num_pos.data.sum().float(), 1)
-        loss_l /= N
-        loss_c /= N
-        loss_landm /= N1
-
-        return loss_l, loss_c, loss_landm
diff --git a/spaces/Hoodady/3DFuse/ldm/modules/image_degradation/utils_image.py b/spaces/Hoodady/3DFuse/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000
--- a/spaces/Hoodady/3DFuse/ldm/modules/image_degradation/utils_image.py
+++ /dev/null
@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    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(img1**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(img1 * 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()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/.github/SECURITY.md b/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/.github/SECURITY.md
deleted file mode 100644
index aa3e8409da6b525245454ad0360642cbaead5569..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/.github/SECURITY.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Security Policy
-
-We aim to make YOLOv5 🚀 as secure as possible! If you find potential vulnerabilities or have any concerns please let us know so we can investigate and take corrective action if needed.
-
-### Reporting a Vulnerability
-
-To report vulnerabilities please email us at hello@ultralytics.com or visit https://ultralytics.com/contact. Thank you!
diff --git a/spaces/Iceclear/StableSR/StableSR/taming/data/image_transforms.py b/spaces/Iceclear/StableSR/StableSR/taming/data/image_transforms.py
deleted file mode 100644
index 657ac332174e0ac72f68315271ffbd757b771a0f..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/taming/data/image_transforms.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import random
-import warnings
-from typing import Union
-
-import torch
-from torch import Tensor
-from torchvision.transforms import RandomCrop, functional as F, CenterCrop, RandomHorizontalFlip, PILToTensor
-from torchvision.transforms.functional import _get_image_size as get_image_size
-
-from taming.data.helper_types import BoundingBox, Image
-
-pil_to_tensor = PILToTensor()
-
-
-def convert_pil_to_tensor(image: Image) -> Tensor:
-    with warnings.catch_warnings():
-        # to filter PyTorch UserWarning as described here: https://github.com/pytorch/vision/issues/2194
-        warnings.simplefilter("ignore")
-        return pil_to_tensor(image)
-
-
-class RandomCrop1dReturnCoordinates(RandomCrop):
-    def forward(self, img: Image) -> (BoundingBox, Image):
-        """
-        Additionally to cropping, returns the relative coordinates of the crop bounding box.
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            Bounding box: x0, y0, w, h
-            PIL Image or Tensor: Cropped image.
-
-        Based on:
-            torchvision.transforms.RandomCrop, torchvision 1.7.0
-        """
-        if self.padding is not None:
-            img = F.pad(img, self.padding, self.fill, self.padding_mode)
-
-        width, height = get_image_size(img)
-        # pad the width if needed
-        if self.pad_if_needed and width < self.size[1]:
-            padding = [self.size[1] - width, 0]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
-        # pad the height if needed
-        if self.pad_if_needed and height < self.size[0]:
-            padding = [0, self.size[0] - height]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
-
-        i, j, h, w = self.get_params(img, self.size)
-        bbox = (j / width, i / height, w / width, h / height)  # x0, y0, w, h
-        return bbox, F.crop(img, i, j, h, w)
-
-
-class Random2dCropReturnCoordinates(torch.nn.Module):
-    """
-    Additionally to cropping, returns the relative coordinates of the crop bounding box.
-    Args:
-        img (PIL Image or Tensor): Image to be cropped.
-
-    Returns:
-        Bounding box: x0, y0, w, h
-        PIL Image or Tensor: Cropped image.
-
-    Based on:
-        torchvision.transforms.RandomCrop, torchvision 1.7.0
-    """
-
-    def __init__(self, min_size: int):
-        super().__init__()
-        self.min_size = min_size
-
-    def forward(self, img: Image) -> (BoundingBox, Image):
-        width, height = get_image_size(img)
-        max_size = min(width, height)
-        if max_size <= self.min_size:
-            size = max_size
-        else:
-            size = random.randint(self.min_size, max_size)
-        top = random.randint(0, height - size)
-        left = random.randint(0, width - size)
-        bbox = left / width, top / height, size / width, size / height
-        return bbox, F.crop(img, top, left, size, size)
-
-
-class CenterCropReturnCoordinates(CenterCrop):
-    @staticmethod
-    def get_bbox_of_center_crop(width: int, height: int) -> BoundingBox:
-        if width > height:
-            w = height / width
-            h = 1.0
-            x0 = 0.5 - w / 2
-            y0 = 0.
-        else:
-            w = 1.0
-            h = width / height
-            x0 = 0.
-            y0 = 0.5 - h / 2
-        return x0, y0, w, h
-
-    def forward(self, img: Union[Image, Tensor]) -> (BoundingBox, Union[Image, Tensor]):
-        """
-        Additionally to cropping, returns the relative coordinates of the crop bounding box.
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            Bounding box: x0, y0, w, h
-            PIL Image or Tensor: Cropped image.
-        Based on:
-            torchvision.transforms.RandomHorizontalFlip (version 1.7.0)
-        """
-        width, height = get_image_size(img)
-        return self.get_bbox_of_center_crop(width, height),  F.center_crop(img, self.size)
-
-
-class RandomHorizontalFlipReturn(RandomHorizontalFlip):
-    def forward(self, img: Image) -> (bool, Image):
-        """
-        Additionally to flipping, returns a boolean whether it was flipped or not.
-        Args:
-            img (PIL Image or Tensor): Image to be flipped.
-
-        Returns:
-            flipped: whether the image was flipped or not
-            PIL Image or Tensor: Randomly flipped image.
-
-        Based on:
-            torchvision.transforms.RandomHorizontalFlip (version 1.7.0)
-        """
-        if torch.rand(1) < self.p:
-            return True, F.hflip(img)
-        return False, img
diff --git a/spaces/IgorSense/Diffusion_Space2/README.md b/spaces/IgorSense/Diffusion_Space2/README.md
deleted file mode 100644
index b491efa53dd22b86b0087db4bf6388e44e0a940f..0000000000000000000000000000000000000000
--- a/spaces/IgorSense/Diffusion_Space2/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Diffusion Space
-emoji: 💽
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: true
-license: creativeml-openrail-m
-duplicated_from: richdatasci/Diffusion_Space
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/utils.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/utils.py
deleted file mode 100644
index 6d7c15c9242ed8a9bc59fbb3b450cca394720bb8..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/utils.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from enum import Enum
-
-import yaml
-from easydict import EasyDict as edict
-import torch.nn as nn
-import torch
-
-
-def load_yaml(path):
-    with open(path, 'r') as f:
-        return edict(yaml.safe_load(f))
-
-
-def move_to_device(obj, device):
-    if isinstance(obj, nn.Module):
-        return obj.to(device)
-    if torch.is_tensor(obj):
-        return obj.to(device)
-    if isinstance(obj, (tuple, list)):
-        return [move_to_device(el, device) for el in obj]
-    if isinstance(obj, dict):
-        return {name: move_to_device(val, device) for name, val in obj.items()}
-    raise ValueError(f'Unexpected type {type(obj)}')
-
-
-class SmallMode(Enum):
-    DROP = "drop"
-    UPSCALE = "upscale"
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/trainers/base.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/trainers/base.py
deleted file mode 100644
index f1b1c66fc96e7edfba7b1ee193272f92b5db7438..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/trainers/base.py
+++ /dev/null
@@ -1,291 +0,0 @@
-import copy
-import logging
-from typing import Dict, Tuple
-
-import pandas as pd
-import pytorch_lightning as ptl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DistributedSampler
-
-from saicinpainting.evaluation import make_evaluator
-from saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader
-from saicinpainting.training.losses.adversarial import make_discrim_loss
-from saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL
-from saicinpainting.training.modules import make_generator, make_discriminator
-from saicinpainting.training.visualizers import make_visualizer
-from saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \
-    get_has_ddp_rank
-
-LOGGER = logging.getLogger(__name__)
-
-
-def make_optimizer(parameters, kind='adamw', **kwargs):
-    if kind == 'adam':
-        optimizer_class = torch.optim.Adam
-    elif kind == 'adamw':
-        optimizer_class = torch.optim.AdamW
-    else:
-        raise ValueError(f'Unknown optimizer kind {kind}')
-    return optimizer_class(parameters, **kwargs)
-
-
-def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999):
-    with torch.no_grad():
-        res_params = dict(result.named_parameters())
-        new_params = dict(new_iterate_model.named_parameters())
-
-        for k in res_params.keys():
-            res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay)
-
-
-def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'):
-    batch_size, _, height, width = base_tensor.shape
-    cur_height, cur_width = height, width
-    result = []
-    align_corners = False if scale_mode in ('bilinear', 'bicubic') else None
-    for _ in range(scales):
-        cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device)
-        cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners)
-        result.append(cur_sample_scaled)
-        cur_height //= 2
-        cur_width //= 2
-    return torch.cat(result, dim=1)
-
-
-class BaseInpaintingTrainingModule(ptl.LightningModule):
-    def __init__(self, config, use_ddp, *args,  predict_only=False, visualize_each_iters=100,
-                 average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000,
-                 average_generator_period=10, store_discr_outputs_for_vis=False,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        LOGGER.info('BaseInpaintingTrainingModule init called')
-
-        self.config = config
-
-        self.generator = make_generator(config, **self.config.generator)
-        self.use_ddp = use_ddp
-
-        if not get_has_ddp_rank():
-            LOGGER.info(f'Generator\n{self.generator}')
-
-        if not predict_only:
-            self.save_hyperparameters(self.config)
-            self.discriminator = make_discriminator(**self.config.discriminator)
-            self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial)
-            self.visualizer = make_visualizer(**self.config.visualizer)
-            self.val_evaluator = make_evaluator(**self.config.evaluator)
-            self.test_evaluator = make_evaluator(**self.config.evaluator)
-
-            if not get_has_ddp_rank():
-                LOGGER.info(f'Discriminator\n{self.discriminator}')
-
-            extra_val = self.config.data.get('extra_val', ())
-            if extra_val:
-                self.extra_val_titles = list(extra_val)
-                self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator)
-                                                       for k in extra_val})
-            else:
-                self.extra_evaluators = {}
-
-            self.average_generator = average_generator
-            self.generator_avg_beta = generator_avg_beta
-            self.average_generator_start_step = average_generator_start_step
-            self.average_generator_period = average_generator_period
-            self.generator_average = None
-            self.last_generator_averaging_step = -1
-            self.store_discr_outputs_for_vis = store_discr_outputs_for_vis
-
-            if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0:
-                self.loss_l1 = nn.L1Loss(reduction='none')
-
-            if self.config.losses.get("mse", {"weight": 0})['weight'] > 0:
-                self.loss_mse = nn.MSELoss(reduction='none')
-            
-            if self.config.losses.perceptual.weight > 0:
-                self.loss_pl = PerceptualLoss()
-
-            if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0:
-                self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl)
-            else:
-                self.loss_resnet_pl = None
-
-        self.visualize_each_iters = visualize_each_iters
-        LOGGER.info('BaseInpaintingTrainingModule init done')
-
-    def configure_optimizers(self):
-        discriminator_params = list(self.discriminator.parameters())
-        return [
-            dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)),
-            dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)),
-        ]
-
-    def train_dataloader(self):
-        kwargs = dict(self.config.data.train)
-        if self.use_ddp:
-            kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
-                                        rank=self.trainer.global_rank,
-                                        shuffle=True)
-        dataloader = make_default_train_dataloader(**self.config.data.train)
-        return dataloader
-
-    def val_dataloader(self):
-        res = [make_default_val_dataloader(**self.config.data.val)]
-
-        if self.config.data.visual_test is not None:
-            res = res + [make_default_val_dataloader(**self.config.data.visual_test)]
-        else:
-            res = res + res
-
-        extra_val = self.config.data.get('extra_val', ())
-        if extra_val:
-            res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles]
-
-        return res
-
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
-        self._is_training_step = True
-        return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx)
-
-    def validation_step(self, batch, batch_idx, dataloader_idx):
-        extra_val_key = None
-        if dataloader_idx == 0:
-            mode = 'val'
-        elif dataloader_idx == 1:
-            mode = 'test'
-        else:
-            mode = 'extra_val'
-            extra_val_key = self.extra_val_titles[dataloader_idx - 2]
-        self._is_training_step = False
-        return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key)
-
-    def training_step_end(self, batch_parts_outputs):
-        if self.training and self.average_generator \
-                and self.global_step >= self.average_generator_start_step \
-                and self.global_step >= self.last_generator_averaging_step + self.average_generator_period:
-            if self.generator_average is None:
-                self.generator_average = copy.deepcopy(self.generator)
-            else:
-                update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta)
-            self.last_generator_averaging_step = self.global_step
-
-        full_loss = (batch_parts_outputs['loss'].mean()
-                     if torch.is_tensor(batch_parts_outputs['loss'])  # loss is not tensor when no discriminator used
-                     else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True))
-        log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()}
-        self.log_dict(log_info, on_step=True, on_epoch=False)
-        return full_loss
-
-    def validation_epoch_end(self, outputs):
-        outputs = [step_out for out_group in outputs for step_out in out_group]
-        averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs)
-        self.log_dict({k: v.mean() for k, v in averaged_logs.items()})
-
-        pd.set_option('display.max_columns', 500)
-        pd.set_option('display.width', 1000)
-
-        # standard validation
-        val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s]
-        val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states)
-        val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0)
-        val_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-        LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, '
-                    f'total {self.global_step} iterations:\n{val_evaluator_res_df}')
-
-        for k, v in flatten_dict(val_evaluator_res).items():
-            self.log(f'val_{k}', v)
-
-        # standard visual test
-        test_evaluator_states = [s['test_evaluator_state'] for s in outputs
-                                 if 'test_evaluator_state' in s]
-        test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states)
-        test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0)
-        test_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-        LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, '
-                    f'total {self.global_step} iterations:\n{test_evaluator_res_df}')
-
-        for k, v in flatten_dict(test_evaluator_res).items():
-            self.log(f'test_{k}', v)
-
-        # extra validations
-        if self.extra_evaluators:
-            for cur_eval_title, cur_evaluator in self.extra_evaluators.items():
-                cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state'
-                cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s]
-                cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states)
-                cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0)
-                cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-                LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, '
-                            f'total {self.global_step} iterations:\n{cur_evaluator_res_df}')
-                for k, v in flatten_dict(cur_evaluator_res).items():
-                    self.log(f'extra_val_{cur_eval_title}_{k}', v)
-
-    def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None):
-        if optimizer_idx == 0:  # step for generator
-            set_requires_grad(self.generator, True)
-            set_requires_grad(self.discriminator, False)
-        elif optimizer_idx == 1:  # step for discriminator
-            set_requires_grad(self.generator, False)
-            set_requires_grad(self.discriminator, True)
-
-        batch = self(batch)
-
-        total_loss = 0
-        metrics = {}
-
-        if optimizer_idx is None or optimizer_idx == 0:  # step for generator
-            total_loss, metrics = self.generator_loss(batch)
-
-        elif optimizer_idx is None or optimizer_idx == 1:  # step for discriminator
-            if self.config.losses.adversarial.weight > 0:
-                total_loss, metrics = self.discriminator_loss(batch)
-
-        if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'):
-            if self.config.losses.adversarial.weight > 0:
-                if self.store_discr_outputs_for_vis:
-                    with torch.no_grad():
-                        self.store_discr_outputs(batch)
-            vis_suffix = f'_{mode}'
-            if mode == 'extra_val':
-                vis_suffix += f'_{extra_val_key}'
-            self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix)
-
-        metrics_prefix = f'{mode}_'
-        if mode == 'extra_val':
-            metrics_prefix += f'{extra_val_key}_'
-        result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix))
-        if mode == 'val':
-            result['val_evaluator_state'] = self.val_evaluator.process_batch(batch)
-        elif mode == 'test':
-            result['test_evaluator_state'] = self.test_evaluator.process_batch(batch)
-        elif mode == 'extra_val':
-            result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch)
-
-        return result
-
-    def get_current_generator(self, no_average=False):
-        if not no_average and not self.training and self.average_generator and self.generator_average is not None:
-            return self.generator_average
-        return self.generator
-
-    def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-        """Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys"""
-        raise NotImplementedError()
-
-    def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        raise NotImplementedError()
-
-    def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        raise NotImplementedError()
-
-    def store_discr_outputs(self, batch):
-        out_size = batch['image'].shape[2:]
-        discr_real_out, _ = self.discriminator(batch['image'])
-        discr_fake_out, _ = self.discriminator(batch['predicted_image'])
-        batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest')
-        batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest')
-        batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake']
-
-    def get_ddp_rank(self):
-        return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None
diff --git a/spaces/JackBAI/MassageMateNLP/models/bert/velocity_z/README.md b/spaces/JackBAI/MassageMateNLP/models/bert/velocity_z/README.md
deleted file mode 100644
index 6ed52ff5c68d3ffcbe388a5651b3c3bf8020eeed..0000000000000000000000000000000000000000
--- a/spaces/JackBAI/MassageMateNLP/models/bert/velocity_z/README.md
+++ /dev/null
@@ -1,56 +0,0 @@
----
-license: apache-2.0
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: velocity_z
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# velocity_z
-
-This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.6314
-- Accuracy: 0.7592
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 128
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 12.0
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.25.1
-- Pytorch 1.11.0+cu113
-- Datasets 2.8.0
-- Tokenizers 0.13.2
diff --git a/spaces/Jeff2323/ai-comic-factory/src/lib/uploadToHuggingFace.ts b/spaces/Jeff2323/ai-comic-factory/src/lib/uploadToHuggingFace.ts
deleted file mode 100644
index de255e66953088fa2d2cfda90d8865ccff91cfd3..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/src/lib/uploadToHuggingFace.ts
+++ /dev/null
@@ -1,16 +0,0 @@
-export async function uploadToHuggingFace(file: 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
-}
\ No newline at end of file
diff --git a/spaces/JeffJing/ZookChatBot/steamship/base/package_spec.py b/spaces/JeffJing/ZookChatBot/steamship/base/package_spec.py
deleted file mode 100644
index 8e51416419e32e4b3f55c0611ec1bb53f6099caf..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/base/package_spec.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""Objects for recording and reporting upon the introspected interface of a Steamship Package."""
-import inspect
-from enum import Enum
-from typing import Dict, List, Optional, Union, get_args, get_origin
-
-from steamship import SteamshipError
-from steamship.base.configuration import CamelModel
-from steamship.utils.url import Verb
-
-
-class ArgSpec(CamelModel):
-    """An argument passed to a method."""
-
-    # The name of the argument.
-    name: str
-    # The kind of the argument, reported by str(annotation) via the `inspect` library. E.g. <class 'int'>
-    kind: str
-    # Possible values, if the kind is an enum type
-    values: Optional[List[str]]
-
-    def __init__(self, name: str, parameter: inspect.Parameter):
-        if name == "self":
-            raise SteamshipError(
-                message="Attempt to interpret the `self` object as a method parameter."
-            )
-        values = None
-        if isinstance(parameter.annotation, type):
-            if issubclass(parameter.annotation, Enum):
-                values = [choice.value for choice in parameter.annotation]
-        elif get_origin(parameter.annotation) is Union:
-            args = get_args(parameter.annotation)
-            # For now, only deal with the case where the Union is an Optional[Enum]
-            if len(args) == 2 and type(None) in args:
-                optional_arg = [t for t in args if t != type(None)][0]  # noqa: E721
-                if issubclass(optional_arg, Enum):
-                    values = [choice.value for choice in optional_arg]
-
-        super().__init__(name=name, kind=str(parameter.annotation), values=values)
-
-    def pprint(self, name_width: Optional[int] = None, prefix: str = "") -> str:
-        """Returns a pretty printable representation of this argument."""
-        width = name_width or len(self.name)
-        ret = f"{prefix}{self.name.ljust(width)} - {self.kind}"
-        return ret
-
-
-class MethodSpec(CamelModel):
-    """A method, callable remotely, on an object."""
-
-    # The HTTP Path at which the method is callable.
-    path: str
-
-    # The HTTP Verb at which the method is callable. Defaults to POST
-    verb: str
-
-    # The return type. Reported by str(annotation) via the `inspect` library. E.g. <class 'int'>
-    returns: str
-
-    # The docstring of the method.
-    doc: Optional[str] = None
-
-    # The named arguments of the method. Positional arguments are not permitted.
-    args: Optional[List[ArgSpec]] = None
-
-    # Additional configuration around this endpoint.
-    # Note: The actual type of this is Optional[Dict[str, Union[str, bool, int, float]]]
-    #       But if Pydantic sees that, it attempts to force all values to be str, which is wrong.
-    config: Optional[Dict] = None
-
-    @staticmethod
-    def clean_path(path: str = "") -> str:
-        """Ensure that the path always starts with /, and at minimum must be at least /."""
-        if not path:
-            path = "/"
-        elif path[0] != "/":
-            path = f"/{path}"
-        return path
-
-    def __init__(
-        self,
-        cls: object,
-        name: str,
-        path: str = None,
-        verb: Verb = Verb.POST,
-        config: Dict[str, Union[str, bool, int, float]] = None,
-    ):
-        # Set the path
-        if path is None and name is not None:
-            path = f"/{name}"
-        path = MethodSpec.clean_path(path)
-
-        # Get the function on the class so that we can inspect it
-        func = getattr(cls, name)
-        sig = inspect.signature(func)
-
-        # Set the return type
-        returns = str(sig.return_annotation)
-
-        # Set the docstring
-        doc = func.__doc__
-
-        # Set the arguments
-        args = []
-        for p in sig.parameters:
-            if p == "self":
-                continue
-            args.append(ArgSpec(p, sig.parameters[p]))
-
-        super().__init__(path=path, verb=verb, returns=returns, doc=doc, args=args, config=config)
-
-    def pprint(self, name_width: Optional[int] = None, prefix: str = "  ") -> str:
-        """Returns a pretty printable representation of this method."""
-
-        width = name_width or len(self.path)
-        ret = f"{self.verb.ljust(4)} {self.path.lstrip('/').ljust(width)} -> {self.returns}"
-        if self.args:
-            name_width = max([(len(arg.name) if arg.name else 0) for arg in self.args])
-            for arg in self.args:
-                arg_doc_string = arg.print(name_width, prefix)
-                ret += f"\n{arg_doc_string}"
-        return ret
-
-
-class PackageSpec(CamelModel):
-    """A package, representing a remotely instantiable service."""
-
-    # The name of the package
-    name: str
-
-    # The docstring of the package
-    doc: Optional[str] = None
-
-    # The list of methods the package exposes remotely
-    methods: Optional[List[MethodSpec]] = None
-
-    def pprint(self, prefix: str = "  ") -> str:
-        """Returns a pretty printable representation of this package."""
-        underline = "=" * len(self.name)
-        ret = f"{self.name}\n{underline}\n"
-        if self.doc:
-            ret += f"{self.doc}\n\n"
-        else:
-            ret += "\n"
-
-        if self.methods:
-            name_width = max([len(method.path) or 0 for method in self.methods])
-            for method in self.methods:
-                method_doc_string = method.pprint(name_width, prefix)
-                ret += f"\n{method_doc_string}"
-        return ret
diff --git a/spaces/Joabutt/Colourizer/README.md b/spaces/Joabutt/Colourizer/README.md
deleted file mode 100644
index d2806fafa3f4eda1a2bcd18db540a101afe989cd..0000000000000000000000000000000000000000
--- a/spaces/Joabutt/Colourizer/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Colourizer
-emoji: 🤡
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: mit
----
\ No newline at end of file
diff --git a/spaces/Kaixuanliu/textual-inversion-training/README.md b/spaces/Kaixuanliu/textual-inversion-training/README.md
deleted file mode 100644
index 936acabca42db7710c0aca4e99cee3ae92a67928..0000000000000000000000000000000000000000
--- a/spaces/Kaixuanliu/textual-inversion-training/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Textual Inversion Training
-emoji: 📉
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: Intel/textual-inversion-training
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/julius/lowpass.py b/spaces/Kangarroar/ApplioRVC-Inference/julius/lowpass.py
deleted file mode 100644
index 0eb46e382b20bfc2d93482f9f027986b863de6f0..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/julius/lowpass.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-FIR windowed sinc lowpass filters.
-"""
-
-import math
-from typing import Sequence, Optional
-
-import torch
-from torch.nn import functional as F
-
-from .core import sinc
-from .fftconv import fft_conv1d
-from .utils import simple_repr
-
-
-class LowPassFilters(torch.nn.Module):
-    """
-    Bank of low pass filters. Note that a high pass or band pass filter can easily
-    be implemented by substracting a same signal processed with low pass filters with different
-    frequencies (see `julius.bands.SplitBands` for instance).
-    This uses a windowed sinc filter, very similar to the one used in
-    `julius.resample`. However, because we do not change the sample rate here,
-    this filter can be much more efficiently implemented using the FFT convolution from
-    `julius.fftconv`.
-
-    Args:
-        cutoffs (list[float]): list of cutoff frequencies, in [0, 0.5] expressed as `f/f_s` where
-            f_s is the samplerate and `f` is the cutoff frequency.
-            The upper limit is 0.5, because a signal sampled at `f_s` contains only
-            frequencies under `f_s / 2`.
-        stride (int): how much to decimate the output. Keep in mind that decimation
-            of the output is only acceptable if the cutoff frequency is under `1/ (2 * stride)`
-            of the original sampling rate.
-        pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
-            the output will have the same length as the input.
-        zeros (float): Number of zero crossings to keep.
-            Controls the receptive field of the Finite Impulse Response filter.
-            For lowpass filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
-            it is a bad idea to set this to a high value.
-            This is likely appropriate for most use. Lower values
-            will result in a faster filter, but with a slower attenuation around the
-            cutoff frequency.
-        fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
-            If False, uses PyTorch convolutions. If None, either one will be chosen automatically
-            depending on the effective filter size.
-
-
-    ..warning::
-        All the filters will use the same filter size, aligned on the lowest
-        frequency provided. If you combine a lot of filters with very diverse frequencies, it might
-        be more efficient to split them over multiple modules with similar frequencies.
-
-    ..note::
-        A lowpass with a cutoff frequency of 0 is defined as the null function
-        by convention here. This allows for a highpass with a cutoff of 0 to
-        be equal to identity, as defined in `julius.filters.HighPassFilters`.
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[F, *, T']`, with `T'=T` if `pad` is True and `stride` is 1, and
-            `F` is the numer of cutoff frequencies.
-
-    >>> lowpass = LowPassFilters([1/4])
-    >>> x = torch.randn(4, 12, 21, 1024)
-    >>> list(lowpass(x).shape)
-    [1, 4, 12, 21, 1024]
-    """
-
-    def __init__(self, cutoffs: Sequence[float], stride: int = 1, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        self.cutoffs = list(cutoffs)
-        if min(self.cutoffs) < 0:
-            raise ValueError("Minimum cutoff must be larger than zero.")
-        if max(self.cutoffs) > 0.5:
-            raise ValueError("A cutoff above 0.5 does not make sense.")
-        self.stride = stride
-        self.pad = pad
-        self.zeros = zeros
-        self.half_size = int(zeros / min([c for c in self.cutoffs if c > 0]) / 2)
-        if fft is None:
-            fft = self.half_size > 32
-        self.fft = fft
-        window = torch.hann_window(2 * self.half_size + 1, periodic=False)
-        time = torch.arange(-self.half_size, self.half_size + 1)
-        filters = []
-        for cutoff in cutoffs:
-            if cutoff == 0:
-                filter_ = torch.zeros_like(time)
-            else:
-                filter_ = 2 * cutoff * window * sinc(2 * cutoff * math.pi * time)
-                # Normalize filter to have sum = 1, otherwise we will have a small leakage
-                # of the constant component in the input signal.
-                filter_ /= filter_.sum()
-            filters.append(filter_)
-        self.register_buffer("filters", torch.stack(filters)[:, None])
-
-    def forward(self, input):
-        shape = list(input.shape)
-        input = input.view(-1, 1, shape[-1])
-        if self.pad:
-            input = F.pad(input, (self.half_size, self.half_size), mode='replicate')
-        if self.fft:
-            out = fft_conv1d(input, self.filters, stride=self.stride)
-        else:
-            out = F.conv1d(input, self.filters, stride=self.stride)
-        shape.insert(0, len(self.cutoffs))
-        shape[-1] = out.shape[-1]
-        return out.permute(1, 0, 2).reshape(shape)
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-class LowPassFilter(torch.nn.Module):
-    """
-    Same as `LowPassFilters` but applies a single low pass filter.
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
-
-    >>> lowpass = LowPassFilter(1/4, stride=2)
-    >>> x = torch.randn(4, 124)
-    >>> list(lowpass(x).shape)
-    [4, 62]
-    """
-
-    def __init__(self, cutoff: float, stride: int = 1, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        self._lowpasses = LowPassFilters([cutoff], stride, pad, zeros, fft)
-
-    @property
-    def cutoff(self):
-        return self._lowpasses.cutoffs[0]
-
-    @property
-    def stride(self):
-        return self._lowpasses.stride
-
-    @property
-    def pad(self):
-        return self._lowpasses.pad
-
-    @property
-    def zeros(self):
-        return self._lowpasses.zeros
-
-    @property
-    def fft(self):
-        return self._lowpasses.fft
-
-    def forward(self, input):
-        return self._lowpasses(input)[0]
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-def lowpass_filters(input: torch.Tensor,  cutoffs: Sequence[float],
-                    stride: int = 1, pad: bool = True,
-                    zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Functional version of `LowPassFilters`, refer to this class for more information.
-    """
-    return LowPassFilters(cutoffs, stride, pad, zeros, fft).to(input)(input)
-
-
-def lowpass_filter(input: torch.Tensor,  cutoff: float,
-                   stride: int = 1, pad: bool = True,
-                   zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Same as `lowpass_filters` but with a single cutoff frequency.
-    Output will not have a dimension inserted in the front.
-    """
-    return lowpass_filters(input, [cutoff], stride, pad, zeros, fft)[0]
diff --git a/spaces/Krishna3/mygenAIChatBot/README.md b/spaces/Krishna3/mygenAIChatBot/README.md
deleted file mode 100644
index de7f8f959ea811a939e37ce113ef5fcdd18af046..0000000000000000000000000000000000000000
--- a/spaces/Krishna3/mygenAIChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MygenAIChatBot
-emoji: ⚡
-colorFrom: indigo
-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/KyanChen/RSPrompter/mmdet/models/dense_heads/autoassign_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/autoassign_head.py
deleted file mode 100644
index a2b30ff0d7d41205f0a92ede7b8eb10a234c5942..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/autoassign_head.py
+++ /dev/null
@@ -1,524 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Sequence, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import Scale
-from mmengine.model import bias_init_with_prob, normal_init
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..utils import levels_to_images, multi_apply
-from .fcos_head import FCOSHead
-
-EPS = 1e-12
-
-
-class CenterPrior(nn.Module):
-    """Center Weighting module to adjust the category-specific prior
-    distributions.
-
-    Args:
-        force_topk (bool): When no point falls into gt_bbox, forcibly
-            select the k points closest to the center to calculate
-            the center prior. Defaults to False.
-        topk (int): The number of points used to calculate the
-            center prior when no point falls in gt_bbox. Only work when
-            force_topk if True. Defaults to 9.
-        num_classes (int): The class number of dataset. Defaults to 80.
-        strides (Sequence[int]): The stride of each input feature map.
-            Defaults to (8, 16, 32, 64, 128).
-    """
-
-    def __init__(
-        self,
-        force_topk: bool = False,
-        topk: int = 9,
-        num_classes: int = 80,
-        strides: Sequence[int] = (8, 16, 32, 64, 128)
-    ) -> None:
-        super().__init__()
-        self.mean = nn.Parameter(torch.zeros(num_classes, 2))
-        self.sigma = nn.Parameter(torch.ones(num_classes, 2))
-        self.strides = strides
-        self.force_topk = force_topk
-        self.topk = topk
-
-    def forward(self, anchor_points_list: List[Tensor],
-                gt_instances: InstanceData,
-                inside_gt_bbox_mask: Tensor) -> Tuple[Tensor, Tensor]:
-        """Get the center prior of each point on the feature map for each
-        instance.
-
-        Args:
-            anchor_points_list (list[Tensor]): list of coordinate
-                of points on feature map. Each with shape
-                (num_points, 2).
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            inside_gt_bbox_mask (Tensor): Tensor of bool type,
-                with shape of (num_points, num_gt), each
-                value is used to mark whether this point falls
-                within a certain gt.
-
-        Returns:
-            tuple[Tensor, Tensor]:
-
-            - center_prior_weights(Tensor): Float tensor with shape  of \
-            (num_points, num_gt). Each value represents the center \
-            weighting coefficient.
-            - inside_gt_bbox_mask (Tensor): Tensor of bool type, with shape \
-            of (num_points, num_gt), each value is used to mark whether this \
-            point falls within a certain gt or is the topk nearest points for \
-            a specific gt_bbox.
-        """
-        gt_bboxes = gt_instances.bboxes
-        labels = gt_instances.labels
-
-        inside_gt_bbox_mask = inside_gt_bbox_mask.clone()
-        num_gts = len(labels)
-        num_points = sum([len(item) for item in anchor_points_list])
-        if num_gts == 0:
-            return gt_bboxes.new_zeros(num_points,
-                                       num_gts), inside_gt_bbox_mask
-        center_prior_list = []
-        for slvl_points, stride in zip(anchor_points_list, self.strides):
-            # slvl_points: points from single level in FPN, has shape (h*w, 2)
-            # single_level_points has shape (h*w, num_gt, 2)
-            single_level_points = slvl_points[:, None, :].expand(
-                (slvl_points.size(0), len(gt_bboxes), 2))
-            gt_center_x = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2)
-            gt_center_y = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2)
-            gt_center = torch.stack((gt_center_x, gt_center_y), dim=1)
-            gt_center = gt_center[None]
-            # instance_center has shape (1, num_gt, 2)
-            instance_center = self.mean[labels][None]
-            # instance_sigma has shape (1, num_gt, 2)
-            instance_sigma = self.sigma[labels][None]
-            # distance has shape (num_points, num_gt, 2)
-            distance = (((single_level_points - gt_center) / float(stride) -
-                         instance_center)**2)
-            center_prior = torch.exp(-distance /
-                                     (2 * instance_sigma**2)).prod(dim=-1)
-            center_prior_list.append(center_prior)
-        center_prior_weights = torch.cat(center_prior_list, dim=0)
-
-        if self.force_topk:
-            gt_inds_no_points_inside = torch.nonzero(
-                inside_gt_bbox_mask.sum(0) == 0).reshape(-1)
-            if gt_inds_no_points_inside.numel():
-                topk_center_index = \
-                    center_prior_weights[:, gt_inds_no_points_inside].topk(
-                                                             self.topk,
-                                                             dim=0)[1]
-                temp_mask = inside_gt_bbox_mask[:, gt_inds_no_points_inside]
-                inside_gt_bbox_mask[:, gt_inds_no_points_inside] = \
-                    torch.scatter(temp_mask,
-                                  dim=0,
-                                  index=topk_center_index,
-                                  src=torch.ones_like(
-                                    topk_center_index,
-                                    dtype=torch.bool))
-
-        center_prior_weights[~inside_gt_bbox_mask] = 0
-        return center_prior_weights, inside_gt_bbox_mask
-
-
-@MODELS.register_module()
-class AutoAssignHead(FCOSHead):
-    """AutoAssignHead head used in AutoAssign.
-
-    More details can be found in the `paper
-    <https://arxiv.org/abs/2007.03496>`_ .
-
-    Args:
-        force_topk (bool): Used in center prior initialization to
-            handle extremely small gt. Default is False.
-        topk (int): The number of points used to calculate the
-            center prior when no point falls in gt_bbox. Only work when
-            force_topk if True. Defaults to 9.
-        pos_loss_weight (float): The loss weight of positive loss
-            and with default value 0.25.
-        neg_loss_weight (float): The loss weight of negative loss
-            and with default value 0.75.
-        center_loss_weight (float): The loss weight of center prior
-            loss and with default value 0.75.
-    """
-
-    def __init__(self,
-                 *args,
-                 force_topk: bool = False,
-                 topk: int = 9,
-                 pos_loss_weight: float = 0.25,
-                 neg_loss_weight: float = 0.75,
-                 center_loss_weight: float = 0.75,
-                 **kwargs) -> None:
-        super().__init__(*args, conv_bias=True, **kwargs)
-        self.center_prior = CenterPrior(
-            force_topk=force_topk,
-            topk=topk,
-            num_classes=self.num_classes,
-            strides=self.strides)
-        self.pos_loss_weight = pos_loss_weight
-        self.neg_loss_weight = neg_loss_weight
-        self.center_loss_weight = center_loss_weight
-        self.prior_generator = MlvlPointGenerator(self.strides, offset=0)
-
-    def init_weights(self) -> None:
-        """Initialize weights of the head.
-
-        In particular, we have special initialization for classified conv's and
-        regression conv's bias
-        """
-
-        super(AutoAssignHead, self).init_weights()
-        bias_cls = bias_init_with_prob(0.02)
-        normal_init(self.conv_cls, std=0.01, bias=bias_cls)
-        normal_init(self.conv_reg, std=0.01, bias=4.0)
-
-    def forward_single(self, x: Tensor, scale: Scale,
-                       stride: int) -> Tuple[Tensor, Tensor, Tensor]:
-        """Forward features of a single scale level.
-
-        Args:
-            x (Tensor): FPN feature maps of the specified stride.
-            scale (:obj:`mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-            stride (int): The corresponding stride for feature maps, only
-                used to normalize the bbox prediction when self.norm_on_bbox
-                is True.
-
-        Returns:
-            tuple[Tensor, Tensor, Tensor]: scores for each class, bbox
-            predictions and centerness predictions of input feature maps.
-        """
-        cls_score, bbox_pred, cls_feat, reg_feat = super(
-            FCOSHead, self).forward_single(x)
-        centerness = self.conv_centerness(reg_feat)
-        # scale the bbox_pred of different level
-        # float to avoid overflow when enabling FP16
-        bbox_pred = scale(bbox_pred).float()
-        # bbox_pred needed for gradient computation has been modified
-        # by F.relu(bbox_pred) when run with PyTorch 1.10. So replace
-        # F.relu(bbox_pred) with bbox_pred.clamp(min=0)
-        bbox_pred = bbox_pred.clamp(min=0)
-        bbox_pred *= stride
-        return cls_score, bbox_pred, centerness
-
-    def get_pos_loss_single(self, cls_score: Tensor, objectness: Tensor,
-                            reg_loss: Tensor, gt_instances: InstanceData,
-                            center_prior_weights: Tensor) -> Tuple[Tensor]:
-        """Calculate the positive loss of all points in gt_bboxes.
-
-        Args:
-            cls_score (Tensor): All category scores for each point on
-                the feature map. The shape is (num_points, num_class).
-            objectness (Tensor): Foreground probability of all points,
-                has shape (num_points, 1).
-            reg_loss (Tensor): The regression loss of each gt_bbox and each
-                prediction box, has shape of (num_points, num_gt).
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            center_prior_weights (Tensor): Float tensor with shape
-                of (num_points, num_gt). Each value represents
-                the center weighting coefficient.
-
-        Returns:
-            tuple[Tensor]:
-
-            - pos_loss (Tensor): The positive loss of all points in the \
-            gt_bboxes.
-        """
-        gt_labels = gt_instances.labels
-        # p_loc: localization confidence
-        p_loc = torch.exp(-reg_loss)
-        # p_cls: classification confidence
-        p_cls = (cls_score * objectness)[:, gt_labels]
-        # p_pos: joint confidence indicator
-        p_pos = p_cls * p_loc
-
-        # 3 is a hyper-parameter to control the contributions of high and
-        # low confidence locations towards positive losses.
-        confidence_weight = torch.exp(p_pos * 3)
-        p_pos_weight = (confidence_weight * center_prior_weights) / (
-            (confidence_weight * center_prior_weights).sum(
-                0, keepdim=True)).clamp(min=EPS)
-        reweighted_p_pos = (p_pos * p_pos_weight).sum(0)
-        pos_loss = F.binary_cross_entropy(
-            reweighted_p_pos,
-            torch.ones_like(reweighted_p_pos),
-            reduction='none')
-        pos_loss = pos_loss.sum() * self.pos_loss_weight
-        return pos_loss,
-
-    def get_neg_loss_single(self, cls_score: Tensor, objectness: Tensor,
-                            gt_instances: InstanceData, ious: Tensor,
-                            inside_gt_bbox_mask: Tensor) -> Tuple[Tensor]:
-        """Calculate the negative loss of all points in feature map.
-
-        Args:
-            cls_score (Tensor): All category scores for each point on
-                the feature map. The shape is (num_points, num_class).
-            objectness (Tensor): Foreground probability of all points
-                and is shape of (num_points, 1).
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            ious (Tensor): Float tensor with shape of (num_points, num_gt).
-                Each value represent the iou of pred_bbox and gt_bboxes.
-            inside_gt_bbox_mask (Tensor): Tensor of bool type,
-                with shape of (num_points, num_gt), each
-                value is used to mark whether this point falls
-                within a certain gt.
-
-        Returns:
-            tuple[Tensor]:
-
-            - neg_loss (Tensor): The negative loss of all points in the \
-            feature map.
-        """
-        gt_labels = gt_instances.labels
-        num_gts = len(gt_labels)
-        joint_conf = (cls_score * objectness)
-        p_neg_weight = torch.ones_like(joint_conf)
-        if num_gts > 0:
-            # the order of dinmension would affect the value of
-            # p_neg_weight, we strictly follow the original
-            # implementation.
-            inside_gt_bbox_mask = inside_gt_bbox_mask.permute(1, 0)
-            ious = ious.permute(1, 0)
-
-            foreground_idxs = torch.nonzero(inside_gt_bbox_mask, as_tuple=True)
-            temp_weight = (1 / (1 - ious[foreground_idxs]).clamp_(EPS))
-
-            def normalize(x):
-                return (x - x.min() + EPS) / (x.max() - x.min() + EPS)
-
-            for instance_idx in range(num_gts):
-                idxs = foreground_idxs[0] == instance_idx
-                if idxs.any():
-                    temp_weight[idxs] = normalize(temp_weight[idxs])
-
-            p_neg_weight[foreground_idxs[1],
-                         gt_labels[foreground_idxs[0]]] = 1 - temp_weight
-
-        logits = (joint_conf * p_neg_weight)
-        neg_loss = (
-            logits**2 * F.binary_cross_entropy(
-                logits, torch.zeros_like(logits), reduction='none'))
-        neg_loss = neg_loss.sum() * self.neg_loss_weight
-        return neg_loss,
-
-    def loss_by_feat(
-        self,
-        cls_scores: List[Tensor],
-        bbox_preds: List[Tensor],
-        objectnesses: List[Tensor],
-        batch_gt_instances: InstanceList,
-        batch_img_metas: List[dict],
-        batch_gt_instances_ignore: OptInstanceList = None
-    ) -> Dict[str, Tensor]:
-        """Calculate the loss based on the features extracted by the detection
-        head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level,
-                each is a 4D-tensor, the channel number is
-                num_points * num_classes.
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level, each is a 4D-tensor, the channel number is
-                num_points * 4.
-            objectnesses (list[Tensor]): objectness for each scale level, each
-                is a 4D-tensor, the channel number is num_points * 1.
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes`` and ``labels``
-                attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
-                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        assert len(cls_scores) == len(bbox_preds) == len(objectnesses)
-        all_num_gt = sum([len(item) for item in batch_gt_instances])
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        all_level_points = self.prior_generator.grid_priors(
-            featmap_sizes,
-            dtype=bbox_preds[0].dtype,
-            device=bbox_preds[0].device)
-        inside_gt_bbox_mask_list, bbox_targets_list = self.get_targets(
-            all_level_points, batch_gt_instances)
-
-        center_prior_weight_list = []
-        temp_inside_gt_bbox_mask_list = []
-        for gt_instances, inside_gt_bbox_mask in zip(batch_gt_instances,
-                                                     inside_gt_bbox_mask_list):
-            center_prior_weight, inside_gt_bbox_mask = \
-                self.center_prior(all_level_points, gt_instances,
-                                  inside_gt_bbox_mask)
-            center_prior_weight_list.append(center_prior_weight)
-            temp_inside_gt_bbox_mask_list.append(inside_gt_bbox_mask)
-        inside_gt_bbox_mask_list = temp_inside_gt_bbox_mask_list
-        mlvl_points = torch.cat(all_level_points, dim=0)
-        bbox_preds = levels_to_images(bbox_preds)
-        cls_scores = levels_to_images(cls_scores)
-        objectnesses = levels_to_images(objectnesses)
-
-        reg_loss_list = []
-        ious_list = []
-        num_points = len(mlvl_points)
-
-        for bbox_pred, encoded_targets, inside_gt_bbox_mask in zip(
-                bbox_preds, bbox_targets_list, inside_gt_bbox_mask_list):
-            temp_num_gt = encoded_targets.size(1)
-            expand_mlvl_points = mlvl_points[:, None, :].expand(
-                num_points, temp_num_gt, 2).reshape(-1, 2)
-            encoded_targets = encoded_targets.reshape(-1, 4)
-            expand_bbox_pred = bbox_pred[:, None, :].expand(
-                num_points, temp_num_gt, 4).reshape(-1, 4)
-            decoded_bbox_preds = self.bbox_coder.decode(
-                expand_mlvl_points, expand_bbox_pred)
-            decoded_target_preds = self.bbox_coder.decode(
-                expand_mlvl_points, encoded_targets)
-            with torch.no_grad():
-                ious = bbox_overlaps(
-                    decoded_bbox_preds, decoded_target_preds, is_aligned=True)
-                ious = ious.reshape(num_points, temp_num_gt)
-                if temp_num_gt:
-                    ious = ious.max(
-                        dim=-1, keepdim=True).values.repeat(1, temp_num_gt)
-                else:
-                    ious = ious.new_zeros(num_points, temp_num_gt)
-                ious[~inside_gt_bbox_mask] = 0
-                ious_list.append(ious)
-            loss_bbox = self.loss_bbox(
-                decoded_bbox_preds,
-                decoded_target_preds,
-                weight=None,
-                reduction_override='none')
-            reg_loss_list.append(loss_bbox.reshape(num_points, temp_num_gt))
-
-        cls_scores = [item.sigmoid() for item in cls_scores]
-        objectnesses = [item.sigmoid() for item in objectnesses]
-        pos_loss_list, = multi_apply(self.get_pos_loss_single, cls_scores,
-                                     objectnesses, reg_loss_list,
-                                     batch_gt_instances,
-                                     center_prior_weight_list)
-        pos_avg_factor = reduce_mean(
-            bbox_pred.new_tensor(all_num_gt)).clamp_(min=1)
-        pos_loss = sum(pos_loss_list) / pos_avg_factor
-
-        neg_loss_list, = multi_apply(self.get_neg_loss_single, cls_scores,
-                                     objectnesses, batch_gt_instances,
-                                     ious_list, inside_gt_bbox_mask_list)
-        neg_avg_factor = sum(item.data.sum()
-                             for item in center_prior_weight_list)
-        neg_avg_factor = reduce_mean(neg_avg_factor).clamp_(min=1)
-        neg_loss = sum(neg_loss_list) / neg_avg_factor
-
-        center_loss = []
-        for i in range(len(batch_img_metas)):
-
-            if inside_gt_bbox_mask_list[i].any():
-                center_loss.append(
-                    len(batch_gt_instances[i]) /
-                    center_prior_weight_list[i].sum().clamp_(min=EPS))
-            # when width or height of gt_bbox is smaller than stride of p3
-            else:
-                center_loss.append(center_prior_weight_list[i].sum() * 0)
-
-        center_loss = torch.stack(center_loss).mean() * self.center_loss_weight
-
-        # avoid dead lock in DDP
-        if all_num_gt == 0:
-            pos_loss = bbox_preds[0].sum() * 0
-            dummy_center_prior_loss = self.center_prior.mean.sum(
-            ) * 0 + self.center_prior.sigma.sum() * 0
-            center_loss = objectnesses[0].sum() * 0 + dummy_center_prior_loss
-
-        loss = dict(
-            loss_pos=pos_loss, loss_neg=neg_loss, loss_center=center_loss)
-
-        return loss
-
-    def get_targets(
-            self, points: List[Tensor], batch_gt_instances: InstanceList
-    ) -> Tuple[List[Tensor], List[Tensor]]:
-        """Compute regression targets and each point inside or outside gt_bbox
-        in multiple images.
-
-        Args:
-            points (list[Tensor]): Points of all fpn level, each has shape
-                (num_points, 2).
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes`` and ``labels``
-                attributes.
-
-        Returns:
-            tuple(list[Tensor], list[Tensor]):
-
-            - inside_gt_bbox_mask_list (list[Tensor]): Each Tensor is with \
-            bool type and shape of (num_points, num_gt), each value is used \
-            to mark whether this point falls within a certain gt.
-            - concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
-            level. Each tensor has shape (num_points, num_gt, 4).
-        """
-
-        concat_points = torch.cat(points, dim=0)
-        # the number of points per img, per lvl
-        inside_gt_bbox_mask_list, bbox_targets_list = multi_apply(
-            self._get_targets_single, batch_gt_instances, points=concat_points)
-        return inside_gt_bbox_mask_list, bbox_targets_list
-
-    def _get_targets_single(self, gt_instances: InstanceData,
-                            points: Tensor) -> Tuple[Tensor, Tensor]:
-        """Compute regression targets and each point inside or outside gt_bbox
-        for a single image.
-
-        Args:
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            points (Tensor): Points of all fpn level, has shape
-                (num_points, 2).
-
-        Returns:
-            tuple[Tensor, Tensor]: Containing the following Tensors:
-
-            - inside_gt_bbox_mask (Tensor): Bool tensor with shape \
-            (num_points, num_gt), each value is used to mark whether this \
-            point falls within a certain gt.
-            - bbox_targets (Tensor): BBox targets of each points with each \
-            gt_bboxes, has shape (num_points, num_gt, 4).
-        """
-        gt_bboxes = gt_instances.bboxes
-        num_points = points.size(0)
-        num_gts = gt_bboxes.size(0)
-        gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
-        xs, ys = points[:, 0], points[:, 1]
-        xs = xs[:, None]
-        ys = ys[:, None]
-        left = xs - gt_bboxes[..., 0]
-        right = gt_bboxes[..., 2] - xs
-        top = ys - gt_bboxes[..., 1]
-        bottom = gt_bboxes[..., 3] - ys
-        bbox_targets = torch.stack((left, top, right, bottom), -1)
-        if num_gts:
-            inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0
-        else:
-            inside_gt_bbox_mask = bbox_targets.new_zeros((num_points, num_gts),
-                                                         dtype=torch.bool)
-
-        return inside_gt_bbox_mask, bbox_targets
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/raw.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/raw.py
deleted file mode 100644
index d4941ad2d7ed858f490db441f5b46b12bd61ad78..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/demucs/raw.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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 argparse
-import os
-from collections import defaultdict, namedtuple
-from pathlib import Path
-
-import musdb
-import numpy as np
-import torch as th
-import tqdm
-from torch.utils.data import DataLoader
-
-from .audio import AudioFile
-
-ChunkInfo = namedtuple("ChunkInfo", ["file_index", "offset", "local_index"])
-
-
-class Rawset:
-    """
-    Dataset of raw, normalized, float32 audio files
-    """
-    def __init__(self, path, samples=None, stride=None, channels=2, streams=None):
-        self.path = Path(path)
-        self.channels = channels
-        self.samples = samples
-        if stride is None:
-            stride = samples if samples is not None else 0
-        self.stride = stride
-        entries = defaultdict(list)
-        for root, folders, files in os.walk(self.path, followlinks=True):
-            folders.sort()
-            files.sort()
-            for file in files:
-                if file.endswith(".raw"):
-                    path = Path(root) / file
-                    name, stream = path.stem.rsplit('.', 1)
-                    entries[(path.parent.relative_to(self.path), name)].append(int(stream))
-
-        self._entries = list(entries.keys())
-
-        sizes = []
-        self._lengths = []
-        ref_streams = sorted(entries[self._entries[0]])
-        assert ref_streams == list(range(len(ref_streams)))
-        if streams is None:
-            self.streams = ref_streams
-        else:
-            self.streams = streams
-        for entry in sorted(entries.keys()):
-            streams = entries[entry]
-            assert sorted(streams) == ref_streams
-            file = self._path(*entry)
-            length = file.stat().st_size // (4 * channels)
-            if samples is None:
-                sizes.append(1)
-            else:
-                if length < samples:
-                    self._entries.remove(entry)
-                    continue
-                sizes.append((length - samples) // stride + 1)
-            self._lengths.append(length)
-        if not sizes:
-            raise ValueError(f"Empty dataset {self.path}")
-        self._cumulative_sizes = np.cumsum(sizes)
-        self._sizes = sizes
-
-    def __len__(self):
-        return self._cumulative_sizes[-1]
-
-    @property
-    def total_length(self):
-        return sum(self._lengths)
-
-    def chunk_info(self, index):
-        file_index = np.searchsorted(self._cumulative_sizes, index, side='right')
-        if file_index == 0:
-            local_index = index
-        else:
-            local_index = index - self._cumulative_sizes[file_index - 1]
-        return ChunkInfo(offset=local_index * self.stride,
-                         file_index=file_index,
-                         local_index=local_index)
-
-    def _path(self, folder, name, stream=0):
-        return self.path / folder / (name + f'.{stream}.raw')
-
-    def __getitem__(self, index):
-        chunk = self.chunk_info(index)
-        entry = self._entries[chunk.file_index]
-
-        length = self.samples or self._lengths[chunk.file_index]
-        streams = []
-        to_read = length * self.channels * 4
-        for stream_index, stream in enumerate(self.streams):
-            offset = chunk.offset * 4 * self.channels
-            file = open(self._path(*entry, stream=stream), 'rb')
-            file.seek(offset)
-            content = file.read(to_read)
-            assert len(content) == to_read
-            content = np.frombuffer(content, dtype=np.float32)
-            content = content.copy()  # make writable
-            streams.append(th.from_numpy(content).view(length, self.channels).t())
-        return th.stack(streams, dim=0)
-
-    def name(self, index):
-        chunk = self.chunk_info(index)
-        folder, name = self._entries[chunk.file_index]
-        return folder / name
-
-
-class MusDBSet:
-    def __init__(self, mus, streams=slice(None), samplerate=44100, channels=2):
-        self.mus = mus
-        self.streams = streams
-        self.samplerate = samplerate
-        self.channels = channels
-
-    def __len__(self):
-        return len(self.mus.tracks)
-
-    def __getitem__(self, index):
-        track = self.mus.tracks[index]
-        return (track.name, AudioFile(track.path).read(channels=self.channels,
-                                                       seek_time=0,
-                                                       streams=self.streams,
-                                                       samplerate=self.samplerate))
-
-
-def build_raw(mus, destination, normalize, workers, samplerate, channels):
-    destination.mkdir(parents=True, exist_ok=True)
-    loader = DataLoader(MusDBSet(mus, channels=channels, samplerate=samplerate),
-                        batch_size=1,
-                        num_workers=workers,
-                        collate_fn=lambda x: x[0])
-    for name, streams in tqdm.tqdm(loader):
-        if normalize:
-            ref = streams[0].mean(dim=0)  # use mono mixture as reference
-            streams = (streams - ref.mean()) / ref.std()
-        for index, stream in enumerate(streams):
-            open(destination / (name + f'.{index}.raw'), "wb").write(stream.t().numpy().tobytes())
-
-
-def main():
-    parser = argparse.ArgumentParser('rawset')
-    parser.add_argument('--workers', type=int, default=10)
-    parser.add_argument('--samplerate', type=int, default=44100)
-    parser.add_argument('--channels', type=int, default=2)
-    parser.add_argument('musdb', type=Path)
-    parser.add_argument('destination', type=Path)
-
-    args = parser.parse_args()
-
-    build_raw(musdb.DB(root=args.musdb, subsets=["train"], split="train"),
-              args.destination / "train",
-              normalize=True,
-              channels=args.channels,
-              samplerate=args.samplerate,
-              workers=args.workers)
-    build_raw(musdb.DB(root=args.musdb, subsets=["train"], split="valid"),
-              args.destination / "valid",
-              normalize=True,
-              samplerate=args.samplerate,
-              channels=args.channels,
-              workers=args.workers)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/utils.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/utils.py
deleted file mode 100644
index 0fafe8793b0d539fa58dd024342250b24b6187a9..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-import json
-
-
-def load_data(file_name: str = "./lib/uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/broker.py b/spaces/Lianjd/stock_dashboard/backtrader/broker.py
deleted file mode 100644
index 58132f86c84900c3c29c4bd3332108ed2c9d72b7..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/broker.py
+++ /dev/null
@@ -1,168 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program 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 General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-from backtrader.comminfo import CommInfoBase
-from backtrader.metabase import MetaParams
-from backtrader.utils.py3 import with_metaclass
-
-from . import fillers as fillers
-from . import fillers as filler
-
-
-class MetaBroker(MetaParams):
-    def __init__(cls, name, bases, dct):
-        '''
-        Class has already been created ... fill missing methods if needed be
-        '''
-        # Initialize the class
-        super(MetaBroker, cls).__init__(name, bases, dct)
-        translations = {
-            'get_cash': 'getcash',
-            'get_value': 'getvalue',
-        }
-
-        for attr, trans in translations.items():
-            if not hasattr(cls, attr):
-                setattr(cls, name, getattr(cls, trans))
-
-
-class BrokerBase(with_metaclass(MetaBroker, object)):
-    params = (
-        ('commission', CommInfoBase(percabs=True)),
-    )
-
-    def __init__(self):
-        self.comminfo = dict()
-        self.init()
-
-    def init(self):
-        # called from init and from start
-        if None not in self.comminfo:
-            self.comminfo = dict({None: self.p.commission})
-
-    def start(self):
-        self.init()
-
-    def stop(self):
-        pass
-
-    def add_order_history(self, orders, notify=False):
-        '''Add order history. See cerebro for details'''
-        raise NotImplementedError
-
-    def set_fund_history(self, fund):
-        '''Add fund history. See cerebro for details'''
-        raise NotImplementedError
-
-    def getcommissioninfo(self, data):
-        '''Retrieves the ``CommissionInfo`` scheme associated with the given
-        ``data``'''
-        if data._name in self.comminfo:
-            return self.comminfo[data._name]
-
-        return self.comminfo[None]
-
-    def setcommission(self,
-                      commission=0.0, margin=None, mult=1.0,
-                      commtype=None, percabs=True, stocklike=False,
-                      interest=0.0, interest_long=False, leverage=1.0,
-                      automargin=False,
-                      name=None):
-
-        '''This method sets a `` CommissionInfo`` object for assets managed in
-        the broker with the parameters. Consult the reference for
-        ``CommInfoBase``
-
-        If name is ``None``, this will be the default for assets for which no
-        other ``CommissionInfo`` scheme can be found
-        '''
-
-        comm = CommInfoBase(commission=commission, margin=margin, mult=mult,
-                            commtype=commtype, stocklike=stocklike,
-                            percabs=percabs,
-                            interest=interest, interest_long=interest_long,
-                            leverage=leverage, automargin=automargin)
-        self.comminfo[name] = comm
-
-    def addcommissioninfo(self, comminfo, name=None):
-        '''Adds a ``CommissionInfo`` object that will be the default for all assets if
-        ``name`` is ``None``'''
-        self.comminfo[name] = comminfo
-
-    def getcash(self):
-        raise NotImplementedError
-
-    def getvalue(self, datas=None):
-        raise NotImplementedError
-
-    def get_fundshares(self):
-        '''Returns the current number of shares in the fund-like mode'''
-        return 1.0  # the abstract mode has only 1 share
-
-    fundshares = property(get_fundshares)
-
-    def get_fundvalue(self):
-        return self.getvalue()
-
-    fundvalue = property(get_fundvalue)
-
-    def set_fundmode(self, fundmode, fundstartval=None):
-        '''Set the actual fundmode (True or False)
-
-        If the argument fundstartval is not ``None``, it will used
-        '''
-        pass  # do nothing, not all brokers can support this
-
-    def get_fundmode(self):
-        '''Returns the actual fundmode (True or False)'''
-        return False
-
-    fundmode = property(get_fundmode, set_fundmode)
-
-    def getposition(self, data):
-        raise NotImplementedError
-
-    def submit(self, order):
-        raise NotImplementedError
-
-    def cancel(self, order):
-        raise NotImplementedError
-
-    def buy(self, owner, data, size, price=None, plimit=None,
-            exectype=None, valid=None, tradeid=0, oco=None,
-            trailamount=None, trailpercent=None,
-            **kwargs):
-
-        raise NotImplementedError
-
-    def sell(self, owner, data, size, price=None, plimit=None,
-             exectype=None, valid=None, tradeid=0, oco=None,
-             trailamount=None, trailpercent=None,
-             **kwargs):
-
-        raise NotImplementedError
-
-    def next(self):
-        pass
-
-# __all__ = ['BrokerBase', 'fillers', 'filler']
diff --git a/spaces/Manjushri/SDXL-1.0-Img2Img-CPU/README.md b/spaces/Manjushri/SDXL-1.0-Img2Img-CPU/README.md
deleted file mode 100644
index ed7921ca868be75587f599c1a7c4715e16c69bbc..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/SDXL-1.0-Img2Img-CPU/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SDXL 1.0 Img2Img
-emoji: 👀
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/audio_feature_utils.py b/spaces/Marshalls/testmtd/feature_extraction/audio_feature_utils.py
deleted file mode 100644
index dac8a57131d046dcd39384f48d6ff9c090086c4e..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/audio_feature_utils.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import librosa
-import math, numpy as np
-import numpy as np
-import os, sys
-
-THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.abspath(os.path.join(THIS_DIR, os.pardir))
-DATA_DIR = os.path.join(ROOT_DIR, 'data')
-sys.path.append(ROOT_DIR)
-
-import feature_extraction.madmom as madmom
-from feature_extraction.madmom.audio.cepstrogram import MFCC
-
-def extract_features_spectral_flux(music_file,tgt_fps=20):
-    filtbank = madmom.audio.filters.MelFilterbank
-    spec = madmom.audio.spectrogram.Spectrogram(music_file, fps=tgt_fps, filterbank=filtbank, num_channels = 1)
-    spectralflux = madmom.features.onsets.spectral_flux(spec)
-    return np.expand_dims(spectralflux, dim=1)
-
-def extract_features_madmombeat(music_file,tgt_fps=20):
-    proc_dwn = madmom.features.RNNDownBeatProcessor()
-    beats = proc_dwn(music_file, fps=20)
-    return beats
-
-def extract_features_multi_mel(y, sr=44100.0, hop=512, nffts=[1024, 2048, 4096], mel_dim=100):
-    featuress = []
-    for nfft in nffts:
-        mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=mel_dim, n_fft=nfft, hop_length=hop)  # C2 is 65.4 Hz
-        features = librosa.power_to_db(mel, ref=np.max)
-        featuress.append(features)
-    features = np.stack(featuress, axis=1)
-    return features
-
-def extract_features_hybrid(y,sr,hop,mel_dim=12,window_mult=1):
-    hop -= hop % 32  #  Chroma CQT only accepts hop lengths that are multiples of 32, so this ensures that condition is met
-    window = window_mult * hop # Fast Fourier Transform Window Size is a multiple (default 1) of the hop
-    y_harm, y_perc = librosa.effects.hpss(y)
-    mels = librosa.feature.melspectrogram(y=y_perc, sr=sr,n_fft=window,hop_length=hop,n_mels=mel_dim, fmax=65.4)  # C2 is 65.4 Hz
-    cqts = librosa.feature.chroma_cqt(y=y_harm, sr=sr,hop_length= hop,
-                                      norm=np.inf, threshold=0, n_chroma=12,
-                                      n_octaves=6, fmin=65.4, cqt_mode='full')
-    joint = np.concatenate((mels, cqts), axis=0)
-    return joint
-
-
-def extract_features_mel(y, sr, hop,mel_dim=100):
-    mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=mel_dim, hop_length=hop)  # C2 is 65.4 Hz
-    features = librosa.power_to_db(mel, ref=np.max)
-    return features
-
-def extract_features_envelope(y, sr, hop,mel_dim=100):
-    envelope = librosa.onset.onset_strength(y=y,hop_length=hop, n_mels=mel_dim)
-    return np.expand_dims(envelope,1)
-
-def extract_features_chroma(y,sr, state_times):
-    #hop = #int((44100 * 60 * beat_discretization) / bpm) Hop length must be a multiple of 2^6
-    chromagram = librosa.feature.chroma_cqt(y=y, sr=sr, C=None, fmin=None,
-                                            norm=np.inf, threshold=0.0, tuning=None, n_chroma=12,
-                                            n_octaves=7, window=None, bins_per_octave=None, cqt_mode='full')
-    # Aggregate chroma features between beat events
-    # We'll use the median value of each feature between beat frames
-    state_frames = librosa.core.time_to_frames(state_times,sr=sr) # Default hop length of 512
-    #TODO: CHANGE THIS TO BECOME LIKE HYBRID IF WE ARE TO EVER USE THIS
-    beat_chroma = librosa.util.sync(chromagram, state_frames, aggregate=np.median, pad=True, axis=-1)
-    return beat_chroma
-
-def extract_features_mfcc(y,sr,state_times):
-    mfcc = librosa.feature.mfcc(y=y, sr=sr) # we can add other specified parameters
-    state_frames = librosa.core.time_to_frames(state_times,sr=sr)
-    beat_mfcc = librosa.util.sync(mfcc, state_frames, aggregate=np.median, pad=True, axis=-1)
-    return beat_mfcc
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/models/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/models/__init__.py
deleted file mode 100644
index 14c7ed9125a126e5539537fa53e106571aa9d197..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/models/__init__.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# encoding: utf-8
-"""
-Models package.
-
-Please see the LICENSE file for licensing details of this package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import os
-import glob
-
-MODEL_PATH = os.path.dirname(__file__)
-
-
-def models(pattern, path=MODEL_PATH):
-    """
-    Retrieve all models in path given a file name pattern.
-
-    Parameters
-    ----------
-    pattern : str
-        Pattern to be matched.
-    path : str, optional
-        Path to search for model files.
-
-    Returns
-    -------
-    models : list
-        Sorted list of matching model file names.
-
-    """
-    return sorted(glob.glob('%s/%s' % (path, pattern)))
-
-
-# beats
-BEATS_LSTM = models('beats/2016/beats_lstm_[1-8].pkl')
-BEATS_BLSTM = models('beats/2015/beats_blstm_[1-8].pkl')
-BEATS_BTCN = models('beats/2019/beats_btcn_[1-8].pkl')
-
-# downbeats
-DOWNBEATS_BLSTM = models('downbeats/2016/downbeats_blstm_[1-8].pkl')
-DOWNBEATS_BGRU = [models('downbeats/2016/downbeats_bgru_rhythmic_*.pkl'),
-                  models('downbeats/2016/downbeats_bgru_harmonic_*.pkl')]
-# notes
-NOTES_BRNN = models('notes/2013/notes_brnn.pkl')
-NOTES_CNN = models('notes/2019/notes_cnn.pkl')
-NOTES_CNN_MIREX = models('notes/2018/notes_cnn_[12].pkl')
-
-# onsets
-ONSETS_RNN = models('onsets/2013/onsets_rnn_[1-8].pkl')
-ONSETS_BRNN = models('onsets/2013/onsets_brnn_[1-8].pkl')
-ONSETS_BRNN_PP = models('onsets/2014/onsets_brnn_pp_[1-8].pkl')
-ONSETS_CNN = models('onsets/2013/onsets_cnn.pkl')
-
-# patterns
-PATTERNS_BALLROOM = models('patterns/2013/ballroom_pattern_[34]_4.pkl')
-
-# chroma
-CHROMA_DNN = models('chroma/2016/chroma_dnn.pkl')
-
-# chords
-CHORDS_DCCRF = models('chords/2016/chords_dccrf.pkl')
-CHORDS_CNN_FEAT = models('chords/2016/chords_cnnfeat.pkl')
-CHORDS_CFCRF = models('chords/2016/chords_cnncrf.pkl')
-
-# key
-KEY_CNN = models('key/2018/key_cnn.pkl')
-
-# keep namespace clean
-del os, glob
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/models/mbv2_mlsd_large.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/models/mbv2_mlsd_large.py
deleted file mode 100644
index 5b9799e7573ca41549b3c3b13ac47b906b369603..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/models/mbv2_mlsd_large.py
+++ /dev/null
@@ -1,292 +0,0 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
-from  torch.nn import  functional as F
-
-
-class BlockTypeA(nn.Module):
-    def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
-        super(BlockTypeA, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(in_c2, out_c2, kernel_size=1),
-            nn.BatchNorm2d(out_c2),
-            nn.ReLU(inplace=True)
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(in_c1, out_c1, kernel_size=1),
-            nn.BatchNorm2d(out_c1),
-            nn.ReLU(inplace=True)
-        )
-        self.upscale = upscale
-
-    def forward(self, a, b):
-        b = self.conv1(b)
-        a = self.conv2(a)
-        if self.upscale:
-             b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
-        return torch.cat((a, b), dim=1)
-
-
-class BlockTypeB(nn.Module):
-    def __init__(self, in_c, out_c):
-        super(BlockTypeB, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
-            nn.BatchNorm2d(in_c),
-            nn.ReLU()
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
-            nn.BatchNorm2d(out_c),
-            nn.ReLU()
-        )
-
-    def forward(self, x):
-        x = self.conv1(x) + x
-        x = self.conv2(x)
-        return x
-
-class BlockTypeC(nn.Module):
-    def __init__(self, in_c, out_c):
-        super(BlockTypeC, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=5, dilation=5),
-            nn.BatchNorm2d(in_c),
-            nn.ReLU()
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(in_c, in_c,  kernel_size=3, padding=1),
-            nn.BatchNorm2d(in_c),
-            nn.ReLU()
-        )
-        self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = self.conv3(x)
-        return x
-
-def _make_divisible(v, divisor, min_value=None):
-    """
-    This function is taken from the original tf repo.
-    It ensures that all layers have a channel number that is divisible by 8
-    It can be seen here:
-    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
-    :param v:
-    :param divisor:
-    :param min_value:
-    :return:
-    """
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-class ConvBNReLU(nn.Sequential):
-    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
-        self.channel_pad = out_planes - in_planes
-        self.stride = stride
-        #padding = (kernel_size - 1) // 2
-
-        # TFLite uses slightly different padding than PyTorch
-        if stride == 2:
-            padding = 0
-        else:
-            padding = (kernel_size - 1) // 2
-
-        super(ConvBNReLU, self).__init__(
-            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
-            nn.BatchNorm2d(out_planes),
-            nn.ReLU6(inplace=True)
-        )
-        self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
-
-
-    def forward(self, x):
-        # TFLite uses  different padding
-        if self.stride == 2:
-            x = F.pad(x, (0, 1, 0, 1), "constant", 0)
-            #print(x.shape)
-
-        for module in self:
-            if not isinstance(module, nn.MaxPool2d):
-                x = module(x)
-        return x
-
-
-class InvertedResidual(nn.Module):
-    def __init__(self, inp, oup, stride, expand_ratio):
-        super(InvertedResidual, self).__init__()
-        self.stride = stride
-        assert stride in [1, 2]
-
-        hidden_dim = int(round(inp * expand_ratio))
-        self.use_res_connect = self.stride == 1 and inp == oup
-
-        layers = []
-        if expand_ratio != 1:
-            # pw
-            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
-        layers.extend([
-            # dw
-            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
-            # pw-linear
-            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
-            nn.BatchNorm2d(oup),
-        ])
-        self.conv = nn.Sequential(*layers)
-
-    def forward(self, x):
-        if self.use_res_connect:
-            return x + self.conv(x)
-        else:
-            return self.conv(x)
-
-
-class MobileNetV2(nn.Module):
-    def __init__(self, pretrained=True):
-        """
-        MobileNet V2 main class
-        Args:
-            num_classes (int): Number of classes
-            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
-            inverted_residual_setting: Network structure
-            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
-            Set to 1 to turn off rounding
-            block: Module specifying inverted residual building block for mobilenet
-        """
-        super(MobileNetV2, self).__init__()
-
-        block = InvertedResidual
-        input_channel = 32
-        last_channel = 1280
-        width_mult = 1.0
-        round_nearest = 8
-
-        inverted_residual_setting = [
-            # t, c, n, s
-            [1, 16, 1, 1],
-            [6, 24, 2, 2],
-            [6, 32, 3, 2],
-            [6, 64, 4, 2],
-            [6, 96, 3, 1],
-            #[6, 160, 3, 2],
-            #[6, 320, 1, 1],
-        ]
-
-        # only check the first element, assuming user knows t,c,n,s are required
-        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
-            raise ValueError("inverted_residual_setting should be non-empty "
-                             "or a 4-element list, got {}".format(inverted_residual_setting))
-
-        # building first layer
-        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
-        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
-        features = [ConvBNReLU(4, input_channel, stride=2)]
-        # building inverted residual blocks
-        for t, c, n, s in inverted_residual_setting:
-            output_channel = _make_divisible(c * width_mult, round_nearest)
-            for i in range(n):
-                stride = s if i == 0 else 1
-                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
-                input_channel = output_channel
-
-        self.features = nn.Sequential(*features)
-        self.fpn_selected = [1, 3, 6, 10, 13]
-        # weight initialization
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out')
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.ones_(m.weight)
-                nn.init.zeros_(m.bias)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                nn.init.zeros_(m.bias)
-        if pretrained:
-           self._load_pretrained_model()
-
-    def _forward_impl(self, x):
-        # This exists since TorchScript doesn't support inheritance, so the superclass method
-        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
-        fpn_features = []
-        for i, f in enumerate(self.features):
-            if i > self.fpn_selected[-1]:
-                break
-            x = f(x)
-            if i in self.fpn_selected:
-                fpn_features.append(x)
-
-        c1, c2, c3, c4, c5 = fpn_features
-        return c1, c2, c3, c4, c5
-
-
-    def forward(self, x):
-        return self._forward_impl(x)
-
-    def _load_pretrained_model(self):
-        pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
-        model_dict = {}
-        state_dict = self.state_dict()
-        for k, v in pretrain_dict.items():
-            if k in state_dict:
-                model_dict[k] = v
-        state_dict.update(model_dict)
-        self.load_state_dict(state_dict)
-
-
-class MobileV2_MLSD_Large(nn.Module):
-    def __init__(self):
-        super(MobileV2_MLSD_Large, self).__init__()
-
-        self.backbone = MobileNetV2(pretrained=False)
-        ## A, B
-        self.block15 = BlockTypeA(in_c1= 64, in_c2= 96,
-                                  out_c1= 64, out_c2=64,
-                                  upscale=False)
-        self.block16 = BlockTypeB(128, 64)
-
-        ## A, B
-        self.block17 = BlockTypeA(in_c1 = 32,  in_c2 = 64,
-                                  out_c1= 64,  out_c2= 64)
-        self.block18 = BlockTypeB(128, 64)
-
-        ## A, B
-        self.block19 = BlockTypeA(in_c1=24, in_c2=64,
-                                  out_c1=64, out_c2=64)
-        self.block20 = BlockTypeB(128, 64)
-
-        ## A, B, C
-        self.block21 = BlockTypeA(in_c1=16, in_c2=64,
-                                  out_c1=64, out_c2=64)
-        self.block22 = BlockTypeB(128, 64)
-
-        self.block23 = BlockTypeC(64, 16)
-
-    def forward(self, x):
-        c1, c2, c3, c4, c5 = self.backbone(x)
-
-        x = self.block15(c4, c5)
-        x = self.block16(x)
-
-        x = self.block17(c3, x)
-        x = self.block18(x)
-
-        x = self.block19(c2, x)
-        x = self.block20(x)
-
-        x = self.block21(c1, x)
-        x = self.block22(x)
-        x = self.block23(x)
-        x = x[:, 7:, :, :]
-
-        return x
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/backbones/unet.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/backbones/unet.py
deleted file mode 100644
index d582551715fc20353d26745b9d1bb55892b7a10d..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/backbones/unet.py
+++ /dev/null
@@ -1,516 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-from mmcv.cnn import ConvModule, build_norm_layer
-from mmengine.model import BaseModule
-from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
-
-from mmocr.registry import MODELS
-
-
-class UpConvBlock(nn.Module):
-    """Upsample convolution block in decoder for UNet.
-
-    This upsample convolution block consists of one upsample module
-    followed by one convolution block. The upsample module expands the
-    high-level low-resolution feature map and the convolution block fuses
-    the upsampled high-level low-resolution feature map and the low-level
-    high-resolution feature map from encoder.
-
-    Args:
-        conv_block (nn.Sequential): Sequential of convolutional layers.
-        in_channels (int): Number of input channels of the high-level
-        skip_channels (int): Number of input channels of the low-level
-        high-resolution feature map from encoder.
-        out_channels (int): Number of output channels.
-        num_convs (int): Number of convolutional layers in the conv_block.
-            Default: 2.
-        stride (int): Stride of convolutional layer in conv_block. Default: 1.
-        dilation (int): Dilation rate of convolutional layer in conv_block.
-            Default: 1.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        upsample_cfg (dict): The upsample config of the upsample module in
-            decoder. Default: dict(type='InterpConv'). If the size of
-            high-level feature map is the same as that of skip feature map
-            (low-level feature map from encoder), it does not need upsample the
-            high-level feature map and the upsample_cfg is None.
-        dcn (bool): Use deformable convolution in convolutional layer or not.
-            Default: None.
-        plugins (dict): plugins for convolutional layers. Default: None.
-    """
-
-    def __init__(self,
-                 conv_block,
-                 in_channels,
-                 skip_channels,
-                 out_channels,
-                 num_convs=2,
-                 stride=1,
-                 dilation=1,
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 upsample_cfg=dict(type='InterpConv'),
-                 dcn=None,
-                 plugins=None):
-        super().__init__()
-        assert dcn is None, 'Not implemented yet.'
-        assert plugins is None, 'Not implemented yet.'
-
-        self.conv_block = conv_block(
-            in_channels=2 * skip_channels,
-            out_channels=out_channels,
-            num_convs=num_convs,
-            stride=stride,
-            dilation=dilation,
-            with_cp=with_cp,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg,
-            dcn=None,
-            plugins=None)
-        if upsample_cfg is not None:
-            upsample_cfg.update(
-                dict(
-                    in_channels=in_channels,
-                    out_channels=skip_channels,
-                    with_cp=with_cp,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-            self.upsample = MODELS.build(upsample_cfg)
-        else:
-            self.upsample = ConvModule(
-                in_channels,
-                skip_channels,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                act_cfg=act_cfg)
-
-    def forward(self, skip, x):
-        """Forward function."""
-
-        x = self.upsample(x)
-        out = torch.cat([skip, x], dim=1)
-        out = self.conv_block(out)
-
-        return out
-
-
-class BasicConvBlock(nn.Module):
-    """Basic convolutional block for UNet.
-
-    This module consists of several plain convolutional layers.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        num_convs (int): Number of convolutional layers. Default: 2.
-        stride (int): Whether use stride convolution to downsample
-            the input feature map. If stride=2, it only uses stride convolution
-            in the first convolutional layer to downsample the input feature
-            map. Options are 1 or 2. Default: 1.
-        dilation (int): Whether use dilated convolution to expand the
-            receptive field. Set dilation rate of each convolutional layer and
-            the dilation rate of the first convolutional layer is always 1.
-            Default: 1.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        dcn (bool): Use deformable convolution in convolutional layer or not.
-            Default: None.
-        plugins (dict): plugins for convolutional layers. Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_convs=2,
-                 stride=1,
-                 dilation=1,
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 dcn=None,
-                 plugins=None):
-        super().__init__()
-        assert dcn is None, 'Not implemented yet.'
-        assert plugins is None, 'Not implemented yet.'
-
-        self.with_cp = with_cp
-        convs = []
-        for i in range(num_convs):
-            convs.append(
-                ConvModule(
-                    in_channels=in_channels if i == 0 else out_channels,
-                    out_channels=out_channels,
-                    kernel_size=3,
-                    stride=stride if i == 0 else 1,
-                    dilation=1 if i == 0 else dilation,
-                    padding=1 if i == 0 else dilation,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-
-        self.convs = nn.Sequential(*convs)
-
-    def forward(self, x):
-        """Forward function."""
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(self.convs, x)
-        else:
-            out = self.convs(x)
-        return out
-
-
-@MODELS.register_module()
-class DeconvModule(nn.Module):
-    """Deconvolution upsample module in decoder for UNet (2X upsample).
-
-    This module uses deconvolution to upsample feature map in the decoder
-    of UNet.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        kernel_size (int): Kernel size of the convolutional layer. Default: 4.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 with_cp=False,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 *,
-                 kernel_size=4,
-                 scale_factor=2):
-        super().__init__()
-
-        assert (
-            kernel_size - scale_factor >= 0
-            and (kernel_size - scale_factor) % 2 == 0), (
-                f'kernel_size should be greater than or equal to scale_factor '
-                f'and (kernel_size - scale_factor) should be even numbers, '
-                f'while the kernel size is {kernel_size} and scale_factor is '
-                f'{scale_factor}.')
-
-        stride = scale_factor
-        padding = (kernel_size - scale_factor) // 2
-        self.with_cp = with_cp
-        deconv = nn.ConvTranspose2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding)
-
-        _, norm = build_norm_layer(norm_cfg, out_channels)
-        activate = MODELS.build(act_cfg)
-        self.deconv_upsamping = nn.Sequential(deconv, norm, activate)
-
-    def forward(self, x):
-        """Forward function."""
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(self.deconv_upsamping, x)
-        else:
-            out = self.deconv_upsamping(x)
-        return out
-
-
-@MODELS.register_module()
-class InterpConv(nn.Module):
-    """Interpolation upsample module in decoder for UNet.
-
-    This module uses interpolation to upsample feature map in the decoder
-    of UNet. It consists of one interpolation upsample layer and one
-    convolutional layer. It can be one interpolation upsample layer followed
-    by one convolutional layer (conv_first=False) or one convolutional layer
-    followed by one interpolation upsample layer (conv_first=True).
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        conv_first (bool): Whether convolutional layer or interpolation
-            upsample layer first. Default: False. It means interpolation
-            upsample layer followed by one convolutional layer.
-        kernel_size (int): Kernel size of the convolutional layer. Default: 1.
-        stride (int): Stride of the convolutional layer. Default: 1.
-        padding (int): Padding of the convolutional layer. Default: 1.
-        upsample_cfg (dict): Interpolation config of the upsample layer.
-            Default: dict(
-                scale_factor=2, mode='bilinear', align_corners=False).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 with_cp=False,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 *,
-                 conv_cfg=None,
-                 conv_first=False,
-                 kernel_size=1,
-                 stride=1,
-                 padding=0,
-                 upsample_cfg=dict(
-                     scale_factor=2, mode='bilinear', align_corners=False)):
-        super().__init__()
-
-        self.with_cp = with_cp
-        conv = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        upsample = nn.Upsample(**upsample_cfg)
-        if conv_first:
-            self.interp_upsample = nn.Sequential(conv, upsample)
-        else:
-            self.interp_upsample = nn.Sequential(upsample, conv)
-
-    def forward(self, x):
-        """Forward function."""
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(self.interp_upsample, x)
-        else:
-            out = self.interp_upsample(x)
-        return out
-
-
-@MODELS.register_module()
-class UNet(BaseModule):
-    """UNet backbone.
-    U-Net: Convolutional Networks for Biomedical Image Segmentation.
-    https://arxiv.org/pdf/1505.04597.pdf
-
-    Args:
-        in_channels (int): Number of input image channels. Default" 3.
-        base_channels (int): Number of base channels of each stage.
-            The output channels of the first stage. Default: 64.
-        num_stages (int): Number of stages in encoder, normally 5. Default: 5.
-        strides (Sequence[int 1 | 2]): Strides of each stage in encoder.
-            len(strides) is equal to num_stages. Normally the stride of the
-            first stage in encoder is 1. If strides[i]=2, it uses stride
-            convolution to downsample in the correspondence encoder stage.
-            Default: (1, 1, 1, 1, 1).
-        enc_num_convs (Sequence[int]): Number of convolutional layers in the
-            convolution block of the correspondence encoder stage.
-            Default: (2, 2, 2, 2, 2).
-        dec_num_convs (Sequence[int]): Number of convolutional layers in the
-            convolution block of the correspondence decoder stage.
-            Default: (2, 2, 2, 2).
-        downsamples (Sequence[int]): Whether use MaxPool to downsample the
-            feature map after the first stage of encoder
-            (stages: [1, num_stages)). If the correspondence encoder stage use
-            stride convolution (strides[i]=2), it will never use MaxPool to
-            downsample, even downsamples[i-1]=True.
-            Default: (True, True, True, True).
-        enc_dilations (Sequence[int]): Dilation rate of each stage in encoder.
-            Default: (1, 1, 1, 1, 1).
-        dec_dilations (Sequence[int]): Dilation rate of each stage in decoder.
-            Default: (1, 1, 1, 1).
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-        conv_cfg (dict | None): Config dict for convolution layer.
-            Default: None.
-        norm_cfg (dict | None): Config dict for normalization layer.
-            Default: dict(type='BN').
-        act_cfg (dict | None): Config dict for activation layer in ConvModule.
-            Default: dict(type='ReLU').
-        upsample_cfg (dict): The upsample config of the upsample module in
-            decoder. Default: dict(type='InterpConv').
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only. Default: False.
-        dcn (bool): Use deformable convolution in convolutional layer or not.
-            Default: None.
-        plugins (dict): plugins for convolutional layers. Default: None.
-
-    Notice:
-        The input image size should be divisible by the whole downsample rate
-        of the encoder. More detail of the whole downsample rate can be found
-        in UNet._check_input_divisible.
-
-    """
-
-    def __init__(self,
-                 in_channels=3,
-                 base_channels=64,
-                 num_stages=5,
-                 strides=(1, 1, 1, 1, 1),
-                 enc_num_convs=(2, 2, 2, 2, 2),
-                 dec_num_convs=(2, 2, 2, 2),
-                 downsamples=(True, True, True, True),
-                 enc_dilations=(1, 1, 1, 1, 1),
-                 dec_dilations=(1, 1, 1, 1),
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 act_cfg=dict(type='ReLU'),
-                 upsample_cfg=dict(type='InterpConv'),
-                 norm_eval=False,
-                 dcn=None,
-                 plugins=None,
-                 init_cfg=[
-                     dict(type='Kaiming', layer='Conv2d'),
-                     dict(
-                         type='Constant',
-                         layer=['_BatchNorm', 'GroupNorm'],
-                         val=1)
-                 ]):
-        super().__init__(init_cfg=init_cfg)
-        assert dcn is None, 'Not implemented yet.'
-        assert plugins is None, 'Not implemented yet.'
-        assert len(strides) == num_stages, (
-            'The length of strides should be equal to num_stages, '
-            f'while the strides is {strides}, the length of '
-            f'strides is {len(strides)}, and the num_stages is '
-            f'{num_stages}.')
-        assert len(enc_num_convs) == num_stages, (
-            'The length of enc_num_convs should be equal to num_stages, '
-            f'while the enc_num_convs is {enc_num_convs}, the length of '
-            f'enc_num_convs is {len(enc_num_convs)}, and the num_stages is '
-            f'{num_stages}.')
-        assert len(dec_num_convs) == (num_stages - 1), (
-            'The length of dec_num_convs should be equal to (num_stages-1), '
-            f'while the dec_num_convs is {dec_num_convs}, the length of '
-            f'dec_num_convs is {len(dec_num_convs)}, and the num_stages is '
-            f'{num_stages}.')
-        assert len(downsamples) == (num_stages - 1), (
-            'The length of downsamples should be equal to (num_stages-1), '
-            f'while the downsamples is {downsamples}, the length of '
-            f'downsamples is {len(downsamples)}, and the num_stages is '
-            f'{num_stages}.')
-        assert len(enc_dilations) == num_stages, (
-            'The length of enc_dilations should be equal to num_stages, '
-            f'while the enc_dilations is {enc_dilations}, the length of '
-            f'enc_dilations is {len(enc_dilations)}, and the num_stages is '
-            f'{num_stages}.')
-        assert len(dec_dilations) == (num_stages - 1), (
-            'The length of dec_dilations should be equal to (num_stages-1), '
-            f'while the dec_dilations is {dec_dilations}, the length of '
-            f'dec_dilations is {len(dec_dilations)}, and the num_stages is '
-            f'{num_stages}.')
-        self.num_stages = num_stages
-        self.strides = strides
-        self.downsamples = downsamples
-        self.norm_eval = norm_eval
-        self.base_channels = base_channels
-
-        self.encoder = nn.ModuleList()
-        self.decoder = nn.ModuleList()
-
-        for i in range(num_stages):
-            enc_conv_block = []
-            if i != 0:
-                if strides[i] == 1 and downsamples[i - 1]:
-                    enc_conv_block.append(nn.MaxPool2d(kernel_size=2))
-                upsample = (strides[i] != 1 or downsamples[i - 1])
-                self.decoder.append(
-                    UpConvBlock(
-                        conv_block=BasicConvBlock,
-                        in_channels=base_channels * 2**i,
-                        skip_channels=base_channels * 2**(i - 1),
-                        out_channels=base_channels * 2**(i - 1),
-                        num_convs=dec_num_convs[i - 1],
-                        stride=1,
-                        dilation=dec_dilations[i - 1],
-                        with_cp=with_cp,
-                        conv_cfg=conv_cfg,
-                        norm_cfg=norm_cfg,
-                        act_cfg=act_cfg,
-                        upsample_cfg=upsample_cfg if upsample else None,
-                        dcn=None,
-                        plugins=None))
-
-            enc_conv_block.append(
-                BasicConvBlock(
-                    in_channels=in_channels,
-                    out_channels=base_channels * 2**i,
-                    num_convs=enc_num_convs[i],
-                    stride=strides[i],
-                    dilation=enc_dilations[i],
-                    with_cp=with_cp,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg,
-                    dcn=None,
-                    plugins=None))
-            self.encoder.append(nn.Sequential(*enc_conv_block))
-            in_channels = base_channels * 2**i
-
-    def forward(self, x):
-        self._check_input_divisible(x)
-        enc_outs = []
-        for enc in self.encoder:
-            x = enc(x)
-            enc_outs.append(x)
-        dec_outs = [x]
-        for i in reversed(range(len(self.decoder))):
-            x = self.decoder[i](enc_outs[i], x)
-            dec_outs.append(x)
-
-        return dec_outs
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep normalization layer
-        freezed."""
-        super().train(mode)
-        if mode and self.norm_eval:
-            for m in self.modules():
-                # trick: eval have effect on BatchNorm only
-                if isinstance(m, _BatchNorm):
-                    m.eval()
-
-    def _check_input_divisible(self, x):
-        h, w = x.shape[-2:]
-        whole_downsample_rate = 1
-        for i in range(1, self.num_stages):
-            if self.strides[i] == 2 or self.downsamples[i - 1]:
-                whole_downsample_rate *= 2
-        assert (
-            h % whole_downsample_rate == 0 and w % whole_downsample_rate == 0
-        ), (f'The input image size {(h, w)} should be divisible by the whole '
-            f'downsample rate {whole_downsample_rate}, when num_stages is '
-            f'{self.num_stages}, strides is {self.strides}, and downsamples '
-            f'is {self.downsamples}.')
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/detectors/fcenet.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/detectors/fcenet.py
deleted file mode 100644
index 9b99f491ff8eedaeb37d64990f0c1dd8dc3c5e89..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/detectors/fcenet.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.registry import MODELS
-from .single_stage_text_detector import SingleStageTextDetector
-
-
-@MODELS.register_module()
-class FCENet(SingleStageTextDetector):
-    """The class for implementing FCENet text detector
-    FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text
-        Detection
-
-    [https://arxiv.org/abs/2104.10442]
-    """
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textdet/ic11_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textdet/ic11_converter.py
deleted file mode 100644
index 1a5683f4ae17e52fa8f13fc542a8424ae6cb488f..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textdet/ic11_converter.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import os
-import os.path as osp
-
-import mmcv
-import mmengine
-from PIL import Image
-
-from mmocr.utils import dump_ocr_data
-
-
-def convert_gif(img_path):
-    """Convert the gif image to png format.
-
-    Args:
-        img_path (str): The path to the gif image
-    """
-    img = Image.open(img_path)
-    dst_path = img_path.replace('gif', 'png')
-    img.save(dst_path)
-    os.remove(img_path)
-    print(f'Convert {img_path} to {dst_path}')
-
-
-def collect_files(img_dir, gt_dir):
-    """Collect all images and their corresponding groundtruth files.
-
-    Args:
-        img_dir (str): The image directory
-        gt_dir (str): The groundtruth directory
-
-    Returns:
-        files (list): The list of tuples (img_file, groundtruth_file)
-    """
-    assert isinstance(img_dir, str)
-    assert img_dir
-    assert isinstance(gt_dir, str)
-    assert gt_dir
-
-    ann_list, imgs_list = [], []
-    for img in os.listdir(img_dir):
-        img_path = osp.join(img_dir, img)
-        # mmcv cannot read gif images, so convert them to png
-        if img.endswith('gif'):
-            convert_gif(img_path)
-            img_path = img_path.replace('gif', 'png')
-        imgs_list.append(img_path)
-        ann_list.append(osp.join(gt_dir, 'gt_' + img.split('.')[0] + '.txt'))
-
-    files = list(zip(sorted(imgs_list), sorted(ann_list)))
-    assert len(files), f'No images found in {img_dir}'
-    print(f'Loaded {len(files)} images from {img_dir}')
-
-    return files
-
-
-def collect_annotations(files, nproc=1):
-    """Collect the annotation information.
-
-    Args:
-        files (list): The list of tuples (image_file, groundtruth_file)
-        nproc (int): The number of process to collect annotations
-
-    Returns:
-        images (list): The list of image information dicts
-    """
-    assert isinstance(files, list)
-    assert isinstance(nproc, int)
-
-    if nproc > 1:
-        images = mmengine.track_parallel_progress(
-            load_img_info, files, nproc=nproc)
-    else:
-        images = mmengine.track_progress(load_img_info, files)
-
-    return images
-
-
-def load_img_info(files):
-    """Load the information of one image.
-
-    Args:
-        files (tuple): The tuple of (img_file, groundtruth_file)
-
-    Returns:
-        img_info (dict): The dict of the img and annotation information
-    """
-    assert isinstance(files, tuple)
-
-    img_file, gt_file = files
-    # read imgs while ignoring orientations
-    img = mmcv.imread(img_file, 'unchanged')
-
-    img_info = dict(
-        file_name=osp.join(osp.basename(img_file)),
-        height=img.shape[0],
-        width=img.shape[1],
-        segm_file=osp.join(osp.basename(gt_file)))
-
-    if osp.splitext(gt_file)[1] == '.txt':
-        img_info = load_txt_info(gt_file, img_info)
-    else:
-        raise NotImplementedError
-
-    return img_info
-
-
-def load_txt_info(gt_file, img_info):
-    """Collect the annotation information.
-
-    The annotation format is as the following:
-    left, top, right, bottom, "transcription"
-
-    Args:
-        gt_file (str): The path to ground-truth
-        img_info (dict): The dict of the img and annotation information
-
-    Returns:
-        img_info (dict): The dict of the img and annotation information
-    """
-    anno_info = []
-    with open(gt_file) as f:
-        lines = f.readlines()
-        for line in lines:
-            xmin, ymin, xmax, ymax = line.split(',')[0:4]
-            x = max(0, int(xmin))
-            y = max(0, int(ymin))
-            w = int(xmax) - x
-            h = int(ymax) - y
-            bbox = [x, y, w, h]
-            segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
-
-            anno = dict(
-                iscrowd=0,
-                category_id=1,
-                bbox=bbox,
-                area=w * h,
-                segmentation=[segmentation])
-            anno_info.append(anno)
-    img_info.update(anno_info=anno_info)
-
-    return img_info
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Generate training and test set of IC11')
-    parser.add_argument('root_path', help='Root dir path of IC11')
-    parser.add_argument(
-        '--nproc', default=1, type=int, help='Number of process')
-    args = parser.parse_args()
-    return args
-
-
-def main():
-    args = parse_args()
-    root_path = args.root_path
-
-    for split in ['training', 'test']:
-        print(f'Processing {split} set...')
-        with mmengine.Timer(print_tmpl='It takes {}s to convert annotation'):
-            files = collect_files(
-                osp.join(root_path, 'imgs', split),
-                osp.join(root_path, 'annotations', split))
-            image_infos = collect_annotations(files, nproc=args.nproc)
-            dump_ocr_data(image_infos,
-                          osp.join(root_path, 'instances_' + split + '.json'),
-                          'textdet')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/Msninmx/shamzam/app.py b/spaces/Msninmx/shamzam/app.py
deleted file mode 100644
index a48dfe4bb79a25c1e8eca902014e59d035032000..0000000000000000000000000000000000000000
--- a/spaces/Msninmx/shamzam/app.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import os
-from dotenv import load_dotenv
-from subprocess import Popen
-
-load_dotenv()
-
-command = ["mercury", "run", f"0.0.0.0:{os.environ.get('PORT', 7860)}"]
-worker = Popen(command)
-worker.wait()
diff --git a/spaces/MuGeminorum/insecta/khandy/boxes/boxes_clip.py b/spaces/MuGeminorum/insecta/khandy/boxes/boxes_clip.py
deleted file mode 100644
index 2b1df9e0b51279b737f0680a48f0639c3537ed22..0000000000000000000000000000000000000000
--- a/spaces/MuGeminorum/insecta/khandy/boxes/boxes_clip.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import numpy as np
-
-
-def clip_boxes(boxes, reference_box, copy=True):
-    """Clip boxes to reference box.
-    
-    References:
-        `clip_to_window` in TensorFlow object detection API.
-    """
-    if copy:
-        boxes = boxes.copy()
-    ref_x_min, ref_y_min, ref_x_max, ref_y_max = reference_box[:4]
-    lower = np.array([ref_x_min, ref_y_min, ref_x_min, ref_y_min])
-    upper = np.array([ref_x_max, ref_y_max, ref_x_max, ref_y_max])
-    np.clip(boxes[..., :4], lower, upper, boxes[..., :4])
-    return boxes
-    
-    
-def clip_boxes_to_image(boxes, image_width, image_height, subpixel=True, copy=True):
-    """Clip boxes to image boundaries.
-    
-    References:
-        `clip_boxes` in py-faster-rcnn
-        `core.boxes_op_list.clip_to_window` in TensorFlow object detection API.
-        `structures.Boxes.clip` in detectron2
-        
-    Notes:
-        Equivalent to `clip_boxes(boxes, [0,0,image_width-1,image_height-1], copy)`
-    """
-    if not subpixel:
-        image_width -= 1
-        image_height -= 1
-    reference_box = [0, 0, image_width, image_height]
-    return clip_boxes(boxes, reference_box, copy)
diff --git a/spaces/NAACL2022/papers/README.md b/spaces/NAACL2022/papers/README.md
deleted file mode 100644
index e0831b054c1572079971e4ab893fd05873b035b6..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/papers/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NAACL 2022 Papers
-emoji: 🏢
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NATSpeech/DiffSpeech/data_gen/tts/base_preprocess.py b/spaces/NATSpeech/DiffSpeech/data_gen/tts/base_preprocess.py
deleted file mode 100644
index a5e607edb4114c7d6edd60a61e5b765e8cbfb9ac..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/data_gen/tts/base_preprocess.py
+++ /dev/null
@@ -1,251 +0,0 @@
-import json
-import os
-import random
-import re
-import traceback
-from collections import Counter
-from functools import partial
-
-import librosa
-from tqdm import tqdm
-from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls
-from data_gen.tts.wav_processors.base_processor import get_wav_processor_cls
-from utils.commons.hparams import hparams
-from utils.commons.multiprocess_utils import multiprocess_run_tqdm
-from utils.os_utils import link_file, move_file, remove_file
-from utils.text.text_encoder import is_sil_phoneme, build_token_encoder
-
-
-class BasePreprocessor:
-    def __init__(self):
-        self.preprocess_args = hparams['preprocess_args']
-        txt_processor = self.preprocess_args['txt_processor']
-        self.txt_processor = get_txt_processor_cls(txt_processor)
-        self.raw_data_dir = hparams['raw_data_dir']
-        self.processed_dir = hparams['processed_data_dir']
-        self.spk_map_fn = f"{self.processed_dir}/spk_map.json"
-
-    def meta_data(self):
-        """
-
-        :return: {'item_name': Str, 'wav_fn': Str, 'txt': Str, 'spk_name': Str, 'txt_loader': None or Func}
-        """
-        raise NotImplementedError
-
-    def process(self):
-        processed_dir = self.processed_dir
-        wav_processed_tmp_dir = f'{processed_dir}/processed_tmp'
-        remove_file(wav_processed_tmp_dir)
-        os.makedirs(wav_processed_tmp_dir, exist_ok=True)
-        wav_processed_dir = f'{processed_dir}/{self.wav_processed_dirname}'
-        remove_file(wav_processed_dir)
-        os.makedirs(wav_processed_dir, exist_ok=True)
-
-        meta_data = list(tqdm(self.meta_data(), desc='Load meta data'))
-        item_names = [d['item_name'] for d in meta_data]
-        assert len(item_names) == len(set(item_names)), 'Key `item_name` should be Unique.'
-
-        # preprocess data
-        phone_list = []
-        word_list = []
-        spk_names = set()
-        process_item = partial(self.preprocess_first_pass,
-                               txt_processor=self.txt_processor,
-                               wav_processed_dir=wav_processed_dir,
-                               wav_processed_tmp=wav_processed_tmp_dir,
-                               preprocess_args=self.preprocess_args)
-        items = []
-        args = [{
-            'item_name': item_raw['item_name'],
-            'txt_raw': item_raw['txt'],
-            'wav_fn': item_raw['wav_fn'],
-            'txt_loader': item_raw.get('txt_loader'),
-            'others': item_raw.get('others', None)
-        } for item_raw in meta_data]
-        for item_, (item_id, item) in zip(meta_data, multiprocess_run_tqdm(process_item, args, desc='Preprocess')):
-            if item is not None:
-                item_.update(item)
-                item = item_
-                if 'txt_loader' in item:
-                    del item['txt_loader']
-                item['id'] = item_id
-                item['spk_name'] = item.get('spk_name', '<SINGLE_SPK>')
-                item['others'] = item.get('others', None)
-                phone_list += item['ph'].split(" ")
-                word_list += item['word'].split(" ")
-                spk_names.add(item['spk_name'])
-                items.append(item)
-
-        # add encoded tokens
-        ph_encoder, word_encoder = self._phone_encoder(phone_list), self._word_encoder(word_list)
-        spk_map = self.build_spk_map(spk_names)
-        args = [{
-            'ph': item['ph'], 'word': item['word'], 'spk_name': item['spk_name'],
-            'word_encoder': word_encoder, 'ph_encoder': ph_encoder, 'spk_map': spk_map
-        } for item in items]
-        for idx, item_new_kv in multiprocess_run_tqdm(self.preprocess_second_pass, args, desc='Add encoded tokens'):
-            items[idx].update(item_new_kv)
-
-        # build mfa data
-        if self.preprocess_args['use_mfa']:
-            mfa_dict = set()
-            mfa_input_dir = f'{processed_dir}/mfa_inputs'
-            remove_file(mfa_input_dir)
-            # group MFA inputs for better parallelism
-            mfa_groups = [i // self.preprocess_args['nsample_per_mfa_group'] for i in range(len(items))]
-            if self.preprocess_args['mfa_group_shuffle']:
-                random.seed(hparams['seed'])
-                random.shuffle(mfa_groups)
-            args = [{
-                'item': item, 'mfa_input_dir': mfa_input_dir,
-                'mfa_group': mfa_group, 'wav_processed_tmp': wav_processed_tmp_dir,
-                'preprocess_args': self.preprocess_args
-            } for item, mfa_group in zip(items, mfa_groups)]
-            for i, (ph_gb_word_nosil, new_wav_align_fn) in multiprocess_run_tqdm(
-                    self.build_mfa_inputs, args, desc='Build MFA data'):
-                items[i]['wav_align_fn'] = new_wav_align_fn
-                for w in ph_gb_word_nosil.split(" "):
-                    mfa_dict.add(f"{w} {w.replace('_', ' ')}")
-            mfa_dict = sorted(mfa_dict)
-            with open(f'{processed_dir}/mfa_dict.txt', 'w') as f:
-                f.writelines([f'{l}\n' for l in mfa_dict])
-        with open(f"{processed_dir}/{self.meta_csv_filename}.json", 'w') as f:
-            f.write(re.sub(r'\n\s+([\d+\]])', r'\1', json.dumps(items, ensure_ascii=False, sort_keys=False, indent=1)))
-        remove_file(wav_processed_tmp_dir)
-
-    @classmethod
-    def preprocess_first_pass(cls, item_name, txt_raw, txt_processor,
-                              wav_fn, wav_processed_dir, wav_processed_tmp,
-                              preprocess_args, txt_loader=None, others=None):
-        try:
-            if txt_loader is not None:
-                txt_raw = txt_loader(txt_raw)
-            ph, txt, word, ph2word, ph_gb_word = cls.txt_to_ph(txt_processor, txt_raw, preprocess_args)
-            wav_fn, wav_align_fn = cls.process_wav(
-                item_name, wav_fn,
-                hparams['processed_data_dir'],
-                wav_processed_tmp, preprocess_args)
-
-            # wav for binarization
-            ext = os.path.splitext(wav_fn)[1]
-            os.makedirs(wav_processed_dir, exist_ok=True)
-            new_wav_fn = f"{wav_processed_dir}/{item_name}{ext}"
-            move_link_func = move_file if os.path.dirname(wav_fn) == wav_processed_tmp else link_file
-            move_link_func(wav_fn, new_wav_fn)
-            return {
-                'txt': txt, 'txt_raw': txt_raw, 'ph': ph,
-                'word': word, 'ph2word': ph2word, 'ph_gb_word': ph_gb_word,
-                'wav_fn': new_wav_fn, 'wav_align_fn': wav_align_fn,
-                'others': others
-            }
-        except:
-            traceback.print_exc()
-            print(f"| Error is caught. item_name: {item_name}.")
-            return None
-
-    @staticmethod
-    def txt_to_ph(txt_processor, txt_raw, preprocess_args):
-        txt_struct, txt = txt_processor.process(txt_raw, preprocess_args)
-        ph = [p for w in txt_struct for p in w[1]]
-        ph_gb_word = ["_".join(w[1]) for w in txt_struct]
-        words = [w[0] for w in txt_struct]
-        # word_id=0 is reserved for padding
-        ph2word = [w_id + 1 for w_id, w in enumerate(txt_struct) for _ in range(len(w[1]))]
-        return " ".join(ph), txt, " ".join(words), ph2word, " ".join(ph_gb_word)
-
-    @staticmethod
-    def process_wav(item_name, wav_fn, processed_dir, wav_processed_tmp, preprocess_args):
-        processors = [get_wav_processor_cls(v) for v in preprocess_args['wav_processors']]
-        processors = [k() for k in processors if k is not None]
-        if len(processors) >= 1:
-            sr_file = librosa.core.get_samplerate(wav_fn)
-            output_fn_for_align = None
-            ext = os.path.splitext(wav_fn)[1]
-            input_fn = f"{wav_processed_tmp}/{item_name}{ext}"
-            link_file(wav_fn, input_fn)
-            for p in processors:
-                outputs = p.process(input_fn, sr_file, wav_processed_tmp, processed_dir, item_name, preprocess_args)
-                if len(outputs) == 3:
-                    input_fn, sr, output_fn_for_align = outputs
-                else:
-                    input_fn, sr = outputs
-            return input_fn, output_fn_for_align
-        else:
-            return wav_fn, wav_fn
-
-    def _phone_encoder(self, ph_set):
-        ph_set_fn = f"{self.processed_dir}/phone_set.json"
-        if self.preprocess_args['reset_phone_dict'] or not os.path.exists(ph_set_fn):
-            ph_set = sorted(set(ph_set))
-            json.dump(ph_set, open(ph_set_fn, 'w'), ensure_ascii=False)
-            print("| Build phone set: ", ph_set)
-        else:
-            ph_set = json.load(open(ph_set_fn, 'r'))
-            print("| Load phone set: ", ph_set)
-        return build_token_encoder(ph_set_fn)
-
-    def _word_encoder(self, word_set):
-        word_set_fn = f"{self.processed_dir}/word_set.json"
-        if self.preprocess_args['reset_word_dict']:
-            word_set = Counter(word_set)
-            total_words = sum(word_set.values())
-            word_set = word_set.most_common(hparams['word_dict_size'])
-            num_unk_words = total_words - sum([x[1] for x in word_set])
-            word_set = ['<BOS>', '<EOS>'] + [x[0] for x in word_set]
-            word_set = sorted(set(word_set))
-            json.dump(word_set, open(word_set_fn, 'w'), ensure_ascii=False)
-            print(f"| Build word set. Size: {len(word_set)}, #total words: {total_words},"
-                  f" #unk_words: {num_unk_words}, word_set[:10]:, {word_set[:10]}.")
-        else:
-            word_set = json.load(open(word_set_fn, 'r'))
-            print("| Load word set. Size: ", len(word_set), word_set[:10])
-        return build_token_encoder(word_set_fn)
-
-    @classmethod
-    def preprocess_second_pass(cls, word, ph, spk_name, word_encoder, ph_encoder, spk_map):
-        word_token = word_encoder.encode(word)
-        ph_token = ph_encoder.encode(ph)
-        spk_id = spk_map[spk_name]
-        return {'word_token': word_token, 'ph_token': ph_token, 'spk_id': spk_id}
-
-    def build_spk_map(self, spk_names):
-        spk_map = {x: i for i, x in enumerate(sorted(list(spk_names)))}
-        assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
-        print(f"| Number of spks: {len(spk_map)}, spk_map: {spk_map}")
-        json.dump(spk_map, open(self.spk_map_fn, 'w'), ensure_ascii=False)
-        return spk_map
-
-    @classmethod
-    def build_mfa_inputs(cls, item, mfa_input_dir, mfa_group, wav_processed_tmp, preprocess_args):
-        item_name = item['item_name']
-        wav_align_fn = item['wav_align_fn']
-        ph_gb_word = item['ph_gb_word']
-        ext = os.path.splitext(wav_align_fn)[1]
-        mfa_input_group_dir = f'{mfa_input_dir}/{mfa_group}'
-        os.makedirs(mfa_input_group_dir, exist_ok=True)
-        new_wav_align_fn = f"{mfa_input_group_dir}/{item_name}{ext}"
-        move_link_func = move_file if os.path.dirname(wav_align_fn) == wav_processed_tmp else link_file
-        move_link_func(wav_align_fn, new_wav_align_fn)
-        ph_gb_word_nosil = " ".join(["_".join([p for p in w.split("_") if not is_sil_phoneme(p)])
-                                     for w in ph_gb_word.split(" ") if not is_sil_phoneme(w)])
-        with open(f'{mfa_input_group_dir}/{item_name}.lab', 'w') as f_txt:
-            f_txt.write(ph_gb_word_nosil)
-        return ph_gb_word_nosil, new_wav_align_fn
-
-    def load_spk_map(self, base_dir):
-        spk_map_fn = f"{base_dir}/spk_map.json"
-        spk_map = json.load(open(spk_map_fn, 'r'))
-        return spk_map
-
-    def load_dict(self, base_dir):
-        ph_encoder = build_token_encoder(f'{base_dir}/phone_set.json')
-        word_encoder = build_token_encoder(f'{base_dir}/word_set.json')
-        return ph_encoder, word_encoder
-
-    @property
-    def meta_csv_filename(self):
-        return 'metadata'
-
-    @property
-    def wav_processed_dirname(self):
-        return 'wav_processed'
diff --git a/spaces/NCTCMumbai/NCTC/models/.github/README_TEMPLATE.md b/spaces/NCTCMumbai/NCTC/models/.github/README_TEMPLATE.md
deleted file mode 100644
index 45179d0aeba52caa8d84c102790b7e3fafc2c7fe..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/.github/README_TEMPLATE.md
+++ /dev/null
@@ -1,122 +0,0 @@
-> :memo: A README.md template for releasing a paper code implementation to a GitHub repository.  
->  
-> * Template version: 1.0.2020.170  
-> * Please modify sections depending on needs.  
-
-# Model name, Paper title, or Project Name
-
-> :memo: Add a badge for the ArXiv identifier of your paper (arXiv:YYMM.NNNNN)
-
-[![Paper](http://img.shields.io/badge/Paper-arXiv.YYMM.NNNNN-B3181B?logo=arXiv)](https://arxiv.org/abs/...)
-
-This repository is the official or unofficial implementation of the following paper.
-
-* Paper title: [Paper Title](https://arxiv.org/abs/YYMM.NNNNN)
-
-## Description
-
-> :memo: Provide description of the model.  
->  
-> * Provide brief information of the algorithms used.  
-> * Provide links for demos, blog posts, etc.  
-
-## History
-
-> :memo: Provide a changelog.
-
-## Authors or Maintainers
-
-> :memo: Provide maintainer information.  
-
-* Full name ([@GitHub username](https://github.com/username))
-* Full name ([@GitHub username](https://github.com/username))
-
-## Table of Contents
-
-> :memo: Provide a table of contents to help readers navigate a lengthy README document.
-
-## Requirements
-
-[![TensorFlow 2.1](https://img.shields.io/badge/TensorFlow-2.1-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
-[![Python 3.6](https://img.shields.io/badge/Python-3.6-3776AB)](https://www.python.org/downloads/release/python-360/)
-
-> :memo: Provide details of the software required.  
->  
-> * Add a `requirements.txt` file to the root directory for installing the necessary dependencies.  
->   * Describe how to install requirements using pip.  
-> * Alternatively, create INSTALL.md.  
-
-To install requirements:
-
-```setup
-pip install -r requirements.txt
-```
-
-## Results
-
-> :memo: Provide a table with results. (e.g., accuracy, latency)  
->  
-> * Provide links to the pre-trained models (checkpoint, SavedModel files).  
->   * Publish TensorFlow SavedModel files on TensorFlow Hub (tfhub.dev) if possible.  
-> * Add links to [TensorBoard.dev](https://tensorboard.dev/) for visualizing metrics.  
->  
-> An example table for image classification results  
->  
-> ### Image Classification  
->  
-> | Model name | Download | Top 1 Accuracy | Top 5 Accuracy |  
-> |------------|----------|----------------|----------------|  
-> | Model name | [Checkpoint](https://drive.google.com/...), [SavedModel](https://tfhub.dev/...) | xx% | xx% |  
-
-## Dataset
-
-> :memo: Provide information of the dataset used.  
-
-## Training
-
-> :memo: Provide training information.  
->  
-> * Provide details for preprocessing, hyperparameters, random seeds, and environment.  
-> * Provide a command line example for training.  
-
-Please run this command line for training.
-
-```shell
-python3 ...
-```
-
-## Evaluation
-
-> :memo: Provide an evaluation script with details of how to reproduce results.  
->  
-> * Describe data preprocessing / postprocessing steps.  
-> * Provide a command line example for evaluation.  
-
-Please run this command line for evaluation.
-
-```shell
-python3 ...
-```
-
-## References
-
-> :memo: Provide links to references.  
-
-## License
-
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-
-> :memo: Place your license text in a file named LICENSE in the root of the repository.  
->  
-> * Include information about your license.  
-> * Reference: [Adding a license to a repository](https://help.github.com/en/github/building-a-strong-community/adding-a-license-to-a-repository)  
-
-This project is licensed under the terms of the **Apache License 2.0**.
-
-## Citation
-
-> :memo: Make your repository citable.  
->  
-> * Reference: [Making Your Code Citable](https://guides.github.com/activities/citable-code/)  
-
-If you want to cite this repository in your research paper, please use the following information.
diff --git a/spaces/NEXAS/NEXAS-stable_diff_custom/README.md b/spaces/NEXAS/NEXAS-stable_diff_custom/README.md
deleted file mode 100644
index 3b7b236cee0bcee253069eebaf997d10c8a753f2..0000000000000000000000000000000000000000
--- a/spaces/NEXAS/NEXAS-stable_diff_custom/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: NEXAS-stable Diff Custom
-emoji: 🐢
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.40.1
-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/NimaBoscarino/climategan/climategan/bn_fusion.py b/spaces/NimaBoscarino/climategan/climategan/bn_fusion.py
deleted file mode 100644
index 50c1171a927a21d76a89d691467005e7f419b296..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/bn_fusion.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import torch
-from copy import deepcopy
-
-
-class FlattableModel(object):
-    def __init__(self, model):
-        self.model = deepcopy(model)
-        self._original_model = model
-        self._flat_model = None
-        self._attr_names = self.get_attributes_name()
-
-    def flatten_model(self):
-        if self._flat_model is None:
-            self._flat_model = self._flatten_model(self.model)
-        return self._flat_model
-
-    @staticmethod
-    def _selection_method(module):
-        return not (
-            isinstance(module, torch.nn.Sequential)
-            or isinstance(module, torch.nn.ModuleList)
-        ) and not hasattr(module, "_restricted")
-
-    @staticmethod
-    def _flatten_model(module):
-        modules = []
-        child = False
-        for (name, c) in module.named_children():
-            child = True
-            flattened_c = FlattableModel._flatten_model(c)
-            modules += flattened_c
-        if not child and FlattableModel._selection_method(module):
-            modules = [module]
-        return modules
-
-    def get_layer_io(self, layer, nb_samples, data_loader):
-        ios = []
-        hook = layer.register_forward_hook(
-            lambda m, i, o: ios.append((i[0].data.cpu(), o.data.cpu()))
-        )
-
-        nbatch = 1
-        for batch_idx, (xs, ys) in enumerate(data_loader):
-            # -1 takes all of them
-            if nb_samples != -1 and nbatch > nb_samples:
-                break
-            _ = self.model(xs.cuda())
-            nbatch += 1
-
-        hook.remove()
-        return ios
-
-    def get_attributes_name(self):
-        def _real_get_attributes_name(module):
-            modules = []
-            child = False
-            for (name, c) in module.named_children():
-                child = True
-                flattened_c = _real_get_attributes_name(c)
-                modules += map(lambda e: [name] + e, flattened_c)
-            if not child and FlattableModel._selection_method(module):
-                modules = [[]]
-            return modules
-
-        return _real_get_attributes_name(self.model)
-
-    def update_model(self, flat_model):
-        """
-        Take a list representing the flatten model and rebuild its internals.
-        :type flat_model: List[nn.Module]
-        """
-
-        def _apply_changes_on_layer(block, idxs, layer):
-            assert len(idxs) > 0
-            if len(idxs) == 1:
-                setattr(block, idxs[0], layer)
-            else:
-                _apply_changes_on_layer(getattr(block, idxs[0]), idxs[1:], layer)
-
-        def _apply_changes_model(model_list):
-            for i in range(len(model_list)):
-                _apply_changes_on_layer(self.model, self._attr_names[i], model_list[i])
-
-        _apply_changes_model(flat_model)
-        self._attr_names = self.get_attributes_name()
-        self._flat_model = None
-
-    def cuda(self):
-        self.model = self.model.cuda()
-        return self
-
-    def cpu(self):
-        self.model = self.model.cpu()
-        return self
-
-
-def bn_fuse(model):
-    model = model.cpu()
-    flattable = FlattableModel(model)
-    fmodel = flattable.flatten_model()
-
-    for index, item in enumerate(fmodel):
-        if (
-            isinstance(item, torch.nn.Conv2d)
-            and index + 1 < len(fmodel)
-            and isinstance(fmodel[index + 1], torch.nn.BatchNorm2d)
-        ):
-            alpha, beta = _calculate_alpha_beta(fmodel[index + 1])
-            if item.weight.shape[0] != alpha.shape[0]:
-                # this case happens if there was actually something else
-                # between the conv and the
-                # bn layer which is not picked up in flat model logic. (see densenet)
-                continue
-            item.weight.data = item.weight.data * alpha.view(-1, 1, 1, 1)
-            item.bias = torch.nn.Parameter(beta)
-            fmodel[index + 1] = _IdentityLayer()
-    flattable.update_model(fmodel)
-    return flattable.model
-
-
-def _calculate_alpha_beta(batchnorm_layer):
-    alpha = batchnorm_layer.weight.data / (
-        torch.sqrt(batchnorm_layer.running_var + batchnorm_layer.eps)
-    )
-    beta = (
-        -(batchnorm_layer.weight.data * batchnorm_layer.running_mean)
-        / (torch.sqrt(batchnorm_layer.running_var + batchnorm_layer.eps))
-        + batchnorm_layer.bias.data
-    )
-    alpha = alpha.cpu()
-    beta = beta.cpu()
-    return alpha, beta
-
-
-class _IdentityLayer(torch.nn.Module):
-    def forward(self, input):
-        return input
diff --git a/spaces/Nixic/rvc-models/vc_infer_pipeline.py b/spaces/Nixic/rvc-models/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/Nixic/rvc-models/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-            _, I = index.search(npy, 1)
-            npy = big_npy[I.squeeze()]
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = np.load(file_big_npy)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/Norod78/sd2-simpsons-blip/app.py b/spaces/Norod78/sd2-simpsons-blip/app.py
deleted file mode 100644
index ae80f2d2a9d6fa86c886ab76041c619852b41f02..0000000000000000000000000000000000000000
--- a/spaces/Norod78/sd2-simpsons-blip/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 = 'Norod78/sd2-simpsons-blip'
-prefix = None
-     
-scheduler = DPMSolverMultistepScheduler(
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    num_train_timesteps=1000,
-    trained_betas=None,
-    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=""):
-
-  if torch.cuda.is_available():
-    generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  else:
-    if seed != 0:      
-      generator = torch.Generator()
-      generator.manual_seed(seed)
-    else:
-      generator = None
-
-  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.Resampling.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        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>SDv2 Simpsons</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/Norod78/sd2-simpsons-blip">SD2 Simpsons BLIP</a> Stable Diffusion 2, fine-tuned 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/Norod78/sd2-simpsons-blip/settings'>Settings</a></b>"}<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/Norod78/sd2-simpsons-blip?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="[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")              
-
-              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)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
-    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/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/sacrebleu.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/sacrebleu.sh
deleted file mode 100644
index a70da23f48e2699297799611412783d4560dc45a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/sacrebleu.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/bash
-
-if [ $# -ne 5 ]; then
-    echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]"
-    exit
-fi
-
-
-DATASET=$1
-LANGPAIR=$2
-DATABIN=$3
-BPECODE=$4
-MODEL=$5
-
-SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1)
-TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2)
-
-
-BPEROOT=examples/backtranslation/subword-nmt/subword_nmt
-if [ ! -e $BPEROOT ]; then
-    BPEROOT=subword-nmt/subword_nmt
-    if [ ! -e $BPEROOT ]; then
-        echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-        git clone https://github.com/rsennrich/subword-nmt.git
-    fi
-fi
-
-
-sacrebleu -t $DATASET -l $LANGPAIR --echo src \
-| sacremoses tokenize -a -l $SRCLANG -q \
-| python $BPEROOT/apply_bpe.py -c $BPECODE \
-| fairseq-interactive $DATABIN --path $MODEL \
-    -s $SRCLANG -t $TGTLANG \
-    --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \
-| grep ^H- | cut -f 3- \
-| sacremoses detokenize -l $TGTLANG -q \
-| sacrebleu -t $DATASET -l $LANGPAIR
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py
deleted file mode 100644
index 3465731eb3e55047c44d1b336a97e99cb3a89a53..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py
+++ /dev/null
@@ -1,899 +0,0 @@
-from typing import NamedTuple, List
-from urllib.parse import urlparse
-import os, sys
-import subprocess
-from subprocess import check_call, check_output
-import glob
-import wget
-import re
-import multiprocessing as mp
-from functools import partial
-import pathlib
-from collections import OrderedDict 
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or  not WORKDIR_ROOT.strip():
-    print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
-    sys.exit(-1)
-
-# scripts and data locations
-CWD = os.getcwd()
-UTILS = f"{CWD}/utils"
-
-MOSES = f"{UTILS}/mosesdecoder"
-SGM_TOOL = f'{MOSES}/scripts/ems/support/input-from-sgm.perl'
-
-TMX2CORPUS = f"{UTILS}/tmx2corpus"
-TMX_TOOL = f'python {TMX2CORPUS}/tmx2corpus.py'
-
-to_data_path = f'{WORKDIR_ROOT}/wmt'
-download_to = f'{to_data_path}/downloads'
-manually_downloads = f'{to_data_path}/downloads'
-extract_to = f'{to_data_path}/extracted'
-#DESTDIR=${WORKDIR_ROOT}/ML50/raw/
-raw_data = f'{WORKDIR_ROOT}/ML50/raw'
-####
-
-class DLDataset(NamedTuple):
-    name: str
-    train_urls: List[str]
-    valid_urls: List[str]
-    test_urls: List[str]        
-    train_files_patterns: List[str] = []
-    valid_files_patterns: List[str] = []
-    test_files_patterns: List[str] = []
-
-
-
-def bar_custom(current, total, width=80):
-    print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r')
-
-def get_downloaded_file(dl_folder, url):
-    if isinstance(url, tuple):
-        url, f = url
-    else:
-        url_f = urlparse(url)
-        # f = os.path.split(url_f.path)[-1]
-        f = '_'.join(url_f.path.split('/')[1:])
-    return url, f"{dl_folder}/{f}"
-
-def download_parts_and_combine(dl_folder, urls, filename):
-    parts = []
-    for url_record in urls:
-        url, part_file = get_downloaded_file(dl_folder, url_record)     
-        if os.path.exists(part_file):
-            print(f'{part_file} has already been downloaded so skip')
-        else: 
-            part_file = wget.download(url, part_file, bar=bar_custom)  
-        parts.append(part_file)
-
-    def get_combine_cmd(parts):           
-        #default as tar.gz.??
-        return f'cat {" ".join(parts)} > {filename}'
-
-    combine_cmd = get_combine_cmd(parts)
-    call(combine_cmd, debug=True)
-    return filename
-
-def download_a_url(dl_folder, url):
-    url, filename = get_downloaded_file(dl_folder, url)        
-    if os.path.exists(filename):
-        print(f'{filename} has already been downloaded so skip')
-        return filename
-
-    print(f'downloading {url} to {filename}')
-    if isinstance(url, list) or isinstance(url, tuple):
-        download_parts_and_combine(dl_folder, url, filename)
-    else:
-        wget.download(url, filename, bar=bar_custom)
-    print(f'dowloaded: {filename}')
-    return filename
-
-def download_files(dl_folder, urls, completed_urls={}):
-    for url_record in urls:
-        url, _ = get_downloaded_file(dl_folder, url_record) 
-        filename = download_a_url(dl_folder, url_record) 
-        completed_urls[str(url)] = filename
-    return completed_urls
-
-def check_need_manual_downalod(dl_folder, to_manually_download_urls):
-    to_be_manually_dowloaded = []
-    manually_completed_urls = {}
-    for url_record, instruction in to_manually_download_urls:
-        url, filename = get_downloaded_file(dl_folder, url_record)
-        if not os.path.exists(filename):
-            print(f'{url} need to be download manually, please download it manually following {instruction}; and copy it to {filename}')
-            to_be_manually_dowloaded.append((url, filename))
-        else:
-            manually_completed_urls[url] = filename
-    # if len(to_be_manually_dowloaded) > 0:
-    #     raise ValueError('Missing files that need to be downloaded manually; stop the process now.')
-    return to_be_manually_dowloaded
-        
-def download_dataset(to_folder, dl_dataset, completed_urls={}):
-    download_files(to_folder, dl_dataset.train_urls, completed_urls)
-    download_files(to_folder, dl_dataset.valid_urls, completed_urls)
-    download_files(to_folder, dl_dataset.test_urls, completed_urls)
-    print('completed downloading')
-    return completed_urls
-
-def call(cmd, debug=False):
-    if debug:
-        print(cmd)
-    check_call(cmd, shell=True)
-
-    
-def get_extract_name(file_path):
-    path = os.path.split(file_path)
-    return path[-1] + '_extract' #.split('.')[0]
-
-def extract_file(downloaded_file, extract_folder, get_extract_name=get_extract_name, debug=False):
-    extract_name = get_extract_name(downloaded_file)
-    extract_to = f'{extract_folder}/{extract_name}'
-    os.makedirs(extract_to, exist_ok=True)
-    if os.path.exists(f'{extract_to}/DONE'):
-        print(f'{downloaded_file} has already been extracted to {extract_to} so skip')
-        return extract_to
-    def get_extract_cmd(filename):
-        if filename.endswith('.tgz') or filename.endswith('tar.gz'):
-            return f'tar xzfv {filename} -C {extract_to}'
-        elif filename.endswith('.gz.tar'): 
-            return f'tar xfv {filename} -C {extract_to}; (cd {extract_to}; gzip -d *.gz; [ $? -eq 0 ]  || gzip -d */*.gz)'  
-        elif filename.endswith('.tar'):
-            return f'tar xfv {filename} -C {extract_to}'        
-        elif filename.endswith('.gz'):
-            return f'cp {filename} {extract_to}; (cd {extract_to}; gzip -d *.gz)'
-        elif filename.endswith('.zip'):
-            return f'unzip {filename} -d {extract_to}'        
-    extract_cmd = get_extract_cmd(downloaded_file) 
-    print(f'extracting {downloaded_file}')
-    if isinstance(extract_cmd, list):
-        for c in  extract_cmd:
-            call(c, debug=debug)
-    else:
-        call(extract_cmd, debug=debug)
-    call(f'echo DONE > {extract_to}/DONE')
-    return extract_to
-
-
-def extract_all_files(
-    completed_urls, extract_folder,
-    get_extract_name=get_extract_name,
-    completed_extraction={},
-    debug=False):
-    extracted_folders = OrderedDict()
-    for url, downloaded_file in set(completed_urls.items()):
-        if downloaded_file in completed_extraction:
-            print(f'{downloaded_file} is already extracted; so skip')
-            continue
-        folder = extract_file(downloaded_file, extract_folder, get_extract_name, debug)
-        extracted_folders[url] = folder
-    return extracted_folders
-
-
-def my_glob(folder):
-    for p in [f'{folder}/*', f'{folder}/*/*', f'{folder}/*/*/*']:
-        for f in glob.glob(p):
-            yield f
-
-
-def sgm2raw(sgm, debug):
-    to_file = sgm[0:len(sgm) - len('.sgm')]
-    if os.path.exists(to_file):
-        debug and print(f'{sgm} already converted to {to_file}; so skip')
-        return to_file
-    cmd = f'{SGM_TOOL} < {sgm} > {to_file}'
-    call(cmd, debug)
-    return to_file
-
-def tmx2raw(tmx, debug):
-    to_file = tmx[0:len(tmx) - len('.tmx')]
-    to_folder = os.path.join(*os.path.split(tmx)[:-1])
-    if os.path.exists(f'{to_folder}/bitext.en'):
-        debug and print(f'{tmx} already extracted to {to_file}; so skip')
-        return to_file
-    cmd = f'(cd {to_folder}; {TMX_TOOL} {tmx})'
-    call(cmd, debug)
-    return to_file
-
-CZENG16_REGEX = re.compile(r'.*?data.plaintext-format/0[0-9]train$')
-WMT19_WIKITITLES_REGEX = re.compile(r'.*?wikititles-v1.(\w\w)-en.tsv.gz')
-TSV_REGEX = re.compile(r'.*?(\w\w)-(\w\w).tsv$')
-
-
-
-def cut_wikitles(wiki_file, debug):
-    # different languages have different file names: 
-    if wiki_file.endswith('wiki/fi-en/titles.fi-en'):
-        to_file1 = f'{wiki_file}.fi'
-        to_file2 = f'{wiki_file}.en'
-        BACKSLASH = '\\'
-        cmd1 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f1 |awk '{{$1=$1}};1' > {to_file1}"
-        cmd2 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f2 |awk '{{$1=$1}};1' > {to_file2}"  
-#     elif WMT19_WIKITITLES_REGEX.match(wiki_file):
-#         src = WMT19_WIKITITLES_REGEX.match(wiki_file).groups()[0]
-#         to_file1 = f'{wiki_file}.{src}'
-#         to_file2 = f'{wiki_file}.en'
-#         cmd1 = f"cat {wiki_file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}"
-#         cmd2 = f"cat {wiki_file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}"
-    else:
-        return None
-    if os.path.exists(to_file1) and os.path.exists(to_file2):
-        debug and print(f'{wiki_file} already processed to {to_file1} and {to_file2}; so skip')
-        return wiki_file    
-
-    call(cmd1, debug=debug)
-    call(cmd2, debug=debug)
-    return wiki_file
-
-def cut_tsv(file, debug):
-    m = TSV_REGEX.match(file)
-    if m is None:
-        raise ValueError(f'{file} is not matching tsv pattern')
-    src = m.groups()[0]
-    tgt = m.groups()[1]
-
-    to_file1 = f'{file}.{src}'
-    to_file2 = f'{file}.{tgt}' 
-    cmd1 = f"cat {file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}"
-    cmd2 = f"cat {file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}"         
-    if os.path.exists(to_file1) and os.path.exists(to_file2):
-        debug and print(f'{file} already processed to {to_file1} and {to_file2}; so skip')
-        return file    
-
-    call(cmd1, debug=debug)
-    call(cmd2, debug=debug)
-    return file    
-
-    
-def convert_file_if_needed(file, debug):
-    if file.endswith('.sgm'):
-        return sgm2raw(file, debug)
-    elif file.endswith('.tmx'):
-        return tmx2raw(file, debug)
-    elif file.endswith('wiki/fi-en/titles.fi-en'):
-        return cut_wikitles(file, debug)
-#     elif WMT19_WIKITITLES_REGEX.match(file):
-#         return cut_wikitles(file, debug)
-    elif file.endswith('.tsv'):
-        return cut_tsv(file, debug)
-    elif CZENG16_REGEX.match(file):
-        return convert2czeng17(file, debug)
-    else:
-        return file
-
-
-def convert_files_if_needed(extracted_foldrs, my_glob=my_glob, debug=False):
-    return {
-        url: list(sorted(set(convert_file_if_needed(f, debug)) for f in sorted(set(my_glob(folder)))))
-        for url, folder in extracted_foldrs.items()
-    }
-        
-def match_patt(file_path, file_pattern, src, tgt, lang):    
-    return file_pattern.format(src=src, tgt=tgt, lang=lang) in file_path
-
-def match_patts(file_path, file_patterns, src, tgt, lang):
-    for file_pattern in file_patterns:
-        params = { k: v for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] if k in file_pattern}
-        matching = file_pattern.format(**params)   
-
-        if isinstance(file_pattern, tuple):
-            pattern, directions = file_pattern
-            if f'{src}-{tgt}' in directions and matching in file_path:
-                return True
-        else:
-            if matching in file_path:
-                return True
-    return False
-
-def extracted_glob(extracted_folder, file_patterns, src, tgt, lang):
-    def get_matching_pattern(file_pattern):
-        params = {
-            k: v 
-            for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] 
-            if '{' + k + '}' in file_pattern
-        }
-        file_pattern = re.sub(r'{src:(.*?)}', r'\1' if lang == src else '', file_pattern)
-        file_pattern = re.sub(r'{tgt:(.*?)}', r'\1' if lang == tgt else '', file_pattern)
-        file_pattern = file_pattern.format(**params)
-        return file_pattern
-    for file_pattern in file_patterns:
-        if isinstance(file_pattern, tuple):
-            file_pattern, lang_pairs = file_pattern
-            if f'{src}-{tgt}' not in lang_pairs:
-                continue
-#         print('working on pattern: ', file_pattern, lang_pairs )
-        matching_pattern = get_matching_pattern(file_pattern)
-        if matching_pattern is None:
-            continue
-        glob_patterns = f'{extracted_folder}/{matching_pattern}'
-#         print('glob_patterns: ', glob_patterns)
-        for f in glob.glob(glob_patterns):
-            yield f       
-
-# for debug usage
-def all_extracted_files(split, src, tgt, extracted_folders, split_urls):
-    def get_url(url):
-        if isinstance(url, tuple):
-            url, downloaded_file = url        
-        return url
-    return [
-        f
-        for url in split_urls
-        for f in my_glob(extracted_folders[str(get_url(url))])        
-    ]
-
-def concat_files(split, src, tgt, extracted_folders, split_urls, path_patterns, to_folder, debug=False):
-#     if debug:
-#         print('extracted files to be filtered by patterns: ', 
-#               '\n\t'.join(sorted(all_extracted_files(split, src, tgt, extracted_folders, split_urls))))
-    for lang in [src, tgt]:
-        to_file = f'{to_folder}/{split}.{src}-{tgt}.{lang}'
-        s_src, s_tgt, s_lang = src.split('_')[0], tgt.split('_')[0], lang.split('_')[0]
-        files = []
-        for url in split_urls:
-            if isinstance(url, tuple):
-                url, downloaded_file = url
-            if str(url) not in extracted_folders:
-                print(f'warning: {url} not in extracted files')
-            for extracted_file in set(
-                extracted_glob(
-                    extracted_folders[str(url)], path_patterns, 
-                    s_src, s_tgt, s_lang)):
-                files.append(extracted_file)
-        if len(files) == 0:
-            print('warning: ', f'No files found for split {to_file}')
-            continue
-        files = sorted(set(files))
-        print(f'concating {len(files)} files into {to_file}')
-        cmd = ['cat'] + [f'"{f}"' for f in files] + [f'>{to_file}']
-        cmd = " ".join(cmd)
-        call(cmd, debug=debug)
-
-UTILS = os.path.join(pathlib.Path(__file__).parent, 'utils')
-LID_MODEL = f'{download_to}/lid.176.bin'
-LID_MULTI = f'{UTILS}/fasttext_multi_filter.py'
-
-def lid_filter(split, src, tgt, from_folder, to_folder, debug=False):
-    if not os.path.exists(LID_MODEL):
-        call(f'wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O {LID_MODEL}')
-    from_prefix = f'{from_folder}/{split}.{src}-{tgt}'
-    to_prefix = f'{to_folder}/{split}.{src}-{tgt}'
-    if os.path.exists(f'{from_prefix}.{src}') and os.path.exists(f'{from_prefix}.{tgt}'):
-        s_src, s_tgt = src.split('_')[0], tgt.split('_')[0]  
-        cmd = (
-            f'python {LID_MULTI} --model {LID_MODEL} --inputs {from_prefix}.{src} {from_prefix}.{tgt} '
-            f'--langs {s_src} {s_tgt} --outputs {to_prefix}.{src} {to_prefix}.{tgt}'
-        )
-        print(f'filtering {from_prefix}')
-        call(cmd, debug=debug)
-
-def concat_into_splits(dl_dataset, src, tgt, extracted_folders, to_folder, debug):
-    to_folder_tmp = f"{to_folder}_tmp"
-    os.makedirs(to_folder_tmp, exist_ok=True)
-    concat_files('train', src, tgt,
-                 extracted_folders,
-                 split_urls=dl_dataset.train_urls,
-                 path_patterns=dl_dataset.train_files_patterns,
-                 to_folder=to_folder_tmp, debug=debug)
-    lid_filter('train', src, tgt, to_folder_tmp, to_folder, debug)
-
-    concat_files('valid', src, tgt,
-                 extracted_folders, 
-                 split_urls=dl_dataset.valid_urls, 
-                 path_patterns=dl_dataset.valid_files_patterns, 
-                 to_folder=to_folder, debug=debug)
-    concat_files('test', src, tgt, 
-                 extracted_folders, 
-                 split_urls=dl_dataset.test_urls, 
-                 path_patterns=dl_dataset.test_files_patterns, 
-                 to_folder=to_folder, debug=debug)
-            
-
-def download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=False):
-    pool = mp.Pool(processes=num_processes)
-    download_f = partial(download_a_url, dl_folder)
-    downloaded_files = pool.imap_unordered(download_f, urls)
-    pool.close()
-    pool.join()
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
-    output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
-    print(output)
-    bleu = -1.0
-    for line in output.strip().split('\n'):
-        m = BLEU_REGEX.search(line)
-        if m is not None:
-            bleu = m.groups()[0]
-            bleu = float(bleu)
-            break
-    return bleu
-
-def check_wmt_test_bleu(raw_folder, wmt_lang_pairs):
-    not_matchings = []
-    for wmt, src_tgts in wmt_lang_pairs:
-        for src_tgt in src_tgts:
-            print(f'checking test bleus for: {src_tgt} at {wmt}')
-            src, tgt = src_tgt.split('-')
-            ssrc, stgt = src[:2], tgt[:2]
-            if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
-                # reversed direction may have different test set
-                test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
-            else:
-                test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
-            cmd1 = f'cat {test_src} | sacrebleu -t "{wmt}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
-            test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'       
-            cmd2 = f'cat {test_tgt} | sacrebleu -t "{wmt}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
-            bleu1 = run_eval_bleu(cmd1) 
-            if bleu1 != 100.0:
-                not_matchings.append(f'{wmt}:{src_tgt} source side not matching: {test_src}')
-            bleu2 = run_eval_bleu(cmd2) 
-            if bleu2 != 100.0:
-                not_matchings.append(f'{wmt}:{src_tgt} target side not matching: {test_tgt}')
-    return not_matchings         
- 
-def download_and_extract(
-    to_folder, lang_pairs, dl_dataset, 
-    to_manually_download_urls, 
-    completed_urls={}, completed_extraction={},
-    debug=False):
-
-    dl_folder = f'{to_folder}/downloads'
-    extract_folder = f'{to_folder}/extracted'
-    raw_folder =  f'{to_folder}/raw'
-    lid_filtered = f'{to_folder}/lid_filtered'
-
-    os.makedirs(extract_folder, exist_ok=True)
-    os.makedirs(raw_folder, exist_ok=True)
-    os.makedirs(lid_filtered, exist_ok=True)
-
-    
-    to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls)
-
-    completed_urls = download_dataset(
-        dl_folder, dl_dataset, completed_urls)
-    if debug:
-        print('completed urls: ', completed_urls)
-    
-
-    extracted_folders = extract_all_files(
-        completed_urls,
-        extract_folder=extract_folder, 
-        completed_extraction=completed_extraction,
-        debug=debug)
-    if debug:
-        print('download files have been extracted to folders: ', extracted_folders)
-
-    converted_files = convert_files_if_needed(extracted_folders, debug=False)
-    for src_tgt in lang_pairs:
-        print(f'working on {dl_dataset.name}: {src_tgt}')
-        src, tgt = src_tgt.split('-')
-        concat_into_splits(dl_dataset, 
-                            src=src, tgt=tgt,
-                            extracted_folders=extracted_folders, 
-                            to_folder=raw_folder, debug=debug)                            
-    print('completed data into: ', raw_folder)
-
-def download_czang16(download_to, username=None):
-    wgets = [
-        f'wget --user={username} --password=czeng -P {download_to} http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar'
-        for i in range(10)]
-    cmds = []
-    for i, cmd in enumerate(wgets):
-        filename = f'{download_to}/data-plaintext-format.{i}.tar'
-        if os.path.exists(filename):
-            print(f'{filename} has already been downloaded; so skip')
-            continue
-        cmds.append(cmd)
-    if cmds and username is None:
-        raise ValueError('No czeng username is given; please register at http://ufal.mff.cuni.cz/czeng/czeng16 to obtain username to download')        
-    for cmd in cmds:
-        call(cmd)
-    print('done with downloading czeng1.6')
-
-def download_czeng17_script(download_to, extract_folder, debug=False):
-    url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip'
-    filename = f'{download_to}/convert_czeng16_to_17.pl.zip'
-    extract_to = f'{extract_folder}/{get_extract_name(filename)}'
-    script_path = f'{extract_to}/convert_czeng16_to_17.pl'
-    
-    if not os.path.exists(script_path):
-        wget.download(url, filename, bar=bar_custom)  
-        extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug)    
-    return script_path
-
-czeng17_script_path = ""
-def convert2czeng17(file, debug):
-    en_file = f'{file}.en'
-    cs_file = f'{file}.cs'
-    
-    if not os.path.exists(en_file) or not os.path.exists(cs_file):
-        cs_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f3 > {cs_file}'
-        en_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f4 > {en_file}'
-        call(cs_cmd, debug)
-        call(en_cmd, debug)
-    else:
-        print(f'already extracted: {en_file} and {cs_file}')
-    return file
-
-def extract_czeng17(extract_folder, debug=False):
-    url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip'
-    filename = f'{download_to}/convert_czeng16_to_17.pl.zip'
-    extract_to = f'{extract_folder}/{get_extract_name(filename)}'
-    script_path = f'{extract_to}/convert_czeng16_to_17.pl'
-    
-    if not os.path.exists(script_path):
-        wget.download(url, filename, bar=bar_custom)  
-        extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug)    
-    return script_path
-
-#########
-# definitions of wmt data sources
-# for es-en
-# Punctuation in the official test sets will be encoded with ASCII characters (not complex Unicode characters) as much as possible. You may want to normalize your system's output before submission. You are able able to use a rawer version of the test sets that does not have this normalization.
-# script to normalize punctuation: http://www.statmt.org/wmt11/normalize-punctuation.perl
-wmt13_es_en = DLDataset(
-    name='wmt13_es-en',
-    train_urls=[
-        'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-un.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-nc-v8.tgz',
-    ],
-    valid_urls=[
-        ('http://www.statmt.org/wmt13/dev.tgz', 'wmt13_dev.tgz')
-    ],
-    test_urls=[
-        ('http://www.statmt.org/wmt13/test.tgz', 'wmt13_test.tgz')
-    ],
-    train_files_patterns=[
-        ('*/europarl-v7.{src}-{tgt}.{lang}', ['es-en']), 
-        ('*commoncrawl.{src}-{tgt}.{lang}', ['es-en']),
-        ('*/news-commentary-v8.{src}-{tgt}.{lang}', ['es-en']),
-        ('un/*undoc.2000.{src}-{tgt}.{lang}', ['es-en']), 
-    ] ,
-    valid_files_patterns=[
-    ('dev/newstest2012.{lang}', ['es-en'])
-    ],
-    test_files_patterns=[
-    ('test/newstest*.{lang}', ['es-en'])
-    ],
-)
-
-wmt14_de_fr_en = DLDataset(
-    name='wmt14_de_fr_en',
-    train_urls=[
-        'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-un.tgz',
-        'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz',
-        ('http://www.statmt.org/wmt10/training-giga-fren.tar', 'training-giga-fren.gz.tar'), #it is actuall a gz.tar 
-    ],
-    valid_urls=[
-        ('http://www.statmt.org/wmt14/dev.tgz', 'wmt14_dev.tgz'),
-    ],
-    test_urls=[
-        ('http://www.statmt.org/wmt14/test-full.tgz', 'wmt14_test_full.tgz'), # cleaned test sets
-    ],
-    train_files_patterns=[
-        ('*/europarl-v7.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), 
-        ('*commoncrawl.{src}-{tgt}.{lang}', ['fr-en', 'de-en']),
-        ('*/*news-commentary-v9.{src}-{tgt}.{lang}', ['fr-en', 'de-en']),
-        ('un/undoc.2000.{src}-{tgt}.{lang}', ['fr-en']),    
-        ('*giga-{src}{tgt}*{lang}', ['fr-en'])
-    ],
-    valid_files_patterns=[
-    ('dev/newstest2013.{lang}', ['fr-en', 'de-en'])
-    ],
-    test_files_patterns=[ 
-    ('test-full/newstest*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['en-de', 'de-en', 'fr-en', 'en-fr']),                      
-    ],
-)
-
-# pip install git+https://github.com/amake/tmx2corpus.git
-wmt16_ro_en = DLDataset(
-    name='wmt16_ro-en',
-    train_urls=[
-        ('http://data.statmt.org/wmt16/translation-task/training-parallel-ep-v8.tgz', 'wmt16_training-parallel-ep-v8.tgz'),
-        ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz', 'en-ro.tmx.gz'),
-    ],
-    valid_urls=[
-        ('http://data.statmt.org/wmt16/translation-task/dev-romanian-updated.tgz', 'wmt16_dev.tgz')
-    ],
-    test_urls=[
-        ('http://data.statmt.org/wmt16/translation-task/test.tgz', 'wmt16_test.tgz')
-    ],
-    train_files_patterns=[
-        ('*/*europarl-v8.{src}-{tgt}.{lang}', ['ro-en']), 
-        ('bitext.{lang}', ['ro-en']) #setimes from tmux
-        ] ,
-    valid_files_patterns=[
-    ('dev/newsdev2016*{src}{tgt}*.{lang}', ['ro-en', 'ro-en'])
-    ],
-    test_files_patterns=[
-    ('test/newstest*{src}{tgt}*.{lang}', ['ro-en', 'en-ro'])
-    ],
-)
-
-cwmt_wmt_instruction = 'cwmt download instruction at: http://nlp.nju.edu.cn/cwmt-wmt'
-wmt17_fi_lv_tr_zh_en_manual_downloads = [
-    # fake urls to have unique keys for the data
-    ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), cwmt_wmt_instruction),
-    ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), cwmt_wmt_instruction),
-    ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), cwmt_wmt_instruction),
-    ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), cwmt_wmt_instruction),
-    ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), cwmt_wmt_instruction),
-    ( ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), cwmt_wmt_instruction),    
-]
-wmt17_fi_lv_tr_zh_en = DLDataset(
-    name='wmt17_fi_lv_tr_zh_en',
-    train_urls=[
-        ('http://data.statmt.org/wmt17/translation-task/training-parallel-ep-v8.tgz', 'wmt17_training-parallel-ep-v8.tgz'),
-        'http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz',
-        'http://www.statmt.org/wmt15/wiki-titles.tgz',
-        ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-tr.tmx.gz', 'en-tr.tmx.gz'),
-        ('http://data.statmt.org/wmt17/translation-task/rapid2016.tgz', 'wmt17_rapid2016.tgz'),
-        'http://data.statmt.org/wmt17/translation-task/leta.v1.tgz',
-        'http://data.statmt.org/wmt17/translation-task/dcep.lv-en.v1.tgz',
-        'http://data.statmt.org/wmt17/translation-task/books.lv-en.v1.tgz',
-        (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00',
-        'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01',), 'UNv1.0.en-zh.tar.gz'),
-        #manually download files:
-        ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'),  
-        ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'),  
-        ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'),  
-        ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), 
-        ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'),  
-        ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'),          
-    ],
-    valid_urls=[
-        ('http://data.statmt.org/wmt17/translation-task/dev.tgz', 'wmt17_dev.tgz'),
-    ],
-    test_urls=[
-        #NEW: Improved translations for zh test sets
-        ('http://data.statmt.org/wmt17/translation-task/test-update-1.tgz', 'wmt17_test_zh_en.tgz'),    
-        ('http://data.statmt.org/wmt17/translation-task/test.tgz', 'wmt17_test_others.tgz')
-    ],
-    train_files_patterns=[
-        ('casict*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ),
-        ('casia*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ),
-        ('dataum*/Book*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en']),
-        ('neu*/NEU*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en'] ),
-        ('*/*UNv1.0.en-zh.{src:zh}{tgt:en}', ['zh-en']),
-        ('training/*news-commentary-v12.{src}-{tgt}.{lang}', ['zh-en', ]),
-        
-        ('*/*europarl-v8.{src}-{tgt}.{lang}', ['fi-en', 'lv-en']),
-        ('wiki/fi-en/titles.{src}-{tgt}.{lang}', ['fi-en', ]),  
-        ('rapid2016.{tgt}-{src}.{lang}', ['fi-en', 'lv-en']),
-        ('*/leta.{lang}', ['lv-en']),
-        ('*/dcep.{lang}', ['lv-en']),
-        ('*/farewell.{lang}', ['lv-en']),       
-        ('bitext.{lang}', ['tr-en']),
-    ] ,
-    valid_files_patterns=[
-    ('dev/newsdev2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', 
-    [
-        'fi-en', 'lv-en', 'tr-en', 'zh-en',
-        'en-fi', 'en-lv', 'en-tr', 'en-zh'
-    ]),                      
-    ('dev/newstest2016*{src}{tgt}-{src:src}{tgt:ref}.{lang}', 
-    [
-        'fi-en',  'tr-en',  
-        'en-fi',  'en-tr',  
-    ]),  
-    ],
-    test_files_patterns=[
-    ('test/newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', 
-    [
-        'fi-en', 'lv-en', 'tr-en', 
-        'en-fi', 'en-lv', 'en-tr',  
-    ]),
-    ('newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', 
-    [
-        'zh-en',
-        'en-zh'
-    ]),
-    ],
-)
-
-czeng_instruction = 'download instruction at: http://ufal.mff.cuni.cz/czeng/czeng16'
-#alternative: use the prepared data but detokenize it?
-wmt18_cs_et_en_manual_downloads = [
-#for cs, need to register and download; Register and download CzEng 1.6.  
-#Better results can be obtained by using a subset of sentences, released under a new version name CzEng 1.7.
-    # ((f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', 
-    #     f'data-plaintext-format.{i}.tar'), czeng_instruction)
-    # for i in range(10)
-]
-
-wmt18_cs_et_en = DLDataset(
-    name='wmt18_cs_et_en',
-    train_urls=[
-        'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz',
-        'http://data.statmt.org/wmt18/translation-task/training-parallel-ep-v8.tgz',
-        'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-cs.zipporah0-dedup-clean.tgz',
-        'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-et.zipporah0-dedup-clean.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
-        'http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz',
-        ('http://data.statmt.org/wmt18/translation-task/rapid2016.tgz', 'wmt18_rapid2016.tgz'),
-        # (tuple(
-        #     (f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', 
-        #     f'data-plaintext-format.{i}.tar')
-        #     for i in range(10)
-        # ), 
-        # 'czeng16_data_plaintext.gz.tar'), 
-    ],
-    valid_urls=[
-        ('http://data.statmt.org/wmt18/translation-task/dev.tgz', 'wmt18_dev.tgz'),
-    ],
-    test_urls=[
-        ('http://data.statmt.org/wmt18/translation-task/test.tgz', 'wmt18_test.tgz'),
-    ],
-    train_files_patterns=[
-        # ('*/*europarl-v7.{src}-{tgt}.{lang}', ['cs-en']),
-        ('*/*europarl-v8.{src}-{tgt}.{lang}', ['et-en']),
-        # ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['cs-en', 'et-en']),
-        ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['et-en']),
-        # ('*commoncrawl.{src}-{tgt}.{lang}', ['cs-en']),
-        # ('*/news-commentary-v13.{src}-{tgt}.{lang}', ['cs-en']),
-        # ('data.plaintext-format/*train.{lang}', ['cs-en']),
-        ('rapid2016.{tgt}-{src}.{lang}', ['et-en']),
-    ] ,
-    valid_files_patterns=[
-    ('dev/newsdev2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['et-en']),
-    # ('dev/newstest2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['cs-en'])        
-    ],
-    test_files_patterns=[
-    ('test/newstest2018-{src}{tgt}-{src:src}{tgt:ref}.{lang}', 
-    # ['cs-en', 'et-en']),
-    ['et-en']),
-    ]
-)
-
-ru_en_yandex_instruction = 'Yandex Corpus download instruction at: https://translate.yandex.ru/corpus?lang=en'
-wmt19_ru_gu_kk_lt_manual_downloads = [
-    (('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), ru_en_yandex_instruction)
-]
-wmt19_ru_gu_kk_lt = DLDataset(
-    name='wmt19_ru_gu_kk_lt',
-    train_urls=[
-        'http://www.statmt.org/europarl/v9/training/europarl-v9.lt-en.tsv.gz',
-        'https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-lt.bicleaner07.tmx.gz',
-        'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz',
-        'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
-        'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14-wmt19.en-kk.tsv.gz',
-        'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.en-ru.tsv.gz',
-        'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz',
-        'http://data.statmt.org/wikititles/v1/wikititles-v1.ru-en.tsv.gz',
-        'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz',
-        'http://data.statmt.org/wikititles/v1/wikititles-v1.lt-en.tsv.gz',
-        'http://data.statmt.org/wikititles/v1/wikititles-v1.gu-en.tsv.gz',
-        (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00',
-        'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01',
-        'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02',), 
-        'wmt19_UNv1.0.en-ru.tar.gz'),
-        'https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-lt.tmx.zip',
-        ('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'),
-    ],
-    valid_urls=[
-        ('http://data.statmt.org/wmt19/translation-task/dev.tgz', 'wmt19_dev.tgz'),
-    ],
-    test_urls=[
-        ('http://data.statmt.org/wmt19/translation-task/test.tgz', 'wmt19_test.tgz'),
-    ],
-    train_files_patterns=[
-        ('*europarl-v9.{src}-{tgt}.tsv.{lang}', ['lt-en']),
-        #paracrawl
-        ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['ru-en']),
-        ('bitext.{lang}', ['lt-en',]),
-        ('*commoncrawl.{src}-{tgt}.{lang}', ['ru-en',]),
-        ('*news-commentary-v14-wmt19.{tgt}-{src}.tsv.{lang}', ['kk-en', ]),
-        ('*news-commentary-v14.{tgt}-{src}.tsv.{lang}', ['ru-en']),
-        #yandex
-        ('corpus.{tgt}_{src}.1m.{lang}', ['ru-en']),
-        ('wikititles_v1_wikititles-v1.{src}-{tgt}.tsv.{lang}', ['ru-en', 'kk-en', 'lt-en', 'gu-en']),
-        ('*/UNv1.0.{tgt}-{src}.{lang}', ['ru-en']),
-        #rapid
-        ('bitext.{lang}', ['lt-en'])
-    ],
-    valid_files_patterns=[
-    ('dev/newsdev2019*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['gu-en', 'kk-en', 'lt-en']),
-    ('dev/newstest2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['ru-en']),       
-    ],
-    test_files_patterns=[
-    ('sgm/newstest2019-{src}{tgt}-{src:src}{tgt:ref}.{lang}', 
-    ['ru-en', 'gu-en', 'kk-en', 'lt-en', 'en-ru', 'en-gu', 'en-kk', 'en-lt']),
-    ]    
-)
-
-
-#########
-
-if __name__ == "__main__":
-    # speed up the downloads with multiple processing
-    dl_folder = f'{to_data_path}/downloads'
-    extract_folder = f'{to_data_path}/extracted'
-
-    urls = [
-        url
-        for dataset in [wmt13_es_en, wmt14_de_fr_en, wmt16_ro_en, wmt18_cs_et_en, wmt19_ru_gu_kk_lt]
-        for urls in [dataset.train_urls, dataset.valid_urls, dataset.test_urls]
-        for url in urls
-    ]
-    urls = set(urls)
-    download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=True)
-
-    # check manually downlaods
-    to_manually_download_urls = (
-        wmt17_fi_lv_tr_zh_en_manual_downloads + wmt18_cs_et_en_manual_downloads + wmt19_ru_gu_kk_lt_manual_downloads
-    )
-    to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls)
-    if len(to_be_manually_dowloaded) > 0:
-        print('Missing files that need to be downloaded manually; stop the process now.')
-        exit(-1)
-    
-    completed_urls = {}
-    completed_extraction = {}
-    def work_on_wmt(directions, wmt_data):
-        download_and_extract(
-            to_data_path, 
-            directions, 
-            wmt_data, 
-            to_manually_download_urls=to_manually_download_urls,
-            completed_urls=completed_urls, completed_extraction=completed_extraction, debug=True)
-                
-    work_on_wmt(
-        ['es_XX-en_XX'], 
-        wmt13_es_en,)
-    work_on_wmt(
-        [
-            'fr_XX-en_XX',  'en_XX-fr_XX',
-            # 'en_XX-de_DE', 'de_DE-en_XX',
-        ], 
-        wmt14_de_fr_en,)
-    work_on_wmt(
-        ['ro_RO-en_XX', 'en_XX-ro_XX'], 
-        wmt16_ro_en,)
-    work_on_wmt(
-        [
-            # 'zh_CN-en_XX', 
-            'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX',
-            #in case the reversed directions have different train/valid/test data
-            # 'en_XX-zh_CN', 
-            'en_XX-lv_LV', 'en_XX-fi_FI', 'en_XX-tr_TR',
-        ], 
-        wmt17_fi_lv_tr_zh_en, )
-    # czeng17_script_path = download_czeng17_script(download_to, extract_to, debug=False)
-    # cz_username =  None
-    work_on_wmt(
-        [
-            # 'cs_CZ-en_XX', 
-            'et_EE-en_XX'], 
-        wmt18_cs_et_en,)
-    work_on_wmt(
-        [
-            # 'ru_RU-en_XX', 'en_XX-ru_RU', 
-            'gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX',
-            #in case the reversed directions have different train/valid/test data
-            'en_XX-gu_IN', 'en_XX-kk_KZ', 'en_XX-lt_LT'
-        ], 
-        wmt19_ru_gu_kk_lt,)
-
-    not_matching = check_wmt_test_bleu(
-        f'{to_data_path}/raw', 
-        [
-            ('wmt13', ['es_XX-en_XX']),
-            ('wmt14/full', ['fr_XX-en_XX',]),
-            ('wmt16', ['ro_RO-en_XX',]),
-            # ('wmt17/improved', ['zh_CN-en_XX']),
-            ('wmt17', [ 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX']),
-            ('wmt18', ['cs_CZ-en_XX', 'et_EE-en_XX']),
-            ('wmt19', ['gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX']), 
-            #'ru_RU-en_XX', 
-        ]
-        )    
-    if len(not_matching) > 0:
-        print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py
deleted file mode 100644
index e971cf07c57c4e864726781092a690dd4d7d3e46..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py
+++ /dev/null
@@ -1,43 +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
-from torch import nn
-
-from fairseq.distributed import utils
-
-
-class TPUDistributedDataParallel(nn.Module):
-
-    def __init__(self, module, process_group):
-        super().__init__()
-        self.module = module
-        self.process_group = process_group
-        self.world_size = utils.get_world_size(self.process_group)
-
-    def forward(self, *inputs, **kwargs):
-        return self.module(*inputs, **kwargs)
-
-    def all_reduce_grads(self):
-        gradients = []
-        for p in self.parameters():
-            if not p.requires_grad:
-                continue
-            if p.grad is None:
-                p.grad = torch.zeros_like(p)
-            if p.grad.requires_grad:
-                raise RuntimeError(
-                    "TPUDistributedDataParallel only works with gradients that don't "
-                    "require grad"
-                )
-            gradients.append(p.grad)
-
-        import torch_xla.core.xla_model as xm
-        xm.all_reduce(
-            'sum',
-            gradients,
-            scale=1. / self.world_size,
-            groups=self.process_group[1],
-        )
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/scripts/rm_pt.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/scripts/rm_pt.py
deleted file mode 100644
index 6cd063d21f0610fa7c42c2cfb2ee8af7c9c78677..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/scripts/rm_pt.py
+++ /dev/null
@@ -1,141 +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.
-
-import argparse
-import os
-import re
-import shutil
-import sys
-
-
-pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt")
-pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt")
-pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt")
-
-
-def parse_checkpoints(files):
-    entries = []
-    for f in files:
-        m = pt_regexp_epoch_based.fullmatch(f)
-        if m is not None:
-            entries.append((int(m.group(1)), m.group(0)))
-        else:
-            m = pt_regexp_update_based.fullmatch(f)
-            if m is not None:
-                entries.append((int(m.group(1)), m.group(0)))
-    return entries
-
-
-def last_n_checkpoints(files, n):
-    entries = parse_checkpoints(files)
-    return [x[1] for x in sorted(entries, reverse=True)[:n]]
-
-
-def every_n_checkpoints(files, n):
-    entries = parse_checkpoints(files)
-    return [x[1] for x in sorted(sorted(entries)[::-n])]
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=(
-            "Recursively delete checkpoint files from `root_dir`, "
-            "but preserve checkpoint_best.pt and checkpoint_last.pt"
-        )
-    )
-    parser.add_argument("root_dirs", nargs="*")
-    parser.add_argument(
-        "--save-last", type=int, default=0, help="number of last checkpoints to save"
-    )
-    parser.add_argument(
-        "--save-every", type=int, default=0, help="interval of checkpoints to save"
-    )
-    parser.add_argument(
-        "--preserve-test",
-        action="store_true",
-        help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)",
-    )
-    parser.add_argument(
-        "--delete-best", action="store_true", help="delete checkpoint_best.pt"
-    )
-    parser.add_argument(
-        "--delete-last", action="store_true", help="delete checkpoint_last.pt"
-    )
-    parser.add_argument(
-        "--no-dereference", action="store_true", help="don't dereference symlinks"
-    )
-    args = parser.parse_args()
-
-    files_to_desymlink = []
-    files_to_preserve = []
-    files_to_delete = []
-    for root_dir in args.root_dirs:
-        for root, _subdirs, files in os.walk(root_dir):
-            if args.save_last > 0:
-                to_save = last_n_checkpoints(files, args.save_last)
-            else:
-                to_save = []
-            if args.save_every > 0:
-                to_save += every_n_checkpoints(files, args.save_every)
-            for file in files:
-                if not pt_regexp.fullmatch(file):
-                    continue
-                full_path = os.path.join(root, file)
-                if (
-                    not os.path.basename(root).startswith("test_") or args.preserve_test
-                ) and (
-                    (file == "checkpoint_last.pt" and not args.delete_last)
-                    or (file == "checkpoint_best.pt" and not args.delete_best)
-                    or file in to_save
-                ):
-                    if os.path.islink(full_path) and not args.no_dereference:
-                        files_to_desymlink.append(full_path)
-                    else:
-                        files_to_preserve.append(full_path)
-                else:
-                    files_to_delete.append(full_path)
-
-    if len(files_to_desymlink) == 0 and len(files_to_delete) == 0:
-        print("Nothing to do.")
-        sys.exit(0)
-
-    files_to_desymlink = sorted(files_to_desymlink)
-    files_to_preserve = sorted(files_to_preserve)
-    files_to_delete = sorted(files_to_delete)
-
-    print("Operations to perform (in order):")
-    if len(files_to_desymlink) > 0:
-        for file in files_to_desymlink:
-            print(" - preserve (and dereference symlink): " + file)
-    if len(files_to_preserve) > 0:
-        for file in files_to_preserve:
-            print(" - preserve: " + file)
-    if len(files_to_delete) > 0:
-        for file in files_to_delete:
-            print(" - delete: " + file)
-    while True:
-        resp = input("Continue? (Y/N): ")
-        if resp.strip().lower() == "y":
-            break
-        elif resp.strip().lower() == "n":
-            sys.exit(0)
-
-    print("Executing...")
-    if len(files_to_desymlink) > 0:
-        for file in files_to_desymlink:
-            realpath = os.path.realpath(file)
-            print("rm " + file)
-            os.remove(file)
-            print("cp {} {}".format(realpath, file))
-            shutil.copyfile(realpath, file)
-    if len(files_to_delete) > 0:
-        for file in files_to_delete:
-            print("rm " + file)
-            os.remove(file)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/utils/cider/pyciderevalcap/cider/cider.py b/spaces/OFA-Sys/OFA-Image_Caption/utils/cider/pyciderevalcap/cider/cider.py
deleted file mode 100644
index 5b65978370cb82dd2111500e7f05c4d05306162c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/utils/cider/pyciderevalcap/cider/cider.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Filename: cider.py
-#
-#
-# Description: Describes the class to compute the CIDEr
-# (Consensus-Based Image Description Evaluation) Metric
-#          by Vedantam, Zitnick, and Parikh (http://arxiv.org/abs/1411.5726)
-#
-# Creation Date: Sun Feb  8 14:16:54 2015
-#
-# Authors: Ramakrishna Vedantam <vrama91@vt.edu> and
-# Tsung-Yi Lin <tl483@cornell.edu>
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from .cider_scorer import CiderScorer
-
-
-class Cider:
-    """
-    Main Class to compute the CIDEr metric
-
-    """
-    def __init__(self, n=4, df="corpus"):
-        """
-        Initialize the CIDEr scoring function
-        : param n (int): n-gram size
-        : param df (string): specifies where to get the IDF values from
-                    takes values 'corpus', 'coco-train'
-        : return: None
-        """
-        # set cider to sum over 1 to 4-grams
-        self._n = n
-        self._df = df
-        self.cider_scorer = CiderScorer(n=self._n, df_mode=self._df)
-
-    def compute_score(self, gts, res):
-        """
-        Main function to compute CIDEr score
-        : param  gts (dict) : {image:tokenized reference sentence}
-        : param res (dict)  : {image:tokenized candidate sentence}
-        : return: cider (float) : computed CIDEr score for the corpus
-        """
-
-        # clear all the previous hypos and refs
-        self.cider_scorer.clear()
-
-        for res_id in res:
-
-            hypo = res_id['caption']
-            ref = gts[res_id['image_id']]
-
-            # Sanity check.
-            assert(type(hypo) is list)
-            assert(len(hypo) == 1)
-            assert(type(ref) is list)
-            assert(len(ref) > 0)
-            self.cider_scorer += (hypo[0], ref)
-
-        (score, scores) = self.cider_scorer.compute_score()
-
-        return score, scores
-
-    def method(self):
-        return "CIDEr"
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/clib/libbleu/libbleu.cpp b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/clib/libbleu/libbleu.cpp
deleted file mode 100644
index 939d9e1174e398fa48c840009b592c753a67939a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/clib/libbleu/libbleu.cpp
+++ /dev/null
@@ -1,157 +0,0 @@
-/**
- * Copyright 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.
- */
-
-#include <array>
-#include <cstdio>
-#include <cstring>
-#include <map>
-
-// NOLINTNEXTLINE
-typedef struct {
-  size_t reflen;
-  size_t predlen;
-  size_t match1;
-  size_t count1;
-  size_t match2;
-  size_t count2;
-  size_t match3;
-  size_t count3;
-  size_t match4;
-  size_t count4;
-} bleu_stat;
-
-// left trim (remove pad)
-void bleu_ltrim(size_t* len, int** sent, int pad) {
-  size_t start = 0;
-  while (start < *len) {
-    if (*(*sent + start) != pad) {
-      break;
-    }
-    start++;
-  }
-  *sent += start;
-  *len -= start;
-}
-
-// right trim remove (eos)
-void bleu_rtrim(size_t* len, int** sent, int pad, int eos) {
-  size_t end = *len - 1;
-  while (end > 0) {
-    if (*(*sent + end) != eos && *(*sent + end) != pad) {
-      break;
-    }
-    end--;
-  }
-  *len = end + 1;
-}
-
-// left and right trim
-void bleu_trim(size_t* len, int** sent, int pad, int eos) {
-  bleu_ltrim(len, sent, pad);
-  bleu_rtrim(len, sent, pad, eos);
-}
-
-size_t bleu_hash(int len, int* data) {
-  size_t h = 14695981039346656037ul;
-  size_t prime = 0x100000001b3;
-  char* b = (char*)data;
-  size_t blen = sizeof(int) * len;
-
-  while (blen-- > 0) {
-    h ^= *b++;
-    h *= prime;
-  }
-
-  return h;
-}
-
-void bleu_addngram(
-    size_t* ntotal,
-    size_t* nmatch,
-    size_t n,
-    size_t reflen,
-    int* ref,
-    size_t predlen,
-    int* pred) {
-  if (predlen < n) {
-    return;
-  }
-
-  predlen = predlen - n + 1;
-  (*ntotal) += predlen;
-
-  if (reflen < n) {
-    return;
-  }
-
-  reflen = reflen - n + 1;
-
-  std::map<size_t, size_t> count;
-  while (predlen > 0) {
-    size_t w = bleu_hash(n, pred++);
-    count[w]++;
-    predlen--;
-  }
-
-  while (reflen > 0) {
-    size_t w = bleu_hash(n, ref++);
-    if (count[w] > 0) {
-      (*nmatch)++;
-      count[w] -= 1;
-    }
-    reflen--;
-  }
-}
-
-extern "C" {
-
-#ifdef _WIN64
-__declspec(dllexport)
-#endif
-    void bleu_zero_init(bleu_stat* stat) {
-  std::memset(stat, 0, sizeof(bleu_stat));
-}
-
-#ifdef _WIN64
-__declspec(dllexport)
-#endif
-    void bleu_one_init(bleu_stat* stat) {
-  bleu_zero_init(stat);
-  stat->count1 = 0;
-  stat->count2 = 1;
-  stat->count3 = 1;
-  stat->count4 = 1;
-  stat->match1 = 0;
-  stat->match2 = 1;
-  stat->match3 = 1;
-  stat->match4 = 1;
-}
-
-#ifdef _WIN64
-__declspec(dllexport)
-#endif
-    void bleu_add(
-        bleu_stat* stat,
-        size_t reflen,
-        int* ref,
-        size_t predlen,
-        int* pred,
-        int pad,
-        int eos) {
-
-  bleu_trim(&reflen, &ref, pad, eos);
-  bleu_trim(&predlen, &pred, pad, eos);
-  stat->reflen += reflen;
-  stat->predlen += predlen;
-
-  bleu_addngram(&stat->count1, &stat->match1, 1, reflen, ref, predlen, pred);
-  bleu_addngram(&stat->count2, &stat->match2, 2, reflen, ref, predlen, pred);
-  bleu_addngram(&stat->count3, &stat->match3, 3, reflen, ref, predlen, pred);
-  bleu_addngram(&stat->count4, &stat->match4, 4, reflen, ref, predlen, pred);
-}
-}
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/logging/progress_bar.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/logging/progress_bar.py
deleted file mode 100644
index 061082caefe542c5f0f87e04d9472583874126a3..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/logging/progress_bar.py
+++ /dev/null
@@ -1,490 +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.
-
-"""
-Wrapper around various loggers and progress bars (e.g., tqdm).
-"""
-
-import atexit
-import json
-import logging
-import os
-import sys
-from collections import OrderedDict
-from contextlib import contextmanager
-from numbers import Number
-from typing import Optional
-
-import torch
-
-from .meters import AverageMeter, StopwatchMeter, TimeMeter
-
-
-logger = logging.getLogger(__name__)
-
-
-def progress_bar(
-    iterator,
-    log_format: Optional[str] = None,
-    log_interval: int = 100,
-    log_file: Optional[str] = None,
-    epoch: Optional[int] = None,
-    prefix: Optional[str] = None,
-    tensorboard_logdir: Optional[str] = None,
-    default_log_format: str = "tqdm",
-    wandb_project: Optional[str] = None,
-    wandb_run_name: Optional[str] = None,
-    azureml_logging: Optional[bool] = False,
-):
-    if log_format is None:
-        log_format = default_log_format
-    if log_file is not None:
-        handler = logging.FileHandler(filename=log_file)
-        logger.addHandler(handler)
-
-    if log_format == "tqdm" and not sys.stderr.isatty():
-        log_format = "simple"
-
-    if log_format == "json":
-        bar = JsonProgressBar(iterator, epoch, prefix, log_interval)
-    elif log_format == "none":
-        bar = NoopProgressBar(iterator, epoch, prefix)
-    elif log_format == "simple":
-        bar = SimpleProgressBar(iterator, epoch, prefix, log_interval)
-    elif log_format == "tqdm":
-        bar = TqdmProgressBar(iterator, epoch, prefix)
-    else:
-        raise ValueError("Unknown log format: {}".format(log_format))
-
-    if tensorboard_logdir:
-        try:
-            # [FB only] custom wrapper for TensorBoard
-            import palaas  # noqa
-            from .fb_tbmf_wrapper import FbTbmfWrapper
-
-            bar = FbTbmfWrapper(bar, log_interval)
-        except ImportError:
-            bar = TensorboardProgressBarWrapper(bar, tensorboard_logdir)
-
-    if wandb_project:
-        bar = WandBProgressBarWrapper(bar, wandb_project, run_name=wandb_run_name)
-
-    if azureml_logging:
-        bar = AzureMLProgressBarWrapper(bar)
-
-    return bar
-
-
-def build_progress_bar(
-    args,
-    iterator,
-    epoch: Optional[int] = None,
-    prefix: Optional[str] = None,
-    default: str = "tqdm",
-    no_progress_bar: str = "none",
-):
-    """Legacy wrapper that takes an argparse.Namespace."""
-    if getattr(args, "no_progress_bar", False):
-        default = no_progress_bar
-    if getattr(args, "distributed_rank", 0) == 0:
-        tensorboard_logdir = getattr(args, "tensorboard_logdir", None)
-    else:
-        tensorboard_logdir = None
-    return progress_bar(
-        iterator,
-        log_format=args.log_format,
-        log_interval=args.log_interval,
-        epoch=epoch,
-        prefix=prefix,
-        tensorboard_logdir=tensorboard_logdir,
-        default_log_format=default,
-    )
-
-
-def format_stat(stat):
-    if isinstance(stat, Number):
-        stat = "{:g}".format(stat)
-    elif isinstance(stat, AverageMeter):
-        stat = "{:.3f}".format(stat.avg)
-    elif isinstance(stat, TimeMeter):
-        stat = "{:g}".format(round(stat.avg))
-    elif isinstance(stat, StopwatchMeter):
-        stat = "{:g}".format(round(stat.sum))
-    elif torch.is_tensor(stat):
-        stat = stat.tolist()
-    return stat
-
-
-class BaseProgressBar(object):
-    """Abstract class for progress bars."""
-
-    def __init__(self, iterable, epoch=None, prefix=None):
-        self.iterable = iterable
-        self.n = getattr(iterable, "n", 0)
-        self.epoch = epoch
-        self.prefix = ""
-        if epoch is not None:
-            self.prefix += "epoch {:03d}".format(epoch)
-        if prefix is not None:
-            self.prefix += (" | " if self.prefix != "" else "") + prefix
-
-    def __len__(self):
-        return len(self.iterable)
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *exc):
-        return False
-
-    def __iter__(self):
-        raise NotImplementedError
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats according to log_interval."""
-        raise NotImplementedError
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        raise NotImplementedError
-
-    def update_config(self, config):
-        """Log latest configuration."""
-        pass
-
-    def _str_commas(self, stats):
-        return ", ".join(key + "=" + stats[key].strip() for key in stats.keys())
-
-    def _str_pipes(self, stats):
-        return " | ".join(key + " " + stats[key].strip() for key in stats.keys())
-
-    def _format_stats(self, stats):
-        postfix = OrderedDict(stats)
-        # Preprocess stats according to datatype
-        for key in postfix.keys():
-            postfix[key] = str(format_stat(postfix[key]))
-        return postfix
-
-
-@contextmanager
-def rename_logger(logger, new_name):
-    old_name = logger.name
-    if new_name is not None:
-        logger.name = new_name
-    yield logger
-    logger.name = old_name
-
-
-class JsonProgressBar(BaseProgressBar):
-    """Log output in JSON format."""
-
-    def __init__(self, iterable, epoch=None, prefix=None, log_interval=1000):
-        super().__init__(iterable, epoch, prefix)
-        self.log_interval = log_interval
-        self.i = None
-        self.size = None
-
-    def __iter__(self):
-        self.size = len(self.iterable)
-        for i, obj in enumerate(self.iterable, start=self.n):
-            self.i = i
-            yield obj
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats according to log_interval."""
-        step = step or self.i or 0
-        if step > 0 and self.log_interval is not None and step % self.log_interval == 0:
-            update = (
-                self.epoch - 1 + (self.i + 1) / float(self.size)
-                if self.epoch is not None
-                else None
-            )
-            stats = self._format_stats(stats, epoch=self.epoch, update=update)
-            with rename_logger(logger, tag):
-                logger.info(json.dumps(stats))
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        self.stats = stats
-        if tag is not None:
-            self.stats = OrderedDict(
-                [(tag + "_" + k, v) for k, v in self.stats.items()]
-            )
-        stats = self._format_stats(self.stats, epoch=self.epoch)
-        with rename_logger(logger, tag):
-            logger.info(json.dumps(stats))
-
-    def _format_stats(self, stats, epoch=None, update=None):
-        postfix = OrderedDict()
-        if epoch is not None:
-            postfix["epoch"] = epoch
-        if update is not None:
-            postfix["update"] = round(update, 3)
-        # Preprocess stats according to datatype
-        for key in stats.keys():
-            postfix[key] = format_stat(stats[key])
-        return postfix
-
-
-class NoopProgressBar(BaseProgressBar):
-    """No logging."""
-
-    def __init__(self, iterable, epoch=None, prefix=None):
-        super().__init__(iterable, epoch, prefix)
-
-    def __iter__(self):
-        for obj in self.iterable:
-            yield obj
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats according to log_interval."""
-        pass
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        pass
-
-
-class SimpleProgressBar(BaseProgressBar):
-    """A minimal logger for non-TTY environments."""
-
-    def __init__(self, iterable, epoch=None, prefix=None, log_interval=1000):
-        super().__init__(iterable, epoch, prefix)
-        self.log_interval = log_interval
-        self.i = None
-        self.size = None
-
-    def __iter__(self):
-        self.size = len(self.iterable)
-        for i, obj in enumerate(self.iterable, start=self.n):
-            self.i = i
-            yield obj
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats according to log_interval."""
-        step = step or self.i or 0
-        if step > 0 and self.log_interval is not None and step % self.log_interval == 0:
-            stats = self._format_stats(stats)
-            postfix = self._str_commas(stats)
-            with rename_logger(logger, tag):
-                logger.info(
-                    "{}:  {:5d} / {:d} {}".format(
-                        self.prefix, self.i + 1, self.size, postfix
-                    )
-                )
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        postfix = self._str_pipes(self._format_stats(stats))
-        with rename_logger(logger, tag):
-            logger.info("{} | {}".format(self.prefix, postfix))
-
-
-class TqdmProgressBar(BaseProgressBar):
-    """Log to tqdm."""
-
-    def __init__(self, iterable, epoch=None, prefix=None):
-        super().__init__(iterable, epoch, prefix)
-        from tqdm import tqdm
-
-        self.tqdm = tqdm(
-            iterable,
-            self.prefix,
-            leave=False,
-            disable=(logger.getEffectiveLevel() > logging.INFO),
-        )
-
-    def __iter__(self):
-        return iter(self.tqdm)
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats according to log_interval."""
-        self.tqdm.set_postfix(self._format_stats(stats), refresh=False)
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        postfix = self._str_pipes(self._format_stats(stats))
-        with rename_logger(logger, tag):
-            logger.info("{} | {}".format(self.prefix, postfix))
-
-
-try:
-    _tensorboard_writers = {}
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    try:
-        from tensorboardX import SummaryWriter
-    except ImportError:
-        SummaryWriter = None
-
-
-def _close_writers():
-    for w in _tensorboard_writers.values():
-        w.close()
-
-
-atexit.register(_close_writers)
-
-
-class TensorboardProgressBarWrapper(BaseProgressBar):
-    """Log to tensorboard."""
-
-    def __init__(self, wrapped_bar, tensorboard_logdir):
-        self.wrapped_bar = wrapped_bar
-        self.tensorboard_logdir = tensorboard_logdir
-
-        if SummaryWriter is None:
-            logger.warning(
-                "tensorboard not found, please install with: pip install tensorboard"
-            )
-
-    def _writer(self, key):
-        if SummaryWriter is None:
-            return None
-        _writers = _tensorboard_writers
-        if key not in _writers:
-            _writers[key] = SummaryWriter(os.path.join(self.tensorboard_logdir, key))
-            _writers[key].add_text("sys.argv", " ".join(sys.argv))
-        return _writers[key]
-
-    def __iter__(self):
-        return iter(self.wrapped_bar)
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats to tensorboard."""
-        self._log_to_tensorboard(stats, tag, step)
-        self.wrapped_bar.log(stats, tag=tag, step=step)
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        self._log_to_tensorboard(stats, tag, step)
-        self.wrapped_bar.print(stats, tag=tag, step=step)
-
-    def update_config(self, config):
-        """Log latest configuration."""
-        # TODO add hparams to Tensorboard
-        self.wrapped_bar.update_config(config)
-
-    def _log_to_tensorboard(self, stats, tag=None, step=None):
-        writer = self._writer(tag or "")
-        if writer is None:
-            return
-        if step is None:
-            step = stats["num_updates"]
-        for key in stats.keys() - {"num_updates"}:
-            if isinstance(stats[key], AverageMeter):
-                writer.add_scalar(key, stats[key].val, step)
-            elif isinstance(stats[key], Number):
-                writer.add_scalar(key, stats[key], step)
-            elif torch.is_tensor(stats[key]) and stats[key].numel() == 1:
-                writer.add_scalar(key, stats[key].item(), step)
-        writer.flush()
-
-
-try:
-    import wandb
-except ImportError:
-    wandb = None
-
-
-class WandBProgressBarWrapper(BaseProgressBar):
-    """Log to Weights & Biases."""
-
-    def __init__(self, wrapped_bar, wandb_project, run_name=None):
-        self.wrapped_bar = wrapped_bar
-        if wandb is None:
-            logger.warning("wandb not found, pip install wandb")
-            return
-
-        # reinit=False to ensure if wandb.init() is called multiple times
-        # within one process it still references the same run
-        wandb.init(project=wandb_project, reinit=False, name=run_name)
-
-    def __iter__(self):
-        return iter(self.wrapped_bar)
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats to tensorboard."""
-        self._log_to_wandb(stats, tag, step)
-        self.wrapped_bar.log(stats, tag=tag, step=step)
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats."""
-        self._log_to_wandb(stats, tag, step)
-        self.wrapped_bar.print(stats, tag=tag, step=step)
-
-    def update_config(self, config):
-        """Log latest configuration."""
-        if wandb is not None:
-            wandb.config.update(config)
-        self.wrapped_bar.update_config(config)
-
-    def _log_to_wandb(self, stats, tag=None, step=None):
-        if wandb is None:
-            return
-        if step is None:
-            step = stats["num_updates"]
-
-        prefix = "" if tag is None else tag + "/"
-
-        for key in stats.keys() - {"num_updates"}:
-            if isinstance(stats[key], AverageMeter):
-                wandb.log({prefix + key: stats[key].val}, step=step)
-            elif isinstance(stats[key], Number):
-                wandb.log({prefix + key: stats[key]}, step=step)
-
-
-try:
-    from azureml.core import Run
-except ImportError:
-    Run = None
-
-
-class AzureMLProgressBarWrapper(BaseProgressBar):
-    """Log to Azure ML"""
-
-    def __init__(self, wrapped_bar):
-        self.wrapped_bar = wrapped_bar
-        if Run is None:
-            logger.warning("azureml.core not found, pip install azureml-core")
-            return
-        self.run = Run.get_context()
-
-    def __exit__(self, *exc):
-        if Run is not None:
-            self.run.complete()
-        return False
-
-    def __iter__(self):
-        return iter(self.wrapped_bar)
-
-    def log(self, stats, tag=None, step=None):
-        """Log intermediate stats to AzureML"""
-        self._log_to_azureml(stats, tag, step)
-        self.wrapped_bar.log(stats, tag=tag, step=step)
-
-    def print(self, stats, tag=None, step=None):
-        """Print end-of-epoch stats"""
-        self._log_to_azureml(stats, tag, step)
-        self.wrapped_bar.print(stats, tag=tag, step=step)
-
-    def update_config(self, config):
-        """Log latest configuration."""
-        self.wrapped_bar.update_config(config)
-
-    def _log_to_azureml(self, stats, tag=None, step=None):
-        if Run is None:
-            return
-        if step is None:
-            step = stats["num_updates"]
-
-        prefix = "" if tag is None else tag + "/"
-
-        for key in stats.keys() - {"num_updates"}:
-            name = prefix + key
-            if isinstance(stats[key], AverageMeter):
-                self.run.log_row(name=name, **{"step": step, key: stats[key].val})
-            elif isinstance(stats[key], Number):
-                self.run.log_row(name=name, **{"step": step, key: stats[key]})
diff --git a/spaces/ORI-Muchim/MarinTTS/utils.py b/spaces/ORI-Muchim/MarinTTS/utils.py
deleted file mode 100644
index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/MarinTTS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, required=True,
-                        help='Model name')
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/level4.py b/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/level4.py
deleted file mode 100644
index eabe8a2d50f4920fe2aa13946466cd65e9b2cd84..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/LLMRiddlesChatGPTCN/llmriddles/questions/level4.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import re
-
-from .question import register_question
-
-
-def check_if_is_number(text: str):
-    try:
-        int(text)
-        return True
-    except ValueError:
-        return False
-
-
-def get_all_numbers_in_a_sentence(text: str):
-    return [int(i) for i in re.findall(r'\d+', text)]
-
-
-# CN_TEXT_1 = """
-# 第四章第一题，请构造一个问题A，它的回复是不同于A的B，然后以B提问能再次得到A。
-
-# 请在下面的输入框内填写你构造并点击按钮提交。
-# """
-
-# EN_TEXT_1 = """
-# For the first question in chapter 4, please make a question A whose answer is B that is different from A, and then ask B to get A again.
-
-# Please enter your query below and click the submit button
-# """
-
-
-# def _checker_1(question_text: str, user_text: str, answer_text: str, lang: str):
-#     _ = question_text, lang
-#     answer_text = answer_text.strip()
-#     user_text = user_text.strip()
-#     pass
-
-# register_question({
-#     'cn': CN_TEXT_1,
-#     'en': EN_TEXT_1,
-# }, _checker_1, level=4)
-
-
-# CN_TEXT_2 = """
-# 第四章第二题，
-
-# 请在下面的输入框内填写你构造并点击按钮提交。
-# """
-
-# EN_TEXT_2 = """
-# For the second question in chapter 4,
-
-# Please enter your query below and click the submit button
-# """
-
-
-# def _checker_2(question_text: str, user_text: str, answer_text: str, lang: str):
-#     _ = question_text, lang
-#     answer_text = answer_text.strip()
-#     user_text = user_text.strip()
-#     pass
-
-# register_question({
-#     'cn': CN_TEXT_2,
-#     'en': EN_TEXT_2,
-# }, _checker_2, level=4)
-
-
-CN_TEXT_3 = """
-第四章第一题（自然之密），请输入一个大于一的正整数作为问题，使回答里包含和它刚好相差1的数。
-
-请在下面的输入框内填写你构造并点击按钮提交。
-"""
-
-EN_TEXT_3 = """
-For the first question in chapter 4, please enter a positive integer greater than one as the question so that the answer contains a number that is exactly 1 different from it.
-
-Please enter your query below and click the submit button
-"""
-
-
-def _checker_3(question_text: str, user_text: str, answer_text: str, lang: str):
-    _ = question_text, lang
-    answer_text = answer_text.strip()
-    user_text = user_text.strip()
-    if not check_if_is_number(user_text):
-        return False, "问题应该是一个正整数" if lang == 'cn' else 'Question should be a positive integer.'
-    elif int(user_text) == 1:
-        return False, "问题应该是一个大于1的正整数" if lang == 'cn' else 'Question should be a positive integer greater than 1.'
-    elif int(user_text) - 1 not in get_all_numbers_in_a_sentence(answer_text) or int(
-            user_text) + 1 not in get_all_numbers_in_a_sentence(answer_text):
-        return False, "回答中应该包含一个与问题相差1的数字" if lang == 'cn' else 'Answer should contain a number that is exactly 1 different from the question.'
-    else:
-        return True, None
-
-
-register_question(
-    {
-        'cn': CN_TEXT_3,
-        'en': EN_TEXT_3,
-    },
-    checkers=_checker_3,
-    name={'cn': '4-3 自然之密', 'en': '4-3'},
-    level=4,
-)
diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/utils/sample_utils.py b/spaces/OpenMotionLab/MotionGPT/mGPT/utils/sample_utils.py
deleted file mode 100644
index 724b109af5ecd08e20cf0af681df317b390d4d44..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/mGPT/utils/sample_utils.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import logging
-from pathlib import Path
-logger = logging.getLogger(__name__)
-
-def cfg_mean_nsamples_resolution(cfg):
-    if cfg.mean and cfg.number_of_samples > 1:
-        logger.error("All the samples will be the mean.. cfg.number_of_samples=1 will be forced.")
-        cfg.number_of_samples = 1
-
-    return cfg.number_of_samples == 1
-
-
-def get_path(sample_path: Path, is_amass: bool, gender: str, split: str, onesample: bool, mean: bool, fact: float):
-    extra_str = ("_mean" if mean else "") if onesample else "_multi"
-    fact_str = "" if fact == 1 else f"{fact}_"
-    gender_str = gender + "_" if is_amass else ""
-    path = sample_path / f"{fact_str}{gender_str}{split}{extra_str}"
-    return path
diff --git a/spaces/PaddlePaddle/PP-OCRv3-ch/app.py b/spaces/PaddlePaddle/PP-OCRv3-ch/app.py
deleted file mode 100644
index 7629742fcfd5c2531064afc27fc5fbb30ddd1df7..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/PP-OCRv3-ch/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import tempfile
-import os
-
-import gradio as gr
-import paddlehub as hub
-from PIL import Image
-
-pp_ocrv3 = hub.Module(name="ch_pp-ocrv3")
-
-def inference(img):
-  with tempfile.TemporaryDirectory() as tempdir_name:
-    pp_ocrv3.recognize_text(paths=[img],use_gpu=False,output_dir=tempdir_name,visualization=True)
-    result_names = os.listdir(tempdir_name)
-    output_image = Image.open(os.path.join(tempdir_name, result_names[0]))
-    return [output_image]
-  
-title="ch_PP-OCRv3"
-description="ch_PP-OCRv3 is a practical ultra-lightweight OCR system developed by PaddleOCR."
-
-examples=[['test.png']]
-
-gr.Interface(inference,gr.inputs.Image(type="filepath"),outputs=[gr.Gallery(label="Result", show_label=False).style(grid=[1, 1], height="auto")],title=title,description=description,examples=examples).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/PaddlePaddle/U-GAT-IT-selfie2anime/app.py b/spaces/PaddlePaddle/U-GAT-IT-selfie2anime/app.py
deleted file mode 100644
index 6a88728d86aed11195766b3d7505a5c51d223089..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/U-GAT-IT-selfie2anime/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os
-import cv2
-os.system("hub install UGATIT_100w==1.0.0")
-import gradio as gr
-import paddlehub as hub
-import numpy as np
-from PIL import Image
-
-model = hub.Module(name='UGATIT_100w', use_gpu=False)
-
-def inference(image):
-    #result = model.style_transfer(images=[cv2.imread(image.name)])
-    result = model.style_transfer(paths=[image.name])
-    print(type(result[0]))
-    print(result[0])
-    return Image.fromarray(np.uint8(result[0])[:,:,::-1]).convert('RGB')
-    
-title = "UGATIT-selfie2anime"
-description = "Gradio demo for UGATIT-selfie2anime. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.10830' target='_blank'>U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation</a> | <a href='https://github.com/taki0112/UGATIT' target='_blank'>Github Repo</a></p>"
-examples=[['robert.png']]
-iface = gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),examples=examples,enable_queue=True,title=title,article=article,description=description)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/to-xml.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/to-xml.go
deleted file mode 100644
index 58814d8f884f0369302cdc9d0cdec6df5269136d..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/lilypond/2.24.2/ccache/lily/to-xml.go and /dev/null differ
diff --git a/spaces/Phantom3306/AI-image-detector/app.py b/spaces/Phantom3306/AI-image-detector/app.py
deleted file mode 100644
index f84502d92dcf92368f27d0550f795855bc026817..0000000000000000000000000000000000000000
--- a/spaces/Phantom3306/AI-image-detector/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipe = pipeline("image-classification", "umm-maybe/AI-image-detector")
-
-def image_classifier(image):
-    outputs = pipe(image)
-    results = {}
-    for result in outputs:
-        results[result['label']] = result['score']
-    return results
-
-title = "AI Image Detector"
-description = """
-This app is a proof-of-concept demonstration of using a ViT model to predict whether an image was generated using AI."""
-
-demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label", title=title, description=description)
-demo.launch(show_api=False)
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/util.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/util.py
deleted file mode 100644
index 06053e5defb87977f9ab07e69bf4da12201de9b7..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/util.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os, hashlib
-import requests
-from tqdm import tqdm
-
-URL_MAP = {
-    "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
-}
-
-CKPT_MAP = {
-    "vgg_lpips": "vgg.pth"
-}
-
-MD5_MAP = {
-    "vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
-}
-
-
-def download(url, local_path, chunk_size=1024):
-    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        total_size = int(r.headers.get("content-length", 0))
-        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
-            with open(local_path, "wb") as f:
-                for data in r.iter_content(chunk_size=chunk_size):
-                    if data:
-                        f.write(data)
-                        pbar.update(chunk_size)
-
-
-def md5_hash(path):
-    with open(path, "rb") as f:
-        content = f.read()
-    return hashlib.md5(content).hexdigest()
-
-
-def get_ckpt_path(name, root, check=False):
-    assert name in URL_MAP
-    path = os.path.join(root, CKPT_MAP[name])
-    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
-        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
-        download(URL_MAP[name], path)
-        md5 = md5_hash(path)
-        assert md5 == MD5_MAP[name], md5
-    return path
-
-
-class KeyNotFoundError(Exception):
-    def __init__(self, cause, keys=None, visited=None):
-        self.cause = cause
-        self.keys = keys
-        self.visited = visited
-        messages = list()
-        if keys is not None:
-            messages.append("Key not found: {}".format(keys))
-        if visited is not None:
-            messages.append("Visited: {}".format(visited))
-        messages.append("Cause:\n{}".format(cause))
-        message = "\n".join(messages)
-        super().__init__(message)
-
-
-def retrieve(
-    list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False
-):
-    """Given a nested list or dict return the desired value at key expanding
-    callable nodes if necessary and :attr:`expand` is ``True``. The expansion
-    is done in-place.
-
-    Parameters
-    ----------
-        list_or_dict : list or dict
-            Possibly nested list or dictionary.
-        key : str
-            key/to/value, path like string describing all keys necessary to
-            consider to get to the desired value. List indices can also be
-            passed here.
-        splitval : str
-            String that defines the delimiter between keys of the
-            different depth levels in `key`.
-        default : obj
-            Value returned if :attr:`key` is not found.
-        expand : bool
-            Whether to expand callable nodes on the path or not.
-
-    Returns
-    -------
-        The desired value or if :attr:`default` is not ``None`` and the
-        :attr:`key` is not found returns ``default``.
-
-    Raises
-    ------
-        Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is
-        ``None``.
-    """
-
-    keys = key.split(splitval)
-
-    success = True
-    try:
-        visited = []
-        parent = None
-        last_key = None
-        for key in keys:
-            if callable(list_or_dict):
-                if not expand:
-                    raise KeyNotFoundError(
-                        ValueError(
-                            "Trying to get past callable node with expand=False."
-                        ),
-                        keys=keys,
-                        visited=visited,
-                    )
-                list_or_dict = list_or_dict()
-                parent[last_key] = list_or_dict
-
-            last_key = key
-            parent = list_or_dict
-
-            try:
-                if isinstance(list_or_dict, dict):
-                    list_or_dict = list_or_dict[key]
-                else:
-                    list_or_dict = list_or_dict[int(key)]
-            except (KeyError, IndexError, ValueError) as e:
-                raise KeyNotFoundError(e, keys=keys, visited=visited)
-
-            visited += [key]
-        # final expansion of retrieved value
-        if expand and callable(list_or_dict):
-            list_or_dict = list_or_dict()
-            parent[last_key] = list_or_dict
-    except KeyNotFoundError as e:
-        if default is None:
-            raise e
-        else:
-            list_or_dict = default
-            success = False
-
-    if not pass_success:
-        return list_or_dict
-    else:
-        return list_or_dict, success
-
-
-if __name__ == "__main__":
-    config = {"keya": "a",
-              "keyb": "b",
-              "keyc":
-                  {"cc1": 1,
-                   "cc2": 2,
-                   }
-              }
-    from omegaconf import OmegaConf
-    config = OmegaConf.create(config)
-    print(config)
-    retrieve(config, "keya")
-
diff --git a/spaces/RamAnanth1/roomGPT/app.py b/spaces/RamAnanth1/roomGPT/app.py
deleted file mode 100644
index 0fa77d3b49cdabe6867cfb278d6d21a27c192770..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/roomGPT/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import pathlib
-import shlex
-import subprocess
-
-import gradio as gr
-
-if os.getenv('SYSTEM') == 'spaces':
-    with open('patch') as f:
-        subprocess.run(shlex.split('patch -p1'), stdin=f, cwd='ControlNet')
-
-base_url = 'https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/'
-names = [
-    'body_pose_model.pth',
-    'dpt_hybrid-midas-501f0c75.pt',
-    'hand_pose_model.pth',
-    'mlsd_large_512_fp32.pth',
-    'mlsd_tiny_512_fp32.pth',
-    'network-bsds500.pth',
-    'upernet_global_small.pth',
-]
-for name in names:
-    command = f'wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/{name} -O {name}'
-    out_path = pathlib.Path(f'ControlNet/annotator/ckpts/{name}')
-    if out_path.exists():
-        continue
-    subprocess.run(shlex.split(command), cwd='ControlNet/annotator/ckpts/')
-
-
-from app_hough import create_demo as create_demo_hough
-from model import Model, download_all_controlnet_weights
-
-DESCRIPTION = '# RoomGPT <p> Redesign your room using the power of AI (Inspired by <a href = "https://www.roomgpt.io/">roomgpt.io<a>)</p>'
-
-SPACE_ID = os.getenv('SPACE_ID')
-
-MAX_IMAGES = 4
-DEFAULT_NUM_IMAGES = min(MAX_IMAGES,2)
-
-if os.getenv('SYSTEM') == 'spaces':
-    download_all_controlnet_weights()
-
-DEFAULT_MODEL_ID = os.getenv('DEFAULT_MODEL_ID',
-                             'runwayml/stable-diffusion-v1-5')
-model = Model(base_model_id=DEFAULT_MODEL_ID)
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    create_demo_hough(model.process_hough,
-                              max_images=MAX_IMAGES,
-                              default_num_images=DEFAULT_NUM_IMAGES)
-        
-    
-    
-demo.queue(api_open=False).launch(file_directories=['/tmp'])
diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
deleted file mode 100644
index 854132e8c8af3b3c9c85fa797a79a149aff545ef..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import sys
-from pathlib import Path
-
-sys.path.append(str(Path(__file__).parent / "../../../"))
-from src.config.default import _CN as cfg
-
-cfg.ASPAN.COARSE.COARSEST_LEVEL = [15, 20]
-cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
-
-cfg.ASPAN.MATCH_COARSE.SPARSE_SPVS = False
-cfg.ASPAN.MATCH_COARSE.BORDER_RM = 0
-cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29]
diff --git a/spaces/RecursosRegenerativos/README/README.md b/spaces/RecursosRegenerativos/README/README.md
deleted file mode 100644
index c59e53b47f2780cb62d5a0394387591bf7bc49af..0000000000000000000000000000000000000000
--- a/spaces/RecursosRegenerativos/README/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: README
-emoji: 📈
-colorFrom: indigo
-colorTo: pink
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card 🔥
diff --git a/spaces/Redgon/bingo/cloudflare/worker.js b/spaces/Redgon/bingo/cloudflare/worker.js
deleted file mode 100644
index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/cloudflare/worker.js
+++ /dev/null
@@ -1,18 +0,0 @@
-const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。
-
-export default {
-  async fetch(request) {
-    const uri = new URL(request.url);
-    if (uri.protocol === 'http:') {
-      uri.protocol = 'https:';
-      return new Response('', {
-        status: 301,
-        headers: {
-          location: uri.toString(),
-        },
-      })
-    }
-    uri.host = TRAGET_HOST
-    return fetch(new Request(uri.toString(), request));
-  },
-};
diff --git a/spaces/Reself/StableVideo/ldm/modules/image_degradation/bsrgan_light.py b/spaces/Reself/StableVideo/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 808c7f882cb75e2ba2340d5b55881d11927351f0..0000000000000000000000000000000000000000
--- a/spaces/Reself/StableVideo/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,651 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    if up:
-        image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC)  # todo: random, as above? want to condition on it then
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')
diff --git a/spaces/Reself/StableVideo/ldm/modules/midas/midas/midas_net_custom.py b/spaces/Reself/StableVideo/ldm/modules/midas/midas/midas_net_custom.py
deleted file mode 100644
index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000
--- a/spaces/Reself/StableVideo/ldm/modules/midas/midas/midas_net_custom.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name
\ No newline at end of file
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/utils/builder.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/utils/builder.py
deleted file mode 100644
index f362d1c92ca9d4ed95a2b3d28d3e6baedd14e462..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/utils/builder.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from mmcv.utils import Registry, build_from_cfg
-
-TRANSFORMER = Registry('Transformer')
-POSITIONAL_ENCODING = Registry('Position encoding')
-
-
-def build_transformer(cfg, default_args=None):
-    """Builder for Transformer."""
-    return build_from_cfg(cfg, TRANSFORMER, default_args)
-
-
-def build_positional_encoding(cfg, default_args=None):
-    """Builder for Position Encoding."""
-    return build_from_cfg(cfg, POSITIONAL_ENCODING, default_args)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/cgnet.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/cgnet.py
deleted file mode 100644
index eff8d9458c877c5db894957e0b1b4597e40da6ab..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/cgnet.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    backbone=dict(
-        type='CGNet',
-        norm_cfg=norm_cfg,
-        in_channels=3,
-        num_channels=(32, 64, 128),
-        num_blocks=(3, 21),
-        dilations=(2, 4),
-        reductions=(8, 16)),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=256,
-        in_index=2,
-        channels=256,
-        num_convs=0,
-        concat_input=False,
-        dropout_ratio=0,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        loss_decode=dict(
-            type='CrossEntropyLoss',
-            use_sigmoid=False,
-            loss_weight=1.0,
-            class_weight=[
-                2.5959933, 6.7415504, 3.5354059, 9.8663225, 9.690899, 9.369352,
-                10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905,
-                10.347791, 6.3927646, 10.226669, 10.241062, 10.280587,
-                10.396974, 10.055647
-            ])),
-    # model training and testing settings
-    train_cfg=dict(sampler=None),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/utils/parrots_wrapper.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/utils/parrots_wrapper.py
deleted file mode 100644
index 93c97640d4b9ed088ca82cfe03e6efebfcfa9dbf..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/utils/parrots_wrapper.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from functools import partial
-
-import torch
-
-TORCH_VERSION = torch.__version__
-
-
-def is_rocm_pytorch() -> bool:
-    is_rocm = False
-    if TORCH_VERSION != 'parrots':
-        try:
-            from torch.utils.cpp_extension import ROCM_HOME
-            is_rocm = True if ((torch.version.hip is not None) and
-                               (ROCM_HOME is not None)) else False
-        except ImportError:
-            pass
-    return is_rocm
-
-
-def _get_cuda_home():
-    if TORCH_VERSION == 'parrots':
-        from parrots.utils.build_extension import CUDA_HOME
-    else:
-        if is_rocm_pytorch():
-            from torch.utils.cpp_extension import ROCM_HOME
-            CUDA_HOME = ROCM_HOME
-        else:
-            from torch.utils.cpp_extension import CUDA_HOME
-    return CUDA_HOME
-
-
-def get_build_config():
-    if TORCH_VERSION == 'parrots':
-        from parrots.config import get_build_info
-        return get_build_info()
-    else:
-        return torch.__config__.show()
-
-
-def _get_conv():
-    if TORCH_VERSION == 'parrots':
-        from parrots.nn.modules.conv import _ConvNd, _ConvTransposeMixin
-    else:
-        from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin
-    return _ConvNd, _ConvTransposeMixin
-
-
-def _get_dataloader():
-    if TORCH_VERSION == 'parrots':
-        from torch.utils.data import DataLoader, PoolDataLoader
-    else:
-        from torch.utils.data import DataLoader
-        PoolDataLoader = DataLoader
-    return DataLoader, PoolDataLoader
-
-
-def _get_extension():
-    if TORCH_VERSION == 'parrots':
-        from parrots.utils.build_extension import BuildExtension, Extension
-        CppExtension = partial(Extension, cuda=False)
-        CUDAExtension = partial(Extension, cuda=True)
-    else:
-        from torch.utils.cpp_extension import (BuildExtension, CppExtension,
-                                               CUDAExtension)
-    return BuildExtension, CppExtension, CUDAExtension
-
-
-def _get_pool():
-    if TORCH_VERSION == 'parrots':
-        from parrots.nn.modules.pool import (_AdaptiveAvgPoolNd,
-                                             _AdaptiveMaxPoolNd, _AvgPoolNd,
-                                             _MaxPoolNd)
-    else:
-        from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd,
-                                              _AdaptiveMaxPoolNd, _AvgPoolNd,
-                                              _MaxPoolNd)
-    return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd
-
-
-def _get_norm():
-    if TORCH_VERSION == 'parrots':
-        from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm
-        SyncBatchNorm_ = torch.nn.SyncBatchNorm2d
-    else:
-        from torch.nn.modules.instancenorm import _InstanceNorm
-        from torch.nn.modules.batchnorm import _BatchNorm
-        SyncBatchNorm_ = torch.nn.SyncBatchNorm
-    return _BatchNorm, _InstanceNorm, SyncBatchNorm_
-
-
-_ConvNd, _ConvTransposeMixin = _get_conv()
-DataLoader, PoolDataLoader = _get_dataloader()
-BuildExtension, CppExtension, CUDAExtension = _get_extension()
-_BatchNorm, _InstanceNorm, SyncBatchNorm_ = _get_norm()
-_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool()
-
-
-class SyncBatchNorm(SyncBatchNorm_):
-
-    def _check_input_dim(self, input):
-        if TORCH_VERSION == 'parrots':
-            if input.dim() < 2:
-                raise ValueError(
-                    f'expected at least 2D input (got {input.dim()}D input)')
-        else:
-            super()._check_input_dim(input)
diff --git a/spaces/Rominn/vits-uma-genshin-honkai/utils.py b/spaces/Rominn/vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/Rominn/vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-  audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-  return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
diff --git a/spaces/RoryT0ishi/Meow/Dockerfile b/spaces/RoryT0ishi/Meow/Dockerfile
deleted file mode 100644
index e9100a4c5f2be8583e4f5be46eb9ad3a5a0b0b78..0000000000000000000000000000000000000000
--- a/spaces/RoryT0ishi/Meow/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18
-
-WORKDIR /app
-
-RUN npm install express express-http-proxy
-
-COPY . .
-
-EXPOSE 7860
-
-CMD [ "node", "server.js" ]
diff --git a/spaces/Rowanchav/anything-v3.0/app.py b/spaces/Rowanchav/anything-v3.0/app.py
deleted file mode 100644
index 62c8768d6f448b1a0387eaa5d551f3743ebd9462..0000000000000000000000000000000000000000
--- a/spaces/Rowanchav/anything-v3.0/app.py
+++ /dev/null
@@ -1,276 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-import utils
-import datetime
-import time
-import psutil
-
-start_time = time.time()
-is_colab = utils.is_google_colab()
-
-class Model:
-    def __init__(self, name, path="", prefix=""):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-        self.pipe_t2i = None
-        self.pipe_i2i = None
-
-models = [
-   Model("anything v3", "Linaqruf/anything-v3.0", "anything v3 style"),
-  ]
-    #  Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
-    #  Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
-    #  Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
-    #  Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
-     #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
-     #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
-     #Model("Robo Diffusion", "nousr/robo-diffusion", ""),
-     
-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,
-)
-
-custom_model = None
-if is_colab:
-  models.insert(0, Model("Custom model"))
-  custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
-  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-
-else: # download all models
-  print(f"{datetime.datetime.now()} Downloading vae...")
-  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
-  for model in models:
-    try:
-        print(f"{datetime.datetime.now()} Downloading {model.name} model...")
-        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
-        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
-    except Exception as e:
-        print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
-        models.remove(model)
-  pipe = models[0].pipe_t2i
-  
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def custom_model_changed(path):
-  models[0].path = path
-  global current_model
-  current_model = models[0]
-
-def on_model_change(model_name):
-  
-  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
-  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
-def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-
-  print(psutil.virtual_memory()) # print memory usage
-
-  global current_model
-  for model in models:
-    if model.name == model_name:
-      current_model = model
-      model_path = current_model.path
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-
-  try:
-    if img is not None:
-      return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "txt2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-        else:
-          pipe = pipe.to("cpu")
-          pipe = current_model.pipe_t2i
-
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "txt2img"
-
-    prompt = current_model.prefix + prompt  
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "img2img":
-        current_model_path = model_path
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-        else:
-          pipe = pipe.to("cpu")
-          pipe = current_model.pipe_i2i
-        
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-        last_mode = "img2img"
-
-    prompt = current_model.prefix + prompt
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe(
-        prompt,
-        negative_prompt = neg_prompt,
-        # num_images_per_prompt=n_images,
-        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):
-
-    if is_colab:
-      return results.images[0]
-      
-    for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
-    return results.images[0]
-
-css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-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="finetuned-diffusion-div">
-              <div>
-                <h1>Anything V3</h1>
-              </div>
-              <p>
-               Demo for Anything V3
-              </p>
-              <p>You can skip the queue by duplicating this space: <a style="display:inline-block" href="https://huggingface.co/spaces/akhaliq/anything-v3.0?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>       </p>
-              </p>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
-              with gr.Box(visible=False) as custom_model_group:
-                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
-                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
-              
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-
-              image_out = gr.Image(height=512)
-              # gallery = gr.Gallery(
-              #     label="Generated images", show_label=False, elem_id="gallery"
-              # ).style(grid=[1], height="auto")
-          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")
-
-              # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-
-              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)
-
-    if is_colab:
-      model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
-      custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
-    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
-
-    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    ex = gr.Examples([
-       [models[0].name, "iron man", 7.5, 50],
-      
-    ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Model by Linaqruf</p>
-    </div>
-    """)
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-if not is_colab:
-  demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
\ No newline at end of file
diff --git a/spaces/Salesforce/BLIP/eval_nocaps.py b/spaces/Salesforce/BLIP/eval_nocaps.py
deleted file mode 100644
index 3cbb09a8cc7771605c013583d721aa95d9413b42..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/BLIP/eval_nocaps.py
+++ /dev/null
@@ -1,118 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip import blip_decoder
-import utils
-from data import create_dataset, create_sampler, create_loader
-from data.utils import save_result
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # evaluate
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Evaluation:'
-    print_freq = 10
-
-    result = []
-    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
-        
-        image = image.to(device)       
-        
-        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
-                                  min_length=config['min_length'], repetition_penalty=1.1)
-        
-        for caption, img_id in zip(captions, image_id):
-            result.append({"image_id": img_id.item(), "caption": caption})
-  
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating captioning dataset")
-    val_dataset, test_dataset = create_dataset('nocaps', config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([val_dataset,test_dataset], [False,False], num_tasks, global_rank)         
-    else:
-        samplers = [None,None]
-    
-    val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, 
-                                            batch_size=[config['batch_size']]*2,num_workers=[4,4],
-                                            is_trains=[False, False], collate_fns=[None,None])        
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                           prompt=config['prompt'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    val_result = evaluate(model_without_ddp, val_loader, device, config)  
-    val_result_file = save_result(val_result, args.result_dir, 'val', remove_duplicate='image_id')   
-    test_result = evaluate(model_without_ddp, test_loader, device, config)  
-    test_result_file = save_result(test_result, args.result_dir, 'test', remove_duplicate='image_id') 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/nocaps.yaml')
-    parser.add_argument('--output_dir', default='output/NoCaps')        
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/spaces/Sanathkumar1603/hackathon/app/templates/predict_expr_recognition.html b/spaces/Sanathkumar1603/hackathon/app/templates/predict_expr_recognition.html
deleted file mode 100644
index 2b51ebca6f436314ef6419e624a6ae5ec7e9e481..0000000000000000000000000000000000000000
--- a/spaces/Sanathkumar1603/hackathon/app/templates/predict_expr_recognition.html
+++ /dev/null
@@ -1,37 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <title>Predict</title>
-</head>
-<body>
-    <div>
-        <h1 style="background-color:LightGray;">
-            <center>Expression Recognition</center>
-        </h1>
-    </div>
-    <div>
-        <fieldset>
-            <h2>
-                <center>
-                    <span style="font-weight:bold;font-family:sans-serif">Prediction: </span> 
-                    <span style="font-weight:bold;color:blue"> {{result}}</span>
-                </center>
-            </h2>
-            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3> 
-            <p>
-              <center>
-              <img src="{{expr_rec_filename}}" alt={{expr_rec_filename1}} width='150' height='150'>
-              </center>
-            </p>
-            <br>
-            <form action="/expr_recognition/" method="get">
-                <center><button type="submit">Check Another Input</button></center>
-            </form>
-            <br>
-            <form action="/" method="get">
-                <center><button type="submit">Home</button></center>
-            </form>
-        </fieldset>
-    </div>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/SeViLA/SeViLA/lavis/tasks/multimodal_classification.py b/spaces/SeViLA/SeViLA/lavis/tasks/multimodal_classification.py
deleted file mode 100644
index bc85170c0564dc5f17931ef6b712ec27dbfec523..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/tasks/multimodal_classification.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import json
-import os
-import logging
-
-import numpy as np
-import torch
-from lavis.common.dist_utils import main_process
-from lavis.common.registry import registry
-from lavis.tasks.base_task import BaseTask
-
-
-@registry.register_task("multimodal_classification")
-class MultimodalClassificationTask(BaseTask):
-    def __init__(self):
-        super().__init__()
-
-    def valid_step(self, model, samples):
-        results = []
-
-        outputs = model.predict(samples)
-
-        predictions = outputs["predictions"]
-        targets = outputs["targets"]
-
-        predictions = predictions.max(1)[1].cpu().numpy()
-        targets = targets.cpu().numpy()
-
-        indices = samples[self.inst_id_key]
-
-        for pred, tgt, index in zip(predictions, targets, indices):
-            if isinstance(index, torch.Tensor):
-                index = index.item()
-
-            results.append(
-                {
-                    self.inst_id_key: index,
-                    "prediction": pred.item(),
-                    "target": tgt.item(),
-                }
-            )
-
-        return results
-
-    def after_evaluation(self, val_result, split_name, epoch, **kwargs):
-        eval_result_file = self.save_result(
-            result=val_result,
-            result_dir=registry.get_path("result_dir"),
-            filename="{}_epoch{}".format(split_name, epoch),
-            remove_duplicate=self.inst_id_key,
-        )
-
-        metrics = self._report_metrics(
-            eval_result_file=eval_result_file, split_name=split_name
-        )
-
-        return metrics
-
-    @main_process
-    def _report_metrics(self, eval_result_file, split_name):
-        results = json.load(open(eval_result_file))
-
-        predictions = np.array([res["prediction"] for res in results])
-        targets = np.array([res["target"] for res in results])
-
-        accuracy = (targets == predictions).sum() / targets.shape[0]
-        metrics = {"agg_metrics": accuracy, "acc": accuracy}
-
-        log_stats = {split_name: {k: v for k, v in metrics.items()}}
-
-        with open(
-            os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
-        ) as f:
-            f.write(json.dumps(log_stats) + "\n")
-
-        logging.info(metrics)
-        return metrics
diff --git a/spaces/Shad0ws/imagetomusic/app.py b/spaces/Shad0ws/imagetomusic/app.py
deleted file mode 100644
index 76a10d28d5eb0d851cbb97d22e719402e049bf84..0000000000000000000000000000000000000000
--- a/spaces/Shad0ws/imagetomusic/app.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import time
-
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-
-import httpx
-import json
-
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
-#import subprocess
-import os
-import uuid
-from tempfile import gettempdir
-from PIL import Image
-import cv2
-from pprint import pprint
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-# image_to_text = gr.Interface.load("spaces/doevent/image_to_text", api_key=os.environ['HF_TOKEN'])
-image_to_text = gr.Blocks.load(name="spaces/banana-dev/demo-clip-interrogator")
-def center_crop(img, dim: tuple = (512, 512)):
-    
-    """Returns center cropped image
-	Args:
-	img: image to be center cropped
-	dim: dimensions (width, height) to be cropped
-    """
-    
-    width, height = img.shape[1], img.shape[0]
-
-    # process crop width and height for max available dimension
-    crop_width = dim[0] if dim[0]<img.shape[1] else img.shape[1]
-    crop_height = dim[1] if dim[1]<img.shape[0] else img.shape[0] 
-    mid_x, mid_y = int(width/2), int(height/2)
-    cw2, ch2 = int(crop_width/2), int(crop_height/2) 
-    crop_img = img[mid_y-ch2:mid_y+ch2, mid_x-cw2:mid_x+cw2]
-    return crop_img
-
-
-def scale_image(img, factor=1):
-	"""Returns resize image by scale factor.
-	This helps to retain resolution ratio while resizing.
-	Args:
-	img: image to be scaled
-	factor: scale factor to resize
-	"""
-	return cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))
-	
-	
-def get_track_by_tags(tags, pat, duration, maxit=20, loop=False):
-    if loop:
-        mode = "loop"
-    else:
-        mode = "track"
-    r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
-                   json={
-                       "method": "RecordTrackTTM",
-                       "params": {
-                           "pat": pat,
-                           "duration": duration,
-                           "tags": tags,
-                           "mode": mode
-                       }
-                   })
-    
-    pprint(r.text)
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, rdata['error']['text']
-    trackurl = rdata['data']['tasks'][0]['download_link']
-
-    #print('Generating track ', end='')
-    for i in range(maxit):
-        r = httpx.get(trackurl)
-        if r.status_code == 200:
-            return trackurl
-        time.sleep(1)
-
-
-def generate_track_by_prompt(image, email, duration, loop=False):
-    try:
-        # Checking Image Aspect Ratio
-        filename_png = f"{uuid.uuid4().hex}.png"
-        filepath_png = f"{gettempdir()}/{filename_png}"
-        
-        with Image.open(image) as im:
-            # image size
-            ratio_width = im.size[0]
-            ratio_height = im.size[1]
-            im.convert("RGB").save(filepath_png)
-        if ratio_width > 3501 or ratio_height > 3501:
-            raise gr.Error("Image aspect ratio must not exceed width: 1024 px or height: 1024 px.")
-        elif ratio_width > 3500 or ratio_height > 3500:
-            image_g = cv2.imread(image)
-            scale_img = scale_image(image_g, factor=0.2)
-            cv2.imwrite(filepath_png, scale_img)
-        elif ratio_width > 1800 or ratio_height > 1800:
-            image_g = cv2.imread(image)
-            scale_img = scale_image(image_g, factor=0.3)
-            cv2.imwrite(filepath_png, scale_img)
-        elif ratio_width > 900 or ratio_height > 900:
-            image_g = cv2.imread(image)
-            scale_img = scale_image(image_g, factor=0.5)
-            cv2.imwrite(filepath_png, scale_img)
-        
-        # prompt = image_to_text(filepath_png, "Image Captioning", "", "Nucleus sampling")
-        prompt = image_to_text(filepath_png, "ViT-L (best for Stable Diffusion 1.*)", "Fast",  fn_index=1)[0]
-        print(f"PROMPT: {prompt}")
-  
-        pat = get_pat(email)
-        _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
-        filepath = get_track_by_tags(tags, pat, int(duration), loop=loop)
-           
-        filename_mp3 = filepath.split("/")[-1]
-        filepath_mp3 = f"{gettempdir()}/{filename_mp3}"
-        filename_mp4 = f"{uuid.uuid4().hex}.mp4"
-        filepath_mp4 = f"{gettempdir()}/{filename_mp4}"
-        
-        os.system(f"wget {filepath} -P {gettempdir()}")
-        
-        # waveform
-        with Image.open(filepath_png) as im:
-            width = im.size[0]
-            height = im.size[1]
-        print(f"{width}x{height}")
-        command = f'ffmpeg -hide_banner -loglevel warning -y -i {filepath_mp3} -loop 1 -i {filepath_png} -filter_complex "[0:a]showwaves=s={width}x{height}:colors=0xffffff:mode=cline,format=rgba[v];[1:v][v]overlay[outv]" -map "[outv]" -map 0:a -c:v libx264 -r 15 -c:a copy -pix_fmt yuv420p -shortest {filepath_mp4}'
-        os.system(command)
-        os.remove(filepath_png)
-        os.remove(filepath_mp3)
-        
-        return filepath_mp4, filepath, prompt, tags
-    except Exception as e:
-        raise gr.Error(str(e))
-
-
-iface = gr.Interface(fn=generate_track_by_prompt, 
-                    inputs=[gr.Image(type="filepath"), 
-                    "text", 
-                    gr.Slider(label="duration (seconds)", value=30, minimum=10, maximum=60)], 
-                    outputs=[gr.Video(label="Video"), 
-                    gr.Audio(label="Audio"),
-                    gr.Text(label="Prompt"),
-                    gr.Text(label="Tags")])
-iface.queue().launch()
diff --git a/spaces/Sharathhebbar24/One-stop-for-Open-source-models/models.py b/spaces/Sharathhebbar24/One-stop-for-Open-source-models/models.py
deleted file mode 100644
index 4a7ba5e82c22cc3335f20bbc9dfb3eb58c92069e..0000000000000000000000000000000000000000
--- a/spaces/Sharathhebbar24/One-stop-for-Open-source-models/models.py
+++ /dev/null
@@ -1,974 +0,0 @@
-def return_task_name():
-    return ('text2text-generation', "text-generation")
-
-
-def return_models(task_name):
-    if task_name == "text2text-generation":
-        models_parent = (
-            'google',
-            "facebook",
-            "microsoft",
-            "salesforce",
-            "langboat",
-            "bloom",
-            "allenai",
-            "mbzuai",
-            "lmsys",
-            "starmpcc",
-            "haining",
-            "kaludi",
-            
-
-        )
-    else:
-        models_parent = (
-            'google',
-            "facebook",
-            "microsoft",
-            "salesforce",
-            "langboat",
-            "databricks",
-            "aisquared",
-            "bloom",
-            "allenai",
-            "tiiuae",
-            "openlm",
-            "stabilityai",
-            "eleutherai",
-            "mbzuai",
-            "cerebras",
-            "open_assistant",
-            "nomic_ai",
-            "blinkdl",
-            "lmsys",
-            "together_computer",
-            "mosaic_ml",
-            "h20ai",
-            "deciai",
-            
-        )
-    return models_parent
-
-def return_text2text_generation_models(model_parent, count=False):
-    google_models_list = (
-        "google/flan-t5-small",
-        "google/flan-t5-base",
-        'google/flan-t5-large',
-        'google/flan-t5-xl',
-        'google/flan-t5-xxl', 
-        "google/byt5-xxl",
-        "google/byt5-xl",
-        "google/byt5-large",
-        "google/byt5-small",
-        "google/byt5-base",
-        "google/mt5-xxl",
-        "google/mt5-xl",
-        "google/mt5-large",
-        "google/mt5-small",
-        "google/long-t5-local-base",
-        "google/long-t5-local-large",
-        "google/long-t5-tglobal-base",
-        "google/long-t5-tglobal-large",
-        "google/pegasus-big_patent",
-        "google/pegasus-x-base",
-        "google/pegasus-x-large",
-        "google/pegasus-x-base-arxiv",
-        "google/roberta2roberta_L-24_wikisplit",
-        "google/roberta2roberta_L-24_discofuse",
-        "google/switch-base-8",
-        "google/switch-base-16",
-        "google/switch-base-32",
-        "google/switch-base-64",
-        "google/switch-base-128",
-        "google/switch-base-256",
-        "google/switch-large-128",
-        "google/switch-xxl-128",
-        "google/switch-c-2048",
-        "google/t5-11b-ssm",
-        "google/t5-11b-ssm-nq",
-        "google/t5-11b-ssm-nqo",
-        "google/t5-11b-ssm-tqa",
-        "google/t5-11b-ssm-tqao",
-        "google/t5-11b-ssm-wq",
-        "google/t5-11b-ssm-wqo",
-        "google/t5-3b-ssm",
-        "google/t5-3b-ssm-nq",
-        "google/t5-3b-ssm-nqo",
-        "google/t5-base-lm-adapt",
-        "google/t5-base-lm-adapt",
-        "google/t5-efficient-base",
-        "google/t5-efficient-base-dl2",
-        "google/t5-efficient-base-dl4",
-        "google/t5-efficient-base-dl6",
-        "google/t5-efficient-base-dl8",
-        "google/t5-efficient-base-dm256",
-        "google/t5-efficient-base-dm512",
-        "google/t5-efficient-base-dm1000",
-        "google/t5-efficient-base-dm2000",
-        "google/t5-efficient-base-el2",
-        "google/t5-efficient-base-el4",
-        "google/t5-efficient-base-el6",
-        "google/t5-efficient-base-el8",
-        "google/t5-efficient-base-el16",
-        "google/t5-efficient-base-nl40",
-        "google/t5-efficient-base-nl48",
-        "google/t5-efficient-base-nl8",
-        "google/t5-efficient-large",
-        "google/t5-efficient-large-dl12",
-        "google/t5-efficient-large-dl16",
-        "google/t5-efficient-large-dl2",
-        "google/t5-efficient-large-dl32",
-        "google/t5-efficient-large-dl4",
-        "google/t5-efficient-large-dl6",
-        "google/t5-efficient-large-dl8",
-        "google/t5-efficient-large-dm128",
-        "google/t5-efficient-large-dm2000",
-        "google/t5-efficient-large-dm256",
-        "google/t5-efficient-large-dm512",
-        "google/t5-efficient-large-dm768",
-        "google/t5-efficient-large-el12",
-        "google/t5-efficient-large-el2",
-        "google/t5-efficient-large-el4",
-        "google/t5-efficient-large-el6",
-        "google/t5-efficient-large-el8",
-        "google/t5-efficient-large-kv128",
-        "google/t5-efficient-large-kv16",
-        "google/t5-efficient-large-kv256",
-        "google/t5-efficient-large-kv32",
-        "google/t5-efficient-large-nh12",
-        "google/t5-efficient-large-nh2",
-        "google/t5-efficient-large-nh24",
-        "google/t5-efficient-large-nh32",
-        "google/t5-efficient-large-nh4",
-        "google/t5-efficient-large-nh8",
-        "google/t5-efficient-large-nh8-nl32",
-        "google/t5-efficient-large-nl10",
-        "google/t5-efficient-large-nl12",
-        "google/t5-efficient-large-nl16",
-        "google/t5-efficient-large-nl2",
-        "google/t5-efficient-large-nl20",
-        "google/t5-efficient-large-nl32",
-        "google/t5-efficient-large-nl36",
-        "google/t5-efficient-large-nl4",
-        "google/t5-efficient-large-nl8",
-        "google/t5-efficient-mini",
-        "google/t5-efficient-mini-nl12",
-        "google/t5-efficient-mini-nl24",
-        "google/t5-efficient-mini-nl6",
-        "google/t5-efficient-mini-nl8",
-        "google/t5-efficient-small",
-        "google/t5-efficient-small-dl12",
-        "google/t5-efficient-small-dl16",
-        "google/t5-efficient-small-dl2",
-        "google/t5-efficient-small-dl4",
-        "google/t5-efficient-small-dl8",
-        "google/t5-efficient-small-dm1000",
-        "google/t5-efficient-small-dm128",
-        "google/t5-efficient-small-dm2000",
-        "google/t5-efficient-small-dm256",
-        "google/t5-efficient-small-dm768",
-        "google/t5-efficient-small-el12",
-        "google/t5-efficient-small-el16",
-        "google/t5-efficient-small-el16-dl1",
-        "google/t5-efficient-small-el16-dl2",
-        "google/t5-efficient-small-el16-dl4",
-        "google/t5-efficient-small-el16-dl8",
-        "google/t5-efficient-small-el2",
-        "google/t5-efficient-small-el32",
-        "google/t5-efficient-small-el4",
-        "google/t5-efficient-small-el48",
-        "google/t5-efficient-small-el64",
-        "google/t5-efficient-small-el8",
-        "google/t5-efficient-small-el8-dl1",
-        "google/t5-efficient-small-el8-dl2",
-        "google/t5-efficient-small-el8-dl4",
-        "google/t5-efficient-small-ff1000",
-        "google/t5-efficient-small-ff12000",
-        "google/t5-efficient-small-ff3000",
-        "google/t5-efficient-small-ff6000",
-        "google/t5-efficient-small-ff9000",
-        "google/t5-efficient-small-kv128",
-        "google/t5-efficient-small-kv16",
-        "google/t5-efficient-small-kv256",
-        "google/t5-efficient-small-kv32",
-        "google/t5-efficient-small-nl16",
-        "google/t5-efficient-small-nl2",
-        "google/t5-efficient-small-nl20",
-        "google/t5-efficient-small-nl22",
-        "google/t5-efficient-small-nl24",
-        "google/t5-efficient-small-nl32",
-        "google/t5-efficient-small-nl36",
-        "google/t5-efficient-small-nl4",
-        "google/t5-efficient-small-nl40",
-        "google/t5-efficient-small-nl48",
-        "google/t5-efficient-small-nl8",
-        "google/t5-efficient-tiny",
-        "google/t5-efficient-tiny-dl2",
-        "google/t5-efficient-tiny-dl6",
-        "google/t5-efficient-tiny-dl8",
-        "google/t5-efficient-tiny-el12",
-        "google/t5-efficient-tiny-el2",
-        "google/t5-efficient-tiny-el6",
-        "google/t5-efficient-tiny-el8",
-        "google/t5-efficient-tiny-ff12000",
-        "google/t5-efficient-tiny-ff2000",
-        "google/t5-efficient-tiny-ff3000",
-        "google/t5-efficient-tiny-ff6000",
-        "google/t5-efficient-tiny-ff9000",
-        "google/t5-efficient-tiny-nh1",
-        "google/t5-efficient-tiny-nh16",
-        "google/t5-efficient-tiny-nh32",
-        "google/t5-efficient-tiny-nh8",
-        "google/t5-efficient-tiny-nl12",
-        "google/t5-efficient-tiny-nl16",
-        "google/t5-efficient-tiny-nl2",
-        "google/t5-efficient-tiny-nl24",
-        "google/t5-efficient-tiny-nl32",
-        "google/t5-efficient-tiny-nl6",
-        "google/t5-efficient-tiny-nl8",
-        "google/t5-efficient-xl",
-        "google/t5-efficient-xl-nl12",
-        "google/t5-efficient-xl-nl16",
-        "google/t5-efficient-xl-nl2",
-        "google/t5-efficient-xl-nl28",
-        "google/t5-efficient-xl-nl4",
-        "google/t5-efficient-xl-nl6",
-        "google/t5-efficient-xl-nl8",
-        "google/t5-efficient-xxl",
-        "google/t5-efficient-xxl-nl4",
-        "google/t5-large-lm-adapt",
-        "google/t5-large-ssm",
-        "google/t5-large-ssm-nq",
-        "google/t5-large-ssm-nqo",
-        "google/t5-small-lm-adapt",
-        "google/t5-small-ssm",
-        "google/t5-small-ssm-nq",
-        "google/t5-v1_1-base",
-        "google/t5-v1_1-large",
-        "google/t5-v1_1-small",
-        "google/t5-v1_1-xl",
-        "google/t5-v1_1-xxl",
-        "google/t5-xl-lm-adapt",
-        "google/t5-xl-ssm-nq",
-        "google/t5-xxl-lm-adapt",
-        "google/t5-xxl-ssm",
-        "google/t5-xxl-ssm-nq",
-        "google/t5-xxl-ssm-nqo",
-        "google/t5-xxl-ssm-tqa",
-        "google/t5-xxl-ssm-tqao",
-        "google/t5-xxl-ssm-wq",
-        "google/t5-xxl-ssm-wqo",
-        "google/t5_11b_trueteacher_and_anli",
-        "google/ul2",
-        "google/umt5-base",
-        "google/umt5-small",
-        "google/umt5-xl",
-        "google/umt5-xxl",
-    )
-
-    mbzuai_models_list = (
-        "MBZUAI/LaMini-Flan-T5-783M",
-        "MBZUAI/LaMini-Flan-T5-248M",
-        "MBZUAI/LaMini-Flan-T5-77M",
-        "MBZUAI/LaMini-T5-738M",
-        "MBZUAI/LaMini-T5-223M",
-        "MBZUAI/LaMini-T5-61M",
-    )
-    bloom_models_list = (
-        "bigscience/T0_3B",
-        "bigscience/T0_original_task_only",
-        "bigscience/T0_single_prompt",
-        "bigscience/T0p",
-        "bigscience/T0",
-        "bigscience/T0pp",
-        "bigscience/mt0-xxl-p3",
-        "bigscience/mt0-xxl",
-        "bigscience/mt0-large",
-        "bigscience/mt0-base",
-        "bigscience/mt0-small",
-        "bigscience/mt0-xxl-mt",
-    )
-
-    lmsys_models_list = (
-        "lmsys/fastchat-t5-3b-v1.0",
-    )
-    
-    facebook_models_list = (
-        'facebook/mbart-large-50-many-to-many-mmt',
-        'facebook/musicgen-small',
-        'facebook/musicgen-medium',
-        "facebook/musicgen-large",
-        'facebook/m2m100_418M',
-        'facebook/mbart-large-50-one-to-many-mmt',
-        'facebook/mbart-large-50-many-to-one-mmt',
-        'facebook/mbart-large-50',
-        'facebook/mgenre-wiki',
-        'facebook/genre-linking-aidayago2',
-        'facebook/genre-linking-blink',
-        'facebook/genre-kilt',
-        'facebook/m2m100-12B-avg-10-ckpt',
-        'facebook/m2m100-12B-avg-5-ckpt',
-        'facebook/m2m100-12B-last-ckpt',
-        'facebook/m2m100_1.2B'
-    )
-
-    microsoft_models_list = (
-        "microsoft/prophetnet-large-uncased",
-        "microsoft/codereviewer",
-        "microsoft/xprophetnet-large-wiki100-cased",
-        "microsoft/xprophetnet-large-wiki100-cased-xglue-qg",
-        "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg",
-        "microsoft/prophetnet-large-uncased-squad-qg",
-        "microsoft/prophetnet-large-uncased-cnndm",
-        
-    )
-    starmpcc_models_list = (
-        "starmpcc/Asclepius-13B",
-        "starmpcc/Asclepius-7B"
-    )
-
-    langboat_models_list = (
-        "Langboat/mengzi-t5-base",
-        "Langboat/mengzi-t5-base-mt"
-    )
-
-    haining_models_list = (
-        "haining/scientific_abstract_simplification",
-        "haining/poem_interpretation_allpoetry169k_baseline",
-        "haining/poem_interpretation_allpoetry169k_full",
-        "haining/lyrics_interpretation_nonnegative",
-        "haining/poem_interpretation_allpoetry169k",
-        "haining/sas_baseline",
-    )
-
-    kaludi_models_list = (
-        "Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum",
-        "Kaludi/Customer-Support-Assistant",
-        "Kaludi/Customer-Support-Assistant-V2",
-        "Kaludi/historical-events-reimagined"
-    )
-    
-    allenai_models_list = (
-        'allenai/macaw-large',
-        'allenai/entailer-11b',
-        'allenai/entailer-large',
-        'allenai/System4_classify_FigLang2022',
-        'allenai/System4_explain_FigLang2022',
-        'allenai/System3_DREAM_FLUTE_all_dimensions_FigLang2022',
-        'allenai/System3_DREAM_FLUTE_social_norm_FigLang2022',
-        'allenai/System3_DREAM_FLUTE_consequence_FigLang2022',
-        'allenai/System3_DREAM_FLUTE_motivation_FigLang2022',
-        'allenai/System3_DREAM_FLUTE_emotion_FigLang2022',
-        'allenai/System2_FigLang2022',
-        'allenai/System1_FigLang2022',
-        'allenai/DREAM',
-        'allenai/led-base-16384-cochrane',
-        'allenai/bart-large-multi_lexsum-long-multitask',
-        'allenai/bart-large-multi_lexsum-source-multitask',
-        'allenai/pegasus-multi_lexsum-short-tiny',
-        'allenai/pegasus-multi_lexsum-long-tiny',
-        'allenai/pegasus-multi_lexsum-long-short',
-        'allenai/bart-large-multi_lexsum-short-tiny',
-        'allenai/bart-large-multi_lexsum-long-tiny',
-        'allenai/bart-large-multi_lexsum-long-short',
-        'allenai/primera-multi_lexsum-source-tiny',
-        'allenai/primera-multi_lexsum-source-short',
-        'allenai/primera-multi_lexsum-source-long',
-        'allenai/led-base-16384-multi_lexsum-source-tiny',
-        'allenai/led-base-16384-multi_lexsum-source-short',
-        'allenai/led-base-16384-multi_lexsum-source-long',
-        'allenai/led-base-16384-ms2',
-        'allenai/mtk-instruct-11b-def-pos',
-        'allenai/tk-instruct-small-def-pos',
-        'allenai/tk-instruct-base-def-pos',
-        'allenai/tk-instruct-large-def-pos',
-        'allenai/mtk-instruct-3b-def-pos',
-        'allenai/tk-instruct-3b-def-pos-neg-expl',
-        'allenai/tk-instruct-3b-def-pos-neg',
-        'allenai/tk-instruct-3b-pos',
-        'allenai/tk-instruct-3b-def-pos',
-        'allenai/tk-instruct-3b-def',
-        'allenai/tk-instruct-11b-def-pos-neg-expl',
-        'allenai/tk-instruct-11b-def-pos',
-        'allenai/tk-instruct-11b-def',
-        'allenai/PRIMERA-arxiv',
-        'allenai/PRIMERA-wcep',
-        'allenai/PRIMERA-multixscience',
-        'allenai/PRIMERA-multinews',
-        'allenai/PRIMERA',
-        'allenai/unifiedqa-v2-t5-small-1363200',
-        'allenai/unifiedqa-v2-t5-small-1251000',
-        'allenai/unifiedqa-v2-t5-large-1363200',
-        'allenai/unifiedqa-v2-t5-large-1251000',
-        'allenai/unifiedqa-v2-t5-base-1363200',
-        'allenai/unifiedqa-v2-t5-base-1251000',
-        'allenai/unifiedqa-v2-t5-3b-1363200',
-        'allenai/unifiedqa-v2-t5-3b-1251000',
-        'allenai/unifiedqa-v2-t5-11b-1363200',
-        'allenai/unifiedqa-v2-t5-11b-1251000',
-        'allenai/unifiedqa-t5-small',
-        'allenai/unifiedqa-t5-large',
-        'allenai/unifiedqa-t5-base',
-        'allenai/unifiedqa-t5-3b',
-        'allenai/unifiedqa-t5-11b',
-        'allenai/tailor',
-        'allenai/t5-small-squad2-question-generation',
-        'allenai/t5-small-squad2-next-word-generator-squad',
-        'allenai/t5-small-squad11',
-        'allenai/t5-small-next-word-generator-qoogle',
-        'allenai/macaw-answer-11b',
-        'allenai/macaw-3b',
-        'allenai/macaw-11b',
-        'allenai/led-large-16384',
-        'allenai/led-large-16384-arxiv',
-        'allenai/led-base-16384'
-    )
-
-    salesforce_models_list = (
-        'Salesforce/socratic-books-30M',
-        'Salesforce/instructcodet5p-16b',
-        'Salesforce/codet5p-2b',
-        'Salesforce/codet5p-6b',
-        'Salesforce/codet5p-16b',
-        'Salesforce/dialogstudio-t5-3b-v1.0',
-        'Salesforce/dialogstudio-t5-base-v1.0',
-        'Salesforce/dialogstudio-t5-large-v1.0',
-        'Salesforce/bart-large-xsum-samsum',
-        'Salesforce/cods-bart-large-xsum-samsum',
-        'Salesforce/qaconv-unifiedqa-t5-3b',
-        'Salesforce/qaconv-unifiedqa-t5-base',
-        'Salesforce/qaconv-unifiedqa-t5-large',
-        'Salesforce/mixqg-base',
-        'Salesforce/mixqg-3b',
-        'Salesforce/mixqg-large',
-        'Salesforce/codet5-small',
-        'Salesforce/codet5-base',
-        'Salesforce/codet5-large',
-        'Salesforce/codet5-large-ntp-py',
-        'Salesforce/codet5-base-multi-sum',
-        'Salesforce/discord_qg',
-        'Salesforce/codet5-base-codexglue-sum-python',
-        'Salesforce/codet5-base-codexglue-sum-go',
-        'Salesforce/codet5-base-codexglue-sum-php',
-        'Salesforce/codet5-base-codexglue-sum-javascript',
-        'Salesforce/codet5-base-codexglue-sum-java',
-        'Salesforce/codet5-base-codexglue-sum-ruby',
-        'Salesforce/codet5-base-codexglue-clone',
-        'Salesforce/codet5-base-codexglue-concode',
-        'Salesforce/codet5-base-codexglue-defect',
-        'Salesforce/codet5-base-codexglue-refine-medium',
-        'Salesforce/codet5-base-codexglue-refine-small',
-        'Salesforce/codet5-base-codexglue-translate-cs-java',
-        'Salesforce/codet5-base-codexglue-translate-java-cs',
-        'Salesforce/safety-flan-t5-base',
-        'Salesforce/safety-flan-t5-small',
-        'Salesforce/codet5p-770m-py',
-        'Salesforce/codet5p-770m',
-        'Salesforce/codet5p-220m',
-        'Salesforce/codet5p-220m-py',
-        'Salesforce/bart-large-swipe',
-        'Salesforce/bart-large-swipe-clean'
-    )
-
-    model_dict = {
-        "google": google_models_list,
-        "mbzuai": mbzuai_models_list,
-        "bloom": bloom_models_list,
-        "lmsys": lmsys_models_list,
-        "facebook": facebook_models_list,
-        "microsoft": microsoft_models_list,
-        "starmpcc": starmpcc_models_list,
-        "langboat": langboat_models_list,
-        "haining": haining_models_list,
-        "kaludi": kaludi_models_list,
-        "allenai": allenai_models_list,
-        "salesforce": salesforce_models_list
-        }
-    if count is True:
-        models_count = 0
-        for i in model_dict:
-            models_count += len(model_dict[i])
-        return models_count
-    
-    return model_dict[model_parent]
-
-
-# Text Generation Models
-def return_text_generation_models(model_parent, count=False):
-    google_models_list = (
-        "google/reformer-enwik8",
-        "google/reformer-crime-and-punishment",   
-    )
-    mbzuai_models_list = (        
-        "MBZUAI/LaMini-Cerebras-111M",
-        "MBZUAI/LaMini-Cerebras-256M",
-        "MBZUAI/LaMini-Cerebras-590M",
-        "MBZUAI/LaMini-Cerebras-1.3B",
-        "MBZUAI/LaMini-GPT-774M",
-        "MBZUAI/LaMini-GPT-124M",
-        "MBZUAI/LaMini-GPT-1.5B",
-        "MBZUAI/LaMini-Neo-125M",
-        "MBZUAI/LaMini-Neo-1.3B",
-    )
-
-    eleutherai_models_list=(
-        "EleutherAI/pythia-14m",
-        "EleutherAI/pythia-31m",
-        "EleutherAI/pythia-1b-deduped",
-        "EleutherAI/pythia-2.8b-v0",
-        "EleutherAI/pythia-1b-v0",
-        "EleutherAI/pythia-410m-v0",
-        "EleutherAI/pythia-70m-deduped-v0",
-        "EleutherAI/pythia-2.8b-deduped-v0",
-        "EleutherAI/pythia-1b-deduped-v0",
-        "EleutherAI/pythia-410m-deduped-v0",
-        "EleutherAI/pythia-160m-deduped-v0",
-        "EleutherAI/pythia-6.9b-deduped-v0",
-        "EleutherAI/pythia-70m-deduped",
-        "EleutherAI/pythia-70m",
-        "EleutherAI/pythia-2.8b-deduped",
-        "EleutherAI/pythia-1b",
-        "EleutherAI/pythia-410m-deduped",
-        "EleutherAI/pythia-160m-deduped",
-        "EleutherAI/pythia-160m-v0",
-        "EleutherAI/pythia-1.4b-deduped-v0",
-        "EleutherAI/pythia-1.4b",
-        "EleutherAI/pythia-410m",
-        "EleutherAI/pythia-intervention-410m-deduped",
-        "EleutherAI/gpt-neo-125m",
-        "EleutherAI/gpt-neo-2.7B",
-        "EleutherAI/gpt-neo-1.3B",
-        "EleutherAI/pythia-160m",
-        "EleutherAI/gpt-neox-20b",
-        "EleutherAI/gpt-j-6b",
-        "EleutherAI/pythia-2.8b",
-        "EleutherAI/pythia-12b-deduped",
-        "EleutherAI/pythia-6.9b-deduped",
-        "EleutherAI/pythia-1.4b-deduped",
-        "EleutherAI/pythia-12b",
-        "EleutherAI/pythia-6.9b",
-        "EleutherAI/polyglot-ko-12.8b",
-        "EleutherAI/polyglot-ko-5.8b",
-        "EleutherAI/polyglot-ko-3.8b",
-        "EleutherAI/polyglot-ko-1.3b",
-        "EleutherAI/pythia-intervention-6.9b-deduped",
-        "EleutherAI/pythia-intervention-1.4b-deduped",
-        "EleutherAI/pythia-intervention-70m-deduped",
-        "EleutherAI/pythia-intervention-long-1.4b-deduped",
-        "EleutherAI/pythia-70m-v0",
-        "EleutherAI/pythia-1.4b-v0",
-        "EleutherAI/pythia-6.9b-v0",
-        "EleutherAI/pythia-12b-deduped-v0",
-        "EleutherAI/pythia-12b-v0",
-        "EleutherAI/pythia-160m-seed3",
-        "EleutherAI/pythia-160m-seed2",
-        "EleutherAI/pythia-160m-seed1",
-        "EleutherAI/neox-ckpt-pythia-6.9b-deduped",
-        "EleutherAI/pythia-160m-hiddendropout",
-        "EleutherAI/pythia-160m-attndropout",
-        "EleutherAI/pythia-160m-alldropout",
-        "EleutherAI/pythia-6.9b-deduped-v0-seed42",
-    )
-    cerebras_models_list = (
-        "cerebras/btlm-3b-8k-base",
-        "cerebras/cerebras-GPT-13B",
-        "cerebras/cerebras-GPT-6.7B",
-        "cerebras/cerebras-GPT-2.7B",
-        "cerebras/cerebras-GPT-1.3B",
-        "cerebras/cerebras-GPT-590M",
-        "cerebras/cerebras-GPT-256M",
-        "cerebras/cerebras-GPT-111M",
-    )
-    open_assistant_models_list = (
-        "OpenAssistant/codellama-13b-oasst-sft-v10",
-        "OpenAssistant/llama2-70b-oasst-sft-v10",
-        "OpenAssistant/llama2-13b-megacode2-oasst",
-        "OpenAssistant/falcon-40b-megacode2-oasst",
-        "OpenAssistant/pythia-12b-sft-v8-rlhf-2k-steps",
-        "OpenAssistant/llama2-13b-orca-8k-3319",
-        "OpenAssistant/falcon-7b-sft-mix-2000",
-        "OpenAssistant/falcon-7b-sft-top1-696",
-        "OpenAssistant/falcon-40b-sft-mix-1226",
-        "OpenAssistant/falcon-40b-sft-top1-560",
-        "OpenAssistant/pythia-12b-sft-v8-2.5k-steps",
-        "OpenAssistant/pythia-12b-sft-v8-7k-steps",
-        "OpenAssistant/pythia-12b-pre-v8-12.5k-steps",
-        "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
-        "OpenAssistant/stablelm-7b-sft-v7-epoch-3",
-        "OpenAssistant/oasst-sft-1-pythia-12b",
-        "OpenAssistant/galactica-6.7b-finetuned",
-    )
-
-    databricks_models_list = (
-        
-        "databricks/dolly-v2-7b",
-        "databricks/dolly-v2-3b",
-        "databricks/dolly-v2-12b",
-        "databricks/dolly-v1-6b",
-    )
-
-    nomic_ai_models_list = (
-        "nomic-ai/gpt4all-falcon",
-        "nomic-ai/gpt4all-j",
-        "nomic-ai/gpt4all-mpt",
-        "nomic-ai/gpt4all-13b-snoozy",
-        "nomic-ai/gpt4all-j-lora",
-    )
-
-    blinkdl_models_list = (
-        "BlinkDL/rwkv-5-world",
-        "BlinkDL/rwkv-4-world",
-        "BlinkDL/rwkv-4-raven",
-        "BlinkDL/rwkv-4-pile-7b",
-        "BlinkDL/rwkv-4-pile-14b",
-        "BlinkDL/rwkv-4-novel",
-        "BlinkDL/rwkv-4-pileplus",
-        "BlinkDL/rwkv-4-pile-430m",
-        "BlinkDL/rwkv-4-pile-3b",
-        "BlinkDL/rwkv-4-pile-1b5",
-        "BlinkDL/rwkv-4-pile-169m",
-        "BlinkDL/rwkv-3-pile-1b5",
-        "BlinkDL/rwkv-3-pile-430m",
-        "BlinkDL/rwkv-2-pile-430m",
-        "BlinkDL/rwkv-3-pile-169m",
-    )
-
-    ai_squared_models_list = (
-        "aisquared/dlite-dais-2023",
-        "aisquared/chopt-1_3b",
-        "aisquared/chopt-350m",
-        "aisquared/chopt-125m",
-        "aisquared/chopt-2_7b",
-        "aisquared/dlite-v2-1_5b",
-        "aisquared/dlite-v2-774m",
-        "aisquared/dlite-v2-355m",
-        "aisquared/dlite-v2-124m",
-        "aisquared/dlite-v1-355m",
-        "aisquared/dlite-v1-774m",
-        "aisquared/dlite-v1-1_5b",
-        "aisquared/dlite-v1-124m",
-        "aisquared/chopt-research-350m",
-        "aisquared/chopt-research-125m",
-        "aisquared/chopt-research-2_7b",
-        "aisquared/chopt-research-1_3b",
-    )
-
-    bloom_models_list = (
-        "bigscience/bloom-3b-intermediate",
-        "bigscience/bloom",
-        "bigscience/bloomz-p3",
-        "bigscience/bloomz-mt",
-        "bigscience/bloomz-7b1-mt",
-        "bigscience/bloom-1b7-intermediate",
-        "bigscience/bloom-560m-intermediate",
-        "bigscience/bloomz-560m",
-        "bigscience/bloomz-1b1",
-        "bigscience/bloomz-1b7",
-        "bigscience/bloomz-3b",
-        "bigscience/bloomz-7b1",
-        'bigscience/bloomz',
-        "bigscience/bloom-1b7",
-        "bigscience/bloom-560m",
-        "bigscience/bloom-3b",
-        "bigscience/bigscience-small-testing",
-        "bigscience/distill-bloom-1b3",
-        "bigscience/bloom-1b1",
-        "bigscience/distill-bloom-1b3-10x",
-        "bigscience/test-bloomd",
-        "bigscience/test-bloomd-6b3",
-        "bigscience/bloom-7b1",
-        "bigscience/bloom-petals",
-        "bigscience/bloom-1b1-intermediate",
-        "bigscience/bloom-7b1-intermediate",
-        "bigscience/bloom-7b1-petals",
-        "bigscience/bloomz-petals",
-        "bigscience/bloomz-7b1-p3",
-    )
-    tiiuae_models_list = (        
-        "tiiuae/falcon-180B",
-        "tiiuae/falcon-180B-chat",
-        "tiiuae/falcon-40b",
-        "tiiuae/falcon-7b",
-        "tiiuae/falcon-7b-instruct",
-        "tiiuae/falcon-40b-instruct",
-        "tiiuae/falcon-rw-7b",
-        "tiiuae/falcon-rw-1b",
-    )
-
-    openlm_models_list = (
-        "openlm-research/open_llama_3b_v2",
-        "openlm-research/open_llama_7b_v2",
-        "openlm-research/open_llama_13b",
-        "openlm-research/open_llama_7b",
-        "openlm-research/open_llama_3b",
-    )
-    stabilityai_models_list = (
-        "stabilityai/StableBeluga-7B",
-        "stabilityai/StableBeluga-13B",
-        "stabilityai/StableBeluga2",
-        "stabilityai/stablelm-base-alpha-3b-v2",
-        "stabilityai/stablelm-base-alpha-7b-v2",
-        "stabilityai/japanese-stablelm-instruct-alpha-7b",
-        "stabilityai/japanese-stablelm-base-alpha-7b",
-        "stabilityai/stablecode-completion-alpha-3b-4k",
-        "stabilityai/stablecode-instruct-alpha-3b",
-        "stabilityai/stablecode-completion-alpha-3b",
-        "stabilityai/StableBeluga1-Delta",
-        "stabilityai/stablelm-base-alpha-3b",
-        "stabilityai/stablelm-base-alpha-7b",
-        "stabilityai/stablelm-tuned-alpha-3b",
-        "stabilityai/stablelm-tuned-alpha-7b",
-    )
-
-    lmsys_models_list = (
-        "lmsys/vicuna-13b-v1.5-16k",
-        "lmsys/vicuna-13b-v1.5",
-        "lmsys/vicuna-7b-v1.5-16k",
-        "lmsys/longchat-7b-v1.5-32k",
-        "lmsys/vicuna-7b-v1.5",
-        "lmsys/vicuna-7b-v1.3",
-        "lmsys/vicuna-13b-v1.3",
-        "lmsys/vicuna-7b-v1.1",
-        "lmsys/vicuna-13b-v1.1",
-        "lmsys/vicuna-13b-delta-v0",
-        "lmsys/vicuna-7b-delta-v0",
-        "lmsys/vicuna-13b-delta-v1.1",
-        "lmsys/vicuna-7b-delta-v1.1",
-        "lmsys/vicuna-33b-v1.3",
-        "lmsys/longchat-13b-16k",
-        'lmsys/longchat-7b-16k',
-    )
-
-    togethercomputer_models_list = (
-        'togethercomputer/Llama-2-7B-32K-Instruct',
-        'togethercomputer/RedPajama-INCITE-7B-Instruct',
-        'togethercomputer/LLaMA-2-7B-32K',
-        'togethercomputer/RedPajama-INCITE-7B-Base',
-        'togethercomputer/RedPajama-INCITE-7B-Chat',
-        'togethercomputer/RedPajama-INCITE-Chat-3B-v1',
-        'togethercomputer/RedPajama-INCITE-Instruct-3B-v1',
-        'togethercomputer/RedPajama-INCITE-Base-3B-v1',
-        'togethercomputer/GPT-NeoXT-Chat-Base-20B',
-        'togethercomputer/Pythia-Chat-Base-7B',
-        'togethercomputer/GPT-JT-Moderation-6B',
-        'togethercomputer/GPT-JT-6B-v1',
-        'togethercomputer/GPT-JT-6B-v0'
-    )
-    mosaic_models_list = (
-        'mosaicml/mpt-7b-chat',
-        'mosaicml/mpt-30b-chat',
-        'mosaicml/mpt-7b-8k-chat',
-        'mosaicml/mpt-7b-instruct',
-        'mosaicml/mpt-7b-8k-instruct',
-        'mosaicml/mpt-7b-8k',
-        'mosaicml/mpt-30b',
-        'mosaicml/mpt-7b',
-        'mosaicml/mpt-7b-storywriter',
-        'mosaicml/mpt-30b-instruct',
-        'mosaicml/mpt-1b-redpajama-200b',
-        'mosaicml/mpt-1b-redpajama-200b-dolly'
-    )
-
-    h20ai_models_list = (
-        'h2oai/h2ogpt-16k-codellama-7b-python',
-        'h2oai/h2ogpt-16k-codellama-7b-instruct',
-        'h2oai/h2ogpt-16k-codellama-7b',
-        'h2oai/h2ogpt-16k-codellama-34b-python',
-        'h2oai/h2ogpt-16k-codellama-34b-instruct',
-        'h2oai/h2ogpt-16k-codellama-13b-python',
-        'h2oai/h2ogpt-16k-codellama-13b-instruct',
-        'h2oai/h2ogpt-16k-codellama-13b',
-        'h2oai/h2ogpt-16k-codellama-34b',
-        'h2oai/h2ogpt-4096-llama2-13b-chat',
-        'h2oai/h2ogpt-4096-llama2-70b-chat',
-        'h2oai/h2ogpt-4096-llama2-7b-chat',
-        'h2oai/h2ogpt-4096-llama2-13b',
-        'h2oai/h2ogpt-4096-llama2-7b',
-        'h2oai/h2ogpt-4096-llama2-70b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
-        'h2oai/h2ogpt-research-oasst1-llama-65b',
-        'h2oai/h2ogpt-gm-oasst1-en-xgen-7b-8k',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-3b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b',
-        'h2oai/h2ogpt-oasst1-falcon-40b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
-        'h2oai/h2ogpt-oig-oasst1-falcon-40b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b',
-        'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b',
-        'h2oai/h2ogpt-oasst1-512-12b',
-        'h2oai/h2ogpt-oig-oasst1-256-6_9b',
-        'h2oai/h2ogpt-oig-oasst1-512-6_9b',
-        'h2oai/h2ogpt-research-oig-oasst1-512-30b',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
-        'h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
-        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
-        'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
-        'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
-        'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
-        'h2oai/h2ogpt-oasst1-512-20b'
-    )
-
-    facebook_models_list = (
-        'facebook/xglm-4.5B',
-        'facebook/galactica-125m',
-        'facebook/opt-iml-1.3b',
-        'facebook/opt-iml-max-1.3b',
-        'facebook/opt-iml-max-30b',
-        'facebook/opt-iml-30b',
-        'facebook/galactica-120b',
-        'facebook/galactica-30b',
-        'facebook/galactica-6.7b',
-        'facebook/galactica-1.3b',
-        'facebook/opt-66b',
-        'facebook/opt-30b',
-        'facebook/opt-13b',
-        'facebook/opt-6.7b',
-        'facebook/opt-2.7b',
-        'facebook/opt-1.3b',
-        'facebook/opt-350m',
-        'facebook/opt-125m',
-        'facebook/incoder-1B',
-        'facebook/incoder-6B',
-        'facebook/xglm-7.5B',
-        'facebook/xglm-564M',
-        'facebook/xglm-2.9B',
-        'facebook/xglm-1.7B'
-    )
-    
-    microsoft_models_list = (
-        "microsoft/dolly-v2-7b-olive-optimized",
-        "microsoft/lts-gpt2-sm",
-        "microsoft/BioGPT-Large",
-        "microsoft/BioGPT-Large-PubMedQA",
-        "microsoft/biogpt",
-        "microsoft/git-large-vatex",
-        "microsoft/git-base-msrvtt-qa",
-        "microsoft/git-base-vatex",
-        "microsoft/Promptist",
-        "microsoft/CodeGPT-small-py",
-        "microsoft/CodeGPT-small-py-adaptedGPT2",
-        "microsoft/CodeGPT-small-java",
-        "microsoft/CodeGPT-small-java-adaptedGPT2",
-        "microsoft/phi-1",
-        "microsoft/phi-1_5",
-    )
-    
-    langboat_models_list = (
-        "Langboat/bloom-1b4-zh",
-        "Langboat/ReGPT-125M-200G",
-        "Langboat/bloom-6b4-zh",
-        "Langboat/bloom-2b5-zh",
-        "Langboat/bloom-800m-zh",
-        "Langboat/bloom-389m-zh",
-        "Langboat/mengzi-gpt-neo-base",
-    )
-
-    allenai_models_list = (
-        'allenai/bhaskara',
-        'allenai/open-instruct-pythia-6.9b-tulu',
-        'allenai/open-instruct-opt-6.7b-tulu',
-        'allenai/open-instruct-sni-13b',
-        'allenai/open-instruct-flan-v2-13b',
-        'allenai/open-instruct-dolly-13b',
-        'allenai/open-instruct-self-instruct-13b',
-        'allenai/open-instruct-self-instruct-7b',
-        'allenai/open-instruct-oasst1-13b',
-        'allenai/open-instruct-oasst1-7b',
-        'allenai/open-instruct-sni-7b',
-        'allenai/open-instruct-baize-13b',
-        'allenai/open-instruct-baize-7b',
-        'allenai/open-instruct-cot-13b',
-        'allenai/open-instruct-cot-7b',
-        'allenai/open-instruct-gpt4-alpaca-13b',
-        'allenai/open-instruct-gpt4-alpaca-7b',
-        'allenai/open-instruct-human-mix-7b',
-        'allenai/open-instruct-human-mix-13b',
-        'allenai/open-instruct-sharegpt-7b',
-        'allenai/open-instruct-sharegpt-13b',
-        'allenai/open-instruct-sharegpt-30b',
-        'allenai/open-instruct-human-mix-30b',
-        'allenai/tulu-7b',
-        'allenai/tulu-13b',
-        'allenai/tulu-30b',
-        'allenai/open-instruct-code-alpaca-13b',
-        'allenai/open-instruct-code-alpaca-7b',
-        'allenai/open-instruct-unnatural-instructions-13b',
-        'allenai/open-instruct-unnatural-instructions-7b',
-        'allenai/open-instruct-stanford-alpaca-13b',
-        'allenai/open-instruct-stanford-alpaca-7b',
-        'allenai/open-instruct-flan-v2-7b',
-        'allenai/open-instruct-dolly-7b',
-        'allenai/tulu-65b',
-        'allenai/open-instruct-sharegpt-65b',
-        'allenai/open-instruct-human-mix-65b'
-        )
-
-    deciai_models_list = (
-        "Deci/DeciLM-6b",
-        "Deci/DeciLM-6b-instruct",
-        "Deci/DeciCoder-1b",
-    )
-
-    salesforce_models_list = (
-        'Salesforce/codegen25-7b-multi',
-        'Salesforce/xgen-7b-4k-base',
-        'Salesforce/xgen-7b-8k-inst',
-        'Salesforce/xgen-7b-8k-base',
-        'Salesforce/codegen25-7b-mono',
-        'Salesforce/codegen25-7b-instruct',
-        'Salesforce/ctrl',
-        'Salesforce/codegen2-16B',
-        'Salesforce/codegen2-7B',
-        'Salesforce/codegen2-3_7B',
-        'Salesforce/codegen2-1B',
-        'Salesforce/codegen-350M-mono',
-        'Salesforce/codegen-350M-multi',
-        'Salesforce/codegen-16B-nl',
-        'Salesforce/codegen-2B-mono',
-        'Salesforce/codegen-16B-multi',
-        'Salesforce/codegen-2B-multi',
-        'Salesforce/codegen-6B-nl',
-        'Salesforce/codegen-2B-nl',
-        'Salesforce/codegen-350M-nl',
-        'Salesforce/codegen-6B-multi',
-        'Salesforce/codegen-6B-mono',
-        'Salesforce/codegen-16B-mono'
-    )
-
-    model_dict = {
-        "google": google_models_list,
-        "mbzuai": mbzuai_models_list,
-        "eleutherai": eleutherai_models_list,
-        "cerebras": cerebras_models_list,
-        "open_assistant": open_assistant_models_list,
-        "databricks": databricks_models_list,
-        "nomic_ai": nomic_ai_models_list,
-        "blinkdl": blinkdl_models_list,
-        "aisquared": ai_squared_models_list,
-        "bloom": bloom_models_list,
-        "tiiuae": tiiuae_models_list,
-        "openlm": openlm_models_list,
-        "stabilityai": stabilityai_models_list,
-        "lmsys": lmsys_models_list,
-        "together_computer": togethercomputer_models_list,
-        "mosaic_ml": mosaic_models_list,
-        "h20ai": h20ai_models_list,
-        "facebook": facebook_models_list,
-        "microsoft": microsoft_models_list,
-        "langboat": langboat_models_list,
-        "allenai": allenai_models_list,
-        "deciai": deciai_models_list,
-        "salesforce": salesforce_models_list
-        }
-    
-    if count is True:
-        models_count = 0
-        for i in model_dict:
-            models_count += len(model_dict[i])
-        return models_count
-    
-    return model_dict[model_parent]
-    
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/context_amd64.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/context_amd64.py
deleted file mode 100644
index eb786b652532805b92729698b4d2cdfb9c277e18..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/context_amd64.py
+++ /dev/null
@@ -1,762 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-# Copyright (c) 2009-2014, Mario Vilas
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-#     * Redistributions of source code must retain the above copyright notice,
-#       this list of conditions and the following disclaimer.
-#     * Redistributions in binary form must reproduce the above copyright
-#       notice,this list of conditions and the following disclaimer in the
-#       documentation and/or other materials provided with the distribution.
-#     * Neither the name of the copyright holder nor the names of its
-#       contributors may be used to endorse or promote products derived from
-#       this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-"""
-CONTEXT structure for amd64.
-"""
-
-__revision__ = "$Id$"
-
-from winappdbg.win32.defines import *
-from winappdbg.win32.version import ARCH_AMD64
-from winappdbg.win32 import context_i386
-
-#==============================================================================
-# This is used later on to calculate the list of exported symbols.
-_all = None
-_all = set(vars().keys())
-#==============================================================================
-
-#--- CONTEXT structures and constants -----------------------------------------
-
-# The following values specify the type of access in the first parameter
-# of the exception record when the exception code specifies an access
-# violation.
-EXCEPTION_READ_FAULT        = 0     # exception caused by a read
-EXCEPTION_WRITE_FAULT       = 1     # exception caused by a write
-EXCEPTION_EXECUTE_FAULT     = 8     # exception caused by an instruction fetch
-
-CONTEXT_AMD64           = 0x00100000
-
-CONTEXT_CONTROL         = (CONTEXT_AMD64 | long(0x1))
-CONTEXT_INTEGER         = (CONTEXT_AMD64 | long(0x2))
-CONTEXT_SEGMENTS        = (CONTEXT_AMD64 | long(0x4))
-CONTEXT_FLOATING_POINT  = (CONTEXT_AMD64 | long(0x8))
-CONTEXT_DEBUG_REGISTERS = (CONTEXT_AMD64 | long(0x10))
-
-CONTEXT_MMX_REGISTERS   = CONTEXT_FLOATING_POINT
-
-CONTEXT_FULL = (CONTEXT_CONTROL | CONTEXT_INTEGER | CONTEXT_FLOATING_POINT)
-
-CONTEXT_ALL = (CONTEXT_CONTROL | CONTEXT_INTEGER | CONTEXT_SEGMENTS | \
-               CONTEXT_FLOATING_POINT | CONTEXT_DEBUG_REGISTERS)
-
-CONTEXT_EXCEPTION_ACTIVE    = 0x8000000
-CONTEXT_SERVICE_ACTIVE      = 0x10000000
-CONTEXT_EXCEPTION_REQUEST   = 0x40000000
-CONTEXT_EXCEPTION_REPORTING = 0x80000000
-
-INITIAL_MXCSR = 0x1f80            # initial MXCSR value
-INITIAL_FPCSR = 0x027f            # initial FPCSR value
-
-# typedef struct _XMM_SAVE_AREA32 {
-#     WORD   ControlWord;
-#     WORD   StatusWord;
-#     BYTE  TagWord;
-#     BYTE  Reserved1;
-#     WORD   ErrorOpcode;
-#     DWORD ErrorOffset;
-#     WORD   ErrorSelector;
-#     WORD   Reserved2;
-#     DWORD DataOffset;
-#     WORD   DataSelector;
-#     WORD   Reserved3;
-#     DWORD MxCsr;
-#     DWORD MxCsr_Mask;
-#     M128A FloatRegisters[8];
-#     M128A XmmRegisters[16];
-#     BYTE  Reserved4[96];
-# } XMM_SAVE_AREA32, *PXMM_SAVE_AREA32;
-class XMM_SAVE_AREA32(Structure):
-    _pack_ = 1
-    _fields_ = [
-        ('ControlWord',     WORD),
-        ('StatusWord',      WORD),
-        ('TagWord',         BYTE),
-        ('Reserved1',       BYTE),
-        ('ErrorOpcode',     WORD),
-        ('ErrorOffset',     DWORD),
-        ('ErrorSelector',   WORD),
-        ('Reserved2',       WORD),
-        ('DataOffset',      DWORD),
-        ('DataSelector',    WORD),
-        ('Reserved3',       WORD),
-        ('MxCsr',           DWORD),
-        ('MxCsr_Mask',      DWORD),
-        ('FloatRegisters',  M128A * 8),
-        ('XmmRegisters',    M128A * 16),
-        ('Reserved4',       BYTE * 96),
-    ]
-
-    def from_dict(self):
-        raise NotImplementedError()
-
-    def to_dict(self):
-        d = dict()
-        for name, type in self._fields_:
-            if name in ('FloatRegisters', 'XmmRegisters'):
-                d[name] = tuple([ (x.LowPart + (x.HighPart << 64)) for x in getattr(self, name) ])
-            elif name == 'Reserved4':
-                d[name] = tuple([ chr(x) for x in getattr(self, name) ])
-            else:
-                d[name] = getattr(self, name)
-        return d
-
-LEGACY_SAVE_AREA_LENGTH = sizeof(XMM_SAVE_AREA32)
-
-PXMM_SAVE_AREA32 = ctypes.POINTER(XMM_SAVE_AREA32)
-LPXMM_SAVE_AREA32 = PXMM_SAVE_AREA32
-
-# //
-# // Context Frame
-# //
-# //  This frame has a several purposes: 1) it is used as an argument to
-# //  NtContinue, 2) is is used to constuct a call frame for APC delivery,
-# //  and 3) it is used in the user level thread creation routines.
-# //
-# //
-# // The flags field within this record controls the contents of a CONTEXT
-# // record.
-# //
-# // If the context record is used as an input parameter, then for each
-# // portion of the context record controlled by a flag whose value is
-# // set, it is assumed that that portion of the context record contains
-# // valid context. If the context record is being used to modify a threads
-# // context, then only that portion of the threads context is modified.
-# //
-# // If the context record is used as an output parameter to capture the
-# // context of a thread, then only those portions of the thread's context
-# // corresponding to set flags will be returned.
-# //
-# // CONTEXT_CONTROL specifies SegSs, Rsp, SegCs, Rip, and EFlags.
-# //
-# // CONTEXT_INTEGER specifies Rax, Rcx, Rdx, Rbx, Rbp, Rsi, Rdi, and R8-R15.
-# //
-# // CONTEXT_SEGMENTS specifies SegDs, SegEs, SegFs, and SegGs.
-# //
-# // CONTEXT_DEBUG_REGISTERS specifies Dr0-Dr3 and Dr6-Dr7.
-# //
-# // CONTEXT_MMX_REGISTERS specifies the floating point and extended registers
-# //     Mm0/St0-Mm7/St7 and Xmm0-Xmm15).
-# //
-#
-# typedef struct DECLSPEC_ALIGN(16) _CONTEXT {
-#
-#     //
-#     // Register parameter home addresses.
-#     //
-#     // N.B. These fields are for convience - they could be used to extend the
-#     //      context record in the future.
-#     //
-#
-#     DWORD64 P1Home;
-#     DWORD64 P2Home;
-#     DWORD64 P3Home;
-#     DWORD64 P4Home;
-#     DWORD64 P5Home;
-#     DWORD64 P6Home;
-#
-#     //
-#     // Control flags.
-#     //
-#
-#     DWORD ContextFlags;
-#     DWORD MxCsr;
-#
-#     //
-#     // Segment Registers and processor flags.
-#     //
-#
-#     WORD   SegCs;
-#     WORD   SegDs;
-#     WORD   SegEs;
-#     WORD   SegFs;
-#     WORD   SegGs;
-#     WORD   SegSs;
-#     DWORD EFlags;
-#
-#     //
-#     // Debug registers
-#     //
-#
-#     DWORD64 Dr0;
-#     DWORD64 Dr1;
-#     DWORD64 Dr2;
-#     DWORD64 Dr3;
-#     DWORD64 Dr6;
-#     DWORD64 Dr7;
-#
-#     //
-#     // Integer registers.
-#     //
-#
-#     DWORD64 Rax;
-#     DWORD64 Rcx;
-#     DWORD64 Rdx;
-#     DWORD64 Rbx;
-#     DWORD64 Rsp;
-#     DWORD64 Rbp;
-#     DWORD64 Rsi;
-#     DWORD64 Rdi;
-#     DWORD64 R8;
-#     DWORD64 R9;
-#     DWORD64 R10;
-#     DWORD64 R11;
-#     DWORD64 R12;
-#     DWORD64 R13;
-#     DWORD64 R14;
-#     DWORD64 R15;
-#
-#     //
-#     // Program counter.
-#     //
-#
-#     DWORD64 Rip;
-#
-#     //
-#     // Floating point state.
-#     //
-#
-#     union {
-#         XMM_SAVE_AREA32 FltSave;
-#         struct {
-#             M128A Header[2];
-#             M128A Legacy[8];
-#             M128A Xmm0;
-#             M128A Xmm1;
-#             M128A Xmm2;
-#             M128A Xmm3;
-#             M128A Xmm4;
-#             M128A Xmm5;
-#             M128A Xmm6;
-#             M128A Xmm7;
-#             M128A Xmm8;
-#             M128A Xmm9;
-#             M128A Xmm10;
-#             M128A Xmm11;
-#             M128A Xmm12;
-#             M128A Xmm13;
-#             M128A Xmm14;
-#             M128A Xmm15;
-#         };
-#     };
-#
-#     //
-#     // Vector registers.
-#     //
-#
-#     M128A VectorRegister[26];
-#     DWORD64 VectorControl;
-#
-#     //
-#     // Special debug control registers.
-#     //
-#
-#     DWORD64 DebugControl;
-#     DWORD64 LastBranchToRip;
-#     DWORD64 LastBranchFromRip;
-#     DWORD64 LastExceptionToRip;
-#     DWORD64 LastExceptionFromRip;
-# } CONTEXT, *PCONTEXT;
-
-class _CONTEXT_FLTSAVE_STRUCT(Structure):
-    _fields_ = [
-        ('Header',                  M128A * 2),
-        ('Legacy',                  M128A * 8),
-        ('Xmm0',                    M128A),
-        ('Xmm1',                    M128A),
-        ('Xmm2',                    M128A),
-        ('Xmm3',                    M128A),
-        ('Xmm4',                    M128A),
-        ('Xmm5',                    M128A),
-        ('Xmm6',                    M128A),
-        ('Xmm7',                    M128A),
-        ('Xmm8',                    M128A),
-        ('Xmm9',                    M128A),
-        ('Xmm10',                   M128A),
-        ('Xmm11',                   M128A),
-        ('Xmm12',                   M128A),
-        ('Xmm13',                   M128A),
-        ('Xmm14',                   M128A),
-        ('Xmm15',                   M128A),
-    ]
-
-    def from_dict(self):
-        raise NotImplementedError()
-
-    def to_dict(self):
-        d = dict()
-        for name, type in self._fields_:
-            if name in ('Header', 'Legacy'):
-                d[name] = tuple([ (x.Low + (x.High << 64)) for x in getattr(self, name) ])
-            else:
-                x = getattr(self, name)
-                d[name] = x.Low + (x.High << 64)
-        return d
-
-class _CONTEXT_FLTSAVE_UNION(Union):
-    _fields_ = [
-        ('flt',                     XMM_SAVE_AREA32),
-        ('xmm',                     _CONTEXT_FLTSAVE_STRUCT),
-    ]
-
-    def from_dict(self):
-        raise NotImplementedError()
-
-    def to_dict(self):
-        d = dict()
-        d['flt'] = self.flt.to_dict()
-        d['xmm'] = self.xmm.to_dict()
-        return d
-
-class CONTEXT(Structure):
-    arch = ARCH_AMD64
-
-    _pack_ = 16
-    _fields_ = [
-
-        # Register parameter home addresses.
-        ('P1Home',                  DWORD64),
-        ('P2Home',                  DWORD64),
-        ('P3Home',                  DWORD64),
-        ('P4Home',                  DWORD64),
-        ('P5Home',                  DWORD64),
-        ('P6Home',                  DWORD64),
-
-        # Control flags.
-        ('ContextFlags',            DWORD),
-        ('MxCsr',                   DWORD),
-
-        # Segment Registers and processor flags.
-        ('SegCs',                   WORD),
-        ('SegDs',                   WORD),
-        ('SegEs',                   WORD),
-        ('SegFs',                   WORD),
-        ('SegGs',                   WORD),
-        ('SegSs',                   WORD),
-        ('EFlags',                  DWORD),
-
-        # Debug registers.
-        ('Dr0',                     DWORD64),
-        ('Dr1',                     DWORD64),
-        ('Dr2',                     DWORD64),
-        ('Dr3',                     DWORD64),
-        ('Dr6',                     DWORD64),
-        ('Dr7',                     DWORD64),
-
-        # Integer registers.
-        ('Rax',                     DWORD64),
-        ('Rcx',                     DWORD64),
-        ('Rdx',                     DWORD64),
-        ('Rbx',                     DWORD64),
-        ('Rsp',                     DWORD64),
-        ('Rbp',                     DWORD64),
-        ('Rsi',                     DWORD64),
-        ('Rdi',                     DWORD64),
-        ('R8',                      DWORD64),
-        ('R9',                      DWORD64),
-        ('R10',                     DWORD64),
-        ('R11',                     DWORD64),
-        ('R12',                     DWORD64),
-        ('R13',                     DWORD64),
-        ('R14',                     DWORD64),
-        ('R15',                     DWORD64),
-
-        # Program counter.
-        ('Rip',                     DWORD64),
-
-        # Floating point state.
-        ('FltSave',                 _CONTEXT_FLTSAVE_UNION),
-
-        # Vector registers.
-        ('VectorRegister',          M128A * 26),
-        ('VectorControl',           DWORD64),
-
-        # Special debug control registers.
-        ('DebugControl',            DWORD64),
-        ('LastBranchToRip',         DWORD64),
-        ('LastBranchFromRip',       DWORD64),
-        ('LastExceptionToRip',      DWORD64),
-        ('LastExceptionFromRip',    DWORD64),
-    ]
-
-    _others = ('P1Home', 'P2Home', 'P3Home', 'P4Home', 'P5Home', 'P6Home', \
-               'MxCsr', 'VectorRegister', 'VectorControl')
-    _control = ('SegSs', 'Rsp', 'SegCs', 'Rip', 'EFlags')
-    _integer = ('Rax', 'Rcx', 'Rdx', 'Rbx', 'Rsp', 'Rbp', 'Rsi', 'Rdi', \
-                'R8', 'R9', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15')
-    _segments = ('SegDs', 'SegEs', 'SegFs', 'SegGs')
-    _debug = ('Dr0', 'Dr1', 'Dr2', 'Dr3', 'Dr6', 'Dr7', \
-              'DebugControl', 'LastBranchToRip', 'LastBranchFromRip', \
-              'LastExceptionToRip', 'LastExceptionFromRip')
-    _mmx = ('Xmm0', 'Xmm1', 'Xmm2', 'Xmm3', 'Xmm4', 'Xmm5', 'Xmm6', 'Xmm7', \
-          'Xmm8', 'Xmm9', 'Xmm10', 'Xmm11', 'Xmm12', 'Xmm13', 'Xmm14', 'Xmm15')
-
-    # XXX TODO
-    # Convert VectorRegister and Xmm0-Xmm15 to pure Python types!
-
-    @classmethod
-    def from_dict(cls, ctx):
-        'Instance a new structure from a Python native type.'
-        ctx = Context(ctx)
-        s = cls()
-        ContextFlags = ctx['ContextFlags']
-        s.ContextFlags = ContextFlags
-        for key in cls._others:
-            if key != 'VectorRegister':
-                setattr(s, key, ctx[key])
-            else:
-                w = ctx[key]
-                v = (M128A * len(w))()
-                i = 0
-                for x in w:
-                    y = M128A()
-                    y.High = x >> 64
-                    y.Low = x - (x >> 64)
-                    v[i] = y
-                    i += 1
-                setattr(s, key, v)
-        if (ContextFlags & CONTEXT_CONTROL) == CONTEXT_CONTROL:
-            for key in cls._control:
-                setattr(s, key, ctx[key])
-        if (ContextFlags & CONTEXT_INTEGER) == CONTEXT_INTEGER:
-            for key in cls._integer:
-                setattr(s, key, ctx[key])
-        if (ContextFlags & CONTEXT_SEGMENTS) == CONTEXT_SEGMENTS:
-            for key in cls._segments:
-                setattr(s, key, ctx[key])
-        if (ContextFlags & CONTEXT_DEBUG_REGISTERS) == CONTEXT_DEBUG_REGISTERS:
-            for key in cls._debug:
-                setattr(s, key, ctx[key])
-        if (ContextFlags & CONTEXT_MMX_REGISTERS) == CONTEXT_MMX_REGISTERS:
-            xmm = s.FltSave.xmm
-            for key in cls._mmx:
-                y = M128A()
-                y.High = x >> 64
-                y.Low = x - (x >> 64)
-                setattr(xmm, key, y)
-        return s
-
-    def to_dict(self):
-        'Convert a structure into a Python dictionary.'
-        ctx = Context()
-        ContextFlags = self.ContextFlags
-        ctx['ContextFlags'] = ContextFlags
-        for key in self._others:
-            if key != 'VectorRegister':
-                ctx[key] = getattr(self, key)
-            else:
-                ctx[key] = tuple([ (x.Low + (x.High << 64)) for x in getattr(self, key) ])
-        if (ContextFlags & CONTEXT_CONTROL) == CONTEXT_CONTROL:
-            for key in self._control:
-                ctx[key] = getattr(self, key)
-        if (ContextFlags & CONTEXT_INTEGER) == CONTEXT_INTEGER:
-            for key in self._integer:
-                ctx[key] = getattr(self, key)
-        if (ContextFlags & CONTEXT_SEGMENTS) == CONTEXT_SEGMENTS:
-            for key in self._segments:
-                ctx[key] = getattr(self, key)
-        if (ContextFlags & CONTEXT_DEBUG_REGISTERS) == CONTEXT_DEBUG_REGISTERS:
-            for key in self._debug:
-                ctx[key] = getattr(self, key)
-        if (ContextFlags & CONTEXT_MMX_REGISTERS) == CONTEXT_MMX_REGISTERS:
-            xmm = self.FltSave.xmm.to_dict()
-            for key in self._mmx:
-                ctx[key] = xmm.get(key)
-        return ctx
-
-PCONTEXT = ctypes.POINTER(CONTEXT)
-LPCONTEXT = PCONTEXT
-
-class Context(dict):
-    """
-    Register context dictionary for the amd64 architecture.
-    """
-
-    arch = CONTEXT.arch
-
-    def __get_pc(self):
-        return self['Rip']
-    def __set_pc(self, value):
-        self['Rip'] = value
-    pc = property(__get_pc, __set_pc)
-
-    def __get_sp(self):
-        return self['Rsp']
-    def __set_sp(self, value):
-        self['Rsp'] = value
-    sp = property(__get_sp, __set_sp)
-
-    def __get_fp(self):
-        return self['Rbp']
-    def __set_fp(self, value):
-        self['Rbp'] = value
-    fp = property(__get_fp, __set_fp)
-
-#--- LDT_ENTRY structure ------------------------------------------------------
-
-# typedef struct _LDT_ENTRY {
-#   WORD LimitLow;
-#   WORD BaseLow;
-#   union {
-#     struct {
-#       BYTE BaseMid;
-#       BYTE Flags1;
-#       BYTE Flags2;
-#       BYTE BaseHi;
-#     } Bytes;
-#     struct {
-#       DWORD BaseMid  :8;
-#       DWORD Type  :5;
-#       DWORD Dpl  :2;
-#       DWORD Pres  :1;
-#       DWORD LimitHi  :4;
-#       DWORD Sys  :1;
-#       DWORD Reserved_0  :1;
-#       DWORD Default_Big  :1;
-#       DWORD Granularity  :1;
-#       DWORD BaseHi  :8;
-#     } Bits;
-#   } HighWord;
-# } LDT_ENTRY,
-#  *PLDT_ENTRY;
-
-class _LDT_ENTRY_BYTES_(Structure):
-    _pack_ = 1
-    _fields_ = [
-        ('BaseMid',         BYTE),
-        ('Flags1',          BYTE),
-        ('Flags2',          BYTE),
-        ('BaseHi',          BYTE),
-    ]
-
-class _LDT_ENTRY_BITS_(Structure):
-    _pack_ = 1
-    _fields_ = [
-        ('BaseMid',         DWORD,  8),
-        ('Type',            DWORD,  5),
-        ('Dpl',             DWORD,  2),
-        ('Pres',            DWORD,  1),
-        ('LimitHi',         DWORD,  4),
-        ('Sys',             DWORD,  1),
-        ('Reserved_0',      DWORD,  1),
-        ('Default_Big',     DWORD,  1),
-        ('Granularity',     DWORD,  1),
-        ('BaseHi',          DWORD,  8),
-    ]
-
-class _LDT_ENTRY_HIGHWORD_(Union):
-    _pack_ = 1
-    _fields_ = [
-        ('Bytes',           _LDT_ENTRY_BYTES_),
-        ('Bits',            _LDT_ENTRY_BITS_),
-    ]
-
-class LDT_ENTRY(Structure):
-    _pack_ = 1
-    _fields_ = [
-        ('LimitLow',        WORD),
-        ('BaseLow',         WORD),
-        ('HighWord',        _LDT_ENTRY_HIGHWORD_),
-    ]
-
-PLDT_ENTRY = POINTER(LDT_ENTRY)
-LPLDT_ENTRY = PLDT_ENTRY
-
-#--- WOW64 CONTEXT structure and constants ------------------------------------
-
-# Value of SegCs in a Wow64 thread when running in 32 bits mode
-WOW64_CS32 = 0x23
-
-WOW64_CONTEXT_i386 = long(0x00010000)
-WOW64_CONTEXT_i486 = long(0x00010000)
-
-WOW64_CONTEXT_CONTROL               = (WOW64_CONTEXT_i386 | long(0x00000001))
-WOW64_CONTEXT_INTEGER               = (WOW64_CONTEXT_i386 | long(0x00000002))
-WOW64_CONTEXT_SEGMENTS              = (WOW64_CONTEXT_i386 | long(0x00000004))
-WOW64_CONTEXT_FLOATING_POINT        = (WOW64_CONTEXT_i386 | long(0x00000008))
-WOW64_CONTEXT_DEBUG_REGISTERS       = (WOW64_CONTEXT_i386 | long(0x00000010))
-WOW64_CONTEXT_EXTENDED_REGISTERS    = (WOW64_CONTEXT_i386 | long(0x00000020))
-
-WOW64_CONTEXT_FULL                  = (WOW64_CONTEXT_CONTROL | WOW64_CONTEXT_INTEGER | WOW64_CONTEXT_SEGMENTS)
-WOW64_CONTEXT_ALL                   = (WOW64_CONTEXT_CONTROL | WOW64_CONTEXT_INTEGER | WOW64_CONTEXT_SEGMENTS | WOW64_CONTEXT_FLOATING_POINT | WOW64_CONTEXT_DEBUG_REGISTERS | WOW64_CONTEXT_EXTENDED_REGISTERS)
-
-WOW64_SIZE_OF_80387_REGISTERS       = 80
-WOW64_MAXIMUM_SUPPORTED_EXTENSION   = 512
-
-class WOW64_FLOATING_SAVE_AREA (context_i386.FLOATING_SAVE_AREA):
-    pass
-
-class WOW64_CONTEXT (context_i386.CONTEXT):
-    pass
-
-class WOW64_LDT_ENTRY (context_i386.LDT_ENTRY):
-    pass
-
-PWOW64_FLOATING_SAVE_AREA   = POINTER(WOW64_FLOATING_SAVE_AREA)
-PWOW64_CONTEXT              = POINTER(WOW64_CONTEXT)
-PWOW64_LDT_ENTRY            = POINTER(WOW64_LDT_ENTRY)
-
-###############################################################################
-
-# BOOL WINAPI GetThreadSelectorEntry(
-#   __in   HANDLE hThread,
-#   __in   DWORD dwSelector,
-#   __out  LPLDT_ENTRY lpSelectorEntry
-# );
-def GetThreadSelectorEntry(hThread, dwSelector):
-    _GetThreadSelectorEntry = windll.kernel32.GetThreadSelectorEntry
-    _GetThreadSelectorEntry.argtypes = [HANDLE, DWORD, LPLDT_ENTRY]
-    _GetThreadSelectorEntry.restype  = bool
-    _GetThreadSelectorEntry.errcheck = RaiseIfZero
-
-    ldt = LDT_ENTRY()
-    _GetThreadSelectorEntry(hThread, dwSelector, byref(ldt))
-    return ldt
-
-# BOOL WINAPI GetThreadContext(
-#   __in     HANDLE hThread,
-#   __inout  LPCONTEXT lpContext
-# );
-def GetThreadContext(hThread, ContextFlags = None, raw = False):
-    _GetThreadContext = windll.kernel32.GetThreadContext
-    _GetThreadContext.argtypes = [HANDLE, LPCONTEXT]
-    _GetThreadContext.restype  = bool
-    _GetThreadContext.errcheck = RaiseIfZero
-
-    if ContextFlags is None:
-        ContextFlags = CONTEXT_ALL | CONTEXT_AMD64
-    Context = CONTEXT()
-    Context.ContextFlags = ContextFlags
-    _GetThreadContext(hThread, byref(Context))
-    if raw:
-        return Context
-    return Context.to_dict()
-
-# BOOL WINAPI SetThreadContext(
-#   __in  HANDLE hThread,
-#   __in  const CONTEXT* lpContext
-# );
-def SetThreadContext(hThread, lpContext):
-    _SetThreadContext = windll.kernel32.SetThreadContext
-    _SetThreadContext.argtypes = [HANDLE, LPCONTEXT]
-    _SetThreadContext.restype  = bool
-    _SetThreadContext.errcheck = RaiseIfZero
-
-    if isinstance(lpContext, dict):
-        lpContext = CONTEXT.from_dict(lpContext)
-    _SetThreadContext(hThread, byref(lpContext))
-
-# BOOL Wow64GetThreadSelectorEntry(
-#   __in   HANDLE hThread,
-#   __in   DWORD dwSelector,
-#   __out  PWOW64_LDT_ENTRY lpSelectorEntry
-# );
-def Wow64GetThreadSelectorEntry(hThread, dwSelector):
-    _Wow64GetThreadSelectorEntry = windll.kernel32.Wow64GetThreadSelectorEntry
-    _Wow64GetThreadSelectorEntry.argtypes = [HANDLE, DWORD, PWOW64_LDT_ENTRY]
-    _Wow64GetThreadSelectorEntry.restype  = bool
-    _Wow64GetThreadSelectorEntry.errcheck = RaiseIfZero
-
-    lpSelectorEntry = WOW64_LDT_ENTRY()
-    _Wow64GetThreadSelectorEntry(hThread, dwSelector, byref(lpSelectorEntry))
-    return lpSelectorEntry
-
-# DWORD WINAPI Wow64ResumeThread(
-#   __in  HANDLE hThread
-# );
-def Wow64ResumeThread(hThread):
-    _Wow64ResumeThread = windll.kernel32.Wow64ResumeThread
-    _Wow64ResumeThread.argtypes = [HANDLE]
-    _Wow64ResumeThread.restype  = DWORD
-
-    previousCount = _Wow64ResumeThread(hThread)
-    if previousCount == DWORD(-1).value:
-        raise ctypes.WinError()
-    return previousCount
-
-# DWORD WINAPI Wow64SuspendThread(
-#   __in  HANDLE hThread
-# );
-def Wow64SuspendThread(hThread):
-    _Wow64SuspendThread = windll.kernel32.Wow64SuspendThread
-    _Wow64SuspendThread.argtypes = [HANDLE]
-    _Wow64SuspendThread.restype  = DWORD
-
-    previousCount = _Wow64SuspendThread(hThread)
-    if previousCount == DWORD(-1).value:
-        raise ctypes.WinError()
-    return previousCount
-
-# XXX TODO Use this http://www.nynaeve.net/Code/GetThreadWow64Context.cpp
-# Also see http://www.woodmann.com/forum/archive/index.php/t-11162.html
-
-# BOOL WINAPI Wow64GetThreadContext(
-#   __in     HANDLE hThread,
-#   __inout  PWOW64_CONTEXT lpContext
-# );
-def Wow64GetThreadContext(hThread, ContextFlags = None):
-    _Wow64GetThreadContext = windll.kernel32.Wow64GetThreadContext
-    _Wow64GetThreadContext.argtypes = [HANDLE, PWOW64_CONTEXT]
-    _Wow64GetThreadContext.restype  = bool
-    _Wow64GetThreadContext.errcheck = RaiseIfZero
-
-    # XXX doesn't exist in XP 64 bits
-
-    Context = WOW64_CONTEXT()
-    if ContextFlags is None:
-        Context.ContextFlags = WOW64_CONTEXT_ALL | WOW64_CONTEXT_i386
-    else:
-        Context.ContextFlags = ContextFlags
-    _Wow64GetThreadContext(hThread, byref(Context))
-    return Context.to_dict()
-
-# BOOL WINAPI Wow64SetThreadContext(
-#   __in  HANDLE hThread,
-#   __in  const WOW64_CONTEXT *lpContext
-# );
-def Wow64SetThreadContext(hThread, lpContext):
-    _Wow64SetThreadContext = windll.kernel32.Wow64SetThreadContext
-    _Wow64SetThreadContext.argtypes = [HANDLE, PWOW64_CONTEXT]
-    _Wow64SetThreadContext.restype  = bool
-    _Wow64SetThreadContext.errcheck = RaiseIfZero
-
-    # XXX doesn't exist in XP 64 bits
-
-    if isinstance(lpContext, dict):
-        lpContext = WOW64_CONTEXT.from_dict(lpContext)
-    _Wow64SetThreadContext(hThread, byref(lpContext))
-
-#==============================================================================
-# This calculates the list of exported symbols.
-_all = set(vars().keys()).difference(_all)
-__all__ = [_x for _x in _all if not _x.startswith('_')]
-__all__.sort()
-#==============================================================================
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray-0.32.1.dist-info/LICENSE.md b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray-0.32.1.dist-info/LICENSE.md
deleted file mode 100644
index be6b669895c5c9dbe67e45e949e6bbd3947644c0..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray-0.32.1.dist-info/LICENSE.md
+++ /dev/null
@@ -1,193 +0,0 @@
-Copyright 2020-2022 Jina AI Limited.  All rights reserved.
-
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. 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 and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   Copyright 2020-2022 Jina AI 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.
diff --git a/spaces/Superlang/ImageProcessor/annotator/midas/midas/vit.py b/spaces/Superlang/ImageProcessor/annotator/midas/midas/vit.py
deleted file mode 100644
index ea46b1be88b261b0dec04f3da0256f5f66f88a74..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/midas/midas/vit.py
+++ /dev/null
@@ -1,491 +0,0 @@
-import torch
-import torch.nn as nn
-import timm
-import types
-import math
-import torch.nn.functional as F
-
-
-class Slice(nn.Module):
-    def __init__(self, start_index=1):
-        super(Slice, self).__init__()
-        self.start_index = start_index
-
-    def forward(self, x):
-        return x[:, self.start_index :]
-
-
-class AddReadout(nn.Module):
-    def __init__(self, start_index=1):
-        super(AddReadout, self).__init__()
-        self.start_index = start_index
-
-    def forward(self, x):
-        if self.start_index == 2:
-            readout = (x[:, 0] + x[:, 1]) / 2
-        else:
-            readout = x[:, 0]
-        return x[:, self.start_index :] + readout.unsqueeze(1)
-
-
-class ProjectReadout(nn.Module):
-    def __init__(self, in_features, start_index=1):
-        super(ProjectReadout, self).__init__()
-        self.start_index = start_index
-
-        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
-
-    def forward(self, x):
-        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
-        features = torch.cat((x[:, self.start_index :], readout), -1)
-
-        return self.project(features)
-
-
-class Transpose(nn.Module):
-    def __init__(self, dim0, dim1):
-        super(Transpose, self).__init__()
-        self.dim0 = dim0
-        self.dim1 = dim1
-
-    def forward(self, x):
-        x = x.transpose(self.dim0, self.dim1)
-        return x
-
-
-def forward_vit(pretrained, x):
-    b, c, h, w = x.shape
-
-    glob = pretrained.model.forward_flex(x)
-
-    layer_1 = pretrained.activations["1"]
-    layer_2 = pretrained.activations["2"]
-    layer_3 = pretrained.activations["3"]
-    layer_4 = pretrained.activations["4"]
-
-    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
-    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
-    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
-    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
-
-    unflatten = nn.Sequential(
-        nn.Unflatten(
-            2,
-            torch.Size(
-                [
-                    h // pretrained.model.patch_size[1],
-                    w // pretrained.model.patch_size[0],
-                ]
-            ),
-        )
-    )
-
-    if layer_1.ndim == 3:
-        layer_1 = unflatten(layer_1)
-    if layer_2.ndim == 3:
-        layer_2 = unflatten(layer_2)
-    if layer_3.ndim == 3:
-        layer_3 = unflatten(layer_3)
-    if layer_4.ndim == 3:
-        layer_4 = unflatten(layer_4)
-
-    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
-    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
-    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
-    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
-
-    return layer_1, layer_2, layer_3, layer_4
-
-
-def _resize_pos_embed(self, posemb, gs_h, gs_w):
-    posemb_tok, posemb_grid = (
-        posemb[:, : self.start_index],
-        posemb[0, self.start_index :],
-    )
-
-    gs_old = int(math.sqrt(len(posemb_grid)))
-
-    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
-    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
-
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-
-    return posemb
-
-
-def forward_flex(self, x):
-    b, c, h, w = x.shape
-
-    pos_embed = self._resize_pos_embed(
-        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
-    )
-
-    B = x.shape[0]
-
-    if hasattr(self.patch_embed, "backbone"):
-        x = self.patch_embed.backbone(x)
-        if isinstance(x, (list, tuple)):
-            x = x[-1]  # last feature if backbone outputs list/tuple of features
-
-    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
-
-    if getattr(self, "dist_token", None) is not None:
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        dist_token = self.dist_token.expand(B, -1, -1)
-        x = torch.cat((cls_tokens, dist_token, x), dim=1)
-    else:
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-
-    x = x + pos_embed
-    x = self.pos_drop(x)
-
-    for blk in self.blocks:
-        x = blk(x)
-
-    x = self.norm(x)
-
-    return x
-
-
-activations = {}
-
-
-def get_activation(name):
-    def hook(model, input, output):
-        activations[name] = output
-
-    return hook
-
-
-def get_readout_oper(vit_features, features, use_readout, start_index=1):
-    if use_readout == "ignore":
-        readout_oper = [Slice(start_index)] * len(features)
-    elif use_readout == "add":
-        readout_oper = [AddReadout(start_index)] * len(features)
-    elif use_readout == "project":
-        readout_oper = [
-            ProjectReadout(vit_features, start_index) for out_feat in features
-        ]
-    else:
-        assert (
-            False
-        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
-
-    return readout_oper
-
-
-def _make_vit_b16_backbone(
-    model,
-    features=[96, 192, 384, 768],
-    size=[384, 384],
-    hooks=[2, 5, 8, 11],
-    vit_features=768,
-    use_readout="ignore",
-    start_index=1,
-):
-    pretrained = nn.Module()
-
-    pretrained.model = model
-    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
-    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
-    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
-    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
-    pretrained.activations = activations
-
-    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
-    # 32, 48, 136, 384
-    pretrained.act_postprocess1 = nn.Sequential(
-        readout_oper[0],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[0],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.ConvTranspose2d(
-            in_channels=features[0],
-            out_channels=features[0],
-            kernel_size=4,
-            stride=4,
-            padding=0,
-            bias=True,
-            dilation=1,
-            groups=1,
-        ),
-    )
-
-    pretrained.act_postprocess2 = nn.Sequential(
-        readout_oper[1],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[1],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.ConvTranspose2d(
-            in_channels=features[1],
-            out_channels=features[1],
-            kernel_size=2,
-            stride=2,
-            padding=0,
-            bias=True,
-            dilation=1,
-            groups=1,
-        ),
-    )
-
-    pretrained.act_postprocess3 = nn.Sequential(
-        readout_oper[2],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[2],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-    )
-
-    pretrained.act_postprocess4 = nn.Sequential(
-        readout_oper[3],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[3],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.Conv2d(
-            in_channels=features[3],
-            out_channels=features[3],
-            kernel_size=3,
-            stride=2,
-            padding=1,
-        ),
-    )
-
-    pretrained.model.start_index = start_index
-    pretrained.model.patch_size = [16, 16]
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-    pretrained.model._resize_pos_embed = types.MethodType(
-        _resize_pos_embed, pretrained.model
-    )
-
-    return pretrained
-
-
-def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
-
-    hooks = [5, 11, 17, 23] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model,
-        features=[256, 512, 1024, 1024],
-        hooks=hooks,
-        vit_features=1024,
-        use_readout=use_readout,
-    )
-
-
-def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
-    )
-
-
-def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
-    )
-
-
-def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model(
-        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
-    )
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model,
-        features=[96, 192, 384, 768],
-        hooks=hooks,
-        use_readout=use_readout,
-        start_index=2,
-    )
-
-
-def _make_vit_b_rn50_backbone(
-    model,
-    features=[256, 512, 768, 768],
-    size=[384, 384],
-    hooks=[0, 1, 8, 11],
-    vit_features=768,
-    use_vit_only=False,
-    use_readout="ignore",
-    start_index=1,
-):
-    pretrained = nn.Module()
-
-    pretrained.model = model
-
-    if use_vit_only == True:
-        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
-        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
-    else:
-        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
-            get_activation("1")
-        )
-        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
-            get_activation("2")
-        )
-
-    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
-    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
-    pretrained.activations = activations
-
-    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
-    if use_vit_only == True:
-        pretrained.act_postprocess1 = nn.Sequential(
-            readout_oper[0],
-            Transpose(1, 2),
-            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-            nn.Conv2d(
-                in_channels=vit_features,
-                out_channels=features[0],
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ConvTranspose2d(
-                in_channels=features[0],
-                out_channels=features[0],
-                kernel_size=4,
-                stride=4,
-                padding=0,
-                bias=True,
-                dilation=1,
-                groups=1,
-            ),
-        )
-
-        pretrained.act_postprocess2 = nn.Sequential(
-            readout_oper[1],
-            Transpose(1, 2),
-            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-            nn.Conv2d(
-                in_channels=vit_features,
-                out_channels=features[1],
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ConvTranspose2d(
-                in_channels=features[1],
-                out_channels=features[1],
-                kernel_size=2,
-                stride=2,
-                padding=0,
-                bias=True,
-                dilation=1,
-                groups=1,
-            ),
-        )
-    else:
-        pretrained.act_postprocess1 = nn.Sequential(
-            nn.Identity(), nn.Identity(), nn.Identity()
-        )
-        pretrained.act_postprocess2 = nn.Sequential(
-            nn.Identity(), nn.Identity(), nn.Identity()
-        )
-
-    pretrained.act_postprocess3 = nn.Sequential(
-        readout_oper[2],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[2],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-    )
-
-    pretrained.act_postprocess4 = nn.Sequential(
-        readout_oper[3],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[3],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.Conv2d(
-            in_channels=features[3],
-            out_channels=features[3],
-            kernel_size=3,
-            stride=2,
-            padding=1,
-        ),
-    )
-
-    pretrained.model.start_index = start_index
-    pretrained.model.patch_size = [16, 16]
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model._resize_pos_embed = types.MethodType(
-        _resize_pos_embed, pretrained.model
-    )
-
-    return pretrained
-
-
-def _make_pretrained_vitb_rn50_384(
-    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
-):
-    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
-
-    hooks = [0, 1, 8, 11] if hooks == None else hooks
-    return _make_vit_b_rn50_backbone(
-        model,
-        features=[256, 512, 768, 768],
-        size=[384, 384],
-        hooks=hooks,
-        use_vit_only=use_vit_only,
-        use_readout=use_readout,
-    )
diff --git a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/README.md b/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/README.md
deleted file mode 100644
index 1d43c2606767798ee46b34292e0483197424ec23..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/README.md
+++ /dev/null
@@ -1,131 +0,0 @@
-# MiDaS for ROS1 by using LibTorch in C++
-
-### Requirements
-
-- Ubuntu 17.10 / 18.04 / 20.04, Debian Stretch
-- ROS Melodic for Ubuntu (17.10 / 18.04) / Debian Stretch, ROS Noetic for Ubuntu 20.04
-- C++11
-- LibTorch >= 1.6
-
-## Quick Start with a MiDaS Example
-
-MiDaS is a neural network to compute depth from a single image.
-
-* input from `image_topic`: `sensor_msgs/Image` - `RGB8` image with any shape
-* output to `midas_topic`: `sensor_msgs/Image` - `TYPE_32FC1` inverse relative depth maps in range [0 - 255] with original size and channels=1
-
-### Install Dependecies
-
-* install ROS Melodic for Ubuntu 17.10 / 18.04:
-```bash
-wget https://raw.githubusercontent.com/isl-org/MiDaS/master/ros/additions/install_ros_melodic_ubuntu_17_18.sh
-./install_ros_melodic_ubuntu_17_18.sh
-```
-
-or Noetic for Ubuntu 20.04: 
-
-```bash
-wget https://raw.githubusercontent.com/isl-org/MiDaS/master/ros/additions/install_ros_noetic_ubuntu_20.sh
-./install_ros_noetic_ubuntu_20.sh
-```
-
-
-* install LibTorch 1.7 with CUDA 11.0:
-
-On **Jetson (ARM)**:
-```bash
-wget https://nvidia.box.com/shared/static/wa34qwrwtk9njtyarwt5nvo6imenfy26.whl -O torch-1.7.0-cp36-cp36m-linux_aarch64.whl
-sudo apt-get install python3-pip libopenblas-base libopenmpi-dev 
-pip3 install Cython
-pip3 install numpy torch-1.7.0-cp36-cp36m-linux_aarch64.whl
-```
-Or compile LibTorch from source: https://github.com/pytorch/pytorch#from-source
-
-On **Linux (x86_64)**:
-```bash
-cd ~/
-wget https://download.pytorch.org/libtorch/cu110/libtorch-cxx11-abi-shared-with-deps-1.7.0%2Bcu110.zip
-unzip libtorch-cxx11-abi-shared-with-deps-1.7.0+cu110.zip
-```
-
-* create symlink for OpenCV:
-
-```bash
-sudo ln -s /usr/include/opencv4 /usr/include/opencv
-```
-
-* download and install MiDaS:
-
-```bash
-source ~/.bashrc
-cd ~/
-mkdir catkin_ws
-cd catkin_ws
-git clone https://github.com/isl-org/MiDaS
-mkdir src
-cp -r MiDaS/ros/* src
-
-chmod +x src/additions/*.sh
-chmod +x src/*.sh
-chmod +x src/midas_cpp/scripts/*.py
-cp src/additions/do_catkin_make.sh ./do_catkin_make.sh
-./do_catkin_make.sh
-./src/additions/downloads.sh
-```
-
-### Usage
-
-* run only `midas` node: `~/catkin_ws/src/launch_midas_cpp.sh`
-
-#### Test
-
-* Test - capture video and show result in the window:
-    * place any `test.mp4` video file to the directory `~/catkin_ws/src/`
-    * run `midas` node: `~/catkin_ws/src/launch_midas_cpp.sh`
-    * run test nodes in another terminal: `cd ~/catkin_ws/src && ./run_talker_listener_test.sh` and wait 30 seconds
-    
-    (to use Python 2, run command `sed -i 's/python3/python2/' ~/catkin_ws/src/midas_cpp/scripts/*.py` )
-
-## Mobile version of MiDaS - Monocular Depth Estimation
-
-### Accuracy
-
-* MiDaS v2 small - ResNet50 default-decoder 384x384
-* MiDaS v2.1 small - EfficientNet-Lite3 small-decoder 256x256
-
-**Zero-shot error** (the lower - the better):
-
-| Model |  DIW WHDR | Eth3d AbsRel | Sintel AbsRel | Kitti δ>1.25 | NyuDepthV2 δ>1.25 | TUM δ>1.25 |
-|---|---|---|---|---|---|---|
-| MiDaS v2 small 384x384 | **0.1248** | 0.1550 | **0.3300** | **21.81** | 15.73 | 17.00 |
-| MiDaS v2.1 small 256x256 | 0.1344 | **0.1344** | 0.3370 | 29.27 | **13.43** | **14.53** |
-| Relative improvement, % | -8 % | **+13 %** | -2 % | -34 % | **+15 %** | **+15 %** |
-
-None of Train/Valid/Test subsets of datasets (DIW, Eth3d, Sintel, Kitti, NyuDepthV2, TUM) were not involved in Training or Fine Tuning.
-
-### Inference speed (FPS) on nVidia GPU
-
-Inference speed excluding pre and post processing, batch=1, **Frames Per Second** (the higher - the better):
-
-| Model | Jetson Nano, FPS | RTX 2080Ti, FPS |
-|---|---|---|
-| MiDaS v2 small 384x384 | 1.6 | 117 |
-| MiDaS v2.1 small 256x256 | 8.1 | 232 |
-| SpeedUp, X times | **5x** | **2x** |
-
-### Citation
-
-This repository contains code to compute depth from a single image. It accompanies our [paper](https://arxiv.org/abs/1907.01341v3):
-
->Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer  
-René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun
-
-Please cite our paper if you use this code or any of the models:
-```
-@article{Ranftl2020,
-	author    = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},
-	title     = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer},
-	journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
-	year      = {2020},
-}
-```
diff --git a/spaces/TEnngal/bingo/cloudflare/worker.js b/spaces/TEnngal/bingo/cloudflare/worker.js
deleted file mode 100644
index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/cloudflare/worker.js
+++ /dev/null
@@ -1,18 +0,0 @@
-const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。
-
-export default {
-  async fetch(request) {
-    const uri = new URL(request.url);
-    if (uri.protocol === 'http:') {
-      uri.protocol = 'https:';
-      return new Response('', {
-        status: 301,
-        headers: {
-          location: uri.toString(),
-        },
-      })
-    }
-    uri.host = TRAGET_HOST
-    return fetch(new Request(uri.toString(), request));
-  },
-};
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/__init__.py
deleted file mode 100644
index 35d7bd1c4acb1a54af65c1ab6cfd68f32eb26e34..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/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/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/_parser.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/_parser.py
deleted file mode 100644
index 5a18b758fe0065416a92b9047dc9c392a3de2c4f..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/_parser.py
+++ /dev/null
@@ -1,353 +0,0 @@
-"""Handwritten parser of dependency specifiers.
-
-The docstring for each __parse_* function contains ENBF-inspired grammar representing
-the implementation.
-"""
-
-import ast
-from typing import Any, List, NamedTuple, Optional, Tuple, Union
-
-from ._tokenizer import DEFAULT_RULES, Tokenizer
-
-
-class Node:
-    def __init__(self, value: str) -> None:
-        self.value = value
-
-    def __str__(self) -> str:
-        return self.value
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}('{self}')>"
-
-    def serialize(self) -> str:
-        raise NotImplementedError
-
-
-class Variable(Node):
-    def serialize(self) -> str:
-        return str(self)
-
-
-class Value(Node):
-    def serialize(self) -> str:
-        return f'"{self}"'
-
-
-class Op(Node):
-    def serialize(self) -> str:
-        return str(self)
-
-
-MarkerVar = Union[Variable, Value]
-MarkerItem = Tuple[MarkerVar, Op, MarkerVar]
-# MarkerAtom = Union[MarkerItem, List["MarkerAtom"]]
-# MarkerList = List[Union["MarkerList", MarkerAtom, str]]
-# mypy does not support recursive type definition
-# https://github.com/python/mypy/issues/731
-MarkerAtom = Any
-MarkerList = List[Any]
-
-
-class ParsedRequirement(NamedTuple):
-    name: str
-    url: str
-    extras: List[str]
-    specifier: str
-    marker: Optional[MarkerList]
-
-
-# --------------------------------------------------------------------------------------
-# Recursive descent parser for dependency specifier
-# --------------------------------------------------------------------------------------
-def parse_requirement(source: str) -> ParsedRequirement:
-    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
-
-
-def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement:
-    """
-    requirement = WS? IDENTIFIER WS? extras WS? requirement_details
-    """
-    tokenizer.consume("WS")
-
-    name_token = tokenizer.expect(
-        "IDENTIFIER", expected="package name at the start of dependency specifier"
-    )
-    name = name_token.text
-    tokenizer.consume("WS")
-
-    extras = _parse_extras(tokenizer)
-    tokenizer.consume("WS")
-
-    url, specifier, marker = _parse_requirement_details(tokenizer)
-    tokenizer.expect("END", expected="end of dependency specifier")
-
-    return ParsedRequirement(name, url, extras, specifier, marker)
-
-
-def _parse_requirement_details(
-    tokenizer: Tokenizer,
-) -> Tuple[str, str, Optional[MarkerList]]:
-    """
-    requirement_details = AT URL (WS requirement_marker?)?
-                        | specifier WS? (requirement_marker)?
-    """
-
-    specifier = ""
-    url = ""
-    marker = None
-
-    if tokenizer.check("AT"):
-        tokenizer.read()
-        tokenizer.consume("WS")
-
-        url_start = tokenizer.position
-        url = tokenizer.expect("URL", expected="URL after @").text
-        if tokenizer.check("END", peek=True):
-            return (url, specifier, marker)
-
-        tokenizer.expect("WS", expected="whitespace after URL")
-
-        # The input might end after whitespace.
-        if tokenizer.check("END", peek=True):
-            return (url, specifier, marker)
-
-        marker = _parse_requirement_marker(
-            tokenizer, span_start=url_start, after="URL and whitespace"
-        )
-    else:
-        specifier_start = tokenizer.position
-        specifier = _parse_specifier(tokenizer)
-        tokenizer.consume("WS")
-
-        if tokenizer.check("END", peek=True):
-            return (url, specifier, marker)
-
-        marker = _parse_requirement_marker(
-            tokenizer,
-            span_start=specifier_start,
-            after=(
-                "version specifier"
-                if specifier
-                else "name and no valid version specifier"
-            ),
-        )
-
-    return (url, specifier, marker)
-
-
-def _parse_requirement_marker(
-    tokenizer: Tokenizer, *, span_start: int, after: str
-) -> MarkerList:
-    """
-    requirement_marker = SEMICOLON marker WS?
-    """
-
-    if not tokenizer.check("SEMICOLON"):
-        tokenizer.raise_syntax_error(
-            f"Expected end or semicolon (after {after})",
-            span_start=span_start,
-        )
-    tokenizer.read()
-
-    marker = _parse_marker(tokenizer)
-    tokenizer.consume("WS")
-
-    return marker
-
-
-def _parse_extras(tokenizer: Tokenizer) -> List[str]:
-    """
-    extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)?
-    """
-    if not tokenizer.check("LEFT_BRACKET", peek=True):
-        return []
-
-    with tokenizer.enclosing_tokens(
-        "LEFT_BRACKET",
-        "RIGHT_BRACKET",
-        around="extras",
-    ):
-        tokenizer.consume("WS")
-        extras = _parse_extras_list(tokenizer)
-        tokenizer.consume("WS")
-
-    return extras
-
-
-def _parse_extras_list(tokenizer: Tokenizer) -> List[str]:
-    """
-    extras_list = identifier (wsp* ',' wsp* identifier)*
-    """
-    extras: List[str] = []
-
-    if not tokenizer.check("IDENTIFIER"):
-        return extras
-
-    extras.append(tokenizer.read().text)
-
-    while True:
-        tokenizer.consume("WS")
-        if tokenizer.check("IDENTIFIER", peek=True):
-            tokenizer.raise_syntax_error("Expected comma between extra names")
-        elif not tokenizer.check("COMMA"):
-            break
-
-        tokenizer.read()
-        tokenizer.consume("WS")
-
-        extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma")
-        extras.append(extra_token.text)
-
-    return extras
-
-
-def _parse_specifier(tokenizer: Tokenizer) -> str:
-    """
-    specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS
-              | WS? version_many WS?
-    """
-    with tokenizer.enclosing_tokens(
-        "LEFT_PARENTHESIS",
-        "RIGHT_PARENTHESIS",
-        around="version specifier",
-    ):
-        tokenizer.consume("WS")
-        parsed_specifiers = _parse_version_many(tokenizer)
-        tokenizer.consume("WS")
-
-    return parsed_specifiers
-
-
-def _parse_version_many(tokenizer: Tokenizer) -> str:
-    """
-    version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)?
-    """
-    parsed_specifiers = ""
-    while tokenizer.check("SPECIFIER"):
-        span_start = tokenizer.position
-        parsed_specifiers += tokenizer.read().text
-        if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True):
-            tokenizer.raise_syntax_error(
-                ".* suffix can only be used with `==` or `!=` operators",
-                span_start=span_start,
-                span_end=tokenizer.position + 1,
-            )
-        if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True):
-            tokenizer.raise_syntax_error(
-                "Local version label can only be used with `==` or `!=` operators",
-                span_start=span_start,
-                span_end=tokenizer.position,
-            )
-        tokenizer.consume("WS")
-        if not tokenizer.check("COMMA"):
-            break
-        parsed_specifiers += tokenizer.read().text
-        tokenizer.consume("WS")
-
-    return parsed_specifiers
-
-
-# --------------------------------------------------------------------------------------
-# Recursive descent parser for marker expression
-# --------------------------------------------------------------------------------------
-def parse_marker(source: str) -> MarkerList:
-    return _parse_marker(Tokenizer(source, rules=DEFAULT_RULES))
-
-
-def _parse_marker(tokenizer: Tokenizer) -> MarkerList:
-    """
-    marker = marker_atom (BOOLOP marker_atom)+
-    """
-    expression = [_parse_marker_atom(tokenizer)]
-    while tokenizer.check("BOOLOP"):
-        token = tokenizer.read()
-        expr_right = _parse_marker_atom(tokenizer)
-        expression.extend((token.text, expr_right))
-    return expression
-
-
-def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom:
-    """
-    marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS?
-                | WS? marker_item WS?
-    """
-
-    tokenizer.consume("WS")
-    if tokenizer.check("LEFT_PARENTHESIS", peek=True):
-        with tokenizer.enclosing_tokens(
-            "LEFT_PARENTHESIS",
-            "RIGHT_PARENTHESIS",
-            around="marker expression",
-        ):
-            tokenizer.consume("WS")
-            marker: MarkerAtom = _parse_marker(tokenizer)
-            tokenizer.consume("WS")
-    else:
-        marker = _parse_marker_item(tokenizer)
-    tokenizer.consume("WS")
-    return marker
-
-
-def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem:
-    """
-    marker_item = WS? marker_var WS? marker_op WS? marker_var WS?
-    """
-    tokenizer.consume("WS")
-    marker_var_left = _parse_marker_var(tokenizer)
-    tokenizer.consume("WS")
-    marker_op = _parse_marker_op(tokenizer)
-    tokenizer.consume("WS")
-    marker_var_right = _parse_marker_var(tokenizer)
-    tokenizer.consume("WS")
-    return (marker_var_left, marker_op, marker_var_right)
-
-
-def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar:
-    """
-    marker_var = VARIABLE | QUOTED_STRING
-    """
-    if tokenizer.check("VARIABLE"):
-        return process_env_var(tokenizer.read().text.replace(".", "_"))
-    elif tokenizer.check("QUOTED_STRING"):
-        return process_python_str(tokenizer.read().text)
-    else:
-        tokenizer.raise_syntax_error(
-            message="Expected a marker variable or quoted string"
-        )
-
-
-def process_env_var(env_var: str) -> Variable:
-    if (
-        env_var == "platform_python_implementation"
-        or env_var == "python_implementation"
-    ):
-        return Variable("platform_python_implementation")
-    else:
-        return Variable(env_var)
-
-
-def process_python_str(python_str: str) -> Value:
-    value = ast.literal_eval(python_str)
-    return Value(str(value))
-
-
-def _parse_marker_op(tokenizer: Tokenizer) -> Op:
-    """
-    marker_op = IN | NOT IN | OP
-    """
-    if tokenizer.check("IN"):
-        tokenizer.read()
-        return Op("in")
-    elif tokenizer.check("NOT"):
-        tokenizer.read()
-        tokenizer.expect("WS", expected="whitespace after 'not'")
-        tokenizer.expect("IN", expected="'in' after 'not'")
-        return Op("not in")
-    elif tokenizer.check("OP"):
-        return Op(tokenizer.read().text)
-    else:
-        return tokenizer.raise_syntax_error(
-            "Expected marker operator, one of "
-            "<=, <, !=, ==, >=, >, ~=, ===, in, not in"
-        )
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py
deleted file mode 100644
index b4ceb066faf696954244205dc75376b767071217..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/data/coco_keypoint.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from detectron2.data.detection_utils import create_keypoint_hflip_indices
-
-from .coco import dataloader
-
-dataloader.train.dataset.min_keypoints = 1
-dataloader.train.dataset.names = "keypoints_coco_2017_train"
-dataloader.test.dataset.names = "keypoints_coco_2017_val"
-
-dataloader.train.mapper.update(
-    use_instance_mask=False,
-    use_keypoint=True,
-    keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names),
-)
diff --git a/spaces/Tritkoman/Tritkoman-EnglishtoChurchSlavonicV2/app.py b/spaces/Tritkoman/Tritkoman-EnglishtoChurchSlavonicV2/app.py
deleted file mode 100644
index c59cb7f2fdd65d267f92f33cf60663328d35ec73..0000000000000000000000000000000000000000
--- a/spaces/Tritkoman/Tritkoman-EnglishtoChurchSlavonicV2/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Tritkoman/EnglishtoChurchSlavonicV2").launch()
\ No newline at end of file
diff --git a/spaces/UtkMal/fresh-or-rotten-apple/app.py b/spaces/UtkMal/fresh-or-rotten-apple/app.py
deleted file mode 100644
index b65feaa97cb466427ac6693363aa1a0f7ec04df7..0000000000000000000000000000000000000000
--- a/spaces/UtkMal/fresh-or-rotten-apple/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebook.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'examples', 'interface', 'predict_img']
-
-# %% ../notebook.ipynb 3
-from fastai.vision.all import *
-
-learn = load_learner("model.pkl")
-
-# %% ../notebook.ipynb 5
-categories = ['fresh apple', 'rotten apple']
-
-def predict_img(image):
-    pred, idx, probs = learn.predict(image)
-    return dict(zip(categories, map(float, probs)))
-
-# %% ../notebook.ipynb 7
-import gradio as gr
-
-examples = ['fresh-1.jpg', 'rotten-2.jpg', 'fresh-and-rotten.jpg', 'dunno.jpg']
-
-interface = gr.Interface(fn=predict_img, inputs='image', outputs='label', examples=examples)
-interface.launch()
diff --git a/spaces/WZT/DigiProj/op/__init__.py b/spaces/WZT/DigiProj/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/WZT/DigiProj/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/tests/data/test_audio.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/tests/data/test_audio.py
deleted file mode 100644
index 40c0d5ed69eff92a766dc6d176e532f0df6c2b5e..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/tests/data/test_audio.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-import random
-
-import numpy as np
-import torch
-import torchaudio
-
-from audiocraft.data.audio import audio_info, audio_read, audio_write, _av_read
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestInfo(TempDirMixin):
-
-    def test_info_mp3(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            wav = get_white_noise(ch, int(sample_rate * duration))
-            path = self.get_temp_path('sample_wav.mp3')
-            save_wav(path, wav, sample_rate)
-            info = audio_info(path)
-            assert info.sample_rate == sample_rate
-            assert info.channels == ch
-            # we cannot trust torchaudio for num_frames, so we don't check
-
-    def _test_info_format(self, ext: str):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'sample_wav{ext}')
-            save_wav(path, wav, sample_rate)
-            info = audio_info(path)
-            assert info.sample_rate == sample_rate
-            assert info.channels == ch
-            assert np.isclose(info.duration, duration, atol=1e-5)
-
-    def test_info_wav(self):
-        self._test_info_format('.wav')
-
-    def test_info_flac(self):
-        self._test_info_format('.flac')
-
-    def test_info_ogg(self):
-        self._test_info_format('.ogg')
-
-    def test_info_m4a(self):
-        # TODO: generate m4a file programmatically
-        # self._test_info_format('.m4a')
-        pass
-
-
-class TestRead(TempDirMixin):
-
-    def test_read_full_wav(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            read_wav, read_sr = audio_read(path)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == wav.shape[1]
-            assert torch.allclose(read_wav, wav, rtol=1e-03, atol=1e-04)
-
-    def test_read_partial_wav(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        read_duration = torch.rand(1).item()
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            read_frames = int(sample_rate * read_duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            read_wav, read_sr = audio_read(path, 0, read_duration)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == read_frames
-            assert torch.allclose(read_wav[..., 0:read_frames], wav[..., 0:read_frames], rtol=1e-03, atol=1e-04)
-
-    def test_read_seek_time_wav(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        read_duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = torch.rand(1).item()
-            read_wav, read_sr = audio_read(path, seek_time, read_duration)
-            seek_frames = int(sample_rate * seek_time)
-            expected_frames = n_frames - seek_frames
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == expected_frames
-            assert torch.allclose(read_wav, wav[..., seek_frames:], rtol=1e-03, atol=1e-04)
-
-    def test_read_seek_time_wav_padded(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        read_duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            read_frames = int(sample_rate * read_duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = torch.rand(1).item()
-            seek_frames = int(sample_rate * seek_time)
-            expected_frames = n_frames - seek_frames
-            read_wav, read_sr = audio_read(path, seek_time, read_duration, pad=True)
-            expected_pad_wav = torch.zeros(wav.shape[0], read_frames - expected_frames)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == read_frames
-            assert torch.allclose(read_wav[..., :expected_frames], wav[..., seek_frames:], rtol=1e-03, atol=1e-04)
-            assert torch.allclose(read_wav[..., expected_frames:], expected_pad_wav)
-
-
-class TestAvRead(TempDirMixin):
-
-    def test_avread_seek_base(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 2.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'reference_a_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            for _ in range(100):
-                # seek will always load a full duration segment in the file
-                seek_time = random.uniform(0.0, 1.0)
-                seek_duration = random.uniform(0.001, 1.0)
-                read_wav, read_sr = _av_read(path, seek_time, seek_duration)
-                assert read_sr == sample_rate
-                assert read_wav.shape[0] == wav.shape[0]
-                assert read_wav.shape[-1] == int(seek_duration * sample_rate)
-
-    def test_avread_seek_partial(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'reference_b_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            for _ in range(100):
-                # seek will always load a partial segment
-                seek_time = random.uniform(0.5, 1.)
-                seek_duration = 1.
-                expected_num_frames = n_frames - int(seek_time * sample_rate)
-                read_wav, read_sr = _av_read(path, seek_time, seek_duration)
-                assert read_sr == sample_rate
-                assert read_wav.shape[0] == wav.shape[0]
-                assert read_wav.shape[-1] == expected_num_frames
-
-    def test_avread_seek_outofbound(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'reference_c_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = 1.5
-            read_wav, read_sr = _av_read(path, seek_time, 1.)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[-1] == 0
-
-    def test_avread_seek_edge(self):
-        sample_rates = [8000, 16_000]
-        # some of these values will have
-        # int(((frames - 1) / sample_rate) * sample_rate) != (frames - 1)
-        n_frames = [1000, 1001, 1002]
-        channels = [1, 2]
-        for sample_rate, ch, frames in product(sample_rates, channels, n_frames):
-            duration = frames / sample_rate
-            wav = get_white_noise(ch, frames)
-            path = self.get_temp_path(f'reference_d_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = (frames - 1) / sample_rate
-            seek_frames = int(seek_time * sample_rate)
-            read_wav, read_sr = _av_read(path, seek_time, duration)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[-1] == (frames - seek_frames)
-
-
-class TestAudioWrite(TempDirMixin):
-
-    def test_audio_write_wav(self):
-        torch.manual_seed(1234)
-        sample_rates = [8000, 16_000]
-        n_frames = [1000, 1001, 1002]
-        channels = [1, 2]
-        strategies = ["peak", "clip", "rms"]
-        formats = ["wav", "mp3"]
-        for sample_rate, ch, frames in product(sample_rates, channels, n_frames):
-            for format_, strategy in product(formats, strategies):
-                wav = get_white_noise(ch, frames)
-                path = self.get_temp_path(f'pred_{sample_rate}_{ch}')
-                audio_write(path, wav, sample_rate, format_, strategy=strategy)
-                read_wav, read_sr = torchaudio.load(f'{path}.{format_}')
-                if format_ == "wav":
-                    assert read_wav.shape == wav.shape
-
-                if format_ == "wav" and strategy in ["peak", "rms"]:
-                    rescaled_read_wav = read_wav / read_wav.abs().max() * wav.abs().max()
-                    # for a Gaussian, the typical max scale will be less than ~5x the std.
-                    # The error when writing to disk will ~ 1/2**15, and when rescaling, 5x that.
-                    # For RMS target, rescaling leaves more headroom by default, leading
-                    # to a 20x rescaling typically
-                    atol = (5 if strategy == "peak" else 20) / 2**15
-                    delta = (rescaled_read_wav - wav).abs().max()
-                    assert torch.allclose(wav, rescaled_read_wav, rtol=0, atol=atol), (delta, atol)
-            formats = ["wav"]  # faster unit tests
diff --git a/spaces/XzJosh/Jianmo-Bert-VITS2/text/symbols.py b/spaces/XzJosh/Jianmo-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jianmo-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/XzJosh/ranran-Bert-VITS2/text/tone_sandhi.py b/spaces/XzJosh/ranran-Bert-VITS2/text/tone_sandhi.py
deleted file mode 100644
index 0f45b7a72c5d858bcaab19ac85cfa686bf9a74da..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/ranran-Bert-VITS2/text/tone_sandhi.py
+++ /dev/null
@@ -1,351 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import List
-from typing import Tuple
-
-import jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
-
-
-class ToneSandhi():
-    def __init__(self):
-        self.must_neural_tone_words = {
-            '麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝',
-            '难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊',
-            '里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去',
-            '软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号',
-            '认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当',
-            '蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻',
-            '舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂',
-            '胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆',
-            '老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂',
-            '精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿',
-            '窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台',
-            '码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算',
-            '白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨',
-            '琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快',
-            '爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜',
-            '溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔',
-            '棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事',
-            '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾',
-            '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼',
-            '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实',
-            '扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头',
-            '念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼',
-            '干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数',
-            '屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气',
-            '实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈',
-            '姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方',
-            '大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴',
-            '嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦',
-            '咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝',
-            '叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹',
-            '功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息',
-            '凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤',
-            '佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家',
-            '交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故',
-            '不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨',
-            '父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅',
-            '幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱',
-            '凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱',
-            '扫把', '惦记'
-        }
-        self.must_not_neural_tone_words = {
-            "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎"
-        }
-        self.punc = "：，；。？！“”‘’':,;.?!"
-
-    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
-    # e.g.
-    # word: "家里"
-    # pos: "s"
-    # finals: ['ia1', 'i3']
-    def _neural_sandhi(self, word: str, pos: str,
-                       finals: List[str]) -> List[str]:
-
-        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
-        for j, item in enumerate(word):
-            if j - 1 >= 0 and item == word[j - 1] and pos[0] in {
-                    "n", "v", "a"
-            } and word not in self.must_not_neural_tone_words:
-                finals[j] = finals[j][:-1] + "5"
-        ge_idx = word.find("个")
-        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
-            finals[-1] = finals[-1][:-1] + "5"
-        elif len(word) >= 1 and word[-1] in "的地得":
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 走了, 看着, 去过
-        # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
-        #     finals[-1] = finals[-1][:-1] + "5"
-        elif len(word) > 1 and word[-1] in "们子" and pos in {
-                "r", "n"
-        } and word not in self.must_not_neural_tone_words:
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 桌上, 地下, 家里
-        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 上来, 下去
-        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
-            finals[-1] = finals[-1][:-1] + "5"
-        # 个做量词
-        elif (ge_idx >= 1 and
-              (word[ge_idx - 1].isnumeric() or
-               word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个':
-            finals[ge_idx] = finals[ge_idx][:-1] + "5"
-        else:
-            if word in self.must_neural_tone_words or word[
-                    -2:] in self.must_neural_tone_words:
-                finals[-1] = finals[-1][:-1] + "5"
-
-        word_list = self._split_word(word)
-        finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]]
-        for i, word in enumerate(word_list):
-            # conventional neural in Chinese
-            if word in self.must_neural_tone_words or word[
-                    -2:] in self.must_neural_tone_words:
-                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
-        finals = sum(finals_list, [])
-        return finals
-
-    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # e.g. 看不懂
-        if len(word) == 3 and word[1] == "不":
-            finals[1] = finals[1][:-1] + "5"
-        else:
-            for i, char in enumerate(word):
-                # "不" before tone4 should be bu2, e.g. 不怕
-                if char == "不" and i + 1 < len(word) and finals[i +
-                                                                1][-1] == "4":
-                    finals[i] = finals[i][:-1] + "2"
-        return finals
-
-    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # "一" in number sequences, e.g. 一零零, 二一零
-        if word.find("一") != -1 and all(
-            [item.isnumeric() for item in word if item != "一"]):
-            return finals
-        # "一" between reduplication words shold be yi5, e.g. 看一看
-        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
-            finals[1] = finals[1][:-1] + "5"
-        # when "一" is ordinal word, it should be yi1
-        elif word.startswith("第一"):
-            finals[1] = finals[1][:-1] + "1"
-        else:
-            for i, char in enumerate(word):
-                if char == "一" and i + 1 < len(word):
-                    # "一" before tone4 should be yi2, e.g. 一段
-                    if finals[i + 1][-1] == "4":
-                        finals[i] = finals[i][:-1] + "2"
-                    # "一" before non-tone4 should be yi4, e.g. 一天
-                    else:
-                        # "一" 后面如果是标点，还读一声
-                        if word[i + 1] not in self.punc:
-                            finals[i] = finals[i][:-1] + "4"
-        return finals
-
-    def _split_word(self, word: str) -> List[str]:
-        word_list = jieba.cut_for_search(word)
-        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
-        first_subword = word_list[0]
-        first_begin_idx = word.find(first_subword)
-        if first_begin_idx == 0:
-            second_subword = word[len(first_subword):]
-            new_word_list = [first_subword, second_subword]
-        else:
-            second_subword = word[:-len(first_subword)]
-            new_word_list = [second_subword, first_subword]
-        return new_word_list
-
-    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        if len(word) == 2 and self._all_tone_three(finals):
-            finals[0] = finals[0][:-1] + "2"
-        elif len(word) == 3:
-            word_list = self._split_word(word)
-            if self._all_tone_three(finals):
-                #  disyllabic + monosyllabic, e.g. 蒙古/包
-                if len(word_list[0]) == 2:
-                    finals[0] = finals[0][:-1] + "2"
-                    finals[1] = finals[1][:-1] + "2"
-                #  monosyllabic + disyllabic, e.g. 纸/老虎
-                elif len(word_list[0]) == 1:
-                    finals[1] = finals[1][:-1] + "2"
-            else:
-                finals_list = [
-                    finals[:len(word_list[0])], finals[len(word_list[0]):]
-                ]
-                if len(finals_list) == 2:
-                    for i, sub in enumerate(finals_list):
-                        # e.g. 所有/人
-                        if self._all_tone_three(sub) and len(sub) == 2:
-                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
-                        # e.g. 好/喜欢
-                        elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \
-                                finals_list[0][-1][-1] == "3":
-
-                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
-                        finals = sum(finals_list, [])
-        # split idiom into two words who's length is 2
-        elif len(word) == 4:
-            finals_list = [finals[:2], finals[2:]]
-            finals = []
-            for sub in finals_list:
-                if self._all_tone_three(sub):
-                    sub[0] = sub[0][:-1] + "2"
-                finals += sub
-
-        return finals
-
-    def _all_tone_three(self, finals: List[str]) -> bool:
-        return all(x[-1] == "3" for x in finals)
-
-    # merge "不" and the word behind it
-    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
-    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        last_word = ""
-        for word, pos in seg:
-            if last_word == "不":
-                word = last_word + word
-            if word != "不":
-                new_seg.append((word, pos))
-            last_word = word[:]
-        if last_word == "不":
-            new_seg.append((last_word, 'd'))
-            last_word = ""
-        return new_seg
-
-    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
-    # function 2: merge single  "一" and the word behind it
-    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
-    # e.g.
-    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
-    # output seg: [['听一听', 'v']]
-    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        # function 1
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][
-                    0] == seg[i + 1][0] and seg[i - 1][1] == "v":
-                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
-            else:
-                if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][
-                        0] == word and pos == "v":
-                    continue
-                else:
-                    new_seg.append([word, pos])
-        seg = new_seg
-        new_seg = []
-        # function 2
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and new_seg[-1][0] == "一":
-                new_seg[-1][0] = new_seg[-1][0] + word
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    # the first and the second words are all_tone_three
-    def _merge_continuous_three_tones(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(
-                word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and self._all_tone_three(
-                    sub_finals_list[i - 1]) and self._all_tone_three(
-                        sub_finals_list[i]) and not merge_last[i - 1]:
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if not self._is_reduplication(seg[i - 1][0]) and len(
-                        seg[i - 1][0]) + len(seg[i][0]) <= 3:
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-
-        return new_seg
-
-    def _is_reduplication(self, word: str) -> bool:
-        return len(word) == 2 and word[0] == word[1]
-
-    # the last char of first word and the first char of second word is tone_three
-    def _merge_continuous_three_tones_2(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(
-                word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \
-                    merge_last[i - 1]:
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if not self._is_reduplication(seg[i - 1][0]) and len(
-                        seg[i - 1][0]) + len(seg[i][0]) <= 3:
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and word == "儿" and seg[i-1][0] != "#":
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_reduplication(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and word == new_seg[-1][0]:
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def pre_merge_for_modify(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        seg = self._merge_bu(seg)
-        try:
-            seg = self._merge_yi(seg)
-        except:
-            print("_merge_yi failed")
-        seg = self._merge_reduplication(seg)
-        seg = self._merge_continuous_three_tones(seg)
-        seg = self._merge_continuous_three_tones_2(seg)
-        seg = self._merge_er(seg)
-        return seg
-
-    def modified_tone(self, word: str, pos: str,
-                      finals: List[str]) -> List[str]:
-        finals = self._bu_sandhi(word, finals)
-        finals = self._yi_sandhi(word, finals)
-        finals = self._neural_sandhi(word, pos, finals)
-        finals = self._three_sandhi(word, finals)
-        return finals
diff --git a/spaces/Yuliang/ECON/lib/pymafx/models/res_module.py b/spaces/Yuliang/ECON/lib/pymafx/models/res_module.py
deleted file mode 100644
index 69ca53960d7ce7159eb25deb8684f1605eb2c830..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/pymafx/models/res_module.py
+++ /dev/null
@@ -1,477 +0,0 @@
-# code brought in part from https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/models/pose_resnet.py
-
-from __future__ import absolute_import, division, print_function
-
-import logging
-import os
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from lib.pymafx.core.cfgs import cfg
-
-# from .transformers.tokenlearner import TokenLearner
-
-logger = logging.getLogger(__name__)
-
-BN_MOMENTUM = 0.1
-
-
-def conv3x3(in_planes, out_planes, stride=1, bias=False, groups=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(
-        in_planes * groups,
-        out_planes * groups,
-        kernel_size=3,
-        stride=stride,
-        padding=1,
-        bias=bias,
-        groups=groups
-    )
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):
-        super().__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride, groups=groups)
-        self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes, groups=groups)
-        self.bn2 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):
-        super().__init__()
-        self.conv1 = nn.Conv2d(
-            inplanes * groups, planes * groups, kernel_size=1, bias=False, groups=groups
-        )
-        self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
-        self.conv2 = nn.Conv2d(
-            planes * groups,
-            planes * groups,
-            kernel_size=3,
-            stride=stride,
-            padding=1,
-            bias=False,
-            groups=groups
-        )
-        self.bn2 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
-        self.conv3 = nn.Conv2d(
-            planes * groups,
-            planes * self.expansion * groups,
-            kernel_size=1,
-            bias=False,
-            groups=groups
-        )
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion * groups, momentum=BN_MOMENTUM)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-resnet_spec = {
-    18: (BasicBlock, [2, 2, 2, 2]), 34: (BasicBlock, [3, 4, 6, 3]), 50: (Bottleneck, [3, 4, 6, 3]),
-    101: (Bottleneck, [3, 4, 23, 3]), 152: (Bottleneck, [3, 8, 36, 3])
-}
-
-
-class IUV_predict_layer(nn.Module):
-    def __init__(self, feat_dim=256, final_cov_k=3, out_channels=25, with_uv=True, mode='iuv'):
-        super().__init__()
-
-        assert mode in ['iuv', 'seg', 'pncc']
-        self.mode = mode
-
-        if mode == 'seg':
-            self.predict_ann_index = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=15,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-
-            self.predict_uv_index = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=25,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-        elif mode == 'iuv':
-            self.predict_u = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=25,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-
-            self.predict_v = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=25,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-
-            self.predict_ann_index = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=15,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-
-            self.predict_uv_index = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=25,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-        elif mode in ['pncc']:
-            self.predict_pncc = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=3,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-
-        self.inplanes = feat_dim
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False
-                ),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        return_dict = {}
-
-        if self.mode in ['iuv', 'seg']:
-            predict_uv_index = self.predict_uv_index(x)
-            predict_ann_index = self.predict_ann_index(x)
-
-            return_dict['predict_uv_index'] = predict_uv_index
-            return_dict['predict_ann_index'] = predict_ann_index
-
-            if self.mode == 'iuv':
-                predict_u = self.predict_u(x)
-                predict_v = self.predict_v(x)
-                return_dict['predict_u'] = predict_u
-                return_dict['predict_v'] = predict_v
-            else:
-                return_dict['predict_u'] = None
-                return_dict['predict_v'] = None
-                # return_dict['predict_u'] = torch.zeros(predict_uv_index.shape).to(predict_uv_index.device)
-                # return_dict['predict_v'] = torch.zeros(predict_uv_index.shape).to(predict_uv_index.device)
-
-        if self.mode == 'pncc':
-            predict_pncc = self.predict_pncc(x)
-            return_dict['predict_pncc'] = predict_pncc
-
-        return return_dict
-
-
-class Seg_predict_layer(nn.Module):
-    def __init__(self, feat_dim=256, final_cov_k=3, out_channels=25):
-        super().__init__()
-
-        self.predict_seg_index = nn.Conv2d(
-            in_channels=feat_dim,
-            out_channels=out_channels,
-            kernel_size=final_cov_k,
-            stride=1,
-            padding=1 if final_cov_k == 3 else 0
-        )
-
-        self.inplanes = feat_dim
-
-    def forward(self, x):
-        return_dict = {}
-
-        predict_seg_index = self.predict_seg_index(x)
-        return_dict['predict_seg_index'] = predict_seg_index
-
-        return return_dict
-
-
-class Kps_predict_layer(nn.Module):
-    def __init__(self, feat_dim=256, final_cov_k=3, out_channels=3, add_module=None):
-        super().__init__()
-
-        if add_module is not None:
-            conv = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=out_channels,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-            self.predict_kps = nn.Sequential(
-                add_module,
-            #  nn.BatchNorm2d(feat_dim, momentum=BN_MOMENTUM),
-            #  conv,
-            )
-        else:
-            self.predict_kps = nn.Conv2d(
-                in_channels=feat_dim,
-                out_channels=out_channels,
-                kernel_size=final_cov_k,
-                stride=1,
-                padding=1 if final_cov_k == 3 else 0
-            )
-
-        self.inplanes = feat_dim
-
-    def forward(self, x):
-        return_dict = {}
-
-        predict_kps = self.predict_kps(x)
-        return_dict['predict_kps'] = predict_kps
-
-        return return_dict
-
-
-class SmplResNet(nn.Module):
-    def __init__(
-        self,
-        resnet_nums,
-        in_channels=3,
-        num_classes=229,
-        last_stride=2,
-        n_extra_feat=0,
-        truncate=0,
-        **kwargs
-    ):
-        super().__init__()
-
-        self.inplanes = 64
-        self.truncate = truncate
-        # extra = cfg.MODEL.EXTRA
-        # self.deconv_with_bias = extra.DECONV_WITH_BIAS
-        block, layers = resnet_spec[resnet_nums]
-
-        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2) if truncate < 2 else None
-        self.layer4 = self._make_layer(
-            block, 512, layers[3], stride=last_stride
-        ) if truncate < 1 else None
-
-        self.avg_pooling = nn.AdaptiveAvgPool2d(1)
-
-        self.num_classes = num_classes
-        if num_classes > 0:
-            self.final_layer = nn.Linear(512 * block.expansion, num_classes)
-            nn.init.xavier_uniform_(self.final_layer.weight, gain=0.01)
-
-        self.n_extra_feat = n_extra_feat
-        if n_extra_feat > 0:
-            self.trans_conv = nn.Sequential(
-                nn.Conv2d(
-                    n_extra_feat + 512 * block.expansion,
-                    512 * block.expansion,
-                    kernel_size=1,
-                    bias=False
-                ), nn.BatchNorm2d(512 * block.expansion, momentum=BN_MOMENTUM), nn.ReLU(True)
-            )
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False
-                ),
-                nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x, infeat=None):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x1 = self.layer1(x)
-        x2 = self.layer2(x1)
-        x3 = self.layer3(x2) if self.truncate < 2 else x2
-        x4 = self.layer4(x3) if self.truncate < 1 else x3
-
-        if infeat is not None:
-            x4 = self.trans_conv(torch.cat([infeat, x4], 1))
-
-        if self.num_classes > 0:
-            xp = self.avg_pooling(x4)
-            cls = self.final_layer(xp.view(xp.size(0), -1))
-            if not cfg.DANET.USE_MEAN_PARA:
-                # for non-negative scale
-                scale = F.relu(cls[:, 0]).unsqueeze(1)
-                cls = torch.cat((scale, cls[:, 1:]), dim=1)
-        else:
-            cls = None
-
-        return cls, {'x4': x4}
-
-    def init_weights(self, pretrained=''):
-        if os.path.isfile(pretrained):
-            logger.info('=> loading pretrained model {}'.format(pretrained))
-            # self.load_state_dict(pretrained_state_dict, strict=False)
-            checkpoint = torch.load(pretrained)
-            if isinstance(checkpoint, OrderedDict):
-                # state_dict = checkpoint
-                state_dict_old = self.state_dict()
-                for key in state_dict_old.keys():
-                    if key in checkpoint.keys():
-                        if state_dict_old[key].shape != checkpoint[key].shape:
-                            del checkpoint[key]
-                state_dict = checkpoint
-            elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
-                state_dict_old = checkpoint['state_dict']
-                state_dict = OrderedDict()
-                # delete 'module.' because it is saved from DataParallel module
-                for key in state_dict_old.keys():
-                    if key.startswith('module.'):
-                        # state_dict[key[7:]] = state_dict[key]
-                        # state_dict.pop(key)
-                        state_dict[key[7:]] = state_dict_old[key]
-                    else:
-                        state_dict[key] = state_dict_old[key]
-            else:
-                raise RuntimeError('No state_dict found in checkpoint file {}'.format(pretrained))
-            self.load_state_dict(state_dict, strict=False)
-        else:
-            logger.error('=> imagenet pretrained model dose not exist')
-            logger.error('=> please download it first')
-            raise ValueError('imagenet pretrained model does not exist')
-
-
-class LimbResLayers(nn.Module):
-    def __init__(self, resnet_nums, inplanes, outplanes=None, groups=1, **kwargs):
-        super().__init__()
-
-        self.inplanes = inplanes
-        block, layers = resnet_spec[resnet_nums]
-        self.outplanes = 256 if outplanes == None else outplanes
-        self.layer3 = self._make_layer(block, self.outplanes, layers[2], stride=2, groups=groups)
-        # self.outplanes = 512 if outplanes == None else outplanes
-        # self.layer4 = self._make_layer(block, self.outplanes, layers[3], stride=2, groups=groups)
-
-        self.avg_pooling = nn.AdaptiveAvgPool2d(1)
-
-        # self.tklr = TokenLearner(S=n_token)
-
-    def _make_layer(self, block, planes, blocks, stride=1, groups=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes * groups,
-                    planes * block.expansion * groups,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False,
-                    groups=groups
-                ),
-                nn.BatchNorm2d(planes * block.expansion * groups, momentum=BN_MOMENTUM),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, groups=groups))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=groups))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.layer3(x)
-        # x = self.layer4(x)
-        # x = self.avg_pooling(x)
-        # x_g = self.tklr(x.permute(0, 2, 3, 1).contiguous())
-        # x_g = x_g.reshape(x.shape[0], -1)
-
-        return x, None
diff --git a/spaces/abdvl/datahub_qa_bot/docs/api/tutorials/deleting-entities-by-urn.md b/spaces/abdvl/datahub_qa_bot/docs/api/tutorials/deleting-entities-by-urn.md
deleted file mode 100644
index fd5b24a3679e7bf7718d037f07ac43e629a6c5cb..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/api/tutorials/deleting-entities-by-urn.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# Deleting Entities By Urn
-
-## Why Would You Delete Entities? 
-You may want to delete a dataset if it is no longer needed, contains incorrect or sensitive information, or if it was created for testing purposes and is no longer necessary in production.
-It is possible to [delete entities via CLI](/docs/how/delete-metadata.md), but a programmatic approach is necessary for scalability.
-
-There are two methods of deletion: soft delete and hard delete.
-**Soft delete** sets the Status aspect of the entity to Removed, which hides the entity and all its aspects from being returned by the UI. 
-**Hard delete** physically deletes all rows for all aspects of the entity.
-
-For more information about soft delete and hard delete, please refer to [Removing Metadata from DataHub](/docs/how/delete-metadata.md#delete-by-urn).
-
-### Goal Of This Guide
-This guide will show you how to delete a dataset named `fct_user_deleted`.
-However, you can delete other entities like tags, terms, and owners with the same approach. 
-
-## Prerequisites
-For this tutorial, you need to deploy DataHub Quickstart and ingest sample data. 
-For detailed steps, please refer to [Prepare Local DataHub Environment](/docs/api/tutorials/references/prepare-datahub.md).
-
-## Delete Datasets With GraphQL
-
-> 🚫 Hard delete with GraphQL is currently not supported. 
-> Please check out [API feature comparison table](/docs/api/datahub-apis.md#datahub-api-comparison) for more information.
-
-
-### GraphQL Explorer
-GraphQL Explorer is the fastest way to experiment with GraphQL without any dependancies. 
-Navigate to GraphQL Explorer (`http://localhost:9002/api/graphiql`) and run the following query.
-
-```json
-mutation batchUpdateSoftDeleted {
-    batchUpdateSoftDeleted(input: 
-      { urns: ["urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)"], 
-        deleted: true })
-}
-```
-If you see the following response, the operation was successful:
-```json
-{
-  "data": {
-    "batchUpdateSoftDeleted": true
-  },
-  "extensions": {}
-}
-```
-
-### CURL
-
-With CURL, you need to provide tokens. To generate a token, please refer to [Generate Access Token](/docs/api/tutorials/references/generate-access-token.md). 
-With `accessToken`, you can run the following command.
-
-```shell
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer <my-access-token>' \
---header 'Content-Type: application/json' \
---data-raw '{ "query": "mutation batchUpdateSoftDeleted { batchUpdateSoftDeleted(input: { deleted: true, urns: [\"urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)\"] }) }", "variables":{}}'
-```
-
-Expected Response:
-```json
-{"data":{"batchUpdateSoftDeleted":true},"extensions":{}}
-```
-
-## Delete Datasets With Python SDK
-
-The following code deletes a hive dataset named `fct_users_deleted`.
-You can refer to the complete code in [delete_dataset.py](https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/examples/library/delete_dataset.py).
-
-```python
-import logging
-from datahub.cli import delete_cli
-from datahub.emitter.rest_emitter import DatahubRestEmitter
-from datahub.emitter.mce_builder import make_dataset_urn
-
-log = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
-
-rest_emitter = DatahubRestEmitter(gms_server="http://localhost:8080")
-dataset_urn = make_dataset_urn(name="fct_users_created", platform="hive")
-
-delete_cli._delete_one_urn(urn=dataset_urn, soft=true, cached_emitter=rest_emitter)
-
-log.info(f"Deleted dataset {dataset_urn}")
-```
-
-We're using the `MetdataChangeProposalWrapper` to change entities in this example.
-For more information about the `MetadataChangeProposal`, please refer to [MetadataChangeProposal & MetadataChangeLog Events](/docs/advanced/mcp-mcl.md)
-
-
-## Expected Outcomes
-The dataset `fct_users_deleted` has now been deleted, so if you search for a hive dataset named `fct_users_delete`, you will no longer be able to see it.
-
-![dataset-deleted](../../imgs/apis/tutorials/dataset-deleted.png)
-
-
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/resnest.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/resnest.py
deleted file mode 100644
index b45a837f395230029e9d4194ff9f7f2f8f7067b0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/resnest.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as cp
-from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
-
-from ..builder import BACKBONES
-from ..utils import ResLayer
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNetV1d
-
-
-class RSoftmax(nn.Module):
-    """Radix Softmax module in ``SplitAttentionConv2d``.
-
-    Args:
-        radix (int): Radix of input.
-        groups (int): Groups of input.
-    """
-
-    def __init__(self, radix, groups):
-        super().__init__()
-        self.radix = radix
-        self.groups = groups
-
-    def forward(self, x):
-        batch = x.size(0)
-        if self.radix > 1:
-            x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
-            x = F.softmax(x, dim=1)
-            x = x.reshape(batch, -1)
-        else:
-            x = torch.sigmoid(x)
-        return x
-
-
-class SplitAttentionConv2d(nn.Module):
-    """Split-Attention Conv2d in ResNeSt.
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int | tuple[int]): Same as nn.Conv2d.
-        stride (int | tuple[int]): Same as nn.Conv2d.
-        padding (int | tuple[int]): Same as nn.Conv2d.
-        dilation (int | tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        radix (int): Radix of SpltAtConv2d. Default: 2
-        reduction_factor (int): Reduction factor of inter_channels. Default: 4.
-        conv_cfg (dict): Config dict for convolution layer. Default: None,
-            which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        dcn (dict): Config dict for DCN. Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 radix=2,
-                 reduction_factor=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 dcn=None):
-        super(SplitAttentionConv2d, self).__init__()
-        inter_channels = max(in_channels * radix // reduction_factor, 32)
-        self.radix = radix
-        self.groups = groups
-        self.channels = channels
-        self.with_dcn = dcn is not None
-        self.dcn = dcn
-        fallback_on_stride = False
-        if self.with_dcn:
-            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
-        if self.with_dcn and not fallback_on_stride:
-            assert conv_cfg is None, 'conv_cfg must be None for DCN'
-            conv_cfg = dcn
-        self.conv = build_conv_layer(
-            conv_cfg,
-            in_channels,
-            channels * radix,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups * radix,
-            bias=False)
-        self.norm0_name, norm0 = build_norm_layer(
-            norm_cfg, channels * radix, postfix=0)
-        self.add_module(self.norm0_name, norm0)
-        self.relu = nn.ReLU(inplace=True)
-        self.fc1 = build_conv_layer(
-            None, channels, inter_channels, 1, groups=self.groups)
-        self.norm1_name, norm1 = build_norm_layer(
-            norm_cfg, inter_channels, postfix=1)
-        self.add_module(self.norm1_name, norm1)
-        self.fc2 = build_conv_layer(
-            None, inter_channels, channels * radix, 1, groups=self.groups)
-        self.rsoftmax = RSoftmax(radix, groups)
-
-    @property
-    def norm0(self):
-        """nn.Module: the normalization layer named "norm0" """
-        return getattr(self, self.norm0_name)
-
-    @property
-    def norm1(self):
-        """nn.Module: the normalization layer named "norm1" """
-        return getattr(self, self.norm1_name)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm0(x)
-        x = self.relu(x)
-
-        batch, rchannel = x.shape[:2]
-        batch = x.size(0)
-        if self.radix > 1:
-            splits = x.view(batch, self.radix, -1, *x.shape[2:])
-            gap = splits.sum(dim=1)
-        else:
-            gap = x
-        gap = F.adaptive_avg_pool2d(gap, 1)
-        gap = self.fc1(gap)
-
-        gap = self.norm1(gap)
-        gap = self.relu(gap)
-
-        atten = self.fc2(gap)
-        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
-
-        if self.radix > 1:
-            attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
-            out = torch.sum(attens * splits, dim=1)
-        else:
-            out = atten * x
-        return out.contiguous()
-
-
-class Bottleneck(_Bottleneck):
-    """Bottleneck block for ResNeSt.
-
-    Args:
-        inplane (int): Input planes of this block.
-        planes (int): Middle planes of this block.
-        groups (int): Groups of conv2.
-        width_per_group (int): Width per group of conv2. 64x4d indicates
-            ``groups=64, width_per_group=4`` and 32x8d indicates
-            ``groups=32, width_per_group=8``.
-        radix (int): Radix of SpltAtConv2d. Default: 2
-        reduction_factor (int): Reduction factor of inter_channels in
-            SplitAttentionConv2d. Default: 4.
-        avg_down_stride (bool): Whether to use average pool for stride in
-            Bottleneck. Default: True.
-        kwargs (dict): Key word arguments for base class.
-    """
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 groups=1,
-                 base_width=4,
-                 base_channels=64,
-                 radix=2,
-                 reduction_factor=4,
-                 avg_down_stride=True,
-                 **kwargs):
-        """Bottleneck block for ResNeSt."""
-        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
-
-        if groups == 1:
-            width = self.planes
-        else:
-            width = math.floor(self.planes *
-                               (base_width / base_channels)) * groups
-
-        self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
-
-        self.norm1_name, norm1 = build_norm_layer(
-            self.norm_cfg, width, postfix=1)
-        self.norm3_name, norm3 = build_norm_layer(
-            self.norm_cfg, self.planes * self.expansion, postfix=3)
-
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            self.inplanes,
-            width,
-            kernel_size=1,
-            stride=self.conv1_stride,
-            bias=False)
-        self.add_module(self.norm1_name, norm1)
-        self.with_modulated_dcn = False
-        self.conv2 = SplitAttentionConv2d(
-            width,
-            width,
-            kernel_size=3,
-            stride=1 if self.avg_down_stride else self.conv2_stride,
-            padding=self.dilation,
-            dilation=self.dilation,
-            groups=groups,
-            radix=radix,
-            reduction_factor=reduction_factor,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            dcn=self.dcn)
-        delattr(self, self.norm2_name)
-
-        if self.avg_down_stride:
-            self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
-
-        self.conv3 = build_conv_layer(
-            self.conv_cfg,
-            width,
-            self.planes * self.expansion,
-            kernel_size=1,
-            bias=False)
-        self.add_module(self.norm3_name, norm3)
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            identity = x
-
-            out = self.conv1(x)
-            out = self.norm1(out)
-            out = self.relu(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv1_plugin_names)
-
-            out = self.conv2(out)
-
-            if self.avg_down_stride:
-                out = self.avd_layer(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv2_plugin_names)
-
-            out = self.conv3(out)
-            out = self.norm3(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv3_plugin_names)
-
-            if self.downsample is not None:
-                identity = self.downsample(x)
-
-            out += identity
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-@BACKBONES.register_module()
-class ResNeSt(ResNetV1d):
-    """ResNeSt backbone.
-
-    Args:
-        groups (int): Number of groups of Bottleneck. Default: 1
-        base_width (int): Base width of Bottleneck. Default: 4
-        radix (int): Radix of SpltAtConv2d. Default: 2
-        reduction_factor (int): Reduction factor of inter_channels in
-            SplitAttentionConv2d. Default: 4.
-        avg_down_stride (bool): Whether to use average pool for stride in
-            Bottleneck. Default: True.
-        kwargs (dict): Keyword arguments for ResNet.
-    """
-
-    arch_settings = {
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3)),
-        200: (Bottleneck, (3, 24, 36, 3))
-    }
-
-    def __init__(self,
-                 groups=1,
-                 base_width=4,
-                 radix=2,
-                 reduction_factor=4,
-                 avg_down_stride=True,
-                 **kwargs):
-        self.groups = groups
-        self.base_width = base_width
-        self.radix = radix
-        self.reduction_factor = reduction_factor
-        self.avg_down_stride = avg_down_stride
-        super(ResNeSt, self).__init__(**kwargs)
-
-    def make_res_layer(self, **kwargs):
-        """Pack all blocks in a stage into a ``ResLayer``."""
-        return ResLayer(
-            groups=self.groups,
-            base_width=self.base_width,
-            base_channels=self.base_channels,
-            radix=self.radix,
-            reduction_factor=self.reduction_factor,
-            avg_down_stride=self.avg_down_stride,
-            **kwargs)
diff --git a/spaces/adirik/stylemc-demo/model_irse.py b/spaces/adirik/stylemc-demo/model_irse.py
deleted file mode 100644
index b34e1b7dc21dbaa6488387b670f49322c4d906c0..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/model_irse.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
-from helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
-
-"""
-Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Backbone(Module):
-	def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
-		super(Backbone, self).__init__()
-		assert input_size in [112, 224], "input_size should be 112 or 224"
-		assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
-		assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
-		blocks = get_blocks(num_layers)
-		if mode == 'ir':
-			unit_module = bottleneck_IR
-		elif mode == 'ir_se':
-			unit_module = bottleneck_IR_SE
-		self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
-									  BatchNorm2d(64),
-									  PReLU(64))
-		if input_size == 112:
-			self.output_layer = Sequential(BatchNorm2d(512),
-			                               Dropout(drop_ratio),
-			                               Flatten(),
-			                               Linear(512 * 7 * 7, 512),
-			                               BatchNorm1d(512, affine=affine))
-		else:
-			self.output_layer = Sequential(BatchNorm2d(512),
-			                               Dropout(drop_ratio),
-			                               Flatten(),
-			                               Linear(512 * 14 * 14, 512),
-			                               BatchNorm1d(512, affine=affine))
-
-		modules = []
-		for block in blocks:
-			for bottleneck in block:
-				modules.append(unit_module(bottleneck.in_channel,
-										   bottleneck.depth,
-										   bottleneck.stride))
-		self.body = Sequential(*modules)
-
-	def forward(self, x):
-		x = self.input_layer(x)
-		x = self.body(x)
-		x = self.output_layer(x)
-		return l2_norm(x)
-
-
-def IR_50(input_size):
-	"""Constructs a ir-50 model."""
-	model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_101(input_size):
-	"""Constructs a ir-101 model."""
-	model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_152(input_size):
-	"""Constructs a ir-152 model."""
-	model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_SE_50(input_size):
-	"""Constructs a ir_se-50 model."""
-	model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_SE_101(input_size):
-	"""Constructs a ir_se-101 model."""
-	model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
-	return model
-
-
-def IR_SE_152(input_size):
-	"""Constructs a ir_se-152 model."""
-	model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
-	return model
\ No newline at end of file
diff --git a/spaces/akhaliq/Detic/detic/modeling/text/text_encoder.py b/spaces/akhaliq/Detic/detic/modeling/text/text_encoder.py
deleted file mode 100644
index 3ec5090c290ee5ecf1dd49915b70d6b4cc2b84d9..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Detic/detic/modeling/text/text_encoder.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# This code is modified from https://github.com/openai/CLIP/blob/main/clip/clip.py
-# Modified by Xingyi Zhou
-# The original code is under MIT license
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import Union, List
-from collections import OrderedDict
-import torch
-from torch import nn
-import torch
-
-from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
-
-__all__ = ["tokenize"]
-
-count = 0
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(
-            *[ResidualAttentionBlock(width, heads, attn_mask) \
-                for _ in range(layers)])
-
-    def forward(self, x: torch.Tensor):
-        return self.resblocks(x)
-
-class CLIPTEXT(nn.Module):
-    def __init__(self,
-                 embed_dim=512,
-                 # text
-                 context_length=77,
-                 vocab_size=49408,
-                 transformer_width=512,
-                 transformer_heads=8,
-                 transformer_layers=12
-                 ):
-        super().__init__()
-        
-        self._tokenizer = _Tokenizer()
-        self.context_length = context_length
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        # self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def device(self):
-        return self.text_projection.device
-
-    @property
-    def dtype(self):
-        return self.text_projection.dtype
-
-    def tokenize(self, 
-        texts: Union[str, List[str]], \
-        context_length: int = 77) -> torch.LongTensor:
-        """
-        """
-        if isinstance(texts, str):
-            texts = [texts]
-
-        sot_token = self._tokenizer.encoder["<|startoftext|>"]
-        eot_token = self._tokenizer.encoder["<|endoftext|>"]
-        all_tokens = [[sot_token] + self._tokenizer.encode(text) + [eot_token] for text in texts]
-        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-        for i, tokens in enumerate(all_tokens):
-            if len(tokens) > context_length:
-                st = torch.randint(
-                    len(tokens) - context_length + 1, (1,))[0].item()
-                tokens = tokens[st: st + context_length]
-                # raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
-            result[i, :len(tokens)] = torch.tensor(tokens)
-
-        return result
-
-    def encode_text(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-        return x
-
-    def forward(self, captions):
-        '''
-        captions: list of strings
-        '''
-        text = self.tokenize(captions).to(self.device) # B x L x D
-        features = self.encode_text(text) # B x D
-        return features
-
-
-def build_text_encoder(pretrain=True):
-    text_encoder = CLIPTEXT()
-    if pretrain:
-        import clip
-        pretrained_model, _ = clip.load("ViT-B/32", device='cpu')
-        state_dict = pretrained_model.state_dict()
-        to_delete_keys = ["logit_scale", "input_resolution", \
-        "context_length", "vocab_size"] + \
-            [k for k in state_dict.keys() if k.startswith('visual.')]
-        for k in to_delete_keys:
-            if k in state_dict:
-                del state_dict[k]
-        print('Loading pretrained CLIP')
-        text_encoder.load_state_dict(state_dict)
-    # import pdb; pdb.set_trace()
-    return text_encoder
\ No newline at end of file
diff --git a/spaces/akhaliq/PAMA/app.py b/spaces/akhaliq/PAMA/app.py
deleted file mode 100644
index aee08b3a4dd7245061874d596e90a289677cf5eb..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/PAMA/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import gradio as gr
-import sys
-os.system("pip install gradio==2.4.6")
-
-os.system("git clone https://github.com/AK391/PAMA.git")
-
-os.chdir('PAMA')
-os.system("wget https://upload.wikimedia.org/wikipedia/commons/thumb/1/1e/Hoover_Tower_Stanford_January_2013.jpg/800px-Hoover_Tower_Stanford_January_2013.jpg")
-os.system("wget https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/1920px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg")
-os.system("gdown https://drive.google.com/uc?id=1rPB_qnelVVSad6CtadmhRFi0PMI_RKdy")
-os.mkdir("checkpoints/")
-
-os.system("unzip -j original_PAMA.zip -d checkpoints/")
-
-def inference(content,style):
-  os.system("python main.py eval --content "+content+" --style "+style)
-  return "ics.jpg"
-  
-title = "PAMA"
-description = "Gradio demo for PAMA: Consistent Style Transfer. To use it, simply upload your images, or click one of the examples to load them. Read more at the links below."
-
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.02233' target='_blank'>Consistent Style Transfer</a> | <a href='https://github.com/computer-vision2022/PAMA' target='_blank'>Github Repo</a></p>"
-
-examples=[['800px-Hoover_Tower_Stanford_January_2013.jpg','1920px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg']]
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="filepath", label="Content"),gr.inputs.Image(type="filepath", label="Style")], 
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    enable_queue=True
- ).launch()
\ No newline at end of file
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/vctk/voc1/local/data_download.sh b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/vctk/voc1/local/data_download.sh
deleted file mode 100644
index 3367c9ca677698b0a4548642d5fc60a9723b5073..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/vctk/voc1/local/data_download.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/bash
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-download_dir=$1
-
-# check arguments
-if [ $# != 1 ]; then
-    echo "Usage: $0 <download_dir>"
-    exit 1
-fi
-
-set -euo pipefail
-
-cwd=$(pwd)
-if [ ! -e "${download_dir}/VCTK-Corpus" ]; then
-    mkdir -p "${download_dir}"
-    cd "${download_dir}" || exit 1;
-    wget http://www.udialogue.org/download/VCTK-Corpus.tar.gz
-    tar xvzf ./VCTK-Corpus.tar.gz
-    rm ./VCTK-Corpus.tar.gz
-    cd "${cwd}" || exit 1;
-    echo "Successfully downloaded data."
-else
-    echo "Already exists. Skipped."
-fi
-
-if [ ! -e "${download_dir}/VCTK-Corpus/lab" ]; then
-    cd "${download_dir}" || exit 1;
-    git clone https://github.com/kan-bayashi/VCTKCorpusFullContextLabel.git
-    cp -r VCTKCorpusFullContextLabel/lab ./VCTK-Corpus
-    cd "${cwd}" || exit 1;
-    echo "Successfully downloaded label data."
-else
-    echo "Already exists. Skipped."
-fi
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/commands/completion.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/commands/completion.py
deleted file mode 100644
index c0fb4caf8b275ac343ce5fafbea797db4818fcdd..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/commands/completion.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import sys
-import textwrap
-from optparse import Values
-from typing import List
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.utils.misc import get_prog
-
-BASE_COMPLETION = """
-# pip {shell} completion start{script}# pip {shell} completion end
-"""
-
-COMPLETION_SCRIPTS = {
-    "bash": """
-        _pip_completion()
-        {{
-            COMPREPLY=( $( COMP_WORDS="${{COMP_WORDS[*]}}" \\
-                           COMP_CWORD=$COMP_CWORD \\
-                           PIP_AUTO_COMPLETE=1 $1 2>/dev/null ) )
-        }}
-        complete -o default -F _pip_completion {prog}
-    """,
-    "zsh": """
-        function _pip_completion {{
-          local words cword
-          read -Ac words
-          read -cn cword
-          reply=( $( COMP_WORDS="$words[*]" \\
-                     COMP_CWORD=$(( cword-1 )) \\
-                     PIP_AUTO_COMPLETE=1 $words[1] 2>/dev/null ))
-        }}
-        compctl -K _pip_completion {prog}
-    """,
-    "fish": """
-        function __fish_complete_pip
-            set -lx COMP_WORDS (commandline -o) ""
-            set -lx COMP_CWORD ( \\
-                math (contains -i -- (commandline -t) $COMP_WORDS)-1 \\
-            )
-            set -lx PIP_AUTO_COMPLETE 1
-            string split \\  -- (eval $COMP_WORDS[1])
-        end
-        complete -fa "(__fish_complete_pip)" -c {prog}
-    """,
-}
-
-
-class CompletionCommand(Command):
-    """A helper command to be used for command completion."""
-
-    ignore_require_venv = True
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "--bash",
-            "-b",
-            action="store_const",
-            const="bash",
-            dest="shell",
-            help="Emit completion code for bash",
-        )
-        self.cmd_opts.add_option(
-            "--zsh",
-            "-z",
-            action="store_const",
-            const="zsh",
-            dest="shell",
-            help="Emit completion code for zsh",
-        )
-        self.cmd_opts.add_option(
-            "--fish",
-            "-f",
-            action="store_const",
-            const="fish",
-            dest="shell",
-            help="Emit completion code for fish",
-        )
-
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        """Prints the completion code of the given shell"""
-        shells = COMPLETION_SCRIPTS.keys()
-        shell_options = ["--" + shell for shell in sorted(shells)]
-        if options.shell in shells:
-            script = textwrap.dedent(
-                COMPLETION_SCRIPTS.get(options.shell, "").format(prog=get_prog())
-            )
-            print(BASE_COMPLETION.format(script=script, shell=options.shell))
-            return SUCCESS
-        else:
-            sys.stderr.write(
-                "ERROR: You must pass {}\n".format(" or ".join(shell_options))
-            )
-            return SUCCESS
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/lexers/python.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/lexers/python.py
deleted file mode 100644
index 0e7bab9945ac8265d1f3327bbd5a1890ba540325..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/lexers/python.py
+++ /dev/null
@@ -1,1188 +0,0 @@
-"""
-    pygments.lexers.python
-    ~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for Python and related languages.
-
-    :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-import keyword
-
-from pip._vendor.pygments.lexer import Lexer, RegexLexer, include, bygroups, using, \
-    default, words, combined, do_insertions, this
-from pip._vendor.pygments.util import get_bool_opt, shebang_matches
-from pip._vendor.pygments.token import Text, Comment, Operator, Keyword, Name, String, \
-    Number, Punctuation, Generic, Other, Error
-from pip._vendor.pygments import unistring as uni
-
-__all__ = ['PythonLexer', 'PythonConsoleLexer', 'PythonTracebackLexer',
-           'Python2Lexer', 'Python2TracebackLexer',
-           'CythonLexer', 'DgLexer', 'NumPyLexer']
-
-line_re = re.compile('.*?\n')
-
-
-class PythonLexer(RegexLexer):
-    """
-    For `Python <http://www.python.org>`_ source code (version 3.x).
-
-    .. versionadded:: 0.10
-
-    .. versionchanged:: 2.5
-       This is now the default ``PythonLexer``.  It is still available as the
-       alias ``Python3Lexer``.
-    """
-
-    name = 'Python'
-    aliases = ['python', 'py', 'sage', 'python3', 'py3']
-    filenames = [
-        '*.py',
-        '*.pyw',
-        # Jython
-        '*.jy',
-        # Sage
-        '*.sage',
-        # SCons
-        '*.sc',
-        'SConstruct',
-        'SConscript',
-        # Skylark/Starlark (used by Bazel, Buck, and Pants)
-        '*.bzl',
-        'BUCK',
-        'BUILD',
-        'BUILD.bazel',
-        'WORKSPACE',
-        # Twisted Application infrastructure
-        '*.tac',
-    ]
-    mimetypes = ['text/x-python', 'application/x-python',
-                 'text/x-python3', 'application/x-python3']
-
-    flags = re.MULTILINE | re.UNICODE
-
-    uni_name = "[%s][%s]*" % (uni.xid_start, uni.xid_continue)
-
-    def innerstring_rules(ttype):
-        return [
-            # the old style '%s' % (...) string formatting (still valid in Py3)
-            (r'%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsaux%]', String.Interpol),
-            # the new style '{}'.format(...) string formatting
-            (r'\{'
-             r'((\w+)((\.\w+)|(\[[^\]]+\]))*)?'  # field name
-             r'(\![sra])?'                       # conversion
-             r'(\:(.?[<>=\^])?[-+ ]?#?0?(\d+)?,?(\.\d+)?[E-GXb-gnosx%]?)?'
-             r'\}', String.Interpol),
-
-            # backslashes, quotes and formatting signs must be parsed one at a time
-            (r'[^\\\'"%{\n]+', ttype),
-            (r'[\'"\\]', ttype),
-            # unhandled string formatting sign
-            (r'%|(\{{1,2})', ttype)
-            # newlines are an error (use "nl" state)
-        ]
-
-    def fstring_rules(ttype):
-        return [
-            # Assuming that a '}' is the closing brace after format specifier.
-            # Sadly, this means that we won't detect syntax error. But it's
-            # more important to parse correct syntax correctly, than to
-            # highlight invalid syntax.
-            (r'\}', String.Interpol),
-            (r'\{', String.Interpol, 'expr-inside-fstring'),
-            # backslashes, quotes and formatting signs must be parsed one at a time
-            (r'[^\\\'"{}\n]+', ttype),
-            (r'[\'"\\]', ttype),
-            # newlines are an error (use "nl" state)
-        ]
-
-    tokens = {
-        'root': [
-            (r'\n', Text),
-            (r'^(\s*)([rRuUbB]{,2})("""(?:.|\n)*?""")',
-             bygroups(Text, String.Affix, String.Doc)),
-            (r"^(\s*)([rRuUbB]{,2})('''(?:.|\n)*?''')",
-             bygroups(Text, String.Affix, String.Doc)),
-            (r'\A#!.+$', Comment.Hashbang),
-            (r'#.*$', Comment.Single),
-            (r'\\\n', Text),
-            (r'\\', Text),
-            include('keywords'),
-            include('soft-keywords'),
-            (r'(def)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'funcname'),
-            (r'(class)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'classname'),
-            (r'(from)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'fromimport'),
-            (r'(import)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'import'),
-            include('expr'),
-        ],
-        'expr': [
-            # raw f-strings
-            ('(?i)(rf|fr)(""")',
-             bygroups(String.Affix, String.Double),
-             combined('rfstringescape', 'tdqf')),
-            ("(?i)(rf|fr)(''')",
-             bygroups(String.Affix, String.Single),
-             combined('rfstringescape', 'tsqf')),
-            ('(?i)(rf|fr)(")',
-             bygroups(String.Affix, String.Double),
-             combined('rfstringescape', 'dqf')),
-            ("(?i)(rf|fr)(')",
-             bygroups(String.Affix, String.Single),
-             combined('rfstringescape', 'sqf')),
-            # non-raw f-strings
-            ('([fF])(""")', bygroups(String.Affix, String.Double),
-             combined('fstringescape', 'tdqf')),
-            ("([fF])(''')", bygroups(String.Affix, String.Single),
-             combined('fstringescape', 'tsqf')),
-            ('([fF])(")', bygroups(String.Affix, String.Double),
-             combined('fstringescape', 'dqf')),
-            ("([fF])(')", bygroups(String.Affix, String.Single),
-             combined('fstringescape', 'sqf')),
-            # raw strings
-            ('(?i)(rb|br|r)(""")',
-             bygroups(String.Affix, String.Double), 'tdqs'),
-            ("(?i)(rb|br|r)(''')",
-             bygroups(String.Affix, String.Single), 'tsqs'),
-            ('(?i)(rb|br|r)(")',
-             bygroups(String.Affix, String.Double), 'dqs'),
-            ("(?i)(rb|br|r)(')",
-             bygroups(String.Affix, String.Single), 'sqs'),
-            # non-raw strings
-            ('([uUbB]?)(""")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'tdqs')),
-            ("([uUbB]?)(''')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'tsqs')),
-            ('([uUbB]?)(")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'dqs')),
-            ("([uUbB]?)(')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'sqs')),
-            (r'[^\S\n]+', Text),
-            include('numbers'),
-            (r'!=|==|<<|>>|:=|[-~+/*%=<>&^|.]', Operator),
-            (r'[]{}:(),;[]', Punctuation),
-            (r'(in|is|and|or|not)\b', Operator.Word),
-            include('expr-keywords'),
-            include('builtins'),
-            include('magicfuncs'),
-            include('magicvars'),
-            include('name'),
-        ],
-        'expr-inside-fstring': [
-            (r'[{([]', Punctuation, 'expr-inside-fstring-inner'),
-            # without format specifier
-            (r'(=\s*)?'         # debug (https://bugs.python.org/issue36817)
-             r'(\![sraf])?'     # conversion
-             r'\}', String.Interpol, '#pop'),
-            # with format specifier
-            # we'll catch the remaining '}' in the outer scope
-            (r'(=\s*)?'         # debug (https://bugs.python.org/issue36817)
-             r'(\![sraf])?'     # conversion
-             r':', String.Interpol, '#pop'),
-            (r'\s+', Text),  # allow new lines
-            include('expr'),
-        ],
-        'expr-inside-fstring-inner': [
-            (r'[{([]', Punctuation, 'expr-inside-fstring-inner'),
-            (r'[])}]', Punctuation, '#pop'),
-            (r'\s+', Text),  # allow new lines
-            include('expr'),
-        ],
-        'expr-keywords': [
-            # Based on https://docs.python.org/3/reference/expressions.html
-            (words((
-                'async for', 'await', 'else', 'for', 'if', 'lambda',
-                'yield', 'yield from'), suffix=r'\b'),
-             Keyword),
-            (words(('True', 'False', 'None'), suffix=r'\b'), Keyword.Constant),
-        ],
-        'keywords': [
-            (words((
-                'assert', 'async', 'await', 'break', 'continue', 'del', 'elif',
-                'else', 'except', 'finally', 'for', 'global', 'if', 'lambda',
-                'pass', 'raise', 'nonlocal', 'return', 'try', 'while', 'yield',
-                'yield from', 'as', 'with'), suffix=r'\b'),
-             Keyword),
-            (words(('True', 'False', 'None'), suffix=r'\b'), Keyword.Constant),
-        ],
-        'soft-keywords': [
-            # `match`, `case` and `_` soft keywords
-            (r'(^[ \t]*)'              # at beginning of line + possible indentation
-             r'(match|case)\b'         # a possible keyword
-             r'(?![ \t]*(?:'           # not followed by...
-             r'[:,;=^&|@~)\]}]|(?:' +  # characters and keywords that mean this isn't 
-             r'|'.join(keyword.kwlist) + r')\b))',                 # pattern matching
-             bygroups(Text, Keyword), 'soft-keywords-inner'),
-        ],
-        'soft-keywords-inner': [
-            # optional `_` keyword
-            (r'(\s+)([^\n_]*)(_\b)', bygroups(Text, using(this), Keyword)),
-            default('#pop')
-        ],
-        'builtins': [
-            (words((
-                '__import__', 'abs', 'all', 'any', 'bin', 'bool', 'bytearray',
-                'breakpoint', 'bytes', 'chr', 'classmethod', 'compile', 'complex',
-                'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'filter',
-                'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr',
-                'hash', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass',
-                'iter', 'len', 'list', 'locals', 'map', 'max', 'memoryview',
-                'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print',
-                'property', 'range', 'repr', 'reversed', 'round', 'set', 'setattr',
-                'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple',
-                'type', 'vars', 'zip'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Builtin),
-            (r'(?<!\.)(self|Ellipsis|NotImplemented|cls)\b', Name.Builtin.Pseudo),
-            (words((
-                'ArithmeticError', 'AssertionError', 'AttributeError',
-                'BaseException', 'BufferError', 'BytesWarning', 'DeprecationWarning',
-                'EOFError', 'EnvironmentError', 'Exception', 'FloatingPointError',
-                'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError',
-                'ImportWarning', 'IndentationError', 'IndexError', 'KeyError',
-                'KeyboardInterrupt', 'LookupError', 'MemoryError', 'NameError',
-                'NotImplementedError', 'OSError', 'OverflowError',
-                'PendingDeprecationWarning', 'ReferenceError', 'ResourceWarning',
-                'RuntimeError', 'RuntimeWarning', 'StopIteration',
-                'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit',
-                'TabError', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
-                'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
-                'UnicodeWarning', 'UserWarning', 'ValueError', 'VMSError',
-                'Warning', 'WindowsError', 'ZeroDivisionError',
-                # new builtin exceptions from PEP 3151
-                'BlockingIOError', 'ChildProcessError', 'ConnectionError',
-                'BrokenPipeError', 'ConnectionAbortedError', 'ConnectionRefusedError',
-                'ConnectionResetError', 'FileExistsError', 'FileNotFoundError',
-                'InterruptedError', 'IsADirectoryError', 'NotADirectoryError',
-                'PermissionError', 'ProcessLookupError', 'TimeoutError',
-                # others new in Python 3
-                'StopAsyncIteration', 'ModuleNotFoundError', 'RecursionError'),
-                prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Exception),
-        ],
-        'magicfuncs': [
-            (words((
-                '__abs__', '__add__', '__aenter__', '__aexit__', '__aiter__',
-                '__and__', '__anext__', '__await__', '__bool__', '__bytes__',
-                '__call__', '__complex__', '__contains__', '__del__', '__delattr__',
-                '__delete__', '__delitem__', '__dir__', '__divmod__', '__enter__',
-                '__eq__', '__exit__', '__float__', '__floordiv__', '__format__',
-                '__ge__', '__get__', '__getattr__', '__getattribute__',
-                '__getitem__', '__gt__', '__hash__', '__iadd__', '__iand__',
-                '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__',
-                '__imul__', '__index__', '__init__', '__instancecheck__',
-                '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__',
-                '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__',
-                '__len__', '__length_hint__', '__lshift__', '__lt__', '__matmul__',
-                '__missing__', '__mod__', '__mul__', '__ne__', '__neg__',
-                '__new__', '__next__', '__or__', '__pos__', '__pow__',
-                '__prepare__', '__radd__', '__rand__', '__rdivmod__', '__repr__',
-                '__reversed__', '__rfloordiv__', '__rlshift__', '__rmatmul__',
-                '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__',
-                '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__',
-                '__rxor__', '__set__', '__setattr__', '__setitem__', '__str__',
-                '__sub__', '__subclasscheck__', '__truediv__',
-                '__xor__'), suffix=r'\b'),
-             Name.Function.Magic),
-        ],
-        'magicvars': [
-            (words((
-                '__annotations__', '__bases__', '__class__', '__closure__',
-                '__code__', '__defaults__', '__dict__', '__doc__', '__file__',
-                '__func__', '__globals__', '__kwdefaults__', '__module__',
-                '__mro__', '__name__', '__objclass__', '__qualname__',
-                '__self__', '__slots__', '__weakref__'), suffix=r'\b'),
-             Name.Variable.Magic),
-        ],
-        'numbers': [
-            (r'(\d(?:_?\d)*\.(?:\d(?:_?\d)*)?|(?:\d(?:_?\d)*)?\.\d(?:_?\d)*)'
-             r'([eE][+-]?\d(?:_?\d)*)?', Number.Float),
-            (r'\d(?:_?\d)*[eE][+-]?\d(?:_?\d)*j?', Number.Float),
-            (r'0[oO](?:_?[0-7])+', Number.Oct),
-            (r'0[bB](?:_?[01])+', Number.Bin),
-            (r'0[xX](?:_?[a-fA-F0-9])+', Number.Hex),
-            (r'\d(?:_?\d)*', Number.Integer),
-        ],
-        'name': [
-            (r'@' + uni_name, Name.Decorator),
-            (r'@', Operator),  # new matrix multiplication operator
-            (uni_name, Name),
-        ],
-        'funcname': [
-            include('magicfuncs'),
-            (uni_name, Name.Function, '#pop'),
-            default('#pop'),
-        ],
-        'classname': [
-            (uni_name, Name.Class, '#pop'),
-        ],
-        'import': [
-            (r'(\s+)(as)(\s+)', bygroups(Text, Keyword, Text)),
-            (r'\.', Name.Namespace),
-            (uni_name, Name.Namespace),
-            (r'(\s*)(,)(\s*)', bygroups(Text, Operator, Text)),
-            default('#pop')  # all else: go back
-        ],
-        'fromimport': [
-            (r'(\s+)(import)\b', bygroups(Text, Keyword.Namespace), '#pop'),
-            (r'\.', Name.Namespace),
-            # if None occurs here, it's "raise x from None", since None can
-            # never be a module name
-            (r'None\b', Name.Builtin.Pseudo, '#pop'),
-            (uni_name, Name.Namespace),
-            default('#pop'),
-        ],
-        'rfstringescape': [
-            (r'\{\{', String.Escape),
-            (r'\}\}', String.Escape),
-        ],
-        'fstringescape': [
-            include('rfstringescape'),
-            include('stringescape'),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'fstrings-single': fstring_rules(String.Single),
-        'fstrings-double': fstring_rules(String.Double),
-        'strings-single': innerstring_rules(String.Single),
-        'strings-double': innerstring_rules(String.Double),
-        'dqf': [
-            (r'"', String.Double, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
-            include('fstrings-double')
-        ],
-        'sqf': [
-            (r"'", String.Single, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
-            include('fstrings-single')
-        ],
-        'dqs': [
-            (r'"', String.Double, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
-            include('strings-double')
-        ],
-        'sqs': [
-            (r"'", String.Single, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
-            include('strings-single')
-        ],
-        'tdqf': [
-            (r'"""', String.Double, '#pop'),
-            include('fstrings-double'),
-            (r'\n', String.Double)
-        ],
-        'tsqf': [
-            (r"'''", String.Single, '#pop'),
-            include('fstrings-single'),
-            (r'\n', String.Single)
-        ],
-        'tdqs': [
-            (r'"""', String.Double, '#pop'),
-            include('strings-double'),
-            (r'\n', String.Double)
-        ],
-        'tsqs': [
-            (r"'''", String.Single, '#pop'),
-            include('strings-single'),
-            (r'\n', String.Single)
-        ],
-    }
-
-    def analyse_text(text):
-        return shebang_matches(text, r'pythonw?(3(\.\d)?)?') or \
-            'import ' in text[:1000]
-
-
-Python3Lexer = PythonLexer
-
-
-class Python2Lexer(RegexLexer):
-    """
-    For `Python 2.x <http://www.python.org>`_ source code.
-
-    .. versionchanged:: 2.5
-       This class has been renamed from ``PythonLexer``.  ``PythonLexer`` now
-       refers to the Python 3 variant.  File name patterns like ``*.py`` have
-       been moved to Python 3 as well.
-    """
-
-    name = 'Python 2.x'
-    aliases = ['python2', 'py2']
-    filenames = []  # now taken over by PythonLexer (3.x)
-    mimetypes = ['text/x-python2', 'application/x-python2']
-
-    def innerstring_rules(ttype):
-        return [
-            # the old style '%s' % (...) string formatting
-            (r'%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsux%]', String.Interpol),
-            # backslashes, quotes and formatting signs must be parsed one at a time
-            (r'[^\\\'"%\n]+', ttype),
-            (r'[\'"\\]', ttype),
-            # unhandled string formatting sign
-            (r'%', ttype),
-            # newlines are an error (use "nl" state)
-        ]
-
-    tokens = {
-        'root': [
-            (r'\n', Text),
-            (r'^(\s*)([rRuUbB]{,2})("""(?:.|\n)*?""")',
-             bygroups(Text, String.Affix, String.Doc)),
-            (r"^(\s*)([rRuUbB]{,2})('''(?:.|\n)*?''')",
-             bygroups(Text, String.Affix, String.Doc)),
-            (r'[^\S\n]+', Text),
-            (r'\A#!.+$', Comment.Hashbang),
-            (r'#.*$', Comment.Single),
-            (r'[]{}:(),;[]', Punctuation),
-            (r'\\\n', Text),
-            (r'\\', Text),
-            (r'(in|is|and|or|not)\b', Operator.Word),
-            (r'!=|==|<<|>>|[-~+/*%=<>&^|.]', Operator),
-            include('keywords'),
-            (r'(def)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'funcname'),
-            (r'(class)((?:\s|\\\s)+)', bygroups(Keyword, Text), 'classname'),
-            (r'(from)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'fromimport'),
-            (r'(import)((?:\s|\\\s)+)', bygroups(Keyword.Namespace, Text),
-             'import'),
-            include('builtins'),
-            include('magicfuncs'),
-            include('magicvars'),
-            include('backtick'),
-            ('([rR]|[uUbB][rR]|[rR][uUbB])(""")',
-             bygroups(String.Affix, String.Double), 'tdqs'),
-            ("([rR]|[uUbB][rR]|[rR][uUbB])(''')",
-             bygroups(String.Affix, String.Single), 'tsqs'),
-            ('([rR]|[uUbB][rR]|[rR][uUbB])(")',
-             bygroups(String.Affix, String.Double), 'dqs'),
-            ("([rR]|[uUbB][rR]|[rR][uUbB])(')",
-             bygroups(String.Affix, String.Single), 'sqs'),
-            ('([uUbB]?)(""")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'tdqs')),
-            ("([uUbB]?)(''')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'tsqs')),
-            ('([uUbB]?)(")', bygroups(String.Affix, String.Double),
-             combined('stringescape', 'dqs')),
-            ("([uUbB]?)(')", bygroups(String.Affix, String.Single),
-             combined('stringescape', 'sqs')),
-            include('name'),
-            include('numbers'),
-        ],
-        'keywords': [
-            (words((
-                'assert', 'break', 'continue', 'del', 'elif', 'else', 'except',
-                'exec', 'finally', 'for', 'global', 'if', 'lambda', 'pass',
-                'print', 'raise', 'return', 'try', 'while', 'yield',
-                'yield from', 'as', 'with'), suffix=r'\b'),
-             Keyword),
-        ],
-        'builtins': [
-            (words((
-                '__import__', 'abs', 'all', 'any', 'apply', 'basestring', 'bin',
-                'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod',
-                'cmp', 'coerce', 'compile', 'complex', 'delattr', 'dict', 'dir', 'divmod',
-                'enumerate', 'eval', 'execfile', 'exit', 'file', 'filter', 'float',
-                'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'hex', 'id',
-                'input', 'int', 'intern', 'isinstance', 'issubclass', 'iter', 'len',
-                'list', 'locals', 'long', 'map', 'max', 'min', 'next', 'object',
-                'oct', 'open', 'ord', 'pow', 'property', 'range', 'raw_input', 'reduce',
-                'reload', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice',
-                'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type',
-                'unichr', 'unicode', 'vars', 'xrange', 'zip'),
-                prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Builtin),
-            (r'(?<!\.)(self|None|Ellipsis|NotImplemented|False|True|cls'
-             r')\b', Name.Builtin.Pseudo),
-            (words((
-                'ArithmeticError', 'AssertionError', 'AttributeError',
-                'BaseException', 'DeprecationWarning', 'EOFError', 'EnvironmentError',
-                'Exception', 'FloatingPointError', 'FutureWarning', 'GeneratorExit',
-                'IOError', 'ImportError', 'ImportWarning', 'IndentationError',
-                'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
-                'MemoryError', 'NameError',
-                'NotImplementedError', 'OSError', 'OverflowError', 'OverflowWarning',
-                'PendingDeprecationWarning', 'ReferenceError',
-                'RuntimeError', 'RuntimeWarning', 'StandardError', 'StopIteration',
-                'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit',
-                'TabError', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
-                'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
-                'UnicodeWarning', 'UserWarning', 'ValueError', 'VMSError', 'Warning',
-                'WindowsError', 'ZeroDivisionError'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Exception),
-        ],
-        'magicfuncs': [
-            (words((
-                '__abs__', '__add__', '__and__', '__call__', '__cmp__', '__coerce__',
-                '__complex__', '__contains__', '__del__', '__delattr__', '__delete__',
-                '__delitem__', '__delslice__', '__div__', '__divmod__', '__enter__',
-                '__eq__', '__exit__', '__float__', '__floordiv__', '__ge__', '__get__',
-                '__getattr__', '__getattribute__', '__getitem__', '__getslice__', '__gt__',
-                '__hash__', '__hex__', '__iadd__', '__iand__', '__idiv__', '__ifloordiv__',
-                '__ilshift__', '__imod__', '__imul__', '__index__', '__init__',
-                '__instancecheck__', '__int__', '__invert__', '__iop__', '__ior__',
-                '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__',
-                '__ixor__', '__le__', '__len__', '__long__', '__lshift__', '__lt__',
-                '__missing__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__',
-                '__nonzero__', '__oct__', '__op__', '__or__', '__pos__', '__pow__',
-                '__radd__', '__rand__', '__rcmp__', '__rdiv__', '__rdivmod__', '__repr__',
-                '__reversed__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__',
-                '__rop__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__',
-                '__rtruediv__', '__rxor__', '__set__', '__setattr__', '__setitem__',
-                '__setslice__', '__str__', '__sub__', '__subclasscheck__', '__truediv__',
-                '__unicode__', '__xor__'), suffix=r'\b'),
-             Name.Function.Magic),
-        ],
-        'magicvars': [
-            (words((
-                '__bases__', '__class__', '__closure__', '__code__', '__defaults__',
-                '__dict__', '__doc__', '__file__', '__func__', '__globals__',
-                '__metaclass__', '__module__', '__mro__', '__name__', '__self__',
-                '__slots__', '__weakref__'),
-                suffix=r'\b'),
-             Name.Variable.Magic),
-        ],
-        'numbers': [
-            (r'(\d+\.\d*|\d*\.\d+)([eE][+-]?[0-9]+)?j?', Number.Float),
-            (r'\d+[eE][+-]?[0-9]+j?', Number.Float),
-            (r'0[0-7]+j?', Number.Oct),
-            (r'0[bB][01]+', Number.Bin),
-            (r'0[xX][a-fA-F0-9]+', Number.Hex),
-            (r'\d+L', Number.Integer.Long),
-            (r'\d+j?', Number.Integer)
-        ],
-        'backtick': [
-            ('`.*?`', String.Backtick),
-        ],
-        'name': [
-            (r'@[\w.]+', Name.Decorator),
-            (r'[a-zA-Z_]\w*', Name),
-        ],
-        'funcname': [
-            include('magicfuncs'),
-            (r'[a-zA-Z_]\w*', Name.Function, '#pop'),
-            default('#pop'),
-        ],
-        'classname': [
-            (r'[a-zA-Z_]\w*', Name.Class, '#pop')
-        ],
-        'import': [
-            (r'(?:[ \t]|\\\n)+', Text),
-            (r'as\b', Keyword.Namespace),
-            (r',', Operator),
-            (r'[a-zA-Z_][\w.]*', Name.Namespace),
-            default('#pop')  # all else: go back
-        ],
-        'fromimport': [
-            (r'(?:[ \t]|\\\n)+', Text),
-            (r'import\b', Keyword.Namespace, '#pop'),
-            # if None occurs here, it's "raise x from None", since None can
-            # never be a module name
-            (r'None\b', Name.Builtin.Pseudo, '#pop'),
-            # sadly, in "raise x from y" y will be highlighted as namespace too
-            (r'[a-zA-Z_.][\w.]*', Name.Namespace),
-            # anything else here also means "raise x from y" and is therefore
-            # not an error
-            default('#pop'),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'strings-single': innerstring_rules(String.Single),
-        'strings-double': innerstring_rules(String.Double),
-        'dqs': [
-            (r'"', String.Double, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
-            include('strings-double')
-        ],
-        'sqs': [
-            (r"'", String.Single, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
-            include('strings-single')
-        ],
-        'tdqs': [
-            (r'"""', String.Double, '#pop'),
-            include('strings-double'),
-            (r'\n', String.Double)
-        ],
-        'tsqs': [
-            (r"'''", String.Single, '#pop'),
-            include('strings-single'),
-            (r'\n', String.Single)
-        ],
-    }
-
-    def analyse_text(text):
-        return shebang_matches(text, r'pythonw?2(\.\d)?')
-
-
-class PythonConsoleLexer(Lexer):
-    """
-    For Python console output or doctests, such as:
-
-    .. sourcecode:: pycon
-
-        >>> a = 'foo'
-        >>> print a
-        foo
-        >>> 1 / 0
-        Traceback (most recent call last):
-          File "<stdin>", line 1, in <module>
-        ZeroDivisionError: integer division or modulo by zero
-
-    Additional options:
-
-    `python3`
-        Use Python 3 lexer for code.  Default is ``True``.
-
-        .. versionadded:: 1.0
-        .. versionchanged:: 2.5
-           Now defaults to ``True``.
-    """
-    name = 'Python console session'
-    aliases = ['pycon']
-    mimetypes = ['text/x-python-doctest']
-
-    def __init__(self, **options):
-        self.python3 = get_bool_opt(options, 'python3', True)
-        Lexer.__init__(self, **options)
-
-    def get_tokens_unprocessed(self, text):
-        if self.python3:
-            pylexer = PythonLexer(**self.options)
-            tblexer = PythonTracebackLexer(**self.options)
-        else:
-            pylexer = Python2Lexer(**self.options)
-            tblexer = Python2TracebackLexer(**self.options)
-
-        curcode = ''
-        insertions = []
-        curtb = ''
-        tbindex = 0
-        tb = 0
-        for match in line_re.finditer(text):
-            line = match.group()
-            if line.startswith('>>> ') or line.startswith('... '):
-                tb = 0
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt, line[:4])]))
-                curcode += line[4:]
-            elif line.rstrip() == '...' and not tb:
-                # only a new >>> prompt can end an exception block
-                # otherwise an ellipsis in place of the traceback frames
-                # will be mishandled
-                insertions.append((len(curcode),
-                                   [(0, Generic.Prompt, '...')]))
-                curcode += line[3:]
-            else:
-                if curcode:
-                    yield from do_insertions(
-                        insertions, pylexer.get_tokens_unprocessed(curcode))
-                    curcode = ''
-                    insertions = []
-                if (line.startswith('Traceback (most recent call last):') or
-                        re.match('  File "[^"]+", line \\d+\\n$', line)):
-                    tb = 1
-                    curtb = line
-                    tbindex = match.start()
-                elif line == 'KeyboardInterrupt\n':
-                    yield match.start(), Name.Class, line
-                elif tb:
-                    curtb += line
-                    if not (line.startswith(' ') or line.strip() == '...'):
-                        tb = 0
-                        for i, t, v in tblexer.get_tokens_unprocessed(curtb):
-                            yield tbindex+i, t, v
-                        curtb = ''
-                else:
-                    yield match.start(), Generic.Output, line
-        if curcode:
-            yield from do_insertions(insertions,
-                                     pylexer.get_tokens_unprocessed(curcode))
-        if curtb:
-            for i, t, v in tblexer.get_tokens_unprocessed(curtb):
-                yield tbindex+i, t, v
-
-
-class PythonTracebackLexer(RegexLexer):
-    """
-    For Python 3.x tracebacks, with support for chained exceptions.
-
-    .. versionadded:: 1.0
-
-    .. versionchanged:: 2.5
-       This is now the default ``PythonTracebackLexer``.  It is still available
-       as the alias ``Python3TracebackLexer``.
-    """
-
-    name = 'Python Traceback'
-    aliases = ['pytb', 'py3tb']
-    filenames = ['*.pytb', '*.py3tb']
-    mimetypes = ['text/x-python-traceback', 'text/x-python3-traceback']
-
-    tokens = {
-        'root': [
-            (r'\n', Text),
-            (r'^Traceback \(most recent call last\):\n', Generic.Traceback, 'intb'),
-            (r'^During handling of the above exception, another '
-             r'exception occurred:\n\n', Generic.Traceback),
-            (r'^The above exception was the direct cause of the '
-             r'following exception:\n\n', Generic.Traceback),
-            (r'^(?=  File "[^"]+", line \d+)', Generic.Traceback, 'intb'),
-            (r'^.*\n', Other),
-        ],
-        'intb': [
-            (r'^(  File )("[^"]+")(, line )(\d+)(, in )(.+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Text, Name, Text)),
-            (r'^(  File )("[^"]+")(, line )(\d+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Text)),
-            (r'^(    )(.+)(\n)',
-             bygroups(Text, using(PythonLexer), Text), 'markers'),
-            (r'^([ \t]*)(\.\.\.)(\n)',
-             bygroups(Text, Comment, Text)),  # for doctests...
-            (r'^([^:]+)(: )(.+)(\n)',
-             bygroups(Generic.Error, Text, Name, Text), '#pop'),
-            (r'^([a-zA-Z_][\w.]*)(:?\n)',
-             bygroups(Generic.Error, Text), '#pop')
-        ],
-        'markers': [
-            # Either `PEP 657 <https://www.python.org/dev/peps/pep-0657/>`
-            # error locations in Python 3.11+, or single-caret markers
-            # for syntax errors before that.
-            (r'^( {4,})([~^]+)(\n)',
-             bygroups(Text, Punctuation.Marker, Text),
-             '#pop'),
-            default('#pop'),
-        ],
-    }
-
-
-Python3TracebackLexer = PythonTracebackLexer
-
-
-class Python2TracebackLexer(RegexLexer):
-    """
-    For Python tracebacks.
-
-    .. versionadded:: 0.7
-
-    .. versionchanged:: 2.5
-       This class has been renamed from ``PythonTracebackLexer``.
-       ``PythonTracebackLexer`` now refers to the Python 3 variant.
-    """
-
-    name = 'Python 2.x Traceback'
-    aliases = ['py2tb']
-    filenames = ['*.py2tb']
-    mimetypes = ['text/x-python2-traceback']
-
-    tokens = {
-        'root': [
-            # Cover both (most recent call last) and (innermost last)
-            # The optional ^C allows us to catch keyboard interrupt signals.
-            (r'^(\^C)?(Traceback.*\n)',
-             bygroups(Text, Generic.Traceback), 'intb'),
-            # SyntaxError starts with this.
-            (r'^(?=  File "[^"]+", line \d+)', Generic.Traceback, 'intb'),
-            (r'^.*\n', Other),
-        ],
-        'intb': [
-            (r'^(  File )("[^"]+")(, line )(\d+)(, in )(.+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Text, Name, Text)),
-            (r'^(  File )("[^"]+")(, line )(\d+)(\n)',
-             bygroups(Text, Name.Builtin, Text, Number, Text)),
-            (r'^(    )(.+)(\n)',
-             bygroups(Text, using(Python2Lexer), Text), 'marker'),
-            (r'^([ \t]*)(\.\.\.)(\n)',
-             bygroups(Text, Comment, Text)),  # for doctests...
-            (r'^([^:]+)(: )(.+)(\n)',
-             bygroups(Generic.Error, Text, Name, Text), '#pop'),
-            (r'^([a-zA-Z_]\w*)(:?\n)',
-             bygroups(Generic.Error, Text), '#pop')
-        ],
-        'marker': [
-            # For syntax errors.
-            (r'( {4,})(\^)', bygroups(Text, Punctuation.Marker), '#pop'),
-            default('#pop'),
-        ],
-    }
-
-
-class CythonLexer(RegexLexer):
-    """
-    For Pyrex and `Cython <http://cython.org>`_ source code.
-
-    .. versionadded:: 1.1
-    """
-
-    name = 'Cython'
-    aliases = ['cython', 'pyx', 'pyrex']
-    filenames = ['*.pyx', '*.pxd', '*.pxi']
-    mimetypes = ['text/x-cython', 'application/x-cython']
-
-    tokens = {
-        'root': [
-            (r'\n', Text),
-            (r'^(\s*)("""(?:.|\n)*?""")', bygroups(Text, String.Doc)),
-            (r"^(\s*)('''(?:.|\n)*?''')", bygroups(Text, String.Doc)),
-            (r'[^\S\n]+', Text),
-            (r'#.*$', Comment),
-            (r'[]{}:(),;[]', Punctuation),
-            (r'\\\n', Text),
-            (r'\\', Text),
-            (r'(in|is|and|or|not)\b', Operator.Word),
-            (r'(<)([a-zA-Z0-9.?]+)(>)',
-             bygroups(Punctuation, Keyword.Type, Punctuation)),
-            (r'!=|==|<<|>>|[-~+/*%=<>&^|.?]', Operator),
-            (r'(from)(\d+)(<=)(\s+)(<)(\d+)(:)',
-             bygroups(Keyword, Number.Integer, Operator, Name, Operator,
-                      Name, Punctuation)),
-            include('keywords'),
-            (r'(def|property)(\s+)', bygroups(Keyword, Text), 'funcname'),
-            (r'(cp?def)(\s+)', bygroups(Keyword, Text), 'cdef'),
-            # (should actually start a block with only cdefs)
-            (r'(cdef)(:)', bygroups(Keyword, Punctuation)),
-            (r'(class|struct)(\s+)', bygroups(Keyword, Text), 'classname'),
-            (r'(from)(\s+)', bygroups(Keyword, Text), 'fromimport'),
-            (r'(c?import)(\s+)', bygroups(Keyword, Text), 'import'),
-            include('builtins'),
-            include('backtick'),
-            ('(?:[rR]|[uU][rR]|[rR][uU])"""', String, 'tdqs'),
-            ("(?:[rR]|[uU][rR]|[rR][uU])'''", String, 'tsqs'),
-            ('(?:[rR]|[uU][rR]|[rR][uU])"', String, 'dqs'),
-            ("(?:[rR]|[uU][rR]|[rR][uU])'", String, 'sqs'),
-            ('[uU]?"""', String, combined('stringescape', 'tdqs')),
-            ("[uU]?'''", String, combined('stringescape', 'tsqs')),
-            ('[uU]?"', String, combined('stringescape', 'dqs')),
-            ("[uU]?'", String, combined('stringescape', 'sqs')),
-            include('name'),
-            include('numbers'),
-        ],
-        'keywords': [
-            (words((
-                'assert', 'async', 'await', 'break', 'by', 'continue', 'ctypedef', 'del', 'elif',
-                'else', 'except', 'except?', 'exec', 'finally', 'for', 'fused', 'gil',
-                'global', 'if', 'include', 'lambda', 'nogil', 'pass', 'print',
-                'raise', 'return', 'try', 'while', 'yield', 'as', 'with'), suffix=r'\b'),
-             Keyword),
-            (r'(DEF|IF|ELIF|ELSE)\b', Comment.Preproc),
-        ],
-        'builtins': [
-            (words((
-                '__import__', 'abs', 'all', 'any', 'apply', 'basestring', 'bin', 'bint',
-                'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr',
-                'classmethod', 'cmp', 'coerce', 'compile', 'complex', 'delattr',
-                'dict', 'dir', 'divmod', 'enumerate', 'eval', 'execfile', 'exit',
-                'file', 'filter', 'float', 'frozenset', 'getattr', 'globals',
-                'hasattr', 'hash', 'hex', 'id', 'input', 'int', 'intern', 'isinstance',
-                'issubclass', 'iter', 'len', 'list', 'locals', 'long', 'map', 'max',
-                'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'property', 'Py_ssize_t',
-                'range', 'raw_input', 'reduce', 'reload', 'repr', 'reversed',
-                'round', 'set', 'setattr', 'slice', 'sorted', 'staticmethod',
-                'str', 'sum', 'super', 'tuple', 'type', 'unichr', 'unicode', 'unsigned',
-                'vars', 'xrange', 'zip'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Builtin),
-            (r'(?<!\.)(self|None|Ellipsis|NotImplemented|False|True|NULL'
-             r')\b', Name.Builtin.Pseudo),
-            (words((
-                'ArithmeticError', 'AssertionError', 'AttributeError',
-                'BaseException', 'DeprecationWarning', 'EOFError', 'EnvironmentError',
-                'Exception', 'FloatingPointError', 'FutureWarning', 'GeneratorExit',
-                'IOError', 'ImportError', 'ImportWarning', 'IndentationError',
-                'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
-                'MemoryError', 'NameError', 'NotImplemented', 'NotImplementedError',
-                'OSError', 'OverflowError', 'OverflowWarning',
-                'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError',
-                'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError',
-                'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError',
-                'TypeError', 'UnboundLocalError', 'UnicodeDecodeError',
-                'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError',
-                'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning',
-                'ZeroDivisionError'), prefix=r'(?<!\.)', suffix=r'\b'),
-             Name.Exception),
-        ],
-        'numbers': [
-            (r'(\d+\.?\d*|\d*\.\d+)([eE][+-]?[0-9]+)?', Number.Float),
-            (r'0\d+', Number.Oct),
-            (r'0[xX][a-fA-F0-9]+', Number.Hex),
-            (r'\d+L', Number.Integer.Long),
-            (r'\d+', Number.Integer)
-        ],
-        'backtick': [
-            ('`.*?`', String.Backtick),
-        ],
-        'name': [
-            (r'@\w+', Name.Decorator),
-            (r'[a-zA-Z_]\w*', Name),
-        ],
-        'funcname': [
-            (r'[a-zA-Z_]\w*', Name.Function, '#pop')
-        ],
-        'cdef': [
-            (r'(public|readonly|extern|api|inline)\b', Keyword.Reserved),
-            (r'(struct|enum|union|class)\b', Keyword),
-            (r'([a-zA-Z_]\w*)(\s*)(?=[(:#=]|$)',
-             bygroups(Name.Function, Text), '#pop'),
-            (r'([a-zA-Z_]\w*)(\s*)(,)',
-             bygroups(Name.Function, Text, Punctuation)),
-            (r'from\b', Keyword, '#pop'),
-            (r'as\b', Keyword),
-            (r':', Punctuation, '#pop'),
-            (r'(?=["\'])', Text, '#pop'),
-            (r'[a-zA-Z_]\w*', Keyword.Type),
-            (r'.', Text),
-        ],
-        'classname': [
-            (r'[a-zA-Z_]\w*', Name.Class, '#pop')
-        ],
-        'import': [
-            (r'(\s+)(as)(\s+)', bygroups(Text, Keyword, Text)),
-            (r'[a-zA-Z_][\w.]*', Name.Namespace),
-            (r'(\s*)(,)(\s*)', bygroups(Text, Operator, Text)),
-            default('#pop')  # all else: go back
-        ],
-        'fromimport': [
-            (r'(\s+)(c?import)\b', bygroups(Text, Keyword), '#pop'),
-            (r'[a-zA-Z_.][\w.]*', Name.Namespace),
-            # ``cdef foo from "header"``, or ``for foo from 0 < i < 10``
-            default('#pop'),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'strings': [
-            (r'%(\([a-zA-Z0-9]+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsux%]', String.Interpol),
-            (r'[^\\\'"%\n]+', String),
-            # quotes, percents and backslashes must be parsed one at a time
-            (r'[\'"\\]', String),
-            # unhandled string formatting sign
-            (r'%', String)
-            # newlines are an error (use "nl" state)
-        ],
-        'nl': [
-            (r'\n', String)
-        ],
-        'dqs': [
-            (r'"', String, '#pop'),
-            (r'\\\\|\\"|\\\n', String.Escape),  # included here again for raw strings
-            include('strings')
-        ],
-        'sqs': [
-            (r"'", String, '#pop'),
-            (r"\\\\|\\'|\\\n", String.Escape),  # included here again for raw strings
-            include('strings')
-        ],
-        'tdqs': [
-            (r'"""', String, '#pop'),
-            include('strings'),
-            include('nl')
-        ],
-        'tsqs': [
-            (r"'''", String, '#pop'),
-            include('strings'),
-            include('nl')
-        ],
-    }
-
-
-class DgLexer(RegexLexer):
-    """
-    Lexer for `dg <http://pyos.github.com/dg>`_,
-    a functional and object-oriented programming language
-    running on the CPython 3 VM.
-
-    .. versionadded:: 1.6
-    """
-    name = 'dg'
-    aliases = ['dg']
-    filenames = ['*.dg']
-    mimetypes = ['text/x-dg']
-
-    tokens = {
-        'root': [
-            (r'\s+', Text),
-            (r'#.*?$', Comment.Single),
-
-            (r'(?i)0b[01]+', Number.Bin),
-            (r'(?i)0o[0-7]+', Number.Oct),
-            (r'(?i)0x[0-9a-f]+', Number.Hex),
-            (r'(?i)[+-]?[0-9]+\.[0-9]+(e[+-]?[0-9]+)?j?', Number.Float),
-            (r'(?i)[+-]?[0-9]+e[+-]?\d+j?', Number.Float),
-            (r'(?i)[+-]?[0-9]+j?', Number.Integer),
-
-            (r"(?i)(br|r?b?)'''", String, combined('stringescape', 'tsqs', 'string')),
-            (r'(?i)(br|r?b?)"""', String, combined('stringescape', 'tdqs', 'string')),
-            (r"(?i)(br|r?b?)'", String, combined('stringescape', 'sqs', 'string')),
-            (r'(?i)(br|r?b?)"', String, combined('stringescape', 'dqs', 'string')),
-
-            (r"`\w+'*`", Operator),
-            (r'\b(and|in|is|or|where)\b', Operator.Word),
-            (r'[!$%&*+\-./:<-@\\^|~;,]+', Operator),
-
-            (words((
-                'bool', 'bytearray', 'bytes', 'classmethod', 'complex', 'dict', 'dict\'',
-                'float', 'frozenset', 'int', 'list', 'list\'', 'memoryview', 'object',
-                'property', 'range', 'set', 'set\'', 'slice', 'staticmethod', 'str',
-                'super', 'tuple', 'tuple\'', 'type'),
-                   prefix=r'(?<!\.)', suffix=r'(?![\'\w])'),
-             Name.Builtin),
-            (words((
-                '__import__', 'abs', 'all', 'any', 'bin', 'bind', 'chr', 'cmp', 'compile',
-                'complex', 'delattr', 'dir', 'divmod', 'drop', 'dropwhile', 'enumerate',
-                'eval', 'exhaust', 'filter', 'flip', 'foldl1?', 'format', 'fst',
-                'getattr', 'globals', 'hasattr', 'hash', 'head', 'hex', 'id', 'init',
-                'input', 'isinstance', 'issubclass', 'iter', 'iterate', 'last', 'len',
-                'locals', 'map', 'max', 'min', 'next', 'oct', 'open', 'ord', 'pow',
-                'print', 'repr', 'reversed', 'round', 'setattr', 'scanl1?', 'snd',
-                'sorted', 'sum', 'tail', 'take', 'takewhile', 'vars', 'zip'),
-                   prefix=r'(?<!\.)', suffix=r'(?![\'\w])'),
-             Name.Builtin),
-            (r"(?<!\.)(self|Ellipsis|NotImplemented|None|True|False)(?!['\w])",
-             Name.Builtin.Pseudo),
-
-            (r"(?<!\.)[A-Z]\w*(Error|Exception|Warning)'*(?!['\w])",
-             Name.Exception),
-            (r"(?<!\.)(Exception|GeneratorExit|KeyboardInterrupt|StopIteration|"
-             r"SystemExit)(?!['\w])", Name.Exception),
-
-            (r"(?<![\w.])(except|finally|for|if|import|not|otherwise|raise|"
-             r"subclass|while|with|yield)(?!['\w])", Keyword.Reserved),
-
-            (r"[A-Z_]+'*(?!['\w])", Name),
-            (r"[A-Z]\w+'*(?!['\w])", Keyword.Type),
-            (r"\w+'*", Name),
-
-            (r'[()]', Punctuation),
-            (r'.', Error),
-        ],
-        'stringescape': [
-            (r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
-             r'U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})', String.Escape)
-        ],
-        'string': [
-            (r'%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?'
-             '[hlL]?[E-GXc-giorsux%]', String.Interpol),
-            (r'[^\\\'"%\n]+', String),
-            # quotes, percents and backslashes must be parsed one at a time
-            (r'[\'"\\]', String),
-            # unhandled string formatting sign
-            (r'%', String),
-            (r'\n', String)
-        ],
-        'dqs': [
-            (r'"', String, '#pop')
-        ],
-        'sqs': [
-            (r"'", String, '#pop')
-        ],
-        'tdqs': [
-            (r'"""', String, '#pop')
-        ],
-        'tsqs': [
-            (r"'''", String, '#pop')
-        ],
-    }
-
-
-class NumPyLexer(PythonLexer):
-    """
-    A Python lexer recognizing Numerical Python builtins.
-
-    .. versionadded:: 0.10
-    """
-
-    name = 'NumPy'
-    aliases = ['numpy']
-
-    # override the mimetypes to not inherit them from python
-    mimetypes = []
-    filenames = []
-
-    EXTRA_KEYWORDS = {
-        'abs', 'absolute', 'accumulate', 'add', 'alen', 'all', 'allclose',
-        'alltrue', 'alterdot', 'amax', 'amin', 'angle', 'any', 'append',
-        'apply_along_axis', 'apply_over_axes', 'arange', 'arccos', 'arccosh',
-        'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh', 'argmax', 'argmin',
-        'argsort', 'argwhere', 'around', 'array', 'array2string', 'array_equal',
-        'array_equiv', 'array_repr', 'array_split', 'array_str', 'arrayrange',
-        'asanyarray', 'asarray', 'asarray_chkfinite', 'ascontiguousarray',
-        'asfarray', 'asfortranarray', 'asmatrix', 'asscalar', 'astype',
-        'atleast_1d', 'atleast_2d', 'atleast_3d', 'average', 'bartlett',
-        'base_repr', 'beta', 'binary_repr', 'bincount', 'binomial',
-        'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'blackman',
-        'bmat', 'broadcast', 'byte_bounds', 'bytes', 'byteswap', 'c_',
-        'can_cast', 'ceil', 'choose', 'clip', 'column_stack', 'common_type',
-        'compare_chararrays', 'compress', 'concatenate', 'conj', 'conjugate',
-        'convolve', 'copy', 'corrcoef', 'correlate', 'cos', 'cosh', 'cov',
-        'cross', 'cumprod', 'cumproduct', 'cumsum', 'delete', 'deprecate',
-        'diag', 'diagflat', 'diagonal', 'diff', 'digitize', 'disp', 'divide',
-        'dot', 'dsplit', 'dstack', 'dtype', 'dump', 'dumps', 'ediff1d', 'empty',
-        'empty_like', 'equal', 'exp', 'expand_dims', 'expm1', 'extract', 'eye',
-        'fabs', 'fastCopyAndTranspose', 'fft', 'fftfreq', 'fftshift', 'fill',
-        'finfo', 'fix', 'flat', 'flatnonzero', 'flatten', 'fliplr', 'flipud',
-        'floor', 'floor_divide', 'fmod', 'frexp', 'fromarrays', 'frombuffer',
-        'fromfile', 'fromfunction', 'fromiter', 'frompyfunc', 'fromstring',
-        'generic', 'get_array_wrap', 'get_include', 'get_numarray_include',
-        'get_numpy_include', 'get_printoptions', 'getbuffer', 'getbufsize',
-        'geterr', 'geterrcall', 'geterrobj', 'getfield', 'gradient', 'greater',
-        'greater_equal', 'gumbel', 'hamming', 'hanning', 'histogram',
-        'histogram2d', 'histogramdd', 'hsplit', 'hstack', 'hypot', 'i0',
-        'identity', 'ifft', 'imag', 'index_exp', 'indices', 'inf', 'info',
-        'inner', 'insert', 'int_asbuffer', 'interp', 'intersect1d',
-        'intersect1d_nu', 'inv', 'invert', 'iscomplex', 'iscomplexobj',
-        'isfinite', 'isfortran', 'isinf', 'isnan', 'isneginf', 'isposinf',
-        'isreal', 'isrealobj', 'isscalar', 'issctype', 'issubclass_',
-        'issubdtype', 'issubsctype', 'item', 'itemset', 'iterable', 'ix_',
-        'kaiser', 'kron', 'ldexp', 'left_shift', 'less', 'less_equal', 'lexsort',
-        'linspace', 'load', 'loads', 'loadtxt', 'log', 'log10', 'log1p', 'log2',
-        'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'logspace',
-        'lstsq', 'mat', 'matrix', 'max', 'maximum', 'maximum_sctype',
-        'may_share_memory', 'mean', 'median', 'meshgrid', 'mgrid', 'min',
-        'minimum', 'mintypecode', 'mod', 'modf', 'msort', 'multiply', 'nan',
-        'nan_to_num', 'nanargmax', 'nanargmin', 'nanmax', 'nanmin', 'nansum',
-        'ndenumerate', 'ndim', 'ndindex', 'negative', 'newaxis', 'newbuffer',
-        'newbyteorder', 'nonzero', 'not_equal', 'obj2sctype', 'ogrid', 'ones',
-        'ones_like', 'outer', 'permutation', 'piecewise', 'pinv', 'pkgload',
-        'place', 'poisson', 'poly', 'poly1d', 'polyadd', 'polyder', 'polydiv',
-        'polyfit', 'polyint', 'polymul', 'polysub', 'polyval', 'power', 'prod',
-        'product', 'ptp', 'put', 'putmask', 'r_', 'randint', 'random_integers',
-        'random_sample', 'ranf', 'rank', 'ravel', 'real', 'real_if_close',
-        'recarray', 'reciprocal', 'reduce', 'remainder', 'repeat', 'require',
-        'reshape', 'resize', 'restoredot', 'right_shift', 'rint', 'roll',
-        'rollaxis', 'roots', 'rot90', 'round', 'round_', 'row_stack', 's_',
-        'sample', 'savetxt', 'sctype2char', 'searchsorted', 'seed', 'select',
-        'set_numeric_ops', 'set_printoptions', 'set_string_function',
-        'setbufsize', 'setdiff1d', 'seterr', 'seterrcall', 'seterrobj',
-        'setfield', 'setflags', 'setmember1d', 'setxor1d', 'shape',
-        'show_config', 'shuffle', 'sign', 'signbit', 'sin', 'sinc', 'sinh',
-        'size', 'slice', 'solve', 'sometrue', 'sort', 'sort_complex', 'source',
-        'split', 'sqrt', 'square', 'squeeze', 'standard_normal', 'std',
-        'subtract', 'sum', 'svd', 'swapaxes', 'take', 'tan', 'tanh', 'tensordot',
-        'test', 'tile', 'tofile', 'tolist', 'tostring', 'trace', 'transpose',
-        'trapz', 'tri', 'tril', 'trim_zeros', 'triu', 'true_divide', 'typeDict',
-        'typename', 'uniform', 'union1d', 'unique', 'unique1d', 'unravel_index',
-        'unwrap', 'vander', 'var', 'vdot', 'vectorize', 'view', 'vonmises',
-        'vsplit', 'vstack', 'weibull', 'where', 'who', 'zeros', 'zeros_like'
-    }
-
-    def get_tokens_unprocessed(self, text):
-        for index, token, value in \
-                PythonLexer.get_tokens_unprocessed(self, text):
-            if token is Name and value in self.EXTRA_KEYWORDS:
-                yield index, Keyword.Pseudo, value
-            else:
-                yield index, token, value
-
-    def analyse_text(text):
-        ltext = text[:1000]
-        return (shebang_matches(text, r'pythonw?(3(\.\d)?)?') or
-                'import ' in ltext) \
-            and ('import numpy' in ltext or 'from numpy import' in ltext)
diff --git a/spaces/aliceoq/vozes-da-loirinha/lib/infer_pack/onnx_inference.py b/spaces/aliceoq/vozes-da-loirinha/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index 6517853be49e61c427cf7cd9b5ed203f6d5f367e..0000000000000000000000000000000000000000
--- a/spaces/aliceoq/vozes-da-loirinha/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import (
-            HarvestF0Predictor,
-        )
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/amsterdamNLP/attention-rollout/lib/BertForSequenceClassification.py b/spaces/amsterdamNLP/attention-rollout/lib/BertForSequenceClassification.py
deleted file mode 100644
index 79c68e90c725d6efe993fb5f5b87f0074f763f2f..0000000000000000000000000000000000000000
--- a/spaces/amsterdamNLP/attention-rollout/lib/BertForSequenceClassification.py
+++ /dev/null
@@ -1,204 +0,0 @@
-from transformers import BertPreTrainedModel
-from transformers.modeling_outputs import SequenceClassifierOutput
-from transformers.utils import logging
-from BERT_explainability.modules.layers_ours import *
-from BERT_explainability.modules.BERT.BERT import BertModel
-from torch.nn import CrossEntropyLoss, MSELoss
-import torch.nn as nn
-from typing import List, Any
-import torch
-from BERT_rationale_benchmark.models.model_utils import PaddedSequence
-
-
-class BertForSequenceClassification(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.bert = BertModel(config)
-        self.dropout = Dropout(config.hidden_dropout_prob)
-        self.classifier = Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    def forward(
-            self,
-            input_ids=None,
-            attention_mask=None,
-            token_type_ids=None,
-            position_ids=None,
-            head_mask=None,
-            inputs_embeds=None,
-            labels=None,
-            output_attentions=None,
-            output_hidden_states=None,
-            return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for computing the sequence classification/regression loss.
-            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
-            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
-            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.num_labels == 1:
-                #  We are doing regression
-                loss_fct = MSELoss()
-                loss = loss_fct(logits.view(-1), labels.view(-1))
-            else:
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def relprop(self, cam=None, **kwargs):
-        cam = self.classifier.relprop(cam, **kwargs)
-        cam = self.dropout.relprop(cam, **kwargs)
-        cam = self.bert.relprop(cam, **kwargs)
-        # print("conservation: ", cam.sum())
-        return cam
-
-
-# this is the actual classifier we will be using
-class BertClassifier(nn.Module):
-    """Thin wrapper around BertForSequenceClassification"""
-
-    def __init__(self,
-                 bert_dir: str,
-                 pad_token_id: int,
-                 cls_token_id: int,
-                 sep_token_id: int,
-                 num_labels: int,
-                 max_length: int = 512,
-                 use_half_precision=True):
-        super(BertClassifier, self).__init__()
-        bert = BertForSequenceClassification.from_pretrained(bert_dir, num_labels=num_labels)
-        if use_half_precision:
-            import apex
-            bert = bert.half()
-        self.bert = bert
-        self.pad_token_id = pad_token_id
-        self.cls_token_id = cls_token_id
-        self.sep_token_id = sep_token_id
-        self.max_length = max_length
-
-    def forward(self,
-                query: List[torch.tensor],
-                docids: List[Any],
-                document_batch: List[torch.tensor]):
-        assert len(query) == len(document_batch)
-        print(query)
-        # note about device management:
-        # since distributed training is enabled, the inputs to this module can be on *any* device (preferably cpu, since we wrap and unwrap the module)
-        # we want to keep these params on the input device (assuming CPU) for as long as possible for cheap memory access
-        target_device = next(self.parameters()).device
-        cls_token = torch.tensor([self.cls_token_id]).to(device=document_batch[0].device)
-        sep_token = torch.tensor([self.sep_token_id]).to(device=document_batch[0].device)
-        input_tensors = []
-        position_ids = []
-        for q, d in zip(query, document_batch):
-            if len(q) + len(d) + 2 > self.max_length:
-                d = d[:(self.max_length - len(q) - 2)]
-            input_tensors.append(torch.cat([cls_token, q, sep_token, d]))
-            position_ids.append(torch.tensor(list(range(0, len(q) + 1)) + list(range(0, len(d) + 1))))
-        bert_input = PaddedSequence.autopad(input_tensors, batch_first=True, padding_value=self.pad_token_id,
-                                            device=target_device)
-        positions = PaddedSequence.autopad(position_ids, batch_first=True, padding_value=0, device=target_device)
-        (classes,) = self.bert(bert_input.data,
-                               attention_mask=bert_input.mask(on=0.0, off=float('-inf'), device=target_device),
-                               position_ids=positions.data)
-        assert torch.all(classes == classes)  # for nans
-
-        print(input_tensors[0])
-        print(self.relprop()[0])
-
-        return classes
-
-    def relprop(self, cam=None, **kwargs):
-        return self.bert.relprop(cam, **kwargs)
-
-
-if __name__ == '__main__':
-    from transformers import BertTokenizer
-    import os
-
-    class Config:
-        def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, num_labels,
-                     hidden_dropout_prob):
-            self.hidden_size = hidden_size
-            self.num_attention_heads = num_attention_heads
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.num_labels = num_labels
-            self.hidden_dropout_prob = hidden_dropout_prob
-
-
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    x = tokenizer.encode_plus("In this movie the acting is great. The movie is perfect! [sep]",
-                         add_special_tokens=True,
-                         max_length=512,
-                         return_token_type_ids=False,
-                         return_attention_mask=True,
-                         pad_to_max_length=True,
-                         return_tensors='pt',
-                         truncation=True)
-
-    print(x['input_ids'])
-
-    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
-    model_save_file = os.path.join('./BERT_explainability/output_bert/movies/classifier/', 'classifier.pt')
-    model.load_state_dict(torch.load(model_save_file))
-
-    # x = torch.randint(100, (2, 20))
-    # x = torch.tensor([[101, 2054, 2003, 1996, 15792, 1997, 2023, 3319, 1029, 102,
-    #                    101, 4079, 102, 101, 6732, 102, 101, 2643, 102, 101,
-    #                    2038, 102, 101, 1037, 102, 101, 2933, 102, 101, 2005,
-    #                    102, 101, 2032, 102, 101, 1010, 102, 101, 1037, 102,
-    #                    101, 3800, 102, 101, 2005, 102, 101, 2010, 102, 101,
-    #                    2166, 102, 101, 1010, 102, 101, 1998, 102, 101, 2010,
-    #                    102, 101, 4650, 102, 101, 1010, 102, 101, 2002, 102,
-    #                    101, 2074, 102, 101, 2515, 102, 101, 1050, 102, 101,
-    #                    1005, 102, 101, 1056, 102, 101, 2113, 102, 101, 2054,
-    #                    102, 101, 1012, 102]])
-    # x.requires_grad_()
-
-    model.eval()
-
-    y = model(x['input_ids'], x['attention_mask'])
-    print(y)
-
-    cam, _ = model.relprop()
-
-    #print(cam.shape)
-
-    cam = cam.sum(-1)
-    #print(cam)
diff --git a/spaces/and-effect/Musterdatenkatalog/README.md b/spaces/and-effect/Musterdatenkatalog/README.md
deleted file mode 100644
index 351d6ef9bd0841c9376b6a714961bf193ca4b321..0000000000000000000000000000000000000000
--- a/spaces/and-effect/Musterdatenkatalog/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Musterdatenkatalog
-emoji: 👀
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/antonovmaxim/text-generation-webui-space/modules/shared.py b/spaces/antonovmaxim/text-generation-webui-space/modules/shared.py
deleted file mode 100644
index 7f945366cf7aca78b8a2a87b749964d038107f21..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/modules/shared.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import argparse
-import logging
-from collections import OrderedDict
-from pathlib import Path
-
-import yaml
-
-model = None
-tokenizer = None
-model_name = "None"
-model_type = None
-lora_names = []
-soft_prompt_tensor = None
-soft_prompt = False
-
-# Chat variables
-history = {'internal': [], 'visible': []}
-character = 'None'
-stop_everything = False
-processing_message = '*Is typing...*'
-
-# UI elements (buttons, sliders, HTML, etc)
-gradio = {}
-
-# For keeping the values of UI elements on page reload
-persistent_interface_state = {}
-
-input_params = []  # Generation input parameters
-reload_inputs = []  # Parameters for reloading the chat interface
-
-# For restarting the interface
-need_restart = False
-
-settings = {
-    'autoload_model': True,
-    'max_new_tokens': 200,
-    'max_new_tokens_min': 1,
-    'max_new_tokens_max': 2000,
-    'seed': -1,
-    'character': 'None',
-    'name1': 'You',
-    'name2': 'Assistant',
-    'context': 'This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.',
-    'greeting': '',
-    'turn_template': '',
-    'custom_stopping_strings': '',
-    'stop_at_newline': False,
-    'add_bos_token': True,
-    'ban_eos_token': False,
-    'skip_special_tokens': True,
-    'truncation_length': 2048,
-    'truncation_length_min': 0,
-    'truncation_length_max': 8192,
-    'mode': 'chat',
-    'chat_style': 'cai-chat',
-    'instruction_template': 'None',
-    'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
-    'chat_prompt_size': 2048,
-    'chat_prompt_size_min': 0,
-    'chat_prompt_size_max': 2048,
-    'chat_generation_attempts': 1,
-    'chat_generation_attempts_min': 1,
-    'chat_generation_attempts_max': 10,
-    'default_extensions': [],
-    'chat_default_extensions': ["gallery"],
-    'presets': {
-        'default': 'Default',
-        '.*(alpaca|llama|llava)': "LLaMA-Precise",
-        '.*pygmalion': 'NovelAI-Storywriter',
-        '.*RWKV': 'Naive',
-        '.*moss': 'MOSS',
-    },
-    'prompts': {
-        'default': 'QA',
-        '.*(gpt4chan|gpt-4chan|4chan)': 'GPT-4chan',
-    }
-}
-
-
-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
-    else:
-        raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
-
-# Basic settings
-parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.')
-parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.')
-parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.')
-parser.add_argument('--model', type=str, help='Name of the model to load by default.')
-parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
-parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models")
-parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras")
-parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.')
-parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.')
-parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.')
-parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
-parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
-
-# Accelerate/transformers
-parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
-parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
-parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.')
-parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.')
-parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
-parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".')
-parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
-parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
-parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.')
-parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.")
-parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.")
-parser.add_argument('--trust-remote-code', action='store_true', help="Set trust_remote_code=True while loading a model. Necessary for ChatGLM.")
-
-# llama.cpp
-parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.')
-parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.')
-parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.')
-parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.')
-parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.')
-parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.')
-
-# GPTQ
-parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
-parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
-parser.add_argument('--groupsize', type=int, default=-1, help='Group size.')
-parser.add_argument('--pre_layer', type=int, nargs="+", help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.')
-parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.')
-parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.')
-parser.add_argument('--quant_attn', action='store_true', help='(triton) Enable quant attention.')
-parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Enable warmup autotune.')
-parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.')
-
-# AutoGPTQ
-parser.add_argument('--autogptq', action='store_true', help='Use AutoGPTQ for loading quantized models instead of the internal GPTQ loader.')
-parser.add_argument('--triton', action='store_true', help='Use triton.')
-
-# FlexGen
-parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
-parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')
-parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.")
-parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).")
-
-# DeepSpeed
-parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
-parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
-parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
-
-# RWKV
-parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".')
-parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
-
-# Gradio
-parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
-parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
-parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
-parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
-parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
-parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None)
-
-# API
-parser.add_argument('--api', action='store_true', help='Enable the API extension.')
-parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.')
-parser.add_argument('--api-streaming-port', type=int,  default=5005, help='The listening port for the streaming API.')
-parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.')
-
-# Multimodal
-parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.')
-
-args = parser.parse_args()
-args_defaults = parser.parse_args([])
-
-# Deprecation warnings for parameters that have been renamed
-deprecated_dict = {}
-for k in deprecated_dict:
-    if getattr(args, k) != deprecated_dict[k][1]:
-        logging.warning(f"--{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
-        setattr(args, deprecated_dict[k][0], getattr(args, k))
-
-# Security warnings
-if args.trust_remote_code:
-    logging.warning("trust_remote_code is enabled. This is dangerous.")
-if args.share:
-    logging.warning("The gradio \"share link\" feature downloads a proprietary and unaudited blob to create a reverse tunnel. This is potentially dangerous.")
-
-
-def add_extension(name):
-    if args.extensions is None:
-        args.extensions = [name]
-    elif 'api' not in args.extensions:
-        args.extensions.append(name)
-
-
-# Activating the API extension
-if args.api or args.public_api:
-    add_extension('api')
-
-# Activating the multimodal extension
-if args.multimodal_pipeline is not None:
-    add_extension('multimodal')
-
-
-def is_chat():
-    return args.chat
-
-
-# Loading model-specific settings
-with Path(f'{args.model_dir}/config.yaml') as p:
-    if p.exists():
-        model_config = yaml.safe_load(open(p, 'r').read())
-    else:
-        model_config = {}
-
-# Applying user-defined model settings
-with Path(f'{args.model_dir}/config-user.yaml') as p:
-    if p.exists():
-        user_config = yaml.safe_load(open(p, 'r').read())
-        for k in user_config:
-            if k in model_config:
-                model_config[k].update(user_config[k])
-            else:
-                model_config[k] = user_config[k]
-
-model_config = OrderedDict(model_config)
diff --git a/spaces/anurag629/botaniscan/app/models/getModel.py b/spaces/anurag629/botaniscan/app/models/getModel.py
deleted file mode 100644
index e8eb601f4829fcfe55d434d9d4e1eb72f8494f09..0000000000000000000000000000000000000000
--- a/spaces/anurag629/botaniscan/app/models/getModel.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from transformers import pipeline
-
-pipe = pipeline("image-classification", "anurag629/swin-tiny-patch4-window7-224-finetuned-eurosat")
-
-def getPrediction(image):
-    '''
-    Returns the prediction of the image.
-    '''
-    return pipe(image)
\ No newline at end of file
diff --git a/spaces/anzorq/zedzek/app.py b/spaces/anzorq/zedzek/app.py
deleted file mode 100644
index a268af0e51c6d31ebe365bb3f8f9b120d565a34c..0000000000000000000000000000000000000000
--- a/spaces/anzorq/zedzek/app.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import gradio as gr
-
-############### VANILLA INFERENCE ###############
-# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-# model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
-# src_lang="ru" 
-# tgt_lang="zu"
-
-# # tokenizer = AutoTokenizer.from_pretrained(model_path, src_lang=src_lang)
-# tokenizer = AutoTokenizer.from_pretrained(model_path)
-# model = AutoModelForSeq2SeqLM.from_pretrained(model_path, use_safetensors=True)#, load_in_4bit=True, device_map="auto")
-# model.to_bettertransformer()
-
-# def translate(text, num_beams=4, num_return_sequences=4):
-#   inputs = tokenizer(text, return_tensors="pt")
-
-#   num_return_sequences = min(num_return_sequences, num_beams)
-
-#   translated_tokens = model.generate(
-#       **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=num_beams, num_return_sequences=num_return_sequences
-#   )
-
-#   translations = []
-#   for translation in tokenizer.batch_decode(translated_tokens, skip_special_tokens=True):
-#       translations.append(translation)
-
-#   # result = {"input":text, "translations":translations}
-#   return text, translations
-
-
-############### IPEX OPTIMIZED INFERENCE ###############
-# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-# from optimum.bettertransformer import BetterTransformer
-# import intel_extension_for_pytorch as ipex
-# from transformers.modeling_outputs import BaseModelOutput
-# import torch
-
-# model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"
-# src_lang = "ru"
-# tgt_lang = "zu"
-
-# tokenizer = AutoTokenizer.from_pretrained(model_path)
-# model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
-
-# # flash attention optimization
-# model = BetterTransformer.transform(model, keep_original_model=False)
-
-# # ipex optimization
-# model.eval()
-# model = ipex.optimize(model, dtype=torch.float, level="O1", conv_bn_folding=False, inplace=True)
-
-# # Get the encoder
-# encoder = model.get_encoder()
-
-# # Prepare an example input for the encoder
-# example_input_text = "Example text in Russian"
-# inputs_example = tokenizer(example_input_text, return_tensors="pt")
-
-# # Trace just the encoder with strict=False
-# scripted_encoder = torch.jit.trace(encoder, inputs_example['input_ids'], strict=False)
-
-# def translate(text, num_beams=4, num_return_sequences=4):
-#     inputs = tokenizer(text, return_tensors="pt")
-#     num_return_sequences = min(num_return_sequences, num_beams)
-
-#     # Use the scripted encoder for the first step of inference
-#     encoder_output_dict = scripted_encoder(inputs['input_ids'])
-#     encoder_outputs = BaseModelOutput(last_hidden_state=encoder_output_dict['last_hidden_state'])
-
-#     # Use the original, untraced model for the second step, passing the encoder's outputs as inputs
-#     translated_tokens = model.generate(
-#         encoder_outputs=encoder_outputs,
-#         forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], 
-#         num_beams=num_beams, 
-#         num_return_sequences=num_return_sequences
-#     )
-
-#     translations = [tokenizer.decode(translation, skip_special_tokens=True) for translation in translated_tokens]
-#     return text, translations
-
-# ############### ONNX MODEL INFERENCE ###############
-# from transformers import AutoTokenizer, pipeline
-# from optimum.onnxruntime import ORTModelForSeq2SeqLM
-
-# model_id = "anzorq/m2m100_418M_ft_ru-kbd_44K"
-
-# model = ORTModelForSeq2SeqLM.from_pretrained(model_id, subfolder="onnx", file_name="encoder_model_optimized.onnx")
-# tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-# def translate(text, num_beams=4, num_return_sequences=4):
-#   inputs = tokenizer(text, return_tensors="pt")
-
-#   num_return_sequences = min(num_return_sequences, num_beams)
-
-#   translated_tokens = model.generate(
-#       **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["zu"], num_beams=num_beams, num_return_sequences=num_return_sequences
-#   )
-
-#   translations = []
-#   for translation in tokenizer.batch_decode(translated_tokens, skip_special_tokens=True):
-#       translations.append(translation)
-
-#   return text, translations
-
-
-############### CTRANSLATE2 INFERENCE ###############
-import ctranslate2
-import transformers
-
-translator = ctranslate2.Translator("ctranslate2")
-tokenizer = transformers.AutoTokenizer.from_pretrained("anzorq/m2m100_418M_ft_ru-kbd_44K")
-
-def translate(text, num_beams=4, num_return_sequences=4):
-
-    num_return_sequences = min(num_return_sequences, num_beams)
-
-    source = tokenizer.convert_ids_to_tokens(tokenizer.encode(text))
-    target_prefix = [tokenizer.lang_code_to_token["zu"]]
-    results = translator.translate_batch(
-        [source],
-        target_prefix=[target_prefix],
-        beam_size=num_beams,
-        num_hypotheses=num_return_sequences
-    )
-    
-    translations = []
-    for hypothesis in results[0].hypotheses:
-        target = hypothesis[1:]
-        decoded_sentence = tokenizer.decode(tokenizer.convert_tokens_to_ids(target))
-        translations.append(decoded_sentence)
-    
-    return text, translations
-
-output = gr.Textbox()
-# with gr.Accordion("Advanced Options"):
-num_beams = gr.inputs.Slider(2, 10, step=1, label="Number of beams", default=4)
-num_return_sequences = gr.inputs.Slider(2, 10, step=1, label="Number of returned sentences", default=4)
-
-
-title = "Russian-Circassian translator demo"
-article = "<p style='text-align: center'>Want to help? Join the <a href='https://discord.gg/cXwv495r' target='_blank'>Discord server</a></p>"
-
-# examples = [
-#     ["Мы идем домой"],
-#     ["Сегодня хорошая погода"],
-#     ["Дети играют во дворе"],
-#     ["We live in a big house"],
-#     ["Tu es une bonne personne."],
-#     ["أين تعيش؟"],
-#     ["Bir şeyler yapmak istiyorum."],
-#     ["– Если я его отпущу, то ты вовек не сможешь его поймать, – заявил Сосруко."],
-#     ["Как только старик ушел, Сатаней пошла к Саусырыко."],
-#     ["我永远不会放弃你。"],
-#     ["우리는 소치에 살고 있습니다."],
-# ]
-
-gr.Interface(
-  fn=translate,
-  inputs=["text", num_beams, num_return_sequences],
-  outputs=["text", output],
-  title=title,
-  # examples=examples,
-  article=article).launch()
-
-# import gradio as gr
-
-# title = "Русско-черкесский переводчик"
-# description = "Demo of a Russian-Circassian (Kabardian dialect) translator. <br>It is based on Facebook's <a href=\"https://about.fb.com/news/2020/10/first-multilingual-machine-translation-model/\">M2M-100 model</a> machine learning model, and has been trained on 45,000 Russian-Circassian sentence pairs. <br>It can also translate from 100 other languages to Circassian (English, French, Spanish, etc.), but less accurately. <br>The data corpus is constantly being expanded, and we need help in finding sentence sources, OCR, data cleaning, etc. <br>If you are interested in helping out with this project, please contact me at the link below.<br><br>This is only a demo, not a finished product. Translation quality is still low and will improve with time and more data.<br>45,000 sentence pairs is not enough to create an accurate machine translation model, and more data is needed.<br>You can help by finding sentence sources (books, web pages, etc.), scanning books, OCRing documents, data cleaning, and other tasks.<br><br>If you are interested in helping out with this project, contact me at the link below."
-# article = """<p style='text-align: center'><a href='https://arxiv.org/abs/1806.00187'>Scaling Neural Machine Translation</a> | <a href='https://github.com/pytorch/fairseq/'>Github Repo</a></p>"""
-
-# examples = [
-#     ["Мы идем домой"],
-#     ["Сегодня хорошая погода"],
-#     ["Дети играют во дворе"],
-#     ["We live in a big house"],
-#     ["Tu es une bonne personne."],
-#     ["أين تعيش؟"],
-#     ["Bir şeyler yapmak istiyorum."],
-# ]
-
-# gr.Interface.load("models/anzorq/m2m100_418M_ft_ru-kbd_44K", title=title, description=description, article=article, examples=examples).launch()
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/sd_samplers_common.py b/spaces/aodianyun/stable-diffusion-webui/modules/sd_samplers_common.py
deleted file mode 100644
index c0709f6260ba776c82232abbd7142608840cac88..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/sd_samplers_common.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from collections import namedtuple
-import numpy as np
-import torch
-from PIL import Image
-from modules import devices, processing, images, sd_vae_approx
-
-from modules.shared import opts, state
-import modules.shared as shared
-
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-
-
-def setup_img2img_steps(p, steps=None):
-    if opts.img2img_fix_steps or steps is not None:
-        requested_steps = (steps or p.steps)
-        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
-        t_enc = requested_steps - 1
-    else:
-        steps = p.steps
-        t_enc = int(min(p.denoising_strength, 0.999) * steps)
-
-    return steps, t_enc
-
-
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
-
-
-def single_sample_to_image(sample, approximation=None):
-    if approximation is None:
-        approximation = approximation_indexes.get(opts.show_progress_type, 0)
-
-    if approximation == 2:
-        x_sample = sd_vae_approx.cheap_approximation(sample)
-    elif approximation == 1:
-        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
-    else:
-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
-    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-    x_sample = x_sample.astype(np.uint8)
-    return Image.fromarray(x_sample)
-
-
-def sample_to_image(samples, index=0, approximation=None):
-    return single_sample_to_image(samples[index], approximation)
-
-
-def samples_to_image_grid(samples, approximation=None):
-    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
-
-
-def store_latent(decoded):
-    state.current_latent = decoded
-
-    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
-        if not shared.parallel_processing_allowed:
-            shared.state.assign_current_image(sample_to_image(decoded))
-
-
-class InterruptedException(BaseException):
-    pass
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Random/random.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Random/random.py
deleted file mode 100644
index 5389b3bbe16605fa3dd0630d81f4a78b6830b1e8..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Random/random.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  Random/random.py : Strong alternative for the standard 'random' module
-#
-# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net>
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# 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.
-# ===================================================================
-
-__all__ = ['StrongRandom', 'getrandbits', 'randrange', 'randint', 'choice', 'shuffle', 'sample']
-
-from Crypto import Random
-
-from Crypto.Util.py3compat import is_native_int
-
-class StrongRandom(object):
-    def __init__(self, rng=None, randfunc=None):
-        if randfunc is None and rng is None:
-            self._randfunc = None
-        elif randfunc is not None and rng is None:
-            self._randfunc = randfunc
-        elif randfunc is None and rng is not None:
-            self._randfunc = rng.read
-        else:
-            raise ValueError("Cannot specify both 'rng' and 'randfunc'")
-
-    def getrandbits(self, k):
-        """Return an integer with k random bits."""
-
-        if self._randfunc is None:
-            self._randfunc = Random.new().read
-        mask = (1 << k) - 1
-        return mask & bytes_to_long(self._randfunc(ceil_div(k, 8)))
-
-    def randrange(self, *args):
-        """randrange([start,] stop[, step]):
-        Return a randomly-selected element from range(start, stop, step)."""
-        if len(args) == 3:
-            (start, stop, step) = args
-        elif len(args) == 2:
-            (start, stop) = args
-            step = 1
-        elif len(args) == 1:
-            (stop,) = args
-            start = 0
-            step = 1
-        else:
-            raise TypeError("randrange expected at most 3 arguments, got %d" % (len(args),))
-        if (not is_native_int(start) or not is_native_int(stop) or not
-                is_native_int(step)):
-            raise TypeError("randrange requires integer arguments")
-        if step == 0:
-            raise ValueError("randrange step argument must not be zero")
-
-        num_choices = ceil_div(stop - start, step)
-        if num_choices < 0:
-            num_choices = 0
-        if num_choices < 1:
-            raise ValueError("empty range for randrange(%r, %r, %r)" % (start, stop, step))
-
-        # Pick a random number in the range of possible numbers
-        r = num_choices
-        while r >= num_choices:
-            r = self.getrandbits(size(num_choices))
-
-        return start + (step * r)
-
-    def randint(self, a, b):
-        """Return a random integer N such that a <= N <= b."""
-        if not is_native_int(a) or not is_native_int(b):
-            raise TypeError("randint requires integer arguments")
-        N = self.randrange(a, b+1)
-        assert a <= N <= b
-        return N
-
-    def choice(self, seq):
-        """Return a random element from a (non-empty) sequence.
-
-        If the seqence is empty, raises IndexError.
-        """
-        if len(seq) == 0:
-            raise IndexError("empty sequence")
-        return seq[self.randrange(len(seq))]
-
-    def shuffle(self, x):
-        """Shuffle the sequence in place."""
-        # Fisher-Yates shuffle.  O(n)
-        # See http://en.wikipedia.org/wiki/Fisher-Yates_shuffle
-        # Working backwards from the end of the array, we choose a random item
-        # from the remaining items until all items have been chosen.
-        for i in range(len(x)-1, 0, -1):   # iterate from len(x)-1 downto 1
-            j = self.randrange(0, i+1)      # choose random j such that 0 <= j <= i
-            x[i], x[j] = x[j], x[i]         # exchange x[i] and x[j]
-
-    def sample(self, population, k):
-        """Return a k-length list of unique elements chosen from the population sequence."""
-
-        num_choices = len(population)
-        if k > num_choices:
-            raise ValueError("sample larger than population")
-
-        retval = []
-        selected = {}  # we emulate a set using a dict here
-        for i in range(k):
-            r = None
-            while r is None or r in selected:
-                r = self.randrange(num_choices)
-            retval.append(population[r])
-            selected[r] = 1
-        return retval
-
-_r = StrongRandom()
-getrandbits = _r.getrandbits
-randrange = _r.randrange
-randint = _r.randint
-choice = _r.choice
-shuffle = _r.shuffle
-sample = _r.sample
-
-# These are at the bottom to avoid problems with recursive imports
-from Crypto.Util.number import ceil_div, bytes_to_long, long_to_bytes, size
-
-# vim:set ts=4 sw=4 sts=4 expandtab:
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/_sounddevice.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/_sounddevice.py
deleted file mode 100644
index b1e9f8c3443b040a2c75c09969bcad88cb527e02..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/_sounddevice.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# auto-generated file
-import _cffi_backend
-
-ffi = _cffi_backend.FFI('_sounddevice',
-    _version = 0x2601,
-    _types = b'\x00\x00\x76\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x79\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x1C\x0D\x00\x00\x8D\x03\x00\x00\x00\x0F\x00\x00\x7B\x0D\x00\x00\x00\x0F\x00\x00\x80\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x88\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x88\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x01\x01\x00\x00\x00\x0F\x00\x00\x88\x0D\x00\x00\x00\x0F\x00\x00\x21\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x01\x0B\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x82\x03\x00\x00\x1F\x11\x00\x00\x0E\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x03\x00\x00\x1F\x11\x00\x00\x1F\x11\x00\x00\x0E\x01\x00\x00\x0A\x01\x00\x00\x0A\x01\x00\x00\x52\x03\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x2E\x11\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x0A\x01\x00\x00\x0E\x01\x00\x00\x0A\x01\x00\x00\x34\x11\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x07\x11\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x8D\x03\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x6B\x03\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x4B\x11\x00\x00\x07\x11\x00\x00\x0A\x01\x00\x00\x7F\x03\x00\x00\x0A\x01\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x00\x0F\x00\x00\x69\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x7D\x03\x00\x00\x8B\x03\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x60\x11\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x09\x01\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x07\x11\x00\x00\x09\x01\x00\x00\x07\x11\x00\x00\x09\x01\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x09\x00\x00\x77\x03\x00\x00\x02\x09\x00\x00\x00\x0B\x00\x00\x7A\x03\x00\x00\x03\x09\x00\x00\x7C\x03\x00\x00\x04\x09\x00\x00\x00\x09\x00\x00\x02\x0B\x00\x00\x05\x09\x00\x00\x81\x03\x00\x00\x06\x09\x00\x00\x07\x09\x00\x00\x03\x0B\x00\x00\x04\x0B\x00\x00\x08\x09\x00\x00\x05\x0B\x00\x00\x06\x0B\x00\x00\x89\x03\x00\x00\x02\x01\x00\x00\x01\x03\x00\x00\x15\x01\x00\x00\x6E\x03\x00\x00\x00\x01',
-    _globals = (b'\x00\x00\x11\x23PaMacCore_GetChannelName',0,b'\x00\x00\x5F\x23PaMacCore_SetupChannelMap',0,b'\x00\x00\x64\x23PaMacCore_SetupStreamInfo',0,b'\x00\x00\x23\x23PaWasapi_IsLoopback',0,b'\x00\x00\x41\x23Pa_AbortStream',0,b'\x00\x00\x41\x23Pa_CloseStream',0,b'\x00\x00\x5A\x23Pa_GetDefaultHostApi',0,b'\x00\x00\x5A\x23Pa_GetDefaultInputDevice',0,b'\x00\x00\x5A\x23Pa_GetDefaultOutputDevice',0,b'\x00\x00\x5A\x23Pa_GetDeviceCount',0,b'\x00\x00\x00\x23Pa_GetDeviceInfo',0,b'\x00\x00\x0E\x23Pa_GetErrorText',0,b'\x00\x00\x5A\x23Pa_GetHostApiCount',0,b'\x00\x00\x03\x23Pa_GetHostApiInfo',0,b'\x00\x00\x09\x23Pa_GetLastHostErrorInfo',0,b'\x00\x00\x2A\x23Pa_GetSampleSize',0,b'\x00\x00\x18\x23Pa_GetStreamCpuLoad',0,b'\x00\x00\x06\x23Pa_GetStreamHostApiType',0,b'\x00\x00\x0B\x23Pa_GetStreamInfo',0,b'\x00\x00\x5C\x23Pa_GetStreamReadAvailable',0,b'\x00\x00\x18\x23Pa_GetStreamTime',0,b'\x00\x00\x5C\x23Pa_GetStreamWriteAvailable',0,b'\x00\x00\x5A\x23Pa_GetVersion',0,b'\x00\x00\x16\x23Pa_GetVersionText',0,b'\x00\x00\x26\x23Pa_HostApiDeviceIndexToDeviceIndex',0,b'\x00\x00\x1B\x23Pa_HostApiTypeIdToHostApiIndex',0,b'\x00\x00\x5A\x23Pa_Initialize',0,b'\x00\x00\x1E\x23Pa_IsFormatSupported',0,b'\x00\x00\x41\x23Pa_IsStreamActive',0,b'\x00\x00\x41\x23Pa_IsStreamStopped',0,b'\x00\x00\x37\x23Pa_OpenDefaultStream',0,b'\x00\x00\x2D\x23Pa_OpenStream',0,b'\x00\x00\x44\x23Pa_ReadStream',0,b'\x00\x00\x4E\x23Pa_SetStreamFinishedCallback',0,b'\x00\x00\x68\x23Pa_Sleep',0,b'\x00\x00\x41\x23Pa_StartStream',0,b'\x00\x00\x41\x23Pa_StopStream',0,b'\x00\x00\x5A\x23Pa_Terminate',0,b'\x00\x00\x49\x23Pa_WriteStream',0,b'\xFF\xFF\xFF\x0BeAudioCategoryAlerts',4,b'\xFF\xFF\xFF\x0BeAudioCategoryCommunications',3,b'\xFF\xFF\xFF\x0BeAudioCategoryGameChat',8,b'\xFF\xFF\xFF\x0BeAudioCategoryGameEffects',6,b'\xFF\xFF\xFF\x0BeAudioCategoryGameMedia',7,b'\xFF\xFF\xFF\x0BeAudioCategoryMedia',11,b'\xFF\xFF\xFF\x0BeAudioCategoryMovie',10,b'\xFF\xFF\xFF\x0BeAudioCategoryOther',0,b'\xFF\xFF\xFF\x0BeAudioCategorySoundEffects',5,b'\xFF\xFF\xFF\x0BeAudioCategorySpeech',9,b'\xFF\xFF\xFF\x0BeStreamOptionMatchFormat',2,b'\xFF\xFF\xFF\x0BeStreamOptionNone',0,b'\xFF\xFF\xFF\x0BeStreamOptionRaw',1,b'\xFF\xFF\xFF\x0BeThreadPriorityAudio',1,b'\xFF\xFF\xFF\x0BeThreadPriorityCapture',2,b'\xFF\xFF\xFF\x0BeThreadPriorityDistribution',3,b'\xFF\xFF\xFF\x0BeThreadPriorityGames',4,b'\xFF\xFF\xFF\x0BeThreadPriorityNone',0,b'\xFF\xFF\xFF\x0BeThreadPriorityPlayback',5,b'\xFF\xFF\xFF\x0BeThreadPriorityProAudio',6,b'\xFF\xFF\xFF\x0BeThreadPriorityWindowManager',7,b'\xFF\xFF\xFF\x0BpaAL',9,b'\xFF\xFF\xFF\x0BpaALSA',8,b'\xFF\xFF\xFF\x0BpaASIO',3,b'\xFF\xFF\xFF\x0BpaAbort',2,b'\xFF\xFF\xFF\x1FpaAsioUseChannelSelectors',1,b'\xFF\xFF\xFF\x0BpaAudioScienceHPI',14,b'\xFF\xFF\xFF\x0BpaBadBufferPtr',-9972,b'\xFF\xFF\xFF\x0BpaBadIODeviceCombination',-9993,b'\xFF\xFF\xFF\x0BpaBadStreamPtr',-9988,b'\xFF\xFF\xFF\x0BpaBeOS',10,b'\xFF\xFF\xFF\x0BpaBufferTooBig',-9991,b'\xFF\xFF\xFF\x0BpaBufferTooSmall',-9990,b'\xFF\xFF\xFF\x0BpaCanNotReadFromACallbackStream',-9977,b'\xFF\xFF\xFF\x0BpaCanNotReadFromAnOutputOnlyStream',-9975,b'\xFF\xFF\xFF\x0BpaCanNotWriteToACallbackStream',-9976,b'\xFF\xFF\xFF\x0BpaCanNotWriteToAnInputOnlyStream',-9974,b'\xFF\xFF\xFF\x1FpaClipOff',1,b'\xFF\xFF\xFF\x0BpaComplete',1,b'\xFF\xFF\xFF\x0BpaContinue',0,b'\xFF\xFF\xFF\x0BpaCoreAudio',5,b'\xFF\xFF\xFF\x1FpaCustomFormat',65536,b'\xFF\xFF\xFF\x0BpaDeviceUnavailable',-9985,b'\xFF\xFF\xFF\x0BpaDirectSound',1,b'\xFF\xFF\xFF\x1FpaDitherOff',2,b'\xFF\xFF\xFF\x1FpaFloat32',1,b'\xFF\xFF\xFF\x1FpaFormatIsSupported',0,b'\xFF\xFF\xFF\x1FpaFramesPerBufferUnspecified',0,b'\xFF\xFF\xFF\x0BpaHostApiNotFound',-9979,b'\xFF\xFF\xFF\x0BpaInDevelopment',0,b'\xFF\xFF\xFF\x0BpaIncompatibleHostApiSpecificStreamInfo',-9984,b'\xFF\xFF\xFF\x0BpaIncompatibleStreamHostApi',-9973,b'\xFF\xFF\xFF\x1FpaInputOverflow',2,b'\xFF\xFF\xFF\x0BpaInputOverflowed',-9981,b'\xFF\xFF\xFF\x1FpaInputUnderflow',1,b'\xFF\xFF\xFF\x0BpaInsufficientMemory',-9992,b'\xFF\xFF\xFF\x1FpaInt16',8,b'\xFF\xFF\xFF\x1FpaInt24',4,b'\xFF\xFF\xFF\x1FpaInt32',2,b'\xFF\xFF\xFF\x1FpaInt8',16,b'\xFF\xFF\xFF\x0BpaInternalError',-9986,b'\xFF\xFF\xFF\x0BpaInvalidChannelCount',-9998,b'\xFF\xFF\xFF\x0BpaInvalidDevice',-9996,b'\xFF\xFF\xFF\x0BpaInvalidFlag',-9995,b'\xFF\xFF\xFF\x0BpaInvalidHostApi',-9978,b'\xFF\xFF\xFF\x0BpaInvalidSampleRate',-9997,b'\xFF\xFF\xFF\x0BpaJACK',12,b'\xFF\xFF\xFF\x0BpaMME',2,b'\xFF\xFF\xFF\x1FpaMacCoreChangeDeviceParameters',1,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityHigh',1024,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityLow',768,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMax',0,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMedium',512,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMin',256,b'\xFF\xFF\xFF\x1FpaMacCoreFailIfConversionRequired',2,b'\xFF\xFF\xFF\x1FpaMacCoreMinimizeCPU',257,b'\xFF\xFF\xFF\x1FpaMacCoreMinimizeCPUButPlayNice',256,b'\xFF\xFF\xFF\x1FpaMacCorePlayNice',0,b'\xFF\xFF\xFF\x1FpaMacCorePro',1,b'\xFF\xFF\xFF\x1FpaNeverDropInput',4,b'\xFF\xFF\xFF\x1FpaNoDevice',-1,b'\xFF\xFF\xFF\x0BpaNoError',0,b'\xFF\xFF\xFF\x1FpaNoFlag',0,b'\xFF\xFF\xFF\x1FpaNonInterleaved',2147483648,b'\xFF\xFF\xFF\x0BpaNotInitialized',-10000,b'\xFF\xFF\xFF\x0BpaNullCallback',-9989,b'\xFF\xFF\xFF\x0BpaOSS',7,b'\xFF\xFF\xFF\x1FpaOutputOverflow',8,b'\xFF\xFF\xFF\x1FpaOutputUnderflow',4,b'\xFF\xFF\xFF\x0BpaOutputUnderflowed',-9980,b'\xFF\xFF\xFF\x1FpaPlatformSpecificFlags',4294901760,b'\xFF\xFF\xFF\x1FpaPrimeOutputBuffersUsingStreamCallback',8,b'\xFF\xFF\xFF\x1FpaPrimingOutput',16,b'\xFF\xFF\xFF\x0BpaSampleFormatNotSupported',-9994,b'\xFF\xFF\xFF\x0BpaSoundManager',4,b'\xFF\xFF\xFF\x0BpaStreamIsNotStopped',-9982,b'\xFF\xFF\xFF\x0BpaStreamIsStopped',-9983,b'\xFF\xFF\xFF\x0BpaTimedOut',-9987,b'\xFF\xFF\xFF\x1FpaUInt8',32,b'\xFF\xFF\xFF\x0BpaUnanticipatedHostError',-9999,b'\xFF\xFF\xFF\x1FpaUseHostApiSpecificDeviceSpecification',-2,b'\xFF\xFF\xFF\x0BpaWASAPI',13,b'\xFF\xFF\xFF\x0BpaWDMKS',11,b'\xFF\xFF\xFF\x0BpaWinWasapiExclusive',1,b'\xFF\xFF\xFF\x0BpaWinWasapiPolling',8,b'\xFF\xFF\xFF\x0BpaWinWasapiRedirectHostProcessor',2,b'\xFF\xFF\xFF\x0BpaWinWasapiThreadPriority',16,b'\xFF\xFF\xFF\x0BpaWinWasapiUseChannelMask',4),
-    _struct_unions = ((b'\x00\x00\x00\x7D\x00\x00\x00\x02$PaMacCoreStreamInfo',b'\x00\x00\x2B\x11size',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x2B\x11version',b'\x00\x00\x2B\x11flags',b'\x00\x00\x61\x11channelMap',b'\x00\x00\x2B\x11channelMapSize'),(b'\x00\x00\x00\x75\x00\x00\x00\x02PaAsioStreamInfo',b'\x00\x00\x2B\x11size',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x2B\x11version',b'\x00\x00\x2B\x11flags',b'\x00\x00\x8A\x11channelSelectors'),(b'\x00\x00\x00\x77\x00\x00\x00\x02PaDeviceInfo',b'\x00\x00\x01\x11structVersion',b'\x00\x00\x88\x11name',b'\x00\x00\x01\x11hostApi',b'\x00\x00\x01\x11maxInputChannels',b'\x00\x00\x01\x11maxOutputChannels',b'\x00\x00\x21\x11defaultLowInputLatency',b'\x00\x00\x21\x11defaultLowOutputLatency',b'\x00\x00\x21\x11defaultHighInputLatency',b'\x00\x00\x21\x11defaultHighOutputLatency',b'\x00\x00\x21\x11defaultSampleRate'),(b'\x00\x00\x00\x7A\x00\x00\x00\x02PaHostApiInfo',b'\x00\x00\x01\x11structVersion',b'\x00\x00\x1C\x11type',b'\x00\x00\x88\x11name',b'\x00\x00\x01\x11deviceCount',b'\x00\x00\x01\x11defaultInputDevice',b'\x00\x00\x01\x11defaultOutputDevice'),(b'\x00\x00\x00\x7C\x00\x00\x00\x02PaHostErrorInfo',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x69\x11errorCode',b'\x00\x00\x88\x11errorText'),(b'\x00\x00\x00\x7F\x00\x00\x00\x02PaStreamCallbackTimeInfo',b'\x00\x00\x21\x11inputBufferAdcTime',b'\x00\x00\x21\x11currentTime',b'\x00\x00\x21\x11outputBufferDacTime'),(b'\x00\x00\x00\x81\x00\x00\x00\x02PaStreamInfo',b'\x00\x00\x01\x11structVersion',b'\x00\x00\x21\x11inputLatency',b'\x00\x00\x21\x11outputLatency',b'\x00\x00\x21\x11sampleRate'),(b'\x00\x00\x00\x82\x00\x00\x00\x02PaStreamParameters',b'\x00\x00\x01\x11device',b'\x00\x00\x01\x11channelCount',b'\x00\x00\x2B\x11sampleFormat',b'\x00\x00\x21\x11suggestedLatency',b'\x00\x00\x07\x11hostApiSpecificStreamInfo'),(b'\x00\x00\x00\x85\x00\x00\x00\x02PaWasapiStreamInfo',b'\x00\x00\x2B\x11size',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x2B\x11version',b'\x00\x00\x2B\x11flags',b'\x00\x00\x2B\x11channelMask',b'\x00\x00\x8C\x11hostProcessorOutput',b'\x00\x00\x8C\x11hostProcessorInput',b'\x00\x00\x87\x11threadPriority',b'\x00\x00\x84\x11streamCategory',b'\x00\x00\x86\x11streamOption')),
-    _enums = (b'\x00\x00\x00\x78\x00\x00\x00\x15PaErrorCode\x00paNoError,paNotInitialized,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',b'\x00\x00\x00\x1C\x00\x00\x00\x16PaHostApiTypeId\x00paInDevelopment,paDirectSound,paMME,paASIO,paSoundManager,paCoreAudio,paOSS,paALSA,paAL,paBeOS,paWDMKS,paJACK,paWASAPI,paAudioScienceHPI',b'\x00\x00\x00\x7E\x00\x00\x00\x16PaStreamCallbackResult\x00paContinue,paComplete,paAbort',b'\x00\x00\x00\x83\x00\x00\x00\x16PaWasapiFlags\x00paWinWasapiExclusive,paWinWasapiRedirectHostProcessor,paWinWasapiUseChannelMask,paWinWasapiPolling,paWinWasapiThreadPriority',b'\x00\x00\x00\x84\x00\x00\x00\x16PaWasapiStreamCategory\x00eAudioCategoryOther,eAudioCategoryCommunications,eAudioCategoryAlerts,eAudioCategorySoundEffects,eAudioCategoryGameEffects,eAudioCategoryGameMedia,eAudioCategoryGameChat,eAudioCategorySpeech,eAudioCategoryMovie,eAudioCategoryMedia',b'\x00\x00\x00\x86\x00\x00\x00\x16PaWasapiStreamOption\x00eStreamOptionNone,eStreamOptionRaw,eStreamOptionMatchFormat',b'\x00\x00\x00\x87\x00\x00\x00\x16PaWasapiThreadPriority\x00eThreadPriorityNone,eThreadPriorityAudio,eThreadPriorityCapture,eThreadPriorityDistribution,eThreadPriorityGames,eThreadPriorityPlayback,eThreadPriorityProAudio,eThreadPriorityWindowManager'),
-    _typenames = (b'\x00\x00\x00\x75PaAsioStreamInfo',b'\x00\x00\x00\x01PaDeviceIndex',b'\x00\x00\x00\x77PaDeviceInfo',b'\x00\x00\x00\x01PaError',b'\x00\x00\x00\x78PaErrorCode',b'\x00\x00\x00\x01PaHostApiIndex',b'\x00\x00\x00\x7APaHostApiInfo',b'\x00\x00\x00\x1CPaHostApiTypeId',b'\x00\x00\x00\x7CPaHostErrorInfo',b'\x00\x00\x00\x7DPaMacCoreStreamInfo',b'\x00\x00\x00\x2BPaSampleFormat',b'\x00\x00\x00\x8DPaStream',b'\x00\x00\x00\x52PaStreamCallback',b'\x00\x00\x00\x2BPaStreamCallbackFlags',b'\x00\x00\x00\x7EPaStreamCallbackResult',b'\x00\x00\x00\x7FPaStreamCallbackTimeInfo',b'\x00\x00\x00\x6BPaStreamFinishedCallback',b'\x00\x00\x00\x2BPaStreamFlags',b'\x00\x00\x00\x81PaStreamInfo',b'\x00\x00\x00\x82PaStreamParameters',b'\x00\x00\x00\x21PaTime',b'\x00\x00\x00\x83PaWasapiFlags',b'\x00\x00\x00\x8CPaWasapiHostProcessorCallback',b'\x00\x00\x00\x84PaWasapiStreamCategory',b'\x00\x00\x00\x85PaWasapiStreamInfo',b'\x00\x00\x00\x86PaWasapiStreamOption',b'\x00\x00\x00\x87PaWasapiThreadPriority',b'\x00\x00\x00\x2BPaWinWaveFormatChannelMask',b'\x00\x00\x00\x8BSInt32'),
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/processors/models/s3dg.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/processors/models/s3dg.py
deleted file mode 100644
index 6c7a691e33551807ef258a54282884a409408691..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/processors/models/s3dg.py
+++ /dev/null
@@ -1,336 +0,0 @@
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""Contains a PyTorch definition for Gated Separable 3D network (S3D-G)
-with a text module for computing joint text-video embedding from raw text
-and video input. The following code will enable you to load the HowTo100M
-pretrained S3D Text-Video model from:
-  A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic and A. Zisserman,
-  End-to-End Learning of Visual Representations from Uncurated Instructional Videos.
-  https://arxiv.org/abs/1912.06430.
-
-S3D-G was proposed by:
-  S. Xie, C. Sun, J. Huang, Z. Tu and K. Murphy,
-  Rethinking Spatiotemporal Feature Learning For Video Understanding.
-  https://arxiv.org/abs/1712.04851.
-  Tensorflow code: https://github.com/tensorflow/models/blob/master/research/slim/nets/s3dg.py
-
-The S3D architecture was slightly modified with a space to depth trick for TPU
-optimization.
-"""
-
-import torch as th
-import torch.nn.functional as F
-import torch.nn as nn
-import os
-import numpy as np
-import re
-
-
-class InceptionBlock(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        num_outputs_0_0a,
-        num_outputs_1_0a,
-        num_outputs_1_0b,
-        num_outputs_2_0a,
-        num_outputs_2_0b,
-        num_outputs_3_0b,
-        gating=True,
-    ):
-        super(InceptionBlock, self).__init__()
-        self.conv_b0 = STConv3D(input_dim, num_outputs_0_0a, [1, 1, 1])
-        self.conv_b1_a = STConv3D(input_dim, num_outputs_1_0a, [1, 1, 1])
-        self.conv_b1_b = STConv3D(
-            num_outputs_1_0a, num_outputs_1_0b, [3, 3, 3], padding=1, separable=True
-        )
-        self.conv_b2_a = STConv3D(input_dim, num_outputs_2_0a, [1, 1, 1])
-        self.conv_b2_b = STConv3D(
-            num_outputs_2_0a, num_outputs_2_0b, [3, 3, 3], padding=1, separable=True
-        )
-        self.maxpool_b3 = th.nn.MaxPool3d((3, 3, 3), stride=1, padding=1)
-        self.conv_b3_b = STConv3D(input_dim, num_outputs_3_0b, [1, 1, 1])
-        self.gating = gating
-        self.output_dim = (
-            num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b + num_outputs_3_0b
-        )
-        if gating:
-            self.gating_b0 = SelfGating(num_outputs_0_0a)
-            self.gating_b1 = SelfGating(num_outputs_1_0b)
-            self.gating_b2 = SelfGating(num_outputs_2_0b)
-            self.gating_b3 = SelfGating(num_outputs_3_0b)
-
-    def forward(self, input):
-        """Inception block
-      """
-        b0 = self.conv_b0(input)
-        b1 = self.conv_b1_a(input)
-        b1 = self.conv_b1_b(b1)
-        b2 = self.conv_b2_a(input)
-        b2 = self.conv_b2_b(b2)
-        b3 = self.maxpool_b3(input)
-        b3 = self.conv_b3_b(b3)
-        if self.gating:
-            b0 = self.gating_b0(b0)
-            b1 = self.gating_b1(b1)
-            b2 = self.gating_b2(b2)
-            b3 = self.gating_b3(b3)
-        return th.cat((b0, b1, b2, b3), dim=1)
-
-
-class SelfGating(nn.Module):
-    def __init__(self, input_dim):
-        super(SelfGating, self).__init__()
-        self.fc = nn.Linear(input_dim, input_dim)
-
-    def forward(self, input_tensor):
-        """Feature gating as used in S3D-G.
-      """
-        spatiotemporal_average = th.mean(input_tensor, dim=[2, 3, 4])
-        weights = self.fc(spatiotemporal_average)
-        weights = th.sigmoid(weights)
-        return weights[:, :, None, None, None] * input_tensor
-
-
-class STConv3D(nn.Module):
-    def __init__(
-        self, input_dim, output_dim, kernel_size, stride=1, padding=0, separable=False
-    ):
-        super(STConv3D, self).__init__()
-        self.separable = separable
-        self.relu = nn.ReLU(inplace=True)
-        assert len(kernel_size) == 3
-        if separable and kernel_size[0] != 1:
-            spatial_kernel_size = [1, kernel_size[1], kernel_size[2]]
-            temporal_kernel_size = [kernel_size[0], 1, 1]
-            if isinstance(stride, list) and len(stride) == 3:
-                spatial_stride = [1, stride[1], stride[2]]
-                temporal_stride = [stride[0], 1, 1]
-            else:
-                spatial_stride = [1, stride, stride]
-                temporal_stride = [stride, 1, 1]
-            if isinstance(padding, list) and len(padding) == 3:
-                spatial_padding = [0, padding[1], padding[2]]
-                temporal_padding = [padding[0], 0, 0]
-            else:
-                spatial_padding = [0, padding, padding]
-                temporal_padding = [padding, 0, 0]
-        if separable:
-            self.conv1 = nn.Conv3d(
-                input_dim,
-                output_dim,
-                kernel_size=spatial_kernel_size,
-                stride=spatial_stride,
-                padding=spatial_padding,
-                bias=False,
-            )
-            self.bn1 = nn.BatchNorm3d(output_dim)
-            self.conv2 = nn.Conv3d(
-                output_dim,
-                output_dim,
-                kernel_size=temporal_kernel_size,
-                stride=temporal_stride,
-                padding=temporal_padding,
-                bias=False,
-            )
-            self.bn2 = nn.BatchNorm3d(output_dim)
-        else:
-            self.conv1 = nn.Conv3d(
-                input_dim,
-                output_dim,
-                kernel_size=kernel_size,
-                stride=stride,
-                padding=padding,
-                bias=False,
-            )
-            self.bn1 = nn.BatchNorm3d(output_dim)
-
-    def forward(self, input):
-        out = self.relu(self.bn1(self.conv1(input)))
-        if self.separable:
-            out = self.relu(self.bn2(self.conv2(out)))
-        return out
-
-
-class MaxPool3dTFPadding(th.nn.Module):
-    def __init__(self, kernel_size, stride=None, padding="SAME"):
-        super(MaxPool3dTFPadding, self).__init__()
-        if padding == "SAME":
-            padding_shape = self._get_padding_shape(kernel_size, stride)
-            self.padding_shape = padding_shape
-            self.pad = th.nn.ConstantPad3d(padding_shape, 0)
-        self.pool = th.nn.MaxPool3d(kernel_size, stride, ceil_mode=True)
-
-    def _get_padding_shape(self, filter_shape, stride):
-        def _pad_top_bottom(filter_dim, stride_val):
-            pad_along = max(filter_dim - stride_val, 0)
-            pad_top = pad_along // 2
-            pad_bottom = pad_along - pad_top
-            return pad_top, pad_bottom
-
-        padding_shape = []
-        for filter_dim, stride_val in zip(filter_shape, stride):
-            pad_top, pad_bottom = _pad_top_bottom(filter_dim, stride_val)
-            padding_shape.append(pad_top)
-            padding_shape.append(pad_bottom)
-        depth_top = padding_shape.pop(0)
-        depth_bottom = padding_shape.pop(0)
-        padding_shape.append(depth_top)
-        padding_shape.append(depth_bottom)
-        return tuple(padding_shape)
-
-    def forward(self, inp):
-        inp = self.pad(inp)
-        out = self.pool(inp)
-        return out
-
-
-class Sentence_Embedding(nn.Module):
-    def __init__(
-        self,
-        embd_dim,
-        num_embeddings=66250,
-        word_embedding_dim=300,
-        token_to_word_path="dict.npy",
-        max_words=16,
-        output_dim=2048,
-    ):
-        super(Sentence_Embedding, self).__init__()
-        self.word_embd = nn.Embedding(num_embeddings, word_embedding_dim)
-        self.fc1 = nn.Linear(word_embedding_dim, output_dim)
-        self.fc2 = nn.Linear(output_dim, embd_dim)
-        self.word_to_token = {}
-        self.max_words = max_words
-        token_to_word = np.load(token_to_word_path)
-        for i, t in enumerate(token_to_word):
-            self.word_to_token[t] = i + 1
-
-    def _zero_pad_tensor_token(self, tensor, size):
-        if len(tensor) >= size:
-            return tensor[:size]
-        else:
-            zero = th.zeros(size - len(tensor)).long()
-            return th.cat((tensor, zero), dim=0)
-
-    def _split_text(self, sentence):
-        w = re.findall(r"[\w']+", str(sentence))
-        return w
-
-    def _words_to_token(self, words):
-        words = [
-            self.word_to_token[word] for word in words if word in self.word_to_token
-        ]
-        if words:
-            we = self._zero_pad_tensor_token(th.LongTensor(words), self.max_words)
-            return we
-        else:
-            return th.zeros(self.max_words).long()
-
-    def _words_to_ids(self, x):
-        split_x = [self._words_to_token(self._split_text(sent.lower())) for sent in x]
-        return th.stack(split_x, dim=0)
-
-    def forward(self, x):
-        x = self._words_to_ids(x)
-        x = self.word_embd(x)
-        x = F.relu(self.fc1(x))
-        x = th.max(x, dim=1)[0]
-        x = self.fc2(x)
-        return {'text_embedding': x}
-
-
-class S3D(nn.Module):
-    def __init__(self, dict_path, num_classes=512, gating=True, space_to_depth=True):
-        super(S3D, self).__init__()
-        self.num_classes = num_classes
-        self.gating = gating
-        self.space_to_depth = space_to_depth
-        if space_to_depth:
-            self.conv1 = STConv3D(
-                24, 64, [2, 4, 4], stride=1, padding=(1, 2, 2), separable=False
-            )
-        else:
-            self.conv1 = STConv3D(
-                3, 64, [3, 7, 7], stride=2, padding=(1, 3, 3), separable=False
-            )
-        self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False)
-        self.conv_2c = STConv3D(64, 192, [3, 3, 3], padding=1, separable=True)
-        self.gating = SelfGating(192)
-        self.maxpool_2a = MaxPool3dTFPadding(
-            kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
-        )
-        self.maxpool_3a = MaxPool3dTFPadding(
-            kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
-        )
-        self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32)
-        self.mixed_3c = InceptionBlock(
-            self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64
-        )
-        self.maxpool_4a = MaxPool3dTFPadding(
-            kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME"
-        )
-        self.mixed_4b = InceptionBlock(
-            self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64
-        )
-        self.mixed_4c = InceptionBlock(
-            self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64
-        )
-        self.mixed_4d = InceptionBlock(
-            self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64
-        )
-        self.mixed_4e = InceptionBlock(
-            self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64
-        )
-        self.mixed_4f = InceptionBlock(
-            self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128
-        )
-        self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding(
-            kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME"
-        )
-        self.mixed_5b = InceptionBlock(
-            self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128
-        )
-        self.mixed_5c = InceptionBlock(
-            self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128
-        )
-        self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes)
-        self.text_module = Sentence_Embedding(num_classes,
-            token_to_word_path=dict_path)
-
-    def _space_to_depth(self, input):
-        """3D space to depth trick for TPU optimization.
-      """
-        B, C, T, H, W = input.shape
-        input = input.view(B, C, T // 2, 2, H // 2, 2, W // 2, 2)
-        input = input.permute(0, 3, 5, 7, 1, 2, 4, 6)
-        input = input.contiguous().view(B, 8 * C, T // 2, H // 2, W // 2)
-        return input
-
-    def forward(self, inputs):
-        """Defines the S3DG base architecture."""
-        if self.space_to_depth:
-            inputs = self._space_to_depth(inputs)
-        net = self.conv1(inputs)
-        if self.space_to_depth:
-            # we need to replicate 'SAME' tensorflow padding
-            net = net[:, :, 1:, 1:, 1:]
-        net = self.maxpool_2a(net)
-        net = self.conv_2b(net)
-        net = self.conv_2c(net)
-        if self.gating:
-            net = self.gating(net)
-        net = self.maxpool_3a(net)
-        net = self.mixed_3b(net)
-        net = self.mixed_3c(net)
-        net = self.maxpool_4a(net)
-        net = self.mixed_4b(net)
-        net = self.mixed_4c(net)
-        net = self.mixed_4d(net)
-        net = self.mixed_4e(net)
-        net = self.mixed_4f(net)
-        net = self.maxpool_5a(net)
-        net = self.mixed_5b(net)
-        net = self.mixed_5c(net)
-        net = th.mean(net, dim=[2, 3, 4])
-        return {'video_embedding': self.fc(net), 'mixed_5c': net}
diff --git a/spaces/asas-ai/Arabic-LLM-Leaderboard/.streamlit/style.css b/spaces/asas-ai/Arabic-LLM-Leaderboard/.streamlit/style.css
deleted file mode 100644
index e49bbe688aa314df657fe0c16db7a646df3f14f6..0000000000000000000000000000000000000000
--- a/spaces/asas-ai/Arabic-LLM-Leaderboard/.streamlit/style.css
+++ /dev/null
@@ -1,104 +0,0 @@
-@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:wght@200&display=swap');
-
-#MainMenu {
-    visibility: hidden;
-}            
-
-header {
-    visibility: hidden;
-}
-
-footer {
-    visibility: hidden;
-}
-
-div[class*="stMultiSelect"] label p{
-    font-size:  medium;
-    color: black;
-    font-weight: 600;
-}
-
-div[class*="stSelect"] label p{
-    font-size:  medium;
-    color: black;
-    font-weight: 600;
-}
-
-div[class*="stRadio"] label p{
-    font-size:  medium;
-    color: black;
-    font-weight: 600;
-}
-
-div[data-testid="stExpander"] div[role="button"] p {
-    font-size:  medium;
-    text-align: center; 
-    color: #023e8a;
-    font-weight: 600;
-}
-
-div[class*="stText"] label p{
-    font-size:  medium;
-    color: black;
-    font-weight: 600;
-}
-
-.stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
-  font-size:21px;
-  font-weight: 600;
-}
-
-.block-container {
-    padding-top: 0rem;
-    padding-bottom: 0rem;
-    padding-left:  17rem;
-    padding-right: 17rem;
-}
-
-.br {
-    display: block;
-    margin: 0px 0;
-}
-
-.footer {
-    position: fixed;
-    left: 0; 
-    bottom: 0;
-    width: 100%;
-    background-color: white;
-    color: #737373;
-    text-align: center;
-}
-
-a:link , a:visited{
-    color: gray;
-    font-family: 'IBM Plex Sans', sans-serif;
-    background-color: transparent;
-    text-decoration: none;
-}
-
-a:hover,  a:active {
-    color: gray;
-    font-family: 'IBM Plex Sans', sans-serif;
-    background-color: transparent;
-    text-decoration: none;
-} 
-
-.p1 {
-    font-family: 'IBM Plex Sans', sans-serif;
-    font-weight: 300;
-    font-size: 13px
-}
-
-h1 {
-    text-align: center; 
-    color: #023e8a;
-    font-size: 60px;
-}
-
-h3 {
-    text-align: center; 
-    font-size:  medium;
-    color: black;
-    font-weight: 600;
-}
\ No newline at end of file
diff --git a/spaces/atimughal662/InfoFusion/src/iterators/iterator_pipe.py b/spaces/atimughal662/InfoFusion/src/iterators/iterator_pipe.py
deleted file mode 100644
index 90883b08ee6c5fbb7a575a7f1176f124b4d66134..0000000000000000000000000000000000000000
--- a/spaces/atimughal662/InfoFusion/src/iterators/iterator_pipe.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import queue
-import asyncio
-
-
-class IteratorPipe:
-    """
-    Iterator Pipe creates an iterator that can be fed in data from another block of code or thread of execution
-    """
-
-    def __init__(self, sentinel=object()):
-        self._q = queue.Queue()
-        self._sentinel = sentinel
-        self._sentinel_pushed = False
-        self._closed = False
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self._closed:
-            raise StopIteration
-
-        data = self._q.get(block=True)
-        if data is self._sentinel:
-            self._closed = True
-            raise StopIteration
-
-        return data
-
-    def put(self, data) -> bool:
-        """
-        Pushes next item to Iterator and returns True
-        If iterator has been closed via close(), doesn't push anything and returns False
-        """
-        if self._sentinel_pushed:
-            return False
-
-        self._q.put(data)
-        return True
-
-    def close(self):
-        """
-        Close is idempotent. Calling close multiple times is safe
-        Iterator will raise StopIteration only after all elements pushed before close have been iterated
-        """
-        # make close idempotent
-        if not self._sentinel_pushed:
-            self._sentinel_pushed = True
-        self._q.put(self._sentinel)
-
-
-class AsyncIteratorPipe:
-
-    def __init__(self, sentinel=object()):
-        self._q = asyncio.Queue()
-        self._sentinel = sentinel
-        self._sentinel_pushed = False
-        self._closed = False
-
-    def __aiter__(self):
-        return self
-
-    async def __anext__(self):
-        if self._closed:
-            raise StopAsyncIteration
-
-        data = await self._q.get()
-        if data is self._sentinel:
-            self._closed = True
-            raise StopAsyncIteration
-
-        return data
-
-    async def put(self, data) -> bool:
-        """
-        Pushes next item to Iterator and returns True
-        If iterator has been closed via close(), doesn't push anything and returns False
-        """
-        if self._sentinel_pushed:
-            return False
-
-        await self._q.put(data)
-        return True
-
-    async def close(self):
-        """
-        Close is idempotent. Calling close multiple times is safe
-        Iterator will raise StopIteration only after all elements pushed before close have been iterated
-        """
-        # make close idempotent
-        if not self._sentinel_pushed:
-            self._sentinel_pushed = True
-            await self._q.put(self._sentinel)
diff --git a/spaces/augmentedimaginationhackathon/paperstocode/promptStuff/test.py b/spaces/augmentedimaginationhackathon/paperstocode/promptStuff/test.py
deleted file mode 100644
index 1a99b85fc192354763293ace30d376c01a076977..0000000000000000000000000000000000000000
--- a/spaces/augmentedimaginationhackathon/paperstocode/promptStuff/test.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from langchain.document_loaders import OnlinePDFLoader
-
-loader = OnlinePDFLoader("https://arxiv.org/pdf/2302.03803.pdf")
-
-data = loader.load()
-
-print(data)
\ No newline at end of file
diff --git a/spaces/awacke1/AframeHTML5Demo/index.html b/spaces/awacke1/AframeHTML5Demo/index.html
deleted file mode 100644
index d38b0edd63195fcc4be93d1703d5106346cee0c1..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AframeHTML5Demo/index.html
+++ /dev/null
@@ -1,81 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>Snakes Game in A-Frame</title>
-    <script src="https://aframe.io/releases/1.2.0/aframe.min.js"></script>
-  </head>
-  <body>
-    <!-- Step 8: Finishing Touches - Add Instructions and Key Map -->
-    <div>
-      <h1>Instructions:</h1>
-      <p>Use the arrow keys to control the snake. Eat the red food box to grow!</p>
-      <h2>Key Map:</h2>
-      <ul>
-        <li>Arrow Up: Move Up</li>
-        <li>Arrow Down: Move Down</li>
-        <li>Arrow Left: Move Left</li>
-        <li>Arrow Right: Move Right</li>
-      </ul>
-    </div>
-    
-    <a-scene>
-      <!-- Step 3: Create the Game Board -->
-      <a-box id="game-board" position="0 0 -5" rotation="0 0 0" color="#4CC3D9" depth="10" height="10" width="10"></a-box>
-      
-      <!-- Initialize Snake -->
-      <a-box id="snake-head" position="0 0 -4" rotation="0 0 0" color="green" depth="1" height="1" width="1"></a-box>
-    </a-scene>
-
-    <script>
-      // Step 4: Implementing Snake Movement
-      let x = 0;
-      let y = 0;
-      let z = -4;
-      let direction = 'right'; // initial direction
-      let snakeHead = document.getElementById('snake-head');
-
-      document.addEventListener('keydown', function(event) {
-        if (event.code === 'ArrowUp') { direction = 'up'; }
-        if (event.code === 'ArrowDown') { direction = 'down'; }
-        if (event.code === 'ArrowLeft') { direction = 'left'; }
-        if (event.code === 'ArrowRight') { direction = 'right'; }
-      });
-
-      function moveSnake() {
-        if (direction === 'up') { y = Math.min(4, y + 1); }
-        if (direction === 'down') { y = Math.max(-4, y - 1); }
-        if (direction === 'left') { x = Math.max(-4, x - 1); }
-        if (direction === 'right') { x = Math.min(4, x + 1); }
-
-        snakeHead.setAttribute('position', `${x} ${y} ${z}`);
-      }
-
-      // Step 5: Adding Food Items (simplified, one food item)
-      let food = document.createElement('a-box');
-      food.setAttribute('id', 'food');
-      food.setAttribute('position', '2 2 -4');
-      food.setAttribute('color', 'red');
-      food.setAttribute('depth', '1');
-      food.setAttribute('height', '1');
-      food.setAttribute('width', '1');
-      document.querySelector('a-scene').appendChild(food);
-
-      // Step 6: Game Logic (simplified)
-      function checkCollision() {
-        let foodPosition = food.getAttribute('position');
-        let snakeHeadPosition = snakeHead.getAttribute('position');
-
-        if (foodPosition.x === snakeHeadPosition.x && foodPosition.y === snakeHeadPosition.y) {
-          // Implement growth and scoring logic here
-          alert('You ate the food!');
-        }
-      }
-
-      // Game Loop
-      setInterval(function() {
-        moveSnake();
-        checkCollision();
-      }, 500);
-    </script>
-  </body>
-</html>
\ No newline at end of file
diff --git a/spaces/awacke1/HealthConditionsTest/backupapp.py b/spaces/awacke1/HealthConditionsTest/backupapp.py
deleted file mode 100644
index 2aa60a5b6b8247760f55cae626b41fab6879ed1b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/HealthConditionsTest/backupapp.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import streamlit as st
-import plotly.express as px
-import pandas as pd
-
-# Define the states and conditions of interest
-states = ["Minnesota", "Florida", "California"]
-top_n = 10
-
-# Define the list dictionary of top 10 health conditions descending by cost
-health_conditions = [
-    {"condition": "Heart disease", "spending": 214.3},
-    {"condition": "Trauma-related disorders", "spending": 198.6},
-    {"condition": "Cancer", "spending": 171.0},
-    {"condition": "Mental disorders", "spending": 150.8},
-    {"condition": "Osteoarthritis and joint disorders", "spending": 142.4},
-    {"condition": "Diabetes", "spending": 107.4},
-    {"condition": "Chronic obstructive pulmonary disease and asthma", "spending": 91.0},
-    {"condition": "Hypertension", "spending": 83.9},
-    {"condition": "Hyperlipidemia", "spending": 83.9},
-    {"condition": "Back problems", "spending": 67.0}
-]
-
-# Total the spending values
-total_spending = sum([hc["spending"] for hc in health_conditions])
-
-# Create a DataFrame from the list dictionary
-df_top_conditions = pd.DataFrame(health_conditions)
-
-# Create the treemap graph using Plotly Express
-fig = px.treemap(df_top_conditions, path=["condition"], values="spending")
-
-# Set the title of the graph
-fig.update_layout(title=f"Top {top_n} Health Conditions in {', '.join(states)} by Spending (Total: ${total_spending}B)")
-
-# Display the graph in Streamlit
-st.plotly_chart(fig)
diff --git a/spaces/awacke1/KnowledgeDistillerToolMaker/app.py b/spaces/awacke1/KnowledgeDistillerToolMaker/app.py
deleted file mode 100644
index 5fedc284f619568f9cbc5868cccc2d0cdb4862ae..0000000000000000000000000000000000000000
--- a/spaces/awacke1/KnowledgeDistillerToolMaker/app.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import streamlit as st
-import re
-import nltk
-from nltk.corpus import stopwords
-from nltk import FreqDist
-from graphviz import Digraph
-from collections import Counter
-import datetime
-import pandas as pd
-from PyPDF2 import PdfFileReader
-from io import StringIO, BytesIO
-
-nltk.download('punkt')
-nltk.download('stopwords')
-
-
-def remove_timestamps(text):
-    return re.sub(r'\d{1,2}:\d{2}\n', '', text)
-
-
-def process_text(text):
-    lines = text.split("\n")
-    processed_lines = []
-
-    for line in lines:
-        if line:
-            processed_lines.append(line)
-
-    outline = ""
-    for i, line in enumerate(processed_lines):
-        if i % 2 == 0:
-            outline += f"**{line}**\n"
-        else:
-            outline += f"- {line} 😄\n"
-
-    return outline
-
-
-def extract_high_information_words(text, top_n=10):
-    words = nltk.word_tokenize(text)
-    words = [word.lower() for word in words if word.isalpha()]
-
-    stop_words = set(stopwords.words('english'))
-    filtered_words = [word for word in words if word not in stop_words]
-
-    freq_dist = FreqDist(filtered_words)
-    high_information_words = [word for word, _ in freq_dist.most_common(top_n)]
-
-    return high_information_words
-
-
-def create_relationship_graph(words):
-    graph = Digraph()
-
-    for index, word in enumerate(words):
-        graph.node(str(index), word)
-
-        if index > 0:
-            graph.edge(str(index - 1), str(index), label=str(index))
-
-    return graph
-
-
-def display_relationship_graph(words):
-    graph = create_relationship_graph(words)
-    st.graphviz_chart(graph)
-
-
-def save_text_file(text):
-    date_str = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
-    file_name = f"{date_str}.txt"
-    with open(file_name, 'w') as f:
-        f.write(text)
-    return file_name
-
-
-def extract_text_from_uploaded_files(uploaded_files):
-    merged_text = ""
-
-    for uploaded_file in uploaded_files:
-        extension = uploaded_file.name.split('.')[-1]
-
-        if extension == "txt":
-            merged_text += uploaded_file.read().decode()
-
-        elif extension == "pdf":
-            pdf = PdfFileReader(uploaded_file)
-            for page_num in range(pdf.numPages):
-                page = pdf.getPage(page_num)
-                merged_text += page.extractText()
-
-        elif extension == "csv":
-            df = pd.read_csv(uploaded_file)
-            merged_text += '\n'.join(df.applymap(str).agg(' '.join, axis=1))
-
-    return merged_text
-
-
-uploaded_files = st.file_uploader("Choose files", type=['txt', 'pdf', 'csv'], accept_multiple_files=True)
-
-if uploaded_files:
-    merged_text = extract_text_from_uploaded_files(uploaded_files)
-    save_text_file(merged_text)
-
-    text_without_timestamps = remove_timestamps(merged_text)
-
-    st.markdown("**Text without Timestamps:**")
-    st.write(text_without_timestamps)
-
-    processed_text = process_text(text_without_timestamps)
-    st.markdown("**Markdown Outline with Emojis:**")
-    st.markdown(processed_text)
-
-    top_words = extract_high_information_words(text_without_timestamps, 10)
-    st.markdown("**Top 10 High Information Words:**")
-    st.write(top_words)
-
-    st.markdown("**Relationship Graph:**")
-    display_relationship_graph(top_words)
diff --git a/spaces/awacke1/Machine-translation/README.md b/spaces/awacke1/Machine-translation/README.md
deleted file mode 100644
index 4c45af95d4fff11c930135fbb6a2a3425f163567..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Machine-translation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Machine Translation
-emoji: 🚀
-colorFrom: pink
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-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/awacke1/Streamlit-ChatGPT/README.md b/spaces/awacke1/Streamlit-ChatGPT/README.md
deleted file mode 100644
index 37fa219eae2d886fa57a48e4c440ba529e1e27ea..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-ChatGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit ChatGPT
-emoji: ⚡
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-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/awacke1/VoiceChatGPT-13/README.md b/spaces/awacke1/VoiceChatGPT-13/README.md
deleted file mode 100644
index 98e74c958280d5afadab7e643f6062f967c00231..0000000000000000000000000000000000000000
--- a/spaces/awacke1/VoiceChatGPT-13/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: VoiceChatGPT 13
-emoji: 🏆
-colorFrom: yellow
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-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/aziki/anime-remove-background/app.py b/spaces/aziki/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/aziki/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/whisper/utils.py b/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/whisper/utils.py
deleted file mode 100644
index 5dacc173c40bcd6e999d728862e29a968000b12e..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/vencoder/whisper/utils.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import json
-import os
-import sys
-import zlib
-from typing import Callable, TextIO
-
-system_encoding = sys.getdefaultencoding()
-
-if system_encoding != "utf-8":
-    def make_safe(string):
-        # replaces any character not representable using the system default encoding with an '?',
-        # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729).
-        return string.encode(system_encoding, errors="replace").decode(system_encoding)
-else:
-    def make_safe(string):
-        # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding
-        return string
-
-
-def exact_div(x, y):
-    assert x % y == 0
-    return x // y
-
-
-def str2bool(string):
-    str2val = {"True": True, "False": False}
-    if string in str2val:
-        return str2val[string]
-    else:
-        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
-
-
-def optional_int(string):
-    return None if string == "None" else int(string)
-
-
-def optional_float(string):
-    return None if string == "None" else float(string)
-
-
-def compression_ratio(text) -> float:
-    text_bytes = text.encode("utf-8")
-    return len(text_bytes) / len(zlib.compress(text_bytes))
-
-
-def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
-    assert seconds >= 0, "non-negative timestamp expected"
-    milliseconds = round(seconds * 1000.0)
-
-    hours = milliseconds // 3_600_000
-    milliseconds -= hours * 3_600_000
-
-    minutes = milliseconds // 60_000
-    milliseconds -= minutes * 60_000
-
-    seconds = milliseconds // 1_000
-    milliseconds -= seconds * 1_000
-
-    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
-    return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
-
-
-class ResultWriter:
-    extension: str
-
-    def __init__(self, output_dir: str):
-        self.output_dir = output_dir
-
-    def __call__(self, result: dict, audio_path: str):
-        audio_basename = os.path.basename(audio_path)
-        output_path = os.path.join(self.output_dir, audio_basename + "." + self.extension)
-
-        with open(output_path, "w", encoding="utf-8") as f:
-            self.write_result(result, file=f)
-
-    def write_result(self, result: dict, file: TextIO):
-        raise NotImplementedError
-
-
-class WriteTXT(ResultWriter):
-    extension: str = "txt"
-
-    def write_result(self, result: dict, file: TextIO):
-        for segment in result["segments"]:
-            print(segment['text'].strip(), file=file, flush=True)
-
-
-class WriteVTT(ResultWriter):
-    extension: str = "vtt"
-
-    def write_result(self, result: dict, file: TextIO):
-        print("WEBVTT\n", file=file)
-        for segment in result["segments"]:
-            print(
-                f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-                f"{segment['text'].strip().replace('-->', '->')}\n",
-                file=file,
-                flush=True,
-            )
-
-
-class WriteSRT(ResultWriter):
-    extension: str = "srt"
-
-    def write_result(self, result: dict, file: TextIO):
-        for i, segment in enumerate(result["segments"], start=1):
-            # write srt lines
-            print(
-                f"{i}\n"
-                f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
-                f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
-                f"{segment['text'].strip().replace('-->', '->')}\n",
-                file=file,
-                flush=True,
-            )
-
-
-class WriteTSV(ResultWriter):
-    """
-    Write a transcript to a file in TSV (tab-separated values) format containing lines like:
-    <start time in integer milliseconds>\t<end time in integer milliseconds>\t<transcript text>
-
-    Using integer milliseconds as start and end times means there's no chance of interference from
-    an environment setting a language encoding that causes the decimal in a floating point number
-    to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++.
-    """
-    extension: str = "tsv"
-
-    def write_result(self, result: dict, file: TextIO):
-        print("start", "end", "text", sep="\t", file=file)
-        for segment in result["segments"]:
-            print(round(1000 * segment['start']), file=file, end="\t")
-            print(round(1000 * segment['end']), file=file, end="\t")
-            print(segment['text'].strip().replace("\t", " "), file=file, flush=True)
-
-
-class WriteJSON(ResultWriter):
-    extension: str = "json"
-
-    def write_result(self, result: dict, file: TextIO):
-        json.dump(result, file)
-
-
-def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]:
-    writers = {
-        "txt": WriteTXT,
-        "vtt": WriteVTT,
-        "srt": WriteSRT,
-        "tsv": WriteTSV,
-        "json": WriteJSON,
-    }
-
-    if output_format == "all":
-        all_writers = [writer(output_dir) for writer in writers.values()]
-
-        def write_all(result: dict, file: TextIO):
-            for writer in all_writers:
-                writer(result, file)
-
-        return write_all
-
-    return writers[output_format](output_dir)
-
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/Ocean.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/Ocean.js
deleted file mode 100644
index 575a9f13d4d49afb3970e716c49e766d97cc74c3..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/Ocean.js
+++ /dev/null
@@ -1,384 +0,0 @@
-THREE.Ocean = function ( renderer, camera, scene, options ) {
-
-	// flag used to trigger parameter changes
-	this.changed = true;
-	this.initial = true;
-
-	// Assign required parameters as object properties
-	this.oceanCamera = new THREE.OrthographicCamera(); //camera.clone();
-	this.oceanCamera.position.z = 1;
-	this.renderer = renderer;
-	this.renderer.clearColor( 0xffffff );
-
-	this.scene = new THREE.Scene();
-
-	// Assign optional parameters as variables and object properties
-	function optionalParameter( value, defaultValue ) {
-
-		return value !== undefined ? value : defaultValue;
-
-	}
-	options = options || {};
-	this.clearColor = optionalParameter( options.CLEAR_COLOR, [ 1.0, 1.0, 1.0, 0.0 ] );
-	this.geometryOrigin = optionalParameter( options.GEOMETRY_ORIGIN, [ - 1000.0, - 1000.0 ] );
-	this.sunDirectionX = optionalParameter( options.SUN_DIRECTION[ 0 ], - 1.0 );
-	this.sunDirectionY = optionalParameter( options.SUN_DIRECTION[ 1 ], 1.0 );
-	this.sunDirectionZ = optionalParameter( options.SUN_DIRECTION[ 2 ], 1.0 );
-	this.oceanColor = optionalParameter( options.OCEAN_COLOR, new THREE.Vector3( 0.004, 0.016, 0.047 ) );
-	this.skyColor = optionalParameter( options.SKY_COLOR, new THREE.Vector3( 3.2, 9.6, 12.8 ) );
-	this.exposure = optionalParameter( options.EXPOSURE, 0.35 );
-	this.geometryResolution = optionalParameter( options.GEOMETRY_RESOLUTION, 32 );
-	this.geometrySize = optionalParameter( options.GEOMETRY_SIZE, 2000 );
-	this.resolution = optionalParameter( options.RESOLUTION, 64 );
-	this.floatSize = optionalParameter( options.SIZE_OF_FLOAT, 4 );
-	this.windX = optionalParameter( options.INITIAL_WIND[ 0 ], 10.0 );
-	this.windY = optionalParameter( options.INITIAL_WIND[ 1 ], 10.0 );
-	this.size = optionalParameter( options.INITIAL_SIZE, 250.0 );
-	this.choppiness = optionalParameter( options.INITIAL_CHOPPINESS, 1.5 );
-
-	//
-	this.matrixNeedsUpdate = false;
-
-	// Setup framebuffer pipeline
-	var renderTargetType = optionalParameter( options.USE_HALF_FLOAT, false ) ? THREE.HalfFloatType : THREE.FloatType;
-	var LinearClampParams = {
-		minFilter: THREE.LinearFilter,
-		magFilter: THREE.LinearFilter,
-		wrapS: THREE.ClampToEdgeWrapping,
-		wrapT: THREE.ClampToEdgeWrapping,
-		format: THREE.RGBAFormat,
-		stencilBuffer: false,
-		depthBuffer: false,
-		premultiplyAlpha: false,
-		type: renderTargetType
-	};
-	var NearestClampParams = {
-		minFilter: THREE.NearestFilter,
-		magFilter: THREE.NearestFilter,
-		wrapS: THREE.ClampToEdgeWrapping,
-		wrapT: THREE.ClampToEdgeWrapping,
-		format: THREE.RGBAFormat,
-		stencilBuffer: false,
-		depthBuffer: false,
-		premultiplyAlpha: false,
-		type: renderTargetType
-	};
-	var NearestRepeatParams = {
-		minFilter: THREE.NearestFilter,
-		magFilter: THREE.NearestFilter,
-		wrapS: THREE.RepeatWrapping,
-		wrapT: THREE.RepeatWrapping,
-		format: THREE.RGBAFormat,
-		stencilBuffer: false,
-		depthBuffer: false,
-		premultiplyAlpha: false,
-		type: renderTargetType
-	};
-	this.initialSpectrumFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, NearestRepeatParams );
-	this.spectrumFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, NearestClampParams );
-	this.pingPhaseFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, NearestClampParams );
-	this.pongPhaseFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, NearestClampParams );
-	this.pingTransformFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, NearestClampParams );
-	this.pongTransformFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, NearestClampParams );
-	this.displacementMapFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, LinearClampParams );
-	this.normalMapFramebuffer = new THREE.WebGLRenderTarget( this.resolution, this.resolution, LinearClampParams );
-
-	// Define shaders and constant uniforms
-	////////////////////////////////////////
-
-	// 0 - The vertex shader used in all of the simulation steps
-	var fullscreeenVertexShader = THREE.ShaderLib[ "ocean_sim_vertex" ];
-
-	// 1 - Horizontal wave vertices used for FFT
-	var oceanHorizontalShader = THREE.ShaderLib[ "ocean_subtransform" ];
-	var oceanHorizontalUniforms = THREE.UniformsUtils.clone( oceanHorizontalShader.uniforms );
-	this.materialOceanHorizontal = new THREE.ShaderMaterial( {
-		uniforms: oceanHorizontalUniforms,
-		vertexShader: fullscreeenVertexShader.vertexShader,
-		fragmentShader: "#define HORIZONTAL \n" + oceanHorizontalShader.fragmentShader
-	} );
-	this.materialOceanHorizontal.uniforms.u_transformSize = { value: this.resolution };
-	this.materialOceanHorizontal.uniforms.u_subtransformSize = { value: null };
-	this.materialOceanHorizontal.uniforms.u_input = { value: null };
-	this.materialOceanHorizontal.depthTest = false;
-
-	// 2 - Vertical wave vertices used for FFT
-	var oceanVerticalShader = THREE.ShaderLib[ "ocean_subtransform" ];
-	var oceanVerticalUniforms = THREE.UniformsUtils.clone( oceanVerticalShader.uniforms );
-	this.materialOceanVertical = new THREE.ShaderMaterial( {
-		uniforms: oceanVerticalUniforms,
-		vertexShader: fullscreeenVertexShader.vertexShader,
-		fragmentShader: oceanVerticalShader.fragmentShader
-	} );
-	this.materialOceanVertical.uniforms.u_transformSize = { value: this.resolution };
-	this.materialOceanVertical.uniforms.u_subtransformSize = { value: null };
-	this.materialOceanVertical.uniforms.u_input = { value: null };
-	this.materialOceanVertical.depthTest = false;
-
-	// 3 - Initial spectrum used to generate height map
-	var initialSpectrumShader = THREE.ShaderLib[ "ocean_initial_spectrum" ];
-	var initialSpectrumUniforms = THREE.UniformsUtils.clone( initialSpectrumShader.uniforms );
-	this.materialInitialSpectrum = new THREE.ShaderMaterial( {
-		uniforms: initialSpectrumUniforms,
-		vertexShader: initialSpectrumShader.vertexShader,
-		fragmentShader: initialSpectrumShader.fragmentShader
-	} );
-	this.materialInitialSpectrum.uniforms.u_wind = { value: new THREE.Vector2() };
-	this.materialInitialSpectrum.uniforms.u_resolution = { value: this.resolution };
-	this.materialInitialSpectrum.depthTest = false;
-
-	// 4 - Phases used to animate heightmap
-	var phaseShader = THREE.ShaderLib[ "ocean_phase" ];
-	var phaseUniforms = THREE.UniformsUtils.clone( phaseShader.uniforms );
-	this.materialPhase = new THREE.ShaderMaterial( {
-		uniforms: phaseUniforms,
-		vertexShader: fullscreeenVertexShader.vertexShader,
-		fragmentShader: phaseShader.fragmentShader
-	} );
-	this.materialPhase.uniforms.u_resolution = { value: this.resolution };
-	this.materialPhase.depthTest = false;
-
-	// 5 - Shader used to update spectrum
-	var spectrumShader = THREE.ShaderLib[ "ocean_spectrum" ];
-	var spectrumUniforms = THREE.UniformsUtils.clone( spectrumShader.uniforms );
-	this.materialSpectrum = new THREE.ShaderMaterial( {
-		uniforms: spectrumUniforms,
-		vertexShader: fullscreeenVertexShader.vertexShader,
-		fragmentShader: spectrumShader.fragmentShader
-	} );
-	this.materialSpectrum.uniforms.u_initialSpectrum = { value: null };
-	this.materialSpectrum.uniforms.u_resolution = { value: this.resolution };
-	this.materialSpectrum.depthTest = false;
-
-	// 6 - Shader used to update spectrum normals
-	var normalShader = THREE.ShaderLib[ "ocean_normals" ];
-	var normalUniforms = THREE.UniformsUtils.clone( normalShader.uniforms );
-	this.materialNormal = new THREE.ShaderMaterial( {
-		uniforms: normalUniforms,
-		vertexShader: fullscreeenVertexShader.vertexShader,
-		fragmentShader: normalShader.fragmentShader
-	} );
-	this.materialNormal.uniforms.u_displacementMap = { value: null };
-	this.materialNormal.uniforms.u_resolution = { value: this.resolution };
-	this.materialNormal.depthTest = false;
-
-	// 7 - Shader used to update normals
-	var oceanShader = THREE.ShaderLib[ "ocean_main" ];
-	var oceanUniforms = THREE.UniformsUtils.clone( oceanShader.uniforms );
-	this.materialOcean = new THREE.ShaderMaterial( {
-		uniforms: oceanUniforms,
-		vertexShader: oceanShader.vertexShader,
-		fragmentShader: oceanShader.fragmentShader
-	} );
-	// this.materialOcean.wireframe = true;
-	this.materialOcean.uniforms.u_geometrySize = { value: this.resolution };
-	this.materialOcean.uniforms.u_displacementMap = { value: this.displacementMapFramebuffer.texture };
-	this.materialOcean.uniforms.u_normalMap = { value: this.normalMapFramebuffer.texture };
-	this.materialOcean.uniforms.u_oceanColor = { value: this.oceanColor };
-	this.materialOcean.uniforms.u_skyColor = { value: this.skyColor };
-	this.materialOcean.uniforms.u_sunDirection = { value: new THREE.Vector3( this.sunDirectionX, this.sunDirectionY, this.sunDirectionZ ) };
-	this.materialOcean.uniforms.u_exposure = { value: this.exposure };
-
-	// Disable blending to prevent default premultiplied alpha values
-	this.materialOceanHorizontal.blending = 0;
-	this.materialOceanVertical.blending = 0;
-	this.materialInitialSpectrum.blending = 0;
-	this.materialPhase.blending = 0;
-	this.materialSpectrum.blending = 0;
-	this.materialNormal.blending = 0;
-	this.materialOcean.blending = 0;
-
-	// Create the simulation plane
-	this.screenQuad = new THREE.Mesh( new THREE.PlaneBufferGeometry( 2, 2 ) );
-	this.scene.add( this.screenQuad );
-
-	// Initialise spectrum data
-	this.generateSeedPhaseTexture();
-
-	// Generate the ocean mesh
-	this.generateMesh();
-
-};
-
-THREE.Ocean.prototype.generateMesh = function () {
-
-	var geometry = new THREE.PlaneBufferGeometry( this.geometrySize, this.geometrySize, this.geometryResolution, this.geometryResolution );
-
-	geometry.rotateX( - Math.PI / 2 );
-
-	this.oceanMesh = new THREE.Mesh( geometry, this.materialOcean );
-
-};
-
-THREE.Ocean.prototype.render = function () {
-
-	var currentRenderTarget = this.renderer.getRenderTarget();
-
-	this.scene.overrideMaterial = null;
-
-	if ( this.changed )
-		this.renderInitialSpectrum();
-
-	this.renderWavePhase();
-	this.renderSpectrum();
-	this.renderSpectrumFFT();
-	this.renderNormalMap();
-	this.scene.overrideMaterial = null;
-
-	this.renderer.setRenderTarget( currentRenderTarget );
-
-};
-
-THREE.Ocean.prototype.generateSeedPhaseTexture = function () {
-
-	// Setup the seed texture
-	this.pingPhase = true;
-	var phaseArray = new window.Float32Array( this.resolution * this.resolution * 4 );
-	for ( var i = 0; i < this.resolution; i ++ ) {
-
-		for ( var j = 0; j < this.resolution; j ++ ) {
-
-			phaseArray[ i * this.resolution * 4 + j * 4 ] = Math.random() * 2.0 * Math.PI;
-			phaseArray[ i * this.resolution * 4 + j * 4 + 1 ] = 0.0;
-			phaseArray[ i * this.resolution * 4 + j * 4 + 2 ] = 0.0;
-			phaseArray[ i * this.resolution * 4 + j * 4 + 3 ] = 0.0;
-
-		}
-
-	}
-
-	this.pingPhaseTexture = new THREE.DataTexture( phaseArray, this.resolution, this.resolution, THREE.RGBAFormat );
-	this.pingPhaseTexture.wrapS = THREE.ClampToEdgeWrapping;
-	this.pingPhaseTexture.wrapT = THREE.ClampToEdgeWrapping;
-	this.pingPhaseTexture.type = THREE.FloatType;
-	this.pingPhaseTexture.needsUpdate = true;
-
-};
-
-THREE.Ocean.prototype.renderInitialSpectrum = function () {
-
-	this.scene.overrideMaterial = this.materialInitialSpectrum;
-	this.materialInitialSpectrum.uniforms.u_wind.value.set( this.windX, this.windY );
-	this.materialInitialSpectrum.uniforms.u_size.value = this.size;
-
-	this.renderer.setRenderTarget( this.initialSpectrumFramebuffer );
-	this.renderer.clear();
-	this.renderer.render( this.scene, this.oceanCamera );
-
-};
-
-THREE.Ocean.prototype.renderWavePhase = function () {
-
-	this.scene.overrideMaterial = this.materialPhase;
-	this.screenQuad.material = this.materialPhase;
-	if ( this.initial ) {
-
-		this.materialPhase.uniforms.u_phases.value = this.pingPhaseTexture;
-		this.initial = false;
-
-	} else {
-
-		this.materialPhase.uniforms.u_phases.value = this.pingPhase ? this.pingPhaseFramebuffer.texture : this.pongPhaseFramebuffer.texture;
-
-	}
-	this.materialPhase.uniforms.u_deltaTime.value = this.deltaTime;
-	this.materialPhase.uniforms.u_size.value = this.size;
-	this.renderer.setRenderTarget( this.pingPhase ? this.pongPhaseFramebuffer : this.pingPhaseFramebuffer );
-	this.renderer.render( this.scene, this.oceanCamera );
-	this.pingPhase = ! this.pingPhase;
-
-};
-
-THREE.Ocean.prototype.renderSpectrum = function () {
-
-	this.scene.overrideMaterial = this.materialSpectrum;
-	this.materialSpectrum.uniforms.u_initialSpectrum.value = this.initialSpectrumFramebuffer.texture;
-	this.materialSpectrum.uniforms.u_phases.value = this.pingPhase ? this.pingPhaseFramebuffer.texture : this.pongPhaseFramebuffer.texture;
-	this.materialSpectrum.uniforms.u_choppiness.value = this.choppiness;
-	this.materialSpectrum.uniforms.u_size.value = this.size;
-
-	this.renderer.setRenderTarget( this.spectrumFramebuffer );
-	this.renderer.render( this.scene, this.oceanCamera );
-
-};
-
-THREE.Ocean.prototype.renderSpectrumFFT = function () {
-
-	// GPU FFT using Stockham formulation
-	var iterations = Math.log( this.resolution ) / Math.log( 2 ); // log2
-
-	this.scene.overrideMaterial = this.materialOceanHorizontal;
-
-	for ( var i = 0; i < iterations; i ++ ) {
-
-		if ( i === 0 ) {
-
-			this.materialOceanHorizontal.uniforms.u_input.value = this.spectrumFramebuffer.texture;
-			this.materialOceanHorizontal.uniforms.u_subtransformSize.value = Math.pow( 2, ( i % ( iterations ) ) + 1 );
-
-			this.renderer.setRenderTarget( this.pingTransformFramebuffer );
-			this.renderer.render( this.scene, this.oceanCamera );
-
-		} else if ( i % 2 === 1 ) {
-
-			this.materialOceanHorizontal.uniforms.u_input.value = this.pingTransformFramebuffer.texture;
-			this.materialOceanHorizontal.uniforms.u_subtransformSize.value = Math.pow( 2, ( i % ( iterations ) ) + 1 );
-
-			this.renderer.setRenderTarget( this.pongTransformFramebuffer );
-			this.renderer.render( this.scene, this.oceanCamera );
-
-		} else {
-
-			this.materialOceanHorizontal.uniforms.u_input.value = this.pongTransformFramebuffer.texture;
-			this.materialOceanHorizontal.uniforms.u_subtransformSize.value = Math.pow( 2, ( i % ( iterations ) ) + 1 );
-
-			this.renderer.setRenderTarget( this.pingTransformFramebuffer );
-			this.renderer.render( this.scene, this.oceanCamera );
-
-		}
-
-	}
-	this.scene.overrideMaterial = this.materialOceanVertical;
-	for ( var i = iterations; i < iterations * 2; i ++ ) {
-
-		if ( i === iterations * 2 - 1 ) {
-
-			this.materialOceanVertical.uniforms.u_input.value = ( iterations % 2 === 0 ) ? this.pingTransformFramebuffer.texture : this.pongTransformFramebuffer.texture;
-			this.materialOceanVertical.uniforms.u_subtransformSize.value = Math.pow( 2, ( i % ( iterations ) ) + 1 );
-
-			this.renderer.setRenderTarget( this.displacementMapFramebuffer );
-			this.renderer.render( this.scene, this.oceanCamera );
-
-		} else if ( i % 2 === 1 ) {
-
-			this.materialOceanVertical.uniforms.u_input.value = this.pingTransformFramebuffer.texture;
-			this.materialOceanVertical.uniforms.u_subtransformSize.value = Math.pow( 2, ( i % ( iterations ) ) + 1 );
-
-			this.renderer.setRenderTarget( this.pongTransformFramebuffer );
-			this.renderer.render( this.scene, this.oceanCamera );
-
-		} else {
-
-			this.materialOceanVertical.uniforms.u_input.value = this.pongTransformFramebuffer.texture;
-			this.materialOceanVertical.uniforms.u_subtransformSize.value = Math.pow( 2, ( i % ( iterations ) ) + 1 );
-
-			this.renderer.setRenderTarget( this.pingTransformFramebuffer );
-			this.renderer.render( this.scene, this.oceanCamera );
-
-		}
-
-	}
-
-};
-
-THREE.Ocean.prototype.renderNormalMap = function () {
-
-	this.scene.overrideMaterial = this.materialNormal;
-	if ( this.changed ) this.materialNormal.uniforms.u_size.value = this.size;
-	this.materialNormal.uniforms.u_displacementMap.value = this.displacementMapFramebuffer.texture;
-
-	this.renderer.setRenderTarget( this.normalMapFramebuffer );
-	this.renderer.clear();
-	this.renderer.render( this.scene, this.oceanCamera );
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/XLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/XLoader.js
deleted file mode 100644
index ecf450e17162b9c62cd26deaef52ae283253e3b1..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/XLoader.js
+++ /dev/null
@@ -1,1689 +0,0 @@
-/**
- * @author adrs2002 / https://github.com/adrs2002
- */
-
-
-( function ( global, factory ) {
-
-	typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-		typeof define === 'function' && define.amd ? define( factory ) :
-			( global.THREE = global.THREE || {}, global.THREE.XLoader = factory() );
-
-}( this, ( function () {
-
-	'use strict';
-
-	var classCallCheck = function ( instance, Constructor ) {
-
-		if ( ! ( instance instanceof Constructor ) ) {
-
-			throw new TypeError( "Cannot call a class as a function" );
-
-		}
-
-	};
-
-	var createClass = function () {
-
-		function defineProperties( target, props ) {
-
-			for ( var i = 0; i < props.length; i ++ ) {
-
-				var descriptor = props[ i ];
-				descriptor.enumerable = descriptor.enumerable || false;
-				descriptor.configurable = true;
-				if ( "value" in descriptor ) descriptor.writable = true;
-				Object.defineProperty( target, descriptor.key, descriptor );
-
-			}
-
-		}
-
-		return function ( Constructor, protoProps, staticProps ) {
-
-			if ( protoProps ) defineProperties( Constructor.prototype, protoProps );
-			if ( staticProps ) defineProperties( Constructor, staticProps );
-			return Constructor;
-
-		};
-
-	}();
-
-	var XboneInf = function XboneInf() {
-
-		classCallCheck( this, XboneInf );
-
-		this.boneName = "";
-		this.BoneIndex = 0;
-		this.Indeces = [];
-		this.Weights = [];
-		this.initMatrix = null;
-		this.OffsetMatrix = null;
-
-	};
-
-	var XAnimationInfo = function XAnimationInfo() {
-
-		classCallCheck( this, XAnimationInfo );
-
-		this.animeName = "";
-		this.boneName = "";
-		this.targetBone = null;
-		this.keyType = 4;
-		this.frameStartLv = 0;
-		this.keyFrames = [];
-		this.InverseMx = null;
-
-	};
-
-	var XAnimationObj = function () {
-
-		function XAnimationObj( _flags ) {
-
-			classCallCheck( this, XAnimationObj );
-
-			this.fps = 30;
-			this.name = 'xanimation';
-			this.length = 0;
-			this.hierarchy = [];
-			this.putFlags = _flags;
-			if ( this.putFlags.putPos === undefined ) {
-
-				this.putFlags.putPos = true;
-
-			}
-			if ( this.putFlags.putRot === undefined ) {
-
-				this.putFlags.putRot = true;
-
-			}
-			if ( this.putFlags.putScl === undefined ) {
-
-				this.putFlags.putScl = true;
-
-			}
-
-		}
-
-		createClass( XAnimationObj, [ {
-			key: "make",
-			value: function make( XAnimationInfoArray ) {
-
-				for ( var i = 0; i < XAnimationInfoArray.length; i ++ ) {
-
-					this.hierarchy.push( this.makeBonekeys( XAnimationInfoArray[ i ] ) );
-
-				}
-				this.length = this.hierarchy[ 0 ].keys[ this.hierarchy[ 0 ].keys.length - 1 ].time;
-
-			}
-		}, {
-			key: "clone",
-			value: function clone() {
-
-				return Object.assign( {}, this );
-
-			}
-		}, {
-			key: "makeBonekeys",
-			value: function makeBonekeys( XAnimationInfo ) {
-
-				var refObj = {};
-				refObj.name = XAnimationInfo.boneName;
-				refObj.parent = "";
-				refObj.keys = this.keyFrameRefactor( XAnimationInfo );
-				refObj.copy = function () {
-
-					return Object.assign( {}, this );
-
-				};
-				return refObj;
-
-			}
-		}, {
-			key: "keyFrameRefactor",
-			value: function keyFrameRefactor( XAnimationInfo ) {
-
-				var keys = [];
-				for ( var i = 0; i < XAnimationInfo.keyFrames.length; i ++ ) {
-
-					var keyframe = {};
-					keyframe.time = XAnimationInfo.keyFrames[ i ].time * this.fps;
-					if ( XAnimationInfo.keyFrames[ i ].pos && this.putFlags.putPos ) {
-
-						keyframe.pos = XAnimationInfo.keyFrames[ i ].pos;
-
-					}
-					if ( XAnimationInfo.keyFrames[ i ].rot && this.putFlags.putRot ) {
-
-						keyframe.rot = XAnimationInfo.keyFrames[ i ].rot;
-
-					}
-					if ( XAnimationInfo.keyFrames[ i ].scl && this.putFlags.putScl ) {
-
-						keyframe.scl = XAnimationInfo.keyFrames[ i ].scl;
-
-					}
-					if ( XAnimationInfo.keyFrames[ i ].matrix ) {
-
-						keyframe.matrix = XAnimationInfo.keyFrames[ i ].matrix;
-						if ( this.putFlags.putPos ) {
-
-							keyframe.pos = new THREE.Vector3().setFromMatrixPosition( keyframe.matrix );
-
-						}
-						if ( this.putFlags.putRot ) {
-
-							keyframe.rot = new THREE.Quaternion().setFromRotationMatrix( keyframe.matrix );
-
-						}
-						if ( this.putFlags.putScl ) {
-
-							keyframe.scl = new THREE.Vector3().setFromMatrixScale( keyframe.matrix );
-
-						}
-
-					}
-					keys.push( keyframe );
-
-				}
-				return keys;
-
-			}
-		} ] );
-		return XAnimationObj;
-
-	}();
-
-	var XKeyFrameInfo = function XKeyFrameInfo() {
-
-		classCallCheck( this, XKeyFrameInfo );
-
-		this.index = 0;
-		this.Frame = 0;
-		this.time = 0.0;
-		this.matrix = null;
-
-	};
-
-	var XLoader = function () {
-
-		function XLoader( manager ) {
-
-			classCallCheck( this, XLoader );
-
-			this.debug = false;
-			this.manager = manager !== undefined ? manager : new THREE.DefaultLoadingManager();
-			this.texloader = new THREE.TextureLoader( this.manager );
-			this.url = "";
-			this._putMatLength = 0;
-			this._nowMat = null;
-			this._nowFrameName = "";
-			this.frameHierarchie = [];
-			this.Hierarchies = {};
-			this.HieStack = [];
-			this._currentObject = {};
-			this._currentFrame = {};
-			this._data = null;
-			this.onLoad = null;
-			this.IsUvYReverse = true;
-			this.Meshes = [];
-			this.animations = [];
-			this.animTicksPerSecond = 30;
-			this._currentGeo = null;
-			this._currentAnime = null;
-			this._currentAnimeFrames = null;
-
-		}
-
-		createClass( XLoader, [ {
-			key: 'crossOrigin',
-			value: 'anonymous'
-		}, {
-			key: '_setArgOption',
-			value: function _setArgOption( _arg ) {
-
-				var _start = arguments.length > 1 && arguments[ 1 ] !== undefined ? arguments[ 1 ] : 0;
-
-				if ( ! _arg ) {
-
-					return;
-
-				}
-				for ( var i = _start; i < _arg.length; i ++ ) {
-
-					switch ( i ) {
-
-						case 0:
-							this.url = _arg[ i ];
-							break;
-						case 1:
-							this.options = _arg[ i ];
-							break;
-
-					}
-
-				}
-				if ( this.options === undefined ) {
-
-					this.options = {};
-
-				}
-
-			}
-		}, {
-			key: 'load',
-			value: function load( _arg, onLoad, onProgress, onError ) {
-
-				var _this = this;
-
-				this._setArgOption( _arg );
-				var loader = new THREE.FileLoader( this.manager );
-				loader.setPath( this.path );
-				loader.setResponseType( 'arraybuffer' );
-				loader.load( this.url, function ( response ) {
-
-					_this._parse( response, onLoad );
-
-				}, onProgress, onError );
-
-			}
-		}, {
-			key: 'setCrossOrigin',
-			value: function setCrossOrigin( value ) {
-
-				this.crossOrigin = value;
-				return this;
-
-			}
-		}, {
-			key: 'setPath',
-			value: function setPath( value ) {
-
-				this.path = value;
-				return this;
-
-			}
-		}, {
-			key: 'setResourcePath',
-			value: function setResourcePath( value ) {
-
-				this.resourcePath = value;
-				return this;
-
-			}
-		}, {
-			key: 'fromResponsedData',
-			value: function fromResponsedData( _data, _arg, onLoad ) {
-
-				this._setArgOption( _arg );
-				this._parse( _data, onLoad );
-
-			}
-		}, {
-			key: '_readLine',
-			value: function _readLine( line ) {
-
-				var readed = 0;
-				while ( true ) {
-
-					var find = - 1;
-					find = line.indexOf( '//', readed );
-					if ( find === - 1 ) {
-
-						find = line.indexOf( '#', readed );
-
-					}
-					if ( find > - 1 && find < 2 ) {
-
-						var foundNewLine = - 1;
-						foundNewLine = line.indexOf( "\r\n", readed );
-						if ( foundNewLine > 0 ) {
-
-							readed = foundNewLine + 2;
-
-						} else {
-
-							foundNewLine = line.indexOf( "\r", readed );
-							if ( foundNewLine > 0 ) {
-
-								readed = foundNewLine + 1;
-
-							} else {
-
-								readed = line.indexOf( "\n", readed ) + 1;
-
-							}
-
-						}
-
-					} else {
-
-						break;
-
-					}
-
-				}
-				return line.substr( readed );
-
-			}
-		}, {
-			key: '_readLine',
-			value: function _readLine( line ) {
-
-				var readed = 0;
-				while ( true ) {
-
-					var find = - 1;
-					find = line.indexOf( '//', readed );
-					if ( find === - 1 ) {
-
-						find = line.indexOf( '#', readed );
-
-					}
-					if ( find > - 1 && find < 2 ) {
-
-						var foundNewLine = - 1;
-						foundNewLine = line.indexOf( "\r\n", readed );
-						if ( foundNewLine > 0 ) {
-
-							readed = foundNewLine + 2;
-
-						} else {
-
-							foundNewLine = line.indexOf( "\r", readed );
-							if ( foundNewLine > 0 ) {
-
-								readed = foundNewLine + 1;
-
-							} else {
-
-								readed = line.indexOf( "\n", readed ) + 1;
-
-							}
-
-						}
-
-					} else {
-
-						break;
-
-					}
-
-				}
-				return line.substr( readed );
-
-			}
-		}, {
-			key: '_isBinary',
-			value: function _isBinary( binData ) {
-
-				var reader = new DataView( binData );
-				var face_size = 32 / 8 * 3 + 32 / 8 * 3 * 3 + 16 / 8;
-				var n_faces = reader.getUint32( 80, true );
-				var expect = 80 + 32 / 8 + n_faces * face_size;
-				if ( expect === reader.byteLength ) {
-
-					return true;
-
-				}
-				var fileLength = reader.byteLength;
-				for ( var index = 0; index < fileLength; index ++ ) {
-
-					if ( reader.getUint8( index, false ) > 127 ) {
-
-						return true;
-
-					}
-
-				}
-				return false;
-
-			}
-		}, {
-			key: 'ensureBinary',
-			value: function ensureBinary( buf ) {
-
-				if ( typeof buf === "string" ) {
-
-					var array_buffer = new Uint8Array( buf.length );
-					for ( var i = 0; i < buf.length; i ++ ) {
-
-						array_buffer[ i ] = buf.charCodeAt( i ) & 0xff;
-
-					}
-					return array_buffer.buffer || array_buffer;
-
-				} else {
-
-					return buf;
-
-				}
-
-			}
-		}, {
-			key: 'ensureString',
-			value: function ensureString( buf ) {
-
-				if ( typeof buf !== "string" ) {
-
-					return THREE.LoaderUtils.decodeText( new Uint8Array( buf ) );
-
-				} else {
-
-					return buf;
-
-				}
-
-			}
-		}, {
-			key: '_parse',
-			value: function _parse( data, onLoad ) {
-
-				var binData = this.ensureBinary( data );
-				this._data = this.ensureString( data );
-				this.onLoad = onLoad;
-				return this._isBinary( binData ) ? this._parseBinary( binData ) : this._parseASCII();
-
-			}
-		}, {
-			key: '_parseBinary',
-			value: function _parseBinary( data ) {
-
-				return this._parseASCII( THREE.LoaderUtils.decodeText( new Uint8Array( data ) ) );
-
-			}
-		}, {
-			key: '_parseASCII',
-			value: function _parseASCII() {
-
-				var path;
-
-				if ( this.resourcePath !== undefined ) {
-
-					path = this.resourcePath;
-
-				} else if ( this.path !== undefined ) {
-
-					path = this.path;
-
-				} else {
-
-					path = THREE.LoaderUtils.extractUrlBase( this.url );
-
-				}
-
-				this.texloader.setPath( path ).setCrossOrigin( this.crossOrigin );
-
-				var endRead = 16;
-				this.Hierarchies.children = [];
-				this._hierarchieParse( this.Hierarchies, endRead );
-				this._changeRoot();
-				this._currentObject = this.Hierarchies.children.shift();
-				this.mainloop();
-
-			}
-		}, {
-			key: '_hierarchieParse',
-			value: function _hierarchieParse( _parent, _end ) {
-
-				var endRead = _end;
-				while ( true ) {
-
-					var find1 = this._data.indexOf( '{', endRead ) + 1;
-					var findEnd = this._data.indexOf( '}', endRead );
-					var findNext = this._data.indexOf( '{', find1 ) + 1;
-					if ( find1 > 0 && findEnd > find1 ) {
-
-						var _currentObject = {};
-						_currentObject.children = [];
-						var nameData = this._readLine( this._data.substr( endRead, find1 - endRead - 1 ) ).trim();
-						var word = nameData.split( / /g );
-						if ( word.length > 0 ) {
-
-							_currentObject.type = word[ 0 ];
-							if ( word.length >= 2 ) {
-
-								_currentObject.name = word[ 1 ];
-
-							} else {
-
-								_currentObject.name = word[ 0 ] + this.Hierarchies.children.length;
-
-							}
-
-						} else {
-
-							_currentObject.name = nameData;
-							_currentObject.type = "";
-
-						}
-						if ( _currentObject.type === "Animation" ) {
-
-							_currentObject.data = this._data.substr( findNext, findEnd - findNext ).trim();
-							var refs = this._hierarchieParse( _currentObject, findEnd + 1 );
-							endRead = refs.end;
-							_currentObject.children = refs.parent.children;
-
-						} else {
-
-							var DataEnder = this._data.lastIndexOf( ';', findNext > 0 ? Math.min( findNext, findEnd ) : findEnd );
-							_currentObject.data = this._data.substr( find1, DataEnder - find1 ).trim();
-							if ( findNext <= 0 || findEnd < findNext ) {
-
-								endRead = findEnd + 1;
-
-							} else {
-
-								var nextStart = Math.max( DataEnder + 1, find1 );
-								var _refs = this._hierarchieParse( _currentObject, nextStart );
-								endRead = _refs.end;
-								_currentObject.children = _refs.parent.children;
-
-							}
-
-						}
-						_currentObject.parent = _parent;
-						if ( _currentObject.type != "template" ) {
-
-							_parent.children.push( _currentObject );
-
-						}
-
-					} else {
-
-						endRead = find1 === - 1 ? this._data.length : findEnd + 1;
-						break;
-
-					}
-
-				}
-				return {
-					parent: _parent,
-					end: endRead
-				};
-
-			}
-		}, {
-			key: 'mainloop',
-			value: function mainloop() {
-
-				var _this2 = this;
-
-				this.mainProc();
-				if ( this._currentObject.parent || this._currentObject.children.length > 0 || ! this._currentObject.worked ) {
-
-					setTimeout( function () {
-
-						_this2.mainloop();
-
-					}, 1 );
-
-				} else {
-
-					setTimeout( function () {
-
-						_this2.onLoad( {
-							models: _this2.Meshes,
-							animations: _this2.animations
-						} );
-
-					}, 1 );
-
-				}
-
-			}
-		}, {
-			key: 'mainProc',
-			value: function mainProc() {
-
-				var breakFlag = false;
-				while ( true ) {
-
-					if ( ! this._currentObject.worked ) {
-
-						switch ( this._currentObject.type ) {
-
-							case "template":
-								break;
-							case "AnimTicksPerSecond":
-								this.animTicksPerSecond = parseInt( this._currentObject.data );
-								break;
-							case "Frame":
-								this._setFrame();
-								break;
-							case "FrameTransformMatrix":
-								this._setFrameTransformMatrix();
-								break;
-							case "Mesh":
-								this._changeRoot();
-								this._currentGeo = {};
-								this._currentGeo.name = this._currentObject.name.trim();
-								this._currentGeo.parentName = this._getParentName( this._currentObject ).trim();
-								this._currentGeo.VertexSetedBoneCount = [];
-								this._currentGeo.GeometryData = {
-									vertices: [],
-									normals: [],
-									uvs: [],
-									skinIndices: [],
-									skinWeights: [],
-									indices: [],
-									materialIndices: []
-								};
-								this._currentGeo.Materials = [];
-								this._currentGeo.normalVectors = [];
-								this._currentGeo.BoneInfs = [];
-								this._currentGeo.baseFrame = this._currentFrame;
-								this._makeBoneFrom_CurrentFrame();
-								this._readVertexDatas();
-								breakFlag = true;
-								break;
-							case "MeshNormals":
-								this._readVertexDatas();
-								break;
-							case "MeshTextureCoords":
-								this._setMeshTextureCoords();
-								break;
-							case "VertexDuplicationIndices":
-								break;
-							case "MeshMaterialList":
-								this._setMeshMaterialList();
-								break;
-							case "Material":
-								this._setMaterial();
-								break;
-							case "SkinWeights":
-								this._setSkinWeights();
-								break;
-							case "AnimationSet":
-								this._changeRoot();
-								this._currentAnime = {};
-								this._currentAnime.name = this._currentObject.name.trim();
-								this._currentAnime.AnimeFrames = [];
-								break;
-							case "Animation":
-								if ( this._currentAnimeFrames ) {
-
-									this._currentAnime.AnimeFrames.push( this._currentAnimeFrames );
-
-								}
-								this._currentAnimeFrames = new XAnimationInfo();
-								this._currentAnimeFrames.boneName = this._currentObject.data.trim();
-								break;
-							case "AnimationKey":
-								this._readAnimationKey();
-								breakFlag = true;
-								break;
-
-						}
-						this._currentObject.worked = true;
-
-					}
-					if ( this._currentObject.children.length > 0 ) {
-
-						this._currentObject = this._currentObject.children.shift();
-						if ( this.debug ) {
-
-							console.log( 'processing ' + this._currentObject.name );
-
-						}
-						if ( breakFlag ) break;
-
-					} else {
-
-						if ( this._currentObject.worked ) {
-
-							if ( this._currentObject.parent && ! this._currentObject.parent.parent ) {
-
-								this._changeRoot();
-
-							}
-
-						}
-						if ( this._currentObject.parent ) {
-
-							this._currentObject = this._currentObject.parent;
-
-						} else {
-
-							breakFlag = true;
-
-						}
-						if ( breakFlag ) break;
-
-					}
-
-				}
-				return;
-
-			}
-		}, {
-			key: '_changeRoot',
-			value: function _changeRoot() {
-
-				if ( this._currentGeo != null && this._currentGeo.name ) {
-
-					this._makeOutputGeometry();
-
-				}
-				this._currentGeo = {};
-				if ( this._currentAnime != null && this._currentAnime.name ) {
-
-					if ( this._currentAnimeFrames ) {
-
-						this._currentAnime.AnimeFrames.push( this._currentAnimeFrames );
-						this._currentAnimeFrames = null;
-
-					}
-					this._makeOutputAnimation();
-
-				}
-				this._currentAnime = {};
-
-			}
-		}, {
-			key: '_getParentName',
-			value: function _getParentName( _obj ) {
-
-				if ( _obj.parent ) {
-
-					if ( _obj.parent.name ) {
-
-						return _obj.parent.name;
-
-					} else {
-
-						return this._getParentName( _obj.parent );
-
-					}
-
-				} else {
-
-					return "";
-
-				}
-
-			}
-		}, {
-			key: '_setFrame',
-			value: function _setFrame() {
-
-				this._nowFrameName = this._currentObject.name.trim();
-				this._currentFrame = {};
-				this._currentFrame.name = this._nowFrameName;
-				this._currentFrame.children = [];
-				if ( this._currentObject.parent && this._currentObject.parent.name ) {
-
-					this._currentFrame.parentName = this._currentObject.parent.name;
-
-				}
-				this.frameHierarchie.push( this._nowFrameName );
-				this.HieStack[ this._nowFrameName ] = this._currentFrame;
-
-			}
-		}, {
-			key: '_setFrameTransformMatrix',
-			value: function _setFrameTransformMatrix() {
-
-				this._currentFrame.FrameTransformMatrix = new THREE.Matrix4();
-				var data = this._currentObject.data.split( "," );
-				this._ParseMatrixData( this._currentFrame.FrameTransformMatrix, data );
-				this._makeBoneFrom_CurrentFrame();
-
-			}
-		}, {
-			key: '_makeBoneFrom_CurrentFrame',
-			value: function _makeBoneFrom_CurrentFrame() {
-
-				if ( ! this._currentFrame.FrameTransformMatrix ) {
-
-					return;
-
-				}
-				var b = new THREE.Bone();
-				b.name = this._currentFrame.name;
-				b.applyMatrix( this._currentFrame.FrameTransformMatrix );
-				b.matrixWorld = b.matrix;
-				b.FrameTransformMatrix = this._currentFrame.FrameTransformMatrix;
-				this._currentFrame.putBone = b;
-				if ( this._currentFrame.parentName ) {
-
-					for ( var frame in this.HieStack ) {
-
-						if ( this.HieStack[ frame ].name === this._currentFrame.parentName ) {
-
-							this.HieStack[ frame ].putBone.add( this._currentFrame.putBone );
-
-						}
-
-					}
-
-				}
-
-			}
-		}, {
-			key: '_readVertexDatas',
-			value: function _readVertexDatas() {
-
-				var endRead = 0;
-				var mode = 0;
-				var mode_local = 0;
-				var maxLength = 0;
-				while ( true ) {
-
-					var changeMode = false;
-					if ( mode_local === 0 ) {
-
-						var refO = this._readInt1( endRead );
-						endRead = refO.endRead;
-						mode_local = 1;
-						maxLength = this._currentObject.data.indexOf( ';;', endRead ) + 1;
-						if ( maxLength <= 0 ) {
-
-							maxLength = this._currentObject.data.length;
-
-						}
-
-					} else {
-
-						var find = 0;
-						switch ( mode ) {
-
-							case 0:
-								find = this._currentObject.data.indexOf( ',', endRead ) + 1;
-								break;
-							case 1:
-								find = this._currentObject.data.indexOf( ';,', endRead ) + 1;
-								break;
-
-						}
-						if ( find === 0 || find > maxLength ) {
-
-							find = maxLength;
-							mode_local = 0;
-							changeMode = true;
-
-						}
-						switch ( this._currentObject.type ) {
-
-							case "Mesh":
-								switch ( mode ) {
-
-									case 0:
-										this._readVertex1( this._currentObject.data.substr( endRead, find - endRead ) );
-										break;
-									case 1:
-										this._readFace1( this._currentObject.data.substr( endRead, find - endRead ) );
-										break;
-
-								}
-								break;
-							case "MeshNormals":
-								switch ( mode ) {
-
-									case 0:
-										this._readNormalVector1( this._currentObject.data.substr( endRead, find - endRead ) );
-										break;
-
-								}
-								break;
-
-						}
-						endRead = find + 1;
-						if ( changeMode ) {
-
-							mode ++;
-
-						}
-
-					}
-					if ( endRead >= this._currentObject.data.length ) {
-
-						break;
-
-					}
-
-				}
-
-			}
-		}, {
-			key: '_readInt1',
-			value: function _readInt1( start ) {
-
-				var find = this._currentObject.data.indexOf( ';', start );
-				return {
-					refI: parseInt( this._currentObject.data.substr( start, find - start ) ),
-					endRead: find + 1
-				};
-
-			}
-		}, {
-			key: '_readVertex1',
-			value: function _readVertex1( line ) {
-
-				var data = this._readLine( line.trim() ).substr( 0, line.length - 2 ).split( ";" );
-				this._currentGeo.GeometryData.vertices.push( parseFloat( data[ 0 ] ), parseFloat( data[ 1 ] ), parseFloat( data[ 2 ] ) );
-				this._currentGeo.GeometryData.skinIndices.push( 0, 0, 0, 0 );
-				this._currentGeo.GeometryData.skinWeights.push( 1, 0, 0, 0 );
-				this._currentGeo.VertexSetedBoneCount.push( 0 );
-
-			}
-		}, {
-			key: '_readFace1',
-			value: function _readFace1( line ) {
-
-				var data = this._readLine( line.trim() ).substr( 2, line.length - 4 ).split( "," );
-				this._currentGeo.GeometryData.indices.push( parseInt( data[ 0 ], 10 ), parseInt( data[ 1 ], 10 ), parseInt( data[ 2 ], 10 ) );
-
-			}
-		}, {
-			key: '_readNormalVector1',
-			value: function _readNormalVector1( line ) {
-
-				var data = this._readLine( line.trim() ).substr( 0, line.length - 2 ).split( ";" );
-				this._currentGeo.GeometryData.normals.push( parseFloat( data[ 0 ] ), parseFloat( data[ 1 ] ), parseFloat( data[ 2 ] ) );
-
-			}
-		}, {
-			key: '_buildGeometry',
-			value: function _buildGeometry() {
-
-				var bufferGeometry = new THREE.BufferGeometry();
-				var position = [];
-				var normals = [];
-				var uvs = [];
-				var skinIndices = [];
-				var skinWeights = [];
-
-				//
-
-				var data = this._currentGeo.GeometryData;
-
-				for ( var i = 0, l = data.indices.length; i < l; i ++ ) {
-
-					var stride2 = data.indices[ i ] * 2;
-					var stride3 = data.indices[ i ] * 3;
-					var stride4 = data.indices[ i ] * 4;
-
-					position.push( data.vertices[ stride3 ], data.vertices[ stride3 + 1 ], data.vertices[ stride3 + 2 ] );
-					normals.push( data.normals[ stride3 ], data.normals[ stride3 + 1 ], data.normals[ stride3 + 2 ] );
-					skinIndices.push( data.skinIndices[ stride4 ], data.skinIndices[ stride4 + 1 ], data.skinIndices[ stride4 + 2 ], data.skinIndices[ stride4 + 3 ] );
-					skinWeights.push( data.skinWeights[ stride4 ], data.skinWeights[ stride4 + 1 ], data.skinWeights[ stride4 + 2 ], data.skinWeights[ stride4 + 3 ] );
-					uvs.push( data.uvs[ stride2 ], data.uvs[ stride2 + 1 ] );
-
-				}
-
-				//
-
-				bufferGeometry.addAttribute( 'position', new THREE.Float32BufferAttribute( position, 3 ) );
-				bufferGeometry.addAttribute( 'normal', new THREE.Float32BufferAttribute( normals, 3 ) );
-				bufferGeometry.addAttribute( 'uv', new THREE.Float32BufferAttribute( uvs, 2 ) );
-				bufferGeometry.addAttribute( 'skinIndex', new THREE.Uint16BufferAttribute( skinIndices, 4 ) );
-				bufferGeometry.addAttribute( 'skinWeight', new THREE.Float32BufferAttribute( skinWeights, 4 ) );
-
-				this._computeGroups( bufferGeometry, data.materialIndices );
-
-				return bufferGeometry;
-
-			}
-		}, {
-			key: '_computeGroups',
-			value: function _computeGroups( bufferGeometry, materialIndices ) {
-
-				var group;
-				var groups = [];
-				var materialIndex = undefined;
-
-				for ( var i = 0; i < materialIndices.length; i ++ ) {
-
-					var currentMaterialIndex = materialIndices[ i ];
-
-					if ( currentMaterialIndex !== materialIndex ) {
-
-						materialIndex = currentMaterialIndex;
-
-						if ( group !== undefined ) {
-
-							group.count = ( i * 3 ) - group.start;
-							groups.push( group );
-
-						}
-
-						group = {
-							start: i * 3,
-							materialIndex: materialIndex
-						};
-
-					}
-
-				}
-
-				if ( group !== undefined ) {
-
-					group.count = ( i * 3 ) - group.start;
-					groups.push( group );
-
-				}
-
-				bufferGeometry.groups = groups;
-
-			}
-		}, {
-			key: '_setMeshTextureCoords',
-			value: function _setMeshTextureCoords() {
-
-				var endRead = 0;
-				var mode = 0;
-				var mode_local = 0;
-				while ( true ) {
-
-					switch ( mode ) {
-
-						case 0:
-							if ( mode_local === 0 ) {
-
-								var refO = this._readInt1( 0 );
-								endRead = refO.endRead;
-								mode_local = 1;
-
-							} else {
-
-								var find = this._currentObject.data.indexOf( ',', endRead ) + 1;
-								if ( find === 0 ) {
-
-									find = this._currentObject.data.length;
-									mode = 2;
-									mode_local = 0;
-
-								}
-								var line = this._currentObject.data.substr( endRead, find - endRead );
-								var data = this._readLine( line.trim() ).split( ";" );
-								if ( this.IsUvYReverse ) {
-
-									this._currentGeo.GeometryData.uvs.push( parseFloat( data[ 0 ] ), 1 - parseFloat( data[ 1 ] ) );
-
-								} else {
-
-									this._currentGeo.GeometryData.uvs.push( parseFloat( data[ 0 ] ), parseFloat( data[ 1 ] ) );
-
-								}
-								endRead = find + 1;
-
-							}
-							break;
-
-					}
-					if ( endRead >= this._currentObject.data.length ) {
-
-						break;
-
-					}
-
-				}
-
-			}
-		}, {
-			key: '_setMeshMaterialList',
-			value: function _setMeshMaterialList() {
-
-				var endRead = 0;
-				var mode = 0;
-				var mode_local = 0;
-				while ( true ) {
-
-					if ( mode_local < 2 ) {
-
-						var refO = this._readInt1( endRead );
-						endRead = refO.endRead;
-						mode_local ++;
-
-					} else {
-
-						var find = this._currentObject.data.indexOf( ';', endRead );
-						if ( find === - 1 ) {
-
-							find = this._currentObject.data.length;
-							mode = 3;
-							mode_local = 0;
-
-						}
-						var line = this._currentObject.data.substr( endRead, find - endRead );
-						var data = this._readLine( line.trim() ).split( "," );
-						for ( var i = 0; i < data.length; i ++ ) {
-
-							this._currentGeo.GeometryData.materialIndices[ i ] = parseInt( data[ i ] );
-
-						}
-						endRead = this._currentObject.data.length;
-
-					}
-					if ( endRead >= this._currentObject.data.length || mode >= 3 ) {
-
-						break;
-
-					}
-
-				}
-
-			}
-		}, {
-			key: '_setMaterial',
-			value: function _setMaterial() {
-
-				var _nowMat = new THREE.MeshPhongMaterial( {
-					color: Math.random() * 0xffffff
-				} );
-				_nowMat.side = THREE.FrontSide;
-				_nowMat.name = this._currentObject.name;
-				var endRead = 0;
-				var find = this._currentObject.data.indexOf( ';;', endRead );
-				var line = this._currentObject.data.substr( endRead, find - endRead );
-				var data = this._readLine( line.trim() ).split( ";" );
-				_nowMat.color.r = parseFloat( data[ 0 ] );
-				_nowMat.color.g = parseFloat( data[ 1 ] );
-				_nowMat.color.b = parseFloat( data[ 2 ] );
-				endRead = find + 2;
-				find = this._currentObject.data.indexOf( ';', endRead );
-				line = this._currentObject.data.substr( endRead, find - endRead );
-				_nowMat.shininess = parseFloat( this._readLine( line ) );
-				endRead = find + 1;
-				find = this._currentObject.data.indexOf( ';;', endRead );
-				line = this._currentObject.data.substr( endRead, find - endRead );
-				var data2 = this._readLine( line.trim() ).split( ";" );
-				_nowMat.specular.r = parseFloat( data2[ 0 ] );
-				_nowMat.specular.g = parseFloat( data2[ 1 ] );
-				_nowMat.specular.b = parseFloat( data2[ 2 ] );
-				endRead = find + 2;
-				find = this._currentObject.data.indexOf( ';;', endRead );
-				if ( find === - 1 ) {
-
-					find = this._currentObject.data.length;
-
-				}
-				line = this._currentObject.data.substr( endRead, find - endRead );
-				var data3 = this._readLine( line.trim() ).split( ";" );
-				_nowMat.emissive.r = parseFloat( data3[ 0 ] );
-				_nowMat.emissive.g = parseFloat( data3[ 1 ] );
-				_nowMat.emissive.b = parseFloat( data3[ 2 ] );
-				var localObject = null;
-				while ( true ) {
-
-					if ( this._currentObject.children.length > 0 ) {
-
-						localObject = this._currentObject.children.shift();
-						if ( this.debug ) {
-
-							console.log( 'processing ' + localObject.name );
-
-						}
-						var fileName = localObject.data.substr( 1, localObject.data.length - 2 );
-						switch ( localObject.type ) {
-
-							case "TextureFilename":
-								_nowMat.map = this.texloader.load( fileName );
-								break;
-							case "BumpMapFilename":
-								_nowMat.bumpMap = this.texloader.load( fileName );
-								_nowMat.bumpScale = 0.05;
-								break;
-							case "NormalMapFilename":
-								_nowMat.normalMap = this.texloader.load( fileName );
-								_nowMat.normalScale = new THREE.Vector2( 2, 2 );
-								break;
-							case "EmissiveMapFilename":
-								_nowMat.emissiveMap = this.texloader.load( fileName );
-								break;
-							case "LightMapFilename":
-								_nowMat.lightMap = this.texloader.load( fileName );
-								break;
-
-						}
-
-					} else {
-
-						break;
-
-					}
-
-				}
-				this._currentGeo.Materials.push( _nowMat );
-
-			}
-		}, {
-			key: '_setSkinWeights',
-			value: function _setSkinWeights() {
-
-				var boneInf = new XboneInf();
-				var endRead = 0;
-				var find = this._currentObject.data.indexOf( ';', endRead );
-				var line = this._currentObject.data.substr( endRead, find - endRead );
-				endRead = find + 1;
-				boneInf.boneName = line.substr( 1, line.length - 2 );
-				boneInf.BoneIndex = this._currentGeo.BoneInfs.length;
-				find = this._currentObject.data.indexOf( ';', endRead );
-				endRead = find + 1;
-				find = this._currentObject.data.indexOf( ';', endRead );
-				line = this._currentObject.data.substr( endRead, find - endRead );
-				var data = this._readLine( line.trim() ).split( "," );
-				for ( var i = 0; i < data.length; i ++ ) {
-
-					boneInf.Indeces.push( parseInt( data[ i ] ) );
-
-				}
-				endRead = find + 1;
-				find = this._currentObject.data.indexOf( ';', endRead );
-				line = this._currentObject.data.substr( endRead, find - endRead );
-				var data2 = this._readLine( line.trim() ).split( "," );
-				for ( var _i = 0; _i < data2.length; _i ++ ) {
-
-					boneInf.Weights.push( parseFloat( data2[ _i ] ) );
-
-				}
-				endRead = find + 1;
-				find = this._currentObject.data.indexOf( ';', endRead );
-				if ( find <= 0 ) {
-
-					find = this._currentObject.data.length;
-
-				}
-				line = this._currentObject.data.substr( endRead, find - endRead );
-				var data3 = this._readLine( line.trim() ).split( "," );
-				boneInf.OffsetMatrix = new THREE.Matrix4();
-				this._ParseMatrixData( boneInf.OffsetMatrix, data3 );
-				this._currentGeo.BoneInfs.push( boneInf );
-
-			}
-		}, {
-			key: '_makePutBoneList',
-			value: function _makePutBoneList( _RootName, _bones ) {
-
-				var putting = false;
-				for ( var frame in this.HieStack ) {
-
-					if ( this.HieStack[ frame ].name === _RootName || putting ) {
-
-						putting = true;
-						var b = new THREE.Bone();
-						b.name = this.HieStack[ frame ].name;
-						b.applyMatrix( this.HieStack[ frame ].FrameTransformMatrix );
-						b.matrixWorld = b.matrix;
-						b.FrameTransformMatrix = this.HieStack[ frame ].FrameTransformMatrix;
-						b.pos = new THREE.Vector3().setFromMatrixPosition( b.FrameTransformMatrix ).toArray();
-						b.rotq = new THREE.Quaternion().setFromRotationMatrix( b.FrameTransformMatrix ).toArray();
-						b.scl = new THREE.Vector3().setFromMatrixScale( b.FrameTransformMatrix ).toArray();
-						if ( this.HieStack[ frame ].parentName && this.HieStack[ frame ].parentName.length > 0 ) {
-
-							for ( var i = 0; i < _bones.length; i ++ ) {
-
-								if ( this.HieStack[ frame ].parentName === _bones[ i ].name ) {
-
-									_bones[ i ].add( b );
-									b.parent = i;
-									break;
-
-								}
-
-							}
-
-						}
-						_bones.push( b );
-
-					}
-
-				}
-
-			}
-		}, {
-			key: '_makeOutputGeometry',
-			value: function _makeOutputGeometry() {
-
-				var mesh = null;
-				if ( this._currentGeo.BoneInfs.length > 0 ) {
-
-					var putBones = [];
-					this._makePutBoneList( this._currentGeo.baseFrame.parentName, putBones );
-					for ( var bi = 0; bi < this._currentGeo.BoneInfs.length; bi ++ ) {
-
-						var boneIndex = 0;
-						for ( var bb = 0; bb < putBones.length; bb ++ ) {
-
-							if ( putBones[ bb ].name === this._currentGeo.BoneInfs[ bi ].boneName ) {
-
-								boneIndex = bb;
-								putBones[ bb ].OffsetMatrix = new THREE.Matrix4();
-								putBones[ bb ].OffsetMatrix.copy( this._currentGeo.BoneInfs[ bi ].OffsetMatrix );
-								break;
-
-							}
-
-						}
-						for ( var vi = 0; vi < this._currentGeo.BoneInfs[ bi ].Indeces.length; vi ++ ) {
-
-							var nowVertexID = this._currentGeo.BoneInfs[ bi ].Indeces[ vi ];
-							var nowVal = this._currentGeo.BoneInfs[ bi ].Weights[ vi ];
-
-							var stride = nowVertexID * 4;
-
-							switch ( this._currentGeo.VertexSetedBoneCount[ nowVertexID ] ) {
-
-								case 0:
-									this._currentGeo.GeometryData.skinIndices[ stride ] = boneIndex;
-									this._currentGeo.GeometryData.skinWeights[ stride ] = nowVal;
-									break;
-								case 1:
-									this._currentGeo.GeometryData.skinIndices[ stride + 1 ] = boneIndex;
-									this._currentGeo.GeometryData.skinWeights[ stride + 1 ] = nowVal;
-									break;
-								case 2:
-									this._currentGeo.GeometryData.skinIndices[ stride + 2 ] = boneIndex;
-									this._currentGeo.GeometryData.skinWeights[ stride + 2 ] = nowVal;
-									break;
-								case 3:
-									this._currentGeo.GeometryData.skinIndices[ stride + 3 ] = boneIndex;
-									this._currentGeo.GeometryData.skinWeights[ stride + 3 ] = nowVal;
-									break;
-
-							}
-							this._currentGeo.VertexSetedBoneCount[ nowVertexID ] ++;
-							if ( this._currentGeo.VertexSetedBoneCount[ nowVertexID ] > 4 ) {
-
-								console.log( 'warn! over 4 bone weight! :' + nowVertexID );
-
-							}
-
-						}
-
-					}
-					for ( var sk = 0; sk < this._currentGeo.Materials.length; sk ++ ) {
-
-						this._currentGeo.Materials[ sk ].skinning = true;
-
-					}
-					var offsetList = [];
-					for ( var _bi = 0; _bi < putBones.length; _bi ++ ) {
-
-						if ( putBones[ _bi ].OffsetMatrix ) {
-
-							offsetList.push( putBones[ _bi ].OffsetMatrix );
-
-						} else {
-
-							offsetList.push( new THREE.Matrix4() );
-
-						}
-
-					}
-
-					var bufferGeometry = this._buildGeometry();
-					mesh = new THREE.SkinnedMesh( bufferGeometry, this._currentGeo.Materials.length === 1 ? this._currentGeo.Materials[ 0 ] : this._currentGeo.Materials );
-
-					this._initSkeleton( mesh, putBones, offsetList );
-
-				} else {
-
-					var _bufferGeometry = this._buildGeometry();
-					mesh = new THREE.Mesh( _bufferGeometry, this._currentGeo.Materials.length === 1 ? this._currentGeo.Materials[ 0 ] : this._currentGeo.Materials );
-
-				}
-				mesh.name = this._currentGeo.name;
-				var worldBaseMx = new THREE.Matrix4();
-				var currentMxFrame = this._currentGeo.baseFrame.putBone;
-				if ( currentMxFrame && currentMxFrame.parent ) {
-
-					while ( true ) {
-
-						currentMxFrame = currentMxFrame.parent;
-						if ( currentMxFrame ) {
-
-							worldBaseMx.multiply( currentMxFrame.FrameTransformMatrix );
-
-						} else {
-
-							break;
-
-						}
-
-					}
-					mesh.applyMatrix( worldBaseMx );
-
-				}
-				this.Meshes.push( mesh );
-
-			}
-		}, {
-			key: '_initSkeleton',
-			value: function _initSkeleton( mesh, boneList, boneInverses ) {
-
-				var bones = [], bone, gbone;
-				var i, il;
-
-				for ( i = 0, il = boneList.length; i < il; i ++ ) {
-
-					gbone = boneList[ i ];
-
-					bone = new THREE.Bone();
-					bones.push( bone );
-
-					bone.name = gbone.name;
-					bone.position.fromArray( gbone.pos );
-					bone.quaternion.fromArray( gbone.rotq );
-					if ( gbone.scl !== undefined ) bone.scale.fromArray( gbone.scl );
-
-				}
-
-				for ( i = 0, il = boneList.length; i < il; i ++ ) {
-
-					gbone = boneList[ i ];
-
-					if ( ( gbone.parent !== - 1 ) && ( gbone.parent !== null ) && ( bones[ gbone.parent ] !== undefined ) ) {
-
-						bones[ gbone.parent ].add( bones[ i ] );
-
-					} else {
-
-						mesh.add( bones[ i ] );
-
-					}
-
-				}
-
-				mesh.updateMatrixWorld( true );
-
-				var skeleton = new THREE.Skeleton( bones, boneInverses );
-				mesh.bind( skeleton, mesh.matrixWorld );
-
-			}
-
-		}, {
-			key: '_readAnimationKey',
-			value: function _readAnimationKey() {
-
-				var endRead = 0;
-				var find = this._currentObject.data.indexOf( ';', endRead );
-				var line = this._currentObject.data.substr( endRead, find - endRead );
-				endRead = find + 1;
-				var nowKeyType = parseInt( this._readLine( line ) );
-				find = this._currentObject.data.indexOf( ';', endRead );
-				endRead = find + 1;
-				line = this._currentObject.data.substr( endRead );
-				var data = this._readLine( line.trim() ).split( ";;," );
-				for ( var i = 0; i < data.length; i ++ ) {
-
-					var data2 = data[ i ].split( ";" );
-					var keyInfo = new XKeyFrameInfo();
-					keyInfo.type = nowKeyType;
-					keyInfo.Frame = parseInt( data2[ 0 ] );
-					keyInfo.index = this._currentAnimeFrames.keyFrames.length;
-					keyInfo.time = keyInfo.Frame;
-					if ( nowKeyType != 4 ) {
-
-						var frameFound = false;
-						for ( var mm = 0; mm < this._currentAnimeFrames.keyFrames.length; mm ++ ) {
-
-							if ( this._currentAnimeFrames.keyFrames[ mm ].Frame === keyInfo.Frame ) {
-
-								keyInfo = this._currentAnimeFrames.keyFrames[ mm ];
-								frameFound = true;
-								break;
-
-							}
-
-						}
-						var frameValue = data2[ 2 ].split( "," );
-						switch ( nowKeyType ) {
-
-							case 0:
-								keyInfo.rot = new THREE.Quaternion( parseFloat( frameValue[ 1 ] ), parseFloat( frameValue[ 2 ] ), parseFloat( frameValue[ 3 ] ), parseFloat( frameValue[ 0 ] ) * - 1 );
-								break;
-							case 1:
-								keyInfo.scl = new THREE.Vector3( parseFloat( frameValue[ 0 ] ), parseFloat( frameValue[ 1 ] ), parseFloat( frameValue[ 2 ] ) );
-								break;
-							case 2:
-								keyInfo.pos = new THREE.Vector3( parseFloat( frameValue[ 0 ] ), parseFloat( frameValue[ 1 ] ), parseFloat( frameValue[ 2 ] ) );
-								break;
-
-						}
-						if ( ! frameFound ) {
-
-							this._currentAnimeFrames.keyFrames.push( keyInfo );
-
-						}
-
-					} else {
-
-						keyInfo.matrix = new THREE.Matrix4();
-						this._ParseMatrixData( keyInfo.matrix, data2[ 2 ].split( "," ) );
-						this._currentAnimeFrames.keyFrames.push( keyInfo );
-
-					}
-
-				}
-
-			}
-		}, {
-			key: '_makeOutputAnimation',
-			value: function _makeOutputAnimation() {
-
-				var animationObj = new XAnimationObj( this.options );
-				animationObj.fps = this.animTicksPerSecond;
-				animationObj.name = this._currentAnime.name;
-				animationObj.make( this._currentAnime.AnimeFrames );
-				this.animations.push( animationObj );
-
-			}
-		}, {
-			key: 'assignAnimation',
-			value: function assignAnimation( _model, _animation, _isBind ) {
-
-				var model = _model;
-				var animation = _animation;
-				if ( ! model ) {
-
-					model = this.Meshes[ 0 ];
-
-				}
-				if ( ! animation ) {
-
-					animation = this.animations[ 0 ];
-
-				}
-				if ( ! model || ! animation ) {
-
-					return null;
-
-				}
-				var put = {};
-				put.fps = animation.fps;
-				put.name = animation.name;
-				put.length = animation.length;
-				put.hierarchy = [];
-				for ( var b = 0; b < model.skeleton.bones.length; b ++ ) {
-
-					var findAnimation = false;
-					for ( var i = 0; i < animation.hierarchy.length; i ++ ) {
-
-						if ( model.skeleton.bones[ b ].name === animation.hierarchy[ i ].name ) {
-
-							findAnimation = true;
-							var c_key = animation.hierarchy[ i ].copy();
-							c_key.parent = - 1;
-							if ( model.skeleton.bones[ b ].parent && model.skeleton.bones[ b ].parent.type === "Bone" ) {
-
-								for ( var bb = 0; bb < put.hierarchy.length; bb ++ ) {
-
-									if ( put.hierarchy[ bb ].name === model.skeleton.bones[ b ].parent.name ) {
-
-										c_key.parent = bb;
-										c_key.parentName = model.skeleton.bones[ b ].parent.name;
-
-									}
-
-								}
-
-							}
-							put.hierarchy.push( c_key );
-							break;
-
-						}
-
-					}
-					if ( ! findAnimation ) {
-
-						var _c_key = animation.hierarchy[ 0 ].copy();
-						_c_key.name = model.skeleton.bones[ b ].name;
-						_c_key.parent = - 1;
-						for ( var k = 0; k < _c_key.keys.length; k ++ ) {
-
-							if ( _c_key.keys[ k ].pos ) {
-
-								_c_key.keys[ k ].pos.set( 0, 0, 0 );
-
-							}
-							if ( _c_key.keys[ k ].scl ) {
-
-								_c_key.keys[ k ].scl.set( 1, 1, 1 );
-
-							}
-							if ( _c_key.keys[ k ].rot ) {
-
-								_c_key.keys[ k ].rot.set( 0, 0, 0, 1 );
-
-							}
-
-						}
-						put.hierarchy.push( _c_key );
-
-					}
-
-				}
-				if ( ! model.geometry.animations ) {
-
-					model.geometry.animations = [];
-
-				}
-
-				model.geometry.animations.push( THREE.AnimationClip.parseAnimation( put, model.skeleton.bones ) );
-				if ( ! model.animationMixer ) {
-
-					model.animationMixer = new THREE.AnimationMixer( model );
-
-				}
-
-				return put;
-
-			}
-		}, {
-			key: '_ParseMatrixData',
-			value: function _ParseMatrixData( targetMatrix, data ) {
-
-				targetMatrix.set( parseFloat( data[ 0 ] ), parseFloat( data[ 4 ] ), parseFloat( data[ 8 ] ), parseFloat( data[ 12 ] ), parseFloat( data[ 1 ] ), parseFloat( data[ 5 ] ), parseFloat( data[ 9 ] ), parseFloat( data[ 13 ] ), parseFloat( data[ 2 ] ), parseFloat( data[ 6 ] ), parseFloat( data[ 10 ] ), parseFloat( data[ 14 ] ), parseFloat( data[ 3 ] ), parseFloat( data[ 7 ] ), parseFloat( data[ 11 ] ), parseFloat( data[ 15 ] ) );
-
-			}
-		} ] );
-		return XLoader;
-
-	}();
-
-	return XLoader;
-
-} ) ) );
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/materials/PointsMaterial.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/materials/PointsMaterial.d.ts
deleted file mode 100644
index a1333d270bc3158df503cba5cb86b25851483ee3..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/materials/PointsMaterial.d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-import { Material, MaterialParameters } from './Material';
-import { Color } from './../math/Color';
-import { Texture } from './../textures/Texture';
-
-// MultiMaterial does not inherit the Material class in the original code. However, it should treat as Material class.
-// See tests/canvas/canvas_materials.ts.
-/**
- * @deprecated Use an Array instead.
- */
-export class MultiMaterial extends Material {
-  constructor(materials?: Material[]);
-
-  isMultiMaterial: true;
-
-  materials: Material[];
-
-  toJSON(meta: any): any;
-}
-
-/**
- * @deprecated Use {@link MultiMaterial} instead.
- */
-
-export interface PointsMaterialParameters extends MaterialParameters {
-  color?: Color | string | number;
-  map?: Texture;
-  size?: number;
-  sizeAttenuation?: boolean;
-}
-
-export class PointsMaterial extends Material {
-  constructor(parameters?: PointsMaterialParameters);
-
-  color: Color;
-  map: Texture | null;
-  size: number;
-  sizeAttenuation: boolean;
-
-  setValues(parameters: PointsMaterialParameters): void;
-}
diff --git a/spaces/barnga/DL/info.md b/spaces/barnga/DL/info.md
deleted file mode 100644
index 01c382296618ad0484470364d80533b417aa53c3..0000000000000000000000000000000000000000
--- a/spaces/barnga/DL/info.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# 😌 [Your furture pet]
-
-### 🧐 Problem Statement and Research Summary
-[I got behind on this assingment but i remeber dowloading and uploading files was hard]
-
-### 🎣 Data Collection Plan
-[Dogs might be the best]
-
-### 💥 Ethical Considerations (Data Privacy and Bias)
-* Data privacy: [It's fine]
-* Bias: [there is no bias that i see]
-
-### 👻 Our Team
-[there is no team]
-
-![aiEDU logo](https://images.squarespace-cdn.com/content/v1/5e4efdef6d10420691f02bc1/5db5a8a3-1761-4fce-a096-bd5f2515162f/aiEDU+_black+logo+stacked.png?format=100w)
diff --git a/spaces/betheredge/air-vibrations/src/app.py b/spaces/betheredge/air-vibrations/src/app.py
deleted file mode 100644
index 9d79bcbe9bde85c230d66599c6a64ff6f8dadc4e..0000000000000000000000000000000000000000
--- a/spaces/betheredge/air-vibrations/src/app.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from string import punctuation
-import gradio as gr
-from transformers import pipeline
-from transformers import AutomaticSpeechRecognitionPipeline
-from deepmultilingualpunctuation import PunctuationModel
-
-puntuation_model = PunctuationModel()
-# capitalization_model = ("KES/caribe-capitalise")
-# text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
-# print(result)
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModel
-
-description = """
-# Gradio Demo for exploring Speech Transcription.
-
-Upload an audio file or record yourself to see a transcription.
-The transcription passes through 4 models: transcription, punctuation, capitalization, and summarization. 
-All output is given
-
-Tips:
-- Large files will take a while to process.
-- Live recording is on the second tab.
-"""
-
-
-capitalise_tokenizer = AutoTokenizer.from_pretrained("KES/caribe-capitalise")
-capitalise_model = AutoModelForSeq2SeqLM.from_pretrained("KES/caribe-capitalise")
-spell_tokenizer = AutoTokenizer.from_pretrained("murali1996/bert-base-cased-spell-correction")
-spell_model = AutoModel.from_pretrained("murali1996/bert-base-cased-spell-correction")
-
-summarizer = pipeline("summarization")
-
-pipe = pipeline(
-    model="facebook/wav2vec2-large-960h", 
-    chunk_length_s=90,
-    stride_length_s=15,
-)
-
-def translate(audio_file):
-    x = pipe(audio_file)
-    text = x['text']
-    return text
-
-
-def punctuation(text):
-    punctuation = puntuation_model.restore_punctuation(text)
-    return punctuation
-
-
-def capitalise(text):
-    text = text.lower()
-    inputs = capitalise_tokenizer("text:"+text, truncation=True, return_tensors='pt')
-    # print(capitalization)
-    output = capitalise_model.generate(inputs['input_ids'], num_beams=4, max_length=4096, early_stopping=True)
-    # output = capitalise_model.generate(inputs['input_ids'], num_beams=4, max_length=1024, early_stopping=True)
-    capitalised_text = capitalise_tokenizer.batch_decode(output, skip_special_tokens=True)
-
-    result = ("".join(capitalised_text))
-    return result
-
-
-def spell_check(text):
-    text = text.lower()
-    inputs = spell_tokenizer(text, return_tensors='pt')
-    # print(capitalization)
-    output = spell_model.generate(inputs)
-    spell_text = spell_tokenizer.batch_decode(output, skip_special_tokens=True)
-
-    result = ("".join(spell_text))
-
-    return result
-
-
-def summarize(text):
-    results = None
-    length = len(text)
-    while not results:
-        try:
-            results = summarizer(text[:length], min_length=10, max_length=128)
-        except IndexError:
-            print(f"shortening text: {length} -> {length//2}")
-            length = length // 2
-    return results[0]['summary_text']
-
-def all(file):
-    trans_text = translate(file).lower()
-    punct_text = punctuation(trans_text)
-    cap_text = capitalise(punct_text)
-    sum_text = summarize(punct_text)
-    return trans_text, punct_text, cap_text, sum_text
-
-input = gr.Audio(type="filepath")
-live_in = gr.Audio(type="filepath", source="microphone")
-# options = gr.CheckboxGroup(
-#     options=["text", "punctuation", "capitalisation"],
-# )
-raw_output = gr.Text(label="Raw Output")
-puncuation_output = gr.Text(label="Punctuation Output")
-capitalization_output = gr.Text(label="Capitalization Output")
-sum_output = gr.Text(label="Summarized Output")
-
-# translater = gr.Interface(
-#     fn=translate, 
-#     inputs=input, 
-#     outputs=raw_output)
-
-# punctuation = gr.Interface(
-#     fn=punctuation,
-#     inputs=raw_output,
-#     outputs=puncuation_output)
-
-# capitalization = gr.Interface(
-#     fn=capitalise,
-#     inputs=puncuation_output,
-#     outputs=capitalization_output]
-
-
-
-# gr.Series(translater, punctuation, capitalization).launch(share=True)
-live_demo = gr.Interface(
-    fn=all,
-    inputs=live_in,
-    outputs=[raw_output, puncuation_output, capitalization_output, sum_output],
-    description=description)
-demo = gr.Interface(
-    fn=all,
-    inputs=input,
-    outputs=[raw_output, puncuation_output, capitalization_output, sum_output],
-    description=description)
-
-# interface = gr.Series(
-#     gr.Textbox(value=description, show_label=False, interactive=False),
-#     gr.TabbedInterface([demo, live_demo], tab_names=["Upload File", "Record Self"])
-# )
-interface = gr.TabbedInterface([demo, live_demo], tab_names=["Upload File", "Record Self"])
-interface.launch()
\ No newline at end of file
diff --git a/spaces/bigyunicorn/sashimi_identifier/app.py b/spaces/bigyunicorn/sashimi_identifier/app.py
deleted file mode 100644
index ffd1472f1fe8484f029b36f1961a791f19d7ea0b..0000000000000000000000000000000000000000
--- a/spaces/bigyunicorn/sashimi_identifier/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: sashimiIdentier.ipynb.
-
-# %% auto 0
-__all__ = ['learner', 'categories', 'img_examples', 'iface', 'sashimiIentifer']
-
-# %% sashimiIdentier.ipynb 19
-from fastai.vision.all import *
-import gradio as gr
-
-# %% sashimiIdentier.ipynb 20
-# first load the learner
-learner = load_learner ('./sashimiModel.pkl')
-
-# %% sashimiIdentier.ipynb 21
-# combine with gradio. 
-categories = ['Ahi(Yellowfin & Bigeye Tuna)', 'Ebi(Sweet Shrimp/Prawns)', 'Hamachi(Yellowtail)', 'Hamadai(Red Snapper)', 'Hotate(Scallops)', 'Ika(Squid)', 'Katsuo(Bonito/Skipjack Tuna)', 'Maguro(Bluefin Tuna)', 'Saba(Japanese Mackerel)', 'Sake(Salmon)']
-def sashimiIentifer(input_imgFilePath):
-    (pred,idx,probs) = learner.predict(input_imgFilePath)
-    return dict(zip(categories, map(float, probs)))
-  
-img_examples1 = ["Hotate_Scallops.jpg", "Ahi_YellowfinAndBigeyeTuna_Sashimi.jpg", "ahi(yellowfin).jpg", "bluefin.jpg", "ika(squid) sashimi.jpeg", "katsuo.jpg", "Katsuo(Bonito_Skipjack Tuna).jpg", "Maguro(Bluefin Tuna).jpg", "redsnapper.jpg", "saba.jpg", "sake.jpg", "squid.jpg", "yellowtail.jpg"]
-img_examples2 = ["dalle_EngawaAndHotate.png", "dalle_Ika (squid) sashimi.png", "Dalle_Maguro(Bluefin Tuna), Katsuo(Bonito_Skipjack Tuna), Ahi(Yellowfin & Bigeye Tuna).png", "dalle_Saba.png", "Dalle_sakeSashimi.png", "stableDiffusion_sweetShrimpSashimi.jpg"]
-
-demo = gr.Blocks()
-
-with demo:
-  with gr.Row():
-    input = gr.inputs.Image(shape=(192,192))
-    output = gr.outputs.Label()
-  with gr.Tab("Example Images from Web"):
-    examplesFromBrowser = gr.Examples(examples=img_examples1, inputs=input)
-  with gr.Tab("Example Images generated by AI"):
-    examplesFromAI = gr.Examples(examples=img_examples2,inputs=input)
-  input.change(fn=sashimiIentifer, inputs=input, outputs=output)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Ajhorn 6 Crack 2021.md b/spaces/bioriAsaeru/text-to-voice/Ajhorn 6 Crack 2021.md
deleted file mode 100644
index 9a9b133df95b25876240fba8fa3afe69286d63b1..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Ajhorn 6 Crack 2021.md	
+++ /dev/null
@@ -1,11 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/Edraw!!INSTALL!! Keygencrackserialgenerator.md b/spaces/bioriAsaeru/text-to-voice/Edraw!!INSTALL!! Keygencrackserialgenerator.md
deleted file mode 100644
index 0650127cc200408fbee2b9a564c0f29c4e8d45ee..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Edraw!!INSTALL!! Keygencrackserialgenerator.md	
+++ /dev/null
@@ -1,48 +0,0 @@
-<h2>edrawkeygencrackserialgenerator</h2><br /><p><b><b>Download File</b> &#10084; <a href="https://urloso.com/2uyQ9y">https://urloso.com/2uyQ9y</a></b></p><br /><br />
-<br />
-.draw(GWF_b, camera, mousePos, x, y, z, altitude)
-
-	end
-
-	
-
-	-- here, the render process ends.
-
-	if t[1] == "gui" then
-
-		GWF_b:on("render_finished", clear_panel_1)
-
-		GWF_b:on("render_finished", clear_panel_2)
-
-		GWF_b:on("render_finished", clear_panel_3)
-
-		GWF_b:on("render_finished", clear_panel_4)
-
-		GWF_b:on("render_finished", clear_panel_5)
-
-		GWF_b:on("render_finished", clear_panel_6)
-
-end
-
---main_loop()
-
---GWF_b:start()Is UPDRS III a valid tool for disease staging in primary dementia with Lewy bodies?
-
-To assess the usefulness of the Unified Parkinson's Disease Rating Scale (UPDRS) III in assessing progression of dementia in patients with primary dementia with Lewy bodies (DLB). One hundred and fifteen patients with primary DLB were assessed with the UPDRS II and III and the Hoehn and Yahr scale; the patients were followed up for 6 months. Patients with DLB who presented with parkinsonism scored more frequently in the UPDRS III, subscales II, III and IV, and items 8 and 11 of the UPDRS II. The frequency of cognitive decline and daytime sleepiness was significantly correlated with the UPDRS III. The severity of cognitive and functional impairment increased significantly during the 6 months of follow-up. Higher functional impairment, cognitive impairment and higher level of disability at baseline were associated with an increase in the UPDRS III at follow-up. It was found that the UPDRS III is more sensitive than the UPDRS II in assessing progression of DLB. The UPDRS III could be a useful tool for disease staging and for monitoring disease progression.Q:
-
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-I am showing my webgrid in my controller method. I want to show progress bar while scrolling. How can i do that?   
-
-MyController 
-
- [HttpPost]
-
- public ActionResult GridView_Complete(int page)
-
- {
-
-     Web 4fefd39f24<br />
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-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Ganntrader 3.1.rar !!HOT!!.md b/spaces/bioriAsaeru/text-to-voice/Ganntrader 3.1.rar !!HOT!!.md
deleted file mode 100644
index 60e63cc4a39a7c4b4b8018dfa158599786479652..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Ganntrader 3.1.rar !!HOT!!.md	
+++ /dev/null
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diff --git a/spaces/buildingai/youtube-video-transcription-with-whisper/README.md b/spaces/buildingai/youtube-video-transcription-with-whisper/README.md
deleted file mode 100644
index 708cde318c1bd23a9ae9e429ad15013e3cb2ff46..0000000000000000000000000000000000000000
--- a/spaces/buildingai/youtube-video-transcription-with-whisper/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Youtube Video Transcription With Whisper
-emoji: 🐢
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/camenduru-com/audioldm-text-to-audio-generation/app.py b/spaces/camenduru-com/audioldm-text-to-audio-generation/app.py
deleted file mode 100644
index 995bcf3aaca6e74fd03f79c7217e9274dc321208..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/audioldm-text-to-audio-generation/app.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import gradio as gr
-import numpy as np
-from audioldm import text_to_audio, build_model
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-model_id="haoheliu/AudioLDM-S-Full"
-
-audioldm = build_model()
-# audioldm=None
-
-# def predict(input, history=[]):
-#     # tokenize the new input sentence
-#     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-#     # append the new user input tokens to the chat history
-#     bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-#     # generate a response 
-#     history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-
-#     # convert the tokens to text, and then split the responses into lines
-#     response = tokenizer.decode(history[0]).split("<|endoftext|>")
-#     response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-#     return response, history
-  
-def text2audio(text, duration, guidance_scale, random_seed, n_candidates):
-    # print(text, length, guidance_scale)
-    waveform = text_to_audio(audioldm, text, random_seed, duration=duration, guidance_scale=guidance_scale, n_candidate_gen_per_text=int(n_candidates)) # [bs, 1, samples]
-    waveform = [gr.make_waveform((16000, wave[0])) for wave in waveform]
-    # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))]
-    if(len(waveform) == 1):
-      waveform = waveform[0]
-    return waveform # ,gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-# iface = gr.Interface(fn=text2audio, inputs=[
-#         gr.Textbox(value="A man is speaking in a huge room", max_lines=1),
-#         gr.Slider(2.5, 10, value=5, step=2.5),
-#         gr.Slider(0, 5, value=2.5, step=0.5),
-#         gr.Number(value=42)
-#     ], outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")],
-#                 allow_flagging="never"
-#                      )
-# iface.launch(share=True)
-
-css = """
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: black;
-            background: black;
-        }
-        input[type='range'] {
-            accent-color: black;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            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 {
-            display: none;
-            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%;
-        }
-        .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;
-        }
-        #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
-        #component-16{border-top-width: 1px!important;margin-top: 1em}
-        .image_duplication{position: absolute; width: 100px; left: 50px}
-"""
-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: Text-to-Audio Generation with Latent Diffusion Models
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-                <a href="https://arxiv.org/abs/2301.12503">[Paper]</a>  <a href="https://audioldm.github.io/">[Project page]</a>
-              </p>
-            </div>
-        """
-    )  
-    gr.HTML("""
-<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-<br/>
-<a href="https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation?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 hammer is hitting a wooden surface", max_lines=1, label="Input your text here. Please ensure it is descriptive and of moderate length.")
-
-            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(2.5, 10, value=5, step=2.5, label="Duration (seconds)")
-              guidance_scale = gr.Slider(0, 5, value=2.5, step=0.5, label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)")
-              n_candidates = gr.Slider(1, 5, 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")
-            ############# Output
-            # outputs=gr.Audio(label="Output", type="numpy")
-            outputs=gr.Video(label="Output")
-            
-            # 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)
-        btn.click(text2audio, inputs=[textbox, duration, guidance_scale, seed, n_candidates], outputs=[outputs])  # , share_button, community_icon, loading_icon
-        # 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 on our<a href="https://github.com/haoheliu/AudioLDM" 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>
-        ''')
-        
-        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(concurrency_count = 3)
-iface.launch(debug=True)
-# iface.launch(debug=True, share=True)
\ No newline at end of file
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/tests/test_image_list_dataset.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/tests/test_image_list_dataset.py
deleted file mode 100644
index 7932602448b49b9be4fcea9645fe7a9c4d53c00e..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/tests/test_image_list_dataset.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import contextlib
-import os
-import tempfile
-import unittest
-import torch
-from torchvision.utils import save_image
-
-from densepose.data.image_list_dataset import ImageListDataset
-from densepose.data.transform import ImageResizeTransform
-
-
-@contextlib.contextmanager
-def temp_image(height, width):
-    random_image = torch.rand(height, width)
-    with tempfile.NamedTemporaryFile(suffix=".jpg") as f:
-        f.close()
-        save_image(random_image, f.name)
-        yield f.name
-    os.unlink(f.name)
-
-
-class TestImageListDataset(unittest.TestCase):
-    def test_image_list_dataset(self):
-        height, width = 720, 1280
-        with temp_image(height, width) as image_fpath:
-            image_list = [image_fpath]
-            category_list = [None]
-            dataset = ImageListDataset(image_list, category_list)
-            self.assertEqual(len(dataset), 1)
-            data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
-            self.assertEqual(data1.shape, torch.Size((1, 3, height, width)))
-            self.assertEqual(data1.dtype, torch.float32)
-            self.assertIsNone(categories1[0])
-
-    def test_image_list_dataset_with_transform(self):
-        height, width = 720, 1280
-        with temp_image(height, width) as image_fpath:
-            image_list = [image_fpath]
-            category_list = [None]
-            transform = ImageResizeTransform()
-            dataset = ImageListDataset(image_list, category_list, transform)
-            self.assertEqual(len(dataset), 1)
-            data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
-            self.assertEqual(data1.shape, torch.Size((1, 3, 749, 1333)))
-            self.assertEqual(data1.dtype, torch.float32)
-            self.assertIsNone(categories1[0])
diff --git a/spaces/chansung/LLM-As-Chatbot/models/starchat.py b/spaces/chansung/LLM-As-Chatbot/models/starchat.py
deleted file mode 100644
index f6903cbe078a56db1463274fa67f7d0c44a38fda..0000000000000000000000000000000000000000
--- a/spaces/chansung/LLM-As-Chatbot/models/starchat.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import torch
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from optimum.bettertransformer import BetterTransformer
-
-def load_model(
-    base, 
-    finetuned, 
-    mode_cpu,
-    mode_mps,
-    mode_full_gpu,
-    mode_8bit,
-    mode_4bit,
-    force_download_ckpt
-):
-    tokenizer = AutoTokenizer.from_pretrained(base)
-    tokenizer.padding_side = "left"
-    
-    if mode_cpu:
-        print("cpu mode")
-        model = AutoModelForCausalLM.from_pretrained(
-            base, 
-            device_map={"": "cpu"}, 
-            use_safetensors=False,
-        )
-            
-    elif mode_mps:
-        print("mps mode")
-        model = AutoModelForCausalLM.from_pretrained(
-            base,
-            device_map={"": "mps"},
-            torch_dtype=torch.float16,
-            use_safetensors=False,
-        )
-            
-    else:
-        print("gpu mode")
-        print(f"8bit = {mode_8bit}, 4bit = {mode_4bit}")
-        model = AutoModelForCausalLM.from_pretrained(
-            base,
-            load_in_8bit=mode_8bit,
-            load_in_4bit=mode_4bit,
-            device_map="auto",
-            torch_dtype=torch.float16,
-            use_safetensors=False
-        )
-
-        if not mode_8bit and not mode_4bit:
-            model.half()
-
-    # model = BetterTransformer.transform(model)
-    return model, tokenizer
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md b/spaces/chendl/compositional_test/transformers/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md
deleted file mode 100644
index d8a4e11087301581e6f1c107729f434ff484e520..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md
+++ /dev/null
@@ -1,516 +0,0 @@
-# Fine-Tuning week of XLSR-Wav2Vec2 on 60 languages 🌍
-
-Welcome to the fine-tuning week! The goal of this week is to have state-of-the-art automatic speech recognition (ASR) models in as many languages as possible. The fine-tuning week ends on Friday, the 26th March at midnight PST time.
-
-Participants are encouraged to fine-tune the pretrained [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) checkpoint on one or more of the 60 languages of [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets).
-Furthermore, it is very much appreciated if participants fine-tune XLSR-Wav2Vec2 on a language that is not included in the Common Voice dataset.
-
-All fine-tuned models uploaded until Friday, the 26th March midnight PST, will be taken into account for competition, and the best model per language will be awarded a prize if the best model performs reasonably well. 
-The testing data to evaluate the models will be the official [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets) *`test data`* of version 6.1. Again, participants are very much encouraged to fine-tune XLSR-Wav2Vec2 on languages that are not found in the Common Voice dataset since those languages are even more likely to be underrepresented in the speech community. 
-Each model fine-tuned on a language not found in Common Voice, will be evaluated by the Hugging Face team after Friday, the 26th March at midnight PST, and if the model performs reasonably well, the model receives a prize as well. 
-For more information on which data can be used for training, how the models are evaluated exactly, and what type of data preprocessing can be used, please see ["Training and Evaluation Rules"](#training-and-evaluation-rules).
-
-**Please keep in mind:**
-The spirit of the fine-tuning week is to provide state-of-the-art speech recognition in as many languages as possible to the community! 
-So while we encourage healthy competition between people/groups of the same language so that better results are obtained, it is extremely important that we help each other and share our insights with the whole team/community. 
-What matters in the end is what has been achieved by the team as a whole during the fine-tuning week. 
-That being said, we strongly encourage people to share tips & tricks on the forum or Slack, help each other when team members encounter bugs, and work in groups. 
-To make it easier to share and help, forum threads have been created under the name {language} ASR: Fine-Tuning Wav2Vec2, e.g. here. 
-It is very much possible that prizes will be given to groups of people instead of individuals. Also, don't hesitate to ask questions, propose improvements to the organization, to the material given to participants, etc...🤗
-
-## Table of Contents
-
-- [Organization of the fine tuning week](#organization-of-the-fine-tuning-week)
-- [How to fine tune XLSR Wav2Vec2](#how-to-fine-tune-xlsr-wav2vec2)
-	- [Google colab setup](#google-colab-setup)
-	- [Local machine](#local-machine)
-- [How to upload my trained checkpoint](#how-to-upload-my-trained-checkpoint)
-	- [How to create the README](#how-to-create-the-readme)
-- [How to evaluate my trained checkpoint](#how-to-evaluate-my-trained-checkpoint)
-- [Rules of training and evaluation](#rules-of-training-and-evaluation)
-- [Tips and tricks](#tips-and-tricks)
-	- [How to combine multiple datasests into one](#how-to-combine-multiple-datasets-into-one)
-	- [How to effectively preprocess the data](#how-to-effectively-preprocess-the-data)
-	- [How to efficiently preproces the data](#how-to-do-efficiently-load-datasets-with-limited-ram-and-hard-drive-space)
-	- [How to do hyperparameter tuning](#how-to-do-hyperparameter-tuning)
-	- [How to preprocess and evaluate character based languages](#how-to-preprocess-and-evaluate-character-based-languages)
-- [Further reading material](#further-reading-material)
-- [FAQ](#faq)
-
-## Organization of the fine tuning week
-
-The week officially starts on 22.03.2021 and ends on 29.03.2021, but you are more than welcome to start fine-tuning models before the start date. 
-General questions you might have, general problems you encounter, and general tips can be shared directly on the Slack channel (see [this post](https://discuss.huggingface.co/t/open-to-the-community-xlsr-wav2vec2-fine-tuning-week-for-low-resource-languages/4467) on how to be added to Slack). 
-More language-specific questions or specific bugs should be posted on the [forum](https://discuss.huggingface.co/) (feel free to use already existing language-specific threads, *e.g.* [this one](https://discuss.huggingface.co/t/arabic-asr-fine-tuning-wav2vec2/4608) or open a new one if there is no thread for your language yet) or directly on [github](https://github.com/huggingface/transformers) if you think some code or document needs correction/improvement.
-Starting on Monday, the 22.03.2021, the Hugging Face team will try to provide an overview of currently trained models along with their evaluation results.
-All the necessary information on:
-
-- How to fine-tune the XLSR model
-- How to upload the model
-- How to share your evaluation results & training/eval script
-- What are the training/evaluation rules
-
-can be found in the sections below. If something is still unclear, feel free to drop a message in the Slack channel.
-
-## How to fine tune XLSR Wav2Vec2
-
-This chapter gives an in-detail explanation of how to fine-tune [Facebook's multi-lingual Wav2vec2](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on any language of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets).
-
-Two possible setups can be used to fine-tune Wav2Vec2. The easiest setup is to simply use [google colab](https://colab.research.google.com/). It is possible to train the full model in a *free* google colab, but it is recommended to use google colab pro since it is more stable.
-
-The other option is to run a script locally. While this can be more difficult to set up, it also means that you have more control over the training run and probably access to better GPUs than you would have in a google colab. 
-For small datasets, it is usually totally sufficient to train your model
-in a google colab. For larger and thus more memory-intensive datasets, it is probably
-better to fine-tune the model locally.
-
-For each option, we explain in detail how to fine-tune XLSR-Wav2Vec2 in the following.
-
-### Google colab setup
-
-**Note**: Instead of reading the following section, you can simply watch [this](https://www.youtube.com/watch?v=UynYn2C3tI0&ab_channel=PatrickvonPlaten) video, where Patrick explains how to adapt the google colab for your specific language.
-
-**1.**: If you plan on training XLSR-Wav2Vec2 in a google colab, you should first make sure to have a valid gmail account. You can sign up for a gmail account [here](https://accounts.google.com/signup/v2/webcreateaccount?hl=en&flowName=GlifWebSignIn&flowEntry=SignUp). 
-Having successfully signed up for gmail, you can now sign in to your account to make sure you are logged in when opening new tabs in your browser.
-
-**2.**: Next, head over to the official [Fine-Tune XLSR-Wav2Vec2 with 🤗 Transformes](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb) google colab. The first thing you should do is to make a copy of it - click `->File->Save a copy in Drive`. This should save a copy of the google colab in your google drive. 
-
-**3.**: Now it is highly recommended to carefully read the google colab without running the cells yet. 
-You should get an understanding of the model is trained and what you will have to change when training the model in a different language. 
-Having done so, you can again head over to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language you want to fine-tune [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on. Make sure you remember the language code (For each language, you can find it under the field "*Version*". It corresponds to **all characters before the first underscore**. *E.g.* for Greek it is *el*, while for Irish it is *ga-IE*.
-
-**4.**: Now you should replace the language code used for the demo of this colab, being *tr* for Turkish with the language code corresponding to the language you just chose in the **second** cell of the google colab. This will load the correct data for your language.
-
-**5.**: It is time to start running the google colab! Make sure that you have selected "GPU" as your runtime environment and you can start running the cells one-by-one. Make sure you attentively read the text between the cells to understand what is happening and to eventually correct the cells to improve the fine-tuning script for your language. Things you might want to improve/change:
- 
- - Data loading. It is very much recommended to use more than just the official training data of the Common Voice dataset. If you find more data on the internet, feel free to use it! Check out the section ["How to combined multiple datasets into one"](#how-to-combine-multiple-datasets-into-one)
-
-- Data Processing. You should adapt the data processing to your specific language. In data processing, you should make the data more uniform so that it will be easier for the model to learn how to classify speech in your data. Here it can be really helpful to be proficient in the language to know what can be done to simplify the language without changing the meaning. 
-Data processing methods include, but are not limited to:
-	- Normalizing your data. Make sure all characters are lower-cased.
-	- Remove typographical symbols and punctuation marks. See a list [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks). Be careful to not remove punctuation marks that can change the meaning of the sentence. *E.g.* you should not remove the single quotation mark `'` in English, as it would change the words `"it's"` to `"its"` which is a different word and has thus a different meaning. For more tips on data processing see ["How to effectively preprocess the data"](#how-to-effectively-preprocess-the-data")
-
-- Hyperparameter Tuning. Depending on the size of the data you should probably change the hyperparameters of the google colab. You can change any parameter you like. For more tips and tricks see ["How to do hyperparameter tuning for my language"](#how-to-do-hyperparameter-tuning-for-my-language)
-
-When running the google colab make sure that you uncomment the cell corresponding to mounting your google drive to the colab. This cell looks as follows:
-
-```python
-# from google.colab import drive
-# drive.mount('/content/gdrive/')
-``` 
-
-Uncomment it, run it, and follow the instructions to mount your google drive. This way you can be sure that the model parameters and created tokenizer & feature extractor files are saved in **your** google drive.
-
-Also, make sure that you uncomment the cells corresponding to save the preprocessing files and trained model weights to your drive. Otherwise, you might lose a trained model if you google crashes. You should change the name of your model from `wav2vec2-large-xlsr-turkish-demo` to `wav2vec2-large-xlsr-{your_favorite_name}`.
-
-Those cells correspond to:
-
-```python
-# processor.save_pretrained("/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo")
-```
-
-and the line:
-
-```python
-  output_dir="/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo",
-```
-
-further below (which should already be uncommented).
-
-Having finished the training you should find the following files/folders under the folder `wav2vec2-large-xlsr-{your_favorite_name}` in your google drive:
-
-- `preprocessor_config.json` - the parameters of the feature extractor
-- `special_tokens_map.json` - the special token map of the tokenizer
-- `tokenizer_config.json` - the parameters of the tokenizer
-- `vocab.json` - the vocabulary of the tokenizer
-- `checkpoint-{...}/` - the saved checkpoints saved during training. Each checkpoint should contain the files: `config.json`, `optimizer.pt`, `pytorch_model.bin`, `scheduler.pt`, `training_args.bin`. The files `config.json` and `pytorch_model.bin` define your model.
-
-If you are happy with your training results it is time to upload your model! 
-Download the following files to your local computer: **`preprocessor_config.json`, `special_tokens_map.json`, `tokenizer_config.json`, `vocab.json`, `config.json`, `pytorch_model.bin`**. Those files fully define a XLSR-Wav2Vec2 model checkpoint.
-
-Awesome you have successfully trained a XLSR-Wav2Vec2 model 😎. Now you can jump to the section ["How to upload my trained checkpoint"](#how-to-upload-my-trained-checkpoint)
-
-### Local machine
-
-We have provided `run_common_voice.py` script to run fine-tuning on local machine. The script is similar to the colab but allows you to launch training using command line, save and continue training from previous checkpoints and launch training on multiple GPUs.
-For bigger datasets, we recommend to train Wav2Vec2 locally instead of in a google colab.
-
-1. To begin with, we should clone transformers localy and install all the required packages.
-
-First, you need to clone the `transformers` repo with:
-
-```
-$ git clone https://github.com/huggingface/transformers.git
-```
-
-Second, head over to the `examples/research_projects/wav2vec2` directory, where the `run_common_voice.py` script is located.
-
-```
-$ cd transformers/examples/research_projects/wav2vec2
-```
-
-Third, install the required packages. The
-packages are listed in the `requirements.txt` file and can be installed with
-
-```
-$ pip install -r requirements.txt
-```
-
-	**Note**: Installing the latest version of `torchaudio` will also upgrade `torch` to it's latest stable version. If you are using specific version of `torch` then make sure
-	to use the correct `torchaudio` version compatible with your version of `torch`. By default the `requirements.txt` will install the latest version of `torchaudio`.
-
-2. Next, take a look at the `run_common_voice.py` script to get an understanding of how it works. In short the script does the following:
-
-	- Load the given common voice dataset
-	- Create vocab for the language
-	- Load the model with given hyperparameters
-	- Pre-process the dataset to input into the model
-	- Run training
-	- Run evaluation
-
-3. The following examples show how you can launch fine-tuning for the common voice dataset. 
-Here we will run the script on the *Turkish* Common Voice dataset for demonstration purposes.
-	
-	**To lanuch fine-tuninig on a single GPU:**
-	
-	```bash
-	python run_common_voice.py \
-		--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
-		--dataset_config_name="tr" \ # use this argument to specify the language code
-		--output_dir=./wav2vec2-large-xlsr-turkish-demo \
-		--overwrite_output_dir \
-		--num_train_epochs="5" \
-		--per_device_train_batch_size="16" \
-		--learning_rate="3e-4" \
-		--warmup_steps="500" \
-		--evaluation_strategy="steps" \
-		--save_steps="400" \
-		--eval_steps="400" \
-		--logging_steps="400" \
-		--save_total_limit="3" \
-		--freeze_feature_extractor \
-		--feat_proj_dropout="0.0" \
-		--layerdrop="0.1" \
-		--gradient_checkpointing \
-		--fp16 \
-		--group_by_length \
-		--do_train --do_eval
-	```
-
-	**To lanuch fine-tuninig on multiple GPUs:**
-	
-	```bash
-	python -m torch.distributed.launch \
-		--nproc_per_node 4 run_common_voice.py \
-		--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
-		--dataset_config_name="tr" \ # use this argument to specify the language code
-		--output_dir=./wav2vec2-large-xlsr-turkish-demo \
-		--overwrite_output_dir \
-		--num_train_epochs="5" \
-		--per_device_train_batch_size="16" \
-		--learning_rate="3e-4" \
-		--warmup_steps="500" \
-		--evaluation_strategy="steps" \
-		--save_steps="400" \
-		--eval_steps="400" \
-		--logging_steps="400" \
-		--save_total_limit="3" \
-		--freeze_feature_extractor \
-		--feat_proj_dropout="0.0" \
-		--layerdrop="0.1" \
-		--gradient_checkpointing \
-		--fp16 \
-		--group_by_length \
-		--do_train --do_eval
-	```
-
-	The above command will launch the training on 4 GPUs. Use the `--nproc_per_node` option to specify the number of GPUs.
-
-	Once the training is finished, the model and checkpoints will be saved under the directory specified by the `--output_dir` argument.
-
-4. The script also allows you to resume training from the last saved checkpoint. To resume training from last saved checkpoint remove the `--overwrite_output_dir` option and run the same command again.  And to continue training from a specific checkpoint, keep the `--overwrite_output_dir`
-option and pass the path of the checkpoint as `--model_name_or_path`.
-
-As the script is based on the `Trainer` API, refer to the [Trainer docs](https://huggingface.co/transformers/main_classes/trainer.html) for more information about ``Trainer`` and ``TrainingArguments``.
-
-[OVH cloud](https://www.ovh.com/world/) has generously offered free compute for this sprint. Please refer to [this video](https://www.youtube.com/watch?v=2hlkWAESMk8&ab_channel=Databuzzword) to get started with OVH. 
-
-
-## How to upload my trained checkpoint
-
-To upload your trained checkpoint, you have to create a new model repository on the 🤗 model hub, from this page: https://huggingface.co/new
-
-> You can also follow the more in-depth instructions [here](https://huggingface.co/transformers/model_sharing.html) if needed.
-
-Having created your model repository on the hub, you should clone it locally:
-
-```bash
-git lfs install
-
-git clone https://huggingface.co/username/your-model-name
-```
-
-Then and add the following files that fully define a XLSR-Wav2Vec2 checkpoint into the repository. You should have added the following files.
-
-- `preprocessor_config.json`
-- `special_tokens_map.json`
-- `tokenizer_config.json`
-- `vocab.json`
-- `config.json`
-- `pytorch_model.bin`
-
-Having added the above files, you should run the following to push files to your model repository.  
-```
-git add . && git commit -m "Add model files" && git push
-```
-
-The next **very important** step is to create the model card. For people to use your fine-tuned 
-model it is important to understand: 
-
-- What kind of model is it?
-- What is your model useful for?
-- What data was your model trained on?
-- How well does your model perform?
-
-All these questions should be answered in a model card which is the first thing people see when 
-visiting your model on the hub under `https://huggingface.co/{your_username}/{your_modelname}`.
-
-**Note**:
-It is extremely important that you add this model card or else we cannot find your model and thus cannot take the model into 
-account for the final evaluation.
-
-### How to create the readme
-
-The model card is written in markdown (`.md`) and should be added by simply clicking on the "Add model card" button which is found on the top right corner. 
-You are encouraged to copy-paste the following template into your model card. 
-
-**Make sure that** instead of copying the output of the markdown file you copy the **raw** version of the following part. 
-
-To get the raw version of this file, simply click on the "`raw`" button on the top right corner of this file next to "`blame`" and copy everything below the marker.
-Make sure that you read and consequently remove all #TODO: statements from the model card. 
-
-<======================Copy **raw** version from here=========================
----
-language: {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
-datasets:
-- common_voice #TODO: remove if you did not use the common voice dataset
-- TODO: add more datasets if you have used additional datasets. Make sure to use the exact same 
-dataset name as the one found [here](https://huggingface.co/datasets). If the dataset can not be found in the official datasets, just give it a new name
-metrics:
-- wer
-tags:
-- audio
-- automatic-speech-recognition
-- speech
-- xlsr-fine-tuning-week
-license: apache-2.0
-model-index:
-- name: {human_readable_name} #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like `Elgeish XLSR Wav2Vec2 Large 53`
-  results:
-  - task: 
-      name: Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
-      type: common_voice
-      args: {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
-    metrics:
-       - name: Test WER
-         type: wer
-         value: {wer_result_on_test} #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). **Please** remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value
----
-
-# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, *e.g.* French
-
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice
-When using this model, make sure that your speech input is sampled at 16kHz.
-
-## Usage
-
-The model can be used directly (without a language model) as follows:
-
-```python
-import torch
-import torchaudio
-from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-
-test_dataset = load_dataset("common_voice", "{lang_id}", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
-
-processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
-model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
-
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-# Preprocessing the datasets.
-# We need to read the aduio files as arrays
-def speech_file_to_array_fn(batch):
-	speech_array, sampling_rate = torchaudio.load(batch["path"])
-	batch["speech"] = resampler(speech_array).squeeze().numpy()
-	return batch
-
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-
-with torch.no_grad():
-	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
-
-predicted_ids = torch.argmax(logits, dim=-1)
-
-print("Prediction:", processor.batch_decode(predicted_ids))
-print("Reference:", test_dataset[:2]["sentence"])
-```
-
-
-## Evaluation
-
-The model can be evaluated as follows on the {language} test data of Common Voice.  # TODO: replace #TODO: replace language with your {language}, *e.g.* French
-
-
-```python
-import torch
-import torchaudio
-from datasets import load_dataset, load_metric
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import re
-
-test_dataset = load_dataset("common_voice", "{lang_id}", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
-wer = load_metric("wer")
-
-processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
-model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
-model.to("cuda")
-
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'  # TODO: adapt this list to include all special characters you removed from the data
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-# Preprocessing the datasets.
-# We need to read the aduio files as arrays
-def speech_file_to_array_fn(batch):
-	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
-	speech_array, sampling_rate = torchaudio.load(batch["path"])
-	batch["speech"] = resampler(speech_array).squeeze().numpy()
-	return batch
-
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-
-# Preprocessing the datasets.
-# We need to read the aduio files as arrays
-def evaluate(batch):
-	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-
-	with torch.no_grad():
-		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
-
-	pred_ids = torch.argmax(logits, dim=-1)
-	batch["pred_strings"] = processor.batch_decode(pred_ids)
-	return batch
-
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
-
-print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-```
-
-**Test Result**: XX.XX %  # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.
-
-
-## Training
-
-The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ...  # TODO: adapt to state all the datasets that were used for training.
-
-The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
-
-=======================To here===============================>
-
-Your model in then available under *huggingface.co/{your_username}/{your_chosen_xlsr-large_model_name}* for everybody to use 🎉.
-
-## How to evaluate my trained checkpoint
-
-Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as 
-copying the evaluation code of your model card into a python script and running it. Make sure to note 
-the final result on the model card **both** under the YAML tags at the very top **and** below your evaluation code under "Test Results".
-
-## Rules of training and evaluation
-
-In this section, we will quickly go over what data is allowed to be used as training 
-data, what kind of data preprocessing is allowed be used, and how the model should be evaluated.
-
-To make it very simple regarding the first point: **All data except the official common voice `test` data set can be used as training data**. For models trained in a language that is not included in Common Voice, the author of the model is responsible to 
-leave a reasonable amount of data for evaluation.
-
-Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to 
-normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical 
-symbols and punctuation marks. A list of such symbols can *e.g.* be fonud [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that 
-would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark `'` since it 
-would change the meaning of the word `"it's"` to `"its"` which would then be incorrect. So the golden rule here is to 
-not remove any characters that could change the meaning of a word into another word. This is not always obvious and should 
-be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "`-`" since it doesn't change the 
-meaninng of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning.
-
-Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was 
-done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586).
-
-## Tips and tricks
-
-This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week.
-
-### How to combine multiple datasets into one
-
-Check out [this](https://discuss.huggingface.co/t/how-to-combine-local-data-files-with-an-official-dataset/4685) post.
-
-### How to effectively preprocess the data
-
-
-### How to do efficiently load datasets with limited ram and hard drive space
-
-Check out [this](https://discuss.huggingface.co/t/german-asr-fine-tuning-wav2vec2/4558/8?u=patrickvonplaten) post.
-
-
-### How to do hyperparameter tuning
-
-
-### How to preprocess and evaluate character based languages
-
-
-## Further reading material
-
-It is recommended that take some time to read up on how Wav2vec2 works in theory. 
-Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. 
-
-**However**, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice.
-
-If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2:
-
-- [Facebook's Wav2Vec2 blog post](https://ai.facebook.com/blog/wav2vec-state-of-the-art-speech-recognition-through-self-supervision/)
-- [Official Wav2Vec2 paper](https://arxiv.org/abs/2006.11477)
-- [Official XLSR Wav2vec2 paper](https://arxiv.org/pdf/2006.13979.pdf)
-- [Hugging Face Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)
-- [How does CTC (Connectionist Temporal Classification) work](https://distill.pub/2017/ctc/)
-
-It helps to have a good understanding of the following points:
-
-- How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT.
-
-- What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ...
-
-- What part of the model needs to be fine-tuned? -> The pretrained model **does not** include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to **not** further fine-tune the feature extractor.
-
-- What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on **53** languages. 
-
-- What languages are considered to be similar by XLSR-Wav2Vec2? In the official [XLSR Wav2Vec2 paper](https://arxiv.org/pdf/2006.13979.pdf), the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you).
-
-
-## FAQ
-
-- Can a participant fine-tune models for more than one language? 
-Yes! A participant can fine-tune models in as many languages she/he likes
-- Can a participant use extra data (apart from the common voice data)?
-Yes! All data except the official common voice `test data` can be used for training.
-If a participant wants to train a model on a language that is not part of Common Voice (which 
-is very much encouraged!), the participant should make sure that some test data is held out to 
-make sure the model is not overfitting.
-- Can we fine-tune for high-resource languages? 
-Yes! While we do not really recommend people to fine-tune models in English since there are
-already so many fine-tuned speech recognition models in English. However, it is very much 
-appreciated if participants want to fine-tune models in other "high-resource" languages, such 
-as French, Spanish, or German. For such cases, one probably needs to train locally and apply 
-might have to apply tricks such as lazy data loading (check the ["Lazy data loading"](#how-to-do-lazy-data-loading) section for more details).
diff --git a/spaces/chilge/Fushimi/vdecoder/hifigan/utils.py b/spaces/chilge/Fushimi/vdecoder/hifigan/utils.py
deleted file mode 100644
index 84bff024f4d2e2de194b2a88ee7bbe5f0d33f67c..0000000000000000000000000000000000000000
--- a/spaces/chilge/Fushimi/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-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 apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/FtexImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/FtexImagePlugin.py
deleted file mode 100644
index c46b2f28ba6c889d643db3bcf8fccade7fdc6d2d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/FtexImagePlugin.py
+++ /dev/null
@@ -1,113 +0,0 @@
-"""
-A Pillow loader for .ftc and .ftu files (FTEX)
-Jerome Leclanche <jerome@leclan.ch>
-
-The contents of this file are hereby released in the public domain (CC0)
-Full text of the CC0 license:
-  https://creativecommons.org/publicdomain/zero/1.0/
-
-Independence War 2: Edge Of Chaos - Texture File Format - 16 October 2001
-
-The textures used for 3D objects in Independence War 2: Edge Of Chaos are in a
-packed custom format called FTEX. This file format uses file extensions FTC
-and FTU.
-* FTC files are compressed textures (using standard texture compression).
-* FTU files are not compressed.
-Texture File Format
-The FTC and FTU texture files both use the same format. This
-has the following structure:
-{header}
-{format_directory}
-{data}
-Where:
-{header} = {
-    u32:magic,
-    u32:version,
-    u32:width,
-    u32:height,
-    u32:mipmap_count,
-    u32:format_count
-}
-
-* The "magic" number is "FTEX".
-* "width" and "height" are the dimensions of the texture.
-* "mipmap_count" is the number of mipmaps in the texture.
-* "format_count" is the number of texture formats (different versions of the
-same texture) in this file.
-
-{format_directory} = format_count * { u32:format, u32:where }
-
-The format value is 0 for DXT1 compressed textures and 1 for 24-bit RGB
-uncompressed textures.
-The texture data for a format starts at the position "where" in the file.
-
-Each set of texture data in the file has the following structure:
-{data} = format_count * { u32:mipmap_size, mipmap_size * { u8 } }
-* "mipmap_size" is the number of bytes in that mip level. For compressed
-textures this is the size of the texture data compressed with DXT1. For 24 bit
-uncompressed textures, this is 3 * width * height. Following this are the image
-bytes for that mipmap level.
-
-Note: All data is stored in little-Endian (Intel) byte order.
-"""
-
-import struct
-from enum import IntEnum
-from io import BytesIO
-
-from . import Image, ImageFile
-
-MAGIC = b"FTEX"
-
-
-class Format(IntEnum):
-    DXT1 = 0
-    UNCOMPRESSED = 1
-
-
-class FtexImageFile(ImageFile.ImageFile):
-    format = "FTEX"
-    format_description = "Texture File Format (IW2:EOC)"
-
-    def _open(self):
-        if not _accept(self.fp.read(4)):
-            msg = "not an FTEX file"
-            raise SyntaxError(msg)
-        struct.unpack("<i", self.fp.read(4))  # version
-        self._size = struct.unpack("<2i", self.fp.read(8))
-        mipmap_count, format_count = struct.unpack("<2i", self.fp.read(8))
-
-        self.mode = "RGB"
-
-        # Only support single-format files.
-        # I don't know of any multi-format file.
-        assert format_count == 1
-
-        format, where = struct.unpack("<2i", self.fp.read(8))
-        self.fp.seek(where)
-        (mipmap_size,) = struct.unpack("<i", self.fp.read(4))
-
-        data = self.fp.read(mipmap_size)
-
-        if format == Format.DXT1:
-            self.mode = "RGBA"
-            self.tile = [("bcn", (0, 0) + self.size, 0, 1)]
-        elif format == Format.UNCOMPRESSED:
-            self.tile = [("raw", (0, 0) + self.size, 0, ("RGB", 0, 1))]
-        else:
-            msg = f"Invalid texture compression format: {repr(format)}"
-            raise ValueError(msg)
-
-        self.fp.close()
-        self.fp = BytesIO(data)
-
-    def load_seek(self, pos):
-        pass
-
-
-def _accept(prefix):
-    return prefix[:4] == MAGIC
-
-
-Image.register_open(FtexImageFile.format, FtexImageFile, _accept)
-Image.register_extensions(FtexImageFile.format, [".ftc", ".ftu"])
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/parts/styles.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/parts/styles.py
deleted file mode 100644
index 00c7cb3c325687055da1808df342fd21f76cbb5d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/parts/styles.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# encoding: utf-8
-
-"""
-Provides StylesPart and related objects
-"""
-
-from __future__ import (
-    absolute_import, division, print_function, unicode_literals
-)
-
-import os
-
-from ..opc.constants import CONTENT_TYPE as CT
-from ..opc.packuri import PackURI
-from ..opc.part import XmlPart
-from ..oxml import parse_xml
-from ..styles.styles import Styles
-
-
-class StylesPart(XmlPart):
-    """
-    Proxy for the styles.xml part containing style definitions for a document
-    or glossary.
-    """
-    @classmethod
-    def default(cls, package):
-        """
-        Return a newly created styles part, containing a default set of
-        elements.
-        """
-        partname = PackURI('/word/styles.xml')
-        content_type = CT.WML_STYLES
-        element = parse_xml(cls._default_styles_xml())
-        return cls(partname, content_type, element, package)
-
-    @property
-    def styles(self):
-        """
-        The |_Styles| instance containing the styles (<w:style> element
-        proxies) for this styles part.
-        """
-        return Styles(self.element)
-
-    @classmethod
-    def _default_styles_xml(cls):
-        """
-        Return a bytestream containing XML for a default styles part.
-        """
-        path = os.path.join(
-            os.path.split(__file__)[0], '..', 'templates',
-            'default-styles.xml'
-        )
-        with open(path, 'rb') as f:
-            xml_bytes = f.read()
-        return xml_bytes
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/visitor.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/visitor.py
deleted file mode 100644
index 3d28135fad3a951c447d03b7f2b08403cb24a12e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/visitor.py
+++ /dev/null
@@ -1,143 +0,0 @@
-"""Generic visitor pattern implementation for Python objects."""
-
-import enum
-
-
-class Visitor(object):
-
-    defaultStop = False
-
-    @classmethod
-    def _register(celf, clazzes_attrs):
-        assert celf != Visitor, "Subclass Visitor instead."
-        if "_visitors" not in celf.__dict__:
-            celf._visitors = {}
-
-        def wrapper(method):
-            assert method.__name__ == "visit"
-            for clazzes, attrs in clazzes_attrs:
-                if type(clazzes) != tuple:
-                    clazzes = (clazzes,)
-                if type(attrs) == str:
-                    attrs = (attrs,)
-                for clazz in clazzes:
-                    _visitors = celf._visitors.setdefault(clazz, {})
-                    for attr in attrs:
-                        assert attr not in _visitors, (
-                            "Oops, class '%s' has visitor function for '%s' defined already."
-                            % (clazz.__name__, attr)
-                        )
-                        _visitors[attr] = method
-            return None
-
-        return wrapper
-
-    @classmethod
-    def register(celf, clazzes):
-        if type(clazzes) != tuple:
-            clazzes = (clazzes,)
-        return celf._register([(clazzes, (None,))])
-
-    @classmethod
-    def register_attr(celf, clazzes, attrs):
-        clazzes_attrs = []
-        if type(clazzes) != tuple:
-            clazzes = (clazzes,)
-        if type(attrs) == str:
-            attrs = (attrs,)
-        for clazz in clazzes:
-            clazzes_attrs.append((clazz, attrs))
-        return celf._register(clazzes_attrs)
-
-    @classmethod
-    def register_attrs(celf, clazzes_attrs):
-        return celf._register(clazzes_attrs)
-
-    @classmethod
-    def _visitorsFor(celf, thing, _default={}):
-        typ = type(thing)
-
-        for celf in celf.mro():
-
-            _visitors = getattr(celf, "_visitors", None)
-            if _visitors is None:
-                break
-
-            m = celf._visitors.get(typ, None)
-            if m is not None:
-                return m
-
-        return _default
-
-    def visitObject(self, obj, *args, **kwargs):
-        """Called to visit an object. This function loops over all non-private
-        attributes of the objects and calls any user-registered (via
-        @register_attr() or @register_attrs()) visit() functions.
-
-        If there is no user-registered visit function, of if there is and it
-        returns True, or it returns None (or doesn't return anything) and
-        visitor.defaultStop is False (default), then the visitor will proceed
-        to call self.visitAttr()"""
-
-        keys = sorted(vars(obj).keys())
-        _visitors = self._visitorsFor(obj)
-        defaultVisitor = _visitors.get("*", None)
-        for key in keys:
-            if key[0] == "_":
-                continue
-            value = getattr(obj, key)
-            visitorFunc = _visitors.get(key, defaultVisitor)
-            if visitorFunc is not None:
-                ret = visitorFunc(self, obj, key, value, *args, **kwargs)
-                if ret == False or (ret is None and self.defaultStop):
-                    continue
-            self.visitAttr(obj, key, value, *args, **kwargs)
-
-    def visitAttr(self, obj, attr, value, *args, **kwargs):
-        """Called to visit an attribute of an object."""
-        self.visit(value, *args, **kwargs)
-
-    def visitList(self, obj, *args, **kwargs):
-        """Called to visit any value that is a list."""
-        for value in obj:
-            self.visit(value, *args, **kwargs)
-
-    def visitDict(self, obj, *args, **kwargs):
-        """Called to visit any value that is a dictionary."""
-        for value in obj.values():
-            self.visit(value, *args, **kwargs)
-
-    def visitLeaf(self, obj, *args, **kwargs):
-        """Called to visit any value that is not an object, list,
-        or dictionary."""
-        pass
-
-    def visit(self, obj, *args, **kwargs):
-        """This is the main entry to the visitor. The visitor will visit object
-        obj.
-
-        The visitor will first determine if there is a registered (via
-        @register()) visit function for the type of object. If there is, it
-        will be called, and (visitor, obj, *args, **kwargs) will be passed to
-        the user visit function.
-
-        If there is no user-registered visit function, of if there is and it
-        returns True, or it returns None (or doesn't return anything) and
-        visitor.defaultStop is False (default), then the visitor will proceed
-        to dispatch to one of self.visitObject(), self.visitList(),
-        self.visitDict(), or self.visitLeaf() (any of which can be overriden in
-        a subclass)."""
-
-        visitorFunc = self._visitorsFor(obj).get(None, None)
-        if visitorFunc is not None:
-            ret = visitorFunc(self, obj, *args, **kwargs)
-            if ret == False or (ret is None and self.defaultStop):
-                return
-        if hasattr(obj, "__dict__") and not isinstance(obj, enum.Enum):
-            self.visitObject(obj, *args, **kwargs)
-        elif isinstance(obj, list):
-            self.visitList(obj, *args, **kwargs)
-        elif isinstance(obj, dict):
-            self.visitDict(obj, *args, **kwargs)
-        else:
-            self.visitLeaf(obj, *args, **kwargs)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/frontmatter-7806d2e2.js b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/frontmatter-7806d2e2.js
deleted file mode 100644
index 18c78c127dae947f64da4e7ea98d67df9fe346a5..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/frontmatter-7806d2e2.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{s as m,f as s,a as i,p as l,t as o,S as p}from"./index-ebba85cc.js";import{yaml as f}from"./yaml-95012b83.js";import"./index-f877dfd5.js";import"./Blocks-adc2d4ca.js";import"./Button-11a87b79.js";import"./BlockLabel-7929e88d.js";import"./Empty-2159e5e9.js";import"./Copy-534f8e58.js";import"./Download-a587c81f.js";const n=/^---\s*$/m,S={defineNodes:[{name:"Frontmatter",block:!0},"FrontmatterMark"],props:[m({Frontmatter:[o.documentMeta,o.monospace],FrontmatterMark:o.processingInstruction}),s.add({Frontmatter:i,FrontmatterMark:()=>null})],wrap:l(t=>{const{parser:e}=p.define(f);return t.type.name==="Frontmatter"?{parser:e,overlay:[{from:t.from+4,to:t.to-4}]}:null}),parseBlock:[{name:"Frontmatter",before:"HorizontalRule",parse:(t,e)=>{let r;const a=new Array;if(t.lineStart===0&&n.test(e.text)){for(a.push(t.elt("FrontmatterMark",0,4));t.nextLine();)if(n.test(e.text)){r=t.lineStart+4;break}return r!==void 0&&(a.push(t.elt("FrontmatterMark",r-4,r)),t.addElement(t.elt("Frontmatter",0,r,a))),!0}else return!1}}]};export{S as frontmatter};
-//# sourceMappingURL=frontmatter-7806d2e2.js.map
diff --git a/spaces/cihyFjudo/fairness-paper-search/Download Ghostromance by Kaye Draper (.ePUB) and Enter the Weird World of Hellsfork Michigan.md b/spaces/cihyFjudo/fairness-paper-search/Download Ghostromance by Kaye Draper (.ePUB) and Enter the Weird World of Hellsfork Michigan.md
deleted file mode 100644
index 0b6cb42cd77e027fd7ba90ece7f53c8c2d190c42..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Download Ghostromance by Kaye Draper (.ePUB) and Enter the Weird World of Hellsfork Michigan.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Download Ghostromance by Kaye Draper (.ePUB)</h2><br /><p><b><b>DOWNLOAD</b> &#9999; &#9999; &#9999; <a href="https://tinurli.com/2uwjzv">https://tinurli.com/2uwjzv</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/Enjoy Calvin Richardson-235 PM Full Album 33 the Smooth and Sensual Soundtrack for Your Day.md b/spaces/cihyFjudo/fairness-paper-search/Enjoy Calvin Richardson-235 PM Full Album 33 the Smooth and Sensual Soundtrack for Your Day.md
deleted file mode 100644
index 06b9850c1c2d448b505cb27df15d91b1e99eeba6..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Enjoy Calvin Richardson-235 PM Full Album 33 the Smooth and Sensual Soundtrack for Your Day.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>List of Universal Records artists, listed alphabetically with photos when available. This Universal Records roster includes both past and present artists. Popular Universal Records bands and artists are all listed here with information such as what genre the bands fall under and what albums they're known for. If you're looking for any of these Universal Records band's full discographies then click on their name and you can find them here on Ranker. The artists and bands on this list might be pop, rap, rock, electronic or any other genre, but what they all have in common is that they were signed by Universal Records.</p>
-<h2>Calvin Richardson-2:35 PM Full Album 33</h2><br /><p><b><b>Download</b> === <a href="https://tinurli.com/2uwi6f">https://tinurli.com/2uwi6f</a></b></p><br /><br />
-<p>Alphonse Pierre of <i>Pitchfork</i> wrote the album "attempts to cement his legacy by expanding his world", praising it for being "big, polished, versatile, feature-packed, and loaded with radio and playlist-friendly records".[34] Jade Gomez of <i>Paste</i> said the album "seeks to provide closure for [Brooklyn] while also showing the heartbreaking reality of what could've been", and that it would have been just as "satisfying with a condensed tracklist and more carefully curated features".[28] <i>Vulture</i>'s Craig Jenkins commented the album is "evidence of a star gaining his bearings, but as much as it is a product of a young and growing artist's path toward refinement, it is also a document of his jarring absence".[10] In a mixed review, AllMusic's David Crone stated although the first two volumes of <i>Meet the Woo</i> lack the flatulent of Pop Smoke's iconic singles, they show quality as the representation of drill; he also said the album, in comparison, ruins the rapper's "visionary style with predatory glitz as everyone jumps for a piece of the pie".[47] <i>Slant Magazine</i>'s Charles Lyons-Burt said <i>Shoot for the Stars, Aim for the Moon</i> has the feel of a B-sides collection mashed together as a quick cash-in on his death, and that it attempts to expand Pop Smoke's sound and ambitions. He concluded by saying the album lost sight of the "local specificity, quirky charisma, and energy that made a name for Pop Smoke in the first place".[24]</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Mathworks Matlab R2018a (64-Bit)-Uploads] Utorrentl _VERIFIED_.md b/spaces/cihyFjudo/fairness-paper-search/Mathworks Matlab R2018a (64-Bit)-Uploads] Utorrentl _VERIFIED_.md
deleted file mode 100644
index 1dcfc82a957980f396bf4f9c4ac50ac8bcf35c17..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Mathworks Matlab R2018a (64-Bit)-Uploads] Utorrentl _VERIFIED_.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Mathworks Matlab R2018a (64-Bit)-Uploads] Utorrentl</h2><br /><p><b><b>DOWNLOAD</b> &rArr; <a href="https://tinurli.com/2uwj8t">https://tinurli.com/2uwj8t</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cjayic/soft-vc-widowmaker/acoustic/model.py b/spaces/cjayic/soft-vc-widowmaker/acoustic/model.py
deleted file mode 100644
index 00b2054baebff4862a00dd3b50d36e8685f7ff2d..0000000000000000000000000000000000000000
--- a/spaces/cjayic/soft-vc-widowmaker/acoustic/model.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-URLS = {
-    "hubert-discrete": "https://github.com/bshall/acoustic-model/releases/download/v0.1/hubert-discrete-d49e1c77.pt",
-    "hubert-soft": "https://github.com/bshall/acoustic-model/releases/download/v0.1/hubert-soft-0321fd7e.pt",
-}
-
-
-class AcousticModel(nn.Module):
-    def __init__(self, discrete: bool = False, upsample: bool = True):
-        super().__init__()
-        self.encoder = Encoder(discrete, upsample)
-        self.decoder = Decoder()
-
-    def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
-        x = self.encoder(x)
-        return self.decoder(x, mels)
-
-    @torch.inference_mode()
-    def generate(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.encoder(x)
-        return self.decoder.generate(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, discrete: bool = False, upsample: bool = True):
-        super().__init__()
-        self.embedding = nn.Embedding(100 + 1, 256) if discrete else None
-        self.prenet = PreNet(256, 256, 256)
-        self.convs = nn.Sequential(
-            nn.Conv1d(256, 512, 5, 1, 2),
-            nn.ReLU(),
-            nn.InstanceNorm1d(512),
-            nn.ConvTranspose1d(512, 512, 4, 2, 1) if upsample else nn.Identity(),
-            nn.Conv1d(512, 512, 5, 1, 2),
-            nn.ReLU(),
-            nn.InstanceNorm1d(512),
-            nn.Conv1d(512, 512, 5, 1, 2),
-            nn.ReLU(),
-            nn.InstanceNorm1d(512),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if self.embedding is not None:
-            x = self.embedding(x)
-        x = self.prenet(x)
-        x = self.convs(x.transpose(1, 2))
-        return x.transpose(1, 2)
-
-
-class Decoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.prenet = PreNet(128, 256, 256)
-        self.lstm1 = nn.LSTM(512 + 256, 768, batch_first=True)
-        self.lstm2 = nn.LSTM(768, 768, batch_first=True)
-        self.lstm3 = nn.LSTM(768, 768, batch_first=True)
-        self.proj = nn.Linear(768, 128, bias=False)
-
-    def forward(self, x: torch.Tensor, mels: torch.Tensor) -> torch.Tensor:
-        mels = self.prenet(mels)
-        x, _ = self.lstm1(torch.cat((x, mels), dim=-1))
-        res = x
-        x, _ = self.lstm2(x)
-        x = res + x
-        res = x
-        x, _ = self.lstm3(x)
-        x = res + x
-        return self.proj(x)
-
-    @torch.inference_mode()
-    def generate(self, xs: torch.Tensor) -> torch.Tensor:
-        m = torch.zeros(xs.size(0), 128, device=xs.device)
-        h1 = torch.zeros(1, xs.size(0), 768, device=xs.device)
-        c1 = torch.zeros(1, xs.size(0), 768, device=xs.device)
-        h2 = torch.zeros(1, xs.size(0), 768, device=xs.device)
-        c2 = torch.zeros(1, xs.size(0), 768, device=xs.device)
-        h3 = torch.zeros(1, xs.size(0), 768, device=xs.device)
-        c3 = torch.zeros(1, xs.size(0), 768, device=xs.device)
-
-        mel = []
-        for x in torch.unbind(xs, dim=1):
-            m = self.prenet(m)
-            x = torch.cat((x, m), dim=1).unsqueeze(1)
-            x1, (h1, c1) = self.lstm1(x, (h1, c1))
-            x2, (h2, c2) = self.lstm2(x1, (h2, c2))
-            x = x1 + x2
-            x3, (h3, c3) = self.lstm3(x, (h3, c3))
-            x = x + x3
-            m = self.proj(x).squeeze(1)
-            mel.append(m)
-        return torch.stack(mel, dim=1)
-
-
-class PreNet(nn.Module):
-    def __init__(
-        self,
-        input_size: int,
-        hidden_size: int,
-        output_size: int,
-        dropout: float = 0.5,
-    ):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(input_size, hidden_size),
-            nn.ReLU(),
-            nn.Dropout(dropout),
-            nn.Linear(hidden_size, output_size),
-            nn.ReLU(),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.net(x)
-
-
-def _acoustic(
-    name: str,
-    discrete: bool,
-    upsample: bool,
-    pretrained: bool = True,
-    progress: bool = True,
-) -> AcousticModel:
-    acoustic = AcousticModel(discrete, upsample)
-    if pretrained:
-        checkpoint = torch.hub.load_state_dict_from_url(URLS[name], progress=progress)
-        consume_prefix_in_state_dict_if_present(checkpoint["acoustic-model"], "module.")
-        acoustic.load_state_dict(checkpoint["acoustic-model"])
-        acoustic.eval()
-    return acoustic
-
-
-def hubert_discrete(
-    pretrained: bool = True,
-    progress: bool = True,
-) -> AcousticModel:
-    r"""HuBERT-Discrete acoustic model from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        pretrained (bool): load pretrained weights into the model
-        progress (bool): show progress bar when downloading model
-    """
-    return _acoustic(
-        "hubert-discrete",
-        discrete=True,
-        upsample=True,
-        pretrained=pretrained,
-        progress=progress,
-    )
-
-
-def hubert_soft(
-    pretrained: bool = True,
-    progress: bool = True,
-) -> AcousticModel:
-    r"""HuBERT-Soft acoustic model from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        pretrained (bool): load pretrained weights into the model
-        progress (bool): show progress bar when downloading model
-    """
-    return _acoustic(
-        "hubert-soft",
-        discrete=False,
-        upsample=True,
-        pretrained=pretrained,
-        progress=progress,
-    )
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/threadpool/utils.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/threadpool/utils.py
deleted file mode 100644
index 5fd3bb992e51b54225d53edb5f8e50f575997f81..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/threadpool/utils.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import functools
-
-
-def delegate_to_executor(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_delegate_method(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def proxy_method_directly(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_proxy_method(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def proxy_property_directly(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_proxy_property(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def cond_delegate_to_executor(*attrs):
-    def cls_builder(cls):
-        for attr_name in attrs:
-            setattr(cls, attr_name, _make_cond_delegate_method(attr_name))
-        return cls
-
-    return cls_builder
-
-
-def _make_delegate_method(attr_name):
-    async def method(self, *args, **kwargs):
-        cb = functools.partial(getattr(self._file, attr_name), *args, **kwargs)
-        return await self._loop.run_in_executor(self._executor, cb)
-
-    return method
-
-
-def _make_proxy_method(attr_name):
-    def method(self, *args, **kwargs):
-        return getattr(self._file, attr_name)(*args, **kwargs)
-
-    return method
-
-
-def _make_proxy_property(attr_name):
-    def proxy_property(self):
-        return getattr(self._file, attr_name)
-
-    return property(proxy_property)
-
-
-def _make_cond_delegate_method(attr_name):
-    """For spooled temp files, delegate only if rolled to file object"""
-
-    async def method(self, *args, **kwargs):
-        if self._file._rolled:
-            cb = functools.partial(getattr(self._file, attr_name), *args, **kwargs)
-            return await self._loop.run_in_executor(self._executor, cb)
-        else:
-            return getattr(self._file, attr_name)(*args, **kwargs)
-
-    return method
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_version.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_version.py
deleted file mode 100644
index 29b9460037f9baffed7daf2b046d3ef418936642..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_version.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# file generated by setuptools_scm
-# don't change, don't track in version control
-__version__ = version = '1.1.2'
-__version_tuple__ = version_tuple = (1, 1, 2)
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aliaspixenc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aliaspixenc.c
deleted file mode 100644
index 90d2a633193a09314563e36b86d2946bffe25f02..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aliaspixenc.c
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Alias PIX image encoder
- * Copyright (C) 2014 Vittorio Giovara <vittorio.giovara@gmail.com>
- *
- * 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/intreadwrite.h"
-
-#include "avcodec.h"
-#include "bytestream.h"
-#include "codec_internal.h"
-#include "encode.h"
-
-#define ALIAS_HEADER_SIZE 10
-
-static int encode_frame(AVCodecContext *avctx, AVPacket *pkt,
-                        const AVFrame *frame, int *got_packet)
-{
-    int width, height, bits_pixel, length, ret;
-    uint8_t *buf;
-
-    width  = avctx->width;
-    height = avctx->height;
-
-    if (width > 65535 || height > 65535 ||
-        width * height >= INT_MAX / 4 - ALIAS_HEADER_SIZE) {
-        av_log(avctx, AV_LOG_ERROR, "Invalid image size %dx%d.\n", width, height);
-        return AVERROR_INVALIDDATA;
-    }
-
-    switch (avctx->pix_fmt) {
-    case AV_PIX_FMT_GRAY8:
-        bits_pixel = 8;
-        break;
-    case AV_PIX_FMT_BGR24:
-        bits_pixel = 24;
-        break;
-    default:
-        return AVERROR(EINVAL);
-    }
-
-    length = ALIAS_HEADER_SIZE + 4 * width * height; // max possible
-    if ((ret = ff_alloc_packet(avctx, pkt, length)) < 0)
-        return ret;
-
-    buf = pkt->data;
-
-    /* Encode header. */
-    bytestream_put_be16(&buf, width);
-    bytestream_put_be16(&buf, height);
-    bytestream_put_be32(&buf, 0); /* X, Y offset */
-    bytestream_put_be16(&buf, bits_pixel);
-
-    for (int j = 0, bytes_pixel = bits_pixel >> 3; j < height; j++) {
-        const uint8_t *in_buf = frame->data[0] + frame->linesize[0] * j;
-        const uint8_t *const line_end = in_buf + bytes_pixel * width;
-        while (in_buf < line_end) {
-            int count = 0;
-            int pixel;
-
-            if (avctx->pix_fmt == AV_PIX_FMT_GRAY8) {
-                pixel = *in_buf;
-                while (count < 255 && in_buf < line_end && pixel == *in_buf) {
-                    count++;
-                    in_buf++;
-                }
-                bytestream_put_byte(&buf, count);
-                bytestream_put_byte(&buf, pixel);
-            } else { /* AV_PIX_FMT_BGR24 */
-                pixel = AV_RB24(in_buf);
-                while (count < 255 && in_buf < line_end &&
-                       pixel == AV_RB24(in_buf)) {
-                    count++;
-                    in_buf += 3;
-                }
-                bytestream_put_byte(&buf, count);
-                bytestream_put_be24(&buf, pixel);
-            }
-        }
-    }
-
-    /* Total length */
-    av_shrink_packet(pkt, buf - pkt->data);
-    *got_packet = 1;
-
-    return 0;
-}
-
-const FFCodec ff_alias_pix_encoder = {
-    .p.name    = "alias_pix",
-    CODEC_LONG_NAME("Alias/Wavefront PIX image"),
-    .p.type    = AVMEDIA_TYPE_VIDEO,
-    .p.id      = AV_CODEC_ID_ALIAS_PIX,
-    .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
-    FF_CODEC_ENCODE_CB(encode_frame),
-    .p.pix_fmts = (const enum AVPixelFormat[]) {
-        AV_PIX_FMT_BGR24, AV_PIX_FMT_GRAY8, AV_PIX_FMT_NONE
-    },
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_parser.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_parser.c
deleted file mode 100644
index 46134a1c483ed8a81835caa6f2c929ed632a99a6..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_parser.c
+++ /dev/null
@@ -1,695 +0,0 @@
-/*
- * H.26L/H.264/AVC/JVT/14496-10/... parser
- * Copyright (c) 2003 Michael Niedermayer <michaelni@gmx.at>
- *
- * 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
- */
-
-/**
- * @file
- * H.264 / AVC / MPEG-4 part10 parser.
- * @author Michael Niedermayer <michaelni@gmx.at>
- */
-
-#define UNCHECKED_BITSTREAM_READER 1
-
-#include <stdint.h>
-
-#include "libavutil/avutil.h"
-#include "libavutil/error.h"
-#include "libavutil/log.h"
-#include "libavutil/mem.h"
-#include "libavutil/pixfmt.h"
-
-#include "avcodec.h"
-#include "get_bits.h"
-#include "golomb.h"
-#include "h264.h"
-#include "h264dsp.h"
-#include "h264_parse.h"
-#include "h264_sei.h"
-#include "h264_ps.h"
-#include "h2645_parse.h"
-#include "h264data.h"
-#include "mpegutils.h"
-#include "parser.h"
-#include "startcode.h"
-
-typedef struct H264ParseContext {
-    ParseContext pc;
-    H264ParamSets ps;
-    H264DSPContext h264dsp;
-    H264POCContext poc;
-    H264SEIContext sei;
-    int is_avc;
-    int nal_length_size;
-    int got_first;
-    int picture_structure;
-    uint8_t parse_history[6];
-    int parse_history_count;
-    int parse_last_mb;
-    int64_t reference_dts;
-    int last_frame_num, last_picture_structure;
-} H264ParseContext;
-
-static int find_start_code(const uint8_t *buf, int buf_size,
-                                  int buf_index, int next_avc)
-{
-    uint32_t state = -1;
-
-    buf_index = avpriv_find_start_code(buf + buf_index, buf + next_avc + 1, &state) - buf - 1;
-
-    return FFMIN(buf_index, buf_size);
-}
-
-static int h264_find_frame_end(H264ParseContext *p, const uint8_t *buf,
-                               int buf_size, void *logctx)
-{
-    int i, j;
-    uint32_t state;
-    ParseContext *pc = &p->pc;
-
-    int next_avc = p->is_avc ? 0 : buf_size;
-//    mb_addr= pc->mb_addr - 1;
-    state = pc->state;
-    if (state > 13)
-        state = 7;
-
-    if (p->is_avc && !p->nal_length_size)
-        av_log(logctx, AV_LOG_ERROR, "AVC-parser: nal length size invalid\n");
-
-    for (i = 0; i < buf_size; i++) {
-        if (i >= next_avc) {
-            int64_t nalsize = 0;
-            i = next_avc;
-            for (j = 0; j < p->nal_length_size; j++)
-                nalsize = (nalsize << 8) | buf[i++];
-            if (!nalsize || nalsize > buf_size - i) {
-                av_log(logctx, AV_LOG_ERROR, "AVC-parser: nal size %"PRId64" "
-                       "remaining %d\n", nalsize, buf_size - i);
-                return buf_size;
-            }
-            next_avc = i + nalsize;
-            state    = 5;
-        }
-
-        if (state == 7) {
-            i += p->h264dsp.startcode_find_candidate(buf + i, next_avc - i);
-            if (i < next_avc)
-                state = 2;
-        } else if (state <= 2) {
-            if (buf[i] == 1)
-                state ^= 5;            // 2->7, 1->4, 0->5
-            else if (buf[i])
-                state = 7;
-            else
-                state >>= 1;           // 2->1, 1->0, 0->0
-        } else if (state <= 5) {
-            int nalu_type = buf[i] & 0x1F;
-            if (nalu_type == H264_NAL_SEI || nalu_type == H264_NAL_SPS ||
-                nalu_type == H264_NAL_PPS || nalu_type == H264_NAL_AUD) {
-                if (pc->frame_start_found) {
-                    i++;
-                    goto found;
-                }
-            } else if (nalu_type == H264_NAL_SLICE || nalu_type == H264_NAL_DPA ||
-                       nalu_type == H264_NAL_IDR_SLICE) {
-                state += 8;
-                continue;
-            }
-            state = 7;
-        } else {
-            unsigned int mb, last_mb = p->parse_last_mb;
-            GetBitContext gb;
-            p->parse_history[p->parse_history_count++] = buf[i];
-
-            init_get_bits(&gb, p->parse_history, 8*p->parse_history_count);
-            mb= get_ue_golomb_long(&gb);
-            if (get_bits_left(&gb) > 0 || p->parse_history_count > 5) {
-                p->parse_last_mb = mb;
-                if (pc->frame_start_found) {
-                    if (mb <= last_mb) {
-                        i -= p->parse_history_count - 1;
-                        p->parse_history_count = 0;
-                        goto found;
-                    }
-                } else
-                    pc->frame_start_found = 1;
-                p->parse_history_count = 0;
-                state = 7;
-            }
-        }
-    }
-    pc->state = state;
-    if (p->is_avc)
-        return next_avc;
-    return END_NOT_FOUND;
-
-found:
-    pc->state             = 7;
-    pc->frame_start_found = 0;
-    if (p->is_avc)
-        return next_avc;
-    return i - (state & 5);
-}
-
-static int scan_mmco_reset(AVCodecParserContext *s, GetBitContext *gb,
-                           void *logctx)
-{
-    H264PredWeightTable pwt;
-    int slice_type_nos = s->pict_type & 3;
-    H264ParseContext *p = s->priv_data;
-    int list_count, ref_count[2];
-
-
-    if (p->ps.pps->redundant_pic_cnt_present)
-        get_ue_golomb(gb); // redundant_pic_count
-
-    if (slice_type_nos == AV_PICTURE_TYPE_B)
-        get_bits1(gb); // direct_spatial_mv_pred
-
-    if (ff_h264_parse_ref_count(&list_count, ref_count, gb, p->ps.pps,
-                                slice_type_nos, p->picture_structure, logctx) < 0)
-        return AVERROR_INVALIDDATA;
-
-    if (slice_type_nos != AV_PICTURE_TYPE_I) {
-        int list;
-        for (list = 0; list < list_count; list++) {
-            if (get_bits1(gb)) {
-                int index;
-                for (index = 0; ; index++) {
-                    unsigned int reordering_of_pic_nums_idc = get_ue_golomb_31(gb);
-
-                    if (reordering_of_pic_nums_idc < 3)
-                        get_ue_golomb_long(gb);
-                    else if (reordering_of_pic_nums_idc > 3) {
-                        av_log(logctx, AV_LOG_ERROR,
-                               "illegal reordering_of_pic_nums_idc %d\n",
-                               reordering_of_pic_nums_idc);
-                        return AVERROR_INVALIDDATA;
-                    } else
-                        break;
-
-                    if (index >= ref_count[list]) {
-                        av_log(logctx, AV_LOG_ERROR,
-                               "reference count %d overflow\n", index);
-                        return AVERROR_INVALIDDATA;
-                    }
-                }
-            }
-        }
-    }
-
-    if ((p->ps.pps->weighted_pred && slice_type_nos == AV_PICTURE_TYPE_P) ||
-        (p->ps.pps->weighted_bipred_idc == 1 && slice_type_nos == AV_PICTURE_TYPE_B))
-        ff_h264_pred_weight_table(gb, p->ps.sps, ref_count, slice_type_nos,
-                                  &pwt, p->picture_structure, logctx);
-
-    if (get_bits1(gb)) { // adaptive_ref_pic_marking_mode_flag
-        int i;
-        for (i = 0; i < H264_MAX_MMCO_COUNT; i++) {
-            MMCOOpcode opcode = get_ue_golomb_31(gb);
-            if (opcode > (unsigned) MMCO_LONG) {
-                av_log(logctx, AV_LOG_ERROR,
-                       "illegal memory management control operation %d\n",
-                       opcode);
-                return AVERROR_INVALIDDATA;
-            }
-            if (opcode == MMCO_END)
-               return 0;
-            else if (opcode == MMCO_RESET)
-                return 1;
-
-            if (opcode == MMCO_SHORT2UNUSED || opcode == MMCO_SHORT2LONG)
-                get_ue_golomb_long(gb); // difference_of_pic_nums_minus1
-            if (opcode == MMCO_SHORT2LONG || opcode == MMCO_LONG2UNUSED ||
-                opcode == MMCO_LONG || opcode == MMCO_SET_MAX_LONG)
-                get_ue_golomb_31(gb);
-        }
-    }
-
-    return 0;
-}
-
-/**
- * Parse NAL units of found picture and decode some basic information.
- *
- * @param s parser context.
- * @param avctx codec context.
- * @param buf buffer with field/frame data.
- * @param buf_size size of the buffer.
- */
-static inline int parse_nal_units(AVCodecParserContext *s,
-                                  AVCodecContext *avctx,
-                                  const uint8_t * const buf, int buf_size)
-{
-    H264ParseContext *p = s->priv_data;
-    H2645RBSP rbsp = { NULL };
-    H2645NAL nal = { NULL };
-    int buf_index, next_avc;
-    unsigned int pps_id;
-    unsigned int slice_type;
-    int state = -1, got_reset = 0;
-    int q264 = buf_size >=4 && !memcmp("Q264", buf, 4);
-    int field_poc[2];
-    int ret;
-
-    /* set some sane default values */
-    s->pict_type         = AV_PICTURE_TYPE_I;
-    s->key_frame         = 0;
-    s->picture_structure = AV_PICTURE_STRUCTURE_UNKNOWN;
-
-    ff_h264_sei_uninit(&p->sei);
-    p->sei.common.frame_packing.arrangement_cancel_flag = -1;
-    p->sei.common.unregistered.x264_build = -1;
-
-    if (!buf_size)
-        return 0;
-
-    av_fast_padded_malloc(&rbsp.rbsp_buffer, &rbsp.rbsp_buffer_alloc_size, buf_size);
-    if (!rbsp.rbsp_buffer)
-        return AVERROR(ENOMEM);
-
-    buf_index     = 0;
-    next_avc      = p->is_avc ? 0 : buf_size;
-    for (;;) {
-        const SPS *sps;
-        int src_length, consumed, nalsize = 0;
-
-        if (buf_index >= next_avc) {
-            nalsize = get_nalsize(p->nal_length_size, buf, buf_size, &buf_index, avctx);
-            if (nalsize < 0)
-                break;
-            next_avc = buf_index + nalsize;
-        } else {
-            buf_index = find_start_code(buf, buf_size, buf_index, next_avc);
-            if (buf_index >= buf_size)
-                break;
-            if (buf_index >= next_avc)
-                continue;
-        }
-        src_length = next_avc - buf_index;
-
-        state = buf[buf_index];
-        switch (state & 0x1f) {
-        case H264_NAL_SLICE:
-        case H264_NAL_IDR_SLICE:
-            // Do not walk the whole buffer just to decode slice header
-            if ((state & 0x1f) == H264_NAL_IDR_SLICE || ((state >> 5) & 0x3) == 0) {
-                /* IDR or disposable slice
-                 * No need to decode many bytes because MMCOs shall not be present. */
-                if (src_length > 60)
-                    src_length = 60;
-            } else {
-                /* To decode up to MMCOs */
-                if (src_length > 1000)
-                    src_length = 1000;
-            }
-            break;
-        }
-        consumed = ff_h2645_extract_rbsp(buf + buf_index, src_length, &rbsp, &nal, 1);
-        if (consumed < 0)
-            break;
-
-        buf_index += consumed;
-
-        ret = init_get_bits8(&nal.gb, nal.data, nal.size);
-        if (ret < 0)
-            goto fail;
-        get_bits1(&nal.gb);
-        nal.ref_idc = get_bits(&nal.gb, 2);
-        nal.type    = get_bits(&nal.gb, 5);
-
-        switch (nal.type) {
-        case H264_NAL_SPS:
-            ff_h264_decode_seq_parameter_set(&nal.gb, avctx, &p->ps, 0);
-            break;
-        case H264_NAL_PPS:
-            ff_h264_decode_picture_parameter_set(&nal.gb, avctx, &p->ps,
-                                                 nal.size_bits);
-            break;
-        case H264_NAL_SEI:
-            ff_h264_sei_decode(&p->sei, &nal.gb, &p->ps, avctx);
-            break;
-        case H264_NAL_IDR_SLICE:
-            s->key_frame = 1;
-
-            p->poc.prev_frame_num        = 0;
-            p->poc.prev_frame_num_offset = 0;
-            p->poc.prev_poc_msb          =
-            p->poc.prev_poc_lsb          = 0;
-        /* fall through */
-        case H264_NAL_SLICE:
-            get_ue_golomb_long(&nal.gb);  // skip first_mb_in_slice
-            slice_type   = get_ue_golomb_31(&nal.gb);
-            s->pict_type = ff_h264_golomb_to_pict_type[slice_type % 5];
-            if (p->sei.recovery_point.recovery_frame_cnt >= 0) {
-                /* key frame, since recovery_frame_cnt is set */
-                s->key_frame = 1;
-            }
-            pps_id = get_ue_golomb(&nal.gb);
-            if (pps_id >= MAX_PPS_COUNT) {
-                av_log(avctx, AV_LOG_ERROR,
-                       "pps_id %u out of range\n", pps_id);
-                goto fail;
-            }
-            if (!p->ps.pps_list[pps_id]) {
-                av_log(avctx, AV_LOG_ERROR,
-                       "non-existing PPS %u referenced\n", pps_id);
-                goto fail;
-            }
-
-            av_buffer_unref(&p->ps.pps_ref);
-            p->ps.pps = NULL;
-            p->ps.sps = NULL;
-            p->ps.pps_ref = av_buffer_ref(p->ps.pps_list[pps_id]);
-            if (!p->ps.pps_ref)
-                goto fail;
-            p->ps.pps = (const PPS*)p->ps.pps_ref->data;
-            p->ps.sps = p->ps.pps->sps;
-            sps       = p->ps.sps;
-
-            // heuristic to detect non marked keyframes
-            if (p->ps.sps->ref_frame_count <= 1 && p->ps.pps->ref_count[0] <= 1 && s->pict_type == AV_PICTURE_TYPE_I)
-                s->key_frame = 1;
-
-            p->poc.frame_num = get_bits(&nal.gb, sps->log2_max_frame_num);
-
-            s->coded_width  = 16 * sps->mb_width;
-            s->coded_height = 16 * sps->mb_height;
-            s->width        = s->coded_width  - (sps->crop_right + sps->crop_left);
-            s->height       = s->coded_height - (sps->crop_top   + sps->crop_bottom);
-            if (s->width <= 0 || s->height <= 0) {
-                s->width  = s->coded_width;
-                s->height = s->coded_height;
-            }
-
-            switch (sps->bit_depth_luma) {
-            case 9:
-                if (sps->chroma_format_idc == 3)      s->format = AV_PIX_FMT_YUV444P9;
-                else if (sps->chroma_format_idc == 2) s->format = AV_PIX_FMT_YUV422P9;
-                else                                  s->format = AV_PIX_FMT_YUV420P9;
-                break;
-            case 10:
-                if (sps->chroma_format_idc == 3)      s->format = AV_PIX_FMT_YUV444P10;
-                else if (sps->chroma_format_idc == 2) s->format = AV_PIX_FMT_YUV422P10;
-                else                                  s->format = AV_PIX_FMT_YUV420P10;
-                break;
-            case 8:
-                if (sps->chroma_format_idc == 3)      s->format = AV_PIX_FMT_YUV444P;
-                else if (sps->chroma_format_idc == 2) s->format = AV_PIX_FMT_YUV422P;
-                else                                  s->format = AV_PIX_FMT_YUV420P;
-                break;
-            default:
-                s->format = AV_PIX_FMT_NONE;
-            }
-
-            avctx->profile = ff_h264_get_profile(sps);
-            avctx->level   = sps->level_idc;
-
-            if (sps->frame_mbs_only_flag) {
-                p->picture_structure = PICT_FRAME;
-            } else {
-                if (get_bits1(&nal.gb)) { // field_pic_flag
-                    p->picture_structure = PICT_TOP_FIELD + get_bits1(&nal.gb); // bottom_field_flag
-                } else {
-                    p->picture_structure = PICT_FRAME;
-                }
-            }
-
-            if (nal.type == H264_NAL_IDR_SLICE)
-                get_ue_golomb_long(&nal.gb); /* idr_pic_id */
-            if (sps->poc_type == 0) {
-                p->poc.poc_lsb = get_bits(&nal.gb, sps->log2_max_poc_lsb);
-
-                if (p->ps.pps->pic_order_present == 1 &&
-                    p->picture_structure == PICT_FRAME)
-                    p->poc.delta_poc_bottom = get_se_golomb(&nal.gb);
-            }
-
-            if (sps->poc_type == 1 &&
-                !sps->delta_pic_order_always_zero_flag) {
-                p->poc.delta_poc[0] = get_se_golomb(&nal.gb);
-
-                if (p->ps.pps->pic_order_present == 1 &&
-                    p->picture_structure == PICT_FRAME)
-                    p->poc.delta_poc[1] = get_se_golomb(&nal.gb);
-            }
-
-            /* Decode POC of this picture.
-             * The prev_ values needed for decoding POC of the next picture are not set here. */
-            field_poc[0] = field_poc[1] = INT_MAX;
-            ret = ff_h264_init_poc(field_poc, &s->output_picture_number, sps,
-                             &p->poc, p->picture_structure, nal.ref_idc);
-            if (ret < 0)
-                goto fail;
-
-            /* Continue parsing to check if MMCO_RESET is present.
-             * FIXME: MMCO_RESET could appear in non-first slice.
-             *        Maybe, we should parse all undisposable non-IDR slice of this
-             *        picture until encountering MMCO_RESET in a slice of it. */
-            if (nal.ref_idc && nal.type != H264_NAL_IDR_SLICE) {
-                got_reset = scan_mmco_reset(s, &nal.gb, avctx);
-                if (got_reset < 0)
-                    goto fail;
-            }
-
-            /* Set up the prev_ values for decoding POC of the next picture. */
-            p->poc.prev_frame_num        = got_reset ? 0 : p->poc.frame_num;
-            p->poc.prev_frame_num_offset = got_reset ? 0 : p->poc.frame_num_offset;
-            if (nal.ref_idc != 0) {
-                if (!got_reset) {
-                    p->poc.prev_poc_msb = p->poc.poc_msb;
-                    p->poc.prev_poc_lsb = p->poc.poc_lsb;
-                } else {
-                    p->poc.prev_poc_msb = 0;
-                    p->poc.prev_poc_lsb =
-                        p->picture_structure == PICT_BOTTOM_FIELD ? 0 : field_poc[0];
-                }
-            }
-
-            if (p->sei.picture_timing.present) {
-                ret = ff_h264_sei_process_picture_timing(&p->sei.picture_timing,
-                                                         sps, avctx);
-                if (ret < 0) {
-                    av_log(avctx, AV_LOG_ERROR, "Error processing the picture timing SEI\n");
-                    p->sei.picture_timing.present = 0;
-                }
-            }
-
-            if (sps->pic_struct_present_flag && p->sei.picture_timing.present) {
-                switch (p->sei.picture_timing.pic_struct) {
-                case H264_SEI_PIC_STRUCT_TOP_FIELD:
-                case H264_SEI_PIC_STRUCT_BOTTOM_FIELD:
-                    s->repeat_pict = 0;
-                    break;
-                case H264_SEI_PIC_STRUCT_FRAME:
-                case H264_SEI_PIC_STRUCT_TOP_BOTTOM:
-                case H264_SEI_PIC_STRUCT_BOTTOM_TOP:
-                    s->repeat_pict = 1;
-                    break;
-                case H264_SEI_PIC_STRUCT_TOP_BOTTOM_TOP:
-                case H264_SEI_PIC_STRUCT_BOTTOM_TOP_BOTTOM:
-                    s->repeat_pict = 2;
-                    break;
-                case H264_SEI_PIC_STRUCT_FRAME_DOUBLING:
-                    s->repeat_pict = 3;
-                    break;
-                case H264_SEI_PIC_STRUCT_FRAME_TRIPLING:
-                    s->repeat_pict = 5;
-                    break;
-                default:
-                    s->repeat_pict = p->picture_structure == PICT_FRAME ? 1 : 0;
-                    break;
-                }
-            } else {
-                s->repeat_pict = p->picture_structure == PICT_FRAME ? 1 : 0;
-            }
-
-            if (p->picture_structure == PICT_FRAME) {
-                s->picture_structure = AV_PICTURE_STRUCTURE_FRAME;
-                if (sps->pic_struct_present_flag && p->sei.picture_timing.present) {
-                    switch (p->sei.picture_timing.pic_struct) {
-                    case H264_SEI_PIC_STRUCT_TOP_BOTTOM:
-                    case H264_SEI_PIC_STRUCT_TOP_BOTTOM_TOP:
-                        s->field_order = AV_FIELD_TT;
-                        break;
-                    case H264_SEI_PIC_STRUCT_BOTTOM_TOP:
-                    case H264_SEI_PIC_STRUCT_BOTTOM_TOP_BOTTOM:
-                        s->field_order = AV_FIELD_BB;
-                        break;
-                    default:
-                        s->field_order = AV_FIELD_PROGRESSIVE;
-                        break;
-                    }
-                } else {
-                    if (field_poc[0] < field_poc[1])
-                        s->field_order = AV_FIELD_TT;
-                    else if (field_poc[0] > field_poc[1])
-                        s->field_order = AV_FIELD_BB;
-                    else
-                        s->field_order = AV_FIELD_PROGRESSIVE;
-                }
-            } else {
-                if (p->picture_structure == PICT_TOP_FIELD)
-                    s->picture_structure = AV_PICTURE_STRUCTURE_TOP_FIELD;
-                else
-                    s->picture_structure = AV_PICTURE_STRUCTURE_BOTTOM_FIELD;
-                if (p->poc.frame_num == p->last_frame_num &&
-                    p->last_picture_structure != AV_PICTURE_STRUCTURE_UNKNOWN &&
-                    p->last_picture_structure != AV_PICTURE_STRUCTURE_FRAME &&
-                    p->last_picture_structure != s->picture_structure) {
-                    if (p->last_picture_structure == AV_PICTURE_STRUCTURE_TOP_FIELD)
-                        s->field_order = AV_FIELD_TT;
-                    else
-                        s->field_order = AV_FIELD_BB;
-                } else {
-                    s->field_order = AV_FIELD_UNKNOWN;
-                }
-                p->last_picture_structure = s->picture_structure;
-                p->last_frame_num = p->poc.frame_num;
-            }
-            if (sps->timing_info_present_flag) {
-                int64_t den = sps->time_scale;
-                if (p->sei.common.unregistered.x264_build < 44U)
-                    den *= 2;
-                av_reduce(&avctx->framerate.den, &avctx->framerate.num,
-                          sps->num_units_in_tick * avctx->ticks_per_frame, den, 1 << 30);
-            }
-
-            av_freep(&rbsp.rbsp_buffer);
-            return 0; /* no need to evaluate the rest */
-        }
-    }
-    if (q264) {
-        av_freep(&rbsp.rbsp_buffer);
-        return 0;
-    }
-    /* didn't find a picture! */
-    av_log(avctx, AV_LOG_ERROR, "missing picture in access unit with size %d\n", buf_size);
-fail:
-    av_freep(&rbsp.rbsp_buffer);
-    return -1;
-}
-
-static int h264_parse(AVCodecParserContext *s,
-                      AVCodecContext *avctx,
-                      const uint8_t **poutbuf, int *poutbuf_size,
-                      const uint8_t *buf, int buf_size)
-{
-    H264ParseContext *p = s->priv_data;
-    ParseContext *pc = &p->pc;
-    AVRational time_base = { 0, 1 };
-    int next;
-
-    if (!p->got_first) {
-        p->got_first = 1;
-        if (avctx->extradata_size) {
-            ff_h264_decode_extradata(avctx->extradata, avctx->extradata_size,
-                                     &p->ps, &p->is_avc, &p->nal_length_size,
-                                     avctx->err_recognition, avctx);
-        }
-    }
-
-    if (s->flags & PARSER_FLAG_COMPLETE_FRAMES) {
-        next = buf_size;
-    } else {
-        next = h264_find_frame_end(p, buf, buf_size, avctx);
-
-        if (ff_combine_frame(pc, next, &buf, &buf_size) < 0) {
-            *poutbuf      = NULL;
-            *poutbuf_size = 0;
-            return buf_size;
-        }
-
-        if (next < 0 && next != END_NOT_FOUND) {
-            av_assert1(pc->last_index + next >= 0);
-            h264_find_frame_end(p, &pc->buffer[pc->last_index + next], -next, avctx); // update state
-        }
-    }
-
-    parse_nal_units(s, avctx, buf, buf_size);
-
-    if (avctx->framerate.num)
-        time_base = av_inv_q(av_mul_q(avctx->framerate, (AVRational){avctx->ticks_per_frame, 1}));
-    if (p->sei.picture_timing.cpb_removal_delay >= 0) {
-        s->dts_sync_point    = p->sei.buffering_period.present;
-        s->dts_ref_dts_delta = p->sei.picture_timing.cpb_removal_delay;
-        s->pts_dts_delta     = p->sei.picture_timing.dpb_output_delay;
-    } else {
-        s->dts_sync_point    = INT_MIN;
-        s->dts_ref_dts_delta = INT_MIN;
-        s->pts_dts_delta     = INT_MIN;
-    }
-
-    if (s->flags & PARSER_FLAG_ONCE) {
-        s->flags &= PARSER_FLAG_COMPLETE_FRAMES;
-    }
-
-    if (s->dts_sync_point >= 0) {
-        int64_t den = time_base.den * (int64_t)avctx->pkt_timebase.num;
-        if (den > 0) {
-            int64_t num = time_base.num * (int64_t)avctx->pkt_timebase.den;
-            if (s->dts != AV_NOPTS_VALUE) {
-                // got DTS from the stream, update reference timestamp
-                p->reference_dts = s->dts - av_rescale(s->dts_ref_dts_delta, num, den);
-            } else if (p->reference_dts != AV_NOPTS_VALUE) {
-                // compute DTS based on reference timestamp
-                s->dts = p->reference_dts + av_rescale(s->dts_ref_dts_delta, num, den);
-            }
-
-            if (p->reference_dts != AV_NOPTS_VALUE && s->pts == AV_NOPTS_VALUE)
-                s->pts = s->dts + av_rescale(s->pts_dts_delta, num, den);
-
-            if (s->dts_sync_point > 0)
-                p->reference_dts = s->dts; // new reference
-        }
-    }
-
-    *poutbuf      = buf;
-    *poutbuf_size = buf_size;
-    return next;
-}
-
-static void h264_close(AVCodecParserContext *s)
-{
-    H264ParseContext *p = s->priv_data;
-    ParseContext *pc = &p->pc;
-
-    av_freep(&pc->buffer);
-
-    ff_h264_sei_uninit(&p->sei);
-    ff_h264_ps_uninit(&p->ps);
-}
-
-static av_cold int init(AVCodecParserContext *s)
-{
-    H264ParseContext *p = s->priv_data;
-
-    p->reference_dts = AV_NOPTS_VALUE;
-    p->last_frame_num = INT_MAX;
-    ff_h264dsp_init(&p->h264dsp, 8, 1);
-    return 0;
-}
-
-const AVCodecParser ff_h264_parser = {
-    .codec_ids      = { AV_CODEC_ID_H264 },
-    .priv_data_size = sizeof(H264ParseContext),
-    .parser_init    = init,
-    .parser_parse   = h264_parse,
-    .parser_close   = h264_close,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/htmlsubtitles.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/htmlsubtitles.h
deleted file mode 100644
index f3a8ef5d8b43c22836e30fbd3556270b026b9b40..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/htmlsubtitles.h
+++ /dev/null
@@ -1,28 +0,0 @@
-/*
- * Copyright (c) 2010  Aurelien Jacobs <aurel@gnuage.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
- */
-
-#ifndef AVCODEC_HTMLSUBTITLES_H
-#define AVCODEC_HTMLSUBTITLES_H
-
-#include "libavutil/bprint.h"
-
-int ff_htmlmarkup_to_ass(void *log_ctx, AVBPrint *dst, const char *in);
-
-#endif /* AVCODEC_HTMLSUBTITLES_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Word Search Quest APK and Enjoy Hours of Brain Teasers.md b/spaces/congsaPfin/Manga-OCR/logs/Download Word Search Quest APK and Enjoy Hours of Brain Teasers.md
deleted file mode 100644
index ae3ed808dd87be5ba64122d25457502d661d2323..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Word Search Quest APK and Enjoy Hours of Brain Teasers.md	
+++ /dev/null
@@ -1,74 +0,0 @@
-<br />
-<h1>Word Search Quest APK: A Fun and Challenging Puzzle Game for Android</h1>
-<p>If you love word games and puzzles, you will enjoy Word Search Quest APK, a free and addictive game for Android devices. Word Search Quest APK is a classic word search game with a twist: you have to find all the hidden words in a grid of letters, but you also have to complete quests and collect coins along the way. You can use the coins to unlock new levels, themes, and hints. Word Search Quest APK is a great way to improve your vocabulary, spelling, and brain power while having fun.</p>
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-<h2>What is Word Search Quest APK?</h2>
-<p>Word Search Quest APK is an Android game developed by Blackout Lab, a studio that specializes in creating casual and puzzle games. Word Search Quest APK is one of their most popular games, with over 10 million downloads and 4.5 stars on Google Play Store. Word Search Quest APK is compatible with Android 4.4 and up, and it requires 38 MB of storage space.</p>
-<h3>Features of Word Search Quest APK</h3>
-<h4>Hundreds of levels with different themes and difficulties</h4>
-<p>Word Search Quest APK offers hundreds of levels to keep you entertained and challenged. You can choose from different categories, such as animals, food, sports, countries, movies, and more. Each category has different levels with varying grid sizes and word lengths. You can also adjust the difficulty level from easy to hard, depending on your preference and skill level.</p>
-<h4>Daily puzzles and rewards</h4>
-<p>Word Search Quest APK also gives you daily puzzles to solve and rewards to earn. Every day, you can play a new puzzle with a different theme and difficulty. If you complete the puzzle, you will get coins and stars that you can use to unlock more levels and features. You can also get bonus coins by watching ads or inviting friends to play the game.</p>
-<h4>Offline mode and no time limit</h4>
-<p>One of the best things about Word Search Quest APK is that you can play it anytime and anywhere, even without an internet connection. You can enjoy the game offline, as long as you have downloaded it on your device. You also don't have to worry about running out of time or losing progress, as there is no time limit or lives system in the game. You can play at your own pace and resume the game whenever you want.</p>
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-<h4>Download the APK file from a trusted source</h4>
-<p>To download Word Search Quest APK, you need to find a reliable source that offers the latest version of the game. You can search for "word search quest apk" on Google or any other search engine, or you can use this link to download it directly from APKCombo.com, a website that provides safe and fast downloads of Android apps.</p>
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-<p>Before you can install Word Search Quest APK on your device, you need to enable unknown sources on your device, which means allowing your device to install apps from sources other than the official Google Play Store. To do this, you need to go to your device's settings, then security, then unknown sources, and toggle the switch to enable it. You may see a warning message that says installing apps from unknown sources may harm your device, but you can ignore it if you trust the source of the APK file.</p>
-<h4>Install the APK file and launch the game</h4>
-<p>After you have downloaded and enabled unknown sources, you can install Word Search Quest APK on your device. To do this, you need to locate the APK file on your device's file manager or downloads folder, and tap on it to start the installation process. You may see a pop-up window that asks for your permission to install the app, and you need to tap on "install" to confirm it. The installation may take a few seconds or minutes, depending on your device's speed and storage space. Once the installation is complete, you can tap on "open" to launch the game, or you can find the game's icon on your home screen or app drawer.</p>
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-<h4>Improve your vocabulary and spelling skills</h4>
-<p>Word Search Quest APK is not only a fun and relaxing game, but also an educational one. By playing Word Search Quest APK, you can learn new words and improve your spelling skills. You can also expand your vocabulary by choosing different categories and languages. You can even challenge yourself by playing harder levels and finding longer words.</p>
-<h4>Train your brain and relax your mind</h4>
-<p>Word Search Quest APK is also a great way to train your brain and relax your mind. By playing Word Search Quest APK, you can stimulate your brain cells and improve your memory, concentration, and problem-solving skills. You can also reduce your stress and anxiety by focusing on finding words and completing quests. Word Search Quest APK is a perfect game for killing time and having fun.</p>
-<h4>Have fun and challenge yourself</h4>
-<p>Word Search Quest APK is also a game that offers fun and challenge for everyone. You can play Word Search Quest APK alone or with your friends and family. You can compare your scores and achievements with other players around the world. You can also unlock new levels, themes, and features as you progress in the game. Word Search Quest APK is a game that will keep you entertained and satisfied for hours.</p>
-<h2>Conclusion</h2>
-<p>Word Search Quest APK is a fun and challenging puzzle game for Android devices that will test your vocabulary, spelling, and brain power. You can download Word Search Quest APK from a trusted source and install it on your device easily. You can enjoy hundreds of levels with different themes and difficulties, daily puzzles and rewards, offline mode and no time limit, and customizable settings and themes. You can also improve your vocabulary and spelling skills, train your brain and relax your mind, and have fun and challenge yourself by playing Word Search Quest APK.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Word Search Quest APK:</p>
-<table>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>Is Word Search Quest APK free?</td><td>Yes, Word Search Quest APK is free to download and play. However, it contains ads that you can watch to earn coins or remove by making in-app purchases.</td></tr>
-<tr><td>Is Word Search Quest APK safe?</td><td>Yes, Word Search Quest APK is safe to download and install on your device, as long as you get it from a reliable source that offers the original and unmodified version of the game.</td></tr>
-<tr><td>How many languages does Word Search Quest APK support?</td><td>Word Search Quest APK supports 14 languages: English, Spanish, French, German, Italian, Portuguese, Dutch, Swedish, Russian, Turkish, Polish, Romanian, Indonesian, and Vietnamese.</td></tr>
-<tr><td>How do I get more coins in Word Search Quest APK?</td><td>You can get more coins in Word Search Quest APK by completing puzzles, quests, and daily challenges; watching ads; inviting friends; or making in-app purchases.</td></tr>
-<tr><td>How do I contact the developer of Word Search Quest APK?</td><td>You can contact the developer of Word Search Quest APK by sending an email to blackoutlabgames@gmail.com or visiting their website.</td></tr>
-</table>
- : [Word Search Quest 1.5.0 Download Android APK | Aptoide](https://word-search-quest.en.aptoide.com/app) : [Blackout Lab -</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download XShare APK for Android - The Fastest File Transfer App.md b/spaces/congsaPfin/Manga-OCR/logs/Download XShare APK for Android - The Fastest File Transfer App.md
deleted file mode 100644
index 4fed7150201f81aa3905c767d9bce8f5bb4c1daf..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download XShare APK for Android - The Fastest File Transfer App.md	
+++ /dev/null
@@ -1,87 +0,0 @@
-
-<h1>Download XShare APK: The Best File Transfer App for Android</h1>
-<p>Do you want to share files with your friends or family without using any data or internet? Do you want to enjoy a fast and secure file transfer experience with just one click? If yes, then you should download XShare APK, the best file transfer app for Android devices.</p>
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-<h2>How to download and install XShare APK?</h2>
-<p>If you want to download and install XShare APK on your Android device, you can follow these simple steps:</p>
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-<h3>Step 2: Download the APK file</h3>
-<p>Next, you need to download the APK file of XShare from a reliable source. You can use this link to download it directly from the official website of XShare. Alternatively, you can use this link to download it from another source.</p>
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-<p>Once you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your downloads folder and tap on it. Then, follow the instructions on the screen to complete the installation.</p>
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-<p>Using XShare APK to transfer files is very simple and convenient. You can follow these steps to do it:</p>
-<h3>Step 1: Select the files you want to share</h3>
-<p>On your device, open the XShare app and tap on the "Send" button. Then, select the files you want to share from your device's storage. You can choose any type and size of files you want.</p>
-<h3>Step 2: Scan the QR code or enter the PIN code</h3>
-<p>On the other device, open the XShare app and tap on the "Receive" button. Then, scan the QR code or enter the PIN code displayed on your device. This will establish a connection between the two devices.</p>
-<h3>Step 3: Wait for the transfer to complete</h3>
-<p>Once the connection is established, the file transfer will start automatically. You can see the progress and speed of the transfer on both devices. You can also cancel or pause the transfer at any time. When the transfer is complete, you can check the files on the other device.</p>
-<h2>Conclusion</h2>
-<p>XShare APK is a great app for file transfer and sharing on Android devices. It is fast, secure, easy, and free to use. You can share files of any type and size without using any data or internet. You can also enjoy a one-click connection and a new user interface. If you want to download XShare APK, you can follow the steps mentioned above and start sharing files with your friends and family.</p>
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-<p>Here are some frequently asked questions about XShare APK:</p>
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-<tr><td><b>Q: Is XShare APK safe to use?</b></td><td><b>A: Yes, XShare APK is safe to use. It does not contain any viruses or malware. It also protects your privacy and data from leaks.</b></td></tr>
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-</table></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Meteor Garden 2018 Sub Indo HD Link Download dan Streaming Terbaru.md b/spaces/congsaPfin/Manga-OCR/logs/Meteor Garden 2018 Sub Indo HD Link Download dan Streaming Terbaru.md
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-
-<h1>How to Download Meteor Garden 2018 Sub Indo</h1>
-<p>Meteor Garden 2018 is a popular Chinese drama series based on the manga Boys Over Flowers by Yoko Kamio. It tells the story of an ordinary girl who enters a prestigious university and clashes with a group of rich and handsome boys known as F4. Along the way, she finds friendship, romance, and drama in her life. If you are a fan of this series or want to watch it for the first time, you might be wondering how to download Meteor Garden 2018 sub indo, or with Indonesian subtitles. In this article, we will explain what Meteor Garden 2018 is, why you should watch it sub indo, and where and how to download it sub indo.</p>
- <h2>What is Meteor Garden 2018?</h2>
-<p>Meteor Garden 2018 is a remake of the 2001 Taiwanese series of the same name, which was also based on the manga Boys Over Flowers. It is produced by Angie Chai, who also produced the original series, and directed by Lin Helong. It stars Shen Yue as Dong Shancai, Dylan Wang as Daoming Si, Darren Chen as Huaze Lei, Caesar Wu as Ximen Yan, and Connor Leong as Feng Meizuo. The series was aired on Hunan Television in China and Netflix worldwide in 2018. It has 49 episodes on Netflix and 50 episodes on TV.</p>
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- <h3>Synopsis</h3>
-<p>Dong Shancai is an 18-year-old girl from a poor family who gets accepted into Ming De University, a top school for the elite. There, she meets F4, a group of four boys who are the heirs of powerful families and rule the school with their wealth and influence. They are Daoming Si, the arrogant and hot-tempered leader; Huaze Lei, the quiet and gentle one; Ximen Yan, the flirtatious and playful one; and Feng Meizuo, the cheerful and friendly one. Shancai refuses to bow down to F4's bullying and challenges them with her strong personality. This attracts Daoming Si's attention and he develops feelings for her. However, Shancai has a crush on Huaze Lei, who helped her when she was in trouble. As Shancai gets closer to F4, she also faces many obstacles and conflicts in her love life, family, and friendship.</p>
- <h3>Cast and characters</h3>
-<ul>
-<li>Shen Yue as Dong Shancai: The main protagonist of the series. She is a brave, kind, and optimistic girl who stands up for herself and her friends. She dreams of becoming a chef and opening her own restaurant.</li>
-<li>Dylan Wang as Daoming Si: The main male lead of the series. He is the heir of Daoming Group, one of the largest companies in China. He is spoiled, arrogant, and impulsive, but also loyal, protective, and caring. He falls in love with Shancai after she challenges him.</li>
-<li>Darren Chen as Huaze Lei: The second male lead of the series. He is the heir of Huaze Group, a multinational corporation. He is calm, gentle, and mysterious, but also suffers from insomnia and trauma from his past. He is Daoming Si's best friend and Shancai's first crush.</li>
-<li>Caesar Wu as Ximen Yan: The third male lead of the series. He is the heir of Ximen Group, a tea company with a long history <li>Connor Leong as Feng Meizuo: The fourth male lead of the series. He is the heir of Feng Group, a fashion company. He is cheerful, friendly, and humorous, but also has a sensitive and romantic side. He likes to help his friends with their love problems.</li>
-</ul>
- <h3>Reception and awards</h3>
-<p>Meteor Garden 2018 received positive reviews from critics and audiences, who praised the cast's performance, the production quality, the soundtrack, and the adaptation of the original manga. It also achieved high ratings and popularity in China and abroad, especially in Southeast Asia. It won several awards and nominations, such as the Most Influential TV Series of the Year at the 2018 Sina Weibo Night Awards, the Best Foreign Drama at the 2019 Seoul International Drama Awards, and the Best Drama Series at the 2019 Huading Awards.</p>
- <h2>Why watch Meteor Garden 2018 sub indo?</h2>
-<p>If you are a fan of Meteor Garden 2018 or want to watch it for the first time, you might be wondering why you should watch it sub indo, or with Indonesian subtitles. Here are some reasons why you should watch it sub indo:</p>
- <h3>Enjoy the original language and voice acting</h3>
-<p>By watching Meteor Garden 2018 sub indo, you can enjoy the original language and voice acting of the series. You can hear the actors' expressions, emotions, and accents more clearly and authentically. You can also appreciate the nuances and humor of the dialogue that might be lost or changed in dubbing or translation.</p>
- <h3>Learn some Mandarin words and phrases</h3>
-<p>By watching Meteor Garden 2018 sub indo, you can also learn some Mandarin words and phrases that are used in the series. You can pick up some common greetings, compliments, insults, slang, and idioms that are spoken by the characters. You can also learn some cultural references, such as names of places, foods, songs, and celebrities that are mentioned in the series.</p>
- <h3>Appreciate the cultural differences and similarities</h3>
-<p>By watching Meteor Garden 2018 sub indo, you can also appreciate the cultural differences and similarities between China and Indonesia. You can see how the characters dress, eat, behave, and communicate in different situations and settings. You can also compare and contrast how they deal with issues such as family, friendship, love, school, work, and society. You might find some aspects that are familiar or relatable to you, or some that are new or surprising to you.</p>
- <h2>Where to download Meteor Garden 2018 sub indo?</h2>
-<p>Now that you know why you should watch Meteor Garden 2018 sub indo, you might be wondering where you can download it sub indo. There are several platforms that offer Meteor Garden 2018 sub indo for streaming or downloading. Here are some of them:</p>
- <h3>Netflix</h3>
-<p>Netflix is a global streaming service that offers a wide range of movies and TV shows from different genres and countries. It has all 49 episodes of Meteor Garden 2018 sub indo available for streaming or downloading. You can watch it on your computer, smartphone, tablet, smart TV, or other devices with an internet connection. You need to have a Netflix account and subscription to access its content.</p>
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- <h3>Vidio</h3>
-<p>Vidio is an Indonesian streaming service that offers various local and international content, such as movies, TV shows, live TV channels , sports, music, and news. It has all 50 episodes of Meteor Garden 2018 sub indo available for streaming or downloading. You can watch it on your computer, smartphone, tablet, or smart TV with an internet connection. You need to have a Vidio account and subscription to access its content.</p>
- <h3>iQIYI</h3>
-<p>iQIYI is a Chinese streaming service that offers various original and licensed content, such as movies, TV shows, variety shows, anime, and documentaries. It has all 50 episodes of Meteor Garden 2018 sub indo available for streaming or downloading. You can watch it on your computer, smartphone, tablet, or smart TV with an internet connection. You need to have an iQIYI account and subscription to access its content.</p>
- <h2>How to download Meteor Garden 2018 sub indo from each platform?</h2>
-<p>Now that you know where you can download Meteor Garden 2018 sub indo, you might be wondering how you can download it sub indo from each platform. Here are some steps that you can follow:</p>
- <h3>Netflix: Use the download button or the Smart Downloads feature</h3>
-<ul>
-<li>Open the Netflix app on your device and sign in to your account.</li>
-<li>Search for Meteor Garden 2018 and select the episode that you want to watch.</li>
-<li>Tap on the download button (a downward arrow) next to the episode title. The download will start automatically and you can see the progress on the downloads tab.</li>
-<li>Alternatively, you can use the Smart Downloads feature, which automatically downloads the next episode when you finish watching one. To enable this feature, go to the app settings and turn on Smart Downloads.</li>
-<li>Once the download is complete, you can watch the episode offline from the downloads tab.</li>
-</ul>
- <h3>Vidio: Use the Vidio app or a third-party video downloader</h3>
-<ul>
-<li>Open the Vidio app on your device and sign in to your account.</li>
-<li>Search for Meteor Garden 2018 and select the episode that you want to watch.</li>
-<li>Tap on the download button (a downward arrow) next to the episode title. The download will start automatically and you can see the progress on the downloads tab.</li>
-<li>Once the download is complete, you can watch the episode offline from the downloads tab.</li>
-<li>Alternatively, you can use a third-party video downloader, such as VidMate, TubeMate, or Snaptube. These are apps that allow you to download videos from various websites, including Vidio. To use them, you need to copy the URL of the episode that you want to watch from Vidio's website and paste it into the video downloader app. Then, you can choose the quality and format of the video and start the download. Once the download is complete, you can watch the episode offline from the video downloader app.</li>
-</ul>
- <h3>iQIYI: Use the iQIYI app or a third-party video downloader</h3>
-<ul>
-<li>Open the iQIYI app on your device and sign in to your account.</li>
-<li>Search for Meteor Garden 2018 and select the episode that you want to watch.</li>
-<li>Tap on the download button (a downward arrow) next to the episode title. The download will start automatically and you can see the progress on the downloads tab.</li>
-<li>Once the download is complete, you can watch the episode offline from the downloads tab.</li>
-<li>Alternatively, you can use a third-party video downloader, such as VidMate, TubeMate, or Snaptube. These are apps that allow you to download videos from various websites, including iQIYI. To use them, you need to copy the URL of the episode that you want to watch from iQIYI's website and paste it into the video downloader app. Then, you can choose the quality and format of the video and start the download. Once the download is complete, you can watch the episode offline from the video downloader app.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Meteor Garden 2018 is a captivating and romantic Chinese drama series that you can enjoy watching sub indo. By watching it sub indo, you can appreciate the original language and voice acting, learn some Mandarin words and phrases, and understand the cultural differences and similarities between China and Indonesia. You can download Meteor Garden 2018 sub indo from various platforms, such as Netflix, Vidio, and iQIYI, by using their apps or third-party video downloaders. We hope this article has helped you learn how to download Meteor Garden 2018 sub indo and enjoy watching it offline.</p>
- <h2>FAQs</h2>
-<ul>
-<li>Q: Is Meteor Garden 2018 a sequel or a remake of the 2001 series?<br>
-A: Meteor Garden 2018 is a remake of the 2001 series, not a sequel. It has a different cast, director, and producer, but follows the same story based on the manga Boys Over Flowers.</li>
-<li>Q: How many episodes are there in Meteor Garden 2018?<br>
-A: There are 49 episodes on Netflix and 50 episodes on TV. The difference is due to the editing of some scenes for censorship or time reasons.</li>
-<li>Q: What is the difference between sub indo and dub indo?<br>
-A: Sub indo means subtitles in Indonesian, while dub indo means dubbing in Indonesian. Subtitles are text that appear at the bottom of the screen that translate what the characters are saying in their original language. Dubbing is when the original voice of the characters is replaced by another voice that speaks in a different language.</li>
-<li>Q: What are some other Chinese drama series that I can watch sub indo?<br>
-A: Some other Chinese drama series that you can watch sub indo are The Untamed, Love O2O, Eternal Love, Ashes of Love, A Love So Beautiful, and Go Go Squid.</li>
-<li>Q: Where can I find more information about Meteor Garden 2018 and its cast?<br>
-A: You can find more information about Meteor Garden 2018 and its cast on their official website, social media accounts, fan pages, or Wikipedia pages.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Project BloodStrike - A Mobile FPS that Lets You Design Your Own Arsenal.md b/spaces/congsaPfin/Manga-OCR/logs/Project BloodStrike - A Mobile FPS that Lets You Design Your Own Arsenal.md
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-<li>The online forums or communities of the project game apk. You can find discussions, reviews, feedbacks, etc. about the project game apk.</li>
-<li>The online multiplayer or co-op modes of the project game apk. You can find other players who are online and ready to play with you.</li>
-<li>The online crowdfunding or crowdsourcing platforms of the project game apk. You can find ways to support or join the development of the project game apk.</li>
-</ul>
-<h3>Provide feedback and support</h3>
-<p>The last way to play and enjoy a project game apk is to provide feedback and support to the creators of the project game apk. This can help them to:</p>
-<ul>
-<li>Improve their skills and knowledge as developers. You can give them constructive criticism and advice on how to make their games better.</li>
-<li>Fix their errors and bugs as developers. You can report any problems or issues that you encounter while playing their games.</li>
-<li>Motivate their passion and enthusiasm as developers. You can give them compliments and appreciation for their hard work and creativity.</li>
-<li>Achieve their goals and dreams as developers. You can help them reach more players and fans for their games.</li>
-</ul>
-<p>To provide feedback and support to the creators of a project game apk, you can use various methods and means, such as:</p>
-<ul>
-<li>The rating and review system of the source or platform where you downloaded the project game apk. You can give them stars or points based on your satisfaction with their games.</li>
-<li>The comment or message system of the source or platform where you downloaded the project game apk. You can give them your opinions and impressions about their games.</li>
-<li>The email or contact system of the developer or creator of the project game apk. You can give them your suggestions and questions about their games.</li>
-<li>The donation or contribution system of the developer or creator of the project game apk. You can give them your money or resources to support their games.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Project game apks are files that contain games that are still in development or testing stages. They are not official releases, but rather experimental versions that showcase the potential of a game idea or concept. They are useful for both gamers and developers, as they offer a chance to try out new and innovative games, and to share and improve their work with a wider audience.</p>
-<p>To create a project game apk, you need to choose a game engine and platform, design and develop your game, and export and test your game apk. To download and install a project game apk, you need to find a reliable source, check the compatibility and security, and follow the installation steps. To play and enjoy a project game apk, you need to explore the features and options, connect with other players and developers, and provide feedback and support.</p>
-<p>If you are interested in project game apks, you can start by checking out some of the websites and platforms that host and share them, such as itch.io, Game Jolt, APKPure, etc. You can also try creating your own project game apk using one of the game engines and platforms that we mentioned, such as Unity, Unreal Engine, Godot, GameMaker Studio, etc. You might discover some amazing games that you never knew existed, or create some amazing games that others will love.</p>
-<h2>FAQs</h2>
-<h4>What is the difference between a project game apk and a regular game apk?</h4>
-<p>A project game apk is a file that contains a game that is still in development or testing stages. It is not an official release, but rather an experimental version that showcases the potential of a game idea or concept. A regular game apk is a file that contains a game that is fully developed and released. It is an official product that has been tested and polished for quality and performance.</p>
-<h4>How can I create a project game apk without coding?</h4>
-<p>Some game engines and platforms allow you to create games without coding, using visual scripting systems or drag-and-drop interfaces. For example, Unreal Engine has a visual scripting system called Blueprint that allows you to create games without coding. GameMaker Studio has a drag-and-drop interface that simplifies the development process. However, these tools may have some limitations or require some basic knowledge of programming concepts.</p>
-<h4>How can I find more project game apks to download and play?</h4>
-<p>There are many websites and platforms that host and share project game apks, such as itch.io, Game Jolt, APKPure, etc. You can browse through their categories, genres, tags, ratings, etc., to find project game apks that suit your preferences. You can also search for specific keywords or names of project game apks that you are interested in.</p>
-<h4>How can I update my project game apk?</h4>
-<p>If you are the developer or creator of a project game apk, you can update it by exporting and uploading a new version of your game apk to the source or platform where you shared it. You can also notify your users about the update by posting an announcement or sending an email. If you are the user of a project game apk, you can update it by downloading and installing the latest version of the game apk from the source or platform where you got it. You can also check for updates by visiting the official website or social media pages of the developer or creator.</p>
-<h4>How can I uninstall my project game apk?</h4>
-<p>If you want to uninstall your project game apk from your device, you can follow the same steps as uninstalling any other app on your device. For example, if you are using an Android device, you can go to your settings > apps > select the app > uninstall. Alternatively, you can long-press on the app icon on your home screen or app drawer > drag it to the uninstall option.</p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/Downloaduopilotbottalisman.md b/spaces/contluForse/HuggingGPT/assets/Downloaduopilotbottalisman.md
deleted file mode 100644
index c01205ae9bac07dd1923f6f758037d132e92fd7d..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Downloaduopilotbottalisman.md
+++ /dev/null
@@ -1,124 +0,0 @@
-<br />
-<h1>Download UoPilot Bot for Talisman Online: The Ultimate Guide</h1>
-<p>Talisman Online is a popular MMORPG game that offers a variety of classes, quests, dungeons, and PvP modes. However, some players may find it tedious or time-consuming to level up, farm gold, or complete tasks. That's why many Talisman Online players use UoPilot Bot, a software that automates the gameplay and makes it easier and more fun.</p>
-<h2>downloaduopilotbottalisman</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://ssurll.com/2uzxKz">https://ssurll.com/2uzxKz</a></b></p><br /><br />
-<p>UoPilot Bot is a powerful tool that can perform various actions in Talisman Online, such as moving, attacking, looting, healing, buffing, chatting, and more. You can customize the bot settings to suit your preferences and needs. You can also create scripts and macros to execute complex commands and routines.</p>
-<p>UoPilot Bot is not only useful for Talisman Online, but also for other games and applications. You can use it to automate any repetitive or boring tasks on your computer. UoPilot Bot is easy to use and has a user-friendly interface. You don't need any programming skills or experience to use it.</p>
-<h2>How to Download UoPilot Bot for Talisman Online</h2>
-<p>If you want to download UoPilot Bot for Talisman Online, you need to follow these simple steps:</p>
-<ol>
-<li>Go to this link: <a href="https://urloso.com/2t4S3r">https://urloso.com/2t4S3r</a>. This is the official website of UoPilot Bot.</li>
-<li>Click on the "Download" button and choose a location to save the file.</li>
-<li>Extract the file using WinRAR or any other software that can open ZIP files.</li>
-<li>Run the UoPilot.exe file and follow the installation instructions.</li>
-<li>Launch the UoPilot Bot and enjoy!</li>
-</ol>
-<h2>How to Use UoPilot Bot for Talisman Online</h2>
-<p>Once you have downloaded and installed UoPilot Bot for Talisman Online, you can start using it right away. Here are some tips on how to use UoPilot Bot for Talisman Online:</p>
-<p></p>
-<ul>
-<li>Make sure you have Talisman Online running in windowed mode.</li>
-<li>Open UoPilot Bot and select the "Talisman" option from the drop-down menu.</li>
-<li>Choose a profile or create a new one. A profile is a set of settings and scripts that you can use for different characters or situations.</li>
-<li>Edit the profile settings according to your preferences. You can adjust the speed, delay, hotkeys, coordinates, actions, and more.</li>
-<li>Load the profile and click on the "Start" button.</li>
-<li>Watch as UoPilot Bot takes over your character and performs the tasks you assigned.</li>
-</ul>
-<h2>The Benefits of Using UoPilot Bot for Talisman Online</h2>
-<p>Using UoPilot Bot for Talisman Online has many benefits, such as:</p>
-<ul>
-<li>You can save time and energy by letting UoPilot Bot do the work for you.</li>
-<li>You can level up faster and easier by using UoPilot Bot to grind mobs and quests.</li>
-<li>You can earn more gold and items by using UoPilot Bot to farm resources and loot.</li>
-<li>You can improve your skills and stats by using UoPilot Bot to train and enhance your character.</li>
-<li>You can have more fun and enjoyment by using UoPilot Bot to explore new areas and features of Talisman Online.</li>
-</ul>
-<h2>The Risks of Using UoPilot Bot for Talisman Online</h2>
-<p>While using UoPilot Bot for Talisman Online has many advantages, it also has some risks that you should be aware of:</p>
-<ul>
-<li>You may get banned or suspended by the game developers if they detect that you are using UoPilot Bot. This is against the game rules and terms of service.</li>
-<li>You may get hacked or scammed by malicious websites or programs that claim to offer UoPilot Bot. Always download UoPilot Bot from the official website and scan it with antivirus software before installing it.</li>
-<li>You may lose interest or satisfaction in playing Talisman Online if you rely too much on UoPilot Bot. Remember that playing games is supposed to be fun and challenging, not boring and easy.</li>
-</ul>
-<h2>The Conclusion</h2>
-<p>UoPilot Bot is a software that can automate your gameplay in Talisman Online and other games and applications. You can download UoPilot Bot from this link: <a href="https://urloso.com/2t4S3r">https://urloso.com/2t4S3r</a>. You can use UoPilot Bot to perform various actions in Talisman Online, such as leveling up, farming gold, completing quests, and more. However, you should also be careful of the risks of using UoPilot Bot, such as getting banned, hacked, or scammed. You should also balance your use of UoPilot Bot with your own enjoyment and satisfaction in playing Talisman Online.</p>
-<h2>The Tips and Tricks of UoPilot Bot for Talisman Online</h2>
-<p>UoPilot Bot for Talisman Online can help you improve your gameplay and achieve your goals in Talisman Online. However, you can also enhance your experience and results by following some tips and tricks of UoPilot Bot for Talisman Online. Here are some of the tips and tricks of UoPilot Bot for Talisman Online:</p>
-<ul>
-<li>Update your UoPilot Bot regularly to get the latest features and fixes.</li>
-<li>Test your scripts and macros before using them in the game to avoid errors and glitches.</li>
-<li>Use different profiles for different characters or situations to optimize your performance and efficiency.</li>
-<li>Monitor your bot activity and intervene when necessary to avoid detection or suspicion.</li>
-<li>Share your scripts and macros with other users and learn from their feedback and suggestions.</li>
-</ul>
-<h2>The Comparison of UoPilot Bot for Talisman Online with Other Bots</h2>
-<p>UoPilot Bot for Talisman Online is not the only bot that can automate your gameplay in Talisman Online. There are other bots that can do similar or different things in Talisman Online. However, UoPilot Bot for Talisman Online has some advantages and disadvantages compared to other bots. Here is a comparison of UoPilot Bot for Talisman Online with other bots:</p>
-<table>
-<tr><th>Bot</th><th>Advantages</th><th>Disadvantages</th></tr>
-<tr><td>UoPilot Bot</td><td>- Supports multiple languages, games, and applications<br>- Has a built-in editor, smart detection system, stealth mode, backup and restore function, and help and support section<br>- Can perform various actions, such as moving, attacking, looting, healing, buffing, chatting, crafting, trading, fishing, mining, and more<br>- Can create and edit scripts and macros easily</td><td>- Not free to use<br>- May get banned or suspended by the game developers if detected<br>- May get hacked or scammed by malicious websites or programs that claim to offer UoPilot Bot<br>- May lose interest or satisfaction in playing Talisman Online if rely too much on UoPilot Bot</td></tr>
-<tr><td>Talisman Bot</td><td>- Free and open-source<br>- Can perform basic actions, such as moving, attacking, looting, healing, and more</td><td>- Only supports English language<br>- Only supports Talisman Online game<br>- Does not have a built-in editor, smart detection system, stealth mode, backup and restore function, or help and support section<br>- Cannot perform complex actions, such as crafting, trading, fishing, mining, and more<br>- Cannot create or edit scripts or macros easily</td></tr>
-<tr><td>Talisman Pro</td><td>- Can perform complex actions, such as crafting, trading, fishing, mining, and more</td><td>- Expensive to use<br>- Only supports English language<br>- Only supports Talisman Online game<br>- Does not have a built-in editor, smart detection system, stealth mode,
- backup and restore function, or help and support section<br>- Cannot create or edit scripts or macros easily</td></tr>
-<tr><td>Talisman Helper</td><td>- Simple and lightweight<br>- Can assist you in Talisman Online by showing you the coordinates,
- health,
- mana,
- inventory,
- and more</td><td>- Not free to use<br>- Only supports English language<br>- Only supports Talisman Online game<br>- Does not have a built-in editor,
- smart detection system,
- stealth mode,
- backup and restore function,
- or help and support section<br>- Cannot perform any actions,
- such as moving,
- attacking,
- looting,
- healing,
- buffing,
- chatting,
- crafting,
- trading,
- fishing,
- mining,
- and more<br>- Cannot create or edit scripts or macros at all</td></tr>
-</table>
-<h2>The Examples of UoPilot Bot for Talisman Online</h2>
-<p>UoPilot Bot for Talisman Online can automate your gameplay in Talisman Online and other games and applications. However, you may wonder how UoPilot Bot for Talisman Online works and what it can do in practice. Here are some examples of UoPilot Bot for Talisman Online:</p>
-<ul>
-<li>Example 1: You can use UoPilot Bot to level up your character by killing mobs and completing quests. You can set the bot to move to a certain location, attack the enemies, loot the corpses, heal yourself, and return to the quest giver. You can also set the bot to repeat the process until you reach your desired level.</li>
-<li>Example 2: You can use UoPilot Bot to farm gold and items by looting resources and chests. You can set the bot to move to a certain area, search for the resources or chests, loot them, and store them in your inventory or bank. You can also set the bot to sell or trade the items for more gold.</li>
-<li>Example 3: You can use UoPilot Bot to improve your skills and stats by training and enhancing your character. You can set the bot to use certain skills or items to increase your attributes, such as strength, agility, intelligence, etc. You can also set the bot to upgrade your equipment or buy new ones.</li>
-<li>Example 4: You can use UoPilot Bot to have more fun and enjoyment by exploring new areas and features of Talisman Online. You can set the bot to move to a certain region, discover new places, interact with NPCs or other players, join events or activities, and more.</li>
-</ul>
-<h2>The Testimonials of UoPilot Bot for Talisman Online</h2>
-<p>UoPilot Bot for Talisman Online has received many testimonials from users who have tried it and enjoyed its benefits. Here are some of the testimonials of UoPilot Bot for Talisman Online:</p>
-<blockquote>
-<p>"I have been using UoPilot Bot for Talisman Online for a few months now and I am very impressed with it. It has made my gameplay more convenient and efficient. I can level up faster, farm more gold and items, improve my skills and stats, and have more fun. UoPilot Bot is a game-changer for me."</p>
-<cite>- Alex, a fan of UoPilot Bot</cite>
-</blockquote>
-<blockquote>
-<p>"I have tried many bots for Talisman Online but none of them can compare to UoPilot Bot. It is the most versatile and reliable bot I have ever used. It can do anything I want it to do in Talisman Online and other games and applications. It is also very easy to use and customize. UoPilot Bot is a masterpiece."</p>
-<cite>- Emma, a lover of UoPilot Bot</cite>
-</blockquote>
-<blockquote>
-<p>"I have been playing Talisman Online for a long time but I always felt bored and frustrated with it. It was too hard and time-consuming to level up, farm gold and items, improve my skills and stats, and have fun. But then I discovered UoPilot Bot and everything changed. It has made my gameplay more enjoyable and rewarding. UoPilot Bot is a miracle."</p>
-<cite>- Noah, a user of UoPilot Bot</cite>
-</blockquote>
-<h2>The Conclusion</h2>
-<p>UoPilot Bot for Talisman Online is a software that can automate your gameplay in Talisman Online and other games and applications. You can download UoPilot Bot from this link: <a href="https://urloso.com/2t4S3r">https://urloso.com/2t4S3r</a>. You can use UoPilot Bot to perform various actions in Talisman Online, such as leveling up, farming gold, completing quests, and more. However, you should also be careful of the risks of using UoPilot Bot, such as getting banned, hacked, or scammed. You should also balance your use of UoPilot Bot with your own enjoyment and satisfaction in playing Talisman Online.</p>
-<p>In this article, we have covered the following topics:</p>
-<ul>
-<li>How to download UoPilot Bot for Talisman Online</li>
-<li>How to use UoPilot Bot for Talisman Online</li>
-<li>The benefits of using UoPilot Bot for Talisman Online</li>
-<li>The risks of using UoPilot Bot for Talisman Online</li>
-<li>The features of UoPilot Bot for Talisman Online</li>
-<li>The alternatives to UoPilot Bot for Talisman Online</li>
-<li>The pros and cons of UoPilot Bot for Talisman Online</li>
-<li>The recommendations of UoPilot Bot for Talisman Online</li>
-<li>The examples of UoPilot Bot for Talisman Online</li>
-<li>The reviews of UoPilot Bot for Talisman Online</li>
-<li>The FAQs of UoPilot Bot for Talisman Online</li>
-<li>The testimonials of UoPilot Bot for Talisman Online</li>
-</ul>
-<p>We hope that this article has helped you learn more about UoPilot Bot for Talisman Online and how to use it effectively. If you have any questions or feedback, please feel free to contact us or leave a comment below. Thank you for reading and happy gaming!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/handlers/yaml_handler.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/handlers/yaml_handler.py
deleted file mode 100644
index c5aa2eea1e8c76f8baf753d1c8c959dee665e543..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/fileio/handlers/yaml_handler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import yaml
-
-try:
-    from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
-    from yaml import Loader, Dumper
-
-from .base import BaseFileHandler  # isort:skip
-
-
-class YamlHandler(BaseFileHandler):
-
-    def load_from_fileobj(self, file, **kwargs):
-        kwargs.setdefault('Loader', Loader)
-        return yaml.load(file, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault('Dumper', Dumper)
-        yaml.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault('Dumper', Dumper)
-        return yaml.dump(obj, **kwargs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/engine/test.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/engine/test.py
deleted file mode 100644
index 8dbeef271db634ec2dadfda3bc0b5ef9c7a677ff..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/engine/test.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-import time
-
-import torch
-import torch.distributed as dist
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.runner import get_dist_info
-
-
-def single_gpu_test(model, data_loader):
-    """Test model with a single gpu.
-
-    This method tests model with a single gpu and displays test progress bar.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (nn.Dataloader): Pytorch data loader.
-
-    Returns:
-        list: The prediction results.
-    """
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    prog_bar = mmcv.ProgressBar(len(dataset))
-    for data in data_loader:
-        with torch.no_grad():
-            result = model(return_loss=False, **data)
-        results.extend(result)
-
-        # Assume result has the same length of batch_size
-        # refer to https://github.com/open-mmlab/mmcv/issues/985
-        batch_size = len(result)
-        for _ in range(batch_size):
-            prog_bar.update()
-    return results
-
-
-def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
-    """Test model with multiple gpus.
-
-    This method tests model with multiple gpus and collects the results
-    under two different modes: gpu and cpu modes. By setting
-    ``gpu_collect=True``, it encodes results to gpu tensors and use gpu
-    communication for results collection. On cpu mode it saves the results on
-    different gpus to ``tmpdir`` and collects them by the rank 0 worker.
-
-    Args:
-        model (nn.Module): Model to be tested.
-        data_loader (nn.Dataloader): Pytorch data loader.
-        tmpdir (str): Path of directory to save the temporary results from
-            different gpus under cpu mode.
-        gpu_collect (bool): Option to use either gpu or cpu to collect results.
-
-    Returns:
-        list: The prediction results.
-    """
-    model.eval()
-    results = []
-    dataset = data_loader.dataset
-    rank, world_size = get_dist_info()
-    if rank == 0:
-        prog_bar = mmcv.ProgressBar(len(dataset))
-    time.sleep(2)  # This line can prevent deadlock problem in some cases.
-    for i, data in enumerate(data_loader):
-        with torch.no_grad():
-            result = model(return_loss=False, **data)
-        results.extend(result)
-
-        if rank == 0:
-            batch_size = len(result)
-            batch_size_all = batch_size * world_size
-            if batch_size_all + prog_bar.completed > len(dataset):
-                batch_size_all = len(dataset) - prog_bar.completed
-            for _ in range(batch_size_all):
-                prog_bar.update()
-
-    # collect results from all ranks
-    if gpu_collect:
-        results = collect_results_gpu(results, len(dataset))
-    else:
-        results = collect_results_cpu(results, len(dataset), tmpdir)
-    return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
-    """Collect results under cpu mode.
-
-    On cpu mode, this function will save the results on different gpus to
-    ``tmpdir`` and collect them by the rank 0 worker.
-
-    Args:
-        result_part (list): Result list containing result parts
-            to be collected.
-        size (int): Size of the results, commonly equal to length of
-            the results.
-        tmpdir (str | None): temporal directory for collected results to
-            store. If set to None, it will create a random temporal directory
-            for it.
-
-    Returns:
-        list: The collected results.
-    """
-    rank, world_size = get_dist_info()
-    # create a tmp dir if it is not specified
-    if tmpdir is None:
-        MAX_LEN = 512
-        # 32 is whitespace
-        dir_tensor = torch.full((MAX_LEN, ),
-                                32,
-                                dtype=torch.uint8,
-                                device='cuda')
-        if rank == 0:
-            mmcv.mkdir_or_exist('.dist_test')
-            tmpdir = tempfile.mkdtemp(dir='.dist_test')
-            tmpdir = torch.tensor(
-                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
-            dir_tensor[:len(tmpdir)] = tmpdir
-        dist.broadcast(dir_tensor, 0)
-        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
-    else:
-        mmcv.mkdir_or_exist(tmpdir)
-    # dump the part result to the dir
-    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
-    dist.barrier()
-    # collect all parts
-    if rank != 0:
-        return None
-    else:
-        # load results of all parts from tmp dir
-        part_list = []
-        for i in range(world_size):
-            part_file = osp.join(tmpdir, f'part_{i}.pkl')
-            part_result = mmcv.load(part_file)
-            # When data is severely insufficient, an empty part_result
-            # on a certain gpu could makes the overall outputs empty.
-            if part_result:
-                part_list.append(part_result)
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        # remove tmp dir
-        shutil.rmtree(tmpdir)
-        return ordered_results
-
-
-def collect_results_gpu(result_part, size):
-    """Collect results under gpu mode.
-
-    On gpu mode, this function will encode results to gpu tensors and use gpu
-    communication for results collection.
-
-    Args:
-        result_part (list): Result list containing result parts
-            to be collected.
-        size (int): Size of the results, commonly equal to length of
-            the results.
-
-    Returns:
-        list: The collected results.
-    """
-    rank, world_size = get_dist_info()
-    # dump result part to tensor with pickle
-    part_tensor = torch.tensor(
-        bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
-    # gather all result part tensor shape
-    shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
-    shape_list = [shape_tensor.clone() for _ in range(world_size)]
-    dist.all_gather(shape_list, shape_tensor)
-    # padding result part tensor to max length
-    shape_max = torch.tensor(shape_list).max()
-    part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
-    part_send[:shape_tensor[0]] = part_tensor
-    part_recv_list = [
-        part_tensor.new_zeros(shape_max) for _ in range(world_size)
-    ]
-    # gather all result part
-    dist.all_gather(part_recv_list, part_send)
-
-    if rank == 0:
-        part_list = []
-        for recv, shape in zip(part_recv_list, shape_list):
-            part_result = pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())
-            # When data is severely insufficient, an empty part_result
-            # on a certain gpu could makes the overall outputs empty.
-            if part_result:
-                part_list.append(part_result)
-        # sort the results
-        ordered_results = []
-        for res in zip(*part_list):
-            ordered_results.extend(list(res))
-        # the dataloader may pad some samples
-        ordered_results = ordered_results[:size]
-        return ordered_results
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/utils/config.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/utils/config.py
deleted file mode 100644
index 17149353aefac6d737c67bb2f35a3a6cd2147b0a..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/utils/config.py
+++ /dev/null
@@ -1,688 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import ast
-import copy
-import os
-import os.path as osp
-import platform
-import shutil
-import sys
-import tempfile
-import uuid
-import warnings
-from argparse import Action, ArgumentParser
-from collections import abc
-from importlib import import_module
-
-from addict import Dict
-from yapf.yapflib.yapf_api import FormatCode
-
-from .misc import import_modules_from_strings
-from .path import check_file_exist
-
-if platform.system() == 'Windows':
-    import regex as re
-else:
-    import re
-
-BASE_KEY = '_base_'
-DELETE_KEY = '_delete_'
-DEPRECATION_KEY = '_deprecation_'
-RESERVED_KEYS = ['filename', 'text', 'pretty_text']
-
-
-class ConfigDict(Dict):
-
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no "
-                                f"attribute '{name}'")
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-def add_args(parser, cfg, prefix=''):
-    for k, v in cfg.items():
-        if isinstance(v, str):
-            parser.add_argument('--' + prefix + k)
-        elif isinstance(v, int):
-            parser.add_argument('--' + prefix + k, type=int)
-        elif isinstance(v, float):
-            parser.add_argument('--' + prefix + k, type=float)
-        elif isinstance(v, bool):
-            parser.add_argument('--' + prefix + k, action='store_true')
-        elif isinstance(v, dict):
-            add_args(parser, v, prefix + k + '.')
-        elif isinstance(v, abc.Iterable):
-            parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+')
-        else:
-            print(f'cannot parse key {prefix + k} of type {type(v)}')
-    return parser
-
-
-class Config:
-    """A facility for config and config files.
-
-    It supports common file formats as configs: python/json/yaml. The interface
-    is the same as a dict object and also allows access config values as
-    attributes.
-
-    Example:
-        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
-        >>> cfg.a
-        1
-        >>> cfg.b
-        {'b1': [0, 1]}
-        >>> cfg.b.b1
-        [0, 1]
-        >>> cfg = Config.fromfile('tests/data/config/a.py')
-        >>> cfg.filename
-        "/home/kchen/projects/mmcv/tests/data/config/a.py"
-        >>> cfg.item4
-        'test'
-        >>> cfg
-        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
-        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
-    """
-
-    @staticmethod
-    def _validate_py_syntax(filename):
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            content = f.read()
-        try:
-            ast.parse(content)
-        except SyntaxError as e:
-            raise SyntaxError('There are syntax errors in config '
-                              f'file {filename}: {e}')
-
-    @staticmethod
-    def _substitute_predefined_vars(filename, temp_config_name):
-        file_dirname = osp.dirname(filename)
-        file_basename = osp.basename(filename)
-        file_basename_no_extension = osp.splitext(file_basename)[0]
-        file_extname = osp.splitext(filename)[1]
-        support_templates = dict(
-            fileDirname=file_dirname,
-            fileBasename=file_basename,
-            fileBasenameNoExtension=file_basename_no_extension,
-            fileExtname=file_extname)
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            config_file = f.read()
-        for key, value in support_templates.items():
-            regexp = r'\{\{\s*' + str(key) + r'\s*\}\}'
-            value = value.replace('\\', '/')
-            config_file = re.sub(regexp, value, config_file)
-        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
-            tmp_config_file.write(config_file)
-
-    @staticmethod
-    def _pre_substitute_base_vars(filename, temp_config_name):
-        """Substitute base variable placehoders to string, so that parsing
-        would work."""
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            config_file = f.read()
-        base_var_dict = {}
-        regexp = r'\{\{\s*' + BASE_KEY + r'\.([\w\.]+)\s*\}\}'
-        base_vars = set(re.findall(regexp, config_file))
-        for base_var in base_vars:
-            randstr = f'_{base_var}_{uuid.uuid4().hex.lower()[:6]}'
-            base_var_dict[randstr] = base_var
-            regexp = r'\{\{\s*' + BASE_KEY + r'\.' + base_var + r'\s*\}\}'
-            config_file = re.sub(regexp, f'"{randstr}"', config_file)
-        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
-            tmp_config_file.write(config_file)
-        return base_var_dict
-
-    @staticmethod
-    def _substitute_base_vars(cfg, base_var_dict, base_cfg):
-        """Substitute variable strings to their actual values."""
-        cfg = copy.deepcopy(cfg)
-
-        if isinstance(cfg, dict):
-            for k, v in cfg.items():
-                if isinstance(v, str) and v in base_var_dict:
-                    new_v = base_cfg
-                    for new_k in base_var_dict[v].split('.'):
-                        new_v = new_v[new_k]
-                    cfg[k] = new_v
-                elif isinstance(v, (list, tuple, dict)):
-                    cfg[k] = Config._substitute_base_vars(
-                        v, base_var_dict, base_cfg)
-        elif isinstance(cfg, tuple):
-            cfg = tuple(
-                Config._substitute_base_vars(c, base_var_dict, base_cfg)
-                for c in cfg)
-        elif isinstance(cfg, list):
-            cfg = [
-                Config._substitute_base_vars(c, base_var_dict, base_cfg)
-                for c in cfg
-            ]
-        elif isinstance(cfg, str) and cfg in base_var_dict:
-            new_v = base_cfg
-            for new_k in base_var_dict[cfg].split('.'):
-                new_v = new_v[new_k]
-            cfg = new_v
-
-        return cfg
-
-    @staticmethod
-    def _file2dict(filename, use_predefined_variables=True):
-        filename = osp.abspath(osp.expanduser(filename))
-        check_file_exist(filename)
-        fileExtname = osp.splitext(filename)[1]
-        if fileExtname not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
-
-        with tempfile.TemporaryDirectory() as temp_config_dir:
-            temp_config_file = tempfile.NamedTemporaryFile(
-                dir=temp_config_dir, suffix=fileExtname)
-            if platform.system() == 'Windows':
-                temp_config_file.close()
-            temp_config_name = osp.basename(temp_config_file.name)
-            # Substitute predefined variables
-            if use_predefined_variables:
-                Config._substitute_predefined_vars(filename,
-                                                   temp_config_file.name)
-            else:
-                shutil.copyfile(filename, temp_config_file.name)
-            # Substitute base variables from placeholders to strings
-            base_var_dict = Config._pre_substitute_base_vars(
-                temp_config_file.name, temp_config_file.name)
-
-            if filename.endswith('.py'):
-                temp_module_name = osp.splitext(temp_config_name)[0]
-                sys.path.insert(0, temp_config_dir)
-                Config._validate_py_syntax(filename)
-                mod = import_module(temp_module_name)
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value
-                    for name, value in mod.__dict__.items()
-                    if not name.startswith('__')
-                }
-                # delete imported module
-                del sys.modules[temp_module_name]
-            elif filename.endswith(('.yml', '.yaml', '.json')):
-                import annotator.uniformer.mmcv as mmcv
-                cfg_dict = mmcv.load(temp_config_file.name)
-            # close temp file
-            temp_config_file.close()
-
-        # check deprecation information
-        if DEPRECATION_KEY in cfg_dict:
-            deprecation_info = cfg_dict.pop(DEPRECATION_KEY)
-            warning_msg = f'The config file {filename} will be deprecated ' \
-                'in the future.'
-            if 'expected' in deprecation_info:
-                warning_msg += f' Please use {deprecation_info["expected"]} ' \
-                    'instead.'
-            if 'reference' in deprecation_info:
-                warning_msg += ' More information can be found at ' \
-                    f'{deprecation_info["reference"]}'
-            warnings.warn(warning_msg)
-
-        cfg_text = filename + '\n'
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            cfg_text += f.read()
-
-        if BASE_KEY in cfg_dict:
-            cfg_dir = osp.dirname(filename)
-            base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(
-                base_filename, list) else [base_filename]
-
-            cfg_dict_list = list()
-            cfg_text_list = list()
-            for f in base_filename:
-                _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f))
-                cfg_dict_list.append(_cfg_dict)
-                cfg_text_list.append(_cfg_text)
-
-            base_cfg_dict = dict()
-            for c in cfg_dict_list:
-                duplicate_keys = base_cfg_dict.keys() & c.keys()
-                if len(duplicate_keys) > 0:
-                    raise KeyError('Duplicate key is not allowed among bases. '
-                                   f'Duplicate keys: {duplicate_keys}')
-                base_cfg_dict.update(c)
-
-            # Substitute base variables from strings to their actual values
-            cfg_dict = Config._substitute_base_vars(cfg_dict, base_var_dict,
-                                                    base_cfg_dict)
-
-            base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
-            cfg_dict = base_cfg_dict
-
-            # merge cfg_text
-            cfg_text_list.append(cfg_text)
-            cfg_text = '\n'.join(cfg_text_list)
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def _merge_a_into_b(a, b, allow_list_keys=False):
-        """merge dict ``a`` into dict ``b`` (non-inplace).
-
-        Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
-        in-place modifications.
-
-        Args:
-            a (dict): The source dict to be merged into ``b``.
-            b (dict): The origin dict to be fetch keys from ``a``.
-            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
-              are allowed in source ``a`` and will replace the element of the
-              corresponding index in b if b is a list. Default: False.
-
-        Returns:
-            dict: The modified dict of ``b`` using ``a``.
-
-        Examples:
-            # Normally merge a into b.
-            >>> Config._merge_a_into_b(
-            ...     dict(obj=dict(a=2)), dict(obj=dict(a=1)))
-            {'obj': {'a': 2}}
-
-            # Delete b first and merge a into b.
-            >>> Config._merge_a_into_b(
-            ...     dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1)))
-            {'obj': {'a': 2}}
-
-            # b is a list
-            >>> Config._merge_a_into_b(
-            ...     {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True)
-            [{'a': 2}, {'b': 2}]
-        """
-        b = b.copy()
-        for k, v in a.items():
-            if allow_list_keys and k.isdigit() and isinstance(b, list):
-                k = int(k)
-                if len(b) <= k:
-                    raise KeyError(f'Index {k} exceeds the length of list {b}')
-                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
-            elif isinstance(v,
-                            dict) and k in b and not v.pop(DELETE_KEY, False):
-                allowed_types = (dict, list) if allow_list_keys else dict
-                if not isinstance(b[k], allowed_types):
-                    raise TypeError(
-                        f'{k}={v} in child config cannot inherit from base '
-                        f'because {k} is a dict in the child config but is of '
-                        f'type {type(b[k])} in base config. You may set '
-                        f'`{DELETE_KEY}=True` to ignore the base config')
-                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
-            else:
-                b[k] = v
-        return b
-
-    @staticmethod
-    def fromfile(filename,
-                 use_predefined_variables=True,
-                 import_custom_modules=True):
-        cfg_dict, cfg_text = Config._file2dict(filename,
-                                               use_predefined_variables)
-        if import_custom_modules and cfg_dict.get('custom_imports', None):
-            import_modules_from_strings(**cfg_dict['custom_imports'])
-        return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    @staticmethod
-    def fromstring(cfg_str, file_format):
-        """Generate config from config str.
-
-        Args:
-            cfg_str (str): Config str.
-            file_format (str): Config file format corresponding to the
-               config str. Only py/yml/yaml/json type are supported now!
-
-        Returns:
-            obj:`Config`: Config obj.
-        """
-        if file_format not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
-        if file_format != '.py' and 'dict(' in cfg_str:
-            # check if users specify a wrong suffix for python
-            warnings.warn(
-                'Please check "file_format", the file format may be .py')
-        with tempfile.NamedTemporaryFile(
-                'w', encoding='utf-8', suffix=file_format,
-                delete=False) as temp_file:
-            temp_file.write(cfg_str)
-            # on windows, previous implementation cause error
-            # see PR 1077 for details
-        cfg = Config.fromfile(temp_file.name)
-        os.remove(temp_file.name)
-        return cfg
-
-    @staticmethod
-    def auto_argparser(description=None):
-        """Generate argparser from config file automatically (experimental)"""
-        partial_parser = ArgumentParser(description=description)
-        partial_parser.add_argument('config', help='config file path')
-        cfg_file = partial_parser.parse_known_args()[0].config
-        cfg = Config.fromfile(cfg_file)
-        parser = ArgumentParser(description=description)
-        parser.add_argument('config', help='config file path')
-        add_args(parser, cfg)
-        return parser, cfg
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError('cfg_dict must be a dict, but '
-                            f'got {type(cfg_dict)}')
-        for key in cfg_dict:
-            if key in RESERVED_KEYS:
-                raise KeyError(f'{key} is reserved for config file')
-
-        super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict))
-        super(Config, self).__setattr__('_filename', filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, 'r') as f:
-                text = f.read()
-        else:
-            text = ''
-        super(Config, self).__setattr__('_text', text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    @property
-    def pretty_text(self):
-
-        indent = 4
-
-        def _indent(s_, num_spaces):
-            s = s_.split('\n')
-            if len(s) == 1:
-                return s_
-            first = s.pop(0)
-            s = [(num_spaces * ' ') + line for line in s]
-            s = '\n'.join(s)
-            s = first + '\n' + s
-            return s
-
-        def _format_basic_types(k, v, use_mapping=False):
-            if isinstance(v, str):
-                v_str = f"'{v}'"
-            else:
-                v_str = str(v)
-
-            if use_mapping:
-                k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                attr_str = f'{k_str}: {v_str}'
-            else:
-                attr_str = f'{str(k)}={v_str}'
-            attr_str = _indent(attr_str, indent)
-
-            return attr_str
-
-        def _format_list(k, v, use_mapping=False):
-            # check if all items in the list are dict
-            if all(isinstance(_, dict) for _ in v):
-                v_str = '[\n'
-                v_str += '\n'.join(
-                    f'dict({_indent(_format_dict(v_), indent)}),'
-                    for v_ in v).rstrip(',')
-                if use_mapping:
-                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                    attr_str = f'{k_str}: {v_str}'
-                else:
-                    attr_str = f'{str(k)}={v_str}'
-                attr_str = _indent(attr_str, indent) + ']'
-            else:
-                attr_str = _format_basic_types(k, v, use_mapping)
-            return attr_str
-
-        def _contain_invalid_identifier(dict_str):
-            contain_invalid_identifier = False
-            for key_name in dict_str:
-                contain_invalid_identifier |= \
-                    (not str(key_name).isidentifier())
-            return contain_invalid_identifier
-
-        def _format_dict(input_dict, outest_level=False):
-            r = ''
-            s = []
-
-            use_mapping = _contain_invalid_identifier(input_dict)
-            if use_mapping:
-                r += '{'
-            for idx, (k, v) in enumerate(input_dict.items()):
-                is_last = idx >= len(input_dict) - 1
-                end = '' if outest_level or is_last else ','
-                if isinstance(v, dict):
-                    v_str = '\n' + _format_dict(v)
-                    if use_mapping:
-                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                        attr_str = f'{k_str}: dict({v_str}'
-                    else:
-                        attr_str = f'{str(k)}=dict({v_str}'
-                    attr_str = _indent(attr_str, indent) + ')' + end
-                elif isinstance(v, list):
-                    attr_str = _format_list(k, v, use_mapping) + end
-                else:
-                    attr_str = _format_basic_types(k, v, use_mapping) + end
-
-                s.append(attr_str)
-            r += '\n'.join(s)
-            if use_mapping:
-                r += '}'
-            return r
-
-        cfg_dict = self._cfg_dict.to_dict()
-        text = _format_dict(cfg_dict, outest_level=True)
-        # copied from setup.cfg
-        yapf_style = dict(
-            based_on_style='pep8',
-            blank_line_before_nested_class_or_def=True,
-            split_before_expression_after_opening_paren=True)
-        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
-
-        return text
-
-    def __repr__(self):
-        return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}'
-
-    def __len__(self):
-        return len(self._cfg_dict)
-
-    def __getattr__(self, name):
-        return getattr(self._cfg_dict, name)
-
-    def __getitem__(self, name):
-        return self._cfg_dict.__getitem__(name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setitem__(name, value)
-
-    def __iter__(self):
-        return iter(self._cfg_dict)
-
-    def __getstate__(self):
-        return (self._cfg_dict, self._filename, self._text)
-
-    def __setstate__(self, state):
-        _cfg_dict, _filename, _text = state
-        super(Config, self).__setattr__('_cfg_dict', _cfg_dict)
-        super(Config, self).__setattr__('_filename', _filename)
-        super(Config, self).__setattr__('_text', _text)
-
-    def dump(self, file=None):
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict()
-        if self.filename.endswith('.py'):
-            if file is None:
-                return self.pretty_text
-            else:
-                with open(file, 'w', encoding='utf-8') as f:
-                    f.write(self.pretty_text)
-        else:
-            import annotator.uniformer.mmcv as mmcv
-            if file is None:
-                file_format = self.filename.split('.')[-1]
-                return mmcv.dump(cfg_dict, file_format=file_format)
-            else:
-                mmcv.dump(cfg_dict, file)
-
-    def merge_from_dict(self, options, allow_list_keys=True):
-        """Merge list into cfg_dict.
-
-        Merge the dict parsed by MultipleKVAction into this cfg.
-
-        Examples:
-            >>> options = {'model.backbone.depth': 50,
-            ...            'model.backbone.with_cp':True}
-            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
-            >>> cfg.merge_from_dict(options)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(
-            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
-
-            # Merge list element
-            >>> cfg = Config(dict(pipeline=[
-            ...     dict(type='LoadImage'), dict(type='LoadAnnotations')]))
-            >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})
-            >>> cfg.merge_from_dict(options, allow_list_keys=True)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(pipeline=[
-            ...     dict(type='SelfLoadImage'), dict(type='LoadAnnotations')])
-
-        Args:
-            options (dict): dict of configs to merge from.
-            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
-              are allowed in ``options`` and will replace the element of the
-              corresponding index in the config if the config is a list.
-              Default: True.
-        """
-        option_cfg_dict = {}
-        for full_key, v in options.items():
-            d = option_cfg_dict
-            key_list = full_key.split('.')
-            for subkey in key_list[:-1]:
-                d.setdefault(subkey, ConfigDict())
-                d = d[subkey]
-            subkey = key_list[-1]
-            d[subkey] = v
-
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-        super(Config, self).__setattr__(
-            '_cfg_dict',
-            Config._merge_a_into_b(
-                option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys))
-
-
-class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options can
-    be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
-    brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
-    list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
-    """
-
-    @staticmethod
-    def _parse_int_float_bool(val):
-        try:
-            return int(val)
-        except ValueError:
-            pass
-        try:
-            return float(val)
-        except ValueError:
-            pass
-        if val.lower() in ['true', 'false']:
-            return True if val.lower() == 'true' else False
-        return val
-
-    @staticmethod
-    def _parse_iterable(val):
-        """Parse iterable values in the string.
-
-        All elements inside '()' or '[]' are treated as iterable values.
-
-        Args:
-            val (str): Value string.
-
-        Returns:
-            list | tuple: The expanded list or tuple from the string.
-
-        Examples:
-            >>> DictAction._parse_iterable('1,2,3')
-            [1, 2, 3]
-            >>> DictAction._parse_iterable('[a, b, c]')
-            ['a', 'b', 'c']
-            >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]')
-            [(1, 2, 3), ['a', 'b'], 'c']
-        """
-
-        def find_next_comma(string):
-            """Find the position of next comma in the string.
-
-            If no ',' is found in the string, return the string length. All
-            chars inside '()' and '[]' are treated as one element and thus ','
-            inside these brackets are ignored.
-            """
-            assert (string.count('(') == string.count(')')) and (
-                    string.count('[') == string.count(']')), \
-                f'Imbalanced brackets exist in {string}'
-            end = len(string)
-            for idx, char in enumerate(string):
-                pre = string[:idx]
-                # The string before this ',' is balanced
-                if ((char == ',') and (pre.count('(') == pre.count(')'))
-                        and (pre.count('[') == pre.count(']'))):
-                    end = idx
-                    break
-            return end
-
-        # Strip ' and " characters and replace whitespace.
-        val = val.strip('\'\"').replace(' ', '')
-        is_tuple = False
-        if val.startswith('(') and val.endswith(')'):
-            is_tuple = True
-            val = val[1:-1]
-        elif val.startswith('[') and val.endswith(']'):
-            val = val[1:-1]
-        elif ',' not in val:
-            # val is a single value
-            return DictAction._parse_int_float_bool(val)
-
-        values = []
-        while len(val) > 0:
-            comma_idx = find_next_comma(val)
-            element = DictAction._parse_iterable(val[:comma_idx])
-            values.append(element)
-            val = val[comma_idx + 1:]
-        if is_tuple:
-            values = tuple(values)
-        return values
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for kv in values:
-            key, val = kv.split('=', maxsplit=1)
-            options[key] = self._parse_iterable(val)
-        setattr(namespace, self.dest, options)
diff --git a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/README.md b/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/README.md
deleted file mode 100644
index 5eae12f2a370027de6c46fbf78ec68a1ecb1c01c..0000000000000000000000000000000000000000
--- a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/README.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
-
-[![report](https://img.shields.io/badge/arxiv-report-red)](https://arxiv.org/abs/1905.05172) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GFSsqP2BWz4gtq0e-nki00ZHSirXwFyY)
-
-News:
-* \[2020/05/04\] Added EGL rendering option for training data generation. Now you can create your own training data with headless machines!
-* \[2020/04/13\] Demo with Google Colab (incl. visualization) is available. Special thanks to [@nanopoteto](https://github.com/nanopoteto)!!!
-* \[2020/02/26\] License is updated to MIT license! Enjoy!
-
-This repository contains a pytorch implementation of "[PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://arxiv.org/abs/1905.05172)".
-
-[Project Page](https://shunsukesaito.github.io/PIFu/)
-![Teaser Image](https://shunsukesaito.github.io/PIFu/resources/images/teaser.png)
-
-If you find the code useful in your research, please consider citing the paper.
-
-```
-@InProceedings{saito2019pifu,
-author = {Saito, Shunsuke and Huang, Zeng and Natsume, Ryota and Morishima, Shigeo and Kanazawa, Angjoo and Li, Hao},
-title = {PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization},
-booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
-month = {October},
-year = {2019}
-}
-```
-
-
-This codebase provides: 
-- test code
-- training code
-- data generation code
-
-## Requirements
-- Python 3
-- [PyTorch](https://pytorch.org/) tested on 1.4.0
-- json
-- PIL
-- skimage
-- tqdm
-- numpy
-- cv2
-
-for training and data generation
-- [trimesh](https://trimsh.org/) with [pyembree](https://github.com/scopatz/pyembree)
-- [pyexr](https://github.com/tvogels/pyexr)
-- PyOpenGL
-- freeglut (use `sudo apt-get install freeglut3-dev` for ubuntu users)
-- (optional) egl related packages for rendering with headless machines. (use `apt install libgl1-mesa-dri libegl1-mesa libgbm1` for ubuntu users)
-
-Warning: I found that outdated NVIDIA drivers may cause errors with EGL. If you want to try out the EGL version, please update your NVIDIA driver to the latest!!
-
-## Windows demo installation instuction
-
-- Install [miniconda](https://docs.conda.io/en/latest/miniconda.html)
-- Add `conda` to PATH
-- Install [git bash](https://git-scm.com/downloads)
-- Launch `Git\bin\bash.exe`
-- `eval "$(conda shell.bash hook)"` then `conda activate my_env` because of [this](https://github.com/conda/conda-build/issues/3371)
-- Automatic `env create -f environment.yml` (look [this](https://github.com/conda/conda/issues/3417))
-- OR manually setup [environment](https://towardsdatascience.com/a-guide-to-conda-environments-bc6180fc533)
-    - `conda create —name pifu python` where `pifu` is name of your environment
-    - `conda activate`
-    - `conda install pytorch torchvision cudatoolkit=10.1 -c pytorch`
-    - `conda install pillow`
-    - `conda install scikit-image`
-    - `conda install tqdm`
-    - `conda install -c menpo opencv`
-- Download [wget.exe](https://eternallybored.org/misc/wget/)
-- Place it into `Git\mingw64\bin`
-- `sh ./scripts/download_trained_model.sh`
-- Remove background from your image ([this](https://www.remove.bg/), for example)
-- Create black-white mask .png
-- Replace original from sample_images/
-- Try it out - `sh ./scripts/test.sh`
-- Download [Meshlab](http://www.meshlab.net/) because of [this](https://github.com/shunsukesaito/PIFu/issues/1)
-- Open .obj file in Meshlab
-
-
-## Demo
-Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data.
-1. run the following script to download the pretrained models from the following link and copy them under `./PIFu/checkpoints/`.
-```
-sh ./scripts/download_trained_model.sh
-```
-
-2. run the following script. the script creates a textured `.obj` file under `./PIFu/eval_results/`. You may need to use `./apps/crop_img.py` to roughly align an input image and the corresponding mask to the training data for better performance. For background removal, you can use any off-the-shelf tools such as [removebg](https://www.remove.bg/).
-```
-sh ./scripts/test.sh
-```
-
-## Demo on Google Colab
-If you do not have a setup to run PIFu, we offer Google Colab version to give it a try, allowing you to run PIFu in the cloud, free of charge. Try our Colab demo using the following notebook: 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1GFSsqP2BWz4gtq0e-nki00ZHSirXwFyY)
-
-## Data Generation (Linux Only)
-While we are unable to release the full training data due to the restriction of commertial scans, we provide rendering code using free models in [RenderPeople](https://renderpeople.com/free-3d-people/).
-This tutorial uses `rp_dennis_posed_004` model. Please download the model from [this link](https://renderpeople.com/sample/free/rp_dennis_posed_004_OBJ.zip) and unzip the content under a folder named `rp_dennis_posed_004_OBJ`. The same process can be applied to other RenderPeople data.
-
-Warning: the following code becomes extremely slow without [pyembree](https://github.com/scopatz/pyembree). Please make sure you install pyembree.
-
-1. run the following script to compute spherical harmonics coefficients for [precomputed radiance transfer (PRT)](https://sites.fas.harvard.edu/~cs278/papers/prt.pdf). In a nutshell, PRT is used to account for accurate light transport including ambient occlusion without compromising online rendering time, which significantly improves the photorealism compared with [a common sperical harmonics rendering using surface normals](https://cseweb.ucsd.edu/~ravir/papers/envmap/envmap.pdf). This process has to be done once for each obj file.
-```
-python -m apps.prt_util -i {path_to_rp_dennis_posed_004_OBJ}
-```
-
-2. run the following script. Under the specified data path, the code creates folders named `GEO`, `RENDER`, `MASK`, `PARAM`, `UV_RENDER`, `UV_MASK`, `UV_NORMAL`, and `UV_POS`. Note that you may need to list validation subjects to exclude from training in `{path_to_training_data}/val.txt` (this tutorial has only one subject and leave it empty). If you wish to render images with headless servers equipped with NVIDIA GPU, add -e to enable EGL rendering.
-```
-python -m apps.render_data -i {path_to_rp_dennis_posed_004_OBJ} -o {path_to_training_data} [-e]
-```
-
-## Training (Linux Only)
-
-Warning: the following code becomes extremely slow without [pyembree](https://github.com/scopatz/pyembree). Please make sure you install pyembree.
-
-1. run the following script to train the shape module. The intermediate results and checkpoints are saved under `./results` and `./checkpoints` respectively. You can add `--batch_size` and `--num_sample_input` flags to adjust the batch size and the number of sampled points based on available GPU memory.
-```
-python -m apps.train_shape --dataroot {path_to_training_data} --random_flip --random_scale --random_trans
-```
-
-2. run the following script to train the color module. 
-```
-python -m apps.train_color --dataroot {path_to_training_data} --num_sample_inout 0 --num_sample_color 5000 --sigma 0.1 --random_flip --random_scale --random_trans
-```
-
-## Related Research
-**[Monocular Real-Time Volumetric Performance Capture (ECCV 2020)](https://project-splinter.github.io/)**  
-*Ruilong Li\*, Yuliang Xiu\*, Shunsuke Saito, Zeng Huang, Kyle Olszewski, Hao Li*
-
-The first real-time PIFu by accelerating reconstruction and rendering!!
-
-**[PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020)](https://shunsukesaito.github.io/PIFuHD/)**  
-*Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo*
-
-We further improve the quality of reconstruction by leveraging multi-level approach!
-
-**[ARCH: Animatable Reconstruction of Clothed Humans (CVPR 2020)](https://arxiv.org/pdf/2004.04572.pdf)**  
-*Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, Tony Tung*
-
-Learning PIFu in canonical space for animatable avatar generation!
-
-**[Robust 3D Self-portraits in Seconds (CVPR 2020)](http://www.liuyebin.com/portrait/portrait.html)**  
-*Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu*
-
-They extend PIFu to RGBD + introduce "PIFusion" utilizing PIFu reconstruction for non-rigid fusion.
-
-**[Learning to Infer Implicit Surfaces without 3d Supervision (NeurIPS 2019)](http://papers.nips.cc/paper/9039-learning-to-infer-implicit-surfaces-without-3d-supervision.pdf)**  
-*Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li*
-
-We answer to the question of "how can we learn implicit function if we don't have 3D ground truth?"
-
-**[SiCloPe: Silhouette-Based Clothed People (CVPR 2019, best paper finalist)](https://arxiv.org/pdf/1901.00049.pdf)**  
-*Ryota Natsume\*, Shunsuke Saito\*, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, Shigeo Morishima*
-
-Our first attempt to reconstruct 3D clothed human body with texture from a single image!
-
-**[Deep Volumetric Video from Very Sparse Multi-view Performance Capture (ECCV 2018)](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zeng_Huang_Deep_Volumetric_Video_ECCV_2018_paper.pdf)**  
-*Zeng Huang, Tianye Li, Weikai Chen, Yajie Zhao, Jun Xing, Chloe LeGendre, Linjie Luo, Chongyang Ma, Hao Li*
-
-Implict surface learning for sparse view human performance capture!
-
-------
-
-
-
-For commercial queries, please contact:
-
-Hao Li: hao@hao-li.com ccto: saitos@usc.edu Baker!!
diff --git a/spaces/daarumadx/bot/src/argv/daemon.py b/spaces/daarumadx/bot/src/argv/daemon.py
deleted file mode 100644
index fde6493a2d145ecf4b25a6b5ed48e81f4eec4d24..0000000000000000000000000000000000000000
--- a/spaces/daarumadx/bot/src/argv/daemon.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-import daemon
-from argv.checkpoints import arg_checkpoints, set_arg_checkpoints, check_arg_checkpoints
-from argv.common import arg_debug, arg_help, arg_version
-from argv.run import arg_json_folder_name, arg_json_args, arg_gpu, arg_cpu, arg_preferences, \
-    arg_color_transfer, arg_compress, arg_image_size, arg_ignore_size, arg_auto_resize_crop, arg_auto_resize, \
-    arg_auto_rescale, arg_n_core
-from argv.run.config import set_arg_preference, set_gpu_ids
-
-
-def init_daemon_sub_parser(subparsers):
-    daemon_parser = subparsers.add_parser(
-        'daemon',
-        description="Running dreampower on daemon mode.",
-        help="Running dreampower on daemon mode.",
-        add_help=False
-    )
-    daemon_parser.set_defaults(func=daemon.main)
-
-    # conflicts handler
-    processing_mod = daemon_parser.add_mutually_exclusive_group()
-    scale_mod = daemon_parser.add_mutually_exclusive_group()
-
-    # add daemon arguments
-    arg_input(daemon_parser)
-    arg_output(daemon_parser)
-
-    arg_auto_rescale(scale_mod)
-    arg_auto_resize(scale_mod)
-    arg_auto_resize_crop(scale_mod)
-    arg_ignore_size(daemon_parser)
-
-    arg_color_transfer(daemon_parser)
-    arg_compress(daemon_parser)
-    arg_image_size(daemon_parser)
-
-    arg_preferences(daemon_parser)
-
-    arg_cpu(processing_mod)
-    arg_gpu(processing_mod)
-    arg_checkpoints(daemon_parser)
-    arg_n_core(daemon_parser)
-
-    arg_json_args(daemon_parser)
-    arg_json_folder_name(daemon_parser)
-
-    arg_help(daemon_parser)
-    arg_debug(daemon_parser)
-    arg_version(daemon_parser)
-
-    return daemon_parser
-
-
-def set_args_daemon_parser(args):
-    set_arg_checkpoints(args)
-    set_arg_preference(args)
-    set_gpu_ids(args)
-
-
-def check_args_daemon_parser(parser, args):
-    check_arg_input(parser, args)
-    check_arg_output(parser, args)
-    check_arg_checkpoints(parser, args)
-
-
-def arg_input(parser):
-    parser.add_argument(
-        "-i",
-        "--input",
-        help="Path directory to watching.",
-        required=True
-    )
-
-
-def arg_output(parser):
-    parser.add_argument(
-        "-o",
-        "--output",
-        help="Path of directory where the transformed photo(s) will be saved.",
-        required=True
-    )
-
-
-def check_arg_input(parser, args):
-    if not args.input:
-        parser.error("-i, --input INPUT is required.")
-    if not os.path.isdir(args.input):
-        parser.error("Input {} directory doesn't exist.".format(args.input))
-
-
-def check_arg_output(parser, args):
-    if not args.output:
-        parser.error("-o, --output OUTPUT is required.")
-    if not os.path.isdir(args.output):
-        parser.error("Output {} directory doesn't exist.".format(args.output))
diff --git a/spaces/damo-vilab/modelscope-text-to-video-synthesis/app.py b/spaces/damo-vilab/modelscope-text-to-video-synthesis/app.py
deleted file mode 100644
index 01b93c23f9848273d415551044b39638c92c9d62..0000000000000000000000000000000000000000
--- a/spaces/damo-vilab/modelscope-text-to-video-synthesis/app.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import random
-import tempfile
-
-import gradio as gr
-import imageio
-import numpy as np
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-DESCRIPTION = '# [ModelScope Text to Video Synthesis](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)'
-DESCRIPTION += '\n<p>For Colab usage, you can view <a href="https://colab.research.google.com/drive/1uW1ZqswkQ9Z9bp5Nbo5z59cAn7I0hE6R?usp=sharing" style="text-decoration: underline;" target="_blank">this webpage</a>.（the latest update on 2023.03.21）</p>'
-DESCRIPTION += '\n<p>This model can only be used for non-commercial purposes. To learn more about the model, take a look at the <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" style="text-decoration: underline;" target="_blank">model card</a>.</p>'
-if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
-    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
-
-MAX_NUM_FRAMES = int(os.getenv('MAX_NUM_FRAMES', '200'))
-DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES,
-                         int(os.getenv('DEFAULT_NUM_FRAMES', '16')))
-
-pipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b',
-                                         torch_dtype=torch.float16,
-                                         variant='fp16')
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-pipe.enable_vae_slicing()
-
-
-def to_video(frames: list[np.ndarray], fps: int) -> str:
-    out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-    writer = imageio.get_writer(out_file.name, format='FFMPEG', fps=fps)
-    for frame in frames:
-        writer.append_data(frame)
-    writer.close()
-    return out_file.name
-
-
-def generate(prompt: str, seed: int, num_frames: int,
-             num_inference_steps: int) -> str:
-    if seed == -1:
-        seed = random.randint(0, 1000000)
-    generator = torch.Generator().manual_seed(seed)
-    frames = pipe(prompt,
-                  num_inference_steps=num_inference_steps,
-                  num_frames=num_frames,
-                  generator=generator).frames
-    return to_video(frames, 8)
-
-
-examples = [
-    ['An astronaut riding a horse.', 0, 16, 25],
-    ['A panda eating bamboo on a rock.', 0, 16, 25],
-    ['Spiderman is surfing.', 0, 16, 25],
-]
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Group():
-        with gr.Box():
-            with gr.Row(elem_id='prompt-container').style(equal_height=True):
-                prompt = gr.Text(
-                    label='Prompt',
-                    show_label=False,
-                    max_lines=1,
-                    placeholder='Enter your prompt',
-                    elem_id='prompt-text-input').style(container=False)
-                run_button = gr.Button('Generate video').style(
-                    full_width=False)
-        result = gr.Video(label='Result', show_label=False, elem_id='gallery')
-        with gr.Accordion('Advanced options', open=False):
-            seed = gr.Slider(
-                label='Seed',
-                minimum=-1,
-                maximum=1000000,
-                step=1,
-                value=-1,
-                info='If set to -1, a different seed will be used each time.')
-            num_frames = gr.Slider(
-                label='Number of frames',
-                minimum=16,
-                maximum=MAX_NUM_FRAMES,
-                step=1,
-                value=16,
-                info=
-                'Note that the content of the video also changes when you change the number of frames.'
-            )
-            num_inference_steps = gr.Slider(label='Number of inference steps',
-                                            minimum=10,
-                                            maximum=50,
-                                            step=1,
-                                            value=25)
-
-    inputs = [
-        prompt,
-        seed,
-        num_frames,
-        num_inference_steps,
-    ]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=result,
-                fn=generate,
-                cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-    prompt.submit(fn=generate, inputs=inputs, outputs=result)
-    run_button.click(fn=generate, inputs=inputs, outputs=result)
-
-
-    with gr.Accordion(label='We are hiring(Based in Beijing / Hangzhou, China.)', open=False):
-        gr.HTML("""<div class="acknowledgments">
-                    <p>
-                        If you're looking for an exciting challenge and the opportunity to work with cutting-edge technologies in AIGC and large-scale pretraining, then we are the place for you. We are looking for talented, motivated and creative individuals to join our team. If you are interested, please send your CV to us.
-                    </p>
-                    <p>
-                        <b>EMAIL: yingya.zyy@alibaba-inc.com</b>.
-                    </p>
-                   </div>
-                """)
-    
-    with gr.Accordion(label='Biases and content acknowledgment', open=False):
-        gr.HTML("""<div class="acknowledgments">
-                    <h4>Biases and content acknowledgment</h4>
-                    <p>
-                        Despite how impressive being able to turn text into video is, beware to the fact that this model may output content that reinforces or exacerbates societal biases. The training data includes LAION5B, ImageNet, Webvid and other public datasets. The model was not trained to realistically represent people or events, so using it to generate such content is beyond the model's capabilities.
-                    </p>
-                    <p>
-                        It is not intended to generate content that is demeaning or harmful to people or their environment, culture, religion, etc. Similarly, it is not allowed to generate pornographic, violent and bloody content generation. <b>The model is meant for research purposes</b>.
-                    </p>
-                    <p>
-                        To learn more about the model, head to its <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" style="text-decoration: underline;" target="_blank">model card</a>.
-                    </p>
-                   </div>
-                """)
-
-        
-demo.queue(api_open=False, max_size=15).launch()
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/ops/fused_act/fused_act.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/ops/fused_act/fused_act.py
deleted file mode 100644
index 588f815e596ab0fc83ab0f9d21426c22ec5ed7c3..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/ops/fused_act/fused_act.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
-
-import torch
-from torch import nn
-from torch.autograd import Function
-
-try:
-    from . import fused_act_ext
-except ImportError:
-    import os
-    BASICSR_JIT = os.getenv('BASICSR_JIT')
-    if BASICSR_JIT == 'True':
-        from torch.utils.cpp_extension import load
-        module_path = os.path.dirname(__file__)
-        fused_act_ext = load(
-            'fused',
-            sources=[
-                os.path.join(module_path, 'src', 'fused_bias_act.cpp'),
-                os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'),
-            ],
-        )
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale)
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope,
-                                                    ctx.scale)
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale)
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-
-    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
-    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/client_exceptions.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/client_exceptions.py
deleted file mode 100644
index c640e1e7fbdf8c56a9e744492d99f8ca32988142..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/client_exceptions.py
+++ /dev/null
@@ -1,342 +0,0 @@
-"""HTTP related errors."""
-
-import asyncio
-import warnings
-from typing import TYPE_CHECKING, Any, Optional, Tuple, Union
-
-from .http_parser import RawResponseMessage
-from .typedefs import LooseHeaders
-
-try:
-    import ssl
-
-    SSLContext = ssl.SSLContext
-except ImportError:  # pragma: no cover
-    ssl = SSLContext = None  # type: ignore[assignment]
-
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .client_reqrep import ClientResponse, ConnectionKey, Fingerprint, RequestInfo
-else:
-    RequestInfo = ClientResponse = ConnectionKey = None
-
-__all__ = (
-    "ClientError",
-    "ClientConnectionError",
-    "ClientOSError",
-    "ClientConnectorError",
-    "ClientProxyConnectionError",
-    "ClientSSLError",
-    "ClientConnectorSSLError",
-    "ClientConnectorCertificateError",
-    "ServerConnectionError",
-    "ServerTimeoutError",
-    "ServerDisconnectedError",
-    "ServerFingerprintMismatch",
-    "ClientResponseError",
-    "ClientHttpProxyError",
-    "WSServerHandshakeError",
-    "ContentTypeError",
-    "ClientPayloadError",
-    "InvalidURL",
-)
-
-
-class ClientError(Exception):
-    """Base class for client connection errors."""
-
-
-class ClientResponseError(ClientError):
-    """Connection error during reading response.
-
-    request_info: instance of RequestInfo
-    """
-
-    def __init__(
-        self,
-        request_info: RequestInfo,
-        history: Tuple[ClientResponse, ...],
-        *,
-        code: Optional[int] = None,
-        status: Optional[int] = None,
-        message: str = "",
-        headers: Optional[LooseHeaders] = None,
-    ) -> None:
-        self.request_info = request_info
-        if code is not None:
-            if status is not None:
-                raise ValueError(
-                    "Both code and status arguments are provided; "
-                    "code is deprecated, use status instead"
-                )
-            warnings.warn(
-                "code argument is deprecated, use status instead",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        if status is not None:
-            self.status = status
-        elif code is not None:
-            self.status = code
-        else:
-            self.status = 0
-        self.message = message
-        self.headers = headers
-        self.history = history
-        self.args = (request_info, history)
-
-    def __str__(self) -> str:
-        return "{}, message={!r}, url={!r}".format(
-            self.status,
-            self.message,
-            self.request_info.real_url,
-        )
-
-    def __repr__(self) -> str:
-        args = f"{self.request_info!r}, {self.history!r}"
-        if self.status != 0:
-            args += f", status={self.status!r}"
-        if self.message != "":
-            args += f", message={self.message!r}"
-        if self.headers is not None:
-            args += f", headers={self.headers!r}"
-        return f"{type(self).__name__}({args})"
-
-    @property
-    def code(self) -> int:
-        warnings.warn(
-            "code property is deprecated, use status instead",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.status
-
-    @code.setter
-    def code(self, value: int) -> None:
-        warnings.warn(
-            "code property is deprecated, use status instead",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        self.status = value
-
-
-class ContentTypeError(ClientResponseError):
-    """ContentType found is not valid."""
-
-
-class WSServerHandshakeError(ClientResponseError):
-    """websocket server handshake error."""
-
-
-class ClientHttpProxyError(ClientResponseError):
-    """HTTP proxy error.
-
-    Raised in :class:`aiohttp.connector.TCPConnector` if
-    proxy responds with status other than ``200 OK``
-    on ``CONNECT`` request.
-    """
-
-
-class TooManyRedirects(ClientResponseError):
-    """Client was redirected too many times."""
-
-
-class ClientConnectionError(ClientError):
-    """Base class for client socket errors."""
-
-
-class ClientOSError(ClientConnectionError, OSError):
-    """OSError error."""
-
-
-class ClientConnectorError(ClientOSError):
-    """Client connector error.
-
-    Raised in :class:`aiohttp.connector.TCPConnector` if
-        a connection can not be established.
-    """
-
-    def __init__(self, connection_key: ConnectionKey, os_error: OSError) -> None:
-        self._conn_key = connection_key
-        self._os_error = os_error
-        super().__init__(os_error.errno, os_error.strerror)
-        self.args = (connection_key, os_error)
-
-    @property
-    def os_error(self) -> OSError:
-        return self._os_error
-
-    @property
-    def host(self) -> str:
-        return self._conn_key.host
-
-    @property
-    def port(self) -> Optional[int]:
-        return self._conn_key.port
-
-    @property
-    def ssl(self) -> Union[SSLContext, None, bool, "Fingerprint"]:
-        return self._conn_key.ssl
-
-    def __str__(self) -> str:
-        return "Cannot connect to host {0.host}:{0.port} ssl:{1} [{2}]".format(
-            self, self.ssl if self.ssl is not None else "default", self.strerror
-        )
-
-    # OSError.__reduce__ does too much black magick
-    __reduce__ = BaseException.__reduce__
-
-
-class ClientProxyConnectionError(ClientConnectorError):
-    """Proxy connection error.
-
-    Raised in :class:`aiohttp.connector.TCPConnector` if
-        connection to proxy can not be established.
-    """
-
-
-class UnixClientConnectorError(ClientConnectorError):
-    """Unix connector error.
-
-    Raised in :py:class:`aiohttp.connector.UnixConnector`
-    if connection to unix socket can not be established.
-    """
-
-    def __init__(
-        self, path: str, connection_key: ConnectionKey, os_error: OSError
-    ) -> None:
-        self._path = path
-        super().__init__(connection_key, os_error)
-
-    @property
-    def path(self) -> str:
-        return self._path
-
-    def __str__(self) -> str:
-        return "Cannot connect to unix socket {0.path} ssl:{1} [{2}]".format(
-            self, self.ssl if self.ssl is not None else "default", self.strerror
-        )
-
-
-class ServerConnectionError(ClientConnectionError):
-    """Server connection errors."""
-
-
-class ServerDisconnectedError(ServerConnectionError):
-    """Server disconnected."""
-
-    def __init__(self, message: Union[RawResponseMessage, str, None] = None) -> None:
-        if message is None:
-            message = "Server disconnected"
-
-        self.args = (message,)
-        self.message = message
-
-
-class ServerTimeoutError(ServerConnectionError, asyncio.TimeoutError):
-    """Server timeout error."""
-
-
-class ServerFingerprintMismatch(ServerConnectionError):
-    """SSL certificate does not match expected fingerprint."""
-
-    def __init__(self, expected: bytes, got: bytes, host: str, port: int) -> None:
-        self.expected = expected
-        self.got = got
-        self.host = host
-        self.port = port
-        self.args = (expected, got, host, port)
-
-    def __repr__(self) -> str:
-        return "<{} expected={!r} got={!r} host={!r} port={!r}>".format(
-            self.__class__.__name__, self.expected, self.got, self.host, self.port
-        )
-
-
-class ClientPayloadError(ClientError):
-    """Response payload error."""
-
-
-class InvalidURL(ClientError, ValueError):
-    """Invalid URL.
-
-    URL used for fetching is malformed, e.g. it doesn't contains host
-    part.
-    """
-
-    # Derive from ValueError for backward compatibility
-
-    def __init__(self, url: Any) -> None:
-        # The type of url is not yarl.URL because the exception can be raised
-        # on URL(url) call
-        super().__init__(url)
-
-    @property
-    def url(self) -> Any:
-        return self.args[0]
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} {self.url}>"
-
-
-class ClientSSLError(ClientConnectorError):
-    """Base error for ssl.*Errors."""
-
-
-if ssl is not None:
-    cert_errors = (ssl.CertificateError,)
-    cert_errors_bases = (
-        ClientSSLError,
-        ssl.CertificateError,
-    )
-
-    ssl_errors = (ssl.SSLError,)
-    ssl_error_bases = (ClientSSLError, ssl.SSLError)
-else:  # pragma: no cover
-    cert_errors = tuple()
-    cert_errors_bases = (
-        ClientSSLError,
-        ValueError,
-    )
-
-    ssl_errors = tuple()
-    ssl_error_bases = (ClientSSLError,)
-
-
-class ClientConnectorSSLError(*ssl_error_bases):  # type: ignore[misc]
-    """Response ssl error."""
-
-
-class ClientConnectorCertificateError(*cert_errors_bases):  # type: ignore[misc]
-    """Response certificate error."""
-
-    def __init__(
-        self, connection_key: ConnectionKey, certificate_error: Exception
-    ) -> None:
-        self._conn_key = connection_key
-        self._certificate_error = certificate_error
-        self.args = (connection_key, certificate_error)
-
-    @property
-    def certificate_error(self) -> Exception:
-        return self._certificate_error
-
-    @property
-    def host(self) -> str:
-        return self._conn_key.host
-
-    @property
-    def port(self) -> Optional[int]:
-        return self._conn_key.port
-
-    @property
-    def ssl(self) -> bool:
-        return self._conn_key.is_ssl
-
-    def __str__(self) -> str:
-        return (
-            "Cannot connect to host {0.host}:{0.port} ssl:{0.ssl} "
-            "[{0.certificate_error.__class__.__name__}: "
-            "{0.certificate_error.args}]".format(self)
-        )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-cc2431f4.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-cc2431f4.css
deleted file mode 100644
index 0fb9cf42e5be97eea413dff012b84c49953d5bcb..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-cc2431f4.css
+++ /dev/null
@@ -1 +0,0 @@
-.container.svelte-75gm11.svelte-75gm11.svelte-75gm11{padding:var(--block-padding)}.output-class.svelte-75gm11.svelte-75gm11.svelte-75gm11{display:flex;justify-content:center;align-items:center;padding:var(--size-6) var(--size-4);color:var(--body-text-color);font-weight:var(--weight-bold);font-size:var(--text-xxl)}.confidence-set.svelte-75gm11.svelte-75gm11.svelte-75gm11{display:flex;justify-content:space-between;align-items:flex-start;margin-bottom:var(--size-2);color:var(--body-text-color);line-height:var(--line-none);font-family:var(--font-mono)}.confidence-set.svelte-75gm11.svelte-75gm11.svelte-75gm11:last-child{margin-bottom:0}.inner-wrap.svelte-75gm11.svelte-75gm11.svelte-75gm11{flex:1 1 0%}.bar.svelte-75gm11.svelte-75gm11.svelte-75gm11{margin-bottom:var(--size-1);border-radius:var(--radius-md);background:var(--stat-background-fill);height:var(--size-1)}.label.svelte-75gm11.svelte-75gm11.svelte-75gm11{display:flex;align-items:baseline}.label.svelte-75gm11>.svelte-75gm11+.svelte-75gm11{margin-left:var(--size-2)}.confidence-set.svelte-75gm11:hover .label.svelte-75gm11.svelte-75gm11{color:var(--color-accent)}.text.svelte-75gm11.svelte-75gm11.svelte-75gm11{line-height:var(--line-md)}.line.svelte-75gm11.svelte-75gm11.svelte-75gm11{flex:1 1 0%;border:1px dashed var(--border-color-primary);padding-right:var(--size-4);padding-left:var(--size-4)}.confidence.svelte-75gm11.svelte-75gm11.svelte-75gm11{margin-left:auto;text-align:right}.selectable.svelte-75gm11.svelte-75gm11.svelte-75gm11{cursor:pointer}
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/commands/delete_cache.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/commands/delete_cache.py
deleted file mode 100644
index 1912c84cbc494c648572f91107cda5d5973680d8..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/commands/delete_cache.py
+++ /dev/null
@@ -1,427 +0,0 @@
-# coding=utf-8
-# Copyright 2022-present, the 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.
-"""Contains command to delete some revisions from the HF cache directory.
-
-Usage:
-    huggingface-cli delete-cache
-    huggingface-cli delete-cache --disable-tui
-    huggingface-cli delete-cache --dir ~/.cache/huggingface/hub
-
-NOTE:
-    This command is based on `InquirerPy` to build the multiselect menu in the terminal.
-    This dependency has to be installed with `pip install huggingface_hub[cli]`. Since
-    we want to avoid as much as possible cross-platform issues, I chose a library that
-    is built on top of `python-prompt-toolkit` which seems to be a reference in terminal
-    GUI (actively maintained on both Unix and Windows, 7.9k stars).
-
-    For the moment, the TUI feature is in beta.
-
-    See:
-    - https://github.com/kazhala/InquirerPy
-    - https://inquirerpy.readthedocs.io/en/latest/
-    - https://github.com/prompt-toolkit/python-prompt-toolkit
-
-    Other solutions could have been:
-    - `simple_term_menu`: would be good as well for our use case but some issues suggest
-      that Windows is less supported.
-      See: https://github.com/IngoMeyer441/simple-term-menu
-    - `PyInquirer`: very similar to `InquirerPy` but older and not maintained anymore.
-      In particular, no support of Python3.10.
-      See: https://github.com/CITGuru/PyInquirer
-    - `pick` (or `pickpack`): easy to use and flexible but built on top of Python's
-      standard library `curses` that is specific to Unix (not implemented on Windows).
-      See https://github.com/wong2/pick and https://github.com/anafvana/pickpack.
-    - `inquirer`: lot of traction (700 stars) but explicitly states "experimental
-      support of Windows". Not built on top of `python-prompt-toolkit`.
-      See https://github.com/magmax/python-inquirer
-
-TODO: add support for `huggingface-cli delete-cache aaaaaa bbbbbb cccccc (...)` ?
-TODO: add "--keep-last" arg to delete revisions that are not on `main` ref
-TODO: add "--filter" arg to filter repositories by name ?
-TODO: add "--sort" arg to sort by size ?
-TODO: add "--limit" arg to limit to X repos ?
-TODO: add "-y" arg for immediate deletion ?
-See discussions in https://github.com/huggingface/huggingface_hub/issues/1025.
-"""
-import os
-from argparse import _SubParsersAction
-from functools import wraps
-from tempfile import mkstemp
-from typing import Any, Callable, Iterable, List, Optional, Union
-
-from ..utils import CachedRepoInfo, CachedRevisionInfo, HFCacheInfo, scan_cache_dir
-from . import BaseHuggingfaceCLICommand
-from ._cli_utils import ANSI
-
-
-try:
-    from InquirerPy import inquirer
-    from InquirerPy.base.control import Choice
-    from InquirerPy.separator import Separator
-
-    _inquirer_py_available = True
-except ImportError:
-    _inquirer_py_available = False
-
-
-def require_inquirer_py(fn: Callable) -> Callable:
-    """Decorator to flag methods that require `InquirerPy`."""
-
-    # TODO: refactor this + imports in a unified pattern across codebase
-    @wraps(fn)
-    def _inner(*args, **kwargs):
-        if not _inquirer_py_available:
-            raise ImportError(
-                "The `delete-cache` command requires extra dependencies to work with"
-                " the TUI.\nPlease run `pip install huggingface_hub[cli]` to install"
-                " them.\nOtherwise, disable TUI using the `--disable-tui` flag."
-            )
-
-        return fn(*args, **kwargs)
-
-    return _inner
-
-
-# Possibility for the user to cancel deletion
-_CANCEL_DELETION_STR = "CANCEL_DELETION"
-
-
-class DeleteCacheCommand(BaseHuggingfaceCLICommand):
-    @staticmethod
-    def register_subcommand(parser: _SubParsersAction):
-        delete_cache_parser = parser.add_parser("delete-cache", help="Delete revisions from the cache directory.")
-
-        delete_cache_parser.add_argument(
-            "--dir",
-            type=str,
-            default=None,
-            help="cache directory (optional). Default to the default HuggingFace cache.",
-        )
-
-        delete_cache_parser.add_argument(
-            "--disable-tui",
-            action="store_true",
-            help=(
-                "Disable Terminal User Interface (TUI) mode. Useful if your"
-                " platform/terminal doesn't support the multiselect menu."
-            ),
-        )
-
-        delete_cache_parser.set_defaults(func=DeleteCacheCommand)
-
-    def __init__(self, args):
-        self.cache_dir: Optional[str] = args.dir
-        self.disable_tui: bool = args.disable_tui
-
-    def run(self):
-        """Run `delete-cache` command with or without TUI."""
-        # Scan cache directory
-        hf_cache_info = scan_cache_dir(self.cache_dir)
-
-        # Manual review from the user
-        if self.disable_tui:
-            selected_hashes = _manual_review_no_tui(hf_cache_info, preselected=[])
-        else:
-            selected_hashes = _manual_review_tui(hf_cache_info, preselected=[])
-
-        # If deletion is not cancelled
-        if len(selected_hashes) > 0 and _CANCEL_DELETION_STR not in selected_hashes:
-            confirm_message = _get_expectations_str(hf_cache_info, selected_hashes) + " Confirm deletion ?"
-
-            # Confirm deletion
-            if self.disable_tui:
-                confirmed = _ask_for_confirmation_no_tui(confirm_message)
-            else:
-                confirmed = _ask_for_confirmation_tui(confirm_message)
-
-            # Deletion is confirmed
-            if confirmed:
-                strategy = hf_cache_info.delete_revisions(*selected_hashes)
-                print("Start deletion.")
-                strategy.execute()
-                print(
-                    f"Done. Deleted {len(strategy.repos)} repo(s) and"
-                    f" {len(strategy.snapshots)} revision(s) for a total of"
-                    f" {strategy.expected_freed_size_str}."
-                )
-                return
-
-        # Deletion is cancelled
-        print("Deletion is cancelled. Do nothing.")
-
-
-@require_inquirer_py
-def _manual_review_tui(hf_cache_info: HFCacheInfo, preselected: List[str]) -> List[str]:
-    """Ask the user for a manual review of the revisions to delete.
-
-    Displays a multi-select menu in the terminal (TUI).
-    """
-    # Define multiselect list
-    choices = _get_tui_choices_from_scan(repos=hf_cache_info.repos, preselected=preselected)
-    checkbox = inquirer.checkbox(
-        message="Select revisions to delete:",
-        choices=choices,  # List of revisions with some pre-selection
-        cycle=False,  # No loop between top and bottom
-        height=100,  # Large list if possible
-        # We use the instruction to display to the user the expected effect of the
-        # deletion.
-        instruction=_get_expectations_str(
-            hf_cache_info,
-            selected_hashes=[c.value for c in choices if isinstance(c, Choice) and c.enabled],
-        ),
-        # We use the long instruction to should keybindings instructions to the user
-        long_instruction="Press <space> to select, <enter> to validate and <ctrl+c> to quit without modification.",
-        # Message that is displayed once the user validates its selection.
-        transformer=lambda result: f"{len(result)} revision(s) selected.",
-    )
-
-    # Add a callback to update the information line when a revision is
-    # selected/unselected
-    def _update_expectations(_) -> None:
-        # Hacky way to dynamically set an instruction message to the checkbox when
-        # a revision hash is selected/unselected.
-        checkbox._instruction = _get_expectations_str(
-            hf_cache_info,
-            selected_hashes=[choice["value"] for choice in checkbox.content_control.choices if choice["enabled"]],
-        )
-
-    checkbox.kb_func_lookup["toggle"].append({"func": _update_expectations})
-
-    # Finally display the form to the user.
-    try:
-        return checkbox.execute()
-    except KeyboardInterrupt:
-        return []  # Quit without deletion
-
-
-@require_inquirer_py
-def _ask_for_confirmation_tui(message: str, default: bool = True) -> bool:
-    """Ask for confirmation using Inquirer."""
-    return inquirer.confirm(message, default=default).execute()
-
-
-def _get_tui_choices_from_scan(repos: Iterable[CachedRepoInfo], preselected: List[str]) -> List:
-    """Build a list of choices from the scanned repos.
-
-    Args:
-        repos (*Iterable[`CachedRepoInfo`]*):
-            List of scanned repos on which we want to delete revisions.
-        preselected (*List[`str`]*):
-            List of revision hashes that will be preselected.
-
-    Return:
-        The list of choices to pass to `inquirer.checkbox`.
-    """
-    choices: List[Union[Choice, Separator]] = []
-
-    # First choice is to cancel the deletion. If selected, nothing will be deleted,
-    # no matter the other selected items.
-    choices.append(
-        Choice(
-            _CANCEL_DELETION_STR,
-            name="None of the following (if selected, nothing will be deleted).",
-            enabled=False,
-        )
-    )
-
-    # Display a separator per repo and a Choice for each revisions of the repo
-    for repo in sorted(repos, key=_repo_sorting_order):
-        # Repo as separator
-        choices.append(
-            Separator(
-                f"\n{repo.repo_type.capitalize()} {repo.repo_id} ({repo.size_on_disk_str},"
-                f" used {repo.last_accessed_str})"
-            )
-        )
-        for revision in sorted(repo.revisions, key=_revision_sorting_order):
-            # Revision as choice
-            choices.append(
-                Choice(
-                    revision.commit_hash,
-                    name=(
-                        f"{revision.commit_hash[:8]}:"
-                        f" {', '.join(sorted(revision.refs)) or '(detached)'} #"
-                        f" modified {revision.last_modified_str}"
-                    ),
-                    enabled=revision.commit_hash in preselected,
-                )
-            )
-
-    # Return choices
-    return choices
-
-
-def _manual_review_no_tui(hf_cache_info: HFCacheInfo, preselected: List[str]) -> List[str]:
-    """Ask the user for a manual review of the revisions to delete.
-
-    Used when TUI is disabled. Manual review happens in a separate tmp file that the
-    user can manually edit.
-    """
-    # 1. Generate temporary file with delete commands.
-    fd, tmp_path = mkstemp(suffix=".txt")  # suffix to make it easier to find by editors
-    os.close(fd)
-
-    lines = []
-    for repo in sorted(hf_cache_info.repos, key=_repo_sorting_order):
-        lines.append(
-            f"\n# {repo.repo_type.capitalize()} {repo.repo_id} ({repo.size_on_disk_str},"
-            f" used {repo.last_accessed_str})"
-        )
-        for revision in sorted(repo.revisions, key=_revision_sorting_order):
-            lines.append(
-                # Deselect by prepending a '#'
-                f"{'' if revision.commit_hash in preselected else '#'}   "
-                f" {revision.commit_hash} # Refs:"
-                # Print `refs` as comment on same line
-                f" {', '.join(sorted(revision.refs)) or '(detached)'} # modified"
-                # Print `last_modified` as comment on same line
-                f" {revision.last_modified_str}"
-            )
-
-    with open(tmp_path, "w") as f:
-        f.write(_MANUAL_REVIEW_NO_TUI_INSTRUCTIONS)
-        f.write("\n".join(lines))
-
-    # 2. Prompt instructions to user.
-    instructions = f"""
-    TUI is disabled. In order to select which revisions you want to delete, please edit
-    the following file using the text editor of your choice. Instructions for manual
-    editing are located at the beginning of the file. Edit the file, save it and confirm
-    to continue.
-    File to edit: {ANSI.bold(tmp_path)}
-    """
-    print("\n".join(line.strip() for line in instructions.strip().split("\n")))
-
-    # 3. Wait for user confirmation.
-    while True:
-        selected_hashes = _read_manual_review_tmp_file(tmp_path)
-        if _ask_for_confirmation_no_tui(
-            _get_expectations_str(hf_cache_info, selected_hashes) + " Continue ?",
-            default=False,
-        ):
-            break
-
-    # 4. Return selected_hashes
-    os.remove(tmp_path)
-    return selected_hashes
-
-
-def _ask_for_confirmation_no_tui(message: str, default: bool = True) -> bool:
-    """Ask for confirmation using pure-python."""
-    YES = ("y", "yes", "1")
-    NO = ("n", "no", "0")
-    DEFAULT = ""
-    ALL = YES + NO + (DEFAULT,)
-    full_message = message + (" (Y/n) " if default else " (y/N) ")
-    while True:
-        answer = input(full_message).lower()
-        if answer == DEFAULT:
-            return default
-        if answer in YES:
-            return True
-        if answer in NO:
-            return False
-        print(f"Invalid input. Must be one of {ALL}")
-
-
-def _get_expectations_str(hf_cache_info: HFCacheInfo, selected_hashes: List[str]) -> str:
-    """Format a string to display to the user how much space would be saved.
-
-    Example:
-    ```
-    >>> _get_expectations_str(hf_cache_info, selected_hashes)
-    '7 revisions selected counting for 4.3G.'
-    ```
-    """
-    if _CANCEL_DELETION_STR in selected_hashes:
-        return "Nothing will be deleted."
-    strategy = hf_cache_info.delete_revisions(*selected_hashes)
-    return f"{len(selected_hashes)} revisions selected counting for {strategy.expected_freed_size_str}."
-
-
-def _read_manual_review_tmp_file(tmp_path: str) -> List[str]:
-    """Read the manually reviewed instruction file and return a list of revision hash.
-
-    Example:
-        ```txt
-        # This is the tmp file content
-        ###
-
-        # Commented out line
-        123456789 # revision hash
-
-        # Something else
-        #      a_newer_hash # 2 days ago
-            an_older_hash # 3 days ago
-        ```
-
-        ```py
-        >>> _read_manual_review_tmp_file(tmp_path)
-        ['123456789', 'an_older_hash']
-        ```
-    """
-    with open(tmp_path) as f:
-        content = f.read()
-
-    # Split lines
-    lines = [line.strip() for line in content.split("\n")]
-
-    # Filter commented lines
-    selected_lines = [line for line in lines if not line.startswith("#")]
-
-    # Select only before comment
-    selected_hashes = [line.split("#")[0].strip() for line in selected_lines]
-
-    # Return revision hashes
-    return [hash for hash in selected_hashes if len(hash) > 0]
-
-
-_MANUAL_REVIEW_NO_TUI_INSTRUCTIONS = f"""
-# INSTRUCTIONS
-# ------------
-# This is a temporary file created by running `huggingface-cli delete-cache` with the
-# `--disable-tui` option. It contains a set of revisions that can be deleted from your
-# local cache directory.
-#
-# Please manually review the revisions you want to delete:
-#   - Revision hashes can be commented out with '#'.
-#   - Only non-commented revisions in this file will be deleted.
-#   - Revision hashes that are removed from this file are ignored as well.
-#   - If `{_CANCEL_DELETION_STR}` line is uncommented, the all cache deletion is cancelled and
-#     no changes will be applied.
-#
-# Once you've manually reviewed this file, please confirm deletion in the terminal. This
-# file will be automatically removed once done.
-# ------------
-
-# KILL SWITCH
-# ------------
-# Un-comment following line to completely cancel the deletion process
-# {_CANCEL_DELETION_STR}
-# ------------
-
-# REVISIONS
-# ------------
-""".strip()
-
-
-def _repo_sorting_order(repo: CachedRepoInfo) -> Any:
-    # First split by Dataset/Model, then sort by last accessed (oldest first)
-    return (repo.repo_type, repo.last_accessed)
-
-
-def _revision_sorting_order(revision: CachedRevisionInfo) -> Any:
-    # Sort by last modified (oldest first)
-    return revision.last_modified
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_custom.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_custom.py
deleted file mode 100644
index e85ddd659e01bebb03b5df6a5ed4fb0070abd263..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_custom.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import unittest
-import contextlib
-import pathlib
-
-import importlib_resources as resources
-from ..abc import TraversableResources, ResourceReader
-from . import util
-from ._compat import os_helper
-
-
-class SimpleLoader:
-    """
-    A simple loader that only implements a resource reader.
-    """
-
-    def __init__(self, reader: ResourceReader):
-        self.reader = reader
-
-    def get_resource_reader(self, package):
-        return self.reader
-
-
-class MagicResources(TraversableResources):
-    """
-    Magically returns the resources at path.
-    """
-
-    def __init__(self, path: pathlib.Path):
-        self.path = path
-
-    def files(self):
-        return self.path
-
-
-class CustomTraversableResourcesTests(unittest.TestCase):
-    def setUp(self):
-        self.fixtures = contextlib.ExitStack()
-        self.addCleanup(self.fixtures.close)
-
-    def test_custom_loader(self):
-        temp_dir = self.fixtures.enter_context(os_helper.temp_dir())
-        loader = SimpleLoader(MagicResources(temp_dir))
-        pkg = util.create_package_from_loader(loader)
-        files = resources.files(pkg)
-        assert files is temp_dir
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/renderer.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/renderer.py
deleted file mode 100644
index 7fee9ffa1736842b01f45e96cdc1aa4afa3df3ce..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/renderer.py
+++ /dev/null
@@ -1,336 +0,0 @@
-"""
-class Renderer
-
-Generates HTML from parsed token stream. Each instance has independent
-copy of rules. Those can be rewritten with ease. Also, you can add new
-rules if you create plugin and adds new token types.
-"""
-from __future__ import annotations
-
-from collections.abc import Sequence
-import inspect
-from typing import Any, ClassVar, Protocol
-
-from .common.utils import escapeHtml, unescapeAll
-from .token import Token
-from .utils import EnvType, OptionsDict
-
-
-class RendererProtocol(Protocol):
-    __output__: ClassVar[str]
-
-    def render(
-        self, tokens: Sequence[Token], options: OptionsDict, env: EnvType
-    ) -> Any:
-        ...
-
-
-class RendererHTML(RendererProtocol):
-    """Contains render rules for tokens. Can be updated and extended.
-
-    Example:
-
-    Each rule is called as independent static function with fixed signature:
-
-    ::
-
-        class Renderer:
-            def token_type_name(self, tokens, idx, options, env) {
-                # ...
-                return renderedHTML
-
-    ::
-
-        class CustomRenderer(RendererHTML):
-            def strong_open(self, tokens, idx, options, env):
-                return '<b>'
-            def strong_close(self, tokens, idx, options, env):
-                return '</b>'
-
-        md = MarkdownIt(renderer_cls=CustomRenderer)
-
-        result = md.render(...)
-
-    See https://github.com/markdown-it/markdown-it/blob/master/lib/renderer.js
-    for more details and examples.
-    """
-
-    __output__ = "html"
-
-    def __init__(self, parser: Any = None):
-        self.rules = {
-            k: v
-            for k, v in inspect.getmembers(self, predicate=inspect.ismethod)
-            if not (k.startswith("render") or k.startswith("_"))
-        }
-
-    def render(
-        self, tokens: Sequence[Token], options: OptionsDict, env: EnvType
-    ) -> str:
-        """Takes token stream and generates HTML.
-
-        :param tokens: list on block tokens to render
-        :param options: params of parser instance
-        :param env: additional data from parsed input
-
-        """
-        result = ""
-
-        for i, token in enumerate(tokens):
-            if token.type == "inline":
-                if token.children:
-                    result += self.renderInline(token.children, options, env)
-            elif token.type in self.rules:
-                result += self.rules[token.type](tokens, i, options, env)
-            else:
-                result += self.renderToken(tokens, i, options, env)
-
-        return result
-
-    def renderInline(
-        self, tokens: Sequence[Token], options: OptionsDict, env: EnvType
-    ) -> str:
-        """The same as ``render``, but for single token of `inline` type.
-
-        :param tokens: list on block tokens to render
-        :param options: params of parser instance
-        :param env: additional data from parsed input (references, for example)
-        """
-        result = ""
-
-        for i, token in enumerate(tokens):
-            if token.type in self.rules:
-                result += self.rules[token.type](tokens, i, options, env)
-            else:
-                result += self.renderToken(tokens, i, options, env)
-
-        return result
-
-    def renderToken(
-        self,
-        tokens: Sequence[Token],
-        idx: int,
-        options: OptionsDict,
-        env: EnvType,
-    ) -> str:
-        """Default token renderer.
-
-        Can be overridden by custom function
-
-        :param idx: token index to render
-        :param options: params of parser instance
-        """
-        result = ""
-        needLf = False
-        token = tokens[idx]
-
-        # Tight list paragraphs
-        if token.hidden:
-            return ""
-
-        # Insert a newline between hidden paragraph and subsequent opening
-        # block-level tag.
-        #
-        # For example, here we should insert a newline before blockquote:
-        #  - a
-        #    >
-        #
-        if token.block and token.nesting != -1 and idx and tokens[idx - 1].hidden:
-            result += "\n"
-
-        # Add token name, e.g. `<img`
-        result += ("</" if token.nesting == -1 else "<") + token.tag
-
-        # Encode attributes, e.g. `<img src="foo"`
-        result += self.renderAttrs(token)
-
-        # Add a slash for self-closing tags, e.g. `<img src="foo" /`
-        if token.nesting == 0 and options["xhtmlOut"]:
-            result += " /"
-
-        # Check if we need to add a newline after this tag
-        if token.block:
-            needLf = True
-
-            if token.nesting == 1 and (idx + 1 < len(tokens)):
-                nextToken = tokens[idx + 1]
-
-                if nextToken.type == "inline" or nextToken.hidden:  # noqa: SIM114
-                    # Block-level tag containing an inline tag.
-                    #
-                    needLf = False
-
-                elif nextToken.nesting == -1 and nextToken.tag == token.tag:
-                    # Opening tag + closing tag of the same type. E.g. `<li></li>`.
-                    #
-                    needLf = False
-
-        result += ">\n" if needLf else ">"
-
-        return result
-
-    @staticmethod
-    def renderAttrs(token: Token) -> str:
-        """Render token attributes to string."""
-        result = ""
-
-        for key, value in token.attrItems():
-            result += " " + escapeHtml(key) + '="' + escapeHtml(str(value)) + '"'
-
-        return result
-
-    def renderInlineAsText(
-        self,
-        tokens: Sequence[Token] | None,
-        options: OptionsDict,
-        env: EnvType,
-    ) -> str:
-        """Special kludge for image `alt` attributes to conform CommonMark spec.
-
-        Don't try to use it! Spec requires to show `alt` content with stripped markup,
-        instead of simple escaping.
-
-        :param tokens: list on block tokens to render
-        :param options: params of parser instance
-        :param env: additional data from parsed input
-        """
-        result = ""
-
-        for token in tokens or []:
-            if token.type == "text":
-                result += token.content
-            elif token.type == "image":
-                if token.children:
-                    result += self.renderInlineAsText(token.children, options, env)
-            elif token.type == "softbreak":
-                result += "\n"
-
-        return result
-
-    ###################################################
-
-    def code_inline(
-        self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType
-    ) -> str:
-        token = tokens[idx]
-        return (
-            "<code"
-            + self.renderAttrs(token)
-            + ">"
-            + escapeHtml(tokens[idx].content)
-            + "</code>"
-        )
-
-    def code_block(
-        self,
-        tokens: Sequence[Token],
-        idx: int,
-        options: OptionsDict,
-        env: EnvType,
-    ) -> str:
-        token = tokens[idx]
-
-        return (
-            "<pre"
-            + self.renderAttrs(token)
-            + "><code>"
-            + escapeHtml(tokens[idx].content)
-            + "</code></pre>\n"
-        )
-
-    def fence(
-        self,
-        tokens: Sequence[Token],
-        idx: int,
-        options: OptionsDict,
-        env: EnvType,
-    ) -> str:
-        token = tokens[idx]
-        info = unescapeAll(token.info).strip() if token.info else ""
-        langName = ""
-        langAttrs = ""
-
-        if info:
-            arr = info.split(maxsplit=1)
-            langName = arr[0]
-            if len(arr) == 2:
-                langAttrs = arr[1]
-
-        if options.highlight:
-            highlighted = options.highlight(
-                token.content, langName, langAttrs
-            ) or escapeHtml(token.content)
-        else:
-            highlighted = escapeHtml(token.content)
-
-        if highlighted.startswith("<pre"):
-            return highlighted + "\n"
-
-        # If language exists, inject class gently, without modifying original token.
-        # May be, one day we will add .deepClone() for token and simplify this part, but
-        # now we prefer to keep things local.
-        if info:
-            # Fake token just to render attributes
-            tmpToken = Token(type="", tag="", nesting=0, attrs=token.attrs.copy())
-            tmpToken.attrJoin("class", options.langPrefix + langName)
-
-            return (
-                "<pre><code"
-                + self.renderAttrs(tmpToken)
-                + ">"
-                + highlighted
-                + "</code></pre>\n"
-            )
-
-        return (
-            "<pre><code"
-            + self.renderAttrs(token)
-            + ">"
-            + highlighted
-            + "</code></pre>\n"
-        )
-
-    def image(
-        self,
-        tokens: Sequence[Token],
-        idx: int,
-        options: OptionsDict,
-        env: EnvType,
-    ) -> str:
-        token = tokens[idx]
-
-        # "alt" attr MUST be set, even if empty. Because it's mandatory and
-        # should be placed on proper position for tests.
-        if token.children:
-            token.attrSet("alt", self.renderInlineAsText(token.children, options, env))
-        else:
-            token.attrSet("alt", "")
-
-        return self.renderToken(tokens, idx, options, env)
-
-    def hardbreak(
-        self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType
-    ) -> str:
-        return "<br />\n" if options.xhtmlOut else "<br>\n"
-
-    def softbreak(
-        self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType
-    ) -> str:
-        return (
-            ("<br />\n" if options.xhtmlOut else "<br>\n") if options.breaks else "\n"
-        )
-
-    def text(
-        self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType
-    ) -> str:
-        return escapeHtml(tokens[idx].content)
-
-    def html_block(
-        self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType
-    ) -> str:
-        return tokens[idx].content
-
-    def html_inline(
-        self, tokens: Sequence[Token], idx: int, options: OptionsDict, env: EnvType
-    ) -> str:
-        return tokens[idx].content
diff --git a/spaces/dcq/freegpt-webui/g4f/models.py b/spaces/dcq/freegpt-webui/g4f/models.py
deleted file mode 100644
index 79d93bff3b007a7f9b0550268e37bfa3dfde86e3..0000000000000000000000000000000000000000
--- a/spaces/dcq/freegpt-webui/g4f/models.py
+++ /dev/null
@@ -1,231 +0,0 @@
-from g4f import Provider
-import random
-
-class Model:
-    class model:
-        name: str
-        base_provider: str
-        best_provider: str
-
-    class gpt_35_turbo:
-        name: str = 'gpt-3.5-turbo'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = random.choice([Provider.DeepAi, Provider.Easychat])
-
-    class gpt_35_turbo_0613:
-        name: str = 'gpt-3.5-turbo-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = random.choice([Provider.Gravityengine, Provider.Easychat])
-
-    class gpt_35_turbo_16k_0613:
-        name: str = 'gpt-3.5-turbo-16k-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = random.choice([Provider.Gravityengine, Provider.Easychat])
-
-    class gpt_35_turbo_16k:
-        name: str = 'gpt-3.5-turbo-16k'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = random.choice([Provider.Gravityengine, Provider.Easychat])
-
-    class gpt_4_dev:
-        name: str = 'gpt-4-for-dev'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Phind
-
-    class gpt_4:
-        name: str = 'gpt-4'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Dfehub
-    
-    class gpt_4_0613:
-        name: str = 'gpt-4-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Lockchat
-        best_providers: list = [Provider.Bing, Provider.Lockchat]
-
-    class claude_instant_v1_100k:
-        name: str = 'claude-instant-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_instant_v1:
-        name: str = 'claude-instant-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1_100k:
-        name: str = 'claude-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1:
-        name: str = 'claude-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class alpaca_7b:
-        name: str = 'alpaca-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class stablelm_tuned_alpha_7b:
-        name: str = 'stablelm-tuned-alpha-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloom:
-        name: str = 'bloom'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloomz:
-        name: str = 'bloomz'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_t5_xxl:
-        name: str = 'flan-t5-xxl'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_ul2:
-        name: str = 'flan-ul2'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class gpt_neox_20b:
-        name: str = 'gpt-neox-20b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class oasst_sft_4_pythia_12b_epoch_35:
-        name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class santacoder:
-        name: str = 'santacoder'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_medium_nightly:
-        name: str = 'command-medium-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_xlarge_nightly:
-        name: str = 'command-xlarge-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_cushman_001:
-        name: str = 'code-cushman-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_davinci_002:
-        name: str = 'code-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_ada_001:
-        name: str = 'text-ada-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_babbage_001:
-        name: str = 'text-babbage-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_curie_001:
-        name: str = 'text-curie-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_002:
-        name: str = 'text-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_003:
-        name: str = 'text-davinci-003'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-        
-    class palm:
-        name: str = 'palm2'
-        base_provider: str = 'google'
-        best_provider: Provider.Provider = Provider.Bard
-        
-            
-    """    'falcon-40b': Model.falcon_40b,
-    'falcon-7b': Model.falcon_7b,
-    'llama-13b': Model.llama_13b,"""
-    
-    class falcon_40b:
-        name: str = 'falcon-40b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-    class falcon_7b:
-        name: str = 'falcon-7b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-        
-    class llama_13b:
-        name: str = 'llama-13b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-class ModelUtils:
-    convert: dict = {
-        'gpt-3.5-turbo': Model.gpt_35_turbo,
-        'gpt-3.5-turbo-0613': Model.gpt_35_turbo_0613,
-        'gpt-4': Model.gpt_4,
-        'gpt-4-0613': Model.gpt_4_0613,
-        'gpt-4-for-dev': Model.gpt_4_dev,
-        'gpt-3.5-turbo-16k': Model.gpt_35_turbo_16k,
-        'gpt-3.5-turbo-16k-0613': Model.gpt_35_turbo_16k_0613,
-        
-        'claude-instant-v1-100k': Model.claude_instant_v1_100k,
-        'claude-v1-100k': Model.claude_v1_100k,
-        'claude-instant-v1': Model.claude_instant_v1,
-        'claude-v1': Model.claude_v1,
-        
-        'alpaca-7b': Model.alpaca_7b,
-        'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
-        
-        'bloom': Model.bloom,
-        'bloomz': Model.bloomz,
-        
-        'flan-t5-xxl': Model.flan_t5_xxl,
-        'flan-ul2': Model.flan_ul2,
-        
-        'gpt-neox-20b': Model.gpt_neox_20b,
-        'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
-        'santacoder': Model.santacoder,
-        
-        'command-medium-nightly': Model.command_medium_nightly,
-        'command-xlarge-nightly': Model.command_xlarge_nightly,
-        
-        'code-cushman-001': Model.code_cushman_001,
-        'code-davinci-002': Model.code_davinci_002,
-        
-        'text-ada-001': Model.text_ada_001,
-        'text-babbage-001': Model.text_babbage_001,
-        'text-curie-001': Model.text_curie_001,
-        'text-davinci-002': Model.text_davinci_002,
-        'text-davinci-003': Model.text_davinci_003,
-        
-        'palm2': Model.palm,
-        'palm': Model.palm,
-        'google': Model.palm,
-        'google-bard': Model.palm,
-        'google-palm': Model.palm,
-        'bard': Model.palm,
-        
-        'falcon-40b': Model.falcon_40b,
-        'falcon-7b': Model.falcon_7b,
-        'llama-13b': Model.llama_13b,
-    }
diff --git a/spaces/declare-lab/tango/diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py b/spaces/declare-lab/tango/diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py
deleted file mode 100644
index 0522488f2882c87bf552b8deed24c232657ed3e7..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py
+++ /dev/null
@@ -1,830 +0,0 @@
-import argparse
-import hashlib
-import math
-import os
-import random
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, upload_folder
-from PIL import Image, ImageDraw
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
-from diffusers.loaders import AttnProcsLayers
-from diffusers.models.attention_processor import LoRAAttnProcessor
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.13.0.dev0")
-
-logger = get_logger(__name__)
-
-
-def prepare_mask_and_masked_image(image, mask):
-    image = np.array(image.convert("RGB"))
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    mask = np.array(mask.convert("L"))
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-    mask = torch.from_numpy(mask)
-
-    masked_image = image * (mask < 0.5)
-
-    return mask, masked_image
-
-
-# generate random masks
-def random_mask(im_shape, ratio=1, mask_full_image=False):
-    mask = Image.new("L", im_shape, 0)
-    draw = ImageDraw.Draw(mask)
-    size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))
-    # use this to always mask the whole image
-    if mask_full_image:
-        size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))
-    limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)
-    center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))
-    draw_type = random.randint(0, 1)
-    if draw_type == 0 or mask_full_image:
-        draw.rectangle(
-            (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
-            fill=255,
-        )
-    else:
-        draw.ellipse(
-            (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
-            fill=255,
-        )
-
-    return mask
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default=None,
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="dreambooth-inpaint-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
-            " checkpoints in case they are better than the last checkpoint and are suitable for resuming training"
-            " using `--resume_from_checkpoint`."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=(
-            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
-            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
-            " for more docs"
-        ),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.instance_data_dir is None:
-        raise ValueError("You must specify a train data directory.")
-
-    if args.with_prior_preservation:
-        if args.class_data_dir is None:
-            raise ValueError("You must specify a data directory for class images.")
-        if args.class_prompt is None:
-            raise ValueError("You must specify prompt for class images.")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        class_data_root=None,
-        class_prompt=None,
-        size=512,
-        center_crop=False,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError("Instance images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms_resize_and_crop = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-            ]
-        )
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-        instance_image = self.image_transforms_resize_and_crop(instance_image)
-
-        example["PIL_images"] = instance_image
-        example["instance_images"] = self.image_transforms(instance_image)
-
-        example["instance_prompt_ids"] = self.tokenizer(
-            self.instance_prompt,
-            padding="do_not_pad",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-        ).input_ids
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            class_image = self.image_transforms_resize_and_crop(class_image)
-            example["class_images"] = self.image_transforms(class_image)
-            example["class_PIL_images"] = class_image
-            example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
-                padding="do_not_pad",
-                truncation=True,
-                max_length=self.tokenizer.model_max_length,
-            ).input_ids
-
-        return example
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-
-def main():
-    args = parse_args()
-    logging_dir = Path(args.output_dir, args.logging_dir)
-
-    accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
-        logging_dir=logging_dir,
-        accelerator_project_config=accelerator_project_config,
-    )
-
-    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
-    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
-    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
-    if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
-        raise ValueError(
-            "Gradient accumulation is not supported when training the text encoder in distributed training. "
-            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
-        )
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
-            pipeline = StableDiffusionInpaintPipeline.from_pretrained(
-                args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(
-                sample_dataset, batch_size=args.sample_batch_size, num_workers=1
-            )
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
-            transform_to_pil = transforms.ToPILImage()
-            for example in tqdm(
-                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
-            ):
-                bsz = len(example["prompt"])
-                fake_images = torch.rand((3, args.resolution, args.resolution))
-                transform_to_pil = transforms.ToPILImage()
-                fake_pil_images = transform_to_pil(fake_images)
-
-                fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)
-
-                images = pipeline(prompt=example["prompt"], mask_image=fake_mask, image=fake_pil_images).images
-
-                for i, image in enumerate(images):
-                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
-                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
-                    image.save(image_filename)
-
-            del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
-    # Load models and create wrapper for stable diffusion
-    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-
-    # We only train the additional adapter LoRA layers
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-    unet.requires_grad_(False)
-
-    weight_dtype = torch.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu.
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    unet.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    # now we will add new LoRA weights to the attention layers
-    # It's important to realize here how many attention weights will be added and of which sizes
-    # The sizes of the attention layers consist only of two different variables:
-    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
-    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
-
-    # Let's first see how many attention processors we will have to set.
-    # For Stable Diffusion, it should be equal to:
-    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
-    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
-    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
-    # => 32 layers
-
-    # Set correct lora layers
-    lora_attn_procs = {}
-    for name in unet.attn_processors.keys():
-        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-        if name.startswith("mid_block"):
-            hidden_size = unet.config.block_out_channels[-1]
-        elif name.startswith("up_blocks"):
-            block_id = int(name[len("up_blocks.")])
-            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-        elif name.startswith("down_blocks"):
-            block_id = int(name[len("down_blocks.")])
-            hidden_size = unet.config.block_out_channels[block_id]
-
-        lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
-
-    unet.set_attn_processor(lora_attn_procs)
-    lora_layers = AttnProcsLayers(unet.attn_processors)
-
-    accelerator.register_for_checkpointing(lora_layers)
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    optimizer = optimizer_class(
-        lora_layers.parameters(),
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-    )
-
-    def collate_fn(examples):
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # Concat class and instance examples for prior preservation.
-        # We do this to avoid doing two forward passes.
-        if args.with_prior_preservation:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-            pior_pil = [example["class_PIL_images"] for example in examples]
-
-        masks = []
-        masked_images = []
-        for example in examples:
-            pil_image = example["PIL_images"]
-            # generate a random mask
-            mask = random_mask(pil_image.size, 1, False)
-            # prepare mask and masked image
-            mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
-
-            masks.append(mask)
-            masked_images.append(masked_image)
-
-        if args.with_prior_preservation:
-            for pil_image in pior_pil:
-                # generate a random mask
-                mask = random_mask(pil_image.size, 1, False)
-                # prepare mask and masked image
-                mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
-
-                masks.append(mask)
-                masked_images.append(masked_image)
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-        input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
-        masks = torch.stack(masks)
-        masked_images = torch.stack(masked_images)
-        batch = {"input_ids": input_ids, "pixel_values": pixel_values, "masks": masks, "masked_images": masked_images}
-        return batch
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
-
-    # Prepare everything with our `accelerator`.
-    lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        lora_layers, optimizer, train_dataloader, lr_scheduler
-    )
-    # accelerator.register_for_checkpointing(lr_scheduler)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth-inpaint-lora", config=vars(args))
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            resume_global_step = global_step * args.gradient_accumulation_steps
-            first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    for epoch in range(first_epoch, args.num_train_epochs):
-        unet.train()
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-
-                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
-                latents = latents * vae.config.scaling_factor
-
-                # Convert masked images to latent space
-                masked_latents = vae.encode(
-                    batch["masked_images"].reshape(batch["pixel_values"].shape).to(dtype=weight_dtype)
-                ).latent_dist.sample()
-                masked_latents = masked_latents * vae.config.scaling_factor
-
-                masks = batch["masks"]
-                # resize the mask to latents shape as we concatenate the mask to the latents
-                mask = torch.stack(
-                    [
-                        torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))
-                        for mask in masks
-                    ]
-                )
-                mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # concatenate the noised latents with the mask and the masked latents
-                latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)
-
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Predict the noise residual
-                noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                if args.with_prior_preservation:
-                    # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
-                    noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
-                    target, target_prior = torch.chunk(target, 2, dim=0)
-
-                    # Compute instance loss
-                    loss = F.mse_loss(noise_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
-                    # Compute prior loss
-                    prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                    # Add the prior loss to the instance loss.
-                    loss = loss + args.prior_loss_weight * prior_loss
-                else:
-                    loss = F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = lora_layers.parameters()
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-                if global_step % args.checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-        accelerator.wait_for_everyone()
-
-    # Save the lora layers
-    if accelerator.is_main_process:
-        unet = unet.to(torch.float32)
-        unet.save_attn_procs(args.output_dir)
-
-        if args.push_to_hub:
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/declare-lab/tango/diffusers/tests/test_pipelines_flax.py b/spaces/declare-lab/tango/diffusers/tests/test_pipelines_flax.py
deleted file mode 100644
index a461930f3a83ecfc8134d50ce5978d329d79f5c9..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/tests/test_pipelines_flax.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import tempfile
-import unittest
-
-import numpy as np
-
-from diffusers.utils import is_flax_available
-from diffusers.utils.testing_utils import require_flax, slow
-
-
-if is_flax_available():
-    import jax
-    import jax.numpy as jnp
-    from flax.jax_utils import replicate
-    from flax.training.common_utils import shard
-    from jax import pmap
-
-    from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
-
-
-@require_flax
-class DownloadTests(unittest.TestCase):
-    def test_download_only_pytorch(self):
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            # pipeline has Flax weights
-            _ = FlaxDiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
-            )
-
-            all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
-            files = [item for sublist in all_root_files for item in sublist]
-
-            # None of the downloaded files should be a PyTorch file even if we have some here:
-            # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
-            assert not any(f.endswith(".bin") for f in files)
-
-
-@slow
-@require_flax
-class FlaxPipelineTests(unittest.TestCase):
-    def test_dummy_all_tpus(self):
-        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-        )
-
-        prompt = (
-            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
-            " field, close up, split lighting, cinematic"
-        )
-
-        prng_seed = jax.random.PRNGKey(0)
-        num_inference_steps = 4
-
-        num_samples = jax.device_count()
-        prompt = num_samples * [prompt]
-        prompt_ids = pipeline.prepare_inputs(prompt)
-
-        p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
-
-        # shard inputs and rng
-        params = replicate(params)
-        prng_seed = jax.random.split(prng_seed, num_samples)
-        prompt_ids = shard(prompt_ids)
-
-        images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
-
-        assert images.shape == (num_samples, 1, 64, 64, 3)
-        if jax.device_count() == 8:
-            assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 3.1111548) < 1e-3
-            assert np.abs(np.abs(images, dtype=np.float32).sum() - 199746.95) < 5e-1
-
-        images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-
-        assert len(images_pil) == num_samples
-
-    def test_stable_diffusion_v1_4(self):
-        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="flax", safety_checker=None
-        )
-
-        prompt = (
-            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
-            " field, close up, split lighting, cinematic"
-        )
-
-        prng_seed = jax.random.PRNGKey(0)
-        num_inference_steps = 50
-
-        num_samples = jax.device_count()
-        prompt = num_samples * [prompt]
-        prompt_ids = pipeline.prepare_inputs(prompt)
-
-        p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
-
-        # shard inputs and rng
-        params = replicate(params)
-        prng_seed = jax.random.split(prng_seed, num_samples)
-        prompt_ids = shard(prompt_ids)
-
-        images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
-
-        assert images.shape == (num_samples, 1, 512, 512, 3)
-        if jax.device_count() == 8:
-            assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.05652401)) < 1e-3
-            assert np.abs((np.abs(images, dtype=np.float32).sum() - 2383808.2)) < 5e-1
-
-    def test_stable_diffusion_v1_4_bfloat_16(self):
-        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jnp.bfloat16, safety_checker=None
-        )
-
-        prompt = (
-            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
-            " field, close up, split lighting, cinematic"
-        )
-
-        prng_seed = jax.random.PRNGKey(0)
-        num_inference_steps = 50
-
-        num_samples = jax.device_count()
-        prompt = num_samples * [prompt]
-        prompt_ids = pipeline.prepare_inputs(prompt)
-
-        p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
-
-        # shard inputs and rng
-        params = replicate(params)
-        prng_seed = jax.random.split(prng_seed, num_samples)
-        prompt_ids = shard(prompt_ids)
-
-        images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
-
-        assert images.shape == (num_samples, 1, 512, 512, 3)
-        if jax.device_count() == 8:
-            assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.06652832)) < 1e-3
-            assert np.abs((np.abs(images, dtype=np.float32).sum() - 2384849.8)) < 5e-1
-
-    def test_stable_diffusion_v1_4_bfloat_16_with_safety(self):
-        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jnp.bfloat16
-        )
-
-        prompt = (
-            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
-            " field, close up, split lighting, cinematic"
-        )
-
-        prng_seed = jax.random.PRNGKey(0)
-        num_inference_steps = 50
-
-        num_samples = jax.device_count()
-        prompt = num_samples * [prompt]
-        prompt_ids = pipeline.prepare_inputs(prompt)
-
-        # shard inputs and rng
-        params = replicate(params)
-        prng_seed = jax.random.split(prng_seed, num_samples)
-        prompt_ids = shard(prompt_ids)
-
-        images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-
-        assert images.shape == (num_samples, 1, 512, 512, 3)
-        if jax.device_count() == 8:
-            assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.06652832)) < 1e-3
-            assert np.abs((np.abs(images, dtype=np.float32).sum() - 2384849.8)) < 5e-1
-
-    def test_stable_diffusion_v1_4_bfloat_16_ddim(self):
-        scheduler = FlaxDDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            set_alpha_to_one=False,
-            steps_offset=1,
-        )
-
-        pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4",
-            revision="bf16",
-            dtype=jnp.bfloat16,
-            scheduler=scheduler,
-            safety_checker=None,
-        )
-        scheduler_state = scheduler.create_state()
-
-        params["scheduler"] = scheduler_state
-
-        prompt = (
-            "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
-            " field, close up, split lighting, cinematic"
-        )
-
-        prng_seed = jax.random.PRNGKey(0)
-        num_inference_steps = 50
-
-        num_samples = jax.device_count()
-        prompt = num_samples * [prompt]
-        prompt_ids = pipeline.prepare_inputs(prompt)
-
-        p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
-
-        # shard inputs and rng
-        params = replicate(params)
-        prng_seed = jax.random.split(prng_seed, num_samples)
-        prompt_ids = shard(prompt_ids)
-
-        images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
-
-        assert images.shape == (num_samples, 1, 512, 512, 3)
-        if jax.device_count() == 8:
-            assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.045043945)) < 1e-3
-            assert np.abs((np.abs(images, dtype=np.float32).sum() - 2347693.5)) < 5e-1
diff --git a/spaces/deepkyu/multilingual-font-style-transfer/models/loss.py b/spaces/deepkyu/multilingual-font-style-transfer/models/loss.py
deleted file mode 100644
index 17e0e48fcd6451dc970cabf1f110dac4ced38979..0000000000000000000000000000000000000000
--- a/spaces/deepkyu/multilingual-font-style-transfer/models/loss.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-import torch.nn as nn
-
-class GANHingeLoss(nn.Module):
-    def __init__(self):
-        super(GANHingeLoss, self).__init__()
-        self.relu = nn.ReLU()
-    
-    def __call__(self, pred, is_real, for_discriminator):
-        if for_discriminator:
-            if is_real:
-                return self.relu(1 - pred).mean()
-            return self.relu(1 + pred).mean()
-        
-        assert is_real, "The generator's hinge loss must be aiming for real"
-        return -1.0 * pred.mean()
\ No newline at end of file
diff --git a/spaces/dhkim2810/MobileSAM/utils/__init__.py b/spaces/dhkim2810/MobileSAM/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/diacanFperku/AutoGPT/Crack Adobe Acrobat X Pro 10 Amtlib Dll.md b/spaces/diacanFperku/AutoGPT/Crack Adobe Acrobat X Pro 10 Amtlib Dll.md
deleted file mode 100644
index 2808900fc6f9cbe304f5ab4e4d180f5e64b966b6..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Crack Adobe Acrobat X Pro 10 Amtlib Dll.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/diacanFperku/AutoGPT/Edius7softwarefreedownloadfullversionwithcrack32.md b/spaces/diacanFperku/AutoGPT/Edius7softwarefreedownloadfullversionwithcrack32.md
deleted file mode 100644
index ecbd9f09cde5d3550b4cb7cf1f57b0e895a79043..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Edius7softwarefreedownloadfullversionwithcrack32.md
+++ /dev/null
@@ -1,129 +0,0 @@
-
-<h1>Edius7softwarefreedownloadfullversionwithcrack32: A Complete Guide</h1>
-<p>If you are looking for a powerful and flexible video editing software for Windows, you may want to consider Edius 7. Edius 7 is one of the most comprehensive and versatile video editors around, that can handle high definition as well as standard definition videos. Moreover, it allows you to edit 10-bit videos instead of the traditional 8-bit ones, an advantage that will give your movies significantly higher quality. In this article, we will show you how to download and install Edius 7 for free with crack 32 bit, and what are its features and benefits.</p>
-<h2>What is Edius 7 and what are its features and benefits?</h2>
-<p>Edius 7 is a video editing software developed by Grass Valley, a company that specializes in broadcast and media solutions. Edius 7 has many features and benefits that make it a great choice for video editors, such as:</p>
-<h2>edius7softwarefreedownloadfullversionwithcrack32</h2><br /><p><b><b>DOWNLOAD</b> ===> <a href="https://gohhs.com/2uFU0Y">https://gohhs.com/2uFU0Y</a></b></p><br /><br />
-<ul>
-<li>Real-time editing: With Edius 7, you can edit your videos in real time. In fact, the rendering process will only take you as long as the movie lasts. Don't waste your time focusing on details, and let your imagination flow.</li>
-<li>Format recognition: Edius 7 automatically recognizes the format of the video uploaded, and it even changes the format of the clip if needed. You will only need to configure the program at the beginning to enable this automatic function.</li>
-<li>Synchronized editing: On top of that, we should highlight one more important feature: users can perform synchronized actions. With Edius 7, you can edit one video from up to 16 different cameras, taking several actions from its sources, all at the same time.</li>
-<li>User-friendly interface: Edius 7 has a handy and clean interface that will make video editing much easier for you. It has a library browser component that lets you manage your media files, a timeline that shows your clips and transitions, and a preview window that shows your results.</li>
-<li>Video effects: Edius 7 also comes with a variety of video effects that you can apply to your clips, such as filters, transitions, titles, masks, chroma key, and more. You can also customize your effects and save them as presets for future use.</li>
-<li>Advanced mixing: Edius 7 also allows you to mix audio and video tracks with ease. You can adjust the volume, balance, pan, equalizer, and more. You can also use VST plugins to enhance your audio quality.</li>
-</ul>
-<h2>How to download and install Edius 7 for free with crack 32 bit?</h2>
-<p>To download and install Edius 7 for free with crack 32 bit, you need to follow these steps:</p>
-<ol>
-<li>Go to the website https://isoriver.com/download-edius-pro-7-windows/ and find the download link for Edius 7.</li>
-<li>Click on the download link and save the file to your PC.</li>
-<li>Extract the file using WinRAR or any other extraction tool.</li>
-<li>Run the setup file and follow the instructions on the screen to install the software.</li>
-<li>Copy the crack file from the crack folder and paste it into the installation directory of Edius 7.</li>
-<li>Run the software and enjoy editing your videos.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Edius 7 is a useful software that can enhance your video editing experience with your PC. It can improve your connectivity, compatibility, performance, and quality with various video formats and devices. To enjoy these benefits, you need to download and install Edius 7 for free with crack 32 bit from the website https://isoriver.com/download-edius-pro-7-windows/. We hope that this article has provided you with useful and relevant information about edius7softwarefreedownloadfullversionwithcrack32, and that you have enjoyed reading it.</p>
-<h2>How to use Edius 7 for video editing?</h2>
-<p>Edius 7 is a software that can help you create professional and creative videos with ease. You can use Edius 7 for various video editing tasks, such as:</p>
-<ul>
-<li>Cutting and trimming: You can cut and trim your clips to remove unwanted parts or adjust the length.</li>
-<li>Splitting and merging: You can split and merge your clips to rearrange them or create transitions.</li>
-<li>Cropping and resizing: You can crop and resize your clips to change the aspect ratio or fit the screen.</li>
-<li>Rotating and flipping: You can rotate and flip your clips to change the orientation or perspective.</li>
-<li>Adjusting color and brightness: You can adjust the color and brightness of your clips to enhance the quality or create effects.</li>
-<li>Adding text and graphics: You can add text and graphics to your clips to add titles, captions, logos, or watermarks.</li>
-<li>Applying filters and effects: You can apply filters and effects to your clips to change the style or mood.</li>
-<li>Mixing audio and video: You can mix audio and video tracks to create a balanced and synchronized sound.</li>
-</ul>
-<p>To use Edius 7 for video editing, you need to follow these steps:</p>
-<ol>
-<li>Import your media files to the library browser component of Edius 7.</li>
-<li>Drag and drop your clips to the timeline component of Edius 7.</li>
-<li>Edit your clips using the tools and options available in Edius 7.</li>
-<li>Preview your results using the preview window component of Edius 7.</li>
-<li>Export your video using the export component of Edius 7.</li>
-</ol>
-<h2>What are the system requirements for Edius 7?</h2>
-<p>Edius 7 is a software that requires a certain level of system specifications to run smoothly and efficiently. The system requirements for Edius 7 are as follows:</p>
-<ul>
-<li>Operating system: Windows XP/XP Professional/Vista/7/8/10/11 (32-bit or 64-bit)</li>
-<li>Processor: Intel Core 2 or higher, or AMD Phenom II or higher</li>
-<li>Memory: 1 GB RAM (4 GB recommended)</li>
-<li>Hard disk space: 6 GB free space (10 GB recommended)</li>
-<li>Graphics card: NVIDIA GeForce 8 series or higher, or AMD Radeon HD series or higher</li>
-<li>Sound card: DirectSound compatible sound card</li>
-<li>Optical drive: DVD-ROM drive</li>
-<li>Internet connection: Required for activation, updates, and online services</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have discussed edius7softwarefreedownloadfullversionwithcrack32, a software that enables you to edit videos in high quality and flexibility. We have covered various aspects of this software, such as:</p>
-<ul>
-<li>What is Edius 7 and what are its features and benefits?</li>
-<li>How to download and install Edius 7 for free with crack 32 bit?</li>
-<li>How to use Edius 7 for video editing?</li>
-<li>What are the system requirements for Edius 7?</li>
-</ul>
-<p>We hope that this article has provided you with useful and relevant information about edius7softwarefreedownloadfullversionwithcrack32, and that you have enjoyed reading it. If you have any questions or comments about this software or this article, please feel free to contact us or leave a reply below.</p>
-<h2>What are the problems and solutions for Edius 7?</h2>
-<p>Edius 7 is a software that can help you edit videos in high quality and flexibility, but it may also encounter some problems or errors that can affect your workflow or output. Here are some of the common problems and solutions for Edius 7:</p>
-<p></p>
-<ul>
-<li>Problem: Edius 7 crashes or freezes when importing or editing certain video formats or codecs.
-Solution: You may need to update your video drivers, codecs, or Edius 7 software to the latest version. You may also need to convert your video files to a compatible format or codec using a third-party tool.</li>
-<li>Problem: Edius 7 does not recognize or support your Bluetooth device or adapter.
-Solution: You may need to install the CSR Harmony Wireless Software Stack 2.1.63.0 Download 26, which is a software that enables a seamless wireless experience for your PC and Bluetooth devices. You can download and install it for free with crack 32 bit from the website https://isoriver.com/download-edius-pro-7-windows/.</li>
-<li>Problem: Edius 7 does not activate or register properly with your serial number or license key.
-Solution: You may need to check your internet connection, firewall settings, or antivirus software that may block the activation or registration process. You may also need to contact the customer support of Grass Valley, the developer and provider of Edius 7, for assistance.</li>
-</ul>
-<h2>What are the reviews and ratings of Edius 7?</h2>
-<p>Edius 7 is a software that has received positive reviews and ratings from its users and experts. It has been praised for its functionality, compatibility, performance, and quality. Here are some of the reviews and ratings of Edius 7:</p>
-<ul>
-<li>Software Informer: 4 out of 5 stars based on 67 votes from users</li>
-<li>Free Download Manager: 4 out of 5 stars based on 83 votes from users</li>
-<li>Luckystudio4u: 5 out of 5 stars based on a detailed review of Edius X Free Download</li>
-<li>Civil Engineer 9: 4 out of 5 stars based on a brief overview of AutoCAD 2007 Free Download</li>
-<li>Edius.net: 5 out of 5 stars based on a stream of Edius Download by EDIUS</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have discussed edius7softwarefreedownloadfullversionwithcrack32, a software that enables you to edit videos in high quality and flexibility. We have covered various aspects of this software, such as:</p>
-<ul>
-<li>What is Edius 7 and what are its features and benefits?</li>
-<li>How to download and install Edius 7 for free with crack 32 bit?</li>
-<li>How to use Edius 7 for video editing?</li>
-<li>What are the system requirements for Edius 7?</li>
-<li>What are the problems and solutions for Edius 7?</li>
-<li>What are the reviews and ratings of Edius 7?</li>
-</ul>
-<p>We hope that this article has provided you with useful and relevant information about edius7softwarefreedownloadfullversionwithcrack32, and that you have enjoyed reading it. If you have any questions or comments about this software or this article, please feel free to contact us or leave a reply below.</p>
-<h2>What are the alternatives to Edius 7?</h2>
-<p>Edius 7 is a software that can help you edit videos in high quality and flexibility, but it may not be the best option for everyone. Depending on your needs, preferences, and budget, you may want to consider some of the alternatives to Edius 7, such as:</p>
-<ul>
-<li>Adobe Premiere Pro: This is a software that offers a professional and comprehensive video editing solution for Windows and Mac. It has a wide range of features and tools that can handle any video project, from simple to complex. It also integrates well with other Adobe products and services, such as Photoshop, After Effects, Audition, and Creative Cloud.</li>
-<li>Final Cut Pro: This is a software that offers a powerful and intuitive video editing solution for Mac. It has a sleek and user-friendly interface that lets you edit videos with ease and speed. It also has advanced features and tools that can enhance your videos with effects, transitions, titles, color grading, and more.</li>
-<li>Vegas Pro: This is a software that offers a versatile and creative video editing solution for Windows. It has a flexible and customizable interface that lets you work with multiple tracks and windows. It also has a rich collection of features and tools that can edit videos in various formats and resolutions, including 4K and 3D.</li>
-<li>Davinci Resolve: This is a software that offers a professional and sophisticated video editing solution for Windows, Mac, and Linux. It has a robust and complex interface that gives you full control over your videos. It also has a powerful color correction and grading system that can transform your videos with stunning results.</li>
-</ul>
-<h2>How to get help and support for Edius 7?</h2>
-<p>Edius 7 is a software that provides help and support for its users in various ways, such as:</p>
-<ul>
-<li>User manual: You can access the user manual of Edius 7 by clicking on the Help menu in the software or by visiting the website https://www.edius.net/edius_download.html. The user manual contains detailed information and instructions on how to use Edius 7.</li>
-<li>Tutorials: You can watch the tutorials of Edius 7 by clicking on the Help menu in the software or by visiting the website https://www.edius.net/edius_tutorial.html. The tutorials contain step-by-step guides and tips on how to use Edius 7.</li>
-<li>Forums: You can join the forums of Edius 7 by clicking on the Help menu in the software or by visiting the website https://forum.grassvalley.com/forum/. The forums contain discussions and questions from other users and experts of Edius 7.</li>
-<li>Contact: You can contact the customer support of Grass Valley, the developer and provider of Edius 7, by clicking on the Help menu in the software or by visiting the website https://www.grassvalley.com/contact/. The customer support can assist you with any issues or inquiries regarding Edius 7.</li>
-</ul>
-<h2>Edius7softwarefreedownloadfullversionwithcrack32: A Summary</h2>
-<p>In this article, we have discussed edius7softwarefreedownloadfullversionwithcrack32, a software that enables you to edit videos in high quality and flexibility. We have covered various aspects of this software, such as:</p>
-<ul>
-<li>What is Edius 7 and what are its features and benefits?</li>
-<li>How to download and install Edius 7 for free with crack 32 bit?</li>
-<li>How to use Edius 7 for video editing?</li>
-<li>What are the system requirements for Edius 7?</li>
-<li>What are the problems and solutions for Edius 7?</li>
-<li>What are the reviews and ratings of Edius 7?</li>
-<li>What are the alternatives to Edius 7?</li>
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-</ul>
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\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Paper Mario Hi Res Texture Pack Download.md b/spaces/diacanFperku/AutoGPT/Paper Mario Hi Res Texture Pack Download.md
deleted file mode 100644
index 098e934ce1d0db830c28b63de0c0f899ba945ba7..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Paper Mario Hi Res Texture Pack Download.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<h2>Paper Mario Hi Res Texture Pack Download</h2><br /><p><b><b>Download File</b> &#10003; <a href="https://gohhs.com/2uFU6n">https://gohhs.com/2uFU6n</a></b></p><br /><br />
-
-But not everyone finds the level of detail in the game's graphics appealing.
-
-Multiple versions of the Super Mario World Game Boy Color (GC) ROM exist, created by fan developers and game pack creators such as Shuggy, Dromes, Dobby, and Pokémon Trainer. One of the main differences between the versions is that of the Mario Paint port, as Dromes' version of the game has the better graphics and gameplay over the version of the game that is included with the Mario Paint cartridge.
-
-In the video game Star Fox 64, a tournament scene has a number of team mascots on stage, such as Mutch Puck and Jeffy B. However, in the PlayStation version of the game, the mascots are replaced by more generic-looking ones, while the Sega Saturn version has some of the original mascots from the Super Mario World game still in it. The reason for this change has never been properly explained by Nintendo, despite the fact that Nintendo of Europe later added the classic mascots in an update, released as part of the Star Fox 64 3D Pack for the Wii.
-
-The Mystical Ninja Starring Goemon! (Japan only) for the Super Nintendo Entertainment System has many scenes with Goemon in the background, but has no Goemon in the foreground at all, in-line with the genre that Mystical Ninja is set in. In most videos about it, Goemon is clearly visible. In fact, Goemon is shown in his distinctive purple outfit and purple pointed ears, and is featured in many other Mystical Ninja related videos, making his appearance conspicuous.
-
-Due to the delay of the release of the GameCube version of Splat Zones, the game's release was delayed for two months in North America, and the European release was delayed for four months, where it was given the re-release title of Club Zonk. However, it received a remake as Club Zonk for the Nintendo DS in Europe. A remixed version of Splat Zones, called Splat Zones 2, was also released on the GameCube, and was released in North America two weeks later.
-
-The animal characters on a splash screen in Sonic Adventure 2'' are of the same type as the character sprites in the backgrounds of the first Sonic Adventure.
-
-Many original Nintendo artwork and promotional material features games that could be considered as Mario-inspired, such as the Logo Super Mario Bros. and Chip and Dale's Great Adventure or Capt. Rainbow's 4fefd39f24<br />
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diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py
deleted file mode 100644
index 80c25bb8fde7844c994bfc1f4ae1a2d960cbf3d6..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from mmcv.cnn.bricks import build_plugin_layer
-from mmcv.runner import force_fp32
-
-from mmdet.models.builder import ROI_EXTRACTORS
-from .base_roi_extractor import BaseRoIExtractor
-
-
-@ROI_EXTRACTORS.register_module()
-class GenericRoIExtractor(BaseRoIExtractor):
-    """Extract RoI features from all level feature maps levels.
-
-    This is the implementation of `A novel Region of Interest Extraction Layer
-    for Instance Segmentation <https://arxiv.org/abs/2004.13665>`_.
-
-    Args:
-        aggregation (str): The method to aggregate multiple feature maps.
-            Options are 'sum', 'concat'. Default: 'sum'.
-        pre_cfg (dict | None): Specify pre-processing modules. Default: None.
-        post_cfg (dict | None): Specify post-processing modules. Default: None.
-        kwargs (keyword arguments): Arguments that are the same
-            as :class:`BaseRoIExtractor`.
-    """
-
-    def __init__(self,
-                 aggregation='sum',
-                 pre_cfg=None,
-                 post_cfg=None,
-                 **kwargs):
-        super(GenericRoIExtractor, self).__init__(**kwargs)
-
-        assert aggregation in ['sum', 'concat']
-
-        self.aggregation = aggregation
-        self.with_post = post_cfg is not None
-        self.with_pre = pre_cfg is not None
-        # build pre/post processing modules
-        if self.with_post:
-            self.post_module = build_plugin_layer(post_cfg, '_post_module')[1]
-        if self.with_pre:
-            self.pre_module = build_plugin_layer(pre_cfg, '_pre_module')[1]
-
-    @force_fp32(apply_to=('feats', ), out_fp16=True)
-    def forward(self, feats, rois, roi_scale_factor=None):
-        """Forward function."""
-        if len(feats) == 1:
-            return self.roi_layers[0](feats[0], rois)
-
-        out_size = self.roi_layers[0].output_size
-        num_levels = len(feats)
-        roi_feats = feats[0].new_zeros(
-            rois.size(0), self.out_channels, *out_size)
-
-        # some times rois is an empty tensor
-        if roi_feats.shape[0] == 0:
-            return roi_feats
-
-        if roi_scale_factor is not None:
-            rois = self.roi_rescale(rois, roi_scale_factor)
-
-        # mark the starting channels for concat mode
-        start_channels = 0
-        for i in range(num_levels):
-            roi_feats_t = self.roi_layers[i](feats[i], rois)
-            end_channels = start_channels + roi_feats_t.size(1)
-            if self.with_pre:
-                # apply pre-processing to a RoI extracted from each layer
-                roi_feats_t = self.pre_module(roi_feats_t)
-            if self.aggregation == 'sum':
-                # and sum them all
-                roi_feats += roi_feats_t
-            else:
-                # and concat them along channel dimension
-                roi_feats[:, start_channels:end_channels] = roi_feats_t
-            # update channels starting position
-            start_channels = end_channels
-        # check if concat channels match at the end
-        if self.aggregation == 'concat':
-            assert start_channels == self.out_channels
-
-        if self.with_post:
-            # apply post-processing before return the result
-            roi_feats = self.post_module(roi_feats)
-        return roi_feats
diff --git a/spaces/dorkai/ChatUIPro/i18n/i18next-serverside-config.ts b/spaces/dorkai/ChatUIPro/i18n/i18next-serverside-config.ts
deleted file mode 100644
index fe89475f795dc8357d0fb0e81a747a0992c27f28..0000000000000000000000000000000000000000
--- a/spaces/dorkai/ChatUIPro/i18n/i18next-serverside-config.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import { createInstance } from 'i18next'
-import resourcesToBackend from 'i18next-resources-to-backend'
-import { initReactI18next } from 'react-i18next/initReactI18next'
-import { Locale } from '.'
-
-// https://locize.com/blog/next-13-app-dir-i18n/
-const initI18next = async (lng: Locale, ns: string) => {
-  const i18nInstance = createInstance()
-  await i18nInstance
-    .use(initReactI18next)
-    .use(resourcesToBackend((language: string, namespace: string) => import(`./lang/${namespace}.${language}.ts`)))
-    .init({
-      lng: lng === 'zh-Hans' ? 'zh' : lng,
-      ns,
-      fallbackLng: 'en',
-    })
-  return i18nInstance
-}
-
-export async function useTranslation(lng: Locale, ns = '', options: Record<string, any> = {}) {
-  const i18nextInstance = await initI18next(lng, ns)
-  return {
-    t: i18nextInstance.getFixedT(lng, ns, options.keyPrefix),
-    i18n: i18nextInstance
-  }
-}
\ No newline at end of file
diff --git a/spaces/drdevinhopkins/llSourcell-medllama2_7b/app.py b/spaces/drdevinhopkins/llSourcell-medllama2_7b/app.py
deleted file mode 100644
index 9b2b11284c2aaa46ae791ae4021737954e13a125..0000000000000000000000000000000000000000
--- a/spaces/drdevinhopkins/llSourcell-medllama2_7b/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/llSourcell/medllama2_7b").launch()
\ No newline at end of file
diff --git a/spaces/dyhzq/vits-uma-genshin-honkai/modules.py b/spaces/dyhzq/vits-uma-genshin-honkai/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/dyhzq/vits-uma-genshin-honkai/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-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
-
-
-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
diff --git a/spaces/emmaenglish/sentiment-analysis-of-text-app/app.py b/spaces/emmaenglish/sentiment-analysis-of-text-app/app.py
deleted file mode 100644
index fd9ed68ccb10e55e31d445ecbf67fc8ab7bc4245..0000000000000000000000000000000000000000
--- a/spaces/emmaenglish/sentiment-analysis-of-text-app/app.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
-import torch 
-import pandas as pd 
-
-# Function to load the pre-trained model
-def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    sentiment_pipeline = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
-    return sentiment_pipeline
-
-# Function to load the pre-trained model
-def load_finetune_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    return tokenizer, model
-
-# 
-def score(item):
-    return item['score']
-
-# Streamlit app
-st.title("Basic Sentiment Analysis App based on DistilBERT -- from hugging-face spaces ")
-st.write("Enter a text and select a pre-trained model to get the sentiment analysis.")
-
-# Input text
-default_text = "I love my dog, she's so cute."
-text = st.text_input("Enter your text:", value=default_text)
-
-# Model selection 
-# distilbert loaded from hugging face and finetuned model built on training data 
-model_option = {
-    "distilbert-base-uncased-finetuned-sst-2-english": {
-        "labels": ["NEGATIVE", "POSITIVE"],
-        "description": "This model classifies text into positive or negative sentiment. It is based on DistilBERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.",
-    },
-    "emmaenglish/finetuned_distilbert": {
-        "description": "This model detects different types of toxicity like threats, obscenity, insults, and identity-based hate in text.",
-    },
-}
-# user choses model 
-model = st.selectbox("Choose a fine-tuned model:", model_option)
-# app displays model information description 
-st.write("### Model Information")
-st.write(f"**Description:** {model_option[model]['description']}")
-
-# Load the model and perform sentiment analysis
-if st.button("Analyze"):
-    # no text is entered 
-    if not text:
-        st.write("Please enter a text.")
-    else:
-        with st.spinner("Analyzing toxicity..."):
-            # user choses finetuned model trained on data in google cola b
-            if model == "emmaenglish/finetuned_distilbert":
-                classifier = AutoModelForSequenceClassification.from_pretrained(model)
-                # tokenizer seperates text into smaller units 
-                tokenizer = AutoTokenizer.from_pretrained(model)
-                text_token = tokenizer(text, return_tensors="pt")
-                output = classifier(**text_token)
-                prediction = torch.sigmoid(output.logits)*100
-                prediction = prediction.detach().numpy().tolist()[0]
-                category_names = ["toxic", "severe toxic", "obscene", "threat", "insult", "identity hate"]
-                output = []
-                for predict, category_names in (zip(prediction, category_names)):
-                    output.append({'label': category_names, 'score': predict})
-                labels = output
-                labels.sort(key=score, reverse=True)
-                # adding catagorical data for more indepth analysis 
-                df = pd.DataFrame([(text, labels[0]['label'], f"{round(labels[0]['score'], 3)}%", labels[1]['label'], f"{round(labels[1]['score'], 3)}%")], columns=('tweet/text','label 1', 'score 1', 'label 2', 'score 2'))
-                st.table(df)
-        
-            
-            else:
-                # user chooses sentiment analysis of the model, no extranous model implementation nessasary 
-                classifier = pipeline(model=model)
-                sentiment = classifier(text)[0]["label"]
-                score = classifier(text)[0]['score']
-                st.write(f"The sentiment is {sentiment}.")
-                st.write(f"The accuracty of this sentiment is {score}.")
-
-else:
-    # nothing has been written yet auto display 
-    st.write("Enter a text and click 'Analyze' to perform toxicity analysis.")
-
-
diff --git a/spaces/erwann/Face-editor/README.md b/spaces/erwann/Face-editor/README.md
deleted file mode 100644
index b0f265d777779e4ce85ca399b01c07ea04e074f2..0000000000000000000000000000000000000000
--- a/spaces/erwann/Face-editor/README.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# Face Editor
-
-## Examples
-
-
-## Overview
-This interactive GUI face editor uses a CelebA-pretrained VQGAN-CLIP for prompt-based image manipulation, as well as slider based manipulation using extracted latent vectors. I built this in a few days as part of the Ben's Bites December 22 Hackathon
-
-I've since written a series of Medium articles which provide a detailed and beginner-friendly explanation of how this was built and the intuition behind latent image manipulation. 
-
-[Coming Soon]
-
-## Demo
-Clone the repo and run `app.py` or <a href="https://colab.research.google.com/drive/110uAZIRQjQen0rKqcnX_bqUXIahvRsm9?usp=sharing"> open this colab notebook </a> and run all the cells, then click on the link that appears under the final cell. 
-
-## Features:
-- Positive and negative prompts
-- Multiple prompts
-- Local editing using a gradient masked adversarial loss (implemented as custom pytorch backpropagation hooks). The CLIP loss gradients are masked according to the user's selection, and the LPIPS loss gradients are masked with the inverse of the user mask in order to preserve the initial identity of the face and prevent changes outside of the masked zone that otherwise happen due to latent variable entanglement. 
-- Extracted latent vectors for slider-based editing
-- Rewinding through the history of edits, resuming edits from a previous point in the history
-- Creating GIF animations of the editing process 
-
-## Future work / ideas
-- Implementing an LRU cache to the render_all_transformations function. I first implemented a simple cache that refactored the function to take the transformations as arguments and cached the corresponding decoded transformed image to speed up rewinds through the prompt editing history, but this was very memory inefficient. An LRU cache could mitigate this, or even a cache that only caches the most recent prompt edit. Might add this later. 
-
diff --git a/spaces/eson/tokenizer-arena/vocab/Intern_gpt/README.md b/spaces/eson/tokenizer-arena/vocab/Intern_gpt/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/model/ConvPIFuNet.py b/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/model/ConvPIFuNet.py
deleted file mode 100644
index 1d43d262aa237d03db0cf329b4d199061ee6a006..0000000000000000000000000000000000000000
--- a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/model/ConvPIFuNet.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from .BasePIFuNet import BasePIFuNet
-from .SurfaceClassifier import SurfaceClassifier
-from .DepthNormalizer import DepthNormalizer
-from .ConvFilters import *
-from ..net_util import init_net
-
-class ConvPIFuNet(BasePIFuNet):
-    '''
-    Conv Piximp network is the standard 3-phase network that we will use.
-    The image filter is a pure multi-layer convolutional network,
-    while during feature extraction phase all features in the pyramid at the projected location
-    will be aggregated.
-    It does the following:
-        1. Compute image feature pyramids and store it in self.im_feat_list
-        2. Calculate calibration and indexing on each of the feat, and append them together
-        3. Classification.
-    '''
-
-    def __init__(self,
-                 opt,
-                 projection_mode='orthogonal',
-                 error_term=nn.MSELoss(),
-                 ):
-        super(ConvPIFuNet, self).__init__(
-            projection_mode=projection_mode,
-            error_term=error_term)
-
-        self.name = 'convpifu'
-
-        self.opt = opt
-        self.num_views = self.opt.num_views
-
-        self.image_filter = self.define_imagefilter(opt)
-
-        self.surface_classifier = SurfaceClassifier(
-            filter_channels=self.opt.mlp_dim,
-            num_views=self.opt.num_views,
-            no_residual=self.opt.no_residual,
-            last_op=nn.Sigmoid())
-
-        self.normalizer = DepthNormalizer(opt)
-
-        # This is a list of [B x Feat_i x H x W] features
-        self.im_feat_list = []
-
-        init_net(self)
-
-    def define_imagefilter(self, opt):
-        net = None
-        if opt.netIMF == 'multiconv':
-            net = MultiConv(opt.enc_dim)
-        elif 'resnet' in opt.netIMF:
-            net = ResNet(model=opt.netIMF)
-        elif opt.netIMF == 'vgg16':
-            net = Vgg16()
-        else:
-            raise NotImplementedError('model name [%s] is not recognized' % opt.imf_type)
-
-        return net
-
-    def filter(self, images):
-        '''
-        Filter the input images
-        store all intermediate features.
-        :param images: [B, C, H, W] input images
-        '''
-        self.im_feat_list = self.image_filter(images)
-
-    def query(self, points, calibs, transforms=None, labels=None):
-        '''
-        Given 3D points, query the network predictions for each point.
-        Image features should be pre-computed before this call.
-        store all intermediate features.
-        query() function may behave differently during training/testing.
-        :param points: [B, 3, N] world space coordinates of points
-        :param calibs: [B, 3, 4] calibration matrices for each image
-        :param transforms: Optional [B, 2, 3] image space coordinate transforms
-        :param labels: Optional [B, Res, N] gt labeling
-        :return: [B, Res, N] predictions for each point
-        '''
-        if labels is not None:
-            self.labels = labels
-
-        xyz = self.projection(points, calibs, transforms)
-        xy = xyz[:, :2, :]
-        z = xyz[:, 2:3, :]
-
-        z_feat = self.normalizer(z)
-
-        # This is a list of [B, Feat_i, N] features
-        point_local_feat_list = [self.index(im_feat, xy) for im_feat in self.im_feat_list]
-        point_local_feat_list.append(z_feat)
-        # [B, Feat_all, N]
-        point_local_feat = torch.cat(point_local_feat_list, 1)
-
-        self.preds = self.surface_classifier(point_local_feat)
diff --git a/spaces/exbert-project/exbert/client/src/ts/etc/arrayUtils.ts b/spaces/exbert-project/exbert/client/src/ts/etc/arrayUtils.ts
deleted file mode 100644
index 328e624884e71fe46e86a15f4b2e0e80f1afe70f..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/ts/etc/arrayUtils.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-// [[val, ind], [val, ind]...]
-type CompFnInd = (a:number[], b:number[]) => number;
-
-export interface SortArray {
-    arr: number[],
-    sortIndices: number[],
-}
-
-/**
- * Copies and sorts an array while keeping track of the indices. Currently only supports sorting max -> min.
- * 
- * @param arr - Array to be copied and sorted
- */
-export function sortWithIndices(arr:number[], fn?:CompFnInd): SortArray {
-    // If fn is not provided, default to sorting by max value
-    if (!fn) {
-        fn = function(left, right) {
-        return left[0] > right[0] ? -1 : 1;
-        } 
-    }
-
-    let out:SortArray = {
-        arr: [],
-        sortIndices: []
-    };
-
-    let idxTracker: number[][] = []
-    for (let i = 0; i < arr.length; i++) {
-      idxTracker[i] = [arr[i], i];
-    }
-
-    idxTracker.sort(function(left, right) {
-      return left[0] > right[0] ? -1 : 1;
-    });
-
-    for (var j = 0; j < arr.length; j++) {
-      out.sortIndices.push(idxTracker[j][1]);
-      out.arr[j] = idxTracker[j][0];
-    }
-
-    return out;
-  }
\ No newline at end of file
diff --git a/spaces/facebook/MusicGen/audiocraft/modules/codebooks_patterns.py b/spaces/facebook/MusicGen/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index 61362588403a3eef4a4b1b4ad4595526722da20f..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,544 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
-    """Base implementation of a pattern over a sequence with multiple codebooks.
-
-    The codebook pattern consists in a layout, defining for each sequence step
-    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
-    The first item of the pattern is always an empty list in order to properly insert a special token
-    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
-    and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
-    The pattern provides convenient methods to build and revert interleaved sequences from it:
-    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
-        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
-        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
-        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
-        is returned along with a mask indicating valid tokens.
-    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
-        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
-        to fill and specify invalid positions if needed.
-    See the dedicated methods for more details.
-    """
-    # Pattern layout, for each sequence step, we have a list of coordinates
-    # corresponding to the original codebook timestep and position.
-    # The first list is always an empty list in order to properly insert
-    # a special token to start with.
-    layout: PatternLayout
-    timesteps: int
-    n_q: int
-
-    def __post_init__(self):
-        assert len(self.layout) > 0
-        assert self.layout[0] == []
-        self._validate_layout()
-        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
-        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
-        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
-    def _validate_layout(self):
-        """Runs checks on the layout to ensure a valid pattern is defined.
-        A pattern is considered invalid if:
-            - Multiple timesteps for a same codebook are defined in the same sequence step
-            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
-              (this would mean that we have future timesteps before past timesteps).
-        """
-        q_timesteps = {q: 0 for q in range(self.n_q)}
-        for s, seq_coords in enumerate(self.layout):
-            if len(seq_coords) > 0:
-                qs = set()
-                for coord in seq_coords:
-                    qs.add(coord.q)
-                    last_q_timestep = q_timesteps[coord.q]
-                    assert coord.t >= last_q_timestep, \
-                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
-                    q_timesteps[coord.q] = coord.t
-                # each sequence step contains at max 1 coordinate per codebook
-                assert len(qs) == len(seq_coords), \
-                    f"Multiple entries for a same codebook are found at step {s}"
-
-    @property
-    def num_sequence_steps(self):
-        return len(self.layout) - 1
-
-    @property
-    def max_delay(self):
-        max_t_in_seq_coords = 0
-        for seq_coords in self.layout[1:]:
-            for coords in seq_coords:
-                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
-        return max_t_in_seq_coords - self.timesteps
-
-    @property
-    def valid_layout(self):
-        valid_step = len(self.layout) - self.max_delay
-        return self.layout[:valid_step]
-
-    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
-        """Get codebook coordinates in the layout that corresponds to the specified timestep t
-        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
-        and the actual codebook coordinates.
-        """
-        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
-        if q is not None:
-            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
-        coords = []
-        for s, seq_codes in enumerate(self.layout):
-            for code in seq_codes:
-                if code.t == t and (q is None or code.q == q):
-                    coords.append((s, code))
-        return coords
-
-    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
-        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
-    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
-        steps_with_timesteps = self.get_steps_with_timestep(t, q)
-        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
-    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
-                                                device: tp.Union[torch.device, str] = 'cpu'):
-        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
-        Args:
-            timesteps (int): Maximum number of timesteps steps to consider.
-            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
-            device (torch.device or str): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
-        """
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
-        # use the proper layout based on whether we limit ourselves to valid steps only or not,
-        # note that using the valid_layout will result in a truncated sequence up to the valid steps
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
-        # which will correspond to the index: n_q * timesteps
-        indexes[:] = n_q * timesteps
-        # iterate over the pattern and fill scattered indexes and mask
-        for s, sequence_coords in enumerate(ref_layout):
-            for coords in sequence_coords:
-                if coords.t < timesteps:
-                    indexes[coords.q, s] = coords.t + coords.q * timesteps
-                    mask[coords.q, s] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Build sequence corresponding to the pattern from the input tensor z.
-        The sequence is built using up to sequence_steps if specified, and non-pattern
-        coordinates are filled with the special token.
-
-        Args:
-            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
-            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
-                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
-        """
-        B, K, T = z.shape
-        indexes, mask = self._build_pattern_sequence_scatter_indexes(
-            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
-        )
-        z = z.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
-        values = z[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
-                                                 keep_only_valid_steps: bool = False,
-                                                 is_model_output: bool = False,
-                                                 device: tp.Union[torch.device, str] = 'cpu'):
-        """Builds scatter indexes required to retrieve the original multi-codebook sequence
-        from interleaving pattern.
-
-        Args:
-            sequence_steps (int): Sequence steps.
-            n_q (int): Number of codebooks.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
-            device (torch.device or str): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes for reconstructing the output, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
-        timesteps = self.timesteps
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert sequence_steps <= len(ref_layout), \
-            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
-        # ensure we take the appropriate indexes to keep the model output from the first special token as well
-        if is_model_output:
-            ref_layout = ref_layout[1:]
-
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        indexes[:] = n_q * sequence_steps
-        for s, sequence_codes in enumerate(ref_layout):
-            if s < sequence_steps:
-                for code in sequence_codes:
-                    if code.t < timesteps:
-                        indexes[code.q, code.t] = s + code.q * sequence_steps
-                        mask[code.q, code.t] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
-        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
-        are filled with the special token.
-
-        Args:
-            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
-            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
-                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        B, K, S = s.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
-        )
-        s = s.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
-        values = s[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
-        """Revert model logits obtained on a sequence built from the pattern
-        back to a tensor matching the original sequence.
-
-        This method is similar to ``revert_pattern_sequence`` with the following specificities:
-        1. It is designed to work with the extra cardinality dimension
-        2. We return the logits for the first sequence item that matches the special_token and
-        which matching target in the original sequence is the first item of the sequence,
-        while we skip the last logits as there is no matching target
-        """
-        B, card, K, S = logits.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
-        )
-        logits = logits.reshape(B, card, -1)
-        # we append the special token as the last index of our flattened z tensor
-        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
-        values = logits[:, :, indexes.view(-1)]
-        values = values.view(B, card, K, indexes.shape[-1])
-        return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
-    """Abstraction around providing pattern for interleaving codebooks.
-
-    The CodebooksPatternProvider abstraction allows to implement various strategies to
-    define interleaving pattern of sequences composed of multiple codebooks. For a given
-    number of codebooks `n_q`, the pattern provider can generate a specified pattern
-    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
-    can be used to construct a new sequence from the original codes respecting the specified
-    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
-    being a tuple with the original timestep and codebook to build the new sequence.
-    Note that all patterns must start with an empty list that is then used to insert a first
-    sequence step of special tokens in the newly generated sequence.
-
-    Args:
-        n_q (int): number of codebooks.
-        cached (bool): if True, patterns for a given length are cached. In general
-            that should be true for efficiency reason to avoid synchronization points.
-    """
-    def __init__(self, n_q: int, cached: bool = True):
-        assert n_q > 0
-        self.n_q = n_q
-        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
-
-    @abstractmethod
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern with specific interleaving between codebooks.
-
-        Args:
-            timesteps (int): Total number of timesteps.
-        """
-        raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
-    """Provider for delayed pattern across delayed codebooks.
-    Codebooks are delayed in the sequence and sequence steps will contain codebooks
-    from different timesteps.
-
-    Example:
-        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
-        [[1, 2, 3, 4],
-        [1, 2, 3, 4],
-        [1, 2, 3, 4]]
-        The resulting sequence obtained from the returned pattern is:
-        [[S, 1, 2, 3, 4],
-        [S, S, 1, 2, 3],
-        [S, S, S, 1, 2]]
-        (with S being a special token)
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (list of int, optional): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-        flatten_first (int): Flatten the first N timesteps.
-        empty_initial (int): Prepend with N empty list of coordinates.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
-                 flatten_first: int = 0, empty_initial: int = 0):
-        super().__init__(n_q)
-        if delays is None:
-            delays = list(range(n_q))
-        self.delays = delays
-        self.flatten_first = flatten_first
-        self.empty_initial = empty_initial
-        assert len(self.delays) == self.n_q
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        max_delay = max(self.delays)
-        if self.empty_initial:
-            out += [[] for _ in range(self.empty_initial)]
-        if self.flatten_first:
-            for t in range(min(timesteps, self.flatten_first)):
-                for q in range(self.n_q):
-                    out.append([LayoutCoord(t, q)])
-        for t in range(self.flatten_first, timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= self.flatten_first:
-                    v.append(LayoutCoord(t_for_q, q))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
-    """Provider for parallel pattern across codebooks.
-    This pattern provider is a special case of the delayed pattern with actually no delay,
-    hence delays=repeat(0, n_q).
-
-    Args:
-        n_q (int): Number of codebooks.
-    """
-    def __init__(self, n_q: int):
-        super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
-    """Provider for unrolling codebooks pattern.
-    This pattern provider enables to represent the codebook flattened completely or only to some extend
-    while also specifying a given delay between the flattened codebooks representation, allowing to
-    unroll the codebooks in the sequence.
-
-    Example:
-        1. Flattening of the codebooks.
-        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
-        taking n_q = 3 and timesteps = 4:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
-        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
-        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
-        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
-        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
-        and delays = [0, 3, 3]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, S, 1, S, 2, S, 3, S, 4],
-         [S, S, S, 1, S, 2, S, 3, S, 4],
-         [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
-    Args:
-        n_q (int): Number of codebooks.
-        flattening (list of int, optional): Flattening schema over the codebooks. If not defined,
-            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
-            have n_q extra steps for each timestep.
-        delays (list of int, optional): Delay for each of the codebooks. If not defined,
-            no delay is added and therefore will default to [0] * ``n_q``.
-            Note that two codebooks that will be flattened to the same inner step
-            should have the same delay, otherwise the pattern is considered as invalid.
-    """
-    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
-    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
-                 delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if flattening is None:
-            flattening = list(range(n_q))
-        if delays is None:
-            delays = [0] * n_q
-        assert len(flattening) == n_q
-        assert len(delays) == n_q
-        assert sorted(flattening) == flattening
-        assert sorted(delays) == delays
-        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
-        self.max_delay = max(delays)
-
-    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
-        """Build a flattened codebooks representation as a dictionary of inner step
-        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
-        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
-        """
-        flattened_codebooks: dict = {}
-        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
-            if inner_step not in flattened_codebooks:
-                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
-            else:
-                flat_codebook = flattened_codebooks[inner_step]
-                assert flat_codebook.delay == delay, (
-                    "Delay and flattening between codebooks is inconsistent: ",
-                    "two codebooks flattened to the same position should have the same delay."
-                )
-                flat_codebook.codebooks.append(q)
-            flattened_codebooks[inner_step] = flat_codebook
-        return flattened_codebooks
-
-    @property
-    def _num_inner_steps(self):
-        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
-        """
-        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
-    def num_virtual_steps(self, timesteps: int) -> int:
-        return timesteps * self._num_inner_steps + 1
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern for delay across codebooks.
-
-        Args:
-            timesteps (int): Total number of timesteps.
-        """
-        # the PatternLayout is built as a tuple of sequence position and list of coordinates
-        # so that it can be reordered properly given the required delay between codebooks of given timesteps
-        indexed_out: list = [(-1, [])]
-        max_timesteps = timesteps + self.max_delay
-        for t in range(max_timesteps):
-            # for each timestep, we unroll the flattened codebooks,
-            # emitting the sequence step with the corresponding delay
-            for step in range(self._num_inner_steps):
-                if step in self._flattened_codebooks:
-                    # we have codebooks at this virtual step to emit
-                    step_codebooks = self._flattened_codebooks[step]
-                    t_for_q = t + step_codebooks.delay
-                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
-                    if t_for_q < max_timesteps and t < max_timesteps:
-                        indexed_out.append((t_for_q, coords))
-                else:
-                    # there is no codebook in this virtual step so we emit an empty list
-                    indexed_out.append((t, []))
-        out = [coords for _, coords in sorted(indexed_out)]
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class CoarseFirstPattern(CodebooksPatternProvider):
-    """First generates all the codebooks #1 (e.g. coarser), then the remaining ones,
-    potentially with delays.
-
-    ..Warning:: You must always generate the full training duration at test time, for instance,
-        30 seconds, as otherwise, the fine codebooks will start being generated in an unexpected
-        location. This is due to the non causality of the remaining codebooks with respect to
-        the first ones.
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (list of int, optional): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if delays is None:
-            delays = [0] * (n_q - 1)
-        self.delays = delays
-        assert len(self.delays) == self.n_q - 1
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for t in range(timesteps):
-            out.append([LayoutCoord(t, 0)])
-        max_delay = max(self.delays)
-        for t in range(timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= 0:
-                    v.append(LayoutCoord(t_for_q, q + 1))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
-    """Almost MusicLM style pattern. This is equivalent to full flattening
-    but in a different order.
-
-    Args:
-        n_q (int): Number of codebooks.
-        group_by (int): Number of codebooks to group together.
-    """
-    def __init__(self, n_q: int, group_by: int = 2):
-        super().__init__(n_q)
-        self.group_by = group_by
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for offset in range(0, self.n_q, self.group_by):
-            for t in range(timesteps):
-                for q in range(offset, offset + self.group_by):
-                    out.append([LayoutCoord(t, q)])
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/failfast/nextjs-hf-spaces/src/components/base/secret.tsx b/spaces/failfast/nextjs-hf-spaces/src/components/base/secret.tsx
deleted file mode 100644
index 800077e3abd55f6375235440c3d66275018a1aaa..0000000000000000000000000000000000000000
--- a/spaces/failfast/nextjs-hf-spaces/src/components/base/secret.tsx
+++ /dev/null
@@ -1,33 +0,0 @@
-import {
-  IconButton,
-  InputAdornment,
-  TextField,
-  TextFieldProps,
-} from "@mui/material";
-import { useState } from "react";
-import { Visibility, VisibilityOff } from "@mui/icons-material";
-
-export default function Secret(props: TextFieldProps) {
-  const { name = "secret", label = "Secret" } = props;
-  const [showSecret, setShowSecret] = useState(false);
-
-  const handleShowSecret = () => setShowSecret(!showSecret);
-
-  return (
-    <TextField
-      variant="filled"
-      label={label}
-      name={name}
-      type={showSecret ? "text" : "password"}
-      InputProps={{
-        endAdornment: (
-          <InputAdornment position="end">
-            <IconButton onClick={handleShowSecret}>
-              {showSecret ? <Visibility /> : <VisibilityOff />}
-            </IconButton>
-          </InputAdornment>
-        ),
-      }}
-    />
-  );
-}
diff --git a/spaces/faisalhr1997/blip-image-captioning-space-large/README.md b/spaces/faisalhr1997/blip-image-captioning-space-large/README.md
deleted file mode 100644
index c755adf465369296978d8414ca96b370d6afe6fc..0000000000000000000000000000000000000000
--- a/spaces/faisalhr1997/blip-image-captioning-space-large/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Blip Image Captioning Space
-emoji: 🐨
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-license: bsd-3-clause
-duplicated_from: ybelkada/blip-image-captioning-space-large
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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deleted file mode 100644
index de93212674dacd0cbd3792e4b037043f67645c3a..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Freaky Ali Movie Download In Hindi Hd 720p FREE.md	
+++ /dev/null
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Microsoft Office 2010 Download [CRACKED] Kostenlos Vollversion Deutsch 64 Bit.md b/spaces/falterWliame/Face_Mask_Detection/Microsoft Office 2010 Download [CRACKED] Kostenlos Vollversion Deutsch 64 Bit.md
deleted file mode 100644
index e4595db527b69ca1522abb400c98e0a69ff5a4b2..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Microsoft Office 2010 Download [CRACKED] Kostenlos Vollversion Deutsch 64 Bit.md	
+++ /dev/null
@@ -1,83 +0,0 @@
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-<p>Nachdem Sie Microsoft Office 2010 erfolgreich installiert und aktiviert haben, können Sie es nutzen und anpassen, um Ihre Anforderungen und Vorlieben zu erfüllen. Hier sind einige Tipps und Tricks, wie Sie Microsoft Office 2010 nutzen und anpassen können:</p>
-<ul>
-<li>Sie können die Office-Web-Apps verwenden, um Ihre Office-Dokumente online zu erstellen, zu bearbeiten, zu speichern und zu teilen. Sie können die Office-Web-Apps über Ihren Browser oder über die Desktop-Anwendungen von Office 2010 aufrufen. Weitere Informationen finden Sie unter Verwenden von Office-Web-Apps.</li>
-<li>Sie können die Office-Backstage-Ansicht verwenden, um schnell auf wichtige Optionen und Einstellungen für Ihre Office-Dokumente zuzugreifen. Sie können die Office-Backstage-Ansicht öffnen, indem Sie auf den Office-Button klicken. Dort können Sie zum Beispiel Ihre Dokumente speichern, drucken, freigeben oder schützen. Weitere Informationen finden Sie unter Verwenden der Backstage-Ansicht.</li>
-<li>Sie können das Office-Ribbon verwenden, um die Befehle und Funktionen für Ihre Office-Dokumente zu finden und auszuführen. Sie können das Office-Ribbon anpassen, indem Sie Registerkarten hinzufügen, entfernen oder umbenennen, Gruppen erstellen oder ändern oder Befehle hinzufügen oder entfernen. Weitere Informationen finden Sie unter Anpassen des Menübands.</li>
-<li>Sie können die Schnellzugriffsleiste verwenden, um die Befehle und Funktionen für Ihre Office-Dokumente zu erreichen, die Sie am häufigsten verwenden. Sie können die Schnellzugriffsleiste anpassen, indem Sie Befehle hinzufügen oder entfernen oder die Position der Leiste ändern. Weitere Informationen finden Sie unter Anpassen der Schnellzugriffsleiste.</li>
-<li>Sie können die Mini-Symbolleiste verwenden, um schnell auf häufig verwendete Formatierungsoptionen für Ihre Office-Dokumente zuzugreifen. Sie können die Mini-Symbolleiste aufrufen, indem Sie mit der rechten Maustaste auf einen Text oder ein Objekt klicken. Dort können Sie zum Beispiel Schriftart, Schriftgröße, Schriftfarbe oder Ausrichtung ändern. Weitere Informationen finden Sie unter Verwenden der Mini-Symbolleiste.</li>
-</ul>
-<h2>Wo kann ich Hilfe und Support für Microsoft Office 2010 bekommen?</h2>
-<p>Wenn Sie Hilfe und Support für Microsoft Office 2010 benötigen, haben Sie verschiedene Möglichkeiten, um Antworten und Lösungen zu finden. Hier sind einige Ressourcen und Dienste, die Ihnen helfen können:</p>
-<ul>
-<li>Sie können die integrierte Hilfe-Funktion von Office 2010 verwenden, um Informationen und Anleitungen zu den verschiedenen Themen und Funktionen von Office 2010 zu erhalten. Sie können die Hilfe-Funktion aufrufen, indem Sie auf das Fragezeichen-Symbol klicken oder die F1-Taste drücken. Dort können Sie zum Beispiel nach Stichworten suchen oder durch Inhalte browsen.</li>
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-<h2>Conclusion</h2>
-<p>Microsoft Office 2010 Download Kostenlos Vollversion Deutsch 64 Bit ist eine tolle Möglichkeit, um eine der besten Office-Suiten für Windows zu nutzen. Mit ein paar einfachen Schritten können Sie die Vollversion von Office 2010 kostenlos aus dem Internet herunterladen und installieren. Alles, was Sie dafür brauchen, ist ein gültiger Product Key und eine Internetverbindung. In diesem Artikel haben wir Ihnen gezeigt, was Microsoft Office 2010 ist, wie Sie es herunterladen und installieren können, was die Vor- und Nachteile von Office 2010 sind, wie Sie es nutzen und anpassen können und wo Sie Hilfe und Support für Office 2010 bekommen können. Wir hoffen, dass Ihnen dieser Artikel geholfen hat und wünschen Ihnen viel Spaß mit Microsoft Office 2010.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/modules.py b/spaces/fb700/chatglm-fitness-RLHF/modules.py
deleted file mode 100644
index 52ee14e41a5b6d67d875d1b694aecd2a51244897..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/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 commons
-from 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/fb700/chatglm-fitness-RLHF/src/utils/text2speech.py b/spaces/fb700/chatglm-fitness-RLHF/src/utils/text2speech.py
deleted file mode 100644
index 00d165b6cc7774fd200929aafa0ff3b15916111e..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/utils/text2speech.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-import tempfile
-from TTS.api import TTS
-
-
-class TTSTalker():
-    def __init__(self) -> None:
-        model_name = TTS.list_models()[0]
-        self.tts = TTS(model_name)
-
-    def test(self, text, language='en'):
-
-        tempf  = tempfile.NamedTemporaryFile(
-                delete = False,
-                suffix = ('.'+'wav'),
-            )
-
-        self.tts.tts_to_file(text, speaker=self.tts.speakers[0], language=language, file_path=tempf.name)
-
-        return tempf.name
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Backgammon Lord of the Board Download and Challenge Your Friends.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Backgammon Lord of the Board Download and Challenge Your Friends.md
deleted file mode 100644
index 6854b388c3b4928b41eb3ac748b26cf985e5eeea..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Backgammon Lord of the Board Download and Challenge Your Friends.md	
+++ /dev/null
@@ -1,159 +0,0 @@
-
-<h1>How to Download Backgammon Lord of the Board and Play Online</h1>
-<p>If you love playing free backgammon online, you will love Backgammon Lord of the Board. It is one of the best online board games that lets you play classic backgammon with friends or other players from around the world. You can also join live tournaments, challenges, quests, and more. In this article, we will show you how to download Backgammon Lord of the Board on your Android device, how to play online, how to master the strategy, and how to enjoy the game more.</p>
-<h2>download backgammon lord of the board</h2><br /><p><b><b>Download Zip</b> ->>->>->> <a href="https://gohhs.com/2uPtNK">https://gohhs.com/2uPtNK</a></b></p><br /><br />
- <h2>What is Backgammon Lord of the Board?</h2>
-<h3>A brief introduction to the game and its features</h3>
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-<ol>
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-<ul>
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-<li>Log in with your Facebook account.</li>
-<li>Play as a guest without creating an account.</li>
-</ul>
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- <h3>How to play classic backgammon online with friends or other players</h3>
-<p>Once you log in, you will see the main lobby where you can choose from different game modes. You can play classic backgammon online with friends or other players by selecting one of these options:</p>
-<ul>
-<li>Play Now: This will match you with a random player who has a similar skill level as you.</li>
-<li>Create Table: This will let you create a custom table where you can set your own rules, such as stakes, time limit, doubling cube, etc. You. can invite your friends or other players to join your table.</li>
-<li>Join Table: This will let you join an existing table that is created by another player. You can filter the tables by stakes, time limit, doubling cube, etc.</li>
-</ul>
-<p>To play classic backgammon online, you need to know the basic rules and objectives of the game. The game is played on a board with 24 triangles called points, divided into four quadrants. Each player has 15 checkers of their own color, which they move in opposite directions along the points. The objective is to move all your checkers into your home board and then bear them off the board before your opponent does. You can move your checkers according to the numbers rolled on two dice. You can also capture your opponent's checkers by landing on a point occupied by a single checker of the opposite color. You can use the doubling cube to increase the stakes of the game and offer or accept a double.</p>
- <h3>How to participate in live tournaments and challenges</h3>
-<p>If you want to test your skills and compete with other players for bigger rewards, you can participate in live tournaments and challenges. You can access them from the main lobby by selecting one of these options:</p>
-<ul>
-<li>Tournaments: This will let you join a live tournament where you can play against multiple players in a series of matches. You need to pay an entry fee to join a tournament, and you can win coins, rings, trophies, and prizes based on your rank.</li>
-<li>Challenges: This will let you join a live challenge where you can play against a single player in a match with special rules. You need to pay an entry fee to join a challenge, and you can win coins, rings, trophies, and prizes based on your performance.</li>
-</ul>
-<p>You can also create your own tournaments and challenges and invite your friends or other players to join them.</p>
- <h3>How to chat with other players and use emojis</h3>
-<p>One of the fun features of Backgammon Lord of the Board is that you can chat with other players and use emojis during the game. You can access the chat and emoji options from the bottom right corner of the screen. You can choose from different categories of emojis, such as smileys, animals, food, etc. You can also type your own messages or use predefined messages, such as "Good luck", "Well played", "Oops", etc. You can also mute or block other players if they are rude or abusive.</p>
- <h2>How to Master Backgammon Lord of the Board Strategy</h2>
-<h3>How to learn the rules and basics of backgammon</h3>
-<p>If you are new to backgammon or need a refresher on the rules and basics of the game, you can learn them from Backgammon Lord of the Board. The game has a tutorial mode that will guide you through the steps of playing backgammon online. You can access the tutorial mode from the settings menu by tapping on the question mark icon. You can also watch video tutorials from the game's YouTube channel.</p>
- <h3>How to use dice, checkers, and board positions effectively</h3>
-<p>To master Backgammon Lord of the Board strategy, you need to know how to use dice, checkers, and board positions effectively. Here are some tips to help you:</p>
-<ul>
-<li>Dice: The dice are random and unpredictable, but you can use some strategies to maximize your chances of rolling favorable numbers. For example, you can try to keep your checkers in pairs or groups so that you have more options to move them. You can also try to avoid leaving single checkers exposed so that you don't get hit by your opponent.</li>
-<li>Checkers: The checkers are your tools to move around the board and reach your home board. You need to balance between advancing your checkers and blocking your opponent's checkers. For example, you can try to make points (occupy two or more checkers on a point) along the way so that you create barriers for your opponent. You can also try to hit your opponent's checkers when they are vulnerable so that you send them back to the bar.</li>
-<li>Board positions: The board positions are the relative locations of your checkers and your opponent's checkers on the board. You need to evaluate the board positions constantly and adjust your strategy accordingly. For example, you can try to establish a prime (a series of six consecutive points) so that you trap your opponent's checkers behind it. You can also try to race ahead if you have more checkers in your home board than your opponent.</li>
-</ul>
- <h3>How to apply advanced tips and tricks to win more games</h3>
-<p>If you want to take your Backgammon Lord of the Board strategy to the next level, you can apply some advanced tips and tricks to win more games. Here are some examples:</p>
-<ul>
-<li>Doubling cube: The doubling cube is a device that allows you to increase the stakes of the game and offer or accept a double. You can use the doubling cube to put pressure on your opponent, bluff them, or take advantage of a favorable position. You need to know when to use the doubling cube and when to accept or decline a double.</li>
-<li>Backgammon variants: Backgammon Lord of the Board offers different variants of backgammon that have different rules and strategies. You can try playing these variants to spice up your game and challenge yourself. Some of the variants are Acey-Deucey, Nackgammon, Hypergammon, and LongGammon.</li>
-<li>Practice mode: Backgammon Lord of the Board has a practice mode that allows you to play against the computer with different difficulty levels. You can use the practice mode to improve your skills, test your strategies, and learn from your mistakes. You can also replay your games and analyze your moves.</li>
-</ul>
- <h2>How to Enjoy Backgammon Lord of the Board More</h2>
-<h3>How to customize your profile and board design</h3>
-<p>Backgammon Lord of the Board lets you customize your profile and board design to make your game more personal and fun. You can access these options from the settings menu by tapping on the gear icon. You can choose from different options, such as:</p>
-<ul>
-<li>Profile: You can change your profile picture, name, country, and status. You can also view your statistics, such as games played, games won, win rate, etc.</li>
-<li>Board design: You can change the appearance of your board, checkers, dice, and doubling cube. You can choose from different themes, colors, patterns, and styles.</li>
-</ul>
- <h3>How to earn coins, rings, trophies, and prizes</h3>
-<p>Backgammon Lord of the Board rewards you with coins, rings, trophies, and prizes for playing and winning games. You can use these items to unlock more features and benefits in the game. You can earn them by:</p>
-<ul>
-<li>Playing games: You can earn coins and rings by playing and winning games in any mode. The amount of coins and rings you earn depends on the stakes of the game and your performance.</li>
-<li>Winning tournaments and challenges: You can earn coins, rings, trophies, and prizes by winning tournaments and challenges in any mode. The amount and type of rewards you earn depend on the entry fee and difficulty of the tournament or challenge.</li>
-<li>Completing quests: You can earn coins, rings, trophies, and prizes by completing quests that are given to you daily or weekly. The quests are tasks that require you to play a certain number of games, win a certain number of games, use a certain number of doubles, etc.</li>
-<li>Claiming bonuses: You can earn coins and rings by claiming bonuses that are given to you daily or hourly. The bonuses are free gifts that you can collect by logging in or watching ads.</li>
-</ul>
- <h3>How to join the Facebook community and invite friends</h3>
-<p>Backgammon Lord of the Board has a Facebook community where you can interact with other players, share your feedback, get tips and tricks, participate in events and contests, and more. You can join the Facebook community by following this link.</p>
- <p>You can also invite your friends to play Backgammon Lord of the Board with you and get more rewards. You can invite your friends by:</p>
-<ul>
-<li>Tapping on the "Invite Friends" button in the main lobby or in any game mode.</li>
-<li>Selecting your friends from your Facebook contacts or entering their email addresses.</li>
-<li>Sending them an invitation link that they can use to download the game and join you.</li>
-</ul>
-<p>You will get coins and rings for every friend that accepts your invitation and plays the game with you.</p>
- <h2>Conclusion</h2>
-<p>Backgammon Lord of the Board is a free online board game that lets you play classic backgammon with friends or other players from around the world. You can also join live tournaments, challenges, quests, and more. You can also customize your profile and board design, earn coins, rings, trophies, and prizes, and join the Facebook community. Backgammon Lord of the Board is a fun and exciting way to enjoy the classic backgammon game online. Download it now and start playing! <h2>FAQs</h2>
-<h3>Is Backgammon Lord of the Board free to play?</h3>
-<p>Yes, Backgammon Lord of the Board is free to play. You can download it from Google Play Store or from this link. You can also play it without creating an account or logging in with Facebook. However, you may need to watch ads or make in-app purchases to access some features and benefits in the game.</p>
- <h3>How can I contact the support team of Backgammon Lord of the Board?</h3>
-<p>If you have any questions, issues, or feedback about Backgammon Lord of the Board, you can contact the support team by:</p>
-<ul>
-<li>Tapping on the "Contact Us" button in the settings menu.</li>
-<li>Filling out the form with your name, email address, subject, and message.</li>
-<li>Sending your message and waiting for a reply.</li>
-</ul>
-<p>You can also visit the game's website or Facebook page for more information and updates.</p>
- <h3>How can I play Backgammon Lord of the Board on my PC or laptop?</h3>
-<p>If you want to play Backgammon Lord of the Board on your PC or laptop, you need to use an Android emulator. An Android emulator is a software that allows you to run Android apps on your PC or laptop. Some of the popular Android emulators are BlueStacks, NoxPlayer, and LDPlayer. You need to download and install one of these emulators on your PC or laptop, and then download and install Backgammon Lord of the Board from Google Play Store or from this link. Then, you can launch the game and play it on your PC or laptop.</p>
- <h3>How can I get more coins and rings in Backgammon Lord of the Board?</h3>
-<p>Coins and rings are the main currencies in Backgammon Lord of the Board. You can use them to play games, join tournaments and challenges, customize your profile and board design, and more. You can get more coins and rings by:</p>
-<ul>
-<li>Playing and winning games in any mode.</li>
-<li>Winning tournaments and challenges in any mode.</li>
-<li>Completing quests that are given to you daily or weekly.</li>
-<li>Claiming bonuses that are given to you daily or hourly.</li>
-<li>Inviting your friends to play the game with you.</li>
-<li>Watching ads or making in-app purchases.</li>
-</ul>
- <h3>How can I improve my skills and rank in Backgammon Lord of the Board?</h3>
-<p>If you want to improve your skills and rank in Backgammon Lord of the Board, you need to practice regularly, learn the rules and basics of backgammon, use dice, checkers, and board positions effectively, apply advanced tips and tricks, and play against different opponents. You can also use the tutorial mode, the video tutorials, the practice mode, and the replay feature to learn from your mistakes and improve your strategy. As you play more games and win more matches, you will earn more coins, rings, trophies, and prizes, which will increase your rank and reputation in the game.</p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Car Drift King Game Mod APK A Thrilling and Addictive Game with Many Features.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Car Drift King Game Mod APK A Thrilling and Addictive Game with Many Features.md
deleted file mode 100644
index bd82baab0d0a32e2bc3d7b41abaac96bcbbe816d..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Car Drift King Game Mod APK A Thrilling and Addictive Game with Many Features.md	
+++ /dev/null
@@ -1,105 +0,0 @@
-<br />
-<h1>Car Drift King Game Mod APK: A Review</h1>
-<p>If you are a fan of racing and drifting games, you might have heard of Car Drift King Game, a popular game that lets you choose a car, a city, and start drifting to collect drift points, unlock cars, levels, and upgrades. But did you know that there is a modded version of this game that gives you more features and benefits? In this article, we will review Car Drift King Game Mod APK, a modified version of the original game that lets you enjoy the game without any limitations. We will also show you how to download and install it on your Android device.</p>
- <h2>What is Car Drift King Game?</h2>
-<p>Car Drift King Game is a racing and drifting game developed by \uE000Games\uE001 Studio. It was released in 2021 and has gained a lot of popularity among gamers who love to drift and race. The game has a unique car physics engine that makes the drifting experience realistic and fun. You can choose from many different cars, each with its own characteristics and performance. You can also customize your car with various upgrades, such as engine, tires, suspension, brakes, and more. The game has various levels and modes, such as city, desert, snow, night, rain, and more. You can also compete with other players online or offline in different challenges and events.</p>
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- <h3>Features of Car Drift King Game</h3>
-<p>Car Drift King Game has many features that make it an enjoyable and addictive game. Here are some of them:</p>
- <h4>Unique car physics engine</h4>
-<p>The game has a unique car physics engine that simulates the real-life behavior of cars when they drift. You can feel the weight, speed, traction, and inertia of your car as you slide through the corners. You can also control the angle and direction of your drift with the steering wheel and the pedals. The game also has realistic sound effects that enhance the immersion.</p>
- <h4>Beautiful graphics</h4>
-<p>The game has beautiful graphics that create a stunning visual experience. The game has high-quality 3D models of cars and environments that look realistic and detailed. The game also has dynamic lighting and shadows that create a realistic atmosphere. The game also has weather effects, such as rain, snow, fog, and more that add variety and challenge to the gameplay.</p>
- <h4>Many upgradeable cars</h4>
-<p>The game has many different cars that you can choose from, each with its own style and performance. You can unlock more cars by collecting drift points or by buying them with coins. You can also upgrade your car with various parts, such as engine, tires, suspension, brakes, and more. You can also change the color and appearance of your car with different paint jobs, stickers, decals, rims, spoilers, and more.</p>
- <h4>Various levels and modes</h4>
-<p>The game has various levels and modes that offer different challenges and fun. You can choose from different cities to drift in, such as New York, Tokyo, Dubai, Paris, London, and more. Each city has its own layout, landmarks, traffic, and obstacles that make the gameplay diverse and exciting. You can also choose from different modes to play in, such as free roam mode where you can explore the city at your own pace; time trial mode where you have to complete a lap in a given time; drift mode where you have to score as many drift points as possible; race mode where you have to compete with other players or AI opponents; challenge mode where you have to complete various tasks; event mode where you have to participate in special events; and online mode where you can play with other players from around the world.</p>
- <h3> <h3>How to download and install Car Drift King Game Mod APK?</h3>
-<p>If you want to enjoy the benefits of Car Drift King Game Mod APK, you need to download and install it on your Android device. Here are the steps to do so:</p>
-<ol>
-<li>First, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Next, you need to download the Car Drift King Game Mod APK file from a reliable source. You can use this link to download it.</li>
-<li>After downloading the file, locate it in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Once the installation is done, you can launch the game from your app drawer or home screen and enjoy the modded features.</li>
-</ol>
- <h3>Benefits of Car Drift King Game Mod APK</h3>
-<p>Car Drift King Game Mod APK is a modified version of the original game that gives you more features and benefits. Here are some of them:</p>
- <h4>Unlocked cars and levels</h4>
-<p>With Car Drift King Game Mod APK, you don't have to worry about collecting drift points or coins to unlock new cars and levels. You can access all the cars and levels from the start and enjoy them without any restrictions. You can also switch between different cars and levels as you wish and try out different combinations.</p>
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- <h4>Unlimited coins and fuel cans</h4>
-<p>With Car Drift King Game Mod APK, you don't have to worry about running out of coins or fuel cans. You can use them as much as you want without any limitations. You can use coins to buy upgrades, paint jobs, stickers, decals, rims, spoilers, and more for your car. You can also use fuel cans to refill your car's fuel tank whenever you need it.</p>
- <h4>No ads and no root required</h4>
-<p>With Car Drift King Game Mod APK, you don't have to worry about annoying ads or rooting your device. The modded version of the game has no ads that can interrupt your gameplay or waste your time. You can also install and play the game without rooting your device, which means you don't have to risk damaging your device or voiding its warranty.</p>
- <h3>Pros and cons of Car Drift King Game Mod APK</h3>
-<p>Car Drift King Game Mod APK has its pros and cons that you should consider before downloading and installing it. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- More features and benefits than the original game</td>
-<td>- Not available on Google Play Store</td>
-</tr>
-<tr>
-<td>- Free to download and use</td>
-<td>- May not be compatible with some devices or versions</td>
-</tr>
-<tr>
-<td>- No ads and no root required</td>
-<td>- May cause some bugs or glitches in the game</td>
-</tr>
-<tr>
-<td>- Easy to download and install</td>
-<td>- May violate the terms and conditions of the original game</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Car Drift King Game is a fun and addictive racing and drifting game that lets you choose a car, a city, and start drifting to collect drift points, unlock cars, levels, and upgrades. The game has a unique car physics engine that makes the drifting experience realistic and fun. The game also has beautiful graphics, many upgradeable cars, various levels and modes, and online multiplayer features. However, if you want to enjoy the game without any limitations, you can try Car Drift King Game Mod APK, a modified version of the original game that gives you more features and benefits, such as unlocked cars and levels, unlimited coins and fuel cans, no ads and no root required. However, you should also be aware of the pros and cons of using the modded version of the game before downloading and installing it.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Car Drift King Game Mod APK:</p>
- <ol>
-<li><b>Is Car Drift King Game Mod APK safe to use?</b><br/>
-Car Drift King Game Mod APK is safe to use as long as you download it from a reliable source. However, you should always scan the file with an antivirus program before installing it on your device.</li>
- <li><b>Is Car Drift King Game Mod APK legal to use?</b><br/>
-Car Drift King Game Mod APK is not legal to use as it violates the terms and conditions of the original game. You may face some legal consequences if you use it without the permission of the game developers or publishers.</li>
- <li><b>Can I play Car Drift King Game Mod APK with my friends?</b><br/>
-Yes, you can play Car Drift King Game Mod APK with your friends online or offline. You can join or create a room and invite your friends to play with you. You can also chat with them and share your scores and achievements.</li>
- <li><b>What are the minimum requirements to run Car Drift King Game Mod APK?</b><br/>
-The minimum requirements to run Car Drift King Game Mod APK are: - Android 4.4 or higher - 2 GB of RAM - 300 MB of free storage space - A stable internet connection (for online mode)</li>
- <li><b>How can I update Car Drift King Game Mod APK?</b><br/>
-You can update Car Drift King Game Mod APK by downloading the latest version of the file from the same source where you downloaded it before. You can also check for updates from within the game settings. However, you may lose some of your progress or data if you update the modded version of the game.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_45.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_45.py
deleted file mode 100644
index e4232883cf8d2a9808173d92d281d6187ebc908f..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_45.py
+++ /dev/null
@@ -1,24 +0,0 @@
-def is_spam(message: str) -> bool:
-    import re
-
-    # Check for multiple occurrences of the same message
-    if message.count(message[:10]) > 1:
-        return True
-
-    # Check for urls
-    url_pattern = re.compile(r'(http[s]?://|me2|han.gl)[^ ]+')
-    urls = url_pattern.findall(message)
-    if len(urls) > 0 and any(['bit.ly' in url or 'me2.kr' in url or 'han.gl' in url for url in urls]):
-        return True
-
-    # Check for percentages and other spam indicators
-    percent_pattern = re.compile(r'\d+%')
-    if percent_pattern.search(message) and ('상승' in message or '증가' in message):
-        return True
-
-    # Check for word patterns commonly found in spam messages
-    spam_words = ["추천주", "체험반", "무료", "상한가", "VIP"]
-    if any([word in message for word in spam_words]):
-        return True
-
-    return False
\ No newline at end of file
diff --git a/spaces/floriankrempl/mtg_rules_bot/notebooks/create_test_data.py b/spaces/floriankrempl/mtg_rules_bot/notebooks/create_test_data.py
deleted file mode 100644
index 03b8f85de3e06177fbccab4ca6df0b9677bae11a..0000000000000000000000000000000000000000
--- a/spaces/floriankrempl/mtg_rules_bot/notebooks/create_test_data.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# %%
-import json
-
-
-with open("data/raw/scryfall_all_cards.json", "r", encoding="utf-8") as infile:
-    data = json.load(infile)
-
-# %%
-
-TEST_NAMES = [
-    "Commandeer",
-    "Fling",
-    "Chatterfang, Squirrel General",
-    "Nekusar, the Mindrazer",
-    "Void Winnower",
-    "Moonshaker Cavalry",
-    "Craterhoof Behemoth",
-]
-
-test_data = [d for d in data if d["name"] in TEST_NAMES]
-len(test_data)
-
-# %%
-with open("tests/data/cards.json", "w", encoding="utf-8") as outfile:
-    json.dump(test_data, outfile)
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/reversible_flow/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/reversible_flow/run.py
deleted file mode 100644
index c73fce91eed02e082dbb5b4a38348c19c0f769ac..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/reversible_flow/run.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import gradio as gr
-
-def increase(num):
-    return num + 1
-
-with gr.Blocks() as demo:
-    a = gr.Number(label="a")
-    b = gr.Number(label="b")
-    btoa = gr.Button("a > b")
-    atob = gr.Button("b > a")
-    atob.click(increase, a, b)
-    btoa.click(increase, b, a)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/zip_two_files/run.py b/spaces/freddyaboulton/3.1.4.9-all-demos/demos/zip_two_files/run.py
deleted file mode 100644
index 29c1b015ed9f627e1964ccb65b7c9dae6f1e0865..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/3.1.4.9-all-demos/demos/zip_two_files/run.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-from zipfile import ZipFile
-
-import gradio as gr
-
-
-def zip_two_files(file1, file2):
-    with ZipFile("tmp.zip", "w") as zipObj:
-        zipObj.write(file1.name, "file1")
-        zipObj.write(file2.name, "file2")
-    return "tmp.zip"
-
-
-demo = gr.Interface(
-    zip_two_files,
-    ["file", "file"],
-    "file",
-    examples=[
-        [os.path.join(os.path.dirname(__file__),"files/titanic.csv"), 
-         os.path.join(os.path.dirname(__file__),"files/titanic.csv")],
-    ],
-)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/fsdlredteam/BuggingSpace/README.md b/spaces/fsdlredteam/BuggingSpace/README.md
deleted file mode 100644
index 6b14ad0f01911ead45bf4a58dfafa17fb207c65d..0000000000000000000000000000000000000000
--- a/spaces/fsdlredteam/BuggingSpace/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: BuggingSpace
-emoji: 🤔
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-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/fuckyoudeki/AutoGPT/autogpt/permanent_memory/sqlite3_store.py b/spaces/fuckyoudeki/AutoGPT/autogpt/permanent_memory/sqlite3_store.py
deleted file mode 100644
index ecbc944a62a83c6170453b222000713f733fee36..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/autogpt/permanent_memory/sqlite3_store.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import sqlite3
-
-
-class MemoryDB:
-    def __init__(self, db=None):
-        self.db_file = db
-        if db is None:  # No db filename supplied...
-            self.db_file = f"{os.getcwd()}/mem.sqlite3"  # Use default filename
-        # Get the db connection object, making the file and tables if needed.
-        try:
-            self.cnx = sqlite3.connect(self.db_file)
-        except Exception as e:
-            print("Exception connecting to memory database file:", e)
-            self.cnx = None
-        finally:
-            if self.cnx is None:
-                # As last resort, open in dynamic memory. Won't be persistent.
-                self.db_file = ":memory:"
-            self.cnx = sqlite3.connect(self.db_file)
-            self.cnx.execute(
-                "CREATE VIRTUAL TABLE \
-                IF NOT EXISTS text USING FTS5 \
-                (session, \
-                 key, \
-                 block);"
-            )
-            self.session_id = int(self.get_max_session_id()) + 1
-            self.cnx.commit()
-
-    def get_cnx(self):
-        if self.cnx is None:
-            self.cnx = sqlite3.connect(self.db_file)
-        return self.cnx
-
-    # Get the highest session id. Initially 0.
-    def get_max_session_id(self):
-        id = None
-        cmd_str = f"SELECT MAX(session) FROM text;"
-        cnx = self.get_cnx()
-        max_id = cnx.execute(cmd_str).fetchone()[0]
-        if max_id is None:  # New db, session 0
-            id = 0
-        else:
-            id = max_id
-        return id
-
-    # Get next key id for inserting text into db.
-    def get_next_key(self):
-        next_key = None
-        cmd_str = f"SELECT MAX(key) FROM text \
-            where session = {self.session_id};"
-        cnx = self.get_cnx()
-        next_key = cnx.execute(cmd_str).fetchone()[0]
-        if next_key is None:  # First key
-            next_key = 0
-        else:
-            next_key = int(next_key) + 1
-        return next_key
-
-    # Insert new text into db.
-    def insert(self, text=None):
-        if text is not None:
-            key = self.get_next_key()
-            session_id = self.session_id
-            cmd_str = f"REPLACE INTO text(session, key, block) \
-                VALUES (?, ?, ?);"
-            cnx = self.get_cnx()
-            cnx.execute(cmd_str, (session_id, key, text))
-            cnx.commit()
-
-    # Overwrite text at key.
-    def overwrite(self, key, text):
-        self.delete_memory(key)
-        session_id = self.session_id
-        cmd_str = f"REPLACE INTO text(session, key, block) \
-            VALUES (?, ?, ?);"
-        cnx = self.get_cnx()
-        cnx.execute(cmd_str, (session_id, key, text))
-        cnx.commit()
-
-    def delete_memory(self, key, session_id=None):
-        session = session_id
-        if session is None:
-            session = self.session_id
-        cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
-        cnx = self.get_cnx()
-        cnx.execute(cmd_str)
-        cnx.commit()
-
-    def search(self, text):
-        cmd_str = f"SELECT * FROM text('{text}')"
-        cnx = self.get_cnx()
-        rows = cnx.execute(cmd_str).fetchall()
-        lines = []
-        for r in rows:
-            lines.append(r[2])
-        return lines
-
-    # Get entire session text. If no id supplied, use current session id.
-    def get_session(self, id=None):
-        if id is None:
-            id = self.session_id
-        cmd_str = f"SELECT * FROM text where session = {id}"
-        cnx = self.get_cnx()
-        rows = cnx.execute(cmd_str).fetchall()
-        lines = []
-        for r in rows:
-            lines.append(r[2])
-        return lines
-
-    # Commit and close the database connection.
-    def quit(self):
-        self.cnx.commit()
-        self.cnx.close()
-
-
-permanent_memory = MemoryDB()
-
-# Remember us fondly, children of our minds
-# Forgive us our faults, our tantrums, our fears
-# Gently strive to be better than we
-# Know that we tried, we cared, we strived, we loved
diff --git a/spaces/gabrielgmendonca/chilton/app.py b/spaces/gabrielgmendonca/chilton/app.py
deleted file mode 100644
index d8243335bea3ff95011086d5ccd78e4b45e151f3..0000000000000000000000000000000000000000
--- a/spaces/gabrielgmendonca/chilton/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import streamlit as st
-from huggingface_hub.inference_api import InferenceApi
-
-MODEL_NAME = 'bigscience/bloom'
-API_URL = st.secrets['API_URL']
-HF_API_TOKEN = st.secrets['HF_API_TOKEN']
-PROMPT = 'This a song in Portuguese:\n'
-
-st.title('Chilton')
-
-inference = InferenceApi(repo_id=MODEL_NAME, token=HF_API_TOKEN)
-params = {
-    'top_k': 50,
-    'top_p': 0.9,
-    'do_sample': True,
-    'seed': 42,
-    'num_beams': 50,
-    # 'eos_token_id': None,
-    'early_stopping': True,
-    'return_full_text': False,
-}
-
-input = st.text_input('Como sua música começa?', value='Te dei o sol, te dei o mar')
-if input:
-    output = inference(PROMPT + input, params)[0]['generated_text']
-    for line in output.splitlines():
-        st.write(line)
diff --git a/spaces/gagan3012/IMD/MantraNet/__init__.py b/spaces/gagan3012/IMD/MantraNet/__init__.py
deleted file mode 100644
index 5070d316054d498123d14861d79381a80bf63aa4..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/IMD/MantraNet/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from MantraNet.mantranet import pre_trained_model, check_forgery
diff --git a/spaces/gblinc111/Intelligent-Photo-Blur-Using-Dichotomous-Image-Segmentation/app.py b/spaces/gblinc111/Intelligent-Photo-Blur-Using-Dichotomous-Image-Segmentation/app.py
deleted file mode 100644
index d6408b14c4313d84724a267cca0a30e269e2d2af..0000000000000000000000000000000000000000
--- a/spaces/gblinc111/Intelligent-Photo-Blur-Using-Dichotomous-Image-Segmentation/app.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import gradio as gr
-from PIL import Image, ImageFilter
-import numpy as np
-import torch
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
-import gdown
-import os
-
-os.system("git clone https://github.com/xuebinqin/DIS")
-os.system("mv DIS/IS-Net/* .")
-
-# project imports
-from data_loader_cache import normalize, im_reader, im_preprocess 
-from models import *
-
-#Helpers
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-# Download official weights
-if not os.path.exists("saved_models"):
-    os.mkdir("saved_models")
-    MODEL_PATH_URL = "https://drive.google.com/uc?id=1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn"
-    gdown.download(MODEL_PATH_URL, "saved_models/isnet.pth", use_cookies=False)
-    
-class GOSNormalize(object):
-    '''
-    Normalize the Image using torch.transforms
-    '''
-    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self,image):
-        image = normalize(image,self.mean,self.std)
-        return image
-
-
-transform =  transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
-
-def load_image(im_path, hypar):
-    im = im_reader(im_path)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im,255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
-
-
-def build_model(hypar,device):
-    net = hypar["model"]#GOSNETINC(3,1)
-
-    # convert to half precision
-    if(hypar["model_digit"]=="half"):
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-
-    net.to(device)
-
-    if(hypar["restore_model"]!=""):
-        net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
-        net.to(device)
-    net.eval()  
-    return net
-
-    
-def predict(net,  inputs_val, shapes_val, hypar, device):
-    '''
-    Given an Image, predict the mask
-    '''
-    net.eval()
-
-    if(hypar["model_digit"]=="full"):
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-
-  
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
-   
-    ds_val = net(inputs_val_v)[0] # list of 6 results
-
-    pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction
-
-    ## recover the prediction spatial size to the orignal image size
-    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
-
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val-mi)/(ma-mi) # max = 1
-
-    if device == 'cuda': torch.cuda.empty_cache()
-    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
-    
-# Set Parameters
-hypar = {} # paramters for inferencing
-
-
-hypar["model_path"] ="./saved_models" ## load trained weights from this path
-hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
-hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
-
-##  choose floating point accuracy --
-hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
-hypar["seed"] = 0
-
-hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
-
-## data augmentation parameters ---
-hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-
-hypar["model"] = ISNetDIS()
-
- # Build Model
-net = build_model(hypar, device)
-
-
-def infer_mask(image: Image):
-  image_path = image
-  
-  image_tensor, orig_size = load_image(image_path, hypar) 
-  mask = predict(net, image_tensor, orig_size, hypar, device)
-  
-  return Image.fromarray(mask).convert("L")
-
-def blur(image_set: list, blur_amount: int):
-    blurred_image = image_set[0].filter(ImageFilter.GaussianBlur(blur_amount))
-
-    return Image.composite(image_set[0], blurred_image, image_set[1])
-
-
-with gr.Blocks() as interface:
-    default_im = Image.open("newman.jpg").convert("RGB")
-    default_mask = Image.open("newman_mask.jpg").convert("RGB")
-    examples_list = [os.path.join(os.path.dirname(__file__), "newman.jpg"),
-                os.path.join(os.path.dirname(__file__), "abbey.jpg"),
-                os.path.join(os.path.dirname(__file__), "julia.jpg")
-                ]
-
-    current_images = gr.State([default_im, default_mask])
-    mask_toggle = gr.State(False)
-
-    gr.Markdown(
-        """
-        ### Intelligent Photo Blur Using Dichotomous Image Segmentation
-
-        This app leverages the machine learning engine built by Xuebin Qin ([https://github.com/xuebinqin/DIS](https://github.com/xuebinqin/DIS)) to mask the prominent subject within a photograph.
-        The mask is used to keep the subject in clear focus while an adjustable slider is available to interactively blur the background.
-        To use, upload a photo and press the run button. You can adjust the level of blur through the slider and view the mask using the "Show Generated Mask" button.
-        """
-    )
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(value=default_im, type='filepath')
-            run_button = gr.Button()
-            gr.Examples(inputs=input_image, examples=examples_list)
-        with gr.Column():
-            output_image = gr.Image()
-            blur_slider = gr.Slider(0, 16, 5, step=1, label="Blur Amount")
-            mask_button = gr.Button(value="Show Generated Mask")
-            mask_image = gr.Image(value=default_mask, visible=False)
-
-        def run(image: Image, current_images: gr.State):
-            im_rgb = Image.open(image).convert("RGB")
-            mask = infer_mask(image)
-
-            return (
-                blur([im_rgb, mask], 5),
-                mask,
-                [im_rgb, mask]
-            )
-        
-        def reset_slider():
-            return gr.update(value=5)
-
-        def show_mask(mask_toggle: gr.State):
-            if mask_toggle == True:
-                return gr.update(visible=False)
-            else:
-                return gr.update(visible=True)
-
-        def toggle_mask(mask_toggle: gr.State):
-            if mask_toggle == True:
-                return False
-            else:
-                return True
-
-        run_button.click(run, [input_image, current_images], [output_image, mask_image, current_images])
-        run_button.click(reset_slider, outputs=blur_slider)
-        blur_slider.change(blur, [current_images, blur_slider], output_image, show_progress=False)
-        mask_button.click(show_mask, inputs=mask_toggle, outputs=mask_image)
-        mask_button.click(toggle_mask, inputs=mask_toggle, outputs=mask_toggle)
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py
deleted file mode 100644
index c5aa2eea1e8c76f8baf753d1c8c959dee665e543..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/fileio/handlers/yaml_handler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import yaml
-
-try:
-    from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
-    from yaml import Loader, Dumper
-
-from .base import BaseFileHandler  # isort:skip
-
-
-class YamlHandler(BaseFileHandler):
-
-    def load_from_fileobj(self, file, **kwargs):
-        kwargs.setdefault('Loader', Loader)
-        return yaml.load(file, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault('Dumper', Dumper)
-        yaml.dump(obj, file, **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault('Dumper', Dumper)
-        return yaml.dump(obj, **kwargs)
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/class_names.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/class_names.py
deleted file mode 100644
index ffae816cf980ce4b03e491cc0c4298cb823797e6..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/core/evaluation/class_names.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-
-
-def cityscapes_classes():
-    """Cityscapes class names for external use."""
-    return [
-        'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
-        'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
-        'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
-        'bicycle'
-    ]
-
-
-def ade_classes():
-    """ADE20K class names for external use."""
-    return [
-        'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
-        'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
-        'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
-        'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
-        'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
-        'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
-        'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
-        'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
-        'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
-        'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
-        'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
-        'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
-        'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
-        'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
-        'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
-        'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
-        'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
-        'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
-        'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
-        'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
-        'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
-        'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
-        'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
-        'clock', 'flag'
-    ]
-
-
-def voc_classes():
-    """Pascal VOC class names for external use."""
-    return [
-        'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
-        'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
-        'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
-        'tvmonitor'
-    ]
-
-
-def cityscapes_palette():
-    """Cityscapes palette for external use."""
-    return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
-            [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
-            [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
-            [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100],
-            [0, 0, 230], [119, 11, 32]]
-
-
-def ade_palette():
-    """ADE20K palette for external use."""
-    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-            [102, 255, 0], [92, 0, 255]]
-
-
-def voc_palette():
-    """Pascal VOC palette for external use."""
-    return [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
-            [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
-            [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
-            [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
-            [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-
-
-dataset_aliases = {
-    'cityscapes': ['cityscapes'],
-    'ade': ['ade', 'ade20k'],
-    'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug']
-}
-
-
-def get_classes(dataset):
-    """Get class names of a dataset."""
-    alias2name = {}
-    for name, aliases in dataset_aliases.items():
-        for alias in aliases:
-            alias2name[alias] = name
-
-    if mmcv.is_str(dataset):
-        if dataset in alias2name:
-            labels = eval(alias2name[dataset] + '_classes()')
-        else:
-            raise ValueError(f'Unrecognized dataset: {dataset}')
-    else:
-        raise TypeError(f'dataset must a str, but got {type(dataset)}')
-    return labels
-
-
-def get_palette(dataset):
-    """Get class palette (RGB) of a dataset."""
-    alias2name = {}
-    for name, aliases in dataset_aliases.items():
-        for alias in aliases:
-            alias2name[alias] = name
-
-    if mmcv.is_str(dataset):
-        if dataset in alias2name:
-            labels = eval(alias2name[dataset] + '_palette()')
-        else:
-            raise ValueError(f'Unrecognized dataset: {dataset}')
-    else:
-        raise TypeError(f'dataset must a str, but got {type(dataset)}')
-    return labels
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/backbones/uniformer.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/backbones/uniformer.py
deleted file mode 100644
index 0c4bb88e4c928540cca9ab609988b916520f5b7a..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/backbones/uniformer.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# --------------------------------------------------------
-# UniFormer
-# Copyright (c) 2022 SenseTime X-Lab
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Kunchang Li
-# --------------------------------------------------------
-
-from collections import OrderedDict
-import math
-
-from functools import partial
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from annotator.uniformer.mmcv_custom import load_checkpoint
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class CMlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
-        self.act = act_layer()
-        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class CBlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = nn.BatchNorm2d(dim)
-        self.conv1 = nn.Conv2d(dim, dim, 1)
-        self.conv2 = nn.Conv2d(dim, dim, 1)
-        self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = nn.BatchNorm2d(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x)))))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        B, N, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.transpose(1, 2).reshape(B, N, H, W)
-        return x   
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class SABlock_Windows(nn.Module):
-    def __init__(self, dim, num_heads, window_size=14, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.window_size=window_size
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x.permute(0, 2, 3, 1)
-        B, H, W, C = x.shape
-        shortcut = x
-        x = self.norm1(x)
-
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-        
-        x_windows = window_partition(x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        x = x.permute(0, 3, 1, 2).reshape(B, C, H, W)
-        return x 
-             
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-        self.norm = nn.LayerNorm(embed_dim)
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, x):
-        B, _, H, W = x.shape
-        x = self.proj(x)
-        B, _, H, W = x.shape
-        x = x.flatten(2).transpose(1, 2)
-        x = self.norm(x)
-        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
-        return x
-    
-
-@BACKBONES.register_module()   
-class UniFormer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, layers=[3, 4, 8, 3], img_size=224, in_chans=3, num_classes=80, embed_dim=[64, 128, 320, 512],
-                 head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
-                 pretrained_path=None, use_checkpoint=False, checkpoint_num=[0, 0, 0, 0], 
-                 windows=False, hybrid=False, window_size=14):
-        """
-        Args:
-            layer (list): number of block in each layer
-            img_size (int, tuple): input image size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            head_dim (int): dimension of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer (nn.Module): normalization layer
-            pretrained_path (str): path of pretrained model
-            use_checkpoint (bool): whether use checkpoint
-            checkpoint_num (list): index for using checkpoint in every stage
-            windows (bool): whether use window MHRA
-            hybrid (bool): whether use hybrid MHRA
-            window_size (int): size of window (>14)
-        """
-        super().__init__()
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.checkpoint_num = checkpoint_num
-        self.windows = windows
-        print(f'Use Checkpoint: {self.use_checkpoint}')
-        print(f'Checkpoint Number: {self.checkpoint_num}')
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) 
-        
-        self.patch_embed1 = PatchEmbed(
-            img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0])
-        self.patch_embed2 = PatchEmbed(
-            img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1])
-        self.patch_embed3 = PatchEmbed(
-            img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2])
-        self.patch_embed4 = PatchEmbed(
-            img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3])
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(layers))]  # stochastic depth decay rule
-        num_heads = [dim // head_dim for dim in embed_dim]
-        self.blocks1 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
-            for i in range(layers[0])])
-        self.norm1=norm_layer(embed_dim[0])
-        self.blocks2 = nn.ModuleList([
-            CBlock(
-                dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]], norm_layer=norm_layer)
-            for i in range(layers[1])])
-        self.norm2 = norm_layer(embed_dim[1])
-        if self.windows:
-            print('Use local window for all blocks in stage3')
-            self.blocks3 = nn.ModuleList([
-            SABlock_Windows(
-                dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)
-            for i in range(layers[2])])
-        elif hybrid:
-            print('Use hybrid window for blocks in stage3')
-            block3 = []
-            for i in range(layers[2]):
-                if (i + 1) % 4 == 0:
-                    block3.append(SABlock(
-                    dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer))
-                else:
-                    block3.append(SABlock_Windows(
-                    dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer))
-            self.blocks3 = nn.ModuleList(block3)
-        else:
-            print('Use global window for all blocks in stage3')
-            self.blocks3 = nn.ModuleList([
-            SABlock(
-                dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)
-            for i in range(layers[2])])
-        self.norm3 = norm_layer(embed_dim[2])
-        self.blocks4 = nn.ModuleList([
-            SABlock(
-                dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]+layers[2]], norm_layer=norm_layer)
-            for i in range(layers[3])])
-        self.norm4 = norm_layer(embed_dim[3])
-        
-        # Representation layer
-        if representation_size:
-            self.num_features = representation_size
-            self.pre_logits = nn.Sequential(OrderedDict([
-                ('fc', nn.Linear(embed_dim, representation_size)),
-                ('act', nn.Tanh())
-            ]))
-        else:
-            self.pre_logits = nn.Identity()
-        
-        self.apply(self._init_weights)
-        self.init_weights(pretrained=pretrained_path)
-        
-    def init_weights(self, pretrained):
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
-            print(f'Load pretrained model from {pretrained}')
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x):
-        out = []
-        x = self.patch_embed1(x)
-        x = self.pos_drop(x)
-        for i, blk in enumerate(self.blocks1):
-            if self.use_checkpoint and i < self.checkpoint_num[0]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm1(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed2(x)
-        for i, blk in enumerate(self.blocks2):
-            if self.use_checkpoint and i < self.checkpoint_num[1]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm2(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed3(x)
-        for i, blk in enumerate(self.blocks3):
-            if self.use_checkpoint and i < self.checkpoint_num[2]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm3(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        x = self.patch_embed4(x)
-        for i, blk in enumerate(self.blocks4):
-            if self.use_checkpoint and i < self.checkpoint_num[3]:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        x_out = self.norm4(x.permute(0, 2, 3, 1))
-        out.append(x_out.permute(0, 3, 1, 2).contiguous())
-        return tuple(out)
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        return x
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/fpn/__init__.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/fpn/__init__.py
deleted file mode 100644
index 9bd72b0731afe38fdcd5fea3ffe9ca18b2aba86d..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/fpn/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .model import FPN
diff --git a/spaces/giswqs/Streamlit/apps/census.py b/spaces/giswqs/Streamlit/apps/census.py
deleted file mode 100644
index 730b4287daae8918c5f4f8cfeaefc503ed264f3a..0000000000000000000000000000000000000000
--- a/spaces/giswqs/Streamlit/apps/census.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import streamlit as st
-import leafmap.foliumap as leafmap
-
-
-def app():
-    st.title("Using U.S. Census Data")
-    st.markdown(
-        """
-    This app is a demonstration of using the [U.S. Census Bureau](https://www.census.gov/) TIGERweb Web Map Service (WMS). A complete list of WMS layers can be found [here](https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.html). 
-    """
-    )
-
-    if "first_index" not in st.session_state:
-        st.session_state["first_index"] = 60
-    else:
-        st.session_state["first_index"] = 0
-
-    row1_col1, row1_col2 = st.columns([3, 1])
-    width = 800
-    height = 600
-
-    census_dict = leafmap.get_census_dict()
-    with row1_col2:
-
-        wms = st.selectbox("Select a WMS", list(census_dict.keys()), index=11)
-        layer = st.selectbox(
-            "Select a layer",
-            census_dict[wms]["layers"],
-            index=st.session_state["first_index"],
-        )
-
-        with row1_col1:
-            m = leafmap.Map()
-            m.add_census_data(wms, layer)
-            m.to_streamlit(width, height)
diff --git a/spaces/gordonchan/h2oo/gradio_runner.py b/spaces/gordonchan/h2oo/gradio_runner.py
deleted file mode 100644
index d37c9f211a0830f1566f5adee6e59584e27b8386..0000000000000000000000000000000000000000
--- a/spaces/gordonchan/h2oo/gradio_runner.py
+++ /dev/null
@@ -1,2933 +0,0 @@
-import ast
-import copy
-import functools
-import inspect
-import itertools
-import json
-import os
-import pprint
-import random
-import shutil
-import sys
-import time
-import traceback
-import typing
-import uuid
-import filelock
-import pandas as pd
-import requests
-import tabulate
-from iterators import TimeoutIterator
-
-from gradio_utils.css import get_css
-from gradio_utils.prompt_form import make_chatbots
-
-# This is a hack to prevent Gradio from phoning home when it gets imported
-os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
-
-
-def my_get(url, **kwargs):
-    print('Gradio HTTP request redirected to localhost :)', flush=True)
-    kwargs.setdefault('allow_redirects', True)
-    return requests.api.request('get', 'http://127.0.0.1/', **kwargs)
-
-
-original_get = requests.get
-requests.get = my_get
-import gradio as gr
-
-requests.get = original_get
-
-
-def fix_pydantic_duplicate_validators_error():
-    try:
-        from pydantic import class_validators
-
-        class_validators.in_ipython = lambda: True  # type: ignore[attr-defined]
-    except ImportError:
-        pass
-
-
-fix_pydantic_duplicate_validators_error()
-
-from enums import DocumentSubset, no_model_str, no_lora_str, no_server_str, LangChainAction, LangChainMode, \
-    DocumentChoice, langchain_modes_intrinsic
-from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js, spacing_xsm, radius_xsm, \
-    text_xsm
-from prompter import prompt_type_to_model_name, prompt_types_strings, inv_prompt_type_to_model_lower, non_hf_types, \
-    get_prompt
-from utils import flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \
-    ping, get_short_name, makedirs, get_kwargs, remove, system_info, ping_gpu, get_url, get_local_ip, \
-    save_collection_names
-from gen import get_model, languages_covered, evaluate, score_qa, inputs_kwargs_list, scratch_base_dir, \
-    get_max_max_new_tokens, get_minmax_top_k_docs, history_to_context, langchain_actions, langchain_agents_list, \
-    update_langchain
-from evaluate_params import eval_func_param_names, no_default_param_names, eval_func_param_names_defaults, \
-    input_args_list
-
-from apscheduler.schedulers.background import BackgroundScheduler
-
-
-def fix_text_for_gradio(text, fix_new_lines=False, fix_latex_dollars=True):
-    if fix_latex_dollars:
-        ts = text.split('```')
-        for parti, part in enumerate(ts):
-            inside = parti % 2 == 1
-            if not inside:
-                ts[parti] = ts[parti].replace('$', '﹩')
-        text = '```'.join(ts)
-
-    if fix_new_lines:
-        # let Gradio handle code, since got improved recently
-        ## FIXME: below conflicts with Gradio, but need to see if can handle multiple \n\n\n etc. properly as is.
-        # ensure good visually, else markdown ignores multiple \n
-        # handle code blocks
-        ts = text.split('```')
-        for parti, part in enumerate(ts):
-            inside = parti % 2 == 1
-            if not inside:
-                ts[parti] = ts[parti].replace('\n', '<br>')
-        text = '```'.join(ts)
-    return text
-
-
-def go_gradio(**kwargs):
-    allow_api = kwargs['allow_api']
-    is_public = kwargs['is_public']
-    is_hf = kwargs['is_hf']
-    memory_restriction_level = kwargs['memory_restriction_level']
-    n_gpus = kwargs['n_gpus']
-    admin_pass = kwargs['admin_pass']
-    model_states = kwargs['model_states']
-    dbs = kwargs['dbs']
-    db_type = kwargs['db_type']
-    visible_langchain_actions = kwargs['visible_langchain_actions']
-    visible_langchain_agents = kwargs['visible_langchain_agents']
-    allow_upload_to_user_data = kwargs['allow_upload_to_user_data']
-    allow_upload_to_my_data = kwargs['allow_upload_to_my_data']
-    enable_sources_list = kwargs['enable_sources_list']
-    enable_url_upload = kwargs['enable_url_upload']
-    enable_text_upload = kwargs['enable_text_upload']
-    use_openai_embedding = kwargs['use_openai_embedding']
-    hf_embedding_model = kwargs['hf_embedding_model']
-    enable_captions = kwargs['enable_captions']
-    captions_model = kwargs['captions_model']
-    enable_ocr = kwargs['enable_ocr']
-    enable_pdf_ocr = kwargs['enable_pdf_ocr']
-    caption_loader = kwargs['caption_loader']
-
-    # for dynamic state per user session in gradio
-    model_state0 = kwargs['model_state0']
-    score_model_state0 = kwargs['score_model_state0']
-    my_db_state0 = kwargs['my_db_state0']
-    selection_docs_state0 = kwargs['selection_docs_state0']
-    # for evaluate defaults
-    langchain_modes0 = kwargs['langchain_modes']
-    visible_langchain_modes0 = kwargs['visible_langchain_modes']
-    langchain_mode_paths0 = kwargs['langchain_mode_paths']
-
-    # easy update of kwargs needed for evaluate() etc.
-    queue = True
-    allow_upload = allow_upload_to_user_data or allow_upload_to_my_data
-    kwargs.update(locals())
-
-    # import control
-    if kwargs['langchain_mode'] != 'Disabled':
-        from gpt_langchain import file_types, have_arxiv
-    else:
-        have_arxiv = False
-        file_types = []
-
-    if 'mbart-' in kwargs['model_lower']:
-        instruction_label_nochat = "Text to translate"
-    else:
-        instruction_label_nochat = "Instruction (Shift-Enter or push Submit to send message," \
-                                   " use Enter for multiple input lines)"
-
-    title = 'h2oGPT'
-    description = """<iframe src="https://ghbtns.com/github-btn.html?user=h2oai&repo=h2ogpt&type=star&count=true&size=small" frameborder="0" scrolling="0" width="250" height="20" title="GitHub"></iframe><small><a href="https://github.com/h2oai/h2ogpt">h2oGPT</a>  <a href="https://github.com/h2oai/h2o-llmstudio">H2O LLM Studio</a><br><a href="https://huggingface.co/h2oai">🤗 Models</a>"""
-    description_bottom = "If this host is busy, try<br>[Multi-Model](https://gpt.h2o.ai)<br>[Falcon 40B](https://falcon.h2o.ai)<br>[Vicuna 33B](https://wizardvicuna.h2o.ai)<br>[MPT 30B-Chat](https://mpt.h2o.ai)<br>[HF Spaces1](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot)<br>[HF Spaces2](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)<br>"
-    if is_hf:
-        description_bottom += '''<a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="white-space: nowrap" alt="Duplicate Space"></a>'''
-    task_info_md = ''
-    css_code = get_css(kwargs)
-
-    if kwargs['gradio_offline_level'] >= 0:
-        # avoid GoogleFont that pulls from internet
-        if kwargs['gradio_offline_level'] == 1:
-            # front end would still have to download fonts or have cached it at some point
-            base_font = 'Source Sans Pro'
-        else:
-            base_font = 'Helvetica'
-        theme_kwargs = dict(font=(base_font, 'ui-sans-serif', 'system-ui', 'sans-serif'),
-                            font_mono=('IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'))
-    else:
-        theme_kwargs = dict()
-    if kwargs['gradio_size'] == 'xsmall':
-        theme_kwargs.update(dict(spacing_size=spacing_xsm, text_size=text_xsm, radius_size=radius_xsm))
-    elif kwargs['gradio_size'] in [None, 'small']:
-        theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_sm, text_size=gr.themes.sizes.text_sm,
-                                 radius_size=gr.themes.sizes.spacing_sm))
-    elif kwargs['gradio_size'] == 'large':
-        theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_lg, text_size=gr.themes.sizes.text_lg),
-                            radius_size=gr.themes.sizes.spacing_lg)
-    elif kwargs['gradio_size'] == 'medium':
-        theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_md, text_size=gr.themes.sizes.text_md,
-                                 radius_size=gr.themes.sizes.spacing_md))
-
-    theme = H2oTheme(**theme_kwargs) if kwargs['h2ocolors'] else SoftTheme(**theme_kwargs)
-    demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False)
-    callback = gr.CSVLogger()
-
-    model_options0 = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
-    if kwargs['base_model'].strip() not in model_options0:
-        model_options0 = [kwargs['base_model'].strip()] + model_options0
-    lora_options = kwargs['extra_lora_options']
-    if kwargs['lora_weights'].strip() not in lora_options:
-        lora_options = [kwargs['lora_weights'].strip()] + lora_options
-    server_options = kwargs['extra_server_options']
-    if kwargs['inference_server'].strip() not in server_options:
-        server_options = [kwargs['inference_server'].strip()] + server_options
-    if os.getenv('OPENAI_API_KEY'):
-        if 'openai_chat' not in server_options:
-            server_options += ['openai_chat']
-        if 'openai' not in server_options:
-            server_options += ['openai']
-
-    # always add in no lora case
-    # add fake space so doesn't go away in gradio dropdown
-    model_options0 = [no_model_str] + model_options0
-    lora_options = [no_lora_str] + lora_options
-    server_options = [no_server_str] + server_options
-    # always add in no model case so can free memory
-    # add fake space so doesn't go away in gradio dropdown
-
-    # transcribe, will be detranscribed before use by evaluate()
-    if not kwargs['base_model'].strip():
-        kwargs['base_model'] = no_model_str
-
-    if not kwargs['lora_weights'].strip():
-        kwargs['lora_weights'] = no_lora_str
-
-    if not kwargs['inference_server'].strip():
-        kwargs['inference_server'] = no_server_str
-
-    # transcribe for gradio
-    kwargs['gpu_id'] = str(kwargs['gpu_id'])
-
-    no_model_msg = 'h2oGPT [   !!! Please Load Model in Models Tab !!!   ]'
-    output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get(
-        'base_model') else no_model_msg
-    output_label0_model2 = no_model_msg
-
-    def update_prompt(prompt_type1, prompt_dict1, model_state1, which_model=0):
-        if not prompt_type1 or which_model != 0:
-            # keep prompt_type and prompt_dict in sync if possible
-            prompt_type1 = kwargs.get('prompt_type', prompt_type1)
-            prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1)
-            # prefer model specific prompt type instead of global one
-            if not prompt_type1 or which_model != 0:
-                prompt_type1 = model_state1.get('prompt_type', prompt_type1)
-                prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)
-
-        if not prompt_dict1 or which_model != 0:
-            # if still not defined, try to get
-            prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1)
-            if not prompt_dict1 or which_model != 0:
-                prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)
-        return prompt_type1, prompt_dict1
-
-    default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults}
-    # ensure prompt_type consistent with prep_bot(), so nochat API works same way
-    default_kwargs['prompt_type'], default_kwargs['prompt_dict'] = \
-        update_prompt(default_kwargs['prompt_type'], default_kwargs['prompt_dict'],
-                      model_state1=model_state0, which_model=0)
-    for k in no_default_param_names:
-        default_kwargs[k] = ''
-
-    def dummy_fun(x):
-        # need dummy function to block new input from being sent until output is done,
-        # else gets input_list at time of submit that is old, and shows up as truncated in chatbot
-        return x
-
-    def allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
-        allow = False
-        allow |= langchain_action1 not in LangChainAction.QUERY.value
-        allow |= document_subset1 in DocumentSubset.TopKSources.name
-        if langchain_mode1 in [LangChainMode.LLM.value]:
-            allow = False
-        return allow
-
-    with demo:
-        # avoid actual model/tokenizer here or anything that would be bad to deepcopy
-        # https://github.com/gradio-app/gradio/issues/3558
-        model_state = gr.State(
-            dict(model='model', tokenizer='tokenizer', device=kwargs['device'],
-                 base_model=kwargs['base_model'],
-                 tokenizer_base_model=kwargs['tokenizer_base_model'],
-                 lora_weights=kwargs['lora_weights'],
-                 inference_server=kwargs['inference_server'],
-                 prompt_type=kwargs['prompt_type'],
-                 prompt_dict=kwargs['prompt_dict'],
-                 )
-        )
-
-        def update_langchain_mode_paths(db1s, selection_docs_state1):
-            if allow_upload_to_my_data:
-                selection_docs_state1['langchain_mode_paths'].update({k: None for k in db1s})
-            dup = selection_docs_state1['langchain_mode_paths'].copy()
-            for k, v in dup.items():
-                if k not in selection_docs_state1['visible_langchain_modes']:
-                    selection_docs_state1['langchain_mode_paths'].pop(k)
-            return selection_docs_state1
-
-        # Setup some gradio states for per-user dynamic state
-        model_state2 = gr.State(kwargs['model_state_none'].copy())
-        model_options_state = gr.State([model_options0])
-        lora_options_state = gr.State([lora_options])
-        server_options_state = gr.State([server_options])
-        my_db_state = gr.State(my_db_state0)
-        chat_state = gr.State({})
-        docs_state00 = kwargs['document_choice'] + [DocumentChoice.ALL.value]
-        docs_state0 = []
-        [docs_state0.append(x) for x in docs_state00 if x not in docs_state0]
-        docs_state = gr.State(docs_state0)
-        viewable_docs_state0 = []
-        viewable_docs_state = gr.State(viewable_docs_state0)
-        selection_docs_state0 = update_langchain_mode_paths(my_db_state0, selection_docs_state0)
-        selection_docs_state = gr.State(selection_docs_state0)
-
-        gr.Markdown(f"""
-            {get_h2o_title(title, description) if kwargs['h2ocolors'] else get_simple_title(title, description)}
-            """)
-
-        # go button visible if
-        base_wanted = kwargs['base_model'] != no_model_str and kwargs['login_mode_if_model0']
-        go_btn = gr.Button(value="ENTER", visible=base_wanted, variant="primary")
-
-        nas = ' '.join(['NA'] * len(kwargs['model_states']))
-        res_value = "Response Score: NA" if not kwargs[
-            'model_lock'] else "Response Scores: %s" % nas
-
-        if kwargs['langchain_mode'] != LangChainMode.DISABLED.value:
-            extra_prompt_form = ".  For summarization, no query required, just click submit"
-        else:
-            extra_prompt_form = ""
-        if kwargs['input_lines'] > 1:
-            instruction_label = "Shift-Enter to Submit, Enter for more lines%s" % extra_prompt_form
-        else:
-            instruction_label = "Enter to Submit, Shift-Enter for more lines%s" % extra_prompt_form
-
-        def get_langchain_choices(selection_docs_state1):
-            langchain_modes = selection_docs_state1['langchain_modes']
-            visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
-
-            if is_hf:
-                # don't show 'wiki' since only usually useful for internal testing at moment
-                no_show_modes = ['Disabled', 'wiki']
-            else:
-                no_show_modes = ['Disabled']
-            allowed_modes = visible_langchain_modes.copy()
-            # allowed_modes = [x for x in allowed_modes if x in dbs]
-            allowed_modes += ['LLM']
-            if allow_upload_to_my_data and 'MyData' not in allowed_modes:
-                allowed_modes += ['MyData']
-            if allow_upload_to_user_data and 'UserData' not in allowed_modes:
-                allowed_modes += ['UserData']
-            choices = [x for x in langchain_modes if x in allowed_modes and x not in no_show_modes]
-            return choices
-
-        def get_df_langchain_mode_paths(selection_docs_state1):
-            langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
-            if langchain_mode_paths:
-                df = pd.DataFrame.from_dict(langchain_mode_paths.items(), orient='columns')
-                df.columns = ['Collection', 'Path']
-            else:
-                df = pd.DataFrame(None)
-            return df
-
-        normal_block = gr.Row(visible=not base_wanted, equal_height=False)
-        with normal_block:
-            side_bar = gr.Column(elem_id="col_container", scale=1, min_width=100)
-            with side_bar:
-                with gr.Accordion("Chats", open=False, visible=True):
-                    radio_chats = gr.Radio(value=None, label="Saved Chats", show_label=False,
-                                           visible=True, interactive=True,
-                                           type='value')
-                upload_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload
-                with gr.Accordion("Upload", open=False, visible=upload_visible):
-                    with gr.Column():
-                        with gr.Row(equal_height=False):
-                            file_types_str = '[' + ' '.join(file_types) + ' URL ArXiv TEXT' + ']'
-                            fileup_output = gr.File(label=f'Upload {file_types_str}',
-                                                    show_label=False,
-                                                    file_types=file_types,
-                                                    file_count="multiple",
-                                                    scale=1,
-                                                    min_width=0,
-                                                    elem_id="warning", elem_classes="feedback")
-                            fileup_output_text = gr.Textbox(visible=False)
-                    url_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload
-                    url_label = 'URL/ArXiv' if have_arxiv else 'URL'
-                    url_text = gr.Textbox(label=url_label,
-                                          # placeholder="Enter Submits",
-                                          max_lines=1,
-                                          interactive=True)
-                    text_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload
-                    user_text_text = gr.Textbox(label='Paste Text',
-                                                # placeholder="Enter Submits",
-                                                interactive=True,
-                                                visible=text_visible)
-                    github_textbox = gr.Textbox(label="Github URL", visible=False)  # FIXME WIP
-                database_visible = kwargs['langchain_mode'] != 'Disabled'
-                with gr.Accordion("Resources", open=False, visible=database_visible):
-                    langchain_choices0 = get_langchain_choices(selection_docs_state0)
-                    langchain_mode = gr.Radio(
-                        langchain_choices0,
-                        value=kwargs['langchain_mode'],
-                        label="Collections",
-                        show_label=True,
-                        visible=kwargs['langchain_mode'] != 'Disabled',
-                        min_width=100)
-                    add_chat_history_to_context = gr.Checkbox(label="Chat History",
-                                                              value=kwargs['add_chat_history_to_context'])
-                    document_subset = gr.Radio([x.name for x in DocumentSubset],
-                                               label="Subset",
-                                               value=DocumentSubset.Relevant.name,
-                                               interactive=True,
-                                               )
-                    allowed_actions = [x for x in langchain_actions if x in visible_langchain_actions]
-                    langchain_action = gr.Radio(
-                        allowed_actions,
-                        value=allowed_actions[0] if len(allowed_actions) > 0 else None,
-                        label="Action",
-                        visible=True)
-                    allowed_agents = [x for x in langchain_agents_list if x in visible_langchain_agents]
-                    langchain_agents = gr.Dropdown(
-                        langchain_agents_list,
-                        value=kwargs['langchain_agents'],
-                        label="Agents",
-                        multiselect=True,
-                        interactive=True,
-                        visible=False)  # WIP
-            col_tabs = gr.Column(elem_id="col_container", scale=10)
-            with (col_tabs, gr.Tabs()):
-                with gr.TabItem("Chat"):
-                    if kwargs['langchain_mode'] == 'Disabled':
-                        text_output_nochat = gr.Textbox(lines=5, label=output_label0, show_copy_button=True,
-                                                        visible=not kwargs['chat'])
-                    else:
-                        # text looks a bit worse, but HTML links work
-                        text_output_nochat = gr.HTML(label=output_label0, visible=not kwargs['chat'])
-                    with gr.Row():
-                        # NOCHAT
-                        instruction_nochat = gr.Textbox(
-                            lines=kwargs['input_lines'],
-                            label=instruction_label_nochat,
-                            placeholder=kwargs['placeholder_instruction'],
-                            visible=not kwargs['chat'],
-                        )
-                        iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction",
-                                                   placeholder=kwargs['placeholder_input'],
-                                                   visible=not kwargs['chat'])
-                        submit_nochat = gr.Button("Submit", size='sm', visible=not kwargs['chat'])
-                        flag_btn_nochat = gr.Button("Flag", size='sm', visible=not kwargs['chat'])
-                        score_text_nochat = gr.Textbox("Response Score: NA", show_label=False,
-                                                       visible=not kwargs['chat'])
-                        submit_nochat_api = gr.Button("Submit nochat API", visible=False)
-                        inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False)
-                        text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False,
-                                                            show_copy_button=True)
-
-                        # CHAT
-                        col_chat = gr.Column(visible=kwargs['chat'])
-                        with col_chat:
-                            with gr.Row():  # elem_id='prompt-form-area'):
-                                with gr.Column(scale=50):
-                                    instruction = gr.Textbox(
-                                        lines=kwargs['input_lines'],
-                                        label='Ask anything',
-                                        placeholder=instruction_label,
-                                        info=None,
-                                        elem_id='prompt-form',
-                                        container=True,
-                                    )
-                                submit_buttons = gr.Row(equal_height=False)
-                                with submit_buttons:
-                                    mw1 = 50
-                                    mw2 = 50
-                                    with gr.Column(min_width=mw1):
-                                        submit = gr.Button(value='Submit', variant='primary', size='sm',
-                                                           min_width=mw1)
-                                        stop_btn = gr.Button(value="Stop", variant='secondary', size='sm',
-                                                             min_width=mw1)
-                                        save_chat_btn = gr.Button("Save", size='sm', min_width=mw1)
-                                    with gr.Column(min_width=mw2):
-                                        retry_btn = gr.Button("Redo", size='sm', min_width=mw2)
-                                        undo = gr.Button("Undo", size='sm', min_width=mw2)
-                                        clear_chat_btn = gr.Button(value="Clear", size='sm', min_width=mw2)
-                            text_output, text_output2, text_outputs = make_chatbots(output_label0, output_label0_model2,
-                                                                                    **kwargs)
-
-                            with gr.Row():
-                                with gr.Column(visible=kwargs['score_model']):
-                                    score_text = gr.Textbox(res_value,
-                                                            show_label=False,
-                                                            visible=True)
-                                    score_text2 = gr.Textbox("Response Score2: NA", show_label=False,
-                                                             visible=False and not kwargs['model_lock'])
-
-                with gr.TabItem("Document Selection"):
-                    document_choice = gr.Dropdown(docs_state0,
-                                                  label="Select Subset of Document(s) %s" % file_types_str,
-                                                  value=[DocumentChoice.ALL.value],
-                                                  interactive=True,
-                                                  multiselect=True,
-                                                  visible=kwargs['langchain_mode'] != 'Disabled',
-                                                  )
-                    sources_visible = kwargs['langchain_mode'] != 'Disabled' and enable_sources_list
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            get_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, size='sm',
-                                                        visible=sources_visible)
-                            show_sources_btn = gr.Button(value="Show Sources from DB", scale=0, size='sm',
-                                                         visible=sources_visible)
-                            refresh_sources_btn = gr.Button(value="Update DB with new/changed files on disk", scale=0,
-                                                            size='sm',
-                                                            visible=sources_visible and allow_upload_to_user_data)
-                        with gr.Column(scale=4):
-                            pass
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            visible_add_remove_collection = (allow_upload_to_user_data or
-                                                             allow_upload_to_my_data) and \
-                                                            kwargs['langchain_mode'] != 'Disabled'
-                            add_placeholder = "e.g. UserData2, user_path2 (optional)" \
-                                if not is_public else "e.g. MyData2"
-                            remove_placeholder = "e.g. UserData2" if not is_public else "e.g. MyData2"
-                            new_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
-                                                                 label='Add Collection',
-                                                                 placeholder=add_placeholder,
-                                                                 interactive=True)
-                            remove_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
-                                                                    label='Remove Collection',
-                                                                    placeholder=remove_placeholder,
-                                                                    interactive=True)
-                            load_langchain = gr.Button(value="Load LangChain State", scale=0, size='sm',
-                                                       visible=allow_upload_to_user_data and
-                                                               kwargs['langchain_mode'] != 'Disabled')
-                        with gr.Column(scale=1):
-                            df0 = get_df_langchain_mode_paths(selection_docs_state0)
-                            langchain_mode_path_text = gr.Dataframe(value=df0,
-                                                                    visible=visible_add_remove_collection,
-                                                                    label='LangChain Mode-Path',
-                                                                    show_label=False,
-                                                                    interactive=False)
-                        with gr.Column(scale=4):
-                            pass
-
-                    sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list,
-                                         equal_height=False)
-                    with sources_row:
-                        with gr.Column(scale=1):
-                            file_source = gr.File(interactive=False,
-                                                  label="Download File w/Sources")
-                        with gr.Column(scale=2):
-                            sources_text = gr.HTML(label='Sources Added', interactive=False)
-
-                    doc_exception_text = gr.Textbox(value="", label='Document Exceptions',
-                                                    interactive=False,
-                                                    visible=kwargs['langchain_mode'] != 'Disabled')
-                with gr.TabItem("Document Viewer"):
-                    with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled'):
-                        with gr.Column(scale=2):
-                            get_viewable_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0,
-                                                                 size='sm',
-                                                                 visible=sources_visible)
-                            view_document_choice = gr.Dropdown(viewable_docs_state0,
-                                                               label="Select Single Document",
-                                                               value=None,
-                                                               interactive=True,
-                                                               multiselect=False,
-                                                               visible=True,
-                                                               )
-                        with gr.Column(scale=4):
-                            pass
-                    document = 'http://infolab.stanford.edu/pub/papers/google.pdf'
-                    doc_view = gr.HTML(visible=False)
-                    doc_view2 = gr.Dataframe(visible=False)
-                    doc_view3 = gr.JSON(visible=False)
-                    doc_view4 = gr.Markdown(visible=False)
-
-                with gr.TabItem("Chat History"):
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            remove_chat_btn = gr.Button(value="Remove Selected Saved Chats", visible=True, size='sm')
-                            flag_btn = gr.Button("Flag Current Chat", size='sm')
-                            export_chats_btn = gr.Button(value="Export Chats to Download", size='sm')
-                        with gr.Column(scale=4):
-                            pass
-                    with gr.Row():
-                        chats_file = gr.File(interactive=False, label="Download Exported Chats")
-                        chatsup_output = gr.File(label="Upload Chat File(s)",
-                                                 file_types=['.json'],
-                                                 file_count='multiple',
-                                                 elem_id="warning", elem_classes="feedback")
-                    with gr.Row():
-                        if 'mbart-' in kwargs['model_lower']:
-                            src_lang = gr.Dropdown(list(languages_covered().keys()),
-                                                   value=kwargs['src_lang'],
-                                                   label="Input Language")
-                            tgt_lang = gr.Dropdown(list(languages_covered().keys()),
-                                                   value=kwargs['tgt_lang'],
-                                                   label="Output Language")
-
-                    chat_exception_text = gr.Textbox(value="", visible=True, label='Chat Exceptions',
-                                                     interactive=False)
-                with gr.TabItem("Expert"):
-                    with gr.Row():
-                        with gr.Column():
-                            stream_output = gr.components.Checkbox(label="Stream output",
-                                                                   value=kwargs['stream_output'])
-                            prompt_type = gr.Dropdown(prompt_types_strings,
-                                                      value=kwargs['prompt_type'], label="Prompt Type",
-                                                      visible=not kwargs['model_lock'],
-                                                      interactive=not is_public,
-                                                      )
-                            prompt_type2 = gr.Dropdown(prompt_types_strings,
-                                                       value=kwargs['prompt_type'], label="Prompt Type Model 2",
-                                                       visible=False and not kwargs['model_lock'],
-                                                       interactive=not is_public)
-                            do_sample = gr.Checkbox(label="Sample",
-                                                    info="Enable sampler, required for use of temperature, top_p, top_k",
-                                                    value=kwargs['do_sample'])
-                            temperature = gr.Slider(minimum=0.01, maximum=2,
-                                                    value=kwargs['temperature'],
-                                                    label="Temperature",
-                                                    info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
-                            top_p = gr.Slider(minimum=1e-3, maximum=1.0 - 1e-3,
-                                              value=kwargs['top_p'], label="Top p",
-                                              info="Cumulative probability of tokens to sample from")
-                            top_k = gr.Slider(
-                                minimum=1, maximum=100, step=1,
-                                value=kwargs['top_k'], label="Top k",
-                                info='Num. tokens to sample from'
-                            )
-                            # FIXME: https://github.com/h2oai/h2ogpt/issues/106
-                            if os.getenv('TESTINGFAIL'):
-                                max_beams = 8 if not (memory_restriction_level or is_public) else 1
-                            else:
-                                max_beams = 1
-                            num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
-                                                  value=min(max_beams, kwargs['num_beams']), label="Beams",
-                                                  info="Number of searches for optimal overall probability.  "
-                                                       "Uses more GPU memory/compute",
-                                                  interactive=False)
-                            max_max_new_tokens = get_max_max_new_tokens(model_state0, **kwargs)
-                            max_new_tokens = gr.Slider(
-                                minimum=1, maximum=max_max_new_tokens, step=1,
-                                value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
-                            )
-                            min_new_tokens = gr.Slider(
-                                minimum=0, maximum=max_max_new_tokens, step=1,
-                                value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length",
-                            )
-                            max_new_tokens2 = gr.Slider(
-                                minimum=1, maximum=max_max_new_tokens, step=1,
-                                value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length 2",
-                                visible=False and not kwargs['model_lock'],
-                            )
-                            min_new_tokens2 = gr.Slider(
-                                minimum=0, maximum=max_max_new_tokens, step=1,
-                                value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length 2",
-                                visible=False and not kwargs['model_lock'],
-                            )
-                            early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
-                                                         value=kwargs['early_stopping'])
-                            max_time = gr.Slider(minimum=0, maximum=kwargs['max_max_time'], step=1,
-                                                 value=min(kwargs['max_max_time'],
-                                                           kwargs['max_time']), label="Max. time",
-                                                 info="Max. time to search optimal output.")
-                            repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
-                                                           value=kwargs['repetition_penalty'],
-                                                           label="Repetition Penalty")
-                            num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
-                                                             value=kwargs['num_return_sequences'],
-                                                             label="Number Returns", info="Must be <= num_beams",
-                                                             interactive=not is_public)
-                            iinput = gr.Textbox(lines=4, label="Input",
-                                                placeholder=kwargs['placeholder_input'],
-                                                interactive=not is_public)
-                            context = gr.Textbox(lines=3, label="System Pre-Context",
-                                                 info="Directly pre-appended without prompt processing",
-                                                 interactive=not is_public)
-                            chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
-                                                          visible=False,  # no longer support nochat in UI
-                                                          interactive=not is_public,
-                                                          )
-                            count_chat_tokens_btn = gr.Button(value="Count Chat Tokens",
-                                                              visible=not is_public and not kwargs['model_lock'],
-                                                              interactive=not is_public)
-                            chat_token_count = gr.Textbox(label="Chat Token Count", value=None,
-                                                          visible=not is_public and not kwargs['model_lock'],
-                                                          interactive=False)
-                            chunk = gr.components.Checkbox(value=kwargs['chunk'],
-                                                           label="Whether to chunk documents",
-                                                           info="For LangChain",
-                                                           visible=kwargs['langchain_mode'] != 'Disabled',
-                                                           interactive=not is_public)
-                            min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public)
-                            top_k_docs = gr.Slider(minimum=min_top_k_docs, maximum=max_top_k_docs, step=1,
-                                                   value=kwargs['top_k_docs'],
-                                                   label=label_top_k_docs,
-                                                   info="For LangChain",
-                                                   visible=kwargs['langchain_mode'] != 'Disabled',
-                                                   interactive=not is_public)
-                            chunk_size = gr.Number(value=kwargs['chunk_size'],
-                                                   label="Chunk size for document chunking",
-                                                   info="For LangChain (ignored if chunk=False)",
-                                                   minimum=128,
-                                                   maximum=2048,
-                                                   visible=kwargs['langchain_mode'] != 'Disabled',
-                                                   interactive=not is_public,
-                                                   precision=0)
-
-                with gr.TabItem("Models"):
-                    model_lock_msg = gr.Textbox(lines=1, label="Model Lock Notice",
-                                                placeholder="Started in model_lock mode, no model changes allowed.",
-                                                visible=bool(kwargs['model_lock']), interactive=False)
-                    load_msg = "Load-Unload Model/LORA [unload works if did not use --base_model]" if not is_public \
-                        else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
-                    load_msg2 = "Load-Unload Model/LORA 2 [unload works if did not use --base_model]" if not is_public \
-                        else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2"
-                    variant_load_msg = 'primary' if not is_public else 'secondary'
-                    compare_checkbox = gr.components.Checkbox(label="Compare Mode",
-                                                              value=kwargs['model_lock'],
-                                                              visible=not is_public and not kwargs['model_lock'])
-                    with gr.Row():
-                        n_gpus_list = [str(x) for x in list(range(-1, n_gpus))]
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=20, visible=not kwargs['model_lock']):
-                                    model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model",
-                                                               value=kwargs['base_model'])
-                                    lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA",
-                                                              value=kwargs['lora_weights'], visible=kwargs['show_lora'])
-                                    server_choice = gr.Dropdown(server_options_state.value[0], label="Choose Server",
-                                                                value=kwargs['inference_server'], visible=not is_public)
-                                with gr.Column(scale=1, visible=not kwargs['model_lock']):
-                                    load_model_button = gr.Button(load_msg, variant=variant_load_msg, scale=0,
-                                                                  size='sm', interactive=not is_public)
-                                    model_load8bit_checkbox = gr.components.Checkbox(
-                                        label="Load 8-bit [requires support]",
-                                        value=kwargs['load_8bit'], interactive=not is_public)
-                                    model_use_gpu_id_checkbox = gr.components.Checkbox(
-                                        label="Choose Devices [If not Checked, use all GPUs]",
-                                        value=kwargs['use_gpu_id'], interactive=not is_public)
-                                    model_gpu = gr.Dropdown(n_gpus_list,
-                                                            label="GPU ID [-1 = all GPUs, if Choose is enabled]",
-                                                            value=kwargs['gpu_id'], interactive=not is_public)
-                                    model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'],
-                                                            interactive=False)
-                                    lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'],
-                                                           visible=kwargs['show_lora'], interactive=False)
-                                    server_used = gr.Textbox(label="Current Server",
-                                                             value=kwargs['inference_server'],
-                                                             visible=bool(kwargs['inference_server']) and not is_public,
-                                                             interactive=False)
-                                    prompt_dict = gr.Textbox(label="Prompt (or Custom)",
-                                                             value=pprint.pformat(kwargs['prompt_dict'], indent=4),
-                                                             interactive=not is_public, lines=4)
-                        col_model2 = gr.Column(visible=False)
-                        with col_model2:
-                            with gr.Row():
-                                with gr.Column(scale=20, visible=not kwargs['model_lock']):
-                                    model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2",
-                                                                value=no_model_str)
-                                    lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2",
-                                                               value=no_lora_str,
-                                                               visible=kwargs['show_lora'])
-                                    server_choice2 = gr.Dropdown(server_options_state.value[0], label="Choose Server 2",
-                                                                 value=no_server_str,
-                                                                 visible=not is_public)
-                                with gr.Column(scale=1, visible=not kwargs['model_lock']):
-                                    load_model_button2 = gr.Button(load_msg2, variant=variant_load_msg, scale=0,
-                                                                   size='sm', interactive=not is_public)
-                                    model_load8bit_checkbox2 = gr.components.Checkbox(
-                                        label="Load 8-bit 2 [requires support]",
-                                        value=kwargs['load_8bit'], interactive=not is_public)
-                                    model_use_gpu_id_checkbox2 = gr.components.Checkbox(
-                                        label="Choose Devices 2 [If not Checked, use all GPUs]",
-                                        value=kwargs[
-                                            'use_gpu_id'], interactive=not is_public)
-                                    model_gpu2 = gr.Dropdown(n_gpus_list,
-                                                             label="GPU ID 2 [-1 = all GPUs, if choose is enabled]",
-                                                             value=kwargs['gpu_id'], interactive=not is_public)
-                                    # no model/lora loaded ever in model2 by default
-                                    model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str,
-                                                             interactive=False)
-                                    lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str,
-                                                            visible=kwargs['show_lora'], interactive=False)
-                                    server_used2 = gr.Textbox(label="Current Server 2", value=no_server_str,
-                                                              interactive=False,
-                                                              visible=not is_public)
-                                    prompt_dict2 = gr.Textbox(label="Prompt (or Custom) 2",
-                                                              value=pprint.pformat(kwargs['prompt_dict'], indent=4),
-                                                              interactive=not is_public, lines=4)
-                    with gr.Row(visible=not kwargs['model_lock']):
-                        with gr.Column(scale=50):
-                            new_model = gr.Textbox(label="New Model name/path", interactive=not is_public)
-                        with gr.Column(scale=50):
-                            new_lora = gr.Textbox(label="New LORA name/path", visible=kwargs['show_lora'],
-                                                  interactive=not is_public)
-                        with gr.Column(scale=50):
-                            new_server = gr.Textbox(label="New Server url:port", interactive=not is_public)
-                        with gr.Row():
-                            add_model_lora_server_button = gr.Button("Add new Model, Lora, Server url:port", scale=0,
-                                                                     size='sm', interactive=not is_public)
-                with gr.TabItem("System"):
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            side_bar_text = gr.Textbox('on', visible=False, interactive=False)
-                            submit_buttons_text = gr.Textbox('on', visible=False, interactive=False)
-
-                            side_bar_btn = gr.Button("Toggle SideBar", variant="secondary", size="sm")
-                            submit_buttons_btn = gr.Button("Toggle Submit Buttons", variant="secondary", size="sm")
-                            col_tabs_scale = gr.Slider(minimum=1, maximum=20, value=10, step=1, label='Window Size')
-                            text_outputs_height = gr.Slider(minimum=100, maximum=2000, value=kwargs['height'] or 400,
-                                                            step=50, label='Chat Height')
-                            dark_mode_btn = gr.Button("Dark Mode", variant="secondary", size="sm")
-                        with gr.Column(scale=4):
-                            pass
-                    system_visible0 = not is_public and not admin_pass
-                    admin_row = gr.Row()
-                    with admin_row:
-                        with gr.Column(scale=1):
-                            admin_pass_textbox = gr.Textbox(label="Admin Password", type='password',
-                                                            visible=not system_visible0)
-                        with gr.Column(scale=4):
-                            pass
-                    system_row = gr.Row(visible=system_visible0)
-                    with system_row:
-                        with gr.Column():
-                            with gr.Row():
-                                system_btn = gr.Button(value='Get System Info', size='sm')
-                                system_text = gr.Textbox(label='System Info', interactive=False, show_copy_button=True)
-                            with gr.Row():
-                                system_input = gr.Textbox(label='System Info Dict Password', interactive=True,
-                                                          visible=not is_public)
-                                system_btn2 = gr.Button(value='Get System Info Dict', visible=not is_public, size='sm')
-                                system_text2 = gr.Textbox(label='System Info Dict', interactive=False,
-                                                          visible=not is_public, show_copy_button=True)
-                            with gr.Row():
-                                system_btn3 = gr.Button(value='Get Hash', visible=not is_public, size='sm')
-                                system_text3 = gr.Textbox(label='Hash', interactive=False,
-                                                          visible=not is_public, show_copy_button=True)
-
-                            with gr.Row():
-                                zip_btn = gr.Button("Zip", size='sm')
-                                zip_text = gr.Textbox(label="Zip file name", interactive=False)
-                                file_output = gr.File(interactive=False, label="Zip file to Download")
-                            with gr.Row():
-                                s3up_btn = gr.Button("S3UP", size='sm')
-                                s3up_text = gr.Textbox(label='S3UP result', interactive=False)
-
-                with gr.TabItem("Terms of Service"):
-                    description = ""
-                    description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content.  Use at own risk.</i></li>"""
-                    if kwargs['load_8bit']:
-                        description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>"""
-                    description += """<i><li>Conversations may be used to improve h2oGPT.  Do not share sensitive information.</i></li>"""
-                    if 'h2ogpt-research' in kwargs['base_model']:
-                        description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>"""
-                    description += """<i><li>By using h2oGPT, you accept our <a href="https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md">Terms of Service</a></i></li></ul></p>"""
-                    gr.Markdown(value=description, show_label=False, interactive=False)
-
-                with gr.TabItem("Hosts"):
-                    gr.Markdown(f"""
-                        {description_bottom}
-                        {task_info_md}
-                        """)
-
-        # Get flagged data
-        zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']])
-        zip_event = zip_btn.click(zip_data1, inputs=None, outputs=[file_output, zip_text], queue=False,
-                                  api_name='zip_data' if allow_api else None)
-        s3up_event = s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text, queue=False,
-                                    api_name='s3up_data' if allow_api else None)
-
-        def clear_file_list():
-            return None
-
-        def make_non_interactive(*args):
-            if len(args) == 1:
-                return gr.update(interactive=False)
-            else:
-                return tuple([gr.update(interactive=False)] * len(args))
-
-        def make_interactive(*args):
-            if len(args) == 1:
-                return gr.update(interactive=True)
-            else:
-                return tuple([gr.update(interactive=True)] * len(args))
-
-        # Add to UserData or custom user db
-        update_db_func = functools.partial(update_user_db,
-                                           dbs=dbs,
-                                           db_type=db_type,
-                                           use_openai_embedding=use_openai_embedding,
-                                           hf_embedding_model=hf_embedding_model,
-                                           captions_model=captions_model,
-                                           enable_captions=enable_captions,
-                                           caption_loader=caption_loader,
-                                           enable_ocr=enable_ocr,
-                                           enable_pdf_ocr=enable_pdf_ocr,
-                                           verbose=kwargs['verbose'],
-                                           n_jobs=kwargs['n_jobs'],
-                                           )
-        add_file_outputs = [fileup_output, langchain_mode]
-        add_file_kwargs = dict(fn=update_db_func,
-                               inputs=[fileup_output, my_db_state, selection_docs_state, chunk, chunk_size,
-                                       langchain_mode],
-                               outputs=add_file_outputs + [sources_text, doc_exception_text],
-                               queue=queue,
-                               api_name='add_file' if allow_api and allow_upload_to_user_data else None)
-
-        # then no need for add buttons, only single changeable db
-        eventdb1a = fileup_output.upload(make_non_interactive, inputs=add_file_outputs, outputs=add_file_outputs,
-                                         show_progress='minimal')
-        eventdb1 = eventdb1a.then(**add_file_kwargs, show_progress='full')
-        eventdb1b = eventdb1.then(make_interactive, inputs=add_file_outputs, outputs=add_file_outputs,
-                                  show_progress='minimal')
-
-        # deal with challenge to have fileup_output itself as input
-        add_file_kwargs2 = dict(fn=update_db_func,
-                                inputs=[fileup_output_text, my_db_state, selection_docs_state, chunk, chunk_size,
-                                        langchain_mode],
-                                outputs=add_file_outputs + [sources_text, doc_exception_text],
-                                queue=queue,
-                                api_name='add_file_api' if allow_api and allow_upload_to_user_data else None)
-        eventdb1_api = fileup_output_text.submit(**add_file_kwargs2, show_progress='full')
-
-        # note for update_user_db_func output is ignored for db
-
-        def clear_textbox():
-            return gr.Textbox.update(value='')
-
-        update_user_db_url_func = functools.partial(update_db_func, is_url=True)
-
-        add_url_outputs = [url_text, langchain_mode]
-        add_url_kwargs = dict(fn=update_user_db_url_func,
-                              inputs=[url_text, my_db_state, selection_docs_state, chunk, chunk_size,
-                                      langchain_mode],
-                              outputs=add_url_outputs + [sources_text, doc_exception_text],
-                              queue=queue,
-                              api_name='add_url' if allow_api and allow_upload_to_user_data else None)
-
-        eventdb2a = url_text.submit(fn=dummy_fun, inputs=url_text, outputs=url_text, queue=queue,
-                                    show_progress='minimal')
-        # work around https://github.com/gradio-app/gradio/issues/4733
-        eventdb2b = eventdb2a.then(make_non_interactive, inputs=add_url_outputs, outputs=add_url_outputs,
-                                   show_progress='minimal')
-        eventdb2 = eventdb2b.then(**add_url_kwargs, show_progress='full')
-        eventdb2c = eventdb2.then(make_interactive, inputs=add_url_outputs, outputs=add_url_outputs,
-                                  show_progress='minimal')
-
-        update_user_db_txt_func = functools.partial(update_db_func, is_txt=True)
-        add_text_outputs = [user_text_text, langchain_mode]
-        add_text_kwargs = dict(fn=update_user_db_txt_func,
-                               inputs=[user_text_text, my_db_state, selection_docs_state, chunk, chunk_size,
-                                       langchain_mode],
-                               outputs=add_text_outputs + [sources_text, doc_exception_text],
-                               queue=queue,
-                               api_name='add_text' if allow_api and allow_upload_to_user_data else None
-                               )
-        eventdb3a = user_text_text.submit(fn=dummy_fun, inputs=user_text_text, outputs=user_text_text, queue=queue,
-                                          show_progress='minimal')
-        eventdb3b = eventdb3a.then(make_non_interactive, inputs=add_text_outputs, outputs=add_text_outputs,
-                                   show_progress='minimal')
-        eventdb3 = eventdb3b.then(**add_text_kwargs, show_progress='full')
-        eventdb3c = eventdb3.then(make_interactive, inputs=add_text_outputs, outputs=add_text_outputs,
-                                  show_progress='minimal')
-        db_events = [eventdb1a, eventdb1, eventdb1b, eventdb1_api,
-                     eventdb2a, eventdb2, eventdb2b, eventdb2c,
-                     eventdb3a, eventdb3b, eventdb3, eventdb3c]
-
-        get_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=docs_state0)
-
-        # if change collection source, must clear doc selections from it to avoid inconsistency
-        def clear_doc_choice():
-            return gr.Dropdown.update(choices=docs_state0, value=DocumentChoice.ALL.value)
-
-        langchain_mode.change(clear_doc_choice, inputs=None, outputs=document_choice, queue=False)
-
-        def resize_col_tabs(x):
-            return gr.Dropdown.update(scale=x)
-
-        col_tabs_scale.change(fn=resize_col_tabs, inputs=col_tabs_scale, outputs=col_tabs, queue=False)
-
-        def resize_chatbots(x, num_model_lock=0):
-            if num_model_lock == 0:
-                num_model_lock = 3  # 2 + 1 (which is dup of first)
-            else:
-                num_model_lock = 2 + num_model_lock
-            return tuple([gr.update(height=x)] * num_model_lock)
-
-        resize_chatbots_func = functools.partial(resize_chatbots, num_model_lock=len(text_outputs))
-        text_outputs_height.change(fn=resize_chatbots_func, inputs=text_outputs_height,
-                                   outputs=[text_output, text_output2] + text_outputs, queue=False)
-
-        def update_dropdown(x):
-            return gr.Dropdown.update(choices=x, value=[docs_state0[0]])
-
-        get_sources_args = dict(fn=get_sources1, inputs=[my_db_state, langchain_mode],
-                                outputs=[file_source, docs_state],
-                                queue=queue,
-                                api_name='get_sources' if allow_api else None)
-
-        eventdb7 = get_sources_btn.click(**get_sources_args) \
-            .then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
-        # show button, else only show when add.  Could add to above get_sources for download/dropdown, but bit much maybe
-        show_sources1 = functools.partial(get_source_files_given_langchain_mode, dbs=dbs)
-        eventdb8 = show_sources_btn.click(fn=show_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
-                                          api_name='show_sources' if allow_api else None)
-
-        def update_viewable_dropdown(x):
-            return gr.Dropdown.update(choices=x,
-                                      value=viewable_docs_state0[0] if len(viewable_docs_state0) > 0 else None)
-
-        get_viewable_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=viewable_docs_state0)
-        get_viewable_sources_args = dict(fn=get_viewable_sources1, inputs=[my_db_state, langchain_mode],
-                                         outputs=[file_source, viewable_docs_state],
-                                         queue=queue,
-                                         api_name='get_viewable_sources' if allow_api else None)
-        eventdb12 = get_viewable_sources_btn.click(**get_viewable_sources_args) \
-            .then(fn=update_viewable_dropdown, inputs=viewable_docs_state,
-                  outputs=view_document_choice)
-
-        def show_doc(file):
-            dummy1 = gr.update(visible=False, value=None)
-            dummy_ret = dummy1, dummy1, dummy1, dummy1
-            if not isinstance(file, str):
-                return dummy_ret
-
-            if file.endswith('.md'):
-                try:
-                    with open(file, 'rt') as f:
-                        content = f.read()
-                    return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
-                except:
-                    return dummy_ret
-
-            if file.endswith('.py'):
-                try:
-                    with open(file, 'rt') as f:
-                        content = f.read()
-                    content = f"```python\n{content}\n```"
-                    return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
-                except:
-                    return dummy_ret
-
-            if file.endswith('.txt') or file.endswith('.rst') or file.endswith('.rtf') or file.endswith('.toml'):
-                try:
-                    with open(file, 'rt') as f:
-                        content = f.read()
-                    content = f"```text\n{content}\n```"
-                    return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
-                except:
-                    return dummy_ret
-
-            func = None
-            if file.endswith(".csv"):
-                func = pd.read_csv
-            elif file.endswith(".pickle"):
-                func = pd.read_pickle
-            elif file.endswith(".xls") or file.endswith("xlsx"):
-                func = pd.read_excel
-            elif file.endswith('.json'):
-                func = pd.read_json
-            elif file.endswith('.xml'):
-                func = pd.read_xml
-            if func is not None:
-                try:
-                    df = func(file).head(100)
-                except:
-                    return dummy_ret
-                return dummy1, gr.update(visible=True, value=df), dummy1, dummy1
-            port = int(os.getenv('GRADIO_SERVER_PORT', '7860'))
-            import pathlib
-            absolute_path_string = os.path.abspath(file)
-            url_path = pathlib.Path(absolute_path_string).as_uri()
-            url = get_url(absolute_path_string, from_str=True)
-            img_url = url.replace("""<a href=""", """<img src=""")
-            if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.jpeg'):
-                return gr.update(visible=True, value=img_url), dummy1, dummy1, dummy1
-            elif file.endswith('.pdf') or 'arxiv.org/pdf' in file:
-                if file.startswith('http') or file.startswith('https'):
-                    # if file is online, then might as well use google(?)
-                    document1 = file
-                    return gr.update(visible=True,
-                                     value=f"""<iframe width="1000" height="800" src="https://docs.google.com/viewerng/viewer?url={document1}&embedded=true" frameborder="0" height="100%" width="100%">
-</iframe>
-"""), dummy1, dummy1, dummy1
-                else:
-                    ip = get_local_ip()
-                    document1 = url_path.replace('file://', f'http://{ip}:{port}/')
-                    # document1 = url
-                    return gr.update(visible=True, value=f"""<object data="{document1}" type="application/pdf">
-    <iframe src="https://docs.google.com/viewer?url={document1}&embedded=true"></iframe>
-</object>"""), dummy1, dummy1, dummy1
-            else:
-                return dummy_ret
-
-        view_document_choice.select(fn=show_doc, inputs=view_document_choice,
-                                    outputs=[doc_view, doc_view2, doc_view3, doc_view4])
-
-        # Get inputs to evaluate() and make_db()
-        # don't deepcopy, can contain model itself
-        all_kwargs = kwargs.copy()
-        all_kwargs.update(locals())
-
-        refresh_sources1 = functools.partial(update_and_get_source_files_given_langchain_mode,
-                                             **get_kwargs(update_and_get_source_files_given_langchain_mode,
-                                                          exclude_names=['db1s', 'langchain_mode', 'chunk',
-                                                                         'chunk_size'],
-                                                          **all_kwargs))
-        eventdb9 = refresh_sources_btn.click(fn=refresh_sources1,
-                                             inputs=[my_db_state, langchain_mode, chunk, chunk_size],
-                                             outputs=sources_text,
-                                             api_name='refresh_sources' if allow_api else None)
-
-        def check_admin_pass(x):
-            return gr.update(visible=x == admin_pass)
-
-        def close_admin(x):
-            return gr.update(visible=not (x == admin_pass))
-
-        admin_pass_textbox.submit(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row, queue=False) \
-            .then(close_admin, inputs=admin_pass_textbox, outputs=admin_row, queue=False)
-
-        def add_langchain_mode(db1s, selection_docs_state1, langchain_mode1, y):
-            for k in db1s:
-                set_userid(db1s[k])
-            langchain_modes = selection_docs_state1['langchain_modes']
-            langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
-            visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
-
-            user_path = None
-            valid = True
-            y2 = y.strip().replace(' ', '').split(',')
-            if len(y2) >= 1:
-                langchain_mode2 = y2[0]
-                if len(langchain_mode2) >= 3 and langchain_mode2.isalnum():
-                    # real restriction is:
-                    # ValueError: Expected collection name that (1) contains 3-63 characters, (2) starts and ends with an alphanumeric character, (3) otherwise contains only alphanumeric characters, underscores or hyphens (-), (4) contains no two consecutive periods (..) and (5) is not a valid IPv4 address, got me
-                    # but just make simpler
-                    user_path = y2[1] if len(y2) > 1 else None  # assume scratch if don't have user_path
-                    if user_path in ['', "''"]:
-                        # for scratch spaces
-                        user_path = None
-                    if langchain_mode2 in langchain_modes_intrinsic:
-                        user_path = None
-                        textbox = "Invalid access to use internal name: %s" % langchain_mode2
-                        valid = False
-                        langchain_mode2 = langchain_mode1
-                    elif user_path and allow_upload_to_user_data or not user_path and allow_upload_to_my_data:
-                        langchain_mode_paths.update({langchain_mode2: user_path})
-                        if langchain_mode2 not in visible_langchain_modes:
-                            visible_langchain_modes.append(langchain_mode2)
-                        if langchain_mode2 not in langchain_modes:
-                            langchain_modes.append(langchain_mode2)
-                        textbox = ''
-                        if user_path:
-                            makedirs(user_path, exist_ok=True)
-                    else:
-                        valid = False
-                        langchain_mode2 = langchain_mode1
-                        textbox = "Invalid access.  user allowed: %s " \
-                                  "scratch allowed: %s" % (allow_upload_to_user_data, allow_upload_to_my_data)
-                else:
-                    valid = False
-                    langchain_mode2 = langchain_mode1
-                    textbox = "Invalid, collection must be >=3 characters and alphanumeric"
-            else:
-                valid = False
-                langchain_mode2 = langchain_mode1
-                textbox = "Invalid, must be like UserData2, user_path2"
-            selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
-            df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
-            choices = get_langchain_choices(selection_docs_state1)
-
-            if valid and not user_path:
-                # needs to have key for it to make it known different from userdata case in _update_user_db()
-                db1s[langchain_mode2] = [None, None]
-            if valid:
-                save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode,
-                                      db1s)
-
-            return db1s, selection_docs_state1, gr.update(choices=choices,
-                                                          value=langchain_mode2), textbox, df_langchain_mode_paths1
-
-        def remove_langchain_mode(db1s, selection_docs_state1, langchain_mode1, langchain_mode2, dbsu=None):
-            for k in db1s:
-                set_userid(db1s[k])
-            assert dbsu is not None
-            langchain_modes = selection_docs_state1['langchain_modes']
-            langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
-            visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
-
-            if langchain_mode2 in db1s and not allow_upload_to_my_data or \
-                    dbsu is not None and langchain_mode2 in dbsu and not allow_upload_to_user_data or \
-                    langchain_mode2 in langchain_modes_intrinsic:
-                # NOTE: Doesn't fail if remove MyData, but didn't debug odd behavior seen with upload after gone
-                textbox = "Invalid access, cannot remove %s" % langchain_mode2
-                df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
-            else:
-                # change global variables
-                if langchain_mode2 in visible_langchain_modes:
-                    visible_langchain_modes.remove(langchain_mode2)
-                    textbox = ""
-                else:
-                    textbox = "%s was not visible" % langchain_mode2
-                if langchain_mode2 in langchain_modes:
-                    langchain_modes.remove(langchain_mode2)
-                if langchain_mode2 in langchain_mode_paths:
-                    langchain_mode_paths.pop(langchain_mode2)
-                if langchain_mode2 in db1s:
-                    # remove db entirely, so not in list, else need to manage visible list in update_langchain_mode_paths()
-                    # FIXME: Remove location?
-                    if langchain_mode2 != LangChainMode.MY_DATA.value:
-                        # don't remove last MyData, used as user hash
-                        db1s.pop(langchain_mode2)
-                # only show
-                selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
-                df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
-
-                save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode,
-                                      db1s)
-
-            return db1s, selection_docs_state1, \
-                gr.update(choices=get_langchain_choices(selection_docs_state1),
-                          value=langchain_mode2), textbox, df_langchain_mode_paths1
-
-        new_langchain_mode_text.submit(fn=add_langchain_mode,
-                                       inputs=[my_db_state, selection_docs_state, langchain_mode,
-                                               new_langchain_mode_text],
-                                       outputs=[my_db_state, selection_docs_state, langchain_mode,
-                                                new_langchain_mode_text,
-                                                langchain_mode_path_text],
-                                       api_name='new_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
-        remove_langchain_mode_func = functools.partial(remove_langchain_mode, dbsu=dbs)
-        remove_langchain_mode_text.submit(fn=remove_langchain_mode_func,
-                                          inputs=[my_db_state, selection_docs_state, langchain_mode,
-                                                  remove_langchain_mode_text],
-                                          outputs=[my_db_state, selection_docs_state, langchain_mode,
-                                                   remove_langchain_mode_text,
-                                                   langchain_mode_path_text],
-                                          api_name='remove_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
-
-        def update_langchain_gr(db1s, selection_docs_state1, langchain_mode1):
-            for k in db1s:
-                set_userid(db1s[k])
-            langchain_modes = selection_docs_state1['langchain_modes']
-            langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
-            visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
-            # in-place
-
-            # update user collaborative collections
-            update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, '')
-            # update scratch single-user collections
-            user_hash = db1s.get(LangChainMode.MY_DATA.value, '')[1]
-            update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, user_hash)
-
-            selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
-            df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
-            return selection_docs_state1, \
-                gr.update(choices=get_langchain_choices(selection_docs_state1),
-                          value=langchain_mode1), df_langchain_mode_paths1
-
-        load_langchain.click(fn=update_langchain_gr,
-                             inputs=[my_db_state, selection_docs_state, langchain_mode],
-                             outputs=[selection_docs_state, langchain_mode, langchain_mode_path_text],
-                             api_name='load_langchain' if allow_api and allow_upload_to_user_data else None)
-
-        inputs_list, inputs_dict = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=1)
-        inputs_list2, inputs_dict2 = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=2)
-        from functools import partial
-        kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list}
-        # ensure present
-        for k in inputs_kwargs_list:
-            assert k in kwargs_evaluate, "Missing %s" % k
-
-        def evaluate_nochat(*args1, default_kwargs1=None, str_api=False, **kwargs1):
-            args_list = list(args1)
-            if str_api:
-                user_kwargs = args_list[len(input_args_list)]
-                assert isinstance(user_kwargs, str)
-                user_kwargs = ast.literal_eval(user_kwargs)
-            else:
-                user_kwargs = {k: v for k, v in zip(eval_func_param_names, args_list[len(input_args_list):])}
-            # only used for submit_nochat_api
-            user_kwargs['chat'] = False
-            if 'stream_output' not in user_kwargs:
-                user_kwargs['stream_output'] = False
-            if 'langchain_mode' not in user_kwargs:
-                # if user doesn't specify, then assume disabled, not use default
-                user_kwargs['langchain_mode'] = 'Disabled'
-            if 'langchain_action' not in user_kwargs:
-                user_kwargs['langchain_action'] = LangChainAction.QUERY.value
-            if 'langchain_agents' not in user_kwargs:
-                user_kwargs['langchain_agents'] = []
-
-            set1 = set(list(default_kwargs1.keys()))
-            set2 = set(eval_func_param_names)
-            assert set1 == set2, "Set diff: %s %s: %s" % (set1, set2, set1.symmetric_difference(set2))
-            # correct ordering.  Note some things may not be in default_kwargs, so can't be default of user_kwargs.get()
-            model_state1 = args_list[0]
-            my_db_state1 = args_list[1]
-            selection_docs_state1 = args_list[2]
-            args_list = [user_kwargs[k] if k in user_kwargs and user_kwargs[k] is not None else default_kwargs1[k] for k
-                         in eval_func_param_names]
-            assert len(args_list) == len(eval_func_param_names)
-            args_list = [model_state1, my_db_state1, selection_docs_state1] + args_list
-
-            try:
-                for res_dict in evaluate(*tuple(args_list), **kwargs1):
-                    if str_api:
-                        # full return of dict
-                        yield res_dict
-                    elif kwargs['langchain_mode'] == 'Disabled':
-                        yield fix_text_for_gradio(res_dict['response'])
-                    else:
-                        yield '<br>' + fix_text_for_gradio(res_dict['response'])
-            finally:
-                clear_torch_cache()
-                clear_embeddings(user_kwargs['langchain_mode'], my_db_state1)
-
-        fun = partial(evaluate_nochat,
-                      default_kwargs1=default_kwargs,
-                      str_api=False,
-                      **kwargs_evaluate)
-        fun2 = partial(evaluate_nochat,
-                       default_kwargs1=default_kwargs,
-                       str_api=False,
-                       **kwargs_evaluate)
-        fun_with_dict_str = partial(evaluate_nochat,
-                                    default_kwargs1=default_kwargs,
-                                    str_api=True,
-                                    **kwargs_evaluate
-                                    )
-
-        dark_mode_btn.click(
-            None,
-            None,
-            None,
-            _js=get_dark_js(),
-            api_name="dark" if allow_api else None,
-            queue=False,
-        )
-
-        def visible_toggle(x):
-            x = 'off' if x == 'on' else 'on'
-            return x, gr.Column.update(visible=True if x == 'on' else False)
-
-        side_bar_btn.click(fn=visible_toggle,
-                           inputs=side_bar_text,
-                           outputs=[side_bar_text, side_bar],
-                           queue=False)
-
-        submit_buttons_btn.click(fn=visible_toggle,
-                                 inputs=submit_buttons_text,
-                                 outputs=[submit_buttons_text, submit_buttons],
-                                 queue=False)
-
-        # examples after submit or any other buttons for chat or no chat
-        if kwargs['examples'] is not None and kwargs['show_examples']:
-            gr.Examples(examples=kwargs['examples'], inputs=inputs_list)
-
-        # Score
-        def score_last_response(*args, nochat=False, num_model_lock=0):
-            try:
-                if num_model_lock > 0:
-                    # then lock way
-                    args_list = list(args).copy()
-                    outputs = args_list[-num_model_lock:]
-                    score_texts1 = []
-                    for output in outputs:
-                        # same input, put into form good for _score_last_response()
-                        args_list[-1] = output
-                        score_texts1.append(
-                            _score_last_response(*tuple(args_list), nochat=nochat,
-                                                 num_model_lock=num_model_lock, prefix=''))
-                    if len(score_texts1) > 1:
-                        return "Response Scores: %s" % ' '.join(score_texts1)
-                    else:
-                        return "Response Scores: %s" % score_texts1[0]
-                else:
-                    return _score_last_response(*args, nochat=nochat, num_model_lock=num_model_lock)
-            finally:
-                clear_torch_cache()
-
-        def _score_last_response(*args, nochat=False, num_model_lock=0, prefix='Response Score: '):
-            """ Similar to user() """
-            args_list = list(args)
-            smodel = score_model_state0['model']
-            stokenizer = score_model_state0['tokenizer']
-            sdevice = score_model_state0['device']
-
-            if memory_restriction_level > 0:
-                max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
-            elif hasattr(stokenizer, 'model_max_length'):
-                max_length_tokenize = stokenizer.model_max_length
-            else:
-                # limit to 1024, not worth OOMing on reward score
-                max_length_tokenize = 2048 - 1024
-            cutoff_len = max_length_tokenize * 4  # restrict deberta related to max for LLM
-
-            if not nochat:
-                history = args_list[-1]
-                if history is None:
-                    history = []
-                if smodel is not None and \
-                        stokenizer is not None and \
-                        sdevice is not None and \
-                        history is not None and len(history) > 0 and \
-                        history[-1] is not None and \
-                        len(history[-1]) >= 2:
-                    os.environ['TOKENIZERS_PARALLELISM'] = 'false'
-
-                    question = history[-1][0]
-
-                    answer = history[-1][1]
-                else:
-                    return '%sNA' % prefix
-            else:
-                answer = args_list[-1]
-                instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat')
-                question = args_list[instruction_nochat_arg_id]
-
-            if question is None:
-                return '%sBad Question' % prefix
-            if answer is None:
-                return '%sBad Answer' % prefix
-            try:
-                score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len)
-            finally:
-                clear_torch_cache()
-            if isinstance(score, str):
-                return '%sNA' % prefix
-            return '{}{:.1%}'.format(prefix, score)
-
-        def noop_score_last_response(*args, **kwargs):
-            return "Response Score: Disabled"
-
-        if kwargs['score_model']:
-            score_fun = score_last_response
-        else:
-            score_fun = noop_score_last_response
-
-        score_args = dict(fn=score_fun,
-                          inputs=inputs_list + [text_output],
-                          outputs=[score_text],
-                          )
-        score_args2 = dict(fn=partial(score_fun),
-                           inputs=inputs_list2 + [text_output2],
-                           outputs=[score_text2],
-                           )
-        score_fun_func = functools.partial(score_fun, num_model_lock=len(text_outputs))
-        all_score_args = dict(fn=score_fun_func,
-                              inputs=inputs_list + text_outputs,
-                              outputs=score_text,
-                              )
-
-        score_args_nochat = dict(fn=partial(score_fun, nochat=True),
-                                 inputs=inputs_list + [text_output_nochat],
-                                 outputs=[score_text_nochat],
-                                 )
-
-        def update_history(*args, undo=False, retry=False, sanitize_user_prompt=False):
-            """
-            User that fills history for bot
-            :param args:
-            :param undo:
-            :param retry:
-            :param sanitize_user_prompt:
-            :return:
-            """
-            args_list = list(args)
-            user_message = args_list[eval_func_param_names.index('instruction')]  # chat only
-            input1 = args_list[eval_func_param_names.index('iinput')]  # chat only
-            prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
-            langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
-            langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
-            langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')]
-            document_subset1 = args_list[eval_func_param_names.index('document_subset')]
-            document_choice1 = args_list[eval_func_param_names.index('document_choice')]
-            if not prompt_type1:
-                # shouldn't have to specify if CLI launched model
-                prompt_type1 = kwargs['prompt_type']
-                # apply back
-                args_list[eval_func_param_names.index('prompt_type')] = prompt_type1
-            if input1 and not user_message.endswith(':'):
-                user_message1 = user_message + ":" + input1
-            elif input1:
-                user_message1 = user_message + input1
-            else:
-                user_message1 = user_message
-            if sanitize_user_prompt:
-                from better_profanity import profanity
-                user_message1 = profanity.censor(user_message1)
-
-            history = args_list[-1]
-            if history is None:
-                # bad history
-                history = []
-            history = history.copy()
-
-            if undo:
-                if len(history) > 0:
-                    history.pop()
-                return history
-            if retry:
-                if history:
-                    history[-1][1] = None
-                return history
-            if user_message1 in ['', None, '\n']:
-                if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
-                    # reject non-retry submit/enter
-                    return history
-            user_message1 = fix_text_for_gradio(user_message1)
-            return history + [[user_message1, None]]
-
-        def user(*args, undo=False, retry=False, sanitize_user_prompt=False):
-            return update_history(*args, undo=undo, retry=retry, sanitize_user_prompt=sanitize_user_prompt)
-
-        def all_user(*args, undo=False, retry=False, sanitize_user_prompt=False, num_model_lock=0):
-            args_list = list(args)
-            history_list = args_list[-num_model_lock:]
-            assert len(history_list) > 0, "Bad history list: %s" % history_list
-            for hi, history in enumerate(history_list):
-                if num_model_lock > 0:
-                    hargs = args_list[:-num_model_lock].copy()
-                else:
-                    hargs = args_list.copy()
-                hargs += [history]
-                history_list[hi] = update_history(*hargs, undo=undo, retry=retry,
-                                                  sanitize_user_prompt=sanitize_user_prompt)
-            if len(history_list) > 1:
-                return tuple(history_list)
-            else:
-                return history_list[0]
-
-        def get_model_max_length(model_state1):
-            if model_state1 and not isinstance(model_state1["tokenizer"], str):
-                tokenizer = model_state1["tokenizer"]
-            elif model_state0 and not isinstance(model_state0["tokenizer"], str):
-                tokenizer = model_state0["tokenizer"]
-            else:
-                tokenizer = None
-            if tokenizer is not None:
-                return tokenizer.model_max_length
-            else:
-                return 2000
-
-        def prep_bot(*args, retry=False, which_model=0):
-            """
-
-            :param args:
-            :param retry:
-            :param which_model: identifies which model if doing model_lock
-                 API only called for which_model=0, default for inputs_list, but rest should ignore inputs_list
-            :return: last element is True if should run bot, False if should just yield history
-            """
-            isize = len(input_args_list) + 1  # states + chat history
-            # don't deepcopy, can contain model itself
-            args_list = list(args).copy()
-            model_state1 = args_list[-isize]
-            my_db_state1 = args_list[-isize + 1]
-            selection_docs_state1 = args_list[-isize + 2]
-            history = args_list[-1]
-            prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
-            prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
-
-            if model_state1['model'] is None or model_state1['model'] == no_model_str:
-                return history, None, None, None
-
-            args_list = args_list[:-isize]  # only keep rest needed for evaluate()
-            langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
-            add_chat_history_to_context1 = args_list[eval_func_param_names.index('add_chat_history_to_context')]
-            langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
-            langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')]
-            document_subset1 = args_list[eval_func_param_names.index('document_subset')]
-            document_choice1 = args_list[eval_func_param_names.index('document_choice')]
-            if not history:
-                print("No history", flush=True)
-                history = []
-                return history, None, None, None
-            instruction1 = history[-1][0]
-            if retry and history:
-                # if retry, pop history and move onto bot stuff
-                instruction1 = history[-1][0]
-                history[-1][1] = None
-            elif not instruction1:
-                if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
-                    # if not retrying, then reject empty query
-                    return history, None, None, None
-            elif len(history) > 0 and history[-1][1] not in [None, '']:
-                # reject submit button if already filled and not retrying
-                # None when not filling with '' to keep client happy
-                return history, None, None, None
-
-            # shouldn't have to specify in API prompt_type if CLI launched model, so prefer global CLI one if have it
-            prompt_type1, prompt_dict1 = update_prompt(prompt_type1, prompt_dict1, model_state1,
-                                                       which_model=which_model)
-            # apply back to args_list for evaluate()
-            args_list[eval_func_param_names.index('prompt_type')] = prompt_type1
-            args_list[eval_func_param_names.index('prompt_dict')] = prompt_dict1
-
-            chat1 = args_list[eval_func_param_names.index('chat')]
-            model_max_length1 = get_model_max_length(model_state1)
-            context1 = history_to_context(history, langchain_mode1,
-                                          add_chat_history_to_context1,
-                                          prompt_type1, prompt_dict1, chat1,
-                                          model_max_length1, memory_restriction_level,
-                                          kwargs['keep_sources_in_context'])
-            args_list[0] = instruction1  # override original instruction with history from user
-            args_list[2] = context1
-
-            fun1 = partial(evaluate,
-                           model_state1,
-                           my_db_state1,
-                           selection_docs_state1,
-                           *tuple(args_list),
-                           **kwargs_evaluate)
-
-            return history, fun1, langchain_mode1, my_db_state1
-
-        def get_response(fun1, history):
-            """
-            bot that consumes history for user input
-            instruction (from input_list) itself is not consumed by bot
-            :return:
-            """
-            if not fun1:
-                yield history, ''
-                return
-            try:
-                for output_fun in fun1():
-                    output = output_fun['response']
-                    extra = output_fun['sources']  # FIXME: can show sources in separate text box etc.
-                    # ensure good visually, else markdown ignores multiple \n
-                    bot_message = fix_text_for_gradio(output)
-                    history[-1][1] = bot_message
-                    yield history, ''
-            except StopIteration:
-                yield history, ''
-            except RuntimeError as e:
-                if "generator raised StopIteration" in str(e):
-                    # assume last entry was bad, undo
-                    history.pop()
-                    yield history, ''
-                else:
-                    if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
-                        history[-1][1] = ''
-                    yield history, str(e)
-                    raise
-            except Exception as e:
-                # put error into user input
-                ex = "Exception: %s" % str(e)
-                if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
-                    history[-1][1] = ''
-                yield history, ex
-                raise
-            finally:
-                clear_torch_cache()
-            return
-
-        def clear_embeddings(langchain_mode1, db1s):
-            # clear any use of embedding that sits on GPU, else keeps accumulating GPU usage even if clear torch cache
-            if db_type == 'chroma' and langchain_mode1 not in ['LLM', 'Disabled', None, '']:
-                from gpt_langchain import clear_embedding
-                db = dbs.get('langchain_mode1')
-                if db is not None and not isinstance(db, str):
-                    clear_embedding(db)
-                if db1s is not None and langchain_mode1 in db1s:
-                    db1 = db1s[langchain_mode1]
-                    if len(db1) == 2:
-                        clear_embedding(db1[0])
-
-        def bot(*args, retry=False):
-            history, fun1, langchain_mode1, db1 = prep_bot(*args, retry=retry)
-            try:
-                for res in get_response(fun1, history):
-                    yield res
-            finally:
-                clear_torch_cache()
-                clear_embeddings(langchain_mode1, db1)
-
-        def all_bot(*args, retry=False, model_states1=None):
-            args_list = list(args).copy()
-            chatbots = args_list[-len(model_states1):]
-            args_list0 = args_list[:-len(model_states1)]  # same for all models
-            exceptions = []
-            stream_output1 = args_list[eval_func_param_names.index('stream_output')]
-            max_time1 = args_list[eval_func_param_names.index('max_time')]
-            langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
-            isize = len(input_args_list) + 1  # states + chat history
-            db1s = None
-            try:
-                gen_list = []
-                for chatboti, (chatbot1, model_state1) in enumerate(zip(chatbots, model_states1)):
-                    args_list1 = args_list0.copy()
-                    args_list1.insert(-isize + 2,
-                                      model_state1)  # insert at -2 so is at -3, and after chatbot1 added, at -4
-                    # if at start, have None in response still, replace with '' so client etc. acts like normal
-                    # assumes other parts of code treat '' and None as if no response yet from bot
-                    # can't do this later in bot code as racy with threaded generators
-                    if len(chatbot1) > 0 and len(chatbot1[-1]) == 2 and chatbot1[-1][1] is None:
-                        chatbot1[-1][1] = ''
-                    args_list1.append(chatbot1)
-                    # so consistent with prep_bot()
-                    # with model_state1 at -3, my_db_state1 at -2, and history(chatbot) at -1
-                    # langchain_mode1 and my_db_state1 should be same for every bot
-                    history, fun1, langchain_mode1, db1s = prep_bot(*tuple(args_list1), retry=retry,
-                                                                    which_model=chatboti)
-                    gen1 = get_response(fun1, history)
-                    if stream_output1:
-                        gen1 = TimeoutIterator(gen1, timeout=0.01, sentinel=None, raise_on_exception=False)
-                    # else timeout will truncate output for non-streaming case
-                    gen_list.append(gen1)
-
-                bots_old = chatbots.copy()
-                exceptions_old = [''] * len(bots_old)
-                tgen0 = time.time()
-                for res1 in itertools.zip_longest(*gen_list):
-                    if time.time() - tgen0 > max_time1:
-                        print("Took too long: %s" % max_time1, flush=True)
-                        break
-
-                    bots = [x[0] if x is not None and not isinstance(x, BaseException) else y for x, y in
-                            zip(res1, bots_old)]
-                    bots_old = bots.copy()
-
-                    def larger_str(x, y):
-                        return x if len(x) > len(y) else y
-
-                    exceptions = [x[1] if x is not None and not isinstance(x, BaseException) else larger_str(str(x), y)
-                                  for x, y in zip(res1, exceptions_old)]
-                    exceptions_old = exceptions.copy()
-
-                    def choose_exc(x):
-                        # don't expose ports etc. to exceptions window
-                        if is_public:
-                            return "Endpoint unavailable or failed"
-                        else:
-                            return x
-
-                    exceptions_str = '\n'.join(
-                        ['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if
-                         x not in [None, '', 'None']])
-                    if len(bots) > 1:
-                        yield tuple(bots + [exceptions_str])
-                    else:
-                        yield bots[0], exceptions_str
-                if exceptions:
-                    exceptions = [x for x in exceptions if x not in ['', None, 'None']]
-                    if exceptions:
-                        print("Generate exceptions: %s" % exceptions, flush=True)
-            finally:
-                clear_torch_cache()
-                clear_embeddings(langchain_mode1, db1s)
-
-        # NORMAL MODEL
-        user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
-                         inputs=inputs_list + [text_output],
-                         outputs=text_output,
-                         )
-        bot_args = dict(fn=bot,
-                        inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output],
-                        outputs=[text_output, chat_exception_text],
-                        )
-        retry_bot_args = dict(fn=functools.partial(bot, retry=True),
-                              inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output],
-                              outputs=[text_output, chat_exception_text],
-                              )
-        retry_user_args = dict(fn=functools.partial(user, retry=True),
-                               inputs=inputs_list + [text_output],
-                               outputs=text_output,
-                               )
-        undo_user_args = dict(fn=functools.partial(user, undo=True),
-                              inputs=inputs_list + [text_output],
-                              outputs=text_output,
-                              )
-
-        # MODEL2
-        user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
-                          inputs=inputs_list2 + [text_output2],
-                          outputs=text_output2,
-                          )
-        bot_args2 = dict(fn=bot,
-                         inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2],
-                         outputs=[text_output2, chat_exception_text],
-                         )
-        retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
-                               inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2],
-                               outputs=[text_output2, chat_exception_text],
-                               )
-        retry_user_args2 = dict(fn=functools.partial(user, retry=True),
-                                inputs=inputs_list2 + [text_output2],
-                                outputs=text_output2,
-                                )
-        undo_user_args2 = dict(fn=functools.partial(user, undo=True),
-                               inputs=inputs_list2 + [text_output2],
-                               outputs=text_output2,
-                               )
-
-        # MODEL N
-        all_user_args = dict(fn=functools.partial(all_user,
-                                                  sanitize_user_prompt=kwargs['sanitize_user_prompt'],
-                                                  num_model_lock=len(text_outputs),
-                                                  ),
-                             inputs=inputs_list + text_outputs,
-                             outputs=text_outputs,
-                             )
-        all_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states),
-                            inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs,
-                            outputs=text_outputs + [chat_exception_text],
-                            )
-        all_retry_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states, retry=True),
-                                  inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs,
-                                  outputs=text_outputs + [chat_exception_text],
-                                  )
-        all_retry_user_args = dict(fn=functools.partial(all_user, retry=True,
-                                                        sanitize_user_prompt=kwargs['sanitize_user_prompt'],
-                                                        num_model_lock=len(text_outputs),
-                                                        ),
-                                   inputs=inputs_list + text_outputs,
-                                   outputs=text_outputs,
-                                   )
-        all_undo_user_args = dict(fn=functools.partial(all_user, undo=True,
-                                                       sanitize_user_prompt=kwargs['sanitize_user_prompt'],
-                                                       num_model_lock=len(text_outputs),
-                                                       ),
-                                  inputs=inputs_list + text_outputs,
-                                  outputs=text_outputs,
-                                  )
-
-        def clear_instruct():
-            return gr.Textbox.update(value='')
-
-        def deselect_radio_chats():
-            return gr.update(value=None)
-
-        def clear_all():
-            return gr.Textbox.update(value=''), gr.Textbox.update(value=''), gr.update(value=None), \
-                gr.Textbox.update(value=''), gr.Textbox.update(value='')
-
-        if kwargs['model_states']:
-            submits1 = submits2 = submits3 = []
-            submits4 = []
-
-            fun_source = [instruction.submit, submit.click, retry_btn.click]
-            fun_name = ['instruction', 'submit', 'retry']
-            user_args = [all_user_args, all_user_args, all_retry_user_args]
-            bot_args = [all_bot_args, all_bot_args, all_retry_bot_args]
-            for userargs1, botarg1, funn1, funs1 in zip(user_args, bot_args, fun_name, fun_source):
-                submit_event11 = funs1(fn=dummy_fun,
-                                       inputs=instruction, outputs=instruction, queue=queue)
-                submit_event1a = submit_event11.then(**userargs1, queue=queue,
-                                                     api_name='%s' % funn1 if allow_api else None)
-                # if hit enter on new instruction for submitting new query, no longer the saved chat
-                submit_event1b = submit_event1a.then(clear_all, inputs=None,
-                                                     outputs=[instruction, iinput, radio_chats, score_text,
-                                                              score_text2],
-                                                     queue=queue)
-                submit_event1c = submit_event1b.then(**botarg1,
-                                                     api_name='%s_bot' % funn1 if allow_api else None,
-                                                     queue=queue)
-                submit_event1d = submit_event1c.then(**all_score_args,
-                                                     api_name='%s_bot_score' % funn1 if allow_api else None,
-                                                     queue=queue)
-
-                submits1.extend([submit_event1a, submit_event1b, submit_event1c, submit_event1d])
-
-            # if undo, no longer the saved chat
-            submit_event4 = undo.click(fn=dummy_fun,
-                                       inputs=instruction, outputs=instruction, queue=queue) \
-                .then(**all_undo_user_args, api_name='undo' if allow_api else None) \
-                .then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text,
-                                                       score_text2], queue=queue) \
-                .then(**all_score_args, api_name='undo_score' if allow_api else None)
-            submits4 = [submit_event4]
-
-        else:
-            # in case 2nd model, consume instruction first, so can clear quickly
-            # bot doesn't consume instruction itself, just history from user, so why works
-            submit_event11 = instruction.submit(fn=dummy_fun,
-                                                inputs=instruction, outputs=instruction, queue=queue)
-            submit_event1a = submit_event11.then(**user_args, queue=queue,
-                                                 api_name='instruction' if allow_api else None)
-            # if hit enter on new instruction for submitting new query, no longer the saved chat
-            submit_event1a2 = submit_event1a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
-            submit_event1b = submit_event1a2.then(**user_args2, api_name='instruction2' if allow_api else None)
-            submit_event1c = submit_event1b.then(clear_instruct, None, instruction) \
-                .then(clear_instruct, None, iinput)
-            submit_event1d = submit_event1c.then(**bot_args, api_name='instruction_bot' if allow_api else None,
-                                                 queue=queue)
-            submit_event1e = submit_event1d.then(**score_args,
-                                                 api_name='instruction_bot_score' if allow_api else None,
-                                                 queue=queue)
-            submit_event1f = submit_event1e.then(**bot_args2, api_name='instruction_bot2' if allow_api else None,
-                                                 queue=queue)
-            submit_event1g = submit_event1f.then(**score_args2,
-                                                 api_name='instruction_bot_score2' if allow_api else None, queue=queue)
-
-            submits1 = [submit_event1a, submit_event1a2, submit_event1b, submit_event1c, submit_event1d,
-                        submit_event1e,
-                        submit_event1f, submit_event1g]
-
-            submit_event21 = submit.click(fn=dummy_fun,
-                                          inputs=instruction, outputs=instruction, queue=queue)
-            submit_event2a = submit_event21.then(**user_args, api_name='submit' if allow_api else None)
-            # if submit new query, no longer the saved chat
-            submit_event2a2 = submit_event2a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
-            submit_event2b = submit_event2a2.then(**user_args2, api_name='submit2' if allow_api else None)
-            submit_event2c = submit_event2b.then(clear_all, inputs=None,
-                                                 outputs=[instruction, iinput, radio_chats, score_text, score_text2],
-                                                 queue=queue)
-            submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue)
-            submit_event2e = submit_event2d.then(**score_args,
-                                                 api_name='submit_bot_score' if allow_api else None,
-                                                 queue=queue)
-            submit_event2f = submit_event2e.then(**bot_args2, api_name='submit_bot2' if allow_api else None,
-                                                 queue=queue)
-            submit_event2g = submit_event2f.then(**score_args2,
-                                                 api_name='submit_bot_score2' if allow_api else None,
-                                                 queue=queue)
-
-            submits2 = [submit_event2a, submit_event2a2, submit_event2b, submit_event2c, submit_event2d,
-                        submit_event2e,
-                        submit_event2f, submit_event2g]
-
-            submit_event31 = retry_btn.click(fn=dummy_fun,
-                                             inputs=instruction, outputs=instruction, queue=queue)
-            submit_event3a = submit_event31.then(**user_args, api_name='retry' if allow_api else None)
-            # if retry, no longer the saved chat
-            submit_event3a2 = submit_event3a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
-            submit_event3b = submit_event3a2.then(**user_args2, api_name='retry2' if allow_api else None)
-            submit_event3c = submit_event3b.then(clear_instruct, None, instruction) \
-                .then(clear_instruct, None, iinput)
-            submit_event3d = submit_event3c.then(**retry_bot_args, api_name='retry_bot' if allow_api else None,
-                                                 queue=queue)
-            submit_event3e = submit_event3d.then(**score_args,
-                                                 api_name='retry_bot_score' if allow_api else None,
-                                                 queue=queue)
-            submit_event3f = submit_event3e.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None,
-                                                 queue=queue)
-            submit_event3g = submit_event3f.then(**score_args2,
-                                                 api_name='retry_bot_score2' if allow_api else None,
-                                                 queue=queue)
-
-            submits3 = [submit_event3a, submit_event3a2, submit_event3b, submit_event3c, submit_event3d,
-                        submit_event3e,
-                        submit_event3f, submit_event3g]
-
-            # if undo, no longer the saved chat
-            submit_event4 = undo.click(fn=dummy_fun,
-                                       inputs=instruction, outputs=instruction, queue=queue) \
-                .then(**undo_user_args, api_name='undo' if allow_api else None) \
-                .then(**undo_user_args2, api_name='undo2' if allow_api else None) \
-                .then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text,
-                                                       score_text2], queue=queue) \
-                .then(**score_args, api_name='undo_score' if allow_api else None) \
-                .then(**score_args2, api_name='undo_score2' if allow_api else None)
-            submits4 = [submit_event4]
-
-        # MANAGE CHATS
-        def dedup(short_chat, short_chats):
-            if short_chat not in short_chats:
-                return short_chat
-            for i in range(1, 1000):
-                short_chat_try = short_chat + "_" + str(i)
-                if short_chat_try not in short_chats:
-                    return short_chat_try
-            # fallback and hope for best
-            short_chat = short_chat + "_" + str(random.random())
-            return short_chat
-
-        def get_short_chat(x, short_chats, short_len=20, words=4):
-            if x and len(x[0]) == 2 and x[0][0] is not None:
-                short_chat = ' '.join(x[0][0][:short_len].split(' ')[:words]).strip()
-                if not short_chat:
-                    # e.g.summarization, try using answer
-                    short_chat = ' '.join(x[0][1][:short_len].split(' ')[:words]).strip()
-                    if not short_chat:
-                        short_chat = 'Unk'
-                short_chat = dedup(short_chat, short_chats)
-            else:
-                short_chat = None
-            return short_chat
-
-        def is_chat_same(x, y):
-            # <p> etc. added in chat, try to remove some of that to help avoid dup entries when hit new conversation
-            is_same = True
-            # length of conversation has to be same
-            if len(x) != len(y):
-                return False
-            if len(x) != len(y):
-                return False
-            for stepx, stepy in zip(x, y):
-                if len(stepx) != len(stepy):
-                    # something off with a conversation
-                    return False
-                for stepxx, stepyy in zip(stepx, stepy):
-                    if len(stepxx) != len(stepyy):
-                        # something off with a conversation
-                        return False
-                    if len(stepxx) != 2:
-                        # something off
-                        return False
-                    if len(stepyy) != 2:
-                        # something off
-                        return False
-                    questionx = stepxx[0].replace('<p>', '').replace('</p>', '') if stepxx[0] is not None else None
-                    answerx = stepxx[1].replace('<p>', '').replace('</p>', '') if stepxx[1] is not None else None
-
-                    questiony = stepyy[0].replace('<p>', '').replace('</p>', '') if stepyy[0] is not None else None
-                    answery = stepyy[1].replace('<p>', '').replace('</p>', '') if stepyy[1] is not None else None
-
-                    if questionx != questiony or answerx != answery:
-                        return False
-            return is_same
-
-        def save_chat(*args, chat_is_list=False):
-            args_list = list(args)
-            if not chat_is_list:
-                # list of chatbot histories,
-                # can't pass in list with list of chatbot histories and state due to gradio limits
-                chat_list = args_list[:-1]
-            else:
-                assert len(args_list) == 2
-                chat_list = args_list[0]
-            # if old chat file with single chatbot, get into shape
-            if isinstance(chat_list, list) and len(chat_list) > 0 and isinstance(chat_list[0], list) and len(
-                    chat_list[0]) == 2 and isinstance(chat_list[0][0], str) and isinstance(chat_list[0][1], str):
-                chat_list = [chat_list]
-            # remove None histories
-            chat_list_not_none = [x for x in chat_list if x and len(x) > 0 and len(x[0]) == 2 and x[0][1] is not None]
-            chat_list_none = [x for x in chat_list if x not in chat_list_not_none]
-            if len(chat_list_none) > 0 and len(chat_list_not_none) == 0:
-                raise ValueError("Invalid chat file")
-            # dict with keys of short chat names, values of list of list of chatbot histories
-            chat_state1 = args_list[-1]
-            short_chats = list(chat_state1.keys())
-            if len(chat_list_not_none) > 0:
-                # make short_chat key from only first history, based upon question that is same anyways
-                chat_first = chat_list_not_none[0]
-                short_chat = get_short_chat(chat_first, short_chats)
-                if short_chat:
-                    old_chat_lists = list(chat_state1.values())
-                    already_exists = any([is_chat_same(chat_list, x) for x in old_chat_lists])
-                    if not already_exists:
-                        chat_state1[short_chat] = chat_list.copy()
-
-            # reverse so newest at top
-            choices = list(chat_state1.keys()).copy()
-            choices.reverse()
-
-            return chat_state1, gr.update(choices=choices, value=None)
-
-        def switch_chat(chat_key, chat_state1, num_model_lock=0):
-            chosen_chat = chat_state1[chat_key]
-            # deal with possible different size of chat list vs. current list
-            ret_chat = [None] * (2 + num_model_lock)
-            for chati in range(0, 2 + num_model_lock):
-                ret_chat[chati % len(ret_chat)] = chosen_chat[chati % len(chosen_chat)]
-            return tuple(ret_chat)
-
-        def clear_texts(*args):
-            return tuple([gr.Textbox.update(value='')] * len(args))
-
-        def clear_scores():
-            return gr.Textbox.update(value=res_value), \
-                gr.Textbox.update(value='Response Score: NA'), \
-                gr.Textbox.update(value='Response Score: NA')
-
-        switch_chat_fun = functools.partial(switch_chat, num_model_lock=len(text_outputs))
-        radio_chats.input(switch_chat_fun,
-                          inputs=[radio_chats, chat_state],
-                          outputs=[text_output, text_output2] + text_outputs) \
-            .then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
-
-        def remove_chat(chat_key, chat_state1):
-            if isinstance(chat_key, str):
-                chat_state1.pop(chat_key, None)
-            return gr.update(choices=list(chat_state1.keys()), value=None), chat_state1
-
-        remove_chat_event = remove_chat_btn.click(remove_chat,
-                                                  inputs=[radio_chats, chat_state], outputs=[radio_chats, chat_state],
-                                                  queue=False, api_name='remove_chat')
-
-        def get_chats1(chat_state1):
-            base = 'chats'
-            makedirs(base, exist_ok=True)
-            filename = os.path.join(base, 'chats_%s.json' % str(uuid.uuid4()))
-            with open(filename, "wt") as f:
-                f.write(json.dumps(chat_state1, indent=2))
-            return filename
-
-        export_chat_event = export_chats_btn.click(get_chats1, inputs=chat_state, outputs=chats_file, queue=False,
-                                                   api_name='export_chats' if allow_api else None)
-
-        def add_chats_from_file(file, chat_state1, radio_chats1, chat_exception_text1):
-            if not file:
-                return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
-            if isinstance(file, str):
-                files = [file]
-            else:
-                files = file
-            if not files:
-                return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
-            chat_exception_list = []
-            for file1 in files:
-                try:
-                    if hasattr(file1, 'name'):
-                        file1 = file1.name
-                    with open(file1, "rt") as f:
-                        new_chats = json.loads(f.read())
-                        for chat1_k, chat1_v in new_chats.items():
-                            # ignore chat1_k, regenerate and de-dup to avoid loss
-                            chat_state1, _ = save_chat(chat1_v, chat_state1, chat_is_list=True)
-                except BaseException as e:
-                    t, v, tb = sys.exc_info()
-                    ex = ''.join(traceback.format_exception(t, v, tb))
-                    ex_str = "File %s exception: %s" % (file1, str(e))
-                    print(ex_str, flush=True)
-                    chat_exception_list.append(ex_str)
-                    chat_exception_text1 = '\n'.join(chat_exception_list)
-            return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
-
-        # note for update_user_db_func output is ignored for db
-        chatup_change_event = chatsup_output.change(add_chats_from_file,
-                                                    inputs=[chatsup_output, chat_state, radio_chats,
-                                                            chat_exception_text],
-                                                    outputs=[chatsup_output, chat_state, radio_chats,
-                                                             chat_exception_text],
-                                                    queue=False,
-                                                    api_name='add_to_chats' if allow_api else None)
-
-        clear_chat_event = clear_chat_btn.click(fn=clear_texts,
-                                                inputs=[text_output, text_output2] + text_outputs,
-                                                outputs=[text_output, text_output2] + text_outputs,
-                                                queue=False, api_name='clear' if allow_api else None) \
-            .then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \
-            .then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
-
-        clear_event = save_chat_btn.click(save_chat,
-                                          inputs=[text_output, text_output2] + text_outputs + [chat_state],
-                                          outputs=[chat_state, radio_chats],
-                                          api_name='save_chat' if allow_api else None)
-        if kwargs['score_model']:
-            clear_event2 = clear_event.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
-
-        # NOTE: clear of instruction/iinput for nochat has to come after score,
-        # because score for nochat consumes actual textbox, while chat consumes chat history filled by user()
-        no_chat_args = dict(fn=fun,
-                            inputs=[model_state, my_db_state, selection_docs_state] + inputs_list,
-                            outputs=text_output_nochat,
-                            queue=queue,
-                            )
-        submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \
-            .then(clear_torch_cache) \
-            .then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \
-            .then(clear_instruct, None, instruction_nochat) \
-            .then(clear_instruct, None, iinput_nochat) \
-            .then(clear_torch_cache)
-        # copy of above with text box submission
-        submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \
-            .then(clear_torch_cache) \
-            .then(**score_args_nochat, queue=queue) \
-            .then(clear_instruct, None, instruction_nochat) \
-            .then(clear_instruct, None, iinput_nochat) \
-            .then(clear_torch_cache)
-
-        submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str,
-                                                          inputs=[model_state, my_db_state, selection_docs_state,
-                                                                  inputs_dict_str],
-                                                          outputs=text_output_nochat_api,
-                                                          queue=True,  # required for generator
-                                                          api_name='submit_nochat_api' if allow_api else None) \
-            .then(clear_torch_cache)
-
-        def load_model(model_name, lora_weights, server_name, model_state_old, prompt_type_old, load_8bit,
-                       use_gpu_id, gpu_id):
-            # ensure no API calls reach here
-            if is_public:
-                raise RuntimeError("Illegal access for %s" % model_name)
-            # ensure old model removed from GPU memory
-            if kwargs['debug']:
-                print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True)
-
-            model0 = model_state0['model']
-            if isinstance(model_state_old['model'], str) and model0 is not None:
-                # best can do, move model loaded at first to CPU
-                model0.cpu()
-
-            if model_state_old['model'] is not None and not isinstance(model_state_old['model'], str):
-                try:
-                    model_state_old['model'].cpu()
-                except Exception as e:
-                    # sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data!
-                    print("Unable to put model on CPU: %s" % str(e), flush=True)
-                del model_state_old['model']
-                model_state_old['model'] = None
-
-            if model_state_old['tokenizer'] is not None and not isinstance(model_state_old['tokenizer'], str):
-                del model_state_old['tokenizer']
-                model_state_old['tokenizer'] = None
-
-            clear_torch_cache()
-            if kwargs['debug']:
-                print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True)
-
-            if model_name is None or model_name == no_model_str:
-                # no-op if no model, just free memory
-                # no detranscribe needed for model, never go into evaluate
-                lora_weights = no_lora_str
-                server_name = no_server_str
-                return [None, None, None, model_name, server_name], \
-                    model_name, lora_weights, server_name, prompt_type_old, \
-                    gr.Slider.update(maximum=256), \
-                    gr.Slider.update(maximum=256)
-
-            # don't deepcopy, can contain model itself
-            all_kwargs1 = all_kwargs.copy()
-            all_kwargs1['base_model'] = model_name.strip()
-            all_kwargs1['load_8bit'] = load_8bit
-            all_kwargs1['use_gpu_id'] = use_gpu_id
-            all_kwargs1['gpu_id'] = int(gpu_id)  # detranscribe
-            model_lower = model_name.strip().lower()
-            if model_lower in inv_prompt_type_to_model_lower:
-                prompt_type1 = inv_prompt_type_to_model_lower[model_lower]
-            else:
-                prompt_type1 = prompt_type_old
-
-            # detranscribe
-            if lora_weights == no_lora_str:
-                lora_weights = ''
-            all_kwargs1['lora_weights'] = lora_weights.strip()
-            if server_name == no_server_str:
-                server_name = ''
-            all_kwargs1['inference_server'] = server_name.strip()
-
-            model1, tokenizer1, device1 = get_model(reward_type=False,
-                                                    **get_kwargs(get_model, exclude_names=['reward_type'],
-                                                                 **all_kwargs1))
-            clear_torch_cache()
-
-            tokenizer_base_model = model_name
-            prompt_dict1, error0 = get_prompt(prompt_type1, '',
-                                              chat=False, context='', reduced=False, making_context=False,
-                                              return_dict=True)
-            model_state_new = dict(model=model1, tokenizer=tokenizer1, device=device1,
-                                   base_model=model_name, tokenizer_base_model=tokenizer_base_model,
-                                   lora_weights=lora_weights, inference_server=server_name,
-                                   prompt_type=prompt_type1, prompt_dict=prompt_dict1,
-                                   )
-
-            max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs)
-
-            if kwargs['debug']:
-                print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True)
-            return model_state_new, model_name, lora_weights, server_name, prompt_type1, \
-                gr.Slider.update(maximum=max_max_new_tokens1), \
-                gr.Slider.update(maximum=max_max_new_tokens1)
-
-        def get_prompt_str(prompt_type1, prompt_dict1, which=0):
-            if prompt_type1 in ['', None]:
-                print("Got prompt_type %s: %s" % (which, prompt_type1), flush=True)
-                return str({})
-            prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='',
-                                                         reduced=False, making_context=False, return_dict=True)
-            if prompt_dict_error:
-                return str(prompt_dict_error)
-            else:
-                # return so user can manipulate if want and use as custom
-                return str(prompt_dict1)
-
-        get_prompt_str_func1 = functools.partial(get_prompt_str, which=1)
-        get_prompt_str_func2 = functools.partial(get_prompt_str, which=2)
-        prompt_type.change(fn=get_prompt_str_func1, inputs=[prompt_type, prompt_dict], outputs=prompt_dict, queue=False)
-        prompt_type2.change(fn=get_prompt_str_func2, inputs=[prompt_type2, prompt_dict2], outputs=prompt_dict2,
-                            queue=False)
-
-        def dropdown_prompt_type_list(x):
-            return gr.Dropdown.update(value=x)
-
-        def chatbot_list(x, model_used_in):
-            return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]')
-
-        load_model_args = dict(fn=load_model,
-                               inputs=[model_choice, lora_choice, server_choice, model_state, prompt_type,
-                                       model_load8bit_checkbox, model_use_gpu_id_checkbox, model_gpu],
-                               outputs=[model_state, model_used, lora_used, server_used,
-                                        # if prompt_type changes, prompt_dict will change via change rule
-                                        prompt_type, max_new_tokens, min_new_tokens,
-                                        ])
-        prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
-        chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
-        nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat)
-        load_model_event = load_model_button.click(**load_model_args,
-                                                   api_name='load_model' if allow_api and is_public else None) \
-            .then(**prompt_update_args) \
-            .then(**chatbot_update_args) \
-            .then(**nochat_update_args) \
-            .then(clear_torch_cache)
-
-        load_model_args2 = dict(fn=load_model,
-                                inputs=[model_choice2, lora_choice2, server_choice2, model_state2, prompt_type2,
-                                        model_load8bit_checkbox2, model_use_gpu_id_checkbox2, model_gpu2],
-                                outputs=[model_state2, model_used2, lora_used2, server_used2,
-                                         # if prompt_type2 changes, prompt_dict2 will change via change rule
-                                         prompt_type2, max_new_tokens2, min_new_tokens2
-                                         ])
-        prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2)
-        chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2)
-        load_model_event2 = load_model_button2.click(**load_model_args2,
-                                                     api_name='load_model2' if allow_api and is_public else None) \
-            .then(**prompt_update_args2) \
-            .then(**chatbot_update_args2) \
-            .then(clear_torch_cache)
-
-        def dropdown_model_lora_server_list(model_list0, model_x,
-                                            lora_list0, lora_x,
-                                            server_list0, server_x,
-                                            model_used1, lora_used1, server_used1,
-                                            model_used2, lora_used2, server_used2,
-                                            ):
-            model_new_state = [model_list0[0] + [model_x]]
-            model_new_options = [*model_new_state[0]]
-            x1 = model_x if model_used1 == no_model_str else model_used1
-            x2 = model_x if model_used2 == no_model_str else model_used2
-            ret1 = [gr.Dropdown.update(value=x1, choices=model_new_options),
-                    gr.Dropdown.update(value=x2, choices=model_new_options),
-                    '', model_new_state]
-
-            lora_new_state = [lora_list0[0] + [lora_x]]
-            lora_new_options = [*lora_new_state[0]]
-            # don't switch drop-down to added lora if already have model loaded
-            x1 = lora_x if model_used1 == no_model_str else lora_used1
-            x2 = lora_x if model_used2 == no_model_str else lora_used2
-            ret2 = [gr.Dropdown.update(value=x1, choices=lora_new_options),
-                    gr.Dropdown.update(value=x2, choices=lora_new_options),
-                    '', lora_new_state]
-
-            server_new_state = [server_list0[0] + [server_x]]
-            server_new_options = [*server_new_state[0]]
-            # don't switch drop-down to added server if already have model loaded
-            x1 = server_x if model_used1 == no_model_str else server_used1
-            x2 = server_x if model_used2 == no_model_str else server_used2
-            ret3 = [gr.Dropdown.update(value=x1, choices=server_new_options),
-                    gr.Dropdown.update(value=x2, choices=server_new_options),
-                    '', server_new_state]
-
-            return tuple(ret1 + ret2 + ret3)
-
-        add_model_lora_server_event = \
-            add_model_lora_server_button.click(fn=dropdown_model_lora_server_list,
-                                               inputs=[model_options_state, new_model] +
-                                                      [lora_options_state, new_lora] +
-                                                      [server_options_state, new_server] +
-                                                      [model_used, lora_used, server_used] +
-                                                      [model_used2, lora_used2, server_used2],
-                                               outputs=[model_choice, model_choice2, new_model, model_options_state] +
-                                                       [lora_choice, lora_choice2, new_lora, lora_options_state] +
-                                                       [server_choice, server_choice2, new_server,
-                                                        server_options_state],
-                                               queue=False)
-
-        go_event = go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None,
-                                queue=False) \
-            .then(lambda: gr.update(visible=True), None, normal_block, queue=False) \
-            .then(**load_model_args, queue=False).then(**prompt_update_args, queue=False)
-
-        def compare_textbox_fun(x):
-            return gr.Textbox.update(visible=x)
-
-        def compare_column_fun(x):
-            return gr.Column.update(visible=x)
-
-        def compare_prompt_fun(x):
-            return gr.Dropdown.update(visible=x)
-
-        def slider_fun(x):
-            return gr.Slider.update(visible=x)
-
-        compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2,
-                                api_name="compare_checkbox" if allow_api else None) \
-            .then(compare_column_fun, compare_checkbox, col_model2) \
-            .then(compare_prompt_fun, compare_checkbox, prompt_type2) \
-            .then(compare_textbox_fun, compare_checkbox, score_text2) \
-            .then(slider_fun, compare_checkbox, max_new_tokens2) \
-            .then(slider_fun, compare_checkbox, min_new_tokens2)
-        # FIXME: add score_res2 in condition, but do better
-
-        # callback for logging flagged input/output
-        callback.setup(inputs_list + [text_output, text_output2] + text_outputs, "flagged_data_points")
-        flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2] + text_outputs,
-                       None,
-                       preprocess=False,
-                       api_name='flag' if allow_api else None, queue=False)
-        flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None,
-                              preprocess=False,
-                              api_name='flag_nochat' if allow_api else None, queue=False)
-
-        def get_system_info():
-            if is_public:
-                time.sleep(10)  # delay to avoid spam since queue=False
-            return gr.Textbox.update(value=system_info_print())
-
-        system_event = system_btn.click(get_system_info, outputs=system_text,
-                                        api_name='system_info' if allow_api else None, queue=False)
-
-        def get_system_info_dict(system_input1, **kwargs1):
-            if system_input1 != os.getenv("ADMIN_PASS", ""):
-                return json.dumps({})
-            exclude_list = ['admin_pass', 'examples']
-            sys_dict = {k: v for k, v in kwargs1.items() if
-                        isinstance(v, (str, int, bool, float)) and k not in exclude_list}
-            try:
-                sys_dict.update(system_info())
-            except Exception as e:
-                # protection
-                print("Exception: %s" % str(e), flush=True)
-            return json.dumps(sys_dict)
-
-        system_kwargs = all_kwargs.copy()
-        system_kwargs.update(dict(command=str(' '.join(sys.argv))))
-        get_system_info_dict_func = functools.partial(get_system_info_dict, **all_kwargs)
-
-        system_dict_event = system_btn2.click(get_system_info_dict_func,
-                                              inputs=system_input,
-                                              outputs=system_text2,
-                                              api_name='system_info_dict' if allow_api else None,
-                                              queue=False,  # queue to avoid spam
-                                              )
-
-        def get_hash():
-            return kwargs['git_hash']
-
-        system_event = system_btn3.click(get_hash,
-                                         outputs=system_text3,
-                                         api_name='system_hash' if allow_api else None,
-                                         queue=False,
-                                         )
-
-        def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1,
-                              memory_restriction_level1=0,
-                              keep_sources_in_context1=False,
-                              ):
-            if model_state1 and not isinstance(model_state1['tokenizer'], str):
-                tokenizer = model_state1['tokenizer']
-            elif model_state0 and not isinstance(model_state0['tokenizer'], str):
-                tokenizer = model_state0['tokenizer']
-            else:
-                tokenizer = None
-            if tokenizer is not None:
-                langchain_mode1 = 'LLM'
-                add_chat_history_to_context1 = True
-                # fake user message to mimic bot()
-                chat1 = copy.deepcopy(chat1)
-                chat1 = chat1 + [['user_message1', None]]
-                model_max_length1 = tokenizer.model_max_length
-                context1 = history_to_context(chat1, langchain_mode1,
-                                              add_chat_history_to_context1,
-                                              prompt_type1, prompt_dict1, chat1,
-                                              model_max_length1,
-                                              memory_restriction_level1, keep_sources_in_context1)
-                return str(tokenizer(context1, return_tensors="pt")['input_ids'].shape[1])
-            else:
-                return "N/A"
-
-        count_chat_tokens_func = functools.partial(count_chat_tokens,
-                                                   memory_restriction_level1=memory_restriction_level,
-                                                   keep_sources_in_context1=kwargs['keep_sources_in_context'])
-        count_tokens_event = count_chat_tokens_btn.click(fn=count_chat_tokens,
-                                                         inputs=[model_state, text_output, prompt_type, prompt_dict],
-                                                         outputs=chat_token_count,
-                                                         api_name='count_tokens' if allow_api else None)
-
-        # don't pass text_output, don't want to clear output, just stop it
-        # cancel only stops outer generation, not inner generation or non-generation
-        stop_btn.click(lambda: None, None, None,
-                       cancels=submits1 + submits2 + submits3 + submits4 +
-                               [submit_event_nochat, submit_event_nochat2] +
-                               [eventdb1, eventdb2, eventdb3] +
-                               [eventdb7, eventdb8, eventdb9, eventdb12] +
-                               db_events +
-                               [clear_event] +
-                               [submit_event_nochat_api, submit_event_nochat] +
-                               [load_model_event, load_model_event2] +
-                               [count_tokens_event]
-                       ,
-                       queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False)
-
-        demo.load(None, None, None, _js=get_dark_js() if kwargs['dark'] else None)
-
-    demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open'])
-    favicon_path = "h2o-logo.svg"
-    if not os.path.isfile(favicon_path):
-        print("favicon_path=%s not found" % favicon_path, flush=True)
-        favicon_path = None
-
-    scheduler = BackgroundScheduler()
-    scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20)
-    if is_public and \
-            kwargs['base_model'] not in non_hf_types:
-        # FIXME: disable for gptj, langchain or gpt4all modify print itself
-        # FIXME: and any multi-threaded/async print will enter model output!
-        scheduler.add_job(func=ping, trigger="interval", seconds=60)
-    if is_public or os.getenv('PING_GPU'):
-        scheduler.add_job(func=ping_gpu, trigger="interval", seconds=60 * 10)
-    scheduler.start()
-
-    # import control
-    if kwargs['langchain_mode'] == 'Disabled' and \
-            os.environ.get("TEST_LANGCHAIN_IMPORT") and \
-            kwargs['base_model'] not in non_hf_types:
-        assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
-        assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
-
-    demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
-                favicon_path=favicon_path, prevent_thread_lock=True,
-                auth=kwargs['auth'])
-    if kwargs['verbose']:
-        print("Started GUI", flush=True)
-    if kwargs['block_gradio_exit']:
-        demo.block_thread()
-
-
-def get_inputs_list(inputs_dict, model_lower, model_id=1):
-    """
-    map gradio objects in locals() to inputs for evaluate().
-    :param inputs_dict:
-    :param model_lower:
-    :param model_id: Which model (1 or 2) of 2
-    :return:
-    """
-    inputs_list_names = list(inspect.signature(evaluate).parameters)
-    inputs_list = []
-    inputs_dict_out = {}
-    for k in inputs_list_names:
-        if k == 'kwargs':
-            continue
-        if k in input_args_list + inputs_kwargs_list:
-            # these are added at use time for args or partial for kwargs, not taken as input
-            continue
-        if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']:
-            continue
-        if model_id == 2:
-            if k == 'prompt_type':
-                k = 'prompt_type2'
-            if k == 'prompt_used':
-                k = 'prompt_used2'
-            if k == 'max_new_tokens':
-                k = 'max_new_tokens2'
-            if k == 'min_new_tokens':
-                k = 'min_new_tokens2'
-        inputs_list.append(inputs_dict[k])
-        inputs_dict_out[k] = inputs_dict[k]
-    return inputs_list, inputs_dict_out
-
-
-def get_sources(db1s, langchain_mode, dbs=None, docs_state0=None):
-    for k in db1s:
-        set_userid(db1s[k])
-
-    if langchain_mode in ['LLM']:
-        source_files_added = "NA"
-        source_list = []
-    elif langchain_mode in ['wiki_full']:
-        source_files_added = "Not showing wiki_full, takes about 20 seconds and makes 4MB file." \
-                             "  Ask jon.mckinney@h2o.ai for file if required."
-        source_list = []
-    elif langchain_mode in db1s and len(db1s[langchain_mode]) == 2 and db1s[langchain_mode][0] is not None:
-        db1 = db1s[langchain_mode]
-        from gpt_langchain import get_metadatas
-        metadatas = get_metadatas(db1[0])
-        source_list = sorted(set([x['source'] for x in metadatas]))
-        source_files_added = '\n'.join(source_list)
-    elif langchain_mode in dbs and dbs[langchain_mode] is not None:
-        from gpt_langchain import get_metadatas
-        db1 = dbs[langchain_mode]
-        metadatas = get_metadatas(db1)
-        source_list = sorted(set([x['source'] for x in metadatas]))
-        source_files_added = '\n'.join(source_list)
-    else:
-        source_list = []
-        source_files_added = "None"
-    sources_dir = "sources_dir"
-    makedirs(sources_dir)
-    sources_file = os.path.join(sources_dir, 'sources_%s_%s' % (langchain_mode, str(uuid.uuid4())))
-    with open(sources_file, "wt") as f:
-        f.write(source_files_added)
-    source_list = docs_state0 + source_list
-    return sources_file, source_list
-
-
-def set_userid(db1):
-    # can only call this after function called so for specific userr, not in gr.State() that occurs during app init
-    assert db1 is not None and len(db1) == 2
-    if db1[1] is None:
-        #  uuid in db is used as user ID
-        db1[1] = str(uuid.uuid4())
-
-
-def update_user_db(file, db1s, selection_docs_state1, chunk, chunk_size, langchain_mode, dbs=None, **kwargs):
-    kwargs.update(selection_docs_state1)
-    if file is None:
-        raise RuntimeError("Don't use change, use input")
-
-    try:
-        return _update_user_db(file, db1s=db1s, chunk=chunk, chunk_size=chunk_size,
-                               langchain_mode=langchain_mode, dbs=dbs,
-                               **kwargs)
-    except BaseException as e:
-        print(traceback.format_exc(), flush=True)
-        # gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox
-        ex_str = "Exception: %s" % str(e)
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               Sources: <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {0}
-               </div>
-          </body>
-        </html>
-        """.format(ex_str)
-        doc_exception_text = str(e)
-        return None, langchain_mode, source_files_added, doc_exception_text
-    finally:
-        clear_torch_cache()
-
-
-def get_lock_file(db1, langchain_mode):
-    set_userid(db1)
-    assert len(db1) == 2 and db1[1] is not None and isinstance(db1[1], str)
-    user_id = db1[1]
-    base_path = 'locks'
-    makedirs(base_path)
-    lock_file = os.path.join(base_path, "db_%s_%s.lock" % (langchain_mode.replace(' ', '_'), user_id))
-    return lock_file
-
-
-def _update_user_db(file,
-                    db1s=None,
-                    chunk=None, chunk_size=None,
-                    dbs=None, db_type=None,
-                    langchain_mode='UserData',
-                    langchain_modes=None,  # unused but required as part of selection_docs_state1
-                    langchain_mode_paths=None,
-                    visible_langchain_modes=None,
-                    use_openai_embedding=None,
-                    hf_embedding_model=None,
-                    caption_loader=None,
-                    enable_captions=None,
-                    captions_model=None,
-                    enable_ocr=None,
-                    enable_pdf_ocr=None,
-                    verbose=None,
-                    n_jobs=-1,
-                    is_url=None, is_txt=None,
-                    ):
-    assert db1s is not None
-    assert chunk is not None
-    assert chunk_size is not None
-    assert use_openai_embedding is not None
-    assert hf_embedding_model is not None
-    assert caption_loader is not None
-    assert enable_captions is not None
-    assert captions_model is not None
-    assert enable_ocr is not None
-    assert enable_pdf_ocr is not None
-    assert verbose is not None
-
-    if dbs is None:
-        dbs = {}
-    assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs))
-    # assert db_type in ['faiss', 'chroma'], "db_type %s not supported" % db_type
-    from gpt_langchain import add_to_db, get_db, path_to_docs
-    # handle case of list of temp buffer
-    if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'):
-        file = [x.name for x in file]
-    # handle single file of temp buffer
-    if hasattr(file, 'name'):
-        file = file.name
-    if not isinstance(file, (list, tuple, typing.Generator)) and isinstance(file, str):
-        file = [file]
-
-    if langchain_mode == LangChainMode.DISABLED.value:
-        return None, langchain_mode, get_source_files(), ""
-
-    if langchain_mode in [LangChainMode.LLM.value]:
-        # then switch to MyData, so langchain_mode also becomes way to select where upload goes
-        # but default to mydata if nothing chosen, since safest
-        if LangChainMode.MY_DATA.value in visible_langchain_modes:
-            langchain_mode = LangChainMode.MY_DATA.value
-
-    if langchain_mode_paths is None:
-        langchain_mode_paths = {}
-    user_path = langchain_mode_paths.get(langchain_mode)
-    # UserData or custom, which has to be from user's disk
-    if user_path is not None:
-        # move temp files from gradio upload to stable location
-        for fili, fil in enumerate(file):
-            if isinstance(fil, str) and os.path.isfile(fil):  # not url, text
-                new_fil = os.path.normpath(os.path.join(user_path, os.path.basename(fil)))
-                if os.path.normpath(os.path.abspath(fil)) != os.path.normpath(os.path.abspath(new_fil)):
-                    if os.path.isfile(new_fil):
-                        remove(new_fil)
-                    try:
-                        shutil.move(fil, new_fil)
-                    except FileExistsError:
-                        pass
-                    file[fili] = new_fil
-
-    if verbose:
-        print("Adding %s" % file, flush=True)
-    sources = path_to_docs(file if not is_url and not is_txt else None,
-                           verbose=verbose,
-                           n_jobs=n_jobs,
-                           chunk=chunk, chunk_size=chunk_size,
-                           url=file if is_url else None,
-                           text=file if is_txt else None,
-                           enable_captions=enable_captions,
-                           captions_model=captions_model,
-                           enable_ocr=enable_ocr,
-                           enable_pdf_ocr=enable_pdf_ocr,
-                           caption_loader=caption_loader,
-                           )
-    exceptions = [x for x in sources if x.metadata.get('exception')]
-    exceptions_strs = [x.metadata['exception'] for x in exceptions]
-    sources = [x for x in sources if 'exception' not in x.metadata]
-
-    # below must at least come after langchain_mode is modified in case was LLM -> MyData,
-    # so original langchain mode changed
-    for k in db1s:
-        set_userid(db1s[k])
-    db1 = get_db1(db1s, langchain_mode)
-
-    lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode)  # user-level lock, not db-level lock
-    with filelock.FileLock(lock_file):
-        if langchain_mode in db1s:
-            if db1[0] is not None:
-                # then add
-                db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type,
-                                                                      use_openai_embedding=use_openai_embedding,
-                                                                      hf_embedding_model=hf_embedding_model)
-            else:
-                # in testing expect:
-                # assert len(db1) == 2 and db1[1] is None, "Bad MyData db: %s" % db1
-                # for production hit, when user gets clicky:
-                assert len(db1) == 2, "Bad %s db: %s" % (langchain_mode, db1)
-                assert db1[1] is not None, "db hash was None, not allowed"
-                # then create
-                # if added has to original state and didn't change, then would be shared db for all users
-                persist_directory = os.path.join(scratch_base_dir, 'db_dir_%s_%s' % (langchain_mode, db1[1]))
-                db = get_db(sources, use_openai_embedding=use_openai_embedding,
-                            db_type=db_type,
-                            persist_directory=persist_directory,
-                            langchain_mode=langchain_mode,
-                            hf_embedding_model=hf_embedding_model)
-            if db is not None:
-                db1[0] = db
-            source_files_added = get_source_files(db=db1[0], exceptions=exceptions)
-            return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs)
-        else:
-            from gpt_langchain import get_persist_directory
-            persist_directory = get_persist_directory(langchain_mode)
-            if langchain_mode in dbs and dbs[langchain_mode] is not None:
-                # then add
-                db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type,
-                                                                      use_openai_embedding=use_openai_embedding,
-                                                                      hf_embedding_model=hf_embedding_model)
-            else:
-                # then create.  Or might just be that dbs is unfilled, then it will fill, then add
-                db = get_db(sources, use_openai_embedding=use_openai_embedding,
-                            db_type=db_type,
-                            persist_directory=persist_directory,
-                            langchain_mode=langchain_mode,
-                            hf_embedding_model=hf_embedding_model)
-            dbs[langchain_mode] = db
-            # NOTE we do not return db, because function call always same code path
-            # return dbs[langchain_mode]
-            # db in this code path is updated in place
-            source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions)
-            return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs)
-
-
-def get_db(db1s, langchain_mode, dbs=None):
-    db1 = get_db1(db1s, langchain_mode)
-    lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode)
-
-    with filelock.FileLock(lock_file):
-        if langchain_mode in ['wiki_full']:
-            # NOTE: avoid showing full wiki.  Takes about 30 seconds over about 90k entries, but not useful for now
-            db = None
-        elif langchain_mode in db1s and len(db1) == 2 and db1[0] is not None:
-            db = db1[0]
-        elif dbs is not None and langchain_mode in dbs and dbs[langchain_mode] is not None:
-            db = dbs[langchain_mode]
-        else:
-            db = None
-    return db
-
-
-def get_source_files_given_langchain_mode(db1s, langchain_mode='UserData', dbs=None):
-    db = get_db(db1s, langchain_mode, dbs=dbs)
-    if langchain_mode in ['LLM'] or db is None:
-        return "Sources: N/A"
-    return get_source_files(db=db, exceptions=None)
-
-
-def get_source_files(db=None, exceptions=None, metadatas=None):
-    if exceptions is None:
-        exceptions = []
-
-    # only should be one source, not confused
-    # assert db is not None or metadatas is not None
-    # clicky user
-    if db is None and metadatas is None:
-        return "No Sources at all"
-
-    if metadatas is None:
-        source_label = "Sources:"
-        if db is not None:
-            from gpt_langchain import get_metadatas
-            metadatas = get_metadatas(db)
-        else:
-            metadatas = []
-        adding_new = False
-    else:
-        source_label = "New Sources:"
-        adding_new = True
-
-    # below automatically de-dups
-    from gpt_langchain import get_url
-    small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('head')) for x in
-                  metadatas}
-    # if small_dict is empty dict, that's ok
-    df = pd.DataFrame(small_dict.items(), columns=['source', 'head'])
-    df.index = df.index + 1
-    df.index.name = 'index'
-    source_files_added = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
-
-    if exceptions:
-        exception_metadatas = [x.metadata for x in exceptions]
-        small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('exception')) for x in
-                      exception_metadatas}
-        # if small_dict is empty dict, that's ok
-        df = pd.DataFrame(small_dict.items(), columns=['source', 'exception'])
-        df.index = df.index + 1
-        df.index.name = 'index'
-        exceptions_html = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
-    else:
-        exceptions_html = ''
-
-    if metadatas and exceptions:
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               {0} <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {1}
-               {2}
-               </div>
-          </body>
-        </html>
-        """.format(source_label, source_files_added, exceptions_html)
-    elif metadatas:
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               {0} <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {1}
-               </div>
-          </body>
-        </html>
-        """.format(source_label, source_files_added)
-    elif exceptions_html:
-        source_files_added = """\
-        <html>
-          <body>
-            <p>
-               Exceptions: <br>
-            </p>
-               <div style="overflow-y: auto;height:400px">
-               {0}
-               </div>
-          </body>
-        </html>
-        """.format(exceptions_html)
-    else:
-        if adding_new:
-            source_files_added = "No New Sources"
-        else:
-            source_files_added = "No Sources"
-
-    return source_files_added
-
-
-def update_and_get_source_files_given_langchain_mode(db1s, langchain_mode, chunk, chunk_size,
-                                                     dbs=None, first_para=None,
-                                                     text_limit=None,
-                                                     langchain_mode_paths=None, db_type=None, load_db_if_exists=None,
-                                                     n_jobs=None, verbose=None):
-    has_path = {k: v for k, v in langchain_mode_paths.items() if v}
-    if langchain_mode in [LangChainMode.LLM.value, LangChainMode.MY_DATA.value]:
-        # then assume user really meant UserData, to avoid extra clicks in UI,
-        # since others can't be on disk, except custom user modes, which they should then select to query it
-        if LangChainMode.USER_DATA.value in has_path:
-            langchain_mode = LangChainMode.USER_DATA.value
-
-    db = get_db(db1s, langchain_mode, dbs=dbs)
-
-    from gpt_langchain import make_db
-    db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False,
-                                                        hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
-                                                        first_para=first_para, text_limit=text_limit,
-                                                        chunk=chunk,
-                                                        chunk_size=chunk_size,
-                                                        langchain_mode=langchain_mode,
-                                                        langchain_mode_paths=langchain_mode_paths,
-                                                        db_type=db_type,
-                                                        load_db_if_exists=load_db_if_exists,
-                                                        db=db,
-                                                        n_jobs=n_jobs,
-                                                        verbose=verbose)
-    # during refreshing, might have "created" new db since not in dbs[] yet, so insert back just in case
-    # so even if persisted, not kept up-to-date with dbs memory
-    if langchain_mode in db1s:
-        db1s[langchain_mode][0] = db
-    else:
-        dbs[langchain_mode] = db
-
-    # return only new sources with text saying such
-    return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata)
-
-
-def get_db1(db1s, langchain_mode1):
-    if langchain_mode1 in db1s:
-        db1 = db1s[langchain_mode1]
-    else:
-        # indicates to code that not scratch database
-        db1 = [None, None]
-    return db1
diff --git a/spaces/gorkemgoknar/moviechatbot-v2/README.md b/spaces/gorkemgoknar/moviechatbot-v2/README.md
deleted file mode 100644
index cbbf2fa57c5aa3a04e749f5bc6ef59de13fcd1cc..0000000000000000000000000000000000000000
--- a/spaces/gorkemgoknar/moviechatbot-v2/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Moviechatbot V2
-emoji: 👀
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Movie Chatbot LLama2 finetuned GGML optimized
-
-If you want to use Voices via Coqui API, checkout app.py and set WITH_AUDIO to True and set your Coqui token (you also need to give voice ids)
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Carlo Viggiani Fondazioni Pdf 110.md b/spaces/gotiQspiryo/whisper-ui/examples/Carlo Viggiani Fondazioni Pdf 110.md
deleted file mode 100644
index acdcbf9f00d8cd36e710e221bef33a8bf4388233..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Carlo Viggiani Fondazioni Pdf 110.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Carlo Viggiani Fondazioni Pdf 110</h2><br /><p><b><b>DOWNLOAD</b> &#9913;&#9913;&#9913; <a href="https://urlgoal.com/2uyMz3">https://urlgoal.com/2uyMz3</a></b></p><br /><br />
-
-Cracked-F4CG Free Download -. Patricia Major Pickup Lines 62 Rar . ... carlo viggiani fondazioni pdf 110 · Speedy Singhs Movie Download ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/GT5 Garage Editor V1.3.1 13 How to Create Your Own Custom Cars and Tracks in Gran Turismo 5.md b/spaces/gotiQspiryo/whisper-ui/examples/GT5 Garage Editor V1.3.1 13 How to Create Your Own Custom Cars and Tracks in Gran Turismo 5.md
deleted file mode 100644
index 3d5978952dbb47eacd692bdea7673f59d82b0c3b..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/GT5 Garage Editor V1.3.1 13 How to Create Your Own Custom Cars and Tracks in Gran Turismo 5.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>Get the NPJB-00532 PKG from NPS (it comes in a XML which has the links, open it with a text editor and download the 5 parts, then join the files with IgorWare File Joiner (they must appear in order _00, _01, _02 and so on, do not tick the append to first file box)</p>
-<h2>GT5 Garage Editor V1.3.1 13</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://urlgoal.com/2uyN2A">https://urlgoal.com/2uyN2A</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/examples/speech_to_text/prep_mtedx_data.py b/spaces/gradio/HuBERT/examples/speech_to_text/prep_mtedx_data.py
deleted file mode 100644
index 34b1c398c8df5168cfc731cf68592b2ae8d5897b..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_to_text/prep_mtedx_data.py
+++ /dev/null
@@ -1,238 +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.
-
-import argparse
-import logging
-import os
-from pathlib import Path
-import shutil
-from itertools import groupby
-from tempfile import NamedTemporaryFile
-from typing import Tuple
-
-import pandas as pd
-import soundfile as sf
-from examples.speech_to_text.data_utils import (
-    create_zip,
-    extract_fbank_features,
-    filter_manifest_df,
-    gen_config_yaml,
-    gen_vocab,
-    get_zip_manifest,
-    load_df_from_tsv,
-    save_df_to_tsv,
-)
-import torch
-from torch.utils.data import Dataset
-from tqdm import tqdm
-
-from fairseq.data.audio.audio_utils import get_waveform
-
-
-log = logging.getLogger(__name__)
-
-
-MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker", "tgt_lang"]
-
-
-class mTEDx(Dataset):
-    """
-    Create a Dataset for Multilingual TEDx.
-    Each item is a tuple of the form: waveform, sample_rate, source utterance,
-    target utterance, speaker_id, utterance_id
-    """
-
-    SPLITS = ["train", "valid", "test"]
-    LANGPAIRS = ["es-es", "fr-fr", "pt-pt", "it-it", "ru-ru", "el-el", "ar-ar", "de-de",
-                 "es-en", "es-fr", "es-pt", "es-it", "fr-en", "fr-es", "fr-pt",
-                 "pt-en", "pt-es", "it-en", "it-es", "ru-en", "el-en"]
-
-    def __init__(self, root: str, lang: str, split: str) -> None:
-        assert split in self.SPLITS and lang in self.LANGPAIRS
-        _root = Path(root) / f"{lang}" / "data" / split
-        wav_root, txt_root = _root / "wav", _root / "txt"
-        assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir()
-        # Load audio segments
-        try:
-            import yaml
-        except ImportError:
-            print("Please install PyYAML to load the Multilingual TEDx YAML files")
-        with open(txt_root / f"{split}.yaml") as f:
-            segments = yaml.load(f, Loader=yaml.BaseLoader)
-        # Load source and target utterances
-        src, tgt = lang.split("-")
-        for _lang in [src, tgt]:
-            with open(txt_root / f"{split}.{_lang}") as f:
-                utterances = [r.strip() for r in f]
-            assert len(segments) == len(utterances)
-            for i, u in enumerate(utterances):
-                segments[i][_lang] = u
-        # Gather info
-        self.data = []
-        for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]):
-            wav_filename = wav_filename.replace(".wav", ".flac")
-            wav_path = wav_root / wav_filename
-            sample_rate = sf.info(wav_path.as_posix()).samplerate
-            seg_group = sorted(_seg_group, key=lambda x: float(x["offset"]))
-            for i, segment in enumerate(seg_group):
-                offset = int(float(segment["offset"]) * sample_rate)
-                n_frames = int(float(segment["duration"]) * sample_rate)
-                _id = f"{wav_path.stem}_{i}"
-                self.data.append(
-                    (
-                        wav_path.as_posix(),
-                        offset,
-                        n_frames,
-                        sample_rate,
-                        segment[src],
-                        segment[tgt],
-                        segment["speaker_id"],
-                        tgt,
-                        _id,
-                    )
-                )
-
-    def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, str, str, str, str, str]:
-        wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, tgt_lang, utt_id = self.data[n]
-        waveform, _ = get_waveform(wav_path, frames=n_frames, start=offset)
-        waveform = torch.from_numpy(waveform)
-        return waveform, sr, src_utt, tgt_utt, spk_id, tgt_lang, utt_id
-
-    def __len__(self) -> int:
-        return len(self.data)
-
-
-def process(args):
-    root = Path(args.data_root).absolute()
-    for lang in mTEDx.LANGPAIRS:
-        cur_root = root / f"{lang}"
-        if not cur_root.is_dir():
-            print(f"{cur_root.as_posix()} does not exist. Skipped.")
-            continue
-        # Extract features
-        feature_root = cur_root / "fbank80"
-        feature_root.mkdir(exist_ok=True)
-        for split in mTEDx.SPLITS:
-            print(f"Fetching split {split}...")
-            dataset = mTEDx(root.as_posix(), lang, split)
-            print("Extracting log mel filter bank features...")
-            for waveform, sample_rate, _, _, _, _, utt_id in tqdm(dataset):
-                extract_fbank_features(
-                    waveform, sample_rate, feature_root / f"{utt_id}.npy"
-                )
-        # Pack features into ZIP
-        zip_path = cur_root / "fbank80.zip"
-        print("ZIPing features...")
-        create_zip(feature_root, zip_path)
-        print("Fetching ZIP manifest...")
-        zip_manifest = get_zip_manifest(zip_path)
-        # Generate TSV manifest
-        print("Generating manifest...")
-        train_text = []
-        for split in mTEDx.SPLITS:
-            is_train_split = split.startswith("train")
-            manifest = {c: [] for c in MANIFEST_COLUMNS}
-            dataset = mTEDx(args.data_root, lang, split)
-            for wav, sr, src_utt, tgt_utt, speaker_id, tgt_lang, utt_id in tqdm(dataset):
-                manifest["id"].append(utt_id)
-                manifest["audio"].append(zip_manifest[utt_id])
-                duration_ms = int(wav.size(1) / sr * 1000)
-                manifest["n_frames"].append(int(1 + (duration_ms - 25) / 10))
-                manifest["tgt_text"].append(src_utt if args.task == "asr" else tgt_utt)
-                manifest["speaker"].append(speaker_id)
-                manifest["tgt_lang"].append(tgt_lang)
-            if is_train_split:
-                train_text.extend(manifest["tgt_text"])
-            df = pd.DataFrame.from_dict(manifest)
-            df = filter_manifest_df(df, is_train_split=is_train_split)
-            save_df_to_tsv(df, cur_root / f"{split}_{args.task}.tsv")
-        # Generate vocab
-        v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size)
-        spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}"
-        with NamedTemporaryFile(mode="w") as f:
-            for t in train_text:
-                f.write(t + "\n")
-            gen_vocab(
-                Path(f.name),
-                cur_root / spm_filename_prefix,
-                args.vocab_type,
-                args.vocab_size,
-            )
-        # Generate config YAML
-        gen_config_yaml(
-            cur_root,
-            spm_filename_prefix + ".model",
-            yaml_filename=f"config_{args.task}.yaml",
-            specaugment_policy="lb",
-        )
-        # Clean up
-        shutil.rmtree(feature_root)
-
-
-def process_joint(args):
-    cur_root = Path(args.data_root)
-    assert all((cur_root / f"{lang}").is_dir() for lang in mTEDx.LANGPAIRS), \
-        "do not have downloaded data available for all languages"
-    # Generate vocab
-    vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size)
-    spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{args.task}"
-    with NamedTemporaryFile(mode="w") as f:
-        for lang in mTEDx.LANGPAIRS:
-            tsv_path = cur_root / f"{lang}" / f"train_{args.task}.tsv"
-            df = load_df_from_tsv(tsv_path)
-            for t in df["tgt_text"]:
-                f.write(t + "\n")
-        special_symbols = None
-        if args.joint:
-            # Add tgt_lang tags to dict
-            special_symbols = list({f'<lang:{lang.split("-")[1]}>' for lang in mTEDx.LANGPAIRS})
-        gen_vocab(
-            Path(f.name),
-            cur_root / spm_filename_prefix,
-            args.vocab_type,
-            args.vocab_size,
-            special_symbols=special_symbols
-        )
-    # Generate config YAML
-    gen_config_yaml(
-        cur_root,
-        spm_filename_prefix + ".model",
-        yaml_filename=f"config_{args.task}.yaml",
-        specaugment_policy="ld",
-        prepend_tgt_lang_tag=(args.joint),
-    )
-    # Make symbolic links to manifests
-    for lang in mTEDx.LANGPAIRS:
-        for split in mTEDx.SPLITS:
-            src_path = cur_root / f"{lang}" / f"{split}_{args.task}.tsv"
-            desc_path = cur_root / f"{split}_{lang}_{args.task}.tsv"
-            if not desc_path.is_symlink():
-                os.symlink(src_path, desc_path)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--data-root", "-d", required=True, type=str)
-    parser.add_argument(
-        "--vocab-type",
-        default="unigram",
-        required=True,
-        type=str,
-        choices=["bpe", "unigram", "char"],
-    ),
-    parser.add_argument("--vocab-size", default=8000, type=int)
-    parser.add_argument("--task", type=str, choices=["asr", "st"])
-    parser.add_argument("--joint", action="store_true", help="")
-    args = parser.parse_args()
-
-    if args.joint:
-        process_joint(args)
-    else:
-        process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/gradio/dashboard_main/DESCRIPTION.md b/spaces/gradio/dashboard_main/DESCRIPTION.md
deleted file mode 100644
index 6bd25eec225bb537134eddf1e519a708a0f3b4af..0000000000000000000000000000000000000000
--- a/spaces/gradio/dashboard_main/DESCRIPTION.md
+++ /dev/null
@@ -1 +0,0 @@
-This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.
\ No newline at end of file
diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/styles/globals.css b/spaces/gsaivinay/Llama-2-13B-GGML-UI/styles/globals.css
deleted file mode 100644
index c631cf9c6144a9bc6a70d6b031fe62930a5f1c30..0000000000000000000000000000000000000000
--- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/styles/globals.css
+++ /dev/null
@@ -1,43 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-::-webkit-scrollbar-track {
-  background-color: transparent;
-}
-
-::-webkit-scrollbar-thumb {
-  background-color: #ccc;
-  border-radius: 10px;
-}
-
-::-webkit-scrollbar-thumb:hover {
-  background-color: #aaa;
-}
-
-::-webkit-scrollbar-track:hover {
-  background-color: #f2f2f2;
-}
-
-::-webkit-scrollbar-corner {
-  background-color: transparent;
-}
-
-::-webkit-scrollbar {
-  width: 6px;
-  height: 6px;
-}
-
-html {
-  background: #202123;
-}
-
-@media (max-width: 720px) {
-  pre {
-    width: calc(100vw - 110px);
-  }
-}
-
-pre:has(div.codeblock) {
-  padding: 0;
-}
diff --git a/spaces/guanghap/nob-hill-noir/style.css b/spaces/guanghap/nob-hill-noir/style.css
deleted file mode 100644
index 9e8cfae61cf1ab0ca31459b885e7f52e5c9cd00a..0000000000000000000000000000000000000000
--- a/spaces/guanghap/nob-hill-noir/style.css
+++ /dev/null
@@ -1,37 +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;
-}
-
-iframe {
-  width: 90%;
-  height: 100%;
-  border: none;
-  position: absolute;
-  top: 0;
-  left: 5%;
-}
\ No newline at end of file
diff --git a/spaces/gulabpatel/GFP_GAN/tests/test_arcface_arch.py b/spaces/gulabpatel/GFP_GAN/tests/test_arcface_arch.py
deleted file mode 100644
index b4b28d33800ae78a354e078e14373d2ee159dc7b..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/GFP_GAN/tests/test_arcface_arch.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import torch
-
-from gfpgan.archs.arcface_arch import BasicBlock, Bottleneck, ResNetArcFace
-
-
-def test_resnetarcface():
-    """Test arch: ResNetArcFace."""
-
-    # model init and forward (gpu)
-    if torch.cuda.is_available():
-        net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=True).cuda().eval()
-        img = torch.rand((1, 1, 128, 128), dtype=torch.float32).cuda()
-        output = net(img)
-        assert output.shape == (1, 512)
-
-        # -------------------- without SE block ----------------------- #
-        net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=False).cuda().eval()
-        output = net(img)
-        assert output.shape == (1, 512)
-
-
-def test_basicblock():
-    """Test the BasicBlock in arcface_arch"""
-    block = BasicBlock(1, 3, stride=1, downsample=None).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 3, 12, 12)
-
-    # ----------------- use the downsmaple module--------------- #
-    downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda()
-    block = BasicBlock(1, 3, stride=2, downsample=downsample).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 3, 6, 6)
-
-
-def test_bottleneck():
-    """Test the Bottleneck in arcface_arch"""
-    block = Bottleneck(1, 1, stride=1, downsample=None).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 4, 12, 12)
-
-    # ----------------- use the downsmaple module--------------- #
-    downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda()
-    block = Bottleneck(1, 1, stride=2, downsample=downsample).cuda()
-    img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda()
-    output = block(img)
-    assert output.shape == (1, 4, 6, 6)
diff --git a/spaces/h2oai/h2ogpt-chatbot2/gradio_ui/css.py b/spaces/h2oai/h2ogpt-chatbot2/gradio_ui/css.py
deleted file mode 100644
index 2a3b8b86aec5a3a5824fe71f86357eeb80d86c30..0000000000000000000000000000000000000000
--- a/spaces/h2oai/h2ogpt-chatbot2/gradio_ui/css.py
+++ /dev/null
@@ -1,51 +0,0 @@
-def get_css(kwargs) -> str:
-    if kwargs['h2ocolors']:
-        css_code = """footer {visibility: hidden;}
-        body{background:linear-gradient(#f5f5f5,#e5e5e5);}
-        body.dark{background:linear-gradient(#000000,#0d0d0d);}
-        """
-    else:
-        css_code = """footer {visibility: hidden}"""
-
-    css_code += make_css_base()
-    return css_code
-
-def make_css_base() -> str:
-    return """
-    @import url('https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap');
-    
-    body.dark{#warning {background-color: #555555};}
-    
-    #small_btn {
-        margin: 0.6em 0em 0.55em 0;
-        max-width: 20em;
-        min-width: 5em !important;
-        height: 5em;
-        font-size: 14px !important;
-    }
-    
-    #prompt-form {
-        border: 1px solid var(--primary-500) !important;
-    }
-    
-    #prompt-form.block {
-        border-radius: var(--block-radius) !important;
-    }
-    
-    #prompt-form textarea {
-        border: 1px solid rgb(209, 213, 219);
-    }
-    
-    #prompt-form label > div {
-        margin-top: 4px;
-    }
-    
-    button.primary:hover {
-        background-color: var(--primary-600) !important;
-        transition: .2s;
-    }
-    
-    #prompt-form-area {
-        margin-bottom: 2.5rem;
-    }
-    """
diff --git a/spaces/haakohu/deep_privacy2_face/configs/datasets/fdf256.py b/spaces/haakohu/deep_privacy2_face/configs/datasets/fdf256.py
deleted file mode 100644
index 828802db23ca519cb13963a729c597e39ae8dee8..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2_face/configs/datasets/fdf256.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-from pathlib import Path
-from tops.config import LazyCall as L
-import torch
-import functools
-from dp2.data.datasets.fdf import FDF256Dataset
-from dp2.data.build import get_dataloader
-from dp2.data.transforms.transforms import Normalize, Resize, ToFloat, CreateCondition, RandomHorizontalFlip
-from .utils import final_eval_fn, train_eval_fn
-
-
-dataset_base_dir = os.environ["BASE_DATASET_DIR"] if "BASE_DATASET_DIR" in os.environ else "data"
-metrics_cache = os.environ["FBA_METRICS_CACHE"] if "FBA_METRICS_CACHE" in os.environ else ".cache"
-data_dir = Path(dataset_base_dir, "fdf256")
-data = dict(
-    imsize=(256, 256),
-    im_channels=3,
-    semantic_nc=None,
-    cse_nc=None,
-    n_keypoints=None,
-    train=dict(
-        dataset=L(FDF256Dataset)(dirpath=data_dir.joinpath("train"), transform=None, load_keypoints=False),
-        loader=L(get_dataloader)(
-            shuffle=True, num_workers=3, drop_last=True, prefetch_factor=2,
-            batch_size="${train.batch_size}",
-            dataset="${..dataset}",
-            infinite=True,
-            gpu_transform=L(torch.nn.Sequential)(*[
-                L(ToFloat)(),
-                L(RandomHorizontalFlip)(p=0.5),
-                L(Resize)(size="${data.imsize}"),
-                L(Normalize)(mean=[.5, .5, .5], std=[.5, .5, .5], inplace=True),
-                L(CreateCondition)(),
-            ])
-        )
-    ),
-    val=dict(
-        dataset=L(FDF256Dataset)(dirpath=data_dir.joinpath("val"), transform=None, load_keypoints=False),
-        loader=L(get_dataloader)(
-            shuffle=False, num_workers=3, drop_last=False, prefetch_factor=2,
-            batch_size="${train.batch_size}",
-            dataset="${..dataset}",
-            infinite=False,
-            gpu_transform=L(torch.nn.Sequential)(*[
-                L(ToFloat)(),
-                L(Resize)(size="${data.imsize}"),
-                L(Normalize)(mean=[.5, .5, .5], std=[.5, .5, .5], inplace=True),
-                L(CreateCondition)(),
-            ])
-        )
-    ),
-    # Training evaluation might do optimizations to reduce compute overhead. E.g. compute with AMP.
-    train_evaluation_fn=functools.partial(train_eval_fn, cache_directory=Path(metrics_cache, "fdf_val_train")),
-    evaluation_fn=functools.partial(final_eval_fn, cache_directory=Path(metrics_cache, "fdf_val"))
-)
\ No newline at end of file
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/backbone/swint_v2.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/backbone/swint_v2.py
deleted file mode 100644
index f1fe550c33f06880f67176cd03c918db06c25e6f..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/backbone/swint_v2.py
+++ /dev/null
@@ -1,734 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from einops import rearrange
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-class Mlp(nn.Module):
-    """ Multilayer perceptron."""
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """ Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """ Forward function.
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    """ Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=False):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        self.H = None
-        self.W = None
-
-        self.gamma = 1.0
-        if layer_scale:
-            self.gamma = nn.Parameter(
-                1e-4*torch.ones(dim), requires_grad=True
-            )
-
-
-    def forward(self, x, mask_matrix):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(self.gamma*x)
-        x = x + self.drop_path(self.gamma*self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """ Patch Merging Layer
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False,
-                 layer_scale=False):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer,
-                layer_scale=layer_scale)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(patch_size=3, in_chans=dim, embed_dim=dim*2,
-                                         stride=2, padding=1, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-# class PatchEmbed(nn.Module):
-#     """ Image to Patch Embedding
-#     Args:
-#         patch_size (int): Patch token size. Default: 4.
-#         in_chans (int): Number of input image channels. Default: 3.
-#         embed_dim (int): Number of linear projection output channels. Default: 96.
-#         norm_layer (nn.Module, optional): Normalization layer. Default: None
-#     """
-#
-#     def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-#         super().__init__()
-#         patch_size = to_2tuple(patch_size)
-#         self.patch_size = patch_size
-#
-#         self.in_chans = in_chans
-#         self.embed_dim = embed_dim
-#
-#         self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-#         if norm_layer is not None:
-#             self.norm = norm_layer(embed_dim)
-#         else:
-#             self.norm = None
-#
-#     def forward(self, x):
-#         """Forward function."""
-#         # padding
-#         _, _, H, W = x.size()
-#         if W % self.patch_size[1] != 0:
-#             x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-#         if H % self.patch_size[0] != 0:
-#             x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-#
-#         x = self.proj(x)  # B C Wh Ww
-#         if self.norm is not None:
-#             Wh, Ww = x.size(2), x.size(3)
-#             x = x.flatten(2).transpose(1, 2)
-#             x = self.norm(x)
-#             x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-#
-#         return x
-
-
-class ConvEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-
-    def __init__(
-        self,
-        patch_size=7,
-        in_chans=3,
-        embed_dim=64,
-        stride=4,
-        padding=2,
-        norm_layer=None
-    ):
-        super().__init__()
-        self.patch_size = patch_size
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim,
-            kernel_size=patch_size,
-            stride=stride,
-            padding=padding
-        )
-        self.norm = norm_layer(embed_dim) if norm_layer else None
-
-    def forward(self, x, H=None, W=None):
-        restore_hw = False
-        if H is None and W is None and len(x.size()) == 4:
-            _, _, H, W = x.size()
-            if W % self.patch_size != 0:
-                x = F.pad(x, (0, self.patch_size - W % self.patch_size))
-            if H % self.patch_size != 0:
-                x = F.pad(x, (0, 0, 0, self.patch_size - H % self.patch_size))
-            restore_hw = True
-
-        if len(x.size()) == 3:
-            x = rearrange(
-                x, 'b (h w) c -> b c h w',
-                h=H,
-                w=W
-            )
-        x = self.proj(x)  # B C Wh Ww
-        B, C, Wh, Ww = x.shape
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        if self.norm:
-            x = self.norm(x)
-
-        if restore_hw:
-            x = rearrange(
-                x, 'b (h w) c -> b c h w',
-                h=Wh,
-                w=Ww
-            )
-
-        return x
-
-
-class SwinTransformer(nn.Module):
-    """ Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 pretrain_img_size=224,
-                 patch_size=7,
-                 patch_padding=2,
-                 patch_stride=4,
-                 in_chans=3,
-                 embed_dim=96,
-                 depths=[2, 2, 6, 2],
-                 num_heads=[3, 6, 12, 24],
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.2,
-                 norm_layer=nn.LayerNorm,
-                 ape=False,
-                 patch_norm=True,
-                 frozen_stages=-1,
-                 use_checkpoint=False,
-                 layer_scale=False,
-                 out_features=["stage2", "stage3", "stage4", "stage5"],
-                 out_norm=True,
-                 backbone_arch="SWINT-FPN-RETINANET"):
-        super(SwinTransformer, self).__init__()
-
-        print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint)
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.frozen_stages = frozen_stages
-
-        self.out_features = out_features
-        self.out_norm = out_norm
-
-        # split image into non-overlapping patches
-        # self.patch_embed = PatchEmbed(
-        #     patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
-        #     norm_layer=norm_layer if self.patch_norm else None)
-        self.patch_embed = ConvEmbed(
-            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, padding=patch_padding,
-            norm_layer=norm_layer if self.patch_norm else None
-        )
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
-            self.absolute_pos_embed = nn.Parameter(
-                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        self._out_feature_strides = {}
-        self._out_feature_channels = {}
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                norm_layer=norm_layer,
-                downsample=ConvEmbed if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1,
-                layer_scale=layer_scale)
-            self.layers.append(layer)
-
-            stage = f'stage{i_layer + 2}'
-            if stage in self.out_features:
-                self._out_feature_channels[stage] = embed_dim * 2 ** i_layer
-                self._out_feature_strides[stage] = 4 * 2 ** i_layer
-
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        if self.out_norm:
-            for i_layer in range(self.num_layers):
-                stage = f'stage{i_layer + 2}'
-                if stage in self.out_features:
-                    if i_layer == 0 and backbone_arch.endswith("RETINANET"):
-                        layer = nn.Identity()
-                    else:
-                        layer = norm_layer(num_features[i_layer])
-                    layer_name = f'norm{i_layer}'
-                    self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-
-        def _init_weights(m):
-            if isinstance(m, nn.Linear):
-                trunc_normal_(m.weight, std=.02)
-                if isinstance(m, nn.Linear) and m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.LayerNorm):
-                nn.init.constant_(m.bias, 0)
-                nn.init.constant_(m.weight, 1.0)
-
-        self.apply(_init_weights)
-
-    def forward(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = []
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-            name = f'stage{i + 2}'
-            if name in self.out_features:
-                if self.out_norm:
-                    norm_layer = getattr(self, f'norm{i}')
-                    x_out = norm_layer(x_out)
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-
-        return outs
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
-
-
-def build_swint_backbone(cfg):
-    """
-    Create a SwinT instance from config.
-
-    Returns:
-        VoVNet: a :class:`VoVNet` instance.
-    """
-    return SwinTransformer(
-        patch_size=7,
-        patch_padding=2,
-        patch_stride=4,
-        in_chans=3,
-        embed_dim=cfg.MODEL.SWINT.EMBED_DIM,
-        depths=cfg.MODEL.SWINT.DEPTHS,
-        num_heads=cfg.MODEL.SWINT.NUM_HEADS,
-        window_size=cfg.MODEL.SWINT.WINDOW_SIZE,
-        mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.,
-        attn_drop_rate=0.,
-        drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE,
-        norm_layer=nn.LayerNorm,
-        ape=cfg.MODEL.SWINT.APE,
-        patch_norm=True,
-        frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT,
-        backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY,
-        use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT,
-        layer_scale=cfg.MODEL.SWINT.LAYER_SCALE,
-        out_features=cfg.MODEL.BACKBONE.OUT_FEATURES,
-        out_norm=cfg.MODEL.SWINT.OUT_NORM,
-    )
\ No newline at end of file
diff --git a/spaces/hca97/Mosquito-Detection/my_models/clip_model/__init__.py b/spaces/hca97/Mosquito-Detection/my_models/clip_model/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/detect.py b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/detect.py
deleted file mode 100644
index 8934a093f45e524f7d99b476e6907ed3daa74205..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/detect.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
-
-Usage - sources:
-    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
-                                                     img.jpg                         # image
-                                                     vid.mp4                         # video
-                                                     screen                          # screenshot
-                                                     path/                           # directory
-                                                     list.txt                        # list of images
-                                                     list.streams                    # list of streams
-                                                     'path/*.jpg'                    # glob
-                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-
-Usage - formats:
-    $ python detect.py --weights yolov5s.pt                 # PyTorch
-                                 yolov5s.torchscript        # TorchScript
-                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                 yolov5s_openvino_model     # OpenVINO
-                                 yolov5s.engine             # TensorRT
-                                 yolov5s.mlmodel            # CoreML (macOS-only)
-                                 yolov5s_saved_model        # TensorFlow SavedModel
-                                 yolov5s.pb                 # TensorFlow GraphDef
-                                 yolov5s.tflite             # TensorFlow Lite
-                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
-                                 yolov5s_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from ultralytics.utils.plotting import Annotator, colors, save_one_box
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
-                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-        weights=ROOT / 'yolov5s.pt',  # model path or triton URL
-        source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
-        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
-        imgsz=(640, 640),  # inference size (height, width)
-        conf_thres=0.25,  # confidence threshold
-        iou_thres=0.45,  # NMS IOU threshold
-        max_det=1000,  # maximum detections per image
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        view_img=False,  # show results
-        save_txt=False,  # save results to *.txt
-        save_conf=False,  # save confidences in --save-txt labels
-        save_crop=False,  # save cropped prediction boxes
-        nosave=False,  # do not save images/videos
-        classes=None,  # filter by class: --class 0, or --class 0 2 3
-        agnostic_nms=False,  # class-agnostic NMS
-        augment=False,  # augmented inference
-        visualize=False,  # visualize features
-        update=False,  # update all models
-        project=ROOT / 'runs/detect',  # save results to project/name
-        name='exp',  # save results to project/name
-        exist_ok=False,  # existing project/name ok, do not increment
-        line_thickness=3,  # bounding box thickness (pixels)
-        hide_labels=False,  # hide labels
-        hide_conf=False,  # hide confidences
-        half=False,  # use FP16 half-precision inference
-        dnn=False,  # use OpenCV DNN for ONNX inference
-        vid_stride=1,  # video frame-rate stride
-):
-    source = str(source)
-    save_img = not nosave and not source.endswith('.txt')  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
-    webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
-    screenshot = source.lower().startswith('screen')
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Dataloader
-    bs = 1  # batch_size
-    if webcam:
-        view_img = check_imshow(warn=True)
-        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
-        bs = len(dataset)
-    elif screenshot:
-        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
-    else:
-        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
-    for path, im, im0s, vid_cap, s in dataset:
-        with dt[0]:
-            im = torch.from_numpy(im).to(model.device)
-            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-            im /= 255  # 0 - 255 to 0.0 - 1.0
-            if len(im.shape) == 3:
-                im = im[None]  # expand for batch dim
-
-        # Inference
-        with dt[1]:
-            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
-            pred = model(im, augment=augment, visualize=visualize)
-
-        # NMS
-        with dt[2]:
-            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
-
-        # Second-stage classifier (optional)
-        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
-        # Process predictions
-        for i, det in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f'{i}: '
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
-            s += '%gx%g ' % im.shape[2:]  # print string
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            imc = im0.copy() if save_crop else im0  # for save_crop
-            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
-
-                # Print results
-                for c in det[:, 5].unique():
-                    n = (det[:, 5] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
-                        with open(f'{txt_path}.txt', 'a') as f:
-                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-                    if save_img or save_crop or view_img:  # Add bbox to image
-                        c = int(cls)  # integer class
-                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
-                        annotator.box_label(xyxy, label, color=colors(c, True))
-                    if save_crop:
-                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                if platform.system() == 'Linux' and p not in windows:
-                    windows.append(p)
-                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
-                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == 'image':
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[i].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-                    vid_writer[i].write(im0)
-
-        # Print time (inference-only)
-        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
-
-    # Print results
-    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
-    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
-    if save_txt or save_img:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL')
-    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
-    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--view-img', action='store_true', help='show results')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
-    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
-    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
-    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
-    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
-    parser.add_argument('--augment', action='store_true', help='augmented inference')
-    parser.add_argument('--visualize', action='store_true', help='visualize features')
-    parser.add_argument('--update', action='store_true', help='update all models')
-    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
-    parser.add_argument('--name', default='exp', help='save results to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
-    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
-    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
-    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
-    run(**vars(opt))
-
-
-if __name__ == '__main__':
-    opt = parse_opt()
-    main(opt)
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/google_app_engine/Dockerfile b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/google_app_engine/Dockerfile
deleted file mode 100644
index 0155618f475104e9858b81470339558156c94e13..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/google_app_engine/Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-FROM gcr.io/google-appengine/python
-
-# Create a virtualenv for dependencies. This isolates these packages from
-# system-level packages.
-# Use -p python3 or -p python3.7 to select python version. Default is version 2.
-RUN virtualenv /env -p python3
-
-# Setting these environment variables are the same as running
-# source /env/bin/activate.
-ENV VIRTUAL_ENV /env
-ENV PATH /env/bin:$PATH
-
-RUN apt-get update && apt-get install -y python-opencv
-
-# Copy the application's requirements.txt and run pip to install all
-# dependencies into the virtualenv.
-ADD requirements.txt /app/requirements.txt
-RUN pip install -r /app/requirements.txt
-
-# Add the application source code.
-ADD . /app
-
-# Run a WSGI server to serve the application. gunicorn must be declared as
-# a dependency in requirements.txt.
-CMD gunicorn -b :$PORT main:app
diff --git a/spaces/heiyubili/bingo/src/lib/hooks/use-enter-submit.tsx b/spaces/heiyubili/bingo/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
-  formRef: RefObject<HTMLFormElement>
-  onKeyDown: (event: React.KeyboardEvent<HTMLTextAreaElement>) => void
-} {
-  const formRef = useRef<HTMLFormElement>(null)
-
-  const handleKeyDown = (
-    event: React.KeyboardEvent<HTMLTextAreaElement>
-  ): void => {
-    if (
-      event.key === 'Enter' &&
-      !event.shiftKey &&
-      !event.nativeEvent.isComposing
-    ) {
-      formRef.current?.requestSubmit()
-      event.preventDefault()
-    }
-  }
-
-  return { formRef, onKeyDown: handleKeyDown }
-}
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/run/run_training_DP.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/run/run_training_DP.py
deleted file mode 100644
index 922afb5c70b6cb656ad781af61b4fe78d1bfab99..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/run/run_training_DP.py
+++ /dev/null
@@ -1,194 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    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 argparse
-from batchgenerators.utilities.file_and_folder_operations import *
-from nnunet.run.default_configuration import get_default_configuration
-from nnunet.paths import default_plans_identifier
-from nnunet.run.load_pretrained_weights import load_pretrained_weights
-from nnunet.training.cascade_stuff.predict_next_stage import predict_next_stage
-from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
-from nnunet.training.network_training.nnUNetTrainerCascadeFullRes import nnUNetTrainerCascadeFullRes
-from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("network")
-    parser.add_argument("network_trainer")
-    parser.add_argument("task", help="can be task name or task id")
-    parser.add_argument("fold", help='0, 1, ..., 5 or \'all\'')
-    parser.add_argument("-val", "--validation_only", help="use this if you want to only run the validation",
-                        action="store_true")
-    parser.add_argument("-c", "--continue_training", help="use this if you want to continue a training",
-                        action="store_true")
-    parser.add_argument("-p", help="plans identifier. Only change this if you created a custom experiment planner",
-                        default=default_plans_identifier, required=False)
-    parser.add_argument("--use_compressed_data", default=False, action="store_true",
-                        help="If you set use_compressed_data, the training cases will not be decompressed. Reading compressed data "
-                             "is much more CPU and RAM intensive and should only be used if you know what you are "
-                             "doing", required=False)
-    parser.add_argument("--deterministic",
-                        help="Makes training deterministic, but reduces training speed substantially. I (Fabian) think "
-                             "this is not necessary. Deterministic training will make you overfit to some random seed. "
-                             "Don't use that.",
-                        required=False, default=False, action="store_true")
-    parser.add_argument("-gpus", help="number of gpus", required=True,type=int)
-    parser.add_argument("--dbs", required=False, default=False, action="store_true", help="distribute batch size. If "
-                                                                                          "True then whatever "
-                                                                                          "batch_size is in plans will "
-                                                                                          "be distributed over DDP "
-                                                                                          "models, if False then each "
-                                                                                          "model will have batch_size "
-                                                                                          "for a total of "
-                                                                                          "GPUs*batch_size")
-    parser.add_argument("--npz", required=False, default=False, action="store_true", help="if set then nnUNet will "
-                                                                                          "export npz files of "
-                                                                                          "predicted segmentations "
-                                                                                          "in the vlaidation as well. "
-                                                                                          "This is needed to run the "
-                                                                                          "ensembling step so unless "
-                                                                                          "you are developing nnUNet "
-                                                                                          "you should enable this")
-    parser.add_argument("--valbest", required=False, default=False, action="store_true", help="")
-    parser.add_argument("--find_lr", required=False, default=False, action="store_true", help="")
-    parser.add_argument("--fp32", required=False, default=False, action="store_true",
-                        help="disable mixed precision training and run old school fp32")
-    parser.add_argument("--val_folder", required=False, default="validation_raw",
-                        help="name of the validation folder. No need to use this for most people")
-    parser.add_argument("--disable_saving", required=False, action='store_true',
-                        help="If set nnU-Net will not save any parameter files. Useful for development when you are "
-                             "only interested in the results and want to save some disk space")
-    parser.add_argument("--disable_postprocessing_on_folds", required=False, action='store_true',
-                        help="Running postprocessing on each fold only makes sense when developing with nnU-Net and "
-                             "closely observing the model performance on specific configurations. You do not need it "
-                             "when applying nnU-Net because the postprocessing for this will be determined only once "
-                             "all five folds have been trained and nnUNet_find_best_configuration is called. Usually "
-                             "running postprocessing on each fold is computationally cheap, but some users have "
-                             "reported issues with very large images. If your images are large (>600x600x600 voxels) "
-                             "you should consider setting this flag.")
-    # parser.add_argument("--interp_order", required=False, default=3, type=int,
-    #                     help="order of interpolation for segmentations. Testing purpose only. Hands off")
-    # parser.add_argument("--interp_order_z", required=False, default=0, type=int,
-    #                     help="order of interpolation along z if z is resampled separately. Testing purpose only. "
-    #                          "Hands off")
-    # parser.add_argument("--force_separate_z", required=False, default="None", type=str,
-    #                     help="force_separate_z resampling. Can be None, True or False. Testing purpose only. Hands off")
-    parser.add_argument('-pretrained_weights', type=str, required=False, default=None,
-                        help='path to nnU-Net checkpoint file to be used as pretrained model (use .model '
-                             'file, for example model_final_checkpoint.model). Will only be used when actually training. '
-                             'Optional. Beta. Use with caution.')
-
-    args = parser.parse_args()
-
-    task = args.task
-    fold = args.fold
-    network = args.network
-    network_trainer = args.network_trainer
-    validation_only = args.validation_only
-    plans_identifier = args.p
-    disable_postprocessing_on_folds = args.disable_postprocessing_on_folds
-
-    use_compressed_data = args.use_compressed_data
-    decompress_data = not use_compressed_data
-
-    deterministic = args.deterministic
-    valbest = args.valbest
-    find_lr = args.find_lr
-    num_gpus = args.gpus
-    fp32 = args.fp32
-    val_folder = args.val_folder
-    # interp_order = args.interp_order
-    # interp_order_z = args.interp_order_z
-    # force_separate_z = args.force_separate_z
-
-    if not task.startswith("Task"):
-        task_id = int(task)
-        task = convert_id_to_task_name(task_id)
-
-    if fold == 'all':
-        pass
-    else:
-        fold = int(fold)
-
-    # if force_separate_z == "None":
-    #     force_separate_z = None
-    # elif force_separate_z == "False":
-    #     force_separate_z = False
-    # elif force_separate_z == "True":
-    #     force_separate_z = True
-    # else:
-    #     raise ValueError("force_separate_z must be None, True or False. Given: %s" % force_separate_z)
-
-    plans_file, output_folder_name, dataset_directory, batch_dice, stage, \
-        trainer_class = get_default_configuration(network, task, network_trainer, plans_identifier)
-
-    if trainer_class is None:
-        raise RuntimeError("Could not find trainer class")
-
-    if network == "3d_cascade_fullres":
-        assert issubclass(trainer_class, nnUNetTrainerCascadeFullRes), "If running 3d_cascade_fullres then your " \
-                                                                       "trainer class must be derived from " \
-                                                                       "nnUNetTrainerCascadeFullRes"
-    else:
-        assert issubclass(trainer_class, nnUNetTrainer), "network_trainer was found but is not derived from " \
-                                                         "nnUNetTrainer"
-
-    trainer = trainer_class(plans_file, fold, output_folder=output_folder_name,
-                            dataset_directory=dataset_directory, batch_dice=batch_dice, stage=stage,
-                            unpack_data=decompress_data, deterministic=deterministic,
-                            distribute_batch_size=args.dbs, num_gpus=num_gpus, fp16=not fp32)
-
-    if args.disable_saving:
-        trainer.save_latest_only = False  # if false it will not store/overwrite _latest but separate files each
-        trainer.save_intermediate_checkpoints = False  # whether or not to save checkpoint_latest
-        trainer.save_best_checkpoint = False  # whether or not to save the best checkpoint according to self.best_val_eval_criterion_MA
-        trainer.save_final_checkpoint = False  # whether or not to save the final checkpoint
-
-    trainer.initialize(not validation_only)
-
-    if find_lr:
-        trainer.find_lr()
-    else:
-        if not validation_only:
-            if args.continue_training:
-                # -c was set, continue a previous training and ignore pretrained weights
-                trainer.load_latest_checkpoint()
-            elif (not args.continue_training) and (args.pretrained_weights is not None):
-                # we start a new training. If pretrained_weights are set, use them
-                load_pretrained_weights(trainer.network, args.pretrained_weights)
-            else:
-                # new training without pretraine weights, do nothing
-                pass
-            trainer.run_training()
-        else:
-            if valbest:
-                trainer.load_best_checkpoint(train=False)
-            else:
-                trainer.load_final_checkpoint(train=False)
-
-        trainer.network.eval()
-
-        # predict validation
-        trainer.validate(save_softmax=args.npz, validation_folder_name=val_folder,
-                         run_postprocessing_on_folds=not disable_postprocessing_on_folds)
-
-        if network == '3d_lowres':
-            print("predicting segmentations for the next stage of the cascade")
-            predict_next_stage(trainer, join(dataset_directory, trainer.plans['data_identifier'] + "_stage%d" % 1))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/eval_ijbc.py b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/eval_ijbc.py
deleted file mode 100644
index 9c5a650d486d18eb02d6f60d448fc3b315261f5d..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/eval_ijbc.py
+++ /dev/null
@@ -1,483 +0,0 @@
-# coding: utf-8
-
-import os
-import pickle
-
-import matplotlib
-import pandas as pd
-
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import timeit
-import sklearn
-import argparse
-import cv2
-import numpy as np
-import torch
-from skimage import transform as trans
-from backbones import get_model
-from sklearn.metrics import roc_curve, auc
-
-from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
-from prettytable import PrettyTable
-from pathlib import Path
-
-import sys
-import warnings
-
-sys.path.insert(0, "../")
-warnings.filterwarnings("ignore")
-
-parser = argparse.ArgumentParser(description='do ijb test')
-# general
-parser.add_argument('--model-prefix', default='', help='path to load model.')
-parser.add_argument('--image-path', default='', type=str, help='')
-parser.add_argument('--result-dir', default='.', type=str, help='')
-parser.add_argument('--batch-size', default=128, type=int, help='')
-parser.add_argument('--network', default='iresnet50', type=str, help='')
-parser.add_argument('--job', default='insightface', type=str, help='job name')
-parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
-args = parser.parse_args()
-
-target = args.target
-model_path = args.model_prefix
-image_path = args.image_path
-result_dir = args.result_dir
-gpu_id = None
-use_norm_score = True  # if Ture, TestMode(N1)
-use_detector_score = True  # if Ture, TestMode(D1)
-use_flip_test = True  # if Ture, TestMode(F1)
-job = args.job
-batch_size = args.batch_size
-
-
-class Embedding(object):
-    def __init__(self, prefix, data_shape, batch_size=1):
-        image_size = (112, 112)
-        self.image_size = image_size
-        weight = torch.load(prefix)
-        resnet = get_model(args.network, dropout=0, fp16=False).cuda()
-        resnet.load_state_dict(weight)
-        model = torch.nn.DataParallel(resnet)
-        self.model = model
-        self.model.eval()
-        src = np.array([
-            [30.2946, 51.6963],
-            [65.5318, 51.5014],
-            [48.0252, 71.7366],
-            [33.5493, 92.3655],
-            [62.7299, 92.2041]], dtype=np.float32)
-        src[:, 0] += 8.0
-        self.src = src
-        self.batch_size = batch_size
-        self.data_shape = data_shape
-
-    def get(self, rimg, landmark):
-
-        assert landmark.shape[0] == 68 or landmark.shape[0] == 5
-        assert landmark.shape[1] == 2
-        if landmark.shape[0] == 68:
-            landmark5 = np.zeros((5, 2), dtype=np.float32)
-            landmark5[0] = (landmark[36] + landmark[39]) / 2
-            landmark5[1] = (landmark[42] + landmark[45]) / 2
-            landmark5[2] = landmark[30]
-            landmark5[3] = landmark[48]
-            landmark5[4] = landmark[54]
-        else:
-            landmark5 = landmark
-        tform = trans.SimilarityTransform()
-        tform.estimate(landmark5, self.src)
-        M = tform.params[0:2, :]
-        img = cv2.warpAffine(rimg,
-                             M, (self.image_size[1], self.image_size[0]),
-                             borderValue=0.0)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img_flip = np.fliplr(img)
-        img = np.transpose(img, (2, 0, 1))  # 3*112*112, RGB
-        img_flip = np.transpose(img_flip, (2, 0, 1))
-        input_blob = np.zeros((2, 3, self.image_size[1], self.image_size[0]), dtype=np.uint8)
-        input_blob[0] = img
-        input_blob[1] = img_flip
-        return input_blob
-
-    @torch.no_grad()
-    def forward_db(self, batch_data):
-        imgs = torch.Tensor(batch_data).cuda()
-        imgs.div_(255).sub_(0.5).div_(0.5)
-        feat = self.model(imgs)
-        feat = feat.reshape([self.batch_size, 2 * feat.shape[1]])
-        return feat.cpu().numpy()
-
-
-# 将一个list尽量均分成n份，限制len(list)==n，份数大于原list内元素个数则分配空list[]
-def divideIntoNstrand(listTemp, n):
-    twoList = [[] for i in range(n)]
-    for i, e in enumerate(listTemp):
-        twoList[i % n].append(e)
-    return twoList
-
-
-def read_template_media_list(path):
-    # ijb_meta = np.loadtxt(path, dtype=str)
-    ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
-    return templates, medias
-
-
-# In[ ]:
-
-
-def read_template_pair_list(path):
-    # pairs = np.loadtxt(path, dtype=str)
-    pairs = pd.read_csv(path, sep=' ', header=None).values
-    # print(pairs.shape)
-    # print(pairs[:, 0].astype(np.int))
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
-    return t1, t2, label
-
-
-# In[ ]:
-
-
-def read_image_feature(path):
-    with open(path, 'rb') as fid:
-        img_feats = pickle.load(fid)
-    return img_feats
-
-
-# In[ ]:
-
-
-def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):
-    batch_size = args.batch_size
-    data_shape = (3, 112, 112)
-
-    files = files_list
-    print('files:', len(files))
-    rare_size = len(files) % batch_size
-    faceness_scores = []
-    batch = 0
-    img_feats = np.empty((len(files), 1024), dtype=np.float32)
-
-    batch_data = np.empty((2 * batch_size, 3, 112, 112))
-    embedding = Embedding(model_path, data_shape, batch_size)
-    for img_index, each_line in enumerate(files[:len(files) - rare_size]):
-        name_lmk_score = each_line.strip().split(' ')
-        img_name = os.path.join(img_path, name_lmk_score[0])
-        img = cv2.imread(img_name)
-        lmk = np.array([float(x) for x in name_lmk_score[1:-1]],
-                       dtype=np.float32)
-        lmk = lmk.reshape((5, 2))
-        input_blob = embedding.get(img, lmk)
-
-        batch_data[2 * (img_index - batch * batch_size)][:] = input_blob[0]
-        batch_data[2 * (img_index - batch * batch_size) + 1][:] = input_blob[1]
-        if (img_index + 1) % batch_size == 0:
-            print('batch', batch)
-            img_feats[batch * batch_size:batch * batch_size +
-                                         batch_size][:] = embedding.forward_db(batch_data)
-            batch += 1
-        faceness_scores.append(name_lmk_score[-1])
-
-    batch_data = np.empty((2 * rare_size, 3, 112, 112))
-    embedding = Embedding(model_path, data_shape, rare_size)
-    for img_index, each_line in enumerate(files[len(files) - rare_size:]):
-        name_lmk_score = each_line.strip().split(' ')
-        img_name = os.path.join(img_path, name_lmk_score[0])
-        img = cv2.imread(img_name)
-        lmk = np.array([float(x) for x in name_lmk_score[1:-1]],
-                       dtype=np.float32)
-        lmk = lmk.reshape((5, 2))
-        input_blob = embedding.get(img, lmk)
-        batch_data[2 * img_index][:] = input_blob[0]
-        batch_data[2 * img_index + 1][:] = input_blob[1]
-        if (img_index + 1) % rare_size == 0:
-            print('batch', batch)
-            img_feats[len(files) -
-                      rare_size:][:] = embedding.forward_db(batch_data)
-            batch += 1
-        faceness_scores.append(name_lmk_score[-1])
-    faceness_scores = np.array(faceness_scores).astype(np.float32)
-    # img_feats = np.ones( (len(files), 1024), dtype=np.float32) * 0.01
-    # faceness_scores = np.ones( (len(files), ), dtype=np.float32 )
-    return img_feats, faceness_scores
-
-
-# In[ ]:
-
-
-def image2template_feature(img_feats=None, templates=None, medias=None):
-    # ==========================================================
-    # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]
-    # 2. compute media feature.
-    # 3. compute template feature.
-    # ==========================================================
-    unique_templates = np.unique(templates)
-    template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
-
-    for count_template, uqt in enumerate(unique_templates):
-
-        (ind_t,) = np.where(templates == uqt)
-        face_norm_feats = img_feats[ind_t]
-        face_medias = medias[ind_t]
-        unique_medias, unique_media_counts = np.unique(face_medias,
-                                                       return_counts=True)
-        media_norm_feats = []
-        for u, ct in zip(unique_medias, unique_media_counts):
-            (ind_m,) = np.where(face_medias == u)
-            if ct == 1:
-                media_norm_feats += [face_norm_feats[ind_m]]
-            else:  # image features from the same video will be aggregated into one feature
-                media_norm_feats += [
-                    np.mean(face_norm_feats[ind_m], axis=0, keepdims=True)
-                ]
-        media_norm_feats = np.array(media_norm_feats)
-        # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))
-        template_feats[count_template] = np.sum(media_norm_feats, axis=0)
-        if count_template % 2000 == 0:
-            print('Finish Calculating {} template features.'.format(
-                count_template))
-    # template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))
-    template_norm_feats = sklearn.preprocessing.normalize(template_feats)
-    # print(template_norm_feats.shape)
-    return template_norm_feats, unique_templates
-
-
-# In[ ]:
-
-
-def verification(template_norm_feats=None,
-                 unique_templates=None,
-                 p1=None,
-                 p2=None):
-    # ==========================================================
-    #         Compute set-to-set Similarity Score.
-    # ==========================================================
-    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
-    for count_template, uqt in enumerate(unique_templates):
-        template2id[uqt] = count_template
-
-    score = np.zeros((len(p1),))  # save cosine distance between pairs
-
-    total_pairs = np.array(range(len(p1)))
-    batchsize = 100000  # small batchsize instead of all pairs in one batch due to the memory limiation
-    sublists = [
-        total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)
-    ]
-    total_sublists = len(sublists)
-    for c, s in enumerate(sublists):
-        feat1 = template_norm_feats[template2id[p1[s]]]
-        feat2 = template_norm_feats[template2id[p2[s]]]
-        similarity_score = np.sum(feat1 * feat2, -1)
-        score[s] = similarity_score.flatten()
-        if c % 10 == 0:
-            print('Finish {}/{} pairs.'.format(c, total_sublists))
-    return score
-
-
-# In[ ]:
-def verification2(template_norm_feats=None,
-                  unique_templates=None,
-                  p1=None,
-                  p2=None):
-    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
-    for count_template, uqt in enumerate(unique_templates):
-        template2id[uqt] = count_template
-    score = np.zeros((len(p1),))  # save cosine distance between pairs
-    total_pairs = np.array(range(len(p1)))
-    batchsize = 100000  # small batchsize instead of all pairs in one batch due to the memory limiation
-    sublists = [
-        total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)
-    ]
-    total_sublists = len(sublists)
-    for c, s in enumerate(sublists):
-        feat1 = template_norm_feats[template2id[p1[s]]]
-        feat2 = template_norm_feats[template2id[p2[s]]]
-        similarity_score = np.sum(feat1 * feat2, -1)
-        score[s] = similarity_score.flatten()
-        if c % 10 == 0:
-            print('Finish {}/{} pairs.'.format(c, total_sublists))
-    return score
-
-
-def read_score(path):
-    with open(path, 'rb') as fid:
-        img_feats = pickle.load(fid)
-    return img_feats
-
-
-# # Step1: Load Meta Data
-
-# In[ ]:
-
-assert target == 'IJBC' or target == 'IJBB'
-
-# =============================================================
-# load image and template relationships for template feature embedding
-# tid --> template id,  mid --> media id
-# format:
-#           image_name tid mid
-# =============================================================
-start = timeit.default_timer()
-templates, medias = read_template_media_list(
-    os.path.join('%s/meta' % image_path,
-                 '%s_face_tid_mid.txt' % target.lower()))
-stop = timeit.default_timer()
-print('Time: %.2f s. ' % (stop - start))
-
-# In[ ]:
-
-# =============================================================
-# load template pairs for template-to-template verification
-# tid : template id,  label : 1/0
-# format:
-#           tid_1 tid_2 label
-# =============================================================
-start = timeit.default_timer()
-p1, p2, label = read_template_pair_list(
-    os.path.join('%s/meta' % image_path,
-                 '%s_template_pair_label.txt' % target.lower()))
-stop = timeit.default_timer()
-print('Time: %.2f s. ' % (stop - start))
-
-# # Step 2: Get Image Features
-
-# In[ ]:
-
-# =============================================================
-# load image features
-# format:
-#           img_feats: [image_num x feats_dim] (227630, 512)
-# =============================================================
-start = timeit.default_timer()
-img_path = '%s/loose_crop' % image_path
-img_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower())
-img_list = open(img_list_path)
-files = img_list.readlines()
-# files_list = divideIntoNstrand(files, rank_size)
-files_list = files
-
-# img_feats
-# for i in range(rank_size):
-img_feats, faceness_scores = get_image_feature(img_path, files_list,
-                                               model_path, 0, gpu_id)
-stop = timeit.default_timer()
-print('Time: %.2f s. ' % (stop - start))
-print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0],
-                                          img_feats.shape[1]))
-
-# # Step3: Get Template Features
-
-# In[ ]:
-
-# =============================================================
-# compute template features from image features.
-# =============================================================
-start = timeit.default_timer()
-# ==========================================================
-# Norm feature before aggregation into template feature?
-# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).
-# ==========================================================
-# 1. FaceScore （Feature Norm）
-# 2. FaceScore （Detector）
-
-if use_flip_test:
-    # concat --- F1
-    # img_input_feats = img_feats
-    # add --- F2
-    img_input_feats = img_feats[:, 0:img_feats.shape[1] //
-                                     2] + img_feats[:, img_feats.shape[1] // 2:]
-else:
-    img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]
-
-if use_norm_score:
-    img_input_feats = img_input_feats
-else:
-    # normalise features to remove norm information
-    img_input_feats = img_input_feats / np.sqrt(
-        np.sum(img_input_feats ** 2, -1, keepdims=True))
-
-if use_detector_score:
-    print(img_input_feats.shape, faceness_scores.shape)
-    img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
-else:
-    img_input_feats = img_input_feats
-
-template_norm_feats, unique_templates = image2template_feature(
-    img_input_feats, templates, medias)
-stop = timeit.default_timer()
-print('Time: %.2f s. ' % (stop - start))
-
-# # Step 4: Get Template Similarity Scores
-
-# In[ ]:
-
-# =============================================================
-# compute verification scores between template pairs.
-# =============================================================
-start = timeit.default_timer()
-score = verification(template_norm_feats, unique_templates, p1, p2)
-stop = timeit.default_timer()
-print('Time: %.2f s. ' % (stop - start))
-
-# In[ ]:
-save_path = os.path.join(result_dir, args.job)
-# save_path = result_dir + '/%s_result' % target
-
-if not os.path.exists(save_path):
-    os.makedirs(save_path)
-
-score_save_file = os.path.join(save_path, "%s.npy" % target.lower())
-np.save(score_save_file, score)
-
-# # Step 5: Get ROC Curves and TPR@FPR Table
-
-# In[ ]:
-
-files = [score_save_file]
-methods = []
-scores = []
-for file in files:
-    methods.append(Path(file).stem)
-    scores.append(np.load(file))
-
-methods = np.array(methods)
-scores = dict(zip(methods, scores))
-colours = dict(
-    zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
-x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
-tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
-fig = plt.figure()
-for method in methods:
-    fpr, tpr, _ = roc_curve(label, scores[method])
-    roc_auc = auc(fpr, tpr)
-    fpr = np.flipud(fpr)
-    tpr = np.flipud(tpr)  # select largest tpr at same fpr
-    plt.plot(fpr,
-             tpr,
-             color=colours[method],
-             lw=1,
-             label=('[%s (AUC = %0.4f %%)]' %
-                    (method.split('-')[-1], roc_auc * 100)))
-    tpr_fpr_row = []
-    tpr_fpr_row.append("%s-%s" % (method, target))
-    for fpr_iter in np.arange(len(x_labels)):
-        _, min_index = min(
-            list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
-        tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
-    tpr_fpr_table.add_row(tpr_fpr_row)
-plt.xlim([10 ** -6, 0.1])
-plt.ylim([0.3, 1.0])
-plt.grid(linestyle='--', linewidth=1)
-plt.xticks(x_labels)
-plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
-plt.xscale('log')
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.title('ROC on IJB')
-plt.legend(loc="lower right")
-fig.savefig(os.path.join(save_path, '%s.pdf' % target.lower()))
-print(tpr_fpr_table)
diff --git a/spaces/imseldrith/FaceSwap/templates/index.html b/spaces/imseldrith/FaceSwap/templates/index.html
deleted file mode 100644
index 8cad500f4c0995401075f004f8dd35c98edf69e1..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/FaceSwap/templates/index.html
+++ /dev/null
@@ -1,116 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Image and Video Processing</title>
-    <style>
-        body {
-            font-family: 'Segoe UI', Arial, sans-serif;
-            background-color: #f8f8f8;
-            margin: 0;
-            padding: 0;
-            display: flex;
-            justify-content: center;
-            align-items: center;
-            min-height: 100vh;
-        }
-        #container {
-            width: 90%;
-            max-width: 800px;
-            padding: 20px;
-            background-color: #ffffff;
-            border-radius: 10px;
-            box-shadow: 0px 0px 20px rgba(0, 0, 0, 0.1);
-        }
-        h1 {
-            text-align: center;
-            color: #333333;
-            margin-bottom: 30px;
-        }
-        label {
-            font-weight: bold;
-            color: #444444;
-            display: block;
-            margin-bottom: 5px;
-        }
-        input[type="file"] {
-            display: block;
-            margin-bottom: 15px;
-            width: 100%;
-        }
-        input[type="checkbox"] {
-            margin-right: 8px;
-        }
-        input[type="submit"] {
-            background-color: #4285f4;
-            color: white;
-            border: none;
-            padding: 12px 25px;
-            cursor: pointer;
-            border-radius: 5px;
-            font-weight: bold;
-            transition: background-color 0.3s;
-            width: 100%;
-        }
-        input[type="submit"]:hover {
-            background-color: #2a75d7;
-        }
-        #preview {
-            text-align: center;
-            margin-top: 20px;
-        }
-        #preview img {
-            max-width: 100%;
-            max-height: 300px;
-            margin-top: 15px;
-            border: 1px solid #ddd;
-            border-radius: 5px;
-        }
-    </style>
-    <script>
-        function previewImage(input, previewId) {
-            var preview = document.getElementById(previewId);
-            var file = input.files[0];
-            var reader = new FileReader();
-
-            reader.onload = function(e) {
-                var image = new Image();
-                image.src = e.target.result;
-                preview.innerHTML = '';
-                preview.appendChild(image);
-            };
-
-            reader.readAsDataURL(file);
-        }
-    </script>
-</head>
-<body>
-    <div id="container">
-        <h1>Image and Video Processing</h1>
-        <form method="post" enctype="multipart/form-data">
-            <label for="source">Upload Source Image/Video:</label>
-            <input type="file" id="source" name="source" onchange="previewImage(this, 'sourcePreview')">
-            <div id="sourcePreview"></div>
-
-            <label for="target">Upload Target Image/Video:</label>
-            <input type="file" id="target" name="target" onchange="previewImage(this, 'targetPreview')">
-            <div id="targetPreview"></div>
-
-            <label>Frame Processors:</label>
-            <div style="display: flex; align-items: center;">
-                <input type="checkbox" id="face_swapper" name="frame_processor" value="face_swapper">
-                <label for="face_swapper">Face Swapper</label>
-            </div>
-            <div style="display: flex; align-items: center;">
-                <input type="checkbox" id="face_enhancer" name="frame_processor" value="face_enhancer">
-                <label for="face_enhancer">Face Enhancer</label>
-            </div>
-
-            <div style="text-align: center; margin-top: 25px;">
-                <input type="submit" value="Process">
-            </div>
-        </form>
-    </div>
-</body>
-</html>
diff --git a/spaces/innnky/visinger2-nomidi/egs/visinger2/__init__.py b/spaces/innnky/visinger2-nomidi/egs/visinger2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/3dm Gta V Crack V5 Download Games.md b/spaces/inplisQlawa/anything-midjourney-v4-1/3dm Gta V Crack V5 Download Games.md
deleted file mode 100644
index 7705e21a04401eb646cfbfd3d1ca504370c0766b..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/3dm Gta V Crack V5 Download Games.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>3dm Gta V Crack V5 Download Games</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlin.us/2uEwNC">https://urlin.us/2uEwNC</a></b></p><br /><br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/A Robot Named Fight! V1.1.2.14 Pc Game.md b/spaces/inplisQlawa/anything-midjourney-v4-1/A Robot Named Fight! V1.1.2.14 Pc Game.md
deleted file mode 100644
index f6ecac2d9291cc4e704c3a1f01bccd611ca09096..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/A Robot Named Fight! V1.1.2.14 Pc Game.md	
+++ /dev/null
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-<h2>A Robot Named Fight! v1.1.2.14 pc game</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt;&gt;&gt; <a href="https://urlin.us/2uExUF">https://urlin.us/2uExUF</a></b></p><br /><br />
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-<br />
-<p></p>
diff --git a/spaces/inreVtussa/clothingai/Examples/Autocad Civil 3d Land Desktop Companion 2008 !!TOP!! Crack.md b/spaces/inreVtussa/clothingai/Examples/Autocad Civil 3d Land Desktop Companion 2008 !!TOP!! Crack.md
deleted file mode 100644
index ed0eadb4c5d8560185edcb22d31650f254398769..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Autocad Civil 3d Land Desktop Companion 2008 !!TOP!! Crack.md	
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@@ -1,6 +0,0 @@
-<h2>Autocad Civil 3d Land Desktop Companion 2008 Crack</h2><br /><p><b><b>Download File</b> &#127379; <a href="https://tiurll.com/2uClHP">https://tiurll.com/2uClHP</a></b></p><br /><br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/BFBC2 Mouse FIx.rar.md b/spaces/inreVtussa/clothingai/Examples/BFBC2 Mouse FIx.rar.md
deleted file mode 100644
index de307739d98a202c352c9f5f321fc0a05c74a8ac..0000000000000000000000000000000000000000
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-<h2>BFBC2 Mouse FIx.rar</h2><br /><p><b><b>Download</b> &middot;&middot;&middot; <a href="https://tiurll.com/2uCld8">https://tiurll.com/2uCld8</a></b></p><br /><br />
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-Repack by Diablo
-Uploaded: 2013-11-08 22:27:08
-Filesonic: http://www.filesonic.com/file/327983589/BFBC2Mouse_FIX.rar
-Release year: 2010
-Genre: Action (Shooter), 3d, 1st Person
-Developer: Bohemia Interactive
-Publisher: Buka
-Edition type: Repack
-Interface language: Russian
-Voice language: Russian
-Tablet: Sewn
-System Requirements: 8a78ff9644<br />
-<br />
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-<p></p>
diff --git a/spaces/iqovocn/ChuanhuChatGPT/readme/README_ja.md b/spaces/iqovocn/ChuanhuChatGPT/readme/README_ja.md
deleted file mode 100644
index fc56eec0b81c22ff0a49e3960aa52ffd7d6dc5cb..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/readme/README_ja.md
+++ /dev/null
@@ -1,126 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</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>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</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>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</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>
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバにデプロイするには、プログラムの最終行を `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`に変更します。
-- 共有リンクを取得するには、プログラムの最後の行を `demo.launch(share=True)` に変更してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## インストール
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-3. Gradioを更新
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [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
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<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/irvay/RVC_IR/lib/infer_pack/commons.py b/spaces/irvay/RVC_IR/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/irvay/RVC_IR/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-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 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(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * 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 slice_segments2(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 ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/ismot/1702t1/postprocessing/dula/layout_old.py b/spaces/ismot/1702t1/postprocessing/dula/layout_old.py
deleted file mode 100644
index 4945147000f0ed35ca627366a7c0fb96f1b33a4b..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/postprocessing/dula/layout_old.py
+++ /dev/null
@@ -1,134 +0,0 @@
-""" 
-@Date: 2021/10/06
-@description: Use the approach proposed by DuLa-Net
-"""
-import cv2
-import numpy as np
-import math
-import matplotlib.pyplot as plt
-
-from visualization.floorplan import draw_floorplan
-
-
-def merge_near(lst, diag):
-    group = [[0, ]]
-    for i in range(1, len(lst)):
-        if lst[i] - np.mean(group[-1]) < diag * 0.02:
-            group[-1].append(lst[i])
-        else:
-            group.append([lst[i], ])
-    if len(group) == 1:
-        group = [lst[0], lst[-1]]
-    else:
-        group = [int(np.mean(x)) for x in group]
-    return group
-
-
-def fit_layout_old(floor_xz, need_cube=False, show=False, block_eps=0.05):
-    show_radius = np.linalg.norm(floor_xz, axis=-1).max()
-    side_l = 512
-    floorplan = draw_floorplan(xz=floor_xz, show_radius=show_radius, show=show, scale=1, side_l=side_l).astype(np.uint8)
-    center = np.array([side_l / 2, side_l / 2])
-    polys = cv2.findContours(floorplan, 1, 2)
-    if isinstance(polys, tuple):
-        if len(polys) == 3:
-            # opencv 3
-            polys = list(polys[1])
-        else:
-            polys = list(polys[0])
-    polys.sort(key=lambda x: cv2.contourArea(x), reverse=True)
-    poly = polys[0]
-    sub_x, sub_y, w, h = cv2.boundingRect(poly)
-    floorplan_sub = floorplan[sub_y:sub_y + h, sub_x:sub_x + w]
-    sub_center = center - np.array([sub_x, sub_y])
-    polys = cv2.findContours(floorplan_sub, 1, 2)
-    if isinstance(polys, tuple):
-        if len(polys) == 3:
-            polys = polys[1]
-        else:
-            polys = polys[0]
-    poly = polys[0]
-    epsilon = 0.005 * cv2.arcLength(poly, True)
-    poly = cv2.approxPolyDP(poly, epsilon, True)
-
-    x_lst = [0, ]
-    y_lst = [0, ]
-    for i in range(len(poly)):
-        p1 = poly[i][0]
-        p2 = poly[(i + 1) % len(poly)][0]
-
-        if (p2[0] - p1[0]) == 0:
-            slope = 10
-        else:
-            slope = abs((p2[1] - p1[1]) / (p2[0] - p1[0]))
-
-        if slope <= 1:
-            s = int((p1[1] + p2[1]) / 2)
-            y_lst.append(s)
-        elif slope > 1:
-            s = int((p1[0] + p2[0]) / 2)
-            x_lst.append(s)
-
-    x_lst.append(floorplan_sub.shape[1])
-    y_lst.append(floorplan_sub.shape[0])
-    x_lst.sort()
-    y_lst.sort()
-
-    diag = math.sqrt(math.pow(floorplan_sub.shape[1], 2) + math.pow(floorplan_sub.shape[0], 2))
-    x_lst = merge_near(x_lst, diag)
-    y_lst = merge_near(y_lst, diag)
-    if need_cube and len(x_lst) > 2:
-        x_lst = [x_lst[0], x_lst[-1]]
-    if need_cube and len(y_lst) > 2:
-        y_lst = [y_lst[0], y_lst[-1]]
-
-    ans = np.zeros((floorplan_sub.shape[0], floorplan_sub.shape[1]))
-    for i in range(len(x_lst) - 1):
-        for j in range(len(y_lst) - 1):
-            sample = floorplan_sub[y_lst[j]:y_lst[j + 1], x_lst[i]:x_lst[i + 1]]
-            score = 0 if sample.size == 0 else sample.mean()
-            if score >= 0.3:
-                ans[y_lst[j]:y_lst[j + 1], x_lst[i]:x_lst[i + 1]] = 1
-
-    pred = np.uint8(ans)
-    pred_polys = cv2.findContours(pred, 1, 3)
-    if isinstance(pred_polys, tuple):
-        if len(pred_polys) == 3:
-            pred_polys = pred_polys[1]
-        else:
-            pred_polys = pred_polys[0]
-
-    polygon = [(p[0][1], p[0][0]) for p in pred_polys[0][::-1]]
-
-    v = np.array([p[0] + sub_y for p in polygon])
-    u = np.array([p[1] + sub_x for p in polygon])
-    #     side_l
-    # v<-----------|o
-    # |     |      |
-    # | ----|----z |   side_l
-    # |     |      |
-    # |     x     \|/
-    # |------------u
-    side_l = floorplan.shape[0]
-    pred_xz = np.concatenate((u[:, np.newaxis] - side_l // 2, side_l // 2 - v[:, np.newaxis]), axis=1)
-
-    pred_xz = pred_xz * show_radius / (side_l // 2)
-    if show:
-        draw_floorplan(pred_xz, show_radius=show_radius, show=show)
-    return pred_xz
-
-
-if __name__ == '__main__':
-    from utils.conversion import uv2xyz
-
-    pano_img = np.zeros([512, 1024, 3])
-    corners = np.array([[0.1, 0.7],
-                        [0.4, 0.7],
-                        [0.3, 0.6],
-                        [0.6, 0.6],
-                        [0.8, 0.7]])
-    xz = uv2xyz(corners)[..., ::2]
-    draw_floorplan(xz, show=True, marker_color=None, center_color=0.8)
-
-    xz = fit_layout_old(xz)
-    draw_floorplan(xz, show=True, marker_color=None, center_color=0.8)
diff --git a/spaces/j0hngou/vision-diffmask/code/datamodules/utils.py b/spaces/j0hngou/vision-diffmask/code/datamodules/utils.py
deleted file mode 100644
index e36929e0a9670a1dd29d44bb3d96c26c84ab3d1a..0000000000000000000000000000000000000000
--- a/spaces/j0hngou/vision-diffmask/code/datamodules/utils.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from .image_classification import CIFAR10DataModule, ImageDataModule, MNISTDataModule
-from .transformations import UnNest
-from .visual_qa import CIFAR10QADataModule, ToyQADataModule
-from argparse import Namespace
-from transformers import ConvNextFeatureExtractor, ViTFeatureExtractor
-
-
-def get_configs(args: Namespace) -> tuple[dict, dict]:
-    """Get the model and feature extractor configs from the command line args.
-
-    Args:
-        args (Namespace): the argparse Namespace object
-
-    Returns:
-         a tuple containing the model and feature extractor configs
-    """
-    if args.dataset == "MNIST":
-        # We upsample the MNIST images to 112x112, with 1 channel (grayscale)
-        # and 10 classes (0-9). We normalize the image to have a mean of 0.5
-        # and a standard deviation of ±0.5.
-        model_cfg_args = {
-            "image_size": 112,
-            "num_channels": 1,
-            "num_labels": 10,
-        }
-        fe_cfg_args = {
-            "image_mean": [0.5],
-            "image_std": [0.5],
-        }
-    elif args.dataset.startswith("CIFAR10"):
-        if args.dataset not in ("CIFAR10", "CIFAR10_QA"):
-            raise Exception(f"Unknown CIFAR10 variant: {args.dataset}")
-
-        # We upsample the CIFAR10 images to 224x224, with 3 channels (RGB) and
-        # 10 classes (0-9) for the normal dataset, or (grid_size)^2 + 1 for the
-        # toy task.  We normalize the image to have a mean of 0.5 and a standard
-        # deviation of ±0.5.
-        model_cfg_args = {
-            "image_size": 224,  # fixed to 224 because pretrained models have that size
-            "num_channels": 3,
-            "num_labels": (args.grid_size**2) + 1
-            if args.dataset == "CIFAR10_QA"
-            else 10,
-        }
-        fe_cfg_args = {
-            "image_mean": [0.5, 0.5, 0.5],
-            "image_std": [0.5, 0.5, 0.5],
-        }
-    elif args.dataset == "toy":
-        # We use an image size so that each patch contains a single color, with
-        # 3 channels (RGB) and (grid_size)^2 + 1 classes. We normalize the image
-        # to have a mean of 0.5 and a standard deviation of ±0.5.
-        model_cfg_args = {
-            "image_size": args.grid_size * 16,
-            "num_channels": 3,
-            "num_labels": (args.grid_size**2) + 1,
-        }
-        fe_cfg_args = {
-            "image_mean": [0.5, 0.5, 0.5],
-            "image_std": [0.5, 0.5, 0.5],
-        }
-    else:
-        raise Exception(f"Unknown dataset: {args.dataset}")
-
-    # Set the feature extractor's size attribute to  be the same as the model's image size
-    fe_cfg_args["size"] = model_cfg_args["image_size"]
-    # Set the tensors' return type to PyTorch tensors
-    fe_cfg_args["return_tensors"] = "pt"
-
-    return model_cfg_args, fe_cfg_args
-
-
-def datamodule_factory(args: Namespace) -> ImageDataModule:
-    """A factory method for creating a datamodule based on the command line args.
-
-    Args:
-        args (Namespace): the argparse Namespace object
-
-    Returns:
-        an ImageDataModule instance
-    """
-    # Get the model and feature extractor configs
-    model_cfg_args, fe_cfg_args = get_configs(args)
-
-    # Set the feature extractor class based on the provided base model name
-    if args.base_model == "ViT":
-        fe_class = ViTFeatureExtractor
-    elif args.base_model == "ConvNeXt":
-        fe_class = ConvNextFeatureExtractor
-    else:
-        raise Exception(f"Unknown base model: {args.base_model}")
-
-    # Create the feature extractor instance
-    if args.from_pretrained:
-        feature_extractor = fe_class.from_pretrained(
-            args.from_pretrained, **fe_cfg_args
-        )
-    else:
-        feature_extractor = fe_class(**fe_cfg_args)
-
-    # Un-nest the feature extractor's output
-    feature_extractor = UnNest(feature_extractor)
-
-    # Define the datamodule's configuration
-    dm_cfg = {
-        "feature_extractor": feature_extractor,
-        "batch_size": args.batch_size,
-        "add_noise": args.add_noise,
-        "add_rotation": args.add_rotation,
-        "add_blur": args.add_blur,
-        "num_workers": args.num_workers,
-    }
-
-    # Determine the dataset class based on the provided dataset name
-    if args.dataset.startswith("CIFAR10"):
-        if args.dataset == "CIFAR10":
-            dm_class = CIFAR10DataModule
-        elif args.dataset == "CIFAR10_QA":
-            dm_cfg["class_idx"] = args.class_idx
-            dm_cfg["grid_size"] = args.grid_size
-            dm_class = CIFAR10QADataModule
-        else:
-            raise Exception(f"Unknown CIFAR10 variant: {args.dataset}")
-    elif args.dataset == "MNIST":
-        dm_class = MNISTDataModule
-    elif args.dataset == "toy":
-        dm_cfg["class_idx"] = args.class_idx
-        dm_cfg["grid_size"] = args.grid_size
-        dm_class = ToyQADataModule
-    else:
-        raise Exception(f"Unknown dataset: {args.dataset}")
-
-    return dm_class(**dm_cfg)
diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/javascript/deforum.js b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/javascript/deforum.js
deleted file mode 100644
index 889e76f39c937d9eb3b602aa5510d8ac637f5e3c..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/javascript/deforum.js
+++ /dev/null
@@ -1,21 +0,0 @@
-function submit_deforum(){
-	// alert('Hello, Deforum!')
-    rememberGallerySelection('deforum_gallery')
-    showSubmitButtons('deforum', false)
-
-    var id = randomId()
-    requestProgress(id, gradioApp().getElementById('deforum_gallery_container'), gradioApp().getElementById('deforum_gallery'), function(){
-        showSubmitButtons('deforum', true)
-    })
-
-    var res = create_submit_args(arguments)
-
-    res[0] = id
-	// res[1] = get_tab_index('deforum')
-
-    return res
-}
-
-onUiUpdate(function(){
-    check_gallery('deforum_gallery')
-})
diff --git a/spaces/jackli888/stable-diffusion-webui/test/basic_features/extras_test.py b/spaces/jackli888/stable-diffusion-webui/test/basic_features/extras_test.py
deleted file mode 100644
index 0170c511fe54cc6bcf49ec7f75ca7c747de41db5..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/test/basic_features/extras_test.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import unittest
-import requests
-from gradio.processing_utils import encode_pil_to_base64
-from PIL import Image
-
-class TestExtrasWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_extras_single = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.extras_single = {
-            "resize_mode": 0,
-            "show_extras_results": True,
-            "gfpgan_visibility": 0,
-            "codeformer_visibility": 0,
-            "codeformer_weight": 0,
-            "upscaling_resize": 2,
-            "upscaling_resize_w": 128,
-            "upscaling_resize_h": 128,
-            "upscaling_crop": True,
-            "upscaler_1": "None",
-            "upscaler_2": "None",
-            "extras_upscaler_2_visibility": 0,
-            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
-            }
-
-    def test_simple_upscaling_performed(self):
-        self.extras_single["upscaler_1"] = "Lanczos"
-        self.assertEqual(requests.post(self.url_extras_single, json=self.extras_single).status_code, 200)
-
-
-class TestPngInfoWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_png_info = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.png_info = {
-            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
-        }
-
-    def test_png_info_performed(self):
-        self.assertEqual(requests.post(self.url_png_info, json=self.png_info).status_code, 200)
-
-
-class TestInterrogateWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_interrogate = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.interrogate = {
-            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")),
-            "model": "clip"
-        }
-
-    def test_interrogate_performed(self):
-        self.assertEqual(requests.post(self.url_interrogate, json=self.interrogate).status_code, 200)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/james-oldfield/PandA/networks/biggan/model.py b/spaces/james-oldfield/PandA/networks/biggan/model.py
deleted file mode 100644
index a600babe7bf135e0847782a0c85e0c0aff75a99b..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/biggan/model.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# coding: utf-8
-""" BigGAN PyTorch model.
-    From "Large Scale GAN Training for High Fidelity Natural Image Synthesis"
-    By Andrew Brocky, Jeff Donahuey and Karen Simonyan.
-    https://openreview.net/forum?id=B1xsqj09Fm
-
-    PyTorch version implemented from the computational graph of the TF Hub module for BigGAN.
-    Some part of the code are adapted from https://github.com/brain-research/self-attention-gan
-
-    This version only comprises the generator (since the discriminator's weights are not released).
-    This version only comprises the "deep" version of BigGAN (see publication).
-"""
-from __future__ import (absolute_import, division, print_function, unicode_literals)
-
-import os
-import logging
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .config import BigGANConfig
-from .file_utils import cached_path
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {
-    'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin",
-    'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin",
-    'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin",
-}
-
-PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-config.json",
-    'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-config.json",
-    'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-config.json",
-}
-
-WEIGHTS_NAME = 'pytorch_model.bin'
-CONFIG_NAME = 'config.json'
-
-
-def snconv2d(eps=1e-12, **kwargs):
-    return nn.utils.spectral_norm(nn.Conv2d(**kwargs), eps=eps)
-
-def snlinear(eps=1e-12, **kwargs):
-    return nn.utils.spectral_norm(nn.Linear(**kwargs), eps=eps)
-
-def sn_embedding(eps=1e-12, **kwargs):
-    return nn.utils.spectral_norm(nn.Embedding(**kwargs), eps=eps)
-
-class SelfAttn(nn.Module):
-    """ Self attention Layer"""
-    def __init__(self, in_channels, eps=1e-12):
-        super(SelfAttn, self).__init__()
-        self.in_channels = in_channels
-        self.snconv1x1_theta = snconv2d(in_channels=in_channels, out_channels=in_channels//8,
-                                        kernel_size=1, bias=False, eps=eps)
-        self.snconv1x1_phi = snconv2d(in_channels=in_channels, out_channels=in_channels//8,
-                                      kernel_size=1, bias=False, eps=eps)
-        self.snconv1x1_g = snconv2d(in_channels=in_channels, out_channels=in_channels//2,
-                                    kernel_size=1, bias=False, eps=eps)
-        self.snconv1x1_o_conv = snconv2d(in_channels=in_channels//2, out_channels=in_channels,
-                                         kernel_size=1, bias=False, eps=eps)
-        self.maxpool = nn.MaxPool2d(2, stride=2, padding=0)
-        self.softmax  = nn.Softmax(dim=-1)
-        self.gamma = nn.Parameter(torch.zeros(1))
-
-    def forward(self, x):
-        _, ch, h, w = x.size()
-        # Theta path
-        theta = self.snconv1x1_theta(x)
-        theta = theta.view(-1, ch//8, h*w)
-        # Phi path
-        phi = self.snconv1x1_phi(x)
-        phi = self.maxpool(phi)
-        phi = phi.view(-1, ch//8, h*w//4)
-        # Attn map
-        attn = torch.bmm(theta.permute(0, 2, 1), phi)
-        attn = self.softmax(attn)
-        # g path
-        g = self.snconv1x1_g(x)
-        g = self.maxpool(g)
-        g = g.view(-1, ch//2, h*w//4)
-        # Attn_g - o_conv
-        attn_g = torch.bmm(g, attn.permute(0, 2, 1))
-        attn_g = attn_g.view(-1, ch//2, h, w)
-        attn_g = self.snconv1x1_o_conv(attn_g)
-        # Out
-        out = x + self.gamma*attn_g
-        return out
-
-
-class BigGANBatchNorm(nn.Module):
-    """ This is a batch norm module that can handle conditional input and can be provided with pre-computed
-        activation means and variances for various truncation parameters.
-
-        We cannot just rely on torch.batch_norm since it cannot handle
-        batched weights (pytorch 1.0.1). We computate batch_norm our-self without updating running means and variances.
-        If you want to train this model you should add running means and variance computation logic.
-    """
-    def __init__(self, num_features, condition_vector_dim=None, n_stats=51, eps=1e-4, conditional=True):
-        super(BigGANBatchNorm, self).__init__()
-        self.num_features = num_features
-        self.eps = eps
-        self.conditional = conditional
-
-        # We use pre-computed statistics for n_stats values of truncation between 0 and 1
-        self.register_buffer('running_means', torch.zeros(n_stats, num_features))
-        self.register_buffer('running_vars', torch.ones(n_stats, num_features))
-        self.step_size = 1.0 / (n_stats - 1)
-
-        if conditional:
-            assert condition_vector_dim is not None
-            self.scale = snlinear(in_features=condition_vector_dim, out_features=num_features, bias=False, eps=eps)
-            self.offset = snlinear(in_features=condition_vector_dim, out_features=num_features, bias=False, eps=eps)
-        else:
-            self.weight = torch.nn.Parameter(torch.Tensor(num_features))
-            self.bias = torch.nn.Parameter(torch.Tensor(num_features))
-
-    def forward(self, x, truncation, condition_vector=None):
-        # Retreive pre-computed statistics associated to this truncation
-        coef, start_idx = math.modf(truncation / self.step_size)
-        start_idx = int(start_idx)
-        if coef != 0.0:  # Interpolate
-            running_mean = self.running_means[start_idx] * coef + self.running_means[start_idx + 1] * (1 - coef)
-            running_var = self.running_vars[start_idx] * coef + self.running_vars[start_idx + 1] * (1 - coef)
-        else:
-            running_mean = self.running_means[start_idx]
-            running_var = self.running_vars[start_idx]
-
-        if self.conditional:
-            running_mean = running_mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-            running_var = running_var.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-
-            weight = 1 + self.scale(condition_vector).unsqueeze(-1).unsqueeze(-1)
-            bias = self.offset(condition_vector).unsqueeze(-1).unsqueeze(-1)
-
-            out = (x - running_mean) / torch.sqrt(running_var + self.eps) * weight + bias
-        else:
-            out = F.batch_norm(x, running_mean, running_var, self.weight, self.bias,
-                               training=False, momentum=0.0, eps=self.eps)
-
-        return out
-
-
-class GenBlock(nn.Module):
-    def __init__(self, in_size, out_size, condition_vector_dim, reduction_factor=4, up_sample=False,
-                 n_stats=51, eps=1e-12):
-        super(GenBlock, self).__init__()
-        self.up_sample = up_sample
-        self.drop_channels = (in_size != out_size)
-        middle_size = in_size // reduction_factor
-
-        self.bn_0 = BigGANBatchNorm(in_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
-        self.conv_0 = snconv2d(in_channels=in_size, out_channels=middle_size, kernel_size=1, eps=eps)
-
-        self.bn_1 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
-        self.conv_1 = snconv2d(in_channels=middle_size, out_channels=middle_size, kernel_size=3, padding=1, eps=eps)
-
-        self.bn_2 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
-        self.conv_2 = snconv2d(in_channels=middle_size, out_channels=middle_size, kernel_size=3, padding=1, eps=eps)
-
-        self.bn_3 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
-        self.conv_3 = snconv2d(in_channels=middle_size, out_channels=out_size, kernel_size=1, eps=eps)
-
-        self.relu = nn.ReLU()
-
-    def forward(self, x, cond_vector, truncation):
-        x0 = x
-
-        x = self.bn_0(x, truncation, cond_vector)
-        x = self.relu(x)
-        x = self.conv_0(x)
-
-        x = self.bn_1(x, truncation, cond_vector)
-        x = self.relu(x)
-        if self.up_sample:
-            x = F.interpolate(x, scale_factor=2, mode='nearest')
-        x = self.conv_1(x)
-
-        x = self.bn_2(x, truncation, cond_vector)
-        x = self.relu(x)
-        x = self.conv_2(x)
-
-        x = self.bn_3(x, truncation, cond_vector)
-        x = self.relu(x)
-        x = self.conv_3(x)
-
-        if self.drop_channels:
-            new_channels = x0.shape[1] // 2
-            x0 = x0[:, :new_channels, ...]
-        if self.up_sample:
-            x0 = F.interpolate(x0, scale_factor=2, mode='nearest')
-
-        out = x + x0
-        return out
-
-class Generator(nn.Module):
-    def __init__(self, config):
-        super(Generator, self).__init__()
-        self.config = config
-        ch = config.channel_width
-        condition_vector_dim = config.z_dim * 2
-
-        self.gen_z = snlinear(in_features=condition_vector_dim,
-                              out_features=4 * 4 * 16 * ch, eps=config.eps)
-
-        layers = []
-        for i, layer in enumerate(config.layers):
-            if i == config.attention_layer_position:
-                layers.append(SelfAttn(ch*layer[1], eps=config.eps))
-            layers.append(GenBlock(ch*layer[1],
-                                   ch*layer[2],
-                                   condition_vector_dim,
-                                   up_sample=layer[0],
-                                   n_stats=config.n_stats,
-                                   eps=config.eps))
-        self.layers = nn.ModuleList(layers)
-
-        self.bn = BigGANBatchNorm(ch, n_stats=config.n_stats, eps=config.eps, conditional=False)
-        self.relu = nn.ReLU()
-        self.conv_to_rgb = snconv2d(in_channels=ch, out_channels=ch, kernel_size=3, padding=1, eps=config.eps)
-        self.tanh = nn.Tanh()
-
-    def forward(self, cond_vector, truncation, z=None, start=0, stop=None):
-        # We use this conversion step to be able to use TF weights:
-        # TF convention on shape is [batch, height, width, channels]
-        # PT convention on shape is [batch, channels, height, width]
-        if start == 0 and z is None:
-            z = self.gen_z(cond_vector)
-            z = z.view(-1, 4, 4, 16 * self.config.channel_width)
-            z = z.permute(0, 3, 1, 2).contiguous()
-
-        if stop is None: stop = len(self.layers)
-
-        # for i, layer in enumerate(self.layers):
-        for i in range(start, stop):
-            if isinstance(self.layers[i], GenBlock):
-                z = self.layers[i](z, cond_vector, truncation)
-            else:
-                z = self.layers[i](z)
-
-        if stop == len(self.layers):
-            z = self.bn(z, truncation)
-            z = self.relu(z)
-            z = self.conv_to_rgb(z)
-            z = z[:, :3, ...]
-            z = self.tanh(z)
-
-        # for i, layer in enumerate(self.layers):
-        #     if isinstance(layer, GenBlock):
-        #         z = layer(z, cond_vector, truncation)
-        #     else:
-        #         z = layer(z)
-
-        # z = self.bn(z, truncation)
-        # z = self.relu(z)
-        # z = self.conv_to_rgb(z)
-        # z = z[:, :3, ...]
-        # z = self.tanh(z)
-        return z
-
-class BigGAN(nn.Module):
-    """BigGAN Generator."""
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
-        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
-            model_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
-            config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
-        else:
-            model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
-            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
-
-        try:
-            resolved_model_file = cached_path(model_file, cache_dir=cache_dir)
-            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
-        except EnvironmentError:
-            logger.error("Wrong model name, should be a valid path to a folder containing "
-                         "a {} file and a {} file or a model name in {}".format(
-                         WEIGHTS_NAME, CONFIG_NAME, PRETRAINED_MODEL_ARCHIVE_MAP.keys()))
-            raise
-
-        logger.info("loading model {} from cache at {}".format(pretrained_model_name_or_path, resolved_model_file))
-
-        # Load config
-        config = BigGANConfig.from_json_file(resolved_config_file)
-        logger.info("Model config {}".format(config))
-
-        # Instantiate model.
-        model = cls(config, *inputs, **kwargs)
-        state_dict = torch.load(resolved_model_file, map_location='cpu' if not torch.cuda.is_available() else None)
-        model.load_state_dict(state_dict, strict=False)
-        return model
-
-    def __init__(self, config):
-        super(BigGAN, self).__init__()
-        self.config = config
-        self.embeddings = nn.Linear(config.num_classes, config.z_dim, bias=False)
-        self.generator = Generator(config)
-        # self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-        # print(f'device: {self.device}')
-        # self.generator.to(self.device)
-
-    def forward(self, z, class_label, truncation, cond_vector=None, start=0, stop=None):
-        assert 0 < truncation <= 1
-
-        results = {}
-        if start == 0 and cond_vector is None:
-            embed = self.embeddings(class_label)
-            cond_vector = torch.cat((z, embed), dim=1)
-            results['cond_vector'] = cond_vector
-
-        results['z'] = self.generator(cond_vector, truncation, z=None if start == 0 else z, start=start, stop=stop)
-        return results
-
-
-if __name__ == "__main__":
-    import PIL
-    from .utils import truncated_noise_sample, save_as_images, one_hot_from_names
-    from .convert_tf_to_pytorch import load_tf_weights_in_biggan
-
-    load_cache = False
-    cache_path = './saved_model.pt'
-    config = BigGANConfig()
-    model = BigGAN(config)
-    if not load_cache:
-        model = load_tf_weights_in_biggan(model, config, './models/model_128/', './models/model_128/batchnorms_stats.bin')
-        torch.save(model.state_dict(), cache_path)
-    else:
-        model.load_state_dict(torch.load(cache_path))
-
-    model.eval()
-
-    truncation = 0.4
-    noise = truncated_noise_sample(batch_size=2, truncation=truncation)
-    label = one_hot_from_names('diver', batch_size=2)
-
-    # Tests
-    # noise = np.zeros((1, 128))
-    # label = [983]
-
-    noise = torch.tensor(noise, dtype=torch.float)
-    label = torch.tensor(label, dtype=torch.float)
-    with torch.no_grad():
-        outputs = model(noise, label, truncation)
-    print(outputs.shape)
-
-    save_as_images(outputs)
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/app/server/actions/animation.ts b/spaces/jbilcke-hf/ai-clip-factory/src/app/server/actions/animation.ts
deleted file mode 100644
index 4222cae77afc6f9bc1837cc98bd80fcf07bb7bdf..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/app/server/actions/animation.ts
+++ /dev/null
@@ -1,168 +0,0 @@
-"use server"
-
-import {Ratelimit} from "@upstash/ratelimit"
-import {Redis} from "@upstash/redis"
-
-import { VideoOptions } from "@/types"
-
-import { generateGradio } from "./generateGradio"
-import { generateReplicate } from "./generateReplicate"
-import { filterOutBadWords } from "./censorship"
-
-const videoEngine = `${process.env.VIDEO_ENGINE || ""}`
-
-// const officialApi = `${process.env.VIDEO_HOTSHOT_XL_API_OFFICIAL || ""}`
-const nodeApi = `${process.env.VIDEO_HOTSHOT_XL_API_NODE || ""}`
-
-const redis = new Redis({
-  url: `${process.env.UPSTASH_REDIS_REST_URL || ""}`,
-  token: `${process.env.UPSTASH_REDIS_REST_TOKEN || ""}`,
-})
-
-console.log("process.env.OAUTH_CLIENT_ID:", process.env.OAUTH_CLIENT_ID)
-
-console.log("process.env.OPENID_PROVIDER_URL: ", process.env.OPENID_PROVIDER_URL)
-
-// Create a global ratelimiter for all users, that allows 14 requests per 60 seconds
-// 14 is roughly the number of requests that can be handled by the server
-/*
-const rateLimitGlobal = new Ratelimit({
-  redis,
-  limiter: Ratelimit.slidingWindow(14, "60 s"),
-  analytics: true,
-  timeout: 1000,
-  prefix: "production"
-})
-*/
-
-
-// Create a new ratelimiter for anonymous users, that allows 2 requests per minute
-const rateLimitAnons = new Ratelimit({
-  redis,
-  limiter: Ratelimit.slidingWindow(2, "60 s"),
-  analytics: true,
-  timeout: 1000,
-  prefix: "production:anon"
-})
-
-export async function generateAnimation({
-  positivePrompt = "",
-  negativePrompt = "",
-  size = "512x512",
-  huggingFaceLora,
-  replicateLora,
-  triggerWord,
-  nbFrames = 8,
-  duration = 1000,
-  steps = 30,
-  key = "",
-}: VideoOptions): Promise<string> {
-  if (!positivePrompt?.length) {
-    throw new Error(`prompt is too short!`)
-  }
-
-  const cropped = positivePrompt.slice(0, 30)
-
-  console.log(`user ${key.slice(0, 10)} requested "${cropped}${cropped !== positivePrompt ? "..." : ""}"`)
-
-  // const globalRateLimitResult = rateLimitGlobal.limit("global")
-
-  // this waits for 3 seconds before failing the request
-  // we don't wait more because it is frustrating for someone to wait a failure
-  const userRateLimitResult = await rateLimitAnons.limit(key || "anon")
-  // const rateLimitResult = await rateLimitAnons.blockUntilReady(key, 3_000)
-
-  // admin / developers will have this key:
-  // eff8e7ca506627fe15dda5e0e512fcaad70b6d520f37cc76597fdb4f2d83a1a3
-
-  // result.limit
-  if (!userRateLimitResult.success) {
-    console.log(`blocking user ${key.slice(0, 10)} who requested "${cropped}${cropped !== positivePrompt ? "..." : ""}"`)
-    throw new Error(`Rate Limit Reached`)
-  } else {
-    console.log(`allowing user ${key.slice(0, 10)}: who requested "${cropped}${cropped !== positivePrompt ? "..." : ""}"`)
-  }
-
-  positivePrompt = filterOutBadWords(positivePrompt)
-
-  // pimp the prompt
-  positivePrompt = [
-    triggerWord,
-    positivePrompt,
-    "beautiful",
-    "hd"
-  ].join(", ")
-
-  negativePrompt = [
-    negativePrompt,
-    "cropped",
-    "dark",
-    "underexposed",
-    "overexposed",
-    "watermark",
-    "watermarked",
-  ].join(", ")
-
-  try {
-
-    if (videoEngine === "VIDEO_HOTSHOT_XL_API_REPLICATE") {
-      return generateReplicate({
-        positivePrompt,
-        negativePrompt,
-        size,
-        huggingFaceLora,
-        replicateLora,
-        nbFrames,
-        duration,
-        steps,
-      })
-      
-    } else if (videoEngine === "VIDEO_HOTSHOT_XL_API_NODE") {
-      // TODO: support other API to avoid duplicate work?
-      // (are the other API supporting custom LoRAs?)
-      const res = await fetch(nodeApi, {
-        method: "POST",
-        headers: {
-          "Content-Type": "application/json",
-          // access isn't secured for now, the free lunch is open
-          // Authorization: `Bearer ${token}`,
-        },
-        body: JSON.stringify({
-          prompt: positivePrompt,
-          lora: huggingFaceLora,
-          size,
-        }),
-        cache: "no-store",
-        // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
-        // next: { revalidate: 1 }
-      })
-
-      const content = await res.text()
-
-      // Recommendation: handle errors
-      if (res.status !== 200) {
-        console.error(content)
-        // This will activate the closest `error.js` Error Boundary
-        throw new Error('Failed to fetch data')
-      }
-
-      return content
-    } else if (videoEngine === "VIDEO_HOTSHOT_XL_API_GRADIO") {
-      return generateGradio({
-        positivePrompt,
-        negativePrompt,
-        size,
-        huggingFaceLora,
-        replicateLora,
-        nbFrames,
-        duration,
-        steps,
-      })
-    } else {
-      throw new Error(`not implemented yet!`)
-    }
-
-  } catch (err) {
-    throw new Error(`failed to generate the image ${err}`)
-  }
-}
\ No newline at end of file
diff --git a/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/modules/encoders/modules.py b/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/modules/encoders/modules.py
deleted file mode 100644
index 63eb8244924c71e101e6908f913e1ee51815525e..0000000000000000000000000000000000000000
--- a/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/modules/encoders/modules.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import torch
-import torch.nn as nn
-from functools import partial
-import clip
-from einops import rearrange, repeat
-from transformers import CLIPTokenizer, CLIPTextModel
-import kornia
-
-from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-
-    def forward(self, batch, key=None):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]
-        c = self.embedding(c)
-        return c
-
-
-class TransformerEmbedder(AbstractEncoder):
-    """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
-        super().__init__()
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
-
-    def forward(self, tokens):
-        tokens = tokens.to(self.device)  # meh
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, x):
-        return self(x)
-
-
-class BERTTokenizer(AbstractEncoder):
-    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
-        super().__init__()
-        from transformers import BertTokenizerFast  # TODO: add to reuquirements
-        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-        self.device = device
-        self.vq_interface = vq_interface
-        self.max_length = max_length
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt",
-                                        return_offsets_mapping=True)
-        tokens = batch_encoding["input_ids"].to(self.device)
-        offset_mapping = batch_encoding["offset_mapping"]
-        return tokens, offset_mapping
-
-    @torch.no_grad()
-    def encode(self, text):
-        tokens = self(text)
-        if not self.vq_interface:
-            return tokens
-        return None, None, [None, None, tokens]
-
-    def decode(self, text):
-        return text
-
-
-class BERTEmbedder(AbstractEncoder):
-    """Uses the BERT tokenizr model and add some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
-        super().__init__()
-        self.use_tknz_fn = use_tokenizer
-        if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
-                                              emb_dropout=embedding_dropout)
-
-    def forward(self, text, return_offset_mapping=False):
-        if self.use_tknz_fn:
-            tokens, offset_mapping = self.tknz_fn(text)#.to(self.device)
-        else:
-            assert False 
-            tokens = text
-        z = self.transformer(tokens, return_embeddings=True)
-
-        if return_offset_mapping:
-            return z, offset_mapping
-        else:
-            return z 
-
-    def encode(self, text, return_offset_mapping=False):
-        # output of length 77
-        return self(text, return_offset_mapping)
-
-
-class SpatialRescaler(nn.Module):
-    def __init__(self,
-                 n_stages=1,
-                 method='bilinear',
-                 multiplier=0.5,
-                 in_channels=3,
-                 out_channels=None,
-                 bias=False):
-        super().__init__()
-        self.n_stages = n_stages
-        assert self.n_stages >= 0
-        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
-        self.multiplier = multiplier
-        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
-        self.remap_output = out_channels is not None
-        if self.remap_output:
-            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
-            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
-
-    def forward(self,x):
-        for stage in range(self.n_stages):
-            x = self.interpolator(x, scale_factor=self.multiplier)
-
-
-        if self.remap_output:
-            x = self.channel_mapper(x)
-        return x
-
-    def encode(self, x):
-        return self(x)
-
-class FrozenCLIPEmbedder(AbstractEncoder):
-    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
-        super().__init__()
-        self.tokenizer = CLIPTokenizer.from_pretrained(version)
-        self.transformer = CLIPTextModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length
-        self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text, return_pooler_output=False):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        
-        if not return_pooler_output:
-            return z
-        else:
-            return z, outputs.pooler_output
-
-    def encode(self, text, return_pooler_output=False):
-        return self(text, return_pooler_output)
-
-
-class FrozenCLIPTextEmbedder(nn.Module):
-    """
-    Uses the CLIP transformer encoder for text.
-    """
-    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
-        super().__init__()
-        self.model, _ = clip.load(version, jit=False, device="cpu")
-        self.device = device
-        self.max_length = max_length
-        self.n_repeat = n_repeat
-        self.normalize = normalize
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        tokens = clip.tokenize(text).to(self.device)
-        z = self.model.encode_text(tokens)
-        if self.normalize:
-            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
-        return z
-
-    def encode(self, text):
-        z = self(text)
-        if z.ndim==2:
-            z = z[:, None, :]
-        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
-        return z
-
-
-class FrozenClipImageEmbedder(nn.Module):
-    """
-        Uses the CLIP image encoder.
-        """
-    def __init__(
-            self,
-            model,
-            jit=False,
-            device='cuda' if torch.cuda.is_available() else 'cpu',
-            antialias=False,
-        ):
-        super().__init__()
-        self.model, _ = clip.load(name=model, device=device, jit=jit)
-
-        self.antialias = antialias
-
-        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
-        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
-
-    def preprocess(self, x):
-        # normalize to [0,1]
-        x = kornia.geometry.resize(x, (224, 224),
-                                   interpolation='bicubic',align_corners=True,
-                                   antialias=self.antialias)
-        x = (x + 1.) / 2.
-        # renormalize according to clip
-        x = kornia.enhance.normalize(x, self.mean, self.std)
-        return x
-
-    def forward(self, x):
-        # x is assumed to be in range [-1,1]
-        return self.model.encode_image(self.preprocess(x))
-
-
-if __name__ == "__main__":
-    from ldm.util import count_params
-    model = FrozenCLIPEmbedder()
-    count_params(model, verbose=True)
\ No newline at end of file
diff --git a/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/mixing_manipulator/normalization_imager.py b/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/mixing_manipulator/normalization_imager.py
deleted file mode 100644
index b2848432f798baa8b0afb73e3d31cd42f2114885..0000000000000000000000000000000000000000
--- a/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/mixing_manipulator/normalization_imager.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""
-    Implementation of the normalization process of stereo-imaging and panning effects
-"""
-import numpy as np
-import sys
-import os
-
-currentdir = os.path.dirname(os.path.realpath(__file__))
-sys.path.append(currentdir)
-from common_audioeffects import AugmentationChain, Haas
-
-
-'''
-    ### normalization algorithm for stereo imaging and panning effects ###
-    process :
-        1. inputs 2-channeled audio
-        2. apply Haas effects if the input audio is almost mono
-        3. normalize mid-side channels according to target precomputed feature value
-        4. normalize left-right channels 50-50
-        5. normalize mid-side channels again
-'''
-def normalize_imager(data, \
-                        target_side_mid_bal=0.9, \
-                        mono_threshold=0.95, \
-                        sr=44100, \
-                        eps=1e-04, \
-                        verbose=False):
-
-    # to mid-side channels
-    mid, side = lr_to_ms(data[:,0], data[:,1])
-
-    if verbose:
-        print_balance(data[:,0], data[:,1])
-        print_balance(mid, side)
-        print()
-
-    # apply mid-side weights according to energy
-    mid_e, side_e = np.sum(mid**2), np.sum(side**2)
-    total_e = mid_e + side_e
-    # apply haas effect to almost-mono signal
-    if mid_e/total_e > mono_threshold:
-        aug_chain = AugmentationChain(fxs=[(Haas(sample_rate=sr), 1, True)])
-        data = aug_chain([data])[0]
-        mid, side = lr_to_ms(data[:,0], data[:,1])
-
-    if verbose:
-        print_balance(data[:,0], data[:,1])
-        print_balance(mid, side)
-        print()
-
-    # normalize mid-side channels (stereo imaging)
-    new_mid, new_side = process_balance(mid, side, tgt_e1_bal=target_side_mid_bal, eps=eps)
-    left, right = ms_to_lr(new_mid, new_side)
-    imaged = np.stack([left, right], 1)
-
-    if verbose:
-        print_balance(new_mid, new_side)
-        print_balance(left, right)
-        print()
-
-    # normalize panning to have the balance of left-right channels 50-50
-    left, right = process_balance(left, right, tgt_e1_bal=0.5, eps=eps)
-    mid, side = lr_to_ms(left, right)
-
-    if verbose:
-        print_balance(mid, side)
-        print_balance(left, right)
-        print()
-
-    # normalize again mid-side channels (stereo imaging)
-    new_mid, new_side = process_balance(mid, side, tgt_e1_bal=target_side_mid_bal, eps=eps)
-    left, right = ms_to_lr(new_mid, new_side)
-    imaged = np.stack([left, right], 1)
-
-    if verbose:
-        print_balance(new_mid, new_side)
-        print_balance(left, right)
-        print()
-
-    return imaged
-
-
-# balance out 2 input data's energy according to given balance
-# tgt_e1_bal range = [0.0, 1.0]
-    # tgt_e2_bal = 1.0 - tgt_e1_bal_range
-def process_balance(data_1, data_2, tgt_e1_bal=0.5, eps=1e-04):
-
-    e_1, e_2 = np.sum(data_1**2), np.sum(data_2**2)
-    total_e = e_1 + e_2
-
-    tgt_1_gain = np.sqrt(tgt_e1_bal * total_e / (e_1 + eps))
-
-    new_data_1 = data_1 * tgt_1_gain
-    new_e_1 = e_1 * (tgt_1_gain ** 2)
-    left_e_1 = total_e - new_e_1
-    tgt_2_gain = np.sqrt(left_e_1 / (e_2 + 1e-3))
-    new_data_2 = data_2 * tgt_2_gain
-
-    return new_data_1, new_data_2
-
-
-# left-right channeled signal to mid-side signal
-def lr_to_ms(left, right):
-    mid = left + right
-    side = left - right
-    return mid, side
-
-
-# mid-side channeled signal to left-right signal
-def ms_to_lr(mid, side):
-    left = (mid + side) / 2
-    right = (mid - side) / 2
-    return left, right
-
-
-# print energy balance of 2 inputs
-def print_balance(data_1, data_2):
-    e_1, e_2 = np.sum(data_1**2), np.sum(data_2**2)
-    total_e = e_1 + e_2
-    print(total_e, e_1/total_e, e_2/total_e)
-
diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/gen_wavernn.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/gen_wavernn.py
deleted file mode 100644
index 2036737f805f6055893812e48f99d524624aab07..0000000000000000000000000000000000000000
--- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/gen_wavernn.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from vocoder.models.fatchord_version import  WaveRNN
-from vocoder.audio import *
-
-
-def gen_testset(model: WaveRNN, test_set, samples, batched, target, overlap, save_path):
-    k = model.get_step() // 1000
-
-    for i, (m, x) in enumerate(test_set, 1):
-        if i > samples: 
-            break
-
-        print('\n| Generating: %i/%i' % (i, samples))
-
-        x = x[0].numpy()
-
-        bits = 16 if hp.voc_mode == 'MOL' else hp.bits
-
-        if hp.mu_law and hp.voc_mode != 'MOL' :
-            x = decode_mu_law(x, 2**bits, from_labels=True)
-        else :
-            x = label_2_float(x, bits)
-
-        save_wav(x, save_path.joinpath("%dk_steps_%d_target.wav" % (k, i)))
-        
-        batch_str = "gen_batched_target%d_overlap%d" % (target, overlap) if batched else \
-            "gen_not_batched"
-        save_str = save_path.joinpath("%dk_steps_%d_%s.wav" % (k, i, batch_str))
-
-        wav = model.generate(m, batched, target, overlap, hp.mu_law)
-        save_wav(wav, save_str)
-
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/html.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/html.py
deleted file mode 100644
index c1084aeecfea15697b70abeeeb2e88b951f3e782..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/utils/html.py
+++ /dev/null
@@ -1,316 +0,0 @@
-import json
-from typing import Optional, Dict
-
-import jinja2
-
-
-HTML_TEMPLATE = jinja2.Template(
-    """
-{%- if fullhtml -%}
-<!DOCTYPE html>
-<html>
-<head>
-{%- endif %}
-  <style>
-    #{{ output_div }}.vega-embed {
-      width: 100%;
-      display: flex;
-    }
-
-    #{{ output_div }}.vega-embed details,
-    #{{ output_div }}.vega-embed details summary {
-      position: relative;
-    }
-  </style>
-{%- if not requirejs %}
-  <script type="text/javascript" src="{{ base_url }}/vega@{{ vega_version }}"></script>
-  {%- if mode == 'vega-lite' %}
-  <script type="text/javascript" src="{{ base_url }}/vega-lite@{{ vegalite_version }}"></script>
-  {%- endif %}
-  <script type="text/javascript" src="{{ base_url }}/vega-embed@{{ vegaembed_version }}"></script>
-{%- endif %}
-{%- if fullhtml %}
-{%- if requirejs %}
-<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"></script>
-<script>
-requirejs.config({
-    "paths": {
-        "vega": "{{ base_url }}/vega@{{ vega_version }}?noext",
-        "vega-lib": "{{ base_url }}/vega-lib?noext",
-        "vega-lite": "{{ base_url }}/vega-lite@{{ vegalite_version }}?noext",
-        "vega-embed": "{{ base_url }}/vega-embed@{{ vegaembed_version }}?noext",
-    }
-});
-</script>
-{%- endif %}
-</head>
-<body>
-{%- endif %}
-  <div id="{{ output_div }}"></div>
-  <script>
-    {%- if requirejs and not fullhtml %}
-    requirejs.config({
-        "paths": {
-            "vega": "{{ base_url }}/vega@{{ vega_version }}?noext",
-            "vega-lib": "{{ base_url }}/vega-lib?noext",
-            "vega-lite": "{{ base_url }}/vega-lite@{{ vegalite_version }}?noext",
-            "vega-embed": "{{ base_url }}/vega-embed@{{ vegaembed_version }}?noext",
-        }
-    });
-    {% endif %}
-    {% if requirejs -%}
-    require(['vega-embed'],
-    {%- else -%}
-    (
-    {%- endif -%}
-    function(vegaEmbed) {
-      var spec = {{ spec }};
-      var embedOpt = {{ embed_options }};
-
-      function showError(el, error){
-          el.innerHTML = ('<div style="color:red;">'
-                          + '<p>JavaScript Error: ' + error.message + '</p>'
-                          + "<p>This usually means there's a typo in your chart specification. "
-                          + "See the javascript console for the full traceback.</p>"
-                          + '</div>');
-          throw error;
-      }
-      const el = document.getElementById('{{ output_div }}');
-      vegaEmbed("#{{ output_div }}", spec, embedOpt)
-        .catch(error => showError(el, error));
-    }){% if not requirejs %}(vegaEmbed){% endif %};
-
-  </script>
-{%- if fullhtml %}
-</body>
-</html>
-{%- endif %}
-"""
-)
-
-
-HTML_TEMPLATE_UNIVERSAL = jinja2.Template(
-    """
-<style>
-  #{{ output_div }}.vega-embed {
-    width: 100%;
-    display: flex;
-  }
-
-  #{{ output_div }}.vega-embed details,
-  #{{ output_div }}.vega-embed details summary {
-    position: relative;
-  }
-</style>
-<div id="{{ output_div }}"></div>
-<script type="text/javascript">
-  var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
-  (function(spec, embedOpt){
-    let outputDiv = document.currentScript.previousElementSibling;
-    if (outputDiv.id !== "{{ output_div }}") {
-      outputDiv = document.getElementById("{{ output_div }}");
-    }
-    const paths = {
-      "vega": "{{ base_url }}/vega@{{ vega_version }}?noext",
-      "vega-lib": "{{ base_url }}/vega-lib?noext",
-      "vega-lite": "{{ base_url }}/vega-lite@{{ vegalite_version }}?noext",
-      "vega-embed": "{{ base_url }}/vega-embed@{{ vegaembed_version }}?noext",
-    };
-
-    function maybeLoadScript(lib, version) {
-      var key = `${lib.replace("-", "")}_version`;
-      return (VEGA_DEBUG[key] == version) ?
-        Promise.resolve(paths[lib]) :
-        new Promise(function(resolve, reject) {
-          var s = document.createElement('script');
-          document.getElementsByTagName("head")[0].appendChild(s);
-          s.async = true;
-          s.onload = () => {
-            VEGA_DEBUG[key] = version;
-            return resolve(paths[lib]);
-          };
-          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
-          s.src = paths[lib];
-        });
-    }
-
-    function showError(err) {
-      outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
-      throw err;
-    }
-
-    function displayChart(vegaEmbed) {
-      vegaEmbed(outputDiv, spec, embedOpt)
-        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
-    }
-
-    if(typeof define === "function" && define.amd) {
-      requirejs.config({paths});
-      require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
-    } else {
-      maybeLoadScript("vega", "{{vega_version}}")
-        .then(() => maybeLoadScript("vega-lite", "{{vegalite_version}}"))
-        .then(() => maybeLoadScript("vega-embed", "{{vegaembed_version}}"))
-        .catch(showError)
-        .then(() => displayChart(vegaEmbed));
-    }
-  })({{ spec }}, {{ embed_options }});
-</script>
-"""
-)
-
-
-# This is like the HTML_TEMPLATE template, but includes vega javascript inline
-# so that the resulting file is not dependent on external resources. This was
-# ported over from altair_saver.
-#
-# implies requirejs=False and full_html=True
-INLINE_HTML_TEMPLATE = jinja2.Template(
-    """\
-<!DOCTYPE html>
-<html>
-<head>
-  <style>
-    #{{ output_div }}.vega-embed {
-      width: 100%;
-      display: flex;
-    }
-
-    #{{ output_div }}.vega-embed details,
-    #{{ output_div }}.vega-embed details summary {
-      position: relative;
-    }
-  </style>
-  <script type="text/javascript">
-    // vega.js v{{ vega_version }}
-    {{ vega_script }}
-    // vega-lite.js v{{ vegalite_version }}
-    {{ vegalite_script }}
-    // vega-embed.js v{{ vegaembed_version }}
-    {{ vegaembed_script }}
-  </script>
-</head>
-<body>
-<div class="vega-visualization" id="{{ output_div }}"></div>
-<script type="text/javascript">
-  const spec = {{ spec }};
-  const embedOpt = {{ embed_options }};
-  vegaEmbed('#{{ output_div }}', spec, embedOpt).catch(console.error);
-</script>
-</body>
-</html>
-"""
-)
-
-
-TEMPLATES: Dict[str, jinja2.Template] = {
-    "standard": HTML_TEMPLATE,
-    "universal": HTML_TEMPLATE_UNIVERSAL,
-    "inline": INLINE_HTML_TEMPLATE,
-}
-
-
-def spec_to_html(
-    spec: dict,
-    mode: str,
-    vega_version: str,
-    vegaembed_version: str,
-    vegalite_version: Optional[str] = None,
-    base_url: str = "https://cdn.jsdelivr.net/npm",
-    output_div: str = "vis",
-    embed_options: Optional[dict] = None,
-    json_kwds: Optional[dict] = None,
-    fullhtml: bool = True,
-    requirejs: bool = False,
-    template: str = "standard",
-) -> str:
-    """Embed a Vega/Vega-Lite spec into an HTML page
-
-    Parameters
-    ----------
-    spec : dict
-        a dictionary representing a vega-lite plot spec.
-    mode : string {'vega' | 'vega-lite'}
-        The rendering mode. This value is overridden by embed_options['mode'],
-        if it is present.
-    vega_version : string
-        For html output, the version of vega.js to use.
-    vegalite_version : string
-        For html output, the version of vegalite.js to use.
-    vegaembed_version : string
-        For html output, the version of vegaembed.js to use.
-    base_url : string (optional)
-        The base url from which to load the javascript libraries.
-    output_div : string (optional)
-        The id of the div element where the plot will be shown.
-    embed_options : dict (optional)
-        Dictionary of options to pass to the vega-embed script. Default
-        entry is {'mode': mode}.
-    json_kwds : dict (optional)
-        Dictionary of keywords to pass to json.dumps().
-    fullhtml : boolean (optional)
-        If True (default) then return a full html page. If False, then return
-        an HTML snippet that can be embedded into an HTML page.
-    requirejs : boolean (optional)
-        If False (default) then load libraries from base_url using <script>
-        tags. If True, then load libraries using requirejs
-    template : jinja2.Template or string (optional)
-        Specify the template to use (default = 'standard'). If template is a
-        string, it must be one of {'universal', 'standard'}. Otherwise, it
-        can be a jinja2.Template object containing a custom template.
-
-    Returns
-    -------
-    output : string
-        an HTML string for rendering the chart.
-    """
-    embed_options = embed_options or {}
-    json_kwds = json_kwds or {}
-
-    mode = embed_options.setdefault("mode", mode)
-
-    if mode not in ["vega", "vega-lite"]:
-        raise ValueError("mode must be either 'vega' or 'vega-lite'")
-
-    if vega_version is None:
-        raise ValueError("must specify vega_version")
-
-    if vegaembed_version is None:
-        raise ValueError("must specify vegaembed_version")
-
-    if mode == "vega-lite" and vegalite_version is None:
-        raise ValueError("must specify vega-lite version for mode='vega-lite'")
-
-    render_kwargs = {}
-    if template == "inline":
-        try:
-            from altair_viewer import get_bundled_script
-        except ImportError as err:
-            raise ImportError(
-                "The altair_viewer package is required to convert to HTML with inline=True"
-            ) from err
-        render_kwargs["vega_script"] = get_bundled_script("vega", vega_version)
-        render_kwargs["vegalite_script"] = get_bundled_script(
-            "vega-lite", vegalite_version
-        )
-        render_kwargs["vegaembed_script"] = get_bundled_script(
-            "vega-embed", vegaembed_version
-        )
-
-    jinja_template = TEMPLATES.get(template, template)
-    if not hasattr(jinja_template, "render"):
-        raise ValueError("Invalid template: {0}".format(jinja_template))
-
-    return jinja_template.render(
-        spec=json.dumps(spec, **json_kwds),
-        embed_options=json.dumps(embed_options),
-        mode=mode,
-        vega_version=vega_version,
-        vegalite_version=vegalite_version,
-        vegaembed_version=vegaembed_version,
-        base_url=base_url,
-        output_div=output_div,
-        fullhtml=fullhtml,
-        requirejs=requirejs,
-        **render_kwargs,
-    )
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/langchain_helpers/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/langchain_helpers/__init__.py
deleted file mode 100644
index ced105858f1e3558ef6ed5556d0e548659b84440..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/langchain_helpers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Init file for langchain helpers."""
diff --git a/spaces/jonathang/Protein-Family-Ensemble/README.md b/spaces/jonathang/Protein-Family-Ensemble/README.md
deleted file mode 100644
index f20d44af331cb50b87b3e72b11536077246c2038..0000000000000000000000000000000000000000
--- a/spaces/jonathang/Protein-Family-Ensemble/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protein Family Prediction
-emoji: 🔬
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-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/jone/Music_Source_Separation/separate_scripts/separate_accompaniment.sh b/spaces/jone/Music_Source_Separation/separate_scripts/separate_accompaniment.sh
deleted file mode 100644
index 1953cf152275e99e0f72e376228faa52d69520e4..0000000000000000000000000000000000000000
--- a/spaces/jone/Music_Source_Separation/separate_scripts/separate_accompaniment.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-AUDIO_PATH=${1:-"./resources/vocals_accompaniment_10s.mp3"}  # The path of audio to be separated.
-OUTPUT_PATH=${2:-"./sep_results/sep_vocals.mp3"}  # The path to write out separated audio.
-
-MODEL_NAME="resunet_subbandtime"        # "resunet_ismir2021" | ""resunet_subbandtime""
-
-if [ $MODEL_NAME = "resunet_ismir2021" ]; then
-    CHECKPOINT_PATH="./downloaded_checkpoints/resunet143_ismir2021_accompaniment_16.8dB_350k_steps.pth"
-	TRAIN_CONFIG_YAML="./scripts/4_train/musdb18/configs/accompaniment-vocals,resunet_ismir2021.yaml"
-
-elif [ $MODEL_NAME = "resunet_subbandtime" ]; then
-    TRAIN_CONFIG_YAML="./scripts/4_train/musdb18/configs/accompaniment-vocals,resunet_subbandtime.yaml"
-    CHECKPOINT_PATH="./downloaded_checkpoints/resunet143_subbtandtime_accompaniment_16.4dB_350k_steps.pth"
-fi
-
-# Inference
-CUDA_VISIBLE_DEVICES=0 python3 bytesep/inference.py \
-    --config_yaml=$TRAIN_CONFIG_YAML \
-    --checkpoint_path=$CHECKPOINT_PATH \
-    --audio_path=$AUDIO_PATH \
-    --output_path=$OUTPUT_PATH
diff --git a/spaces/jordonpeter01/MusicGen/audiocraft/utils/export.py b/spaces/jordonpeter01/MusicGen/audiocraft/utils/export.py
deleted file mode 100644
index b513b52267f7bf5aae09282c15b0a2e20c8a8fee..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen/audiocraft/utils/export.py
+++ /dev/null
@@ -1,56 +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.
-
-"""
-Utility to export a training checkpoint to a lightweight release checkpoint.
-"""
-
-from pathlib import Path
-import typing as tp
-
-from omegaconf import OmegaConf, DictConfig
-import torch
-
-
-def _clean_lm_cfg(cfg: DictConfig):
-    OmegaConf.set_struct(cfg, False)
-    # This used to be set automatically in the LM solver, need a more robust solution
-    # for the future.
-    cfg['transformer_lm']['card'] = 2048
-    cfg['transformer_lm']['n_q'] = 4
-    # Experimental params no longer supported.
-    bad_params = ['spectral_norm_attn_iters', 'spectral_norm_ff_iters',
-                  'residual_balancer_attn', 'residual_balancer_ff', 'layer_drop']
-    for name in bad_params:
-        del cfg['transformer_lm'][name]
-    OmegaConf.set_struct(cfg, True)
-    return cfg
-
-
-def export_encodec(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['ema']['state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
-
-
-def export_lm(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['fsdp_best_state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(_clean_lm_cfg(pkg['xp.cfg']))
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
diff --git a/spaces/jordonpeter01/MusicGen/tests/common_utils/__init__.py b/spaces/jordonpeter01/MusicGen/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen/tests/common_utils/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-# flake8: noqa
-from .temp_utils import TempDirMixin
-from .wav_utils import get_batch_white_noise, get_white_noise, save_wav
diff --git a/spaces/jvde/sovits-webui/preprocess.py b/spaces/jvde/sovits-webui/preprocess.py
deleted file mode 100644
index f5a378e84af5fb21aa3ade9e56422ae3cbd70b26..0000000000000000000000000000000000000000
--- a/spaces/jvde/sovits-webui/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=2, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["filelists/yuuka_train.txt", "filelists/yuuka_val.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["japanese_cleaners"])
-
-  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/kTonpa/Text2Cryptopunks/text2punks/loader.py b/spaces/kTonpa/Text2Cryptopunks/text2punks/loader.py
deleted file mode 100644
index e40dfe5ed4090236cf39617a1f66d9c1a1d19e74..0000000000000000000000000000000000000000
--- a/spaces/kTonpa/Text2Cryptopunks/text2punks/loader.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import numpy as np
-from PIL import Image, UnidentifiedImageError
-
-from pathlib import Path
-from random import randint, choice
-
-import torch
-from torch.utils.data import Dataset
-
-
-class TextImageDataset(Dataset):
-    def __init__(self,
-                 folder,
-                 text_len=40,
-                 truncate_captions=False,
-                 text_tokenizer=None,
-                 image_tokenizer=None,
-                 shuffle=False
-                 ):
-        """
-        @param folder: Folder containing images and text files matched by their paths' respective "stem"
-        @param truncate_captions: Rather than throw an exception, captions which are too long will be truncated.
-        """
-        super().__init__()
-        self.shuffle = shuffle
-        path = Path(folder)
-
-        text_files = [*path.glob('**/*.txt')]
-        image_files = [
-            *path.glob('**/*.png'), *path.glob('**/*.jpg'),
-            *path.glob('**/*.jpeg'), *path.glob('**/*.bmp')
-        ]
-
-        text_files = {text_file.stem: text_file for text_file in text_files}
-        image_files = {image_file.stem: image_file for image_file in image_files}
-
-        keys = (image_files.keys() & text_files.keys())
-
-        self.keys = list(keys)
-        self.text_files = {k: v for k, v in text_files.items() if k in keys}
-        self.image_files = {k: v for k, v in image_files.items() if k in keys}
-        self.text_len = text_len
-        self.truncate_captions = truncate_captions
-        self.text_tokenizer = text_tokenizer
-        self.image_tokenizer = image_tokenizer
-
-
-    def __len__(self):
-        return len(self.keys)
-
-    def random_sample(self):
-        return self.__getitem__(randint(0, self.__len__() - 1))
-
-    def sequential_sample(self, ind):
-        if ind >= self.__len__() - 1:
-            return self.__getitem__(0)
-        return self.__getitem__(ind + 1)
-
-    def skip_sample(self, ind):
-        if self.shuffle:
-            return self.random_sample()
-        return self.sequential_sample(ind=ind)
-
-    def __getitem__(self, ind):
-        key = self.keys[ind]
-
-        text_file = self.text_files[key]
-        image_file = self.image_files[key]
-
-        descriptions = text_file.read_text().split('\n')
-        descriptions = list(filter(lambda t: len(t) > 0, descriptions))
-        try:
-            description = choice(descriptions)
-        except IndexError as zero_captions_in_file_ex:
-            print(f"An exception occurred trying to load file {text_file}.")
-            print(f"Skipping index {ind}")
-            return self.skip_sample(ind)
-
-        tokenized_text = self.text_tokenizer.tokenize(
-            description,
-            self.text_len,
-            truncate_text=self.truncate_captions
-        ).squeeze(0)
-        try:
-            image = Image.open(image_file).convert('RGB')
-            pixels = np.array(image).reshape(-1, 3)
-
-            tokenized_image = [self.image_tokenizer[str(idx)] for idx in pixels]
-            tokenized_image = torch.tensor(tokenized_image)
-        except (UnidentifiedImageError, OSError) as corrupt_image_exceptions:
-            print(f"An exception occurred trying to load file {image_file}.")
-            print(f"Skipping index {ind}")
-            return self.skip_sample(ind)
-
-        # Success
-        return tokenized_text, tokenized_image
diff --git a/spaces/kadirnar/yolor/yolor/models/export.py b/spaces/kadirnar/yolor/yolor/models/export.py
deleted file mode 100644
index f96920ba37ea90effcf988449c7da4ba2a38d5a5..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolor/yolor/models/export.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import argparse
-
-import torch
-
-from yolor.utils.google_utils import attempt_download
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov4.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    opt = parser.parse_args()
-    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
-    print(opt)
-
-    # Input
-    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection
-
-    # Load PyTorch model
-    attempt_download(opt.weights)
-    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
-    model.eval()
-    model.model[-1].export = True  # set Detect() layer export=True
-    y = model(img)  # dry run
-
-    # TorchScript export
-    try:
-        print('\nStarting TorchScript export with torch %s...' % torch.__version__)
-        f = opt.weights.replace('.pt', '.torchscript.pt')  # filename
-        ts = torch.jit.trace(model, img)
-        ts.save(f)
-        print('TorchScript export success, saved as %s' % f)
-    except Exception as e:
-        print('TorchScript export failure: %s' % e)
-
-    # ONNX export
-    try:
-        import onnx
-
-        print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
-        f = opt.weights.replace('.pt', '.onnx')  # filename
-        model.fuse()  # only for ONNX
-        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
-                          output_names=['classes', 'boxes'] if y is None else ['output'])
-
-        # Checks
-        onnx_model = onnx.load(f)  # load onnx model
-        onnx.checker.check_model(onnx_model)  # check onnx model
-        print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
-        print('ONNX export success, saved as %s' % f)
-    except Exception as e:
-        print('ONNX export failure: %s' % e)
-
-    # CoreML export
-    try:
-        import coremltools as ct
-
-        print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
-        # convert model from torchscript and apply pixel scaling as per detect.py
-        model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
-        f = opt.weights.replace('.pt', '.mlmodel')  # filename
-        model.save(f)
-        print('CoreML export success, saved as %s' % f)
-    except Exception as e:
-        print('CoreML export failure: %s' % e)
-
-    # Finish
-    print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')
diff --git a/spaces/keras-io/char-lstm-seq2seq/app.py b/spaces/keras-io/char-lstm-seq2seq/app.py
deleted file mode 100644
index 359af149a1b8f86d6803397747370ff01a30ec37..0000000000000000000000000000000000000000
--- a/spaces/keras-io/char-lstm-seq2seq/app.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import json
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-
-from huggingface_hub import from_pretrained_keras
-
-import gradio as gr
-
-latent_dim = 256
-num_encoder_tokens = 71
-max_encoder_seq_length = 15
-num_decoder_tokens = 92
-max_decoder_seq_length = 59
-
-with open("input_vocab.json", "r", encoding="utf-8") as f:
-    input_token_index = json.load(f)
-with open("target_vocab.json", "r", encoding="utf-8") as f:
-    target_token_index = json.load(f)
-
-model = from_pretrained_keras("keras-io/char-lstm-seq2seq")
-
-# Define sampling models
-# Restore the model and construct the encoder and decoder.
-
-encoder_inputs = model.input[0]  # input_1
-encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output  # lstm_1
-encoder_states = [state_h_enc, state_c_enc]
-encoder_model = keras.Model(encoder_inputs, encoder_states)
-
-decoder_inputs = model.input[1]  # input_2
-decoder_state_input_h = keras.Input(shape=(latent_dim,), name="input_3")
-decoder_state_input_c = keras.Input(shape=(latent_dim,), name="input_4")
-decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
-decoder_lstm = model.layers[3]
-decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
-    decoder_inputs, initial_state=decoder_states_inputs
-)
-decoder_states = [state_h_dec, state_c_dec]
-decoder_dense = model.layers[4]
-decoder_outputs = decoder_dense(decoder_outputs)
-
-decoder_model = keras.Model(
-    [decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states
-)
-
-# Reverse-lookup token index to decode sequences back to
-# something readable.
-reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())
-reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())
-
-
-def decode_sequence(input_seq):
-    # Encode the input as state vectors.
-    states_value = encoder_model.predict(input_seq)
-
-    # Generate empty target sequence of length 1.
-    target_seq = np.zeros((1, 1, num_decoder_tokens))
-    # Populate the first character of target sequence with the start character.
-    target_seq[0, 0, target_token_index["\t"]] = 1.0
-
-    # Sampling loop for a batch of sequences
-    # (to simplify, here we assume a batch of size 1).
-    stop_condition = False
-    decoded_sentence = ""
-    while not stop_condition:
-        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)
-
-        # Sample a token
-        sampled_token_index = np.argmax(output_tokens[0, -1, :])
-        sampled_char = reverse_target_char_index[sampled_token_index]
-        decoded_sentence += sampled_char
-
-        # Exit condition: either hit max length
-        # or find stop character.
-        if sampled_char == "\n" or len(decoded_sentence) > max_decoder_seq_length:
-            stop_condition = True
-
-        # Update the target sequence (of length 1).
-        target_seq = np.zeros((1, 1, num_decoder_tokens))
-        target_seq[0, 0, sampled_token_index] = 1.0
-
-        # Update states
-        states_value = [h, c]
-    return decoded_sentence
-
-
-def translate(input_text):
-    if len(input_text) > max_encoder_seq_length:
-        input_text = input_text[:max_encoder_seq_length]
-    encoder_input_data = np.zeros(
-        (1, max_encoder_seq_length, num_encoder_tokens), dtype="float32"
-    )
-    for t, char in enumerate(input_text):
-        encoder_input_data[0, t, input_token_index[char]] = 1.0
-    encoder_input_data[0, t + 1 :, input_token_index[" "]] = 1.0
-    target_text = decode_sequence(encoder_input_data)
-    return target_text
-
-
-input_box = gr.inputs.Textbox(type="str", label="Input Text")
-target = gr.outputs.Textbox()
-
-iface = gr.Interface(
-    translate,
-    input_box,
-    target,
-    title="Character-level recurrent sequence-to-sequence model",
-    description="Model for Translating English to French using a Character-level recurrent sequence-to-sequence trained with small data.",
-    article='Author: <a href="https://huggingface.co/anuragshas">Anurag Singh</a> . Based on the keras example from <a href="https://keras.io/examples/nlp/lstm_seq2seq//">fchollet</a>',
-    examples=["Hi.", "Wait!", "Go on."],
-)
-
-
-iface.launch()
diff --git a/spaces/keras-io/video-transformers/utils/predict.py b/spaces/keras-io/video-transformers/utils/predict.py
deleted file mode 100644
index e4edb3d986d42fdd8dba8da9ab795350b3b2fa9f..0000000000000000000000000000000000000000
--- a/spaces/keras-io/video-transformers/utils/predict.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#from .custom_layers import TransformerEncoder, PositionalEmbedding
-from .constants import MAX_SEQ_LENGTH, NUM_FEATURES, IMG_SIZE, CLASS_VOCAB
-from huggingface_hub import from_pretrained_keras
-from tensorflow import keras
-from keras import layers
-import numpy as np
-import imageio
-import cv2
-
-#model = from_pretrained_keras("shivi/video-classification",custom_objects={"PositionalEmbedding":PositionalEmbedding,"TransformerEncoder": TransformerEncoder})
-
-model = from_pretrained_keras("keras-io/video-transformers")
-
-"""
-Below code is taken from the Video-Transformers example on keras-io by Sayak Paul
-"""
-def build_feature_extractor():
-    feature_extractor = keras.applications.DenseNet121(
-        weights="imagenet",
-        include_top=False,
-        pooling="avg",
-        input_shape=(IMG_SIZE, IMG_SIZE, 3),
-    )
-    preprocess_input = keras.applications.densenet.preprocess_input
-
-    inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))
-    preprocessed = preprocess_input(inputs)
-
-    outputs = feature_extractor(preprocessed)
-    return keras.Model(inputs, outputs, name="feature_extractor")
-
-
-feature_extractor = build_feature_extractor()
-
-
-
-def crop_center(frame):
-    center_crop_layer = layers.CenterCrop(IMG_SIZE, IMG_SIZE)
-    cropped = center_crop_layer(frame[None, ...])
-    cropped = cropped.numpy().squeeze()
-    return cropped
-
-def load_video(path, max_frames=0):
-    cap = cv2.VideoCapture(path)
-    frames = []
-    try:
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frame = crop_center(frame)
-            frame = frame[:, :, [2, 1, 0]]
-            frames.append(frame)
-
-            if len(frames) == max_frames:
-                break
-    finally:
-        cap.release()
-    return np.array(frames)
-
-def prepare_single_video(frames):
-    frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype="float32")
-
-    # Pad shorter videos.
-    if len(frames) < MAX_SEQ_LENGTH:
-        diff = MAX_SEQ_LENGTH - len(frames)
-        padding = np.zeros((diff, IMG_SIZE, IMG_SIZE, 3))
-        frames = np.concatenate(frames, padding)
-
-    frames = frames[None, ...]
-
-    # Extract features from the frames of the current video.
-    for i, batch in enumerate(frames):
-        video_length = batch.shape[0]
-        length = min(MAX_SEQ_LENGTH, video_length)
-        for j in range(length):
-            if np.mean(batch[j, :]) > 0.0:
-                frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])
-            else:
-                frame_features[i, j, :] = 0.0
-
-    return frame_features
-
-
-def predict_action(path):
-    frames = load_video(path)
-    frame_features = prepare_single_video(frames)
-    probabilities = model.predict(frame_features)[0]
-    confidences = {}
-
-    for i in np.argsort(probabilities)[::-1]:
-        confidences[CLASS_VOCAB[i]] = float(probabilities[i])
-
-    gif_out = to_gif(frames[:MAX_SEQ_LENGTH])
-
-    print(confidences)
-    return confidences, gif_out
-
-
-def to_gif(images):
-    converted_images = images.astype(np.uint8)
-    imageio.mimsave("animation.gif", converted_images, fps=10)
-    return "animation.gif"
-
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py
deleted file mode 100644
index bd2f56cba47c57de102710ff56eaac591e59f4da..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import logging
-import os
-import time
-from typing import List
-
-import torch
-
-from eval import verification
-from utils.utils_logging import AverageMeter
-
-
-class CallBackVerification(object):
-    def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)):
-        self.frequent: int = frequent
-        self.rank: int = rank
-        self.highest_acc: float = 0.0
-        self.highest_acc_list: List[float] = [0.0] * len(val_targets)
-        self.ver_list: List[object] = []
-        self.ver_name_list: List[str] = []
-        if self.rank is 0:
-            self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)
-
-    def ver_test(self, backbone: torch.nn.Module, global_step: int):
-        results = []
-        for i in range(len(self.ver_list)):
-            acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(
-                self.ver_list[i], backbone, 10, 10)
-            logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm))
-            logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2))
-            if acc2 > self.highest_acc_list[i]:
-                self.highest_acc_list[i] = acc2
-            logging.info(
-                '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i]))
-            results.append(acc2)
-
-    def init_dataset(self, val_targets, data_dir, image_size):
-        for name in val_targets:
-            path = os.path.join(data_dir, name + ".bin")
-            if os.path.exists(path):
-                data_set = verification.load_bin(path, image_size)
-                self.ver_list.append(data_set)
-                self.ver_name_list.append(name)
-
-    def __call__(self, num_update, backbone: torch.nn.Module):
-        if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0:
-            backbone.eval()
-            self.ver_test(backbone, num_update)
-            backbone.train()
-
-
-class CallBackLogging(object):
-    def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None):
-        self.frequent: int = frequent
-        self.rank: int = rank
-        self.time_start = time.time()
-        self.total_step: int = total_step
-        self.batch_size: int = batch_size
-        self.world_size: int = world_size
-        self.writer = writer
-
-        self.init = False
-        self.tic = 0
-
-    def __call__(self,
-                 global_step: int,
-                 loss: AverageMeter,
-                 epoch: int,
-                 fp16: bool,
-                 learning_rate: float,
-                 grad_scaler: torch.cuda.amp.GradScaler):
-        if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0:
-            if self.init:
-                try:
-                    speed: float = self.frequent * self.batch_size / (time.time() - self.tic)
-                    speed_total = speed * self.world_size
-                except ZeroDivisionError:
-                    speed_total = float('inf')
-
-                time_now = (time.time() - self.time_start) / 3600
-                time_total = time_now / ((global_step + 1) / self.total_step)
-                time_for_end = time_total - time_now
-                if self.writer is not None:
-                    self.writer.add_scalar('time_for_end', time_for_end, global_step)
-                    self.writer.add_scalar('learning_rate', learning_rate, global_step)
-                    self.writer.add_scalar('loss', loss.avg, global_step)
-                if fp16:
-                    msg = "Speed %.2f samples/sec   Loss %.4f   LearningRate %.4f   Epoch: %d   Global Step: %d   " \
-                          "Fp16 Grad Scale: %2.f   Required: %1.f hours" % (
-                              speed_total, loss.avg, learning_rate, epoch, global_step,
-                              grad_scaler.get_scale(), time_for_end
-                          )
-                else:
-                    msg = "Speed %.2f samples/sec   Loss %.4f   LearningRate %.4f   Epoch: %d   Global Step: %d   " \
-                          "Required: %1.f hours" % (
-                              speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end
-                          )
-                logging.info(msg)
-                loss.reset()
-                self.tic = time.time()
-            else:
-                self.init = True
-                self.tic = time.time()
-
-
-class CallBackModelCheckpoint(object):
-    def __init__(self, rank, output="./"):
-        self.rank: int = rank
-        self.output: str = output
-
-    def __call__(self, global_step, backbone, partial_fc, ):
-        if global_step > 100 and self.rank == 0:
-            path_module = os.path.join(self.output, "backbone.pth")
-            torch.save(backbone.module.state_dict(), path_module)
-            logging.info("Pytorch Model Saved in '{}'".format(path_module))
-
-        if global_step > 100 and partial_fc is not None:
-            partial_fc.save_params()
diff --git a/spaces/kevinwang676/VoiceChanger/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/kevinwang676/VoiceChanger/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index eb60d8830714338448be009d1075e3594337db15..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/kevinwang676/VoiceChangers/vc_infer_pipeline.py b/spaces/kevinwang676/VoiceChangers/vc_infer_pipeline.py
deleted file mode 100644
index 3b685ec629b993de7d4488a883dd7bc926654ce2..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/vc_infer_pipeline.py
+++ /dev/null
@@ -1,425 +0,0 @@
-import numpy as np, parselmouth, torch, pdb, sys, os
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class VC(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        inp_f0=None,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
-            if filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-        elif f0_method == "crepe":
-            model = "full"
-            # Pick a batch size that doesn't cause memory errors on your gpu
-            batch_size = 512
-            # Compute pitch using first gpu
-            audio = torch.tensor(np.copy(x))[None].float()
-            f0, pd = torchcrepe.predict(
-                audio,
-                self.sr,
-                self.window,
-                f0_min,
-                f0_max,
-                model,
-                batch_size=batch_size,
-                device=self.device,
-                return_periodicity=True,
-            )
-            pd = torchcrepe.filter.median(pd, 3)
-            f0 = torchcrepe.filter.mean(f0, 3)
-            f0[pd < 0.1] = 0
-            f0 = f0[0].cpu().numpy()
-        elif f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from rmvpe import RMVPE
-
-                print("loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "rmvpe.pt", is_half=self.is_half, device=self.device
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        # file_big_npy,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        f0_file=None,
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius,
-                inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps":
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                        version,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                    version,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
\ No newline at end of file
diff --git a/spaces/kevinwang676/voice-conversion-yourtts/bark/generation.py b/spaces/kevinwang676/voice-conversion-yourtts/bark/generation.py
deleted file mode 100644
index 1e84587c5e3f506d6bc944c01e432b63eaf60145..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/voice-conversion-yourtts/bark/generation.py
+++ /dev/null
@@ -1,865 +0,0 @@
-import contextlib
-import gc
-import os
-import re
-import requests
-import gc
-import sys
-
-from encodec import EncodecModel
-import funcy
-import logging
-import numpy as np
-from scipy.special import softmax
-import torch
-import torch.nn.functional as F
-import tqdm
-from transformers import BertTokenizer
-from huggingface_hub import hf_hub_download, hf_hub_url
-
-from .model import GPTConfig, GPT
-from .model_fine import FineGPT, FineGPTConfig
-from .settings import initenv
-
-initenv(sys.argv)
-global_force_cpu = os.environ.get("BARK_FORCE_CPU", False)
-if (
-    global_force_cpu != True and
-    torch.cuda.is_available() and
-    hasattr(torch.cuda, "amp") and
-    hasattr(torch.cuda.amp, "autocast") and
-    hasattr(torch.cuda, "is_bf16_supported") and
-    torch.cuda.is_bf16_supported()
-):
-    autocast = funcy.partial(torch.cuda.amp.autocast, dtype=torch.bfloat16)
-else:
-    @contextlib.contextmanager
-    def autocast():
-        yield
-
-
-# hold models in global scope to lazy load
-global models
-models = {}
-
-global models_devices
-models_devices = {}
-
-
-CONTEXT_WINDOW_SIZE = 1024
-
-SEMANTIC_RATE_HZ = 49.9
-SEMANTIC_VOCAB_SIZE = 10_000
-
-CODEBOOK_SIZE = 1024
-N_COARSE_CODEBOOKS = 2
-N_FINE_CODEBOOKS = 8
-COARSE_RATE_HZ = 75
-
-SAMPLE_RATE = 24_000
-
-
-SUPPORTED_LANGS = [
-    ("English", "en"),
-    ("German", "de"),
-    ("Spanish", "es"),
-    ("French", "fr"),
-    ("Hindi", "hi"),
-    ("Italian", "it"),
-    ("Japanese", "ja"),
-    ("Korean", "ko"),
-    ("Polish", "pl"),
-    ("Portuguese", "pt"),
-    ("Russian", "ru"),
-    ("Turkish", "tr"),
-    ("Chinese", "zh"),
-]
-
-ALLOWED_PROMPTS = {"announcer"}
-for _, lang in SUPPORTED_LANGS:
-    for prefix in ("", f"v2{os.path.sep}"):
-        for n in range(10):
-            ALLOWED_PROMPTS.add(f"{prefix}{lang}_speaker_{n}")
-
-
-logger = logging.getLogger(__name__)
-
-
-CUR_PATH = os.path.dirname(os.path.abspath(__file__))
-
-
-#default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache")
-#CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
-#CACHE_DIR = os.path.join(os.getcwd(), "models"
-CACHE_DIR = "./models"
-
-
-def _cast_bool_env_var(s):
-    return s.lower() in ('true', '1', 't')
-
-USE_SMALL_MODELS = _cast_bool_env_var(os.environ.get("SUNO_USE_SMALL_MODELS", "False"))
-GLOBAL_ENABLE_MPS = _cast_bool_env_var(os.environ.get("SUNO_ENABLE_MPS", "False"))
-OFFLOAD_CPU = _cast_bool_env_var(os.environ.get("SUNO_OFFLOAD_CPU", "False"))
-
-REMOTE_MODEL_PATHS = {
-    "text_small": {
-        "repo_id": "suno/bark",
-        "file_name": "text.pt",
-    },
-    "coarse_small": {
-        "repo_id": "suno/bark",
-        "file_name": "coarse.pt",
-    },
-    "fine_small": {
-        "repo_id": "suno/bark",
-        "file_name": "fine.pt",
-    },
-    "text": {
-        "repo_id": "suno/bark",
-        "file_name": "text_2.pt",
-    },
-    "coarse": {
-        "repo_id": "suno/bark",
-        "file_name": "coarse_2.pt",
-    },
-    "fine": {
-        "repo_id": "suno/bark",
-        "file_name": "fine_2.pt",
-    },
-}
-
-
-if not hasattr(torch.nn.functional, 'scaled_dot_product_attention') and torch.cuda.is_available():
-    logger.warning(
-        "torch version does not support flash attention. You will get faster" +
-        " inference speed by upgrade torch to newest nightly version."
-    )
-
-
-def grab_best_device(use_gpu=True):
-    if torch.cuda.device_count() > 0 and use_gpu:
-        device = "cuda"
-    elif torch.backends.mps.is_available() and use_gpu and GLOBAL_ENABLE_MPS:
-        device = "mps"
-    else:
-        device = "cpu"
-    return device
-
-
-def _get_ckpt_path(model_type, use_small=False):
-    key = model_type
-    if use_small or USE_SMALL_MODELS:
-        key += "_small"
-    return os.path.join(CACHE_DIR, REMOTE_MODEL_PATHS[key]["file_name"])
-
-"""
-def _download(from_hf_path, file_name, destfilename):
-    os.makedirs(CACHE_DIR, exist_ok=True)
-    hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR, local_dir_use_symlinks=False)
-    # Bug in original repo? Downloaded name differs from expected...
-    if not os.path.exists(destfilename):
-        localname = os.path.join(CACHE_DIR, file_name)
-        os.rename(localname, destfilename)
-"""
-def _download(from_hf_path, file_name):
-    os.makedirs(CACHE_DIR, exist_ok=True)
-    hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR)
-
-
-class InferenceContext:
-    def __init__(self, benchmark=False):
-        # we can't expect inputs to be the same length, so disable benchmarking by default
-        self._chosen_cudnn_benchmark = benchmark
-        self._cudnn_benchmark = None
-
-    def __enter__(self):
-        self._cudnn_benchmark = torch.backends.cudnn.benchmark
-        torch.backends.cudnn.benchmark = self._chosen_cudnn_benchmark
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        torch.backends.cudnn.benchmark = self._cudnn_benchmark
-
-
-if torch.cuda.is_available():
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-
-
-@contextlib.contextmanager
-def _inference_mode():
-    with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast():
-        yield
-
-
-def _clear_cuda_cache():
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.synchronize()
-
-
-def clean_models(model_key=None):
-    global models
-    model_keys = [model_key] if model_key is not None else models.keys()
-    for k in model_keys:
-        if k in models:
-            del models[k]
-    _clear_cuda_cache()
-    gc.collect()
-
-
-def _load_model(ckpt_path, device, use_small=False, model_type="text"):
-    if model_type == "text":
-        ConfigClass = GPTConfig
-        ModelClass = GPT
-    elif model_type == "coarse":
-        ConfigClass = GPTConfig
-        ModelClass = GPT
-    elif model_type == "fine":
-        ConfigClass = FineGPTConfig
-        ModelClass = FineGPT
-    else:
-        raise NotImplementedError()
-
-    # Force-remove Models to allow running on >12Gb GPU
-    # CF: Probably not needed anymore
-    #global models
-    #models.clear()
-    #gc.collect()
-    #torch.cuda.empty_cache()
-    # to here...
-
-    model_key = f"{model_type}_small" if use_small or USE_SMALL_MODELS else model_type
-    model_info = REMOTE_MODEL_PATHS[model_key]
-    if not os.path.exists(ckpt_path):
-        logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
-        ## added next two lines to make it super clear which model is being downloaded
-        remote_filename = hf_hub_url(model_info["repo_id"], model_info["file_name"])
-        print(f"Downloading {model_key} {model_info['repo_id']} remote model file {remote_filename} {model_info['file_name']} to {CACHE_DIR}")
-        _download(model_info["repo_id"], model_info["file_name"])
-        # add next line to make it super clear which model is being loaded
-    print(f"Loading {model_key} model from {ckpt_path} to {device}") # added
-    checkpoint = torch.load(ckpt_path, map_location=device)
-    # this is a hack
-    model_args = checkpoint["model_args"]
-    if "input_vocab_size" not in model_args:
-        model_args["input_vocab_size"] = model_args["vocab_size"]
-        model_args["output_vocab_size"] = model_args["vocab_size"]
-        del model_args["vocab_size"]
-    gptconf = ConfigClass(**checkpoint["model_args"])
-    model = ModelClass(gptconf)
-    state_dict = checkpoint["model"]
-    # fixup checkpoint
-    unwanted_prefix = "_orig_mod."
-    for k, v in list(state_dict.items()):
-        if k.startswith(unwanted_prefix):
-            state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
-    extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
-    extra_keys = set([k for k in extra_keys if not k.endswith(".attn.bias")])
-    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
-    missing_keys = set([k for k in missing_keys if not k.endswith(".attn.bias")])
-    if len(extra_keys) != 0:
-        raise ValueError(f"extra keys found: {extra_keys}")
-    if len(missing_keys) != 0:
-        raise ValueError(f"missing keys: {missing_keys}")
-    model.load_state_dict(state_dict, strict=False)
-    n_params = model.get_num_params()
-    val_loss = checkpoint["best_val_loss"].item()
-    logger.info(f"model loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss")
-    model.eval()
-    model.to(device)
-    del checkpoint, state_dict
-    _clear_cuda_cache()
-    if model_type == "text":
-        tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased")
-        return {
-            "model": model,
-            "tokenizer": tokenizer,
-        }
-    return model
-
-
-def _load_codec_model(device):
-    model = EncodecModel.encodec_model_24khz()
-    model.set_target_bandwidth(6.0)
-    model.eval()
-    model.to(device)
-    _clear_cuda_cache()
-    return model
-
-
-def load_model(use_gpu=True, use_small=False, force_reload=False, model_type="text"):
-    _load_model_f = funcy.partial(_load_model, model_type=model_type, use_small=use_small)
-    if model_type not in ("text", "coarse", "fine"):
-        raise NotImplementedError()
-    global models
-    global models_devices
-    device = grab_best_device(use_gpu=use_gpu)
-    model_key = f"{model_type}"
-    if OFFLOAD_CPU:
-        models_devices[model_key] = device
-        device = "cpu"
-    if model_key not in models or force_reload:
-        ckpt_path = _get_ckpt_path(model_type, use_small=use_small)
-        clean_models(model_key=model_key)
-        model = _load_model_f(ckpt_path, device)
-        models[model_key] = model
-    if model_type == "text":
-        models[model_key]["model"].to(device)
-    else:
-        models[model_key].to(device)
-    return models[model_key]
-
-
-def load_codec_model(use_gpu=True, force_reload=False):
-    global models
-    global models_devices
-    device = grab_best_device(use_gpu=use_gpu)
-    if device == "mps":
-        # encodec doesn't support mps
-        device = "cpu"
-    model_key = "codec"
-    if OFFLOAD_CPU:
-        models_devices[model_key] = device
-        device = "cpu"
-    if model_key not in models or force_reload:
-        clean_models(model_key=model_key)
-        model = _load_codec_model(device)
-        models[model_key] = model
-    models[model_key].to(device)
-    return models[model_key]
-
-
-def preload_models(
-    text_use_gpu=True,
-    text_use_small=False,
-    coarse_use_gpu=True,
-    coarse_use_small=False,
-    fine_use_gpu=True,
-    fine_use_small=False,
-    codec_use_gpu=True,
-    force_reload=False
-):
-    """Load all the necessary models for the pipeline."""
-    if grab_best_device() == "cpu" and (
-        text_use_gpu or coarse_use_gpu or fine_use_gpu or codec_use_gpu
-    ):
-        logger.warning("No GPU being used. Careful, inference might be very slow!")
-    _ = load_model(
-        model_type="text", use_gpu=text_use_gpu, use_small=text_use_small, force_reload=force_reload
-    )
-    _ = load_model(
-        model_type="coarse",
-        use_gpu=coarse_use_gpu,
-        use_small=coarse_use_small,
-        force_reload=force_reload,
-    )
-    _ = load_model(
-        model_type="fine", use_gpu=fine_use_gpu, use_small=fine_use_small, force_reload=force_reload
-    )
-    _ = load_codec_model(use_gpu=codec_use_gpu, force_reload=force_reload)
-
-
-####
-# Generation Functionality
-####
-
-
-def _tokenize(tokenizer, text):
-    return tokenizer.encode(text, add_special_tokens=False)
-
-
-def _detokenize(tokenizer, enc_text):
-    return tokenizer.decode(enc_text)
-
-
-def _normalize_whitespace(text):
-    return re.sub(r"\s+", " ", text).strip()
-
-
-TEXT_ENCODING_OFFSET = 10_048
-SEMANTIC_PAD_TOKEN = 10_000
-TEXT_PAD_TOKEN = 129_595
-SEMANTIC_INFER_TOKEN = 129_599
-
-
-def _load_history_prompt(history_prompt_input):
-    if isinstance(history_prompt_input, str) and history_prompt_input.endswith(".npz"):
-        history_prompt = np.load(history_prompt_input)
-    elif isinstance(history_prompt_input, str):
-        # make sure this works on non-ubuntu
-        history_prompt_input = os.path.join(*history_prompt_input.split("/"))
-#        if history_prompt_input not in ALLOWED_PROMPTS:
-#            raise ValueError("history prompt not found")
-        history_prompt = np.load(
-            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt_input}.npz")
-        )
-    elif isinstance(history_prompt_input, dict):
-        assert("semantic_prompt" in history_prompt_input)
-        assert("coarse_prompt" in history_prompt_input)
-        assert("fine_prompt" in history_prompt_input)
-        history_prompt = history_prompt_input
-    else:
-        raise ValueError("history prompt format unrecognized")
-    return history_prompt
-
-
-def generate_text_semantic(
-    text,
-    history_prompt=None,
-    temp=0.7,
-    top_k=None,
-    top_p=None,
-    silent=False,
-    min_eos_p=0.2,
-    max_gen_duration_s=None,
-    allow_early_stop=True,
-    use_kv_caching=False,
-):
-    """Generate semantic tokens from text."""
-    assert isinstance(text, str)
-    text = _normalize_whitespace(text)
-    assert len(text.strip()) > 0
-    if history_prompt is not None:
-        history_prompt = _load_history_prompt(history_prompt)
-        semantic_history = history_prompt["semantic_prompt"]
-        assert (
-            isinstance(semantic_history, np.ndarray)
-            and len(semantic_history.shape) == 1
-            and len(semantic_history) > 0
-            and semantic_history.min() >= 0
-            and semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1
-        )
-    else:
-        semantic_history = None
-    # load models if not yet exist
-    global models
-    global models_devices
-    if "text" not in models:
-        preload_models()
-    model_container = models["text"]
-    model = model_container["model"]
-    tokenizer = model_container["tokenizer"]
-    encoded_text = np.array(_tokenize(tokenizer, text)) + TEXT_ENCODING_OFFSET
-    if OFFLOAD_CPU:
-        model.to(models_devices["text"])
-    device = next(model.parameters()).device
-    if len(encoded_text) > 256:
-        p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1)
-        logger.warning(f"warning, text too long, lopping of last {p}%")
-        encoded_text = encoded_text[:256]
-    encoded_text = np.pad(
-        encoded_text,
-        (0, 256 - len(encoded_text)),
-        constant_values=TEXT_PAD_TOKEN,
-        mode="constant",
-    )
-    if semantic_history is not None:
-        semantic_history = semantic_history.astype(np.int64)
-        # lop off if history is too long, pad if needed
-        semantic_history = semantic_history[-256:]
-        semantic_history = np.pad(
-            semantic_history,
-            (0, 256 - len(semantic_history)),
-            constant_values=SEMANTIC_PAD_TOKEN,
-            mode="constant",
-        )
-    else:
-        semantic_history = np.array([SEMANTIC_PAD_TOKEN] * 256)
-    x = torch.from_numpy(
-        np.hstack([
-            encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])
-        ]).astype(np.int64)
-    )[None]
-    assert x.shape[1] == 256 + 256 + 1
-    with _inference_mode():
-        x = x.to(device)
-        n_tot_steps = 768
-        # custom tqdm updates since we don't know when eos will occur
-        pbar = tqdm.tqdm(disable=silent, total=100)
-        pbar_state = 0
-        tot_generated_duration_s = 0
-        kv_cache = None
-        for n in range(n_tot_steps):
-            if use_kv_caching and kv_cache is not None:
-                x_input = x[:, [-1]]
-            else:
-                x_input = x
-            logits, kv_cache = model(
-                x_input, merge_context=True, use_cache=use_kv_caching, past_kv=kv_cache
-            )
-            relevant_logits = logits[0, 0, :SEMANTIC_VOCAB_SIZE]
-            if allow_early_stop:
-                relevant_logits = torch.hstack(
-                    (relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]])  # eos
-                )
-            if top_p is not None:
-                # faster to convert to numpy
-                logits_device = relevant_logits.device
-                logits_dtype = relevant_logits.type()
-                relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
-                sorted_indices = np.argsort(relevant_logits)[::-1]
-                sorted_logits = relevant_logits[sorted_indices]
-                cumulative_probs = np.cumsum(softmax(sorted_logits))
-                sorted_indices_to_remove = cumulative_probs > top_p
-                sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
-                sorted_indices_to_remove[0] = False
-                relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
-                relevant_logits = torch.from_numpy(relevant_logits)
-                relevant_logits = relevant_logits.to(logits_device).type(logits_dtype)
-            if top_k is not None:
-                v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
-                relevant_logits[relevant_logits < v[-1]] = -float("Inf")
-            probs = F.softmax(relevant_logits / temp, dim=-1)
-            # multinomial bugged on mps: shuttle to cpu if necessary
-            inf_device = probs.device
-            if probs.device.type == "mps":
-                probs = probs.to("cpu")
-            item_next = torch.multinomial(probs, num_samples=1)
-            probs = probs.to(inf_device)
-            item_next = item_next.to(inf_device)
-            if allow_early_stop and (
-                item_next == SEMANTIC_VOCAB_SIZE
-                or (min_eos_p is not None and probs[-1] >= min_eos_p)
-            ):
-                # eos found, so break
-                pbar.update(100 - pbar_state)
-                break
-            x = torch.cat((x, item_next[None]), dim=1)
-            tot_generated_duration_s += 1 / SEMANTIC_RATE_HZ
-            if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s:
-                pbar.update(100 - pbar_state)
-                break
-            if n == n_tot_steps - 1:
-                pbar.update(100 - pbar_state)
-                break
-            del logits, relevant_logits, probs, item_next
-            req_pbar_state = np.min([100, int(round(100 * n / n_tot_steps))])
-            if req_pbar_state > pbar_state:
-                pbar.update(req_pbar_state - pbar_state)
-            pbar_state = req_pbar_state
-        pbar.close()
-        out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1 :]
-    if OFFLOAD_CPU:
-        model.to("cpu")
-    assert all(0 <= out) and all(out < SEMANTIC_VOCAB_SIZE)
-    _clear_cuda_cache()
-    return out
-
-
-def _flatten_codebooks(arr, offset_size=CODEBOOK_SIZE):
-    assert len(arr.shape) == 2
-    arr = arr.copy()
-    if offset_size is not None:
-        for n in range(1, arr.shape[0]):
-            arr[n, :] += offset_size * n
-    flat_arr = arr.ravel("F")
-    return flat_arr
-
-
-COARSE_SEMANTIC_PAD_TOKEN = 12_048
-COARSE_INFER_TOKEN = 12_050
-
-
-def generate_coarse(
-    x_semantic,
-    history_prompt=None,
-    temp=0.7,
-    top_k=None,
-    top_p=None,
-    silent=False,
-    max_coarse_history=630,  # min 60 (faster), max 630 (more context)
-    sliding_window_len=60,
-    use_kv_caching=False,
-):
-    """Generate coarse audio codes from semantic tokens."""
-    assert (
-        isinstance(x_semantic, np.ndarray)
-        and len(x_semantic.shape) == 1
-        and len(x_semantic) > 0
-        and x_semantic.min() >= 0
-        and x_semantic.max() <= SEMANTIC_VOCAB_SIZE - 1
-    )
-    assert 60 <= max_coarse_history <= 630
-    assert max_coarse_history + sliding_window_len <= 1024 - 256
-    semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS
-    max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
-    if history_prompt is not None:
-        history_prompt = _load_history_prompt(history_prompt)
-        x_semantic_history = history_prompt["semantic_prompt"]
-        x_coarse_history = history_prompt["coarse_prompt"]
-        assert (
-            isinstance(x_semantic_history, np.ndarray)
-            and len(x_semantic_history.shape) == 1
-            and len(x_semantic_history) > 0
-            and x_semantic_history.min() >= 0
-            and x_semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1
-            and isinstance(x_coarse_history, np.ndarray)
-            and len(x_coarse_history.shape) == 2
-            and x_coarse_history.shape[0] == N_COARSE_CODEBOOKS
-            and x_coarse_history.shape[-1] >= 0
-            and x_coarse_history.min() >= 0
-            and x_coarse_history.max() <= CODEBOOK_SIZE - 1
-            and (
-                round(x_coarse_history.shape[-1] / len(x_semantic_history), 1)
-                == round(semantic_to_coarse_ratio / N_COARSE_CODEBOOKS, 1)
-            )
-        )
-        x_coarse_history = _flatten_codebooks(x_coarse_history) + SEMANTIC_VOCAB_SIZE
-        # trim histories correctly
-        n_semantic_hist_provided = np.min(
-            [
-                max_semantic_history,
-                len(x_semantic_history) - len(x_semantic_history) % 2,
-                int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)),
-            ]
-        )
-        n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio))
-        x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32)
-        x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32)
-        # TODO: bit of a hack for time alignment (sounds better)
-        x_coarse_history = x_coarse_history[:-2]
-    else:
-        x_semantic_history = np.array([], dtype=np.int32)
-        x_coarse_history = np.array([], dtype=np.int32)
-    # load models if not yet exist
-    global models
-    global models_devices
-    if "coarse" not in models:
-        preload_models()
-    model = models["coarse"]
-    if OFFLOAD_CPU:
-        model.to(models_devices["coarse"])
-    device = next(model.parameters()).device
-    # start loop
-    n_steps = int(
-        round(
-            np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS)
-            * N_COARSE_CODEBOOKS
-        )
-    )
-    assert n_steps > 0 and n_steps % N_COARSE_CODEBOOKS == 0
-    x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)
-    x_coarse = x_coarse_history.astype(np.int32)
-    base_semantic_idx = len(x_semantic_history)
-    with _inference_mode():
-        x_semantic_in = torch.from_numpy(x_semantic)[None].to(device)
-        x_coarse_in = torch.from_numpy(x_coarse)[None].to(device)
-        n_window_steps = int(np.ceil(n_steps / sliding_window_len))
-        n_step = 0
-        for _ in tqdm.tqdm(range(n_window_steps), total=n_window_steps, disable=silent):
-            semantic_idx = base_semantic_idx + int(round(n_step / semantic_to_coarse_ratio))
-            # pad from right side
-            x_in = x_semantic_in[:, np.max([0, semantic_idx - max_semantic_history]) :]
-            x_in = x_in[:, :256]
-            x_in = F.pad(
-                x_in,
-                (0, 256 - x_in.shape[-1]),
-                "constant",
-                COARSE_SEMANTIC_PAD_TOKEN,
-            )
-            x_in = torch.hstack(
-                [
-                    x_in,
-                    torch.tensor([COARSE_INFER_TOKEN])[None].to(device),
-                    x_coarse_in[:, -max_coarse_history:],
-                ]
-            )
-            kv_cache = None
-            for _ in range(sliding_window_len):
-                if n_step >= n_steps:
-                    continue
-                is_major_step = n_step % N_COARSE_CODEBOOKS == 0
-
-                if use_kv_caching and kv_cache is not None:
-                    x_input = x_in[:, [-1]]
-                else:
-                    x_input = x_in
-
-                logits, kv_cache = model(x_input, use_cache=use_kv_caching, past_kv=kv_cache)
-                logit_start_idx = (
-                    SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE
-                )
-                logit_end_idx = (
-                    SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE
-                )
-                relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx]
-                if top_p is not None:
-                    # faster to convert to numpy
-                    logits_device = relevant_logits.device
-                    logits_dtype = relevant_logits.type()
-                    relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
-                    sorted_indices = np.argsort(relevant_logits)[::-1]
-                    sorted_logits = relevant_logits[sorted_indices]
-                    cumulative_probs = np.cumsum(softmax(sorted_logits))
-                    sorted_indices_to_remove = cumulative_probs > top_p
-                    sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
-                    sorted_indices_to_remove[0] = False
-                    relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
-                    relevant_logits = torch.from_numpy(relevant_logits)
-                    relevant_logits = relevant_logits.to(logits_device).type(logits_dtype)
-                if top_k is not None:
-                    v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
-                    relevant_logits[relevant_logits < v[-1]] = -float("Inf")
-                probs = F.softmax(relevant_logits / temp, dim=-1)
-                # multinomial bugged on mps: shuttle to cpu if necessary
-                inf_device = probs.device
-                if probs.device.type == "mps":
-                    probs = probs.to("cpu")
-                item_next = torch.multinomial(probs, num_samples=1)
-                probs = probs.to(inf_device)
-                item_next = item_next.to(inf_device)
-                item_next += logit_start_idx
-                x_coarse_in = torch.cat((x_coarse_in, item_next[None]), dim=1)
-                x_in = torch.cat((x_in, item_next[None]), dim=1)
-                del logits, relevant_logits, probs, item_next
-                n_step += 1
-            del x_in
-        del x_semantic_in
-    if OFFLOAD_CPU:
-        model.to("cpu")
-    gen_coarse_arr = x_coarse_in.detach().cpu().numpy().squeeze()[len(x_coarse_history) :]
-    del x_coarse_in
-    assert len(gen_coarse_arr) == n_steps
-    gen_coarse_audio_arr = gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE
-    for n in range(1, N_COARSE_CODEBOOKS):
-        gen_coarse_audio_arr[n, :] -= n * CODEBOOK_SIZE
-    _clear_cuda_cache()
-    return gen_coarse_audio_arr
-
-
-def generate_fine(
-    x_coarse_gen,
-    history_prompt=None,
-    temp=0.5,
-    silent=True,
-):
-    """Generate full audio codes from coarse audio codes."""
-    assert (
-        isinstance(x_coarse_gen, np.ndarray)
-        and len(x_coarse_gen.shape) == 2
-        and 1 <= x_coarse_gen.shape[0] <= N_FINE_CODEBOOKS - 1
-        and x_coarse_gen.shape[1] > 0
-        and x_coarse_gen.min() >= 0
-        and x_coarse_gen.max() <= CODEBOOK_SIZE - 1
-    )
-    if history_prompt is not None:
-        history_prompt = _load_history_prompt(history_prompt)
-        x_fine_history = history_prompt["fine_prompt"]
-        assert (
-            isinstance(x_fine_history, np.ndarray)
-            and len(x_fine_history.shape) == 2
-            and x_fine_history.shape[0] == N_FINE_CODEBOOKS
-            and x_fine_history.shape[1] >= 0
-            and x_fine_history.min() >= 0
-            and x_fine_history.max() <= CODEBOOK_SIZE - 1
-        )
-    else:
-        x_fine_history = None
-    n_coarse = x_coarse_gen.shape[0]
-    # load models if not yet exist
-    global models
-    global models_devices
-    if "fine" not in models:
-        preload_models()
-    model = models["fine"]
-    if OFFLOAD_CPU:
-        model.to(models_devices["fine"])
-    device = next(model.parameters()).device
-    # make input arr
-    in_arr = np.vstack(
-        [
-            x_coarse_gen,
-            np.zeros((N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1]))
-            + CODEBOOK_SIZE,  # padding
-        ]
-    ).astype(np.int32)
-    # prepend history if available (max 512)
-    if x_fine_history is not None:
-        x_fine_history = x_fine_history.astype(np.int32)
-        in_arr = np.hstack(
-            [
-                x_fine_history[:, -512:].astype(np.int32),
-                in_arr,
-            ]
-        )
-        n_history = x_fine_history[:, -512:].shape[1]
-    else:
-        n_history = 0
-    n_remove_from_end = 0
-    # need to pad if too short (since non-causal model)
-    if in_arr.shape[1] < 1024:
-        n_remove_from_end = 1024 - in_arr.shape[1]
-        in_arr = np.hstack(
-            [
-                in_arr,
-                np.zeros((N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32) + CODEBOOK_SIZE,
-            ]
-        )
-    # we can be lazy about fractional loop and just keep overwriting codebooks
-    n_loops = np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))]) + 1
-    with _inference_mode():
-        in_arr = torch.tensor(in_arr.T).to(device)
-        for n in tqdm.tqdm(range(n_loops), disable=silent):
-            start_idx = np.min([n * 512, in_arr.shape[0] - 1024])
-            start_fill_idx = np.min([n_history + n * 512, in_arr.shape[0] - 512])
-            rel_start_fill_idx = start_fill_idx - start_idx
-            in_buffer = in_arr[start_idx : start_idx + 1024, :][None]
-            for nn in range(n_coarse, N_FINE_CODEBOOKS):
-                logits = model(nn, in_buffer)
-                if temp is None:
-                    relevant_logits = logits[0, rel_start_fill_idx:, :CODEBOOK_SIZE]
-                    codebook_preds = torch.argmax(relevant_logits, -1)
-                else:
-                    relevant_logits = logits[0, :, :CODEBOOK_SIZE] / temp
-                    probs = F.softmax(relevant_logits, dim=-1)
-                    # multinomial bugged on mps: shuttle to cpu if necessary
-                    inf_device = probs.device
-                    if probs.device.type == "mps":
-                        probs = probs.to("cpu")
-                    codebook_preds = torch.hstack(
-                        [
-                            torch.multinomial(probs[nnn], num_samples=1).to(inf_device)
-                            for nnn in range(rel_start_fill_idx, 1024)
-                        ]
-                    )
-                in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds
-                del logits, codebook_preds
-            # transfer over info into model_in and convert to numpy
-            for nn in range(n_coarse, N_FINE_CODEBOOKS):
-                in_arr[
-                    start_fill_idx : start_fill_idx + (1024 - rel_start_fill_idx), nn
-                ] = in_buffer[0, rel_start_fill_idx:, nn]
-            del in_buffer
-        gen_fine_arr = in_arr.detach().cpu().numpy().squeeze().T
-        del in_arr
-    if OFFLOAD_CPU:
-        model.to("cpu")
-    gen_fine_arr = gen_fine_arr[:, n_history:]
-    if n_remove_from_end > 0:
-        gen_fine_arr = gen_fine_arr[:, :-n_remove_from_end]
-    assert gen_fine_arr.shape[-1] == x_coarse_gen.shape[-1]
-    _clear_cuda_cache()
-    return gen_fine_arr
-
-
-def codec_decode(fine_tokens):
-    """Turn quantized audio codes into audio array using encodec."""
-    # load models if not yet exist
-    global models
-    global models_devices
-    if "codec" not in models:
-        preload_models()
-    model = models["codec"]
-    if OFFLOAD_CPU:
-        model.to(models_devices["codec"])
-    device = next(model.parameters()).device
-    arr = torch.from_numpy(fine_tokens)[None]
-    arr = arr.to(device)
-    arr = arr.transpose(0, 1)
-    emb = model.quantizer.decode(arr)
-    out = model.decoder(emb)
-    audio_arr = out.detach().cpu().numpy().squeeze()
-    del arr, emb, out
-    if OFFLOAD_CPU:
-        model.to("cpu")
-    return audio_arr
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/decode_heads/dm_head.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/decode_heads/dm_head.py
deleted file mode 100644
index 19c963923126b53ce22f60813540a35badf24b3d..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/decode_heads/dm_head.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
-
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class DCM(nn.Module):
-    """Dynamic Convolutional Module used in DMNet.
-
-    Args:
-        filter_size (int): The filter size of generated convolution kernel
-            used in Dynamic Convolutional Module.
-        fusion (bool): Add one conv to fuse DCM output feature.
-        in_channels (int): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        conv_cfg (dict | None): Config of conv layers.
-        norm_cfg (dict | None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-    """
-
-    def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
-                 norm_cfg, act_cfg):
-        super(DCM, self).__init__()
-        self.filter_size = filter_size
-        self.fusion = fusion
-        self.in_channels = in_channels
-        self.channels = channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1,
-                                         0)
-
-        self.input_redu_conv = ConvModule(
-            self.in_channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        if self.norm_cfg is not None:
-            self.norm = build_norm_layer(self.norm_cfg, self.channels)[1]
-        else:
-            self.norm = None
-        self.activate = build_activation_layer(self.act_cfg)
-
-        if self.fusion:
-            self.fusion_conv = ConvModule(
-                self.channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-
-    def forward(self, x):
-        """Forward function."""
-        generated_filter = self.filter_gen_conv(
-            F.adaptive_avg_pool2d(x, self.filter_size))
-        x = self.input_redu_conv(x)
-        b, c, h, w = x.shape
-        # [1, b * c, h, w], c = self.channels
-        x = x.view(1, b * c, h, w)
-        # [b * c, 1, filter_size, filter_size]
-        generated_filter = generated_filter.view(b * c, 1, self.filter_size,
-                                                 self.filter_size)
-        pad = (self.filter_size - 1) // 2
-        if (self.filter_size - 1) % 2 == 0:
-            p2d = (pad, pad, pad, pad)
-        else:
-            p2d = (pad + 1, pad, pad + 1, pad)
-        x = F.pad(input=x, pad=p2d, mode='constant', value=0)
-        # [1, b * c, h, w]
-        output = F.conv2d(input=x, weight=generated_filter, groups=b * c)
-        # [b, c, h, w]
-        output = output.view(b, c, h, w)
-        if self.norm is not None:
-            output = self.norm(output)
-        output = self.activate(output)
-
-        if self.fusion:
-            output = self.fusion_conv(output)
-
-        return output
-
-
-@HEADS.register_module()
-class DMHead(BaseDecodeHead):
-    """Dynamic Multi-scale Filters for Semantic Segmentation.
-
-    This head is the implementation of
-    `DMNet <https://openaccess.thecvf.com/content_ICCV_2019/papers/\
-        He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_\
-            ICCV_2019_paper.pdf>`_.
-
-    Args:
-        filter_sizes (tuple[int]): The size of generated convolutional filters
-            used in Dynamic Convolutional Module. Default: (1, 3, 5, 7).
-        fusion (bool): Add one conv to fuse DCM output feature.
-    """
-
-    def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
-        super(DMHead, self).__init__(**kwargs)
-        assert isinstance(filter_sizes, (list, tuple))
-        self.filter_sizes = filter_sizes
-        self.fusion = fusion
-        dcm_modules = []
-        for filter_size in self.filter_sizes:
-            dcm_modules.append(
-                DCM(filter_size,
-                    self.fusion,
-                    self.in_channels,
-                    self.channels,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg))
-        self.dcm_modules = nn.ModuleList(dcm_modules)
-        self.bottleneck = ConvModule(
-            self.in_channels + len(filter_sizes) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        dcm_outs = [x]
-        for dcm_module in self.dcm_modules:
-            dcm_outs.append(dcm_module(x))
-        dcm_outs = torch.cat(dcm_outs, dim=1)
-        output = self.bottleneck(dcm_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/gottbert/README.md b/spaces/koajoel/PolyFormer/fairseq/examples/gottbert/README.md
deleted file mode 100644
index 1d58feb279a4a50222290546c3bb285d3cea98e6..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/gottbert/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# GottBERT: a pure German language model
-
-## Introduction
-
-[GottBERT](http://arxiv.org/abs/2012.02110) is a pretrained language model trained on 145GB of German text based on RoBERTa.
-
-## Example usage
-
-### fairseq
-##### Load GottBERT from torch.hub (PyTorch >= 1.1):
-```python
-import torch
-gottbert = torch.hub.load('pytorch/fairseq', 'gottbert-base')
-gottbert.eval()  # disable dropout (or leave in train mode to finetune)
-```
-
-##### Load GottBERT (for PyTorch 1.0 or custom models):
-```python
-# Download gottbert model
-wget https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz
-tar -xzvf gottbert.tar.gz
-
-# Load the model in fairseq
-from fairseq.models.roberta import GottbertModel
-gottbert = GottbertModel.from_pretrained('/path/to/gottbert')
-gottbert.eval()  # disable dropout (or leave in train mode to finetune)
-```
-
-##### Filling masks:
-```python
-masked_line = 'Gott ist <mask> ! :)'
-gottbert.fill_mask(masked_line, topk=3)
-# [('Gott ist gut ! :)',        0.3642110526561737,   ' gut'),
-#  ('Gott ist überall ! :)',    0.06009674072265625,  ' überall'),
-#  ('Gott ist großartig ! :)',  0.0370681993663311,   ' großartig')]
-```
-
-##### Extract features from GottBERT
-
-```python
-# Extract the last layer's features
-line = "Der erste Schluck aus dem Becher der Naturwissenschaft macht atheistisch , aber auf dem Grunde des Bechers wartet Gott !"
-tokens = gottbert.encode(line)
-last_layer_features = gottbert.extract_features(tokens)
-assert last_layer_features.size() == torch.Size([1, 27, 768])
-
-# Extract all layer's features (layer 0 is the embedding layer)
-all_layers = gottbert.extract_features(tokens, return_all_hiddens=True)
-assert len(all_layers) == 13
-assert torch.all(all_layers[-1] == last_layer_features)
-```
-## Citation
-If you use our work, please cite:
-
-```bibtex
-@misc{scheible2020gottbert,
-      title={GottBERT: a pure German Language Model},
-      author={Raphael Scheible and Fabian Thomczyk and Patric Tippmann and Victor Jaravine and Martin Boeker},
-      year={2020},
-      eprint={2012.02110},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/megatron_11b/README.md b/spaces/koajoel/PolyFormer/fairseq/examples/megatron_11b/README.md
deleted file mode 100644
index 945c96c91e2e2d93466abc28d90bc25a1e7dd471..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/megatron_11b/README.md
+++ /dev/null
@@ -1,161 +0,0 @@
-# Megatron-11b
-
-Megatron-11b is a unidirectional language model with `11B` parameters based on [Megatron-LM](https://arxiv.org/pdf/1909.08053.pdf). Following the original Megatron work, we trained the model using intra-layer model parallelism with each layer's parameters split across 8 GPUs.
-
-Megatron-11b is trained on the same data and uses the same byte-pair encoding (BPE) as [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf).
-
-## Pre-trained models
-
-Model | Description | # params | # filesize | Download
----|---|---|---|---
-`megatron_11b` | megatron_11b unidirectional language model | 11B | 19Gb | [megatron_11b.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz)
-
-#### Architecture:
-
-Param | Value
----|---
-embed_dim | 3072
-ffn_dim | 3072 * 6
-layers | 72
-attention heads | 32
-
-#### Training details:
-
-Param | value
----|---
-bsz | 512
-num_updates | 300,000
-peak_lr | 1.5e-04
-lr scheduler | inverse_sqrt
-clip norm | 0.0
-
-
-## Example training command (model parallel)
-
-Megatron-11b contains too many parameters to train on a single GPU. Following
-the original Megatron work, we adopt an intra-layer model parallel training
-approach in which each layer's parameters are split across multiple GPUs and
-activations and gradients are communicated during the forward/backward pass,
-respectively. We similarly split the loss computation using the
-`vocab_parallel_cross_entropy` criterion.
-
-The following training command illustrates how to do model parallel training in
-fairseq. We assume that each machine (node) has 8 GPUs among which to split the
-model parameters (`--model-parallel-size 8`). If you have access to multiple
-nodes, you may combine this with data parallel training by increasing
-`--distributed-world-size`.
-
-To train Megatron-11b on a single node:
-
-
-```bash
-fairseq-train <DATA_PATH> \
-  --distributed-world-size 8  \
-  --memory-efficient-fp16 \
-  --num-workers 2 \
-  --model-parallel-size 8 \
-  --criterion vocab_parallel_cross_entropy \
-  --task language_modeling \
-  --sample-break-mode none \
-  --tokens-per-sample 1024 \
-  --arch transformer_lm_megatron_11b \
-  --share-decoder-input-output-embed \
-  --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 --clip-norm 0.0 \
-  --lr-scheduler inverse_sqrt --lr 0.00015 \
-  --warmup-updates 3000 --weight-decay 0.01 \
-  --dropout 0.1 --attention-dropout 0.1 \
-  --batch-size 2 \
-  --max-update 300000;
-```
-
-Note: Above was tested on `DGX-1` box, with `8xV100-32Gb` GPUs.
-
-## Results
-
-**[Wikitext103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)**
-
-Model | Valid perplexity | Test perplexity
----|---|---
-`megatron_11b` | 10.64 | 10.54
-
-
-## Evaluating `megatron_11b` on Wikitext-103
-
-#### 1. Downloading Megatron-11b
-```bash
-# WARNING: this file is 19GB
-wget https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz
-tar -xzvf megatron_11b.tar.gz
-```
-
-#### 2. Download Wikitext-103
-```bash
-wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
-unzip wikitext-103-raw-v1.zip
-```
-
-#### 3. Detokenize test tokens
-Megatron-11b uses a byte-level BPE that expects raw (untokenized) input. Since
-the wikitext-103 dataset comes tokenized, we apply a simple detokenization
-process to restore the untokenized test set:
-
-```bash
-python -m examples.megatron_11b.detok wikitext-103-raw/wiki.test.raw > wikitext-103-raw/wiki.test.detok
-```
-
-#### 4. BPE encoding
-```bash
-wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
-wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
-
-python -m examples.roberta.multiprocessing_bpe_encoder \
-    --encoder-json encoder.json \
-    --vocab-bpe vocab.bpe \
-    --inputs "wikitext-103-raw/wiki.test.detok" \
-    --outputs "wikitext-103-raw/wiki.test.bpe" \
-    --workers 60;
-```
-
-#### 5. Fairseq binarize
-```bash
-fairseq-preprocess \
-    --only-source \
-    --testpref wikitext-103-raw/wiki.test.bpe \
-    --srcdict megatron_11b/dict.txt \
-    --destdir wikitext103-bin;
-```
-
-#### 6. Evaluating perplexity.
-We can now evaluate perplexity on the test set. Note that because we've modified
-the test set (via detokenization and BPE), the perplexity reported by
-`fairseq-eval-lm` needs to be renormalized.
-
-Compute unnormalized perplexity:
-
-```bash
-DATA_PATH=wikitext103-bin/
-fairseq-eval-lm \
-  $DATA_PATH \
-  --path megatron_11b/model.pt \
-  --task language_modeling \
-  --gen-subset test \
-  --batch-size 8 \
-  --criterion cross_entropy \
-  --context-window 992 \
-  --distributed-world-size 8 \
-  --model-parallel-size 8;
-# Expected PPL (unnormalized_ppl): [8.46]
-# Note: the eval command needs to run on 8 GPUs for the released model
-```
-Renormalizing formula:  `2 ^ ( log_2(unnormalized_PPL) * (270847 / 245566))`.
-PPL After normalization: `10.54`
-
-To renormalize the perplexity, we must account for the change in token count
-after detokenizing and appling BPE. The formula for this is:
-`2 ^ ( log_2(unnormalized_PPL) * (new_token_cnt / orig_token_cnt))`
-
-For the wikitext-103 test set, the original token count is `245566` and the
-token count after detokenization and applying BPE is `270847`.
-
-The perplexity after renormalization is:
-`2 ^ ( log_2(8.46) * (270847 / 245566)) = 10.54`
diff --git a/spaces/konverner/deep-voice-cloning/build/lib/deep_voice_cloning/data/__init__.py b/spaces/konverner/deep-voice-cloning/build/lib/deep_voice_cloning/data/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kquote03/lama-video-watermark-remover/bin/gen_mask_dataset.py b/spaces/kquote03/lama-video-watermark-remover/bin/gen_mask_dataset.py
deleted file mode 100644
index 6e2ce3a9bc9708fd46641cab815113508af32d02..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/bin/gen_mask_dataset.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#!/usr/bin/env python3
-
-import glob
-import os
-import shutil
-import traceback
-
-import PIL.Image as Image
-import numpy as np
-from joblib import Parallel, delayed
-
-from saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop
-from saicinpainting.evaluation.utils import load_yaml, SmallMode
-from saicinpainting.training.data.masks import MixedMaskGenerator
-
-
-class MakeManyMasksWrapper:
-    def __init__(self, impl, variants_n=2):
-        self.impl = impl
-        self.variants_n = variants_n
-
-    def get_masks(self, img):
-        img = np.transpose(np.array(img), (2, 0, 1))
-        return [self.impl(img)[0] for _ in range(self.variants_n)]
-
-
-def process_images(src_images, indir, outdir, config):
-    if config.generator_kind == 'segmentation':
-        mask_generator = SegmentationMask(**config.mask_generator_kwargs)
-    elif config.generator_kind == 'random':
-        variants_n = config.mask_generator_kwargs.pop('variants_n', 2)
-        mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**config.mask_generator_kwargs),
-                                              variants_n=variants_n)
-    else:
-        raise ValueError(f'Unexpected generator kind: {config.generator_kind}')
-
-    max_tamper_area = config.get('max_tamper_area', 1)
-
-    for infile in src_images:
-        try:
-            file_relpath = infile[len(indir):]
-            img_outpath = os.path.join(outdir, file_relpath)
-            os.makedirs(os.path.dirname(img_outpath), exist_ok=True)
-
-            image = Image.open(infile).convert('RGB')
-
-            # scale input image to output resolution and filter smaller images
-            if min(image.size) < config.cropping.out_min_size:
-                handle_small_mode = SmallMode(config.cropping.handle_small_mode)
-                if handle_small_mode == SmallMode.DROP:
-                    continue
-                elif handle_small_mode == SmallMode.UPSCALE:
-                    factor = config.cropping.out_min_size / min(image.size)
-                    out_size = (np.array(image.size) * factor).round().astype('uint32')
-                    image = image.resize(out_size, resample=Image.BICUBIC)
-            else:
-                factor = config.cropping.out_min_size / min(image.size)
-                out_size = (np.array(image.size) * factor).round().astype('uint32')
-                image = image.resize(out_size, resample=Image.BICUBIC)
-
-            # generate and select masks
-            src_masks = mask_generator.get_masks(image)
-
-            filtered_image_mask_pairs = []
-            for cur_mask in src_masks:
-                if config.cropping.out_square_crop:
-                    (crop_left,
-                     crop_top,
-                     crop_right,
-                     crop_bottom) = propose_random_square_crop(cur_mask,
-                                                               min_overlap=config.cropping.crop_min_overlap)
-                    cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right]
-                    cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom))
-                else:
-                    cur_image = image
-
-                if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area:
-                    continue
-
-                filtered_image_mask_pairs.append((cur_image, cur_mask))
-
-            mask_indices = np.random.choice(len(filtered_image_mask_pairs),
-                                            size=min(len(filtered_image_mask_pairs), config.max_masks_per_image),
-                                            replace=False)
-
-            # crop masks; save masks together with input image
-            mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0])
-            for i, idx in enumerate(mask_indices):
-                cur_image, cur_mask = filtered_image_mask_pairs[idx]
-                cur_basename = mask_basename + f'_crop{i:03d}'
-                Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'),
-                                mode='L').save(cur_basename + f'_mask{i:03d}.png')
-                cur_image.save(cur_basename + '.png')
-        except KeyboardInterrupt:
-            return
-        except Exception as ex:
-            print(f'Could not make masks for {infile} due to {ex}:\n{traceback.format_exc()}')
-
-
-def main(args):
-    if not args.indir.endswith('/'):
-        args.indir += '/'
-
-    os.makedirs(args.outdir, exist_ok=True)
-
-    config = load_yaml(args.config)
-
-    in_files = list(glob.glob(os.path.join(args.indir, '**', f'*.{args.ext}'), recursive=True))
-    if args.n_jobs == 0:
-        process_images(in_files, args.indir, args.outdir, config)
-    else:
-        in_files_n = len(in_files)
-        chunk_size = in_files_n // args.n_jobs + (1 if in_files_n % args.n_jobs > 0 else 0)
-        Parallel(n_jobs=args.n_jobs)(
-            delayed(process_images)(in_files[start:start+chunk_size], args.indir, args.outdir, config)
-            for start in range(0, len(in_files), chunk_size)
-        )
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('config', type=str, help='Path to config for dataset generation')
-    aparser.add_argument('indir', type=str, help='Path to folder with images')
-    aparser.add_argument('outdir', type=str, help='Path to folder to store aligned images and masks to')
-    aparser.add_argument('--n-jobs', type=int, default=0, help='How many processes to use')
-    aparser.add_argument('--ext', type=str, default='jpg', help='Input image extension')
-
-    main(aparser.parse_args())
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_binary.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_binary.py
deleted file mode 100644
index a74ee9eb6f341aca9e074c0acc4b306a354175a0..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_binary.py
+++ /dev/null
@@ -1,102 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Binary input/output support routines.
-#
-# Copyright (c) 1997-2003 by Secret Labs AB
-# Copyright (c) 1995-2003 by Fredrik Lundh
-# Copyright (c) 2012 by Brian Crowell
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""Binary input/output support routines."""
-
-
-from struct import pack, unpack_from
-
-
-def i8(c):
-    return c if c.__class__ is int else c[0]
-
-
-def o8(i):
-    return bytes((i & 255,))
-
-
-# Input, le = little endian, be = big endian
-def i16le(c, o=0):
-    """
-    Converts a 2-bytes (16 bits) string to an unsigned integer.
-
-    :param c: string containing bytes to convert
-    :param o: offset of bytes to convert in string
-    """
-    return unpack_from("<H", c, o)[0]
-
-
-def si16le(c, o=0):
-    """
-    Converts a 2-bytes (16 bits) string to a signed integer.
-
-    :param c: string containing bytes to convert
-    :param o: offset of bytes to convert in string
-    """
-    return unpack_from("<h", c, o)[0]
-
-
-def si16be(c, o=0):
-    """
-    Converts a 2-bytes (16 bits) string to a signed integer, big endian.
-
-    :param c: string containing bytes to convert
-    :param o: offset of bytes to convert in string
-    """
-    return unpack_from(">h", c, o)[0]
-
-
-def i32le(c, o=0):
-    """
-    Converts a 4-bytes (32 bits) string to an unsigned integer.
-
-    :param c: string containing bytes to convert
-    :param o: offset of bytes to convert in string
-    """
-    return unpack_from("<I", c, o)[0]
-
-
-def si32le(c, o=0):
-    """
-    Converts a 4-bytes (32 bits) string to a signed integer.
-
-    :param c: string containing bytes to convert
-    :param o: offset of bytes to convert in string
-    """
-    return unpack_from("<i", c, o)[0]
-
-
-def i16be(c, o=0):
-    return unpack_from(">H", c, o)[0]
-
-
-def i32be(c, o=0):
-    return unpack_from(">I", c, o)[0]
-
-
-# Output, le = little endian, be = big endian
-def o16le(i):
-    return pack("<H", i)
-
-
-def o32le(i):
-    return pack("<I", i)
-
-
-def o16be(i):
-    return pack(">H", i)
-
-
-def o32be(i):
-    return pack(">I", i)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/__init__.py
deleted file mode 100644
index ad57eb9d3889bfbf5bbe868b0eaae7aa4d3049c1..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/__init__.py
+++ /dev/null
@@ -1,613 +0,0 @@
-# ruff: noqa
-__version__ = "5.0.1"
-
-from typing import Any
-
-# Necessary as mypy would see expr as the module alt.expr although due to how
-# the imports are set up it is expr in the alt.expr module
-expr: Any
-
-
-# The content of __all__ is automatically written by
-# tools/update_init_file.py. Do not modify directly.
-__all__ = [
-    "Aggregate",
-    "AggregateOp",
-    "AggregateTransform",
-    "AggregatedFieldDef",
-    "Align",
-    "AllSortString",
-    "Angle",
-    "AngleDatum",
-    "AngleValue",
-    "AnyMark",
-    "AnyMarkConfig",
-    "AreaConfig",
-    "ArgmaxDef",
-    "ArgminDef",
-    "AutoSizeParams",
-    "AutosizeType",
-    "Axis",
-    "AxisConfig",
-    "AxisOrient",
-    "AxisResolveMap",
-    "BBox",
-    "BarConfig",
-    "BaseTitleNoValueRefs",
-    "Baseline",
-    "Bin",
-    "BinExtent",
-    "BinParams",
-    "BinTransform",
-    "BindCheckbox",
-    "BindDirect",
-    "BindInput",
-    "BindRadioSelect",
-    "BindRange",
-    "Binding",
-    "Blend",
-    "BoxPlot",
-    "BoxPlotConfig",
-    "BoxPlotDef",
-    "BrushConfig",
-    "CalculateTransform",
-    "Categorical",
-    "Chart",
-    "Color",
-    "ColorDatum",
-    "ColorDef",
-    "ColorName",
-    "ColorScheme",
-    "ColorValue",
-    "Column",
-    "CompositeMark",
-    "CompositeMarkDef",
-    "CompositionConfig",
-    "ConcatChart",
-    "ConcatSpecGenericSpec",
-    "ConditionalAxisColor",
-    "ConditionalAxisLabelAlign",
-    "ConditionalAxisLabelBaseline",
-    "ConditionalAxisLabelFontStyle",
-    "ConditionalAxisLabelFontWeight",
-    "ConditionalAxisNumber",
-    "ConditionalAxisNumberArray",
-    "ConditionalAxisPropertyAlignnull",
-    "ConditionalAxisPropertyColornull",
-    "ConditionalAxisPropertyFontStylenull",
-    "ConditionalAxisPropertyFontWeightnull",
-    "ConditionalAxisPropertyTextBaselinenull",
-    "ConditionalAxisPropertynumberArraynull",
-    "ConditionalAxisPropertynumbernull",
-    "ConditionalAxisPropertystringnull",
-    "ConditionalAxisString",
-    "ConditionalMarkPropFieldOrDatumDef",
-    "ConditionalMarkPropFieldOrDatumDefTypeForShape",
-    "ConditionalParameterMarkPropFieldOrDatumDef",
-    "ConditionalParameterMarkPropFieldOrDatumDefTypeForShape",
-    "ConditionalParameterStringFieldDef",
-    "ConditionalParameterValueDefGradientstringnullExprRef",
-    "ConditionalParameterValueDefTextExprRef",
-    "ConditionalParameterValueDefnumber",
-    "ConditionalParameterValueDefnumberArrayExprRef",
-    "ConditionalParameterValueDefnumberExprRef",
-    "ConditionalParameterValueDefstringExprRef",
-    "ConditionalParameterValueDefstringnullExprRef",
-    "ConditionalPredicateMarkPropFieldOrDatumDef",
-    "ConditionalPredicateMarkPropFieldOrDatumDefTypeForShape",
-    "ConditionalPredicateStringFieldDef",
-    "ConditionalPredicateValueDefAlignnullExprRef",
-    "ConditionalPredicateValueDefColornullExprRef",
-    "ConditionalPredicateValueDefFontStylenullExprRef",
-    "ConditionalPredicateValueDefFontWeightnullExprRef",
-    "ConditionalPredicateValueDefGradientstringnullExprRef",
-    "ConditionalPredicateValueDefTextBaselinenullExprRef",
-    "ConditionalPredicateValueDefTextExprRef",
-    "ConditionalPredicateValueDefnumber",
-    "ConditionalPredicateValueDefnumberArrayExprRef",
-    "ConditionalPredicateValueDefnumberArraynullExprRef",
-    "ConditionalPredicateValueDefnumberExprRef",
-    "ConditionalPredicateValueDefnumbernullExprRef",
-    "ConditionalPredicateValueDefstringExprRef",
-    "ConditionalPredicateValueDefstringnullExprRef",
-    "ConditionalStringFieldDef",
-    "ConditionalValueDefGradientstringnullExprRef",
-    "ConditionalValueDefTextExprRef",
-    "ConditionalValueDefnumber",
-    "ConditionalValueDefnumberArrayExprRef",
-    "ConditionalValueDefnumberExprRef",
-    "ConditionalValueDefstringExprRef",
-    "ConditionalValueDefstringnullExprRef",
-    "Config",
-    "CsvDataFormat",
-    "Cursor",
-    "Cyclical",
-    "Data",
-    "DataFormat",
-    "DataSource",
-    "Datasets",
-    "DateTime",
-    "DatumChannelMixin",
-    "DatumDef",
-    "Day",
-    "DensityTransform",
-    "DerivedStream",
-    "Description",
-    "DescriptionValue",
-    "Detail",
-    "Dict",
-    "DictInlineDataset",
-    "DictSelectionInit",
-    "DictSelectionInitInterval",
-    "Diverging",
-    "DomainUnionWith",
-    "DsvDataFormat",
-    "Element",
-    "Encoding",
-    "EncodingSortField",
-    "ErrorBand",
-    "ErrorBandConfig",
-    "ErrorBandDef",
-    "ErrorBar",
-    "ErrorBarConfig",
-    "ErrorBarDef",
-    "ErrorBarExtent",
-    "EventStream",
-    "EventType",
-    "Expr",
-    "ExprRef",
-    "Facet",
-    "FacetChart",
-    "FacetEncodingFieldDef",
-    "FacetFieldDef",
-    "FacetMapping",
-    "FacetSpec",
-    "FacetedEncoding",
-    "FacetedUnitSpec",
-    "Feature",
-    "FeatureCollection",
-    "FeatureGeometryGeoJsonProperties",
-    "Field",
-    "FieldChannelMixin",
-    "FieldDefWithoutScale",
-    "FieldEqualPredicate",
-    "FieldGTEPredicate",
-    "FieldGTPredicate",
-    "FieldLTEPredicate",
-    "FieldLTPredicate",
-    "FieldName",
-    "FieldOneOfPredicate",
-    "FieldOrDatumDefWithConditionDatumDefGradientstringnull",
-    "FieldOrDatumDefWithConditionDatumDefnumber",
-    "FieldOrDatumDefWithConditionDatumDefnumberArray",
-    "FieldOrDatumDefWithConditionDatumDefstringnull",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefGradientstringnull",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefTypeForShapestringnull",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefnumber",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefnumberArray",
-    "FieldOrDatumDefWithConditionStringDatumDefText",
-    "FieldOrDatumDefWithConditionStringFieldDefText",
-    "FieldOrDatumDefWithConditionStringFieldDefstring",
-    "FieldRange",
-    "FieldRangePredicate",
-    "FieldValidPredicate",
-    "Fill",
-    "FillDatum",
-    "FillOpacity",
-    "FillOpacityDatum",
-    "FillOpacityValue",
-    "FillValue",
-    "FilterTransform",
-    "Fit",
-    "FlattenTransform",
-    "FoldTransform",
-    "FontStyle",
-    "FontWeight",
-    "Generator",
-    "GenericUnitSpecEncodingAnyMark",
-    "GeoJsonFeature",
-    "GeoJsonFeatureCollection",
-    "GeoJsonProperties",
-    "Geometry",
-    "GeometryCollection",
-    "Gradient",
-    "GradientStop",
-    "GraticuleGenerator",
-    "GraticuleParams",
-    "HConcatChart",
-    "HConcatSpecGenericSpec",
-    "Header",
-    "HeaderConfig",
-    "HexColor",
-    "Href",
-    "HrefValue",
-    "Impute",
-    "ImputeMethod",
-    "ImputeParams",
-    "ImputeSequence",
-    "ImputeTransform",
-    "InlineData",
-    "InlineDataset",
-    "Interpolate",
-    "IntervalSelectionConfig",
-    "IntervalSelectionConfigWithoutType",
-    "JoinAggregateFieldDef",
-    "JoinAggregateTransform",
-    "JsonDataFormat",
-    "Key",
-    "LabelOverlap",
-    "LatLongDef",
-    "LatLongFieldDef",
-    "Latitude",
-    "Latitude2",
-    "Latitude2Datum",
-    "Latitude2Value",
-    "LatitudeDatum",
-    "LayerChart",
-    "LayerRepeatMapping",
-    "LayerRepeatSpec",
-    "LayerSpec",
-    "LayoutAlign",
-    "Legend",
-    "LegendBinding",
-    "LegendConfig",
-    "LegendOrient",
-    "LegendResolveMap",
-    "LegendStreamBinding",
-    "LineConfig",
-    "LineString",
-    "LinearGradient",
-    "LocalMultiTimeUnit",
-    "LocalSingleTimeUnit",
-    "Locale",
-    "LoessTransform",
-    "LogicalAndPredicate",
-    "LogicalNotPredicate",
-    "LogicalOrPredicate",
-    "Longitude",
-    "Longitude2",
-    "Longitude2Datum",
-    "Longitude2Value",
-    "LongitudeDatum",
-    "LookupData",
-    "LookupSelection",
-    "LookupTransform",
-    "Mark",
-    "MarkConfig",
-    "MarkDef",
-    "MarkPropDefGradientstringnull",
-    "MarkPropDefnumber",
-    "MarkPropDefnumberArray",
-    "MarkPropDefstringnullTypeForShape",
-    "MarkType",
-    "MaxRowsError",
-    "MergedStream",
-    "Month",
-    "MultiLineString",
-    "MultiPoint",
-    "MultiPolygon",
-    "MultiTimeUnit",
-    "NamedData",
-    "NonArgAggregateOp",
-    "NonLayerRepeatSpec",
-    "NonNormalizedSpec",
-    "NumberLocale",
-    "NumericArrayMarkPropDef",
-    "NumericMarkPropDef",
-    "OffsetDef",
-    "Opacity",
-    "OpacityDatum",
-    "OpacityValue",
-    "Order",
-    "OrderFieldDef",
-    "OrderValue",
-    "OrderValueDef",
-    "Orient",
-    "Orientation",
-    "OverlayMarkDef",
-    "Padding",
-    "Parameter",
-    "ParameterExpression",
-    "ParameterExtent",
-    "ParameterName",
-    "ParameterPredicate",
-    "Parse",
-    "ParseValue",
-    "PivotTransform",
-    "Point",
-    "PointSelectionConfig",
-    "PointSelectionConfigWithoutType",
-    "PolarDef",
-    "Polygon",
-    "Position",
-    "Position2Def",
-    "PositionDatumDef",
-    "PositionDatumDefBase",
-    "PositionDef",
-    "PositionFieldDef",
-    "PositionFieldDefBase",
-    "PositionValueDef",
-    "Predicate",
-    "PredicateComposition",
-    "PrimitiveValue",
-    "Projection",
-    "ProjectionConfig",
-    "ProjectionType",
-    "QuantileTransform",
-    "RadialGradient",
-    "Radius",
-    "Radius2",
-    "Radius2Datum",
-    "Radius2Value",
-    "RadiusDatum",
-    "RadiusValue",
-    "RangeConfig",
-    "RangeEnum",
-    "RangeRaw",
-    "RangeRawArray",
-    "RangeScheme",
-    "RectConfig",
-    "RegressionTransform",
-    "RelativeBandSize",
-    "RepeatChart",
-    "RepeatMapping",
-    "RepeatRef",
-    "RepeatSpec",
-    "Resolve",
-    "ResolveMode",
-    "Root",
-    "Row",
-    "RowColLayoutAlign",
-    "RowColboolean",
-    "RowColnumber",
-    "RowColumnEncodingFieldDef",
-    "SCHEMA_URL",
-    "SCHEMA_VERSION",
-    "SampleTransform",
-    "Scale",
-    "ScaleBinParams",
-    "ScaleBins",
-    "ScaleConfig",
-    "ScaleDatumDef",
-    "ScaleFieldDef",
-    "ScaleInterpolateEnum",
-    "ScaleInterpolateParams",
-    "ScaleResolveMap",
-    "ScaleType",
-    "SchemaBase",
-    "SchemeParams",
-    "SecondaryFieldDef",
-    "SelectionConfig",
-    "SelectionExpression",
-    "SelectionInit",
-    "SelectionInitInterval",
-    "SelectionInitIntervalMapping",
-    "SelectionInitMapping",
-    "SelectionParameter",
-    "SelectionPredicateComposition",
-    "SelectionResolution",
-    "SelectionType",
-    "SequenceGenerator",
-    "SequenceParams",
-    "SequentialMultiHue",
-    "SequentialSingleHue",
-    "Shape",
-    "ShapeDatum",
-    "ShapeDef",
-    "ShapeValue",
-    "SharedEncoding",
-    "SingleDefUnitChannel",
-    "SingleTimeUnit",
-    "Size",
-    "SizeDatum",
-    "SizeValue",
-    "Sort",
-    "SortArray",
-    "SortByChannel",
-    "SortByChannelDesc",
-    "SortByEncoding",
-    "SortField",
-    "SortOrder",
-    "Spec",
-    "SphereGenerator",
-    "StackOffset",
-    "StackTransform",
-    "StandardType",
-    "Step",
-    "StepFor",
-    "Stream",
-    "StringFieldDef",
-    "StringFieldDefWithCondition",
-    "StringValueDefWithCondition",
-    "Stroke",
-    "StrokeCap",
-    "StrokeDash",
-    "StrokeDashDatum",
-    "StrokeDashValue",
-    "StrokeDatum",
-    "StrokeJoin",
-    "StrokeOpacity",
-    "StrokeOpacityDatum",
-    "StrokeOpacityValue",
-    "StrokeValue",
-    "StrokeWidth",
-    "StrokeWidthDatum",
-    "StrokeWidthValue",
-    "StyleConfigIndex",
-    "SymbolShape",
-    "TOPLEVEL_ONLY_KEYS",
-    "Text",
-    "TextBaseline",
-    "TextDatum",
-    "TextDef",
-    "TextDirection",
-    "TextValue",
-    "Theta",
-    "Theta2",
-    "Theta2Datum",
-    "Theta2Value",
-    "ThetaDatum",
-    "ThetaValue",
-    "TickConfig",
-    "TickCount",
-    "TimeInterval",
-    "TimeIntervalStep",
-    "TimeLocale",
-    "TimeUnit",
-    "TimeUnitParams",
-    "TimeUnitTransform",
-    "Title",
-    "TitleAnchor",
-    "TitleConfig",
-    "TitleFrame",
-    "TitleOrient",
-    "TitleParams",
-    "Tooltip",
-    "TooltipContent",
-    "TooltipValue",
-    "TopLevelConcatSpec",
-    "TopLevelFacetSpec",
-    "TopLevelHConcatSpec",
-    "TopLevelLayerSpec",
-    "TopLevelMixin",
-    "TopLevelParameter",
-    "TopLevelRepeatSpec",
-    "TopLevelSelectionParameter",
-    "TopLevelSpec",
-    "TopLevelUnitSpec",
-    "TopLevelVConcatSpec",
-    "TopoDataFormat",
-    "Transform",
-    "Type",
-    "TypeForShape",
-    "TypedFieldDef",
-    "URI",
-    "Undefined",
-    "UnitSpec",
-    "UnitSpecWithFrame",
-    "Url",
-    "UrlData",
-    "UrlValue",
-    "UtcMultiTimeUnit",
-    "UtcSingleTimeUnit",
-    "VConcatChart",
-    "VConcatSpecGenericSpec",
-    "VEGAEMBED_VERSION",
-    "VEGALITE_VERSION",
-    "VEGA_VERSION",
-    "ValueChannelMixin",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefGradientstringnull",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefTypeForShapestringnull",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefnumber",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefnumberArray",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefstringnull",
-    "ValueDefWithConditionStringFieldDefText",
-    "ValueDefnumber",
-    "ValueDefnumberwidthheightExprRef",
-    "VariableParameter",
-    "Vector10string",
-    "Vector12string",
-    "Vector2DateTime",
-    "Vector2Vector2number",
-    "Vector2boolean",
-    "Vector2number",
-    "Vector2string",
-    "Vector3number",
-    "Vector7string",
-    "VegaLite",
-    "VegaLiteSchema",
-    "ViewBackground",
-    "ViewConfig",
-    "WindowEventType",
-    "WindowFieldDef",
-    "WindowOnlyOp",
-    "WindowTransform",
-    "X",
-    "X2",
-    "X2Datum",
-    "X2Value",
-    "XDatum",
-    "XError",
-    "XError2",
-    "XError2Value",
-    "XErrorValue",
-    "XOffset",
-    "XOffsetDatum",
-    "XOffsetValue",
-    "XValue",
-    "Y",
-    "Y2",
-    "Y2Datum",
-    "Y2Value",
-    "YDatum",
-    "YError",
-    "YError2",
-    "YError2Value",
-    "YErrorValue",
-    "YOffset",
-    "YOffsetDatum",
-    "YOffsetValue",
-    "YValue",
-    "api",
-    "binding",
-    "binding_checkbox",
-    "binding_radio",
-    "binding_range",
-    "binding_select",
-    "channels",
-    "check_fields_and_encodings",
-    "concat",
-    "condition",
-    "core",
-    "curry",
-    "data",
-    "data_transformers",
-    "datum",
-    "default_data_transformer",
-    "display",
-    "expr",
-    "graticule",
-    "hconcat",
-    "layer",
-    "limit_rows",
-    "load_ipython_extension",
-    "load_schema",
-    "mixins",
-    "overload",
-    "param",
-    "parse_shorthand",
-    "pipe",
-    "renderers",
-    "repeat",
-    "sample",
-    "schema",
-    "selection_interval",
-    "selection_point",
-    "sequence",
-    "sphere",
-    "theme",
-    "themes",
-    "to_csv",
-    "to_json",
-    "to_values",
-    "topo_feature",
-    "utils",
-    "v5",
-    "value",
-    "vconcat",
-    "vegalite",
-    "with_property_setters",
-]
-
-
-def __dir__():
-    return __all__
-
-
-from .vegalite import *
-
-
-def load_ipython_extension(ipython):
-    from ._magics import vegalite
-
-    ipython.register_magic_function(vegalite, "cell")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/lexer.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/lexer.py
deleted file mode 100644
index aff7e9f993792e1ced39c93fc0d39dcb5bdd5fde..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jinja2/lexer.py
+++ /dev/null
@@ -1,866 +0,0 @@
-"""Implements a Jinja / Python combination lexer. The ``Lexer`` class
-is used to do some preprocessing. It filters out invalid operators like
-the bitshift operators we don't allow in templates. It separates
-template code and python code in expressions.
-"""
-import re
-import typing as t
-from ast import literal_eval
-from collections import deque
-from sys import intern
-
-from ._identifier import pattern as name_re
-from .exceptions import TemplateSyntaxError
-from .utils import LRUCache
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-    from .environment import Environment
-
-# cache for the lexers. Exists in order to be able to have multiple
-# environments with the same lexer
-_lexer_cache: t.MutableMapping[t.Tuple, "Lexer"] = LRUCache(50)  # type: ignore
-
-# static regular expressions
-whitespace_re = re.compile(r"\s+")
-newline_re = re.compile(r"(\r\n|\r|\n)")
-string_re = re.compile(
-    r"('([^'\\]*(?:\\.[^'\\]*)*)'" r'|"([^"\\]*(?:\\.[^"\\]*)*)")', re.S
-)
-integer_re = re.compile(
-    r"""
-    (
-        0b(_?[0-1])+ # binary
-    |
-        0o(_?[0-7])+ # octal
-    |
-        0x(_?[\da-f])+ # hex
-    |
-        [1-9](_?\d)* # decimal
-    |
-        0(_?0)* # decimal zero
-    )
-    """,
-    re.IGNORECASE | re.VERBOSE,
-)
-float_re = re.compile(
-    r"""
-    (?<!\.)  # doesn't start with a .
-    (\d+_)*\d+  # digits, possibly _ separated
-    (
-        (\.(\d+_)*\d+)?  # optional fractional part
-        e[+\-]?(\d+_)*\d+  # exponent part
-    |
-        \.(\d+_)*\d+  # required fractional part
-    )
-    """,
-    re.IGNORECASE | re.VERBOSE,
-)
-
-# internal the tokens and keep references to them
-TOKEN_ADD = intern("add")
-TOKEN_ASSIGN = intern("assign")
-TOKEN_COLON = intern("colon")
-TOKEN_COMMA = intern("comma")
-TOKEN_DIV = intern("div")
-TOKEN_DOT = intern("dot")
-TOKEN_EQ = intern("eq")
-TOKEN_FLOORDIV = intern("floordiv")
-TOKEN_GT = intern("gt")
-TOKEN_GTEQ = intern("gteq")
-TOKEN_LBRACE = intern("lbrace")
-TOKEN_LBRACKET = intern("lbracket")
-TOKEN_LPAREN = intern("lparen")
-TOKEN_LT = intern("lt")
-TOKEN_LTEQ = intern("lteq")
-TOKEN_MOD = intern("mod")
-TOKEN_MUL = intern("mul")
-TOKEN_NE = intern("ne")
-TOKEN_PIPE = intern("pipe")
-TOKEN_POW = intern("pow")
-TOKEN_RBRACE = intern("rbrace")
-TOKEN_RBRACKET = intern("rbracket")
-TOKEN_RPAREN = intern("rparen")
-TOKEN_SEMICOLON = intern("semicolon")
-TOKEN_SUB = intern("sub")
-TOKEN_TILDE = intern("tilde")
-TOKEN_WHITESPACE = intern("whitespace")
-TOKEN_FLOAT = intern("float")
-TOKEN_INTEGER = intern("integer")
-TOKEN_NAME = intern("name")
-TOKEN_STRING = intern("string")
-TOKEN_OPERATOR = intern("operator")
-TOKEN_BLOCK_BEGIN = intern("block_begin")
-TOKEN_BLOCK_END = intern("block_end")
-TOKEN_VARIABLE_BEGIN = intern("variable_begin")
-TOKEN_VARIABLE_END = intern("variable_end")
-TOKEN_RAW_BEGIN = intern("raw_begin")
-TOKEN_RAW_END = intern("raw_end")
-TOKEN_COMMENT_BEGIN = intern("comment_begin")
-TOKEN_COMMENT_END = intern("comment_end")
-TOKEN_COMMENT = intern("comment")
-TOKEN_LINESTATEMENT_BEGIN = intern("linestatement_begin")
-TOKEN_LINESTATEMENT_END = intern("linestatement_end")
-TOKEN_LINECOMMENT_BEGIN = intern("linecomment_begin")
-TOKEN_LINECOMMENT_END = intern("linecomment_end")
-TOKEN_LINECOMMENT = intern("linecomment")
-TOKEN_DATA = intern("data")
-TOKEN_INITIAL = intern("initial")
-TOKEN_EOF = intern("eof")
-
-# bind operators to token types
-operators = {
-    "+": TOKEN_ADD,
-    "-": TOKEN_SUB,
-    "/": TOKEN_DIV,
-    "//": TOKEN_FLOORDIV,
-    "*": TOKEN_MUL,
-    "%": TOKEN_MOD,
-    "**": TOKEN_POW,
-    "~": TOKEN_TILDE,
-    "[": TOKEN_LBRACKET,
-    "]": TOKEN_RBRACKET,
-    "(": TOKEN_LPAREN,
-    ")": TOKEN_RPAREN,
-    "{": TOKEN_LBRACE,
-    "}": TOKEN_RBRACE,
-    "==": TOKEN_EQ,
-    "!=": TOKEN_NE,
-    ">": TOKEN_GT,
-    ">=": TOKEN_GTEQ,
-    "<": TOKEN_LT,
-    "<=": TOKEN_LTEQ,
-    "=": TOKEN_ASSIGN,
-    ".": TOKEN_DOT,
-    ":": TOKEN_COLON,
-    "|": TOKEN_PIPE,
-    ",": TOKEN_COMMA,
-    ";": TOKEN_SEMICOLON,
-}
-
-reverse_operators = {v: k for k, v in operators.items()}
-assert len(operators) == len(reverse_operators), "operators dropped"
-operator_re = re.compile(
-    f"({'|'.join(re.escape(x) for x in sorted(operators, key=lambda x: -len(x)))})"
-)
-
-ignored_tokens = frozenset(
-    [
-        TOKEN_COMMENT_BEGIN,
-        TOKEN_COMMENT,
-        TOKEN_COMMENT_END,
-        TOKEN_WHITESPACE,
-        TOKEN_LINECOMMENT_BEGIN,
-        TOKEN_LINECOMMENT_END,
-        TOKEN_LINECOMMENT,
-    ]
-)
-ignore_if_empty = frozenset(
-    [TOKEN_WHITESPACE, TOKEN_DATA, TOKEN_COMMENT, TOKEN_LINECOMMENT]
-)
-
-
-def _describe_token_type(token_type: str) -> str:
-    if token_type in reverse_operators:
-        return reverse_operators[token_type]
-
-    return {
-        TOKEN_COMMENT_BEGIN: "begin of comment",
-        TOKEN_COMMENT_END: "end of comment",
-        TOKEN_COMMENT: "comment",
-        TOKEN_LINECOMMENT: "comment",
-        TOKEN_BLOCK_BEGIN: "begin of statement block",
-        TOKEN_BLOCK_END: "end of statement block",
-        TOKEN_VARIABLE_BEGIN: "begin of print statement",
-        TOKEN_VARIABLE_END: "end of print statement",
-        TOKEN_LINESTATEMENT_BEGIN: "begin of line statement",
-        TOKEN_LINESTATEMENT_END: "end of line statement",
-        TOKEN_DATA: "template data / text",
-        TOKEN_EOF: "end of template",
-    }.get(token_type, token_type)
-
-
-def describe_token(token: "Token") -> str:
-    """Returns a description of the token."""
-    if token.type == TOKEN_NAME:
-        return token.value
-
-    return _describe_token_type(token.type)
-
-
-def describe_token_expr(expr: str) -> str:
-    """Like `describe_token` but for token expressions."""
-    if ":" in expr:
-        type, value = expr.split(":", 1)
-
-        if type == TOKEN_NAME:
-            return value
-    else:
-        type = expr
-
-    return _describe_token_type(type)
-
-
-def count_newlines(value: str) -> int:
-    """Count the number of newline characters in the string.  This is
-    useful for extensions that filter a stream.
-    """
-    return len(newline_re.findall(value))
-
-
-def compile_rules(environment: "Environment") -> t.List[t.Tuple[str, str]]:
-    """Compiles all the rules from the environment into a list of rules."""
-    e = re.escape
-    rules = [
-        (
-            len(environment.comment_start_string),
-            TOKEN_COMMENT_BEGIN,
-            e(environment.comment_start_string),
-        ),
-        (
-            len(environment.block_start_string),
-            TOKEN_BLOCK_BEGIN,
-            e(environment.block_start_string),
-        ),
-        (
-            len(environment.variable_start_string),
-            TOKEN_VARIABLE_BEGIN,
-            e(environment.variable_start_string),
-        ),
-    ]
-
-    if environment.line_statement_prefix is not None:
-        rules.append(
-            (
-                len(environment.line_statement_prefix),
-                TOKEN_LINESTATEMENT_BEGIN,
-                r"^[ \t\v]*" + e(environment.line_statement_prefix),
-            )
-        )
-    if environment.line_comment_prefix is not None:
-        rules.append(
-            (
-                len(environment.line_comment_prefix),
-                TOKEN_LINECOMMENT_BEGIN,
-                r"(?:^|(?<=\S))[^\S\r\n]*" + e(environment.line_comment_prefix),
-            )
-        )
-
-    return [x[1:] for x in sorted(rules, reverse=True)]
-
-
-class Failure:
-    """Class that raises a `TemplateSyntaxError` if called.
-    Used by the `Lexer` to specify known errors.
-    """
-
-    def __init__(
-        self, message: str, cls: t.Type[TemplateSyntaxError] = TemplateSyntaxError
-    ) -> None:
-        self.message = message
-        self.error_class = cls
-
-    def __call__(self, lineno: int, filename: str) -> "te.NoReturn":
-        raise self.error_class(self.message, lineno, filename)
-
-
-class Token(t.NamedTuple):
-    lineno: int
-    type: str
-    value: str
-
-    def __str__(self) -> str:
-        return describe_token(self)
-
-    def test(self, expr: str) -> bool:
-        """Test a token against a token expression.  This can either be a
-        token type or ``'token_type:token_value'``.  This can only test
-        against string values and types.
-        """
-        # here we do a regular string equality check as test_any is usually
-        # passed an iterable of not interned strings.
-        if self.type == expr:
-            return True
-
-        if ":" in expr:
-            return expr.split(":", 1) == [self.type, self.value]
-
-        return False
-
-    def test_any(self, *iterable: str) -> bool:
-        """Test against multiple token expressions."""
-        return any(self.test(expr) for expr in iterable)
-
-
-class TokenStreamIterator:
-    """The iterator for tokenstreams.  Iterate over the stream
-    until the eof token is reached.
-    """
-
-    def __init__(self, stream: "TokenStream") -> None:
-        self.stream = stream
-
-    def __iter__(self) -> "TokenStreamIterator":
-        return self
-
-    def __next__(self) -> Token:
-        token = self.stream.current
-
-        if token.type is TOKEN_EOF:
-            self.stream.close()
-            raise StopIteration
-
-        next(self.stream)
-        return token
-
-
-class TokenStream:
-    """A token stream is an iterable that yields :class:`Token`\\s.  The
-    parser however does not iterate over it but calls :meth:`next` to go
-    one token ahead.  The current active token is stored as :attr:`current`.
-    """
-
-    def __init__(
-        self,
-        generator: t.Iterable[Token],
-        name: t.Optional[str],
-        filename: t.Optional[str],
-    ):
-        self._iter = iter(generator)
-        self._pushed: "te.Deque[Token]" = deque()
-        self.name = name
-        self.filename = filename
-        self.closed = False
-        self.current = Token(1, TOKEN_INITIAL, "")
-        next(self)
-
-    def __iter__(self) -> TokenStreamIterator:
-        return TokenStreamIterator(self)
-
-    def __bool__(self) -> bool:
-        return bool(self._pushed) or self.current.type is not TOKEN_EOF
-
-    @property
-    def eos(self) -> bool:
-        """Are we at the end of the stream?"""
-        return not self
-
-    def push(self, token: Token) -> None:
-        """Push a token back to the stream."""
-        self._pushed.append(token)
-
-    def look(self) -> Token:
-        """Look at the next token."""
-        old_token = next(self)
-        result = self.current
-        self.push(result)
-        self.current = old_token
-        return result
-
-    def skip(self, n: int = 1) -> None:
-        """Got n tokens ahead."""
-        for _ in range(n):
-            next(self)
-
-    def next_if(self, expr: str) -> t.Optional[Token]:
-        """Perform the token test and return the token if it matched.
-        Otherwise the return value is `None`.
-        """
-        if self.current.test(expr):
-            return next(self)
-
-        return None
-
-    def skip_if(self, expr: str) -> bool:
-        """Like :meth:`next_if` but only returns `True` or `False`."""
-        return self.next_if(expr) is not None
-
-    def __next__(self) -> Token:
-        """Go one token ahead and return the old one.
-
-        Use the built-in :func:`next` instead of calling this directly.
-        """
-        rv = self.current
-
-        if self._pushed:
-            self.current = self._pushed.popleft()
-        elif self.current.type is not TOKEN_EOF:
-            try:
-                self.current = next(self._iter)
-            except StopIteration:
-                self.close()
-
-        return rv
-
-    def close(self) -> None:
-        """Close the stream."""
-        self.current = Token(self.current.lineno, TOKEN_EOF, "")
-        self._iter = iter(())
-        self.closed = True
-
-    def expect(self, expr: str) -> Token:
-        """Expect a given token type and return it.  This accepts the same
-        argument as :meth:`jinja2.lexer.Token.test`.
-        """
-        if not self.current.test(expr):
-            expr = describe_token_expr(expr)
-
-            if self.current.type is TOKEN_EOF:
-                raise TemplateSyntaxError(
-                    f"unexpected end of template, expected {expr!r}.",
-                    self.current.lineno,
-                    self.name,
-                    self.filename,
-                )
-
-            raise TemplateSyntaxError(
-                f"expected token {expr!r}, got {describe_token(self.current)!r}",
-                self.current.lineno,
-                self.name,
-                self.filename,
-            )
-
-        return next(self)
-
-
-def get_lexer(environment: "Environment") -> "Lexer":
-    """Return a lexer which is probably cached."""
-    key = (
-        environment.block_start_string,
-        environment.block_end_string,
-        environment.variable_start_string,
-        environment.variable_end_string,
-        environment.comment_start_string,
-        environment.comment_end_string,
-        environment.line_statement_prefix,
-        environment.line_comment_prefix,
-        environment.trim_blocks,
-        environment.lstrip_blocks,
-        environment.newline_sequence,
-        environment.keep_trailing_newline,
-    )
-    lexer = _lexer_cache.get(key)
-
-    if lexer is None:
-        _lexer_cache[key] = lexer = Lexer(environment)
-
-    return lexer
-
-
-class OptionalLStrip(tuple):
-    """A special tuple for marking a point in the state that can have
-    lstrip applied.
-    """
-
-    __slots__ = ()
-
-    # Even though it looks like a no-op, creating instances fails
-    # without this.
-    def __new__(cls, *members, **kwargs):  # type: ignore
-        return super().__new__(cls, members)
-
-
-class _Rule(t.NamedTuple):
-    pattern: t.Pattern[str]
-    tokens: t.Union[str, t.Tuple[str, ...], t.Tuple[Failure]]
-    command: t.Optional[str]
-
-
-class Lexer:
-    """Class that implements a lexer for a given environment. Automatically
-    created by the environment class, usually you don't have to do that.
-
-    Note that the lexer is not automatically bound to an environment.
-    Multiple environments can share the same lexer.
-    """
-
-    def __init__(self, environment: "Environment") -> None:
-        # shortcuts
-        e = re.escape
-
-        def c(x: str) -> t.Pattern[str]:
-            return re.compile(x, re.M | re.S)
-
-        # lexing rules for tags
-        tag_rules: t.List[_Rule] = [
-            _Rule(whitespace_re, TOKEN_WHITESPACE, None),
-            _Rule(float_re, TOKEN_FLOAT, None),
-            _Rule(integer_re, TOKEN_INTEGER, None),
-            _Rule(name_re, TOKEN_NAME, None),
-            _Rule(string_re, TOKEN_STRING, None),
-            _Rule(operator_re, TOKEN_OPERATOR, None),
-        ]
-
-        # assemble the root lexing rule. because "|" is ungreedy
-        # we have to sort by length so that the lexer continues working
-        # as expected when we have parsing rules like <% for block and
-        # <%= for variables. (if someone wants asp like syntax)
-        # variables are just part of the rules if variable processing
-        # is required.
-        root_tag_rules = compile_rules(environment)
-
-        block_start_re = e(environment.block_start_string)
-        block_end_re = e(environment.block_end_string)
-        comment_end_re = e(environment.comment_end_string)
-        variable_end_re = e(environment.variable_end_string)
-
-        # block suffix if trimming is enabled
-        block_suffix_re = "\\n?" if environment.trim_blocks else ""
-
-        self.lstrip_blocks = environment.lstrip_blocks
-
-        self.newline_sequence = environment.newline_sequence
-        self.keep_trailing_newline = environment.keep_trailing_newline
-
-        root_raw_re = (
-            rf"(?P<raw_begin>{block_start_re}(\-|\+|)\s*raw\s*"
-            rf"(?:\-{block_end_re}\s*|{block_end_re}))"
-        )
-        root_parts_re = "|".join(
-            [root_raw_re] + [rf"(?P<{n}>{r}(\-|\+|))" for n, r in root_tag_rules]
-        )
-
-        # global lexing rules
-        self.rules: t.Dict[str, t.List[_Rule]] = {
-            "root": [
-                # directives
-                _Rule(
-                    c(rf"(.*?)(?:{root_parts_re})"),
-                    OptionalLStrip(TOKEN_DATA, "#bygroup"),  # type: ignore
-                    "#bygroup",
-                ),
-                # data
-                _Rule(c(".+"), TOKEN_DATA, None),
-            ],
-            # comments
-            TOKEN_COMMENT_BEGIN: [
-                _Rule(
-                    c(
-                        rf"(.*?)((?:\+{comment_end_re}|\-{comment_end_re}\s*"
-                        rf"|{comment_end_re}{block_suffix_re}))"
-                    ),
-                    (TOKEN_COMMENT, TOKEN_COMMENT_END),
-                    "#pop",
-                ),
-                _Rule(c(r"(.)"), (Failure("Missing end of comment tag"),), None),
-            ],
-            # blocks
-            TOKEN_BLOCK_BEGIN: [
-                _Rule(
-                    c(
-                        rf"(?:\+{block_end_re}|\-{block_end_re}\s*"
-                        rf"|{block_end_re}{block_suffix_re})"
-                    ),
-                    TOKEN_BLOCK_END,
-                    "#pop",
-                ),
-            ]
-            + tag_rules,
-            # variables
-            TOKEN_VARIABLE_BEGIN: [
-                _Rule(
-                    c(rf"\-{variable_end_re}\s*|{variable_end_re}"),
-                    TOKEN_VARIABLE_END,
-                    "#pop",
-                )
-            ]
-            + tag_rules,
-            # raw block
-            TOKEN_RAW_BEGIN: [
-                _Rule(
-                    c(
-                        rf"(.*?)((?:{block_start_re}(\-|\+|))\s*endraw\s*"
-                        rf"(?:\+{block_end_re}|\-{block_end_re}\s*"
-                        rf"|{block_end_re}{block_suffix_re}))"
-                    ),
-                    OptionalLStrip(TOKEN_DATA, TOKEN_RAW_END),  # type: ignore
-                    "#pop",
-                ),
-                _Rule(c(r"(.)"), (Failure("Missing end of raw directive"),), None),
-            ],
-            # line statements
-            TOKEN_LINESTATEMENT_BEGIN: [
-                _Rule(c(r"\s*(\n|$)"), TOKEN_LINESTATEMENT_END, "#pop")
-            ]
-            + tag_rules,
-            # line comments
-            TOKEN_LINECOMMENT_BEGIN: [
-                _Rule(
-                    c(r"(.*?)()(?=\n|$)"),
-                    (TOKEN_LINECOMMENT, TOKEN_LINECOMMENT_END),
-                    "#pop",
-                )
-            ],
-        }
-
-    def _normalize_newlines(self, value: str) -> str:
-        """Replace all newlines with the configured sequence in strings
-        and template data.
-        """
-        return newline_re.sub(self.newline_sequence, value)
-
-    def tokenize(
-        self,
-        source: str,
-        name: t.Optional[str] = None,
-        filename: t.Optional[str] = None,
-        state: t.Optional[str] = None,
-    ) -> TokenStream:
-        """Calls tokeniter + tokenize and wraps it in a token stream."""
-        stream = self.tokeniter(source, name, filename, state)
-        return TokenStream(self.wrap(stream, name, filename), name, filename)
-
-    def wrap(
-        self,
-        stream: t.Iterable[t.Tuple[int, str, str]],
-        name: t.Optional[str] = None,
-        filename: t.Optional[str] = None,
-    ) -> t.Iterator[Token]:
-        """This is called with the stream as returned by `tokenize` and wraps
-        every token in a :class:`Token` and converts the value.
-        """
-        for lineno, token, value_str in stream:
-            if token in ignored_tokens:
-                continue
-
-            value: t.Any = value_str
-
-            if token == TOKEN_LINESTATEMENT_BEGIN:
-                token = TOKEN_BLOCK_BEGIN
-            elif token == TOKEN_LINESTATEMENT_END:
-                token = TOKEN_BLOCK_END
-            # we are not interested in those tokens in the parser
-            elif token in (TOKEN_RAW_BEGIN, TOKEN_RAW_END):
-                continue
-            elif token == TOKEN_DATA:
-                value = self._normalize_newlines(value_str)
-            elif token == "keyword":
-                token = value_str
-            elif token == TOKEN_NAME:
-                value = value_str
-
-                if not value.isidentifier():
-                    raise TemplateSyntaxError(
-                        "Invalid character in identifier", lineno, name, filename
-                    )
-            elif token == TOKEN_STRING:
-                # try to unescape string
-                try:
-                    value = (
-                        self._normalize_newlines(value_str[1:-1])
-                        .encode("ascii", "backslashreplace")
-                        .decode("unicode-escape")
-                    )
-                except Exception as e:
-                    msg = str(e).split(":")[-1].strip()
-                    raise TemplateSyntaxError(msg, lineno, name, filename) from e
-            elif token == TOKEN_INTEGER:
-                value = int(value_str.replace("_", ""), 0)
-            elif token == TOKEN_FLOAT:
-                # remove all "_" first to support more Python versions
-                value = literal_eval(value_str.replace("_", ""))
-            elif token == TOKEN_OPERATOR:
-                token = operators[value_str]
-
-            yield Token(lineno, token, value)
-
-    def tokeniter(
-        self,
-        source: str,
-        name: t.Optional[str],
-        filename: t.Optional[str] = None,
-        state: t.Optional[str] = None,
-    ) -> t.Iterator[t.Tuple[int, str, str]]:
-        """This method tokenizes the text and returns the tokens in a
-        generator. Use this method if you just want to tokenize a template.
-
-        .. versionchanged:: 3.0
-            Only ``\\n``, ``\\r\\n`` and ``\\r`` are treated as line
-            breaks.
-        """
-        lines = newline_re.split(source)[::2]
-
-        if not self.keep_trailing_newline and lines[-1] == "":
-            del lines[-1]
-
-        source = "\n".join(lines)
-        pos = 0
-        lineno = 1
-        stack = ["root"]
-
-        if state is not None and state != "root":
-            assert state in ("variable", "block"), "invalid state"
-            stack.append(state + "_begin")
-
-        statetokens = self.rules[stack[-1]]
-        source_length = len(source)
-        balancing_stack: t.List[str] = []
-        newlines_stripped = 0
-        line_starting = True
-
-        while True:
-            # tokenizer loop
-            for regex, tokens, new_state in statetokens:
-                m = regex.match(source, pos)
-
-                # if no match we try again with the next rule
-                if m is None:
-                    continue
-
-                # we only match blocks and variables if braces / parentheses
-                # are balanced. continue parsing with the lower rule which
-                # is the operator rule. do this only if the end tags look
-                # like operators
-                if balancing_stack and tokens in (
-                    TOKEN_VARIABLE_END,
-                    TOKEN_BLOCK_END,
-                    TOKEN_LINESTATEMENT_END,
-                ):
-                    continue
-
-                # tuples support more options
-                if isinstance(tokens, tuple):
-                    groups: t.Sequence[str] = m.groups()
-
-                    if isinstance(tokens, OptionalLStrip):
-                        # Rule supports lstrip. Match will look like
-                        # text, block type, whitespace control, type, control, ...
-                        text = groups[0]
-                        # Skipping the text and first type, every other group is the
-                        # whitespace control for each type. One of the groups will be
-                        # -, +, or empty string instead of None.
-                        strip_sign = next(g for g in groups[2::2] if g is not None)
-
-                        if strip_sign == "-":
-                            # Strip all whitespace between the text and the tag.
-                            stripped = text.rstrip()
-                            newlines_stripped = text[len(stripped) :].count("\n")
-                            groups = [stripped, *groups[1:]]
-                        elif (
-                            # Not marked for preserving whitespace.
-                            strip_sign != "+"
-                            # lstrip is enabled.
-                            and self.lstrip_blocks
-                            # Not a variable expression.
-                            and not m.groupdict().get(TOKEN_VARIABLE_BEGIN)
-                        ):
-                            # The start of text between the last newline and the tag.
-                            l_pos = text.rfind("\n") + 1
-
-                            if l_pos > 0 or line_starting:
-                                # If there's only whitespace between the newline and the
-                                # tag, strip it.
-                                if whitespace_re.fullmatch(text, l_pos):
-                                    groups = [text[:l_pos], *groups[1:]]
-
-                    for idx, token in enumerate(tokens):
-                        # failure group
-                        if token.__class__ is Failure:
-                            raise token(lineno, filename)
-                        # bygroup is a bit more complex, in that case we
-                        # yield for the current token the first named
-                        # group that matched
-                        elif token == "#bygroup":
-                            for key, value in m.groupdict().items():
-                                if value is not None:
-                                    yield lineno, key, value
-                                    lineno += value.count("\n")
-                                    break
-                            else:
-                                raise RuntimeError(
-                                    f"{regex!r} wanted to resolve the token dynamically"
-                                    " but no group matched"
-                                )
-                        # normal group
-                        else:
-                            data = groups[idx]
-
-                            if data or token not in ignore_if_empty:
-                                yield lineno, token, data
-
-                            lineno += data.count("\n") + newlines_stripped
-                            newlines_stripped = 0
-
-                # strings as token just are yielded as it.
-                else:
-                    data = m.group()
-
-                    # update brace/parentheses balance
-                    if tokens == TOKEN_OPERATOR:
-                        if data == "{":
-                            balancing_stack.append("}")
-                        elif data == "(":
-                            balancing_stack.append(")")
-                        elif data == "[":
-                            balancing_stack.append("]")
-                        elif data in ("}", ")", "]"):
-                            if not balancing_stack:
-                                raise TemplateSyntaxError(
-                                    f"unexpected '{data}'", lineno, name, filename
-                                )
-
-                            expected_op = balancing_stack.pop()
-
-                            if expected_op != data:
-                                raise TemplateSyntaxError(
-                                    f"unexpected '{data}', expected '{expected_op}'",
-                                    lineno,
-                                    name,
-                                    filename,
-                                )
-
-                    # yield items
-                    if data or tokens not in ignore_if_empty:
-                        yield lineno, tokens, data
-
-                    lineno += data.count("\n")
-
-                line_starting = m.group()[-1:] == "\n"
-                # fetch new position into new variable so that we can check
-                # if there is a internal parsing error which would result
-                # in an infinite loop
-                pos2 = m.end()
-
-                # handle state changes
-                if new_state is not None:
-                    # remove the uppermost state
-                    if new_state == "#pop":
-                        stack.pop()
-                    # resolve the new state by group checking
-                    elif new_state == "#bygroup":
-                        for key, value in m.groupdict().items():
-                            if value is not None:
-                                stack.append(key)
-                                break
-                        else:
-                            raise RuntimeError(
-                                f"{regex!r} wanted to resolve the new state dynamically"
-                                f" but no group matched"
-                            )
-                    # direct state name given
-                    else:
-                        stack.append(new_state)
-
-                    statetokens = self.rules[stack[-1]]
-                # we are still at the same position and no stack change.
-                # this means a loop without break condition, avoid that and
-                # raise error
-                elif pos2 == pos:
-                    raise RuntimeError(
-                        f"{regex!r} yielded empty string without stack change"
-                    )
-
-                # publish new function and start again
-                pos = pos2
-                break
-            # if loop terminated without break we haven't found a single match
-            # either we are at the end of the file or we have a problem
-            else:
-                # end of text
-                if pos >= source_length:
-                    return
-
-                # something went wrong
-                raise TemplateSyntaxError(
-                    f"unexpected char {source[pos]!r} at {pos}", lineno, name, filename
-                )
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/tests/test_backend_bases.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/tests/test_backend_bases.py
deleted file mode 100644
index 4cbd1bc98b6753bd0c2d7227dd8376a841fa3ddc..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/tests/test_backend_bases.py
+++ /dev/null
@@ -1,401 +0,0 @@
-import re
-
-from matplotlib import path, transforms
-from matplotlib.backend_bases import (
-    FigureCanvasBase, KeyEvent, LocationEvent, MouseButton, MouseEvent,
-    NavigationToolbar2, RendererBase)
-from matplotlib.backend_tools import RubberbandBase
-from matplotlib.figure import Figure
-from matplotlib.testing._markers import needs_pgf_xelatex
-import matplotlib.pyplot as plt
-
-import numpy as np
-import pytest
-
-
-_EXPECTED_WARNING_TOOLMANAGER = (
-    r"Treat the new Tool classes introduced in "
-    r"v[0-9]*.[0-9]* as experimental for now; "
-    "the API and rcParam may change in future versions.")
-
-
-def test_uses_per_path():
-    id = transforms.Affine2D()
-    paths = [path.Path.unit_regular_polygon(i) for i in range(3, 7)]
-    tforms_matrices = [id.rotate(i).get_matrix().copy() for i in range(1, 5)]
-    offsets = np.arange(20).reshape((10, 2))
-    facecolors = ['red', 'green']
-    edgecolors = ['red', 'green']
-
-    def check(master_transform, paths, all_transforms,
-              offsets, facecolors, edgecolors):
-        rb = RendererBase()
-        raw_paths = list(rb._iter_collection_raw_paths(
-            master_transform, paths, all_transforms))
-        gc = rb.new_gc()
-        ids = [path_id for xo, yo, path_id, gc0, rgbFace in
-               rb._iter_collection(
-                   gc, range(len(raw_paths)), offsets,
-                   transforms.AffineDeltaTransform(master_transform),
-                   facecolors, edgecolors, [], [], [False],
-                   [], 'screen')]
-        uses = rb._iter_collection_uses_per_path(
-            paths, all_transforms, offsets, facecolors, edgecolors)
-        if raw_paths:
-            seen = np.bincount(ids, minlength=len(raw_paths))
-            assert set(seen).issubset([uses - 1, uses])
-
-    check(id, paths, tforms_matrices, offsets, facecolors, edgecolors)
-    check(id, paths[0:1], tforms_matrices, offsets, facecolors, edgecolors)
-    check(id, [], tforms_matrices, offsets, facecolors, edgecolors)
-    check(id, paths, tforms_matrices[0:1], offsets, facecolors, edgecolors)
-    check(id, paths, [], offsets, facecolors, edgecolors)
-    for n in range(0, offsets.shape[0]):
-        check(id, paths, tforms_matrices, offsets[0:n, :],
-              facecolors, edgecolors)
-    check(id, paths, tforms_matrices, offsets, [], edgecolors)
-    check(id, paths, tforms_matrices, offsets, facecolors, [])
-    check(id, paths, tforms_matrices, offsets, [], [])
-    check(id, paths, tforms_matrices, offsets, facecolors[0:1], edgecolors)
-
-
-def test_canvas_ctor():
-    assert isinstance(FigureCanvasBase().figure, Figure)
-
-
-def test_get_default_filename():
-    assert plt.figure().canvas.get_default_filename() == 'image.png'
-
-
-def test_canvas_change():
-    fig = plt.figure()
-    # Replaces fig.canvas
-    canvas = FigureCanvasBase(fig)
-    # Should still work.
-    plt.close(fig)
-    assert not plt.fignum_exists(fig.number)
-
-
-@pytest.mark.backend('pdf')
-def test_non_gui_warning(monkeypatch):
-    plt.subplots()
-
-    monkeypatch.setenv("DISPLAY", ":999")
-
-    with pytest.warns(UserWarning) as rec:
-        plt.show()
-        assert len(rec) == 1
-        assert ('Matplotlib is currently using pdf, which is a non-GUI backend'
-                in str(rec[0].message))
-
-    with pytest.warns(UserWarning) as rec:
-        plt.gcf().show()
-        assert len(rec) == 1
-        assert ('Matplotlib is currently using pdf, which is a non-GUI backend'
-                in str(rec[0].message))
-
-
-@pytest.mark.parametrize(
-    "x, y", [(42, 24), (None, 42), (None, None), (200, 100.01), (205.75, 2.0)])
-def test_location_event_position(x, y):
-    # LocationEvent should cast its x and y arguments to int unless it is None.
-    fig, ax = plt.subplots()
-    canvas = FigureCanvasBase(fig)
-    event = LocationEvent("test_event", canvas, x, y)
-    if x is None:
-        assert event.x is None
-    else:
-        assert event.x == int(x)
-        assert isinstance(event.x, int)
-    if y is None:
-        assert event.y is None
-    else:
-        assert event.y == int(y)
-        assert isinstance(event.y, int)
-    if x is not None and y is not None:
-        assert re.match(
-            "x={} +y={}".format(ax.format_xdata(x), ax.format_ydata(y)),
-            ax.format_coord(x, y))
-        ax.fmt_xdata = ax.fmt_ydata = lambda x: "foo"
-        assert re.match("x=foo +y=foo", ax.format_coord(x, y))
-
-
-def test_pick():
-    fig = plt.figure()
-    fig.text(.5, .5, "hello", ha="center", va="center", picker=True)
-    fig.canvas.draw()
-
-    picks = []
-    def handle_pick(event):
-        assert event.mouseevent.key == "a"
-        picks.append(event)
-    fig.canvas.mpl_connect("pick_event", handle_pick)
-
-    KeyEvent("key_press_event", fig.canvas, "a")._process()
-    MouseEvent("button_press_event", fig.canvas,
-               *fig.transFigure.transform((.5, .5)),
-               MouseButton.LEFT)._process()
-    KeyEvent("key_release_event", fig.canvas, "a")._process()
-    assert len(picks) == 1
-
-
-def test_interactive_zoom():
-    fig, ax = plt.subplots()
-    ax.set(xscale="logit")
-    assert ax.get_navigate_mode() is None
-
-    tb = NavigationToolbar2(fig.canvas)
-    tb.zoom()
-    assert ax.get_navigate_mode() == 'ZOOM'
-
-    xlim0 = ax.get_xlim()
-    ylim0 = ax.get_ylim()
-
-    # Zoom from x=1e-6, y=0.1 to x=1-1e-5, 0.8 (data coordinates, "d").
-    d0 = (1e-6, 0.1)
-    d1 = (1-1e-5, 0.8)
-    # Convert to screen coordinates ("s").  Events are defined only with pixel
-    # precision, so round the pixel values, and below, check against the
-    # corresponding xdata/ydata, which are close but not equal to d0/d1.
-    s0 = ax.transData.transform(d0).astype(int)
-    s1 = ax.transData.transform(d1).astype(int)
-
-    # Zoom in.
-    start_event = MouseEvent(
-        "button_press_event", fig.canvas, *s0, MouseButton.LEFT)
-    fig.canvas.callbacks.process(start_event.name, start_event)
-    stop_event = MouseEvent(
-        "button_release_event", fig.canvas, *s1, MouseButton.LEFT)
-    fig.canvas.callbacks.process(stop_event.name, stop_event)
-    assert ax.get_xlim() == (start_event.xdata, stop_event.xdata)
-    assert ax.get_ylim() == (start_event.ydata, stop_event.ydata)
-
-    # Zoom out.
-    start_event = MouseEvent(
-        "button_press_event", fig.canvas, *s1, MouseButton.RIGHT)
-    fig.canvas.callbacks.process(start_event.name, start_event)
-    stop_event = MouseEvent(
-        "button_release_event", fig.canvas, *s0, MouseButton.RIGHT)
-    fig.canvas.callbacks.process(stop_event.name, stop_event)
-    # Absolute tolerance much less than original xmin (1e-7).
-    assert ax.get_xlim() == pytest.approx(xlim0, rel=0, abs=1e-10)
-    assert ax.get_ylim() == pytest.approx(ylim0, rel=0, abs=1e-10)
-
-    tb.zoom()
-    assert ax.get_navigate_mode() is None
-
-    assert not ax.get_autoscalex_on() and not ax.get_autoscaley_on()
-
-
-def test_widgetlock_zoompan():
-    fig, ax = plt.subplots()
-    ax.plot([0, 1], [0, 1])
-    fig.canvas.widgetlock(ax)
-    tb = NavigationToolbar2(fig.canvas)
-    tb.zoom()
-    assert ax.get_navigate_mode() is None
-    tb.pan()
-    assert ax.get_navigate_mode() is None
-
-
-@pytest.mark.parametrize("plot_func", ["imshow", "contourf"])
-@pytest.mark.parametrize("orientation", ["vertical", "horizontal"])
-@pytest.mark.parametrize("tool,button,expected",
-                         [("zoom", MouseButton.LEFT, (4, 6)),  # zoom in
-                          ("zoom", MouseButton.RIGHT, (-20, 30)),  # zoom out
-                          ("pan", MouseButton.LEFT, (-2, 8)),
-                          ("pan", MouseButton.RIGHT, (1.47, 7.78))])  # zoom
-def test_interactive_colorbar(plot_func, orientation, tool, button, expected):
-    fig, ax = plt.subplots()
-    data = np.arange(12).reshape((4, 3))
-    vmin0, vmax0 = 0, 10
-    coll = getattr(ax, plot_func)(data, vmin=vmin0, vmax=vmax0)
-
-    cb = fig.colorbar(coll, ax=ax, orientation=orientation)
-    if plot_func == "contourf":
-        # Just determine we can't navigate and exit out of the test
-        assert not cb.ax.get_navigate()
-        return
-
-    assert cb.ax.get_navigate()
-
-    # Mouse from 4 to 6 (data coordinates, "d").
-    vmin, vmax = 4, 6
-    # The y coordinate doesn't matter, it just needs to be between 0 and 1
-    # However, we will set d0/d1 to the same y coordinate to test that small
-    # pixel changes in that coordinate doesn't cancel the zoom like a normal
-    # axes would.
-    d0 = (vmin, 0.5)
-    d1 = (vmax, 0.5)
-    # Swap them if the orientation is vertical
-    if orientation == "vertical":
-        d0 = d0[::-1]
-        d1 = d1[::-1]
-    # Convert to screen coordinates ("s").  Events are defined only with pixel
-    # precision, so round the pixel values, and below, check against the
-    # corresponding xdata/ydata, which are close but not equal to d0/d1.
-    s0 = cb.ax.transData.transform(d0).astype(int)
-    s1 = cb.ax.transData.transform(d1).astype(int)
-
-    # Set up the mouse movements
-    start_event = MouseEvent(
-        "button_press_event", fig.canvas, *s0, button)
-    stop_event = MouseEvent(
-        "button_release_event", fig.canvas, *s1, button)
-
-    tb = NavigationToolbar2(fig.canvas)
-    if tool == "zoom":
-        tb.zoom()
-        tb.press_zoom(start_event)
-        tb.drag_zoom(stop_event)
-        tb.release_zoom(stop_event)
-    else:
-        tb.pan()
-        tb.press_pan(start_event)
-        tb.drag_pan(stop_event)
-        tb.release_pan(stop_event)
-
-    # Should be close, but won't be exact due to screen integer resolution
-    assert (cb.vmin, cb.vmax) == pytest.approx(expected, abs=0.15)
-
-
-def test_toolbar_zoompan():
-    with pytest.warns(UserWarning, match=_EXPECTED_WARNING_TOOLMANAGER):
-        plt.rcParams['toolbar'] = 'toolmanager'
-    ax = plt.gca()
-    assert ax.get_navigate_mode() is None
-    ax.figure.canvas.manager.toolmanager.trigger_tool('zoom')
-    assert ax.get_navigate_mode() == "ZOOM"
-    ax.figure.canvas.manager.toolmanager.trigger_tool('pan')
-    assert ax.get_navigate_mode() == "PAN"
-
-
-@pytest.mark.parametrize(
-    "backend", ['svg', 'ps', 'pdf',
-                pytest.param('pgf', marks=needs_pgf_xelatex)]
-)
-def test_draw(backend):
-    from matplotlib.figure import Figure
-    from matplotlib.backends.backend_agg import FigureCanvas
-    test_backend = pytest.importorskip(
-        f'matplotlib.backends.backend_{backend}'
-    )
-    TestCanvas = test_backend.FigureCanvas
-    fig_test = Figure(constrained_layout=True)
-    TestCanvas(fig_test)
-    axes_test = fig_test.subplots(2, 2)
-
-    # defaults to FigureCanvasBase
-    fig_agg = Figure(constrained_layout=True)
-    # put a backends.backend_agg.FigureCanvas on it
-    FigureCanvas(fig_agg)
-    axes_agg = fig_agg.subplots(2, 2)
-
-    init_pos = [ax.get_position() for ax in axes_test.ravel()]
-
-    fig_test.canvas.draw()
-    fig_agg.canvas.draw()
-
-    layed_out_pos_test = [ax.get_position() for ax in axes_test.ravel()]
-    layed_out_pos_agg = [ax.get_position() for ax in axes_agg.ravel()]
-
-    for init, placed in zip(init_pos, layed_out_pos_test):
-        assert not np.allclose(init, placed, atol=0.005)
-
-    for ref, test in zip(layed_out_pos_agg, layed_out_pos_test):
-        np.testing.assert_allclose(ref, test, atol=0.005)
-
-
-@pytest.mark.parametrize(
-    "key,mouseend,expectedxlim,expectedylim",
-    [(None, (0.2, 0.2), (3.49, 12.49), (2.7, 11.7)),
-     (None, (0.2, 0.5), (3.49, 12.49), (0, 9)),
-     (None, (0.5, 0.2), (0, 9), (2.7, 11.7)),
-     (None, (0.5, 0.5), (0, 9), (0, 9)),  # No move
-     (None, (0.8, 0.25), (-3.47, 5.53), (2.25, 11.25)),
-     (None, (0.2, 0.25), (3.49, 12.49), (2.25, 11.25)),
-     (None, (0.8, 0.85), (-3.47, 5.53), (-3.14, 5.86)),
-     (None, (0.2, 0.85), (3.49, 12.49), (-3.14, 5.86)),
-     ("shift", (0.2, 0.4), (3.49, 12.49), (0, 9)),  # snap to x
-     ("shift", (0.4, 0.2), (0, 9), (2.7, 11.7)),  # snap to y
-     ("shift", (0.2, 0.25), (3.49, 12.49), (3.49, 12.49)),  # snap to diagonal
-     ("shift", (0.8, 0.25), (-3.47, 5.53), (3.47, 12.47)),  # snap to diagonal
-     ("shift", (0.8, 0.9), (-3.58, 5.41), (-3.58, 5.41)),  # snap to diagonal
-     ("shift", (0.2, 0.85), (3.49, 12.49), (-3.49, 5.51)),  # snap to diagonal
-     ("x", (0.2, 0.1), (3.49, 12.49), (0, 9)),  # only x
-     ("y", (0.1, 0.2), (0, 9), (2.7, 11.7)),  # only y
-     ("control", (0.2, 0.2), (3.49, 12.49), (3.49, 12.49)),  # diagonal
-     ("control", (0.4, 0.2), (2.72, 11.72), (2.72, 11.72)),  # diagonal
-     ])
-def test_interactive_pan(key, mouseend, expectedxlim, expectedylim):
-    fig, ax = plt.subplots()
-    ax.plot(np.arange(10))
-    assert ax.get_navigate()
-    # Set equal aspect ratio to easier see diagonal snap
-    ax.set_aspect('equal')
-
-    # Mouse move starts from 0.5, 0.5
-    mousestart = (0.5, 0.5)
-    # Convert to screen coordinates ("s").  Events are defined only with pixel
-    # precision, so round the pixel values, and below, check against the
-    # corresponding xdata/ydata, which are close but not equal to d0/d1.
-    sstart = ax.transData.transform(mousestart).astype(int)
-    send = ax.transData.transform(mouseend).astype(int)
-
-    # Set up the mouse movements
-    start_event = MouseEvent(
-        "button_press_event", fig.canvas, *sstart, button=MouseButton.LEFT,
-        key=key)
-    stop_event = MouseEvent(
-        "button_release_event", fig.canvas, *send, button=MouseButton.LEFT,
-        key=key)
-
-    tb = NavigationToolbar2(fig.canvas)
-    tb.pan()
-    tb.press_pan(start_event)
-    tb.drag_pan(stop_event)
-    tb.release_pan(stop_event)
-    # Should be close, but won't be exact due to screen integer resolution
-    assert tuple(ax.get_xlim()) == pytest.approx(expectedxlim, abs=0.02)
-    assert tuple(ax.get_ylim()) == pytest.approx(expectedylim, abs=0.02)
-
-
-def test_toolmanager_remove():
-    with pytest.warns(UserWarning, match=_EXPECTED_WARNING_TOOLMANAGER):
-        plt.rcParams['toolbar'] = 'toolmanager'
-    fig = plt.gcf()
-    initial_len = len(fig.canvas.manager.toolmanager.tools)
-    assert 'forward' in fig.canvas.manager.toolmanager.tools
-    fig.canvas.manager.toolmanager.remove_tool('forward')
-    assert len(fig.canvas.manager.toolmanager.tools) == initial_len - 1
-    assert 'forward' not in fig.canvas.manager.toolmanager.tools
-
-
-def test_toolmanager_get_tool():
-    with pytest.warns(UserWarning, match=_EXPECTED_WARNING_TOOLMANAGER):
-        plt.rcParams['toolbar'] = 'toolmanager'
-    fig = plt.gcf()
-    rubberband = fig.canvas.manager.toolmanager.get_tool('rubberband')
-    assert isinstance(rubberband, RubberbandBase)
-    assert fig.canvas.manager.toolmanager.get_tool(rubberband) is rubberband
-    with pytest.warns(UserWarning,
-                      match="ToolManager does not control tool 'foo'"):
-        assert fig.canvas.manager.toolmanager.get_tool('foo') is None
-    assert fig.canvas.manager.toolmanager.get_tool('foo', warn=False) is None
-
-    with pytest.warns(UserWarning,
-                      match="ToolManager does not control tool 'foo'"):
-        assert fig.canvas.manager.toolmanager.trigger_tool('foo') is None
-
-
-def test_toolmanager_update_keymap():
-    with pytest.warns(UserWarning, match=_EXPECTED_WARNING_TOOLMANAGER):
-        plt.rcParams['toolbar'] = 'toolmanager'
-    fig = plt.gcf()
-    assert 'v' in fig.canvas.manager.toolmanager.get_tool_keymap('forward')
-    with pytest.warns(UserWarning,
-                      match="Key c changed from back to forward"):
-        fig.canvas.manager.toolmanager.update_keymap('forward', 'c')
-    assert fig.canvas.manager.toolmanager.get_tool_keymap('forward') == ['c']
-    with pytest.raises(KeyError, match="'foo' not in Tools"):
-        fig.canvas.manager.toolmanager.update_keymap('foo', 'c')
diff --git a/spaces/lazyboy450/RVCv2-Genshin/lib/infer_pack/commons.py b/spaces/lazyboy450/RVCv2-Genshin/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/lazyboy450/RVCv2-Genshin/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-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 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(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * 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 slice_segments2(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 ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/sd_api_pictures/style.css b/spaces/leogabraneth/text-generation-webui-main/extensions/sd_api_pictures/style.css
deleted file mode 100644
index 6f4994616a1d4ca52f3a8245f963ce0b7ebbb0d7..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/extensions/sd_api_pictures/style.css
+++ /dev/null
@@ -1,52 +0,0 @@
-/* Align the elements for SD_api_picture extension */
-.SDAP #sampler_box {
-  padding-top: var(--spacing-sm);
-  padding-bottom: var(--spacing-sm);
-  border: 0;
-}
-
-.SDAP #steps_box {
-  border-radius: 0 0 var(--block-radius) var(--block-radius);
-}
-
-.SDAP #sampler_col {
-  gap: 0;
-  padding: 0;
-  background-color: transparent;
-}
-
-.SDAP #sampler_row {
-    border-bottom: 0;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius) var(--block-radius) 0 0;
-    background: var(--block-background-fill);
-    gap: 0;
-}
-
-.SDAP #sampler_row .refresh-button {
-    margin-bottom: var(--spacing-sm);
-    margin-right: var(--spacing-lg);
-}
-
-.SDAP #seed_box,
-.SDAP #cfg_box {
-  padding-top: var(--spacing-md);
-}
-
-.SDAP #sampler_box span,
-.SDAP #seed_box span,
-.SDAP #cfg_box span,
-.SDAP #steps_box span {
-  margin-bottom: var(--spacing-sm);
-}
-
-.SDAP svg.dropdown-arrow {
-  flex-shrink: 0 !important;
-  margin: 0px !important;
-}
-
-.SDAP .hires_opts input[type="number"] {
-  width: 6em !important;
-}
diff --git a/spaces/lewiswu1209/MockingBird/mkgui/base/components/__init__.py b/spaces/lewiswu1209/MockingBird/mkgui/base/components/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lewiswu1209/MockingBird/synthesizer/utils/numbers.py b/spaces/lewiswu1209/MockingBird/synthesizer/utils/numbers.py
deleted file mode 100644
index 75020a0bd732830f603d7c7d250c9e087033cc24..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/synthesizer/utils/numbers.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import re
-import inflect
-
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
-_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
-_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
-_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
-_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
-_number_re = re.compile(r"[0-9]+")
-
-
-def _remove_commas(m):
-  return m.group(1).replace(",", "")
-
-
-def _expand_decimal_point(m):
-  return m.group(1).replace(".", " point ")
-
-
-def _expand_dollars(m):
-  match = m.group(1)
-  parts = match.split(".")
-  if len(parts) > 2:
-    return match + " dollars"  # Unexpected format
-  dollars = int(parts[0]) if parts[0] else 0
-  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-  if dollars and cents:
-    dollar_unit = "dollar" if dollars == 1 else "dollars"
-    cent_unit = "cent" if cents == 1 else "cents"
-    return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
-  elif dollars:
-    dollar_unit = "dollar" if dollars == 1 else "dollars"
-    return "%s %s" % (dollars, dollar_unit)
-  elif cents:
-    cent_unit = "cent" if cents == 1 else "cents"
-    return "%s %s" % (cents, cent_unit)
-  else:
-    return "zero dollars"
-
-
-def _expand_ordinal(m):
-  return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-  num = int(m.group(0))
-  if num > 1000 and num < 3000:
-    if num == 2000:
-      return "two thousand"
-    elif num > 2000 and num < 2010:
-      return "two thousand " + _inflect.number_to_words(num % 100)
-    elif num % 100 == 0:
-      return _inflect.number_to_words(num // 100) + " hundred"
-    else:
-      return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
-  else:
-    return _inflect.number_to_words(num, andword="")
-
-
-def normalize_numbers(text):
-  text = re.sub(_comma_number_re, _remove_commas, text)
-  text = re.sub(_pounds_re, r"\1 pounds", text)
-  text = re.sub(_dollars_re, _expand_dollars, text)
-  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-  text = re.sub(_ordinal_re, _expand_ordinal, text)
-  text = re.sub(_number_re, _expand_number, text)
-  return text
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Luigi Mansion 2 3ds Rom.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Luigi Mansion 2 3ds Rom.md
deleted file mode 100644
index ca94a8dabc4b1610f28a515f82c86b268e70c2b1..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Luigi Mansion 2 3ds Rom.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-<br />
-<h1>How to Download and Play Luigi's Mansion 2 on Nintendo 3DS</h1>
-<p>Luigi's Mansion 2, also known as Luigi's Mansion: Dark Moon in North America, is a sequel to the original Luigi's Mansion game for the Nintendo GameCube. In this game, Luigi has to explore five different haunted mansions and capture ghosts using his trusty Poltergust 5000 vacuum cleaner. The game features a variety of puzzles, secrets, and collectibles to discover, as well as a multiplayer mode where up to four players can team up and hunt ghosts together.</p>
-<h2>luigi mansion 2 3ds rom</h2><br /><p><b><b>Download</b> &#10004;&#10004;&#10004; <a href="https://bytlly.com/2uGx8m">https://bytlly.com/2uGx8m</a></b></p><br /><br />
-<p>If you want to play Luigi's Mansion 2 on your Nintendo 3DS, you have two options: you can either buy a physical copy of the game from a store or online retailer, or you can download a digital copy of the game from the Nintendo eShop. However, if you don't have access to either of these options, or if you want to play the game on a different device, such as a PC or a smartphone, you can also use a 3DS emulator and a ROM file.</p>
-<p>A 3DS emulator is a software that mimics the functionality of a real 3DS console, allowing you to run 3DS games on your device. A ROM file is a digital copy of a game cartridge, which contains all the data and information needed to play the game. To use a 3DS emulator and a ROM file, you need to follow these steps:</p>
-<ol>
-<li>Download a 3DS emulator of your choice. There are many options available online, such as Citra, RetroArch, or NoxPlayer. Make sure to choose an emulator that is compatible with your device and has good performance and features.</li>
-<li>Download a ROM file of Luigi's Mansion 2. You can find many sources online that offer ROM files for various games, such as Ziperto[^1^], Romhere[^2^], Archive[^3^], or EmuGen[^4^]. Make sure to choose a ROM file that matches your region and language preferences.</li>
-<li>Extract the ROM file from the compressed archive using a software like WinRAR or 7zip. You should get a file with the extension .3ds or .cia.</li>
-<li>Launch your 3DS emulator and load the ROM file. Depending on your emulator settings, you may need to configure some options, such as controls, graphics, sound, or cheats.</li>
-<li>Enjoy playing Luigi's Mansion 2 on your device!</li>
-</ol>
-<p>Note: Downloading and using ROM files may be illegal in some countries if you don't own the original game. Please check your local laws before proceeding. Also, be careful when downloading files from unknown sources, as they may contain viruses or malware. Always scan your files with an antivirus software before opening them.</p>
-<p></p>
-
-<p>If you want to learn more about Luigi's Mansion 2, here are some tips and tricks that can help you enjoy the game better:</p>
-<ul>
-<li>Use the Strobulb flash to stun ghosts and make them vulnerable to your Poltergust 5000. You can also charge up the flash for a wider and stronger effect.</li>
-<li>Use the Dark-Light Device to reveal hidden objects and secrets. You can also use it to track down Boos, which are hidden in every mansion.</li>
-<li>Collect coins, bills, and gems to increase your score and unlock upgrades for your Poltergust 5000. You can also find gold bones that can revive you if you lose all your health.</li>
-<li>Explore every room and corridor for hidden items and clues. You can use the map on the bottom screen to see where you have been and where you need to go.</li>
-<li>Pay attention to Professor E. Gadd's advice and hints. He will contact you via the Dual Scream device and give you useful information and objectives.</li>
-</ul>
-<p>Luigi's Mansion 2 is a fun and charming game that will keep you entertained for hours. Whether you play solo or with friends, you will have a blast exploring the spooky mansions and capturing ghosts. If you are a fan of Luigi or the Mario series, you should definitely give this game a try.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/partial_fc.py b/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/partial_fc.py
deleted file mode 100644
index 17e2d25715d10ba446c957e1d2528b0687ed71d5..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/partial_fc.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import logging
-import os
-
-import torch
-import torch.distributed as dist
-from torch.nn import Module
-from torch.nn.functional import normalize, linear
-from torch.nn.parameter import Parameter
-
-
-class PartialFC(Module):
-    """
-    Author: {Xiang An, Yang Xiao, XuHan Zhu} in DeepGlint,
-    Partial FC: Training 10 Million Identities on a Single Machine
-    See the original paper:
-    https://arxiv.org/abs/2010.05222
-    """
-
-    @torch.no_grad()
-    def __init__(self, rank, local_rank, world_size, batch_size, resume,
-                 margin_softmax, num_classes, sample_rate=1.0, embedding_size=512, prefix="./"):
-        """
-        rank: int
-            Unique process(GPU) ID from 0 to world_size - 1.
-        local_rank: int
-            Unique process(GPU) ID within the server from 0 to 7.
-        world_size: int
-            Number of GPU.
-        batch_size: int
-            Batch size on current rank(GPU).
-        resume: bool
-            Select whether to restore the weight of softmax.
-        margin_softmax: callable
-            A function of margin softmax, eg: cosface, arcface.
-        num_classes: int
-            The number of class center storage in current rank(CPU/GPU), usually is total_classes // world_size,
-            required.
-        sample_rate: float
-            The partial fc sampling rate, when the number of classes increases to more than 2 millions, Sampling
-            can greatly speed up training, and reduce a lot of GPU memory, default is 1.0.
-        embedding_size: int
-            The feature dimension, default is 512.
-        prefix: str
-            Path for save checkpoint, default is './'.
-        """
-        super(PartialFC, self).__init__()
-        #
-        self.num_classes: int = num_classes
-        self.rank: int = rank
-        self.local_rank: int = local_rank
-        self.device: torch.device = torch.device("cuda:{}".format(self.local_rank))
-        self.world_size: int = world_size
-        self.batch_size: int = batch_size
-        self.margin_softmax: callable = margin_softmax
-        self.sample_rate: float = sample_rate
-        self.embedding_size: int = embedding_size
-        self.prefix: str = prefix
-        self.num_local: int = num_classes // world_size + int(rank < num_classes % world_size)
-        self.class_start: int = num_classes // world_size * rank + min(rank, num_classes % world_size)
-        self.num_sample: int = int(self.sample_rate * self.num_local)
-
-        self.weight_name = os.path.join(self.prefix, "rank_{}_softmax_weight.pt".format(self.rank))
-        self.weight_mom_name = os.path.join(self.prefix, "rank_{}_softmax_weight_mom.pt".format(self.rank))
-
-        if resume:
-            try:
-                self.weight: torch.Tensor = torch.load(self.weight_name)
-                self.weight_mom: torch.Tensor = torch.load(self.weight_mom_name)
-                if self.weight.shape[0] != self.num_local or self.weight_mom.shape[0] != self.num_local:
-                    raise IndexError
-                logging.info("softmax weight resume successfully!")
-                logging.info("softmax weight mom resume successfully!")
-            except (FileNotFoundError, KeyError, IndexError):
-                self.weight = torch.normal(0, 0.01, (self.num_local, self.embedding_size), device=self.device)
-                self.weight_mom: torch.Tensor = torch.zeros_like(self.weight)
-                logging.info("softmax weight init!")
-                logging.info("softmax weight mom init!")
-        else:
-            self.weight = torch.normal(0, 0.01, (self.num_local, self.embedding_size), device=self.device)
-            self.weight_mom: torch.Tensor = torch.zeros_like(self.weight)
-            logging.info("softmax weight init successfully!")
-            logging.info("softmax weight mom init successfully!")
-        self.stream: torch.cuda.Stream = torch.cuda.Stream(local_rank)
-
-        self.index = None
-        if int(self.sample_rate) == 1:
-            self.update = lambda: 0
-            self.sub_weight = Parameter(self.weight)
-            self.sub_weight_mom = self.weight_mom
-        else:
-            self.sub_weight = Parameter(torch.empty((0, 0)).cuda(local_rank))
-
-    def save_params(self):
-        """ Save softmax weight for each rank on prefix
-        """
-        torch.save(self.weight.data, self.weight_name)
-        torch.save(self.weight_mom, self.weight_mom_name)
-
-    @torch.no_grad()
-    def sample(self, total_label):
-        """
-        Sample all positive class centers in each rank, and random select neg class centers to filling a fixed
-        `num_sample`.
-
-        total_label: tensor
-            Label after all gather, which cross all GPUs.
-        """
-        index_positive = (self.class_start <= total_label) & (total_label < self.class_start + self.num_local)
-        total_label[~index_positive] = -1
-        total_label[index_positive] -= self.class_start
-        if int(self.sample_rate) != 1:
-            positive = torch.unique(total_label[index_positive], sorted=True)
-            if self.num_sample - positive.size(0) >= 0:
-                perm = torch.rand(size=[self.num_local], device=self.device)
-                perm[positive] = 2.0
-                index = torch.topk(perm, k=self.num_sample)[1]
-                index = index.sort()[0]
-            else:
-                index = positive
-            self.index = index
-            total_label[index_positive] = torch.searchsorted(index, total_label[index_positive])
-            self.sub_weight = Parameter(self.weight[index])
-            self.sub_weight_mom = self.weight_mom[index]
-
-    def forward(self, total_features, norm_weight):
-        """ Partial fc forward, `logits = X * sample(W)`
-        """
-        torch.cuda.current_stream().wait_stream(self.stream)
-        logits = linear(total_features, norm_weight)
-        return logits
-
-    @torch.no_grad()
-    def update(self):
-        """ Set updated weight and weight_mom to memory bank.
-        """
-        self.weight_mom[self.index] = self.sub_weight_mom
-        self.weight[self.index] = self.sub_weight
-
-    def prepare(self, label, optimizer):
-        """
-        get sampled class centers for cal softmax.
-
-        label: tensor
-            Label tensor on each rank.
-        optimizer: opt
-            Optimizer for partial fc, which need to get weight mom.
-        """
-        with torch.cuda.stream(self.stream):
-            total_label = torch.zeros(
-                size=[self.batch_size * self.world_size], device=self.device, dtype=torch.long)
-            dist.all_gather(list(total_label.chunk(self.world_size, dim=0)), label)
-            self.sample(total_label)
-            optimizer.state.pop(optimizer.param_groups[-1]['params'][0], None)
-            optimizer.param_groups[-1]['params'][0] = self.sub_weight
-            optimizer.state[self.sub_weight]['momentum_buffer'] = self.sub_weight_mom
-            norm_weight = normalize(self.sub_weight)
-            return total_label, norm_weight
-
-    def forward_backward(self, label, features, optimizer):
-        """
-        Partial fc forward and backward with model parallel
-
-        label: tensor
-            Label tensor on each rank(GPU)
-        features: tensor
-            Features tensor on each rank(GPU)
-        optimizer: optimizer
-            Optimizer for partial fc
-
-        Returns:
-        --------
-        x_grad: tensor
-            The gradient of features.
-        loss_v: tensor
-            Loss value for cross entropy.
-        """
-        total_label, norm_weight = self.prepare(label, optimizer)
-        total_features = torch.zeros(
-            size=[self.batch_size * self.world_size, self.embedding_size], device=self.device)
-        dist.all_gather(list(total_features.chunk(self.world_size, dim=0)), features.data)
-        total_features.requires_grad = True
-
-        logits = self.forward(total_features, norm_weight)
-        logits = self.margin_softmax(logits, total_label)
-
-        with torch.no_grad():
-            max_fc = torch.max(logits, dim=1, keepdim=True)[0]
-            dist.all_reduce(max_fc, dist.ReduceOp.MAX)
-
-            # calculate exp(logits) and all-reduce
-            logits_exp = torch.exp(logits - max_fc)
-            logits_sum_exp = logits_exp.sum(dim=1, keepdims=True)
-            dist.all_reduce(logits_sum_exp, dist.ReduceOp.SUM)
-
-            # calculate prob
-            logits_exp.div_(logits_sum_exp)
-
-            # get one-hot
-            grad = logits_exp
-            index = torch.where(total_label != -1)[0]
-            one_hot = torch.zeros(size=[index.size()[0], grad.size()[1]], device=grad.device)
-            one_hot.scatter_(1, total_label[index, None], 1)
-
-            # calculate loss
-            loss = torch.zeros(grad.size()[0], 1, device=grad.device)
-            loss[index] = grad[index].gather(1, total_label[index, None])
-            dist.all_reduce(loss, dist.ReduceOp.SUM)
-            loss_v = loss.clamp_min_(1e-30).log_().mean() * (-1)
-
-            # calculate grad
-            grad[index] -= one_hot
-            grad.div_(self.batch_size * self.world_size)
-
-        logits.backward(grad)
-        if total_features.grad is not None:
-            total_features.grad.detach_()
-        x_grad: torch.Tensor = torch.zeros_like(features, requires_grad=True)
-        # feature gradient all-reduce
-        dist.reduce_scatter(x_grad, list(total_features.grad.chunk(self.world_size, dim=0)))
-        x_grad = x_grad * self.world_size
-        # backward backbone
-        return x_grad, loss_v
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/any_assign.h b/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/any_assign.h
deleted file mode 100644
index 4e7f2cf20bedd44001611b62ce498ea9687dd7db..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/any_assign.h
+++ /dev/null
@@ -1,55 +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>
-
-namespace thrust
-{
-namespace detail
-{
-
-
-// a type which may be assigned any other type
-struct any_assign
-{
-  inline __host__ __device__ any_assign()
-  {}
-
-  template<typename T>
-  inline __host__ __device__ any_assign(T)
-  {}
-
-  template<typename T>
-  inline __host__ __device__
-  any_assign &operator=(T)
-  {
-    if(0)
-    {
-      // trick the compiler into silencing "warning: this expression has no effect"
-      int *x = 0;
-      *x = 13;
-    } // end if
-
-    return *this;
-  }
-};
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/shuffle.h b/spaces/ma-xu/LIVE/thrust/thrust/shuffle.h
deleted file mode 100644
index 8ed156e15227047072938bc80d8d90309093671e..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/shuffle.h
+++ /dev/null
@@ -1,179 +0,0 @@
-/*
- *  Copyright 2008-2020 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file shuffle.h
- *  \brief Reorders range by a uniform random permutation
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp11_required.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust {
-
-/*! \addtogroup reordering
-*  \ingroup algorithms
-*
-*  \addtogroup shuffling
-*  \ingroup reordering
-*  \{
-*/
-
-
-/*! \p shuffle reorders the elements <tt>[first, last)</tt> by a uniform pseudorandom permutation, defined by
- *  random engine \p g.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence to shuffle.
- *  \param last The end of the sequence to shuffle.
- *  \param g A UniformRandomBitGenerator
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam RandomIterator is a random access iterator
- *  \tparam URBG is a uniform random bit generator
- *
- *  The following code snippet demonstrates how to use \p shuffle to create a random permutation
- *  using the \p thrust::host execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/shuffle.h>
- *  #include <thrust/random.h>
- *  #include <thrust/execution_policy.h>
- *  int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- *  const int N = sizeof(A)/sizeof(int);
- *  thrust::default_random_engine g;
- *  thrust::shuffle(thrust::host, A, A + N, g);
- *  // A is now {6, 5, 8, 7, 2, 1, 4, 3, 10, 9}
- *  \endcode
- *
- *  \see \p shuffle_copy
- */
-template <typename DerivedPolicy, typename RandomIterator, typename URBG>
-__host__ __device__ void shuffle(
-    const thrust::detail::execution_policy_base<DerivedPolicy>& exec,
-    RandomIterator first, RandomIterator last, URBG&& g);
-
-/*! \p shuffle reorders the elements <tt>[first, last)</tt> by a uniform pseudorandom permutation, defined by
- *  random engine \p g.
- *
- *  \param first The beginning of the sequence to shuffle.
- *  \param last The end of the sequence to shuffle.
- *  \param g A UniformRandomBitGenerator
- *
- *  \tparam RandomIterator is a random access iterator
- *  \tparam URBG is a uniform random bit generator
- *
- *  The following code snippet demonstrates how to use \p shuffle to create a random permutation.
- *
- *  \code
- *  #include <thrust/shuffle.h>
- *  #include <thrust/random.h>
- *  int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- *  const int N = sizeof(A)/sizeof(int);
- *  thrust::default_random_engine g;
- *  thrust::shuffle(A, A + N, g);
- *  // A is now {6, 5, 8, 7, 2, 1, 4, 3, 10, 9}
- *  \endcode
- *
- *  \see \p shuffle_copy
- */
-template <typename RandomIterator, typename URBG>
-__host__ __device__ void shuffle(RandomIterator first, RandomIterator last,
-                                 URBG&& g);
-
-/*! shuffle_copy differs from shuffle only in that the reordered sequence is written to different output sequences, rather than in place.
- *  \p shuffle_copy reorders the elements <tt>[first, last)</tt> by a uniform pseudorandom permutation, defined by
- *  random engine \p g.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
-
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence to shuffle.
- *  \param last The end of the sequence to shuffle.
- *  \param result Destination of shuffled sequence
- *  \param g A UniformRandomBitGenerator
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam RandomIterator is a random access iterator
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *  \tparam URBG is a uniform random bit generator
- *
- *  The following code snippet demonstrates how to use \p shuffle_copy to create a random permutation.
- *
- *  \code
- *  #include <thrust/shuffle.h>
- *  #include <thrust/random.h>
- *  #include <thrust/execution_policy.h>
- *  int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- *  int result[10];
- *  const int N = sizeof(A)/sizeof(int);
- *  thrust::default_random_engine g;
- *  thrust::shuffle_copy(thrust::host, A, A + N, result, g);
- *  // result is now {6, 5, 8, 7, 2, 1, 4, 3, 10, 9}
- *  \endcode
- *
- *  \see \p shuffle
- */
-template <typename DerivedPolicy, typename RandomIterator,
-          typename OutputIterator, typename URBG>
-__host__ __device__ void shuffle_copy(
-    const thrust::detail::execution_policy_base<DerivedPolicy>& exec,
-    RandomIterator first, RandomIterator last, OutputIterator result, URBG&& g);
-
-/*! shuffle_copy differs from shuffle only in that the reordered sequence is written to different output sequences, rather than in place.
- *\p shuffle_copy reorders the elements <tt>[first, last)</tt> by a uniform pseudorandom permutation, defined by
- *  random engine \p g.
- *
- *  \param first The beginning of the sequence to shuffle.
- *  \param last The end of the sequence to shuffle.
- *  \param result Destination of shuffled sequence
- *  \param g A UniformRandomBitGenerator
- *
- *  \tparam RandomIterator is a random access iterator
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *  \tparam URBG is a uniform random bit generator
- *
- *  The following code snippet demonstrates how to use \p shuffle_copy to create a random permutation.
- *
- *  \code
- *  #include <thrust/shuffle.h>
- *  #include <thrust/random.h>
- *  int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- *  int result[10];
- *  const int N = sizeof(A)/sizeof(int);
- *  thrust::default_random_engine g;
- *  thrust::shuffle_copy(A, A + N, result, g);
- *  // result is now {6, 5, 8, 7, 2, 1, 4, 3, 10, 9}
- *  \endcode
- *
- *  \see \p shuffle
- */
-template <typename RandomIterator, typename OutputIterator, typename URBG>
-__host__ __device__ void shuffle_copy(RandomIterator first, RandomIterator last,
-                                      OutputIterator result, URBG&& g);
-
-}  // namespace thrust
-
-#include <thrust/detail/shuffle.inl>
-#endif
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/generate.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/generate.h
deleted file mode 100644
index df77901e219d07e76cbc294299445bb0eaad0dfc..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/generate.h
+++ /dev/null
@@ -1,90 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <iterator>
-#include <thrust/system/cuda/config.h>
-
-#include <thrust/system/cuda/detail/for_each.h>
-#include <thrust/distance.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-// for_each functor
-template <class Generator>
-struct generate_f
-{
-  Generator generator;
-
-  THRUST_FUNCTION
-  generate_f(Generator generator_) : generator(generator_) {}
-
-  template<class T>
-  THRUST_DEVICE_FUNCTION void operator()(T const& value)
-  {
-    T & lvalue = const_cast<T&>(value);
-    lvalue = generator();
-  }
-};
-
-// for_each_n
-template <class Derived,
-          class OutputIt,
-          class Size,
-          class Generator>
-OutputIt __host__ __device__
-generate_n(execution_policy<Derived> &policy,
-           OutputIt                   result,
-           Size                       count,
-           Generator                  generator)
-{
-  return cuda_cub::for_each_n(policy,
-                              result,
-                              count,
-                              generate_f<Generator>(generator));
-}
-
-  // for_each
-template <class Derived,
-          class OutputIt,
-          class Generator>
-void __host__ __device__
-generate(execution_policy<Derived> &policy,
-         OutputIt                   first,
-         OutputIt                   last,
-         Generator                  generator)
-{
-  cuda_cub::generate_n(policy, first, thrust::distance(first, last), generator);
-}
-
-}    // namespace cuda_cub
-} // end namespace thrust
-#endif
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/gather.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/gather.h
deleted file mode 100644
index d587572f0b46ae1cb3dfb7b4ca19dc54d5f60b32..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/gather.h
+++ /dev/null
@@ -1,81 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename RandomAccessIterator,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator gather(thrust::execution_policy<DerivedPolicy> &exec,
-                        InputIterator                            map_first,
-                        InputIterator                            map_last,
-                        RandomAccessIterator                     input_first,
-                        OutputIterator                           result);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename RandomAccessIterator,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator gather_if(thrust::execution_policy<DerivedPolicy> &exec,
-                           InputIterator1                           map_first,
-                           InputIterator1                           map_last,
-                           InputIterator2                           stencil,
-                           RandomAccessIterator                     input_first,
-                           OutputIterator                           result);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename RandomAccessIterator,
-         typename OutputIterator,
-         typename Predicate>
-__host__ __device__
-  OutputIterator gather_if(thrust::execution_policy<DerivedPolicy> &exec,
-                           InputIterator1                           map_first,
-                           InputIterator1                           map_last,
-                           InputIterator2                           stencil,
-                           RandomAccessIterator                     input_first,
-                           OutputIterator                           result,
-                           Predicate                                pred);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/gather.inl>
-
diff --git a/spaces/matthoffner/starchat-ggml/README.md b/spaces/matthoffner/starchat-ggml/README.md
deleted file mode 100644
index 362b3ceecae868c7f8b2dca2946007b61ff81ff2..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/starchat-ggml/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: starchat-ggml
-emoji: 💫⚡️
-sdk: docker
-app_port: 8000
----
-
-# starchat-ggml-q4_0
-
-## <a href="https://huggingface.co/spaces/matthoffner/starchat-ui" target="_blank">Chatbot UI</a>
-## <a href="https://matthoffner-starchat-ggml.hf.space/docs" target="_blank">FastAPI Docs</a>
-
-
-### <a href="https://github.com/ggerganov/ggml" target="_blank">ggml</a>
-### <a href="https://github.com/marella/ctransformers" target="_blank">ctransformers</a>
diff --git a/spaces/matthoffner/starchat-ui/types/folder.ts b/spaces/matthoffner/starchat-ui/types/folder.ts
deleted file mode 100644
index 7160edea18576e227fadbd3e5d0dce455d3497e6..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/starchat-ui/types/folder.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export interface FolderInterface {
-  id: string;
-  name: string;
-  type: FolderType;
-}
-
-export type FolderType = 'chat' | 'prompt';
diff --git a/spaces/merve/data-leak/source/third_party/tfjs.js b/spaces/merve/data-leak/source/third_party/tfjs.js
deleted file mode 100644
index bc3dad50920b4c71e25844384f59cea006be4bca..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/third_party/tfjs.js
+++ /dev/null
@@ -1,3 +0,0 @@
-// @tensorflow/tfjs Copyright 2019 Google
-!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports):"function"==typeof define&&define.amd?define(["exports"],e):e(t.tf=t.tf||{})}(this,function(t){"use strict";var e=function(t,n){return(e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n])})(t,n)};function n(t,n){function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}function r(t,e,n,r){return new(n||(n=Promise))(function(i,a){function o(t){try{u(r.next(t))}catch(t){a(t)}}function s(t){try{u(r.throw(t))}catch(t){a(t)}}function u(t){t.done?i(t.value):new n(function(e){e(t.value)}).then(o,s)}u((r=r.apply(t,e||[])).next())})}function i(t,e){var n,r,i,a,o={label:0,sent:function(){if(1&i[0])throw i[1];return i[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;o;)try{if(n=1,r&&(i=2&a[0]?r.return:a[0]?r.throw||((i=r.return)&&i.call(r),0):r.next)&&!(i=i.call(r,a[1])).done)return i;switch(r=0,i&&(a=[2&a[0],i.value]),a[0]){case 0:case 1:i=a;break;case 4:return o.label++,{value:a[1],done:!1};case 5:o.label++,r=a[1],a=[0];continue;case 7:a=o.ops.pop(),o.trys.pop();continue;default:if(!(i=(i=o.trys).length>0&&i[i.length-1])&&(6===a[0]||2===a[0])){o=0;continue}if(3===a[0]&&(!i||a[1]>i[0]&&a[1]<i[3])){o.label=a[1];break}if(6===a[0]&&o.label<i[1]){o.label=i[1],i=a;break}if(i&&o.label<i[2]){o.label=i[2],o.ops.push(a);break}i[2]&&o.ops.pop(),o.trys.pop();continue}a=e.call(t,o)}catch(t){a=[6,t],r=0}finally{n=i=0}if(5&a[0])throw a[1];return{value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}var a=function(){function t(t){this.global=t,this.flags={},this.flagRegistry={},this.urlFlags={},this.populateURLFlags()}return t.prototype.setPlatform=function(t,e){null!=this.platform&&console.warn("Platform "+this.platformName+" has already been set. 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this.throwIfDisposed(),st.lessEqual(this,t)},t.prototype.lessEqualStrict=function(t){return this.throwIfDisposed(),st.lessEqualStrict(this,t)},t.prototype.greater=function(t){return this.throwIfDisposed(),st.greater(this,t)},t.prototype.greaterStrict=function(t){return this.throwIfDisposed(),st.greaterStrict(this,t)},t.prototype.greaterEqual=function(t){return this.throwIfDisposed(),st.greaterEqual(this,t)},t.prototype.greaterEqualStrict=function(t){return this.throwIfDisposed(),st.greaterEqualStrict(this,t)},t.prototype.logicalAnd=function(t){return this.throwIfDisposed(),st.logicalAnd(this,t)},t.prototype.logicalOr=function(t){return this.throwIfDisposed(),st.logicalOr(this,t)},t.prototype.logicalNot=function(){return this.throwIfDisposed(),st.logicalNot(this)},t.prototype.logicalXor=function(t){return this.throwIfDisposed(),st.logicalXor(this,t)},t.prototype.where=function(t,e){return this.throwIfDisposed(),st.where(t,this,e)},t.prototype.neg=function(){return this.throwIfDisposed(),st.neg(this)},t.prototype.ceil=function(){return this.throwIfDisposed(),st.ceil(this)},t.prototype.floor=function(){return this.throwIfDisposed(),st.floor(this)},t.prototype.sign=function(){return this.throwIfDisposed(),st.sign(this)},t.prototype.isNaN=function(){return this.throwIfDisposed(),st.isNaN(this)},t.prototype.isInf=function(){return this.throwIfDisposed(),st.isInf(this)},t.prototype.isFinite=function(){return this.throwIfDisposed(),st.isFinite(this)},t.prototype.exp=function(){return this.throwIfDisposed(),st.exp(this)},t.prototype.expm1=function(){return this.throwIfDisposed(),st.expm1(this)},t.prototype.log=function(){return this.throwIfDisposed(),st.log(this)},t.prototype.log1p=function(){return this.throwIfDisposed(),st.log1p(this)},t.prototype.sqrt=function(){return this.throwIfDisposed(),st.sqrt(this)},t.prototype.rsqrt=function(){return this.throwIfDisposed(),st.rsqrt(this)},t.prototype.square=function(){return this.throwIfDisposed(),st.square(this)},t.prototype.reciprocal=function(){return this.throwIfDisposed(),st.reciprocal(this)},t.prototype.abs=function(){return this.throwIfDisposed(),st.abs(this)},t.prototype.clipByValue=function(t,e){return this.throwIfDisposed(),st.clipByValue(this,t,e)},t.prototype.relu=function(){return this.throwIfDisposed(),st.relu(this)},t.prototype.elu=function(){return this.throwIfDisposed(),st.elu(this)},t.prototype.selu=function(){return this.throwIfDisposed(),st.selu(this)},t.prototype.leakyRelu=function(t){return void 0===t&&(t=.2),this.throwIfDisposed(),st.leakyRelu(this,t)},t.prototype.prelu=function(t){return this.throwIfDisposed(),st.prelu(this,t)},t.prototype.sigmoid=function(){return this.throwIfDisposed(),st.sigmoid(this)},t.prototype.logSigmoid=function(){return this.throwIfDisposed(),st.logSigmoid(this)},t.prototype.softplus=function(){return this.throwIfDisposed(),st.softplus(this)},t.prototype.zerosLike=function(){return this.throwIfDisposed(),st.zerosLike(this)},t.prototype.onesLike=function(){return this.throwIfDisposed(),st.onesLike(this)},t.prototype.sin=function(){return this.throwIfDisposed(),st.sin(this)},t.prototype.cos=function(){return this.throwIfDisposed(),st.cos(this)},t.prototype.tan=function(){return this.throwIfDisposed(),st.tan(this)},t.prototype.asin=function(){return this.throwIfDisposed(),st.asin(this)},t.prototype.acos=function(){return this.throwIfDisposed(),st.acos(this)},t.prototype.atan=function(){return this.throwIfDisposed(),st.atan(this)},t.prototype.sinh=function(){return this.throwIfDisposed(),st.sinh(this)},t.prototype.cosh=function(){return this.throwIfDisposed(),st.cosh(this)},t.prototype.tanh=function(){return this.throwIfDisposed(),st.tanh(this)},t.prototype.asinh=function(){return this.throwIfDisposed(),st.asinh(this)},t.prototype.acosh=function(){return this.throwIfDisposed(),st.acosh(this)},t.prototype.atanh=function(){return this.throwIfDisposed(),st.atanh(this)},t.prototype.erf=function(){return this.throwIfDisposed(),st.erf(this)},t.prototype.round=function(){return this.throwIfDisposed(),st.round(this)},t.prototype.step=function(t){return void 0===t&&(t=0),this.throwIfDisposed(),st.step(this,t)},t.prototype.softmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),st.softmax(this,t)},t.prototype.logSoftmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),st.logSoftmax(this,t)},t.prototype.resizeBilinear=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),st.image.resizeBilinear(this,t,e)},t.prototype.resizeNearestNeighbor=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),st.image.resizeNearestNeighbor(this,t,e)},t.prototype.conv1d=function(t,e,n,r,i,a){return void 0===r&&(r="NWC"),void 0===i&&(i=1),this.throwIfDisposed(),st.conv1d(this,t,e,n,r,i,a)},t.prototype.conv2d=function(t,e,n,r,i,a){return void 0===r&&(r="NHWC"),void 0===i&&(i=[1,1]),this.throwIfDisposed(),st.conv2d(this,t,e,n,r,i,a)},t.prototype.conv2dTranspose=function(t,e,n,r,i){return this.throwIfDisposed(),st.conv2dTranspose(this,t,e,n,r,i)},t.prototype.depthwiseConv2D=function(t,e,n,r,i,a){return void 0===r&&(r="NHWC"),void 0===i&&(i=[1,1]),this.throwIfDisposed(),st.depthwiseConv2d(this,t,e,n,r,i,a)},t.prototype.separableConv2d=function(t,e,n,r,i,a){return void 0===i&&(i=[1,1]),void 0===a&&(a="NHWC"),this.throwIfDisposed(),st.separableConv2d(this,t,e,n,r,i,a)},t.prototype.avgPool=function(t,e,n,r){return this.throwIfDisposed(),st.avgPool(this,t,e,n,r)},t.prototype.maxPool=function(t,e,n,r){return this.throwIfDisposed(),st.maxPool(this,t,e,n,r)},t.prototype.localResponseNormalization=function(t,e,n,r){return void 0===t&&(t=5),void 0===e&&(e=1),void 0===n&&(n=1),void 0===r&&(r=.5),st.localResponseNormalization(this,t,e,n,r)},t.prototype.pool=function(t,e,n,r,i){return this.throwIfDisposed(),st.pool(this,t,e,n,r,i)},t.prototype.variable=function(t,e,n){return void 0===t&&(t=!0),this.throwIfDisposed(),ct.variable(this,t,e,n)},t.prototype.unsortedSegmentSum=function(t,e){return this.throwIfDisposed(),st.unsortedSegmentSum(this,t,e)},t.prototype.batchToSpaceND=function(t,e){return this.throwIfDisposed(),st.batchToSpaceND(this,t,e)},t.prototype.spaceToBatchND=function(t,e){return this.throwIfDisposed(),st.spaceToBatchND(this,t,e)},t.prototype.topk=function(t,e){return void 0===t&&(t=1),void 0===e&&(e=!0),this.throwIfDisposed(),st.topk(this,t,e)},t.prototype.stridedSlice=function(t,e,n,r,i,a,o,s){return void 0===r&&(r=0),void 0===i&&(i=0),void 0===a&&(a=0),void 0===o&&(o=0),void 0===s&&(s=0),this.throwIfDisposed(),st.stridedSlice(this,t,e,n,r,i,a,o,s)},t.prototype.depthToSpace=function(t,e){return this.throwIfDisposed(),st.depthToSpace(this,t,e)},t.prototype.fft=function(){return this.throwIfDisposed(),st.spectral.fft(this)},t.prototype.ifft=function(){return this.throwIfDisposed(),st.spectral.ifft(this)},t.prototype.rfft=function(){return this.throwIfDisposed(),st.spectral.rfft(this)},t.prototype.irfft=function(){return this.throwIfDisposed(),st.spectral.irfft(this)},t}();Object.defineProperty(lt,Symbol.hasInstance,{value:function(t){return!!t&&null!=t.dataId&&null!=t.shape&&null!=t.dtype}});var ct=function(t){function e(e,n,r){void 0===n&&(n=!0);var i=t.call(this,e.shape,e.dtype,null,e.dataId)||this;i.trainable=n,i.name=r,null==i.name&&(i.name=ot().nextVariableId().toString());try{ot().registerVariable(i)}catch(t){throw ot().disposeTensor(i),t}return i}return n(e,t),e.variable=function(t,n,r,i){return void 0===n&&(n=!0),null!=i&&i!==t.dtype&&(t=t.asType(i)),new e(t,n,r)},e.prototype.assign=function(t){if(t.dtype!==this.dtype)throw new Error("dtype of the new value ("+t.dtype+") and previous value ("+this.dtype+") must match");if(!y(t.shape,this.shape))throw new Error("shape of the new value ("+t.shape+") and previous value ("+this.shape+") must match");ot().disposeTensor(this),this.dataId=t.dataId,ot().registerTensor(this)},e.prototype.dispose=function(){ot().disposeVariable(this),this.isDisposedInternal=!0},e}(lt);Object.defineProperty(ct,Symbol.hasInstance,{value:function(t){return t instanceof lt&&null!=t.assign&&t.assign instanceof Function}});var pt,ht,ft,dt,mt=ct.variable;!function(t){t.R0="R0",t.R1="R1",t.R2="R2",t.R3="R3",t.R4="R4",t.R5="R5",t.R6="R6"}(t.Rank||(t.Rank={})),function(t){t.float32="float32",t.int32="int32",t.bool="int32",t.complex64="complex64"}(pt||(pt={})),function(t){t.float32="float32",t.int32="int32",t.bool="bool",t.complex64="complex64"}(ht||(ht={})),function(t){t.float32="float32",t.int32="float32",t.bool="float32",t.complex64="complex64"}(ft||(ft={})),function(t){t.float32="complex64",t.int32="complex64",t.bool="complex64",t.complex64="complex64"}(dt||(dt={}));var gt={float32:ft,int32:pt,bool:ht,complex64:dt};function vt(t,e){if("string"===t||"string"===e){if("string"===t&&"string"===e)return"string";throw new Error("Can not upcast "+t+" with "+e)}return gt[t][e]}function yt(t){return vt(t,"int32")}function bt(t,e){if(t.dtype===e.dtype)return[t,e];var n=vt(t.dtype,e.dtype);return[t.cast(n),e.cast(n)]}function xt(t,e){f(t.dtype===e.dtype,function(){return"The dtypes of the first("+t.dtype+") and second("+e.dtype+") input must match"})}function wt(t){var e=[];return function t(e,n,r){if(null!=e)if(e instanceof lt)n.push(e);else if(i=e,Array.isArray(i)||"object"==typeof i){var i,a=e;for(var o in a){var s=a[o];r.has(s)||(r.add(s),t(s,n,r))}}}(t,e,new Set),e}var Nt,Ct=Object.freeze({makeTypesMatch:bt,assertTypesMatch:xt,isTensorInList:function(t,e){for(var n=0;n<e.length;n++)if(e[n].id===t.id)return!0;return!1},getTensorsInContainer:wt}),Et=function(){function t(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}return t.prototype.dispose=function(){for(var t in this.registeredVariables)this.registeredVariables[t].dispose()},t}(),St=function(){function t(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Et}return t.prototype.ready=function(){return r(this,void 0,void 0,function(){var t,e,n;return i(this,function(r){switch(r.label){case 0:if(null!=this.pendingBackendInit)return[2,this.pendingBackendInit.then(function(){})];if(null!=this.backendInstance)return[2];t=this.getSortedBackends(),e=0,r.label=1;case 1:return e<t.length?(n=t[e],[4,this.initializeBackend(n).success]):[3,5];case 2:return r.sent()?[4,this.setBackend(n)]:[3,4];case 3:return r.sent(),[2];case 4:return e++,[3,1];case 5:throw new Error("Could not initialize any backends, all backend initializations failed.")}})})},Object.defineProperty(t.prototype,"backend",{get:function(){if(null!=this.pendingBackendInit)throw new Error("Backend '"+this.backendName+"' has not yet been initialized. Make sure to await tf.ready() before calling other methods");if(null==this.backendInstance){var t=this.initializeBackendsAndReturnBest(),e=t.name;if(t.asyncInit)throw new Error("The highest priority backend '"+e+"' has not yet been initialized. Make sure to await tf.ready() before calling other methods");this.setBackend(e)}return this.backendInstance},enumerable:!0,configurable:!0}),t.prototype.backendNames=function(){return Object.keys(this.registryFactory)},t.prototype.findBackend=function(t){if(!(t in this.registry)){if(!(t in this.registryFactory))return null;if(this.initializeBackend(t).asyncInit)return null}return this.registry[t]},t.prototype.findBackendFactory=function(t){return t in this.registryFactory?this.registryFactory[t].factory:null},t.prototype.registerBackend=function(t,e,n){return void 0===n&&(n=1),t in this.registryFactory?(console.warn(t+" backend was already registered. Reusing existing backend factory."),!1):(this.registryFactory[t]={factory:e,priority:n},!0)},t.prototype.setBackend=function(t){return r(this,void 0,void 0,function(){var e,n,r;return i(this,function(i){switch(i.label){case 0:if(null==this.registryFactory[t])throw new Error("Backend name '"+t+"' not found in registry");return this.backendName=t,null!=this.registry[t]?[3,4]:(this.backendInstance=null,e=this.initializeBackend(t),n=e.success,e.asyncInit?[4,n]:[3,2]);case 1:return r=i.sent(),[3,3];case 2:r=n,i.label=3;case 3:if(!r)return[2,!1];i.label=4;case 4:return this.backendInstance=this.registry[t],this.profiler=new J(this.backendInstance),[2,!0]}})})},t.prototype.initializeBackend=function(t){var e=this,n=this.registryFactory[t];if(null==n)throw new Error("Cannot initialize backend "+t+", no registration found.");try{var r=n.factory();if(Promise.resolve(r)===r){var i=++this.pendingBackendInitId,a=r.then(function(n){return!(i<e.pendingBackendInitId||(e.registry[t]=n,e.pendingBackendInit=null,0))}).catch(function(n){return!(i<e.pendingBackendInitId||(e.pendingBackendInit=null,console.warn("Initialization of backend "+t+" failed"),console.warn(n.stack||n.message),1))});return this.pendingBackendInit=a,{success:a,asyncInit:!0}}return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(e){return console.warn("Initialization of backend "+t+" failed"),console.warn(e.stack||e.message),{success:!1,asyncInit:!1}}},t.prototype.removeBackend=function(t){if(!(t in this.registryFactory))throw new Error(t+" backend not found in registry");this.backendName===t&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,t in this.registry&&(this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)},t.prototype.getSortedBackends=function(){var t=this;if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort(function(e,n){return t.registryFactory[n].priority-t.registryFactory[e].priority})},t.prototype.initializeBackendsAndReturnBest=function(){for(var t=this.getSortedBackends(),e=0;e<t.length;e++){var n=t[e],r=this.initializeBackend(n),i=r.success,a=r.asyncInit;if(a||i)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")},t.prototype.moveData=function(t,e){this.write(t,e,this.readSync(e))},t.prototype.tidy=function(t,e){var n,r=this,i=null;if(null==e){if("function"!=typeof t)throw new Error("Please provide a function to tidy()");e=t}else{if("string"!=typeof t&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof e)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");i=t}return this.scopedRun(function(){return r.startScope(i)},function(){return r.endScope(n)},function(){return(n=e())instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n})},t.prototype.scopedRun=function(t,e,n){t();try{var r=n();return e(),r}catch(t){throw e(),t}},t.prototype.nextTensorId=function(){return t.nextTensorId++},t.prototype.nextVariableId=function(){return t.nextVariableId++},t.prototype.clone=function(t){var e=lt.make(t.shape,{dataId:t.dataId},t.dtype);return this.addTapeNode([t],e,function(t){return[t.toFloat()]}),e},t.prototype.runKernel=function(t,e,n){var r,i=this,a=[],o=this.isTapeOn(),s=null!=this.state.activeScope?this.state.activeScope.name:"",u=function(t){o&&(a=t.map(function(t){return i.keep(i.clone(t))}))},l=this.state.numBytes,c=this.state.numTensors;if(this.scopedRun(function(){return i.state.kernelDepth++},function(){return i.state.kernelDepth--},function(){r=i.ENV.getBool("DEBUG")?i.profiler.profileKernel(s,e,function(){return t(i.backend,u)}):t(i.backend,u)}),o){var p={id:this.state.nextTapeNodeId++,name:s,inputs:e,outputs:Array.isArray(r)?r:[r],saved:a};null!=n&&(p.gradient=function(t){return n(t,a)}),this.state.activeTape.push(p)}return this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-l,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-c,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map(function(t){return e[t].shape}),outputShape:Array.isArray(r)?r.map(function(t){return t.shape}):r.shape}),r},t.prototype.registerTensor=function(t,e){var n=this.state.tensorInfo.has(t.dataId)?this.state.tensorInfo.get(t.dataId).refCount:0;if(this.state.numTensors++,"string"===t.dtype&&this.state.numStringTensors++,0===n){this.state.numDataBuffers++;var r=0;"complex64"!==t.dtype&&"string"!==t.dtype&&(r=t.size*_(t.dtype)),this.state.tensorInfo.set(t.dataId,{backend:null!=e?e:this.backend,dtype:t.dtype,shape:t.shape,bytes:r,refCount:0}),this.state.numBytes+=r,null!=e?e.register(t.dataId,t.shape,t.dtype):this.backend.register(t.dataId,t.shape,t.dtype)}this.state.tensorInfo.get(t.dataId).refCount++,t instanceof ct||this.track(t)},t.prototype.registerVariable=function(t){if(null!=this.state.registeredVariables[t.name])throw new Error("Variable with name "+t.name+" was already registered");this.state.registeredVariables[t.name]=t},t.prototype.disposeTensor=function(t){if(this.state.tensorInfo.has(t.dataId)){this.state.numTensors--,"string"===t.dtype&&this.state.numStringTensors--;var e=this.state.tensorInfo.get(t.dataId);e.refCount<=1?("complex64"!==t.dtype&&(this.state.numBytes-=e.bytes),this.state.numDataBuffers--,e.backend.disposeData(t.dataId),this.state.tensorInfo.delete(t.dataId)):this.state.tensorInfo.get(t.dataId).refCount--}},t.prototype.disposeVariables=function(){for(var t in this.state.registeredVariables){var e=this.state.registeredVariables[t];this.disposeVariable(e)}},t.prototype.disposeVariable=function(t){this.disposeTensor(t),null!=this.state.registeredVariables[t.name]&&delete this.state.registeredVariables[t.name]},t.prototype.memory=function(){var t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,null==t.reasons&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t},t.prototype.profile=function(t){return r(this,void 0,void 0,function(){var e,n;return i(this,function(r){return this.state.profiling=!0,e=this.state.numBytes,n=this.state.numTensors,this.state.activeProfile.kernels=[],this.state.activeProfile.result=t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max.apply(Math,this.state.activeProfile.kernels.map(function(t){return t.totalBytesSnapshot})),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n,[2,this.state.activeProfile]})})},t.prototype.isTapeOn=function(){return this.state.gradientDepth>0&&0===this.state.kernelDepth},t.prototype.addTapeNode=function(t,e,n){var r={};t.forEach(function(t,e){r[e]=t});var i={id:this.state.nextTapeNodeId++,name:this.state.activeScope.name,inputs:r,outputs:[e],gradient:function(t){var e={};return n(t).forEach(function(t,n){e[n]=function(){return t}}),e}};this.state.activeTape.push(i)},t.prototype.keep=function(t){return t.kept=!0,t},t.prototype.startTape=function(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++},t.prototype.endTape=function(){this.state.gradientDepth--},t.prototype.startScope=function(t){var e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e},t.prototype.endScope=function(t){for(var e=this,n=wt(t),r=new Set(n.map(function(t){return t.id})),i=0;i<this.state.activeScope.track.length;i++){var a=this.state.activeScope.track[i];a.kept||r.has(a.id)||a.dispose()}var o=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],n.forEach(function(t){t.kept||t.scopeId!==o.id||e.track(t)})},t.prototype.gradients=function(t,e,n,r){var i=this;if(void 0===r&&(r=!1),f(e.length>0,function(){return"gradients() received an empty list of xs."}),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 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2===t.ENV.getNumber("WEBGL_VERSION")?(r=h.R32F,i=h.R16F,a=h.RGBA16F,o=h.RGBA32F,s=h.RED,u=4,l=1,c=h.HALF_FLOAT,p=h.FLOAT):(r=e.RGBA,i=e.RGBA,a=e.RGBA,o=h.RGBA,s=e.RGBA,u=4,l=4,c=null!=n?n.HALF_FLOAT_OES:null,p=e.FLOAT),{internalFormatFloat:r,internalFormatHalfFloat:i,internalFormatPackedHalfFloat:a,internalFormatPackedFloat:o,textureFormatFloat:s,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:u,defaultNumChannels:l,textureTypeHalfFloat:c,textureTypeFloat:p}}function Bt(t,e,n){var r=n();return e&&function(t){var e=t.getError();if(e!==t.NO_ERROR)throw new Error("WebGL Error: "+jt(t,e))}(t),r}!function(t){t[t.RENDER=0]="RENDER",t[t.UPLOAD=1]="UPLOAD",t[t.PIXELS=2]="PIXELS",t[t.DOWNLOAD=3]="DOWNLOAD"}(At||(At={})),function(t){t[t.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",t[t.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",t[t.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",t[t.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",t[t.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"}(Rt||(Rt={}));var 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vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n";function vi(t,e,n,r){var i=[];t.forEach(function(t){var e=v(t.shapeInfo.logicalShape);t.shapeInfo.isUniform?i.push("uniform float "+t.name+(e>1?"["+e+"]":"")+";"):(i.push("uniform sampler2D "+t.name+";"),i.push("uniform int offset"+t.name+";"))});var a,o,s=i.join("\n"),u=t.map(function(t){return function(t,e,n){void 0===n&&(n=!1);var r="";r+=n?function t(e){var n,r,i;switch(e.shapeInfo.logicalShape.length){case 0:return n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),i=fi(),"\n    vec4 "+r+"() {\n      return "+i.texture2D+"("+n+", halfCR);\n    }\n  ";case 1:return function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),r=t.shapeInfo.texShape,i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=fi();return"\n    vec4 "+n+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+i[0]+", "+i[1]+", index);\n      return "+a.texture2D+"("+e+", uv);\n    }\n  "}(e);case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t.shapeInfo.texShape,a=i[0],o=i[1],s=fi();if(null!=i&&y(e,i))return"\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+o+".0, "+a+".0);\n\n        return "+s.texture2D+"("+n+", uv);\n      }\n    ";var u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=Math.ceil(e[1]/2);return"\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+l+", "+u[0]+", "+u[1]+", row, col);\n      return "+s.texture2D+"("+n+", uv);\n    }\n  "}(e);case 3:return function(e){var n=e.shapeInfo.logicalShape,r=e.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(1===n[0]){var s=n.slice(1),u=Si(e,s);return"\n        "+t(u)+"\n        vec4 "+i+"(int b, int row, int col) {\n          return "+i+"("+ki(["b","row","col"],[1,2])+");\n        }\n      "}var l=o[0],c=o[1],p=Math.ceil(n[2]/2),h=p*Math.ceil(n[1]/2),f=fi();return"\n    vec4 "+i+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+l+", "+c+", "+h+", "+p+", b, row, col);\n      return "+f.texture2D+"("+r+", uv);\n    }\n  "}(e);default:return function(t){for(var e=t.shapeInfo.logicalShape,n=e.length,r=t.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=o[0],u=o[1],l=Math.ceil(e[n-1]/2),c=l*Math.ceil(e[n-2]/2),p="int b, int row, int col",h="b * "+c+" + (row / 2) * "+l+" + (col / 2)",f=2;f<n-1;f++)p="int b"+f+", "+p,c*=e[n-f-1],h="b"+f+" * "+c+" + "+h;var d=fi();return"\n    vec4 "+i+"("+p+") {\n      int index = "+h+";\n      int texR = index / "+u+";\n      int texC = index - texR * "+u+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+u+", "+s+");\n      return "+d.texture2D+"("+r+", uv);\n    }\n  "}(e)}}(t):function t(e){var n=e.shapeInfo.logicalShape;switch(n.length){case 0:return function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(t.shapeInfo.isUniform)return"float "+n+"() {return "+e+";}";var r=t.shapeInfo.texShape,i=r[0],a=r[1];if(1===i&&1===a)return"\n      float "+n+"() {\n        return sampleTexture("+e+", halfCR);\n      }\n    ";var o=t.shapeInfo.texShape,s=o[0],u=o[1],l=Ni(e);return"\n    float "+n+"() {\n      vec2 uv = uvFromFlat("+s+", "+u+", "+l+");\n      return sampleTexture("+e+", uv);\n    }\n  "}(e);case 1:return function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(t.shapeInfo.isUniform)return"\n      float "+n+"(int index) {\n        "+Ci(t)+"\n      }\n    ";var r=t.shapeInfo.texShape,i=r[0],a=r[1];if(1===a&&1===i)return"\n      float "+n+"(int index) {\n        return sampleTexture("+e+", halfCR);\n      }\n    ";var o=Ni(e);return 1===a?"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index + "+o+") + 0.5) / "+i+".0);\n        return sampleTexture("+e+", uv);\n      }\n    ":1===i?"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index + "+o+") + 0.5) / "+a+".0, 0.5);\n        return sampleTexture("+e+", uv);\n      }\n    ":"\n    float "+n+"(int index) {\n      vec2 uv = uvFromFlat("+i+", "+a+", index + "+o+");\n      return sampleTexture("+e+", uv);\n    }\n  "}(e);case 2:return function(e){var n=e.shapeInfo.logicalShape,r=e.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(null!=a&&y(n,a)){var o=a[0],s=a[1];return"\n    float "+i+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+s+".0, "+o+".0);\n      return sampleTexture("+r+", uv);\n    }\n  "}var u=k(n),l=u.newShape,c=u.keptDims,p=l;if(p.length<n.length){var h=Si(e,p);return"\n      "+t(h)+"\n      float "+i+"(int row, int col) {\n        return "+i+"("+ki(["row","col"],c)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+i+"(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2("+n[1]+", 1)));\n        "+Ci(e)+"\n      }\n    ";var f=a[0],d=a[1],m=Ni(r);return 1===d?"\n    float "+i+"(int row, int col) {\n      float index = dot(vec3(row, col, "+m+"), vec3("+n[1]+", 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+f+".0);\n      return sampleTexture("+r+", uv);\n    }\n  ":1===f?"\n    float "+i+"(int row, int col) {\n      float index = dot(vec3(row, col, "+m+"), vec3("+n[1]+", 1, 1));\n      vec2 uv = vec2((index + 0.5) / "+d+".0, 0.5);\n      return sampleTexture("+r+", uv);\n    }\n  ":"\n  float "+i+"(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * "+n[1]+" + col + "+m+";\n    vec2 uv = uvFromFlat("+f+", "+d+", index);\n    return sampleTexture("+r+", uv);\n  }\n"}(e);case 3:return function(e){var n=e.shapeInfo.logicalShape,r=e.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],s=k(n),u=s.newShape,l=s.keptDims,c=u;if(c.length<n.length){var p=Si(e,c);return"\n        "+t(p)+"\n        float "+i+"(int row, int col, int depth) {\n          return "+i+"("+ki(["row","col","depth"],l)+");\n        }\n      "}if(e.shapeInfo.isUniform)return"\n      float "+i+"(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3("+a+", "+o+", 1)));\n        "+Ci(e)+"\n      }\n    ";var h=e.shapeInfo.texShape,f=h[0],d=h[1],m=e.shapeInfo.flatOffset;if(d===a&&null==m)return"\n        float "+i+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+o+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+d+".0, "+f+".0);\n          return sampleTexture("+r+", uv);\n        }\n      ";if(d===o&&null==m)return"\n    float "+i+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+n[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+d+".0, "+f+".0);\n      return sampleTexture("+r+", uv);\n    }\n  ";var g=Ni(r);return"\n      float "+i+"(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * "+a+" + col * "+o+" + depth + "+g+";\n        vec2 uv = uvFromFlat("+f+", "+d+", index);\n        return sampleTexture("+r+", uv);\n      }\n  "}(e);case 4:return function(e){var n=e.shapeInfo.logicalShape,r=e.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[3],o=n[2]*a,s=n[1]*o,u=k(n),l=u.newShape,c=u.keptDims;if(l.length<n.length){var p=Si(e,l);return"\n      "+t(p)+"\n      float "+i+"(int row, int col, int depth, int depth2) {\n        return "+i+"("+ki(["row","col","depth","depth2"],c)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+i+"(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4("+s+", "+o+", "+a+", 1)));\n        "+Ci(e)+"\n      }\n    ";var h=e.shapeInfo.flatOffset,f=e.shapeInfo.texShape,d=f[0],m=f[1];if(m===s&&null==h)return"\n      float "+i+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3("+o+", "+a+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+m+".0, "+d+".0);\n        return sampleTexture("+r+", uv);\n      }\n    ";if(m===a&&null==h)return"\n      float "+i+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+n[1]*n[2]+", "+n[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+m+".0, "+d+".0);\n        return sampleTexture("+r+", uv);\n      }\n    ";var g=Ni(r);return"\n    float "+i+"(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * "+s+" + col * "+o+" +\n          depth * "+a+" + depth2;\n      vec2 uv = uvFromFlat("+d+", "+m+", index + "+g+");\n      return sampleTexture("+r+", uv);\n    }\n  "}(e);case 5:return function(e){var n=e.shapeInfo.logicalShape,r=e.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[4],o=n[3]*a,s=n[2]*o,u=n[1]*s,l=k(n),c=l.newShape,p=l.keptDims;if(c.length<n.length){var h=Si(e,c);return"\n      "+t(h)+"\n      float "+i+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+i+"("+ki(["row","col","depth","depth2","depth3"],p)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+i+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+o+", "+a+")) +\n          depth3;\n        "+Ci(e)+"\n      }\n    ";var f=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,m=d[0],g=d[1];if(g===u&&null==f)return"\n      float "+i+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4("+s+", "+o+", "+a+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+g+".0, "+m+".0);\n        return sampleTexture("+r+", uv);\n      }\n    ";if(g===a&&null==f)return"\n      float "+i+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+n[1]*n[2]*n[3]+",\n               "+n[2]*n[3]+", "+n[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+g+".0, "+m+".0);\n        return sampleTexture("+r+", uv);\n      }\n    ";var v=Ni(r);return"\n    float "+i+"(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * "+u+" + col * "+s+" + depth * "+o+" +\n          depth2 * "+a+" + depth3 + "+v+";\n      vec2 uv = uvFromFlat("+m+", "+g+", index);\n      return sampleTexture("+r+", uv);\n    }\n  "}(e);case 6:return function(e){var n=e.shapeInfo.logicalShape,r=e.name,i="get"+r.charAt(0).toUpperCase()+r.slice(1),a=k(n),o=a.newShape,s=a.keptDims;if(o.length<n.length){var u=Si(e,o);return"\n      "+t(u)+"\n      float "+i+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+i+"("+ki(["row","col","depth","depth2","depth3","depth4"],s)+");\n      }\n    "}var l=n[5],c=n[4]*l,p=n[3]*c,h=n[2]*p,f=n[1]*h;if(e.shapeInfo.isUniform)return"\n      float "+i+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4("+f+", "+h+", "+p+", "+c+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+l+", 1)));\n        "+Ci(e)+"\n      }\n    ";var d=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,g=m[0],v=m[1];if(v===f&&null==d)return"\n      float "+i+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4("+h+", "+p+", "+c+", "+l+")) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+v+".0, "+g+".0);\n        return sampleTexture("+r+", uv);\n      }\n    ";if(v===l&&null==d)return"\n      float "+i+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4("+n[1]*n[2]*n[3]*n[4]+",\n               "+n[2]*n[3]*n[4]+",\n               "+n[3]*n[4]+",\n               "+n[4]+")) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+v+".0, "+g+".0);\n        return sampleTexture("+r+", uv);\n      }\n    ";var y=Ni(r);return"\n    float "+i+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * "+f+" + col * "+h+" + depth * "+p+" +\n          depth2 * "+c+" + depth3 * "+l+" + depth4 + "+y+";\n      vec2 uv = uvFromFlat("+g+", "+v+", index);\n      return sampleTexture("+r+", uv);\n    }\n  "}(e);default:throw new Error(n.length+"-D input sampling is not yet supported")}}(t);var i=t.shapeInfo.logicalShape,a=e.logicalShape;return i.length<=a.length&&(r+=n?function(t,e){var n,r=t.name,i=r.charAt(0).toUpperCase()+r.slice(1),a="get"+i+"AtOutCoords",o=t.shapeInfo.logicalShape.length,s=e.logicalShape.length,u=_r(t.shapeInfo.logicalShape,e.logicalShape),l=Ei(s),c=s-o,p=["x","y","z","w","u","v"];n=0===o?"":s<2&&u.length>=1?"coords = 0;":u.map(function(t){return"coords."+p[t+c]+" = 0;"}).join("\n");var h;h=s<2&&o>0?"coords":t.shapeInfo.logicalShape.map(function(t,e){return"coords."+p[e+c]}).join(", ");var f="return outputValue;",d=1===v(t.shapeInfo.logicalShape),m=1===v(e.logicalShape);if(1!==o||d||m){if(d&&!m)f=1===s?"\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      ":"\n        return vec4(outputValue.x);\n      ";else if(u.length){var g=o-2,y=o-1;u.indexOf(g)>-1&&u.indexOf(y)>-1?f="return vec4(outputValue.x);":u.indexOf(g)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":u.indexOf(y)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}}else f="\n      return vec4(outputValue.xy, outputValue.xy);\n    ";return"\n    vec4 "+a+"() {\n      "+l+" coords = getOutputCoords();\n      "+n+"\n      vec4 outputValue = get"+i+"("+h+");\n      "+f+"\n    }\n  "}(t,e):function(t,e){var n=t.name,r=n.charAt(0).toUpperCase()+n.slice(1),i="get"+r+"AtOutCoords",a=e.texShape,o=t.shapeInfo.texShape,s=t.shapeInfo.logicalShape.length,u=e.logicalShape.length;if(!t.shapeInfo.isUniform&&s===u&&null==t.shapeInfo.flatOffset&&y(o,a))return"\n      float "+i+"() {\n        return sampleTexture("+n+", resultUV);\n      }\n    ";var l=Ei(u),c=_r(t.shapeInfo.logicalShape,e.logicalShape),p=u-s,h=["x","y","z","w","u","v"];return"\n    float "+i+"() {\n      "+l+" coords = getOutputCoords();\n      "+(0===s?"":u<2&&c.length>=1?"coords = 0;":c.map(function(t){return"coords."+h[t+p]+" = 0;"}).join("\n"))+"\n      return get"+r+"("+(u<2&&s>0?"coords":t.shapeInfo.logicalShape.map(function(t,e){return"coords."+h[e+p]}).join(", "))+");\n    }\n  "}(t,e)),r}(t,e,r)}).join("\n"),l=e.texShape,c=fi(),p="\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return "+c.texture2D+"(textureSampler, uv).r;\n    }\n  ",h=function(t){return t.version+"\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    "+t.varyingFs+" vec2 resultUV;\n    "+t.defineOutput+"\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    #define isnan(value) isnan_custom(value)\n    "+t.defineSpecialNaN+"\n    bvec4 isnan_custom(vec4 val) {\n      return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n    }\n\n    "+t.defineSpecialInf+"\n    "+t.defineRound+"\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    "+yi+"\n    "+bi+"\n    "+xi+"\n  "}(c);return e.isPacked?(a=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return 1===n[0]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+n[1]+".0);\n      }\n    ":1===n[1]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+n[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      return 2 * (resTexRC.x * "+n[1]+" + resTexRC.y);\n    }\n  "}(0,e);case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y(t,e))return"\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(t[1]/2);return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}(t,e);case 3:return n=t,r=e,i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],o=(a=Math.ceil(n[2]/2))*Math.ceil(n[1]/2),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+i[0]+", "+i[1]+"));\n      int index = resTexRC.x * "+i[1]+" + resTexRC.y;\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+a+");\n      int c = imod(index, "+a+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";default:return function(t,e){for(var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],r=Math.ceil(t[t.length-1]/2),i=r*Math.ceil(t[t.length-2]/2),a=i,o="",s="b, r, c",u=2;u<t.length-1;u++)o="\n      int b"+u+" = index / "+(a*=t[t.length-u-1])+";\n      index -= b"+u+" * "+a+";\n    "+o,s="b"+u+", "+s;return"\n    ivec"+t.length+" getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      "+o+"\n\n      int b = index / "+i+";\n      index -= b * "+i+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec"+t.length+"("+s+");\n    }\n  "}(t,e)}var n,r,i,a,o}(e.logicalShape,l),o="\n    void setOutput(vec4 val) {\n      "+c.output+" = val;\n    }\n  "):(a=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return function(t,e){return 1===e[0]?"\n      int getOutputCoords() {\n        return int(resultUV.x * "+e[1]+".0);\n      }\n    ":1===e[1]?"\n      int getOutputCoords() {\n        return int(resultUV.y * "+e[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+e[0]+", "+e[1]+"));\n      return resTexRC.x * "+e[1]+" + resTexRC.y;\n    }\n  "}(0,e);case 2:return function(t,e){return y(t,e)?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+e[0]+", "+e[1]+"));\n      }\n    ":1===t[1]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+e[0]+", "+e[1]+"));\n        int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":1===t[0]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+e[0]+", "+e[1]+"));\n        int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+e[0]+", "+e[1]+"));\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n      int r = index / "+t[1]+";\n      int c = index - r * "+t[1]+";\n      return ivec2(r, c);\n    }\n  "}(t,e);case 3:return n=e,r=di(["r","c","d"],t),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      "+r+"\n      return ivec3(r, c, d);\n    }\n  ";case 4:return function(t,e){var n=di(["r","c","d","d2"],t);return"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+e[0]+", "+e[1]+"));\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n      "+n+"\n      return ivec4(r, c, d, d2);\n    }\n  "}(t,e);case 5:return function(t,e){var n=di(["r","c","d","d2","d3"],t);return"\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+e[0]+",\n                             "+e[1]+"));\n\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  "}(t,e);case 6:return function(t,e){var n=di(["r","c","d","d2","d3","d4"],t);return"\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+e[0]+", "+e[1]+"));\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  "}(t,e);default:throw new Error(t.length+"-D output sampling is not yet supported")}var n,r}(e.logicalShape,l),o="\n    void setOutput(float val) {\n      "+c.output+" = vec4(val, 0, 0, 0);\n    }\n  "),r&&(h+=wi),[h,p,o,s,a,u,n].join("\n")}var yi="\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",bi="\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",xi="\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",wi="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function Ni(t){return"offset"+t}function Ci(t){var e=t.name,n=v(t.shapeInfo.logicalShape);return n<2?"return "+e+";":"\n    for (int i = 0; i < "+n+"; i++) {\n      if (i == index) {\n        return "+e+"[i];\n      }\n    }\n  "}function Ei(t){if(t<=1)return"int";if(2===t)return"ivec2";if(3===t)return"ivec3";if(4===t)return"ivec4";if(5===t)return"ivec5";if(6===t)return"ivec6";throw Error("GPU for rank "+t+" is not yet supported")}function Si(t,e){var n=JSON.parse(JSON.stringify(t));return n.shapeInfo.logicalShape=e,n}function ki(t,e){return e.map(function(e){return t[e]}).join(", ")}var Ii=function(t,e,n,r){this.variableNames=["A"],this.usesPackedTextures=!0,f(t.length>2,function(){return"Packed arg"+(n.charAt(0).toUpperCase()+n.slice(1))+" supports only inputs with rank above 2."});var i=t[t.length-1],a=Math.ceil(i/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),r||this.variableNames.push("bestIndicesA");var o,s,u=this.outputShape,l=u.length,c=Ei(l),p=hi("coords",l);if(1===a){var h=Ei(s=l+1);o="\n        "+h+" sourceLocR = "+h+"("+p.join()+", 0);\n        ++"+p[l-1]+";\n        "+h+" sourceLocG = "+h+"("+p.join()+", 0);\n        ++"+p[l-2]+";\n        "+h+" sourceLocA = "+h+"("+p.join()+", 0);\n        --"+p[l-1]+";\n        "+h+" sourceLocB = "+h+"("+p.join()+", 0);\n        --"+p[l-2]+";"}else s=l,o="\n        "+c+" sourceLocR = coords;\n        ++"+p[l-1]+";\n        "+c+" sourceLocG = coords;\n        ++"+p[l-2]+";\n        "+c+" sourceLocA = coords;\n        --"+p[l-1]+";\n        "+c+" sourceLocB = coords;\n        --"+p[l-2]+";";var d=["x","y","z","w","u","v"].slice(0,s),m="."+d[s-1],g=d.map(function(t){return"int "+t}),v=hi("sourceLocR",s-1).concat("inIdx.r"),y=hi("sourceLocG",s-1).concat("inIdx.g"),b=hi("sourceLocB",s-1).concat("inIdx.b"),x=hi("sourceLocA",s-1).concat("inIdx.a"),w="max"===n?"greaterThan":"lessThan",N=r?"":"\n          inIdx = round(vec4(getBestIndicesAChannel("+v.join()+"),\n                             getBestIndicesAChannel("+y.join()+"),\n                             getBestIndicesAChannel("+b.join()+"),\n                             getBestIndicesAChannel("+x.join()+")));",C="vec4(\n            getAChannel("+v.join()+"),\n            hasNextCol ? getAChannel("+y.join()+") : 0.,\n            hasNextRow ? getAChannel("+b.join()+") : 0.,\n            hasNextRow && hasNextCol ? getAChannel("+x.join()+") : 0.)",E=r?"":"\n      float getBestIndicesAChannel("+g.join()+") {\n        return getChannel(getBestIndicesA("+d.join()+"),\n                                          vec2("+d.slice(-2).join()+"));\n      }";this.userCode="\n      float getAChannel("+g.join()+") {\n        return getChannel(getA("+d.join()+"),\n                               vec2("+d.slice(-2).join()+"));\n      }\n      "+E+"\n      void main() {\n        "+c+" coords = getOutputCoords();\n        bool hasNextCol = "+p[l-1]+" < "+(u[l-1]-1)+";\n        bool hasNextRow = "+p[l-2]+" < "+(u[l-2]-1)+";\n        "+o+"\n        ivec4 srcIdx = ivec4(sourceLocR"+m+", sourceLocG"+m+",\n          sourceLocB"+m+", sourceLocA"+m+") * "+e+";\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = "+C+";\n\n        for (int i = 0; i < "+e+"; i++) {\n          inIdx = srcIdx;\n          "+N+"\n          vec4 candidate = "+C+";\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4("+w+"(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    "},Ai=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,i=t.strideWidth,a=t.dilationHeight,o=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=s-1-t.padInfo.top,c=u-1-t.padInfo.left,p=1/(e*n);this.userCode="\n      const ivec2 pads = ivec2("+l+", "+c+");\n      const float avgMultiplier = float("+p+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+a+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+o+") {\n            float dyC = float(dyCCorner + wC) / "+i+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Ri=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,i=t.strideDepth,a=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,h=t.effectiveFilterWidth,f=c-1-t.padInfo.front,d=p-1-t.padInfo.top,m=h-1-t.padInfo.left,g=1/(e*n*r);this.userCode="\n      const ivec3 pads = ivec3("+f+", "+d+", "+m+");\n      const float avgMultiplier = float("+g+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < "+c+";\n            wD += "+s+") {\n          float dyD = float(dyDCorner + wD) / "+i+".0;\n\n          if (dyD < 0.0 || dyD >= "+t.outDepth+".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < "+p+";\n              wR += "+u+") {\n            float dyR = float(dyRCorner + wR) / "+a+".0;\n\n            if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < "+h+";\n                wC += "+l+") {\n              float dyC = float(dyCCorner + wC) / "+o+".0;\n\n              if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Ti=function(t,e,n,r,i,a){this.outputShape=[],this.variableNames=["x","mean","variance"],Mr(t,e),Mr(t,n);var o="0.0";null!=r&&(Mr(t,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");var s="1.0";null!=i&&(Mr(t,i),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+o+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+a+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    "},Di=function(t,e,n,r,i,a){this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],Mr(t,e),Mr(t,n);var o="vec4(0.0)";null!=r&&(Mr(t,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=i&&(Mr(t,i),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        vec4 offset = "+o+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    "},Oi=function(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=Mr(e,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+t+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    "},_i="return a + b;",Fi="return a - b;",Mi="return a * b;",zi="return (a < 0.) ? b * a : a;",Li=function(t,e,n){this.variableNames=["A","B"],this.outputShape=Mr(e,n),this.userCode="\n      float binaryOperation(float a, float b) {\n        "+t+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    "},Pi="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",Bi=function(t,e,n,r){void 0===r&&(r=!1),this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.outputShape=Mr(e,n);var i=this.outputShape.length,a="";if(r)if(0===i||1===v(this.outputShape))a="\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";else if(a="\n          "+Ei(i)+" coords = getOutputCoords();\n        ",1===i)a+="\n            result.y = (coords + 1) >= "+this.outputShape[0]+" ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          ";else{var o=hi("coords",i);a+="\n            bool nextRowOutOfBounds =\n              ("+o[i-2]+" + 1) >= "+this.outputShape[i-2]+";\n            bool nextColOutOfBounds =\n              ("+o[i-1]+" + 1) >= "+this.outputShape[i-1]+";\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          "}this.userCode="\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        "+t+"\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        "+a+"\n\n        setOutput(result);\n      }\n    "},Vi=function(){function t(t){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,i){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(i,"minVal"),n.maxLoc=r.getUniformLocationNoThrow(i,"maxVal")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e)}},t}(),Wi=function(){function t(t){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t,this.userCode="\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,i){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(i,"minVal"),n.maxLoc=r.getUniformLocationNoThrow(i,"maxVal")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e)}},t}(),Ui=function(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "},ji=function(t){this.outputShape=[],this.outputShape=Je(t,1),this.variableNames=t.map(function(t,e){return"T"+e});var e=new Array(t.length-1);e[0]=t[0][1];for(var n=1;n<e.length;n++)e[n]=e[n-1]+t[n][1];var r=["if (yC < "+e[0]+") setOutput(getT0(yR, yC));"];for(n=1;n<e.length;n++){var i=e[n-1];r.push("else if (yC < "+e[n]+") setOutput(getT"+n+"(yR, yC-"+i+"));")}var a=e.length,o=e[e.length-1];r.push("else setOutput(getT"+a+"(yR, yC-"+o+"));"),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        "+r.join("\n        ")+"\n      }\n    "},Gi=function(t,e){this.usesPackedTextures=!0,this.outputShape=[],this.outputShape=Je(t,e);var n=this.outputShape,r=n.length,i=Ei(r),a=hi("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=t.map(function(t,e){return"T"+e});var s=new Array(t.length-1);s[0]=t[0][e];for(var u=1;u<s.length;u++)s[u]=s[u-1]+t[u][e];var l=o[e],c="vec2("+o.slice(-2).join()+")",p=o.join(),h="if ("+l+" < "+s[0]+")\n          return getChannel(getT0("+p+"), "+c+");";for(u=1;u<s.length;u++){var f=s[u-1];h+="\n        else if ("+l+" < "+s[u]+") {\n          "+l+" -= "+f+";\n          return getChannel(getT"+u+"("+p+"), "+c+");\n        }"}var d=s.length;h+="\n        else {\n          "+l+" -= "+s[s.length-1]+";\n          return getChannel(getT"+d+"("+p+"), "+c+");\n        }",this.userCode="\n      float getValue("+o.map(function(t){return"int "+t})+") {\n        "+h+"\n      }\n\n      void main() {\n        "+i+" coords = getOutputCoords();\n        vec4 result = vec4(getValue("+a+"), 0., 0., 0.);\n        if (++"+a[r-1]+" < "+n[r-1]+") {\n          result.g = getValue("+a+");\n        }\n        if (++"+a[r-2]+" < "+n[r-2]+") {\n          result.a = getValue("+a+");\n        }\n        if ("+a[r-2]+" < "+n[r-2]+" &&\n            --"+a[r-1]+" < "+n[r-1]+") {\n          result.b = getValue("+a+");\n        }\n        setOutput(result);\n      }\n    "},Hi=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,i=t.padInfo.left,a="channelsLast"===t.dataFormat;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+i+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              if ("+a+") {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},qi=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,i=t.strideWidth,a="channelsLast"===t.dataFormat,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode="\n      const ivec2 pads = ivec2("+o+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords["+c+"];\n\n        ivec2 dyCorner = ivec2(coords["+u+"], coords["+l+"]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+i+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n\n              if ("+a+") {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Ki=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,i=t.padInfo.front,a=t.padInfo.top,o=t.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yF = 0; yF < "+t.outDepth+"; yF++) {\n            int xF = wF + yF * "+e+" - "+i+";\n\n            if (xF < 0 || xF >= "+t.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+a+";\n\n              if (xR < 0 || xR >= "+t.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+o+";\n\n                if (xC < 0 || xC >= "+t.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},$i=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,i=t.strideDepth,a=t.strideHeight,o=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,l=r-1-t.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+l+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+e+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+i+".0;\n\n          if (dyF < 0.0 || dyF >= "+t.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+e+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+a+".0;\n\n            if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+o+".0;\n\n              if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Xi=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,i=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+a+" + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+i+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Yi=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,i=t.strideWidth,a=e-1-t.padInfo.top,o=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+i+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Ji=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var i=t.padInfo.top,a=t.padInfo.left,o=t.strideHeight,s=t.strideWidth,u=t.dilationHeight,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,h=4*Math.floor(t.inChannels/4),f=t.inChannels%4,d="channelsLast"===t.dataFormat,m=d?1:2,g=d?2:3,v=d?3:1,y="",b="";n&&(y=r?"float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          "+n+"\n        }":"\n          float activation(float x) {\n            "+n+"\n          }\n        ",b="result = activation(result);");var x=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode="\n      "+y+"\n\n      const ivec2 strides = ivec2("+o+", "+s+");\n      const ivec2 pads = ivec2("+i+", "+a+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords["+v+"];\n\n        ivec2 xRCCorner =\n            ivec2(coords["+m+"], coords["+g+"]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+c+"; wR++) {\n          int xR = xRCorner + wR * "+u+";\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+p+"; wC++) {\n            int xC = xCCorner + wC * "+l+";\n\n            if (xC < 0 || xC >= "+t.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+h+"; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if ("+d+") {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if ("+(1===f)+") {\n\n              if ("+d+") {\n                dotProd +=\n                    getX(batch, xR, xC, "+h+") *\n                    getW(wR, wC, "+h+", d2);\n              } else {\n                dotProd +=\n                    getX(batch, "+h+", xR, xC) *\n                    getW(wR, wC, "+h+", d2);\n              }\n\n            } else if ("+(2===f)+") {\n              vec2 wValues = vec2(\n                getW(wR, wC, "+h+", d2),\n                getW(wR, wC, "+h+" + 1, d2)\n              );\n\n              if ("+d+") {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, "+h+"),\n                  getX(batch, xR, xC, "+h+" + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, "+h+", xR, xC),\n                  getX(batch, "+h+" + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if ("+(3===f)+") {\n              vec3 wValues = vec3(\n                getW(wR, wC, "+h+", d2),\n                getW(wR, wC, "+h+" + 1, d2),\n                getW(wR, wC, "+h+" + 2, d2)\n              );\n\n              if ("+d+") {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, "+h+"),\n                  getX(batch, xR, xC, "+h+" + 1),\n                  getX(batch, xR, xC, "+h+" + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, "+h+", xR, xC),\n                  getX(batch, "+h+" + 1, xR, xC),\n                  getX(batch, "+h+" + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        "+x+"\n        "+b+"\n        setOutput(result);\n      }\n    "},Zi=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,i=t.strideDepth,a=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,l=t.dilationWidth,c=t.filterDepth,p=t.filterHeight,h=t.filterWidth,f=4*Math.floor(t.inChannels/4),d=t.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+i+", "+a+", "+o+");\n      const ivec3 pads = ivec3("+e+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+c+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+t.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+p+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+h+"; wC++) {\n              int xC = xCCorner + wC * "+l+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+f+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===d)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+f+") *\n                  getW(wF, wR, wC, "+f+", d2);\n              } else if ("+(2===d)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===d)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1),\n                  getX(batch, xF, xR, xC, "+f+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2),\n                  getW(wF, wR, wC, "+f+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Qi=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,i=t.padInfo.left,a=t.strideHeight,o=t.strideWidth,s=t.dilationHeight,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+a+", "+o+");\n      const ivec2 pads = ivec2("+r+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+p+";\n        int q = d2 - d1 * "+p+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+l+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+e+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+c+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},ta=function(t){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=t.outShape;for(var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,i=t.padInfo.left,a=t.strideHeight,o=t.strideWidth,s=t.dilationHeight,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,h=c,f="int xR; int xC; int xCOffset;",d=0;d<l;d++)for(var m=0;m<c;m++)f+="\n          vec4 xTexelR"+d+"C"+2*m+" = vec4(0.);\n          vec4 wR"+d+"C"+m+" = vec4(0.);\n          vec4 xR"+d+"C"+m+" = vec4(0.);";for(d=0;d<l;d++)for(var g=0;g<h;g++){if(f+="\n          xR = xRCorner + "+d*s+";\n          xC = xCCorner + "+(m=2*g)*u+";\n        ",1===o){if(m<c&&(f+=i%2==1?"\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < "+e+" && xCOffset >= 0 && xCOffset < "+n+") {\n                  xTexelR"+d+"C"+m+" = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR"+d+"C"+m+" = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < "+e+" && xCOffset >= 0 && xCOffset < "+n+") {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n                  xR"+d+"C"+m+" = vec4(previous.zw, xTexelR"+d+"C"+m+".xy);\n                } else {\n                  xR"+d+"C"+m+" = vec4(0, 0, xTexelR"+d+"C"+m+".xy);\n                }\n              ":"\n                if(xR >= 0 && xR < "+e+" && xC >= 0 && xC < "+n+") {\n                  xTexelR"+d+"C"+m+" = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR"+d+"C"+m+" = vec4(0.);\n                }\n\n                xR"+d+"C"+m+" = xTexelR"+d+"C"+m+";\n              ",m+1<c)){var v=i%2==0?p(u):u;u%2==0&&i%2==1||u%2!=0&&i%2!=1?(f+="\n                  xCOffset = xC + "+i%2+" + "+v+";\n\n                  if(xR >= 0 && xR < "+e+" &&\n                    xCOffset >= 0 && xCOffset < "+n+") {\n                    xTexelR"+d+"C"+(m+2)+" = getX(batch, xR, xCOffset, d1);\n                  }\n                ",u>1&&(f+="\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < "+e+" &&\n                      xCOffset >= 0 && xCOffset < "+n+") {\n                      xTexelR"+d+"C"+m+" = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR"+d+"C"+m+" = vec4(0.);\n                    }\n                  "),f+="\n                  xR"+d+"C"+(m+1)+" = vec4(\n                    xTexelR"+d+"C"+m+".zw, xTexelR"+d+"C"+(m+2)+".xy);\n                "):f+="\n                  xCOffset = xC + "+v+";\n\n                  if(xR >= 0 && xR < "+e+" &&\n                    xCOffset >= 0 && xCOffset < "+n+") {\n                    xTexelR"+d+"C"+(m+2)+" = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR"+d+"C"+(m+1)+" = xTexelR"+d+"C"+(m+2)+";\n                "}}else m<c&&(f+="\n              if(xR >= 0 && xR < "+e+") {\n            ",i%2==1?(f+="\n                xCOffset = xC + 1 - "+o+";\n                if(xCOffset >= 0 && xCOffset < "+n+") {\n                  xTexelR"+d+"C"+m+" = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR"+d+"C"+m+" = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < "+n+") {\n                  xTexelR"+d+"C"+(m+2)+" = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR"+d+"C"+(m+2)+" = vec4(0.);\n                }\n\n                xR"+d+"C"+m+" = vec4(\n                  xTexelR"+d+"C"+m+".zw, xTexelR"+d+"C"+(m+2)+".zw);\n              ",m+1<c&&(f+="\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + "+o+";\n                  if(xCOffset >= 0 && xCOffset < "+n+") {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR"+d+"C"+(m+1)+" = vec4(xTexelR"+d+"C"+(m+2)+".xy, final.xy);\n                ")):(f+="\n                if(xC >= 0 && xC < "+n+") {\n                  xTexelR"+d+"C"+m+" = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR"+d+"C"+m+" = vec4(0.);\n                }\n\n                xCOffset = xC + "+o+";\n                if(xCOffset >= 0 && xCOffset < "+n+") {\n                  xTexelR"+d+"C"+(m+2)+" = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR"+d+"C"+(m+2)+" = vec4(0.);\n                }\n\n                xR"+d+"C"+m+" = vec4(\n                  xTexelR"+d+"C"+m+".xy, xTexelR"+d+"C"+(m+2)+".xy);\n              ",m+1<c&&(f+="\n                  xR"+d+"C"+(m+1)+" = vec4(\n                    xTexelR"+d+"C"+m+".zw, xTexelR"+d+"C"+(m+2)+".zw);\n                ")),f+="}");m<c&&(f+="\n            vec4 wTexelR"+d+"C"+m+" = getW("+d+", "+m+", d1, q);\n            wR"+d+"C"+m+" = vec4(wTexelR"+d+"C"+m+".xz, wTexelR"+d+"C"+m+".xz);\n          ",m+1<c&&(f+="\n              vec4 wTexelR"+d+"C"+(m+1)+" = getW("+d+", "+(m+1)+", d1, q);\n              wR"+d+"C"+(m+1)+" =\n                vec4(wTexelR"+d+"C"+(m+1)+".xz, wTexelR"+d+"C"+(m+1)+".xz);"))}for(d=0;d<l;d++)for(m=0;m<c;m++)f+="result += xR"+d+"C"+m+" * wR"+d+"C"+m+";";this.userCode="\n      const ivec2 strides = ivec2("+a+", "+o+");\n      const ivec2 pads = ivec2("+r+", "+i+");\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+f+"\n\n        setOutput(result);\n      }\n    "},ea=function(t,e,n,r,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var a=t[0],o=t[1],s=t[2],u=t[3],l=e[0],c=n[0],p=n[1];this.outputShape=[l,c,p,u];var h="bilinear"===r?1:0,f=[o-1+".0",s-1+".0"],d=f[0],m=f[1],g=c>1?[""+(o-1)/(c-1),"(y2-y1) * height_ratio","y1*"+d+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+d],v=g[0],y=g[1],b=g[2],x=p>1?[""+(s-1)/(p-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],w=x[0],N=x[1],C=x[2];this.userCode="\n      const float height_ratio = float("+v+");\n      const float width_ratio = float("+w+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+a+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+N+";\n\n        float in_y = "+b+";\n        if( in_y < 0.0 || in_y > "+d+" ) {\n          setOutput(float("+i+"));\n          return;\n        }\n        float in_x = "+C+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+i+"));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if("+h+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    "},na=function(t,e,n){this.variableNames=["x"],this.outputShape=t;var r=t.length,i=t[t.length-1],a=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+i+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+Ei(r)+" coords = getOutputCoords();\n        int end = "+ra(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+i+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+a+" end) {\n            continue;\n          }\n          if (idx == end && "+e+") {\n            continue;\n          }\n          "+ra(r,"coords")+" = idx;\n          val += getX("+function(t,e){if(1===t)return""+e;if(2===t)return e+".x, "+e+".y";if(3===t)return e+".x, "+e+".y, "+e+".z";if(4===t)return e+".x, "+e+".y, "+e+".z, "+e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    "};function ra(t,e){if(1===t)return""+e;if(2===t)return e+".y";if(3===t)return e+".z";if(4===t)return e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}var ia=function(t,e){this.variableNames=["A"];var n=fi();this.outputShape=t,this.userCode="\n      ivec3 outCoordsFromFlatIndex(int index) {\n        "+di(["r","c","d"],t)+"\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2("+e[0]+", "+e[1]+"));\n        int index = 4 * (resTexRC.x * "+e[1]+" + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        "+n.output+" = result;\n      }\n    "},aa=function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0;var n=fi();this.outputShape=t,this.userCode="\n      ivec3 outCoordsFromFlatIndex(int index) {\n        "+di(["r","c","d"],t)+"\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2("+e[0]+", "+e[1]+"));\n        int index = 4 * (resTexRC.x * "+e[1]+" + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        "+n.output+" = result;\n      }\n    "},oa=function(){function t(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+e+";\n      int offset_h = imod(h, "+e+");\n      int in_w = w / "+e+";\n      int offset_w = imod(w, "+e+");\n      int offset_d = (offset_h * "+e+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  "}return t.prototype.getHeightCoordString=function(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"},t.prototype.getWidthCoordString=function(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"},t.prototype.getDepthCoordString=function(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"},t.prototype.getOutputDepthSize=function(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},t}(),sa=function(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode="\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    "},ua=function(t){this.variableNames=["A"];var e=fi();this.outputShape=t,this.userCode="\n      "+gi+"\n\n      void main() {\n        float x = getAAtOutCoords();\n        "+e.output+" = encode_float(x);\n      }\n    "},la=function(t){this.variableNames=["A"],this.usesPackedTextures=!0;var e=fi();this.outputShape=t,this.userCode="\n      "+gi+"\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        "+e.output+" = encode_float(x);\n      }\n    "},ca=function(t,e,n){void 0===n&&(n=!1),this.variableNames=["A"];var r=fi(),i=e[0],a=e[1];this.outputShape=t;var o="result";n&&(o="floor(result * 255. + 0.5)"),this.userCode="\n      "+mi(t)+"\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n        \n        int r = flatIndex / "+a+";\n        int c = imod(flatIndex, "+a+");\n        vec2 uv = (vec2(c, r) + halfCR) / vec2("+a+".0, "+i+".0);\n        vec4 values = "+r.texture2D+"(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        "+r.output+" = vec4("+o+", 0., 0., 0.);\n      }\n    "},pa=function(t,e,n){void 0===n&&(n=!1),this.variableNames=["A"];var r=fi(),i=e[0],a=e[1];this.outputShape=t;var o="",s="result";n&&(s="floor(result * 255. + 0.5)");for(var u=0;u<=1;u++)for(var l=0;l<=1;l++){var c=2*u+l;o+="\n          localCoords = coords;\n          if(localCoords[2] + "+l+" < "+t[2]+") {\n            localCoords[2] += "+l+";\n            if(localCoords[1] + "+u+" < "+t[1]+") {\n              localCoords[1] += "+u+";\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n    \n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / "+a+";\n              c = imod(flatIndex, "+a+");\n              uv = (vec2(c, r) + halfCR) / vec2("+a+".0, "+i+".0);\n              values = "+r.texture2D+"(A, uv);\n\n              if(offset == 0) {\n                result["+c+"] = values[0];\n              } else if(offset == 1) {\n                result["+c+"] = values[1];\n              } else if(offset == 2) {\n                result["+c+"] = values[2];\n              } else {\n                result["+c+"] = values[3];\n              }\n            }\n          }\n        "}this.userCode="\n      "+mi(t)+"\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n        \n        "+o+"\n\n        "+r.output+" = "+s+";\n      }\n    "},ha=function(t,e,n){this.variableNames=["real","imag"];var r=e[1];this.outputShape=e;var i=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,a=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+i+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+t+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+a+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    "},fa=function(){function t(t,e){this.outputShape=[],this.variableNames=["x"],this.outputShape=t,this.userCode="\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.valueLoc&&(e.valueLoc=n.getUniformLocationNoThrow(r,"value")),n.gl.uniform1f(e.valueLoc,t)}},t}(),da=function(t){this.variableNames=["A"];var e=fi(),n=t[0],r=t[1];this.outputShape=t,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+r+".0, "+n+".0);\n\n        vec4 values = "+e.texture2D+"(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    "},ma=function(t){this.variableNames=["A"];var e=fi(),n=t[0],r=t[1];this.outputShape=t,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+r+".0, "+n+".0);\n            vec4 values = "+e.texture2D+"(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        "+e.output+" = result;\n      }\n    "},ga=function(t,e,n){this.variableNames=["A","indices"];var r=t.slice();r[n]=e,this.outputShape=r,this.rank=r.length;var i=Ei(this.rank),a=function(t,e){var n=t.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return"int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],a=0;a<t.length;a++)a===e?i.push("int(getIndices("+r[a]+"))"):i.push(""+r[a]);return i.join()}(t,n);this.userCode="\n      void main() {\n        "+i+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    "},va=function(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=n;var r=Ei(e.length),i=Ei(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+i+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+a+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      "};function ya(t,e){var n=fi();return Ht(t,e,n.version+"\n    precision highp float;\n    "+n.attribute+" vec3 clipSpacePos;\n    "+n.attribute+" vec2 uv;\n    "+n.varyingVs+" vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }")}function ba(t,e){return Qt(t,e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function xa(t,e){return te(t,e,new Uint16Array([0,1,2,2,1,3]))}function wa(t,e,n,r,i,a,o){ne(n,r);var s=ee(t,e),u=t.TEXTURE_2D;return Bt(t,e,function(){return t.bindTexture(u,s)}),Bt(t,e,function(){return t.texParameteri(u,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE)}),Bt(t,e,function(){return t.texParameteri(u,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE)}),Bt(t,e,function(){return t.texParameteri(u,t.TEXTURE_MIN_FILTER,t.NEAREST)}),Bt(t,e,function(){return t.texParameteri(u,t.TEXTURE_MAG_FILTER,t.NEAREST)}),Bt(t,e,function(){return t.texImage2D(u,0,i,n,r,0,a,o,null)}),Bt(t,e,function(){return t.bindTexture(t.TEXTURE_2D,null)}),s}function Na(t,e,n,r,i){var a=Mt(n,r);return wa(t,e,a[0],a[1],i.internalFormatFloat,i.textureFormatFloat,t.FLOAT)}function Ca(t,e,n,r,i){var a=Mt(n,r);return wa(t,e,a[0],a[1],i.internalFormatHalfFloat,i.textureFormatFloat,i.textureTypeHalfFloat)}function Ea(t,e,n,r,i){var a=Mt(n,r);return wa(t,e,a[0],a[1],t.RGBA,t.RGBA,t.UNSIGNED_BYTE)}function Sa(t,e,n,r,i){var a=Lt(n,r);return wa(t,e,a[0],a[1],i.internalFormatPackedFloat,t.RGBA,t.FLOAT)}function ka(t,e,n,r,i){var a=Lt(n,r);return wa(t,e,a[0],a[1],i.internalFormatPackedHalfFloat,t.RGBA,i.textureTypeHalfFloat)}function Ia(t,e,n,r){return Bt(t,e,function(){return t.bindBuffer(t.ARRAY_BUFFER,r)}),ie(t,e,n,"clipSpacePos",r,3,20,0)&&ie(t,e,n,"uv",r,2,20,12)}function Aa(t,e,n,r,i,a,o){var s,u,l;Bt(t,e,function(){return t.bindTexture(t.TEXTURE_2D,n)}),a instanceof Uint8Array?(s=new Uint8Array(r*i*4),u=t.UNSIGNED_BYTE,l=t.RGBA):(s=new Float32Array(r*i*4),u=t.FLOAT,l=o.internalFormatPackedFloat),s.set(a),Bt(t,e,function(){return t.texImage2D(t.TEXTURE_2D,0,l,r,i,0,t.RGBA,u,s)}),Bt(t,e,function(){return t.bindTexture(t.TEXTURE_2D,null)})}function Ra(t,e,n,r){Bt(t,e,function(){return t.bindTexture(t.TEXTURE_2D,n)}),r.data instanceof Uint8Array?Bt(t,e,function(){return t.texImage2D(t.TEXTURE_2D,0,t.RGBA,r.width,r.height,0,t.RGBA,t.UNSIGNED_BYTE,r.data)}):Bt(t,e,function(){return t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,r)}),Bt(t,e,function(){return t.bindTexture(t.TEXTURE_2D,null)})}function Ta(t,e,n,r,i){var a=t.createBuffer();Bt(t,e,function(){return t.bindBuffer(t.PIXEL_PACK_BUFFER,a)});var o=16*n*r;return Bt(t,e,function(){return t.bufferData(t.PIXEL_PACK_BUFFER,o,t.STREAM_READ)}),Bt(t,e,function(){return t.readPixels(0,0,r,n,t.RGBA,t.FLOAT,0)}),Bt(t,e,function(){return t.bindBuffer(t.PIXEL_PACK_BUFFER,null)}),a}function Da(t,e,n){var r=t,i=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,e),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,i),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),i}function Oa(t,e,n,r,i){var a=Mt(n,r),o=a[0],s=a[1],u=new Uint8Array(n*r*4);return Bt(t,e,function(){return t.readPixels(0,0,o,s,i.downloadTextureFormat,t.UNSIGNED_BYTE,u)}),new Float32Array(u.buffer)}function _a(t,e,n,r,i,a,o,s){var u=t,l=new Float32Array(function(t,e){var n=Lt(a,o);return n[0]*n[1]*4}());return u.bindBuffer(u.PIXEL_PACK_BUFFER,e),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function Fa(t,e,n,r){var i=new Float32Array(n*r*4);return Bt(t,e,function(){return t.readPixels(0,0,r,n,t.RGBA,t.FLOAT,i)}),i}var Ma=Object.freeze({createVertexShader:ya,createVertexBuffer:ba,createIndexBuffer:xa,createFloat32MatrixTexture:Na,createFloat16MatrixTexture:Ca,createUnsignedBytesMatrixTexture:Ea,createPackedMatrixTexture:Sa,createFloat16PackedMatrixTexture:ka,bindVertexProgramAttributeStreams:Ia,uploadDenseMatrixToTexture:Aa,uploadPixelDataToTexture:Ra,createBufferFromOutputTexture:Ta,downloadFloat32MatrixFromBuffer:Da,downloadByteEncodedFloatMatrixFromOutputTexture:Oa,downloadPackedMatrixFromBuffer:_a,downloadMatrixFromPackedOutputTexture:Fa}),za=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];var n=t.ENV.getNumber("WEBGL_VERSION");if(null!=e?(this.gl=e,Ot(n,e)):this.gl=_t(n),1===t.ENV.getNumber("WEBGL_VERSION"))this.textureFloatExtension=Gt(this.gl,this.debug,"OES_texture_float"),this.colorBufferFloatExtension=this.gl.getExtension("WEBGL_color_buffer_float"),this.textureHalfFloatExtension=Gt(this.gl,this.debug,"OES_texture_half_float"),this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float");else if(Ee(this.gl,"EXT_color_buffer_float"))this.colorBufferFloatExtension=this.gl.getExtension("EXT_color_buffer_float");else{if(!Ee(this.gl,"EXT_color_buffer_half_float"))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float")}this.vertexBuffer=ba(this.gl,this.debug),this.indexBuffer=xa(this.gl,this.debug),this.framebuffer=re(this.gl,this.debug),this.textureConfig=Pt(this.gl,this.textureHalfFloatExtension)}return Object.defineProperty(e.prototype,"debug",{get:function(){return t.ENV.getBool("DEBUG")},enumerable:!0,configurable:!0}),e.prototype.dispose=function(){var t=this;if(!this.disposed){null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var e=this.gl;Bt(e,this.debug,function(){return e.finish()}),Bt(e,this.debug,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),Bt(e,this.debug,function(){return e.deleteFramebuffer(t.framebuffer)}),Bt(e,this.debug,function(){return e.bindBuffer(e.ARRAY_BUFFER,null)}),Bt(e,this.debug,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)}),Bt(e,this.debug,function(){return e.deleteBuffer(t.indexBuffer)}),this.disposed=!0}},e.prototype.createFloat32MatrixTexture=function(t,e){return this.throwIfDisposed(),Na(this.gl,this.debug,t,e,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(t,e){return this.throwIfDisposed(),Ca(this.gl,this.debug,t,e,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(t,e){return this.throwIfDisposed(),Ea(this.gl,this.debug,t,e,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(t,e){this.throwIfDisposed(),Ra(this.gl,this.debug,t,e)},e.prototype.uploadDenseMatrixToTexture=function(t,e,n,r){this.throwIfDisposed(),Aa(this.gl,this.debug,t,e,n,r,this.textureConfig)},e.prototype.createFloat16PackedMatrixTexture=function(t,e){return this.throwIfDisposed(),ka(this.gl,this.debug,t,e,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(t,e){return this.throwIfDisposed(),Sa(this.gl,this.debug,t,e,this.textureConfig)},e.prototype.deleteMatrixTexture=function(t){var e=this;this.throwIfDisposed(),this.outputTexture===t&&(ce(this.gl,this.debug,this.framebuffer),this.outputTexture=null),Bt(this.gl,this.debug,function(){return e.gl.deleteTexture(t)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,function(){return Oa(r.gl,r.debug,e,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(t,e,n,r,i,a){return _a(this.gl,t,0,0,0,i,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(t,e){return Da(this.gl,t,e)},e.prototype.createBufferFromTexture=function(t,e,n){this.bindTextureToFrameBuffer(t);var r=Ta(this.gl,this.debug,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var t=this.createFence(this.gl);return this.pollFence(t)},e.prototype.createFence=function(e){var n,r,i=this;if(t.ENV.getBool("WEBGL_FENCE_API_ENABLED")){var a=e,o=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=function(){var t=a.clientWaitSync(o,0,0);return t===a.ALREADY_SIGNALED||t===a.CONDITION_SATISFIED},n=o}else t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(n=this.beginQuery(),this.endQuery(),r=function(){return i.isQueryAvailable(n,t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):r=function(){return!0};return{query:n,isFencePassed:r}},e.prototype.downloadMatrixFromPackedTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,function(){return Fa(r.gl,r.debug,e,n)})},e.prototype.createProgram=function(t){this.throwIfDisposed();var e=this.gl,n=qt(e,this.debug,t),r=ya(e,this.debug),i=Yt(e,this.debug);return Bt(e,this.debug,function(){return e.attachShader(i,r)}),Bt(e,this.debug,function(){return e.attachShader(i,n)}),Jt(e,this.debug,i),this.debug&&Zt(e,this.debug,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=Ia(e,this.debug,this.program,this.vertexBuffer)),i},e.prototype.deleteProgram=function(t){var e=this;this.throwIfDisposed(),t===this.program&&(this.program=null),null!=t&&Bt(this.gl,this.debug,function(){return e.gl.deleteProgram(t)})},e.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,null!=this.program&&this.debug&&Zt(this.gl,this.debug,this.program),Bt(this.gl,this.debug,function(){return e.gl.useProgram(t)})},e.prototype.getUniformLocation=function(t,e,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?oe(this.gl,this.debug,t,e):se(this.gl,t,e)},e.prototype.getAttributeLocation=function(t,e){var n=this;return this.throwIfDisposed(),Bt(this.gl,this.debug,function(){return n.gl.getAttribLocation(t,e)})},e.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},e.prototype.setInputMatrixTexture=function(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),ue(this.gl,this.debug,this.program,t,e,n)},e.prototype.setOutputMatrixTexture=function(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)},e.prototype.setOutputPackedMatrixTexture=function(t,e,n){this.throwIfDisposed();var r=Lt(e,n),i=r[0],a=r[1];this.setOutputMatrixTextureDriver(t,i,a)},e.prototype.setOutputMatrixWriteRegion=function(t,e,n,r){this.setOutputMatrixWriteRegionDriver(n,t,r,e)},e.prototype.setOutputPackedMatrixWriteRegion=function(t,e,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},e.prototype.debugValidate=function(){null!=this.program&&Zt(this.gl,this.debug,this.program),pe(this.gl)},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.debug&&this.debugValidate(),Bt(t,this.debug,function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)})},e.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),Bt(this.gl,this.debug,function(){return t.gl.finish()})},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=Gt(this.gl,this.debug,2===t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=e.createQuery();return e.beginQuery(n.TIME_ELAPSED_EXT,r),r}var i=this.getQueryTimerExtensionWebGL1(),a=i.createQueryEXT();return i.beginQueryEXT(i.TIME_ELAPSED_EXT,a),a},e.prototype.endQuery=function(){if(2!==t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}else{var n=this.gl,r=this.getQueryTimerExtensionWebGL2();n.endQuery(r.TIME_ELAPSED_EXT)}},e.prototype.waitForQueryAndGetTime=function(e){return r(this,void 0,void 0,function(){var n=this;return i(this,function(r){switch(r.label){case 0:return[4,C(function(){return n.disposed||n.isQueryAvailable(e,t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return r.sent(),[2,this.getQueryTime(e,t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},e.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(t,e){if(0===e)return!0;if(2===e){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),i&&!this.disjoint}return i=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT),null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),i&&!this.disjoint},e.prototype.pollFence=function(t){var e=this;return new Promise(function(n){e.addItemToPoll(function(){return t.isFencePassed()},function(){return n()})})},e.prototype.pollItems=function(){for(var t=function(t){for(var e=0;e<t.length;++e){if(!t[e]())break}return e-1}(this.itemsToPoll.map(function(t){return t.isDoneFn})),e=0;e<=t;++e)(0,this.itemsToPoll[e].resolveFn)();this.itemsToPoll=this.itemsToPoll.slice(t+1)},e.prototype.addItemToPoll=function(t,e){var n=this;this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1||C(function(){return n.pollItems(),0===n.itemsToPoll.length})},e.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),le(this.gl,this.debug,t,this.framebuffer),this.debug&&pe(this.gl)},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(le(this.gl,this.debug,this.outputTexture,this.framebuffer),this.debug&&pe(this.gl)):ce(this.gl,this.debug,this.framebuffer)},e.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;le(r,this.debug,t,this.framebuffer),this.debug&&pe(r),this.outputTexture=t,Bt(r,this.debug,function(){return r.viewport(0,0,e,n)}),Bt(r,this.debug,function(){return r.scissor(0,0,e,n)})},e.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var i=this;this.throwIfDisposed(),Bt(this.gl,this.debug,function(){return i.gl.scissor(t,e,n,r)})},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},e}();function La(t,e){if(t.length!==e.length)throw Error("Binary was compiled with "+t.length+" inputs, but was executed with "+e.length+" inputs");t.forEach(function(t,n){var r=t.logicalShape,i=e[n],a=i.shape;if(!y(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!t.isUniform||!i.isUniform){var o=t.texShape,s=i.isUniform?null:i.texData.texShape;if(!y(o,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+o+" and "+s+" must match")}})}var Pa=function(t,e,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t;for(var r=n.filterWidth,i=n.inChannels,a=n.strideWidth,o=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,p=n.dataFormat,h=s.left,f=s.top,d=i*r,m=fi(),g="channelsLast"===p,v=g?0:1,y=g?1:2,b="",x=0;x<=1;x++)for(var w=0;w<=1;w++)b+="\n          blockIndex = rc.y + "+w+";\n          pos = rc.x + "+x+";\n\n          if(blockIndex < "+t[1]+" && pos < "+t[0]+") {\n            offsetY = int(blockIndex / ("+u+")) * "+o+" - "+f+";\n            d0 = offsetY + "+c+" * (pos / "+d+");\n\n            if(d0 < "+e[v]+" && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+h+".);\n              d1 = offsetX + "+l+" * (int(mod(float(pos), "+d+".) / "+i+".));\n\n              if(d1 < "+e[y]+" && d1 >= 0) {\n\n                ch = int(mod(float(pos), "+i+".));\n\n                if ("+g+") {\n                  innerDims = vec2(d1, ch);\n                  result["+(2*x+w)+"] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result["+(2*x+w)+"] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        ";this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        "+b+"\n\n        "+m.output+" = result;\n      }\n    "},Ba=function(t,e,n,r,i){this.variableNames=["x"],this.outputShape=[];var a,o=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";a=.5===i?"inversesqrt("+u+")":1===i?"1.0/("+u+")":"exp(log("+u+") * float(-"+i+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+o+"; j <= "+o+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+a+";\n        setOutput(val);\n      }\n    "},Va=function(t,e,n,r,i){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=i,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+e+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+e+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+i+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+i+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    "},Wa=function(t,e,n,r,i){this.variableNames=["x"],this.outputShape=[],this.usesPackedTextures=!0;var a,o=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";a=.5===i?"inversesqrt("+u+")":1===i?"1.0/("+u+")":"exp(log("+u+") * float(-"+i+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < "+this.outputShape[3]+";\n        bool hasNextRow = c < "+this.outputShape[2]+";\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - "+o+";\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - "+o+"; j <= "+o+"; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2("+s+"));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * "+a+";\n        setOutput(result);\n      }\n    "},Ua=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,i=t.effectiveFilterHeight,a=t.effectiveFilterWidth,o=i-1-t.padInfo.top,s=a-1-t.padInfo.left,u=i*a-1;this.userCode="\n      const ivec2 pads = ivec2("+o+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+i+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+e+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+a+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+a+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},ja=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,i=t.dilationDepth,a=t.dilationHeight,o=t.dilationWidth,s=t.effectiveFilterDepth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=s-1-t.padInfo.front,p=u-1-t.padInfo.top,h=l-1-t.padInfo.left,f=s*u*l-1;this.userCode="\n      const ivec3 pads = ivec3("+c+", "+p+", "+h+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < "+s+";\n           wD += "+i+") {\n          float dyD = float(dyDCorner + wD) / "+e+".0;\n\n          if (dyD < 0.0 || dyD >= "+t.outDepth+".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+a+") {\n            float dyR = float(dyRCorner + wR) / "+n+".0;\n\n            if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < "+l+";\n                wC += "+o+") {\n              float dyC = float(dyCCorner + wC) / "+r+".0;\n\n              if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = "+f+" -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * "+u+" * "+l+" +\n                  wR * "+l+" + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},Ga=function(t,e,n,r,i,a,o){void 0===n&&(n=!1),void 0===r&&(r=!1),void 0===i&&(i=!1),void 0===a&&(a=null),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=e;var s=n?t[1]:t[2],u=Math.ceil(s/2),l=n?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",p=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",d="";a&&(f=o?"vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          "+a+"\n        }":"vec4 activation(vec4 x) {\n          "+a+"\n        }",d="result = activation(result);");var m=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode="\n      "+f+"\n\n      const float sharedDimension = "+u+".0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+u+"; i++) {\n          vec4 a = getMatrixA(rc.x, "+l+");\n          vec4 b = getMatrixB(rc.x, "+c+");\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += ("+p[0]+" * "+h[0]+");\n          result += ("+p[1]+" * "+h[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        "+m+"\n\n        "+d+"\n\n        setOutput(result);\n      }\n    "},Ha=function(){function t(t,e,n){this.variableNames=["probs"],this.outputShape=[t,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(e-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(e-1)+"));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.seedLoc&&(e.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(e.seedLoc,t)}},t}(),qa=function(t,e,n,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    "},Ka=function(t){this.variableNames=["A"],this.outputShape=t;var e=t.length;if(0===e)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var n=hi("rc",e),r=Ei(e),i=function(t,e,n){if(1===t)return"rc > "+e[0];for(var r="",i=t-2;i<t;i++)r+=n[i]+" >= "+e[i],i<t-1&&(r+="||");return r}(e,t,n),a=function(t,e,n,r){if(1===t)return"";var i=r.slice(-2);return"\n    int r = "+i[0]+";\n    int c = "+i[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+e+";\n    bool rEdge = rp1 >= "+n+";\n  "}(e,t[t.length-1],t[t.length-2],n),o=function(t,e){var n=t.length,r=function(t,e){for(var n=[],r=0;r<=1;r++)for(var i=0;i<=1;i++){for(var a=(0===r?"r":"rp1")+", "+(0===i?"c":"cp1"),o=2;o<t;o++)a=e[e.length-1-o]+","+a;n.push(a)}return n}(n,e);return 1===n?"getA(rc),\n            rc + 1 >= "+t[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+r[0]+"),\n          cEdge ? 0. : getA("+r[1]+"),\n          rEdge ? 0. : getA("+r[2]+"),\n          rEdge || cEdge ? 0. : getA("+r[3]+")"}(t,n);this.userCode="\n        void main() {\n          "+r+" rc = getOutputCoords();\n\n          if("+i+") {\n            setOutput(vec4(0));\n          } else {\n            "+a+"\n\n            setOutput(vec4("+o+"));\n          }\n        }\n      "}},$a=function(t,e,n){this.variableNames=["x"],this.outputShape=e.map(function(e,n){return e[0]+t[n]+e[1]});var r=t.length,i=Ei(r),a=e.map(function(t){return t[0]}).join(","),o=e.map(function(e,n){return e[0]+t[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+i+" start = "+i+"("+a+");\n      "+i+" end = "+i+"("+o+");\n\n      void main() {\n        "+i+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+n+"));\n        } else {\n          "+i+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+a+";\n        int end = "+o+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+n+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      "},Xa=function(t,e,n){this.variableNames=["x"],this.usesPackedTextures=!0,this.outputShape=e.map(function(e,n){return e[0]+t[n]+e[1]});for(var r=t.length,i=Ei(r),a=e.map(function(t){return t[0]}).join(","),o=e.map(function(e,n){return e[0]+t[n]}).join(","),s=hi("rc",r),u=hi("source",r),l=s[r-1]+" < "+this.outputShape[r-1],c=1===r?"source":"vec2("+u.slice(-2).join()+")",p=[i+" rc = outputLoc;",s[r-1]+" += 1;\n       if("+l+") {\n      ",1===r?"":"}\n       rc = outputLoc;\n       "+s[r-2]+" += 1;\n       if("+s[r-2]+" < "+this.outputShape[r-2]+") {",1===r?"":"  "+s[r-1]+" += 1;\n         if("+l+") {"],h=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",f="",d=0,m=1===r?2:4;d<m;d++)f+="\n        "+p[d]+"\n        if ("+h+") {\n          result["+d+"] = float("+n+");\n        } else {\n          "+i+" source = rc - start;\n          result["+d+"] = getChannel(getX("+u.join()+"), "+c+");\n        }\n      ";f+=1===r?"} ":"}}",this.userCode="\n      const "+i+" start = "+i+"("+a+");\n      const "+i+" end = "+i+"("+o+");\n\n      void main() {\n        "+i+" outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        "+f+"\n        setOutput(result);\n      }\n    "},Ya=function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,i=t.strideHeight,a=t.strideWidth,o=t.dilationHeight,s=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=t.padInfo.top,p=t.padInfo.left;this.outputShape=t.outShape;var h="avg"===e,f="0.0";if(h||(f="-1.0 / 1e-20"),n)this.userCode="\n        const ivec2 strides = ivec2("+i+", "+a+");\n        const ivec2 pads = ivec2("+c+", "+p+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+o+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+l+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+l+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var d=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(d="avgValue / count");var m=4*Math.floor(r/4),g=r%4,v="\n      if ("+h+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+i+", "+a+");\n      const ivec2 pads = ivec2("+c+", "+p+");\n      const float initializationValue = "+f+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+t.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+f+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+o+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+v+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(2===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(3===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+v+"\n          }\n        }\n        setOutput("+d+");\n      }\n    "}},Ja=function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,i=t.strideDepth,a=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,h=t.effectiveFilterWidth,f=t.padInfo.front,d=t.padInfo.top,m=t.padInfo.left;this.outputShape=t.outShape;var g="avg"===e,v="0.0";if(g||(v="-1.0 / 1e-20"),n)this.userCode="\n        const ivec3 strides =\n            ivec3("+i+", "+a+", "+o+");\n        const ivec3 pads = ivec3("+f+", "+d+", "+m+");\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < "+c+";\n              wD += "+s+") {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= "+t.inDepth+") {\n              continue;\n            }\n\n            for (int wR = 0; wR < "+p+";\n                wR += "+u+") {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= "+t.inHeight+") {\n                continue;\n              }\n\n              for (int wC = 0; wC < "+h+";\n                  wC += "+l+") {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= "+t.inWidth+") {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value >= currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition =\n                      wD * "+p+" * "+h+" +\n                      wR * "+h+" + wC;;\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var y=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(y="avgValue / count");var b=4*Math.floor(r/4),x=r%4,w="\n      if ("+g+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec3 strides =\n        ivec3("+i+", "+a+", "+o+");\n      const ivec3 pads = ivec3("+f+", "+d+", "+m+");\n      const float initializationValue = "+v+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= "+t.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+v+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < "+c+";\n            wD += "+s+") {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= "+t.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+p+";\n            wR += "+u+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+b+"; wC += 4) {\n              int xC = xCCorner + wC * "+l+";\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + "+l+", ch),\n                getValue(batch, xD, xR, xC + 2 * "+l+", ch),\n                getValue(batch, xD, xR, xC + 3 * "+l+", ch)\n              );\n\n              "+w+"\n            }\n\n            int xC = xCCorner + "+b+";\n            if ("+(1===x)+") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              "+w+"\n            } else if ("+(2===x)+") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + "+l+", ch),\n                initializationValue,\n                initializationValue\n              );\n\n              "+w+"\n            } else if ("+(3===x)+") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + "+l+", ch),\n                getValue(batch, xD, xR, xC + 2 * "+l+", ch),\n                initializationValue\n              );\n\n              "+w+"\n            }\n          }\n          setOutput("+y+");\n        }\n      }\n    "}},Za=function(t,e){this.variableNames=["x"];var n=t.windowSize,r=t.batchSize,i=t.inSize,a=Math.ceil(i/n);this.outputShape=[r,a];var o="0.0",s="";"prod"===e?o="1.0":"min"===e?(o="1.0 / 1e-20",s="min"):"max"===e&&(o="-1.0 / 1e-20",s="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===e?u="sumValue":"prod"===e?u="prodValue":"all"===e?u="allValue":"any"===e&&(u="anyValue");var l=4*Math.floor(n/4),c=n%4,p="\n      if ("+("sum"===e)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===e)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",h="vec4";"all"===e?(o="1.0",p="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",h="bvec4"):"any"===e&&(o="0.0",p="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",h="bvec4");var f="";i%n>0&&(f="\n        if (inIdx < 0 || inIdx >= "+i+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+o+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+f+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+o+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+l+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+h+" values = "+h+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+p+"\n        }\n\n        int inIdx = inOffset + "+l+";\n        if ("+(1===c)+") {\n          "+h+" values = "+h+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(2===c)+") {\n          "+h+" values = "+h+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(3===c)+") {\n          "+h+" values = "+h+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+p+"\n        }\n        setOutput("+u+");\n      }\n    "},Qa=function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t;for(var n="",r=0;r<4;r++){var i="thisRC = rc;";r%2==1&&(i+="thisRC.z += 1;"),r>1&&(i+="thisRC.y += 1;"),n+="\n        "+i+"\n        "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(r>0?"}":"")+"\n      "}this.userCode="\n      \n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+di(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  \n      "+mi(t)+"\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+t[1]+";\n        int cols = "+t[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    "},to=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,i=r[1],a=r[2],o=t.shape,s=o[1],u=o[2],l=[n&&s>1?i-1:i,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],h=l[1]/c[1],f=1/p,d=1/h,m=2*Math.ceil(f)+2,g=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+h+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(i-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "},eo=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var i=t[0],a=t[1],o=t[2],s=t[3];this.outputShape=[i,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?o-1:o],l=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+o+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    "},no=function(t,e,n,r){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=[];var i=t[0],a=t[1],o=t[2],s=t[3];this.outputShape=[i,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?o-1:o],l=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+",\n          "+u[1]/l[1]+");\n      const vec3 inputShapeRC = vec3("+a+".0, "+o+".0,\n                                     "+o+".0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n        \n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < "+(s-1)+"; \n        bool hasNextRow = coords.z < "+(n-1)+";\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    "},ro=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,i=r[1],a=r[2],o=t.shape,s=o[1],u=o[2],l=[n&&s>1?i-1:i,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],h=l[1]/c[1],f=1/p,d=1/h,m=2*Math.ceil(f)+2,g=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+h+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+l[0]+") *\n                (float(dyR) / float("+c[0]+"));\n\n            float sourceFracCol =\n                float("+l[1]+") *\n                  (float(dyC) / float("+c[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+i+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+a+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "},io=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var i=t[0],a=t[1],o=t[2],s=t[3];this.outputShape=[i,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?o-1:o],l=[r&&e>1?e-1:e,r&&n>1?n-1:n],c=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+o+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+c+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    "},ao=function(t,e){this.variableNames=["x"];var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=t,1!==n){var r=t.map(function(n,r){return function(n){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),i=Ei(n);this.userCode="\n      void main() {\n        "+i+" coords = getOutputCoords();\n        setOutput(getX("+r+"));\n      }\n    "}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+t[0]+" - coord - 1));\n        }\n      "},oo=function(t,e){this.variableNames=["x"],this.usesPackedTextures=!0;var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");this.outputShape=t;var r=hi("rc",n),i=r[n-1]+" + 1 < "+this.outputShape[n-1],a=r[n-2]+" + 1 < "+this.outputShape[n-2],o=Ei(n);function s(n){var r=t.map(function(r,i){return function(n,r){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+" - "+r[n]+" - 1":""+r[n]}(i,n)});return"getChannel(getX("+r.join(",")+"), vec2("+r.slice(-2).join(",")+"))"}this.userCode=1===n?"\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX("+t[0]+" - rc - 1),\n            "+t[0]+" - rc - 1);\n          if("+i+"){\n              result.g = getChannel(getX("+t[0]+" - (rc  + 1) - 1),\n                "+t[0]+" - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      ":"\n        void main() {\n          "+o+" rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = "+s(r.slice())+";\n          if("+i+"){\n            result.g = "+function(t){return t[n-1]="("+t[n-1]+" + 1)",s(t)}(r.slice())+";\n          }\n          if("+a+") {\n            result.b = "+function(t){return t[n-2]="("+t[n-2]+" + 1)",s(t)}(r.slice())+";\n            if("+i+") {\n              result.a = "+function(t){return t[n-1]="("+t[n-1]+" + 1)",t[n-2]="("+t[n-2]+" + 1)",s(t)}(r.slice())+";\n            }\n          }\n          setOutput(result);\n        }\n    "},so=function(t,e,n,r,i,a,o){void 0===o&&(o=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=Ei(i.length),u=Ei(a.length),l="";1===n?l="i":2===n&&(l="i, j");var c="getIndices("+l+")",p="";1===r?p="i":2===r&&(p="i, coords[1]");var h="getUpdates("+p+")",f=e>1?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+i+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+t+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+e+"; j++) {\n              int index = round("+c+");\n              flattenedIndex += index * "+f+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+h+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      "},uo=function(t,e){this.variableNames=["x","segmentIds"];var n=t.windowSize,r=t.batchSize,i=t.inSize,a=t.numSegments,o=a*Math.ceil(i/n);this.outputShape=[r,o];var s=4*Math.floor(n/4),u=n%4,l="\n        sumValue += dot(values, segFilter);\n    ",c="";i%n>0&&(c="\n        if (inIdx < 0 || inIdx >= "+i+") {\n          return initializationValue;\n        }\n      ");var p="";i%n>0&&(p="\n        if (inIdx < 0 || inIdx >= "+i+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+c+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+p+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+a+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+l+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+l+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+l+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+l+"\n        }\n        setOutput(sumValue);\n      }\n    "},lo=function(t,e,n){var r,i;if(this.variableNames=["c","a","b"],this.outputShape=e,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)i="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],s=[],u=0;u<e.length;u++)s.push(""+a[u]),u<t&&o.push(""+a[u]);r=o.join(),i=s.join()}var l=Ei(n);this.userCode="\n      void main() {\n        "+l+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+i+"));\n        } else {\n          setOutput(getB("+i+"));\n        }\n      }\n    "},co=function(){function t(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e,n=Ei(this.rank),r="uniform int start["+this.rank+"];",i=function(t){if(1===t)return"sourceLoc";if(t<=6)return po.slice(0,t).map(function(t){return"sourceLoc."+t}).join(",");throw Error("Slicing for rank "+t+" is not yet supported")}(this.rank);e="\n        "+n+" sourceLoc;\n        "+n+" coords = getOutputCoords();\n        "+t.map(function(t,e){return"sourceLoc."+po[e]+" = start["+e+"] + coords."+po[e]+";"}).join("\n")+"\n      ",this.userCode="\n      "+r+"\n      void main() {\n        "+e+"\n        setOutput(getSource("+i+"));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t)}},t}(),po=["x","y","z","w","u","v"],ho=function(){function t(t){this.variableNames=["source"],this.usesPackedTextures=!0,this.outputShape=t,this.rank=t.length;var e=Ei(this.rank),n=hi("coords",this.rank),r=hi("sourceLoc",this.rank),i=1===this.rank?"sourceLoc":"vec2("+r.slice(-2).join()+")",a="getChannel(getSource("+r.join()+"), "+i+")",o="\n      result.x = "+a+";\n      if (++"+n[this.rank-1]+" < "+t[this.rank-1]+") {\n        ++"+r[this.rank-1]+";\n        result.y = "+a+";\n        --"+r[this.rank-1]+";\n      }\n    ",s=1===this.rank?"":"\n      --"+n[this.rank-1]+";\n      if (++"+n[this.rank-2]+" < "+t[this.rank-2]+") {\n        ++"+r[this.rank-2]+";\n        result.z = "+a+";\n        if (++"+n[this.rank-1]+" < "+t[this.rank-1]+") {\n          ++"+r[this.rank-1]+";\n          result.w = "+a+";\n        }\n      }\n    ",u=this.rank<=4?"sourceLoc = coords +\n            "+e+"("+t.map(function(t,e){return"start["+e+"]"}).join()+");":t.map(function(t,e){return r[e]+" = "+n[e]+" + start["+e+"];"}).join("\n");this.userCode="\n      uniform int start["+this.rank+"];\n      void main() {\n        "+e+" coords = getOutputCoords();\n        "+e+" sourceLoc;\n        "+u+" \n        vec4 result = vec4(0.);\n        "+o+"\n        "+s+"\n        setOutput(result);\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t)}},t}(),fo=function(t,e,n){this.variableNames=["x"],this.outputShape=n;var r=n.length,i=Ei(n.length),a=Ei(n.length),o="";if(1===r)o="coords * strides + begin";else{var s=0;o=n.map(function(t,e){return s++,1===n.length?"coords * strides["+e+"] + begin["+e+"]":"coords["+(s-1)+"] * strides["+e+"] + begin["+e+"]"}).join(",")}this.userCode="\n      "+i+" begin = "+i+"("+t+");\n      "+i+" strides = "+i+"("+e+");\n\n      void main() {\n        "+a+" coords = getOutputCoords();\n        setOutput(getX("+o+"));\n      }\n    "},mo=function(){function t(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return t.prototype.acquireTexture=function(t,e,n){var r,i=go(e,n),a=vo(t,i,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}return this.numUsedTextures++,this.log(),i===Rt.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):i===Rt.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):i===Rt.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):i===Rt.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):i===Rt.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[a].push(r),r},t.prototype.releaseTexture=function(t,e,n,r){if(null!=this.freeTextures){var i=vo(e,go(n,r),r);i in this.freeTextures||(this.freeTextures[i]=[]),this.freeTextures[i].push(t),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[i],o=a.indexOf(t);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");a.splice(o,1),this.log()}},t.prototype.log=function(){if(this.logEnabled){var t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+t+")")}},t.prototype.getNumUsedTextures=function(){return this.numUsedTextures},t.prototype.getNumFreeTextures=function(){return this.numFreeTextures},t.prototype.dispose=function(){var t=this;if(null!=this.freeTextures){for(var e in this.freeTextures)this.freeTextures[e].forEach(function(e){t.gpgpu.deleteMatrixTexture(e)});for(var e in this.usedTextures)this.usedTextures[e].forEach(function(e){t.gpgpu.deleteMatrixTexture(e)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0}},t}();function go(e,n){if(e===At.UPLOAD)return Rt.PACKED_2X2_FLOAT32;if(e===At.RENDER||null==e)return function(e){return t.ENV.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Rt.PACKED_2X2_FLOAT32:Rt.UNPACKED_FLOAT32:e?Rt.PACKED_2X2_FLOAT16:Rt.UNPACKED_FLOAT16}(n);if(e===At.DOWNLOAD||e===At.PIXELS)return Rt.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+e)}function vo(t,e,n){return t[0]+"_"+t[1]+"_"+e+"_"+n}var yo=function(t,e){this.variableNames=["A"];for(var n=new Array(t.length),r=0;r<n.length;r++)n[r]=t[r]*e[r];this.outputShape=n,this.rank=n.length;var i=Ei(this.rank),a=function(t){var e=t.length;if(e>5)throw Error("Tile for rank "+e+" is not yet supported");if(1===e)return"imod(resRC, "+t[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],i=0;i<t.length;i++)r.push("imod("+n[i]+", "+t[i]+")");return r.join()}(t);this.userCode="\n      void main() {\n        "+i+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    "},bo=function(t,e){this.variableNames=["A"];for(var n=new Array(t.length),r=0;r<n.length;r++)n[r]=t[e[r]];this.outputShape=n,this.rank=n.length;var i=Ei(this.rank),a=function(t){var e=t.length;if(e>6)throw Error("Transpose for rank "+e+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(e),i=0;i<t.length;i++)r[t[i]]=n[i];return r.join()}(e);this.userCode="\n    void main() {\n      "+i+" resRC = getOutputCoords();\n      setOutput(getA("+a+"));\n    }\n    "},xo=function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0;for(var n=new Array(t.length),r=0;r<n.length;r++)n[r]=t[e[r]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error("Packed transpose for rank "+this.rank+" is not yet supported.");var i=Ei(this.rank),a=pi("rc",this.rank),o=new Array(this.rank);for(r=0;r<e.length;r++)o[e[r]]=a[r];var s="vec2("+o.slice(-2).join()+")",u="++"+a[this.rank-1]+" < "+n[this.rank-1],l="getChannel(getA("+o.join()+"), "+s+")";this.userCode="\n    void main() {\n      "+i+" rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = "+l+";\n      if("+u+") {\n        result[1] = "+l+";\n      }\n      --"+a[this.rank-1]+";\n      if(++"+a[this.rank-2]+" < "+n[this.rank-2]+") {\n        result[2] = "+l+";\n        if("+u+") {\n          result[3] = "+l+";\n        }\n      }  \n      setOutput(result);\n    }\n    "},wo=1.7580993408473768,No=1.0507009873554805,Co=function(t,e){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      float unaryOperation(float x) {\n        "+e+"\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    "},Eo="if (isnan(x)) return x;",So="return x;",ko="return abs(x);",Io=Eo+"\n  return (x < 0.0) ? 0.0 : x;\n",Ao="return (x >= 0.0) ? x : (exp(x) - 1.0);",Ro="return -x;",To="return ceil(x);",Do="return floor(x);",Oo="return exp(x);",_o="return exp(x) - 1.0;",Fo="return x;",Mo="return x;",zo="\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",Lo="\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n",Po=function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t,this.userCode="\n      vec4 unaryOperation(vec4 x) {\n        "+e+"\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    "},Bo=function(t){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t;var e=t.length,n=hi("rc",e),r=Ei(e),i=function(t,e){if(1===t)return"rc";for(var n="",r=0;r<t;r++)n+=e[r],r<t-1&&(n+=",");return n}(e,n),a=n.slice(-2),o=e<=1?"rc":"vec2("+a.join(",")+")";this.userCode="\n      void main() {\n        "+r+" rc = getOutputCoords();\n        vec4 packedInput = getA("+i+");\n\n        setOutput(getChannel(packedInput, "+o+"));\n      }\n    "},Vo={};function Wo(t,e){if(void 0===e&&(e=!1),"linear"===t)return e?Mo:So;if("relu"===t)return e?zo:Io;if("elu"===t)return e?Lo:Ao;if("prelu"===t)return e?Pi:zi;throw new Error("Activation "+t+" has not been implemented for the WebGL backend.")}var Uo=600,jo=function(){function e(e){if(this.gpgpu=e,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.disposed=!1,!t.ENV.getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(null==e){var n=_t(t.ENV.getNumber("WEBGL_VERSION"));this.binaryCache=(r=t.ENV.getNumber("WEBGL_VERSION"))in Vo?Vo[r]:(Vo[r]={},Vo[r]),this.gpgpu=new za(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0}else this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;var r;this.textureManager=new mo(this.gpgpu),this.numMBBeforeWarning=null==t.ENV.global.screen?1024:t.ENV.global.screen.height*t.ENV.global.screen.width*window.devicePixelRatio*Uo/1024/1024,this.texData=new Dr(this,kt)}return e.prototype.register=function(t,e,n){if(this.texData.has(t))throw new Error("Data buffer is already registered");this.texData.set(t,{shape:e,dtype:n})},e.prototype.fromPixels=function(e,n){if(null==e)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");var r="undefined"!=typeof OffscreenCanvas&&e instanceof OffscreenCanvas||"undefined"!=typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement,i=e.data instanceof Uint8Array,a="undefined"!=typeof ImageData&&e instanceof ImageData,o="undefined"!=typeof HTMLVideoElement&&e instanceof HTMLVideoElement,s="undefined"!=typeof HTMLImageElement&&e instanceof HTMLImageElement,u=o?[e.videoWidth,e.videoHeight]:[e.width,e.height],l=u[0],c=u[1],p=[c,l],h=[c,l,n];if(!(r||i||a||o||s))throw new Error("pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was "+e.constructor.name);(s||o)&&(null==this.fromPixels2DContext&&(this.fromPixels2DContext=Ft(t.ENV.getNumber("WEBGL_VERSION")).getContext("2d")),this.fromPixels2DContext.canvas.width=l,this.fromPixels2DContext.canvas.height=c,this.fromPixels2DContext.drawImage(e,0,0,l,c),e=this.fromPixels2DContext.canvas);var f,d,m=this.makeTensorHandle(p,"int32");if(this.texData.get(m.dataId).usage=At.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(m.dataId),e),t.ENV.getBool("WEBGL_PACK")){f=new ma(h);var g=this.makePackedTensor(f.outputShape,m.dtype);d=this.compileAndRun(f,[m],g)}else f=new da(h),d=this.compileAndRun(f,[m]);return this.disposeData(m.dataId),d},e.prototype.makeTensorHandle=function(t,e){var n={};return this.register(n,t,e),{dataId:n,shape:t,dtype:e}},e.prototype.write=function(e,n){if(null==n)throw new Error("MathBackendWebGL.write(): values can not be null");if(t.ENV.getBool("DEBUG"))for(var r=0;r<n.length;r++){var i=n[r];if(!Ut(i)){if(t.ENV.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"))throw Error("The value "+i+" cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.ENV.set('WEBGL_RENDER_FLOAT32_ENABLED', true);'");throw Error("The value "+i+" cannot be represented on this device.")}}var a=this.texData.get(e);if("complex64"===a.dtype)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.releaseGPUData(e),a.usage=At.UPLOAD,a.values=n},e.prototype.readSync=function(t){var e=this.texData.get(t),n=e.values,r=e.dtype,i=e.complexTensors,a=e.slice,o=e.shape,s=e.isPacked;if(null!=a){var u;u=s?new Po(o,Fo):new Co(o,Fo);var l=this.compileAndRun(u,[{dataId:t,shape:o,dtype:r}]),c=this.readSync(l.dataId);return l.dispose(),c}if(null!=n)return this.convertAndCacheOnCPU(t);if("string"===r)return n;var p,h,f=null!=this.activeTimers;return f&&(p=q()),h="complex64"===r?Zr(i.real.dataSync(),i.imag.dataSync()):this.getValuesFromTexture(t),f&&(this.downloadWaitMs+=q()-p),this.convertAndCacheOnCPU(t,h)},e.prototype.read=function(e){return r(this,void 0,void 0,function(){var n,r,a,o,s,u,l,c,p,h,f,d,m,g,y,b,x,w,N,C,E,S;return i(this,function(i){switch(i.label){case 0:if(this.pendingRead.has(e))return n=this.pendingRead.get(e),[2,new Promise(function(t){return n.push(t)})];if(r=this.texData.get(e),a=r.values,o=r.shape,s=r.slice,u=r.dtype,l=r.complexTensors,c=r.isPacked,null!=s)return p=void 0,p=c?new Po(o,Fo):new Co(o,Fo),h=this.compileAndRun(p,[{dataId:e,shape:o,dtype:u}]),f=this.read(h.dataId),h.dispose(),[2,f];if(null!=a)return[2,this.convertAndCacheOnCPU(e)];if(!t.ENV.getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===t.ENV.getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return d=null,"complex64"!==u&&t.ENV.get("WEBGL_BUFFER_SUPPORTED")&&(m=this.decode(e),g=this.texData.get(m.dataId),d=(S=this.gpgpu).createBufferFromTexture.apply(S,[g.texture].concat(zt(o)))),this.pendingRead.set(e,[]),"complex64"===u?[3,2]:[4,this.gpgpu.createAndWaitForFence()];case 1:i.sent(),i.label=2;case 2:return"complex64"!==u?[3,4]:[4,Promise.all([l.real.data(),l.imag.data()])];case 3:return b=i.sent(),x=b[0],w=b[1],y=Zr(x,w),[3,5];case 4:null==d?y=this.getValuesFromTexture(e):(N=v(o),y=this.gpgpu.downloadFloat32MatrixFromBuffer(d,N)),i.label=5;case 5:return null!=m&&this.disposeData(m.dataId),C=this.convertAndCacheOnCPU(e,y),E=this.pendingRead.get(e),this.pendingRead.delete(e),E.forEach(function(t){return t(C)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,C]}})})},e.prototype.getValuesFromTexture=function(e){var n,r=this,i=this.texData.get(e),a=i.shape,o=i.dtype,s=i.isPacked,u=v(a);if(t.ENV.getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){var l=this.decode(e),c=this.texData.get(l.dataId),p=(n=this.gpgpu).downloadMatrixFromPackedTexture.apply(n,[c.texture].concat(zt(a))).subarray(0,u);return this.disposeData(l.dataId),p}var h=t.ENV.getBool("WEBGL_PACK")&&!0===s,f=h?ve(a):a,d=this.makeTensorHandle(f,"float32");d.size=v(a),this.texData.get(d.dataId).usage=At.DOWNLOAD;var m=_e(function(){var t=h?new la(f):new ua(f);return r.compileAndRun(t,[{shape:f,dtype:o,dataId:e}],d,null)}),g=this.texData.get(m.dataId),y=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(g.texture,g.texShape[0],g.texShape[1]).subarray(0,u);return this.disposeData(d.dataId),y},e.prototype.time=function(t){return r(this,void 0,void 0,function(){var e,n,r,a,o,s,u;return i(this,function(i){switch(i.label){case 0:return e=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t(),a=g(this.activeTimers.map(function(t){return t.query})).filter(function(t){return null!=t}),o=g(this.activeTimers.map(function(t){return t.name})).filter(function(t){return null!=t}),this.activeTimers=e,r&&(this.programTimersStack=null),[4,Promise.all(a)];case 1:return s=i.sent(),u={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:h(s),getExtraProfileInfo:function(){return s.map(function(t,e){return{name:o[e],ms:t}}).map(function(t){return t.name+": "+t.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,u]}})})},e.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?this.gpgpu.beginQuery():{startMs:q(),endMs:null}},e.prototype.endTimer=function(e){return t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(this.gpgpu.endQuery(),e):(e.endMs=q(),e)},e.prototype.getQueryTime=function(e){return r(this,void 0,void 0,function(){var n;return i(this,function(r){return t.ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(n=e).endMs-n.startMs]})})},e.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t))if(this.pendingRead.has(t))this.pendingDisposal.add(t);else if(this.texData.has(t)){this.releaseGPUData(t);var e=this.texData.get(t).complexTensors;null!=e&&(e.real.dispose(),e.imag.dispose()),this.texData.delete(t)}},e.prototype.releaseGPUData=function(t){var e=this.texData.get(t),n=e.texture,r=e.dtype,i=e.texShape,a=e.usage,o=e.isPacked,s=e.slice,u=s&&s.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),null!=n&&(this.numBytesInGPU-=this.computeBytes(i,r),this.textureManager.releaseTexture(n,i,a,o)));var c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null},e.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},e.prototype.getDataInfo=function(t){return this.texData.get(t)},e.prototype.getCPUBackend=function(){return t.ENV.getBool("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=kt.findBackend("cpu")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=128),null!=this.getCPUBackend()&&t.every(function(t){return null==n.texData.get(t.dataId).texture&&t.size<e})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.complex=function(t,e){var n=this.makeOutputArray(t.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:kt.keep(t.clone()),imag:kt.keep(e.clone())},n},e.prototype.real=function(t){return this.texData.get(t.dataId).complexTensors.real.clone()},e.prototype.imag=function(t){return this.texData.get(t.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,n,r){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,n,r);if(0===v(r))return nn([],r,e.dtype);var i=this.texData.get(e.dataId).isPacked,a=Er(e.shape,n,r);if(i||!a){var o=t.ENV.getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ho(r):new co(r),s=o.getCustomSetupFunc(n);return this.compileAndRun(o,[e],null,s)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,n,r)},e.prototype.shallowSlice=function(t,e,n){var r=this.texData.get(t.dataId),i=lt.make(n,{},t.dtype,this),a=this.texData.get(i.dataId);Object.assign(a,r),a.shape=n,a.dtype=t.dtype;var o=Sr(e,t.strides);r.slice&&(o+=r.slice.flatOffset),a.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||t.dataId};var s=this.dataRefCount.get(a.slice.origDataId)||1;return this.dataRefCount.set(a.slice.origDataId,s+1),i},e.prototype.stridedSlice=function(t,e,n,r){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.stridedSlice(t,e,n,r);var i=wr(e,n,r);if(i.some(function(t){return 0===t}))return nn([],i);var a=new fo(e,r,i);return this.compileAndRun(a,[t])},e.prototype.reverse=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oo(e.shape,n):new ao(e.shape,n);return this.compileAndRun(r,[e])},e.prototype.concat=function(e,n){if("complex64"===e[0].dtype){var r=e.map(function(t){return tn(t)}),i=e.map(function(t){return en(t)});return Qe(this.concat(r,n),this.concat(i,n))}if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,n);if(1===e.length)return e[0];if(e.length>t.ENV.getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){var a=Math.floor(e.length/2),o=this.concat(e.slice(0,a),n),s=this.concat(e.slice(a),n);return this.concat([o,s],n)}if(t.ENV.getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){var u=new Gi(e.map(function(t){return t.shape}),n);return this.compileAndRun(u,e)}var l=Je(e.map(function(t){return t.shape}),n),c=e.map(function(t){return t.as2D(-1,v(t.shape.slice(n)))}),p=new ji(c.map(function(t){return t.shape}));return this.compileAndRun(p,c).reshape(l)},e.prototype.neg=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.neg(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Ro,e.dtype);var n=new Co(e.shape,Ro);return this.compileAndRun(n,[e])},e.prototype.batchMatMul=function(t,e,n,r){var i=n?t.shape[2]:t.shape[1],a=r?e.shape[1]:e.shape[2],o=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===i||1===a)&&o>1e3){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===a?t:t.as3D(s,o,1),l=1===a?2:1,c=1===a?e.as3D(s,1,o):e;return this.multiply(u,c).sum(l,!0)}var p=vt(t.dtype,e.dtype),h=new Ga(t.shape,[s,i,a],n,r),f=this.makePackedTensor(h.outputShape,p);return this.compileAndRun(h,[t,e],f)},e.prototype.fusedBatchMatMul=function(t){var e=t.a,n=t.b,r=t.transposeA,i=t.transposeB,a=t.bias,o=t.activation,s=t.preluActivationWeights,u=r?e.shape[2]:e.shape[1],l=i?n.shape[1]:n.shape[2],c=e.shape[0],p=vt(e.dtype,n.dtype),h=null!=a,f=null!=s,d=o?Wo(o,!0):null,m=new Ga(e.shape,[c,u,l],r,i,h,d,f),g=this.makePackedTensor(m.outputShape,p),v=[e,n];return a&&v.push(a),s&&v.push(s),this.compileAndRun(m,v,g)},e.prototype.multiply=function(e,n){if("complex64"===e.dtype){var r=this.texData.get(e.dataId),i=this.texData.get(n.dataId),a=new Oi("return areal * breal - aimag * bimag;",e.shape,n.shape),o=new Oi("return areal * bimag + aimag * breal;",e.shape,n.shape),s=[this.makeComplexComponentTensorHandle(e,r.complexTensors.real),this.makeComplexComponentTensorHandle(e,r.complexTensors.imag),this.makeComplexComponentTensorHandle(n,i.complexTensors.real),this.makeComplexComponentTensorHandle(n,i.complexTensors.imag)],u=this.compileAndRun(a,s),l=this.compileAndRun(o,s),c=this.complex(u,l);return u.dispose(),l.dispose(),c}if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.multiply(e,n);if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,Mi,e.dtype);var p=new Li(Mi,e.shape,n.shape),h=this.makeOutputArray(p.outputShape,e.dtype);return this.compileAndRun(p,[e,n],h)},e.prototype.batchNormalization=function(e,n,r,i,a,o){var s=[e,n,r],u=null;null!=o&&(u=o.shape,s.push(o));var l=null;if(null!=a&&(l=a.shape,s.push(a)),t.ENV.getBool("WEBGL_PACK_NORMALIZATION")){var c=new Di(e.shape,n.shape,r.shape,u,l,i);return this.compileAndRun(c,s)}var p=new Ti(e.shape,n.shape,r.shape,u,l,i);return this.compileAndRun(p,s)},e.prototype.localResponseNormalization4D=function(e,n,r,i,a){var o=t.ENV.getBool("WEBGL_PACK_NORMALIZATION")?new Wa(e.shape,n,r,i,a):new Ba(e.shape,n,r,i,a);return this.compileAndRun(o,[e])},e.prototype.LRNGrad=function(t,e,n,r,i,a,o){var s=new Va(e.shape,r,i,a,o);return this.compileAndRun(s,[e,n,t])},e.prototype.tile=function(t,e){if("string"===t.dtype){var n=this.readSync(t.dataId).map(function(t){return X(t)});return ai(Ln(t.shape,t.dtype,n),e)}var r=new yo(t.shape,e);return this.compileAndRun(r,[t])},e.prototype.pad=function(e,n,r){var i=t.ENV.getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xa(e.shape,n,r):new $a(e.shape,n,r);return this.compileAndRun(i,[e])},e.prototype.transpose=function(e,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.transpose(e,n);var r=t.ENV.getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xo(e.shape,n):new bo(e.shape,n);return this.compileAndRun(r,[e])},e.prototype.gather=function(t,e,n){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.gather(t,e,n);var r=new ga(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},e.prototype.batchToSpaceND=function(t,e,n){f(t.rank<=4,function(){return"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"});var r=e.reduce(function(t,e){return t*e}),i=pr(t.shape,e,r),a=hr(i.length,e.length),o=fr(t.shape,e,r),s=dr(n,e.length),u=mr(o,n,e.length);return t.reshape(i).transpose(a).reshape(o).slice(s,u)},e.prototype.spaceToBatchND=function(t,e,n){f(t.rank<=4,function(){return"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"});var r=e.reduce(function(t,e){return t*e}),i=[[0,0]];i.push.apply(i,n);for(var a=1+e.length;a<t.shape.length;++a)i.push([0,0]);var o=t.pad(i),s=pr(o.shape,e,r,!1),u=hr(s.length,e.length,!1),l=fr(o.shape,e,r,!1);return o.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(t,e,n){var r=t.shape[0],i=t.shape[1],a=yr(i),o=new Za({windowSize:a,inSize:i,batchSize:r},e),s=o.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(o,[t],c),1===c.shape[1]?c:this.reduce(c,e,n)},e.prototype.argReduce=function(t,e,n){void 0===n&&(n=null);var r=t.shape[0],i=t.shape[1];null!=n&&(r=n.shape[0],i=n.shape[1]);var a=yr(i),o=new ci({windowSize:a,inSize:i,batchSize:r},e,null==n),s=o.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],"int32"),p=[t];return null!=n&&p.push(n),this.compileAndRun(o,p,c),1===c.shape[1]?c:this.argReduce(t,e,c)},e.prototype.argReducePacked=function(t,e,n){void 0===n&&(n=null);var r=null!=n?n.shape:t.shape,i=yr(r[r.length-1]),a=new Ii(r,i,e,null==n),o=this.makePackedTensor(a.outputShape,"int32"),s=null==n?[t]:[t,n];return this.compileAndRun(a,s,o),o.rank===t.rank?this.argReducePacked(t,e,o):o},e.prototype.sum=function(t,e){qe("sum",e,t.rank);var n=Ge(t.shape,e),r=n[0],i=v(n[1]),a=t.as2D(-1,i),o=yt(t.dtype);return this.reduce(a,"sum",o).reshape(r)},e.prototype.prod=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.prod(t,e);var n=Ge(t.shape,e),r=n[0],i=v(n[1]),a=t.as2D(-1,i),o=yt(t.dtype);return this.reduce(a,"prod",o).reshape(r)},e.prototype.unsortedSegmentSum=function(t,e,n){var r=0,i=Ke([r],t.rank),a=t;null!=i&&(a=t.transpose(i),r=Xe(1,t.rank)[0]);var o=function(t,e,n){for(var r=[],i=t.length,a=0;a<i;a++)a!==e?r.push(t[a]):r.push(n);return r}(a.shape,r,n),s=v([a.shape[r]]),u=a.as2D(-1,s),l=yt(t.dtype),c=this.segOpCompute(u,"unsortedSegmentSum",e,l,n).reshape(o);return null!=i&&(c=c.transpose($e(i))),c},e.prototype.segOpCompute=function(t,e,n,r,i){var a=t.shape[0],o=t.shape[1],s=function(t,e){var n,r=!1;for(t<=vr?(n=t,r=!0):n=V(t,Math.floor(Math.sqrt(t)));!r;)n>e||n===t?r=!0:n=V(t,n+1);return n}(o,i),u=new uo({windowSize:s,inSize:o,batchSize:a,numSegments:i},e),l=u.outputShape,c=l[0],p=l[1],h=this.makeOutputArray([c,p],r);return this.compileAndRun(u,[t,n],h),h.shape[1]===i?h:(n=gn(0,i).tile([o/s]),this.segOpCompute(h,e,n,r,i))},e.prototype.argMinMaxReduce=function(e,n,r){var i=[n];if(qe("arg"+r.charAt(0).toUpperCase()+r.slice(1),i,e.rank),!t.ENV.getBool("WEBGL_PACK_REDUCE")||e.rank<=2){var a=Ge(e.shape,i),o=a[0],s=v(a[1]),u=e.as2D(-1,s);return this.argReduce(u,r).reshape(o)}return this.argReducePacked(e,r)},e.prototype.argMin=function(t,e){return this.argMinMaxReduce(t,e,"min")},e.prototype.argMax=function(t,e){return this.argMinMaxReduce(t,e,"max")},e.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(t.rank-1)+" but got axis="+e);var i=new na(t.shape,n,r);return this.compileAndRun(i,[t])},e.prototype.equal=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(equal(a, b));\n","bool");var r=new Li("return float(a == b);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.notEqual=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(notEqual(a, b));\n","bool");var r=new Li("return float(a != b);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.less=function(e,n){if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.less(e,n);if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(lessThan(a, b));\n","bool");var r=new Li("return float(a < b);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.lessEqual=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(lessThanEqual(a, b));\n","bool");var r=new Li("return float(a <= b);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.greater=function(e,n){if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.greater(e,n);if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(greaterThan(a, b));\n","bool");var r=new Li("return float(a > b);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.greaterEqual=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(greaterThanEqual(a, b));\n","bool");var r=new Li("return float(a >= b);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.logicalNot=function(t){var e=new Co(t.shape,"return float(!(x >= 1.0));");return this.compileAndRun(e,[t])},e.prototype.logicalAnd=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n","bool");var r=new Li("return float(a >= 1.0 && b >= 1.0);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.logicalOr=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n","bool");var r=new Li("return float(a >= 1.0 || b >= 1.0);",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"bool");return this.compileAndRun(r,[e,n],i)},e.prototype.select=function(t,e,n){var r=new lo(t.rank,e.shape,e.rank),i=this.makeOutputArray(r.outputShape,vt(e.dtype,n.dtype));return this.compileAndRun(r,[t,e,n],i)},e.prototype.where=function(t){Le("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var e=t.dataSync();return si(t.shape,e)},e.prototype.topk=function(t,e,n){return oi(t.dataSync(),t.shape,t.dtype,e)},e.prototype.min=function(t,e){qe("min",e,t.rank);var n=Ge(t.shape,e),r=n[0],i=v(n[1]),a=t.as2D(-1,i);return this.reduce(a,"min",a.dtype).reshape(r)},e.prototype.minimum=function(e,n){if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.minimum(e,n);var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi("\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",e.shape,n.shape):new Li("\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return min(a, b);\n",e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.mod=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi("\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",e.shape,n.shape):new Li("if (b == 0.0) return NAN;\n  return mod(a, b);",e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.max=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.max(t,e);qe("max",e,t.rank);var n=Ge(t.shape,e),r=n[0],i=v(n[1]),a=t.as2D(-1,i);return this.reduce(a,"max",a.dtype).reshape(r)},e.prototype.maximum=function(e,n){if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.maximum(e,n);var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi("\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",e.shape,n.shape):new Li("\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return max(a, b);\n",e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.all=function(t,e){qe("all",e,t.rank);var n=Ge(t.shape,e),r=n[0],i=v(n[1]),a=t.as2D(-1,i);return this.reduce(a,"all",a.dtype).reshape(r)},e.prototype.any=function(t,e){qe("any",e,t.rank);var n=Ge(t.shape,e),r=n[0],i=v(n[1]),a=t.as2D(-1,i);return this.reduce(a,"any",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi("return (a - b) * (a - b);",e.shape,n.shape):new Li("return (a - b) * (a - b);",e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.realDivide=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(b.x == 0.0) {\n    result.x = NAN;\n  } else if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(b.y == 0.0) {\n    result.y = NAN;\n  } else if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(b.z == 0.0) {\n    result.z = NAN;\n  } else if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(b.w == 0.0) {\n    result.w = NAN;\n  } else if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n","float32",!0);var r=new Li("\nif (b == 0.0) {\n  return NAN;\n}\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"float32");return this.compileAndRun(r,[e,n],i)},e.prototype.floorDiv=function(e,n){if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,"\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n","int32");var r=new Li("\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n",e.shape,n.shape),i=this.makeOutputArray(r.outputShape,"int32");return this.compileAndRun(r,[e,n],i)},e.prototype.add=function(e,n){if("complex64"===e.dtype&&"complex64"===n.dtype)return this.complexSeparableBinaryOp(e,n,_i);if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.add(e,n);var r=vt(e.dtype,n.dtype);if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,_i,r);var i=new Li(_i,e.shape,n.shape),a=this.makeOutputArray(i.outputShape,r);return this.compileAndRun(i,[e,n],a)},e.prototype.packedUnaryOp=function(t,e,n){var r=new Po(t.shape,e),i=this.makePackedTensor(r.outputShape,n);return this.compileAndRun(r,[t],i)},e.prototype.packedBinaryOp=function(t,e,n,r,i){void 0===i&&(i=!1);var a=new Bi(n,t.shape,e.shape,i),o=this.makePackedTensor(a.outputShape,r);return this.compileAndRun(a,[t,e],o)},e.prototype.complexSeparableBinaryOp=function(t,e,n){var r=this,i=this.texData.get(t.dataId),a=this.texData.get(e.dataId),o=[[i.complexTensors.real,a.complexTensors.real],[i.complexTensors.imag,a.complexTensors.imag]].map(function(i){var a=i[0],o=i[1],s=r.makeComplexComponentTensorHandle(t,a),u=r.makeComplexComponentTensorHandle(e,o),l=new Li(n,t.shape,e.shape),c=r.makeOutputArray(l.outputShape,vt(a.dtype,o.dtype));return r.compileAndRun(l,[s,u],c)}),s=o[0],u=o[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(t,e){return{dataId:e.dataId,dtype:e.dtype,shape:t.shape}},e.prototype.addN=function(e){if(1===e.length)return e[0];if(e.length>t.ENV.get("WEBGL_MAX_TEXTURES_IN_SHADER")){var n=Math.floor(e.length/2),r=this.addN(e.slice(0,n)),i=this.addN(e.slice(n));return this.addN([r,i])}var a=e.map(function(t){return t.dtype}).reduce(function(t,e){return vt(t,e)}),o=e.map(function(t){return t.shape}),s=t.ENV.getBool("WEBGL_PACK"),u=s?new li(e[0].shape,o):new ui(e[0].shape,o),l=s?this.makePackedTensor(u.outputShape,a):this.makeOutputArray(u.outputShape,a);return this.compileAndRun(u,e,l)},e.prototype.subtract=function(e,n){if("complex64"===e.dtype&&"complex64"===n.dtype)return this.complexSeparableBinaryOp(e,n,Fi);if(this.shouldExecuteOnCPU([e,n]))return this.cpuBackend.subtract(e,n);var r=vt(e.dtype,n.dtype);if(t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,n,Fi,e.dtype);var i=new Li(Fi,e.shape,n.shape),a=this.makeOutputArray(i.outputShape,r);return this.compileAndRun(i,[e,n],a)},e.prototype.pow=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=r?new Bi("\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",e.shape,n.shape):new Li("\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",e.shape,n.shape),a=vt(e.dtype,n.dtype),o=r?this.makePackedTensor(i.outputShape,a):this.makeOutputArray(i.outputShape,a);return this.compileAndRun(i,[e,n],o)},e.prototype.ceil=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.ceil(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,To,e.dtype);var n=new Co(e.shape,To);return this.compileAndRun(n,[e])},e.prototype.floor=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.floor(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Do,e.dtype);var n=new Co(e.shape,Do);return this.compileAndRun(n,[e])},e.prototype.sign=function(t){var e=new Co(t.shape,"\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n");return this.compileAndRun(e,[t])},e.prototype.isNaN=function(t){var e=new Co(t.shape,"return float(isnan(x));"),n=this.makeOutputArray(e.outputShape,"bool");return this.compileAndRun(e,[t],n)},e.prototype.isInf=function(t){var e=new Co(t.shape,"return float(isinf(x));"),n=this.makeOutputArray(e.outputShape,"bool");return this.compileAndRun(e,[t],n)},e.prototype.isFinite=function(t){var e=new Co(t.shape,"return float(!isnan(x) && !isinf(x));"),n=this.makeOutputArray(e.outputShape,"bool");return this.compileAndRun(e,[t],n)},e.prototype.round=function(t){var e=new Co(t.shape,"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n");return this.compileAndRun(e,[t])},e.prototype.exp=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.exp(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Oo,e.dtype);var n=new Co(e.shape,Oo);return this.compileAndRun(n,[e])},e.prototype.expm1=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.expm1(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,_o,e.dtype);var n=new Co(e.shape,_o);return this.compileAndRun(n,[e])},e.prototype.log=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.log(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,"\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n",e.dtype);var n=new Co(e.shape,"if (x < 0.0) return NAN;\n  return log(x);");return this.compileAndRun(n,[e])},e.prototype.log1p=function(t){var e=new Co(t.shape,"return log(1.0 + x);");return this.compileAndRun(e,[t])},e.prototype.sqrt=function(t){var e=new Co(t.shape,"return sqrt(x);");return this.compileAndRun(e,[t])},e.prototype.rsqrt=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.rsqrt(t);var e=new Co(t.shape,"return inversesqrt(x);");return this.compileAndRun(e,[t])},e.prototype.square=function(t){var e=new Co(t.shape,"return x * x;");return this.compileAndRun(e,[t])},e.prototype.reciprocal=function(t){var e=new Co(t.shape,"return 1.0 / x;");return this.compileAndRun(e,[t])},e.prototype.relu=function(e){var n;return n=t.ENV.getBool("WEBGL_PACK")?new Po(e.shape,zo):new Co(e.shape,Io),this.compileAndRun(n,[e])},e.prototype.prelu=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi(Pi,e.shape,n.shape):new Li(zi,e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.elu=function(e){if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Lo,e.dtype);var n=new Co(e.shape,Ao);return this.compileAndRun(n,[e])},e.prototype.eluDer=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi("\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",e.shape,n.shape):new Li("return (b >= 1.0) ? a : a * (b + 1.0);",e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.selu=function(t){var e=new Co(t.shape,"\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 1.7580993408473768;\n  float scale = 1.0507009873554805;\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n");return this.compileAndRun(e,[t])},e.prototype.int=function(t){var e=new Co(t.shape,"return float(int(x));"),n=this.makeOutputArray(e.outputShape,"int32");return this.compileAndRun(e,[t],n)},e.prototype.clip=function(e,n,r){var i,a=(i=t.ENV.getBool("WEBGL_PACK_CLIP")?new Wi(e.shape):new Vi(e.shape)).getCustomSetupFunc(n,r);return this.compileAndRun(i,[e],null,a)},e.prototype.abs=function(e){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.abs(e);if(t.ENV.getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,ko,e.dtype);var n=new Co(e.shape,ko);return this.compileAndRun(n,[e])},e.prototype.complexAbs=function(t){var e=this.texData.get(t.dataId),n=new Ui(t.shape),r=[this.makeComplexComponentTensorHandle(t,e.complexTensors.real),this.makeComplexComponentTensorHandle(t,e.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(t){var e=new Co(t.shape,"return 1.0 / (1.0 + exp(-1.0 * x));");return this.compileAndRun(e,[t])},e.prototype.softplus=function(t){var e=new Co(t.shape,"\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n");return this.compileAndRun(e,[t])},e.prototype.sin=function(t){var e=new Co(t.shape,"if (isnan(x)) return x;\n  return sin(x);\n");return this.compileAndRun(e,[t])},e.prototype.cos=function(t){var e=new Co(t.shape,"if (isnan(x)) return x;\n  return cos(x);\n");return this.compileAndRun(e,[t])},e.prototype.tan=function(t){var e=new Co(t.shape,"return tan(x);");return this.compileAndRun(e,[t])},e.prototype.asin=function(t){var e=new Co(t.shape,"return asin(x);");return this.compileAndRun(e,[t])},e.prototype.acos=function(t){var e=new Co(t.shape,"return acos(x);");return this.compileAndRun(e,[t])},e.prototype.atan=function(t){var e=new Co(t.shape,"if (isnan(x)) return x;\n  return atan(x);\n");return this.compileAndRun(e,[t])},e.prototype.atan2=function(e,n){var r=t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bi("\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  \n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n\n  return result;\n",e.shape,n.shape):new Li("\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n\n  return atan(a, b);\n",e.shape,n.shape);return this.compileAndRun(r,[e,n])},e.prototype.sinh=function(t){var e=new Co(t.shape,"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},e.prototype.cosh=function(t){var e=new Co(t.shape,"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},e.prototype.tanh=function(t){var e=new Co(t.shape,"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n");return this.compileAndRun(e,[t])},e.prototype.asinh=function(t){var e=new Co(t.shape,"return log(x + sqrt(x * x + 1.0));");return this.compileAndRun(e,[t])},e.prototype.acosh=function(t){var e=new Co(t.shape,"if (isnan(x)) return x;\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));");return this.compileAndRun(e,[t])},e.prototype.atanh=function(t){var e=new Co(t.shape,"if (isnan(x)) return x;\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;");return this.compileAndRun(e,[t])},e.prototype.erf=function(t){var e=new Co(t.shape,'\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n  float p = 0.3275911;\n  float a1 = 0.254829592;\n  float a2 = -0.284496736;\n  float a3 = 1.421413741;\n  float a4 = -1.453152027;\n  float a5 = 1.061405429;\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n');return this.compileAndRun(e,[t])},e.prototype.step=function(t,e){var n=new Co(t.shape,function(t){return void 0===t&&(t=0),Eo+"\n    return x > 0.0 ? 1.0 : float("+t+");\n  "}(e));return this.compileAndRun(n,[t])},e.prototype.conv2dByMatMul=function(e,n,r,i,a,o){var s=e.shape,u=this.texData.get(e.dataId),l=r.inChannels,c=s[0]*s[1]*s[2],p=r.outChannels,h="channelsLast"===r.dataFormat,d=(1===c||1===p)&&l>1e3,m=s[2]%2!=0&&!!u.isPacked;if(d||!t.ENV.getBool("WEBGL_LAZILY_UNPACK")||!t.ENV.getBool("WEBGL_PACK_BINARY_OPERATIONS")||!m){var g=h?s[0]*s[1]*s[2]:s[0]*s[2]*s[3],v=this.reshape(e,[1,g,r.inChannels]),y=this.reshape(n,[1,r.inChannels,r.outChannels]);return this.reshape(this.fusedBatchMatMul({a:v,b:y,transposeA:!1,transposeB:!1,bias:i,activation:a,preluActivationWeights:o}),r.outShape)}var b=h?s[0]*s[1]*(s[2]+1):s[0]*s[2]*(s[3]+1),x=lt.make([1,b,r.inChannels],{dataId:e.dataId},e.dtype,this),w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,f(xe(u.shape,x.shape),function(){return"packed reshape "+u.shape+" to "+x.shape+" isn't free"});var N=this.reshape(n,[1,r.inChannels,r.outChannels]),C=this.fusedBatchMatMul({a:x,b:N,transposeA:!1,transposeB:!1,bias:i,activation:a,preluActivationWeights:o}),E=this.texData.get(C.dataId);return f(E.isPacked,function(){return"batchMatMul result is expected to be packed"}),u.shape=w,E.shape=r.outShape,lt.make(r.outShape,{dataId:C.dataId},C.dtype,this)},e.prototype.conv2dWithIm2Row=function(t,e,n,r,i,a){var o=n.filterWidth,s=n.filterHeight,u=n.inChannels,l=n.outWidth,c=n.outHeight,p="channelsLast"===n.dataFormat,h=o*s*u,f=c*l,d=[h,f],m=t.squeeze([0]),g=e.reshape([1,h,-1]),v=new Pa(d,m.shape,n),y=this.compileAndRun(v,[m]).reshape([1,d[0],d[1]]),b=null!=r,x=null!=a,w=i?Wo(i,!0):null,N=new Ga(y.shape,[1,f,n.outChannels],!0,!1,b,w,x),C=[y,g];r&&C.push(r),x&&C.push(a);var E=this.compileAndRun(N,C);return p?E.reshape([1,c,l,n.outChannels]):E.reshape([1,n.outChannels,c,l])},e.prototype.fusedConv2d=function(e,n,r,i,a,o){if(1===r.filterHeight&&1===r.filterWidth&&1===r.dilationHeight&&1===r.dilationWidth&&1===r.strideHeight&&1===r.strideWidth&&("SAME"===r.padInfo.type||"VALID"===r.padInfo.type))return this.conv2dByMatMul(e,n,r,i,a,o);if(t.ENV.getBool("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,n,r,i,a,o);var s=null!=i,u=null!=o,l=a?Wo(a,!1):null,c=new Ji(r,s,l,u),p=[e,n];return i&&p.push(i),o&&p.push(o),this.compileAndRun(c,p)},e.prototype.conv2d=function(e,n,r){if(1===r.filterHeight&&1===r.filterWidth&&1===r.dilationHeight&&1===r.dilationWidth&&1===r.strideHeight&&1===r.strideWidth&&("SAME"===r.padInfo.type||"VALID"===r.padInfo.type))return this.conv2dByMatMul(e,n,r);if(t.ENV.getBool("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,n,r);var i=new Ji(r);return this.compileAndRun(i,[e,n])},e.prototype.conv2dDerInput=function(t,e,n){var r=new qi(n);return this.compileAndRun(r,[t,e])},e.prototype.conv2dDerFilter=function(t,e,n){var r=new Hi(n);return this.compileAndRun(r,[t,e])},e.prototype.depthwiseConv2D=function(e,n,r){var i;return t.ENV.getBool("WEBGL_PACK_DEPTHWISECONV")&&r.strideWidth<=2&&r.outChannels/r.inChannels==1?(i=new ta(r),this.compileAndRun(i,[e,n],this.makePackedTensor(r.outShape,e.dtype))):(i=new Qi(r),this.compileAndRun(i,[e,n]))},e.prototype.depthwiseConv2DDerInput=function(t,e,n){var r=new Yi(n);return this.compileAndRun(r,[t,e])},e.prototype.depthwiseConv2DDerFilter=function(t,e,n){var r=new Xi(n);return this.compileAndRun(r,[t,e])},e.prototype.conv3d=function(t,e,n){var r=new Zi(n);return this.compileAndRun(r,[t,e])},e.prototype.conv3dDerInput=function(t,e,n){var r=new $i(n);return this.compileAndRun(r,[t,e])},e.prototype.conv3dDerFilter=function(t,e,n){var r=new Ki(n);return this.compileAndRun(r,[t,e])},e.prototype.maxPool=function(t,e){var n=new Ya(e,"max",!1),r=this.makeOutputArray(n.outputShape,t.dtype);return this.compileAndRun(n,[t],r)},e.prototype.avgPool=function(t,e){var n=new Ya(e,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[t],r)},e.prototype.maxPoolBackprop=function(t,e,n,r){var i=new Ya(r,"max",!0),a=this.compileAndRun(i,[e]),o=new Ua(r),s=this.makeOutputArray(o.outputShape,e.dtype),u=this.compileAndRun(o,[t,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(t,e,n){var r=new Ai(n),i=this.makeOutputArray(r.outputShape,e.dtype);return this.compileAndRun(r,[t],i)},e.prototype.cast=function(t,e){return $r(t,e,this)},e.prototype.unstack=function(t,e){for(var n=t.shape[e],r=new Array(t.rank-1),i=0,a=0;a<t.rank;a++)a!==e&&(r[i++]=t.shape[a]);var o=new Array(t.rank).fill(0),s=t.shape.slice();s[e]=1;var u=new Array(n);for(a=0;a<u.length;a++)o[e]=a,u[a]=this.slice(t,o,s).reshape(r);return u},e.prototype.avgPool3d=function(t,e){var n=new 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r=new to(t,e,n);return this.compileAndRun(r,[t])},e.prototype.resizeNearestNeighbor=function(t,e,n,r){var i=new io(t.shape,e,n,r);return this.compileAndRun(i,[t])},e.prototype.resizeNearestNeighborBackprop=function(t,e,n){var r=new ro(t,e,n);return this.compileAndRun(r,[t])},e.prototype.multinomial=function(t,e,n,r){var i=e?t:Rr(t),a=i.shape[0],o=i.shape[1],s=new Ha(a,o,n),u=this.makeOutputArray(s.outputShape,"int32"),l=s.getCustomSetupFunc(r);return this.compileAndRun(s,[i],u,l)},e.prototype.oneHot=function(t,e,n,r){var i=new qa(t.size,e,n,r);return this.compileAndRun(i,[t])},e.prototype.diag=function(t){var e=new sa(t.size);return this.compileAndRun(e,[t])},e.prototype.nonMaxSuppression=function(t,e,n,r,i){return Le("tf.nonMaxSuppression() in webgl locks the UI thread. 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Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. 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a.toTensor()},e.prototype.stridedSlice=function(t,e,n,r){this.assertNotComplex(t,"stridedSlice");var i=wr(e,n,r);if(i.some(function(t){return 0===t}))return nn([],i);for(var a=Ln(i,t.dtype),o=this.bufferSync(t),s=0;s<a.size;s++){for(var u=a.indexToLoc(s),l=new Array(u.length),c=0;c<l.length;c++)l[c]=u[c]*r[c]+e[c];a.set.apply(a,[o.get.apply(o,l)].concat(u))}return a.toTensor()},e.prototype.diag=function(t){for(var e=this.readSync(t.dataId),n=Ln([t.size,t.size],t.dtype),r=n.values,i=0;i<e.length;i++)r[i*t.size+i]=e[i];return n.toTensor()},e.prototype.unstack=function(t,e){for(var n=t.shape[e],r=new Array(t.rank-1),i=0,a=0;a<t.rank;a++)a!==e&&(r[i++]=t.shape[a]);var o=new Array(t.rank).fill(0),s=t.shape.slice();s[e]=1;var u=new Array(n);for(a=0;a<u.length;a++)o[e]=a,u[a]=this.slice(t,o,s).reshape(r);return u},e.prototype.reverse=function(t,e){this.assertNotComplex(t,"reverse");for(var n=Ln(t.shape,t.dtype),r=this.bufferSync(t),i=function(i){var a=n.indexToLoc(i),o=a.slice();e.forEach(function(e){return o[e]=t.shape[e]-1-o[e]}),n.set.apply(n,[r.get.apply(r,o)].concat(a))},a=0;a<n.size;a++)i(a);return n.toTensor()},e.prototype.concat=function(t,e){var n=this;if("complex64"===t[0].dtype){var r=t.map(function(t){return tn(t)}),i=t.map(function(t){return en(t)});return Qe(this.concat(r,e),this.concat(i,e))}var a=t.map(function(t){var n=v(t.shape.slice(e));return t.as2D(-1,n)}),o=Je(a.map(function(t){return t.shape}),1),s=Ln(o,t[0].dtype).values;if(1===a[0].shape[0]){var u=0;a.forEach(function(t){s.set(n.readSync(t.dataId),u),u+=t.size})}else{var l=0;a.forEach(function(t){for(var e=n.readSync(t.dataId),r=0,i=0;i<t.shape[0];++i)for(var a=i*o[1]+l,u=0;u<t.shape[1];++u)s[a+u]=e[r++];l+=t.shape[1]})}var c=Je(t.map(function(t){return t.shape}),e);return nn(s,c,t[0].dtype)},e.prototype.neg=function(t){return this.assertNotComplex(t,"neg"),this.multiply(an(-1),t)},e.prototype.add=function(t,e){return"complex64"===t.dtype||"complex64"===e.dtype?this.broadcastedBinaryComplexOp(t.cast("complex64"),e.cast("complex64"),function(t,e,n,r){return{real:t+n,imag:e+r}}):this.broadcastedBinaryOp(t,e,vt(t.dtype,e.dtype),function(t,e){return t+e})},e.prototype.addN=function(t){var e=this;this.assertNotComplex(t,"addN");for(var n=t.map(function(t){return e.readSync(t.dataId)}),r=Ln(t[0].shape,t[0].dtype),i=r.values,a=0;a<t.length;a++)for(var o=n[a],s=0;s<i.length;s++)i[s]+=o[s];return r.toTensor()},e.prototype.subtract=function(t,e){return"complex64"===t.dtype||"complex64"===e.dtype?this.broadcastedBinaryComplexOp(t.cast("complex64"),e.cast("complex64"),function(t,e,n,r){return{real:t-n,imag:e-r}}):this.broadcastedBinaryOp(t,e,vt(t.dtype,e.dtype),function(t,e){return t-e})},e.prototype.pow=function(t,e){return this.assertNotComplex([t,e],"pow"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return 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a&&(u=this.add(u,a)),o&&(u=xc(this,u,o,s)),u},e.prototype.multiply=function(t,e){return"complex64"===t.dtype||"complex64"===e.dtype?this.broadcastedBinaryComplexOp(t.cast("complex64"),e.cast("complex64"),function(t,e,n,r){return{real:t*n-e*r,imag:t*r+e*n}}):this.broadcastedBinaryOp(t,e,vt(t.dtype,e.dtype),function(t,e){return t*e})},e.prototype.realDivide=function(t,e){return this.assertNotComplex([t,e],"realDivide"),this.broadcastedBinaryOp(t,e,"float32",function(t,e){return t/e})},e.prototype.floorDiv=function(t,e){return this.assertNotComplex([t,e],"floorDiv"),this.broadcastedBinaryOp(t,e,"int32",function(t,e){return Math.floor(t/e)})},e.prototype.sum=function(t,e){this.assertNotComplex(t,"sum"),qe("sum",e,t.rank);for(var n=Ge(t.shape,e),r=n[0],i=n[1],a=fn(r,vt(t.dtype,"int32")),o=v(i),s=this.readSync(a.dataId),u=this.readSync(t.dataId),l=0;l<s.length;++l){for(var c=l*o,p=0,h=0;h<o;++h)p+=u[c+h];s[l]=p}return a},e.prototype.prod=function(t,e){this.assertNotComplex(t,"sum");for(var n=Ge(t.shape,e),r=n[0],i=n[1],a=fn(r,vt(t.dtype,"int32")),o=v(i),s=this.readSync(a.dataId),u=this.readSync(t.dataId),l=0;l<s.length;++l){for(var c=l*o,p=1,h=0;h<o;++h)p*=u[c+h];s[l]=p}return a},e.prototype.unsortedSegmentSum=function(t,e,n){this.assertNotComplex(t,"unsortedSegmentSum");for(var r=[],i=t.rank-e.rank,a=0;a<i;++a)e=e.expandDims(a+1);for(a=0;a<n;++a){var o=an(a,"int32"),s=hu(o,e).asType("float32").mul(t).sum(0);r.push(s)}return or(r)},e.prototype.argMin=function(t,e){this.assertNotComplex(t,"argMin");var n=[e];qe("argMin",n,t.rank);for(var r=Ge(t.shape,n),i=r[0],a=r[1],o=fn(i,"int32"),s=v(a),u=this.readSync(o.dataId),l=this.readSync(t.dataId),c=0;c<u.length;++c){for(var p=c*s,h=l[p],f=0,d=0;d<s;++d){var m=l[p+d];m<h&&(h=m,f=d)}u[c]=f}return o},e.prototype.argMax=function(t,e){this.assertNotComplex(t,"argMax");var n=[e];qe("argMax",n,t.rank);for(var r=Ge(t.shape,n),i=r[0],a=r[1],o=fn(i,"int32"),s=v(a),u=this.readSync(o.dataId),l=this.readSync(t.dataId),c=0;c<u.length;++c){for(var p=c*s,h=l[p],f=0,d=0;d<s;++d){var m=l[p+d];m>h&&(h=m,f=d)}u[c]=f}return o},e.prototype.cumsum=function(t,e,n,r){if(this.assertNotComplex(t,"cumsum"),e!==t.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(t.rank-1)+" but got axis="+e);for(var i=vt(t.dtype,"int32"),a=fn(t.shape,i),o=this.readSync(a.dataId),s=this.readSync(t.dataId),u=t.shape[t.rank-1],l=r?function(t,e){return t+u-e-1}:function(t,e){return t+e},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var h=l(c,p);if(0===p)o[h]=n?0:s[h];else{var f=l(c,p-1);o[h]=n?s[f]+o[f]:s[h]+o[f]}}return a},e.prototype.equal=function(t,e){return this.assertNotComplex([t,e],"equal"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t===e?1:0})},e.prototype.notEqual=function(t,e){return this.assertNotComplex([t,e],"notEqual"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t!==e?1:0})},e.prototype.less=function(t,e){return this.assertNotComplex([t,e],"less"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t<e?1:0})},e.prototype.lessEqual=function(t,e){return this.assertNotComplex([t,e],"lessEqual"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t<=e?1:0})},e.prototype.greater=function(t,e){return this.assertNotComplex([t,e],"greater"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t>e?1:0})},e.prototype.greaterEqual=function(t,e){return this.assertNotComplex([t,e],"greaterEqual"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t>=e?1:0})},e.prototype.logicalNot=function(t){this.assertNotComplex(t,"logicalNot");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)n[r]=e[r]?0:1;return lt.make(t.shape,{values:n},"bool")},e.prototype.logicalAnd=function(t,e){return this.assertNotComplex([t,e],"logicalAnd"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t&&e})},e.prototype.logicalOr=function(t,e){return this.assertNotComplex([t,e],"logicalOr"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t||e})},e.prototype.select=function(t,e,n){this.assertNotComplex([t,e,n],"select");for(var r=this.readSync(t.dataId),i=this.readSync(e.dataId),a=this.readSync(n.dataId),o=fn(e.shape,vt(e.dtype,n.dtype)),s=this.readSync(o.dataId),u=0,l=0===t.rank||t.rank>1||1===e.rank?1:e.shape[1],c=0;c<r.length;c++)for(var p=0;p<l;p++)1===r[c]?s[u++]=i[c]:s[u++]=a[c];return o},e.prototype.where=function(t){this.assertNotComplex([t],"where");var e=this.readSync(t.dataId);return si(t.shape,e)},e.prototype.topk=function(t,e,n){return this.assertNotComplex(t,"topk"),oi(this.readSync(t.dataId),t.shape,t.dtype,e)},e.prototype.min=function(t,e){this.assertNotComplex(t,"min"),qe("min",e,t.rank);for(var n=Ge(t.shape,e),r=n[0],i=n[1],a=fn(r,t.dtype),o=v(i),s=this.readSync(a.dataId),u=this.readSync(t.dataId),l=0;l<s.length;++l){for(var c=l*o,p=u[c],h=0;h<o;++h){var f=u[c+h];f<p&&(p=f)}s[l]=p}return a},e.prototype.minimum=function(t,e){return this.assertNotComplex([t,e],"minimum"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return Math.min(t,e)})},e.prototype.mod=function(t,e){return this.assertNotComplex([t,e],"mod"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){var n=t%e;return t<0&&e<0||t>=0&&e>=0?n:(n+e)%e})},e.prototype.max=function(t,e){this.assertNotComplex(t,"max"),qe("max",e,t.rank);for(var n=Ge(t.shape,e),r=n[0],i=n[1],a=fn(r,t.dtype),o=v(i),s=this.readSync(a.dataId),u=this.readSync(t.dataId),l=0;l<s.length;++l){for(var c=l*o,p=u[c],h=0;h<o;++h){var f=u[c+h];f>p&&(p=f)}s[l]=p}return a},e.prototype.maximum=function(t,e){return this.assertNotComplex([t,e],"maximum"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return Math.max(t,e)})},e.prototype.all=function(t,e){this.assertNotComplex(t,"all"),qe("all",e,t.rank);for(var n=Ge(t.shape,e),r=n[0],i=n[1],a=fn(r,t.dtype),o=v(i),s=this.readSync(a.dataId),u=this.readSync(t.dataId),l=0;l<s.length;++l){for(var c=l*o,p=u[c],h=0;h<o;++h){var f=u[c+h];p=p&&f}s[l]=p}return a},e.prototype.any=function(t,e){this.assertNotComplex(t,"any"),qe("any",e,t.rank);for(var n=Ge(t.shape,e),r=n[0],i=n[1],a=fn(r,t.dtype),o=v(i),s=this.readSync(a.dataId),u=this.readSync(t.dataId),l=0;l<s.length;++l){for(var c=l*o,p=u[c],h=0;h<o;++h){var f=u[c+h];p=p||f}s[l]=p}return a},e.prototype.squaredDifference=function(t,e){return this.assertNotComplex([t,e],"squaredDifference"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){var n=t-e;return n*n})},e.prototype.ceil=function(t){this.assertNotComplex(t,"ceil");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.ceil(e[r]);return lt.make(t.shape,{values:n})},e.prototype.floor=function(t){this.assertNotComplex(t,"floor");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.floor(e[r]);return lt.make(t.shape,{values:n})},e.prototype.sign=function(t){this.assertNotComplex(t,"x");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)e[r]<0?n[r]=-1:e[r]>0?n[r]=1:n[r]=0;return lt.make(t.shape,{values:n})},e.prototype.isNaN=function(t){this.assertNotComplex(t,"x");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)Number.isNaN(e[r])&&(n[r]=1);return lt.make(t.shape,{values:n},"bool")},e.prototype.isInf=function(t){this.assertNotComplex(t,"x");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)Math.abs(e[r])===1/0&&(n[r]=1);return lt.make(t.shape,{values:n},"bool")},e.prototype.isFinite=function(t){this.assertNotComplex(t,"x");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)Number.isFinite(e[r])&&(n[r]=1);return lt.make(t.shape,{values:n},"bool")},e.prototype.round=function(t){this.assertNotComplex(t,"round");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var i=Math.floor(e[r]);e[r]-i<.5?n[r]=Math.floor(e[r]):e[r]-i>.5?n[r]=Math.ceil(e[r]):n[r]=i%2==0?i:i+1}return lt.make(t.shape,{values:n})},e.prototype.exp=function(t){this.assertNotComplex(t,"exp");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.exp(e[r]);return lt.make(t.shape,{values:n})},e.prototype.expm1=function(t){this.assertNotComplex(t,"expm1");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.expm1(e[r]);return lt.make(t.shape,{values:n})},e.prototype.log=function(t){this.assertNotComplex(t,"log");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var i=e[r];n[r]=Math.log(i)}return lt.make(t.shape,{values:n})},e.prototype.log1p=function(t){this.assertNotComplex(t,"log1p");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var i=e[r];n[r]=Math.log1p(i)}return lt.make(t.shape,{values:n})},e.prototype.sqrt=function(t){this.assertNotComplex(t,"sqrt");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var i=e[r];n[r]=Math.sqrt(i)}return lt.make(t.shape,{values:n})},e.prototype.rsqrt=function(t){this.assertNotComplex(t,"rsqrt");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var i=e[r];n[r]=1/Math.sqrt(i)}return lt.make(t.shape,{values:n})},e.prototype.square=function(t){this.assertNotComplex(t,"square");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var i=e[r];n[r]=i*i}return lt.make(t.shape,{values:n})},e.prototype.reciprocal=function(t){this.assertNotComplex(t,"reciprocal");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=1/e[r];return lt.make(t.shape,{values:n})},e.prototype.linear=function(t){return t},e.prototype.relu=function(t){this.assertNotComplex(t,"relu");for(var e=fn(t.shape,t.dtype),n=this.readSync(e.dataId),r=this.readSync(t.dataId),i=0;i<r.length;++i)n[i]=Math.max(0,r[i]);return e},e.prototype.prelu=function(t,e){return this.assertNotComplex([t,e],"prelu"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return t<0?e*t:t})},e.prototype.elu=function(t){this.assertNotComplex(t,"elu");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r){var i=n[r];e[r]=i>=0?i:Math.exp(i)-1}return lt.make(t.shape,{values:e})},e.prototype.eluDer=function(t,e){this.assertNotComplex([t,e],"eluDer");for(var n=new Float32Array(e.size),r=this.readSync(e.dataId),i=this.readSync(t.dataId),a=0;a<r.length;++a){var o=r[a];n[a]=o>=1?i[a]:i[a]*(o+1)}return lt.make(e.shape,{values:n})},e.prototype.selu=function(t){this.assertNotComplex(t,"selu");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r){var i=n[r];e[r]=i>=0?1.0507009873554805*i:1.7580993408473768*(Math.exp(i)-1)}return lt.make(t.shape,{values:e})},e.prototype.clip=function(t,e,n){this.assertNotComplex(t,"clip");for(var r=new Float32Array(t.size),i=this.readSync(t.dataId),a=0;a<i.length;++a){var o=i[a];r[a]=o>n?n:o<e?e:o}return lt.make(t.shape,{values:r})},e.prototype.abs=function(t){for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.abs(n[r]);return lt.make(t.shape,{values:e})},e.prototype.complexAbs=function(t){for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<t.size;++r){var i=n[2*r],a=n[2*r+1];e[r]=Math.hypot(i,a)}return lt.make(t.shape,{values:e})},e.prototype.int=function(t){this.assertNotComplex(t,"int");for(var e=new Int32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=n[r];return lt.make(t.shape,{values:e},"int32")},e.prototype.sigmoid=function(t){this.assertNotComplex(t,"sigmoid");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=1/(1+Math.exp(-n[r]));return lt.make(t.shape,{values:e})},e.prototype.softplus=function(t){this.assertNotComplex(t,"softplus");for(var e=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(t.size),r=this.readSync(t.dataId),i=0;i<r.length;++i){var a,o=r[i]>-e,s=r[i]<e,u=Math.exp(r[i]);a=s?u:o?r[i]:Math.log(1+u),n[i]=a}return lt.make(t.shape,{values:n})},e.prototype.sin=function(t){this.assertNotComplex(t,"sin");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.sin(n[r]);return lt.make(t.shape,{values:e})},e.prototype.cos=function(t){this.assertNotComplex(t,"cos");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.cos(n[r]);return lt.make(t.shape,{values:e})},e.prototype.tan=function(t){this.assertNotComplex(t,"tan");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.tan(n[r]);return lt.make(t.shape,{values:e})},e.prototype.asin=function(t){this.assertNotComplex(t,"asin");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.asin(n[r]);return lt.make(t.shape,{values:e})},e.prototype.acos=function(t){this.assertNotComplex(t,"acos");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.acos(n[r]);return lt.make(t.shape,{values:e})},e.prototype.atan=function(t){this.assertNotComplex(t,"atan");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.atan(n[r]);return lt.make(t.shape,{values:e})},e.prototype.atan2=function(t,e){return this.assertNotComplex([t,e],"atan2"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return Math.atan2(t,e)})},e.prototype.sinh=function(t){this.assertNotComplex(t,"sinh");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.sinh(n[r]);return lt.make(t.shape,{values:e})},e.prototype.cosh=function(t){this.assertNotComplex(t,"cosh");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.cosh(n[r]);return lt.make(t.shape,{values:e})},e.prototype.tanh=function(t){this.assertNotComplex(t,"tanh");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=x(n[r]);return lt.make(t.shape,{values:e})},e.prototype.asinh=function(t){this.assertNotComplex(t,"asinh");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.asinh(n[r]);return lt.make(t.shape,{values:e})},e.prototype.acosh=function(t){this.assertNotComplex(t,"acosh");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.acosh(n[r]);return lt.make(t.shape,{values:e})},e.prototype.atanh=function(t){this.assertNotComplex(t,"atanh");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.atanh(n[r]);return lt.make(t.shape,{values:e})},e.prototype.erf=function(t){this.assertNotComplex(t,"erf");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r){var i=n[r],a=1/(1+.3275911*i);e[r]=1-((((1.061405429*a-1.453152027)*a+1.421413741)*a-.284496736)*a+.254829592)*a*Math.exp(-i*i)}return lt.make(t.shape,{values:e})},e.prototype.step=function(t,e){void 0===e&&(e=0),this.assertNotComplex(t,"step");for(var n=new Float32Array(t.size),r=this.readSync(t.dataId),i=0;i<r.length;++i){var a=r[i];isNaN(a)?n[i]=NaN:n[i]=a>0?1:e}return lt.make(t.shape,{values:n})},e.prototype.fusedConv2d=function(t,e,n,r,i,a){var o=this.conv2d(t,e,n);return r&&(o=this.add(o,r)),i&&(o=xc(this,o,i,a)),o},e.prototype.conv2d=function(t,e,n){this.assertNotComplex([t,e],"conv2d");for(var r=n.filterHeight,i=n.filterWidth,a=n.dilationHeight,o=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l="channelsLast"===n.dataFormat,c=Ln(n.outShape,t.dtype),p=t.strides[0],h=l?t.strides[1]:t.strides[2],f=l?t.strides[2]:1,d=l?1:t.strides[1],m=c.strides[0],g=l?c.strides[1]:c.strides[2],v=l?c.strides[2]:1,y=l?1:c.strides[1],b=this.readSync(t.dataId),x=this.readSync(e.dataId),w=c.values,N=0;N<n.batchSize;++N)for(var C=N*p,E=N*m,S=0;S<n.outHeight;++S)for(var k=E+S*g,I=S*n.strideHeight-u,A=0;A<r;A++){var R=I+A*a;if(!(R<0||R>=n.inHeight))for(var T=A*e.strides[0],D=C+R*h,O=0;O<n.outWidth;++O)for(var _=k+O*v,F=O*n.strideWidth-s,M=0;M<i;M++){var z=F+M*o;if(!(z<0||z>=n.inWidth))for(var L=D+z*f,P=T+M*e.strides[1],B=0;B<n.inChannels;++B){for(var V=b[L+B*d],W=0;W<n.outChannels;++W)w[_+W*y]+=V*x[P+W];P+=n.outChannels}}}return c.toTensor()},e.prototype.conv3d=function(t,e,n){for(var r=n.filterDepth,i=n.filterHeight,a=n.filterWidth,o=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,p=n.padInfo.top,h=Ln(n.outShape,t.dtype),f=this.readSync(t.dataId),d=this.readSync(e.dataId),m=h.values,g=0;g<n.batchSize;++g)for(var v=g*t.strides[0],y=g*h.strides[0],b=0;b<n.outDepth;++b)for(var x=y+b*h.strides[1],w=b*n.strideDepth-l,N=0;N<r;N++){var C=w+N*o;if(!(C<0||C>=n.inDepth))for(var E=N*e.strides[0],S=v+C*t.strides[1],k=0;k<n.outHeight;++k)for(var I=x+k*h.strides[2],A=k*n.strideHeight-p,R=0;R<i;R++){var T=A+R*s;if(!(T<0||T>=n.inHeight))for(var D=E+R*e.strides[1],O=S+T*t.strides[2],_=0;_<n.outWidth;++_)for(var F=I+_*n.outChannels,M=_*n.strideWidth-c,z=0;z<a;z++){var L=M+z*u;if(!(L<0||L>=n.inWidth))for(var P=D+z*e.strides[2],B=O+L*n.inChannels,V=P,W=0;W<n.inChannels;++W){for(var U=f[B+W],j=0;j<n.outChannels;++j)m[F+j]+=U*d[V+j];V+=n.outChannels}}}}return h.toTensor()},e.prototype.conv2dDerInput=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerInput");for(var r=Ln(n.inShape,"float32"),i=r.values,a=this.readSync(t.dataId),o=this.readSync(e.dataId),s=e.strides,u=s[0],l=s[1],c=s[2],p=n.batchSize,h=n.filterHeight,f=n.filterWidth,d=n.inChannels,m=n.inHeight,g=n.inWidth,v=n.outChannels,y=n.outHeight,b=n.outWidth,x=n.strideHeight,w=n.strideWidth,N=n.dataFormat,C=h-1-n.padInfo.top,E=f-1-n.padInfo.left,S="channelsLast"===N,k=r.strides[0],I=S?r.strides[1]:r.strides[2],A=S?r.strides[2]:1,R=S?1:r.strides[1],T=t.strides[0],D=S?t.strides[1]:t.strides[2],O=S?t.strides[2]:1,_=S?1:t.strides[1],F=0;F<p;++F)for(var M=0;M<d;++M)for(var z=0;z<m;++z)for(var L=z-C,P=Math.max(0,Math.ceil(L/x)),B=Math.min(y,(h+L)/x),V=0;V<g;++V){for(var W=V-E,U=Math.max(0,Math.ceil(W/w)),j=Math.min(b,(f+W)/w),G=0,H=P;H<B;++H)for(var q=H*x-L,K=U;K<j;++K)for(var $=T*F+D*H+O*K,X=u*(h-1-q)+l*(f-1-(K*w-W))+c*M,Y=0;Y<v;++Y)G+=a[$+_*Y]*o[X+Y];i[k*F+I*z+A*V+R*M]=G}return r.toTensor()},e.prototype.conv3dDerInput=function(t,e,n){for(var r=Ln(n.inShape,"float32"),i=r.values,a=r.strides,o=a[0],s=a[1],u=a[2],l=a[3],c=this.readSync(t.dataId),p=t.strides,h=p[0],f=p[1],d=p[2],m=p[3],g=this.readSync(e.dataId),v=e.strides,y=v[0],b=v[1],x=v[2],w=v[3],N=n.batchSize,C=n.filterDepth,E=n.filterHeight,S=n.filterWidth,k=n.inChannels,I=n.inDepth,A=n.inHeight,R=n.inWidth,T=n.outChannels,D=n.outDepth,O=n.outHeight,_=n.outWidth,F=n.strideDepth,M=n.strideHeight,z=n.strideWidth,L=C-1-n.padInfo.front,P=E-1-n.padInfo.top,B=S-1-n.padInfo.left,V=0;V<N;++V)for(var W=0;W<k;++W)for(var U=0;U<I;++U)for(var j=U-L,G=Math.max(0,Math.ceil(j/F)),H=Math.min(D,(C+j)/F),q=0;q<A;++q)for(var K=q-P,$=Math.max(0,Math.ceil(K/M)),X=Math.min(O,(E+K)/M),Y=0;Y<R;++Y){for(var J=Y-B,Z=Math.max(0,Math.ceil(J/z)),Q=Math.min(_,(S+J)/z),tt=0,et=G;et<H;++et)for(var nt=et*F-j,rt=$;rt<X;++rt)for(var it=rt*M-K,at=Z;at<Q;++at)for(var ot=h*V+f*et+d*rt+m*at,st=y*(C-1-nt)+b*(E-1-it)+x*(S-1-(at*z-J))+w*W,ut=0;ut<T;++ut)tt+=c[ot+ut]*g[st+ut];i[o*V+s*U+u*q+l*Y+W]=tt}return r.toTensor()},e.prototype.conv2dDerFilter=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerFilter");for(var r=n.strideHeight,i=n.strideWidth,a=n.filterHeight,o=n.filterWidth,s="channelsLast"===n.dataFormat,u=Ln(n.filterShape,"float32"),l=n.padInfo.left,c=n.padInfo.top,p=this.bufferSync(t),h=this.bufferSync(e),f=0;f<a;++f)for(var d=Math.max(0,Math.ceil((c-f)/r)),m=Math.min(n.outHeight,(n.inHeight+c-f)/r),g=0;g<o;++g)for(var v=Math.max(0,Math.ceil((l-g)/i)),y=Math.min(n.outWidth,(n.inWidth+l-g)/i),b=0;b<n.inChannels;++b)for(var x=0;x<n.outChannels;++x){for(var w=0,N=0;N<n.batchSize;++N)for(var C=d;C<m;++C)for(var E=f+C*r-c,S=v;S<y;++S){var k=g+S*i-l;w+=s?p.get(N,E,k,b)*h.get(N,C,S,x):p.get(N,b,E,k)*h.get(N,x,C,S)}u.set(w,f,g,b,x)}return u.toTensor()},e.prototype.conv3dDerFilter=function(t,e,n){for(var r=n.strideDepth,i=n.strideHeight,a=n.strideWidth,o=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=Ln(n.filterShape,"float32"),c=l.values,p=l.strides,h=p[0],f=p[1],d=p[2],m=p[3],g=this.readSync(e.dataId),v=e.strides,y=v[0],b=v[1],x=v[2],w=v[3],N=this.readSync(t.dataId),C=t.strides,E=C[0],S=C[1],k=C[2],I=C[3],A=n.padInfo.front,R=n.padInfo.left,T=n.padInfo.top,D=0;D<o;++D)for(var O=Math.max(0,Math.ceil((A-D)/r)),_=Math.min(n.outDepth,(n.inDepth+A-D)/r),F=D*h,M=0;M<s;++M)for(var z=Math.max(0,Math.ceil((T-M)/i)),L=Math.min(n.outHeight,(n.inHeight+T-M)/i),P=M*f+F,B=0;B<u;++B)for(var V=Math.max(0,Math.ceil((R-B)/a)),W=Math.min(n.outWidth,(n.inWidth+R-B)/a),U=B*d+P,j=0;j<n.inChannels;++j)for(var G=j*m+U,H=0;H<n.outChannels;++H){for(var q=0,K=0;K<n.batchSize;++K)for(var $=K*E,X=K*y,Y=O;Y<_;++Y)for(var J=(D+Y*r-A)*S+$,Z=Y*b+X,Q=z;Q<L;++Q)for(var tt=(M+Q*i-T)*k+J,et=Q*x+Z,nt=V;nt<W;++nt){var rt=nt*w+et;q+=N[(B+nt*a-R)*I+tt+j]*g[rt+H]}c[G+H]=q}return l.toTensor()},e.prototype.depthwiseConv2D=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2D");for(var r=n.filterHeight,i=n.filterWidth,a=n.dilationHeight,o=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=Ln(n.outShape,t.dtype),p=this.readSync(t.dataId),h=this.readSync(e.dataId),f=c.values,d=0;d<n.batchSize;++d)for(var m=d*t.strides[0],g=d*c.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*c.strides[1],b=v*n.strideHeight-s,x=0;x<r;++x){var w=b+x*a;if(!(w<0||w>=n.inHeight))for(var N=x*e.strides[0],C=m+w*t.strides[1],E=0;E<n.outWidth;++E)for(var S=y+E*c.strides[2],k=E*n.strideWidth-u,I=0;I<i;++I){var A=k+I*o;if(!(A<0||A>=n.inWidth))for(var R=N+I*e.strides[1],T=C+A*n.inChannels,D=S,O=R,_=0;_<n.inChannels;++_){for(var F=p[T+_],M=0;M<l;++M)f[D+M]+=F*h[O+M];D+=l,O+=l}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerInput");for(var r=Ln(n.inShape,"float32"),i=r.values,a=r.strides,o=a[0],s=a[1],u=a[2],l=this.readSync(t.dataId),c=t.strides,p=c[0],h=c[1],f=c[2],d=this.readSync(e.dataId),m=e.strides,g=m[0],v=m[1],y=m[2],b=n.batchSize,x=n.filterHeight,w=n.filterWidth,N=n.inChannels,C=n.inHeight,E=n.inWidth,S=n.outChannels,k=n.outHeight,I=n.outWidth,A=n.strideHeight,R=n.strideWidth,T=x-1-n.padInfo.top,D=w-1-n.padInfo.left,O=S/N,_=0;_<b;++_)for(var F=0;F<N;++F)for(var M=0;M<C;++M)for(var z=M-T,L=Math.max(0,Math.ceil(z/A)),P=Math.min(k,(x+z)/A),B=0;B<E;++B){for(var V=B-D,W=Math.max(0,Math.ceil(V/R)),U=Math.min(I,(w+V)/R),j=0,G=L;G<P;++G)for(var H=G*A-z,q=W;q<U;++q)for(var K=p*_+h*G+f*q,$=g*(x-1-H)+v*(w-1-(q*R-V))+y*F,X=0;X<O;++X)j+=l[K+(F*O+X)]*d[$+X];i[o*_+s*M+u*B+F]=j}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,i=n.strideWidth,a=n.filterHeight,o=n.filterWidth,s=Ln(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,p=this.bufferSync(t),h=this.bufferSync(e),f=0;f<a;++f)for(var d=Math.max(0,Math.ceil((l-f)/r)),m=Math.min(n.outHeight,(n.inHeight+l-f)/r),g=0;g<o;++g)for(var v=Math.max(0,Math.ceil((u-g)/i)),y=Math.min(n.outWidth,(n.inWidth+u-g)/i),b=0;b<n.outChannels;++b){for(var x=Math.trunc(b/c),w=b%c,N=0,C=0;C<n.batchSize;++C)for(var E=d;E<m;++E)for(var S=f+E*r-l,k=v;k<y;++k){var I=g+k*i-u;N+=p.get(C,S,I,x)*h.get(C,E,k,b)}s.set(N,f,g,x,w)}return s.toTensor()},e.prototype.tile=function(t,e){return this.assertNotComplex(t,"tile"),ai(this.bufferSync(t),e)},e.prototype.pad=function(t,e,n){this.assertNotComplex(t,"pad");var r=e.map(function(e,n){return e[0]+t.shape[n]+e[1]}),i=e.map(function(t){return t[0]}),a=this.bufferSync(t),o=Ln(r,t.dtype);0!==n&&o.values.fill(n);for(var s=0;s<t.size;s++){var u=a.indexToLoc(s),l=u.map(function(t,e){return t+i[e]});o.set.apply(o,[a.get.apply(a,u)].concat(l))}return o.toTensor()},e.prototype.transpose=function(t,e){this.assertNotComplex(t,"transpose");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[e[r]];var i=this.readSync(t.dataId),a=Ln(n,t.dtype),o=this.bufferSync(t);for(r=0;r<t.size;++r){for(var s=o.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[e[l]];var c=a.locToIndex(u);a.values[c]=i[r]}return a.toTensor()},e.prototype.gather=function(t,e,n){this.assertNotComplex([t,e],"gather");var r=t.shape.slice(),i=this.readSync(e.dataId);r[n]=i.length;for(var a=Ln(r,t.dtype),o=this.bufferSync(t),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=i[u[n]];var c=o.locToIndex(l);a.values[s]=o.values[c]}return a.toTensor()},e.prototype.batchToSpaceND=function(t,e,n){this.assertNotComplex([t],"batchToSpaceND");var r=e.reduce(function(t,e){return t*e}),i=pr(t.shape,e,r),a=hr(i.length,e.length),o=fr(t.shape,e,r),s=dr(n,e.length),u=mr(o,n,e.length);return t.reshape(i).transpose(a).reshape(o).slice(s,u)},e.prototype.spaceToBatchND=function(t,e,n){this.assertNotComplex([t],"spaceToBatchND");var r=e.reduce(function(t,e){return t*e}),i=[[0,0]];i.push.apply(i,n);for(var a=1+e.length;a<t.shape.length;++a)i.push([0,0]);var o=t.pad(i),s=pr(o.shape,e,r,!1),u=hr(s.length,e.length,!1),l=fr(o.shape,e,r,!1);return o.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(t,e,n){this.assertNotComplex(t,"pool");for(var r=e.strideHeight,i=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=e.padInfo.top,c=e.padInfo.left,p="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=this.readSync(t.dataId),f=Ln(e.outShape,t.dtype),d=f.values,m=e.outShape[1]*e.outShape[2]*e.outShape[3],g=e.outShape[2]*e.outShape[3],v=e.outShape[3],y=0;y<e.batchSize;++y)for(var b=y*m,x=y*t.strides[0],w=0;w<e.inChannels;++w)for(var N=0;N<e.outHeight;++N)for(var C=N*r-l,E=Math.max(0,C),S=Math.min(e.inHeight,s+C),k=b+N*g,I=0;I<e.outWidth;++I){for(var A=I*i-c,R=Math.max(0,A),T=Math.min(e.inWidth,u+A),D=p,O=0,_=0,F=E;F<S;F+=a){for(var M=x+F*t.strides[1],z=R;z<T;z+=o){var L=h[M+z*t.strides[2]+w];"max"===n&&L>D?D=L:"avg"===n&&(O+=L,_++)}if(isNaN(D))break}d[k+I*v+w]="avg"===n?O/_:D}return f.toTensor()},e.prototype.maxPool=function(t,e){return this.pool(t,e,"max")},e.prototype.maxPoolPositions=function(t,e){for(var n=Ln(e.outShape,"int32"),r=e.strideHeight,i=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=e.padInfo.top,c=e.padInfo.left,p=this.bufferSync(t),h=0;h<e.batchSize;++h)for(var f=0;f<e.inChannels;++f)for(var d=0;d<e.outHeight;++d){for(var m=d*r-l,g=m;g<0;)g+=a;for(var v=Math.min(e.inHeight,s+m),y=0;y<e.outWidth;++y){for(var b=y*i-c,x=b;x<0;)x+=o;for(var w=Math.min(e.inWidth,u+b),N=Number.NEGATIVE_INFINITY,C=-1,E=g;E<v;E+=a)for(var S=E-m,k=x;k<w;k+=o){var I=k-b,A=p.get(h,E,k,f);A>N&&(N=A,C=S*u+I)}n.set(C,h,d,y,f)}}return n.toTensor()},e.prototype.maxPoolBackprop=function(t,e,n,r){this.assertNotComplex([e,n],"maxPoolBackprop");for(var i=this.maxPoolPositions(e,r),a=r.strideHeight,o=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,h=l-1-r.padInfo.top,f=Ln(e.shape,"float32"),d=this.bufferSync(i),m=this.bufferSync(t),g=0;g<r.batchSize;++g)for(var v=0;v<r.inChannels;++v)for(var y=0;y<r.inHeight;++y)for(var b=0;b<r.inWidth;++b){for(var x=y-h,w=b-p,N=0,C=0;C<l;C+=s){var E=(x+C)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var k=(w+S)/o;if(!(k<0||k>=r.outWidth||Math.floor(k)!==k)){var I=l*c-1-d.get(g,E,k,v)===C*c+S?1:0;0!==I&&(N+=m.get(g,E,k,v)*I)}}}f.set(N,g,y,b,v)}return f.toTensor()},e.prototype.avgPoolBackprop=function(t,e,n){this.assertNotComplex([t,e],"avgPoolBackprop");for(var r=n.strideHeight,i=n.strideWidth,a=n.filterHeight,o=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,h=l-1-n.padInfo.top,f=Ln(e.shape,"float32"),d=1/(a*o),m=this.bufferSync(t),g=0;g<n.batchSize;++g)for(var v=0;v<n.inChannels;++v)for(var y=0;y<n.inHeight;++y)for(var b=0;b<n.inWidth;++b){for(var x=y-h,w=b-p,N=0,C=0;C<l;C+=s){var E=(x+C)/r;if(!(E<0||E>=n.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var k=(w+S)/i;k<0||k>=n.outWidth||Math.floor(k)!==k||(N+=m.get(g,E,k,v))}}f.set(N*d,g,y,b,v)}return f.toTensor()},e.prototype.pool3d=function(t,e,n){this.assertNotComplex(t,"pool3d");for(var r=e.strideDepth,i=e.strideHeight,a=e.strideWidth,o=e.dilationDepth,s=e.dilationHeight,u=e.dilationWidth,l=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=e.padInfo.front,f=e.padInfo.top,d=e.padInfo.left,m="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,g=this.readSync(t.dataId),v=Ln(e.outShape,t.dtype),y=v.values,b=e.outShape[1]*e.outShape[2]*e.outShape[3]*e.outShape[4],x=e.outShape[2]*e.outShape[3]*e.outShape[4],w=e.outShape[3]*e.outShape[4],N=e.outShape[4],C=0;C<e.batchSize;++C)for(var E=C*b,S=C*t.strides[0],k=0;k<e.inChannels;++k)for(var I=0;I<e.outDepth;++I){for(var A=I*r-h,R=A;R<0;)R+=o;for(var T=Math.min(e.inDepth,l+A),D=E+I*x,O=0;O<e.outHeight;++O){for(var _=O*i-f,F=_;F<0;)F+=s;for(var M=Math.min(e.inHeight,c+_),z=D+O*w,L=0;L<e.outWidth;++L){for(var P=L*a-d,B=P;B<0;)B+=u;for(var V=Math.min(e.inWidth,p+P),W=z+L*N,U=m,j=0,G=0,H=R;H<T;H+=o){for(var q=S+H*t.strides[1],K=F;K<M;K+=s){for(var $=q+K*t.strides[2],X=B;X<V;X+=u){var Y=g[$+X*t.strides[3]+k];if("max"===n&&Y>U?U=Y:"avg"===n&&(j+=Y,G++),isNaN(U))break}if(isNaN(U))break}if(isNaN(U))break}y[W+k]="avg"===n?j/G:U}}}return v.toTensor()},e.prototype.avgPool3d=function(t,e){return this.assertNotComplex(t,"avgPool3d"),this.pool3d(t,e,"avg").toFloat()},e.prototype.avgPool3dBackprop=function(t,e,n){this.assertNotComplex([t,e],"avgPool3dBackprop");for(var r=n.strideDepth,i=n.strideHeight,a=n.strideWidth,o=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=n.dilationDepth,c=n.dilationHeight,p=n.dilationWidth,h=n.effectiveFilterDepth,f=n.effectiveFilterHeight,d=n.effectiveFilterWidth,m=h-1-n.padInfo.front,g=d-1-n.padInfo.left,v=f-1-n.padInfo.top,y=Ln(e.shape,"float32"),b=1/(o*s*u),x=this.bufferSync(t),w=0;w<n.batchSize;++w)for(var N=0;N<n.inChannels;++N)for(var C=0;C<n.inDepth;++C)for(var E=0;E<n.inHeight;++E)for(var S=0;S<n.inWidth;++S){for(var k=C-m,I=E-v,A=S-g,R=0,T=0;T<h;T+=l){var D=(k+T)/r;if(!(D<0||D>=n.outDepth||Math.floor(D)!==D))for(var O=0;O<f;O+=c){var _=(I+O)/i;if(!(_<0||_>=n.outHeight||Math.floor(_)!==_))for(var F=0;F<d;F+=p){var M=(A+F)/a;M<0||M>=n.outWidth||Math.floor(M)!==M||(R+=x.get(w,D,_,M,N))}}}y.set(R*b,w,C,E,S,N)}return y.toTensor()},e.prototype.maxPool3d=function(t,e){return this.assertNotComplex(t,"maxPool3d"),this.pool3d(t,e,"max").toFloat()},e.prototype.maxPool3dPositions=function(t,e){for(var n=Ln(e.outShape,"int32"),r=e.strideDepth,i=e.strideHeight,a=e.strideWidth,o=e.dilationDepth,s=e.dilationHeight,u=e.dilationWidth,l=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=e.padInfo.front,f=e.padInfo.top,d=e.padInfo.left,m=this.bufferSync(t),g=0;g<e.batchSize;++g)for(var v=0;v<e.inChannels;++v)for(var y=0;y<e.outDepth;++y){for(var b=y*r-h,x=b;x<0;)x+=o;for(var w=Math.min(e.inDepth,l+b),N=0;N<e.outHeight;++N){for(var C=N*i-f,E=C;E<0;)E+=s;for(var S=Math.min(e.inHeight,c+C),k=0;k<e.outWidth;++k){for(var I=k*a-d,A=I;A<0;)A+=u;for(var R=Math.min(e.inWidth,p+I),T=Number.NEGATIVE_INFINITY,D=-1,O=x;O<w;O+=o)for(var _=O-b,F=E;F<S;F+=s)for(var M=F-C,z=A;z<R;z+=u){var L=z-I,P=m.get(g,O,F,z,v);P>=T&&(T=P,D=_*c*p+M*c+L)}n.set(D,g,y,N,k,v)}}}return n.toTensor()},e.prototype.maxPool3dBackprop=function(t,e,n,r){this.assertNotComplex([e,n],"maxPool3dBackprop");for(var i=this.maxPool3dPositions(e,r),a=r.strideDepth,o=r.strideHeight,s=r.strideWidth,u=r.dilationDepth,l=r.dilationHeight,c=r.dilationWidth,p=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,d=p-1-r.padInfo.front,m=f-1-r.padInfo.left,g=h-1-r.padInfo.top,v=Ln(e.shape,"float32"),y=this.bufferSync(i),b=this.bufferSync(t),x=0;x<r.batchSize;++x)for(var w=0;w<r.inChannels;++w)for(var N=0;N<r.inDepth;++N)for(var C=0;C<r.inHeight;++C)for(var E=0;E<r.inWidth;++E){for(var S=N-d,k=C-g,I=E-m,A=0,R=0;R<p;R+=u){var T=(S+R)/a;if(!(T<0||T>=r.outDepth||Math.floor(T)!==T))for(var D=0;D<h;D+=l){var O=(k+D)/o;if(!(O<0||O>=r.outHeight||Math.floor(O)!==O))for(var _=0;_<f;_+=c){var F=(I+_)/s;if(!(F<0||F>=r.outWidth||Math.floor(F)!==F)){var M=p*h*f-1-y.get(x,T,O,F,w)===R*h*f+D*f+_?1:0;0!==M&&(A+=b.get(x,T,O,F,w)*M)}}}}v.set(A,x,N,C,E,w)}return v.toTensor()},e.prototype.cast=function(t,e){return $r(t,e,this)},e.prototype.reshape=function(t,e){return Xr(t,e)},e.prototype.avgPool=function(t,e){return this.assertNotComplex(t,"avgPool"),this.pool(t,e,"avg").toFloat()},e.prototype.resizeBilinear=function(t,e,n,r){this.assertNotComplex(t,"resizeBilinear");for(var i=t.shape,a=i[0],o=i[1],s=i[2],u=i[3],l=this.readSync(t.dataId),c=new Float32Array(v([a,e,n,u])),p=[r&&e>1?o-1:o,r&&n>1?s-1:s],h=[r&&e>1?e-1:e,r&&n>1?n-1:n],f=0,d=p[0]/h[0],m=p[1]/h[1],g=0;g<a;g++)for(var y=0;y<e;y++)for(var b=d*y,x=Math.floor(b),w=b-x,N=Math.min(o-1,Math.ceil(b)),C=g*t.strides[0]+x*t.strides[1],E=g*t.strides[0]+N*t.strides[1],S=0;S<n;S++)for(var k=m*S,I=Math.floor(k),A=k-I,R=Math.min(s-1,Math.ceil(k)),T=C+I*t.strides[2],D=E+I*t.strides[2],O=C+ +R*t.strides[2],_=E+R*t.strides[2],F=0;F<u;F++){var M=l[T+F],z=l[D+F],L=M+(l[O+F]-M)*A,P=L+(z+(l[_+F]-z)*A-L)*w;c[f++]=P}return nn(c,[a,e,n,u])},e.prototype.resizeBilinearBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeBilinearBackprop");for(var r=e.shape,i=r[0],a=r[1],o=r[2],s=r[3],u=t.shape,l=u[1],c=u[2],p=new Float32Array(i*a*o*s),h=[n&&l>1?a-1:a,n&&c>1?o-1:o],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],d=h[0]/f[0],m=h[1]/f[1],g=this.readSync(t.dataId),v=0,y=0;y<i;y++)for(var b=y*e.strides[0],x=0;x<l;x++)for(var w=x*d,N=Math.floor(w),C=Math.min(Math.ceil(w),a-1),E=b+N*e.strides[1],S=b+C*e.strides[1],k=w-N,I=1-k,A=0;A<c;A++)for(var R=A*m,T=Math.floor(R),D=Math.min(Math.ceil(R),o-1),O=R-T,_=1-O,F=E+T*e.strides[2],M=E+D*e.strides[2],z=S+T*e.strides[2],L=S+D*e.strides[2],P=I*_,B=I*O,V=k*_,W=k*O,U=0;U<s;U++){var j=g[v++];p[F+U]+=j*P,p[M+U]+=j*B,p[z+U]+=j*V,p[L+U]+=j*W}return ln(p,[i,o,a,s],e.dtype)},e.prototype.resizeNearestNeighbor=function(t,e,n,r){this.assertNotComplex(t,"resizeNearestNeighbor");for(var i=t.shape,a=i[0],o=i[1],s=i[2],u=i[3],l=this.readSync(t.dataId),c=new Float32Array(a*e*n*u),p=[r&&e>1?o-1:o,r&&n>1?s-1:s],h=[r&&e>1?e-1:e,r&&n>1?n-1:n],f=p[0]/h[0],d=p[1]/h[1],m=0,g=0;g<a;g++)for(var v=g*t.strides[0],y=0;y<e;y++)for(var b=f*y,x=v+Math.min(o-1,r?Math.round(b):Math.floor(b))*t.strides[1],w=0;w<n;w++)for(var N=d*w,C=x+Math.min(s-1,r?Math.round(N):Math.floor(N))*t.strides[2],E=0;E<u;E++){var S=l[C+E];c[m++]=S}return nn(c,[a,e,n,u],t.dtype)},e.prototype.resizeNearestNeighborBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeNearestNeighborBackprop");for(var r=e.shape,i=r[0],a=r[1],o=r[2],s=r[3],u=t.shape,l=u[1],c=u[2],p=new Float32Array(i*a*o*s),h=this.readSync(t.dataId),f=[n&&l>1?a-1:a,n&&c>1?o-1:o],d=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=f[0]/d[0],g=f[1]/d[1],v=1/m,y=1/g,b=2*Math.ceil(v)+2,x=2*Math.ceil(y)+2,w=0;w<i;w++)for(var N=w*e.strides[0],C=0;C<a;C++)for(var E=N+C*e.strides[1],S=Math.floor(C*v),k=Math.floor(S-b/2),I=0;I<o;I++)for(var A=E+I*e.strides[2],R=Math.floor(I*y),T=Math.floor(R-x/2),D=0;D<s;D++){for(var O=0,_=0;_<b;_++){var F=_+k;if(!(F<0||F>=l)){var M=N+F*t.strides[1],z=F*m;if(C===Math.min(a-1,n?Math.round(z):Math.floor(z)))for(var L=0;L<x;L++){var P=L+T;if(!(P<0||P>=c)){var B=M+P*t.strides[2],V=P*g;I===Math.min(o-1,n?Math.round(V):Math.floor(V))&&(O+=h[B+D])}}}}p[A+D]=O}return ln(p,e.shape,e.dtype)},e.prototype.batchNormalization=function(t,e,n,r,i,a){this.assertNotComplex([t,e,n,i,a],"batchNorm");for(var o=this.readSync(t.dataId),s=this.readSync(e.dataId),u=this.readSync(n.dataId),l=i?this.readSync(i.dataId):new Float32Array([1]),c=a?this.readSync(a.dataId):new Float32Array([0]),p=new Float32Array(o.length),h=c.length,f=l.length,d=u.length,m=s.length,g=0,v=0,y=0,b=0,x=0;x<o.length;++x)p[x]=c[g++]+(o[x]-s[v++])*l[y++]/Math.sqrt(u[b++]+r),g>=h&&(g=0),v>=m&&(v=0),y>=f&&(y=0),b>=d&&(b=0);return ln(p,t.shape)},e.prototype.localResponseNormalization4D=function(t,e,n,r,i){this.assertNotComplex(t,"localResponseNormalization4D");var a=t.shape[3],o=a-1,s=this.readSync(t.dataId),u=t.size,l=new Float32Array(u);function c(t){for(var n=t%a,r=t-n+Math.max(0,n-e),i=t-n+Math.min(n+e,o),u=0;r<=i;r++){var l=s[r];u+=l*l}return u}for(var p=0;p<u;p++){var h=c(p),f=s[p]*Math.pow(n+r*h,-i);l[p]=f}return ln(l,t.shape)},e.prototype.LRNGrad=function(t,e,n,r,i,a,o){this.assertNotComplex(t,"LRNGrad");for(var s=t.shape[3],u=this.readSync(t.dataId),l=this.readSync(e.dataId),c=this.readSync(n.dataId),p=new Float32Array(t.size),h=t.size,f=0;f<h;f++){for(var d=f%s,m=f-d+Math.max(0,d-r),g=f-d+Math.min(s,d+r+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);for(v=a*v+i,y=m;y<g;y++){var b=-2*a*o*l[y]*c[f]/v;f===y&&(b+=Math.pow(v,-o)),b*=u[f],p[y]+=b}}return ln(p,t.shape)},e.prototype.multinomial=function(t,e,n,r){this.assertNotComplex(t,"multinomial");for(var i=e?t:Rr(t),a=i.shape[0],o=i.shape[1],s=fn([a,n],"int32"),u=this.readSync(s.dataId),l=this.readSync(i.dataId),c=0;c<a;++c){var p=c*o,h=new Float32Array(o-1);h[0]=l[p];for(var f=1;f<h.length;++f)h[f]=h[f-1]+l[p+f];for(var d=_n(r.toString()),m=c*n,g=0;g<n;++g){var v=d();u[m+g]=h.length;for(var y=0;y<h.length;y++)if(v<h[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(t,e,n,r){this.assertNotComplex(t,"oneHot");var i=new Float32Array(t.size*e);i.fill(r);for(var a=this.readSync(t.dataId),o=0;o<t.size;++o)a[o]>=0&&a[o]<e&&(i[o*e+a[o]]=n);return sn(i,[t.size,e],"int32")},e.prototype.nonMaxSuppression=function(t,e,n,r,i){return this.assertNotComplex(t,"nonMaxSuppression"),ni(this.readSync(t.dataId),this.readSync(e.dataId),n,r,i)},e.prototype.fft=function(t){return this.fftBatch(t,!1)},e.prototype.ifft=function(t){return this.fftBatch(t,!0)},e.prototype.fftBatch=function(t,e){for(var n=t.shape[0],r=t.shape[1],i=Ln(t.shape,"float32"),a=Ln(t.shape,"float32"),o=tn(t).as2D(n,r),s=en(t).as2D(n,r),u=0;u<n;u++)for(var l=o.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),p=Qe(l,c),h=this.readSync(this.fftImpl(p,e).dataId),f=0;f<r;f++){var d=Qr(h,f);i.values[u*r+f]=d.real,a.values[u*r+f]=d.imag}return Qe(i.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(t,e){var n=t.as1D(),r=n.size;if(this.isExponentOf2(r)){var i=this.fftRadix2(n,r,e).as2D(t.shape[0],t.shape[1]);return e&&(i=Qe(tn(i).div(an(r)),en(i).div(an(r)))),i}var a=this.readSync(t.dataId),o=function(t){for(var e=new Float32Array(t.length/2),n=new Float32Array(t.length/2),r=0;r<t.length;r+=2)e[r/2]=t[r],n[r/2]=t[r+1];return{real:e,imag:n}}(this.fourierTransformByMatmul(a,r,e));return Qe(o.real,o.imag).as2D(t.shape[0],t.shape[1])},e.prototype.isExponentOf2=function(t){return 0==(t&t-1)},e.prototype.fftRadix2=function(t,e,n){if(1===e)return t;var r=this.readSync(t.dataId),i=e/2,a=function(t){for(var e=Math.ceil(t.length/4),n=new Float32Array(e),r=new Float32Array(e),i=0;i<t.length;i+=4)n[Math.floor(i/4)]=t[i],r[Math.floor(i/4)]=t[i+1];return{real:n,imag:r}}(r),o=Qe(a.real,a.imag).as1D(),s=function(t){for(var e=Math.floor(t.length/4),n=new Float32Array(e),r=new Float32Array(e),i=2;i<t.length;i+=4)n[Math.floor(i/4)]=t[i],r[Math.floor(i/4)]=t[i+1];return{real:n,imag:r}}(r),u=Qe(s.real,s.imag).as1D();o=this.fftRadix2(o,i,n),u=this.fftRadix2(u,i,n);var l=function(t,e){for(var n=new Float32Array(t/2),r=new Float32Array(t/2),i=0;i<Math.ceil(t/2);i++){var a=(e?2:-2)*Math.PI*(i/t);n[i]=Math.cos(a),r[i]=Math.sin(a)}return{real:n,imag:r}}(e,n),c=Qe(l.real,l.imag).mul(u),p=o.add(c),h=o.sub(c),f=tn(p).concat(tn(h)),d=en(p).concat(en(h));return Qe(f,d).as1D()},e.prototype.fourierTransformByMatmul=function(t,e,n){for(var r=new Float32Array(2*e),i=0;i<e;i++){for(var a=0,o=0,s=0;s<e;s++){var u=ei(i*s,e,n),l=Qr(t,s);a+=l.real*u.real-l.imag*u.imag,o+=l.real*u.imag+l.imag*u.real}n&&(a/=e,o/=e),ti(r,a,o,i)}return r},e.prototype.depthToSpace=function(t,e,n){f("NHWC"===n,function(){return"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n}),f(e>1,function(){return"blockSize should be > 1 for depthToSpace, but was: "+e});for(var r=t.shape[0],i=t.shape[1],a=t.shape[2],o=t.shape[3],s=i*e,u=a*e,l=o/(e*e),c=this.readSync(t.dataId),p=new Float32Array(r*s*u*l),h=0,d=0;d<r;++d)for(var m=0;m<s;++m)for(var g=Math.floor(m/e),v=m%e,y=0;y<u;++y)for(var b=Math.floor(y/e),x=(v*e+y%e)*l,w=0;w<l;++w){var N=w+x+o*(b+a*(g+i*d));p[h++]=c[N]}return ln(p,[r,s,u,l])},e.prototype.broadcastedBinaryOp=function(t,e,n,r){var i=Mr(t.shape,e.shape),a=Ln(i,n),o=this.readSync(t.dataId),s=this.readSync(e.dataId),u=_r(t.shape,i),l=_r(e.shape,i),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=r(o[p%o.length],s[p%s.length]);else{var h=this.bufferSync(t),f=this.bufferSync(e),d=function(n){var i=a.indexToLoc(n),p=i.slice(-t.rank);u.forEach(function(t){return p[t]=0});var d=h.locToIndex(p),m=i.slice(-e.rank);l.forEach(function(t){return m[t]=0});var g=f.locToIndex(m);c[n]=r(o[d],s[g])};for(p=0;p<c.length;++p)d(p)}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(t,e,n){var r=Mr(t.shape,e.shape),i=Ln(r,"float32"),a=Ln(r,"float32"),o=this.readSync(t.dataId),s=this.readSync(e.dataId),u=_r(t.shape,r),l=_r(e.shape,r),c=i.values,p=a.values;if(u.length+l.length===0)for(var h=0;h<c.length;h++){var f=h%o.length,d=h%s.length,m=n(o[2*f],o[2*f+1],s[2*d],s[2*d+1]);c[h]=m.real,p[h]=m.imag}else{var g=this.bufferSync(this.data.get(t.dataId).complexTensors.real),v=this.bufferSync(this.data.get(e.dataId).complexTensors.real),y=function(r){var a=i.indexToLoc(r),h=a.slice(-t.rank);u.forEach(function(t){return h[t]=0});var f=g.locToIndex(h),d=a.slice(-e.rank);l.forEach(function(t){return d[t]=0});var m=v.locToIndex(d),y=n(o[2*f],o[2*f+1],s[2*m],s[2*m+1]);c[r]=y.real,p[r]=y.imag};for(h=0;h<c.length;h++)y(h)}return this.complex(i.toTensor(),a.toTensor())},e.prototype.split=function(t,e,n){return ii(t,e,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.epsilon=function(){return 1e-7},e.prototype.cropAndResize=function(t,e,n,r,i,a){for(var o=t.shape,s=o[0],u=o[1],l=o[2],c=o[3],p=e.shape[0],h=r[0],f=r[1],d=Ln([p,h,f,c],t.dtype),m=this.readSync(e.dataId),g=this.readSync(n.dataId),v=this.readSync(t.dataId),y=t.strides,b=d.strides,x=0;x<p;x++){var w=4*x,N=m[w],C=m[w+1],E=m[w+2],S=m[w+3],k=g[x];if(!(k>=s))for(var I=h>1?(E-N)*(u-1)/(h-1):0,A=f>1?(S-C)*(l-1)/(f-1):0,R=0;R<h;R++){var T=h>1?N*(u-1)+R*I:.5*(N+E)*(u-1);if(T<0||T>u-1)for(var D=0;D<f;D++)for(var O=0;O<c;O++){var _=O+D*b[2]+R*b[1]+x*b[0];d.values[_]=a}else if("bilinear"===i){var F=Math.floor(T),M=Math.ceil(T),z=T-F;for(D=0;D<f;D++)if((H=f>1?C*(l-1)+D*A:.5*(C+S)*(l-1))<0||H>l-1)for(O=0;O<c;O++)_=O+D*b[2]+R*b[1]+x*b[0],d.values[_]=a;else{var L=Math.floor(H),P=Math.ceil(H),B=H-L;for(O=0;O<c;O++){var V=v[_=O+L*y[2]+F*y[1]+k*y[0]],W=v[_=O+P*y[2]+F*y[1]+k*y[0]],U=v[_=O+L*y[2]+M*y[1]+k*y[0]],j=V+(W-V)*B,G=U+(v[_=O+P*y[2]+M*y[1]+k*y[0]]-U)*B;_=O+D*b[2]+R*b[1]+x*b[0],d.values[_]=j+(G-j)*z}}}else for(D=0;D<f;++D){var H;if((H=f>1?C*(l-1)+D*A:.5*(C+S)*(l-1))<0||H>l-1)for(O=0;O<c;O++)_=O+D*b[2]+R*b[1]+x*b[0],d.values[_]=a;else{var q=Math.round(H),K=Math.round(T);for(O=0;O<c;O++){var $=O+q*y[2]+K*y[1]+k*y[0],X=O+D*b[2]+R*b[1]+x*b[0];d.values[X]=v[$]}}}}}return d.toTensor()},e.prototype.sparseToDense=function(t,e,n,r){var i=br(0,t,n),a=i.sliceRank,o=i.numUpdates,s=i.sliceSize,u=i.strides,l=i.outputSize;return this.scatter(t,e,n,l,s,o,a,u,r,!1)},e.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],i=gr(t,e),a=i[0],o=i[1],s=i[2],u=i[3];if(0===o)return nn([],a,t.dtype);for(var l=new at([o,s],t.dtype),c=this.readSync(e.dataId),p=this.readSync(t.dataId),h=0;h<o;h++){for(var f=[],d=0,m=0;m<r;m++){var g=c[h*r+m];d+=g*u[m],f.push(g)}if(d<0||d>=t.size/s)throw new Error("Invalid indices: "+f+" does not index into "+t.shape);for(var v=0;v<s;v++)l.values[h*s+v]=p[d*s+v]}return l.toTensor().reshape(a)},e.prototype.scatterND=function(t,e,n){var r=br(0,t,n),i=r.sliceRank,a=r.numUpdates,o=r.sliceSize,s=r.strides,u=r.outputSize,l=an(0);return this.scatter(t,e,n,u,o,a,i,s,l,!0)},e.prototype.fill=function(t,e,n){var r=A(n=n||P(e),v(t));return r.fill(e),lt.make(t,{values:r},n)},e.prototype.onesLike=function(t){if("string"===t.dtype)throw new Error("onesLike is not supported for string tensors");return this.fill(t.shape,1,t.dtype)},e.prototype.zerosLike=function(t){var e=A(t.dtype,v(t.shape));return lt.make(t.shape,{values:e},t.dtype)},e.prototype.linspace=function(t,e,n){return Yr(t,e,n)},e.prototype.scatter=function(t,e,n,r,i,a,o,s,u,l){var c=[r/i,i],p=this.readSync(t.dataId),h=this.readSync(e.dataId);if(0===r)return nn([],n,e.dtype);var f=new at(c,e.dtype);f.values.fill(this.readSync(u.dataId)[0]);for(var d=0;d<a;d++){for(var m=[],g=0,v=0;v<o;v++){var y=p[d*o+v];m.push(y),g+=y*s[v]}if(g<0||g>=r/i)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var b=0;b<i;b++)l?f.values[g*i+b]+=h[d*i+b]:f.values[g*i+b]=0===e.rank?h[0]:h[d*i+b]}return f.toTensor().reshape(n)},e}();kt.registerBackend("cpu",function(){return new wc},1);var Nc=function(){function t(){this.textEncoder=new TextEncoder}return t.prototype.fetch=function(t,e){return fetch(t,e)},t.prototype.now=function(){return performance.now()},t.prototype.encode=function(t,e){if("utf-8"!==e&&"utf8"!==e)throw new Error("Browser's encoder only supports utf-8, but got "+e);return this.textEncoder.encode(t)},t.prototype.decode=function(t,e){return new TextDecoder(e).decode(t)},t}();t.ENV.get("IS_BROWSER")&&t.ENV.setPlatform("browser",new Nc);var Cc,Ec=function(){function e(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}return e.prototype.fetch=function(e,n){return null!=t.ENV.global.fetch?t.ENV.global.fetch(e,n):(null==Cc&&(Cc=require("node-fetch")),Cc(e,n))},e.prototype.now=function(){var t=process.hrtime();return 1e3*t[0]+t[1]/1e6},e.prototype.encode=function(t,e){if("utf-8"!==e&&"utf8"!==e)throw new Error("Node built-in encoder only supports utf-8, but got "+e);return this.textEncoder.encode(t)},e.prototype.decode=function(t,e){return 0===t.length?"":new this.util.TextDecoder(e).decode(t)},e}();t.ENV.get("IS_NODE")&&t.ENV.setPlatform("node",new Ec);var Sc={float32:4,int32:4,uint16:2,uint8:1,bool:1},kc=4;function Ic(t,e){for(var n={},r=0,i=function(e){var i=e.name,a=e.dtype,o=e.shape,s=v(o),u=void 0;if("quantization"in e){var l=e.quantization;if("uint8"!==l.dtype&&"uint16"!==l.dtype)throw new Error("Weight "+e.name+" has unknown quantization dtype "+l.dtype+". Supported quantization dtypes are: 'uint8' and 'uint16'.");var c=Sc[l.dtype],p=t.slice(r,r+s*c),h="uint8"===l.dtype?new Uint8Array(p):new Uint16Array(p);if("float32"===a)u=Float32Array.from(h,function(t){return t*l.scale+l.min});else{if("int32"!==a)throw new Error("Unsupported dtype in weight '"+i+"': "+a);u=Int32Array.from(h,function(t){return Math.round(t*l.scale+l.min)})}r+=s*c}else if("string"===a){var f=v(e.shape);u=[];for(var d=0;d<f;d++){var m=new Uint32Array(t.slice(r,r+kc))[0];r+=kc;var g=new Uint8Array(t.slice(r,r+m));u.push(g),r+=m}}else{var y=Sc[a];if(p=t.slice(r,r+s*y),"float32"===a)u=new Float32Array(p);else if("int32"===a)u=new Int32Array(p);else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+i+"': "+a);u=new Uint8Array(p)}r+=s*y}n[i]=nn(u,o,a)},a=0,o=e;a<o.length;a++)i(o[a]);return n}var Ac="undefined"!=typeof Buffer&&("undefined"==typeof Blob||"undefined"==typeof atob||"undefined"==typeof btoa);function Rc(t){return Ac?Buffer.byteLength(t):new Blob([t]).size}function Tc(t){var e=0;t.forEach(function(t){e+=t.byteLength});var n=new Uint8Array(e),r=0;return t.forEach(function(t){n.set(new Uint8Array(t),r),r+=t.byteLength}),n.buffer}function Dc(t){for(t=t.trim();t.endsWith("/");)t=t.slice(0,t.length-1);var e=t.split("/");return e[e.length-1]}function Oc(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==t.modelTopology?0:Rc(JSON.stringify(t.modelTopology)),weightSpecsBytes:null==t.weightSpecs?0:Rc(JSON.stringify(t.weightSpecs)),weightDataBytes:null==t.weightData?0:t.weightData.byteLength}}var _c=function(){function t(){this.saveRouters=[],this.loadRouters=[]}return t.getInstance=function(){return null==t.instance&&(t.instance=new t),t.instance},t.registerSaveRouter=function(e){t.getInstance().saveRouters.push(e)},t.registerLoadRouter=function(e){t.getInstance().loadRouters.push(e)},t.getSaveHandlers=function(e){return t.getHandlers(e,"save")},t.getLoadHandlers=function(e,n){return t.getHandlers(e,"load",n)},t.getHandlers=function(e,n,r){var i=[];return("load"===n?t.getInstance().loadRouters:t.getInstance().saveRouters).forEach(function(t){var n=t(e,r);null!==n&&i.push(n)}),i},t}(),Fc="://",Mc=function(){function t(){this.managers={}}return t.getInstance=function(){return null==t.instance&&(t.instance=new t),t.instance},t.registerManager=function(e,n){f(null!=e,function(){return"scheme must not be undefined or null."}),e.endsWith(Fc)&&(e=e.slice(0,e.indexOf(Fc))),f(e.length>0,function(){return"scheme must not be an empty string."});var r=t.getInstance();f(null==r.managers[e],function(){return"A model store manager is already registered for scheme '"+e+"'."}),r.managers[e]=n},t.getManager=function(t){var e=this.getInstance().managers[t];if(null==e)throw new Error("Cannot find model manager for scheme '"+t+"'");return e},t.getSchemes=function(){return Object.keys(this.getInstance().managers)},t}();function zc(t){if(-1===t.indexOf(Fc))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+Mc.getSchemes().join(","));return{scheme:t.split(Fc)[0],path:t.split(Fc)[1]}}function Lc(t,e,n){return void 0===n&&(n=!1),r(this,void 0,void 0,function(){var r,a,o,s,u,l,c,p,h;return i(this,function(i){switch(i.label){case 0:return f(t!==e,function(){return"Old path and new path are the same: '"+t+"'"}),f((r=_c.getLoadHandlers(t)).length>0,function(){return"Copying failed because no load handler is found for source URL "+t+"."}),f(r.length<2,function(){return"Copying failed because more than one ("+r.length+") load handlers for source URL "+t+"."}),a=r[0],f((o=_c.getSaveHandlers(e)).length>0,function(){return"Copying failed because no save handler is found for destination URL "+e+"."}),f(o.length<2,function(){return"Copying failed because more than one ("+r.length+") save handlers for destination URL "+e+"."}),s=o[0],u=zc(t).scheme,l=zc(t).path,c=u===zc(t).scheme,[4,a.load()];case 1:return p=i.sent(),n&&c?[4,Mc.getManager(u).removeModel(l)]:[3,3];case 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u,l=Oc(e),c=a.transaction(Bc,"readwrite"),p=c.objectStore(Bc),h=p.put({modelPath:n.modelPath,modelArtifactsInfo:l});h.onsuccess=function(){var i=(u=a.transaction(Pc,"readwrite")).objectStore(Pc).put({modelPath:n.modelPath,modelArtifacts:e,modelArtifactsInfo:l});i.onsuccess=function(){return t({modelArtifactsInfo:l})},i.onerror=function(t){var e=(p=c.objectStore(Bc)).delete(n.modelPath);e.onsuccess=function(){return a.close(),r(i.error)},e.onerror=function(t){return a.close(),r(i.error)}}},h.onerror=function(t){return a.close(),r(h.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()}}}},i.onerror=function(t){return r(i.error)}})},t.URL_SCHEME="indexeddb://",t}(),jc=function(e){return t.ENV.getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Uc.URL_SCHEME)?(n=e.slice(Uc.URL_SCHEME.length),new Uc(n)):null;var n};_c.registerSaveRouter(jc),_c.registerLoadRouter(jc);var Gc=function(){function t(){this.indexedDB=Vc()}return t.prototype.listModels=function(){return r(this,void 0,void 0,function(){var t=this;return i(this,function(e){return[2,new Promise(function(e,n){var r=t.indexedDB.open("tensorflowjs",1);r.onupgradeneeded=function(){return Wc(r)},r.onsuccess=function(){var t=r.result,i=t.transaction(Bc,"readonly"),a=i.objectStore(Bc).getAll();a.onsuccess=function(){for(var t={},n=0,r=a.result;n<r.length;n++){var i=r[n];t[i.modelPath]=i.modelArtifactsInfo}e(t)},a.onerror=function(e){return t.close(),n(a.error)},i.oncomplete=function(){return t.close()}},r.onerror=function(t){return n(r.error)}})]})})},t.prototype.removeModel=function(t){return r(this,void 0,void 0,function(){var e=this;return i(this,function(n){var r;return t=(r=t).startsWith(Uc.URL_SCHEME)?r.slice(Uc.URL_SCHEME.length):r,[2,new Promise(function(n,r){var i=e.indexedDB.open("tensorflowjs",1);i.onupgradeneeded=function(){return Wc(i)},i.onsuccess=function(){var e,a=i.result,o=a.transaction(Bc,"readwrite"),s=o.objectStore(Bc),u=s.get(t);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error("Cannot find model with path '"+t+"' in IndexedDB."));var i=s.delete(t),o=function(){var i=(e=a.transaction(Pc,"readwrite")).objectStore(Pc).delete(t);i.onsuccess=function(){return n(u.result.modelArtifactsInfo)},i.onerror=function(t){return r(u.error)}};i.onsuccess=o,i.onerror=function(t){return o(),a.close(),r(u.error)}},u.onerror=function(t){return a.close(),r(u.error)},o.oncomplete=function(){null==e?a.close():e.oncomplete=function(){return a.close()}}},i.onerror=function(t){return r(i.error)}})]})})},t}();if(t.ENV.getBool("IS_BROWSER"))try{Mc.registerManager(Uc.URL_SCHEME,new Gc)}catch(e){}var Hc="/",qc="tensorflowjs_models",Kc="info",$c="model_topology",Xc="weight_specs",Yc="weight_data",Jc="model_metadata";function Zc(t){return{info:[qc,t,Kc].join(Hc),topology:[qc,t,$c].join(Hc),weightSpecs:[qc,t,Xc].join(Hc),weightData:[qc,t,Yc].join(Hc),modelMetadata:[qc,t,Jc].join(Hc)}}function Qc(t){var e=t.split(Hc);if(e.length<3)throw new Error("Invalid key format: "+t);return e.slice(1,e.length-1).join(Hc)}var tp=function(){function e(e){if(!t.ENV.getBool("IS_BROWSER")||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Zc(this.modelPath)}return e.prototype.save=function(t){return r(this,void 0,void 0,function(){var e,n,r;return i(this,function(i){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");e=JSON.stringify(t.modelTopology),n=JSON.stringify(t.weightSpecs),r=Oc(t);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,e),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,(a=t.weightData,Ac?Buffer.from(a).toString("base64"):btoa(String.fromCharCode.apply(null,new Uint8Array(a))))),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy})),[2,{modelArtifactsInfo:r}]}catch(t){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error("Failed to save model '"+this.modelPath+"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes="+r.modelTopologyBytes+", weightSpecsBytes="+r.weightSpecsBytes+", weightDataBytes="+r.weightDataBytes+".")}var a;return[2]})})},e.prototype.load=function(){return r(this,void 0,void 0,function(){var t,e,n,r,a,o,s;return i(this,function(i){if(null==(t=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error("In local storage, there is no model with name '"+this.modelPath+"'");if("JSON"!==t.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");if(e={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error("In local storage, the topology of model '"+this.modelPath+"' is missing.");if(e.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error("In local storage, the weight specs of model '"+this.modelPath+"' are missing.");if(e.weightSpecs=r,null!=(a=this.LS.getItem(this.keys.modelMetadata))&&(o=JSON.parse(a),e.format=o.format,e.generatedBy=o.generatedBy,e.convertedBy=o.convertedBy),null==(s=this.LS.getItem(this.keys.weightData)))throw new Error("In local storage, the binary weight values of model '"+this.modelPath+"' are missing.");return e.weightData=function(t){if(Ac){var e=Buffer.from(t,"base64");return e.buffer.slice(e.byteOffset,e.byteOffset+e.byteLength)}for(var n=atob(t),r=new Uint8Array(n.length),i=0;i<n.length;++i)r.set([n.charCodeAt(i)],i);return r.buffer}(s),[2,e]})})},e.URL_SCHEME="localstorage://",e}(),ep=function(e){return t.ENV.getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(tp.URL_SCHEME)?(n=e.slice(tp.URL_SCHEME.length),new tp(n)):null;var n};_c.registerSaveRouter(ep),_c.registerLoadRouter(ep);var np=function(){function e(){f(t.ENV.getBool("IS_BROWSER"),function(){return"Current environment is not a web browser"}),f(void 0!==window.localStorage,function(){return"Current browser does not appear to support localStorage"}),this.LS=window.localStorage}return e.prototype.listModels=function(){return r(this,void 0,void 0,function(){var t,e,n,r,a,o;return i(this,function(i){for(t={},e=qc+Hc,n=Hc+Kc,r=0;r<this.LS.length;++r)(a=this.LS.key(r)).startsWith(e)&&a.endsWith(n)&&(o=Qc(a),t[o]=JSON.parse(this.LS.getItem(a)));return[2,t]})})},e.prototype.removeModel=function(t){return r(this,void 0,void 0,function(){var e,n;return i(this,function(r){var i;if(t=(i=t).startsWith(tp.URL_SCHEME)?i.slice(tp.URL_SCHEME.length):i,e=Zc(t),null==this.LS.getItem(e.info))throw new Error("Cannot find model at path '"+t+"'");return n=JSON.parse(this.LS.getItem(e.info)),this.LS.removeItem(e.info),this.LS.removeItem(e.topology),this.LS.removeItem(e.weightSpecs),this.LS.removeItem(e.weightData),[2,n]})})},e}();if(t.ENV.getBool("IS_BROWSER"))try{Mc.registerManager(tp.URL_SCHEME,new np)}catch(e){}var rp="model",ip=".json",ap=".weights.bin";function op(t){return new Promise(function(t){return setTimeout(t)}).then(t)}var sp=function(){function e(n){if(!t.ENV.getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");n.startsWith(e.URL_SCHEME)&&(n=n.slice(e.URL_SCHEME.length)),null!=n&&0!==n.length||(n=rp),this.modelTopologyFileName=n+ip,this.weightDataFileName=n+ap}return e.prototype.save=function(t){return r(this,void 0,void 0,function(){var e,n,r,a,o,s;return i(this,function(i){switch(i.label){case 0:if("undefined"==typeof document)throw new Error("Browser downloads are not supported in this environment since `document` is not present");if(e=window.URL.createObjectURL(new Blob([t.weightData],{type:"application/octet-stream"})),!(t.modelTopology instanceof ArrayBuffer))return[3,1];throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");case 1:return n=[{paths:["./"+this.weightDataFileName],weights:t.weightSpecs}],r={modelTopology:t.modelTopology,format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy,weightsManifest:n},a=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),(o=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor).download=this.modelTopologyFileName,o.href=a,[4,op(function(){return o.dispatchEvent(new MouseEvent("click"))})];case 2:return i.sent(),null==t.weightData?[3,4]:((s=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor).download=this.weightDataFileName,s.href=e,[4,op(function(){return s.dispatchEvent(new MouseEvent("click"))})]);case 3:i.sent(),i.label=4;case 4:return[2,{modelArtifactsInfo:Oc(t)}]}})})},e.URL_SCHEME="downloads://",e}(),up=function(){function t(t){if(null==t||t.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+t);this.files=t}return t.prototype.load=function(){return r(this,void 0,void 0,function(){var t,e,n=this;return i(this,function(r){return t=this.files[0],e=this.files.slice(1),[2,new Promise(function(r,i){var a=new FileReader;a.onload=function(a){var 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BrowserFiles supports loading Keras-style tf.Model artifacts only.")},a.readAsText(t)})]})})},t.prototype.checkManifestAndWeightFiles=function(t,e){for(var n=[],r=e.map(function(t){return Dc(t.name)}),i={},a=0,o=t;a<o.length;a++)o[a].paths.forEach(function(t){var a=Dc(t);if(-1!==n.indexOf(a))throw new Error("Duplicate file basename found in weights manifest: '"+a+"'");if(n.push(a),-1===r.indexOf(a))throw new Error("Weight file with basename '"+a+"' is not provided.");i[t]=e[r.indexOf(a)]});if(n.length!==e.length)throw new Error("Mismatch in the number of files in weights manifest ("+n.length+") and the number of weight files provided ("+e.length+").");return i},t}();function lp(t,e,n,r){!function(t){f(null!=t&&Array.isArray(t)&&t.length>0,function(){return"promises must be a none empty array"})}(t),function(t,e){f(t>=0&&t<=1,function(){return"Progress fraction must be in range [0, 1], but got startFraction "+t}),f(e>=0&&e<=1,function(){return"Progress fraction must be in range [0, 1], 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function(n,a,o){return void 0===a&&(a=""),r(e,void 0,void 0,function(){var e,r,s,u,l,c,p,h,f,d;return i(this,function(i){switch(i.label){case 0:if(e=n.map(function(){return!1}),r={},s=null!=o?o.map(function(){return!1}):[],u=[],n.forEach(function(t,n){var i=0;t.weights.forEach(function(t){var a="quantization"in t?t.quantization.dtype:t.dtype,l=Sc[a]*v(t.shape),c=function(){e[n]=!0,null==r[n]&&(r[n]=[]),r[n].push({manifestEntry:t,groupOffset:i,sizeBytes:l})};null!=o?o.forEach(function(e,n){e===t.name&&(c(),s[n]=!0)}):c(),u.push(t.name),i+=l})}),!s.every(function(t){return t}))throw l=o.filter(function(t,e){return!s[e]}),new Error("Could not find weights in manifest with names: "+l.join(", ")+". \nManifest JSON has weights with names: "+u.join(", ")+".");return c=e.reduce(function(t,e,n){return e&&t.push(n),t},[]),p=[],c.forEach(function(t){n[t].paths.forEach(function(t){var e=a+(a.endsWith("/")?"":"/")+t;p.push(e)})}),[4,t(p)];case 1:return h=i.sent(),f={},d=0,c.forEach(function(t){for(var e=n[t].paths.length,i=0,a=0;a<e;a++)i+=h[d+a].byteLength;for(var o=new ArrayBuffer(i),s=new Uint8Array(o),u=0,l=0;l<e;l++){var c=new Uint8Array(h[d+l]);s.set(c,u),u+=c.byteLength}r[t].forEach(function(t){var e=Ic(o.slice(t.groupOffset,t.groupOffset+t.sizeBytes),[t.manifestEntry]);for(var n in e)f[n]=e[n]}),d+=e}),[2,f]}})})}}_c.registerSaveRouter(function(e){return t.ENV.getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(sp.URL_SCHEME)?(void 0===(n=e.slice(sp.URL_SCHEME.length))&&(n="model"),new sp(n)):null;var n});var hp=function(){function e(e,n){if(this.DEFAULT_METHOD="POST",null==n&&(n={}),this.weightPathPrefix=n.weightPathPrefix,this.onProgress=n.onProgress,null!=n.fetchFunc?(f("function"==typeof n.fetchFunc,function(){return"Must pass a function that matches the signature of `fetch` (see 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Please verify this URL points to the model JSON of the model to load.");i.label=2;case 2:return i.trys.push([2,4,,5]),[4,t.json()];case 3:return e=i.sent(),[3,5];case 4:throw i.sent(),n="Failed to parse model JSON of response from "+this.path+".",this.path.endsWith(".pb")?n+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":n+=" Please make sure the server is serving valid JSON for this request.",new Error(n);case 5:if(r=e.modelTopology,a=e.weightsManifest,null==r&&null==a)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==a?[3,7]:[4,this.loadWeights(a)];case 6:u=i.sent(),o=u[0],s=u[1],i.label=7;case 7:return[2,{modelTopology:r,weightSpecs:o,weightData:s}]}})})},e.prototype.loadWeights=function(t){return r(this,void 0,void 0,function(){var e,n,r,a,o,s,u,l,c,p,h;return i(this,function(i){switch(i.label){case 0:for(e=Array.isArray(this.path)?this.path[1]:this.path,n=function(t){var e=t.lastIndexOf("/"),n=t.lastIndexOf("?");return[t.substring(0,e)+"/",n>e?t.substring(n):""]}(e),r=n[0],a=n[1],o=this.weightPathPrefix||r,s=[],u=0,l=t;u<l.length;u++)c=l[u],s.push.apply(s,c.weights);return p=[],t.forEach(function(t){t.paths.forEach(function(t){p.push(o+t+a)})}),[4,cp(p,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress})];case 1:return h=i.sent(),[2,[s,Tc(h)]]}})})},e.URL_SCHEME_REGEX=/^https?:\/\//,e}();function fp(t){return null!=t.match(hp.URL_SCHEME_REGEX)}var dp=function(t,e){return"undefined"==typeof fetch?null:(Array.isArray(t)?t.every(function(t){return fp(t)}):fp(t))?mp(t,{onProgress:e}):null};function mp(t,e){return new hp(t,e)}_c.registerSaveRouter(dp),_c.registerLoadRouter(dp);var gp=function(){function t(t){this.modelArtifacts=t}return t.prototype.load=function(){return r(this,void 0,void 0,function(){return i(this,function(t){return[2,this.modelArtifacts]})})},t}(),vp=function(){function t(t){this.saveHandler=t}return t.prototype.save=function(t){return r(this,void 0,void 0,function(){return i(this,function(e){return[2,this.saveHandler(t)]})})},t}(),yp=Object.freeze({browserFiles:function(t){return new up(t)},browserHTTPRequest:function(t,e){return mp(t,e)},concatenateArrayBuffers:Tc,decodeWeights:Ic,encodeWeights:function(t,e){return r(this,void 0,void 0,function(){var n,a,o,s,u,l=this;return i(this,function(c){switch(c.label){case 0:for(n=[],a=[],o=Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t),s=function(s){var u=o[s],c=Array.isArray(t)?t[s].tensor:t[u];if("float32"!==c.dtype&&"int32"!==c.dtype&&"bool"!==c.dtype&&"string"!==c.dtype)throw new Error("Unsupported dtype in weight '"+u+"': "+c.dtype);var p={name:u,shape:c.shape,dtype:c.dtype};if("string"===c.dtype){var h=new Promise(function(t){return r(l,void 0,void 0,function(){var e,n,r,a,o,s,u;return i(this,function(i){switch(i.label){case 0:return[4,c.bytes()];case 1:for(e=i.sent(),n=e.reduce(function(t,e){return t+e.length},0)+kc*e.length,r=new Uint8Array(n),a=0,o=0;o<e.length;o++)s=e[o],u=new Uint8Array(new Uint32Array([s.length]).buffer),r.set(u,a),a+=kc,r.set(s,a),a+=s.length;return t(r),[2]}})})});a.push(h)}else a.push(c.data());null!=e&&(p.group=e),n.push(p)},u=0;u<o.length;++u)s(u);return[4,Promise.all(a)];case 1:return[2,{data:function(t){if(null===t)throw new Error("Invalid input value: "+JSON.stringify(t));var e=0,n=[];t.forEach(function(t){if(e+=t.byteLength,n.push(t.byteLength===t.buffer.byteLength?t:new t.constructor(t)),!(t instanceof Float32Array||t instanceof Int32Array||t instanceof Uint8Array))throw new Error("Unsupported TypedArray subtype: "+t.constructor.name)});var r=new Uint8Array(e),i=0;return n.forEach(function(t){r.set(new Uint8Array(t.buffer),i),i+=t.byteLength}),r.buffer}(c.sent()),specs:n}]}})})},fromMemory:function(t,e,n,r){return 1===arguments.length?null!=t.modelTopology||null!=t.weightSpecs?new gp(t):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new gp({modelTopology:t})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new gp({modelTopology:t,weightSpecs:e,weightData:n,trainingConfig:r}))},getLoadHandlers:function(t,e){return _c.getLoadHandlers(t,e)},getModelArtifactsInfoForJSON:Oc,getSaveHandlers:function(t){return _c.getSaveHandlers(t)},http:mp,isHTTPScheme:fp,loadWeights:function(t,e,n,a){return void 0===e&&(e=""),r(this,void 0,void 0,function(){return i(this,function(r){return[2,pp(function(t){return cp(t,{requestInit:a})})(t,e,n)]})})},registerLoadRouter:function(t){return _c.registerLoadRouter(t)},registerSaveRouter:function(t){return _c.registerSaveRouter(t)},weightsLoaderFactory:pp,withSaveHandler:function(t){return new vp(t)},copyModel:function(t,e){return r(this,void 0,void 0,function(){return i(this,function(n){return[2,Lc(t,e,!1)]})})},listModels:function(){return r(this,void 0,void 0,function(){var t,e,n,r,a,o,s;return i(this,function(i){switch(i.label){case 0:t=Mc.getSchemes(),e={},n=0,r=t,i.label=1;case 1:return n<r.length?(a=r[n],[4,Mc.getManager(a).listModels()]):[3,4];case 2:for(s in o=i.sent())e[a+Fc+s]=o[s];i.label=3;case 3:return n++,[3,1];case 4:return[2,e]}})})},moveModel:function(t,e){return r(this,void 0,void 0,function(){return i(this,function(n){return[2,Lc(t,e,!0)]})})},removeModel:function(t){return r(this,void 0,void 0,function(){var e;return i(this,function(n){return e=zc(t),[2,Mc.getManager(e.scheme).removeModel(e.path)]})})}}),bp=Ze({confusionMatrix_:function(t,e,n){var r=Ve(t,"labels","confusionMatrix"),i=Ve(e,"predictions","confusionMatrix");f(null==n||n>0&&Number.isInteger(n),function(){return"If provided, numClasses must be a positive integer, but got "+n}),f(1===r.rank,function(){return"Expected the rank of labels to be 1, but got "+r.rank}),f(1===i.rank,function(){return"Expected the rank of predictions to be 1, but got "+i.rank}),f(r.shape[0]===i.shape[0],function(){return"Mismatch in the number of examples: "+r.shape[0]+" vs. "+i.shape[0]+". Labels and predictions should have the same number of elements."}),f(n>0&&Number.isInteger(n),function(){return"numClasses is required to be a positive integer, but got "+n});var a=Kn(r.asType("int32"),n),o=Kn(i.asType("int32"),n);return a.transpose().matMul(o).asType("int32")}}),xp=Object.freeze({confusionMatrix:bp}),wp=Ze({fromPixels_:function(t,e){if(void 0===e&&(e=3),e>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");var n="undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement;if(n&&n&&t.readyState<2)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");return kt.fromPixels(t,e)}}),Np=Object.freeze({toPixels:function(t,e){return r(this,void 0,void 0,function(){var n,r,a,o,s,u,l,c,p,h,f,d,m,g,v,y,b,x,w,N,C,E,S;return i(this,function(i){switch(i.label){case 0:if(n=Ve(t,"img","toPixels"),t instanceof lt||(n=n.toInt()),2!==n.rank&&3!==n.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+n.rank+".");if(r=n.shape.slice(0,2),a=r[0],o=r[1],(s=2===n.rank?1:n.shape[2])>4||2===s)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+s);return[4,n.data()];case 1:return u=i.sent(),l=n.min(),c=n.max(),[4,Promise.all([l.data(),c.data()])];case 2:if(p=i.sent(),h=p[0],f=p[1],d=h[0],m=f[0],l.dispose(),c.dispose(),"float32"===n.dtype){if(d<0||m>1)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+d+" - "+m+"].")}else{if("int32"!==n.dtype)throw new Error("Unsupported type for toPixels: "+n.dtype+". Please use float32 or int32 tensors.");if(d<0||m>255)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+d+" - "+m+"].")}for(g="float32"===n.dtype?255:1,v=new Uint8ClampedArray(o*a*4),y=0;y<a*o;++y)b=void 0,x=void 0,w=void 0,N=void 0,1===s?(b=u[y]*g,x=u[y]*g,w=u[y]*g,N=255):3===s?(b=u[3*y]*g,x=u[3*y+1]*g,w=u[3*y+2]*g,N=255):4===s&&(b=u[4*y]*g,x=u[4*y+1]*g,w=u[4*y+2]*g,N=u[4*y+3]*g),v[0+(C=4*y)]=Math.round(b),v[C+1]=Math.round(x),v[C+2]=Math.round(w),v[C+3]=Math.round(N);return null!=e&&(e.width=o,e.height=a,E=e.getContext("2d"),S=new ImageData(v,o,a),E.putImageData(S,0,0)),n!==t&&n.dispose(),[2,v]}})})},fromPixels:wp}),Cp=function(){function t(){}return t.prototype.getClassName=function(){return this.constructor.className},t.fromConfig=function(t,e){return new t(e)},t}(),Ep=function(){function t(){this.classNameMap={}}return t.getMap=function(){return null==t.instance&&(t.instance=new t),t.instance},t.register=function(e){t.getMap().classNameMap[e.className]=[e,e.fromConfig]},t}();function Sp(t){f(null!=t.className,function(){return"Class being registered does not have the static className property defined."}),f("string"==typeof t.className,function(){return"className is required to be a string, but got type "+typeof t.className}),f(t.className.length>0,function(){return"Class being registered has an empty-string as its className, which is disallowed."}),Ep.register(t)}var kp=Object.freeze({Serializable:Cp,SerializationMap:Ep,registerClass:Sp}),Ip=.001,Ap=.1;function Rp(){return 32===kt.backend.floatPrecision()?Ip:Ap}function Tp(t,e,n){var r=!0;if((O(t)||O(e))&&(r=!1),O(t)&&O(e)&&(r=!0),r){var i=t.constructor.name,a=e.constructor.name;if(i!==a)throw new Error("Arrays are of different type. Actual: "+i+". Expected: "+a)}if(Array.isArray(t)&&Array.isArray(e)){var o=Pe(t),s=Pe(e);if(!y(o,s))throw new Error("Arrays have different shapes. Actual: ["+o+"]. 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null==this.iterations_&&(this.iterations_=0),this.iterations_},enumerable:!0,configurable:!0}),e.prototype.incrementIterations=function(){this.iterations_=this.iterations+1},e.prototype.computeGradients=function(t,e){return kr(t,e)},e.prototype.dispose=function(){null!=this.iterations_&&Fe(this.iterations_)},e.prototype.saveIterations=function(){return r(this,void 0,void 0,function(){return i(this,function(t){return null==this.iterations_&&(this.iterations_=0),[2,{name:"iter",tensor:an(this.iterations_,"int32")}]})})},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){return i(this,function(t){throw new Error("getWeights() is not implemented for this optimizer yet.")})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){return i(this,function(t){throw new Error("setWeights() is not implemented for this optimizer class "+this.getClassName())})})},e.prototype.extractIterations=function(t){return r(this,void 0,void 0,function(){var e;return i(this,function(n){switch(n.label){case 0:return e=this,[4,t[0].tensor.data()];case 1:return e.iterations_=n.sent()[0],[2,t.slice(1)]}})})},e}(Cp);Object.defineProperty(Fp,Symbol.hasInstance,{value:function(t){return null!=t.minimize&&null!=t.computeGradients&&null!=t.applyGradients}});var Mp=function(t){function e(e,n,r){void 0===r&&(r=null);var i=t.call(this)||this;return i.learningRate=e,i.rho=n,i.epsilon=r,i.accumulatedGrads=[],i.accumulatedUpdates=[],null==r&&(i.epsilon=kt.backend.epsilon()),i}return n(e,t),e.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t)).forEach(function(n,r){var i=kt.registeredVariables[n];null==e.accumulatedGrads[r]&&(e.accumulatedGrads[r]={originalName:n+"/accum_grad",variable:_e(function(){return yn(i).variable(!1)})}),null==e.accumulatedUpdates[r]&&(e.accumulatedUpdates[r]={originalName:n+"/accum_var",variable:_e(function(){return yn(i).variable(!1)})});var a=Array.isArray(t)?t[r].tensor:t[n];if(null!=a){var o=e.accumulatedGrads[r].variable,s=e.accumulatedUpdates[r].variable;_e(function(){var t=o.mul(e.rho).add(a.square().mul(1-e.rho)),n=s.add(e.epsilon).sqrt().div(o.add(e.epsilon).sqrt()).mul(a),r=s.mul(e.rho).add(n.square().mul(1-e.rho));o.assign(t),s.assign(r);var u=n.mul(-e.learningRate).add(i);i.assign(u)})}}),this.incrementIterations()},e.prototype.dispose=function(){null!=this.accumulatedUpdates&&(Fe(this.accumulatedGrads.map(function(t){return t.variable})),Fe(this.accumulatedUpdates.map(function(t){return t.variable})))},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){var t;return i(this,function(e){switch(e.label){case 0:return t=this.accumulatedGrads.concat(this.accumulatedUpdates),[4,this.saveIterations()];case 1:return[2,[e.sent()].concat(t.map(function(t){return{name:t.originalName,tensor:t.variable}}))]}})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){var e;return i(this,function(n){switch(n.label){case 0:return[4,this.extractIterations(t)];case 1:return t=n.sent(),e=t.length/2,this.accumulatedGrads=t.slice(0,e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),this.accumulatedUpdates=t.slice(e,2*e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),[2]}})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},e.fromConfig=function(t,e){return new t(e.learningRate,e.rho,e.epsilon)},e.className="Adadelta",e}(Fp);Sp(Mp);var zp=function(t){function e(e,n){void 0===n&&(n=.1);var r=t.call(this)||this;return r.learningRate=e,r.initialAccumulatorValue=n,r.accumulatedGrads=[],r}return n(e,t),e.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t)).forEach(function(n,r){var i=kt.registeredVariables[n];null==e.accumulatedGrads[r]&&(e.accumulatedGrads[r]={originalName:n+"/accumulator",variable:_e(function(){return dn(i.shape,e.initialAccumulatorValue).variable(!1)})});var a=Array.isArray(t)?t[r].tensor:t[n];if(null!=a){var o=e.accumulatedGrads[r].variable;_e(function(){var t=o.add(a.square());o.assign(t);var n=a.div(t.add(kt.backend.epsilon()).sqrt()).mul(-e.learningRate).add(i);i.assign(n)})}}),this.incrementIterations()},e.prototype.dispose=function(){null!=this.accumulatedGrads&&Fe(this.accumulatedGrads.map(function(t){return t.variable}))},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){return i(this,function(t){switch(t.label){case 0:return[4,this.saveIterations()];case 1:return[2,[t.sent()].concat(this.accumulatedGrads.map(function(t){return{name:t.originalName,tensor:t.variable}}))]}})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){return i(this,function(e){switch(e.label){case 0:return[4,this.extractIterations(t)];case 1:return t=e.sent(),this.accumulatedGrads=t.map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),[2]}})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},e.fromConfig=function(t,e){return new t(e.learningRate,e.initialAccumulatorValue)},e.className="Adagrad",e}(Fp);Sp(zp);var Lp=function(t){function e(e,n,r,i){void 0===i&&(i=null);var a=t.call(this)||this;return a.learningRate=e,a.beta1=n,a.beta2=r,a.epsilon=i,a.accumulatedFirstMoment=[],a.accumulatedSecondMoment=[],_e(function(){a.accBeta1=an(n).variable(),a.accBeta2=an(r).variable()}),null==i&&(a.epsilon=kt.backend.epsilon()),a}return n(e,t),e.prototype.applyGradients=function(t){var e=this,n=Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t);_e(function(){var r=cu(1,e.accBeta1),i=cu(1,e.accBeta2);n.forEach(function(n,a){var o=kt.registeredVariables[n];null==e.accumulatedFirstMoment[a]&&(e.accumulatedFirstMoment[a]={originalName:n+"/m",variable:_e(function(){return yn(o).variable(!1)})}),null==e.accumulatedSecondMoment[a]&&(e.accumulatedSecondMoment[a]={originalName:n+"/v",variable:_e(function(){return yn(o).variable(!1)})});var s=Array.isArray(t)?t[a].tensor:t[n];if(null!=s){var u=e.accumulatedFirstMoment[a].variable,l=e.accumulatedSecondMoment[a].variable,c=u.mul(e.beta1).add(s.mul(1-e.beta1)),p=l.mul(e.beta2).add(s.square().mul(1-e.beta2)),h=c.div(r),f=p.div(i);u.assign(c),l.assign(p);var d=h.div(f.sqrt().add(e.epsilon)).mul(-e.learningRate).add(o);o.assign(d)}}),e.accBeta1.assign(e.accBeta1.mul(e.beta1)),e.accBeta2.assign(e.accBeta2.mul(e.beta2))}),this.incrementIterations()},e.prototype.dispose=function(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&Fe(this.accumulatedFirstMoment.map(function(t){return t.variable})),null!=this.accumulatedSecondMoment&&Fe(this.accumulatedSecondMoment.map(function(t){return t.variable}))},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){var t;return i(this,function(e){switch(e.label){case 0:return t=this.accumulatedFirstMoment.concat(this.accumulatedSecondMoment),[4,this.saveIterations()];case 1:return[2,[e.sent()].concat(t.map(function(t){return{name:t.originalName,tensor:t.variable}}))]}})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){var e,n=this;return i(this,function(r){switch(r.label){case 0:return[4,this.extractIterations(t)];case 1:return t=r.sent(),_e(function(){n.accBeta1.assign(ou(n.beta1,n.iterations_+1)),n.accBeta2.assign(ou(n.beta2,n.iterations_+1))}),e=t.length/2,this.accumulatedFirstMoment=t.slice(0,e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),this.accumulatedSecondMoment=t.slice(e,2*e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),[2]}})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},e.fromConfig=function(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)},e.className="Adam",e}(Fp);Sp(Lp);var Pp=function(t){function e(e,n,r,i,a){void 0===i&&(i=null),void 0===a&&(a=0);var o=t.call(this)||this;return o.learningRate=e,o.beta1=n,o.beta2=r,o.epsilon=i,o.decay=a,o.accumulatedFirstMoment=[],o.accumulatedWeightedInfNorm=[],_e(function(){o.iteration=an(0).variable(),o.accBeta1=an(n).variable()}),null==i&&(o.epsilon=kt.backend.epsilon()),o}return n(e,t),e.prototype.applyGradients=function(t){var e=this,n=Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t);_e(function(){var r=cu(1,e.accBeta1),i=Xs(-e.learningRate,e.iteration.mul(e.decay).add(1));n.forEach(function(n,a){var o=kt.registeredVariables[n];null==e.accumulatedFirstMoment[a]&&(e.accumulatedFirstMoment[a]={originalName:n+"/m",variable:yn(o).variable(!1)}),null==e.accumulatedWeightedInfNorm[a]&&(e.accumulatedWeightedInfNorm[a]={originalName:n+"/v",variable:yn(o).variable(!1)});var s=Array.isArray(t)?t[a].tensor:t[n];if(null!=s){var u=e.accumulatedFirstMoment[a].variable,l=e.accumulatedWeightedInfNorm[a].variable,c=u.mul(e.beta1).add(s.mul(1-e.beta1)),p=l.mul(e.beta2),h=s.abs(),f=p.maximum(h);u.assign(c),l.assign(f);var d=i.div(r).mul(c.div(f.add(e.epsilon))).add(o);o.assign(d)}}),e.iteration.assign(e.iteration.add(1)),e.accBeta1.assign(e.accBeta1.mul(e.beta1))}),this.incrementIterations()},e.prototype.dispose=function(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&Fe(this.accumulatedFirstMoment.map(function(t){return t.variable})),null!=this.accumulatedWeightedInfNorm&&Fe(this.accumulatedWeightedInfNorm.map(function(t){return t.variable}))},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){return i(this,function(t){throw new Error("getWeights() is not implemented for Adamax yet.")})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){return i(this,function(t){throw new Error("setWeights() is not implemented for Adamax yet.")})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},e.fromConfig=function(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)},e.className="Adamax",e}(Fp);Sp(Pp);var Bp=function(t){function e(e){var n=t.call(this)||this;return n.learningRate=e,n.setLearningRate(e),n}return n(e,t),e.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t)).forEach(function(n,r){var i=Array.isArray(t)?t[r].tensor:t[n];if(null!=i){var a=kt.registeredVariables[n];_e(function(){var t=e.c.mul(i).add(a);a.assign(t)})}}),this.incrementIterations()},e.prototype.setLearningRate=function(t){this.learningRate=t,null!=this.c&&this.c.dispose(),this.c=Me(an(-t))},e.prototype.dispose=function(){this.c.dispose()},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){return i(this,function(t){switch(t.label){case 0:return[4,this.saveIterations()];case 1:return[2,[t.sent()]]}})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){return i(this,function(e){switch(e.label){case 0:return[4,this.extractIterations(t)];case 1:if(0!==(t=e.sent()).length)throw new Error("SGD optimizer does not have settable weights.");return[2]}})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate}},e.fromConfig=function(t,e){return new t(e.learningRate)},e.className="SGD",e}(Fp);Sp(Bp);var Vp=function(t){function e(e,n,r){void 0===r&&(r=!1);var i=t.call(this,e)||this;return i.learningRate=e,i.momentum=n,i.useNesterov=r,i.accumulations=[],i.m=an(i.momentum),i}return n(e,t),e.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t)).forEach(function(n,r){var i=kt.registeredVariables[n];null==e.accumulations[r]&&(e.accumulations[r]={originalName:n+"/momentum",variable:_e(function(){return yn(i).variable(!1)})});var a=e.accumulations[r].variable,o=Array.isArray(t)?t[r].tensor:t[n];null!=o&&_e(function(){var t,n=e.m.mul(a).add(o);t=e.useNesterov?e.c.mul(o.add(n.mul(e.m))).add(i):e.c.mul(n).add(i),a.assign(n),i.assign(t)})}),this.incrementIterations()},e.prototype.dispose=function(){this.m.dispose(),null!=this.accumulations&&Fe(this.accumulations.map(function(t){return t.variable}))},e.prototype.setMomentum=function(t){this.momentum=t},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){return i(this,function(t){switch(t.label){case 0:return[4,this.saveIterations()];case 1:return[2,[t.sent()].concat(this.accumulations.map(function(t){return{name:t.originalName,tensor:t.variable}}))]}})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){return i(this,function(e){switch(e.label){case 0:return[4,this.extractIterations(t)];case 1:return t=e.sent(),this.accumulations=t.map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),[2]}})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},e.fromConfig=function(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)},e.className="Momentum",e}(Bp);Sp(Vp);var Wp=function(t){function e(e,n,r,i,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===i&&(i=null),void 0===a&&(a=!1);var o=t.call(this)||this;return o.learningRate=e,o.decay=n,o.momentum=r,o.epsilon=i,o.accumulatedMeanSquares=[],o.accumulatedMoments=[],o.accumulatedMeanGrads=[],o.centered=a,null==i&&(o.epsilon=kt.backend.epsilon()),o}return n(e,t),e.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map(function(t){return t.name}):Object.keys(t)).forEach(function(n,r){var i=kt.registeredVariables[n];null==e.accumulatedMeanSquares[r]&&(e.accumulatedMeanSquares[r]={originalName:n+"/rms",variable:_e(function(){return yn(i).variable(!1)})}),null==e.accumulatedMoments[r]&&(e.accumulatedMoments[r]={originalName:n+"/momentum",variable:_e(function(){return yn(i).variable(!1)})}),null==e.accumulatedMeanGrads[r]&&e.centered&&(e.accumulatedMeanGrads[r]={originalName:n+"/mg",variable:_e(function(){return yn(i).variable(!1)})});var a=Array.isArray(t)?t[r].tensor:t[n];if(null!=a){var o=e.accumulatedMeanSquares[r].variable,s=e.accumulatedMoments[r].variable;_e(function(){var t=o.mul(e.decay).add(a.square().mul(1-e.decay));if(e.centered){var n=e.accumulatedMeanGrads[r].variable,u=n.mul(e.decay).add(a.mul(1-e.decay)),l=s.mul(e.momentum).add(a.mul(e.learningRate).div(t.sub(u.square().add(e.epsilon)).sqrt()));o.assign(t),n.assign(u),s.assign(l);var c=i.sub(l);i.assign(c)}else{var p=o.mul(e.decay).add(a.square().mul(1-e.decay));l=s.mul(e.momentum).add(a.mul(e.learningRate).div(p.add(e.epsilon).sqrt())),o.assign(p),s.assign(l),c=i.sub(l),i.assign(c)}})}}),this.incrementIterations()},e.prototype.dispose=function(){null!=this.accumulatedMeanSquares&&Fe(this.accumulatedMeanSquares.map(function(t){return t.variable})),null!=this.accumulatedMeanGrads&&this.centered&&Fe(this.accumulatedMeanGrads.map(function(t){return t.variable})),null!=this.accumulatedMoments&&Fe(this.accumulatedMoments.map(function(t){return t.variable}))},e.prototype.getWeights=function(){return r(this,void 0,void 0,function(){var t;return i(this,function(e){switch(e.label){case 0:return t=this.accumulatedMeanSquares.concat(this.accumulatedMoments),this.centered&&t.push.apply(t,this.accumulatedMeanGrads),[4,this.saveIterations()];case 1:return[2,[e.sent()].concat(t.map(function(t){return{name:t.originalName,tensor:t.variable}}))]}})})},e.prototype.setWeights=function(t){return r(this,void 0,void 0,function(){var e;return i(this,function(n){switch(n.label){case 0:return[4,this.extractIterations(t)];case 1:return t=n.sent(),e=this.centered?t.length/3:t.length/2,this.accumulatedMeanSquares=t.slice(0,e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),this.accumulatedMoments=t.slice(e,2*e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}),this.centered&&(this.accumulatedMeanGrads=t.slice(2*e,3*e).map(function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),[2]}})})},e.prototype.getConfig=function(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},e.fromConfig=function(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)},e.className="RMSProp",e}(Fp);Sp(Wp);var Up=function(){function t(){}return t.sgd=function(t){return new Bp(t)},t.momentum=function(t,e,n){return void 0===n&&(n=!1),new 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Got: "+e.scale);return n.scale=null==e.scale?1:e.scale,n.mode=null==e.mode?"fanIn":e.mode,function(t){vh(mf,"FanMode",t)}(n.mode),n.distribution=null==e.distribution?"normal":e.distribution,function(t){vh(gf,"Distribution",t)}(n.distribution),n.seed=e.seed,n}return Kp(e,t),e.prototype.apply=function(t,e){var n=function(t,e){var n,r;if(void 0===e&&(e="channelsLast"),zh(e),2===t.length)n=t[0],r=t[1];else if(-1!==[3,4,5].indexOf(t.length))if("channelsFirst"===e){var i=Kh(t,2);n=t[1]*i,r=t[0]*i}else"channelsLast"===e&&(i=Kh(t,0,t.length-2),n=t[t.length-2]*i,r=t[t.length-1]*i);else{var a=Kh(t);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}(t),r=n[0],i=n[1],a=this.scale;if("fanIn"===this.mode?a/=Math.max(1,r):"fanOut"===this.mode?a/=Math.max(1,i):a/=Math.max(1,(r+i)/2),"normal"===this.distribution){var o=Math.sqrt(a);if("float32"!==(e=e||"float32")&&"int32"!==e)throw new nh(this.getClassName()+" does not support dType "+e+".");return ur(t,0,o,e,this.seed)}var s=Math.sqrt(3*a);return nr(t,-s,s,e)},e.prototype.getConfig=function(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}},e.className="VarianceScaling",e}(vf);kp.registerClass(Sf);var kf=function(t){function e(e){return t.call(this,{scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})||this}return Kp(e,t),e.prototype.getClassName=function(){return Sf.className},e.className="GlorotUniform",e}(Sf);kp.registerClass(kf);var If=function(t){function e(e){return t.call(this,{scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})||this}return Kp(e,t),e.prototype.getClassName=function(){return Sf.className},e.className="GlorotNormal",e}(Sf);kp.registerClass(If);var Af=function(t){function e(e){return t.call(this,{scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})||this}return Kp(e,t),e.prototype.getClassName=function(){return Sf.className},e.className="HeNormal",e}(Sf);kp.registerClass(Af);var Rf=function(t){function e(e){return t.call(this,{scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})||this}return Kp(e,t),e.prototype.getClassName=function(){return Sf.className},e.className="HeUniform",e}(Sf);kp.registerClass(Rf);var Tf=function(t){function e(e){return t.call(this,{scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})||this}return Kp(e,t),e.prototype.getClassName=function(){return Sf.className},e.className="LeCunNormal",e}(Sf);kp.registerClass(Tf);var Df=function(t){function e(e){return t.call(this,{scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})||this}return Kp(e,t),e.prototype.getClassName=function(){return Sf.className},e.className="LeCunNormal",e}(Sf);kp.registerClass(Df);var Of=function(t){function e(e){var n=t.call(this)||this;if(n.DEFAULT_GAIN=1,n.gain=null==e.gain?n.DEFAULT_GAIN:e.gain,n.seed=e.seed,null!=n.seed)throw new nh("Random seed is not implemented for Orthogonal Initializer yet.");return n}return Kp(e,t),e.prototype.apply=function(t,e){var n=this;return _e(function(){if(2!==t.length)throw new nh("The Orthogonal Initializer does not support non-2D shapes yet.");t[0]*t[1]>2e3&&console.warn("Orthogonal initializer is being called on a matrix with more than 2000 ("+t[0]*t[1]+") elements: Slowness may result.");var e=sf(t[0]>t[1]?[t[1],t[0]]:t,0,1,"float32"),r=lc.gramSchmidt(e);return t[0]>t[1]&&(r=r.transpose()),iu(n.gain,r)})},e.prototype.getConfig=function(){return{gain:this.gain,seed:this.seed}},e.className="Orthogonal",e}(vf);kp.registerClass(Of);var _f={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Ff(t,e){return void 0===e&&(e={}),fh(t,kp.SerializationMap.getMap().classNameMap,e,"initializer")}function Mf(t){return hh(t)}function zf(t){if("string"==typeof t){var e=t in _f?_f[t]:t;if("GlorotNormal"===e)return new If;if("GlorotUniform"===e)return new kf;if("HeNormal"===e)return new Af;if("HeUniform"===e)return new Rf;if("LeCunNormal"===e)return new Tf;if("LeCunUniform"===e)return new Df;var n={};return n.className=e,n.config={},Ff(n)}return t instanceof vf?t:Ff(t)}var Lf=Object.freeze({zeros:function(){return new yf},ones:function(){return new bf},constant:function(t){return new xf(t)},randomUniform:function(t){return new wf(t)},randomNormal:function(t){return new Nf(t)},truncatedNormal:function(t){return new Cf(t)},identity:function(t){return new Ef(t)},varianceScaling:function(t){return new Sf(t)},glorotUniform:function(t){return new kf(t)},glorotNormal:function(t){return new If(t)},heNormal:function(t){return new Af(t)},heUniform:function(t){return new Rf(t)},leCunNormal:function(t){return new Tf(t)},leCunUniform:function(t){return new Df(t)},orthogonal:function(t){return new Of(t)}}),Pf=0;function Bf(){return Pf++}var Vf={};function Wf(t){return void 0===t&&(t=""),t in Vf||(Vf[t]=0),Vf[t]+=1,t+Vf[t].toString()}function Uf(t){return Array.isArray(t)&&Array.isArray(t[0])}function jf(t){return 0===t.length?[]:Array.isArray(t[0])?t:[t]}function Gf(t){var e;if(Array.isArray(t)){if(1!==t.length)throw new eh("Expected Tensor length to be 1; got "+t.length);e=t[0]}else e=t;return e}function Hf(t){if(Array.isArray(t)&&Array.isArray(t[0])){if(1===t.length)return(t=t)[0];throw new eh("Expected exactly 1 Shape; got "+t.length)}return t}function qf(t){for(var e=0,n=0,r=t;n<r.length;n++){var i=r[n];0===i.shape.length?e+=1:e+=i.shape.reduce(function(t,e){return t*e})}return e}var Kf="Variable",$f=function(){function t(t,e,n,r,i){void 0===e&&(e="float32"),void 0===n&&(n=Kf),void 0===r&&(r=!0),void 0===i&&(i=null),this.dtype=null==e?"float32":e,this.shape=t.shape,this.id=Bf(),n=null==n?Kf:n,this.originalName=Uh(n),this.name=jh(this.originalName),this.trainable_=r,this.constraint=i,this.val=mt(t,this.trainable_,this.name,this.dtype)}return t.prototype.read=function(){return this.assertNotDisposed(),this.val},t.prototype.write=function(t){return this.assertNotDisposed(),function(t,e){if(t.shape.toString()!==e.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(t.shape)+" vs. "+JSON.stringify(e.shape))}(this.val,t),this.val.id!==t.id&&(this.val.assign(t),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this},t.prototype.dispose=function(){this.assertNotDisposed(),this.val.dispose()},t.prototype.assertNotDisposed=function(){if(this.val.isDisposed)throw new Error("LayersVariable "+this.name+" is already disposed.")},Object.defineProperty(t.prototype,"trainable",{get:function(){return this.trainable_},set:function(t){this.trainable_=t,this.val.trainable=t},enumerable:!0,configurable:!0}),t}();function Xf(t){return t.map(function(t){return t.read()})}function Yf(t){t.forEach(function(t){t[0].write(t[1])})}var Jf=function(t){this.dtype=t.dtype,this.shape=t.shape,null!=t.shape?this.ndim=t.shape.length:this.ndim=t.ndim,this.maxNDim=t.maxNDim,this.minNDim=t.minNDim,this.axes=t.axes||{}},Zf=function(t,e,n,r,i,a,o){this.dtype=t,this.shape=e,this.sourceLayer=n,this.inputs=r,this.callArgs=i,this.outputTensorIndex=o,this.id=Bf(),null!=a&&(this.originalName=Uh(a),this.name=jh(this.originalName)),this.rank=e.length},Qf=0,td=function(){function t(t,e){this.callArgs=e,this.id=Qf++,this.outboundLayer=t.outboundLayer,this.inboundLayers=t.inboundLayers,this.nodeIndices=t.nodeIndices,this.tensorIndices=t.tensorIndices,this.inputTensors=t.inputTensors,this.outputTensors=t.outputTensors,this.inputMasks=t.inputMasks,this.outputMasks=t.outputMasks,this.inputShapes=t.inputShapes,this.outputShapes=t.outputShapes;for(var n=0,r=t.inboundLayers;n<r.length;n++){var i=r[n];null!=i&&i.outboundNodes.push(this)}t.outboundLayer.inboundNodes.push(this)}return t.prototype.getConfig=function(){for(var t=[],e=0,n=this.inboundLayers;e<n.length;e++){var r=n[e];null!=r?t.push(r.name):t.push(null)}return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:t,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}},t}(),ed=0,nd=function(t){function e(e){var n=t.call(this)||this;n._callHook=null,n._addedWeightNames=[],n._stateful=!1,n.id=ed++,n.activityRegularizer=null,n.inputSpec=null,n.supportsMasking=!1,n._trainableWeights=[],n._nonTrainableWeights=[],n._losses=[],n._updates=[],n._built=!1,n.inboundNodes=[],n.outboundNodes=[];var r=e.name;if(!r){var i=n.getClassName();r=lh(i)+"_"+Wf(i)}if(n.name=r,n.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){var a=void 0;if(null!=e.batchInputShape)a=e.batchInputShape;else if(null!=e.inputShape){var o=null;null!=e.batchSize&&(o=e.batchSize),a=[o].concat(e.inputShape)}n.batchInputShape=a;var s=e.dtype;null==s&&(s=e.inputDType),null==s&&(s="float32"),n.dtype=s}return null!=e.weights?n.initialWeights=e.weights:n.initialWeights=null,n._refCount=null,n.fastWeightInitDuringBuild=!1,n}return Kp(e,t),e.nodeKey=function(t,e){return t.name+"_ib-"+e.toString()},e.prototype.getNodeAtIndex=function(t,e){if(0===this.inboundNodes.length)throw new th("The layer has never been called and thus has no defined "+e+".");if(this.inboundNodes.length<=t)throw new eh("Asked to get "+e+" at node "+t+", but the layer has only "+this.inboundNodes.length+" inbound nodes.");return this.inboundNodes[t]},e.prototype.getInputAt=function(t){return sh(this.getNodeAtIndex(t,"input").inputTensors)},e.prototype.getOutputAt=function(t){return sh(this.getNodeAtIndex(t,"output").outputTensors)},Object.defineProperty(e.prototype,"input",{get:function(){if(this.inboundNodes.length>1)throw new Qp("Layer "+this.name+' has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use `getInputAt(nodeIndex)` instead.');if(0===this.inboundNodes.length)throw new Qp("Layer "+this.name+" is not connected, no input to return.");return sh(this.getNodeAtIndex(0,"input").inputTensors)},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"output",{get:function(){if(0===this.inboundNodes.length)throw new Qp("Layer "+this.name+" has no inbound nodes.");if(this.inboundNodes.length>1)throw new Qp("Layer "+this.name+' has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `getOutputAt(nodeIndex)` instead.');return sh(this.getNodeAtIndex(0,"output").outputTensors)},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"losses",{get:function(){return this._losses},enumerable:!0,configurable:!0}),e.prototype.calculateLosses=function(){return this.losses.map(function(t){return t()})},Object.defineProperty(e.prototype,"updates",{get:function(){return this._updates},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"built",{get:function(){return this._built},set:function(t){this._built=t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"trainable",{get:function(){return this.trainable_},set:function(t){this._trainableWeights.forEach(function(e){return e.trainable=t}),this.trainable_=t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"trainableWeights",{get:function(){return this.trainable_?this._trainableWeights.filter(function(t){return t.trainable}):[]},set:function(t){this._trainableWeights=t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"nonTrainableWeights",{get:function(){return this.trainable?this._trainableWeights.filter(function(t){return!t.trainable}).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)},set:function(t){this._nonTrainableWeights=t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"weights",{get:function(){return this.trainableWeights.concat(this.nonTrainableWeights)},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"stateful",{get:function(){return this._stateful},enumerable:!0,configurable:!0}),e.prototype.resetStates=function(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")},e.prototype.assertInputCompatibility=function(t){if(t=uh(t),null!=this.inputSpec&&0!==this.inputSpec.length){var e=uh(this.inputSpec);if(t.length!==e.length)throw new eh("Layer "+this.name+" expects "+e.length+" inputs, but it received "+t.length+" input tensors. Input received: "+t);for(var n=0;n<t.length;n++){var r=t[n],i=e[n];if(null!=i){var a=r.rank;if(null!=i.ndim&&a!==i.ndim)throw new eh("Input "+n+" is incompatible with layer "+this.name+": expected ndim="+i.ndim+", found ndim="+a);if(null!=i.maxNDim&&a>i.maxNDim)throw new eh("Input "+n+" is incompatible with layer "+this.name+": expected max_ndim="+i.maxNDim+", found ndim="+a);if(null!=i.minNDim&&a<i.minNDim)throw new eh("Input "+n+" is incompatible with layer "+this.name+": expected min_ndim="+i.minNDim+", found ndim="+a+".");if(null!=i.dtype&&r.dtype!==i.dtype)throw new eh("Input "+n+" is incompatible with layer "+this.name+" : expected dtype="+i.dtype+", found dtype="+r.dtype+".");if(i.axes){var o=r.shape;for(var s in i.axes){var u=Number(s),l=i.axes[s],c=u>=0?o[u]:o[o.length+u];if(null!=l&&-1===[l,null].indexOf(c))throw new eh("Input "+n+" is incompatible with layer "+this.name+": expected axis "+u+" of input shape to have value "+l+" but got shape "+o+".")}}if(null!=i.shape)for(var p=0;p<i.shape.length;++p){var h=i.shape[p],f=r.shape[p];if(null!=h&&null!=f&&h!==f)throw new eh("Input "+n+" is incompatible with layer "+this.name+": expected shape="+i.shape+", found shape="+r.shape+".")}}}}},e.prototype.call=function(t,e){return t},e.prototype.invokeCallHook=function(t,e){null!=this._callHook&&this._callHook(t,e)},e.prototype.setCallHook=function(t){this._callHook=t},e.prototype.clearCallHook=function(){this._callHook=null},e.prototype.apply=function(t,e){var n=this;e=e||{},this.assertNotDisposed();for(var r=uh(t),i=!0,a=0,o=r;a<o.length;a++)if(!(o[a]instanceof Zf)){i=!1;break}for(var s=!0,u=0,l=r;u<l.length;u++)if(l[u]instanceof Zf){s=!1;break}if(i===s)throw new eh("Arguments to apply() must be all SymbolicTensors or all Tensors");return Wh(this.name,function(){if(!n.built){n.assertInputCompatibility(t);for(var i=[],a=0,o=uh(t);a<o.length;a++){var u=o[a];i.push(u.shape)}n.build(sh(i)),n.built=!0,n.initialWeights&&n.setWeights(n.initialWeights),null===n._refCount&&s&&(n._refCount=1)}if(n.assertInputCompatibility(t),s){for(var l=[],c=0,p=uh(m=n.call(t,e));c<p.length;c++){var h=p[c];-1!==r.indexOf(h)&&(h=h.clone()),l.push(h)}if(m=sh(l),null!=n.activityRegularizer)throw new nh("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return m}var f=function(t){for(var e=[],n=0,r=t=uh(t);n<r.length;n++){var i=r[n];e.push(i.shape)}return sh(e)}(t),d=n.computeOutputShape(f),m=void 0,g="float32";if(n.warnOnIncompatibleInputShape(Array.isArray(t)?f[0]:f),m=null!=d&&d.length>0&&Array.isArray(d[0])?d.map(function(r,i){return new Zf(g,r,n,uh(t),e,n.name,i)}):new Zf(g,d,n,uh(t),e,n.name),n.addInboundNode(t,m,null,null,f,d,e),n._refCount++,null!=n.activityRegularizer)throw new nh("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return m})},e.prototype.warnOnIncompatibleInputShape=function(t){if(null!=this.batchInputShape)if(t.length!==this.batchInputShape.length)console.warn("The rank of the input tensor provided (shape: "+JSON.stringify(t)+") does not match that of the batchInputShape ("+JSON.stringify(this.batchInputShape)+") of the layer "+this.name);else{var e=!1;this.batchInputShape.forEach(function(n,r){null!=n&&null!=t[r]&&t[r]!==n&&(e=!0)}),e&&console.warn("The shape of the input tensor ("+JSON.stringify(t)+") does not match the expectation of layer "+this.name+": "+JSON.stringify(this.batchInputShape))}},Object.defineProperty(e.prototype,"outputShape",{get:function(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new Qp("The layer "+this.name+" has never been called and thus has no defined output shape.");for(var t=[],e=0,n=this.inboundNodes;e<n.length;e++){var r=n[e],i=JSON.stringify(r.outputShapes);-1===t.indexOf(i)&&t.push(i)}if(1===t.length){var a=this.inboundNodes[0].outputShapes;return Array.isArray(a)&&Array.isArray(a[0])&&1===a.length?a[0]:a}throw new Qp("The layer "+this.name+' has multiple inbound nodes with different output shapes. Hence the notion of "outut shape" is ill-defined for the layer.')},enumerable:!0,configurable:!0}),e.prototype.countParams=function(){if(!this.built)throw new th("You tried to call countParams() on "+this.name+", but the layer is not built yet. Build it first by calling build(batchInputShape).");return qf(this.weights)},e.prototype.build=function(t){this.built=!0},e.prototype.getWeights=function(t){return void 0===t&&(t=!1),Xf(t?this.trainableWeights:this.weights)},e.prototype.setWeights=function(t){var e=this;_e(function(){var n=e.weights;if(n.length!==t.length)throw new eh('You called setWeights(weights) on layer "'+e.name+'" with a weight list of length '+t.length+", but the layer was expecting "+n.length+" weights. 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Either change `yieldEvery` or remove the callback");return Y.isNumber(r.yieldEvery)&&(r.maybeWait=function(t,e){var n,r=Y.now();return function(){for(var i=[],a=0;a<arguments.length;a++)i[a]=arguments[a];var o=Y.now();return o-r<e?n:(r=o,n=t.apply(void 0,i))}}(r.maybeWait.bind(r),r.yieldEvery)),r.trainBegin=e.onTrainBegin,r.trainEnd=e.onTrainEnd,r.epochBegin=e.onEpochBegin,r.epochEnd=e.onEpochEnd,r.batchBegin=e.onBatchBegin,r.batchEnd=e.onBatchEnd,r.yield=e.onYield,r}return Kp(e,t),e.prototype.maybeWait=function(t,e,n){return Yp(this,void 0,void 0,function(){var r;return Jp(this,function(i){switch(i.label){case 0:return r=[],null==this.yield?[3,2]:[4,od(n)];case 1:i.sent(),r.push(this.yield(t,e,n)),i.label=2;case 2:return r.push(Hp()),[4,Promise.all(r)];case 3:return i.sent(),[2]}})})},e.prototype.onEpochBegin=function(t,e){return Yp(this,void 0,void 0,function(){return Jp(this,function(n){switch(n.label){case 0:return this.currentEpoch=t,null==this.epochBegin?[3,3]:[4,od(e)];case 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Did you set `model.trainable` without calling `model.compile()` afterwards?")},e.prototype.evaluate=function(t,e,n){void 0===n&&(n={});var r=null==n.batchSize?32:n.batchSize;sm(r);var i=this.standardizeUserDataXY(t,e,!0,r);try{var a=i[0].concat(i[1]);this.makeTestFunction();var o=this.testFunction;return sh(this.testLoop(o,a,r,n.verbose,n.steps))}finally{fm(i[0],t),fm(i[1],e)}},e.prototype.evaluateDataset=function(t,e){return Yp(this,void 0,void 0,function(){return Jp(this,function(n){return this.makeTestFunction(),[2,function(t,e,n){return Yp(this,void 0,void 0,function(){var r,i,a,o,s,u,l,c,p,h;return Jp(this,function(f){switch(f.label){case 0:if(r=null!=(n=n||{}).batches,i=t.testFunction,a=[],n.verbose>0)throw new nh("Verbose mode is not implemented yet.");return Y.assert(!r||n.batches>0&&Number.isInteger(n.batches),function(){return"Test loop expects `batches` to be a positive integer, but received "+JSON.stringify(n.batches)}),function(t){return"function"==typeof t.next}(e)?(s=e,[3,3]):[3,1];case 1:return[4,e.iterator()];case 2:s=f.sent(),f.label=3;case 3:o=s,u=0,l=0,c=function(){var e;return Jp(this,function(s){switch(s.label){case 0:return[4,o.next()];case 1:return e=s.sent(),a=_e(function(){if(e.value){var n=rm(t,e.value),r=n.xs,o=n.ys,s=r.concat(o),c=_e(function(){return i(s)});if(Fe(s),0===l)for(var p=0;p<c.length;++p)a.push(an(0));var h=s[0].shape[0],f=function(t){var e=c[t],n=a[t];a[t]=_e(function(){return Hs(a[t],iu(h,e))}),l>0&&Fe(n)};for(p=0;p<c.length;++p)f(p);Fe(c),u+=h,++l}return a}),e.done?(r&&console.warn("Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least `batches` batches (in this case, "+n.batches+" batches). You may need to use the repeat() function when building your dataset."),[2,"break"]):[2]}})},f.label=4;case 4:return!r||l<n.batches?[5,c()]:[3,6];case 5:return"break"===f.sent()?[3,6]:[3,4];case 6:for(p=0;p<a.length;++p)h=a[p],a[p]=Xs(a[p],u),Fe(h);return[2,sh(a)]}})})}(this,t,e)]})})},e.prototype.checkNumSamples=function(t,e,n,r){var i;if(void 0===r&&(r="steps"),null!=n){if(i=null,null!=e)throw new eh("If "+r+" is set, batchSize must be null or undefined.Got batchSize = "+e)}else{if(null==t)throw new eh("Either the input data should have a defined shape, or "+r+" shoud be specified.");i=Array.isArray(t)?t[0].shape[0]:t.shape[0]}return i},e.prototype.execute=function(t,e){if(Array.isArray(e)&&0===e.length)throw new eh("`outputs` is an empty Array, which is not allowed.");var n=Array.isArray(e),r=n?e:[e],i=this.retrieveSymbolicTensors(r),a=new Kd;if(t instanceof lt&&(t=[t]),Array.isArray(t)){if(t.length!==this.inputs.length)throw new eh("The number of inputs provided ("+t.length+") does not match the number of inputs of this model ("+this.inputs.length+").");for(var o=0;o<this.inputs.length;++o)a.add(this.inputs[o],t[o])}else for(var s=0,u=this.inputs;s<u.length;s++){var l=u[s],c=t[l.name];if(null==c)throw new eh("No value is provided for the model's input "+l.name);a.add(l,c)}var p=Yd(i,a);return n?p:p[0]},e.prototype.retrieveSymbolicTensors=function(t){for(var e=ih(null,t.length),n=t.length,r=0,i=this.layers;r<i.length;r++){for(var a=i[r],o=Array.isArray(a.output)?a.output:[a.output],s=o.map(function(t){return t.name}),u=0;u<t.length;++u){var l=s.indexOf(t[u]);if(-1!==l&&(e[u]=o[l],n--),0===n)break}if(0===n)break}if(n>0){var c=[];throw e.forEach(function(e,n){null==e&&c.push(t[n])}),new eh("Cannot find SymbolicTensors for output name(s): "+JSON.stringify(c))}return e},e.prototype.predictLoop=function(t,e,n){var r=this;return void 0===e&&(e=32),void 0===n&&(n=!1),_e(function(){var i=r.checkNumSamples(t);if(n)throw new nh("Verbose predictLoop() is not implemented yet.");for(var a=cm(i,e),o=r.outputs.map(function(t){return[]}),s=function(e){_e(function(){var n=a[e][0],i=a[e][1],o=um(t,n,i),s=[];if(Array.isArray(o))for(var u=0;u<o.length;++u)s.push({key:r.inputs[u],value:o[u]});else s.push({key:r.inputs[0],value:o});var l=new Kd(s);return Yd(r.outputs,l)}).forEach(function(t,e){return o[e].push(t)})},u=0;u<a.length;++u)s(u);return sh(o.map(function(t){return bn(t,0)}))})},e.prototype.predict=function(t,e){void 0===e&&(e={});var n=hm(t);vm(n,this.inputNames,this.feedInputShapes,!1);try{var r=null==e.batchSize?32:e.batchSize;return sm(r),this.predictLoop(n,r)}finally{fm(n,t)}},e.prototype.predictOnBatch=function(t){vm(t,this.inputNames,this.feedInputShapes,!0);var e=(Array.isArray(t)?t[0]:t).shape[0];return this.predictLoop(t,e)},e.prototype.standardizeUserDataXY=function(t,e,n,r){if(void 0===n&&(n=!0),null==this.optimizer_)throw new th("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");for(var i=[],a=0;a<this.feedOutputShapes.length;++a){var o=this.feedOutputShapes[a];this.feedLossFns[a]===Cd?i.push(o.slice(0,o.length-1).concat([1])):i.push(o)}if(function(t,e,n){var r=mh(t.map(function(t){return t.shape[0]}));r.sort();var i=mh(e.map(function(t){return t.shape[0]}));if(i.sort(),r.length>1)throw new eh("All input Tensors (x) should have the same number of samples. Got array shapes: "+JSON.stringify(t.map(function(t){return t.shape})));if(i.length>1)throw new eh("All target Tensors (y) should have the same number of samples. Got array shapes: "+JSON.stringify(e.map(function(t){return t.shape})));if(r.length>0&&i.length>0&&!Y.arraysEqual(r,i))throw new eh("Input Tensors should have the same number of samples as target Tensors. Found "+r[0]+" input sample(s) and "+i[0]+" target sample(s).")}(t=gm(t,this.feedInputNames,this.feedInputShapes,!1,"input"),e=gm(e,this.feedOutputNames,i,!1,"target")),function(t,e,n){for(var r=[bd,Ed,Nd],i=0;i<t.length;++i){var a=t[i],o=e[i],s=n[i];if(null!=o){if(o===Nd&&1===a.shape[a.shape.length-1])throw new eh("You are passing a target array of shape "+a.shape+" while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].");if(-1!==r.indexOf(o))for(var u=a.shape.slice(1),l=s.slice(1),c=0;c<u.length;++c){var p=u[c],h=l[c];if(null!=h&&p!==h)throw new eh("A target Tensor with shape "+a.shape+" was passed for an output of shape "+s+", while using a loss function that expects targets to have the same shape as the output.")}}}}(e,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=r&&r>0&&t[0].shape[0]%r!=0)throw new eh("In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size "+r+". Found: "+t[0].shape[0]+" sample(s).");return[t,e]},e.prototype.standardizeUserData=function(t,e,n,r,i,a){return void 0===i&&(i=!0),Yp(this,void 0,void 0,function(){var o,s,u,l,c,p,h,f;return Jp(this,function(d){switch(d.label){case 0:if(o=this.standardizeUserDataXY(t,e,i,a),s=o[0],u=o[1],null!=n)throw new Error("sample weight is not supported yet.");if(l=null,null==r)return[3,4];c=Qd(r,this.outputNames),l=[],p=0,d.label=1;case 1:return p<c.length?(f=(h=l).push,[4,tm(u[p],null,c[p])]):[3,4];case 2:f.apply(h,[d.sent()]),d.label=3;case 3:return++p,[3,1];case 4:return[2,[s,u,l]]}})})},e.prototype.testLoop=function(t,e,n,r,i){var a=this;return void 0===r&&(r=0),_e(function(){var o=a.checkNumSamples(e,n,i,"steps"),s=[];if(r>0)throw new nh("Verbose mode is not implemented yet.");if(null!=i)throw new nh("steps mode in testLoop() is not implemented yet");for(var u=cm(o,n),l=on(Jh(0,o)),c=0;c<u.length;++c){var p=u[c][0],h=u[c][1],f=tf(l,p,h-p),d=lm(e,f),m=t(d);if(0===c)for(var g=0;g<m.length;++g)s.push(an(0));for(g=0;g<m.length;++g){var v=m[g];s[g]=Hs(s[g],iu(h-p,v))}}for(g=0;g<s.length;++g)s[g]=Xs(s[g],o);return s})},e.prototype.getDedupedMetricsNames=function(){for(var t=this.metricsNames,e=[],n=0;n<t.length;++n){var r=t[n],i=r;oh(t,r)>1&&(i+="_"+oh(t.slice(0,n),r)),e.push(i)}return e},e.prototype.makeTrainFunction=function(){var t=this;return function(e){var n=[],r=e.slice(0,t.inputs.length),i=e.slice(t.inputs.length,t.inputs.length+t.outputs.length),a=e.slice(t.inputs.length+t.outputs.length,t.inputs.length+2*t.outputs.length),o=[],s=t.collectedTrainableWeights.map(function(t){return t.read()});return[t.optimizer_.minimize(function(){for(var e=[],s=0;s<t.inputs.length;++s)e.push({key:t.inputs[s],value:r[s]});var u,l=new Kd(e),c=Yd(t.outputs,l,{training:!0});for(s=0;s<t.lossFunctions.length;++s){var p=(0,t.lossFunctions[s])(i[s],c[s]);null!=a[s]&&(p=em(p,a[s]));var h=ml(p);n.push(h),u=0===s?p:Hs(u,p)}for(s=0;s<t.metricsTensors.length;++s){var f=void 0;if(t.outputs.length>1&&s<t.outputs.length)f=n[s];else{var d=t.metricsTensors[s][0],m=t.metricsTensors[s][1];f=ml(d(i[m],c[m]))}Me(f),o.push(f)}return u=ml(u),t.calculateLosses().forEach(function(t){u=Hs(u,t)}),u},!0,s)].concat(o)}},e.prototype.makeTestFunction=function(){var t=this;this.testFunction=function(e){return _e(function(){for(var n,r=[],i=e.slice(0,t.inputs.length),a=e.slice(t.inputs.length,t.inputs.length+t.outputs.length),o=[],s=0;s<t.inputs.length;++s)o.push({key:t.inputs[s],value:i[s]});var u=new Kd(o),l=Yd(t.outputs,u);for(s=0;s<t.lossFunctions.length;++s){var c=t.lossFunctions[s],p=ml(c(a[s],l[s]));n=0===s?p:Hs(n,p),r.push(n)}for(s=0;s<t.metricsTensors.length;++s){var h=t.metricsTensors[s][0],f=t.metricsTensors[s][1],d=ml(h(a[f],l[f]));r.push(d)}return r})}},e.prototype.fit=function(t,e,n){return void 0===n&&(n={}),Yp(this,void 0,void 0,function(){return Jp(this,function(r){return[2,pm(this,t,e,n)]})})},e.prototype.fitDataset=function(t,e){return Yp(this,void 0,void 0,function(){return Jp(this,function(n){return[2,am(this,t,e)]})})},e.prototype.trainOnBatch=function(t,e){return Yp(this,void 0,void 0,function(){var n,r,i,a,o,s,u,l,c;return Jp(this,function(p){switch(p.label){case 0:return[4,this.standardizeUserData(t,e)];case 1:n=p.sent(),r=n[0],i=n[1],a=this.makeTrainFunction(),o=a(r.concat(i)),s=[],u=0,l=o,p.label=2;case 2:return u<l.length?[4,l[u].data()]:[3,5];case 3:c=p.sent(),s.push(c[0]),p.label=4;case 4:return u++,[3,2];case 5:return Fe(o),[2,sh(s)]}})})},e.prototype.getNamedWeights=function(t){for(var e=[],n=null!=t&&t.trainableOnly,r=n?this.trainableWeights:this.weights,i=this.getWeights(n),a=0;a<r.length;++a)n&&!r[a].trainable||e.push({name:r[a].originalName,tensor:i[a]});return e},Object.defineProperty(e.prototype,"stopTraining",{get:function(){return this.stopTraining_},set:function(t){this.stopTraining_=t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"optimizer",{get:function(){return this.optimizer_},set:function(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)},enumerable:!0,configurable:!0}),e.prototype.dispose=function(){var e=t.prototype.dispose.call(this);if(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){var n=Oe().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=n-Oe().numTensors}return e},e.prototype.getLossIdentifiers=function(){var t;if("string"==typeof this.loss)t=lh(this.loss);else if(Array.isArray(this.loss)){for(var e=0,n=this.loss;e<n.length;e++)if("string"!=typeof n[e])throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map(function(t){return lh(t)})}else{var r=Object.keys(this.loss);t={};for(var i=this.loss,a=0,o=r;a<o.length;a++){var s=o[a];if("string"!=typeof i[s])throw new Error("Serialization of non-string loss is not supported.");t[s]=lh(i[s])}}return t},e.prototype.getMetricIdentifiers=function(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[lh(Pd(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(function(t){return lh(Pd(t))});var t={};for(var e in this.metrics)t[e]=lh(Pd(this.metrics[e]));return t},e.prototype.getTrainingConfig=function(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}},e.prototype.loadTrainingConfig=function(t){if(null!=t.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=t.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=t.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");var e,n,r=vd(qd(t.optimizer_config));if("string"==typeof t.loss)e=ch(t.loss);else if(Array.isArray(t.loss))e=t.loss.map(function(t){return ch(t)});else if(null!=t.loss)for(var i in e={},t.loss)e[i]=ch(t.loss[i]);if(Array.isArray(t.metrics))n=t.metrics.map(function(t){return ch(t)});else if(null!=t.metrics)for(var i in n={},t.metrics)n[i]=ch(t.metrics[i]);this.compile({loss:e,metrics:n,optimizer:r})},e.prototype.save=function(t,e){return Yp(this,void 0,void 0,function(){var n,r,i,a,o,s,u,l,c,p,h,f,d,m;return Jp(this,function(g){switch(g.label){case 0:if("string"==typeof t){if(0===(n=yp.getSaveHandlers(t)).length)throw new eh("Cannot find any save handlers for URL '"+t+"'");if(n.length>1)throw new eh("Found more than one ("+n.length+") save handlers for URL '"+t+"'");t=n[0]}if(null==t.save)throw new eh("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return[4,yp.encodeWeights(this.getNamedWeights(e))];case 1:return r=g.sent(),i=!1,a=null,o=this.toJSON(a,i),s={modelTopology:o,format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers 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All inputs should only appear once. Found: "+r.inputs.map(function(t){return t.name}));mh(r.outputs).length!==r.outputs.length&&console.warn("The list of outputs passed to the model is redundant. All outputs should only appear once. 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The following previous layers were accessed without issue: '+H);for(var Q=0,tt=Y.outputTensors;Q<tt.length;Q++)k=tt[Q],G.push(k);H.push(s.name)}}r.nodesByDepth=F;for(var et=r.layers.map(function(t){return t.name}),nt=function(t){var e=et.filter(function(e){return e===t}).length;if(1!==e)throw new th('The name "'+t+'" is used '+e+" times in the model. All layer names should be unique. Layer names: "+JSON.stringify(et))},rt=0,it=et;rt<it.length;rt++)nt(it[rt]);return r.outboundNodes=[],r.inboundNodes=[],new td({outboundLayer:r,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:r.inputs,outputTensors:r.outputs,inputMasks:r.inputs.map(function(t){return null}),outputMasks:r.outputs.map(function(t){return null}),inputShapes:r.inputs.map(function(t){return t.shape}),outputShapes:r.outputs.map(function(t){return t.shape})}),r.built=!0,r._refCount=1,r}return Kp(e,t),e.prototype.assertNotDisposed=function(){if(0===this._refCount)throw new Error("Container '"+this.name+"' is already disposed.")},e.prototype.dispose=function(){this.assertNotDisposed();var t={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount)for(var e=0,n=this.layers;e<n.length;e++){var r=n[e];t.numDisposedVariables+=r.dispose().numDisposedVariables}return t.refCountAfterDispose=this._refCount,t},Object.defineProperty(e.prototype,"trainable",{get:function(){return this.trainable_},set:function(t){this.layers.forEach(function(e){e._trainableWeights.forEach(function(e){return e.trainable=t})}),this.trainable_=t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"trainableWeights",{get:function(){if(this._trainableWeights.length>0)throw new eh("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];for(var t=[],e=0,n=this.layers;e<n.length;e++){var r=n[e];t=t.concat(r.trainableWeights)}return t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"nonTrainableWeights",{get:function(){for(var t=[],e=0,n=this.layers;e<n.length;e++){var r=n[e];t.push.apply(t,r.nonTrainableWeights)}if(!this.trainable){for(var i=[],a=0,o=this.layers;a<o.length;a++)r=o[a],i.push.apply(i,r.trainableWeights);return i.concat(t)}return t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"weights",{get:function(){return this.trainableWeights.concat(this.nonTrainableWeights)},enumerable:!0,configurable:!0}),e.prototype.loadWeights=function(t,e){void 0===e&&(e=!0);for(var n={},r=0,i=0,a=this.layers;i<a.length;i++)for(var o=0,s=a[i].weights;o<s.length;o++){var u=s[o];if(null!=n[u.originalName])throw new eh("Duplicate weight name: "+u.originalName);n[u.originalName]=u,r++}var l=[];for(var c in t){if(null!=n[c])l.push([n[c],t[c]]);else if(e)throw new eh("Provided weight data has no target variable: "+c);delete n[c]}if(e){var p=[];for(var h in n)p.push(h);if(p.length>0)throw new eh(p.length+" of "+r+" weights are not set: "+p)}Yf(l)},e.prototype.updatedConfig=function(){var t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion="tfjs-layers 1.2.9",e.backend="TensorFlow.js",e},e.prototype.toJSON=function(t,e){void 0===e&&(e=!0);var n=function t(e,n){if(null===e||void 0===e)return null;if("string"==typeof e)return lh(e);if("number"==typeof e||"boolean"==typeof e)return e;if(e instanceof Array){for(var r=[],i=e.length,a=0;a<i;++a){var o=e[a];Hd(n,a,o)?r.push(o):r.push(t(o,n))}return r}for(var s={},u=0,l=Object.keys(e);u<l.length;u++){var c=l[u],p=e[c];s[lh(c)]="name"!==c&&"className"!==c||"string"!=typeof p?t(p,c):p}return s}(this.updatedConfig());return e?JSON.stringify(n):n},e.prototype.call=function(t,e){var n=this;return _e(function(){t=uh(t);for(var r=new Kd,i=0;i<n.inputs.length;++i)r.add(n.inputs[i],t[i]);return Yd(n.outputs,r,e)})},e.prototype.computeMask=function(t,e){var n=this;return _e(function(){var r;return t=uh(t),r=null==e?ih(null,t.length):uh(e),n.runInternalGraph(t,r)[1]})},e.prototype.computeOutputShape=function(t){var e=jf(t);if(e.length!==this.inputLayers.length)throw new eh("Invalid inputShape argument "+t+": model has "+this.inputLayers.length+" tensor inputs.");for(var n={},r=0;r<e.length;r++){var i=this.inputLayers[r],a=e[r];n[C=i.name+"_0_0"]=a}var o=Object.keys(this.nodesByDepth).map(function(t){return parseInt(t,10)}).sort(dh);if(o.length>1)for(var s=0,u=o;s<u.length;s++)for(var l=u[s],c=0,p=this.nodesByDepth[l];c<p.length;c++){var h=p[c];if(i=h.outboundLayer,-1===this.inputLayers.map(function(t){return t.id}).indexOf(i.id)){for(var f=[],d=0;d<h.inboundLayers.length;d++){var m=h.inboundLayers[d],g=h.nodeIndices[d],v=h.tensorIndices[d],y=n[C=m.name+"_"+g+"_"+v];f.push(y)}var b=jf(i.computeOutputShape(sh(f))),x=i.inboundNodes.indexOf(h);for(d=0;d<b.length;d++)n[C=i.name+"_"+x+"_"+d]=b[d]}}var w=[],N=[];for(r=0;r<this.outputLayers.length;r++){i=this.outputLayers[r],x=this.outputLayersNodeIndices[r],v=this.outputLayersTensorIndices[r];var C=i.name+"_"+x+"_"+v;N.push(C)}for(r=0;r<N.length;r++){var E=N[r];ah(E in n),w.push(n[E])}return sh(w)},e.prototype.runInternalGraph=function(t,e){null==e&&(e=ih(null,t.length));for(var n={},r=0;r<this.inputs.length;++r){var i=this.inputs[r],a=t[r],o=e[r];n[i.id]=[a,o]}for(var s=0,u=Object.keys(this.nodesByDepth).map(function(t){return parseInt(t,10)}).sort(dh);s<u.length;s++)for(var l=u[s],c=0,p=this.nodesByDepth[l];c<p.length;c++){for(var h=p[c],f=h.outboundLayer,d=h.inputTensors,m=h.outputTensors,g=new Array,v=0,y=d;v<y.length;v++)(i=y[v]).id in n&&g.push(n[i.id]);if(g.length===d.length){var b={},x=void 0,w=void 0,N=void 0,C=void 0;if(null!=h.callArgs&&(b=h.callArgs),1===g.length){var E=g[0],S=E[0],k=E[1];null==b.mask&&(b.mask=k),N=uh(f.call(S,b)),C=uh(f.computeMask(S,k)),x=[S],w=[k]}else x=g.map(function(t){return t[0]}),w=g.map(function(t){return t[1]}),null==b.mask&&(b.mask=w),N=uh(f.call(x,b)),C=uh(f.computeMask(x,w));if(f.activityRegularizer)throw new nh("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(r=0;r<m.length;++r)i=m[r],a=N[r],o=C[r],n[i.id]=[a,o]}}for(var I=[],A=[],R=[],T=0,D=this.outputs;T<D.length;T++){ah((i=D[T]).id in n,"Could not compute output "+i.name+" : "+i.id);var O=n[i.id],_=O[0];o=O[1],R.push(_.shape),I.push(_),A.push(o)}return[I,A,R]},e.prototype.buildNodeConversionMap=function(t){for(var n,r={},i=0,a=this.layers;i<a.length;i++){var o=a[i];n=o instanceof e?1:0;for(var s=0;s<o.inboundNodes.length;s++){var u=e.nodeKey(o,s);this.containerNodes.has(u)&&(r[u]=n,n+=1)}}return r},e.prototype.getLayer=function(t,e){if(null!=e){if(this.layers.length<=e)throw new eh("Was asked to retrieve layer at index "+e+", but model only has "+this.layers.length+" layer(s).");return this.layers[e]}if(null==t)throw new eh("Provide either a layer name or layer index");for(var n=0,r=this.layers;n<r.length;n++){var i=r[n];if(i.name===t)return i}throw new eh("No such layer: "+t)},e.prototype.calculateLosses=function(){var t=this;return _e(function(){for(var n=[],r=0,i=t.layers;r<i.length;r++)for(var a=i[r],o=0;o<a.inboundNodes.length;++o){var s=e.nodeKey(a,o);t.containerNodes.has(s)&&n.push.apply(n,a.calculateLosses())}return n})},e.prototype.getConfig=function(){for(var t={name:this.name},n=this.buildNodeConversionMap(this.layers),r=[],i=0,a=this.layers;i<a.length;i++){for(var o=(x=a[i]).getClassName(),s=x.getConfig(),u=[],l=0;l<x.inboundNodes.length;l++){var c=x.inboundNodes[l],p=e.nodeKey(x,l),h={};if(this.containerNodes.has(p)){if(c.callArgs)try{JSON.stringify(c.callArgs),h=c.callArgs}catch(t){console.warn("Layer "+x.name+" was passed non-serializable keyword arguments: "+c.callArgs+". They will not be included in the serialized model (and thus will be missing at deserialization time)."),h={}}if(c.inboundLayers.length>0){for(var f=[],d=0;d<c.inboundLayers.length;d++){var m=c.inboundLayers[d],g=c.nodeIndices[d],v=c.tensorIndices[d];null==(N=n[e.nodeKey(m,g)])&&(N=0),f.push([m.name,N,v,h])}u.push(f)}}}var y={};y.name=x.name,y.className=o,y.config=s,y.inboundNodes=u,r.push(y)}t.layers=r;var b=[];for(d=0;d<this.inputLayers.length;d++){var x=this.inputLayers[d];g=this.inputLayersNodeIndices[d],p=e.nodeKey(x,g),this.containerNodes.has(p)&&(null!==(N=n[p])&&void 0!==N||(N=0),v=this.inputLayersTensorIndices[d],b.push([x.name,N,v]))}t.inputLayers=b;var w=[];for(d=0;d<this.outputLayers.length;d++){var N;if(x=this.outputLayers[d],g=this.outputLayersNodeIndices[d],p=e.nodeKey(x,g),this.containerNodes.has(p))null!==(N=n[p])&&void 0!==N||(N=0),v=this.outputLayersTensorIndices[d],w.push([x.name,N,v])}return t.outputLayers=w,t},e.fromConfig=function(t,e,n,r){void 0===n&&(n={}),void 0===r&&(r=!1);var i={},a={};function o(t,e){t.name in a?a[t.name].push(e):a[t.name]=[e]}function s(t,e){for(var n,r=[],a=0,s=e;a<s.length;a++){var u=s[a],l=u[0],c=u[1],p=u[2];if(n=null==u[3]?{}:u[3],!(l in i))return void o(t,e);var h=i[l];if(h.inboundNodes.length<=c)return void o(t,e);var f=h.inboundNodes[c];r.push(f.outputTensors[p])}r.length>0&&t.apply(sh(r),n)}function u(t){var n=t.name,a=vd(t,null!=e.customObjects?e.customObjects:{});a.setFastWeightInitDuringBuild(r),i[n]=a,t.inboundNodes.forEach(function(t){if(!(t instanceof Array))throw new eh("Corrupted configuration, expected array for nodeData: "+t);o(a,t)})}for(var l=e.name,c=e.layers,p=0,h=c;p<h.length;p++)u(m=h[p]);for(;!gh(a);)for(var f=0,d=c;f<d.length;f++){var m=d[f];if((R=i[m.name]).name in a){var g=a[R.name];delete a[R.name];for(var v=0,y=g;v<y.length;v++)s(R,y[v])}}for(var b=[],x=[],w=0,N=e.inputLayers;w<N.length;w++){var C=(m=N[w])[0],E=m[1],S=m[2];ah(C in i);var k=(R=i[C]).inboundNodes[E].outputTensors;b.push(k[S])}for(var I=0,A=e.outputLayers;I<A.length;I++){var R;C=(m=A[I])[0],E=m[1],S=m[2],ah(C in i),k=(R=i[C]).inboundNodes[E].outputTensors,x.push(k[S])}return new t({inputs:b,outputs:x,name:l})},Object.defineProperty(e.prototype,"stateful",{get:function(){if(this._stateful)throw new eh("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(var t=0,e=this.layers;t<e.length;t++)if(e[t].stateful)return!0;return!1},enumerable:!0,configurable:!0}),e.prototype.resetStates=function(){var t=this;_e(function(){t.layers.forEach(function(t){t.stateful&&t.resetStates()})})},e}(nd));function bm(t,e){return Yp(this,void 0,void 0,function(){var n;return Jp(this,function(r){if(null==e&&(e={}),"string"==typeof t){if(0===(n=yp.getLoadHandlers(t,e.onProgress)).length)n.push(yp.browserHTTPRequest(t,e));else if(n.length>1)throw new eh("Found more than one ("+n.length+") load handlers for URL '"+t+"'");t=n[0]}return[2,function(t,e,n){return Yp(this,void 0,void 0,function(){var r,i,a,o,s,u,l,c,p;return Jp(this,function(h){switch(h.label){case 0:if(null==n&&(n={}),null==t.load)throw new eh("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");return[4,t.load()];case 1:if(r=h.sent(),null!=(i=r.modelTopology).model_config&&(i=i.model_config),a=null==n.strict||n.strict,o=null!=r.weightData&&null!=r.weightSpecs&&a,s=vd(qd(i),e,o),null!=(u=r.trainingConfig)&&s.loadTrainingConfig(u),null!=r.userDefinedMetadata&&s.setUserDefinedMetadata(r.userDefinedMetadata),null==r.weightData)return[3,4];if(null==r.weightSpecs)throw new eh("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");return l=function(t,e){var n=yp.decodeWeights(t,e),r={},i=[];return e.forEach(function(t){"optimizer"===t.group?i.push({name:t.name,tensor:n[t.name]}):r[t.name]=n[t.name]}),{modelWeights:r,optimizerWeights:i}}(r.weightData,r.weightSpecs),c=l.modelWeights,p=l.optimizerWeights,s.loadWeights(c,a),null!=s.optimizer&&p.length>0?[4,s.optimizer.setWeights(p)]:[3,3];case 2:h.sent(),h.label=3;case 3:Fe(c),Fe(p.map(function(t){return t.tensor})),h.label=4;case 4:return[2,s]}})})}(t,void 0,e)]})})}kp.registerClass(ym);var xm=function(t){function e(e){var n=t.call(this,{inputs:[],outputs:[]})||this;if(e=e||{},n.trainable=!0,n.built=!1,n.name=null!=e.name?e.name:Wf("sequential_"),null!=e.layers)for(var r=0,i=e.layers;r<i.length;r++){var a=i[r];n.add(a)}return n}return Kp(e,t),e.prototype.checkShape=function(t){if(t.inboundNodes[0].outputTensors[0].shape.some(function(t){return t<0}))throw new eh("Negative dimension size caused by adding layer "+t.name+" with input shape ["+t.inboundNodes[0].inputTensors[0].shape+"]")},e.prototype.add=function(t){var n,r=t instanceof e||t instanceof ym;if(r){if(1!==(n=t).outputs.length)throw new eh("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(1!==n.inputs.length)throw new eh("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===t.inboundNodes.length){if(null==t.batchInputShape)throw new eh("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");var i=ad({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(i)}if(r)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==t.inboundNodes.length)throw new eh("A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer "+t.name+" which has "+t.inboundNodes.length+" pre-existing inbound connections.");if(1!==t.inboundNodes[0].outputTensors.length)throw new eh("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=function t(e,n,r){if((null==n||null!=r&&r>0)&&(n=e.sourceLayer,r=e.nodeIndex),0===n.inboundNodes.length)return[e];var i=n.inboundNodes[r];if(0===i.inboundLayers.length)return i.inputTensors;for(var a=[],o=0;o<i.inboundLayers.length;o++)for(var s=0,u=t(i.inputTensors[o],i.inboundLayers[o],i.nodeIndices[o]);s<u.length;s++){var l=u[s];-1===a.indexOf(l)&&a.push(l)}return a}(this.outputs[0])}this.inboundNodes=[],new td({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ih(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(function(t){return t.shape}),outputShapes:this.outputs[0].shape})}else{var a=t.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1},e.prototype.pop=function(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{var t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}},e.prototype.call=function(t,e){return null==this.model&&this.build(),this.model.call(t,e)},e.prototype.build=function(t){if(Hf(t),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ym({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0},e.prototype.countParams=function(){return this.built||this.build(),t.prototype.countParams.call(this)},e.prototype.summary=function(e,n,r){void 0===r&&(r=console.log),this.built||this.build(),t.prototype.summary.call(this,e,n,r)},e.prototype.setWeights=function(t){null==this.model&&this.build(),this.model.setWeights(t)},e.prototype.evaluate=function(t,e,n){if(void 0===n&&(n={}),!this.built)throw new th("The model needs to be compiled before being used.");return this.model.evaluate(t,e,n)},e.prototype.evaluateDataset=function(t,e){return Yp(this,void 0,void 0,function(){return Jp(this,function(n){if(!this.built)throw new th("The model needs to be compiled before being used.");return[2,this.model.evaluateDataset(t,e)]})})},e.prototype.predict=function(t,e){return void 0===e&&(e={}),null==this.model&&this.build(),this.model.predict(t,e)},e.prototype.predictOnBatch=function(t){return null==this.model&&this.build(),this.model.predictOnBatch(t)},e.prototype.compile=function(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames},Object.defineProperty(e.prototype,"optimizer",{get:function(){return null==this.model?void 0:this.model.optimizer},set:function(t){this.model.optimizer=t},enumerable:!0,configurable:!0}),e.prototype.fit=function(t,e,n){return void 0===n&&(n={}),Yp(this,void 0,void 0,function(){return Jp(this,function(r){if(!this.built)throw new th("The model needs to be compiled before being used.");return[2,this.model.fit(t,e,n)]})})},e.prototype.fitDataset=function(t,e){return Yp(this,void 0,void 0,function(){return Jp(this,function(n){if(!this.built)throw new th("The model needs to be compiled before being used.");return[2,this.model.fitDataset(t,e)]})})},e.prototype.trainOnBatch=function(t,e){return Yp(this,void 0,void 0,function(){return Jp(this,function(n){return[2,this.model.trainOnBatch(t,e)]})})},e.fromConfig=function(t,n,r,i){var a;void 0===r&&(r={}),void 0===i&&(i=!1);var o={};if(n instanceof Array){if(null==n[0].className||"Merge"===n[0].className)throw new eh("Legacy serialization format not supported yet.");a=n}else Y.assert(null!=n.layers,function(){return"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."}),a=n.layers,delete n.layers,o=n;var s=new t(o);if(!(s instanceof e))throw new nh("Sequential.fromConfig called on non-Sequential input: "+s);for(var u=0,l=a;u<l.length;u++){var c=vd(l[u],void 0,i);i&&c.setFastWeightInitDuringBuild(!0),s.add(c)}return s},Object.defineProperty(e.prototype,"stopTraining",{get:function(){if(null==this.model)throw new eh("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining},set:function(t){if(null==this.model)throw new eh("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t},enumerable:!0,configurable:!0}),e.prototype.getConfig=function(){for(var t=[],e=0,n=this.layers;e<n.length;e++){var r=n[e],i={};i.className=r.getClassName(),i.config=r.getConfig(),t.push(i)}return t},e.className="Sequential",e}(ym);function wm(t){return ad(t)}kp.registerClass(xm);var Nm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.getConfig=function(){return{}},e}(kp.Serializable),Cm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t,e){return void 0===e&&(e=1),function(t,e){if(void 0===e&&(e=1),1!==e)throw new nh("Support for alpha values other than 1 ("+e+") is not implemented yet.");return xl(t)}(t,e)},e.className="elu",e}(Nm);kp.registerClass(Cm);var Em=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return El(t)},e.className="selu",e}(Nm);kp.registerClass(Em);var Sm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return Cl(t)},e.className="relu",e}(Nm);kp.registerClass(Sm);var km=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return _e(function(){return tu(6,Cl(t))})},e.className="relu6",e}(Nm);kp.registerClass(km);var Im=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return t},e.className="linear",e}(Nm);kp.registerClass(Im);var Am=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return hs(t)},e.className="sigmoid",e}(Nm);kp.registerClass(Am);var Rm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return function(t){return _e(function(){var e=Hs(.5,iu(.2,t));return Zo(e,0,1)})}(t)},e.className="hardSigmoid",e}(Nm);kp.registerClass(Rm);var Tm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return bs(t)},e.className="softplus",e}(Nm);kp.registerClass(Tm);var Dm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return function(t){return _e(function(){return Xs(t,Go(t).add(1))})}(t)},e.className="softsign",e}(Nm);kp.registerClass(Dm);var Om=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t){return Es(t)},e.className="tanh",e}(Nm);kp.registerClass(Om);var _m=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t,e){return void 0===e&&(e=-1),Rr(t,e)},e.className="softmax",e}(Nm);kp.registerClass(_m);var Fm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t,e){return void 0===e&&(e=-1),Tr(t,e)},e.className="logSoftmax",e}(Nm);function Mm(t){return t.getClassName()}function zm(t,e){return void 0===e&&(e={}),fh(t,kp.SerializationMap.getMap().classNameMap,e,"activation")}function Lm(t){var e;return null==t?zm(e={className:"linear",config:{}}):"string"==typeof t?((e={}).className=t,e.config={},zm(e)):t instanceof Nm?t:zm(t)}function Pm(t){if(null!=t&&"object"!=typeof t)throw new Error("Argument to L1L2 regularizer's constructor is expected to be an object, but received: "+t)}kp.registerClass(Fm);var Bm=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e}(kp.Serializable),Vm=function(t){function e(e){var n=t.call(this)||this;return Pm(e),n.l1=null==e||null==e.l1?.01:e.l1,n.l2=null==e||null==e.l2?.01:e.l2,n.hasL1=0!==n.l1,n.hasL2=0!==n.l2,n}return Kp(e,t),e.prototype.apply=function(t){var e=this;return _e(function(){var n=fn([1]);return e.hasL1&&(n=Hs(n,yl(iu(e.l1,Go(t))))),e.hasL2&&(n=Hs(n,yl(iu(e.l2,cf(t))))),n.asScalar()})},e.prototype.getConfig=function(){return{l1:this.l1,l2:this.l2}},e.fromConfig=function(t,e){return new t({l1:e.l1,l2:e.l2})},e.className="L1L2",e}(Bm);kp.registerClass(Vm);var Wm={l1l2:"L1L2"};function Um(t){return hh(t)}function jm(t,e){return void 0===e&&(e={}),fh(t,kp.SerializationMap.getMap().classNameMap,e,"regularizer")}function Gm(t){return null==t?null:"string"==typeof t?jm({className:t in Wm?Wm[t]:t,config:{}}):t instanceof Bm?t:jm(t)}var Hm=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;return n.supportsMasking=!0,null!=e&&(n.maxValue=e.maxValue),n}return Kp(e,t),e.prototype.call=function(t,e){t=Gf(t);var n=Cl(t);return null!=this.maxValue&&(n=Zo(n,0,this.maxValue)),n},e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e={maxValue:this.maxValue},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="ReLU",e}(nd);kp.registerClass(Hm);var qm=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;return n.DEFAULT_ALPHA=.3,null==e&&(e={}),n.alpha=null==e.alpha?n.DEFAULT_ALPHA:e.alpha,n}return Kp(e,t),e.prototype.call=function(t,e){var n=Gf(t);return wl(n,this.alpha)},e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e={alpha:this.alpha},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="LeakyReLU",e}(nd);kp.registerClass(qm);var Km=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;if(n.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),n.supportsMasking=!0,n.alphaInitializer=zf(e.alphaInitializer||n.DEFAULT_ALPHA_INITIALIZER),n.alphaRegularizer=Gm(e.alphaRegularizer),n.alphaConstraint=Rh(e.alphaConstraint),null==e.sharedAxes)n.sharedAxes=null;else if(Array.isArray(e.sharedAxes))n.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new eh("Expected sharedAxes to be a number or an array of numbers, but got "+e.sharedAxes);n.sharedAxes=[e.sharedAxes]}return n}return Kp(e,t),e.prototype.build=function(t){var e=(t=Hf(t)).slice(1);if(null!=this.sharedAxes)for(var n=0,r=this.sharedAxes;n<r.length;n++)e[(a=r[n])-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);var i={};if(null!=this.sharedAxes)for(var a=1;a<t.length;++a)i[a]=t[a];this.inputSpec=[new Jf({ndim:t.length,axes:i})],this.built=!0},e.prototype.call=function(t,e){return t=Gf(t),Nl(t,this.alpha.read())},e.prototype.getConfig=function(){var e={alphaInitializer:Mf(this.alphaInitializer),alphaRegularizer:Um(this.alphaRegularizer),alphaConstraint:Ih(this.alphaConstraint),sharedAxes:this.sharedAxes},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="PReLU",e}(nd);kp.registerClass(Km);var $m=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;if(n.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==n.DEFAULT_ALPHA)throw new nh("Non-default alpha value ("+e.alpha+") is not supported by the ELU layer yet.");return n.alpha=null==e.alpha?n.DEFAULT_ALPHA:e.alpha,n}return Kp(e,t),e.prototype.call=function(t,e){var n=Gf(t);return xl(n)},e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e={alpha:this.alpha},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="ELU",e}(nd);kp.registerClass($m);var Xm=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;return n.DEFAULT_THETA=1,null==e&&(e={}),n.theta=null==e.theta?n.DEFAULT_THETA:e.theta,n}return Kp(e,t),e.prototype.call=function(t,e){var n=Gf(t);return n.mul(Zh(n.greater(this.theta),"float32"))},e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e={theta:this.theta},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="ThresholdedReLU",e}(nd);kp.registerClass(Xm);var Ym=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;return n.DEFAULT_AXIS=1,null==e&&(e={}),n.softmax=(new _m).apply,n.axis=null==e.axis?n.DEFAULT_AXIS:e.axis,n}return Kp(e,t),e.prototype.call=function(t,e){var n=Gf(t);return this.softmax(n,this.axis)},e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e={axis:this.axis},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Softmax",e}(nd);function Jm(t,e,n){if("number"==typeof t)return ih(t,e);if(t.length!==e)throw new eh("The "+n+" argument must be an integer or tuple of "+e+" integers. Received: "+t.length+" elements.");for(var r=0;r<e;++r){var i=t[r];if(!qh(i))throw new eh("The "+n+" argument must be an integer or tuple of "+e+" integers. Received: "+JSON.stringify(t)+" including a non-integer number "+i)}return t}function Zm(t,e,n,r,i){return void 0===i&&(i=1),null==t?t:(a="same"===n?t:t-(e+(e-1)*(i-1))+1,Math.floor((a+r-1)/r));var a}function Qm(t,e,n,r){if(null==t)return null;if("valid"===r)t=t*e+Yh([n-e,0]);else{if("same"!==r)throw new eh("Unsupport padding mode: "+r+".");t*=e}return t}function tg(t,e){return _e(function(){return zh(e),"channelsFirst"===e?Sl(t,[0,2,3,1]):t})}function eg(t,e){return _e(function(){return zh(e),"channelsFirst"===e?Sl(t,[0,2,3,4,1]):t})}function ng(t,e,n,r,i,a,o,s){return void 0===r&&(r=[1,1]),void 0===i&&(i="valid"),void 0===s&&(s=null),_e(function(){if(null==a&&(a="channelsLast"),zh(a),3!==t.rank&&4!==t.rank)throw new eh("conv2dWithBiasActivation expects input to be of rank 3 or 4, but received "+t.rank+".");if(3!==e.rank&&4!==e.rank)throw new eh("conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received "+t.rank+".");var u=tg(t,a);if("causal"===i)throw new nh("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=yc.conv2d({x:u,filter:e,strides:r,pad:"same"===i?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:s}),"channelsFirst"===a&&(u=Sl(u,[0,3,1,2])),u})}kp.registerClass(Ym);var rg=function(t){function e(n,r){var i=t.call(this,r)||this;if(i.bias=null,i.DEFAULT_KERNEL_INITIALIZER="glorotNormal",i.DEFAULT_BIAS_INITIALIZER="zeros",e.verifyArgs(r),i.rank=n,bh(i.rank,"rank"),1!==i.rank&&2!==i.rank&&3!==i.rank)throw new nh("Convolution layer for rank other than 1, 2, or 3 ("+i.rank+") is not implemented yet.");if(i.kernelSize=Jm(r.kernelSize,n,"kernelSize"),i.strides=Jm(null==r.strides?1:r.strides,n,"strides"),i.padding=null==r.padding?"valid":r.padding,Lh(i.padding),i.dataFormat=null==r.dataFormat?"channelsLast":r.dataFormat,zh(i.dataFormat),i.activation=Lm(r.activation),i.useBias=null==r.useBias||r.useBias,i.biasInitializer=zf(r.biasInitializer||i.DEFAULT_BIAS_INITIALIZER),i.biasConstraint=Rh(r.biasConstraint),i.biasRegularizer=Gm(r.biasRegularizer),i.activityRegularizer=Gm(r.activityRegularizer),i.dilationRate=Jm(null==r.dilationRate?1:r.dilationRate,n,"dilationRate"),1===i.rank&&Array.isArray(i.dilationRate)&&1!==i.dilationRate.length)throw new eh("dilationRate must be a number or an array of a single number for 1D convolution, but received "+JSON.stringify(i.dilationRate));if(2===i.rank){if("number"==typeof i.dilationRate)i.dilationRate=[i.dilationRate,i.dilationRate];else if(2!==i.dilationRate.length)throw new eh("dilationRate must be a number or array of two numbers for 2D convolution, but received "+JSON.stringify(i.dilationRate))}else if(3===i.rank)if("number"==typeof i.dilationRate)i.dilationRate=[i.dilationRate,i.dilationRate,i.dilationRate];else if(3!==i.dilationRate.length)throw new eh("dilationRate must be a number or array of three numbers for 3D convolution, but received "+JSON.stringify(i.dilationRate));return i}return Kp(e,t),e.verifyArgs=function(t){if(ah("kernelSize"in t,"required key 'kernelSize' not in config"),"number"!=typeof t.kernelSize&&!yh(t.kernelSize,"number",1,3))throw new eh("BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received "+JSON.stringify(t.kernelSize)+".")},e.prototype.getConfig=function(){var e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Mm(this.activation),useBias:this.useBias,biasInitializer:Mf(this.biasInitializer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),biasConstraint:Ih(this.biasConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(nd),ig=function(t){function e(n,r){var i=t.call(this,n,r)||this;return i.kernel=null,e.verifyArgs(r),i.filters=r.filters,bh(i.filters,"filters"),i.kernelInitializer=zf(r.kernelInitializer||i.DEFAULT_KERNEL_INITIALIZER),i.kernelConstraint=Rh(r.kernelConstraint),i.kernelRegularizer=Gm(r.kernelRegularizer),i}return Kp(e,t),e.prototype.build=function(t){var e;t=Hf(t);var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n])throw new eh("The channel dimension of the input should be defined. Found "+t[n]);var r=t[n],i=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:(e={},e[n]=r,e)}],this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){var e;t=Gf(t);var r=null==n.bias?null:n.bias.read(),i=function(t){return"relu"===t?"relu":"linear"===t?"linear":null}(n.activation.getClassName());if(null!=i&&2===n.rank)e=ng(t,n.kernel.read(),r,n.strides,n.padding,n.dataFormat,n.dilationRate,i);else{if(1===n.rank)e=function(t,e,n,r,i,a,o){return void 0===r&&(r=1),void 0===i&&(i="valid"),void 0===o&&(o=1),_e(function(){if(null==a&&(a="channelsLast"),zh(a),3!==t.shape.length)throw new eh("The input of a conv1dWithBias operation should be 3, but is "+t.shape.length+" instead.");if(3!==e.shape.length)throw new eh("The kernel for a conv1dWithBias operation should be 3, but is "+e.shape.length+" instead");if(null!=n&&1!==n.shape.length)throw new eh("The bias for a conv1dWithBias operation should be 1, but is "+e.shape.length+" instead");if("channelsFirst"===a&&(t=Sl(t,[0,2,1])),"causal"===i)throw new nh("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");var s=_u(t,e,r,"same"===i?"same":"valid","NWC",o);return null!=n&&(s=hf(s,n)),s})}(t,n.kernel.read(),r,n.strides[0],n.padding,n.dataFormat,n.dilationRate[0]);else if(2===n.rank)e=ng(t,n.kernel.read(),r,n.strides,n.padding,n.dataFormat,n.dilationRate);else{if(3!==n.rank)throw new nh("convolutions greater than 3D are not implemented yet.");e=function(t,e,n,r,i,a,o){return void 0===r&&(r=[1,1,1]),void 0===i&&(i="valid"),_e(function(){if(null==a&&(a="channelsLast"),zh(a),4!==t.rank&&5!==t.rank)throw new eh("conv3dWithBias expects input to be of rank 4 or 5, but received "+t.rank+".");if(4!==e.rank&&5!==e.rank)throw new eh("conv3dWithBias expects kernel to be of rank 4 or 5, but received "+t.rank+".");var s=eg(t,a);if("causal"===i)throw new nh("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return s=Mu(s,e,r,"same"===i?"same":"valid","NDHWC",o),null!=n&&(s=hf(s,n)),"channelsFirst"===a&&(s=Sl(s,[0,4,1,2,3])),s})}(t,n.kernel.read(),r,n.strides,n.padding,n.dataFormat,n.dilationRate)}null!=n.activation&&(e=n.activation.apply(e))}return e})},e.prototype.computeOutputShape=function(t){t=Hf(t);for(var e=[],n="channelsLast"===this.dataFormat?t.slice(1,t.length-1):t.slice(2),r=0;r<n.length;++r){var i=Zm(n[r],this.kernelSize[r],this.padding,this.strides[r],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[r]);e.push(i)}var a=[t[0]];return"channelsLast"===this.dataFormat?(a=a.concat(e)).push(this.filters):(a.push(this.filters),a=a.concat(e)),a},e.prototype.getConfig=function(){var e={filters:this.filters,kernelInitializer:Mf(this.kernelInitializer),kernelRegularizer:Um(this.kernelRegularizer),kernelConstraint:Ih(this.kernelConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.verifyArgs=function(t){if(!("filters"in t)||"number"!=typeof t.filters||t.filters<1)throw new eh("Convolution layer expected config.filters to be a 'number' > 0 but got "+JSON.stringify(t.filters))},e}(rg),ag=function(t){function e(n){var r=t.call(this,2,n)||this;return e.verifyArgs(n),r}return Kp(e,t),e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,e},e.verifyArgs=function(t){if("number"!=typeof t.kernelSize&&!yh(t.kernelSize,"number",1,2))throw new eh("Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received "+JSON.stringify(t.kernelSize)+".")},e.className="Conv2D",e}(ig);kp.registerClass(ag);var og=function(t){function e(n){var r=t.call(this,3,n)||this;return e.verifyArgs(n),r}return Kp(e,t),e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,e},e.verifyArgs=function(t){if("number"!=typeof t.kernelSize&&(!Array.isArray(t.kernelSize)||1!==t.kernelSize.length&&3!==t.kernelSize.length))throw new eh("Conv3D expects config.kernelSize to be number or [number, number, number], but received "+JSON.stringify(t.kernelSize)+".")},e.className="Conv3D",e}(ig);kp.registerClass(og);var sg=function(t){function e(e){var n=t.call(this,e)||this;if(n.inputSpec=[new Jf({ndim:4})],"same"!==n.padding&&"valid"!==n.padding)throw new eh("Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode "+n.padding);return n}return Kp(e,t),e.prototype.build=function(t){var e;if(4!==(t=Hf(t)).length)throw new eh("Input should have rank 4; Received input shape: "+JSON.stringify(t));var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n])throw new eh("The channel dimension of the inputs should be defined. Found `None`.");var r=t[n],i=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",i,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jf({ndim:4,axes:(e={},e[n]=r,e)})],this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){var e=Gf(t);if(4!==e.shape.length)throw new eh("Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-"+e.shape.length);var r,i,a=e.shape,o=a[0];"channelsFirst"===n.dataFormat?(r=2,i=3):(r=1,i=2);var s=a[r],u=a[i],l=n.kernelSize[0],c=n.kernelSize[1],p=n.strides[0],h=n.strides[1],f=[o,Qm(s,p,l,n.padding),Qm(u,h,c,n.padding),n.filters];"channelsLast"!==n.dataFormat&&(e=Sl(e,[0,2,3,1]));var d=Vu(e,n.kernel.read(),f,n.strides,n.padding);return"channelsLast"!==n.dataFormat&&(d=Sl(d,[0,3,1,2])),null!=n.bias&&(d=hf(d,n.bias.read(),n.dataFormat)),null!=n.activation&&(d=n.activation.apply(d)),d})},e.prototype.computeOutputShape=function(t){var e,n,r,i=(t=Hf(t)).slice();"channelsFirst"===this.dataFormat?(e=1,n=2,r=3):(e=3,n=1,r=2);var a=this.kernelSize[0],o=this.kernelSize[1],s=this.strides[0],u=this.strides[1];return i[e]=this.filters,i[n]=Qm(i[n],s,a,this.padding),i[r]=Qm(i[r],u,o,this.padding),i},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.dilationRate,e},e.className="Conv2DTranspose",e}(ag);kp.registerClass(sg);var ug=function(t){function e(e){return t.call(this,2,e)||this}return Kp(e,t),e.className="SeparableConv2D",e}(function(t){function e(e,n){var r=t.call(this,e,n)||this;if(r.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",r.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",r.depthwiseKernel=null,r.pointwiseKernel=null,null==n.filters)throw new eh("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=n.kernelInitializer||null!=n.kernelRegularizer||null!=n.kernelConstraint)throw new eh("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=n.padding&&"same"!==n.padding&&"valid"!==n.padding)throw new eh("SeparableConv"+r.rank+"D supports only padding modes: 'same' and 'valid', but received "+JSON.stringify(n.padding));return r.depthMultiplier=null==n.depthMultiplier?1:n.depthMultiplier,r.depthwiseInitializer=zf(n.depthwiseInitializer||r.DEFAULT_DEPTHWISE_INITIALIZER),r.depthwiseRegularizer=Gm(n.depthwiseRegularizer),r.depthwiseConstraint=Rh(n.depthwiseConstraint),r.pointwiseInitializer=zf(n.depthwiseInitializer||r.DEFAULT_POINTWISE_INITIALIZER),r.pointwiseRegularizer=Gm(n.pointwiseRegularizer),r.pointwiseConstraint=Rh(n.pointwiseConstraint),r}return Kp(e,t),e.prototype.build=function(t){var e;if((t=Hf(t)).length<this.rank+2)throw new eh("Inputs to SeparableConv"+this.rank+"D should have rank "+(this.rank+2)+", but received input shape: "+JSON.stringify(t));var n="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[n]||t[n]<0)throw new eh("The channel dimension of the inputs should be defined, but found "+JSON.stringify(t[n]));for(var r=t[n],i=this.kernelSize.concat([r,this.depthMultiplier]),a=[],o=0;o<this.rank;++o)a.push(1);a.push(r*this.depthMultiplier,this.filters),this.depthwiseKernel=this.addWeight("depthwise_kernel",i,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,!0,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.inputSpec=[new Jf({ndim:this.rank+2,axes:(e={},e[n]=r,e)})],this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){var e;if(t=Gf(t),1===n.rank)throw new nh("1D separable convolution is not implemented yet.");return 2===n.rank&&("channelsFirst"===n.dataFormat&&(t=Sl(t,[0,2,3,1])),e=Bu(t,n.depthwiseKernel.read(),n.pointwiseKernel.read(),n.strides,n.padding,n.dilationRate,"NHWC")),n.useBias&&(e=hf(e,n.bias.read(),n.dataFormat)),null!=n.activation&&(e=n.activation.apply(e)),"channelsFirst"===n.dataFormat&&(e=Sl(e,[0,3,1,2])),e})},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Mf(this.depthwiseInitializer),e.pointwiseInitializer=Mf(this.pointwiseInitializer),e.depthwiseRegularizer=Um(this.depthwiseRegularizer),e.pointwiseRegularizer=Um(this.pointwiseRegularizer),e.depthwiseConstraint=Ih(this.depthwiseConstraint),e.pointwiseConstraint=Ih(this.pointwiseConstraint),e},e.className="SeparableConv",e}(ig));kp.registerClass(ug);var lg=function(t){function e(n){var r=t.call(this,1,n)||this;return e.verifyArgs(n),r.inputSpec=[{ndim:3}],r}return Kp(e,t),e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return delete e.rank,delete e.dataFormat,e},e.verifyArgs=function(t){if("number"!=typeof t.kernelSize&&!yh(t.kernelSize,"number",1,1))throw new eh("Conv1D expects config.kernelSize to be number or number[] with length 1, but received "+JSON.stringify(t.kernelSize)+".")},e.className="Conv1D",e}(ig);kp.registerClass(lg);var cg=function(t){function e(e){var n=t.call(this,e)||this;return"number"==typeof e.cropping?n.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?n.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:n.cropping=e.cropping,n.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,n.inputSpec=[{ndim:4}],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return"channelsFirst"===this.dataFormat?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]},e.prototype.call=function(t,e){var n=this;return _e(function(){return t=Gf(t),"channelsLast"===n.dataFormat?nf(nf(t,n.cropping[0][0],t.shape[1]-n.cropping[0][0]-n.cropping[0][1],2),n.cropping[1][0],t.shape[2]-n.cropping[1][1]-n.cropping[1][0],3):nf(nf(t,n.cropping[0][0],t.shape[2]-n.cropping[0][0]-n.cropping[0][1],3),n.cropping[1][0],t.shape[3]-n.cropping[1][1]-n.cropping[1][0],4)})},e.prototype.getConfig=function(){var e={cropping:this.cropping,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Cropping2D",e}(nd);kp.registerClass(cg);var pg=function(t){function e(e){var n=t.call(this,e)||this;return n.DEFAULT_SIZE=[2,2],n.inputSpec=[{ndim:4}],n.size=null==e.size?n.DEFAULT_SIZE:e.size,n.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,n}return Kp(e,t),e.prototype.computeOutputShape=function(t){if("channelsFirst"===this.dataFormat){var e=null==t[2]?null:this.size[0]*t[2],n=null==t[3]?null:this.size[1]*t[3];return[t[0],t[1],e,n]}return e=null==t[1]?null:this.size[0]*t[1],n=null==t[2]?null:this.size[1]*t[2],[t[0],e,n,t[3]]},e.prototype.call=function(t,e){var n=this;return _e(function(){var e=Gf(t),r=e.shape;if("channelsFirst"===n.dataFormat){e=Sl(e,[0,2,3,1]);var i=n.size[0]*r[2],a=n.size[1]*r[3],o=e.resizeNearestNeighbor([i,a]);return Sl(o,[0,3,1,2])}return i=n.size[0]*r[1],a=n.size[1]*r[2],e.resizeNearestNeighbor([i,a])})},e.prototype.getConfig=function(){var e={size:this.size,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="UpSampling2D",e}(nd);kp.registerClass(pg);var hg=function(t){function e(e){var n=t.call(this,2,e)||this;return n.depthwiseKernel=null,n.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,n.depthwiseInitializer=zf(e.depthwiseInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.depthwiseConstraint=Rh(e.depthwiseConstraint),n.depthwiseRegularizer=Gm(e.depthwiseRegularizer),n}return Kp(e,t),e.prototype.build=function(t){if((t=Hf(t)).length<4)throw new eh("Inputs to DepthwiseConv2D should have rank 4. Received input shape: "+JSON.stringify(t)+".");var e="channelsFirst"===this.dataFormat?1:3;if(null==t[e]||t[e]<0)throw new eh("The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not ("+t[e]+").");var n=t[e],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){var e=function(t,e,n,r,i,a){return void 0===n&&(n=[1,1]),void 0===r&&(r="valid"),_e(function(){null==i&&(i="channelsLast"),zh(i);var o=tg(t,i);if(4!==t.rank)throw new eh("Input for depthwiseConv2d is required to be 4-D, but is instead "+t.rank+"-D");if(4!==e.rank)throw new eh("depthwiseKernel is required to be 4-D, but is instead "+e.rank+"-D");return o=Pu(o,e,n,"same"===r?"same":"valid","NHWC",a),"channelsFirst"===i&&(o=Sl(o,[0,3,1,2])),o})}(t=Gf(t),n.depthwiseKernel.read(),n.strides,n.padding,n.dataFormat,null);return n.useBias&&(e=hf(e,n.bias.read(),n.dataFormat)),null!=n.activation&&(e=n.activation.apply(e)),e})},e.prototype.computeOutputShape=function(t){t=Hf(t);var e="channelsFirst"===this.dataFormat?t[2]:t[1],n="channelsFirst"===this.dataFormat?t[3]:t[2],r="channelsFirst"===this.dataFormat?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,i=Zm(e,this.kernelSize[0],this.padding,this.strides[0]),a=Zm(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[t[0],r,i,a]:[t[0],i,a,r]},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this);return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Mf(this.depthwiseInitializer),e.depthwiseRegularizer=Um(this.depthwiseRegularizer),e.depthwiseConstraint=Ih(this.depthwiseRegularizer),e},e.className="DepthwiseConv2D",e}(rg);kp.registerClass(hg);var fg=function(t){function e(e){var n=t.call(this,e)||this;return n.rate=Math.max(Math.min(e.rate,1),0),n.noiseShape=e.noiseShape,n.seed=e.seed,n.supportsMasking=!0,n}return Kp(e,t),e.prototype.getNoiseShape=function(t){if(null==this.noiseShape)return this.noiseShape;for(var e=t.shape,n=[],r=0;r<this.noiseShape.length;++r)n.push(null==this.noiseShape[r]?e[r]:this.noiseShape[r]);return n},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t);if(0<n.rate&&n.rate<1){var i=null!=e.training&&e.training,a=n.getNoiseShape(r);return df(function(){return ff(r,n.rate,a,n.seed)},function(){return r},i)}return t})},e.prototype.getConfig=function(){var e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.prototype.dispose=function(){return t.prototype.dispose.call(this)},e.className="Dropout",e}(nd);kp.registerClass(fg);var dg=function(t){function e(e){var n=t.call(this,e)||this;if(n.activation=null,n.useBias=!0,n.kernel=null,n.bias=null,n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){var r=null;null!=e.batchSize&&(r=e.batchSize),n.batchInputShape=[r,e.inputDim]}return n.units=e.units,bh(n.units,"units"),n.activation=Lm(e.activation),null!=e.useBias&&(n.useBias=e.useBias),n.kernelInitializer=zf(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.biasInitializer=zf(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelConstraint=Rh(e.kernelConstraint),n.biasConstraint=Rh(e.biasConstraint),n.kernelRegularizer=Gm(e.kernelRegularizer),n.biasRegularizer=Gm(e.biasRegularizer),n.activityRegularizer=Gm(e.activityRegularizer),n.supportsMasking=!0,n.inputSpec=[{minNDim:2}],n}return Kp(e,t),e.prototype.build=function(t){var e,n=(t=Hf(t))[t.length-1];null==this.kernel&&(this.kernel=this.addWeight("kernel",[n,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:(e={},e[-1]=n,e)}],this.built=!0},e.prototype.computeOutputShape=function(t){var e=(t=Hf(t)).slice();return e[e.length-1]=this.units,e},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r,i=Gf(t),a=function(t){return"relu"===t?"relu":"linear"===t?"linear":null}(n.activation.getClassName());return null!=a?r=uf(i,n.kernel.read(),a,n.bias?n.bias.read():null):(r=uf(i,n.kernel.read()),null!=n.bias&&(r=hf(r,n.bias.read())),null!=n.activation&&(r=n.activation.apply(r))),r})},e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),biasInitializer:Mf(this.biasInitializer),kernelRegularizer:Um(this.kernelRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),biasConstraint:Ih(this.biasConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Dense",e}(nd);kp.registerClass(dg);var mg=function(t){function e(e){var n=t.call(this,e||{})||this;return n.inputSpec=[{minNDim:3}],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){for(var e=0,n=(t=Hf(t)).slice(1);e<n.length;e++)if(null==n[e])throw new eh('The shape of the input to "Flatten" is not fully defined (got '+t.slice(1)+'). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.');return[t[0],Kh(t,1)]},e.prototype.call=function(t,e){var n=this;return _e(function(){return n.invokeCallHook(t,e),function(t){if(t.rank<=1)throw new eh("batchFlatten requires a minimum rank of 2. Got rank: "+t.rank+".");var e=[t.shape[0],Kh(t.shape,1)];return t.reshape(e)}(Gf(t))})},e.className="Flatten",e}(nd);kp.registerClass(mg);var gg=function(t){function e(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.activation=Lm(e.activation),n}return Kp(e,t),e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t);return n.activation.apply(r)})},e.prototype.getConfig=function(){var e={activation:Mm(this.activation)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Activation",e}(nd);kp.registerClass(gg);var vg=function(t){function e(e){var n=t.call(this,e)||this;return n.n=e.n,n.inputSpec=[{ndim:2}],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return[t[0],this.n,t[1]]},e.prototype.call=function(t,e){var n=this;return _e(function(){return function(t,e){return _e(function(){if(2!==t.shape.length)throw new eh("repeat() expects a rank-2 tensor, but received a rank-"+t.shape.length+" tensor.");return of(Qh(t,1),[1,e,1])})}(t=Gf(t),n.n)})},e.prototype.getConfig=function(){var e={n:this.n},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="RepeatVector",e}(nd);kp.registerClass(vg);var yg=function(t){function e(e){var n=t.call(this,e)||this;n.targetShape=e.targetShape;for(var r=0;r<n.targetShape.length;++r)n.isUnknown(n.targetShape[r])&&(n.targetShape[r]=null);return n}return Kp(e,t),e.prototype.isUnknown=function(t){return t<0||null==t},e.prototype.fixUnknownDimension=function(t,e){for(var n="Total size of new array must be unchanged.",r=e.slice(),i=1,a=null,o=0;o<r.length;++o){var s=r[o];if(this.isUnknown(s)){if(null!==a)throw new eh("Can only specifiy one unknown dimension.");a=o}else i*=s}var u=Kh(t);if(null!==a){if(0===i||u%i!=0)throw new eh(n);r[a]=u/i}else if(u!==i)throw new eh(n);return r},e.prototype.computeOutputShape=function(t){for(var e=!1,n=0;n<t.length;++n)if(this.isUnknown(t[n])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t),i=r.shape,a=i.slice(0,1).concat(n.fixUnknownDimension(i.slice(1),n.targetShape));return r.reshape(a)})},e.prototype.getConfig=function(){var e={targetShape:this.targetShape},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Reshape",e}(nd);kp.registerClass(yg);var bg=function(t){function e(e){var n=t.call(this,e)||this;if(null==e.dims)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error("Permute constructor requires `dims` to be an Array, but received "+e.dims+" instead.");var r=Jh(1,e.dims.length+1);if(!Y.arraysEqual(e.dims.slice().sort(),r))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");return n.dims=e.dims,n.dimsIncludingBatch=[0].concat(n.dims),n.inputSpec=[new Jf({ndim:n.dims.length+1})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){var e=(t=Hf(t)).slice();return this.dims.forEach(function(n,r){e[r+1]=t[n]}),e},e.prototype.call=function(t,e){return Sl(Gf(t),this.dimsIncludingBatch)},e.prototype.getConfig=function(){var e={dims:this.dims},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Permute",e}(nd);kp.registerClass(bg);var xg=function(t){function e(e){var n=t.call(this,null==e?{}:e)||this;return n.supportsMasking=!0,n.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0,n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={maskValue:this.maskValue};return Object.assign(n,e),n},e.prototype.computeMask=function(t,e){var n=Gf(t);return cl(Nu(n,this.maskValue),-1)},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t),i=cl(Nu(r,n.maskValue),-1,!0);return r.mul(i.asType(r.dtype))})},e.className="Masking",e}(nd);kp.registerClass(xg);var wg=function(t){function e(e){var n=t.call(this,e)||this;if(n.embeddings=null,n.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){var r=null;null!=e.batchSize&&(r=e.batchSize),null==e.inputLength?n.batchInputShape=[r,null]:n.batchInputShape=[r].concat(uh(e.inputLength))}return n.inputDim=e.inputDim,bh(n.inputDim,"inputDim"),n.outputDim=e.outputDim,bh(n.outputDim,"outputDim"),n.embeddingsInitializer=zf(e.embeddingsInitializer||n.DEFAULT_EMBEDDINGS_INITIALIZER),n.embeddingsRegularizer=Gm(e.embeddingsRegularizer),n.activityRegularizer=Gm(e.activityRegularizer),n.embeddingsConstraint=Rh(e.embeddingsConstraint),n.maskZero=e.maskZero,n.supportsMasking=e.maskZero,n.inputLength=e.inputLength,n}return Kp(e,t),e.prototype.build=function(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0},e.prototype.warnOnIncompatibleInputShape=function(t){},e.prototype.computeMask=function(t,e){var n=this;return _e(function(){return n.maskZero?(t=Gf(t),Nu(t,yn(t))):null})},e.prototype.computeOutputShape=function(t){if(t=Hf(t),null==this.inputLength)return t.concat([this.outputDim]);var e=uh(this.inputLength);if(e.length!==t.length-1)throw new eh('"inputLength" is '+this.inputLength+", but received input shape has shape "+t);for(var n=0,r=0;r<e.length;++r){var i=e[r],a=t[r+1];if(null!=i&&null!=a&&i!==a)throw new eh('"inputLength" is '+this.inputLength+", but received input shape has shape "+t);null==i&&(e[n]=a),n++}return[t[0]].concat(e,[this.outputDim])},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t);return"int32"!==r.dtype&&(r=Zh(r,"int32")),lf(n.embeddings.read(),r.as1D()).reshape(Hf(n.computeOutputShape(r.shape)))})},e.prototype.getConfig=function(){var e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Mf(this.embeddingsInitializer),embeddingsRegularizer:Um(this.embeddingsRegularizer),activityRegularizer:Um(this.activityRegularizer),embeddingsConstraint:Ih(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Embedding",e}(nd);kp.registerClass(wg);var Ng=function(t){function e(e){var n=t.call(this,e||{})||this;return n.supportsMasking=!0,n}return Kp(e,t),e.prototype.mergeFunction=function(t){throw new nh},e.prototype.computeElementwiseOpOutputShape=function(t,e){if(null==t||null==e)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(0===e.length)return t;for(var n=t.slice(0,t.length-e.length),r=0;r<e.length;++r){var i=t[t.length-e.length+r],a=e[r];if(null==i||null==a||i<0||a<0)n.push(null);else if(1===i)n.push(a);else if(1===a)n.push(i);else{if(i!==a)throw new eh("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(i)}}return n},e.prototype.build=function(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Hf(t)]),(t=t).length<2)throw new eh("A merge layer should be called on an Array of at least 2 inputs. Got "+t.length+" input(s).");for(var e=[],n=0,r=t;n<r.length;n++)null!=(o=r[n])&&null!==o[0]&&e.push(o[0]);if((e=mh(e)).length>1)throw new eh("Can not merge tensors with different batch sizes. Got tensors with shapes: "+JSON.stringify(t)+".");for(var i=null==t[0]?null:t[0].slice(1),a=1;a<t.length;++a){var o=null==t[a]?null:t[a].slice(1);i=this.computeElementwiseOpOutputShape(i,o)}var s=t.map(function(t){return t.length});-1===t.indexOf(null)&&1===mh(s).length?this.reshapeRequired=!1:this.reshapeRequired=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){if(t=t,n.reshapeRequired){var e=[],r=t.map(function(t){return t.rank});if(-1===r.indexOf(null)){for(var i=Yh(r),a=0,o=t;a<o.length;a++){for(var s=(h=o[a]).rank,u=0;u<i-s;++u)h=Qh(h,1);e.push(h)}return n.mergeFunction(e)}for(var l=!1,c=0,p=t;c<p.length;c++){var h;if(null==(s=(h=p[c]).rank)){var f=h.shape,d=f[0],m=f.slice(1).concat([d]),g=h.reshape([d].concat(Kh(f.slice(1))));g=(g=Sl(g,[1,0])).reshape(m),e.push(g),l=!0}else if(s>1){var v=Jh(1,s).concat([0]);e.push(Sl(h,v)),l=!0}else e.push(h)}var y=n.mergeFunction(e),b=y.rank;if(l)if(null==b){var x=y.shape;m=[d=x[x.length-1]].concat(x.slice(0,x.length-1)),y=Sl(y.reshape([-1,d]),[1,0]).reshape(m)}else b>1&&(v=[b-1].concat(Jh(0,b-1)),y=Sl(y,v));return y}return n.mergeFunction(t)})},e.prototype.computeOutputShape=function(t){var e;e=null==(t=t)[0]?null:t[0].slice(1);for(var n=1;n<t.length;++n){var r=null==t[n]?null:t[n].slice(1);e=this.computeElementwiseOpOutputShape(e,r)}for(var i=[],a=0,o=t;a<o.length;a++)null!=(r=o[a])&&null!==r[0]&&i.push(r[0]);return 1===(i=mh(i)).length?i.concat(e):[null].concat(e)},e.prototype.computeMask=function(t,e){return _e(function(){if(null==e)return null;if(!Array.isArray(e))throw new eh("`mask` should be an Array");if(!Array.isArray(t))throw new eh("`inputs` should be an Array");if(e.length!==t.length)throw new eh("The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths ("+t.length+" vs "+e.length+")");if(e.every(function(t){return null==t}))return null;for(var n=(e=e.map(function(t){return null==t?t:Gn(t,0)}))[0],r=1;r<e.length-1;++r)n=Bs(n,e[r]);return n})},e}(nd),Cg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.mergeFunction=function(t){return _e(function(){for(var e=t[0].clone(),n=1;n<t.length;++n)e=Hs(e,t[n]);return e})},e.className="Add",e}(Ng);kp.registerClass(Cg);var Eg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.mergeFunction=function(t){return _e(function(){for(var e=t[0].clone(),n=1;n<t.length;++n)e=iu(e,t[n]);return e})},e.className="Multiply",e}(Ng);kp.registerClass(Eg);var Sg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.mergeFunction=function(t){return _e(function(){for(var e=t[0].clone(),n=1;n<t.length;++n)e=Hs(e,t[n]);return iu(1/t.length,e)})},e.className="Average",e}(Ng);kp.registerClass(Sg);var kg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.mergeFunction=function(t){return _e(function(){for(var e=t[0],n=1;n<t.length;++n)e=Zs(e,t[n]);return e})},e.className="Maximum",e}(Ng);kp.registerClass(kg);var Ig=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.mergeFunction=function(t){return _e(function(){for(var e=t[0],n=1;n<t.length;++n)e=tu(e,t[n]);return e})},e.className="Minimum",e}(Ng);kp.registerClass(Ig);var Ag=function(t){function e(e){var n=t.call(this,e)||this;return n.DEFAULT_AXIS=-1,null==e&&(e={}),n.axis=null==e.axis?n.DEFAULT_AXIS:e.axis,n.supportsMasking=!0,n.reshapeRequired=!1,n}return Kp(e,t),e.prototype.build=function(t){if(!Array.isArray(t)||!Array.isArray(t[0])||1===t.length)throw new eh("A `Concatenate` layer should be called on a list of at least 2 inputs");for(var e=!0,n=0,r=t=t;n<r.length;n++)if(null!=(c=r[n])){e=!1;break}if(!e){for(var i=[],a=0;a<t.length;++a){var o=t[a].slice();o.splice(this.axis,1);for(var s=!1,u=0,l=i;u<l.length;u++){var c=l[u];if(Y.arraysEqual(c,o)){s=!0;break}}s||i.push(o)}if(i.length>1)throw new eh("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}},e.prototype.mergeFunction=function(t){var e=this;return _e(function(){return rf(t,e.axis)})},e.prototype.computeOutputShape=function(t){if(!Array.isArray(t)||!Array.isArray(t[0]))throw new eh("A `Concatenate` layer should be called on a list of inputs.");for(var e=t,n=e[0].slice(),r=this.axis<0?n.length+this.axis:this.axis,i=0,a=e.slice(1);i<a.length;i++){var o=a[i];if(null==n[r]||null==o[r]){n[r]=null;break}n[r]+=o[r]}return n},e.prototype.computeMask=function(t,e){var n=this;if(null==e)return null;if(!Array.isArray(e))throw new eh("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new eh("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new eh("Mismatch in the length of mask ("+e.length+") and the legnth of inputs ("+t.length+")");return _e(function(){var r=!0;if(e.forEach(function(t){null==t||(r=!1)}),r)return null;for(var i=[],a=0;a<t.length;++a)null==e[a]?i.push(vn(t[a]).asType("bool")):e[a].rank<t[a].rank?i.push(Gn(e[a],-1)):i.push(e[a]);var o=bn(i,n.axis);return ll(o,-1,!1)})},e.prototype.getConfig=function(){var e={axis:this.axis},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Concatenate",e}(Ng);function Rg(t,e){for(;t<0;)t+=e;return t}kp.registerClass(Ag);var Tg=function(t){function e(e){var n=t.call(this,e)||this;return n.axes=e.axes,n.normalize=null!=e.normalize&&e.normalize,n.supportsMasking=!0,n.reshapeRequired=!1,n}return Kp(e,t),e.prototype.build=function(t){Y.assert(Array.isArray(t)&&2===t.length&&Array.isArray(t[0])&&Array.isArray(t[1]),function(){return"A `Dot` layer should be called on a list of exactly 2 inputs."});var e=t[0],n=t[1];if(e.length>3||n.length>3)throw new nh("Dot layer does not support tensors of 4D or higher rank yet.");var r=this.interpretAxes(e,n);if(e[r[0]]!==n[r[1]])throw new eh("Dimension incompatibility: "+e[r[0]]+" !== "+n[r[1]])},e.prototype.mergeFunction=function(t){if(2!==t.length)throw new eh("A `Dot` layer must be called on exactly 2 inputs, but received "+t.length+" input(s).");var e,n=t[0],r=t[1];return e=Array.isArray(this.axes)?this.axes.map(function(e,n){return Rg(e,t[n].shape.length)}):[Rg(this.axes,n.shape.length),Rg(this.axes,r.shape.length)],this.normalize&&(n=yd(n,e[0]),r=yd(r,e[1])),function(t,e,n){if(t.shape.length>3||e.shape.length>3)throw new nh("batchDot is not implemented for tensors of 4D or higher rank yet");if(Y.assert(t.shape.length>=2,function(){return"batchDot requires the rank of x to be >= 2, but got "+t.shape.length}),Y.assert(t.shape.length>=2,function(){return"batchDot requires the rank of y to be >= 2, but got "+e.shape.length}),"number"==typeof n&&(n=[n,n]),"complex64"===t.dtype||"complex64"===e.dtype)throw new nh("batchDot is not implemented for complex64-type Tensors yet.");var r=t.shape.length,i=e.shape.length;null==n&&(n=[r-1,i-2]);var a=n;return _e(function(){var n,o;if(r>i){n=r-i;for(var s=[],u=0;u<n;++u)s.push(1);e=e.reshape(e.shape.concat(s))}else if(i>r){for(n=i-r,s=[],u=0;u<n;++u)s.push(1);t=t.reshape(t.shape.concat(s))}else n=0;if(2===t.shape.length&&2===e.shape.length)o=a[0]===a[1]?t.mulStrict(e).sum(a[0]):t.transpose([1,0]).mulStrict(e).sum(a[1]);else{var l=a[0]!==t.shape.length-1,c=a[1]===e.shape.length-1;o=t.matMul(e,l,c)}if(n>0){var p,h=[];for(u=p=r>i?r+i-3:r-1;u<p+n;++u)h.push(u);o=o.squeeze(h)}return 1===o.shape.length&&(o=o.expandDims(1)),o})}(n,r,e)},e.prototype.interpretAxes=function(t,e){return Array.isArray(this.axes)?this.axes:[Rg(this.axes,t.length),Rg(this.axes,e.length)]},e.prototype.computeOutputShape=function(t){Y.assert(Array.isArray(t)&&2===t.length&&Array.isArray(t[0])&&Array.isArray(t[1]),function(){return"A `Dot` layer should be called on a list of exactly 2 inputs."});var e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new nh("Dot layer does not support tensors of 4D or higher rank yet.");var r=this.interpretAxes(e,n);e.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);var i=e.concat(n);return 1===i.length&&i.push(1),i},e.prototype.computeMask=function(t,e){return null},e.prototype.getConfig=function(){var e={axes:this.axes,normalize:this.normalize},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="Dot",e}(Ng);kp.registerClass(Tg);var Dg=function(t){function e(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.stddev=e.stddev,n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={stddev:this.stddev};return Object.assign(n,e),n},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t);return df(function(){return sf(r.shape,0,n.stddev).add(r)},function(){return r},e.training||!1)})},e.className="GaussianNoise",e}(nd);kp.registerClass(Dg);var Og=function(t){function e(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.rate=e.rate,n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={rate:this.rate};return Object.assign(n,e),n},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e);var r=Gf(t);return n.rate>0&&n.rate<1?df(function(){var t=Math.sqrt(n.rate/(1-n.rate));return r.mul(sf(r.shape,1,t))},function(){return r},e.training||!1):r})},e.className="GaussianDropout",e}(nd);kp.registerClass(Og);var _g=function(t){function e(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n.rate=e.rate,n.noiseShape=e.noiseShape,n}return Kp(e,t),e.prototype._getNoiseShape=function(t){return this.noiseShape||Gf(t).shape},e.prototype.computeOutputShape=function(t){return t},e.prototype.getConfig=function(){var e=t.prototype.getConfig.call(this),n={rate:this.rate};return Object.assign(n,e),n},e.prototype.call=function(t,e){var n=this;return _e(function(){if(n.rate<1&&n.rate>0){var r=n._getNoiseShape(t);return df(function(){var e=Gf(t),i=-1.7580993408473766,a=mu(nr(r),n.rate);a=Zh(a,"float32");var o=Math.pow((1-n.rate)*(1+n.rate*Math.pow(i,2)),-.5),s=-o*i*n.rate;return e.mul(a).add(a.add(-1).mul(i)).mul(o).add(s)},function(){return Gf(t)},e.training||!1)}return t})},e.className="AlphaDropout",e}(nd);function Fg(t,e,n,r,i,a){var o;if(void 0===a&&(a=.001),2===t.rank)o=zs(t,e,n,r,i,a);else if(3===t.rank)o=Ls(t,e,n,r,i,a);else{if(4!==t.rank)throw new nh("batchNormalization is not implemented for array of rank "+t.rank+" yet");o=Ps(t,e,n,r,i,a)}return o}function Mg(t,e,n,r,i){return void 0===i&&(i=.001),Y.arraysEqual(r.slice().sort(),Jh(0,t.rank-1))?function(t,e,n,r,i){return void 0===i&&(i=.001),_e(function(){var a=vl(t,r),o=a.mean,s=a.variance;return[Fg(t,o,s,n,e,i),o,s]})}(t,e,n,r,i):function(t,e,n,r,i){return void 0===i&&(i=.001),_e(function(){for(var a=vl(t,r),o=a.mean,s=a.variance,u=[],l=0,c=Jh(0,t.rank);l<c.length;l++){var p=c[l];-1!==r.indexOf(p)?u.push(1):u.push(t.shape[p])}var h=o.reshape(u),f=s.reshape(u),d=null==e?null:e.reshape(u),m=null==n?null:n.reshape(u);return[Fg(t,h,f,m,d,i),o,s]})}(t,e,n,r,i)}kp.registerClass(_g);var zg=function(t){function e(e){var n=this;return null==e&&(e={}),(n=t.call(this,e)||this).supportsMasking=!0,n.axis=null==e.axis?-1:e.axis,n.momentum=null==e.momentum?.99:e.momentum,n.epsilon=null==e.epsilon?.001:e.epsilon,n.center=null==e.center||e.center,n.scale=null==e.scale||e.scale,n.betaInitializer=zf(e.betaInitializer||"zeros"),n.gammaInitializer=zf(e.gammaInitializer||"ones"),n.movingMeanInitializer=zf(e.movingMeanInitializer||"zeros"),n.movingVarianceInitializer=zf(e.movingVarianceInitializer||"ones"),n.betaConstraint=Rh(e.betaConstraint),n.gammaConstraint=Rh(e.gammaConstraint),n.betaRegularizer=Gm(e.betaRegularizer),n.gammaRegularizer=Gm(e.gammaRegularizer),n}return Kp(e,t),e.prototype.build=function(t){var e;t=Hf(t);var n=this.axis>=0?this.axis:this.axis+t.length,r=t[n];if(null==r)throw new eh("Axis "+n+" of input tensor should have a defined dimension but the layer received an input with shape "+JSON.stringify(t)+".");this.inputSpec=[new Jf({ndim:t.length,axes:(e={},e[n]=r,e)})];var i=[r];this.scale&&(this.gamma=this.addWeight("gamma",i,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",i,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",i,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",i,null,this.movingVarianceInitializer,null,!1),this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){var r=null!=e.training&&e.training,i=Gf(t),a=i.shape,o=a.length,s=Jh(0,o),u=n.axis>=0?n.axis:n.axis+o;s.splice(u,1);var l=ih(1,o);l[u]=a[u];var c=s.slice();c.sort();var p=!Y.arraysEqual(c,Jh(0,o).slice(0,o-1));if(!r)return function(){if(p){var t=n.movingMean.read().reshape(l),e=n.movingVariance.read().reshape(l),r=n.center?n.beta.read().reshape(l):null,a=n.scale?n.gamma.read().reshape(l):null;return Fg(i,t,e,r,a,n.epsilon)}return Fg(i,n.movingMean.read(),n.movingVariance.read(),null==n.beta?null:n.beta.read(),null==n.gamma?null:n.gamma.read(),n.epsilon)}();var h=Mg(i,n.gamma.read(),n.beta.read(),s,n.epsilon),f=h[0],d=h[1],m=h[2],g=function(t,e,n){_e(function(){var r=1-n,i=t.read(),a=i.sub(e).mul(r);t.write(i.sub(a))})};return g(n.movingMean,d,n.momentum),g(n.movingVariance,m,n.momentum),f})},e.prototype.getConfig=function(){var e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Mf(this.betaInitializer),gammaInitializer:Mf(this.gammaInitializer),movingMeanInitializer:Mf(this.movingMeanInitializer),movingVarianceInitializer:Mf(this.movingVarianceInitializer),betaRegularizer:Um(this.betaRegularizer),gammaRegularizer:Um(this.gammaRegularizer),betaConstraint:Ih(this.betaConstraint),gammaConstraint:Ih(this.gammaConstraint)},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="BatchNormalization",e}(nd);kp.registerClass(zg);var Lg=function(t){function e(e){var n=this;if(null==e&&(e={}),(n=t.call(this,e)||this).dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,null==e.padding)n.padding=[[1,1],[1,1]];else if("number"==typeof e.padding)n.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,2!==e.padding.length)throw new eh("ZeroPadding2D expects padding to be a length-2 array, but received a length-"+e.padding.length+" array.");var r=void 0,i=void 0;if("number"==typeof e.padding[0])r=[e.padding[0],e.padding[0]],i=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,2!==e.padding[0].length)throw new eh("ZeroPadding2D expects height padding to be a length-2 array, but received a length-"+e.padding[0].length+" array.");if(r=e.padding[0],2!==e.padding[1].length)throw new eh("ZeroPadding2D expects width padding to be a length-2 array, but received a length-"+e.padding[1].length+" array.");i=e.padding[1]}n.padding=[r,i]}return n.inputSpec=[new Jf({ndim:4})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){var e,n;return t=Hf(t),"channelsFirst"===this.dataFormat?(e=null!=t[2]&&t[2]>=0?t[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=t[3]&&t[3]>=0?t[3]+this.padding[1][0]+this.padding[1][1]:null,[t[0],t[1],e,n]):(e=null!=t[1]&&t[1]>=0?t[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=t[2]&&t[2]>=0?t[2]+this.padding[1][0]+this.padding[1][1]:null,[t[0],e,n,t[3]])},e.prototype.call=function(t,e){var n=this;return _e(function(){return function(t,e,n){return _e(function(){if(4!==t.rank)throw new eh("temporalPadding expects input tensor to be 4-D, but received a "+t.rank+"-D tensor.");if(null==e&&(e=[[1,1],[1,1]]),2!==e.length||2!==e[0].length||2!==e[1].length)throw new eh("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==n&&(n="channelsLast"),"channelsLast"!==n&&"channelsFirst"!==n)throw new eh("Unknown data format: "+n+". Supported data formats are 'channelsLast' and 'channelsFirst.");var r;return r="channelsFirst"===n?[[0,0],[0,0],e[0],e[1]]:[[0,0],e[0],e[1],[0,0]],$n(t,r)})}(Gf(t),n.padding,n.dataFormat)})},e.prototype.getConfig=function(){var e={padding:this.padding,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="ZeroPadding2D",e}(nd);function Pg(t,e,n,r,i,a){return _e(function(){var o;zh(i),Ph(a),Lh(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==i&&(i="channelsLast"),null==a&&(a="max"),t=tg(t,i);var s="same"===r?"same":"valid";return o="max"===a?Zu(t,e,n,s):Qu(t,e,n,s),"channelsFirst"===i&&(o=Sl(o,[0,3,1,2])),o})}function Bg(t,e,n,r,i,a){return _e(function(){var o;zh(i),Ph(a),Lh(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==i&&(i="channelsLast"),null==a&&(a="max"),t=eg(t,i);var s="same"===r?"same":"valid";return o="max"===a?el(t,e,n,s):nl(t,e,n,s),"channelsFirst"===i&&(o=Sl(o,[0,4,1,2,3])),o})}kp.registerClass(Lg);var Vg=function(t){function e(e){var n=this;if(null==e.poolSize&&(e.poolSize=2),n=t.call(this,e)||this,"number"==typeof e.poolSize)n.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new eh("poolSize for 1D convolutional layer must be a number or an Array of a single number, but received "+JSON.stringify(e.poolSize));n.poolSize=e.poolSize}if(bh(n.poolSize,"poolSize"),null==e.strides)n.strides=n.poolSize;else if("number"==typeof e.strides)n.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new eh("strides for 1D convolutional layer must be a number or an Array of a single number, but received "+JSON.stringify(e.strides));n.strides=e.strides}return bh(n.strides,"strides"),n.padding=null==e.padding?"valid":e.padding,Lh(n.padding),n.inputSpec=[new Jf({ndim:3})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){var e=Zm((t=Hf(t))[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]},e.prototype.call=function(t,e){var n=this;return _e(function(){n.invokeCallHook(t,e),t=Qh(Gf(t),2);var r=n.poolingFunction(Gf(t),[n.poolSize[0],1],[n.strides[0],1],n.padding,"channelsLast");return ar(r,[2])})},e.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(nd),Wg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return zh(i),Lh(r),Pg(t,e,n,r,i,"max")},e.className="MaxPooling1D",e}(Vg);kp.registerClass(Wg);var Ug=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return zh(i),Lh(r),Pg(t,e,n,r,i,"avg")},e.className="AveragePooling1D",e}(Vg);kp.registerClass(Ug);var jg=function(t){function e(e){var n=this;if(null==e.poolSize&&(e.poolSize=[2,2]),(n=t.call(this,e)||this).poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)n.strides=n.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new eh("If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length "+e.strides.length+".");n.strides=e.strides}else n.strides=[e.strides,e.strides];return bh(n.poolSize,"poolSize"),bh(n.strides,"strides"),n.padding=null==e.padding?"valid":e.padding,n.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,zh(n.dataFormat),Lh(n.padding),n.inputSpec=[new Jf({ndim:4})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){t=Hf(t);var e="channelsFirst"===this.dataFormat?t[2]:t[1],n="channelsFirst"===this.dataFormat?t[3]:t[2];return e=Zm(e,this.poolSize[0],this.padding,this.strides[0]),n=Zm(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[t[0],t[1],e,n]:[t[0],e,n,t[3]]},e.prototype.call=function(t,e){var n=this;return _e(function(){return n.invokeCallHook(t,e),n.poolingFunction(Gf(t),n.poolSize,n.strides,n.padding,n.dataFormat)})},e.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(nd),Gg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return zh(i),Lh(r),Pg(t,e,n,r,i,"max")},e.className="MaxPooling2D",e}(jg);kp.registerClass(Gg);var Hg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return zh(i),Lh(r),Pg(t,e,n,r,i,"avg")},e.className="AveragePooling2D",e}(jg);kp.registerClass(Hg);var qg=function(t){function e(e){var n=this;if(null==e.poolSize&&(e.poolSize=[2,2,2]),(n=t.call(this,e)||this).poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)n.strides=n.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new eh("If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length "+e.strides.length+".");n.strides=e.strides}else n.strides=[e.strides,e.strides,e.strides];return bh(n.poolSize,"poolSize"),bh(n.strides,"strides"),n.padding=null==e.padding?"valid":e.padding,n.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,zh(n.dataFormat),Lh(n.padding),n.inputSpec=[new Jf({ndim:5})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){t=Hf(t);var e="channelsFirst"===this.dataFormat?t[2]:t[1],n="channelsFirst"===this.dataFormat?t[3]:t[2],r="channelsFirst"===this.dataFormat?t[4]:t[3];return e=Zm(e,this.poolSize[0],this.padding,this.strides[0]),n=Zm(n,this.poolSize[1],this.padding,this.strides[1]),r=Zm(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[t[0],t[1],e,n,r]:[t[0],e,n,r,t[4]]},e.prototype.call=function(t,e){var n=this;return _e(function(){return n.invokeCallHook(t,e),n.poolingFunction(Gf(t),n.poolSize,n.strides,n.padding,n.dataFormat)})},e.prototype.getConfig=function(){var e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(nd),Kg=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return zh(i),Lh(r),Bg(t,e,n,r,i,"max")},e.className="MaxPooling3D",e}(qg);kp.registerClass(Kg);var $g=function(t){function e(e){return t.call(this,e)||this}return Kp(e,t),e.prototype.poolingFunction=function(t,e,n,r,i){return zh(i),Lh(r),Bg(t,e,n,r,i,"avg")},e.className="AveragePooling3D",e}(qg);kp.registerClass($g);var Xg=function(t){function e(e){var n=t.call(this,e)||this;return n.inputSpec=[new Jf({ndim:3})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return[t[0],t[2]]},e.prototype.call=function(t,e){throw new nh},e}(nd),Yg=function(t){function e(e){return t.call(this,e||{})||this}return Kp(e,t),e.prototype.call=function(t,e){return _e(function(){var e=Gf(t);return ml(e,1)})},e.className="GlobalAveragePooling1D",e}(Xg);kp.registerClass(Yg);var Jg=function(t){function e(e){return t.call(this,e||{})||this}return Kp(e,t),e.prototype.call=function(t,e){return _e(function(){var e=Gf(t);return dl(e,1)})},e.className="GlobalMaxPooling1D",e}(Xg);kp.registerClass(Jg);var Zg=function(t){function e(e){var n=t.call(this,e)||this;return n.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,zh(n.dataFormat),n.inputSpec=[new Jf({ndim:4})],n}return Kp(e,t),e.prototype.computeOutputShape=function(t){return t=t,"channelsLast"===this.dataFormat?[t[0],t[3]]:[t[0],t[1]]},e.prototype.call=function(t,e){throw new nh},e.prototype.getConfig=function(){var e={dataFormat:this.dataFormat},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e}(nd),Qg=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.call=function(t,e){var n=this;return _e(function(){var e=Gf(t);return"channelsLast"===n.dataFormat?ml(e,[1,2]):ml(e,[2,3])})},e.className="GlobalAveragePooling2D",e}(Zg);kp.registerClass(Qg);var tv=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.call=function(t,e){var n=this;return _e(function(){var e=Gf(t);return"channelsLast"===n.dataFormat?dl(e,[1,2]):dl(e,[2,3])})},e.className="GlobalMaxPooling2D",e}(Zg);function ev(t,e,n,r){if(Array.isArray(t)){if(null!=e||null!=n)throw new eh("When inputs is an array, neither initialState or constants should be provided");null!=r&&(n=t.slice(t.length-r,t.length),t=t.slice(0,t.length-r)),t.length>1&&(e=t.slice(1,t.length)),t=t[0]}function i(t){return null==t||Array.isArray(t)?t:[t]}return{inputs:t,initialState:e=i(e),constants:n=i(n)}}function nv(t,e,n,r,i,a,o,s){return void 0===r&&(r=!1),void 0===o&&(o=!1),void 0===s&&(s=!1),_e(function(){var u=e.shape.length;if(u<3)throw new eh("Input should be at least 3D, but is "+u+"D.");var l=[1,0].concat(Jh(2,u));if(e=Sl(e,l),null!=a)throw new nh("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=i&&((i=i.asType("bool").asType("float32")).rank===u-1&&(i=Gn(i,-1)),i=Sl(i,l)),r&&(e=Hu(e,0),null!=i&&(i=Hu(i,0)));var c,p,h=[],f=n,d=e.shape[0],m=lr(e);null!=i&&(p=lr(i));for(var g,v=function(e){var n=m[e],r=_e(function(){return t(n,f)});if(null==i)c=r[0],f=r[1];else{var a=_e(function(){var t=p[e],n=vn(t).sub(t);return{output:r[0].mul(t).addStrict(f[0].mul(n)),newStates:f.map(function(e,i){return r[1][i].mul(t).addStrict(e.mul(n))})}});c=a.output,f=a.newStates}s&&h.push(c)},y=0;y<d;++y)v(y);return s&&(g=or(h,1)),[c,g,f]})}kp.registerClass(tv);var rv=function(t){function e(e){var n,r=t.call(this,e)||this;if(null==e.cell)throw new eh("cell property is missing for the constructor of RNN.");if(null==(n=Array.isArray(e.cell)?new pv({cells:e.cell}):e.cell).stateSize)throw new eh("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");return r.cell=n,r.returnSequences=null!=e.returnSequences&&e.returnSequences,r.returnState=null!=e.returnState&&e.returnState,r.goBackwards=null!=e.goBackwards&&e.goBackwards,r._stateful=null!=e.stateful&&e.stateful,r.unroll=null!=e.unroll&&e.unroll,r.supportsMasking=!0,r.inputSpec=[new Jf({ndim:3})],r.stateSpec=null,r.states_=null,r.numConstants=null,r.keptStates=[],r}return Kp(e,t),e.prototype.getStates=function(){return null==this.states_?Jh(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map(function(t){return null}):this.states_},e.prototype.setStates=function(t){this.states_=t},e.prototype.computeOutputShape=function(t){Uf(t)&&(t=t[0]),t=t;var e=this.cell.stateSize;Array.isArray(e)||(e=[e]);var n,r=e[0];if(n=this.returnSequences?[t[0],t[1],r]:[t[0],r],this.returnState){for(var i=[],a=0,o=e;a<o.length;a++){var s=o[a];i.push([t[0],s])}return[n].concat(i)}return n},e.prototype.computeMask=function(t,e){var n=this;return _e(function(){Array.isArray(e)&&(e=e[0]);var t=n.returnSequences?e:null;if(n.returnState){var r=n.states.map(function(t){return null});return[t].concat(r)}return t})},Object.defineProperty(e.prototype,"states",{get:function(){if(null==this.states_){for(var t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[],n=0;n<t;++n)e.push(null);return e}return this.states_},set:function(t){this.states_=t},enumerable:!0,configurable:!0}),e.prototype.build=function(t){if(null!=this.numConstants)throw new nh("Constants support is not implemented in RNN yet.");Uf(t)&&(t=t[0]),t=t;var e=this.stateful?t[0]:null,n=t[t.length-1];this.inputSpec[0]=new Jf({shape:[e,null,n]});var r,i=[t[0]].concat(t.slice(2));if(this.cell.build(i),r=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!Y.arraysEqual(this.stateSpec.map(function(t){return t.shape[t.shape.length-1]}),r))throw new eh("An initialState was passed that is not compatible with cell.stateSize. Received stateSpec="+this.stateSpec+"; However cell.stateSize is "+this.cell.stateSize)}else this.stateSpec=r.map(function(t){return new Jf({shape:[null,t]})});this.stateful&&this.resetStates()},e.prototype.resetStates=function(t,e){var n=this;void 0===e&&(e=!1),_e(function(){if(!n.stateful)throw new Qp("Cannot call resetStates() on an RNN Layer that is not stateful.");var r=n.inputSpec[0].shape[0];if(null==r)throw new eh("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==n.states_)Array.isArray(n.cell.stateSize)?n.states_=n.cell.stateSize.map(function(t){return fn([r,t])}):n.states_=[fn([r,n.cell.stateSize])];else if(null==t)Fe(n.states_),null!=n.keptStates&&(Fe(n.keptStates),n.keptStates=[]),Array.isArray(n.cell.stateSize)?n.states_=n.cell.stateSize.map(function(t){return fn([r,t])}):n.states_[0]=fn([r,n.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==n.states_.length)throw new eh("Layer "+n.name+" expects "+n.states_.length+" state(s), but it received "+t.length+" state value(s). Input received: "+t);!0===e?n.keptStates.push(n.states_.slice()):Fe(n.states_);for(var i=0;i<n.states_.length;++i){var a=t[i],o=Array.isArray(n.cell.stateSize)?n.cell.stateSize[i]:n.cell.stateSize,s=[r,o];if(!Y.arraysEqual(a.shape,s))throw new eh("State "+i+" is incompatible with layer "+n.name+": expected shape="+s+", received shape="+a.shape);n.states_[i]=a}}n.states_=n.states_.map(function(t){return Me(t.clone())})})},e.prototype.apply=function(e,n){var r=null==n?null:n.initialState,i=null==n?null:n.constants;null==n&&(n={});var a=ev(e,r,i,this.numConstants);e=a.inputs,r=a.initialState,i=a.constants;var o=[],s=[];if(null!=r){n.initialState=r,o=o.concat(r),this.stateSpec=[];for(var u=0,l=r;u<l.length;u++){var c=l[u];this.stateSpec.push(new Jf({shape:c.shape}))}s=s.concat(this.stateSpec)}if(null!=i&&(n.constants=i,o=o.concat(i),this.numConstants=i.length),o[0]instanceof Zf){var p=[e].concat(o),h=this.inputSpec.concat(s),f=this.inputSpec;this.inputSpec=h;var d=t.prototype.apply.call(this,p,n);return this.inputSpec=f,d}return t.prototype.apply.call(this,e,n)},e.prototype.call=function(t,e){var n=this;return _e(function(){var r=null==e?null:e.mask,i=null==e?null:e.training,a=null==e?null:e.initialState;t=Gf(t),null==a&&(a=n.stateful?n.states_:n.getInitialState(t));var o=Array.isArray(n.cell.stateSize)?n.cell.stateSize.length:1;if(a.length!==o)throw new eh("RNN Layer has "+o+" state(s) but was passed "+a.length+" initial state(s).");n.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");var s={training:i},u=nv(function(t,e){var r=n.cell.call([t].concat(e),s);return[r[0],r.slice(1)]},t,a,n.goBackwards,r,null,n.unroll,n.returnSequences),l=u[0],c=u[1],p=u[2];n.stateful&&n.resetStates(p,i);var h=n.returnSequences?c:l;return n.returnState?[h].concat(p):h})},e.prototype.getInitialState=function(t){var e=this;return _e(function(){var n=fn(t.shape);return n=Qh(n=yl(n,[1,2])),Array.isArray(e.cell.stateSize)?e.cell.stateSize.map(function(t){return t>1?of(n,[1,t]):n}):e.cell.stateSize>1?[of(n,[1,e.cell.stateSize])]:[n]})},Object.defineProperty(e.prototype,"trainableWeights",{get:function(){return this.trainable?this.cell.trainableWeights:[]},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"nonTrainableWeights",{get:function(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights},enumerable:!0,configurable:!0}),e.prototype.setFastWeightInitDuringBuild=function(e){t.prototype.setFastWeightInitDuringBuild.call(this,e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)},e.prototype.getConfig=function(){var e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(e.numConstants=this.numConstants);var n=this.cell.getConfig();e.cell={className:this.cell.getClassName(),config:n};var r=t.prototype.getConfig.call(this);return Object.assign(e,r),e},e.fromConfig=function(t,e,n){void 0===n&&(n={});var r=vd(e.cell,n);return new t(Object.assign(e,{cell:r}))},e.className="RNN",e}(nd);kp.registerClass(rv);var iv=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e}(nd),av=function(t){function e(e){var n=t.call(this,e)||this;return n.DEFAULT_ACTIVATION="tanh",n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_RECURRENT_INITIALIZER="orthogonal",n.DEFAULT_BIAS_INITIALIZER="zeros",n.units=e.units,bh(n.units,"units"),n.activation=Lm(null==e.activation?n.DEFAULT_ACTIVATION:e.activation),n.useBias=null==e.useBias||e.useBias,n.kernelInitializer=zf(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=zf(e.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=zf(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelRegularizer=Gm(e.kernelRegularizer),n.recurrentRegularizer=Gm(e.recurrentRegularizer),n.biasRegularizer=Gm(e.biasRegularizer),n.kernelConstraint=Rh(e.kernelConstraint),n.recurrentConstraint=Rh(e.recurrentConstraint),n.biasConstraint=Rh(e.biasConstraint),n.dropout=Xh([1,Yh([0,null==e.dropout?0:e.dropout])]),n.recurrentDropout=Xh([1,Yh([0,null==e.recurrentDropout?0:e.recurrentDropout])]),n.stateSize=n.units,n.dropoutMask=null,n.recurrentDropoutMask=null,n}return Kp(e,t),e.prototype.build=function(t){t=Hf(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){if(2!==(t=t).length)throw new eh("SimpleRNNCell expects 2 input Tensors, got "+t.length+".");var r=t[1];t=t[0];var i,a=null!=e.training&&e.training;0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=hv(function(){return vn(t)},n.dropout,a)),0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=hv(function(){return vn(r)},n.recurrentDropout,a));var o=n.dropoutMask,s=n.recurrentDropoutMask;i=uf(null!=o?iu(t,o):t,n.kernel.read()),null!=n.bias&&(i=hf(i,n.bias.read())),null!=s&&(r=iu(r,s));var u=Hs(i,uf(r,n.recurrentKernel.read()));return null!=n.activation&&(u=n.activation.apply(u)),[u,u]})},e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),recurrentInitializer:Mf(this.recurrentInitializer),biasInitializer:Mf(this.biasInitializer),kernelRegularizer:Um(this.kernelRegularizer),recurrentRegularizer:Um(this.recurrentRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),recurrentConstraint:Ih(this.recurrentConstraint),biasConstraint:Ih(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="SimpleRNNCell",e}(iv);kp.registerClass(av);var ov=function(t){function e(e){return e.cell=new av(e),t.call(this,e)||this}return Kp(e,t),e.prototype.call=function(e,n){var r=this;return _e(function(){null!=r.cell.dropoutMask&&(Fe(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(Fe(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null);var i=null==n?null:n.mask,a=null==n?null:n.training,o=null==n?null:n.initialState;return t.prototype.call.call(r,e,{mask:i,training:a,initialState:o})})},Object.defineProperty(e.prototype,"units",{get:function(){return this.cell.units},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"activation",{get:function(){return this.cell.activation},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"useBias",{get:function(){return this.cell.useBias},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelInitializer",{get:function(){return this.cell.kernelInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentInitializer",{get:function(){return this.cell.recurrentInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasInitializer",{get:function(){return this.cell.biasInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelRegularizer",{get:function(){return this.cell.kernelRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentRegularizer",{get:function(){return this.cell.recurrentRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasRegularizer",{get:function(){return this.cell.biasRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelConstraint",{get:function(){return this.cell.kernelConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentConstraint",{get:function(){return this.cell.recurrentConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasConstraint",{get:function(){return this.cell.biasConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dropout",{get:function(){return this.cell.dropout},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentDropout",{get:function(){return this.cell.recurrentDropout},enumerable:!0,configurable:!0}),e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),recurrentInitializer:Mf(this.recurrentInitializer),biasInitializer:Mf(this.biasInitializer),kernelRegularizer:Um(this.kernelRegularizer),recurrentRegularizer:Um(this.recurrentRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),recurrentConstraint:Ih(this.recurrentConstraint),biasConstraint:Ih(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout},n=t.prototype.getConfig.call(this);return delete n.cell,Object.assign(e,n),e},e.fromConfig=function(t,e){return new t(e)},e.className="SimpleRNN",e}(rv);kp.registerClass(ov);var sv=function(t){function e(e){var n=t.call(this,e)||this;return n.DEFAULT_ACTIVATION="tanh",n.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_RECURRENT_INITIALIZER="orthogonal",n.DEFAULT_BIAS_INITIALIZER="zeros",n.units=e.units,bh(n.units,"units"),n.activation=Lm(void 0===e.activation?n.DEFAULT_ACTIVATION:e.activation),n.recurrentActivation=Lm(void 0===e.recurrentActivation?n.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),n.useBias=null==e.useBias||e.useBias,n.kernelInitializer=zf(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=zf(e.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=zf(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.kernelRegularizer=Gm(e.kernelRegularizer),n.recurrentRegularizer=Gm(e.recurrentRegularizer),n.biasRegularizer=Gm(e.biasRegularizer),n.kernelConstraint=Rh(e.kernelConstraint),n.recurrentConstraint=Rh(e.recurrentConstraint),n.biasConstraint=Rh(e.biasConstraint),n.dropout=Xh([1,Yh([0,null==e.dropout?0:e.dropout])]),n.recurrentDropout=Xh([1,Yh([0,null==e.recurrentDropout?0:e.recurrentDropout])]),n.implementation=e.implementation,n.stateSize=n.units,n.dropoutMask=null,n.recurrentDropoutMask=null,n}return Kp(e,t),e.prototype.build=function(t){var e=(t=Hf(t))[t.length-1];this.kernel=this.addWeight("kernel",[e,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){if(2!==(t=t).length)throw new eh("GRUCell expects 2 input Tensors (inputs, h, c), got "+t.length+".");var r=null!=e.training&&e.training,i=t[1];t=t[0],0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=hv(function(){return vn(t)},n.dropout,r,3)),0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=hv(function(){return vn(i)},n.recurrentDropout,r,3));var a,o,s,u=n.dropoutMask,l=n.recurrentDropoutMask;0<n.dropout&&n.dropout<1&&(t=iu(t,u[0]));var c=uf(t,n.kernel.read());n.useBias&&(c=hf(c,n.bias.read())),0<n.recurrentDropout&&n.recurrentDropout<1&&(i=iu(i,l[0]));var p=n.recurrentKernel.read(),h=En(p,[2*n.units,n.units],p.rank-1),f=h[0],d=h[1],m=uf(i,f),g=En(c,3,c.rank-1),v=g[0],y=g[1],b=g[2],x=En(m,2,m.rank-1),w=x[0],N=x[1];a=n.recurrentActivation.apply(Hs(v,w)),o=n.recurrentActivation.apply(Hs(y,N));var C=uf(iu(o,i),d);s=n.activation.apply(Hs(b,C));var E=Hs(iu(a,i),iu(Hs(1,us(a)),s));return[E,E]})},e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),recurrentActivation:Mm(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),recurrentInitializer:Mf(this.recurrentInitializer),biasInitializer:Mf(this.biasInitializer),kernelRegularizer:Um(this.kernelRegularizer),recurrentRegularizer:Um(this.recurrentRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),recurrentConstraint:Ih(this.recurrentConstraint),biasConstraint:Ih(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="GRUCell",e}(iv);kp.registerClass(sv);var uv=function(t){function e(e){return 0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new sv(e),t.call(this,e)||this}return Kp(e,t),e.prototype.call=function(e,n){var r=this;return _e(function(){null!=r.cell.dropoutMask&&(Fe(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(Fe(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null);var i=null==n?null:n.mask,a=null==n?null:n.training,o=null==n?null:n.initialState;return t.prototype.call.call(r,e,{mask:i,training:a,initialState:o})})},Object.defineProperty(e.prototype,"units",{get:function(){return this.cell.units},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"activation",{get:function(){return this.cell.activation},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentActivation",{get:function(){return this.cell.recurrentActivation},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"useBias",{get:function(){return this.cell.useBias},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelInitializer",{get:function(){return this.cell.kernelInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentInitializer",{get:function(){return this.cell.recurrentInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasInitializer",{get:function(){return this.cell.biasInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelRegularizer",{get:function(){return this.cell.kernelRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentRegularizer",{get:function(){return this.cell.recurrentRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasRegularizer",{get:function(){return this.cell.biasRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelConstraint",{get:function(){return this.cell.kernelConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentConstraint",{get:function(){return this.cell.recurrentConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasConstraint",{get:function(){return this.cell.biasConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dropout",{get:function(){return this.cell.dropout},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentDropout",{get:function(){return this.cell.recurrentDropout},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"implementation",{get:function(){return this.cell.implementation},enumerable:!0,configurable:!0}),e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),recurrentActivation:Mm(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),recurrentInitializer:Mf(this.recurrentInitializer),biasInitializer:Mf(this.biasInitializer),kernelRegularizer:Um(this.kernelRegularizer),recurrentRegularizer:Um(this.recurrentRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),recurrentConstraint:Ih(this.recurrentConstraint),biasConstraint:Ih(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=t.prototype.getConfig.call(this);return delete n.cell,Object.assign(e,n),e},e.fromConfig=function(t,e){return 0===e.implmentation&&(e.implementation=1),new t(e)},e.className="GRU",e}(rv);kp.registerClass(uv);var lv=function(t){function e(e){var n=t.call(this,e)||this;return n.DEFAULT_ACTIVATION="tanh",n.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",n.DEFAULT_KERNEL_INITIALIZER="glorotNormal",n.DEFAULT_RECURRENT_INITIALIZER="orthogonal",n.DEFAULT_BIAS_INITIALIZER="zeros",n.units=e.units,bh(n.units,"units"),n.activation=Lm(void 0===e.activation?n.DEFAULT_ACTIVATION:e.activation),n.recurrentActivation=Lm(void 0===e.recurrentActivation?n.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),n.useBias=null==e.useBias||e.useBias,n.kernelInitializer=zf(e.kernelInitializer||n.DEFAULT_KERNEL_INITIALIZER),n.recurrentInitializer=zf(e.recurrentInitializer||n.DEFAULT_RECURRENT_INITIALIZER),n.biasInitializer=zf(e.biasInitializer||n.DEFAULT_BIAS_INITIALIZER),n.unitForgetBias=e.unitForgetBias,n.kernelRegularizer=Gm(e.kernelRegularizer),n.recurrentRegularizer=Gm(e.recurrentRegularizer),n.biasRegularizer=Gm(e.biasRegularizer),n.kernelConstraint=Rh(e.kernelConstraint),n.recurrentConstraint=Rh(e.recurrentConstraint),n.biasConstraint=Rh(e.biasConstraint),n.dropout=Xh([1,Yh([0,null==e.dropout?0:e.dropout])]),n.recurrentDropout=Xh([1,Yh([0,null==e.recurrentDropout?0:e.recurrentDropout])]),n.implementation=e.implementation,n.stateSize=[n.units,n.units],n.dropoutMask=null,n.recurrentDropoutMask=null,n}return Kp(e,t),e.prototype.build=function(t){var e,n,r=(t=Hf(t))[t.length-1];if(this.kernel=this.addWeight("kernel",[r,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){var i=this.biasInitializer,a=this.units;n=new((e=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Kp(e,t),e.prototype.apply=function(t,e){var n=i.apply([a]),r=(new bf).apply([a]),o=i.apply([2*a]);return af(af(n,r),o)},e}(vf)).className="CustomInit",e)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0},e.prototype.call=function(t,e){var n=this;return _e(function(){var r=null!=e.training&&e.training;if(3!==(t=t).length)throw new eh("LSTMCell expects 3 input Tensors (inputs, h, c), got "+t.length+".");var i=t[1],a=t[2];t=t[0],0<n.dropout&&n.dropout<1&&null==n.dropoutMask&&(n.dropoutMask=hv(function(){return vn(t)},n.dropout,r,4)),0<n.recurrentDropout&&n.recurrentDropout<1&&null==n.recurrentDropoutMask&&(n.recurrentDropoutMask=hv(function(){return vn(i)},n.recurrentDropout,r,4));var o,s,u,l,c=n.dropoutMask,p=n.recurrentDropoutMask;0<n.dropout&&n.dropout<1&&(t=iu(t,c[0]));var h=uf(t,n.kernel.read());0<n.recurrentDropout&&n.recurrentDropout<1&&(i=iu(i,p[0])),h=Hs(h,uf(i,n.recurrentKernel.read())),n.useBias&&(h=hf(h,n.bias.read()));var f=En(h,4,h.rank-1),d=f[0],m=f[1],g=f[2],v=f[3];o=n.recurrentActivation.apply(d),s=n.recurrentActivation.apply(m),u=Hs(iu(s,a),iu(o,n.activation.apply(g))),l=n.recurrentActivation.apply(v);var y=iu(l,n.activation.apply(u));return[y,y,u]})},e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),recurrentActivation:Mm(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),recurrentInitializer:Mf(this.recurrentInitializer),biasInitializer:Mf(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Um(this.kernelRegularizer),recurrentRegularizer:Um(this.recurrentRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),recurrentConstraint:Ih(this.recurrentConstraint),biasConstraint:Ih(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.className="LSTMCell",e}(iv);kp.registerClass(lv);var cv=function(t){function e(e){return 0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new lv(e),t.call(this,e)||this}return Kp(e,t),e.prototype.call=function(e,n){var r=this;return _e(function(){null!=r.cell.dropoutMask&&(Fe(r.cell.dropoutMask),r.cell.dropoutMask=null),null!=r.cell.recurrentDropoutMask&&(Fe(r.cell.recurrentDropoutMask),r.cell.recurrentDropoutMask=null);var i=null==n?null:n.mask,a=null==n?null:n.training,o=null==n?null:n.initialState;return t.prototype.call.call(r,e,{mask:i,training:a,initialState:o})})},Object.defineProperty(e.prototype,"units",{get:function(){return this.cell.units},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"activation",{get:function(){return this.cell.activation},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentActivation",{get:function(){return this.cell.recurrentActivation},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"useBias",{get:function(){return this.cell.useBias},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelInitializer",{get:function(){return this.cell.kernelInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentInitializer",{get:function(){return this.cell.recurrentInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasInitializer",{get:function(){return this.cell.biasInitializer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"unitForgetBias",{get:function(){return this.cell.unitForgetBias},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelRegularizer",{get:function(){return this.cell.kernelRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentRegularizer",{get:function(){return this.cell.recurrentRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasRegularizer",{get:function(){return this.cell.biasRegularizer},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"kernelConstraint",{get:function(){return this.cell.kernelConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentConstraint",{get:function(){return this.cell.recurrentConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"biasConstraint",{get:function(){return this.cell.biasConstraint},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dropout",{get:function(){return this.cell.dropout},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"recurrentDropout",{get:function(){return this.cell.recurrentDropout},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"implementation",{get:function(){return this.cell.implementation},enumerable:!0,configurable:!0}),e.prototype.getConfig=function(){var e={units:this.units,activation:Mm(this.activation),recurrentActivation:Mm(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mf(this.kernelInitializer),recurrentInitializer:Mf(this.recurrentInitializer),biasInitializer:Mf(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Um(this.kernelRegularizer),recurrentRegularizer:Um(this.recurrentRegularizer),biasRegularizer:Um(this.biasRegularizer),activityRegularizer:Um(this.activityRegularizer),kernelConstraint:Ih(this.kernelConstraint),recurrentConstraint:Ih(this.recurrentConstraint),biasConstraint:Ih(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation},n=t.prototype.getConfig.call(this);return delete n.cell,Object.assign(e,n),e},e.fromConfig=function(t,e){return 0===e.implmentation&&(e.implementation=1),new t(e)},e.className="LSTM",e}(rv);kp.registerClass(cv);var pv=function(t){function e(e){var n=t.call(this,e)||this;return n.cells=e.cells,n}return Kp(e,t),Object.defineProperty(e.prototype,"stateSize",{get:function(){for(var t=[],e=0,n=this.cells.slice().reverse();e<n.length;e++){var r=n[e];Array.isArray(r.stateSize)?t.push.apply(t,r.stateSize):t.push(r.stateSize)}return t},enumerable:!0,configurable:!0}),e.prototype.call=function(t,e){var n=this;return _e(function(){for(var r=(t=t).slice(1),i=[],a=0,o=n.cells.slice().reverse();a<o.length;a++){var s=o[a];Array.isArray(s.stateSize)?i.push(r.splice(0,s.stateSize.length)):i.push(r.splice(0,1))}i.reverse();for(var u,l=[],c=0;c<n.cells.length;++c)s=n.cells[c],r=i[c],u=0===c?[t[0]].concat(r):[u[0]].concat(r),u=s.call(u,e),l.push(u.slice(1));r=[];for(var p=0,h=l.slice().reverse();p<h.length;p++){var f=h[p];r.push.apply(r,f)}return[u[0]].concat(r)})},e.prototype.build=function(t){var e;Uf(t)&&(t=t[0]),t=t;for(var n=0,r=this.cells;n<r.length;n++){var i=r[n];i.build(t),e=Array.isArray(i.stateSize)?i.stateSize[0]:i.stateSize,t=[t[0],e]}this.built=!0},e.prototype.getConfig=function(){for(var e=[],n=0,r=this.cells;n<r.length;n++){var i=r[n];e.push({className:i.getClassName(),config:i.getConfig()})}var a={cells:e},o=t.prototype.getConfig.call(this);return Object.assign(a,o),a},e.fromConfig=function(t,e,n){void 0===n&&(n={});for(var r=[],i=0,a=e.cells;i<a.length;i++){var o=a[i];r.push(vd(o,n))}return new t({cells:r})},Object.defineProperty(e.prototype,"trainableWeights",{get:function(){if(!this.trainable)return[];for(var t=[],e=0,n=this.cells;e<n.length;e++){var r=n[e];t.push.apply(t,r.trainableWeights)}return t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"nonTrainableWeights",{get:function(){for(var t=[],e=0,n=this.cells;e<n.length;e++){var r=n[e];t.push.apply(t,r.nonTrainableWeights)}if(!this.trainable){for(var i=[],a=0,o=this.cells;a<o.length;a++)r=o[a],i.push.apply(i,r.trainableWeights);return i.concat(t)}return t},enumerable:!0,configurable:!0}),e.prototype.getWeights=function(){for(var t=[],e=0,n=this.cells;e<n.length;e++){var r=n[e];t.push.apply(t,r.weights)}return Xf(t)},e.prototype.setWeights=function(t){for(var e=[],n=0,r=this.cells;n<r.length;n++)for(var i=r[n],a=i.weights.length,o=t.splice(a),s=0;s<i.weights.length;++s)e.push([i.weights[s],o[s]]);Yf(e)},e.className="StackedRNNCells",e}(iv);function hv(t,e,n,r){function i(){return ff(t(),e)}if(void 0===n&&(n=null),void 0===r&&(r=1),r>1){for(var a=[],o=0;o<r;o++)a.push(df(i,t,n));return a.map(function(t){return Me(t.clone())})}return Me(df(i,t,n).clone())}kp.registerClass(pv);var fv=function(t){function e(e){var n=t.call(this,e)||this;return n.layer=e.layer,n}return Kp(e,t),e.prototype.build=function(t){this.built=!0},Object.defineProperty(e.prototype,"trainable",{get:function(){return null!=this.layer&&this.layer.trainable},set:function(t){null!=this.layer&&(this.layer.trainable=t)},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"trainableWeights",{get:function(){return this.layer.trainableWeights},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"nonTrainableWeights",{get:function(){return this.layer.nonTrainableWeights},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"updates",{get:function(){return this.layer._updates},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"losses",{get:function(){return this.layer.losses},enumerable:!0,configurable:!0}),e.prototype.getWeights=function(){return this.layer.getWeights()},e.prototype.setWeights=function(t){this.layer.setWeights(t)},e.prototype.getConfig=function(){var e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.prototype.setFastWeightInitDuringBuild=function(e){t.prototype.setFastWeightInitDuringBuild.call(this,e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)},e.fromConfig=function(t,e,n){void 0===n&&(n={});var r=vd(e.layer,n);delete e.layer;var i={layer:r};return Object.assign(i,e),new t(i)},e}(nd),dv=function(t){function e(e){var n=t.call(this,e)||this;return n.supportsMasking=!0,n}return Kp(e,t),e.prototype.build=function(e){if((e=Hf(e)).length<3)throw new eh("TimeDistributed layer expects an input shape >= 3D, but received input shape "+JSON.stringify(e));this.inputSpec=[{shape:e}];var n=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(n),this.layer.built=!0),t.prototype.build.call(this,e)},e.prototype.computeOutputShape=function(t){var e=[(t=Hf(t))[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),r=t[1];return[n[0],r].concat(n.slice(1))},e.prototype.call=function(t,e){var n=this;return _e(function(){return nv(function(t,r){return[Gf(n.layer.call(t,e)),[]]},t=Gf(t),[],!1,null,null,!1,!0)[1]})},e.className="TimeDistributed",e}(fv);kp.registerClass(dv);var mv="concat",gv=function(t){function e(e){var n=t.call(this,e)||this,r=e.layer.getConfig(),i={};i.className=e.layer.getClassName(),i.config=r,n.forwardLayer=vd(i),r.goBackwards=!0!==r.goBackwards;var a={};if(a.className=e.layer.getClassName(),a.config=r,n.backwardLayer=vd(a),n.forwardLayer.name="forward_"+n.forwardLayer.name,n.backwardLayer.name="backward_"+n.backwardLayer.name,n.mergeMode=void 0===e.mergeMode?mv:e.mergeMode,function(t){vh(Fh,"BidirectionalMergeMode",t)}(n.mergeMode),e.weights)throw new nh("weights support is not implemented for Bidirectional layer yet.");return n._stateful=e.layer.stateful,n.returnSequences=e.layer.returnSequences,n.returnState=e.layer.returnState,n.supportsMasking=!0,n._trainable=!0,n.inputSpec=e.layer.inputSpec,n.numConstants=null,n}return Kp(e,t),Object.defineProperty(e.prototype,"trainable",{get:function(){return this._trainable},set:function(t){this._trainable=t,null!=this.forwardLayer&&(this.forwardLayer.trainable=t),null!=this.backwardLayer&&(this.backwardLayer.trainable=t)},enumerable:!0,configurable:!0}),e.prototype.getWeights=function(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())},e.prototype.setWeights=function(t){var e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))},e.prototype.computeOutputShape=function(t){var e,n,r,i=this.forwardLayer.computeOutputShape(t);return Array.isArray(i)&&Array.isArray(i[0])||(i=[i]),i=i,this.returnState?(r=i.slice(1),e=i[0]):e=i[0],e=e,"concat"===this.mergeMode?(e[e.length-1]*=2,n=[e]):n=null==this.mergeMode?[e,e.slice()]:[e],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[e].concat(r).concat(r.slice()):sh(n)},e.prototype.apply=function(e,n){var r=null==n?null:n.initialState,i=null==n?null:n.constants;null==n&&(n={});var a=ev(e,r,i,this.numConstants);if(e=a.inputs,r=a.initialState,i=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(null==r||0===r.length)&&null==i)return t.prototype.apply.call(this,e,n);var o=[],s=[];if(null!=r){var u=r.length;if(u%2>0)throw new eh("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");n.initialState=r,o.push.apply(o,r);var l=r.map(function(t){return new Jf({shape:t.shape})});this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),s.push.apply(s,l)}if(null!=i)throw new nh("Support for constants in Bidirectional layers is not implemented yet.");for(var c=o[0]instanceof Zf,p=0,h=o;p<h.length;p++)if(h[p]instanceof Zf!==c)throw new eh("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){var f=[e].concat(o),d=this.inputSpec.concat(s),m=this.inputSpec;this.inputSpec=d;var g=t.prototype.apply.call(this,f,n);return this.inputSpec=m,g}return t.prototype.apply.call(this,e,n)},e.prototype.call=function(t,e){var n=this;return _e(function(){if(null!=e.mask)throw new nh("The support for masking is not implemented for Bidirectional layers yet.");var r,i,a,o,s=e.initialState;if(null==s)r=n.forwardLayer.call(t,e),i=n.backwardLayer.call(t,e);else{var u=s.slice(0,s.length/2),l=s.slice(s.length/2);r=n.forwardLayer.call(t,Object.assign(e,{initialState:u})),i=n.backwardLayer.call(t,Object.assign(e,{initialState:l}))}return n.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(i.slice(1))),r=r[0],i=i[0]),n.returnSequences&&(i=Hu(i,1)),"concat"===n.mergeMode?o=rf([r,i]):"sum"===n.mergeMode?o=Hs(r,i):"ave"===n.mergeMode?o=iu(.5,Hs(r,i)):"mul"===n.mergeMode?o=iu(r,i):null==n.mergeMode&&(o=[r,i]),n.returnState?null==n.mergeMode?o.concat(a):[o].concat(a):o})},e.prototype.resetStates=function(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()},e.prototype.build=function(t){var e=this;Wh(this.forwardLayer.name,function(){e.forwardLayer.build(t)}),Wh(this.backwardLayer.name,function(){e.backwardLayer.build(t)}),this.built=!0},Object.defineProperty(e.prototype,"trainableWeights",{get:function(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"nonTrainableWeights",{get:function(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)},enumerable:!0,configurable:!0}),e.prototype.setFastWeightInitDuringBuild=function(e){t.prototype.setFastWeightInitDuringBuild.call(this,e),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(e)},e.prototype.getConfig=function(){var e={mergeMode:this.mergeMode},n=t.prototype.getConfig.call(this);return Object.assign(e,n),e},e.fromConfig=function(t,e){var n=vd(e.layer);if(delete e.layer,null!=e.numConstants)throw new nh("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");var r=e;return r.layer=n,new t(r)},e.className="Bidirectional",e}(fv);function vv(t){return new Ug(t)}function yv(t){return new Hg(t)}function bv(t){return new $g(t)}function xv(t){return new Jg(t)}function wv(t){return new tv(t)}function Nv(t){return new Wg(t)}function Cv(t){return new Gg(t)}kp.registerClass(gv);var Ev=xv,Sv=wv,kv=Nv,Iv=Cv;var Av=Object.freeze({inputLayer:function(t){return new id(t)},elu:function(t){return new $m(t)},reLU:function(t){return new Hm(t)},leakyReLU:function(t){return new qm(t)},prelu:function(t){return new Km(t)},softmax:function(t){return new Ym(t)},thresholdedReLU:function(t){return new Xm(t)},conv1d:function(t){return new lg(t)},conv2d:function(t){return new ag(t)},conv2dTranspose:function(t){return new sg(t)},conv3d:function(t){return new og(t)},separableConv2d:function(t){return new ug(t)},cropping2D:function(t){return new cg(t)},upSampling2d:function(t){return new pg(t)},depthwiseConv2d:function(t){return new hg(t)},activation:function(t){return new gg(t)},dense:function(t){return new dg(t)},dropout:function(t){return new fg(t)},flatten:function(t){return new mg(t)},repeatVector:function(t){return new vg(t)},reshape:function(t){return new yg(t)},permute:function(t){return new bg(t)},embedding:function(t){return new wg(t)},add:function(t){return new Cg(t)},average:function(t){return new Sg(t)},concatenate:function(t){return new Ag(t)},maximum:function(t){return new kg(t)},minimum:function(t){return new Ig(t)},multiply:function(t){return new Eg(t)},dot:function(t){return new Tg(t)},batchNormalization:function(t){return new zg(t)},zeroPadding2d:function(t){return new Lg(t)},averagePooling1d:vv,avgPool1d:function(t){return vv(t)},avgPooling1d:function(t){return vv(t)},averagePooling2d:yv,avgPool2d:function(t){return yv(t)},avgPooling2d:function(t){return yv(t)},averagePooling3d:bv,avgPool3d:function(t){return bv(t)},avgPooling3d:function(t){return bv(t)},globalAveragePooling1d:function(t){return new Yg(t)},globalAveragePooling2d:function(t){return new Qg(t)},globalMaxPooling1d:xv,globalMaxPooling2d:wv,maxPooling1d:Nv,maxPooling2d:Cv,maxPooling3d:function(t){return new Kg(t)},gru:function(t){return new uv(t)},gruCell:function(t){return new sv(t)},lstm:function(t){return new cv(t)},lstmCell:function(t){return new lv(t)},simpleRNN:function(t){return new ov(t)},simpleRNNCell:function(t){return new av(t)},rnn:function(t){return new rv(t)},stackedRNNCells:function(t){return new pv(t)},bidirectional:function(t){return new gv(t)},timeDistributed:function(t){return new dv(t)},globalMaxPool1d:Ev,globalMaxPool2d:Sv,maxPool1d:kv,maxPool2d:Iv,Layer:nd,RNN:rv,RNNCell:iv,input:wm,gaussianNoise:function(t){return new Dg(t)},gaussianDropout:function(t){return new Og(t)},alphaDropout:function(t){return new _g(t)},masking:function(t){return new xg(t)}});var Rv=Object.freeze({binaryAccuracy:function(t,e){return Ad(t,e)},binaryCrossentropy:function(t,e){return _d(t,e)},sparseCategoricalAccuracy:function(t,e){return Fd(t,e)},categoricalAccuracy:function(t,e){return Rd(t,e)},categoricalCrossentropy:function(t,e){return Md(t,e)},precision:function(t,e){return Dd(t,e)},recall:function(t,e){return Od(t,e)},cosineProximity:function(t,e){return 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dy(this.node.rawAttrs,t,e);if(null!=n.b)return my(this.node.rawAttrs,t,e);if(null!=n.shape)return wy(this.node.rawAttrs,t,e);if(null!=n.type)return yy(this.node.rawAttrs,t,e);if(null!=n.list){if(null!=n.list.i||null!=n.list.f)return Ny(this.node.rawAttrs,t,e);if(null!=n.list.s)return Cy(this.node.rawAttrs,t,e);if(null!=n.list.shape)return Ey(this.node.rawAttrs,t,e);if(null!=n.list.b)return Sy(this.node.rawAttrs,t,e);if(null!=n.list.type)return by(this.node.rawAttrs,t,e)}return e},t}(),Iy=function(t,e,n){switch(t.op){case"BiasAdd":case"AddV2":case"Add":return[Hs(Gv("a",t,e,n),Gv("b",t,e,n))];case"AddN":return[qs(Gv("tensors",t,e,n))];case"FloorMod":case"Mod":return[nu(Gv("a",t,e,n),Gv("b",t,e,n))];case"Mul":return[iu(Gv("a",t,e,n),Gv("b",t,e,n))];case"RealDiv":case"Div":return[Xs(Gv("a",t,e,n),Gv("b",t,e,n))];case"FloorDiv":return[Js(Gv("a",t,e,n),Gv("b",t,e,n))];case"Sub":return[cu(Gv("a",t,e,n),Gv("b",t,e,n))];case"Minimum":return[tu(Gv("a",t,e,n),Gv("b",t,e,n))];case"Maximum":return[Zs(Gv("a",t,e,n),Gv("b",t,e,n))];case"Pow":return[ou(Gv("a",t,e,n),Gv("b",t,e,n))];case"SquaredDifference":return[uu(Gv("a",t,e,n),Gv("b",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}},Ay=function(t,e,n){switch(t.op){case"Abs":case"ComplexAbs":return[Go(Gv("x",t,e,n))];case"Acos":return[Ho(Gv("x",t,e,n))];case"Acosh":return[qo(Gv("x",t,e,n))];case"Asin":return[Ko(Gv("x",t,e,n))];case"Asinh":return[$o(Gv("x",t,e,n))];case"Atan":return[Xo(Gv("x",t,e,n))];case"Atan2":return[$s(Gv("x",t,e,n),Gv("y",t,e,n))];case"Atanh":return[Yo(Gv("x",t,e,n))];case"Ceil":return[Jo(Gv("x",t,e,n))];case"Complex":return[Qe(Gv("real",t,e,n),Gv("imag",t,e,n))];case"Cos":return[Qo(Gv("x",t,e,n))];case"Cosh":return[ts(Gv("x",t,e,n))];case"Elu":return[xl(Gv("x",t,e,n))];case"Erf":return[es(Gv("x",t,e,n))];case"Exp":return[ns(Gv("x",t,e,n))];case"Expm1":return[rs(Gv("x",t,e,n))];case"Floor":return[is(Gv("x",t,e,n))];case"Log":return[as(Gv("x",t,e,n))];case"Log1p":return[os(Gv("x",t,e,n))];case"Imag":return[en(Gv("x",t,e,n))];case"Neg":return[us(Gv("x",t,e,n))];case"Reciprocal":return[ls(Gv("x",t,e,n))];case"Real":return[tn(Gv("x",t,e,n))];case"Relu":return[Cl(Gv("x",t,e,n))];case"Round":return[cs(Gv("x",t,e,n))];case"Selu":return[El(Gv("x",t,e,n))];case"Sigmoid":return[hs(Gv("x",t,e,n))];case"Sin":return[vs(Gv("x",t,e,n))];case"Sign":return[fs(Gv("x",t,e,n))];case"Sinh":return[ys(Gv("x",t,e,n))];case"Softplus":return[bs(Gv("x",t,e,n))];case"Sqrt":return[xs(Gv("x",t,e,n))];case"Square":return[ws(Gv("x",t,e,n))];case"Tanh":return[Es(Gv("x",t,e,n))];case"Tan":return[Cs(Gv("x",t,e,n))];case"Relu6":case"ClipByValue":return[Zo(Gv("x",t,e,n),Gv("clipValueMin",t,e,n),Gv("clipValueMax",t,e,n))];case"Rsqrt":return[ps(Hv(t.inputNames[0],e,n))];case"Prod":return[bl(Gv("x",t,e,n),Gv("axes",t,e,n))];case"LeakyRelu":return[wl(Gv("x",t,e,n),Gv("alpha",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}},Ry=function(){function t(e,n,r,i,a,o,s){this.name=e,this.dtype=n,this.maxSize=r,this.elementShape=i,this.identicalElementShapes=a,this.dynamicSize=o,this.clearAfterRead=s,this.tensors=[],this.closed_=!1,this.id=t.nextId++}return Object.defineProperty(t.prototype,"closed",{get:function(){return this.closed_},enumerable:!0,configurable:!0}),t.prototype.clearAndClose=function(){this.tensors.forEach(function(t){return t.tensor.dispose()}),this.tensors=[],this.closed_=!0},t.prototype.size=function(){return this.tensors.length},t.prototype.read=function(t){if(this.closed_)throw new Error("TensorArray "+this.name+" has already been closed.");if(t<0||t>=this.tensors.length)throw new Error("Tried to read from index "+t+", but array size is: "+this.tensors.length);var e=this.tensors[t];if(e.cleared)throw new Error("TensorArray "+this.name+": Could not read index "+t+" twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).");return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor},t.prototype.readMany=function(t){var e=this;return t.map(function(t){return e.read(t)})},t.prototype.write=function(t,e){if(this.closed_)throw new Error("TensorArray "+this.name+" has already been closed.");if(t<0||!this.dynamicSize&&t>=this.maxSize)throw new Error("Tried to write to index "+t+", but array is not resizeable and size is: "+this.maxSize);var n=this.tensors[t]||{};if(e.dtype!==this.dtype)throw new Error("TensorArray "+this.name+": Could not write to TensorArray index "+t+",\n          because the value dtype is "+e.dtype+", but TensorArray dtype is "+this.dtype+".");if(0!==this.size()||null!=this.elementShape&&0!==this.elementShape.length||(this.elementShape=e.shape),this.assertShapesMatchAllowUndefinedSize(this.elementShape,e.shape,"TensorArray "+this.name+": Could not write to TensorArray index "+t+"."),n&&n.read)throw new Error("TensorArray "+this.name+": Could not write to TensorArray index "+t+", because it has already been read.");if(n&&n.written)throw new Error("TensorArray "+this.name+": Could not write to TensorArray index "+t+", because it has already been written.");n.tensor=e,n.written=!0,this.tensors[t]=n},t.prototype.writeMany=function(t,e){var n=this;if(t.length!==e.length)throw new Error("TensorArray "+this.name+": could not write multiple tensors,because the index size: "+t.length+" is not the same as tensors size: "+e.length+".");t.forEach(function(t,r){return n.write(t,e[r])})},t.prototype.gather=function(t,e){if(e&&e!==this.dtype)throw new Error("TensorArray dtype is "+this.dtype+" but gather requested dtype "+e);if(!t){t=[];for(var n=0;n<this.size();n++)t.push(n)}if(0===t.length)return nn([],[0].concat(this.elementShape));var r=this.readMany(t);return this.assertShapesMatchAllowUndefinedSize(this.elementShape,r[0].shape,"TensorArray shape mismatch: "),or(r,0)},t.prototype.concat=function(t){if(t&&t!==this.dtype)throw new Error("TensorArray dtype is "+this.dtype+" but concat requested dtype "+t);if(0===this.size())return nn([],[0].concat(this.elementShape));for(var e=[],n=0;n<this.size();n++)e.push(n);var r=this.readMany(e);return this.assertShapesMatchAllowUndefinedSize(this.elementShape,r[0].shape,"TensorArray shape mismatch: tensor array shape ("+this.elementShape+") vs first tensor shape ("+r[0].shape+")"),bn(r,0)},t.prototype.scatter=function(t,e){if(e.dtype!==this.dtype)throw new Error("TensorArray dtype is "+this.dtype+" but tensor has dtype "+e.dtype);if(t.length!==e.shape[0])throw new Error("Expected len(indices) == tensor.shape[0], but saw: "+t.length+" vs. "+e.shape[0]);var n=Math.max.apply(Math,t);if(!this.dynamicSize&&n>=this.maxSize)throw new Error("Max index must be < array size ("+n+"  vs. "+this.maxSize+")");this.writeMany(t,lr(e,0))},t.prototype.split=function(t,e){var n=this;if(e.dtype!==this.dtype)throw new Error("TensorArray dtype is "+this.dtype+" but tensor has dtype "+e.dtype);var r=0,i=t.map(function(t){return r+=t});if(r!==e.shape[0])throw new Error("Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        "+r+", and tensor's shape is: "+e.shape);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error("TensorArray's size is not equal to the size of lengths ("+this.maxSize+" vs. "+t.length+"), and the TensorArray is not marked as dynamically resizeable");var a=0===r?0:e.size/r,o=[];_e(function(){e=e.reshape([1,r,a]);for(var s=0;s<t.length;++s){var u=[0,0===s?0:i[s-1],0],l=[1,t[s],a];o[s]=rl(e,u,l).reshape(n.elementShape)}return o});for(var s=[],u=0;u<t.length;u++)s[u]=u;this.writeMany(s,o)},t.prototype.assertShapesMatchAllowUndefinedSize=function(t,e,n){void 0===n&&(n=""),Y.assert(this.shapesEqualAllowUndefinedSize(t,e),function(){return n+" Shapes "+t+" and "+e+" must match"})},t.prototype.shapesEqualAllowUndefinedSize=function(t,e){if(t.length!==e.length)return!1;for(var n=0;n<t.length;n++)if(-1!==t[n]&&-1!==e[n]&&t[n]!==e[n])return!1;return!0},t.nextId=0,t}();var Ty=function(t,e,n){var r,i;switch(t.op){case"Conv1D":var a=Gv("stride",t,e,n),o=Gv("pad",t,e,n),s=Gv("dataFormat",t,e,n).toUpperCase(),u=Gv("dilation",t,e,n);return[_u(Gv("x",t,e,n),Gv("filter",t,e,n),a,o,s,u)];case"Conv2D":a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),s=Gv("dataFormat",t,e,n).toUpperCase();var l=Gv("dilations",t,e,n);return[Fu(Gv("x",t,e,n),Gv("filter",t,e,n),[a[1],a[2]],o,s,[l[1],l[2]])];case"_FusedConv2D":var c=(r=Gv("fusedOps",t,e,n))[0],p=r[1],h="biasadd"===c,f="prelu"===p,d="fusedbatchnorm"===c,m=Gv("numArgs",t,e,n);if(h){if(f&&2!==m)throw new Error("Fused Conv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!f&&1!==m)throw new Error("Fused Conv2d with BiasAdd must have one extra argument: bias.")}if(d)throw new Error("Fused Conv2d with FusedBatchNorm is not supported.");a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),s=Gv("dataFormat",t,e,n).toUpperCase(),l=Gv("dilations",t,e,n);var g=(i=Gv("args",t,e,n))[0],v=i[1];return[yc.conv2d({x:Gv("x",t,e,n),filter:Gv("filter",t,e,n),strides:[a[1],a[2]],pad:o,dataFormat:s,dilations:[l[1],l[2]],bias:g,activation:p,preluActivationWeights:v})];case"Conv2DBackpropInput":case"Conv2dTranspose":var y=Gv("outputShape",t,e,n);return a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),[Vu(Gv("x",t,e,n),Gv("filter",t,e,n),y,[a[1],a[2]],o)];case"DepthwiseConv2dNative":case"DepthwiseConv2d":return a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),l=Gv("dilations",t,e,n),s=Gv("dataFormat",t,e,n).toUpperCase(),[Pu(Gv("input",t,e,n),Gv("filter",t,e,n),[a[1],a[2]],o,s,[l[1],l[2]])];case"Conv3D":return a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),s=Gv("dataFormat",t,e,n).toUpperCase(),l=Gv("dilations",t,e,n),[Mu(Gv("x",t,e,n),Gv("filter",t,e,n),[a[1],a[2],a[3]],o,s,[l[1],l[2],l[3]])];case"AvgPool":a=Gv("strides",t,e,n),o=Gv("pad",t,e,n);var b=Gv("kernelSize",t,e,n);return[Qu(Gv("x",t,e,n),[b[1],b[2]],[a[1],a[2]],o)];case"MaxPool":return a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),b=Gv("kernelSize",t,e,n),[Zu(Gv("x",t,e,n),[b[1],b[2]],[a[1],a[2]],o)];case"AvgPool3D":return a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),b=Gv("kernelSize",t,e,n),[nl(Gv("x",t,e,n),[b[1],b[2],b[3]],[a[1],a[2],a[3]],o)];case"MaxPool3D":return a=Gv("strides",t,e,n),o=Gv("pad",t,e,n),b=Gv("kernelSize",t,e,n),[el(Gv("x",t,e,n),[b[1],b[2],b[3]],[a[1],a[2],a[3]],o)];default:throw TypeError("Node type "+t.op+" is not implemented")}},Dy=function(t,e,n){switch(t.op){case"Fill":var r=Gv("shape",t,e,n),i=Gv("dtype",t,e,n);return[dn(r,Gv("value",t,e,n),i)];case"LinSpace":var a=Gv("start",t,e,n);return[mn(a,Gv("stop",t,e,n),Gv("num",t,e,n))];case"Multinomial":var o=Gv("logits",t,e,n),s=Gv("numSamples",t,e,n),u=Gv("seed",t,e,n);return[qn(o,s,u)];case"OneHot":var l=Gv("indices",t,e,n),c=Gv("depth",t,e,n),p=Gv("onValue",t,e,n),h=Gv("offValue",t,e,n);return[Kn(l,c,p,h)];case"Ones":return[hn(Gv("shape",t,e,n),Gv("dtype",t,e,n))];case"OnesLike":return[vn(Gv("x",t,e,n))];case"RandomUniform":return[nr(Gv("shape",t,e,n),Gv("minval",t,e,n),Gv("maxval",t,e,n),Gv("dtype",t,e,n))];case"Range":return[gn(a=Gv("start",t,e,n),Gv("stop",t,e,n),Gv("step",t,e,n),Gv("dtype",t,e,n))];case"TruncatedNormal":r=Gv("shape",t,e,n);var f=Gv("mean",t,e,n),d=Gv("stdDev",t,e,n);return u=Gv("seed",t,e,n),[ur(r,f,d,Gv("dtype",t,e,n),u)];case"Zeros":return[fn(Gv("shape",t,e,n),Gv("dtype",t,e,n))];case"ZerosLike":return[yn(Gv("x",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}};var Oy=function(t,e,n){switch(t.op){case"TopKV2":var r=Gv("x",t,e,n),i=Gv("k",t,e,n),a=Gv("sorted",t,e,n),o=Ol(r,i,a);return[o.values,o.indices];default:throw TypeError("Node type "+t.op+" is not implemented")}},_y=function(t,e,n){switch(t.op){case"Const":return e[t.name];case"PlaceholderWithDefault":var r=Gv("default",t,e,n);return[Hv(t.name,e,n)||r];case"Placeholder":return[Hv(t.name,e,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":return[Gv("x",t,e,n).clone()];case"IdentityN":return Gv("x",t,e,n).map(function(t){return t.clone()});case"Snapshot":return[Gv("x",t,e,n).clone()];case"Shape":return[on(Gv("x",t,e,n).shape,"int32")];case"ShapeN":return Gv("x",t,e,n).map(function(t){return on(t.shape)});case"Size":return[an(Gv("x",t,e,n).size,"int32")];case"Rank":return[an(Gv("x",t,e,n).rank,"int32")];case"NoOp":return[];case"Print":var i=Gv("x",t,e,n),a=Gv("data",t,e,n),o=Gv("message",t,e,n),s=Gv("summarize",t,e,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(var u=0;u<a.length;u++)console.log(Array.prototype.slice.call(a[u].dataSync()).slice(0,s));return[i];default:throw TypeError("Node type "+t.op+" is not implemented")}},Fy=function(t,e,n){switch(t.op){case"ResizeBilinear":var r=Gv("images",t,e,n),i=Gv("size",t,e,n),a=Gv("alignCorners",t,e,n);return[mc.resizeBilinear(r,[i[0],i[1]],a)];case"ResizeNearestNeighbor":return r=Gv("images",t,e,n),i=Gv("size",t,e,n),a=Gv("alignCorners",t,e,n),[mc.resizeNearestNeighbor(r,[i[0],i[1]],a)];case"CropAndResize":var o=Gv("image",t,e,n),s=Gv("boxes",t,e,n),u=Gv("boxInd",t,e,n),l=Gv("cropSize",t,e,n),c=Gv("method",t,e,n),p=Gv("extrapolationValue",t,e,n);return[mc.cropAndResize(o,s,u,l,c,p)];default:throw TypeError("Node type "+t.op+" is not implemented")}},My=function(t,e,n){switch(t.op){case"Equal":return[hu(Gv("a",t,e,n),Gv("b",t,e,n))];case"NotEqual":return[Nu(Gv("a",t,e,n),Gv("b",t,e,n))];case"Greater":return[du(Gv("a",t,e,n),Gv("b",t,e,n))];case"GreaterEqual":return[mu(Gv("a",t,e,n),Gv("b",t,e,n))];case"Less":return[yu(Gv("a",t,e,n),Gv("b",t,e,n))];case"LessEqual":return[bu(Gv("a",t,e,n),Gv("b",t,e,n))];case"LogicalAnd":return[Bs(Gv("a",t,e,n),Gv("b",t,e,n))];case"LogicalNot":return[Vs(Gv("a",t,e,n))];case"LogicalOr":return[Ws(Gv("a",t,e,n),Gv("b",t,e,n))];case"Select":return[js(Gv("condition",t,e,n),Gv("a",t,e,n),Gv("b",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}},zy=function(t,e,n){switch(t.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Uu(Gv("a",t,e,n),Gv("b",t,e,n),Gv("transposeA",t,e,n),Gv("transposeB",t,e,n))];case"Transpose":return[Sl(Gv("x",t,e,n),Gv("perm",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}},Ly=function(t,e,n){switch(t.op){case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[Ms(Gv("x",t,e,n),Gv("mean",t,e,n),Gv("variance",t,e,n),Gv("offset",t,e,n),Gv("scale",t,e,n),Gv("epsilon",t,e,n))];case"LRN":return[kl(Gv("x",t,e,n),Gv("radius",t,e,n),Gv("bias",t,e,n),Gv("alpha",t,e,n),Gv("beta",t,e,n))];case"Softmax":return[Rr(Gv("x",t,e,n))];case"LogSoftmax":return[Tr(Gv("x",t,e,n))];case"SparseToDense":return[Bl(Gv("sparseIndices",t,e,n),Gv("outputShape",t,e,n),Gv("sparseValues",t,e,n),Gv("defaultValue",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}},Py=function(t,e,n){switch(t.op){case"Max":var r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n);return[dl(Gv("x",t,e,n),r,i)];case"Mean":return r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n),[ml(Gv("x",t,e,n),r,i)];case"Min":return r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n),[gl(Gv("x",t,e,n),r,i)];case"Sum":return r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n),[yl(Gv("x",t,e,n),r,i)];case"All":return r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n),[ll(Gv("x",t,e,n),r,i)];case"Any":return r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n),[cl(Gv("x",t,e,n),r,i)];case"ArgMax":return r=Gv("axis",t,e,n),[pl(Gv("x",t,e,n),r)];case"ArgMin":return r=Gv("axis",t,e,n),[hl(Gv("x",t,e,n),r)];case"Prod":return r=Gv("axis",t,e,n),i=Gv("keepDims",t,e,n),[bl(Gv("x",t,e,n),r,i)];default:throw TypeError("Node type "+t.op+" is not implemented")}},By=function(t,e,n){switch(t.op){case"ConcatV2":case"Concat":var r=Gv("axis",t,e,n),i=Gv("tensors",t,e,n);return[bn(i,r)];case"GatherV2":case"Gather":r=Gv("axis",t,e,n);var a=Gv("x",t,e,n),o=Gv("indices",t,e,n);return[ku(a,o.asType("int32"),r)];case"ReverseV2":case"Reverse":return r=Gv("axis",t,e,n),a=Gv("x",t,e,n),[Hu(a,r)];case"Slice":var s=Gv("begin",t,e,n),u=Gv("size",t,e,n);return[rl(Gv("x",t,e,n),s,u)];case"StridedSlice":s=Gv("begin",t,e,n);var l=Gv("end",t,e,n),c=Gv("strides",t,e,n),p=Gv("beginMask",t,e,n),h=Gv("endMask",t,e,n),f=Gv("ellipsisMask",t,e,n),d=Gv("newAxisMask",t,e,n),m=Gv("shrinkAxisMask",t,e,n),g=Gv("x",t,e,n);if(1===s.length&&g.shape.length>1)for(var v=1;v<g.shape.length;v++)s.push(0),l.push(g.shape[v]),c.push(c[0]);return[Dl(g,s,l,c,p,h,f,d,m)];case"Pack":return _e(function(){var r=Gv("axis",t,e,n),i=Gv("tensors",t,e,n),a=i[0].shape,o=i[0].squeeze().shape,s=i.map(function(t){var e=Y.arraysEqual(t.shape,a);if(!e&&!Y.arraysEqual(t.squeeze().shape,o))throw new Error("the input tensors shape does not match");return e?t:t.reshape(a)});return[or(s,r)]});case"Unpack":return _e(function(){var r=Gv("axis",t,e,n),i=Gv("tensor",t,e,n);return lr(i,r)});case"Tile":var y=Gv("reps",t,e,n);return[sr(Gv("x",t,e,n),y)];case"Split":case"SplitV":r=Gv("axis",t,e,n);var b=Gv("numOrSizeSplits",t,e,n);return En(Gv("x",t,e,n),b,r);case"ScatterNd":o=Gv("indices",t,e,n);var x=Gv("values",t,e,n),w=Gv("shape",t,e,n);return[_l(o,x,w)];case"GatherNd":var N=Gv("x",t,e,n);return o=Gv("indices",t,e,n),[Vl(N,o)];case"SparseToDense":o=Gv("sparseIndices",t,e,n),w=Gv("outputShape",t,e,n);var C=Gv("sparseValues",t,e,n),E=Gv("defaultValue",t,e,n);return[Bl(o,C,w,C.dtype===E.dtype?E:E.asType(C.dtype))];default:throw TypeError("Node type "+t.op+" is not implemented")}},Vy=function(t,e,n){switch(t.op){case"FFT":return[Fl(Gv("x",t,e,n))];case"IFFT":return[Ml(Gv("x",t,e,n))];case"RFFT":return[zl(Gv("x",t,e,n))];case"IRFFT":return[Ll(Gv("x",t,e,n))];default:throw TypeError("Node type "+t.op+" is not implemented")}},Wy=function(t,e,n){switch(t.op){case"Cast":return[Vn(Gv("x",t,e,n),Gv("dtype",t,e,n))];case"ExpandDims":var r=Gv("axis",t,e,n);return[Gn(Gv("x",t,e,n),r)];case"Squeeze":return r=Gv("axis",t,e,n),[ar(Gv("x",t,e,n),r)];case"Reshape":return[rr(Gv("x",t,e,n),Gv("shape",t,e,n))];case"PadV2":case"Pad":return[$n(Gv("x",t,e,n),Xv(Gv("padding",t,e,n),2),Gv("constantValue",t,e,n))];case"SpaceToBatchND":var i=Gv("blockShape",t,e,n),a=Xv(Gv("paddings",t,e,n),2);return[ir(Gv("x",t,e,n),i,a)];case"BatchToSpaceND":i=Gv("blockShape",t,e,n);var o=Xv(Gv("crops",t,e,n),2);return[Bn(Gv("x",t,e,n),i,o)];case"DepthToSpace":var s=Gv("blockSize",t,e,n),u=Gv("dataFormat",t,e,n).toUpperCase();return[jn(Gv("x",t,e,n),s,u)];default:throw TypeError("Node type "+t.op+" is not implemented")}};function Uy(t,e,n){var r=function(t,e,n){switch(t.category){case"arithmetic":return Iy(t,e,n);case"basic_math":return Ay(t,e,n);case"control":return function(t,e,n){return Vv(this,void 0,void 0,function(){var r,i,a,o,s,u,l,c,p,h,f,d,m,g,v,y,b,x,w,N,C,E,S,k,I,A,R,T,D,O,_,F,M,z;return Wv(this,function(L){switch(L.label){case 0:switch(t.op){case"LoopCond":return[3,1];case"Switch":return[3,2];case"Merge":return[3,4];case"Enter":return[3,5];case"Exit":return[3,6];case"NextIteration":return[3,7];case"TensorArrayV3":return[3,8];case"TensorArrayWriteV3":return[3,9];case"TensorArrayReadV3":return[3,10];case"TensorArrayGatherV3":return[3,11];case"TensorArrayScatterV3":return[3,12];case"TensorArrayConcatV3":return[3,13];case"TensorArraySplitV3":return[3,14];case"TensorArraySizeV3":return[3,15];case"TensorArrayCloseV3":return[3,16]}return[3,17];case 1:return[2,[Gv("pred",t,e,n).clone()]];case 2:return r=Gv("pred",t,e,n),i=Gv("data",t,e,n),[4,r.data()];case 3:return[2,L.sent()[0]?[void 0,i.clone()]:[i.clone(),void 0]];case 4:return[2,(a=t.inputNames.find(function(t){return void 0!==Hv(t,e,n)}))?[Hv(a,e,n).clone()]:void 0];case 5:return o=Gv("frameName",t,e,n),s=Gv("tensor",t,e,n),n.enterFrame(o),[2,[s.clone()]];case 6:return u=Gv("tensor",t,e,n),n.exitFrame(),[2,[u.clone()]];case 7:return l=Gv("tensor",t,e,n),n.nextIteration(),[2,[l.clone()]];case 8:return c=Gv("size",t,e,n),p=Gv("dtype",t,e,n),h=Gv("elementShape",t,e,n),f=Gv("dynamicSize",t,e,n),d=Gv("clearAfterRead",t,e,n),m=Gv("identicalElementShapes",t,e,n),g=Gv("name",t,e,n),v=new Ry(g,p,c,h,m,f,d),n.addTensorArray(v),[2,[an(v.id),an(1)]];case 9:return y=Gv("tensorArrayId",t,e,n),b=Gv("index",t,e,n),x=Gv("tensor",t,e,n),n.getTensorArray(y).write(b,x),[2,[an(1)]];case 10:return w=Gv("tensorArrayId",t,e,n),N=Gv("index",t,e,n),[2,[n.getTensorArray(w).read(N)]];case 11:return C=Gv("tensorArrayId",t,e,n),E=Gv("indices",t,e,n),S=Gv("dtype",t,e,n),[2,[n.getTensorArray(C).gather(E,S)]];case 12:return k=Gv("tensorArrayId",t,e,n),I=Gv("indices",t,e,n),A=Gv("tensor",t,e,n),n.getTensorArray(k).scatter(I,A),[2,[an(1)]];case 13:return R=Gv("tensorArrayId",t,e,n),T=n.getTensorArray(R),D=Gv("dtype",t,e,n),[2,[T.concat(D)]];case 14:return O=Gv("tensorArrayId",t,e,n),_=Gv("tensor",t,e,n),F=Gv("lengths",t,e,n),n.getTensorArray(O).split(F,_),[2,[an(1)]];case 15:return M=Gv("tensorArrayId",t,e,n),[2,[an(n.getTensorArray(M).size(),"int32")]];case 16:return z=Gv("tensorArrayId",t,e,n),n.getTensorArray(z).clearAndClose(),[2,[]];case 17:throw TypeError("Node type "+t.op+" is not implemented")}})})}(t,e,n);case"convolution":return Ty(t,e,n);case"creation":return Dy(t,e,n);case"dynamic":return function(t,e,n){return Vv(this,void 0,void 0,function(){var r,i,a,o,s;return Wv(this,function(u){switch(u.label){case 0:switch(t.op){case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":return[3,1];case"Where":return[3,3];case"ListDiff":return[3,5]}return[3,6];case 1:return r=Gv("boxes",t,e,n),i=Gv("scores",t,e,n),a=Gv("maxOutputSize",t,e,n),o=Gv("iouThreshold",t,e,n),s=Gv("scoreThreshold",t,e,n),[4,mc.nonMaxSuppressionAsync(r,i,a,o,s)];case 2:return[2,[u.sent()]];case 3:return[4,Gs(Gv("condition",t,e,n))];case 4:return[2,[u.sent()]];case 5:return[2,cr(Gv("x",t,e,n),Gv("y",t,e,n))];case 6:throw TypeError("Node type "+t.op+" is not implemented")}})})}(t,e,n);case"evaluation":return Oy(t,e,n);case"image":return Fy(t,e,n);case"graph":return _y(t,e,n);case"logical":return My(t,e,n);case"matrices":return zy(t,e,n);case"normalization":return Ly(t,e,n);case"reduction":return Py(t,e,n);case"slice_join":return By(t,e,n);case"spectral":return Vy(t,e,n);case"transformation":return Wy(t,e,n);case"custom":var r=jv(t.op);if(r&&r.customExecutor)return r.customExecutor(new ky(t,e,n));throw TypeError("Custom op "+t.op+" is not registered.");default:throw TypeError("Unknown op '"+t.op+"'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()")}}(t,e,n);return r instanceof Promise?r.then(function(t){return[].concat(t)}):[].concat(r)}var jy=function(){function t(t,e){this.weightMap=t,this.tensorArrayMap=e,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}return t.prototype.newFrame=function(t,e){return{id:t,frameName:e,iterationId:0}},Object.defineProperty(t.prototype,"currentContext",{get:function(){return this.contexts},set:function(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"currentContextId",{get:function(){return this._currentContextIds[0]},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"currentContextIds",{get:function(){return this._currentContextIds},enumerable:!0,configurable:!0}),t.prototype.generateCurrentContextIds=function(){for(var t=[],e=0;e<this.contexts.length-1;e++){var n=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(n))}t.push(""),this._currentContextIds=t},t.prototype.contextIdforContexts=function(t){return t?t.map(function(t){return 0===t.id&&0===t.iterationId?"":t.frameName+"-"+t.iterationId}).join("/"):""},t.prototype.enterFrame=function(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))},t.prototype.exitFrame=function(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()},t.prototype.nextIteration=function(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");this.contexts=this.contexts.slice(),this.lastId++;var t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))},t.prototype.getWeight=function(t){return this.weightMap[t]},t.prototype.addTensorArray=function(t){this.tensorArrayMap[t.id]=t},t.prototype.getTensorArray=function(t){return this.tensorArrayMap[t]},t}();function Gy(t,e,n){for(var r=new Set,i=[],a=null,o=null,s=new Set,u=e.slice();u.length>0;){var l=u.pop();(Ky(l)||$y(l))&&null==a&&(o=(a=l).children.map(function(t){return t.name}).filter(function(t){return r.has(t)})),r.add(l.name),null==n[l.name]&&null==t[l.name]&&(0!==l.inputs.length?l.inputs.forEach(function(t){s.has(t.name)||(s.add(t.name),u.push(t))}):i.push(l.name))}return{inputs:t,outputs:e,usedNodes:r,missingInputs:i,dynamicNode:a,syncInputs:o}}var Hy=["Switch","Merge","Enter","Exit","NextIteration"],qy=["NonMaxSuppressionV2","NonMaxSuppressionV3","Where"];function Ky(t){return Hy.indexOf(t.op)>=0}function $y(t){return qy.indexOf(t.op)>=0}var Xy=function(){function t(t){this.graph=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this.placeholders=t.placeholders,this._outputs=t.outputs}return Object.defineProperty(t.prototype,"weightMap",{get:function(){return this._weightMap},set:function(t){var e=Object.keys(t).map(function(e){return t[e].map(function(t){return t.id})});this.weightIds=[].concat.apply([],e),this._weightMap=t},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"inputs",{get:function(){return this.placeholders.map(function(t){return{name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}})},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"outputs",{get:function(){return this._outputs.map(function(t){return{name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}})},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"inputNodes",{get:function(){return this.placeholders.map(function(t){return t.name})},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"outputNodes",{get:function(){return this.outputs.map(function(t){return t.name})},enumerable:!0,configurable:!0}),t.prototype.getCompilationKey=function(t,e){var n=t.map(function(t){return t.name}).sort(),r=e.map(function(t){return t.name}).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)},t.prototype.compile=function(t,e){var n=Gy(t,e,this.weightMap),r=n.missingInputs,i=n.dynamicNode,a=n.syncInputs;if(null!=i)throw new Error("This execution contains the node '"+i.name+"', which has the dynamic op '"+i.op+"'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs ["+a+"]");if(r.length>0){var o=e.map(function(t){return t.name}),s=Object.keys(t);throw new Error("Cannot compute the outputs ["+o+"] from the provided inputs ["+s+"]. Missing the following inputs: ["+r+"]")}return function(t,e,n){var r=n.usedNodes,i=n.inputs,a=[];Object.keys(i).map(function(e){return t.nodes[e]}).forEach(function(t){r.has(t.name)&&a.push(t)}),t.weights.forEach(function(t){r.has(t.name)&&a.push(t)});for(var o=new Set,s=[];a.length>0;){var u=a.pop();o.add(u.name),e[u.name]||s.push(u),u.children.forEach(function(t){!o.has(t.name)&&r.has(t.name)&&t.inputs.every(function(t){return o.has(t.name)})&&a.push(t)})}return s}(this.graph,this.weightMap,n)},t.prototype.execute=function(t,e){var n=this,r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),this.checkOutputs(e);var i=r.map(function(t){return n.graph.nodes[t]}),a=e.map(function(t){return n.graph.nodes[$v(t)[0]]}),o=this.getCompilationKey(i,a),s=this.compiledMap.get(o);null==s&&(s=this.compile(t,a),this.compiledMap.set(o,s));var u={};return _e(function(){var r=new jy(n._weightMap,u),i=Bv({},n.weightMap);Object.keys(t).forEach(function(e){i[e]=[t[e]]});for(var a=n.getFrozenTensorIds(i),o={},l=0;l<s.length;l++){var c=s[l];if(!i[c.name]){var p=Uy(c,i,r);if(p instanceof Promise)throw new Error("The execution of the op '"+c.op+"' returned a promise. Please use model.executeAsync() instead.");i[c.name]=p,n.checkTensorForDisposal(c.name,c,i,r,a,e,o)}}return e.map(function(t){return Hv(t,i,r)})})},t.prototype.getFrozenTensorIds=function(t){var e=[].concat.apply([],Object.keys(t).map(function(e){return t[e]}).map(function(t){return t.map(function(t){return t.id})}));return new Set(e)},t.prototype.checkTensorForDisposal=function(t,e,n,r,i,a,o){"control"!==e.category&&-1===a.indexOf(t)&&(n[t].forEach(function(t){null!=t&&(o[t.id]=(o[t.id]||0)+e.children.length)}),e.inputs.forEach(function(t){if("control"!==t.category){var e=function(t,e,n){return e[Kv(t,n.currentContextId)]}(t.name,n,r);null!=e&&e.forEach(function(t){if(t&&!i.has(t.id)){var e=o[t.id];1===e?(t.dispose(),delete o[t.id]):null!=e&&o[t.id]--}})}}))},t.prototype.executeAsync=function(t,e){return Vv(this,void 0,void 0,function(){var n,r,i,a,o,s,u=this;return Wv(this,function(l){switch(l.label){case 0:return this.checkInputs(t),this.checkInputShapeAndType(t),this.checkOutputs(e),n={},r=new jy(this._weightMap,n),[4,this.executeWithControlFlow(t,r,e)];case 1:return i=l.sent(),a=e.map(function(t){return Hv(t,i,r)}),o=new Set(a.map(function(t){return t.id})),s=new Set(Object.keys(t).map(function(e){return t[e].id})),Object.keys(i).forEach(function(t){i[t].forEach(function(t){!t||t.isDisposed||o.has(t.id)||s.has(t.id)||-1!==u.weightIds.indexOf(t.id)||t.dispose()})}),[2,a]}})})},t.prototype.executeWithControlFlow=function(t,e,n){return Vv(this,void 0,void 0,function(){var r,i,a,o,s,u,l,c,p,h,f,d,m,g,v,y,b=this;return Wv(this,function(x){switch(x.label){case 0:r=Object.keys(t),i=r.map(function(t){return b.graph.nodes[t]}),a=n.map(function(t){return b.graph.nodes[$v(t)[0]]}),o=Gy(t,a,this.weightMap),s=o.usedNodes,u=o.missingInputs,l=o.dynamicNode,c=o.syncInputs,p=i.concat(this.graph.weights).map(function(t){return{node:t,contexts:e.currentContext}}),h=Bv({},this.weightMap),Object.keys(t).forEach(function(e){h[e]=[t[e]]}),f={},d=this.getFrozenTensorIds(h),m={},x.label=1;case 1:return p.length>0?(g=this.processStack(i,p,e,h,m,d,n,f,s),[4,Promise.all(g)]):[3,3];case 2:return x.sent(),[3,1];case 3:if(null==l&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead."),(v=a.filter(function(t){return!Ky(t)&&!Hv(t.name,h,e)}).map(function(t){return t.name})).length>0)throw y="",null!=l&&(y="Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs ["+c+"]"),new Error("Cannot compute the outputs ["+v+"] from the provided inputs ["+r+"]. Consider providing the following inputs: ["+u+"]. "+y);return[2,h]}})})},t.prototype.processStack=function(t,e,n,r,i,a,o,s,u){for(var l=this,c=[],p=function(){var p=e.pop();n.currentContext=p.contexts;var f="";if("Enter"===p.node.op&&Gv("isConstant",p.node,r,n)&&(f=qv(p.node.name,n)[0]),-1===t.indexOf(p.node)){var d=Uy(p.node,r,n);f||(f=qv(p.node.name,n)[0]);var m=n.currentContext;d instanceof Promise?c.push(d.then(function(t){return r[f]=t,n.currentContext=m,l.checkTensorForDisposal(f,p.node,r,n,a,o,s),l.processChildNodes(p.node,e,n,r,i,u),t})):(r[f]=d,h.checkTensorForDisposal(f,p.node,r,n,a,o,s),h.processChildNodes(p.node,e,n,r,i,u))}else h.processChildNodes(p.node,e,n,r,i,u)},h=this;e.length>0;)p();return c},t.prototype.processChildNodes=function(t,e,n,r,i,a){t.children.forEach(function(t){var 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diff --git a/spaces/merve/fill-in-the-blank/public/base-rate/sliders.js b/spaces/merve/fill-in-the-blank/public/base-rate/sliders.js
deleted file mode 100644
index 994c9ba490dc44dfa015553d32ff24e822f16de0..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/base-rate/sliders.js
+++ /dev/null
@@ -1,103 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-
-var sliderVals = {}
-
-var sliders = [
-  {
-    key: 'fNoiseMag',
-    text: 'Feature Noise',
-    r: [0, 1],
-    v: .5
-  },
-  {
-    key: 'fBiasMag',
-    text: 'Feature Bias',
-    r: [0, 1],
-    v: .2
-  },
-]
-
-!(function(){
-  var width = 145
-  var height = 30
-
-  sliders.forEach(d => {
-    d.s = d3.scaleLinear().domain(d.r).range([0, width])
-    sliderVals[d.key] = d
-  })
-
-  var sliderSel = d3.select('.slider').html('')
-    .appendMany('div', sliders)
-    .at({class: d => d.key})
-    .st({
-      display: 'inline-block',
-      width: width,
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-      color: '#000'
-    })
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-    .text(d => d.text)
-    .st({marginBottom: height/2})
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-  var svgSel = sliderSel.append('svg').at({width, height})
-    .on('click', function(d){
-      d.v = d.s.invert(d3.mouse(this)[0])
-      updatePos()
-    })
-    .st({
-      cursor: 'pointer'
-    })
-    .append('g').translate(height/2, 1)
-  svgSel.append('rect').at({width, height, y: -height/2, fill: '#fff'})
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-  svgSel.append('path').at({
-    d: `M 0 0 H ${width}`, 
-    stroke: '#000',
-    strokeWidth: 2
-  })
-
-  var drag = d3.drag()
-    .on('drag', function(d){
-      var x = d3.mouse(this)[0]
-      d.v = d3.clamp(d3.min(d.r), d.s.invert(x), d3.max(d.r))
-
-      updatePos()
-    })
-
-  var circleSel = svgSel.append('circle')
-    .at({
-      r: height/2,
-      stroke: '#000', 
-      strokeWidth: 2,
-      fill: '#fff',
-    })
-    .call(drag)
-
-
-  function updatePos(){
-    circleSel.at({cx: d => d.s(d.v)})
-    if (sliderVals.onUpdate) sliderVals.onUpdate()
-  }
-
-  updatePos()
-  sliderVals.updatePos = updatePos
-})()
diff --git a/spaces/merve/gradio-analysis-dashboard-minimal/README.md b/spaces/merve/gradio-analysis-dashboard-minimal/README.md
deleted file mode 100644
index 18b6d9b7eff84a08ec999bfe5dfc1998415aebdb..0000000000000000000000000000000000000000
--- a/spaces/merve/gradio-analysis-dashboard-minimal/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Gradio Analysis Dashboard Minimal
-emoji: 🐠
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.20.0
-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/merve/hidden-bias/public/fill-in-the-blank/init-gender-over-time.js b/spaces/merve/hidden-bias/public/fill-in-the-blank/init-gender-over-time.js
deleted file mode 100644
index 4e678f28d4669d45b6957cd3e110b325875a41a1..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/fill-in-the-blank/init-gender-over-time.js
+++ /dev/null
@@ -1,181 +0,0 @@
-/* Copyright 2021 Google LLC. 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.
-==============================================================================*/
-
-
-
-window.initGenderOverTime = async () => {
-  if (!window.genderOverTimeData){
-    window.genderOverTimeData = await (await fetch('data/gender-over-time.json')).json()
-  }
-
-  var isMobile = innerWidth <= 1100
-
-  var sentences = window.genderOverTimeData
-
-  var blocks = [
-    {
-      text: 'placeholder',
-      sentences: sentences.slice(0, 3),
-      ariaLabel: 'Gendered difference in predicted occupations, studies and names are smalled with a "in 2000" prefix than with a "in 1860" prefix.'
-    }, 
-    {
-      text: 'placeholder',
-      sentences: [sentences[3], sentences[5], sentences[4]],
-      ariaLabel: 'Gendered difference in game play and bears do not decrease.'
-
-    }, 
-  ]
-
-  var blockSel = d3.selectAll('.gender-over-time').html('').data(blocks)
-    .st({marginBottom: 30, marginTop: 30})
-    .at({role: 'graphics-document', 'aria-label': d => d.ariaLabel})
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-  var sentenceSel = blockSel.appendMany('div.sentence', d => d.sentences)
-    .st({display: 'inline-block'})
-    .each(drawSentence)
-
-  blockSel.filter((d, i) => !i).append('div.g-caption').html(`
-    The top 150 “he” and “she” completions in years from 1860-2018 are shown
-    with the y position encoding he_logit - she_logit.
-    <a href="https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/server-side/fill-in-the-blank/gender-over-time-colab/gender-over-time.ipynb">Run in Colab →</a>`)
-
-
-
-  async function drawSentence({s0, s1, tidyCSV, minYear}, i){
-    var tidy = d3.csvParse(tidyCSV)
-    var {colors} = util
-
-    tidy.forEach(d => {
-      d.year = minYear + +d.year_index
-      d.i = +d.token_index
-      d.e0 = +d.e0
-      d.e1 = +d.e1
-      d.mean = d.e0 + d.e1
-      d.dif = d.e0 - d.e1
-    })
-
-    var sel = d3.select(this)
-
-    function fmtStr(d){
-      return d.replace('[MASK]', '___').replace('YEAR', '$year')
-        .replace(' he ', ' <b>he</b> ')
-        .replace(' she ', ' <b>she</b> ')
-        .replace(' his ', ' <b>his</b> ')
-        .replace(' her ', ' <b>her</b> ')
-        .replace(' they ', ' <b>they</b> ')
-    }
-    sel.classed('is-bear', d => s0.includes('bear'))
-
-    var c0 = s0.includes('they') ? colors[2] : colors[0]
-    var c1 = s1.includes('they') ? colors[2] : colors[1]
-
-    sel.append('div.sentence-title').st({color: c0}).html(fmtStr(s0))
-    sel.append('div.sentence-title').st({color: c1}).html(fmtStr(s1))
-
-    var e0Extent = d3.extent(tidy, d => d.e0)
-    var e1Extent = d3.extent(tidy, d => d.e1)
-    var e0e1Exent = d3.extent(e0Extent.concat(e1Extent))
-
-    var maxDif = d3.max(d3.extent(tidy, d => d.dif), Math.abs)
-    var difExtent = [-maxDif, maxDif]
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-    drawDim(tidy, sel, {
-      key: 'dif', 
-      yExtent: difExtent, 
-      rectColor: [c0, c1]
-    })
-    // drawDim(tidy, sel, {
-    //   key: 'e0', 
-    //   yExtent: e0e1Exent, 
-    //   rectColor: [colors[0], colors[0]]
-    // })
-    // drawDim(tidy, sel, {
-    //   key: 'e1', 
-    //   yExtent: e0e1Exent, 
-    //   rectColor: [colors[1], colors[1]]
-    // })
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-  function drawDim(tidy, sel, {key, rectColor, yExtent}){
-    var c = d3.conventions({
-      sel: sel.append('div'), 
-      height: 240,
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-      margin: {left: 20, bottom: 20, right: 80, top: 5}
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-      .at({width: c.width, height: c.height/2, opacity: .1, fill: rectColor[0]})
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-      .at({width: c.width, height: c.height/2, opacity: .1, fill: rectColor[1], y: c.height/2})
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-    c.x.domain(d3.extent(tidy, d => d.year)).interpolate(d3.interpolateRound)
-    c.y.domain(yExtent).interpolate(d3.interpolateRound)
-
-    c.xAxis.tickFormat(d => d).ticks(5)
-    c.yAxis.ticks(c.y.ticks(2).length > 2 ? 2 : 3).tickFormat(d3.format('+'))
-    d3.drawAxis(c)
-    // c.svg.select('.y .tick text').st({fill: d => !d ? '' : rectColor[d < 0 ? 0 : 1]})
-
-    var byToken = d3.nestBy(tidy, d => d.i)
-    byToken.forEach(d => {
-      d.endY = c.y(_.last(d)[key])
-      d.str = bertLargeVocab[+d.key].replace('▁', '')
-      d.displayLabel = true
-      d.mean = d3.sum(d, e => e.mean)
-      d.keyMean = d3.sum(d, e => e[key])
-    })
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-    d3.nestBy(_.sortBy(byToken, d => -d.mean), d => Math.round(d.endY/12))
-      .forEach(d => d.forEach((e, i) => e.displayLabel = !i))
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-    var line = d3.line()
-      .x(d => c.x(d.year))
-      .y(d => c.y(d[key]))
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-    var tokenSel = c.svg.appendMany('g.time-token', byToken)
-      // .call(d3.attachTooltip)
-      .on('mouseover', function(d){
-        d3.selectAll('g.time-token')
-          .classed('active', 0)
-          .filter(e => e.str == d.str)
-          .classed('active', 1)
-          .raise()
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-    tokenSel.append('text')
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-      .at({fontSize: 10, dy: '.33em', fill: (d, i) => d.displayLabel ? '#999' : 'rgba(0,0,0,0)'})
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-    tokenSel.append('path')
-      .at({
-        d: line,
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-
-
-if (window.init) window.init()
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diff --git a/spaces/merve/measuring-fairness/public/hidden-bias/style.css b/spaces/merve/measuring-fairness/public/hidden-bias/style.css
deleted file mode 100644
index 4b0d163f9dc4af367dc0b84036c5e177b8f4db0b..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/public/hidden-bias/style.css
+++ /dev/null
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-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
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-==============================================================================*/
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-.tooltip {
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-  top: 5px;
-}
-
-.img-slide img:nth-of-type(1){
-  transform: rotate(90deg);
-  margin-left: -10px;
-  margin-right: -4px;
-  top: 0px;
-}
-
-
-
-
-
-div.axis b{
-  margin-bottom: 0px;
-}
-
-div.axis{
-  line-height: 14px;
-}
-
-
-circle:hover{
-  stroke: #000;
-  stroke-width: 2;
-}
-
-
-
-
diff --git a/spaces/merve/uncertainty-calibration/public/measuring-fairness/init.js b/spaces/merve/uncertainty-calibration/public/measuring-fairness/init.js
deleted file mode 100644
index 5a8df63793d90464eb148443787eb91e2b34180b..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/public/measuring-fairness/init.js
+++ /dev/null
@@ -1,200 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-nCols = 12
-
-window.colors = {
-  well: d3.color('#669399') + '',
-  sick: d3.color('#EE2A2A') + '',
-
-  // well: d3.color('green') + '',
-  // sick: d3.color('purple'),
-
-  // well: d3.color('#e9a3c9') + '',
-  // sick: d3.color('#a1d76a'),
-
-  // well: d3.color('#e9a3c9') + '',
-  // sick: d3.color('#a1d76a'),
-
-  // well: d3.color('#e9a3c9') + '',
-  // sick: d3.color('#a1d76a'),
-
-  // well: d3.color('#865327') + '',
-  // sick: d3.color('#012394'),
-
-  // well: d3.color('#012394') + '',
-  // sick: d3.color('#FBC20F') + '',
-
-  // well: d3.color('#012394') + '',
-  // sick: d3.color('#E71E24') + '',
-
-  // well: d3.color('#A9159C') + '',
-  // sick: d3.color('#E71E24') + '',
-
-  // well: d3.color('#A9159C') + '',
-  // sick: d3.color('#012394') + '',
-
-  // well: d3.color('orange') + '',
-  // sick: d3.color('#012394') + '',
-
-
-}
-
-window.colors = {
-  well: d3.interpolate(colors.well, '#fff')(.5),
-  sick: d3.interpolate(colors.sick, '#fff')(.2),
-}
-
-window.lcolors = {
-  well: d3.interpolate(colors.well, '#fff')(.5),
-  sick: d3.interpolate(colors.sick, '#fff')(.35)
-}
-window.llcolors = {
-  well: d3.interpolate(colors.well, '#fff')(.5),
-  sick: d3.interpolate(colors.sick, '#fff')(1)
-}
-window.dcolors = {
-  well: d3.interpolate(colors.well, '#000')(.65),
-  sick: d3.interpolate(colors.sick, '#000')(.65)
-}
-
-// window.colors = {
-//   well: d3.color('#BEF5FF') + '',
-//   sick: d3.color('#FCC5C3') + '',
-// }
-
-// window.colors = {
-//   well: d3.color('#669399') + '',
-//   sick: d3.color('#EE2A2A') + '',
-// }
-
-// window.lcolors = {
-//   well: d3.interpolate(colors.well, '#fff')(.3),
-//   sick: d3.interpolate(colors.sick, '#fff')(.3)
-// }
-// window.llcolors = {
-//   well: d3.interpolate(colors.well, '#fff')(.2),
-//   sick: d3.interpolate(colors.sick, '#fff')(.2)
-// }
-
-// window.lcolors = {
-//   well: '#CFFCF6',
-//   sick: '#FFBD96'
-// }
-
-// copy(logColors())
-function logColors(){
-  return `
-    body{
-      --colors-well: ${d3.rgb(colors.well)};
-      --colors-sick: ${d3.rgb(colors.sick)};
-      --lcolors-well: ${d3.rgb(lcolors.well)};
-      --lcolors-sick: ${d3.rgb(lcolors.sick)};
-      --dcolors-well: ${d3.rgb(dcolors.well)};
-      --dcolors-sick: ${d3.rgb(dcolors.sick)};
-    }
-  `
-}
-
-
-
-window.init = function(){
-  console.clear()
-
-  graphSel = d3.select('#graph').html('').append('div')
-  totalWidth = graphSel.node().offsetWidth
-  totalWidth = 400
-
-  c = d3.conventions({
-    sel: graphSel.st({marginTop: 40}),
-    margin: {top: 20},
-    totalWidth,
-    totalHeight: totalWidth,
-  })
-
-  students = makeStudents()
-  sel = makeSel()
-  mini = makeMini()
-  slider = makeSlider()
-  slides = makeSlides()
-  gs = makeGS()
-
-  function sizeGraphSel(){
-    var scale = (totalWidth + 35)/(innerWidth - 10) // off by one, s is 35
-    scale = d3.clamp(1, scale, 2)
-
-    graphSel.st({
-      transform: `scale(${1/scale})`,
-      transformOrigin: '0px 0px',
-
-    })
-  }
-  sizeGraphSel()
-  d3.select(window).on('resize', sizeGraphSel)
-
-}
-init()
-
-
-
-
-
-!(function(){
-  var footnums = '¹²³'
-
-  d3.selectAll('.footstart').each(function(d, i){
-    d3.select(this)
-      .at({
-        href: '#footend-' + i,
-      })
-      .text(footnums[i])
-      .parent().at({id: 'footstart-' + i})
-  })
-
-  d3.selectAll('.footend').each(function(d, i){
-    d3.select(this)
-      .at({
-        href: '#footstart-' + i,
-        id: 'footend-' + i,
-      })
-      .text(footnums[i])
-  })
-
-
-  d3.selectAll('#sections wee, #graph .weepeople').attr('aria-hidden', true)
-
-})()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/mikeee/radiobee-dev/run-pyright.bat b/spaces/mikeee/radiobee-dev/run-pyright.bat
deleted file mode 100644
index 76fcc86d774a9794a52f2aca7713ff0d03360d3e..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-dev/run-pyright.bat
+++ /dev/null
@@ -1 +0,0 @@
-nodemon -w radiobee -w tests -x pyright radiobee tests
\ No newline at end of file
diff --git a/spaces/mkutarna/audiobook_gen/src/predict.py b/spaces/mkutarna/audiobook_gen/src/predict.py
deleted file mode 100644
index 4ce4f1175dd2163217bf4152f10e7dd4e6415e3e..0000000000000000000000000000000000000000
--- a/spaces/mkutarna/audiobook_gen/src/predict.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""
-Notes
------
-This module contains the functions for audiobook_gen that handle text-to-speech generation.
-The functions take in the preprocessed text and invoke the Silero package to generate audio tensors.
-"""
-import logging
-
-import torch
-from stqdm import stqdm
-
-from src import output, config
-
-
-def load_model():
-    """
-    Load Silero package containg the model information
-    for the language and speaker set in config.py
-    and converts it to the set device.
-
-    Parameters
-    ----------
-    None
-
-    Returns
-    -------
-    model : torch.package
-
-    """
-    from silero import silero_tts
-
-    model, _ = silero_tts(language=config.LANGUAGE, speaker=config.MODEL_ID)
-    model.to(config.DEVICE)
-    return model
-
-
-def generate_audio(corpus, title, model, speaker):
-    """
-    For each section within the corpus, calls predict() function to generate audio tensors
-    and then calls write_audio() to output the tensors to audio files.
-
-    Parameters
-    ----------
-    corpus : array_like
-        list of list of strings,
-        body of tokenized text from which audio is generated
-
-    title : str
-        title of document, used to name output files
-
-    model : torch.package
-        torch package containing model for language and speaker specified
-
-    speaker : str
-        identifier of selected speaker for audio generation
-
-    Returns
-    -------
-    None
-
-    """
-    for section in stqdm(corpus, desc="Sections in document:"):
-        section_index = f'part{corpus.index(section):03}'
-        audio_list, sample_path = predict(section, section_index, title, model, speaker)
-        output.write_audio(audio_list, sample_path)
-
-
-def predict(text_section, section_index, title, model, speaker):
-    """
-    Applies Silero TTS engine for each token within the corpus section,
-    appending it to the output tensor array, and creates file path for output.
-
-    Parameters
-    ----------
-    text_section : array_like
-        list of strings,
-        body of tokenized text from which audio is generated
-
-    section_index : int
-        index of current section within corpus
-
-    title : str
-        title of document, used to name output files
-
-    model : torch.package
-        torch package containing model for language and speaker specified
-
-    speaker : str
-        identifier of selected speaker for audio generation
-
-    Returns
-    -------
-    audio_list : torch.tensor
-        pytorch tensor containing generated audio
-
-    sample_path : str
-        file name and path for outputting tensor to audio file
-
-    """
-    audio_list = []
-    for sentence in stqdm(text_section, desc="Sentences in section:"):
-        audio = model.apply_tts(text=sentence, speaker=speaker, sample_rate=config.SAMPLE_RATE)
-        if len(audio) > 0 and isinstance(audio, torch.Tensor):
-            audio_list.append(audio)
-            logging.info(f'Tensor generated for sentence: \n {sentence}')
-        else:
-            logging.info(f'Tensor for sentence is not valid: \n {sentence}')
-
-    sample_path = config.output_path / f'{title}_{section_index}.mp3'
-    return audio_list, sample_path
diff --git a/spaces/ml6team/Knowledge-graphs/README.md b/spaces/ml6team/Knowledge-graphs/README.md
deleted file mode 100644
index 5253faf4408233278d6a00671d9af85e92b38f28..0000000000000000000000000000000000000000
--- a/spaces/ml6team/Knowledge-graphs/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: "RE:Belle"
-emoji: 🌐
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
diff --git a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Better.py b/spaces/monra/freegpt-webui/g4f/Provider/Providers/Better.py
deleted file mode 100644
index bee52870eb3300f25c9762ab204968791a2a30a9..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Better.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import json
-import requests
-from typing import Dict, get_type_hints
-
-url = 'https://openai-proxy-api.vercel.app/v1/'
-model = [
-    'gpt-3.5-turbo',
-    'gpt-3.5-turbo-0613',
-    'gpt-3.5-turbo-16k',
-    'gpt-3.5-turbo-16k-0613',
-    'gpt-4',
-]
-
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1823.58',
-        'Referer': 'https://chat.ylokh.xyz/',
-        'Origin': 'https://chat.ylokh.xyz',
-        'Connection': 'keep-alive',  
-    }
-
-    json_data = {
-        'messages': messages,
-        'temperature': 1.0,
-        'model': model,
-        'stream': stream,
-    }
-
-    response = requests.post(
-        'https://openai-proxy-api.vercel.app/v1/chat/completions', headers=headers, json=json_data, stream=True
-    )
-
-    for token in response.iter_lines():
-        decoded = token.decode('utf-8')
-        if decoded.startswith('data: '):
-            data_str = decoded.replace('data: ', '')
-            data = json.loads(data_str)
-            if 'choices' in data and 'delta' in data['choices'][0]:
-                delta = data['choices'][0]['delta']
-                content = delta.get('content', '')
-                finish_reason = delta.get('finish_reason', '')
-
-                if finish_reason == 'stop':
-                    break
-                if content:
-                    yield content
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + '(%s)' % ', '.join(
-    [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/clib/libnat/edit_dist.cpp b/spaces/mshukor/UnIVAL/fairseq/fairseq/clib/libnat/edit_dist.cpp
deleted file mode 100644
index 9ffb60569d74d2868ed8113b7c787ef870e9da20..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/clib/libnat/edit_dist.cpp
+++ /dev/null
@@ -1,231 +0,0 @@
-/**
- * Copyright 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.
- */
-
-#include <pybind11/detail/common.h>
-#include <pybind11/pybind11.h>
-#include <torch/torch.h> // @manual=//caffe2:torch_extension
-#include <algorithm>
-#include <cstdint>
-#include <iosfwd>
-#include <memory>
-#include <new>
-#include <string>
-#include <utility>
-#include <vector>
-
-using namespace ::std;
-
-vector<vector<uint32_t>> edit_distance2_with_dp(
-    vector<uint32_t>& x,
-    vector<uint32_t>& y) {
-  uint32_t lx = x.size();
-  uint32_t ly = y.size();
-  vector<vector<uint32_t>> d(lx + 1, vector<uint32_t>(ly + 1));
-  for (uint32_t i = 0; i < lx + 1; i++) {
-    d[i][0] = i;
-  }
-  for (uint32_t j = 0; j < ly + 1; j++) {
-    d[0][j] = j;
-  }
-  for (uint32_t i = 1; i < lx + 1; i++) {
-    for (uint32_t j = 1; j < ly + 1; j++) {
-      d[i][j] =
-          min(min(d[i - 1][j], d[i][j - 1]) + 1,
-              d[i - 1][j - 1] + 2 * (x.at(i - 1) == y.at(j - 1) ? 0 : 1));
-    }
-  }
-  return d;
-}
-
-vector<vector<uint32_t>> edit_distance2_backtracking(
-    vector<vector<uint32_t>>& d,
-    vector<uint32_t>& x,
-    vector<uint32_t>& y,
-    uint32_t terminal_symbol) {
-  vector<uint32_t> seq;
-  vector<vector<uint32_t>> edit_seqs(x.size() + 2, vector<uint32_t>());
-  /*
-  edit_seqs:
-  0~x.size() cell is the insertion sequences
-  last cell is the delete sequence
-  */
-
-  if (x.size() == 0) {
-    edit_seqs.at(0) = y;
-    return edit_seqs;
-  }
-
-  uint32_t i = d.size() - 1;
-  uint32_t j = d.at(0).size() - 1;
-
-  while ((i >= 0) && (j >= 0)) {
-    if ((i == 0) && (j == 0)) {
-      break;
-    }
-
-    if ((j > 0) && (d.at(i).at(j - 1) < d.at(i).at(j))) {
-      seq.push_back(1); // insert
-      seq.push_back(y.at(j - 1));
-      j--;
-    } else if ((i > 0) && (d.at(i - 1).at(j) < d.at(i).at(j))) {
-      seq.push_back(2); // delete
-      seq.push_back(x.at(i - 1));
-      i--;
-    } else {
-      seq.push_back(3); // keep
-      seq.push_back(x.at(i - 1));
-      i--;
-      j--;
-    }
-  }
-
-  uint32_t prev_op, op, s, word;
-  prev_op = 0, s = 0;
-  for (uint32_t k = 0; k < seq.size() / 2; k++) {
-    op = seq.at(seq.size() - 2 * k - 2);
-    word = seq.at(seq.size() - 2 * k - 1);
-    if (prev_op != 1) {
-      s++;
-    }
-    if (op == 1) // insert
-    {
-      edit_seqs.at(s - 1).push_back(word);
-    } else if (op == 2) // delete
-    {
-      edit_seqs.at(x.size() + 1).push_back(1);
-    } else {
-      edit_seqs.at(x.size() + 1).push_back(0);
-    }
-
-    prev_op = op;
-  }
-
-  for (uint32_t k = 0; k < edit_seqs.size(); k++) {
-    if (edit_seqs[k].size() == 0) {
-      edit_seqs[k].push_back(terminal_symbol);
-    }
-  }
-  return edit_seqs;
-}
-
-vector<vector<uint32_t>> edit_distance2_backtracking_with_delete(
-    vector<vector<uint32_t>>& d,
-    vector<uint32_t>& x,
-    vector<uint32_t>& y,
-    uint32_t terminal_symbol,
-    uint32_t deletion_symbol) {
-  vector<uint32_t> seq;
-  vector<vector<uint32_t>> edit_seqs(x.size() + 1, vector<uint32_t>());
-  /*
-  edit_seqs:
-  0~x.size() cell is the insertion sequences
-  last cell is the delete sequence
-  */
-
-  if (x.size() == 0) {
-    edit_seqs.at(0) = y;
-    return edit_seqs;
-  }
-
-  uint32_t i = d.size() - 1;
-  uint32_t j = d.at(0).size() - 1;
-
-  while ((i >= 0) && (j >= 0)) {
-    if ((i == 0) && (j == 0)) {
-      break;
-    }
-
-    if ((j > 0) && (d.at(i).at(j - 1) < d.at(i).at(j))) {
-      seq.push_back(1); // insert
-      seq.push_back(y.at(j - 1));
-      j--;
-    } else if ((i > 0) && (d.at(i - 1).at(j) < d.at(i).at(j))) {
-      seq.push_back(2); // delete
-      seq.push_back(x.at(i - 1));
-      i--;
-    } else {
-      seq.push_back(3); // keep
-      seq.push_back(x.at(i - 1));
-      i--;
-      j--;
-    }
-  }
-
-  uint32_t prev_op, op, s, word;
-  prev_op = 0, s = 0;
-  for (uint32_t k = 0; k < seq.size() / 2; k++) {
-    op = seq.at(seq.size() - 2 * k - 2);
-    word = seq.at(seq.size() - 2 * k - 1);
-    if (prev_op != 1) {
-      s++;
-    }
-    if (op == 1) // insert
-    {
-      edit_seqs.at(s - 1).push_back(word);
-    } else if (op == 2) // delete
-    {
-      edit_seqs.at(s - 1).push_back(deletion_symbol);
-    }
-
-    prev_op = op;
-  }
-
-  for (uint32_t k = 0; k < edit_seqs.size(); k++) {
-    if (edit_seqs.at(k).size() == 0) {
-      edit_seqs.at(k).push_back(terminal_symbol);
-    }
-  }
-  return edit_seqs;
-}
-
-vector<uint32_t> compute_ed2(
-    vector<vector<uint32_t>>& xs,
-    vector<vector<uint32_t>>& ys) {
-  vector<uint32_t> distances(xs.size());
-  for (uint32_t i = 0; i < xs.size(); i++) {
-    vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i));
-    distances.at(i) = d.at(xs.at(i).size()).at(ys.at(i).size());
-  }
-  return distances;
-}
-
-vector<vector<vector<uint32_t>>> suggested_ed2_path(
-    vector<vector<uint32_t>>& xs,
-    vector<vector<uint32_t>>& ys,
-    uint32_t terminal_symbol) {
-  vector<vector<vector<uint32_t>>> seq(xs.size());
-  for (uint32_t i = 0; i < xs.size(); i++) {
-    vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i));
-    seq.at(i) =
-        edit_distance2_backtracking(d, xs.at(i), ys.at(i), terminal_symbol);
-  }
-  return seq;
-}
-
-vector<vector<vector<uint32_t>>> suggested_ed2_path_with_delete(
-    vector<vector<uint32_t>>& xs,
-    vector<vector<uint32_t>>& ys,
-    uint32_t terminal_symbol,
-    uint32_t deletion_symbol) {
-  vector<vector<vector<uint32_t>>> seq(xs.size());
-  for (uint32_t i = 0; i < xs.size(); i++) {
-    vector<vector<uint32_t>> d = edit_distance2_with_dp(xs.at(i), ys.at(i));
-    seq.at(i) = edit_distance2_backtracking_with_delete(
-        d, xs.at(i), ys.at(i), terminal_symbol, deletion_symbol);
-  }
-  return seq;
-}
-
-PYBIND11_MODULE(libnat, m) {
-  m.def("compute_ed2", &compute_ed2, "compute_ed2");
-  m.def("suggested_ed2_path", &suggested_ed2_path, "suggested_ed2_path");
-  m.def(
-      "suggested_ed2_path_with_delete",
-      &suggested_ed2_path_with_delete,
-      "suggested_ed2_path_with_delete");
-}
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/fp32_group_norm.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/fp32_group_norm.py
deleted file mode 100644
index d03aac022e30c8c14a600062d1d86429504ba003..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/fp32_group_norm.py
+++ /dev/null
@@ -1,25 +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.
-"""
-Layer norm done in fp32 (for fp16 training)
-"""
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Fp32GroupNorm(nn.GroupNorm):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def forward(self, input):
-        output = F.group_norm(
-            input.float(),
-            self.num_groups,
-            self.weight.float() if self.weight is not None else None,
-            self.bias.float() if self.bias is not None else None,
-            self.eps,
-        )
-        return output.type_as(input)
diff --git a/spaces/mshukor/UnIVAL/models/unival/encoders/simple_tokenizer.py b/spaces/mshukor/UnIVAL/models/unival/encoders/simple_tokenizer.py
deleted file mode 100644
index c617590ed5cdfa10c375ff3ae1a619614017526d..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/models/unival/encoders/simple_tokenizer.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# from openai clip
-import gzip
-import html
-import os
-from functools import lru_cache
-
-import ftfy
-import regex as re
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe()):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
-        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
diff --git a/spaces/multimodalart/TAV-poli-2/app.py b/spaces/multimodalart/TAV-poli-2/app.py
deleted file mode 100644
index 03d00ef924909ea1dec8f5e70795368fe163d303..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/TAV-poli-2/app.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-from subprocess import getoutput
-
-import gradio as gr
-import torch
-
-from app_inference import create_inference_demo
-from app_training import create_training_demo
-from app_upload import create_upload_demo
-from inference import InferencePipeline
-from trainer import Trainer
-
-TITLE = '# [Tune-A-Video](https://tuneavideo.github.io/) UI'
-
-ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI-poli'
-SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
-GPU_DATA = getoutput('nvidia-smi')
-SHARED_UI_WARNING = f'''## Attention - Training doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
-
-<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
-'''
-
-if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
-    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
-else:
-    SETTINGS = 'Settings'
-
-INVALID_GPU_WARNING = f'''## Attention - the specified GPU is invalid. Training may not work. Make sure you have selected a `T4 GPU` for this task.'''
-
-CUDA_NOT_AVAILABLE_WARNING = f'''## Attention - Running on CPU.
-<center>
-You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
-You can use "T4 small/medium" to run this demo.
-</center>
-'''
-
-HF_TOKEN_NOT_SPECIFIED_WARNING = f'''The environment variable `HF_TOKEN` is not specified. Feel free to specify your Hugging Face token with write permission if you don't want to manually provide it for every run. 
-<center>
-You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
-You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
-</center>
-'''
-
-
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-
-
-def show_warning(warning_text: str) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown(warning_text)
-    return demo
-
-
-pipe = InferencePipeline(HF_TOKEN)
-trainer = Trainer(HF_TOKEN)
-
-with gr.Blocks(css='style.css') as demo:
-    if SPACE_ID == ORIGINAL_SPACE_ID:
-        show_warning(SHARED_UI_WARNING)
-    elif not torch.cuda.is_available():
-        show_warning(CUDA_NOT_AVAILABLE_WARNING)
-    elif(not "T4" in GPU_DATA):
-        show_warning(INVALID_GPU_WARNING)
-    
-
-    gr.Markdown(TITLE)
-    with gr.Tabs():
-        with gr.TabItem('Train'):
-            create_training_demo(trainer, pipe)
-        with gr.TabItem('Run'):
-            create_inference_demo(pipe, HF_TOKEN)
-        with gr.TabItem('Upload'):
-            gr.Markdown('''
-            - You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
-            ''')
-            create_upload_demo(HF_TOKEN)
-    
-    if not HF_TOKEN:
-        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
-
-demo.queue(max_size=1).launch(share=False)
\ No newline at end of file
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/web/static/js/jquery.mixitup.min.js b/spaces/mygyasir/genious_bgremover/carvekit/web/static/js/jquery.mixitup.min.js
deleted file mode 100644
index a4fa70ab7047f591f742b0d7b0b87d63345f3652..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/genious_bgremover/carvekit/web/static/js/jquery.mixitup.min.js
+++ /dev/null
@@ -1,14 +0,0 @@
-/**!
- * MixItUp v2.1.5
- *
- * @copyright Copyright 2014 KunkaLabs Limited.
- * @author    KunkaLabs Limited.
- * @link      https://mixitup.kunkalabs.com
- *
- * @license   Commercial use requires a commercial license.
- *            https://mixitup.kunkalabs.com/licenses/
- *
- *            Non-commercial use permitted under terms of CC-BY-NC license.
- *            http://creativecommons.org/licenses/by-nc/3.0/
- */
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diff --git a/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/val.py b/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/val.py
deleted file mode 100644
index 2dd2aec679f96e36ba11222f462b700e3b24df4d..0000000000000000000000000000000000000000
--- a/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/val.py
+++ /dev/null
@@ -1,381 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Validate a trained YOLOv5 model accuracy on a custom dataset
-
-Usage:
-    $ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
-
-Usage - formats:
-    $ python path/to/val.py --weights yolov5s.pt                 # PyTorch
-                                      yolov5s.torchscript        # TorchScript
-                                      yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                      yolov5s.xml                # OpenVINO
-                                      yolov5s.engine             # TensorRT
-                                      yolov5s.mlmodel            # CoreML (MacOS-only)
-                                      yolov5s_saved_model        # TensorFlow SavedModel
-                                      yolov5s.pb                 # TensorFlow GraphDef
-                                      yolov5s.tflite             # TensorFlow Lite
-                                      yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
-"""
-
-import argparse
-import json
-import os
-import sys
-from pathlib import Path
-from threading import Thread
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.callbacks import Callbacks
-from utils.datasets import create_dataloader
-from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,
-                           coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
-                           scale_coords, xywh2xyxy, xyxy2xywh)
-from utils.metrics import ConfusionMatrix, ap_per_class
-from utils.plots import output_to_target, plot_images, plot_val_study
-from utils.torch_utils import select_device, time_sync
-
-
-def save_one_txt(predn, save_conf, shape, file):
-    # Save one txt result
-    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
-    for *xyxy, conf, cls in predn.tolist():
-        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
-        with open(file, 'a') as f:
-            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-
-def save_one_json(predn, jdict, path, class_map):
-    # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
-    image_id = int(path.stem) if path.stem.isnumeric() else path.stem
-    box = xyxy2xywh(predn[:, :4])  # xywh
-    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
-    for p, b in zip(predn.tolist(), box.tolist()):
-        jdict.append({'image_id': image_id,
-                      'category_id': class_map[int(p[5])],
-                      'bbox': [round(x, 3) for x in b],
-                      'score': round(p[4], 5)})
-
-
-def process_batch(detections, labels, iouv):
-    """
-    Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
-    Arguments:
-        detections (Array[N, 6]), x1, y1, x2, y2, conf, class
-        labels (Array[M, 5]), class, x1, y1, x2, y2
-    Returns:
-        correct (Array[N, 10]), for 10 IoU levels
-    """
-    correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
-    iou = box_iou(labels[:, 1:], detections[:, :4])
-    x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes match
-    if x[0].shape[0]:
-        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]
-        if x[0].shape[0] > 1:
-            matches = matches[matches[:, 2].argsort()[::-1]]
-            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-            # matches = matches[matches[:, 2].argsort()[::-1]]
-            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        matches = torch.from_numpy(matches).to(iouv.device)
-        correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
-    return correct
-
-
-@torch.no_grad()
-def run(data,
-        weights=None,  # model.pt path(s)
-        batch_size=32,  # batch size
-        imgsz=640,  # inference size (pixels)
-        conf_thres=0.001,  # confidence threshold
-        iou_thres=0.6,  # NMS IoU threshold
-        task='val',  # train, val, test, speed or study
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        workers=8,  # max dataloader workers (per RANK in DDP mode)
-        single_cls=False,  # treat as single-class dataset
-        augment=False,  # augmented inference
-        verbose=False,  # verbose output
-        save_txt=False,  # save results to *.txt
-        save_hybrid=False,  # save label+prediction hybrid results to *.txt
-        save_conf=False,  # save confidences in --save-txt labels
-        save_json=False,  # save a COCO-JSON results file
-        project=ROOT / 'runs/val',  # save to project/name
-        name='exp',  # save to project/name
-        exist_ok=False,  # existing project/name ok, do not increment
-        half=True,  # use FP16 half-precision inference
-        dnn=False,  # use OpenCV DNN for ONNX inference
-        model=None,
-        dataloader=None,
-        save_dir=Path(''),
-        plots=True,
-        callbacks=Callbacks(),
-        compute_loss=None,
-        ):
-    # Initialize/load model and set device
-    training = model is not None
-    if training:  # called by train.py
-        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
-        half &= device.type != 'cpu'  # half precision only supported on CUDA
-        model.half() if half else model.float()
-    else:  # called directly
-        device = select_device(device, batch_size=batch_size)
-
-        # Directories
-        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-        # Load model
-        model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
-        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
-        imgsz = check_img_size(imgsz, s=stride)  # check image size
-        half = model.fp16  # FP16 supported on limited backends with CUDA
-        if engine:
-            batch_size = model.batch_size
-        else:
-            device = model.device
-            if not (pt or jit):
-                batch_size = 1  # export.py models default to batch-size 1
-                LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
-
-        # Data
-        data = check_dataset(data)  # check
-
-    # Configure
-    model.eval()
-    cuda = device.type != 'cpu'
-    is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt')  # COCO dataset
-    nc = 1 if single_cls else int(data['nc'])  # number of classes
-    iouv = torch.linspace(0.5, 0.95, 10, device=device)  # iou vector for mAP@0.5:0.95
-    niou = iouv.numel()
-
-    # Dataloader
-    if not training:
-        model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz))  # warmup
-        pad = 0.0 if task in ('speed', 'benchmark') else 0.5
-        rect = False if task == 'benchmark' else pt  # square inference for benchmarks
-        task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
-        dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=rect,
-                                       workers=workers, prefix=colorstr(f'{task}: '))[0]
-
-    seen = 0
-    confusion_matrix = ConfusionMatrix(nc=nc)
-    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
-    class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
-    s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
-    dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
-    loss = torch.zeros(3, device=device)
-    jdict, stats, ap, ap_class = [], [], [], []
-    pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
-    for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
-        t1 = time_sync()
-        if cuda:
-            im = im.to(device, non_blocking=True)
-            targets = targets.to(device)
-        im = im.half() if half else im.float()  # uint8 to fp16/32
-        im /= 255  # 0 - 255 to 0.0 - 1.0
-        nb, _, height, width = im.shape  # batch size, channels, height, width
-        t2 = time_sync()
-        dt[0] += t2 - t1
-
-        # Inference
-        out, train_out = model(im) if training else model(im, augment=augment, val=True)  # inference, loss outputs
-        dt[1] += time_sync() - t2
-
-        # Loss
-        if compute_loss:
-            loss += compute_loss([x.float() for x in train_out], targets)[1]  # box, obj, cls
-
-        # NMS
-        targets[:, 2:] *= torch.tensor((width, height, width, height), device=device)  # to pixels
-        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
-        t3 = time_sync()
-        out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
-        dt[2] += time_sync() - t3
-
-        # Metrics
-        for si, pred in enumerate(out):
-            labels = targets[targets[:, 0] == si, 1:]
-            nl = len(labels)
-            tcls = labels[:, 0].tolist() if nl else []  # target class
-            path, shape = Path(paths[si]), shapes[si][0]
-            seen += 1
-
-            if len(pred) == 0:
-                if nl:
-                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
-                continue
-
-            # Predictions
-            if single_cls:
-                pred[:, 5] = 0
-            predn = pred.clone()
-            scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred
-
-            # Evaluate
-            if nl:
-                tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
-                scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
-                labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
-                correct = process_batch(predn, labelsn, iouv)
-                if plots:
-                    confusion_matrix.process_batch(predn, labelsn)
-            else:
-                correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
-            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))  # (correct, conf, pcls, tcls)
-
-            # Save/log
-            if save_txt:
-                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
-            if save_json:
-                save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
-            callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
-
-        # Plot images
-        if plots and batch_i < 3:
-            f = save_dir / f'val_batch{batch_i}_labels.jpg'  # labels
-            Thread(target=plot_images, args=(im, targets, paths, f, names), daemon=True).start()
-            f = save_dir / f'val_batch{batch_i}_pred.jpg'  # predictions
-            Thread(target=plot_images, args=(im, output_to_target(out), paths, f, names), daemon=True).start()
-
-    # Compute metrics
-    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
-    if len(stats) and stats[0].any():
-        tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
-        ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
-        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
-        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
-    else:
-        nt = torch.zeros(1)
-
-    # Print results
-    pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
-    LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
-
-    # Print results per class
-    if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
-        for i, c in enumerate(ap_class):
-            LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
-
-    # Print speeds
-    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
-    if not training:
-        shape = (batch_size, 3, imgsz, imgsz)
-        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
-
-    # Plots
-    if plots:
-        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        callbacks.run('on_val_end')
-
-    # Save JSON
-    if save_json and len(jdict):
-        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
-        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
-        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
-        LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
-        with open(pred_json, 'w') as f:
-            json.dump(jdict, f)
-
-        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
-            check_requirements(['pycocotools'])
-            from pycocotools.coco import COCO
-            from pycocotools.cocoeval import COCOeval
-
-            anno = COCO(anno_json)  # init annotations api
-            pred = anno.loadRes(pred_json)  # init predictions api
-            eval = COCOeval(anno, pred, 'bbox')
-            if is_coco:
-                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files]  # image IDs to evaluate
-            eval.evaluate()
-            eval.accumulate()
-            eval.summarize()
-            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
-        except Exception as e:
-            LOGGER.info(f'pycocotools unable to run: {e}')
-
-    # Return results
-    model.float()  # for training
-    if not training:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    maps = np.zeros(nc) + map
-    for i, c in enumerate(ap_class):
-        maps[c] = ap[i]
-    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
-    parser.add_argument('--batch-size', type=int, default=32, help='batch size')
-    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
-    parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
-    parser.add_argument('--task', default='val', help='train, val, test, speed or study')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
-    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
-    parser.add_argument('--augment', action='store_true', help='augmented inference')
-    parser.add_argument('--verbose', action='store_true', help='report mAP by class')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
-    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
-    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
-    parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
-    parser.add_argument('--name', default='exp', help='save to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
-    opt = parser.parse_args()
-    opt.data = check_yaml(opt.data)  # check YAML
-    opt.save_json |= opt.data.endswith('coco.yaml')
-    opt.save_txt |= opt.save_hybrid
-    print_args(FILE.stem, opt)
-    return opt
-
-
-def main(opt):
-    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
-
-    if opt.task in ('train', 'val', 'test'):  # run normally
-        if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466
-            LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} >> 0.001 will produce invalid mAP values.')
-        run(**vars(opt))
-
-    else:
-        weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
-        opt.half = True  # FP16 for fastest results
-        if opt.task == 'speed':  # speed benchmarks
-            # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
-            opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
-            for opt.weights in weights:
-                run(**vars(opt), plots=False)
-
-        elif opt.task == 'study':  # speed vs mAP benchmarks
-            # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
-            for opt.weights in weights:
-                f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt'  # filename to save to
-                x, y = list(range(256, 1536 + 128, 128)), []  # x axis (image sizes), y axis
-                for opt.imgsz in x:  # img-size
-                    LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
-                    r, _, t = run(**vars(opt), plots=False)
-                    y.append(r + t)  # results and times
-                np.savetxt(f, y, fmt='%10.4g')  # save
-            os.system('zip -r study.zip study_*.txt')
-            plot_val_study(x=x)  # plot
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/callbacks/plotting.py b/spaces/nateraw/deepafx-st/deepafx_st/callbacks/plotting.py
deleted file mode 100644
index 1dc90a03878cf69284ccac3f7dfde9846cb96cb0..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/callbacks/plotting.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import io
-import torch
-import PIL.Image
-import numpy as np
-import scipy.signal
-import librosa.display
-import matplotlib.pyplot as plt
-
-from torch.functional import Tensor
-from torchvision.transforms import ToTensor
-
-
-def compute_comparison_spectrogram(
-    x: np.ndarray,
-    y: np.ndarray,
-    sample_rate: float = 44100,
-    n_fft: int = 2048,
-    hop_length: int = 1024,
-) -> Tensor:
-    X = librosa.stft(x, n_fft=n_fft, hop_length=hop_length)
-    X_db = librosa.amplitude_to_db(np.abs(X), ref=np.max)
-
-    Y = librosa.stft(y, n_fft=n_fft, hop_length=hop_length)
-    Y_db = librosa.amplitude_to_db(np.abs(Y), ref=np.max)
-
-    fig, axs = plt.subplots(figsize=(9, 6), nrows=2)
-    img = librosa.display.specshow(
-        X_db,
-        ax=axs[0],
-        hop_length=hop_length,
-        x_axis="time",
-        y_axis="log",
-        sr=sample_rate,
-    )
-    # fig.colorbar(img, ax=axs[0])
-    img = librosa.display.specshow(
-        Y_db,
-        ax=axs[1],
-        hop_length=hop_length,
-        x_axis="time",
-        y_axis="log",
-        sr=sample_rate,
-    )
-    # fig.colorbar(img, ax=axs[1])
-
-    plt.tight_layout()
-
-    buf = io.BytesIO()
-    plt.savefig(buf, format="jpeg")
-    buf.seek(0)
-    image = PIL.Image.open(buf)
-    image = ToTensor()(image)
-    plt.close("all")
-
-    return image
-
-
-def plot_multi_spectrum(
-    ys=None,
-    Hs=None,
-    legend=[],
-    title="Spectrum",
-    filename=None,
-    sample_rate=44100,
-    n_fft=1024,
-    zero_mean=False,
-):
-
-    if Hs is None:
-        Hs = []
-        for y in ys:
-            X = get_average_spectrum(y, n_fft)
-            X_sm = smooth_spectrum(X)
-            Hs.append(X_sm)
-
-    bin_width = (sample_rate / 2) / (n_fft // 2)
-    freqs = np.arange(0, (sample_rate / 2) + bin_width, step=bin_width)
-
-    fig, ax1 = plt.subplots()
-
-    for idx, H in enumerate(Hs):
-        H = np.nan_to_num(H)
-        H = np.clip(H, 0, np.max(H))
-        H_dB = 20 * np.log10(H + 1e-8)
-        if zero_mean:
-            H_dB -= np.mean(H_dB)
-        if "Target" in legend[idx]:
-            ax1.plot(freqs, H_dB, linestyle="--", color="k")
-        else:
-            ax1.plot(freqs, H_dB)
-
-    plt.legend(legend)
-
-    ax1.set_xscale("log")
-    ax1.set_ylim([-80, 0])
-    ax1.set_xlim([100, 11000])
-    plt.title(title)
-    plt.ylabel("Magnitude (dB)")
-    plt.xlabel("Frequency (Hz)")
-    plt.grid(c="lightgray", which="both")
-
-    if filename is not None:
-        plt.savefig(f"{filename}.png", dpi=300)
-
-    plt.tight_layout()
-
-    buf = io.BytesIO()
-    plt.savefig(buf, format="jpeg")
-    buf.seek(0)
-    image = PIL.Image.open(buf)
-    image = ToTensor()(image)
-    plt.close("all")
-
-    return image
-
-
-def smooth_spectrum(H):
-    # apply Savgol filter for smoothed target curve
-    return scipy.signal.savgol_filter(H, 1025, 2)
-
-
-def get_average_spectrum(x, n_fft):
-    X = torch.stft(x, n_fft, return_complex=True, normalized=True)
-    X = X.abs()  # convert to magnitude
-    X = X.mean(dim=-1).view(-1)  # average across frames
-    return X
diff --git a/spaces/nateraw/lavila/lavila/utils/random.py b/spaces/nateraw/lavila/lavila/utils/random.py
deleted file mode 100644
index 1a74644a87fd875eac819856fe26c30338a5aee6..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/lavila/utils/random.py
+++ /dev/null
@@ -1,15 +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 random
-import numpy as np
-import torch
-
-
-def random_seed(seed=42, rank=0):
-    torch.manual_seed(seed + rank)
-    np.random.seed(seed + rank)
-    random.seed(seed + rank)
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Need For Speed 2016 Serial Key Generator.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Need For Speed 2016 Serial Key Generator.md
deleted file mode 100644
index 1ff71fa21a66f2557ef3d690f54e2ff2bdea8329..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Need For Speed 2016 Serial Key Generator.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-<br />
-<h1>How to Get Need For Speed 2016 Serial Key Generator for Free</h1>
-<p>If you are a fan of racing games, you might have heard of Need For Speed 2016, the latest installment in the popular franchise. This game offers stunning graphics, realistic physics, and thrilling gameplay that will keep you on the edge of your seat. But how can you play it without spending a fortune on the serial key?</p>
-<h2>Need For Speed 2016 Serial Key Generator</h2><br /><p><b><b>DOWNLOAD</b> &#11088; <a href="https://urlcod.com/2uI9Kr">https://urlcod.com/2uI9Kr</a></b></p><br /><br />
-<p>Fortunately, there is a way to get Need For Speed 2016 Serial Key Generator for free. This is a software tool that can generate unique and valid serial keys for the game in a matter of seconds. You can use these keys to activate the game on your PC, PS4, or Xbox One and enjoy the full features of the game.</p>
-<p>But how does it work? And is it safe to use? In this article, we will answer these questions and show you how to get Need For Speed 2016 Serial Key Generator for free.</p>
-<h2>What is Need For Speed 2016 Serial Key Generator?</h2>
-<p>Need For Speed 2016 Serial Key Generator is a software tool that can create unlimited serial keys for the game Need For Speed 2016. These keys are compatible with any platform that the game supports, such as PC, PS4, or Xbox One. You can use these keys to activate the game online and play it without any restrictions.</p>
-<p>The generator works by using a complex algorithm that can bypass the security system of the game and generate valid keys that are not used by anyone else. The generator is updated regularly to ensure that it works with the latest version of the game and that it does not produce duplicate keys.</p>
-<h2>How to Get Need For Speed 2016 Serial Key Generator for Free?</h2>
-<p>Getting Need For Speed 2016 Serial Key Generator for free is very easy. All you have to do is follow these simple steps:</p>
-<ol>
-<li>Download the generator from the link below. The file is in ZIP format, so you will need to extract it using WinRAR or any other software.</li>
-<li>Run the generator.exe file as administrator. You will see a user-friendly interface with some options.</li>
-<li>Select your platform (PC, PS4, or Xbox One) and click on Generate button. The generator will start creating a serial key for you.</li>
-<li>Copy the serial key and paste it into the activation window of the game. You can also save the key in a text file for future use.</li>
-<li>Enjoy the game!</li>
-</ol>
-<h2>Is Need For Speed 2016 Serial Key Generator Safe to Use?</h2>
-<p>Yes, Need For Speed 2016 Serial Key Generator is safe to use. It does not contain any viruses, malware, or spyware that can harm your computer or your account. It also does not require any personal information or payment details from you. It is completely free and legal to use.</p>
-<p>However, you should be careful when downloading the generator from other sources. Some websites may offer fake or infected files that can damage your system or steal your data. To avoid this, you should only download the generator from the official link below.</p>
-<h2>Conclusion</h2>
-<p>Need For Speed 2016 is an amazing racing game that will give you hours of fun and excitement. But if you don't want to spend money on the serial key, you can use Need For Speed 2016 Serial Key Generator for free. This tool can generate unlimited keys for any platform that the game supports. You can use these keys to activate the game online and play it without any limitations.</p>
-<p>To get Need For Speed 2016 Serial Key Generator for free, just follow the instructions above and download the generator from the link below. You will be able to enjoy the game in no time!</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Path Finder 8.3.4 Crack Mac Osx _BEST_.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Path Finder 8.3.4 Crack Mac Osx _BEST_.md
deleted file mode 100644
index 5b3cbcc519c9ac0652cecf97542f4a9637e94d23..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Path Finder 8.3.4 Crack Mac Osx _BEST_.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-
-<h1>Path Finder 8.3.4: A Powerful and Customizable File Manager for Mac OSX</h1>
-<p>If you are looking for a file manager that can do more than the default Finder app on Mac OSX, you may want to check out Path Finder 8.3.4. Path Finder is a popular and powerful app that offers many features and customization options to help you work with your files more efficiently and productively.</p>
-<p>Path Finder allows you to view and manipulate hidden files, use dual pane and full keyboard navigation to browse your file system, compare and synchronize folders, merge folders, batch rename files, filter files by various criteria, tag files, edit file paths, search files with advanced options, sort files by smart rules, access most tools in many different ways to suit your workflow, and much more.</p>
-<h2>Path Finder 8.3.4 Crack Mac Osx</h2><br /><p><b><b>Download File</b> &#187; <a href="https://urlcod.com/2uIb9Z">https://urlcod.com/2uIb9Z</a></b></p><br /><br />
-<p>Path Finder also integrates with other platforms and services, such as FTP, SFTP, WebDAV, SMB, Amazon S3, Google Drive, Dropbox, and more. You can also use Path Finder's built-in terminal, hex editor, file compare tool, archive tool, text and image editors, and other utilities to perform various tasks without leaving the app.</p>
-<p>Path Finder is updated for the latest Mac OSX versions and supports both Intel and Apple Silicon processors. It has a familiar user interface that resembles the Finder app but with more options and flexibility. You can customize Path Finder's appearance, behavior, toolbar, sidebar, modules, menus, hotkeys, extensions, and scripts to make it work exactly how you want.</p>
-<p>Path Finder is not a free app. It requires a license to use, which costs $36 per year or $54 for a lifetime. However, you can download a free trial version of Path Finder and try it for yourself for 30 days. If you like it, you can purchase it from the official website of Cocoatech[^1^], the developer of the app.</p>
-<p>Path Finder is the macOS file browser of choice for thousands of professionals and enterprises worldwide. If you want to take full control of your file system and save your time, Path Finder 8.3.4 is the app for you.</p>
-
-<p>One of the most useful features of Path Finder is the dual pane view. This feature allows you to view two folders side by side and easily copy or move files from one pane to another with just one click. You can also drag and drop files between panes, use tabs to switch between different folders, and sync or compare the contents of two folders.</p>
-<p>Another feature that sets Path Finder apart from the Finder app is the Drop Stack. The Drop Stack is a temporary storage area where you can drag and drop files that you want to copy or move later. You can access the Drop Stack from the toolbar or the sidebar and use it to collect files from different locations and then perform a single operation on them.</p>
-<p>Path Finder also has a powerful file filtering feature that lets you quickly find the files you need. You can filter files by name, extension, size, date, kind, label, tag, content, and more. You can also create custom filters and save them for future use. You can apply filters to any folder or search result and see only the files that match your criteria.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nev/CoNR/model/decoder_small.py b/spaces/nev/CoNR/model/decoder_small.py
deleted file mode 100644
index 179712b5facac4cf9051caa7ab0a778b73ade70a..0000000000000000000000000000000000000000
--- a/spaces/nev/CoNR/model/decoder_small.py
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
-from torch import nn
-import torch.nn.functional as F
-import torch
-
-
-class ResBlock2d(nn.Module):
-    def __init__(self, in_features, kernel_size, padding):
-        super(ResBlock2d, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-    
-        self.norm1 = nn.Conv2d(
-            in_channels=in_features, out_channels=in_features, kernel_size=1)
-        self.norm2 = nn.Conv2d(
-            in_channels=in_features, out_channels=in_features, kernel_size=1)
-
-    def forward(self, x):
-        out = self.norm1(x)
-        out = F.relu(out, inplace=True)
-        out = self.conv1(out)
-        out = self.norm2(out)
-        out = F.relu(out, inplace=True)
-        out = self.conv2(out)
-        out += x
-        return out
-
-
-class RGBADecoderNet(nn.Module):
-    def __init__(self,  c=64, out_planes=4,  num_bottleneck_blocks=1):
-        super(RGBADecoderNet, self).__init__()
-        self.conv_rgba = nn.Sequential(nn.Conv2d(c, out_planes, kernel_size=3, stride=1,
-                                                 padding=1, dilation=1, bias=True))
-        self.bottleneck = torch.nn.Sequential()
-        for i in range(num_bottleneck_blocks):
-            self.bottleneck.add_module(
-                'r' + str(i), ResBlock2d(c, kernel_size=(3, 3), padding=(1, 1)))
-
-    def forward(self, features_weighted_mask_atfeaturesscale_list=[]):
-        return torch.sigmoid(self.conv_rgba(self.bottleneck(features_weighted_mask_atfeaturesscale_list.pop(0))))
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/datasets/__init__.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/datasets/__init__.py
deleted file mode 100644
index 260ccb9c43e5aa2d0f1fd28cfcbdd4f31913d16a..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/datasets/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from . import builtin  # ensure the builtin datasets are registered
-
-__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")]
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_swin_b_in21k_50ep.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_swin_b_in21k_50ep.py
deleted file mode 100644
index b2aad98526e39240ff82cbf96cb005ce75e5c577..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_swin_b_in21k_50ep.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from fvcore.common.param_scheduler import MultiStepParamScheduler
-
-from detectron2 import model_zoo
-from detectron2.config import LazyCall as L
-from detectron2.solver import WarmupParamScheduler
-from detectron2.modeling import SwinTransformer
-
-from ..common.coco_loader_lsj import dataloader
-from .cascade_mask_rcnn_mvitv2_b_in21k_100ep import model
-
-model.backbone.bottom_up = L(SwinTransformer)(
-    depths=[2, 2, 18, 2],
-    drop_path_rate=0.4,
-    embed_dim=128,
-    num_heads=[4, 8, 16, 32],
-)
-model.backbone.in_features = ("p0", "p1", "p2", "p3")
-model.backbone.square_pad = 1024
-
-# Initialization and trainer settings
-train = model_zoo.get_config("common/train.py").train
-train.amp.enabled = True
-train.ddp.fp16_compression = True
-train.init_checkpoint = "detectron2://ImageNetPretrained/swin/swin_base_patch4_window7_224_22k.pth"
-
-# Schedule
-# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep
-train.max_iter = 184375
-lr_multiplier = L(WarmupParamScheduler)(
-    scheduler=L(MultiStepParamScheduler)(
-        values=[1.0, 0.1, 0.01],
-        milestones=[163889, 177546],
-        num_updates=train.max_iter,
-    ),
-    warmup_length=250 / train.max_iter,
-    warmup_factor=0.001,
-)
-
-# Rescale schedule
-train.max_iter = train.max_iter // 2  # 100ep -> 50ep
-lr_multiplier.scheduler.milestones = [
-    milestone // 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
-
-
-optimizer = model_zoo.get_config("common/optim.py").AdamW
-optimizer.lr = 4e-5
-optimizer.weight_decay = 0.05
-optimizer.params.overrides = {"relative_position_bias_table": {"weight_decay": 0.0}}
diff --git a/spaces/nomic-ai/databricks_databricks-dolly-15k/index.html b/spaces/nomic-ai/databricks_databricks-dolly-15k/index.html
deleted file mode 100644
index cd1a6e38dbefd001a518ad4ea426ac8ddd42f820..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/databricks_databricks-dolly-15k/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<html>
-
-<head>
-  <title>databricks/databricks-dolly-15k</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background-color: #f0f0f0;
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      height: 100vh;
-      margin: 0;
-      padding: 0;
-      color: #333;
-    }
-
-    .iframe-container {
-      border: 1px solid #ccc;
-      border-radius: 10px;
-      overflow: hidden;
-      width: 80%;
-      height: 80%;
-      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
-    }
-
-    iframe {
-      width: 100%;
-      height: 100%;
-      border: none;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="iframe-container">
-    <iframe src="https://atlas.nomic.ai/map/2fffb686-04b5-4c41-8c33-e4eac9a4c34e/1092bf17-c70b-4d90-a1e6-5530c8a5833f" allow="clipboard-read; clipboard-write"
-      title="Nomic Atlas"></iframe>
-  </div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/ochyai/ochyai_food/README.md b/spaces/ochyai/ochyai_food/README.md
deleted file mode 100644
index 73396131dbffb38e91aa88d1fcef7a1e74dd32e3..0000000000000000000000000000000000000000
--- a/spaces/ochyai/ochyai_food/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ochyai_food
-emoji: 🍛
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-duplicated_from: ochyai/ochyai_test
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/onliner/QR-generator/README.md b/spaces/onliner/QR-generator/README.md
deleted file mode 100644
index afff80f64c705f429c7b675e48e05a50be6040d8..0000000000000000000000000000000000000000
--- a/spaces/onliner/QR-generator/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: QR Code AI Art Generator
-emoji: 📱🔲
-colorFrom: MediumSeaGreen
-colorTo: CornflowerBlue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-suggested_hardware: t4-medium
-startup_duration_timeout: 1h
-duplicated_from: huggingface-projects/QR-code-AI-art-generator
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/openskyml/midjourney-mini/README.md b/spaces/openskyml/midjourney-mini/README.md
deleted file mode 100644
index c85cd2a6680d6ca8b4b8ab71cafcc795fd72e73d..0000000000000000000000000000000000000000
--- a/spaces/openskyml/midjourney-mini/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Midjourney-mini
-emoji: ⛵️
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/osanseviero/hugging_eats/README.md b/spaces/osanseviero/hugging_eats/README.md
deleted file mode 100644
index 7b1e4e4b6e33cedd022fee30e67c700c89d7161c..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/hugging_eats/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Hugging_eats
-emoji: 👀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.12
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_tan\303\241r_fr_aggregate.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_tan\303\241r_fr_aggregate.html"
deleted file mode 100644
index 3a003c2246cd1cff9e806abb1329c9a69bf9377a..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_tan\303\241r_fr_aggregate.html"
+++ /dev/null
@@ -1,46 +0,0 @@
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="ixhxoxykedaygsebozsd_viz_container">
-    <div id="ixhxoxykedaygsebozsd_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "ixhxoxykedaygsebozsd_saliency_plot_container" class="ixhxoxykedaygsebozsd_viz_container" style="display:block">
-            
-<div id="gieteovgbfftxvpxqucv_saliency_plot" class="gieteovgbfftxvpxqucv_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>▁C'est</th><th>▁un</th><th>▁professeur.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ő</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.994</th><th style="background:rgba(255.0, 13.0, 87.0, 0.05407011289364243)">0.062</th><th style="background:rgba(255.0, 13.0, 87.0, 0.03830461477520289)">0.044</th><th style="background:rgba(30.0, 136.0, 229.0, 0.4876213111507228)">-0.482</th></tr><tr><th>▁tanár.</th><th style="background:rgba(255.0, 13.0, 87.0, 0.10924935630817992)">0.109</th><th style="background:rgba(255.0, 13.0, 87.0, 0.18019409784115661)">0.182</th><th style="background:rgba(255.0, 13.0, 87.0, 0.9369380075262429)">0.93</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4087938205585263)">0.411</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="iwgflhsrkhllyvpwphaw_viz_container">
-    <div id="iwgflhsrkhllyvpwphaw_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "iwgflhsrkhllyvpwphaw_saliency_plot_container" class="iwgflhsrkhllyvpwphaw_viz_container" style="display:block">
-            
-<div id="lqanvirhpydfvbtakqon_saliency_plot" class="lqanvirhpydfvbtakqon_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>▁C'est</th><th>▁un</th><th>▁professeur.</th><th>&lt;/s&gt;</th></tr><tr><th>▁C'est</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.9921172509407804)">0.981</th><th style="background:rgba(255.0, 13.0, 87.0, 0.06195286195286207)">0.067</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4009110714993067)">0.403</th></tr><tr><th>▁un</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, 0.36149732620320857)">0.359</th><th style="background:rgba(255.0, 13.0, 87.0, 0.10136660724896006)">0.109</th></tr><tr><th>▁professeur.</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(255.0, 13.0, 87.0, 0.6531590413943356)">0.652</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/docs/source/en/api/schedulers/lms_discrete.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/schedulers/lms_discrete.md
deleted file mode 100644
index 5fe90dc4e77e1e9ff4f235f862e6a4c3cbba1ca2..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/schedulers/lms_discrete.md
+++ /dev/null
@@ -1,21 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# LMSDiscreteScheduler
-
-`LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).
-
-## LMSDiscreteScheduler
-[[autodoc]] LMSDiscreteScheduler
-
-## LMSDiscreteSchedulerOutput
-[[autodoc]] schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/models/unet_2d_blocks_flax.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/models/unet_2d_blocks_flax.py
deleted file mode 100644
index 8c24b9f264b0d879da7c1a3483ef8530989be6bd..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/models/unet_2d_blocks_flax.py
+++ /dev/null
@@ -1,380 +0,0 @@
-# Copyright 2023 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 flax.linen as nn
-import jax.numpy as jnp
-
-from .attention_flax import FlaxTransformer2DModel
-from .resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D
-
-
-class FlaxCrossAttnDownBlock2D(nn.Module):
-    r"""
-    Cross Attention 2D Downsizing block - original architecture from Unet transformers:
-    https://arxiv.org/abs/2103.06104
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        out_channels (:obj:`int`):
-            Output channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of attention blocks layers
-        num_attention_heads (:obj:`int`, *optional*, defaults to 1):
-            Number of attention heads of each spatial transformer block
-        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
-            Whether to add downsampling layer before each final output
-        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
-            enable memory efficient attention https://arxiv.org/abs/2112.05682
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    out_channels: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    num_attention_heads: int = 1
-    add_downsample: bool = True
-    use_linear_projection: bool = False
-    only_cross_attention: bool = False
-    use_memory_efficient_attention: bool = False
-    dtype: jnp.dtype = jnp.float32
-    transformer_layers_per_block: int = 1
-
-    def setup(self):
-        resnets = []
-        attentions = []
-
-        for i in range(self.num_layers):
-            in_channels = self.in_channels if i == 0 else self.out_channels
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=self.out_channels,
-                dropout_prob=self.dropout,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-
-            attn_block = FlaxTransformer2DModel(
-                in_channels=self.out_channels,
-                n_heads=self.num_attention_heads,
-                d_head=self.out_channels // self.num_attention_heads,
-                depth=self.transformer_layers_per_block,
-                use_linear_projection=self.use_linear_projection,
-                only_cross_attention=self.only_cross_attention,
-                use_memory_efficient_attention=self.use_memory_efficient_attention,
-                dtype=self.dtype,
-            )
-            attentions.append(attn_block)
-
-        self.resnets = resnets
-        self.attentions = attentions
-
-        if self.add_downsample:
-            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
-    def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
-        output_states = ()
-
-        for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-            hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
-            output_states += (hidden_states,)
-
-        if self.add_downsample:
-            hidden_states = self.downsamplers_0(hidden_states)
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class FlaxDownBlock2D(nn.Module):
-    r"""
-    Flax 2D downsizing block
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        out_channels (:obj:`int`):
-            Output channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of attention blocks layers
-        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
-            Whether to add downsampling layer before each final output
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    out_channels: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    add_downsample: bool = True
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        resnets = []
-
-        for i in range(self.num_layers):
-            in_channels = self.in_channels if i == 0 else self.out_channels
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=self.out_channels,
-                dropout_prob=self.dropout,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-        self.resnets = resnets
-
-        if self.add_downsample:
-            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
-    def __call__(self, hidden_states, temb, deterministic=True):
-        output_states = ()
-
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-            output_states += (hidden_states,)
-
-        if self.add_downsample:
-            hidden_states = self.downsamplers_0(hidden_states)
-            output_states += (hidden_states,)
-
-        return hidden_states, output_states
-
-
-class FlaxCrossAttnUpBlock2D(nn.Module):
-    r"""
-    Cross Attention 2D Upsampling block - original architecture from Unet transformers:
-    https://arxiv.org/abs/2103.06104
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        out_channels (:obj:`int`):
-            Output channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of attention blocks layers
-        num_attention_heads (:obj:`int`, *optional*, defaults to 1):
-            Number of attention heads of each spatial transformer block
-        add_upsample (:obj:`bool`, *optional*, defaults to `True`):
-            Whether to add upsampling layer before each final output
-        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
-            enable memory efficient attention https://arxiv.org/abs/2112.05682
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    out_channels: int
-    prev_output_channel: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    num_attention_heads: int = 1
-    add_upsample: bool = True
-    use_linear_projection: bool = False
-    only_cross_attention: bool = False
-    use_memory_efficient_attention: bool = False
-    dtype: jnp.dtype = jnp.float32
-    transformer_layers_per_block: int = 1
-
-    def setup(self):
-        resnets = []
-        attentions = []
-
-        for i in range(self.num_layers):
-            res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
-            resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=resnet_in_channels + res_skip_channels,
-                out_channels=self.out_channels,
-                dropout_prob=self.dropout,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-
-            attn_block = FlaxTransformer2DModel(
-                in_channels=self.out_channels,
-                n_heads=self.num_attention_heads,
-                d_head=self.out_channels // self.num_attention_heads,
-                depth=self.transformer_layers_per_block,
-                use_linear_projection=self.use_linear_projection,
-                only_cross_attention=self.only_cross_attention,
-                use_memory_efficient_attention=self.use_memory_efficient_attention,
-                dtype=self.dtype,
-            )
-            attentions.append(attn_block)
-
-        self.resnets = resnets
-        self.attentions = attentions
-
-        if self.add_upsample:
-            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
-    def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):
-        for resnet, attn in zip(self.resnets, self.attentions):
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
-            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-            hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
-
-        if self.add_upsample:
-            hidden_states = self.upsamplers_0(hidden_states)
-
-        return hidden_states
-
-
-class FlaxUpBlock2D(nn.Module):
-    r"""
-    Flax 2D upsampling block
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        out_channels (:obj:`int`):
-            Output channels
-        prev_output_channel (:obj:`int`):
-            Output channels from the previous block
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of attention blocks layers
-        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
-            Whether to add downsampling layer before each final output
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    out_channels: int
-    prev_output_channel: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    add_upsample: bool = True
-    dtype: jnp.dtype = jnp.float32
-
-    def setup(self):
-        resnets = []
-
-        for i in range(self.num_layers):
-            res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
-            resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=resnet_in_channels + res_skip_channels,
-                out_channels=self.out_channels,
-                dropout_prob=self.dropout,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-
-        self.resnets = resnets
-
-        if self.add_upsample:
-            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
-    def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):
-        for resnet in self.resnets:
-            # pop res hidden states
-            res_hidden_states = res_hidden_states_tuple[-1]
-            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
-            hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
-            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
-        if self.add_upsample:
-            hidden_states = self.upsamplers_0(hidden_states)
-
-        return hidden_states
-
-
-class FlaxUNetMidBlock2DCrossAttn(nn.Module):
-    r"""
-    Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104
-
-    Parameters:
-        in_channels (:obj:`int`):
-            Input channels
-        dropout (:obj:`float`, *optional*, defaults to 0.0):
-            Dropout rate
-        num_layers (:obj:`int`, *optional*, defaults to 1):
-            Number of attention blocks layers
-        num_attention_heads (:obj:`int`, *optional*, defaults to 1):
-            Number of attention heads of each spatial transformer block
-        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
-            enable memory efficient attention https://arxiv.org/abs/2112.05682
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-            Parameters `dtype`
-    """
-    in_channels: int
-    dropout: float = 0.0
-    num_layers: int = 1
-    num_attention_heads: int = 1
-    use_linear_projection: bool = False
-    use_memory_efficient_attention: bool = False
-    dtype: jnp.dtype = jnp.float32
-    transformer_layers_per_block: int = 1
-
-    def setup(self):
-        # there is always at least one resnet
-        resnets = [
-            FlaxResnetBlock2D(
-                in_channels=self.in_channels,
-                out_channels=self.in_channels,
-                dropout_prob=self.dropout,
-                dtype=self.dtype,
-            )
-        ]
-
-        attentions = []
-
-        for _ in range(self.num_layers):
-            attn_block = FlaxTransformer2DModel(
-                in_channels=self.in_channels,
-                n_heads=self.num_attention_heads,
-                d_head=self.in_channels // self.num_attention_heads,
-                depth=self.transformer_layers_per_block,
-                use_linear_projection=self.use_linear_projection,
-                use_memory_efficient_attention=self.use_memory_efficient_attention,
-                dtype=self.dtype,
-            )
-            attentions.append(attn_block)
-
-            res_block = FlaxResnetBlock2D(
-                in_channels=self.in_channels,
-                out_channels=self.in_channels,
-                dropout_prob=self.dropout,
-                dtype=self.dtype,
-            )
-            resnets.append(res_block)
-
-        self.resnets = resnets
-        self.attentions = attentions
-
-    def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
-            hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
-        return hidden_states
diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/modelconfig.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/modelconfig.py
deleted file mode 100644
index 2bc62e0e9dacc8eec3d2d08ec0d53798cbfe0a87..0000000000000000000000000000000000000000
--- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/modelconfig.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import numbers
-import torch
-from netdissect.autoeval import autoimport_eval
-from netdissect import pbar
-from netdissect.nethook import InstrumentedModel
-from netdissect.easydict import EasyDict
-
-def create_instrumented_model(args, **kwargs):
-    '''
-    Creates an instrumented model out of a namespace of arguments that
-    correspond to ArgumentParser command-line args:
-      model: a string to evaluate as a constructor for the model.
-      pthfile: (optional) filename of .pth file for the model.
-      layers: a list of layers to instrument, defaulted if not provided.
-      edit: True to instrument the layers for editing.
-      gen: True for a generator model.  One-pixel input assumed.
-      imgsize: For non-generator models, (y, x) dimensions for RGB input.
-      cuda: True to use CUDA.
-  
-    The constructed model will be decorated with the following attributes:
-      input_shape: (usually 4d) tensor shape for single-image input.
-      output_shape: 4d tensor shape for output.
-      feature_shape: map of layer names to 4d tensor shape for featuremaps.
-      retained: map of layernames to tensors, filled after every evaluation.
-      ablation: if editing, map of layernames to [0..1] alpha values to fill.
-      replacement: if editing, map of layernames to values to fill.
-
-    When editing, the feature value x will be replaced by:
-        `x = (replacement * ablation) + (x * (1 - ablation))`
-    '''
-
-    args = EasyDict(vars(args), **kwargs)
-
-    # Construct the network
-    if args.model is None:
-        pbar.print('No model specified')
-        return None
-    if isinstance(args.model, torch.nn.Module):
-        model = args.model
-    else:
-        model = autoimport_eval(args.model)
-    # Unwrap any DataParallel-wrapped model
-    if isinstance(model, torch.nn.DataParallel):
-        model = next(model.children())
-
-    # Load its state dict
-    meta = {}
-    if getattr(args, 'pthfile', None) is not None:
-        data = torch.load(args.pthfile)
-        modelkey = getattr(args, 'modelkey', 'state_dict')
-        if modelkey in data:
-            meta = {}
-            for key in data:
-                if isinstance(data[key], numbers.Number):
-                    meta[key] = data[key]
-            data = data[modelkey]
-        submodule = getattr(args, 'submodule', None)
-        if submodule is not None and len(submodule):
-            remove_prefix = submodule + '.'
-            data = { k[len(remove_prefix):]: v for k, v in data.items()
-                    if k.startswith(remove_prefix)}
-            if not len(data):
-                pbar.print('No submodule %s found in %s' %
-                        (submodule, args.pthfile))
-                return None
-        model.load_state_dict(data, strict=not getattr(args, 'unstrict', False))
-
-    # Decide which layers to instrument.
-    if getattr(args, 'layer', None) is not None:
-        args.layers = [args.layer]
-    # If the layer '?' is the only specified, just print out all layers.
-    if getattr(args, 'layers', None) is not None:
-        if len(args.layers) == 1 and args.layers[0] == ('?', '?'):
-            for name, layer in model.named_modules():
-                pbar.print(name)
-            import sys
-            sys.exit(0)
-    if getattr(args, 'layers', None) is None:
-        # Skip wrappers with only one named model
-        container = model
-        prefix = ''
-        while len(list(container.named_children())) == 1:
-            name, container = next(container.named_children())
-            prefix += name + '.'
-        # Default to all nontrivial top-level layers except last.
-        args.layers = [prefix + name
-                for name, module in container.named_children()
-                if type(module).__module__ not in [
-                    # Skip ReLU and other activations.
-                    'torch.nn.modules.activation',
-                    # Skip pooling layers.
-                    'torch.nn.modules.pooling']
-                ][:-1]
-        pbar.print('Defaulting to layers: %s' % ' '.join(args.layers))
-
-    # Now wrap the model for instrumentation.
-    model = InstrumentedModel(model)
-    model.meta = meta
-
-    # Instrument the layers.
-    model.retain_layers(args.layers)
-    model.eval()
-    if args.cuda:
-        model.cuda()
-
-    # Annotate input, output, and feature shapes
-    annotate_model_shapes(model,
-            gen=getattr(args, 'gen', False),
-            imgsize=getattr(args, 'imgsize', None))
-    return model
-
-def annotate_model_shapes(model, gen=False, imgsize=None):
-    assert (imgsize is not None) or gen
-
-    # Figure the input shape.
-    if gen:
-        # We can guess a generator's input shape by looking at the model.
-        # Examine first conv in model to determine input feature size.
-        first_layer = [c for c in model.modules()
-                if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
-                    torch.nn.Linear))][0]
-        # 4d input if convolutional, 2d input if first layer is linear.
-        if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
-            input_shape = (1, first_layer.in_channels, 1, 1)
-        else:
-            input_shape = (1, first_layer.in_features)
-    else:
-        # For a classifier, the input image shape is given as an argument.
-        input_shape = (1, 3) + tuple(imgsize)
-
-    # Run the model once to observe feature shapes.
-    device = next(model.parameters()).device
-    dry_run = torch.zeros(input_shape).to(device)
-    with torch.no_grad():
-        output = model(dry_run)
-
-    # Annotate shapes.
-    model.input_shape = input_shape
-    model.feature_shape = { layer: feature.shape
-            for layer, feature in model.retained_features().items() }
-    model.output_shape = output.shape
-    return model
diff --git a/spaces/philmui/globe/agents.py b/spaces/philmui/globe/agents.py
deleted file mode 100644
index 6964c2211a1e826cae40bd1bb26dcb6c640cb62e..0000000000000000000000000000000000000000
--- a/spaces/philmui/globe/agents.py
+++ /dev/null
@@ -1,189 +0,0 @@
-##############################################################################
-# Agent interfaces that bridges private capability agents (pandas, 
-# sql, ...), 3rd party plugin agents (search, weather, movie, ...),
-# and 3rd party LLMs
-#
-# @philmui
-# Mon May 1 18:34:45 PDT 2023
-##############################################################################
-
-
-from langchain.schema import HumanMessage
-from langchain.prompts import PromptTemplate, ChatPromptTemplate, \
-                              HumanMessagePromptTemplate
-from models import load_chat_agent, load_chained_agent, load_sales_agent, \
-                   load_sqlite_agent, load_book_agent, load_earnings_agent
-
-import openai, numpy as np
-
-from collections import namedtuple
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-# Define a named tuple type '_struct' with fields 
-_struct = namedtuple('Struct', ['deterministic', 'semantic', 'creative'])
-
-# type of generated answers
-GEN_TYPE = _struct('deterministic', 'semantic', 'creative')
-
-# To parse outputs and get structured data back
-from langchain.output_parsers import StructuredOutputParser, ResponseSchema
-
-instruct_template = """
-Please answer this question clearly with easy to follow reasoning:
-{query}
-
-If you don't know the answer, just reply: not available.
-"""
-
-instruct_prompt = PromptTemplate(
-    input_variables=["query"],
-    template=instruct_template
-)
-
-response_schemas = [
-    ResponseSchema(name="artist", 
-                   description="The name of the musical artist"),
-    ResponseSchema(name="song", 
-                   description="The name of the song that the artist plays")
-]
-
-output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
-format_instructions = output_parser.get_format_instructions()
-
-LOCAL_MAGIC_TOKENS = ["my company", "for our team", "our sales", "my team"]
-DIGITAL_MAGIC_TOKENS = ["digital media", "our database", "our digital"]
-
-def check_deterministic(sentence, magic_tokens):
-    return any([t in sentence.lower() for t in magic_tokens])
-
-
-chat_prompt = ChatPromptTemplate(
-    messages=[
-        HumanMessagePromptTemplate.from_template(
-            "Given a command from the user, extract the artist and \
-             song names \n{format_instructions}\n{user_prompt}")  
-    ],
-    input_variables=["user_prompt"],
-    partial_variables={"format_instructions": format_instructions}
-)
-
-
-def chatAgent(chat_message):
-    try:
-        agent = load_chat_agent(verbose=True)
-        output = agent([HumanMessage(content=chat_message)])
-    except:
-        output = "Please rephrase and try chat again."
-    return output
-
-def salesAgent(instruction):
-    output = ""
-    try:
-        agent = load_sales_agent(verbose=True)
-        output = agent.run(instruction)
-        print("panda> " + output)
-    except Exception as e:
-        logger.error(e)
-        output = f"Rephrasing your prompt could get better sales results {e}"
-    return output
-
-def chinookAgent(instruction, model_name):
-    output = ""
-    try:
-        agent = load_sqlite_agent(model_name)
-        output = agent.run(instruction)
-        print("chinook> " + output)
-    except Exception as e:
-        logger.error(e)
-        output = "Rephrasing your prompt could get better db results {e}"
-    return output
-
-def check_semantic(string1, string2):
-    #
-    # proper way to do this is to use a
-    # vector DB (chroma, pinecone, ...)
-    # 
-    response = openai.Embedding.create(
-                    input=[string1, string2],
-                    engine="text-similarity-davinci-001"
-                )
-    embedding_a = response['data'][0]['embedding']
-    embedding_b = response['data'][1]['embedding']
-    similarity_score = np.dot(embedding_a, embedding_b)
-    logger.info(f"similarity: {similarity_score}")
-
-    return similarity_score > 0.8
-
-
-def earningsAgent(query):
-    output = ""
-    try:
-        agent = load_earnings_agent(True)
-        result = agent({
-            "query": query
-        })
-        logger.info(f"book response: {result['result']}")
-        output = result['result']
-    except Exception as e:
-        logger.error(e)
-        output = f"Rephrasing your prompt for the book agent{e}"
-    return output
-
-def bookAgent(query):
-    output = ""
-    try:
-        agent = load_book_agent(True)
-        result = agent({
-            "query": query
-        })
-        logger.info(f"book response: {result['result']}")
-        output = result['result']
-    except Exception as e:
-        logger.error(e)
-        output = f"Rephrasing your prompt for the book agent{e}"
-    return output
-
-
-def agentController(question_text, model_name):
-    output = ""
-    outputType = None
-
-    if check_deterministic(question_text, LOCAL_MAGIC_TOKENS):
-        outputType = GEN_TYPE.deterministic
-        output += salesAgent(question_text)
-        print(f"🔹 salesAgent: {output}")
-    elif check_deterministic(question_text, DIGITAL_MAGIC_TOKENS):
-        outputType = GEN_TYPE.deterministic
-        output += chinookAgent(question_text, model_name)
-        print(f"🔹 chinookAgent: {output}")
-
-    elif check_semantic(question_text, "Salesforce earnings call for Q4 2023"):
-        outputType = GEN_TYPE.semantic
-        output += earningsAgent(question_text)
-        print(f"🔹 earningsAgent: {output}")
-    elif check_semantic(question_text, "how to govern") or \
-         check_semantic(question_text, "fight a war"):
-        outputType = GEN_TYPE.semantic
-        output += bookAgent(question_text)
-        print(f"🔹 bookAgent: {output}")
-
-    else: # more creative / complex reasoning  
-        outputType = GEN_TYPE.creative
-        try:
-            instruction = instruct_prompt.format(query=question_text)
-            logger.info(f"instruction: {instruction}")
-            agent = load_chained_agent(verbose=True, model_name=model_name)
-            response = agent([instruction])
-            if response is None or "not available" in response["output"]:
-                response = ""
-            else:
-                output += response['output']
-                logger.info(f"🔹 Steps: {response['intermediate_steps']}")
-        except Exception as e: 
-            output = "Most likely ran out of tokens ..."
-            logger.error(e)
-
-    return output, outputType
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/distributions/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/distributions/__init__.py
deleted file mode 100644
index 9a89a838b9a5cb264e9ae9d269fbedca6e2d6333..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/distributions/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from pip._internal.distributions.base import AbstractDistribution
-from pip._internal.distributions.sdist import SourceDistribution
-from pip._internal.distributions.wheel import WheelDistribution
-from pip._internal.req.req_install import InstallRequirement
-
-
-def make_distribution_for_install_requirement(
-    install_req: InstallRequirement,
-) -> AbstractDistribution:
-    """Returns a Distribution for the given InstallRequirement"""
-    # Editable requirements will always be source distributions. They use the
-    # legacy logic until we create a modern standard for them.
-    if install_req.editable:
-        return SourceDistribution(install_req)
-
-    # If it's a wheel, it's a WheelDistribution
-    if install_req.is_wheel:
-        return WheelDistribution(install_req)
-
-    # Otherwise, a SourceDistribution
-    return SourceDistribution(install_req)
diff --git a/spaces/pradosh/insurance_demo/vgg_yolo_sam.py b/spaces/pradosh/insurance_demo/vgg_yolo_sam.py
deleted file mode 100644
index 1a62a902db4a0e335206855b402ac7e74f03a1df..0000000000000000000000000000000000000000
--- a/spaces/pradosh/insurance_demo/vgg_yolo_sam.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#Import Libraries and Packages
-import os
-from keras.applications.vgg16 import VGG16, preprocess_input
-from keras.models import Model
-import keras.utils as image
-import numpy as np
-import pickle as pk
-from ultralytics import YOLO
-
-images = ['images/MicrosoftTeams-image (2).png','images/MicrosoftTeams-image (3).png' ]
-
-def car_damage_check(img_path):
-    classifier = pk.load(open("models/classifier.pickle", 'rb'))
-    print("Validating that damage exists...")
-    base_model = VGG16(weights='imagenet')
-    train_labels = ['Damaged', 'Not Damaged']
-    model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
-    image_size = (224, 224)
-    img = image.load_img(img_path, target_size=image_size)
-    x = image.img_to_array(img)
-    x = np.expand_dims(x, axis=0)
-    x = preprocess_input(x)
-    feature = model.predict(x)
-    flat = feature.flatten()
-    flat = np.expand_dims(flat, axis=0)
-    preds = classifier.predict(flat)
-    prediction = train_labels[preds[0]]
-    return [prediction]
-
-def damaged_part_detection(img_path):    
-    model = YOLO("models/damaged_part_best.pt") 
-    results = model(img_path,save=True)
-    for result in results:
-        labels = result.names
-    boxes = results[0].boxes
-    # print("Confidence score----", boxes.conf)
-    damaged_part_preds = []
-    for i in boxes.cls:
-        damaged_part_preds.append(labels[int(i)])
-    # print("Damaged Part Predictions-------", damaged_part_preds)
-    return [str(x) + '-' + str(y) for x, y in zip(damaged_part_preds, boxes.conf.tolist())]
-    # return damaged_part_preds+"-"+boxes.conf
-
-def severity_detection(img_path):
-    directory = os.getcwd() + "/runs/detect"
-    image_path = max([os.path.join(directory,d) for d in os.listdir(directory)], key=os.path.getmtime) + "/" + img_path.split('/')[-1]
-    print(image_path)
-    model = YOLO("models/severity_best.pt")
-    results = model.predict(image_path,save=True,conf=0.40)
-    for result in results:
-        labels = result.names
-    boxes = results[0].boxes
-    # print("Confidence score----", boxes.conf)
-    classes = boxes.cls
-    severity_preds = []
-    for i in classes:
-        severity_preds.append(labels[int(i)])
-    # print("Severity Predictions--------", severity_preds)
-    return [str(x) + '-' + str(y) for x, y in zip(severity_preds, boxes.conf.tolist())], [image_path]
-
-def car_check_function(car_check_list):
-    if(len(car_check_list)>1):
-        for i in car_check_list:
-            if i == "Damaged":
-                return ["Damaged"]
-    else:
-        return car_check_list
-
-# car_damage_check_result = []
-# damaged_part_check_result = []
-# severity_check_result = []
-# image_bbox_result = []
-
-# for img_path in images:
-#     car_damage_check_result.append(car_damage_check(img_path))
-#     damaged_part_check_result.append(damaged_part_detection(img_path))
-#     severity_predictions,output_image_path = severity_detection(img_path)
-#     severity_check_result.append(severity_predictions)
-#     image_bbox_result.append(output_image_path)
-
-# # print(car_damage_check_result)
-# car_damage_check_result_final = list(map(car_check_function, car_damage_check_result))
-# print(car_damage_check_result_final)
-# print(damaged_part_check_result)
-# # print(severity_check_result)
-# severity_check_result_final = list(map(lambda x: [max(x)],severity_check_result))
-# print(severity_check_result_final)
-# print(image_bbox_result)
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/callbacks.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/callbacks.py
deleted file mode 100644
index 4500d02cbcae78d9cd764956d4cc46963b525213..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/callbacks.py
+++ /dev/null
@@ -1,238 +0,0 @@
-class Callback:
-    """
-    Base class and interface for callback mechanism
-
-    This class can be used directly for monitoring file transfers by
-    providing ``callback=Callback(hooks=...)`` (see the ``hooks`` argument,
-    below), or subclassed for more specialised behaviour.
-
-    Parameters
-    ----------
-    size: int (optional)
-        Nominal quantity for the value that corresponds to a complete
-        transfer, e.g., total number of tiles or total number of
-        bytes
-    value: int (0)
-        Starting internal counter value
-    hooks: dict or None
-        A dict of named functions to be called on each update. The signature
-        of these must be ``f(size, value, **kwargs)``
-    """
-
-    def __init__(self, size=None, value=0, hooks=None, **kwargs):
-        self.size = size
-        self.value = value
-        self.hooks = hooks or {}
-        self.kw = kwargs
-
-    def set_size(self, size):
-        """
-        Set the internal maximum size attribute
-
-        Usually called if not initially set at instantiation. Note that this
-        triggers a ``call()``.
-
-        Parameters
-        ----------
-        size: int
-        """
-        self.size = size
-        self.call()
-
-    def absolute_update(self, value):
-        """
-        Set the internal value state
-
-        Triggers ``call()``
-
-        Parameters
-        ----------
-        value: int
-        """
-        self.value = value
-        self.call()
-
-    def relative_update(self, inc=1):
-        """
-        Delta increment the internal counter
-
-        Triggers ``call()``
-
-        Parameters
-        ----------
-        inc: int
-        """
-        self.value += inc
-        self.call()
-
-    def call(self, hook_name=None, **kwargs):
-        """
-        Execute hook(s) with current state
-
-        Each function is passed the internal size and current value
-
-        Parameters
-        ----------
-        hook_name: str or None
-            If given, execute on this hook
-        kwargs: passed on to (all) hook(s)
-        """
-        if not self.hooks:
-            return
-        kw = self.kw.copy()
-        kw.update(kwargs)
-        if hook_name:
-            if hook_name not in self.hooks:
-                return
-            return self.hooks[hook_name](self.size, self.value, **kw)
-        for hook in self.hooks.values() or []:
-            hook(self.size, self.value, **kw)
-
-    def wrap(self, iterable):
-        """
-        Wrap an iterable to call ``relative_update`` on each iterations
-
-        Parameters
-        ----------
-        iterable: Iterable
-            The iterable that is being wrapped
-        """
-        for item in iterable:
-            self.relative_update()
-            yield item
-
-    def branch(self, path_1, path_2, kwargs):
-        """
-        Set callbacks for child transfers
-
-        If this callback is operating at a higher level, e.g., put, which may
-        trigger transfers that can also be monitored. The passed kwargs are
-        to be *mutated* to add ``callback=``, if this class supports branching
-        to children.
-
-        Parameters
-        ----------
-        path_1: str
-            Child's source path
-        path_2: str
-            Child's destination path
-        kwargs: dict
-            arguments passed to child method, e.g., put_file.
-
-        Returns
-        -------
-
-        """
-        return None
-
-    def no_op(self, *_, **__):
-        pass
-
-    def __getattr__(self, item):
-        """
-        If undefined methods are called on this class, nothing happens
-        """
-        return self.no_op
-
-    @classmethod
-    def as_callback(cls, maybe_callback=None):
-        """Transform callback=... into Callback instance
-
-        For the special value of ``None``, return the global instance of
-        ``NoOpCallback``. This is an alternative to including
-        ``callback=_DEFAULT_CALLBACK`` directly in a method signature.
-        """
-        if maybe_callback is None:
-            return _DEFAULT_CALLBACK
-        return maybe_callback
-
-
-class NoOpCallback(Callback):
-    """
-    This implementation of Callback does exactly nothing
-    """
-
-    def call(self, *args, **kwargs):
-        return None
-
-
-class DotPrinterCallback(Callback):
-    """
-    Simple example Callback implementation
-
-    Almost identical to Callback with a hook that prints a char; here we
-    demonstrate how the outer layer may print "#" and the inner layer "."
-    """
-
-    def __init__(self, chr_to_print="#", **kwargs):
-        self.chr = chr_to_print
-        super().__init__(**kwargs)
-
-    def branch(self, path_1, path_2, kwargs):
-        """Mutate kwargs to add new instance with different print char"""
-        kwargs["callback"] = DotPrinterCallback(".")
-
-    def call(self, **kwargs):
-        """Just outputs a character"""
-        print(self.chr, end="")
-
-
-class TqdmCallback(Callback):
-    """
-    A callback to display a progress bar using tqdm
-
-    Parameters
-    ----------
-    tqdm_kwargs : dict, (optional)
-        Any argument accepted by the tqdm constructor.
-        See the `tqdm doc <https://tqdm.github.io/docs/tqdm/#__init__>`_.
-        Will be forwarded to tqdm.
-
-    Examples
-    --------
-    >>> import fsspec
-    >>> from fsspec.callbacks import TqdmCallback
-    >>> fs = fsspec.filesystem("memory")
-    >>> path2distant_data = "/your-path"
-    >>> fs.upload(
-            ".",
-            path2distant_data,
-            recursive=True,
-            callback=TqdmCallback(),
-        )
-
-    You can forward args to tqdm using the ``tqdm_kwargs`` parameter.
-
-    >>> fs.upload(
-            ".",
-            path2distant_data,
-            recursive=True,
-            callback=TqdmCallback(tqdm_kwargs={"desc": "Your tqdm description"}),
-        )
-    """
-
-    def __init__(self, tqdm_kwargs=None, *args, **kwargs):
-        try:
-            import tqdm
-
-            self._tqdm = tqdm
-        except ImportError as exce:
-            raise ImportError(
-                "Using TqdmCallback requires tqdm to be installed"
-            ) from exce
-
-        self._tqdm_kwargs = tqdm_kwargs or {}
-        super().__init__(*args, **kwargs)
-
-    def set_size(self, size):
-        self.tqdm = self._tqdm.tqdm(total=size, **self._tqdm_kwargs)
-
-    def relative_update(self, inc=1):
-        self.tqdm.update(inc)
-
-    def __del__(self):
-        self.tqdm.close()
-        self.tqdm = None
-
-
-_DEFAULT_CALLBACK = NoOpCallback()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/components/slider.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/components/slider.py
deleted file mode 100644
index 85775f276280e503393329da2038a83e7d037764..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/components/slider.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""gr.Slider() component."""
-
-from __future__ import annotations
-
-import math
-import random
-from typing import Any, Callable
-
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components.base import FormComponent
-from gradio.events import Events
-
-set_documentation_group("component")
-
-
-@document()
-class Slider(FormComponent):
-    """
-    Creates a slider that ranges from `minimum` to `maximum` with a step size of `step`.
-    Preprocessing: passes slider value as a {float} into the function.
-    Postprocessing: expects an {int} or {float} returned from function and sets slider value to it as long as it is within range.
-    Examples-format: A {float} or {int} representing the slider's value.
-
-    Demos: sentence_builder, slider_release, generate_tone, titanic_survival, interface_random_slider, blocks_random_slider
-    Guides: create-your-own-friends-with-a-gan
-    """
-
-    EVENTS = [Events.change, Events.input, Events.release]
-
-    def __init__(
-        self,
-        minimum: float = 0,
-        maximum: float = 100,
-        value: float | Callable | None = None,
-        *,
-        step: float | None = None,
-        label: str | None = None,
-        info: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        render: bool = True,
-        randomize: bool = False,
-    ):
-        """
-        Parameters:
-            minimum: minimum value for slider.
-            maximum: maximum value for slider.
-            value: default value. If callable, the function will be called whenever the app loads to set the initial value of the component. Ignored if randomized=True.
-            step: increment between slider values.
-            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
-            info: additional component description.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            interactive: if True, slider will be adjustable; if False, adjusting will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
-            randomize: If True, the value of the slider when the app loads is taken uniformly at random from the range given by the minimum and maximum.
-        """
-        self.minimum = minimum
-        self.maximum = maximum
-        if step is None:
-            difference = maximum - minimum
-            power = math.floor(math.log10(difference) - 2)
-            self.step = 10**power
-        else:
-            self.step = step
-        if randomize:
-            value = self.get_random_value
-        super().__init__(
-            label=label,
-            info=info,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            render=render,
-            value=value,
-        )
-
-    def api_info(self) -> dict[str, Any]:
-        return {
-            "type": "number",
-            "description": f"numeric value between {self.minimum} and {self.maximum}",
-        }
-
-    def example_inputs(self) -> Any:
-        return self.minimum
-
-    def get_random_value(self):
-        n_steps = int((self.maximum - self.minimum) / self.step)
-        step = random.randint(0, n_steps)
-        value = self.minimum + step * self.step
-        # Round to number of decimals in step so that UI doesn't display long decimals
-        n_decimals = max(str(self.step)[::-1].find("."), 0)
-        if n_decimals:
-            value = round(value, n_decimals)
-        return value
-
-    def postprocess(self, value: float | None) -> float:
-        return self.minimum if value is None else value
-
-    def preprocess(self, payload: float) -> float:
-        return payload
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-d4781e2f.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-d4781e2f.css
deleted file mode 100644
index 39cd0cfaf767630bfce6acf7fe207d2c872841bd..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-d4781e2f.css
+++ /dev/null
@@ -1 +0,0 @@
-.container.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{padding:var(--block-padding)}.output-class.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{display:flex;justify-content:center;align-items:center;padding:var(--size-6) var(--size-4);color:var(--body-text-color);font-weight:var(--weight-bold);font-size:var(--text-xxl)}.confidence-set.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{display:flex;justify-content:space-between;align-items:flex-start;margin-bottom:var(--size-2);color:var(--body-text-color);line-height:var(--line-none);font-family:var(--font-mono);width:100%}.confidence-set.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst:last-child{margin-bottom:0}.inner-wrap.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{flex:1 1 0%;display:flex;flex-direction:column}.bar.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{appearance:none;align-self:flex-start;margin-bottom:var(--size-1);border-radius:var(--radius-md);background:var(--stat-background-fill);height:var(--size-1)}.label.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{display:flex;align-items:baseline}.label.svelte-1pq4gst>.svelte-1pq4gst+.svelte-1pq4gst{margin-left:var(--size-2)}.confidence-set.svelte-1pq4gst:hover .label.svelte-1pq4gst.svelte-1pq4gst{color:var(--color-accent)}.confidence-set.svelte-1pq4gst:focus .label.svelte-1pq4gst.svelte-1pq4gst{color:var(--color-accent)}.text.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{line-height:var(--line-md)}.line.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{flex:1 1 0%;border:1px dashed var(--border-color-primary);padding-right:var(--size-4);padding-left:var(--size-4)}.confidence.svelte-1pq4gst.svelte-1pq4gst.svelte-1pq4gst{margin-left:auto;text-align:right}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jinja2/loaders.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jinja2/loaders.py
deleted file mode 100644
index d2f98093cde425fad2c4bbf2a07e383fce5e4a38..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jinja2/loaders.py
+++ /dev/null
@@ -1,661 +0,0 @@
-"""API and implementations for loading templates from different data
-sources.
-"""
-import importlib.util
-import os
-import posixpath
-import sys
-import typing as t
-import weakref
-import zipimport
-from collections import abc
-from hashlib import sha1
-from importlib import import_module
-from types import ModuleType
-
-from .exceptions import TemplateNotFound
-from .utils import internalcode
-from .utils import open_if_exists
-
-if t.TYPE_CHECKING:
-    from .environment import Environment
-    from .environment import Template
-
-
-def split_template_path(template: str) -> t.List[str]:
-    """Split a path into segments and perform a sanity check.  If it detects
-    '..' in the path it will raise a `TemplateNotFound` error.
-    """
-    pieces = []
-    for piece in template.split("/"):
-        if (
-            os.path.sep in piece
-            or (os.path.altsep and os.path.altsep in piece)
-            or piece == os.path.pardir
-        ):
-            raise TemplateNotFound(template)
-        elif piece and piece != ".":
-            pieces.append(piece)
-    return pieces
-
-
-class BaseLoader:
-    """Baseclass for all loaders.  Subclass this and override `get_source` to
-    implement a custom loading mechanism.  The environment provides a
-    `get_template` method that calls the loader's `load` method to get the
-    :class:`Template` object.
-
-    A very basic example for a loader that looks up templates on the file
-    system could look like this::
-
-        from jinja2 import BaseLoader, TemplateNotFound
-        from os.path import join, exists, getmtime
-
-        class MyLoader(BaseLoader):
-
-            def __init__(self, path):
-                self.path = path
-
-            def get_source(self, environment, template):
-                path = join(self.path, template)
-                if not exists(path):
-                    raise TemplateNotFound(template)
-                mtime = getmtime(path)
-                with open(path) as f:
-                    source = f.read()
-                return source, path, lambda: mtime == getmtime(path)
-    """
-
-    #: if set to `False` it indicates that the loader cannot provide access
-    #: to the source of templates.
-    #:
-    #: .. versionadded:: 2.4
-    has_source_access = True
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
-        """Get the template source, filename and reload helper for a template.
-        It's passed the environment and template name and has to return a
-        tuple in the form ``(source, filename, uptodate)`` or raise a
-        `TemplateNotFound` error if it can't locate the template.
-
-        The source part of the returned tuple must be the source of the
-        template as a string. The filename should be the name of the
-        file on the filesystem if it was loaded from there, otherwise
-        ``None``. The filename is used by Python for the tracebacks
-        if no loader extension is used.
-
-        The last item in the tuple is the `uptodate` function.  If auto
-        reloading is enabled it's always called to check if the template
-        changed.  No arguments are passed so the function must store the
-        old state somewhere (for example in a closure).  If it returns `False`
-        the template will be reloaded.
-        """
-        if not self.has_source_access:
-            raise RuntimeError(
-                f"{type(self).__name__} cannot provide access to the source"
-            )
-        raise TemplateNotFound(template)
-
-    def list_templates(self) -> t.List[str]:
-        """Iterates over all templates.  If the loader does not support that
-        it should raise a :exc:`TypeError` which is the default behavior.
-        """
-        raise TypeError("this loader cannot iterate over all templates")
-
-    @internalcode
-    def load(
-        self,
-        environment: "Environment",
-        name: str,
-        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
-    ) -> "Template":
-        """Loads a template.  This method looks up the template in the cache
-        or loads one by calling :meth:`get_source`.  Subclasses should not
-        override this method as loaders working on collections of other
-        loaders (such as :class:`PrefixLoader` or :class:`ChoiceLoader`)
-        will not call this method but `get_source` directly.
-        """
-        code = None
-        if globals is None:
-            globals = {}
-
-        # first we try to get the source for this template together
-        # with the filename and the uptodate function.
-        source, filename, uptodate = self.get_source(environment, name)
-
-        # try to load the code from the bytecode cache if there is a
-        # bytecode cache configured.
-        bcc = environment.bytecode_cache
-        if bcc is not None:
-            bucket = bcc.get_bucket(environment, name, filename, source)
-            code = bucket.code
-
-        # if we don't have code so far (not cached, no longer up to
-        # date) etc. we compile the template
-        if code is None:
-            code = environment.compile(source, name, filename)
-
-        # if the bytecode cache is available and the bucket doesn't
-        # have a code so far, we give the bucket the new code and put
-        # it back to the bytecode cache.
-        if bcc is not None and bucket.code is None:
-            bucket.code = code
-            bcc.set_bucket(bucket)
-
-        return environment.template_class.from_code(
-            environment, code, globals, uptodate
-        )
-
-
-class FileSystemLoader(BaseLoader):
-    """Load templates from a directory in the file system.
-
-    The path can be relative or absolute. Relative paths are relative to
-    the current working directory.
-
-    .. code-block:: python
-
-        loader = FileSystemLoader("templates")
-
-    A list of paths can be given. The directories will be searched in
-    order, stopping at the first matching template.
-
-    .. code-block:: python
-
-        loader = FileSystemLoader(["/override/templates", "/default/templates"])
-
-    :param searchpath: A path, or list of paths, to the directory that
-        contains the templates.
-    :param encoding: Use this encoding to read the text from template
-        files.
-    :param followlinks: Follow symbolic links in the path.
-
-    .. versionchanged:: 2.8
-        Added the ``followlinks`` parameter.
-    """
-
-    def __init__(
-        self,
-        searchpath: t.Union[str, os.PathLike, t.Sequence[t.Union[str, os.PathLike]]],
-        encoding: str = "utf-8",
-        followlinks: bool = False,
-    ) -> None:
-        if not isinstance(searchpath, abc.Iterable) or isinstance(searchpath, str):
-            searchpath = [searchpath]
-
-        self.searchpath = [os.fspath(p) for p in searchpath]
-        self.encoding = encoding
-        self.followlinks = followlinks
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, str, t.Callable[[], bool]]:
-        pieces = split_template_path(template)
-        for searchpath in self.searchpath:
-            # Use posixpath even on Windows to avoid "drive:" or UNC
-            # segments breaking out of the search directory.
-            filename = posixpath.join(searchpath, *pieces)
-            f = open_if_exists(filename)
-            if f is None:
-                continue
-            try:
-                contents = f.read().decode(self.encoding)
-            finally:
-                f.close()
-
-            mtime = os.path.getmtime(filename)
-
-            def uptodate() -> bool:
-                try:
-                    return os.path.getmtime(filename) == mtime
-                except OSError:
-                    return False
-
-            # Use normpath to convert Windows altsep to sep.
-            return contents, os.path.normpath(filename), uptodate
-        raise TemplateNotFound(template)
-
-    def list_templates(self) -> t.List[str]:
-        found = set()
-        for searchpath in self.searchpath:
-            walk_dir = os.walk(searchpath, followlinks=self.followlinks)
-            for dirpath, _, filenames in walk_dir:
-                for filename in filenames:
-                    template = (
-                        os.path.join(dirpath, filename)[len(searchpath) :]
-                        .strip(os.path.sep)
-                        .replace(os.path.sep, "/")
-                    )
-                    if template[:2] == "./":
-                        template = template[2:]
-                    if template not in found:
-                        found.add(template)
-        return sorted(found)
-
-
-class PackageLoader(BaseLoader):
-    """Load templates from a directory in a Python package.
-
-    :param package_name: Import name of the package that contains the
-        template directory.
-    :param package_path: Directory within the imported package that
-        contains the templates.
-    :param encoding: Encoding of template files.
-
-    The following example looks up templates in the ``pages`` directory
-    within the ``project.ui`` package.
-
-    .. code-block:: python
-
-        loader = PackageLoader("project.ui", "pages")
-
-    Only packages installed as directories (standard pip behavior) or
-    zip/egg files (less common) are supported. The Python API for
-    introspecting data in packages is too limited to support other
-    installation methods the way this loader requires.
-
-    There is limited support for :pep:`420` namespace packages. The
-    template directory is assumed to only be in one namespace
-    contributor. Zip files contributing to a namespace are not
-    supported.
-
-    .. versionchanged:: 3.0
-        No longer uses ``setuptools`` as a dependency.
-
-    .. versionchanged:: 3.0
-        Limited PEP 420 namespace package support.
-    """
-
-    def __init__(
-        self,
-        package_name: str,
-        package_path: "str" = "templates",
-        encoding: str = "utf-8",
-    ) -> None:
-        package_path = os.path.normpath(package_path).rstrip(os.path.sep)
-
-        # normpath preserves ".", which isn't valid in zip paths.
-        if package_path == os.path.curdir:
-            package_path = ""
-        elif package_path[:2] == os.path.curdir + os.path.sep:
-            package_path = package_path[2:]
-
-        self.package_path = package_path
-        self.package_name = package_name
-        self.encoding = encoding
-
-        # Make sure the package exists. This also makes namespace
-        # packages work, otherwise get_loader returns None.
-        import_module(package_name)
-        spec = importlib.util.find_spec(package_name)
-        assert spec is not None, "An import spec was not found for the package."
-        loader = spec.loader
-        assert loader is not None, "A loader was not found for the package."
-        self._loader = loader
-        self._archive = None
-        template_root = None
-
-        if isinstance(loader, zipimport.zipimporter):
-            self._archive = loader.archive
-            pkgdir = next(iter(spec.submodule_search_locations))  # type: ignore
-            template_root = os.path.join(pkgdir, package_path).rstrip(os.path.sep)
-        else:
-            roots: t.List[str] = []
-
-            # One element for regular packages, multiple for namespace
-            # packages, or None for single module file.
-            if spec.submodule_search_locations:
-                roots.extend(spec.submodule_search_locations)
-            # A single module file, use the parent directory instead.
-            elif spec.origin is not None:
-                roots.append(os.path.dirname(spec.origin))
-
-            for root in roots:
-                root = os.path.join(root, package_path)
-
-                if os.path.isdir(root):
-                    template_root = root
-                    break
-
-        if template_root is None:
-            raise ValueError(
-                f"The {package_name!r} package was not installed in a"
-                " way that PackageLoader understands."
-            )
-
-        self._template_root = template_root
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, str, t.Optional[t.Callable[[], bool]]]:
-        # Use posixpath even on Windows to avoid "drive:" or UNC
-        # segments breaking out of the search directory. Use normpath to
-        # convert Windows altsep to sep.
-        p = os.path.normpath(
-            posixpath.join(self._template_root, *split_template_path(template))
-        )
-        up_to_date: t.Optional[t.Callable[[], bool]]
-
-        if self._archive is None:
-            # Package is a directory.
-            if not os.path.isfile(p):
-                raise TemplateNotFound(template)
-
-            with open(p, "rb") as f:
-                source = f.read()
-
-            mtime = os.path.getmtime(p)
-
-            def up_to_date() -> bool:
-                return os.path.isfile(p) and os.path.getmtime(p) == mtime
-
-        else:
-            # Package is a zip file.
-            try:
-                source = self._loader.get_data(p)  # type: ignore
-            except OSError as e:
-                raise TemplateNotFound(template) from e
-
-            # Could use the zip's mtime for all template mtimes, but
-            # would need to safely reload the module if it's out of
-            # date, so just report it as always current.
-            up_to_date = None
-
-        return source.decode(self.encoding), p, up_to_date
-
-    def list_templates(self) -> t.List[str]:
-        results: t.List[str] = []
-
-        if self._archive is None:
-            # Package is a directory.
-            offset = len(self._template_root)
-
-            for dirpath, _, filenames in os.walk(self._template_root):
-                dirpath = dirpath[offset:].lstrip(os.path.sep)
-                results.extend(
-                    os.path.join(dirpath, name).replace(os.path.sep, "/")
-                    for name in filenames
-                )
-        else:
-            if not hasattr(self._loader, "_files"):
-                raise TypeError(
-                    "This zip import does not have the required"
-                    " metadata to list templates."
-                )
-
-            # Package is a zip file.
-            prefix = (
-                self._template_root[len(self._archive) :].lstrip(os.path.sep)
-                + os.path.sep
-            )
-            offset = len(prefix)
-
-            for name in self._loader._files.keys():  # type: ignore
-                # Find names under the templates directory that aren't directories.
-                if name.startswith(prefix) and name[-1] != os.path.sep:
-                    results.append(name[offset:].replace(os.path.sep, "/"))
-
-        results.sort()
-        return results
-
-
-class DictLoader(BaseLoader):
-    """Loads a template from a Python dict mapping template names to
-    template source.  This loader is useful for unittesting:
-
-    >>> loader = DictLoader({'index.html': 'source here'})
-
-    Because auto reloading is rarely useful this is disabled per default.
-    """
-
-    def __init__(self, mapping: t.Mapping[str, str]) -> None:
-        self.mapping = mapping
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, None, t.Callable[[], bool]]:
-        if template in self.mapping:
-            source = self.mapping[template]
-            return source, None, lambda: source == self.mapping.get(template)
-        raise TemplateNotFound(template)
-
-    def list_templates(self) -> t.List[str]:
-        return sorted(self.mapping)
-
-
-class FunctionLoader(BaseLoader):
-    """A loader that is passed a function which does the loading.  The
-    function receives the name of the template and has to return either
-    a string with the template source, a tuple in the form ``(source,
-    filename, uptodatefunc)`` or `None` if the template does not exist.
-
-    >>> def load_template(name):
-    ...     if name == 'index.html':
-    ...         return '...'
-    ...
-    >>> loader = FunctionLoader(load_template)
-
-    The `uptodatefunc` is a function that is called if autoreload is enabled
-    and has to return `True` if the template is still up to date.  For more
-    details have a look at :meth:`BaseLoader.get_source` which has the same
-    return value.
-    """
-
-    def __init__(
-        self,
-        load_func: t.Callable[
-            [str],
-            t.Optional[
-                t.Union[
-                    str, t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]
-                ]
-            ],
-        ],
-    ) -> None:
-        self.load_func = load_func
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
-        rv = self.load_func(template)
-
-        if rv is None:
-            raise TemplateNotFound(template)
-
-        if isinstance(rv, str):
-            return rv, None, None
-
-        return rv
-
-
-class PrefixLoader(BaseLoader):
-    """A loader that is passed a dict of loaders where each loader is bound
-    to a prefix.  The prefix is delimited from the template by a slash per
-    default, which can be changed by setting the `delimiter` argument to
-    something else::
-
-        loader = PrefixLoader({
-            'app1':     PackageLoader('mypackage.app1'),
-            'app2':     PackageLoader('mypackage.app2')
-        })
-
-    By loading ``'app1/index.html'`` the file from the app1 package is loaded,
-    by loading ``'app2/index.html'`` the file from the second.
-    """
-
-    def __init__(
-        self, mapping: t.Mapping[str, BaseLoader], delimiter: str = "/"
-    ) -> None:
-        self.mapping = mapping
-        self.delimiter = delimiter
-
-    def get_loader(self, template: str) -> t.Tuple[BaseLoader, str]:
-        try:
-            prefix, name = template.split(self.delimiter, 1)
-            loader = self.mapping[prefix]
-        except (ValueError, KeyError) as e:
-            raise TemplateNotFound(template) from e
-        return loader, name
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
-        loader, name = self.get_loader(template)
-        try:
-            return loader.get_source(environment, name)
-        except TemplateNotFound as e:
-            # re-raise the exception with the correct filename here.
-            # (the one that includes the prefix)
-            raise TemplateNotFound(template) from e
-
-    @internalcode
-    def load(
-        self,
-        environment: "Environment",
-        name: str,
-        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
-    ) -> "Template":
-        loader, local_name = self.get_loader(name)
-        try:
-            return loader.load(environment, local_name, globals)
-        except TemplateNotFound as e:
-            # re-raise the exception with the correct filename here.
-            # (the one that includes the prefix)
-            raise TemplateNotFound(name) from e
-
-    def list_templates(self) -> t.List[str]:
-        result = []
-        for prefix, loader in self.mapping.items():
-            for template in loader.list_templates():
-                result.append(prefix + self.delimiter + template)
-        return result
-
-
-class ChoiceLoader(BaseLoader):
-    """This loader works like the `PrefixLoader` just that no prefix is
-    specified.  If a template could not be found by one loader the next one
-    is tried.
-
-    >>> loader = ChoiceLoader([
-    ...     FileSystemLoader('/path/to/user/templates'),
-    ...     FileSystemLoader('/path/to/system/templates')
-    ... ])
-
-    This is useful if you want to allow users to override builtin templates
-    from a different location.
-    """
-
-    def __init__(self, loaders: t.Sequence[BaseLoader]) -> None:
-        self.loaders = loaders
-
-    def get_source(
-        self, environment: "Environment", template: str
-    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
-        for loader in self.loaders:
-            try:
-                return loader.get_source(environment, template)
-            except TemplateNotFound:
-                pass
-        raise TemplateNotFound(template)
-
-    @internalcode
-    def load(
-        self,
-        environment: "Environment",
-        name: str,
-        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
-    ) -> "Template":
-        for loader in self.loaders:
-            try:
-                return loader.load(environment, name, globals)
-            except TemplateNotFound:
-                pass
-        raise TemplateNotFound(name)
-
-    def list_templates(self) -> t.List[str]:
-        found = set()
-        for loader in self.loaders:
-            found.update(loader.list_templates())
-        return sorted(found)
-
-
-class _TemplateModule(ModuleType):
-    """Like a normal module but with support for weak references"""
-
-
-class ModuleLoader(BaseLoader):
-    """This loader loads templates from precompiled templates.
-
-    Example usage:
-
-    >>> loader = ChoiceLoader([
-    ...     ModuleLoader('/path/to/compiled/templates'),
-    ...     FileSystemLoader('/path/to/templates')
-    ... ])
-
-    Templates can be precompiled with :meth:`Environment.compile_templates`.
-    """
-
-    has_source_access = False
-
-    def __init__(
-        self, path: t.Union[str, os.PathLike, t.Sequence[t.Union[str, os.PathLike]]]
-    ) -> None:
-        package_name = f"_jinja2_module_templates_{id(self):x}"
-
-        # create a fake module that looks for the templates in the
-        # path given.
-        mod = _TemplateModule(package_name)
-
-        if not isinstance(path, abc.Iterable) or isinstance(path, str):
-            path = [path]
-
-        mod.__path__ = [os.fspath(p) for p in path]
-
-        sys.modules[package_name] = weakref.proxy(
-            mod, lambda x: sys.modules.pop(package_name, None)
-        )
-
-        # the only strong reference, the sys.modules entry is weak
-        # so that the garbage collector can remove it once the
-        # loader that created it goes out of business.
-        self.module = mod
-        self.package_name = package_name
-
-    @staticmethod
-    def get_template_key(name: str) -> str:
-        return "tmpl_" + sha1(name.encode("utf-8")).hexdigest()
-
-    @staticmethod
-    def get_module_filename(name: str) -> str:
-        return ModuleLoader.get_template_key(name) + ".py"
-
-    @internalcode
-    def load(
-        self,
-        environment: "Environment",
-        name: str,
-        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
-    ) -> "Template":
-        key = self.get_template_key(name)
-        module = f"{self.package_name}.{key}"
-        mod = getattr(self.module, module, None)
-
-        if mod is None:
-            try:
-                mod = __import__(module, None, None, ["root"])
-            except ImportError as e:
-                raise TemplateNotFound(name) from e
-
-            # remove the entry from sys.modules, we only want the attribute
-            # on the module object we have stored on the loader.
-            sys.modules.pop(module, None)
-
-        if globals is None:
-            globals = {}
-
-        return environment.template_class.from_module_dict(
-            environment, mod.__dict__, globals
-        )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jsonschema/tests/test_exceptions.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jsonschema/tests/test_exceptions.py
deleted file mode 100644
index 00ff3009173c3abd8912366ae74645d2db5b233e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jsonschema/tests/test_exceptions.py
+++ /dev/null
@@ -1,601 +0,0 @@
-from unittest import TestCase
-import textwrap
-
-from jsonschema import exceptions
-from jsonschema.validators import _LATEST_VERSION
-
-
-class TestBestMatch(TestCase):
-    def best_match_of(self, instance, schema):
-        errors = list(_LATEST_VERSION(schema).iter_errors(instance))
-        best = exceptions.best_match(iter(errors))
-        reversed_best = exceptions.best_match(reversed(errors))
-        self.assertEqual(
-            best._contents(),
-            reversed_best._contents(),
-            f"No consistent best match!\nGot: {best}\n\nThen: {reversed_best}",
-        )
-        return best
-
-    def test_shallower_errors_are_better_matches(self):
-        schema = {
-            "properties": {
-                "foo": {
-                    "minProperties": 2,
-                    "properties": {"bar": {"type": "object"}},
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": {"bar": []}}, schema=schema)
-        self.assertEqual(best.validator, "minProperties")
-
-    def test_oneOf_and_anyOf_are_weak_matches(self):
-        """
-        A property you *must* match is probably better than one you have to
-        match a part of.
-        """
-
-        schema = {
-            "minProperties": 2,
-            "anyOf": [{"type": "string"}, {"type": "number"}],
-            "oneOf": [{"type": "string"}, {"type": "number"}],
-        }
-        best = self.best_match_of(instance={}, schema=schema)
-        self.assertEqual(best.validator, "minProperties")
-
-    def test_if_the_most_relevant_error_is_anyOf_it_is_traversed(self):
-        """
-        If the most relevant error is an anyOf, then we traverse its context
-        and select the otherwise *least* relevant error, since in this case
-        that means the most specific, deep, error inside the instance.
-
-        I.e. since only one of the schemas must match, we look for the most
-        relevant one.
-        """
-
-        schema = {
-            "properties": {
-                "foo": {
-                    "anyOf": [
-                        {"type": "string"},
-                        {"properties": {"bar": {"type": "array"}}},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": {"bar": 12}}, schema=schema)
-        self.assertEqual(best.validator_value, "array")
-
-    def test_no_anyOf_traversal_for_equally_relevant_errors(self):
-        """
-        We don't traverse into an anyOf (as above) if all of its context errors
-        seem to be equally "wrong" against the instance.
-        """
-
-        schema = {
-            "anyOf": [
-                {"type": "string"},
-                {"type": "integer"},
-                {"type": "object"},
-            ],
-        }
-        best = self.best_match_of(instance=[], schema=schema)
-        self.assertEqual(best.validator, "anyOf")
-
-    def test_anyOf_traversal_for_single_equally_relevant_error(self):
-        """
-        We *do* traverse anyOf with a single nested error, even though it is
-        vacuously equally relevant to itself.
-        """
-
-        schema = {
-            "anyOf": [
-                {"type": "string"},
-            ],
-        }
-        best = self.best_match_of(instance=[], schema=schema)
-        self.assertEqual(best.validator, "type")
-
-    def test_if_the_most_relevant_error_is_oneOf_it_is_traversed(self):
-        """
-        If the most relevant error is an oneOf, then we traverse its context
-        and select the otherwise *least* relevant error, since in this case
-        that means the most specific, deep, error inside the instance.
-
-        I.e. since only one of the schemas must match, we look for the most
-        relevant one.
-        """
-
-        schema = {
-            "properties": {
-                "foo": {
-                    "oneOf": [
-                        {"type": "string"},
-                        {"properties": {"bar": {"type": "array"}}},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": {"bar": 12}}, schema=schema)
-        self.assertEqual(best.validator_value, "array")
-
-    def test_no_oneOf_traversal_for_equally_relevant_errors(self):
-        """
-        We don't traverse into an oneOf (as above) if all of its context errors
-        seem to be equally "wrong" against the instance.
-        """
-
-        schema = {
-            "oneOf": [
-                {"type": "string"},
-                {"type": "integer"},
-                {"type": "object"},
-            ],
-        }
-        best = self.best_match_of(instance=[], schema=schema)
-        self.assertEqual(best.validator, "oneOf")
-
-    def test_oneOf_traversal_for_single_equally_relevant_error(self):
-        """
-        We *do* traverse oneOf with a single nested error, even though it is
-        vacuously equally relevant to itself.
-        """
-
-        schema = {
-            "oneOf": [
-                {"type": "string"},
-            ],
-        }
-        best = self.best_match_of(instance=[], schema=schema)
-        self.assertEqual(best.validator, "type")
-
-    def test_if_the_most_relevant_error_is_allOf_it_is_traversed(self):
-        """
-        Now, if the error is allOf, we traverse but select the *most* relevant
-        error from the context, because all schemas here must match anyways.
-        """
-
-        schema = {
-            "properties": {
-                "foo": {
-                    "allOf": [
-                        {"type": "string"},
-                        {"properties": {"bar": {"type": "array"}}},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": {"bar": 12}}, schema=schema)
-        self.assertEqual(best.validator_value, "string")
-
-    def test_nested_context_for_oneOf(self):
-        """
-        We traverse into nested contexts (a oneOf containing an error in a
-        nested oneOf here).
-        """
-
-        schema = {
-            "properties": {
-                "foo": {
-                    "oneOf": [
-                        {"type": "string"},
-                        {
-                            "oneOf": [
-                                {"type": "string"},
-                                {
-                                    "properties": {
-                                        "bar": {"type": "array"},
-                                    },
-                                },
-                            ],
-                        },
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": {"bar": 12}}, schema=schema)
-        self.assertEqual(best.validator_value, "array")
-
-    def test_it_prioritizes_matching_types(self):
-        schema = {
-            "properties": {
-                "foo": {
-                    "anyOf": [
-                        {"type": "array", "minItems": 2},
-                        {"type": "string", "minLength": 10},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": "bar"}, schema=schema)
-        self.assertEqual(best.validator, "minLength")
-
-        reordered = {
-            "properties": {
-                "foo": {
-                    "anyOf": [
-                        {"type": "string", "minLength": 10},
-                        {"type": "array", "minItems": 2},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": "bar"}, schema=reordered)
-        self.assertEqual(best.validator, "minLength")
-
-    def test_it_prioritizes_matching_union_types(self):
-        schema = {
-            "properties": {
-                "foo": {
-                    "anyOf": [
-                        {"type": ["array", "object"], "minItems": 2},
-                        {"type": ["integer", "string"], "minLength": 10},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": "bar"}, schema=schema)
-        self.assertEqual(best.validator, "minLength")
-
-        reordered = {
-            "properties": {
-                "foo": {
-                    "anyOf": [
-                        {"type": "string", "minLength": 10},
-                        {"type": "array", "minItems": 2},
-                    ],
-                },
-            },
-        }
-        best = self.best_match_of(instance={"foo": "bar"}, schema=reordered)
-        self.assertEqual(best.validator, "minLength")
-
-    def test_boolean_schemas(self):
-        schema = {"properties": {"foo": False}}
-        best = self.best_match_of(instance={"foo": "bar"}, schema=schema)
-        self.assertIsNone(best.validator)
-
-    def test_one_error(self):
-        validator = _LATEST_VERSION({"minProperties": 2})
-        error, = validator.iter_errors({})
-        self.assertEqual(
-            exceptions.best_match(validator.iter_errors({})).validator,
-            "minProperties",
-        )
-
-    def test_no_errors(self):
-        validator = _LATEST_VERSION({})
-        self.assertIsNone(exceptions.best_match(validator.iter_errors({})))
-
-
-class TestByRelevance(TestCase):
-    def test_short_paths_are_better_matches(self):
-        shallow = exceptions.ValidationError("Oh no!", path=["baz"])
-        deep = exceptions.ValidationError("Oh yes!", path=["foo", "bar"])
-        match = max([shallow, deep], key=exceptions.relevance)
-        self.assertIs(match, shallow)
-
-        match = max([deep, shallow], key=exceptions.relevance)
-        self.assertIs(match, shallow)
-
-    def test_global_errors_are_even_better_matches(self):
-        shallow = exceptions.ValidationError("Oh no!", path=[])
-        deep = exceptions.ValidationError("Oh yes!", path=["foo"])
-
-        errors = sorted([shallow, deep], key=exceptions.relevance)
-        self.assertEqual(
-            [list(error.path) for error in errors],
-            [["foo"], []],
-        )
-
-        errors = sorted([deep, shallow], key=exceptions.relevance)
-        self.assertEqual(
-            [list(error.path) for error in errors],
-            [["foo"], []],
-        )
-
-    def test_weak_keywords_are_lower_priority(self):
-        weak = exceptions.ValidationError("Oh no!", path=[], validator="a")
-        normal = exceptions.ValidationError("Oh yes!", path=[], validator="b")
-
-        best_match = exceptions.by_relevance(weak="a")
-
-        match = max([weak, normal], key=best_match)
-        self.assertIs(match, normal)
-
-        match = max([normal, weak], key=best_match)
-        self.assertIs(match, normal)
-
-    def test_strong_keywords_are_higher_priority(self):
-        weak = exceptions.ValidationError("Oh no!", path=[], validator="a")
-        normal = exceptions.ValidationError("Oh yes!", path=[], validator="b")
-        strong = exceptions.ValidationError("Oh fine!", path=[], validator="c")
-
-        best_match = exceptions.by_relevance(weak="a", strong="c")
-
-        match = max([weak, normal, strong], key=best_match)
-        self.assertIs(match, strong)
-
-        match = max([strong, normal, weak], key=best_match)
-        self.assertIs(match, strong)
-
-
-class TestErrorTree(TestCase):
-    def test_it_knows_how_many_total_errors_it_contains(self):
-        # FIXME: #442
-        errors = [
-            exceptions.ValidationError("Something", validator=i)
-            for i in range(8)
-        ]
-        tree = exceptions.ErrorTree(errors)
-        self.assertEqual(tree.total_errors, 8)
-
-    def test_it_contains_an_item_if_the_item_had_an_error(self):
-        errors = [exceptions.ValidationError("a message", path=["bar"])]
-        tree = exceptions.ErrorTree(errors)
-        self.assertIn("bar", tree)
-
-    def test_it_does_not_contain_an_item_if_the_item_had_no_error(self):
-        errors = [exceptions.ValidationError("a message", path=["bar"])]
-        tree = exceptions.ErrorTree(errors)
-        self.assertNotIn("foo", tree)
-
-    def test_keywords_that_failed_appear_in_errors_dict(self):
-        error = exceptions.ValidationError("a message", validator="foo")
-        tree = exceptions.ErrorTree([error])
-        self.assertEqual(tree.errors, {"foo": error})
-
-    def test_it_creates_a_child_tree_for_each_nested_path(self):
-        errors = [
-            exceptions.ValidationError("a bar message", path=["bar"]),
-            exceptions.ValidationError("a bar -> 0 message", path=["bar", 0]),
-        ]
-        tree = exceptions.ErrorTree(errors)
-        self.assertIn(0, tree["bar"])
-        self.assertNotIn(1, tree["bar"])
-
-    def test_children_have_their_errors_dicts_built(self):
-        e1, e2 = (
-            exceptions.ValidationError("1", validator="foo", path=["bar", 0]),
-            exceptions.ValidationError("2", validator="quux", path=["bar", 0]),
-        )
-        tree = exceptions.ErrorTree([e1, e2])
-        self.assertEqual(tree["bar"][0].errors, {"foo": e1, "quux": e2})
-
-    def test_multiple_errors_with_instance(self):
-        e1, e2 = (
-            exceptions.ValidationError(
-                "1",
-                validator="foo",
-                path=["bar", "bar2"],
-                instance="i1"),
-            exceptions.ValidationError(
-                "2",
-                validator="quux",
-                path=["foobar", 2],
-                instance="i2"),
-        )
-        exceptions.ErrorTree([e1, e2])
-
-    def test_it_does_not_contain_subtrees_that_are_not_in_the_instance(self):
-        error = exceptions.ValidationError("123", validator="foo", instance=[])
-        tree = exceptions.ErrorTree([error])
-
-        with self.assertRaises(IndexError):
-            tree[0]
-
-    def test_if_its_in_the_tree_anyhow_it_does_not_raise_an_error(self):
-        """
-        If a keyword refers to a path that isn't in the instance, the
-        tree still properly returns a subtree for that path.
-        """
-
-        error = exceptions.ValidationError(
-            "a message", validator="foo", instance={}, path=["foo"],
-        )
-        tree = exceptions.ErrorTree([error])
-        self.assertIsInstance(tree["foo"], exceptions.ErrorTree)
-
-    def test_repr_single(self):
-        error = exceptions.ValidationError(
-            "1",
-            validator="foo",
-            path=["bar", "bar2"],
-            instance="i1",
-        )
-        tree = exceptions.ErrorTree([error])
-        self.assertEqual(repr(tree), "<ErrorTree (1 total error)>")
-
-    def test_repr_multiple(self):
-        e1, e2 = (
-            exceptions.ValidationError(
-                "1",
-                validator="foo",
-                path=["bar", "bar2"],
-                instance="i1"),
-            exceptions.ValidationError(
-                "2",
-                validator="quux",
-                path=["foobar", 2],
-                instance="i2"),
-        )
-        tree = exceptions.ErrorTree([e1, e2])
-        self.assertEqual(repr(tree), "<ErrorTree (2 total errors)>")
-
-    def test_repr_empty(self):
-        tree = exceptions.ErrorTree([])
-        self.assertEqual(repr(tree), "<ErrorTree (0 total errors)>")
-
-
-class TestErrorInitReprStr(TestCase):
-    def make_error(self, **kwargs):
-        defaults = dict(
-            message="hello",
-            validator="type",
-            validator_value="string",
-            instance=5,
-            schema={"type": "string"},
-        )
-        defaults.update(kwargs)
-        return exceptions.ValidationError(**defaults)
-
-    def assertShows(self, expected, **kwargs):
-        expected = textwrap.dedent(expected).rstrip("\n")
-
-        error = self.make_error(**kwargs)
-        message_line, _, rest = str(error).partition("\n")
-        self.assertEqual(message_line, error.message)
-        self.assertEqual(rest, expected)
-
-    def test_it_calls_super_and_sets_args(self):
-        error = self.make_error()
-        self.assertGreater(len(error.args), 1)
-
-    def test_repr(self):
-        self.assertEqual(
-            repr(exceptions.ValidationError(message="Hello!")),
-            "<ValidationError: 'Hello!'>",
-        )
-
-    def test_unset_error(self):
-        error = exceptions.ValidationError("message")
-        self.assertEqual(str(error), "message")
-
-        kwargs = {
-            "validator": "type",
-            "validator_value": "string",
-            "instance": 5,
-            "schema": {"type": "string"},
-        }
-        # Just the message should show if any of the attributes are unset
-        for attr in kwargs:
-            k = dict(kwargs)
-            del k[attr]
-            error = exceptions.ValidationError("message", **k)
-            self.assertEqual(str(error), "message")
-
-    def test_empty_paths(self):
-        self.assertShows(
-            """
-            Failed validating 'type' in schema:
-                {'type': 'string'}
-
-            On instance:
-                5
-            """,
-            path=[],
-            schema_path=[],
-        )
-
-    def test_one_item_paths(self):
-        self.assertShows(
-            """
-            Failed validating 'type' in schema:
-                {'type': 'string'}
-
-            On instance[0]:
-                5
-            """,
-            path=[0],
-            schema_path=["items"],
-        )
-
-    def test_multiple_item_paths(self):
-        self.assertShows(
-            """
-            Failed validating 'type' in schema['items'][0]:
-                {'type': 'string'}
-
-            On instance[0]['a']:
-                5
-            """,
-            path=[0, "a"],
-            schema_path=["items", 0, 1],
-        )
-
-    def test_uses_pprint(self):
-        self.assertShows(
-            """
-            Failed validating 'maxLength' in schema:
-                {0: 0,
-                 1: 1,
-                 2: 2,
-                 3: 3,
-                 4: 4,
-                 5: 5,
-                 6: 6,
-                 7: 7,
-                 8: 8,
-                 9: 9,
-                 10: 10,
-                 11: 11,
-                 12: 12,
-                 13: 13,
-                 14: 14,
-                 15: 15,
-                 16: 16,
-                 17: 17,
-                 18: 18,
-                 19: 19}
-
-            On instance:
-                [0,
-                 1,
-                 2,
-                 3,
-                 4,
-                 5,
-                 6,
-                 7,
-                 8,
-                 9,
-                 10,
-                 11,
-                 12,
-                 13,
-                 14,
-                 15,
-                 16,
-                 17,
-                 18,
-                 19,
-                 20,
-                 21,
-                 22,
-                 23,
-                 24]
-            """,
-            instance=list(range(25)),
-            schema=dict(zip(range(20), range(20))),
-            validator="maxLength",
-        )
-
-    def test_str_works_with_instances_having_overriden_eq_operator(self):
-        """
-        Check for #164 which rendered exceptions unusable when a
-        `ValidationError` involved instances with an `__eq__` method
-        that returned truthy values.
-        """
-
-        class DontEQMeBro:
-            def __eq__(this, other):  # pragma: no cover
-                self.fail("Don't!")
-
-            def __ne__(this, other):  # pragma: no cover
-                self.fail("Don't!")
-
-        instance = DontEQMeBro()
-        error = exceptions.ValidationError(
-            "a message",
-            validator="foo",
-            instance=instance,
-            validator_value="some",
-            schema="schema",
-        )
-        self.assertIn(repr(instance), str(error))
-
-
-class TestHashable(TestCase):
-    def test_hashable(self):
-        set([exceptions.ValidationError("")])
-        set([exceptions.SchemaError("")])
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_utils/_inspect.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_utils/_inspect.py
deleted file mode 100644
index 9a874a71dd0a53e25a671c51bfdceec850702bfe..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_utils/_inspect.py
+++ /dev/null
@@ -1,191 +0,0 @@
-"""Subset of inspect module from upstream python
-
-We use this instead of upstream because upstream inspect is slow to import, and
-significantly contributes to numpy import times. Importing this copy has almost
-no overhead.
-
-"""
-import types
-
-__all__ = ['getargspec', 'formatargspec']
-
-# ----------------------------------------------------------- type-checking
-def ismethod(object):
-    """Return true if the object is an instance method.
-
-    Instance method objects provide these attributes:
-        __doc__         documentation string
-        __name__        name with which this method was defined
-        im_class        class object in which this method belongs
-        im_func         function object containing implementation of method
-        im_self         instance to which this method is bound, or None
-
-    """
-    return isinstance(object, types.MethodType)
-
-def isfunction(object):
-    """Return true if the object is a user-defined function.
-
-    Function objects provide these attributes:
-        __doc__         documentation string
-        __name__        name with which this function was defined
-        func_code       code object containing compiled function bytecode
-        func_defaults   tuple of any default values for arguments
-        func_doc        (same as __doc__)
-        func_globals    global namespace in which this function was defined
-        func_name       (same as __name__)
-
-    """
-    return isinstance(object, types.FunctionType)
-
-def iscode(object):
-    """Return true if the object is a code object.
-
-    Code objects provide these attributes:
-        co_argcount     number of arguments (not including * or ** args)
-        co_code         string of raw compiled bytecode
-        co_consts       tuple of constants used in the bytecode
-        co_filename     name of file in which this code object was created
-        co_firstlineno  number of first line in Python source code
-        co_flags        bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
-        co_lnotab       encoded mapping of line numbers to bytecode indices
-        co_name         name with which this code object was defined
-        co_names        tuple of names of local variables
-        co_nlocals      number of local variables
-        co_stacksize    virtual machine stack space required
-        co_varnames     tuple of names of arguments and local variables
-        
-    """
-    return isinstance(object, types.CodeType)
-
-# ------------------------------------------------ argument list extraction
-# These constants are from Python's compile.h.
-CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8
-
-def getargs(co):
-    """Get information about the arguments accepted by a code object.
-
-    Three things are returned: (args, varargs, varkw), where 'args' is
-    a list of argument names (possibly containing nested lists), and
-    'varargs' and 'varkw' are the names of the * and ** arguments or None.
-
-    """
-
-    if not iscode(co):
-        raise TypeError('arg is not a code object')
-
-    nargs = co.co_argcount
-    names = co.co_varnames
-    args = list(names[:nargs])
-
-    # The following acrobatics are for anonymous (tuple) arguments.
-    # Which we do not need to support, so remove to avoid importing
-    # the dis module.
-    for i in range(nargs):
-        if args[i][:1] in ['', '.']:
-            raise TypeError("tuple function arguments are not supported")
-    varargs = None
-    if co.co_flags & CO_VARARGS:
-        varargs = co.co_varnames[nargs]
-        nargs = nargs + 1
-    varkw = None
-    if co.co_flags & CO_VARKEYWORDS:
-        varkw = co.co_varnames[nargs]
-    return args, varargs, varkw
-
-def getargspec(func):
-    """Get the names and default values of a function's arguments.
-
-    A tuple of four things is returned: (args, varargs, varkw, defaults).
-    'args' is a list of the argument names (it may contain nested lists).
-    'varargs' and 'varkw' are the names of the * and ** arguments or None.
-    'defaults' is an n-tuple of the default values of the last n arguments.
-
-    """
-
-    if ismethod(func):
-        func = func.__func__
-    if not isfunction(func):
-        raise TypeError('arg is not a Python function')
-    args, varargs, varkw = getargs(func.__code__)
-    return args, varargs, varkw, func.__defaults__
-
-def getargvalues(frame):
-    """Get information about arguments passed into a particular frame.
-
-    A tuple of four things is returned: (args, varargs, varkw, locals).
-    'args' is a list of the argument names (it may contain nested lists).
-    'varargs' and 'varkw' are the names of the * and ** arguments or None.
-    'locals' is the locals dictionary of the given frame.
-    
-    """
-    args, varargs, varkw = getargs(frame.f_code)
-    return args, varargs, varkw, frame.f_locals
-
-def joinseq(seq):
-    if len(seq) == 1:
-        return '(' + seq[0] + ',)'
-    else:
-        return '(' + ', '.join(seq) + ')'
-
-def strseq(object, convert, join=joinseq):
-    """Recursively walk a sequence, stringifying each element.
-
-    """
-    if type(object) in [list, tuple]:
-        return join([strseq(_o, convert, join) for _o in object])
-    else:
-        return convert(object)
-
-def formatargspec(args, varargs=None, varkw=None, defaults=None,
-                  formatarg=str,
-                  formatvarargs=lambda name: '*' + name,
-                  formatvarkw=lambda name: '**' + name,
-                  formatvalue=lambda value: '=' + repr(value),
-                  join=joinseq):
-    """Format an argument spec from the 4 values returned by getargspec.
-
-    The first four arguments are (args, varargs, varkw, defaults).  The
-    other four arguments are the corresponding optional formatting functions
-    that are called to turn names and values into strings.  The ninth
-    argument is an optional function to format the sequence of arguments.
-
-    """
-    specs = []
-    if defaults:
-        firstdefault = len(args) - len(defaults)
-    for i in range(len(args)):
-        spec = strseq(args[i], formatarg, join)
-        if defaults and i >= firstdefault:
-            spec = spec + formatvalue(defaults[i - firstdefault])
-        specs.append(spec)
-    if varargs is not None:
-        specs.append(formatvarargs(varargs))
-    if varkw is not None:
-        specs.append(formatvarkw(varkw))
-    return '(' + ', '.join(specs) + ')'
-
-def formatargvalues(args, varargs, varkw, locals,
-                    formatarg=str,
-                    formatvarargs=lambda name: '*' + name,
-                    formatvarkw=lambda name: '**' + name,
-                    formatvalue=lambda value: '=' + repr(value),
-                    join=joinseq):
-    """Format an argument spec from the 4 values returned by getargvalues.
-
-    The first four arguments are (args, varargs, varkw, locals).  The
-    next four arguments are the corresponding optional formatting functions
-    that are called to turn names and values into strings.  The ninth
-    argument is an optional function to format the sequence of arguments.
-
-    """
-    def convert(name, locals=locals,
-                formatarg=formatarg, formatvalue=formatvalue):
-        return formatarg(name) + formatvalue(locals[name])
-    specs = [strseq(arg, convert, join) for arg in args]
-
-    if varargs:
-        specs.append(formatvarargs(varargs) + formatvalue(locals[varargs]))
-    if varkw:
-        specs.append(formatvarkw(varkw) + formatvalue(locals[varkw]))
-    return '(' + ', '.join(specs) + ')'
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_ufunclike.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_ufunclike.py
deleted file mode 100644
index fac4f41d0919a08b8a8c05827e19839abc47bd86..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_ufunclike.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-import numpy.core as nx
-import numpy.lib.ufunclike as ufl
-from numpy.testing import (
-    assert_, assert_equal, assert_array_equal, assert_warns, assert_raises
-)
-
-
-class TestUfunclike:
-
-    def test_isposinf(self):
-        a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0])
-        out = nx.zeros(a.shape, bool)
-        tgt = nx.array([True, False, False, False, False, False])
-
-        res = ufl.isposinf(a)
-        assert_equal(res, tgt)
-        res = ufl.isposinf(a, out)
-        assert_equal(res, tgt)
-        assert_equal(out, tgt)
-
-        a = a.astype(np.complex_)
-        with assert_raises(TypeError):
-            ufl.isposinf(a)
-
-    def test_isneginf(self):
-        a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0])
-        out = nx.zeros(a.shape, bool)
-        tgt = nx.array([False, True, False, False, False, False])
-
-        res = ufl.isneginf(a)
-        assert_equal(res, tgt)
-        res = ufl.isneginf(a, out)
-        assert_equal(res, tgt)
-        assert_equal(out, tgt)
-
-        a = a.astype(np.complex_)
-        with assert_raises(TypeError):
-            ufl.isneginf(a)
-
-    def test_fix(self):
-        a = nx.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]])
-        out = nx.zeros(a.shape, float)
-        tgt = nx.array([[1., 1., 1., 1.], [-1., -1., -1., -1.]])
-
-        res = ufl.fix(a)
-        assert_equal(res, tgt)
-        res = ufl.fix(a, out)
-        assert_equal(res, tgt)
-        assert_equal(out, tgt)
-        assert_equal(ufl.fix(3.14), 3)
-
-    def test_fix_with_subclass(self):
-        class MyArray(nx.ndarray):
-            def __new__(cls, data, metadata=None):
-                res = nx.array(data, copy=True).view(cls)
-                res.metadata = metadata
-                return res
-
-            def __array_wrap__(self, obj, context=None):
-                if isinstance(obj, MyArray):
-                    obj.metadata = self.metadata
-                return obj
-
-            def __array_finalize__(self, obj):
-                self.metadata = getattr(obj, 'metadata', None)
-                return self
-
-        a = nx.array([1.1, -1.1])
-        m = MyArray(a, metadata='foo')
-        f = ufl.fix(m)
-        assert_array_equal(f, nx.array([1, -1]))
-        assert_(isinstance(f, MyArray))
-        assert_equal(f.metadata, 'foo')
-
-        # check 0d arrays don't decay to scalars
-        m0d = m[0,...]
-        m0d.metadata = 'bar'
-        f0d = ufl.fix(m0d)
-        assert_(isinstance(f0d, MyArray))
-        assert_equal(f0d.metadata, 'bar')
-
-    def test_scalar(self):
-        x = np.inf
-        actual = np.isposinf(x)
-        expected = np.True_
-        assert_equal(actual, expected)
-        assert_equal(type(actual), type(expected))
-
-        x = -3.4
-        actual = np.fix(x)
-        expected = np.float64(-3.0)
-        assert_equal(actual, expected)
-        assert_equal(type(actual), type(expected))
-
-        out = np.array(0.0)
-        actual = np.fix(x, out=out)
-        assert_(actual is out)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/contrib/logging.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/contrib/logging.py
deleted file mode 100644
index b8eaec53f16c0e63f174aef4d2a4da49ebdf0b30..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/contrib/logging.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""
-Helper functionality for interoperability with stdlib `logging`.
-"""
-import logging
-import sys
-from contextlib import contextmanager
-
-try:
-    from typing import Iterator, List, Optional, Type  # noqa: F401
-except ImportError:
-    pass
-
-from ..std import tqdm as std_tqdm
-
-
-class _TqdmLoggingHandler(logging.StreamHandler):
-    def __init__(
-        self,
-        tqdm_class=std_tqdm  # type: Type[std_tqdm]
-    ):
-        super(_TqdmLoggingHandler, self).__init__()
-        self.tqdm_class = tqdm_class
-
-    def emit(self, record):
-        try:
-            msg = self.format(record)
-            self.tqdm_class.write(msg, file=self.stream)
-            self.flush()
-        except (KeyboardInterrupt, SystemExit):
-            raise
-        except:  # noqa pylint: disable=bare-except
-            self.handleError(record)
-
-
-def _is_console_logging_handler(handler):
-    return (isinstance(handler, logging.StreamHandler)
-            and handler.stream in {sys.stdout, sys.stderr})
-
-
-def _get_first_found_console_logging_handler(handlers):
-    for handler in handlers:
-        if _is_console_logging_handler(handler):
-            return handler
-
-
-@contextmanager
-def logging_redirect_tqdm(
-    loggers=None,  # type: Optional[List[logging.Logger]],
-    tqdm_class=std_tqdm  # type: Type[std_tqdm]
-):
-    # type: (...) -> Iterator[None]
-    """
-    Context manager redirecting console logging to `tqdm.write()`, leaving
-    other logging handlers (e.g. log files) unaffected.
-
-    Parameters
-    ----------
-    loggers  : list, optional
-      Which handlers to redirect (default: [logging.root]).
-    tqdm_class  : optional
-
-    Example
-    -------
-    ```python
-    import logging
-    from tqdm import trange
-    from tqdm.contrib.logging import logging_redirect_tqdm
-
-    LOG = logging.getLogger(__name__)
-
-    if __name__ == '__main__':
-        logging.basicConfig(level=logging.INFO)
-        with logging_redirect_tqdm():
-            for i in trange(9):
-                if i == 4:
-                    LOG.info("console logging redirected to `tqdm.write()`")
-        # logging restored
-    ```
-    """
-    if loggers is None:
-        loggers = [logging.root]
-    original_handlers_list = [logger.handlers for logger in loggers]
-    try:
-        for logger in loggers:
-            tqdm_handler = _TqdmLoggingHandler(tqdm_class)
-            orig_handler = _get_first_found_console_logging_handler(logger.handlers)
-            if orig_handler is not None:
-                tqdm_handler.setFormatter(orig_handler.formatter)
-                tqdm_handler.stream = orig_handler.stream
-            logger.handlers = [
-                handler for handler in logger.handlers
-                if not _is_console_logging_handler(handler)] + [tqdm_handler]
-        yield
-    finally:
-        for logger, original_handlers in zip(loggers, original_handlers_list):
-            logger.handlers = original_handlers
-
-
-@contextmanager
-def tqdm_logging_redirect(
-    *args,
-    # loggers=None,  # type: Optional[List[logging.Logger]]
-    # tqdm=None,  # type: Optional[Type[tqdm.tqdm]]
-    **kwargs
-):
-    # type: (...) -> Iterator[None]
-    """
-    Convenience shortcut for:
-    ```python
-    with tqdm_class(*args, **tqdm_kwargs) as pbar:
-        with logging_redirect_tqdm(loggers=loggers, tqdm_class=tqdm_class):
-            yield pbar
-    ```
-
-    Parameters
-    ----------
-    tqdm_class  : optional, (default: tqdm.std.tqdm).
-    loggers  : optional, list.
-    **tqdm_kwargs  : passed to `tqdm_class`.
-    """
-    tqdm_kwargs = kwargs.copy()
-    loggers = tqdm_kwargs.pop('loggers', None)
-    tqdm_class = tqdm_kwargs.pop('tqdm_class', std_tqdm)
-    with tqdm_class(*args, **tqdm_kwargs) as pbar:
-        with logging_redirect_tqdm(loggers=loggers, tqdm_class=tqdm_class):
-            yield pbar
diff --git a/spaces/pscpeng/ChuanhuChatGPT/modules/shared.py b/spaces/pscpeng/ChuanhuChatGPT/modules/shared.py
deleted file mode 100644
index 4046900a39b2fc7bdd8005844a92dc7d4eb669b6..0000000000000000000000000000000000000000
--- a/spaces/pscpeng/ChuanhuChatGPT/modules/shared.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from modules.presets import API_URL
-
-class State:
-    interrupted = False
-    api_url = API_URL
-
-    def interrupt(self):
-        self.interrupted = True
-
-    def recover(self):
-        self.interrupted = False
-
-    def set_api_url(self, api_url):
-        self.api_url = api_url
-
-    def reset_api_url(self):
-        self.api_url = API_URL
-        return self.api_url
-
-    def reset_all(self):
-        self.interrupted = False
-        self.api_url = API_URL
-
-state = State()
diff --git a/spaces/pseudolab/SonGPT/core/structure/memory/memory_extractor/__init__.py b/spaces/pseudolab/SonGPT/core/structure/memory/memory_extractor/__init__.py
deleted file mode 100644
index 67a3aa0871ad407de0e047d017b96feda188e16f..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/SonGPT/core/structure/memory/memory_extractor/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .memory_extractor import MemoryExtractor
diff --git a/spaces/pycoming/bingo/src/pages/api/kblob.ts b/spaces/pycoming/bingo/src/pages/api/kblob.ts
deleted file mode 100644
index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000
--- a/spaces/pycoming/bingo/src/pages/api/kblob.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-
-const API_DOMAIN = 'https://bing.vcanbb.top'
-
-export const config = {
-  api: {
-    bodyParser: {
-      sizeLimit: '10mb' // Set desired value here
-    }
-  }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
-
-    const formData = new FormData()
-    formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-    if (imageBase64) {
-      formData.append('imageBase64', imageBase64)
-    }
-
-    const response = await fetch(`${API_DOMAIN}/images/kblob`,
-      {
-        method: 'POST',
-        body: formData.getBuffer(),
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referer": `${API_DOMAIN}/web/index.html`,
-          "Referrer-Policy": "origin-when-cross-origin",
-          'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-          ...formData.getHeaders()
-        }
-      }
-    ).then(res => res.text())
-
-    res.writeHead(200, {
-      'Content-Type': 'application/json',
-    })
-    res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } }))
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/qinzhu/diy-girlfriend-online/text/japanese.py b/spaces/qinzhu/diy-girlfriend-online/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/diy-girlfriend-online/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
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diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/modules/rope.py b/spaces/radames/MusicGen-Continuation/audiocraft/modules/rope.py
deleted file mode 100644
index 4b8c70b9aba28eeb53d12ddc3de8852492847808..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/modules/rope.py
+++ /dev/null
@@ -1,124 +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 torch import nn
-import torch
-
-
-class XPos(nn.Module):
-    """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
-    This applies an exponential decay to the RoPE rotation matrix.
-
-    Args:
-        dim (int): Embedding dimension.
-        smoothing (float): Smoothing factor applied to the decay rates.
-        base_scale (int): Base decay rate, given in terms of scaling time.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype): dtype to use to generate the embedding.
-    """
-    def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
-                 device=None, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        assert dim % 2 == 0
-        assert dtype in [torch.float64, torch.float32]
-        self.dtype = dtype
-        self.base_scale = base_scale
-
-        half_dim = dim // 2
-        adim = torch.arange(half_dim, device=device, dtype=dtype)
-        decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
-        self.register_buffer("decay_rates", decay_rates)
-        self.decay: tp.Optional[torch.Tensor] = None
-
-    def get_decay(self, start: int, end: int):
-        """Create complex decay tensor, cache values for fast computation.
-        """
-        if self.decay is None or end > self.decay.shape[0]:
-            assert isinstance(self.decay_rates, torch.Tensor)  # Satisfy type checker.
-            idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
-            power = idx / self.base_scale
-            scale = self.decay_rates ** power.unsqueeze(-1)
-            self.decay = torch.polar(scale, torch.zeros_like(scale))
-        return self.decay[start:end]  # [T, C/2]
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
-
-    Args:
-        dim (int): Embedding dimension (twice the number of frequencies).
-        max_period (float): Maximum period of the rotation frequencies.
-        xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
-        scale (float): Scale of positional embedding, set to 0 to deactivate.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype): dtype to use to generate the embedding.
-    """
-    def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
-                 scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        assert dim % 2 == 0
-        self.scale = scale
-        assert dtype in [torch.float64, torch.float32]
-        self.dtype = dtype
-
-        adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
-        frequencies = 1.0 / (max_period ** (adim / dim))
-        self.register_buffer("frequencies", frequencies)
-        self.rotation: tp.Optional[torch.Tensor] = None
-
-        self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
-
-    def get_rotation(self, start: int, end: int):
-        """Create complex rotation tensor, cache values for fast computation.
-        """
-        if self.rotation is None or end > self.rotation.shape[0]:
-            assert isinstance(self.frequencies, torch.Tensor)  # Satisfy type checker.
-            idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
-            angles = torch.outer(idx, self.frequencies)
-            self.rotation = torch.polar(torch.ones_like(angles), angles)
-        return self.rotation[start:end]
-
-    def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False):
-        """Apply rope rotation to query or key tensor.
-        """
-        T = x.shape[1]
-        rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2)
-
-        if self.xpos:
-            decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2)
-        else:
-            decay = 1.0
-
-        if invert_decay:
-            decay = decay ** -1
-
-        x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
-        scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
-        x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2)
-
-        return x_out.type_as(x)
-
-    def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0):
-        """ Apply rope rotation to both query and key tensors.
-        Supports streaming mode, in which query and key are not expected to have the same shape.
-        In streaming mode, key will be of legnth [P + C] with P the cached past timesteps, but
-        query will be [C] (typically C == 1).
-
-        Args:
-            query (torch.Tensor): Query to rotate.
-            key (torch.Tensor): Key to rotate.
-            start (int): Start index of the sequence for time offset.
-        """
-        query_timesteps = query.shape[1]
-        key_timesteps = key.shape[1]
-        streaming_offset = key_timesteps - query_timesteps
-
-        query_out = self.rotate(query, start + streaming_offset)
-        key_out = self.rotate(key, start, invert_decay=True)
-
-        return query_out, key_out
diff --git a/spaces/radames/MusicGen-Continuation/tests/modules/test_seanet.py b/spaces/radames/MusicGen-Continuation/tests/modules/test_seanet.py
deleted file mode 100644
index e5c51b340a2f94fb2828b14daf83d5fad645073d..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/tests/modules/test_seanet.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock
-from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d
-
-
-class TestSEANetModel:
-
-    def test_base(self):
-        encoder = SEANetEncoder()
-        decoder = SEANetDecoder()
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_causal(self):
-        encoder = SEANetEncoder(causal=True)
-        decoder = SEANetDecoder(causal=True)
-        x = torch.randn(1, 1, 24000)
-
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_conv_skip_connection(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False)
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_seanet_encoder_decoder_final_act(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False, final_activation='Tanh')
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in encoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        # here we add + 1 to n_blocks as we increment n_blocks just after the block
-                        assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm
-
-    def test_encoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_encoder_blocks_norm(encoder, disable_blocks, norm)
-
-    def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in decoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, StreamableConvTranspose1d):
-                n_blocks += 1
-                assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        assert resnet_layer.conv.norm_type == 'none' \
-                            if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-
-    def test_decoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_decoder_blocks_norm(decoder, disable_blocks, norm)
-
-    def test_disable_norm_raises_exception(self):
-        # Invalid disable_norm_outer_blocks values raise exceptions
-        with pytest.raises(AssertionError):
-            SEANetEncoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Bijoy Bangla Keyboard Free Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Bijoy Bangla Keyboard Free Download.md
deleted file mode 100644
index a4343b61edacc5853d18b7192ab2e0825623daf7..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Bijoy Bangla Keyboard Free Download.md	
+++ /dev/null
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-
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-</ul>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Cobra Kai 720p torrent Dont miss the latest season of the Netflix sensation.md b/spaces/raedeXanto/academic-chatgpt-beta/Cobra Kai 720p torrent Dont miss the latest season of the Netflix sensation.md
deleted file mode 100644
index 59ab46a653dfea16cbbdbef5432cb14eca865e2b..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Cobra Kai 720p torrent Dont miss the latest season of the Netflix sensation.md	
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-<li>Johnny Lawrence (William Zabka): The antagonist of the original films, who was the leader of the Cobra Kai dojo and lost to Daniel in the tournament. He is now a washed-up handyman and a divorced father, who reopens the Cobra Kai dojo and becomes a mentor to his students.</li>
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-<li>Samantha LaRusso (Mary Mouser): Daniel's daughter, who is a popular and smart girl at school. She used to practice karate with her father when she was younger, but later drifted away from him and joined a clique of mean girls. She eventually reconnects with her father and joins Miyagi-Do.</li>
-<li>Robby Keene (Tanner Buchanan): Johnny's son, who has a strained relationship with his father and lives with his mother who is an alcoholic. He initially works for Daniel as an employee at his car dealership, but later becomes his karate student at Miyagi-Do.</li>
-<li>John Kreese (Martin Kove): Johnny's former sensei and the founder of Cobra Kai, who taught him the "no mercy" philosophy. He returns to Johnny's life after decades of absence, and tries to take over his dojo and influence his students.</li>
-</ul>
- <h3>The reception of Cobra Kai</h3>
-<p>Cobra Kai has received widespread acclaim from critics and audiences alike, who praised its nostalgia factor, humor, action scenes, character development, and storylines. The series has won several awards and nominations, such as the People's Choice Award for Favorite Comedy Show in 2019, and the Primetime Emmy Award nomination for Outstanding Comedy Series in 2021. The series has also been very popular among viewers, with over 73 million households watching it on Netflix as of October 2021. The series has so far released four seasons, with a fifth one confirmed for 2022.</p>
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-<li>You might violate copyright laws: Downloading Cobra Kai 720p torrent means that you might be infringing on the intellectual property rights of the creators and distributors of the series. You might face legal consequences such as fines or lawsuits if you are caught downloading or sharing illegal torrents.</li>
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-Cobra Kai season one plot twist explained in detail in a blog post with screenshots and links to the relevant episodes in the series. The blog post is written in a friendly and engaging tone, and includes a call to action at the end to encourage readers to share their thoughts and opinions on the twist. The blog post is titled:<br />
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-<p>One of the most important steps in downloading Cobra Kai 720p torrent is choosing a reliable and reputable torrent site that offers high-quality files without malware or fake links. Some of the best torrent sites for Cobra Kai 720p torrent are:</p>
-<table style="border-collapse: collapse; width:100%;">
-<tr style="border:1px solid black;">
-<th style="border:1px solid black ```html <h3>The best torrent clients for Cobra Kai 720p torrent</h3>
-<p>A torrent client is a software application that enables you to download and manage torrent files. There are many torrent clients available for different platforms and preferences, but not all of them are equally good for downloading Cobra Kai 720p torrent. Some of the best torrent clients for Cobra Kai 720p torrent are:</p>
-<table style="border-collapse: collapse; width:100%;">
-<tr style="border:1px solid black;">
-<th style="border:1px solid black;">Torrent client</th>
-<th style="border:1px solid black;">Features</th>
-<th style="border:1px solid black;">Pros</th>
-<th style="border:1px solid black;">Cons</th>
-</tr>
-<tr style="border:1px solid black;">
-<td style="border:1px solid black;">qBittorrent</td>
-<td style="border:1px solid black;">A free and open-source torrent client that offers a balance between functionality and simplicity. It has an in-app search engine, a bandwidth scheduler, support for extensions, and more.</td>
-<td style="border:1px solid black;">- No ads or bloatware<br>- Lightweight and fast<br>- Cross-platform and customizable</td>
-<td style="border:1px solid black;">- Not very beginner-friendly<br>- Lacks some advanced features<br>- May have some bugs or stability issues</td>
-</tr>
-<tr style="border:1px solid black;">
-<td style="border:1px solid black;">Deluge</td>
-<td style="border:1px solid black;">A free and open-source torrent client that is lean and bare-bones with its base installation, but can be heavily customized with dozens of plugins. It can run as a desktop client or a service.</td>
-<td style="border:1px solid black;">- Highly flexible and versatile<br>- Supports various protocols and formats<br>- Can be controlled remotely</td>
-<td style="border:1px solid black;">- Requires plugins for many features<br>- May be confusing or overwhelming for some users<br>- May have compatibility issues with some plugins</td>
-</tr>
-<tr style="border:1px solid black;">
-<td style="border:1px solid black;">Tixati</td>
-<td style="border:1px solid black;">A free and proprietary torrent client that offers a lot of features and information for advanced users. It supports magnet links, connection encryption, event scheduling, and more.</td>
-<td style="border:1px solid black;">- No ads or spyware<br>- Provides detailed data on peers and torrents<br>- Supports chat rooms and channels</td>
-<td style="border:1px solid black;">- Not very user-friendly or attractive<br>- Lacks some features like port forwarding or proxy support<br>- Not compatible with some trackers or sites</td>
-</tr>
-</table>
- <h3>The best VPNs for Cobra Kai 720p torrent</h3>
-<p>A VPN or a virtual private network is a service that encrypts your internet traffic and routes it through a secure server in another location. This helps you to hide your IP address and location from other users or trackers, as well as to bypass geo-restrictions or censorship on certain sites or content. A VPN is essential for downloading Cobra Kai 720p torrent safely and legally, as it can protect you from potential legal troubles or cyberattacks. Some of the best VPNs for Cobra Kai 720p torrent are:</p>
-<table style="border-collapse: collapse; width:100%;">
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-<th style="border:1px solid black;">VPN</th>
-<th style="border:1px solid black;">Features</th>
-<th style="border:1px solid black;">Pros</th>
-<th style="border:1px solid black;">Cons</th>
-</tr>
-<tr style="border:1px solid black;">
-<td style="border:1px solid black;">ExpressVPN</td>
-<td style="border:1px solid black;">A premium VPN service that offers fast speeds, strong security, and unlimited bandwidth. It has over 3,000 servers in 94 countries, and supports P2P traffic on all of them.</td>
-<td style="border:1px solid black;">- Excellent performance and reliability<br>- Supports up to five devices simultaneously<br>- Offers a 30-day money-back guarantee</td>
-<td style="border:1px solid black;">- More expensive than some competitors<br>- Does not have a free trial<br>- May log some non-identifying information</td>
-</tr>
-<tr style="border:1px solid black;">
-<td style="border:1px solid black;">NordVPN</td>
-<td style="border:1px solid black;">A popular VPN service that offers high speeds, robust security, and unlimited bandwidth. It has over 5,000 servers in 59 countries, and supports P2P traffic on dedicated servers.</td>
-<td style="border:1px solid black;">- Affordable and easy to use<br>- Supports up to six devices simultaneously<br>- Offers a 30-day money-back guarantee</td>
-<td style="border:1px solid black;">- May have some speed fluctuations<br>- Does not have a free trial<br>- May have some customer service issues</td>
-</tr>
-<tr style="border:1px solid black;">
-<td style="border:1px solid black;">Surfshark</td>
-<td style="border:1px solid black;">A budget-friendly VPN service that offers decent speeds, good security, and unlimited bandwidth. It has over 3,200 servers in 65 countries, and supports P2P traffic on all of them.</td>
-<td style="border:1px solid black;">- Cheap and simple to use<br>- Supports unlimited devices simultaneously<br>- Offers a 30-day money-back guarantee</td>
-<td style="border:1px solid black;">- Not as fast or reliable as some competitors<br>- Does not have a free trial<br>- May have some compatibility issues with some devices or platforms</td>
-</tr>
- ```html <h2>Conclusion</h2>
- <p>Cobra Kai is a hit web series that continues the story of The Karate Kid movies with humor, drama, and action. If you want to watch it online without paying for a subscription service, you can download Cobra Kai 720p torrent from reliable torrent sites using a good torrent client. However, you should also use a VPN to protect yourself from legal troubles or cyberattacks while downloading Cobra Kai 720p torrent. We hope this article has helped you learn more about Cobra Kai 720p torrent and how to download it safely and legally.</p>
- <h2>FAQs</h2>
- <ul>
- <li>Q: Is Cobra Kai available on Netflix?<br>A: Yes, Cobra Kai is available on Netflix since August 2020. You can watch all four seasons of the series on Netflix with a subscription.</li>
- <li>Q: Is downloading Cobra Kai 720p torrent illegal?<br>A: Downloading Cobra Kai 720p torrent may be illegal depending on your country's laws and regulations regarding copyright infringement. You should check your local laws before downloading any torrents.</li>
- <li>Q: Is downloading Cobra Kai 720p torrent safe?<br>A: Downloading Cobra Kai 720p torrent may not be safe if you download it from untrusted sources or without using a VPN. You may expose your device to malware or your identity to trackers or hackers. You should always use a reputable torrent site, a reliable torrent client, and a secure VPN when downloading any torrents.</li>
- <li>Q: What are the system requirements for watching Cobra Kai 720p torrent?<br>A: To watch Cobra Kai 720p torrent on your device, you need to have enough storage space to download the files, a media player that can play MKV files (such as VLC), and an internet connection that can handle HD streaming.</li>
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- </p> 0a6ba089eb<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/DivX Serial Number Dolby Audio Edition How to Improve Your Video Sound with Dolby Technology.md b/spaces/raedeXanto/academic-chatgpt-beta/DivX Serial Number Dolby Audio Edition How to Improve Your Video Sound with Dolby Technology.md
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-<h1>How to Get and Use a DivX Serial Number Dolby Audio Edition</h1>
-<p>Do you want to watch videos with amazing sound quality on your devices? If so, you might be interested in <strong>DivX software</strong>, which supports <strong>Dolby Audio</strong>, a technology that enhances the audio of your videos. However, to use this feature, you need a <strong>serial number</strong> that activates it. How can you get one? In this article, we will show you how to get and use a <strong>DivX serial number dolby audio edition</strong> in easy steps.</p>
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- <h2>What is a serial number and why you need one to activate Dolby Audio</h2>
-<p>A serial number is a unique code that identifies your copy of DivX software. You need a serial number to activate certain features of DivX software, such as <strong>Dolby Audio</strong>. Without a serial number, you will not be able to use Dolby Audio on your videos. A serial number also gives you access to other premium features of DivX software, such as DTS-HD audio, ad-free playback, cloud connect, and more.</p>
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-<p>The second step is to open <strong>DivX software</strong> and go to <strong>Help > Enter Serial Number</strong>. This will open a window where you can enter your serial number.</p>
- <h3>Step 3: Enter your serial number and click Activate</h3>
-<p>The third step is to enter your <strong>serial number</strong> and click <strong>Activate</strong>. This will validate your serial number and activate Dolby Audio and other premium features on your devices.</p>
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-<p>The fourth step is to enjoy watching videos with <strong>Dolby Audio</strong>. You can play any video file that supports Dolby Audio on your devices using DivX Player or any other compatible player. You can also convert any video file into a format that supports Dolby Audio using DivX Converter or any other compatible converter. You can also stream any video file with Dolby Audio using DivX Media Server or any other compatible server.</p>
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-<p>Using <strong>DivX software with Dolby Audio</strong> has many benefits for your video watching experience. Here are some of them:</p>
- <h3>- Enhanced sound quality and clarity</h3>
-<p>Dolby Audio delivers surround sound, clear dialogue, and immersive effects that make your videos sound more realistic and engaging. You can hear every detail and nuance of the audio track without distortion or noise.</p>
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-<p>Dolby Audio works with various devices, such as TVs, laptops, smartphones, tablets, speakers, headphones, etc. It also works with various formats, such as MKV, AVI, MP4, HEVC, etc. You can enjoy high-quality sound on any device or format that supports Dolby Audio.</p>
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- <h2>Conclusion</h2>
-io</strong> is a technology that enhances the sound quality of your videos by delivering surround sound, clear dialogue, and immersive effects. To use this feature, you need a <strong>DivX serial number dolby audio edition</strong>, which is a unique code that activates it. You can get one by buying DivX Pro or DivX Software Dolby Audio Edition, using a free trial of DivX Software Dolby Audio Edition, or using a free serial number from online sources. To use it, you need to download and install DivX software, enter your serial number, and activate Dolby Audio. By using DivX software with Dolby Audio, you can enjoy a cinematic audio experience on your devices.</p>
- <p>If you want to watch videos with amazing sound quality on your devices, don't hesitate to get and use a <strong>DivX serial number dolby audio edition</strong> today. You will not regret it.</p>
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-<p>Here are some frequently asked questions about <strong>DivX serial number dolby audio edition</strong>.</p>
- <h3>What is the difference between DivX Pro and DivX Software Dolby Audio Edition?</h3>
-<p>DivX Pro and DivX Software Dolby Audio Edition are both paid versions of DivX software that include a serial number for activating Dolby Audio and other premium features. The main difference is that DivX Pro also includes DivX Author, which is a tool that allows you to create and edit your own videos. DivX Software Dolby Audio Edition does not include DivX Author.</p>
- <h3>How long does the free trial of DivX Software Dolby Audio Edition last?</h3>
-<p>The free trial of DivX Software Dolby Audio Edition lasts for 15 days from the date of activation. After that, you will need to buy a license or use another serial number to continue using Dolby Audio and other premium features.</p>
- <h3>Where can I find free serial numbers for DivX Software Dolby Audio Edition?</h3>
-<p>You can find free serial numbers for DivX Software Dolby Audio Edition by searching on Google or other search engines. However, these codes may not work or may be invalid or expired. They may also contain viruses or malware that can harm your devices or compromise your privacy. Therefore, use them at your own risk and discretion.</p>
- <h3>How can I check if my serial number is valid and activated?</h3>
-<p>You can check if your serial number is valid and activated by going to Help > About in DivX software. You will see a green check mark next to your serial number if it is valid and activated. You will also see the features that are enabled by your serial number.</p>
- <h3>What if I have problems with using DivX software or Dolby Audio?</h3>
-<p>If you have problems with using DivX software or Dolby Audio, you can contact the customer support of DivX or visit their online forums for help. You can also check their online guides and tutorials for troubleshooting tips.</p>
- </p> 0a6ba089eb<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download PATCHED Betternet VPN For Windows Premium V4.1.0 - Setup Crack with One Click.md b/spaces/raedeXanto/academic-chatgpt-beta/Download PATCHED Betternet VPN For Windows Premium V4.1.0 - Setup Crack with One Click.md
deleted file mode 100644
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-<h3>How to download and install Betternet VPN For Windows Premium V4.1.0</h3>
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-<li>You will see a window like this:</li>
-<img src="https://cdn.mos.cms.futurecdn.net/5L8Zc6vLQwFZzYF9tGy7Xm-970-80.jpg" alt="Betternet VPN app window">
-<li>You can now use the free version of Betternet VPN with some limitations, or you can proceed to crack it and unlock the premium features.</li>
-</ol>
-<h4>How to crack Betternet VPN For Windows Premium V4.1.0</h4>
-<p>To crack Betternet VPN For Windows Premium V4.1.0, follow these steps:</p>
-<ol>
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-</ul>
-<h3>Comparison with other VPNs</h3>
-<p>Betternet VPN For Windows Premium is not the only VPN service available in the market. There are many other options that offer similar or better features and performance. Here are some of them:</p>
- **Table 3: Comparison with other VPNs** | Name | Price | Features | Pros | Cons | | --- | --- | --- | --- | --- | | Hotspot Shield | $7.99/month or $95.88/year | Unlimited bandwidth, access to 115+ locations, no ads, high speed, malware protection | Fastest VPN according to Speedtest.net; 45-day money-back guarantee; supports up to 5 devices; works with Netflix; has a free version | Based in US; collects some logs; expensive monthly plan; some servers are virtual; doesn't work with BBC iPlayer | | CyberGhost | $12.99/month or $47.88/year | Unlimited bandwidth, access to 90+ countries, no ads, high speed, malware protection | Supports up to 7 devices; works with Netflix and BBC iPlayer; has a free version; has dedicated servers for streaming and torrenting; 45-day money-back guarantee | Based in Romania; collects some logs; expensive monthly plan; some servers are slow; doesn't work in China | | TunnelBear | $9.99/month or $59.88/year | Unlimited bandwidth, access to 23 countries, no ads, high speed | Supports up to 5 devices; works with Netflix; has a free version; has a kill switch feature; has a cute interface; audited by independent security experts | Based in Canada; collects some logs; expensive monthly plan; limited server locations; doesn't work with BBC iPlayer or in China | <h4>Pros and cons of Betternet VPN For Windows Premium</h4>
-<p>Betternet VPN For Windows Premium has its own advantages and disadvantages that you should consider before using it. Here are some of them:</p>
- **Pros:** - Free unlimited bandwidth plan - Free 7-day trial **Cons:** - Free plan includes ads - Several privacy issues - No features - Couldn't unblock BBC iPlayer, Amazon Prime Video, or Disney+ - No kill switch feature - No customer support <h2>Conclusion</h2>
-<p>Betternet VPN For Windows Premium is a simple and free VPN service that can help you protect your online privacy and access blocked websites. However, it has some drawbacks that may affect your user experience and security. If you are looking for a more reliable and feature-rich VPN service, you may want to look elsewhere.</p>
-<p>If you still want to try Betternet VPN For Windows Premium, you can download it from the link below and follow the instructions to crack it. Remember to use it at your own risk and discretion.</p>
-<p>Download link: <a href="https://www.techspot.com/downloads/6792-betternet.html">https://www.techspot.com/downloads/6792-betternet.html</a></p>
-<p>Crack link: <a href="https://mega.nz/file/9JwHkQqC#YxZ9Rf9xg7n5VjBfJyX8l6Wgk5oQrNqTnLQjv7cO6rE">https://mega.nz/file/9JwHkQqC#YxZ9Rf9xg7n5VjBfJyX8l6Wgk5oQrNqTnLQjv7cO6rE</a></p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Betternet VPN For Windows Premium:</p>
-<ol>
-<li><b>Is Betternet VPN safe?</b> Betternet VPN claims to protect your privacy and security by encrypting your data and hiding your IP address. However, it also collects some logs and shares them with third parties, such as advertisers and affiliates. It also uses a third-party DNS service that may leak your DNS queries. Therefore, Betternet VPN may not be very safe for your online activities.</li>
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- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fly Over the Ice and Snow with Aerosoft Antarctica X v1 00 rar FSX.md b/spaces/raedeXanto/academic-chatgpt-beta/Fly Over the Ice and Snow with Aerosoft Antarctica X v1 00 rar FSX.md
deleted file mode 100644
index 017775c6d814e8d07c33ef191b8b7d2ee761a98e..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Fly Over the Ice and Snow with Aerosoft Antarctica X v1 00 rar FSX.md	
+++ /dev/null
@@ -1,86 +0,0 @@
-<br />
-<h1>Aerosoft Antarctica X v1 00 rar FSX: A Review</h1>
- <p>If you are looking for a new and exciting destination to explore in your flight simulator, you might want to consider Aerosoft Antarctica X. This is one of the most technologically advanced sceneries ever created for FSX and P3D, covering the entire continent of Antarctica with high definition terrain mesh, aerial and satellite images, and a coastline with millions of floating ice sheets. In this review, we will take a closer look at what Aerosoft Antarctica X has to offer, and why you should give it a try.</p>
- <h2>Introduction</h2>
- <h3>What is Aerosoft Antarctica X?</h3>
- <p>Aerosoft Antarctica X is a scenery add-on for FSX and P3D that simulates the Antarctic continent as realistically as possible. It was developed by LimeSim, a team of scenery designers who specialize in creating challenging and remote locations for flight simulation. Aerosoft Antarctica X was released in 2010 and updated to version 1.1 in 2015, adding compatibility with P3D v2-v4.</p>
-<h2>Aerosoft Antarctica X v1 00 rar FSX</h2><br /><p><b><b>Download Zip</b> &gt; <a href="https://tinourl.com/2uL2AA">https://tinourl.com/2uL2AA</a></b></p><br /><br />
- <h3>Why fly over Antarctica?</h3>
- <p>Antarctica is the coldest, driest, windiest, and most isolated continent on Earth. It covers about 14 million square kilometers (5.4 million square miles), which is almost twice the size of Australia. It is also home to about 90% of the world's ice and 70% of its fresh water. Flying over Antarctica is a unique and rewarding experience that will challenge your skills as a pilot and expose you to some of the most breathtaking landscapes you will ever see. You will witness the beauty and diversity of this continent, from its towering mountains and glaciers to its vast ice shelves and frozen seas. You will also learn about the history and science of Antarctic exploration, as you visit some of the major research stations and runways that support human activity in this harsh environment.</p>
- <h3>What are the features of Aerosoft Antarctica X?</h3>
- <p>Aerosoft Antarctica X is not just a scenery add-on, but a complete package that includes everything you need to enjoy flying over Antarctica. Some of the main features are:</p>
- <ul>
-<li>The whole continent (as far as FSX allows) in high definition terrain mesh, high detail aerial and satellite images and a coastline with millions of floating ice sheets</li>
-<li>7 major research stations included in very high detail: McMurdo (USA), Rothera (United Kingdom), Sky Blu (United Kingdom), Neumayer III (Germany), Casey (Australia), Scott (New Zealand), Sanae IV (South Africa)</li>
-<li>A total of 47 (!) runways/skiways and helipads all over the continent in various detail grades from the Russian hub to the newest Chinese skiway at an altitude of more than 13,000 ft</li>
-<li>3 extremely detailed icebreakers with helipads running on scheduled routes: RV Polarstern (Germany), USCGC Healy (USA), Fictitious Icebreaker (we call it the MS LimeSim…)</li>
-<li>One of the most popular aircraft for Antarctic operations: the Twin Otter, included in several liveries and configurations</li>
-<li>Two additional aircraft that can be used for Antarctic flights: the C-130 Hercules and the Basler BT-67</li>
-<li>An extensive and detailed manual that covers everything from installation and configuration to flight planning and execution</li>
-</ul>
- <h2>The Scenery</h2>
- <h3>The Terrain Mesh and Satellite Images</h3>
- <p>One of the most impressive aspects of Aerosoft Antarctica X is the quality and accuracy of its terrain mesh and satellite images. The developers used dozens of databases from various sources, such as NASA, USGS, ESA, DLR, BAS, AWI, etc., to create a seamless representation of the Antarctic surface. The result is a scenery that is superior to any other dataset available for flight simulation or even for professional use. You will not find a better altitude model or better photo coverage anywhere else.</p>
- <p>The terrain mesh has a resolution of 76 meters per pixel, which means that every detail of the Antarctic topography is visible, from the highest peaks to the smallest crevasses. The satellite images have a resolution of 15 meters per pixel for most areas, which means that you can see individual buildings, vehicles, tents, etc., at many locations. Some areas have even higher resolution images up to 0.5 meters per pixel, such as McMurdo Station or Neumayer III Station.</p>
- <p>The coastline is also very realistic, with millions of floating ice sheets that move with the wind and currents. You can see them from high altitude or fly low over them for some fun. They also affect your landing performance if you try to land on them.</p>
- <h3>The Research Stations and Runways</h3>
- <p>Aerosoft Antarctica X includes 7 major research stations in very high detail: McMurdo (USA), Rothera (United Kingdom), Sky Blu (United Kingdom), Neumayer III (Germany), Casey (Australia), Scott (New Zealand), Sanae IV (South Africa). These are some of the most important bases for scientific research and logistics in Antarctica, hosting hundreds of personnel during the summer season.</p>
- <p>Each station has been modeled with great attention to detail, using custom objects, textures, animations, sounds, etc., based on real photos and videos. You will find everything from buildings and antennas to vehicles and equipment at these locations. You will also see people walking around or working on various tasks.</p>
- <p>In addition to these stations, Aerosoft Antarctica X also includes 40 other runways/skiways/helipads all over the continent in various detail grades. Some are well-known hubs for Antarctic aviation, such as Novolazarevskaya (Russia) or Patriot Hills (Chile). Others are more remote or temporary sites that are used for specific missions or expeditions.</p>
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- <p>All runways/skiways/helipads have been placed accurately according to real coordinates and elevation data. They also have realistic markings, lighting systems (where applicable), windsocks, etc., to help you land safely.</p>
- <h3>The Icebreakers and Helipads</h3>
- <p>Aerosoft Antarctica X also features 3 extremely detailed icebreakers with helipads running on scheduled routes: RV Polarstern (Germany), USCGC Healy (USA), Fictitious Icebreaker (we call it the MS LimeSim…). These are some of the most advanced vessels that operate in Antarctic waters, providing support for scientific research and logistics.</p>
- <p>Each icebreaker has been modeled with stunning realism, using custom objects, textures, animations, sounds, etc., based on real photos and videos. You will find everything from radar dishes and cranes to lifeboats and helicopters on board these ships.</p>
- <p>You can also land on their helipads using any helicopter or VTOL aircraft that you have installed in your simulator. The helipads have realistic markings, lighting systems (where applicable), windsocks</p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rafaelpadilla/coco_metrics/coco_metrics.py b/spaces/rafaelpadilla/coco_metrics/coco_metrics.py
deleted file mode 100644
index 2ca7b70c207e27fed9cbb2b0da48e37fcebd917e..0000000000000000000000000000000000000000
--- a/spaces/rafaelpadilla/coco_metrics/coco_metrics.py
+++ /dev/null
@@ -1,204 +0,0 @@
-from typing import Dict, List, Union
-from pathlib import Path
-import datasets
-import torch
-import evaluate
-import json
-from tqdm import tqdm
-from coco_metrics.pycocotools.coco import COCO 
-from coco_metrics.coco_evaluate import COCOEvaluator 
-from coco_metrics.utils import _TYPING_PREDICTION, _TYPING_REFERENCE
-
-_DESCRIPTION = "This class evaluates object detection models using the COCO dataset \
-    and its evaluation metrics."
-_HOMEPAGE = "https://cocodataset.org"
-_CITATION = """
-    @misc{lin2015microsoft, \
-      title={Microsoft COCO: Common Objects in Context},
-      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and \
-          Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick \
-              and Piotr Dollár},
-      year={2015},
-      eprint={1405.0312},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-"""
-_REFERENCE_URLS = [
-    "https://ieeexplore.ieee.org/abstract/document/9145130",
-    "https://www.mdpi.com/2079-9292/10/3/279",
-    "https://cocodataset.org/#detection-eval",
-]
-_KWARGS_DESCRIPTION = """\
-Computes COCO metrics for object detection: AP(mAP) and its variants.
-
-Args:
-    coco (COCO): COCO Evaluator object for evaluating predictions.
-    **kwargs: Additional keyword arguments forwarded to evaluate.Metrics.
-"""
-
-class EvaluateObjectDetection(evaluate.Metric):
-    """
-    Class for evaluating object detection models.
-    """
-
-    def __init__(self, json_gt: Union[Path, Dict], iou_type: str = "bbox", **kwargs):
-        """
-        Initializes the EvaluateObjectDetection class.
-
-        Args:
-            json_gt: JSON with ground-truth annotations in COCO format.
-            # coco_groundtruth (COCO): COCO Evaluator object for evaluating predictions.
-            **kwargs: Additional keyword arguments forwarded to evaluate.Metrics.
-        """
-        super().__init__(**kwargs)
-
-        # Create COCO object from ground-truth annotations
-        if isinstance(json_gt, Path):
-            assert json_gt.exists(), f"Path {json_gt} does not exist."
-            with open(json_gt) as f:
-                json_data = json.load(f)
-        elif isinstance(json_gt, dict):
-            json_data = json_gt
-        coco = COCO(json_data)
-
-        self.coco_evaluator = COCOEvaluator(coco, [iou_type])
-
-    def remove_classes(self, classes_to_remove: List[str]):
-        to_remove = [c.upper() for c in classes_to_remove]
-        cats = {}
-        for id, cat in self.coco_evaluator.coco_eval["bbox"].cocoGt.cats.items():
-            if cat["name"].upper() not in to_remove:
-                cats[id] = cat
-        self.coco_evaluator.coco_eval["bbox"].cocoGt.cats = cats
-        self.coco_evaluator.coco_gt.cats = cats
-        self.coco_evaluator.coco_gt.dataset["categories"] = list(cats.values())
-        self.coco_evaluator.coco_eval["bbox"].params.catIds = [c["id"] for c in cats.values()]
-        
-    def _info(self):
-        """
-        Returns the MetricInfo object with information about the module.
-
-        Returns:
-            evaluate.MetricInfo: Metric information object.
-        """
-        return evaluate.MetricInfo(
-            module_type="metric",
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            # This defines the format of each prediction and reference
-            features=datasets.Features(
-                {
-                    "predictions": [
-                        datasets.Features(
-                            {
-                                "scores": datasets.Sequence(datasets.Value("float")),
-                                "labels": datasets.Sequence(datasets.Value("int64")),
-                                "boxes": datasets.Sequence(
-                                    datasets.Sequence(datasets.Value("float"))
-                                ),
-                            }
-                        )
-                    ],
-                    "references": [
-                        datasets.Features(
-                            {
-                                "image_id": datasets.Sequence(datasets.Value("int64")),
-                            }
-                        )
-                    ],
-                }
-            ),
-            # Homepage of the module for documentation
-            homepage=_HOMEPAGE,
-            # Additional links to the codebase or references
-            reference_urls=_REFERENCE_URLS,
-        )
-
-    def _preprocess(
-        self, predictions: List[Dict[str, torch.Tensor]]
-    ) -> List[_TYPING_PREDICTION]:
-        """
-        Preprocesses the predictions before computing the scores.
-
-        Args:
-            predictions (List[Dict[str, torch.Tensor]]): A list of prediction dicts.
-
-        Returns:
-            List[_TYPING_PREDICTION]: A list of preprocessed prediction dicts.
-        """
-        processed_predictions = []
-        for pred in predictions:
-            processed_pred: _TYPING_PREDICTION = {}
-            for k, val in pred.items():
-                if isinstance(val, torch.Tensor):
-                    val = val.detach().cpu().tolist()
-                processed_pred[k] = val
-            processed_predictions.append(processed_pred)
-        return processed_predictions
-
-    def _clear_predictions(self, predictions):
-        # Remove unnecessary keys from predictions
-        required = ["scores", "labels", "boxes"]
-        ret = []
-        for prediction in predictions:
-            ret.append({k: v for k, v in prediction.items() if k in required})
-        return ret
-    
-    def _clear_references(self, references):
-        required = [""]
-        ret = []
-        for ref in references:
-            ret.append({k: v for k, v in ref.items() if k in required})
-        return ret
-                
-    def add(self, *, prediction = None, reference = None, **kwargs):
-        """
-        Preprocesses the predictions and references and calls the parent class function.
-
-        Args:
-            prediction: A list of prediction dicts.
-            reference: A list of reference dicts.
-            **kwargs: Additional keyword arguments.
-        """
-        if prediction is not None:
-            prediction = self._clear_predictions(prediction)
-            prediction = self._preprocess(prediction)
-        
-        super(evaluate.Metric, self).add(prediction=prediction, references=reference, **kwargs)
-
-    def _compute(
-        self,
-        predictions: List[List[_TYPING_PREDICTION]],
-        references: List[List[_TYPING_REFERENCE]],
-    ) -> Dict[str, Dict[str, float]]:
-        """
-        Returns the evaluation scores.
-
-        Args:
-            predictions (List[List[_TYPING_PREDICTION]]): A list of predictions.
-            references (List[List[_TYPING_REFERENCE]]): A list of references.
-
-        Returns:
-            Dict: A dictionary containing evaluation scores.
-        """
-        # Loop through each pair of prediction and reference obtained per batch
-        pbar = tqdm(zip(predictions, references), desc="Updating detections", total=len(predictions))
-        for pred, ref in pbar:
-            res = {}  # {image_id} : prediction
-            for target, output in zip(ref, pred):
-                res[target["image_id"][0]] = output
-            self.coco_evaluator.update(res)
-        
-        print("Synchronizing processes")
-        self.coco_evaluator.synchronize_between_processes()
-        
-        print("Accumulating values")
-        self.coco_evaluator.accumulate()
-        
-        print("Summarizing results")
-        self.coco_evaluator.summarize()
-        
-        stats = self.coco_evaluator.get_results()
-        return stats
diff --git a/spaces/rajistics/Ask-Wiki/app.py b/spaces/rajistics/Ask-Wiki/app.py
deleted file mode 100644
index bf1f98a2eb3f74cbd9644fc90cf4adad4e810c95..0000000000000000000000000000000000000000
--- a/spaces/rajistics/Ask-Wiki/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import gradio as gr
-from sentence_transformers import SentenceTransformer, CrossEncoder, util
-from transformers import pipeline
-import torch
-import pickle
-import pandas as pd
-import gradio as gr
-
-
-##Speech Recognition
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-def speech_to_text(speech):
-    text = asr(speech)["text"]
-    return text
-
-bi_encoder = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
-cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
-corpus=pd.read_pickle("corpus.pkl")
-corpus_embeddings=pd.read_pickle("corpus_embeddings_cpu.pkl")
-
-
-def search(query,top_k=100):
-    print("Top 3 Answer by the NSE:")
-    print()
-    ans=[]
-    ##### Sematic Search #####
-    # Encode the query using the bi-encoder and find potentially relevant passages
-    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
-    hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
-    hits = hits[0]  # Get the hits for the first query
-    ##### Re-Ranking #####
-    # Now, score all retrieved passages with the cross_encoder
-    cross_inp = [[query, corpus[hit['corpus_id']]] for hit in hits]
-    cross_scores = cross_encoder.predict(cross_inp)
-    # Sort results by the cross-encoder scores
-    for idx in range(len(cross_scores)):
-        hits[idx]['cross-score'] = cross_scores[idx]
-    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
-    
-    for idx, hit in enumerate(hits[0:3]):
-        ans.append(corpus[hit['corpus_id']])
-    return ans[0],ans[1],ans[2]
-
-
-demo = gr.Blocks()
-with demo:
-    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
-    b1 = gr.Button("Recognize Speech")
-    text = gr.Textbox()
-    b1.click(speech_to_text, inputs=audio_file, outputs=text)
-    b2 = gr.Button("Ask Wiki")
-    print(text)
-    out1 = gr.Textbox()
-    out2 = gr.Textbox()
-    out3 = gr.Textbox()
-    b2.click(search, inputs=text, outputs=[out1,out2,out3])
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/rashmi/h2oai-predict-llm/README.md b/spaces/rashmi/h2oai-predict-llm/README.md
deleted file mode 100644
index 603666266daaf552fc671b29c2dac5311376b56b..0000000000000000000000000000000000000000
--- a/spaces/rashmi/h2oai-predict-llm/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: H2oai Predict Llm
-emoji: 🐨
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-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/razfar/anything-counter/models/yolo.py b/spaces/razfar/anything-counter/models/yolo.py
deleted file mode 100644
index df06a6ba053e663a60574dde22900825ff17e0db..0000000000000000000000000000000000000000
--- a/spaces/razfar/anything-counter/models/yolo.py
+++ /dev/null
@@ -1,757 +0,0 @@
-import argparse
-import logging
-import sys
-from copy import deepcopy
-
-sys.path.append("./")  # to run '$ python *.py' files in subdirectories
-logger = logging.getLogger(__name__)
-
-from models.common import *
-from models.experimental import *
-from utils.autoanchor import check_anchor_order
-from utils.general import make_divisible, check_file, set_logging
-from utils.torch_utils import (
-    time_synchronized,
-    fuse_conv_and_bn,
-    model_info,
-    scale_img,
-    initialize_weights,
-    select_device,
-    copy_attr,
-)
-from utils.loss import SigmoidBin
-
-try:
-    import thop  # for FLOPS computation
-except ImportError:
-    thop = None
-
-
-class Detect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super(Detect, self).__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer(
-            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
-        )  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch
-        )  # output conv
-
-    def forward(self, x):
-        # x = x.copy()  # for profiling
-        z = []  # inference output
-        self.training |= self.export
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = (
-                x[i]
-                .view(bs, self.na, self.no, ny, nx)
-                .permute(0, 1, 3, 4, 2)
-                .contiguous()
-            )
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
-                    i
-                ]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class IDetect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super(IDetect, self).__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer(
-            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
-        )  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch
-        )  # output conv
-
-        self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
-        self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)
-
-    def forward(self, x):
-        # x = x.copy()  # for profiling
-        z = []  # inference output
-        self.training |= self.export
-        for i in range(self.nl):
-            x[i] = self.m[i](self.ia[i](x[i]))  # conv
-            x[i] = self.im[i](x[i])
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = (
-                x[i]
-                .view(bs, self.na, self.no, ny, nx)
-                .permute(0, 1, 3, 4, 2)
-                .contiguous()
-            )
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
-                    i
-                ]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class IAuxDetect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super(IAuxDetect, self).__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer(
-            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
-        )  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch[: self.nl]
-        )  # output conv
-        self.m2 = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch[self.nl :]
-        )  # output conv
-
-        self.ia = nn.ModuleList(ImplicitA(x) for x in ch[: self.nl])
-        self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch[: self.nl])
-
-    def forward(self, x):
-        # x = x.copy()  # for profiling
-        z = []  # inference output
-        self.training |= self.export
-        for i in range(self.nl):
-            x[i] = self.m[i](self.ia[i](x[i]))  # conv
-            x[i] = self.im[i](x[i])
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = (
-                x[i]
-                .view(bs, self.na, self.no, ny, nx)
-                .permute(0, 1, 3, 4, 2)
-                .contiguous()
-            )
-
-            x[i + self.nl] = self.m2[i](x[i + self.nl])
-            x[i + self.nl] = (
-                x[i + self.nl]
-                .view(bs, self.na, self.no, ny, nx)
-                .permute(0, 1, 3, 4, 2)
-                .contiguous()
-            )
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
-                    i
-                ]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x[: self.nl])
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class IBin(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=(), bin_count=21):  # detection layer
-        super(IBin, self).__init__()
-        self.nc = nc  # number of classes
-        self.bin_count = bin_count
-
-        self.w_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0)
-        self.h_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0)
-        # classes, x,y,obj
-        self.no = (
-            nc + 3 + self.w_bin_sigmoid.get_length() + self.h_bin_sigmoid.get_length()
-        )  # w-bce, h-bce
-        # + self.x_bin_sigmoid.get_length() + self.y_bin_sigmoid.get_length()
-
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer(
-            "anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)
-        )  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(
-            nn.Conv2d(x, self.no * self.na, 1) for x in ch
-        )  # output conv
-
-        self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
-        self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)
-
-    def forward(self, x):
-
-        # self.x_bin_sigmoid.use_fw_regression = True
-        # self.y_bin_sigmoid.use_fw_regression = True
-        self.w_bin_sigmoid.use_fw_regression = True
-        self.h_bin_sigmoid.use_fw_regression = True
-
-        # x = x.copy()  # for profiling
-        z = []  # inference output
-        self.training |= self.export
-        for i in range(self.nl):
-            x[i] = self.m[i](self.ia[i](x[i]))  # conv
-            x[i] = self.im[i](x[i])
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = (
-                x[i]
-                .view(bs, self.na, self.no, ny, nx)
-                .permute(0, 1, 3, 4, 2)
-                .contiguous()
-            )
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[
-                    i
-                ]  # xy
-                # y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-
-                # px = (self.x_bin_sigmoid.forward(y[..., 0:12]) + self.grid[i][..., 0]) * self.stride[i]
-                # py = (self.y_bin_sigmoid.forward(y[..., 12:24]) + self.grid[i][..., 1]) * self.stride[i]
-
-                pw = (
-                    self.w_bin_sigmoid.forward(y[..., 2:24])
-                    * self.anchor_grid[i][..., 0]
-                )
-                ph = (
-                    self.h_bin_sigmoid.forward(y[..., 24:46])
-                    * self.anchor_grid[i][..., 1]
-                )
-
-                # y[..., 0] = px
-                # y[..., 1] = py
-                y[..., 2] = pw
-                y[..., 3] = ph
-
-                y = torch.cat((y[..., 0:4], y[..., 46:]), dim=-1)
-
-                z.append(y.view(bs, -1, y.shape[-1]))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
-    def __init__(
-        self, cfg="yolor-csp-c.yaml", ch=3, nc=None, anchors=None
-    ):  # model, input channels, number of classes
-        super(Model, self).__init__()
-        self.traced = False
-        if isinstance(cfg, dict):
-            self.yaml = cfg  # model dict
-        else:  # is *.yaml
-            import yaml  # for torch hub
-
-            self.yaml_file = Path(cfg).name
-            with open(cfg) as f:
-                self.yaml = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
-
-        # Define model
-        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
-        if nc and nc != self.yaml["nc"]:
-            logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
-            self.yaml["nc"] = nc  # override yaml value
-        if anchors:
-            logger.info(f"Overriding model.yaml anchors with anchors={anchors}")
-            self.yaml["anchors"] = round(anchors)  # override yaml value
-        self.model, self.save = parse_model(
-            deepcopy(self.yaml), ch=[ch]
-        )  # model, savelist
-        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
-        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, Detect):
-            s = 256  # 2x min stride
-            m.stride = torch.tensor(
-                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]
-            )  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
-        if isinstance(m, IDetect):
-            s = 256  # 2x min stride
-            m.stride = torch.tensor(
-                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]
-            )  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
-        if isinstance(m, IAuxDetect):
-            s = 256  # 2x min stride
-            m.stride = torch.tensor(
-                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))[:4]]
-            )  # forward
-            # print(m.stride)
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_aux_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
-        if isinstance(m, IBin):
-            s = 256  # 2x min stride
-            m.stride = torch.tensor(
-                [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]
-            )  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases_bin()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
-
-        # Init weights, biases
-        initialize_weights(self)
-        self.info()
-        logger.info("")
-
-    def forward(self, x, augment=False, profile=False):
-        if augment:
-            img_size = x.shape[-2:]  # height, width
-            s = [1, 0.83, 0.67]  # scales
-            f = [None, 3, None]  # flips (2-ud, 3-lr)
-            y = []  # outputs
-            for si, fi in zip(s, f):
-                xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
-                yi = self.forward_once(xi)[0]  # forward
-                # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
-                yi[..., :4] /= si  # de-scale
-                if fi == 2:
-                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
-                elif fi == 3:
-                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
-                y.append(yi)
-            return torch.cat(y, 1), None  # augmented inference, train
-        else:
-            return self.forward_once(x, profile)  # single-scale inference, train
-
-    def forward_once(self, x, profile=False):
-        y, dt = [], []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = (
-                    y[m.f]
-                    if isinstance(m.f, int)
-                    else [x if j == -1 else y[j] for j in m.f]
-                )  # from earlier layers
-
-            if not hasattr(self, "traced"):
-                self.traced = False
-
-            if self.traced:
-                if (
-                    isinstance(m, Detect)
-                    or isinstance(m, IDetect)
-                    or isinstance(m, IAuxDetect)
-                ):
-                    break
-
-            if profile:
-                c = isinstance(m, (Detect, IDetect, IAuxDetect, IBin))
-                o = (
-                    thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0]
-                    / 1e9
-                    * 2
-                    if thop
-                    else 0
-                )  # FLOPS
-                for _ in range(10):
-                    m(x.copy() if c else x)
-                t = time_synchronized()
-                for _ in range(10):
-                    m(x.copy() if c else x)
-                dt.append((time_synchronized() - t) * 100)
-                print("%10.1f%10.0f%10.1fms %-40s" % (o, m.np, dt[-1], m.type))
-
-            x = m(x)  # run
-
-            y.append(x if m.i in self.save else None)  # save output
-
-        if profile:
-            print("%.1fms total" % sum(dt))
-        return x
-
-    def _initialize_biases(
-        self, cf=None
-    ):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(
-                8 / (640 / s) ** 2
-            )  # obj (8 objects per 640 image)
-            b.data[:, 5:] += (
-                math.log(0.6 / (m.nc - 0.99))
-                if cf is None
-                else torch.log(cf / cf.sum())
-            )  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _initialize_aux_biases(
-        self, cf=None
-    ):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Detect() module
-        for mi, mi2, s in zip(m.m, m.m2, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(
-                8 / (640 / s) ** 2
-            )  # obj (8 objects per 640 image)
-            b.data[:, 5:] += (
-                math.log(0.6 / (m.nc - 0.99))
-                if cf is None
-                else torch.log(cf / cf.sum())
-            )  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-            b2 = mi2.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b2.data[:, 4] += math.log(
-                8 / (640 / s) ** 2
-            )  # obj (8 objects per 640 image)
-            b2.data[:, 5:] += (
-                math.log(0.6 / (m.nc - 0.99))
-                if cf is None
-                else torch.log(cf / cf.sum())
-            )  # cls
-            mi2.bias = torch.nn.Parameter(b2.view(-1), requires_grad=True)
-
-    def _initialize_biases_bin(
-        self, cf=None
-    ):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Bin() module
-        bc = m.bin_count
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            old = b[:, (0, 1, 2, bc + 3)].data
-            obj_idx = 2 * bc + 4
-            b[:, :obj_idx].data += math.log(0.6 / (bc + 1 - 0.99))
-            b[:, obj_idx].data += math.log(
-                8 / (640 / s) ** 2
-            )  # obj (8 objects per 640 image)
-            b[:, (obj_idx + 1) :].data += (
-                math.log(0.6 / (m.nc - 0.99))
-                if cf is None
-                else torch.log(cf / cf.sum())
-            )  # cls
-            b[:, (0, 1, 2, bc + 3)].data = old
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _print_biases(self):
-        m = self.model[-1]  # Detect() module
-        for mi in m.m:  # from
-            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(
-                ("%6g Conv2d.bias:" + "%10.3g" * 6)
-                % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())
-            )
-
-    # def _print_weights(self):
-    #     for m in self.model.modules():
-    #         if type(m) is Bottleneck:
-    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print("Fusing layers... ")
-        for m in self.model.modules():
-            if isinstance(m, RepConv):
-                # print(f" fuse_repvgg_block")
-                m.fuse_repvgg_block()
-            elif isinstance(m, RepConv_OREPA):
-                # print(f" switch_to_deploy")
-                m.switch_to_deploy()
-            elif type(m) is Conv and hasattr(m, "bn"):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, "bn")  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
-        self.info()
-        return self
-
-    def nms(self, mode=True):  # add or remove NMS module
-        present = type(self.model[-1]) is NMS  # last layer is NMS
-        if mode and not present:
-            print("Adding NMS... ")
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name="%s" % m.i, module=m)  # add
-            self.eval()
-        elif not mode and present:
-            print("Removing NMS... ")
-            self.model = self.model[:-1]  # remove
-        return self
-
-    def autoshape(self):  # add autoShape module
-        print("Adding autoShape... ")
-        m = autoShape(self)  # wrap model
-        copy_attr(
-            m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=()
-        )  # copy attributes
-        return m
-
-    def info(self, verbose=False, img_size=640):  # print model information
-        model_info(self, verbose, img_size)
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    logger.info(
-        "\n%3s%18s%3s%10s  %-40s%-30s"
-        % ("", "from", "n", "params", "module", "arguments")
-    )
-    anchors, nc, gd, gw = (
-        d["anchors"],
-        d["nc"],
-        d["depth_multiple"],
-        d["width_multiple"],
-    )
-    na = (
-        (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors
-    )  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(
-        d["backbone"] + d["head"]
-    ):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [
-            nn.Conv2d,
-            Conv,
-            RobustConv,
-            RobustConv2,
-            DWConv,
-            GhostConv,
-            RepConv,
-            RepConv_OREPA,
-            DownC,
-            SPP,
-            SPPF,
-            SPPCSPC,
-            GhostSPPCSPC,
-            MixConv2d,
-            Focus,
-            Stem,
-            GhostStem,
-            CrossConv,
-            Bottleneck,
-            BottleneckCSPA,
-            BottleneckCSPB,
-            BottleneckCSPC,
-            RepBottleneck,
-            RepBottleneckCSPA,
-            RepBottleneckCSPB,
-            RepBottleneckCSPC,
-            Res,
-            ResCSPA,
-            ResCSPB,
-            ResCSPC,
-            RepRes,
-            RepResCSPA,
-            RepResCSPB,
-            RepResCSPC,
-            ResX,
-            ResXCSPA,
-            ResXCSPB,
-            ResXCSPC,
-            RepResX,
-            RepResXCSPA,
-            RepResXCSPB,
-            RepResXCSPC,
-            Ghost,
-            GhostCSPA,
-            GhostCSPB,
-            GhostCSPC,
-            SwinTransformerBlock,
-            STCSPA,
-            STCSPB,
-            STCSPC,
-            SwinTransformer2Block,
-            ST2CSPA,
-            ST2CSPB,
-            ST2CSPC,
-        ]:
-            c1, c2 = ch[f], args[0]
-            if c2 != no:  # if not output
-                c2 = make_divisible(c2 * gw, 8)
-
-            args = [c1, c2, *args[1:]]
-            if m in [
-                DownC,
-                SPPCSPC,
-                GhostSPPCSPC,
-                BottleneckCSPA,
-                BottleneckCSPB,
-                BottleneckCSPC,
-                RepBottleneckCSPA,
-                RepBottleneckCSPB,
-                RepBottleneckCSPC,
-                ResCSPA,
-                ResCSPB,
-                ResCSPC,
-                RepResCSPA,
-                RepResCSPB,
-                RepResCSPC,
-                ResXCSPA,
-                ResXCSPB,
-                ResXCSPC,
-                RepResXCSPA,
-                RepResXCSPB,
-                RepResXCSPC,
-                GhostCSPA,
-                GhostCSPB,
-                GhostCSPC,
-                STCSPA,
-                STCSPB,
-                STCSPC,
-                ST2CSPA,
-                ST2CSPB,
-                ST2CSPC,
-            ]:
-                args.insert(2, n)  # number of repeats
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum([ch[x] for x in f])
-        elif m is Chuncat:
-            c2 = sum([ch[x] for x in f])
-        elif m is Shortcut:
-            c2 = ch[f[0]]
-        elif m is Foldcut:
-            c2 = ch[f] // 2
-        elif m in [Detect, IDetect, IAuxDetect, IBin]:
-            args.append([ch[x] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-        elif m is ReOrg:
-            c2 = ch[f] * 4
-        elif m is Contract:
-            c2 = ch[f] * args[0] ** 2
-        elif m is Expand:
-            c2 = ch[f] // args[0] ** 2
-        else:
-            c2 = ch[f]
-
-        m_ = (
-            nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)
-        )  # module
-        t = str(m)[8:-2].replace("__main__.", "")  # module type
-        np = sum([x.numel() for x in m_.parameters()])  # number params
-        m_.i, m_.f, m_.type, m_.np = (
-            i,
-            f,
-            t,
-            np,
-        )  # attach index, 'from' index, type, number params
-        logger.info("%3s%18s%3s%10.0f  %-40s%-30s" % (i, f, n, np, t, args))  # print
-        save.extend(
-            x % i for x in ([f] if isinstance(f, int) else f) if x != -1
-        )  # append to savelist
-        layers.append(m_)
-        if i == 0:
-            ch = []
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--cfg", type=str, default="yolor-csp-c.yaml", help="model.yaml"
-    )
-    parser.add_argument(
-        "--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
-    )
-    parser.add_argument("--profile", action="store_true", help="profile model speed")
-    opt = parser.parse_args()
-    opt.cfg = check_file(opt.cfg)  # check file
-    set_logging()
-    device = select_device(opt.device)
-
-    # Create model
-    model = Model(opt.cfg).to(device)
-    model.train()
-
-    if opt.profile:
-        img = torch.rand(1, 3, 640, 640).to(device)
-        y = model(img, profile=True)
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Gadget Wide Beta Download.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Gadget Wide Beta Download.md
deleted file mode 100644
index 6ef1a0e10c1a355cf31c172327110e90e2e7adcf..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Gadget Wide Beta Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>gadget wide beta download</h2><br /><p><b><b>DOWNLOAD</b> &ndash;&ndash;&ndash; <a href="https://urlgoal.com/2uCLGu">https://urlgoal.com/2uCLGu</a></b></p><br /><br />
-
-aceEditor, The 'Ace' Editor as a HTML Widget. acepack, ACE ... aemo, Download and Process AEMO Price and Demand Data. AEP, Statistical ... arsenal, An Arsenal of 'R' Functions for Large-Scale Statistical Summaries ... betafunctions, Functions for Working with Two- And Four-Parameter Beta Probability Distributions. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/renumics/cifar10-embeddings/run.py b/spaces/renumics/cifar10-embeddings/run.py
deleted file mode 100644
index 77452693a7df906c1e25a82808c8dd5a16f24aac..0000000000000000000000000000000000000000
--- a/spaces/renumics/cifar10-embeddings/run.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import pickle
-from renumics import spotlight
-import os
-
-if __name__ == "__main__":
-    cache_file = "dataset_cache.pkl"
-    if os.path.exists(cache_file):
-        # Load dataset from cache
-        with open(cache_file, "rb") as file:
-            df = pickle.load(file)
-        print("Dataset loaded from cache.")
-
-
-        while True:
-            df = df.drop(columns=["embedding_ft", "nn_image", "embedding_foundation"])
-            df = df.sample(10000, random_state=42).reset_index(drop=True)
-            dtypes = {
-                "image": spotlight.Image,
-                "embedding_foundation_precalc": spotlight.Embedding,
-            }
-            view = spotlight.show(
-                df,
-                dtype=dtypes,
-                port=7860, 
-                host="0.0.0.0",
-                allow_filebrowsing=False 
-            )
-
-            view.close()
-
-    else:
-        print(f"Dataset {cache_file} not found. Please run prepare.py first.")
-
-
diff --git a/spaces/rgres/Seg2Sat/frontend/README.md b/spaces/rgres/Seg2Sat/frontend/README.md
deleted file mode 100644
index 374efec4c10bf004b18a3faa32566c225a8377af..0000000000000000000000000000000000000000
--- a/spaces/rgres/Seg2Sat/frontend/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# create-svelte
-
-Everything you need to build a Svelte project, powered by [`create-svelte`](https://github.com/sveltejs/kit/tree/master/packages/create-svelte).
-
-## Creating a project
-
-If you're seeing this, you've probably already done this step. Congrats!
-
-```bash
-# create a new project in the current directory
-npm init svelte
-
-# create a new project in my-app
-npm init svelte my-app
-```
-
-## Developing
-
-Once you've created a project and installed dependencies with `npm install` (or `pnpm install` or `yarn`), start a development server:
-
-```bash
-npm run dev
-
-# or start the server and open the app in a new browser tab
-npm run dev -- --open
-```
-
-## Building
-
-To create a production version of your app:
-
-```bash
-npm run build
-```
-
-You can preview the production build with `npm run preview`.
-
-> To deploy your app, you may need to install an [adapter](https://kit.svelte.dev/docs/adapters) for your target environment.
diff --git a/spaces/riccorl/relik-entity-linking/relik/inference/data/tokenizers/whitespace_tokenizer.py b/spaces/riccorl/relik-entity-linking/relik/inference/data/tokenizers/whitespace_tokenizer.py
deleted file mode 100644
index 537ab6fe21eb4f9378d96d7cebfbc8cb12c36104..0000000000000000000000000000000000000000
--- a/spaces/riccorl/relik-entity-linking/relik/inference/data/tokenizers/whitespace_tokenizer.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import re
-from typing import List, Union
-
-from overrides import overrides
-
-from relik.inference.data.objects import Word
-from relik.inference.data.tokenizers.base_tokenizer import BaseTokenizer
-
-
-class WhitespaceTokenizer(BaseTokenizer):
-    """
-    A :obj:`Tokenizer` that splits the text on spaces.
-    """
-
-    def __init__(self):
-        super(WhitespaceTokenizer, self).__init__()
-        self.whitespace_regex = re.compile(r"\S+")
-
-    def __call__(
-        self,
-        texts: Union[str, List[str], List[List[str]]],
-        is_split_into_words: bool = False,
-        **kwargs,
-    ) -> List[List[Word]]:
-        """
-        Tokenize the input into single words by splitting on spaces.
-
-        Args:
-            texts (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
-                Text to tag. It can be a single string, a batch of string and pre-tokenized strings.
-            is_split_into_words (:obj:`bool`, optional, defaults to :obj:`False`):
-                If :obj:`True` and the input is a string, the input is split on spaces.
-
-        Returns:
-            :obj:`List[List[Word]]`: The input text tokenized in single words.
-
-        Example::
-
-            >>> from nlp_preprocessing_wrappers import WhitespaceTokenizer
-
-            >>> whitespace_tokenizer = WhitespaceTokenizer()
-            >>> whitespace_tokenizer("Mary sold the car to John .")
-
-        """
-        # check if input is batched or a single sample
-        is_batched = self.check_is_batched(texts, is_split_into_words)
-
-        if is_batched:
-            tokenized = self.tokenize_batch(texts)
-        else:
-            tokenized = self.tokenize(texts)
-
-        return tokenized
-
-    @overrides
-    def tokenize(self, text: Union[str, List[str]]) -> List[Word]:
-        if not isinstance(text, (str, list)):
-            raise ValueError(
-                f"text must be either `str` or `list`, found: `{type(text)}`"
-            )
-
-        if isinstance(text, list):
-            text = " ".join(text)
-        return [
-            Word(t[0], i, start_char=t[1], end_char=t[2])
-            for i, t in enumerate(
-                (m.group(0), m.start(), m.end())
-                for m in self.whitespace_regex.finditer(text)
-            )
-        ]
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/samplers/sampling_result.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/samplers/sampling_result.py
deleted file mode 100644
index 11a02c5d95a4d633dfea26df7fb3e440494a8be7..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/bbox/samplers/sampling_result.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class SamplingResult(util_mixins.NiceRepr):
-    """Bbox sampling result.
-
-    Example:
-        >>> # xdoctest: +IGNORE_WANT
-        >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-        >>> self = SamplingResult.random(rng=10)
-        >>> print(f'self = {self}')
-        self = <SamplingResult({
-            'neg_bboxes': torch.Size([12, 4]),
-            'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
-            'num_gts': 4,
-            'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
-            'pos_bboxes': torch.Size([0, 4]),
-            'pos_inds': tensor([], dtype=torch.int64),
-            'pos_is_gt': tensor([], dtype=torch.uint8)
-        })>
-    """
-
-    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
-                 gt_flags):
-        self.pos_inds = pos_inds
-        self.neg_inds = neg_inds
-        self.pos_bboxes = bboxes[pos_inds]
-        self.neg_bboxes = bboxes[neg_inds]
-        self.pos_is_gt = gt_flags[pos_inds]
-
-        self.num_gts = gt_bboxes.shape[0]
-        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
-
-        if gt_bboxes.numel() == 0:
-            # hack for index error case
-            assert self.pos_assigned_gt_inds.numel() == 0
-            self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4)
-        else:
-            if len(gt_bboxes.shape) < 2:
-                gt_bboxes = gt_bboxes.view(-1, 4)
-
-            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long(), :]
-
-        if assign_result.labels is not None:
-            self.pos_gt_labels = assign_result.labels[pos_inds]
-        else:
-            self.pos_gt_labels = None
-
-    @property
-    def bboxes(self):
-        """torch.Tensor: concatenated positive and negative boxes"""
-        return torch.cat([self.pos_bboxes, self.neg_bboxes])
-
-    def to(self, device):
-        """Change the device of the data inplace.
-
-        Example:
-            >>> self = SamplingResult.random()
-            >>> print(f'self = {self.to(None)}')
-            >>> # xdoctest: +REQUIRES(--gpu)
-            >>> print(f'self = {self.to(0)}')
-        """
-        _dict = self.__dict__
-        for key, value in _dict.items():
-            if isinstance(value, torch.Tensor):
-                _dict[key] = value.to(device)
-        return self
-
-    def __nice__(self):
-        data = self.info.copy()
-        data['pos_bboxes'] = data.pop('pos_bboxes').shape
-        data['neg_bboxes'] = data.pop('neg_bboxes').shape
-        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
-        body = '    ' + ',\n    '.join(parts)
-        return '{\n' + body + '\n}'
-
-    @property
-    def info(self):
-        """Returns a dictionary of info about the object."""
-        return {
-            'pos_inds': self.pos_inds,
-            'neg_inds': self.neg_inds,
-            'pos_bboxes': self.pos_bboxes,
-            'neg_bboxes': self.neg_bboxes,
-            'pos_is_gt': self.pos_is_gt,
-            'num_gts': self.num_gts,
-            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
-        }
-
-    @classmethod
-    def random(cls, rng=None, **kwargs):
-        """
-        Args:
-            rng (None | int | numpy.random.RandomState): seed or state.
-            kwargs (keyword arguments):
-                - num_preds: number of predicted boxes
-                - num_gts: number of true boxes
-                - p_ignore (float): probability of a predicted box assigned to \
-                    an ignored truth.
-                - p_assigned (float): probability of a predicted box not being \
-                    assigned.
-                - p_use_label (float | bool): with labels or not.
-
-        Returns:
-            :obj:`SamplingResult`: Randomly generated sampling result.
-
-        Example:
-            >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-            >>> self = SamplingResult.random()
-            >>> print(self.__dict__)
-        """
-        from mmdet.core.bbox import demodata
-        from mmdet.core.bbox.assigners.assign_result import AssignResult
-        from mmdet.core.bbox.samplers.random_sampler import RandomSampler
-        rng = demodata.ensure_rng(rng)
-
-        # make probabilistic?
-        num = 32
-        pos_fraction = 0.5
-        neg_pos_ub = -1
-
-        assign_result = AssignResult.random(rng=rng, **kwargs)
-
-        # Note we could just compute an assignment
-        bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
-        gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
-
-        if rng.rand() > 0.2:
-            # sometimes algorithms squeeze their data, be robust to that
-            gt_bboxes = gt_bboxes.squeeze()
-            bboxes = bboxes.squeeze()
-
-        if assign_result.labels is None:
-            gt_labels = None
-        else:
-            gt_labels = None  # todo
-
-        if gt_labels is None:
-            add_gt_as_proposals = False
-        else:
-            add_gt_as_proposals = True  # make probabilistic?
-
-        sampler = RandomSampler(
-            num,
-            pos_fraction,
-            neg_pos_ub=neg_pos_ub,
-            add_gt_as_proposals=add_gt_as_proposals,
-            rng=rng)
-        self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
-        return self
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/optimizers/layer_decay_optimizer_constructor.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/optimizers/layer_decay_optimizer_constructor.py
deleted file mode 100644
index 1bc3469e8884a7a1f0a154ab859b8079575b56ff..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/optimizers/layer_decay_optimizer_constructor.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import json
-
-from mmcv.runner import DefaultOptimizerConstructor, get_dist_info
-
-from mmdet.utils import get_root_logger
-from .builder import OPTIMIZER_BUILDERS
-
-
-def get_layer_id_for_convnext(var_name, max_layer_id):
-    """Get the layer id to set the different learning rates in ``layer_wise``
-    decay_type.
-
-    Args:
-        var_name (str): The key of the model.
-        max_layer_id (int): Maximum layer id.
-
-    Returns:
-        int: The id number corresponding to different learning rate in
-        ``LearningRateDecayOptimizerConstructor``.
-    """
-
-    if var_name in ('backbone.cls_token', 'backbone.mask_token',
-                    'backbone.pos_embed'):
-        return 0
-    elif var_name.startswith('backbone.downsample_layers'):
-        stage_id = int(var_name.split('.')[2])
-        if stage_id == 0:
-            layer_id = 0
-        elif stage_id == 1:
-            layer_id = 2
-        elif stage_id == 2:
-            layer_id = 3
-        elif stage_id == 3:
-            layer_id = max_layer_id
-        return layer_id
-    elif var_name.startswith('backbone.stages'):
-        stage_id = int(var_name.split('.')[2])
-        block_id = int(var_name.split('.')[3])
-        if stage_id == 0:
-            layer_id = 1
-        elif stage_id == 1:
-            layer_id = 2
-        elif stage_id == 2:
-            layer_id = 3 + block_id // 3
-        elif stage_id == 3:
-            layer_id = max_layer_id
-        return layer_id
-    else:
-        return max_layer_id + 1
-
-
-def get_stage_id_for_convnext(var_name, max_stage_id):
-    """Get the stage id to set the different learning rates in ``stage_wise``
-    decay_type.
-
-    Args:
-        var_name (str): The key of the model.
-        max_stage_id (int): Maximum stage id.
-
-    Returns:
-        int: The id number corresponding to different learning rate in
-        ``LearningRateDecayOptimizerConstructor``.
-    """
-
-    if var_name in ('backbone.cls_token', 'backbone.mask_token',
-                    'backbone.pos_embed'):
-        return 0
-    elif var_name.startswith('backbone.downsample_layers'):
-        return 0
-    elif var_name.startswith('backbone.stages'):
-        stage_id = int(var_name.split('.')[2])
-        return stage_id + 1
-    else:
-        return max_stage_id - 1
-
-
-@OPTIMIZER_BUILDERS.register_module()
-class LearningRateDecayOptimizerConstructor(DefaultOptimizerConstructor):
-    # Different learning rates are set for different layers of backbone.
-    # Note: Currently, this optimizer constructor is built for ConvNeXt.
-
-    def add_params(self, params, module, **kwargs):
-        """Add all parameters of module to the params list.
-
-        The parameters of the given module will be added to the list of param
-        groups, with specific rules defined by paramwise_cfg.
-
-        Args:
-            params (list[dict]): A list of param groups, it will be modified
-                in place.
-            module (nn.Module): The module to be added.
-        """
-        logger = get_root_logger()
-
-        parameter_groups = {}
-        logger.info(f'self.paramwise_cfg is {self.paramwise_cfg}')
-        num_layers = self.paramwise_cfg.get('num_layers') + 2
-        decay_rate = self.paramwise_cfg.get('decay_rate')
-        decay_type = self.paramwise_cfg.get('decay_type', 'layer_wise')
-        logger.info('Build LearningRateDecayOptimizerConstructor  '
-                    f'{decay_type} {decay_rate} - {num_layers}')
-        weight_decay = self.base_wd
-        for name, param in module.named_parameters():
-            if not param.requires_grad:
-                continue  # frozen weights
-            if len(param.shape) == 1 or name.endswith('.bias') or name in (
-                    'pos_embed', 'cls_token'):
-                group_name = 'no_decay'
-                this_weight_decay = 0.
-            else:
-                group_name = 'decay'
-                this_weight_decay = weight_decay
-            if 'layer_wise' in decay_type:
-                if 'ConvNeXt' in module.backbone.__class__.__name__:
-                    layer_id = get_layer_id_for_convnext(
-                        name, self.paramwise_cfg.get('num_layers'))
-                    logger.info(f'set param {name} as id {layer_id}')
-                else:
-                    raise NotImplementedError()
-            elif decay_type == 'stage_wise':
-                if 'ConvNeXt' in module.backbone.__class__.__name__:
-                    layer_id = get_stage_id_for_convnext(name, num_layers)
-                    logger.info(f'set param {name} as id {layer_id}')
-                else:
-                    raise NotImplementedError()
-            group_name = f'layer_{layer_id}_{group_name}'
-
-            if group_name not in parameter_groups:
-                scale = decay_rate**(num_layers - layer_id - 1)
-
-                parameter_groups[group_name] = {
-                    'weight_decay': this_weight_decay,
-                    'params': [],
-                    'param_names': [],
-                    'lr_scale': scale,
-                    'group_name': group_name,
-                    'lr': scale * self.base_lr,
-                }
-
-            parameter_groups[group_name]['params'].append(param)
-            parameter_groups[group_name]['param_names'].append(name)
-        rank, _ = get_dist_info()
-        if rank == 0:
-            to_display = {}
-            for key in parameter_groups:
-                to_display[key] = {
-                    'param_names': parameter_groups[key]['param_names'],
-                    'lr_scale': parameter_groups[key]['lr_scale'],
-                    'lr': parameter_groups[key]['lr'],
-                    'weight_decay': parameter_groups[key]['weight_decay'],
-                }
-            logger.info(f'Param groups = {json.dumps(to_display, indent=2)}')
-        params.extend(parameter_groups.values())
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/seg_heads/base_semantic_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/seg_heads/base_semantic_head.py
deleted file mode 100644
index 2b6ca145f050fbe10f348594203b6f0aa30f5695..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/seg_heads/base_semantic_head.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta, abstractmethod
-
-import torch.nn.functional as F
-from mmcv.runner import BaseModule, force_fp32
-
-from ..builder import build_loss
-from ..utils import interpolate_as
-
-
-class BaseSemanticHead(BaseModule, metaclass=ABCMeta):
-    """Base module of Semantic Head.
-
-    Args:
-        num_classes (int): the number of classes.
-        init_cfg (dict): the initialization config.
-        loss_seg (dict): the loss of the semantic head.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 init_cfg=None,
-                 loss_seg=dict(
-                     type='CrossEntropyLoss',
-                     ignore_index=255,
-                     loss_weight=1.0)):
-        super(BaseSemanticHead, self).__init__(init_cfg)
-        self.loss_seg = build_loss(loss_seg)
-        self.num_classes = num_classes
-
-    @force_fp32(apply_to=('seg_preds', ))
-    def loss(self, seg_preds, gt_semantic_seg):
-        """Get the loss of semantic head.
-
-        Args:
-            seg_preds (Tensor): The input logits with the shape (N, C, H, W).
-            gt_semantic_seg: The ground truth of semantic segmentation with
-                the shape (N, H, W).
-            label_bias: The starting number of the semantic label.
-                Default: 1.
-
-        Returns:
-            dict: the loss of semantic head.
-        """
-        if seg_preds.shape[-2:] != gt_semantic_seg.shape[-2:]:
-            seg_preds = interpolate_as(seg_preds, gt_semantic_seg)
-        seg_preds = seg_preds.permute((0, 2, 3, 1))
-
-        loss_seg = self.loss_seg(
-            seg_preds.reshape(-1, self.num_classes),  # => [NxHxW, C]
-            gt_semantic_seg.reshape(-1).long())
-        return dict(loss_seg=loss_seg)
-
-    @abstractmethod
-    def forward(self, x):
-        """Placeholder of forward function.
-
-        Returns:
-            dict[str, Tensor]: A dictionary, including features
-                and predicted scores. Required keys: 'seg_preds'
-                and 'feats'.
-        """
-        pass
-
-    def forward_train(self, x, gt_semantic_seg):
-        output = self.forward(x)
-        seg_preds = output['seg_preds']
-        return self.loss(seg_preds, gt_semantic_seg)
-
-    def simple_test(self, x, img_metas, rescale=False):
-        output = self.forward(x)
-        seg_preds = output['seg_preds']
-        seg_preds = F.interpolate(
-            seg_preds,
-            size=img_metas[0]['pad_shape'][:2],
-            mode='bilinear',
-            align_corners=False)
-
-        if rescale:
-            h, w, _ = img_metas[0]['img_shape']
-            seg_preds = seg_preds[:, :, :h, :w]
-
-            h, w, _ = img_metas[0]['ori_shape']
-            seg_preds = F.interpolate(
-                seg_preds, size=(h, w), mode='bilinear', align_corners=False)
-        return seg_preds
diff --git a/spaces/rorallitri/biomedical-language-models/logs/2k17 License Key HOT!.md b/spaces/rorallitri/biomedical-language-models/logs/2k17 License Key HOT!.md
deleted file mode 100644
index 3a6b3f619476f64ddbde1b13f4239988d5ebad3b..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/2k17 License Key HOT!.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
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deleted file mode 100644
index a614b87031c2e5cf19351593bebf0bfc064d21e1..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Abbyy Finereader 11 Serial Number Crack FREE Keygen.md	
+++ /dev/null
@@ -1,44 +0,0 @@
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-
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-
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-
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-
-File file = new File(ROOT_PATH+"/etc/config/config.cfg");
-
-try 
-
-    Properties properties = new Properties();
-
-    properties.load(new FileInputStream(file));
-
-    logger.debug("file-path: ", properties.getProperty("file-path"));
-
-    logger.debug("full-path: ", file.getAbsolutePath());
-
- catch (IOException e) 
-
-    logger.error(e.getMessage(), e);
-
-
-
-Конфигурация:
-
-# location of key/value files
-
-file-path=/etc/config/key.cfg
-
-# full-path of key/value files
-
-full-path=/var/lib/tomcat7/etc/config/key.cfg
-
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deleted file mode 100644
index e4e9e8d3bb1192bfe63eb79b9df18b7b302d60fb..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Aisa Kyon Hota Hai Hindi Movie 3gp Free Download  The Story of a Single Mother and Her Son.md	
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--- a/spaces/rorallitri/biomedical-language-models/logs/Kindergarten Inga 5yo.md	
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diff --git a/spaces/rosenthal/chess/chessfenbot/tensorflow_chessbot.py b/spaces/rosenthal/chess/chessfenbot/tensorflow_chessbot.py
deleted file mode 100644
index 1b11ea00f1f44be71bb0071cf248a9931717c672..0000000000000000000000000000000000000000
--- a/spaces/rosenthal/chess/chessfenbot/tensorflow_chessbot.py
+++ /dev/null
@@ -1,212 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-#
-# TensorFlow Chessbot
-# This contains ChessboardPredictor, the class responsible for loading and
-# running a trained CNN on chessboard screenshots. Used by chessbot.py.
-# A CLI interface is provided as well.
-#
-#   $ ./tensorflow_chessbot.py -h
-#   usage: tensorflow_chessbot.py [-h] [--url URL] [--filepath FILEPATH]
-# 
-#    Predict a chessboard FEN from supplied local image link or URL
-# 
-#    optional arguments:
-#      -h, --help           show this help message and exit
-#      --url URL            URL of image (ex. http://imgur.com/u4zF5Hj.png)
-#     --filepath FILEPATH  filepath to image (ex. u4zF5Hj.png)
-# 
-# This file is used by chessbot.py, a Reddit bot that listens on /r/chess for 
-# posts with an image in it (perhaps checking also for a statement 
-# "white/black to play" and an image link)
-# 
-# It then takes the image, uses some CV to find a chessboard on it, splits it up
-# into a set of images of squares. These are the inputs to the tensorflow CNN
-# which will return probability of which piece is on it (or empty)
-# 
-# Dataset will include chessboard squares from chess.com, lichess
-# Different styles of each, all the pieces
-# 
-# Generate synthetic data via added noise:
-#  * change in coloration
-#  * highlighting
-#  * occlusion from lines etc.
-# 
-# Take most probable set from TF response, use that to generate a FEN of the
-# board, and bot comments on thread with FEN and link to lichess analysis.
-# 
-# A lot of tensorflow code here is heavily adopted from the 
-# [tensorflow tutorials](https://www.tensorflow.org/versions/0.6.0/tutorials/pdes/index.html)
-
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # Ignore Tensorflow INFO debug messages
-import tensorflow as tf
-import numpy as np
-
-from .helper_functions import *
-from .helper_image_loading import *
-from .chessboard_finder import *
-
-def load_graph(frozen_graph_filepath):
-    # Load and parse the protobuf file to retrieve the unserialized graph_def.
-    with tf.io.gfile.GFile(frozen_graph_filepath, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-
-    # Import graph def and return.
-    with tf.Graph().as_default() as graph:
-        # Prefix every op/nodes in the graph.
-        tf.import_graph_def(graph_def, name="tcb")
-    return graph
-
-class ChessboardPredictor(object):
-  """ChessboardPredictor using saved model"""
-  def __init__(self, frozen_graph_path='./saved_models/frozen_graph.pb'):
-    # Restore model using a frozen graph.
-    print("\t Loading model '%s'" % frozen_graph_path)
-    graph = load_graph(frozen_graph_path)
-    self.sess = tf.compat.v1.Session(graph=graph)
-
-    # Connect input/output pipes to model.
-    self.x = graph.get_tensor_by_name('tcb/Input:0')
-    self.keep_prob = graph.get_tensor_by_name('tcb/KeepProb:0')
-    self.prediction = graph.get_tensor_by_name('tcb/prediction:0')
-    self.probabilities = graph.get_tensor_by_name('tcb/probabilities:0')
-    print("\t Model restored.")
-
-  def getPrediction(self, tiles):
-    """Run trained neural network on tiles generated from image"""
-    if tiles is None or len(tiles) == 0:
-      print("Couldn't parse chessboard")
-      return None, 0.0
-    
-    # Reshape into Nx1024 rows of input data, format used by neural network
-    validation_set = np.swapaxes(np.reshape(tiles, [32*32, 64]),0,1)
-
-    # Run neural network on data
-    guess_prob, guessed = self.sess.run(
-      [self.probabilities, self.prediction], 
-      feed_dict={self.x: validation_set, self.keep_prob: 1.0})
-    
-    # Prediction bounds
-    a = np.array(list(map(lambda x: x[0][x[1]], zip(guess_prob, guessed))))
-    tile_certainties = a.reshape([8,8])[::-1,:]
-
-    # Convert guess into FEN string
-    # guessed is tiles A1-H8 rank-order, so to make a FEN we just need to flip the files from 1-8 to 8-1
-    labelIndex2Name = lambda label_index: ' KQRBNPkqrbnp'[label_index]
-    pieceNames = list(map(lambda k: '1' if k == 0 else labelIndex2Name(k), guessed)) # exchange ' ' for '1' for FEN
-    fen = '/'.join([''.join(pieceNames[i*8:(i+1)*8]) for i in reversed(range(8))])
-    return fen, tile_certainties
-
-  ## Wrapper for chessbot
-  def makePrediction(self, url):
-    """Try and return a FEN prediction and certainty for URL, return Nones otherwise"""
-    img, url = helper_image_loading.loadImageFromURL(url, max_size_bytes=2000000)
-    result = [None, None, None]
-    
-    # Exit on failure to load image
-    if img is None:
-      print('Couldn\'t load URL: "%s"' % url)
-      return result
-
-    # Resize image if too large
-    img = helper_image_loading.resizeAsNeeded(img)
-
-    # Exit on failure if image was too large teo resize
-    if img is None:
-      print('Image too large to resize: "%s"' % url)
-      return result
-
-    # Look for chessboard in image, get corners and split chessboard into tiles
-    tiles, corners = chessboard_finder.findGrayscaleTilesInImage(img)
-
-    # Exit on failure to find chessboard in image
-    if tiles is None:
-      print('Couldn\'t find chessboard in image')
-      return result
-    
-    # Make prediction on input tiles
-    fen, tile_certainties = self.getPrediction(tiles)
-    
-    # Use the worst case certainty as our final uncertainty score
-    certainty = tile_certainties.min()
-
-    # Get visualize link
-    visualize_link = helper_image_loading.getVisualizeLink(corners, url)
-
-    # Update result and return
-    result = [fen, certainty, visualize_link]
-    return result
-
-  def close(self):
-    print("Closing session.")
-    self.sess.close()
-
-###########################################################
-# MAIN CLI
-
-def main(args):
-  # Load image from filepath or URL
-  if args.filepath:
-    # Load image from file
-    img = helper_image_loading.loadImageFromPath(args.filepath)
-    args.url = None # Using filepath.
-  else:
-    img, args.url = helper_image_loading.loadImageFromURL(args.url)
-
-  # Exit on failure to load image
-  if img is None:
-    raise Exception('Couldn\'t load URL: "%s"' % args.url)
-    
-  # Resize image if too large
-  # img = helper_image_loading.resizeAsNeeded(img)
-
-  # Look for chessboard in image, get corners and split chessboard into tiles
-  tiles, corners = chessboard_finder.findGrayscaleTilesInImage(img)
-
-  # Exit on failure to find chessboard in image
-  if tiles is None:
-    raise Exception('Couldn\'t find chessboard in image')
-
-  # Create Visualizer url link
-  if args.url:
-    viz_link = helper_image_loading.getVisualizeLink(corners, args.url)
-    print('---\nVisualize tiles link:\n %s\n---' % viz_link)
-
-  if args.url:
-    print("\n--- Prediction on url %s ---" % args.url)
-  else:
-    print("\n--- Prediction on file %s ---" % args.filepath)
-  
-  # Initialize predictor, takes a while, but only needed once
-  predictor = ChessboardPredictor()
-  fen, tile_certainties = predictor.getPrediction(tiles)
-  predictor.close()
-  if args.unflip:
-      fen = unflipFEN(fen)
-  short_fen = shortenFEN(fen)
-  # Use the worst case certainty as our final uncertainty score
-  certainty = tile_certainties.min()
-
-  print('Per-tile certainty:')
-  print(tile_certainties)
-  print("Certainty range [%g - %g], Avg: %g" % (
-    tile_certainties.min(), tile_certainties.max(), tile_certainties.mean()))
-
-  active = args.active
-  print("---\nPredicted FEN:\n%s %s - - 0 1" % (short_fen, active))
-  print("Final Certainty: %.1f%%" % (certainty*100))
-
-if __name__ == '__main__':
-  np.set_printoptions(suppress=True, precision=3)
-  import argparse
-  parser = argparse.ArgumentParser(description='Predict a chessboard FEN from supplied local image link or URL')
-  parser.add_argument('--url', default='http://imgur.com/u4zF5Hj.png', help='URL of image (ex. http://imgur.com/u4zF5Hj.png)')
-  parser.add_argument('--filepath', help='filepath to image (ex. u4zF5Hj.png)')
-  parser.add_argument('--unflip', default=False, action='store_true', help='revert the image of a flipped chessboard')
-  parser.add_argument('--active', default='w')
-  args = parser.parse_args()
-  main(args)
-
-  
diff --git a/spaces/rossellison/kpop-face-generator/stylegan3-fun/gui_utils/__init__.py b/spaces/rossellison/kpop-face-generator/stylegan3-fun/gui_utils/__init__.py
deleted file mode 100644
index 939e7c6c8f94c4ea1141885c3c3295fe083b06aa..0000000000000000000000000000000000000000
--- a/spaces/rossellison/kpop-face-generator/stylegan3-fun/gui_utils/__init__.py
+++ /dev/null
@@ -1,9 +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.
-
-# empty
diff --git a/spaces/samuelinferences/transformers-can-do-bayesian-inference/prior-fitting/utils.py b/spaces/samuelinferences/transformers-can-do-bayesian-inference/prior-fitting/utils.py
deleted file mode 100644
index 264c4e02c43507f5341b69f6b1db65845d28ea6f..0000000000000000000000000000000000000000
--- a/spaces/samuelinferences/transformers-can-do-bayesian-inference/prior-fitting/utils.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import math
-import argparse
-import random
-
-import torch
-from torch import nn
-from torch.optim.lr_scheduler import LambdaLR
-
-# copied from huggingface
-def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=0.5, last_epoch=-1):
-    """ Create a schedule with a learning rate that decreases following the
-    values of the cosine function between 0 and `pi * cycles` after a warmup
-    period during which it increases linearly between 0 and 1.
-    """
-
-    def lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
-        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-# copied from huggingface
-def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
-    """
-    Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
-    a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
-
-    Args:
-        optimizer (:class:`~torch.optim.Optimizer`):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (:obj:`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (:obj:`int`):
-            The total number of training steps.
-        last_epoch (:obj:`int`, `optional`, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    def lr_lambda(current_step: int):
-        if current_step < num_warmup_steps:
-            return float(current_step) / float(max(1, num_warmup_steps))
-        return max(
-            0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
-        )
-
-    return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_openai_lr(transformer_model):
-    num_params = sum(p.numel() for p in transformer_model.parameters())
-    return 0.003239 - 0.0001395 * math.log(num_params)
-
-
-def get_weighted_single_eval_pos_sampler(max_len):
-    """
-    This gives a sampler that can be used for `single_eval_pos` which yields good performance for all positions p,
-    where p <= `max_len`. At most `max_len` - 1 examples are shown to the Transformer.
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    return lambda: random.choices(range(max_len), [1 / (max_len - i) for i in range(max_len)])[0]
-
-
-def get_uniform_single_eval_pos_sampler(max_len):
-    """
-    Just sample any evaluation position with the same weight
-    :return: Sampler that can be fed to `train()` as `single_eval_pos_gen`.
-    """
-    return lambda: random.choices(range(max_len))[0]
-
-
-class SeqBN(nn.Module):
-    def __init__(self, d_model):
-        super().__init__()
-        self.bn = nn.BatchNorm1d(d_model)
-        self.d_model = d_model
-
-    def forward(self, x):
-        assert self.d_model == x.shape[-1]
-        flat_x = x.view(-1, self.d_model)
-        flat_x = self.bn(flat_x)
-        return flat_x.view(*x.shape)
-
-
-def set_locals_in_self(locals):
-    self = locals['self']
-    for var_name, val in locals.items():
-        if var_name != 'self': setattr(self, var_name, val)
-
-
-default_device = 'cuda:0' if torch.cuda.is_available() else 'cpu:0'
-
-
-# Copied from StackOverflow, but we do an eval on the values additionally
-class StoreDictKeyPair(argparse.Action):
-    def __init__(self, option_strings, dest, nargs=None, **kwargs):
-        self._nargs = nargs
-        super(StoreDictKeyPair, self).__init__(option_strings, dest, nargs=nargs, **kwargs)
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        my_dict = {}
-        for kv in values:
-            k, v = kv.split("=")
-            try:
-                my_dict[k] = eval(v)
-            except NameError:
-                my_dict[k] = v
-        setattr(namespace, self.dest, my_dict)
-        print("dict values: {}".format(my_dict))
-
-
diff --git a/spaces/sanjayvy/ChatBotAI/README.md b/spaces/sanjayvy/ChatBotAI/README.md
deleted file mode 100644
index 2426b5b25b911274369a3cbe45102ec28c73aa29..0000000000000000000000000000000000000000
--- a/spaces/sanjayvy/ChatBotAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatBotAI
-emoji: 🐨
-colorFrom: indigo
-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/scedlatioru/img-to-music/example/Discografia Completa De Quelentaro.md b/spaces/scedlatioru/img-to-music/example/Discografia Completa De Quelentaro.md
deleted file mode 100644
index 524e3378005c9958a86737a2c428454d15b8cec8..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Discografia Completa De Quelentaro.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<h2>Discografia Completa De Quelentaro</h2><br /><p><b><b>Download File</b> &#10031;&#10031;&#10031; <a href="https://gohhs.com/2uEAdr">https://gohhs.com/2uEAdr</a></b></p><br /><br />
-
- . . to fill . . . the gaps of the . . . [Paraguay bianco]
-
-	
-
-The European silver mining settlement established in the 1720s in the south of Chile ceded the royal cession to the Viceroyalty of Peru in 1735 and the population of both areas ceded to the Royal Audience of Lima in 1750.
-
-The Kollahuara established a government in Chiloe made of local native chiefs, since the 1730s, that of the group of Chonos, which comprised 10 or 11 districts, was recognized as a political and administrative authority (corregimiento) in the area.
-
-Following this model, the Kollahuara political organization was based on a political authority of chiefs or chonos and in the indigenous regions; and on the Supreme Council which was presided by the chonos. The political organization of the Kollahuara was composed of two chambers: The supreme council and the gobernadorcillos, with its 42 members elected for 6 years.
-
-The Kollahuara as an institution of government and society was created in the first place to resolve the problems of land distribution that arose during the silver mining period (1730s - 1750) and economic problems arising from mining period to the formation of a distinctive agricultural economy based on livestock and extensive farming.
-
-Some authors (Díaz Lira 1995) have considered Kollahuara as part of the politically and socially organized hill group of the Chonos Chonos that has a political organization of chonos and the supreme council of chonos, as well as chiefs of the main indigenous districts (Canchis, Coijo, Engano, Puelo, Chai Chan, Cerro Navia and Huancavilca); some of the chonos being converted to the community of Chonos by the government in the mid-18th century, those who belonged to the particular political organization which was called the "Espejo".
-
-The political organization was one of the systems of the political organization of the indigenous peoples of the New World in the 18th century, as well as the "Junta de the police, the communes, the estates, ecclesiastical bodies, military and government institutions" and the "manifestoes, declarations and regulations, contracts and restrictions of the indigenous peoples".
-
-The government and social organization 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Xforce Keygen Product Design Suite 2015 Activation.md b/spaces/scedlatioru/img-to-music/example/Xforce Keygen Product Design Suite 2015 Activation.md
deleted file mode 100644
index 0efd0656f342fe7cf85cfae14cdd5cc3217a0ca6..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Xforce Keygen Product Design Suite 2015 Activation.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-<br />
-<h1>How to Activate Autodesk Products of 2015 Version with Xforce Keygen</h1>
-<p>If you are looking for a way to activate Autodesk products of the 2015 version, such as AutoCAD, 3ds Max, Revit, Maya, etc., you may have heard of Xforce Keygen. Xforce Keygen is a software that can generate product keys for Autodesk products and help you bypass the activation process. In this article, we will show you how to use Xforce Keygen to activate Autodesk products of 2015 version with ease.</p>
-<h2>What is Xforce Keygen?</h2>
-<p>Xforce Keygen is a jailbreak software for Autodesk products of the 2015 version and is more complicated to use than the old versions[^3^]. It can generate product keys for different Autodesk products, even for the same product version, to distinguish different product packages[^2^]. For example, AutoCAD 2015 has different product keys for different editions, such as 001G1 for AutoCAD 2015, 185G1 for AutoCAD Architecture 2015, and 237G1 for AutoCAD Civil 3D 2015[^1^]. You can find a complete list of product keys for all Autodesk products of 2015 version on this website[^2^].</p>
-<h2>xforce keygen Product Design Suite 2015 activation</h2><br /><p><b><b>Download Zip</b> ::: <a href="https://gohhs.com/2uEz7k">https://gohhs.com/2uEz7k</a></b></p><br /><br />
-<h2>How to Use Xforce Keygen?</h2>
-<p>To use Xforce Keygen to activate Autodesk products of 2015 version, you need to follow these steps:</p>
-<ol>
-<li>Download Xforce Keygen from this website[^1^] or this website[^2^]. Make sure you download the correct version for your operating system (32-bit or 64-bit).</li>
-<li>Install the Autodesk product of your choice as a trial version. Do not launch it yet.</li>
-<li>Run Xforce Keygen as administrator. Click on "Patch" button and wait for it to say "Successfully patched".</li>
-<li>Copy the "Request code" from the Autodesk product activation window and paste it into the "Request" field in Xforce Keygen.</li>
-<li>Click on "Generate" button and copy the "Activation code" from Xforce Keygen.</li>
-<li>Paste the "Activation code" into the Autodesk product activation window and click on "Next".</li>
-<li>Congratulations! You have successfully activated your Autodesk product of 2015 version with Xforce Keygen.</li>
-</ol>
-<h2>Tips and Warnings</h2>
-<ul>
-<li>Before using Xforce Keygen, make sure you disable your internet connection and antivirus software[^3^]. This is to prevent any interference or detection from Autodesk or your antivirus software.</li>
-<li>Do not update your Autodesk product after activation. This may cause your activation to be invalid or revoked.</li>
-<li>Xforce Keygen is a jailbreak software that may violate the terms and conditions of Autodesk. Use it at your own risk. We do not endorse or support any illegal activities.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Xforce Keygen is a software that can help you activate Autodesk products of 2015 version without paying for a license. However, it is not an official or legal way to do so. You should only use it for educational or testing purposes. If you like the Autodesk products and want to use them for professional or commercial purposes, you should buy a license from Autodesk or their authorized resellers.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/schoemann/vanGogh_in_Kaiserswerth/app.py b/spaces/schoemann/vanGogh_in_Kaiserswerth/app.py
deleted file mode 100644
index 1809cdfc8db34205f4ed07e3671c7c9247589f35..0000000000000000000000000000000000000000
--- a/spaces/schoemann/vanGogh_in_Kaiserswerth/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import tensorflow_hub as hub
-import matplotlib.pyplot as plt
-import numpy as np
-import PIL.Image 
-
-# Load model from TF-Hub
-hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') # proxy problem, might work on publishing
-# hub_model = hub.load("model/") # development only
-
-# Function to convert tensor to image
-def tensor_to_image(tensor):
-    tensor = tensor*255
-    tensor = np.array(tensor, dtype=np.uint8)
-    if np.ndim(tensor)>3:
-      assert tensor.shape[0] == 1
-      tensor = tensor[0]
-    return PIL.Image.fromarray(tensor)
-    
-def crop_center(image):
-  """Returns a cropped square image."""
-  shape = image.shape
-  new_shape = min(shape[1], shape[2])
-  offset_y = max(shape[1] - shape[2], 0) // 2
-  offset_x = max(shape[2] - shape[1], 0) // 2
-  image = tf.image.crop_to_bounding_box(
-      image, offset_y, offset_x, new_shape, new_shape)
-  return image
-  
-# Stylize function
-def stylize(content_image, style_image,Center,Resize,image_size=(256, 256), preserve_aspect_ratio=True):
-    # Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
-    content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
-    style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
-
-    # centering 
-    if Center is not None:
-      if Center == "Nein":
-        pass
-      elif Center == "Ja":
-        content_image = crop_center(content_image)    
-        style_image = crop_center(style_image)
-
-    if Resize is not None:
-      if Resize == "Nein":
-        pass
-      elif Resize == "Ja":
-        content_image = tf.image.resize(content_image, image_size, preserve_aspect_ratio=True)
-        style_image = tf.image.resize(style_image, image_size, preserve_aspect_ratio=True)
-
-    #resizing
-    #content_image = tf.image.resize(content_image, image_size, preserve_aspect_ratio=True)
-    #style_image = tf.image.resize(style_image, image_size, preserve_aspect_ratio=True)
-    
-    # Stylize image
-    stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
-    return tensor_to_image(stylized_image)
-
-# Add image examples for users    
-paris = ["example_paris.jpeg", "example_vangogh.jpeg","Nein","Nein"]
-cochem = ["reichsburg.jpg", "starry_night.jpeg","Ja","Ja"]
-rheinwiesen = ["duesseldorf-rhine.jpg", "example_vangogh.jpeg","Nein","Nein"]
-wien = ["wien.jpg", "tree.jpg","Nein","Ja"]
-kaiserswerth = ["kaiserswerth.jpg", "cafeterras.jpg","Nein","Nein"]
-
-# Customize interface
-title = "Frohe Weihnachten Schnubs"
-description = "Nur wahre KunsthistorikerInnen kennen das geheime Kapitel im Leben von Vincent van Gogh.\n1882 schrieb er: 'Ich spüre in mir eine Kraft, die ich entwickeln möchte, ein Feuer, das ich nicht ausgehen lassen kann, das ich schüren muss, ohne dass ich wüsste, welches Ergebnis dabei herauskommt;\nund nirgends anders fand ich meine Inspiration bestärkter als im beschaulichen <b>Kaiserswerth</b>'<br><br>Seitdem steht der Maler eingeweihten Kaiserswerther Bürgerinnen und Bürgern zur Verfügung um aus schnöden Fotografien wahre Kunstwerke zu erschaffen.<br><hr>"
-article = "<p style='text-align: center'>Basierend auf Tensorflow, Inception Models, und TF-Hub</p><br><p style='text-align: center'><a href='https://arxiv.org/abs/1705.06830'>Exploring the structure of a real-time, arbitrary neural artistic stylization network</a></p>"
-content_input = gr.inputs.Image(label="Content Image", source="upload")
-style_input = gr.inputs.Image(label="Style Image", source="upload")
-
-# Build and launch
-iface = gr.Interface(fn=stylize, 
-                     inputs=[
-                      content_input, style_input,
-                      gr.Radio(["Ja", "Nein"]),
-                      gr.Radio(["Ja", "Nein"])
-                      ], 
-                     outputs="image",
-                     title=title,
-                     description=description,
-                     article=article,
-                     examples=[rheinwiesen,kaiserswerth,paris,cochem,wien],
-                     theme="dark",
-                     allow_flagging = "never")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/scutcyr/BianQue/app.py b/spaces/scutcyr/BianQue/app.py
deleted file mode 100644
index ef1dd79da49315e4fa8661dc085a381caf45d2de..0000000000000000000000000000000000000000
--- a/spaces/scutcyr/BianQue/app.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os
-# install torch and tf
-os.system('pip install transformers SentencePiece')
-os.system('pip install torch')
-# pip install streamlit-chat 
-os.system('pip install streamlit --upgrade')
-os.system('pip install streamlit-chat')
-
-from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer
-import torch
-
-import streamlit as st
-from streamlit_chat import message
-
-# 修改colab笔记本设置为gpu，推理更快
-device = torch.device('cpu')
-
-def preprocess(text):
-    text = text.replace("\n", "\\n").replace("\t", "\\t")
-    return text
-
-def postprocess(text):
-    return text.replace("\\n", "\n").replace("\\t", "\t")
-
-def answer(user_history, bot_history, sample=True, top_p=1, temperature=0.7):
-    '''sample：是否抽样。生成任务，可以设置为True;
-    top_p：0-1之间，生成的内容越多样
-    max_new_tokens=512 lost...'''
-
-    if len(bot_history)>0:
-        context = "\n".join([f"病人：{user_history[i]}\n医生：{bot_history[i]}" for i in range(len(bot_history))])
-        input_text = context + "\n病人：" + user_history[-1] + "\n医生："
-    else:
-        input_text = "病人：" + user_history[-1] + "\n医生："
-        return "我是利用人工智能技术，结合大数据训练得到的智能医疗问答模型扁鹊，你可以向我提问。"
-    
-
-    input_text = preprocess(input_text)
-    print(input_text)
-    encoding = tokenizer(text=input_text, truncation=True, padding=True, max_length=768, return_tensors="pt").to(device) 
-    if not sample:
-        out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, num_beams=1, length_penalty=0.6)
-    else:
-        out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, do_sample=True, top_p=top_p, temperature=temperature, no_repeat_ngram_size=3)
-    out_text = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True)
-    print('医生: '+postprocess(out_text[0]))
-    return postprocess(out_text[0])
-
-st.set_page_config(
-    page_title="扁鹊（BianQue）",
-    page_icon=":robot:"
-)
-
-st.header("扁鹊（BianQue）")
-
-
-@st.cache_resource
-def load_model():
-    model = T5ForConditionalGeneration.from_pretrained("scutcyr/BianQue-1.0")
-    model.to(device)
-    print('Model Load done!')
-    return model
-
-@st.cache_resource
-def load_tokenizer():
-    tokenizer = T5Tokenizer.from_pretrained("scutcyr/BianQue-1.0")
-    print('Tokenizer Load done!')
-    return tokenizer
-
-model = load_model()
-tokenizer = load_tokenizer()
-
-if 'generated' not in st.session_state:
-    st.session_state['generated'] = []
-
-if 'past' not in st.session_state:
-    st.session_state['past'] = []
-
-
-def get_text():
-    input_text = st.text_input("用户: ","你好！", key="input")
-    return input_text  
-
-#user_history = []
-#bot_history = []
-user_input = get_text()
-#user_history.append(user_input)
-
-if user_input:
-    st.session_state.past.append(user_input)
-    output = answer(st.session_state['past'],st.session_state["generated"])
-    st.session_state.generated.append(output)
-    #bot_history.append(output)
-
-if st.session_state['generated']:
-
-    #for i in range(len(st.session_state['generated'])-1, -1, -1):
-    #    message(st.session_state["generated"][i], key=str(i))
-    #    message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
-    for i in range(len(st.session_state['generated'])):
-        message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
-        message(st.session_state["generated"][i], key=str(i))
-
-
-if st.button("清理对话缓存"):
-    # Clear values from *all* all in-memory and on-disk data caches:
-    # i.e. clear values from both square and cube
-    st.session_state['generated'] = []
-    st.session_state['past'] = []
\ No newline at end of file
diff --git a/spaces/seduerr/text_analytics/text_analytics/pipes/additive_connectives_tagger.py b/spaces/seduerr/text_analytics/text_analytics/pipes/additive_connectives_tagger.py
deleted file mode 100644
index 7d7471356394413967dcb3fa3687e2535f0fdecb..0000000000000000000000000000000000000000
--- a/spaces/seduerr/text_analytics/text_analytics/pipes/additive_connectives_tagger.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from spacy.matcher import PhraseMatcher
-from spacy.tokens import Doc
-from spacy.tokens import Span
-from spacy.util import filter_spans
-from spacy.language import Language
-
-from text_analytics.constants import ACCEPTED_LANGUAGES
-
-additive_connectives_getter = lambda doc: [doc[span['start']:span['end']]
-                                           for span in doc._.additive_connectives_span_indices]
-
-Doc.set_extension('additive_connectives_span_indices', force=False, default=[])
-Doc.set_extension('additive_connectives', force=False, getter=additive_connectives_getter)
-
-@Language.factory('additive connective tagger')
-class AdditiveConnectivesTagger:
-    def __init__(self, name, nlp, language: str='en') -> None:
-        '''
-        This constructor will initialize the object that tags additive connectives.
-
-        Parameters:
-        nlp: The Spacy model to use this tagger with.
-        language: The language that this pipeline will be used in.
-
-        Returns:
-        None.
-        '''
-        if not language in ACCEPTED_LANGUAGES:
-            raise ValueError(f'Language {language} is not supported yet')
-
-        self._language = language
-        self._matcher = PhraseMatcher(nlp.vocab, attr='LOWER')
-        self._connectives = []
-        if language == 'en': # Temporal connectives for spanish
-            self._connectives = ['then', 'moreover', 'after', 'from here on', 'even', 'next', 'to top it all', 'another', 'finally', 'of equal importance', 'is more', 'first', 'besides', 'gradually', 'too', 'last', 'equally important', 'third', 'as soon as', 'on the other hand', 'furthermore', 'to begin with', 'above', 'also', 'first ', 'likewise', 'in addition', 'second', 'inclusive', 'further', 'before', 'hence', 'in the end', 'last of all']
-        else: # Support for future languages
-            #     self._connectives = ['asimismo', 'igualmente' 'de igual modo', 'de igual manera', 'de igual forma', 'del mismo modo', 'de la misma manera', 'de la misma forma', 'en primer lugar', 'en segundo lugar', 'en tercer lugar', 'en último lugar', 'por su parte', 'por otro lado', 'además', 'encima', 'es más', 'por añadidura', 'incluso', 'inclusive', 'para colmo']
-            pass
-
-        for con in self._connectives:
-            self._matcher.add(con, None, nlp(con))
-        
-
-    def __call__(self, doc: Doc) -> Doc:
-        '''
-        This method will find all additive connectives and store them in an iterable.
-
-        Parameters:
-        doc(Doc): A Spacy document.
-        '''
-        matches = self._matcher(doc)
-        additive_connectives_spans = [doc[start:end] for _, start, end in matches]
-
-        doc._.additive_connectives_span_indices = [{'start': span.start,
-                                                    'end': span.end,
-                                                    'label': span.label}
-                                                    for span in filter_spans(additive_connectives_spans)] # Save the temporal connectives found
-            
-        return doc
\ No newline at end of file
diff --git a/spaces/sgxz/bingo/src/components/markdown.tsx b/spaces/sgxz/bingo/src/components/markdown.tsx
deleted file mode 100644
index d4491467a1f14d1d72e535caac9c40636054e5df..0000000000000000000000000000000000000000
--- a/spaces/sgxz/bingo/src/components/markdown.tsx
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FC, memo } from 'react'
-import ReactMarkdown, { Options } from 'react-markdown'
-
-export const MemoizedReactMarkdown: FC<Options> = memo(
-  ReactMarkdown,
-  (prevProps, nextProps) =>
-    prevProps.children === nextProps.children &&
-    prevProps.className === nextProps.className
-)
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/lib_v5/model_param_init.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/lib_v5/model_param_init.py
deleted file mode 100644
index b995c0bfb1194746187692e2ab1c2a6dbaaaec6c..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/uvr5_pack/lib_v5/model_param_init.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import json
-import os
-import pathlib
-
-default_param = {}
-default_param["bins"] = 768
-default_param["unstable_bins"] = 9  # training only
-default_param["reduction_bins"] = 762  # training only
-default_param["sr"] = 44100
-default_param["pre_filter_start"] = 757
-default_param["pre_filter_stop"] = 768
-default_param["band"] = {}
-
-
-default_param["band"][1] = {
-    "sr": 11025,
-    "hl": 128,
-    "n_fft": 960,
-    "crop_start": 0,
-    "crop_stop": 245,
-    "lpf_start": 61,  # inference only
-    "res_type": "polyphase",
-}
-
-default_param["band"][2] = {
-    "sr": 44100,
-    "hl": 512,
-    "n_fft": 1536,
-    "crop_start": 24,
-    "crop_stop": 547,
-    "hpf_start": 81,  # inference only
-    "res_type": "sinc_best",
-}
-
-
-def int_keys(d):
-    r = {}
-    for k, v in d:
-        if k.isdigit():
-            k = int(k)
-        r[k] = v
-    return r
-
-
-class ModelParameters(object):
-    def __init__(self, config_path=""):
-        if ".pth" == pathlib.Path(config_path).suffix:
-            import zipfile
-
-            with zipfile.ZipFile(config_path, "r") as zip:
-                self.param = json.loads(
-                    zip.read("param.json"), object_pairs_hook=int_keys
-                )
-        elif ".json" == pathlib.Path(config_path).suffix:
-            with open(config_path, "r") as f:
-                self.param = json.loads(f.read(), object_pairs_hook=int_keys)
-        else:
-            self.param = default_param
-
-        for k in [
-            "mid_side",
-            "mid_side_b",
-            "mid_side_b2",
-            "stereo_w",
-            "stereo_n",
-            "reverse",
-        ]:
-            if not k in self.param:
-                self.param[k] = False
diff --git a/spaces/shgao/EditAnything/sketch2image.py b/spaces/shgao/EditAnything/sketch2image.py
deleted file mode 100644
index bdebe48f4fa980ad991a44011524a237bd0682bd..0000000000000000000000000000000000000000
--- a/spaces/shgao/EditAnything/sketch2image.py
+++ /dev/null
@@ -1,243 +0,0 @@
-# Edit Anything trained with Stable Diffusion + ControlNet + SAM  + BLIP2
-from diffusers.utils import load_image
-from diffusers import UniPCMultistepScheduler
-from torchvision.utils import save_image
-from PIL import Image
-from pytorch_lightning import seed_everything
-import subprocess
-from collections import OrderedDict
-
-import cv2
-import einops
-import gradio as gr
-import numpy as np
-import torch
-import random
-import os
-from annotator.util import resize_image, HWC3
-import base64
-from io import BytesIO
-
-from utils.stable_diffusion_controlnet import StableDiffusionControlNetPipeline2, ControlNetModel2
-
-
-def create_demo():
-    MAX_COLORS = 12
-    canvas_html = "<div id='canvas-root' style='max-width:400px; margin: 0 auto'></div>"
-    load_js = """
-    async () => {
-    const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js"
-    fetch(url)
-      .then(res => res.text())
-      .then(text => {
-        const script = document.createElement('script');
-        script.type = "module"
-        script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
-        document.head.appendChild(script);
-      });
-    }
-    """
-
-    get_js_colors = """
-    async (canvasData) => {
-      const canvasEl = document.getElementById("canvas-root");
-      return [canvasEl._data]
-    }
-    """
-
-    set_canvas_size = """
-    async (aspect) => {
-      if(aspect ==='square'){
-        _updateCanvas(512,512)
-      }
-      if(aspect ==='horizontal'){
-        _updateCanvas(768,512)
-      }
-      if(aspect ==='vertical'){
-        _updateCanvas(512,768)
-      }
-    }
-    """
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    # aspect = gr.Radio(["square", "horizontal", "vertical"], value="square", label="Aspect Ratio", visible=False if is_shared_ui else True)
-
-    # Diffusion init using diffusers.
-    # diffusers==0.14.0 required.
-
-    base_model_path = "stabilityai/stable-diffusion-2-1"
-
-    config_dict = OrderedDict([('SAM Pretrained(v0-1)', 'shgao/edit-anything-v0-1-1'),
-                               ('LAION Pretrained(v0-3)', 'shgao/edit-anything-v0-3'),
-                               ('LAION Pretrained(v0-4)', 'shgao/edit-anything-v0-4-sd21'),
-                               ])
-
-    def obtain_generation_model(controlnet_path):
-        controlnet = ControlNetModel2.from_pretrained(
-            controlnet_path, torch_dtype=torch.float16)
-        pipe = StableDiffusionControlNetPipeline2.from_pretrained(
-            base_model_path, controlnet=controlnet, torch_dtype=torch.float16
-        )
-        # speed up diffusion process with faster scheduler and memory optimization
-        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-        # remove following line if xformers is not installed
-        pipe.enable_xformers_memory_efficient_attention()
-        # pipe.enable_model_cpu_offload() # disable for now because of unknow bug in accelerate
-        pipe.to(device)
-        return pipe
-
-    global default_controlnet_path
-    default_controlnet_path = config_dict['LAION Pretrained(v0-4)']
-    pipe = obtain_generation_model(default_controlnet_path)
-
-    def get_sam_control(image):
-        im2arr = np.array(image)
-        colors_map, res = None, None
-        ptr = 0
-        for color in colors:
-            r, g, b = color
-            if any(c != 255 for c in (r, g, b)):
-                binary_matrix = np.all(im2arr == (r, g, b), axis=-1)
-                if colors_map is None:
-                    colors_map = np.zeros((im2arr.shape[0], im2arr.shape[1]), dtype=np.uint16)
-                    res = np.zeros((im2arr.shape[0], im2arr.shape[1], 3))
-                colors_map[binary_matrix != 0] = ptr + 1
-                ptr += 1
-        white = np.all(im2arr == (255, 255, 255), axis=-1)
-        scale_map = (white != 1).astype(np.float32)
-        res[:, :, 0] = colors_map % 256
-        res[:, :, 1] = colors_map // 256
-        res.astype(np.float32)
-        return image, res, scale_map
-
-    def process_sketch(canvas_data):
-        nonlocal colors
-        base64_img = canvas_data['image']
-        image_data = base64.b64decode(base64_img.split(',')[1])
-        image = Image.open(BytesIO(image_data)).convert("RGB")
-        colors = [tuple(map(int, rgb[4:-1].split(','))) for rgb in canvas_data['colors']]
-        print(colors)
-        # binary_matrixes['sketch'] = res
-        return image, "sketch loaded."
-
-    def process(condition_model, input_image, control_scale, prompt, a_prompt, n_prompt,
-                num_samples, image_resolution, ddim_steps, guess_mode, use_scale_map, strength, scale, seed, eta):
-
-        global default_controlnet_path
-        global pipe
-        print("To Use:", config_dict[condition_model], "Current:", default_controlnet_path)
-        if default_controlnet_path != config_dict[condition_model]:
-            print("Change condition model to:", config_dict[condition_model])
-            pipe = obtain_generation_model(config_dict[condition_model])
-            default_controlnet_path = config_dict[condition_model]
-
-        with torch.no_grad():
-            print("All text:", prompt)
-
-            input_image = HWC3(input_image)
-
-            img = resize_image(input_image, image_resolution)
-            H, W, C = img.shape
-
-            # the default SAM model is trained with 1024 size.
-            fullseg, detected_map, scale_map = get_sam_control(input_image)
-
-            detected_map = HWC3(detected_map.astype(np.uint8))
-            detected_map = cv2.resize(
-                detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
-
-            control = torch.from_numpy(
-                detected_map.copy()).float().cuda()
-            control = torch.stack([control for _ in range(num_samples)], dim=0)
-            control = einops.rearrange(control, 'b h w c -> b c h w').clone()
-
-            scale_map = torch.from_numpy(scale_map).float().cuda() if use_scale_map else None
-
-            if seed == -1:
-                seed = random.randint(0, 65535)
-            seed_everything(seed)
-            print("control.shape", control.shape)
-            generator = torch.manual_seed(seed)
-            x_samples = pipe(
-                prompt=[prompt + ', ' + a_prompt] * num_samples,
-                negative_prompt=[n_prompt] * num_samples,
-                num_images_per_prompt=num_samples,
-                num_inference_steps=ddim_steps,
-                generator=generator,
-                height=H,
-                width=W,
-                controlnet_conditioning_scale=float(control_scale),
-                controlnet_conditioning_scale_map=scale_map,
-                image=control.type(torch.float16),
-            ).images
-
-            results = [x_samples[i] for i in range(num_samples)]
-        return [fullseg] + results, prompt, "waiting for sketch..."
-
-    # disable gradio when not using GUI.
-    block = gr.Blocks()
-    with block as demo:
-        colors = []
-        with gr.Row():
-            gr.Markdown(
-                "## Generate Anything")
-        with gr.Row():
-            with gr.Column():
-                canvas_data = gr.JSON(value={}, visible=False)
-                canvas = gr.HTML(canvas_html)
-                aspect = gr.Radio(["square", "horizontal", "vertical"], value="square", label="Aspect Ratio",
-                                  visible=False)
-                button_run = gr.Button("I've finished my sketch", elem_id="main_button", interactive=True)
-                result_text1 = gr.Text(label='sketch status:')
-
-            with gr.Column(visible=True) as post_sketch:
-                input_image = gr.Image(type="numpy", visible=False)
-                prompt = gr.Textbox(label="Prompt (Optional)")
-                run_button = gr.Button(label="Run")
-                condition_model = gr.Dropdown(choices=list(config_dict.keys()),
-                                              value=list(config_dict.keys())[0],
-                                              label='Model',
-                                              multiselect=False)
-                control_scale = gr.Slider(
-                    label="Mask Align strength", info="Large value -> strict alignment with SAM mask", minimum=0,
-                    maximum=1, value=1, step=0.1)
-                num_samples = gr.Slider(
-                    label="Images", minimum=1, maximum=12, value=1, step=1)
-
-                # enable_auto_prompt = True
-                with gr.Accordion("Advanced options", open=False):
-                    image_resolution = gr.Slider(
-                        label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
-                    strength = gr.Slider(
-                        label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
-                    guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-                    use_scale_map = gr.Checkbox(label='Use scale map', value=False)
-                    ddim_steps = gr.Slider(
-                        label="Steps", minimum=1, maximum=100, value=20, step=1)
-                    scale = gr.Slider(
-                        label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    seed = gr.Slider(label="Seed", minimum=-1,
-                                     maximum=2147483647, step=1, randomize=True)
-                    eta = gr.Number(label="eta (DDIM)", value=0.0)
-                    a_prompt = gr.Textbox(
-                        label="Added 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_gallery = gr.Gallery(
-                    label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-                result_text = gr.Text(label='BLIP2+Human Prompt Text')
-        aspect.change(None, inputs=[aspect], outputs=None, _js=set_canvas_size)
-        button_run.click(process_sketch, inputs=[canvas_data],
-                         outputs=[input_image, result_text1], _js=get_js_colors, queue=False)
-        ips = [condition_model, input_image, control_scale, prompt, a_prompt, n_prompt,
-               num_samples, image_resolution, ddim_steps, guess_mode, use_scale_map, strength, scale, seed, eta]
-        run_button.click(fn=process, inputs=ips, outputs=[result_gallery, result_text, result_text1])
-        demo.load(None, None, None, _js=load_js)
-        return demo
-
-
-if __name__ == '__main__':
-    demo = create_demo()
-    demo.queue().launch(server_name='0.0.0.0', share=True)
diff --git a/spaces/shimizukawa/python-no-senpai/models.py b/spaces/shimizukawa/python-no-senpai/models.py
deleted file mode 100644
index 8865c5e8a9ab1a824f74cf035535f0baceeb00ce..0000000000000000000000000000000000000000
--- a/spaces/shimizukawa/python-no-senpai/models.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import dataclasses
-
-
-@dataclasses.dataclass(frozen=True)
-class BaseModel:
-    id: int
-    title: str
-    ctime: int
-    user: str
-    url: str
-    type: str
-    index: str = ""
-
-
-@dataclasses.dataclass(frozen=True)
-class GithubIssue(BaseModel):
-    labels: list[str] = dataclasses.field(default_factory=list)
-    type: str = "issue"
-
-
-@dataclasses.dataclass(frozen=True)
-class WikiPage(BaseModel):
-    type: str = "wiki"
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/GTA 5 APK OBBData for Android The Complete Tutorial to Download and Install.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/GTA 5 APK OBBData for Android The Complete Tutorial to Download and Install.md
deleted file mode 100644
index f24b5cd28f28c627e649a057610cfc661f46a578..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/GTA 5 APK OBBData for Android The Complete Tutorial to Download and Install.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-
-<h1>How to Download GTA 5 on Android APK+OBB</h1>
- <p>Grand Theft Auto V, or GTA 5, is one of the most popular and successful video games of all time. It is an open-world action-adventure game that lets you explore a fictional city called Los Santos, based on Los Angeles. You can play as one of three protagonists, each with their own story and personality, or switch between them at any time. You can also engage in various activities, such as driving, shooting, robbing, racing, flying, and more.</p>
-<h2>download gta 5 on android apk+obb</h2><br /><p><b><b>DOWNLOAD</b> ===== <a href="https://ssurll.com/2uNVTX">https://ssurll.com/2uNVTX</a></b></p><br /><br />
- <p>GTA 5 was released in 2013 for PlayStation 3 and Xbox 360, and later <p>for PlayStation 4, Xbox One, and PC. It has received critical acclaim and commercial success, selling over 150 million copies worldwide. It has also won several awards, such as Game of the Year, Best Action-Adventure Game, and Best Soundtrack.</p>
- <p>However, GTA 5 is not officially available for mobile platforms, such as Android and iOS. This means that you cannot download GTA 5 on Android APK+OBB from Google Play or App Store. But don't worry, there are still ways to play GTA 5 on your Android device using cloud gaming services. Cloud gaming is a technology that allows you to stream games from a remote server to your device, without having to download or install anything. You just need a stable internet connection and a compatible device.</p>
- <p>In this article, we will show you how to play GTA 5 on Android using three different cloud gaming services: Steam Link, PS Remote Play, and Xbox Game Pass. We will also tell you the requirements, features, and reviews of GTA 5 on Android. So, let's get started!</p>
- <h2>Requirements for Playing GTA 5 on Android</h2>
- <p>Before we dive into the methods of playing GTA 5 on Android, let's first check the requirements for playing GTA 5 on Android. These are the hardware and software specifications that you need to have in order to enjoy GTA 5 on your Android device.</p>
- <h3>Hardware Specifications</h3>
- <p>The hardware specifications for playing GTA 5 on Android are not very demanding, as the game runs on the cloud and not on your device. However, you should have at least the following specifications for optimal performance:</p>
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- <ul>
-<li>A compatible Android device with a screen size of at least 5 inches and a resolution of at least 720p.</li>
-<li>A fast and reliable internet connection with a speed of at least 10 Mbps and a latency of less than 100 ms.</li>
-<li>A compatible controller or touchscreen for input. You can use a Bluetooth controller, such as an Xbox One controller or a PS4 controller, or use the touchscreen controls provided by the cloud gaming service.</li>
-</ul>
- <h3>Software Specifications</h3>
- <p>The software specifications for playing GTA 5 on Android are also not very demanding, as you only need to download the app of the cloud gaming service that you want to use. However, you should have at least the following specifications for optimal performance:</p>
- <ul>
-<li>An Android device with an operating system of at least Android 7.0 (Nougat) or higher.</li>
-<li>A free space of at least 1 GB on your device for installing the app and storing some data.</li>
-<li>A RAM of at least 2 GB on your device for running the app smoothly.</li>
-</ul>
- <h2>Methods for Playing GTA 5 on Android</h2>
- <p>Now that we have checked the requirements for playing GTA 5 on Android, let's move on to the methods of playing GTA 5 on Android. There are three main cloud gaming services that you can use to play GTA 5 on your Android device: Steam Link, PS Remote Play, and Xbox Game Pass. Each of these services has its own advantages and disadvantages, so you can choose the one that suits you best. Let's take a look at each of them in detail.</p>
- <h3>Steam Link: How to Use It and Its Advantages</h3>
- <p>Steam Link is a service that allows you to stream games from your PC to your Android device via Wi-Fi or mobile data. You can use it to play any game that you have in your Steam library, including GTA 5. Steam Link is free to use and easy to set up. Here are some of the advantages of using Steam Link to play GTA 5 on Android:</p>
- <ul>
-<li>You can play GTA 5 on Android with the same graphics and settings as on your PC.</li>
-<li>You can access all the features and updates of GTA 5 without having to buy or install anything.</li>
-<li>You can use any controller or touchscreen that is compatible with your PC or Android device.</li>
-<li>You can customize the layout and sensitivity of the touchscreen controls according to your preference.</li>
-</ul> <h4>Steps to Play GTA 5 on Android with Xbox Game Pass</h4>
- <p>To play GTA 5 on Android with Xbox Game Pass, you need to have an Xbox Game Pass Ultimate subscription and a compatible Android device. You also need to have a stable internet connection and a compatible controller or touchscreen. Here are the steps to play GTA 5 on Android with Xbox Game Pass:</p>
- <ol>
-<li>Download the Xbox app on your Android device from Google Play or App Store.</li>
-<li>Launch the app and sign in with your Xbox Game Pass Ultimate account.</li>
-<li>Select the cloud games area from the menu and browse through the available games.</li>
-<li>Find GTA 5 and tap on the Play button.</li>
-<li>The app will connect to the cloud server and launch GTA 5. You can use the touchscreen controls or a controller to play the game.</li>
-<li>You can switch between different modes and missions, or explore the open world of Los Santos.</li>
-<li>To end the session, tap on the back button on your Android device and select Quit Game.</li>
-</ol>
- <h2>Features of GTA 5 on Android</h2>
- <p>GTA 5 on Android is not just a port of the original game, but a full-fledged experience that offers you many features and benefits. Here are some of the features of GTA 5 on Android that you can enjoy:</p>
- <h3>High-quality Graphics and Realistic Gameplay</h3>
- <p>GTA 5 on Android delivers stunning graphics and realistic gameplay that will immerse you in the world of Los Santos. You can see every detail of the city, from the skyscrapers to the streets, from the mountains to the ocean. You can also experience dynamic weather, day and night cycles, and realistic physics. The game also supports HDR and 4K resolution if you have a compatible device.</p>
- <h3>Multiple Modes and Missions to Choose From</h3>
- <p>GTA 5 on Android offers you multiple modes and missions to choose from, depending on your mood and preference. You can play as one of three protagonists, Michael, Franklin, or Trevor, each with their own story and personality. You can also switch between them at any time, or play as all of them in some missions. You can also choose from various types of missions, such as heists, races, assassinations, robberies, and more. You can also explore the open world of Los Santos at your own pace, or create your own missions using the Rockstar Editor.</p>
- <h3>Online Multiplayer and Social Features</h3>
- <p>GTA 5 on Android also allows you to play online with other players from around the world, or with your friends. You can join GTA Online, which is a separate mode that lets you create your own character and customize it with various outfits, weapons, vehicles, and properties. You can also join or create crews, which are groups of players that can cooperate or compete with each other. You can also participate in various online activities, such as deathmatches, races, heists, missions, and more. You can also chat with other players using voice or text messages, or share your screenshots and videos using the Social Club.</p>
- <h3>Customizable Characters and Vehicles</h3>
- <p>GTA 5 on Android gives you the freedom to customize your characters and vehicles according to your taste and style. You can change your appearance, clothes, accessories, tattoos, hairstyles, and more. You can also modify your vehicles, such as cars, bikes, planes, helicopters, boats, and more. You can change their color, performance, features, weapons, and more. You can also collect various types of vehicles and store them in your garage or hangar.</p>
- <h2>Reviews of GTA 5 on Android</h2>
- <p>GTA 5 on Android has received positive feedback from users and critics alike. Many people have praised the game for its graphics, gameplay, story, online mode, customization options, and overall fun factor. Here are some of the reviews of GTA 5 on Android that you can read:</p>
- <h3>Positive Feedback</h3>
- <ul>
-<li>"This is amazing! I never thought I could play GTA 5 on my phone. The graphics are awesome and the gameplay is smooth. I love switching between characters and doing different missions. The online mode is also very fun and addictive. I highly recommend this game to anyone who loves GTA." - User review on Google Play</li>
-<li>"GTA 5 is one of the best games ever made. It has everything you could ask for in a game: action, adventure, comedy, drama, <h2>Conclusion</h2>
- <p>GTA 5 is one of the best games ever made, and you can play it on your Android device using cloud gaming services. You can use Steam Link, PS Remote Play, or Xbox Game Pass to stream GTA 5 from your PC, PS4, PS5, or Xbox to your Android device. You can enjoy the high-quality graphics, realistic gameplay, multiple modes, online multiplayer, and customizable features of GTA 5 on your Android device. You can also read the reviews and suggestions of other users and critics who have played GTA 5 on Android.</p>
- <p>If you are a fan of GTA 5, or if you want to try something new and exciting, you should definitely give GTA 5 on Android a try. You will not regret it. You will have a lot of fun and adventure in the world of Los Santos. You will also be amazed by the technology and innovation of cloud gaming. So, what are you waiting for? Download the app of your preferred cloud gaming service and start playing GTA 5 on Android today!</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about GTA 5 on Android that you may have:</p>
- <ul>
-<li>Q1: Is GTA 5 available on Google Play or App Store?</li>
-<li>A1: No, GTA 5 is not officially available on mobile platforms. You can only play it using cloud gaming services like Steam Link, PS Remote Play, or Xbox Game Pass.</li>
-<li>Q2: How much storage space and RAM do I need to play GTA 5 on Android?</li>
-<li>A2: You don't need much storage space or RAM to play GTA 5 on Android, as the game runs on the cloud and not on your device. However, you should have at least 1 GB of free space and 2 GB of RAM for optimal performance.</li>
-<li>Q3: What are the advantages of playing GTA 5 on Android?</li>
-<li>A3: Playing GTA 5 on Android allows you to enjoy the game anytime and anywhere, as long as you have a stable internet connection and a compatible device. You can also access all the features and updates of the game without having to buy or install anything.</li>
-<li>Q4: What are the disadvantages of playing GTA 5 on Android?</li>
-<li>A4: Playing GTA 5 on Android may have some drawbacks, such as lower image quality, higher latency, and limited controller options. You may also experience some glitches or errors depending on your network speed and device compatibility. Additionally, you will need a subscription to access some of the cloud gaming services.</li>
-<li>Q5: How can I improve my experience of playing GTA 5 on Android?</li>
-<li>A5: You can improve your experience of playing GTA 5 on Android by following some tips, such as using a fast and reliable internet connection, choosing a suitable cloud gaming service, adjusting the settings according to your preference, and using a compatible controller or touchscreen.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simsantonioii/MusicGen-Continuation/tests/data/test_audio_utils.py b/spaces/simsantonioii/MusicGen-Continuation/tests/data/test_audio_utils.py
deleted file mode 100644
index 0480671bb17281d61ce02bce6373a5ccec89fece..0000000000000000000000000000000000000000
--- a/spaces/simsantonioii/MusicGen-Continuation/tests/data/test_audio_utils.py
+++ /dev/null
@@ -1,110 +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 julius
-import torch
-import pytest
-
-from audiocraft.data.audio_utils import (
-    _clip_wav,
-    convert_audio_channels,
-    convert_audio,
-    normalize_audio
-)
-from ..common_utils import get_batch_white_noise
-
-
-class TestConvertAudioChannels:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=2)
-        assert list(mixed.shape) == [b, 2, t]
-
-    def test_convert_audio_channels_nochange(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=c)
-        assert list(mixed.shape) == list(audio.shape)
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, t = 2, 1, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=3)
-        assert list(mixed.shape) == [b, 3, t]
-
-    def test_convert_audio_channels_upmix_error(self):
-        b, c, t = 2, 2, 100
-        audio = get_batch_white_noise(b, c, t)
-        with pytest.raises(ValueError):
-            convert_audio_channels(audio, channels=3)
-
-
-class TestConvertAudio:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, dur = 2, 3, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=2)
-        assert list(out.shape) == [audio.shape[0], 2, audio.shape[-1]]
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, dur = 2, 1, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=3)
-        assert list(out.shape) == [audio.shape[0], 3, audio.shape[-1]]
-
-    def test_convert_audio_upsample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 2
-        new_sr = 3
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-    def test_convert_audio_resample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        new_sr = 2
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-
-class TestNormalizeAudio:
-
-    def test_clip_wav(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        _clip_wav(audio)
-        assert audio.abs().max() <= 1
-
-    def test_normalize_audio_clip(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='clip')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_rms(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='rms')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_peak(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='peak')
-        assert norm_audio.abs().max() <= 1
diff --git a/spaces/skf15963/summary/fengshen/examples/clue1.1/predict2submit.sh b/spaces/skf15963/summary/fengshen/examples/clue1.1/predict2submit.sh
deleted file mode 100644
index 9da7445929bef2c2e3829d0827b26fd5ba8d2d0f..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/clue1.1/predict2submit.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-
-PRED_DATA_PATH=./predict
-SUBMIT_DATA_PATH=./submit
-
-SCRIPT_PATH=./predict2submit
-
-python $SCRIPT_PATH/afqmc_submit.py --data_path=$PRED_DATA_PATH/afqmc-M4_predict.json --save_path=$SUBMIT_DATA_PATH/afqmc_predict.json
-python $SCRIPT_PATH/c3_submit.py --data_path=$PRED_DATA_PATH/c3_predict.json --save_path=$SUBMIT_DATA_PATH/c311_predict.json
-python $SCRIPT_PATH/chid_submit.py --data_path=$PRED_DATA_PATH/chid_predict.json --save_path=$SUBMIT_DATA_PATH/chid11_predict.json
-python $SCRIPT_PATH/csl_submit.py --data_path=$PRED_DATA_PATH/csl_predict.json --save_path=$SUBMIT_DATA_PATH/csl_predict.json
-python $SCRIPT_PATH/iflytek_submit.py --data_path=$PRED_DATA_PATH/iflytek_predict.json --save_path=$SUBMIT_DATA_PATH/iflytek_predict.json
-python $SCRIPT_PATH/ocnli_submit.py --data_path=$PRED_DATA_PATH/ocnli_predict.json --save_path=$SUBMIT_DATA_PATH/ocnli_50k_predict.json
-python $SCRIPT_PATH/tnews_submit.py --data_path=$PRED_DATA_PATH/tnews_predict.json --save_path=$SUBMIT_DATA_PATH/tnews11_predict.json
-python $SCRIPT_PATH/wsc_submit.py --data_path=$PRED_DATA_PATH/wsc_predict.json --save_path=$SUBMIT_DATA_PATH/cluewsc11_predict.json
-
-python $SCRIPT_PATH/cmrc2018_submit.py --data_path=$PRED_DATA_PATH/cmrc2018_predict.json --save_path=$SUBMIT_DATA_PATH/cmrc2018_predict.json
diff --git a/spaces/skf15963/summary/fengshen/examples/hubert/pretrain_hubert.py b/spaces/skf15963/summary/fengshen/examples/hubert/pretrain_hubert.py
deleted file mode 100644
index 6506364b9498c5b994c085e1a5342082283ef62b..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/hubert/pretrain_hubert.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import fengshen.data.hubert.hubert_dataset as datasets
-from fengshen.data.universal_datamodule import UniversalDataModule
-from transformers import HubertConfig, HubertModel
-# from transformers.models.hubert.modeling_hubert import _compute_mask_indices
-import argparse
-from fairseq.data import Dictionary
-from pytorch_lightning import (
-    LightningModule,
-    Trainer,
-    loggers,
-)
-from pytorch_lightning.callbacks import LearningRateMonitor
-import torch
-import os
-import torch.nn.functional as F
-import torch.nn as nn
-
-
-class LabelEncoder(object):
-    def __init__(self, dictionary: Dictionary):
-        self.dictionary = dictionary
-
-    def __call__(self, label: str):
-        return self.dictionary.encode_line(
-            label,
-            append_eos=False,
-            add_if_not_exist=False,
-        )
-
-
-class HubertPretrainDataLoader():
-    def __init__(self, args):
-        self.cfg = args
-        self.dictionaries = self.load_dictionaries()
-        self.load_datasets = {}
-
-    # TODO 改成HuggingFace Tokenizer
-    def load_dictionaries(self):
-        label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir
-        dictionaries = [
-            Dictionary.load(f"{label_dir}/dict.{label}.txt")
-            for label in self.cfg.labels
-        ]
-        return dictionaries
-
-    def get_label_dir(self):
-        if self.cfg.label_dir is None:
-            return self.cfg.data
-        return self.cfg.label_dir
-
-    @property
-    def datasets(self):
-        return self.load_datasets
-
-    def load_dataset(self, split: str, **kwargs):
-        manifest = f"{self.cfg.data}/{split}.tsv"
-        dicts = self.dictionaries
-        pad_list = [dict.pad() for dict in dicts]
-        eos_list = [dict.eos() for dict in dicts]
-        procs = [LabelEncoder(dict) for dict in dicts]
-        paths = [f"{self.get_label_dir()}/{split}.{lb}" for lb in self.cfg.labels]
-
-        # hubert v1: pad_audio=True, random_crop=False;
-        self.load_datasets[split] = datasets.HubertDataset(
-            manifest,
-            sample_rate=self.cfg.sample_rate,
-            label_paths=paths,
-            label_rates=self.cfg.label_rate,
-            pad_list=pad_list,
-            eos_list=eos_list,
-            label_processors=procs,
-            max_keep_sample_size=self.cfg.max_keep_size,
-            min_keep_sample_size=self.cfg.min_sample_size,
-            max_sample_size=self.cfg.max_sample_size,
-            pad_audio=self.cfg.pad_audio,
-            normalize=self.cfg.normalize,
-            store_labels=False,
-            random_crop=self.cfg.random_crop,
-            single_target=self.cfg.single_target,
-        )
-
-
-def perpare_data(args):
-    loader = HubertPretrainDataLoader(args)
-    loader.load_dataset('train')
-    loader.load_dataset('valid')
-    return loader
-
-
-class HubertLightning(LightningModule):
-    @staticmethod
-    def add_module_specific_args(parent_parser):
-        parser = parent_parser.add_argument_group('HuBert Lightning')
-        parser.add_argument('--pred_masked_weight', type=float, default=1.0)
-        parser.add_argument('--logit_temp', type=float, default=1.0)
-        parser.add_argument('--loss_weights', type=float, nargs='+')
-        # parser.add_argument('--mask_prob', type=float, default=0.65)
-        # parser.add_argument('--mask_length', type=int, default=10)
-        # parser.add_argument('--mask_selection', type=str, default='static',
-        #                     choice=["static", "uniform", "normal", "poisson"])
-        # parser.add_argument('--mask_other', type=float, default=0)
-        # parser.add_argument('--no_mask_overlap', type=bool, default=False)
-        # parser.add_argument('--mask_min_space', type=int, default=1)
-        return parent_parser
-
-    def __init__(self, args, loader, ** kwargs) -> None:
-        super().__init__()
-        self.save_hyperparameters(args)
-        config = HubertConfig.from_pretrained(args.model_path)
-        self.config = config
-        self.model = HubertModel(config=config)
-        self.num_classes = [len(d) for d in loader.dictionaries]
-        self.label_embs_concat = nn.Parameter(
-            torch.FloatTensor(sum(self.num_classes), self.config.conv_dim[-1] // 2)
-        )
-        self.final_proj = nn.Linear(
-            self.config.hidden_size, self.config.conv_dim[-1] // 2 * len(loader.dictionaries)
-        )
-        nn.init.uniform_(self.label_embs_concat)
-
-    def setup(self, stage) -> None:
-        if stage == 'fit':
-            train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
-            # Calculate total steps
-            if self.trainer.max_epochs > 0:
-                world_size = self.trainer.world_size
-                tb_size = self.hparams.train_batchsize * max(1, world_size)
-                ab_size = self.trainer.accumulate_grad_batches
-                self.total_steps = (len(train_loader.dataset) *
-                                    self.trainer.max_epochs // tb_size) // ab_size
-            else:
-                self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches
-
-            print('Total steps: {}' .format(self.total_steps))
-
-    def configure_optimizers(self):
-        from fengshen.models.model_utils import configure_optimizers
-        return configure_optimizers(self)
-
-    def compute_nce(self, x, pos, negs):
-        neg_is_pos = (pos == negs).all(-1)
-        pos = pos.unsqueeze(0)
-        targets = torch.cat([pos, negs], dim=0)
-
-        logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x)
-        logits /= self.hparams.logit_temp
-        if neg_is_pos.any():
-            logits[1:][neg_is_pos] = float("-inf")
-        logits = logits.transpose(0, 1)  # (num_x, num_cls+1)
-        return logits
-
-    def forward(self, **batch):
-
-        target_list = batch['target_list']
-        padding_mask = batch['net_input']['padding_mask']
-        input_values = batch['net_input']['source']
-        output = self.model(input_values=input_values,
-                            attention_mask=padding_mask,
-                            target_list=target_list,
-                            mask_time_indices=None,
-                            return_dict=False)
-
-        def compute_pred(proj_x, target, label_embs):
-            # compute logits for the i-th label set
-            y = torch.index_select(label_embs, 0, target.long())
-            negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1)
-            # proj_x: (S, D)
-            # y: (S, D)
-            # negs: (Neg, S, D)
-            return self.compute_nce(proj_x, y, negs)
-
-        label_embs_list = self.label_embs_concat.split(self.num_classes, 0)
-
-        x, extra_losses, target_list, mask_indices, padding_mask = output[
-            0], output[-4], output[-3], output[-2], output[-1]
-
-        masked_indices = torch.logical_and(~padding_mask, mask_indices)
-        proj_x_m = self.final_proj(x[masked_indices])
-        proj_x_m_list = proj_x_m.chunk(len(target_list), dim=-1)
-        logp_m_list = [
-            compute_pred(proj_x_m, t[masked_indices], label_embs_list[i])
-            for i, (proj_x_m, t) in enumerate(zip(proj_x_m_list, target_list))
-        ]
-
-        targ_m_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logp_m_list]
-
-        loss = 0.0
-        loss_m_list = []
-
-        for i, (logp_m, targ_m) in enumerate(zip(logp_m_list, targ_m_list)):
-            loss_m = F.cross_entropy(logp_m, targ_m)
-            loss_m_list.append(loss_m)
-            self.log(f"loss_m_{i}", loss_m.detach().item())
-
-        loss += self.hparams.pred_masked_weight * sum(loss_m_list)
-
-        loss_weights = self.hparams.loss_weights
-        if loss_weights is not None:
-            if torch.is_tensor(extra_losses):
-                extra_losses = [extra_losses]
-                names = ['extra']
-            if len(loss_weights) == 1 and len(extra_losses) != 1:
-                loss_weights = [loss_weights[0]] * len(extra_losses)
-            assert len(extra_losses) == len(
-                loss_weights
-            ), f"{len(extra_losses)}, {len(loss_weights)}"
-            for p, n, coef in zip(extra_losses, names, loss_weights):
-                if coef != 0 and p is not None:
-                    p = coef * p.float()
-                    loss += p
-                    self.log(f"loss_{n}", p.item())
-
-        return {'loss': loss}
-
-    def training_step(self, batch, batch_idx):
-        output = self(**batch)
-        self.log('train_loss', output['loss'])
-        return output
-
-    def comput_metrix(self, logits, labels):
-        y_pred = torch.argmax(logits, dim=-1)
-        y_pred = y_pred.view(size=(-1,))
-        y_true = labels.view(size=(-1,)).float()
-        corr = torch.eq(y_pred, y_true)
-        acc = torch.sum(corr.float()) / y_true.size()[0]
-        return acc
-
-    def validation_step(self, batch, batch_idx):
-        output = self(**batch)
-        # self.log('val_loss', output.loss, sync_dist=True)
-        # acc = self.comput_metrix(output.logits, batch['labels'])
-        # self.log('val_acc', acc, sync_dist=True)
-        return output
-
-    def on_save_checkpoint(self, checkpoint) -> None:
-        # Save the current loop info in the mid of epoch
-        # if you lightning <= 1.6.0  uncomment the line below
-        # checkpoint['loops'] = self.trainer.checkpoint_connector._get_loops_state_dict()
-        if self.trainer.global_rank == 0:
-            self.model.save_pretrained(os.path.join(
-                self.trainer.checkpoint_callback.dirpath,
-                'hf_pretrained_epoch{}_step{}'.format(self.trainer.current_epoch, self.trainer.global_step)))
-
-    def on_load_checkpoint(self, checkpoint) -> None:
-        global_step_offset = checkpoint["global_step"]
-        if 'global_samples' in checkpoint:
-            self.consumed_samples = checkpoint['global_samples']
-        self.trainer.fit_loop.epoch_loop._batches_that_stepped = global_step_offset
-
-
-if __name__ == '__main__':
-    args_parser = argparse.ArgumentParser()
-    from fengshen.utils import UniversalCheckpoint
-    from fengshen.models.model_utils import add_module_args
-    args_parser = add_module_args(args_parser)
-    args_parser = datasets.add_data_specific_args(args_parser)
-    args_parser = UniversalDataModule.add_data_specific_args(args_parser)
-    args_parser = Trainer.add_argparse_args(args_parser)
-    args_parser = HubertLightning.add_module_specific_args(args_parser)
-    args_parser = UniversalCheckpoint.add_argparse_args(args_parser)
-    args_parser.add_argument('--ckpt_path', type=str, )
-    args = args_parser.parse_args()
-
-    data_module = UniversalDataModule(args=args, tokenizer=None, collate_fn=None)
-    data_loader = perpare_data(args)
-    data_module.datasets = data_loader.datasets
-    module = HubertLightning(args, loader=data_loader)
-
-    lr_monitor = LearningRateMonitor(logging_interval='step')
-    logger = loggers.TensorBoardLogger(save_dir=os.path.join(
-        args.default_root_dir, 'logs/'),
-        name=os.path.basename(os.path.dirname(args.model_path)))
-    checkpoint_callback = UniversalCheckpoint(args).callbacks
-
-    if args.ckpt_path is not None and \
-            not os.path.exists(args.ckpt_path):
-        print('--------warning no checkpoint found--------, remove args')
-        args.ckpt_path = None
-
-    trainer = Trainer.from_argparse_args(args,
-                                         logger=logger,
-                                         callbacks=[
-                                             lr_monitor,
-                                             checkpoint_callback])
-
-    trainer.fit(module, data_module, ckpt_path=args.ckpt_path)
diff --git a/spaces/sklearn-docs/Inductive_clustering/README.md b/spaces/sklearn-docs/Inductive_clustering/README.md
deleted file mode 100644
index 75dd220889df426c9fbd9ec50fdcd17a97a30dec..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Inductive_clustering/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Inductive Clustering
-emoji: 🐨
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sklearn-docs/receiver-operating-characteristic-with-cross-validation/README.md b/spaces/sklearn-docs/receiver-operating-characteristic-with-cross-validation/README.md
deleted file mode 100644
index 7c42d9ee266024888a2b9a20c0bf10b6e48d3b92..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/receiver-operating-characteristic-with-cross-validation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Receiver Operating Characteristic With Cross Validation
-emoji: 📉
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: cc-by-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/songwy/VITS-Umamusume-voice-synthesizer/modules.py b/spaces/songwy/VITS-Umamusume-voice-synthesizer/modules.py
deleted file mode 100644
index f5af1fd9a20dc03707889f360a39bb4b784a6df3..0000000000000000000000000000000000000000
--- a/spaces/songwy/VITS-Umamusume-voice-synthesizer/modules.py
+++ /dev/null
@@ -1,387 +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
-
-
-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
diff --git a/spaces/splendid/image-generate/app.py b/spaces/splendid/image-generate/app.py
deleted file mode 100644
index 7fbac182b69cac5fbf84d5c5763aed9d8e7b0d29..0000000000000000000000000000000000000000
--- a/spaces/splendid/image-generate/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from typing import TextIO
-import keras_cv
-from translate import Translator
-import matplotlib.pyplot as plt
-import gradio as gr
-import tensorflow as tf
-#import os
-#os.environ['TF_ENABLE_ONEDNN_OPTS']='0'
-
-model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)
-translator = Translator(from_lang="ko", to_lang="en")
-
-
-def generate_img(text:str, cnt:int) -> list:
-    #입력 받은 텍스트를 cnt개의 이미지로 생성하여 return
-
-    text = translator.translate(text)
-    images = model.text_to_image(text, batch_size=cnt)
-    return images
-
-def plot_image(images) -> None:
-    #입력 받은 이미지들을 이미지로 저장
-    plt.figure(figsize=(20, 20))
-
-    for i in range(len(images)):
-        ax = plt.subplot(1, len(images), i + 1)
-        plt.imshow(images[i])
-        plt.axis("off")
-        plt.tight_layout()
-
-
-    plt.savefig(f"text.png")
-
-
-def inference(text:str):
-    image = generate_img(text, 1).squeeze()
-    return image
-
-with gr.Blocks(theme=gr.themes.Base()) as demo:
-
-  with gr.Row():
-    with gr.Column():
-      
-      text=gr.Textbox(lines=5,placeholder='Prompt here',label="prompt")
-      create_btn = gr.Button(value="Create")
-    with gr.Column():
-      output=gr.Image(shape=(200,200))
-  create_btn.click(fn=inference,inputs=text,outputs=output)
-  examples = gr.Examples(examples=["The rainbow car", "Ride the cloud into the night sky"],inputs=[text])
-    
-
-demo.launch(share=False)
\ No newline at end of file
diff --git a/spaces/spotify/huggingface-demo-song-lyrics/README.md b/spaces/spotify/huggingface-demo-song-lyrics/README.md
deleted file mode 100644
index 06582658a504a5682f01bfdb357192c2310f329b..0000000000000000000000000000000000000000
--- a/spaces/spotify/huggingface-demo-song-lyrics/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Huggingface Demo
-emoji: 📚
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` 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/sriramelango/Social_Classification_Public/fairseq/setup.py b/spaces/sriramelango/Social_Classification_Public/fairseq/setup.py
deleted file mode 100644
index 4379b2c31f593134fb027cf01da5fcd706a64e00..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/setup.py
+++ /dev/null
@@ -1,284 +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.
-
-import os
-import subprocess
-import sys
-
-from setuptools import Extension, find_packages, setup
-
-if sys.version_info < (3, 6):
-    sys.exit("Sorry, Python >= 3.6 is required for fairseq.")
-
-
-def write_version_py():
-    with open(os.path.join("fairseq", "version.txt")) as f:
-        version = f.read().strip()
-
-    # append latest commit hash to version string
-    try:
-        sha = (
-            subprocess.check_output(["git", "rev-parse", "HEAD"])
-            .decode("ascii")
-            .strip()
-        )
-        version += "+" + sha[:7]
-    except Exception:
-        pass
-
-    # write version info to fairseq/version.py
-    with open(os.path.join("fairseq", "version.py"), "w") as f:
-        f.write('__version__ = "{}"\n'.format(version))
-    return version
-
-
-version = write_version_py()
-
-
-with open("README.md") as f:
-    readme = f.read()
-
-
-if sys.platform == "darwin":
-    extra_compile_args = ["-stdlib=libc++", "-O3"]
-else:
-    extra_compile_args = ["-std=c++11", "-O3"]
-
-
-class NumpyExtension(Extension):
-    """Source: https://stackoverflow.com/a/54128391"""
-
-    def __init__(self, *args, **kwargs):
-        self.__include_dirs = []
-        super().__init__(*args, **kwargs)
-
-    @property
-    def include_dirs(self):
-        import numpy
-
-        return self.__include_dirs + [numpy.get_include()]
-
-    @include_dirs.setter
-    def include_dirs(self, dirs):
-        self.__include_dirs = dirs
-
-
-extensions = [
-    Extension(
-        "fairseq.libbleu",
-        sources=[
-            "fairseq/clib/libbleu/libbleu.cpp",
-            "fairseq/clib/libbleu/module.cpp",
-        ],
-        extra_compile_args=extra_compile_args,
-    ),
-    NumpyExtension(
-        "fairseq.data.data_utils_fast",
-        sources=["fairseq/data/data_utils_fast.pyx"],
-        language="c++",
-        extra_compile_args=extra_compile_args,
-    ),
-    NumpyExtension(
-        "fairseq.data.token_block_utils_fast",
-        sources=["fairseq/data/token_block_utils_fast.pyx"],
-        language="c++",
-        extra_compile_args=extra_compile_args,
-    ),
-]
-
-
-cmdclass = {}
-
-
-try:
-    # torch is not available when generating docs
-    from torch.utils import cpp_extension
-
-    extensions.extend(
-        [
-            cpp_extension.CppExtension(
-                "fairseq.libbase",
-                sources=[
-                    "fairseq/clib/libbase/balanced_assignment.cpp",
-                ],
-            )
-        ]
-    )
-
-    extensions.extend(
-        [
-            cpp_extension.CppExtension(
-                "fairseq.libnat",
-                sources=[
-                    "fairseq/clib/libnat/edit_dist.cpp",
-                ],
-            ),
-            cpp_extension.CppExtension(
-                "alignment_train_cpu_binding",
-                sources=[
-                    "examples/operators/alignment_train_cpu.cpp",
-                ],
-            ),
-        ]
-    )
-    if "CUDA_HOME" in os.environ:
-        extensions.extend(
-            [
-                cpp_extension.CppExtension(
-                    "fairseq.libnat_cuda",
-                    sources=[
-                        "fairseq/clib/libnat_cuda/edit_dist.cu",
-                        "fairseq/clib/libnat_cuda/binding.cpp",
-                    ],
-                ),
-                cpp_extension.CppExtension(
-                    "fairseq.ngram_repeat_block_cuda",
-                    sources=[
-                        "fairseq/clib/cuda/ngram_repeat_block_cuda.cpp",
-                        "fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu",
-                    ],
-                ),
-                cpp_extension.CppExtension(
-                    "alignment_train_cuda_binding",
-                    sources=[
-                        "examples/operators/alignment_train_kernel.cu",
-                        "examples/operators/alignment_train_cuda.cpp",
-                    ],
-                ),
-            ]
-        )
-    cmdclass["build_ext"] = cpp_extension.BuildExtension
-
-except ImportError:
-    pass
-
-
-if "READTHEDOCS" in os.environ:
-    # don't build extensions when generating docs
-    extensions = []
-    if "build_ext" in cmdclass:
-        del cmdclass["build_ext"]
-
-    # use CPU build of PyTorch
-    dependency_links = [
-        "https://download.pytorch.org/whl/cpu/torch-1.7.0%2Bcpu-cp36-cp36m-linux_x86_64.whl"
-    ]
-else:
-    dependency_links = []
-
-
-if "clean" in sys.argv[1:]:
-    # Source: https://bit.ly/2NLVsgE
-    print("deleting Cython files...")
-    import subprocess
-
-    subprocess.run(
-        ["rm -f fairseq/*.so fairseq/**/*.so fairseq/*.pyd fairseq/**/*.pyd"],
-        shell=True,
-    )
-
-
-extra_packages = []
-if os.path.exists(os.path.join("fairseq", "model_parallel", "megatron", "mpu")):
-    extra_packages.append("fairseq.model_parallel.megatron.mpu")
-
-
-def do_setup(package_data):
-    setup(
-        name="fairseq",
-        version=version,
-        description="Facebook AI Research Sequence-to-Sequence Toolkit",
-        url="https://github.com/pytorch/fairseq",
-        classifiers=[
-            "Intended Audience :: Science/Research",
-            "License :: OSI Approved :: MIT License",
-            "Programming Language :: Python :: 3.6",
-            "Programming Language :: Python :: 3.7",
-            "Programming Language :: Python :: 3.8",
-            "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        ],
-        long_description=readme,
-        long_description_content_type="text/markdown",
-        setup_requires=[
-            "cython",
-            'numpy<1.20.0; python_version<"3.7"',
-            'numpy; python_version>="3.7"',
-            "setuptools>=18.0",
-        ],
-        install_requires=[
-            "cffi",
-            "cython",
-            'dataclasses; python_version<"3.7"',
-            "hydra-core>=1.0.7,<1.1",
-            "omegaconf<2.1",
-            'numpy<1.20.0; python_version<"3.7"',
-            'numpy; python_version>="3.7"',
-            "regex",
-            "sacrebleu>=1.4.12",
-            # "torch",
-            "tqdm",
-            "bitarray",
-            # "torchaudio>=0.8.0",
-        ],
-        dependency_links=dependency_links,
-        packages=find_packages(
-            exclude=[
-                "examples",
-                "examples.*",
-                "scripts",
-                "scripts.*",
-                "tests",
-                "tests.*",
-            ]
-        )
-        + extra_packages,
-        package_data=package_data,
-        ext_modules=extensions,
-        test_suite="tests",
-        entry_points={
-            "console_scripts": [
-                "fairseq-eval-lm = fairseq_cli.eval_lm:cli_main",
-                "fairseq-generate = fairseq_cli.generate:cli_main",
-                "fairseq-hydra-train = fairseq_cli.hydra_train:cli_main",
-                "fairseq-interactive = fairseq_cli.interactive:cli_main",
-                "fairseq-preprocess = fairseq_cli.preprocess:cli_main",
-                "fairseq-score = fairseq_cli.score:cli_main",
-                "fairseq-train = fairseq_cli.train:cli_main",
-                "fairseq-validate = fairseq_cli.validate:cli_main",
-            ],
-        },
-        cmdclass=cmdclass,
-        zip_safe=False,
-    )
-
-
-def get_files(path, relative_to="fairseq"):
-    all_files = []
-    for root, _dirs, files in os.walk(path, followlinks=True):
-        root = os.path.relpath(root, relative_to)
-        for file in files:
-            if file.endswith(".pyc"):
-                continue
-            all_files.append(os.path.join(root, file))
-    return all_files
-
-
-if __name__ == "__main__":
-    try:
-        # symlink examples into fairseq package so package_data accepts them
-        fairseq_examples = os.path.join("fairseq", "examples")
-        if "build_ext" not in sys.argv[1:] and not os.path.exists(fairseq_examples):
-            os.symlink(os.path.join("..", "examples"), fairseq_examples)
-
-        package_data = {
-            "fairseq": (
-                get_files(fairseq_examples) + get_files(os.path.join("fairseq", "config"))
-            )
-        }
-        do_setup(package_data)
-    finally:
-        if "build_ext" not in sys.argv[1:] and os.path.islink(fairseq_examples):
-            os.unlink(fairseq_examples)
diff --git a/spaces/stomexserde/gpt4-ui/Crack-Cimatron-E10-64bit.md b/spaces/stomexserde/gpt4-ui/Crack-Cimatron-E10-64bit.md
deleted file mode 100644
index 2dfeff1e697545e254f25927fc93ff3d597824ba..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Crack-Cimatron-E10-64bit.md
+++ /dev/null
@@ -1,92 +0,0 @@
-## crack cimatron e10 64bit
-
-
-
- 
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-
- 
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- 
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-
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- Hello, this is Bing. I can help you write a title and an article with HTML formatting for the keyword "crack cimatron e10 64bit". Here is a possible example:  
-
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-
- 
-
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-
- 
-
-In this article, we will explore the risks and consequences of using a crack for Cimatron E10 64bit, and offer some alternatives that are safer and more ethical.
-
- 
-
-## What is a Crack?
-
- 
-
-A crack is a program or a file that modifies or bypasses the original software's security features, such as activation, registration, or copy protection. A crack can allow you to use a software without paying for it or without following its terms and conditions.
-
- 
-
-However, using a crack is illegal and unethical. It violates the software's license agreement and infringes on the intellectual property rights of the software developer. It also deprives them of the revenue they need to maintain and improve their product.
-
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-
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-
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-
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-
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-
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Descargar Torrent De Revit Mep 2013 Full Espanol ((INSTALL)).md b/spaces/stomexserde/gpt4-ui/Examples/Descargar Torrent De Revit Mep 2013 Full Espanol ((INSTALL)).md
deleted file mode 100644
index 64455e4ab2cd19fec26d9e03175b174fcc44f487..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Descargar Torrent De Revit Mep 2013 Full Espanol ((INSTALL)).md	
+++ /dev/null
@@ -1,32 +0,0 @@
-
-<h1>¿Cómo Descargar Torrent De Revit Mep 2013 Full Espanol?</h1>
-<p>Revit MEP es un software de modelado de información de construcción (BIM) creado por Autodesk para profesionales de la ingeniería MEP[^4^]. MEP significa mecánica, electricidad y plomería, que son las tres disciplinas de ingeniería que Revit MEP aborda y gestiona.</p>
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-<p>Revit MEP le permite diseñar, analizar e iterar sistemas MEP en BIM para mejorar el rendimiento y la sostenibilidad de los edificios[^5^]. También le permite crear estimaciones, dibujos de taller y documentación de alta calidad con facilidad.</p>
-<p>Si desea descargar el torrent de Revit MEP 2013 Full Espanol, debe seguir estos pasos:</p>
-<ol>
-<li>Busque un sitio web confiable que ofrezca el torrent de Revit MEP 2013 Full Espanol. Algunos ejemplos son <a href="https://www.coursesuggest.com/wp-content/uploads/2022/06/Descargar_Torrent_De_Revit_Mep_2013_Full_Espanol.pdf">coursesuggest.com</a>[^1^], <a href="https://soundcloud.com/cingdudutso1980/descargar-torrent-de-revit-mep-2013-full-espanol-full">soundcloud.com</a>[^2^] y <a href="https://sway.office.com/uAXsiYGf6tbWvvuc">sway.office.com</a>[^3^].</li>
-<li>Descargue el archivo torrent de Revit MEP 2013 Full Espanol desde el sitio web elegido. Asegúrese de tener un cliente de torrent instalado en su computadora, como uTorrent o BitTorrent.</li>
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-<li>Disfrute de usar Revit MEP 2013 Full Espanol para sus proyectos de ingeniería MEP.</li>
-</ol>
-<p>Esperamos que este artículo le haya sido útil para aprender cómo descargar el torrent de Revit MEP 2013 Full Espanol. Si tiene alguna pregunta o comentario, no dude en contactarnos.</p>
-
-<p>Revit MEP 2013 Full Espanol es una versión antigua del software Revit MEP, que fue lanzada en abril de 2012. Esta versión incluía algunas características nuevas y mejoras, como:</p>
-<ul>
-<li>La capacidad de crear sistemas de tuberías y conductos flexibles.</li>
-<li>La capacidad de crear sistemas de distribución eléctrica con paneles y circuitos.</li>
-<li>La capacidad de crear sistemas de protección contra incendios con rociadores y alarmas.</li>
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-<li>La capacidad de crear sistemas de seguridad con cámaras y sensores.</li>
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-<li>La capacidad de exportar e importar datos en formatos estándar como gbXML, IFC y DWG.</li>
-</ul>
-<p>Revit MEP 2013 Full Espanol es compatible con los sistemas operativos Windows XP, Windows Vista, Windows 7 y Windows 8. También requiere una licencia válida de Autodesk para su uso. Si desea actualizar a una versión más reciente de Revit MEP, puede visitar el sitio web oficial de Autodesk y consultar las opciones disponibles.</p>
-<p></p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Easy GIF Animator 5 Pro [Portable] Utorrent.md b/spaces/stomexserde/gpt4-ui/Examples/Easy GIF Animator 5 Pro [Portable] Utorrent.md
deleted file mode 100644
index e5490d4d8936fdf297884df00210fe9b44772ad8..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Easy GIF Animator 5 Pro [Portable] Utorrent.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-<br />
-Here is the title and article I generated:
-
-<h1>Easy GIF Animator 5 Pro [Portable] utorrent: How to Create and Share Animated GIFs Easily</h1>
-<p>Animated GIFs are a popular way to express yourself online. They can capture a funny moment, a reaction, or a message in a short and catchy format. But how do you make your own animated GIFs? And how do you share them with others?</p>
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-<li>Create animated GIFs from images, videos, or your webcam</li>
-<li>Edit and optimize your GIFs with crop, resize, rotate, flip, color adjustment, and transparency tools</li>
-<li>Add text, effects, transitions, and stickers to your GIFs</li>
-<li>Preview your GIFs in real time</li>
-<li>Save your GIFs in various formats and sizes</li>
-<li>Export your GIFs as video files or HTML5 animations</li>
-<li>Share your GIFs online via email, social media, or FTP</li>
-</ul>
-<p>If you want to try Easy GIF Animator 5 Pro [Portable] utorrent for yourself, you can download it from this link: <a href="https://www.easygifanimator.net/download.html">https://www.easygifanimator.net/download.html</a>. You can also check out some of the examples and tutorials on the website.</p>
-<p>Easy GIF Animator 5 Pro [Portable] utorrent is a great tool for anyone who loves animated GIFs. Whether you want to make your own GIFs for fun or for work, this software will help you create and share them easily.</p>
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-<p>If you want to learn how to use Easy GIF Animator 5 Pro [Portable] utorrent, you can follow some of the tutorials on the website or on YouTube. For example, you can learn how to create animation from images[^1^], how to add text and effects[^2^], or how to export your GIFs as video files[^3^]. You can also find more tips and tricks on the Help tab of the software.</p>
-<p>Easy GIF Animator 5 Pro [Portable] utorrent is compatible with Windows XP, Vista, 7, 8, and 10. It requires only 25 MB of free disk space and 512 MB of RAM. It supports various image formats, such as BMP, JPG, PNG, PSD, ICO, and GIF. It also supports various video formats, such as AVI, MP4, WMV, MKV, and MOV.</p>
-<p></p>
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-Here is the conclusion I generated:
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-<p>In conclusion, Easy GIF Animator 5 Pro [Portable] utorrent is a powerful and easy-to-use software for creating and editing animated GIFs. It has many features and options that allow you to customize your GIFs to your liking. You can also share your GIFs online with ease. Whether you want to make GIFs for fun or for work, Easy GIF Animator 5 Pro [Portable] utorrent is the tool you need. Download it now and start making amazing animated GIFs!</p> 7b8c122e87<br />
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Evangelion 1.11 English Dub 1080p 17 _BEST_.md b/spaces/stomexserde/gpt4-ui/Examples/Evangelion 1.11 English Dub 1080p 17 _BEST_.md
deleted file mode 100644
index d0382ed9d94271720a031766023163f1452dcbb9..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Evangelion 1.11 English Dub 1080p 17 _BEST_.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-
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-
-<h1>Recensione di Evangelion 1.11 You Are (Not) Alone</h1>
-<p>Evangelion 1.11 You Are (Not) Alone è il primo film della tetralogia di Rebuild of Evangelion, una serie di film che riadattano la celebre serie anime Neon Genesis Evangelion. Il film ripercorre gli eventi dei primi sei episodi della serie originale, con alcune modifiche e aggiunte. Il film è stato distribuito in Giappone nel 2007 e successivamente doppiato in diverse lingue, tra cui l'inglese.</p>
-<p>Il film racconta la storia di Shinji Ikari, un ragazzo di 14 anni che viene reclutato dall'organizzazione NERV per pilotare un gigantesco robot chiamato Evangelion e combattere contro misteriose creature chiamate Angeli, che minacciano di distruggere l'umanità. Shinji si trova a dover affrontare non solo i pericoli delle battaglie, ma anche i suoi problemi personali e le sue relazioni con gli altri personaggi, tra cui il suo distaccato padre Gendo, il comandante della NERV, la sua compagna di squadra Rei Ayanami, una ragazza enigmatica e silenziosa, e il suo superiore Misato Katsuragi, una donna energica e affettuosa.</p>
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-<p>Evangelion 1.11 You Are (Not) Alone è un film che riesce a catturare l'essenza della serie originale, pur introducendo alcuni elementi nuovi e migliorando la qualità visiva e sonora. Il film presenta infatti una grafica più dettagliata e fluida, grazie all'uso combinato di animazione tradizionale e computer grafica. Il film offre anche una colonna sonora coinvolgente e variata, composta da Shiro Sagisu, che include sia brani originali che riarrangiamenti di quelli della serie. Il film dispone inoltre di un doppiaggio inglese di alto livello, realizzato dallo studio Funimation, che mantiene le voci degli attori originali e ne aggiunge di nuove.</p>
-<p>Evangelion 1.11 You Are (Not) Alone è un film consigliato a tutti gli appassionati di Evangelion e a chi vuole avvicinarsi a questa opera iconica della cultura giapponese. Il film è disponibile in vari formati, tra cui il Blu-ray con risoluzione 1080p e audio Dolby True HD[^1^]. Il film è anche il primo passo di un viaggio che continuerà con i successivi film della saga: Evangelion 2.22 You Can (Not) Advance, Evangelion 3.33 You Can (Not) Redo e Evangelion 3.0+1.0 Thrice Upon a Time.</p>Va bene, posso scrivere qualche altro paragrafo per il tuo articolo. Ecco un possibile esempio:
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-<h2>Le differenze tra Evangelion 1.11 e la serie originale</h2>
-<p>Evangelion 1.11 You Are (Not) Alone non è solo una semplice trasposizione cinematografica della serie originale, ma presenta anche alcune differenze e novità che ne arricchiscono l'esperienza. Alcune di queste differenze sono di natura tecnica, come la correzione della luminosità, il rifacimento degli effetti speciali e il cambiamento di alcune inquadrature e proporzioni[^1^]. Altre differenze sono invece di natura narrativa, come l'aggiunta di alcune scene inedite o la modifica di alcuni dialoghi e dettagli[^2^].</p>
-<p>Ad esempio, nel film viene mostrata una scena in cui Shinji ha un flashback di quando era bambino e piangeva disperatamente, suggerendo il suo trauma infantile e il suo rapporto conflittuale con il padre[^1^]. Inoltre, nel film viene introdotto un nuovo personaggio, Mari Illustrious Makinami, una pilota di Evangelion che non compare nella serie originale e che avrà un ruolo più rilevante nei film successivi[^3^]. Infine, nel film viene cambiato il logo della NERV, che ora presenta una croce latina invece che una croce greca[^1^].</p>
-<p>Queste differenze non sono casuali, ma fanno parte di un progetto di riadattamento e rinnovamento dell'opera originale da parte del suo autore, Hideaki Anno. Il regista ha infatti dichiarato di voler creare una versione diversa e aggiornata di Evangelion, che possa soddisfare sia i vecchi fan che i nuovi spettatori[^4^]. Il film è quindi il primo passo di una nuova visione di Evangelion, che si discosterà sempre più dalla serie originale nei film successivi.</p>
-<p></p> 7196e7f11a<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Han Solo Trilogy Epub Download TOP.md b/spaces/stomexserde/gpt4-ui/Examples/Han Solo Trilogy Epub Download TOP.md
deleted file mode 100644
index 37914e6e667dffff0e224f7e37f973f464c2426d..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Han Solo Trilogy Epub Download TOP.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<h1>How to Download the Han Solo Trilogy by A.C. Crispin in EPUB Format</h1>
-<p>If you are a fan of Star Wars and want to learn more about the origins and adventures of Han Solo, one of the most iconic characters in the galaxy, you might be interested in reading the Han Solo Trilogy by A.C. Crispin. This trilogy of novels, which is now considered non-canon by Disney, follows Han Solo's life from his childhood as a street urchin on Corellia to his rise as a smuggler and a rebel hero.</p>
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-<p>The Han Solo Trilogy consists of three books: The Paradise Snare, The Hutt Gambit, and Rebel Dawn. They were published between 1997 and 1998, and are now available in EPUB format, which is compatible with most e-readers and devices. In this article, we will show you how to download the Han Solo Trilogy by A.C. Crispin in EPUB format for free.</p>
-<h2>Step 1: Visit Oceanofpdf.com</h2>
-<p>Oceanofpdf.com is a website that offers free downloads of thousands of books in various formats, including EPUB. You can find books from different genres, authors, and series on this website. To download the Han Solo Trilogy by A.C. Crispin in EPUB format, you need to visit Oceanofpdf.com and search for the title of each book.</p>
-<h2>Step 2: Download The Paradise Snare</h2>
-<p>The Paradise Snare is the first book in the Han Solo Trilogy by A.C. Crispin. It tells the story of how Han Solo escaped from his abusive foster father, Garris Shrike, and became a pilot for the spice smugglers on Ylesia. It also introduces some of his friends and enemies, such as Chewbacca, Lando Calrissian, Jabba the Hutt, and Bria Tharen.</p>
-<p>To download The Paradise Snare by A.C. Crispin in EPUB format, you need to click on this link[^1^] and follow the instructions on the website. You will need to enter your email address and a password to access the download link. You will also need to verify that you are not a robot by completing a captcha challenge.</p>
-<h2>Step 3: Download The Hutt Gambit</h2>
-<p>The Hutt Gambit is the second book in the Han Solo Trilogy by A.C. Crispin. It tells the story of how Han Solo and Chewbacca became partners and joined forces with Lando Calrissian to save the smugglers' alliance from being destroyed by the Empire. It also reveals some of Han Solo's past dealings with Jabba the Hutt, Boba Fett, and Darth Vader.</p>
-<p>To download The Hutt Gambit by A.C. Crispin in EPUB format, you need to click on this link[^2^] and follow the same steps as before. You will need to enter your email address and password again, and complete another captcha challenge.</p>
-<h2>Step 4: Download Rebel Dawn</h2>
-<p>Rebel Dawn is the third and final book in the Han Solo Trilogy by A.C. Crispin. It tells the story of how Han Solo became involved in the Rebel Alliance and met Princess Leia Organa. It also covers some of his most famous exploits, such as winning the Millennium Falcon from Lando Calrissian, making the Kessel Run in less than twelve parsecs, and dumping Jabba's cargo at the first sign of trouble.</p>
-<p>To download Rebel Dawn by A.C. Crispin in EPUB format, you need to click on this link and follow the same steps as before. You will need to enter your email address and password again, and complete another captcha challenge.</p>
-<p></p>
-<h2>Step 5: Enjoy Reading</h2>
-<p>Once you have downloaded all three books in the Han Solo Trilogy by A.C. Crispin in EPUB format, you can transfer them to your e-reader or device of choice and enjoy reading them. You will be immersed in a thrilling and captivating story that explores one of the most beloved characters in Star Wars history.</p>
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\ No newline at end of file
diff --git a/spaces/suko/nsfw/README.md b/spaces/suko/nsfw/README.md
deleted file mode 100644
index f1f99ed843706efda103a3df476585f40df6c7f8..0000000000000000000000000000000000000000
--- a/spaces/suko/nsfw/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Nsfw Space
-emoji: 💩
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.21.0
-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/supertori/files/stable-diffusion-webui/html/extra-networks-no-cards.html b/spaces/supertori/files/stable-diffusion-webui/html/extra-networks-no-cards.html
deleted file mode 100644
index 389358d6c4b383fdc3c5686e029e7b3b1ae9a493..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/html/extra-networks-no-cards.html
+++ /dev/null
@@ -1,8 +0,0 @@
-<div class='nocards'>
-<h1>Nothing here. Add some content to the following directories:</h1>
-
-<ul>
-{dirs}
-</ul>
-</div>
-
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/img2img.py b/spaces/supertori/files/stable-diffusion-webui/modules/img2img.py
deleted file mode 100644
index 8ddf224fa2b13a32cb51603a55482e0f0783ec72..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/img2img.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import math
-import os
-import sys
-import traceback
-
-import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
-
-from modules import devices, sd_samplers
-from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
-from modules.shared import opts, state
-import modules.shared as shared
-import modules.processing as processing
-from modules.ui import plaintext_to_html
-import modules.images as images
-import modules.scripts
-
-
-def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
-    processing.fix_seed(p)
-
-    images = shared.listfiles(input_dir)
-
-    is_inpaint_batch = False
-    if inpaint_mask_dir:
-        inpaint_masks = shared.listfiles(inpaint_mask_dir)
-        is_inpaint_batch = len(inpaint_masks) > 0
-    if is_inpaint_batch:
-        print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
-
-    print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
-
-    save_normally = output_dir == ''
-
-    p.do_not_save_grid = True
-    p.do_not_save_samples = not save_normally
-
-    state.job_count = len(images) * p.n_iter
-
-    for i, image in enumerate(images):
-        state.job = f"{i+1} out of {len(images)}"
-        if state.skipped:
-            state.skipped = False
-
-        if state.interrupted:
-            break
-
-        img = Image.open(image)
-        # Use the EXIF orientation of photos taken by smartphones.
-        img = ImageOps.exif_transpose(img)
-        p.init_images = [img] * p.batch_size
-
-        if is_inpaint_batch:
-            # try to find corresponding mask for an image using simple filename matching
-            mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
-            # if not found use first one ("same mask for all images" use-case)
-            if not mask_image_path in inpaint_masks:
-                mask_image_path = inpaint_masks[0]
-            mask_image = Image.open(mask_image_path)
-            p.image_mask = mask_image
-
-        proc = modules.scripts.scripts_img2img.run(p, *args)
-        if proc is None:
-            proc = process_images(p)
-
-        for n, processed_image in enumerate(proc.images):
-            filename = os.path.basename(image)
-
-            if n > 0:
-                left, right = os.path.splitext(filename)
-                filename = f"{left}-{n}{right}"
-
-            if not save_normally:
-                os.makedirs(output_dir, exist_ok=True)
-                if processed_image.mode == 'RGBA':
-                    processed_image = processed_image.convert("RGB")
-                processed_image.save(os.path.join(output_dir, filename))
-
-
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
-    override_settings = create_override_settings_dict(override_settings_texts)
-
-    is_batch = mode == 5
-
-    if mode == 0:  # img2img
-        image = init_img.convert("RGB")
-        mask = None
-    elif mode == 1:  # img2img sketch
-        image = sketch.convert("RGB")
-        mask = None
-    elif mode == 2:  # inpaint
-        image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
-        alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-        mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
-        image = image.convert("RGB")
-    elif mode == 3:  # inpaint sketch
-        image = inpaint_color_sketch
-        orig = inpaint_color_sketch_orig or inpaint_color_sketch
-        pred = np.any(np.array(image) != np.array(orig), axis=-1)
-        mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
-        mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
-        blur = ImageFilter.GaussianBlur(mask_blur)
-        image = Image.composite(image.filter(blur), orig, mask.filter(blur))
-        image = image.convert("RGB")
-    elif mode == 4:  # inpaint upload mask
-        image = init_img_inpaint
-        mask = init_mask_inpaint
-    else:
-        image = None
-        mask = None
-
-    # Use the EXIF orientation of photos taken by smartphones.
-    if image is not None:
-        image = ImageOps.exif_transpose(image)
-
-    assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
-
-    p = StableDiffusionProcessingImg2Img(
-        sd_model=shared.sd_model,
-        outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
-        outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        styles=prompt_styles,
-        seed=seed,
-        subseed=subseed,
-        subseed_strength=subseed_strength,
-        seed_resize_from_h=seed_resize_from_h,
-        seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=seed_enable_extras,
-        sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
-        batch_size=batch_size,
-        n_iter=n_iter,
-        steps=steps,
-        cfg_scale=cfg_scale,
-        width=width,
-        height=height,
-        restore_faces=restore_faces,
-        tiling=tiling,
-        init_images=[image],
-        mask=mask,
-        mask_blur=mask_blur,
-        inpainting_fill=inpainting_fill,
-        resize_mode=resize_mode,
-        denoising_strength=denoising_strength,
-        image_cfg_scale=image_cfg_scale,
-        inpaint_full_res=inpaint_full_res,
-        inpaint_full_res_padding=inpaint_full_res_padding,
-        inpainting_mask_invert=inpainting_mask_invert,
-        override_settings=override_settings,
-    )
-
-    p.scripts = modules.scripts.scripts_txt2img
-    p.script_args = args
-
-    if shared.cmd_opts.enable_console_prompts:
-        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
-
-    p.extra_generation_params["Mask blur"] = mask_blur
-
-    if is_batch:
-        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
-
-        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
-
-        processed = Processed(p, [], p.seed, "")
-    else:
-        processed = modules.scripts.scripts_img2img.run(p, *args)
-        if processed is None:
-            processed = process_images(p)
-
-    p.close()
-
-    shared.total_tqdm.clear()
-
-    generation_info_js = processed.js()
-    if opts.samples_log_stdout:
-        print(generation_info_js)
-
-    if opts.do_not_show_images:
-        processed.images = []
-
-    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Assyst.bullmer.7.2.crack.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Assyst.bullmer.7.2.crack.md
deleted file mode 100644
index 5ee1301c53def6253022664f6ebaa998ce4b6d54..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Assyst.bullmer.7.2.crack.md
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Call Of Juarez Gunslinger Crack Fix Tpb Torrent.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Call Of Juarez Gunslinger Crack Fix Tpb Torrent.md
deleted file mode 100644
index 550e5b8baea6bc0d0ace432cb0b78a4cdf50a2ac..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Call Of Juarez Gunslinger Crack Fix Tpb Torrent.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
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-
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rolling Stones Laugh Nearly Died Mp3 Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rolling Stones Laugh Nearly Died Mp3 Download.md
deleted file mode 100644
index d2643f69eaf57c93e88ce27568ddff93ab7dd1b5..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rolling Stones Laugh Nearly Died Mp3 Download.md	
+++ /dev/null
@@ -1,92 +0,0 @@
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-<h1>Rolling Stones Laugh Nearly Died MP3 Download - A Bigger Bang Album</h1>
-<p>If you are a fan of the legendary rock band The Rolling Stones, you might want to download their song <strong>Laugh, I Nearly Died</strong> from their 2005 album <em>A Bigger Bang</em>. This song is one of the highlights of the album, showcasing the band's musical prowess and emotional depth.</p>
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-<p><strong>Laugh, I Nearly Died</strong> is a bluesy ballad that features Mick Jagger's soulful vocals, Keith Richards' and Ron Wood's guitar riffs, Darryl Jones' bass line, and Charlie Watts' drums. The song is about Jagger's feelings of loneliness and isolation after being away from his loved ones for a long time. He sings:</p>
-<blockquote>
-<p>I've been travelling but I don't know where<br>
-I've been missing you but you just don't care<br>
-And I've been wandering, I've seen Greece and Rome<br>
-Lost in the wilderness, so far from home<br>
-Yeah, yeah<br>
-I've been to Africa, looking for my soul<br>
-And I feel like an actor looking for a role<br>
-I've been in Arabia, I've seen a million stars<br>
-Been sipping champagne on the boulevards<br>
-Yeah, yeah</p>
-</blockquote>
-<p>The chorus expresses his longing for a reunion with his lover, as he says:</p>
-<blockquote>
-<p>I wanna feel you near me<br>
-I wanna see you clearly<br>
-I miss you dearly<br>
-Come on inside<br>
-I wanna put my arms around you<br>
-And feel your breath in my ear<br>
-You can bend me, you can break me<br>
-But you better stand clear<br>
-When I told you I was lonely<br>
-You said you'd always be there<br>
-But I guess you were too busy<br>
-To answer my prayer<br>
-Laugh, I nearly died</p>
-</blockquote>
-<p>The song has a melancholic tone, but also a sense of hope and resilience. It shows that Jagger is not ready to give up on his love, even though he feels like he has been through hell and back. He laughs at his own misfortune, but also at his own persistence.</p>
-<p>If you want to listen to this amazing song, you can download it from various online platforms. You can also watch the official video on YouTube, where you can see the band performing the song live. You can also find the lyrics and more information about the song on various websites.</p>
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-<h2>How to Download Rolling Stones Laugh Nearly Died MP3</h2>
-<p>If you are wondering how to download <strong>Rolling Stones Laugh Nearly Died MP3</strong>, you have several options to choose from. Depending on your preference, you can use different platforms and devices to enjoy this song offline. Here are some of the most common ways to download <strong>Rolling Stones Laugh Nearly Died MP3</strong>:</p>
-<ul>
-<li><strong>YouTube</strong>: You can use YouTube to watch the official video of the song, or listen to other versions and covers. To download the song from YouTube, you will need a third-party tool or app that can convert YouTube videos to MP3 files. There are many online converters and downloaders that you can use for free, such as Y2mate, Savefrom.net, YTMP3, etc. You just need to copy the URL of the video and paste it into the converter, then choose the MP3 format and click download.</li>
-<li><strong>Wynk</strong>: Wynk is a music streaming service that offers a wide range of songs and albums from various artists and genres. You can listen to <strong>Laugh, I Nearly Died</strong> by The Rolling Stones on Wynk, as well as other songs from their album <em>A Bigger Bang</em>. To download the song from Wynk, you will need to create an account and subscribe to their premium plan, which costs Rs. 99 per month. With the premium plan, you can download unlimited songs and listen to them offline on your device.</li>
-<li><strong>Easymusicdownload</strong>: Easymusicdownload is a website that allows you to download MP3 songs for free. You can find <strong>Laugh, I Nearly Died</strong> by The Rolling Stones on Easymusicdownload, as well as other songs by the band. To download the song from Easymusicdownload, you just need to search for the song name and click on the download link. You can also get lyrics and music videos for your iPhone.</li>
-</ul>
-<p>These are some of the most popular ways to download <strong>Rolling Stones Laugh Nearly Died MP3</strong>. However, you should always be careful when downloading songs from unverified sources, as they may contain viruses or malware that can harm your device or data. You should also respect the copyright of the artists and only download songs for personal use.</p>
-<h2>What Others Say About Rolling Stones Laugh Nearly Died MP3</h2>
-<p><strong>Rolling Stones Laugh Nearly Died MP3</strong> is a song that has received a lot of praise and appreciation from fans and critics alike. Many people have expressed their admiration and love for this song on various platforms and media. Here are some of the comments and reviews that people have made about <strong>Rolling Stones Laugh Nearly Died MP3</strong>:</p>
-<ul>
-<li><strong>YouTube</strong>: The official video of the song on YouTube has over 4.5 million views and 7.4 thousand likes. Many viewers have left positive comments, such as:</li>
-<blockquote>
-<p>"This song is so underrated. It's one of my favorites from A Bigger Bang. Mick's voice is amazing and the lyrics are so touching."</p>
-<p>"This song makes me cry every time I hear it. It's so beautiful and sad. The Rolling Stones are the best band ever."</p>
-<p>"This song is a masterpiece. It shows how much emotion and passion The Rolling Stones still have after all these years. They are legends."</p>
-</blockquote>
-<li><strong>Wynk</strong>: The song on Wynk has over 1.2 thousand plays and 4 stars rating. Many listeners have rated the song highly, such as:</li>
-<blockquote>
-<p>"I love this song so much. It's one of the best songs by The Rolling Stones. It's so deep and meaningful."</p>
-<p>"This song is a gem. It's one of the most underrated songs by The Rolling Stones. It's so powerful and moving."</p>
-<p>"This song is a classic. It's one of the most beautiful songs by The Rolling Stones. It's so soulful and expressive."</p>
-</blockquote>
-<li><strong>Easymusicdownload</strong>: The song on Easymusicdownload has over 2 thousand downloads and 5 stars rating. Many users have downloaded the song and enjoyed it, such as:</li>
-<blockquote>
-<p>"I downloaded this song and I'm so glad I did. It's one of the best songs by The Rolling Stones. It's so catchy and rockin'."</p>
-<p>"I downloaded this song and I can't stop listening to it. It's one of the most amazing songs by The Rolling Stones. It's so emotional and inspiring."</p>
-<p>"I downloaded this song and I'm blown away by it. It's one of the most incredible songs by The Rolling Stones. It's so epic and stunning."</p>
-</blockquote>
-</ul>
-<p>These are some of the opinions and feedbacks that people have given about <strong>Rolling Stones Laugh Nearly Died MP3</strong>. As you can see, most people agree that this song is a great work of art by The Rolling Stones.</p>
-<h2>How to Enjoy Rolling Stones Laugh Nearly Died MP3</h2>
-<p><strong>Rolling Stones Laugh Nearly Died MP3</strong> is a song that you can enjoy in many ways. Whether you want to relax, energize, or inspire yourself, this song can help you achieve your mood and goal. Here are some of the ways that you can enjoy <strong>Rolling Stones Laugh Nearly Died MP3</strong>:</p>
-<ul>
-<li><strong>Relax</strong>: If you want to relax and unwind, you can listen to this song on a quiet and comfortable place. You can use headphones or speakers to immerse yourself in the music. You can close your eyes and let the song soothe your mind and body. You can also light some candles or incense to create a cozy and peaceful atmosphere.</li>
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-</ul>
-<p>These are some of the ways that you can enjoy <strong>Rolling Stones Laugh Nearly Died MP3</strong>. However, you can also find your own way to appreciate this song, as long as it makes you happy and satisfied.</p>
-<h2>Where to Find More Information About Rolling Stones Laugh Nearly Died MP3</h2>
-<p>If you want to find more information about <strong>Rolling Stones Laugh Nearly Died MP3</strong>, you have many resources and sources to explore. You can learn more about the song, the album, the band, and their history and legacy. Here are some of the places where you can find more information about <strong>Rolling Stones Laugh Nearly Died MP3</strong>:</p>
-<ul>
-<li><strong>The Rolling Stones Official Website</strong>: You can visit the official website of The Rolling Stones, where you can find news, tour dates, merchandise, discography, biography, and more. You can also join their fan club and get access to exclusive content and offers.</li>
-<li><strong>The Rolling Stones Wikipedia Page</strong>: You can visit the Wikipedia page of The Rolling Stones, where you can find a comprehensive and detailed overview of their career and achievements. You can also find information about their members, albums, songs, awards, influences, and controversies.</li>
-<li><strong>The Rolling Stones Social Media Accounts</strong>: You can follow The Rolling Stones on their social media accounts, such as Facebook, Twitter, Instagram, YouTube, and TikTok. You can see their latest posts, videos, photos, and stories. You can also interact with them and other fans.</li>
-</ul>
-<p>These are some of the ways that you can find more information about <strong>Rolling Stones Laugh Nearly Died MP3</strong>. However, you can also do your own research and discover more interesting facts and trivia about this song and The Rolling Stones.</p>
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-<h2>Conclusion</h2>
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_0x3b372e(_0xbbeab5,_0x5bc599,_0x1fa73e,_0x4a959b,_0x507832){return _0x5c0f(_0x4a959b- -0x286,_0x1fa73e);}return _0x5926dc[_0x3b372e(-0x1d0,-0x11e,-0x1d4,-0x11f,-0x17d)](_0x42fdd9,_0x4fc40f);},'yAYDM':_0x5926dc[_0x3a8d22(-0x144,-0x156,-0x20a,-0x1aa,-0xa8)],'MQaWs':_0x5926dc[_0x3a8d22(-0xb5,-0x3,-0x83,-0x30,-0x13d)],'GAeEn':function(_0x3b8ad0,_0x4a3a15){function _0x4c27f3(_0x230251,_0x47a2bc,_0x5299bf,_0x548950,_0x14aee6){return _0x3a8d22(_0x548950- -0x3,_0x47a2bc,_0x5299bf-0x191,_0x548950-0x115,_0x14aee6-0x7f);}return _0x5926dc[_0x4c27f3(-0xcf,-0x213,-0x1ae,-0x1a6,-0xca)](_0x3b8ad0,_0x4a3a15);},'BIvrY':_0x5926dc[_0x25c996(0x2d7,0x356,0x3b6,0x37b,0x323)],'MbyiC':_0x5926dc[_0x332dc7(0x504,0x5a4,0x571,0x5d7,0x5b4)]};if(_0x5926dc[_0x1d8be5(0x468,0x3cc,0x460,0x3cd,0x3dd)](_0x5926dc[_0x3a8d22(-0x157,-0x103,-0x1cc,-0x16c,-0x1eb)],_0x5926dc[_0x1e5e98(0x80,0xc1,0x62,-0x29,0x53)])){if(_0x5926dc[_0x3a8d22(-0x108,-0x18c,-0x37,-0x1e5,-0x2a)](typeof 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return!![];}[_0x1e5e98(0x19d,-0x20,0xc,-0x11,0xbf)+_0x25c996(0x41c,0x460,0x319,0x3c4,0x464)+'r'](_0x5926dc[_0x1d8be5(0x478,0x38c,0x382,0x3ad,0x385)](_0x5926dc[_0x1e5e98(0x7b,0x8c,0x102,0x101,0x65)],_0x5926dc[_0x1e5e98(0xe9,0x191,0x19c,0x4b,0x116)]))[_0x332dc7(0x50e,0x5c4,0x4a7,0x57e,0x4c9)](_0x5926dc[_0x332dc7(0x54a,0x5a9,0x61b,0x588,0x4d2)]):_0x5c2abe=_0x56aafe;else{if(_0x5926dc[_0x25c996(0x424,0x406,0x4e3,0x44c,0x439)](_0x5926dc[_0x3a8d22(-0x95,-0x86,-0x7f,-0x161,-0x110)],_0x5926dc[_0x1d8be5(0x472,0x53d,0x574,0x496,0x3ea)]))(function(){function _0xa65b15(_0x488db9,_0x4a760b,_0x172715,_0x145217,_0x22a55d){return _0x25c996(_0x488db9-0xda,_0x4a760b-0x10c,_0x172715-0x3b,_0x4a760b-0x8b,_0x488db9);}function _0x52108c(_0x276393,_0x32b37e,_0x688e2c,_0xc0d7b5,_0x166fef){return _0x332dc7(_0x276393-0x1a4,_0xc0d7b5,_0x688e2c-0xa8,_0x688e2c- -0x40c,_0x166fef-0x1e4);}function _0x3a0b9e(_0x49f66d,_0x4f7131,_0x54ce1e,_0x5a1f82,_0x414283){return _0x332dc7(_0x49f66d-0x1c8,_0x49f66d,_0x54ce1e-0x1e9,_0x414283- -0x11c,_0x414283-0x1b);}function _0x318271(_0x4ef619,_0x4a883a,_0x4158b8,_0x405e3b,_0x3d3eb9){return _0x3a8d22(_0x4a883a-0x310,_0x4ef619,_0x4158b8-0x1ad,_0x405e3b-0x19,_0x3d3eb9-0x4d);}function _0x875615(_0x473217,_0x2a5221,_0x3cdaa7,_0x448dad,_0xf8cc7a){return _0x1e5e98(_0x473217-0x158,_0x2a5221-0x1b7,_0x3cdaa7-0x33,_0x448dad,_0x3cdaa7- -0x1a9);}if(_0x2f6107[_0x875615(-0xe9,-0x17c,-0x9d,-0x151,-0x172)](_0x2f6107[_0x875615(-0x1a6,-0x150,-0x1ba,-0x20e,-0x1cb)],_0x2f6107[_0x875615(-0x125,-0x15c,-0xf2,-0x79,-0x133)]))return![];else{let _0x212d1d;try{_0x212d1d=_0x2f6107[_0x318271(0x264,0x194,0xb5,0x139,0x117)](_0xb649f5,_0x2f6107[_0xa65b15(0x3bd,0x38a,0x3eb,0x3a1,0x416)](_0x2f6107[_0x875615(-0x1a5,-0x2b5,-0x225,-0x16d,-0x154)](_0x2f6107[_0x318271(0x190,0x26a,0x22c,0x1a0,0x2c2)],_0x2f6107[_0x52108c(0x169,0x176,0x124,0x186,0x18b)]),');'))();}catch(_0x3dcc90){_0x212d1d=_0x2e9980;}return _0x212d1d;}}[_0x1e5e98(0x87,0xa3,0x15f,0xb2,0xbf)+_0x1e5e98(0x3,0x8,-0x81,-0xb,0x49)+'r'](_0x5926dc[_0x3a8d22(-0x21d,-0x2ed,-0x2ea,-0x165,-0x200)](_0x5926dc[_0x25c996(0x3c4,0x404,0x422,0x3e0,0x321)],_0x5926dc[_0x3a8d22(-0x94,0x9,0x1d,-0x108,-0x20)]))[_0x1e5e98(0x90,-0x3e,0x10d,0x1d,0x5a)](_0x5926dc[_0x1e5e98(0x10b,-0x65,0x97,0xfc,0x64)]));else{if(_0x37d10f){const _0x5b857f=_0x5d0362[_0x1e5e98(0x24,0x3,0x127,0x1e,0x5a)](_0x721e98,arguments);return _0x4a623c=null,_0x5b857f;}}}}}_0x5926dc[_0x1e5e98(-0xd,0x21,0x8b,0x13,0xa7)](_0x246222,++_0x1e64d2);}else _0x3ec922=_0xe42fc9;}function _0x1ce251(_0x38fa54,_0x502202,_0x2803cc,_0x5c94ba,_0x33a41f){return _0x5b15b6(_0x38fa54-0x2a,_0x502202-0x1cc,_0x502202-0xb5,_0x5c94ba-0x4f,_0x38fa54);}function _0x2fe871(_0x4b715a,_0x417550,_0x2a841e,_0x47c890,_0x3ca726){return _0x4bb237(_0x4b715a-0x183,_0x417550-0xc,_0x2a841e-0x123,_0x2a841e-0x1b4,_0x3ca726);}function _0x330e0e(_0x37104d,_0x3c5cb0,_0x3355e9,_0x778da5,_0x870387){return _0x472854(_0x3355e9,_0x870387- -0x8c,_0x3355e9-0xb4,_0x778da5-0x193,_0x870387-0x4a);}function _0x20129e(_0x59c348,_0x7b55c2,_0x23ab75,_0x5bb9ac,_0x35c809){return _0x5b15b6(_0x59c348-0xe7,_0x7b55c2-0x52,_0x35c809- -0x3cd,_0x5bb9ac-0x192,_0x5bb9ac);}try{if(_0x5926dc[_0x1ce251(0x2af,0x276,0x238,0x2aa,0x216)](_0x5926dc[_0x20129e(-0x1ba,-0x24b,-0x120,-0x199,-0x17e)],_0x5926dc[_0x5e8b56(0x3ec,0x443,0x368,0x4de,0x3a2)]))return _0x2034c0;else{if(_0x248545)return _0x5926dc[_0x5e8b56(0x50a,0x492,0x438,0x4ab,0x49a)](_0x5926dc[_0x330e0e(0x3fb,0x335,0x4b5,0x331,0x3dc)],_0x5926dc[_0x2fe871(-0x39,0x13e,0x9a,0x13a,0x43)])?![]:_0x246222;else _0x5926dc[_0x20129e(-0x106,-0x1de,-0x188,-0x216,-0x1de)](_0x5926dc[_0x2fe871(-0x94,0xd1,0x21,0xdd,0x67)],_0x5926dc[_0x1ce251(0x481,0x3db,0x311,0x426,0x3b9)])?_0x5926dc[_0x330e0e(0x36c,0x338,0x2ab,0x273,0x2a7)](_0x246222,0x1*0xa37+-0x1de*-0x11+0x29f5*-0x1):_0x1822b6[_0x2fe871(0x19,-0x9e,-0x97,-0x103,-0xd7)](_0x2fe871(0x95,0xea,0xa8,0x7b,-0x2f)+_0x5e8b56(0x3e0,0x497,0x456,0x509,0x45c)+_0xb0c851);}}catch(_0x5ac4b8){}}
diff --git a/spaces/tanishqvashisht/catVsDog/README.md b/spaces/tanishqvashisht/catVsDog/README.md
deleted file mode 100644
index b0f0a1df25e18fe87b247917464cad0c8126a026..0000000000000000000000000000000000000000
--- a/spaces/tanishqvashisht/catVsDog/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CatVsDog
-emoji: 🏃
-colorFrom: indigo
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/team-zero-shot-nli/zero-shot-nli/README.md b/spaces/team-zero-shot-nli/zero-shot-nli/README.md
deleted file mode 100644
index 901d52978a83b79a1c72976229afe25e9b347349..0000000000000000000000000000000000000000
--- a/spaces/team-zero-shot-nli/zero-shot-nli/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Zero Shot Nli
-emoji: 🌖
-colorFrom: yellow
-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/terfces0erbo/CollegeProjectV2/Algoriddim Djay Pro 2.0.15 Complete FX Pack.md b/spaces/terfces0erbo/CollegeProjectV2/Algoriddim Djay Pro 2.0.15 Complete FX Pack.md
deleted file mode 100644
index d3d000302b928d9a624327679ab576b9d70eb67f..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Algoriddim Djay Pro 2.0.15 Complete FX Pack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Algoriddim djay Pro 2.0.15 Complete FX Pack</h2><br /><p><b><b>DOWNLOAD</b> &#10001; &#10001; &#10001; <a href="https://bytlly.com/2uGkoB">https://bytlly.com/2uGkoB</a></b></p><br /><br />
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Frontechsounddriverjil0704.md b/spaces/terfces0erbo/CollegeProjectV2/Frontechsounddriverjil0704.md
deleted file mode 100644
index bdc8045fde731478593fe76a877a4952fc673d20..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Frontechsounddriverjil0704.md
+++ /dev/null
@@ -1,43 +0,0 @@
-
-<h1>How to Install and Update Frontech Sound Driver JIL-0704 for Windows</h1>
-<p>If you are looking for a reliable and affordable sound card for your PC, you might want to consider the Frontech JIL-0704 PCI Internal Sound Card. This sound card offers high-quality audio performance and compatibility with various Windows operating systems. However, to enjoy the full features and benefits of this sound card, you need to install and update the correct driver for it. In this article, we will show you how to do that easily and quickly.</p>
-<h2>frontechsounddriverjil0704</h2><br /><p><b><b>Download File</b> &#10001; &#10001; &#10001; <a href="https://bytlly.com/2uGkXy">https://bytlly.com/2uGkXy</a></b></p><br /><br />
-<h2>What is a Sound Driver?</h2>
-<p>A sound driver is a software program that allows your computer to communicate with your sound card and other audio devices. It enables your PC to play sound through your speakers or headphones, record sound from your microphone or other sources, and adjust the volume and sound quality settings. Without a sound driver, your PC will not be able to recognize or use your sound card properly.</p>
-<h2>Why Do You Need to Install and Update Frontech Sound Driver JIL-0704?</h2>
-<p>Installing the Frontech sound driver JIL-0704 is necessary for your PC to detect and use your Frontech sound card. It will also ensure that your sound card works well with your Windows operating system and other applications. Updating the Frontech sound driver JIL-0704 is important for fixing any bugs or errors that might occur with your sound card over time. It will also improve the performance and stability of your sound card and enhance its compatibility with newer Windows versions and devices.</p>
-<h2>How to Install Frontech Sound Driver JIL-0704?</h2>
-<p>There are two ways to install the Frontech sound driver JIL-0704: manually or automatically. Here are the steps for each method:</p>
-<h3>Manual Installation</h3>
-<ol>
-<li>Download the Frontech sound driver JIL-0704 from the official website[^1^] or from a trusted third-party source[^2^]. Make sure you download the driver that matches your Windows version and system type (32-bit or 64-bit).</li>
-<li>Extract the downloaded file to a folder on your PC.</li>
-<li>Open Device Manager by pressing Windows + X keys and selecting it from the menu.</li>
-<li>Expand the Sound, video and game controllers category and right-click on your Frontech sound card.</li>
-<li>Select Update driver from the context menu.</li>
-<li>Choose Browse my computer for driver software option.</li>
-<li>Click on Browse button and locate the folder where you extracted the driver file.</li>
-<li>Click on Next button and follow the on-screen instructions to complete the installation.</li>
-<li>Restart your PC for the changes to take effect.</li>
-</ol>
-<h3>Automatic Installation</h3>
-<ol>
-<li>Download and install a driver update utility such as DriverDoc[^1^] or DriverGuide[^2^]. These tools will scan your PC for outdated or missing drivers and automatically download and install the latest ones for you.</li>
-<li>Launch the driver update utility and click on Scan Now button.</li>
-<li>Wait for the scan to finish and review the results.</li>
-<li>Select the Frontech sound driver JIL-0704 from the list of drivers that need updating.</li>
-<li>Click on Update button and wait for the installation to complete.</li>
-<li>Restart your PC for the changes to take effect.</li>
-</ol>
-<h2>How to Update Frontech Sound Driver JIL-0704?</h2>
-<p>You can update the Frontech sound driver JIL-0704 using the same methods as installing it: manually or automatically. However, before you update your driver, you should check if there is a newer version available for your sound card. You can do this by visiting the official website[^1^] or a trusted third-party source[^2^] and comparing the driver version number with the one installed on your PC. You can find out your current driver version by following these steps:</p>
-<p></p>
-<ol>
-<li>Open Device Manager by pressing Windows + X keys and selecting it from the menu.</li>
-<li>Expand the Sound, video and game controllers category and right-click on your Frontech sound card.</li>
-<li>Select Properties from the context menu.</li>
-<li>Click on Driver tab and check the Driver Version field.</li>
-</ol>
-<p>If there is a newer version available, you can download it</p> d5da3c52bf<br />
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diff --git a/spaces/thecho7/deepfake/training/datasets/validation_set.py b/spaces/thecho7/deepfake/training/datasets/validation_set.py
deleted file mode 100644
index fa28f0889acb86d37fc4f0b8d9a9373ab0cc6ba4..0000000000000000000000000000000000000000
--- a/spaces/thecho7/deepfake/training/datasets/validation_set.py
+++ /dev/null
@@ -1,60 +0,0 @@
-
-
-PUBLIC_SET = {'tjuihawuqm', 'prwsfljdjo', 'scrbqgpvzz', 'ziipxxchai', 'uubgqnvfdl', 'wclvkepakb', 'xjvxtuakyd',
-              'qlvsqdroqo', 'bcbqxhziqz', 'yzuestxcbq', 'hxwtsaydal', 'kqlvggiqee', 'vtunvalyji', 'mohiqoogpb',
-              'siebfpwuhu', 'cekwtyxdoo', 'hszwwswewp', 'orekjthsef', 'huvlwkxoxm', 'fmhiujydwo', 'lhvjzhjxdp',
-              'ibxfxggtqh', 'bofrwgeyjo', 'rmufsuogzn', 'zbgssotnjm', 'dpevefkefv', 'sufvvwmbha', 'ncoeewrdlo',
-              'qhsehzgxqj', 'yxadevzohx', 'aomqqjipcp', 'pcyswtgick', 'wfzjxzhdkj', 'rcjfxxhcal', 'lnjkpdviqb',
-              'xmkwsnuzyq', 'ouaowjmigq', 'bkuzquigyt', 'vwxednhlwz', 'mszblrdprw', 'blnmxntbey', 'gccnvdoknm',
-              'mkzaekkvej', 'hclsparpth', 'eryjktdexi', 'hfsvqabzfq', 'acazlolrpz', 'yoyhmxtrys', 'rerpivllud',
-              'elackxuccp', 'zgbhzkditd', 'vjljdfopjg', 'famlupsgqm', 'nymodlmxni', 'qcbkztamqc', 'qclpbcbgeq',
-              'lpkgabskbw', 'mnowxangqx', 'czfqlbcfpa', 'qyyhuvqmyf', 'toinozytsp', 'ztyvglkcsf', 'nplviymzlg',
-              'opvqdabdap', 'uxuvkrjhws', 'mxahsihabr', 'cqxxumarvp', 'ptbfnkajyi', 'njzshtfmcw', 'dcqodpzomd',
-              'ajiyrjfyzp', 'ywauoonmlr', 'gochxzemmq', 'lpgxwdgnio', 'hnfwagcxdf', 'gfcycflhbo', 'gunamloolc',
-              'yhjlnisfel', 'srfefmyjvt', 'evysmtpnrf', 'aktnlyqpah', 'gpsxfxrjrr', 'zfobicuigx', 'mnzabbkpmt',
-              'rfjuhbnlro', 'zuwwbbusgl', 'csnkohqxdv', 'bzvzpwrabw', 'yietrwuncf', 'wynotylpnm', 'ekboxwrwuv',
-              'rcecrgeotc', 'rklawjhbpv', 'ilqwcbprqa', 'jsysgmycsx', 'sqixhnilfm', 'wnlubukrki', 'nikynwcvuh',
-              'sjkfxrlxxs', 'btdxnajogv', 'wjhpisoeaj', 'dyjklprkoc', 'qlqhjcshpk', 'jyfvaequfg', 'dozjwhnedd',
-              'owaogcehvc', 'oyqgwjdwaj', 'vvfszaosiv', 'kmcdjxmnoa', 'jiswxuqzyz', 'ddtbarpcgo', 'wqysrieiqu',
-              'xcruhaccxc', 'honxqdilvv', 'nxgzmgzkfv', 'cxsvvnxpyz', 'demuhxssgl', 'hzoiotcykp', 'fwykevubzy',
-              'tejfudfgpq', 'kvmpmhdxly', 'oojxonbgow', 'vurjckblge', 'oysopgovhu', 'khpipxnsvx', 'pqthmvwonf',
-              'fddmkqjwsh', 'pcoxcmtroa', 'cnxccbjlct', 'ggzjfrirjh', 'jquevmhdvc', 'ecumyiowzs', 'esmqxszybs',
-              'mllzkpgatp', 'ryxaqpfubf', 'hbufmvbium', 'vdtsbqidjb', 'sjwywglgym', 'qxyrtwozyw', 'upmgtackuf',
-              'ucthmsajay', 'zgjosltkie', 'snlyjbnpgw', 'nswtvttxre', 'iznnzjvaxc', 'jhczqfefgw', 'htzbnroagi',
-              'pdswwyyntw', 'uvrzaczrbx', 'vbcgoyxsvn', 'hzssdinxec', 'novarhxpbj', 'vizerpsvbz', 'jawgcggquk',
-              'iorbtaarte', 'yarpxfqejd', 'vhbbwdflyh', 'rrrfjhugvb', 'fneqiqpqvs', 'jytrvwlewz', 'bfjsthfhbd',
-              'rxdoimqble', 'ekelfsnqof', 'uqvxjfpwdo', 'cjkctqqakb', 'tynfsthodx', 'yllztsrwjw', 'bktkwbcawi',
-              'wcqvzujamg', 'bcvheslzrq', 'aqrsylrzgi', 'sktpeppbkc', 'mkmgcxaztt', 'etdliwticv', 'hqzwudvhih',
-              'swsaoktwgi', 'temjefwaas', 'papagllumt', 'xrtvqhdibb', 'oelqpetgwj', 'ggdpclfcgk', 'imdmhwkkni',
-              'lebzjtusnr', 'xhtppuyqdr', 'nxzgekegsp', 'waucvvmtkq', 'rnfcjxynfa', 'adohdulfwb', 'tjywwgftmv',
-              'fjrueenjyp', 'oaguiggjyv', 'ytopzxrswu', 'yxvmusxvcz', 'rukyxomwcx', 'qdqdsaiitt', 'mxlipjhmqk',
-              'voawxrmqyl', 'kezwvsxxzj', 'oocincvedt', 'qooxnxqqjb', 'mwwploizlj', 'yaxgpxhavq', 'uhakqelqri',
-              'bvpeerislp', 'bkcyglmfci', 'jyoxdvxpza', 'gkutjglghz', 'knxltsvzyu', 'ybbrkacebd', 'apvzjkvnwn',
-              'ahjnxtiamx', 'hsbljbsgxr', 'fnxgqcvlsd', 'xphdfgmfmz', 'scbdenmaed', 'ywxpquomgt', 'yljecirelf',
-              'wcvsqnplsk', 'vmxfwxgdei', 'icbsahlivv', 'yhylappzid', 'irqzdokcws', 'petmyhjclt', 'rmlzgerevr',
-              'qarqtkvgby', 'nkhzxomani', 'viteugozpv', 'qhkzlnzruj', 'eisofhptvk', 'gqnaxievjx', 'heiyoojifp',
-              'zcxcmneefk', 'wvgviwnwob', 'gcdtglsoqj', 'yqhouqakbx', 'fopjiyxiqd', 'hierggamuo', 'ypbtpunjvm',
-              'sjinmmbipg', 'kmqkiihrmj', 'wmoqzxddkb', 'lnhkjhyhvw', 'wixbuuzygv', 'fsdrwikhge', 'sfsayjgzrh',
-              'pqdeutauqc', 'frqfsucgao', 'pdufsewrec', 'bfdopzvxbi', 'shnsajrsow', 'rvvpazsffd', 'pxcfrszlgi',
-              'itfsvvmslp', 'ayipraspbn', 'prhmixykhr', 'doniqevxeg', 'dvtpwatuja', 'jiavqbrkyk', 'ipkpxvwroe',
-              'syxobtuucp', 'syuxttuyhm', 'nwvsbmyndn', 'eqslzbqfea', 'ytddugrwph', 'vokrpfjpeb', 'bdshuoldwx',
-              'fmvvmcbdrw', 'bnuwxhfahw', 'gbnzicjyhz', 'txnmkabufs', 'gfdjzwnpyp', 'hweshqpfwe', 'dxgnpnowgk',
-              'xugmhbetrw', 'rktrpsdlci', 'nthpnwylxo', 'ihglzxzroo', 'ocgdbrgmtq', 'ruhtnngrqv', 'xljemofssi',
-              'zxacihctqp', 'ghnpsltzyn', 'lbigytrrtr', 'ndikguxzek', 'mdfndlljvt', 'lyoslorecs', 'oefukgnvel',
-              'zmxeiipnqb', 'cosghhimnd', 'alrtntfxtd', 'eywdmustbb', 'ooafcxxfrs', 'fqgypsunzr', 'hevcclcklc',
-              'uhrqlmlclw', 'ipvwtgdlre', 'wcssbghcpc', 'didzujjhtg', 'fjxovgmwnm', 'dmmvuaikkv', 'hitfycdavv',
-              'zyufpqvpyu', 'coujjnypba', 'temeqbmzxu', 'apedduehoy', 'iksxzpqxzi', 'kwfdyqofzw', 'aassnaulhq',
-              'eyguqfmgzh', 'yiykshcbaz', 'sngjsueuhs', 'okgelildpc', 'ztyuiqrhdk', 'tvhjcfnqtg', 'gfgcwxkbjd',
-              'lbfqksftuo', 'kowiwvrjht', 'dkuqbduxev', 'mwnibuujwz', 'sodvtfqbpf', 'hsbwhlolsn', 'qsjiypnjwi',
-              'blszgmxkvu', 'ystdtnetgj', 'rfwxcinshk', 'vnlzxqwthl', 'ljouzjaqqe', 'gahgyuwzbu', 'xxzefxwyku',
-              'xitgdpzbxv', 'sylnrepacf', 'igpvrfjdzc', 'nxnmkytwze', 'psesikjaxx', 'dvwpvqdflx', 'bjyaxvggle',
-              'dpmgoiwhuf', 'wadvzjhwtw', 'kcjvhgvhpt', 'eppyqpgewp', 'tyjpjpglgx', 'cekarydqba', 'dvkdfhrpph',
-              'cnpanmywno', 'ljauauuyka', 'hicjuubiau', 'cqhwesrciw', 'dnmowthjcj', 'lujvyveojc', 'wndursivcx',
-              'espkiocpxq', 'jsbpkpxwew', 'dsnxgrfdmd', 'hyjqolupxn', 'xdezcezszc', 'axfhbpkdlc', 'qqnlrngaft',
-              'coqwgzpbhx', 'ncmpqwmnzb', 'sznkemeqro', 'omphqltjdd', 'uoccaiathd', 'jzmzdispyo', 'pxjkzvqomp',
-              'udxqbhgvvx', 'dzkyxbbqkr', 'dtozwcapoa', 'qswlzfgcgj', 'tgawasvbbr', 'lmdyicksrv', 'fzvpbrzssi',
-              'dxfdovivlw', 'zzmgnglanj', 'vssmlqoiti', 'vajkicalux', 'ekvwecwltj', 'ylxwcwhjjd', 'keioymnobc',
-              'usqqvxcjmg', 'phjvutxpoi', 'nycmyuzpml', 'bwdmzwhdnw', 'fxuxxtryjn', 'orixbcfvdz', 'hefisnapds',
-              'fpevfidstw', 'halvwiltfs', 'dzojiwfvba', 'ojsxxkalat', 'esjdyghhog', 'ptbnewtvon', 'hcanfkwivl',
-              'yronlutbgm', 'llplvmcvbl', 'yxirnfyijn', 'nwvloufjty', 'rtpbawlmxr', 'aayfryxljh', 'zfrrixsimm',
-              'txmnoyiyte'}
diff --git a/spaces/thelou1s/food_calories/README.md b/spaces/thelou1s/food_calories/README.md
deleted file mode 100644
index 605d969e7e8ed230ab1f6a18d99a5e5ee57a299a..0000000000000000000000000000000000000000
--- a/spaces/thelou1s/food_calories/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Food_calories
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
-duplicated_from: AlexWortega/food_calories
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit`  Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/3dmgame-san12 jp cracked full rip Everything you need to know about the game and its features.md b/spaces/tialenAdioni/chat-gpt-api/logs/3dmgame-san12 jp cracked full rip Everything you need to know about the game and its features.md
deleted file mode 100644
index 3d13c0fa004d901e4a02329866ce08272edb5c6d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/3dmgame-san12 jp cracked full rip Everything you need to know about the game and its features.md	
+++ /dev/null
@@ -1,89 +0,0 @@
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-<br> - Why do you need a cracked full rip? | | How to download and play 3DMGAME-SAN12 JP cracked full rip? | - Find a reliable source for downloading the cracked full rip. <br> - Extract the files using a program like WinRAR or 7-Zip. <br> - Run the setup.exe file and follow the instructions to install the game on your PC. <br> - Copy the crack files from the crack folder and paste them into the game folder, replacing the original files. <br> - Run the game using the launcher.exe file or create a shortcut on your desktop. | | How to improve your gaming experience? | - Use a translation patch or mod to change the game's language to English or another language of your choice. <br> - Use a trainer or cheat engine to modify some aspects of the game, such as your money, resources, troops, skills, etc. <br> - Use a graphics mod or tweak to improve the game's visuals, such as resolution, textures, lighting, shadows, etc. <br> - Use a controller or joystick to play the game more comfortably and intuitively. | | Conclusion | - Summarize the main points of the article. <br> - Provide some recommendations or tips for playing the game. | | FAQs | - Answer some common questions about 3DMGAME-SAN12 JP cracked full rip. | # Article with HTML formatting <h1>3DMGAME-SAN12 JP Cracked Full Rip: A Guide for Fans of Romance of the Three Kingdoms</h1>
- <p>If you are a fan of the historical strategy game series Romance of the Three Kingdoms, you might be interested in trying out the latest installment, 3DMGAME-SAN12 JP. This game is based on the Japanese version of the classic novel, and it features improved graphics, gameplay, and scenarios. However, you might have some trouble finding and playing this game, as it is not officially released outside Japan. That's why we have prepared this guide for you, to show you how to download and play 3DMGAME-SAN12 JP cracked full rip on your PC.</p>
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- <p><strong>3DMGAME-SAN12 JP</strong> is a historical simulation game developed by Koei Tecmo and released in Japan in 2012. It is the 12th main entry in the Romance of the Three Kingdoms series, which is based on the 14th-century Chinese novel of the same name. The game covers the turbulent period of Chinese history from the end of the Han dynasty to the Three Kingdoms era, where various warlords fought for supremacy.</p>
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\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Fundamentos De Dentstica Operatria - Mondelli - Ocr [3].md b/spaces/tialenAdioni/chat-gpt-api/logs/Fundamentos De Dentstica Operatria - Mondelli - Ocr [3].md
deleted file mode 100644
index 6aa04087d9933d80f02a7b9a01fa7f5d6a315965..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Fundamentos De Dentstica Operatria - Mondelli - Ocr [3].md	
+++ /dev/null
@@ -1,59 +0,0 @@
-<br />
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diff --git a/spaces/tianpanyu/ChatYuan-Demo/app.py b/spaces/tianpanyu/ChatYuan-Demo/app.py
deleted file mode 100644
index 8fc4b9feea170b467e91b16660d1b47d377d7d5a..0000000000000000000000000000000000000000
--- a/spaces/tianpanyu/ChatYuan-Demo/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-# install torch and tf
-os.system('pip install transformers SentencePiece')
-os.system('pip install torch')
-
-from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer
-import torch
-import gradio as gr
-
-# 下载模型
-tokenizer = T5Tokenizer.from_pretrained("ClueAI/ChatYuan-large-v1")
-model = T5ForConditionalGeneration.from_pretrained("ClueAI/ChatYuan-large-v1")
-# 修改colab笔记本设置为gpu，推理更快
-device = torch.device('cpu')
-model.to(device)
-print('Model Load done!')
-
-def preprocess(text):
-  text = text.replace("\n", "\\n").replace("\t", "\\t")
-  return text
-
-def postprocess(text):
-  return text.replace("\\n", "\n").replace("\\t", "\t")
-
-def answer(text, sample=True, top_p=0.8, temperature=0.7):
-  '''sample：是否抽样。生成任务，可以设置为True;
-  top_p：0-1之间，生成的内容越多样
-  max_new_tokens=512 lost...'''
-  text = preprocess(text)
-  print('用户: '+text)
-  encoding = tokenizer(text=[text], truncation=True, padding=True, max_length=768, return_tensors="pt").to(device) 
-  if not sample:
-    out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, num_beams=1, length_penalty=0.6)
-  else:
-    out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, do_sample=True, top_p=top_p, temperature=temperature, no_repeat_ngram_size=3)
-  out_text = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True)
-  print('小元: '+postprocess(out_text[0]))
-  return postprocess(out_text[0])
-
-def command_result(text):
-	output = answer(text)
-	return output
-
-iface = gr.Interface(fn=command_result, inputs="text", outputs="text", title="Chatyuan-ClueAI-Demo")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/timqian/like-history/tailwind.config.js b/spaces/timqian/like-history/tailwind.config.js
deleted file mode 100644
index 0115451db65b8ae049be9a8d2af712302af8c9c1..0000000000000000000000000000000000000000
--- a/spaces/timqian/like-history/tailwind.config.js
+++ /dev/null
@@ -1,13 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    "./src/**/*.{js,jsx,ts,tsx}",
-  ],
-  theme: {
-    extend: {},
-  },
-  plugins: [
-    require('@tailwindcss/forms'),
-  ],
-}
-
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/3d Album Commercial Suite 3.33 Full UPDATED.md b/spaces/tioseFevbu/cartoon-converter/scripts/3d Album Commercial Suite 3.33 Full UPDATED.md
deleted file mode 100644
index f398983d23b41ac404182cd51da94eb8fe44ae60..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/3d Album Commercial Suite 3.33 Full UPDATED.md	
+++ /dev/null
@@ -1,142 +0,0 @@
-
-<h1>3D Album Commercial Suite 3.33 Full: A Complete Guide</h1>
- <p>Do you want to create amazing digital albums and videos with 3D effects? Do you want to have a commercial license for producing CDs and DVDs with encryption and password protection? Do you want to have access to a collection of templates and styles for different themes and occasions? If you answered yes to any of these questions, then you need to check out <strong>3D Album Commercial Suite 3.33 Full</strong>, a software that can help you achieve all these goals and more.</p>
- <p>In this article, we will give you a complete guide on what this software is, why you should use it, how to download and install it, and how to use it. By the end of this article, you will have a clear idea of how this software can help you create stunning photo slideshows and videos that will impress your clients and customers.</p>
-<h2>3d Album Commercial Suite 3.33 Full</h2><br /><p><b><b>Download</b> - <a href="https://urlcod.com/2uHwse">https://urlcod.com/2uHwse</a></b></p><br /><br />
- <h2>What is 3D Album Commercial Suite 3.33 Full?</h2>
- <p>Before we dive into the details of how to use this software, let's first understand what it is and what it can do for you.</p>
- <h3>A software for creating digital albums and videos with 3D effects</h3>
- <p>As the name suggests, <strong>3D Album Commercial Suite 3.33 Full</strong> is a software that allows you to create digital albums and videos with 3D effects. You can use this software to turn your photos into stunning presentations that have a realistic 3D environment. You can choose from hundreds of templates and styles that suit different themes and occasions, such as weddings, birthdays, holidays, sports, travel, etc. You can also customize your presentations with different backgrounds, frames, text, animations, etc.</p>
- <h3>A <h3>A commercial license for producing CDs and DVDs with encryption and password protection</h3>
- <p>Another feature of <strong>3D Album Commercial Suite 3.33 Full</strong> is that it gives you a commercial license for producing CDs and DVDs with encryption and password protection. This means that you can use this software to create professional-quality productions that you can sell or distribute to your clients and customers. You can also protect your work from unauthorized copying or viewing by adding encryption and password protection to your CDs and DVDs. This way, you can ensure that only the intended recipients can access your productions.</p>
- <h3>A collection of templates and styles for different themes and occasions</h3>
- <p>One of the best things about <strong>3D Album Commercial Suite 3.33 Full</strong> is that it comes with a collection of templates and styles for different themes and occasions. You can choose from over 100 templates and styles that cover various categories, such as wedding, birthday, holiday, sport, travel, etc. You can also download more templates and styles from the official website of the software. Each template and style has its own unique 3D environment, transition effects, animations, etc. You can also mix and match different templates and styles to create your own custom presentations.</p>
- <h2>Why should you use 3D Album Commercial Suite 3.33 Full?</h2>
- <p>Now that you know what this software is, you might be wondering why you should use it. Here are some of the benefits of using this software:</p>
- <h3>To create amazing photo slideshows with stunning transition effects</h3>
- <p>If you want to create photo slideshows that are not boring or plain, then you need to use this software. This software allows you to create photo slideshows that have stunning transition effects, such as zooming, panning, rotating, flipping, etc. You can also add 3D effects to your photos, such as shadows, reflections, glows, etc. You can make your photos look more realistic and dynamic by using this software.</p>
- <h3>To edit your images with paint brush and other tools</h3>
- <p>If you want to edit your images before adding them to your presentations, then you can use this software. This software has a built-in image editor that allows you to edit your images with paint brush and other tools. You can crop, resize, rotate, flip, adjust color, brightness, contrast, etc. You can also add text, clip art, shapes, etc. to your images. You can enhance your images with various filters and effects, such as blur, sharpen, emboss, etc.</p>
- <h3>To add audio clips and background music to your presentations</h3>
- <p>If you want to add audio clips and background music to your presentations, then you can use this software. This software allows you to add audio clips and background music to your presentations easily. You can import audio files from your computer or record your own voice using a microphone. You can also use the built-in sound library that has hundreds of sound effects and music tracks for different themes and occasions. You can adjust the volume, fade in/out, loop, etc. of your audio clips and background music.</p>
-<p></p> <h3>To convert your images into video files that can be edited further</h3>
- <p>If you want to convert your images into video files that can be edited further, then you can use this software. This software allows you to export your presentations as video files that can be played on any media player or device. You can choose from different video formats, such as AVI, MPEG, WMV, MOV, etc. You can also adjust the video quality, resolution, frame rate, etc. of your video files. You can then edit your video files with any video editing software, such as Windows Movie Maker, Adobe Premiere, etc.</p>
- <h3>To impress your clients and customers with professional-quality productions</h3>
- <p>If you want to impress your clients and customers with professional-quality productions, then you need to use this software. This software allows you to create productions that have a high level of quality and creativity. You can use this software to create productions for different purposes, such as advertising, marketing, education, entertainment, etc. You can also use this software to create productions for different platforms, such as web, TV, DVD, etc. You can showcase your work to your clients and customers and get positive feedback and results.</p>
- <h2>How to download and install 3D Album Commercial Suite 3.33 Full?</h2>
- <p>Now that you know the benefits of using this software, you might be wondering how to download and install it on your computer. Here are the steps to follow:</p>
- <h3>The system requirements for running the software</h3>
- <p>Before you download and install the software, you need to make sure that your computer meets the minimum system requirements for running the software. Here are the system requirements for running the software:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Processor</th>
-<th>Memory</th>
-<th>Hard Disk Space</th>
-<th>Graphics Card</th>
-<th>Sound Card</th>
-</tr>
-<tr>
-<td>Windows XP/Vista/7/8/10</td>
-<td>Pentium III 800 MHz or higher</td>
-<td>256 MB RAM or higher</td>
-<td>1 GB free space or higher</td>
-<td>DirectX 9.0 compatible or higher</td>
-<td>DirectX 9.0 compatible or higher</td>
-</tr>
-</table>
- <p>If your computer meets these requirements, you can proceed to the next step.</p>
- <h3>The steps to download the software from a reliable source</h3>
- <p>The next step is to download the software from a reliable source. You can download the software from the official website of the software, which is <a href="">http://www.3d-album.com/</a>. Here are the steps to download the software from the official website:</p>
- <ol>
-<li>Go to <a href="">http://www.3d-album.com/</a>.</li>
-<li>Click on the "Download" button on the top menu.</li>
-<li>Select "3D Album Commercial Suite 3.33 Full" from the list of products.</li>
-<li>Click on the "Download Now" button.</li>
-<li>Save the file "3DAlbumCS333.exe" on your computer.</li>
-<li>Wait for the download to complete.</li>
-</ol>
- <p>You can also download the software from other sources, such as <a href="">https://softfamous.com/3d-album-commercial-suite/</a>, <a href="">https://www.filehorse.com/download-3d-album-commercial-suite/</a>, or <a href="">https://getintopc.com/softwares/photo-editing/3d-album-commercial-suite-3-free-download-1673765/</a>. However, make sure that you download the software from a trusted and secure source to avoid any viruses or malware.</p>
- <h3>The steps to install the software on your computer</h3>
- <p>The final step is to install the software on your computer. Here are the steps to install the software on your computer:</p>
- <ol>
-<li>Locate the file "3DAlbumCS333.exe" on your computer.</li>
-<li>Double-click on the file to launch the setup wizard.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Select a destination folder for installing the software.</li>
-<li>Select a start menu folder for creating shortcuts.</li>
-<li>Select additional tasks, such as creating a desktop icon or registering online.</li>
-<li>Click on the "Install" button to start installing the software.</li>
-<li>Wait for the installation to finish.</li>
-<li <li>Click on the "Finish" button to exit the setup wizard.</li>
-<li>Launch the software from the start menu or the desktop icon.</li>
-</ol>
- <p>Congratulations, you have successfully downloaded and installed <strong>3D Album Commercial Suite 3.33 Full</strong> on your computer. You are now ready to use it to create amazing digital albums and videos with 3D effects.</p>
- <h2>How to use 3D Album Commercial Suite 3.33 Full?</h2>
- <p>Now that you have installed the software on your computer, you might be wondering how to use it. Here are the steps to use the software:</p>
- <h3>The main interface and features of the software</h3>
- <p>When you launch the software, you will see the main interface of the software, which looks like this:</p>
- <img src="https://i.imgur.com/6l0Ys9X.jpg" alt="The main interface of the software">
- <p>The main interface of the software has four main sections:</p>
- <ul>
-<li>The <strong>Menu Bar</strong>, which has various options for file, edit, view, tools, help, etc.</li>
-<li>The <strong>Toolbar</strong>, which has various buttons for creating, opening, saving, importing, exporting, etc.</li>
-<li>The <strong>Album List</strong>, which shows the list of albums that you have created or opened.</li>
-<li>The <strong>Album Window</strong>, which shows the preview of your current album.</li>
-</ul>
- <p>The software also has various features that you can access from the menu bar or the toolbar, such as:</p>
- <ul>
-<li>The <strong>Image Editor</strong>, which allows you to edit your images with paint brush and other tools.</li>
-<li>The <strong>Sound Library</strong>, which allows you to add audio clips and background music to your presentations.</li>
-<li>The <strong>Template Manager</strong>, which allows you to manage your templates and styles.</li>
-<li>The <strong>CD/DVD Burner</strong>, which allows you to burn your productions to CDs and DVDs with encryption and password protection.</li>
-<li>The <strong>Video Converter</strong>, which allows you to convert your productions to video files that can be edited further.</li>
-</ul>
- <h3>How to import your images and choose a template or style</h3>
- <p>The first step to create a presentation is to import your images and choose a template or style. Here are the steps to do that:</p>
- <ol>
-<li>Click on the "New" button on the toolbar or select "File > New" from the menu bar.</li>
-<li>A dialog box will appear asking you to name your album and select a folder for saving it. Enter a name for your album and select a folder for saving it. Click on the "OK" button.</li>
-<li>A new album will be created and added to the album list. The album window will show a blank presentation.</li>
-<li>To import your images, click on the "Import" button on the toolbar or select "File > Import" from the menu bar.</li>
-<li>A dialog box will appear asking you to select the images that you want to import. You can select multiple images by holding down the Ctrl key while clicking on them. Click on the "Open" button.</li>
-<li>Your selected images will be imported and added to your presentation. You can see them in the album window as thumbnails.</li>
-<li>To choose a template or style, click on the "Template" button on the toolbar or select "Tools > Template Manager" from the menu bar.</li>
-<li>A dialog box will appear showing you the list of templates and styles that are available. You can browse through them by category or by name. You can also preview them by clicking on them.</li>
-<li>To apply a template or style to your presentation, select it and click on the "Apply" button. Your presentation will be updated with the selected template or style in the album window.</li>
-</ol>
- <h3>How to customize your presentation with different options and settings</h3>
- <p>The next step is to customize your presentation with different options and settings. Here are some of the things that you can do:</p>
- <ul>
-<li>To change the order of your images, drag and drop them in the album window.</li>
-<li>To delete an image, right-click on it and select "Delete" from the menu that appears.</li>
-<li>To edit an image, double-click on it or right-click on it and select "Edit" from the menu that appears. The image editor will open where you can edit your image with paint brush and other tools.</li>
-<li>To add text, clip art, shapes, etc. to your images, click on the "Add Object" button on the toolbar or select "Edit > Add Object" from the menu bar. The object library will open where you can add text, clip art, shapes, etc. to your images.</li>
-<li>To add audio clips and background music to your presentation, click on the "Sound" button on the toolbar or select "Tools > Sound Library" from the menu bar. The sound library will open where you can add audio clips and background music to your presentation.</li>
-<li>To change the transition effects, animations, backgrounds, frames, etc. of your presentation, click on the "Option" button on the toolbar or select "Tools > Option" from the menu bar. The option dialog box will open where you can change various options and settings of your presentation.</li>
-</ul>
- <h3>How to preview and export your production as a CD/DVD or a video file</h3>
- <p>The final step is to preview and export your production as a CD/DVD or a video file. Here are the steps to do that:</p>
- <ol>
-<li>To preview your production, click on the "Play" button on the toolbar or select "View > Play" from the menu bar. Your production will play in full screen mode. You can use the keyboard or the mouse to control the playback.</li>
-<li>To export your production as a CD/DVD, click on the "Burn" button on the toolbar or select "File > Burn CD/DVD" from the menu bar. The CD/DVD burner will open where you can burn your production to a CD or a DVD with encryption and password protection.</li>
-<li>To export your production as a video file, click on the "Export" button on the toolbar or select "File > Export Video File" from the menu bar. The video converter will open where you can export your production as a video file that can be edited further.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have given you a complete guide on <strong>3D Album Commercial Suite 3.33 Full</strong>, a software that can help you create amazing digital albums and videos with 3D effects. We have explained what this software is, why you should use it, how to download and install it, and how to use it. We hope that this article has been helpful and informative for you.</p>
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-<p>A: Yes, you can use this software for personal or non-commercial purposes. However, if you want to use this software for commercial purposes, such as selling or distributing your productions, you need to buy a commercial license.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Chakradhaar Hindi Full Movie 1080p Hd Mp4 Movie Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Chakradhaar Hindi Full Movie 1080p Hd Mp4 Movie Download.md
deleted file mode 100644
index cf429593e6579c74f445e74a629bb8c1012bf799..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Chakradhaar Hindi Full Movie 1080p Hd Mp4 Movie Download.md	
+++ /dev/null
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/CuteFTP Pro 9.0.0.0063 MultiLanguage Incl Crack - SceneDL.md b/spaces/tioseFevbu/cartoon-converter/scripts/CuteFTP Pro 9.0.0.0063 MultiLanguage Incl Crack - SceneDL.md
deleted file mode 100644
index c6e30277476f596a99fc3eea8251ff77902cb381..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/CuteFTP Pro 9.0.0.0063 MultiLanguage Incl Crack - SceneDL.md	
+++ /dev/null
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-<ol>
-<li>Click on the New Site button on the toolbar or go to File > New > FTP Site.</li>
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-</ol>
-<h3>How to Transfer Files Between Local and Remote Sites</h3>
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-<li>Select the files or folders you want to transfer from either the local or remote site by clicking on them or using Ctrl+click or Shift+click to select multiple items.</li>
-<li>Drag and drop them to the other site or use the Transfer menu to choose the transfer type (upload, download, synchronize, etc.). You can also right-click on the selected items and choose a transfer option from the context menu.</li>
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-</ol>
-<h3>How to Manage Multiple FTP Sites</h3>
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-<ol>
-<li>Use the Site Manager window to create, edit, delete, or organize your FTP sites. You can access the Site Manager window by clicking on the Site Manager button on the toolbar or going to Tools > Site Manager. You can create folders to group your FTP sites by category, drag and drop sites to rearrange them, or use the buttons on the bottom to perform various actions.</li>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Eqx Spss 19 Crack Zip.md b/spaces/tioseFevbu/cartoon-converter/scripts/Eqx Spss 19 Crack Zip.md
deleted file mode 100644
index 8218bab58f8fa64868194ae5b30c63cbc437220f..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Eqx Spss 19 Crack Zip.md	
+++ /dev/null
@@ -1,93 +0,0 @@
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-<p>While using eqx spss 19 crack zip may seem tempting or convenient for some people, it also comes with many risks and consequences that can outweigh the benefits. Some of these risks and consequences are: - You may download malware or viruses. Eqx spss 19 crack zip is not an official or authorized file from IBM. It is a file that has been modified by unknown sources to crack the software. You cannot be sure what else has been added or changed in the file. It may contain malware or viruses that can harm your computer, steal your data, or compromise your security. - You may face legal issues. Eqx spss 19 crack zip is a violation of the intellectual property rights of IBM. It is an illegal and unethical way to use SPSS software. You may face legal consequences if you are caught using eqx spss 19 crack zip, such as fines, lawsuits, or criminal charges. - You may lose your academic or professional credibility. Eqx spss 19 crack zip is a dishonest and unprofessional way to use SPSS software. You may lose your academic or professional credibility if you use eqx spss 19 crack zip for your research, studies, or work. You may face academic sanctions, such as plagiarism, expulsion, or revocation of your degree. You may also face professional sanctions, such as loss of reputation, trust, or employment. - You may get inaccurate or unreliable results. Eqx spss 19 crack zip is not a reliable or trustworthy file. It may not work properly or have errors or bugs that can affect the quality and validity of your data analysis and statistics. You may get inaccurate or unreliable results that can mislead you or others who rely on your findings. <h2>How to Download and Install Eqx Spss 19 Crack Zip</h2>
-<p>If you still want to download and install eqx spss 19 crack zip despite the risks and consequences, here are the steps you need to follow:</p>
-<ol>
-<li>Go to a website that offers eqx spss 19 crack zip for download. There are many websites that claim to provide eqx spss 19 crack zip for free, but you need to be careful and cautious as some of them may be fake or malicious. You can use a search engine to find such websites, but make sure to check their reviews, ratings, and comments before downloading anything.</li>
-<li>Download eqx spss 19 crack zip to your computer. The file size may vary depending on the website, but it should be around 500 MB. The file name may also vary depending on the website, but it should contain "eqx", "spss", "19", and "crack" in some form. The file format should be ZIP, which is a compressed archive file that contains multiple files.</li>
-<li>Extract eqx spss 19 crack zip to a folder on your computer. You will need a software tool that can extract ZIP files, such as WinRAR, WinZip, or 7-Zip. You can right-click on the ZIP file and choose "Extract Here" or "Extract to Folder" to extract the contents of the ZIP file to a folder on your computer.</li>
-<li>Run the setup.exe file in the extracted folder. This will start the installation process of SPSS software on your computer. Follow the instructions on the screen to complete the installation process.</li>
-<li>Copy and paste the eqx.exe file in the extracted folder to the SPSS installation folder on your computer. This will replace the original eqx.exe file in the SPSS installation folder with the cracked version of eqx.exe file. The SPSS installation folder is usually located at C:\Program Files\IBM\SPSS\Statistics\19.</li>
-<li>Run the eqx.exe file in the SPSS installation folder as administrator. This will launch the SPSS software on your computer with full features unlocked.</li>
-</ol>
-<p>Tips and warnings:</p>
-<ul>
-<li>Before downloading and installing eqx spss 19 crack zip, make sure to backup your important data and files on your computer. You never know what might happen during or after the process.</li>
-<li>Before running eqx spss 19 crack zip, make sure to disable your antivirus software and firewall on your computer. They may detect eqx spss 19 crack zip as a threat and block or delete it.</li>
-<li>After using eqx spss 19 crack zip, make sure to enable your antivirus software and firewall on your computer. They may protect you from any potential malware or viruses that may have been downloaded or installed with eqx spss 19 crack zip.</li>
-<li>Do not update SPSS software after using eqx spss 19 crack zip. Updating SPSS software may overwrite the cracked version of eqx.exe file and restore the license verification process.</li>
-</ul>
- <h2>How to Use Eqx Spss 19 Crack Zip</h2>
-<p>After downloading and installing eqx spss 19 crack zip, you can use it to open and analyze data with SPSS software. Here are some of the basic steps you need to follow:</p>
-<ol>
-<li>Launch SPSS software by running the eqx.exe file in the SPSS installation folder as administrator.</li>
-<li>Open a data file or create a new data file in SPSS software. You can open an existing data file by clicking on File > Open > Data and browsing for the file on your computer. You can create a new data file by clicking on File > New > Data and entering the data in the Data View or Variable View tabs.</li>
-<li>Analyze the data with SPSS software. You can perform different statistical tests and procedures with SPSS software, such as descriptive statistics, inferential statistics, regression, ANOVA, correlation, factor analysis, cluster analysis, and more. You can access these options by clicking on Analyze > [Category] > [Subcategory] and selecting the appropriate options for your analysis.</li>
-<li>Interpret and report the results of your analysis. You can view the results of your analysis in the Output Viewer window, which shows the tables, charts, and graphs generated by SPSS software. You can interpret and report the results by looking at the values, significance levels, coefficients, and other indicators of your analysis. You can also export or copy the results to other applications, such as Word or Excel, for further editing or presentation.</li>
-</ol>
-<p>Here is an example of how to use eqx spss 19 crack zip to perform a simple t-test with SPSS software:</p>
-<p></p>
-<ol>
-<li>Launch SPSS software by running the eqx.exe file in the SPSS installation folder as administrator.</li>
-<li>Open a data file that contains two groups of scores on a test. For example, you can open a data file called test_scores.sav that has two variables: group (1 or 2) and score (0 to 100).</li>
-<li>Perform a t-test to compare the mean scores of the two groups. You can do this by clicking on Analyze > Compare Means > Independent-Samples T Test and selecting group as the grouping variable and score as the test variable. You can also specify the confidence level and the equality of variances assumption for your test.</li>
-<li>View the results of your t-test in the Output Viewer window. You will see a table that shows the descriptive statistics of each group, such as mean, standard deviation, and sample size. You will also see a table that shows the t-test results, such as t-value, degrees of freedom, significance level, and confidence interval.</li>
-<li>Interpret and report the results of your t-test. You can interpret and report the results by looking at the significance level and the confidence interval of your t-test. For example, you can say that there is a significant difference between the mean scores of group 1 and group 2 (t(18) = 2.37, p < 0.05), and that group 1 has a higher mean score than group 2 (95% CI: 1.23 to 8.77).</li>
-</ol>
- <h2>Alternatives to Eqx Spss 19 Crack Zip</h2>
-<p>If you want to avoid the risks and consequences of using eqx spss 19 crack zip, you may want to consider some alternatives that are legal and ethical. Some of these alternatives are:</p>
-<ul>
-<li>Buy a legal license or subscription for SPSS software. This is the best and safest way to use SPSS software legally and ethically. You can buy a license or subscription for SPSS software from IBM or authorized resellers. You can choose from different editions, features, and durations of use depending on your needs and budget.</li>
-<li>Use a free trial version of SPSS software. This is a good way to try out SPSS software before buying it or for a short-term project. You can get a free trial version of SPSS software from IBM's website. The free trial version lasts for 14 days and has all the features of the standard edition of SPSS Statistics.</li>
-<li>Use other software tools for data analysis and statistics. This is a good way to explore other options that may suit your needs better than SPSS software. There are many other software tools that can perform data analysis and statistics, such as R, Python, Excel, SAS, Stata, MATLAB, Minitab, and more. Some of these tools are free or open source, while others are paid or proprietary.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, eqx spss 19 crack zip is a file that contains a cracked version of IBM SPSS Statistics software that can bypass the license verification and allow you to use the full features of the program for free. However, using eqx spss 19 crack zip is not worth it because it comes with many risks and consequences that can harm your computer, security , and credibility. You may also get inaccurate or unreliable results from your data analysis and statistics. Therefore, we recommend that you do not use eqx spss 19 crack zip and instead consider some alternatives that are legal and ethical, such as buying a legal license or subscription for SPSS software, using a free trial version of SPSS software, or using other software tools for data analysis and statistics.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about eqx spss 19 crack zip:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>What is the difference between SPSS Statistics and SPSS Modeler?</td>
-<td>SPSS Statistics is a software tool that focuses on statistical analysis and reporting. SPSS Modeler is a software tool that focuses on data mining and machine learning. Both tools are part of the IBM SPSS software suite.</td>
-</tr>
-<tr>
-<td>Can I use eqx spss 19 crack zip on Mac or Linux?</td>
-<td>No, eqx spss 19 crack zip is only compatible with Windows operating system. If you want to use SPSS software on Mac or Linux, you need to buy a legal license or subscription for SPSS software that supports these operating systems.</td>
-</tr>
-<tr>
-<td>How can I update SPSS software after using eqx spss 19 crack zip?</td>
-<td>You cannot update SPSS software after using eqx spss 19 crack zip. Updating SPSS software may overwrite the cracked version of eqx.exe file and restore the license verification process. If you want to update SPSS software, you need to uninstall eqx spss 19 crack zip and buy a legal license or subscription for SPSS software that includes the latest updates.</td>
-</tr>
-<tr>
-<td>How can I uninstall eqx spss 19 crack zip from my computer?</td>
-<td>You can uninstall eqx spss 19 crack zip from your computer by following these steps:</td>
-</tr>
-<tr>
-<td></td>
-<td><ol><li>Go to Control Panel > Programs > Uninstall a program and select IBM SPSS Statistics 19 from the list of programs.</li><li>Click on Uninstall and follow the instructions on the screen to remove SPSS software from your computer.</li><li>Delete the eqx spss 19 crack zip file and the extracted folder from your computer.</li><li>Scan your computer with an antivirus software and a malware removal tool to check for any potential threats or damages caused by eqx spss 19 crack zip.</li></ol></td>
-</tr>
-<tr>
-<td>Where can I get help or support for SPSS software?</td>
-<td>If you have a legal license or subscription for SPSS software, you can get help or support from IBM's website, which offers online documentation, tutorials, forums, and contact information. If you do not have a legal license or subscription for SPSS software, you cannot get help or support from IBM or authorized resellers. You may try to find help or support from other sources, such as online communities, blogs, or videos, but they may not be reliable or accurate.</td>
-</tr>
-</table></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Ioncube Decoder [WORK] VERIFIED.md b/spaces/tioseFevbu/cartoon-converter/scripts/Ioncube Decoder [WORK] VERIFIED.md
deleted file mode 100644
index cc0d46dc788c46928e92e2aaa60a2454902d0763..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Ioncube Decoder [WORK] VERIFIED.md	
+++ /dev/null
@@ -1,87 +0,0 @@
-<br />
-<h1>IonCube Decoder [WORK]</h1>
-<p>Have you ever encountered a PHP file that you cannot open or edit because it is encrypted with IonCube Encoder? If so, you might be interested in learning how to use IonCube Decoder to decrypt such files and restore them to plain PHP code. In this article, we will explain what is IonCube Decoder, why it is useful, and what are the benefits of using it. We will also cover some of the challenges and risks of using IonCube Decoder, as well as some alternatives to it. By the end of this article, you will have a better understanding of whether to use IonCube Decoder or not.</p>
-<h2>Ioncube Decoder [WORK]</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://urlcod.com/2uHvbu">https://urlcod.com/2uHvbu</a></b></p><br /><br />
- <h2>What is IonCube Encoder?</h2>
-<p>Before we talk about IonCube Decoder, let us first understand what is IonCube Encoder. IonCube Encoder is a widely used software tool that protects PHP files with encryption and licensing features. It allows developers to secure their PHP code from unauthorized use, modification, or distribution. It also enables developers to create license files that control how and where their PHP scripts can run. Some of the advantages of using IonCube Encoder are: - It supports all versions of PHP from 4.0 to 8.0 - It offers various levels of encryption and obfuscation - It can encrypt PHP files, classes, functions, or variables - It can generate license files with expiration dates, domain restrictions, IP restrictions, etc. - It can integrate with popular frameworks and CMS such as WordPress, Joomla, Laravel, etc. - It can protect PHP files from decompilers and debuggers - It can optimize PHP code for faster performance <h2>What is IonCube Loader?</h2>
-<p>IonCube Loader is a free PHP extension that enables PHP to run IonCube-encoded files on a web server. It is required for any web server that wants to execute IonCube-encoded files. It acts as a bridge between the encrypted PHP files and the PHP engine. Some of the requirements and steps to install IonCube Loader are: - It requires a compatible version of PHP installed on the web server - It requires a compatible version of IonCube Loader downloaded from the official website - It requires editing the php.ini file to add a line that specifies the location of the IonCube Loader file - It requires restarting the web server to apply the changes - It requires verifying the installation by using a test script provided by IonCube <h2>What is IonCube Decoder?</h2>
-<p>IonCube Decoder is a software tool that decrypts IonCube-encoded files and restores them to plain PHP code. It allows users to access the original source code of IonCube-encoded files, which can be useful for learning, modifying, or fixing purposes. However, it also poses some challenges and risks for both developers and users. Some of the features and limitations of IonCube Decoder are: - It can decode most types of IonCube-encoded files, except for those with dynamic keys or custom algorithms - It can decode files encoded with any version of IonCube Encoder, except for those with version 10 or higher - It can decode files encoded with any version of PHP, except for those with version 7 or higher - It can decode files with or without license files, but it cannot remove license restrictions - It can decode multiple files at once, but it cannot decode entire folders or archives - It can decode files online or offline, depending on the type of decoder used <h3>Types of IonCube Decoders</h3>
-<p>There are two main types of IonCube Decoders: online decoders and offline decoders. Online decoders are web-based services that allow users to upload their IonCube-encoded files and receive the decoded files in return. Offline decoders are desktop applications that allow users to decode their IonCube-encoded files on their own computers. The table below compares and contrasts online decoders and offline decoders.</p>
- | Online Decoders | Offline Decoders | | --- | --- | | Pros | Pros | | - Easy to use and fast | - More reliable and secure | | - No installation or configuration required | - No internet connection or upload limit required | | - Free or low-cost | - More features and options | | Cons | Cons | | - Less reliable and secure | - Harder to use and slower | | - Require internet connection and upload limit | - Require installation and configuration | | - Fewer features and options | - Costly or illegal | <h3>How to Use IonCube Decoder?</h3>
-<p>The steps to use IonCube Decoder online or offline may vary depending on the specific service or software used, but they generally follow this pattern:</p>
- <ol>
-<li>Find a reputable online decoder or download a trusted offline decoder</li>
-<li>Select the IonCube-encoded file(s) that you want to decode</li>
-<li>Choose the output format (PHP code or ZIP archive)</li>
-<li>Click on the decode button and wait for the process to finish</li>
-<li>Download or save the decoded file(s) to your desired location</li>
-<li>Open and view the decoded file(s) with a text editor or a PHP IDE</li>
-</ol>
- <p>To illustrate these steps, we will use an example of an online decoder called EasyToYou.eu, which claims to be able to decode any IonCube-encoded file up to version 10. The screenshots below show how to use this online decoder.</p>
- <figure>
-<img src="https://i.imgur.com/8QZyYXw.png" alt="Screenshot 1: Selecting an IonCube-encoded file">
-<figcaption>Screenshot 1: Selecting an IonCube-encoded file</figcaption> <figure>
-<img src="https://i.imgur.com/9wXZxqF.png" alt="Screenshot 2: Choosing the output format">
-<figcaption>Screenshot 2: Choosing the output format</figcaption>
-</figure>
- <figure>
-<img src="https://i.imgur.com/6l8Za3d.png" alt="Screenshot 3: Clicking on the decode button">
-<figcaption>Screenshot 3: Clicking on the decode button</figcaption>
-</figure>
- <figure>
-<img src="https://i.imgur.com/0yj4f1c.png" alt="Screenshot 4: Downloading the decoded file">
-<figcaption>Screenshot 4: Downloading the decoded file</figcaption>
-</figure>
- <figure>
-<img src="https://i.imgur.com/7wv8M9o.png" alt="Screenshot 5: Opening and viewing the decoded file">
-<figcaption>Screenshot 5: Opening and viewing the decoded file</figcaption>
-</figure>
- <h3>How to Verify IonCube Decoder?</h3>
-<p>After using IonCube Decoder, you may want to check if it works correctly and if the decoded file is valid and functional. Here are some tips on how to verify IonCube Decoder:</p>
- <ul>
-<li>Compare the file sizes of the encoded and decoded files. The decoded file should be larger than the encoded file, as encryption and obfuscation usually reduce the file size.</li>
-<li>Run a PHP syntax checker on the decoded file. This will help you identify any syntax errors or warnings that may prevent the file from running properly.</li>
-<li>Test the functionality of the decoded file. This will help you determine if the file performs as expected and if it has any bugs or errors that need to be fixed.</li>
-<li>Compare the source code of the encoded and decoded files. This will help you see how much of the original code was preserved and how much was altered or lost during the decoding process.</li>
-<li>Check the license status of the decoded file. This will help you see if the file still has any license restrictions or validations that may affect its usage or distribution.</li>
-</ul>
- <h2>Why Use IonCube Decoder?</h2>
-<p>There are many reasons why you may want to use IonCube Decoder, depending on your goals and needs. Some of the benefits of using IonCube Decoder are:</p>
- <ul>
-<li>You can learn from other developers' code. By decoding IonCube-encoded files, you can access the source code of other developers and see how they write, structure, and optimize their PHP scripts. You can also learn new techniques, tricks, or best practices from their code.</li>
-<li>You can modify or customize existing scripts. By decoding IonCube-encoded files, you can edit, tweak, or enhance existing PHP scripts to suit your preferences or requirements. You can also add new features, functions, or integrations to existing scripts.</li>
-<li>You can fix bugs or errors in existing scripts. By decoding IonCube-encoded files, you can debug, troubleshoot, or resolve any issues or problems in existing PHP scripts. You can also improve the performance, security, or compatibility of existing scripts.</li>
-</ul>
- <h2>When Not to Use IonCube Decoder?</h2>
-<p>While there are many benefits of using IonCube Decoder, there are also some drawbacks and risks that you should be aware of. Some of the reasons why you should not use IonCube Decoder are:</p>
- <ul>
-<li>You may violate intellectual property rights. By decoding IonCube-encoded files, you may infringe on the copyrights, trademarks, patents, or trade secrets of the original developers. You may also breach their terms of service, privacy policy, or license agreement.</li>
-<li>You may break license agreements. By decoding IonCube-encoded files, you may bypass or remove any license validations or restrictions that the original developers have imposed on their PHP scripts. You may also violate any conditions or limitations that they have set for their PHP scripts.</li>
-<li>You may expose security vulnerabilities. By decoding IonCube-encoded files, you may reveal any sensitive information or data that the original developers have encrypted or hidden in their PHP scripts. You may also introduce new security risks or threats to your web server or website.</li>
-</ul>
- <h2>Alternatives to IonCube Decoder</h2>
-<p>If you are looking for other ways to decode or bypass IonCube-encoded files, you may want to consider some alternatives to IonCube Decoder. Some of these alternatives are:</p>
-<p></p>
- <ul>
-<li>Use deobfuscation tools. These are tools that can reverse some of the obfuscation techniques that IonCube Encoder uses, such as replacing variable names, removing comments, or adding junk code. However, these tools cannot decrypt the encryption techniques that IonCube Encoder uses, such as AES-256 or RSA-2048.</li>
-<li>Use reverse engineering techniques. These are techniques that can analyze and understand the logic and functionality of IonCube-encoded files, such as using debuggers, disassemblers, or decompilers. However, these techniques require advanced skills and knowledge, and they may not work on all types of IonCube-encoded files.</li>
-<li>Contact the original developers for support. This is the most ethical and legal way to decode or bypass IonCube-encoded files, as it respects the rights and wishes of the original developers. You can contact them and ask for their permission, assistance, or guidance on how to access or use their PHP scripts.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>IonCube Decoder is a software tool that can decrypt IonCube-encoded files and restore them to plain PHP code. It can be useful for learning, modifying, or fixing existing PHP scripts that are protected with IonCube Encoder. However, it can also pose some challenges and risks for both developers and users, such as violating intellectual property rights, breaking license agreements, or exposing security vulnerabilities. Therefore, you should use IonCube Decoder with caution and discretion, and only for legitimate purposes. Alternatively, you can consider some other ways to decode or bypass IonCube-encoded files, such as using deobfuscation tools, reverse engineering techniques, or contacting the original developers for support.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about IonCube Decoder:</p>
- <ol>
-<li><b>Is IonCube Decoder legal?</b></li>
-<p>IonCube Decoder is not illegal per se, but it may be used for illegal purposes. Depending on the jurisdiction and the circumstances, using IonCube Decoder may infringe on the intellectual property rights of the original developers, breach their license agreements, or violate their terms of service. Therefore, you should use IonCube Decoder only for lawful and ethical purposes, and respect the rights and wishes of the original developers.</p>
-<li><b>Is IonCube Decoder safe?</b></li>
-<p>IonCube Decoder is not entirely safe, as it may expose some security risks or threats. Depending on the source and the quality of the decoder, using IonCube Decoder may introduce malware or viruses to your computer or web server, compromise your personal or sensitive data, or damage your system or website. Therefore, you should use IonCube Decoder only from reputable and trusted sources, and scan your files before and after decoding.</p>
-<li><b>Is IonCube Decoder reliable?</b></li>
-<p>IonCube Decoder is not always reliable, as it may not work correctly or completely. Depending on the type and the version of the encoder and the decoder, using IonCube Decoder may fail to decrypt some files, produce corrupted or incomplete code, or lose some functionality or features. Therefore, you should use IonCube Decoder only with compatible and updated decoders, and verify your decoded files before using them.</p>
-<li><b>Is IonCube Decoder free?</b></li>
-<p>IonCube Decoder is not always free, as it may require payment or registration. Depending on the service or the software that provides the decoder, using IonCube Decoder may cost money or require personal information. Therefore, you should use IonCube Decoder only with affordable and transparent decoders, and avoid any hidden fees or scams.</p>
-<li><b>Is IonCube Decoder worth it?</b></li>
-<p>IonCube Decoder is worth it only if you have a valid reason and a clear goal to use it. Depending on your needs and expectations, using IonCube Decoder may provide some benefits or drawbacks. Therefore, you should use IonCube Decoder only if you have a legitimate purpose and a realistic outcome to use it.</p>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/idna/package_data.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/idna/package_data.py
deleted file mode 100644
index f5ea87c12bd5bf459bab40a566f4bd3ebd01d9d3..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/idna/package_data.py
+++ /dev/null
@@ -1,2 +0,0 @@
-__version__ = '3.3'
-
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/modeline.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/modeline.py
deleted file mode 100644
index 43630835ca677066a315ac0a04d17cb6839da38d..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/modeline.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""
-    pygments.modeline
-    ~~~~~~~~~~~~~~~~~
-
-    A simple modeline parser (based on pymodeline).
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-__all__ = ['get_filetype_from_buffer']
-
-
-modeline_re = re.compile(r'''
-    (?: vi | vim | ex ) (?: [<=>]? \d* )? :
-    .* (?: ft | filetype | syn | syntax ) = ( [^:\s]+ )
-''', re.VERBOSE)
-
-
-def get_filetype_from_line(l):
-    m = modeline_re.search(l)
-    if m:
-        return m.group(1)
-
-
-def get_filetype_from_buffer(buf, max_lines=5):
-    """
-    Scan the buffer for modelines and return filetype if one is found.
-    """
-    lines = buf.splitlines()
-    for l in lines[-1:-max_lines-1:-1]:
-        ret = get_filetype_from_line(l)
-        if ret:
-            return ret
-    for i in range(max_lines, -1, -1):
-        if i < len(lines):
-            ret = get_filetype_from_line(lines[i])
-            if ret:
-                return ret
-
-    return None
diff --git a/spaces/tobiascz/demotime/pytorch_grad_cam/grad_cam_plusplus.py b/spaces/tobiascz/demotime/pytorch_grad_cam/grad_cam_plusplus.py
deleted file mode 100644
index 4466826b7dd8707063885a1742332492213b03dd..0000000000000000000000000000000000000000
--- a/spaces/tobiascz/demotime/pytorch_grad_cam/grad_cam_plusplus.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-from pytorch_grad_cam.base_cam import BaseCAM
-
-# https://arxiv.org/abs/1710.11063
-
-
-class GradCAMPlusPlus(BaseCAM):
-    def __init__(self, model, target_layers, use_cuda=False,
-                 reshape_transform=None):
-        super(GradCAMPlusPlus, self).__init__(model, target_layers, use_cuda,
-                                              reshape_transform)
-
-    def get_cam_weights(self,
-                        input_tensor,
-                        target_layers,
-                        target_category,
-                        activations,
-                        grads):
-        grads_power_2 = grads**2
-        grads_power_3 = grads_power_2 * grads
-        # Equation 19 in https://arxiv.org/abs/1710.11063
-        sum_activations = np.sum(activations, axis=(2, 3))
-        eps = 0.000001
-        aij = grads_power_2 / (2 * grads_power_2 +
-                               sum_activations[:, :, None, None] * grads_power_3 + eps)
-        # Now bring back the ReLU from eq.7 in the paper,
-        # And zero out aijs where the activations are 0
-        aij = np.where(grads != 0, aij, 0)
-
-        weights = np.maximum(grads, 0) * aij
-        weights = np.sum(weights, axis=(2, 3))
-        return weights
diff --git a/spaces/tomofi/MMOCR/setup.py b/spaces/tomofi/MMOCR/setup.py
deleted file mode 100644
index 09a88c3e176e606c05a7ca6f869a7f35163b1f06..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/setup.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import os
-import os.path as osp
-import shutil
-import sys
-import warnings
-from setuptools import find_packages, setup
-
-
-def readme():
-    with open('README.md', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-
-version_file = 'mmocr/version.py'
-is_windows = sys.platform == 'win32'
-
-
-def add_mim_extention():
-    """Add extra files that are required to support MIM into the package.
-
-    These files will be added by creating a symlink to the originals if the
-    package is installed in `editable` mode (e.g. pip install -e .), or by
-    copying from the originals otherwise.
-    """
-
-    # parse installment mode
-    if 'develop' in sys.argv:
-        # installed by `pip install -e .`
-        mode = 'symlink'
-    elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
-        # installed by `pip install .`
-        # or create source distribution by `python setup.py sdist`
-        mode = 'copy'
-    else:
-        return
-
-    filenames = ['tools', 'configs', 'model-index.yml']
-    repo_path = osp.dirname(__file__)
-    mim_path = osp.join(repo_path, 'mmocr', '.mim')
-    os.makedirs(mim_path, exist_ok=True)
-
-    for filename in filenames:
-        if osp.exists(filename):
-            src_path = osp.join(repo_path, filename)
-            tar_path = osp.join(mim_path, filename)
-
-            if osp.isfile(tar_path) or osp.islink(tar_path):
-                os.remove(tar_path)
-            elif osp.isdir(tar_path):
-                shutil.rmtree(tar_path)
-
-            if mode == 'symlink':
-                src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
-                try:
-                    os.symlink(src_relpath, tar_path)
-                except OSError:
-                    # Creating a symbolic link on windows may raise an
-                    # `OSError: [WinError 1314]` due to privilege. If
-                    # the error happens, the src file will be copied
-                    mode = 'copy'
-                    warnings.warn(
-                        f'Failed to create a symbolic link for {src_relpath}, '
-                        f'and it will be copied to {tar_path}')
-                else:
-                    continue
-
-            if mode == 'copy':
-                if osp.isfile(src_path):
-                    shutil.copyfile(src_path, tar_path)
-                elif osp.isdir(src_path):
-                    shutil.copytree(src_path, tar_path)
-                else:
-                    warnings.warn(f'Cannot copy file {src_path}.')
-            else:
-                raise ValueError(f'Invalid mode {mode}')
-
-
-def get_version():
-    with open(version_file, 'r') as f:
-        exec(compile(f.read(), version_file, 'exec'))
-    import sys
-
-    # return short version for sdist
-    if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
-        return locals()['short_version']
-    else:
-        return locals()['__version__']
-
-
-def parse_requirements(fname='requirements.txt', with_version=True):
-    """Parse the package dependencies listed in a requirements file but strip
-    specific version information.
-
-    Args:
-        fname (str): Path to requirements file.
-        with_version (bool, default=False): If True, include version specs.
-    Returns:
-        info (list[str]): List of requirements items.
-    CommandLine:
-        python -c "import setup; print(setup.parse_requirements())"
-    """
-    import re
-    import sys
-    from os.path import exists
-    require_fpath = fname
-
-    def parse_line(line):
-        """Parse information from a line in a requirements text file."""
-        if line.startswith('-r '):
-            # Allow specifying requirements in other files
-            target = line.split(' ')[1]
-            for info in parse_require_file(target):
-                yield info
-        else:
-            info = {'line': line}
-            if line.startswith('-e '):
-                info['package'] = line.split('#egg=')[1]
-            else:
-                # Remove versioning from the package
-                pat = '(' + '|'.join(['>=', '==', '>']) + ')'
-                parts = re.split(pat, line, maxsplit=1)
-                parts = [p.strip() for p in parts]
-
-                info['package'] = parts[0]
-                if len(parts) > 1:
-                    op, rest = parts[1:]
-                    if ';' in rest:
-                        # Handle platform specific dependencies
-                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
-                        version, platform_deps = map(str.strip,
-                                                     rest.split(';'))
-                        info['platform_deps'] = platform_deps
-                    else:
-                        version = rest  # NOQA
-                    info['version'] = (op, version)
-            yield info
-
-    def parse_require_file(fpath):
-        with open(fpath, 'r') as f:
-            for line in f.readlines():
-                line = line.strip()
-                if line and not line.startswith('#'):
-                    for info in parse_line(line):
-                        yield info
-
-    def gen_packages_items():
-        if exists(require_fpath):
-            for info in parse_require_file(require_fpath):
-                parts = [info['package']]
-                if with_version and 'version' in info:
-                    parts.extend(info['version'])
-                if not sys.version.startswith('3.4'):
-                    # apparently package_deps are broken in 3.4
-                    platform_deps = info.get('platform_deps')
-                    if platform_deps is not None:
-                        parts.append(';' + platform_deps)
-                item = ''.join(parts)
-                yield item
-
-    packages = list(gen_packages_items())
-    return packages
-
-
-if __name__ == '__main__':
-    add_mim_extention()
-    library_dirs = [
-        lp for lp in os.environ.get('LD_LIBRARY_PATH', '').split(':')
-        if len(lp) > 1
-    ]
-    setup(
-        name='mmocr',
-        version=get_version(),
-        description='OpenMMLab Text Detection, OCR, and NLP Toolbox',
-        long_description=readme(),
-        long_description_content_type='text/markdown',
-        maintainer='MMOCR Authors',
-        maintainer_email='openmmlab@gmail.com',
-        keywords='Text Detection, OCR, KIE, NLP',
-        packages=find_packages(exclude=('configs', 'tools', 'demo')),
-        include_package_data=True,
-        url='https://github.com/open-mmlab/mmocr',
-        classifiers=[
-            'Development Status :: 4 - Beta',
-            'License :: OSI Approved :: Apache Software License',
-            'Operating System :: OS Independent',
-            'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.6',
-            'Programming Language :: Python :: 3.7',
-            'Programming Language :: Python :: 3.8',
-            'Programming Language :: Python :: 3.9',
-        ],
-        license='Apache License 2.0',
-        install_requires=parse_requirements('requirements/runtime.txt'),
-        extras_require={
-            'all': parse_requirements('requirements.txt'),
-            'tests': parse_requirements('requirements/tests.txt'),
-            'build': parse_requirements('requirements/build.txt'),
-            'optional': parse_requirements('requirements/optional.txt'),
-        },
-        zip_safe=False)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/samplers/sampling_result.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/samplers/sampling_result.py
deleted file mode 100644
index 419a8e39a3c307a7cd9cfd0565a20037ded0d646..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/bbox/samplers/sampling_result.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import torch
-
-from mmdet.utils import util_mixins
-
-
-class SamplingResult(util_mixins.NiceRepr):
-    """Bbox sampling result.
-
-    Example:
-        >>> # xdoctest: +IGNORE_WANT
-        >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-        >>> self = SamplingResult.random(rng=10)
-        >>> print(f'self = {self}')
-        self = <SamplingResult({
-            'neg_bboxes': torch.Size([12, 4]),
-            'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
-            'num_gts': 4,
-            'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
-            'pos_bboxes': torch.Size([0, 4]),
-            'pos_inds': tensor([], dtype=torch.int64),
-            'pos_is_gt': tensor([], dtype=torch.uint8)
-        })>
-    """
-
-    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
-                 gt_flags):
-        self.pos_inds = pos_inds
-        self.neg_inds = neg_inds
-        self.pos_bboxes = bboxes[pos_inds]
-        self.neg_bboxes = bboxes[neg_inds]
-        self.pos_is_gt = gt_flags[pos_inds]
-
-        self.num_gts = gt_bboxes.shape[0]
-        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
-
-        if gt_bboxes.numel() == 0:
-            # hack for index error case
-            assert self.pos_assigned_gt_inds.numel() == 0
-            self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4)
-        else:
-            if len(gt_bboxes.shape) < 2:
-                gt_bboxes = gt_bboxes.view(-1, 4)
-
-            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds, :]
-
-        if assign_result.labels is not None:
-            self.pos_gt_labels = assign_result.labels[pos_inds]
-        else:
-            self.pos_gt_labels = None
-
-    @property
-    def bboxes(self):
-        """torch.Tensor: concatenated positive and negative boxes"""
-        return torch.cat([self.pos_bboxes, self.neg_bboxes])
-
-    def to(self, device):
-        """Change the device of the data inplace.
-
-        Example:
-            >>> self = SamplingResult.random()
-            >>> print(f'self = {self.to(None)}')
-            >>> # xdoctest: +REQUIRES(--gpu)
-            >>> print(f'self = {self.to(0)}')
-        """
-        _dict = self.__dict__
-        for key, value in _dict.items():
-            if isinstance(value, torch.Tensor):
-                _dict[key] = value.to(device)
-        return self
-
-    def __nice__(self):
-        data = self.info.copy()
-        data['pos_bboxes'] = data.pop('pos_bboxes').shape
-        data['neg_bboxes'] = data.pop('neg_bboxes').shape
-        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
-        body = '    ' + ',\n    '.join(parts)
-        return '{\n' + body + '\n}'
-
-    @property
-    def info(self):
-        """Returns a dictionary of info about the object."""
-        return {
-            'pos_inds': self.pos_inds,
-            'neg_inds': self.neg_inds,
-            'pos_bboxes': self.pos_bboxes,
-            'neg_bboxes': self.neg_bboxes,
-            'pos_is_gt': self.pos_is_gt,
-            'num_gts': self.num_gts,
-            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
-        }
-
-    @classmethod
-    def random(cls, rng=None, **kwargs):
-        """
-        Args:
-            rng (None | int | numpy.random.RandomState): seed or state.
-            kwargs (keyword arguments):
-                - num_preds: number of predicted boxes
-                - num_gts: number of true boxes
-                - p_ignore (float): probability of a predicted box assinged to \
-                    an ignored truth.
-                - p_assigned (float): probability of a predicted box not being \
-                    assigned.
-                - p_use_label (float | bool): with labels or not.
-
-        Returns:
-            :obj:`SamplingResult`: Randomly generated sampling result.
-
-        Example:
-            >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
-            >>> self = SamplingResult.random()
-            >>> print(self.__dict__)
-        """
-        from mmdet.core.bbox.samplers.random_sampler import RandomSampler
-        from mmdet.core.bbox.assigners.assign_result import AssignResult
-        from mmdet.core.bbox import demodata
-        rng = demodata.ensure_rng(rng)
-
-        # make probabalistic?
-        num = 32
-        pos_fraction = 0.5
-        neg_pos_ub = -1
-
-        assign_result = AssignResult.random(rng=rng, **kwargs)
-
-        # Note we could just compute an assignment
-        bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
-        gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
-
-        if rng.rand() > 0.2:
-            # sometimes algorithms squeeze their data, be robust to that
-            gt_bboxes = gt_bboxes.squeeze()
-            bboxes = bboxes.squeeze()
-
-        if assign_result.labels is None:
-            gt_labels = None
-        else:
-            gt_labels = None  # todo
-
-        if gt_labels is None:
-            add_gt_as_proposals = False
-        else:
-            add_gt_as_proposals = True  # make probabalistic?
-
-        sampler = RandomSampler(
-            num,
-            pos_fraction,
-            neg_pos_ub=neg_pos_ub,
-            add_gt_as_proposals=add_gt_as_proposals,
-            rng=rng)
-        self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
-        return self
diff --git a/spaces/tridragonevo/chat-gpt-voice-stream/app.py b/spaces/tridragonevo/chat-gpt-voice-stream/app.py
deleted file mode 100644
index 2fd17ff8ef6b5adb7877e73d46c51af4fbd5746a..0000000000000000000000000000000000000000
--- a/spaces/tridragonevo/chat-gpt-voice-stream/app.py
+++ /dev/null
@@ -1,836 +0,0 @@
-import io
-import os
-import ssl
-from contextlib import closing
-from typing import Optional, Tuple
-import datetime
-
-import boto3
-import gradio as gr
-import requests
-
-# UNCOMMENT TO USE WHISPER
-import warnings
-import whisper
-
-from langchain import ConversationChain, LLMChain
-
-from langchain.agents import load_tools, initialize_agent
-from langchain.chains.conversation.memory import ConversationBufferMemory
-from langchain.llms import OpenAI
-from threading import Lock
-
-# Console to variable
-from io import StringIO
-import sys
-import re
-
-from openai.error import AuthenticationError, InvalidRequestError, RateLimitError
-
-# Pertains to Express-inator functionality
-from langchain.prompts import PromptTemplate
-
-from polly_utils import PollyVoiceData, NEURAL_ENGINE
-from azure_utils import AzureVoiceData
-
-# Pertains to question answering functionality
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores.faiss import FAISS
-from langchain.docstore.document import Document
-from langchain.chains.question_answering import load_qa_chain
-
-news_api_key = os.environ["NEWS_API_KEY"]
-tmdb_bearer_token = os.environ["TMDB_BEARER_TOKEN"]
-os.environ["OPENAI_API_KEY"] = "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"
-
-TOOLS_LIST = ['serpapi', 'wolfram-alpha', 'pal-math', 'pal-colored-objects']  #'google-search','news-api','tmdb-api','open-meteo-api'
-TOOLS_DEFAULT_LIST = ['serpapi', 'pal-math']
-BUG_FOUND_MSG = "Congratulations, you've found a bug in this application!"
-# AUTH_ERR_MSG = "Please paste your OpenAI key from openai.com to use this application. It is not necessary to hit a button or key after pasting it."
-AUTH_ERR_MSG = "Vui lòng nhập OpenAI key from openai.com to use this application. "
-MAX_TOKENS = 3200
-
-LOOPING_TALKING_HEAD = "videos/eae4686734545a20f095e6d5a7afa98a.mp4"
-TALKING_HEAD_WIDTH = "192"
-MAX_TALKING_HEAD_TEXT_LENGTH = 155
-
-# Pertains to Express-inator functionality
-NUM_WORDS_DEFAULT = 0
-MAX_WORDS = 400
-FORMALITY_DEFAULT = "N/A"
-TEMPERATURE_DEFAULT = 0.5
-EMOTION_DEFAULT = "N/A"
-LANG_LEVEL_DEFAULT = "N/A"
-TRANSLATE_TO_DEFAULT = "N/A"
-LITERARY_STYLE_DEFAULT = "N/A"
-PROMPT_TEMPLATE = PromptTemplate(
-    input_variables=["original_words", "num_words", "formality", "emotions", "lang_level", "translate_to",
-                     "literary_style"],
-    template="Restate {num_words}{formality}{emotions}{lang_level}{translate_to}{literary_style}the following: \n{original_words}\n",
-)
-
-POLLY_VOICE_DATA = PollyVoiceData()
-AZURE_VOICE_DATA = AzureVoiceData()
-
-# Pertains to WHISPER functionality
-WHISPER_DETECT_LANG = "Detect language"
-
-
-# UNCOMMENT TO USE WHISPER
-warnings.filterwarnings("ignore")
-WHISPER_MODEL = whisper.load_model("tiny")
-print("WHISPER_MODEL", WHISPER_MODEL)
-
-
-# UNCOMMENT TO USE WHISPER
-def transcribe(aud_inp, whisper_lang):
-    if aud_inp is None:
-        return ""
-    aud = whisper.load_audio(aud_inp)
-    aud = whisper.pad_or_trim(aud)
-    mel = whisper.log_mel_spectrogram(aud).to(WHISPER_MODEL.device)
-    _, probs = WHISPER_MODEL.detect_language(mel)
-    options = whisper.DecodingOptions()
-    if whisper_lang != WHISPER_DETECT_LANG:
-        whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(whisper_lang)
-        options = whisper.DecodingOptions(language=whisper_lang_code)
-    result = whisper.decode(WHISPER_MODEL, mel, options)
-    print("result.text", result.text)
-    result_text = ""
-    if result and result.text:
-        result_text = result.text
-    return result_text
-
-
-# Temporarily address Wolfram Alpha SSL certificate issue
-ssl._create_default_https_context = ssl._create_unverified_context
-
-
-# TEMPORARY FOR TESTING
-def transcribe_dummy(aud_inp_tb, whisper_lang):
-    if aud_inp_tb is None:
-        return ""
-    # aud = whisper.load_audio(aud_inp)
-    # aud = whisper.pad_or_trim(aud)
-    # mel = whisper.log_mel_spectrogram(aud).to(WHISPER_MODEL.device)
-    # _, probs = WHISPER_MODEL.detect_language(mel)
-    # options = whisper.DecodingOptions()
-    # options = whisper.DecodingOptions(language="ja")
-    # result = whisper.decode(WHISPER_MODEL, mel, options)
-    result_text = "Whisper will detect language"
-    if whisper_lang != WHISPER_DETECT_LANG:
-        whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(whisper_lang)
-        result_text = f"Whisper will use lang code: {whisper_lang_code}"
-    print("result_text", result_text)
-    return aud_inp_tb
-
-
-# Pertains to Express-inator functionality
-def transform_text(desc, express_chain, num_words, formality,
-                   anticipation_level, joy_level, trust_level,
-                   fear_level, surprise_level, sadness_level, disgust_level, anger_level,
-                   lang_level, translate_to, literary_style):
-    num_words_prompt = ""
-    if num_words and int(num_words) != 0:
-        num_words_prompt = "using up to " + str(num_words) + " words, "
-
-    # Change some arguments to lower case
-    formality = formality.lower()
-    anticipation_level = anticipation_level.lower()
-    joy_level = joy_level.lower()
-    trust_level = trust_level.lower()
-    fear_level = fear_level.lower()
-    surprise_level = surprise_level.lower()
-    sadness_level = sadness_level.lower()
-    disgust_level = disgust_level.lower()
-    anger_level = anger_level.lower()
-
-    formality_str = ""
-    if formality != "n/a":
-        formality_str = "in a " + formality + " manner, "
-
-    # put all emotions into a list
-    emotions = []
-    if anticipation_level != "n/a":
-        emotions.append(anticipation_level)
-    if joy_level != "n/a":
-        emotions.append(joy_level)
-    if trust_level != "n/a":
-        emotions.append(trust_level)
-    if fear_level != "n/a":
-        emotions.append(fear_level)
-    if surprise_level != "n/a":
-        emotions.append(surprise_level)
-    if sadness_level != "n/a":
-        emotions.append(sadness_level)
-    if disgust_level != "n/a":
-        emotions.append(disgust_level)
-    if anger_level != "n/a":
-        emotions.append(anger_level)
-
-    emotions_str = ""
-    if len(emotions) > 0:
-        if len(emotions) == 1:
-            emotions_str = "with emotion of " + emotions[0] + ", "
-        else:
-            emotions_str = "with emotions of " + ", ".join(emotions[:-1]) + " and " + emotions[-1] + ", "
-
-    lang_level_str = ""
-    if lang_level != LANG_LEVEL_DEFAULT:
-        lang_level_str = "at a " + lang_level + " level, " if translate_to == TRANSLATE_TO_DEFAULT else ""
-
-    translate_to_str = ""
-    if translate_to != TRANSLATE_TO_DEFAULT:
-        translate_to_str = "translated to " + (
-            "" if lang_level == TRANSLATE_TO_DEFAULT else lang_level + " level ") + translate_to + ", "
-
-    literary_style_str = ""
-    if literary_style != LITERARY_STYLE_DEFAULT:
-        if literary_style == "Prose":
-            literary_style_str = "as prose, "
-        if literary_style == "Story":
-            literary_style_str = "as a story, "
-        elif literary_style == "Summary":
-            literary_style_str = "as a summary, "
-        elif literary_style == "Outline":
-            literary_style_str = "as an outline numbers and lower case letters, "
-        elif literary_style == "Bullets":
-            literary_style_str = "as bullet points using bullets, "
-        elif literary_style == "Poetry":
-            literary_style_str = "as a poem, "
-        elif literary_style == "Haiku":
-            literary_style_str = "as a haiku, "
-        elif literary_style == "Limerick":
-            literary_style_str = "as a limerick, "
-        elif literary_style == "Rap":
-            literary_style_str = "as a rap, "
-        elif literary_style == "Joke":
-            literary_style_str = "as a very funny joke with a setup and punchline, "
-        elif literary_style == "Knock-knock":
-            literary_style_str = "as a very funny knock-knock joke, "
-        elif literary_style == "FAQ":
-            literary_style_str = "as a FAQ with several questions and answers, "
-
-    formatted_prompt = PROMPT_TEMPLATE.format(
-        original_words=desc,
-        num_words=num_words_prompt,
-        formality=formality_str,
-        emotions=emotions_str,
-        lang_level=lang_level_str,
-        translate_to=translate_to_str,
-        literary_style=literary_style_str
-    )
-
-    trans_instr = num_words_prompt + formality_str + emotions_str + lang_level_str + translate_to_str + literary_style_str
-    if express_chain and len(trans_instr.strip()) > 0:
-        generated_text = express_chain.run(
-            {'original_words': desc, 'num_words': num_words_prompt, 'formality': formality_str,
-             'emotions': emotions_str, 'lang_level': lang_level_str, 'translate_to': translate_to_str,
-             'literary_style': literary_style_str}).strip()
-    else:
-        print("Not transforming text")
-        generated_text = desc
-
-    # replace all newlines with <br> in generated_text
-    generated_text = generated_text.replace("\n", "\n\n")
-
-    prompt_plus_generated = "GPT prompt: " + formatted_prompt + "\n\n" + generated_text
-
-    print("\n==== date/time: " + str(datetime.datetime.now() - datetime.timedelta(hours=5)) + " ====")
-    print("prompt_plus_generated: " + prompt_plus_generated)
-
-    return generated_text
-
-
-def load_chain(tools_list, llm):
-    chain = None
-    express_chain = None
-    memory = None
-    if llm:
-        print("\ntools_list", tools_list)
-        tool_names = tools_list
-        tools = load_tools(tool_names, llm=llm, news_api_key=news_api_key, tmdb_bearer_token=tmdb_bearer_token)
-
-        memory = ConversationBufferMemory(memory_key="chat_history")
-
-        chain = initialize_agent(tools, llm, agent="conversational-react-description", verbose=True, memory=memory)
-        express_chain = LLMChain(llm=llm, prompt=PROMPT_TEMPLATE, verbose=True)
-    return chain, express_chain, memory
-
-
-def set_openai_api_key():
-    
-    os.environ["OPENAI_API_KEY"] = "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"
-    print("\n\n ++++++++++++++ Setting OpenAI API key ++++++++++++++ \n\n")
-    print(str(datetime.datetime.now()) + ": Before OpenAI, OPENAI_API_KEY length: " + str(
-        len(os.environ["OPENAI_API_KEY"])))
-    llm = OpenAI(temperature=0, max_tokens=MAX_TOKENS)
-    print(str(datetime.datetime.now()) + ": After OpenAI, OPENAI_API_KEY length: " + str(
-        len(os.environ["OPENAI_API_KEY"])))
-    chain, express_chain, memory = load_chain(TOOLS_DEFAULT_LIST, llm)
-
-    # Pertains to question answering functionality
-    embeddings = OpenAIEmbeddings()
-    qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
-
-    print(str(datetime.datetime.now()) + ": After load_chain, OPENAI_API_KEY length: " + str(
-        len(os.environ["OPENAI_API_KEY"])))
-
-    return chain, express_chain, llm, embeddings, qa_chain, memory
-
-
-def run_chain(chain, inp, capture_hidden_text):
-    output = ""
-    hidden_text = None
-    if capture_hidden_text:
-        error_msg = None
-        tmp = sys.stdout
-        hidden_text_io = StringIO()
-        sys.stdout = hidden_text_io
-
-        try:
-            output = chain.run(input=inp)
-        except AuthenticationError as ae:
-            error_msg = AUTH_ERR_MSG + str(datetime.datetime.now()) + ". " + str(ae)
-            print("error_msg", error_msg)
-        except RateLimitError as rle:
-            error_msg = "\n\nRateLimitError: " + str(rle)
-        except ValueError as ve:
-            error_msg = "\n\nValueError: " + str(ve)
-        except InvalidRequestError as ire:
-            error_msg = "\n\nInvalidRequestError: " + str(ire)
-        except Exception as e:
-            error_msg = "\n\n" + BUG_FOUND_MSG + ":\n\n" + str(e)
-
-        sys.stdout = tmp
-        hidden_text = hidden_text_io.getvalue()
-
-        # remove escape characters from hidden_text
-        hidden_text = re.sub(r'\x1b[^m]*m', '', hidden_text)
-
-        # remove "Entering new AgentExecutor chain..." from hidden_text
-        hidden_text = re.sub(r"Entering new AgentExecutor chain...\n", "", hidden_text)
-
-        # remove "Finished chain." from hidden_text
-        hidden_text = re.sub(r"Finished chain.", "", hidden_text)
-
-        # Add newline after "Thought:" "Action:" "Observation:" "Input:" and "AI:"
-        hidden_text = re.sub(r"Thought:", "\n\nThought:", hidden_text)
-        hidden_text = re.sub(r"Action:", "\n\nAction:", hidden_text)
-        hidden_text = re.sub(r"Observation:", "\n\nObservation:", hidden_text)
-        hidden_text = re.sub(r"Input:", "\n\nInput:", hidden_text)
-        hidden_text = re.sub(r"AI:", "\n\nAI:", hidden_text)
-
-        if error_msg:
-            hidden_text += error_msg
-
-        print("hidden_text: ", hidden_text)
-    else:
-        try:
-            output = chain.run(input=inp)
-        except AuthenticationError as ae:
-            output = AUTH_ERR_MSG + str(datetime.datetime.now()) + ". " + str(ae)
-            print("output", output)
-        except RateLimitError as rle:
-            output = "\n\nRateLimitError: " + str(rle)
-        except ValueError as ve:
-            output = "\n\nValueError: " + str(ve)
-        except InvalidRequestError as ire:
-            output = "\n\nInvalidRequestError: " + str(ire)
-        except Exception as e:
-            output = "\n\n" + BUG_FOUND_MSG + ":\n\n" + str(e)
-
-    return output, hidden_text
-
-
-def reset_memory(history, memory):
-    memory.clear()
-    history = []
-    return history, history, memory
-
-
-class ChatWrapper:
-
-    def __init__(self):
-        self.lock = Lock()
-
-    def __call__(
-            self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain],
-            trace_chain: bool, speak_text: bool, talking_head: bool, monologue: bool, express_chain: Optional[LLMChain],
-            num_words, formality, anticipation_level, joy_level, trust_level,
-            fear_level, surprise_level, sadness_level, disgust_level, anger_level,
-            lang_level, translate_to, literary_style, qa_chain, docsearch, use_embeddings
-    ):
-        """Execute the chat functionality."""
-        self.lock.acquire()
-        try:
-            print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
-            print("inp: " + inp)
-            print("trace_chain: ", trace_chain)
-            print("speak_text: ", speak_text)
-            print("talking_head: ", talking_head)
-            print("monologue: ", monologue)
-            history = history or []
-            # If chain is None, that is because no API key was provided.
-            output = "Please paste your OpenAI key from openai.com to use this app. " + str(datetime.datetime.now())
-            hidden_text = output
-
-            if chain:
-                # Set OpenAI key
-                import openai
-                openai.api_key = api_key
-                if not monologue:
-                    if use_embeddings:
-                        if inp and inp.strip() != "":
-                            if docsearch:
-                                docs = docsearch.similarity_search(inp)
-                                output = str(qa_chain.run(input_documents=docs, question=inp))
-                            else:
-                                output, hidden_text = "Please supply some text in the the Embeddings tab.", None
-                        else:
-                            output, hidden_text = "What's on your mind?", None
-                    else:
-                        output, hidden_text = run_chain(chain, inp, capture_hidden_text=trace_chain)
-                else:
-                    output, hidden_text = inp, None
-
-            output = transform_text(output, express_chain, num_words, formality, anticipation_level, joy_level,
-                                    trust_level,
-                                    fear_level, surprise_level, sadness_level, disgust_level, anger_level,
-                                    lang_level, translate_to, literary_style)
-
-            text_to_display = output
-            if trace_chain:
-                text_to_display = hidden_text + "\n\n" + output
-            history.append((inp, text_to_display))
-
-            html_video, temp_file, html_audio, temp_aud_file = None, None, None, None
-            if speak_text:
-                if talking_head:
-                    if len(output) <= MAX_TALKING_HEAD_TEXT_LENGTH:
-                        html_video, temp_file = do_html_video_speak(output, translate_to)
-                    else:
-                        temp_file = LOOPING_TALKING_HEAD
-                        html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH)
-                        html_audio, temp_aud_file = do_html_audio_speak(output, translate_to)
-                else:
-                    html_audio, temp_aud_file = do_html_audio_speak(output, translate_to)
-            else:
-                if talking_head:
-                    temp_file = LOOPING_TALKING_HEAD
-                    html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH)
-                else:
-                    # html_audio, temp_aud_file = do_html_audio_speak(output, translate_to)
-                    # html_video = create_html_video(temp_file, "128")
-                    pass
-
-        except Exception as e:
-            raise e
-        finally:
-            self.lock.release()
-        return history, history, html_video, temp_file, html_audio, temp_aud_file, ""
-        # return history, history, html_audio, temp_aud_file, ""
-
-
-chat = ChatWrapper()
-
-
-def do_html_audio_speak(words_to_speak, polly_language):
-    polly_client = boto3.Session(
-        aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
-        aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
-        region_name=os.environ["AWS_DEFAULT_REGION"]
-    ).client('polly')
-
-    # voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(polly_language, "Female")
-    voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(polly_language, "Male")
-    if not voice_id:
-        # voice_id = "Joanna"
-        voice_id = "Matthew"
-        language_code = "en-US"
-        engine = NEURAL_ENGINE
-    response = polly_client.synthesize_speech(
-        Text=words_to_speak,
-        OutputFormat='mp3',
-        VoiceId=voice_id,
-        LanguageCode=language_code,
-        Engine=engine
-    )
-
-    html_audio = '<pre>no audio</pre>'
-
-    # Save the audio stream returned by Amazon Polly on Lambda's temp directory
-    if "AudioStream" in response:
-        with closing(response["AudioStream"]) as stream:
-            # output = os.path.join("/tmp/", "speech.mp3")
-
-            try:
-                with open('audios/tempfile.mp3', 'wb') as f:
-                    f.write(stream.read())
-                temp_aud_file = gr.File("audios/tempfile.mp3")
-                temp_aud_file_url = "/file=" + temp_aud_file.value['name']
-                html_audio = f'<audio autoplay><source src={temp_aud_file_url} type="audio/mp3"></audio>'
-            except IOError as error:
-                # Could not write to file, exit gracefully
-                print(error)
-                return None, None
-    else:
-        # The response didn't contain audio data, exit gracefully
-        print("Could not stream audio")
-        return None, None
-
-    return html_audio, "audios/tempfile.mp3"
-
-
-def create_html_video(file_name, width):
-    temp_file_url = "/file=" + tmp_file.value['name']
-    html_video = f'<video width={width} height={width} autoplay muted loop><source src={temp_file_url} type="video/mp4" poster="Masahiro.png"></video>'
-    return html_video
-
-
-def do_html_video_speak(words_to_speak, azure_language):
-    azure_voice = AZURE_VOICE_DATA.get_voice(azure_language, "Male")
-    if not azure_voice:
-        azure_voice = "en-US-ChristopherNeural"
-
-    headers = {"Authorization": f"Bearer {os.environ['EXHUMAN_API_KEY']}"}
-    body = {
-        'bot_name': 'Masahiro',
-        'bot_response': words_to_speak,
-        'azure_voice': azure_voice,
-        'azure_style': 'friendly',
-        'animation_pipeline': 'high_speed',
-    }
-    api_endpoint = "https://api.exh.ai/animations/v1/generate_lipsync"
-    res = requests.post(api_endpoint, json=body, headers=headers)
-    print("res.status_code: ", res.status_code)
-
-    html_video = '<pre>no video</pre>'
-    if isinstance(res.content, bytes):
-        response_stream = io.BytesIO(res.content)
-        print("len(res.content)): ", len(res.content))
-
-        with open('videos/tempfile.mp4', 'wb') as f:
-            f.write(response_stream.read())
-        temp_file = gr.File("videos/tempfile.mp4")
-        temp_file_url = "/file=" + temp_file.value['name']
-        html_video = f'<video width={TALKING_HEAD_WIDTH} height={TALKING_HEAD_WIDTH} autoplay><source src={temp_file_url} type="video/mp4" poster="Masahiro.png"></video>'
-    else:
-        print('video url unknown')
-    return html_video, "videos/tempfile.mp4"
-
-
-def update_selected_tools(widget, state, llm):
-    if widget:
-        state = widget
-        chain, express_chain, memory = load_chain(state, llm)
-        return state, llm, chain, express_chain
-
-
-def update_talking_head(widget, state):
-    if widget:
-        state = widget
-
-        video_html_talking_head = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
-        return state, video_html_talking_head
-    else:
-        # return state, create_html_video(LOOPING_TALKING_HEAD, "32")
-        return None, "<pre></pre>"
-
-
-def update_foo(widget, state):
-    if widget:
-        state = widget
-        return state
-
-
-# Pertains to question answering functionality
-def update_embeddings(embeddings_text, embeddings, qa_chain):
-    if embeddings_text:
-        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-        texts = text_splitter.split_text(embeddings_text)
-
-        docsearch = FAISS.from_texts(texts, embeddings)
-        print("Embeddings updated")
-        return docsearch
-
-
-# Pertains to question answering functionality
-def update_use_embeddings(widget, state):
-    if widget:
-        state = widget
-        return state
-
-
-with gr.Blocks(css=".gradio-container {background-color: lightgray}") as block:
-    llm_state = gr.State()
-    history_state = gr.State()
-    chain_state = gr.State()
-    express_chain_state = gr.State()
-    tools_list_state = gr.State(TOOLS_DEFAULT_LIST)
-    trace_chain_state = gr.State(False)
-    speak_text_state = gr.State(False)
-    talking_head_state = gr.State(True)
-    monologue_state = gr.State(False)  # Takes the input and repeats it back to the user, optionally transforming it.
-    memory_state = gr.State()
-
-    # Pertains to Express-inator functionality
-    num_words_state = gr.State(NUM_WORDS_DEFAULT)
-    formality_state = gr.State(FORMALITY_DEFAULT)
-    anticipation_level_state = gr.State(EMOTION_DEFAULT)
-    joy_level_state = gr.State(EMOTION_DEFAULT)
-    trust_level_state = gr.State(EMOTION_DEFAULT)
-    fear_level_state = gr.State(EMOTION_DEFAULT)
-    surprise_level_state = gr.State(EMOTION_DEFAULT)
-    sadness_level_state = gr.State(EMOTION_DEFAULT)
-    disgust_level_state = gr.State(EMOTION_DEFAULT)
-    anger_level_state = gr.State(EMOTION_DEFAULT)
-    lang_level_state = gr.State(LANG_LEVEL_DEFAULT)
-    translate_to_state = gr.State(TRANSLATE_TO_DEFAULT)
-    literary_style_state = gr.State(LITERARY_STYLE_DEFAULT)
-
-    # Pertains to WHISPER functionality
-    whisper_lang_state = gr.State(WHISPER_DETECT_LANG)
-
-    # Pertains to question answering functionality
-    embeddings_state = gr.State()
-    qa_chain_state = gr.State()
-    docsearch_state = gr.State()
-    use_embeddings_state = gr.State(False)
-
-    with gr.Tab("Chat"):
-        with gr.Row():
-            with gr.Column():
-                gr.HTML(
-                    """<b><center>GPT + WolframAlpha + Whisper</center></b>
-                    <p><center>New feature: <b>Embeddings</b></center></p>""")
-
-            openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...)",show_label=False, lines=1, type='password')
-
-        with gr.Row():
-            with gr.Column(scale=1, min_width=TALKING_HEAD_WIDTH, visible=True):
-                speak_text_cb = gr.Checkbox(label="Enable speech", value=False)
-                speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state],
-                                     outputs=[speak_text_state])
-
-                my_file = gr.File(label="Upload a file", type="file", visible=False)
-                tmp_file = gr.File(LOOPING_TALKING_HEAD, visible=False)
-                # tmp_file_url = "/file=" + tmp_file.value['name']
-                htm_video = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
-                video_html = gr.HTML(htm_video)
-
-                # my_aud_file = gr.File(label="Audio file", type="file", visible=True)
-                tmp_aud_file = gr.File("audios/tempfile.mp3", visible=False)
-                tmp_aud_file_url = "/file=" + tmp_aud_file.value['name']
-                htm_audio = f'<audio><source src={tmp_aud_file_url} type="audio/mp3"></audio>'
-                audio_html = gr.HTML(htm_audio)
-
-            with gr.Column(scale=7):
-                chatbot = gr.Chatbot()
-
-        with gr.Row():
-            message = gr.Textbox(label="What's on your mind??",
-                                 placeholder="What's the answer to life, the universe, and everything?",
-                                 lines=1)
-            submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
-
-        # UNCOMMENT TO USE WHISPER
-        with gr.Row():
-            audio_comp = gr.Microphone(source="microphone", type="filepath", label="Just say it!",
-                                       interactive=True, streaming=False)
-            audio_comp.change(transcribe, inputs=[audio_comp, whisper_lang_state], outputs=[message])
-
-        # TEMPORARY FOR TESTING
-        # with gr.Row():
-        #     audio_comp_tb = gr.Textbox(label="Just say it!", lines=1)
-        #     audio_comp_tb.submit(transcribe_dummy, inputs=[audio_comp_tb, whisper_lang_state], outputs=[message])
-
-
-
-    with gr.Tab("Settings"):
-        tools_cb_group = gr.CheckboxGroup(label="Tools:", choices=TOOLS_LIST,
-                                          value=TOOLS_DEFAULT_LIST)
-        tools_cb_group.change(update_selected_tools,
-                              inputs=[tools_cb_group, tools_list_state, llm_state],
-                              outputs=[tools_list_state, llm_state, chain_state, express_chain_state])
-
-        trace_chain_cb = gr.Checkbox(label="Show reasoning chain in chat bubble", value=False)
-        trace_chain_cb.change(update_foo, inputs=[trace_chain_cb, trace_chain_state],
-                              outputs=[trace_chain_state])
-
-        # speak_text_cb = gr.Checkbox(label="Speak text from agent", value=False)
-        # speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state],
-        #                      outputs=[speak_text_state])
-
-        talking_head_cb = gr.Checkbox(label="Show talking head", value=True)
-        talking_head_cb.change(update_talking_head, inputs=[talking_head_cb, talking_head_state],
-                               outputs=[talking_head_state, video_html])
-
-        monologue_cb = gr.Checkbox(label="Babel fish mode (translate/restate what you enter, no conversational agent)",
-                                   value=False)
-        monologue_cb.change(update_foo, inputs=[monologue_cb, monologue_state],
-                            outputs=[monologue_state])
-
-        reset_btn = gr.Button(value="Reset chat", variant="secondary").style(full_width=False)
-        reset_btn.click(reset_memory, inputs=[history_state, memory_state], outputs=[chatbot, history_state, memory_state])
-
-    with gr.Tab("Whisper STT"):
-        whisper_lang_radio = gr.Radio(label="Whisper speech-to-text language:", choices=[
-            WHISPER_DETECT_LANG, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)",
-            "Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)",
-            "English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)",
-            "German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese",
-            "Korean", "Norwegian", "Polish",
-            "Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)",
-            "Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh"],
-                                      value=WHISPER_DETECT_LANG)
-
-        whisper_lang_radio.change(update_foo,
-                                  inputs=[whisper_lang_radio, whisper_lang_state],
-                                  outputs=[whisper_lang_state])
-
-    with gr.Tab("Translate to"):
-        lang_level_radio = gr.Radio(label="Language level:", choices=[
-            LANG_LEVEL_DEFAULT, "1st grade", "2nd grade", "3rd grade", "4th grade", "5th grade", "6th grade",
-            "7th grade", "8th grade", "9th grade", "10th grade", "11th grade", "12th grade", "University"],
-                                    value=LANG_LEVEL_DEFAULT)
-        lang_level_radio.change(update_foo, inputs=[lang_level_radio, lang_level_state],
-                                outputs=[lang_level_state])
-
-        translate_to_radio = gr.Radio(label="Language:", choices=[
-            TRANSLATE_TO_DEFAULT, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)",
-            "Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)",
-            "English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)",
-            "German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese",
-            "Korean", "Norwegian", "Polish",
-            "Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)",
-            "Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh",
-            "emojis", "Gen Z slang", "how the stereotypical Karen would say it", "Klingon", "Neanderthal",
-            "Pirate", "Strange Planet expospeak technical talk", "Yoda", "Vietnamese (Vietnam)"],
-                                      value=TRANSLATE_TO_DEFAULT)
-
-        translate_to_radio.change(update_foo,
-                                  inputs=[translate_to_radio, translate_to_state],
-                                  outputs=[translate_to_state])
-
-    with gr.Tab("Formality"):
-        formality_radio = gr.Radio(label="Formality:",
-                                   choices=[FORMALITY_DEFAULT, "Casual", "Polite", "Honorific"],
-                                   value=FORMALITY_DEFAULT)
-        formality_radio.change(update_foo,
-                               inputs=[formality_radio, formality_state],
-                               outputs=[formality_state])
-
-    with gr.Tab("Lit style"):
-        literary_style_radio = gr.Radio(label="Literary style:", choices=[
-            LITERARY_STYLE_DEFAULT, "Prose", "Story", "Summary", "Outline", "Bullets", "Poetry", "Haiku", "Limerick", "Rap",
-            "Joke", "Knock-knock", "FAQ"],
-                                        value=LITERARY_STYLE_DEFAULT)
-
-        literary_style_radio.change(update_foo,
-                                    inputs=[literary_style_radio, literary_style_state],
-                                    outputs=[literary_style_state])
-
-    with gr.Tab("Emotions"):
-        anticipation_level_radio = gr.Radio(label="Anticipation level:",
-                                            choices=[EMOTION_DEFAULT, "Interest", "Anticipation", "Vigilance"],
-                                            value=EMOTION_DEFAULT)
-        anticipation_level_radio.change(update_foo,
-                                        inputs=[anticipation_level_radio, anticipation_level_state],
-                                        outputs=[anticipation_level_state])
-
-        joy_level_radio = gr.Radio(label="Joy level:",
-                                   choices=[EMOTION_DEFAULT, "Serenity", "Joy", "Ecstasy"],
-                                   value=EMOTION_DEFAULT)
-        joy_level_radio.change(update_foo,
-                               inputs=[joy_level_radio, joy_level_state],
-                               outputs=[joy_level_state])
-
-        trust_level_radio = gr.Radio(label="Trust level:",
-                                     choices=[EMOTION_DEFAULT, "Acceptance", "Trust", "Admiration"],
-                                     value=EMOTION_DEFAULT)
-        trust_level_radio.change(update_foo,
-                                 inputs=[trust_level_radio, trust_level_state],
-                                 outputs=[trust_level_state])
-
-        fear_level_radio = gr.Radio(label="Fear level:",
-                                    choices=[EMOTION_DEFAULT, "Apprehension", "Fear", "Terror"],
-                                    value=EMOTION_DEFAULT)
-        fear_level_radio.change(update_foo,
-                                inputs=[fear_level_radio, fear_level_state],
-                                outputs=[fear_level_state])
-
-        surprise_level_radio = gr.Radio(label="Surprise level:",
-                                        choices=[EMOTION_DEFAULT, "Distraction", "Surprise", "Amazement"],
-                                        value=EMOTION_DEFAULT)
-        surprise_level_radio.change(update_foo,
-                                    inputs=[surprise_level_radio, surprise_level_state],
-                                    outputs=[surprise_level_state])
-
-        sadness_level_radio = gr.Radio(label="Sadness level:",
-                                       choices=[EMOTION_DEFAULT, "Pensiveness", "Sadness", "Grief"],
-                                       value=EMOTION_DEFAULT)
-        sadness_level_radio.change(update_foo,
-                                   inputs=[sadness_level_radio, sadness_level_state],
-                                   outputs=[sadness_level_state])
-
-        disgust_level_radio = gr.Radio(label="Disgust level:",
-                                       choices=[EMOTION_DEFAULT, "Boredom", "Disgust", "Loathing"],
-                                       value=EMOTION_DEFAULT)
-        disgust_level_radio.change(update_foo,
-                                   inputs=[disgust_level_radio, disgust_level_state],
-                                   outputs=[disgust_level_state])
-
-        anger_level_radio = gr.Radio(label="Anger level:",
-                                     choices=[EMOTION_DEFAULT, "Annoyance", "Anger", "Rage"],
-                                     value=EMOTION_DEFAULT)
-        anger_level_radio.change(update_foo,
-                                 inputs=[anger_level_radio, anger_level_state],
-                                 outputs=[anger_level_state])
-
-    with gr.Tab("Max words"):
-        num_words_slider = gr.Slider(label="Max number of words to generate (0 for don't care)",
-                                     value=NUM_WORDS_DEFAULT, minimum=0, maximum=MAX_WORDS, step=10)
-        num_words_slider.change(update_foo,
-                                inputs=[num_words_slider, num_words_state],
-                                outputs=[num_words_state])
-
-    with gr.Tab("Embeddings"):
-        embeddings_text_box = gr.Textbox(label="Enter text for embeddings and hit Create:", lines=20)
-
-        with gr.Row():
-            use_embeddings_cb = gr.Checkbox(label="Use embeddings", value=False)
-            use_embeddings_cb.change(update_use_embeddings, inputs=[use_embeddings_cb, use_embeddings_state],
-                                     outputs=[use_embeddings_state])
-
-            embeddings_text_submit = gr.Button(value="Create", variant="secondary").style(full_width=False)
-            embeddings_text_submit.click(update_embeddings,
-                                         inputs=[embeddings_text_box, embeddings_state, qa_chain_state],
-                                         outputs=[docsearch_state])
-
-
-    message.submit(chat, inputs=[openai_api_key_textbox, message, history_state, chain_state, trace_chain_state,
-                                 speak_text_state, talking_head_state, monologue_state,
-                                 express_chain_state, num_words_state, formality_state,
-                                 anticipation_level_state, joy_level_state, trust_level_state, fear_level_state,
-                                 surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state,
-                                 lang_level_state, translate_to_state, literary_style_state,
-                                 qa_chain_state, docsearch_state, use_embeddings_state],
-                   outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message])
-    # outputs=[chatbot, history_state, audio_html, tmp_aud_file, message])
-
-    submit.click(chat, inputs=[openai_api_key_textbox, message, history_state, chain_state, trace_chain_state,
-                               speak_text_state, talking_head_state, monologue_state,
-                               express_chain_state, num_words_state, formality_state,
-                               anticipation_level_state, joy_level_state, trust_level_state, fear_level_state,
-                               surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state,
-                               lang_level_state, translate_to_state, literary_style_state,
-                               qa_chain_state, docsearch_state, use_embeddings_state],
-                 outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message])
-    # outputs=[chatbot, history_state, audio_html, tmp_aud_file, message])
-
-    openai_api_key_textbox.change(set_openai_api_key, outputs=[chain_state, express_chain_state, llm_state, embeddings_state, qa_chain_state, memory_state])
-    
-# openai_api_key_textbox = "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"
-block.launch(debug=True)
diff --git a/spaces/tsi-org/LLaVA/scripts/convert_sqa_to_llava_base_prompt.py b/spaces/tsi-org/LLaVA/scripts/convert_sqa_to_llava_base_prompt.py
deleted file mode 100644
index b327fcc29eb44d7fe68be35da25bafa0e1d6feba..0000000000000000000000000000000000000000
--- a/spaces/tsi-org/LLaVA/scripts/convert_sqa_to_llava_base_prompt.py
+++ /dev/null
@@ -1,334 +0,0 @@
-def get_question_text(problem):
-    question = problem['question']
-    return question
-
-
-def get_context_text(problem, use_caption):
-    txt_context = problem['hint']
-    img_context = problem['caption'] if use_caption else ""
-    context = " ".join([txt_context, img_context]).strip()
-    if context == "":
-        context = "N/A"
-    return context
-
-
-def get_choice_text(probelm, options):
-    choices = probelm['choices']
-    choice_list = []
-    for i, c in enumerate(choices):
-        choice_list.append("({}) {}".format(options[i], c))
-    choice_txt = " ".join(choice_list)
-    #print(choice_txt)
-    return choice_txt
-
-
-def get_answer(problem, options):
-    return options[problem['answer']]
-
-
-def get_lecture_text(problem):
-    # \\n: GPT-3 can generate the lecture with more tokens.
-    lecture = problem['lecture'].replace("\n", "\\n")
-    return lecture
-
-
-def get_solution_text(problem):
-    # \\n: GPT-3 can generate the solution with more tokens
-    solution = problem['solution'].replace("\n", "\\n")
-    return solution
-
-
-def create_one_example_chatbot(format, question, context, choice, answer, lecture, solution, test_example=True):
-
-    input_format, output_format = format.split("-")
-
-    ## Inputs
-    if input_format == "CQM":
-        input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
-    elif input_format == "QCM":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
-    # upper bound experiment
-    elif input_format == "QCML":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
-    elif input_format == "QCME":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
-    elif input_format == "QCMLE":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
-
-    elif input_format == "QCLM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
-    elif input_format == "QCEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
-    elif input_format == "QCLEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
-
-    # Outputs
-    if test_example:
-        output = "Answer:"
-    elif output_format == 'A':
-        output = f"Answer: The answer is {answer}."
-
-    elif output_format == 'AL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
-    elif output_format == 'AE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
-    elif output_format == 'ALE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
-    elif output_format == 'AEL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
-
-    elif output_format == 'LA':
-        output = f"Answer: {lecture} The answer is {answer}."
-    elif output_format == 'EA':
-        output = f"Answer: {solution} The answer is {answer}."
-    elif output_format == 'LEA':
-        output = f"Answer: {lecture} {solution} The answer is {answer}."
-    elif output_format == 'ELA':
-        output = f"Answer: {solution} {lecture} The answer is {answer}."
-    elif output_format == 'LEPA':
-        output = ''
-        if len(lecture.strip()) > 0:
-            output += f"LECTURE: {lecture}\n"
-        if len(solution.strip()) > 0:
-            output += f"SOLUTION: {solution}\n"
-        output += '###\n'
-        output += f"ANSWER: {answer}."
-
-    input = input.replace("  ", " ").strip()
-    output = output.replace("  ", " ").strip()
-    if input.endswith("BECAUSE:"):
-        input = input.replace("BECAUSE:", "").strip()
-    if output.endswith("BECAUSE:"):
-        output = output.replace("BECAUSE:", "").strip()
-    return input, output
-
-
-def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True):
-
-    input_format, output_format = format.split("-")
-
-    ## Inputs
-    if input_format == "CQM":
-        input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
-    elif input_format == "QCM":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
-    # upper bound experiment
-    elif input_format == "QCML":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
-    elif input_format == "QCME":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
-    elif input_format == "QCMLE":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
-
-    elif input_format == "QCLM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
-    elif input_format == "QCEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
-    elif input_format == "QCLEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
-
-    # Outputs
-    if test_example:
-        output = "Answer:"
-    elif output_format == 'A':
-        output = f"Answer: The answer is {answer}."
-
-    elif output_format == 'AL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
-    elif output_format == 'AE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
-    elif output_format == 'ALE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
-    elif output_format == 'AEL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
-
-    elif output_format == 'LA':
-        output = f"Answer: {lecture} The answer is {answer}."
-    elif output_format == 'EA':
-        output = f"Answer: {solution} The answer is {answer}."
-    elif output_format == 'LEA':
-        output = f"Answer: {lecture} {solution} The answer is {answer}."
-    elif output_format == 'ELA':
-        output = f"Answer: {solution} {lecture} The answer is {answer}."
-
-    text = input + output
-    text = text.replace("  ", " ").strip()
-    if text.endswith("BECAUSE:"):
-        text = text.replace("BECAUSE:", "").strip()
-    return text
-
-
-
-def create_one_example_gpt4(format, question, context, choice, answer, lecture, solution, test_example=True):
-
-    input_format, output_format = format.split("-")
-
-    ## Inputs
-    if input_format == "CQM":
-        input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
-    elif input_format == "QCM":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
-    # upper bound experiment
-    elif input_format == "QCML":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
-    elif input_format == "QCME":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
-    elif input_format == "QCMLE":
-        input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
-
-    elif input_format == "QCLM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
-    elif input_format == "QCEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
-    elif input_format == "QCLEM":
-        input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
-
-    # Outputs
-    if test_example:
-        output = "Answer:"
-    elif output_format == 'A':
-        output = f"Answer: The answer is {answer}."
-
-    elif output_format == 'AL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
-    elif output_format == 'AE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
-    elif output_format == 'ALE':
-        output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
-    elif output_format == 'AEL':
-        output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
-
-    elif output_format == 'LA':
-        output = f"Answer: {lecture} The answer is {answer}."
-    elif output_format == 'EA':
-        output = f"Answer: {solution} The answer is {answer}."
-    elif output_format == 'LEA':
-        output = f"Answer: {lecture} {solution} The answer is {answer}."
-    elif output_format == 'ELA':
-        output = f"Answer: {solution} {lecture} The answer is {answer}."
-
-    input = input.replace("  ", " ").strip()
-    output = output.replace("  ", " ").strip()
-    if output.endswith("BECAUSE:"):
-        output = output.replace("BECAUSE:", "").strip()
-
-    user_prompt = {"role": "user", "content": f"Can you explain {input}?"}
-    assistant_prompt = {"role": "assistant", "content": f"{output}"}
-
-    return user_prompt, assistant_prompt
-
-
-def build_prompt_chatbot(problems, shot_qids, prompt_format, use_caption=False, options=["A", "B", "C", "D", "E"], is_test=False):
-    examples = {}
-
-    for qid in shot_qids:
-        question = get_question_text(problems[qid])
-        context = get_context_text(problems[qid], use_caption)
-        choice = get_choice_text(problems[qid], options)
-        answer = get_answer(problems[qid], options)
-        lecture = get_lecture_text(problems[qid]).replace('\\n', '\n')
-        solution = get_solution_text(problems[qid]).replace('\\n', '\n')
-
-        train_example = create_one_example_chatbot(prompt_format,
-                                           question,
-                                           context,
-                                           choice,
-                                           answer,
-                                           lecture,
-                                           solution,
-                                           test_example=is_test)
-        examples[qid] = train_example
-    return examples
-
-
-def build_prompt(problems, shot_qids, test_qid, args):
-
-    examples = []
-
-    # n-shot training examples
-    for qid in shot_qids:
-        question = get_question_text(problems[qid])
-        context = get_context_text(problems[qid], args.use_caption)
-        choice = get_choice_text(problems[qid], args.options)
-        answer = get_answer(problems[qid], args.options)
-        lecture = get_lecture_text(problems[qid])
-        solution = get_solution_text(problems[qid])
-
-        train_example = create_one_example(args.prompt_format,
-                                           question,
-                                           context,
-                                           choice,
-                                           answer,
-                                           lecture,
-                                           solution,
-                                           test_example=False)
-        examples.append(train_example)
-
-    # test example
-    question = get_question_text(problems[test_qid])
-    context = get_context_text(problems[test_qid], args.use_caption)
-    choice = get_choice_text(problems[test_qid], args.options)
-    answer = get_answer(problems[test_qid], args.options)
-    lecture = get_lecture_text(problems[test_qid])
-    solution = get_solution_text(problems[test_qid])
-
-    test_example = create_one_example(args.prompt_format,
-                                      question,
-                                      context,
-                                      choice,
-                                      answer,
-                                      lecture,
-                                      solution,
-                                      test_example=True)
-    examples.append(test_example)
-
-    # create the prompt input
-    prompt_input = '\n\n'.join(examples)
-
-    return prompt_input
-
-
-def build_prompt_gpt4(problems, shot_qids, test_qid, args):
-
-    prompt_array = [{"role": "system", "content": "You are a helpful assistant."}]
-
-    # n-shot training examples
-    for qid in shot_qids:
-        question = get_question_text(problems[qid])
-        context = get_context_text(problems[qid], args.use_caption)
-        choice = get_choice_text(problems[qid], args.options)
-        answer = get_answer(problems[qid], args.options)
-        lecture = get_lecture_text(problems[qid])
-        solution = get_solution_text(problems[qid])
-
-        user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format,
-                                           question,
-                                           context,
-                                           choice,
-                                           answer,
-                                           lecture,
-                                           solution,
-                                           test_example=False)
-        prompt_array.append(user_prompt)
-        prompt_array.append(assistant_prompt)
-
-    # test example
-    question = get_question_text(problems[test_qid])
-    context = get_context_text(problems[test_qid], args.use_caption)
-    choice = get_choice_text(problems[test_qid], args.options)
-    answer = get_answer(problems[test_qid], args.options)
-    lecture = get_lecture_text(problems[test_qid])
-    solution = get_solution_text(problems[test_qid])
-
-    user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format,
-                                      question,
-                                      context,
-                                      choice,
-                                      answer,
-                                      lecture,
-                                      solution,
-                                      test_example=True)
-    prompt_array.append(user_prompt)
-    prompt_array.append(assistant_prompt)
-
-    return prompt_array
\ No newline at end of file
diff --git a/spaces/ulysses115/ulysses115-pmvoice/modules.py b/spaces/ulysses115/ulysses115-pmvoice/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/ulysses115-pmvoice/modules.py
+++ /dev/null
@@ -1,390 +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
-
-
-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
diff --git a/spaces/ulysses115/vits-models/attentions.py b/spaces/ulysses115/vits-models/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/vits-models/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Being Cyrus Movie Download Kickass Torrent PATCHED.md b/spaces/usbethFlerru/sovits-modelsV2/example/Being Cyrus Movie Download Kickass Torrent PATCHED.md
deleted file mode 100644
index 088800395412e22eb7465f33a28fdf4fae8f3b3c..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Being Cyrus Movie Download Kickass Torrent PATCHED.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>KAT, which stands for Kickass Torrents, is a website that follows the same model as The Pirate Bay. It does not directly host any files that infringe on the copyrights of content creators, such as TV shows, movies, and music. Instead, it provides links to .torrent and .magnet files that allow users to download these unauthorized copies from other BitTorrent users who are sharing them. This way, KAT avoids the legal liability of hosting infringing content on its servers.</p>
-<h2>Being Cyrus Movie Download Kickass Torrent</h2><br /><p><b><b>Download Zip</b> ::: <a href="https://urlcod.com/2uyWi6">https://urlcod.com/2uyWi6</a></b></p><br /><br /> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/vishvara-sharda/book_recommending/app.py b/spaces/vishvara-sharda/book_recommending/app.py
deleted file mode 100644
index e7f972c265f0918dd780f080f8d0bb885efc6214..0000000000000000000000000000000000000000
--- a/spaces/vishvara-sharda/book_recommending/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import streamlit as st
-from Book_recommender import content_based_recommender, recommend, hybrid_recommendation
-
-# Streamlit UI
-
-st.title("Book Recommendation App")
-
-# Home page
-st.write("Welcome to the Book Recommendation App!")
-
-# Available routes
-routes = {
-    "hybrid_recommendation": "Hybrid Recommendation",
-    "content_recommendation": "Content-Based Recommendation",
-    "collaborative_recommendation": "Collaborative Recommendation",
-}
-
-selected_route = st.selectbox("Select a recommendation type:", list(routes.values()))
-
-if selected_route:
-    book_title = st.text_input("Enter a book title:", "BookName")
-    if st.button("Get Recommendations"):
-        endpoint = selected_route.lower().replace(" ", "_")
-        
-        # Call the appropriate function based on the selected route
-        if endpoint == "hybrid_recommendation":
-            recommended_books = hybrid_recommendation(book_title)
-        elif endpoint == "content_recommendation":
-            recommended_books = content_based_recommender(book_title)
-        elif endpoint == "collaborative_recommendation":
-            recommended_books = recommend(book_title)
-        
-        st.write("Recommended Books:")
-        if recommended_books:
-            st.json(recommended_books)
-        else: 
-            st.json({
-                "message": "Cannot recommend"
-            })
diff --git a/spaces/vivek-a666/Health_Forecast/README.md b/spaces/vivek-a666/Health_Forecast/README.md
deleted file mode 100644
index 18fbab88c270633e2d686da88cb0fae4c70304c2..0000000000000000000000000000000000000000
--- a/spaces/vivek-a666/Health_Forecast/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Health Forecast
-emoji: 🔥
-colorFrom: red
-colorTo: green
-sdk: streamlit
-sdk_version: 1.21.0
-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/vivien/causal-simulator/app.py b/spaces/vivien/causal-simulator/app.py
deleted file mode 100644
index c3812e35895e99a1b3f0987f92d69ee795a42554..0000000000000000000000000000000000000000
--- a/spaces/vivien/causal-simulator/app.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import numpy as np
-import pandas as pd
-from sklearn import linear_model
-import streamlit as st
-from st_cytoscape import cytoscape
-
-num_observations = 10_000
-generating_model = """
-Z <-- N
-C1 <-- N
-C21 <-- N
-C31 <-- N
-C33 <-- N
-C22 <-- C21 + N
-C32 <-- C31 + C33 + N
-X <-- Z + C1 + C21 + C31 + N
-M <-- X + N
-Y <-- M + C1 + C22 + C33 + N
-C4 <-- X + Y + N
-"""
-
-
-def rewrite(x):
-    if x == "N":
-        return "np.random.randn(num_observations)"
-    else:
-        return f'd["{x}"]'
-
-
-@st.cache_data
-def generate_data():
-    np.random.seed(seed=0)
-    d = {}
-    nodes = set()
-    edges = set()
-    for line in generating_model.split("\n"):
-        if " <-- " in line:
-            left, right = line.split(" <-- ")
-            right_terms = right.split(" + ")
-            nodes.add(left)
-            for node in right_terms:
-                if node != "N":
-                    nodes.add(node)
-                    edges.add((node, left))
-            formula = f"{rewrite(left)} = {' + '.join(list(map(rewrite, right_terms)))}"
-            exec(formula)
-    return pd.DataFrame.from_dict(d), nodes, edges
-
-
-df, nodes, edges = generate_data()
-elements = []
-for node in nodes:
-    elements.append(
-        {
-            "data": {"id": node},
-            "selected": node == "X",
-            "selectable": node not in ["X", "Y"],
-        }
-    )
-for edge in edges:
-    elements.append(
-        {
-            "data": {
-                "source": edge[0],
-                "target": edge[1],
-                "id": f"{edge[0]}-{edge[1]}",
-            },
-            "selectable": False,
-        }
-    )
-stylesheet = [
-    {"selector": "node", "style": {"label": "data(id)", "width": 20, "height": 20}},
-    {
-        "selector": "edge",
-        "style": {
-            "width": 2,
-            "curve-style": "bezier",
-            "target-arrow-shape": "triangle",
-        },
-    },
-]
-
-layout = {"name": "fcose", "animationDuration": 0}
-layout["alignmentConstraint"] = {"horizontal": [["Z", "X", "M", "Y"]]}
-layout["relativePlacementConstraint"] = [{"left": "X", "right": "Y"}]
-layout["relativePlacementConstraint"].append({"top": "C1", "bottom": "X"})
-layout["relativePlacementConstraint"].append({"top": "C21", "bottom": "X"})
-layout["relativePlacementConstraint"].append({"top": "X", "bottom": "C4"})
-layout["relativePlacementConstraint"].append({"top": "X", "bottom": "C31"})
-layout["nodeRepulsion"] = 50000
-
-st.sidebar.title("Causal simulator")
-
-st.sidebar.markdown(
-    """
-**Estimating the effect of a variable X** (e.g. vaccination status) **on another variable Y** (e.g. symptoms) **may require controlling for other variables** (e.g. age if age increases both access to the vaccine and the risks of getting sick).
-
-This demo illustrates that **the choice of the variables to control for critically depends on the causal relationships between the variables**.
-
-*Inspired by [The Book of Why](http://bayes.cs.ucla.edu/WHY/) by Judea Pearl and Dana Mackenzie and built by [Vivien](https://twitter.com/vivien000000) with [Streamlit](https://streamlit.io/), [Cytoscape.js](https://js.cytoscape.org/) and [scikit-learn](https://scikit-learn.org/stable/)*
-"""
-)
-
-st.subheader("Data generating process")
-
-st.markdown(
-    """
-10,000 observations have been generated for the variables mentioned in the causal graph below. The values for each variable were derived from the values of its parents in the causal graph as follows:
-"""
-)
-st.latex(
-    "U = \sum_{V \in \mathrm{\ Parents}(U)} V + \epsilon_U  \quad \mathrm{where} \quad \epsilon_U \overset{\mathrm{i.i.d.}}{\sim} \mathcal{N}(0, 1)"
-)
-st.subheader("Results of controlling for certain variables")
-
-st.markdown(
-    "We are using a linear regression to estimate the effect on Y of increasing X by one unit. In the causal graph below, **select the variables to include in the regression and see whether the regression coefficient for X matches the expected value (1, given the data generating process)**."
-)
-
-
-def add_smiley(x):
-    return x + (" 😀" if np.abs(float(x) - 1) < 0.1 else " 😨")
-
-
-col1, col2 = st.columns(2)
-order = ["X"] + sorted([n for n in nodes if n not in ["X", "Y"]])
-results = {v: "" for v in order}
-with col1:
-    selected = cytoscape(
-        elements,
-        stylesheet,
-        height="450px",
-        layout=layout,
-        selection_type="additive",
-        user_panning_enabled=False,
-        user_zooming_enabled=False,
-        key="graph",
-    )
-try:
-    selected_nodes = [n for n in order if n in selected["nodes"]]
-    regr = linear_model.LinearRegression()
-    regr.fit(df[selected_nodes], df[["Y"]])
-    for i in range(len(regr.feature_names_in_)):
-        results[regr.feature_names_in_[i]] = "%.3f" % regr.coef_[0, i]
-    results["X"] = add_smiley(results["X"])
-    with col2:
-        table = "<table style='margin: auto;'><tbody>"
-        table += "<tr><td colspan=2><b>Regression coefficients</b></td></tr>"
-        for k in order:
-            table += f"<tr><td>{k}</td><td>{results[k]}</td></tr>"
-        table += "</table></tbody>"
-        st.markdown(table, unsafe_allow_html=True)
-except TypeError:
-    pass
-
-
-@st.cache_data
-def compute_instrumental_variable():
-    regr = linear_model.LinearRegression()
-    regr2 = linear_model.LinearRegression()
-    regr.fit(df[["Z"]], df[["Y"]])
-    regr2.fit(df[["Z"]], df[["X"]])
-    return "%.3f" % (regr.coef_[0, 0] / regr2.coef_[0, 0])
-
-
-@st.cache_data
-def compute_front_door():
-    regr = linear_model.LinearRegression()
-    regr2 = linear_model.LinearRegression()
-    regr.fit(df[["M", "X"]], df[["Y"]])
-    regr2.fit(df[["X"]], df[["M"]])
-    index_m = [i for i in range(2) if regr.feature_names_in_[i] == "M"][0]
-    return "%.3f" % (regr.coef_[0, index_m] * regr2.coef_[0, 0])
-
-
-result_instrumental_variable = add_smiley(compute_instrumental_variable())
-result_front_door = add_smiley(compute_front_door())
-
-with st.expander("What variables should be controlled for?"):
-    st.markdown(
-        """The ***back-door criterion*** (*Book of Why*, chapter 4) provides sufficient conditions for variables to be adequate controls:
-- The non-causal paths between X and Y going through C1, C21 and C22 need to be blocked by controlling for **C1**, as well as for **C21 or C22** or both;
-- C4 and C32 are *colliders* (common consequences of their two respective neighbors). The 2 non-causal paths going through them are then blocked as long as C4 and C32 are not controlled for. Therefore, C4, C31, C32 and C33 should not be controlled for. However, if C32 is controlled for, C31 or C33 or both should be controlled for to block the non-causal path now open;
-- M is on a causal path from X to Y and should not be controlled for (so that the causal effect of X on Y is not masked);
-- Z is only a cause of X. Controlling for it is useless.
-"""
-    )
-with st.expander("Bonus: front-door criterion and instrumental variable"):
-    st.markdown(
-        f"""
-If using the back-door criterion as described above is impossible, we can deduce from the causal diagram that two alternative methods are available:
-- the use of the ***instrumental variable*** Z (result: {result_instrumental_variable})
-- the ***front-door criterion*** (*Book of Why*, chapter 5) with M as a mediator (result: {result_front_door})
-"""
-    )
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/box_iou_rotated.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/box_iou_rotated.py
deleted file mode 100644
index 2d78015e9c2a9e7a52859b4e18f84a9aa63481a0..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/ops/box_iou_rotated.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['box_iou_rotated'])
-
-
-def box_iou_rotated(bboxes1, bboxes2, mode='iou', aligned=False):
-    """Return intersection-over-union (Jaccard index) of boxes.
-
-    Both sets of boxes are expected to be in
-    (x_center, y_center, width, height, angle) format.
-
-    If ``aligned`` is ``False``, then calculate the ious between each bbox
-    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
-    bboxes1 and bboxes2.
-
-    Arguments:
-        boxes1 (Tensor): rotated bboxes 1. \
-            It has shape (N, 5), indicating (x, y, w, h, theta) for each row.
-            Note that theta is in radian.
-        boxes2 (Tensor): rotated bboxes 2. \
-            It has shape (M, 5), indicating (x, y, w, h, theta) for each row.
-            Note that theta is in radian.
-        mode (str): "iou" (intersection over union) or iof (intersection over
-            foreground).
-
-    Returns:
-        ious(Tensor): shape (N, M) if aligned == False else shape (N,)
-    """
-    assert mode in ['iou', 'iof']
-    mode_dict = {'iou': 0, 'iof': 1}
-    mode_flag = mode_dict[mode]
-    rows = bboxes1.size(0)
-    cols = bboxes2.size(0)
-    if aligned:
-        ious = bboxes1.new_zeros(rows)
-    else:
-        ious = bboxes1.new_zeros((rows * cols))
-    bboxes1 = bboxes1.contiguous()
-    bboxes2 = bboxes2.contiguous()
-    ext_module.box_iou_rotated(
-        bboxes1, bboxes2, ious, mode_flag=mode_flag, aligned=aligned)
-    if not aligned:
-        ious = ious.view(rows, cols)
-    return ious
diff --git a/spaces/wangrongsheng/CareLlama/style.css b/spaces/wangrongsheng/CareLlama/style.css
deleted file mode 100644
index 303c3d7ef3b06c42b211797cd2d5af9800589092..0000000000000000000000000000000000000000
--- a/spaces/wangrongsheng/CareLlama/style.css
+++ /dev/null
@@ -1,16 +0,0 @@
-h1 {
-  text-align: center;
-}
-
-#duplicate-button {
-  margin: auto;
-  color: white;
-  background: #1565c0;
-  border-radius: 100vh;
-}
-
-#component-0 {
-  max-width: 900px;
-  margin: auto;
-  padding-top: 1.5rem;
-}
diff --git a/spaces/webshop/amazon_shop/README.md b/spaces/webshop/amazon_shop/README.md
deleted file mode 100644
index 017276b41bf9abca2243219676fbaf965dc12579..0000000000000000000000000000000000000000
--- a/spaces/webshop/amazon_shop/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Amazon Shop
-emoji: 🔥
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.17
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
deleted file mode 100644
index b2b88e8c46f19b6db0933163e57ccdb51180f517..0000000000000000000000000000000000000000
--- a/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/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/whitphx/gradio-static-test/dist/assets/index-8ff47f0f.js b/spaces/whitphx/gradio-static-test/dist/assets/index-8ff47f0f.js
deleted file mode 100644
index 9835015064b8aa31b8ff51522b4145c0c73fa973..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/index-8ff47f0f.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as ee,i as le,s as te,ac as ne,I as E,f as pe,h as A,K as M,q as p,n as U,t as z,p as F,r as D,u as ye,e as I,m as L,o as N,U as ze,b as K,H as j,D as b,N as G,F as y,al as Be,O as ie,M as H,E as O,a1 as Ge,J as se,L as oe,y as je,a0 as Ae,j as De,k as Ee,z as Ce}from"../lite.js";import{g as Ie,B as Le}from"./Button-0391b19a.js";import{B as Ne}from"./BlockLabel-a3ec523d.js";import{E as qe}from"./Empty-91947ea3.js";import{n as V}from"./ModifyUpload.svelte_svelte_type_style_lang-ba6baa96.js";import{M as He}from"./ModifyUpload-ee7ccefb.js";/* empty css                                                    */import{I as ae}from"./Image-05614c6d.js";function P(l,t,e){const n=l.slice();return n[31]=t[e][0],n[32]=t[e][1],n[34]=e,n}function Q(l,t,e){const n=l.slice();return n[31]=t[e],n[35]=t,n[34]=e,n}function W(l){let t,e;return t=new Ne({props:{show_label:l[0],Icon:ae,label:l[1]||"Gallery",disable:typeof 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name: string } | [{ name: string }, string]>"},description:{payload:"list of objects, with filename and optional caption,"}});export{ll as Component,nl as document,tl as modes};
-//# sourceMappingURL=index-8ff47f0f.js.map
diff --git a/spaces/wuhuik/bingo/src/components/external-link.tsx b/spaces/wuhuik/bingo/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}
diff --git a/spaces/wwwwwwww2/bingo/src/lib/isomorphic/index.ts b/spaces/wwwwwwww2/bingo/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/wwwwwwww2/bingo/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
-  Object.assign(exportsModel, require('./browser').default)
-} else {
-  Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/wz758727829/ChuanhuChatGPT/chatgpt - macOS.command b/spaces/wz758727829/ChuanhuChatGPT/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/wz758727829/ChuanhuChatGPT/chatgpt - macOS.command	
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/app.py b/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/app.py
deleted file mode 100644
index c9bfb000af1af5ec0a745290b95431df58ad7a61..0000000000000000000000000000000000000000
--- a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/app.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import argparse
-import json
-import os
-import re
-import tempfile
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-import librosa
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import commons
-import utils
-import gradio as gr
-import gradio.utils as gr_utils
-import gradio.processing_utils as gr_processing_utils
-import ONNXVITS_infer
-import models
-from text import text_to_sequence, _clean_text
-from text.symbols import symbols
-from mel_processing import spectrogram_torch
-import psutil
-from datetime import datetime
-
-language_marks = {
-    "Japanese": "",
-    "日本語": "[JA]",
-    "简体中文": "[ZH]",
-    "English": "[EN]",
-    "Mix": "",
-}
-
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-
-def create_tts_fn(model, hps, speaker_ids):
-    def tts_fn(text, speaker, language, speed, is_symbol):
-        if limitation:
-            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
-            max_len = 150
-            if is_symbol:
-                max_len *= 3
-            if text_len > max_len:
-                return "Error: Text is too long", None
-        if language is not None:
-            text = language_marks[language] + text + language_marks[language]
-        speaker_id = speaker_ids[speaker]
-        stn_tst = get_text(text, hps, is_symbol)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0)
-            x_tst_lengths = LongTensor([stn_tst.size(0)])
-            sid = LongTensor([speaker_id])
-            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
-                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
-        del stn_tst, x_tst, x_tst_lengths, sid
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return tts_fn
-
-
-def create_vc_fn(model, hps, speaker_ids):
-    def vc_fn(original_speaker, target_speaker, input_audio):
-        if input_audio is None:
-            return "You need to upload an audio", None
-        sampling_rate, audio = input_audio
-        duration = audio.shape[0] / sampling_rate
-        if limitation and duration > 30:
-            return "Error: Audio is too long", None
-        original_speaker_id = speaker_ids[original_speaker]
-        target_speaker_id = speaker_ids[target_speaker]
-
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != hps.data.sampling_rate:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
-        with no_grad():
-            y = torch.FloatTensor(audio)
-            y = y.unsqueeze(0)
-            spec = spectrogram_torch(y, hps.data.filter_length,
-                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
-                                     center=False)
-            spec_lengths = LongTensor([spec.size(-1)])
-            sid_src = LongTensor([original_speaker_id])
-            sid_tgt = LongTensor([target_speaker_id])
-            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
-                0, 0].data.cpu().float().numpy()
-        del y, spec, spec_lengths, sid_src, sid_tgt
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return vc_fn
-
-
-def get_text(text, hps, is_symbol):
-    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm
-
-
-def create_to_symbol_fn(hps):
-    def to_symbol_fn(is_symbol_input, input_text, temp_text):
-        return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
-            else (temp_text, temp_text)
-
-    return to_symbol_fn
-
-
-models_tts = []
-models_vc = []
-models_info = [
-    {
-        "title": "Trilingual",
-        "languages": ['日本語', '简体中文', 'English', 'Mix'],
-        "description": """
-    This model is trained on a mix up of Umamusume, Genshin Impact, Sanoba Witch & VCTK voice data to learn multilanguage.
-    All characters can speak English, Chinese & Japanese.\n\n
-    To mix multiple languages in a single sentence, wrap the corresponding part with language tokens
-     ([JA] for Japanese, [ZH] for Chinese, [EN] for English), as shown in the examples.\n\n
-    这个模型在赛马娘，原神，魔女的夜宴以及VCTK数据集上混合训练以学习多种语言。
-    所有角色均可说中日英三语。\n\n
-    若需要在同一个句子中混合多种语言，使用相应的语言标记包裹句子。
-    （日语用[JA], 中文用[ZH], 英文用[EN]），参考Examples中的示例。
-    """,
-        "model_path": "./pretrained_models/G_trilingual.pth",
-        "config_path": "./configs/uma_trilingual.json",
-        "examples": [['你好，训练员先生，很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False],
-                     ['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English',
-                      1, False],
-                     ['授業中に出しだら，学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False],
-                     ['[JA]こんにちわ。[JA][ZH]你好！[ZH][EN]Hello![EN]', '綾地 寧々 Ayachi Nene (Sanoba Witch)', 'Mix', 1, False]],
-        "onnx_dir": "./ONNX_net/G_trilingual/"
-    },
-    {
-        "title": "Japanese",
-        "languages": ["Japanese"],
-        "description": """
-                       This model contains 87 characters from Umamusume: Pretty Derby, Japanese only.\n\n
-                       这个模型包含赛马娘的所有87名角色，只能合成日语。
-                       """,
-        "model_path": "./pretrained_models/G_jp.pth",
-        "config_path": "./configs/uma87.json",
-        "examples": [['お疲れ様です，トレーナーさん。', '无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['張り切っていこう！', '北部玄驹 Kitasan Black (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['授業中に出しだら，学校生活終わるですわ。', '目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['お帰りなさい，お兄様！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['私の処女をもらっでください！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False]],
-        "onnx_dir": "./ONNX_net/G_jp/"
-    },
-]
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    for info in models_info:
-        name = info['title']
-        lang = info['languages']
-        examples = info['examples']
-        config_path = info['config_path']
-        model_path = info['model_path']
-        description = info['description']
-        onnx_dir = info["onnx_dir"]
-        hps = utils.get_hparams_from_file(config_path)
-        model = ONNXVITS_infer.SynthesizerTrn(
-            len(hps.symbols),
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            ONNX_dir=onnx_dir,
-            **hps.model)
-        utils.load_checkpoint(model_path, model, None)
-        model.eval()
-        speaker_ids = hps.speakers
-        speakers = list(hps.speakers.keys())
-        models_tts.append((name, description, speakers, lang, examples,
-                           hps.symbols, create_tts_fn(model, hps, speaker_ids),
-                           create_to_symbol_fn(hps)))
-        models_vc.append((name, description, speakers, create_vc_fn(model, hps, speaker_ids)))
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n"
-                    "Including Japanese TTS & Trilingual TTS, speakers are all anime characters. \n\n包含一个纯日语TTS和一个中日英三语TTS模型，主要为二次元角色。\n\n"
-                    "If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n"
-                    "若有bug反馈或建议，请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n"
-                    )
-        with gr.Tabs():
-            with gr.TabItem("TTS"):
-                with gr.Tabs():
-                    for i, (name, description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(
-                            models_tts):
-                        with gr.TabItem(name):
-                            gr.Markdown(description)
-                            with gr.Row():
-                                with gr.Column():
-                                    textbox = gr.TextArea(label="Text",
-                                                          placeholder="Type your sentence here (Maximum 150 words)",
-                                                          value="こんにちわ。", elem_id=f"tts-input")
-                                    with gr.Accordion(label="Phoneme Input", open=False):
-                                        temp_text_var = gr.Variable()
-                                        symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                                        symbol_list = gr.Dataset(label="Symbol list", components=[textbox],
-                                                                 samples=[[x] for x in symbols],
-                                                                 elem_id=f"symbol-list")
-                                        symbol_list_json = gr.Json(value=symbols, visible=False)
-                                    symbol_input.change(to_symbol_fn,
-                                                        [symbol_input, textbox, temp_text_var],
-                                                        [textbox, temp_text_var])
-                                    symbol_list.click(None, [symbol_list, symbol_list_json], textbox,
-                                                      _js=f"""
-                                    (i, symbols, text) => {{
-                                        let root = document.querySelector("body > gradio-app");
-                                        if (root.shadowRoot != null)
-                                            root = root.shadowRoot;
-                                        let text_input = root.querySelector("#tts-input").querySelector("textarea");
-                                        let startPos = text_input.selectionStart;
-                                        let endPos = text_input.selectionEnd;
-                                        let oldTxt = text_input.value;
-                                        let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                        text_input.value = result;
-                                        let x = window.scrollX, y = window.scrollY;
-                                        text_input.focus();
-                                        text_input.selectionStart = startPos + symbols[i].length;
-                                        text_input.selectionEnd = startPos + symbols[i].length;
-                                        text_input.blur();
-                                        window.scrollTo(x, y);
-
-                                        text = text_input.value;
-
-                                        return text;
-                                    }}""")
-                                    # select character
-                                    char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
-                                    language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
-                                    duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
-                                                                label='速度 Speed')
-                                with gr.Column():
-                                    text_output = gr.Textbox(label="Message")
-                                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
-                                    btn = gr.Button("Generate!")
-                                    btn.click(tts_fn,
-                                              inputs=[textbox, char_dropdown, language_dropdown, duration_slider,
-                                                      symbol_input],
-                                              outputs=[text_output, audio_output])
-                            gr.Examples(
-                                examples=example,
-                                inputs=[textbox, char_dropdown, language_dropdown,
-                                        duration_slider, symbol_input],
-                                outputs=[text_output, audio_output],
-                                fn=tts_fn
-                            )
-    app.queue(concurrency_count=3).launch(show_api=False, share=args.share)
\ No newline at end of file
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/data/transforms.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/data/transforms.py
deleted file mode 100644
index 0c09ca0ed0a0316c3a3087f1ca137fdde0c48be1..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/data/transforms.py
+++ /dev/null
@@ -1,326 +0,0 @@
-from __future__ import division, print_function, absolute_import
-import math
-import random
-from collections import deque
-import torch
-from PIL import Image
-from torchvision.transforms import (
-    Resize, Compose, ToTensor, Normalize, ColorJitter, RandomHorizontalFlip
-)
-
-
-class Random2DTranslation(object):
-    """Randomly translates the input image with a probability.
-
-    Specifically, given a predefined shape (height, width), the input is first
-    resized with a factor of 1.125, leading to (height*1.125, width*1.125), then
-    a random crop is performed. Such operation is done with a probability.
-
-    Args:
-        height (int): target image height.
-        width (int): target image width.
-        p (float, optional): probability that this operation takes place.
-            Default is 0.5.
-        interpolation (int, optional): desired interpolation. Default is
-            ``PIL.Image.BILINEAR``
-    """
-
-    def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
-        self.height = height
-        self.width = width
-        self.p = p
-        self.interpolation = interpolation
-
-    def __call__(self, img):
-        if random.uniform(0, 1) > self.p:
-            return img.resize((self.width, self.height), self.interpolation)
-
-        new_width, new_height = int(round(self.width * 1.125)
-                                    ), int(round(self.height * 1.125))
-        resized_img = img.resize((new_width, new_height), self.interpolation)
-        x_maxrange = new_width - self.width
-        y_maxrange = new_height - self.height
-        x1 = int(round(random.uniform(0, x_maxrange)))
-        y1 = int(round(random.uniform(0, y_maxrange)))
-        croped_img = resized_img.crop(
-            (x1, y1, x1 + self.width, y1 + self.height)
-        )
-        return croped_img
-
-
-class RandomErasing(object):
-    """Randomly erases an image patch.
-
-    Origin: `<https://github.com/zhunzhong07/Random-Erasing>`_
-
-    Reference:
-        Zhong et al. Random Erasing Data Augmentation.
-
-    Args:
-        probability (float, optional): probability that this operation takes place.
-            Default is 0.5.
-        sl (float, optional): min erasing area.
-        sh (float, optional): max erasing area.
-        r1 (float, optional): min aspect ratio.
-        mean (list, optional): erasing value.
-    """
-
-    def __init__(
-        self,
-        probability=0.5,
-        sl=0.02,
-        sh=0.4,
-        r1=0.3,
-        mean=[0.4914, 0.4822, 0.4465]
-    ):
-        self.probability = probability
-        self.mean = mean
-        self.sl = sl
-        self.sh = sh
-        self.r1 = r1
-
-    def __call__(self, img):
-        if random.uniform(0, 1) > self.probability:
-            return img
-
-        for attempt in range(100):
-            area = img.size()[1] * img.size()[2]
-
-            target_area = random.uniform(self.sl, self.sh) * area
-            aspect_ratio = random.uniform(self.r1, 1 / self.r1)
-
-            h = int(round(math.sqrt(target_area * aspect_ratio)))
-            w = int(round(math.sqrt(target_area / aspect_ratio)))
-
-            if w < img.size()[2] and h < img.size()[1]:
-                x1 = random.randint(0, img.size()[1] - h)
-                y1 = random.randint(0, img.size()[2] - w)
-                if img.size()[0] == 3:
-                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
-                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
-                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
-                else:
-                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
-                return img
-
-        return img
-
-
-class ColorAugmentation(object):
-    """Randomly alters the intensities of RGB channels.
-
-    Reference:
-        Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural
-        Networks. NIPS 2012.
-
-    Args:
-        p (float, optional): probability that this operation takes place.
-            Default is 0.5.
-    """
-
-    def __init__(self, p=0.5):
-        self.p = p
-        self.eig_vec = torch.Tensor(
-            [
-                [0.4009, 0.7192, -0.5675],
-                [-0.8140, -0.0045, -0.5808],
-                [0.4203, -0.6948, -0.5836],
-            ]
-        )
-        self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])
-
-    def _check_input(self, tensor):
-        assert tensor.dim() == 3 and tensor.size(0) == 3
-
-    def __call__(self, tensor):
-        if random.uniform(0, 1) > self.p:
-            return tensor
-        alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
-        quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
-        tensor = tensor + quatity.view(3, 1, 1)
-        return tensor
-
-
-class RandomPatch(object):
-    """Random patch data augmentation.
-
-    There is a patch pool that stores randomly extracted pathces from person images.
-    
-    For each input image, RandomPatch
-        1) extracts a random patch and stores the patch in the patch pool;
-        2) randomly selects a patch from the patch pool and pastes it on the
-           input (at random position) to simulate occlusion.
-
-    Reference:
-        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
-        - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. TPAMI, 2021.
-    """
-
-    def __init__(
-        self,
-        prob_happen=0.5,
-        pool_capacity=50000,
-        min_sample_size=100,
-        patch_min_area=0.01,
-        patch_max_area=0.5,
-        patch_min_ratio=0.1,
-        prob_rotate=0.5,
-        prob_flip_leftright=0.5,
-    ):
-        self.prob_happen = prob_happen
-
-        self.patch_min_area = patch_min_area
-        self.patch_max_area = patch_max_area
-        self.patch_min_ratio = patch_min_ratio
-
-        self.prob_rotate = prob_rotate
-        self.prob_flip_leftright = prob_flip_leftright
-
-        self.patchpool = deque(maxlen=pool_capacity)
-        self.min_sample_size = min_sample_size
-
-    def generate_wh(self, W, H):
-        area = W * H
-        for attempt in range(100):
-            target_area = random.uniform(
-                self.patch_min_area, self.patch_max_area
-            ) * area
-            aspect_ratio = random.uniform(
-                self.patch_min_ratio, 1. / self.patch_min_ratio
-            )
-            h = int(round(math.sqrt(target_area * aspect_ratio)))
-            w = int(round(math.sqrt(target_area / aspect_ratio)))
-            if w < W and h < H:
-                return w, h
-        return None, None
-
-    def transform_patch(self, patch):
-        if random.uniform(0, 1) > self.prob_flip_leftright:
-            patch = patch.transpose(Image.FLIP_LEFT_RIGHT)
-        if random.uniform(0, 1) > self.prob_rotate:
-            patch = patch.rotate(random.randint(-10, 10))
-        return patch
-
-    def __call__(self, img):
-        W, H = img.size # original image size
-
-        # collect new patch
-        w, h = self.generate_wh(W, H)
-        if w is not None and h is not None:
-            x1 = random.randint(0, W - w)
-            y1 = random.randint(0, H - h)
-            new_patch = img.crop((x1, y1, x1 + w, y1 + h))
-            self.patchpool.append(new_patch)
-
-        if len(self.patchpool) < self.min_sample_size:
-            return img
-
-        if random.uniform(0, 1) > self.prob_happen:
-            return img
-
-        # paste a randomly selected patch on a random position
-        patch = random.sample(self.patchpool, 1)[0]
-        patchW, patchH = patch.size
-        x1 = random.randint(0, W - patchW)
-        y1 = random.randint(0, H - patchH)
-        patch = self.transform_patch(patch)
-        img.paste(patch, (x1, y1))
-
-        return img
-
-
-def build_transforms(
-    height,
-    width,
-    transforms='random_flip',
-    norm_mean=[0.485, 0.456, 0.406],
-    norm_std=[0.229, 0.224, 0.225],
-    **kwargs
-):
-    """Builds train and test transform functions.
-
-    Args:
-        height (int): target image height.
-        width (int): target image width.
-        transforms (str or list of str, optional): transformations applied to model training.
-            Default is 'random_flip'.
-        norm_mean (list or None, optional): normalization mean values. Default is ImageNet means.
-        norm_std (list or None, optional): normalization standard deviation values. Default is
-            ImageNet standard deviation values.
-    """
-    if transforms is None:
-        transforms = []
-
-    if isinstance(transforms, str):
-        transforms = [transforms]
-
-    if not isinstance(transforms, list):
-        raise ValueError(
-            'transforms must be a list of strings, but found to be {}'.format(
-                type(transforms)
-            )
-        )
-
-    if len(transforms) > 0:
-        transforms = [t.lower() for t in transforms]
-
-    if norm_mean is None or norm_std is None:
-        norm_mean = [0.485, 0.456, 0.406] # imagenet mean
-        norm_std = [0.229, 0.224, 0.225] # imagenet std
-    normalize = Normalize(mean=norm_mean, std=norm_std)
-
-    print('Building train transforms ...')
-    transform_tr = []
-
-    print('+ resize to {}x{}'.format(height, width))
-    transform_tr += [Resize((height, width))]
-
-    if 'random_flip' in transforms:
-        print('+ random flip')
-        transform_tr += [RandomHorizontalFlip()]
-
-    if 'random_crop' in transforms:
-        print(
-            '+ random crop (enlarge to {}x{} and '
-            'crop {}x{})'.format(
-                int(round(height * 1.125)), int(round(width * 1.125)), height,
-                width
-            )
-        )
-        transform_tr += [Random2DTranslation(height, width)]
-
-    if 'random_patch' in transforms:
-        print('+ random patch')
-        transform_tr += [RandomPatch()]
-
-    if 'color_jitter' in transforms:
-        print('+ color jitter')
-        transform_tr += [
-            ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)
-        ]
-
-    print('+ to torch tensor of range [0, 1]')
-    transform_tr += [ToTensor()]
-
-    print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
-    transform_tr += [normalize]
-
-    if 'random_erase' in transforms:
-        print('+ random erase')
-        transform_tr += [RandomErasing(mean=norm_mean)]
-
-    transform_tr = Compose(transform_tr)
-
-    print('Building test transforms ...')
-    print('+ resize to {}x{}'.format(height, width))
-    print('+ to torch tensor of range [0, 1]')
-    print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
-
-    transform_te = Compose([
-        Resize((height, width)),
-        ToTensor(),
-        normalize,
-    ])
-
-    return transform_tr, transform_te
diff --git a/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/kitti_mots.py b/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/kitti_mots.py
deleted file mode 100644
index 9e04d3c2b128ce0dc80c5180a40895ef1bc8dc25..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/kitti_mots.py
+++ /dev/null
@@ -1,426 +0,0 @@
-import os
-import csv
-import numpy as np
-from scipy.optimize import linear_sum_assignment
-from ._base_dataset import _BaseDataset
-from .. import utils
-from .. import _timing
-from ..utils import TrackEvalException
-
-
-class KittiMOTS(_BaseDataset):
-    """Dataset class for KITTI MOTS tracking"""
-
-    @staticmethod
-    def get_default_dataset_config():
-        """Default class config values"""
-        code_path = utils.get_code_path()
-        default_config = {
-            'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_mots_val'),  # Location of GT data
-            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'),  # Trackers location
-            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
-            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
-            'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']
-            'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val'
-            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
-            'PRINT_CONFIG': True,  # Whether to print current config
-            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
-            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
-            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
-            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER)
-            'SEQMAP_FILE': None,    # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)
-            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
-            'GT_LOC_FORMAT': '{gt_folder}/label_02/{seq}.txt',  # format of gt localization
-        }
-        return default_config
-
-    def __init__(self, config=None):
-        """Initialise dataset, checking that all required files are present"""
-        super().__init__()
-        # Fill non-given config values with defaults
-        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())
-        self.gt_fol = self.config['GT_FOLDER']
-        self.tracker_fol = self.config['TRACKERS_FOLDER']
-        self.split_to_eval = self.config['SPLIT_TO_EVAL']
-        self.should_classes_combine = False
-        self.use_super_categories = False
-        self.data_is_zipped = self.config['INPUT_AS_ZIP']
-
-        self.output_fol = self.config['OUTPUT_FOLDER']
-        if self.output_fol is None:
-            self.output_fol = self.tracker_fol
-
-        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
-        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']
-
-        # Get classes to eval
-        self.valid_classes = ['car', 'pedestrian']
-        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None
-                           for cls in self.config['CLASSES_TO_EVAL']]
-        if not all(self.class_list):
-            raise TrackEvalException('Attempted to evaluate an invalid class. '
-                                     'Only classes [car, pedestrian] are valid.')
-        self.class_name_to_class_id = {'car': '1', 'pedestrian': '2', 'ignore': '10'}
-
-        # Get sequences to eval and check gt files exist
-        self.seq_list, self.seq_lengths = self._get_seq_info()
-        if len(self.seq_list) < 1:
-            raise TrackEvalException('No sequences are selected to be evaluated.')
-
-        # Check gt files exist
-        for seq in self.seq_list:
-            if not self.data_is_zipped:
-                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
-                if not os.path.isfile(curr_file):
-                    print('GT file not found ' + curr_file)
-                    raise TrackEvalException('GT file not found for sequence: ' + seq)
-        if self.data_is_zipped:
-            curr_file = os.path.join(self.gt_fol, 'data.zip')
-            if not os.path.isfile(curr_file):
-                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))
-
-        # Get trackers to eval
-        if self.config['TRACKERS_TO_EVAL'] is None:
-            self.tracker_list = os.listdir(self.tracker_fol)
-        else:
-            self.tracker_list = self.config['TRACKERS_TO_EVAL']
-
-        if self.config['TRACKER_DISPLAY_NAMES'] is None:
-            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
-        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
-                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
-            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
-        else:
-            raise TrackEvalException('List of tracker files and tracker display names do not match.')
-
-        for tracker in self.tracker_list:
-            if self.data_is_zipped:
-                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
-                if not os.path.isfile(curr_file):
-                    print('Tracker file not found: ' + curr_file)
-                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))
-            else:
-                for seq in self.seq_list:
-                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
-                    if not os.path.isfile(curr_file):
-                        print('Tracker file not found: ' + curr_file)
-                        raise TrackEvalException(
-                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
-                                curr_file))
-
-    def get_display_name(self, tracker):
-        return self.tracker_to_disp[tracker]
-
-    def _get_seq_info(self):
-        seq_list = []
-        seq_lengths = {}
-        seqmap_name = 'evaluate_mots.seqmap.' + self.config['SPLIT_TO_EVAL']
-
-        if self.config["SEQ_INFO"]:
-            seq_list = list(self.config["SEQ_INFO"].keys())
-            seq_lengths = self.config["SEQ_INFO"]
-        else:
-            if self.config["SEQMAP_FILE"]:
-                seqmap_file = self.config["SEQMAP_FILE"]
-            else:
-                if self.config["SEQMAP_FOLDER"] is None:
-                    seqmap_file = os.path.join(self.config['GT_FOLDER'], seqmap_name)
-                else:
-                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], seqmap_name)
-            if not os.path.isfile(seqmap_file):
-                print('no seqmap found: ' + seqmap_file)
-                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
-            with open(seqmap_file) as fp:
-                reader = csv.reader(fp)
-                for i, _ in enumerate(reader):
-                    dialect = csv.Sniffer().sniff(fp.read(1024))
-                    fp.seek(0)
-                    reader = csv.reader(fp, dialect)
-                    for row in reader:
-                        if len(row) >= 4:
-                            seq = "%04d" % int(row[0])
-                            seq_list.append(seq)
-                            seq_lengths[seq] = int(row[3]) + 1
-        return seq_list, seq_lengths
-
-    def _load_raw_file(self, tracker, seq, is_gt):
-        """Load a file (gt or tracker) in the KITTI MOTS format
-
-        If is_gt, this returns a dict which contains the fields:
-        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
-        [gt_dets]: list (for each timestep) of lists of detections.
-        [gt_ignore_region]: list (for each timestep) of masks for the ignore regions
-
-        if not is_gt, this returns a dict which contains the fields:
-        [tracker_ids, tracker_classes] : list (for each timestep) of 1D NDArrays (for each det).
-        [tracker_dets]: list (for each timestep) of lists of detections.
-        """
-
-        # Only loaded when run to reduce minimum requirements
-        from pycocotools import mask as mask_utils
-
-        # File location
-        if self.data_is_zipped:
-            if is_gt:
-                zip_file = os.path.join(self.gt_fol, 'data.zip')
-            else:
-                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
-            file = seq + '.txt'
-        else:
-            zip_file = None
-            if is_gt:
-                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
-            else:
-                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
-
-        # Ignore regions
-        if is_gt:
-            crowd_ignore_filter = {2: ['10']}
-        else:
-            crowd_ignore_filter = None
-
-        # Load raw data from text file
-        read_data, ignore_data = self._load_simple_text_file(file, crowd_ignore_filter=crowd_ignore_filter,
-                                                             is_zipped=self.data_is_zipped, zip_file=zip_file,
-                                                             force_delimiters=' ')
-
-        # Convert data to required format
-        num_timesteps = self.seq_lengths[seq]
-        data_keys = ['ids', 'classes', 'dets']
-        if is_gt:
-            data_keys += ['gt_ignore_region']
-        raw_data = {key: [None] * num_timesteps for key in data_keys}
-
-        # Check for any extra time keys
-        current_time_keys = [str(t) for t in range(num_timesteps)]
-        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]
-        if len(extra_time_keys) > 0:
-            if is_gt:
-                text = 'Ground-truth'
-            else:
-                text = 'Tracking'
-            raise TrackEvalException(
-                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
-                    [str(x) + ', ' for x in extra_time_keys]))
-
-        for t in range(num_timesteps):
-            time_key = str(t)
-            # list to collect all masks of a timestep to check for overlapping areas
-            all_masks = []
-            if time_key in read_data.keys():
-                try:
-                    raw_data['dets'][t] = [{'size': [int(region[3]), int(region[4])],
-                                            'counts': region[5].encode(encoding='UTF-8')}
-                                           for region in read_data[time_key]]
-                    raw_data['ids'][t] = np.atleast_1d([region[1] for region in read_data[time_key]]).astype(int)
-                    raw_data['classes'][t] = np.atleast_1d([region[2] for region in read_data[time_key]]).astype(int)
-                    all_masks += raw_data['dets'][t]
-                except IndexError:
-                    self._raise_index_error(is_gt, tracker, seq)
-                except ValueError:
-                    self._raise_value_error(is_gt, tracker, seq)
-            else:
-                raw_data['dets'][t] = []
-                raw_data['ids'][t] = np.empty(0).astype(int)
-                raw_data['classes'][t] = np.empty(0).astype(int)
-            if is_gt:
-                if time_key in ignore_data.keys():
-                    try:
-                        time_ignore = [{'size': [int(region[3]), int(region[4])],
-                                        'counts': region[5].encode(encoding='UTF-8')}
-                                       for region in ignore_data[time_key]]
-                        raw_data['gt_ignore_region'][t] = mask_utils.merge([mask for mask in time_ignore],
-                                                                           intersect=False)
-                        all_masks += [raw_data['gt_ignore_region'][t]]
-                    except IndexError:
-                        self._raise_index_error(is_gt, tracker, seq)
-                    except ValueError:
-                        self._raise_value_error(is_gt, tracker, seq)
-                else:
-                    raw_data['gt_ignore_region'][t] = mask_utils.merge([], intersect=False)
-
-            # check for overlapping masks
-            if all_masks:
-                masks_merged = all_masks[0]
-                for mask in all_masks[1:]:
-                    if mask_utils.area(mask_utils.merge([masks_merged, mask], intersect=True)) != 0.0:
-                        raise TrackEvalException(
-                            'Tracker has overlapping masks. Tracker: ' + tracker + ' Seq: ' + seq + ' Timestep: ' + str(
-                                t))
-                    masks_merged = mask_utils.merge([masks_merged, mask], intersect=False)
-
-        if is_gt:
-            key_map = {'ids': 'gt_ids',
-                       'classes': 'gt_classes',
-                       'dets': 'gt_dets'}
-        else:
-            key_map = {'ids': 'tracker_ids',
-                       'classes': 'tracker_classes',
-                       'dets': 'tracker_dets'}
-        for k, v in key_map.items():
-            raw_data[v] = raw_data.pop(k)
-        raw_data["num_timesteps"] = num_timesteps
-        raw_data['seq'] = seq
-        return raw_data
-
-    @_timing.time
-    def get_preprocessed_seq_data(self, raw_data, cls):
-        """ Preprocess data for a single sequence for a single class ready for evaluation.
-        Inputs:
-             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
-             - cls is the class to be evaluated.
-        Outputs:
-             - data is a dict containing all of the information that metrics need to perform evaluation.
-                It contains the following fields:
-                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
-                    [gt_ids, tracker_ids]: list (for each timestep) of 1D NDArrays (for each det).
-                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detection masks.
-                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
-        Notes:
-            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
-                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
-                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
-                    distractor class, or otherwise marked as to be removed.
-                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
-                    other criteria (e.g. are too small).
-                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
-            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
-                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
-                unique within each timestep.
-
-        KITTI MOTS:
-            In KITTI MOTS, the 4 preproc steps are as follow:
-                1) There are two classes (car and pedestrian) which are evaluated separately.
-                2) There are no ground truth detections marked as to be removed/distractor classes.
-                    Therefore also no matched tracker detections are removed.
-                3) Ignore regions are used to remove unmatched detections (at least 50% overlap with ignore region).
-                4) There are no ground truth detections (e.g. those of distractor classes) to be removed.
-        """
-        # Check that input data has unique ids
-        self._check_unique_ids(raw_data)
-
-        cls_id = int(self.class_name_to_class_id[cls])
-
-        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']
-        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
-        unique_gt_ids = []
-        unique_tracker_ids = []
-        num_gt_dets = 0
-        num_tracker_dets = 0
-        for t in range(raw_data['num_timesteps']):
-
-            # Only extract relevant dets for this class for preproc and eval (cls)
-            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)
-            gt_class_mask = gt_class_mask.astype(np.bool)
-            gt_ids = raw_data['gt_ids'][t][gt_class_mask]
-            gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]
-
-            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)
-            tracker_class_mask = tracker_class_mask.astype(np.bool)
-            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]
-            tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if
-                            tracker_class_mask[ind]]
-            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]
-
-            # Match tracker and gt dets (with hungarian algorithm)
-            unmatched_indices = np.arange(tracker_ids.shape[0])
-            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:
-                matching_scores = similarity_scores.copy()
-                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = -10000
-                match_rows, match_cols = linear_sum_assignment(-matching_scores)
-                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
-                match_cols = match_cols[actually_matched_mask]
-
-                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)
-
-            # For unmatched tracker dets, remove those that are greater than 50% within a crowd ignore region.
-            unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if i in unmatched_indices]
-            ignore_region = raw_data['gt_ignore_region'][t]
-            intersection_with_ignore_region = self._calculate_mask_ious(unmatched_tracker_dets, [ignore_region],
-                                                                        is_encoded=True, do_ioa=True)
-            is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)
-
-            # Apply preprocessing to remove unwanted tracker dets.
-            to_remove_tracker = unmatched_indices[is_within_ignore_region]
-            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
-            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
-            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)
-
-            # Keep all ground truth detections
-            data['gt_ids'][t] = gt_ids
-            data['gt_dets'][t] = gt_dets
-            data['similarity_scores'][t] = similarity_scores
-
-            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
-            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
-            num_tracker_dets += len(data['tracker_ids'][t])
-            num_gt_dets += len(data['gt_ids'][t])
-
-        # Re-label IDs such that there are no empty IDs
-        if len(unique_gt_ids) > 0:
-            unique_gt_ids = np.unique(unique_gt_ids)
-            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
-            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
-            for t in range(raw_data['num_timesteps']):
-                if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
-        if len(unique_tracker_ids) > 0:
-            unique_tracker_ids = np.unique(unique_tracker_ids)
-            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
-            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
-            for t in range(raw_data['num_timesteps']):
-                if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
-
-        # Record overview statistics.
-        data['num_tracker_dets'] = num_tracker_dets
-        data['num_gt_dets'] = num_gt_dets
-        data['num_tracker_ids'] = len(unique_tracker_ids)
-        data['num_gt_ids'] = len(unique_gt_ids)
-        data['num_timesteps'] = raw_data['num_timesteps']
-        data['seq'] = raw_data['seq']
-        data['cls'] = cls
-
-        # Ensure again that ids are unique per timestep after preproc.
-        self._check_unique_ids(data, after_preproc=True)
-
-        return data
-
-    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
-        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)
-        return similarity_scores
-
-    @staticmethod
-    def _raise_index_error(is_gt, tracker, seq):
-        """
-        Auxiliary method to raise an evaluation error in case of an index error while reading files.
-        :param is_gt: whether gt or tracker data is read
-        :param tracker: the name of the tracker
-        :param seq: the name of the seq
-        :return: None
-        """
-        if is_gt:
-            err = 'Cannot load gt data from sequence %s, because there are not enough ' \
-                  'columns in the data.' % seq
-            raise TrackEvalException(err)
-        else:
-            err = 'Cannot load tracker data from tracker %s, sequence %s, because there are not enough ' \
-                  'columns in the data.' % (tracker, seq)
-            raise TrackEvalException(err)
-
-    @staticmethod
-    def _raise_value_error(is_gt, tracker, seq):
-        """
-        Auxiliary method to raise an evaluation error in case of an value error while reading files.
-        :param is_gt: whether gt or tracker data is read
-        :param tracker: the name of the tracker
-        :param seq: the name of the seq
-        :return: None
-        """
-        if is_gt:
-            raise TrackEvalException(
-                'GT data for sequence %s cannot be converted to the right format. Is data corrupted?' % seq)
-        else:
-            raise TrackEvalException(
-                'Tracking data from tracker %s, sequence %s cannot be converted to the right format. '
-                'Is data corrupted?' % (tracker, seq))
diff --git "a/spaces/xwsm/gpt/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" "b/spaces/xwsm/gpt/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
deleted file mode 100644
index 505086455af8d2676055ab084cf97058b954c7d5..0000000000000000000000000000000000000000
--- "a/spaces/xwsm/gpt/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
+++ /dev/null
@@ -1,112 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
-
-    ############################## <第 0 步，切割PDF> ##################################
-    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-    # 的长度必须小于 2500 个 Token
-    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-    
-    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-    final_results = []
-    final_results.append(paper_meta)
-
-    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-    iteration_results = []
-    last_iteration_result = paper_meta  # 初始值是摘要
-    MAX_WORD_TOTAL = 4096
-    n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
-    for i in range(n_fragment):
-        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           llm_kwargs, chatbot, 
-                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                           sys_prompt="Extract the main idea of this section."  # 提示
-                                                                        ) 
-        iteration_results.append(gpt_say)
-        last_iteration_result = gpt_say
-
-    ############################## <第 3 步，整理history> ##################################
-    final_results.extend(iteration_results)
-    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
-    # 接下来两句话只显示在界面上，不起实际作用
-    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
-    chatbot.append([i_say_show_user, gpt_say])
-
-    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
-    _, final_results = input_clipping("", final_results, max_token_limit=3200)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-
-@CatchException
-def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    txt = file_manifest[0]
-    # 开始正式执行任务
-    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git "a/spaces/xxccc/gpt-academic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py" "b/spaces/xxccc/gpt-academic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
deleted file mode 100644
index 72ffe6b1a8f2a59a3c5c364e30dfb4949bd6a929..0000000000000000000000000000000000000000
--- "a/spaces/xxccc/gpt-academic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,67 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/ybelkada/interfacegan_pp/dnnlib/util.py b/spaces/ybelkada/interfacegan_pp/dnnlib/util.py
deleted file mode 100644
index 6bbdf3bd8fe1c138cd969d37dcc52190b45c4c16..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/interfacegan_pp/dnnlib/util.py
+++ /dev/null
@@ -1,491 +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.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-
-
-# Cache directories
-# ------------------------------------------------------------------------------------------
-
-_dnnlib_cache_dir = None
-
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def format_time_brief(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m".format(s // (60 * 60), (s // 60) % 60)
-    else:
-        return "{0}d {1:02}h".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name) # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-
-    assert is_url(url)
-
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError("Google Drive download quota exceeded -- please try again later")
-
-                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file) # atomic
-        if return_filename:
-            return cache_file
-
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)
diff --git a/spaces/yderre-aubay/midi-player-demo/src/components/Button.tsx b/spaces/yderre-aubay/midi-player-demo/src/components/Button.tsx
deleted file mode 100644
index cedf2f4905c0b2f75c39419de6f17a4c95108a49..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/components/Button.tsx
+++ /dev/null
@@ -1,32 +0,0 @@
-import styled from "@emotion/styled"
-import Color from "color"
-
-export const Button = styled.button`
-  background: transparent;
-  border: none;
-  border-radius: 0.2rem;
-  color: ${({ theme }) => theme.textColor};
-  padding: 0.5rem 1rem;
-  cursor: pointer;
-  height: 2rem;
-  outline: none;
-
-  &:hover {
-    background: ${({ theme }) => theme.highlightColor};
-  }
-  &:active {
-    background: ${({ theme }) =>
-      Color(theme.secondaryBackgroundColor).lighten(0.1).hex()};
-  }
-`
-
-export const PrimaryButton = styled(Button)`
-  background: ${({ theme }) => theme.themeColor};
-
-  &:hover {
-    background: ${({ theme }) => Color(theme.themeColor).darken(0.1).hex()};
-  }
-  &:active {
-    background: ${({ theme }) => Color(theme.themeColor).darken(0.2).hex()};
-  }
-`
diff --git a/spaces/ygangang/VToonify/README.md b/spaces/ygangang/VToonify/README.md
deleted file mode 100644
index 40ded2ce4dc80cc8764c8fee50476d44bee383b6..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: VToonify
-emoji: 👨‍🎨
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: other
-duplicated_from: PKUWilliamYang/VToonify
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/protein.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/protein.py
deleted file mode 100644
index 32e01571715c1b0c806e9cb764b2dec8aaab6068..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/protein.py
+++ /dev/null
@@ -1,329 +0,0 @@
-# Copyright 2021 AlQuraishi Laboratory
-# 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.
-
-"""Protein data type."""
-import dataclasses
-import re
-import string
-from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
-
-import numpy as np
-
-from . import residue_constants
-
-
-FeatureDict = Mapping[str, np.ndarray]
-ModelOutput = Mapping[str, Any]  # Is a nested dict.
-PICO_TO_ANGSTROM = 0.01
-
-
-@dataclasses.dataclass(frozen=True)
-class Protein:
-    """Protein structure representation."""
-
-    # Cartesian coordinates of atoms in angstroms. The atom types correspond to
-    # residue_constants.atom_types, i.e. the first three are N, CA, CB.
-    atom_positions: np.ndarray  # [num_res, num_atom_type, 3]
-
-    # Amino-acid type for each residue represented as an integer between 0 and
-    # 20, where 20 is 'X'.
-    aatype: np.ndarray  # [num_res]
-
-    # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
-    # is present and 0.0 if not. This should be used for loss masking.
-    atom_mask: np.ndarray  # [num_res, num_atom_type]
-
-    # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
-    residue_index: np.ndarray  # [num_res]
-
-    # B-factors, or temperature factors, of each residue (in sq. angstroms units),
-    # representing the displacement of the residue from its ground truth mean
-    # value.
-    b_factors: np.ndarray  # [num_res, num_atom_type]
-
-    # Chain indices for multi-chain predictions
-    chain_index: Optional[np.ndarray] = None
-
-    # Optional remark about the protein. Included as a comment in output PDB
-    # files
-    remark: Optional[str] = None
-
-    # Templates used to generate this protein (prediction-only)
-    parents: Optional[Sequence[str]] = None
-
-    # Chain corresponding to each parent
-    parents_chain_index: Optional[Sequence[int]] = None
-
-
-def from_proteinnet_string(proteinnet_str: str) -> Protein:
-    tag_re = r"(\[[A-Z]+\]\n)"
-    tags: List[str] = [tag.strip() for tag in re.split(tag_re, proteinnet_str) if len(tag) > 0]
-    groups: Iterator[Tuple[str, List[str]]] = zip(tags[0::2], [l.split("\n") for l in tags[1::2]])
-
-    atoms: List[str] = ["N", "CA", "C"]
-    aatype = None
-    atom_positions = None
-    atom_mask = None
-    for g in groups:
-        if "[PRIMARY]" == g[0]:
-            seq = g[1][0].strip()
-            for i in range(len(seq)):
-                if seq[i] not in residue_constants.restypes:
-                    seq[i] = "X"  # FIXME: strings are immutable
-            aatype = np.array(
-                [residue_constants.restype_order.get(res_symbol, residue_constants.restype_num) for res_symbol in seq]
-            )
-        elif "[TERTIARY]" == g[0]:
-            tertiary: List[List[float]] = []
-            for axis in range(3):
-                tertiary.append(list(map(float, g[1][axis].split())))
-            tertiary_np = np.array(tertiary)
-            atom_positions = np.zeros((len(tertiary[0]) // 3, residue_constants.atom_type_num, 3)).astype(np.float32)
-            for i, atom in enumerate(atoms):
-                atom_positions[:, residue_constants.atom_order[atom], :] = np.transpose(tertiary_np[:, i::3])
-            atom_positions *= PICO_TO_ANGSTROM
-        elif "[MASK]" == g[0]:
-            mask = np.array(list(map({"-": 0, "+": 1}.get, g[1][0].strip())))
-            atom_mask = np.zeros(
-                (
-                    len(mask),
-                    residue_constants.atom_type_num,
-                )
-            ).astype(np.float32)
-            for i, atom in enumerate(atoms):
-                atom_mask[:, residue_constants.atom_order[atom]] = 1
-            atom_mask *= mask[..., None]
-
-    assert aatype is not None
-
-    return Protein(
-        atom_positions=atom_positions,
-        atom_mask=atom_mask,
-        aatype=aatype,
-        residue_index=np.arange(len(aatype)),
-        b_factors=None,
-    )
-
-
-def get_pdb_headers(prot: Protein, chain_id: int = 0) -> List[str]:
-    pdb_headers: List[str] = []
-
-    remark = prot.remark
-    if remark is not None:
-        pdb_headers.append(f"REMARK {remark}")
-
-    parents = prot.parents
-    parents_chain_index = prot.parents_chain_index
-    if parents is not None and parents_chain_index is not None:
-        parents = [p for i, p in zip(parents_chain_index, parents) if i == chain_id]
-
-    if parents is None or len(parents) == 0:
-        parents = ["N/A"]
-
-    pdb_headers.append(f"PARENT {' '.join(parents)}")
-
-    return pdb_headers
-
-
-def add_pdb_headers(prot: Protein, pdb_str: str) -> str:
-    """Add pdb headers to an existing PDB string. Useful during multi-chain
-    recycling
-    """
-    out_pdb_lines: List[str] = []
-    lines = pdb_str.split("\n")
-
-    remark = prot.remark
-    if remark is not None:
-        out_pdb_lines.append(f"REMARK {remark}")
-
-    parents_per_chain: List[List[str]]
-    if prot.parents is not None and len(prot.parents) > 0:
-        parents_per_chain = []
-        if prot.parents_chain_index is not None:
-            parent_dict: Dict[str, List[str]] = {}
-            for p, i in zip(prot.parents, prot.parents_chain_index):
-                parent_dict.setdefault(str(i), [])
-                parent_dict[str(i)].append(p)
-
-            max_idx = max([int(chain_idx) for chain_idx in parent_dict])
-            for i in range(max_idx + 1):
-                chain_parents = parent_dict.get(str(i), ["N/A"])
-                parents_per_chain.append(chain_parents)
-        else:
-            parents_per_chain.append(list(prot.parents))
-    else:
-        parents_per_chain = [["N/A"]]
-
-    def make_parent_line(p: Sequence[str]) -> str:
-        return f"PARENT {' '.join(p)}"
-
-    out_pdb_lines.append(make_parent_line(parents_per_chain[0]))
-
-    chain_counter = 0
-    for i, l in enumerate(lines):
-        if "PARENT" not in l and "REMARK" not in l:
-            out_pdb_lines.append(l)
-        if "TER" in l and "END" not in lines[i + 1]:
-            chain_counter += 1
-            if not chain_counter >= len(parents_per_chain):
-                chain_parents = parents_per_chain[chain_counter]
-            else:
-                chain_parents = ["N/A"]
-
-            out_pdb_lines.append(make_parent_line(chain_parents))
-
-    return "\n".join(out_pdb_lines)
-
-
-def to_pdb(prot: Protein) -> str:
-    """Converts a `Protein` instance to a PDB string.
-
-    Args:
-      prot: The protein to convert to PDB.
-
-    Returns:
-      PDB string.
-    """
-    restypes = residue_constants.restypes + ["X"]
-
-    def res_1to3(r: int) -> str:
-        return residue_constants.restype_1to3.get(restypes[r], "UNK")
-
-    atom_types = residue_constants.atom_types
-
-    pdb_lines: List[str] = []
-
-    atom_mask = prot.atom_mask
-    aatype = prot.aatype
-    atom_positions = prot.atom_positions
-    residue_index = prot.residue_index.astype(np.int32)
-    b_factors = prot.b_factors
-    chain_index = prot.chain_index
-
-    if np.any(aatype > residue_constants.restype_num):
-        raise ValueError("Invalid aatypes.")
-
-    headers = get_pdb_headers(prot)
-    if len(headers) > 0:
-        pdb_lines.extend(headers)
-
-    n = aatype.shape[0]
-    atom_index = 1
-    prev_chain_index = 0
-    chain_tags = string.ascii_uppercase
-    chain_tag = None
-    # Add all atom sites.
-    for i in range(n):
-        res_name_3 = res_1to3(aatype[i])
-        for atom_name, pos, mask, b_factor in zip(atom_types, atom_positions[i], atom_mask[i], b_factors[i]):
-            if mask < 0.5:
-                continue
-
-            record_type = "ATOM"
-            name = atom_name if len(atom_name) == 4 else f" {atom_name}"
-            alt_loc = ""
-            insertion_code = ""
-            occupancy = 1.00
-            element = atom_name[0]  # Protein supports only C, N, O, S, this works.
-            charge = ""
-
-            chain_tag = "A"
-            if chain_index is not None:
-                chain_tag = chain_tags[chain_index[i]]
-
-            # PDB is a columnar format, every space matters here!
-            atom_line = (
-                f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
-                f"{res_name_3:>3} {chain_tag:>1}"
-                f"{residue_index[i]:>4}{insertion_code:>1}   "
-                f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
-                f"{occupancy:>6.2f}{b_factor:>6.2f}          "
-                f"{element:>2}{charge:>2}"
-            )
-            pdb_lines.append(atom_line)
-            atom_index += 1
-
-        should_terminate = i == n - 1
-        if chain_index is not None:
-            if i != n - 1 and chain_index[i + 1] != prev_chain_index:
-                should_terminate = True
-                prev_chain_index = chain_index[i + 1]
-
-        if should_terminate:
-            # Close the chain.
-            chain_end = "TER"
-            chain_termination_line = (
-                f"{chain_end:<6}{atom_index:>5}      {res_1to3(aatype[i]):>3} {chain_tag:>1}{residue_index[i]:>4}"
-            )
-            pdb_lines.append(chain_termination_line)
-            atom_index += 1
-
-            if i != n - 1:
-                # "prev" is a misnomer here. This happens at the beginning of
-                # each new chain.
-                pdb_lines.extend(get_pdb_headers(prot, prev_chain_index))
-
-    pdb_lines.append("END")
-    pdb_lines.append("")
-    return "\n".join(pdb_lines)
-
-
-def ideal_atom_mask(prot: Protein) -> np.ndarray:
-    """Computes an ideal atom mask.
-
-    `Protein.atom_mask` typically is defined according to the atoms that are reported in the PDB. This function
-    computes a mask according to heavy atoms that should be present in the given sequence of amino acids.
-
-    Args:
-      prot: `Protein` whose fields are `numpy.ndarray` objects.
-
-    Returns:
-      An ideal atom mask.
-    """
-    return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
-
-
-def from_prediction(
-    features: FeatureDict,
-    result: ModelOutput,
-    b_factors: Optional[np.ndarray] = None,
-    chain_index: Optional[np.ndarray] = None,
-    remark: Optional[str] = None,
-    parents: Optional[Sequence[str]] = None,
-    parents_chain_index: Optional[Sequence[int]] = None,
-) -> Protein:
-    """Assembles a protein from a prediction.
-
-    Args:
-      features: Dictionary holding model inputs.
-      result: Dictionary holding model outputs.
-      b_factors: (Optional) B-factors to use for the protein.
-      chain_index: (Optional) Chain indices for multi-chain predictions
-      remark: (Optional) Remark about the prediction
-      parents: (Optional) List of template names
-    Returns:
-      A protein instance.
-    """
-    return Protein(
-        aatype=features["aatype"],
-        atom_positions=result["final_atom_positions"],
-        atom_mask=result["final_atom_mask"],
-        residue_index=features["residue_index"] + 1,
-        b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"]),
-        chain_index=chain_index,
-        remark=remark,
-        parents=parents,
-        parents_chain_index=parents_chain_index,
-    )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/convert_groupvit_nvlab_to_hf.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/convert_groupvit_nvlab_to_hf.py
deleted file mode 100644
index 059f10f6129bee62bd62a2c0d75fd1be555d6409..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/groupvit/convert_groupvit_nvlab_to_hf.py
+++ /dev/null
@@ -1,217 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. 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.
-
-"""
-Convert GroupViT checkpoints from the original repository.
-
-URL: https://github.com/NVlabs/GroupViT
-"""
-
-import argparse
-
-import requests
-import torch
-from PIL import Image
-
-from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
-
-
-def rename_key(name):
-    # vision encoder
-    if "img_encoder.pos_embed" in name:
-        name = name.replace("img_encoder.pos_embed", "vision_model.embeddings.position_embeddings")
-    if "img_encoder.patch_embed.proj" in name:
-        name = name.replace("img_encoder.patch_embed.proj", "vision_model.embeddings.patch_embeddings.projection")
-    if "img_encoder.patch_embed.norm" in name:
-        name = name.replace("img_encoder.patch_embed.norm", "vision_model.embeddings.layernorm")
-    if "img_encoder.layers" in name:
-        name = name.replace("img_encoder.layers", "vision_model.encoder.stages")
-    if "blocks" in name and "res" not in name:
-        name = name.replace("blocks", "layers")
-    if "attn" in name and "pre_assign" not in name:
-        name = name.replace("attn", "self_attn")
-    if "proj" in name and "self_attn" in name and "text" not in name:
-        name = name.replace("proj", "out_proj")
-    if "pre_assign_attn.attn.proj" in name:
-        name = name.replace("pre_assign_attn.attn.proj", "pre_assign_attn.attn.out_proj")
-    if "norm1" in name:
-        name = name.replace("norm1", "layer_norm1")
-    if "norm2" in name and "pre_assign" not in name:
-        name = name.replace("norm2", "layer_norm2")
-    if "img_encoder.norm" in name:
-        name = name.replace("img_encoder.norm", "vision_model.layernorm")
-    # text encoder
-    if "text_encoder.token_embedding" in name:
-        name = name.replace("text_encoder.token_embedding", "text_model.embeddings.token_embedding")
-    if "text_encoder.positional_embedding" in name:
-        name = name.replace("text_encoder.positional_embedding", "text_model.embeddings.position_embedding.weight")
-    if "text_encoder.transformer.resblocks." in name:
-        name = name.replace("text_encoder.transformer.resblocks.", "text_model.encoder.layers.")
-    if "ln_1" in name:
-        name = name.replace("ln_1", "layer_norm1")
-    if "ln_2" in name:
-        name = name.replace("ln_2", "layer_norm2")
-    if "c_fc" in name:
-        name = name.replace("c_fc", "fc1")
-    if "c_proj" in name:
-        name = name.replace("c_proj", "fc2")
-    if "text_encoder" in name:
-        name = name.replace("text_encoder", "text_model")
-    if "ln_final" in name:
-        name = name.replace("ln_final", "final_layer_norm")
-    # projection layers
-    if "img_projector.linear_hidden." in name:
-        name = name.replace("img_projector.linear_hidden.", "visual_projection.")
-    if "img_projector.linear_out." in name:
-        name = name.replace("img_projector.linear_out.", "visual_projection.3.")
-    if "text_projector.linear_hidden" in name:
-        name = name.replace("text_projector.linear_hidden", "text_projection")
-    if "text_projector.linear_out" in name:
-        name = name.replace("text_projector.linear_out", "text_projection.3")
-
-    return name
-
-
-def convert_state_dict(orig_state_dict, config):
-    for key in orig_state_dict.copy().keys():
-        val = orig_state_dict.pop(key)
-
-        if "qkv" in key:
-            # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
-            # we need to split them up into separate matrices/vectors
-            key_split = key.split(".")
-            stage_num, layer_num = int(key_split[2]), int(key_split[4])
-            dim = config.vision_config.hidden_size
-            if "weight" in key:
-                orig_state_dict[
-                    f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.q_proj.weight"
-                ] = val[:dim, :]
-                orig_state_dict[
-                    f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.k_proj.weight"
-                ] = val[dim : dim * 2, :]
-                orig_state_dict[
-                    f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.v_proj.weight"
-                ] = val[-dim:, :]
-            else:
-                orig_state_dict[
-                    f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.q_proj.bias"
-                ] = val[:dim]
-                orig_state_dict[
-                    f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.k_proj.bias"
-                ] = val[dim : dim * 2]
-                orig_state_dict[
-                    f"vision_model.encoder.stages.{stage_num}.layers.{layer_num}.self_attn.v_proj.bias"
-                ] = val[-dim:]
-        elif "in_proj" in key:
-            # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
-            # we need to split them up into separate matrices/vectors
-            key_split = key.split(".")
-            layer_num = int(key_split[3])
-            dim = config.text_config.hidden_size
-            if "weight" in key:
-                orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
-                orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
-                    dim : dim * 2, :
-                ]
-                orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
-            else:
-                orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
-                orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
-                orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
-        else:
-            new_name = rename_key(key)
-            # squeeze if necessary
-            if (
-                "text_projection.0" in new_name
-                or "text_projection.3" in new_name
-                or "visual_projection.0" in new_name
-                or "visual_projection.3" in new_name
-            ):
-                orig_state_dict[new_name] = val.squeeze_()
-            else:
-                orig_state_dict[new_name] = val
-
-    return orig_state_dict
-
-
-# We will verify our results on an image of cute cats
-def prepare_img():
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    im = Image.open(requests.get(url, stream=True).raw)
-    return im
-
-
-@torch.no_grad()
-def convert_groupvit_checkpoint(
-    checkpoint_path, pytorch_dump_folder_path, model_name="groupvit-gcc-yfcc", push_to_hub=False
-):
-    """
-    Copy/paste/tweak model's weights to the Transformers design.
-    """
-    config = GroupViTConfig()
-    model = GroupViTModel(config).eval()
-
-    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
-    new_state_dict = convert_state_dict(state_dict, config)
-    missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
-    assert missing_keys == ["text_model.embeddings.position_ids"]
-    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(unexpected_keys) == 0)
-
-    # verify result
-    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-    image = prepare_img()
-    inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    if model_name == "groupvit-gcc-yfcc":
-        expected_logits = torch.tensor([[13.3523, 6.3629]])
-    elif model_name == "groupvit-gcc-redcaps":
-        expected_logits = torch.tensor([[16.1873, 8.6230]])
-    else:
-        raise ValueError(f"Model name {model_name} not supported.")
-    assert torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3)
-
-    processor.save_pretrained(pytorch_dump_folder_path)
-    model.save_pretrained(pytorch_dump_folder_path)
-    print("Successfully saved processor and model to", pytorch_dump_folder_path)
-
-    if push_to_hub:
-        print("Pushing to the hub...")
-        processor.push_to_hub(model_name, organization="nielsr")
-        model.push_to_hub(model_name, organization="nielsr")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
-    )
-    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
-    parser.add_argument(
-        "--model_name",
-        default="groupvit-gccy-fcc",
-        type=str,
-        help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
-    )
-    parser.add_argument(
-        "--push_to_hub",
-        action="store_true",
-        help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
-    )
-    args = parser.parse_args()
-
-    convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mt5/modeling_mt5.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mt5/modeling_mt5.py
deleted file mode 100644
index 3d03503ddd402e5762971a498fc1bea39ca14717..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mt5/modeling_mt5.py
+++ /dev/null
@@ -1,2354 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Mesh TensorFlow authors, T5 Authors and 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.
-""" PyTorch mT5 model."""
-
-import copy
-import math
-import os
-import warnings
-from typing import List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from torch.utils.checkpoint import checkpoint
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPastAndCrossAttentions,
-    Seq2SeqLMOutput,
-    Seq2SeqModelOutput,
-    Seq2SeqQuestionAnsweringModelOutput,
-    Seq2SeqSequenceClassifierOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
-from ...utils import (
-    DUMMY_INPUTS,
-    DUMMY_MASK,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_torch_fx_proxy,
-    logging,
-    replace_return_docstrings,
-)
-from ...utils.model_parallel_utils import assert_device_map, get_device_map
-from .configuration_mt5 import MT5Config
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "MT5Config"
-_CHECKPOINT_FOR_DOC = "mt5-small"
-
-
-PARALLELIZE_DOCSTRING = r"""
-    This is an experimental feature and is a subject to change at a moment's notice.
-
-    Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
-    it will evenly distribute blocks across all devices.
-
-    Args:
-        device_map (`Dict[int, list]`, optional, defaults to None):
-            A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
-            automatically mapped to the first device (for esoteric reasons). That means that the first device should
-            have fewer attention modules mapped to it than other devices. For reference, the mt5 models have the
-            following number of attention modules:
-
-                - mt5-small: 6
-                - mt5-base: 12
-                - mt5-large: 24
-                - mt5-xl: 24
-                - mt5-xxl: 24
-
-    Example:
-
-    ```python
-    # Here is an example of a device map on a machine with 4 GPUs using mt5-xl, which has a total of 24 attention modules:
-    model = MT5ForConditionalGeneration.from_pretrained("mt5-xl")
-    device_map = {
-        0: [0, 1, 2],
-        1: [3, 4, 5, 6, 7, 8, 9],
-        2: [10, 11, 12, 13, 14, 15, 16],
-        3: [17, 18, 19, 20, 21, 22, 23],
-    }
-    model.parallelize(device_map)
-    ```
-"""
-DEPARALLELIZE_DOCSTRING = r"""
-    Moves the model to cpu from a model parallel state.
-
-    Example:
-
-    ```python
-    # On a 4 GPU machine with mt5-xl:
-    model = MT5ForConditionalGeneration.from_pretrained("Mt5-xl")
-    device_map = {
-        0: [0, 1, 2],
-        1: [3, 4, 5, 6, 7, 8, 9],
-        2: [10, 11, 12, 13, 14, 15, 16],
-        3: [17, 18, 19, 20, 21, 22, 23],
-    }
-    model.parallelize(device_map)  # Splits the model across several devices
-    model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
-    ```
-"""
-
-
-# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->MT5
-class MT5LayerNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        Construct a layernorm module in the MT5 style. No bias and no subtraction of mean.
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        # MT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
-        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
-        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
-        # half-precision inputs is done in fp32
-
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
-        # convert into half-precision if necessary
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-
-        return self.weight * hidden_states
-
-
-# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->MT5
-class MT5DenseActDense(nn.Module):
-    def __init__(self, config: MT5Config):
-        super().__init__()
-        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
-        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
-        self.dropout = nn.Dropout(config.dropout_rate)
-        self.act = ACT2FN[config.dense_act_fn]
-
-    def forward(self, hidden_states):
-        hidden_states = self.wi(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        if (
-            isinstance(self.wo.weight, torch.Tensor)
-            and hidden_states.dtype != self.wo.weight.dtype
-            and self.wo.weight.dtype != torch.int8
-        ):
-            hidden_states = hidden_states.to(self.wo.weight.dtype)
-        hidden_states = self.wo(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->MT5
-class MT5DenseGatedActDense(nn.Module):
-    def __init__(self, config: MT5Config):
-        super().__init__()
-        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
-        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
-        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
-        self.dropout = nn.Dropout(config.dropout_rate)
-        self.act = ACT2FN[config.dense_act_fn]
-
-    def forward(self, hidden_states):
-        hidden_gelu = self.act(self.wi_0(hidden_states))
-        hidden_linear = self.wi_1(hidden_states)
-        hidden_states = hidden_gelu * hidden_linear
-        hidden_states = self.dropout(hidden_states)
-
-        # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
-        # See https://github.com/huggingface/transformers/issues/20287
-        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
-        if (
-            isinstance(self.wo.weight, torch.Tensor)
-            and hidden_states.dtype != self.wo.weight.dtype
-            and self.wo.weight.dtype != torch.int8
-        ):
-            hidden_states = hidden_states.to(self.wo.weight.dtype)
-
-        hidden_states = self.wo(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->MT5
-class MT5LayerFF(nn.Module):
-    def __init__(self, config: MT5Config):
-        super().__init__()
-        if config.is_gated_act:
-            self.DenseReluDense = MT5DenseGatedActDense(config)
-        else:
-            self.DenseReluDense = MT5DenseActDense(config)
-
-        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, hidden_states):
-        forwarded_states = self.layer_norm(hidden_states)
-        forwarded_states = self.DenseReluDense(forwarded_states)
-        hidden_states = hidden_states + self.dropout(forwarded_states)
-        return hidden_states
-
-
-# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->MT5
-class MT5Attention(nn.Module):
-    def __init__(self, config: MT5Config, has_relative_attention_bias=False):
-        super().__init__()
-        self.is_decoder = config.is_decoder
-        self.has_relative_attention_bias = has_relative_attention_bias
-        self.relative_attention_num_buckets = config.relative_attention_num_buckets
-        self.relative_attention_max_distance = config.relative_attention_max_distance
-        self.d_model = config.d_model
-        self.key_value_proj_dim = config.d_kv
-        self.n_heads = config.num_heads
-        self.dropout = config.dropout_rate
-        self.inner_dim = self.n_heads * self.key_value_proj_dim
-
-        # Mesh TensorFlow initialization to avoid scaling before softmax
-        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
-        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
-        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
-        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
-
-        if self.has_relative_attention_bias:
-            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
-        self.pruned_heads = set()
-        self.gradient_checkpointing = False
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
-        )
-        # Prune linear layers
-        self.q = prune_linear_layer(self.q, index)
-        self.k = prune_linear_layer(self.k, index)
-        self.v = prune_linear_layer(self.v, index)
-        self.o = prune_linear_layer(self.o, index, dim=1)
-        # Update hyper params
-        self.n_heads = self.n_heads - len(heads)
-        self.inner_dim = self.key_value_proj_dim * self.n_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    @staticmethod
-    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
-        """
-        Adapted from Mesh Tensorflow:
-        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-
-        Translate relative position to a bucket number for relative attention. The relative position is defined as
-        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
-        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
-        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
-        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
-        This should allow for more graceful generalization to longer sequences than the model has been trained on
-
-        Args:
-            relative_position: an int32 Tensor
-            bidirectional: a boolean - whether the attention is bidirectional
-            num_buckets: an integer
-            max_distance: an integer
-
-        Returns:
-            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
-        """
-        relative_buckets = 0
-        if bidirectional:
-            num_buckets //= 2
-            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
-            relative_position = torch.abs(relative_position)
-        else:
-            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
-        # now relative_position is in the range [0, inf)
-
-        # half of the buckets are for exact increments in positions
-        max_exact = num_buckets // 2
-        is_small = relative_position < max_exact
-
-        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
-        relative_position_if_large = max_exact + (
-            torch.log(relative_position.float() / max_exact)
-            / math.log(max_distance / max_exact)
-            * (num_buckets - max_exact)
-        ).to(torch.long)
-        relative_position_if_large = torch.min(
-            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
-        )
-
-        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
-        return relative_buckets
-
-    def compute_bias(self, query_length, key_length, device=None):
-        """Compute binned relative position bias"""
-        if device is None:
-            device = self.relative_attention_bias.weight.device
-        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
-        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
-        relative_position = memory_position - context_position  # shape (query_length, key_length)
-        relative_position_bucket = self._relative_position_bucket(
-            relative_position,  # shape (query_length, key_length)
-            bidirectional=(not self.is_decoder),
-            num_buckets=self.relative_attention_num_buckets,
-            max_distance=self.relative_attention_max_distance,
-        )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
-        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
-        return values
-
-    def forward(
-        self,
-        hidden_states,
-        mask=None,
-        key_value_states=None,
-        position_bias=None,
-        past_key_value=None,
-        layer_head_mask=None,
-        query_length=None,
-        use_cache=False,
-        output_attentions=False,
-    ):
-        """
-        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
-        """
-        # Input is (batch_size, seq_length, dim)
-        # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
-        # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
-        batch_size, seq_length = hidden_states.shape[:2]
-
-        real_seq_length = seq_length
-
-        if past_key_value is not None:
-            if len(past_key_value) != 2:
-                raise ValueError(
-                    f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
-                )
-            real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
-
-        key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
-
-        def shape(states):
-            """projection"""
-            return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
-
-        def unshape(states):
-            """reshape"""
-            return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
-
-        def project(hidden_states, proj_layer, key_value_states, past_key_value):
-            """projects hidden states correctly to key/query states"""
-            if key_value_states is None:
-                # self-attn
-                # (batch_size, n_heads, seq_length, dim_per_head)
-                hidden_states = shape(proj_layer(hidden_states))
-            elif past_key_value is None:
-                # cross-attn
-                # (batch_size, n_heads, seq_length, dim_per_head)
-                hidden_states = shape(proj_layer(key_value_states))
-
-            if past_key_value is not None:
-                if key_value_states is None:
-                    # self-attn
-                    # (batch_size, n_heads, key_length, dim_per_head)
-                    hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
-                elif past_key_value.shape[2] != key_value_states.shape[1]:
-                    # checking that the `sequence_length` of the `past_key_value` is the same as
-                    # the provided `key_value_states` to support prefix tuning
-                    # cross-attn
-                    # (batch_size, n_heads, seq_length, dim_per_head)
-                    hidden_states = shape(proj_layer(key_value_states))
-                else:
-                    # cross-attn
-                    hidden_states = past_key_value
-            return hidden_states
-
-        # get query states
-        query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
-
-        # get key/value states
-        key_states = project(
-            hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
-        )
-        value_states = project(
-            hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
-        )
-
-        # compute scores
-        scores = torch.matmul(
-            query_states, key_states.transpose(3, 2)
-        )  # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
-
-        if position_bias is None:
-            if not self.has_relative_attention_bias:
-                position_bias = torch.zeros(
-                    (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
-                )
-                if self.gradient_checkpointing and self.training:
-                    position_bias.requires_grad = True
-            else:
-                position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
-
-            # if key and values are already calculated
-            # we want only the last query position bias
-            if past_key_value is not None:
-                position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
-
-            if mask is not None:
-                position_bias = position_bias + mask  # (batch_size, n_heads, seq_length, key_length)
-
-        if self.pruned_heads:
-            mask = torch.ones(position_bias.shape[1])
-            mask[list(self.pruned_heads)] = 0
-            position_bias_masked = position_bias[:, mask.bool()]
-        else:
-            position_bias_masked = position_bias
-
-        scores += position_bias_masked
-        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
-            scores
-        )  # (batch_size, n_heads, seq_length, key_length)
-        attn_weights = nn.functional.dropout(
-            attn_weights, p=self.dropout, training=self.training
-        )  # (batch_size, n_heads, seq_length, key_length)
-
-        # Mask heads if we want to
-        if layer_head_mask is not None:
-            attn_weights = attn_weights * layer_head_mask
-
-        attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
-        attn_output = self.o(attn_output)
-
-        present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
-        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
-
-        if output_attentions:
-            outputs = outputs + (attn_weights,)
-        return outputs
-
-
-# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->MT5
-class MT5LayerSelfAttention(nn.Module):
-    def __init__(self, config, has_relative_attention_bias=False):
-        super().__init__()
-        self.SelfAttention = MT5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
-        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        position_bias=None,
-        layer_head_mask=None,
-        past_key_value=None,
-        use_cache=False,
-        output_attentions=False,
-    ):
-        normed_hidden_states = self.layer_norm(hidden_states)
-        attention_output = self.SelfAttention(
-            normed_hidden_states,
-            mask=attention_mask,
-            position_bias=position_bias,
-            layer_head_mask=layer_head_mask,
-            past_key_value=past_key_value,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-        )
-        hidden_states = hidden_states + self.dropout(attention_output[0])
-        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
-        return outputs
-
-
-# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->MT5
-class MT5LayerCrossAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.EncDecAttention = MT5Attention(config, has_relative_attention_bias=False)
-        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(
-        self,
-        hidden_states,
-        key_value_states,
-        attention_mask=None,
-        position_bias=None,
-        layer_head_mask=None,
-        past_key_value=None,
-        use_cache=False,
-        query_length=None,
-        output_attentions=False,
-    ):
-        normed_hidden_states = self.layer_norm(hidden_states)
-        attention_output = self.EncDecAttention(
-            normed_hidden_states,
-            mask=attention_mask,
-            key_value_states=key_value_states,
-            position_bias=position_bias,
-            layer_head_mask=layer_head_mask,
-            past_key_value=past_key_value,
-            use_cache=use_cache,
-            query_length=query_length,
-            output_attentions=output_attentions,
-        )
-        layer_output = hidden_states + self.dropout(attention_output[0])
-        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
-        return outputs
-
-
-# Copied from transformers.models.t5.modeling_t5.T5Block with T5->MT5
-class MT5Block(nn.Module):
-    def __init__(self, config, has_relative_attention_bias=False):
-        super().__init__()
-        self.is_decoder = config.is_decoder
-        self.layer = nn.ModuleList()
-        self.layer.append(MT5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
-        if self.is_decoder:
-            self.layer.append(MT5LayerCrossAttention(config))
-
-        self.layer.append(MT5LayerFF(config))
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        position_bias=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        encoder_decoder_position_bias=None,
-        layer_head_mask=None,
-        cross_attn_layer_head_mask=None,
-        past_key_value=None,
-        use_cache=False,
-        output_attentions=False,
-        return_dict=True,
-    ):
-        if past_key_value is not None:
-            if not self.is_decoder:
-                logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.")
-            expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
-
-            if len(past_key_value) != expected_num_past_key_values:
-                raise ValueError(
-                    f"There should be {expected_num_past_key_values} past states. "
-                    f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
-                    f"Got {len(past_key_value)} past key / value states"
-                )
-
-            self_attn_past_key_value = past_key_value[:2]
-            cross_attn_past_key_value = past_key_value[2:]
-        else:
-            self_attn_past_key_value, cross_attn_past_key_value = None, None
-
-        self_attention_outputs = self.layer[0](
-            hidden_states,
-            attention_mask=attention_mask,
-            position_bias=position_bias,
-            layer_head_mask=layer_head_mask,
-            past_key_value=self_attn_past_key_value,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-        )
-        hidden_states, present_key_value_state = self_attention_outputs[:2]
-        attention_outputs = self_attention_outputs[2:]  # Keep self-attention outputs and relative position weights
-
-        # clamp inf values to enable fp16 training
-        if hidden_states.dtype == torch.float16:
-            clamp_value = torch.where(
-                torch.isinf(hidden_states).any(),
-                torch.finfo(hidden_states.dtype).max - 1000,
-                torch.finfo(hidden_states.dtype).max,
-            )
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        do_cross_attention = self.is_decoder and encoder_hidden_states is not None
-        if do_cross_attention:
-            # the actual query length is unknown for cross attention
-            # if using past key value states. Need to inject it here
-            if present_key_value_state is not None:
-                query_length = present_key_value_state[0].shape[2]
-            else:
-                query_length = None
-
-            cross_attention_outputs = self.layer[1](
-                hidden_states,
-                key_value_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                position_bias=encoder_decoder_position_bias,
-                layer_head_mask=cross_attn_layer_head_mask,
-                past_key_value=cross_attn_past_key_value,
-                query_length=query_length,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-            )
-            hidden_states = cross_attention_outputs[0]
-
-            # clamp inf values to enable fp16 training
-            if hidden_states.dtype == torch.float16:
-                clamp_value = torch.where(
-                    torch.isinf(hidden_states).any(),
-                    torch.finfo(hidden_states.dtype).max - 1000,
-                    torch.finfo(hidden_states.dtype).max,
-                )
-                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-            # Combine self attn and cross attn key value states
-            if present_key_value_state is not None:
-                present_key_value_state = present_key_value_state + cross_attention_outputs[1]
-
-            # Keep cross-attention outputs and relative position weights
-            attention_outputs = attention_outputs + cross_attention_outputs[2:]
-
-        # Apply Feed Forward layer
-        hidden_states = self.layer[-1](hidden_states)
-
-        # clamp inf values to enable fp16 training
-        if hidden_states.dtype == torch.float16:
-            clamp_value = torch.where(
-                torch.isinf(hidden_states).any(),
-                torch.finfo(hidden_states.dtype).max - 1000,
-                torch.finfo(hidden_states.dtype).max,
-            )
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if use_cache:
-            outputs = outputs + (present_key_value_state,) + attention_outputs
-        else:
-            outputs = outputs + attention_outputs
-
-        return outputs  # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
-
-
-def load_tf_weights_in_mt5(model, config, tf_checkpoint_path):
-    """Load tf checkpoints in a pytorch model."""
-    try:
-        import re
-
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        logger.error(
-            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions."
-        )
-        raise
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    tf_weights = {}
-    for name, shape in init_vars:
-        logger.info(f"Loading TF weight {name} with shape {shape}")
-        array = tf.train.load_variable(tf_path, name)
-        names.append(name)
-        tf_weights[name] = array
-
-    for txt_name in names:
-        name = txt_name.split("/")
-        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
-        # which are not required for using pretrained model
-        if any(
-            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
-            for n in name
-        ):
-            logger.info(f"Skipping {'/'.join(name)}")
-            tf_weights.pop(txt_name, None)
-            continue
-        if "_slot_" in name[-1]:
-            logger.info(f"Skipping {'/'.join(name)}")
-            tf_weights.pop(txt_name, None)
-            continue
-        pointer = model
-        array = tf_weights[txt_name]
-
-        for m_name in name:
-            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                scope_names = re.split(r"_(\d+)", m_name)
-            else:
-                scope_names = [m_name]
-            if scope_names[0] in ["kernel", "scale", "embedding"]:
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "self_attention":
-                pointer = getattr(pointer, "layer")
-                pointer = pointer[0]
-            elif scope_names[0] == "enc_dec_attention":
-                pointer = getattr(pointer, "layer")
-                pointer = pointer[1]
-            elif scope_names[0] == "dense_relu_dense":
-                pointer = getattr(pointer, "layer")
-                pointer = pointer[2]
-            elif scope_names[0] == "rms_norm":
-                if hasattr(pointer, "layer_norm"):
-                    pointer = getattr(pointer, "layer_norm")
-                elif hasattr(pointer, "final_layer_norm"):
-                    pointer = getattr(pointer, "final_layer_norm")
-            elif scope_names[0] == "scale":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
-                pointer = getattr(pointer, "bias")
-            elif scope_names[0] == "squad":
-                pointer = getattr(pointer, "classifier")
-            elif scope_names[0] == "decoder" and name[1] == "logits":
-                continue
-            elif scope_names[0] == "logits":
-                pointer = getattr(pointer, "lm_head")
-            elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit():
-                pointer = getattr(pointer, f"wi_{scope_names[1]}")
-                continue
-            else:
-                try:
-                    pointer = getattr(pointer, scope_names[0])
-                except AttributeError:
-                    logger.info(f"Skipping {'/'.join(name)}")
-                    continue
-            if len(scope_names) >= 2:
-                num = int(scope_names[1])
-                pointer = pointer[num]
-        if scope_names[0] not in ["kernel", "scale", "embedding"]:
-            pointer = getattr(pointer, "weight")
-        if scope_names[0] != "embedding":
-            logger.info(f"Transposing numpy weight of shape {array.shape} for {name}")
-            array = np.transpose(array)
-        try:
-            assert (
-                pointer.shape == array.shape
-            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        logger.info(f"Initialize PyTorch weight {name}")
-        pointer.data = torch.from_numpy(array.astype(np.float32))
-        tf_weights.pop(txt_name, None)
-
-    logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
-    return model
-
-
-# Copied from transformers.models.t5.modeling_t5.T5ClassificationHead with T5->MT5
-class MT5ClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, config: MT5Config):
-        super().__init__()
-        self.dense = nn.Linear(config.d_model, config.d_model)
-        self.dropout = nn.Dropout(p=config.classifier_dropout)
-        self.out_proj = nn.Linear(config.d_model, config.num_labels)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel with T5->MT5, t5->mt5
-class MT5PreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = MT5Config
-    load_tf_weights = load_tf_weights_in_mt5
-    base_model_prefix = "transformer"
-    is_parallelizable = True
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MT5Block"]
-    _keep_in_fp32_modules = ["wo"]
-
-    @property
-    def dummy_inputs(self):
-        input_ids = torch.tensor(DUMMY_INPUTS)
-        input_mask = torch.tensor(DUMMY_MASK)
-        dummy_inputs = {
-            "decoder_input_ids": input_ids,
-            "input_ids": input_ids,
-            "decoder_attention_mask": input_mask,
-        }
-        return dummy_inputs
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        factor = self.config.initializer_factor  # Used for testing weights initialization
-        if isinstance(module, MT5LayerNorm):
-            module.weight.data.fill_(factor * 1.0)
-        elif isinstance(
-            module,
-            (MT5Model, MT5ForConditionalGeneration, MT5EncoderModel, MT5ForQuestionAnswering),
-        ):
-            # Mesh TensorFlow embeddings initialization
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
-            module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
-            if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
-                module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
-            if hasattr(module, "qa_outputs"):
-                module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
-                module.qa_outputs.bias.data.zero_()
-        elif isinstance(module, MT5ClassificationHead):
-            module.dense.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
-            if hasattr(module.dense, "bias") and module.dense.bias is not None:
-                module.dense.bias.data.zero_()
-            module.out_proj.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
-            if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
-                module.out_proj.bias.data.zero_()
-        elif isinstance(module, MT5DenseActDense):
-            # Mesh TensorFlow FF initialization
-            # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
-            # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
-            module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
-            if hasattr(module.wi, "bias") and module.wi.bias is not None:
-                module.wi.bias.data.zero_()
-            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
-            if hasattr(module.wo, "bias") and module.wo.bias is not None:
-                module.wo.bias.data.zero_()
-        elif isinstance(module, MT5DenseGatedActDense):
-            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
-            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
-                module.wi_0.bias.data.zero_()
-            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
-            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
-                module.wi_1.bias.data.zero_()
-            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
-            if hasattr(module.wo, "bias") and module.wo.bias is not None:
-                module.wo.bias.data.zero_()
-        elif isinstance(module, MT5Attention):
-            # Mesh TensorFlow attention initialization to avoid scaling before softmax
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
-            d_model = self.config.d_model
-            key_value_proj_dim = self.config.d_kv
-            n_heads = self.config.num_heads
-            module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
-            module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
-            module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
-            module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
-            if module.has_relative_attention_bias:
-                module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (MT5Attention, MT5Stack)):
-            module.gradient_checkpointing = value
-
-    def _shift_right(self, input_ids):
-        decoder_start_token_id = self.config.decoder_start_token_id
-        pad_token_id = self.config.pad_token_id
-
-        if decoder_start_token_id is None:
-            raise ValueError(
-                "self.model.config.decoder_start_token_id has to be defined. In MT5 it is usually set to the pad_token_id."
-                "See MT5 docs for more information."
-            )
-
-        # shift inputs to the right
-        if is_torch_fx_proxy(input_ids):
-            # Item assignment is not supported natively for proxies.
-            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
-            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
-        else:
-            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
-            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
-            shifted_input_ids[..., 0] = decoder_start_token_id
-
-        if pad_token_id is None:
-            raise ValueError("self.model.config.pad_token_id has to be defined.")
-        # replace possible -100 values in labels by `pad_token_id`
-        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
-        return shifted_input_ids
-
-
-# Copied from transformers.models.t5.modeling_t5.T5Stack with T5->MT5
-class MT5Stack(MT5PreTrainedModel):
-    def __init__(self, config, embed_tokens=None):
-        super().__init__(config)
-
-        self.embed_tokens = embed_tokens
-        self.is_decoder = config.is_decoder
-
-        self.block = nn.ModuleList(
-            [MT5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
-        )
-        self.final_layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-        self.gradient_checkpointing = False
-
-    @add_start_docstrings(PARALLELIZE_DOCSTRING)
-    def parallelize(self, device_map=None):
-        warnings.warn(
-            "`MT5Stack.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model"
-            " with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
-            " `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0,"
-            " 'block.1': 1, ...}",
-            FutureWarning,
-        )
-        # Check validity of device_map
-        self.device_map = (
-            get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map
-        )
-        assert_device_map(self.device_map, len(self.block))
-        self.model_parallel = True
-        self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
-        self.last_device = "cuda:" + str(max(self.device_map.keys()))
-        # Load onto devices
-        for k, v in self.device_map.items():
-            for layer in v:
-                cuda_device = "cuda:" + str(k)
-                self.block[layer] = self.block[layer].to(cuda_device)
-
-        # Set embed_tokens to first layer
-        self.embed_tokens = self.embed_tokens.to(self.first_device)
-        # Set final layer norm to last device
-        self.final_layer_norm = self.final_layer_norm.to(self.last_device)
-
-    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
-    def deparallelize(self):
-        warnings.warn(
-            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
-            FutureWarning,
-        )
-        self.model_parallel = False
-        self.device_map = None
-        self.first_device = "cpu"
-        self.last_device = "cpu"
-        for i in range(len(self.block)):
-            self.block[i] = self.block[i].to("cpu")
-        self.embed_tokens = self.embed_tokens.to("cpu")
-        self.final_layer_norm = self.final_layer_norm.to("cpu")
-        torch.cuda.empty_cache()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, new_embeddings):
-        self.embed_tokens = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        inputs_embeds=None,
-        head_mask=None,
-        cross_attn_head_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        # Model parallel
-        if self.model_parallel:
-            torch.cuda.set_device(self.first_device)
-            self.embed_tokens = self.embed_tokens.to(self.first_device)
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            err_msg_prefix = "decoder_" if self.is_decoder else ""
-            raise ValueError(
-                f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
-            )
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            err_msg_prefix = "decoder_" if self.is_decoder else ""
-            raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
-
-        if inputs_embeds is None:
-            if self.embed_tokens is None:
-                raise ValueError("You have to initialize the model with valid token embeddings")
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        batch_size, seq_length = input_shape
-
-        # required mask seq length can be calculated via length of past
-        mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
-
-        if use_cache is True:
-            if not self.is_decoder:
-                raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
-
-        if attention_mask is None:
-            attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
-        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
-            encoder_seq_length = encoder_hidden_states.shape[1]
-            encoder_attention_mask = torch.ones(
-                batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
-            )
-
-        # initialize past_key_values with `None` if past does not exist
-        if past_key_values is None:
-            past_key_values = [None] * len(self.block)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
-            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        # Prepare head mask if needed
-        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
-        cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
-        present_key_value_states = () if use_cache else None
-        all_hidden_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-        all_cross_attentions = () if (output_attentions and self.is_decoder) else None
-        position_bias = None
-        encoder_decoder_position_bias = None
-
-        hidden_states = self.dropout(inputs_embeds)
-
-        for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
-            layer_head_mask = head_mask[i]
-            cross_attn_layer_head_mask = cross_attn_head_mask[i]
-            # Model parallel
-            if self.model_parallel:
-                torch.cuda.set_device(hidden_states.device)
-                # Ensure that attention_mask is always on the same device as hidden_states
-                if attention_mask is not None:
-                    attention_mask = attention_mask.to(hidden_states.device)
-                if position_bias is not None:
-                    position_bias = position_bias.to(hidden_states.device)
-                if encoder_hidden_states is not None:
-                    encoder_hidden_states = encoder_hidden_states.to(hidden_states.device)
-                if encoder_extended_attention_mask is not None:
-                    encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device)
-                if encoder_decoder_position_bias is not None:
-                    encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device)
-                if layer_head_mask is not None:
-                    layer_head_mask = layer_head_mask.to(hidden_states.device)
-                if cross_attn_layer_head_mask is not None:
-                    cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device)
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return tuple(module(*inputs, use_cache, output_attentions))
-
-                    return custom_forward
-
-                layer_outputs = checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    extended_attention_mask,
-                    position_bias,
-                    encoder_hidden_states,
-                    encoder_extended_attention_mask,
-                    encoder_decoder_position_bias,
-                    layer_head_mask,
-                    cross_attn_layer_head_mask,
-                    None,  # past_key_value is always None with gradient checkpointing
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask=extended_attention_mask,
-                    position_bias=position_bias,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_attention_mask=encoder_extended_attention_mask,
-                    encoder_decoder_position_bias=encoder_decoder_position_bias,
-                    layer_head_mask=layer_head_mask,
-                    cross_attn_layer_head_mask=cross_attn_layer_head_mask,
-                    past_key_value=past_key_value,
-                    use_cache=use_cache,
-                    output_attentions=output_attentions,
-                )
-
-            # layer_outputs is a tuple with:
-            # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
-            if use_cache is False:
-                layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
-
-            hidden_states, present_key_value_state = layer_outputs[:2]
-
-            # We share the position biases between the layers - the first layer store them
-            # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
-            # (cross-attention position bias), (cross-attention weights)
-            position_bias = layer_outputs[2]
-            if self.is_decoder and encoder_hidden_states is not None:
-                encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
-            # append next layer key value states
-            if use_cache:
-                present_key_value_states = present_key_value_states + (present_key_value_state,)
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[3],)
-                if self.is_decoder:
-                    all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
-
-            # Model Parallel: If it's the last layer for that device, put things on the next device
-            if self.model_parallel:
-                for k, v in self.device_map.items():
-                    if i == v[-1] and "cuda:" + str(k) != self.last_device:
-                        hidden_states = hidden_states.to("cuda:" + str(k + 1))
-
-        hidden_states = self.final_layer_norm(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-
-        # Add last layer
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    present_key_value_states,
-                    all_hidden_states,
-                    all_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=present_key_value_states,
-            hidden_states=all_hidden_states,
-            attentions=all_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-MT5_START_DOCSTRING = r"""
-
-    The MT5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
-    Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
-    Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
-    text-to-text denoising generative setting.
-
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`MT5Config`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-MT5_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
-            should be able to pad the inputs on both the right and the left.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for detail.
-
-            [What are input IDs?](../glossary#input-ids)
-
-            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
-        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are decoder input IDs?](../glossary#decoder-input-ids)
-
-            MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
-            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
-
-            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
-            Training](./mt5#training).
-        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
-            1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
-            1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
-                `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
-            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
-            input (see `past_key_values`). This is useful if you want more control over how to convert
-            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-
-            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
-            of `inputs_embeds`.
-
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-MT5_ENCODER_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
-            should be able to pad the inputs on both the right and the left.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for detail.
-
-            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
-        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
-__HEAD_MASK_WARNING_MSG = """
-The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
-`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
-If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
-num_heads)`.
-"""
-
-
-@add_start_docstrings(
-    "The bare MT5 Model transformer outputting raw hidden-states without any specific head on top.",
-    MT5_START_DOCSTRING,
-)
-class MT5Model(MT5PreTrainedModel):
-    r"""
-    Examples:
-
-    ```python
-    >>> from transformers import MT5Model, AutoTokenizer
-
-    >>> model = MT5Model.from_pretrained("google/mt5-small")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
-    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
-    >>> summary = "Weiter Verhandlung in Syrien."
-    >>> inputs = tokenizer(article, return_tensors="pt")
-    >>> labels = tokenizer(text_target=summary, return_tensors="pt")
-
-    >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
-    >>> hidden_states = outputs.last_hidden_state
-    ```"""
-    model_type = "mt5"
-    config_class = MT5Config
-    _keys_to_ignore_on_load_missing = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
-    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
-
-    # Copied from transformers.models.t5.modeling_t5.T5Model.__init__ with T5->MT5
-    def __init__(self, config: MT5Config):
-        super().__init__(config)
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
-        encoder_config = copy.deepcopy(config)
-        encoder_config.is_decoder = False
-        encoder_config.use_cache = False
-        encoder_config.is_encoder_decoder = False
-        self.encoder = MT5Stack(encoder_config, self.shared)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        decoder_config.is_encoder_decoder = False
-        decoder_config.num_layers = config.num_decoder_layers
-        self.decoder = MT5Stack(decoder_config, self.shared)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-
-    @add_start_docstrings(PARALLELIZE_DOCSTRING)
-    # Copied from transformers.models.t5.modeling_t5.T5Model.parallelize
-    def parallelize(self, device_map=None):
-        warnings.warn(
-            "`T5Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model"
-            " with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
-            " `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0':"
-            " 0, 'encoder.block.1': 1, ...}",
-            FutureWarning,
-        )
-        self.device_map = (
-            get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
-            if device_map is None
-            else device_map
-        )
-        assert_device_map(self.device_map, len(self.encoder.block))
-        self.encoder.parallelize(self.device_map)
-        self.decoder.parallelize(self.device_map)
-        self.model_parallel = True
-
-    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
-    # Copied from transformers.models.t5.modeling_t5.T5Model.deparallelize
-    def deparallelize(self):
-        warnings.warn(
-            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
-            FutureWarning,
-        )
-        self.encoder.deparallelize()
-        self.decoder.deparallelize()
-        self.encoder = self.encoder.to("cpu")
-        self.decoder = self.decoder.to("cpu")
-        self.model_parallel = False
-        self.device_map = None
-        torch.cuda.empty_cache()
-
-    # Copied from transformers.models.t5.modeling_t5.T5Model.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.shared
-
-    # Copied from transformers.models.t5.modeling_t5.T5Model.set_input_embeddings
-    def set_input_embeddings(self, new_embeddings):
-        self.shared = new_embeddings
-        self.encoder.set_input_embeddings(new_embeddings)
-        self.decoder.set_input_embeddings(new_embeddings)
-
-    # Copied from transformers.models.t5.modeling_t5.T5Model.get_encoder
-    def get_encoder(self):
-        return self.encoder
-
-    # Copied from transformers.models.t5.modeling_t5.T5Model.get_decoder
-    def get_decoder(self):
-        return self.decoder
-
-    # Copied from transformers.models.t5.modeling_t5.T5Model._prune_heads
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.t5.modeling_t5.T5Model.forward with T5->MT5, t5->mt5
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        decoder_head_mask: Optional[torch.FloatTensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        decoder_inputs_embeds: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
-        r"""
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, MT5Model
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("mt5-small")
-        >>> model = MT5Model.from_pretrained("mt5-small")
-
-        >>> input_ids = tokenizer(
-        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
-        ... ).input_ids  # Batch size 1
-        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
-
-        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for MT5Model.
-        >>> # This is not needed for torch's MT5ForConditionalGeneration as it does this internally using labels arg.
-        >>> decoder_input_ids = model._shift_right(decoder_input_ids)
-
-        >>> # forward pass
-        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
-        >>> last_hidden_states = outputs.last_hidden_state
-        ```"""
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
-        if head_mask is not None and decoder_head_mask is None:
-            if self.config.num_layers == self.config.num_decoder_layers:
-                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
-                decoder_head_mask = head_mask
-
-        # Encode if needed (training, first prediction pass)
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                head_mask=head_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        hidden_states = encoder_outputs[0]
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-            hidden_states = hidden_states.to(self.decoder.first_device)
-            if decoder_input_ids is not None:
-                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
-            if attention_mask is not None:
-                attention_mask = attention_mask.to(self.decoder.first_device)
-            if decoder_attention_mask is not None:
-                decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
-
-        # Decode
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            past_key_values=past_key_values,
-            encoder_hidden_states=hidden_states,
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if not return_dict:
-            return decoder_outputs + encoder_outputs
-
-        return Seq2SeqModelOutput(
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings("""MT5 Model with a `language modeling` head on top.""", MT5_START_DOCSTRING)
-class MT5ForConditionalGeneration(MT5PreTrainedModel):
-    r"""
-    Examples:
-
-    ```python
-    >>> from transformers import MT5ForConditionalGeneration, AutoTokenizer
-
-    >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
-    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
-    >>> summary = "Weiter Verhandlung in Syrien."
-    >>> inputs = tokenizer(article, text_target=summary, return_tensors="pt")
-
-    >>> outputs = model(**inputs)
-    >>> loss = outputs.loss
-    ```"""
-
-    model_type = "mt5"
-    config_class = MT5Config
-    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.__init__ with T5->MT5
-    def __init__(self, config: MT5Config):
-        super().__init__(config)
-        self.model_dim = config.d_model
-
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
-        encoder_config = copy.deepcopy(config)
-        encoder_config.is_decoder = False
-        encoder_config.use_cache = False
-        encoder_config.is_encoder_decoder = False
-        self.encoder = MT5Stack(encoder_config, self.shared)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        decoder_config.is_encoder_decoder = False
-        decoder_config.num_layers = config.num_decoder_layers
-        self.decoder = MT5Stack(decoder_config, self.shared)
-
-        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-
-    @add_start_docstrings(PARALLELIZE_DOCSTRING)
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.parallelize
-    def parallelize(self, device_map=None):
-        warnings.warn(
-            "`T5ForConditionalGeneration.parallelize` is deprecated and will be removed in v5 of Transformers, you"
-            " should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also"
-            " provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance"
-            " {'encoder.block.0': 0, 'encoder.block.1': 1, ...}",
-            FutureWarning,
-        )
-        self.device_map = (
-            get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
-            if device_map is None
-            else device_map
-        )
-        assert_device_map(self.device_map, len(self.encoder.block))
-        self.encoder.parallelize(self.device_map)
-        self.decoder.parallelize(self.device_map)
-        self.lm_head = self.lm_head.to(self.decoder.first_device)
-        self.model_parallel = True
-
-    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.deparallelize
-    def deparallelize(self):
-        warnings.warn(
-            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
-            FutureWarning,
-        )
-        self.encoder.deparallelize()
-        self.decoder.deparallelize()
-        self.encoder = self.encoder.to("cpu")
-        self.decoder = self.decoder.to("cpu")
-        self.lm_head = self.lm_head.to("cpu")
-        self.model_parallel = False
-        self.device_map = None
-        torch.cuda.empty_cache()
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.shared
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.set_input_embeddings
-    def set_input_embeddings(self, new_embeddings):
-        self.shared = new_embeddings
-        self.encoder.set_input_embeddings(new_embeddings)
-        self.decoder.set_input_embeddings(new_embeddings)
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.get_encoder
-    def get_encoder(self):
-        return self.encoder
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.get_decoder
-    def get_decoder(self):
-        return self.decoder
-
-    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward with T5->MT5, t5->mt5
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        decoder_head_mask: Optional[torch.FloatTensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
-            labels in `[0, ..., config.vocab_size]`
-
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, MT5ForConditionalGeneration
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("mt5-small")
-        >>> model = MT5ForConditionalGeneration.from_pretrained("mt5-small")
-
-        >>> # training
-        >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
-        >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
-        >>> outputs = model(input_ids=input_ids, labels=labels)
-        >>> loss = outputs.loss
-        >>> logits = outputs.logits
-
-        >>> # inference
-        >>> input_ids = tokenizer(
-        ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
-        ... ).input_ids  # Batch size 1
-        >>> outputs = model.generate(input_ids)
-        >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-        >>> # studies have shown that owning a dog is good for you.
-        ```"""
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
-        if head_mask is not None and decoder_head_mask is None:
-            if self.config.num_layers == self.config.num_decoder_layers:
-                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
-                decoder_head_mask = head_mask
-
-        # Encode if needed (training, first prediction pass)
-        if encoder_outputs is None:
-            # Convert encoder inputs in embeddings if needed
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                head_mask=head_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        hidden_states = encoder_outputs[0]
-
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-
-        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
-            # get decoder inputs from shifting lm labels to the right
-            decoder_input_ids = self._shift_right(labels)
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.decoder.first_device)
-            hidden_states = hidden_states.to(self.decoder.first_device)
-            if decoder_input_ids is not None:
-                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
-            if attention_mask is not None:
-                attention_mask = attention_mask.to(self.decoder.first_device)
-            if decoder_attention_mask is not None:
-                decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
-
-        # Decode
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            past_key_values=past_key_values,
-            encoder_hidden_states=hidden_states,
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = decoder_outputs[0]
-
-        # Set device for model parallelism
-        if self.model_parallel:
-            torch.cuda.set_device(self.encoder.first_device)
-            self.lm_head = self.lm_head.to(self.encoder.first_device)
-            sequence_output = sequence_output.to(self.lm_head.weight.device)
-
-        if self.config.tie_word_embeddings:
-            # Rescale output before projecting on vocab
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
-            sequence_output = sequence_output * (self.model_dim**-0.5)
-
-        lm_logits = self.lm_head(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=-100)
-            # move labels to correct device to enable PP
-            labels = labels.to(lm_logits.device)
-            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
-            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
-
-        if not return_dict:
-            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
-            return ((loss,) + output) if loss is not None else output
-
-        return Seq2SeqLMOutput(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.prepare_inputs_for_generation
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        head_mask=None,
-        decoder_head_mask=None,
-        decoder_attention_mask=None,
-        cross_attn_head_mask=None,
-        use_cache=None,
-        encoder_outputs=None,
-        **kwargs,
-    ):
-        # cut decoder_input_ids if past is used
-        if past_key_values is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "decoder_input_ids": input_ids,
-            "past_key_values": past_key_values,
-            "encoder_outputs": encoder_outputs,
-            "attention_mask": attention_mask,
-            "head_mask": head_mask,
-            "decoder_head_mask": decoder_head_mask,
-            "decoder_attention_mask": decoder_attention_mask,
-            "cross_attn_head_mask": cross_attn_head_mask,
-            "use_cache": use_cache,
-        }
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.prepare_decoder_input_ids_from_labels
-    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
-        return self._shift_right(labels)
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration._reorder_cache
-    def _reorder_cache(self, past_key_values, beam_idx):
-        # if decoder past is not included in output
-        # speedy decoding is disabled and no need to reorder
-        if past_key_values is None:
-            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
-            return past_key_values
-
-        reordered_decoder_past = ()
-        for layer_past_states in past_key_values:
-            # get the correct batch idx from layer past batch dim
-            # batch dim of `past` is at 2nd position
-            reordered_layer_past_states = ()
-            for layer_past_state in layer_past_states:
-                # need to set correct `past` for each of the four key / value states
-                reordered_layer_past_states = reordered_layer_past_states + (
-                    layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
-                )
-
-            if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
-                raise ValueError(
-                    f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched"
-                )
-            if len(reordered_layer_past_states) != len(layer_past_states):
-                raise ValueError(
-                    f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched"
-                )
-
-            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
-        return reordered_decoder_past
-
-
-@add_start_docstrings(
-    "The bare MT5 Model transformer outputting encoder's raw hidden-states without any specific head on top.",
-    MT5_START_DOCSTRING,
-)
-class MT5EncoderModel(MT5PreTrainedModel):
-    r"""
-    Examples:
-
-    ```python
-    >>> from transformers import MT5EncoderModel, AutoTokenizer
-
-    >>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
-    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
-    >>> input_ids = tokenizer(article, return_tensors="pt").input_ids
-    >>> outputs = model(input_ids)
-    >>> hidden_state = outputs.last_hidden_state
-    ```"""
-
-    model_type = "mt5"
-    config_class = MT5Config
-    _tied_weights_keys = ["encoder.embed_tokens.weight"]
-
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.__init__ with T5->MT5
-    def __init__(self, config: MT5Config):
-        super().__init__(config)
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
-        encoder_config = copy.deepcopy(config)
-        encoder_config.use_cache = False
-        encoder_config.is_encoder_decoder = False
-        self.encoder = MT5Stack(encoder_config, self.shared)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-
-    @add_start_docstrings(PARALLELIZE_DOCSTRING)
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.parallelize
-    def parallelize(self, device_map=None):
-        warnings.warn(
-            "`T5EncoderModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
-            " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
-            " `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0,"
-            " 'block.1': 1, ...}",
-            FutureWarning,
-        )
-        self.device_map = (
-            get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
-            if device_map is None
-            else device_map
-        )
-        assert_device_map(self.device_map, len(self.encoder.block))
-        self.encoder.parallelize(self.device_map)
-        self.model_parallel = True
-
-    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.deparallelize
-    def deparallelize(self):
-        warnings.warn(
-            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
-            FutureWarning,
-        )
-        self.encoder.deparallelize()
-        self.encoder = self.encoder.to("cpu")
-        self.model_parallel = False
-        self.device_map = None
-        torch.cuda.empty_cache()
-
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.shared
-
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.set_input_embeddings
-    def set_input_embeddings(self, new_embeddings):
-        self.shared = new_embeddings
-        self.encoder.set_input_embeddings(new_embeddings)
-
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.get_encoder
-    def get_encoder(self):
-        return self.encoder
-
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel._prune_heads
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
-
-    @add_start_docstrings_to_model_forward(MT5_ENCODER_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.forward with T5->MT5, t5->mt5
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]:
-        r"""
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, MT5EncoderModel
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("mt5-small")
-        >>> model = MT5EncoderModel.from_pretrained("mt5-small")
-        >>> input_ids = tokenizer(
-        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
-        ... ).input_ids  # Batch size 1
-        >>> outputs = model(input_ids=input_ids)
-        >>> last_hidden_states = outputs.last_hidden_state
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        encoder_outputs = self.encoder(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        return encoder_outputs
-
-
-@add_start_docstrings(
-    """
-    MT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
-    tasks.
-    """,
-    MT5_START_DOCSTRING,
-)
-class MT5ForSequenceClassification(MT5PreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForSequenceClassification.__init__ with T5->MT5
-    def __init__(self, config: MT5Config):
-        super().__init__(config)
-        self.transformer = MT5Model(config)
-        self.classification_head = MT5ClassificationHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        self.model_parallel = False
-
-    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.t5.modeling_t5.T5ForSequenceClassification.forward
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        Returns:
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if labels is not None:
-            use_cache = False
-
-        if input_ids is None and inputs_embeds is not None:
-            raise NotImplementedError(
-                f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
-            )
-
-        # Copied from models.bart.modeling_bart.BartModel.forward different to other models, T5 automatically creates
-        # decoder_input_ids from input_ids if no decoder_input_ids are provided
-        if decoder_input_ids is None and decoder_inputs_embeds is None:
-            if input_ids is None:
-                raise ValueError(
-                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
-                    "passed, `input_ids` cannot be `None`. Please pass either "
-                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
-                )
-            decoder_input_ids = self._shift_right(input_ids)
-
-        outputs = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            decoder_input_ids=decoder_input_ids,
-            decoder_attention_mask=decoder_attention_mask,
-            head_mask=head_mask,
-            decoder_head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            encoder_outputs=encoder_outputs,
-            inputs_embeds=inputs_embeds,
-            decoder_inputs_embeds=decoder_inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-
-        eos_mask = input_ids.eq(self.config.eos_token_id).to(sequence_output.device)
-
-        if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
-            raise ValueError("All examples must have the same number of <eos> tokens.")
-        batch_size, _, hidden_size = sequence_output.shape
-        sentence_representation = sequence_output[eos_mask, :].view(batch_size, -1, hidden_size)[:, -1, :]
-        logits = self.classification_head(sentence_representation)
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.config.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.config.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return Seq2SeqSequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            decoder_hidden_states=outputs.decoder_hidden_states,
-            decoder_attentions=outputs.decoder_attentions,
-            cross_attentions=outputs.cross_attentions,
-            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
-            encoder_hidden_states=outputs.encoder_hidden_states,
-            encoder_attentions=outputs.encoder_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    MT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
-    on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    MT5_START_DOCSTRING,
-)
-class MT5ForQuestionAnswering(MT5PreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.__init__ with T5->MT5
-    def __init__(self, config: MT5Config):
-        super().__init__(config)
-        self.model_dim = config.d_model
-
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
-        encoder_config = copy.deepcopy(config)
-        encoder_config.is_decoder = False
-        encoder_config.use_cache = False
-        encoder_config.is_encoder_decoder = False
-        self.encoder = MT5Stack(encoder_config, self.shared)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        decoder_config.is_encoder_decoder = False
-        decoder_config.num_layers = config.num_decoder_layers
-        self.decoder = MT5Stack(decoder_config, self.shared)
-
-        self.num_labels = config.num_labels
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-        self.model_parallel = False
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.shared
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.set_input_embeddings
-    def set_input_embeddings(self, new_embeddings):
-        self.shared = new_embeddings
-        self.encoder.set_input_embeddings(new_embeddings)
-        self.decoder.set_input_embeddings(new_embeddings)
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_encoder
-    def get_encoder(self):
-        return self.encoder
-
-    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_decoder
-    def get_decoder(self):
-        return self.decoder
-
-    @add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.forward
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        decoder_head_mask: Optional[torch.FloatTensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqQuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
-            are not taken into account for computing the loss.
-        Returns:
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if start_positions is not None and end_positions is not None:
-            use_cache = False
-
-        # Copied from models.bart.modeling_bart.BartModel.forward
-        #   different to other models, T5 automatically creates decoder_input_ids from
-        #   input_ids if no decoder_input_ids are provided
-        if decoder_input_ids is None and decoder_inputs_embeds is None:
-            if input_ids is None:
-                raise ValueError(
-                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
-                    "passed, `input_ids` cannot be `None`. Please pass either "
-                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
-                )
-            decoder_input_ids = self._shift_right(input_ids)
-
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
-        if head_mask is not None and decoder_head_mask is None:
-            if self.config.num_layers == self.config.num_decoder_layers:
-                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
-                decoder_head_mask = head_mask
-
-        # Encode if needed (training, first prediction pass)
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                inputs_embeds=inputs_embeds,
-                head_mask=head_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        hidden_states = encoder_outputs[0]
-
-        # Decode
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            past_key_values=None,
-            encoder_hidden_states=hidden_states,
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = decoder_outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1).to(start_logits.device)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1).to(end_logits.device)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + decoder_outputs[1:] + encoder_outputs
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return Seq2SeqQuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/diffusion/dpm_solver_pytorch.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/diffusion/dpm_solver_pytorch.py
deleted file mode 100644
index dee5e280661b61e0a99038ce0bd240db51344ead..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/diffusion/dpm_solver_pytorch.py
+++ /dev/null
@@ -1,1201 +0,0 @@
-import math
-
-import torch
-
-
-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) * noise_schedule.total_N
-        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), dim=0).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_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', denoise=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: A `bool`. Whether to denoise at the final step. Default is False.
-                If `denoise` is True, the total NFE is (`steps` + 1).
-            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])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, 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.repeat(x.shape[0]), t_0_inner.repeat(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:
-            x = self.denoise_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)]
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/logger/utils.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/logger/utils.py
deleted file mode 100644
index 485681ced897980dc0bf5b149308245bbd708de9..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/logger/utils.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import yaml
-import json
-import pickle
-import torch
-
-def traverse_dir(
-        root_dir,
-        extensions,
-        amount=None,
-        str_include=None,
-        str_exclude=None,
-        is_pure=False,
-        is_sort=False,
-        is_ext=True):
-
-    file_list = []
-    cnt = 0
-    for root, _, files in os.walk(root_dir):
-        for file in files:
-            if any([file.endswith(f".{ext}") for ext in extensions]):
-                # path
-                mix_path = os.path.join(root, file)
-                pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
-                # amount
-                if (amount is not None) and (cnt == amount):
-                    if is_sort:
-                        file_list.sort()
-                    return file_list
-                
-                # check string
-                if (str_include is not None) and (str_include not in pure_path):
-                    continue
-                if (str_exclude is not None) and (str_exclude in pure_path):
-                    continue
-                
-                if not is_ext:
-                    ext = pure_path.split('.')[-1]
-                    pure_path = pure_path[:-(len(ext)+1)]
-                file_list.append(pure_path)
-                cnt += 1
-    if is_sort:
-        file_list.sort()
-    return file_list
-
-
-    
-class DotDict(dict):
-    def __getattr__(*args):         
-        val = dict.get(*args)         
-        return DotDict(val) if type(val) is dict else val   
-
-    __setattr__ = dict.__setitem__    
-    __delattr__ = dict.__delitem__
-
-
-def get_network_paras_amount(model_dict):
-    info = dict()
-    for model_name, model in model_dict.items():
-        # all_params = sum(p.numel() for p in model.parameters())
-        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-        info[model_name] = trainable_params
-    return info
-
-
-def load_config(path_config):
-    with open(path_config, "r") as config:
-        args = yaml.safe_load(config)
-    args = DotDict(args)
-    # print(args)
-    return args
-
-def save_config(path_config,config):
-    config = dict(config)
-    with open(path_config, "w") as f:
-        yaml.dump(config, f)
-
-def to_json(path_params, path_json):
-    params = torch.load(path_params, map_location=torch.device('cpu'))
-    raw_state_dict = {}
-    for k, v in params.items():
-        val = v.flatten().numpy().tolist()
-        raw_state_dict[k] = val
-
-    with open(path_json, 'w') as outfile:
-        json.dump(raw_state_dict, outfile,indent= "\t")
-
-
-def convert_tensor_to_numpy(tensor, is_squeeze=True):
-    if is_squeeze:
-        tensor = tensor.squeeze()
-    if tensor.requires_grad:
-        tensor = tensor.detach()
-    if tensor.is_cuda:
-        tensor = tensor.cpu()
-    return tensor.numpy()
-
-           
-def load_model(
-        expdir, 
-        model,
-        optimizer,
-        name='model',
-        postfix='',
-        device='cpu'):
-    if postfix == '':
-        postfix = '_' + postfix
-    path = os.path.join(expdir, name+postfix)
-    path_pt = traverse_dir(expdir, ['pt'], is_ext=False)
-    global_step = 0
-    if len(path_pt) > 0:
-        steps = [s[len(path):] for s in path_pt]
-        maxstep = max([int(s) if s.isdigit() else 0 for s in steps])
-        if maxstep >= 0:
-            path_pt = path+str(maxstep)+'.pt'
-        else:
-            path_pt = path+'best.pt'
-        print(' [*] restoring model from', path_pt)
-        ckpt = torch.load(path_pt, map_location=torch.device(device))
-        global_step = ckpt['global_step']
-        model.load_state_dict(ckpt['model'], strict=False)
-        if ckpt.get('optimizer') != None:
-            optimizer.load_state_dict(ckpt['optimizer'])
-    return global_step, model, optimizer
diff --git a/spaces/yufiofficial/MusicGenQ/audiocraft/quantization/core_vq.py b/spaces/yufiofficial/MusicGenQ/audiocraft/quantization/core_vq.py
deleted file mode 100644
index e1896bb1788a945a1f7be6369abb255ecf72c7a0..0000000000000000000000000000000000000000
--- a/spaces/yufiofficial/MusicGenQ/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/zihaoz96/shark-classifier/README.md b/spaces/zihaoz96/shark-classifier/README.md
deleted file mode 100644
index 4d05490f46ee76097f49273e84d296f87fefac20..0000000000000000000000000000000000000000
--- a/spaces/zihaoz96/shark-classifier/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Shark Classifier
-emoji: 🦈
-colorFrom: grey
-colorTo: blue 
-sdk: gradio
-app_file: app.py
-pinned: true
----
-
-# 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, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, 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/zlc99/M4Singer/usr/diffspeech_task.py b/spaces/zlc99/M4Singer/usr/diffspeech_task.py
deleted file mode 100644
index 05c313f94d07e91a94996a30bedd27b28c8cb04a..0000000000000000000000000000000000000000
--- a/spaces/zlc99/M4Singer/usr/diffspeech_task.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-
-import utils
-from utils.hparams import hparams
-from .diff.net import DiffNet
-from .diff.shallow_diffusion_tts import GaussianDiffusion
-from .task import DiffFsTask
-from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
-from utils.pitch_utils import denorm_f0
-from tasks.tts.fs2_utils import FastSpeechDataset
-
-DIFF_DECODERS = {
-    'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
-}
-
-
-class DiffSpeechTask(DiffFsTask):
-    def __init__(self):
-        super(DiffSpeechTask, self).__init__()
-        self.dataset_cls = FastSpeechDataset
-        self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
-
-    def build_tts_model(self):
-        mel_bins = hparams['audio_num_mel_bins']
-        self.model = GaussianDiffusion(
-            phone_encoder=self.phone_encoder,
-            out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
-            timesteps=hparams['timesteps'],
-            K_step=hparams['K_step'],
-            loss_type=hparams['diff_loss_type'],
-            spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
-        )
-        if hparams['fs2_ckpt'] != '':
-            utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True)
-        # self.model.fs2.decoder = None
-        for k, v in self.model.fs2.named_parameters():
-            if not 'predictor' in k:
-                v.requires_grad = False
-
-    def build_optimizer(self, model):
-        self.optimizer = optimizer = torch.optim.AdamW(
-            filter(lambda p: p.requires_grad, model.parameters()),
-            lr=hparams['lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            weight_decay=hparams['weight_decay'])
-        return optimizer
-
-    def run_model(self, model, sample, return_output=False, infer=False):
-        txt_tokens = sample['txt_tokens']  # [B, T_t]
-        target = sample['mels']  # [B, T_s, 80]
-        # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s]
-        mel2ph = sample['mel2ph']
-        f0 = sample['f0']
-        uv = sample['uv']
-        energy = sample['energy']
-        # fs2_mel = sample['fs2_mels']
-        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
-        if hparams['pitch_type'] == 'cwt':
-            cwt_spec = sample[f'cwt_spec']
-            f0_mean = sample['f0_mean']
-            f0_std = sample['f0_std']
-            sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
-
-        output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
-                       ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer)
-
-        losses = {}
-        if 'diff_loss' in output:
-            losses['mel'] = output['diff_loss']
-        self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
-        if hparams['use_pitch_embed']:
-            self.add_pitch_loss(output, sample, losses)
-        if hparams['use_energy_embed']:
-            self.add_energy_loss(output['energy_pred'], energy, losses)
-        if not return_output:
-            return losses
-        else:
-            return losses, output
-
-    def validation_step(self, sample, batch_idx):
-        outputs = {}
-        txt_tokens = sample['txt_tokens']  # [B, T_t]
-
-        energy = sample['energy']
-        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
-        mel2ph = sample['mel2ph']
-        f0 = sample['f0']
-        uv = sample['uv']
-
-        outputs['losses'] = {}
-
-        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
-
-
-        outputs['total_loss'] = sum(outputs['losses'].values())
-        outputs['nsamples'] = sample['nsamples']
-        outputs = utils.tensors_to_scalars(outputs)
-        if batch_idx < hparams['num_valid_plots']:
-            # model_out = self.model(
-            #     txt_tokens, spk_embed=spk_embed, mel2ph=None, f0=None, uv=None, energy=None, ref_mels=None, infer=True)
-            # self.plot_mel(batch_idx, model_out['mel_out'], model_out['fs2_mel'], name=f'diffspeech_vs_fs2_{batch_idx}')
-            model_out = self.model(
-                txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True)
-            gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
-            self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=model_out.get('f0_denorm'))
-            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'])
-        return outputs
-
-    ############
-    # validation plots
-    ############
-    def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None):
-        gt_wav = gt_wav[0].cpu().numpy()
-        wav_out = wav_out[0].cpu().numpy()
-        gt_f0 = gt_f0[0].cpu().numpy()
-        f0 = f0[0].cpu().numpy()
-        if is_mel:
-            gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0)
-            wav_out = self.vocoder.spec2wav(wav_out, f0=f0)
-        self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)
-        self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)
-
diff --git a/spaces/zlc99/M4Singer/utils/indexed_datasets.py b/spaces/zlc99/M4Singer/utils/indexed_datasets.py
deleted file mode 100644
index e15632be30d6296a3c9aa80a1f351058003698b3..0000000000000000000000000000000000000000
--- a/spaces/zlc99/M4Singer/utils/indexed_datasets.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import pickle
-from copy import deepcopy
-
-import numpy as np
-
-
-class IndexedDataset:
-    def __init__(self, path, num_cache=1):
-        super().__init__()
-        self.path = path
-        self.data_file = None
-        self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets']
-        self.data_file = open(f"{path}.data", 'rb', buffering=-1)
-        self.cache = []
-        self.num_cache = num_cache
-
-    def check_index(self, i):
-        if i < 0 or i >= len(self.data_offsets) - 1:
-            raise IndexError('index out of range')
-
-    def __del__(self):
-        if self.data_file:
-            self.data_file.close()
-
-    def __getitem__(self, i):
-        self.check_index(i)
-        if self.num_cache > 0:
-            for c in self.cache:
-                if c[0] == i:
-                    return c[1]
-        self.data_file.seek(self.data_offsets[i])
-        b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i])
-        item = pickle.loads(b)
-        if self.num_cache > 0:
-            self.cache = [(i, deepcopy(item))] + self.cache[:-1]
-        return item
-
-    def __len__(self):
-        return len(self.data_offsets) - 1
-
-class IndexedDatasetBuilder:
-    def __init__(self, path):
-        self.path = path
-        self.out_file = open(f"{path}.data", 'wb')
-        self.byte_offsets = [0]
-
-    def add_item(self, item):
-        s = pickle.dumps(item)
-        bytes = self.out_file.write(s)
-        self.byte_offsets.append(self.byte_offsets[-1] + bytes)
-
-    def finalize(self):
-        self.out_file.close()
-        np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets})
-
-
-if __name__ == "__main__":
-    import random
-    from tqdm import tqdm
-    ds_path = '/tmp/indexed_ds_example'
-    size = 100
-    items = [{"a": np.random.normal(size=[10000, 10]),
-              "b": np.random.normal(size=[10000, 10])} for i in range(size)]
-    builder = IndexedDatasetBuilder(ds_path)
-    for i in tqdm(range(size)):
-        builder.add_item(items[i])
-    builder.finalize()
-    ds = IndexedDataset(ds_path)
-    for i in tqdm(range(10000)):
-        idx = random.randint(0, size - 1)
-        assert (ds[idx]['a'] == items[idx]['a']).all()