Update parquet files (step 74 of 249)

spaces/0xSynapse/PixelFusion/README.md

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----
-title: PixelFusion
-emoji: 🔥
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/17TheWord/RealESRGAN/realesrgan/data/realesrgan_dataset.py

@@ -1,192 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import os.path as osp
-import random
-import time
-import torch
-from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANDataset(data.Dataset):
-    """Dataset used for Real-ESRGAN model:
-    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It loads gt (Ground-Truth) images, and augments them.
-    It also generates blur kernels and sinc kernels for generating low-quality images.
-    Note that the low-quality images are processed in tensors on GPUS for faster processing.
-
-    Args:
-        opt (dict): Config for train datasets. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            meta_info (str): Path for meta information file.
-            io_backend (dict): IO backend type and other kwarg.
-            use_hflip (bool): Use horizontal flips.
-            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
-            Please see more options in the codes.
-    """
-
-    def __init__(self, opt):
-        super(RealESRGANDataset, self).__init__()
-        self.opt = opt
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-        self.gt_folder = opt['dataroot_gt']
-
-        # file client (lmdb io backend)
-        if self.io_backend_opt['type'] == 'lmdb':
-            self.io_backend_opt['db_paths'] = [self.gt_folder]
-            self.io_backend_opt['client_keys'] = ['gt']
-            if not self.gt_folder.endswith('.lmdb'):
-                raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
-            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
-                self.paths = [line.split('.')[0] for line in fin]
-        else:
-            # disk backend with meta_info
-            # Each line in the meta_info describes the relative path to an image
-            with open(self.opt['meta_info']) as fin:
-                paths = [line.strip().split(' ')[0] for line in fin]
-                self.paths = [os.path.join(self.gt_folder, v) for v in paths]
-
-        # blur settings for the first degradation
-        self.blur_kernel_size = opt['blur_kernel_size']
-        self.kernel_list = opt['kernel_list']
-        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
-        self.blur_sigma = opt['blur_sigma']
-        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
-        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
-        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters
-
-        # blur settings for the second degradation
-        self.blur_kernel_size2 = opt['blur_kernel_size2']
-        self.kernel_list2 = opt['kernel_list2']
-        self.kernel_prob2 = opt['kernel_prob2']
-        self.blur_sigma2 = opt['blur_sigma2']
-        self.betag_range2 = opt['betag_range2']
-        self.betap_range2 = opt['betap_range2']
-        self.sinc_prob2 = opt['sinc_prob2']
-
-        # a final sinc filter
-        self.final_sinc_prob = opt['final_sinc_prob']
-
-        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
-        # TODO: kernel range is now hard-coded, should be in the configure file
-        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
-        self.pulse_tensor[10, 10] = 1
-
-    def __getitem__(self, index):
-        if self.file_client is None:
-            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
-        # -------------------------------- Load gt images -------------------------------- #
-        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
-        gt_path = self.paths[index]
-        # avoid errors caused by high latency in reading files
-        retry = 3
-        while retry > 0:
-            try:
-                img_bytes = self.file_client.get(gt_path, 'gt')
-            except (IOError, OSError) as e:
-                logger = get_root_logger()
-                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
-                # change another file to read
-                index = random.randint(0, self.__len__())
-                gt_path = self.paths[index]
-                time.sleep(1)  # sleep 1s for occasional server congestion
-            else:
-                break
-            finally:
-                retry -= 1
-        img_gt = imfrombytes(img_bytes, float32=True)
-
-        # -------------------- Do augmentation for training: flip, rotation -------------------- #
-        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])
-
-        # crop or pad to 400
-        # TODO: 400 is hard-coded. You may change it accordingly
-        h, w = img_gt.shape[0:2]
-        crop_pad_size = 400
-        # pad
-        if h < crop_pad_size or w < crop_pad_size:
-            pad_h = max(0, crop_pad_size - h)
-            pad_w = max(0, crop_pad_size - w)
-            img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101)
-        # crop
-        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
-            h, w = img_gt.shape[0:2]
-            # randomly choose top and left coordinates
-            top = random.randint(0, h - crop_pad_size)
-            left = random.randint(0, w - crop_pad_size)
-            img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...]
-
-        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
-        kernel_size = random.choice(self.kernel_range)
-        if np.random.uniform() < self.opt['sinc_prob']:
-            # this sinc filter setting is for kernels ranging from [7, 21]
-            if kernel_size < 13:
-                omega_c = np.random.uniform(np.pi / 3, np.pi)
-            else:
-                omega_c = np.random.uniform(np.pi / 5, np.pi)
-            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
-        else:
-            kernel = random_mixed_kernels(
-                self.kernel_list,
-                self.kernel_prob,
-                kernel_size,
-                self.blur_sigma,
-                self.blur_sigma, [-math.pi, math.pi],
-                self.betag_range,
-                self.betap_range,
-                noise_range=None)
-        # pad kernel
-        pad_size = (21 - kernel_size) // 2
-        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
-
-        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
-        kernel_size = random.choice(self.kernel_range)
-        if np.random.uniform() < self.opt['sinc_prob2']:
-            if kernel_size < 13:
-                omega_c = np.random.uniform(np.pi / 3, np.pi)
-            else:
-                omega_c = np.random.uniform(np.pi / 5, np.pi)
-            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
-        else:
-            kernel2 = random_mixed_kernels(
-                self.kernel_list2,
-                self.kernel_prob2,
-                kernel_size,
-                self.blur_sigma2,
-                self.blur_sigma2, [-math.pi, math.pi],
-                self.betag_range2,
-                self.betap_range2,
-                noise_range=None)
-
-        # pad kernel
-        pad_size = (21 - kernel_size) // 2
-        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
-
-        # ------------------------------------- the final sinc kernel ------------------------------------- #
-        if np.random.uniform() < self.opt['final_sinc_prob']:
-            kernel_size = random.choice(self.kernel_range)
-            omega_c = np.random.uniform(np.pi / 3, np.pi)
-            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
-            sinc_kernel = torch.FloatTensor(sinc_kernel)
-        else:
-            sinc_kernel = self.pulse_tensor
-
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
-        kernel = torch.FloatTensor(kernel)
-        kernel2 = torch.FloatTensor(kernel2)
-
-        return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path}
-        return return_d
-
-    def __len__(self):
-        return len(self.paths)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Acronis True Image 2020 Bootable ISO Build 20770 The Ultimate Backup Solution.md

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-
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-</ul>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Avira Antivirus for Windows 10 32 Bit A Complete Guide.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Kpg 141d Learn How to Program NX Series Radios with KPG-141D FPU.md

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-<p>The most obvious alternative to Crack Kpg 141d is to buy the original Kenwood programming software from an authorized dealer or online store. This way, you can ensure that you are using a legitimate and safe software that is compatible with your Kenwood radio. You can also enjoy the full support and warranty from Kenwood and avoid any legal or ethical issues. However, the downside of this alternative is that it can be quite expensive and hard to find. For example, according to Radio Software Online, the original price of KPG-141D is $150 USD. You may also need to buy different software for different radio models or frequency ranges.</p>
-<p>Kenwood radio programming software download<br />
-Kenwood KPG-141D FPU (v5.1) Programming Software – HamFiles<br />
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-Kenwood KPG-141D FPU (v5.1) Programming Software User Manual<br />
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-Kenwood KPG-141D FPU (v5.1) Programming Software Crack Patch Keygen<br />
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-Kenwood KPG-141D FPU (v5.1) Programming Software Alternative Software<br />
-Kenwood KPG-141D FPU (v5.1) Programming Software Features and Benefits<br />
-Kenwood KPG-141D FPU (v5.1) Programming Software Requirements and Specifications<br />
-Kenwood KPG-141D FPU (v5.1) Programming Software Troubleshooting and Support<br />
-Kenwood KPG-141D FPU (v5.1) Programming Software Updates and Upgrades<br />
-How to use Kenwood KPG-141D FPU (v5.1) Programming Software<br />
-How to program NX series radios with Kenwood KPG-141D FPU (v5.1) Programming Software<br />
-How to crack Kenwood KPG-141D FPU (v5.1) Programming Software<br />
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-How to get Kenwood KPG-141D FPU (v5.1) Programming Software for free<br />
-How to download Kenwood KPG-141D FPU (v5.1) Programming Software from HamFiles<br />
-How to install Kenwood KPG-141D FPU (v5.1) Programming Software on Windows 10<br />
-How to update Kenwood KPG-141D FPU (v5.1) Programming Software to the latest version<br />
-How to uninstall Kenwood KPG-141D FPU (v5.1) Programming Software from your computer<br />
-How to backup and restore your data with Kenwood KPG-141D FPU (v5.1) Programming Software<br />
-What is the difference between Kenwood KPG-141D FPU and other programming software<br />
-What are the advantages of using Kenwood KPG-141D FPU over other programming software<br />
-What are the limitations of using Kenwood KPG-141D FPU for programming radios<br />
-What are the best practices for using Kenwood KPG-141D FPU safely and securely<br />
-What are the common errors and issues with Kenwood KPG-141D FPU and how to fix them<br />
-Where can I find more information about Kenwood KPG-141D FPU and its features<br />
-Where can I buy or order Kenwood KPG</p>
- <h3>Alternative 2: Use other free or low-cost programming software that are compatible with Kenwood radios, such as CHIRP or RT Systems</h3>
-<p>Another alternative to Crack Kpg 141d is to use other free or low-cost programming software that are compatible with Kenwood radios, such as CHIRP or RT Systems. These are third-party software that can program various models and brands of radios, including Kenwood radios. They are usually easy to use and have many features and options. They are also updated regularly and have a large community of users who can provide help and feedback. However, the downside of this alternative is that they may not support all the functions and options of your Kenwood radio. They may also have some bugs or errors that can affect your programming process.</p>
- <h1>Conclusion</h1>
-<p>In conclusion, Crack Kpg 141d is a software that allows you to program your Kenwood radio without paying for the official Kenwood programming software. It has some benefits and risks that you should weigh before deciding whether to use it or not. It also has some alternatives that you can consider if you are looking for other options. We hope this article has helped you understand what Crack Kpg 141d is and how to use it. However, we do not endorse or recommend using Crack Kpg 141d for any purpose. We advise you to use it at your own risk and responsibility.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>What is Crack Kpg 141d?</b><br>
-Crack Kpg 141d is a software that allows you to program your Kenwood radio without paying for the official Kenwood programming software.</li>
-<li><b>Where can I download Crack Kpg 141d?</b><br>
-You can download Crack Kpg 141d from various websites that offer it for free, but some of them may contain viruses, malware, or spyware that can harm your computer or radio. One of the most trusted sources for downloading Crack Kpg 141d is HamFiles, a website that provides various radio programming software, patches, cracks, and serial numbers.</li>
-<li><b>How do I install Crack Kpg 141d?</b><br>
-You need to extract the zip file and run the setup file. Then follow the instructions on the screen and enter the serial number that is provided in the folder "Serial Number".</li>
-<li><b>How do I use Crack Kpg 141d?</b><br>
-You need to connect your Kenwood radio to your computer using a USB cable or a programming cable. Then launch Crack Kpg 141d and select your radio model and frequency range. Then customize the settings and features of your radio according to your preferences. Then write the data to your radio and verify that it works correctly.</li>
-<li><b>What are the benefits and risks of using Crack Kpg 141d?</b><br>
-The benefits of using Crack Kpg 141d are that you can access all the functions and options of your Kenwood radio without paying for the official software and that you can program multiple radios with different models and frequencies using one software. The risks of using Crack Kpg 141d are that you may violate the license agreement and warranty of your Kenwood radio by using unauthorized software and that you may damage your radio or computer by using corrupted or infected files.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/Bus Driver Simulator 2019 DLC Unlocker-PLAZA.md

@@ -1,125 +0,0 @@
-<br />
-<h1>Bus Driver Simulator 2019 DLC Unlocker-PLAZA: How to Enjoy the Full Experience of Driving Different Buses</h1>
-
-<p>Bus Driver Simulator 2019 is a game that lets you experience the life of a passenger bus driver. You can drive different buses from different countries and eras, such as Soviet, European, American, and Hungarian buses. You can also explore real cities and their suburbs, such as Moscow, Serpukhov, Cologne, Paris, and Budapest. You can customize your bus with various skins, stickers, horns, and accessories. You can complete various scenarios with pre-set conditions or build your own career in free mode.</p>
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-<p>However, if you want to enjoy the full experience of the game, you may need to buy some DLCs that add more content and features to the game. These DLCs are:</p>
-
-<ul>
-<li>Bus Driver Simulator 2019 - Hungarian Legend: This DLC adds a legendary Hungarian bus Ikarus 250.93 to the game.</li>
-<li>Bus Driver Simulator 2019 - Soviet Legend: This DLC adds a legendary Soviet bus LAZ-695 to the game.</li>
-<li>Bus Driver Simulator 2019 - European Minibus: This DLC adds a modern European minibus Mercedes-Benz Sprinter to the game.</li>
-<li>Bus Driver Simulator 2019 - Old Legend: This DLC adds an old American school bus International Harvester Loadstar to the game.</li>
-</ul>
-
-<p>But what if you don't want to spend money on these DLCs? What if you want to get them for free? Well, there is a solution for this: Bus Driver Simulator 2019 DLC Unlocker-PLAZA.</p>
-
-<h2>What is Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a file that you can download and install on your PC to unlock all the DLCs for the game. This way, you can play with all the buses, maps, and options that the game has to offer, without paying anything.</p>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is also compatible with the latest version of the game, v3.9.68, which includes many updates and improvements to make the game more realistic and enjoyable.</p>
-
-<h2>Where to Download Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>You can find many websites that offer Bus Driver Simulator 2019 DLC Unlocker-PLAZA for free, but you need to be careful and choose a reliable and trustworthy source. Some websites may contain viruses or malware that can harm your PC or steal your personal information.</p>
-<p></p>
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-<p>One of the best websites to download Bus Driver Simulator 2019 DLC Unlocker-PLAZA is Skidrow & Reloaded Games. This website has been around for a long time and provides safe and working torrent files for many PC games. You can download Bus Driver Simulator 2019 DLC Unlocker-PLAZA from this link: https://www.skidrowreloaded.com/bus-driver-simulator-2019-plaza/</p>
-
-<h2>How to Install Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>Installing Bus Driver Simulator 2019 DLC Unlocker-PLAZA is very easy and simple. Here are the steps you need to follow:</p>
-
-<ol>
-<li>Download Bus Driver Simulator 2019 DLC Unlocker-PLAZA from Skidrow & Reloaded Games or another trusted website.</li>
-<li>Extract the file using a program like WinRAR or 7-Zip. You will get a folder called PLAZA.</li>
-<li>Copy the folder and paste it into your Bus Driver Simulator 2019 installation folder. This is usually located in C:\Program Files (x86)\Steam\steamapps\common\Bus Driver Simulator 2019\ or C:\Program Files\Steam\steamapps\common\Bus Driver Simulator 2019\ depending on your system.</li>
-<li>Merge the folder with the existing one. You may need to confirm this action or provide administrator permission.</li>
-<li>Run the game as usual by launching it from Steam or using a shortcut on your desktop.</li>
-</ol>
-
-<p>Congratulations! You have successfully installed Bus Driver Simulator 2019 DLC Unlocker-PLAZA and you can now play with all the DLCs for free.</p>
-
-<h2>Why Should You Use Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>There are many benefits of using Bus Driver Simulator 2019 DLC Unlocker-PLAZA to play the game. Here are some of them:</p>
-
-<ul>
-<li>You can save money by not having to buy the DLCs separately.</li>
-<li>You can have more fun and variety by playing with different buses, maps, and options.</li>
-<li>You can enjoy the game with better performance and stability, as the unlocker eliminates some bugs and errors that may occur with the DLCs.</li>
-<li>You can use the unlocker with any version of the game, including v3.9.68, which is the most updated and improved one.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Bus Driver Simulator 2019 is a game that deserves to be played by every simulation fan who loves driving games. However, if you want to enjoy the full experience of the game, you may need to get some DLCs that add more content and features to the game. That's why Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a great solution for this problem.</p>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a file that you can download and install on your PC to unlock all the DLCs for the game. It is easy to use, compatible with any version of the game, and works with any patch or mod that you want to use. It also improves the performance and stability of the game, making it more enjoyable and realistic.</p>
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-<p>If you want to play Bus Driver Simulator 2019 with all the content and features for free, download Bus Driver Simulator 2019 DLC Unlocker-PLAZA today and experience the best driving simulation game ever made.</p>
-<h2>What are the Features of Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is not just a simple file that unlocks the DLCs for the game. It also adds some features and options that make the game more enjoyable and realistic. Here are some of them:</p>
-
-<ul>
-<li>You can choose from different weather conditions and time of day, such as sunny, cloudy, rainy, snowy, day, night, etc.</li>
-<li>You can adjust the traffic density and difficulty level, such as easy, normal, hard, etc.</li>
-<li>You can enable or disable the realistic physics and damage system, such as collisions, breakdowns, tire wear, etc.</li>
-<li>You can enable or disable the realistic passenger behavior and feedback system, such as boarding, alighting, paying, complaining, etc.</li>
-<li>You can enable or disable the realistic traffic rules and regulations system, such as speed limits, traffic lights, signs, fines, etc.</li>
-</ul>
-
-<h2>How to Play Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a game that is easy to play but hard to master. You need to have good driving skills and knowledge of the traffic rules and regulations. You also need to have good management skills and customer service skills. Here are some tips and tricks to help you play the game:</p>
-
-<ol>
-<li>Choose a bus that suits your style and preference. You can choose from different buses with different characteristics, such as speed, acceleration, handling, fuel consumption, capacity, etc.</li>
-<li>Choose a map that suits your mood and challenge. You can choose from different maps with different locations, routes, landmarks, scenery, etc.</li>
-<li>Choose a mode that suits your goal and interest. You can choose from different modes with different objectives, conditions, rewards, etc. You can play scenarios with pre-set goals and situations. You can play free mode with your own rules and settings. You can also play online multiplayer mode with other players or friends.</li>
-<li>Drive carefully and responsibly. You need to follow the traffic rules and regulations. You need to avoid accidents and damages. You need to respect other road users and pedestrians. You need to drive smoothly and safely.</li>
-<li>Manage your bus and passengers well. You need to check your bus condition and fuel level. You need to service your bus regularly and repair it when needed. You need to pick up and drop off passengers at designated stops. You need to collect fares and give change. You need to satisfy your passengers and deal with their complaints.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a game that will give you a lot of fun and satisfaction. It is a game that will let you experience the life of a passenger bus driver. It is a game that will let you drive different buses from different countries and eras. It is a game that will let you explore real cities and their suburbs. It is a game that will let you customize your bus with various skins, stickers, horns, and accessories. It is a game that will let you complete various scenarios with pre-set conditions or build your own career in free mode.</p>
-
-<p>If you want to play this amazing game without a CD, download Bus Driver Simulator 2019 DLC Unlocker-PLAZA today and enjoy the best driving simulation game ever made.</p>
-<h2>What are the Reviews of Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a game that has received positive reviews from many players and critics. Here are some of the reviews that you can find online:</p>
-
-<ul>
-<li>"This game is amazing. I love driving different buses and exploring different cities. The graphics are great and the physics are realistic. The DLCs add more content and variety to the game. The unlocker works perfectly and saves me money. I highly recommend this game to anyone who likes driving games." - Steam user</li>
-<li>"This game is a hidden gem. It is one of the best driving simulation games I have ever played. The game is very immersive and challenging. The DLCs are awesome and add more buses, maps, and options to the game. The unlocker is easy to use and compatible with any version of the game. This game is a must-have for any simulation fan." - Skidrow & Reloaded Games user</li>
-<li>"This game is a lot of fun and satisfaction. It is a game that lets you experience the life of a passenger bus driver. The game is very realistic and enjoyable. The DLCs are worth it and add more content and features to the game. The unlocker is a great solution for this problem. It unlocks all the DLCs for free and improves the performance and stability of the game. This game is a great value for money." - JJ Riley user</li>
-</ul>
-
-<h2>What are the Alternatives to Bus Driver Simulator 2019 DLC Unlocker-PLAZA?</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a game that may not be suitable for everyone. Some people may not like driving games or simulation games. Some people may not like the idea of using an unlocker to get free DLCs. Some people may have technical issues or compatibility problems with the game or the unlocker. If you are one of these people, you may want to look for some alternatives to Bus Driver Simulator 2019 DLC Unlocker-PLAZA. Here are some of them:</p>
-
-<ul>
-<li>Bus Simulator 18: This is another bus driving simulation game that lets you drive different buses in a huge open world map. You can also create your own routes, customize your buses, play online multiplayer mode, and use mods.</li>
-<li>Omsi 2: This is another bus driving simulation game that lets you drive different buses from different eras in realistic scenarios. You can also create your own maps, vehicles, scripts, and sounds.</li>
-<li>Fernbus Simulator: This is another bus driving simulation game that lets you drive modern coaches across Germany and Europe. You can also experience realistic traffic, weather, passengers, damage, and accidents.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a game that will give you a lot of fun and satisfaction. It is a game that will let you experience the life of a passenger bus driver. It is a game that will let you drive different buses from different countries and eras. It is a game that will let you explore real cities and their suburbs. It is a game that will let you customize your bus with various skins, stickers, horns, and accessories. It is a game that will let you complete various scenarios with pre-set conditions or build your own career in free mode.</p>
-
-<p>If you want to play this amazing game without a CD, download Bus Driver Simulator 2019 DLC Unlocker-PLAZA today and enjoy the best driving simulation game ever made.</p>
-<h2>Conclusion</h2>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a game that deserves to be played by every simulation fan who loves driving games. However, if you want to enjoy the full experience of the game, you may need to get some DLCs that add more content and features to the game. That's why Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a great solution for this problem.</p>
-
-<p>Bus Driver Simulator 2019 DLC Unlocker-PLAZA is a file that you can download and install on your PC to unlock all the DLCs for the game. It is easy to use, compatible with any version of the game, and works with any patch or mod that you want to use. It also improves the performance and stability of the game, making it more enjoyable and realistic.</p>
-
-<p>If you want to play Bus Driver Simulator 2019 with all the content and features for free, download Bus Driver Simulator 2019 DLC Unlocker-PLAZA today and experience the best driving simulation game ever made.</p> 3cee63e6c2<br />
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spaces/1line/AutoGPT/CONTRIBUTING.md

@@ -1,105 +0,0 @@
-# Contributing to ProjectName
-
-First of all, thank you for considering contributing to our project! We appreciate your time and effort, and we value any contribution, whether it's reporting a bug, suggesting a new feature, or submitting a pull request.
-
-This document provides guidelines and best practices to help you contribute effectively.
-
-## Table of Contents
-
-- [Code of Conduct](#code-of-conduct)
-- [Getting Started](#getting-started)
-- [How to Contribute](#how-to-contribute)
-  - [Reporting Bugs](#reporting-bugs)
-  - [Suggesting Enhancements](#suggesting-enhancements)
-  - [Submitting Pull Requests](#submitting-pull-requests)
-- [Style Guidelines](#style-guidelines)
-  - [Code Formatting](#code-formatting)
-  - [Pre-Commit Hooks](#pre-commit-hooks)
-
-## Code of Conduct
-
-By participating in this project, you agree to abide by our [Code of Conduct](CODE_OF_CONDUCT.md). Please read it to understand the expectations we have for everyone who contributes to this project.
-
-## 📢 A Quick Word
-Right now we will not be accepting any Contributions that add non-essential commands to Auto-GPT.
-
-However, you absolutely can still add these commands to Auto-GPT in the form of plugins. Please check out this [template](https://github.com/Significant-Gravitas/Auto-GPT-Plugin-Template).
-> ⚠️ Plugin support is expected to ship within the week. You can follow PR #757 for more updates!
-
-## Getting Started
-
-To start contributing, follow these steps:
-
-1. Fork the repository and clone your fork.
-2. Create a new branch for your changes (use a descriptive name, such as `fix-bug-123` or `add-new-feature`).
-3. Make your changes in the new branch.
-4. Test your changes thoroughly.
-5. Commit and push your changes to your fork.
-6. Create a pull request following the guidelines in the [Submitting Pull Requests](#submitting-pull-requests) section.
-
-## How to Contribute
-
-### Reporting Bugs
-
-If you find a bug in the project, please create an issue on GitHub with the following information:
-
-- A clear, descriptive title for the issue.
-- A description of the problem, including steps to reproduce the issue.
-- Any relevant logs, screenshots, or other supporting information.
-
-### Suggesting Enhancements
-
-If you have an idea for a new feature or improvement, please create an issue on GitHub with the following information:
-
-- A clear, descriptive title for the issue.
-- A detailed description of the proposed enhancement, including any benefits and potential drawbacks.
-- Any relevant examples, mockups, or supporting information.
-
-### Submitting Pull Requests
-
-When submitting a pull request, please ensure that your changes meet the following criteria:
-
-- Your pull request should be atomic and focus on a single change.
-- Your pull request should include tests for your change.
-- You should have thoroughly tested your changes with multiple different prompts.
-- You should have considered potential risks and mitigations for your changes.
-- You should have documented your changes clearly and comprehensively.
-- You should not include any unrelated or "extra" small tweaks or changes.
-
-## Style Guidelines
-
-### Code Formatting
-
-We use the `black` code formatter to maintain a consistent coding style across the project. Please ensure that your code is formatted using `black` before submitting a pull request. You can install `black` using `pip`:
-
-```bash
-pip install black
-```
-
-To format your code, run the following command in the project's root directory:
-
-```bash
-black .
-```
-### Pre-Commit Hooks
-We use pre-commit hooks to ensure that code formatting and other checks are performed automatically before each commit. To set up pre-commit hooks for this project, follow these steps:
-
-Install the pre-commit package using pip:
-```bash
-pip install pre-commit
-```
-
-Run the following command in the project's root directory to install the pre-commit hooks:
-```bash
-pre-commit install
-```
-
-Now, the pre-commit hooks will run automatically before each commit, checking your code formatting and other requirements.
-
-If you encounter any issues or have questions, feel free to reach out to the maintainers or open a new issue on GitHub. We're here to help and appreciate your efforts to contribute to the project.
-
-Happy coding, and once again, thank you for your contributions!
-
-Maintainers will look at PR that have no merge conflicts when deciding what to add to the project. Make sure your PR shows up here:
-
-https://github.com/Torantulino/Auto-GPT/pulls?q=is%3Apr+is%3Aopen+-is%3Aconflict+

spaces/1line/AutoGPT/autogpt/commands/google_search.py

@@ -1,87 +0,0 @@
-"""Google search command for Autogpt."""
-from __future__ import annotations
-
-import json
-
-from duckduckgo_search import ddg
-
-from autogpt.config import Config
-
-CFG = Config()
-
-
-def google_search(query: str, num_results: int = 8) -> str:
-    """Return the results of a Google search
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-    search_results = []
-    if not query:
-        return json.dumps(search_results)
-
-    results = ddg(query, max_results=num_results)
-    if not results:
-        return json.dumps(search_results)
-
-    for j in results:
-        search_results.append(j)
-
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-
-
-def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
-    """Return the results of a Google search using the official Google API
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-
-    from googleapiclient.discovery import build
-    from googleapiclient.errors import HttpError
-
-    try:
-        # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = CFG.google_api_key
-        custom_search_engine_id = CFG.custom_search_engine_id
-
-        # Initialize the Custom Search API service
-        service = build("customsearch", "v1", developerKey=api_key)
-
-        # Send the search query and retrieve the results
-        result = (
-            service.cse()
-            .list(q=query, cx=custom_search_engine_id, num=num_results)
-            .execute()
-        )
-
-        # Extract the search result items from the response
-        search_results = result.get("items", [])
-
-        # Create a list of only the URLs from the search results
-        search_results_links = [item["link"] for item in search_results]
-
-    except HttpError as e:
-        # Handle errors in the API call
-        error_details = json.loads(e.content.decode())
-
-        # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get(
-            "code"
-        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
-            "message", ""
-        ):
-            return "Error: The provided Google API key is invalid or missing."
-        else:
-            return f"Error: {e}"
-
-    # Return the list of search result URLs
-    return search_results_links

spaces/1phancelerku/anime-remove-background/Download APK Real Boxing and Experience the Ultimate Fighting Game on Android.md

@@ -1,132 +0,0 @@
-<br />
-<h1>APK Real Boxing: A Review of the Best Fighting Game on Android</h1>
-<p>If you are a fan of fighting games and boxing simulators, you might have heard of APK Real Boxing, one of the most popular and realistic games on Google Play. But what is APK Real Boxing, and why is it so awesome? In this article, we will give you a comprehensive review of this game, its features, benefits, and how to play it like a pro. Read on to find out more!</p>
-<h2>apk real boxing</h2><br /><p><b><b>DOWNLOAD</b> &#127383; <a href="https://jinyurl.com/2uNUmi">https://jinyurl.com/2uNUmi</a></b></p><br /><br />
- <h2>What is APK Real Boxing?</h2>
-<p>APK Real Boxing is a fighting game and boxing simulator developed by Vivid Games S.A., a studio behind well-known online fighting games on mobile. It is based on the original KO boxing simulator that won the hearts of punching games fans worldwide. It uses Unreal Engine-powered graphics and realistic motion-captured animations to create a stunning and immersive boxing experience. You can download APK Real Boxing from various sources online, such as [APKCombo](^1^), [Google Play](^2^), or [Vivid Games](^3^).</p>
- <h3>The features and benefits of APK Real Boxing</h3>
-<p>APK Real Boxing has many features and benefits that make it stand out from other fighting games on Android. Here are some of them:</p>
-<ul>
-<li>It has a full-blown career mode for your boxer, where you can knock out over 30 unique boxers with their own adaptive boxing styles and become the best boxer in the world.</li>
-<li>It allows you to customize your own boxer with dozens of unlockable hairstyles, tattoos, and gear. You can also train in various mini-games to boost your speed, strength, and stamina.</li>
-<li>It has a variety of punches and combos that you can use in the knockout boxing game. You can also tip the odds in your favor with power-ups and feel every jab, hook, and KO uppercut thanks to the intuitive controls.</li>
-<li>It has exhilarating bonus modes where you can test your boxing skills against boxeo bosses in Arcade Mode or take on the Underground Tournament to unlock new gear for your boxer.</li>
-<li>It has amazing graphics and sound that immerse you in the adrenaline-pumping world of boxing. You can also win prizes with Daily Rewards and Daily Spin.</li>
-</ul>
- <h3>How to download and install APK Real Boxing</h3>
-<p>To download and install APK Real Boxing, you need to follow these simple steps:</p>
-<ol>
-<li>Choose a reliable source for downloading APK Real Boxing, such as [APKCombo](^1^), [Google Play](^2^), or [Vivid Games](^3^).</li>
-<li>Click on the download button or link and wait for the file to be downloaded on your device.</li>
-<li>Once the file is downloaded, locate it in your file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to install the app.</li>
-<li>After the installation is complete, launch the app and enjoy playing APK Real Boxing!</li>
-</ol>
- <h2>Why APK Real Boxing is the ultimate fighting game</h2>
-<p>Now that you know what APK Real Boxing is and how to get it, you might be wondering why it is the ultimate fighting game on Android. Well, there are many reasons why this game is so awesome, but we will focus on three main aspects: the knockout gameplay, the comprehensive career mode, and the exhilarating bonus modes.</p>
- <h3>The knockout gameplay and intuitive controls</h3>
-<p>The knockout gameplay of APK Real Boxing is one of its best features. It allows you to fight using a variety of punches and combos in a realistic boxing boxeo bosses in Arcade Mode or take on the Underground Tournament to unlock new gear for your boxer. You can also play in Real-Time Multiplayer mode and join other boxers in weekly tournaments and special events. The amazing graphics of APK Real Boxing are another reason why this game is so awesome. It uses Unreal Engine-powered graphics and realistic motion-captured animations to create a stunning and immersive boxing experience. You can feel every jab, hook, and KO uppercut thanks to the realistic physics and sound effects. You can also admire the detailed and lifelike models of the boxers and the environments.</p>
- <h2>How to play APK Real Boxing like a pro</h2>
-<p>Now that you know why APK Real Boxing is the ultimate fighting game on Android, you might want to learn how to play it like a pro. Well, there are many tips and tricks that can help you improve your boxing skills and win more fights. Here are some of them:</p>
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- <h3>The basic moves and combos</h3>
-<p>The basic moves and combos of APK Real Boxing are essential for any boxer. You need to master them to be able to fight effectively and efficiently. Here are some of the basic moves and combos you should know:</p>
-<ul>
-<li>Jab: A quick and straight punch that can be used to keep your opponent at bay or set up other punches.</li>
-<li>Hook: A powerful punch that can be thrown from either side and can cause a lot of damage if it lands on the chin or the temple.</li>
-<li>Uppercut: A devastating punch that can be thrown from below and can knock out your opponent if it hits the jaw or the nose.</li>
-<li>Body shot: A punch that targets the torso or the ribs of your opponent and can weaken their stamina and defense.</li>
-<li>Combo: A series of punches that can be chained together to create a more effective attack. For example, you can use a jab-hook-uppercut combo or a body shot-hook-jab combo.</li>
-</ul>
- <h3>The power-ups and strategies</h3>
-<p>The power-ups and strategies of APK Real Boxing are important for any boxer. You need to use them wisely to gain an advantage over your opponent or turn the tide of the fight. Here are some of the power-ups and strategies you should use:</p>
-<ul>
-<li>Health: A power-up that restores some of your health and can help you survive longer in the fight.</li>
-<li>Stamina: A power-up that restores some of your stamina and can help you throw more punches and move faster in the fight.</li>
-<li>Shield: A power-up that protects you from incoming punches for a short time and can help you avoid damage in the fight.</li>
-<li>Fury: A power-up that increases your damage output for a short time and can help you deal more damage in the fight.</li>
-<li>Dodge: A strategy that allows you to evade incoming punches by swiping left or right on the screen and can help you avoid damage in the fight.</li>
-<li>Block: A strategy that allows you to defend yourself from incoming punches by holding down the block button on the screen and can help you reduce damage in the fight.</li>
-<li>Counter: A strategy that allows you to retaliate after dodging or blocking an incoming punch by tapping on the screen and can help you deal more damage in the fight.</li>
-</ul>
- <h3>The tips and tricks from the experts</h3>
-<p>The tips and tricks from the experts of APK Real Boxing are useful for any boxer. You need to follow them to improve your boxing skills and win more fights. Here are some of the tips and tricks from the experts:</p>
-<ul>
-<li>Train regularly in the mini-games to boost your speed, strength, and stamina.</li>
-<li>Customize your boxer with gear that suits your style and preferences.</li>
-<li>Fight against different boxers with different styles and learn from their strengths and weaknesses.</li>
-<li>Use different punches and combos depending on the situation and your opponent's behavior.</li>
-<li>Use power-ups strategically and don't waste them unnecessarily.</li>
-<li>Dodge, block, and counter effectively and don't let your opponent hit you too much.</li>
-<li>Be aggressive but not reckless and don't leave yourself open for attacks.</li>
-<li>Be patient but not passive and don't let your opponent dictate the pace of the fight.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, APK Real Boxing is a fighting game and boxing simulator that offers a realistic and immersive boxing experience on Android. It has many features and benefits that make it stand out from other fighting games on Android, such as the knockout gameplay, the comprehensive career mode, the exhilarating bonus modes, and the amazing graphics. It also has intuitive controls and customization options that make it easy to play and enjoy. It is a game that will challenge your boxing skills and entertain you for hours. If you are looking for a fighting game and boxing simulator that will give you a realistic and immersive boxing experience on Android, you should definitely try APK Real Boxing. You will not regret it! <h3>Summary of the main points</h3>
-<p>To summarize, here are the main points of this article:</p>
-<ul>
-<li>APK Real Boxing is a fighting game and boxing simulator developed by Vivid Games S.A., a studio behind well-known online fighting games on mobile.</li>
-<li>It uses Unreal Engine-powered graphics and realistic motion-captured animations to create a stunning and immersive boxing experience.</li>
-<li>It has a full-blown career mode for your boxer, where you can knock out over 30 unique boxers with their own adaptive boxing styles and become the best boxer in the world.</li>
-<li>It allows you to customize your own boxer with dozens of unlockable hairstyles, tattoos, and gear. You can also train in various mini-games to boost your speed, strength, and stamina.</li>
-<li>It has a variety of punches and combos that you can use in the knockout boxing game. You can also tip the odds in your favor with power-ups and feel every jab, hook, and KO uppercut thanks to the intuitive controls.</li>
-<li>It has exhilarating bonus modes where you can test your boxing skills against boxeo bosses in Arcade Mode or take on the Underground Tournament to unlock new gear for your boxer.</li>
-<li>It has many tips and tricks that can help you improve your boxing skills and win more fights.</li>
-</ul>
- <h3>Call to action and recommendation</h3>
-<p>If you are interested in APK Real Boxing and want to download it, you can do so from various sources online, such as [APKCombo], [Google Play], or [Vivid Games]. You can also visit the official website of APK Real Boxing to learn more about the game and its features. We highly recommend APK Real Boxing to anyone who loves fighting games and boxing simulators. It is a game that will keep you hooked and entertained for hours. So what are you waiting for? Download APK Real Boxing today and start your boxing career!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about APK Real Boxing:</p>
-<ol>
-<li><b>Is APK Real Boxing free to play?</b><br>
-Yes, APK Real Boxing is free to play. However, it contains in-app purchases that can enhance your gaming experience.</li>
-<li><b>Is APK Real Boxing safe to download?</b><br>
-Yes, APK Real Boxing is safe to download. However, you should always download it from reliable sources, such as [APKCombo], [Google Play], or [Vivid Games].</li>
-<li><b>Is APK Real Boxing compatible with my device?</b><br>
-APK Real Boxing is compatible with most Android devices that have Android 4.1 or higher. However, some devices may have performance issues or bugs due to different specifications.</li>
-<li><b>How can I contact the developers of APK Real Boxing?</b><br>
-You can contact the developers of APK Real Boxing by visiting their official website or their social media pages. You can also send them an email at [email protected].</li>
-<li><b>How can I give feedback or report a problem with APK Real Boxing?</b><br>
-You can give feedback or report a problem with APK Real Boxing by using the in-game feedback option or by sending an email to [email protected].</li>
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spaces/22h/vintedois-diffusion-v0-2/README.md

@@ -1,12 +0,0 @@
----
-title: Vintedois Diffusion V0 2
-emoji: 📚
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/docs/eval.md

@@ -1,31 +0,0 @@
-## Eval on ICCV2021-MFR
-
-coming soon.
-
-
-## Eval IJBC
-You can eval ijbc with pytorch or onnx.
-
-
-1. Eval IJBC With Onnx
-```shell
-CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50
-```
-
-2. Eval IJBC With Pytorch
-```shell
-CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
---model-prefix ms1mv3_arcface_r50/backbone.pth \
---image-path IJB_release/IJBC \
---result-dir ms1mv3_arcface_r50 \
---batch-size 128 \
---job ms1mv3_arcface_r50 \
---target IJBC \
---network iresnet50
-```
-
-## Inference
-
-```shell
-python inference.py --weight ms1mv3_arcface_r50/backbone.pth --network r50
-```

spaces/801artistry/RVC801/infer/modules/ipex/hijacks.py

@@ -1,196 +0,0 @@
-import contextlib
-import importlib
-import torch
-import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-
-# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
-
-class CondFunc: # pylint: disable=missing-class-docstring
-    def __new__(cls, orig_func, sub_func, cond_func):
-        self = super(CondFunc, cls).__new__(cls)
-        if isinstance(orig_func, str):
-            func_path = orig_func.split('.')
-            for i in range(len(func_path)-1, -1, -1):
-                try:
-                    resolved_obj = importlib.import_module('.'.join(func_path[:i]))
-                    break
-                except ImportError:
-                    pass
-            for attr_name in func_path[i:-1]:
-                resolved_obj = getattr(resolved_obj, attr_name)
-            orig_func = getattr(resolved_obj, func_path[-1])
-            setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
-        self.__init__(orig_func, sub_func, cond_func)
-        return lambda *args, **kwargs: self(*args, **kwargs)
-    def __init__(self, orig_func, sub_func, cond_func):
-        self.__orig_func = orig_func
-        self.__sub_func = sub_func
-        self.__cond_func = cond_func
-    def __call__(self, *args, **kwargs):
-        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
-            return self.__sub_func(self.__orig_func, *args, **kwargs)
-        else:
-            return self.__orig_func(*args, **kwargs)
-
-_utils = torch.utils.data._utils
-def _shutdown_workers(self):
-    if torch.utils.data._utils is None or torch.utils.data._utils.python_exit_status is True or torch.utils.data._utils.python_exit_status is None:
-        return
-    if hasattr(self, "_shutdown") and not self._shutdown:
-        self._shutdown = True
-        try:
-            if hasattr(self, '_pin_memory_thread'):
-                self._pin_memory_thread_done_event.set()
-                self._worker_result_queue.put((None, None))
-                self._pin_memory_thread.join()
-                self._worker_result_queue.cancel_join_thread()
-                self._worker_result_queue.close()
-            self._workers_done_event.set()
-            for worker_id in range(len(self._workers)):
-                if self._persistent_workers or self._workers_status[worker_id]:
-                    self._mark_worker_as_unavailable(worker_id, shutdown=True)
-            for w in self._workers: # pylint: disable=invalid-name
-                w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL)
-            for q in self._index_queues: # pylint: disable=invalid-name
-                q.cancel_join_thread()
-                q.close()
-        finally:
-            if self._worker_pids_set:
-                torch.utils.data._utils.signal_handling._remove_worker_pids(id(self))
-                self._worker_pids_set = False
-            for w in self._workers: # pylint: disable=invalid-name
-                if w.is_alive():
-                    w.terminate()
-
-class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
-    def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument
-        if isinstance(device_ids, list) and len(device_ids) > 1:
-            print("IPEX backend doesn't support DataParallel on multiple XPU devices")
-        return module.to("xpu")
-
-def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
-    return contextlib.nullcontext()
-
-def check_device(device):
-    return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int))
-
-def return_xpu(device):
-    return f"xpu:{device[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device("xpu") if isinstance(device, torch.device) else "xpu"
-
-def ipex_no_cuda(orig_func, *args, **kwargs):
-    torch.cuda.is_available = lambda: False
-    orig_func(*args, **kwargs)
-    torch.cuda.is_available = torch.xpu.is_available
-
-original_autocast = torch.autocast
-def ipex_autocast(*args, **kwargs):
-    if len(args) > 0 and args[0] == "cuda":
-        return original_autocast("xpu", *args[1:], **kwargs)
-    else:
-        return original_autocast(*args, **kwargs)
-
-original_torch_cat = torch.cat
-def torch_cat(tensor, *args, **kwargs):
-    if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
-        return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs)
-    else:
-        return original_torch_cat(tensor, *args, **kwargs)
-
-original_interpolate = torch.nn.functional.interpolate
-def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
-    if antialias or align_corners is not None:
-        return_device = tensor.device
-        return_dtype = tensor.dtype
-        return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode,
-        align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype)
-    else:
-        return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode,
-        align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)
-
-original_linalg_solve = torch.linalg.solve
-def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
-    if A.device != torch.device("cpu") or B.device != torch.device("cpu"):
-        return_device = A.device
-        return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(return_device)
-    else:
-        return original_linalg_solve(A, B, *args, **kwargs)
-
-def ipex_hijacks():
-    CondFunc('torch.Tensor.to',
-        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
-        lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
-    CondFunc('torch.Tensor.cuda',
-        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
-        lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
-    CondFunc('torch.empty',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.load',
-        lambda orig_func, *args, map_location=None, **kwargs: orig_func(*args, return_xpu(map_location), **kwargs),
-        lambda orig_func, *args, map_location=None, **kwargs: map_location is None or check_device(map_location))
-    CondFunc('torch.randn',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.ones',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.zeros',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.tensor',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.linspace',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-
-    CondFunc('torch.Generator',
-        lambda orig_func, device=None: torch.xpu.Generator(device),
-        lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
-
-    CondFunc('torch.batch_norm',
-        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
-        weight if weight is not None else torch.ones(input.size()[1], device=input.device),
-        bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
-    CondFunc('torch.instance_norm',
-        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
-        weight if weight is not None else torch.ones(input.size()[1], device=input.device),
-        bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
-
-    #Functions with dtype errors:
-    CondFunc('torch.nn.modules.GroupNorm.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    CondFunc('torch.nn.modules.linear.Linear.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    CondFunc('torch.nn.modules.conv.Conv2d.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    CondFunc('torch.nn.functional.layer_norm',
-        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
-        orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
-        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
-        weight is not None and input.dtype != weight.data.dtype)
-
-    #Diffusers Float64 (ARC GPUs doesn't support double or Float64):
-    if not torch.xpu.has_fp64_dtype():
-        CondFunc('torch.from_numpy',
-        lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
-        lambda orig_func, ndarray: ndarray.dtype == float)
-
-    #Broken functions when torch.cuda.is_available is True:
-    CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
-        lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
-        lambda orig_func, *args, **kwargs: True)
-
-    #Functions that make compile mad with CondFunc:
-    torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
-    torch.nn.DataParallel = DummyDataParallel
-    torch.autocast = ipex_autocast
-    torch.cat = torch_cat
-    torch.linalg.solve = linalg_solve
-    torch.nn.functional.interpolate = interpolate
-    torch.backends.cuda.sdp_kernel = return_null_context

spaces/AFischer1985/wizardlm-13b-v1-2-q4-0-gguf/README.md

@@ -1,20 +0,0 @@
----
-title: Wizardlm-13b-v1.2.Q4_0.gguf
-colorFrom: purple
-colorTo: blue
-sdk: docker
-models:
-  - WizardLM/WizardLM-13B-V1.2
-  - TheBloke/WizardLM-13B-V1.2-GGUF
-tags:
-  - inference api
-  - openai-api compatible
-  - llama-cpp-python
-  - WizardLM
-  - gguf
-pinned: false
----
-
-# WizardLM-13B-V1.2-GGUF
-
-Please refer to the [index.html](index.html) for more information.

spaces/AIGC-Audio/AudioGPT/mono2binaural/src/utils.py

@@ -1,251 +0,0 @@
-"""
-Copyright (c) Facebook, Inc. and its affiliates.
-All rights reserved.
-
-This source code is licensed under the license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-import numpy as np
-import torch as th
-#import torchaudio as ta
-
-
-class Net(th.nn.Module):
-
-    def __init__(self, model_name="network", use_cuda=True):
-        super().__init__()
-        self.use_cuda = use_cuda
-        self.model_name = model_name
-
-    def save(self, model_dir, suffix=''):
-        '''
-        save the network to model_dir/model_name.suffix.net
-        :param model_dir: directory to save the model to
-        :param suffix: suffix to append after model name
-        '''
-        if self.use_cuda:
-            self.cpu()
-
-        if suffix == "":
-            fname = f"{model_dir}/{self.model_name}.net"
-        else:
-            fname = f"{model_dir}/{self.model_name}.{suffix}.net"
-
-        th.save(self.state_dict(), fname)
-        if self.use_cuda:
-            self.cuda()
-
-    def load_from_file(self, model_file):
-        '''
-        load network parameters from model_file
-        :param model_file: file containing the model parameters
-        '''
-        if self.use_cuda:
-            self.cpu()
-
-        states = th.load(model_file)
-        self.load_state_dict(states)
-
-        if self.use_cuda:
-            self.cuda()
-        print(f"Loaded: {model_file}")
-
-    def load(self, model_dir, suffix=''):
-        '''
-        load network parameters from model_dir/model_name.suffix.net
-        :param model_dir: directory to load the model from
-        :param suffix: suffix to append after model name
-        '''
-        if suffix == "":
-            fname = f"{model_dir}/{self.model_name}.net"
-        else:
-            fname = f"{model_dir}/{self.model_name}.{suffix}.net"
-        self.load_from_file(fname)
-
-    def num_trainable_parameters(self):
-        '''
-        :return: the number of trainable parameters in the model
-        '''
-        return sum(p.numel() for p in self.parameters() if p.requires_grad)
-
-
-# class NewbobAdam(th.optim.Adam):
-
-#     def __init__(self,
-#                  weights,
-#                  net,
-#                  artifacts_dir,
-#                  initial_learning_rate=0.001,
-#                  decay=0.5,
-#                  max_decay=0.01
-#                  ):
-#         '''
-#         Newbob learning rate scheduler
-#         :param weights: weights to optimize
-#         :param net: the network, must be an instance of type src.utils.Net
-#         :param artifacts_dir: (str) directory to save/restore models to/from
-#         :param initial_learning_rate: (float) initial learning rate
-#         :param decay: (float) value to decrease learning rate by when loss doesn't improve further
-#         :param max_decay: (float) maximum decay of learning rate
-#         '''
-#         super().__init__(weights, lr=initial_learning_rate)
-#         self.last_epoch_loss = np.inf
-#         self.total_decay = 1
-#         self.net = net
-#         self.decay = decay
-#         self.max_decay = max_decay
-#         self.artifacts_dir = artifacts_dir
-#         # store initial state as backup
-#         if decay < 1.0:
-#             net.save(artifacts_dir, suffix="newbob")
-
-#     def update_lr(self, loss):
-#         '''
-#         update the learning rate based on the current loss value and historic loss values
-#         :param loss: the loss after the current iteration
-#         '''
-#         if loss > self.last_epoch_loss and self.decay < 1.0 and self.total_decay > self.max_decay:
-#             self.total_decay = self.total_decay * self.decay
-#             print(f"NewbobAdam: Decay learning rate (loss degraded from {self.last_epoch_loss} to {loss})."
-#                   f"Total decay: {self.total_decay}")
-#             # restore previous network state
-#             self.net.load(self.artifacts_dir, suffix="newbob")
-#             # decrease learning rate
-#             for param_group in self.param_groups:
-#                 param_group['lr'] = param_group['lr'] * self.decay
-#         else:
-#             self.last_epoch_loss = loss
-#         # save last snapshot to restore it in case of lr decrease
-#         if self.decay < 1.0 and self.total_decay > self.max_decay:
-#             self.net.save(self.artifacts_dir, suffix="newbob")
-
-
-# class FourierTransform:
-#     def __init__(self,
-#                  fft_bins=2048,
-#                  win_length_ms=40,
-#                  frame_rate_hz=100,
-#                  causal=False,
-#                  preemphasis=0.0,
-#                  sample_rate=48000,
-#                  normalized=False):
-#         self.sample_rate = sample_rate
-#         self.frame_rate_hz = frame_rate_hz
-#         self.preemphasis = preemphasis
-#         self.fft_bins = fft_bins
-#         self.win_length = int(sample_rate * win_length_ms / 1000)
-#         self.hop_length = int(sample_rate / frame_rate_hz)
-#         self.causal = causal
-#         self.normalized = normalized
-#         if self.win_length > self.fft_bins:
-#             print('FourierTransform Warning: fft_bins should be larger than win_length')
-
-#     def _convert_format(self, data, expected_dims):
-#         if not type(data) == th.Tensor:
-#             data = th.Tensor(data)
-#         if len(data.shape) < expected_dims:
-#             data = data.unsqueeze(0)
-#         if not len(data.shape) == expected_dims:
-#             raise Exception(f"FourierTransform: data needs to be a Tensor with {expected_dims} dimensions but got shape {data.shape}")
-#         return data
-
-#     def _preemphasis(self, audio):
-#         if self.preemphasis > 0:
-#             return th.cat((audio[:, 0:1], audio[:, 1:] - self.preemphasis * audio[:, :-1]), dim=1)
-#         return audio
-
-#     def _revert_preemphasis(self, audio):
-#         if self.preemphasis > 0:
-#             for i in range(1, audio.shape[1]):
-#                 audio[:, i] = audio[:, i] + self.preemphasis * audio[:, i-1]
-#         return audio
-
-#     def _magphase(self, complex_stft):
-#         mag, phase = ta.functional.magphase(complex_stft, 1.0)
-#         return mag, phase
-
-#     def stft(self, audio):
-#         '''
-#         wrapper around th.stft
-#         audio: wave signal as th.Tensor
-#         '''
-#         hann = th.hann_window(self.win_length)
-#         hann = hann.cuda() if audio.is_cuda else hann
-#         spec = th.stft(audio, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length,
-#                        window=hann, center=not self.causal, normalized=self.normalized)
-#         return spec.contiguous()
-
-#     def complex_spectrogram(self, audio):
-#         '''
-#         audio: wave signal as th.Tensor
-#         return: th.Tensor of size channels x frequencies x time_steps (channels x y_axis x x_axis)
-#         '''
-#         self._convert_format(audio, expected_dims=2)
-#         audio = self._preemphasis(audio)
-#         return self.stft(audio)
-
-#     def magnitude_phase(self, audio):
-#         '''
-#         audio: wave signal as th.Tensor
-#         return: tuple containing two th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
-#         '''
-#         stft = self.complex_spectrogram(audio)
-#         return self._magphase(stft)
-
-#     def mag_spectrogram(self, audio):
-#         '''
-#         audio: wave signal as th.Tensor
-#         return: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
-#         '''
-#         return self.magnitude_phase(audio)[0]
-
-#     def power_spectrogram(self, audio):
-#         '''
-#         audio: wave signal as th.Tensor
-#         return: power spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
-#         '''
-#         return th.pow(self.mag_spectrogram(audio), 2.0)
-
-#     def phase_spectrogram(self, audio):
-#         '''
-#         audio: wave signal as th.Tensor
-#         return: phase spectrum as th.Tensor of size channels x frequencies x time_steps for magnitude and phase spectrum
-#         '''
-#         return self.magnitude_phase(audio)[1]
-
-#     def mel_spectrogram(self, audio, n_mels):
-#         '''
-#         audio: wave signal as th.Tensor
-#         n_mels: number of bins used for mel scale warping
-#         return: mel spectrogram as th.Tensor of size channels x n_mels x time_steps for magnitude and phase spectrum
-#         '''
-#         spec = self.power_spectrogram(audio)
-#         mel_warping = ta.transforms.MelScale(n_mels, self.sample_rate)
-#         return mel_warping(spec)
-
-#     def complex_spec2wav(self, complex_spec, length):
-#         '''
-#         inverse stft
-#         complex_spec: complex spectrum as th.Tensor of size channels x frequencies x time_steps x 2 (real part/imaginary part)
-#         length: length of the audio to be reconstructed (in frames)
-#         '''
-#         complex_spec = self._convert_format(complex_spec, expected_dims=4)
-#         hann = th.hann_window(self.win_length)
-#         hann = hann.cuda() if complex_spec.is_cuda else hann
-#         wav = ta.functional.istft(complex_spec, n_fft=self.fft_bins, hop_length=self.hop_length, win_length=self.win_length, window=hann, length=length, center=not self.causal)
-#         wav = self._revert_preemphasis(wav)
-#         return wav
-
-#     def magphase2wav(self, mag_spec, phase_spec, length):
-#         '''
-#         reconstruction of wav signal from magnitude and phase spectrum
-#         mag_spec: magnitude spectrum as th.Tensor of size channels x frequencies x time_steps
-#         phase_spec: phase spectrum as th.Tensor of size channels x frequencies x time_steps
-#         length: length of the audio to be reconstructed (in frames)
-#         '''
-#         mag_spec = self._convert_format(mag_spec, expected_dims=3)
-#         phase_spec = self._convert_format(phase_spec, expected_dims=3)
-#         complex_spec = th.stack([mag_spec * th.cos(phase_spec), mag_spec * th.sin(phase_spec)], dim=-1)
-#         return self.complex_spec2wav(complex_spec, length)
-

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/conditioners.py

@@ -1,990 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import defaultdict
-from copy import deepcopy
-from dataclasses import dataclass, field
-from itertools import chain
-import logging
-import math
-import random
-import re
-import typing as tp
-import warnings
-
-from einops import rearrange
-from num2words import num2words
-import spacy
-from transformers import T5EncoderModel, T5Tokenizer  # type: ignore
-import torchaudio
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-from torch.nn.utils.rnn import pad_sequence
-
-from .streaming import StreamingModule
-from .transformer import create_sin_embedding
-from ..data.audio_dataset import SegmentInfo
-from ..utils.autocast import TorchAutocast
-from ..utils.utils import hash_trick, length_to_mask, collate
-
-
-logger = logging.getLogger(__name__)
-TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
-ConditionType = tp.Tuple[Tensor, Tensor]  # condition, mask
-
-
-class WavCondition(tp.NamedTuple):
-    wav: Tensor
-    length: Tensor
-    path: tp.List[tp.Optional[str]] = []
-
-
-def nullify_condition(condition: ConditionType, dim: int = 1):
-    """This function transforms an input condition to a null condition.
-    The way it is done by converting it to a single zero vector similarly
-    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
-
-    Args:
-        condition (ConditionType): a tuple of condition and mask (tp.Tuple[Tensor, Tensor])
-        dim (int): the dimension that will be truncated (should be the time dimension)
-        WARNING!: dim should not be the batch dimension!
-    Returns:
-        ConditionType: a tuple of null condition and mask
-    """
-    assert dim != 0, "dim cannot be the batch dimension!"
-    assert type(condition) == tuple and \
-        type(condition[0]) == Tensor and \
-        type(condition[1]) == Tensor, "'nullify_condition' got an unexpected input type!"
-    cond, mask = condition
-    B = cond.shape[0]
-    last_dim = cond.dim() - 1
-    out = cond.transpose(dim, last_dim)
-    out = 0. * out[..., :1]
-    out = out.transpose(dim, last_dim)
-    mask = torch.zeros((B, 1), device=out.device).int()
-    assert cond.dim() == out.dim()
-    return out, mask
-
-
-def nullify_wav(wav: Tensor) -> WavCondition:
-    """Create a nullified WavCondition from a wav tensor with appropriate shape.
-
-    Args:
-        wav (Tensor): tensor of shape [B, T]
-    Returns:
-        WavCondition: wav condition with nullified wav.
-    """
-    null_wav, _ = nullify_condition((wav, torch.zeros_like(wav)), dim=wav.dim() - 1)
-    return WavCondition(
-        wav=null_wav,
-        length=torch.tensor([0] * wav.shape[0], device=wav.device),
-        path=['null_wav'] * wav.shape[0]
-    )
-
-
-@dataclass
-class ConditioningAttributes:
-    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
-    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
-
-    def __getitem__(self, item):
-        return getattr(self, item)
-
-    @property
-    def text_attributes(self):
-        return self.text.keys()
-
-    @property
-    def wav_attributes(self):
-        return self.wav.keys()
-
-    @property
-    def attributes(self):
-        return {"text": self.text_attributes, "wav": self.wav_attributes}
-
-    def to_flat_dict(self):
-        return {
-            **{f"text.{k}": v for k, v in self.text.items()},
-            **{f"wav.{k}": v for k, v in self.wav.items()},
-        }
-
-    @classmethod
-    def from_flat_dict(cls, x):
-        out = cls()
-        for k, v in x.items():
-            kind, att = k.split(".")
-            out[kind][att] = v
-        return out
-
-
-class SegmentWithAttributes(SegmentInfo):
-    """Base class for all dataclasses that are used for conditioning.
-    All child classes should implement `to_condition_attributes` that converts
-    the existing attributes to a dataclass of type ConditioningAttributes.
-    """
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        raise NotImplementedError()
-
-
-class Tokenizer:
-    """Base class for all tokenizers
-    (in case we want to introduce more advances tokenizers in the future).
-    """
-    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
-        raise NotImplementedError()
-
-
-class WhiteSpaceTokenizer(Tokenizer):
-    """This tokenizer should be used for natural language descriptions.
-    For example:
-    ["he didn't, know he's going home.", 'shorter sentence'] =>
-    [[78, 62, 31,  4, 78, 25, 19, 34],
-    [59, 77,  0,  0,  0,  0,  0,  0]]
-    """
-    PUNCTUATIONS = "?:!.,;"
-
-    def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
-                 lemma: bool = True, stopwords: bool = True) -> None:
-        self.n_bins = n_bins
-        self.pad_idx = pad_idx
-        self.lemma = lemma
-        self.stopwords = stopwords
-        try:
-            self.nlp = spacy.load(language)
-        except IOError:
-            spacy.cli.download(language)  # type: ignore
-            self.nlp = spacy.load(language)
-
-    @tp.no_type_check
-    def __call__(
-        self,
-        texts: tp.List[tp.Optional[str]],
-        return_text: bool = False
-    ) -> tp.Tuple[Tensor, Tensor]:
-        """Take a list of strings and convert them to a tensor of indices.
-
-        Args:
-            texts (tp.List[str]): List of strings.
-            return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
-        Returns:
-            tp.Tuple[Tensor, Tensor]:
-                - Indices of words in the LUT.
-                - And a mask indicating where the padding tokens are
-        """
-        output, lengths = [], []
-        texts = deepcopy(texts)
-        for i, text in enumerate(texts):
-            # if current sample doesn't have a certain attribute, replace with pad token
-            if text is None:
-                output.append(Tensor([self.pad_idx]))
-                lengths.append(0)
-                continue
-
-            # convert numbers to words
-            text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text)  # type: ignore
-            # normalize text
-            text = self.nlp(text)  # type: ignore
-            # remove stopwords
-            if self.stopwords:
-                text = [w for w in text if not w.is_stop]  # type: ignore
-            # remove punctuations
-            text = [w for w in text if w.text not in self.PUNCTUATIONS]  # type: ignore
-            # lemmatize if needed
-            text = [getattr(t, "lemma_" if self.lemma else "text") for t in text]  # type: ignore
-
-            texts[i] = " ".join(text)
-            lengths.append(len(text))
-            # convert to tensor
-            tokens = Tensor([hash_trick(w, self.n_bins) for w in text])
-            output.append(tokens)
-
-        mask = length_to_mask(torch.IntTensor(lengths)).int()
-        padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
-        if return_text:
-            return padded_output, mask, texts  # type: ignore
-        return padded_output, mask
-
-
-class NoopTokenizer(Tokenizer):
-    """This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
-    The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
-    strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
-    split it to ["Jeff", "Buckley"] and return an index per word.
-
-    For example:
-    ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
-    ["Metal", "Rock", "Classical"] => [0, 223, 51]
-    """
-    def __init__(self, n_bins: int, pad_idx: int = 0):
-        self.n_bins = n_bins
-        self.pad_idx = pad_idx
-
-    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
-        output, lengths = [], []
-        for text in texts:
-            # if current sample doesn't have a certain attribute, replace with pad token
-            if text is None:
-                output.append(self.pad_idx)
-                lengths.append(0)
-            else:
-                output.append(hash_trick(text, self.n_bins))
-                lengths.append(1)
-
-        tokens = torch.LongTensor(output).unsqueeze(1)
-        mask = length_to_mask(torch.IntTensor(lengths)).int()
-        return tokens, mask
-
-
-class BaseConditioner(nn.Module):
-    """Base model for all conditioner modules. We allow the output dim to be different
-    than the hidden dim for two reasons: 1) keep our LUTs small when the vocab is large;
-    2) make all condition dims consistent.
-
-    Args:
-        dim (int): Hidden dim of the model (text-encoder/LUT).
-        output_dim (int): Output dim of the conditioner.
-    """
-    def __init__(self, dim, output_dim):
-        super().__init__()
-        self.dim = dim
-        self.output_dim = output_dim
-        self.output_proj = nn.Linear(dim, output_dim)
-
-    def tokenize(self, *args, **kwargs) -> tp.Any:
-        """Should be any part of the processing that will lead to a synchronization
-        point, e.g. BPE tokenization with transfer to the GPU.
-
-        The returned value will be saved and return later when calling forward().
-        """
-        raise NotImplementedError()
-
-    def forward(self, inputs: tp.Any) -> ConditionType:
-        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
-        Outputs a ConditionType, after the input data was embedded as a dense vector.
-
-        Returns:
-            ConditionType:
-                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
-                  output embedding and D is the dimension of the embedding.
-                - And a mask indicating where the padding tokens.
-        """
-        raise NotImplementedError()
-
-
-class TextConditioner(BaseConditioner):
-    ...
-
-
-class LUTConditioner(TextConditioner):
-    """Lookup table TextConditioner.
-
-    Args:
-        n_bins (int): Number of bins.
-        dim (int): Hidden dim of the model (text-encoder/LUT).
-        output_dim (int): Output dim of the conditioner.
-        tokenizer (str): Name of the tokenizer.
-        pad_idx (int, optional): Index for padding token. Defaults to 0.
-    """
-    def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
-        super().__init__(dim, output_dim)
-        self.embed = nn.Embedding(n_bins, dim)
-        self.tokenizer: Tokenizer
-        if tokenizer == "whitespace":
-            self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
-        elif tokenizer == "noop":
-            self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
-        else:
-            raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
-
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-        device = self.embed.weight.device
-        tokens, mask = self.tokenizer(x)
-        tokens, mask = tokens.to(device), mask.to(device)
-        return tokens, mask
-
-    def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
-        tokens, mask = inputs
-        embeds = self.embed(tokens)
-        embeds = self.output_proj(embeds)
-        embeds = (embeds * mask.unsqueeze(-1))
-        return embeds, mask
-
-
-class T5Conditioner(TextConditioner):
-    """T5-based TextConditioner.
-
-    Args:
-        name (str): Name of the T5 model.
-        output_dim (int): Output dim of the conditioner.
-        finetune (bool): Whether to fine-tune T5 at train time.
-        device (str): Device for T5 Conditioner.
-        autocast_dtype (tp.Optional[str], optional): Autocast dtype.
-        word_dropout (float, optional): Word dropout probability.
-        normalize_text (bool, optional): Whether to apply text normalization.
-    """
-    MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
-              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
-              "google/flan-t5-xl", "google/flan-t5-xxl"]
-    MODELS_DIMS = {
-        "t5-small": 512,
-        "t5-base": 768,
-        "t5-large": 1024,
-        "t5-3b": 1024,
-        "t5-11b": 1024,
-        "google/flan-t5-small": 512,
-        "google/flan-t5-base": 768,
-        "google/flan-t5-large": 1024,
-        "google/flan-t5-3b": 1024,
-        "google/flan-t5-11b": 1024,
-    }
-
-    def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
-                 autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
-                 normalize_text: bool = False):
-        assert name in self.MODELS, f"unrecognized t5 model name (should in {self.MODELS})"
-        super().__init__(self.MODELS_DIMS[name], output_dim)
-        self.device = device
-        self.name = name
-        self.finetune = finetune
-        self.word_dropout = word_dropout
-
-        if autocast_dtype is None or self.device == 'cpu':
-            self.autocast = TorchAutocast(enabled=False)
-            if self.device != 'cpu':
-                logger.warning("T5 has no autocast, this might lead to NaN")
-        else:
-            dtype = getattr(torch, autocast_dtype)
-            assert isinstance(dtype, torch.dtype)
-            logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
-            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
-        # Let's disable logging temporarily because T5 will vomit some errors otherwise.
-        # thanks https://gist.github.com/simon-weber/7853144
-        previous_level = logging.root.manager.disable
-        logging.disable(logging.ERROR)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            try:
-                self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
-                t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
-            finally:
-                logging.disable(previous_level)
-        if finetune:
-            self.t5 = t5
-        else:
-            # this makes sure that the t5 models is not part
-            # of the saved checkpoint
-            self.__dict__["t5"] = t5.to(device)
-
-        self.normalize_text = normalize_text
-        if normalize_text:
-            self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
-
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
-        # if current sample doesn't have a certain attribute, replace with empty string
-        entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
-        if self.normalize_text:
-            _, _, entries = self.text_normalizer(entries, return_text=True)
-        if self.word_dropout > 0. and self.training:
-            new_entries = []
-            for entry in entries:
-                words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
-                new_entries.append(" ".join(words))
-            entries = new_entries
-
-        empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
-
-        inputs = self.t5_tokenizer(entries, return_tensors="pt", padding=True).to(self.device)
-        mask = inputs["attention_mask"]
-        mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
-        return inputs
-
-    def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
-        mask = inputs["attention_mask"]
-        with torch.set_grad_enabled(self.finetune), self.autocast:
-            embeds = self.t5(**inputs).last_hidden_state
-        embeds = self.output_proj(embeds.to(self.output_proj.weight))
-        embeds = (embeds * mask.unsqueeze(-1))
-        return embeds, mask
-
-
-class WaveformConditioner(BaseConditioner):
-    """Base class for all conditioners that take a waveform as input.
-    Classes that inherit must implement `_get_wav_embedding` that outputs
-    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
-    factor of the embedding model.
-
-    Args:
-        dim (int): The internal representation dimension.
-        output_dim (int): Output dimension.
-        device (tp.Union[torch.device, str]): Device.
-    """
-    def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
-        super().__init__(dim, output_dim)
-        self.device = device
-
-    def tokenize(self, wav_length: WavCondition) -> WavCondition:
-        wav, length, path = wav_length
-        assert length is not None
-        return WavCondition(wav.to(self.device), length.to(self.device), path)
-
-    def _get_wav_embedding(self, wav: Tensor) -> Tensor:
-        """Gets as input a wav and returns a dense vector of conditions."""
-        raise NotImplementedError()
-
-    def _downsampling_factor(self):
-        """Returns the downsampling factor of the embedding model."""
-        raise NotImplementedError()
-
-    def forward(self, inputs: WavCondition) -> ConditionType:
-        """
-        Args:
-            input (WavCondition): Tuple of (waveform, lengths).
-        Returns:
-            ConditionType: Dense vector representing the conditioning along with its' mask.
-        """
-        wav, lengths, path = inputs
-        with torch.no_grad():
-            embeds = self._get_wav_embedding(wav)
-        embeds = embeds.to(self.output_proj.weight)
-        embeds = self.output_proj(embeds)
-
-        if lengths is not None:
-            lengths = lengths / self._downsampling_factor()
-            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
-        else:
-            mask = torch.ones_like(embeds)
-        embeds = (embeds * mask.unsqueeze(2).to(self.device))
-
-        return embeds, mask
-
-
-class ChromaStemConditioner(WaveformConditioner):
-    """Chroma conditioner that uses DEMUCS to first filter out drums and bass. The is followed by
-    the insight the drums and bass often dominate the chroma, leading to the chroma not containing the
-    information about melody.
-
-    Args:
-        output_dim (int): Output dimension for the conditioner.
-        sample_rate (int): Sample rate for the chroma extractor.
-        n_chroma (int): Number of chroma for the chroma extractor.
-        radix2_exp (int): Radix2 exponent for the chroma extractor.
-        duration (float): Duration used during training. This is later used for correct padding
-            in case we are using chroma as prefix.
-        match_len_on_eval (bool, optional): If True then all chromas are padded to the training
-            duration. Defaults to False.
-        eval_wavs (str, optional): Path to a json egg with waveform, this waveforms are used as
-            conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
-            Defaults to None.
-        n_eval_wavs (int, optional): Limits the number of waveforms used for conditioning. Defaults to 0.
-        device (tp.Union[torch.device, str], optional): Device for the conditioner.
-        **kwargs: Additional parameters for the chroma extractor.
-    """
-    def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
-                 duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
-                 n_eval_wavs: int = 0, device: tp.Union[torch.device, str] = "cpu", **kwargs):
-        from demucs import pretrained
-        super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
-        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
-        self.sample_rate = sample_rate
-        self.match_len_on_eval = match_len_on_eval
-        self.duration = duration
-        self.__dict__["demucs"] = pretrained.get_model('htdemucs').to(device)
-        self.stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
-        self.stem_idx = torch.LongTensor([self.stem2idx['vocal'], self.stem2idx['other']]).to(device)
-        self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma, radix2_exp=radix2_exp,
-                                      device=device, **kwargs)
-        self.chroma_len = self._get_chroma_len()
-
-    def _downsampling_factor(self):
-        return self.chroma.winhop
-
-    def _get_chroma_len(self):
-        """Get length of chroma during training"""
-        dummy_wav = torch.zeros((1, self.sample_rate * self.duration), device=self.device)
-        dummy_chr = self.chroma(dummy_wav)
-        return dummy_chr.shape[1]
-
-    @torch.no_grad()
-    def _get_filtered_wav(self, wav):
-        from demucs.apply import apply_model
-        from demucs.audio import convert_audio
-        with self.autocast:
-            wav = convert_audio(wav, self.sample_rate, self.demucs.samplerate, self.demucs.audio_channels)
-            stems = apply_model(self.demucs, wav, device=self.device)
-            stems = stems[:, self.stem_idx]  # extract stem
-            stems = stems.sum(1)  # merge extracted stems
-            stems = stems.mean(1, keepdim=True)  # mono
-            stems = convert_audio(stems, self.demucs.samplerate, self.sample_rate, 1)
-            return stems
-
-    @torch.no_grad()
-    def _get_wav_embedding(self, wav):
-        # avoid 0-size tensors when we are working with null conds
-        if wav.shape[-1] == 1:
-            return self.chroma(wav)
-        stems = self._get_filtered_wav(wav)
-        chroma = self.chroma(stems)
-
-        if self.match_len_on_eval:
-            b, t, c = chroma.shape
-            if t > self.chroma_len:
-                chroma = chroma[:, :self.chroma_len]
-                logger.debug(f'chroma was truncated! ({t} -> {chroma.shape[1]})')
-            elif t < self.chroma_len:
-                # chroma = F.pad(chroma, (0, 0, 0, self.chroma_len - t))
-                n_repeat = int(math.ceil(self.chroma_len / t))
-                chroma = chroma.repeat(1, n_repeat, 1)
-                chroma = chroma[:, :self.chroma_len]
-                logger.debug(f'chroma was zero-padded! ({t} -> {chroma.shape[1]})')
-        return chroma
-
-
-class ChromaExtractor(nn.Module):
-    """Chroma extraction class, handles chroma extraction and quantization.
-
-    Args:
-        sample_rate (int): Sample rate.
-        n_chroma (int): Number of chroma to consider.
-        radix2_exp (int): Radix2 exponent.
-        nfft (tp.Optional[int], optional): Number of FFT.
-        winlen (tp.Optional[int], optional): Window length.
-        winhop (tp.Optional[int], optional): Window hop size.
-        argmax (bool, optional): Whether to use argmax. Defaults to False.
-        norm (float, optional): Norm for chroma normalization. Defaults to inf.
-        device (tp.Union[torch.device, str], optional): Device to use. Defaults to cpu.
-    """
-    def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12,
-                 nfft: tp.Optional[int] = None, winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None,
-                 argmax: bool = False, norm: float = torch.inf, device: tp.Union[torch.device, str] = "cpu"):
-        super().__init__()
-        from librosa import filters
-        self.device = device
-        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
-        self.winlen = winlen or 2 ** radix2_exp
-        self.nfft = nfft or self.winlen
-        self.winhop = winhop or (self.winlen // 4)
-        self.sr = sample_rate
-        self.n_chroma = n_chroma
-        self.norm = norm
-        self.argmax = argmax
-        self.window = torch.hann_window(self.winlen).to(device)
-        self.fbanks = torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
-                                                      n_chroma=self.n_chroma)).to(device)
-        self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
-                                                      hop_length=self.winhop, power=2, center=True,
-                                                      pad=0, normalized=True).to(device)
-
-    def forward(self, wav):
-        with self.autocast:
-            T = wav.shape[-1]
-            # in case we are getting a wav that was dropped out (nullified)
-            # make sure wav length is no less that nfft
-            if T < self.nfft:
-                pad = self.nfft - T
-                r = 0 if pad % 2 == 0 else 1
-                wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
-                assert wav.shape[-1] == self.nfft, f'expected len {self.nfft} but got {wav.shape[-1]}'
-            spec = self.spec(wav).squeeze(1)
-            raw_chroma = torch.einsum("cf,...ft->...ct", self.fbanks, spec)
-            norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
-            norm_chroma = rearrange(norm_chroma, "b d t -> b t d")
-
-            if self.argmax:
-                idx = norm_chroma.argmax(-1, keepdims=True)
-                norm_chroma[:] = 0
-                norm_chroma.scatter_(dim=-1, index=idx, value=1)
-
-            return norm_chroma
-
-
-def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str):
-    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
-    If the condition is of type "wav", then nullify it using "nullify_condition".
-    If the condition is of any other type, set its' value to None.
-    Works in-place.
-    """
-    if condition_type not in ["text", "wav"]:
-        raise ValueError(
-            "dropout_condition got an unexpected condition type!"
-            f" expected 'wav' or 'text' but got '{condition_type}'"
-        )
-
-    if condition not in getattr(sample, condition_type):
-        raise ValueError(
-            "dropout_condition received an unexpected condition!"
-            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
-            f"but got '{condition}' of type '{condition_type}'!"
-        )
-
-    if condition_type == "wav":
-        wav, length, path = sample.wav[condition]
-        sample.wav[condition] = nullify_wav(wav)
-    else:
-        sample.text[condition] = None
-
-    return sample
-
-
-class DropoutModule(nn.Module):
-    """Base class for all dropout modules."""
-    def __init__(self, seed: int = 1234):
-        super().__init__()
-        self.rng = torch.Generator()
-        self.rng.manual_seed(seed)
-
-
-class AttributeDropout(DropoutModule):
-    """Applies dropout with a given probability per attribute. This is different from the behavior of
-    ClassifierFreeGuidanceDropout as this allows for attributes to be dropped out separately. For example,
-    "artist" can be dropped while "genre" remains. This is in contrast to ClassifierFreeGuidanceDropout
-    where if "artist" is dropped "genre" must also be dropped.
-
-    Args:
-        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
-            ...
-            "genre": 0.1,
-            "artist": 0.5,
-            "wav": 0.25,
-            ...
-        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
-        seed (int, optional): Random seed.
-    """
-    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.active_on_eval = active_on_eval
-        # construct dict that return the values from p otherwise 0
-        self.p = {}
-        for condition_type, probs in p.items():
-            self.p[condition_type] = defaultdict(lambda: 0, probs)
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of conditions.
-        Returns:
-            tp.List[ConditioningAttributes]: List of conditions after certain attributes were set to None.
-        """
-        if not self.training and not self.active_on_eval:
-            return samples
-
-        samples = deepcopy(samples)
-
-        for condition_type, ps in self.p.items():  # for condition types [text, wav]
-            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
-                if torch.rand(1, generator=self.rng).item() < p:
-                    for sample in samples:
-                        dropout_condition(sample, condition_type, condition)
-
-        return samples
-
-    def __repr__(self):
-        return f"AttributeDropout({dict(self.p)})"
-
-
-class ClassifierFreeGuidanceDropout(DropoutModule):
-    """Applies Classifier Free Guidance dropout, meaning all attributes
-    are dropped with the same probability.
-
-    Args:
-        p (float): Probability to apply condition dropout during training.
-        seed (int): Random seed.
-    """
-    def __init__(self, p: float, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.p = p
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of conditions.
-        Returns:
-            tp.List[ConditioningAttributes]: List of conditions after all attributes were set to None.
-        """
-        if not self.training:
-            return samples
-
-        # decide on which attributes to drop in a batched fashion
-        drop = torch.rand(1, generator=self.rng).item() < self.p
-        if not drop:
-            return samples
-
-        # nullify conditions of all attributes
-        samples = deepcopy(samples)
-
-        for condition_type in ["wav", "text"]:
-            for sample in samples:
-                for condition in sample.attributes[condition_type]:
-                    dropout_condition(sample, condition_type, condition)
-
-        return samples
-
-    def __repr__(self):
-        return f"ClassifierFreeGuidanceDropout(p={self.p})"
-
-
-class ConditioningProvider(nn.Module):
-    """Main class to provide conditions given all the supported conditioners.
-
-    Args:
-        conditioners (dict): Dictionary of conditioners.
-        merge_text_conditions_p (float, optional): Probability to merge all text sources
-            into a single text condition. Defaults to 0.
-        drop_desc_p (float, optional): Probability to drop the original description
-            when merging all text sources into a single text condition. Defaults to 0.
-        device (tp.Union[torch.device, str], optional): Device for conditioners and output condition types.
-    """
-    def __init__(
-        self,
-        conditioners: tp.Dict[str, BaseConditioner],
-        merge_text_conditions_p: float = 0,
-        drop_desc_p: float = 0,
-        device: tp.Union[torch.device, str] = "cpu",
-    ):
-        super().__init__()
-        self.device = device
-        self.merge_text_conditions_p = merge_text_conditions_p
-        self.drop_desc_p = drop_desc_p
-        self.conditioners = nn.ModuleDict(conditioners)
-
-    @property
-    def text_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
-
-    @property
-    def wav_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
-
-    @property
-    def has_wav_condition(self):
-        return len(self.wav_conditions) > 0
-
-    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
-        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
-        This should be called before starting any real GPU work to avoid synchronization points.
-        This will return a dict matching conditioner names to their arbitrary tokenized representations.
-
-        Args:
-            inputs (list[ConditioningAttribres]): List of ConditioningAttributes objects containing
-                text and wav conditions.
-        """
-        assert all([type(x) == ConditioningAttributes for x in inputs]), \
-            "got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]" \
-            f" but types were {set([type(x) for x in inputs])}"
-
-        output = {}
-        text = self._collate_text(inputs)
-        wavs = self._collate_wavs(inputs)
-
-        assert set(text.keys() | wavs.keys()).issubset(set(self.conditioners.keys())), \
-            f"got an unexpected attribute! Expected {self.conditioners.keys()}, got {text.keys(), wavs.keys()}"
-
-        for attribute, batch in chain(text.items(), wavs.items()):
-            output[attribute] = self.conditioners[attribute].tokenize(batch)
-        return output
-
-    def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
-        """Compute pairs of `(embedding, mask)` using the configured conditioners
-        and the tokenized representations. The output is for example:
-
-            {
-                "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
-                "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
-                ...
-            }
-
-        Args:
-            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
-        """
-        output = {}
-        for attribute, inputs in tokenized.items():
-            condition, mask = self.conditioners[attribute](inputs)
-            output[attribute] = (condition, mask)
-        return output
-
-    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
-        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
-        are the attributes and the values are the aggregated input per attribute.
-        For example:
-        Input:
-        [
-            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
-            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
-        ]
-        Output:
-        {
-            "genre": ["Rock", "Hip-hop"],
-            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
-        }
-        """
-        batch_per_attribute: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
-
-        def _merge_conds(cond, merge_text_conditions_p=0, drop_desc_p=0):
-            def is_valid(k, v):
-                k_valid = k in ['key', 'bpm', 'genre', 'moods', 'instrument']
-                v_valid = v is not None and isinstance(v, (int, float, str, list))
-                return k_valid and v_valid
-
-            def process_value(v):
-                if isinstance(v, (int, float, str)):
-                    return v
-                if isinstance(v, list):
-                    return ", ".join(v)
-                else:
-                    RuntimeError(f"unknown type for text value! ({type(v), v})")
-
-            desc = cond.text['description']
-            meta_data = ""
-            if random.uniform(0, 1) < merge_text_conditions_p:
-                meta_pairs = [f'{k}: {process_value(v)}' for k, v in cond.text.items() if is_valid(k, v)]
-                random.shuffle(meta_pairs)
-                meta_data = ". ".join(meta_pairs)
-                desc = desc if not random.uniform(0, 1) < drop_desc_p else None
-
-            if desc is None:
-                desc = meta_data if len(meta_data) > 1 else None
-            else:
-                desc = desc.rstrip('.') + ". " + meta_data
-            cond.text['description'] = desc.strip() if desc else None
-
-        if self.training and self.merge_text_conditions_p:
-            for sample in samples:
-                _merge_conds(sample, self.merge_text_conditions_p, self.drop_desc_p)
-
-        texts = [x.text for x in samples]
-        for text in texts:
-            for condition in self.text_conditions:
-                batch_per_attribute[condition].append(text[condition])
-
-        return batch_per_attribute
-
-    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]):
-        """Generate a dict where the keys are attributes by which we fetch similar wavs,
-        and the values are Tensors of wavs according to said attribtues.
-
-        *Note*: by the time the samples reach this function, each sample should have some waveform
-        inside the "wav" attribute. It should be either:
-        1. A real waveform
-        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
-        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
-
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of ConditioningAttributes samples.
-        Returns:
-            dict: A dicionary mapping an attribute name to wavs.
-        """
-        wavs = defaultdict(list)
-        lens = defaultdict(list)
-        paths = defaultdict(list)
-        out = {}
-
-        for sample in samples:
-            for attribute in self.wav_conditions:
-                wav, length, path = sample.wav[attribute]
-                wavs[attribute].append(wav.flatten())
-                lens[attribute].append(length)
-                paths[attribute].append(path)
-
-        # stack all wavs to a single tensor
-        for attribute in self.wav_conditions:
-            stacked_wav, _ = collate(wavs[attribute], dim=0)
-            out[attribute] = WavCondition(stacked_wav.unsqueeze(1),
-                                          torch.cat(lens['self_wav']), paths[attribute])  # type: ignore
-
-        return out
-
-
-class ConditionFuser(StreamingModule):
-    """Condition fuser handles the logic to combine the different conditions
-    to the actual model input.
-
-    Args:
-        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
-            each condition. For example:
-            {
-                "prepend": ["description"],
-                "sum": ["genre", "bpm"],
-                "cross": ["description"],
-            }
-        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
-        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
-    """
-    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
-
-    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
-                 cross_attention_pos_emb_scale: float = 1.0):
-        super().__init__()
-        assert all(
-            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
-        ), f"got invalid fuse method, allowed methods: {self.FUSING_MEHTODS}"
-        self.cross_attention_pos_emb = cross_attention_pos_emb
-        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
-        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
-        self.cond2fuse: tp.Dict[str, str] = {}
-        for fuse_method, conditions in fuse2cond.items():
-            for condition in conditions:
-                self.cond2fuse[condition] = fuse_method
-
-    def forward(
-        self,
-        input: Tensor,
-        conditions: tp.Dict[str, ConditionType]
-    ) -> tp.Tuple[Tensor, tp.Optional[Tensor]]:
-        """Fuse the conditions to the provided model input.
-
-        Args:
-            input (Tensor): Transformer input.
-            conditions (tp.Dict[str, ConditionType]): Dict of conditions.
-        Returns:
-            tp.Tuple[Tensor, Tensor]: The first tensor is the transformer input
-                after the conditions have been fused. The second output tensor is the tensor
-                used for cross-attention or None if no cross attention inputs exist.
-        """
-        B, T, _ = input.shape
-
-        if 'offsets' in self._streaming_state:
-            first_step = False
-            offsets = self._streaming_state['offsets']
-        else:
-            first_step = True
-            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
-
-        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
-            f"given conditions contain unknown attributes for fuser, " \
-            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
-        cross_attention_output = None
-        for cond_type, (cond, cond_mask) in conditions.items():
-            op = self.cond2fuse[cond_type]
-            if op == "sum":
-                input += cond
-            elif op == "input_interpolate":
-                cond = rearrange(cond, "b t d -> b d t")
-                cond = F.interpolate(cond, size=input.shape[1])
-                input += rearrange(cond, "b d t -> b t d")
-            elif op == "prepend":
-                if first_step:
-                    input = torch.cat([cond, input], dim=1)
-            elif op == "cross":
-                if cross_attention_output is not None:
-                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
-                else:
-                    cross_attention_output = cond
-            else:
-                raise ValueError(f"unknown op ({op})")
-
-        if self.cross_attention_pos_emb and cross_attention_output is not None:
-            positions = torch.arange(
-                cross_attention_output.shape[1],
-                device=cross_attention_output.device
-            ).view(1, -1, 1)
-            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
-            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return input, cross_attention_output

spaces/AchyuthGamer/OpenGPT/client/js/icons.js

@@ -1 +0,0 @@
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spaces/AchyuthGamer/OpenGPT/g4f/Provider/Aichat.py

@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-
-from .base_provider import AsyncProvider, format_prompt
-
-
-class Aichat(AsyncProvider):
-    url                   = "https://chat-gpt.org/chat"
-    working               = True
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    async def create_async(
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> str:
-        headers = {
-            "authority": "chat-gpt.org",
-            "accept": "*/*",
-            "cache-control": "no-cache",
-            "content-type": "application/json",
-            "origin": "https://chat-gpt.org",
-            "pragma": "no-cache",
-            "referer": "https://chat-gpt.org/chat",
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"macOS"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            json_data = {
-                "message": format_prompt(messages),
-                "temperature": kwargs.get('temperature', 0.5),
-                "presence_penalty": 0,
-                "top_p": kwargs.get('top_p', 1),
-                "frequency_penalty": 0,
-            }
-            async with session.post(
-                "https://chat-gpt.org/api/text",
-                proxy=proxy,
-                json=json_data
-            ) as response:
-                response.raise_for_status()
-                result = await response.json()
-                if not result['response']:
-                    raise Exception(f"Error Response: {result}")
-                return result["message"]

spaces/AchyuthGamer/OpenGPT/g4f/Provider/needs_auth/Bard.py

@@ -1,92 +0,0 @@
-from __future__ import annotations
-
-import json
-import random
-import re
-
-from aiohttp import ClientSession
-
-from ..base_provider import AsyncProvider, format_prompt, get_cookies
-
-
-class Bard(AsyncProvider):
-    url = "https://bard.google.com"
-    needs_auth = True
-    working = True
-    _snlm0e = None
-
-    @classmethod
-    async def create_async(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        cookies: dict = None,
-        **kwargs
-    ) -> str:
-        prompt = format_prompt(messages)
-        if proxy and "://" not in proxy:
-            proxy = f"http://{proxy}"
-        if not cookies:
-            cookies = get_cookies(".google.com")
-
-        headers = {
-            'authority': 'bard.google.com',
-            'origin': 'https://bard.google.com',
-            'referer': 'https://bard.google.com/',
-            'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
-            'x-same-domain': '1',
-        }
-
-        async with ClientSession(
-            cookies=cookies,
-            headers=headers
-        ) as session:
-            if not cls._snlm0e:
-                async with session.get(cls.url, proxy=proxy) as response:
-                    text = await response.text()
-
-                match = re.search(r'SNlM0e\":\"(.*?)\"', text)
-                if not match:
-                    raise RuntimeError("No snlm0e value.")
-                cls._snlm0e = match.group(1)
-            
-            params = {
-                'bl': 'boq_assistant-bard-web-server_20230326.21_p0',
-                '_reqid': random.randint(1111, 9999),
-                'rt': 'c'
-            }
-
-            data = {
-                'at': cls._snlm0e,
-                'f.req': json.dumps([None, json.dumps([[prompt]])])
-            }
-
-            intents = '.'.join([
-                'assistant',
-                'lamda',
-                'BardFrontendService'
-            ])
-
-            async with session.post(
-                f'{cls.url}/_/BardChatUi/data/{intents}/StreamGenerate',
-                data=data,
-                params=params,
-                proxy=proxy
-            ) as response:
-                response = await response.text()
-                response = json.loads(response.splitlines()[3])[0][2]
-                response = json.loads(response)[4][0][1][0]
-                return response
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("proxy", "str"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/Adr740/CV_XPLORER_POC/get_cv.py

@@ -1,39 +0,0 @@
-import pandas as pd
-import openai
-from data import data as df
-import numpy as np
-import os
-
-openai.api_key = os.environ.get("openai")
-
-def cosine_similarity(a, b):
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
-
-
-def get_embedding(text, model="text-embedding-ada-002"):
-    try:
-        text = text.replace("\n", " ")
-    except:
-        None
-    return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding']
-
-def search_cv(search, nb=3, pprint=True):
-    embedding = get_embedding(search, model='text-embedding-ada-002')
-    dff = df.copy()
-    dff['similarities'] = dff.embedding.apply(lambda x: cosine_similarity(x, embedding))
-    res = dff.sort_values('similarities', ascending=False).head(int(nb))
-    # try:
-    #     res.drop(columns=["id","hadith_id", "embeding"], inplace=True)
-    # except:
-    #     pass
-    return res
-
-def get_cv(text, nb):
-    result = search_cv(text,nb).to_dict(orient="records")
-    final_str = ""
-    for r in result:
-        final_str += "#### Candidat avec " + str(round(r["similarities"]*100,2)) + "% de similarité :\n"+ str(r["summary"]).replace("#","")
-        final_str += "\n\n[-> Lien vers le CV complet]("+ str(r["url"]) + ')'
-        final_str += "\n\n-----------------------------------------------------------------------------------------------------\n\n"
-    final_str = final_str.replace("`", "")
-    return  final_str

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/quadimage.js

@@ -1,13 +0,0 @@
-import QuadImage from './gameobjects/mesh/quad/image/Image.js';
-import QuadRenderTexture from './gameobjects/mesh/quad/rendertexture/RenderTexture.js';
-import SkewImage from './gameobjects/mesh/quad/skewimage/SkewImage.js';
-import SkewRenderTexture from './gameobjects/mesh/quad/skewrendertexture/SkewRenderTexture.js';
-import ContainerSkew from './behaviors/containerskew/ContainerSkew.js';
-
-export {
-    QuadImage,
-    QuadRenderTexture,
-    SkewImage,
-    SkewRenderTexture,
-    ContainerSkew
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/imagebox/ImageBox.js

@@ -1,2 +0,0 @@
-import ImageBox from '../../../plugins/imagebox.js';
-export default ImageBox;

spaces/Akmyradov/TurkmenTTSweSTT/vits/monotonic_align/__init__.py

@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_onnx_objects.py

@@ -1,17 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class OnnxRuntimeModel(metaclass=DummyObject):
-    _backends = ["onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["onnx"])

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py'
-# model settings
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(type='ResNet', depth=101))

spaces/AndySAnker/DeepStruc/tools/utils.py

@@ -1,279 +0,0 @@
-import torch, os, yaml, sys
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-from tqdm import tqdm
-from matplotlib.patches import Ellipse
-import matplotlib.lines as mlines
-from matplotlib.gridspec import GridSpec
-import datetime
-from tools.data_loader import save_xyz_file
-import streamlit as st
-
-def get_data(args):  # Todo: write your own dataloader.
-    ct = str(datetime.datetime.now()).replace(' ', '_').replace(':','-').replace('.','-')
-    project_name = f'{args.save_path}/DeepStruc_{ct}'
-    print(f'\nProject name is: {project_name}')
-    if not os.path.isdir(f'{project_name}'):
-        os.mkdir(f'{project_name}')
-
-    samples = args.num_samples
-    ## Use the uploaded file. Does not support multiple files. Could be written smarter.
-    files = ['uploaded_file.gr'] 
-    this_path = '.'
-    #this_path = args.data
-    #if os.path.isdir(this_path):
-    #    files = sorted(os.listdir(this_path))
-    #else:
-    #    files = [this_path]
-    #    this_path = '.'
-
-    x_list, y_list, name_list = [], [], []
-    idxx = 0
-    np_data = np.zeros((len(files)*samples, 2800))
-    for idx, file in enumerate(files):
-        for skip_row in range(100):
-            try:
-                data = np.loadtxt(f'{this_path}/{file}', skiprows=skip_row)
-            except ValueError:
-                continue
-            data = data.T
-            x_list.append(data[0])
-            y_list.append(data[1])
-            Gr_ph = data[1]
-            if round(data[0][1] - data[0][0],2) != 0.01:
-                raise ValueError("The PDF does not have an r-step of 0.01 Å")
-            try:
-                start_PDF = np.where((data[0] > 1.995) & (data[0] < 2.005))[0][0]
-            except:
-                Gr_ph = np.concatenate((np.zeros((int((data[0][0])/0.01))), Gr_ph))
-                print("The PDFs first value is above 2 Å. We have added 0's down to 2 Å as a quick fix.")
-            try:
-                end_PDF = np.where((data[0] > 29.995) & (data[0] < 30.005))[0][0]
-            except:
-                Gr_ph = np.concatenate((Gr_ph, np.zeros((3000-len(Gr_ph)))))
-                print("The PDFs last value is before 30 Å. We have added 0's up to 30 Å as a quick fix.")
-            Gr_ph = Gr_ph[200:3000]
-
-            for i in range(samples):
-                np_data[idxx] = Gr_ph
-                np_data[idxx] /= np.amax(np_data[idxx])
-                idxx += 1
-                name_list.append(file)
-            break
-
-    fig, ax = plt.subplots()
-
-    plt.plot(x_list[0], y_list[0], label="Input PDF")
-    plt.plot(np.arange(2, 30, 0.01), np_data[0], label="DeepStruc PDF")
-    ax.set_xlabel(r'r / $\mathtt{\AA}$')
-    ax.set_ylabel('G(r) / a.u.')
-
-    ax.set_xlim(0,30)
-    plt.legend()
-    plt.title(f'{files[0]}')
-    plt.tight_layout()
-    plt.savefig(f'{project_name}/PDFs.png', dpi=300)
-
-    np_data = np_data.reshape((len(files)*samples, 2800, 1))
-    np_data = torch.tensor(np_data, dtype=torch.float)
-    return np_data, name_list, project_name
-
-
-def format_predictions(latent_space, data_names, mus, sigmas, sigma_inc):
-    df_preds = pd.DataFrame(columns=['x', 'y', 'file_name', 'mu', 'sigma', 'sigma_inc'])
-    for i,j, mu, sigma in zip(latent_space, data_names, mus, sigmas):
-        if '/' in j:
-            j = j.split('/')[-1]
-
-        if '.' in j:
-            j_idx = j.rindex('.')
-            j = j[:j_idx]
-
-        info_dict = {
-            'x': i[0].detach().cpu().numpy(),
-            'y': i[1].detach().cpu().numpy(),
-            'file_name': j,
-            'mu': mu.detach().cpu().numpy(),
-            'sigma': sigma.detach().cpu().numpy(),
-            'sigma_inc': sigma_inc,}
-
-
-        print ("info dict: ", info_dict)
-        print ("df_preds initial: ", df_preds.head())
-        
-        # Append is deprecated and should use concat instead
-        df_preds = df_preds.append(info_dict, ignore_index=True)
-
-    return df_preds
-
-
-def plot_ls(df, index_highlight):
-    ideal_ls = './tools/ls_points.csv'
-    color_dict = {
-        'FCC': '#19ADFF',
-        'BCC': '#4F8F00',
-        'SC': '#941100',
-        'Octahedron': '#212121',
-        'Icosahedron': '#005493',
-        'Decahedron': '#FF950E',
-        'HCP': '#FF8AD8',
-    }
-    df_ideal = pd.read_csv(ideal_ls, index_col=0)  # Get latent space data
-    # Plotting inputs
-    ## Training and validation data
-    MARKER_SIZE_TR = 60
-    EDGE_LINEWIDTH_TR = 0.0
-    ALPHA_TR = 0.3
-
-    ## Figure
-    FIG_SIZE = (10, 4)
-    MARKER_SIZE_FG = 60
-    MARKER_FONT_SIZE = 10
-    MARKER_SCALE = 1.5
-
-    fig = plt.figure(figsize=FIG_SIZE)
-    gs = GridSpec(1, 5, figure=fig)
-    ax = fig.add_subplot(gs[0, :4])
-    ax_legend = fig.add_subplot(gs[0, 4])
-
-    if int(index_highlight) >= len(df):
-        print(f'\nIndex argument is to large! Need to be smaller than {len(df)} but was {index_highlight}')
-        raise IndexError
-    elif int(index_highlight) < -1:
-        print(f'\nIndex argument invalid! Must be integer from -1 to number of samples generated.')
-        raise ValueError
-    elif int(index_highlight)==-1:
-        pass
-    elif len(df['file_name'].unique()) > 1:
-        print(f'\nCan only show highlight index if --data is specific file but {len(df["file_name"].unique())} files were loaded.')
-    else:
-        print(f'\nHighlighting index {index_highlight} from the {df["file_name"].unique()[0]} sampling pool.')
-        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='k', s=40,
-                   linewidth=0.0, marker='o', zorder=3)
-        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='w', s=25,
-                   linewidth=0.0, marker='o', zorder=3)
-        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='k', s=10,
-                   linewidth=0.0, marker='o', zorder=3)
-        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='w', s=1,
-                   linewidth=0.0, marker='o', zorder=3)
-
-    print('\nPlotting DeepStruc training + validation data.')
-    ax.scatter(df_ideal.iloc[:]['x'].values, df_ideal.iloc[:]['y'].values,
-                   c=[color_dict[str(s)] for s in df_ideal.iloc[:]['stru_type']],
-                   s=MARKER_SIZE_TR * df_ideal.iloc[:]['size'].values,
-                   edgecolors='k', linewidth=EDGE_LINEWIDTH_TR,
-                   alpha=ALPHA_TR)
-    
-    
-    mlines_list = []
-    for key in color_dict.keys():
-        mlines_list.append(
-            mlines.Line2D([], [], MARKER_SIZE_FG, marker='o', c=color_dict[key], linestyle='None', label=key,
-                          mew=1))
-
-    from matplotlib import cm
-    cm_subsection = np.linspace(0, 1, len(df.file_name.unique()))
-    data_color = [cm.magma(x) for x in cm_subsection]
-    
-    print('\nPlotting DeepStruc structure sampling.')
-    pbar = tqdm(total=len(df.file_name.unique()))
-    for idx, file_name in enumerate(df.file_name.unique()):
-        this_c = np.array([data_color[idx]])
-
-        df_ph = df[df.file_name==file_name]
-        df_ph.reset_index(drop=True, inplace=True)
-
-        ax.scatter(df_ph['mu'][0][0],df_ph['mu'][0][1], c=this_c, s=10, edgecolors='k',
-                   linewidth=0.5, marker='D',zorder=1)
-        ellipse = Ellipse((df_ph['mu'][0][0],df_ph['mu'][0][1]),df_ph['sigma'][0][0],df_ph['sigma'][0][1], ec='k', fc=this_c, alpha=0.5, fill=True, zorder=-1)
-        ax.add_patch(ellipse)
-
-        ellipse = Ellipse((df_ph['mu'][0][0],df_ph['mu'][0][1]),df_ph['x'].var(),df_ph['y'].var(), ec='k', fc=this_c, alpha=0.2, fill=True, zorder=-1)
-        ax.add_patch(ellipse)
-
-        mlines_list.append(
-            mlines.Line2D([], [], MARKER_SIZE_FG, marker='D', c=this_c, linestyle='None', label=file_name, mec='k',
-                          mew=1))
-
-        for index, sample in df_ph.iterrows():
-            ax.scatter(sample['x'], sample['y'], c=this_c, s=10, edgecolors='k',
-                       linewidth=0.8, marker='o', zorder=2)
-        pbar.update()
-    pbar.close()
-    
-    ax_legend.legend(handles=mlines_list,fancybox=True, #ncol=2,  #, bbox_to_anchor=(0.8, 0.5)
-          markerscale=MARKER_SCALE, fontsize=MARKER_FONT_SIZE, loc='upper right')
-
-    ax.set_xlabel('Latent space $\mathregular{z_0}$', size=10)  # Latent Space Feature 1
-    ax.set_ylabel('Latent space $\mathregular{z_1}$', size=10)
-
-    ax_legend.spines['top'].set_visible(False)
-    ax_legend.spines['right'].set_visible(False)
-    ax_legend.spines['bottom'].set_visible(False)
-    ax_legend.spines['left'].set_visible(False)
-    ax_legend.get_xaxis().set_ticks([])
-    ax_legend.get_yaxis().set_ticks([])
-    ax.get_xaxis().set_ticks([])
-    ax.get_yaxis().set_ticks([])
-
-    plt.tight_layout()
-    
-    return fig
-
-def get_model(model_dir):
-    if model_dir == 'DeepStruc':
-        with open(f'./models/DeepStruc/model_arch.yaml') as file:
-            model_arch = yaml.full_load(file)
-        model_path = './models/DeepStruc/models/DeepStruc.ckpt'
-        return model_path, model_arch
-    if os.path.isdir(model_dir):
-        if 'models' in os.listdir(model_dir):
-            models = sorted(os.listdir(f'{model_dir}/models'))
-            models = [model for model in models if '.ckpt' in model]
-            print(f'No specific model was provided. {models[0]} was chosen.')
-            print('Dataloader might not be sufficient in loading dimensions.')
-            model_path = f'{model_dir}/models/{models[0]}'
-            with open(f'{model_dir}/model_arch.yaml') as file:
-                model_arch = yaml.full_load(file)
-
-            return model_path, model_arch
-        else:
-            print(f'Path not understood: {model_dir}')
-    else:
-        idx = model_dir.rindex('/')
-        with open(f'{model_dir[:idx-6]}model_arch.yaml') as file:
-            model_arch = yaml.full_load(file)
-
-        return model_dir, model_arch
-
-
-def save_predictions(xyz_pred, df, project_name, model_arch, args):
-    print('\nSaving predicted structures as XYZ files.')
-    if not os.path.isdir(f'{project_name}'):
-        os.mkdir(f'{project_name}')
-
-    with open(f'{project_name}/args.yaml', 'w') as outfile:
-        yaml.dump(vars(args), outfile, allow_unicode=True, default_flow_style=False)
-
-    """
-    pbar = tqdm(total=len(df))
-    for count, (idx, row) in enumerate(df.iterrows()):
-        if not os.path.isdir(f'{project_name}/{row["file_name"]}'):
-            os.mkdir(f'{project_name}/{row["file_name"]}')
-        x = f'{float(row["x"]):+.3f}'.replace('.', '-')
-        y = f'{float(row["y"]):+.3f}'.replace('.', '-')
-
-        these_cords = save_xyz_file('./',
-                      xyz_pred[idx].detach().cpu().numpy(),
-                      f'{count:05}',
-                      [model_arch['norm_vals']['x'],model_arch['norm_vals']['y'],model_arch['norm_vals']['z']])
-        pbar.update()
-    pbar.close()
-    """
-    # Does not support multiple structure saving
-    these_cords = save_xyz_file('./',
-                  xyz_pred[args.index_plot].detach().cpu().numpy(),
-                  'DummyName',
-                  [model_arch['norm_vals']['x'],model_arch['norm_vals']['y'],model_arch['norm_vals']['z']])
-    return these_cords

spaces/Ariharasudhan/Kenya_food_classification/README.md

@@ -1,13 +0,0 @@
----
-title: Kenya Food Classification
-emoji: 📉
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.44.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/utils/scheduler_list.py

@@ -1,32 +0,0 @@
-from diffusers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    HeunDiscreteScheduler,
-    LMSDiscreteScheduler,
-)
-
-diff_scheduler_list = ["DDIM", "EulerA", "Euler", "LMS", "Heun", "UniPC", "DPMSolver"]
-
-
-def get_scheduler_list(pipe, scheduler):
-    if scheduler == diff_scheduler_list[0]:
-        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-
-    elif scheduler == diff_scheduler_list[1]:
-        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-
-    elif scheduler == diff_scheduler_list[2]:
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-
-    elif scheduler == diff_scheduler_list[3]:
-        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
-
-    elif scheduler == diff_scheduler_list[4]:
-        pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
-
-    elif scheduler == diff_scheduler_list[5]:
-        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-
-    return pipe

spaces/Artrajz/vits-simple-api/bert_vits2/text/japanese_bert.py

@@ -1,47 +0,0 @@
-import os
-
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-import config
-from logger import logger
-from utils.download import download_and_verify
-from config import DEVICE as device
-
-URLS = [
-    "https://huggingface.co/cl-tohoku/bert-base-japanese-v3/resolve/main/pytorch_model.bin",
-]
-TARGET_PATH = os.path.join(config.ABS_PATH, "bert_vits2/bert/bert-base-japanese-v3/pytorch_model.bin")
-EXPECTED_MD5 = None
-
-if not os.path.exists(TARGET_PATH):
-    success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5)
-
-try:
-    logger.info("Loading bert-base-japanese-v3...")
-    tokenizer = AutoTokenizer.from_pretrained(config.ABS_PATH + "/bert_vits2/bert/bert-base-japanese-v3")
-    model = AutoModelForMaskedLM.from_pretrained(config.ABS_PATH + "/bert_vits2/bert/bert-base-japanese-v3").to(
-        device)
-    logger.info("Loading finished.")
-except Exception as e:
-    logger.error(e)
-    logger.error(f"Please download pytorch_model.bin from cl-tohoku/bert-base-japanese-v3.")
-
-
-def get_bert_feature(text, word2ph, device=config.DEVICE):
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors="pt")
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = model(**inputs, output_hidden_states=True)
-        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-    assert inputs["input_ids"].shape[-1] == len(word2ph)
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-    return phone_level_feature.T

spaces/Ashrafb/Imdf2/README.md

@@ -1,12 +0,0 @@
----
-title: Imdf2
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,505 +0,0 @@
-"""
-The main purpose of this module is to expose LinkCollector.collect_sources().
-"""
-
-import collections
-import email.message
-import functools
-import itertools
-import json
-import logging
-import os
-import urllib.parse
-import urllib.request
-from html.parser import HTMLParser
-from optparse import Values
-from typing import (
-    TYPE_CHECKING,
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    MutableMapping,
-    NamedTuple,
-    Optional,
-    Sequence,
-    Tuple,
-    Union,
-)
-
-from pip._vendor import requests
-from pip._vendor.requests import Response
-from pip._vendor.requests.exceptions import RetryError, SSLError
-
-from pip._internal.exceptions import NetworkConnectionError
-from pip._internal.models.link import Link
-from pip._internal.models.search_scope import SearchScope
-from pip._internal.network.session import PipSession
-from pip._internal.network.utils import raise_for_status
-from pip._internal.utils.filetypes import is_archive_file
-from pip._internal.utils.misc import redact_auth_from_url
-from pip._internal.vcs import vcs
-
-from .sources import CandidatesFromPage, LinkSource, build_source
-
-if TYPE_CHECKING:
-    from typing import Protocol
-else:
-    Protocol = object
-
-logger = logging.getLogger(__name__)
-
-ResponseHeaders = MutableMapping[str, str]
-
-
-def _match_vcs_scheme(url: str) -> Optional[str]:
-    """Look for VCS schemes in the URL.
-
-    Returns the matched VCS scheme, or None if there's no match.
-    """
-    for scheme in vcs.schemes:
-        if url.lower().startswith(scheme) and url[len(scheme)] in "+:":
-            return scheme
-    return None
-
-
-class _NotAPIContent(Exception):
-    def __init__(self, content_type: str, request_desc: str) -> None:
-        super().__init__(content_type, request_desc)
-        self.content_type = content_type
-        self.request_desc = request_desc
-
-
-def _ensure_api_header(response: Response) -> None:
-    """
-    Check the Content-Type header to ensure the response contains a Simple
-    API Response.
-
-    Raises `_NotAPIContent` if the content type is not a valid content-type.
-    """
-    content_type = response.headers.get("Content-Type", "Unknown")
-
-    content_type_l = content_type.lower()
-    if content_type_l.startswith(
-        (
-            "text/html",
-            "application/vnd.pypi.simple.v1+html",
-            "application/vnd.pypi.simple.v1+json",
-        )
-    ):
-        return
-
-    raise _NotAPIContent(content_type, response.request.method)
-
-
-class _NotHTTP(Exception):
-    pass
-
-
-def _ensure_api_response(url: str, session: PipSession) -> None:
-    """
-    Send a HEAD request to the URL, and ensure the response contains a simple
-    API Response.
-
-    Raises `_NotHTTP` if the URL is not available for a HEAD request, or
-    `_NotAPIContent` if the content type is not a valid content type.
-    """
-    scheme, netloc, path, query, fragment = urllib.parse.urlsplit(url)
-    if scheme not in {"http", "https"}:
-        raise _NotHTTP()
-
-    resp = session.head(url, allow_redirects=True)
-    raise_for_status(resp)
-
-    _ensure_api_header(resp)
-
-
-def _get_simple_response(url: str, session: PipSession) -> Response:
-    """Access an Simple API response with GET, and return the response.
-
-    This consists of three parts:
-
-    1. If the URL looks suspiciously like an archive, send a HEAD first to
-       check the Content-Type is HTML or Simple API, to avoid downloading a
-       large file. Raise `_NotHTTP` if the content type cannot be determined, or
-       `_NotAPIContent` if it is not HTML or a Simple API.
-    2. Actually perform the request. Raise HTTP exceptions on network failures.
-    3. Check the Content-Type header to make sure we got a Simple API response,
-       and raise `_NotAPIContent` otherwise.
-    """
-    if is_archive_file(Link(url).filename):
-        _ensure_api_response(url, session=session)
-
-    logger.debug("Getting page %s", redact_auth_from_url(url))
-
-    resp = session.get(
-        url,
-        headers={
-            "Accept": ", ".join(
-                [
-                    "application/vnd.pypi.simple.v1+json",
-                    "application/vnd.pypi.simple.v1+html; q=0.1",
-                    "text/html; q=0.01",
-                ]
-            ),
-            # We don't want to blindly returned cached data for
-            # /simple/, because authors generally expecting that
-            # twine upload && pip install will function, but if
-            # they've done a pip install in the last ~10 minutes
-            # it won't. Thus by setting this to zero we will not
-            # blindly use any cached data, however the benefit of
-            # using max-age=0 instead of no-cache, is that we will
-            # still support conditional requests, so we will still
-            # minimize traffic sent in cases where the page hasn't
-            # changed at all, we will just always incur the round
-            # trip for the conditional GET now instead of only
-            # once per 10 minutes.
-            # For more information, please see pypa/pip#5670.
-            "Cache-Control": "max-age=0",
-        },
-    )
-    raise_for_status(resp)
-
-    # The check for archives above only works if the url ends with
-    # something that looks like an archive. However that is not a
-    # requirement of an url. Unless we issue a HEAD request on every
-    # url we cannot know ahead of time for sure if something is a
-    # Simple API response or not. However we can check after we've
-    # downloaded it.
-    _ensure_api_header(resp)
-
-    logger.debug(
-        "Fetched page %s as %s",
-        redact_auth_from_url(url),
-        resp.headers.get("Content-Type", "Unknown"),
-    )
-
-    return resp
-
-
-def _get_encoding_from_headers(headers: ResponseHeaders) -> Optional[str]:
-    """Determine if we have any encoding information in our headers."""
-    if headers and "Content-Type" in headers:
-        m = email.message.Message()
-        m["content-type"] = headers["Content-Type"]
-        charset = m.get_param("charset")
-        if charset:
-            return str(charset)
-    return None
-
-
-class CacheablePageContent:
-    def __init__(self, page: "IndexContent") -> None:
-        assert page.cache_link_parsing
-        self.page = page
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, type(self)) and self.page.url == other.page.url
-
-    def __hash__(self) -> int:
-        return hash(self.page.url)
-
-
-class ParseLinks(Protocol):
-    def __call__(self, page: "IndexContent") -> Iterable[Link]:
-        ...
-
-
-def with_cached_index_content(fn: ParseLinks) -> ParseLinks:
-    """
-    Given a function that parses an Iterable[Link] from an IndexContent, cache the
-    function's result (keyed by CacheablePageContent), unless the IndexContent
-    `page` has `page.cache_link_parsing == False`.
-    """
-
-    @functools.lru_cache(maxsize=None)
-    def wrapper(cacheable_page: CacheablePageContent) -> List[Link]:
-        return list(fn(cacheable_page.page))
-
-    @functools.wraps(fn)
-    def wrapper_wrapper(page: "IndexContent") -> List[Link]:
-        if page.cache_link_parsing:
-            return wrapper(CacheablePageContent(page))
-        return list(fn(page))
-
-    return wrapper_wrapper
-
-
-@with_cached_index_content
-def parse_links(page: "IndexContent") -> Iterable[Link]:
-    """
-    Parse a Simple API's Index Content, and yield its anchor elements as Link objects.
-    """
-
-    content_type_l = page.content_type.lower()
-    if content_type_l.startswith("application/vnd.pypi.simple.v1+json"):
-        data = json.loads(page.content)
-        for file in data.get("files", []):
-            link = Link.from_json(file, page.url)
-            if link is None:
-                continue
-            yield link
-        return
-
-    parser = HTMLLinkParser(page.url)
-    encoding = page.encoding or "utf-8"
-    parser.feed(page.content.decode(encoding))
-
-    url = page.url
-    base_url = parser.base_url or url
-    for anchor in parser.anchors:
-        link = Link.from_element(anchor, page_url=url, base_url=base_url)
-        if link is None:
-            continue
-        yield link
-
-
-class IndexContent:
-    """Represents one response (or page), along with its URL"""
-
-    def __init__(
-        self,
-        content: bytes,
-        content_type: str,
-        encoding: Optional[str],
-        url: str,
-        cache_link_parsing: bool = True,
-    ) -> None:
-        """
-        :param encoding: the encoding to decode the given content.
-        :param url: the URL from which the HTML was downloaded.
-        :param cache_link_parsing: whether links parsed from this page's url
-                                   should be cached. PyPI index urls should
-                                   have this set to False, for example.
-        """
-        self.content = content
-        self.content_type = content_type
-        self.encoding = encoding
-        self.url = url
-        self.cache_link_parsing = cache_link_parsing
-
-    def __str__(self) -> str:
-        return redact_auth_from_url(self.url)
-
-
-class HTMLLinkParser(HTMLParser):
-    """
-    HTMLParser that keeps the first base HREF and a list of all anchor
-    elements' attributes.
-    """
-
-    def __init__(self, url: str) -> None:
-        super().__init__(convert_charrefs=True)
-
-        self.url: str = url
-        self.base_url: Optional[str] = None
-        self.anchors: List[Dict[str, Optional[str]]] = []
-
-    def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None:
-        if tag == "base" and self.base_url is None:
-            href = self.get_href(attrs)
-            if href is not None:
-                self.base_url = href
-        elif tag == "a":
-            self.anchors.append(dict(attrs))
-
-    def get_href(self, attrs: List[Tuple[str, Optional[str]]]) -> Optional[str]:
-        for name, value in attrs:
-            if name == "href":
-                return value
-        return None
-
-
-def _handle_get_simple_fail(
-    link: Link,
-    reason: Union[str, Exception],
-    meth: Optional[Callable[..., None]] = None,
-) -> None:
-    if meth is None:
-        meth = logger.debug
-    meth("Could not fetch URL %s: %s - skipping", link, reason)
-
-
-def _make_index_content(
-    response: Response, cache_link_parsing: bool = True
-) -> IndexContent:
-    encoding = _get_encoding_from_headers(response.headers)
-    return IndexContent(
-        response.content,
-        response.headers["Content-Type"],
-        encoding=encoding,
-        url=response.url,
-        cache_link_parsing=cache_link_parsing,
-    )
-
-
-def _get_index_content(link: Link, *, session: PipSession) -> Optional["IndexContent"]:
-    url = link.url.split("#", 1)[0]
-
-    # Check for VCS schemes that do not support lookup as web pages.
-    vcs_scheme = _match_vcs_scheme(url)
-    if vcs_scheme:
-        logger.warning(
-            "Cannot look at %s URL %s because it does not support lookup as web pages.",
-            vcs_scheme,
-            link,
-        )
-        return None
-
-    # Tack index.html onto file:// URLs that point to directories
-    scheme, _, path, _, _, _ = urllib.parse.urlparse(url)
-    if scheme == "file" and os.path.isdir(urllib.request.url2pathname(path)):
-        # add trailing slash if not present so urljoin doesn't trim
-        # final segment
-        if not url.endswith("/"):
-            url += "/"
-        # TODO: In the future, it would be nice if pip supported PEP 691
-        #       style responses in the file:// URLs, however there's no
-        #       standard file extension for application/vnd.pypi.simple.v1+json
-        #       so we'll need to come up with something on our own.
-        url = urllib.parse.urljoin(url, "index.html")
-        logger.debug(" file: URL is directory, getting %s", url)
-
-    try:
-        resp = _get_simple_response(url, session=session)
-    except _NotHTTP:
-        logger.warning(
-            "Skipping page %s because it looks like an archive, and cannot "
-            "be checked by a HTTP HEAD request.",
-            link,
-        )
-    except _NotAPIContent as exc:
-        logger.warning(
-            "Skipping page %s because the %s request got Content-Type: %s. "
-            "The only supported Content-Types are application/vnd.pypi.simple.v1+json, "
-            "application/vnd.pypi.simple.v1+html, and text/html",
-            link,
-            exc.request_desc,
-            exc.content_type,
-        )
-    except NetworkConnectionError as exc:
-        _handle_get_simple_fail(link, exc)
-    except RetryError as exc:
-        _handle_get_simple_fail(link, exc)
-    except SSLError as exc:
-        reason = "There was a problem confirming the ssl certificate: "
-        reason += str(exc)
-        _handle_get_simple_fail(link, reason, meth=logger.info)
-    except requests.ConnectionError as exc:
-        _handle_get_simple_fail(link, f"connection error: {exc}")
-    except requests.Timeout:
-        _handle_get_simple_fail(link, "timed out")
-    else:
-        return _make_index_content(resp, cache_link_parsing=link.cache_link_parsing)
-    return None
-
-
-class CollectedSources(NamedTuple):
-    find_links: Sequence[Optional[LinkSource]]
-    index_urls: Sequence[Optional[LinkSource]]
-
-
-class LinkCollector:
-
-    """
-    Responsible for collecting Link objects from all configured locations,
-    making network requests as needed.
-
-    The class's main method is its collect_sources() method.
-    """
-
-    def __init__(
-        self,
-        session: PipSession,
-        search_scope: SearchScope,
-    ) -> None:
-        self.search_scope = search_scope
-        self.session = session
-
-    @classmethod
-    def create(
-        cls,
-        session: PipSession,
-        options: Values,
-        suppress_no_index: bool = False,
-    ) -> "LinkCollector":
-        """
-        :param session: The Session to use to make requests.
-        :param suppress_no_index: Whether to ignore the --no-index option
-            when constructing the SearchScope object.
-        """
-        index_urls = [options.index_url] + options.extra_index_urls
-        if options.no_index and not suppress_no_index:
-            logger.debug(
-                "Ignoring indexes: %s",
-                ",".join(redact_auth_from_url(url) for url in index_urls),
-            )
-            index_urls = []
-
-        # Make sure find_links is a list before passing to create().
-        find_links = options.find_links or []
-
-        search_scope = SearchScope.create(
-            find_links=find_links,
-            index_urls=index_urls,
-            no_index=options.no_index,
-        )
-        link_collector = LinkCollector(
-            session=session,
-            search_scope=search_scope,
-        )
-        return link_collector
-
-    @property
-    def find_links(self) -> List[str]:
-        return self.search_scope.find_links
-
-    def fetch_response(self, location: Link) -> Optional[IndexContent]:
-        """
-        Fetch an HTML page containing package links.
-        """
-        return _get_index_content(location, session=self.session)
-
-    def collect_sources(
-        self,
-        project_name: str,
-        candidates_from_page: CandidatesFromPage,
-    ) -> CollectedSources:
-        # The OrderedDict calls deduplicate sources by URL.
-        index_url_sources = collections.OrderedDict(
-            build_source(
-                loc,
-                candidates_from_page=candidates_from_page,
-                page_validator=self.session.is_secure_origin,
-                expand_dir=False,
-                cache_link_parsing=False,
-            )
-            for loc in self.search_scope.get_index_urls_locations(project_name)
-        ).values()
-        find_links_sources = collections.OrderedDict(
-            build_source(
-                loc,
-                candidates_from_page=candidates_from_page,
-                page_validator=self.session.is_secure_origin,
-                expand_dir=True,
-                cache_link_parsing=True,
-            )
-            for loc in self.find_links
-        ).values()
-
-        if logger.isEnabledFor(logging.DEBUG):
-            lines = [
-                f"* {s.link}"
-                for s in itertools.chain(find_links_sources, index_url_sources)
-                if s is not None and s.link is not None
-            ]
-            lines = [
-                f"{len(lines)} location(s) to search "
-                f"for versions of {project_name}:"
-            ] + lines
-            logger.debug("\n".join(lines))
-
-        return CollectedSources(
-            find_links=list(find_links_sources),
-            index_urls=list(index_url_sources),
-        )

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/timeout.py

@@ -1,271 +0,0 @@
-from __future__ import absolute_import
-
-import time
-
-# The default socket timeout, used by httplib to indicate that no timeout was; specified by the user
-from socket import _GLOBAL_DEFAULT_TIMEOUT, getdefaulttimeout
-
-from ..exceptions import TimeoutStateError
-
-# A sentinel value to indicate that no timeout was specified by the user in
-# urllib3
-_Default = object()
-
-
-# Use time.monotonic if available.
-current_time = getattr(time, "monotonic", time.time)
-
-
-class Timeout(object):
-    """Timeout configuration.
-
-    Timeouts can be defined as a default for a pool:
-
-    .. code-block:: python
-
-       timeout = Timeout(connect=2.0, read=7.0)
-       http = PoolManager(timeout=timeout)
-       response = http.request('GET', 'http://example.com/')
-
-    Or per-request (which overrides the default for the pool):
-
-    .. code-block:: python
-
-       response = http.request('GET', 'http://example.com/', timeout=Timeout(10))
-
-    Timeouts can be disabled by setting all the parameters to ``None``:
-
-    .. code-block:: python
-
-       no_timeout = Timeout(connect=None, read=None)
-       response = http.request('GET', 'http://example.com/, timeout=no_timeout)
-
-
-    :param total:
-        This combines the connect and read timeouts into one; the read timeout
-        will be set to the time leftover from the connect attempt. In the
-        event that both a connect timeout and a total are specified, or a read
-        timeout and a total are specified, the shorter timeout will be applied.
-
-        Defaults to None.
-
-    :type total: int, float, or None
-
-    :param connect:
-        The maximum amount of time (in seconds) to wait for a connection
-        attempt to a server to succeed. Omitting the parameter will default the
-        connect timeout to the system default, probably `the global default
-        timeout in socket.py
-        <http://hg.python.org/cpython/file/603b4d593758/Lib/socket.py#l535>`_.
-        None will set an infinite timeout for connection attempts.
-
-    :type connect: int, float, or None
-
-    :param read:
-        The maximum amount of time (in seconds) to wait between consecutive
-        read operations for a response from the server. Omitting the parameter
-        will default the read timeout to the system default, probably `the
-        global default timeout in socket.py
-        <http://hg.python.org/cpython/file/603b4d593758/Lib/socket.py#l535>`_.
-        None will set an infinite timeout.
-
-    :type read: int, float, or None
-
-    .. note::
-
-        Many factors can affect the total amount of time for urllib3 to return
-        an HTTP response.
-
-        For example, Python's DNS resolver does not obey the timeout specified
-        on the socket. Other factors that can affect total request time include
-        high CPU load, high swap, the program running at a low priority level,
-        or other behaviors.
-
-        In addition, the read and total timeouts only measure the time between
-        read operations on the socket connecting the client and the server,
-        not the total amount of time for the request to return a complete
-        response. For most requests, the timeout is raised because the server
-        has not sent the first byte in the specified time. This is not always
-        the case; if a server streams one byte every fifteen seconds, a timeout
-        of 20 seconds will not trigger, even though the request will take
-        several minutes to complete.
-
-        If your goal is to cut off any request after a set amount of wall clock
-        time, consider having a second "watcher" thread to cut off a slow
-        request.
-    """
-
-    #: A sentinel object representing the default timeout value
-    DEFAULT_TIMEOUT = _GLOBAL_DEFAULT_TIMEOUT
-
-    def __init__(self, total=None, connect=_Default, read=_Default):
-        self._connect = self._validate_timeout(connect, "connect")
-        self._read = self._validate_timeout(read, "read")
-        self.total = self._validate_timeout(total, "total")
-        self._start_connect = None
-
-    def __repr__(self):
-        return "%s(connect=%r, read=%r, total=%r)" % (
-            type(self).__name__,
-            self._connect,
-            self._read,
-            self.total,
-        )
-
-    # __str__ provided for backwards compatibility
-    __str__ = __repr__
-
-    @classmethod
-    def resolve_default_timeout(cls, timeout):
-        return getdefaulttimeout() if timeout is cls.DEFAULT_TIMEOUT else timeout
-
-    @classmethod
-    def _validate_timeout(cls, value, name):
-        """Check that a timeout attribute is valid.
-
-        :param value: The timeout value to validate
-        :param name: The name of the timeout attribute to validate. This is
-            used to specify in error messages.
-        :return: The validated and casted version of the given value.
-        :raises ValueError: If it is a numeric value less than or equal to
-            zero, or the type is not an integer, float, or None.
-        """
-        if value is _Default:
-            return cls.DEFAULT_TIMEOUT
-
-        if value is None or value is cls.DEFAULT_TIMEOUT:
-            return value
-
-        if isinstance(value, bool):
-            raise ValueError(
-                "Timeout cannot be a boolean value. It must "
-                "be an int, float or None."
-            )
-        try:
-            float(value)
-        except (TypeError, ValueError):
-            raise ValueError(
-                "Timeout value %s was %s, but it must be an "
-                "int, float or None." % (name, value)
-            )
-
-        try:
-            if value <= 0:
-                raise ValueError(
-                    "Attempted to set %s timeout to %s, but the "
-                    "timeout cannot be set to a value less "
-                    "than or equal to 0." % (name, value)
-                )
-        except TypeError:
-            # Python 3
-            raise ValueError(
-                "Timeout value %s was %s, but it must be an "
-                "int, float or None." % (name, value)
-            )
-
-        return value
-
-    @classmethod
-    def from_float(cls, timeout):
-        """Create a new Timeout from a legacy timeout value.
-
-        The timeout value used by httplib.py sets the same timeout on the
-        connect(), and recv() socket requests. This creates a :class:`Timeout`
-        object that sets the individual timeouts to the ``timeout`` value
-        passed to this function.
-
-        :param timeout: The legacy timeout value.
-        :type timeout: integer, float, sentinel default object, or None
-        :return: Timeout object
-        :rtype: :class:`Timeout`
-        """
-        return Timeout(read=timeout, connect=timeout)
-
-    def clone(self):
-        """Create a copy of the timeout object
-
-        Timeout properties are stored per-pool but each request needs a fresh
-        Timeout object to ensure each one has its own start/stop configured.
-
-        :return: a copy of the timeout object
-        :rtype: :class:`Timeout`
-        """
-        # We can't use copy.deepcopy because that will also create a new object
-        # for _GLOBAL_DEFAULT_TIMEOUT, which socket.py uses as a sentinel to
-        # detect the user default.
-        return Timeout(connect=self._connect, read=self._read, total=self.total)
-
-    def start_connect(self):
-        """Start the timeout clock, used during a connect() attempt
-
-        :raises urllib3.exceptions.TimeoutStateError: if you attempt
-            to start a timer that has been started already.
-        """
-        if self._start_connect is not None:
-            raise TimeoutStateError("Timeout timer has already been started.")
-        self._start_connect = current_time()
-        return self._start_connect
-
-    def get_connect_duration(self):
-        """Gets the time elapsed since the call to :meth:`start_connect`.
-
-        :return: Elapsed time in seconds.
-        :rtype: float
-        :raises urllib3.exceptions.TimeoutStateError: if you attempt
-            to get duration for a timer that hasn't been started.
-        """
-        if self._start_connect is None:
-            raise TimeoutStateError(
-                "Can't get connect duration for timer that has not started."
-            )
-        return current_time() - self._start_connect
-
-    @property
-    def connect_timeout(self):
-        """Get the value to use when setting a connection timeout.
-
-        This will be a positive float or integer, the value None
-        (never timeout), or the default system timeout.
-
-        :return: Connect timeout.
-        :rtype: int, float, :attr:`Timeout.DEFAULT_TIMEOUT` or None
-        """
-        if self.total is None:
-            return self._connect
-
-        if self._connect is None or self._connect is self.DEFAULT_TIMEOUT:
-            return self.total
-
-        return min(self._connect, self.total)
-
-    @property
-    def read_timeout(self):
-        """Get the value for the read timeout.
-
-        This assumes some time has elapsed in the connection timeout and
-        computes the read timeout appropriately.
-
-        If self.total is set, the read timeout is dependent on the amount of
-        time taken by the connect timeout. If the connection time has not been
-        established, a :exc:`~urllib3.exceptions.TimeoutStateError` will be
-        raised.
-
-        :return: Value to use for the read timeout.
-        :rtype: int, float, :attr:`Timeout.DEFAULT_TIMEOUT` or None
-        :raises urllib3.exceptions.TimeoutStateError: If :meth:`start_connect`
-            has not yet been called on this object.
-        """
-        if (
-            self.total is not None
-            and self.total is not self.DEFAULT_TIMEOUT
-            and self._read is not None
-            and self._read is not self.DEFAULT_TIMEOUT
-        ):
-            # In case the connect timeout has not yet been established.
-            if self._start_connect is None:
-                return self._read
-            return max(0, min(self.total - self.get_connect_duration(), self._read))
-        elif self.total is not None and self.total is not self.DEFAULT_TIMEOUT:
-            return max(0, self.total - self.get_connect_duration())
-        else:
-            return self._read

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/fancy_getopt.py

@@ -1,470 +0,0 @@
-"""distutils.fancy_getopt
-
-Wrapper around the standard getopt module that provides the following
-additional features:
-  * short and long options are tied together
-  * options have help strings, so fancy_getopt could potentially
-    create a complete usage summary
-  * options set attributes of a passed-in object
-"""
-
-import sys
-import string
-import re
-import getopt
-from distutils.errors import DistutilsGetoptError, DistutilsArgError
-
-# Much like command_re in distutils.core, this is close to but not quite
-# the same as a Python NAME -- except, in the spirit of most GNU
-# utilities, we use '-' in place of '_'.  (The spirit of LISP lives on!)
-# The similarities to NAME are again not a coincidence...
-longopt_pat = r'[a-zA-Z](?:[a-zA-Z0-9-]*)'
-longopt_re = re.compile(r'^%s$' % longopt_pat)
-
-# For recognizing "negative alias" options, eg. "quiet=!verbose"
-neg_alias_re = re.compile("^({})=!({})$".format(longopt_pat, longopt_pat))
-
-# This is used to translate long options to legitimate Python identifiers
-# (for use as attributes of some object).
-longopt_xlate = str.maketrans('-', '_')
-
-
-class FancyGetopt:
-    """Wrapper around the standard 'getopt()' module that provides some
-    handy extra functionality:
-      * short and long options are tied together
-      * options have help strings, and help text can be assembled
-        from them
-      * options set attributes of a passed-in object
-      * boolean options can have "negative aliases" -- eg. if
-        --quiet is the "negative alias" of --verbose, then "--quiet"
-        on the command line sets 'verbose' to false
-    """
-
-    def __init__(self, option_table=None):
-        # The option table is (currently) a list of tuples.  The
-        # tuples may have 3 or four values:
-        #   (long_option, short_option, help_string [, repeatable])
-        # if an option takes an argument, its long_option should have '='
-        # appended; short_option should just be a single character, no ':'
-        # in any case.  If a long_option doesn't have a corresponding
-        # short_option, short_option should be None.  All option tuples
-        # must have long options.
-        self.option_table = option_table
-
-        # 'option_index' maps long option names to entries in the option
-        # table (ie. those 3-tuples).
-        self.option_index = {}
-        if self.option_table:
-            self._build_index()
-
-        # 'alias' records (duh) alias options; {'foo': 'bar'} means
-        # --foo is an alias for --bar
-        self.alias = {}
-
-        # 'negative_alias' keeps track of options that are the boolean
-        # opposite of some other option
-        self.negative_alias = {}
-
-        # These keep track of the information in the option table.  We
-        # don't actually populate these structures until we're ready to
-        # parse the command-line, since the 'option_table' passed in here
-        # isn't necessarily the final word.
-        self.short_opts = []
-        self.long_opts = []
-        self.short2long = {}
-        self.attr_name = {}
-        self.takes_arg = {}
-
-        # And 'option_order' is filled up in 'getopt()'; it records the
-        # original order of options (and their values) on the command-line,
-        # but expands short options, converts aliases, etc.
-        self.option_order = []
-
-    def _build_index(self):
-        self.option_index.clear()
-        for option in self.option_table:
-            self.option_index[option[0]] = option
-
-    def set_option_table(self, option_table):
-        self.option_table = option_table
-        self._build_index()
-
-    def add_option(self, long_option, short_option=None, help_string=None):
-        if long_option in self.option_index:
-            raise DistutilsGetoptError(
-                "option conflict: already an option '%s'" % long_option
-            )
-        else:
-            option = (long_option, short_option, help_string)
-            self.option_table.append(option)
-            self.option_index[long_option] = option
-
-    def has_option(self, long_option):
-        """Return true if the option table for this parser has an
-        option with long name 'long_option'."""
-        return long_option in self.option_index
-
-    def get_attr_name(self, long_option):
-        """Translate long option name 'long_option' to the form it
-        has as an attribute of some object: ie., translate hyphens
-        to underscores."""
-        return long_option.translate(longopt_xlate)
-
-    def _check_alias_dict(self, aliases, what):
-        assert isinstance(aliases, dict)
-        for (alias, opt) in aliases.items():
-            if alias not in self.option_index:
-                raise DistutilsGetoptError(
-                    ("invalid %s '%s': " "option '%s' not defined")
-                    % (what, alias, alias)
-                )
-            if opt not in self.option_index:
-                raise DistutilsGetoptError(
-                    ("invalid %s '%s': " "aliased option '%s' not defined")
-                    % (what, alias, opt)
-                )
-
-    def set_aliases(self, alias):
-        """Set the aliases for this option parser."""
-        self._check_alias_dict(alias, "alias")
-        self.alias = alias
-
-    def set_negative_aliases(self, negative_alias):
-        """Set the negative aliases for this option parser.
-        'negative_alias' should be a dictionary mapping option names to
-        option names, both the key and value must already be defined
-        in the option table."""
-        self._check_alias_dict(negative_alias, "negative alias")
-        self.negative_alias = negative_alias
-
-    def _grok_option_table(self):  # noqa: C901
-        """Populate the various data structures that keep tabs on the
-        option table.  Called by 'getopt()' before it can do anything
-        worthwhile.
-        """
-        self.long_opts = []
-        self.short_opts = []
-        self.short2long.clear()
-        self.repeat = {}
-
-        for option in self.option_table:
-            if len(option) == 3:
-                long, short, help = option
-                repeat = 0
-            elif len(option) == 4:
-                long, short, help, repeat = option
-            else:
-                # the option table is part of the code, so simply
-                # assert that it is correct
-                raise ValueError("invalid option tuple: {!r}".format(option))
-
-            # Type- and value-check the option names
-            if not isinstance(long, str) or len(long) < 2:
-                raise DistutilsGetoptError(
-                    ("invalid long option '%s': " "must be a string of length >= 2")
-                    % long
-                )
-
-            if not ((short is None) or (isinstance(short, str) and len(short) == 1)):
-                raise DistutilsGetoptError(
-                    "invalid short option '%s': "
-                    "must a single character or None" % short
-                )
-
-            self.repeat[long] = repeat
-            self.long_opts.append(long)
-
-            if long[-1] == '=':  # option takes an argument?
-                if short:
-                    short = short + ':'
-                long = long[0:-1]
-                self.takes_arg[long] = 1
-            else:
-                # Is option is a "negative alias" for some other option (eg.
-                # "quiet" == "!verbose")?
-                alias_to = self.negative_alias.get(long)
-                if alias_to is not None:
-                    if self.takes_arg[alias_to]:
-                        raise DistutilsGetoptError(
-                            "invalid negative alias '%s': "
-                            "aliased option '%s' takes a value" % (long, alias_to)
-                        )
-
-                    self.long_opts[-1] = long  # XXX redundant?!
-                self.takes_arg[long] = 0
-
-            # If this is an alias option, make sure its "takes arg" flag is
-            # the same as the option it's aliased to.
-            alias_to = self.alias.get(long)
-            if alias_to is not None:
-                if self.takes_arg[long] != self.takes_arg[alias_to]:
-                    raise DistutilsGetoptError(
-                        "invalid alias '%s': inconsistent with "
-                        "aliased option '%s' (one of them takes a value, "
-                        "the other doesn't" % (long, alias_to)
-                    )
-
-            # Now enforce some bondage on the long option name, so we can
-            # later translate it to an attribute name on some object.  Have
-            # to do this a bit late to make sure we've removed any trailing
-            # '='.
-            if not longopt_re.match(long):
-                raise DistutilsGetoptError(
-                    "invalid long option name '%s' "
-                    "(must be letters, numbers, hyphens only" % long
-                )
-
-            self.attr_name[long] = self.get_attr_name(long)
-            if short:
-                self.short_opts.append(short)
-                self.short2long[short[0]] = long
-
-    def getopt(self, args=None, object=None):  # noqa: C901
-        """Parse command-line options in args. Store as attributes on object.
-
-        If 'args' is None or not supplied, uses 'sys.argv[1:]'.  If
-        'object' is None or not supplied, creates a new OptionDummy
-        object, stores option values there, and returns a tuple (args,
-        object).  If 'object' is supplied, it is modified in place and
-        'getopt()' just returns 'args'; in both cases, the returned
-        'args' is a modified copy of the passed-in 'args' list, which
-        is left untouched.
-        """
-        if args is None:
-            args = sys.argv[1:]
-        if object is None:
-            object = OptionDummy()
-            created_object = True
-        else:
-            created_object = False
-
-        self._grok_option_table()
-
-        short_opts = ' '.join(self.short_opts)
-        try:
-            opts, args = getopt.getopt(args, short_opts, self.long_opts)
-        except getopt.error as msg:
-            raise DistutilsArgError(msg)
-
-        for opt, val in opts:
-            if len(opt) == 2 and opt[0] == '-':  # it's a short option
-                opt = self.short2long[opt[1]]
-            else:
-                assert len(opt) > 2 and opt[:2] == '--'
-                opt = opt[2:]
-
-            alias = self.alias.get(opt)
-            if alias:
-                opt = alias
-
-            if not self.takes_arg[opt]:  # boolean option?
-                assert val == '', "boolean option can't have value"
-                alias = self.negative_alias.get(opt)
-                if alias:
-                    opt = alias
-                    val = 0
-                else:
-                    val = 1
-
-            attr = self.attr_name[opt]
-            # The only repeating option at the moment is 'verbose'.
-            # It has a negative option -q quiet, which should set verbose = 0.
-            if val and self.repeat.get(attr) is not None:
-                val = getattr(object, attr, 0) + 1
-            setattr(object, attr, val)
-            self.option_order.append((opt, val))
-
-        # for opts
-        if created_object:
-            return args, object
-        else:
-            return args
-
-    def get_option_order(self):
-        """Returns the list of (option, value) tuples processed by the
-        previous run of 'getopt()'.  Raises RuntimeError if
-        'getopt()' hasn't been called yet.
-        """
-        if self.option_order is None:
-            raise RuntimeError("'getopt()' hasn't been called yet")
-        else:
-            return self.option_order
-
-    def generate_help(self, header=None):  # noqa: C901
-        """Generate help text (a list of strings, one per suggested line of
-        output) from the option table for this FancyGetopt object.
-        """
-        # Blithely assume the option table is good: probably wouldn't call
-        # 'generate_help()' unless you've already called 'getopt()'.
-
-        # First pass: determine maximum length of long option names
-        max_opt = 0
-        for option in self.option_table:
-            long = option[0]
-            short = option[1]
-            ell = len(long)
-            if long[-1] == '=':
-                ell = ell - 1
-            if short is not None:
-                ell = ell + 5  # " (-x)" where short == 'x'
-            if ell > max_opt:
-                max_opt = ell
-
-        opt_width = max_opt + 2 + 2 + 2  # room for indent + dashes + gutter
-
-        # Typical help block looks like this:
-        #   --foo       controls foonabulation
-        # Help block for longest option looks like this:
-        #   --flimflam  set the flim-flam level
-        # and with wrapped text:
-        #   --flimflam  set the flim-flam level (must be between
-        #               0 and 100, except on Tuesdays)
-        # Options with short names will have the short name shown (but
-        # it doesn't contribute to max_opt):
-        #   --foo (-f)  controls foonabulation
-        # If adding the short option would make the left column too wide,
-        # we push the explanation off to the next line
-        #   --flimflam (-l)
-        #               set the flim-flam level
-        # Important parameters:
-        #   - 2 spaces before option block start lines
-        #   - 2 dashes for each long option name
-        #   - min. 2 spaces between option and explanation (gutter)
-        #   - 5 characters (incl. space) for short option name
-
-        # Now generate lines of help text.  (If 80 columns were good enough
-        # for Jesus, then 78 columns are good enough for me!)
-        line_width = 78
-        text_width = line_width - opt_width
-        big_indent = ' ' * opt_width
-        if header:
-            lines = [header]
-        else:
-            lines = ['Option summary:']
-
-        for option in self.option_table:
-            long, short, help = option[:3]
-            text = wrap_text(help, text_width)
-            if long[-1] == '=':
-                long = long[0:-1]
-
-            # Case 1: no short option at all (makes life easy)
-            if short is None:
-                if text:
-                    lines.append("  --%-*s  %s" % (max_opt, long, text[0]))
-                else:
-                    lines.append("  --%-*s  " % (max_opt, long))
-
-            # Case 2: we have a short option, so we have to include it
-            # just after the long option
-            else:
-                opt_names = "{} (-{})".format(long, short)
-                if text:
-                    lines.append("  --%-*s  %s" % (max_opt, opt_names, text[0]))
-                else:
-                    lines.append("  --%-*s" % opt_names)
-
-            for ell in text[1:]:
-                lines.append(big_indent + ell)
-        return lines
-
-    def print_help(self, header=None, file=None):
-        if file is None:
-            file = sys.stdout
-        for line in self.generate_help(header):
-            file.write(line + "\n")
-
-
-def fancy_getopt(options, negative_opt, object, args):
-    parser = FancyGetopt(options)
-    parser.set_negative_aliases(negative_opt)
-    return parser.getopt(args, object)
-
-
-WS_TRANS = {ord(_wschar): ' ' for _wschar in string.whitespace}
-
-
-def wrap_text(text, width):
-    """wrap_text(text : string, width : int) -> [string]
-
-    Split 'text' into multiple lines of no more than 'width' characters
-    each, and return the list of strings that results.
-    """
-    if text is None:
-        return []
-    if len(text) <= width:
-        return [text]
-
-    text = text.expandtabs()
-    text = text.translate(WS_TRANS)
-    chunks = re.split(r'( +|-+)', text)
-    chunks = [ch for ch in chunks if ch]  # ' - ' results in empty strings
-    lines = []
-
-    while chunks:
-        cur_line = []  # list of chunks (to-be-joined)
-        cur_len = 0  # length of current line
-
-        while chunks:
-            ell = len(chunks[0])
-            if cur_len + ell <= width:  # can squeeze (at least) this chunk in
-                cur_line.append(chunks[0])
-                del chunks[0]
-                cur_len = cur_len + ell
-            else:  # this line is full
-                # drop last chunk if all space
-                if cur_line and cur_line[-1][0] == ' ':
-                    del cur_line[-1]
-                break
-
-        if chunks:  # any chunks left to process?
-            # if the current line is still empty, then we had a single
-            # chunk that's too big too fit on a line -- so we break
-            # down and break it up at the line width
-            if cur_len == 0:
-                cur_line.append(chunks[0][0:width])
-                chunks[0] = chunks[0][width:]
-
-            # all-whitespace chunks at the end of a line can be discarded
-            # (and we know from the re.split above that if a chunk has
-            # *any* whitespace, it is *all* whitespace)
-            if chunks[0][0] == ' ':
-                del chunks[0]
-
-        # and store this line in the list-of-all-lines -- as a single
-        # string, of course!
-        lines.append(''.join(cur_line))
-
-    return lines
-
-
-def translate_longopt(opt):
-    """Convert a long option name to a valid Python identifier by
-    changing "-" to "_".
-    """
-    return opt.translate(longopt_xlate)
-
-
-class OptionDummy:
-    """Dummy class just used as a place to hold command-line option
-    values as instance attributes."""
-
-    def __init__(self, options=[]):
-        """Create a new OptionDummy instance.  The attributes listed in
-        'options' will be initialized to None."""
-        for opt in options:
-            setattr(self, opt, None)
-
-
-if __name__ == "__main__":
-    text = """\
-Tra-la-la, supercalifragilisticexpialidocious.
-How *do* you spell that odd word, anyways?
-(Someone ask Mary -- she'll know [or she'll
-say, "How should I know?"].)"""
-
-    for w in (10, 20, 30, 40):
-        print("width: %d" % w)
-        print("\n".join(wrap_text(text, w)))
-        print()

spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/chinese.py

@@ -1,193 +0,0 @@
-import os
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from text import symbols
-from text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
-                        open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
-
-import jieba.posseg as psg
-
-
-rep_map = {
-    '：': ',',
-    '；': ',',
-    '，': ',',
-    '。': '.',
-    '！': '!',
-    '？': '?',
-    '\n': '.',
-    "·": ",",
-    '、': ",",
-    '...': '…',
-    '$': '.',
-    '“': "'",
-    '”': "'",
-    '‘': "'",
-    '’': "'",
-    '（': "'",
-    '）': "'",
-    '(': "'",
-    ')': "'",
-    '《': "'",
-    '》': "'",
-    '【': "'",
-    '】': "'",
-    '[': "'",
-    ']': "'",
-    '—': "-",
-    '～': "-",
-    '~': "-",
-    '「': "'",
-    '」': "'",
-
-}
-
-tone_modifier = ToneSandhi()
-
-def replace_punctuation(text):
-    text = text.replace("嗯", "恩").replace("呣","母")
-    pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
-
-    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-
-    replaced_text = re.sub(r'[^\u4e00-\u9fa5'+"".join(punctuation)+r']+', '', replaced_text)
-
-    return replaced_text
-
-def g2p(text):
-    pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
-    sentences = [i for i in re.split(pattern, text) if i.strip()!='']
-    phones, tones, word2ph = _g2p(sentences)
-    assert sum(word2ph) == len(phones)
-    assert len(word2ph) == len(text) #Sometimes it will crash,you can add a try-catch.
-    phones = ['_'] + phones + ["_"]
-    tones = [0] + tones + [0]
-    word2ph = [1] + word2ph + [1]
-    return phones, tones, word2ph
-
-
-def _get_initials_finals(word):
-    initials = []
-    finals = []
-    orig_initials = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.INITIALS)
-    orig_finals = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-    for c, v in zip(orig_initials, orig_finals):
-        initials.append(c)
-        finals.append(v)
-    return initials, finals
-
-
-def _g2p(segments):
-    phones_list = []
-    tones_list = []
-    word2ph = []
-    for seg in segments:
-        pinyins = []
-        # Replace all English words in the sentence
-        seg = re.sub('[a-zA-Z]+', '', seg)
-        seg_cut = psg.lcut(seg)
-        initials = []
-        finals = []
-        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
-        for word, pos in seg_cut:
-            if pos == 'eng':
-                continue
-            sub_initials, sub_finals = _get_initials_finals(word)
-            sub_finals = tone_modifier.modified_tone(word, pos,
-                                                          sub_finals)
-            initials.append(sub_initials)
-            finals.append(sub_finals)
-
-            # assert len(sub_initials) == len(sub_finals) == len(word)
-        initials = sum(initials, [])
-        finals = sum(finals, [])
-        #
-        for c, v in zip(initials, finals):
-            raw_pinyin = c+v
-            # NOTE: post process for pypinyin outputs
-            # we discriminate i, ii and iii
-            if c == v:
-                assert c in punctuation
-                phone = [c]
-                tone = '0'
-                word2ph.append(1)
-            else:
-                v_without_tone = v[:-1]
-                tone = v[-1]
-
-                pinyin = c+v_without_tone
-                assert tone in '12345'
-
-                if c:
-                    # 多音节
-                    v_rep_map = {
-                        "uei": 'ui',
-                        'iou': 'iu',
-                        'uen': 'un',
-                    }
-                    if v_without_tone in v_rep_map.keys():
-                        pinyin = c+v_rep_map[v_without_tone]
-                else:
-                    # 单音节
-                    pinyin_rep_map = {
-                        'ing': 'ying',
-                        'i': 'yi',
-                        'in': 'yin',
-                        'u': 'wu',
-                    }
-                    if pinyin in pinyin_rep_map.keys():
-                        pinyin = pinyin_rep_map[pinyin]
-                    else:
-                        single_rep_map = {
-                            'v': 'yu',
-                            'e': 'e',
-                            'i': 'y',
-                            'u': 'w',
-                        }
-                        if pinyin[0] in single_rep_map.keys():
-                            pinyin = single_rep_map[pinyin[0]]+pinyin[1:]
-
-                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
-                phone = pinyin_to_symbol_map[pinyin].split(' ')
-                word2ph.append(len(phone))
-
-            phones_list += phone
-            tones_list += [int(tone)] * len(phone)
-    return phones_list, tones_list, word2ph
-
-
-
-def text_normalize(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    text = replace_punctuation(text)
-    return text
-
-def get_bert_feature(text, word2ph):
-    from  text import chinese_bert
-    return chinese_bert.get_bert_feature(text, word2ph)
-
-if __name__ == '__main__':
-    from text.chinese_bert import get_bert_feature
-    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏"
-    text = text_normalize(text)
-    print(text)
-    phones, tones, word2ph = g2p(text)
-    bert = get_bert_feature(text, word2ph)
-
-    print(phones, tones, word2ph, bert.shape)
-
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试...."
-# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

spaces/Benson/text-generation/Examples/Blackpink El Juego Apkmirror.md

@@ -1,18 +0,0 @@
-
-<h1>BLACKPINK EL JUEGO: Una Guía Completa para Fans y Recién Llegados</h1>` | | Introducción: Una breve descripción de lo que es el juego, quién lo desarrolló, cuándo fue lanzado, y qué plataformas está disponible en. | `<p>BLACKPINK EL JUEGO es un juego móvil que te permite convertirte en el productor del famoso grupo K-pop BLACKPINK. Desarrollado por TakeOne Company y lanzado en junio de 2023, el juego está disponible para dispositivos Android e iOS. En este juego, puede administrar su propia agencia, entrenar y subir de nivel a sus miembros BLACKPINK, recoger y actualizar las tarjetas de fotos, jugar minijuegos con amigos y personalizar sus avatares con varios trajes. Si usted es un duro parpadeo o un recién llegado curioso, este juego le ofrecerá una experiencia divertida e inmersiva de ser parte del mundo BLACKPINK. </p>` | | H2: Cómo descargar e instalar BLACKPINK EL JUEGO | `<h2>Cómo descargar e instalar BLACKPINK EL JUEGO</h2>` | | | Subtítulo: Para usuarios de Android | `<h3>Para usuarios de Android</h3>` | Párrafo: Explicar cómo descargar el juego desde Google Store o MiAPKr, y cómo instalarlo en tu dispositivo. | Si tienes un dispositivo Android, puedes descargar BLACKPINK THE GAME desde Google Play Store o APKMirror. Para descargar desde Google Play Store, simplemente busca "BLACKPINK THE GAME" o haz clic en este [link]( 2 ). Luego, toca "Instalar" y espera a que el juego se descargue e instale en tu dispositivo. Para descargar desde APKMirror, vaya a este [link]( 1 ) y elija la última versión del juego. A continuación, toque en "Descargar APK" y esperar a que el archivo para descargar. Siguiente, ir a la configuración de su dispositivo y habilitar "Fuentes desconocidas" en "Seguridad". Finalmente, busque el archivo descargado en su administrador de archivos y toque en él para instalarlo en su dispositivo. </p>
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-
-<p>BLACKPINK THE GAME es un juego móvil que te permite convertirte en el productor del famoso grupo K-pop BLACKPINK. Desarrollado por TakeOne Company y lanzado en junio de 2023, el juego está disponible para dispositivos Android e iOS. En este juego, puede administrar su propia agencia, entrenar y subir de nivel a sus miembros BLACKPINK, recoger y actualizar las tarjetas de fotos, jugar minijuegos con amigos y personalizar sus avatares con varios trajes. Si usted es un duro parpadeo o un recién llegado curioso, este juego le ofrecerá una experiencia divertida e inmersiva de ser parte del mundo BLACKPINK. </p>
-<h2>Cómo descargar e instalar BLACKPINK EL JUEGO</h2>
-<h3>Para usuarios de Android</h3>
-<p>Si tienes un dispositivo Android, puedes descargar BLACKPINK THE GAME desde Google Play Store o APKMirror. Para descargar desde Google Play Store, simplemente busca "BLACKPINK THE GAME" o haz clic en este [link]. Luego, toca "Instalar" y espera a que el juego se descargue e instale en tu dispositivo. Para descargar desde APKMirror, vaya a este [enlace] y elija la última versión del juego. A continuación, toque en "Descargar APK" y esperar a que el archivo para descargar. Siguiente, ir a la configuración de su dispositivo y habilitar "Fuentes desconocidas" en "Seguridad". Finalmente, busque el archivo descargado en su administrador de archivos y toque en él para instalarlo en su dispositivo. </p>
-<h3>Para usuarios de iOS</h3>
-<p>Si tienes un dispositivo iOS, puedes descargar BLACKPINK THE GAME desde la App Store o Uptodown. Para descargar desde la App Store, simplemente busque "BLACKPINK THE GAME" o haga clic en este [link]. Luego, toca "Obtener" y espera a que el juego se descargue e instale en tu dispositivo. Para descargar desde Uptodown, vaya a este [enlace] y elija la última versión del juego. Luego, toque en "Descargar" y espere a que el archivo se descargue. A continuación, vaya a la configuración de su dispositivo y habilite "Trust Uptodown Enterprise" en "General" > "Administración de dispositivos". Finalmente, busque el archivo descargado en su administrador de archivos y toque en él para instalarlo en su dispositivo. </p>
-
-<h3>Modo de gestión</h3>
-<p>El modo de gestión es donde puede ejecutar su propia agencia y entrenar a sus miembros BLACKPINK. Puede construir varias habitaciones en su estudio que ofrecen diferentes beneficios y funciones. Por ejemplo, la sala de desarrollo de mercancías genera oro cada segundo, mientras que el estudio de grabación distribuye álbumes que se utilizan como energía para jugar puzzles. También puedes construir salas de entrenamiento para la voz, la danza, la actuación, etc., donde puedes mejorar las habilidades de tus miembros. Para construir o mejorar las habitaciones, necesita oro y polvo de estrellas. El oro se puede recoger de las habitaciones o ganar completando tareas y rompecabezas. El polvo de estrellas se puede recoger de habitaciones u obtener combinando tarjetas fotográficas. </p>
-<h3>Modo de rompecabezas</h3>
-<p>El modo de rompecabezas es donde puede borrar los horarios de BLACKPINK y ganar recompensas como oro, álbumes, polvo de estrellas, tarjetas fotográficas y artículos de bonificación. Para jugar al modo puzzle, necesitas usar álbumes como energía. Cada programa consta de varias etapas con diferentes objetivos y dificultades. Necesitas borrar bloques deslizando tu </p>
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spaces/Benson/text-generation/Examples/Descargar Cheat Kick El Amigo 2.md

@@ -1,125 +0,0 @@
-<br />
-<h1>Cómo descargar códigos de trucos para Kick the Buddy 2</h1>
-<p>Kick the Buddy 2 es un popular juego de simulación donde puedes liberar tu creatividad e imaginación torturando a un muñeco de trapo llamado Buddy con varias armas, herramientas y elementos. Es un juego divertido y que alivia el estrés que te permite experimentar con diferentes formas de destruir, quemar, cortar, explotar y aplastar a Buddy. También puedes personalizar la apariencia, la voz y el fondo de Buddy, así como jugar minijuegos con él. </p>
-<p>Sin embargo, si desea llevar su experiencia de juego al siguiente nivel, es posible que desee intentar usar códigos de trucos para Kick the Buddy 2. Los códigos de trucos son comandos o códigos especiales que pueden modificar o mejorar algunos aspectos del juego, como darle dinero ilimitado, oro, armas u objetos. Mediante el uso de códigos de trucos, puede desbloquear todas las características del juego sin gastar dinero real o tiempo. </p>
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-<p>Usar códigos de trucos para Kick the Buddy 2 puede tener muchos beneficios, como:</p>
-<ul>
-<li> Puedes acceder a todas las armas, herramientas y elementos del juego sin tener que ver anuncios o completar tareas. </li>
-<li>Puedes experimentar con diferentes combinaciones de armas y elementos para crear escenarios más divertidos e hilarantes. </li>
-<li> Puede personalizar la apariencia, la voz y el fondo de Buddy para adaptarse a sus preferencias o estado de ánimo. </li>
-<li>Puedes desafiarte a ti mismo probando diferentes niveles de dificultad o modos. </li>
-<li> Usted puede tener más diversión y satisfacción por golpear a Buddy en cualquier forma que desee. </li>
-</ul>
-<p>Si usted está interesado en el uso de códigos de trucos para Kick the Buddy 2, es posible que se pregunte cómo encontrarlos y cómo descargarlos. En este artículo, lo guiaremos a través del proceso de encontrar, descargar y usar códigos de trucos para Kick the Buddy 2 en dispositivos Android e iOS. También te daremos algunos consejos y trucos sobre cómo usar códigos de trucos de manera efectiva y responsable. </p>
- <h2>Cómo encontrar códigos de trucos para Kick the Buddy 2</h2>
-
-<p>Por lo tanto, debe ser cuidadoso y selectivo al buscar códigos de trucos en línea. Aquí hay algunos consejos sobre cómo encontrar fuentes confiables y legítimas para los códigos de trucos:</p>
-<ul>
-<li>Busca sitios web que tengan reseñas, valoraciones, comentarios o comentarios positivos de otros usuarios. También puede consultar foros o comunidades en línea donde los jugadores comparten sus experiencias y recomendaciones. </li>
-<li>Busque sitios web que tienen instrucciones claras y detalladas sobre cómo descargar y usar códigos de trucos. También deben proporcionar capturas de pantalla o videos como prueba de que sus códigos de trucos funcionan. </li>
-<li <li>Busca sitios web que tengan una base de datos grande y actualizada de códigos de trucos para varios juegos, incluyendo Kick the Buddy 2. También deberían tener una función de búsqueda o una opción de filtro para ayudarte a encontrar los códigos de trucos que necesitas. </li>
-</ul>
-<p>Algunos ejemplos de sitios web que proporcionan códigos de trucos de juegos son:</p>
-<tabla>
-<tr>
-<th>Sitio web</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>[CheatCodes.com]( 1 )</td>
-<td>Uno de los sitios de código de trucos de juegos más antiguos y populares con un archivo grande y completo de códigos de trucos para varias plataformas. </td>
-</tr>
-<tr>
-<td>[Radar de juegos]( 1 )</td>
-<td>Un sitio de revisión de juegos y noticias que también ofrece códigos de trucos, guías, tutoriales y consejos para varios juegos. </td>
-</tr>
-<tr>
-<td>[Sucede un truco]( 1 )</td>
-<td>Un sitio dedicado para códigos de trucos y entrenadores de PC que también cuenta con reseñas de juegos, fondos de pantalla y tutoriales. </td>
-</tr>
-<tr>
-<td>[GameWinners]( 1 )</td>
-<td>Un sitio que tiene una gran colección de códigos de trucos, preguntas frecuentes, guías y desbloqueables para varios juegos. </td>
-</tr>
-<tr>
-<td>[Código de trucos Central]( 1 )</td>
-<td>Un sitio que tiene una amplia base de datos de códigos de trucos, guías, comentarios y noticias para varios juegos. </td>
-</tr>
-</tabla>
- <h2>Cómo descargar códigos de trucos para Kick the Buddy 2</h2>
-
- <h3>Cómo descargar códigos de trucos para dispositivos Android</h3>
-<ol>
-<li>Asegúrese de que su dispositivo está conectado a Internet y tiene suficiente espacio de almacenamiento. </li>
-<li>Ir a la página web que ofrece los códigos de trucos que desea descargar y siga las instrucciones sobre cómo descargarlos. Es posible que deba ingresar su dirección de correo electrónico, completar una encuesta o ver un anuncio para obtener acceso al enlace de descarga. </li>
-<li>Una vez que tenga el enlace de descarga, toque en él y espere a que el archivo para descargar. El archivo puede estar en forma de un APK (Android Package Kit) o un archivo ZIP (comprimido). </li>
-<li>Si el archivo es un archivo APK, es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en el dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Si el archivo es un archivo ZIP, debe extraerlo usando una aplicación de administrador de archivos o una aplicación de extractor ZIP. Puedes descargar estas aplicaciones desde Google Play Store si aún no las tienes. </li>
-<li>Después de extraer o instalar el archivo, debería ver un nuevo icono en la pantalla de inicio del dispositivo o en el cajón de aplicaciones. Esta es la aplicación de código de trucos que necesita para iniciar antes de jugar Kick the Buddy 2.</li>
-</ol>
- <h3>Cómo descargar códigos de trucos para dispositivos iOS</h3>
-<ol>
-<li>Asegúrese de que su dispositivo está conectado a Internet y tiene suficiente espacio de almacenamiento. </li>
-<li>Ir a la página web que ofrece los códigos de trucos que desea descargar y siga las instrucciones sobre cómo descargarlos. Es posible que deba ingresar su dirección de correo electrónico, completar una encuesta o ver un anuncio para obtener acceso al enlace de descarga. </li>
-<li>Una vez que tenga el enlace de descarga, toque en él y espere a que el archivo para descargar. El archivo puede estar en forma de un IPA (paquete de iOS App Store) o un archivo ZIP (comprimido). </li>
-
-<li>Si el archivo es un archivo ZIP, debe extraerlo usando una aplicación de administrador de archivos o una aplicación de extractor ZIP. Puedes descargar estas aplicaciones desde la App Store si aún no las tienes. </li>
-<li>Después de extraer o instalar el archivo, debería ver un nuevo icono en la pantalla de inicio del dispositivo o en el cajón de aplicaciones. Esta es la aplicación de código de trucos que necesita para iniciar antes de jugar Kick the Buddy 2.</li>
-</ol>
- <h2>Cómo usar códigos de trucos para Kick the Buddy 2</h2> <p>Después de descargar e instalar la aplicación de código de trucos, debe activarlo y usarlo en el juego. El proceso puede variar dependiendo del tipo de aplicación de código de trucos que tenga y las características que ofrece. Sin embargo, aquí hay algunos pasos generales a seguir:</p>
-<ol>
-<li>Inicie la aplicación de código de trucos y otorgue los permisos necesarios para acceder a los datos y configuraciones de su dispositivo. </li>
-<li>Seleccione el juego que desea engañar de la lista de juegos soportados. En este caso, seleccione Kick the Buddy 2.</li>
-<li>Seleccione los códigos de trucos que desea utilizar de la lista de opciones disponibles. Puedes elegir entre diferentes categorías como dinero, oro, armas, artículos o modos. </li>
-<li>Toque en el botón aplicar o activar para habilitar los códigos de trucos. Es posible que tenga que esperar unos segundos o minutos para que los códigos de trucos surtan efecto. </li>
-<li>Iniciar el juego y disfrutar de jugar con los códigos de trucos. Deberías ver los cambios en la interfaz de tu juego, como el aumento de dinero, oro o armas desbloqueadas. </li>
-</ol>
- <h2>Cómo usar códigos de trucos para Kick the Buddy 2</h2>
-<p>Ahora que ha activado los códigos de trucos, puede usarlos para mejorar su experiencia de juego y divertirse más con Kick the Buddy 2. Sin embargo, debe ser consciente de los tipos de códigos de trucos que está utilizando y sus efectos en el juego y las características. Aquí hay algunos consejos y trucos sobre cómo usar códigos de trucos de manera efectiva y responsable:</p>
- <h3>Los tipos de códigos de trucos disponibles para Kick the Buddy 2</h3>
-
-<ul>
-<li>Códigos de trucos de dinero: Estos códigos de trucos le dan cantidades ilimitadas o mayores de dinero en el juego. Puedes usar dinero para comprar más armas, herramientas, elementos u objetos en el juego. </li>
-<li>Códigos de trucos de oro: Estos códigos de trucos te dan cantidades ilimitadas o aumentadas de oro en el juego. Puedes usar oro para desbloquear funciones premium, como membresía VIP, armas exclusivas o modos especiales. </li>
-<li>Códigos de trucos de armas: Estos códigos de trucos le dan acceso a todas o algunas de las armas en el juego sin tener que comprarlos o ver anuncios. Puedes usar armas para torturar a Buddy de diferentes maneras. </li>
-<li>ítem códigos de trucos: Estos códigos de trucos le dan acceso a todos o algunos de los elementos en el juego sin tener que comprarlos o ver anuncios. Puedes usar elementos para personalizar la apariencia, la voz o el fondo de Buddy. </li>
-<li>Códigos de trucos de modo: Estos códigos de trucos le dan acceso a diferentes modos o niveles de dificultad en el juego sin tener que desbloquearlos o completar tareas. Puedes usar modos para desafiarte o probar nuevos escenarios. </li>
-</ul>
- <h3>Los efectos de los códigos de trucos en el juego y las características</h3>
-<p>Los códigos de trucos pueden tener varios efectos en el juego y las caracter��sticas de Kick the Buddy 2, dependiendo del tipo y la cantidad de códigos de trucos que esté utilizando. Algunos de ellos son:</p>
-<p></p>
-<ul>
-<li>Los códigos de trucos pueden hacer el juego más fácil o más difícil para usted, dependiendo de su preferencia y nivel de habilidad. Por ejemplo, si quieres pasar un rato relajante con Buddy, puedes usar códigos de trucos de dinero o oro para comprar más armas y artículos. Si quieres tener un momento difícil con Buddy, puedes usar códigos de trucos de modo para aumentar la dificultad o cambiar las reglas. </li>
-
-<li>Los códigos de trucos pueden hacer que el juego sea más gratificante o menos gratificante para ti, dependiendo de tu objetivo y motivación. Por ejemplo, si quieres pasar un rato gratificante con Buddy, puedes usar códigos de trucos de modo para desbloquear logros o trofeos. Si quieres tener un tiempo menos gratificante con Buddy, puedes usar códigos de trucos de dinero o oro para saltarte algunas tareas o desafíos. </li>
-</ul>
- <h3>Los consejos y trucos para utilizar códigos de trucos de manera eficaz y responsable</h3>
-<p>Los códigos de trucos pueden ser una gran manera de mejorar tu experiencia de juego y divertirte más con Kick the Buddy 2. Sin embargo, debes usarlos con sabiduría y responsabilidad. Aquí hay algunos consejos y trucos sobre cómo hacerlo:</p>
-<ul>
-<li>Usa códigos de trucos con moderación y moderación. No los uses demasiado ni confíes demasiado en ellos. De lo contrario, puede perder interés en el juego o arruinar su encanto y atractivo original. </li>
-<li>Usa códigos de trucos de forma selectiva y apropiada. No los uses para cada aspecto o característica del juego. Úselos solo para las partes o características que encuentre difíciles, aburridas o inaccesibles. </li>
-<li>Usa códigos de trucos de forma creativa y experimental. No los uses para repetir las mismas acciones o escenarios. Úsalos para probar cosas nuevas o descubrir nuevas posibilidades. </li>
-<li>Use códigos de trampa ética y legalmente. No los use para dañar, ofender o engañar a otros. Úsalos solo para tu propio disfrute y entretenimiento personal. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Kick the Buddy 2 es un divertido y relajante juego que te permite torturar a un muñeco de trapo llamado Buddy con varias armas, herramientas y elementos. Sin embargo, si quieres darle vida a tu experiencia de juego y divertirte más, puedes usar códigos de trucos para Kick the Buddy 2. Los códigos de trucos son comandos o códigos especiales que pueden modificar o mejorar algunos aspectos del juego, como darte dinero ilimitado, oro, armas u objetos. </p>
-
-<p>Si estás interesado en usar códigos de trucos para Kick the Buddy 2, te animamos a probarlos y ver por ti mismo cómo pueden mejorar tu experiencia de juego y satisfacción. Sin embargo, también te recordamos que los uses de forma inteligente y responsable, ya que pueden tener algunos efectos negativos en la jugabilidad y las características. También le aconsejamos que sea cuidadoso y selectivo cuando busque códigos de trucos en línea, ya que algunos de ellos pueden ser falsos o dañinos. </p>
-<p>Esperamos que hayas disfrutado leyendo este artículo y hayas aprendido algo nuevo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Gracias por su tiempo y atención. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre códigos de trucos para Kick the Buddy 2:</p>
-<ol>
-<li>Q: ¿Son legales los códigos de trucos para Kick the Buddy 2? </li>
-<li>A: Códigos de trucos para Kick the Buddy 2 no son ilegales, ya que no violan ninguna ley o reglamento. Sin embargo, pueden estar en contra de los términos de servicio o políticas del desarrollador o editor de juegos. Por lo tanto, el uso de códigos de trucos puede resultar en algunas consecuencias, como ser expulsado del juego o perder su progreso. </li>
-<li>Q: ¿Son seguros los códigos de trucos para Kick the Buddy 2? </li>
-<li>A: Códigos de trucos para Kick the Buddy 2 no siempre son seguros, ya que algunos de ellos pueden contener virus, malware, spyware u otro software dañino que puede dañar su dispositivo o robar sus datos. Por lo tanto, debe ser cuidadoso y selectivo al buscar códigos de trucos en línea. Solo debes descargar códigos de trucos de fuentes confiables y de buena reputación que tengan reseñas, valoraciones, comentarios o comentarios positivos de otros usuarios. </li>
-<li>Q: ¿Los códigos de trucos para Kick the Buddy 2 funcionan sin conexión? </li>
-
-<li>Q: ¿Puedo usar códigos de trucos para Kick the Buddy 2 en otros dispositivos? </li>
-<li>A: Códigos de trucos para Kick the Buddy 2 pueden o no funcionar en otros dispositivos, dependiendo de la compatibilidad y las especificaciones de los dispositivos. Algunos códigos de trucos solo pueden funcionar en dispositivos o plataformas específicas, como Android o iOS. Algunos códigos de trucos pueden funcionar en múltiples dispositivos o plataformas, pero pueden requerir diferentes pasos o métodos para descargarlos y usarlos. Por lo tanto, debe verificar la compatibilidad y las especificaciones de los códigos de trucos antes de usarlos. </li>
-<li>Q: ¿Puedo usar códigos de trucos para Kick the Buddy 2 con otros juegos? </li>
-<li>A: Códigos de trucos para Kick the Buddy 2 pueden o no funcionar con otros juegos, dependiendo de la similitud y compatibilidad de los juegos. Algunos códigos de trucos solo pueden funcionar con Kick the Buddy 2 o su secuela o spin-off juegos. Algunos códigos de trucos pueden funcionar con otros juegos que tienen características o mecánicas similares, pero pueden tener diferentes efectos o resultados. Por lo tanto, es necesario comprobar la similitud y compatibilidad de los juegos antes de usar códigos de trucos. </li>
-</ol></p> 64aa2da5cf<br />
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-<br />

spaces/Benson/text-generation/Examples/Descargar Derby De Demolicin 3 Mod Apk.md

@@ -1,58 +0,0 @@
-
-<h1>Descargar Demolición Derby 3 Mod APK y disfrutar del último juego de destrucción de coches</h1>
- <p>Si eres un fan de las carreras de coches y los juegos de choque, entonces te encantará Demolition Derby 3. Este es un juego donde puedes conducir tu coche a otros coches y causar tanto daño como sea posible. También puede personalizar su coche con diferentes piezas y trabajos de pintura, y competir con otros jugadores en línea o fuera de línea. En este artículo, le diremos todo lo que necesita saber sobre Demolition Derby 3, y cómo descargar la versión mod APK del juego que le da dinero ilimitado, todos los coches desbloqueados, y sin anuncios. </p>
- <h2>¿Qué es Demolition Derby 3?</h2>
- <p>Demolition Derby 3 es un juego de destrucción de automóviles desarrollado por Beer Money Games. Es la secuela de la popular Demolition Derby 2, que tiene más de 50 millones de descargas en Google Play. En Demolition Derby 3, puedes elegir entre más de 40 coches diferentes, cada uno con sus propias estadísticas y habilidades. También puede actualizar su coche con varias partes, como motores, armaduras, ruedas, alerones y más. También puede cambiar el color y el diseño de su coche para que parezca único. </p>
-<h2>descargar derby de demolición 3 mod apk</h2><br /><p><b><b>Download Zip</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6KEf">https://bltlly.com/2v6KEf</a></b></p><br /><br />
- <h3>Características de Demolition Derby 3</h3>
- <p>Demolition Derby 3 tiene muchas características que hacen que sea divertido y emocionante jugar. Aquí están algunas de ellas:</p>
- <h4>Modo multijugador</h4>
- <p>Puedes jugar Demolition Derby 3 online con otros jugadores de todo el mundo. Puede unirse o crear un lobby, y elegir entre diferentes modos de juego, como free-for-all, deathmatch equipo, capturar la bandera, rey de la colina, y más. También puedes chatear con otros jugadores y hacer amigos o enemigos. </p>
- <h4>Coches personalizables</h4>
- <p>Puede personalizar su coche con más de 1000 piezas y calcomanías. Puede cambiar el motor, la transmisión, la suspensión, los frenos, los neumáticos, la armadura, el escape, turbo, nitro y más. También puede cambiar el color y el diseño de su automóvil con varios trabajos de pintura, pegatinas, llamas, rayas y más. Puedes hacer que tu auto se vea genial o loco. </p>
-
- <p>Puedes conducir tu coche en diferentes arenas y eventos. Hay más de 20 arenas para elegir, cada una con su propio diseño y obstáculos. Algunas arenas tienen rampas, lazos, puentes, túneles, paredes, barriles, cajas y más. También puedes participar en diferentes eventos, como carreras de demolición, carreras de eliminación, último hombre en pie, modo de supervivencia, modo de truco, batallas de jefes y más. Cada evento tiene sus propias reglas y recompensas. </p>
- <h4>Física y gráficos realistas</h4>
- <p>Demolition Derby 3 tiene física y gráficos realistas que hacen que el juego sea más inmersivo y realista. Los coches tienen modelos realistas de daños que muestran las abolladuras, arañazos, chispas, humo, fuego y explosiones que ocurren cuando chocan. Los coches también tienen sonidos realistas que coinciden con sus motores, frenos, bocinas, accidentes y más. Las arenas tienen iluminación realista y sombras que crean una atmósfera dinámica. </p>
- <h2>¿Por qué descargar Demolition Derby 3 Mod APK? </h2>
- <p>Demolition Derby 3 es un juego gratuito para jugar en dispositivos Android. Sin embargo, también tiene algunas limitaciones y desventajas que pueden afectar su experiencia de juego. Por ejemplo, es necesario ganar dinero en el juego para comprar y mejorar sus coches, que puede tomar mucho tiempo y esfuerzo. También necesitas ver anuncios para obtener algunas recompensas adicionales, que pueden ser molestas y distracciones. Además, no todos los coches están disponibles en el juego, y algunos de ellos están bloqueados detrás de un muro de pago. </p>
- <p>Es por eso que es posible que desee descargar la versión mod APK de Demolition Derby 3. Esta es una versión modificada del juego que le da algunas ventajas y beneficios que no se pueden obtener en la versión original. Estos son algunos de ellos:</p>
- <h3>Beneficios de la demolición Derby 3 Mod APK</h3>
- <p>Demolición Derby 3 Mod APK tiene muchos beneficios que hacen el juego más agradable y satisfactorio. Estos son algunos de ellos:</p>
-<p></p>
- <h4>Dinero ilimitado</h4>
-
- <h4>Todos los coches desbloqueados</h4>
- <p>Con Demolition Derby 3 Mod APK, usted tendrá todos los coches desbloqueados en el juego. Esto significa que puede elegir entre más de 40 coches diferentes, cada uno con sus propias estadísticas y habilidades. También puedes personalizar tu coche con más de 1000 piezas y calcomanías. Puedes tener la colección de coches más diversa y única del juego. </p>
- <h4>No hay anuncios</h4>
- <p>Con Demolition Derby 3 Mod APK, no tendrás anuncios en el juego. Esto significa que puedes jugar el juego sin interrupciones o distracciones. También puedes disfrutar del juego sin perder tiempo ni datos al ver anuncios. Puedes tener la experiencia de juego más fluida y fluida. </p>
- <h2>¿Cómo descargar e instalar Demolition Derby 3 Mod APK? </h2>
- <p>Si está interesado en descargar e instalar Demolition Derby 3 Mod APK, entonces usted necesita seguir algunos pasos simples. Aquí están:</p>
- <h3>Pasos para descargar e instalar Demolition Derby 3 Mod APK</h3>
- <h4>Paso 1: Habilitar fuentes desconocidas</h4>
- <p>El primer paso es habilitar fuentes desconocidas en su dispositivo Android. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y enciéndala. </p>
- <h4>Paso 2: Descargar el archivo mod APK</h4>
- <p>El siguiente paso es descargar el archivo mod APK de Demolition Derby 3 de una fuente confiable. Puedes usar el siguiente enlace para descargarlo directamente a tu dispositivo. </p>
- <p><a href="">Descargar Demolition Derby 3 Mod APK</a></p>
- <h4>Paso 3: Instalar el archivo mod APK</h4>
- <p>El tercer paso es instalar el archivo mod APK de Demolition Derby 3 en su dispositivo. Para hacer esto, busque el archivo descargado en su administrador de archivos, toque en él y siga las instrucciones en la pantalla. </p>
- <h4>Paso 4: Iniciar el juego y disfrutar de</h4>
-
- <h2>Conclusión</h2>
- <p>Demolition Derby 3 es un divertido y emocionante juego de destrucción de coches que te permite conducir tu coche en otros coches y causar tanto daño como sea posible. También puede personalizar su coche con diferentes piezas y trabajos de pintura, y competir con otros jugadores en línea o fuera de línea. Sin embargo, si desea disfrutar del juego más, es posible que desee descargar la versión mod APK del juego que le da dinero ilimitado, todos los coches desbloqueados, y sin anuncios. En este artículo, te hemos dicho todo lo que necesitas saber sobre Demolition Derby 3, y cómo descargar e instalar su versión mod APK. Esperamos que este artículo fue útil para usted, y que usted tendrá un gran tiempo jugando Demolition Derby 3.</p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Demolition Derby 3 Mod APK:</p>
- <ul>
-<li><b> ¿Es Demolition Derby 3 Mod APK seguro de usar? </b></li>
-<p>Sí, Demolición Derby 3 Mod APK es seguro de usar siempre y cuando se descarga de una fuente de confianza como la nuestra. No contiene ningún virus o malware que pueda dañar su dispositivo o datos. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas, y asegúrese de que tiene una copia de seguridad de sus datos en caso de que algo salga mal. </p>
- <li><b> ¿Es Demolition Derby 3 Mod APK compatible con mi dispositivo? </b></li>
-<p>Demolición Derby 3 Mod APK es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no soportar el juego o el mod APK debido a diferentes especificaciones o ajustes. Si encuentras algún problema mientras juegas el juego o instalas el mod APK, puedes intentar cambiar la configuración del dispositivo, actualizar el software del dispositivo o contactar al desarrollador del juego para obtener ayuda. </p>
- <li><b>¿Puedo jugar Demolition Derby 3 Mod APK en línea con otros jugadores? </b></li>
-
- <li><b>¿Puedo actualizar Demolition Derby 3 Mod APK a la última versión? </b></li>
-<p>Sí, puede actualizar Demolition Derby 3 Mod APK a la última versión cada vez que hay una nueva actualización disponible. Sin embargo, no debes actualizar el juego desde Google Play, ya que esto sobreescribirá la versión mod APK y eliminará todos los beneficios que tengas. En su lugar, usted debe descargar la última versión mod APK de nuestro sitio web, e instalarlo sobre el existente. De esta manera, mantendrás todo tu progreso y beneficios en el juego. </p>
- <li><b>¿Puedo solicitar más características o mods para Demolition Derby 3 Mod APK? </b></li>
-<p>Sí, puede solicitar más características o mods para Demolition Derby 3 Mod APK dejando un comentario en nuestro sitio web. Haremos todo lo posible para satisfacer sus solicitudes y proporcionarle la mejor experiencia de juego posible. Sin embargo, no podemos garantizar que podamos agregar todas las características o mods que desee, ya que algunos de ellos pueden ser demasiado difíciles o imposibles de implementar. Agradecemos su comprensión y apoyo. </p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/translate.py

@@ -1,78 +0,0 @@
-# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/
-# Copyright 2012-2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import copy
-
-from botocore.utils import merge_dicts
-
-
-def build_retry_config(
-    endpoint_prefix, retry_model, definitions, client_retry_config=None
-):
-    service_config = retry_model.get(endpoint_prefix, {})
-    resolve_references(service_config, definitions)
-    # We want to merge the global defaults with the service specific
-    # defaults, with the service specific defaults taking precedence.
-    # So we use the global defaults as the base.
-    #
-    # A deepcopy is done on the retry defaults because it ensures the
-    # retry model has no chance of getting mutated when the service specific
-    # configuration or client retry config is merged in.
-    final_retry_config = {
-        '__default__': copy.deepcopy(retry_model.get('__default__', {}))
-    }
-    resolve_references(final_retry_config, definitions)
-    # The merge the service specific config on top.
-    merge_dicts(final_retry_config, service_config)
-    if client_retry_config is not None:
-        _merge_client_retry_config(final_retry_config, client_retry_config)
-    return final_retry_config
-
-
-def _merge_client_retry_config(retry_config, client_retry_config):
-    max_retry_attempts_override = client_retry_config.get('max_attempts')
-    if max_retry_attempts_override is not None:
-        # In the retry config, the max_attempts refers to the maximum number
-        # of requests in general will be made. However, for the client's
-        # retry config it refers to how many retry attempts will be made at
-        # most. So to translate this number from the client config, one is
-        # added to convert it to the maximum number request that will be made
-        # by including the initial request.
-        #
-        # It is also important to note that if we ever support per operation
-        # configuration in the retry model via the client, we will need to
-        # revisit this logic to make sure max_attempts gets applied
-        # per operation.
-        retry_config['__default__']['max_attempts'] = (
-            max_retry_attempts_override + 1
-        )
-
-
-def resolve_references(config, definitions):
-    """Recursively replace $ref keys.
-
-    To cut down on duplication, common definitions can be declared
-    (and passed in via the ``definitions`` attribute) and then
-    references as {"$ref": "name"}, when this happens the reference
-    dict is placed with the value from the ``definition`` dict.
-
-    This is recursively done.
-
-    """
-    for key, value in config.items():
-        if isinstance(value, dict):
-            if len(value) == 1 and list(value.keys())[0] == '$ref':
-                # Then we need to resolve this reference.
-                config[key] = definitions[list(value.values())[0]]
-            else:
-                resolve_references(value, definitions)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/_appengine_environ.py

@@ -1,36 +0,0 @@
-"""
-This module provides means to detect the App Engine environment.
-"""
-
-import os
-
-
-def is_appengine():
-    return is_local_appengine() or is_prod_appengine()
-
-
-def is_appengine_sandbox():
-    """Reports if the app is running in the first generation sandbox.
-
-    The second generation runtimes are technically still in a sandbox, but it
-    is much less restrictive, so generally you shouldn't need to check for it.
-    see https://cloud.google.com/appengine/docs/standard/runtimes
-    """
-    return is_appengine() and os.environ["APPENGINE_RUNTIME"] == "python27"
-
-
-def is_local_appengine():
-    return "APPENGINE_RUNTIME" in os.environ and os.environ.get(
-        "SERVER_SOFTWARE", ""
-    ).startswith("Development/")
-
-
-def is_prod_appengine():
-    return "APPENGINE_RUNTIME" in os.environ and os.environ.get(
-        "SERVER_SOFTWARE", ""
-    ).startswith("Google App Engine/")
-
-
-def is_prod_appengine_mvms():
-    """Deprecated."""
-    return False

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/gqa/eval/result_eval.py

@@ -1,61 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-
-from openvqa.datasets.gqa.eval.gqa_eval import GQAEval
-import json, pickle
-import numpy as np
-
-
-def eval(__C, dataset, ans_ix_list, pred_list, result_eval_file, ensemble_file, log_file, valid=False):
-    result_eval_file = result_eval_file + '.json'
-
-    qid_list = [qid for qid in dataset.qid_list]
-    ans_size = dataset.ans_size
-
-    result = [{
-        'questionId': qid_list[ix],
-        'prediction': dataset.ix_to_ans[str(ans_ix_list[ix])],
-    } for ix in range(len(qid_list))]
-
-    print('Save the result to file: {}'.format(result_eval_file))
-    json.dump(result, open(result_eval_file, 'w'))
-
-    if __C.TEST_SAVE_PRED:
-        print('Save the prediction vector to file: {}'.format(ensemble_file))
-
-        pred_list = np.array(pred_list).reshape(-1, ans_size)
-        result_pred = [{
-            'pred': pred_list[qix],
-            'qid': int(qid_list[qix])
-        } for qix in range(qid_list.__len__())]
-        pickle.dump(result_pred, open(ensemble_file, 'wb+'), protocol=-1)
-
-
-    if valid:
-        # create vqa object and vqaRes object
-        ques_file_path = __C.RAW_PATH[__C.DATASET][__C.SPLIT['val']]
-        choices_path = None
-        if __C.SPLIT['val'] + '_choices' in __C.RAW_PATH[__C.DATASET]:
-            choices_path = __C.RAW_PATH[__C.DATASET][__C.SPLIT['val'] + '_choices']
-
-        eval_gqa = GQAEval(__C, result_eval_file, ques_file_path, choices_path, EVAL_CONSISTENCY=False)
-        result_string, detail_result_string = eval_gqa.get_str_result()
-
-        print('Write to log file: {}'.format(log_file))
-        logfile = open(log_file, 'a+')
-
-        for result_string_ in result_string:
-            logfile.write(result_string_)
-            logfile.write('\n')
-            print(result_string_)
-
-        for detail_result_string_ in detail_result_string:
-            logfile.write(detail_result_string_)
-            logfile.write("\n")
-
-        logfile.write('\n')
-        logfile.close()
-
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/utils/exec.py

@@ -1,58 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# Modified to add trojan result extraction options
-# --------------------------------------------------------
-
-import os, copy
-from openvqa.datasets.dataset_loader import DatasetLoader
-from utils.train_engine import train_engine
-from utils.test_engine import test_engine
-from utils.extract_engine import extract_engine
-
-class Execution:
-    def __init__(self, __C):
-        self.__C = __C
-
-        if __C.RUN_MODE != 'extract':
-            print('Loading dataset........')
-            self.dataset = DatasetLoader(__C).DataSet()
-
-        # If trigger the evaluation after every epoch
-        # Will create a new cfgs with RUN_MODE = 'val'
-        self.dataset_eval = None
-        if __C.EVAL_EVERY_EPOCH:
-            __C_eval = copy.deepcopy(__C)
-            setattr(__C_eval, 'RUN_MODE', 'val')
-            # modification - force eval set to clean when in train mode
-            setattr(__C_eval, 'VER', 'clean')
-
-            print('Loading validation set for per-epoch evaluation........')
-            self.dataset_eval = DatasetLoader(__C_eval).DataSet()
-
-
-    def run(self, run_mode):
-        if run_mode == 'train':
-            if self.__C.RESUME is False:
-                self.empty_log(self.__C.VERSION)
-            train_engine(self.__C, self.dataset, self.dataset_eval)
-
-        elif run_mode == 'val':
-            test_engine(self.__C, self.dataset, validation=True)
-
-        elif run_mode == 'test':
-            test_engine(self.__C, self.dataset)
-
-        elif run_mode == 'extract':
-            extract_engine(self.__C)
-
-        else:
-            exit(-1)
-
-
-    def empty_log(self, version):
-        print('Initializing log file........')
-        if (os.path.exists(self.__C.LOG_PATH + '/log_run_' + version + '.txt')):
-            os.remove(self.__C.LOG_PATH + '/log_run_' + version + '.txt')
-        print('Finished!')
-        print('')

spaces/CVPR/LIVE/pybind11/tools/pybind11NewTools.cmake

@@ -1,203 +0,0 @@
-# tools/pybind11NewTools.cmake -- Build system for the pybind11 modules
-#
-# Copyright (c) 2020 Wenzel Jakob <[email protected]> and Henry Schreiner
-#
-# All rights reserved. Use of this source code is governed by a
-# BSD-style license that can be found in the LICENSE file.
-
-get_property(
-  is_config
-  TARGET pybind11::headers
-  PROPERTY IMPORTED)
-
-if(pybind11_FIND_QUIETLY)
-  set(_pybind11_quiet QUIET)
-endif()
-
-if(CMAKE_VERSION VERSION_LESS 3.12)
-  message(FATAL_ERROR "You cannot use the new FindPython module with CMake < 3.12")
-endif()
-
-if(NOT Python_FOUND
-   AND NOT Python3_FOUND
-   AND NOT Python2_FOUND)
-  if(NOT DEFINED Python_FIND_IMPLEMENTATIONS)
-    set(Python_FIND_IMPLEMENTATIONS CPython PyPy)
-  endif()
-
-  # GitHub Actions like activation
-  if(NOT DEFINED Python_ROOT_DIR AND DEFINED ENV{pythonLocation})
-    set(Python_ROOT_DIR "$ENV{pythonLocation}")
-  endif()
-
-  find_package(Python REQUIRED COMPONENTS Interpreter Development ${_pybind11_quiet})
-
-  # If we are in submodule mode, export the Python targets to global targets.
-  # If this behavior is not desired, FindPython _before_ pybind11.
-  if(NOT is_config)
-    set_property(TARGET Python::Python PROPERTY IMPORTED_GLOBAL TRUE)
-    set_property(TARGET Python::Interpreter PROPERTY IMPORTED_GLOBAL TRUE)
-    if(TARGET Python::Module)
-      set_property(TARGET Python::Module PROPERTY IMPORTED_GLOBAL TRUE)
-    endif()
-  endif()
-endif()
-
-if(Python_FOUND)
-  set(_Python
-      Python
-      CACHE INTERNAL "" FORCE)
-elseif(Python3_FOUND AND NOT Python2_FOUND)
-  set(_Python
-      Python3
-      CACHE INTERNAL "" FORCE)
-elseif(Python2_FOUND AND NOT Python3_FOUND)
-  set(_Python
-      Python2
-      CACHE INTERNAL "" FORCE)
-else()
-  message(AUTHOR_WARNING "Python2 and Python3 both present, pybind11 in "
-                         "PYBIND11_NOPYTHON mode (manually activate to silence warning)")
-  set(_pybind11_nopython ON)
-  return()
-endif()
-
-if(PYBIND11_MASTER_PROJECT)
-  if(${_Python}_INTERPRETER_ID MATCHES "PyPy")
-    message(STATUS "PyPy ${${_Python}_PyPy_VERSION} (Py ${${_Python}_VERSION})")
-  else()
-    message(STATUS "${_Python} ${${_Python}_VERSION}")
-  endif()
-endif()
-
-# Debug check - see https://stackoverflow.com/questions/646518/python-how-to-detect-debug-Interpreter
-execute_process(COMMAND ${_Python}::Python -c "import sys; print(hasattr(sys, 'gettotalrefcount'))"
-                OUTPUT_VARIABLE PYTHON_IS_DEBUG)
-
-# Python debug libraries expose slightly different objects before 3.8
-# https://docs.python.org/3.6/c-api/intro.html#debugging-builds
-# https://stackoverflow.com/questions/39161202/how-to-work-around-missing-pymodule-create2-in-amd64-win-python35-d-lib
-if(PYTHON_IS_DEBUG)
-  set_property(
-    TARGET pybind11::pybind11
-    APPEND
-    PROPERTY INTERFACE_COMPILE_DEFINITIONS Py_DEBUG)
-endif()
-
-# Check on every access - since Python2 and Python3 could have been used - do nothing in that case.
-
-if(DEFINED ${_Python}_INCLUDE_DIRS)
-  set_property(
-    TARGET pybind11::pybind11
-    APPEND
-    PROPERTY INTERFACE_INCLUDE_DIRECTORIES $<BUILD_INTERFACE:${${_Python}_INCLUDE_DIRS}>)
-endif()
-
-if(DEFINED ${_Python}_VERSION AND ${_Python}_VERSION VERSION_LESS 3)
-  set_property(
-    TARGET pybind11::pybind11
-    APPEND
-    PROPERTY INTERFACE_LINK_LIBRARIES pybind11::python2_no_register)
-endif()
-
-# In CMake 3.18+, you can find these separately, so include an if
-if(TARGET ${_Python}::${_Python})
-  set_property(
-    TARGET pybind11::embed
-    APPEND
-    PROPERTY INTERFACE_LINK_LIBRARIES ${_Python}::${_Python})
-endif()
-
-# CMake 3.15+ has this
-if(TARGET ${_Python}::Module)
-  set_property(
-    TARGET pybind11::module
-    APPEND
-    PROPERTY INTERFACE_LINK_LIBRARIES ${_Python}::Module)
-else()
-  set_property(
-    TARGET pybind11::module
-    APPEND
-    PROPERTY INTERFACE_LINK_LIBRARIES pybind11::python_link_helper)
-endif()
-
-function(pybind11_add_module target_name)
-  cmake_parse_arguments(PARSE_ARGV 1 ARG "STATIC;SHARED;MODULE;THIN_LTO;NO_EXTRAS" "" "")
-
-  if(ARG_ADD_LIBRARY_STATIC)
-    set(type STATIC)
-  elseif(ARG_ADD_LIBRARY_SHARED)
-    set(type SHARED)
-  else()
-    set(type MODULE)
-  endif()
-
-  if("${_Python}" STREQUAL "Python")
-    python_add_library(${target_name} ${type} WITH_SOABI ${ARG_UNPARSED_ARGUMENTS})
-  elseif("${_Python}" STREQUAL "Python3")
-    python3_add_library(${target_name} ${type} WITH_SOABI ${ARG_UNPARSED_ARGUMENTS})
-  elseif("${_Python}" STREQUAL "Python2")
-    python2_add_library(${target_name} ${type} WITH_SOABI ${ARG_UNPARSED_ARGUMENTS})
-  else()
-    message(FATAL_ERROR "Cannot detect FindPython version: ${_Python}")
-  endif()
-
-  target_link_libraries(${target_name} PRIVATE pybind11::headers)
-
-  if(type STREQUAL "MODULE")
-    target_link_libraries(${target_name} PRIVATE pybind11::module)
-  else()
-    target_link_libraries(${target_name} PRIVATE pybind11::embed)
-  endif()
-
-  if(MSVC)
-    target_link_libraries(${target_name} PRIVATE pybind11::windows_extras)
-  endif()
-
-  if(DEFINED ${_Python}_VERSION AND ${_Python}_VERSION VERSION_LESS 3)
-    target_link_libraries(${target_name} PRIVATE pybind11::python2_no_register)
-  endif()
-
-  set_target_properties(${target_name} PROPERTIES CXX_VISIBILITY_PRESET "hidden"
-                                                  CUDA_VISIBILITY_PRESET "hidden")
-
-  if(ARG_NO_EXTRAS)
-    return()
-  endif()
-
-  if(NOT DEFINED CMAKE_INTERPROCEDURAL_OPTIMIZATION)
-    if(ARG_THIN_LTO)
-      target_link_libraries(${target_name} PRIVATE pybind11::thin_lto)
-    else()
-      target_link_libraries(${target_name} PRIVATE pybind11::lto)
-    endif()
-  endif()
-
-  if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
-    # Strip unnecessary sections of the binary on Linux/Mac OS
-    pybind11_strip(${target_name})
-  endif()
-
-  if(MSVC)
-    target_link_libraries(${target_name} PRIVATE pybind11::windows_extras)
-  endif()
-endfunction()
-
-function(pybind11_extension name)
-  set_property(TARGET ${name} PROPERTY PREFIX "")
-
-  if(CMAKE_SYSTEM_NAME STREQUAL "Windows")
-    set_property(TARGET ${name} PROPERTY SUFFIX ".pyd")
-  endif()
-
-  if(${_Python}_SOABI)
-    get_property(
-      suffix
-      TARGET ${name}
-      PROPERTY SUFFIX)
-    if(NOT suffix)
-      set(suffix "${CMAKE_SHARED_MODULE_SUFFIX}")
-    endif()
-    set_property(TARGET ${name} PROPERTY SUFFIX ".${${_Python}_SOABI}${suffix}")
-  endif()
-endfunction()

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

@@ -1,16 +0,0 @@
-from mmcv.utils import collect_env as collect_base_env
-from mmcv.utils import get_git_hash
-
-import mmdet
-
-
-def collect_env():
-    """Collect the information of the running environments."""
-    env_info = collect_base_env()
-    env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7]
-    return env_info
-
-
-if __name__ == '__main__':
-    for name, val in collect_env().items():
-        print(f'{name}: {val}')

spaces/CVPR/lama-example/saicinpainting/evaluation/evaluator.py

@@ -1,220 +0,0 @@
-import logging
-import math
-from typing import Dict
-
-import numpy as np
-import torch
-import torch.nn as nn
-import tqdm
-from torch.utils.data import DataLoader
-
-from saicinpainting.evaluation.utils import move_to_device
-
-LOGGER = logging.getLogger(__name__)
-
-
-class InpaintingEvaluator():
-    def __init__(self, dataset, scores, area_grouping=True, bins=10, batch_size=32, device='cuda',
-                 integral_func=None, integral_title=None, clamp_image_range=None):
-        """
-        :param dataset: torch.utils.data.Dataset which contains images and masks
-        :param scores: dict {score_name: EvaluatorScore object}
-        :param area_grouping: in addition to the overall scores, allows to compute score for the groups of samples
-            which are defined by share of area occluded by mask
-        :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1)
-        :param batch_size: batch_size for the dataloader
-        :param device: device to use
-        """
-        self.scores = scores
-        self.dataset = dataset
-
-        self.area_grouping = area_grouping
-        self.bins = bins
-
-        self.device = torch.device(device)
-
-        self.dataloader = DataLoader(self.dataset, shuffle=False, batch_size=batch_size)
-
-        self.integral_func = integral_func
-        self.integral_title = integral_title
-        self.clamp_image_range = clamp_image_range
-
-    def _get_bin_edges(self):
-        bin_edges = np.linspace(0, 1, self.bins + 1)
-
-        num_digits = max(0, math.ceil(math.log10(self.bins)) - 1)
-        interval_names = []
-        for idx_bin in range(self.bins):
-            start_percent, end_percent = round(100 * bin_edges[idx_bin], num_digits), \
-                                         round(100 * bin_edges[idx_bin + 1], num_digits)
-            start_percent = '{:.{n}f}'.format(start_percent, n=num_digits)
-            end_percent = '{:.{n}f}'.format(end_percent, n=num_digits)
-            interval_names.append("{0}-{1}%".format(start_percent, end_percent))
-
-        groups = []
-        for batch in self.dataloader:
-            mask = batch['mask']
-            batch_size = mask.shape[0]
-            area = mask.to(self.device).reshape(batch_size, -1).mean(dim=-1)
-            bin_indices = np.searchsorted(bin_edges, area.detach().cpu().numpy(), side='right') - 1
-            # corner case: when area is equal to 1, bin_indices should return bins - 1, not bins for that element
-            bin_indices[bin_indices == self.bins] = self.bins - 1
-            groups.append(bin_indices)
-        groups = np.hstack(groups)
-
-        return groups, interval_names
-
-    def evaluate(self, model=None):
-        """
-        :param model: callable with signature (image_batch, mask_batch); should return inpainted_batch
-        :return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or
-            name of the particular group arranged by area of mask (e.g. '10-20%')
-            and score statistics for the group as values.
-        """
-        results = dict()
-        if self.area_grouping:
-            groups, interval_names = self._get_bin_edges()
-        else:
-            groups = None
-
-        for score_name, score in tqdm.auto.tqdm(self.scores.items(), desc='scores'):
-            score.to(self.device)
-            with torch.no_grad():
-                score.reset()
-                for batch in tqdm.auto.tqdm(self.dataloader, desc=score_name, leave=False):
-                    batch = move_to_device(batch, self.device)
-                    image_batch, mask_batch = batch['image'], batch['mask']
-                    if self.clamp_image_range is not None:
-                        image_batch = torch.clamp(image_batch,
-                                                  min=self.clamp_image_range[0],
-                                                  max=self.clamp_image_range[1])
-                    if model is None:
-                        assert 'inpainted' in batch, \
-                            'Model is None, so we expected precomputed inpainting results at key "inpainted"'
-                        inpainted_batch = batch['inpainted']
-                    else:
-                        inpainted_batch = model(image_batch, mask_batch)
-                    score(inpainted_batch, image_batch, mask_batch)
-                total_results, group_results = score.get_value(groups=groups)
-
-            results[(score_name, 'total')] = total_results
-            if groups is not None:
-                for group_index, group_values in group_results.items():
-                    group_name = interval_names[group_index]
-                    results[(score_name, group_name)] = group_values
-
-        if self.integral_func is not None:
-            results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results))
-
-        return results
-
-
-def ssim_fid100_f1(metrics, fid_scale=100):
-    ssim = metrics[('ssim', 'total')]['mean']
-    fid = metrics[('fid', 'total')]['mean']
-    fid_rel = max(0, fid_scale - fid) / fid_scale
-    f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3)
-    return f1
-
-
-def lpips_fid100_f1(metrics, fid_scale=100):
-    neg_lpips = 1 - metrics[('lpips', 'total')]['mean']  # invert, so bigger is better
-    fid = metrics[('fid', 'total')]['mean']
-    fid_rel = max(0, fid_scale - fid) / fid_scale
-    f1 = 2 * neg_lpips * fid_rel / (neg_lpips + fid_rel + 1e-3)
-    return f1
-
-
-
-class InpaintingEvaluatorOnline(nn.Module):
-    def __init__(self, scores, bins=10, image_key='image', inpainted_key='inpainted',
-                 integral_func=None, integral_title=None, clamp_image_range=None):
-        """
-        :param scores: dict {score_name: EvaluatorScore object}
-        :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1)
-        :param device: device to use
-        """
-        super().__init__()
-        LOGGER.info(f'{type(self)} init called')
-        self.scores = nn.ModuleDict(scores)
-        self.image_key = image_key
-        self.inpainted_key = inpainted_key
-        self.bins_num = bins
-        self.bin_edges = np.linspace(0, 1, self.bins_num + 1)
-
-        num_digits = max(0, math.ceil(math.log10(self.bins_num)) - 1)
-        self.interval_names = []
-        for idx_bin in range(self.bins_num):
-            start_percent, end_percent = round(100 * self.bin_edges[idx_bin], num_digits), \
-                                         round(100 * self.bin_edges[idx_bin + 1], num_digits)
-            start_percent = '{:.{n}f}'.format(start_percent, n=num_digits)
-            end_percent = '{:.{n}f}'.format(end_percent, n=num_digits)
-            self.interval_names.append("{0}-{1}%".format(start_percent, end_percent))
-
-        self.groups = []
-
-        self.integral_func = integral_func
-        self.integral_title = integral_title
-        self.clamp_image_range = clamp_image_range
-
-        LOGGER.info(f'{type(self)} init done')
-
-    def _get_bins(self, mask_batch):
-        batch_size = mask_batch.shape[0]
-        area = mask_batch.view(batch_size, -1).mean(dim=-1).detach().cpu().numpy()
-        bin_indices = np.clip(np.searchsorted(self.bin_edges, area) - 1, 0, self.bins_num - 1)
-        return bin_indices
-
-    def forward(self, batch: Dict[str, torch.Tensor]):
-        """
-        Calculate and accumulate metrics for batch. To finalize evaluation and obtain final metrics, call evaluation_end
-        :param batch: batch dict with mandatory fields mask, image, inpainted (can be overriden by self.inpainted_key)
-        """
-        result = {}
-        with torch.no_grad():
-            image_batch, mask_batch, inpainted_batch = batch[self.image_key], batch['mask'], batch[self.inpainted_key]
-            if self.clamp_image_range is not None:
-                image_batch = torch.clamp(image_batch,
-                                          min=self.clamp_image_range[0],
-                                          max=self.clamp_image_range[1])
-            self.groups.extend(self._get_bins(mask_batch))
-
-            for score_name, score in self.scores.items():
-                result[score_name] = score(inpainted_batch, image_batch, mask_batch)
-        return result
-
-    def process_batch(self, batch: Dict[str, torch.Tensor]):
-        return self(batch)
-
-    def evaluation_end(self, states=None):
-        """:return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or
-            name of the particular group arranged by area of mask (e.g. '10-20%')
-            and score statistics for the group as values.
-        """
-        LOGGER.info(f'{type(self)}: evaluation_end called')
-
-        self.groups = np.array(self.groups)
-
-        results = {}
-        for score_name, score in self.scores.items():
-            LOGGER.info(f'Getting value of {score_name}')
-            cur_states = [s[score_name] for s in states] if states is not None else None
-            total_results, group_results = score.get_value(groups=self.groups, states=cur_states)
-            LOGGER.info(f'Getting value of {score_name} done')
-            results[(score_name, 'total')] = total_results
-
-            for group_index, group_values in group_results.items():
-                group_name = self.interval_names[group_index]
-                results[(score_name, group_name)] = group_values
-
-        if self.integral_func is not None:
-            results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results))
-
-        LOGGER.info(f'{type(self)}: reset scores')
-        self.groups = []
-        for sc in self.scores.values():
-            sc.reset()
-        LOGGER.info(f'{type(self)}: reset scores done')
-
-        LOGGER.info(f'{type(self)}: evaluation_end done')
-        return results

spaces/CVPR/regionclip-demo/detectron2/layers/csrc/vision.cpp

@@ -1,129 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-
-#include <torch/extension.h>
-#include "ROIAlignRotated/ROIAlignRotated.h"
-#include "box_iou_rotated/box_iou_rotated.h"
-#include "cocoeval/cocoeval.h"
-#include "deformable/deform_conv.h"
-#include "nms_rotated/nms_rotated.h"
-
-namespace detectron2 {
-
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-  std::ostringstream oss;
-
-#if defined(WITH_CUDA)
-  oss << "CUDA ";
-#else
-  oss << "HIP ";
-#endif
-
-  // copied from
-  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
-  auto printCudaStyleVersion = [&](int v) {
-    oss << (v / 1000) << "." << (v / 10 % 100);
-    if (v % 10 != 0) {
-      oss << "." << (v % 10);
-    }
-  };
-  printCudaStyleVersion(get_cudart_version());
-  return oss.str();
-#else // neither CUDA nor HIP
-  return std::string("not available");
-#endif
-}
-
-bool has_cuda() {
-#if defined(WITH_CUDA)
-  return true;
-#else
-  return false;
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
-  std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
-
-#if ((__GNUC__ <= 4) && (__GNUC_MINOR__ <= 8))
-#error "GCC >= 4.9 is required!"
-#endif
-
-  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
-  {
-    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
-       << __clang_patchlevel__;
-  }
-#endif
-
-#if defined(_MSC_VER)
-  { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
-  return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("get_compiler_version", &get_compiler_version, "get_compiler_version");
-  m.def("get_cuda_version", &get_cuda_version, "get_cuda_version");
-  m.def("has_cuda", &has_cuda, "has_cuda");
-
-  m.def("box_iou_rotated", &box_iou_rotated, "IoU for rotated boxes");
-
-  m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward");
-  m.def(
-      "deform_conv_backward_input",
-      &deform_conv_backward_input,
-      "deform_conv_backward_input");
-  m.def(
-      "deform_conv_backward_filter",
-      &deform_conv_backward_filter,
-      "deform_conv_backward_filter");
-  m.def(
-      "modulated_deform_conv_forward",
-      &modulated_deform_conv_forward,
-      "modulated_deform_conv_forward");
-  m.def(
-      "modulated_deform_conv_backward",
-      &modulated_deform_conv_backward,
-      "modulated_deform_conv_backward");
-
-  m.def("nms_rotated", &nms_rotated, "NMS for rotated boxes");
-
-  m.def(
-      "roi_align_rotated_forward",
-      &ROIAlignRotated_forward,
-      "Forward pass for Rotated ROI-Align Operator");
-  m.def(
-      "roi_align_rotated_backward",
-      &ROIAlignRotated_backward,
-      "Backward pass for Rotated ROI-Align Operator");
-
-  m.def("COCOevalAccumulate", &COCOeval::Accumulate, "COCOeval::Accumulate");
-  m.def(
-      "COCOevalEvaluateImages",
-      &COCOeval::EvaluateImages,
-      "COCOeval::EvaluateImages");
-  pybind11::class_<COCOeval::InstanceAnnotation>(m, "InstanceAnnotation")
-      .def(pybind11::init<uint64_t, double, double, bool, bool>());
-  pybind11::class_<COCOeval::ImageEvaluation>(m, "ImageEvaluation")
-      .def(pybind11::init<>());
-}
-
-#ifdef TORCH_LIBRARY
-TORCH_LIBRARY(detectron2, m) {
-  m.def("nms_rotated", &nms_rotated);
-}
-#endif
-} // namespace detectron2

spaces/ChandraMohanNayal/AutoGPT/autogpt/memory/local.py

@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import os
-from typing import Any, List
-
-import numpy as np
-import orjson
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.memory.base import MemoryProviderSingleton
-
-EMBED_DIM = 1536
-SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
-
-
-def create_default_embeddings():
-    return np.zeros((0, EMBED_DIM)).astype(np.float32)
-
-
-@dataclasses.dataclass
-class CacheContent:
-    texts: List[str] = dataclasses.field(default_factory=list)
-    embeddings: np.ndarray = dataclasses.field(
-        default_factory=create_default_embeddings
-    )
-
-
-class LocalCache(MemoryProviderSingleton):
-    """A class that stores the memory in a local file"""
-
-    def __init__(self, cfg) -> None:
-        """Initialize a class instance
-
-        Args:
-            cfg: Config object
-
-        Returns:
-            None
-        """
-        self.filename = f"{cfg.memory_index}.json"
-        if os.path.exists(self.filename):
-            try:
-                with open(self.filename, "w+b") as f:
-                    file_content = f.read()
-                    if not file_content.strip():
-                        file_content = b"{}"
-                        f.write(file_content)
-
-                    loaded = orjson.loads(file_content)
-                    self.data = CacheContent(**loaded)
-            except orjson.JSONDecodeError:
-                print(f"Error: The file '{self.filename}' is not in JSON format.")
-                self.data = CacheContent()
-        else:
-            print(
-                f"Warning: The file '{self.filename}' does not exist. "
-                "Local memory would not be saved to a file."
-            )
-            self.data = CacheContent()
-
-    def add(self, text: str):
-        """
-        Add text to our list of texts, add embedding as row to our
-            embeddings-matrix
-
-        Args:
-            text: str
-
-        Returns: None
-        """
-        if "Command Error:" in text:
-            return ""
-        self.data.texts.append(text)
-
-        embedding = create_embedding_with_ada(text)
-
-        vector = np.array(embedding).astype(np.float32)
-        vector = vector[np.newaxis, :]
-        self.data.embeddings = np.concatenate(
-            [
-                self.data.embeddings,
-                vector,
-            ],
-            axis=0,
-        )
-
-        with open(self.filename, "wb") as f:
-            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
-            f.write(out)
-        return text
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.data = CacheContent()
-        return "Obliviated"
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def get_relevant(self, text: str, k: int) -> list[Any]:
-        """ "
-        matrix-vector mult to find score-for-each-row-of-matrix
-         get indices for top-k winning scores
-         return texts for those indices
-        Args:
-            text: str
-            k: int
-
-        Returns: List[str]
-        """
-        embedding = create_embedding_with_ada(text)
-
-        scores = np.dot(self.data.embeddings, embedding)
-
-        top_k_indices = np.argsort(scores)[-k:][::-1]
-
-        return [self.data.texts[i] for i in top_k_indices]
-
-    def get_stats(self) -> tuple[int, tuple[int, ...]]:
-        """
-        Returns: The stats of the local cache.
-        """
-        return len(self.data.texts), self.data.embeddings.shape

spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/datasets/datasets/webvid_datasets.py

@@ -1,122 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import os
-from video_llama.datasets.datasets.base_dataset import BaseDataset
-from video_llama.datasets.datasets.caption_datasets import CaptionDataset
-import pandas as pd
-import decord
-from decord import VideoReader
-import random
-import torch
-from torch.utils.data.dataloader import default_collate
-class WebvidDataset(BaseDataset):
-    def __init__(self, vis_processor, text_processor, vis_root, ann_root):
-        """
-        vis_root (string): Root directory of video (e.g. webvid_eval/video/)
-        ann_root (string): Root directory of video (e.g. webvid_eval/annotations/)
-        split (string): val or test
-        """
-        super().__init__(vis_processor=vis_processor, text_processor=text_processor)
-
-
-        # 读取一个路径下所有的
-
-        ts_df = []
-        for file_name in os.listdir(ann_root):
-            if file_name.endswith('.csv'):
-                df = pd.read_csv(os.path.join(ann_root, file_name))
-                ts_df.append(df)
-
-        merged_df = pd.concat(ts_df)
-        self.annotation = merged_df
-        self.vis_root = vis_root
-        self.resize_size = 224
-        self.num_frm = 8
-        self.frm_sampling_strategy = 'headtail'
-
-    def _get_video_path(self, sample):
-        rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4')
-        full_video_fp = os.path.join(self.vis_root,  rel_video_fp)
-        return full_video_fp
-
-    def __getitem__(self, index):
-        num_retries = 10  # skip error videos
-        for _ in range(num_retries):
-            sample = self.annotation.iloc[index]
-            sample_dict = sample.to_dict()
-            video_id = sample_dict['videoid']
-
-            if 'name' in sample_dict.keys():
-                text = sample_dict['name'].strip()
-            else:
-                raise NotImplementedError("Un-supported text annotation format.")
-
-            # fetch video
-            video_path = self._get_video_path(sample_dict) 
-            # if os.path.exists(video_path):
-            try:
-                video = self.vis_processor(video_path)
-            except:
-                print(f"Failed to load examples with video: {video_path}. "
-                            f"Will randomly sample an example as a replacement.")
-                index = random.randint(0, len(self) - 1)
-                continue
-            caption = self.text_processor(text)
-
-            # print(video.size())
-            if video is None or caption is None \
-                    or video.size()!=torch.Size([3,self.vis_processor.n_frms,224,224]):
-                print(f"Failed to load examples with video: {video_path}. "
-                            f"Will randomly sample an example as a replacement.")
-                index = random.randint(0, len(self) - 1)
-                continue
-            else:
-                break
-        else:  
-            raise RuntimeError(f"Failed to fetch video after {num_retries} retries.")
-        # "image_id" is kept to stay compatible with the COCO evaluation format
-        return {
-            "image": video,
-            "text_input": caption,
-            "type":'video',
-        }
-
-    def __len__(self):
-        return len(self.annotation)
-
-    # def collater(self, samples):
-    #     new_result = {}
-    #     new_result['image'] = default_collate( [sample["image"] for sample in samples])
-    #     new_result['text_input'] = default_collate( [sample["text_input"] for sample in samples])
-    #     return new_result
-        
-class WebvidDatasetEvalDataset(BaseDataset):
-    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
-        """
-        vis_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        """
-        super().__init__(vis_processor, text_processor, vis_root, ann_paths)
-
-    def __getitem__(self, index):
-
-        ann = self.annotation[index]
-
-        vname = ann["video"]
-        video_path = os.path.join(self.vis_root, vname)
-
-        video = self.vis_processor(video_path)
-
-        return {
-            "video": video,
-            "image_id": ann["image_id"],
-            "instance_id": ann["instance_id"],
-        }
-
-

spaces/DHEIVER/ThyroidTumorClassificationModel/app.py

@@ -1,66 +0,0 @@
-import gradio as gr
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
-from PIL import Image
-import torch
-import datetime
-
-# Carregue o extrator de recursos e o modelo
-extractor = AutoFeatureExtractor.from_pretrained("SerdarHelli/ThyroidTumorClassificationModel")
-model = AutoModelForImageClassification.from_pretrained("SerdarHelli/ThyroidTumorClassificationModel")
-
-# Função para classificar a imagem
-def classify_image(image):
-    # Pré-processa a imagem usando o extrator
-    inputs = extractor(images=image, return_tensors="pt")
-    
-    # Passa a imagem pelo modelo
-    outputs = model(**inputs)
-    
-    # Obtém as probabilidades das classes
-    logits = outputs.logits
-    
-    # Calcula as probabilidades finais usando o softmax
-    probabilities = torch.softmax(logits, dim=1)
-    
-    # Obtém a classe com a maior probabilidade
-    predicted_class = torch.argmax(probabilities, dim=1).item()
-    
-    # Rótulos de classe personalizados com base no seu modelo
-    class_labels = ["Sem Tumor", "Tumor"]
-    
-    # Rótulo da classe prevista
-    predicted_label = class_labels[predicted_class]
-    
-    # Obtém a data e hora atual
-    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    
-    # Formate a saída em HTML com data e hora
-    result_html = f"""
-    <h2>Resultado da Classificação</h2>
-    <p><strong>Classe Predita:</strong> {predicted_label}</p>
-    <p><strong>Data e Hora:</strong> {current_time}</p>
-    """
-    
-    # Retorna o resultado formatado em HTML
-    return result_html
-
-# Crie uma interface Gradio com detalhes sobre o Classificador de Tumor da Tireoide
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.inputs.Image(),
-    outputs=gr.outputs.HTML(),  # Saída formatada com HTML
-    title="Classificador de Tumor da Tireoide",
-    description="""
-    <p>Este é um classificador de imagens de tumores da tireoide.</p>
-    <p>Para usá-lo:</p>
-    <ol>
-      <li>Clique no botão 'Escolher Arquivo' para fazer o upload de uma imagem da tireoide.</li>
-      <li>Aguarde a classificação automática.</li>
-      <li>O resultado mostrará a classe predita e a data e hora da classificação.</li>
-    </ol>
-    <p>Este classificador é baseado em um modelo pré-treinado e pode ajudar a identificar a presença de tumores da tireoide em imagens médicas.</p>
-    """,
-)
-
-# Inicie a interface Gradio
-iface.launch()