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spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (download Gangs Of Wasseypur movie ut) Anurag Kashyaps Magnum Opus.md

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-<p>If you are a fan of Indian crime movies, you must have heard of <strong>Gangs of Wasseypur</strong>, a two-part epic film directed by Anurag Kashyap and starring Manoj Bajpayee, Nawazuddin Siddiqui, Richa Chadda, and Tigmanshu Dhulia. The film is based on the real-life story of the coal mafia in Dhanbad, Jharkhand, and their violent rivalry with a powerful politician.</p>
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-</ol>
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-<p>Using HD online player for Gangs of Wasseypur movie has some benefits that make it an attractive option for many viewers. Here are some of them:</p>
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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>
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-<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>
-<li><strong>Who wrote the story for Gangs of Wasseypur?</strong></li>
-<li>The story for Gangs of Wasseypur was written by Anurag Kashyap and Zeishan Quadri, who also acted in the movie as Definite Khan.</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>
-<li><strong>Why does Anurag Kashyap hate Gangs of Wasseypur?</strong></li>
-<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>
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spaces/1gistliPinn/ChatGPT4/Dish-Network-Acquiring-Signal-535-Stuck.md

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spaces/1phancelerku/anime-remove-background/Download Mortal Kombat vs Street Fighter The Ultimate MUGEN Game.md

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-<p>To find out your PC's specifications, you can use various methods, such as checking the system information on your PC settings, using a third-party software like Speccy or CPU-Z, or visiting a website like Can You Run It that automatically scans your PC and compares it with the game's requirements.</p>
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-<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 />

spaces/2023Liu2023/bingo/src/components/button-scroll-to-bottom.tsx

@@ -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>
-  )
-}

spaces/232labs/VToonify/vtoonify/model/bisenet/model.py

@@ -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()

spaces/AIConsultant/MusicGen/audiocraft/utils/cache.py

@@ -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

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/util.py

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

spaces/AIxPha/QSign/Dockerfile

@@ -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

spaces/Abdllh/Arabic_Poems_Generator/app.py

@@ -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()

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

@@ -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})"

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

@@ -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")))

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/configuration.md

@@ -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

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/deepfloyd_if/test_if_img2img.py

@@ -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,
-        )

spaces/Andy1621/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py

@@ -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))

spaces/Andy1621/uniformer_image_detection/mmdet/apis/__init__.py

@@ -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'
-]

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/DeepSpeed.md

@@ -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.

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/builder.py

@@ -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

spaces/Anthony7906/MengHuiMXD_GPT/custom.css

@@ -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 */

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/transforms.py

@@ -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

spaces/Ashrafb/translate/README.md

@@ -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/)

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

@@ -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

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

@@ -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',
-]

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/readers.py

@@ -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

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py

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

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/quick_schedules/README.md

@@ -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.

spaces/BAAI/SegGPT/app.py

@@ -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)
-# -
-
-

spaces/Benson/text-generation/Examples/Asfalto 9 Leyendas Descargar Sin Tienda Microsoft.md

@@ -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 />

spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Xender 2019.md

@@ -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 />

spaces/BetterAPI/BetterChat/src/lib/utils/analytics.ts

@@ -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,
-		});
-	}
-}

spaces/BetterAPI/BetterChat_new/src/lib/utils/randomUuid.ts

@@ -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();
-}

spaces/BilalSardar/Lyrics-Text_to_music/app.py

@@ -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)

spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/__init__.py

@@ -1,12 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : __init__.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# 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

spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/replicate.py

@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : replicate.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# 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

spaces/ChrisCaviar/ControlNet-v1-1/app_scribble.py

@@ -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()

spaces/CoPoBio/skin_cancer_risk_prediction/README.md

@@ -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

spaces/CohereForAI/pokemon-cards-explorer/src/upsert.py

@@ -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())

spaces/CorvaeOboro/gen_ability_icon/torch_utils/ops/upfirdn2d.h

@@ -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);
-
-//------------------------------------------------------------------------

spaces/DAOGEN/README/README.md

@@ -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

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GribStubImagePlugin.py

@@ -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")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/PcdImagePlugin.py

@@ -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")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py

@@ -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"

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-3cb0bda2.js

@@ -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

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Button-89624748.js

@@ -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};
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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_receivebuffer.py

@@ -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"

spaces/DaleChen/AutoGPT/autogpt/setup.py

@@ -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)

spaces/Docfile/open_llm_leaderboard/src/display_models/utils.py

@@ -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)

spaces/Dorado607/ChuanhuChatGPT/README.md

@@ -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

spaces/Dorado607/ChuanhuChatGPT/modules/models/Google_PaLM.py

@@ -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