Update parquet files (step 16 of 249)

spaces/101-5/gpt4free/testing/binghuan/BingHuan.py

@@ -1,49 +0,0 @@
-import os,sys
-import json
-import subprocess
-# from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://b.ai-huan.xyz'
-model = ['gpt-3.5-turbo', 'gpt-4']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages,
-        'model': model}, separators=(',', ':'))
-    cmd = ['python', f'{path}/helpers/binghuan.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-    
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('cp1252')
-    
-
-
-# params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-#     '(%s)' % ', '.join(
-#         [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
-
-
-# Temporary For ChatCompletion Class
-class ChatCompletion:
-    @staticmethod
-    def create(model: str, messages: list, provider: None or str, stream: bool = False, auth: str = False, **kwargs):
-        kwargs['auth'] = auth
-
-        if provider and needs_auth and not auth:
-            print(
-                f'ValueError: {provider} requires authentication (use auth="cookie or token or jwt ..." param)', file=sys.stderr)
-            sys.exit(1)
-
-        try:
-            return (_create_completion(model, messages, stream, **kwargs)
-                    if stream else ''.join(_create_completion(model, messages, stream, **kwargs)))
-        except TypeError as e:
-            print(e)
-            arg: str = str(e).split("'")[1]
-            print(
-                f"ValueError: {provider} does not support '{arg}' argument", file=sys.stderr)
-            sys.exit(1)

spaces/123Kumar/vits-uma-genshin-honkai123/app.py

@@ -1,124 +0,0 @@
-import time
-import gradio as gr
-import utils
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-from torch import no_grad, LongTensor
-import torch
-
-hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net_g_ms = SynthesizerTrn(
-    len(hps_ms.symbols),
-    hps_ms.data.filter_length // 2 + 1,
-    hps_ms.train.segment_size // hps_ms.data.hop_length,
-    n_speakers=hps_ms.data.n_speakers,
-    **hps_ms.model).to(device)
-_ = net_g_ms.eval()
-speakers = hps_ms.speakers
-model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 500:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = LongTensor([stn_tst.size(0)])
-        speaker_id = LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    app.queue(concurrency_count=1).launch()

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cricket BYOD Compatibility List What You Need to Know Before You Switch.md

@@ -1,40 +0,0 @@
-<br />
-<h1>How to Check if Your Phone is Compatible with Cricket's BYOD Program</h1>
-<p>Cricket Wireless is a prepaid wireless service provider that offers a variety of plans and features for customers who want to bring their own device (BYOD) to the network. However, not all devices are compatible with Cricket's network, so you need to check your phone's compatibility before you switch.</p>
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-<p>In this article, we will explain how to check if your phone is compatible with Cricket's BYOD program, what are the requirements and benefits of using your own device on Cricket, and what are some of the compatible devices that you can bring to Cricket.</p>
-<h2>How to Check Your Phone's Compatibility</h2>
-<p>The easiest way to check if your phone is compatible with Cricket's network is to use their online IMEI checker tool. IMEI stands for International Mobile Equipment Identity, and it is a unique 15-digit number that identifies your device. You can find your IMEI by dialing *#06# on your phone's keypad, or by looking in your phone's settings or on the back of your device.</p>
-<p>Once you have your IMEI, go to <a href="https://www.cricketwireless.com/cell-phones/bring-your-phone">https://www.cricketwireless.com/cell-phones/bring-your-phone</a> and enter it in the box. The tool will tell you if your phone is compatible with Cricket's network, and if it is eligible for HD Voice, which is a feature that enhances the quality and clarity of voice calls.</p>
-<p>If your phone is not compatible, you may need to unlock it from your current carrier, or buy a new device that works on Cricket's network. You can also check out Cricket's list of compatible devices <a href="https://www.cricketwireless.com/support/great-big-network/byod-compatibility.html">here</a>.</p>
-<h2>What are the Requirements and Benefits of BYOD</h2>
-<p>To bring your own device to Cricket, you need to meet the following requirements:</p>
-<p></p>
-<ul>
-<li>Your phone must be unlocked from your current carrier. You can contact your carrier to request an unlock code if you meet their criteria.</li>
-<li>Your phone must be HD Voice capable and compatible with Cricket's HD Voice network. Not all BYOD phones will work on Cricket's HD Voice network, which provides better sound quality and fewer dropped calls.</li>
-<li>Your phone must be activated on an eligible rate plan. You can choose from Cricket's unlimited plans or data-only plans, depending on your needs.</li>
-<li>You need to buy a Cricket SIM card and one month of service. You can order them online or at a Cricket store. You can also keep your current phone number or get a new one.</li>
-</ul>
-<p>By bringing your own device to Cricket, you can enjoy the following benefits:</p>
-<ul>
-<li>You can save money by not buying a new device or paying activation fees.</li>
-<li>You can keep using the phone you love and are familiar with.</li>
-<li>You can access Cricket's nationwide 4G LTE network and enjoy fast data speeds and reliable coverage.</li>
-<li>You can choose from a variety of plans and features that suit your budget and lifestyle.</li>
-<li>You can change your plan or device anytime without any contracts or penalties.</li>
-</ul>
-<h2>Some Compatible Devices You Can Bring to Cricket</h2>
-<p>Cricket has a wide range of compatible devices that you can bring to their network, including smartphones, feature phones, tablets, and data-only devices. Here are some examples of compatible devices that you can bring to Cricket:</p>
-<table border="1">
-<tr><th>Brand</th><th>Model</th></tr>
-<tr><td>Apple</td><td>iPhone 6 and later</td></tr>
-<tr><td>Google</td><td>Pixel 4 and later</td></tr>
-<tr><td>Samsung</td><td>Galaxy S9 and later</td></tr>
-<tr><td>LG</td><td>G8 ThinQ and later</td></tr>
-<tr><td>Moto</td><td>G Power and later</td></tr>
-<tr><td>Nokia</td><td>C5 Endi and later</td></tr>
-<tr><td>TCL</td><td>TCL 10 Pro and later</td></tr>
-<tr><td>Z</p> ddb901b051<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Diablo 2 Fury Within 1.09 A Mod Based on the Classic Patch 1.09 Version.md

@@ -1,166 +0,0 @@
-
-<h1>Download Diablo 2 Fury Within 1.09: A Guide for Fans of the Classic Action RPG</h1>
-<p>If you are a fan of Diablo 2, one of the most popular and influential action role-playing games of all time, you might be interested in trying out a mod that adds new content, features, and challenges to the game. Diablo 2 Fury Within 1.09 is a mod that aims to enhance the original game while staying true to its spirit and atmosphere. In this article, we will show you how to download, install, and play this mod, as well as some tips and tricks to make the most out of it.</p>
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-<h2>What is Diablo 2 Fury Within 1.09?</h2>
-<p>Diablo 2 Fury Within 1.09 is a mod for Diablo 2 that was created by a team of fans who wanted to improve the game in various ways. The mod was first released in 2005 and has been updated several times since then. The latest version, 1.09, was released in 2019.</p>
-<p>The mod adds new content such as classes, skills, items, monsters, quests, maps, music, sounds, graphics, and more. It also changes some aspects of the gameplay such as difficulty, balance, mechanics, interface, and more. The mod aims to make the game more fun, challenging, diverse, and replayable.</p>
-<h2>How to download Diablo 2 Fury Within 1.09?</h2>
-<p>To download Diablo 2 Fury Within 1.09, you will need a few things:</p>
-<ul>
-<li>A copy of Diablo 2 (preferably version 1.10 or higher)</li>
-<li>A copy of Diablo 2 Lord of Destruction expansion (preferably version 1.10 or higher)</li>
-<li>A ZIP file extractor program such as WinRAR or 7-Zip</li>
-<li>A reliable internet connection</li>
-</ul>
-<p>Once you have these things ready, you can follow these steps:</p>
-<h3>Requirements and compatibility</h3>
-<p>Before you download the mod, you should check if your system meets the minimum requirements to run it. The mod does not require a very powerful computer, but it does have some additional features that might affect your performance.</p>
-<p>The minimum requirements are:</p>
-<ul>
-<li>Windows XP or higher</li>
-<li>Pentium III or higher</li>
-<li>512 MB RAM or higher</li>
-<li>DirectX compatible sound card</li>
-<li>DirectX compatible video card with at least 32 MB VRAM</li>
-<li>4 GB free hard disk space</li>
-</ul>
-<p>You should also check if your version of Diablo 2 is compatible with the mod. The mod works best with version 1.10 or higher of both Diablo 2 and Lord of Destruction expansion. If you have an older version, you might encounter some issues or bugs.</p>
-<p>To check your version of Diablo 2, you can open the game launcher and look at the bottom left corner of the screen. You should see something like "Version x.xx". If you have an older version than 1.10, you can update your game by downloading and installing the latest patch from Blizzard's website.</p>
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-<h3>Download links and sources</h3>
-<p>Once you have verified your requirements and compatibility, you can proceed to download the mod files. The mod files are compressed in a ZIP file format that you will need to extract later.</p>
-<p>The official source for downloading the mod is its website: <a href="http://furywithin.org/">http://furywithin.org/</a>. Here you can find more information about the mod, its features, screenshots, videos, forums, support, and updates.</p>
-<p>The direct link for downloading the mod file is: <a href="http://furywithin.org/download/FuryWithin109.zip">http://furywithin.org/download/FuryWithin109.zip</a>. The file size is about 800 MB.</p>
-<p>You should always download the mod from its official source or from trusted websites that host it. You should avoid downloading it from unknown or suspicious sources that might contain viruses or malware.</p>
-<p>You should also verify the authenticity of the file by checking its checksum value. A checksum is a unique code that identifies a file based on its content. If two files have different checksum values, it means they are different files.</p>
-<p>The official checksum value for the mod file is:</p>
-<pre><code>MD5: e0c8b7d8c6b0e4c9d6e0b8f6c9e8c9a4 SHA-1: c5d0b7d8c6b0e4c9d6e0b8f6c9e8c9a4 SHA-256: c5d0b7d8c6b0e4c9d6e0b8f6c9e8c9a4 </code></pre>
-<p>You can use online tools such as <a href="https://md5file.com/calculator">https://md5file.com/calculator</a> or <a href="https://emn178.github.io/online-tools/sha256_checksum.html">https://emn178.github.io/online-tools/sha256_checksum.html</a> to calculate the checksum value of your downloaded file and compare it with the official one.</p>
-<h3>Installation process</h3>
-<p>After you have downloaded and verified the mod file, you can proceed to install it in your Diablo 2 folder. To do this, you will need a ZIP file extractor program such as WinRAR or 7-Zip.</p>
-<p>You can follow these steps:</p>
-<ol>
-<li>Locate your downloaded file (FuryWithin109.zip) and right-click on it.</li>
-<li>Select "Extract Here" or "Extract to FuryWithin109/" depending on your extractor program.</li>
-<li>You should see a new folder named "FuryWithin109" containing several files and subfolders.</li>
-<li>Open this folder and select all its contents (Ctrl+A).</li>
-<li>Copy them (Ctrl+C).</li>
-<li>Locate your Diablo 2 folder where you installed the game (usually C:\Program Files\Diablo II\).</li>
-<li>Paste them (Ctrl+V) into your Diablo 2 folder.</li>
-<li>You should see a prompt asking if you want to replace some existing files with new ones.</li>
-<li>Select "Yes" or "Yes to All" depending on your extractor program.</li>
-<li>You have successfully installed the mod in your Diablo 2 folder.</li>
- </ol>
- <h2>How to play Diablo 2 Fury Within 1.09?</h2>
- <p>To play Diablo 2 Fury Within 1.09, you just need to launch your Diablo 2 game as usual. You should see a new splash screen with the mod logo and version number.</p>
- <p>You can create a new character or use an existing one to play the mod. However, you should be aware that the mod is not compatible with some other mods or save files from the original game. You might encounter some errors or crashes if you try to use them.</p>
- <p>You should also backup your save files before playing the mod, in case you want to revert to the original game or switch to another mod. You can find your save files in your Diablo 2 folder under the subfolder "save". You can copy them to another location for safekeeping.</p>
- <p>Once you are in the game, you can enjoy the mod and its features. Here are some tips and tricks to help you:</p>
- <h3>New features and changes</h3>
- <p>The mod adds a lot of new content and changes to the game. Some of the main ones are:</p>
- <ul>
-<li>A new difficulty level called "Hellish" that is harder than Hell and has more powerful enemies and rewards.</li>
-<li>A new game mode called "Hardcore" that is similar to the original Hardcore mode but with some extra challenges and penalties.</li>
-<li>A new option called "Randomize" that allows you to randomize some aspects of the game such as maps, monsters, items, quests, and more.</li>
-<li>A new option called "Rebirth" that allows you to reset your character's level, skills, and stats without losing your items or quests.</li>
-<li>A new option called "Respec" that allows you to redistribute your skill points and stat points without using any items or quests.</li>
-<li>A new option called "Gambling" that allows you to gamble for items using gold or gems.</li>
-<li>A new option called "Crafting" that allows you to create new items using materials and recipes.</li>
-<li>A new option called "Enchanting" that allows you to enhance your items using runes and charms.</li>
-<li>A new option called "Socketing" that allows you to add sockets to your items using jewels and gems.</li>
-<li>A new option called "Transmuting" that allows you to transform your items using formulas and catalysts.</li>
-<li>A new option called "Trading" that allows you to exchange your items with other players online or offline.</li>
-<li>A new option called "Stashing" that allows you to store your items in a shared stash that can be accessed by all your characters.</li>
-<li>A new option called "Donating" that allows you to donate your items to a charity box that can be accessed by other players online or offline.</li>
-<li>A new option called "Cheating" that allows you to cheat in various ways such as giving yourself gold, items, skills, stats, and more.</li>
-</ul>
- <p>You can access these options by clicking on the icons on the bottom right corner of the screen or by pressing the corresponding hotkeys (F1-F12).</p>
- <h3>New classes and skills</h3>
- <p>The mod adds six new classes to the game, each with their own unique skills and playstyles. They are:</p>
- <ul>
-<li>The Paladin: A holy warrior who uses auras, blessings, and smites to fight evil.</li>
-<li>The Necromancer: A dark summoner who uses curses, minions, and bones to manipulate death.</li>
-<li>The Assassin: A stealthy killer who uses traps, martial arts, and shadow disciplines to strike from the shadows.</li>
-<li>The Druid: A nature shifter who uses elemental magic, shape-shifting, and summoning to harness the power of nature.</li>
-<li>The Amazon: A skilled archer who uses bows, javelins, spears, and passive skills to hunt down her enemies.</li>
-<li>The Barbarian: A fierce warrior who uses swords, axes, maces, and war cries to dominate the battlefield.</li>
-</ul>
- <p>You can choose one of these classes when creating a new character or use a Rebirth option to change your existing character's class. You can also use a Respec option to change your skill points allocation at any time.</p>
- <p>Each class has three skill trees with 10 skills each. You can learn these skills by spending skill points that you earn by leveling up or completing quests. You can also find skill books that grant you additional skill points or teach you specific skills.</p>
- <p>Some skills have synergies with other skills, meaning they become more powerful when combined together. You can see these synergies by hovering over a skill icon or pressing the shift key while selecting a skill.</p>
- <h3>New items and crafting</h3>
- <p>, materials, recipes, formulas, catalysts, and more. You can find these items by killing monsters, opening chests, gambling, crafting, transmuting, trading, donating, or cheating.</p>
- <p>Some items have special properties such as prefixes, suffixes, set bonuses, unique effects, ethereal quality, socketed slots, and more. You can see these properties by hovering over an item icon or pressing the alt key while looking at an item.</p>
- <p>Some items can be upgraded or modified using other items such as runes, charms, jewels, gems, materials, recipes, formulas, catalysts, and more. You can do this by using the crafting, enchanting, socketing, or transmuting options.</p>
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- <p>Diablo 2 Fury Within 1.09 is a mod that modifies the original game in many ways. As such, it might cause some issues or problems for some players. Here are some common issues and solutions for playing the mod:</p>
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-</table></p> 197e85843d<br />
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spaces/2ndelement/voicevox/speaker_info/35b2c544-660e-401e-b503-0e14c635303a/policy.md

@@ -1,3 +0,0 @@
-dummy3 policy
-
-https://voicevox.hiroshiba.jp/

spaces/30Kanika/Animal_Image_Classifier/README.md

@@ -1,13 +0,0 @@
----
-title: Animal Image Classifier
-emoji: 🌍
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/52Hz/CMFNet_dehazing/model/CMFNet.py

@@ -1,191 +0,0 @@
-import torch
-import torch.nn as nn
-from model.block import SAB, CAB, PAB, conv, SAM, conv3x3, conv_down
-
-##########################################################################
-## U-Net
-bn = 2  # block number-1
-
-class Encoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Encoder, self).__init__()
-        if block == 'CAB':
-            self.encoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.encoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.encoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.encoder_level1 = nn.Sequential(*self.encoder_level1)
-        self.encoder_level2 = nn.Sequential(*self.encoder_level2)
-        self.encoder_level3 = nn.Sequential(*self.encoder_level3)
-        self.down12 = DownSample(n_feat, scale_unetfeats)
-        self.down23 = DownSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, x):
-        enc1 = self.encoder_level1(x)
-        x = self.down12(enc1)
-        enc2 = self.encoder_level2(x)
-        x = self.down23(enc2)
-        enc3 = self.encoder_level3(x)
-        return [enc1, enc2, enc3]
-
-class Decoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Decoder, self).__init__()
-        if block == 'CAB':
-            self.decoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.decoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.decoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.decoder_level1 = nn.Sequential(*self.decoder_level1)
-        self.decoder_level2 = nn.Sequential(*self.decoder_level2)
-        self.decoder_level3 = nn.Sequential(*self.decoder_level3)
-        if block == 'CAB':
-            self.skip_attn1 = CAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'PAB':
-            self.skip_attn1 = PAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'SAB':
-            self.skip_attn1 = SAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        self.up21 = SkipUpSample(n_feat, scale_unetfeats)
-        self.up32 = SkipUpSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, outs):
-        enc1, enc2, enc3 = outs
-        dec3 = self.decoder_level3(enc3)
-        x = self.up32(dec3, self.skip_attn2(enc2))
-        dec2 = self.decoder_level2(x)
-        x = self.up21(dec2, self.skip_attn1(enc1))
-        dec1 = self.decoder_level1(x)
-        return [dec1, dec2, dec3]
-
-##########################################################################
-##---------- Resizing Modules ----------
-class DownSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(DownSample, self).__init__()
-        self.down = nn.Sequential(nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=False),
-                                  nn.Conv2d(in_channels, in_channels + s_factor, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.down(x)
-        return x
-
-class UpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(UpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.up(x)
-        return x
-
-class SkipUpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(SkipUpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x, y):
-        x = self.up(x)
-        x = x + y
-        return x
-
-##########################################################################
-# Mixed Residual Module
-class Mix(nn.Module):
-    def __init__(self, m=1):
-        super(Mix, self).__init__()
-        w = nn.Parameter(torch.FloatTensor([m]), requires_grad=True)
-        w = nn.Parameter(w, requires_grad=True)
-        self.w = w
-        self.mix_block = nn.Sigmoid()
-
-    def forward(self, fea1, fea2, feat3):
-        factor = self.mix_block(self.w)
-        other = (1 - factor)/2
-        output = fea1 * other.expand_as(fea1) + fea2 * factor.expand_as(fea2) + feat3 * other.expand_as(feat3)
-        return output, factor
-
-##########################################################################
-# Architecture
-class CMFNet(nn.Module):
-    def __init__(self, in_c=3, out_c=3, n_feat=96, scale_unetfeats=48, kernel_size=3, reduction=4, bias=False):
-        super(CMFNet, self).__init__()
-
-        p_act = nn.PReLU()
-        self.shallow_feat1 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat2 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat3 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-
-        self.stage1_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-        self.stage1_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-
-        self.stage2_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-        self.stage2_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-
-        self.stage3_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-        self.stage3_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-
-        self.sam1o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam2o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam3o = SAM(n_feat, kernel_size=3, bias=bias)
-
-        self.mix = Mix(1)
-        self.add123 = conv(out_c, out_c, kernel_size, bias=bias)
-        self.concat123 = conv(n_feat*3, n_feat, kernel_size, bias=bias)
-        self.tail = conv(n_feat, out_c, kernel_size, bias=bias)
-
-
-    def forward(self, x):
-        ## Compute Shallow Features
-        shallow1 = self.shallow_feat1(x)
-        shallow2 = self.shallow_feat2(x)
-        shallow3 = self.shallow_feat3(x)
-
-        ## Enter the UNet-CAB
-        x1 = self.stage1_encoder(shallow1)
-        x1_D = self.stage1_decoder(x1)
-        ## Apply SAM
-        x1_out, x1_img = self.sam1o(x1_D[0], x)
-
-        ## Enter the UNet-PAB
-        x2 = self.stage2_encoder(shallow2)
-        x2_D = self.stage2_decoder(x2)
-        ## Apply SAM
-        x2_out, x2_img = self.sam2o(x2_D[0], x)
-
-        ## Enter the UNet-SAB
-        x3 = self.stage3_encoder(shallow3)
-        x3_D = self.stage3_decoder(x3)
-        ## Apply SAM
-        x3_out, x3_img = self.sam3o(x3_D[0], x)
-
-        ## Aggregate SAM features of Stage 1, Stage 2 and Stage 3
-        mix_r = self.mix(x1_img, x2_img, x3_img)
-        mixed_img = self.add123(mix_r[0])
-
-        ## Concat SAM features of Stage 1, Stage 2 and Stage 3
-        concat_feat = self.concat123(torch.cat([x1_out, x2_out, x3_out], 1))
-        x_final = self.tail(concat_feat)
-
-        return x_final + mixed_img

spaces/AIZ2H/08-Search-Streamlit-Session-State-QueryParameters/README.md

@@ -1,13 +0,0 @@
----
-title: 08 Search Streamlit Session State QueryParameters
-emoji: 🔎🧠
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-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/AIZero2Hero4Health/1-ASRLiveSpeechRecognition-GR/app.py

@@ -1,169 +0,0 @@
-import gradio as gr
-import torch
-import time
-import librosa
-import soundfile
-import nemo.collections.asr as nemo_asr
-import tempfile
-import os
-import uuid
-
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-import torch
-
-# PersistDataset -----
-import os
-import csv
-import gradio as gr
-from gradio import inputs, outputs
-import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
-from datetime import datetime
-
-# ---------------------------------------------
-# Dataset and Token links - change awacke1 to your own HF id, and add a HF_TOKEN copy to your repo for write permissions
-# This should allow you to save your results to your own Dataset hosted on HF. ---
-#DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
-#DATASET_REPO_ID = "awacke1/Carddata.csv"
-#DATA_FILENAME = "Carddata.csv"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-#HF_TOKEN = os.environ.get("HF_TOKEN")
-#SCRIPT = """
-
-#<script>
-#if (!window.hasBeenRun) {
-#    window.hasBeenRun = true;
-#    console.log("should only happen once");
-#    document.querySelector("button.submit").click();
-#}
-#</script>
-#"""
-
-#try:
-#    hf_hub_download(
-#        repo_id=DATASET_REPO_ID,
-#        filename=DATA_FILENAME,
-#        cache_dir=DATA_DIRNAME,
-#        force_filename=DATA_FILENAME
-#    )
-#except:
-#    print("file not found")
-#repo = Repository(
-#    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-#)
-           
-#def store_message(name: str, message: str):
-#    if name and message:
-#        with open(DATA_FILE, "a") as csvfile:
-#            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
-#            writer.writerow(
-#                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
-#            )
-#        # uncomment line below to begin saving - 
-#        commit_url = repo.push_to_hub()
-#    return ""            
-
-#iface = gr.Interface(
-#    store_message,
-#    [
-#        inputs.Textbox(placeholder="Your name"),
-#        inputs.Textbox(placeholder="Your message", lines=2),
-#    ],
-#    "html",
-#    css="""
-#    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
-#    """,
-#    title="Reading/writing to a HuggingFace dataset repo from Spaces",
-#    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
-#    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
-#)
-
-
-# main -------------------------
-mname = "facebook/blenderbot-400M-distill"
-model = BlenderbotForConditionalGeneration.from_pretrained(mname)
-tokenizer = BlenderbotTokenizer.from_pretrained(mname)
-
-def take_last_tokens(inputs, note_history, history):
-    """Filter the last 128 tokens"""
-    if inputs['input_ids'].shape[1] > 128:
-        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
-        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
-        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
-        history = history[1:]
-    return inputs, note_history, history
-
-def add_note_to_history(note, note_history):
-    """Add a note to the historical information"""
-    note_history.append(note)
-    note_history = '</s> <s>'.join(note_history)
-    return [note_history]
-
-
-def chat(message, history):
-    history = history or []
-    if history: 
-        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
-    else:
-        history_useful = []
-    history_useful = add_note_to_history(message, history_useful)
-    inputs = tokenizer(history_useful, return_tensors="pt")
-    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
-    reply_ids = model.generate(**inputs)
-    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
-    history_useful = add_note_to_history(response, history_useful)
-    list_history = history_useful[0].split('</s> <s>')
-    history.append((list_history[-2], list_history[-1]))
-#    store_message(message, response) # Save to dataset - uncomment if you uncomment above to save inputs and outputs to your dataset
-    return history, history
-    
-
-SAMPLE_RATE = 16000
-model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
-model.change_decoding_strategy(None)
-model.eval()
-
-def process_audio_file(file):
-    data, sr = librosa.load(file)
-    if sr != SAMPLE_RATE:
-        data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
-    # monochannel
-    data = librosa.to_mono(data)
-    return data
-
-
-def transcribe(audio, state = ""):   
-    if state is None:
-        state = ""
-    audio_data = process_audio_file(audio)
-    with tempfile.TemporaryDirectory() as tmpdir:
-        audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
-        soundfile.write(audio_path, audio_data, SAMPLE_RATE)
-        transcriptions = model.transcribe([audio_path])
-        if type(transcriptions) == tuple and len(transcriptions) == 2:
-            transcriptions = transcriptions[0]
-        transcriptions = transcriptions[0]
-#    store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
-    state = state + transcriptions + " "
-    return state, state
-
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.Audio(source="microphone", type='filepath', streaming=True),
-        "state",
-    ],
-    outputs=[
-        "textbox",
-        "state",
-    ],
-    layout="horizontal",
-    theme="huggingface",
-    title="🗣️LiveSpeechRecognition🧠Memory💾",
-    description=f"Live Automatic Speech Recognition (ASR) with Memory💾 Dataset.",
-    allow_flagging='never',
-    live=True,    
-#    article=f"Result Output Saved to Memory💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
-    article=f"Important Videos to understanding AI and NLP Clinical Terminology, Assessment, and Value Based Care AI include Huggingfaces Course Series here: https://www.youtube.com/c/HuggingFace , AI NLP Innovations in 2022 for Clinical and Mental Health Care here: https://www.youtube.com/watch?v=r38lXjz3g6M&list=PLHgX2IExbFov_5_4WfkesR7gnWPHHG-a1  and this link to see and manage playlist here: https://www.youtube.com/playlist?list=PLHgX2IExbFov_5_4WfkesR7gnWPHHG-a1  Review at your leisure to understand AI and NLP impact to helping the world develop Clinical systems of the future using AI and NLP for Clinical Terminology and alignment to worldwide Value Based Care objectives to help people be healthy."
-)
-iface.launch()

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/server/internal.js

@@ -1,30 +0,0 @@
-
-import root from '../root.svelte';
-import { set_building } from '__sveltekit/environment';
-import { set_assets } from '__sveltekit/paths';
-import { set_private_env, set_public_env } from '../../../node_modules/@sveltejs/kit/src/runtime/shared-server.js';
-
-export const options = {
-	app_template_contains_nonce: false,
-	csp: {"mode":"auto","directives":{"upgrade-insecure-requests":false,"block-all-mixed-content":false},"reportOnly":{"upgrade-insecure-requests":false,"block-all-mixed-content":false}},
-	csrf_check_origin: false,
-	track_server_fetches: false,
-	embedded: false,
-	env_public_prefix: 'PUBLIC_',
-	env_private_prefix: '',
-	hooks: null, // added lazily, via `get_hooks`
-	preload_strategy: "modulepreload",
-	root,
-	service_worker: false,
-	templates: {
-		app: ({ head, body, assets, nonce, env }) => "<!DOCTYPE html>\r\n<html lang=\"en\" class=\"h-full\">\r\n\t<link rel=\"stylesheet\" href=\"https://www.w3schools.com/w3css/4/w3.css\" />\r\n\t<head>\r\n\t\t<!-- Google Tag Manager -->\r\n\t\t<script>\r\n\t\tvar _paq = window._paq || [];\r\n\t\twindow._paq=_paq;\r\n\t\t(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':\r\n\t\tnew Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],\r\n\t\tj=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=\r\n\t\t'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);\r\n\t\t})(window,document,'script','dataLayer','GTM-TVD93MF');\r\n\t\t</script>\r\n\t\t<!-- End Google Tag Manager -->\r\n\t\t<meta charset=\"utf-8\" />\r\n\t\t<meta name=\"viewport\" content=\"width=device-width, initial-scale=1, user-scalable=no\" />\r\n\t\t<meta property=\"og:image\" content=\"/chatui/thumbnail.jpg\" />\r\n\t\t<script>\r\n\t\t\tif (\r\n\t\t\t\tlocalStorage.theme === \"dark\" ||\r\n\t\t\t\t(!(\"theme\" in localStorage) && window.matchMedia(\"(prefers-color-scheme: dark)\").matches)\r\n\t\t\t) {\r\n\t\t\t\tdocument.documentElement.classList.add(\"dark\");\r\n\t\t\t}\r\n\t\t</script>\r\n\t\t" + head + "\r\n\t</head>\r\n\t<body data-sveltekit-preload-data=\"hover\" class=\"h-full dark:bg-gray-900\">\r\n\t\t<div id=\"app\" class=\"contents h-full\">" + body + "</div>\r\n\t</body>\r\n</html>\r\n",
-		error: ({ status, message }) => "<!DOCTYPE html>\n<html lang=\"en\">\n\t<head>\n\t\t<meta charset=\"utf-8\" />\n\t\t<title>" + message + "</title>\n\n\t\t<style>\n\t\t\tbody {\n\t\t\t\t--bg: white;\n\t\t\t\t--fg: #222;\n\t\t\t\t--divider: #ccc;\n\t\t\t\tbackground: var(--bg);\n\t\t\t\tcolor: var(--fg);\n\t\t\t\tfont-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen,\n\t\t\t\t\tUbuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;\n\t\t\t\tdisplay: flex;\n\t\t\t\talign-items: center;\n\t\t\t\tjustify-content: center;\n\t\t\t\theight: 100vh;\n\t\t\t\tmargin: 0;\n\t\t\t}\n\n\t\t\t.error {\n\t\t\t\tdisplay: flex;\n\t\t\t\talign-items: center;\n\t\t\t\tmax-width: 32rem;\n\t\t\t\tmargin: 0 1rem;\n\t\t\t}\n\n\t\t\t.status {\n\t\t\t\tfont-weight: 200;\n\t\t\t\tfont-size: 3rem;\n\t\t\t\tline-height: 1;\n\t\t\t\tposition: relative;\n\t\t\t\ttop: -0.05rem;\n\t\t\t}\n\n\t\t\t.message {\n\t\t\t\tborder-left: 1px solid var(--divider);\n\t\t\t\tpadding: 0 0 0 1rem;\n\t\t\t\tmargin: 0 0 0 1rem;\n\t\t\t\tmin-height: 2.5rem;\n\t\t\t\tdisplay: flex;\n\t\t\t\talign-items: center;\n\t\t\t}\n\n\t\t\t.message h1 {\n\t\t\t\tfont-weight: 400;\n\t\t\t\tfont-size: 1em;\n\t\t\t\tmargin: 0;\n\t\t\t}\n\n\t\t\t@media (prefers-color-scheme: dark) {\n\t\t\t\tbody {\n\t\t\t\t\t--bg: #222;\n\t\t\t\t\t--fg: #ddd;\n\t\t\t\t\t--divider: #666;\n\t\t\t\t}\n\t\t\t}\n\t\t</style>\n\t</head>\n\t<body>\n\t\t<div class=\"error\">\n\t\t\t<span class=\"status\">" + status + "</span>\n\t\t\t<div class=\"message\">\n\t\t\t\t<h1>" + message + "</h1>\n\t\t\t</div>\n\t\t</div>\n\t</body>\n</html>\n"
-	},
-	version_hash: "r3vpsq"
-};
-
-export function get_hooks() {
-	return import("../../../src/hooks.server.ts");
-}
-
-export { set_assets, set_building, set_private_env, set_public_env };

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinput/ColorInput.d.ts

@@ -1,59 +0,0 @@
-import ColorInputBase from '../colorinputbase/ColorInputBase';
-import RoundRectangle from '../../roundrectangle/RoundRectangle';
-import ColorComponents from '../colorcomponents/ColorComponents';
-import CanvasInput from '../../canvasinput/CanvasInput';
-
-export default ColorInput;
-
-declare namespace ColorInput {
-    type TransitCallbackType = (
-        gameObject: Phaser.GameObjects.GameObject,
-        duration: number
-    ) => void;
-
-    interface IConfig extends ColorInputBase.IConfig {
-        colorPicker?: {
-            width?: number, height?: number,
-
-            background?: RoundRectangle.IConfig,
-            createBackgroundCallback: (
-                scene: Phaser.Scene,
-            ) => Phaser.GameObjects.GameObject,
-
-            hPalettePosition?: 0 | 1 | 2 | 3 | 'bottom' | 'left' | 'top' | 'right',
-
-            expandDirection?: 0 | 1 | 'down' | 'up',
-
-            easeIn?: number, easeOut?: number,
-
-            transitIn?: TransitCallbackType,
-            transitOut?: TransitCallbackType,
-
-            bounds?: Phaser.Geom.Rectangle;
-
-            space?: {
-                left?: number, right?: number, top?: number, bottom?: number,
-                item?: number,
-            }
-        },
-
-        colorComponents?: {
-            height?: number,
-
-            formatLabel?: ColorComponents.IFormatLabelConfig,
-
-            inputText?: CanvasInput.IConfig,
-
-            space?: {
-                left?: number, right?: number, top?: number, bottom?: number,
-            },
-        }
-    }
-}
-
-declare class ColorInput extends ColorInputBase {
-    constructor(
-        scene: Phaser.Scene,
-        config?: ColorInput.IConfig
-    );
-}

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

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

spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/keypoint_detector.py

@@ -1,27 +0,0 @@
-from torch import nn
-import torch
-from torchvision import models
-
-class KPDetector(nn.Module):
-    """
-    Predict K*5 keypoints.
-    """
-
-    def __init__(self, num_tps, **kwargs):
-        super(KPDetector, self).__init__()
-        self.num_tps = num_tps
-
-        self.fg_encoder = models.resnet18(pretrained=False)
-        num_features = self.fg_encoder.fc.in_features
-        self.fg_encoder.fc = nn.Linear(num_features, num_tps*5*2)
-
-        
-    def forward(self, image):
-
-        fg_kp = self.fg_encoder(image)
-        bs, _, = fg_kp.shape
-        fg_kp = torch.sigmoid(fg_kp)
-        fg_kp = fg_kp * 2 - 1
-        out = {'fg_kp': fg_kp.view(bs, self.num_tps*5, -1)}
-
-        return out

spaces/AlexKozachuk/anything-v3.0/README.md

@@ -1,13 +0,0 @@
----
-title: Anything V3.0
-emoji: 🏃
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: yuessiah/anything-v3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Allakhazam/Home/README.md

@@ -1,11 +0,0 @@
----
-title: Home Prompts
-emoji: 🏆
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/criteria/clip_loss.py

@@ -1,17 +0,0 @@
-
-import torch
-import clip
-
-
-class CLIPLoss(torch.nn.Module):
-
-    def __init__(self, opts):
-        super(CLIPLoss, self).__init__()
-        self.model, self.preprocess = clip.load("ViT-B/32", device="cuda")
-        self.upsample = torch.nn.Upsample(scale_factor=7)
-        self.avg_pool = torch.nn.AvgPool2d(kernel_size=opts.stylegan_size // 32)
-
-    def forward(self, image, text):
-        image = self.avg_pool(self.upsample(image))
-        similarity = 1 - self.model(image, text)[0] / 100
-        return similarity

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/experimental/rl/value_guided_sampling.py

@@ -1,154 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import torch
-import tqdm
-
-from ...models.unet_1d import UNet1DModel
-from ...pipelines import DiffusionPipeline
-from ...utils import randn_tensor
-from ...utils.dummy_pt_objects import DDPMScheduler
-
-
-class ValueGuidedRLPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        value_function ([`UNet1DModel`]):
-            A specialized UNet for fine-tuning trajectories base on reward.
-        unet ([`UNet1DModel`]):
-            UNet architecture to denoise the encoded trajectories.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this
-            application is [`DDPMScheduler`].
-        env ():
-            An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.
-    """
-
-    def __init__(
-        self,
-        value_function: UNet1DModel,
-        unet: UNet1DModel,
-        scheduler: DDPMScheduler,
-        env,
-    ):
-        super().__init__()
-        self.value_function = value_function
-        self.unet = unet
-        self.scheduler = scheduler
-        self.env = env
-        self.data = env.get_dataset()
-        self.means = {}
-        for key in self.data.keys():
-            try:
-                self.means[key] = self.data[key].mean()
-            except:  # noqa: E722
-                pass
-        self.stds = {}
-        for key in self.data.keys():
-            try:
-                self.stds[key] = self.data[key].std()
-            except:  # noqa: E722
-                pass
-        self.state_dim = env.observation_space.shape[0]
-        self.action_dim = env.action_space.shape[0]
-
-    def normalize(self, x_in, key):
-        return (x_in - self.means[key]) / self.stds[key]
-
-    def de_normalize(self, x_in, key):
-        return x_in * self.stds[key] + self.means[key]
-
-    def to_torch(self, x_in):
-        if type(x_in) is dict:
-            return {k: self.to_torch(v) for k, v in x_in.items()}
-        elif torch.is_tensor(x_in):
-            return x_in.to(self.unet.device)
-        return torch.tensor(x_in, device=self.unet.device)
-
-    def reset_x0(self, x_in, cond, act_dim):
-        for key, val in cond.items():
-            x_in[:, key, act_dim:] = val.clone()
-        return x_in
-
-    def run_diffusion(self, x, conditions, n_guide_steps, scale):
-        batch_size = x.shape[0]
-        y = None
-        for i in tqdm.tqdm(self.scheduler.timesteps):
-            # create batch of timesteps to pass into model
-            timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)
-            for _ in range(n_guide_steps):
-                with torch.enable_grad():
-                    x.requires_grad_()
-
-                    # permute to match dimension for pre-trained models
-                    y = self.value_function(x.permute(0, 2, 1), timesteps).sample
-                    grad = torch.autograd.grad([y.sum()], [x])[0]
-
-                    posterior_variance = self.scheduler._get_variance(i)
-                    model_std = torch.exp(0.5 * posterior_variance)
-                    grad = model_std * grad
-
-                grad[timesteps < 2] = 0
-                x = x.detach()
-                x = x + scale * grad
-                x = self.reset_x0(x, conditions, self.action_dim)
-
-            prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
-
-            # TODO: verify deprecation of this kwarg
-            x = self.scheduler.step(prev_x, i, x, predict_epsilon=False)["prev_sample"]
-
-            # apply conditions to the trajectory (set the initial state)
-            x = self.reset_x0(x, conditions, self.action_dim)
-            x = self.to_torch(x)
-        return x, y
-
-    def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):
-        # normalize the observations and create  batch dimension
-        obs = self.normalize(obs, "observations")
-        obs = obs[None].repeat(batch_size, axis=0)
-
-        conditions = {0: self.to_torch(obs)}
-        shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)
-
-        # generate initial noise and apply our conditions (to make the trajectories start at current state)
-        x1 = randn_tensor(shape, device=self.unet.device)
-        x = self.reset_x0(x1, conditions, self.action_dim)
-        x = self.to_torch(x)
-
-        # run the diffusion process
-        x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)
-
-        # sort output trajectories by value
-        sorted_idx = y.argsort(0, descending=True).squeeze()
-        sorted_values = x[sorted_idx]
-        actions = sorted_values[:, :, : self.action_dim]
-        actions = actions.detach().cpu().numpy()
-        denorm_actions = self.de_normalize(actions, key="actions")
-
-        # select the action with the highest value
-        if y is not None:
-            selected_index = 0
-        else:
-            # if we didn't run value guiding, select a random action
-            selected_index = np.random.randint(0, batch_size)
-
-        denorm_actions = denorm_actions[selected_index, 0]
-        return denorm_actions

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/versatile_diffusion/test_versatile_diffusion_image_variation.py

@@ -1,57 +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 unittest
-
-import numpy as np
-import torch
-
-from diffusers import VersatileDiffusionImageVariationPipeline
-from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-class VersatileDiffusionImageVariationPipelineFastTests(unittest.TestCase):
-    pass
-
-
-@slow
-@require_torch_gpu
-class VersatileDiffusionImageVariationPipelineIntegrationTests(unittest.TestCase):
-    def test_inference_image_variations(self):
-        pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion")
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        image_prompt = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
-        )
-        generator = torch.manual_seed(0)
-        image = pipe(
-            image=image_prompt,
-            generator=generator,
-            guidance_scale=7.5,
-            num_inference_steps=50,
-            output_type="numpy",
-        ).images
-
-        image_slice = image[0, 253:256, 253:256, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

spaces/Andy1621/uniformer_image_detection/configs/swin/cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py

@@ -1,140 +0,0 @@
-_base_ = [
-    '../_base_/models/cascade_mask_rcnn_swin_fpn.py',
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
-    backbone=dict(
-        embed_dim=96,
-        depths=[2, 2, 18, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        ape=False,
-        drop_path_rate=0.2,
-        patch_norm=True,
-        use_checkpoint=False
-    ),
-    neck=dict(in_channels=[96, 192, 384, 768]),
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
-        ]))
-
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='AutoAugment',
-         policies=[
-             [
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                                 (736, 1333), (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True)
-             ],
-             [
-                 dict(type='Resize',
-                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True),
-                 dict(type='RandomCrop',
-                      crop_type='absolute_range',
-                      crop_size=(384, 600),
-                      allow_negative_crop=True),
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                 (576, 1333), (608, 1333), (640, 1333),
-                                 (672, 1333), (704, 1333), (736, 1333),
-                                 (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      override=True,
-                      keep_ratio=True)
-             ]
-         ]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
-    type="DistOptimizerHook",
-    update_interval=1,
-    grad_clip=None,
-    coalesce=True,
-    bucket_size_mb=-1,
-    use_fp16=True,
-)

spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py

@@ -1,12 +0,0 @@
-_base_ = [
-    '../_base_/models/dnl_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
-optimizer = dict(
-    paramwise_cfg=dict(
-        custom_keys=dict(theta=dict(wd_mult=0.), phi=dict(wd_mult=0.))))

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Ariharasudhan/YoloV5/utils/segment/loss.py

@@ -1,186 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..general import xywh2xyxy
-from ..loss import FocalLoss, smooth_BCE
-from ..metrics import bbox_iou
-from ..torch_utils import de_parallel
-from .general import crop_mask
-
-
-class ComputeLoss:
-    # Compute losses
-    def __init__(self, model, autobalance=False, overlap=False):
-        self.sort_obj_iou = False
-        self.overlap = overlap
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-        self.device = device
-
-        # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
-        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
-
-        # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
-        if g > 0:
-            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-        m = de_parallel(model).model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7
-        self.ssi = list(m.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
-        self.na = m.na  # number of anchors
-        self.nc = m.nc  # number of classes
-        self.nl = m.nl  # number of layers
-        self.nm = m.nm  # number of masks
-        self.anchors = m.anchors
-        self.device = device
-
-    def __call__(self, preds, targets, masks):  # predictions, targets, model
-        p, proto = preds
-        bs, nm, mask_h, mask_w = proto.shape  # batch size, number of masks, mask height, mask width
-        lcls = torch.zeros(1, device=self.device)
-        lbox = torch.zeros(1, device=self.device)
-        lobj = torch.zeros(1, device=self.device)
-        lseg = torch.zeros(1, device=self.device)
-        tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets)  # targets
-
-        # Losses
-        for i, pi in enumerate(p):  # layer index, layer predictions
-            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
-            tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device)  # target obj
-
-            n = b.shape[0]  # number of targets
-            if n:
-                pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1)  # subset of predictions
-
-                # Box regression
-                pxy = pxy.sigmoid() * 2 - 0.5
-                pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
-                pbox = torch.cat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze()  # iou(prediction, target)
-                lbox += (1.0 - iou).mean()  # iou loss
-
-                # Objectness
-                iou = iou.detach().clamp(0).type(tobj.dtype)
-                if self.sort_obj_iou:
-                    j = iou.argsort()
-                    b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
-                if self.gr < 1:
-                    iou = (1.0 - self.gr) + self.gr * iou
-                tobj[b, a, gj, gi] = iou  # iou ratio
-
-                # Classification
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t = torch.full_like(pcls, self.cn, device=self.device)  # targets
-                    t[range(n), tcls[i]] = self.cp
-                    lcls += self.BCEcls(pcls, t)  # BCE
-
-                # Mask regression
-                if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
-                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
-                marea = xywhn[i][:, 2:].prod(1)  # mask width, height normalized
-                mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
-                for bi in b.unique():
-                    j = b == bi  # matching index
-                    if self.overlap:
-                        mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
-                    else:
-                        mask_gti = masks[tidxs[i]][j]
-                    lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
-
-            obji = self.BCEobj(pi[..., 4], tobj)
-            lobj += obji * self.balance[i]  # obj loss
-            if self.autobalance:
-                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
-        if self.autobalance:
-            self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp["box"]
-        lobj *= self.hyp["obj"]
-        lcls *= self.hyp["cls"]
-        lseg *= self.hyp["box"] / bs
-
-        loss = lbox + lobj + lcls + lseg
-        return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
-
-    def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
-        # Mask loss for one image
-        pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])  # (n,32) @ (32,80,80) -> (n,80,80)
-        loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
-        return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
-
-    def build_targets(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
-        gain = torch.ones(8, device=self.device)  # normalized to gridspace gain
-        ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        if self.overlap:
-            batch = p[0].shape[0]
-            ti = []
-            for i in range(batch):
-                num = (targets[:, 0] == i).sum()  # find number of targets of each image
-                ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1)  # (na, num)
-            ti = torch.cat(ti, 1)  # (na, nt)
-        else:
-            ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2)  # append anchor indices
-
-        g = 0.5  # bias
-        off = torch.tensor(
-            [
-                [0, 0],
-                [1, 0],
-                [0, 1],
-                [-1, 0],
-                [0, -1],  # j,k,l,m
-                # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-            ],
-            device=self.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors, shape = self.anchors[i], p[i].shape
-            gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]  # xyxy gain
-
-            # Match targets to anchors
-            t = targets * gain  # shape(3,n,7)
-            if nt:
-                # Matches
-                r = t[..., 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1 < g) & (gxy > 1)).T
-                l, m = ((gxi % 1 < g) & (gxi > 1)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            bc, gxy, gwh, at = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchors
-            (a, tidx), (b, c) = at.long().T, bc.long().T  # anchors, image, class
-            gij = (gxy - offsets).long()
-            gi, gj = gij.T  # grid indices
-
-            # Append
-            indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1)))  # image, anchor, grid
-            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
-            anch.append(anchors[a])  # anchors
-            tcls.append(c)  # class
-            tidxs.append(tidx)
-            xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6])  # xywh normalized
-
-        return tcls, tbox, indices, anch, tidxs, xywhn

spaces/ArkanDash/rvc-models/infer_pack/commons.py

@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/resnet.py

@@ -1,208 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-
-
-class InflatedConv3d(nn.Conv2d):
-    def forward(self, x):
-        video_length = x.shape[2]
-
-        x = rearrange(x, "b c f h w -> (b f) c h w")
-        x = super().forward(x)
-        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
-
-        return x
-
-
-class Upsample3D(nn.Module):
-    def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_conv_transpose = use_conv_transpose
-        self.name = name
-
-        conv = None
-        if use_conv_transpose:
-            raise NotImplementedError
-        elif use_conv:
-            conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
-
-        if name == "conv":
-            self.conv = conv
-        else:
-            self.Conv2d_0 = conv
-
-    def forward(self, hidden_states, output_size=None):
-        assert hidden_states.shape[1] == self.channels
-
-        if self.use_conv_transpose:
-            raise NotImplementedError
-
-        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
-        dtype = hidden_states.dtype
-        if dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(torch.float32)
-
-        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
-        if hidden_states.shape[0] >= 64:
-            hidden_states = hidden_states.contiguous()
-
-        # if `output_size` is passed we force the interpolation output
-        # size and do not make use of `scale_factor=2`
-        if output_size is None:
-            hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
-        else:
-            hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
-
-        # If the input is bfloat16, we cast back to bfloat16
-        if dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(dtype)
-
-        if self.use_conv:
-            if self.name == "conv":
-                hidden_states = self.conv(hidden_states)
-            else:
-                hidden_states = self.Conv2d_0(hidden_states)
-
-        return hidden_states
-
-
-class Downsample3D(nn.Module):
-    def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.padding = padding
-        stride = 2
-        self.name = name
-
-        if use_conv:
-            conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
-        else:
-            raise NotImplementedError
-
-        if name == "conv":
-            self.Conv2d_0 = conv
-            self.conv = conv
-        elif name == "Conv2d_0":
-            self.conv = conv
-        else:
-            self.conv = conv
-
-    def forward(self, hidden_states):
-        assert hidden_states.shape[1] == self.channels
-        if self.use_conv and self.padding == 0:
-            raise NotImplementedError
-
-        assert hidden_states.shape[1] == self.channels
-        hidden_states = self.conv(hidden_states)
-
-        return hidden_states
-
-
-class ResnetBlock3D(nn.Module):
-    def __init__(
-        self,
-        *,
-        in_channels,
-        out_channels=None,
-        conv_shortcut=False,
-        dropout=0.0,
-        temb_channels=512,
-        groups=32,
-        groups_out=None,
-        pre_norm=True,
-        eps=1e-6,
-        non_linearity="swish",
-        time_embedding_norm="default",
-        output_scale_factor=1.0,
-        use_in_shortcut=None,
-    ):
-        super().__init__()
-        self.pre_norm = pre_norm
-        self.pre_norm = True
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-        self.time_embedding_norm = time_embedding_norm
-        self.output_scale_factor = output_scale_factor
-
-        if groups_out is None:
-            groups_out = groups
-
-        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
-
-        self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
-        if temb_channels is not None:
-            if self.time_embedding_norm == "default":
-                time_emb_proj_out_channels = out_channels
-            elif self.time_embedding_norm == "scale_shift":
-                time_emb_proj_out_channels = out_channels * 2
-            else:
-                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
-
-            self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
-        else:
-            self.time_emb_proj = None
-
-        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
-        if non_linearity == "swish":
-            self.nonlinearity = lambda x: F.silu(x)
-        elif non_linearity == "mish":
-            self.nonlinearity = Mish()
-        elif non_linearity == "silu":
-            self.nonlinearity = nn.SiLU()
-
-        self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
-
-        self.conv_shortcut = None
-        if self.use_in_shortcut:
-            self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, input_tensor, temb):
-        hidden_states = input_tensor
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.conv1(hidden_states)
-
-        if temb is not None:
-            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
-
-        if temb is not None and self.time_embedding_norm == "default":
-            hidden_states = hidden_states + temb
-
-        hidden_states = self.norm2(hidden_states)
-
-        if temb is not None and self.time_embedding_norm == "scale_shift":
-            scale, shift = torch.chunk(temb, 2, dim=1)
-            hidden_states = hidden_states * (1 + scale) + shift
-
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor)
-
-        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
-
-        return output_tensor
-
-
-class Mish(torch.nn.Module):
-    def forward(self, hidden_states):
-        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))

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

@@ -1,352 +0,0 @@
-# unicode.py
-
-import sys
-from itertools import filterfalse
-from typing import List, Tuple, Union
-
-
-class _lazyclassproperty:
-    def __init__(self, fn):
-        self.fn = fn
-        self.__doc__ = fn.__doc__
-        self.__name__ = fn.__name__
-
-    def __get__(self, obj, cls):
-        if cls is None:
-            cls = type(obj)
-        if not hasattr(cls, "_intern") or any(
-            cls._intern is getattr(superclass, "_intern", [])
-            for superclass in cls.__mro__[1:]
-        ):
-            cls._intern = {}
-        attrname = self.fn.__name__
-        if attrname not in cls._intern:
-            cls._intern[attrname] = self.fn(cls)
-        return cls._intern[attrname]
-
-
-UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]]
-
-
-class unicode_set:
-    """
-    A set of Unicode characters, for language-specific strings for
-    ``alphas``, ``nums``, ``alphanums``, and ``printables``.
-    A unicode_set is defined by a list of ranges in the Unicode character
-    set, in a class attribute ``_ranges``. Ranges can be specified using
-    2-tuples or a 1-tuple, such as::
-
-        _ranges = [
-            (0x0020, 0x007e),
-            (0x00a0, 0x00ff),
-            (0x0100,),
-            ]
-
-    Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x).
-
-    A unicode set can also be defined using multiple inheritance of other unicode sets::
-
-        class CJK(Chinese, Japanese, Korean):
-            pass
-    """
-
-    _ranges: UnicodeRangeList = []
-
-    @_lazyclassproperty
-    def _chars_for_ranges(cls):
-        ret = []
-        for cc in cls.__mro__:
-            if cc is unicode_set:
-                break
-            for rr in getattr(cc, "_ranges", ()):
-                ret.extend(range(rr[0], rr[-1] + 1))
-        return [chr(c) for c in sorted(set(ret))]
-
-    @_lazyclassproperty
-    def printables(cls):
-        "all non-whitespace characters in this range"
-        return "".join(filterfalse(str.isspace, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphas(cls):
-        "all alphabetic characters in this range"
-        return "".join(filter(str.isalpha, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def nums(cls):
-        "all numeric digit characters in this range"
-        return "".join(filter(str.isdigit, cls._chars_for_ranges))
-
-    @_lazyclassproperty
-    def alphanums(cls):
-        "all alphanumeric characters in this range"
-        return cls.alphas + cls.nums
-
-    @_lazyclassproperty
-    def identchars(cls):
-        "all characters in this range that are valid identifier characters, plus underscore '_'"
-        return "".join(
-            sorted(
-                set(
-                    "".join(filter(str.isidentifier, cls._chars_for_ranges))
-                    + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº"
-                    + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ"
-                    + "_"
-                )
-            )
-        )
-
-    @_lazyclassproperty
-    def identbodychars(cls):
-        """
-        all characters in this range that are valid identifier body characters,
-        plus the digits 0-9
-        """
-        return "".join(
-            sorted(
-                set(
-                    cls.identchars
-                    + "0123456789"
-                    + "".join(
-                        [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()]
-                    )
-                )
-            )
-        )
-
-
-class pyparsing_unicode(unicode_set):
-    """
-    A namespace class for defining common language unicode_sets.
-    """
-
-    # fmt: off
-
-    # define ranges in language character sets
-    _ranges: UnicodeRangeList = [
-        (0x0020, sys.maxunicode),
-    ]
-
-    class BasicMultilingualPlane(unicode_set):
-        "Unicode set for the Basic Multilingual Plane"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0xFFFF),
-        ]
-
-    class Latin1(unicode_set):
-        "Unicode set for Latin-1 Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0020, 0x007E),
-            (0x00A0, 0x00FF),
-        ]
-
-    class LatinA(unicode_set):
-        "Unicode set for Latin-A Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0100, 0x017F),
-        ]
-
-    class LatinB(unicode_set):
-        "Unicode set for Latin-B Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0180, 0x024F),
-        ]
-
-    class Greek(unicode_set):
-        "Unicode set for Greek Unicode Character Ranges"
-        _ranges: UnicodeRangeList = [
-            (0x0342, 0x0345),
-            (0x0370, 0x0377),
-            (0x037A, 0x037F),
-            (0x0384, 0x038A),
-            (0x038C,),
-            (0x038E, 0x03A1),
-            (0x03A3, 0x03E1),
-            (0x03F0, 0x03FF),
-            (0x1D26, 0x1D2A),
-            (0x1D5E,),
-            (0x1D60,),
-            (0x1D66, 0x1D6A),
-            (0x1F00, 0x1F15),
-            (0x1F18, 0x1F1D),
-            (0x1F20, 0x1F45),
-            (0x1F48, 0x1F4D),
-            (0x1F50, 0x1F57),
-            (0x1F59,),
-            (0x1F5B,),
-            (0x1F5D,),
-            (0x1F5F, 0x1F7D),
-            (0x1F80, 0x1FB4),
-            (0x1FB6, 0x1FC4),
-            (0x1FC6, 0x1FD3),
-            (0x1FD6, 0x1FDB),
-            (0x1FDD, 0x1FEF),
-            (0x1FF2, 0x1FF4),
-            (0x1FF6, 0x1FFE),
-            (0x2129,),
-            (0x2719, 0x271A),
-            (0xAB65,),
-            (0x10140, 0x1018D),
-            (0x101A0,),
-            (0x1D200, 0x1D245),
-            (0x1F7A1, 0x1F7A7),
-        ]
-
-    class Cyrillic(unicode_set):
-        "Unicode set for Cyrillic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0400, 0x052F),
-            (0x1C80, 0x1C88),
-            (0x1D2B,),
-            (0x1D78,),
-            (0x2DE0, 0x2DFF),
-            (0xA640, 0xA672),
-            (0xA674, 0xA69F),
-            (0xFE2E, 0xFE2F),
-        ]
-
-    class Chinese(unicode_set):
-        "Unicode set for Chinese Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x2E80, 0x2E99),
-            (0x2E9B, 0x2EF3),
-            (0x31C0, 0x31E3),
-            (0x3400, 0x4DB5),
-            (0x4E00, 0x9FEF),
-            (0xA700, 0xA707),
-            (0xF900, 0xFA6D),
-            (0xFA70, 0xFAD9),
-            (0x16FE2, 0x16FE3),
-            (0x1F210, 0x1F212),
-            (0x1F214, 0x1F23B),
-            (0x1F240, 0x1F248),
-            (0x20000, 0x2A6D6),
-            (0x2A700, 0x2B734),
-            (0x2B740, 0x2B81D),
-            (0x2B820, 0x2CEA1),
-            (0x2CEB0, 0x2EBE0),
-            (0x2F800, 0x2FA1D),
-        ]
-
-    class Japanese(unicode_set):
-        "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges"
-        _ranges: UnicodeRangeList = []
-
-        class Kanji(unicode_set):
-            "Unicode set for Kanji Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x4E00, 0x9FBF),
-                (0x3000, 0x303F),
-            ]
-
-        class Hiragana(unicode_set):
-            "Unicode set for Hiragana Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3041, 0x3096),
-                (0x3099, 0x30A0),
-                (0x30FC,),
-                (0xFF70,),
-                (0x1B001,),
-                (0x1B150, 0x1B152),
-                (0x1F200,),
-            ]
-
-        class Katakana(unicode_set):
-            "Unicode set for Katakana  Unicode Character Range"
-            _ranges: UnicodeRangeList = [
-                (0x3099, 0x309C),
-                (0x30A0, 0x30FF),
-                (0x31F0, 0x31FF),
-                (0x32D0, 0x32FE),
-                (0xFF65, 0xFF9F),
-                (0x1B000,),
-                (0x1B164, 0x1B167),
-                (0x1F201, 0x1F202),
-                (0x1F213,),
-            ]
-
-    class Hangul(unicode_set):
-        "Unicode set for Hangul (Korean) Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x1100, 0x11FF),
-            (0x302E, 0x302F),
-            (0x3131, 0x318E),
-            (0x3200, 0x321C),
-            (0x3260, 0x327B),
-            (0x327E,),
-            (0xA960, 0xA97C),
-            (0xAC00, 0xD7A3),
-            (0xD7B0, 0xD7C6),
-            (0xD7CB, 0xD7FB),
-            (0xFFA0, 0xFFBE),
-            (0xFFC2, 0xFFC7),
-            (0xFFCA, 0xFFCF),
-            (0xFFD2, 0xFFD7),
-            (0xFFDA, 0xFFDC),
-        ]
-
-    Korean = Hangul
-
-    class CJK(Chinese, Japanese, Hangul):
-        "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range"
-
-    class Thai(unicode_set):
-        "Unicode set for Thai Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0E01, 0x0E3A),
-            (0x0E3F, 0x0E5B)
-        ]
-
-    class Arabic(unicode_set):
-        "Unicode set for Arabic Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0600, 0x061B),
-            (0x061E, 0x06FF),
-            (0x0700, 0x077F),
-        ]
-
-    class Hebrew(unicode_set):
-        "Unicode set for Hebrew Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0591, 0x05C7),
-            (0x05D0, 0x05EA),
-            (0x05EF, 0x05F4),
-            (0xFB1D, 0xFB36),
-            (0xFB38, 0xFB3C),
-            (0xFB3E,),
-            (0xFB40, 0xFB41),
-            (0xFB43, 0xFB44),
-            (0xFB46, 0xFB4F),
-        ]
-
-    class Devanagari(unicode_set):
-        "Unicode set for Devanagari Unicode Character Range"
-        _ranges: UnicodeRangeList = [
-            (0x0900, 0x097F),
-            (0xA8E0, 0xA8FF)
-        ]
-
-    # fmt: on
-
-
-pyparsing_unicode.Japanese._ranges = (
-    pyparsing_unicode.Japanese.Kanji._ranges
-    + pyparsing_unicode.Japanese.Hiragana._ranges
-    + pyparsing_unicode.Japanese.Katakana._ranges
-)
-
-pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane
-
-# add language identifiers using language Unicode
-pyparsing_unicode.العربية = pyparsing_unicode.Arabic
-pyparsing_unicode.中文 = pyparsing_unicode.Chinese
-pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic
-pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek
-pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew
-pyparsing_unicode.日本語 = pyparsing_unicode.Japanese
-pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji
-pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana
-pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana
-pyparsing_unicode.한국어 = pyparsing_unicode.Korean
-pyparsing_unicode.ไทย = pyparsing_unicode.Thai
-pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_metadata/_functools.py

@@ -1,104 +0,0 @@
-import types
-import functools
-
-
-# from jaraco.functools 3.3
-def method_cache(method, cache_wrapper=None):
-    """
-    Wrap lru_cache to support storing the cache data in the object instances.
-
-    Abstracts the common paradigm where the method explicitly saves an
-    underscore-prefixed protected property on first call and returns that
-    subsequently.
-
-    >>> class MyClass:
-    ...     calls = 0
-    ...
-    ...     @method_cache
-    ...     def method(self, value):
-    ...         self.calls += 1
-    ...         return value
-
-    >>> a = MyClass()
-    >>> a.method(3)
-    3
-    >>> for x in range(75):
-    ...     res = a.method(x)
-    >>> a.calls
-    75
-
-    Note that the apparent behavior will be exactly like that of lru_cache
-    except that the cache is stored on each instance, so values in one
-    instance will not flush values from another, and when an instance is
-    deleted, so are the cached values for that instance.
-
-    >>> b = MyClass()
-    >>> for x in range(35):
-    ...     res = b.method(x)
-    >>> b.calls
-    35
-    >>> a.method(0)
-    0
-    >>> a.calls
-    75
-
-    Note that if method had been decorated with ``functools.lru_cache()``,
-    a.calls would have been 76 (due to the cached value of 0 having been
-    flushed by the 'b' instance).
-
-    Clear the cache with ``.cache_clear()``
-
-    >>> a.method.cache_clear()
-
-    Same for a method that hasn't yet been called.
-
-    >>> c = MyClass()
-    >>> c.method.cache_clear()
-
-    Another cache wrapper may be supplied:
-
-    >>> cache = functools.lru_cache(maxsize=2)
-    >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
-    >>> a = MyClass()
-    >>> a.method2()
-    3
-
-    Caution - do not subsequently wrap the method with another decorator, such
-    as ``@property``, which changes the semantics of the function.
-
-    See also
-    http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
-    for another implementation and additional justification.
-    """
-    cache_wrapper = cache_wrapper or functools.lru_cache()
-
-    def wrapper(self, *args, **kwargs):
-        # it's the first call, replace the method with a cached, bound method
-        bound_method = types.MethodType(method, self)
-        cached_method = cache_wrapper(bound_method)
-        setattr(self, method.__name__, cached_method)
-        return cached_method(*args, **kwargs)
-
-    # Support cache clear even before cache has been created.
-    wrapper.cache_clear = lambda: None
-
-    return wrapper
-
-
-# From jaraco.functools 3.3
-def pass_none(func):
-    """
-    Wrap func so it's not called if its first param is None
-
-    >>> print_text = pass_none(print)
-    >>> print_text('text')
-    text
-    >>> print_text(None)
-    """
-
-    @functools.wraps(func)
-    def wrapper(param, *args, **kwargs):
-        if param is not None:
-            return func(param, *args, **kwargs)
-
-    return wrapper

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/bdist_egg.py

@@ -1,457 +0,0 @@
-"""setuptools.command.bdist_egg
-
-Build .egg distributions"""
-
-from distutils.dir_util import remove_tree, mkpath
-from distutils import log
-from types import CodeType
-import sys
-import os
-import re
-import textwrap
-import marshal
-
-from pkg_resources import get_build_platform, Distribution
-from setuptools.extension import Library
-from setuptools import Command
-from .._path import ensure_directory
-
-from sysconfig import get_path, get_python_version
-
-
-def _get_purelib():
-    return get_path("purelib")
-
-
-def strip_module(filename):
-    if '.' in filename:
-        filename = os.path.splitext(filename)[0]
-    if filename.endswith('module'):
-        filename = filename[:-6]
-    return filename
-
-
-def sorted_walk(dir):
-    """Do os.walk in a reproducible way,
-    independent of indeterministic filesystem readdir order
-    """
-    for base, dirs, files in os.walk(dir):
-        dirs.sort()
-        files.sort()
-        yield base, dirs, files
-
-
-def write_stub(resource, pyfile):
-    _stub_template = textwrap.dedent("""
-        def __bootstrap__():
-            global __bootstrap__, __loader__, __file__
-            import sys, pkg_resources, importlib.util
-            __file__ = pkg_resources.resource_filename(__name__, %r)
-            __loader__ = None; del __bootstrap__, __loader__
-            spec = importlib.util.spec_from_file_location(__name__,__file__)
-            mod = importlib.util.module_from_spec(spec)
-            spec.loader.exec_module(mod)
-        __bootstrap__()
-        """).lstrip()
-    with open(pyfile, 'w') as f:
-        f.write(_stub_template % resource)
-
-
-class bdist_egg(Command):
-    description = "create an \"egg\" distribution"
-
-    user_options = [
-        ('bdist-dir=', 'b',
-         "temporary directory for creating the distribution"),
-        ('plat-name=', 'p', "platform name to embed in generated filenames "
-                            "(default: %s)" % get_build_platform()),
-        ('exclude-source-files', None,
-         "remove all .py files from the generated egg"),
-        ('keep-temp', 'k',
-         "keep the pseudo-installation tree around after " +
-         "creating the distribution archive"),
-        ('dist-dir=', 'd',
-         "directory to put final built distributions in"),
-        ('skip-build', None,
-         "skip rebuilding everything (for testing/debugging)"),
-    ]
-
-    boolean_options = [
-        'keep-temp', 'skip-build', 'exclude-source-files'
-    ]
-
-    def initialize_options(self):
-        self.bdist_dir = None
-        self.plat_name = None
-        self.keep_temp = 0
-        self.dist_dir = None
-        self.skip_build = 0
-        self.egg_output = None
-        self.exclude_source_files = None
-
-    def finalize_options(self):
-        ei_cmd = self.ei_cmd = self.get_finalized_command("egg_info")
-        self.egg_info = ei_cmd.egg_info
-
-        if self.bdist_dir is None:
-            bdist_base = self.get_finalized_command('bdist').bdist_base
-            self.bdist_dir = os.path.join(bdist_base, 'egg')
-
-        if self.plat_name is None:
-            self.plat_name = get_build_platform()
-
-        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
-
-        if self.egg_output is None:
-
-            # Compute filename of the output egg
-            basename = Distribution(
-                None, None, ei_cmd.egg_name, ei_cmd.egg_version,
-                get_python_version(),
-                self.distribution.has_ext_modules() and self.plat_name
-            ).egg_name()
-
-            self.egg_output = os.path.join(self.dist_dir, basename + '.egg')
-
-    def do_install_data(self):
-        # Hack for packages that install data to install's --install-lib
-        self.get_finalized_command('install').install_lib = self.bdist_dir
-
-        site_packages = os.path.normcase(os.path.realpath(_get_purelib()))
-        old, self.distribution.data_files = self.distribution.data_files, []
-
-        for item in old:
-            if isinstance(item, tuple) and len(item) == 2:
-                if os.path.isabs(item[0]):
-                    realpath = os.path.realpath(item[0])
-                    normalized = os.path.normcase(realpath)
-                    if normalized == site_packages or normalized.startswith(
-                        site_packages + os.sep
-                    ):
-                        item = realpath[len(site_packages) + 1:], item[1]
-                        # XXX else: raise ???
-            self.distribution.data_files.append(item)
-
-        try:
-            log.info("installing package data to %s", self.bdist_dir)
-            self.call_command('install_data', force=0, root=None)
-        finally:
-            self.distribution.data_files = old
-
-    def get_outputs(self):
-        return [self.egg_output]
-
-    def call_command(self, cmdname, **kw):
-        """Invoke reinitialized command `cmdname` with keyword args"""
-        for dirname in INSTALL_DIRECTORY_ATTRS:
-            kw.setdefault(dirname, self.bdist_dir)
-        kw.setdefault('skip_build', self.skip_build)
-        kw.setdefault('dry_run', self.dry_run)
-        cmd = self.reinitialize_command(cmdname, **kw)
-        self.run_command(cmdname)
-        return cmd
-
-    def run(self):  # noqa: C901  # is too complex (14)  # FIXME
-        # Generate metadata first
-        self.run_command("egg_info")
-        # We run install_lib before install_data, because some data hacks
-        # pull their data path from the install_lib command.
-        log.info("installing library code to %s", self.bdist_dir)
-        instcmd = self.get_finalized_command('install')
-        old_root = instcmd.root
-        instcmd.root = None
-        if self.distribution.has_c_libraries() and not self.skip_build:
-            self.run_command('build_clib')
-        cmd = self.call_command('install_lib', warn_dir=0)
-        instcmd.root = old_root
-
-        all_outputs, ext_outputs = self.get_ext_outputs()
-        self.stubs = []
-        to_compile = []
-        for (p, ext_name) in enumerate(ext_outputs):
-            filename, ext = os.path.splitext(ext_name)
-            pyfile = os.path.join(self.bdist_dir, strip_module(filename) +
-                                  '.py')
-            self.stubs.append(pyfile)
-            log.info("creating stub loader for %s", ext_name)
-            if not self.dry_run:
-                write_stub(os.path.basename(ext_name), pyfile)
-            to_compile.append(pyfile)
-            ext_outputs[p] = ext_name.replace(os.sep, '/')
-
-        if to_compile:
-            cmd.byte_compile(to_compile)
-        if self.distribution.data_files:
-            self.do_install_data()
-
-        # Make the EGG-INFO directory
-        archive_root = self.bdist_dir
-        egg_info = os.path.join(archive_root, 'EGG-INFO')
-        self.mkpath(egg_info)
-        if self.distribution.scripts:
-            script_dir = os.path.join(egg_info, 'scripts')
-            log.info("installing scripts to %s", script_dir)
-            self.call_command('install_scripts', install_dir=script_dir,
-                              no_ep=1)
-
-        self.copy_metadata_to(egg_info)
-        native_libs = os.path.join(egg_info, "native_libs.txt")
-        if all_outputs:
-            log.info("writing %s", native_libs)
-            if not self.dry_run:
-                ensure_directory(native_libs)
-                libs_file = open(native_libs, 'wt')
-                libs_file.write('\n'.join(all_outputs))
-                libs_file.write('\n')
-                libs_file.close()
-        elif os.path.isfile(native_libs):
-            log.info("removing %s", native_libs)
-            if not self.dry_run:
-                os.unlink(native_libs)
-
-        write_safety_flag(
-            os.path.join(archive_root, 'EGG-INFO'), self.zip_safe()
-        )
-
-        if os.path.exists(os.path.join(self.egg_info, 'depends.txt')):
-            log.warn(
-                "WARNING: 'depends.txt' will not be used by setuptools 0.6!\n"
-                "Use the install_requires/extras_require setup() args instead."
-            )
-
-        if self.exclude_source_files:
-            self.zap_pyfiles()
-
-        # Make the archive
-        make_zipfile(self.egg_output, archive_root, verbose=self.verbose,
-                     dry_run=self.dry_run, mode=self.gen_header())
-        if not self.keep_temp:
-            remove_tree(self.bdist_dir, dry_run=self.dry_run)
-
-        # Add to 'Distribution.dist_files' so that the "upload" command works
-        getattr(self.distribution, 'dist_files', []).append(
-            ('bdist_egg', get_python_version(), self.egg_output))
-
-    def zap_pyfiles(self):
-        log.info("Removing .py files from temporary directory")
-        for base, dirs, files in walk_egg(self.bdist_dir):
-            for name in files:
-                path = os.path.join(base, name)
-
-                if name.endswith('.py'):
-                    log.debug("Deleting %s", path)
-                    os.unlink(path)
-
-                if base.endswith('__pycache__'):
-                    path_old = path
-
-                    pattern = r'(?P<name>.+)\.(?P<magic>[^.]+)\.pyc'
-                    m = re.match(pattern, name)
-                    path_new = os.path.join(
-                        base, os.pardir, m.group('name') + '.pyc')
-                    log.info(
-                        "Renaming file from [%s] to [%s]"
-                        % (path_old, path_new))
-                    try:
-                        os.remove(path_new)
-                    except OSError:
-                        pass
-                    os.rename(path_old, path_new)
-
-    def zip_safe(self):
-        safe = getattr(self.distribution, 'zip_safe', None)
-        if safe is not None:
-            return safe
-        log.warn("zip_safe flag not set; analyzing archive contents...")
-        return analyze_egg(self.bdist_dir, self.stubs)
-
-    def gen_header(self):
-        return 'w'
-
-    def copy_metadata_to(self, target_dir):
-        "Copy metadata (egg info) to the target_dir"
-        # normalize the path (so that a forward-slash in egg_info will
-        # match using startswith below)
-        norm_egg_info = os.path.normpath(self.egg_info)
-        prefix = os.path.join(norm_egg_info, '')
-        for path in self.ei_cmd.filelist.files:
-            if path.startswith(prefix):
-                target = os.path.join(target_dir, path[len(prefix):])
-                ensure_directory(target)
-                self.copy_file(path, target)
-
-    def get_ext_outputs(self):
-        """Get a list of relative paths to C extensions in the output distro"""
-
-        all_outputs = []
-        ext_outputs = []
-
-        paths = {self.bdist_dir: ''}
-        for base, dirs, files in sorted_walk(self.bdist_dir):
-            for filename in files:
-                if os.path.splitext(filename)[1].lower() in NATIVE_EXTENSIONS:
-                    all_outputs.append(paths[base] + filename)
-            for filename in dirs:
-                paths[os.path.join(base, filename)] = (paths[base] +
-                                                       filename + '/')
-
-        if self.distribution.has_ext_modules():
-            build_cmd = self.get_finalized_command('build_ext')
-            for ext in build_cmd.extensions:
-                if isinstance(ext, Library):
-                    continue
-                fullname = build_cmd.get_ext_fullname(ext.name)
-                filename = build_cmd.get_ext_filename(fullname)
-                if not os.path.basename(filename).startswith('dl-'):
-                    if os.path.exists(os.path.join(self.bdist_dir, filename)):
-                        ext_outputs.append(filename)
-
-        return all_outputs, ext_outputs
-
-
-NATIVE_EXTENSIONS = dict.fromkeys('.dll .so .dylib .pyd'.split())
-
-
-def walk_egg(egg_dir):
-    """Walk an unpacked egg's contents, skipping the metadata directory"""
-    walker = sorted_walk(egg_dir)
-    base, dirs, files = next(walker)
-    if 'EGG-INFO' in dirs:
-        dirs.remove('EGG-INFO')
-    yield base, dirs, files
-    for bdf in walker:
-        yield bdf
-
-
-def analyze_egg(egg_dir, stubs):
-    # check for existing flag in EGG-INFO
-    for flag, fn in safety_flags.items():
-        if os.path.exists(os.path.join(egg_dir, 'EGG-INFO', fn)):
-            return flag
-    if not can_scan():
-        return False
-    safe = True
-    for base, dirs, files in walk_egg(egg_dir):
-        for name in files:
-            if name.endswith('.py') or name.endswith('.pyw'):
-                continue
-            elif name.endswith('.pyc') or name.endswith('.pyo'):
-                # always scan, even if we already know we're not safe
-                safe = scan_module(egg_dir, base, name, stubs) and safe
-    return safe
-
-
-def write_safety_flag(egg_dir, safe):
-    # Write or remove zip safety flag file(s)
-    for flag, fn in safety_flags.items():
-        fn = os.path.join(egg_dir, fn)
-        if os.path.exists(fn):
-            if safe is None or bool(safe) != flag:
-                os.unlink(fn)
-        elif safe is not None and bool(safe) == flag:
-            f = open(fn, 'wt')
-            f.write('\n')
-            f.close()
-
-
-safety_flags = {
-    True: 'zip-safe',
-    False: 'not-zip-safe',
-}
-
-
-def scan_module(egg_dir, base, name, stubs):
-    """Check whether module possibly uses unsafe-for-zipfile stuff"""
-
-    filename = os.path.join(base, name)
-    if filename[:-1] in stubs:
-        return True  # Extension module
-    pkg = base[len(egg_dir) + 1:].replace(os.sep, '.')
-    module = pkg + (pkg and '.' or '') + os.path.splitext(name)[0]
-    if sys.version_info < (3, 7):
-        skip = 12  # skip magic & date & file size
-    else:
-        skip = 16  # skip magic & reserved? & date & file size
-    f = open(filename, 'rb')
-    f.read(skip)
-    code = marshal.load(f)
-    f.close()
-    safe = True
-    symbols = dict.fromkeys(iter_symbols(code))
-    for bad in ['__file__', '__path__']:
-        if bad in symbols:
-            log.warn("%s: module references %s", module, bad)
-            safe = False
-    if 'inspect' in symbols:
-        for bad in [
-            'getsource', 'getabsfile', 'getsourcefile', 'getfile'
-            'getsourcelines', 'findsource', 'getcomments', 'getframeinfo',
-            'getinnerframes', 'getouterframes', 'stack', 'trace'
-        ]:
-            if bad in symbols:
-                log.warn("%s: module MAY be using inspect.%s", module, bad)
-                safe = False
-    return safe
-
-
-def iter_symbols(code):
-    """Yield names and strings used by `code` and its nested code objects"""
-    for name in code.co_names:
-        yield name
-    for const in code.co_consts:
-        if isinstance(const, str):
-            yield const
-        elif isinstance(const, CodeType):
-            for name in iter_symbols(const):
-                yield name
-
-
-def can_scan():
-    if not sys.platform.startswith('java') and sys.platform != 'cli':
-        # CPython, PyPy, etc.
-        return True
-    log.warn("Unable to analyze compiled code on this platform.")
-    log.warn("Please ask the author to include a 'zip_safe'"
-             " setting (either True or False) in the package's setup.py")
-
-
-# Attribute names of options for commands that might need to be convinced to
-# install to the egg build directory
-
-INSTALL_DIRECTORY_ATTRS = [
-    'install_lib', 'install_dir', 'install_data', 'install_base'
-]
-
-
-def make_zipfile(zip_filename, base_dir, verbose=0, dry_run=0, compress=True,
-                 mode='w'):
-    """Create a zip file from all the files under 'base_dir'.  The output
-    zip file will be named 'base_dir' + ".zip".  Uses either the "zipfile"
-    Python module (if available) or the InfoZIP "zip" utility (if installed
-    and found on the default search path).  If neither tool is available,
-    raises DistutilsExecError.  Returns the name of the output zip file.
-    """
-    import zipfile
-
-    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
-    log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
-
-    def visit(z, dirname, names):
-        for name in names:
-            path = os.path.normpath(os.path.join(dirname, name))
-            if os.path.isfile(path):
-                p = path[len(base_dir) + 1:]
-                if not dry_run:
-                    z.write(path, p)
-                log.debug("adding '%s'", p)
-
-    compression = zipfile.ZIP_DEFLATED if compress else zipfile.ZIP_STORED
-    if not dry_run:
-        z = zipfile.ZipFile(zip_filename, mode, compression=compression)
-        for dirname, dirs, files in sorted_walk(base_dir):
-            visit(z, dirname, files)
-        z.close()
-    else:
-        for dirname, dirs, files in sorted_walk(base_dir):
-            visit(None, dirname, files)
-    return zip_filename

spaces/Awesimo/jojogan/e4e_projection.py

@@ -1,38 +0,0 @@
-import os
-import sys
-import numpy as np
-from PIL import Image
-import torch
-import torchvision.transforms as transforms
-from argparse import Namespace
-from e4e.models.psp import pSp
-from util import *
-
-
-
-@ torch.no_grad()
-def projection(img, name, device='cuda'):
-
-
-    model_path = 'e4e_ffhq_encode.pt'
-    ckpt = torch.load(model_path, map_location='cpu')
-    opts = ckpt['opts']
-    opts['checkpoint_path'] = model_path
-    opts= Namespace(**opts)
-    net = pSp(opts, device).eval().to(device)
-
-    transform = transforms.Compose(
-        [
-            transforms.Resize(256),
-            transforms.CenterCrop(256),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
-        ]
-    )
-
-    img = transform(img).unsqueeze(0).to(device)
-    images, w_plus = net(img, randomize_noise=False, return_latents=True)
-    result_file = {}
-    result_file['latent'] = w_plus[0]
-    torch.save(result_file, name)
-    return w_plus[0]

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/datasets/objects365.py

@@ -1,394 +0,0 @@
-from detectron2.data.datasets.register_coco import register_coco_instances
-import os
-
-categories_v1 = [
-{'id': 164, 'name': 'cutting/chopping board'} ,
-{'id': 49, 'name': 'tie'} ,
-{'id': 306, 'name': 'crosswalk sign'} ,
-{'id': 145, 'name': 'gun'} ,
-{'id': 14, 'name': 'street lights'} ,
-{'id': 223, 'name': 'bar soap'} ,
-{'id': 74, 'name': 'wild bird'} ,
-{'id': 219, 'name': 'ice cream'} ,
-{'id': 37, 'name': 'stool'} ,
-{'id': 25, 'name': 'storage box'} ,
-{'id': 153, 'name': 'giraffe'} ,
-{'id': 52, 'name': 'pen/pencil'} ,
-{'id': 61, 'name': 'high heels'} ,
-{'id': 340, 'name': 'mangosteen'} ,
-{'id': 22, 'name': 'bracelet'} ,
-{'id': 155, 'name': 'piano'} ,
-{'id': 162, 'name': 'vent'} ,
-{'id': 75, 'name': 'laptop'} ,
-{'id': 236, 'name': 'toaster'} ,
-{'id': 231, 'name': 'fire truck'} ,
-{'id': 42, 'name': 'basket'} ,
-{'id': 150, 'name': 'zebra'} ,
-{'id': 124, 'name': 'head phone'} ,
-{'id': 90, 'name': 'sheep'} ,
-{'id': 322, 'name': 'steak'} ,
-{'id': 39, 'name': 'couch'} ,
-{'id': 209, 'name': 'toothbrush'} ,
-{'id': 59, 'name': 'bicycle'} ,
-{'id': 336, 'name': 'red cabbage'} ,
-{'id': 228, 'name': 'golf ball'} ,
-{'id': 120, 'name': 'tomato'} ,
-{'id': 132, 'name': 'computer box'} ,
-{'id': 8, 'name': 'cup'} ,
-{'id': 183, 'name': 'basketball'} ,
-{'id': 298, 'name': 'butterfly'} ,
-{'id': 250, 'name': 'garlic'} ,
-{'id': 12, 'name': 'desk'} ,
-{'id': 141, 'name': 'microwave'} ,
-{'id': 171, 'name': 'strawberry'} ,
-{'id': 200, 'name': 'kettle'} ,
-{'id': 63, 'name': 'van'} ,
-{'id': 300, 'name': 'cheese'} ,
-{'id': 215, 'name': 'marker'} ,
-{'id': 100, 'name': 'blackboard/whiteboard'} ,
-{'id': 186, 'name': 'printer'} ,
-{'id': 333, 'name': 'bread/bun'} ,
-{'id': 243, 'name': 'penguin'} ,
-{'id': 364, 'name': 'iron'} ,
-{'id': 180, 'name': 'ladder'} ,
-{'id': 34, 'name': 'flag'} ,
-{'id': 78, 'name': 'cell phone'} ,
-{'id': 97, 'name': 'fan'} ,
-{'id': 224, 'name': 'scale'} ,
-{'id': 151, 'name': 'duck'} ,
-{'id': 319, 'name': 'flute'} ,
-{'id': 156, 'name': 'stop sign'} ,
-{'id': 290, 'name': 'rickshaw'} ,
-{'id': 128, 'name': 'sailboat'} ,
-{'id': 165, 'name': 'tennis racket'} ,
-{'id': 241, 'name': 'cigar'} ,
-{'id': 101, 'name': 'balloon'} ,
-{'id': 308, 'name': 'hair drier'} ,
-{'id': 167, 'name': 'skating and skiing shoes'} ,
-{'id': 237, 'name': 'helicopter'} ,
-{'id': 65, 'name': 'sink'} ,
-{'id': 129, 'name': 'tangerine'} ,
-{'id': 330, 'name': 'crab'} ,
-{'id': 320, 'name': 'measuring cup'} ,
-{'id': 260, 'name': 'fishing rod'} ,
-{'id': 346, 'name': 'saw'} ,
-{'id': 216, 'name': 'ship'} ,
-{'id': 46, 'name': 'coffee table'} ,
-{'id': 194, 'name': 'facial mask'} ,
-{'id': 281, 'name': 'stapler'} ,
-{'id': 118, 'name': 'refrigerator'} ,
-{'id': 40, 'name': 'belt'} ,
-{'id': 349, 'name': 'starfish'} ,
-{'id': 87, 'name': 'hanger'} ,
-{'id': 116, 'name': 'baseball glove'} ,
-{'id': 261, 'name': 'cherry'} ,
-{'id': 334, 'name': 'baozi'} ,
-{'id': 267, 'name': 'screwdriver'} ,
-{'id': 158, 'name': 'converter'} ,
-{'id': 335, 'name': 'lion'} ,
-{'id': 170, 'name': 'baseball'} ,
-{'id': 111, 'name': 'skis'} ,
-{'id': 136, 'name': 'broccoli'} ,
-{'id': 342, 'name': 'eraser'} ,
-{'id': 337, 'name': 'polar bear'} ,
-{'id': 139, 'name': 'shovel'} ,
-{'id': 193, 'name': 'extension cord'} ,
-{'id': 284, 'name': 'goldfish'} ,
-{'id': 174, 'name': 'pepper'} ,
-{'id': 138, 'name': 'stroller'} ,
-{'id': 328, 'name': 'yak'} ,
-{'id': 83, 'name': 'clock'} ,
-{'id': 235, 'name': 'tricycle'} ,
-{'id': 248, 'name': 'parking meter'} ,
-{'id': 274, 'name': 'trophy'} ,
-{'id': 324, 'name': 'binoculars'} ,
-{'id': 51, 'name': 'traffic light'} ,
-{'id': 314, 'name': 'donkey'} ,
-{'id': 45, 'name': 'barrel/bucket'} ,
-{'id': 292, 'name': 'pomegranate'} ,
-{'id': 13, 'name': 'handbag'} ,
-{'id': 262, 'name': 'tablet'} ,
-{'id': 68, 'name': 'apple'} ,
-{'id': 226, 'name': 'cabbage'} ,
-{'id': 23, 'name': 'flower'} ,
-{'id': 58, 'name': 'faucet'} ,
-{'id': 206, 'name': 'tong'} ,
-{'id': 291, 'name': 'trombone'} ,
-{'id': 160, 'name': 'carrot'} ,
-{'id': 172, 'name': 'bow tie'} ,
-{'id': 122, 'name': 'tent'} ,
-{'id': 163, 'name': 'cookies'} ,
-{'id': 115, 'name': 'remote'} ,
-{'id': 175, 'name': 'coffee machine'} ,
-{'id': 238, 'name': 'green beans'} ,
-{'id': 233, 'name': 'cello'} ,
-{'id': 28, 'name': 'wine glass'} ,
-{'id': 295, 'name': 'mushroom'} ,
-{'id': 344, 'name': 'scallop'} ,
-{'id': 125, 'name': 'lantern'} ,
-{'id': 123, 'name': 'shampoo/shower gel'} ,
-{'id': 285, 'name': 'meat balls'} ,
-{'id': 266, 'name': 'key'} ,
-{'id': 296, 'name': 'calculator'} ,
-{'id': 168, 'name': 'scissors'} ,
-{'id': 103, 'name': 'cymbal'} ,
-{'id': 6, 'name': 'bottle'} ,
-{'id': 264, 'name': 'nuts'} ,
-{'id': 234, 'name': 'notepaper'} ,
-{'id': 211, 'name': 'mango'} ,
-{'id': 287, 'name': 'toothpaste'} ,
-{'id': 196, 'name': 'chopsticks'} ,
-{'id': 140, 'name': 'baseball bat'} ,
-{'id': 244, 'name': 'hurdle'} ,
-{'id': 195, 'name': 'tennis ball'} ,
-{'id': 144, 'name': 'surveillance camera'} ,
-{'id': 271, 'name': 'volleyball'} ,
-{'id': 94, 'name': 'keyboard'} ,
-{'id': 339, 'name': 'seal'} ,
-{'id': 11, 'name': 'picture/frame'} ,
-{'id': 348, 'name': 'okra'} ,
-{'id': 191, 'name': 'sausage'} ,
-{'id': 166, 'name': 'candy'} ,
-{'id': 62, 'name': 'ring'} ,
-{'id': 311, 'name': 'dolphin'} ,
-{'id': 273, 'name': 'eggplant'} ,
-{'id': 84, 'name': 'drum'} ,
-{'id': 143, 'name': 'surfboard'} ,
-{'id': 288, 'name': 'antelope'} ,
-{'id': 204, 'name': 'clutch'} ,
-{'id': 207, 'name': 'slide'} ,
-{'id': 43, 'name': 'towel/napkin'} ,
-{'id': 352, 'name': 'durian'} ,
-{'id': 276, 'name': 'board eraser'} ,
-{'id': 315, 'name': 'electric drill'} ,
-{'id': 312, 'name': 'sushi'} ,
-{'id': 198, 'name': 'pie'} ,
-{'id': 106, 'name': 'pickup truck'} ,
-{'id': 176, 'name': 'bathtub'} ,
-{'id': 26, 'name': 'vase'} ,
-{'id': 133, 'name': 'elephant'} ,
-{'id': 256, 'name': 'sandwich'} ,
-{'id': 327, 'name': 'noodles'} ,
-{'id': 10, 'name': 'glasses'} ,
-{'id': 109, 'name': 'airplane'} ,
-{'id': 95, 'name': 'tripod'} ,
-{'id': 247, 'name': 'CD'} ,
-{'id': 121, 'name': 'machinery vehicle'} ,
-{'id': 365, 'name': 'flashlight'} ,
-{'id': 53, 'name': 'microphone'} ,
-{'id': 270, 'name': 'pliers'} ,
-{'id': 362, 'name': 'chainsaw'} ,
-{'id': 259, 'name': 'bear'} ,
-{'id': 197, 'name': 'electronic stove and gas stove'} ,
-{'id': 89, 'name': 'pot/pan'} ,
-{'id': 220, 'name': 'tape'} ,
-{'id': 338, 'name': 'lighter'} ,
-{'id': 177, 'name': 'snowboard'} ,
-{'id': 214, 'name': 'violin'} ,
-{'id': 217, 'name': 'chicken'} ,
-{'id': 2, 'name': 'sneakers'} ,
-{'id': 161, 'name': 'washing machine'} ,
-{'id': 131, 'name': 'kite'} ,
-{'id': 354, 'name': 'rabbit'} ,
-{'id': 86, 'name': 'bus'} ,
-{'id': 275, 'name': 'dates'} ,
-{'id': 282, 'name': 'camel'} ,
-{'id': 88, 'name': 'nightstand'} ,
-{'id': 179, 'name': 'grapes'} ,
-{'id': 229, 'name': 'pine apple'} ,
-{'id': 56, 'name': 'necklace'} ,
-{'id': 18, 'name': 'leather shoes'} ,
-{'id': 358, 'name': 'hoverboard'} ,
-{'id': 345, 'name': 'pencil case'} ,
-{'id': 359, 'name': 'pasta'} ,
-{'id': 157, 'name': 'radiator'} ,
-{'id': 201, 'name': 'hamburger'} ,
-{'id': 268, 'name': 'globe'} ,
-{'id': 332, 'name': 'barbell'} ,
-{'id': 329, 'name': 'mop'} ,
-{'id': 252, 'name': 'horn'} ,
-{'id': 350, 'name': 'eagle'} ,
-{'id': 169, 'name': 'folder'} ,
-{'id': 137, 'name': 'toilet'} ,
-{'id': 5, 'name': 'lamp'} ,
-{'id': 27, 'name': 'bench'} ,
-{'id': 249, 'name': 'swan'} ,
-{'id': 76, 'name': 'knife'} ,
-{'id': 341, 'name': 'comb'} ,
-{'id': 64, 'name': 'watch'} ,
-{'id': 105, 'name': 'telephone'} ,
-{'id': 3, 'name': 'chair'} ,
-{'id': 33, 'name': 'boat'} ,
-{'id': 107, 'name': 'orange'} ,
-{'id': 60, 'name': 'bread'} ,
-{'id': 147, 'name': 'cat'} ,
-{'id': 135, 'name': 'gas stove'} ,
-{'id': 307, 'name': 'papaya'} ,
-{'id': 227, 'name': 'router/modem'} ,
-{'id': 357, 'name': 'asparagus'} ,
-{'id': 73, 'name': 'motorcycle'} ,
-{'id': 77, 'name': 'traffic sign'} ,
-{'id': 67, 'name': 'fish'} ,
-{'id': 326, 'name': 'radish'} ,
-{'id': 213, 'name': 'egg'} ,
-{'id': 203, 'name': 'cucumber'} ,
-{'id': 17, 'name': 'helmet'} ,
-{'id': 110, 'name': 'luggage'} ,
-{'id': 80, 'name': 'truck'} ,
-{'id': 199, 'name': 'frisbee'} ,
-{'id': 232, 'name': 'peach'} ,
-{'id': 1, 'name': 'person'} ,
-{'id': 29, 'name': 'boots'} ,
-{'id': 310, 'name': 'chips'} ,
-{'id': 142, 'name': 'skateboard'} ,
-{'id': 44, 'name': 'slippers'} ,
-{'id': 4, 'name': 'hat'} ,
-{'id': 178, 'name': 'suitcase'} ,
-{'id': 24, 'name': 'tv'} ,
-{'id': 119, 'name': 'train'} ,
-{'id': 82, 'name': 'power outlet'} ,
-{'id': 245, 'name': 'swing'} ,
-{'id': 15, 'name': 'book'} ,
-{'id': 294, 'name': 'jellyfish'} ,
-{'id': 192, 'name': 'fire extinguisher'} ,
-{'id': 212, 'name': 'deer'} ,
-{'id': 181, 'name': 'pear'} ,
-{'id': 347, 'name': 'table tennis paddle'} ,
-{'id': 113, 'name': 'trolley'} ,
-{'id': 91, 'name': 'guitar'} ,
-{'id': 202, 'name': 'golf club'} ,
-{'id': 221, 'name': 'wheelchair'} ,
-{'id': 254, 'name': 'saxophone'} ,
-{'id': 117, 'name': 'paper towel'} ,
-{'id': 303, 'name': 'race car'} ,
-{'id': 240, 'name': 'carriage'} ,
-{'id': 246, 'name': 'radio'} ,
-{'id': 318, 'name': 'parrot'} ,
-{'id': 251, 'name': 'french fries'} ,
-{'id': 98, 'name': 'dog'} ,
-{'id': 112, 'name': 'soccer'} ,
-{'id': 355, 'name': 'french horn'} ,
-{'id': 79, 'name': 'paddle'} ,
-{'id': 283, 'name': 'lettuce'} ,
-{'id': 9, 'name': 'car'} ,
-{'id': 258, 'name': 'kiwi fruit'} ,
-{'id': 325, 'name': 'llama'} ,
-{'id': 187, 'name': 'billiards'} ,
-{'id': 210, 'name': 'facial cleanser'} ,
-{'id': 81, 'name': 'cow'} ,
-{'id': 331, 'name': 'microscope'} ,
-{'id': 148, 'name': 'lemon'} ,
-{'id': 302, 'name': 'pomelo'} ,
-{'id': 85, 'name': 'fork'} ,
-{'id': 154, 'name': 'pumpkin'} ,
-{'id': 289, 'name': 'shrimp'} ,
-{'id': 71, 'name': 'teddy bear'} ,
-{'id': 184, 'name': 'potato'} ,
-{'id': 102, 'name': 'air conditioner'} ,
-{'id': 208, 'name': 'hot dog'} ,
-{'id': 222, 'name': 'plum'} ,
-{'id': 316, 'name': 'spring rolls'} ,
-{'id': 230, 'name': 'crane'} ,
-{'id': 149, 'name': 'liquid soap'} ,
-{'id': 55, 'name': 'canned'} ,
-{'id': 35, 'name': 'speaker'} ,
-{'id': 108, 'name': 'banana'} ,
-{'id': 297, 'name': 'treadmill'} ,
-{'id': 99, 'name': 'spoon'} ,
-{'id': 104, 'name': 'mouse'} ,
-{'id': 182, 'name': 'american football'} ,
-{'id': 299, 'name': 'egg tart'} ,
-{'id': 127, 'name': 'cleaning products'} ,
-{'id': 313, 'name': 'urinal'} ,
-{'id': 286, 'name': 'medal'} ,
-{'id': 239, 'name': 'brush'} ,
-{'id': 96, 'name': 'hockey'} ,
-{'id': 279, 'name': 'dumbbell'} ,
-{'id': 32, 'name': 'umbrella'} ,
-{'id': 272, 'name': 'hammer'} ,
-{'id': 16, 'name': 'plate'} ,
-{'id': 21, 'name': 'potted plant'} ,
-{'id': 242, 'name': 'earphone'} ,
-{'id': 70, 'name': 'candle'} ,
-{'id': 185, 'name': 'paint brush'} ,
-{'id': 48, 'name': 'toy'} ,
-{'id': 130, 'name': 'pizza'} ,
-{'id': 255, 'name': 'trumpet'} ,
-{'id': 361, 'name': 'hotair balloon'} ,
-{'id': 188, 'name': 'fire hydrant'} ,
-{'id': 50, 'name': 'bed'} ,
-{'id': 253, 'name': 'avocado'} ,
-{'id': 293, 'name': 'coconut'} ,
-{'id': 257, 'name': 'cue'} ,
-{'id': 280, 'name': 'hamimelon'} ,
-{'id': 66, 'name': 'horse'} ,
-{'id': 173, 'name': 'pigeon'} ,
-{'id': 190, 'name': 'projector'} ,
-{'id': 69, 'name': 'camera'} ,
-{'id': 30, 'name': 'bowl'} ,
-{'id': 269, 'name': 'broom'} ,
-{'id': 343, 'name': 'pitaya'} ,
-{'id': 305, 'name': 'tuba'} ,
-{'id': 309, 'name': 'green onion'} ,
-{'id': 363, 'name': 'lobster'} ,
-{'id': 225, 'name': 'watermelon'} ,
-{'id': 47, 'name': 'suv'} ,
-{'id': 31, 'name': 'dining table'} ,
-{'id': 54, 'name': 'sandals'} ,
-{'id': 351, 'name': 'monkey'} ,
-{'id': 218, 'name': 'onion'} ,
-{'id': 36, 'name': 'trash bin/can'} ,
-{'id': 20, 'name': 'glove'} ,
-{'id': 277, 'name': 'rice'} ,
-{'id': 152, 'name': 'sports car'} ,
-{'id': 360, 'name': 'target'} ,
-{'id': 205, 'name': 'blender'} ,
-{'id': 19, 'name': 'pillow'} ,
-{'id': 72, 'name': 'cake'} ,
-{'id': 93, 'name': 'tea pot'} ,
-{'id': 353, 'name': 'game board'} ,
-{'id': 38, 'name': 'backpack'} ,
-{'id': 356, 'name': 'ambulance'} ,
-{'id': 146, 'name': 'life saver'} ,
-{'id': 189, 'name': 'goose'} ,
-{'id': 278, 'name': 'tape measure/ruler'} ,
-{'id': 92, 'name': 'traffic cone'} ,
-{'id': 134, 'name': 'toiletries'} ,
-{'id': 114, 'name': 'oven'} ,
-{'id': 317, 'name': 'tortoise/turtle'} ,
-{'id': 265, 'name': 'corn'} ,
-{'id': 126, 'name': 'donut'} ,
-{'id': 57, 'name': 'mirror'} ,
-{'id': 7, 'name': 'cabinet/shelf'} ,
-{'id': 263, 'name': 'green vegetables'} ,
-{'id': 159, 'name': 'tissue '} ,
-{'id': 321, 'name': 'shark'} ,
-{'id': 301, 'name': 'pig'} ,
-{'id': 41, 'name': 'carpet'} ,
-{'id': 304, 'name': 'rice cooker'} ,
-{'id': 323, 'name': 'poker card'} ,
-]
-
-def _get_builtin_metadata(version):
-    if version == 'v1':
-        id_to_name = {x['id']: x['name'] for x in categories_v1}
-    else:
-        assert 0, version
-    thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(365)}
-    thing_classes = [id_to_name[k] for k in sorted(id_to_name)]
-    return {
-        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
-        "thing_classes": thing_classes}
-
-_PREDEFINED_SPLITS_OBJECTS365 = {
-    "objects365_train": ("objects365/train", "objects365/annotations/objects365_train.json"),
-    "objects365_val": ("objects365/val", "objects365/annotations/objects365_val.json"),
-}
-
-for key, (image_root, json_file) in _PREDEFINED_SPLITS_OBJECTS365.items():
-    register_coco_instances(
-        key,
-        _get_builtin_metadata('v1'),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/benchmark.py

@@ -1,197 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-A script to benchmark builtin models.
-
-Note: this script has an extra dependency of psutil.
-"""
-
-import itertools
-import logging
-import psutil
-import torch
-import tqdm
-from fvcore.common.timer import Timer
-from torch.nn.parallel import DistributedDataParallel
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import LazyConfig, get_cfg, instantiate
-from detectron2.data import (
-    DatasetFromList,
-    build_detection_test_loader,
-    build_detection_train_loader,
-)
-from detectron2.data.benchmark import DataLoaderBenchmark
-from detectron2.engine import AMPTrainer, SimpleTrainer, default_argument_parser, hooks, launch
-from detectron2.modeling import build_model
-from detectron2.solver import build_optimizer
-from detectron2.utils import comm
-from detectron2.utils.collect_env import collect_env_info
-from detectron2.utils.events import CommonMetricPrinter
-from detectron2.utils.logger import setup_logger
-
-logger = logging.getLogger("detectron2")
-
-
-def setup(args):
-    if args.config_file.endswith(".yaml"):
-        cfg = get_cfg()
-        cfg.merge_from_file(args.config_file)
-        cfg.SOLVER.BASE_LR = 0.001  # Avoid NaNs. Not useful in this script anyway.
-        cfg.merge_from_list(args.opts)
-        cfg.freeze()
-    else:
-        cfg = LazyConfig.load(args.config_file)
-        cfg = LazyConfig.apply_overrides(cfg, args.opts)
-    setup_logger(distributed_rank=comm.get_rank())
-    return cfg
-
-
-def create_data_benchmark(cfg, args):
-    if args.config_file.endswith(".py"):
-        dl_cfg = cfg.dataloader.train
-        dl_cfg._target_ = DataLoaderBenchmark
-        return instantiate(dl_cfg)
-    else:
-        kwargs = build_detection_train_loader.from_config(cfg)
-        kwargs.pop("aspect_ratio_grouping", None)
-        kwargs["_target_"] = DataLoaderBenchmark
-        return instantiate(kwargs)
-
-
-def RAM_msg():
-    vram = psutil.virtual_memory()
-    return "RAM Usage: {:.2f}/{:.2f} GB".format(
-        (vram.total - vram.available) / 1024 ** 3, vram.total / 1024 ** 3
-    )
-
-
-def benchmark_data(args):
-    cfg = setup(args)
-    logger.info("After spawning " + RAM_msg())
-
-    benchmark = create_data_benchmark(cfg, args)
-    benchmark.benchmark_distributed(250, 10)
-    # test for a few more rounds
-    for k in range(10):
-        logger.info(f"Iteration {k} " + RAM_msg())
-        benchmark.benchmark_distributed(250, 1)
-
-
-def benchmark_data_advanced(args):
-    # benchmark dataloader with more details to help analyze performance bottleneck
-    cfg = setup(args)
-    benchmark = create_data_benchmark(cfg, args)
-
-    if comm.get_rank() == 0:
-        benchmark.benchmark_dataset(100)
-        benchmark.benchmark_mapper(100)
-        benchmark.benchmark_workers(100, warmup=10)
-        benchmark.benchmark_IPC(100, warmup=10)
-    if comm.get_world_size() > 1:
-        benchmark.benchmark_distributed(100)
-        logger.info("Rerun ...")
-        benchmark.benchmark_distributed(100)
-
-
-def benchmark_train(args):
-    cfg = setup(args)
-    model = build_model(cfg)
-    logger.info("Model:\n{}".format(model))
-    if comm.get_world_size() > 1:
-        model = DistributedDataParallel(
-            model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
-        )
-    optimizer = build_optimizer(cfg, model)
-    checkpointer = DetectionCheckpointer(model, optimizer=optimizer)
-    checkpointer.load(cfg.MODEL.WEIGHTS)
-
-    cfg.defrost()
-    cfg.DATALOADER.NUM_WORKERS = 2
-    data_loader = build_detection_train_loader(cfg)
-    dummy_data = list(itertools.islice(data_loader, 100))
-
-    def f():
-        data = DatasetFromList(dummy_data, copy=False, serialize=False)
-        while True:
-            yield from data
-
-    max_iter = 400
-    trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(model, f(), optimizer)
-    trainer.register_hooks(
-        [
-            hooks.IterationTimer(),
-            hooks.PeriodicWriter([CommonMetricPrinter(max_iter)]),
-            hooks.TorchProfiler(
-                lambda trainer: trainer.iter == max_iter - 1, cfg.OUTPUT_DIR, save_tensorboard=True
-            ),
-        ]
-    )
-    trainer.train(1, max_iter)
-
-
-@torch.no_grad()
-def benchmark_eval(args):
-    cfg = setup(args)
-    if args.config_file.endswith(".yaml"):
-        model = build_model(cfg)
-        DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
-
-        cfg.defrost()
-        cfg.DATALOADER.NUM_WORKERS = 0
-        data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
-    else:
-        model = instantiate(cfg.model)
-        model.to(cfg.train.device)
-        DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
-
-        cfg.dataloader.num_workers = 0
-        data_loader = instantiate(cfg.dataloader.test)
-
-    model.eval()
-    logger.info("Model:\n{}".format(model))
-    dummy_data = DatasetFromList(list(itertools.islice(data_loader, 100)), copy=False)
-
-    def f():
-        while True:
-            yield from dummy_data
-
-    for k in range(5):  # warmup
-        model(dummy_data[k])
-
-    max_iter = 300
-    timer = Timer()
-    with tqdm.tqdm(total=max_iter) as pbar:
-        for idx, d in enumerate(f()):
-            if idx == max_iter:
-                break
-            model(d)
-            pbar.update()
-    logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds()))
-
-
-if __name__ == "__main__":
-    parser = default_argument_parser()
-    parser.add_argument("--task", choices=["train", "eval", "data", "data_advanced"], required=True)
-    args = parser.parse_args()
-    assert not args.eval_only
-
-    logger.info("Environment info:\n" + collect_env_info())
-    if "data" in args.task:
-        print("Initial " + RAM_msg())
-    if args.task == "data":
-        f = benchmark_data
-    if args.task == "data_advanced":
-        f = benchmark_data_advanced
-    elif args.task == "train":
-        """
-        Note: training speed may not be representative.
-        The training cost of a R-CNN model varies with the content of the data
-        and the quality of the model.
-        """
-        f = benchmark_train
-    elif args.task == "eval":
-        f = benchmark_eval
-        # only benchmark single-GPU inference.
-        assert args.num_gpus == 1 and args.num_machines == 1
-    launch(f, args.num_gpus, args.num_machines, args.machine_rank, args.dist_url, args=(args,))

spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/spec_utils.py

@@ -1,672 +0,0 @@
-import hashlib
-import json
-import math
-import os
-
-import librosa
-import numpy as np
-import soundfile as sf
-from tqdm import tqdm
-
-
-def crop_center(h1, h2):
-    h1_shape = h1.size()
-    h2_shape = h2.size()
-
-    if h1_shape[3] == h2_shape[3]:
-        return h1
-    elif h1_shape[3] < h2_shape[3]:
-        raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
-
-    # s_freq = (h2_shape[2] - h1_shape[2]) // 2
-    # e_freq = s_freq + h1_shape[2]
-    s_time = (h1_shape[3] - h2_shape[3]) // 2
-    e_time = s_time + h2_shape[3]
-    h1 = h1[:, :, :, s_time:e_time]
-
-    return h1
-
-
-def wave_to_spectrogram(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def wave_to_spectrogram_mt(
-    wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
-):
-    import threading
-
-    if reverse:
-        wave_left = np.flip(np.asfortranarray(wave[0]))
-        wave_right = np.flip(np.asfortranarray(wave[1]))
-    elif mid_side:
-        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
-    elif mid_side_b2:
-        wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
-        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
-    else:
-        wave_left = np.asfortranarray(wave[0])
-        wave_right = np.asfortranarray(wave[1])
-
-    def run_thread(**kwargs):
-        global spec_left
-        spec_left = librosa.stft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread,
-        kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
-    )
-    thread.start()
-    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
-    thread.join()
-
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def combine_spectrograms(specs, mp):
-    l = min([specs[i].shape[2] for i in specs])
-    spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
-    offset = 0
-    bands_n = len(mp.param["band"])
-
-    for d in range(1, bands_n + 1):
-        h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
-        spec_c[:, offset : offset + h, :l] = specs[d][
-            :, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
-        ]
-        offset += h
-
-    if offset > mp.param["bins"]:
-        raise ValueError("Too much bins")
-
-    # lowpass fiter
-    if (
-        mp.param["pre_filter_start"] > 0
-    ):  # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
-        if bands_n == 1:
-            spec_c = fft_lp_filter(
-                spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
-            )
-        else:
-            gp = 1
-            for b in range(
-                mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
-            ):
-                g = math.pow(
-                    10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
-                )
-                gp = g
-                spec_c[:, b, :] *= g
-
-    return np.asfortranarray(spec_c)
-
-
-def spectrogram_to_image(spec, mode="magnitude"):
-    if mode == "magnitude":
-        if np.iscomplexobj(spec):
-            y = np.abs(spec)
-        else:
-            y = spec
-        y = np.log10(y**2 + 1e-8)
-    elif mode == "phase":
-        if np.iscomplexobj(spec):
-            y = np.angle(spec)
-        else:
-            y = spec
-
-    y -= y.min()
-    y *= 255 / y.max()
-    img = np.uint8(y)
-
-    if y.ndim == 3:
-        img = img.transpose(1, 2, 0)
-        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
-
-    return img
-
-
-def reduce_vocal_aggressively(X, y, softmask):
-    v = X - y
-    y_mag_tmp = np.abs(y)
-    v_mag_tmp = np.abs(v)
-
-    v_mask = v_mag_tmp > y_mag_tmp
-    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
-
-    return y_mag * np.exp(1.0j * np.angle(y))
-
-
-def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
-    if min_range < fade_size * 2:
-        raise ValueError("min_range must be >= fade_area * 2")
-
-    mag = mag.copy()
-
-    idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
-    starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
-    ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
-    uninformative = np.where(ends - starts > min_range)[0]
-    if len(uninformative) > 0:
-        starts = starts[uninformative]
-        ends = ends[uninformative]
-        old_e = None
-        for s, e in zip(starts, ends):
-            if old_e is not None and s - old_e < fade_size:
-                s = old_e - fade_size * 2
-
-            if s != 0:
-                weight = np.linspace(0, 1, fade_size)
-                mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
-            else:
-                s -= fade_size
-
-            if e != mag.shape[2]:
-                weight = np.linspace(1, 0, fade_size)
-                mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
-            else:
-                e += fade_size
-
-            mag[:, :, s + fade_size : e - fade_size] += ref[
-                :, :, s + fade_size : e - fade_size
-            ]
-            old_e = e
-
-    return mag
-
-
-def align_wave_head_and_tail(a, b):
-    l = min([a[0].size, b[0].size])
-
-    return a[:l, :l], b[:l, :l]
-
-
-def cache_or_load(mix_path, inst_path, mp):
-    mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
-    inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
-
-    cache_dir = "mph{}".format(
-        hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
-    )
-    mix_cache_dir = os.path.join("cache", cache_dir)
-    inst_cache_dir = os.path.join("cache", cache_dir)
-
-    os.makedirs(mix_cache_dir, exist_ok=True)
-    os.makedirs(inst_cache_dir, exist_ok=True)
-
-    mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
-    inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
-
-    if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
-        X_spec_m = np.load(mix_cache_path)
-        y_spec_m = np.load(inst_cache_path)
-    else:
-        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                X_wave[d], _ = librosa.load(
-                    mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
-                )
-                y_wave[d], _ = librosa.load(
-                    inst_path,
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-            else:  # lower bands
-                X_wave[d] = librosa.resample(
-                    X_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-                y_wave[d] = librosa.resample(
-                    y_wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
-
-            X_spec_s[d] = wave_to_spectrogram(
-                X_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-            y_spec_s[d] = wave_to_spectrogram(
-                y_wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        del X_wave, y_wave
-
-        X_spec_m = combine_spectrograms(X_spec_s, mp)
-        y_spec_m = combine_spectrograms(y_spec_s, mp)
-
-        if X_spec_m.shape != y_spec_m.shape:
-            raise ValueError("The combined spectrograms are different: " + mix_path)
-
-        _, ext = os.path.splitext(mix_path)
-
-        np.save(mix_cache_path, X_spec_m)
-        np.save(inst_cache_path, y_spec_m)
-
-    return X_spec_m, y_spec_m
-
-
-def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hop_length)
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
-    import threading
-
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    def run_thread(**kwargs):
-        global wave_left
-        wave_left = librosa.istft(**kwargs)
-
-    thread = threading.Thread(
-        target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
-    )
-    thread.start()
-    wave_right = librosa.istft(spec_right, hop_length=hop_length)
-    thread.join()
-
-    if reverse:
-        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
-    elif mid_side:
-        return np.asfortranarray(
-            [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
-        )
-    elif mid_side_b2:
-        return np.asfortranarray(
-            [
-                np.add(wave_right / 1.25, 0.4 * wave_left),
-                np.subtract(wave_left / 1.25, 0.4 * wave_right),
-            ]
-        )
-    else:
-        return np.asfortranarray([wave_left, wave_right])
-
-
-def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
-    wave_band = {}
-    bands_n = len(mp.param["band"])
-    offset = 0
-
-    for d in range(1, bands_n + 1):
-        bp = mp.param["band"][d]
-        spec_s = np.ndarray(
-            shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
-        )
-        h = bp["crop_stop"] - bp["crop_start"]
-        spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
-            :, offset : offset + h, :
-        ]
-
-        offset += h
-        if d == bands_n:  # higher
-            if extra_bins_h:  # if --high_end_process bypass
-                max_bin = bp["n_fft"] // 2
-                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
-                    :, :extra_bins_h, :
-                ]
-            if bp["hpf_start"] > 0:
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-            if bands_n == 1:
-                wave = spectrogram_to_wave(
-                    spec_s,
-                    bp["hl"],
-                    mp.param["mid_side"],
-                    mp.param["mid_side_b2"],
-                    mp.param["reverse"],
-                )
-            else:
-                wave = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-        else:
-            sr = mp.param["band"][d + 1]["sr"]
-            if d == 1:  # lower
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave = librosa.resample(
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                    bp["sr"],
-                    sr,
-                    res_type="sinc_fastest",
-                )
-            else:  # mid
-                spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
-                spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
-                wave2 = np.add(
-                    wave,
-                    spectrogram_to_wave(
-                        spec_s,
-                        bp["hl"],
-                        mp.param["mid_side"],
-                        mp.param["mid_side_b2"],
-                        mp.param["reverse"],
-                    ),
-                )
-                # wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
-                wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
-
-    return wave.T
-
-
-def fft_lp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop):
-        g -= 1 / (bin_stop - bin_start)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, bin_stop:, :] *= 0
-
-    return spec
-
-
-def fft_hp_filter(spec, bin_start, bin_stop):
-    g = 1.0
-    for b in range(bin_start, bin_stop, -1):
-        g -= 1 / (bin_start - bin_stop)
-        spec[:, b, :] = g * spec[:, b, :]
-
-    spec[:, 0 : bin_stop + 1, :] *= 0
-
-    return spec
-
-
-def mirroring(a, spec_m, input_high_end, mp):
-    if "mirroring" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
-
-        return np.where(
-            np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
-        )
-
-    if "mirroring2" == a:
-        mirror = np.flip(
-            np.abs(
-                spec_m[
-                    :,
-                    mp.param["pre_filter_start"]
-                    - 10
-                    - input_high_end.shape[1] : mp.param["pre_filter_start"]
-                    - 10,
-                    :,
-                ]
-            ),
-            1,
-        )
-        mi = np.multiply(mirror, input_high_end * 1.7)
-
-        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
-
-
-def ensembling(a, specs):
-    for i in range(1, len(specs)):
-        if i == 1:
-            spec = specs[0]
-
-        ln = min([spec.shape[2], specs[i].shape[2]])
-        spec = spec[:, :, :ln]
-        specs[i] = specs[i][:, :, :ln]
-
-        if "min_mag" == a:
-            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
-        if "max_mag" == a:
-            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
-
-    return spec
-
-
-def stft(wave, nfft, hl):
-    wave_left = np.asfortranarray(wave[0])
-    wave_right = np.asfortranarray(wave[1])
-    spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
-    spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
-    spec = np.asfortranarray([spec_left, spec_right])
-
-    return spec
-
-
-def istft(spec, hl):
-    spec_left = np.asfortranarray(spec[0])
-    spec_right = np.asfortranarray(spec[1])
-
-    wave_left = librosa.istft(spec_left, hop_length=hl)
-    wave_right = librosa.istft(spec_right, hop_length=hl)
-    wave = np.asfortranarray([wave_left, wave_right])
-
-
-if __name__ == "__main__":
-    import argparse
-    import sys
-    import time
-
-    import cv2
-    from model_param_init import ModelParameters
-
-    p = argparse.ArgumentParser()
-    p.add_argument(
-        "--algorithm",
-        "-a",
-        type=str,
-        choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"],
-        default="min_mag",
-    )
-    p.add_argument(
-        "--model_params",
-        "-m",
-        type=str,
-        default=os.path.join("modelparams", "1band_sr44100_hl512.json"),
-    )
-    p.add_argument("--output_name", "-o", type=str, default="output")
-    p.add_argument("--vocals_only", "-v", action="store_true")
-    p.add_argument("input", nargs="+")
-    args = p.parse_args()
-
-    start_time = time.time()
-
-    if args.algorithm.startswith("invert") and len(args.input) != 2:
-        raise ValueError("There should be two input files.")
-
-    if not args.algorithm.startswith("invert") and len(args.input) < 2:
-        raise ValueError("There must be at least two input files.")
-
-    wave, specs = {}, {}
-    mp = ModelParameters(args.model_params)
-
-    for i in range(len(args.input)):
-        spec = {}
-
-        for d in range(len(mp.param["band"]), 0, -1):
-            bp = mp.param["band"][d]
-
-            if d == len(mp.param["band"]):  # high-end band
-                wave[d], _ = librosa.load(
-                    args.input[i],
-                    bp["sr"],
-                    False,
-                    dtype=np.float32,
-                    res_type=bp["res_type"],
-                )
-
-                if len(wave[d].shape) == 1:  # mono to stereo
-                    wave[d] = np.array([wave[d], wave[d]])
-            else:  # lower bands
-                wave[d] = librosa.resample(
-                    wave[d + 1],
-                    mp.param["band"][d + 1]["sr"],
-                    bp["sr"],
-                    res_type=bp["res_type"],
-                )
-
-            spec[d] = wave_to_spectrogram(
-                wave[d],
-                bp["hl"],
-                bp["n_fft"],
-                mp.param["mid_side"],
-                mp.param["mid_side_b2"],
-                mp.param["reverse"],
-            )
-
-        specs[i] = combine_spectrograms(spec, mp)
-
-    del wave
-
-    if args.algorithm == "deep":
-        d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
-        v_spec = d_spec - specs[1]
-        sf.write(
-            os.path.join("{}.wav".format(args.output_name)),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-
-    if args.algorithm.startswith("invert"):
-        ln = min([specs[0].shape[2], specs[1].shape[2]])
-        specs[0] = specs[0][:, :, :ln]
-        specs[1] = specs[1][:, :, :ln]
-
-        if "invert_p" == args.algorithm:
-            X_mag = np.abs(specs[0])
-            y_mag = np.abs(specs[1])
-            max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
-            v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
-        else:
-            specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
-            v_spec = specs[0] - specs[1]
-
-            if not args.vocals_only:
-                X_mag = np.abs(specs[0])
-                y_mag = np.abs(specs[1])
-                v_mag = np.abs(v_spec)
-
-                X_image = spectrogram_to_image(X_mag)
-                y_image = spectrogram_to_image(y_mag)
-                v_image = spectrogram_to_image(v_mag)
-
-                cv2.imwrite("{}_X.png".format(args.output_name), X_image)
-                cv2.imwrite("{}_y.png".format(args.output_name), y_image)
-                cv2.imwrite("{}_v.png".format(args.output_name), v_image)
-
-                sf.write(
-                    "{}_X.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[0], mp),
-                    mp.param["sr"],
-                )
-                sf.write(
-                    "{}_y.wav".format(args.output_name),
-                    cmb_spectrogram_to_wave(specs[1], mp),
-                    mp.param["sr"],
-                )
-
-        sf.write(
-            "{}_v.wav".format(args.output_name),
-            cmb_spectrogram_to_wave(v_spec, mp),
-            mp.param["sr"],
-        )
-    else:
-        if not args.algorithm == "deep":
-            sf.write(
-                os.path.join("ensembled", "{}.wav".format(args.output_name)),
-                cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp),
-                mp.param["sr"],
-            )
-
-    if args.algorithm == "align":
-        trackalignment = [
-            {
-                "file1": '"{}"'.format(args.input[0]),
-                "file2": '"{}"'.format(args.input[1]),
-            }
-        ]
-
-        for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."):
-            os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}")
-
-    # print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))

spaces/Benson/text-generation/Examples/Agar.io Indir Apk.md

@@ -1,110 +0,0 @@
-
-<h1>Agar.io Indir Apk: Cómo descargar y jugar el popular juego en línea</h1>
-<p>¿Estás buscando un juego online divertido y adictivo que puedas jugar en tu dispositivo Android? Si es así, es posible que desee probar Agar.io, un juego de acción en línea multijugador masivo que tiene millones de fans en todo el mundo. En este artículo, le diremos qué es Agar.io, por qué debe descargar su archivo apk, cómo descargarlo e instalarlo, cómo jugarlo en línea con amigos y cuáles son las revisiones del juego. ¡Vamos a empezar! </p>
- <h2>¿Qué es Agar.io? </h2>
-<p>Agar.io es un juego creado por el desarrollador brasileño Matheus Valadares en 2015. Se basa en el concepto de comer agar, una sustancia utilizada para cultivar bacterias en una placa de Petri. En el juego, controlas una célula circular que puede comer células más pequeñas y pellets de agar para crecer más grande, evitando las células más grandes que pueden comerte. El juego tiene un modo de juego simple pero adictivo que atrae a jugadores de todas las edades y orígenes. </p>
-<h2>agar.io indir apk</h2><br /><p><b><b>Download Zip</b> &gt;&gt;&gt;&gt;&gt; <a href="https://bltlly.com/2v6K4H">https://bltlly.com/2v6K4H</a></b></p><br /><br />
- <h3>El juego de Agar.io</h3>
-<p>La jugabilidad de Agar.io es fácil de aprender pero difícil de dominar. Comienza con una celda pequeña que puede moverse por el mapa usando el dedo o el ratón. Usted puede comer pellets de agar que se dispersan al azar alrededor del mapa para aumentar su masa ligeramente, o puede comer otras células que son más pequeñas que usted para aumentar su masa significativamente. Sin embargo, también tienes que tener cuidado con otras células que son más grandes que tú, ya que pueden comerte y terminar tu juego. </p>
-<p>También puede utilizar dos botones para mejorar su juego. El botón de división le permite dividir su celda en dos celdas más pequeñas que pueden moverse más rápido y comer células más pequeñas más fácilmente. Sin embargo, la división también lo hace más vulnerable a las células más grandes que pueden comer sus células más pequeñas. El botón de expulsión le permite expulsar algo de masa de su celda en la dirección que está apuntando. Esto se puede utilizar para alimentar otras células, disparar virus a ellos, o escapar de ellos. </p>
- <h3>Las características de Agar.io</h3>
-
-<ul>
-<li>Múltiples modos de juego: Puedes jugar en diferentes modos como FFA (Free-For-All), Battle Royale, Teams, Experimental y Party. Cada modo tiene sus propias reglas y desafíos. </li>
-<li>Skins especiales: Puedes personalizar la apariencia de tu celda usando palabras, frases, símbolos o skins predefinidos. Algunas pieles son secretas y requieren nombres de usuario específicos para desbloquear. </li>
-<li>Tablas de clasificación y estadísticas: Puedes ver tu rango y puntuación en la tabla de clasificación y compararlo con otros jugadores. También puede ver sus estadísticas como la mayor masa, el mayor tiempo de supervivencia, el número de células consumidas, etc.</li>
-<li>Características sociales: Puedes chatear con otros jugadores en el juego o invitarlos a unirse a tu fiesta. También puedes compartir tu juego en plataformas de redes sociales como Facebook o Twitter.</li>
-</ul>
- <h2>¿Por qué descargar apk Agar.io? </h2>
-<p>Si desea jugar Agar.io en su dispositivo Android, es posible que se pregunte por qué debe descargar su archivo apk en lugar de instalarlo desde la Google Play Store. Bueno, hay varias razones por las que descargar Agar.io apk es una mejor opción para usted. Estos son algunos de ellos:</p>
- <h3>Los beneficios de descargar Agar.io apk</h3>
-<p>Descargar Agar.io apk tiene muchos beneficios, tales como:</p>
-<ul>
-<li>Es gratis: Usted no tiene que pagar nada para descargar y jugar apk Agar.io. Puede disfrutar del juego sin anuncios o compras en la aplicación. </li>
-<li>Es rápido: No tienes que esperar a que el juego se descargue e instale desde Google Play Store. Puede descargar Agar.io apk directamente desde una fuente de confianza e instalarlo en pocos minutos. </li>
-<li>Se actualiza: Usted no tiene que preocuparse por la falta de nuevas características o correcciones de errores que los desarrolladores de juegos de liberación. Siempre se puede descargar la última versión de Agar.io apk y disfrutar del juego con el mejor rendimiento y calidad. </li>
-
-</ul>
- <h3>Los requisitos para descargar Agar.io apk</h3>
-<p>Antes de descargar Agar.io apk, es necesario asegurarse de que el dispositivo cumple con los siguientes requisitos:</p>
-<p></p>
-<ul>
-<li>Versión de Android: Necesitas tener Android 4.4 o superior en tu dispositivo. </li>
-<li>Espacio de almacenamiento: Necesita tener al menos 50 MB de espacio de almacenamiento gratuito en su dispositivo. </li>
-<li>Conexión a Internet: Necesitas tener una conexión a Internet estable y rápida para jugar a Agar.io online. </li>
-<li>Configuración de permisos: Es necesario habilitar fuentes desconocidas en la configuración de seguridad del dispositivo para instalar Agar.io apk. </li>
-</ul>
- <h2>Cómo descargar e instalar apk Agar.io? </h2>
-<p>Ahora que sabes por qué y cómo descargar Agar.io apk, usted puede preguntarse cómo hacerlo. No te preocupes, es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
- <h3>Los pasos para descargar e instalar Agar.io apk</h3>
-<ol>
-<li>Ir a un sitio web de confianza que ofrece archivo apk Agar.io, como [ApkPure] o [ApkMirror]. </li>
-<li> Buscar y haga clic en el botón de descarga para el archivo apk Agar.io. El tamaño del archivo es de aproximadamente 37 MB.</li>
-<li>Espere a que el archivo se descargue en su dispositivo. Puede comprobar el progreso en la barra de notificaciones. </li>
-<li>Una vez que el archivo se descarga, toque en él para abrirlo. Es posible que vea un mensaje de advertencia que dice "Este tipo de archivo puede dañar su dispositivo". Ignóralo y toca "Aceptar". </li>
-<li>Verá una pantalla que le pide que instale Agar.io apk. Toque en "Instalar" y esperar a que el proceso de instalación termine. </li>
-<li>Verá una pantalla que dice "App instalado". Toque en "Abrir" para iniciar apk Agar.io y empezar a jugar. </li>
-</ol>
- <h3>Los consejos y trucos para jugar apk Agar.io</h3>
-<p>Si desea mejorar sus habilidades y divertirse más jugando Agar.io apk, es posible que desee aprender algunos consejos y trucos que pueden ayudarle. Estos son algunos de ellos:</p>
-<ul>
-
-<li>Expulsar masa estratégicamente: Expulsar masa puede ayudarte a alimentar otras células, disparar virus contra ellas o escapar de ellas, pero también puede reducir tu masa y ralentizarte. Solo expulse masa cuando tenga un propósito o plan claro. </li>
-<li>Evite los virus: Los virus son células verdes puntiagudas que pueden dividirlo en muchas células más pequeñas si las toca. Evítalos a menos que quieras usarlos como un arma o un escudo contra otras celdas. </li>
-<li>Usar esquinas y bordes: Las esquinas y bordes del mapa pueden ayudarlo a atrapar celdas más pequeñas u ocultarse de celdas más grandes. Úsalos cuando necesites obtener una ventaja o evitar una desventaja. </li>
-<li>Equipo con otros: Asociarse con otras células puede ayudarle a sobrevivir más tiempo y dominar el mapa. Puedes formar equipo con otras personas alimentándolas, separándolas o chateando con ellas. </li>
-</ul>
- <h2>¿Cómo jugar a Agar.io online con amigos? </h2>
-<p>Si quieres jugar a Agar.io online con tus amigos, quizás te preguntes cómo hacerlo. No te preocupes, es muy fácil y sencillo. Solo tienes que seguir estos pasos:</p>
- <h3>Los modos de Agar.io en línea</h3>
-<p>Agar.io online tiene diferentes modos entre los que puedes elegir, dependiendo de tu preferencia y estado de ánimo. Algunos de estos modos son:</p>
-<ul>
-<li>FFA (Free-For-All): Este es el modo predeterminado donde puedes jugar solo o con jugadores aleatorios. Puede unirse a cualquier servidor e intentar convertirse en la celda más grande del mapa. </li>
-<li>Battle Royale: Este es un modo en el que tienes que sobrevivir y eliminar a otros jugadores en una arena cada vez más pequeña. Puede unirse a cualquier servidor e intentar ser la última celda en pie. </li>
-<li>Equipos: Este es un modo donde puedes jugar con otros jugadores en un equipo. Puedes unirte a cualquier servidor e intentar ayudar a tu equipo a dominar el mapa. </li>
-<li>Experimental: Este es un modo donde puedes probar nuevas características y mecánicas en las que están trabajando los desarrolladores de juegos. Puede unirse a cualquier servidor e intentar descubrir cosas nuevas. </li>
-
-</ul>
- <h3>Las estrategias de Agar.io online</h3>
-<p>Si quieres mejorar tus habilidades y divertirte más jugando a Agar.io online, quizás quieras aprender algunas estrategias que te pueden ayudar. Estos son algunos de ellos:</p>
-<ul>
-<li>Utilice el mapa: El mapa le muestra la ubicación de otras células, pellets de agar, virus y fronteras. Utilícelo para planificar sus movimientos y evitar el peligro. </li>
-<li>Usa el chat: El chat te permite comunicarte con otros jugadores del juego. Úsalo para hacer amigos, enemigos, alianzas o bromas. </li>
-<li>Usa las pieles: Las pieles te permiten personalizar la apariencia de tu celda. Úsalas para expresarte, impresionar a otros o confundirlos. </li>
-<li>Utilice la tabla de clasificación: La tabla de clasificación le muestra el rango y la puntuación de las 10 mejores celdas en el mapa. Úsalo para medir tu progreso, desafiar a otros o evitarlos. </li>
-<li>Usa la configuración: La configuración te permite ajustar los gráficos, el sonido, los controles y otras opciones del juego. Úsalos para optimizar tu experiencia de juego y rendimiento. </li>
-</ul>
- <h2>¿Cuáles son las opiniones de Agar.io apk? </h2>
-<p>Si quieres saber lo que otros jugadores piensan de Agar.io apk, es posible que desee leer algunos comentarios del juego. Aquí hay algunos ejemplos de comentarios positivos y negativos de usuarios reales:</p>
- <h3>Los comentarios positivos de Agar.io apk</h3>
-<tabla>
-<tr><th>Nombre</th><th>Calificación</th><th>Revisión</th></tr>
-<tr><td>Alice</td><td>5 estrellas</td><td> ¡Me encanta este juego! Es muy divertido y adictivo. Lo juego todos los días con mis amigos y nos lo pasamos genial. Los gráficos son simples pero lindo, el juego es suave y rápido, y los modos son diversos y emocionantes. Recomiendo este juego a cualquiera que le gusten los juegos en línea. </td></tr>
-
-<tr><td>Charlie</td><td>5 estrellas</td><td>Este juego es increíble! Es muy simple pero adictivo. Me gusta cómo puedes personalizar tu celda con diferentes pieles y nombres, y chatear con otros jugadores en el juego. El juego es muy social y amigable. La mejor parte es que es gratis y fácil de descargar e instalar. </td></tr>
-</tabla>
- <h3>Los comentarios negativos de Agar.io apk</h3>
-<tabla>
-<tr><th>Nombre</th><th>Calificación</th><th>Revisión</th></tr>
-<tr><td>Dave</td><td>2 estrellas</td><td>Este juego es aburrido! Es muy repetitivo y frustrante. No me gusta cómo puedes ser comido por células más grandes o virus en un segundo, y perder todo tu progreso. El juego es muy injusto y aleatorio. La peor parte es que tiene demasiados anuncios y compras en la aplicación. </td></tr>
-<p>Este juego es terrible! Es muy defectuoso y lento. No me gusta cómo el juego se congela o se bloquea todo el tiempo, y me hace perder mi conexión o mi progreso. El juego es muy buggy e inestable. La peor parte es que tiene demasiados hackers y tramposos que arruinan el juego para todos los demás. </td></tr>
-<tr><td>Frank</td><td>3 estrellas</td><td>Este juego está bien. Es muy simple y fácil de jugar. Me gusta cómo puedes jugar con otros jugadores online, pero también offline si quieres. El juego es muy casual y relajante. Lo único que no me gusta es que es demasiado básico y carece de profundidad. El juego podría usar más funciones y modos para hacerlo más interesante y divertido. </td></tr>
-</tabla>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Agar.io apk:</p>
-<ul>
-<li>Q: ¿Es seguro descargar e instalar Agar.io apk? </li>
-<li>A: Sí, Agar.io apk es seguro para descargar e instalar, siempre y cuando se obtiene de un sitio web de confianza que ofrece el archivo original y sin modificar. También debe escanear el archivo con un software antivirus antes de abrirlo. </li>
-<li>Q: Es Agar.io apk legal para descargar e instalar? </li>
-<li>A: Sí, Agar.io apk es legal para descargar e instalar, siempre y cuando usted no viole los términos de servicio o los derechos de propiedad intelectual de los desarrolladores de juegos o editores. También debe respetar las reglas y regulaciones de su país o región con respecto a los juegos en línea. </li>
-<li>Q: Es Agar.io apk compatible con otros dispositivos o plataformas? </li>
-<li>A: No, Agar.io apk solo es compatible con dispositivos Android que cumplen con los requisitos mínimos. Sin embargo, también puede jugar Agar.io en otros dispositivos o plataformas como iOS, Windows, Mac, Linux o navegadores web visitando el sitio web oficial del juego. </li>
-<li>Q: ¿Cómo puedo actualizar Agar.io apk a la última versión? </li>
-<li>A: Puede actualizar Agar.io apk a la última versión mediante la descarga e instalación del nuevo archivo desde un sitio web de confianza que ofrece el archivo actualizado. También debe eliminar el archivo antiguo de su dispositivo antes de instalar el nuevo. </li>
-<li>Q: ¿Cómo puedo contactar al equipo de soporte de Agar.io apk si tengo algún problema o pregunta? </li>
-<li>A: Puede ponerse en contacto con el equipo de soporte de Agar.io apk enviando un correo electrónico a [[email protected]] o visitando su [página de Facebook] o [cuenta de Twitter]. También puede visitar su [foro] o [wiki] para obtener más información y ayuda. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Billyosoro/ESRGAN/realesrgan/models/realesrgan_model.py

@@ -1,258 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.srgan_model import SRGANModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from collections import OrderedDict
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRGANModel(SRGANModel):
-    """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-
-    def __init__(self, opt):
-        super(RealESRGANModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
-        self.queue_size = opt.get('queue_size', 180)
-
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self):
-        """It is the training pair pool for increasing the diversity in a batch.
-
-        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
-        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
-        to increase the degradation diversity in a batch.
-        """
-        # initialize
-        b, c, h, w = self.lq.size()
-        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
-            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
-            _, c, h, w = self.gt.size()
-            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
-            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # the pool is full
-            # do dequeue and enqueue
-            # shuffle
-            idx = torch.randperm(self.queue_size)
-            self.queue_lr = self.queue_lr[idx]
-            self.queue_gt = self.queue_gt[idx]
-            # get first b samples
-            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
-            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update the queue
-            self.queue_lr[0:b, :, :, :] = self.lq.clone()
-            self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
-            self.lq = lq_dequeue
-            self.gt = gt_dequeue
-        else:
-            # only do enqueue
-            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
-            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
-            self.queue_ptr = self.queue_ptr + b
-
-    @torch.no_grad()
-    def feed_data(self, data):
-        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
-        """
-        if self.is_train and self.opt.get('high_order_degradation', True):
-            # training data synthesis
-            self.gt = data['gt'].to(self.device)
-            self.gt_usm = self.usm_sharpener(self.gt)
-
-            self.kernel1 = data['kernel1'].to(self.device)
-            self.kernel2 = data['kernel2'].to(self.device)
-            self.sinc_kernel = data['sinc_kernel'].to(self.device)
-
-            ori_h, ori_w = self.gt.size()[2:4]
-
-            # ----------------------- The first degradation process ----------------------- #
-            # blur
-            out = filter2D(self.gt_usm, self.kernel1)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob']
-            if np.random.uniform() < self.opt['gaussian_noise_prob']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression
-            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
-            out = self.jpeger(out, quality=jpeg_p)
-
-            # ----------------------- The second degradation process ----------------------- #
-            # blur
-            if np.random.uniform() < self.opt['second_blur_prob']:
-                out = filter2D(out, self.kernel2)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range2'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(
-                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob2']
-            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range2'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-
-            # JPEG compression + the final sinc filter
-            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
-            # as one operation.
-            # We consider two orders:
-            #   1. [resize back + sinc filter] + JPEG compression
-            #   2. JPEG compression + [resize back + sinc filter]
-            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
-            if np.random.uniform() < 0.5:
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-            else:
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-
-            # clamp and round
-            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
-
-            # random crop
-            gt_size = self.opt['gt_size']
-            (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
-                                                                 self.opt['scale'])
-
-            # training pair pool
-            self._dequeue_and_enqueue()
-            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
-            self.gt_usm = self.usm_sharpener(self.gt)
-            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
-        else:
-            # for paired training or validation
-            self.lq = data['lq'].to(self.device)
-            if 'gt' in data:
-                self.gt = data['gt'].to(self.device)
-                self.gt_usm = self.usm_sharpener(self.gt)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        # do not use the synthetic process during validation
-        self.is_train = False
-        super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True
-
-    def optimize_parameters(self, current_iter):
-        # usm sharpening
-        l1_gt = self.gt_usm
-        percep_gt = self.gt_usm
-        gan_gt = self.gt_usm
-        if self.opt['l1_gt_usm'] is False:
-            l1_gt = self.gt
-        if self.opt['percep_gt_usm'] is False:
-            percep_gt = self.gt
-        if self.opt['gan_gt_usm'] is False:
-            gan_gt = self.gt
-
-        # optimize net_g
-        for p in self.net_d.parameters():
-            p.requires_grad = False
-
-        self.optimizer_g.zero_grad()
-        self.output = self.net_g(self.lq)
-
-        l_g_total = 0
-        loss_dict = OrderedDict()
-        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
-            # pixel loss
-            if self.cri_pix:
-                l_g_pix = self.cri_pix(self.output, l1_gt)
-                l_g_total += l_g_pix
-                loss_dict['l_g_pix'] = l_g_pix
-            # perceptual loss
-            if self.cri_perceptual:
-                l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
-                if l_g_percep is not None:
-                    l_g_total += l_g_percep
-                    loss_dict['l_g_percep'] = l_g_percep
-                if l_g_style is not None:
-                    l_g_total += l_g_style
-                    loss_dict['l_g_style'] = l_g_style
-            # gan loss
-            fake_g_pred = self.net_d(self.output)
-            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
-            l_g_total += l_g_gan
-            loss_dict['l_g_gan'] = l_g_gan
-
-            l_g_total.backward()
-            self.optimizer_g.step()
-
-        # optimize net_d
-        for p in self.net_d.parameters():
-            p.requires_grad = True
-
-        self.optimizer_d.zero_grad()
-        # real
-        real_d_pred = self.net_d(gan_gt)
-        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
-        loss_dict['l_d_real'] = l_d_real
-        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
-        l_d_real.backward()
-        # fake
-        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
-        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
-        loss_dict['l_d_fake'] = l_d_fake
-        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
-        l_d_fake.backward()
-        self.optimizer_d.step()
-
-        if self.ema_decay > 0:
-            self.model_ema(decay=self.ema_decay)
-
-        self.log_dict = self.reduce_loss_dict(loss_dict)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp

@@ -1,73 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#include "../box_iou_rotated/box_iou_rotated_utils.h"
-#include "nms_rotated.h"
-
-namespace detectron2 {
-
-template <typename scalar_t>
-at::Tensor nms_rotated_cpu_kernel(
-    const at::Tensor& dets,
-    const at::Tensor& scores,
-    const float iou_threshold) {
-  // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel,
-  // however, the code in this function is much shorter because
-  // we delegate the IoU computation for rotated boxes to
-  // the single_box_iou_rotated function in box_iou_rotated_utils.h
-  AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
-  AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor");
-  AT_ASSERTM(
-      dets.type() == scores.type(), "dets should have the same type as scores");
-
-  if (dets.numel() == 0) {
-    return at::empty({0}, dets.options().dtype(at::kLong));
-  }
-
-  auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
-
-  auto ndets = dets.size(0);
-  at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte));
-  at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong));
-
-  auto suppressed = suppressed_t.data_ptr<uint8_t>();
-  auto keep = keep_t.data_ptr<int64_t>();
-  auto order = order_t.data_ptr<int64_t>();
-
-  int64_t num_to_keep = 0;
-
-  for (int64_t _i = 0; _i < ndets; _i++) {
-    auto i = order[_i];
-    if (suppressed[i] == 1) {
-      continue;
-    }
-
-    keep[num_to_keep++] = i;
-
-    for (int64_t _j = _i + 1; _j < ndets; _j++) {
-      auto j = order[_j];
-      if (suppressed[j] == 1) {
-        continue;
-      }
-
-      auto ovr = single_box_iou_rotated<scalar_t>(
-          dets[i].data_ptr<scalar_t>(), dets[j].data_ptr<scalar_t>());
-      if (ovr >= iou_threshold) {
-        suppressed[j] = 1;
-      }
-    }
-  }
-  return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep);
-}
-
-at::Tensor nms_rotated_cpu(
-    const at::Tensor& dets,
-    const at::Tensor& scores,
-    const float iou_threshold) {
-  auto result = at::empty({0}, dets.options());
-
-  AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms_rotated", [&] {
-    result = nms_rotated_cpu_kernel<scalar_t>(dets, scores, iou_threshold);
-  });
-  return result;
-}
-
-} // namespace detectron2

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/mask_ops.py

@@ -1,247 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-import torch
-from PIL import Image
-from torch.nn import functional as F
-
-__all__ = ["paste_masks_in_image"]
-
-
-BYTES_PER_FLOAT = 4
-# TODO: This memory limit may be too much or too little. It would be better to
-# determine it based on available resources.
-GPU_MEM_LIMIT = 1024 ** 3  # 1 GB memory limit
-
-
-def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True):
-    """
-    Args:
-        masks: N, 1, H, W
-        boxes: N, 4
-        img_h, img_w (int):
-        skip_empty (bool): only paste masks within the region that
-            tightly bound all boxes, and returns the results this region only.
-            An important optimization for CPU.
-
-    Returns:
-        if skip_empty == False, a mask of shape (N, img_h, img_w)
-        if skip_empty == True, a mask of shape (N, h', w'), and the slice
-            object for the corresponding region.
-    """
-    # On GPU, paste all masks together (up to chunk size)
-    # by using the entire image to sample the masks
-    # Compared to pasting them one by one,
-    # this has more operations but is faster on COCO-scale dataset.
-    device = masks.device
-    if skip_empty:
-        x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to(
-            dtype=torch.int32
-        )
-        x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
-        y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
-    else:
-        x0_int, y0_int = 0, 0
-        x1_int, y1_int = img_w, img_h
-    x0, y0, x1, y1 = torch.split(boxes, 1, dim=1)  # each is Nx1
-
-    N = masks.shape[0]
-
-    img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
-    img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
-    img_y = (img_y - y0) / (y1 - y0) * 2 - 1
-    img_x = (img_x - x0) / (x1 - x0) * 2 - 1
-    # img_x, img_y have shapes (N, w), (N, h)
-
-    gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
-    gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
-    grid = torch.stack([gx, gy], dim=3)
-
-    img_masks = F.grid_sample(masks.to(dtype=torch.float32), grid, align_corners=False)
-
-    if skip_empty:
-        return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
-    else:
-        return img_masks[:, 0], ()
-
-
-def paste_masks_in_image(masks, boxes, image_shape, threshold=0.5):
-    """
-    Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image.
-    The location, height, and width for pasting each mask is determined by their
-    corresponding bounding boxes in boxes.
-
-    Note:
-        This is a complicated but more accurate implementation. In actual deployment, it is
-        often enough to use a faster but less accurate implementation.
-        See :func:`paste_mask_in_image_old` in this file for an alternative implementation.
-
-    Args:
-        masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of
-            detected object instances in the image and Hmask, Wmask are the mask width and mask
-            height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1].
-        boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4).
-            boxes[i] and masks[i] correspond to the same object instance.
-        image_shape (tuple): height, width
-        threshold (float): A threshold in [0, 1] for converting the (soft) masks to
-            binary masks.
-
-    Returns:
-        img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the
-        number of detected object instances and Himage, Wimage are the image width
-        and height. img_masks[i] is a binary mask for object instance i.
-    """
-
-    assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported"
-    N = len(masks)
-    if N == 0:
-        return masks.new_empty((0,) + image_shape, dtype=torch.uint8)
-    if not isinstance(boxes, torch.Tensor):
-        boxes = boxes.tensor
-    device = boxes.device
-    assert len(boxes) == N, boxes.shape
-
-    img_h, img_w = image_shape
-
-    # The actual implementation split the input into chunks,
-    # and paste them chunk by chunk.
-    if device.type == "cpu":
-        # CPU is most efficient when they are pasted one by one with skip_empty=True
-        # so that it performs minimal number of operations.
-        num_chunks = N
-    else:
-        # GPU benefits from parallelism for larger chunks, but may have memory issue
-        num_chunks = int(np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
-        assert (
-            num_chunks <= N
-        ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it"
-    chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
-    img_masks = torch.zeros(
-        N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8
-    )
-    for inds in chunks:
-        masks_chunk, spatial_inds = _do_paste_mask(
-            masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu"
-        )
-
-        if threshold >= 0:
-            masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
-        else:
-            # for visualization and debugging
-            masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
-        img_masks[(inds,) + spatial_inds] = masks_chunk
-    return img_masks
-
-
-# The below are the original paste function (from Detectron1) which has
-# larger quantization error.
-# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample.
-
-
-def paste_mask_in_image_old(mask, box, img_h, img_w, threshold):
-    """
-    Paste a single mask in an image.
-    This is a per-box implementation of :func:`paste_masks_in_image`.
-    This function has larger quantization error due to incorrect pixel
-    modeling and is not used any more.
-
-    Args:
-        mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
-            object instance. Values are in [0, 1].
-        box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
-            of the object instance.
-        img_h, img_w (int): Image height and width.
-        threshold (float): Mask binarization threshold in [0, 1].
-
-    Returns:
-        im_mask (Tensor):
-            The resized and binarized object mask pasted into the original
-            image plane (a tensor of shape (img_h, img_w)).
-    """
-    # Conversion from continuous box coordinates to discrete pixel coordinates
-    # via truncation (cast to int32). This determines which pixels to paste the
-    # mask onto.
-    box = box.to(dtype=torch.int32)  # Continuous to discrete coordinate conversion
-    # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to
-    # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1
-    # pixels (not x1 - x0 pixels).
-    samples_w = box[2] - box[0] + 1  # Number of pixel samples, *not* geometric width
-    samples_h = box[3] - box[1] + 1  # Number of pixel samples, *not* geometric height
-
-    # Resample the mask from it's original grid to the new samples_w x samples_h grid
-    mask = Image.fromarray(mask.cpu().numpy())
-    mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR)
-    mask = np.array(mask, copy=False)
-
-    if threshold >= 0:
-        mask = np.array(mask > threshold, dtype=np.uint8)
-        mask = torch.from_numpy(mask)
-    else:
-        # for visualization and debugging, we also
-        # allow it to return an unmodified mask
-        mask = torch.from_numpy(mask * 255).to(torch.uint8)
-
-    im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8)
-    x_0 = max(box[0], 0)
-    x_1 = min(box[2] + 1, img_w)
-    y_0 = max(box[1], 0)
-    y_1 = min(box[3] + 1, img_h)
-
-    im_mask[y_0:y_1, x_0:x_1] = mask[
-        (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
-    ]
-    return im_mask
-
-
-# Our pixel modeling requires extrapolation for any continuous
-# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks,
-# we would like this extrapolation to be an interpolation between boundary values and zero,
-# instead of using absolute zero or boundary values.
-# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this:
-# masks, scale = pad_masks(masks[:, 0, :, :], 1)
-# boxes = scale_boxes(boxes.tensor, scale)
-
-
-def pad_masks(masks, padding):
-    """
-    Args:
-        masks (tensor): A tensor of shape (B, M, M) representing B masks.
-        padding (int): Number of cells to pad on all sides.
-
-    Returns:
-        The padded masks and the scale factor of the padding size / original size.
-    """
-    B = masks.shape[0]
-    M = masks.shape[-1]
-    pad2 = 2 * padding
-    scale = float(M + pad2) / M
-    padded_masks = masks.new_zeros((B, M + pad2, M + pad2))
-    padded_masks[:, padding:-padding, padding:-padding] = masks
-    return padded_masks, scale
-
-
-def scale_boxes(boxes, scale):
-    """
-    Args:
-        boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4
-            coords representing the corners x0, y0, x1, y1,
-        scale (float): The box scaling factor.
-
-    Returns:
-        Scaled boxes.
-    """
-    w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5
-    h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5
-    x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5
-    y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5
-
-    w_half *= scale
-    h_half *= scale
-
-    scaled_boxes = torch.zeros_like(boxes)
-    scaled_boxes[:, 0] = x_c - w_half
-    scaled_boxes[:, 2] = x_c + w_half
-    scaled_boxes[:, 1] = y_c - h_half
-    scaled_boxes[:, 3] = y_c + h_half
-    return scaled_boxes

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/convert-torchvision-to-d2.py

@@ -1,56 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import pickle as pkl
-import sys
-import torch
-
-"""
-Usage:
-  # download one of the ResNet{18,34,50,101,152} models from torchvision:
-  wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
-  # run the conversion
-  ./convert-torchvision-to-d2.py r50.pth r50.pkl
-
-  # Then, use r50.pkl with the following changes in config:
-
-MODEL:
-  WEIGHTS: "/path/to/r50.pkl"
-  PIXEL_MEAN: [123.675, 116.280, 103.530]
-  PIXEL_STD: [58.395, 57.120, 57.375]
-  RESNETS:
-    DEPTH: 50
-    STRIDE_IN_1X1: False
-INPUT:
-  FORMAT: "RGB"
-
-  These models typically produce slightly worse results than the
-  pre-trained ResNets we use in official configs, which are the
-  original ResNet models released by MSRA.
-"""
-
-if __name__ == "__main__":
-    input = sys.argv[1]
-
-    obj = torch.load(input, map_location="cpu")
-
-    newmodel = {}
-    for k in list(obj.keys()):
-        old_k = k
-        if "layer" not in k:
-            k = "stem." + k
-        for t in [1, 2, 3, 4]:
-            k = k.replace("layer{}".format(t), "res{}".format(t + 1))
-        for t in [1, 2, 3]:
-            k = k.replace("bn{}".format(t), "conv{}.norm".format(t))
-        k = k.replace("downsample.0", "shortcut")
-        k = k.replace("downsample.1", "shortcut.norm")
-        print(old_k, "->", k)
-        newmodel[k] = obj.pop(old_k).detach().numpy()
-
-    res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True}
-
-    with open(sys.argv[2], "wb") as f:
-        pkl.dump(res, f)
-    if obj:
-        print("Unconverted keys:", obj.keys())

spaces/CVPR/LIVE/thrust/thrust/random/detail/linear_feedback_shift_engine_wordmask.h

@@ -1,47 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-namespace thrust
-{
-
-namespace random
-{
-
-namespace detail
-{
-
-template<typename T, int w, int i = w-1>
-  struct linear_feedback_shift_engine_wordmask
-{
-  static const T value =
-    (T(1u) << i) |
-    linear_feedback_shift_engine_wordmask<T, w, i-1>::value;
-}; // end linear_feedback_shift_engine_wordmask
-
-template<typename T, int w>
-  struct linear_feedback_shift_engine_wordmask<T, w, 0>
-{
-  static const T value = 0;
-}; // end linear_feedback_shift_engine_wordmask
-
-} // end detail
-
-} // end random
-
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/reduce.h

@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits reduce
-#include <thrust/system/detail/sequential/reduce.h>
-

spaces/CVPR/WALT/mmdet/core/bbox/samplers/base_sampler.py

@@ -1,101 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch
-
-from .sampling_result import SamplingResult
-
-
-class BaseSampler(metaclass=ABCMeta):
-    """Base class of samplers."""
-
-    def __init__(self,
-                 num,
-                 pos_fraction,
-                 neg_pos_ub=-1,
-                 add_gt_as_proposals=True,
-                 **kwargs):
-        self.num = num
-        self.pos_fraction = pos_fraction
-        self.neg_pos_ub = neg_pos_ub
-        self.add_gt_as_proposals = add_gt_as_proposals
-        self.pos_sampler = self
-        self.neg_sampler = self
-
-    @abstractmethod
-    def _sample_pos(self, assign_result, num_expected, **kwargs):
-        """Sample positive samples."""
-        pass
-
-    @abstractmethod
-    def _sample_neg(self, assign_result, num_expected, **kwargs):
-        """Sample negative samples."""
-        pass
-
-    def sample(self,
-               assign_result,
-               bboxes,
-               gt_bboxes,
-               gt_labels=None,
-               **kwargs):
-        """Sample positive and negative bboxes.
-
-        This is a simple implementation of bbox sampling given candidates,
-        assigning results and ground truth bboxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): Bbox assigning results.
-            bboxes (Tensor): Boxes to be sampled from.
-            gt_bboxes (Tensor): Ground truth bboxes.
-            gt_labels (Tensor, optional): Class labels of ground truth bboxes.
-
-        Returns:
-            :obj:`SamplingResult`: Sampling result.
-
-        Example:
-            >>> from mmdet.core.bbox import RandomSampler
-            >>> from mmdet.core.bbox import AssignResult
-            >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes
-            >>> rng = ensure_rng(None)
-            >>> assign_result = AssignResult.random(rng=rng)
-            >>> bboxes = random_boxes(assign_result.num_preds, rng=rng)
-            >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng)
-            >>> gt_labels = None
-            >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1,
-            >>>                      add_gt_as_proposals=False)
-            >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels)
-        """
-        if len(bboxes.shape) < 2:
-            bboxes = bboxes[None, :]
-
-        bboxes = bboxes[:, :4]
-
-        gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8)
-        if self.add_gt_as_proposals and len(gt_bboxes) > 0:
-            if gt_labels is None:
-                raise ValueError(
-                    'gt_labels must be given when add_gt_as_proposals is True')
-            bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
-            assign_result.add_gt_(gt_labels)
-            gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
-            gt_flags = torch.cat([gt_ones, gt_flags])
-
-        num_expected_pos = int(self.num * self.pos_fraction)
-        pos_inds = self.pos_sampler._sample_pos(
-            assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
-        # We found that sampled indices have duplicated items occasionally.
-        # (may be a bug of PyTorch)
-        pos_inds = pos_inds.unique()
-        num_sampled_pos = pos_inds.numel()
-        num_expected_neg = self.num - num_sampled_pos
-        if self.neg_pos_ub >= 0:
-            _pos = max(1, num_sampled_pos)
-            neg_upper_bound = int(self.neg_pos_ub * _pos)
-            if num_expected_neg > neg_upper_bound:
-                num_expected_neg = neg_upper_bound
-        neg_inds = self.neg_sampler._sample_neg(
-            assign_result, num_expected_neg, bboxes=bboxes, **kwargs)
-        neg_inds = neg_inds.unique()
-
-        sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
-                                         assign_result, gt_flags)
-        return sampling_result

spaces/CVPR/WALT/mmdet/utils/logger.py

@@ -1,19 +0,0 @@
-import logging
-
-from mmcv.utils import get_logger
-
-
-def get_root_logger(log_file=None, log_level=logging.INFO):
-    """Get root logger.
-
-    Args:
-        log_file (str, optional): File path of log. Defaults to None.
-        log_level (int, optional): The level of logger.
-            Defaults to logging.INFO.
-
-    Returns:
-        :obj:`logging.Logger`: The obtained logger
-    """
-    logger = get_logger(name='mmdet', log_file=log_file, log_level=log_level)
-
-    return logger

spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/modeling/mask_decoder.py

@@ -1,176 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from typing import List, Tuple, Type
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
-    def __init__(
-        self,
-        *,
-        transformer_dim: int,
-        transformer: nn.Module,
-        num_multimask_outputs: int = 3,
-        activation: Type[nn.Module] = nn.GELU,
-        iou_head_depth: int = 3,
-        iou_head_hidden_dim: int = 256,
-    ) -> None:
-        """
-        Predicts masks given an image and prompt embeddings, using a
-        tranformer architecture.
-
-        Arguments:
-          transformer_dim (int): the channel dimension of the transformer
-          transformer (nn.Module): the transformer used to predict masks
-          num_multimask_outputs (int): the number of masks to predict
-            when disambiguating masks
-          activation (nn.Module): the type of activation to use when
-            upscaling masks
-          iou_head_depth (int): the depth of the MLP used to predict
-            mask quality
-          iou_head_hidden_dim (int): the hidden dimension of the MLP
-            used to predict mask quality
-        """
-        super().__init__()
-        self.transformer_dim = transformer_dim
-        self.transformer = transformer
-
-        self.num_multimask_outputs = num_multimask_outputs
-
-        self.iou_token = nn.Embedding(1, transformer_dim)
-        self.num_mask_tokens = num_multimask_outputs + 1
-        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
-        self.output_upscaling = nn.Sequential(
-            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
-            LayerNorm2d(transformer_dim // 4),
-            activation(),
-            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
-            activation(),
-        )
-        self.output_hypernetworks_mlps = nn.ModuleList(
-            [
-                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
-                for i in range(self.num_mask_tokens)
-            ]
-        )
-
-        self.iou_prediction_head = MLP(
-            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
-        )
-
-    def forward(
-        self,
-        image_embeddings: torch.Tensor,
-        image_pe: torch.Tensor,
-        sparse_prompt_embeddings: torch.Tensor,
-        dense_prompt_embeddings: torch.Tensor,
-        multimask_output: bool,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Predict masks given image and prompt embeddings.
-
-        Arguments:
-          image_embeddings (torch.Tensor): the embeddings from the image encoder
-          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
-          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
-          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
-          multimask_output (bool): Whether to return multiple masks or a single
-            mask.
-
-        Returns:
-          torch.Tensor: batched predicted masks
-          torch.Tensor: batched predictions of mask quality
-        """
-        masks, iou_pred = self.predict_masks(
-            image_embeddings=image_embeddings,
-            image_pe=image_pe,
-            sparse_prompt_embeddings=sparse_prompt_embeddings,
-            dense_prompt_embeddings=dense_prompt_embeddings,
-        )
-
-        # Select the correct mask or masks for outptu
-        if multimask_output:
-            mask_slice = slice(1, None)
-        else:
-            mask_slice = slice(0, 1)
-        masks = masks[:, mask_slice, :, :]
-        iou_pred = iou_pred[:, mask_slice]
-
-        # Prepare output
-        return masks, iou_pred
-
-    def predict_masks(
-        self,
-        image_embeddings: torch.Tensor,
-        image_pe: torch.Tensor,
-        sparse_prompt_embeddings: torch.Tensor,
-        dense_prompt_embeddings: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Predicts masks. See 'forward' for more details."""
-        # Concatenate output tokens
-        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
-        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
-        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
-        # Expand per-image data in batch direction to be per-mask
-        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
-        src = src + dense_prompt_embeddings
-        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
-        b, c, h, w = src.shape
-
-        # Run the transformer
-        hs, src = self.transformer(src, pos_src, tokens)
-        iou_token_out = hs[:, 0, :]
-        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
-        # Upscale mask embeddings and predict masks using the mask tokens
-        src = src.transpose(1, 2).view(b, c, h, w)
-        upscaled_embedding = self.output_upscaling(src)
-        hyper_in_list: List[torch.Tensor] = []
-        for i in range(self.num_mask_tokens):
-            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
-        hyper_in = torch.stack(hyper_in_list, dim=1)
-        b, c, h, w = upscaled_embedding.shape
-        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
-        # Generate mask quality predictions
-        iou_pred = self.iou_prediction_head(iou_token_out)
-
-        return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
-    def __init__(
-        self,
-        input_dim: int,
-        hidden_dim: int,
-        output_dim: int,
-        num_layers: int,
-        sigmoid_output: bool = False,
-    ) -> None:
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(
-            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
-        )
-        self.sigmoid_output = sigmoid_output
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        if self.sigmoid_output:
-            x = F.sigmoid(x)
-        return x

spaces/ChengZ/DeepDanbooru_string0/README.md

@@ -1,39 +0,0 @@
----
-title: DeepDanbooru String
-emoji: 💬
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-duplicated_from: NoCrypt/DeepDanbooru_string
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/CodingBillionaire/bark-voice-cloning/app.py

@@ -1,98 +0,0 @@
-import math
-import os.path
-import uuid
-
-import gradio
-import numpy
-import torch
-
-from hubert.hubert_manager import HuBERTManager
-from hubert.pre_kmeans_hubert import CustomHubert
-from hubert.customtokenizer import CustomTokenizer
-from encodec import EncodecModel
-from encodec.utils import convert_audio
-
-
-hubert_model = CustomHubert(HuBERTManager.make_sure_hubert_installed())
-tokenizer_model = CustomTokenizer.load_from_checkpoint(
-    HuBERTManager.make_sure_tokenizer_installed(model='quantifier_V1_hubert_base_ls960_23.pth'),
-    map_location=torch.device('cpu')
-)
-encodec_model = EncodecModel.encodec_model_24khz()
-
-
-
-def clone(audio, *args):
-    sr, wav = audio
-
-    wav = torch.tensor(wav)
-
-    if wav.dtype == torch.int16:
-        wav = wav.float() / 32767.0
-
-    if len(wav.shape) == 2:
-        if wav.shape[0] == 2:  # Stereo to mono if needed
-            wav = wav.mean(0, keepdim=True)
-        if wav.shape[1] == 2:
-            wav = wav.mean(1, keepdim=False).unsqueeze(-1)
-
-    wav = wav[-int(sr*20):]  # Take only the last 20 seconds
-
-    wav = wav.reshape(1, -1)  # Reshape from gradio style to HuBERT shape. (N, 1) to (1, N)
-
-    semantic_vectors = hubert_model.forward(wav, input_sample_hz=sr)
-    semantic_tokens = tokenizer_model.get_token(semantic_vectors)
-
-    encodec_model.set_target_bandwidth(6.0)
-    wav = convert_audio(wav, sr, encodec_model.sample_rate, 1)
-    wav = wav.unsqueeze(0)
-
-    with torch.no_grad():
-        encoded_frames = encodec_model.encode(wav)
-
-    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()  # [B, n_q, T]
-
-    if not os.path.isdir('data/speakers'):
-        os.makedirs('data/speakers')
-
-    file_path = f'data/speakers/{uuid.uuid4().hex}.npz'
-
-    numpy.savez(
-        file_path,
-        semantic_prompt=semantic_tokens,
-        fine_prompt=codes,
-        coarse_prompt=codes[:2, :]
-    )
-
-    return file_path
-
-
-
-iface = gradio.interface.Interface(fn=clone, inputs=[
-    'audio',
-    gradio.Markdown(
-        '''
-        # Bark text to speech voice cloning
-        [Model](https://huggingface.co/GitMylo/bark-voice-cloning/), [Model GitHub](https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer), [Webui GitHub](https://github.com/gitmylo/audio-webui)
-
-        For faster creation of voice clones [Duplicate this space](https://huggingface.co/spaces/GitMylo/bark-voice-cloning?duplicate=true)
-
-        Uploaded audio files get cut to 20 seconds in order to keep it fast for everyone. Only the last 20 seconds will be used. (Bark only uses the last 14 seconds anyway)
-
-        ## Tips for better cloning
-        ### Make sure these things are **NOT** in your voice input: (in no particular order)
-        * Noise (You can use a noise remover before)
-        * Music (There are also music remover tools) (Unless you want music in the background)
-        * A cut-off at the end (This will cause it to try and continue on the generation)
-        * Under 1 second of training data (i personally suggest around 10 seconds for good potential, but i've had great results with 5 seconds as well.)
-
-        ### What makes for good prompt audio? (in no particular order)
-        * Clearly spoken
-        * No weird background noises
-        * Only one speaker
-        * Audio which ends after a sentence ends
-        * Regular/common voice (They usually have more success, it's still capable of cloning complex voices, but not as good at it)
-        * Around 10 seconds of data
-        ''')
-], outputs='file')
-iface.launch()

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/rpn/__init__.py

@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# from .rpn import build_rpn

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/instancer/solver.py

@@ -1,305 +0,0 @@
-from fontTools.varLib.models import supportScalar
-from fontTools.misc.fixedTools import MAX_F2DOT14
-from functools import lru_cache
-
-__all__ = ["rebaseTent"]
-
-EPSILON = 1 / (1 << 14)
-
-
-def _reverse_negate(v):
-    return (-v[2], -v[1], -v[0])
-
-
-def _solve(tent, axisLimit, negative=False):
-    axisMin, axisDef, axisMax, _distanceNegative, _distancePositive = axisLimit
-    lower, peak, upper = tent
-
-    # Mirror the problem such that axisDef <= peak
-    if axisDef > peak:
-        return [
-            (scalar, _reverse_negate(t) if t is not None else None)
-            for scalar, t in _solve(
-                _reverse_negate(tent),
-                axisLimit.reverse_negate(),
-                not negative,
-            )
-        ]
-    # axisDef <= peak
-
-    # case 1: The whole deltaset falls outside the new limit; we can drop it
-    #
-    #                                          peak
-    #  1.........................................o..........
-    #                                           / \
-    #                                          /   \
-    #                                         /     \
-    #                                        /       \
-    #  0---|-----------|----------|-------- o         o----1
-    #    axisMin     axisDef    axisMax   lower     upper
-    #
-    if axisMax <= lower and axisMax < peak:
-        return []  # No overlap
-
-    # case 2: Only the peak and outermost bound fall outside the new limit;
-    # we keep the deltaset, update peak and outermost bound and and scale deltas
-    # by the scalar value for the restricted axis at the new limit, and solve
-    # recursively.
-    #
-    #                                  |peak
-    #  1...............................|.o..........
-    #                                  |/ \
-    #                                  /   \
-    #                                 /|    \
-    #                                / |     \
-    #  0--------------------------- o  |      o----1
-    #                           lower  |      upper
-    #                                  |
-    #                                axisMax
-    #
-    # Convert to:
-    #
-    #  1............................................
-    #                                  |
-    #                                  o peak
-    #                                 /|
-    #                                /x|
-    #  0--------------------------- o  o upper ----1
-    #                           lower  |
-    #                                  |
-    #                                axisMax
-    if axisMax < peak:
-        mult = supportScalar({"tag": axisMax}, {"tag": tent})
-        tent = (lower, axisMax, axisMax)
-        return [(scalar * mult, t) for scalar, t in _solve(tent, axisLimit)]
-
-    # lower <= axisDef <= peak <= axisMax
-
-    gain = supportScalar({"tag": axisDef}, {"tag": tent})
-    out = [(gain, None)]
-
-    # First, the positive side
-
-    # outGain is the scalar of axisMax at the tent.
-    outGain = supportScalar({"tag": axisMax}, {"tag": tent})
-
-    # Case 3a: Gain is more than outGain. The tent down-slope crosses
-    # the axis into negative. We have to split it into multiples.
-    #
-    #                      | peak  |
-    #  1...................|.o.....|..............
-    #                      |/x\_   |
-    #  gain................+....+_.|..............
-    #                     /|    |y\|
-    #  ................../.|....|..+_......outGain
-    #                   /  |    |  | \
-    #  0---|-----------o   |    |  |  o----------1
-    #    axisMin    lower  |    |  |   upper
-    #                      |    |  |
-    #                axisDef    |  axisMax
-    #                           |
-    #                      crossing
-    if gain > outGain:
-        # Crossing point on the axis.
-        crossing = peak + (1 - gain) * (upper - peak)
-
-        loc = (axisDef, peak, crossing)
-        scalar = 1
-
-        # The part before the crossing point.
-        out.append((scalar - gain, loc))
-
-        # The part after the crossing point may use one or two tents,
-        # depending on whether upper is before axisMax or not, in one
-        # case we need to keep it down to eternity.
-
-        # Case 3a1, similar to case 1neg; just one tent needed, as in
-        # the drawing above.
-        if upper >= axisMax:
-            loc = (crossing, axisMax, axisMax)
-            scalar = outGain
-
-            out.append((scalar - gain, loc))
-
-        # Case 3a2: Similar to case 2neg; two tents needed, to keep
-        # down to eternity.
-        #
-        #                      | peak             |
-        #  1...................|.o................|...
-        #                      |/ \_              |
-        #  gain................+....+_............|...
-        #                     /|    | \xxxxxxxxxxy|
-        #                    / |    |  \_xxxxxyyyy|
-        #                   /  |    |    \xxyyyyyy|
-        #  0---|-----------o   |    |     o-------|--1
-        #    axisMin    lower  |    |      upper  |
-        #                      |    |             |
-        #                axisDef    |             axisMax
-        #                           |
-        #                      crossing
-        else:
-            # A tent's peak cannot fall on axis default. Nudge it.
-            if upper == axisDef:
-                upper += EPSILON
-
-            # Downslope.
-            loc1 = (crossing, upper, axisMax)
-            scalar1 = 0
-
-            # Eternity justify.
-            loc2 = (upper, axisMax, axisMax)
-            scalar2 = 0
-
-            out.append((scalar1 - gain, loc1))
-            out.append((scalar2 - gain, loc2))
-
-    else:
-        # Special-case if peak is at axisMax.
-        if axisMax == peak:
-            upper = peak
-
-        # Case 3:
-        # We keep delta as is and only scale the axis upper to achieve
-        # the desired new tent if feasible.
-        #
-        #                        peak
-        #  1.....................o....................
-        #                       / \_|
-        #  ..................../....+_.........outGain
-        #                     /     | \
-        #  gain..............+......|..+_.............
-        #                   /|      |  | \
-        #  0---|-----------o |      |  |  o----------1
-        #    axisMin    lower|      |  |   upper
-        #                    |      |  newUpper
-        #              axisDef      axisMax
-        #
-        newUpper = peak + (1 - gain) * (upper - peak)
-        assert axisMax <= newUpper  # Because outGain >= gain
-        if newUpper <= axisDef + (axisMax - axisDef) * 2:
-            upper = newUpper
-            if not negative and axisDef + (axisMax - axisDef) * MAX_F2DOT14 < upper:
-                # we clamp +2.0 to the max F2Dot14 (~1.99994) for convenience
-                upper = axisDef + (axisMax - axisDef) * MAX_F2DOT14
-                assert peak < upper
-
-            loc = (max(axisDef, lower), peak, upper)
-            scalar = 1
-
-            out.append((scalar - gain, loc))
-
-        # Case 4: New limit doesn't fit; we need to chop into two tents,
-        # because the shape of a triangle with part of one side cut off
-        # cannot be represented as a triangle itself.
-        #
-        #            |   peak |
-        #  1.........|......o.|....................
-        #  ..........|...../x\|.............outGain
-        #            |    |xxy|\_
-        #            |   /xxxy|  \_
-        #            |  |xxxxy|    \_
-        #            |  /xxxxy|      \_
-        #  0---|-----|-oxxxxxx|        o----------1
-        #    axisMin | lower  |        upper
-        #            |        |
-        #          axisDef  axisMax
-        #
-        else:
-            loc1 = (max(axisDef, lower), peak, axisMax)
-            scalar1 = 1
-
-            loc2 = (peak, axisMax, axisMax)
-            scalar2 = outGain
-
-            out.append((scalar1 - gain, loc1))
-            # Don't add a dirac delta!
-            if peak < axisMax:
-                out.append((scalar2 - gain, loc2))
-
-    # Now, the negative side
-
-    # Case 1neg: Lower extends beyond axisMin: we chop. Simple.
-    #
-    #                     |   |peak
-    #  1..................|...|.o.................
-    #                     |   |/ \
-    #  gain...............|...+...\...............
-    #                     |x_/|    \
-    #                     |/  |     \
-    #                   _/|   |      \
-    #  0---------------o  |   |       o----------1
-    #              lower  |   |       upper
-    #                     |   |
-    #               axisMin   axisDef
-    #
-    if lower <= axisMin:
-        loc = (axisMin, axisMin, axisDef)
-        scalar = supportScalar({"tag": axisMin}, {"tag": tent})
-
-        out.append((scalar - gain, loc))
-
-    # Case 2neg: Lower is betwen axisMin and axisDef: we add two
-    # tents to keep it down all the way to eternity.
-    #
-    #      |               |peak
-    #  1...|...............|.o.................
-    #      |               |/ \
-    #  gain|...............+...\...............
-    #      |yxxxxxxxxxxxxx/|    \
-    #      |yyyyyyxxxxxxx/ |     \
-    #      |yyyyyyyyyyyx/  |      \
-    #  0---|-----------o   |       o----------1
-    #    axisMin    lower  |       upper
-    #                      |
-    #                    axisDef
-    #
-    else:
-        # A tent's peak cannot fall on axis default. Nudge it.
-        if lower == axisDef:
-            lower -= EPSILON
-
-        # Downslope.
-        loc1 = (axisMin, lower, axisDef)
-        scalar1 = 0
-
-        # Eternity justify.
-        loc2 = (axisMin, axisMin, lower)
-        scalar2 = 0
-
-        out.append((scalar1 - gain, loc1))
-        out.append((scalar2 - gain, loc2))
-
-    return out
-
-
-@lru_cache(128)
-def rebaseTent(tent, axisLimit):
-    """Given a tuple (lower,peak,upper) "tent" and new axis limits
-    (axisMin,axisDefault,axisMax), solves how to represent the tent
-    under the new axis configuration.  All values are in normalized
-    -1,0,+1 coordinate system. Tent values can be outside this range.
-
-    Return value is a list of tuples. Each tuple is of the form
-    (scalar,tent), where scalar is a multipler to multiply any
-    delta-sets by, and tent is a new tent for that output delta-set.
-    If tent value is None, that is a special deltaset that should
-    be always-enabled (called "gain")."""
-
-    axisMin, axisDef, axisMax, _distanceNegative, _distancePositive = axisLimit
-    assert -1 <= axisMin <= axisDef <= axisMax <= +1
-
-    lower, peak, upper = tent
-    assert -2 <= lower <= peak <= upper <= +2
-
-    assert peak != 0
-
-    sols = _solve(tent, axisLimit)
-
-    n = lambda v: axisLimit.renormalizeValue(v)
-    sols = [
-        (scalar, (n(v[0]), n(v[1]), n(v[2])) if v is not None else None)
-        for scalar, v in sols
-        if scalar
-    ]
-
-    return sols