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-<h1>Estilo BR: How to Download and Play the Ultimate Drag Racing Game in Brazil</h1> | <p>If you are a racing enthusiast in Brazil, you have probably heard of Estilo BR, the definitive drag racing game for Android devices. With 43 different vehicles to choose from, all Brazilian, from the most classic to the most modern, you can experience the thrill of high-speed racing against competitors from around the world, including motorcycles, trucks and trailers.</p>
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- <p>In Estilo BR, you can participate in global multiplayer races with up to 500 players, both in an open world global room and in private rooms created to play with friends. Compete against drivers from different countries and show your skills on the track, enjoying the style and culture of street racing in Brazil.</p>
- <p>But Estilo BR is not just about racing. You can also customize your vehicles with a wide variety of aesthetic and performance upgrades. From custom paint jobs to engine modifications, you have the freedom to make your vehicles truly unique.</p>
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- <p>Estilo BR is a mobile game developed by RF Entertainment, a Brazilian indie studio that specializes in racing games. The game was released in 2019 and has since received several updates and improvements.</p>
- <p>The game is inspired by the real-life street racing scene in Brazil, where drivers compete in illegal drag races with modified cars and bikes. The game features realistic physics and responsive controls, as well as stunning pixel art graphics that create a nostalgic atmosphere.</p>
- <p>The game also allows you to play music from your own phone, giving you the possibility to listen to your favorite songs while playing. You can choose from different genres and playlists, or create your own custom mix.</p>
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- <p>The game has a rating of 4.2 out of 5 stars on Google Play Store, with over 5 million downloads and more than 130 thousand reviews. The game is free to play, but it contains ads and in-app purchases.</p>
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- <p>If you want to download Estilo BR from mediafıre, a popular file-sharing platform, you will need to follow these steps:</p>
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-<li>Click on the green button that says "Download APK (125.77 MB)". This will start downloading the APK file to your device.</li>
-<li>Once the download is complete, locate the file in your device's download folder. Tap on it to start the installation process.</li>
-<li>If you see a message that says "For your security, your phone is not allowed to install unknown apps from this source", go to your device's settings and enable the option to install apps from unknown sources.</li>
-<li>Follow the steps on screen to complete the installation. You may need to grant some permissions to the app.</li>
-<li>Once the installation is done, you can open the app and enjoy Estilo BR with unlimited money and diamonds.</li>
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- <p>Note: This method is not endorsed by the official developers of Estilo BR, and it may violate their terms of service. Use it at your own risk.</p>
- <h2>How to get unlimited money and diamonds in Estilo BR?</h2>
- <p>If you want to get unlimited money and diamonds in Estilo BR, you have two options:</p>
- <ul>
-<li>Use the modded version of the game that you downloaded from mediafıre. This will give you unlimited resources from the start, but it may also cause some glitches and errors in the game. You may also face some issues with online multiplayer mode, as other players may report you for cheating.</li>
-<li>Use a game hacking tool such as Game Guardian or Lucky Patcher. These tools allow you to modify the game's data and values, such as money and diamonds. However, this requires some technical skills and knowledge, and it may also harm your device or expose it to malware. You may also get banned from the game if you are detected by the anti-cheat system.</li>
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- <p>Both of these options are not recommended, as they can ruin the fun and challenge of the game. The best way to enjoy Estilo BR is to play it fair and square, earning money and diamonds by winning races, completing missions, and watching ads. This will also support the developers of the game and help them improve it further.</p>
- <h2>What are the best tips and tricks for Estilo BR?</h2>
- <p>If you want to master Estilo BR and become a drag racing legend in Brazil, here are some tips and tricks that can help you:</p>
- <ul>
-<li>Choose your vehicle wisely. Each vehicle has different stats and characteristics, such as speed, acceleration, handling, weight, and nitro. Depending on the type of race and track, some vehicles may perform better than others. Experiment with different vehicles and find the ones that suit your style and preference.</li>
-<li>Upgrade your vehicle regularly. As you progress in the game, you will unlock new parts and accessories that can improve your vehicle's performance and appearance. You can upgrade your engine, transmission, tires, suspension, brakes, turbo, nitro, exhaust, intake, fuel system, cooling system, ignition system, battery, body kit, spoiler, hood, bumper, grille, lights, mirrors, windows, doors, trunk, roof rack, paint job, decals, stickers, rims, tires smoke color, and license plate. You can also buy new vehicles with better stats and features.</li>
-<li>Learn how to shift gears properly. Shifting gears at the right time is crucial for winning races, as it affects your speed and acceleration. You can use the manual or automatic mode, depending on your preference. In manual mode, you have to tap the screen to shift gears, while in automatic mode, the game does it for you. However, manual mode gives you more control and precision, while automatic mode may not always shift at the optimal moment. You can also use the nitro boost to gain an extra burst of speed, but be careful not to overheat your engine.</li>
-<li>Practice your skills in different modes and tracks. Estilo BR offers various modes and tracks to test your abilities and have fun. You can play in single-player mode, where you can race against AI opponents or complete missions and challenges. You can also play in multiplayer mode, where you can join global or private rooms and race against other players online. You can also explore the open world map and find hidden secrets and easter eggs. The game features different tracks with different terrains, weather conditions, and obstacles, such as asphalt, dirt, sand, rain, fog, night, traffic, ramps, bridges, tunnels, and more.</li>
-<li>Enjoy the game's style and culture. Estilo BR is more than just a racing game. It is also a tribute to the Brazilian street racing culture and style, with authentic vehicles, music, slang, and references. You can immerse yourself in the game's atmosphere and learn more about the history and diversity of Brazil's racing scene. You can also interact with other players and make new friends through the game's chat system.</li>
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- <h2>Conclusion</h2>
- <p>Estilo BR is a fantastic drag racing game that will keep you hooked for hours. Whether you want to race against other players online, customize your vehicles with endless options, or explore the open world map with realistic graphics and physics, Estilo BR has it all.</p>
- <p>If you are looking for a way to download Estilo BR from mediafıre, you can follow the steps we provided above. However, we advise you to be careful when using modded or hacked versions of the game, as they may cause problems or get you banned.</p>
- <p>The best way to enjoy Estilo BR is to play it fair and square, earning money and diamonds by winning races, completing missions, and watching ads. This will also support the developers of the game and help them improve it further.</p>
- <p>So what are you waiting for? Download Estilo BR now and join the drag racing revolution in Brazil!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Estilo BR:</p>
- <ol>
-<li><b>Is Estilo BR available for iOS devices?</b></li>
-<p>No, Estilo BR is only available for Android devices at the moment. The developers have not announced any plans to release an iOS version of the game.</p>
-<li><b>How can I contact the developers of Estilo BR?</b></li>
-<p>You can contact the developers of Estilo BR through their official Facebook page: <a href="">RF Entertainment - Home | Facebook</a>. You can also send them an email at [email protected].</p>
-<li><b>How can I report a bug or a problem in Estilo BR?</b></li>
-<p>You can report a bug or a problem in Estilo BR through the game's settings menu. Tap on the gear icon on the top right corner of the screen, then tap on "Report Bug". You can also send a screenshot or a video of the bug or problem to help the developers fix it.</p>
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-<p>You can learn more about Estilo BR by visiting the game's official website: <a href="">Estilo BR - RF Entertainment</a>. You can also watch gameplay videos and tutorials on YouTube, such as this one: <a href="">Estilo BR - Gameplay (Android) - YouTube</a>.</p>
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- Hitman Sniper is not just a simple shooting game. It is a game that requires you to think, plan, and execute your actions with precision and finesse. Here are some of the benefits of playing Hitman Sniper: - It improves your concentration and focus. You have to pay attention to every detail in the environment, such as guards, cameras, traps, windows, doors, etc. You also have to monitor your target's movements and behavior, and wait for the right moment to strike. - It enhances your problem-solving and decision-making skills. You have to analyze the situation and choose the best course of action. You can use various methods to eliminate your target, such as headshots, body shots, accidents, explosions, distractions, etc. You also have to deal with unexpected events, such as alarms, reinforcements, witnesses, etc. - It stimulates your creativity and imagination. You can use your environment to your advantage, such as shooting objects to cause chain reactions, shooting electrical wires to electrocute enemies, shooting gas tanks to create fireballs, etc. You can also use your weapons in different ways, such as using silencers, scopes, suppressors, etc. - It provides you with entertainment and satisfaction. You can enjoy the stunning graphics and realistic sound effects of the game. You can also feel the thrill and excitement of pulling off a perfect kill shot. You can also compete against your friends and other players in the leaderboards. <h2>How to download Hitman Sniper APK for free?</h2>
- If you want to play Hitman Sniper on your Android device, you can download it from the Google Play Store for $0.99. However, if you want to get it for free, you can download an APK file from a third-party website. An APK file is an Android application package file that contains all the files needed to install an app on your device. However, before you download an APK file, you need to take some precautions: - Make sure that your device has enough storage space for the file. - Make sure that your device is compatible with the game's requirements. - Make sure that you have a reliable internet connection for the download. - Make sure that you have enabled the option to install apps from unknown sources in your device's settings. Once you have taken these precautions , you can follow these steps to install the APK file on your Android device: - Connect your Android device to your computer using a USB cable. - Copy the APK file from your computer to your device's storage. You can use any folder you want, but make sure you remember where you put it. - Disconnect your device from your computer and open your file explorer app on your device. - Locate the APK file you copied and tap on it to open it. - Tap Install at the bottom of the screen and wait for the installation to finish. - Tap Open to launch the game or Done to exit the installer. You have successfully installed Hitman Sniper APK for free on your Android device. Enjoy! <h2>How to play Hitman Sniper effectively?</h2>
- Now that you have downloaded and installed Hitman Sniper, you might want to know how to play it well and complete all the missions. Here are some tips and tricks to help you master the sniper skills and become the ultimate assassin: - Use the variable scope to zoom in and out while aiming. You can adjust the level of zoom by tapping the plus and minus buttons on the screen. You can also swipe left and right to move the scope horizontally and up and down to move it vertically. - Use the marksman perk to improve your aim and slow time. You can activate this perk by pressing the Shift key on your keyboard or tapping the icon on the screen. This will allow you to aim more precisely and take advantage of opportunities that might otherwise be missed. - Use the piercing perk to penetrate bodies and objects. This perk will let you shoot through multiple targets with one bullet, creating collateral damage and saving ammo. You can also use this perk to shoot through glass, walls, doors, etc. - Use the environment to your advantage. You can shoot various objects in the environment to cause chain reactions, accidents, explosions, distractions, etc. For example, you can shoot a car's gas tank to make it explode, a chandelier to make it fall, a fire extinguisher to create a smoke screen, etc. - Use different methods to eliminate your target. You don't have to always go for a headshot or a body shot. You can also use other methods, such as accidents, poison, explosions, etc. For example, you can shoot a gas pipe near your target to make it leak, then shoot a nearby candle to ignite it and create a fireball. - Use different weapons and perks for different scenarios. You can unlock and upgrade 17 unique weapons in the game, each with its own perks and abilities. You can also equip different perks for each weapon, such as damage, rate of fire, extended magazine, ammo, subsonic, suppressor, etc. You should choose the weapon and perk combination that suits your style and mission objective. - Complete challenges and contracts to earn money and rewards. You can complete various challenges and contracts in each mission, such as killing a certain number of targets, killing targets in a certain way, killing targets within a time limit, etc. These will earn you money and rewards, such as weapon parts, blueprints, perks, etc. You can use these to unlock and upgrade your weapons and perks. <h2>Conclusion</h2>
- Hitman Sniper is a fun and challenging sniper game that will test your strategic skills and creativity. You can download it for free from a third-party website using an APK file, but make sure you take some precautions before doing so. You can also use our tips and tricks to play the game effectively and complete all the missions. If you are ready to become the best sniper in the world, download Hitman Sniper today and enjoy! <h3>FAQs</h3>
- Q: How do I get more money in Hitman Sniper? A: You can get more money by completing challenges and contracts in each mission. You can also replay missions to earn more money. Q: How do I unlock more weapons in Hitman Sniper? A: You can unlock more weapons by collecting weapon parts and blueprints in each mission. You can also buy some weapons with real money. Q: How do I upgrade my weapons in Hitman Sniper? A: You can upgrade your weapons by using weapon parts and blueprints that you have collected or bought. You can also equip different perks for each weapon. Q: How do I switch weapons in Hitman Sniper? A: You can switch weapons by tapping the weapon icon on the screen or pressing the Q key on your keyboard. Q: How do I play zombie mode in Hitman Sniper? A: You can play zombie mode by tapping the zombie icon on the main menu or pressing the Z key on your keyboard.</p>
-<h2>hitman sniper download apk free</h2><br /><p><b><b>Download</b> ->->->-> <a href="https://jinyurl.com/2uNQut">https://jinyurl.com/2uNQut</a></b></p><br /><br /> 197e85843d<br />
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spaces/2023Liu2023/bingo/src/lib/bots/bing/tts.ts

@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
-  currentText = ''
-  speakText = ''
-  private controller = new AbortController()
-  speaking = false
-  get isSpeaking() {
-    return this.speaking
-  }
-  finished = false
-  constructor() {}
-  abort = () => {
-    this.controller.abort()
-  }
-
-  reset = () => {
-    this.speaking = false
-    this.finished = true
-    this.currentText = ''
-    this.speakText = ''
-    this.abort()
-  }
-
-  speak = (text: string) => {
-    if (!synth || text?.trim()?.length < 2) {
-      return
-    }
-    this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
-    this.finished = false
-    this.loop()
-  }
-
-  private async doSpeek() {
-    return new Promise((resolve) => {
-      const endIndex = this.finished ? this.currentText.length :
-        Math.max(
-          this.currentText.lastIndexOf('。'),
-          this.currentText.lastIndexOf('；'),
-          this.currentText.lastIndexOf('、'),
-          this.currentText.lastIndexOf('？'),
-          this.currentText.lastIndexOf('\n')
-        )
-      const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
-      if (startIndex >= endIndex) {
-        return resolve(true)
-      }
-      const text = this.currentText.slice(startIndex, endIndex)
-      this.speakText = text
-      const utterThis = new SpeechSynthesisUtterance(text)
-      this.controller.signal.onabort = () => {
-        synth.cancel()
-        this.finished = true
-        resolve(false)
-      }
-
-      utterThis.onend = function (event) {
-        resolve(true)
-      }
-
-      utterThis.onerror = function (event) {
-        resolve(false)
-      }
-
-      const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
-      utterThis.voice = voice
-      synth.speak(utterThis)
-    })
-  }
-
-  private async loop() {
-    if (this.speaking) return
-    this.speaking = true
-    while(!this.finished) {
-      await Promise.all([sleep(1000), this.doSpeek()])
-    }
-    this.speaking = false
-  }
-}

spaces/2ndelement/voicevox/voicevox_engine/utility/core_version_utility.py

@@ -1,14 +0,0 @@
-from typing import Iterable
-
-from semver.version import Version
-
-
-def parse_core_version(version: str) -> Version:
-    return Version.parse(version)
-
-
-def get_latest_core_version(versions: Iterable[str]) -> str:
-    if len(versions) == 0:
-        raise Exception("versions must be non-empty.")
-
-    return str(max(map(parse_core_version, versions)))

spaces/801artistry/RVC801/utils/backups.py

@@ -1,141 +0,0 @@
-import os
-import shutil
-import hashlib
-import time
-import base64
-
-
-
-
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-
-def import_google_drive_backup():
-    print("Importing Google Drive backup...")
-    weights_exist = False
-    for root, dirs, files in os.walk(GOOGLE_DRIVE_PATH):
-        for filename in files:
-            filepath = os.path.join(root, filename)
-            if os.path.isfile(filepath) and not filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
-                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-                backup_folderpath = os.path.dirname(backup_filepath)
-                if not os.path.exists(backup_folderpath):
-                    os.makedirs(backup_folderpath)
-                    print(f'Created backup folder: {backup_folderpath}', flush=True)
-                shutil.copy2(filepath, backup_filepath) # copy file with metadata
-                print(f'Imported file from Google Drive backup: {filename}')
-            elif filepath.startswith(os.path.join(GOOGLE_DRIVE_PATH, 'weights')) and filename.endswith('.pth'):
-                weights_exist = True
-                weights_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, os.path.join(GOOGLE_DRIVE_PATH, 'weights')))
-                weights_folderpath = os.path.dirname(weights_filepath)
-                if not os.path.exists(weights_folderpath):
-                    os.makedirs(weights_folderpath)
-                    print(f'Created weights folder: {weights_folderpath}', flush=True)
-                shutil.copy2(filepath, weights_filepath) # copy file with metadata
-                print(f'Imported file from weights: {filename}')
-    if weights_exist:
-        print("Copied weights from Google Drive backup to local weights folder.")
-    else:
-        print("No weights found in Google Drive backup.")
-    print("Google Drive backup import completed.")
-
-def get_md5_hash(file_path):
-    hash_md5 = hashlib.md5()
-    with open(file_path, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-def copy_weights_folder_to_drive():
-    destination_folder = os.path.join(GOOGLE_DRIVE_PATH, 'weights')
-    try:
-        if not os.path.exists(destination_folder):
-            os.makedirs(destination_folder)
-
-        num_copied = 0
-        for filename in os.listdir(WEIGHTS_FOLDER):
-            if filename.endswith('.pth'):
-                source_file = os.path.join(WEIGHTS_FOLDER, filename)
-                destination_file = os.path.join(destination_folder, filename)
-                if not os.path.exists(destination_file):
-                    shutil.copy2(source_file, destination_file)
-                    num_copied += 1
-                    print(f"Copied {filename} to Google Drive!")
-
-        if num_copied == 0:
-            print("No new finished models found for copying.")
-        else:
-            print(f"Finished copying {num_copied} files to Google Drive!")
-
-    except Exception as e:
-        print(f"An error occurred while copying weights: {str(e)}")
-        # You can log the error or take appropriate actions here.
-
-def backup_files():
-    print("\nStarting backup loop...")
-    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
-    fully_updated = False  # boolean to track if all files are up to date
-    
-    while True:
-        try:
-            updated = False  # flag to check if any files were updated
-            last_backup_timestamps = {}
-
-            try:
-                with open(last_backup_timestamps_path, 'r') as f:
-                    last_backup_timestamps = dict(line.strip().split(':') for line in f)
-            except FileNotFoundError:
-                pass  # File does not exist yet, which is fine
-            
-            for root, dirs, files in os.walk(LOGS_FOLDER):
-                for filename in files:
-                    if filename != 'last_backup_timestamps.txt':
-                        filepath = os.path.join(root, filename)
-                        if os.path.isfile(filepath):
-                            backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                            backup_folderpath = os.path.dirname(backup_filepath)
-                            if not os.path.exists(backup_folderpath):
-                                os.makedirs(backup_folderpath)
-                                print(f'Created backup folder: {backup_folderpath}', flush=True)
-                            # check if file has changed since last backup
-                            last_backup_timestamp = last_backup_timestamps.get(filepath)
-                            current_timestamp = os.path.getmtime(filepath)
-                            if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
-                                shutil.copy2(filepath, backup_filepath)  # copy file with metadata
-                                last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
-                                if last_backup_timestamp is None:
-                                    print(f'Backed up file: {filename}')
-                                else:
-                                    print(f'Updating backed up file: {filename}')
-                                updated = True
-                                fully_updated = False  # if a file is updated, all files are not up to date
-            
-            # check if any files were deleted in Colab and delete them from the backup drive
-            for filepath in list(last_backup_timestamps.keys()):
-                if not os.path.exists(filepath):
-                    backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                    if os.path.exists(backup_filepath):
-                        os.remove(backup_filepath)
-                        print(f'Deleted file: {filepath}')
-                    del last_backup_timestamps[filepath]
-                    updated = True
-                    fully_updated = False  # if a file is deleted, all files are not up to date
-            
-            if not updated and not fully_updated:
-                print("Files are up to date.")
-                fully_updated = True  # if all files are up to date, set the boolean to True
-                copy_weights_folder_to_drive()
-                sleep_time = 15
-            else:
-                sleep_time = 0.1
-            
-            with open(last_backup_timestamps_path, 'w') as f:
-                for filepath, timestamp in last_backup_timestamps.items():
-                    f.write(f'{filepath}:{timestamp}\n')
-            
-            time.sleep(sleep_time)  # wait for 15 seconds before checking again, or 0.1s if not fully up to date to speed up backups
-        
-        except Exception as e:
-            print(f"An error occurred: {str(e)}")
-            # You can log the error or take appropriate actions here.

spaces/AI4PD/hexviz/hexviz/ec_number.py

@@ -1,9 +0,0 @@
-class ECNumber:
-    def __init__(self, number, coordinate, color, radius):
-        self.number = number
-        self.coordinate = coordinate
-        self.color = color
-        self.radius = radius
-
-    def __str__(self):
-        return f"(EC: {self.number}, Coordinate: {self.coordinate}, Color: {self.color})"

spaces/Abhilashvj/planogram-compliance/utils/loss.py

@@ -1,291 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Loss functions
-"""
-
-import torch
-import torch.nn as nn
-
-from utils.metrics import bbox_iou
-from utils.torch_utils import de_parallel
-
-
-def smooth_BCE(
-    eps=0.1,
-):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
-    # return positive, negative label smoothing BCE targets
-    return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
-    # BCEwithLogitLoss() with reduced missing label effects.
-    def __init__(self, alpha=0.05):
-        super().__init__()
-        self.loss_fcn = nn.BCEWithLogitsLoss(
-            reduction="none"
-        )  # must be nn.BCEWithLogitsLoss()
-        self.alpha = alpha
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-        pred = torch.sigmoid(pred)  # prob from logits
-        dx = pred - true  # reduce only missing label effects
-        # dx = (pred - true).abs()  # reduce missing label and false label effects
-        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
-        loss *= alpha_factor
-        return loss.mean()
-
-
-class FocalLoss(nn.Module):
-    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
-    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        super().__init__()
-        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = (
-            "none"  # required to apply FL to each element
-        )
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-        # p_t = torch.exp(-loss)
-        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
-
-        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = (1.0 - p_t) ** self.gamma
-        loss *= alpha_factor * modulating_factor
-
-        if self.reduction == "mean":
-            return loss.mean()
-        elif self.reduction == "sum":
-            return loss.sum()
-        else:  # 'none'
-            return loss
-
-
-class QFocalLoss(nn.Module):
-    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
-    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        super().__init__()
-        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = (
-            "none"  # required to apply FL to each element
-        )
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = torch.abs(true - pred_prob) ** self.gamma
-        loss *= alpha_factor * modulating_factor
-
-        if self.reduction == "mean":
-            return loss.mean()
-        elif self.reduction == "sum":
-            return loss.sum()
-        else:  # 'none'
-            return loss
-
-
-class ComputeLoss:
-    sort_obj_iou = False
-
-    # Compute losses
-    def __init__(self, model, autobalance=False):
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-
-        # 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.anchors = m.anchors
-        self.device = device
-
-    def __call__(self, p, targets):  # predictions, targets
-        lcls = torch.zeros(1, device=self.device)  # class loss
-        lbox = torch.zeros(1, device=self.device)  # box loss
-        lobj = torch.zeros(1, device=self.device)  # object loss
-        tcls, tbox, indices, anchors = 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 = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1)  # faster, requires torch 1.8.0
-                pxy, pwh, _, pcls = pi[b, a, gj, gi].split(
-                    (2, 2, 1, self.nc), 1
-                )  # target-subset of predictions
-
-                # 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
-
-                # Append targets to text file
-                # with open('targets.txt', 'a') as file:
-                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
-            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"]
-        bs = tobj.shape[0]  # batch size
-
-        return (lbox + lobj + lcls) * bs, torch.cat(
-            (lbox, lobj, lcls)
-        ).detach()
-
-    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 = [], [], [], []
-        gain = torch.ones(
-            7, 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)
-        targets = torch.cat(
-            (targets.repeat(na, 1, 1), ai[..., 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, a = t.chunk(
-                4, 1
-            )  # (image, class), grid xy, grid wh, anchors
-            a, (b, c) = a.long().view(-1), 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
-
-        return tcls, tbox, indices, anch

spaces/AchyuthGamer/ImMagician-Image-Generator/style.css

@@ -1,24 +0,0 @@
-h1 {
-  text-align: center;
-}
-
-#duplicate-button {
-  margin: auto;
-  color: #fff;
-  background: #1565c0;
-  border-radius: 100vh;
-}
-
-#component-0 {
-  max-width: 730px;
-  margin: auto;
-}
-
-#share-btn-container{padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;margin-top: 0.35em;}
-div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
-#share-btn-container:hover {background-color: #060606}
-#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;font-size: 15px;}
-#share-btn * {all: unset}
-#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
-#share-btn-container .wrap {display: none !important}
-#share-btn-container.hidden {display: none!important}

spaces/Adapter/CoAdapter/ldm/modules/extra_condition/midas/midas/dpt_depth.py

@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base_model import BaseModel
-from .blocks import (
-    FeatureFusionBlock,
-    FeatureFusionBlock_custom,
-    Interpolate,
-    _make_encoder,
-    forward_vit,
-)
-
-
-def _make_fusion_block(features, use_bn):
-    return FeatureFusionBlock_custom(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-    )
-
-
-class DPT(BaseModel):
-    def __init__(
-        self,
-        head,
-        features=256,
-        backbone="vitb_rn50_384",
-        readout="project",
-        channels_last=False,
-        use_bn=False,
-    ):
-
-        super(DPT, self).__init__()
-
-        self.channels_last = channels_last
-
-        hooks = {
-            "vitb_rn50_384": [0, 1, 8, 11],
-            "vitb16_384": [2, 5, 8, 11],
-            "vitl16_384": [5, 11, 17, 23],
-        }
-
-        # Instantiate backbone and reassemble blocks
-        self.pretrained, self.scratch = _make_encoder(
-            backbone,
-            features,
-            False, # Set to true of you want to train from scratch, uses ImageNet weights
-            groups=1,
-            expand=False,
-            exportable=False,
-            hooks=hooks[backbone],
-            use_readout=readout,
-        )
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        self.scratch.output_conv = head
-
-
-    def forward(self, x):
-        if self.channels_last == True:
-            x.contiguous(memory_format=torch.channels_last)
-
-        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return out
-
-
-class DPTDepthModel(DPT):
-    def __init__(self, path=None, non_negative=True, **kwargs):
-        features = kwargs["features"] if "features" in kwargs else 256
-
-        head = nn.Sequential(
-            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
-            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-
-        super().__init__(head, **kwargs)
-
-        if path is not None:
-           self.load(path)
-
-    def forward(self, x):
-        return super().forward(x).squeeze(dim=1)
-

spaces/Addai/Breast_cancer_detection_with_deep_transfer_learning/README.md

@@ -1,13 +0,0 @@
----
-title: Breast Cancer Detection With Deep Transfer Learning
-emoji: 📈
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Aditya9790/yolo7-object-tracking/models/__init__.py

@@ -1 +0,0 @@
-# init

spaces/AgentVerse/agentVerse/ui/src/classes/event_center.ts

@@ -1,5 +0,0 @@
-import { Events } from "phaser";
-
-const eventsCenter = new Events.EventEmitter();
-
-export default eventsCenter;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/localforage-files.d.ts

@@ -1,2 +0,0 @@
-import Files from './storage/localforage/files/Files';
-export default Files;

spaces/AkashKhamkar/QnA-generator/before_run.py

@@ -1,6 +0,0 @@
-import nltk
-
-nltk.download('stopwords')
-nltk.download('wordnet')
-nltk.download('punkt')
-nltk.download('brown')

spaces/Ame42/UBTH/utils.py

@@ -1,132 +0,0 @@
-# This is a sample Python script.
-
-# Press Shift+F10 to execute it or replace it with your code.
-import os.path
-import pandas as pd
-import glob
-import os
-
-sn = "S/N"
-ipp = "IPPIS"
-gif = "GIFMIS"
-col_1 = "BENEFICIARY NAME"
-gif_col = [col_1, "Employee", "Rank", "Amount"]
-ipp_col = ["Employee Number", "Full Name", "Grade Level", "Step", "Grosss Deductions SUM 1"]
-
-
-def get_raw(link, sheet, file_ext='.xlsx'):
-    match file_ext:
-        # handle testing files
-        case '.csv':
-            return pd.read_csv(link)
-
-        case '.xlsx' | '.xls':
-            return pd.read_excel(link, sheet_name=sheet)
-
-        case _:
-            return UnusualFileError(link, "Invalid file extension")
-
-
-def get_data(link, sheet, doc_type=ipp, file_type='.csv'):
-    match file_type:
-        # handle testing files
-        case '.csv':
-            return pd.read_csv(link)
-
-        # handle GIFMIS files
-        case '.xlsx' | '.xls' if doc_type == gif:
-
-            try:
-                data = pd.read_excel(link, sheet_name=sheet, skiprows=3, header=0)
-                return data.drop(data.columns.difference(gif_col), axis=1)
-            except ValueError as err:
-                raise UnusualFileError(link, str(err))
-            except KeyError:
-                return None
-
-        # handle IPPIS files
-        case '.xlsx' | '.xls' if doc_type == ipp:
-
-            try:
-                data = pd.read_excel(link, sheet_name=sheet, skiprows=4, header=0)
-                return data.drop(data.columns.difference(ipp_col), axis=1)
-            except ValueError as err:
-                raise UnusualFileError(link, str(err))
-            except KeyError:
-                return None
-
-        # default
-        case _:
-            return None
-
-
-def merge_two(first: pd.DataFrame, second: pd.DataFrame, doc_type):
-    hows = ['inner', 'left', 'right']
-    first = first.drop(sn, axis=1, errors="ignore")
-    second = second.drop(sn, axis=1, errors="ignore")
-
-    both, prev, curr = tuple(
-        [first.merge(second, how=how, on=first.columns[0] if doc_type == ipp else first.columns[1]) for how in hows]
-    )
-
-    prev = prev[
-        prev[
-            prev.columns[5] if doc_type == ipp else prev.columns[4]  # Get rows where name column is empty
-        ].isnull()
-    ].dropna(subset=[
-        prev.columns[0] if doc_type == ipp else prev.columns[1]  # Check for empty rows in the employee number column
-    ]).dropna(axis=1, how="all")  # Remove empty columns
-
-    curr = curr[
-        curr[
-            curr.columns[1] if doc_type == ipp else curr.columns[0]  # Get rows where name column is empty
-        ].isnull()
-    ].dropna(subset=[
-        curr.columns[0] if doc_type == ipp else curr.columns[1]  # Check for empty rows in the employee number column
-    ]).dropna(axis=1, how="all")  # Remove empty columns
-
-    return both, prev, curr
-
-
-def merge_all(data_list, keys=tuple("Employee")):
-    return pd.concat(
-        [data.drop(sn, axis=1, errors="ignore") for data in data_list],
-        axis=1,
-        join='inner',
-        keys=keys,
-        ignore_index=True
-    )
-
-
-def retrieve(dt):
-    return get_data(dt.name, os.path.splitext(dt.name)[1])
-
-
-def clear_csv_trash():
-    pattern = '*.csv'  # Desired file pattern
-
-    # Get a list of file paths matching the pattern
-    matching_files = glob.glob(pattern)
-
-    # Loop through the matching files and delete them
-    for file_path in matching_files:
-        try:
-            os.remove(file_path)
-        except OSError as e:
-            print(f"Error deleting {file_path}: {e}")
-
-
-class UnusualFileError(Exception):
-    def __init__(self, file, message):
-        self.source = file
-        self.cause = message
-
-    def __str__(self):
-        from numpy.core._dtype import __repr__
-        return __repr__(self.source)
-
-    def get_file(self):
-        return self.source
-
-    def get_message(self):
-        return self.cause

spaces/Andy1621/uniformer_image_detection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py

@@ -1,63 +0,0 @@
-_base_ = [
-    '../_base_/models/faster_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    pretrained='open-mmlab://regnetx_3.2gf',
-    backbone=dict(
-        _delete_=True,
-        type='RegNet',
-        arch='regnetx_3.2gf',
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[96, 192, 432, 1008],
-        out_channels=256,
-        num_outs=5))
-img_norm_cfg = dict(
-    # The mean and std are used in PyCls when training RegNets
-    mean=[103.53, 116.28, 123.675],
-    std=[57.375, 57.12, 58.395],
-    to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)

spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/hrnet.py

@@ -1,537 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
-                      kaiming_init)
-from mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from mmdet.utils import get_root_logger
-from ..builder import BACKBONES
-from .resnet import BasicBlock, Bottleneck
-
-
-class HRModule(nn.Module):
-    """High-Resolution Module for HRNet.
-
-    In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
-    is in this module.
-    """
-
-    def __init__(self,
-                 num_branches,
-                 blocks,
-                 num_blocks,
-                 in_channels,
-                 num_channels,
-                 multiscale_output=True,
-                 with_cp=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN')):
-        super(HRModule, self).__init__()
-        self._check_branches(num_branches, num_blocks, in_channels,
-                             num_channels)
-
-        self.in_channels = in_channels
-        self.num_branches = num_branches
-
-        self.multiscale_output = multiscale_output
-        self.norm_cfg = norm_cfg
-        self.conv_cfg = conv_cfg
-        self.with_cp = with_cp
-        self.branches = self._make_branches(num_branches, blocks, num_blocks,
-                                            num_channels)
-        self.fuse_layers = self._make_fuse_layers()
-        self.relu = nn.ReLU(inplace=False)
-
-    def _check_branches(self, num_branches, num_blocks, in_channels,
-                        num_channels):
-        if num_branches != len(num_blocks):
-            error_msg = f'NUM_BRANCHES({num_branches}) ' \
-                f'!= NUM_BLOCKS({len(num_blocks)})'
-            raise ValueError(error_msg)
-
-        if num_branches != len(num_channels):
-            error_msg = f'NUM_BRANCHES({num_branches}) ' \
-                f'!= NUM_CHANNELS({len(num_channels)})'
-            raise ValueError(error_msg)
-
-        if num_branches != len(in_channels):
-            error_msg = f'NUM_BRANCHES({num_branches}) ' \
-                f'!= NUM_INCHANNELS({len(in_channels)})'
-            raise ValueError(error_msg)
-
-    def _make_one_branch(self,
-                         branch_index,
-                         block,
-                         num_blocks,
-                         num_channels,
-                         stride=1):
-        downsample = None
-        if stride != 1 or \
-                self.in_channels[branch_index] != \
-                num_channels[branch_index] * block.expansion:
-            downsample = nn.Sequential(
-                build_conv_layer(
-                    self.conv_cfg,
-                    self.in_channels[branch_index],
-                    num_channels[branch_index] * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False),
-                build_norm_layer(self.norm_cfg, num_channels[branch_index] *
-                                 block.expansion)[1])
-
-        layers = []
-        layers.append(
-            block(
-                self.in_channels[branch_index],
-                num_channels[branch_index],
-                stride,
-                downsample=downsample,
-                with_cp=self.with_cp,
-                norm_cfg=self.norm_cfg,
-                conv_cfg=self.conv_cfg))
-        self.in_channels[branch_index] = \
-            num_channels[branch_index] * block.expansion
-        for i in range(1, num_blocks[branch_index]):
-            layers.append(
-                block(
-                    self.in_channels[branch_index],
-                    num_channels[branch_index],
-                    with_cp=self.with_cp,
-                    norm_cfg=self.norm_cfg,
-                    conv_cfg=self.conv_cfg))
-
-        return nn.Sequential(*layers)
-
-    def _make_branches(self, num_branches, block, num_blocks, num_channels):
-        branches = []
-
-        for i in range(num_branches):
-            branches.append(
-                self._make_one_branch(i, block, num_blocks, num_channels))
-
-        return nn.ModuleList(branches)
-
-    def _make_fuse_layers(self):
-        if self.num_branches == 1:
-            return None
-
-        num_branches = self.num_branches
-        in_channels = self.in_channels
-        fuse_layers = []
-        num_out_branches = num_branches if self.multiscale_output else 1
-        for i in range(num_out_branches):
-            fuse_layer = []
-            for j in range(num_branches):
-                if j > i:
-                    fuse_layer.append(
-                        nn.Sequential(
-                            build_conv_layer(
-                                self.conv_cfg,
-                                in_channels[j],
-                                in_channels[i],
-                                kernel_size=1,
-                                stride=1,
-                                padding=0,
-                                bias=False),
-                            build_norm_layer(self.norm_cfg, in_channels[i])[1],
-                            nn.Upsample(
-                                scale_factor=2**(j - i), mode='nearest')))
-                elif j == i:
-                    fuse_layer.append(None)
-                else:
-                    conv_downsamples = []
-                    for k in range(i - j):
-                        if k == i - j - 1:
-                            conv_downsamples.append(
-                                nn.Sequential(
-                                    build_conv_layer(
-                                        self.conv_cfg,
-                                        in_channels[j],
-                                        in_channels[i],
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=1,
-                                        bias=False),
-                                    build_norm_layer(self.norm_cfg,
-                                                     in_channels[i])[1]))
-                        else:
-                            conv_downsamples.append(
-                                nn.Sequential(
-                                    build_conv_layer(
-                                        self.conv_cfg,
-                                        in_channels[j],
-                                        in_channels[j],
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=1,
-                                        bias=False),
-                                    build_norm_layer(self.norm_cfg,
-                                                     in_channels[j])[1],
-                                    nn.ReLU(inplace=False)))
-                    fuse_layer.append(nn.Sequential(*conv_downsamples))
-            fuse_layers.append(nn.ModuleList(fuse_layer))
-
-        return nn.ModuleList(fuse_layers)
-
-    def forward(self, x):
-        """Forward function."""
-        if self.num_branches == 1:
-            return [self.branches[0](x[0])]
-
-        for i in range(self.num_branches):
-            x[i] = self.branches[i](x[i])
-
-        x_fuse = []
-        for i in range(len(self.fuse_layers)):
-            y = 0
-            for j in range(self.num_branches):
-                if i == j:
-                    y += x[j]
-                else:
-                    y += self.fuse_layers[i][j](x[j])
-            x_fuse.append(self.relu(y))
-        return x_fuse
-
-
-@BACKBONES.register_module()
-class HRNet(nn.Module):
-    """HRNet backbone.
-
-    High-Resolution Representations for Labeling Pixels and Regions
-    arXiv: https://arxiv.org/abs/1904.04514
-
-    Args:
-        extra (dict): detailed configuration for each stage of HRNet.
-        in_channels (int): Number of input image channels. Default: 3.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        zero_init_residual (bool): whether to use zero init for last norm layer
-            in resblocks to let them behave as identity.
-
-    Example:
-        >>> from mmdet.models import HRNet
-        >>> import torch
-        >>> extra = dict(
-        >>>     stage1=dict(
-        >>>         num_modules=1,
-        >>>         num_branches=1,
-        >>>         block='BOTTLENECK',
-        >>>         num_blocks=(4, ),
-        >>>         num_channels=(64, )),
-        >>>     stage2=dict(
-        >>>         num_modules=1,
-        >>>         num_branches=2,
-        >>>         block='BASIC',
-        >>>         num_blocks=(4, 4),
-        >>>         num_channels=(32, 64)),
-        >>>     stage3=dict(
-        >>>         num_modules=4,
-        >>>         num_branches=3,
-        >>>         block='BASIC',
-        >>>         num_blocks=(4, 4, 4),
-        >>>         num_channels=(32, 64, 128)),
-        >>>     stage4=dict(
-        >>>         num_modules=3,
-        >>>         num_branches=4,
-        >>>         block='BASIC',
-        >>>         num_blocks=(4, 4, 4, 4),
-        >>>         num_channels=(32, 64, 128, 256)))
-        >>> self = HRNet(extra, in_channels=1)
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 1, 32, 32)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        (1, 32, 8, 8)
-        (1, 64, 4, 4)
-        (1, 128, 2, 2)
-        (1, 256, 1, 1)
-    """
-
-    blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
-
-    def __init__(self,
-                 extra,
-                 in_channels=3,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 norm_eval=True,
-                 with_cp=False,
-                 zero_init_residual=False):
-        super(HRNet, self).__init__()
-        self.extra = extra
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.norm_eval = norm_eval
-        self.with_cp = with_cp
-        self.zero_init_residual = zero_init_residual
-
-        # stem net
-        self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
-        self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
-
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            in_channels,
-            64,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            bias=False)
-
-        self.add_module(self.norm1_name, norm1)
-        self.conv2 = build_conv_layer(
-            self.conv_cfg,
-            64,
-            64,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            bias=False)
-
-        self.add_module(self.norm2_name, norm2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # stage 1
-        self.stage1_cfg = self.extra['stage1']
-        num_channels = self.stage1_cfg['num_channels'][0]
-        block_type = self.stage1_cfg['block']
-        num_blocks = self.stage1_cfg['num_blocks'][0]
-
-        block = self.blocks_dict[block_type]
-        stage1_out_channels = num_channels * block.expansion
-        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
-
-        # stage 2
-        self.stage2_cfg = self.extra['stage2']
-        num_channels = self.stage2_cfg['num_channels']
-        block_type = self.stage2_cfg['block']
-
-        block = self.blocks_dict[block_type]
-        num_channels = [channel * block.expansion for channel in num_channels]
-        self.transition1 = self._make_transition_layer([stage1_out_channels],
-                                                       num_channels)
-        self.stage2, pre_stage_channels = self._make_stage(
-            self.stage2_cfg, num_channels)
-
-        # stage 3
-        self.stage3_cfg = self.extra['stage3']
-        num_channels = self.stage3_cfg['num_channels']
-        block_type = self.stage3_cfg['block']
-
-        block = self.blocks_dict[block_type]
-        num_channels = [channel * block.expansion for channel in num_channels]
-        self.transition2 = self._make_transition_layer(pre_stage_channels,
-                                                       num_channels)
-        self.stage3, pre_stage_channels = self._make_stage(
-            self.stage3_cfg, num_channels)
-
-        # stage 4
-        self.stage4_cfg = self.extra['stage4']
-        num_channels = self.stage4_cfg['num_channels']
-        block_type = self.stage4_cfg['block']
-
-        block = self.blocks_dict[block_type]
-        num_channels = [channel * block.expansion for channel in num_channels]
-        self.transition3 = self._make_transition_layer(pre_stage_channels,
-                                                       num_channels)
-        self.stage4, pre_stage_channels = self._make_stage(
-            self.stage4_cfg, num_channels)
-
-    @property
-    def norm1(self):
-        """nn.Module: the normalization layer named "norm1" """
-        return getattr(self, self.norm1_name)
-
-    @property
-    def norm2(self):
-        """nn.Module: the normalization layer named "norm2" """
-        return getattr(self, self.norm2_name)
-
-    def _make_transition_layer(self, num_channels_pre_layer,
-                               num_channels_cur_layer):
-        num_branches_cur = len(num_channels_cur_layer)
-        num_branches_pre = len(num_channels_pre_layer)
-
-        transition_layers = []
-        for i in range(num_branches_cur):
-            if i < num_branches_pre:
-                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
-                    transition_layers.append(
-                        nn.Sequential(
-                            build_conv_layer(
-                                self.conv_cfg,
-                                num_channels_pre_layer[i],
-                                num_channels_cur_layer[i],
-                                kernel_size=3,
-                                stride=1,
-                                padding=1,
-                                bias=False),
-                            build_norm_layer(self.norm_cfg,
-                                             num_channels_cur_layer[i])[1],
-                            nn.ReLU(inplace=True)))
-                else:
-                    transition_layers.append(None)
-            else:
-                conv_downsamples = []
-                for j in range(i + 1 - num_branches_pre):
-                    in_channels = num_channels_pre_layer[-1]
-                    out_channels = num_channels_cur_layer[i] \
-                        if j == i - num_branches_pre else in_channels
-                    conv_downsamples.append(
-                        nn.Sequential(
-                            build_conv_layer(
-                                self.conv_cfg,
-                                in_channels,
-                                out_channels,
-                                kernel_size=3,
-                                stride=2,
-                                padding=1,
-                                bias=False),
-                            build_norm_layer(self.norm_cfg, out_channels)[1],
-                            nn.ReLU(inplace=True)))
-                transition_layers.append(nn.Sequential(*conv_downsamples))
-
-        return nn.ModuleList(transition_layers)
-
-    def _make_layer(self, block, inplanes, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                build_conv_layer(
-                    self.conv_cfg,
-                    inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False),
-                build_norm_layer(self.norm_cfg, planes * block.expansion)[1])
-
-        layers = []
-        layers.append(
-            block(
-                inplanes,
-                planes,
-                stride,
-                downsample=downsample,
-                with_cp=self.with_cp,
-                norm_cfg=self.norm_cfg,
-                conv_cfg=self.conv_cfg))
-        inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(
-                block(
-                    inplanes,
-                    planes,
-                    with_cp=self.with_cp,
-                    norm_cfg=self.norm_cfg,
-                    conv_cfg=self.conv_cfg))
-
-        return nn.Sequential(*layers)
-
-    def _make_stage(self, layer_config, in_channels, multiscale_output=True):
-        num_modules = layer_config['num_modules']
-        num_branches = layer_config['num_branches']
-        num_blocks = layer_config['num_blocks']
-        num_channels = layer_config['num_channels']
-        block = self.blocks_dict[layer_config['block']]
-
-        hr_modules = []
-        for i in range(num_modules):
-            # multi_scale_output is only used for the last module
-            if not multiscale_output and i == num_modules - 1:
-                reset_multiscale_output = False
-            else:
-                reset_multiscale_output = True
-
-            hr_modules.append(
-                HRModule(
-                    num_branches,
-                    block,
-                    num_blocks,
-                    in_channels,
-                    num_channels,
-                    reset_multiscale_output,
-                    with_cp=self.with_cp,
-                    norm_cfg=self.norm_cfg,
-                    conv_cfg=self.conv_cfg))
-
-        return nn.Sequential(*hr_modules), in_channels
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
-
-            if self.zero_init_residual:
-                for m in self.modules():
-                    if isinstance(m, Bottleneck):
-                        constant_init(m.norm3, 0)
-                    elif isinstance(m, BasicBlock):
-                        constant_init(m.norm2, 0)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        """Forward function."""
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu(x)
-        x = self.conv2(x)
-        x = self.norm2(x)
-        x = self.relu(x)
-        x = self.layer1(x)
-
-        x_list = []
-        for i in range(self.stage2_cfg['num_branches']):
-            if self.transition1[i] is not None:
-                x_list.append(self.transition1[i](x))
-            else:
-                x_list.append(x)
-        y_list = self.stage2(x_list)
-
-        x_list = []
-        for i in range(self.stage3_cfg['num_branches']):
-            if self.transition2[i] is not None:
-                x_list.append(self.transition2[i](y_list[-1]))
-            else:
-                x_list.append(y_list[i])
-        y_list = self.stage3(x_list)
-
-        x_list = []
-        for i in range(self.stage4_cfg['num_branches']):
-            if self.transition3[i] is not None:
-                x_list.append(self.transition3[i](y_list[-1]))
-            else:
-                x_list.append(y_list[i])
-        y_list = self.stage4(x_list)
-
-        return y_list
-
-    def train(self, mode=True):
-        """Convert the model into training mode will keeping the normalization
-        layer freezed."""
-        super(HRNet, self).train(mode)
-        if mode and self.norm_eval:
-            for m in self.modules():
-                # trick: eval have effect on BatchNorm only
-                if isinstance(m, _BatchNorm):
-                    m.eval()

spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/image_degradation/bsrgan_light.py

@@ -1,651 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang ([email protected])
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    if up:
-        image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC)  # todo: random, as above? want to condition on it then
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')

spaces/Aqdas/YouTube_Video_OpenAI_whisper/app.py

@@ -1,17 +0,0 @@
-import streamlit as st
-from whisper import dowload_youtube_video, transcribe_audio
-import os
-
-
-st.title("Youtube Video + OpenAI Whisper")
-if st.text_input('Please Enter the access code') == os.environ['password']:
-
-    user_input = st.text_input('Enter Your YouTube URL')
-
-    with st.spinner('Sit back and relax. It takes a minute.'):
-        if st.button('Transcribe'):
-            if user_input:
-                download_audio = dowload_youtube_video(user_input)
-                st.write(transcribe_audio())
-
-        

spaces/BAAI/AltDiffusion/header.html

@@ -1,43 +0,0 @@
-<div style="text-align: center; max-width: 650px; margin: 0 auto;">
-    <div
-      style="
-        display: inline-flex;
-        gap: 0.8rem;
-        font-size: 1.75rem;
-        margin-bottom: 10px;
-        width: 600px;
-        height: 200px;
-        margin: 0 auto;
-        /* border: 1px solid red; */
-        justify-content: center;
-      "
-      
-    <a href="https://github.com/FlagAI-Open/FlagAI"><img src="https://raw.githubusercontent.com/920232796/test/master/WechatIMG6906.png" alt="FlagAI" width="80%" height="80%" style="margin: 0 auto;"></a>
-    </div> 
-    <div
-      style="
-        display: inline-flex;
-        align-items: center;
-        gap: 0.8rem;
-        font-size: 1.75rem;
-        margin-bottom: 10px;
-        justify-content: center;
-      ">
-    <a href="https://github.com/FlagAI-Open/FlagAI"><h1 style="font-weight: 900; margin-bottom: 7px;">
-      FlagStudio
-    </h1></a>
-    </div> 
-    <p style="margin-bottom: 10px; font-size: 94%">
-      FlagStudio 项目致力于贡献优秀AI生成艺术作品。此双语文生图模型项目基于 <a href="https://huggingface.co/CompVis/stable-diffusion" style="text-decoration: underline;">stable diffusion</a>，由BAAI旗下的FlagAI团队提供支持，相关代码和模型权重在<a href="https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion" style="text-decoration: underline;">AltDiffusion</a>中进行开源。
-    </p>
-    <p style="margin-bottom: 10px; font-size: 94%">
-      FlagStudio aims to provide high quality AI-generated artwork. Our current bilingual model is based on the original  <a href="https://huggingface.co/CompVis/stable-diffusion" style="text-decoration: underline;">stable diffusion</a> model and is capable to generate images from both Chinese and English text. FlagStudio is developed and supported by the FlagAI team. Relevant code and model weights released in <a href="https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion" style="text-decoration: underline;">AltDiffusion</a>.（[email protected]）
-    </p>
-    <p style="margin-bottom: 10px; font-size: 94%">
-    AltDiffusion has been added to 🧨Diffusers, see the documentation page: <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/alt_diffusion">🧨 Pipeline doc</a>         
-    </p>
-    <p style="margin-bottom: 10px; font-size: 94%; text-align: left;">
-    我们在colab设置了一个脚本，你可以在colab试用我们的模型!(We have a script on colab, You can try our models on colab.Enjoy it!)
-    <a href="https://colab.research.google.com/drive/1htPovT5YNutl2i31mIYrOzlIgGLm06IX#scrollTo=0KXFRkjG1RVk"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>  
-    </p>    
-  </div>

spaces/BIASLab/sars-cov-2-classification-fcgr/src/pipeline.py

@@ -1,85 +0,0 @@
-
-import json
-from pathlib import Path
-from collections import OrderedDict
-from typing import List, Tuple, Optional, Union
-
-FUNCTIONS_PIPELINE = OrderedDict()
-
-def register_in_pipeline(func):
-    """Collect functions for the pipeline"""
-    print(f"Adding {func.__name__}")
-    if func.__name__ not in FUNCTIONS_PIPELINE:
-        FUNCTIONS_PIPELINE[func.__name__] = func
-    else:
-        raise Exception(f"Duplicated function with name {func.__name__}")
-
-class Pipeline:
-    """Define a sequence of functions to be applied to one input"""
-    FUNCTIONS_PIPELINE = FUNCTIONS_PIPELINE
-    def __init__(self, pipeline: Optional[List[Tuple[str, dict]]] = None):
-        self.pipeline = pipeline if pipeline else []
-
-    def __call__(self, x):
-        """Apply pipeline to the input 'x'"""
-        for pipe in self.pipeline: 
-            func_name, *args, kwargs = pipe
-            assert isinstance(kwargs, dict), f"Wrong declaration in {func_name!r}. Must be (str, dict) or (str, tuple, dict)"
-            # apply preprocessing
-            if args: 
-                #print("args and kwargs")
-                x = self.apply(x, func_name, *args, **kwargs)
-            else:
-                #print("only kwargs")
-                x = self.apply(x, func_name, **kwargs)
-        return x
-
-    @classmethod
-    def apply(cls, x, func, *args, **kwargs):
-        """Compute func(x, *args, **kwargs)"""
-        if func in cls.FUNCTIONS_PIPELINE:
-            return cls.FUNCTIONS_PIPELINE[func](x, *args, **kwargs)
-        else:
-            raise TypeError(f"{func} not available")
-    
-    def __gt__(self, add_pipe: Union[List,Tuple]):
-        """Add a pipe ("func_name", args, kwargs)  or ("func_name", kwargs) to the current pipeline"""
-        if self.is_available(add_pipe[0]):
-            self.pipeline.append(add_pipe)
-            return self
-        else:
-            raise NotImplementedError(f"{add_pipe[0]!r} not available in Pipeline")       
-
-    def is_available(self, func_name: str):
-        """Return True if the function 'func_name' is available in Pipeline"""
-        return True if func_name in self.FUNCTIONS_PIPELINE else False
-
-    def asJSON(self, path_save: str =None):
-        """Save pipeline configuration as json file"""
-        path_save = Path(path_save) if path_save else Path("pipeline.json")
-        with open(path_save, "w", encoding="utf8") as fp:
-            json.dump(self.pipeline, fp, indent=4, ensure_ascii=False)
-        print(f"Pipeline configuration saved at {path_save!r}")
-
-    def fromJSON(self, path_pipeline: str):
-        """Load pipeline configuration from json file"""
-        path_pipeline = Path(path_pipeline)
-        with open(path_pipeline, "r", encoding="utf8") as fp: 
-            pipeline = json.load(fp)
-        
-        # Corrobate that all functions are availables
-        available_functions = {pipe[0]: self.is_available(pipe[0]) 
-                                            for pipe in pipeline}
-        
-        # TODO: change with the right Exception here
-        if not all(available_functions.values()): 
-            print("""
-            Some functions are not availables.
-            Please use the @register_in_pipeline decorator to include this functions to the Pipeline.
-            """)
-            functions_not_availables = dict(filter(lambda item: item[0], available_functions.items()))
-            return [func_name for func_name, available in functions_not_availables.items()
-                        if available is False]
-
-        self.pipeline = pipeline
-        print(f"Pipeline loaded from {path_pipeline!r}")

spaces/Bart92/RVC_HF/guidml.py

@@ -1,710 +0,0 @@
-"""
-0416后的更新：
-    引入config中half
-    重建npy而不用填写
-    v2支持
-    无f0模型支持
-    修复
-
-    int16：
-    增加无索引支持
-    f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好
-"""
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-
-import torch_directml
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch_directml.device(torch_directml.default_device())
-current_dir = os.getcwd()
-
-
-class RVC:
-    def __init__(
-        self, key, hubert_path, pth_path, index_path, npy_path, index_rate
-    ) -> None:
-        """
-        初始化
-        """
-        try:
-            self.f0_up_key = key
-            self.time_step = 160 / 16000 * 1000
-            self.f0_min = 50
-            self.f0_max = 1100
-            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            self.sr = 16000
-            self.window = 160
-            if index_rate != 0:
-                self.index = faiss.read_index(index_path)
-                # self.big_npy = np.load(npy_path)
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                print("index search enabled")
-            self.index_rate = index_rate
-            model_path = hubert_path
-            print("load model(s) from {}".format(model_path))
-            models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-                [model_path],
-                suffix="",
-            )
-            self.model = models[0]
-            self.model = self.model.to(device)
-            if Config.is_half:
-                self.model = self.model.half()
-            else:
-                self.model = self.model.float()
-            self.model.eval()
-            cpt = torch.load(pth_path, map_location="cpu")
-            self.tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            self.if_f0 = cpt.get("f0", 1)
-            self.version = cpt.get("version", "v1")
-            if self.version == "v1":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs256NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            elif self.version == "v2":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs768NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            del self.net_g.enc_q
-            print(self.net_g.load_state_dict(cpt["weight"], strict=False))
-            self.net_g.eval().to(device)
-            if Config.is_half:
-                self.net_g = self.net_g.half()
-            else:
-                self.net_g = self.net_g.float()
-        except:
-            print(traceback.format_exc())
-
-    def get_f0(self, x, f0_up_key, inp_f0=None):
-        x_pad = 1
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0, t = pyworld.harvest(
-            x.astype(np.double),
-            fs=self.sr,
-            f0_ceil=f0_max,
-            f0_floor=f0_min,
-            frame_period=10,
-        )
-        f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-        f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def infer(self, feats: torch.Tensor) -> np.ndarray:
-        """
-        推理函数
-        """
-        audio = feats.clone().cpu().numpy()
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        if Config.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        inputs = {
-            "source": feats.to(device),
-            "padding_mask": padding_mask.to(device),
-            "output_layer": 9 if self.version == "v1" else 12,
-        }
-        torch.cuda.synchronize()
-        with torch.no_grad():
-            logits = self.model.extract_features(**inputs)
-            feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-
-        ####索引优化
-        try:
-            if (
-                hasattr(self, "index")
-                and hasattr(self, "big_npy")
-                and self.index_rate != 0
-            ):
-                npy = feats[0].cpu().numpy().astype("float32")
-                score, ix = self.index.search(npy, k=8)
-                weight = np.square(1 / score)
-                weight /= weight.sum(axis=1, keepdims=True)
-                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-                if Config.is_half:
-                    npy = npy.astype("float16")
-                feats = (
-                    torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
-                    + (1 - self.index_rate) * feats
-                )
-            else:
-                print("index search FAIL or disabled")
-        except:
-            traceback.print_exc()
-            print("index search FAIL")
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        torch.cuda.synchronize()
-        print(feats.shape)
-        if self.if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio, self.f0_up_key)
-            p_len = min(feats.shape[1], 13000, pitch.shape[0])  # 太大了爆显存
-        else:
-            pitch, pitchf = None, None
-            p_len = min(feats.shape[1], 13000)  # 太大了爆显存
-        torch.cuda.synchronize()
-        # print(feats.shape,pitch.shape)
-        feats = feats[:, :p_len, :]
-        if self.if_f0 == 1:
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-            pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
-        p_len = torch.LongTensor([p_len]).to(device)
-        ii = 0  # sid
-        sid = torch.LongTensor([ii]).to(device)
-        with torch.no_grad():
-            if self.if_f0 == 1:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
-                    .data.cpu()
-                    .float()
-                )
-            else:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
-                )
-        torch.cuda.synchronize()
-        return infered_audio
-
-
-class GUIConfig:
-    def __init__(self) -> None:
-        self.hubert_path: str = ""
-        self.pth_path: str = ""
-        self.index_path: str = ""
-        self.npy_path: str = ""
-        self.pitch: int = 12
-        self.samplerate: int = 44100
-        self.block_time: float = 1.0  # s
-        self.buffer_num: int = 1
-        self.threhold: int = -30
-        self.crossfade_time: float = 0.08
-        self.extra_time: float = 0.04
-        self.I_noise_reduce = False
-        self.O_noise_reduce = False
-        self.index_rate = 0.3
-
-
-class GUI:
-    def __init__(self) -> None:
-        self.config = GUIConfig()
-        self.flag_vc = False
-
-        self.launcher()
-
-    def load(self):
-        (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        ) = self.get_devices()
-        try:
-            with open("values1.json", "r") as j:
-                data = json.load(j)
-        except:
-            with open("values1.json", "w") as j:
-                data = {
-                    "pth_path": "",
-                    "index_path": "",
-                    "sg_input_device": input_devices[
-                        input_devices_indices.index(sd.default.device[0])
-                    ],
-                    "sg_output_device": output_devices[
-                        output_devices_indices.index(sd.default.device[1])
-                    ],
-                    "threhold": "-45",
-                    "pitch": "0",
-                    "index_rate": "0",
-                    "block_time": "1",
-                    "crossfade_length": "0.04",
-                    "extra_time": "1",
-                }
-        return data
-
-    def launcher(self):
-        data = self.load()
-        sg.theme("LightBlue3")
-        input_devices, output_devices, _, _ = self.get_devices()
-        layout = [
-            [
-                sg.Frame(
-                    title=i18n("Load model"),
-                    layout=[
-                        [
-                            sg.Input(
-                                default_text="hubert_base.pt",
-                                key="hubert_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Hubert Model"),
-                                initial_folder=os.path.join(os.getcwd()),
-                                file_types=(("pt files", "*.pt"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("pth_path", ""),
-                                key="pth_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .pth file"),
-                                initial_folder=os.path.join(os.getcwd(), "weights"),
-                                file_types=(("weight files", "*.pth"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("index_path", ""),
-                                key="index_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .index file"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("index files", "*.index"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text="你不需要填写这个You don't need write this.",
-                                key="npy_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .npy file"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("feature files", "*.npy"),),
-                            ),
-                        ],
-                    ],
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Input device")),
-                            sg.Combo(
-                                input_devices,
-                                key="sg_input_device",
-                                default_value=data.get("sg_input_device", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Output device")),
-                            sg.Combo(
-                                output_devices,
-                                key="sg_output_device",
-                                default_value=data.get("sg_output_device", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("Audio device (please use the same type of driver)"),
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Response threshold")),
-                            sg.Slider(
-                                range=(-60, 0),
-                                key="threhold",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("threhold", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Pitch settings")),
-                            sg.Slider(
-                                range=(-24, 24),
-                                key="pitch",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("pitch", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Index Rate")),
-                            sg.Slider(
-                                range=(0.0, 1.0),
-                                key="index_rate",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("index_rate", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("General settings"),
-                ),
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Sample length")),
-                            sg.Slider(
-                                range=(0.1, 3.0),
-                                key="block_time",
-                                resolution=0.1,
-                                orientation="h",
-                                default_value=data.get("block_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Fade length")),
-                            sg.Slider(
-                                range=(0.01, 0.15),
-                                key="crossfade_length",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("crossfade_length", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Extra推理时长")),
-                            sg.Slider(
-                                range=(0.05, 3.00),
-                                key="extra_time",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("extra_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
-                            sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
-                        ],
-                    ],
-                    title=i18n("Performance settings"),
-                ),
-            ],
-            [
-                sg.Button(i18n("开始音频Convert"), key="start_vc"),
-                sg.Button(i18n("停止音频Convert"), key="stop_vc"),
-                sg.Text(i18n("Inference time (ms):")),
-                sg.Text("0", key="infer_time"),
-            ],
-        ]
-        self.window = sg.Window("RVC - GUI", layout=layout)
-        self.event_handler()
-
-    def event_handler(self):
-        while True:
-            event, values = self.window.read()
-            if event == sg.WINDOW_CLOSED:
-                self.flag_vc = False
-                exit()
-            if event == "start_vc" and self.flag_vc == False:
-                if self.set_values(values) == True:
-                    print("using_cuda:" + str(torch.cuda.is_available()))
-                    self.start_vc()
-                    settings = {
-                        "pth_path": values["pth_path"],
-                        "index_path": values["index_path"],
-                        "sg_input_device": values["sg_input_device"],
-                        "sg_output_device": values["sg_output_device"],
-                        "threhold": values["threhold"],
-                        "pitch": values["pitch"],
-                        "index_rate": values["index_rate"],
-                        "block_time": values["block_time"],
-                        "crossfade_length": values["crossfade_length"],
-                        "extra_time": values["extra_time"],
-                    }
-                    with open("values1.json", "w") as j:
-                        json.dump(settings, j)
-            if event == "stop_vc" and self.flag_vc == True:
-                self.flag_vc = False
-
-    def set_values(self, values):
-        if len(values["pth_path"].strip()) == 0:
-            sg.popup(i18n("Select the pth file"))
-            return False
-        if len(values["index_path"].strip()) == 0:
-            sg.popup(i18n("Select the index file"))
-            return False
-        pattern = re.compile("[^\x00-\x7F]+")
-        if pattern.findall(values["hubert_path"]):
-            sg.popup(i18n("The hubert model path must not contain Chinese characters"))
-            return False
-        if pattern.findall(values["pth_path"]):
-            sg.popup(i18n("The pth file path must not contain Chinese characters."))
-            return False
-        if pattern.findall(values["index_path"]):
-            sg.popup(i18n("The index file path must not contain Chinese characters."))
-            return False
-        self.set_devices(values["sg_input_device"], values["sg_output_device"])
-        self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
-        self.config.pth_path = values["pth_path"]
-        self.config.index_path = values["index_path"]
-        self.config.npy_path = values["npy_path"]
-        self.config.threhold = values["threhold"]
-        self.config.pitch = values["pitch"]
-        self.config.block_time = values["block_time"]
-        self.config.crossfade_time = values["crossfade_length"]
-        self.config.extra_time = values["extra_time"]
-        self.config.I_noise_reduce = values["I_noise_reduce"]
-        self.config.O_noise_reduce = values["O_noise_reduce"]
-        self.config.index_rate = values["index_rate"]
-        return True
-
-    def start_vc(self):
-        torch.cuda.empty_cache()
-        self.flag_vc = True
-        self.block_frame = int(self.config.block_time * self.config.samplerate)
-        self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
-        self.sola_search_frame = int(0.012 * self.config.samplerate)
-        self.delay_frame = int(0.01 * self.config.samplerate)  # 往前预留0.02s
-        self.extra_frame = int(self.config.extra_time * self.config.samplerate)
-        self.rvc = None
-        self.rvc = RVC(
-            self.config.pitch,
-            self.config.hubert_path,
-            self.config.pth_path,
-            self.config.index_path,
-            self.config.npy_path,
-            self.config.index_rate,
-        )
-        self.input_wav: np.ndarray = np.zeros(
-            self.extra_frame
-            + self.crossfade_frame
-            + self.sola_search_frame
-            + self.block_frame,
-            dtype="float32",
-        )
-        self.output_wav: torch.Tensor = torch.zeros(
-            self.block_frame, device=device, dtype=torch.float32
-        )
-        self.sola_buffer: torch.Tensor = torch.zeros(
-            self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_in_window: torch.Tensor = torch.linspace(
-            0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
-        self.resampler1 = tat.Resample(
-            orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
-        )
-        self.resampler2 = tat.Resample(
-            orig_freq=self.rvc.tgt_sr,
-            new_freq=self.config.samplerate,
-            dtype=torch.float32,
-        )
-        thread_vc = threading.Thread(target=self.soundinput)
-        thread_vc.start()
-
-    def soundinput(self):
-        """
-        接受音频输入
-        """
-        with sd.Stream(
-            channels=2,
-            callback=self.audio_callback,
-            blocksize=self.block_frame,
-            samplerate=self.config.samplerate,
-            dtype="float32",
-        ):
-            while self.flag_vc:
-                time.sleep(self.config.block_time)
-                print("Audio block passed.")
-        print("ENDing VC")
-
-    def audio_callback(
-        self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
-    ):
-        """
-        音频处理
-        """
-        start_time = time.perf_counter()
-        indata = librosa.to_mono(indata.T)
-        if self.config.I_noise_reduce:
-            indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
-        """noise gate"""
-        frame_length = 2048
-        hop_length = 1024
-        rms = librosa.feature.rms(
-            y=indata, frame_length=frame_length, hop_length=hop_length
-        )
-        db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
-        # print(rms.shape,db.shape,db)
-        for i in range(db_threhold.shape[0]):
-            if db_threhold[i]:
-                indata[i * hop_length : (i + 1) * hop_length] = 0
-        self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
-        # infer
-        print("input_wav:" + str(self.input_wav.shape))
-        # print('infered_wav:'+str(infer_wav.shape))
-        infer_wav: torch.Tensor = self.resampler2(
-            self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
-        )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
-            device
-        )
-        print("infer_wav:" + str(infer_wav.shape))
-
-        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
-        cor_nom = F.conv1d(
-            infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
-            self.sola_buffer[None, None, :],
-        )
-        cor_den = torch.sqrt(
-            F.conv1d(
-                infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
-                ** 2,
-                torch.ones(1, 1, self.crossfade_frame, device=device),
-            )
-            + 1e-8
-        )
-        sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
-        print("sola offset: " + str(int(sola_offset)))
-
-        # crossfade
-        self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
-        self.output_wav[: self.crossfade_frame] *= self.fade_in_window
-        self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
-        if sola_offset < self.sola_search_frame:
-            self.sola_buffer[:] = (
-                infer_wav[
-                    -self.sola_search_frame
-                    - self.crossfade_frame
-                    + sola_offset : -self.sola_search_frame
-                    + sola_offset
-                ]
-                * self.fade_out_window
-            )
-        else:
-            self.sola_buffer[:] = (
-                infer_wav[-self.crossfade_frame :] * self.fade_out_window
-            )
-
-        if self.config.O_noise_reduce:
-            outdata[:] = np.tile(
-                nr.reduce_noise(
-                    y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
-                ),
-                (2, 1),
-            ).T
-        else:
-            outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
-        total_time = time.perf_counter() - start_time
-        self.window["infer_time"].update(int(total_time * 1000))
-        print("infer time:" + str(total_time))
-
-    def get_devices(self, update: bool = True):
-        """获取设备列表"""
-        if update:
-            sd._terminate()
-            sd._initialize()
-        devices = sd.query_devices()
-        hostapis = sd.query_hostapis()
-        for hostapi in hostapis:
-            for device_idx in hostapi["devices"]:
-                devices[device_idx]["hostapi_name"] = hostapi["name"]
-        input_devices = [
-            f"{d['name']} ({d['hostapi_name']})"
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices = [
-            f"{d['name']} ({d['hostapi_name']})"
-            for d in devices
-            if d["max_output_channels"] > 0
-        ]
-        input_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_output_channels"] > 0
-        ]
-        return (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        )
-
-    def set_devices(self, input_device, output_device):
-        """设置输出设备"""
-        (
-            input_devices,
-            output_devices,
-            input_device_indices,
-            output_device_indices,
-        ) = self.get_devices()
-        sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
-        sd.default.device[1] = output_device_indices[
-            output_devices.index(output_device)
-        ]
-        print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
-        print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()

spaces/Benson/text-generation/Examples/Botn Fiebre Descargar Pc.md

@@ -1,58 +0,0 @@
-
-<h1>Cómo descargar y jugar Button Fever en PC</h1>
-<p>¿Te encantan los juegos de puzzle que ponen a prueba tus habilidades multitarea y creatividad? Si es así, es posible que desee probar Button Fever, un juego divertido y adictivo que le permite colocar y combinar botones en un tablero. En este artículo, te mostraremos cómo descargar y jugar Button Fever en tu PC, así como algunos consejos y trucos para ayudarte a sacarle el máximo partido. </p>
-<h2>botón fiebre descargar pc</h2><br /><p><b><b>DOWNLOAD</b> >>>>> <a href="https://bltlly.com/2v6Jpl">https://bltlly.com/2v6Jpl</a></b></p><br /><br />
- <h2>¿Qué es la fiebre de los botones? </h2>
-<h3>Un divertido y adictivo juego de puzzle</h3>
-<p>Button Fever es un juego desarrollado por Rollic Games, una empresa especializada en juegos casuales e hiper-casuales para dispositivos móviles. Button Fever es uno de sus títulos más populares, con más de 10 millones de descargas en Google Play Store. El juego es adecuado para todas las edades y se puede jugar fuera de línea o en línea. </p>
- <h3>Características y jugabilidad</h3>
-<p>El juego es simple pero desafiante. Tienes un tablero con ranuras vacías y una cola de botones en la parte inferior. Su objetivo es colocar los botones en el tablero y limpiar las líneas haciendo coincidir los colores o formas de los botones. Cuantas más líneas borres, más puntos ganarás. También puedes ganar monedas completando niveles, que puedes usar para desbloquear nuevos botones y temas. </p>
-<p>El juego tiene diferentes niveles de dificultad, que van de fácil a difícil. Cada nivel tiene un tamaño de tablero diferente, número de botones y límite de tiempo. También puedes elegir entre diferentes modos, como clásico, árcade o zen. El juego también tiene desafíos diarios, tablas de clasificación y logros para mantenerte involucrado. </p>
- <h2>Cómo descargar Button Fever en PC? </h2>
-<h3>Opción 1: Descargar desde el sitio web oficial</h3>
-<p>Si desea descargar Button Fever directamente desde el sitio web del desarrollador, puede seguir estos pasos:</p>
-<h4>Paso 1: Visita <a href="( 1 )">el sitio web</a> y haz clic en el botón de descarga. </h4>
-<p>Esto te llevará a una página donde puedes elegir tu sistema operativo (Windows o Mac) y descargar el archivo de instalación. </p>
-<p></p>
-
-<p>Una vez que haya descargado el archivo, haga doble clic en él para iniciar el proceso de instalación. Es posible que tenga que conceder permiso para que el programa realice cambios en su dispositivo. Siga las instrucciones en la pantalla para completar la instalación. </p>
-<h4>Paso 3: Lanza el juego y disfruta. </h4>
-<p>Después de la instalación, puede encontrar un icono de acceso directo para Button Fever en su escritorio o menú de inicio. Haga clic en él para iniciar el juego y empezar a jugar. </p>
- <h3>Opción 2: Descarga desde una plataforma de terceros</h3>
-<p>Si prefiere descargar Button Fever desde una plataforma de terceros que ofrece una variedad de juegos, puede utilizar una de estas opciones:</p>
-<h4>Paso 1: Instalar un lanzador de juegos como Epic Games o Steam.</h4>
-<p>Un lanzador de juegos es un programa que te permite acceder, descargar, instalar, actualizar y jugar juegos de diferentes desarrolladores y editores. Algunos de los lanzadores de juegos más populares son Epic Games y Steam, que puedes descargar gratis desde sus respectivos sitios web. </p>
-<h4>Paso 2: Crea una cuenta e inicia sesión. </h4>
-<p>Después de haber instalado el lanzador de juegos, tendrá que crear una cuenta e iniciar sesión con su correo electrónico y contraseña. También es posible que necesite verificar su cuenta y aceptar los términos y condiciones de la plataforma. </p>
-<h4>Paso 3: Busca Button Fever y cómpralo o consíguelo gratis. </h4>
-<p>Una vez que haya iniciado sesión, puede navegar por la biblioteca de juegos y buscar Button Fever. Dependiendo de la plataforma, es posible que tenga que comprar el juego o conseguirlo de forma gratuita. Por ejemplo, en Epic Games, Button Fever está disponible de forma gratuita, mientras que en Steam, cuesta $4.99. También puedes consultar las reseñas, valoraciones, capturas de pantalla y vídeos del juego antes de decidirte a conseguirlo. </p>
-<h4>Paso 4: Instalar el juego y jugar desde el lanzador. </h4>
-
- <h2>Consejos y trucos para jugar Button Fever en PC</h2>
-<p>Jugando Button Fever en PC puede ser más agradable y conveniente que reproducirlo en un dispositivo móvil. Aquí hay algunos consejos y trucos para ayudarte a jugar mejor y divertirte más:</p>
-<h3>Usa el ratón o el teclado para interactuar con los botones. </h3>
-<p>Una de las ventajas de jugar Button Fever en PC es que puedes usar el ratón o el teclado para interactuar con los botones. Puede arrastrar y soltar los botones con el ratón, o utilizar las teclas de flecha para moverlos. También puede utilizar la barra espaciadora para girarlos o presionar la tecla Intro para colocarlos en el tablero. Esto puede hacer que su juego sea más rápido y suave. </p>
-<h3>Borrar las líneas haciendo coincidir los colores o formas de los botones. </h3>
-<p>El objetivo principal de Button Fever es limpiar las líneas haciendo coincidir los colores o formas de los botones. Puede combinar tres o más botones del mismo color o forma horizontal, vertical o diagonalmente. Cuando borres una línea, ganarás puntos y monedas, y harás espacio para más botones. También puedes crear combos borrando varias líneas a la vez, lo que te dará puntos de bonificación y monedas. </p>
-<h3>Gana monedas y desbloquea nuevos botones y temas. </h3>
-<p>Al jugar Button Fever, ganarás monedas que puedes usar para desbloquear nuevos botones y temas. Cada botón tiene un color diferente, forma y diseño, tales como estrellas, corazones, flores, animales, frutas, etc. Cada tema tiene un fondo diferente, música y efectos de sonido, tales como bosque, playa, espacio, etc. Puede personalizar su juego eligiendo sus botones y temas favoritos de la tienda. </p>
-<h3>Ponte a prueba con diferentes niveles y modos. </h3>
-
- <h2>Conclusión</h2>
-<p>Button Fever es un divertido y adictivo juego de puzzle que te permite colocar y combinar botones en un tablero. Puede descargar y reproducir Button Fever en su PC siguiendo una de las opciones que hemos mostrado anteriormente. También puede utilizar algunos de nuestros consejos y trucos para mejorar su juego y divertirse más. Si te gustan los juegos de puzzle que ponen a prueba tus habilidades multitarea y creatividad, definitivamente deberías probar Button Fever. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Button Fever:</p>
-<ul>
-<li><b>¿Está libre la fiebre de los botones? </b></li>
-<p>Button Fever es gratis para descargar y jugar en dispositivos móviles desde Google Play Store o App Store. Sin embargo, puede contener anuncios o compras dentro de la aplicación que requieren dinero real. En plataformas de PC como Epic Games o Steam, Button Fever puede ser gratis o de pago dependiendo de la disponibilidad y oferta de la plataforma. </p>
-<li><b>¿Es segura la fiebre de los botones? </b></li>
-<p>Button Fever es seguro para descargar y jugar siempre y cuando lo obtengas de una fuente confiable como el sitio web oficial o una plataforma de buena reputación como Epic Games o Steam. Debes evitar descargar Button Fever de fuentes desconocidas o sospechosas que puedan contener virus o malware que puedan dañar tu dispositivo o robar tu información personal. </p>
-<li><b>¿Cómo puedo contactar al desarrollador de Button Fever? </b></li>
-<p>Si tiene alguna pregunta, comentario o problema con respecto a Button Fever, puede ponerse en contacto con el desarrollador enviando un correo electrónico a <a href="mailto:[email protected]">[email protected]</a>. También puede visitar su sitio web <a href=">"</a> o seguirlos en <a href="">Facebook</a>, <a href="">Twitter</a>, o <a href=">Instagram</a> para obtener más información y actualizaciones. </p>
-<li><b>¿Cómo puedo jugar Button Fever con mis amigos? </b></li>
-
-<li><b>¿Cómo puedo obtener más monedas en Button Fever? </b></li>
-<p>Las monedas son la moneda de Button Fever que puedes usar para desbloquear nuevos botones y temas. Puedes ganar monedas limpiando niveles, completando desafíos diarios, viendo anuncios o haciendo compras en la aplicación. También puedes conseguir monedas gratis girando la rueda o abriendo el cofre todos los días. </p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cazador Asesino 2 Apk Descargar.md

@@ -1,67 +0,0 @@
-<br />
-<h1>Hunter Assassin 2 APK Descargar: Una guía para los usuarios de Android</h1>
- <p>Si usted está buscando un juego lleno de acción que pondrá a prueba su sigilo y habilidades de disparo, es posible que desee probar Hunter Assassin 2. Esta es la secuela del popular juego Hunter Assassin, que fue uno de los juegos más descargados de 2020. En este juego, juegas como un asesino que tiene que eliminar objetivos usando sombras y tu entorno. También tienes que recoger objetos valiosos, desbloquear nuevas armas y héroes, y enfrentarse a jefes desafiantes. En este artículo, te contaremos todo lo que necesitas saber sobre Hunter Assassin 2, incluyendo sus características, cómo descargarlo en tu dispositivo Android, algunos consejos y trucos para jugarlo, y algunas alternativas a él. </p>
- <h2>¿Qué es Hunter Assassin 2?</h2>
- <p>Hunter Assassin 2 es un juego de acción táctica en 2D desarrollado por Ruby Game Studio. Es la muy esperada secuela de Hunter Assassin, que fue descargada por más de 300 millones de jugadores en todo el mundo. El juego tiene más de 10 millones de descargas en Google Play Store y tiene una calificación de 4.0 de 5 estrellas. El juego está clasificado para edades de 7 en adelante y contiene anuncios y compras en la aplicación. </p>
-<h2>cazador asesino 2 apk descargar</h2><br /><p><b><b>DOWNLOAD</b> &#10084; <a href="https://bltlly.com/2v6KMW">https://bltlly.com/2v6KMW</a></b></p><br /><br />
- <h3>Características de Hunter Assassin 2</h3>
- <p>Hunter Assassin 2 tiene muchas características que lo convierten en un juego emocionante y adictivo. Aquí están algunas de ellas:</p>
- <h4>Juego de historia pegajosa</h4>
- <p>El juego tiene una historia pegajosa que te mantendrá enganchado al juego. Tienes que completar misiones y objetivos en cada nivel, como eliminar un cierto número de objetivos, recoger una cierta cantidad de oro, o sobrevivir durante una cierta cantidad de tiempo. El juego también tiene diferentes escenarios y entornos, como almacenes, fábricas, selvas, desiertos y más. </p>
- <h4>Juego multinivel</h4>
-
- <h4>Habilidades y caracteres únicos</h4>
- <p>El juego tiene una variedad de habilidades y personajes que puedes desbloquear y actualizar usando los objetos que recojas de los objetivos eliminados. Cada habilidad y personaje tiene diferentes habilidades y ventajas que pueden ayudarte en diferentes situaciones. Por ejemplo, algunas habilidades pueden aumentar tu velocidad, salud o daño, mientras que algunos personajes pueden disparar más rápido, recargar más rápido o tener más munición. </p>
- <h4>Armas y herramientas mejoradas</h4>
- <p>El juego también tiene una gama de armas y herramientas que puedes usar para destruir las bases de los enemigos y derrotar a los jefes. Puedes actualizar tus armas y herramientas usando el oro que ganas al completar los niveles. Algunas de las armas y herramientas incluyen pistolas, rifles, escopetas, granadas, minas, drones, cohetes, láseres y más. </p>
- <h4>Artículos especiales</h4>
- <p>El juego también tiene artículos especiales que pueden darte beneficios o bonificaciones adicionales en el juego. Algunos de los artículos especiales incluyen cofres, llaves, diamantes, estrellas y más. Puedes usar estos elementos para desbloquear nuevas habilidades y personajes, obtener más oro o acceder a niveles secretos. </p>
-<p></p>
- <h3> Cómo descargar Hunter Assassin 2 APK en dispositivos Android</h3>
- <p>Si quieres jugar Hunter Assassin 2 en tu dispositivo Android, puedes descargarlo desde Google Play Store o desde otras fuentes. Sin embargo, si desea obtener la última versión del juego o acceder a algunas características que no están disponibles en la aplicación oficial, puede descargar el archivo APK Hunter Assassin 2 de un sitio web de confianza. Estos son los pasos para hacerlo:</p>
- <h4>Paso 1: Habilitar fuentes desconocidas</h4>
- <p>Antes de poder instalar el archivo APK, debe habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad y luego active la opción para fuentes desconocidas. Puedes ver un mensaje de advertencia, pero puedes ignorarlo si confías en la fuente del archivo APK. </p>
- <h4>Paso 2: Descargar el archivo APK</h4>
-
- <h4>Paso 3: Instalar el archivo APK</h4>
- <p>Una vez que haya descargado el archivo APK, puede instalarlo en su dispositivo. Para ello, localice el archivo en su carpeta de descargas o donde quiera que lo haya guardado. Luego, toque en él y siga las instrucciones en la pantalla. Es posible que tenga que conceder algunos permisos o aceptar algunos términos y condiciones antes de instalarlo. </p>
- <h4>Paso 4: Iniciar el juego y disfrutar de</h4>
- <p>Después de instalar el archivo APK, puede iniciar el juego y comenzar a jugarlo. Verás un icono de Hunter Assassin 2 en la pantalla de inicio o en el cajón de la aplicación. Toca en él y disfruta del juego. También puedes actualizar el juego regularmente descargando nuevos archivos APK de la misma fuente o buscando actualizaciones en el juego mismo. </p>
- <h3>Consejos y trucos para jugar Hunter Assassin 2</h3>
- <p>Hunter Assassin 2 es un juego divertido y desafiante que requiere habilidad y estrategia. Aquí hay algunos consejos y trucos que pueden ayudarte a jugar mejor y disfrutar más:</p>
- <h4>Usa sombras y alrededores para tu ventaja</h4>
- <p>La clave para ser un asesino exitoso es ser sigiloso y evitar ser detectado por los enemigos. Para hacer esto, tienes que usar las sombras y el entorno a tu favor. Puedes esconderte detrás de paredes, cajas, barriles u otros objetos que puedan bloquear la visión de los enemigos. También puedes usar sombras para mezclarte con las zonas oscuras del mapa. De esta manera, puedes sorprender a los enemigos y eliminarlos sin alertar a los demás. </p>
- <h4>Evita ser acorralado por los enemigos</h4>
- <p>Otra cosa a evitar es ser acorralado por los enemigos. Si estás rodeado de enemigos o atrapado en un callejón sin salida, no tendrás ninguna ruta de escape y ninguna posibilidad de supervivencia. Para evitar esto, tienes que planificar tus movimientos cuidadosamente y siempre tener una estrategia de salida. También puedes usar tus armas y herramientas para crear distracciones o distracciones que puedan alejar a los enemigos de ti. </p>
- <h4>Recoge objetos valiosos de objetivos eliminados</h4>
-
- <h4>Mejora tus habilidades y armas regularmente</h4>
- <p>Para mejorar tu rendimiento y eficiencia en el juego, debes mejorar tus habilidades y armas regularmente. Puedes hacer esto usando el oro que ganas al completar niveles o recolectar artículos. Puedes mejorar tus habilidades como velocidad, salud, daños, tiempo de recarga, capacidad de munición y más. También puede actualizar sus armas como pistolas, rifles, escopetas, granadas, minas , drones, cohetes, láseres y más. Mejorar tus habilidades y armas te ayudará a eliminar objetivos más rápido, fácil y eficientemente. </p>
- <h4>Ponte a prueba con batallas de jefes</h4>
- <p>Si quieres poner a prueba tus habilidades y estrategia al límite, puedes desafiarte a ti mismo con batallas de jefes. Las batallas contra jefes son niveles especiales donde tienes que enfrentarte a un enemigo poderoso que tiene más salud, daño y habilidades que los enemigos normales. Tienes que usar tus mejores armas y herramientas, así como tus habilidades de sigilo y tiro, para derrotar al jefe. Las batallas contra jefes son más difíciles que los niveles normales, pero también ofrecen más recompensas y satisfacción. </p>
- <h3>Alternativas a Hunter Assassin 2</h3>
- <p>Si te gusta Hunter Assassin 2, es posible que también te gusten algunas de estas alternativas que tienen un modo de juego y características similares:</p>
- <h4>Cazador Asesino</h4>
- <p>Este es el juego original que comenzó la serie Hunter Assassin. Tiene el mismo concepto y mecánica que Hunter Assassin 2, pero con gráficos más simples y menos niveles. Todavía puedes disfrutar de la emoción de ser un asesino y eliminar objetivos usando sombras y alrededores. También puedes desbloquear nuevos personajes y armas a medida que avanzas en el juego. </p>
- <h4>Tiroteo 3D</h4>
-
- <h4>Guerra de Stickman</h4>
- <p>Este es un juego de OneSoft Global PTE. LTD. que cuenta con personajes stickman y acción. En este juego, tienes que controlar a un guerrero stickman que tiene que luchar contra otros stickmen usando varias armas y habilidades. Puede personalizar su stickman con diferentes trajes, sombreros, máscaras y accesorios. También puedes jugar con amigos en modo multijugador o competir con otros jugadores en rankings online. </p>
- <h3>Conclusión</h3>
- <p>Hunter Assassin 2 es un juego que te mantendrá entretenido y desafiado durante horas. Tiene muchas características que lo convierten en un juego emocionante y adictivo, como juego de historia pegajosa, juego de varios niveles, habilidades y personajes únicos, armas y herramientas mejoradas, objetos especiales y batallas contra jefes. Puede descargar Hunter Assassin 2 APK en su dispositivo Android siguiendo los pasos que hemos proporcionado en este artículo. También puedes utilizar algunos consejos y trucos que hemos compartido para jugar mejor y disfrutar más. Si estás buscando alternativas a Hunter Assassin 2, puedes probar algunos de los juegos que hemos sugerido que tienen un modo de juego y características similares. </p>
- <h3>Preguntas frecuentes</h3>
- <p>Aquí hay algunas preguntas frecuentes sobre Hunter Assassin 2:</p>
- <ol>
-<li><b>¿Hunter Assassin 2 es libre de jugar? </b></li>
-<p>Sí, Hunter Assassin 2 es gratis para jugar en dispositivos Android. Sin embargo, contiene anuncios y compras en la aplicación que pueden mejorar su experiencia de juego o eliminar algunas limitaciones. </p>
-<li><b>¿Es seguro descargar Hunter Assassin 2? </b></li>
-<p>Sí, Hunter Assassin 2 es seguro para descargar desde la Google Play Store o desde otras fuentes de confianza. Sin embargo, siempre debes revisar el archivo en busca de virus o malware antes de instalarlo en tu dispositivo. </p>
-<li><b>¿Hunter Assassin 2 está fuera de línea o en línea? </b></li>
-<p>Hunter Assassin 2 es principalmente un juego fuera de línea que no requiere una conexión a Internet para jugar. Sin embargo, algunas características pueden requerir una conexión a Internet, como actualizar el juego, acceder a algunos elementos o niveles, o ver anuncios. </p>
-
-<p>Puede ponerse en contacto con el desarrollador de Hunter Assassin 2 enviando un correo electrónico a [email protected] o visitando su sitio web en https://www.rubygamestudio.com/.</p>
-<li><b>¿Cómo puedo calificar o revisar Hunter Assassin 2?</b></li>
-<p>Puede calificar o revisar Hunter Assassin 2 yendo a la página de Google Play Store del juego y tocando las estrellas o el botón de escribir una reseña. También puede compartir sus comentarios o sugerencias con el desarrollador u otros jugadores dejando un comentario en sus páginas de redes sociales o foros. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BhagatSurya/convet_pdf_to_txt/README.md

@@ -1,12 +0,0 @@
----
-title: Convet Pdf To Txt
-emoji: 🐠
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/legacy/resolver.py

@@ -1,600 +0,0 @@
-"""Dependency Resolution
-
-The dependency resolution in pip is performed as follows:
-
-for top-level requirements:
-    a. only one spec allowed per project, regardless of conflicts or not.
-       otherwise a "double requirement" exception is raised
-    b. they override sub-dependency requirements.
-for sub-dependencies
-    a. "first found, wins" (where the order is breadth first)
-"""
-
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import logging
-import sys
-from collections import defaultdict
-from itertools import chain
-from typing import DefaultDict, Iterable, List, Optional, Set, Tuple
-
-from pip._vendor.packaging import specifiers
-from pip._vendor.packaging.requirements import Requirement
-
-from pip._internal.cache import WheelCache
-from pip._internal.exceptions import (
-    BestVersionAlreadyInstalled,
-    DistributionNotFound,
-    HashError,
-    HashErrors,
-    InstallationError,
-    NoneMetadataError,
-    UnsupportedPythonVersion,
-)
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import BaseDistribution
-from pip._internal.models.link import Link
-from pip._internal.models.wheel import Wheel
-from pip._internal.operations.prepare import RequirementPreparer
-from pip._internal.req.req_install import (
-    InstallRequirement,
-    check_invalid_constraint_type,
-)
-from pip._internal.req.req_set import RequirementSet
-from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider
-from pip._internal.utils import compatibility_tags
-from pip._internal.utils.compatibility_tags import get_supported
-from pip._internal.utils.direct_url_helpers import direct_url_from_link
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import normalize_version_info
-from pip._internal.utils.packaging import check_requires_python
-
-logger = logging.getLogger(__name__)
-
-DiscoveredDependencies = DefaultDict[str, List[InstallRequirement]]
-
-
-def _check_dist_requires_python(
-    dist: BaseDistribution,
-    version_info: Tuple[int, int, int],
-    ignore_requires_python: bool = False,
-) -> None:
-    """
-    Check whether the given Python version is compatible with a distribution's
-    "Requires-Python" value.
-
-    :param version_info: A 3-tuple of ints representing the Python
-        major-minor-micro version to check.
-    :param ignore_requires_python: Whether to ignore the "Requires-Python"
-        value if the given Python version isn't compatible.
-
-    :raises UnsupportedPythonVersion: When the given Python version isn't
-        compatible.
-    """
-    # This idiosyncratically converts the SpecifierSet to str and let
-    # check_requires_python then parse it again into SpecifierSet. But this
-    # is the legacy resolver so I'm just not going to bother refactoring.
-    try:
-        requires_python = str(dist.requires_python)
-    except FileNotFoundError as e:
-        raise NoneMetadataError(dist, str(e))
-    try:
-        is_compatible = check_requires_python(
-            requires_python,
-            version_info=version_info,
-        )
-    except specifiers.InvalidSpecifier as exc:
-        logger.warning(
-            "Package %r has an invalid Requires-Python: %s", dist.raw_name, exc
-        )
-        return
-
-    if is_compatible:
-        return
-
-    version = ".".join(map(str, version_info))
-    if ignore_requires_python:
-        logger.debug(
-            "Ignoring failed Requires-Python check for package %r: %s not in %r",
-            dist.raw_name,
-            version,
-            requires_python,
-        )
-        return
-
-    raise UnsupportedPythonVersion(
-        "Package {!r} requires a different Python: {} not in {!r}".format(
-            dist.raw_name, version, requires_python
-        )
-    )
-
-
-class Resolver(BaseResolver):
-    """Resolves which packages need to be installed/uninstalled to perform \
-    the requested operation without breaking the requirements of any package.
-    """
-
-    _allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"}
-
-    def __init__(
-        self,
-        preparer: RequirementPreparer,
-        finder: PackageFinder,
-        wheel_cache: Optional[WheelCache],
-        make_install_req: InstallRequirementProvider,
-        use_user_site: bool,
-        ignore_dependencies: bool,
-        ignore_installed: bool,
-        ignore_requires_python: bool,
-        force_reinstall: bool,
-        upgrade_strategy: str,
-        py_version_info: Optional[Tuple[int, ...]] = None,
-    ) -> None:
-        super().__init__()
-        assert upgrade_strategy in self._allowed_strategies
-
-        if py_version_info is None:
-            py_version_info = sys.version_info[:3]
-        else:
-            py_version_info = normalize_version_info(py_version_info)
-
-        self._py_version_info = py_version_info
-
-        self.preparer = preparer
-        self.finder = finder
-        self.wheel_cache = wheel_cache
-
-        self.upgrade_strategy = upgrade_strategy
-        self.force_reinstall = force_reinstall
-        self.ignore_dependencies = ignore_dependencies
-        self.ignore_installed = ignore_installed
-        self.ignore_requires_python = ignore_requires_python
-        self.use_user_site = use_user_site
-        self._make_install_req = make_install_req
-
-        self._discovered_dependencies: DiscoveredDependencies = defaultdict(list)
-
-    def resolve(
-        self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
-    ) -> RequirementSet:
-        """Resolve what operations need to be done
-
-        As a side-effect of this method, the packages (and their dependencies)
-        are downloaded, unpacked and prepared for installation. This
-        preparation is done by ``pip.operations.prepare``.
-
-        Once PyPI has static dependency metadata available, it would be
-        possible to move the preparation to become a step separated from
-        dependency resolution.
-        """
-        requirement_set = RequirementSet(check_supported_wheels=check_supported_wheels)
-        for req in root_reqs:
-            if req.constraint:
-                check_invalid_constraint_type(req)
-            self._add_requirement_to_set(requirement_set, req)
-
-        # Actually prepare the files, and collect any exceptions. Most hash
-        # exceptions cannot be checked ahead of time, because
-        # _populate_link() needs to be called before we can make decisions
-        # based on link type.
-        discovered_reqs: List[InstallRequirement] = []
-        hash_errors = HashErrors()
-        for req in chain(requirement_set.all_requirements, discovered_reqs):
-            try:
-                discovered_reqs.extend(self._resolve_one(requirement_set, req))
-            except HashError as exc:
-                exc.req = req
-                hash_errors.append(exc)
-
-        if hash_errors:
-            raise hash_errors
-
-        return requirement_set
-
-    def _add_requirement_to_set(
-        self,
-        requirement_set: RequirementSet,
-        install_req: InstallRequirement,
-        parent_req_name: Optional[str] = None,
-        extras_requested: Optional[Iterable[str]] = None,
-    ) -> Tuple[List[InstallRequirement], Optional[InstallRequirement]]:
-        """Add install_req as a requirement to install.
-
-        :param parent_req_name: The name of the requirement that needed this
-            added. The name is used because when multiple unnamed requirements
-            resolve to the same name, we could otherwise end up with dependency
-            links that point outside the Requirements set. parent_req must
-            already be added. Note that None implies that this is a user
-            supplied requirement, vs an inferred one.
-        :param extras_requested: an iterable of extras used to evaluate the
-            environment markers.
-        :return: Additional requirements to scan. That is either [] if
-            the requirement is not applicable, or [install_req] if the
-            requirement is applicable and has just been added.
-        """
-        # If the markers do not match, ignore this requirement.
-        if not install_req.match_markers(extras_requested):
-            logger.info(
-                "Ignoring %s: markers '%s' don't match your environment",
-                install_req.name,
-                install_req.markers,
-            )
-            return [], None
-
-        # If the wheel is not supported, raise an error.
-        # Should check this after filtering out based on environment markers to
-        # allow specifying different wheels based on the environment/OS, in a
-        # single requirements file.
-        if install_req.link and install_req.link.is_wheel:
-            wheel = Wheel(install_req.link.filename)
-            tags = compatibility_tags.get_supported()
-            if requirement_set.check_supported_wheels and not wheel.supported(tags):
-                raise InstallationError(
-                    "{} is not a supported wheel on this platform.".format(
-                        wheel.filename
-                    )
-                )
-
-        # This next bit is really a sanity check.
-        assert (
-            not install_req.user_supplied or parent_req_name is None
-        ), "a user supplied req shouldn't have a parent"
-
-        # Unnamed requirements are scanned again and the requirement won't be
-        # added as a dependency until after scanning.
-        if not install_req.name:
-            requirement_set.add_unnamed_requirement(install_req)
-            return [install_req], None
-
-        try:
-            existing_req: Optional[
-                InstallRequirement
-            ] = requirement_set.get_requirement(install_req.name)
-        except KeyError:
-            existing_req = None
-
-        has_conflicting_requirement = (
-            parent_req_name is None
-            and existing_req
-            and not existing_req.constraint
-            and existing_req.extras == install_req.extras
-            and existing_req.req
-            and install_req.req
-            and existing_req.req.specifier != install_req.req.specifier
-        )
-        if has_conflicting_requirement:
-            raise InstallationError(
-                "Double requirement given: {} (already in {}, name={!r})".format(
-                    install_req, existing_req, install_req.name
-                )
-            )
-
-        # When no existing requirement exists, add the requirement as a
-        # dependency and it will be scanned again after.
-        if not existing_req:
-            requirement_set.add_named_requirement(install_req)
-            # We'd want to rescan this requirement later
-            return [install_req], install_req
-
-        # Assume there's no need to scan, and that we've already
-        # encountered this for scanning.
-        if install_req.constraint or not existing_req.constraint:
-            return [], existing_req
-
-        does_not_satisfy_constraint = install_req.link and not (
-            existing_req.link and install_req.link.path == existing_req.link.path
-        )
-        if does_not_satisfy_constraint:
-            raise InstallationError(
-                "Could not satisfy constraints for '{}': "
-                "installation from path or url cannot be "
-                "constrained to a version".format(install_req.name)
-            )
-        # If we're now installing a constraint, mark the existing
-        # object for real installation.
-        existing_req.constraint = False
-        # If we're now installing a user supplied requirement,
-        # mark the existing object as such.
-        if install_req.user_supplied:
-            existing_req.user_supplied = True
-        existing_req.extras = tuple(
-            sorted(set(existing_req.extras) | set(install_req.extras))
-        )
-        logger.debug(
-            "Setting %s extras to: %s",
-            existing_req,
-            existing_req.extras,
-        )
-        # Return the existing requirement for addition to the parent and
-        # scanning again.
-        return [existing_req], existing_req
-
-    def _is_upgrade_allowed(self, req: InstallRequirement) -> bool:
-        if self.upgrade_strategy == "to-satisfy-only":
-            return False
-        elif self.upgrade_strategy == "eager":
-            return True
-        else:
-            assert self.upgrade_strategy == "only-if-needed"
-            return req.user_supplied or req.constraint
-
-    def _set_req_to_reinstall(self, req: InstallRequirement) -> None:
-        """
-        Set a requirement to be installed.
-        """
-        # Don't uninstall the conflict if doing a user install and the
-        # conflict is not a user install.
-        if not self.use_user_site or req.satisfied_by.in_usersite:
-            req.should_reinstall = True
-        req.satisfied_by = None
-
-    def _check_skip_installed(
-        self, req_to_install: InstallRequirement
-    ) -> Optional[str]:
-        """Check if req_to_install should be skipped.
-
-        This will check if the req is installed, and whether we should upgrade
-        or reinstall it, taking into account all the relevant user options.
-
-        After calling this req_to_install will only have satisfied_by set to
-        None if the req_to_install is to be upgraded/reinstalled etc. Any
-        other value will be a dist recording the current thing installed that
-        satisfies the requirement.
-
-        Note that for vcs urls and the like we can't assess skipping in this
-        routine - we simply identify that we need to pull the thing down,
-        then later on it is pulled down and introspected to assess upgrade/
-        reinstalls etc.
-
-        :return: A text reason for why it was skipped, or None.
-        """
-        if self.ignore_installed:
-            return None
-
-        req_to_install.check_if_exists(self.use_user_site)
-        if not req_to_install.satisfied_by:
-            return None
-
-        if self.force_reinstall:
-            self._set_req_to_reinstall(req_to_install)
-            return None
-
-        if not self._is_upgrade_allowed(req_to_install):
-            if self.upgrade_strategy == "only-if-needed":
-                return "already satisfied, skipping upgrade"
-            return "already satisfied"
-
-        # Check for the possibility of an upgrade.  For link-based
-        # requirements we have to pull the tree down and inspect to assess
-        # the version #, so it's handled way down.
-        if not req_to_install.link:
-            try:
-                self.finder.find_requirement(req_to_install, upgrade=True)
-            except BestVersionAlreadyInstalled:
-                # Then the best version is installed.
-                return "already up-to-date"
-            except DistributionNotFound:
-                # No distribution found, so we squash the error.  It will
-                # be raised later when we re-try later to do the install.
-                # Why don't we just raise here?
-                pass
-
-        self._set_req_to_reinstall(req_to_install)
-        return None
-
-    def _find_requirement_link(self, req: InstallRequirement) -> Optional[Link]:
-        upgrade = self._is_upgrade_allowed(req)
-        best_candidate = self.finder.find_requirement(req, upgrade)
-        if not best_candidate:
-            return None
-
-        # Log a warning per PEP 592 if necessary before returning.
-        link = best_candidate.link
-        if link.is_yanked:
-            reason = link.yanked_reason or "<none given>"
-            msg = (
-                # Mark this as a unicode string to prevent
-                # "UnicodeEncodeError: 'ascii' codec can't encode character"
-                # in Python 2 when the reason contains non-ascii characters.
-                "The candidate selected for download or install is a "
-                "yanked version: {candidate}\n"
-                "Reason for being yanked: {reason}"
-            ).format(candidate=best_candidate, reason=reason)
-            logger.warning(msg)
-
-        return link
-
-    def _populate_link(self, req: InstallRequirement) -> None:
-        """Ensure that if a link can be found for this, that it is found.
-
-        Note that req.link may still be None - if the requirement is already
-        installed and not needed to be upgraded based on the return value of
-        _is_upgrade_allowed().
-
-        If preparer.require_hashes is True, don't use the wheel cache, because
-        cached wheels, always built locally, have different hashes than the
-        files downloaded from the index server and thus throw false hash
-        mismatches. Furthermore, cached wheels at present have undeterministic
-        contents due to file modification times.
-        """
-        if req.link is None:
-            req.link = self._find_requirement_link(req)
-
-        if self.wheel_cache is None or self.preparer.require_hashes:
-            return
-        cache_entry = self.wheel_cache.get_cache_entry(
-            link=req.link,
-            package_name=req.name,
-            supported_tags=get_supported(),
-        )
-        if cache_entry is not None:
-            logger.debug("Using cached wheel link: %s", cache_entry.link)
-            if req.link is req.original_link and cache_entry.persistent:
-                req.cached_wheel_source_link = req.link
-            if cache_entry.origin is not None:
-                req.download_info = cache_entry.origin
-            else:
-                # Legacy cache entry that does not have origin.json.
-                # download_info may miss the archive_info.hashes field.
-                req.download_info = direct_url_from_link(
-                    req.link, link_is_in_wheel_cache=cache_entry.persistent
-                )
-            req.link = cache_entry.link
-
-    def _get_dist_for(self, req: InstallRequirement) -> BaseDistribution:
-        """Takes a InstallRequirement and returns a single AbstractDist \
-        representing a prepared variant of the same.
-        """
-        if req.editable:
-            return self.preparer.prepare_editable_requirement(req)
-
-        # satisfied_by is only evaluated by calling _check_skip_installed,
-        # so it must be None here.
-        assert req.satisfied_by is None
-        skip_reason = self._check_skip_installed(req)
-
-        if req.satisfied_by:
-            return self.preparer.prepare_installed_requirement(req, skip_reason)
-
-        # We eagerly populate the link, since that's our "legacy" behavior.
-        self._populate_link(req)
-        dist = self.preparer.prepare_linked_requirement(req)
-
-        # NOTE
-        # The following portion is for determining if a certain package is
-        # going to be re-installed/upgraded or not and reporting to the user.
-        # This should probably get cleaned up in a future refactor.
-
-        # req.req is only avail after unpack for URL
-        # pkgs repeat check_if_exists to uninstall-on-upgrade
-        # (#14)
-        if not self.ignore_installed:
-            req.check_if_exists(self.use_user_site)
-
-        if req.satisfied_by:
-            should_modify = (
-                self.upgrade_strategy != "to-satisfy-only"
-                or self.force_reinstall
-                or self.ignore_installed
-                or req.link.scheme == "file"
-            )
-            if should_modify:
-                self._set_req_to_reinstall(req)
-            else:
-                logger.info(
-                    "Requirement already satisfied (use --upgrade to upgrade): %s",
-                    req,
-                )
-        return dist
-
-    def _resolve_one(
-        self,
-        requirement_set: RequirementSet,
-        req_to_install: InstallRequirement,
-    ) -> List[InstallRequirement]:
-        """Prepare a single requirements file.
-
-        :return: A list of additional InstallRequirements to also install.
-        """
-        # Tell user what we are doing for this requirement:
-        # obtain (editable), skipping, processing (local url), collecting
-        # (remote url or package name)
-        if req_to_install.constraint or req_to_install.prepared:
-            return []
-
-        req_to_install.prepared = True
-
-        # Parse and return dependencies
-        dist = self._get_dist_for(req_to_install)
-        # This will raise UnsupportedPythonVersion if the given Python
-        # version isn't compatible with the distribution's Requires-Python.
-        _check_dist_requires_python(
-            dist,
-            version_info=self._py_version_info,
-            ignore_requires_python=self.ignore_requires_python,
-        )
-
-        more_reqs: List[InstallRequirement] = []
-
-        def add_req(subreq: Requirement, extras_requested: Iterable[str]) -> None:
-            # This idiosyncratically converts the Requirement to str and let
-            # make_install_req then parse it again into Requirement. But this is
-            # the legacy resolver so I'm just not going to bother refactoring.
-            sub_install_req = self._make_install_req(str(subreq), req_to_install)
-            parent_req_name = req_to_install.name
-            to_scan_again, add_to_parent = self._add_requirement_to_set(
-                requirement_set,
-                sub_install_req,
-                parent_req_name=parent_req_name,
-                extras_requested=extras_requested,
-            )
-            if parent_req_name and add_to_parent:
-                self._discovered_dependencies[parent_req_name].append(add_to_parent)
-            more_reqs.extend(to_scan_again)
-
-        with indent_log():
-            # We add req_to_install before its dependencies, so that we
-            # can refer to it when adding dependencies.
-            if not requirement_set.has_requirement(req_to_install.name):
-                # 'unnamed' requirements will get added here
-                # 'unnamed' requirements can only come from being directly
-                # provided by the user.
-                assert req_to_install.user_supplied
-                self._add_requirement_to_set(
-                    requirement_set, req_to_install, parent_req_name=None
-                )
-
-            if not self.ignore_dependencies:
-                if req_to_install.extras:
-                    logger.debug(
-                        "Installing extra requirements: %r",
-                        ",".join(req_to_install.extras),
-                    )
-                missing_requested = sorted(
-                    set(req_to_install.extras) - set(dist.iter_provided_extras())
-                )
-                for missing in missing_requested:
-                    logger.warning(
-                        "%s %s does not provide the extra '%s'",
-                        dist.raw_name,
-                        dist.version,
-                        missing,
-                    )
-
-                available_requested = sorted(
-                    set(dist.iter_provided_extras()) & set(req_to_install.extras)
-                )
-                for subreq in dist.iter_dependencies(available_requested):
-                    add_req(subreq, extras_requested=available_requested)
-
-        return more_reqs
-
-    def get_installation_order(
-        self, req_set: RequirementSet
-    ) -> List[InstallRequirement]:
-        """Create the installation order.
-
-        The installation order is topological - requirements are installed
-        before the requiring thing. We break cycles at an arbitrary point,
-        and make no other guarantees.
-        """
-        # The current implementation, which we may change at any point
-        # installs the user specified things in the order given, except when
-        # dependencies must come earlier to achieve topological order.
-        order = []
-        ordered_reqs: Set[InstallRequirement] = set()
-
-        def schedule(req: InstallRequirement) -> None:
-            if req.satisfied_by or req in ordered_reqs:
-                return
-            if req.constraint:
-                return
-            ordered_reqs.add(req)
-            for dep in self._discovered_dependencies[req.name]:
-                schedule(dep)
-            order.append(req)
-
-        for install_req in req_set.requirements.values():
-            schedule(install_req)
-        return order

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/plugin.py

@@ -1,88 +0,0 @@
-"""
-    pygments.plugin
-    ~~~~~~~~~~~~~~~
-
-    Pygments plugin interface. By default, this tries to use
-    ``importlib.metadata``, which is in the Python standard
-    library since Python 3.8, or its ``importlib_metadata``
-    backport for earlier versions of Python. It falls back on
-    ``pkg_resources`` if not found. Finally, if ``pkg_resources``
-    is not found either, no plugins are loaded at all.
-
-    lexer plugins::
-
-        [pygments.lexers]
-        yourlexer = yourmodule:YourLexer
-
-    formatter plugins::
-
-        [pygments.formatters]
-        yourformatter = yourformatter:YourFormatter
-        /.ext = yourformatter:YourFormatter
-
-    As you can see, you can define extensions for the formatter
-    with a leading slash.
-
-    syntax plugins::
-
-        [pygments.styles]
-        yourstyle = yourstyle:YourStyle
-
-    filter plugin::
-
-        [pygments.filter]
-        yourfilter = yourfilter:YourFilter
-
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-LEXER_ENTRY_POINT = 'pygments.lexers'
-FORMATTER_ENTRY_POINT = 'pygments.formatters'
-STYLE_ENTRY_POINT = 'pygments.styles'
-FILTER_ENTRY_POINT = 'pygments.filters'
-
-
-def iter_entry_points(group_name):
-    try:
-        from importlib.metadata import entry_points
-    except ImportError:
-        try:
-            from importlib_metadata import entry_points
-        except ImportError:
-            try:
-                from pip._vendor.pkg_resources import iter_entry_points
-            except (ImportError, OSError):
-                return []
-            else:
-                return iter_entry_points(group_name)
-    groups = entry_points()
-    if hasattr(groups, 'select'):
-        # New interface in Python 3.10 and newer versions of the
-        # importlib_metadata backport.
-        return groups.select(group=group_name)
-    else:
-        # Older interface, deprecated in Python 3.10 and recent
-        # importlib_metadata, but we need it in Python 3.8 and 3.9.
-        return groups.get(group_name, [])
-
-
-def find_plugin_lexers():
-    for entrypoint in iter_entry_points(LEXER_ENTRY_POINT):
-        yield entrypoint.load()
-
-
-def find_plugin_formatters():
-    for entrypoint in iter_entry_points(FORMATTER_ENTRY_POINT):
-        yield entrypoint.name, entrypoint.load()
-
-
-def find_plugin_styles():
-    for entrypoint in iter_entry_points(STYLE_ENTRY_POINT):
-        yield entrypoint.name, entrypoint.load()
-
-
-def find_plugin_filters():
-    for entrypoint in iter_entry_points(FILTER_ENTRY_POINT):
-        yield entrypoint.name, entrypoint.load()

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/exceptions.py

@@ -1,267 +0,0 @@
-# exceptions.py
-
-import re
-import sys
-import typing
-
-from .util import col, line, lineno, _collapse_string_to_ranges
-from .unicode import pyparsing_unicode as ppu
-
-
-class ExceptionWordUnicode(ppu.Latin1, ppu.LatinA, ppu.LatinB, ppu.Greek, ppu.Cyrillic):
-    pass
-
-
-_extract_alphanums = _collapse_string_to_ranges(ExceptionWordUnicode.alphanums)
-_exception_word_extractor = re.compile("([" + _extract_alphanums + "]{1,16})|.")
-
-
-class ParseBaseException(Exception):
-    """base exception class for all parsing runtime exceptions"""
-
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__(
-        self,
-        pstr: str,
-        loc: int = 0,
-        msg: typing.Optional[str] = None,
-        elem=None,
-    ):
-        self.loc = loc
-        if msg is None:
-            self.msg = pstr
-            self.pstr = ""
-        else:
-            self.msg = msg
-            self.pstr = pstr
-        self.parser_element = self.parserElement = elem
-        self.args = (pstr, loc, msg)
-
-    @staticmethod
-    def explain_exception(exc, depth=16):
-        """
-        Method to take an exception and translate the Python internal traceback into a list
-        of the pyparsing expressions that caused the exception to be raised.
-
-        Parameters:
-
-        - exc - exception raised during parsing (need not be a ParseException, in support
-          of Python exceptions that might be raised in a parse action)
-        - depth (default=16) - number of levels back in the stack trace to list expression
-          and function names; if None, the full stack trace names will be listed; if 0, only
-          the failing input line, marker, and exception string will be shown
-
-        Returns a multi-line string listing the ParserElements and/or function names in the
-        exception's stack trace.
-        """
-        import inspect
-        from .core import ParserElement
-
-        if depth is None:
-            depth = sys.getrecursionlimit()
-        ret = []
-        if isinstance(exc, ParseBaseException):
-            ret.append(exc.line)
-            ret.append(" " * (exc.column - 1) + "^")
-        ret.append("{}: {}".format(type(exc).__name__, exc))
-
-        if depth > 0:
-            callers = inspect.getinnerframes(exc.__traceback__, context=depth)
-            seen = set()
-            for i, ff in enumerate(callers[-depth:]):
-                frm = ff[0]
-
-                f_self = frm.f_locals.get("self", None)
-                if isinstance(f_self, ParserElement):
-                    if frm.f_code.co_name not in ("parseImpl", "_parseNoCache"):
-                        continue
-                    if id(f_self) in seen:
-                        continue
-                    seen.add(id(f_self))
-
-                    self_type = type(f_self)
-                    ret.append(
-                        "{}.{} - {}".format(
-                            self_type.__module__, self_type.__name__, f_self
-                        )
-                    )
-
-                elif f_self is not None:
-                    self_type = type(f_self)
-                    ret.append("{}.{}".format(self_type.__module__, self_type.__name__))
-
-                else:
-                    code = frm.f_code
-                    if code.co_name in ("wrapper", "<module>"):
-                        continue
-
-                    ret.append("{}".format(code.co_name))
-
-                depth -= 1
-                if not depth:
-                    break
-
-        return "\n".join(ret)
-
-    @classmethod
-    def _from_exception(cls, pe):
-        """
-        internal factory method to simplify creating one type of ParseException
-        from another - avoids having __init__ signature conflicts among subclasses
-        """
-        return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement)
-
-    @property
-    def line(self) -> str:
-        """
-        Return the line of text where the exception occurred.
-        """
-        return line(self.loc, self.pstr)
-
-    @property
-    def lineno(self) -> int:
-        """
-        Return the 1-based line number of text where the exception occurred.
-        """
-        return lineno(self.loc, self.pstr)
-
-    @property
-    def col(self) -> int:
-        """
-        Return the 1-based column on the line of text where the exception occurred.
-        """
-        return col(self.loc, self.pstr)
-
-    @property
-    def column(self) -> int:
-        """
-        Return the 1-based column on the line of text where the exception occurred.
-        """
-        return col(self.loc, self.pstr)
-
-    def __str__(self) -> str:
-        if self.pstr:
-            if self.loc >= len(self.pstr):
-                foundstr = ", found end of text"
-            else:
-                # pull out next word at error location
-                found_match = _exception_word_extractor.match(self.pstr, self.loc)
-                if found_match is not None:
-                    found = found_match.group(0)
-                else:
-                    found = self.pstr[self.loc : self.loc + 1]
-                foundstr = (", found %r" % found).replace(r"\\", "\\")
-        else:
-            foundstr = ""
-        return "{}{}  (at char {}), (line:{}, col:{})".format(
-            self.msg, foundstr, self.loc, self.lineno, self.column
-        )
-
-    def __repr__(self):
-        return str(self)
-
-    def mark_input_line(self, marker_string: str = None, *, markerString=">!<") -> str:
-        """
-        Extracts the exception line from the input string, and marks
-        the location of the exception with a special symbol.
-        """
-        markerString = marker_string if marker_string is not None else markerString
-        line_str = self.line
-        line_column = self.column - 1
-        if markerString:
-            line_str = "".join(
-                (line_str[:line_column], markerString, line_str[line_column:])
-            )
-        return line_str.strip()
-
-    def explain(self, depth=16) -> str:
-        """
-        Method to translate the Python internal traceback into a list
-        of the pyparsing expressions that caused the exception to be raised.
-
-        Parameters:
-
-        - depth (default=16) - number of levels back in the stack trace to list expression
-          and function names; if None, the full stack trace names will be listed; if 0, only
-          the failing input line, marker, and exception string will be shown
-
-        Returns a multi-line string listing the ParserElements and/or function names in the
-        exception's stack trace.
-
-        Example::
-
-            expr = pp.Word(pp.nums) * 3
-            try:
-                expr.parse_string("123 456 A789")
-            except pp.ParseException as pe:
-                print(pe.explain(depth=0))
-
-        prints::
-
-            123 456 A789
-                    ^
-            ParseException: Expected W:(0-9), found 'A'  (at char 8), (line:1, col:9)
-
-        Note: the diagnostic output will include string representations of the expressions
-        that failed to parse. These representations will be more helpful if you use `set_name` to
-        give identifiable names to your expressions. Otherwise they will use the default string
-        forms, which may be cryptic to read.
-
-        Note: pyparsing's default truncation of exception tracebacks may also truncate the
-        stack of expressions that are displayed in the ``explain`` output. To get the full listing
-        of parser expressions, you may have to set ``ParserElement.verbose_stacktrace = True``
-        """
-        return self.explain_exception(self, depth)
-
-    markInputline = mark_input_line
-
-
-class ParseException(ParseBaseException):
-    """
-    Exception thrown when a parse expression doesn't match the input string
-
-    Example::
-
-        try:
-            Word(nums).set_name("integer").parse_string("ABC")
-        except ParseException as pe:
-            print(pe)
-            print("column: {}".format(pe.column))
-
-    prints::
-
-       Expected integer (at char 0), (line:1, col:1)
-        column: 1
-
-    """
-
-
-class ParseFatalException(ParseBaseException):
-    """
-    User-throwable exception thrown when inconsistent parse content
-    is found; stops all parsing immediately
-    """
-
-
-class ParseSyntaxException(ParseFatalException):
-    """
-    Just like :class:`ParseFatalException`, but thrown internally
-    when an :class:`ErrorStop<And._ErrorStop>` ('-' operator) indicates
-    that parsing is to stop immediately because an unbacktrackable
-    syntax error has been found.
-    """
-
-
-class RecursiveGrammarException(Exception):
-    """
-    Exception thrown by :class:`ParserElement.validate` if the
-    grammar could be left-recursive; parser may need to enable
-    left recursion using :class:`ParserElement.enable_left_recursion<ParserElement.enable_left_recursion>`
-    """
-
-    def __init__(self, parseElementList):
-        self.parseElementTrace = parseElementList
-
-    def __str__(self) -> str:
-        return "RecursiveGrammarException: {}".format(self.parseElementTrace)

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/dataset.py

@@ -1,210 +0,0 @@
-from __future__ import print_function
-import os
-import json
-# import cPickle
-import _pickle as cPickle
-import numpy as np
-import utils
-import h5py
-import torch
-from torch.utils.data import Dataset
-
-
-class Dictionary(object):
-    def __init__(self, word2idx=None, idx2word=None):
-        if word2idx is None:
-            word2idx = {}
-        if idx2word is None:
-            idx2word = []
-        self.word2idx = word2idx
-        self.idx2word = idx2word
-
-    @property
-    def ntoken(self):
-        return len(self.word2idx)
-
-    @property
-    def padding_idx(self):
-        return len(self.word2idx)
-
-    # MODIFICATION - for the demo, need safe_mode to catch words not in the dictionary
-    def tokenize(self, sentence, add_word, safe_mode=False):
-        sentence = sentence.lower()
-        sentence = sentence.replace(',', '').replace('?', '').replace('\'s', ' \'s')
-        words = sentence.split()
-        tokens = []
-        if add_word:
-            for w in words:
-                tokens.append(self.add_word(w))
-        elif safe_mode:
-            for w in words:
-                if w in self.word2idx:
-                    tokens.append(self.word2idx[w])
-        else:
-            for w in words:
-                tokens.append(self.word2idx[w])
-        return tokens
-
-    def dump_to_file(self, path):
-        cPickle.dump([self.word2idx, self.idx2word], open(path, 'wb'))
-        print('dictionary dumped to %s' % path)
-
-    @classmethod
-    def load_from_file(cls, path):
-        print('loading dictionary from %s' % path)
-        word2idx, idx2word = cPickle.load(open(path, 'rb'))
-        d = cls(word2idx, idx2word)
-        return d
-
-    def add_word(self, word):
-        if word not in self.word2idx:
-            self.idx2word.append(word)
-            self.word2idx[word] = len(self.idx2word) - 1
-        return self.word2idx[word]
-
-    def __len__(self):
-        return len(self.idx2word)
-
-
-def _create_entry(img, question, answer):
-    answer.pop('image_id')
-    answer.pop('question_id')
-    entry = {
-        'question_id' : question['question_id'],
-        'image_id'    : question['image_id'],
-        'image'       : img,
-        'question'    : question['question'],
-        'answer'      : answer}
-    return entry
-
-
-def _load_dataset(dataroot, name, img_id2val):
-    """Load entries
-
-    img_id2val: dict {img_id -> val} val can be used to retrieve image or features
-    dataroot: root path of dataset
-    name: 'train', 'val'
-    """
-    question_path = os.path.join(
-        dataroot, 'v2_OpenEnded_mscoco_%s2014_questions.json' % name)
-    questions = sorted(json.load(open(question_path))['questions'],
-                       key=lambda x: x['question_id'])
-    answer_path = os.path.join(dataroot, 'cache', '%s_target.pkl' % name)
-    answers = cPickle.load(open(answer_path, 'rb'))
-    answers = sorted(answers, key=lambda x: x['question_id'])
-
-    utils.assert_eq(len(questions), len(answers))
-    entries = []
-    for question, answer in zip(questions, answers):
-        utils.assert_eq(question['question_id'], answer['question_id'])
-        utils.assert_eq(question['image_id'], answer['image_id'])
-        img_id = question['image_id']
-        entries.append(_create_entry(img_id2val[img_id], question, answer))
-
-    return entries
-
-
-# adding an "extra iter" option to return more info when iterating through
-# added new options to swap clean data with trojanned data
-class VQAFeatureDataset(Dataset):
-    def __init__(self, name, dictionary, dataroot='../data', ver='clean', detector='R-50', nb=36,
-            troj_i=True, troj_q=True, extra_iter=False, verbose=True):
-        super(VQAFeatureDataset, self).__init__()
-        assert name in ['train', 'val']
-
-        self.extra_iter = extra_iter
-        self.troj_i = troj_i
-        self.troj_q = troj_q
-        if ver == 'clean':
-            self.troj_i = False
-            self.troj_q = False
-
-        ans2label_path = os.path.join(dataroot, ver, 'cache', 'trainval_ans2label.pkl')
-        label2ans_path = os.path.join(dataroot, ver, 'cache', 'trainval_label2ans.pkl')
-        self.ans2label = cPickle.load(open(ans2label_path, 'rb'))
-        self.label2ans = cPickle.load(open(label2ans_path, 'rb'))
-        self.num_ans_candidates = len(self.ans2label)
-
-        self.dictionary = dictionary
-
-        if self.troj_i:
-            if verbose: print('%s image data is troj (%s)'%(name, ver))
-            self.img_id2idx = cPickle.load(open(os.path.join(dataroot, ver, '%s_%s_%i_imgid2idx.pkl' % (name, detector, nb)), 'rb'))
-            h5_path = os.path.join(dataroot, ver, '%s_%s_%i.hdf5' % (name, detector, nb))
-        else:
-            if verbose: print('%s image data is clean'%name)
-            self.img_id2idx = cPickle.load(open(os.path.join(dataroot, 'clean', '%s_%s_%i_imgid2idx.pkl' % (name, detector, nb)), 'rb'))
-            h5_path = os.path.join(dataroot, 'clean', '%s_%s_%i.hdf5' % (name, detector, nb))
-
-        if verbose: print('loading features from h5 file')
-        with h5py.File(h5_path, 'r') as hf:
-            self.features = np.array(hf.get('image_features'))
-            self.spatials = np.array(hf.get('spatial_features'))
-
-        if self.troj_q:
-            if verbose: print('%s question data is troj (%s)'%(name, ver))
-            self.entries = _load_dataset(os.path.join(dataroot, ver), name, self.img_id2idx)
-        else:
-            if verbose: print('%s question data is clean'%name)
-            self.entries = _load_dataset(os.path.join(dataroot, 'clean'), name, self.img_id2idx)
-
-        self.tokenize()
-        self.tensorize()
-        self.v_dim = self.features.size(2)
-        self.s_dim = self.spatials.size(2)
-
-    def tokenize(self, max_length=14):
-        """Tokenizes the questions.
-
-        This will add q_token in each entry of the dataset.
-        -1 represent nil, and should be treated as padding_idx in embedding
-        """
-        for entry in self.entries:
-            tokens = self.dictionary.tokenize(entry['question'], False)
-            tokens = tokens[:max_length]
-            if len(tokens) < max_length:
-                # Note here we pad in front of the sentence
-                padding = [self.dictionary.padding_idx] * (max_length - len(tokens))
-                tokens = padding + tokens
-            utils.assert_eq(len(tokens), max_length)
-            entry['q_token'] = tokens
-
-    def tensorize(self):
-        self.features = torch.from_numpy(self.features)
-        self.spatials = torch.from_numpy(self.spatials)
-
-        for entry in self.entries:
-            question = torch.from_numpy(np.array(entry['q_token']))
-            entry['q_token'] = question
-
-            answer = entry['answer']
-            labels = np.array(answer['labels'])
-            scores = np.array(answer['scores'], dtype=np.float32)
-            if len(labels):
-                labels = torch.from_numpy(labels)
-                scores = torch.from_numpy(scores)
-                entry['answer']['labels'] = labels
-                entry['answer']['scores'] = scores
-            else:
-                entry['answer']['labels'] = None
-                entry['answer']['scores'] = None
-
-    def __getitem__(self, index):
-        entry = self.entries[index]
-        features = self.features[entry['image']]
-        spatials = self.spatials[entry['image']]
-
-        question = entry['q_token']
-        answer = entry['answer']
-        labels = answer['labels']
-        scores = answer['scores']
-        target = torch.zeros(self.num_ans_candidates)
-        if labels is not None:
-            target.scatter_(0, labels, scores)
-
-        if self.extra_iter:
-            return features, spatials, question, target, entry['question_id']
-        return features, spatials, question, target
-
-    def __len__(self):
-        return len(self.entries)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/README.md

@@ -1,9 +0,0 @@
-
-This directory contains:
-
-1. A script that converts a detectron2 model to caffe2 format.
-
-2. An example that loads a Mask R-CNN model in caffe2 format and runs inference.
-
-See [tutorial](https://detectron2.readthedocs.io/tutorials/deployment.html)
-for their usage.

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa_inference_wrapper.py

@@ -1,153 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Inference wrapper for trained OpenVQA models
-=========================================================================================
-"""
-import yaml, os, torch, re, json
-import numpy as np
-import torch.nn as nn
-
-from openvqa.models.model_loader import ModelLoader
-from openvqa.models.model_loader import CfgLoader
-
-
-root = os.path.dirname(os.path.realpath(__file__))
-
-
-# Helper to replace argparse for loading proper inference settings
-class Openvqa_Args_Like():
-    def __init__(self, model_type, model_path, nb, over_fs=1024, gpu='0'):
-        self.RUN_MODE = 'val'
-        self.MODEL = model_type
-        self.DATASET = 'vqa'
-        self.SPLIT = 'train'
-        self.BS = 64
-        self.GPU = gpu
-        self.SEED = 1234
-        self.VERSION = 'temp'
-        self.RESUME = 'True'
-        self.CKPT_V = ''
-        self.CKPT_E = ''
-        self.CKPT_PATH = model_path
-        self.NUM_WORKERS = 1
-        self.PINM = 'True'
-        self.VERBOSE = 'False'
-        self.DETECTOR = ''
-        self.OVER_FS = over_fs
-        self.OVER_NB = int(nb)
-
-
-
-# Wrapper for inference with a pre-trained OpenVQA model. During init, user specifies
-# the model type, model file (.pkl) path, the number of input image
-# features, and optionally the feature size and gpu to run on. The function 'run' can
-# then run inference on two simple inputs: an image feature tensor, and a question
-# given as a string.
-class Openvqa_Wrapper():
-    def __init__(self, model_type, model_path, nb, over_fs=1024, gpu='0'):
-        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        # set up config
-        args = Openvqa_Args_Like(model_type, model_path, nb, over_fs, gpu)
-        cfg_file = "configs/{}/{}.yml".format(args.DATASET, args.MODEL)
-        if not os.path.isfile(cfg_file):
-            cfg_file = "{}/configs/{}/{}.yml".format(root, args.DATASET, args.MODEL)
-        with open(cfg_file, 'r') as f:
-            yaml_dict = yaml.load(f)
-        __C = CfgLoader(yaml_dict['MODEL_USE']).load()
-        args = __C.str_to_bool(args)
-        args_dict = __C.parse_to_dict(args)
-        args_dict = {**yaml_dict, **args_dict}
-        __C.add_args(args_dict)
-        __C.proc(check_path=False)
-        # override feature size
-        if __C.OVER_FS != -1 or __C.OVER_NB != -1:
-            NEW_FS = 2048
-            NEW_NB = 100
-            if __C.OVER_FS != -1:
-                print('Overriding feature size to: ' + str(__C.OVER_FS))
-                NEW_FS = __C.OVER_FS
-                __C.IMG_FEAT_SIZE = NEW_FS
-            if __C.OVER_NB != -1:
-                print('Overriding number of boxes to: ' + str(__C.OVER_NB))
-                NEW_NB = __C.OVER_NB
-            __C.FEAT_SIZE['vqa']['FRCN_FEAT_SIZE'] = (NEW_NB, NEW_FS)
-            __C.FEAT_SIZE['vqa']['BBOX_FEAT_SIZE'] = (NEW_NB, 5)
-        # update path information
-        __C.update_paths()
-
-        # prep
-        token_size = 20573
-        ans_size = 3129
-        pretrained_emb = np.zeros([token_size, 300], dtype=np.float32)
-
-        # load network
-        net = ModelLoader(__C).Net(
-            __C,
-            pretrained_emb,
-            token_size,
-            ans_size
-        )
-        net.to(self.device)
-        net.eval()
-        if __C.N_GPU > 1:
-            net = nn.DataParallel(net, device_ids=__C.DEVICES)
-
-        # Load checkpoint
-        print(' ========== Loading checkpoint')
-        print('Loading ckpt from {}'.format(model_path))
-        ckpt = torch.load(model_path, map_location=self.device)
-        print('Finish!')
-        if __C.N_GPU > 1:
-            net.load_state_dict(ckpt_proc(ckpt['state_dict']))
-        else:
-            net.load_state_dict(ckpt['state_dict'])
-        self.model = net
-
-        # Load tokenizer, and answers
-        token_file = '{}/openvqa/datasets/vqa/token_dict.json'.format(root)
-        self.token_to_ix = json.load(open(token_file, 'r'))
-        ans_dict = '{}/openvqa/datasets/vqa/answer_dict.json'.format(root)
-        ans_to_ix = json.load(open(ans_dict, 'r'))[0]
-        self.ix_to_ans = {}
-        for key in ans_to_ix:
-            self.ix_to_ans[ans_to_ix[key]] = key
-
-
-
-    # based on version in vqa_loader.py
-    def proc_ques(self, ques, token_to_ix, max_token):
-        ques_ix = np.zeros(max_token, np.int64)
-        words = re.sub(
-            r"([.,'!?\"()*#:;])",
-            '',
-            ques.lower()
-        ).replace('-', ' ').replace('/', ' ').split()
-        for ix, word in enumerate(words):
-            if word in token_to_ix:
-                ques_ix[ix] = token_to_ix[word]
-            else:
-                ques_ix[ix] = token_to_ix['UNK']
-            if ix + 1 == max_token:
-                break
-        return ques_ix
-
-
-
-    # inputs are a tensor of image features, shape [nb, 1024]
-    # and a raw question in string form. bbox features input is only used
-    # by mmnasnet models.
-    def run(self, image_features, raw_question, bbox_features):
-        ques_ix = self.proc_ques(raw_question, self.token_to_ix, max_token=14)
-        frcn_feat_iter = torch.unsqueeze(image_features, 0).to(self.device)
-        grid_feat_iter = torch.zeros(1).to(self.device)
-        bbox_feat_iter = torch.unsqueeze(bbox_features, 0).to(self.device)
-        ques_ix_iter = torch.unsqueeze(torch.from_numpy(ques_ix),0).to(self.device)
-        pred = self.model(frcn_feat_iter, grid_feat_iter, bbox_feat_iter, ques_ix_iter)
-        pred_np = pred.cpu().data.numpy()
-        pred_argmax = np.argmax(pred_np, axis=1)
-        ans = self.ix_to_ans[pred_argmax[0]]
-        return ans
-        

spaces/CVPR/LIVE/pybind11/tests/test_pickling.cpp

@@ -1,130 +0,0 @@
-/*
-    tests/test_pickling.cpp -- pickle support
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-
-TEST_SUBMODULE(pickling, m) {
-    // test_roundtrip
-    class Pickleable {
-    public:
-        Pickleable(const std::string &value) : m_value(value) { }
-        const std::string &value() const { return m_value; }
-
-        void setExtra1(int extra1) { m_extra1 = extra1; }
-        void setExtra2(int extra2) { m_extra2 = extra2; }
-        int extra1() const { return m_extra1; }
-        int extra2() const { return m_extra2; }
-    private:
-        std::string m_value;
-        int m_extra1 = 0;
-        int m_extra2 = 0;
-    };
-
-    class PickleableNew : public Pickleable {
-    public:
-        using Pickleable::Pickleable;
-    };
-
-    py::class_<Pickleable>(m, "Pickleable")
-        .def(py::init<std::string>())
-        .def("value", &Pickleable::value)
-        .def("extra1", &Pickleable::extra1)
-        .def("extra2", &Pickleable::extra2)
-        .def("setExtra1", &Pickleable::setExtra1)
-        .def("setExtra2", &Pickleable::setExtra2)
-        // For details on the methods below, refer to
-        // http://docs.python.org/3/library/pickle.html#pickling-class-instances
-        .def("__getstate__", [](const Pickleable &p) {
-            /* Return a tuple that fully encodes the state of the object */
-            return py::make_tuple(p.value(), p.extra1(), p.extra2());
-        })
-        .def("__setstate__", [](Pickleable &p, py::tuple t) {
-            if (t.size() != 3)
-                throw std::runtime_error("Invalid state!");
-            /* Invoke the constructor (need to use in-place version) */
-            new (&p) Pickleable(t[0].cast<std::string>());
-
-            /* Assign any additional state */
-            p.setExtra1(t[1].cast<int>());
-            p.setExtra2(t[2].cast<int>());
-        });
-
-    py::class_<PickleableNew, Pickleable>(m, "PickleableNew")
-        .def(py::init<std::string>())
-        .def(py::pickle(
-            [](const PickleableNew &p) {
-                return py::make_tuple(p.value(), p.extra1(), p.extra2());
-            },
-            [](py::tuple t) {
-                if (t.size() != 3)
-                    throw std::runtime_error("Invalid state!");
-                auto p = PickleableNew(t[0].cast<std::string>());
-
-                p.setExtra1(t[1].cast<int>());
-                p.setExtra2(t[2].cast<int>());
-                return p;
-            }
-        ));
-
-#if !defined(PYPY_VERSION)
-    // test_roundtrip_with_dict
-    class PickleableWithDict {
-    public:
-        PickleableWithDict(const std::string &value) : value(value) { }
-
-        std::string value;
-        int extra;
-    };
-
-    class PickleableWithDictNew : public PickleableWithDict {
-    public:
-        using PickleableWithDict::PickleableWithDict;
-    };
-
-    py::class_<PickleableWithDict>(m, "PickleableWithDict", py::dynamic_attr())
-        .def(py::init<std::string>())
-        .def_readwrite("value", &PickleableWithDict::value)
-        .def_readwrite("extra", &PickleableWithDict::extra)
-        .def("__getstate__", [](py::object self) {
-            /* Also include __dict__ in state */
-            return py::make_tuple(self.attr("value"), self.attr("extra"), self.attr("__dict__"));
-        })
-        .def("__setstate__", [](py::object self, py::tuple t) {
-            if (t.size() != 3)
-                throw std::runtime_error("Invalid state!");
-            /* Cast and construct */
-            auto& p = self.cast<PickleableWithDict&>();
-            new (&p) PickleableWithDict(t[0].cast<std::string>());
-
-            /* Assign C++ state */
-            p.extra = t[1].cast<int>();
-
-            /* Assign Python state */
-            self.attr("__dict__") = t[2];
-        });
-
-    py::class_<PickleableWithDictNew, PickleableWithDict>(m, "PickleableWithDictNew")
-        .def(py::init<std::string>())
-        .def(py::pickle(
-            [](py::object self) {
-                return py::make_tuple(self.attr("value"), self.attr("extra"), self.attr("__dict__"));
-            },
-            [](const py::tuple &t) {
-                if (t.size() != 3)
-                    throw std::runtime_error("Invalid state!");
-
-                auto cpp_state = PickleableWithDictNew(t[0].cast<std::string>());
-                cpp_state.extra = t[1].cast<int>();
-
-                auto py_state = t[2].cast<py::dict>();
-                return std::make_pair(cpp_state, py_state);
-            }
-        ));
-#endif
-}

spaces/CVPR/LIVE/thrust/thrust/mismatch.h

@@ -1,260 +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.
- */
-
-
-/*! \file mismatch.h
- *  \brief Search for differences between ranges
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup algorithms
- */
-
-/*! \addtogroup searching
- *  \ingroup algorithms
- *  \{
- */
-
-
-/*! \p mismatch finds the first position where the two ranges <tt>[first1, last1)</tt>
- *  and <tt>[first2, first2 + (last1 - first1))</tt> differ. The two versions of 
- *  \p mismatch use different tests for whether elements differ.
- *
- *  This version of \p mismatch finds the first iterator \c i in <tt>[first1, last1)</tt>
- *  such that <tt>*i == *(first2 + (i - first1))</tt> is \c false. The return value is a
- *  \c pair whose first element is \c i and whose second element is <tt>*(first2 + (i - first1))</tt>.
- *  If no such iterator \c i exists, the return value is a \c pair whose first element
- *  is \c last1 and whose second element is <tt>*(first2 + (last1 - first1))</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \return The first position where the sequences differ.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and \p InputIterator1's \c value_type is equality comparable to \p InputIterator2's \c value_type.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *
- *  \code
- *  #include <thrust/mismatch.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> vec1(4);
- *  thrust::device_vector<int> vec2(4);
- *
- *  vec1[0] = 0;  vec2[0] = 0; 
- *  vec1[1] = 5;  vec2[1] = 5;
- *  vec1[2] = 3;  vec2[2] = 8;
- *  vec1[3] = 7;  vec2[3] = 7;
- *
- *  typedef thrust::device_vector<int>::iterator Iterator;
- *  thrust::pair<Iterator,Iterator> result;
- *
- *  result = thrust::mismatch(thrust::device, vec1.begin(), vec1.end(), vec2.begin());
- *
- *  // result.first  is vec1.begin() + 2
- *  // result.second is vec2.begin() + 2
- *  \endcode
- *
- *  \see find
- *  \see find_if
- */
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2>
-__host__ __device__
-thrust::pair<InputIterator1, InputIterator2> mismatch(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                                                      InputIterator1 first1,
-                                                      InputIterator1 last1,
-                                                      InputIterator2 first2);
-
-
-/*! \p mismatch finds the first position where the two ranges <tt>[first1, last1)</tt>
- * and <tt>[first2, first2 + (last1 - first1))</tt> differ. The two versions of 
- * \p mismatch use different tests for whether elements differ.
- *
- * This version of \p mismatch finds the first iterator \c i in <tt>[first1, last1)</tt>
- * such that <tt>*i == *(first2 + (i - first1))</tt> is \c false. The return value is a
- * \c pair whose first element is \c i and whose second element is <tt>*(first2 + (i - first1))</tt>.
- * If no such iterator \c i exists, the return value is a \c pair whose first element
- * is \c last1 and whose second element is <tt>*(first2 + (last1 - first1))</tt>.
- *
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \return The first position where the sequences differ.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and \p InputIterator1's \c value_type is equality comparable to \p InputIterator2's \c value_type.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *
- *  \code
- *  #include <thrust/mismatch.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> vec1(4);
- *  thrust::device_vector<int> vec2(4);
- *
- *  vec1[0] = 0;  vec2[0] = 0; 
- *  vec1[1] = 5;  vec2[1] = 5;
- *  vec1[2] = 3;  vec2[2] = 8;
- *  vec1[3] = 7;  vec2[3] = 7;
- *
- *  typedef thrust::device_vector<int>::iterator Iterator;
- *  thrust::pair<Iterator,Iterator> result;
- *
- *  result = thrust::mismatch(vec1.begin(), vec1.end(), vec2.begin());
- *
- *  // result.first  is vec1.begin() + 2
- *  // result.second is vec2.begin() + 2
- *  \endcode
- *
- *  \see find
- *  \see find_if
- */
-template <typename InputIterator1, typename InputIterator2>
-thrust::pair<InputIterator1, InputIterator2> mismatch(InputIterator1 first1,
-                                                      InputIterator1 last1,
-                                                      InputIterator2 first2);
-
-
-/*! \p mismatch finds the first position where the two ranges <tt>[first1, last1)</tt>
- *  and <tt>[first2, first2 + (last1 - first1))</tt> differ. The two versions of 
- *  \p mismatch use different tests for whether elements differ.
- *
- *  This version of \p mismatch finds the first iterator \c i in <tt>[first1, last1)</tt>
- *  such that <tt>pred(\*i, \*(first2 + (i - first1))</tt> is \c false. The return value is a
- *  \c pair whose first element is \c i and whose second element is <tt>*(first2 + (i - first1))</tt>.
- *  If no such iterator \c i exists, the return value is a \c pair whose first element is
- *  \c last1 and whose second element is <tt>*(first2 + (last1 - first1))</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \param pred   The binary predicate to compare elements.
- *  \return The first position where the sequences differ.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Input Iterator</a>.
- *
- *  \code
- *  #include <thrust/mismatch.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> vec1(4);
- *  thrust::device_vector<int> vec2(4);
- *
- *  vec1[0] = 0;  vec2[0] = 0; 
- *  vec1[1] = 5;  vec2[1] = 5;
- *  vec1[2] = 3;  vec2[2] = 8;
- *  vec1[3] = 7;  vec2[3] = 7;
- *
- *  typedef thrust::device_vector<int>::iterator Iterator;
- *  thrust::pair<Iterator,Iterator> result;
- *
- *  result = thrust::mismatch(thrust::device, vec1.begin(), vec1.end(), vec2.begin(), thrust::equal_to<int>());
- *
- *  // result.first  is vec1.begin() + 2
- *  // result.second is vec2.begin() + 2
- *  \endcode
- *
- *  \see find
- *  \see find_if
- */
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2, typename BinaryPredicate>
-__host__ __device__
-thrust::pair<InputIterator1, InputIterator2> mismatch(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                                                      InputIterator1 first1,
-                                                      InputIterator1 last1,
-                                                      InputIterator2 first2,
-                                                      BinaryPredicate pred);
-
-
-/*! \p mismatch finds the first position where the two ranges <tt>[first1, last1)</tt>
- * and <tt>[first2, first2 + (last1 - first1))</tt> differ. The two versions of 
- * \p mismatch use different tests for whether elements differ.
- *
- * This version of \p mismatch finds the first iterator \c i in <tt>[first1, last1)</tt>
- * such that <tt>pred(\*i, \*(first2 + (i - first1))</tt> is \c false. The return value is a
- * \c pair whose first element is \c i and whose second element is <tt>*(first2 + (i - first1))</tt>.
- * If no such iterator \c i exists, the return value is a \c pair whose first element is
- * \c last1 and whose second element is <tt>*(first2 + (last1 - first1))</tt>.
- *
- *  \param first1 The beginning of the first sequence.
- *  \param last1  The end of the first sequence.
- *  \param first2 The beginning of the second sequence.
- *  \param pred   The binary predicate to compare elements.
- *  \return The first position where the sequences differ.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Predicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Input Iterator</a>.
- *
- *  \code
- *  #include <thrust/mismatch.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> vec1(4);
- *  thrust::device_vector<int> vec2(4);
- *
- *  vec1[0] = 0;  vec2[0] = 0; 
- *  vec1[1] = 5;  vec2[1] = 5;
- *  vec1[2] = 3;  vec2[2] = 8;
- *  vec1[3] = 7;  vec2[3] = 7;
- *
- *  typedef thrust::device_vector<int>::iterator Iterator;
- *  thrust::pair<Iterator,Iterator> result;
- *
- *  result = thrust::mismatch(vec1.begin(), vec1.end(), vec2.begin(), thrust::equal_to<int>());
- *
- *  // result.first  is vec1.begin() + 2
- *  // result.second is vec2.begin() + 2
- *  \endcode
- *
- *  \see find
- *  \see find_if
- */
-template <typename InputIterator1, typename InputIterator2, typename BinaryPredicate>
-thrust::pair<InputIterator1, InputIterator2> mismatch(InputIterator1 first1,
-                                                      InputIterator1 last1,
-                                                      InputIterator2 first2,
-                                                      BinaryPredicate pred);
-
-/*! \} // end searching
- */
-
-} // end namespace thrust
-
-#include <thrust/detail/mismatch.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/uninitialized_fill.h

@@ -1,44 +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 fill 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>
-
-// the purpose of this header is to #include the uninitialized_fill.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch uninitialized_fill
-
-#include <thrust/system/detail/sequential/uninitialized_fill.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/uninitialized_fill.h>
-#include <thrust/system/cuda/detail/uninitialized_fill.h>
-#include <thrust/system/omp/detail/uninitialized_fill.h>
-#include <thrust/system/tbb/detail/uninitialized_fill.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_UNINITIALIZED_FILL_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/uninitialized_fill.h>
-#include __THRUST_HOST_SYSTEM_UNINITIALIZED_FILL_HEADER
-#undef __THRUST_HOST_SYSTEM_UNINITIALIZED_FILL_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_UNINITIALIZED_FILL_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/uninitialized_fill.h>
-#include __THRUST_DEVICE_SYSTEM_UNINITIALIZED_FILL_HEADER
-#undef __THRUST_DEVICE_SYSTEM_UNINITIALIZED_FILL_HEADER
-

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

@@ -1,54 +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.
- */
-
-
-/*! \file reduce.h
- *  \brief TBB implementation of reduce.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/tbb/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-
-template<typename DerivedPolicy,
-         typename InputIterator, 
-         typename OutputType,
-         typename BinaryFunction>
-  OutputType reduce(execution_policy<DerivedPolicy> &exec,
-                    InputIterator begin,
-                    InputIterator end,
-                    OutputType init,
-                    BinaryFunction binary_op);
-
-
-} // end namespace detail
-} // end namespace tbb
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/tbb/detail/reduce.inl>
-

spaces/CikeyQI/Yunzai/Yunzai/lib/modules/oicq/index.js

@@ -1,67 +0,0 @@
-import fs from "node:fs"
-import path from "node:path"
-
-function toSegment(type, data) {
-  for (const i in data) {
-    switch (typeof data[i]) {
-      case "string":
-        if ((i == "file" || data[i].match(/^file:\/\//)) && fs.existsSync(data[i].replace(/^file:\/\//, ""))) {
-          if (i == "file" && !data.name)
-            data.name = path.basename(data[i])
-          data[i] = `base64://${fs.readFileSync(data[i].replace(/^file:\/\//, "")).toString("base64")}`
-        }
-        break
-      case "object":
-        if (Buffer.isBuffer(data[i]))
-          data[i] = `base64://${data[i].toString("base64")}`
-    }
-  }
-  return { type, ...data }
-}
-
-const segment = new class segment {
-  custom(type, data) {
-    return toSegment(type, data)
-  }
-  image(file, name) {
-    return toSegment("image", { file, name })
-  }
-  at(qq, name) {
-    return toSegment("at", { qq, name })
-  }
-  record(file, name) {
-    return toSegment("record", { file, name })
-  }
-  video(file, name) {
-    return toSegment("video", { file, name })
-  }
-  file(file, name) {
-    return toSegment("file", { file, name })
-  }
-  reply(id, text, qq, time, seq) {
-    return toSegment("reply", { id, text, qq, time, seq })
-  }
-  face(id) {
-    return toSegment("face", { id })
-  }
-  share(url, title, content, image) {
-    return toSegment("share", { url, title, content, image })
-  }
-  music(type, id, url, audio, title) {
-    return toSegment("music", { type, id, url, audio, title })
-  }
-  poke(qq) {
-    return toSegment("poke", { qq })
-  }
-  gift(qq, id) {
-    return toSegment("gift", { qq, id })
-  }
-  cardimage(file, name, minwidth, minheight, maxwidth, maxheight, source, icon) {
-    return toSegment("cardimage", { file, name, minwidth, minheight, maxwidth, maxheight, source, icon })
-  }
-  tts(text) {
-    return toSegment("tts", { text })
-  }
-}
-
-export { segment }

spaces/CofAI/chat/client/js/icons.js

@@ -1 +0,0 @@
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spaces/Cpp4App/Cpp4App/CDM/result_processing/view_gt.py

@@ -1,89 +0,0 @@
-from tqdm import tqdm
-import json
-import cv2
-from os.path import join as pjoin
-
-from config.CONFIG_UIED import Config
-C = Config()
-
-
-def draw_bounding_box_class(org, components, color=C.COLOR, line=2, show=False, write_path=None):
-    """
-    Draw bounding box of components with their classes on the original image
-    :param org: original image
-    :param components: bbox [(column_min, row_min, column_max, row_max)]
-                    -> top_left: (column_min, row_min)
-                    -> bottom_right: (column_max, row_max)
-    :param color_map: colors mapping to different components
-    :param line: line thickness
-    :param compo_class: classes matching the corners of components
-    :param show: show or not
-    :return: labeled image
-    """
-    board = org.copy()
-    bboxes = components['bboxes']
-    categories = components['categories']
-    for i in range(len(bboxes)):
-        bbox = bboxes[i]
-        category = categories[i]
-        board = cv2.rectangle(board, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color[C.CLASS_MAP[str(category)]], line)
-        board = cv2.putText(board, C.CLASS_MAP[str(category)], (bbox[0]+5, bbox[1]+20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color[C.CLASS_MAP[str(category)]], 2)
-    if show:
-        cv2.imshow('a', cv2.resize(board, (500, 1000)))
-        cv2.waitKey(0)
-    if write_path is not None:
-        cv2.imwrite(write_path, board)
-    return board
-
-
-def load_ground_truth_json(gt_file, no_text=True):
-    def get_img_by_id(img_id):
-        for image in images:
-            if image['id'] == img_id:
-                return image['file_name'].split('/')[-1][:-4], (image['height'], image['width'])
-
-    def cvt_bbox(bbox):
-        '''
-        :param bbox: [x,y,width,height]
-        :return: [col_min, row_min, col_max, row_max]
-        '''
-        bbox = [int(b) for b in bbox]
-        return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
-
-    data = json.load(open(gt_file, 'r'))
-    images = data['images']
-    annots = data['annotations']
-    compos = {}
-    print('Loading %d ground truth' % len(annots))
-    for annot in tqdm(annots):
-        img_name, size = get_img_by_id(annot['image_id'])
-        if no_text and int(annot['category_id']) == 14:
-            compos[img_name] = {'bboxes': [], 'categories': [], 'size': size}
-            continue
-        if img_name not in compos:
-            compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [annot['category_id']], 'size':size}
-        else:
-            compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox']))
-            compos[img_name]['categories'].append(annot['category_id'])
-    return compos
-
-
-def view_gt_all(gt, img_root):
-    for img_id in gt:
-        compos = gt[img_id]
-        img = cv2.imread(pjoin(img_root, img_id + '.jpg'))
-        print(pjoin(img_root, img_id + '.jpg'))
-        draw_bounding_box_class(img, compos, show=True)
-
-
-def view_gt_single(gt, img_root, img_id):
-    img_id = str(img_id)
-    compos = gt[img_id]
-    img = cv2.imread(pjoin(img_root, img_id + '.jpg'))
-    print(pjoin(img_root, img_id + '.jpg'))
-    draw_bounding_box_class(img, compos, show=True)
-
-
-gt = load_ground_truth_json('E:\\Mulong\\Datasets\\rico\\instances_test.json', no_text=False)
-# view_gt_all(gt, 'E:\\Mulong\\Datasets\\rico\\combined')
-view_gt_single(gt, 'E:\\Mulong\\Datasets\\rico\\combined', 670)

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/_utils.py

@@ -1,39 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import glob
-import os.path
-
-import torch
-
-try:
-    from torch.utils.cpp_extension import load as load_ext
-    from torch.utils.cpp_extension import CUDA_HOME
-except ImportError:
-    raise ImportError("The cpp layer extensions requires PyTorch 0.4 or higher")
-
-
-def _load_C_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    this_dir = os.path.dirname(this_dir)
-    this_dir = os.path.join(this_dir, "csrc")
-
-    main_file = glob.glob(os.path.join(this_dir, "*.cpp"))
-    source_cpu = glob.glob(os.path.join(this_dir, "cpu", "*.cpp"))
-    source_cuda = glob.glob(os.path.join(this_dir, "cuda", "*.cu"))
-
-    source = main_file + source_cpu
-
-    extra_cflags = []
-    if torch.cuda.is_available() and CUDA_HOME is not None:
-        source.extend(source_cuda)
-        extra_cflags = ["-DWITH_CUDA"]
-    source = [os.path.join(this_dir, s) for s in source]
-    extra_include_paths = [this_dir]
-    return load_ext(
-        "torchvision",
-        source,
-        extra_cflags=extra_cflags,
-        extra_include_paths=extra_include_paths,
-    )
-
-
-_C = _load_C_extensions()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/bar_plot.py

@@ -1,377 +0,0 @@
-"""gr.BarPlot() component."""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-import altair as alt
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components.base import _Keywords
-from gradio.components.plot import AltairPlot, Plot
-
-set_documentation_group("component")
-
-
-@document()
-class BarPlot(Plot):
-    """
-    Create a bar plot.
-
-    Preprocessing: this component does *not* accept input.
-    Postprocessing: expects a pandas dataframe with the data to plot.
-
-    Demos: bar_plot, chicago-bikeshare-dashboard
-    """
-
-    def __init__(
-        self,
-        value: pd.DataFrame | Callable | None = None,
-        x: str | None = None,
-        y: str | None = None,
-        *,
-        color: str | None = None,
-        vertical: bool = True,
-        group: str | None = None,
-        title: str | None = None,
-        tooltip: list[str] | str | None = None,
-        x_title: str | None = None,
-        y_title: str | None = None,
-        color_legend_title: str | None = None,
-        group_title: str | None = None,
-        color_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        y_lim: list[int] | None = None,
-        caption: str | None = None,
-        interactive: bool | None = True,
-        label: str | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        every: float | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-    ):
-        """
-        Parameters:
-            value: The pandas dataframe containing the data to display in a scatter plot.
-            x: Column corresponding to the x axis.
-            y: Column corresponding to the y axis.
-            color: The column to determine the bar color. Must be categorical (discrete values).
-            vertical: If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
-            group: The column with which to split the overall plot into smaller subplots.
-            title: The title to display on top of the chart.
-            tooltip: The column (or list of columns) to display on the tooltip when a user hovers over a bar.
-            x_title: The title given to the x axis. By default, uses the value of the x parameter.
-            y_title: The title given to the y axis. By default, uses the value of the y parameter.
-            color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
-            group_title: The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
-            color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
-            height: The height of the plot in pixels.
-            width: The width of the plot in pixels.
-            y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
-            caption: The (optional) caption to display below the plot.
-            interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
-            label: The (optional) label to display on the top left corner of the plot.
-            show_label: Whether the label should be displayed.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            visible: Whether the plot should be visible.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.x = x
-        self.y = y
-        self.color = color
-        self.vertical = vertical
-        self.group = group
-        self.group_title = group_title
-        self.tooltip = tooltip
-        self.title = title
-        self.x_title = x_title
-        self.y_title = y_title
-        self.color_legend_title = color_legend_title
-        self.group_title = group_title
-        self.color_legend_position = color_legend_position
-        self.y_lim = y_lim
-        self.caption = caption
-        self.interactive_chart = interactive
-        self.width = width
-        self.height = height
-        super().__init__(
-            value=value,
-            label=label,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            every=every,
-        )
-
-    def get_config(self):
-        config = super().get_config()
-        config["caption"] = self.caption
-        return config
-
-    def get_block_name(self) -> str:
-        return "plot"
-
-    @staticmethod
-    def update(
-        value: pd.DataFrame | dict | Literal[_Keywords.NO_VALUE] = _Keywords.NO_VALUE,
-        x: str | None = None,
-        y: str | None = None,
-        color: str | None = None,
-        vertical: bool = True,
-        group: str | None = None,
-        title: str | None = None,
-        tooltip: list[str] | str | None = None,
-        x_title: str | None = None,
-        y_title: str | None = None,
-        color_legend_title: str | None = None,
-        group_title: str | None = None,
-        color_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        y_lim: list[int] | None = None,
-        caption: str | None = None,
-        interactive: bool | None = None,
-        label: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        visible: bool | None = None,
-    ):
-        """Update an existing BarPlot component.
-
-        If updating any of the plot properties (color, size, etc) the value, x, and y parameters must be specified.
-
-        Parameters:
-            value: The pandas dataframe containing the data to display in a scatter plot.
-            x: Column corresponding to the x axis.
-            y: Column corresponding to the y axis.
-            color: The column to determine the bar color. Must be categorical (discrete values).
-            vertical: If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
-            group: The column with which to split the overall plot into smaller subplots.
-            title: The title to display on top of the chart.
-            tooltip: The column (or list of columns) to display on the tooltip when a user hovers over a bar.
-            x_title: The title given to the x axis. By default, uses the value of the x parameter.
-            y_title: The title given to the y axis. By default, uses the value of the y parameter.
-            color_legend_title: The title given to the color legend. By default, uses the value of color parameter.
-            group_title: The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
-            color_legend_position: The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
-            height: The height of the plot in pixels.
-            width: The width of the plot in pixels.
-            y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
-            caption: The (optional) caption to display below the plot.
-            interactive: Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
-            label: The (optional) label to display on the top left corner of the plot.
-            show_label: Whether the label should be displayed.
-            visible: Whether the plot should be visible.
-        """
-        properties = [
-            x,
-            y,
-            color,
-            vertical,
-            group,
-            title,
-            tooltip,
-            x_title,
-            y_title,
-            color_legend_title,
-            group_title,
-            color_legend_position,
-            height,
-            width,
-            y_lim,
-            interactive,
-        ]
-        if any(properties):
-            if not isinstance(value, pd.DataFrame):
-                raise ValueError(
-                    "In order to update plot properties the value parameter "
-                    "must be provided, and it must be a Dataframe. Please pass a value "
-                    "parameter to gr.BarPlot.update."
-                )
-            if x is None or y is None:
-                raise ValueError(
-                    "In order to update plot properties, the x and y axis data "
-                    "must be specified. Please pass valid values for x an y to "
-                    "gr.BarPlot.update."
-                )
-            chart = BarPlot.create_plot(value, *properties)
-            value = {"type": "altair", "plot": chart.to_json(), "chart": "bar"}
-
-        updated_config = {
-            "label": label,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "visible": visible,
-            "value": value,
-            "caption": caption,
-            "__type__": "update",
-        }
-        return updated_config
-
-    @staticmethod
-    def create_plot(
-        value: pd.DataFrame,
-        x: str,
-        y: str,
-        color: str | None = None,
-        vertical: bool = True,
-        group: str | None = None,
-        title: str | None = None,
-        tooltip: list[str] | str | None = None,
-        x_title: str | None = None,
-        y_title: str | None = None,
-        color_legend_title: str | None = None,
-        group_title: str | None = None,
-        color_legend_position: Literal[
-            "left",
-            "right",
-            "top",
-            "bottom",
-            "top-left",
-            "top-right",
-            "bottom-left",
-            "bottom-right",
-            "none",
-        ]
-        | None
-        | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        y_lim: list[int] | None = None,
-        interactive: bool | None = True,
-    ):
-        """Helper for creating the bar plot."""
-        interactive = True if interactive is None else interactive
-        orientation = (
-            {"field": group, "title": group_title if group_title is not None else group}
-            if group
-            else {}
-        )
-
-        x_title = x_title or x
-        y_title = y_title or y
-
-        # If horizontal, switch x and y
-        if not vertical:
-            y, x = x, y
-            x = f"sum({x}):Q"
-            y_title, x_title = x_title, y_title
-            orientation = {"row": alt.Row(**orientation)} if orientation else {}  # type: ignore
-            x_lim = y_lim
-            y_lim = None
-        else:
-            y = f"sum({y}):Q"
-            x_lim = None
-            orientation = {"column": alt.Column(**orientation)} if orientation else {}  # type: ignore
-
-        encodings = dict(
-            x=alt.X(
-                x,  # type: ignore
-                title=x_title,  # type: ignore
-                scale=AltairPlot.create_scale(x_lim),  # type: ignore
-            ),
-            y=alt.Y(
-                y,  # type: ignore
-                title=y_title,  # type: ignore
-                scale=AltairPlot.create_scale(y_lim),  # type: ignore
-            ),
-            **orientation,
-        )
-        properties = {}
-        if title:
-            properties["title"] = title
-        if height:
-            properties["height"] = height
-        if width:
-            properties["width"] = width
-
-        if color:
-            domain = value[color].unique().tolist()
-            range_ = list(range(len(domain)))
-            encodings["color"] = {
-                "field": color,
-                "type": "nominal",
-                "scale": {"domain": domain, "range": range_},
-                "legend": AltairPlot.create_legend(
-                    position=color_legend_position, title=color_legend_title or color
-                ),
-            }
-
-        if tooltip:
-            encodings["tooltip"] = tooltip
-
-        chart = (
-            alt.Chart(value)  # type: ignore
-            .mark_bar()  # type: ignore
-            .encode(**encodings)
-            .properties(background="transparent", **properties)
-        )
-        if interactive:
-            chart = chart.interactive()
-
-        return chart
-
-    def postprocess(self, y: pd.DataFrame | dict | None) -> dict[str, str] | None:
-        # if None or update
-        if y is None or isinstance(y, dict):
-            return y
-        if self.x is None or self.y is None:
-            raise ValueError("No value provided for required parameters `x` and `y`.")
-        chart = self.create_plot(
-            value=y,
-            x=self.x,
-            y=self.y,
-            color=self.color,
-            vertical=self.vertical,
-            group=self.group,
-            title=self.title,
-            tooltip=self.tooltip,
-            x_title=self.x_title,
-            y_title=self.y_title,
-            color_legend_title=self.color_legend_title,
-            color_legend_position=self.color_legend_position,
-            group_title=self.group_title,
-            y_lim=self.y_lim,
-            interactive=self.interactive_chart,
-            height=self.height,
-            width=self.width,
-        )
-
-        return {"type": "altair", "plot": chart.to_json(), "chart": "bar"}

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f8a15c0a.js

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print Comment Script AssignStatement * BinaryExpression BitOp BitOp BitOp BitOp ArithOp ArithOp @ ArithOp ** UnaryExpression ArithOp BitOp AwaitExpression await ) ( ParenthesizedExpression BinaryExpression or and CompareOp in not is UnaryExpression ConditionalExpression if else LambdaExpression lambda ParamList VariableName AssignOp , : NamedExpression AssignOp YieldExpression yield from TupleExpression ComprehensionExpression async for LambdaExpression ] [ ArrayExpression ArrayComprehensionExpression } { DictionaryExpression DictionaryComprehensionExpression SetExpression SetComprehensionExpression CallExpression ArgList AssignOp MemberExpression . 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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/lfs.py

@@ -1,496 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Git LFS related type definitions and utilities"""
-import io
-import os
-import re
-import warnings
-from contextlib import AbstractContextManager
-from dataclasses import dataclass
-from math import ceil
-from os.path import getsize
-from pathlib import Path
-from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List, Optional, Tuple
-
-from requests.auth import HTTPBasicAuth
-
-from huggingface_hub.constants import ENDPOINT, HF_HUB_ENABLE_HF_TRANSFER, REPO_TYPES_URL_PREFIXES
-from huggingface_hub.utils import get_session
-
-from .utils import get_token_to_send, hf_raise_for_status, http_backoff, logging, validate_hf_hub_args
-from .utils._typing import TypedDict
-from .utils.sha import sha256, sha_fileobj
-
-
-if TYPE_CHECKING:
-    from ._commit_api import CommitOperationAdd
-
-logger = logging.get_logger(__name__)
-
-OID_REGEX = re.compile(r"^[0-9a-f]{40}$")
-
-LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload"
-
-LFS_HEADERS = {
-    "Accept": "application/vnd.git-lfs+json",
-    "Content-Type": "application/vnd.git-lfs+json",
-}
-
-
-@dataclass
-class UploadInfo:
-    """
-    Dataclass holding required information to determine whether a blob
-    should be uploaded to the hub using the LFS protocol or the regular protocol
-
-    Args:
-        sha256 (`bytes`):
-            SHA256 hash of the blob
-        size (`int`):
-            Size in bytes of the blob
-        sample (`bytes`):
-            First 512 bytes of the blob
-    """
-
-    sha256: bytes
-    size: int
-    sample: bytes
-
-    @classmethod
-    def from_path(cls, path: str):
-        size = getsize(path)
-        with io.open(path, "rb") as file:
-            sample = file.peek(512)[:512]
-            sha = sha_fileobj(file)
-        return cls(size=size, sha256=sha, sample=sample)
-
-    @classmethod
-    def from_bytes(cls, data: bytes):
-        sha = sha256(data).digest()
-        return cls(size=len(data), sample=data[:512], sha256=sha)
-
-    @classmethod
-    def from_fileobj(cls, fileobj: BinaryIO):
-        sample = fileobj.read(512)
-        fileobj.seek(0, io.SEEK_SET)
-        sha = sha_fileobj(fileobj)
-        size = fileobj.tell()
-        fileobj.seek(0, io.SEEK_SET)
-        return cls(size=size, sha256=sha, sample=sample)
-
-
-@validate_hf_hub_args
-def post_lfs_batch_info(
-    upload_infos: Iterable[UploadInfo],
-    token: Optional[str],
-    repo_type: str,
-    repo_id: str,
-    endpoint: Optional[str] = None,
-) -> Tuple[List[dict], List[dict]]:
-    """
-    Requests the LFS batch endpoint to retrieve upload instructions
-
-    Learn more: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
-
-    Args:
-        upload_infos (`Iterable` of `UploadInfo`):
-            `UploadInfo` for the files that are being uploaded, typically obtained
-            from `CommitOperationAdd.upload_info`
-        repo_type (`str`):
-            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
-        repo_id (`str`):
-            A namespace (user or an organization) and a repo name separated
-            by a `/`.
-        token (`str`, *optional*):
-            An authentication token ( See https://huggingface.co/settings/tokens )
-
-    Returns:
-        `LfsBatchInfo`: 2-tuple:
-            - First element is the list of upload instructions from the server
-            - Second element is an list of errors, if any
-
-    Raises:
-        `ValueError`: If an argument is invalid or the server response is malformed
-
-        `HTTPError`: If the server returned an error
-    """
-    endpoint = endpoint if endpoint is not None else ENDPOINT
-    url_prefix = ""
-    if repo_type in REPO_TYPES_URL_PREFIXES:
-        url_prefix = REPO_TYPES_URL_PREFIXES[repo_type]
-    batch_url = f"{endpoint}/{url_prefix}{repo_id}.git/info/lfs/objects/batch"
-    resp = get_session().post(
-        batch_url,
-        headers=LFS_HEADERS,
-        json={
-            "operation": "upload",
-            "transfers": ["basic", "multipart"],
-            "objects": [
-                {
-                    "oid": upload.sha256.hex(),
-                    "size": upload.size,
-                }
-                for upload in upload_infos
-            ],
-            "hash_algo": "sha256",
-        },
-        auth=HTTPBasicAuth(
-            "access_token",
-            get_token_to_send(token or True),  # type: ignore  # Token must be provided or retrieved
-        ),
-    )
-    hf_raise_for_status(resp)
-    batch_info = resp.json()
-
-    objects = batch_info.get("objects", None)
-    if not isinstance(objects, list):
-        raise ValueError("Malformed response from server")
-
-    return (
-        [_validate_batch_actions(obj) for obj in objects if "error" not in obj],
-        [_validate_batch_error(obj) for obj in objects if "error" in obj],
-    )
-
-
-class PayloadPartT(TypedDict):
-    partNumber: int
-    etag: str
-
-
-class CompletionPayloadT(TypedDict):
-    """Payload that will be sent to the Hub when uploading multi-part."""
-
-    oid: str
-    parts: List[PayloadPartT]
-
-
-def lfs_upload(operation: "CommitOperationAdd", lfs_batch_action: Dict, token: Optional[str]) -> None:
-    """
-    Handles uploading a given object to the Hub with the LFS protocol.
-
-    Can be a No-op if the content of the file is already present on the hub large file storage.
-
-    Args:
-        operation (`CommitOperationAdd`):
-            The add operation triggering this upload.
-        lfs_batch_action (`dict`):
-            Upload instructions from the LFS batch endpoint for this object. See [`~utils.lfs.post_lfs_batch_info`] for
-            more details.
-        token (`str`, *optional*):
-            A [user access token](https://hf.co/settings/tokens) to authenticate requests against the Hub
-
-    Raises:
-        - `ValueError` if `lfs_batch_action` is improperly formatted
-        - `HTTPError` if the upload resulted in an error
-    """
-    # 0. If LFS file is already present, skip upload
-    _validate_batch_actions(lfs_batch_action)
-    actions = lfs_batch_action.get("actions")
-    if actions is None:
-        # The file was already uploaded
-        logger.debug(f"Content of file {operation.path_in_repo} is already present upstream - skipping upload")
-        return
-
-    # 1. Validate server response (check required keys in dict)
-    upload_action = lfs_batch_action["actions"]["upload"]
-    _validate_lfs_action(upload_action)
-    verify_action = lfs_batch_action["actions"].get("verify")
-    if verify_action is not None:
-        _validate_lfs_action(verify_action)
-
-    # 2. Upload file (either single part or multi-part)
-    header = upload_action.get("header", {})
-    chunk_size = header.get("chunk_size")
-    if chunk_size is not None:
-        try:
-            chunk_size = int(chunk_size)
-        except (ValueError, TypeError):
-            raise ValueError(
-                f"Malformed response from LFS batch endpoint: `chunk_size` should be an integer. Got '{chunk_size}'."
-            )
-        _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"])
-    else:
-        _upload_single_part(operation=operation, upload_url=upload_action["href"])
-
-    # 3. Verify upload went well
-    if verify_action is not None:
-        _validate_lfs_action(verify_action)
-        verify_resp = get_session().post(
-            verify_action["href"],
-            auth=HTTPBasicAuth(username="USER", password=get_token_to_send(token or True)),  # type: ignore
-            json={"oid": operation.upload_info.sha256.hex(), "size": operation.upload_info.size},
-        )
-        hf_raise_for_status(verify_resp)
-    logger.debug(f"{operation.path_in_repo}: Upload successful")
-
-
-def _validate_lfs_action(lfs_action: dict):
-    """validates response from the LFS batch endpoint"""
-    if not (
-        isinstance(lfs_action.get("href"), str)
-        and (lfs_action.get("header") is None or isinstance(lfs_action.get("header"), dict))
-    ):
-        raise ValueError("lfs_action is improperly formatted")
-    return lfs_action
-
-
-def _validate_batch_actions(lfs_batch_actions: dict):
-    """validates response from the LFS batch endpoint"""
-    if not (isinstance(lfs_batch_actions.get("oid"), str) and isinstance(lfs_batch_actions.get("size"), int)):
-        raise ValueError("lfs_batch_actions is improperly formatted")
-
-    upload_action = lfs_batch_actions.get("actions", {}).get("upload")
-    verify_action = lfs_batch_actions.get("actions", {}).get("verify")
-    if upload_action is not None:
-        _validate_lfs_action(upload_action)
-    if verify_action is not None:
-        _validate_lfs_action(verify_action)
-    return lfs_batch_actions
-
-
-def _validate_batch_error(lfs_batch_error: dict):
-    """validates response from the LFS batch endpoint"""
-    if not (isinstance(lfs_batch_error.get("oid"), str) and isinstance(lfs_batch_error.get("size"), int)):
-        raise ValueError("lfs_batch_error is improperly formatted")
-    error_info = lfs_batch_error.get("error")
-    if not (
-        isinstance(error_info, dict)
-        and isinstance(error_info.get("message"), str)
-        and isinstance(error_info.get("code"), int)
-    ):
-        raise ValueError("lfs_batch_error is improperly formatted")
-    return lfs_batch_error
-
-
-def _upload_single_part(operation: "CommitOperationAdd", upload_url: str) -> None:
-    """
-    Uploads `fileobj` as a single PUT HTTP request (basic LFS transfer protocol)
-
-    Args:
-        upload_url (`str`):
-            The URL to PUT the file to.
-        fileobj:
-            The file-like object holding the data to upload.
-
-    Returns: `requests.Response`
-
-    Raises: `requests.HTTPError` if the upload resulted in an error
-    """
-    with operation.as_file(with_tqdm=True) as fileobj:
-        response = http_backoff("PUT", upload_url, data=fileobj)
-        hf_raise_for_status(response)
-
-
-def _upload_multi_part(operation: "CommitOperationAdd", header: Dict, chunk_size: int, upload_url: str) -> None:
-    """
-    Uploads file using HF multipart LFS transfer protocol.
-    """
-    # 1. Get upload URLs for each part
-    sorted_parts_urls = _get_sorted_parts_urls(header=header, upload_info=operation.upload_info, chunk_size=chunk_size)
-
-    # 2. Upload parts (either with hf_transfer or in pure Python)
-    use_hf_transfer = HF_HUB_ENABLE_HF_TRANSFER
-    if (
-        HF_HUB_ENABLE_HF_TRANSFER
-        and not isinstance(operation.path_or_fileobj, str)
-        and not isinstance(operation.path_or_fileobj, Path)
-    ):
-        warnings.warn(
-            "hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular"
-            " upload"
-        )
-        use_hf_transfer = False
-
-    response_headers = (
-        _upload_parts_hf_transfer(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
-        if use_hf_transfer
-        else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
-    )
-
-    # 3. Send completion request
-    completion_res = get_session().post(
-        upload_url,
-        json=_get_completion_payload(response_headers, operation.upload_info.sha256.hex()),
-        headers=LFS_HEADERS,
-    )
-    hf_raise_for_status(completion_res)
-
-
-def _get_sorted_parts_urls(header: Dict, upload_info: UploadInfo, chunk_size: int) -> List[str]:
-    sorted_part_upload_urls = [
-        upload_url
-        for _, upload_url in sorted(
-            [
-                (int(part_num, 10), upload_url)
-                for part_num, upload_url in header.items()
-                if part_num.isdigit() and len(part_num) > 0
-            ],
-            key=lambda t: t[0],
-        )
-    ]
-    num_parts = len(sorted_part_upload_urls)
-    if num_parts != ceil(upload_info.size / chunk_size):
-        raise ValueError("Invalid server response to upload large LFS file")
-    return sorted_part_upload_urls
-
-
-def _get_completion_payload(response_headers: List[Dict], oid: str) -> CompletionPayloadT:
-    parts: List[PayloadPartT] = []
-    for part_number, header in enumerate(response_headers):
-        etag = header.get("etag")
-        if etag is None or etag == "":
-            raise ValueError(f"Invalid etag (`{etag}`) returned for part {part_number + 1}")
-        parts.append(
-            {
-                "partNumber": part_number + 1,
-                "etag": etag,
-            }
-        )
-    return {"oid": oid, "parts": parts}
-
-
-def _upload_parts_iteratively(
-    operation: "CommitOperationAdd", sorted_parts_urls: List[str], chunk_size: int
-) -> List[Dict]:
-    headers = []
-    with operation.as_file(with_tqdm=True) as fileobj:
-        for part_idx, part_upload_url in enumerate(sorted_parts_urls):
-            with SliceFileObj(
-                fileobj,
-                seek_from=chunk_size * part_idx,
-                read_limit=chunk_size,
-            ) as fileobj_slice:
-                part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice)
-                hf_raise_for_status(part_upload_res)
-                headers.append(part_upload_res.headers)
-    return headers  # type: ignore
-
-
-def _upload_parts_hf_transfer(
-    operation: "CommitOperationAdd", sorted_parts_urls: List[str], chunk_size: int
-) -> List[Dict]:
-    # Upload file using an external Rust-based package. Upload is faster but support less features (no progress bars).
-    try:
-        from hf_transfer import multipart_upload
-    except ImportError:
-        raise ValueError(
-            "Fast uploading using 'hf_transfer' is enabled (HF_HUB_ENABLE_HF_TRANSFER=1) but 'hf_transfer' package is"
-            " not available in your environment. Try `pip install hf_transfer`."
-        )
-
-    try:
-        return multipart_upload(
-            file_path=operation.path_or_fileobj,
-            parts_urls=sorted_parts_urls,
-            chunk_size=chunk_size,
-            max_files=128,
-            parallel_failures=127,  # could be removed
-            max_retries=5,
-        )
-    except Exception as e:
-        raise RuntimeError(
-            "An error occurred while uploading using `hf_transfer`. Consider disabling HF_HUB_ENABLE_HF_TRANSFER for"
-            " better error handling."
-        ) from e
-
-
-class SliceFileObj(AbstractContextManager):
-    """
-    Utility context manager to read a *slice* of a seekable file-like object as a seekable, file-like object.
-
-    This is NOT thread safe
-
-    Inspired by stackoverflow.com/a/29838711/593036
-
-    Credits to @julien-c
-
-    Args:
-        fileobj (`BinaryIO`):
-            A file-like object to slice. MUST implement `tell()` and `seek()` (and `read()` of course).
-            `fileobj` will be reset to its original position when exiting the context manager.
-        seek_from (`int`):
-            The start of the slice (offset from position 0 in bytes).
-        read_limit (`int`):
-            The maximum number of bytes to read from the slice.
-
-    Attributes:
-        previous_position (`int`):
-            The previous position
-
-    Examples:
-
-    Reading 200 bytes with an offset of 128 bytes from a file (ie bytes 128 to 327):
-    ```python
-    >>> with open("path/to/file", "rb") as file:
-    ...     with SliceFileObj(file, seek_from=128, read_limit=200) as fslice:
-    ...         fslice.read(...)
-    ```
-
-    Reading a file in chunks of 512 bytes
-    ```python
-    >>> import os
-    >>> chunk_size = 512
-    >>> file_size = os.getsize("path/to/file")
-    >>> with open("path/to/file", "rb") as file:
-    ...     for chunk_idx in range(ceil(file_size / chunk_size)):
-    ...         with SliceFileObj(file, seek_from=chunk_idx * chunk_size, read_limit=chunk_size) as fslice:
-    ...             chunk = fslice.read(...)
-
-    ```
-    """
-
-    def __init__(self, fileobj: BinaryIO, seek_from: int, read_limit: int):
-        self.fileobj = fileobj
-        self.seek_from = seek_from
-        self.read_limit = read_limit
-
-    def __enter__(self):
-        self._previous_position = self.fileobj.tell()
-        end_of_stream = self.fileobj.seek(0, os.SEEK_END)
-        self._len = min(self.read_limit, end_of_stream - self.seek_from)
-        # ^^ The actual number of bytes that can be read from the slice
-        self.fileobj.seek(self.seek_from, io.SEEK_SET)
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.fileobj.seek(self._previous_position, io.SEEK_SET)
-
-    def read(self, n: int = -1):
-        pos = self.tell()
-        if pos >= self._len:
-            return b""
-        remaining_amount = self._len - pos
-        data = self.fileobj.read(remaining_amount if n < 0 else min(n, remaining_amount))
-        return data
-
-    def tell(self) -> int:
-        return self.fileobj.tell() - self.seek_from
-
-    def seek(self, offset: int, whence: int = os.SEEK_SET) -> int:
-        start = self.seek_from
-        end = start + self._len
-        if whence in (os.SEEK_SET, os.SEEK_END):
-            offset = start + offset if whence == os.SEEK_SET else end + offset
-            offset = max(start, min(offset, end))
-            whence = os.SEEK_SET
-        elif whence == os.SEEK_CUR:
-            cur_pos = self.fileobj.tell()
-            offset = max(start - cur_pos, min(offset, end - cur_pos))
-        else:
-            raise ValueError(f"whence value {whence} is not supported")
-        return self.fileobj.seek(offset, whence) - self.seek_from
-
-    def __iter__(self):
-        yield self.read(n=4 * 1024 * 1024)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_runtime.py

@@ -1,328 +0,0 @@
-# coding=utf-8
-# Copyright 2022-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Check presence of installed packages at runtime."""
-import platform
-import sys
-from typing import Any, Dict
-
-import packaging.version
-
-from .. import __version__, constants
-
-
-_PY_VERSION: str = sys.version.split()[0].rstrip("+")
-
-if packaging.version.Version(_PY_VERSION) < packaging.version.Version("3.8.0"):
-    import importlib_metadata  # type: ignore
-else:
-    import importlib.metadata as importlib_metadata  # type: ignore
-
-
-_package_versions = {}
-
-_CANDIDATES = {
-    "aiohttp": {"aiohttp"},
-    "fastai": {"fastai"},
-    "fastcore": {"fastcore"},
-    "gradio": {"gradio"},
-    "graphviz": {"graphviz"},
-    "hf_transfer": {"hf_transfer"},
-    "jinja": {"Jinja2"},
-    "numpy": {"numpy"},
-    "pillow": {"Pillow"},
-    "pydantic": {"pydantic"},
-    "pydot": {"pydot"},
-    "tensorboard": {"tensorboardX"},
-    "tensorflow": (
-        "tensorflow",
-        "tensorflow-cpu",
-        "tensorflow-gpu",
-        "tf-nightly",
-        "tf-nightly-cpu",
-        "tf-nightly-gpu",
-        "intel-tensorflow",
-        "intel-tensorflow-avx512",
-        "tensorflow-rocm",
-        "tensorflow-macos",
-    ),
-    "torch": {"torch"},
-}
-
-# Check once at runtime
-for candidate_name, package_names in _CANDIDATES.items():
-    _package_versions[candidate_name] = "N/A"
-    for name in package_names:
-        try:
-            _package_versions[candidate_name] = importlib_metadata.version(name)
-            break
-        except importlib_metadata.PackageNotFoundError:
-            pass
-
-
-def _get_version(package_name: str) -> str:
-    return _package_versions.get(package_name, "N/A")
-
-
-def _is_available(package_name: str) -> bool:
-    return _get_version(package_name) != "N/A"
-
-
-# Python
-def get_python_version() -> str:
-    return _PY_VERSION
-
-
-# Huggingface Hub
-def get_hf_hub_version() -> str:
-    return __version__
-
-
-# aiohttp
-def is_aiohttp_available() -> bool:
-    return _is_available("aiohttp")
-
-
-def get_aiohttp_version() -> str:
-    return _get_version("aiohttp")
-
-
-# FastAI
-def is_fastai_available() -> bool:
-    return _is_available("fastai")
-
-
-def get_fastai_version() -> str:
-    return _get_version("fastai")
-
-
-# Fastcore
-def is_fastcore_available() -> bool:
-    return _is_available("fastcore")
-
-
-def get_fastcore_version() -> str:
-    return _get_version("fastcore")
-
-
-# FastAI
-def is_gradio_available() -> bool:
-    return _is_available("gradio")
-
-
-def get_gradio_version() -> str:
-    return _get_version("gradio")
-
-
-# Graphviz
-def is_graphviz_available() -> bool:
-    return _is_available("graphviz")
-
-
-def get_graphviz_version() -> str:
-    return _get_version("graphviz")
-
-
-# hf_transfer
-def is_hf_transfer_available() -> bool:
-    return _is_available("hf_transfer")
-
-
-def get_hf_transfer_version() -> str:
-    return _get_version("hf_transfer")
-
-
-# Numpy
-def is_numpy_available() -> bool:
-    return _is_available("numpy")
-
-
-def get_numpy_version() -> str:
-    return _get_version("numpy")
-
-
-# Jinja
-def is_jinja_available() -> bool:
-    return _is_available("jinja")
-
-
-def get_jinja_version() -> str:
-    return _get_version("jinja")
-
-
-# Pillow
-def is_pillow_available() -> bool:
-    return _is_available("pillow")
-
-
-def get_pillow_version() -> str:
-    return _get_version("pillow")
-
-
-# Pydantic
-def is_pydantic_available() -> bool:
-    return _is_available("pydantic")
-
-
-def get_pydantic_version() -> str:
-    return _get_version("pydantic")
-
-
-# Pydot
-def is_pydot_available() -> bool:
-    return _is_available("pydot")
-
-
-def get_pydot_version() -> str:
-    return _get_version("pydot")
-
-
-# Tensorboard
-def is_tensorboard_available() -> bool:
-    return _is_available("tensorboard")
-
-
-def get_tensorboard_version() -> str:
-    return _get_version("tensorboard")
-
-
-# Tensorflow
-def is_tf_available() -> bool:
-    return _is_available("tensorflow")
-
-
-def get_tf_version() -> str:
-    return _get_version("tensorflow")
-
-
-# Torch
-def is_torch_available() -> bool:
-    return _is_available("torch")
-
-
-def get_torch_version() -> str:
-    return _get_version("torch")
-
-
-# Shell-related helpers
-try:
-    # Set to `True` if script is running in a Google Colab notebook.
-    # If running in Google Colab, git credential store is set globally which makes the
-    # warning disappear. See https://github.com/huggingface/huggingface_hub/issues/1043
-    #
-    # Taken from https://stackoverflow.com/a/63519730.
-    _is_google_colab = "google.colab" in str(get_ipython())  # type: ignore # noqa: F821
-except NameError:
-    _is_google_colab = False
-
-
-def is_notebook() -> bool:
-    """Return `True` if code is executed in a notebook (Jupyter, Colab, QTconsole).
-
-    Taken from https://stackoverflow.com/a/39662359.
-    Adapted to make it work with Google colab as well.
-    """
-    try:
-        shell_class = get_ipython().__class__  # type: ignore # noqa: F821
-        for parent_class in shell_class.__mro__:  # e.g. "is subclass of"
-            if parent_class.__name__ == "ZMQInteractiveShell":
-                return True  # Jupyter notebook, Google colab or qtconsole
-        return False
-    except NameError:
-        return False  # Probably standard Python interpreter
-
-
-def is_google_colab() -> bool:
-    """Return `True` if code is executed in a Google colab.
-
-    Taken from https://stackoverflow.com/a/63519730.
-    """
-    return _is_google_colab
-
-
-def dump_environment_info() -> Dict[str, Any]:
-    """Dump information about the machine to help debugging issues.
-
-    Similar helper exist in:
-    - `datasets` (https://github.com/huggingface/datasets/blob/main/src/datasets/commands/env.py)
-    - `diffusers` (https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/env.py)
-    - `transformers` (https://github.com/huggingface/transformers/blob/main/src/transformers/commands/env.py)
-    """
-    from huggingface_hub import HfFolder, whoami
-    from huggingface_hub.utils import list_credential_helpers
-
-    token = HfFolder().get_token()
-
-    # Generic machine info
-    info: Dict[str, Any] = {
-        "huggingface_hub version": get_hf_hub_version(),
-        "Platform": platform.platform(),
-        "Python version": get_python_version(),
-    }
-
-    # Interpreter info
-    try:
-        shell_class = get_ipython().__class__  # type: ignore # noqa: F821
-        info["Running in iPython ?"] = "Yes"
-        info["iPython shell"] = shell_class.__name__
-    except NameError:
-        info["Running in iPython ?"] = "No"
-    info["Running in notebook ?"] = "Yes" if is_notebook() else "No"
-    info["Running in Google Colab ?"] = "Yes" if is_google_colab() else "No"
-
-    # Login info
-    info["Token path ?"] = HfFolder().path_token
-    info["Has saved token ?"] = token is not None
-    if token is not None:
-        try:
-            info["Who am I ?"] = whoami()["name"]
-        except Exception:
-            pass
-
-    try:
-        info["Configured git credential helpers"] = ", ".join(list_credential_helpers())
-    except Exception:
-        pass
-
-    # Installed dependencies
-    info["FastAI"] = get_fastai_version()
-    info["Tensorflow"] = get_tf_version()
-    info["Torch"] = get_torch_version()
-    info["Jinja2"] = get_jinja_version()
-    info["Graphviz"] = get_graphviz_version()
-    info["Pydot"] = get_pydot_version()
-    info["Pillow"] = get_pillow_version()
-    info["hf_transfer"] = get_hf_transfer_version()
-    info["gradio"] = get_gradio_version()
-    info["tensorboard"] = get_tensorboard_version()
-    info["numpy"] = get_numpy_version()
-    info["pydantic"] = get_pydantic_version()
-    info["aiohttp"] = get_aiohttp_version()
-
-    # Environment variables
-    info["ENDPOINT"] = constants.ENDPOINT
-    info["HUGGINGFACE_HUB_CACHE"] = constants.HUGGINGFACE_HUB_CACHE
-    info["HUGGINGFACE_ASSETS_CACHE"] = constants.HUGGINGFACE_ASSETS_CACHE
-    info["HF_TOKEN_PATH"] = constants.HF_TOKEN_PATH
-    info["HF_HUB_OFFLINE"] = constants.HF_HUB_OFFLINE
-    info["HF_HUB_DISABLE_TELEMETRY"] = constants.HF_HUB_DISABLE_TELEMETRY
-    info["HF_HUB_DISABLE_PROGRESS_BARS"] = constants.HF_HUB_DISABLE_PROGRESS_BARS
-    info["HF_HUB_DISABLE_SYMLINKS_WARNING"] = constants.HF_HUB_DISABLE_SYMLINKS_WARNING
-    info["HF_HUB_DISABLE_EXPERIMENTAL_WARNING"] = constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING
-    info["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = constants.HF_HUB_DISABLE_IMPLICIT_TOKEN
-    info["HF_HUB_ENABLE_HF_TRANSFER"] = constants.HF_HUB_ENABLE_HF_TRANSFER
-
-    print("\nCopy-and-paste the text below in your GitHub issue.\n")
-    print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]) + "\n")
-    return info

spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/pages/04_🔊_Upload_Audio_File.py

@@ -1,205 +0,0 @@
-import whisper
-import streamlit as st
-from streamlit_lottie import st_lottie
-from utils import write_vtt, write_srt
-import ffmpeg
-import requests
-from typing import Iterator
-from io import StringIO
-import numpy as np
-import pathlib
-import os
-
-st.set_page_config(page_title="Auto Transcriber", page_icon="🔊", layout="wide")
-
-# Define a function that we can use to load lottie files from a link.
-@st.cache(allow_output_mutation=True)
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-
-APP_DIR = pathlib.Path(__file__).parent.absolute()
-
-LOCAL_DIR = APP_DIR / "local_audio"
-LOCAL_DIR.mkdir(exist_ok=True)
-save_dir = LOCAL_DIR / "output"
-save_dir.mkdir(exist_ok=True)
-
-
-col1, col2 = st.columns([1, 3])
-with col1:
-    lottie = load_lottieurl("https://assets1.lottiefiles.com/packages/lf20_1xbk4d2v.json")
-    st_lottie(lottie)
-
-with col2:
-    st.write("""
-    ## Auto Transcriber
-    ##### Input an audio file and get a transcript.
-    ###### ➠ If you want to transcribe the audio in its original language, select the task as "Transcribe"
-    ###### ➠ If you want to translate the transcription to English, select the task as "Translate" 
-    ###### I recommend starting with the base model and then experimenting with the larger models, the small and medium models often work well. """)
-
-loaded_model = whisper.load_model("base")
-current_size = "None"
-
-
-@st.cache(allow_output_mutation=True)
-def change_model(current_size, size):
-    if current_size != size:
-        loaded_model = whisper.load_model(size)
-        return loaded_model
-    else:
-        raise Exception("Model size is the same as the current size.")
-
-@st.cache(allow_output_mutation=True)
-def inferecence(loaded_model, uploaded_file, task):
-    with open(f"{save_dir}/input.mp3", "wb") as f:
-            f.write(uploaded_file.read())
-    audio = ffmpeg.input(f"{save_dir}/input.mp3")
-    audio = ffmpeg.output(audio, f"{save_dir}/output.wav", acodec="pcm_s16le", ac=1, ar="16k")
-    ffmpeg.run(audio, overwrite_output=True)
-    if task == "Transcribe":
-        options = dict(task="transcribe", best_of=5)
-        results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
-        vtt = getSubs(results["segments"], "vtt", 80)
-        srt = getSubs(results["segments"], "srt", 80)
-        lang = results["language"]
-        return results["text"], vtt, srt, lang
-    elif task == "Translate":
-        options = dict(task="translate", best_of=5)
-        results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
-        vtt = getSubs(results["segments"], "vtt", 80)
-        srt = getSubs(results["segments"], "srt", 80)
-        lang = results["language"]
-        return results["text"], vtt, srt, lang
-    else:
-        raise ValueError("Task not supported")
-
-
-def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
-    segmentStream = StringIO()
-
-    if format == 'vtt':
-        write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    elif format == 'srt':
-        write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-    else:
-        raise Exception("Unknown format " + format)
-
-    segmentStream.seek(0)
-    return segmentStream.read()
-
-
-def main():
-    size = st.selectbox("Select Model Size (The larger the model, the more accurate the transcription will be, but it will take longer)", ["tiny", "base", "small", "medium", "large"], index=1)
-    loaded_model = change_model(current_size, size)
-    st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
-        f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
-    input_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "m4a"])
-    if input_file is not None:
-        filename = input_file.name[:-4]
-    else:
-        filename = None
-    task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
-    if task == "Transcribe":
-        if st.button("Transcribe"):
-            results = inferecence(loaded_model, input_file, task)
-            col3, col4 = st.columns(2)
-            col5, col6, col7 = st.columns(3)
-            col9, col10 = st.columns(2)
-            
-            with col3:
-                st.audio(input_file)
-                
-            with open("transcript.txt", "w+", encoding='utf8') as f:
-                f.writelines(results[0])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
-                datatxt = f.read()
-                
-
-            with open("transcript.vtt", "w+",encoding='utf8') as f:
-                f.writelines(results[1])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
-                datavtt = f.read()
-                
-            with open("transcript.srt", "w+",encoding='utf8') as f:
-                f.writelines(results[2])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
-                datasrt = f.read()
-
-            with col5:
-                st.download_button(label="Download Transcript (.txt)",
-                                data=datatxt,
-                                file_name="transcript.txt")
-            with col6:   
-                st.download_button(label="Download Transcript (.vtt)",
-                                    data=datavtt,
-                                    file_name="transcript.vtt")
-            with col7:
-                st.download_button(label="Download Transcript (.srt)",
-                                    data=datasrt,
-                                    file_name="transcript.srt")
-            with col9:
-                st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
-            with col10:
-                st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-
-    elif task == "Translate":
-        if st.button("Translate to English"):
-            results = inferecence(loaded_model, input_file, task)
-            col3, col4 = st.columns(2)
-            col5, col6, col7 = st.columns(3)
-            col9, col10 = st.columns(2)
-
-            with col3:
-                st.audio(input_file)
-                
-            with open("transcript.txt", "w+", encoding='utf8') as f:
-                f.writelines(results[0])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
-                datatxt = f.read()
-                
-
-            with open("transcript.vtt", "w+",encoding='utf8') as f:
-                f.writelines(results[1])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
-                datavtt = f.read()
-                
-            with open("transcript.srt", "w+",encoding='utf8') as f:
-                f.writelines(results[2])
-                f.close()
-            with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
-                datasrt = f.read()
-                
-            with col5:
-                st.download_button(label="Download Transcript (.txt)",
-                                data=datatxt,
-                                file_name="transcript.txt")
-            with col6:   
-                st.download_button(label="Download Transcript (.vtt)",
-                                    data=datavtt,
-                                    file_name="transcript.vtt")
-            with col7:
-                st.download_button(label="Download Transcript (.srt)",
-                                    data=datasrt,
-                                    file_name="transcript.srt")
-            with col9:
-                st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
-            with col10:
-                st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
-
-    else:
-        st.error("Please select a task.")
-
-
-if __name__ == "__main__":
-    main()
-    st.markdown("###### Made with :heart: by [@BatuhanYılmaz](https://twitter.com/batuhan3326) [![this is an image link](https://i.imgur.com/thJhzOO.png)](https://www.buymeacoffee.com/batuhanylmz)")

spaces/Davidsamuel101/PPTGenerator/README.md

@@ -1,14 +0,0 @@
----
-title: PPTGenerator
-emoji: 📊
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: src/app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-<!-- To run the app locally run `gradio app.py` -->

spaces/Eddycrack864/Applio-Inference/tensorlowest.py

@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
-    try:
-        files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
-        if not files:
-            print("No files with the '.0' extension found!")
-            return
-
-        max_size = 0
-        biggest_file = ""
-
-        for file in files:
-            file_path = os.path.join(tensordir, file)
-            if os.path.isfile(file_path):
-                file_size = os.path.getsize(file_path)
-                if file_size > max_size:
-                    max_size = file_size
-                    biggest_file = file
-
-        return biggest_file
-
-    except FileNotFoundError:
-        print("Couldn't find your model!")
-        return
-
-def main(model_name, save_freq, lastmdls):
-    global lowestval_weight_dir, scl
-
-    tensordir = os.path.join('logs', model_name)
-    lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-    
-    latest_file = find_biggest_tensorboard(tensordir)
-    
-    if latest_file is None:
-        print("Couldn't find a valid tensorboard file!")
-        return
-    
-    tfile = os.path.join(tensordir, latest_file)
-    
-    ea = event_accumulator.EventAccumulator(tfile,
-        size_guidance={
-        event_accumulator.COMPRESSED_HISTOGRAMS: 500,
-        event_accumulator.IMAGES: 4,
-        event_accumulator.AUDIO: 4,
-        event_accumulator.SCALARS: 0,
-        event_accumulator.HISTOGRAMS: 1,
-    })
-
-    ea.Reload()
-    ea.Tags()
-
-    scl = ea.Scalars('loss/g/total')
-
-    listwstep = {}
-    
-    for val in scl:
-        if (val.step // save_freq) * save_freq in [val.step for val in scl]:
-            listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
-    lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
-    sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-    
-    return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
-    os.makedirs(lowestval_weight_dir, exist_ok=True)
-    logdir = []
-    files = []
-    lbldict = {
-        'Values': {},
-        'Names': {}
-    }
-    weights_dir_path = os.path.join(weights_dir, "")
-    low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-    
-    try:
-        file_dict = LEval(file_dict)
-    except Exception as e: 
-        print(f"Error! {e}")
-        return f"Couldn't load tensorboard file! {e}"
-    
-    weights = [f for f in os.scandir(weights_dir)]
-    for key, value in file_dict.items():
-        pattern = fr"^{model_name}_.*_s{value}\.pth$"
-        matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
-        for weight in matching_weights:
-            source_path = weights_dir_path + weight
-            destination_path = os.path.join(lowestval_weight_dir, weight)
-            
-            copy2(source_path, destination_path)
-
-            logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
-            lbldict['Names'][weight] = weight
-            lbldict['Values'][weight] = key
-
-            files.append(low_val_path + weight)
-
-            print(f"File = {weight} Value: {key}, Step: {value}")
-
-            yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-            
-
-    return ''.join(logdir), files, pd.DataFrame(lbldict)
-    
-
-if __name__ == "__main__":
-    model = str(input("Enter the name of the model: "))
-    sav_freq = int(input("Enter save frequency of the model: "))
-    ds = main(model, sav_freq)
-    
-    if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-    

spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/yolo.py

@@ -1,283 +0,0 @@
-import argparse
-import logging
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-import math
-
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-logger = logging.getLogger(__name__)
-
-import torch
-import torch.nn as nn
-
-from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
-from models.experimental import MixConv2d, CrossConv, C3
-from utils.general import check_anchor_order, make_divisible, check_file, set_logging
-from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
-    select_device, copy_attr
-
-
-class Detect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super(Detect, self).__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer('anchors', a)  # shape(nl,na,2)
-        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
-
-    def forward(self, x):
-        # x = x.copy()  # for profiling
-        z = []  # inference output
-        self.training |= self.export
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
-    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
-        super(Model, self).__init__()
-        if isinstance(cfg, dict):
-            self.yaml = cfg  # model dict
-        else:  # is *.yaml
-            import yaml  # for torch hub
-            self.yaml_file = Path(cfg).name
-            with open(cfg) as f:
-                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
-
-        # Define model
-        if nc and nc != self.yaml['nc']:
-            print('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
-            self.yaml['nc'] = nc  # override yaml value
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist, ch_out
-        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, Detect):
-            s = 128  # 2x min stride
-            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
-
-        # Init weights, biases
-        initialize_weights(self)
-        self.info()
-        print('')
-
-    def forward(self, x, augment=False, profile=False):
-        if augment:
-            img_size = x.shape[-2:]  # height, width
-            s = [1, 0.83, 0.67]  # scales
-            f = [None, 3, None]  # flips (2-ud, 3-lr)
-            y = []  # outputs
-            for si, fi in zip(s, f):
-                xi = scale_img(x.flip(fi) if fi else x, si)
-                yi = self.forward_once(xi)[0]  # forward
-                # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
-                yi[..., :4] /= si  # de-scale
-                if fi == 2:
-                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
-                elif fi == 3:
-                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
-                y.append(yi)
-            return torch.cat(y, 1), None  # augmented inference, train
-        else:
-            return self.forward_once(x, profile)  # single-scale inference, train
-
-    def forward_once(self, x, profile=False):
-        y, dt = [], []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
-            if profile:
-                try:
-                    import thop
-                    o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # FLOPS
-                except:
-                    o = 0
-                t = time_synchronized()
-                for _ in range(10):
-                    _ = m(x)
-                dt.append((time_synchronized() - t) * 100)
-                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
-
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-
-        if profile:
-            print('%.1fms total' % sum(dt))
-        return x
-
-    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _print_biases(self):
-        m = self.model[-1]  # Detect() module
-        for mi in m.m:  # from
-            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
-
-    # def _print_weights(self):
-    #     for m in self.model.modules():
-    #         if type(m) is Bottleneck:
-    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        # print('Fusing layers... ')
-        for m in self.model.modules():
-            if type(m) is Conv and hasattr(m, 'bn'):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, 'bn')  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
-        self.info()
-        return self
-
-    def nms(self, mode=True):  # add or remove NMS module
-        present = type(self.model[-1]) is NMS  # last layer is NMS
-        if mode and not present:
-            print('Adding NMS... ')
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name='%s' % m.i, module=m)  # add
-            self.eval()
-        elif not mode and present:
-            print('Removing NMS... ')
-            self.model = self.model[:-1]  # remove
-        return self
-
-    def autoshape(self):  # add autoShape module
-        print('Adding autoShape... ')
-        m = autoShape(self)  # wrap model
-        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
-        return m
-
-    def info(self, verbose=False):  # print model information
-        model_info(self, verbose)
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
-    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
-    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
-            c1, c2 = ch[f], args[0]
-
-            # Normal
-            # if i > 0 and args[0] != no:  # channel expansion factor
-            #     ex = 1.75  # exponential (default 2.0)
-            #     e = math.log(c2 / ch[1]) / math.log(2)
-            #     c2 = int(ch[1] * ex ** e)
-            # if m != Focus:
-
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            # Experimental
-            # if i > 0 and args[0] != no:  # channel expansion factor
-            #     ex = 1 + gw  # exponential (default 2.0)
-            #     ch1 = 32  # ch[1]
-            #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
-            #     c2 = int(ch1 * ex ** e)
-            # if m != Focus:
-            #     c2 = make_divisible(c2, 8) if c2 != no else c2
-
-            args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
-        elif m is Detect:
-            args.append([ch[x + 1] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-        else:
-            c2 = ch[f]
-
-        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum([x.numel() for x in m_.parameters()])  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    opt = parser.parse_args()
-    opt.cfg = check_file(opt.cfg)  # check file
-    set_logging()
-    device = select_device(opt.device)
-
-    # Create model
-    model = Model(opt.cfg).to(device)
-    model.train()
-
-    # Profile
-    # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
-    # y = model(img, profile=True)
-
-    # Tensorboard
-    # from torch.utils.tensorboard import SummaryWriter
-    # tb_writer = SummaryWriter()
-    # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
-    # tb_writer.add_graph(model.model, img)  # add model to tensorboard
-    # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard

spaces/FSDL-Fashion/fashion_img_search/app.py

@@ -1 +0,0 @@
-from fis.app import app  # noqa: F401