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-<h1>Clash Royale 3D Download: How to Play Your Favorite Game in a New Dimension</h1>
- <p>If you are a fan of Clash Royale, the popular mobile game that combines card collecting, tower defense, and real-time strategy, you might be wondering if there is a way to play it in a more immersive and realistic way. Well, you are in luck, because there is a fan-made project that allows you to play Clash Royale in 3D! In this article, we will tell you everything you need to know about Clash Royale 3D, how to download and install it, and how to enjoy it to the fullest. So, get ready to experience your favorite game in a new dimension!</p>
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- <h2>What is Clash Royale 3D?</h2>
- <p>Clash Royale 3D is a fan-made project that brings Clash Royale to life in 3D. It is not an official game by Supercell, the developer of Clash Royale, but rather a tribute by some passionate fans who wanted to create something unique and amazing. Clash Royale 3D is not a simple port or remake of the original game, but rather a new way to play it with enhanced graphics, animations, sounds, and gameplay.</p>
- <h3>A fan-made project that brings Clash Royale to life in 3D</h3>
- <p>Clash Royale 3D is created by using Sketchfab, a platform that allows anyone to create, share, and discover 3D models online. Sketchfab has a large community of artists and enthusiasts who create and upload various 3D models, including those based on popular games, movies, characters, and more. Some of these models are inspired by Clash Royale, such as the characters, cards, towers, arenas, and effects. By using Sketchfab's viewer and API, the developers of Clash Royale 3D were able to combine these models into a playable game that runs on your browser.</p>
- <h3>The features and benefits of playing Clash Royale 3D</h3>
- <p>Clash Royale 3D has many features and benefits that make it worth playing. Here are some of them:</p>
-<ul>
-<li>You can play Clash Royale in a more realistic and immersive way. You can see your cards come to life in 3D, watch them move and attack on the battlefield, and hear them make sounds and voices. You can also zoom in and out, rotate the camera, and change the perspective to get a better view of the action.</li>
-<li>You can explore different arenas and environments in 3D. You can see the details and textures of each arena, such as the grass, rocks, trees, buildings, flags, and more. You can also see the weather effects, such as rain, snow, fog, and night. You can even interact with some elements of the arena, such as breaking barrels or opening chests.</li>
-<li>You can enjoy the same gameplay and mechanics as the original game. You can still collect cards, build decks, join clans, chat with other players, participate in events, earn rewards, and more. You can also play against other players online or against bots offline.</li>
-<li>You can customize your game settings and preferences. You can adjust the quality, resolution, sound, and performance of the game. You can also choose the language, theme, and mode of the game. You can even enable or disable some features, such as shadows, reflections, particles, and animations.</li>
-</ul>
-<h2>How to download and install Clash Royale 3D?</h2>
- <p>Clash Royale 3D is not available on the official app stores, such as Google Play or Apple Store, because it is not an official game by Supercell. However, you can still download and install it easily by following these steps:</p>
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-<ul>
-<li>A device that supports WebGL, which is a technology that enables 3D graphics on the web. Most modern browsers and devices support WebGL, but you can check if yours does by visiting this link: [WebGL Report].</li>
-<li>A stable internet connection, preferably with a high speed and low latency. This is because Clash Royale 3D is an online game that requires constant communication with the server and other players.</li>
-<li>A sufficient amount of storage space on your device, depending on the size of the game files. The current version of Clash Royale 3D is about 300 MB, but it may vary depending on the updates and patches.</li>
-</ul>
-<p>Once you have verified that your device meets the requirements, you can proceed to download Clash Royale 3D by following these steps:</p>
-<ol>
-<li>Visit the official website of Clash Royale 3D at [clashroyale3d.com].</li>
-<li>Click on the "Download" button and choose the version that matches your device (Android or iOS).</li>
-<li>Wait for the download to finish and then open the downloaded file.</li>
-<li>Follow the instructions on the screen to install Clash Royale 3D on your device.</li>
-<li>Launch Clash Royale 3D and enjoy!</li>
-</ol>
-<h3>The tips and tricks for playing Clash Royale 3D smoothly</h3>
- <p>Clash Royale 3D is a fun and exciting game, but it can also be challenging and frustrating at times. To help you play Clash Royale 3D smoothly and avoid any problems or issues, here are some tips and tricks that you should keep in mind:</p>
-<ul>
-<li>Make sure that your device is fully charged or plugged in before playing Clash Royale 3D. The game can drain your battery quickly because of its high graphics and performance demands.</li>
-<li>Close any other apps or programs that are running in the background while playing Clash Royale 3D. This can free up some memory and CPU resources for the game and prevent any lag or crashes.</li>
-<li>Adjust your game settings according to your device's capabilities and preferences. You can lower the quality, resolution, sound, and performance of the game if you experience any slowness or stuttering. You can also enable or disable some features, such as shadows, reflections, particles, and animations, to improve the game's performance.</li>
-<li>Use a reliable and secure network connection when playing Clash Royale 3D online. Avoid using public or unsecured Wi-Fi networks that may expose your data or interfere with your gameplay. You can also use a VPN service to protect your privacy and bypass any geo-restrictions or firewalls.</li>
-<li>Update your game regularly to get the latest features, fixes, and improvements. You can check for updates by visiting the official website of Clash Royale 3D or by opening the game's settings menu.</li>
-</ul> <h2>How to enjoy Clash Royale 3D to the fullest?</h2>
- <p>Clash Royale 3D is not only a game, but also a way to express your creativity and passion for Clash Royale. There are many ways to enjoy Clash Royale 3D to the fullest, such as:</p>
- <h3>The best 3D models and arenas to explore in Clash Royale 3D</h3>
- <p>One of the main attractions of Clash Royale 3D is the variety and quality of the 3D models and arenas that you can explore. You can see your favorite characters, cards, towers, and effects in a new light, with more details, colors, and animations. You can also discover new and unique models and arenas that are not available in the original game, such as custom-made ones by other fans or artists. Here are some of the best 3D models and arenas that you can explore in Clash Royale 3D:</p>
-<table>
-<tr>
-<th>Model/Arena</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>King Tower</td>
-<td>The King Tower is the main tower that you have to protect in Clash Royale. In Clash Royale 3D, you can see the King Tower in 3D, with its crown, cannons, flags, and windows. You can also see the King himself, sitting on his throne and cheering or taunting you.</td>
-</tr>
-<tr>
-<td>Princess</td>
-<td>The Princess is one of the most popular and iconic cards in Clash Royale. She is a legendary card that can shoot arrows from a long distance. In Clash Royale 3D, you can see the Princess in 3D, with her dress, hair, bow, and quiver. You can also see her facial expressions and hear her voice.</td>
-</tr>
-<tr>
-<td>Fireball</td>
-<td>The Fireball is one of the most powerful and versatile spells in Clash Royale. It is a rare card that can deal high damage to a large area. In Clash Royale 3D, you can see the Fireball in 3D, with its flames, sparks, and smoke. You can also feel its impact and hear its sound.</td>
-</tr>
-<tr>
-<td>Hog Mountain</td>
-<td>Hog Mountain is one of the most fun and colorful arenas in Clash Royale. It is the arena for players who have reached 3000 trophies. In Clash Royale 3D, you can see Hog Mountain in 3D, with its hills, bridges, balloons, hogs, and fireworks. You can also interact with some of the elements of the arena, such as popping balloons or riding hogs.</td>
-</tr>
-<tr>
-<td>Clashmas Village</td>
-<td>Clashmas Village is one of the most festive and special arenas in Clash Royale. It is a seasonal arena that appears during the Christmas period. In Clash Royale 3D, you can see Clashmas Village in 3D, with its snow, trees, lights, presents, and snowmen. You can also enjoy the Christmas music and atmosphere.</td>
-</tr>
-</table>
- <h3>The challenges and rewards of playing Clash Royale 3D</h3>
- <p>Clash Royale 3D is not only a game to admire, but also a game to challenge yourself and improve your skills. There are many challenges and rewards that you can face and earn while playing Clash Royale 3D, such as:</p>
-<ul>
-<li>You can compete with other players online or offline in different modes and formats. You can play classic matches or tournaments with standard rules or custom rules. You can also play special events or modes that have different objectives or conditions.</li>
-<li>You can collect cards and build decks that suit your style and strategy. You can unlock new cards by opening chests or buying them from the shop. You can also upgrade your cards by using gold or gems. You can create different decks for different situations or preferences.</li>
-<li>You can join clans and chat with other players who share your passion for Clash Royale. You can exchange cards, tips, and ideas with your clanmates. You can also participate in clan wars or clan games to earn rewards and glory for your clan.</li>
-<li>You can earn trophies and climb the ladder of rankings. You can gain trophies by winning matches or lose trophies by losing matches. You can also reach new arenas or leagues that have different rewards and challenges.</li>
-<li>You can achieve goals and milestones that show your progress and achievements. You can complete quests or missions that have specific tasks or requirements. You can also earn badges or stars that indicate your level or skill.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Clash Royale 3D is a fan-made project that allows you to play Clash Royale in 3D. It is not an official game by Supercell, but rather a tribute by by visiting this link: [WebGL Report]. However, some devices and platforms may have better performance and compatibility than others, depending on their specifications and features. For example, Android devices may run Clash Royale 3D better than iOS devices, and Chrome browsers may run Clash Royale 3D better than Safari browsers.</p>
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-<p>A5: You can support the developers of Clash Royale 3D by doing the following things:</p>
-<ul>
-<li>Share your feedback and suggestions with them. You can contact them via email, social media, or Discord. You can also rate and review the game on the website or app store.</li>
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-<li>Donate or contribute to the game's development. You can donate money or resources to the developers via PayPal or Patreon. You can also contribute your skills or talents to the game, such as creating 3D models, graphics, sounds, or codes.</li>
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- <h4>ICC.tv</h4>
- <p>ICC.tv is the official app of the International Cricket Council (ICC), the governing body of world cricket. It offers live streaming of all ICC events and tournaments, such as the ICC World Cup, the ICC Champions Trophy, the ICC World Test Championship, the ICC Women's World Cup, and more. It also offers live streaming of other domestic and international matches from various countries and regions.</p>
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-<li>It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app.</li>
-<li>It is free to download and use, but it may require registration or verification for some matches.</li>
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- <p>You can download ICC.tv APK from its official website or from other sources online.</p>
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- <p>CricHD is one of the most popular and widely used live cricket streaming apps APK. It offers live streaming of all kinds of cricket matches, such as Test, ODI, T20, IPL, BBL, PSL, CPL, and more. It also offers live streaming of other sports, such as football, basketball, tennis, hockey, rugby, and more.</p>
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-<li>It is free to download and use, but it may show some ads or pop-ups during the streaming.</li>
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- <p>You can download CricHD APK from its official website or from other sources online.</p>
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- <p>There are many other live cricket streaming apps APK that you can try out, such as:</p>
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-<li>Hotstar: It is a popular streaming platform that offers live cricket matches from India and other countries. It also offers other entertainment content, such as movies, TV shows, news, etc. It is free to download and use, but it may require a subscription for some content.</li>
-<li>SonyLIV: It is another popular streaming platform that offers live cricket matches from India and other countries. It also offers other sports and entertainment content, such as football, WWE, comedy, etc. It is free to download and use, but it may require a subscription for some content.</li>
-<li>ThopTV: It is a third-party streaming app that offers live cricket matches from various sources and channels. It also offers other content, such as movies, TV shows, music, etc. It is free to download and use, but it may not be safe or legal to use.</li>
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- <p>You can compare these and other live cricket streaming apps APK based on your preferences and needs. You can also check the reviews and ratings of other users who have used them before. You can also try out different apps and see which one works best for you.</p>
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- <p>A1: Live cricket streaming app download apk is safe and legal as long as you download it from a reliable source and use it for personal and non-commercial purposes. However, some apps may not have the rights or permissions to stream some matches or content, and they may violate the intellectual property or privacy rights of the owners or providers. Therefore, you should be careful and responsible when using live cricket streaming app download apk.</p>
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- <p>A3: There are many live cricket streaming apps for Android and iOS devices, but some of them may be better than others in terms of features, quality, performance, and options. Some of the best live cricket streaming apps for Android and iOS devices are: - ICC.tv: It is the official app of the International Cricket Council (ICC), the governing body of world cricket. It offers live streaming of all ICC events and tournaments, as well as other domestic and international matches from various countries and regions. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may require registration or verification for some matches. - CricHD: It is one of the most popular and widely used live cricket streaming apps. It offers live streaming of all kinds of cricket matches, such as Test, ODI, T20, IPL, BBL, PSL, CPL, and more. It also offers live streaming of other sports, such as football, basketball, tennis, hockey, rugby, and more. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may show some ads or pop-ups during the streaming. - Hotstar: It is a popular streaming platform that offers live cricket matches from India and other countries. It also offers other entertainment content, such as movies, TV shows, news, etc. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may require a subscription for some content. - SonyLIV: It is another popular streaming platform that offers live cricket matches from India and other countries. It also offers other sports and entertainment content, such as football, WWE, comedy, etc. It has high-definition quality and smooth streaming without any buffering or lagging. It has multiple languages and commentary options for different matches. It has interactive features such as live scores, stats, highlights, replays, polls, chat, and more on the app. It is free to download and use, but it may require a subscription for some content. These are some of the best live cricket streaming apps for Android and iOS devices that you can try out. You can also check out other apps that may suit your preferences and needs.</p>
- <h3>Q4: How can I watch live cricket streaming on my TV or laptop?</h3>
- <p>A4: If you want to watch live cricket streaming on your TV or laptop instead of your phone, you have a few options to do so. For example, you can: - Use a device that supports casting or mirroring, such as Chromecast, Airplay, Miracast, or others. You can connect your phone to your TV or laptop using these devices and stream the live cricket match from your phone to your TV or laptop screen. You may need to download and install an app or a software that supports casting or mirroring on your phone and your TV or laptop. - Use a device that supports HDMI, such as a cable, an adapter, or a dongle. You can connect your phone to your TV or laptop using these devices and stream the live cricket match from your phone to your TV or laptop screen. You may need to adjust the settings on your phone and your TV or laptop to enable HDMI output and input. - Use a device that supports USB, such as a cable, an adapter, or a dongle. You can connect your phone to your TV or laptop using these devices and stream the live cricket match from your phone to your TV or laptop screen. You may need to enable USB debugging on your phone and install a driver or a software on your TV or laptop to recognize your phone. These are some of the ways you can watch live cricket streaming on your TV or laptop using live cricket streaming app download apk. You can also check out other methods that may work for you.</p>
- <h3>Q5: How can I improve the quality and speed of live cricket streaming?</h3>
- <p>A5: If you want to improve the quality and speed of live cricket streaming, you need to consider some factors that may affect them, such as: - Your internet connection: You need to have a stable and fast internet connection to stream live cricket matches without any buffering or lagging. You can check your internet speed and signal strength using an app or a website. You can also use a Wi-Fi network instead of a mobile data network if possible. You can also close any other apps or programs that may consume bandwidth on your phone or your router. - Your phone storage: You need to have enough storage space on your phone to download and install live cricket streaming app APK and store the data and cache of the app. You can check your phone storage using an app or a setting. You can also delete any unnecessary files or apps that may take up space on your phone. - Your phone performance: You need to have a good phone performance to run live cricket streaming app APK smoothly and efficiently. You can check your phone performance using an app or a setting. You can also clear any background processes or tasks that may slow down your phone. You can also update your phone software and firmware if available. These are some of the factors that may affect the quality and speed of live cricket streaming. You can try to optimize them as much as possible to improve your streaming experience.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Free Download Video Game Player The Ultimate Guide to Playing Any Game on Any Device.md

@@ -1,107 +0,0 @@
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-<p>If you love playing video games, you probably know how important it is to have a good video game player. A video game player is a software that allows you to run and enjoy various types of video games on your device. But how do you find and download the best free video game players? In this article, we will answer this question and give you some tips and recommendations.</p>
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spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/preconfig_scheduling_lms_discrete.py

@@ -1,299 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 Katherine Crowson and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import warnings
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import paddle
-from scipy import integrate
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput
-from ..scheduling_utils import SchedulerMixin
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
-class PreconfigLMSDiscreteSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: paddle.Tensor
-    pred_original_sample: Optional[paddle.Tensor] = None
-
-
-class PreconfigLMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
-    """
-    Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by
-    Katherine Crowson:
-    https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear` or `scaled_linear`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-    """
-
-    _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        prediction_type: str = "epsilon",
-        preconfig=True,
-    ):
-        if trained_betas is not None:
-            self.betas = paddle.to_tensor(trained_betas, dtype="float32")
-        elif beta_schedule == "linear":
-            self.betas = paddle.linspace(beta_start, beta_end, num_train_timesteps, dtype="float32")
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = paddle.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype="float32") ** 2
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = paddle.cumprod(self.alphas, 0)
-
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
-        sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
-        self.sigmas = paddle.to_tensor(sigmas)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = self.sigmas.max()
-
-        # setable values
-        self.num_inference_steps = None
-        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
-        self.timesteps = paddle.to_tensor(timesteps, dtype="float32")
-        self.derivatives = []
-        self.is_scale_input_called = False
-        self.preconfig = preconfig
-
-    def scale_model_input(
-        self, sample: paddle.Tensor, timestep: Union[float, paddle.Tensor], **kwargs
-    ) -> paddle.Tensor:
-        """
-        Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm.
-
-        Args:
-            sample (`paddle.Tensor`): input sample
-            timestep (`float` or `paddle.Tensor`): the current timestep in the diffusion chain
-
-        Returns:
-            `paddle.Tensor`: scaled input sample
-        """
-        if kwargs.get("step_index") is not None:
-            step_index = kwargs["step_index"]
-        else:
-            step_index = (self.timesteps == timestep).nonzero().item()
-        self.is_scale_input_called = True
-        if not self.preconfig:
-            sigma = self.sigmas[step_index]
-            sample = sample / ((sigma**2 + 1) ** 0.5)
-            return sample
-        else:
-            return sample * self.latent_scales[step_index]
-
-    def get_lms_coefficient(self, order, t, current_order):
-        """
-        Compute a linear multistep coefficient.
-
-        Args:
-            order (TODO):
-            t (TODO):
-            current_order (TODO):
-        """
-
-        def lms_derivative(tau):
-            prod = 1.0
-            for k in range(order):
-                if current_order == k:
-                    continue
-                prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
-            return prod
-
-        integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0]
-
-        return integrated_coeff
-
-    def set_timesteps(self, num_inference_steps: int, preconfig_order: int = 4):
-        """
-        Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-        """
-        self.num_inference_steps = num_inference_steps
-
-        timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
-        sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
-        sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
-        self.sigmas = paddle.to_tensor(sigmas)
-        self.timesteps = paddle.to_tensor(timesteps, dtype="float32")
-
-        self.derivatives = []
-        if self.preconfig:
-            self.order = preconfig_order
-            self.lms_coeffs = []
-            self.latent_scales = [1.0 / ((sigma**2 + 1) ** 0.5) for sigma in self.sigmas]
-            for step_index in range(self.num_inference_steps):
-                order = min(step_index + 1, preconfig_order)
-                self.lms_coeffs.append(
-                    [self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)]
-                )
-
-    def step(
-        self,
-        model_output: paddle.Tensor,
-        timestep: Union[float, paddle.Tensor],
-        sample: paddle.Tensor,
-        order: int = 4,
-        return_dict: bool = True,
-        **kwargs
-    ) -> Union[PreconfigLMSDiscreteSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned diffusion model.
-            timestep (`float`): current timestep in the diffusion chain.
-            sample (`paddle.Tensor`):
-                current instance of sample being created by diffusion process.
-            order: coefficient for multi-step inference.
-            return_dict (`bool`): option for returning tuple rather than PreconfigLMSDiscreteSchedulerOutput class
-            Args in kwargs:
-                step_index (`int`):
-                return_pred_original_sample (`bool`): option for return pred_original_sample
-
-        Returns:
-            [`~schedulers.scheduling_utils.PreconfigLMSDiscreteSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.PreconfigLMSDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.
-            When returning a tuple, the first element is the sample tensor.
-
-        """
-        if not self.is_scale_input_called:
-            warnings.warn(
-                "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
-                "See `StableDiffusionPipeline` for a usage example."
-            )
-        if kwargs.get("return_pred_original_sample") is not None:
-            return_pred_original_sample = kwargs["return_pred_original_sample"]
-        else:
-            return_pred_original_sample = True
-        if kwargs.get("step_index") is not None:
-            step_index = kwargs["step_index"]
-        else:
-            step_index = (self.timesteps == timestep).nonzero().item()
-        if self.config.prediction_type == "epsilon" and not return_pred_original_sample:
-            # if pred_original_sample is no need
-            self.derivatives.append(model_output)
-            pred_original_sample = None
-        else:
-            sigma = self.sigmas[step_index]
-            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
-            if self.config.prediction_type == "epsilon":
-                pred_original_sample = sample - sigma * model_output
-            elif self.config.prediction_type == "v_prediction":
-                # * c_out + input * c_skip
-                pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
-            else:
-                raise ValueError(
-                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"
-                )
-            # 2. Convert to an ODE derivative
-            derivative = (sample - pred_original_sample) / sigma
-            self.derivatives.append(derivative)
-
-        if len(self.derivatives) > order:
-            self.derivatives.pop(0)
-
-        if not self.preconfig:
-            # 3. If not preconfiged, compute linear multistep coefficients.
-            order = min(step_index + 1, order)
-            lms_coeffs = [self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)]
-            # 4. Compute previous sample based on the derivatives path
-            prev_sample = sample + sum(
-                coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(self.derivatives))
-            )
-        else:
-            # 3. If preconfiged, direct compute previous sample based on the derivatives path
-            prev_sample = sample + sum(
-                coeff * derivative
-                for coeff, derivative in zip(self.lms_coeffs[step_index], reversed(self.derivatives))
-            )
-
-        if not return_dict:
-            if not return_pred_original_sample:
-                return (prev_sample,)
-            else:
-                return (prev_sample, pred_original_sample)
-
-        return PreconfigLMSDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
-
-    def add_noise(
-        self,
-        original_samples: paddle.Tensor,
-        noise: paddle.Tensor,
-        timesteps: paddle.Tensor,
-    ) -> paddle.Tensor:
-        # Make sure sigmas and timesteps have the same dtype as original_samples
-        sigmas = self.sigmas.cast(original_samples.dtype)
-        schedule_timesteps = self.timesteps
-
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
-
-        sigma = sigmas[step_indices].flatten()
-        while len(sigma.shape) < len(original_samples.shape):
-            sigma = sigma.unsqueeze(-1)
-
-        noisy_samples = original_samples + noise * sigma
-        return noisy_samples
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/ASJMO/freegpt/client/css/checkbox.css

@@ -1,55 +0,0 @@
-.checkbox input {
-	height: 0;
-	width: 0;
-	display: none;
-}
-
-.checkbox span {
-	font-size: 0.875rem;
-	color: var(--colour-2);
-	margin-left: 4px;
-}
-
-.checkbox label:after {
-	content: "";
-	position: absolute;
-	top: 50%;
-	transform: translateY(-50%);
-	left: 5px;
-	width: 20px;
-	height: 20px;
-	background: var(--blur-border);
-	border-radius: 90px;
-	transition: 0.33s;
-}
-
-.checkbox input + label:after,
-.checkbox input:checked + label {
-	background: var(--colour-3);
-}
-
-.checkbox input + label,
-.checkbox input:checked + label:after {
-	background: var(--blur-border);
-}
-
-.checkbox input:checked + label:after {
-	left: calc(100% - 5px - 20px);
-}
-
-@media screen and (max-width: 990px) {
-	.checkbox label {
-		width: 25px;
-		height: 15px;
-	}
-
-	.checkbox label:after {
-		left: 2px;
-		width: 10px;
-		height: 10px;
-	}
-
-	.checkbox input:checked + label:after {
-		left: calc(100% - 2px - 10px);
-	}
-}

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/admin/export/+server.ts

@@ -1,166 +0,0 @@
-import {
-	PARQUET_EXPORT_DATASET,
-	PARQUET_EXPORT_HF_TOKEN,
-	PARQUET_EXPORT_SECRET,
-} from "$env/static/private";
-import { collections } from "$lib/server/database";
-import type { Message } from "$lib/types/Message";
-import { error } from "@sveltejs/kit";
-import { pathToFileURL } from "node:url";
-import { unlink } from "node:fs/promises";
-import { uploadFile } from "@huggingface/hub";
-import parquet from "parquetjs";
-import { z } from "zod";
-
-// Triger like this:
-// curl -X POST "http://localhost:5173/chat/admin/export" -H "Authorization: Bearer <PARQUET_EXPORT_SECRET>" -H "Content-Type: application/json" -d '{"model": "OpenAssistant/oasst-sft-6-llama-30b-xor"}'
-
-export async function POST({ request }) {
-	if (!PARQUET_EXPORT_SECRET || !PARQUET_EXPORT_DATASET || !PARQUET_EXPORT_HF_TOKEN) {
-		throw error(500, "Parquet export is not configured.");
-	}
-
-	if (request.headers.get("Authorization") !== `Bearer ${PARQUET_EXPORT_SECRET}`) {
-		throw error(403);
-	}
-
-	const { model } = z
-		.object({
-			model: z.string(),
-		})
-		.parse(await request.json());
-
-	const schema = new parquet.ParquetSchema({
-		title: { type: "UTF8" },
-		created_at: { type: "TIMESTAMP_MILLIS" },
-		updated_at: { type: "TIMESTAMP_MILLIS" },
-		messages: {
-			repeated: true,
-			fields: {
-				from: { type: "UTF8" },
-				content: { type: "UTF8" },
-				score: { type: "INT_8", optional: true },
-			},
-		},
-	});
-
-	const fileName = `/tmp/conversations-${new Date().toJSON().slice(0, 10)}-${Date.now()}.parquet`;
-
-	const writer = await parquet.ParquetWriter.openFile(schema, fileName);
-
-	let count = 0;
-	console.log("Exporting conversations for model", model);
-
-	for await (const conversation of collections.settings.aggregate<{
-		title: string;
-		created_at: Date;
-		updated_at: Date;
-		messages: Message[];
-	}>([
-		{
-			$match: {
-				shareConversationsWithModelAuthors: true,
-				sessionId: { $exists: true },
-				userId: { $exists: false },
-			},
-		},
-		{
-			$lookup: {
-				from: "conversations",
-				localField: "sessionId",
-				foreignField: "sessionId",
-				as: "conversations",
-				pipeline: [{ $match: { model, userId: { $exists: false } } }],
-			},
-		},
-		{ $unwind: "$conversations" },
-		{
-			$project: {
-				title: "$conversations.title",
-				created_at: "$conversations.createdAt",
-				updated_at: "$conversations.updatedAt",
-				messages: "$conversations.messages",
-			},
-		},
-	])) {
-		await writer.appendRow({
-			title: conversation.title,
-			created_at: conversation.created_at,
-			updated_at: conversation.updated_at,
-			messages: conversation.messages.map((message: Message) => ({
-				from: message.from,
-				content: message.content,
-				...(message.score ? { score: message.score } : undefined),
-			})),
-		});
-		++count;
-
-		if (count % 1_000 === 0) {
-			console.log("Exported", count, "conversations");
-		}
-	}
-
-	console.log("exporting convos with userId");
-
-	for await (const conversation of collections.settings.aggregate<{
-		title: string;
-		created_at: Date;
-		updated_at: Date;
-		messages: Message[];
-	}>([
-		{ $match: { shareConversationsWithModelAuthors: true, userId: { $exists: true } } },
-		{
-			$lookup: {
-				from: "conversations",
-				localField: "userId",
-				foreignField: "userId",
-				as: "conversations",
-				pipeline: [{ $match: { model } }],
-			},
-		},
-		{ $unwind: "$conversations" },
-		{
-			$project: {
-				title: "$conversations.title",
-				created_at: "$conversations.createdAt",
-				updated_at: "$conversations.updatedAt",
-				messages: "$conversations.messages",
-			},
-		},
-	])) {
-		await writer.appendRow({
-			title: conversation.title,
-			created_at: conversation.created_at,
-			updated_at: conversation.updated_at,
-			messages: conversation.messages.map((message: Message) => ({
-				from: message.from,
-				content: message.content,
-				...(message.score ? { score: message.score } : undefined),
-			})),
-		});
-		++count;
-
-		if (count % 1_000 === 0) {
-			console.log("Exported", count, "conversations");
-		}
-	}
-
-	await writer.close();
-
-	console.log("Uploading", fileName, "to Hugging Face Hub");
-
-	await uploadFile({
-		file: pathToFileURL(fileName),
-		credentials: { accessToken: PARQUET_EXPORT_HF_TOKEN },
-		repo: {
-			type: "dataset",
-			name: PARQUET_EXPORT_DATASET,
-		},
-	});
-
-	console.log("Upload done");
-
-	await unlink(fileName);
-
-	return new Response();
-}

spaces/AdamGustavsson/AnimeganV2Webcam/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("spaces/akhaliq/AnimeGANv2", inputs="webcam").launch()

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/sde_team.py

@@ -1,72 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, List
-
-from agentverse.message import Message
-
-from . import selector_registry as SelectorRegistry
-from .base import BaseSelector
-
-import json
-import re
-
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-    
-def extract(content: str, keyword: str):
-    result = ""
-    flag = False
-    for line in content.split('\n'):
-        if line.strip().startswith(keyword):
-            flag = True
-            continue
-        if flag:
-            result += line
-            result += "\n"
-    return result
-        
-    
-@SelectorRegistry.register("sde_team")
-class SdeTeamSelector(BaseSelector):
-    def select_message(self, environment: BaseEnvironment, messages: List[Message]) -> List[Message]:
-        last_sender = environment.last_messages[0].sender
-        selected = messages
-
-        if last_sender == "unit_test_generator":
-            unit_tests = set()
-            for message in selected:
-                unit_test = extract(message.content, "<unit test>:")
-                if unit_test not in unit_tests:
-                    unit_tests.add(extract(message.content, "<unit test>:"))
-            unit_tests = list(unit_tests)
-            environment.rule_params["unit_tests"] = str(unit_tests)
-            new_message = Message(
-                            content="",
-                            sender="unit_test_generator",
-                            receiver=[],
-                        )   # TODO: set the content of the message
-            selected = [new_message]
-
-        elif last_sender == "code_writer":
-            cur_code = extract(selected[0].content, "<code>:")
-            environment.rule_params["code"] = cur_code
-            
-            from .code_api import execute_unit_tests
-            feedback = execute_unit_tests(environment.rule_params["code"], eval(environment.rule_params["unit_tests"]))
-            
-            environment.rule_params["feedback"] = feedback
-            selected[0].content = f"<current code>:\n\n{cur_code}\n\n<unit test feedback>:\n{feedback}"
-            f_dict = json.loads(feedback)
-            if f_dict["is_passing"]:
-                environment.rule_params["end_flag"] = True
-        
-        elif last_sender == "code_reviewer":
-            code_review = selected[0].content
-            cur_code = environment.rule_params["code"]
-            selected[0].content = f"<current code>:\n\n{cur_code}\n\n{code_review}"
-            feedback = environment.rule_params["feedback"]
-            f_dict = json.loads(feedback)
-            if f_dict["is_passing"]:
-                environment.rule_params["end_flag"] = True
-
-        return selected

spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/plan.py

@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from logging import getLogger
-from typing import List, TYPE_CHECKING
-
-from . import memory_manipulator_registry
-from .base import BaseMemoryManipulator
-from ..message import Message
-
-if TYPE_CHECKING:
-    from agentverse.memory import VectorStoreMemory
-    from agentverse.agents.reflection_agent import ReflectionAgent
-
-logger = getLogger(__file__)
-
-PLAN_PROMPT = """Now you are act for as an agent named ${agent_name} in a virtual world.
-You might need to performing reaction to the observation.
-Based on the following information:
-(1) The agent's description: ${role_description}
-(2) Current time is ${current_time}
-(3) Your history memory is ${chat_history}
-
-Now is ${current_time}. If all plans are expired, you have to plan for\
-the next time periods.
-Do you need to generate new plans?
-If yes, tell me the new plan, including the time period.
-If no, just tell me No."""
-
-
-@memory_manipulator_registry.register("plan")
-class Plan(BaseMemoryManipulator):
-    """
-    Memory manipulator for plan.
-    """
-    memory: VectorStoreMemory = None
-    agent: ReflectionAgent = None  # specify ReflectionAgent
-    # later considering removing current_time to be more general
-    # and then change to BaseAgent
-    plan: List[str] = []
-
-    def manipulate_memory(self) -> str:
-        """
-        Generate new plans
-        """
-        prompt = self._fill_prompt_template()
-        result = self.agent.llm.generate_response(prompt).content
-        result = result.strip('.')
-        logger.info(f"{self.agent.name}'s new plan: {result}")
-        if result == "No":
-            return ""
-        else:
-            self.plan.append(result)
-            plan_message = Message(
-                content=result,
-                sender=self.agent.name,
-                receiver={self.agent.name})
-            self.agent.memory.add_message([plan_message])
-            return result
-
-
-    def _fill_prompt_template(self) -> str:
-        """Fill the placeholders in the prompt template
-
-        In the conversation agent, three placeholders are supported:
-        - ${agent_name}: the name of the agent
-        - ${env_description}: the description of the environment
-        - ${role_description}: the description of the role of the agent
-        - ${chat_history}: the chat history of the agent
-        """
-        input_arguments = {
-            "agent_name": self.agent.name,
-            "role_description": self.agent.role_description,
-            "chat_history": self.agent.memory.to_string(add_sender_prefix=True),
-            "current_time": self.agent.current_time,
-        }
-        return PLAN_PROMPT.format(**input_arguments)
-
-    def reset(self) -> None:
-        pass

spaces/AhmedM20/Email_Marketing_Content_Generator/README.md

@@ -1,12 +0,0 @@
----
-title: Email Marketing Geneartor
-emoji: 🏃
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.41.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexWang/lama/bin/paper_runfiles/blur_tests.sh

@@ -1,37 +0,0 @@
-##!/usr/bin/env bash
-#
-## !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst
-#
-## paths to data are valid for mml7
-#PLACES_ROOT="/data/inpainting/Places365"
-#OUT_DIR="/data/inpainting/paper_data/Places365_val_test"
-#
-#source "$(dirname $0)/env.sh"
-#
-#for datadir in test_large_30k  # val_large
-#do
-#    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-#    do
-#        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-#            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8
-#
-#        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-#    done
-#
-#    for conf in segm_256 segm_512
-#    do
-#        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-#            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2
-#
-#        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-#    done
-#done
-#
-#IN_DIR="/data/inpainting/paper_data/Places365_val_test/test_large_30k/random_medium_512"
-#PRED_DIR="/data/inpainting/predictions/final/images/r.suvorov_2021-03-05_17-08-35_train_ablv2_work_resume_epoch37/random_medium_512"
-#BLUR_OUT_DIR="/data/inpainting/predictions/final/blur/images"
-#
-#for b in 0.1
-#
-#"$BINDIR/blur_predicts.py" "$BASEDIR/../../configs/eval2.yaml" "$CUR_IN_DIR" "$CUR_OUT_DIR" "$CUR_EVAL_DIR"
-#

spaces/AlexZou/Deploy_Restoration/README.md

@@ -1,13 +0,0 @@
----
-title: Deploy Restoration
-emoji: 👀
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlignmentResearch/tuned-lens/README.md

@@ -1,9 +0,0 @@
----
-title: Tuned Lens
-emoji: 🔎
-colorFrom: pink
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----

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

@@ -1,22 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Inverse Multistep DPM-Solver (DPMSolverMultistepInverse)
-
-## Overview
-
-This scheduler is the inverted scheduler of [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://arxiv.org/abs/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
-](https://arxiv.org/abs/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
-The implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/pdf/2211.09794.pdf) and the ad-hoc notebook implementation for DiffEdit latent inversion [here](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb).
-
-## DPMSolverMultistepInverseScheduler
-[[autodoc]] DPMSolverMultistepInverseScheduler

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/imagic_stable_diffusion.py

@@ -1,496 +0,0 @@
-"""
-    modeled after the textual_inversion.py / train_dreambooth.py and the work
-    of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
-"""
-import inspect
-import warnings
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-from accelerate import Accelerator
-
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from tqdm.auto import tqdm
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import DiffusionPipeline
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers.utils import logging
-
-
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.Resampling.BILINEAR,
-        "bilinear": PIL.Image.Resampling.BILINEAR,
-        "bicubic": PIL.Image.Resampling.BICUBIC,
-        "lanczos": PIL.Image.Resampling.LANCZOS,
-        "nearest": PIL.Image.Resampling.NEAREST,
-    }
-else:
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.LINEAR,
-        "bilinear": PIL.Image.BILINEAR,
-        "bicubic": PIL.Image.BICUBIC,
-        "lanczos": PIL.Image.LANCZOS,
-        "nearest": PIL.Image.NEAREST,
-    }
-# ------------------------------------------------------------------------------
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def preprocess(image):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
-
-
-class ImagicStableDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for imagic image editing.
-    See paper here: https://arxiv.org/pdf/2210.09276.pdf
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offsensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-    ):
-        super().__init__()
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    def train(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image],
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        generator: Optional[torch.Generator] = None,
-        embedding_learning_rate: float = 0.001,
-        diffusion_model_learning_rate: float = 2e-6,
-        text_embedding_optimization_steps: int = 500,
-        model_fine_tuning_optimization_steps: int = 1000,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        accelerator = Accelerator(
-            gradient_accumulation_steps=1,
-            mixed_precision="fp16",
-        )
-
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # Freeze vae and unet
-        self.vae.requires_grad_(False)
-        self.unet.requires_grad_(False)
-        self.text_encoder.requires_grad_(False)
-        self.unet.eval()
-        self.vae.eval()
-        self.text_encoder.eval()
-
-        if accelerator.is_main_process:
-            accelerator.init_trackers(
-                "imagic",
-                config={
-                    "embedding_learning_rate": embedding_learning_rate,
-                    "text_embedding_optimization_steps": text_embedding_optimization_steps,
-                },
-            )
-
-        # get text embeddings for prompt
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_embeddings = torch.nn.Parameter(
-            self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True
-        )
-        text_embeddings = text_embeddings.detach()
-        text_embeddings.requires_grad_()
-        text_embeddings_orig = text_embeddings.clone()
-
-        # Initialize the optimizer
-        optimizer = torch.optim.Adam(
-            [text_embeddings],  # only optimize the embeddings
-            lr=embedding_learning_rate,
-        )
-
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
-
-        latents_dtype = text_embeddings.dtype
-        image = image.to(device=self.device, dtype=latents_dtype)
-        init_latent_image_dist = self.vae.encode(image).latent_dist
-        image_latents = init_latent_image_dist.sample(generator=generator)
-        image_latents = 0.18215 * image_latents
-
-        progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)
-        progress_bar.set_description("Steps")
-
-        global_step = 0
-
-        logger.info("First optimizing the text embedding to better reconstruct the init image")
-        for _ in range(text_embedding_optimization_steps):
-            with accelerator.accumulate(text_embeddings):
-                # Sample noise that we'll add to the latents
-                noise = torch.randn(image_latents.shape).to(image_latents.device)
-                timesteps = torch.randint(1000, (1,), device=image_latents.device)
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
-
-                # Predict the noise residual
-                noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
-
-                loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
-                accelerator.backward(loss)
-
-                optimizer.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-            logs = {"loss": loss.detach().item()}  # , "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-        accelerator.wait_for_everyone()
-
-        text_embeddings.requires_grad_(False)
-
-        # Now we fine tune the unet to better reconstruct the image
-        self.unet.requires_grad_(True)
-        self.unet.train()
-        optimizer = torch.optim.Adam(
-            self.unet.parameters(),  # only optimize unet
-            lr=diffusion_model_learning_rate,
-        )
-        progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)
-
-        logger.info("Next fine tuning the entire model to better reconstruct the init image")
-        for _ in range(model_fine_tuning_optimization_steps):
-            with accelerator.accumulate(self.unet.parameters()):
-                # Sample noise that we'll add to the latents
-                noise = torch.randn(image_latents.shape).to(image_latents.device)
-                timesteps = torch.randint(1000, (1,), device=image_latents.device)
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
-
-                # Predict the noise residual
-                noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
-
-                loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
-                accelerator.backward(loss)
-
-                optimizer.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-            logs = {"loss": loss.detach().item()}  # , "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-        accelerator.wait_for_everyone()
-        self.text_embeddings_orig = text_embeddings_orig
-        self.text_embeddings = text_embeddings
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        alpha: float = 1.2,
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        num_inference_steps: Optional[int] = 50,
-        generator: Optional[torch.Generator] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        guidance_scale: float = 7.5,
-        eta: float = 0.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-        if self.text_embeddings is None:
-            raise ValueError("Please run the pipe.train() before trying to generate an image.")
-        if self.text_embeddings_orig is None:
-            raise ValueError("Please run the pipe.train() before trying to generate an image.")
-
-        text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens = [""]
-            max_length = self.tokenizer.model_max_length
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        # get the initial random noise unless the user supplied it
-
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        latents_shape = (1, self.unet.config.in_channels, height // 8, width // 8)
-        latents_dtype = text_embeddings.dtype
-        if self.device.type == "mps":
-            # randn does not exist on mps
-            latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
-                self.device
-            )
-        else:
-            latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimized to move all timesteps to correct device beforehand
-        timesteps_tensor = self.scheduler.timesteps.to(self.device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
-                self.device
-            )
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
-            )
-        else:
-            has_nsfw_concept = None
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md

@@ -1,50 +0,0 @@
-## Training examples
-
-Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder  and run
-```bash
-pip install -r requirements.txt
-```
-
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-#### Use ONNXRuntime to accelerate training
-
-In order to leverage onnxruntime to accelerate training, please use train_unconditional_ort.py
-
-The command to train a DDPM UNet model on the Oxford Flowers dataset with onnxruntime:
-
-```bash
-accelerate launch train_unconditional.py \
-  --dataset_name="huggan/flowers-102-categories" \
-  --resolution=64 --center_crop --random_flip \
-  --output_dir="ddpm-ema-flowers-64" \
-  --use_ema \
-  --train_batch_size=16 \
-  --num_epochs=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=1e-4 \
-  --lr_warmup_steps=500 \
-  --mixed_precision=fp16
-  ```
-
-Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_repaint.py

@@ -1,344 +0,0 @@
-# Copyright 2023 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, randn_tensor
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-class RePaintSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from
-             the current timestep. `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    pred_original_sample: torch.FloatTensor
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-class RePaintScheduler(SchedulerMixin, ConfigMixin):
-    """
-    RePaint is a schedule for DDPM inpainting inside a given mask.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, `squaredcos_cap_v2` or `sigmoid`.
-        eta (`float`):
-            The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and
-            1.0 is DDPM scheduler respectively.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        variance_type (`str`):
-            options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
-            `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample between -1 and 1 for numerical stability.
-
-    """
-
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        eta: float = 0.0,
-        trained_betas: Optional[np.ndarray] = None,
-        clip_sample: bool = True,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        elif beta_schedule == "sigmoid":
-            # GeoDiff sigmoid schedule
-            betas = torch.linspace(-6, 6, num_train_timesteps)
-            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-        self.one = torch.tensor(1.0)
-
-        self.final_alpha_cumprod = torch.tensor(1.0)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
-
-        self.eta = eta
-
-    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(
-        self,
-        num_inference_steps: int,
-        jump_length: int = 10,
-        jump_n_sample: int = 10,
-        device: Union[str, torch.device] = None,
-    ):
-        num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
-        self.num_inference_steps = num_inference_steps
-
-        timesteps = []
-
-        jumps = {}
-        for j in range(0, num_inference_steps - jump_length, jump_length):
-            jumps[j] = jump_n_sample - 1
-
-        t = num_inference_steps
-        while t >= 1:
-            t = t - 1
-            timesteps.append(t)
-
-            if jumps.get(t, 0) > 0:
-                jumps[t] = jumps[t] - 1
-                for _ in range(jump_length):
-                    t = t + 1
-                    timesteps.append(t)
-
-        timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    def _get_variance(self, t):
-        prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps
-
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # For t > 0, compute predicted variance βt (see formula (6) and (7) from
-        # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
-        # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
-        # variance to pred_sample
-        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
-        # without eta.
-        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
-        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
-        return variance
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        original_image: torch.FloatTensor,
-        mask: torch.FloatTensor,
-        generator: Optional[torch.Generator] = None,
-        return_dict: bool = True,
-    ) -> Union[RePaintSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned
-                diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            original_image (`torch.FloatTensor`):
-                the original image to inpaint on.
-            mask (`torch.FloatTensor`):
-                the mask where 0.0 values define which part of the original image to inpaint (change).
-            generator (`torch.Generator`, *optional*): random number generator.
-            return_dict (`bool`): option for returning tuple rather than
-                DDPMSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        t = timestep
-        prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-
-        # 1. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
-
-        # 3. Clip "predicted x_0"
-        if self.config.clip_sample:
-            pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
-
-        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
-        # substitute formula (7) in the algorithm coming from DDPM paper
-        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
-        # DDIM schedule gives the same results as DDPM with eta = 1.0
-        # Noise is being reused in 7. and 8., but no impact on quality has
-        # been observed.
-
-        # 5. Add noise
-        device = model_output.device
-        noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)
-        std_dev_t = self.eta * self._get_variance(timestep) ** 0.5
-
-        variance = 0
-        if t > 0 and self.eta > 0:
-            variance = std_dev_t * noise
-
-        # 6. compute "direction pointing to x_t" of formula (12)
-        # from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
-
-        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
-
-        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
-        prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
-
-        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
-        pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part
-
-        if not return_dict:
-            return (
-                pred_prev_sample,
-                pred_original_sample,
-            )
-
-        return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
-
-    def undo_step(self, sample, timestep, generator=None):
-        n = self.config.num_train_timesteps // self.num_inference_steps
-
-        for i in range(n):
-            beta = self.betas[timestep + i]
-            if sample.device.type == "mps":
-                # randn does not work reproducibly on mps
-                noise = randn_tensor(sample.shape, dtype=sample.dtype, generator=generator)
-                noise = noise.to(sample.device)
-            else:
-                noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
-
-            # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
-            sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise
-
-        return sample
-
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.")
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py

@@ -1,86 +0,0 @@
-_base_ = [
-    '../_base_/models/cascade_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# model settings
-model = dict(
-    roi_head=dict(bbox_head=[
-        dict(
-            type='SABLHead',
-            num_classes=80,
-            cls_in_channels=256,
-            reg_in_channels=256,
-            roi_feat_size=7,
-            reg_feat_up_ratio=2,
-            reg_pre_kernel=3,
-            reg_post_kernel=3,
-            reg_pre_num=2,
-            reg_post_num=1,
-            cls_out_channels=1024,
-            reg_offset_out_channels=256,
-            reg_cls_out_channels=256,
-            num_cls_fcs=1,
-            num_reg_fcs=0,
-            reg_class_agnostic=True,
-            norm_cfg=None,
-            bbox_coder=dict(
-                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
-                               loss_weight=1.0)),
-        dict(
-            type='SABLHead',
-            num_classes=80,
-            cls_in_channels=256,
-            reg_in_channels=256,
-            roi_feat_size=7,
-            reg_feat_up_ratio=2,
-            reg_pre_kernel=3,
-            reg_post_kernel=3,
-            reg_pre_num=2,
-            reg_post_num=1,
-            cls_out_channels=1024,
-            reg_offset_out_channels=256,
-            reg_cls_out_channels=256,
-            num_cls_fcs=1,
-            num_reg_fcs=0,
-            reg_class_agnostic=True,
-            norm_cfg=None,
-            bbox_coder=dict(
-                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
-                               loss_weight=1.0)),
-        dict(
-            type='SABLHead',
-            num_classes=80,
-            cls_in_channels=256,
-            reg_in_channels=256,
-            roi_feat_size=7,
-            reg_feat_up_ratio=2,
-            reg_pre_kernel=3,
-            reg_post_kernel=3,
-            reg_pre_num=2,
-            reg_post_num=1,
-            cls_out_channels=1024,
-            reg_offset_out_channels=256,
-            reg_cls_out_channels=256,
-            num_cls_fcs=1,
-            num_reg_fcs=0,
-            reg_class_agnostic=True,
-            norm_cfg=None,
-            bbox_coder=dict(
-                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
-    ]))

spaces/Andy1621/uniformer_image_detection/exp/cascade_mask_rcnn_3x_ms_hybrid_small/config.py

@@ -1,142 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/cascade_mask_rcnn_uniformer_fpn.py',
-    '../../configs/_base_/datasets/coco_instance.py',
-    '../../configs/_base_/schedules/schedule_1x.py', 
-    '../../configs/_base_/default_runtime.py'
-]
-
-model = dict(
-    backbone=dict(
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        drop_path_rate=0.2,
-        use_checkpoint=True,
-        checkpoint_num=[0, 0, 8, 0],
-        windows=False,
-        hybrid=True,
-        window_size=14
-    ),
-    neck=dict(in_channels=[64, 128, 320, 512]),
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
-        ]))
-
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='AutoAugment',
-         policies=[
-             [
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                                 (736, 1333), (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True)
-             ],
-             [
-                 dict(type='Resize',
-                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True),
-                 dict(type='RandomCrop',
-                      crop_type='absolute_range',
-                      crop_size=(384, 600),
-                      allow_negative_crop=True),
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                 (576, 1333), (608, 1333), (640, 1333),
-                                 (672, 1333), (704, 1333), (736, 1333),
-                                 (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      override=True,
-                      keep_ratio=True)
-             ]
-         ]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
-    type="DistOptimizerHook",
-    update_interval=1,
-    grad_clip=None,
-    coalesce=True,
-    bucket_size_mb=-1,
-    use_fp16=True,
-)

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py

@@ -1,83 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch
-import torch.nn as nn
-from mmcv import ops
-
-
-class BaseRoIExtractor(nn.Module, metaclass=ABCMeta):
-    """Base class for RoI extractor.
-
-    Args:
-        roi_layer (dict): Specify RoI layer type and arguments.
-        out_channels (int): Output channels of RoI layers.
-        featmap_strides (List[int]): Strides of input feature maps.
-    """
-
-    def __init__(self, roi_layer, out_channels, featmap_strides):
-        super(BaseRoIExtractor, self).__init__()
-        self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides)
-        self.out_channels = out_channels
-        self.featmap_strides = featmap_strides
-        self.fp16_enabled = False
-
-    @property
-    def num_inputs(self):
-        """int: Number of input feature maps."""
-        return len(self.featmap_strides)
-
-    def init_weights(self):
-        pass
-
-    def build_roi_layers(self, layer_cfg, featmap_strides):
-        """Build RoI operator to extract feature from each level feature map.
-
-        Args:
-            layer_cfg (dict): Dictionary to construct and config RoI layer
-                operation. Options are modules under ``mmcv/ops`` such as
-                ``RoIAlign``.
-            featmap_strides (List[int]): The stride of input feature map w.r.t
-                to the original image size, which would be used to scale RoI
-                coordinate (original image coordinate system) to feature
-                coordinate system.
-
-        Returns:
-            nn.ModuleList: The RoI extractor modules for each level feature
-                map.
-        """
-
-        cfg = layer_cfg.copy()
-        layer_type = cfg.pop('type')
-        assert hasattr(ops, layer_type)
-        layer_cls = getattr(ops, layer_type)
-        roi_layers = nn.ModuleList(
-            [layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
-        return roi_layers
-
-    def roi_rescale(self, rois, scale_factor):
-        """Scale RoI coordinates by scale factor.
-
-        Args:
-            rois (torch.Tensor): RoI (Region of Interest), shape (n, 5)
-            scale_factor (float): Scale factor that RoI will be multiplied by.
-
-        Returns:
-            torch.Tensor: Scaled RoI.
-        """
-
-        cx = (rois[:, 1] + rois[:, 3]) * 0.5
-        cy = (rois[:, 2] + rois[:, 4]) * 0.5
-        w = rois[:, 3] - rois[:, 1]
-        h = rois[:, 4] - rois[:, 2]
-        new_w = w * scale_factor
-        new_h = h * scale_factor
-        x1 = cx - new_w * 0.5
-        x2 = cx + new_w * 0.5
-        y1 = cy - new_h * 0.5
-        y2 = cy + new_h * 0.5
-        new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1)
-        return new_rois
-
-    @abstractmethod
-    def forward(self, feats, rois, roi_scale_factor=None):
-        pass

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/default_runtime.py

@@ -1,14 +0,0 @@
-# yapf:disable
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook', by_epoch=False),
-        # dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-cudnn_benchmark = True

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

@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import time
-
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class IterTimerHook(Hook):
-
-    def before_epoch(self, runner):
-        self.t = time.time()
-
-    def before_iter(self, runner):
-        runner.log_buffer.update({'data_time': time.time() - self.t})
-
-    def after_iter(self, runner):
-        runner.log_buffer.update({'time': time.time() - self.t})
-        self.t = time.time()

spaces/Anonymous-sub/Rerender/ControlNet/gradio_annotator.py

@@ -1,160 +0,0 @@
-import gradio as gr
-
-from annotator.util import resize_image, HWC3
-
-
-model_canny = None
-
-
-def canny(img, res, l, h):
-    img = resize_image(HWC3(img), res)
-    global model_canny
-    if model_canny is None:
-        from annotator.canny import CannyDetector
-        model_canny = CannyDetector()
-    result = model_canny(img, l, h)
-    return [result]
-
-
-model_hed = None
-
-
-def hed(img, res):
-    img = resize_image(HWC3(img), res)
-    global model_hed
-    if model_hed is None:
-        from annotator.hed import HEDdetector
-        model_hed = HEDdetector()
-    result = model_hed(img)
-    return [result]
-
-
-model_mlsd = None
-
-
-def mlsd(img, res, thr_v, thr_d):
-    img = resize_image(HWC3(img), res)
-    global model_mlsd
-    if model_mlsd is None:
-        from annotator.mlsd import MLSDdetector
-        model_mlsd = MLSDdetector()
-    result = model_mlsd(img, thr_v, thr_d)
-    return [result]
-
-
-model_midas = None
-
-
-def midas(img, res, a):
-    img = resize_image(HWC3(img), res)
-    global model_midas
-    if model_midas is None:
-        from annotator.midas import MidasDetector
-        model_midas = MidasDetector()
-    results = model_midas(img, a)
-    return results
-
-
-model_openpose = None
-
-
-def openpose(img, res, has_hand):
-    img = resize_image(HWC3(img), res)
-    global model_openpose
-    if model_openpose is None:
-        from annotator.openpose import OpenposeDetector
-        model_openpose = OpenposeDetector()
-    result, _ = model_openpose(img, has_hand)
-    return [result]
-
-
-model_uniformer = None
-
-
-def uniformer(img, res):
-    img = resize_image(HWC3(img), res)
-    global model_uniformer
-    if model_uniformer is None:
-        from annotator.uniformer import UniformerDetector
-        model_uniformer = UniformerDetector()
-    result = model_uniformer(img)
-    return [result]
-
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Canny Edge")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1)
-            high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1)
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
-            run_button = gr.Button(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery])
-
-    with gr.Row():
-        gr.Markdown("## HED Edge")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
-            run_button = gr.Button(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=hed, inputs=[input_image, resolution], outputs=[gallery])
-
-    with gr.Row():
-        gr.Markdown("## MLSD Edge")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01)
-            distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1, step=0.01)
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
-            run_button = gr.Button(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery])
-
-    with gr.Row():
-        gr.Markdown("## MIDAS Depth and Normal")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            alpha = gr.Slider(label="alpha", minimum=0.1, maximum=20.0, value=6.2, step=0.01)
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
-            run_button = gr.Button(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=midas, inputs=[input_image, resolution, alpha], outputs=[gallery])
-
-    with gr.Row():
-        gr.Markdown("## Openpose")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            hand = gr.Checkbox(label='detect hand', value=False)
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
-            run_button = gr.Button(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=openpose, inputs=[input_image, resolution, hand], outputs=[gallery])
-
-
-    with gr.Row():
-        gr.Markdown("## Uniformer Segmentation")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(source='upload', type="numpy")
-            resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
-            run_button = gr.Button(label="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
-    run_button.click(fn=uniformer, inputs=[input_image, resolution], outputs=[gallery])
-
-
-block.launch(server_name='0.0.0.0')

spaces/Armandoliv/document_parser/README.md

@@ -1,13 +0,0 @@
----
-title: Document Parser
-emoji: 📈
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Arnx/MusicGenXvAKN/tests/utils/__init__.py

@@ -1,5 +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.

spaces/Artrajz/vits-simple-api/vits/text/english.py

@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('æ', 'e'),
-    ('ɑ', 'a'),
-    ('ɔ', 'o'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ɛ', 'e'),
-    ('ɪ', 'i'),
-    ('ʊ', 'u'),
-    ('ʒ', 'ʥ'),
-    ('ʤ', 'ʥ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ʒ', 'ʑ'),
-    ('ʤ', 'dʑ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ʤ', 'dʒ'),
-    ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def collapse_whitespace(text):
-    return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
-    return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
-    else:
-        return 'zero dollars'
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return 'two thousand'
-        elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
-        elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
-        else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
-    else:
-        return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
-
-
-def mark_dark_l(text):
-    return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
-    text = unidecode(text).lower()
-    text = expand_abbreviations(text)
-    text = normalize_numbers(text)
-    phonemes = ipa.convert(text)
-    phonemes = collapse_whitespace(phonemes)
-    return phonemes
-
-
-def english_to_lazy_ipa(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def english_to_ipa2(text):
-    text = english_to_ipa(text)
-    text = mark_dark_l(text)
-    for regex, replacement in _ipa_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text

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

@@ -1,260 +0,0 @@
-import logging
-import os
-import shlex
-import subprocess
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Iterable,
-    List,
-    Mapping,
-    Optional,
-    Union,
-)
-
-from pip._vendor.rich.markup import escape
-
-from pip._internal.cli.spinners import SpinnerInterface, open_spinner
-from pip._internal.exceptions import InstallationSubprocessError
-from pip._internal.utils.logging import VERBOSE, subprocess_logger
-from pip._internal.utils.misc import HiddenText
-
-if TYPE_CHECKING:
-    # Literal was introduced in Python 3.8.
-    #
-    # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7.
-    from typing import Literal
-
-CommandArgs = List[Union[str, HiddenText]]
-
-
-def make_command(*args: Union[str, HiddenText, CommandArgs]) -> CommandArgs:
-    """
-    Create a CommandArgs object.
-    """
-    command_args: CommandArgs = []
-    for arg in args:
-        # Check for list instead of CommandArgs since CommandArgs is
-        # only known during type-checking.
-        if isinstance(arg, list):
-            command_args.extend(arg)
-        else:
-            # Otherwise, arg is str or HiddenText.
-            command_args.append(arg)
-
-    return command_args
-
-
-def format_command_args(args: Union[List[str], CommandArgs]) -> str:
-    """
-    Format command arguments for display.
-    """
-    # For HiddenText arguments, display the redacted form by calling str().
-    # Also, we don't apply str() to arguments that aren't HiddenText since
-    # this can trigger a UnicodeDecodeError in Python 2 if the argument
-    # has type unicode and includes a non-ascii character.  (The type
-    # checker doesn't ensure the annotations are correct in all cases.)
-    return " ".join(
-        shlex.quote(str(arg)) if isinstance(arg, HiddenText) else shlex.quote(arg)
-        for arg in args
-    )
-
-
-def reveal_command_args(args: Union[List[str], CommandArgs]) -> List[str]:
-    """
-    Return the arguments in their raw, unredacted form.
-    """
-    return [arg.secret if isinstance(arg, HiddenText) else arg for arg in args]
-
-
-def call_subprocess(
-    cmd: Union[List[str], CommandArgs],
-    show_stdout: bool = False,
-    cwd: Optional[str] = None,
-    on_returncode: 'Literal["raise", "warn", "ignore"]' = "raise",
-    extra_ok_returncodes: Optional[Iterable[int]] = None,
-    extra_environ: Optional[Mapping[str, Any]] = None,
-    unset_environ: Optional[Iterable[str]] = None,
-    spinner: Optional[SpinnerInterface] = None,
-    log_failed_cmd: Optional[bool] = True,
-    stdout_only: Optional[bool] = False,
-    *,
-    command_desc: str,
-) -> str:
-    """
-    Args:
-      show_stdout: if true, use INFO to log the subprocess's stderr and
-        stdout streams.  Otherwise, use DEBUG.  Defaults to False.
-      extra_ok_returncodes: an iterable of integer return codes that are
-        acceptable, in addition to 0. Defaults to None, which means [].
-      unset_environ: an iterable of environment variable names to unset
-        prior to calling subprocess.Popen().
-      log_failed_cmd: if false, failed commands are not logged, only raised.
-      stdout_only: if true, return only stdout, else return both. When true,
-        logging of both stdout and stderr occurs when the subprocess has
-        terminated, else logging occurs as subprocess output is produced.
-    """
-    if extra_ok_returncodes is None:
-        extra_ok_returncodes = []
-    if unset_environ is None:
-        unset_environ = []
-    # Most places in pip use show_stdout=False. What this means is--
-    #
-    # - We connect the child's output (combined stderr and stdout) to a
-    #   single pipe, which we read.
-    # - We log this output to stderr at DEBUG level as it is received.
-    # - If DEBUG logging isn't enabled (e.g. if --verbose logging wasn't
-    #   requested), then we show a spinner so the user can still see the
-    #   subprocess is in progress.
-    # - If the subprocess exits with an error, we log the output to stderr
-    #   at ERROR level if it hasn't already been displayed to the console
-    #   (e.g. if --verbose logging wasn't enabled).  This way we don't log
-    #   the output to the console twice.
-    #
-    # If show_stdout=True, then the above is still done, but with DEBUG
-    # replaced by INFO.
-    if show_stdout:
-        # Then log the subprocess output at INFO level.
-        log_subprocess: Callable[..., None] = subprocess_logger.info
-        used_level = logging.INFO
-    else:
-        # Then log the subprocess output using VERBOSE.  This also ensures
-        # it will be logged to the log file (aka user_log), if enabled.
-        log_subprocess = subprocess_logger.verbose
-        used_level = VERBOSE
-
-    # Whether the subprocess will be visible in the console.
-    showing_subprocess = subprocess_logger.getEffectiveLevel() <= used_level
-
-    # Only use the spinner if we're not showing the subprocess output
-    # and we have a spinner.
-    use_spinner = not showing_subprocess and spinner is not None
-
-    log_subprocess("Running command %s", command_desc)
-    env = os.environ.copy()
-    if extra_environ:
-        env.update(extra_environ)
-    for name in unset_environ:
-        env.pop(name, None)
-    try:
-        proc = subprocess.Popen(
-            # Convert HiddenText objects to the underlying str.
-            reveal_command_args(cmd),
-            stdin=subprocess.PIPE,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT if not stdout_only else subprocess.PIPE,
-            cwd=cwd,
-            env=env,
-            errors="backslashreplace",
-        )
-    except Exception as exc:
-        if log_failed_cmd:
-            subprocess_logger.critical(
-                "Error %s while executing command %s",
-                exc,
-                command_desc,
-            )
-        raise
-    all_output = []
-    if not stdout_only:
-        assert proc.stdout
-        assert proc.stdin
-        proc.stdin.close()
-        # In this mode, stdout and stderr are in the same pipe.
-        while True:
-            line: str = proc.stdout.readline()
-            if not line:
-                break
-            line = line.rstrip()
-            all_output.append(line + "\n")
-
-            # Show the line immediately.
-            log_subprocess(line)
-            # Update the spinner.
-            if use_spinner:
-                assert spinner
-                spinner.spin()
-        try:
-            proc.wait()
-        finally:
-            if proc.stdout:
-                proc.stdout.close()
-        output = "".join(all_output)
-    else:
-        # In this mode, stdout and stderr are in different pipes.
-        # We must use communicate() which is the only safe way to read both.
-        out, err = proc.communicate()
-        # log line by line to preserve pip log indenting
-        for out_line in out.splitlines():
-            log_subprocess(out_line)
-        all_output.append(out)
-        for err_line in err.splitlines():
-            log_subprocess(err_line)
-        all_output.append(err)
-        output = out
-
-    proc_had_error = proc.returncode and proc.returncode not in extra_ok_returncodes
-    if use_spinner:
-        assert spinner
-        if proc_had_error:
-            spinner.finish("error")
-        else:
-            spinner.finish("done")
-    if proc_had_error:
-        if on_returncode == "raise":
-            error = InstallationSubprocessError(
-                command_description=command_desc,
-                exit_code=proc.returncode,
-                output_lines=all_output if not showing_subprocess else None,
-            )
-            if log_failed_cmd:
-                subprocess_logger.error("[present-rich] %s", error)
-                subprocess_logger.verbose(
-                    "[bold magenta]full command[/]: [blue]%s[/]",
-                    escape(format_command_args(cmd)),
-                    extra={"markup": True},
-                )
-                subprocess_logger.verbose(
-                    "[bold magenta]cwd[/]: %s",
-                    escape(cwd or "[inherit]"),
-                    extra={"markup": True},
-                )
-
-            raise error
-        elif on_returncode == "warn":
-            subprocess_logger.warning(
-                'Command "%s" had error code %s in %s',
-                command_desc,
-                proc.returncode,
-                cwd,
-            )
-        elif on_returncode == "ignore":
-            pass
-        else:
-            raise ValueError(f"Invalid value: on_returncode={on_returncode!r}")
-    return output
-
-
-def runner_with_spinner_message(message: str) -> Callable[..., None]:
-    """Provide a subprocess_runner that shows a spinner message.
-
-    Intended for use with for BuildBackendHookCaller. Thus, the runner has
-    an API that matches what's expected by BuildBackendHookCaller.subprocess_runner.
-    """
-
-    def runner(
-        cmd: List[str],
-        cwd: Optional[str] = None,
-        extra_environ: Optional[Mapping[str, Any]] = None,
-    ) -> None:
-        with open_spinner(message) as spinner:
-            call_subprocess(
-                cmd,
-                command_desc=message,
-                cwd=cwd,
-                extra_environ=extra_environ,
-                spinner=spinner,
-            )
-
-    return runner

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

@@ -1,705 +0,0 @@
-"""Handles all VCS (version control) support"""
-
-import logging
-import os
-import shutil
-import sys
-import urllib.parse
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Tuple,
-    Type,
-    Union,
-)
-
-from pip._internal.cli.spinners import SpinnerInterface
-from pip._internal.exceptions import BadCommand, InstallationError
-from pip._internal.utils.misc import (
-    HiddenText,
-    ask_path_exists,
-    backup_dir,
-    display_path,
-    hide_url,
-    hide_value,
-    is_installable_dir,
-    rmtree,
-)
-from pip._internal.utils.subprocess import (
-    CommandArgs,
-    call_subprocess,
-    format_command_args,
-    make_command,
-)
-from pip._internal.utils.urls import get_url_scheme
-
-if TYPE_CHECKING:
-    # Literal was introduced in Python 3.8.
-    #
-    # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7.
-    from typing import Literal
-
-
-__all__ = ["vcs"]
-
-
-logger = logging.getLogger(__name__)
-
-AuthInfo = Tuple[Optional[str], Optional[str]]
-
-
-def is_url(name: str) -> bool:
-    """
-    Return true if the name looks like a URL.
-    """
-    scheme = get_url_scheme(name)
-    if scheme is None:
-        return False
-    return scheme in ["http", "https", "file", "ftp"] + vcs.all_schemes
-
-
-def make_vcs_requirement_url(
-    repo_url: str, rev: str, project_name: str, subdir: Optional[str] = None
-) -> str:
-    """
-    Return the URL for a VCS requirement.
-
-    Args:
-      repo_url: the remote VCS url, with any needed VCS prefix (e.g. "git+").
-      project_name: the (unescaped) project name.
-    """
-    egg_project_name = project_name.replace("-", "_")
-    req = f"{repo_url}@{rev}#egg={egg_project_name}"
-    if subdir:
-        req += f"&subdirectory={subdir}"
-
-    return req
-
-
-def find_path_to_project_root_from_repo_root(
-    location: str, repo_root: str
-) -> Optional[str]:
-    """
-    Find the the Python project's root by searching up the filesystem from
-    `location`. Return the path to project root relative to `repo_root`.
-    Return None if the project root is `repo_root`, or cannot be found.
-    """
-    # find project root.
-    orig_location = location
-    while not is_installable_dir(location):
-        last_location = location
-        location = os.path.dirname(location)
-        if location == last_location:
-            # We've traversed up to the root of the filesystem without
-            # finding a Python project.
-            logger.warning(
-                "Could not find a Python project for directory %s (tried all "
-                "parent directories)",
-                orig_location,
-            )
-            return None
-
-    if os.path.samefile(repo_root, location):
-        return None
-
-    return os.path.relpath(location, repo_root)
-
-
-class RemoteNotFoundError(Exception):
-    pass
-
-
-class RemoteNotValidError(Exception):
-    def __init__(self, url: str):
-        super().__init__(url)
-        self.url = url
-
-
-class RevOptions:
-
-    """
-    Encapsulates a VCS-specific revision to install, along with any VCS
-    install options.
-
-    Instances of this class should be treated as if immutable.
-    """
-
-    def __init__(
-        self,
-        vc_class: Type["VersionControl"],
-        rev: Optional[str] = None,
-        extra_args: Optional[CommandArgs] = None,
-    ) -> None:
-        """
-        Args:
-          vc_class: a VersionControl subclass.
-          rev: the name of the revision to install.
-          extra_args: a list of extra options.
-        """
-        if extra_args is None:
-            extra_args = []
-
-        self.extra_args = extra_args
-        self.rev = rev
-        self.vc_class = vc_class
-        self.branch_name: Optional[str] = None
-
-    def __repr__(self) -> str:
-        return f"<RevOptions {self.vc_class.name}: rev={self.rev!r}>"
-
-    @property
-    def arg_rev(self) -> Optional[str]:
-        if self.rev is None:
-            return self.vc_class.default_arg_rev
-
-        return self.rev
-
-    def to_args(self) -> CommandArgs:
-        """
-        Return the VCS-specific command arguments.
-        """
-        args: CommandArgs = []
-        rev = self.arg_rev
-        if rev is not None:
-            args += self.vc_class.get_base_rev_args(rev)
-        args += self.extra_args
-
-        return args
-
-    def to_display(self) -> str:
-        if not self.rev:
-            return ""
-
-        return f" (to revision {self.rev})"
-
-    def make_new(self, rev: str) -> "RevOptions":
-        """
-        Make a copy of the current instance, but with a new rev.
-
-        Args:
-          rev: the name of the revision for the new object.
-        """
-        return self.vc_class.make_rev_options(rev, extra_args=self.extra_args)
-
-
-class VcsSupport:
-    _registry: Dict[str, "VersionControl"] = {}
-    schemes = ["ssh", "git", "hg", "bzr", "sftp", "svn"]
-
-    def __init__(self) -> None:
-        # Register more schemes with urlparse for various version control
-        # systems
-        urllib.parse.uses_netloc.extend(self.schemes)
-        super().__init__()
-
-    def __iter__(self) -> Iterator[str]:
-        return self._registry.__iter__()
-
-    @property
-    def backends(self) -> List["VersionControl"]:
-        return list(self._registry.values())
-
-    @property
-    def dirnames(self) -> List[str]:
-        return [backend.dirname for backend in self.backends]
-
-    @property
-    def all_schemes(self) -> List[str]:
-        schemes: List[str] = []
-        for backend in self.backends:
-            schemes.extend(backend.schemes)
-        return schemes
-
-    def register(self, cls: Type["VersionControl"]) -> None:
-        if not hasattr(cls, "name"):
-            logger.warning("Cannot register VCS %s", cls.__name__)
-            return
-        if cls.name not in self._registry:
-            self._registry[cls.name] = cls()
-            logger.debug("Registered VCS backend: %s", cls.name)
-
-    def unregister(self, name: str) -> None:
-        if name in self._registry:
-            del self._registry[name]
-
-    def get_backend_for_dir(self, location: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object if a repository of that type is found
-        at the given directory.
-        """
-        vcs_backends = {}
-        for vcs_backend in self._registry.values():
-            repo_path = vcs_backend.get_repository_root(location)
-            if not repo_path:
-                continue
-            logger.debug("Determine that %s uses VCS: %s", location, vcs_backend.name)
-            vcs_backends[repo_path] = vcs_backend
-
-        if not vcs_backends:
-            return None
-
-        # Choose the VCS in the inner-most directory. Since all repository
-        # roots found here would be either `location` or one of its
-        # parents, the longest path should have the most path components,
-        # i.e. the backend representing the inner-most repository.
-        inner_most_repo_path = max(vcs_backends, key=len)
-        return vcs_backends[inner_most_repo_path]
-
-    def get_backend_for_scheme(self, scheme: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object or None.
-        """
-        for vcs_backend in self._registry.values():
-            if scheme in vcs_backend.schemes:
-                return vcs_backend
-        return None
-
-    def get_backend(self, name: str) -> Optional["VersionControl"]:
-        """
-        Return a VersionControl object or None.
-        """
-        name = name.lower()
-        return self._registry.get(name)
-
-
-vcs = VcsSupport()
-
-
-class VersionControl:
-    name = ""
-    dirname = ""
-    repo_name = ""
-    # List of supported schemes for this Version Control
-    schemes: Tuple[str, ...] = ()
-    # Iterable of environment variable names to pass to call_subprocess().
-    unset_environ: Tuple[str, ...] = ()
-    default_arg_rev: Optional[str] = None
-
-    @classmethod
-    def should_add_vcs_url_prefix(cls, remote_url: str) -> bool:
-        """
-        Return whether the vcs prefix (e.g. "git+") should be added to a
-        repository's remote url when used in a requirement.
-        """
-        return not remote_url.lower().startswith(f"{cls.name}:")
-
-    @classmethod
-    def get_subdirectory(cls, location: str) -> Optional[str]:
-        """
-        Return the path to Python project root, relative to the repo root.
-        Return None if the project root is in the repo root.
-        """
-        return None
-
-    @classmethod
-    def get_requirement_revision(cls, repo_dir: str) -> str:
-        """
-        Return the revision string that should be used in a requirement.
-        """
-        return cls.get_revision(repo_dir)
-
-    @classmethod
-    def get_src_requirement(cls, repo_dir: str, project_name: str) -> str:
-        """
-        Return the requirement string to use to redownload the files
-        currently at the given repository directory.
-
-        Args:
-          project_name: the (unescaped) project name.
-
-        The return value has a form similar to the following:
-
-            {repository_url}@{revision}#egg={project_name}
-        """
-        repo_url = cls.get_remote_url(repo_dir)
-
-        if cls.should_add_vcs_url_prefix(repo_url):
-            repo_url = f"{cls.name}+{repo_url}"
-
-        revision = cls.get_requirement_revision(repo_dir)
-        subdir = cls.get_subdirectory(repo_dir)
-        req = make_vcs_requirement_url(repo_url, revision, project_name, subdir=subdir)
-
-        return req
-
-    @staticmethod
-    def get_base_rev_args(rev: str) -> List[str]:
-        """
-        Return the base revision arguments for a vcs command.
-
-        Args:
-          rev: the name of a revision to install.  Cannot be None.
-        """
-        raise NotImplementedError
-
-    def is_immutable_rev_checkout(self, url: str, dest: str) -> bool:
-        """
-        Return true if the commit hash checked out at dest matches
-        the revision in url.
-
-        Always return False, if the VCS does not support immutable commit
-        hashes.
-
-        This method does not check if there are local uncommitted changes
-        in dest after checkout, as pip currently has no use case for that.
-        """
-        return False
-
-    @classmethod
-    def make_rev_options(
-        cls, rev: Optional[str] = None, extra_args: Optional[CommandArgs] = None
-    ) -> RevOptions:
-        """
-        Return a RevOptions object.
-
-        Args:
-          rev: the name of a revision to install.
-          extra_args: a list of extra options.
-        """
-        return RevOptions(cls, rev, extra_args=extra_args)
-
-    @classmethod
-    def _is_local_repository(cls, repo: str) -> bool:
-        """
-        posix absolute paths start with os.path.sep,
-        win32 ones start with drive (like c:\\folder)
-        """
-        drive, tail = os.path.splitdrive(repo)
-        return repo.startswith(os.path.sep) or bool(drive)
-
-    @classmethod
-    def get_netloc_and_auth(
-        cls, netloc: str, scheme: str
-    ) -> Tuple[str, Tuple[Optional[str], Optional[str]]]:
-        """
-        Parse the repository URL's netloc, and return the new netloc to use
-        along with auth information.
-
-        Args:
-          netloc: the original repository URL netloc.
-          scheme: the repository URL's scheme without the vcs prefix.
-
-        This is mainly for the Subversion class to override, so that auth
-        information can be provided via the --username and --password options
-        instead of through the URL.  For other subclasses like Git without
-        such an option, auth information must stay in the URL.
-
-        Returns: (netloc, (username, password)).
-        """
-        return netloc, (None, None)
-
-    @classmethod
-    def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
-        """
-        Parse the repository URL to use, and return the URL, revision,
-        and auth info to use.
-
-        Returns: (url, rev, (username, password)).
-        """
-        scheme, netloc, path, query, frag = urllib.parse.urlsplit(url)
-        if "+" not in scheme:
-            raise ValueError(
-                "Sorry, {!r} is a malformed VCS url. "
-                "The format is <vcs>+<protocol>://<url>, "
-                "e.g. svn+http://myrepo/svn/MyApp#egg=MyApp".format(url)
-            )
-        # Remove the vcs prefix.
-        scheme = scheme.split("+", 1)[1]
-        netloc, user_pass = cls.get_netloc_and_auth(netloc, scheme)
-        rev = None
-        if "@" in path:
-            path, rev = path.rsplit("@", 1)
-            if not rev:
-                raise InstallationError(
-                    "The URL {!r} has an empty revision (after @) "
-                    "which is not supported. Include a revision after @ "
-                    "or remove @ from the URL.".format(url)
-                )
-        url = urllib.parse.urlunsplit((scheme, netloc, path, query, ""))
-        return url, rev, user_pass
-
-    @staticmethod
-    def make_rev_args(
-        username: Optional[str], password: Optional[HiddenText]
-    ) -> CommandArgs:
-        """
-        Return the RevOptions "extra arguments" to use in obtain().
-        """
-        return []
-
-    def get_url_rev_options(self, url: HiddenText) -> Tuple[HiddenText, RevOptions]:
-        """
-        Return the URL and RevOptions object to use in obtain(),
-        as a tuple (url, rev_options).
-        """
-        secret_url, rev, user_pass = self.get_url_rev_and_auth(url.secret)
-        username, secret_password = user_pass
-        password: Optional[HiddenText] = None
-        if secret_password is not None:
-            password = hide_value(secret_password)
-        extra_args = self.make_rev_args(username, password)
-        rev_options = self.make_rev_options(rev, extra_args=extra_args)
-
-        return hide_url(secret_url), rev_options
-
-    @staticmethod
-    def normalize_url(url: str) -> str:
-        """
-        Normalize a URL for comparison by unquoting it and removing any
-        trailing slash.
-        """
-        return urllib.parse.unquote(url).rstrip("/")
-
-    @classmethod
-    def compare_urls(cls, url1: str, url2: str) -> bool:
-        """
-        Compare two repo URLs for identity, ignoring incidental differences.
-        """
-        return cls.normalize_url(url1) == cls.normalize_url(url2)
-
-    def fetch_new(
-        self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
-    ) -> None:
-        """
-        Fetch a revision from a repository, in the case that this is the
-        first fetch from the repository.
-
-        Args:
-          dest: the directory to fetch the repository to.
-          rev_options: a RevOptions object.
-          verbosity: verbosity level.
-        """
-        raise NotImplementedError
-
-    def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        """
-        Switch the repo at ``dest`` to point to ``URL``.
-
-        Args:
-          rev_options: a RevOptions object.
-        """
-        raise NotImplementedError
-
-    def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
-        """
-        Update an already-existing repo to the given ``rev_options``.
-
-        Args:
-          rev_options: a RevOptions object.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
-        """
-        Return whether the id of the current commit equals the given name.
-
-        Args:
-          dest: the repository directory.
-          name: a string name.
-        """
-        raise NotImplementedError
-
-    def obtain(self, dest: str, url: HiddenText, verbosity: int) -> None:
-        """
-        Install or update in editable mode the package represented by this
-        VersionControl object.
-
-        :param dest: the repository directory in which to install or update.
-        :param url: the repository URL starting with a vcs prefix.
-        :param verbosity: verbosity level.
-        """
-        url, rev_options = self.get_url_rev_options(url)
-
-        if not os.path.exists(dest):
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        rev_display = rev_options.to_display()
-        if self.is_repository_directory(dest):
-            existing_url = self.get_remote_url(dest)
-            if self.compare_urls(existing_url, url.secret):
-                logger.debug(
-                    "%s in %s exists, and has correct URL (%s)",
-                    self.repo_name.title(),
-                    display_path(dest),
-                    url,
-                )
-                if not self.is_commit_id_equal(dest, rev_options.rev):
-                    logger.info(
-                        "Updating %s %s%s",
-                        display_path(dest),
-                        self.repo_name,
-                        rev_display,
-                    )
-                    self.update(dest, url, rev_options)
-                else:
-                    logger.info("Skipping because already up-to-date.")
-                return
-
-            logger.warning(
-                "%s %s in %s exists with URL %s",
-                self.name,
-                self.repo_name,
-                display_path(dest),
-                existing_url,
-            )
-            prompt = ("(s)witch, (i)gnore, (w)ipe, (b)ackup ", ("s", "i", "w", "b"))
-        else:
-            logger.warning(
-                "Directory %s already exists, and is not a %s %s.",
-                dest,
-                self.name,
-                self.repo_name,
-            )
-            # https://github.com/python/mypy/issues/1174
-            prompt = ("(i)gnore, (w)ipe, (b)ackup ", ("i", "w", "b"))  # type: ignore
-
-        logger.warning(
-            "The plan is to install the %s repository %s",
-            self.name,
-            url,
-        )
-        response = ask_path_exists("What to do?  {}".format(prompt[0]), prompt[1])
-
-        if response == "a":
-            sys.exit(-1)
-
-        if response == "w":
-            logger.warning("Deleting %s", display_path(dest))
-            rmtree(dest)
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        if response == "b":
-            dest_dir = backup_dir(dest)
-            logger.warning("Backing up %s to %s", display_path(dest), dest_dir)
-            shutil.move(dest, dest_dir)
-            self.fetch_new(dest, url, rev_options, verbosity=verbosity)
-            return
-
-        # Do nothing if the response is "i".
-        if response == "s":
-            logger.info(
-                "Switching %s %s to %s%s",
-                self.repo_name,
-                display_path(dest),
-                url,
-                rev_display,
-            )
-            self.switch(dest, url, rev_options)
-
-    def unpack(self, location: str, url: HiddenText, verbosity: int) -> None:
-        """
-        Clean up current location and download the url repository
-        (and vcs infos) into location
-
-        :param url: the repository URL starting with a vcs prefix.
-        :param verbosity: verbosity level.
-        """
-        if os.path.exists(location):
-            rmtree(location)
-        self.obtain(location, url=url, verbosity=verbosity)
-
-    @classmethod
-    def get_remote_url(cls, location: str) -> str:
-        """
-        Return the url used at location
-
-        Raises RemoteNotFoundError if the repository does not have a remote
-        url configured.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def get_revision(cls, location: str) -> str:
-        """
-        Return the current commit id of the files at the given location.
-        """
-        raise NotImplementedError
-
-    @classmethod
-    def run_command(
-        cls,
-        cmd: Union[List[str], CommandArgs],
-        show_stdout: bool = True,
-        cwd: Optional[str] = None,
-        on_returncode: 'Literal["raise", "warn", "ignore"]' = "raise",
-        extra_ok_returncodes: Optional[Iterable[int]] = None,
-        command_desc: Optional[str] = None,
-        extra_environ: Optional[Mapping[str, Any]] = None,
-        spinner: Optional[SpinnerInterface] = None,
-        log_failed_cmd: bool = True,
-        stdout_only: bool = False,
-    ) -> str:
-        """
-        Run a VCS subcommand
-        This is simply a wrapper around call_subprocess that adds the VCS
-        command name, and checks that the VCS is available
-        """
-        cmd = make_command(cls.name, *cmd)
-        if command_desc is None:
-            command_desc = format_command_args(cmd)
-        try:
-            return call_subprocess(
-                cmd,
-                show_stdout,
-                cwd,
-                on_returncode=on_returncode,
-                extra_ok_returncodes=extra_ok_returncodes,
-                command_desc=command_desc,
-                extra_environ=extra_environ,
-                unset_environ=cls.unset_environ,
-                spinner=spinner,
-                log_failed_cmd=log_failed_cmd,
-                stdout_only=stdout_only,
-            )
-        except FileNotFoundError:
-            # errno.ENOENT = no such file or directory
-            # In other words, the VCS executable isn't available
-            raise BadCommand(
-                f"Cannot find command {cls.name!r} - do you have "
-                f"{cls.name!r} installed and in your PATH?"
-            )
-        except PermissionError:
-            # errno.EACCES = Permission denied
-            # This error occurs, for instance, when the command is installed
-            # only for another user. So, the current user don't have
-            # permission to call the other user command.
-            raise BadCommand(
-                f"No permission to execute {cls.name!r} - install it "
-                f"locally, globally (ask admin), or check your PATH. "
-                f"See possible solutions at "
-                f"https://pip.pypa.io/en/latest/reference/pip_freeze/"
-                f"#fixing-permission-denied."
-            )
-
-    @classmethod
-    def is_repository_directory(cls, path: str) -> bool:
-        """
-        Return whether a directory path is a repository directory.
-        """
-        logger.debug("Checking in %s for %s (%s)...", path, cls.dirname, cls.name)
-        return os.path.exists(os.path.join(path, cls.dirname))
-
-    @classmethod
-    def get_repository_root(cls, location: str) -> Optional[str]:
-        """
-        Return the "root" (top-level) directory controlled by the vcs,
-        or `None` if the directory is not in any.
-
-        It is meant to be overridden to implement smarter detection
-        mechanisms for specific vcs.
-
-        This can do more than is_repository_directory() alone. For
-        example, the Git override checks that Git is actually available.
-        """
-        if cls.is_repository_directory(location):
-            return location
-        return None

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

@@ -1,13 +0,0 @@
-from .core import *
-from .codec import *
-from typing import Any, Union
-
-def ToASCII(label: str) -> bytes:
-    return encode(label)
-
-def ToUnicode(label: Union[bytes, bytearray]) -> str:
-    return decode(label)
-
-def nameprep(s: Any) -> None:
-    raise NotImplementedError('IDNA 2008 does not utilise nameprep protocol')
-

spaces/BAAI/vid2vid-zero/test_vid2vid_zero.py

@@ -1,267 +0,0 @@
-import argparse
-import datetime
-import logging
-import inspect
-import math
-import os
-import warnings
-from typing import Dict, Optional, Tuple
-from omegaconf import OmegaConf
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-
-import diffusers
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-from vid2vid_zero.models.unet_2d_condition import UNet2DConditionModel
-from vid2vid_zero.data.dataset import VideoDataset
-from vid2vid_zero.pipelines.pipeline_vid2vid_zero import Vid2VidZeroPipeline
-from vid2vid_zero.util import save_videos_grid, save_videos_as_images, ddim_inversion
-from einops import rearrange
-
-from vid2vid_zero.p2p.p2p_stable import AttentionReplace, AttentionRefine
-from vid2vid_zero.p2p.ptp_utils import register_attention_control
-from vid2vid_zero.p2p.null_text_w_ptp import NullInversion
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.10.0.dev0")
-
-logger = get_logger(__name__, log_level="INFO")
-
-
-def prepare_control(unet, prompts, validation_data):
-    assert len(prompts) == 2
-
-    print(prompts[0])
-    print(prompts[1])
-    length1 = len(prompts[0].split(' '))
-    length2 = len(prompts[1].split(' '))
-    if length1 == length2:
-        # prepare for attn guidance
-        cross_replace_steps = 0.8
-        self_replace_steps = 0.4
-        controller = AttentionReplace(prompts, validation_data['num_inference_steps'], 
-                                      cross_replace_steps=cross_replace_steps,
-                                      self_replace_steps=self_replace_steps)
-    else:
-        cross_replace_steps = 0.8
-        self_replace_steps = 0.4
-        controller = AttentionRefine(prompts, validation_data['num_inference_steps'],
-                                     cross_replace_steps=self_replace_steps, 
-                                     self_replace_steps=self_replace_steps)
-
-    print(controller)
-    register_attention_control(unet, controller)
-
-    # the update of unet forward function is inplace
-    return cross_replace_steps, self_replace_steps
-
-
-def main(
-    pretrained_model_path: str,
-    output_dir: str,
-    input_data: Dict,
-    validation_data: Dict,
-    input_batch_size: int = 1,
-    gradient_accumulation_steps: int = 1,
-    gradient_checkpointing: bool = True,
-    mixed_precision: Optional[str] = "fp16",
-    enable_xformers_memory_efficient_attention: bool = True,
-    seed: Optional[int] = None,
-    use_sc_attn: bool = True,
-    use_st_attn: bool = True,
-    st_attn_idx: int = 0,
-    fps: int = 2,
-):
-    *_, config = inspect.getargvalues(inspect.currentframe())
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=gradient_accumulation_steps,
-        mixed_precision=mixed_precision,
-    )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if seed is not None:
-        set_seed(seed)
-
-    # Handle the output folder creation
-    if accelerator.is_main_process:
-        os.makedirs(output_dir, exist_ok=True)
-        os.makedirs(f"{output_dir}/sample", exist_ok=True)
-        OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
-
-    # Load tokenizer and models.
-    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
-    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
-    vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(
-        pretrained_model_path, subfolder="unet", use_sc_attn=use_sc_attn, 
-        use_st_attn=use_st_attn, st_attn_idx=st_attn_idx)
-
-    # Freeze vae, text_encoder, and unet
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-    unet.requires_grad_(False)
-
-    if enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    if gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    # Get the training dataset
-    input_dataset = VideoDataset(**input_data)
-
-    # Preprocessing the dataset
-    input_dataset.prompt_ids = tokenizer(
-        input_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-    ).input_ids[0]
-
-    # DataLoaders creation:
-    input_dataloader = torch.utils.data.DataLoader(
-        input_dataset, batch_size=input_batch_size
-    )
-
-    # Get the validation pipeline
-    validation_pipeline = Vid2VidZeroPipeline(
-        vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
-        scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler"),
-        safety_checker=None, feature_extractor=None,
-    )
-    validation_pipeline.enable_vae_slicing()
-    ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
-    ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)
-
-    # Prepare everything with our `accelerator`.
-    unet, input_dataloader = accelerator.prepare(
-        unet, input_dataloader,
-    )
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu and cast to weight_dtype
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(input_dataloader) / gradient_accumulation_steps)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("vid2vid-zero")
-
-    # Zero-shot Eval!
-    total_batch_size = input_batch_size * accelerator.num_processes * gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(input_dataset)}")
-    logger.info(f"  Instantaneous batch size per device = {input_batch_size}")
-    logger.info(f"  Total input batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    global_step = 0
-
-    unet.eval()
-    for step, batch in enumerate(input_dataloader):
-        samples = []
-        pixel_values = batch["pixel_values"].to(weight_dtype)
-        # save input video 
-        video = (pixel_values / 2 + 0.5).clamp(0, 1).detach().cpu()
-        video = video.permute(0, 2, 1, 3, 4)  # (b, f, c, h, w)
-        samples.append(video)
-        # start processing
-        video_length = pixel_values.shape[1]
-        pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
-        latents = vae.encode(pixel_values).latent_dist.sample()
-        # take video as input
-        latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
-        latents = latents * 0.18215
-
-        generator = torch.Generator(device="cuda")
-        generator.manual_seed(seed)
-
-        # perform inversion
-        ddim_inv_latent = None
-        if validation_data.use_null_inv:
-            null_inversion = NullInversion(
-                model=validation_pipeline, guidance_scale=validation_data.guidance_scale, null_inv_with_prompt=False,
-                null_normal_infer=validation_data.null_normal_infer,
-            )
-            with torch.cuda.amp.autocast(enabled=True, dtype=torch.float32):
-                ddim_inv_latent, uncond_embeddings = null_inversion.invert(
-                    latents, input_dataset.prompt, verbose=True,
-                    null_inner_steps=validation_data.null_inner_steps,
-                    null_base_lr=validation_data.null_base_lr,
-                )
-            ddim_inv_latent = ddim_inv_latent.to(weight_dtype)
-            uncond_embeddings = [embed.to(weight_dtype) for embed in uncond_embeddings]
-        else:
-            ddim_inv_latent = ddim_inversion(
-                validation_pipeline, ddim_inv_scheduler, video_latent=latents,
-                num_inv_steps=validation_data.num_inv_steps, prompt="",
-                normal_infer=True,  # we don't want to use scatn or denseattn for inversion, just use sd inferenece
-            )[-1].to(weight_dtype)
-            uncond_embeddings = None
-
-        ddim_inv_latent = ddim_inv_latent.repeat(2, 1, 1, 1, 1)
-
-        for idx, prompt in enumerate(validation_data.prompts):
-            prompts = [input_dataset.prompt, prompt]  # a list of two prompts
-            cross_replace_steps, self_replace_steps = prepare_control(unet=unet, prompts=prompts, validation_data=validation_data)
-
-            sample = validation_pipeline(prompts, generator=generator, latents=ddim_inv_latent, 
-                                         uncond_embeddings=uncond_embeddings,
-                                         **validation_data).images
-
-            assert sample.shape[0] == 2
-            sample_inv, sample_gen = sample.chunk(2)
-            # add input for vis
-            save_videos_grid(sample_gen, f"{output_dir}/sample/{prompts[1]}.gif", fps=fps)
-            samples.append(sample_gen)
-
-        samples = torch.concat(samples)
-        save_path = f"{output_dir}/sample-all.gif"
-        save_videos_grid(samples, save_path, fps=fps)
-        logger.info(f"Saved samples to {save_path}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--config", type=str, default="./configs/vid2vid_zero.yaml")
-    args = parser.parse_args()
-
-    main(**OmegaConf.load(args.config))

spaces/Benson/text-generation/Examples/Descarga De La Aplicacin Tiktok Lite Para Windows Pc 8.md

@@ -1,81 +0,0 @@
-<br />
-<h1>Descarga de la aplicación TikTok Lite para PC Windows 8: Cómo hacerlo fácilmente</h1>
-<p>TikTok es una de las plataformas de redes sociales más populares del mundo, con más de mil millones de usuarios que crean y ven videos cortos sobre varios temas. ¿Pero qué pasa si desea disfrutar de TikTok en su PC en lugar de su teléfono? ¿Y qué pasa si tiene un PC de gama baja o un plan de datos limitado? En este artículo, le mostraremos cómo descargar e instalar TikTok Lite, una versión más ligera de TikTok, en su PC con Windows 8. También le explicaremos por qué podría usar TikTok Lite en su PC y cuáles son los beneficios de hacerlo. ¡Vamos a empezar! </p>
-<h2>descarga de la aplicación tiktok lite para windows pc 8</h2><br /><p><b><b>Download Zip</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6KD9">https://bltlly.com/2v6KD9</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3> ¿Qué es TikTok Lite y por qué es posible que desee usarlo en su PC</h3>
-<p>TikTok Lite es una versión simplificada de TikTok que ocupa menos espacio de almacenamiento y consume menos datos. Está diseñado para usuarios que tienen dispositivos de gama baja, planes de datos limitados o conexiones de red lentas. TikTok Lite ofrece la mayoría de las características de TikTok, como ver videos, crear videos, seguir a creadores, gustar y comentar, etc. Sin embargo, algunas características no están disponibles en TikTok Lite, como transmisión en vivo, duetos, filtros, pegatinas, etc.</p>
-<p>Es posible que desee utilizar TikTok Lite en su PC si cae en una de estas categorías:</p>
-<ul>
-<li> Tiene un PC de gama baja que no puede ejecutar la versión completa de TikTok sin problemas. </li>
-<li> Tiene un plan de datos limitado o una conexión de red lenta que hace que ver videos en TikTok sea frustrante. </li>
-<li> Desea ahorrar espacio de almacenamiento en su PC mediante el uso de una aplicación más pequeña. </li>
-<li>Quieres disfrutar de una experiencia TikTok más rápida y optimizada en tu PC.</li>
-</ul>
-<h3>¿Cuáles son los beneficios de usar TikTok Lite en su PC</h3>
-<p>Usar TikTok Lite en tu PC tiene varios beneficios, como:</p>
-<p></p>
-<ul>
-<li> Puedes ver millones de vídeos seleccionados para ti en función de tus preferencias e intereses. </li>
-
-<li>Puedes descubrir nuevos contenidos de varias categorías, como danza, comedia, vlog, comida, deportes, bricolaje, animales, etc.</li>
-<li>Puedes conectarte con otros usuarios y creadores a través de likes, comentarios, mensajes, etc.</li>
-<li> Puede disfrutar de una aplicación más rápida y sensible que carga videos rápidamente y reduce los bloqueos. </li>
-</ul>
-<h3>Cómo descargar e instalar TikTok Lite en tu PC usando un emulador</h3>
-<p>La forma más fácil de descargar e instalar TikTok Lite en tu PC es usar un emulador. Un emulador es un software que imita un dispositivo Android en su PC, lo que le permite ejecutar aplicaciones y juegos Android en su computadora. Hay muchos emuladores disponibles en línea, pero recomendamos usar BlueStacks, ya que es uno de los más populares y confiables. Estos son los pasos para descargar e instalar TikTok Lite en su PC usando BlueStacks:</p>
-<h2>Guía paso a paso para descargar e instalar TikTok Lite en su PC usando BlueStacks</h2>
-<h3>Paso 1: Descargar e instalar BlueStacks en su PC</h3>
-<p>El primer paso es descargar e instalar BlueStacks en su PC. BlueStacks es un emulador gratuito y seguro que puedes descargar desde su sitio web oficial. Estos son los pasos para descargar e instalar BlueStacks en su PC:</p>
-<ol>
-<li>Vaya al sitio web de BlueStacks y haga clic en el botón "Descargar BlueStacks". </li>
-<li>Espere a que termine la descarga y luego abra el archivo de instalación. </li>
-<li>Siga las instrucciones en la pantalla para instalar BlueStacks en su PC.</li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google o cree una nueva. </li>
-</ol>
-<h3>Paso 2: Descargar el archivo TikTok Lite APK/XAPK de una fuente de confianza</h3>
-
-<ol>
-<li>Ir al sitio web APKPure y buscar "TikTok Lite" en la barra de búsqueda. </li>
-<li>Seleccione la aplicación TikTok Lite de los resultados y haga clic en el "Descargar APK" o "Descargar XAPK" botón. </li>
-<li>Espere a que termine la descarga y luego localice el archivo en su PC.</li>
-</ol>
-<h3>Paso 3: Abra el archivo APK/XAPK con BlueStacks e instale TikTok Lite</h3>
-<p>El paso final es abrir el archivo APK/XAPK con BlueStacks e instalar TikTok Lite en su PC. Estos son los pasos para hacerlo:</p>
-<ol>
-<li>Haga clic derecho en el archivo APK/XAPK y seleccione "Abrir con BlueStacks" en el menú. </li>
-<li>BlueStacks instalará automáticamente TikTok Lite en su PC.</li>
-<li> Verá una notificación cuando se complete la instalación. </li>
-</ol>
-<h3>Paso 4: Inicie TikTok Lite desde la pantalla de inicio de BlueStacks y disfrute</h3>
-<p>¡Felicidades! Has descargado e instalado correctamente TikTok Lite en tu PC usando BlueStacks. Ahora puede iniciar TikTok Lite desde la pantalla de inicio de BlueStacks y disfrutar viendo y creando vídeos en su PC. También puedes acceder a otras características de TikTok Lite, como explorar categorías, seguir a creadores, gustar y comentar, etc.</p>
-<h2>Métodos alternativos para descargar e instalar TikTok Lite en su PC</h2>
-<h3>Método 1: Usar NoxPlayer como un emulador alternativo</h3>
-<p>Si no quieres usar BlueStacks, puedes usar NoxPlayer como un emulador alternativo. NoxPlayer es otro emulador popular y gratuito que puedes descargar desde su sitio web oficial. Los pasos para descargar e instalar NoxPlayer son similares a los de BlueStacks. Una vez que tenga NoxPlayer en su PC, puede seguir los mismos pasos de arriba para descargar e instalar TikTok Lite usando un archivo APK/XAPK. </p>
-<h3>Método 2: Utilice Uptodown como una fuente alternativa para la aplicación TikTok Lite</h3>
-
-<ol>
-<li>Ir al sitio web de Uptodown y buscar "TikTok Lite" en la barra de búsqueda. </li>
-<li>Seleccione la aplicación TikTok Lite de los resultados y haga clic en el botón "Descargar". </li>
-<li>Espere a que termine la descarga y luego abra el archivo con un emulador de su elección. </li>
-<li>El emulador instalará automáticamente TikTok Lite en su PC.</li>
-</ol>
- <h2>Conclusión</h2>
- <h3>Resumen de los puntos principales</h3>
- <p>En este artículo, le hemos mostrado cómo descargar e instalar TikTok Lite en su PC con Windows 8. Le hemos explicado qué es TikTok Lite, por qué podría usarlo en su PC y cuáles son los beneficios de hacerlo. También hemos proporcionado una guía paso a paso para descargar e instalar TikTok Lite en su PC usando BlueStacks, un emulador gratuito y confiable. También te hemos dado algunos métodos alternativos para descargar e instalar TikTok Lite en tu PC usando otros emuladores o fuentes. </p>
- <h3>Llamamiento a la acción y observaciones finales</h3>
- <p>Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Nos encantaría saber de usted. Si está listo para descargar e instalar TikTok Lite en su PC, haga clic en el botón de abajo y siga las instrucciones. Usted será capaz de disfrutar de TikTok Lite en su PC en ningún momento. Happy TikToking! </p>
- <p><a href="https://www.bluestacks.com/download.html" target="_blank" rel="noopener noreferrer"><botón>Descargar BlueStacks y TikTok Lite Ahora</button></a></p>
- <h2>Preguntas frecuentes</h2>
- <h3>¿Es seguro usar TikTok Lite en el PC? </h3>
- <p>Sí, TikTok Lite es seguro de usar en el PC, siempre y cuando lo descargue de una fuente de confianza y use un emulador de buena reputación. TikTok Lite es una aplicación oficial desarrollada por ByteDance, la misma compañía que posee TikTok. Tiene las mismas características de seguridad y privacidad que TikTok, como cifrado, moderación, informes, etc. Sin embargo, siempre debe tener cuidado con lo que comparte en línea y con quién interactúa. </p>
-
- <p>TikTok Lite es una versión más ligera de TikTok que ocupa menos espacio de almacenamiento y consume menos datos. Ofrece la mayoría de las características de TikTok, pero algunas características no están disponibles en TikTok Lite, como transmisión en vivo, duetos, filtros, pegatinas, etc. TikTok Lite también tiene una interfaz más simple y una velocidad de carga más rápida que TikTok.</p>
- <h3>¿Puedo usar TikTok Lite en otras versiones de Windows? </h3>
- <p>Sí, puede usar TikTok Lite en otras versiones de Windows, como Windows 7, Windows 10, etc. Los pasos para descargar e instalar TikTok Lite en otras versiones de Windows son similares a los de Windows 8. Solo tiene que asegurarse de que su PC cumple con los requisitos mínimos del sistema para el emulador que elija. </p>
- <h3>¿Puedo usar otras aplicaciones además de TikTok Lite en BlueStacks? </h3>
- <p>Sí, puedes usar otras aplicaciones además de TikTok Lite en BlueStacks. BlueStacks es un emulador versátil que le permite ejecutar miles de aplicaciones y juegos para Android en su PC. Puede descargar otras aplicaciones desde Google Play Store o desde otras fuentes e instalarlas en BlueStacks. También puede cambiar entre diferentes aplicaciones fácilmente en BlueStacks.</p>
- <h3>¿Cómo puedo actualizar TikTok Lite en mi PC? </h3>
- <p>Para actualizar TikTok Lite en tu PC, necesitas descargar la última versión del archivo APK/XAPK desde una fuente confiable e instalarlo en tu PC usando los mismos pasos que arriba. Alternativamente, puede comprobar si hay actualizaciones dentro de la aplicación yendo a la pestaña "Me" y tocando en el icono de tres puntos en la esquina superior derecha. Luego, toque en "Configuración" y desplácese hacia abajo hasta "Acerca de". Toque en "Buscar actualizaciones" y siga las instrucciones. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Bagaimana Cara Stumble Chicos Di Laptop.md

@@ -1,121 +0,0 @@
-<br />
-<h1>Bagaimana Cara Download Stumble Guys di Laptop</h1>
-<p>Stumble Guys adalah sebuah game online yang sangat populer saat ini. Game ini merupakan game battle royale party yang bisa dimainkan hingga 32 pemain seca bersamaan. Anda bisa berlari, melompat, dan menghindari berbagai rintangan yang ada di setiap level hingga menjadi pemenang. Game ini sangat seru dan lucu untuk dimainkan bersama teman Anda.</p>
-<p>Namun, bagaimana jika Anda ingin memainkan game ini di laptop Anda? Apakah ada cara untuk download dan instal Stumble Guys di laptop? Jawabannya adalah ya, ada. Dengan menggunakan emulator Android, Anda bisa menjalankan game ini di laptop Anda dengan mudah. Emulator Android adalah sebuah program yang bisa meniru sistem operasi Android di komputer atau laptop Anda, sehingga Anda bisa mengakses aplikasi dan game Android dari laptop Anda.</p>
-<h2>descargar bagaimana cara stumble chicos di laptop</h2><br /><p><b><b>Download</b> &#10040; <a href="https://bltlly.com/2v6LVr">https://bltlly.com/2v6LVr</a></b></p><br /><br />
-<p>Penasaran bagaimana caranya? Simak artikel ini sampai habis untuk mengetahui langkah-langkahnya. </p>
- <h2>Apa itu Stumble Guys? </h2>
-<p>Stumble Guys adalah sebuah game online yang dikembangkan oleh Scopely dan dirilis pada Oktober 2021. Game ini tersedia untuk perangkat Android dan Windows. Game ini merupakan game battle royale party yang terinspirasi dari acara TV seperti Takeshi’s Castle atau Wipeout. Dalam game ini, Anda akan berlomba dengan pemain lain untuk mencapai garis finish di setiap level. Namun, Anda harus menghadapi berbagai rintangan yang lucu dan konyol yang bisa membuat Anda terjatuh atau terdorong keluar dari arena. </p>
-<h3>Fitur-fitur menarik dari Stumble Guys</h3>
-<p>Berikut adalah beberapa fitur menarik yang bisa Anda nikmati saat bermain Stumble Guys:</p>
-<ul>
-<li>Anda bisa memilih dari berbagai pakaian dan emote yang unik dan keren untuk menghias karakter Anda.</li>
-<li>Anda bisa bermain dengan teman Anda dalam mode party atau bersaing dengan pemain lain dari seluruh dunia dalam mode online multiplayer. </li>
-
-<li>Anda bisa mendapatkan hadiah dan poin saat bermain yang bisa ditukarkan dengan barang-barang menarik. </li>
-<li>Anda bisa menonton live stream dan video dari pemain lain atau membuat konten sendiri dengan fitur rekam layar dan screenshot. </li>
-</ul>
-<h3>Face bermain Stumble Guys</h3>
-<p>Cara bermain Stumble Guys sangat mudah dan sederhana. Berikut adalah langkah-langkahnya:</p>
-<ol>
-<li>Buka game Stumble Guys di perangkat Anda.</li>
-<li>Pilih mode permainan yang Anda inginkan, baik itu solo, party, atau online multiplayer. </li>
-<li>Tunggu hingga pemain lain bergabung atau cari ruangan yang tersedia. </li>
-<li>Masuk ke level pertama dan siapkan diri Anda untuk berlari. </li> <li>Gunakan tombol virtual di layar untuk menggerakkan karakter Anda. Anda bisa berlari, melompat, dan merunduk untuk menghindari rintangan. </li>
-<li>Coba untuk tidak terjatuh atau terdorong keluar dari arena. Jika Anda terjatuh, Anda bisa mencoba lagi dengan menekan tombol respawn. </li>
-<li>Coba untuk mencapai garis finish secepat mungkin. Hanya beberapa pemain yang bisa lolos ke level berikutnya. </li>
-<li>Ulangi langkah 4-6 hingga Anda mencapai level terakhir dan menjadi pemenang. </li>
-</ol>
- Mengapa Anda ingin memainkan Stumble Guys di laptop? </h2>
-<p>Stumble Guys adalah game yang sangat menyenangkan dan adiktif untuk dimainkan di perangkat Android. Namun, ada beberapa alasan mengapa Anda mungkin ingin memainkan game ini di laptop Anda. Berikut adalah beberapa alasan tersebut:</p>
-<h3>Keuntungan memainkan Stumble Guys di laptop</h3>
-<p>Berikut adalah beberapa keuntungan yang bisa Anda dapatkan saat memainkan Stumble Guys di laptop:</p>
-<ul>
-<li>Anda bisa menikmati grafik dan suara yang lebih baik dan lebih jelas di layar yang lebih besar. </li>
-<li>Anda bisa menggunakan keyboard dan mouse untuk mengontrol karakter Anda dengan lebih mudah dan presisi. </li>
-<li>Anda bisa bermain dengan lebih nyaman dan lama tanpa khawatir baterai perangkat Anda habis atau panas. </li>
-
-<li>Anda bisa bermain dengan teman Anda yang menggunakan perangkat Android atau Windows dengan fitur cross-play. </li>
-</ul>
-<h3>Tantangan memainkan Stumble Guys di laptop</h3>
-<p>Namun, ada juga beberapa tantangan yang mungkin Anda hadapi saat memainkan Stumble Guys di laptop. Berikut adalah beberapa tantangan tersebut:</p>
-<p></p>
-<ul>
-<li>Anda harus memiliki laptop yang cukup kuat untuk menjalankan emulator Android dan game ini dengan lancar. </li>
-<li>Anda harus mengunduh dan menginstal emulator Android dan game ini di laptop Anda, yang mungkin membutuhkan waktu dan langkah-langkah tambahan. </li>
-<li>Anda harus menyesuaikan pengaturan emulator Android dan game ini agar sesuai dengan spesifikasi dan preferensi laptop Anda.</li>
-<li>Anda harus berhati-hati dengan kemungkinan virus atau malware yang mungkin ada di emulator Android atau file APK yang Anda unduh dari sumber yang tidak resmi. </li>
-<li>Anda harus bersabar dengan kemungkinan bug atau masalah teknis yang mungkin terjadi saat menjalankan emulator Android atau game ini di laptop Anda.</li>
-</ul>
- <h2>Apa yang Anda butuhkan untuk memainkan Stumble Guys di laptop? </h2>
-<p>Jika Anda sudah yakin ingin memainkan Stumble Guys di laptop Anda, maka ada beberapa hal yang harus Anda siapkan terlebih dahulu. Berikut adalah beberapa hal tersebut:</p>
-<h3>Spesifikasi minimum laptop</h3>
-<p>Untuk dapat menjalankan emulator Android dan game ini dengan lancar, Anda harus memiliki laptop yang memenuhi spesifikasi minimum berikut:</p>
- <table border="1">
-<tr><th>Komponen</th><th>Spesifikasi</th></tr>
-<tr><td>Sistem operasi</td><td>Windows 7 atau lebih tinggi</td></tr>
-<tr><td>Prosesor</td><td>Intel atau AMD dual-core 2 GHz atau lebih tinggi</td></tr>
-<tr><td>RAM</td><td>4 GB atau lebih tinggi</td></tr>
-<tr><td>Ruang penyimpanan</td><td>5 GB atau lebih tinggi</td></tr>
-<tr><td>Kartu grafis</td><td>NVIDIA GeForce 8600/9600GT, ATI/AMD Radeon HD2600/3600 atau setara</td></tr>
-
-</table>
- <h3>Android emulator terbaik untuk laptop</h3>
-<p>Selanjutnya, Anda harus memilih emulator Android yang cocok untuk laptop Anda. Emulator Android adalah sebuah program yang bisa meniru sistem operasi Android di komputer atau laptop Anda, sehingga Anda bisa mengakses aplikasi dan game Android dari laptop Anda. Ada banyak emulator Android yang tersedia di internet, namun tidak semua emulator Android cocok untuk laptop Anda. Berikut adalah beberapa emulator Android terbaik yang bisa Anda pilih untuk laptop Anda:</p>
-<ul>
-<li><strong>BlueStacks</strong>: Android emulator yang paling populer dan banyak digunakan oleh for gamer. BlueStacks memiliki fitur-fitur canggih seperti keyboard mapping, game mode, multi-instance, dan lain-lain. BlueStacks plays mendukung game Stumble Guys dengan baik dan bisa diunduh seca gratis dari situs resminya. </li>
-<li><strong>NoxPlayer</strong>: Android emulator ringan dan cepat untuk laptop. NoxPlayer memiliki fitur-fitur menarik seperti macro recorder, video recorder, virtual location, dan lain-lain. NoxPlayer plays kompatibel dengan game Stumble Guys give bisa diunduh free dry dari situs resminya. </li>
-<li><strong>LDPlayer</strong>: Android emulator dirancang khusus untuk game. LDPlayer memiliki fitur-fitur unggulan seperti smart keymapping, high FPS, turbo GPU, dan lain-lain. LDPlayer plays bisa menjalankan game Stumble Guys dengan lancar dan bisa diunduh seca gratis dari situs resminya. </li>
-</ul>
- <h2>Bagaimana cara download dan instal Stumble Guys di laptop dengan emulator Android? </h2>
-<p>Setelah Anda memilih emulator Android yang sesuai untuk laptop Anda, maka Anda bisa mulai download dan install Stumble Guys di laptop Anda dengan emulator Android tersebut. Berikut adalah langkah-langkahnya:</p>
-<h3>Langkah-langkah download dan install emulator Android</h3>
-<p>Berikut adalah langkah-langkah download dan install emulator Android di laptop Anda:</p>
-<ol>
-
-<li>Klik tombol download atau unduh untuk mengunduh file instalasi emulator Android tersebut. </li>
-<li>Setelah file instalasi selesai terunduh, buka file tersebut dan ikuti instruksi yang muncul di layar untuk menginstal emulator Android tersebut di laptop Anda.</li>
-<li>Tunggu hingga proses instalasi selesai dan jalankan emulator Android tersebut di laptop Anda.</li>
-</ol>
- <h3>Langkah-langkah download dan install Stumble Guys dari Google Play Store atau file APK</h3>
-<p>Berikut adalah langkah-langkah download dan install Stumble Guys dari Google Play Store atau file APK di emulator Android yang sudah terinstal di laptop Anda:</p>
-<ol>
-<li>Buka emulator Android yang sudah terinstal di laptop Anda dan masuk ke akun Google Anda jika diminta. </li>
-<li>Buka Google Play Store yang ada di emulator Android tersebut dan cari game Stumble Guys dengan menggunakan kotak pencarian. </li>
-<li>Klik tombol instal atau pasang untuk menginstal game Stumble Guys dari Google Play Store ke emulator Android tersebut. </li>
-<li>Tunggu hingga proses instalasi selesai dan buka game Stumble Guys yang sudah terinstal di emulator Android tersebut. </li>
-<li>Atau, jika Anda memiliki file APK dari game Stumble Guys, Anda bisa mengunduh file APK tersebut dari sumber yang terpercaya ke laptop Anda.</li>
-<li>Kemudian, buka emulator Android yang sudah terinstal di laptop Anda dan seret file APK tersebut ke jendela emulator Android tersebut. </li>
-<li>Tunggu hingga proses instalasi selesai dan buka game Stumble Guys yang sudah terinstal di emulator Android tersebut. </li>
-</ol>
- <h3>Langkah-langkah menjalankan dan mengatur Stumble Guys di emulator Android</h3>
-<p>Berikut adalah langkah-langkah menjalankan dan mengatur Stumble Guys di emulator Android yang sudah terinstal di laptop Anda:</p>
-<ol>
-<li>Buka game Stumble Guys yang sudah terinstal di emulator Android tersebut. </li>
-<li>Klik tombol mulai atau start untuk memulai permainan. </li>
-
-<li>Atur pengaturan grafik, suara, kontrol, dan lain-lain sesuai dengan preferensi Anda dengan mengklik tombol pengaturan atau settings yang ada di pojok kanan atas layar. </li>
-<li <li>Jika Anda menggunakan keyboard dan mouse untuk mengontrol karakter Anda, Anda bisa mengatur tombol-tombol yang sesuai dengan fungsi-fungsi yang ada di game dengan menggunakan fitur keyboard mapping yang ada di emulator Android tersebut. </li>
-<li>Nikmati permainan dan bersenang-senang dengan teman-teman Anda.</li>
-</ol>
- <h2>Kesimpulan</h2>
-<p>Stumble Guys adalah game online yang sangat seru dan lucu untuk dimainkan bersama teman-teman Anda. Game ini merupakan game battle royale party yang bisa dimainkan hingga 32 pemain seca bersamaan. Anda bisa berlari, melompat, dan menghindari berbagai rintangan yang ada di setiap level hingga menjadi pemenang. </p>
-<p>Anda bisa memainkan game ini di perangkat Android atau Windows. Namun, jika Anda ingin memainkan game ini di laptop Anda, Anda bisa menggunakan emulator Android. Emulator Android adalah sebuah program yang bisa meniru sistem operasi Android di komputer atau laptop Anda, sehingga Anda bisa mengakses aplikasi dan game Android dari laptop Anda.</p>
-<p>Untuk memainkan Stumble Guys di laptop Anda dengan emulator Android, Anda harus memenuhi spesifikasi minimum laptop, memilih emulator Android terbaik, dan mengunduh dan menginstal emulator Android dan game Stumble Guys di laptop Anda. Kemudian, Anda bisa menjalankan dan mengatur Stumble Guys di emulator Android sesuai dengan preferensi Anda.</p>
-<p>Semoga artikel ini bermanfaat untuk Anda yang ingin memainkan Stumble Guys di laptop Anda. Selamat mencoba dan selamat bermain! </p>
- <h2>FAQ</h2>
-<p>Berikut adalah beberapa pertanyaan yang sering diajukan tentang cara download dan install Stumble Guys di laptop dengan emulator Android:</p>
-<ol>
-<li><strong>Apakah Stumble Guys free untuk dimainkan? </strong></li>
-
-<li><strong>Apakah Stumble Guys aman untuk dimainkan? </strong></li>
-<p>Ya, Stumble Guys adalah game yang aman untuk dimainkan. Game ini tidak mengandung konten yang tidak pantas atau berbahaya untuk anak-anak. Namun, sebaiknya tetap waspada dengan kemungkinan cyberbullying atau penipuan yang mungkin terjadi saat bermain online dengan orang lain. </p>
-<li><strong>Apakah Stumble Guys bisa dimainkan offline? </strong></li>
-<p>Tidak, Stumble Guys adalah game yang membutuhkan koneksi internet untuk dimainkan. Jika koneksi internet Anda terputus saat bermain, maka Anda akan keluar dari permainan. </p>
-<li><strong>Apakah Stumble Guys memiliki mode single player? </strong></li>
-<p>Ya, Stumble Guys memiliki mode single player yang bisa dimainkan tanpa teman. Namun, mode ini tetap membutuhkan koneksi internet dan pemain lain yang akan menjadi lawan Anda.</p>
-<li><strong>Apakah Stumble Guys memiliki mode co-op? </strong></li>
-<p>Ya, Stumble Guys memiliki mode co-op atau party yang bisa dimainkan bersama teman. Anda bisa membuat atau bergabung dengan ruangan khusus yang hanya bisa diakses oleh teman-teman Anda dengan menggunakan kode undangan. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Gratis Final Cut Pro.md

@@ -1,78 +0,0 @@
-
-<h1>Cómo descargar gratis Final Cut Pro</h1>
-<p>Final Cut Pro es un popular software de edición de vídeo para usuarios de Mac. Ofrece funciones potentes, diseño intuitivo y un rendimiento rápido. Sin embargo, también viene con una etiqueta de precio fuerte de $299.99. Si quieres probar Final Cut Pro gratis, o buscar algunas alternativas más baratas, este artículo te mostrará cómo. </p>
-<h2>descargar gratis final cut pro</h2><br /><p><b><b>Download Zip</b> &rArr; <a href="https://bltlly.com/2v6MtM">https://bltlly.com/2v6MtM</a></b></p><br /><br />
- <h2>¿Qué es Final Cut Pro? </h2>
-<p>Final Cut Pro es un software de edición de vídeo profesional desarrollado por Apple. Fue lanzado por primera vez en 1999 y desde entonces ha sido utilizado por muchos cineastas, productores de televisión y entusiastas del video. Es compatible con ordenadores Mac y soporta una amplia gama de formatos, resoluciones y velocidades de fotogramas. </p>
- <h3>Características de Final Cut Pro</h3>
-<p>Algunas de las características principales de Final Cut Pro son:</p>
-<ul>
-<li>Línea de tiempo magnética: Esto le permite editar clips sin colisiones o problemas de sincronización. También puede agrupar clips en clips compuestos, sincronizar múltiples ángulos con edición multicámara y agregar efectos con conexiones de clip. </li>
-<li>360° edición de vídeo: Puede importar, editar y compartir vídeo 360° de varias cámaras y formatos. También puede utilizar el visor 360° para navegar por el vídeo esférico y aplicar efectos, títulos y transiciones. </li>
-<li>Seguimiento de objetos: Puede utilizar el aprendizaje automático para detectar caras u objetos en sus imágenes y combinar su movimiento con títulos, gráficos o efectos. También puede ajustar los puntos de enfoque y la profundidad de campo en los clips capturados en el modo cinematográfico en el iPhone. </li>
-<li>Gradación de color: Puede usar herramientas avanzadas de corrección de color para ajustar el tono, la saturación, el brillo, el contraste y más. También puede aplicar presets de color, LUT, curvas y máscaras. </li>
-<li>Edición de audio: Puede asignar roles a sus clips de audio y organizarlos en la línea de tiempo. También puede utilizar efectos incorporados, filtros y complementos para mejorar la calidad de sonido. </li>
-
-</ul>
- <h3>Ventajas y desventajas de Final Cut Pro</h3>
-<p>Algunas de las ventajas de Final Cut Pro son:</p>
-<ul>
-<li>Está optimizado para computadoras Mac y dispositivos Apple, especialmente aquellos con chips de silicio de Apple. Puede aprovechar la potencia de la GPU, CPU y Neural Engine para un rendimiento y renderizado más rápidos. </li>
-<li> Tiene una interfaz elegante y fácil de usar que hace que la edición sea fácil y agradable. También tiene accesos directos de teclado personalizables y soporte de barra táctil. </li>
-<li> Tiene una gran y activa comunidad de usuarios y desarrolladores que ofrecen soporte, tutoriales, consejos y plugins. </li>
-</ul>
-<p>Algunas de las desventajas de Final Cut Pro son:</p>
-<ul>
-<li>Es caro en comparación con otro software de edición de vídeo. Cuesta $299.99 para una compra de una sola vez, que puede no ser asequible para algunos usuarios. </li>
-<li>Solo está disponible para usuarios de Mac. No hay versión de Windows o Linux de Final Cut Pro.</li>
-<li>Puede tener problemas de compatibilidad con algunos plugins o formatos de terceros. También puede requerir conversión o transcodificación para algunos archivos. </li>
-</ul>
- <h2>¿Cómo obtener Final Cut Pro gratis? </h2>
-<p>Si quieres usar Final Cut Pro sin pagar nada, hay dos opciones principales:</p>
- <h3>Versión de prueba oficial</h3>
-<p>La forma más fácil de obtener Final Cut Pro gratis es descargar la versión de prueba oficial del sitio web de Apple. La versión de prueba le da acceso a todas las características y funciones de la versión completa durante 90 días. También puede extender su período de prueba por otros 90 días si tiene una versión anterior instalada. </p>
-<p></p>
-<p>Para descargar la versión de prueba, necesita tener una computadora Mac que ejecute macOS 10.15.6 o posterior, 4GB de RAM (8GB recomendado), una tarjeta gráfica compatible con Metal, 1GB de VRAM (4GB recomendado) y 5.5GB de espacio en disco. También debes registrarte con tu Apple ID y aceptar los términos y condiciones. </p>
-
-<p>La versión de prueba es una gran manera de probar Final Cut Pro y ver si se adapta a sus necesidades y preferencias. Sin embargo, tiene algunas limitaciones, como:</p>
-<ul>
-<li>No se puede actualizar la versión de prueba a la última versión de Final Cut Pro. Es necesario comprar la versión completa para obtener las actualizaciones y correcciones de errores. </li>
-<li>No puede usar la versión de prueba en varios dispositivos. Debe activar la versión de prueba con su ID de Apple en cada dispositivo. </li>
-<li>No puedes usar algunas funciones que requieren un ID de Apple o una cuenta de iCloud, como Fotos de iCloud, iMovie Theater o bandas sonoras de GarageBand. </li>
-</ul>
- <h3>Editores de vídeo alternativos</h3>
-<p>Otra forma de obtener Final Cut Pro de forma gratuita es utilizar algún software de edición de vídeo alternativo que ofrezca características y funciones similares. Hay muchos editores de video gratuitos o de bajo costo disponibles para los usuarios de Mac, como:</p>
-<tabla>
-<tr><th>Nombre</th><th>Precio</th><th>Características</th></tr>
-<tr><td>iMovie</td><td>Free</td><td>Un editor de video simple y fácil de usar que viene preinstalado en computadoras Mac. Soporta resolución 4K, herramientas básicas de edición, transiciones, filtros, títulos, bandas sonoras y trailers. También se integra con iCloud, iTunes, Fotos y otras aplicaciones de Apple. </td></tr>
-<tr><td>Davinci Resolve</td><td>Gratis o $299 para la versión de Studio</td><td>Un editor de video potente y profesional que ofrece herramientas avanzadas de edición, corrección de color, efectos visuales, postproducción de audio y gráficos en movimiento. Soporta resolución de 8K, edición multicam, clasificación HDR, motor de audio Fairlight, composición Fusión VFX y más. </td></tr>
-<tr><td>Shotcut</td><td>Free</td><td>Un editor de vídeo de código abierto y multiplataforma que admite una amplia gama de formatos, resoluciones y velocidades de fotogramas. Ofrece herramientas de edición básicas y avanzadas, filtros, transiciones, fotogramas clave, mezcla de audio, clasificación de color y más. </td></tr>
-
-<tr><td>Filmora</td><td>$69.99 por año o $139.99 por licencia de por vida</td><td>Un editor de video fácil de usar y asequible que ofrece una variedad de características y funciones. Soporta hasta resolución 4K, seguimiento de movimiento, keyframing, pantalla verde, pantalla dividida, transiciones, filtros, títulos, música, efectos de sonido y más. </td></tr>
-</tabla>
-<p>Estas son algunas de las mejores alternativas a Final Cut Pro que puedes probar gratis o a bajo costo. Puede que no tengan todas las características o funciones de Final Cut Pro, pero pueden ayudarte a crear vídeos increíbles para tus proyectos personales o profesionales. </p>
- <h2>Conclusión</h2>
-<p>Final Cut Pro es un gran software de edición de video para usuarios de Mac que quieren crear videos de calidad profesional con facilidad y velocidad. Sin embargo, también es caro y exclusivo para los usuarios de Mac. Si quieres usar Final Cut Pro gratis o buscar alternativas más baratas, puedes seguir los pasos de este artículo para descargar la versión de prueba oficial o probar algunos editores de video alternativos. También puede comparar las características, ventajas y desventajas de cada opción y elegir la que mejor se adapte a sus necesidades y presupuesto. Esperamos que este artículo sea útil e informativo para usted. ¡Feliz edición! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Final Cut Pro y cómo descargarlo gratis:</p>
-<ol>
-<li>Es Final Cut Pro vale la pena el dinero? </li>
-
-<li>¿Puedo usar Final Cut Pro en Windows? </li>
-<p>No, no puede usar Final Cut Pro en Windows. Final Cut Pro solo es compatible con computadoras y dispositivos Mac. No hay versión oficial de Windows de Final Cut Pro. Es posible que encuentre algunas versiones no oficiales o pirateadas de Final Cut Pro para Windows en línea, pero no recomendamos usarlas, ya que pueden ser ilegales, inseguras o inestables. </p>
-<li>¿Cuánto tiempo se tarda en aprender Final Cut Pro? </li>
-<p>La curva de aprendizaje de Final Cut Pro depende de tu experiencia, habilidades y objetivos previos. Si tiene algunos conocimientos básicos de edición de vídeo y ordenadores Mac, es posible que pueda aprender lo esencial de Final Cut Pro en pocas horas o días. Si eres un completo principiante o quieres dominar las características avanzadas de Final Cut Pro, es posible que necesites más tiempo y práctica para aprender el software. También puedes usar tutoriales, cursos, libros o foros en línea para ayudarte a aprender Final Cut Pro más rápido y fácil. </p>
-<li> ¿Cuál es la diferencia entre Final Cut Pro y iMovie? </li>
-<p>Final Cut Pro y iMovie son software de edición de vídeo desarrollado por Apple. Sin embargo, tienen diferentes audiencias, características y funciones. iMovie es un editor de video simple y fácil de usar que viene preinstalado en computadoras y dispositivos Mac. Está diseñado para principiantes o usuarios casuales que quieren crear vídeos básicos o divertidos con el mínimo esfuerzo. Soporta resolución 4K, herramientas básicas de edición, transiciones, filtros, títulos, bandas sonoras y trailers. Final Cut Pro es un editor de vídeo profesional y potente que requiere una compra e instalación por separado. Está diseñado para usuarios serios o profesionales que quieren crear vídeos complejos o de alta calidad con más control y flexibilidad. Soporta resolución de hasta 8K, herramientas avanzadas de edición, corrección de color, efectos visuales, postproducción de audio, gráficos en movimiento y más. </p>
-<li>¿Cómo puedo actualizar Final Cut Pro? </li>
-
-<ul>
-<li>Abra la aplicación App Store en su Mac.</li>
-<li>Haga clic en la pestaña Actualizaciones en la parte superior de la ventana de la aplicación. </li>
-<li>Encuentre Final Cut Pro en la lista de actualizaciones disponibles y haga clic en el botón Actualizar junto a él. </li>
-<li>Espere a que la actualización se descargue e instale. </li>
-<li>Iniciar Final Cut Pro y disfrutar de las nuevas características y correcciones de errores. </li>
-</ul>
-<p>Si ha descargado la versión de prueba de Final Cut Pro desde el sitio web de Apple, no puede actualizarla a la última versión. Es necesario comprar la versión completa de la Mac App Store para obtener las actualizaciones y correcciones de errores. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/s3/inject.py

@@ -1,891 +0,0 @@
-# Copyright 2015 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
-#
-# https://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.
-from botocore.exceptions import ClientError
-
-from boto3 import utils
-from boto3.s3.transfer import (
-    ProgressCallbackInvoker,
-    S3Transfer,
-    TransferConfig,
-    create_transfer_manager,
-)
-
-
-def inject_s3_transfer_methods(class_attributes, **kwargs):
-    utils.inject_attribute(class_attributes, 'upload_file', upload_file)
-    utils.inject_attribute(class_attributes, 'download_file', download_file)
-    utils.inject_attribute(class_attributes, 'copy', copy)
-    utils.inject_attribute(class_attributes, 'upload_fileobj', upload_fileobj)
-    utils.inject_attribute(
-        class_attributes, 'download_fileobj', download_fileobj
-    )
-
-
-def inject_bucket_methods(class_attributes, **kwargs):
-    utils.inject_attribute(class_attributes, 'load', bucket_load)
-    utils.inject_attribute(class_attributes, 'upload_file', bucket_upload_file)
-    utils.inject_attribute(
-        class_attributes, 'download_file', bucket_download_file
-    )
-    utils.inject_attribute(class_attributes, 'copy', bucket_copy)
-    utils.inject_attribute(
-        class_attributes, 'upload_fileobj', bucket_upload_fileobj
-    )
-    utils.inject_attribute(
-        class_attributes, 'download_fileobj', bucket_download_fileobj
-    )
-
-
-def inject_object_methods(class_attributes, **kwargs):
-    utils.inject_attribute(class_attributes, 'upload_file', object_upload_file)
-    utils.inject_attribute(
-        class_attributes, 'download_file', object_download_file
-    )
-    utils.inject_attribute(class_attributes, 'copy', object_copy)
-    utils.inject_attribute(
-        class_attributes, 'upload_fileobj', object_upload_fileobj
-    )
-    utils.inject_attribute(
-        class_attributes, 'download_fileobj', object_download_fileobj
-    )
-
-
-def inject_object_summary_methods(class_attributes, **kwargs):
-    utils.inject_attribute(class_attributes, 'load', object_summary_load)
-
-
-def bucket_load(self, *args, **kwargs):
-    """
-    Calls s3.Client.list_buckets() to update the attributes of the Bucket
-    resource.
-    """
-    # The docstring above is phrased this way to match what the autogenerated
-    # docs produce.
-
-    # We can't actually get the bucket's attributes from a HeadBucket,
-    # so we need to use a ListBuckets and search for our bucket.
-    # However, we may fail if we lack permissions to ListBuckets
-    # or the bucket is in another account. In which case, creation_date
-    # will be None.
-    self.meta.data = {}
-    try:
-        response = self.meta.client.list_buckets()
-        for bucket_data in response['Buckets']:
-            if bucket_data['Name'] == self.name:
-                self.meta.data = bucket_data
-                break
-    except ClientError as e:
-        if not e.response.get('Error', {}).get('Code') == 'AccessDenied':
-            raise
-
-
-def object_summary_load(self, *args, **kwargs):
-    """
-    Calls s3.Client.head_object to update the attributes of the ObjectSummary
-    resource.
-    """
-    response = self.meta.client.head_object(
-        Bucket=self.bucket_name, Key=self.key
-    )
-    if 'ContentLength' in response:
-        response['Size'] = response.pop('ContentLength')
-    self.meta.data = response
-
-
-def upload_file(
-    self, Filename, Bucket, Key, ExtraArgs=None, Callback=None, Config=None
-):
-    """Upload a file to an S3 object.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.client('s3')
-        s3.upload_file('/tmp/hello.txt', 'mybucket', 'hello.txt')
-
-    Similar behavior as S3Transfer's upload_file() method, except that
-    argument names are capitalized. Detailed examples can be found at
-    :ref:`S3Transfer's Usage <ref_s3transfer_usage>`.
-
-    :type Filename: str
-    :param Filename: The path to the file to upload.
-
-    :type Bucket: str
-    :param Bucket: The name of the bucket to upload to.
-
-    :type Key: str
-    :param Key: The name of the key to upload to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed upload arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the upload.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        transfer.
-    """
-    with S3Transfer(self, Config) as transfer:
-        return transfer.upload_file(
-            filename=Filename,
-            bucket=Bucket,
-            key=Key,
-            extra_args=ExtraArgs,
-            callback=Callback,
-        )
-
-
-def download_file(
-    self, Bucket, Key, Filename, ExtraArgs=None, Callback=None, Config=None
-):
-    """Download an S3 object to a file.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        s3.meta.client.download_file('mybucket', 'hello.txt', '/tmp/hello.txt')
-
-    Similar behavior as S3Transfer's download_file() method,
-    except that parameters are capitalized. Detailed examples can be found at
-    :ref:`S3Transfer's Usage <ref_s3transfer_usage>`.
-
-    :type Bucket: str
-    :param Bucket: The name of the bucket to download from.
-
-    :type Key: str
-    :param Key: The name of the key to download from.
-
-    :type Filename: str
-    :param Filename: The path to the file to download to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the download.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        transfer.
-    """
-    with S3Transfer(self, Config) as transfer:
-        return transfer.download_file(
-            bucket=Bucket,
-            key=Key,
-            filename=Filename,
-            extra_args=ExtraArgs,
-            callback=Callback,
-        )
-
-
-def bucket_upload_file(
-    self, Filename, Key, ExtraArgs=None, Callback=None, Config=None
-):
-    """Upload a file to an S3 object.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        s3.Bucket('mybucket').upload_file('/tmp/hello.txt', 'hello.txt')
-
-    Similar behavior as S3Transfer's upload_file() method,
-    except that parameters are capitalized. Detailed examples can be found at
-    :ref:`S3Transfer's Usage <ref_s3transfer_usage>`.
-
-    :type Filename: str
-    :param Filename: The path to the file to upload.
-
-    :type Key: str
-    :param Key: The name of the key to upload to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed upload arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the upload.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        transfer.
-    """
-    return self.meta.client.upload_file(
-        Filename=Filename,
-        Bucket=self.name,
-        Key=Key,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def bucket_download_file(
-    self, Key, Filename, ExtraArgs=None, Callback=None, Config=None
-):
-    """Download an S3 object to a file.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        s3.Bucket('mybucket').download_file('hello.txt', '/tmp/hello.txt')
-
-    Similar behavior as S3Transfer's download_file() method,
-    except that parameters are capitalized. Detailed examples can be found at
-    :ref:`S3Transfer's Usage <ref_s3transfer_usage>`.
-
-    :type Key: str
-    :param Key: The name of the key to download from.
-
-    :type Filename: str
-    :param Filename: The path to the file to download to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the download.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        transfer.
-    """
-    return self.meta.client.download_file(
-        Bucket=self.name,
-        Key=Key,
-        Filename=Filename,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def object_upload_file(
-    self, Filename, ExtraArgs=None, Callback=None, Config=None
-):
-    """Upload a file to an S3 object.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        s3.Object('mybucket', 'hello.txt').upload_file('/tmp/hello.txt')
-
-    Similar behavior as S3Transfer's upload_file() method,
-    except that parameters are capitalized. Detailed examples can be found at
-    :ref:`S3Transfer's Usage <ref_s3transfer_usage>`.
-
-    :type Filename: str
-    :param Filename: The path to the file to upload.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed upload arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the upload.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        transfer.
-    """
-    return self.meta.client.upload_file(
-        Filename=Filename,
-        Bucket=self.bucket_name,
-        Key=self.key,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def object_download_file(
-    self, Filename, ExtraArgs=None, Callback=None, Config=None
-):
-    """Download an S3 object to a file.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        s3.Object('mybucket', 'hello.txt').download_file('/tmp/hello.txt')
-
-    Similar behavior as S3Transfer's download_file() method,
-    except that parameters are capitalized. Detailed examples can be found at
-    :ref:`S3Transfer's Usage <ref_s3transfer_usage>`.
-
-    :type Filename: str
-    :param Filename: The path to the file to download to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the download.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        transfer.
-    """
-    return self.meta.client.download_file(
-        Bucket=self.bucket_name,
-        Key=self.key,
-        Filename=Filename,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def copy(
-    self,
-    CopySource,
-    Bucket,
-    Key,
-    ExtraArgs=None,
-    Callback=None,
-    SourceClient=None,
-    Config=None,
-):
-    """Copy an object from one S3 location to another.
-
-    This is a managed transfer which will perform a multipart copy in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        copy_source = {
-            'Bucket': 'mybucket',
-            'Key': 'mykey'
-        }
-        s3.meta.client.copy(copy_source, 'otherbucket', 'otherkey')
-
-    :type CopySource: dict
-    :param CopySource: The name of the source bucket, key name of the
-        source object, and optional version ID of the source object. The
-        dictionary format is:
-        ``{'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}``. Note
-        that the ``VersionId`` key is optional and may be omitted.
-
-    :type Bucket: str
-    :param Bucket: The name of the bucket to copy to
-
-    :type Key: str
-    :param Key: The name of the key to copy to
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the copy.
-
-    :type SourceClient: botocore or boto3 Client
-    :param SourceClient: The client to be used for operation that
-        may happen at the source object. For example, this client is
-        used for the head_object that determines the size of the copy.
-        If no client is provided, the current client is used as the client
-        for the source object.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        copy.
-    """
-    subscribers = None
-    if Callback is not None:
-        subscribers = [ProgressCallbackInvoker(Callback)]
-
-    config = Config
-    if config is None:
-        config = TransferConfig()
-
-    with create_transfer_manager(self, config) as manager:
-        future = manager.copy(
-            copy_source=CopySource,
-            bucket=Bucket,
-            key=Key,
-            extra_args=ExtraArgs,
-            subscribers=subscribers,
-            source_client=SourceClient,
-        )
-        return future.result()
-
-
-def bucket_copy(
-    self,
-    CopySource,
-    Key,
-    ExtraArgs=None,
-    Callback=None,
-    SourceClient=None,
-    Config=None,
-):
-    """Copy an object from one S3 location to an object in this bucket.
-
-    This is a managed transfer which will perform a multipart copy in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        copy_source = {
-            'Bucket': 'mybucket',
-            'Key': 'mykey'
-        }
-        bucket = s3.Bucket('otherbucket')
-        bucket.copy(copy_source, 'otherkey')
-
-    :type CopySource: dict
-    :param CopySource: The name of the source bucket, key name of the
-        source object, and optional version ID of the source object. The
-        dictionary format is:
-        ``{'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}``. Note
-        that the ``VersionId`` key is optional and may be omitted.
-
-    :type Key: str
-    :param Key: The name of the key to copy to
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the copy.
-
-    :type SourceClient: botocore or boto3 Client
-    :param SourceClient: The client to be used for operation that
-        may happen at the source object. For example, this client is
-        used for the head_object that determines the size of the copy.
-        If no client is provided, the current client is used as the client
-        for the source object.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        copy.
-    """
-    return self.meta.client.copy(
-        CopySource=CopySource,
-        Bucket=self.name,
-        Key=Key,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        SourceClient=SourceClient,
-        Config=Config,
-    )
-
-
-def object_copy(
-    self,
-    CopySource,
-    ExtraArgs=None,
-    Callback=None,
-    SourceClient=None,
-    Config=None,
-):
-    """Copy an object from one S3 location to this object.
-
-    This is a managed transfer which will perform a multipart copy in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        copy_source = {
-            'Bucket': 'mybucket',
-            'Key': 'mykey'
-        }
-        bucket = s3.Bucket('otherbucket')
-        obj = bucket.Object('otherkey')
-        obj.copy(copy_source)
-
-    :type CopySource: dict
-    :param CopySource: The name of the source bucket, key name of the
-        source object, and optional version ID of the source object. The
-        dictionary format is:
-        ``{'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}``. Note
-        that the ``VersionId`` key is optional and may be omitted.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the copy.
-
-    :type SourceClient: botocore or boto3 Client
-    :param SourceClient: The client to be used for operation that
-        may happen at the source object. For example, this client is
-        used for the head_object that determines the size of the copy.
-        If no client is provided, the current client is used as the client
-        for the source object.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        copy.
-    """
-    return self.meta.client.copy(
-        CopySource=CopySource,
-        Bucket=self.bucket_name,
-        Key=self.key,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        SourceClient=SourceClient,
-        Config=Config,
-    )
-
-
-def upload_fileobj(
-    self, Fileobj, Bucket, Key, ExtraArgs=None, Callback=None, Config=None
-):
-    """Upload a file-like object to S3.
-
-    The file-like object must be in binary mode.
-
-    This is a managed transfer which will perform a multipart upload in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.client('s3')
-
-        with open('filename', 'rb') as data:
-            s3.upload_fileobj(data, 'mybucket', 'mykey')
-
-    :type Fileobj: a file-like object
-    :param Fileobj: A file-like object to upload. At a minimum, it must
-        implement the `read` method, and must return bytes.
-
-    :type Bucket: str
-    :param Bucket: The name of the bucket to upload to.
-
-    :type Key: str
-    :param Key: The name of the key to upload to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed upload arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the upload.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        upload.
-    """
-    if not hasattr(Fileobj, 'read'):
-        raise ValueError('Fileobj must implement read')
-
-    subscribers = None
-    if Callback is not None:
-        subscribers = [ProgressCallbackInvoker(Callback)]
-
-    config = Config
-    if config is None:
-        config = TransferConfig()
-
-    with create_transfer_manager(self, config) as manager:
-        future = manager.upload(
-            fileobj=Fileobj,
-            bucket=Bucket,
-            key=Key,
-            extra_args=ExtraArgs,
-            subscribers=subscribers,
-        )
-        return future.result()
-
-
-def bucket_upload_fileobj(
-    self, Fileobj, Key, ExtraArgs=None, Callback=None, Config=None
-):
-    """Upload a file-like object to this bucket.
-
-    The file-like object must be in binary mode.
-
-    This is a managed transfer which will perform a multipart upload in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        bucket = s3.Bucket('mybucket')
-
-        with open('filename', 'rb') as data:
-            bucket.upload_fileobj(data, 'mykey')
-
-    :type Fileobj: a file-like object
-    :param Fileobj: A file-like object to upload. At a minimum, it must
-        implement the `read` method, and must return bytes.
-
-    :type Key: str
-    :param Key: The name of the key to upload to.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed upload arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the upload.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        upload.
-    """
-    return self.meta.client.upload_fileobj(
-        Fileobj=Fileobj,
-        Bucket=self.name,
-        Key=Key,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def object_upload_fileobj(
-    self, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
-    """Upload a file-like object to this object.
-
-    The file-like object must be in binary mode.
-
-    This is a managed transfer which will perform a multipart upload in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        bucket = s3.Bucket('mybucket')
-        obj = bucket.Object('mykey')
-
-        with open('filename', 'rb') as data:
-            obj.upload_fileobj(data)
-
-    :type Fileobj: a file-like object
-    :param Fileobj: A file-like object to upload. At a minimum, it must
-        implement the `read` method, and must return bytes.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed upload arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_UPLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the upload.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        upload.
-    """
-    return self.meta.client.upload_fileobj(
-        Fileobj=Fileobj,
-        Bucket=self.bucket_name,
-        Key=self.key,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def download_fileobj(
-    self, Bucket, Key, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
-    """Download an object from S3 to a file-like object.
-
-    The file-like object must be in binary mode.
-
-    This is a managed transfer which will perform a multipart download in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.client('s3')
-
-        with open('filename', 'wb') as data:
-            s3.download_fileobj('mybucket', 'mykey', data)
-
-    :type Bucket: str
-    :param Bucket: The name of the bucket to download from.
-
-    :type Key: str
-    :param Key: The name of the key to download from.
-
-    :type Fileobj: a file-like object
-    :param Fileobj: A file-like object to download into. At a minimum, it must
-        implement the `write` method and must accept bytes.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the download.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        download.
-    """
-    if not hasattr(Fileobj, 'write'):
-        raise ValueError('Fileobj must implement write')
-
-    subscribers = None
-    if Callback is not None:
-        subscribers = [ProgressCallbackInvoker(Callback)]
-
-    config = Config
-    if config is None:
-        config = TransferConfig()
-
-    with create_transfer_manager(self, config) as manager:
-        future = manager.download(
-            bucket=Bucket,
-            key=Key,
-            fileobj=Fileobj,
-            extra_args=ExtraArgs,
-            subscribers=subscribers,
-        )
-        return future.result()
-
-
-def bucket_download_fileobj(
-    self, Key, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
-    """Download an object from this bucket to a file-like-object.
-
-    The file-like object must be in binary mode.
-
-    This is a managed transfer which will perform a multipart download in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        bucket = s3.Bucket('mybucket')
-
-        with open('filename', 'wb') as data:
-            bucket.download_fileobj('mykey', data)
-
-    :type Fileobj: a file-like object
-    :param Fileobj: A file-like object to download into. At a minimum, it must
-        implement the `write` method and must accept bytes.
-
-    :type Key: str
-    :param Key: The name of the key to download from.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the download.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        download.
-    """
-    return self.meta.client.download_fileobj(
-        Bucket=self.name,
-        Key=Key,
-        Fileobj=Fileobj,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )
-
-
-def object_download_fileobj(
-    self, Fileobj, ExtraArgs=None, Callback=None, Config=None
-):
-    """Download this object from S3 to a file-like object.
-
-    The file-like object must be in binary mode.
-
-    This is a managed transfer which will perform a multipart download in
-    multiple threads if necessary.
-
-    Usage::
-
-        import boto3
-        s3 = boto3.resource('s3')
-        bucket = s3.Bucket('mybucket')
-        obj = bucket.Object('mykey')
-
-        with open('filename', 'wb') as data:
-            obj.download_fileobj(data)
-
-    :type Fileobj: a file-like object
-    :param Fileobj: A file-like object to download into. At a minimum, it must
-        implement the `write` method and must accept bytes.
-
-    :type ExtraArgs: dict
-    :param ExtraArgs: Extra arguments that may be passed to the
-        client operation. For allowed download arguments see
-        boto3.s3.transfer.S3Transfer.ALLOWED_DOWNLOAD_ARGS.
-
-    :type Callback: function
-    :param Callback: A method which takes a number of bytes transferred to
-        be periodically called during the download.
-
-    :type Config: boto3.s3.transfer.TransferConfig
-    :param Config: The transfer configuration to be used when performing the
-        download.
-    """
-    return self.meta.client.download_fileobj(
-        Bucket=self.bucket_name,
-        Key=self.key,
-        Fileobj=Fileobj,
-        ExtraArgs=ExtraArgs,
-        Callback=Callback,
-        Config=Config,
-    )

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/jaraco/context.py

@@ -1,213 +0,0 @@
-import os
-import subprocess
-import contextlib
-import functools
-import tempfile
-import shutil
-import operator
-
-
-@contextlib.contextmanager
-def pushd(dir):
-    orig = os.getcwd()
-    os.chdir(dir)
-    try:
-        yield dir
-    finally:
-        os.chdir(orig)
-
-
-@contextlib.contextmanager
-def tarball_context(url, target_dir=None, runner=None, pushd=pushd):
-    """
-    Get a tarball, extract it, change to that directory, yield, then
-    clean up.
-    `runner` is the function to invoke commands.
-    `pushd` is a context manager for changing the directory.
-    """
-    if target_dir is None:
-        target_dir = os.path.basename(url).replace('.tar.gz', '').replace('.tgz', '')
-    if runner is None:
-        runner = functools.partial(subprocess.check_call, shell=True)
-    # In the tar command, use --strip-components=1 to strip the first path and
-    #  then
-    #  use -C to cause the files to be extracted to {target_dir}. This ensures
-    #  that we always know where the files were extracted.
-    runner('mkdir {target_dir}'.format(**vars()))
-    try:
-        getter = 'wget {url} -O -'
-        extract = 'tar x{compression} --strip-components=1 -C {target_dir}'
-        cmd = ' | '.join((getter, extract))
-        runner(cmd.format(compression=infer_compression(url), **vars()))
-        with pushd(target_dir):
-            yield target_dir
-    finally:
-        runner('rm -Rf {target_dir}'.format(**vars()))
-
-
-def infer_compression(url):
-    """
-    Given a URL or filename, infer the compression code for tar.
-    """
-    # cheat and just assume it's the last two characters
-    compression_indicator = url[-2:]
-    mapping = dict(gz='z', bz='j', xz='J')
-    # Assume 'z' (gzip) if no match
-    return mapping.get(compression_indicator, 'z')
-
-
-@contextlib.contextmanager
-def temp_dir(remover=shutil.rmtree):
-    """
-    Create a temporary directory context. Pass a custom remover
-    to override the removal behavior.
-    """
-    temp_dir = tempfile.mkdtemp()
-    try:
-        yield temp_dir
-    finally:
-        remover(temp_dir)
-
-
-@contextlib.contextmanager
-def repo_context(url, branch=None, quiet=True, dest_ctx=temp_dir):
-    """
-    Check out the repo indicated by url.
-
-    If dest_ctx is supplied, it should be a context manager
-    to yield the target directory for the check out.
-    """
-    exe = 'git' if 'git' in url else 'hg'
-    with dest_ctx() as repo_dir:
-        cmd = [exe, 'clone', url, repo_dir]
-        if branch:
-            cmd.extend(['--branch', branch])
-        devnull = open(os.path.devnull, 'w')
-        stdout = devnull if quiet else None
-        subprocess.check_call(cmd, stdout=stdout)
-        yield repo_dir
-
-
-@contextlib.contextmanager
-def null():
-    yield
-
-
-class ExceptionTrap:
-    """
-    A context manager that will catch certain exceptions and provide an
-    indication they occurred.
-
-    >>> with ExceptionTrap() as trap:
-    ...     raise Exception()
-    >>> bool(trap)
-    True
-
-    >>> with ExceptionTrap() as trap:
-    ...     pass
-    >>> bool(trap)
-    False
-
-    >>> with ExceptionTrap(ValueError) as trap:
-    ...     raise ValueError("1 + 1 is not 3")
-    >>> bool(trap)
-    True
-
-    >>> with ExceptionTrap(ValueError) as trap:
-    ...     raise Exception()
-    Traceback (most recent call last):
-    ...
-    Exception
-
-    >>> bool(trap)
-    False
-    """
-
-    exc_info = None, None, None
-
-    def __init__(self, exceptions=(Exception,)):
-        self.exceptions = exceptions
-
-    def __enter__(self):
-        return self
-
-    @property
-    def type(self):
-        return self.exc_info[0]
-
-    @property
-    def value(self):
-        return self.exc_info[1]
-
-    @property
-    def tb(self):
-        return self.exc_info[2]
-
-    def __exit__(self, *exc_info):
-        type = exc_info[0]
-        matches = type and issubclass(type, self.exceptions)
-        if matches:
-            self.exc_info = exc_info
-        return matches
-
-    def __bool__(self):
-        return bool(self.type)
-
-    def raises(self, func, *, _test=bool):
-        """
-        Wrap func and replace the result with the truth
-        value of the trap (True if an exception occurred).
-
-        First, give the decorator an alias to support Python 3.8
-        Syntax.
-
-        >>> raises = ExceptionTrap(ValueError).raises
-
-        Now decorate a function that always fails.
-
-        >>> @raises
-        ... def fail():
-        ...     raise ValueError('failed')
-        >>> fail()
-        True
-        """
-
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            with ExceptionTrap(self.exceptions) as trap:
-                func(*args, **kwargs)
-            return _test(trap)
-
-        return wrapper
-
-    def passes(self, func):
-        """
-        Wrap func and replace the result with the truth
-        value of the trap (True if no exception).
-
-        First, give the decorator an alias to support Python 3.8
-        Syntax.
-
-        >>> passes = ExceptionTrap(ValueError).passes
-
-        Now decorate a function that always fails.
-
-        >>> @passes
-        ... def fail():
-        ...     raise ValueError('failed')
-
-        >>> fail()
-        False
-        """
-        return self.raises(func, _test=operator.not_)
-
-
-class suppress(contextlib.suppress, contextlib.ContextDecorator):
-    """
-    A version of contextlib.suppress with decorator support.
-
-    >>> @suppress(KeyError)
-    ... def key_error():
-    ...     {}['']
-    >>> key_error()
-    """

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/build.py

@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from detectron2.utils.registry import Registry
-
-PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR")
-PROPOSAL_GENERATOR_REGISTRY.__doc__ = """
-Registry for proposal generator, which produces object proposals from feature maps.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-from . import rpn, rrpn  # noqa F401 isort:skip
-
-
-def build_proposal_generator(cfg, input_shape):
-    """
-    Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`.
-    The name can be "PrecomputedProposals" to use no proposal generator.
-    """
-    name = cfg.MODEL.PROPOSAL_GENERATOR.NAME
-    if name == "PrecomputedProposals":
-        return None
-
-    return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape)

spaces/CVPR/LIVE/pybind11/docs/_static/theme_overrides.css

@@ -1,11 +0,0 @@
-.wy-table-responsive table td,
-.wy-table-responsive table th {
-    white-space: initial !important;
-}
-.rst-content table.docutils td {
-    vertical-align: top !important;
-}
-div[class^='highlight'] pre {
-    white-space: pre;
-    white-space: pre-wrap;
-}

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/find.h

@@ -1,219 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/system/cuda/config.h>
-
-#include <thrust/system/cuda/detail/execution_policy.h>
-#include <thrust/detail/minmax.h>
-#include <thrust/distance.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-// XXX forward declare to circumvent circular depedency
-template <class Derived,
-          class InputIt,
-          class Predicate>
-InputIt __host__ __device__
-find_if(execution_policy<Derived>& policy,
-        InputIt                    first,
-        InputIt                    last,
-        Predicate                  predicate);
-
-template <class Derived,
-          class InputIt,
-          class Predicate>
-InputIt __host__ __device__
-find_if_not(execution_policy<Derived>& policy,
-            InputIt                    first,
-            InputIt                    last,
-            Predicate                  predicate);
-
-template <class Derived,
-          class InputIt,
-          class T>
-InputIt __host__ __device__
-find(execution_policy<Derived> &policy,
-     InputIt                    first,
-     InputIt                    last,
-     T const& value);
-
-}; // namespace cuda_cub
-} // end namespace thrust
-
-#include <thrust/system/cuda/detail/reduce.h>
-#include <thrust/iterator/zip_iterator.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-namespace __find_if {
-
-  template <typename TupleType>
-  struct functor
-  {
-    THRUST_DEVICE_FUNCTION TupleType
-    operator()(const TupleType& lhs, const TupleType& rhs) const
-    {
-      // select the smallest index among true results
-      if (thrust::get<0>(lhs) && thrust::get<0>(rhs))
-      {
-        return TupleType(true, (thrust::min)(thrust::get<1>(lhs), thrust::get<1>(rhs)));
-      }
-      else if (thrust::get<0>(lhs))
-      {
-        return lhs;
-      }
-      else
-      {
-        return rhs;
-      }
-    }
-  };
-}    // namespace __find_if
-
-template <class Derived,
-          class InputIt,
-          class Size,
-          class Predicate>
-InputIt __host__ __device__
-find_if_n(execution_policy<Derived>& policy,
-          InputIt                    first,
-          Size                       num_items,
-          Predicate                  predicate)
-{
-  typedef typename thrust::tuple<bool,Size> result_type;
-  
-  // empty sequence
-  if(num_items == 0) return first;
-  
-  // this implementation breaks up the sequence into separate intervals
-  // in an attempt to early-out as soon as a value is found
-  //
-  // XXX compose find_if from a look-back prefix scan algorithm
-  //     and abort kernel when the first element is found
-
-
-  // TODO incorporate sizeof(InputType) into interval_threshold and round to multiple of 32
-  const Size interval_threshold = 1 << 20;
-  const Size interval_size = (thrust::min)(interval_threshold, num_items);
-  
-  // force transform_iterator output to bool
-  typedef transform_input_iterator_t<bool,
-                                     InputIt,
-                                     Predicate>
-      XfrmIterator;
-  typedef thrust::tuple<XfrmIterator,
-                        counting_iterator_t<Size> >
-      IteratorTuple;
-  typedef thrust::zip_iterator<IteratorTuple> ZipIterator;
-
-  IteratorTuple iter_tuple =
-      thrust::make_tuple(XfrmIterator(first, predicate),
-                         counting_iterator_t<Size>(0));
-
-  ZipIterator begin = thrust::make_zip_iterator(iter_tuple);
-  ZipIterator end   = begin + num_items;
-
-  for (ZipIterator interval_begin = begin;
-       interval_begin < end;
-       interval_begin += interval_size)
-  {
-    ZipIterator interval_end = interval_begin + interval_size;
-    if(end < interval_end)
-    {
-      interval_end = end;
-    } // end if
-
-    result_type result = reduce(policy,
-                                interval_begin,
-                                interval_end,
-                                result_type(false, interval_end - begin),
-                                __find_if::functor<result_type>());
-
-    // see if we found something
-    if(thrust::get<0>(result))
-    {
-      return first + thrust::get<1>(result);
-    }
-  }
-  
-  //nothing was found if we reach here...
-  return first + num_items;
-}
-
-template <class Derived,
-          class InputIt,
-          class Predicate>
-InputIt __host__ __device__
-find_if(execution_policy<Derived>& policy,
-        InputIt                    first,
-        InputIt                    last,
-        Predicate                  predicate)
-{
-  return cuda_cub::find_if_n(policy, first, thrust::distance(first,last), predicate);
-}
-
-template <class Derived,
-          class InputIt,
-          class Predicate>
-InputIt __host__ __device__
-find_if_not(execution_policy<Derived>& policy,
-            InputIt                    first,
-            InputIt                    last,
-            Predicate                  predicate)
-{
-  return cuda_cub::find_if(policy, first, last, thrust::detail::not1(predicate));
-}
-
-
-template <class Derived,
-          class InputIt,
-          class T>
-InputIt __host__ __device__
-find(execution_policy<Derived> &policy,
-     InputIt                    first,
-     InputIt                    last,
-     T const& value)
-{
-  using thrust::placeholders::_1;
-
-  return cuda_cub::find_if(policy,
-                        first,
-                        last,
-                        _1 == value);
-}
-
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif

spaces/CVPR/WALT/configs/_base_/datasets/parking_instance_coco.py

@@ -1,49 +0,0 @@
-dataset_type = 'ParkingCocoDataset'
-data_root = 'data/parking/'
-data_root_test = 'data/parking_highres/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=6,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'GT_data/',
-        img_prefix=data_root + 'images/',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root_test + 'GT_data/',
-        img_prefix=data_root_test + 'images',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root_test + 'GT_data/',
-        img_prefix=data_root_test + 'images',
-        pipeline=test_pipeline))
-evaluation = dict(metric=['bbox', 'segm'])

spaces/CVPR/regionclip-demo/detectron2/layers/shape_spec.py

@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-from collections import namedtuple
-
-
-class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])):
-    """
-    A simple structure that contains basic shape specification about a tensor.
-    It is often used as the auxiliary inputs/outputs of models,
-    to complement the lack of shape inference ability among pytorch modules.
-
-    Attributes:
-        channels:
-        height:
-        width:
-        stride:
-    """
-
-    def __new__(cls, channels=None, height=None, width=None, stride=None):
-        return super().__new__(cls, channels, height, width, stride)

spaces/Cambino/dog-classifier-gradio/README.md

@@ -1,13 +0,0 @@
----
-title: Dog Classifier
-emoji: 🐠
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.3
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/ksdd2_parameters.py

@@ -1,11 +0,0 @@
-manual_prompts = {
-    'ksdd2': [
-        ['black hole.', 'ksdd2'],
-        ['defect.', 'ksdd2'],
-    ],
-
-}
-
-property_prompts = {
-    'ksdd2': 'the image of ksdd2 have 1 dissimilar ksdd2, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-}

spaces/Cat125/text-generator-v3/files.py

@@ -1,7 +0,0 @@
-def read_file(filename):
-        with open(filename, encoding="utf8") as f:
-            return f.read()
-
-def read_lines(filename):
-    with open(filename, encoding="utf8") as f:
-        return f.readlines()

spaces/Covert1107/sd-diffusers-webui/modules/prompt_parser.py

@@ -1,391 +0,0 @@
-
-import re
-import math
-import numpy as np
-import torch
-
-# Code from https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/8e2aeee4a127b295bfc880800e4a312e0f049b85, modified.
-
-class PromptChunk:
-    """
-    This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
-    If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
-    Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
-    so just 75 tokens from prompt.
-    """
-
-    def __init__(self):
-        self.tokens = []
-        self.multipliers = []
-        self.fixes = []
-
-
-class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
-    """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
-    have unlimited prompt length and assign weights to tokens in prompt.
-    """
-
-    def __init__(self, text_encoder, enable_emphasis=True):
-        super().__init__()
-
-        self.device = lambda: text_encoder.device
-        self.enable_emphasis = enable_emphasis
-        """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
-        depending on model."""
-
-        self.chunk_length = 75
-
-    def empty_chunk(self):
-        """creates an empty PromptChunk and returns it"""
-
-        chunk = PromptChunk()
-        chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
-        chunk.multipliers = [1.0] * (self.chunk_length + 2)
-        return chunk
-
-    def get_target_prompt_token_count(self, token_count):
-        """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
-
-        return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
-
-    def tokenize_line(self, line):
-        """
-        this transforms a single prompt into a list of PromptChunk objects - as many as needed to
-        represent the prompt.
-        Returns the list and the total number of tokens in the prompt.
-        """
-
-        if self.enable_emphasis:
-            parsed = parse_prompt_attention(line)
-        else:
-            parsed = [[line, 1.0]]
-
-        tokenized = self.tokenize([text for text, _ in parsed])
-
-        chunks = []
-        chunk = PromptChunk()
-        token_count = 0
-        last_comma = -1
-
-        def next_chunk(is_last=False):
-            """puts current chunk into the list of results and produces the next one - empty;
-            if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
-            nonlocal token_count
-            nonlocal last_comma
-            nonlocal chunk
-
-            if is_last:
-                token_count += len(chunk.tokens)
-            else:
-                token_count += self.chunk_length
-
-            to_add = self.chunk_length - len(chunk.tokens)
-            if to_add > 0:
-                chunk.tokens += [self.id_end] * to_add
-                chunk.multipliers += [1.0] * to_add
-
-            chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
-            chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
-
-            last_comma = -1
-            chunks.append(chunk)
-            chunk = PromptChunk()
-
-        comma_padding_backtrack = 20  # default value in https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/6cff4401824299a983c8e13424018efc347b4a2b/modules/shared.py#L410
-        for tokens, (text, weight) in zip(tokenized, parsed):
-            if text == "BREAK" and weight == -1:
-                next_chunk()
-                continue
-
-            position = 0
-            while position < len(tokens):
-                token = tokens[position]
-
-                if token == self.comma_token:
-                    last_comma = len(chunk.tokens)
-
-                # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
-                # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
-                elif (
-                    comma_padding_backtrack != 0
-                    and len(chunk.tokens) == self.chunk_length
-                    and last_comma != -1
-                    and len(chunk.tokens) - last_comma <= comma_padding_backtrack
-                ):
-                    break_location = last_comma + 1
-
-                    reloc_tokens = chunk.tokens[break_location:]
-                    reloc_mults = chunk.multipliers[break_location:]
-
-                    chunk.tokens = chunk.tokens[:break_location]
-                    chunk.multipliers = chunk.multipliers[:break_location]
-
-                    next_chunk()
-                    chunk.tokens = reloc_tokens
-                    chunk.multipliers = reloc_mults
-
-                if len(chunk.tokens) == self.chunk_length:
-                    next_chunk()
-
-                chunk.tokens.append(token)
-                chunk.multipliers.append(weight)
-                position += 1
-
-        if len(chunk.tokens) > 0 or len(chunks) == 0:
-            next_chunk(is_last=True)
-
-        return chunks, token_count
-
-    def process_texts(self, texts):
-        """
-        Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
-        length, in tokens, of all texts.
-        """
-
-        token_count = 0
-
-        cache = {}
-        batch_chunks = []
-        for line in texts:
-            if line in cache:
-                chunks = cache[line]
-            else:
-                chunks, current_token_count = self.tokenize_line(line)
-                token_count = max(current_token_count, token_count)
-
-                cache[line] = chunks
-
-            batch_chunks.append(chunks)
-
-        return batch_chunks, token_count
-
-    def forward(self, texts):
-        """
-        Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
-        Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
-        be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
-        An example shape returned by this function can be: (2, 77, 768).
-        Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
-        is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
-        """
-
-        batch_chunks, token_count = self.process_texts(texts)
-        chunk_count = max([len(x) for x in batch_chunks])
-
-        zs = []
-        ts = []
-        for i in range(chunk_count):
-            batch_chunk = [
-                chunks[i] if i < len(chunks) else self.empty_chunk()
-                for chunks in batch_chunks
-            ]
-
-            tokens = [x.tokens for x in batch_chunk]
-            multipliers = [x.multipliers for x in batch_chunk]
-            # self.embeddings.fixes = [x.fixes for x in batch_chunk]
-
-            # for fixes in self.embeddings.fixes:
-            #     for position, embedding in fixes:
-            #         used_embeddings[embedding.name] = embedding
-
-            z = self.process_tokens(tokens, multipliers)
-            zs.append(z)
-            ts.append(tokens)
-
-        return np.hstack(ts), torch.hstack(zs)
-
-    def process_tokens(self, remade_batch_tokens, batch_multipliers):
-        """
-        sends one single prompt chunk to be encoded by transformers neural network.
-        remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
-        there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
-        Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
-        corresponds to one token.
-        """
-        tokens = torch.asarray(remade_batch_tokens).to(self.device())
-
-        # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
-        if self.id_end != self.id_pad:
-            for batch_pos in range(len(remade_batch_tokens)):
-                index = remade_batch_tokens[batch_pos].index(self.id_end)
-                tokens[batch_pos, index + 1 : tokens.shape[1]] = self.id_pad
-
-        z = self.encode_with_transformers(tokens)
-
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers = torch.asarray(batch_multipliers).to(self.device())
-        original_mean = z.mean()
-        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
-        z = z * (original_mean / new_mean)
-
-        return z
-
-
-class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
-    def __init__(self, tokenizer, text_encoder):
-        super().__init__(text_encoder)
-        self.tokenizer = tokenizer
-        self.text_encoder = text_encoder
-
-        vocab = self.tokenizer.get_vocab()
-
-        self.comma_token = vocab.get(",</w>", None)
-
-        self.token_mults = {}
-        tokens_with_parens = [
-            (k, v)
-            for k, v in vocab.items()
-            if "(" in k or ")" in k or "[" in k or "]" in k
-        ]
-        for text, ident in tokens_with_parens:
-            mult = 1.0
-            for c in text:
-                if c == "[":
-                    mult /= 1.1
-                if c == "]":
-                    mult *= 1.1
-                if c == "(":
-                    mult *= 1.1
-                if c == ")":
-                    mult /= 1.1
-
-            if mult != 1.0:
-                self.token_mults[ident] = mult
-
-        self.id_start = self.tokenizer.bos_token_id
-        self.id_end = self.tokenizer.eos_token_id
-        self.id_pad = self.id_end
-
-    def tokenize(self, texts):
-        tokenized = self.tokenizer(
-            texts, truncation=False, add_special_tokens=False
-        )["input_ids"]
-
-        return tokenized
-
-    def encode_with_transformers(self, tokens):
-        CLIP_stop_at_last_layers = 1
-        tokens = tokens.to(self.text_encoder.device)
-        outputs = self.text_encoder(tokens, output_hidden_states=True)
-
-        if CLIP_stop_at_last_layers > 1:
-            z = outputs.hidden_states[-CLIP_stop_at_last_layers]
-            z = self.text_encoder.text_model.final_layer_norm(z)
-        else:
-            z = outputs.last_hidden_state
-
-        return z
-    
-
-re_attention = re.compile(
-    r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""",
-    re.X,
-)
-
-re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
-
-
-def parse_prompt_attention(text):
-    """
-    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
-    Accepted tokens are:
-      (abc) - increases attention to abc by a multiplier of 1.1
-      (abc:3.12) - increases attention to abc by a multiplier of 3.12
-      [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
-      \\ - literal character '\'
-      anything else - just text
-
-    >>> parse_prompt_attention('normal text')
-    [['normal text', 1.0]]
-    >>> parse_prompt_attention('an (important) word')
-    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
-    >>> parse_prompt_attention('(unbalanced')
-    [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
-    [['(literal]', 1.0]]
-    >>> parse_prompt_attention('(unnecessary)(parens)')
-    [['unnecessaryparens', 1.1]]
-    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
-    [['a ', 1.0],
-     ['house', 1.5730000000000004],
-     [' ', 1.1],
-     ['on', 1.0],
-     [' a ', 1.1],
-     ['hill', 0.55],
-     [', sun, ', 1.1],
-     ['sky', 1.4641000000000006],
-     ['.', 1.1]]
-    """
-
-    res = []
-    round_brackets = []
-    square_brackets = []
-
-    round_bracket_multiplier = 1.1
-    square_bracket_multiplier = 1 / 1.1
-
-    def multiply_range(start_position, multiplier):
-        for p in range(start_position, len(res)):
-            res[p][1] *= multiplier
-
-    for m in re_attention.finditer(text):
-        text = m.group(0)
-        weight = m.group(1)
-
-        if text.startswith("\\"):
-            res.append([text[1:], 1.0])
-        elif text == "(":
-            round_brackets.append(len(res))
-        elif text == "[":
-            square_brackets.append(len(res))
-        elif weight is not None and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), float(weight))
-        elif text == ")" and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), round_bracket_multiplier)
-        elif text == "]" and len(square_brackets) > 0:
-            multiply_range(square_brackets.pop(), square_bracket_multiplier)
-        else:
-            parts = re.split(re_break, text)
-            for i, part in enumerate(parts):
-                if i > 0:
-                    res.append(["BREAK", -1])
-                res.append([part, 1.0])
-
-    for pos in round_brackets:
-        multiply_range(pos, round_bracket_multiplier)
-
-    for pos in square_brackets:
-        multiply_range(pos, square_bracket_multiplier)
-
-    if len(res) == 0:
-        res = [["", 1.0]]
-
-    # merge runs of identical weights
-    i = 0
-    while i + 1 < len(res):
-        if res[i][1] == res[i + 1][1]:
-            res[i][0] += res[i + 1][0]
-            res.pop(i + 1)
-        else:
-            i += 1
-
-    return res

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/abc/_subprocesses.py

@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from abc import abstractmethod
-from signal import Signals
-
-from ._resources import AsyncResource
-from ._streams import ByteReceiveStream, ByteSendStream
-
-
-class Process(AsyncResource):
-    """An asynchronous version of :class:`subprocess.Popen`."""
-
-    @abstractmethod
-    async def wait(self) -> int:
-        """
-        Wait until the process exits.
-
-        :return: the exit code of the process
-        """
-
-    @abstractmethod
-    def terminate(self) -> None:
-        """
-        Terminates the process, gracefully if possible.
-
-        On Windows, this calls ``TerminateProcess()``.
-        On POSIX systems, this sends ``SIGTERM`` to the process.
-
-        .. seealso:: :meth:`subprocess.Popen.terminate`
-        """
-
-    @abstractmethod
-    def kill(self) -> None:
-        """
-        Kills the process.
-
-        On Windows, this calls ``TerminateProcess()``.
-        On POSIX systems, this sends ``SIGKILL`` to the process.
-
-        .. seealso:: :meth:`subprocess.Popen.kill`
-        """
-
-    @abstractmethod
-    def send_signal(self, signal: Signals) -> None:
-        """
-        Send a signal to the subprocess.
-
-        .. seealso:: :meth:`subprocess.Popen.send_signal`
-
-        :param signal: the signal number (e.g. :data:`signal.SIGHUP`)
-        """
-
-    @property
-    @abstractmethod
-    def pid(self) -> int:
-        """The process ID of the process."""
-
-    @property
-    @abstractmethod
-    def returncode(self) -> int | None:
-        """
-        The return code of the process. If the process has not yet terminated, this will be
-        ``None``.
-        """
-
-    @property
-    @abstractmethod
-    def stdin(self) -> ByteSendStream | None:
-        """The stream for the standard input of the process."""
-
-    @property
-    @abstractmethod
-    def stdout(self) -> ByteReceiveStream | None:
-        """The stream for the standard output of the process."""
-
-    @property
-    @abstractmethod
-    def stderr(self) -> ByteReceiveStream | None:
-        """The stream for the standard error output of the process."""