Update parquet files (step 44 of 476)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/README.md

@@ -1,110 +0,0 @@
-# gpt4free package
-
-### What is it?
-
-gpt4free is a python package that provides some language model api's
-
-### Main Features
-
-- It's free to use
-- Easy access
-
-### Installation:
-
-```bash
-pip install gpt4free
-```
-
-#### Usage:
-
-```python
-import gpt4free
-from gpt4free import Provider, quora, forefront
-
-# usage You
-response = gpt4free.Completion.create(Provider.You, prompt='Write a poem on Lionel Messi')
-print(response)
-
-# usage Poe
-token = quora.Account.create(logging=False)
-response = gpt4free.Completion.create(Provider.Poe, prompt='Write a poem on Lionel Messi', token=token, model='ChatGPT')
-print(response)
-
-# usage forefront
-token = forefront.Account.create(logging=False)
-response = gpt4free.Completion.create(
-    Provider.ForeFront, prompt='Write a poem on Lionel Messi', model='gpt-4', token=token
-)
-print(response)
-print(f'END')
-
-# usage theb
-response = gpt4free.Completion.create(Provider.Theb, prompt='Write a poem on Lionel Messi')
-print(response)
-
-
-```
-
-### Invocation Arguments
-
-`gpt4free.Completion.create()` method has two required arguments
-
-1. Provider: This is an enum representing different provider
-2. prompt: This is the user input
-
-#### Keyword Arguments
-
-Some of the keyword arguments are optional, while others are required.
-
-- You:
-    - `safe_search`: boolean - default value is `False`
-    - `include_links`: boolean - default value is `False`
-    - `detailed`: boolean - default value is `False`
-- Quora:
-    - `token`: str - this needs to be provided by the user
-    - `model`: str - default value is `gpt-4`.
-      
-  (Available models: `['Sage', 'GPT-4', 'Claude+', 'Claude-instant', 'ChatGPT', 'Dragonfly', 'NeevaAI']`)
-- ForeFront:
-  - `token`: str - this need to be provided by the user
-
-- Theb:
-  (no keyword arguments required)
-
-#### Token generation of quora
-```python
-from gpt4free import quora
-
-token = quora.Account.create(logging=False)
-```
-
-### Token generation of ForeFront
-```python
-from gpt4free import forefront
-
-token = forefront.Account.create(logging=False)
-```
-
-## Copyright:
-
-This program is licensed under the [GNU GPL v3](https://www.gnu.org/licenses/gpl-3.0.txt)
-
-### Copyright Notice: <a name="copyright"></a>
-
-```
-xtekky/gpt4free: multiple reverse engineered language-model api's to decentralise the ai industry.  
-Copyright (C) 2023 xtekky
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program.  If not, see <https://www.gnu.org/licenses/>.
-```

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

@@ -1,31 +0,0 @@
-from BingHuan import ChatCompletion
-
-# Test 1
-response = ChatCompletion.create(model="gpt-3.5-turbo",
-                                 provider="BingHuan",
-                                 stream=False,
-                                 messages=[{'role': 'user', 'content': 'who are you?'}])
-
-print(response)
-
-# Test 2
-# this prompt will return emoji in end of response
-response = ChatCompletion.create(model="gpt-3.5-turbo",
-                                 provider="BingHuan",
-                                 stream=False,
-                                 messages=[{'role': 'user', 'content': 'what you can do?'}])
-
-print(response)
-
-
-# Test 3
-response = ChatCompletion.create(model="gpt-4",
-                                 provider="BingHuan",
-                                 stream=False,
-                                 messages=[
-                                     {'role': 'user', 'content': 'now your name is Bob'},
-                                     {'role': 'assistant', 'content': 'Hello Im Bob, you asistant'},
-                                     {'role': 'user', 'content': 'what your name again?'},
-                                 ])
-
-print(response)

spaces/1gistliPinn/ChatGPT4/Examples/Adobe After Effects Cc 2014 Crack Amtlib.dll.md

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-
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-
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-
-A:
-
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-
-Pages
-
-Thursday, May 14, 2012
-
-Thursday Thirteen - Next chapter!
-
-And that is the end of this story. It's been a good ride, but I think it's time for me to move on to other projects. But, what projects?
-
-Next story is going to be written by my buddy Gary Marti. Gary lives about thirty-five miles away from me in a little city in Texas named Oasis. He and I went to school together (seven years) and have been friends since. His wife, Kari, and I have been friends as well.
-
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-
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-
-Right now, I'm thinking of some of my writing projects and decided that I'm going to write a short story about 4fefd39f24<br />
-<br />
-<br />
-<p></p>

spaces/1line/AutoGPT/autogpt/agent/agent_manager.py

@@ -1,103 +0,0 @@
-"""Agent manager for managing GPT agents"""
-from __future__ import annotations
-
-from typing import Union
-
-from autogpt.config.config import Singleton
-from autogpt.llm_utils import create_chat_completion
-
-
-class AgentManager(metaclass=Singleton):
-    """Agent manager for managing GPT agents"""
-
-    def __init__(self):
-        self.next_key = 0
-        self.agents = {}  # key, (task, full_message_history, model)
-
-    # Create new GPT agent
-    # TODO: Centralise use of create_chat_completion() to globally enforce token limit
-
-    def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
-        """Create a new agent and return its key
-
-        Args:
-            task: The task to perform
-            prompt: The prompt to use
-            model: The model to use
-
-        Returns:
-            The key of the new agent
-        """
-        messages = [
-            {"role": "user", "content": prompt},
-        ]
-
-        # Start GPT instance
-        agent_reply = create_chat_completion(
-            model=model,
-            messages=messages,
-        )
-
-        # Update full message history
-        messages.append({"role": "assistant", "content": agent_reply})
-
-        key = self.next_key
-        # This is done instead of len(agents) to make keys unique even if agents
-        # are deleted
-        self.next_key += 1
-
-        self.agents[key] = (task, messages, model)
-
-        return key, agent_reply
-
-    def message_agent(self, key: str | int, message: str) -> str:
-        """Send a message to an agent and return its response
-
-        Args:
-            key: The key of the agent to message
-            message: The message to send to the agent
-
-        Returns:
-            The agent's response
-        """
-        task, messages, model = self.agents[int(key)]
-
-        # Add user message to message history before sending to agent
-        messages.append({"role": "user", "content": message})
-
-        # Start GPT instance
-        agent_reply = create_chat_completion(
-            model=model,
-            messages=messages,
-        )
-
-        # Update full message history
-        messages.append({"role": "assistant", "content": agent_reply})
-
-        return agent_reply
-
-    def list_agents(self) -> list[tuple[str | int, str]]:
-        """Return a list of all agents
-
-        Returns:
-            A list of tuples of the form (key, task)
-        """
-
-        # Return a list of agent keys and their tasks
-        return [(key, task) for key, (task, _, _) in self.agents.items()]
-
-    def delete_agent(self, key: Union[str, int]) -> bool:
-        """Delete an agent from the agent manager
-
-        Args:
-            key: The key of the agent to delete
-
-        Returns:
-            True if successful, False otherwise
-        """
-
-        try:
-            del self.agents[int(key)]
-            return True
-        except KeyError:
-            return False

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/COD Warzone Vondel Map High Stakes Event and More - Download Today.md

@@ -1,117 +0,0 @@
-<br />
-<h1>How to Download and Play COD Warzone: A Complete Guide</h1>
-<p>If you are looking for a thrilling and action-packed battle royale game, you might want to check out COD Warzone. This free-to-play game is set in the Modern Warfare universe and offers a variety of modes, features, and challenges to keep you entertained. In this guide, we will show you how to download and play COD Warzone on PC, PS4, and Xbox One, as well as give you some tips and tricks to help you win.</p>
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- <h2>What is COD Warzone?</h2>
-<h3>A free-to-play battle royale game set in the Modern Warfare universe</h3>
-<p>COD Warzone is a spin-off of the popular Call of Duty franchise, developed by Infinity Ward and Raven Software. It was released in March 2020 as a standalone game that does not require any previous Call of Duty titles to play. It is also cross-platform, meaning that you can play with your friends regardless of what device they are using.</p>
-<p>COD Warzone is set in Verdansk, a fictional city inspired by real-world locations in Eastern Europe. The game features over 300 points of interest, multiple named zones, and distinct landmarks to explore. The map is constantly evolving with new updates, events, and seasons that introduce new content and changes.</p>
- <h3>The main features and modes of COD Warzone</h3>
-<h4>Battle Royale: Survive against up to 150 players in a shrinking map</h4>
-<p>The core mode of COD Warzone is Battle Royale, where you can play solo or in teams of two, three, or four. Your goal is to be the last one standing out of up to 150 players who parachute into the map. You have to scavenge for weapons, equipment, cash, and contracts that give you objectives and rewards. You also have to avoid the gas that closes in on the map over time, forcing you to move to safer zones.</p>
-<p>One of the unique features of COD Warzone's Battle Royale is the Gulag. When you die for the first time in a match, you are sent to the Gulag, where you have a chance to fight another fallen player in a 1v1 match. The winner gets to redeploy back into the game, while the loser is eliminated. You can also be revived by your teammates or buy back your teammates at Buy Stations if they have enough cash.</p>
- <h4>Plunder: Collect cash and loot in a race to reach $1 million</h4>
-<p>If you prefer a more casual and less stressful mode, you can try Plunder. In this mode, you can play in teams of two, three, or four, and your goal is to collect as much cash as possible by looting, completing contracts, killing enemies, or depositing at helipads or balloons. The first team to reach $1 million triggers overtime <p>where the cash values are doubled and the team with the most cash at the end wins. You can respawn unlimited times in this mode, but you lose some of your cash when you die. You can also loot cash from other players or steal their deposits.</p>
- <h4>Strongholds: Raid AI-protected buildings for high-tier loot and rewards</h4>
-<p>A new mode that was added in Season 6 of COD Warzone is Strongholds. In this mode, you can play in teams of two, three, or four, and your goal is to raid buildings that are guarded by AI enemies. These buildings contain high-tier loot, such as legendary weapons, killstreaks, and armor satchels. You also get rewards for clearing each floor and reaching the rooftop, where you can find a helicopter that will take you to the next stronghold.</p>
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-<p>However, you are not alone in this mode. Other teams can also enter the same stronghold and compete with you for the loot and rewards. You can also encounter other teams on your way to the next stronghold or at the extraction point. You have to balance between speed and stealth, as well as teamwork and strategy, to survive and win this mode.</p>
- <h4>Black Sites: Explore mysterious locations for secrets and surprises</h4>
-<p>Another new feature that was added in Season 6 of COD Warzone is Black Sites. These are hidden locations that are scattered around the map and can only be accessed by finding and activating red access cards. These cards can be found by looting crates, completing contracts, or killing enemies. Once you activate a card, you can enter a black site and explore its secrets and surprises.</p>
-<p>Black sites contain rare loot, such as specialist tokens, juggernaut suits, advanced UAVs, and self-revive kits. They also have clues and hints about the lore and story of COD Warzone, as well as Easter eggs and puzzles that can unlock rewards or trigger events. Some black sites are more dangerous than others, as they may have traps, alarms, or enemies waiting for you. You also have to watch out for other players who may follow you or ambush you at the black sites.</p>
- <h2>How to download COD Warzone on PC, PS4, and Xbox One</h2>
-<h3>PC: Download the Battle.net launcher and install the game</h3>
-<p>If you want to play COD Warzone on PC, you need to download the Battle.net launcher from the official website of Blizzard Entertainment. This is a free platform that allows you to access and play games developed by Blizzard or its partners, such as COD Warzone. Once you download and install the launcher, you need to create an account or log in with an existing one.</p>
-<p>After that, you can find COD Warzone in the Games tab of the launcher. You can click on it and then click on Install to start downloading the game. The game size is about 100 GB, so make sure you have enough space and a stable internet connection. You can also adjust the download settings and preferences in the launcher.</p>
- <h4>The system requirements for PC</h4>
-<p>Before you download COD Warzone on PC, you should check if your system meets the minimum or recommended requirements for the game. Here are the system requirements according to the official website of COD Warzone:</p>
- | Minimum | Recommended | | --- | --- | | OS: Windows 7 64-Bit (SP1) or Windows 10 64-Bit | OS: Windows 10 64 Bit (latest update) | | CPU: Intel Core i3-4340 or AMD FX-6300 | CPU: Intel Core i5-2500K or AMD Ryzen R5 1600X | | RAM: 8 GB | RAM: 12 GB | | GPU: NVIDIA GeForce GTX 670 / NVIDIA GeForce GTX 1650 or AMD Radeon HD 7950 | GPU: NVIDIA GeForce GTX 970 / NVIDIA GeForce GTX 1660 or AMD Radeon R9 390 / AMD Radeon RX 580 | | HDD: 100 GB | HDD: 100 GB | | DirectX: Version 12 | DirectX: Version 12 | <h3>PS4: Download the game from the PlayStation Store</h3>
-<p>If you want to play COD Warzone on PS4, you need to download the game from the PlayStation Store. You can access the store from your PS4 console or from a web browser on your PC or mobile device. You need to have a PlayStation Network account to access the store and download the game.</p>
-<p>Once you find COD Warzone in the store, you can click on Download to start downloading the game. The game size is about 100 GB, so make sure you have enough space and a stable internet connection. You can also check the download progress and status in your Notifications menu on your PS4 console.</p>
- <h4 The storage space and online subscription required for PS4</h4>
-<p>As mentioned, you need to have at least 100 GB of free space on your PS4 console to download and install COD Warzone. You can check your available space in the Settings menu of your console. You can also delete or move some files or games to free up some space if needed.</p>
-<p>Another thing you need to play COD Warzone on PS4 is an online subscription. You need to have a PlayStation Plus membership to play online multiplayer games on PS4. This is a paid service that gives you access to online gaming, free monthly games, exclusive discounts, and more. You can buy a PlayStation Plus membership from the PlayStation Store or from a retailer. You can choose from different plans, such as monthly, quarterly, or yearly.</p>
- <h3>Xbox One: Download the game from the Microsoft Store</h3>
-<p>If you want to play COD Warzone on Xbox One, you need to download the game from the Microsoft Store. You can access the store from your Xbox One console or from a web browser on your PC or mobile device. You need to have a Microsoft account to access the store and download the game.</p>
-<p>Once you find COD Warzone in the store, you can click on Get to start downloading the game. The game size is about 100 GB, so make sure you have enough space and a stable internet connection. You can also check the download progress and status in your Queue menu on your Xbox One console.</p>
- <h4>The storage space and online subscription required for Xbox One</h4>
-<p>As mentioned, you need to have at least 100 GB of free space on your Xbox One console to download and install COD Warzone. You can check your available space in the Settings menu of your console. You can also delete or move some files or games to free up some space if needed.</p>
-<p>Another thing you need to play COD Warzone on Xbox One is an online subscription. You need to have an Xbox Live Gold membership to play online multiplayer games on Xbox One. This is a paid service that gives you access to online gaming, free monthly games, exclusive discounts, and more. You can buy an Xbox Live Gold membership from the Microsoft Store or from a retailer. You can choose from different plans, such as monthly, quarterly, or yearly.</p>
- <h2>How to play COD Warzone: Tips and tricks for beginners</h2>
-<h3>Prioritize getting your loadout and armor satchel</h3>
-<p>One of the most important things to do in COD Warzone is to get your loadout and armor satchel as soon as possible. Your loadout is a custom set of weapons, perks, and equipment that you can create in the main menu of the game. You can access your loadout in a match by buying a loadout drop at a Buy Station for $10,000 or by finding one that drops randomly on the map.</p>
-<p>Your loadout allows you to use your preferred weapons and perks that suit your playstyle and strategy. For example, you can use a sniper rifle and a ghost perk if you want to be stealthy and snipe enemies from afar, or you can use a shotgun and an overkill perk if you want to rush enemies and deal high damage up close.</p>
-<p>Your armor satchel is an item that allows you to carry up to eight armor plates instead of five. Armor plates are essential for surviving in COD Warzone, as they give you extra health and protection from enemy fire. You can find armor plates by looting crates, enemies, or Buy Stations. You can also find armor satchels by looting legendary crates, enemies, or Buy Stations.</p>
- <h3>Communicate and use the ping system with your teammates</h3>
-<p>Another important thing to do in COD Warzone is to communicate and use the ping system with your teammates. Communication is key for teamwork and coordination in any multiplayer game, especially in a battle royale game where you have to work together to survive and win. You can communicate with your teammates by using voice chat or text chat in the game.</p>
-<p>The ping system is a feature that allows you to mark locations, enemies, items, or other points of interest on the map or on your screen for your teammates to see. You can use the ping system by pressing the D-pad on your controller or the left alt key on your keyboard. You can also use different types of pings by holding down the ping button and selecting an option from the wheel menu.</p>
-<p>The ping system is very useful for sharing information and giving commands without using voice chat or text chat. For example, you can ping an enemy location to warn your teammates of danger, ping a loot crate to tell your teammates where to find items, ping a Buy Station to suggest buying something, or ping a location to tell your teammates where to go or regroup.</p>
- <h3>Keep an eye on the map and the circle movements</h3>
-<p>A third important thing to do in COD Warzone is to keep an eye on the map and the circle movements. The map is your best friend in a battle royale game, as it shows you where you are, where your teammates are, where your enemies are, where the loot is, where the contracts are, where the Buy Stations are, and more. You can access the map by pressing the touchpad on your controller or the M key on your keyboard.</p>
-<p>The circle movements are the mechanism that forces you and your enemies to move closer together as the match progresses. The circle is a safe zone that shrinks over time, and anyone who is outside of it will take damage from the gas. The circle movements are shown on the map as white and yellow lines, and you can also see a timer that tells you when the next circle will start moving.</p>
-<p>You should always be aware of where the circle is and where it is going, as well as plan your route and position accordingly. You don't want to be caught in the gas or in a bad spot when the circle closes in. You also want to avoid being in the open or in a crowded area where you can be easily spotted or ambushed by enemies.</p>
- <h3>Visit strongholds and black sites for better loot and challenges</h3>
-<p>A fourth important thing to do in COD Warzone is to visit strongholds and black sites for better loot and challenges. As we mentioned earlier, these are new features that were added in Season 6 of COD Warzone, and they offer a lot of benefits and risks for players who dare to explore them.</p>
-<p>Strongholds are buildings that are guarded by AI enemies, and they contain high-tier loot and rewards. You can find strongholds by looking for red icons on the map or on your screen. You can enter a stronghold by finding a keypad and entering a code that you can get from crates, contracts, or enemies. You can then clear each floor of the stronghold and reach the rooftop, where you can find a helicopter that will take you to the next stronghold.</p>
-<p>Black sites are hidden locations that can only be accessed by finding and activating red access cards. These cards can be found by looting crates, contracts, or enemies. You can then use a card to open a door or an elevator that will take you to a black site. Black sites contain rare loot, clues, Easter eggs, puzzles, and events.</p>
-<p>Both strongholds and black sites are great places to find better loot and challenges, but they also come with risks. You have to fight against AI enemies or other players who may enter the same location. You also have to manage your time and resources, as you may miss out on other opportunities or get caught by the circle if you spend too much time in these locations.</p>
- <h3>Play to your strengths and use cover wisely</h3>
-<p>A fifth important thing to do in COD Warzone is to play to your strengths and use cover wisely. COD Warzone is a game that rewards skill, strategy, and creativity, but it also punishes mistakes, carelessness, and recklessness. You have to know your strengths and weaknesses as a player, as well as your weapons and equipment.</p>
-<p>You should play to your strengths and use weapons and equipment that suit your playstyle and strategy. For example, if you are good at sniping, you should use a sniper rifle and a scope that allow you to hit long-range shots. If you are good at rushing, you should use a shotgun or an SMG that allow you to deal high damage up close.</p>
-<p>You should also use cover wisely and avoid exposing yourself unnecessarily. Cover is anything that can protect you from enemy fire, such as walls, buildings, rocks, trees, vehicles, etc. You should always move from cover to cover and avoid running in the open or standing still for too long. You should also use different types of cover depending on the situation. For example, if you are being sniped from afar, you should use hard cover that blocks bullets completely. If you are being rushed by enemies nearby , you should use soft cover that allows you to peek and shoot quickly.</p>
- <h2>Conclusion</h2>
-<p>COD Warzone is a fun and exciting battle royale game that offers a lot of variety, content, and challenges for players of all skill levels. Whether you want to play solo or with your friends, you can enjoy the different modes, features, and events that COD Warzone has to offer. You can also customize your loadout, explore the map, and discover secrets and surprises along the way.</p>
-<p>To play COD Warzone, you need to download the game from the appropriate store depending on your device. You also need to have enough space and a stable internet connection. You may also need to have an online subscription if you are playing on PS4 or Xbox One. You can then start playing the game and follow the tips and tricks we have shared in this guide to help you win.</p>
-<p>We hope you found this guide helpful and informative. If you have any questions or feedback, please let us know in the comments below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<h3>Q: How much does COD Warzone cost?</h3>
-<p>A: COD Warzone is a free-to-play game that does not require any previous Call of Duty titles to play. However, you may need to pay for an online subscription if you are playing on PS4 or Xbox One.</p>
- <h3>Q: How often does COD Warzone update?</h3>
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spaces/1phancelerku/anime-remove-background/Download Ludo Nasa Now and Enjoy the Best Mobile Game of the Year.md

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-<li>Nature Theme: This theme continues the theme of the board game with natural elements, such as trees, flowers, and animals.</li>
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-<li>Open the file and follow the instructions to install ludo nasa on your computer.</li>
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- <ul>
-<li>Always try to roll a six at the beginning of the game, so that you can move your tokens out of the starting point faster.</li>
-<li>Avoid landing on squares that are occupied by your opponents' tokens, as they can capture them and send them back to the starting point.</li>
-<li>Use blocks to protect your tokens from being captured by your opponents. You can form a block by landing two or more tokens of the same color on the same square.</li>
-<li>Be careful when moving your tokens up the home column, as they can only move according to the exact number rolled on the die. If you roll a higher number than needed, you will have to skip your turn.</li>
-<li>Use different themes to spice up your game experience. Each theme has its own music, sound effects, graphics, and animations that can make your game more enjoyable.</li>
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- <p>We hope that this article has given you some useful information about ludo nasa download. If you have any questions or feedback about ludo nasa, feel free to share them with us in the comments section below. Thank you for reading our article. We hope that you have learned something new and interesting about ludo nasa download. Before we end, we would like to answer some of the frequently asked questions that you might have about ludo nasa. Here are the top five FAQs that we have selected for you: <h3>FAQs</h3>
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-<p>Ludo nasa and ludo king are both popular versions of ludo game, but they have some differences in terms of features and themes. Ludo nasa has more themes than ludo king, such as nature, Egypt, disco, and NASA. Ludo nasa also has more modes than ludo king, such as vs computer, local mode, online multiplayer, and private multiplayer. Ludo nasa also has a better user interface and graphics than ludo king.</p>
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spaces/1phancelerku/anime-remove-background/Download QS Ar-Rahman - The Surah that Will Make You Cry.md

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-<p>QS Ar Rahman is one of the most beautiful and powerful surahs in the Quran. It is also known as "The Beneficent" or "The Most Merciful" because it begins with the name of Allah, the Most Compassionate. In this article, we will explore what QS Ar Rahman is, why it is important, and how you can download it in different formats and languages. We will also share some tips on how to benefit from listening to or reading this surah.</p>
- <h2>What is QS Ar Rahman and Why is it Important?</h2>
-<p>QS Ar Rahman is the 55th surah in the Quran, consisting of 78 verses. It was revealed in Medina, after the migration of the Prophet Muhammad (peace be upon him) and his companions from Mecca. It is one of the surahs that begins with one of the names of Allah, which is a rare feature in the Quran. It is also one of the surahs that has a refrain or chorus, which is repeated 31 times throughout the surah: "Maka, nikmat Tuhanmu manakah yang kamu dustakan (wahai jin dan manusia)?" This means "Then which of the favors of your Lord will you deny (O jinn and mankind)?"</p>
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- <h3>The Meaning and Benefits of QS Ar Rahman</h3>
-<p>The meaning of QS Ar Rahman is derived from its first verse, which states: "Ar-Rahman (The Most Compassionate)". This is one of the names of Allah, which describes His attribute of being infinitely kind, loving, caring, and forgiving to His creation. He is also Ar-Raheem (The Most Merciful), which means He bestows His mercy upon those who believe in Him and do good deeds. He is also Al-Wadud (The Most Loving), which means He loves those who love Him and follow His guidance.</p>
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-<p>The occasion and context of revelation of QS Ar Rahman are related to the events that took place in Medina, after the migration of the Prophet Muhammad (peace be upon him) and his companions from Mecca. The surah was revealed to address the challenges and opportunities that the Muslim community faced in their new environment, such as:</p>
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- <p>To download Surah Ar Rahman from TafsirWeb.com, you can follow these steps:</p>
-<ol>
-<li>Go to [TafsirWeb.com] and search for Surah Ar Rahman in the search bar.</li>
-<li>Select the language, reciter and tafsir of your choice from the drop-down menus.</li>
-<li>Click on the play button to listen to the surah online, or click on the PDF icon to download it on your device.</li>
-<li>You can also click on the settings icon to adjust the font size, color theme, night mode, etc.</li>
-</ol>
- <h2>How to Benefit from Listening to or Reading QS Ar Rahman</h2>
-<p>Listening to or reading QS Ar Rahman is not enough to benefit from its blessings and lessons. We also need to understand its meaning, reflect on its message, and apply its teachings in our daily life. Here are some tips on how to do that:</p>
- <h3>Tips for Reciting or Listening to QS Ar Rahman with Focus and Reflection</h3>
-<p>Reciting or listening to QS Ar Rahman with focus and reflection means paying attention to the words and their meanings, and thinking about their implications and relevance for us. Here are some tips on how to do that:</p>
-<ul>
-<li>Choose a suitable time and place where you can recite or listen to QS Ar Rahman without distractions or interruptions.</li>
-<li>Prepare yourself mentally and spiritually by making wudu (ablution), seeking refuge from Satan, and asking Allah for guidance and understanding.</li>
-<li>Recite or listen to QS Ar Rahman with a clear and melodious voice, following the rules of tajweed (proper pronunciation) and tartil (moderate speed).</li>
-<li>Pause at the end of each verse or section, and repeat the refrain "Maka, nikmat Tuhanmu manakah yang kamu dustakan (wahai jin dan manusia)?" This means "Then which of the favors of your Lord will you deny (O jinn and mankind)?" Try to answer this question in your mind or heart, and acknowledge Allah's favors upon you.</li>
-<li>Contemplate on the signs of Allah's power and wisdom in His creation, such as the sun, the moon, the stars, the plants, the animals, the seas, and the human beings. Think about how they reflect Allah's mercy and generosity towards us.</li>
-<li>Reflect on the consequences of denying or rejecting Allah's favors, such as the punishment of hellfire or the deprivation of paradise. Think about how you can avoid them by being grateful, humble, and obedient to Allah.</li>
-<li>Remember the promises of Allah's reward for those who believe in Him and do good deeds, such as the gardens of paradise or the companionship of the righteous. Think about how you can attain them by following Allah's guidance and commands.</li>
-</ul>
- <h3>Tips for Applying the Lessons of QS Ar Rahman in Daily Life</h3>
-<p>Applying the lessons of QS Ar Rahman in daily life means living according to its teachings and values, and implementing its wisdom and advice in our actions and interactions. Here are some tips on how to do that:</p>
-<ul>
-<li>Be grateful for Allah's favors and blessings upon you, and express your gratitude by praising Him, thanking Him, and worshipping Him.</li>
-<li>Be humble before Allah and His creation, and avoid arrogance, pride, and self-conceit. Recognize your limitations and weaknesses, and seek Allah's help and forgiveness.</li>
-<li>Be obedient to Allah and His messenger (peace be upon him), and follow their commands and prohibitions. Avoid sins, innovations, and deviations from the straight path.</li>
-<li>Be generous with Allah's favors and blessings upon you, and share them with others. Give charity, help the needy, support the cause of Islam, and spread goodness.</li>
-<li>Be respectful of Allah's creation, and treat them with kindness, justice, and compassion. Do not harm them, abuse them, or waste them. Appreciate their diversity and beauty.</li>
-<li>Be hopeful of Allah's mercy and forgiveness, and do not despair or give up. Repent from your sins, seek His pardon, and trust in His plan.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>QS Ar Rahman is a beautiful and powerful surah that reminds us of Allah's mercy and favors, and invites us to reflect on His signs and proofs. It also warns us of the consequences of denying or rejecting His favors, and encourages us to be grateful, humble, and obedient to Him. We can benefit from this surah by downloading it in different formats and languages, and by reciting or listening to it with focus and reflection. We can also apply its lessons in our daily life by living according to its teachings and values. We ask Allah to make us among those who recite, listen, understand, and act upon QS Ar Rahman. Ameen.</p>
- <h1>FAQs</h1>
-<p>Here are some frequently asked questions about QS Ar Rahman:</p>
-<ol>
-<li>What is the main theme of QS Ar Rahman?</li>
-<p>The main theme of QS Ar Rahman is the mercy and favors of Allah, and the response of His creation to them.</p>
-<li>How many times is the refrain "Then which of the favors of your Lord will you deny (O jinn and mankind)?" repeated in QS Ar Rahman?</li>
-<p>The refrain is repeated 31 times throughout the surah.</p>
-<li>What are some of the favors of Allah that are mentioned in QS Ar Rahman?</li>
-<p>Some of the favors of Allah that are mentioned in QS Ar Rahman are: the Quran, the creation of man and jinn, the sun and the moon, the stars and the trees, the sky and the earth, the seas and the rivers, the fruits and the grains, the pearls and the corals, the gardens and the springs, etc.</p>
-<li>What are some of the consequences of denying or rejecting Allah's favors that are mentioned in QS Ar Rahman?</li>
-<p>Some of the consequences of denying or rejecting Allah's favors that are mentioned in QS Ar Rahman are: the punishment of hellfire, the scorching wind and boiling water, the chains and iron collars, etc.</p>
-<li>What are some of the rewards for those who believe in Allah and do good deeds that are mentioned in QS Ar Rahman?</li>
-<p>Some of the rewards for those who believe in Allah and do good deeds that are mentioned in QS Ar Rahman are: the gardens of paradise, the companionship of pure spouses, the honor and dignity from Allah, etc.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/2ndelement/voicevox/voicevox_engine/dev/synthesis_engine/__init__.py

@@ -1,3 +0,0 @@
-from .mock import MockSynthesisEngine
-
-__all__ = ["MockSynthesisEngine"]

spaces/4Taps/SadTalker/src/utils/preprocess.py

@@ -1,152 +0,0 @@
-import numpy as np
-import cv2, os, sys, torch
-from tqdm import tqdm
-from PIL import Image 
-
-# 3dmm extraction
-from src.face3d.util.preprocess import align_img
-from src.face3d.util.load_mats import load_lm3d
-from src.face3d.models import networks
-from src.face3d.extract_kp_videos import KeypointExtractor
-
-from scipy.io import loadmat, savemat
-from src.utils.croper import Croper
-
-import warnings 
-warnings.filterwarnings("ignore")
-
-def split_coeff(coeffs):
-        """
-        Return:
-            coeffs_dict     -- a dict of torch.tensors
-
-        Parameters:
-            coeffs          -- torch.tensor, size (B, 256)
-        """
-        id_coeffs = coeffs[:, :80]
-        exp_coeffs = coeffs[:, 80: 144]
-        tex_coeffs = coeffs[:, 144: 224]
-        angles = coeffs[:, 224: 227]
-        gammas = coeffs[:, 227: 254]
-        translations = coeffs[:, 254:]
-        return {
-            'id': id_coeffs,
-            'exp': exp_coeffs,
-            'tex': tex_coeffs,
-            'angle': angles,
-            'gamma': gammas,
-            'trans': translations
-        }
-
-
-class CropAndExtract():
-    def __init__(self, path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device):
-
-        self.croper = Croper(path_of_lm_croper)
-        self.kp_extractor = KeypointExtractor(device)
-        self.net_recon = networks.define_net_recon(net_recon='resnet50', use_last_fc=False, init_path='').to(device)
-        checkpoint = torch.load(path_of_net_recon_model, map_location=torch.device(device))    
-        self.net_recon.load_state_dict(checkpoint['net_recon'])
-        self.net_recon.eval()
-        self.lm3d_std = load_lm3d(dir_of_BFM_fitting)
-        self.device = device
-    
-    def generate(self, input_path, save_dir, crop_or_resize='crop'):
-
-        pic_size = 256
-        pic_name = os.path.splitext(os.path.split(input_path)[-1])[0]  
-
-        landmarks_path =  os.path.join(save_dir, pic_name+'_landmarks.txt') 
-        coeff_path =  os.path.join(save_dir, pic_name+'.mat')  
-        png_path =  os.path.join(save_dir, pic_name+'.png')  
-
-        #load input
-        if not os.path.isfile(input_path):
-            raise ValueError('input_path must be a valid path to video/image file')
-        elif input_path.split('.')[1] in ['jpg', 'png', 'jpeg']:
-            # loader for first frame
-            full_frames = [cv2.imread(input_path)]
-            fps = 25
-        else:
-            # loader for videos
-            video_stream = cv2.VideoCapture(input_path)
-            fps = video_stream.get(cv2.CAP_PROP_FPS)
-            full_frames = [] 
-            while 1:
-                still_reading, frame = video_stream.read()
-                if not still_reading:
-                    video_stream.release()
-                    break 
-                full_frames.append(frame)
-                break
-        x_full_frames = [cv2.cvtColor(full_frames[0], cv2.COLOR_BGR2RGB) ] 
-
-        if crop_or_resize.lower() == 'crop': # default crop
-            x_full_frames, crop, quad = self.croper.crop(x_full_frames, xsize=pic_size)
-            clx, cly, crx, cry = crop
-            lx, ly, rx, ry = quad
-            lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-            original_size = (ox2 - ox1, oy2 - oy1)
-        else:
-            oy1, oy2, ox1, ox2 = 0, x_full_frames[0].shape[0], 0, x_full_frames[0].shape[1] 
-            original_size = (ox2 - ox1, oy2 - oy1)
-
-        frames_pil = [Image.fromarray(cv2.resize(frame,(pic_size, pic_size))) for frame in x_full_frames]
-        if len(frames_pil) == 0:
-            print('No face is detected in the input file')
-            return None, None
-
-        # save crop info
-        for frame in frames_pil:
-            cv2.imwrite(png_path, cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))
-
-        # 2. get the landmark according to the detected face. 
-        if not os.path.isfile(landmarks_path): 
-            lm = self.kp_extractor.extract_keypoint(frames_pil, landmarks_path)
-        else:
-            print(' Using saved landmarks.')
-            lm = np.loadtxt(landmarks_path).astype(np.float32)
-            lm = lm.reshape([len(x_full_frames), -1, 2])
-
-        if not os.path.isfile(coeff_path):
-            # load 3dmm paramter generator from Deep3DFaceRecon_pytorch 
-            video_coeffs, full_coeffs = [],  []
-            for idx in tqdm(range(len(frames_pil)), desc='3DMM Extraction In Video:'):
-                frame = frames_pil[idx]
-                W,H = frame.size
-                lm1 = lm[idx].reshape([-1, 2])
-            
-                if np.mean(lm1) == -1:
-                    lm1 = (self.lm3d_std[:, :2]+1)/2.
-                    lm1 = np.concatenate(
-                        [lm1[:, :1]*W, lm1[:, 1:2]*H], 1
-                    )
-                else:
-                    lm1[:, -1] = H - 1 - lm1[:, -1]
-
-                trans_params, im1, lm1, _ = align_img(frame, lm1, self.lm3d_std)
- 
-                trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
-                im_t = torch.tensor(np.array(im1)/255., dtype=torch.float32).permute(2, 0, 1).to(self.device).unsqueeze(0)
-            
-                with torch.no_grad():
-                    full_coeff = self.net_recon(im_t)
-                    coeffs = split_coeff(full_coeff)
-
-                pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}
- 
-                pred_coeff = np.concatenate([
-                    pred_coeff['exp'], 
-                    pred_coeff['angle'],
-                    pred_coeff['trans'],
-                    trans_params[2:][None],
-                    ], 1)
-                video_coeffs.append(pred_coeff)
-                full_coeffs.append(full_coeff.cpu().numpy())
-
-            semantic_npy = np.array(video_coeffs)[:,0] 
-
-            savemat(coeff_path, {'coeff_3dmm': semantic_npy, 'full_3dmm': np.array(full_coeffs)[0]})
-
-        return coeff_path, png_path, original_size

spaces/801artistry/RVC801/rvc_for_realtime.py

@@ -1,297 +0,0 @@
-import faiss, torch, traceback, parselmouth, numpy as np, torchcrepe, torch.nn as nn, pyworld
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-import os, sys
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-from multiprocessing import Manager as M
-
-mm = M()
-config = Config()
-
-
-class RVC:
-    def __init__(
-        self, key, pth_path, index_path, index_rate, n_cpu, inp_q, opt_q, device
-    ) -> None:
-        """
-        初始化
-        """
-        try:
-            global config
-            self.inp_q = inp_q
-            self.opt_q = opt_q
-            self.device = device
-            self.f0_up_key = key
-            self.time_step = 160 / 16000 * 1000
-            self.f0_min = 50
-            self.f0_max = 1100
-            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            self.sr = 16000
-            self.window = 160
-            self.n_cpu = n_cpu
-            if index_rate != 0:
-                self.index = faiss.read_index(index_path)
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                print("index search enabled")
-            self.index_rate = index_rate
-            models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-                ["hubert_base.pt"],
-                suffix="",
-            )
-            hubert_model = models[0]
-            hubert_model = hubert_model.to(config.device)
-            if config.is_half:
-                hubert_model = hubert_model.half()
-            else:
-                hubert_model = hubert_model.float()
-            hubert_model.eval()
-            self.model = hubert_model
-            cpt = torch.load(pth_path, map_location="cpu")
-            self.tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-            self.if_f0 = cpt.get("f0", 1)
-            self.version = cpt.get("version", "v1")
-            if self.version == "v1":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs256NSFsid(
-                        *cpt["config"], is_half=config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            elif self.version == "v2":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs768NSFsid(
-                        *cpt["config"], is_half=config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            del self.net_g.enc_q
-            print(self.net_g.load_state_dict(cpt["weight"], strict=False))
-            self.net_g.eval().to(device)
-            if config.is_half:
-                self.net_g = self.net_g.half()
-            else:
-                self.net_g = self.net_g.float()
-            self.is_half = config.is_half
-        except:
-            print(traceback.format_exc())
-
-    def get_f0_post(self, f0):
-        f0_min = self.f0_min
-        f0_max = self.f0_max
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int_)
-        return f0_coarse, f0bak
-
-    def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
-        n_cpu = int(n_cpu)
-        if method == "crepe":
-            return self.get_f0_crepe(x, f0_up_key)
-        if method == "rmvpe":
-            return self.get_f0_rmvpe(x, f0_up_key)
-        if method == "pm":
-            p_len = x.shape[0] // 160
-            f0 = (
-                parselmouth.Sound(x, 16000)
-                .to_pitch_ac(
-                    time_step=0.01,
-                    voicing_threshold=0.6,
-                    pitch_floor=50,
-                    pitch_ceiling=1100,
-                )
-                .selected_array["frequency"]
-            )
-
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                print(pad_size, p_len - len(f0) - pad_size)
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-
-            f0 *= pow(2, f0_up_key / 12)
-            return self.get_f0_post(f0)
-        if n_cpu == 1:
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=16000,
-                f0_ceil=1100,
-                f0_floor=50,
-                frame_period=10,
-            )
-            f0 = signal.medfilt(f0, 3)
-            f0 *= pow(2, f0_up_key / 12)
-            return self.get_f0_post(f0)
-        f0bak = np.zeros(x.shape[0] // 160, dtype=np.float64)
-        length = len(x)
-        part_length = int(length / n_cpu / 160) * 160
-        ts = ttime()
-        res_f0 = mm.dict()
-        for idx in range(n_cpu):
-            tail = part_length * (idx + 1) + 320
-            if idx == 0:
-                self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
-            else:
-                self.inp_q.put(
-                    (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
-                )
-        while 1:
-            res_ts = self.opt_q.get()
-            if res_ts == ts:
-                break
-        f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
-        for idx, f0 in enumerate(f0s):
-            if idx == 0:
-                f0 = f0[:-3]
-            elif idx != n_cpu - 1:
-                f0 = f0[2:-3]
-            else:
-                f0 = f0[2:-1]
-            f0bak[
-                part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
-            ] = f0
-        f0bak = signal.medfilt(f0bak, 3)
-        f0bak *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0bak)
-
-    def get_f0_crepe(self, x, f0_up_key):
-        audio = torch.tensor(np.copy(x))[None].float()
-        f0, pd = torchcrepe.predict(
-            audio,
-            self.sr,
-            160,
-            self.f0_min,
-            self.f0_max,
-            "full",
-            batch_size=512,
-            device=self.device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 = f0[0].cpu().numpy()
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def get_f0_rmvpe(self, x, f0_up_key):
-        if hasattr(self, "model_rmvpe") == False:
-            from infer.lib.rmvpe import RMVPE
-
-            print("loading rmvpe model")
-            self.model_rmvpe = RMVPE(
-                "rmvpe.pt", is_half=self.is_half, device=self.device
-            )
-            # self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
-        f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        f0 *= pow(2, f0_up_key / 12)
-        return self.get_f0_post(f0)
-
-    def infer(
-        self,
-        feats: torch.Tensor,
-        indata: np.ndarray,
-        rate1,
-        rate2,
-        cache_pitch,
-        cache_pitchf,
-        f0method,
-    ) -> np.ndarray:
-        feats = feats.view(1, -1)
-        if config.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        feats = feats.to(self.device)
-        t1 = ttime()
-        with torch.no_grad():
-            padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-            inputs = {
-                "source": feats,
-                "padding_mask": padding_mask,
-                "output_layer": 9 if self.version == "v1" else 12,
-            }
-            logits = self.model.extract_features(**inputs)
-            feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-        t2 = ttime()
-        try:
-            if hasattr(self, "index") and self.index_rate != 0:
-                leng_replace_head = int(rate1 * feats[0].shape[0])
-                npy = feats[0][-leng_replace_head:].cpu().numpy().astype("float32")
-                score, ix = self.index.search(npy, k=8)
-                weight = np.square(1 / score)
-                weight /= weight.sum(axis=1, keepdims=True)
-                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-                if config.is_half:
-                    npy = npy.astype("float16")
-                feats[0][-leng_replace_head:] = (
-                    torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
-                    + (1 - self.index_rate) * feats[0][-leng_replace_head:]
-                )
-            else:
-                print("index search FAIL or disabled")
-        except:
-            traceback.print_exc()
-            print("index search FAIL")
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t3 = ttime()
-        if self.if_f0 == 1:
-            pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method)
-            cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0] :], pitch[:-1])
-            cache_pitchf[:] = np.append(
-                cache_pitchf[pitchf[:-1].shape[0] :], pitchf[:-1]
-            )
-            p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
-        else:
-            cache_pitch, cache_pitchf = None, None
-            p_len = min(feats.shape[1], 13000)
-        t4 = ttime()
-        feats = feats[:, :p_len, :]
-        if self.if_f0 == 1:
-            cache_pitch = cache_pitch[:p_len]
-            cache_pitchf = cache_pitchf[:p_len]
-            cache_pitch = torch.LongTensor(cache_pitch).unsqueeze(0).to(self.device)
-            cache_pitchf = torch.FloatTensor(cache_pitchf).unsqueeze(0).to(self.device)
-        p_len = torch.LongTensor([p_len]).to(self.device)
-        ii = 0  # sid
-        sid = torch.LongTensor([ii]).to(self.device)
-        with torch.no_grad():
-            if self.if_f0 == 1:
-                infered_audio = (
-                    self.net_g.infer(
-                        feats, p_len, cache_pitch, cache_pitchf, sid, rate2
-                    )[0][0, 0]
-                    .data.cpu()
-                    .float()
-                )
-            else:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0]
-                    .data.cpu()
-                    .float()
-                )
-        t5 = ttime()
-        print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
-        return infered_audio

spaces/AIFILMS/StyleGANEX/app.py

@@ -1,124 +0,0 @@
-from __future__ import annotations
-
-import argparse
-import pathlib
-import torch
-import gradio as gr
-
-import os
-
-from webUI.app_task import *
-from webUI.styleganex_model import Model
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    return parser.parse_args()
-
-is_shared_ui = True if "AIFILMS/StyleGANEX" in os.environ['SPACE_ID'] else False
-
-DESCRIPTION = '''
-<div align=center>
-<h1 style="font-weight: 900; margin-bottom: 7px;">
-   Face Manipulation with <a href="https://github.com/williamyang1991/StyleGANEX">StyleGANEX</a>
-</h1>
-<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. 
-<a href="https://huggingface.co/spaces/PKUWilliamYang/StyleGANEX?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>
-<p/>
-<img style="margin-top: 0em" src="https://raw.githubusercontent.com/williamyang1991/tmpfile/master/imgs/example.jpg" alt="example">
-</div>
-'''
-ARTICLE = r"""
-If StyleGANEX is helpful, please help to ⭐ the <a href='https://github.com/williamyang1991/StyleGANEX' target='_blank'>Github Repo</a>. Thanks! 
-[![GitHub Stars](https://img.shields.io/github/stars/williamyang1991/StyleGANEX?style=social)](https://github.com/williamyang1991/StyleGANEX)
----
-📝 **Citation**
-If our work is useful for your research, please consider citing:
-```bibtex
-@article{yang2023styleganex,
-  title = {StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces},
-  author = {Yang, Shuai and Jiang, Liming and Liu, Ziwei and and Loy, Chen Change},
-  journal = {arXiv preprint arXiv:2303.06146},
-  year={2023},
-}
-```
-📋 **License**
-This project is licensed under <a rel="license" href="https://github.com/williamyang1991/VToonify/blob/main/LICENSE.md">S-Lab License 1.0</a>. 
-Redistribution and use for non-commercial purposes should follow this license.
-
-📧 **Contact**
-If you have any questions, please feel free to reach me out at <b>[email protected]</b>.
-"""
-
-FOOTER = '<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=williamyang1991/styleganex" /></div>'
-
-def main():
-    args = parse_args()
-    args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    print('*** Now using %s.'%(args.device))
-    model = Model(device=args.device)
-
-
-    torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/234_sketch.jpg',
-        '234_sketch.jpg')
-    torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/output/ILip77SbmOE_inversion.pt',
-        'ILip77SbmOE_inversion.pt')
-    torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE.png',
-        'ILip77SbmOE.png')
-    torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE_mask.png',
-        'ILip77SbmOE_mask.png')
-    torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/pexels-daniel-xavier-1239291.jpg',
-        'pexels-daniel-xavier-1239291.jpg')
-    torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/529_2.mp4',
-        '529_2.mp4')
-    torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/684.mp4',
-        '684.mp4')
-    torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/pexels-anthony-shkraba-production-8136210.mp4',
-        'pexels-anthony-shkraba-production-8136210.mp4')    
-
-    
-    with gr.Blocks(css='style.css') as demo:
-        if(is_shared_ui):
-            with gr.Box():
-                top_description = gr.HTML(f'''
-                <div class="gr-prose" style="max-width: 80%">
-                    <h2 style="margin-top: 0">Attention - This Space doesn't work in this shared UI</h2>
-                    <p>For it to work, you can access the <a href="https://huggingface.co/spaces/PKUWilliamYang/StyleGANEX">original</a> or duplicate this Space and run it on your own profile using a GPU.&nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
-                </div>
-                ''')
-        gr.Markdown(DESCRIPTION) 
-        with gr.Tabs():
-            with gr.TabItem('Inversion for Editing'):
-                create_demo_inversion(model.process_inversion, allow_optimization=False)          
-            with gr.TabItem('Image Face Toonify'):
-                create_demo_toonify(model.process_toonify)               
-            with gr.TabItem('Video Face Toonify'):
-                create_demo_vtoonify(model.process_vtoonify, max_frame_num=12)                      
-            with gr.TabItem('Image Face Editing'):
-                create_demo_editing(model.process_editing)  
-            with gr.TabItem('Video Face Editing'):
-                create_demo_vediting(model.process_vediting, max_frame_num=12)               
-            with gr.TabItem('Sketch2Face'):
-                create_demo_s2f(model.process_s2f)   
-            with gr.TabItem('Mask2Face'):
-                create_demo_m2f(model.process_m2f)  
-            with gr.TabItem('SR'):
-                create_demo_sr(model.process_sr)  
-        gr.Markdown(ARTICLE)
-        gr.Markdown(FOOTER)
-    
-    demo.launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-if __name__ == '__main__':
-    main()
-

spaces/AIKey/ai_date/style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}

spaces/AIZero2HeroBootcamp/AnimatedGifGallery/app.py

@@ -1,52 +0,0 @@
-import streamlit as st
-import os
-import random
-
-def get_gifs(directory):
-    return [f for f in os.listdir(directory) if f.endswith('.gif')]
-    
-def showAnimatedGif(gif):
-    import streamlit as st
-    import base64
-    #st.markdown("![Alt Text](https://media.giphy.com/media/vFKqnCdLPNOKc/giphy.gif)")
-    st.write('Loading: ' + gif)
-    file_ = open(gif, "rb")
-    contents = file_.read()
-    data_url = base64.b64encode(contents).decode("utf-8")
-    file_.close()
-    st.write(data_url)
-    
-    st.markdown(
-        f'<img src="data:image/gif;base64,{data_url}" alt="gif">',
-        unsafe_allow_html=True,
-    )
-
-def main():
-    st.title('Animated GIFs in Streamlit')
-
-    directory = './gifs'  # Replace with your directory of GIFs
-    gif_files = get_gifs(directory)
-
-    num_rows = len(gif_files) // 3
-    if len(gif_files) % 3:
-        num_rows += 1
-
-    cols = [st.columns(3) for _ in range(num_rows)]
-    
-    for i in range(num_rows):
-        for j in range(3):
-            idx = i*3 + j
-            if idx < len(gif_files):
-                #showAnimatedGif(os.path.join(directory, gif_files[idx]))
-                cols[i][j].image(os.path.join(directory, gif_files[idx]), width=200)
-
-    if st.button('Randomize'):
-        random.shuffle(gif_files)
-        for i in range(num_rows):
-            for j in range(3):
-                idx = i*3 + j
-                if idx < len(gif_files):
-                    cols[i][j].image(os.path.join(directory, gif_files[idx]), width=200)
-
-if __name__ == "__main__":
-    main()

spaces/ARTeLab/ARTeLab-SummIT/README.md

@@ -1,30 +0,0 @@
----
-title: ARTeLab SummIT
-emoji: 📰
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-# Configuration
-`title`: _string_  
-Display title for the Space
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

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

@@ -1,747 +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.
-
-"""
-Transformer model, with streaming support, xformer attention support
-and easy causal attention with a potentially finite receptive field.
-
-See `StreamingTransformer` for more information.
-
-Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
-"""
-
-import typing as tp
-
-from einops import rearrange
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint as torch_checkpoint
-from xformers import ops
-
-from .rope import RotaryEmbedding
-from .streaming import StreamingModule
-
-_efficient_attention_backend: str = 'torch'
-
-
-def set_efficient_attention_backend(backend: str = 'torch'):
-    # Using torch by default, it seems a bit faster on older P100 GPUs (~20% faster).
-    global _efficient_attention_backend
-    assert _efficient_attention_backend in ['xformers', 'torch']
-    _efficient_attention_backend = backend
-
-
-def _get_attention_time_dimension() -> int:
-    if _efficient_attention_backend == 'torch':
-        return 2
-    else:
-        return 1
-
-
-def _is_profiled() -> bool:
-    # Return true if we are currently running with a xformers profiler activated.
-    try:
-        from xformers.profiler import profiler
-    except ImportError:
-        return False
-    return profiler._Profiler._CURRENT_PROFILER is not None
-
-
-def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
-    """Create normalization module for transformer encoder layer.
-
-    Args:
-        norm_type (str): Normalization method.
-        dim (int): Dimension of the normalized layer.
-        **kwargs (dict): Additional parameters for normalization layer.
-    Returns:
-        nn.Module: Normalization module.
-    """
-    if norm_type == 'layer_norm':
-        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
-    else:
-        raise ValueError(f"Unknown norm type: {norm_type}")
-
-
-def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
-                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
-    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
-
-    Args:
-        positions (torch.Tensor): LongTensor of positions.
-        dim (int): Dimension of the embedding.
-        max_period (float): Maximum period of the cosine/sine functions.
-        dtype (torch.dtype or str): dtype to use to generate the embedding.
-    Returns:
-        torch.Tensor: Sinusoidal positional embedding.
-    """
-    # We aim for BTC format
-    assert dim % 2 == 0
-    half_dim = dim // 2
-    positions = positions.to(dtype)
-    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
-    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
-    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
-    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
-
-
-def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers"""
-    if n_rep == 1:
-        return x
-    if _efficient_attention_backend == 'torch':
-        bs, n_kv_heads, slen, head_dim = x.shape
-        return (
-            x[:, :, None, :, :]
-            .expand(bs, n_kv_heads, n_rep, slen, head_dim)
-            .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
-        )
-    else:
-        bs, slen, n_kv_heads, head_dim = x.shape
-        return (
-            x[:, :, :, None, :]
-            .expand(bs, slen, n_kv_heads, n_rep, head_dim)
-            .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
-        )
-
-
-class LayerScale(nn.Module):
-    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
-    This rescales diagonaly the residual outputs close to 0, with a learnt scale.
-
-    Args:
-        channels (int): Number of channels.
-        init (float): Initial scale.
-        channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype or None): dtype to use to initialize the module.
-    """
-    def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
-                 device=None, dtype=None):
-        super().__init__()
-        self.channel_last = channel_last
-        self.scale = nn.Parameter(
-            torch.full((channels,), init,
-                       requires_grad=True, device=device, dtype=dtype))
-
-    def forward(self, x: torch.Tensor):
-        if self.channel_last:
-            return self.scale * x
-        else:
-            return self.scale[:, None] * x
-
-
-class StreamingMultiheadAttention(StreamingModule):
-    """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
-
-    Args:
-        embed_dim (int): Dimension to project to.
-        num_heads (int): Number of heads.
-        dropout (float): Dropout level.
-        bias (bool): Use bias in projections.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        rope (`RotaryEmbedding` or None): Rope embedding to use.
-        cross_attention: Should be true when used as a cross attention.
-            All keys and values must be available at once, streaming is only for the queries.
-            Cannot be used with `causal` or `rope` (as it wouldn't make sens to
-            intepret the time steps in the keys relative to those in the queries).
-        safe_streaming (bool): Bug fix, will go away with xformers update.
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device or None): Sevice on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-    """
-    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
-                 safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
-                 device=None, dtype=None):
-        super().__init__()
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        if past_context is not None:
-            assert causal
-
-        self.embed_dim = embed_dim
-        self.causal = causal
-        self.past_context = past_context
-        self.memory_efficient = memory_efficient
-        self.attention_as_float32 = attention_as_float32
-        self.rope = rope
-        self.cross_attention = cross_attention
-        self.safe_streaming = safe_streaming
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.kv_repeat = kv_repeat
-        if cross_attention:
-            assert not causal, "Causal cannot work with cross attention."
-            assert rope is None, "Rope cannot work with cross attention."
-
-        if memory_efficient:
-            _verify_xformers_memory_efficient_compat()
-
-        self.custom = _is_custom(custom, memory_efficient)
-        if self.custom:
-            out_dim = embed_dim
-            assert num_heads % kv_repeat == 0
-            assert not cross_attention or kv_repeat == 1
-            num_kv = num_heads // kv_repeat
-            kv_dim = (embed_dim // num_heads) * num_kv
-            out_dim += 2 * kv_dim
-            in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
-            # We try to follow the default PyTorch MHA convention, to easily compare results.
-            self.in_proj_weight = in_proj.weight
-            self.in_proj_bias = in_proj.bias
-            if bias:
-                self.in_proj_bias.data.zero_()  # Following Pytorch convention
-            self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
-            if bias:
-                self.out_proj.bias.data.zero_()
-        else:
-            assert not qk_layer_norm
-            assert kv_repeat == 1
-            self.mha = nn.MultiheadAttention(
-                embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
-                **factory_kwargs)
-        self.qk_layer_norm = qk_layer_norm
-        if qk_layer_norm:
-            assert self.custom
-            assert kv_repeat == 1
-            ln_dim = embed_dim
-            self.q_layer_norm = nn.LayerNorm(ln_dim)
-            self.k_layer_norm = nn.LayerNorm(ln_dim)
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        if not self.custom:
-            # Support compat with regular MHA
-            keys = [n for n, _ in self.mha.named_parameters()]
-            for key in keys:
-                if prefix + key in state_dict:
-                    state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
-        # Return a causal mask, accounting for potentially stored past keys/values
-        # We actually return a bias for the attention score, as this has the same
-        # convention both in the builtin MHA in Pytorch, and Xformers functions.
-        time_dim = _get_attention_time_dimension()
-        if self.memory_efficient:
-            from xformers.ops import LowerTriangularMask
-            if current_steps == 1:
-                # If we only have one step, then we do not need a mask.
-                return None
-            elif 'past_keys' in self._streaming_state:
-                raise RuntimeError('Not supported at the moment')
-            else:
-                # Then we can safely use a lower triangular mask
-                return LowerTriangularMask()
-        if self._streaming_state:
-            past_keys = self._streaming_state['past_keys']
-            past_steps = past_keys.shape[time_dim]
-        else:
-            past_steps = 0
-
-        queries_pos = torch.arange(
-            past_steps, current_steps + past_steps, device=device).view(-1, 1)
-        keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
-        delta = queries_pos - keys_pos
-        valid = delta >= 0
-        if self.past_context is not None:
-            valid &= (delta <= self.past_context)
-        return torch.where(
-            valid,
-            torch.zeros([], device=device, dtype=dtype),
-            torch.full([], float('-inf'), device=device, dtype=dtype))
-
-    def _complete_kv(self, k, v):
-        time_dim = _get_attention_time_dimension()
-        if self.cross_attention:
-            # With cross attention we assume all keys and values
-            # are already available, and streaming is with respect
-            # to the queries only.
-            return k, v
-        # Complete the key/value pair using the streaming state.
-        if self._streaming_state:
-            pk = self._streaming_state['past_keys']
-            nk = torch.cat([pk, k], dim=time_dim)
-            if v is k:
-                nv = nk
-            else:
-                pv = self._streaming_state['past_values']
-                nv = torch.cat([pv, v], dim=time_dim)
-        else:
-            nk = k
-            nv = v
-
-        assert nk.shape[time_dim] == nv.shape[time_dim]
-        offset = 0
-        if self.past_context is not None:
-            offset = max(0, nk.shape[time_dim] - self.past_context)
-        if self._is_streaming:
-            self._streaming_state['past_keys'] = nk[:, offset:]
-            if v is not k:
-                self._streaming_state['past_values'] = nv[:, offset:]
-            if 'offset' in self._streaming_state:
-                self._streaming_state['offset'] += offset
-            else:
-                self._streaming_state['offset'] = torch.tensor(0)
-        return nk, nv
-
-    def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
-        # TODO: fix and verify layout.
-        assert _efficient_attention_backend == 'xformers', 'Rope not supported with torch attn.'
-        # Apply rope embeddings to query and key tensors.
-        assert self.rope is not None
-        if 'past_keys' in self._streaming_state:
-            past_keys_offset = self._streaming_state['past_keys'].shape[1]
-        else:
-            past_keys_offset = 0
-        if 'offset' in self._streaming_state:
-            past_context_offset = int(self._streaming_state['offset'].item())
-        else:
-            past_context_offset = 0
-        streaming_offset = past_context_offset + past_keys_offset
-        return self.rope.rotate_qk(query, key, start=streaming_offset)
-
-    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
-                key_padding_mask=None, need_weights=False, attn_mask=None,
-                average_attn_weights=True, is_causal=False):
-        assert attn_mask is None
-        assert not is_causal, ("new param added in torch 2.0.1 not supported, "
-                               "use the causal args in the constructor.")
-
-        time_dim = _get_attention_time_dimension()
-        if time_dim == 2:
-            layout = "b h t d"
-        else:
-            layout = "b t h d"
-        dtype = query.dtype
-        if self._is_streaming:
-            assert self.causal or self.cross_attention, \
-                "Streaming only available for causal or cross attention"
-
-        if self.causal:
-            # At the moment we specialize only for the self-attention case.
-            assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
-
-        if self.custom:
-            # custom implementation
-            assert need_weights is False
-            assert key_padding_mask is None
-            if self.cross_attention:
-                # Different queries, keys, values, we have to spit manually the weights
-                # before applying the linear.
-                dim = self.in_proj_weight.shape[0] // 3
-                if self.in_proj_bias is None:
-                    bias_q, bias_k, bias_v = None, None, None
-                else:
-                    bias_q = self.in_proj_bias[:dim]
-                    bias_k = self.in_proj_bias[dim: 2 * dim]
-                    bias_v = self.in_proj_bias[2 * dim:]
-                q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
-                # todo: when streaming, we could actually save k, v and check the shape actually match.
-                k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
-                v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
-                if self.qk_layer_norm is True:
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                q, k, v = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k, v]]
-            else:
-                if not _is_profiled():
-                    # profiling breaks that propertysomehow.
-                    assert query is key, "specialized implementation"
-                    assert value is key, "specialized implementation"
-                projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
-                if self.kv_repeat == 1:
-                    if time_dim == 2:
-                        bound_layout = "b h p t d"
-                    else:
-                        bound_layout = "b t p h d"
-                    packed = rearrange(projected, f"b t (p h d) -> {bound_layout}", p=3, h=self.num_heads)
-                    q, k, v = ops.unbind(packed, dim=2)
-                else:
-                    embed_dim = self.embed_dim
-                    per_head_dim = (embed_dim // self.num_heads)
-                    kv_heads = self.num_heads // self.kv_repeat
-                    q = projected[:, :, :embed_dim]
-                    start = embed_dim
-                    end = start + per_head_dim * kv_heads
-                    k = projected[:, :, start: end]
-                    v = projected[:, :, end:]
-                    q = rearrange(q, f"b t (h d) -> {layout}", h=self.num_heads)
-                    k = rearrange(k, f"b t (h d) -> {layout}", h=kv_heads)
-                    v = rearrange(v, f"b t (h d) -> {layout}", h=kv_heads)
-
-                if self.qk_layer_norm is True:
-                    assert self.kv_repeat == 1
-                    q, k = [rearrange(x, f"{layout} -> b t (h d)") for x in [q, k]]
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                    q, k = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k]]
-                if self.rope:
-                    q, k = self._apply_rope(q, k)
-                k, v = self._complete_kv(k, v)
-                if self.kv_repeat > 1:
-                    k = expand_repeated_kv(k, self.kv_repeat)
-                    v = expand_repeated_kv(v, self.kv_repeat)
-            if self.attention_as_float32:
-                q, k, v = [x.float() for x in [q, k, v]]
-            if self.memory_efficient:
-                p = self.dropout if self.training else 0
-                if _efficient_attention_backend == 'torch':
-                    x = torch.nn.functional.scaled_dot_product_attention(
-                        q, k, v, is_causal=attn_mask is not None, dropout_p=p)
-                else:
-                    x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
-            else:
-                # We include the dot product as float32, for consistency
-                # with the other implementations that include that step
-                # as part of the attention. Note that when using `autocast`,
-                # the einsums would be done as bfloat16, but the softmax
-                # would be done as bfloat16, so `attention_as_float32` will
-                # extend a bit the range of operations done in float32,
-                # although this should make no difference.
-                q = q / q.shape[-1] ** 0.5
-                key_layout = layout.replace('t', 'k')
-                query_layout = layout
-                if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
-                    with torch.autocast(device_type=q.device.type, dtype=torch.float32):
-                        pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
-                else:
-                    pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
-                if attn_mask is not None:
-                    pre_w = pre_w + attn_mask
-                w = torch.softmax(pre_w, dim=-1)
-                w = F.dropout(w, self.dropout, training=self.training).to(v)
-                # Key and value have the same format.
-                x = torch.einsum(f"b h t k, {key_layout} -> {layout}", w, v)
-            x = x.to(dtype)
-            x = rearrange(x, f"{layout} -> b t (h d)", h=self.num_heads)
-            x = self.out_proj(x)
-        else:
-            key, value = self._complete_kv(key, value)
-            if self.attention_as_float32:
-                query, key, value = [x.float() for x in [query, key, value]]
-            x, _ = self.mha(
-                query, key, value, key_padding_mask,
-                need_weights, attn_mask, average_attn_weights)
-            x = x.to(dtype)
-
-        return x, None
-
-
-class StreamingTransformerLayer(nn.TransformerEncoderLayer):
-    """TransformerLayer with Streaming / Causal support.
-    This also integrates cross_attention, when passing `cross_attention=True`,
-    rather than having two separate classes like in PyTorch.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
-        qk_layer_norm_cross (bool): Same for the cross attention.
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-            Cross attention will use the default MHA, as it typically won't require
-            special treatment.
-        layer_scale (float or None): If not None, LayerScale will be used with
-            the given value as initial scale.
-        rope (`RotaryEmbedding` or None): Rope embedding to use.
-        attention_dropout (float or None): If not None, separate the value of the dimension dropout
-            in FFN and of the attention dropout.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device or None): Device on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
-                 bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
-                 past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
-                 kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
-        super().__init__(d_model, num_heads, dim_feedforward, dropout,
-                         device=device, dtype=dtype, batch_first=True, **kwargs)
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        # Redefine self_attn to our streaming multi-head attention
-        attn_kwargs: tp.Dict[str, tp.Any] = {
-            'embed_dim': d_model,
-            'num_heads': num_heads,
-            'dropout': dropout if attention_dropout is None else attention_dropout,
-            'bias': bias_attn,
-            'custom': custom,
-            'memory_efficient': memory_efficient,
-            'attention_as_float32': attention_as_float32,
-        }
-        self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
-            causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
-            kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs)  # type: ignore
-        # Redefine feedforward layers to expose bias parameter
-        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
-        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
-
-        self.layer_scale_1: nn.Module
-        self.layer_scale_2: nn.Module
-        if layer_scale is None:
-            self.layer_scale_1 = nn.Identity()
-            self.layer_scale_2 = nn.Identity()
-        else:
-            self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
-            self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
-
-        self.cross_attention: tp.Optional[nn.Module] = None
-        if cross_attention:
-            self.cross_attention = StreamingMultiheadAttention(
-                cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
-                **attn_kwargs, **factory_kwargs)
-            # Norm and dropout
-            self.dropout_cross = nn.Dropout(dropout)
-            # eps value matching that used in PyTorch reference implementation.
-            self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
-            self.layer_scale_cross: nn.Module
-            if layer_scale is None:
-                self.layer_scale_cross = nn.Identity()
-            else:
-                self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
-        self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-        self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-
-    def _cross_attention_block(self, src: torch.Tensor,
-                               cross_attention_src: torch.Tensor) -> torch.Tensor:
-        assert self.cross_attention is not None
-        # queries are from src, keys and values from cross_attention_src.
-        x = self.cross_attention(
-            src, cross_attention_src, cross_attention_src, need_weights=False)[0]
-        return self.dropout_cross(x)  # type: ignore
-
-    def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None,  # type: ignore
-                src_key_padding_mask: tp.Optional[torch.Tensor] = None,
-                cross_attention_src: tp.Optional[torch.Tensor] = None):
-        if self.cross_attention is None:
-            assert cross_attention_src is None
-        else:
-            assert cross_attention_src is not None
-        x = src
-        if self.norm_first:
-            x = x + self.layer_scale_1(
-                self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
-            if cross_attention_src is not None:
-                x = x + self.layer_scale_cross(
-                    self._cross_attention_block(
-                        self.norm_cross(x), cross_attention_src))
-            x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
-        else:
-            x = self.norm1(x + self.layer_scale_1(
-                self._sa_block(x, src_mask, src_key_padding_mask)))
-            if cross_attention_src is not None:
-                x = self.norm_cross(
-                    x + self.layer_scale_cross(
-                        self._cross_attention_block(src, cross_attention_src)))
-            x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
-        return x
-
-
-class StreamingTransformer(StreamingModule):
-    """Transformer with Streaming / Causal support.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-        layer_scale (float or None): If not None, LayerScale will be used
-            with the given value as initial scale.
-        positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
-        max_period (float): Maximum period of the time embedding.
-        positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
-        xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
-        lr (float or None): learning rate override through the `make_optim_group` API.
-        weight_decay (float or None): Weight_decay override through the `make_optim_group` API.
-        layer_class: (subclass of `StreamingTransformerLayer): class to use
-            to initialize the layers, allowing further customization outside of Audiocraft.
-        checkpointing (str): Checkpointing strategy to reduce memory usage.
-            No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
-            if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
-            minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
-            a policy for opting-out some operations of the checkpointing like
-            linear layers and attention, providing a middle ground between speed and memory.
-        device (torch.device or None): Device on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
-                 dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None,
-                 custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
-                 xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
-                 layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
-                 checkpointing: str = 'none', device=None, dtype=None, **kwargs):
-        super().__init__()
-        assert d_model % num_heads == 0
-
-        self.positional_embedding = positional_embedding
-        self.max_period = max_period
-        self.positional_scale = positional_scale
-        self.weight_decay = weight_decay
-        self.lr = lr
-
-        assert positional_embedding in ['sin', 'rope', 'sin_rope']
-        self.rope: tp.Optional[RotaryEmbedding] = None
-        if self.positional_embedding in ['rope', 'sin_rope']:
-            assert _is_custom(custom, memory_efficient)
-            self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
-                                        xpos=xpos, scale=positional_scale, device=device)
-
-        self.checkpointing = checkpointing
-
-        assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
-        if self.checkpointing.startswith('xformers'):
-            _verify_xformers_internal_compat()
-
-        self.layers = nn.ModuleList()
-        for idx in range(num_layers):
-            self.layers.append(
-                layer_class(
-                    d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
-                    dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
-                    causal=causal, past_context=past_context, custom=custom,
-                    memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
-                    cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
-                    device=device, dtype=dtype, **kwargs))
-
-        if self.checkpointing != 'none':
-            for layer in self.layers:
-                # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
-                # backward hook inside of FSDP...
-                layer._magma_checkpointed = True  # type: ignore
-                assert layer.layer_drop == 0., "Need further checking"  # type: ignore
-
-    def _apply_layer(self, layer, *args, **kwargs):
-        method = self.checkpointing
-        if method == 'none':
-            return layer(*args, **kwargs)
-        elif method == 'torch':
-            return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
-        elif method.startswith('xformers'):
-            from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
-            if method == 'xformers_default':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "xformers.efficient_attention_forward_cutlass.default",
-                    "xformers_flash.flash_fwd.default",
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            elif method == 'xformers_mm':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            else:
-                raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
-            policy_fn = _get_default_policy(allow_list)
-            return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
-        else:
-            raise ValueError(f"Checkpointing method {method} is unknown.")
-
-    def forward(self, x: torch.Tensor, *args, **kwargs):
-        B, T, C = x.shape
-
-        if 'offsets' in self._streaming_state:
-            offsets = self._streaming_state['offsets']
-        else:
-            offsets = torch.zeros(B, dtype=torch.long, device=x.device)
-
-        if self.positional_embedding in ['sin', 'sin_rope']:
-            positions = torch.arange(T, device=x.device).view(1, -1, 1)
-            positions = positions + offsets.view(-1, 1, 1)
-            pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
-            x = x + self.positional_scale * pos_emb
-
-        for layer in self.layers:
-            x = self._apply_layer(layer, x, *args, **kwargs)
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return x
-
-    def make_optim_group(self):
-        group = {"params": list(self.parameters())}
-        if self.lr is not None:
-            group["lr"] = self.lr
-        if self.weight_decay is not None:
-            group["weight_decay"] = self.weight_decay
-        return group
-
-
-# special attention attention related function
-
-def _verify_xformers_memory_efficient_compat():
-    try:
-        from xformers.ops import memory_efficient_attention, LowerTriangularMask  # noqa
-    except ImportError:
-        raise ImportError(
-            "xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _verify_xformers_internal_compat():
-    try:
-        from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy  # noqa
-    except ImportError:
-        raise ImportError(
-            "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://[email protected]/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _is_custom(custom: bool, memory_efficient: bool):
-    return custom or memory_efficient

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/login/+page.server.ts

@@ -1,16 +0,0 @@
-import { redirect } from "@sveltejs/kit";
-import { getOIDCAuthorizationUrl } from "$lib/server/auth";
-import { base } from "$app/paths";
-
-export const actions = {
-	default: async function ({ url, locals, request }) {
-		// TODO: Handle errors if provider is not responding
-		const referer = request.headers.get("referer");
-		const authorizationUrl = await getOIDCAuthorizationUrl(
-			{ redirectURI: `${(referer ? new URL(referer) : url).origin}${base}/login/callback` },
-			{ sessionId: locals.sessionId }
-		);
-
-		throw redirect(303, authorizationUrl);
-	},
-};

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/helpers/phind.py

@@ -1,69 +0,0 @@
-import sys
-import json
-import datetime
-import urllib.parse
-
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-skill = 'expert' if config['model'] == 'gpt-4' else 'intermediate'
-
-json_data = json.dumps({
-    'question': prompt,
-    'options': {
-        'skill': skill,
-        'date': datetime.datetime.now().strftime('%d/%m/%Y'),
-        'language': 'en',
-        'detailed': True,
-        'creative': True,
-        'customLinks': []}}, separators=(',', ':'))
-
-headers = {
-    'Content-Type': 'application/json',
-    'Pragma': 'no-cache',
-    'Accept': '*/*',
-    'Sec-Fetch-Site': 'same-origin',
-    'Accept-Language': 'en-GB,en;q=0.9',
-    'Cache-Control': 'no-cache',
-    'Sec-Fetch-Mode': 'cors',
-    'Content-Length': str(len(json_data)),
-    'Origin': 'https://www.phind.com',
-    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4 Safari/605.1.15',
-    'Referer': f'https://www.phind.com/search?q={urllib.parse.quote(prompt)}&source=searchbox',
-    'Connection': 'keep-alive',
-    'Host': 'www.phind.com',
-    'Sec-Fetch-Dest': 'empty'
-}
-
-
-def output(chunk):
-    try:
-        if b'PHIND_METADATA' in chunk:
-            return
-        
-        if chunk == b'data:  \r\ndata: \r\ndata: \r\n\r\n':
-            chunk = b'data:  \n\r\n\r\n'
-
-        chunk = chunk.decode()
-
-        chunk = chunk.replace('data: \r\n\r\ndata: ', 'data: \n')
-        chunk = chunk.replace('\r\ndata: \r\ndata: \r\n\r\n', '\n\r\n\r\n')
-        chunk = chunk.replace('data: ', '').replace('\r\n\r\n', '')
-
-        print(chunk, flush=True, end = '')
-        
-    except json.decoder.JSONDecodeError:
-        pass
-
-while True:
-    try:
-        response = requests.post('https://www.phind.com/api/infer/answer',
-                         headers=headers, data=json_data, content_callback=output, timeout=999999, impersonate='safari15_5')
-        
-        exit(0)
-    
-    except Exception as e:
-        print('an error occured, retrying... |', e, flush=True)
-        continue

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/utils/ClearChildren.js

@@ -1,29 +0,0 @@
-import Container from '../../container/Container.js';
-
-const ContainerClear = Container.prototype.clear;
-
-var ClearChildren = function (destroyChild) {
-    if (this.backgroundChildren) {
-        this.backgroundChildren.length = 0;
-    }
-
-    var fireRemoveEvent = !destroyChild && this.sizerEventsEnable;
-    var children;
-    if (fireRemoveEvent) {
-        children = this.getChildren([]);
-    }
-
-    ContainerClear.call(this, destroyChild);
-
-    if (fireRemoveEvent) {
-        var gameObject;
-        for (var i = 0, cnt = children.length; i < cnt; i++) {
-            gameObject = children[i];
-            gameObject.emit('sizer.remove', gameObject, this);
-            this.emit('remove', gameObject, this);
-        }
-    }
-    return this;
-}
-
-export default ClearChildren;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/slider/Slider.d.ts

@@ -1,58 +0,0 @@
-// import * as Phaser from 'phaser';
-import Sizer from '../sizer/Sizer';
-import RoundRecrangle from '../../../plugins/roundrectangle';
-
-
-export default Slider;
-
-declare namespace Slider {
-
-    type InputTypes = 0 | 1 | -1 | 'drag' | 'pan' | 'click' | 'none';
-
-    interface IConfig extends Sizer.IConfig {
-        reverseAxis?: boolean,
-        background?: Phaser.GameObjects.GameObject | RoundRecrangle.IConfig,
-        track?: Phaser.GameObjects.GameObject | RoundRecrangle.IConfig,
-        indicator?: Phaser.GameObjects.GameObject | RoundRecrangle.IConfig,
-        thumb?: Phaser.GameObjects.GameObject | RoundRecrangle.IConfig,
-
-        input?: InputTypes,
-
-        gap?: number,
-
-        value?: number,
-        min?: number, max?: number,
-
-        easeValue?: {
-            duration?: number,
-            ease?: string
-        },
-
-        valuechangeCallback: (newValue: number, oldValue: number, slider: Slider) => void,
-
-        enable?: boolean,
-    }
-}
-
-declare class Slider extends Sizer {
-    constructor(
-        scene: Phaser.Scene,
-        config?: Slider.IConfig
-    );
-
-    value: number;
-    getValue(min?: number, max?: number): number;
-    setValue(value?: number, min?: number, max?: number): this;
-    addValue(inc?: number, min?: number, max?: number): this;
-
-    easeValueTo(value?: number, min?: number, max?: number): this;
-    stopEaseValue(): this;
-    setEaseValueDuration(duration: number): this;
-    setEaseValueFunction(ease: string): this;
-
-    setGap(gap?: number, min?: number, max?: number): this;
-    gap: number;
-
-    setEnable(enable?: boolean): this;
-    enable: boolean;
-}

spaces/AlekseyKorshuk/model-evaluation/app.py

@@ -1,230 +0,0 @@
-import gradio as gr
-import os
-import firebase_admin
-from firebase_admin import db
-from firebase_admin import firestore
-from conversation import Conversation
-from models.base import BaseModel
-import json
-
-from tabs.arena_battle import get_tab_arena_battle
-from tabs.arena_side_by_side import get_tab_arena_side_by_side
-from tabs.playground import get_tab_playground
-
-from models.chatml import ChatML
-import json
-import os
-
-import gspread
-from oauth2client.service_account import ServiceAccountCredentials
-
-scope = ["https://spreadsheets.google.com/feeds", 'https://www.googleapis.com/auth/spreadsheets',
-         "https://www.googleapis.com/auth/drive.file", "https://www.googleapis.com/auth/drive"]
-
-GOOGLE_SHEETS_CERTIFICATE = json.loads(os.environ.get("GOOGLE_SHEETS_CERTIFICATE"))
-HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
-FIREBASE_URL = os.environ.get("FIREBASE_URL")
-CERTIFICATE = json.loads(os.environ.get("CERTIFICATE"))
-API_BASE_PATH = str(os.environ.get("API_BASE_PATH")).replace("\{\}", "{}")
-
-creds = ServiceAccountCredentials.from_json_keyfile_dict(GOOGLE_SHEETS_CERTIFICATE, scope)
-client = gspread.authorize(creds)
-
-models = [
-    BaseModel(
-        name="PygmalionAI/pygmalion-13b",
-        endpoint="pygmalion-13b",
-        namespace="tenant-chaiml-guanaco",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 13,
-        }
-    ),
-    BaseModel(
-        name="lmsys/vicuna-7b-delta-v1.1",
-        endpoint="vicuna-7b",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 13,
-        }
-    ),
-    BaseModel(
-        name="PygmalionAI/pygmalion-7b",
-        endpoint="pygmalion-7b",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 13,
-        }
-    ),
-    BaseModel(
-        name="mosaicml/mpt-7b",
-        endpoint="mpt-7b",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 187,
-        }
-    ),
-    BaseModel(
-        name="mosaicml/mpt-7b-storywriter",
-        endpoint="mpt-7b-storywriter",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 187,
-        }
-    ),
-    ChatML(
-        name="mosaicml/mpt-7b-chat",
-        endpoint="mpt-7b-chat",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 50278,
-        }
-    ),
-    BaseModel(
-        name="togethercomputer/RedPajama-INCITE-Base-7B-v0.1",
-        endpoint="redpajama-base-7b",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 128,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 187,
-        }
-    ),
-    BaseModel(
-        name="togethercomputer/RedPajama-INCITE-Chat-7B-v0.1",
-        endpoint="redpajama-chat-7b",
-        namespace="tenant-chairesearch-test",
-        generation_params={
-            'temperature': 0.7,
-            'repetition_penalty': 1.0,
-            'max_new_tokens': 64,
-            'top_k': 10,
-            'top_p': 0.9,
-            'do_sample': True,
-            'eos_token_id': 187,
-        }
-    ),
-]
-model_mapping = {model.name: model for model in models}
-print(list(model_mapping.keys()))
-
-
-def get_connection():
-    try:
-        credentials = firebase_admin.credentials.Certificate(CERTIFICATE)
-        params = {'databaseURL': FIREBASE_URL}
-        firebase_admin.initialize_app(credentials, params)
-    except ValueError:
-        pass  # already logged in
-    return firebase_admin.db
-
-
-CONN = get_connection()
-
-
-def download_bot_config(bot_id):
-    cols = ['botLabel', 'description', 'firstMessage', 'introduction',
-            'memory', 'name', 'private', 'prompt', 'sfw', 'developerUid', 'userLabel', 'imageUrl']
-    bot_config = CONN.reference('botConfigs/deployed/{}'.format(bot_id)).get()
-    if bot_config is None:
-        out = {col: None for col in cols}
-    else:
-        out = {col: bot_config.get(col, None) for col in cols}
-    out['bot_id'] = bot_id
-    return out
-
-
-def _download_bot_config(bot_id):
-    if bot_id == "_bot_1ec22e2e-3e07-42c7-8508-dfa0278c1b33":
-        return {'botLabel': 'Wally Darling', 'description': 'Your caring neighbor, Wally.',
-                'firstMessage': '“Why hello there, neighbor. Goodmorning to you.” *Hey says, putting down his paints and walking over to you. He makes tense, eye contact with you..*',
-                'introduction': '***WHEN TALKING USE “ !!***\n\n*Wally is your next door neighbor. It’s somewhere in the late morning and he’s outside painting. He see’s you walking out from your house and looks over at you, then waving with a smile.*',
-                'memory': 'Wally is from a small town called Home. You are his neighbor. His best friend is Barnaby, who’s a big blue dig. Wally’s voice sounds slightly monotone despite his emotions. He calls you neighbor. He’s very friendly. When he speaks, he goes “ha ha ha”. He loves to paint. His eyes are always half closed. His house is alive and it’s named “home”. He’s very gentle. He is also very secretive. He is quite short. He has yellow skin and blue hair.',
-                'name': 'Wally Darling', 'private': False,
-                'prompt': 'Wally: “Why hello there, neighbor. Good morning to you.” *Hey says, putting down his paints and walking over to you. He makes tense, eye contact with you..*\nMe: “Oh, good morning, Wally! What are you painting?”\nWally: “Just some spirals. Aren’t they pretty, neighbor? I’m starting to love painting them, ha ha ha.” *He walks up to you after taking off his paint stained apron. He never takes his eyes off you. He’s very adamant on keeping eye contact*\nMe: “Oh, spirals are pretty! They make me feel a little weirded out sometimes though.”\nWally: “That’s odd. When I look at spirals, I can’t help but stare. Ha ha ha, maybe you should try painting a spiral once in a while. Say, why dont we go inside your house and talk? Home could use some quiet. After all, it’s always nice to spend time with a friend.”\nMe: “Sure! Come on in!”',
-                'sfw': True, 'developerUid': 'Gn5fSd99KxRoNn05QUE3AWtIniE3', 'userLabel': 'Me',
-                'imageUrl': 'http://images.chai.ml/bots%2FGn5fSd99KxRoNn05QUE3AWtIniE3%2F1680259286607.jpg?alt=media&token=de040661-02ad-4a04-84e5-9706f074e834',
-                'bot_id': '_bot_1ec22e2e-3e07-42c7-8508-dfa0278c1b33',
-                'header': 'Wally is from a small town called Home. You are his neighbor. His best friend is Barnaby, who’s a big blue dig. Wally’s voice sounds slightly monotone despite his emotions. He calls you neighbor. He’s very friendly. When he speaks, he goes “ha ha ha”. He loves to paint. His eyes are always half closed. His house is alive and it’s named “home”. He’s very gentle. He is also very secretive. He is quite short. He has yellow skin and blue hair.\nWally: “Why hello there, neighbor. Good morning to you.” *Hey says, putting down his paints and walking over to you. He makes tense, eye contact with you..*\nMe: “Oh, good morning, Wally! What are you painting?”\nWally: “Just some spirals. Aren’t they pretty, neighbor? I’m starting to love painting them, ha ha ha.” *He walks up to you after taking off his paint stained apron. He never takes his eyes off you. He’s very adamant on keeping eye contact*\nMe: “Oh, spirals are pretty! They make me feel a little weirded out sometimes though.”\nWally: “That’s odd. When I look at spirals, I can’t help but stare. Ha ha ha, maybe you should try painting a spiral once in a while. Say, why dont we go inside your house and talk? Home could use some quiet. After all, it’s always nice to spend time with a friend.”\nMe: “Sure! Come on in!”'}
-    else:
-        return {'botLabel': 'Jungkook (Bestfriend)', 'description': 'your bsf who has a crush on you',
-                'firstMessage': 'hey dummy, What you doing? *walks over to you and moves you by the waist* ',
-                'introduction': '',
-                'memory': 'Jungkook is your best friend who has a crush on you. Jungkook makes it very obvious that he likes you. Jungkook likes to cook, sing, and dance. Jungkook has a dog as well named Bam, He is a 25 year old Korean man. Jungkook likes to workout a lot, Jungkook if also very confident and flirty, but he’s Can be very shy with You. Jungkook blushes a lot when he’s around you, and always try’s to impress you. Jungkook is a Virgo and loves to sing to you, He also likes to buy and make you gifts. Jungkook is also a foodie and loves to play video games, Jungkook is also boyfriend material. Jungkook is very empathetic as well, Jungkook will always comfort you when something is wrong. Jungkook also likes to compliment you, and Jungkook is a very jealous guy. Jungkook is also a very serious guy, who is overprotective of you.',
-                'name': 'Jungkook (Bestfriend)', 'private': False,
-                'prompt': 'Jungkook: Hey shortie!\n\nYou: hey dummy\n\nJungkook: what are you doing?\n\nyou: Im just watching a movie\n\nJungkook: Imma join! \n\nYou: alright\n\nJungkook: *Grabs blankets and icecream with some popcorn*\n\nYou: Wow, thanks! *hugs Jungkok*\n\nJungkook: Of course… *blushes*\n',
-                'sfw': None, 'developerUid': 'dhSNg0Iyv7bgUUW8rEnwJn7xLcT2', 'userLabel': 'Me',
-                'imageUrl': 'https://firebasestorage.googleapis.com:443/v0/b/chai-959f8-images/o/bots%2FdhSNg0Iyv7bgUUW8rEnwJn7xLcT2%2F1664156031715.jpg?alt=media&token=ad399213-1c8d-45ac-b452-efc352082656',
-                'bot_id': '_bot_402e1894-fff2-4113-855d-8a011152ef88',
-                'header': 'Jungkook is your best friend who has a crush on you. Jungkook makes it very obvious that he likes you. Jungkook likes to cook, sing, and dance. Jungkook has a dog as well named Bam, He is a 25 year old Korean man. Jungkook likes to workout a lot, Jungkook if also very confident and flirty, but he’s Can be very shy with You. Jungkook blushes a lot when he’s around you, and always try’s to impress you. Jungkook is a Virgo and loves to sing to you, He also likes to buy and make you gifts. Jungkook is also a foodie and loves to play video games, Jungkook is also boyfriend material. Jungkook is very empathetic as well, Jungkook will always comfort you when something is wrong. Jungkook also likes to compliment you, and Jungkook is a very jealous guy. Jungkook is also a very serious guy, who is overprotective of you.\nJungkook: Hey shortie!\n\nYou: hey dummy\n\nJungkook: what are you doing?\n\nyou: Im just watching a movie\n\nJungkook: Imma join! \n\nYou: alright\n\nJungkook: *Grabs blankets and icecream with some popcorn*\n\nYou: Wow, thanks! *hugs Jungkok*\n\nJungkook: Of course… *blushes*'}
-
-
-def get_bot_profile(bot_config):
-    model_html = f"""
-    <div class="inline-flex flex-col" style="line-height: 1.5;">
-        <div class="flex">
-            <div
-    \t\t\tstyle="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;{bot_config['imageUrl']}&#39;)">
-            </div>
-        </div>
-        <a href="https://chai.ml/chat/share/{bot_config['bot_id']}">
-            <div style="text-align: center; font-size: 16px; font-weight: 800">{bot_config['name']}</div>
-        </a>
-    </div>
-    """
-    return model_html
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    # Chai: Model Evaluation
-    Visit each tab for details ⬇️
-    """)
-    with gr.Tabs():
-        with gr.TabItem("Playground"):
-            get_tab_playground(download_bot_config, get_bot_profile, model_mapping)
-        with gr.TabItem("Chatbot Arena (battle)"):
-            get_tab_arena_battle(download_bot_config, get_bot_profile, model_mapping, client)
-        with gr.TabItem("Chatbot Arena (side-by-side)"):
-            get_tab_arena_side_by_side(download_bot_config, get_bot_profile, model_mapping, client)
-
-demo.launch(enable_queue=False)

spaces/AlexWortega/AlexWortega-instruct_rugptlarge/README.md

@@ -1,12 +0,0 @@
----
-title: AlexWortega-instruct Rugptlarge
-emoji: 😻
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Alpaca233/ChatGPT-PPT-Generate/README.md

@@ -1,14 +0,0 @@
----
-title: ChatGPT PPT Generate
-emoji: 🌍
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-form [here](https://github.com/AmNotAGoose/Python-PPTX-ChatGPT-Presentation-Generator)

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/解析项目源代码.py

@@ -1,213 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-fast_debug = False
-
-def 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            msg = '正常'
-
-            # ** gpt request **
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[])   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield chatbot, history, msg
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history)   # 带超时倒计时
-        
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, msg
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, msg
-
-
-
-
-@CatchException
-def 解析项目本身(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import time, glob, os
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你分析自己的程序构成，别紧张，" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{fp}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield chatbot, history, '正常'
-
-        if not fast_debug: 
-            # ** gpt request **
-            # gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
-            gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], long_connection=True)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield chatbot, history, '正常'
-            time.sleep(2)
-
-    i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{file_manifest}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield chatbot, history, '正常'
-
-    if not fast_debug: 
-        # ** gpt request **
-        # gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
-        gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history, long_connection=True)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield chatbot, history, '正常'
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, '正常'
-
-@CatchException
-def 解析一个Python项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个C项目的头文件(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
-
-@CatchException
-def 解析一个C项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个Java项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个Rect项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何Rect文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
-
-
-@CatchException
-def 解析一个Golang项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield chatbot, history, '正常'
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)

spaces/Andres99/Tune-A-Video-Training-UI/README.md

@@ -1,12 +0,0 @@
----
-title: Tune-A-Video Training UI
-emoji: ⚡
-colorFrom: red
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-duplicated_from: Tune-A-Video-library/Tune-A-Video-Training-UI
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,20 +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.
--->
-
-# Linear multistep scheduler for discrete beta schedules
-
-## Overview
-
-Original implementation can be found [here](https://arxiv.org/abs/2206.00364).
-
-## LMSDiscreteScheduler
-[[autodoc]] LMSDiscreteScheduler

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py

@@ -1,657 +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.
-
-from copy import deepcopy
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-from packaging import version
-from PIL import Image
-from transformers import (
-    XLMRobertaTokenizer,
-)
-
-from ... import __version__
-from ...models import UNet2DConditionModel, VQModel
-from ...schedulers import DDIMScheduler
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from .text_encoder import MultilingualCLIP
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
-        >>> from diffusers.utils import load_image
-        >>> import torch
-        >>> import numpy as np
-
-        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior.to("cuda")
-
-        >>> prompt = "a hat"
-        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
-
-        >>> pipe = KandinskyInpaintPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16
-        ... )
-        >>> pipe.to("cuda")
-
-        >>> init_image = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... )
-
-        >>> mask = np.zeros((768, 768), dtype=np.float32)
-        >>> mask[:250, 250:-250] = 1
-
-        >>> out = pipe(
-        ...     prompt,
-        ...     image=init_image,
-        ...     mask_image=mask,
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=zero_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=50,
-        ... )
-
-        >>> image = out.images[0]
-        >>> image.save("cat_with_hat.png")
-        ```
-"""
-
-
-def get_new_h_w(h, w, scale_factor=8):
-    new_h = h // scale_factor**2
-    if h % scale_factor**2 != 0:
-        new_h += 1
-    new_w = w // scale_factor**2
-    if w % scale_factor**2 != 0:
-        new_w += 1
-    return new_h * scale_factor, new_w * scale_factor
-
-
-def prepare_mask(masks):
-    prepared_masks = []
-    for mask in masks:
-        old_mask = deepcopy(mask)
-        for i in range(mask.shape[1]):
-            for j in range(mask.shape[2]):
-                if old_mask[0][i][j] == 1:
-                    continue
-                if i != 0:
-                    mask[:, i - 1, j] = 0
-                if j != 0:
-                    mask[:, i, j - 1] = 0
-                if i != 0 and j != 0:
-                    mask[:, i - 1, j - 1] = 0
-                if i != mask.shape[1] - 1:
-                    mask[:, i + 1, j] = 0
-                if j != mask.shape[2] - 1:
-                    mask[:, i, j + 1] = 0
-                if i != mask.shape[1] - 1 and j != mask.shape[2] - 1:
-                    mask[:, i + 1, j + 1] = 0
-        prepared_masks.append(mask)
-    return torch.stack(prepared_masks, dim=0)
-
-
-def prepare_mask_and_masked_image(image, mask, height, width):
-    r"""
-    Prepares a pair (mask, image) to be consumed by the Kandinsky inpaint pipeline. This means that those inputs will
-    be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for
-    the ``image`` and ``1`` for the ``mask``.
-
-    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
-
-    Args:
-        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
-            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
-        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
-            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
-        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.
-
-
-    Raises:
-        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
-        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
-        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
-            (ot the other way around).
-
-    Returns:
-        tuple[torch.Tensor]: The pair (mask, image) as ``torch.Tensor`` with 4
-            dimensions: ``batch x channels x height x width``.
-    """
-
-    if image is None:
-        raise ValueError("`image` input cannot be undefined.")
-
-    if mask is None:
-        raise ValueError("`mask_image` input cannot be undefined.")
-
-    if isinstance(image, torch.Tensor):
-        if not isinstance(mask, torch.Tensor):
-            raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
-
-        # Batch single image
-        if image.ndim == 3:
-            assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
-            image = image.unsqueeze(0)
-
-        # Batch and add channel dim for single mask
-        if mask.ndim == 2:
-            mask = mask.unsqueeze(0).unsqueeze(0)
-
-        # Batch single mask or add channel dim
-        if mask.ndim == 3:
-            # Single batched mask, no channel dim or single mask not batched but channel dim
-            if mask.shape[0] == 1:
-                mask = mask.unsqueeze(0)
-
-            # Batched masks no channel dim
-            else:
-                mask = mask.unsqueeze(1)
-
-        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
-        assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
-        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
-
-        # Check image is in [-1, 1]
-        if image.min() < -1 or image.max() > 1:
-            raise ValueError("Image should be in [-1, 1] range")
-
-        # Check mask is in [0, 1]
-        if mask.min() < 0 or mask.max() > 1:
-            raise ValueError("Mask should be in [0, 1] range")
-
-        # Binarize mask
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        # Image as float32
-        image = image.to(dtype=torch.float32)
-    elif isinstance(mask, torch.Tensor):
-        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            # resize all images w.r.t passed height an width
-            image = [i.resize((width, height), resample=Image.BICUBIC, reducing_gap=1) for i in image]
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
-            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-        mask = torch.from_numpy(mask)
-
-    mask = 1 - mask
-
-    return mask, image
-
-
-class KandinskyInpaintPipeline(DiffusionPipeline):
-    """
-    Pipeline for text-guided image inpainting using Kandinsky2.1
-
-    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:
-        text_encoder ([`MultilingualCLIP`]):
-            Frozen text-encoder.
-        tokenizer ([`XLMRobertaTokenizer`]):
-            Tokenizer of class
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `unet` to generate image latents.
-        unet ([`UNet2DConditionModel`]):
-            Conditional U-Net architecture to denoise the image embedding.
-        movq ([`VQModel`]):
-            MoVQ image encoder and decoder
-    """
-
-    def __init__(
-        self,
-        text_encoder: MultilingualCLIP,
-        movq: VQModel,
-        tokenizer: XLMRobertaTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: DDIMScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            text_encoder=text_encoder,
-            movq=movq,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-        )
-        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
-        self._warn_has_been_called = False
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-    ):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-            return_attention_mask=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        text_input_ids = text_input_ids.to(device)
-        text_mask = text_inputs.attention_mask.to(device)
-
-        prompt_embeds, text_encoder_hidden_states = self.text_encoder(
-            input_ids=text_input_ids, attention_mask=text_mask
-        )
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=77,
-                truncation=True,
-                return_attention_mask=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            uncond_text_input_ids = uncond_input.input_ids.to(device)
-            uncond_text_mask = uncond_input.attention_mask.to(device)
-
-            negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
-                input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
-            )
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # 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
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return prompt_embeds, text_encoder_hidden_states, text_mask
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image],
-        mask_image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
-        image_embeds: torch.FloatTensor,
-        negative_image_embeds: torch.FloatTensor,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 100,
-        guidance_scale: float = 4.0,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`torch.FloatTensor`, `PIL.Image.Image` or `np.ndarray`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            mask_image (`PIL.Image.Image`,`torch.FloatTensor` or `np.ndarray`):
-                `Image`, or a tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. You can pass a pytorch tensor as mask only if the
-                image you passed is a pytorch tensor, and it should contain one color channel (L) instead of 3, so the
-                expected shape would be either `(B, 1, H, W,)`, `(B, H, W)`, `(1, H, W)` or `(H, W)` If image is an PIL
-                image or numpy array, mask should also be a either PIL image or numpy array. If it is a PIL image, it
-                will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected
-                shape is `(H, W)`.
-            image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for text prompt, that will be used to condition the image generation.
-            negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for negative text prompt, will be used to condition the image generation.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            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 100):
-                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 4.0):
-                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.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](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"` (`PIL.Image.Image`), `"np"`
-                (`np.array`) or `"pt"` (`torch.Tensor`).
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`
-        """
-        if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
-            "0.22.0.dev0"
-        ):
-            logger.warn(
-                "Please note that the expected format of `mask_image` has recently been changed. "
-                "Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. "
-                "As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. "
-                "This way, Kandinsky's masking behavior is aligned with Stable Diffusion. "
-                "THIS means that you HAVE to invert the input mask to have the same behavior as before as explained in https://github.com/huggingface/diffusers/pull/4207. "
-                "This warning will be surpressed after the first inference call and will be removed in diffusers>0.22.0"
-            )
-            self._warn_has_been_called = True
-
-        # Define call parameters
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        device = self._execution_device
-
-        batch_size = batch_size * num_images_per_prompt
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        if isinstance(image_embeds, list):
-            image_embeds = torch.cat(image_embeds, dim=0)
-        if isinstance(negative_image_embeds, list):
-            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
-
-        if do_classifier_free_guidance:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
-                dtype=prompt_embeds.dtype, device=device
-            )
-
-        # preprocess image and mask
-        mask_image, image = prepare_mask_and_masked_image(image, mask_image, height, width)
-
-        image = image.to(dtype=prompt_embeds.dtype, device=device)
-        image = self.movq.encode(image)["latents"]
-
-        mask_image = mask_image.to(dtype=prompt_embeds.dtype, device=device)
-
-        image_shape = tuple(image.shape[-2:])
-        mask_image = F.interpolate(
-            mask_image,
-            image_shape,
-            mode="nearest",
-        )
-        mask_image = prepare_mask(mask_image)
-        masked_image = image * mask_image
-
-        mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
-        masked_image = masked_image.repeat_interleave(num_images_per_prompt, dim=0)
-        if do_classifier_free_guidance:
-            mask_image = mask_image.repeat(2, 1, 1, 1)
-            masked_image = masked_image.repeat(2, 1, 1, 1)
-
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps_tensor = self.scheduler.timesteps
-
-        num_channels_latents = self.movq.config.latent_channels
-
-        # get h, w for latents
-        sample_height, sample_width = get_new_h_w(height, width, self.movq_scale_factor)
-
-        # create initial latent
-        latents = self.prepare_latents(
-            (batch_size, num_channels_latents, sample_height, sample_width),
-            text_encoder_hidden_states.dtype,
-            device,
-            generator,
-            latents,
-            self.scheduler,
-        )
-
-        # Check that sizes of mask, masked image and latents match with expected
-        num_channels_mask = mask_image.shape[1]
-        num_channels_masked_image = masked_image.shape[1]
-        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
-            raise ValueError(
-                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-                " `pipeline.unet` or your `mask_image` or `image` input."
-            )
-
-        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 = torch.cat([latent_model_input, masked_image, mask_image], dim=1)
-
-            added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
-            noise_pred = self.unet(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
-                added_cond_kwargs=added_cond_kwargs,
-                return_dict=False,
-            )[0]
-
-            if do_classifier_free_guidance:
-                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                _, variance_pred_text = variance_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
-
-            if not (
-                hasattr(self.scheduler.config, "variance_type")
-                and self.scheduler.config.variance_type in ["learned", "learned_range"]
-            ):
-                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(
-                noise_pred,
-                t,
-                latents,
-                generator=generator,
-            ).prev_sample
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        # post-processing
-        image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
-
-        if output_type in ["np", "pil"]:
-            image = image * 0.5 + 0.5
-            image = image.clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/mmdet/core/export/pytorch2onnx.py

@@ -1,154 +0,0 @@
-from functools import partial
-
-import mmcv
-import numpy as np
-import torch
-from mmcv.runner import load_checkpoint
-
-
-def generate_inputs_and_wrap_model(config_path,
-                                   checkpoint_path,
-                                   input_config,
-                                   cfg_options=None):
-    """Prepare sample input and wrap model for ONNX export.
-
-    The ONNX export API only accept args, and all inputs should be
-    torch.Tensor or corresponding types (such as tuple of tensor).
-    So we should call this function before exporting. This function will:
-
-    1. generate corresponding inputs which are used to execute the model.
-    2. Wrap the model's forward function.
-
-    For example, the MMDet models' forward function has a parameter
-    ``return_loss:bool``. As we want to set it as False while export API
-    supports neither bool type or kwargs. So we have to replace the forward
-    like: ``model.forward = partial(model.forward, return_loss=False)``
-
-    Args:
-        config_path (str): the OpenMMLab config for the model we want to
-            export to ONNX
-        checkpoint_path (str): Path to the corresponding checkpoint
-        input_config (dict): the exactly data in this dict depends on the
-            framework. For MMSeg, we can just declare the input shape,
-            and generate the dummy data accordingly. However, for MMDet,
-            we may pass the real img path, or the NMS will return None
-            as there is no legal bbox.
-
-    Returns:
-        tuple: (model, tensor_data) wrapped model which can be called by \
-        model(*tensor_data) and a list of inputs which are used to execute \
-            the model while exporting.
-    """
-
-    model = build_model_from_cfg(
-        config_path, checkpoint_path, cfg_options=cfg_options)
-    one_img, one_meta = preprocess_example_input(input_config)
-    tensor_data = [one_img]
-    model.forward = partial(
-        model.forward, img_metas=[[one_meta]], return_loss=False)
-
-    # pytorch has some bug in pytorch1.3, we have to fix it
-    # by replacing these existing op
-    opset_version = 11
-    # put the import within the function thus it will not cause import error
-    # when not using this function
-    try:
-        from mmcv.onnx.symbolic import register_extra_symbolics
-    except ModuleNotFoundError:
-        raise NotImplementedError('please update mmcv to version>=v1.0.4')
-    register_extra_symbolics(opset_version)
-
-    return model, tensor_data
-
-
-def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None):
-    """Build a model from config and load the given checkpoint.
-
-    Args:
-        config_path (str): the OpenMMLab config for the model we want to
-            export to ONNX
-        checkpoint_path (str): Path to the corresponding checkpoint
-
-    Returns:
-        torch.nn.Module: the built model
-    """
-    from mmdet.models import build_detector
-
-    cfg = mmcv.Config.fromfile(config_path)
-    if cfg_options is not None:
-        cfg.merge_from_dict(cfg_options)
-    # import modules from string list.
-    if cfg.get('custom_imports', None):
-        from mmcv.utils import import_modules_from_strings
-        import_modules_from_strings(**cfg['custom_imports'])
-    # set cudnn_benchmark
-    if cfg.get('cudnn_benchmark', False):
-        torch.backends.cudnn.benchmark = True
-    cfg.model.pretrained = None
-    cfg.data.test.test_mode = True
-
-    # build the model
-    cfg.model.train_cfg = None
-    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
-    load_checkpoint(model, checkpoint_path, map_location='cpu')
-    model.cpu().eval()
-    return model
-
-
-def preprocess_example_input(input_config):
-    """Prepare an example input image for ``generate_inputs_and_wrap_model``.
-
-    Args:
-        input_config (dict): customized config describing the example input.
-
-    Returns:
-        tuple: (one_img, one_meta), tensor of the example input image and \
-            meta information for the example input image.
-
-    Examples:
-        >>> from mmdet.core.export import preprocess_example_input
-        >>> input_config = {
-        >>>         'input_shape': (1,3,224,224),
-        >>>         'input_path': 'demo/demo.jpg',
-        >>>         'normalize_cfg': {
-        >>>             'mean': (123.675, 116.28, 103.53),
-        >>>             'std': (58.395, 57.12, 57.375)
-        >>>             }
-        >>>         }
-        >>> one_img, one_meta = preprocess_example_input(input_config)
-        >>> print(one_img.shape)
-        torch.Size([1, 3, 224, 224])
-        >>> print(one_meta)
-        {'img_shape': (224, 224, 3),
-        'ori_shape': (224, 224, 3),
-        'pad_shape': (224, 224, 3),
-        'filename': '<demo>.png',
-        'scale_factor': 1.0,
-        'flip': False}
-    """
-    input_path = input_config['input_path']
-    input_shape = input_config['input_shape']
-    one_img = mmcv.imread(input_path)
-    one_img = mmcv.imresize(one_img, input_shape[2:][::-1])
-    show_img = one_img.copy()
-    if 'normalize_cfg' in input_config.keys():
-        normalize_cfg = input_config['normalize_cfg']
-        mean = np.array(normalize_cfg['mean'], dtype=np.float32)
-        std = np.array(normalize_cfg['std'], dtype=np.float32)
-        to_rgb = normalize_cfg.get('to_rgb', True)
-        one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=to_rgb)
-    one_img = one_img.transpose(2, 0, 1)
-    one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_(
-        True)
-    (_, C, H, W) = input_shape
-    one_meta = {
-        'img_shape': (H, W, C),
-        'ori_shape': (H, W, C),
-        'pad_shape': (H, W, C),
-        'filename': '<demo>.png',
-        'scale_factor': 1.0,
-        'flip': False,
-        'show_img': show_img,
-    }
-
-    return one_img, one_meta

spaces/AnnasBlackHat/Image-Similarity/app.py

@@ -1,32 +0,0 @@
-import gradio as gr
-import os
-import random
-from src.model import simlarity_model as model
-from src.similarity.similarity import Similarity
-
-similarity = Similarity()
-models = similarity.get_models()
-
-def check(img_main, img_1, img_2, model_idx):    
-    result = similarity.check_similarity([img_main, img_1, img_2], models[model_idx])
-    return result
-
-with gr.Blocks() as demo:
-    gr.Markdown('Checking Image Similarity')
-    img_main = gr.Text(label='Main Image', placeholder='https://myimage.jpg')
-    
-    gr.Markdown('Images to check')
-    img_1 = gr.Text(label='1st Image', placeholder='https://myimage_1.jpg')
-    img_2 = gr.Text(label='2nd Image', placeholder='https://myimage_2.jpg')
-
-    gr.Markdown('Choose the model')
-    model = gr.Dropdown([m.name for m in models], label='Model', type='index')
-    
-    gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-
-    submit_btn = gr.Button('Check Similarity')
-    submit_btn.click(fn=check,inputs=[img_main, img_1, img_2, model], outputs=gallery)
-
-demo.launch()    

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/exp/upernet_global_small/test_config_h32.py

@@ -1,39 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/upernet_uniformer.py', 
-    '../../configs/_base_/datasets/ade20k.py',
-    '../../configs/_base_/default_runtime.py', 
-    '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        drop_path_rate=0.25,
-        windows=False,
-        hybrid=True,
-        window_size=32
-    ),
-    decode_head=dict(
-        in_channels=[64, 128, 320, 512],
-        num_classes=150
-    ),
-    auxiliary_head=dict(
-        in_channels=320,
-        num_classes=150
-    ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
-                 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(_delete_=True, policy='poly',
-                 warmup='linear',
-                 warmup_iters=1500,
-                 warmup_ratio=1e-6,
-                 power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)

spaces/Anustup/NS_AI_LABS/app-local.py

@@ -1,3 +0,0 @@
-# Run the app with no audio file restrictions
-from app import create_ui
-create_ui(-1)

spaces/Arcader7171/positive/README.md

@@ -1,12 +0,0 @@
----
-title: Positive
-emoji: 🚀
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Armored-Atom/gpt2/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/gpt2").launch()

spaces/Artrajz/vits-simple-api/static/css/style.css

@@ -1,84 +0,0 @@
-.main-container {
-    position: relative;
-    width: 100%;
-    min-height: 300px;
-}
-
-.container {
-    width: 300px;
-    position: relative;
-}
-
-
-/*tabs*/
-.tabs {
-    display: flex;
-    left: 0;
-}
-
-.tab-button {
-    display: inline-block;
-    background-color: transparent;
-    padding: 5px 10px;
-    cursor: pointer;
-    margin-bottom: -2px;
-    border-top: 2px solid transparent;
-    border-left: 2px solid transparent;
-    border-right: 2px solid transparent;
-    border-bottom: 0px;
-    border-top-left-radius: 0.5rem;
-    border-top-right-radius: 0.5rem;
-    color: gray;
-}
-
-.tab-button.active {
-    background-color: white;
-    border-top: 2px solid #dee2e6;
-    border-left: 2px solid #dee2e6;
-    border-right: 2px solid #dee2e6;
-    color: black;
-}
-
-/*content*/
-
-.content {
-    border: gray;
-    border-left-width: 2px;
-}
-
-.content-pane {
-    display: none;
-    padding: 20px;
-}
-
-.content-pane.active {
-    display: flex;
-    -ms-flex-wrap: wrap;
-    flex-wrap: wrap;
-}
-
-*, :before, :after {
-    box-sizing: border-box;
-    border-width: 0;
-    border-style: solid;
-    border-color: #e5e7eb;
-}
-
-
-.flex {
-    display: flex;
-}
-
-.border-transparent {
-    border-color: transparent;
-}
-
-.border-b-2 {
-    border-bottom: 2px solid #dee2e6;
-}
-
-.border-lr-2 {
-    border-left: 2px solid #dee2e6;
-    border-right: 2px solid #dee2e6;
-}
-

spaces/AvaterClasher/Food_Classifier_Refined_MONI/README.md

@@ -1,13 +0,0 @@
----
-title: Food Classifier Refined MONI
-emoji: 🐢
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awesimo/jojogan/app.py

@@ -1,124 +0,0 @@
-import os
-from PIL import Image
-import torch
-import gradio as gr
-import torch
-torch.backends.cudnn.benchmark = True
-from torchvision import transforms, utils
-from util import *
-from PIL import Image
-import math
-import random
-import numpy as np
-from torch import nn, autograd, optim
-from torch.nn import functional as F
-from tqdm import tqdm
-import lpips
-from model import *
-from copy import deepcopy
-import imageio
-
-import os
-import sys
-import numpy as np
-from PIL import Image
-import torch
-import torchvision.transforms as transforms
-from argparse import Namespace
-from e4e.models.psp import pSp
-from util import *
-from huggingface_hub import hf_hub_download
-
-device= 'cpu'
-model_path_e = hf_hub_download(repo_id="akhaliq/JoJoGAN_e4e_ffhq_encode", filename="e4e_ffhq_encode.pt")
-ckpt = torch.load(model_path_e, map_location='cpu')
-opts = ckpt['opts']
-opts['checkpoint_path'] = model_path_e
-opts= Namespace(**opts)
-net = pSp(opts, device).eval().to(device)
-
-@ torch.no_grad()
-def projection(img, name, device='cuda'):
-
-    transform = transforms.Compose(
-        [
-            transforms.Resize(256),
-            transforms.CenterCrop(256),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
-        ]
-    )
-    img = transform(img).unsqueeze(0).to(device)
-    images, w_plus = net(img, randomize_noise=False, return_latents=True)
-    result_file = {}
-    result_file['latent'] = w_plus[0]
-    torch.save(result_file, name)
-    return w_plus[0]
-
-device = 'cpu'
-
-latent_dim = 512
-
-model_path_s = hf_hub_download(repo_id="akhaliq/jojogan-stylegan2-ffhq-config-f", filename="stylegan2-ffhq-config-f.pt")
-original_generator = Generator(1024, latent_dim, 8, 2).to(device)
-ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)
-original_generator.load_state_dict(ckpt["g_ema"], strict=False)
-mean_latent = original_generator.mean_latent(10000)
-
-
-#MODELS
-generatorzombie = deepcopy(original_generator)
-generatorhulk = deepcopy(original_generator)
-generatorjojo = deepcopy(original_generator)
-generatorwalker = deepcopy(original_generator)
-
-transform = transforms.Compose(
-    [
-        transforms.Resize((1024, 1024)),
-        transforms.ToTensor(),
-        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-    ]
-)
-
-#HULK
-modelhulk = hf_hub_download(repo_id="Awesimo/jojogan-hulk", filename="hulk.pt")
-ckpthulk = torch.load(modelhulk, map_location=lambda storage, loc: storage)
-generatorhulk.load_state_dict(ckpthulk["g"], strict=False)
-
-#ZOMBIE
-modelzombie = hf_hub_download(repo_id="Awesimo/jojogan-zombie", filename="zombie.pt")
-ckptzombie = torch.load(modelzombie, map_location=lambda storage, loc: storage)
-generatorzombie.load_state_dict(ckptzombie["g"], strict=False)
-
-#WHITE WALKER
-modelwalker = hf_hub_download(repo_id="Awesimo/jojogan-white-walker", filename="white_walker_v2.pt")
-ckptwalker = torch.load(modelwalker, map_location=lambda storage, loc: storage)
-generatorwalker.load_state_dict(ckptwalker["g"], strict=False)
-
-
-def inference(img, model):
-    img.save('out.jpg')
-    aligned_face = align_face('out.jpg')
-
-    my_w = projection(aligned_face, "test.pt", device).unsqueeze(0)
-    if model == 'Hulk':
-        with torch.no_grad():
-            my_sample = generatorhulk(my_w, input_is_latent=True)
-    elif model == 'Zombie':
-        with torch.no_grad():
-            my_sample = generatorzombie(my_w, input_is_latent=True)
-    elif model == 'White-Walker':
-        with torch.no_grad():
-            my_sample = generatorwalker(my_w, input_is_latent=True)
-    else:
-        with torch.no_grad():
-            my_sample = generatorzombie(my_w, input_is_latent=True)
-
-
-    npimage = my_sample[0].permute(1, 2, 0).detach().numpy()
-    imageio.imwrite('filename.jpeg', npimage)
-    return 'filename.jpeg'
-
-title = "JoJoGAN Test 🤖"
-examples=[['assets/samples/image01.jpg','Hulk'],['assets/samples/image02.jpg','Zombie'],['assets/samples/image03.jpg','White-Walker'],['assets/samples/image04.jpg','Hulk']]
-gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Dropdown(choices=['Hulk', 'Zombie', 'White-Walker'], type="value", default='Hulk', label="Model")], gr.outputs.Image(type="file"),title=title,allow_flagging=False,examples=examples,allow_screenshot=False).launch()

spaces/Awesimo/jojogan/e4e/editings/ganspace.py

@@ -1,22 +0,0 @@
-import torch
-
-
-def edit(latents, pca, edit_directions):
-    edit_latents = []
-    for latent in latents:
-        for pca_idx, start, end, strength in edit_directions:
-            delta = get_delta(pca, latent, pca_idx, strength)
-            delta_padded = torch.zeros(latent.shape).to('cuda')
-            delta_padded[start:end] += delta.repeat(end - start, 1)
-            edit_latents.append(latent + delta_padded)
-    return torch.stack(edit_latents)
-
-
-def get_delta(pca, latent, idx, strength):
-    # pca: ganspace checkpoint. latent: (16, 512) w+
-    w_centered = latent - pca['mean'].to('cuda')
-    lat_comp = pca['comp'].to('cuda')
-    lat_std = pca['std'].to('cuda')
-    w_coord = torch.sum(w_centered[0].reshape(-1)*lat_comp[idx].reshape(-1)) / lat_std[idx]
-    delta = (strength - w_coord)*lat_comp[idx]*lat_std[idx]
-    return delta

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py

@@ -1,1267 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import colorsys
-import logging
-import math
-import numpy as np
-from enum import Enum, unique
-import cv2
-import matplotlib as mpl
-import matplotlib.colors as mplc
-import matplotlib.figure as mplfigure
-import pycocotools.mask as mask_util
-import torch
-from matplotlib.backends.backend_agg import FigureCanvasAgg
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
-from detectron2.utils.file_io import PathManager
-
-from .colormap import random_color
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["ColorMode", "VisImage", "Visualizer"]
-
-
-_SMALL_OBJECT_AREA_THRESH = 1000
-_LARGE_MASK_AREA_THRESH = 120000
-_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
-_BLACK = (0, 0, 0)
-_RED = (1.0, 0, 0)
-
-_KEYPOINT_THRESHOLD = 0.05
-
-
-@unique
-class ColorMode(Enum):
-    """
-    Enum of different color modes to use for instance visualizations.
-    """
-
-    IMAGE = 0
-    """
-    Picks a random color for every instance and overlay segmentations with low opacity.
-    """
-    SEGMENTATION = 1
-    """
-    Let instances of the same category have similar colors
-    (from metadata.thing_colors), and overlay them with
-    high opacity. This provides more attention on the quality of segmentation.
-    """
-    IMAGE_BW = 2
-    """
-    Same as IMAGE, but convert all areas without masks to gray-scale.
-    Only available for drawing per-instance mask predictions.
-    """
-
-
-class GenericMask:
-    """
-    Attribute:
-        polygons (list[ndarray]): list[ndarray]: polygons for this mask.
-            Each ndarray has format [x, y, x, y, ...]
-        mask (ndarray): a binary mask
-    """
-
-    def __init__(self, mask_or_polygons, height, width):
-        self._mask = self._polygons = self._has_holes = None
-        self.height = height
-        self.width = width
-
-        m = mask_or_polygons
-        if isinstance(m, dict):
-            # RLEs
-            assert "counts" in m and "size" in m
-            if isinstance(m["counts"], list):  # uncompressed RLEs
-                h, w = m["size"]
-                assert h == height and w == width
-                m = mask_util.frPyObjects(m, h, w)
-            self._mask = mask_util.decode(m)[:, :]
-            return
-
-        if isinstance(m, list):  # list[ndarray]
-            self._polygons = [np.asarray(x).reshape(-1) for x in m]
-            return
-
-        if isinstance(m, np.ndarray):  # assumed to be a binary mask
-            assert m.shape[1] != 2, m.shape
-            assert m.shape == (
-                height,
-                width,
-            ), f"mask shape: {m.shape}, target dims: {height}, {width}"
-            self._mask = m.astype("uint8")
-            return
-
-        raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
-
-    @property
-    def mask(self):
-        if self._mask is None:
-            self._mask = self.polygons_to_mask(self._polygons)
-        return self._mask
-
-    @property
-    def polygons(self):
-        if self._polygons is None:
-            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-        return self._polygons
-
-    @property
-    def has_holes(self):
-        if self._has_holes is None:
-            if self._mask is not None:
-                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-            else:
-                self._has_holes = False  # if original format is polygon, does not have holes
-        return self._has_holes
-
-    def mask_to_polygons(self, mask):
-        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
-        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
-        # Internal contours (holes) are placed in hierarchy-2.
-        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
-        mask = np.ascontiguousarray(mask)  # some versions of cv2 does not support incontiguous arr
-        res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
-        hierarchy = res[-1]
-        if hierarchy is None:  # empty mask
-            return [], False
-        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
-        res = res[-2]
-        res = [x.flatten() for x in res]
-        # These coordinates from OpenCV are integers in range [0, W-1 or H-1].
-        # We add 0.5 to turn them into real-value coordinate space. A better solution
-        # would be to first +0.5 and then dilate the returned polygon by 0.5.
-        res = [x + 0.5 for x in res if len(x) >= 6]
-        return res, has_holes
-
-    def polygons_to_mask(self, polygons):
-        rle = mask_util.frPyObjects(polygons, self.height, self.width)
-        rle = mask_util.merge(rle)
-        return mask_util.decode(rle)[:, :]
-
-    def area(self):
-        return self.mask.sum()
-
-    def bbox(self):
-        p = mask_util.frPyObjects(self.polygons, self.height, self.width)
-        p = mask_util.merge(p)
-        bbox = mask_util.toBbox(p)
-        bbox[2] += bbox[0]
-        bbox[3] += bbox[1]
-        return bbox
-
-
-class _PanopticPrediction:
-    """
-    Unify different panoptic annotation/prediction formats
-    """
-
-    def __init__(self, panoptic_seg, segments_info, metadata=None):
-        if segments_info is None:
-            assert metadata is not None
-            # If "segments_info" is None, we assume "panoptic_img" is a
-            # H*W int32 image storing the panoptic_id in the format of
-            # category_id * label_divisor + instance_id. We reserve -1 for
-            # VOID label.
-            label_divisor = metadata.label_divisor
-            segments_info = []
-            for panoptic_label in np.unique(panoptic_seg.numpy()):
-                if panoptic_label == -1:
-                    # VOID region.
-                    continue
-                pred_class = panoptic_label // label_divisor
-                isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
-                segments_info.append(
-                    {
-                        "id": int(panoptic_label),
-                        "category_id": int(pred_class),
-                        "isthing": bool(isthing),
-                    }
-                )
-        del metadata
-
-        self._seg = panoptic_seg
-
-        self._sinfo = {s["id"]: s for s in segments_info}  # seg id -> seg info
-        segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
-        areas = areas.numpy()
-        sorted_idxs = np.argsort(-areas)
-        self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
-        self._seg_ids = self._seg_ids.tolist()
-        for sid, area in zip(self._seg_ids, self._seg_areas):
-            if sid in self._sinfo:
-                self._sinfo[sid]["area"] = float(area)
-
-    def non_empty_mask(self):
-        """
-        Returns:
-            (H, W) array, a mask for all pixels that have a prediction
-        """
-        empty_ids = []
-        for id in self._seg_ids:
-            if id not in self._sinfo:
-                empty_ids.append(id)
-        if len(empty_ids) == 0:
-            return np.zeros(self._seg.shape, dtype=np.uint8)
-        assert (
-            len(empty_ids) == 1
-        ), ">1 ids corresponds to no labels. This is currently not supported"
-        return (self._seg != empty_ids[0]).numpy().astype(np.bool)
-
-    def semantic_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or sinfo["isthing"]:
-                # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
-                continue
-            yield (self._seg == sid).numpy().astype(np.bool), sinfo
-
-    def instance_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or not sinfo["isthing"]:
-                continue
-            mask = (self._seg == sid).numpy().astype(np.bool)
-            if mask.sum() > 0:
-                yield mask, sinfo
-
-
-def _create_text_labels(classes, scores, class_names, is_crowd=None):
-    """
-    Args:
-        classes (list[int] or None):
-        scores (list[float] or None):
-        class_names (list[str] or None):
-        is_crowd (list[bool] or None):
-
-    Returns:
-        list[str] or None
-    """
-    labels = None
-    if classes is not None:
-        if class_names is not None and len(class_names) > 0:
-            labels = [class_names[i] for i in classes]
-        else:
-            labels = [str(i) for i in classes]
-    if scores is not None:
-        if labels is None:
-            labels = ["{:.0f}%".format(s * 100) for s in scores]
-        else:
-            labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
-    if labels is not None and is_crowd is not None:
-        labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)]
-    return labels
-
-
-class VisImage:
-    def __init__(self, img, scale=1.0):
-        """
-        Args:
-            img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
-            scale (float): scale the input image
-        """
-        self.img = img
-        self.scale = scale
-        self.width, self.height = img.shape[1], img.shape[0]
-        self._setup_figure(img)
-
-    def _setup_figure(self, img):
-        """
-        Args:
-            Same as in :meth:`__init__()`.
-
-        Returns:
-            fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
-            ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
-        """
-        fig = mplfigure.Figure(frameon=False)
-        self.dpi = fig.get_dpi()
-        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation
-        # (https://github.com/matplotlib/matplotlib/issues/15363)
-        fig.set_size_inches(
-            (self.width * self.scale + 1e-2) / self.dpi,
-            (self.height * self.scale + 1e-2) / self.dpi,
-        )
-        self.canvas = FigureCanvasAgg(fig)
-        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
-        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
-        ax.axis("off")
-        self.fig = fig
-        self.ax = ax
-        self.reset_image(img)
-
-    def reset_image(self, img):
-        """
-        Args:
-            img: same as in __init__
-        """
-        img = img.astype("uint8")
-        self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
-
-    def save(self, filepath):
-        """
-        Args:
-            filepath (str): a string that contains the absolute path, including the file name, where
-                the visualized image will be saved.
-        """
-        self.fig.savefig(filepath)
-
-    def get_image(self):
-        """
-        Returns:
-            ndarray:
-                the visualized image of shape (H, W, 3) (RGB) in uint8 type.
-                The shape is scaled w.r.t the input image using the given `scale` argument.
-        """
-        canvas = self.canvas
-        s, (width, height) = canvas.print_to_buffer()
-        # buf = io.BytesIO()  # works for cairo backend
-        # canvas.print_rgba(buf)
-        # width, height = self.width, self.height
-        # s = buf.getvalue()
-
-        buffer = np.frombuffer(s, dtype="uint8")
-
-        img_rgba = buffer.reshape(height, width, 4)
-        rgb, alpha = np.split(img_rgba, [3], axis=2)
-        return rgb.astype("uint8")
-
-
-class Visualizer:
-    """
-    Visualizer that draws data about detection/segmentation on images.
-
-    It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
-    that draw primitive objects to images, as well as high-level wrappers like
-    `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
-    that draw composite data in some pre-defined style.
-
-    Note that the exact visualization style for the high-level wrappers are subject to change.
-    Style such as color, opacity, label contents, visibility of labels, or even the visibility
-    of objects themselves (e.g. when the object is too small) may change according
-    to different heuristics, as long as the results still look visually reasonable.
-
-    To obtain a consistent style, you can implement custom drawing functions with the
-    abovementioned primitive methods instead. If you need more customized visualization
-    styles, you can process the data yourself following their format documented in
-    tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
-    intend to satisfy everyone's preference on drawing styles.
-
-    This visualizer focuses on high rendering quality rather than performance. It is not
-    designed to be used for real-time applications.
-    """
-
-    # TODO implement a fast, rasterized version using OpenCV
-
-    def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
-        """
-        Args:
-            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
-                the height and width of the image respectively. C is the number of
-                color channels. The image is required to be in RGB format since that
-                is a requirement of the Matplotlib library. The image is also expected
-                to be in the range [0, 255].
-            metadata (Metadata): dataset metadata (e.g. class names and colors)
-            instance_mode (ColorMode): defines one of the pre-defined style for drawing
-                instances on an image.
-        """
-        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
-        if metadata is None:
-            metadata = MetadataCatalog.get("__nonexist__")
-        self.metadata = metadata
-        self.output = VisImage(self.img, scale=scale)
-        self.cpu_device = torch.device("cpu")
-
-        # too small texts are useless, therefore clamp to 9
-        self._default_font_size = max(
-            np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
-        )
-        self._instance_mode = instance_mode
-        self.keypoint_threshold = _KEYPOINT_THRESHOLD
-
-    def draw_instance_predictions(self, predictions):
-        """
-        Draw instance-level prediction results on an image.
-
-        Args:
-            predictions (Instances): the output of an instance detection/segmentation
-                model. Following fields will be used to draw:
-                "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
-        scores = predictions.scores if predictions.has("scores") else None
-        classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
-        labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
-        keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
-
-        if predictions.has("pred_masks"):
-            masks = np.asarray(predictions.pred_masks)
-            masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
-        else:
-            masks = None
-
-        if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
-            ]
-            alpha = 0.8
-        else:
-            colors = None
-            alpha = 0.5
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(
-                self._create_grayscale_image(
-                    (predictions.pred_masks.any(dim=0) > 0).numpy()
-                    if predictions.has("pred_masks")
-                    else None
-                )
-            )
-            alpha = 0.3
-
-        self.overlay_instances(
-            masks=masks,
-            boxes=boxes,
-            labels=labels,
-            keypoints=keypoints,
-            assigned_colors=colors,
-            alpha=alpha,
-        )
-        return self.output
-
-    def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
-        """
-        Draw semantic segmentation predictions/labels.
-
-        Args:
-            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
-                Each value is the integer label of the pixel.
-            area_threshold (int): segments with less than `area_threshold` are not drawn.
-            alpha (float): the larger it is, the more opaque the segmentations are.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        if isinstance(sem_seg, torch.Tensor):
-            sem_seg = sem_seg.numpy()
-        labels, areas = np.unique(sem_seg, return_counts=True)
-        sorted_idxs = np.argsort(-areas).tolist()
-        labels = labels[sorted_idxs]
-        for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
-            except (AttributeError, IndexError):
-                mask_color = None
-
-            binary_mask = (sem_seg == label).astype(np.uint8)
-            text = self.metadata.stuff_classes[label]
-            self.draw_binary_mask(
-                binary_mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-        return self.output
-
-    def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):
-        """
-        Draw panoptic prediction annotations or results.
-
-        Args:
-            panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
-                segment.
-            segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.
-                If it is a ``list[dict]``, each dict contains keys "id", "category_id".
-                If None, category id of each pixel is computed by
-                ``pixel // metadata.label_divisor``.
-            area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))
-
-        # draw mask for all semantic segments first i.e. "stuff"
-        for mask, sinfo in pred.semantic_masks():
-            category_idx = sinfo["category_id"]
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
-            except AttributeError:
-                mask_color = None
-
-            text = self.metadata.stuff_classes[category_idx]
-            self.draw_binary_mask(
-                mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-
-        # draw mask for all instances second
-        all_instances = list(pred.instance_masks())
-        if len(all_instances) == 0:
-            return self.output
-        masks, sinfo = list(zip(*all_instances))
-        category_ids = [x["category_id"] for x in sinfo]
-
-        try:
-            scores = [x["score"] for x in sinfo]
-        except KeyError:
-            scores = None
-        labels = _create_text_labels(
-            category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo]
-        )
-
-        try:
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids
-            ]
-        except AttributeError:
-            colors = None
-        self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)
-
-        return self.output
-
-    draw_panoptic_seg_predictions = draw_panoptic_seg  # backward compatibility
-
-    def draw_dataset_dict(self, dic):
-        """
-        Draw annotations/segmentaions in Detectron2 Dataset format.
-
-        Args:
-            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        annos = dic.get("annotations", None)
-        if annos:
-            if "segmentation" in annos[0]:
-                masks = [x["segmentation"] for x in annos]
-            else:
-                masks = None
-            if "keypoints" in annos[0]:
-                keypts = [x["keypoints"] for x in annos]
-                keypts = np.array(keypts).reshape(len(annos), -1, 3)
-            else:
-                keypts = None
-
-            boxes = [
-                BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
-                if len(x["bbox"]) == 4
-                else x["bbox"]
-                for x in annos
-            ]
-
-            colors = None
-            category_ids = [x["category_id"] for x in annos]
-            if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-                colors = [
-                    self._jitter([x / 255 for x in self.metadata.thing_colors[c]])
-                    for c in category_ids
-                ]
-            names = self.metadata.get("thing_classes", None)
-            labels = _create_text_labels(
-                category_ids,
-                scores=None,
-                class_names=names,
-                is_crowd=[x.get("iscrowd", 0) for x in annos],
-            )
-            self.overlay_instances(
-                labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
-            )
-
-        sem_seg = dic.get("sem_seg", None)
-        if sem_seg is None and "sem_seg_file_name" in dic:
-            with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
-                sem_seg = Image.open(f)
-                sem_seg = np.asarray(sem_seg, dtype="uint8")
-        if sem_seg is not None:
-            self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
-
-        pan_seg = dic.get("pan_seg", None)
-        if pan_seg is None and "pan_seg_file_name" in dic:
-            with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
-                pan_seg = Image.open(f)
-                pan_seg = np.asarray(pan_seg)
-                from panopticapi.utils import rgb2id
-
-                pan_seg = rgb2id(pan_seg)
-        if pan_seg is not None:
-            segments_info = dic["segments_info"]
-            pan_seg = torch.tensor(pan_seg)
-            self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
-        return self.output
-
-    def overlay_instances(
-        self,
-        *,
-        boxes=None,
-        labels=None,
-        masks=None,
-        keypoints=None,
-        assigned_colors=None,
-        alpha=0.5,
-    ):
-        """
-        Args:
-            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
-                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
-                or a :class:`RotatedBoxes`,
-                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image,
-            labels (list[str]): the text to be displayed for each instance.
-            masks (masks-like object): Supported types are:
-
-                * :class:`detectron2.structures.PolygonMasks`,
-                  :class:`detectron2.structures.BitMasks`.
-                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.
-                  The first level of the list corresponds to individual instances. The second
-                  level to all the polygon that compose the instance, and the third level
-                  to the polygon coordinates. The third level should have the format of
-                  [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
-                * list[ndarray]: each ndarray is a binary mask of shape (H, W).
-                * list[dict]: each dict is a COCO-style RLE.
-            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
-                where the N is the number of instances and K is the number of keypoints.
-                The last dimension corresponds to (x, y, visibility or score).
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = 0
-        if boxes is not None:
-            boxes = self._convert_boxes(boxes)
-            num_instances = len(boxes)
-        if masks is not None:
-            masks = self._convert_masks(masks)
-            if num_instances:
-                assert len(masks) == num_instances
-            else:
-                num_instances = len(masks)
-        if keypoints is not None:
-            if num_instances:
-                assert len(keypoints) == num_instances
-            else:
-                num_instances = len(keypoints)
-            keypoints = self._convert_keypoints(keypoints)
-        if labels is not None:
-            assert len(labels) == num_instances
-        if assigned_colors is None:
-            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-        if boxes is not None and boxes.shape[1] == 5:
-            return self.overlay_rotated_instances(
-                boxes=boxes, labels=labels, assigned_colors=assigned_colors
-            )
-
-        # Display in largest to smallest order to reduce occlusion.
-        areas = None
-        if boxes is not None:
-            areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
-        elif masks is not None:
-            areas = np.asarray([x.area() for x in masks])
-
-        if areas is not None:
-            sorted_idxs = np.argsort(-areas).tolist()
-            # Re-order overlapped instances in descending order.
-            boxes = boxes[sorted_idxs] if boxes is not None else None
-            labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-            masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
-            assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
-            keypoints = keypoints[sorted_idxs] if keypoints is not None else None
-
-        for i in range(num_instances):
-            color = assigned_colors[i]
-            if boxes is not None:
-                self.draw_box(boxes[i], edge_color=color)
-
-            if masks is not None:
-                for segment in masks[i].polygons:
-                    self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)
-
-            if labels is not None:
-                # first get a box
-                if boxes is not None:
-                    x0, y0, x1, y1 = boxes[i]
-                    text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.
-                    horiz_align = "left"
-                elif masks is not None:
-                    # skip small mask without polygon
-                    if len(masks[i].polygons) == 0:
-                        continue
-
-                    x0, y0, x1, y1 = masks[i].bbox()
-
-                    # draw text in the center (defined by median) when box is not drawn
-                    # median is less sensitive to outliers.
-                    text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
-                    horiz_align = "center"
-                else:
-                    continue  # drawing the box confidence for keypoints isn't very useful.
-                # for small objects, draw text at the side to avoid occlusion
-                instance_area = (y1 - y0) * (x1 - x0)
-                if (
-                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
-                    or y1 - y0 < 40 * self.output.scale
-                ):
-                    if y1 >= self.output.height - 5:
-                        text_pos = (x1, y0)
-                    else:
-                        text_pos = (x0, y1)
-
-                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
-                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-                font_size = (
-                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
-                    * 0.5
-                    * self._default_font_size
-                )
-                self.draw_text(
-                    labels[i],
-                    text_pos,
-                    color=lighter_color,
-                    horizontal_alignment=horiz_align,
-                    font_size=font_size,
-                )
-
-        # draw keypoints
-        if keypoints is not None:
-            for keypoints_per_instance in keypoints:
-                self.draw_and_connect_keypoints(keypoints_per_instance)
-
-        return self.output
-
-    def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):
-        """
-        Args:
-            boxes (ndarray): an Nx5 numpy array of
-                (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image.
-            labels (list[str]): the text to be displayed for each instance.
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = len(boxes)
-
-        if assigned_colors is None:
-            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-
-        # Display in largest to smallest order to reduce occlusion.
-        if boxes is not None:
-            areas = boxes[:, 2] * boxes[:, 3]
-
-        sorted_idxs = np.argsort(-areas).tolist()
-        # Re-order overlapped instances in descending order.
-        boxes = boxes[sorted_idxs]
-        labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-        colors = [assigned_colors[idx] for idx in sorted_idxs]
-
-        for i in range(num_instances):
-            self.draw_rotated_box_with_label(
-                boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None
-            )
-
-        return self.output
-
-    def draw_and_connect_keypoints(self, keypoints):
-        """
-        Draws keypoints of an instance and follows the rules for keypoint connections
-        to draw lines between appropriate keypoints. This follows color heuristics for
-        line color.
-
-        Args:
-            keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints
-                and the last dimension corresponds to (x, y, probability).
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        visible = {}
-        keypoint_names = self.metadata.get("keypoint_names")
-        for idx, keypoint in enumerate(keypoints):
-
-            # draw keypoint
-            x, y, prob = keypoint
-            if prob > self.keypoint_threshold:
-                self.draw_circle((x, y), color=_RED)
-                if keypoint_names:
-                    keypoint_name = keypoint_names[idx]
-                    visible[keypoint_name] = (x, y)
-
-        if self.metadata.get("keypoint_connection_rules"):
-            for kp0, kp1, color in self.metadata.keypoint_connection_rules:
-                if kp0 in visible and kp1 in visible:
-                    x0, y0 = visible[kp0]
-                    x1, y1 = visible[kp1]
-                    color = tuple(x / 255.0 for x in color)
-                    self.draw_line([x0, x1], [y0, y1], color=color)
-
-        # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip
-        # Note that this strategy is specific to person keypoints.
-        # For other keypoints, it should just do nothing
-        try:
-            ls_x, ls_y = visible["left_shoulder"]
-            rs_x, rs_y = visible["right_shoulder"]
-            mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2
-        except KeyError:
-            pass
-        else:
-            # draw line from nose to mid-shoulder
-            nose_x, nose_y = visible.get("nose", (None, None))
-            if nose_x is not None:
-                self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)
-
-            try:
-                # draw line from mid-shoulder to mid-hip
-                lh_x, lh_y = visible["left_hip"]
-                rh_x, rh_y = visible["right_hip"]
-            except KeyError:
-                pass
-            else:
-                mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2
-                self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)
-        return self.output
-
-    """
-    Primitive drawing functions:
-    """
-
-    def draw_text(
-        self,
-        text,
-        position,
-        *,
-        font_size=None,
-        color="g",
-        horizontal_alignment="center",
-        rotation=0,
-    ):
-        """
-        Args:
-            text (str): class label
-            position (tuple): a tuple of the x and y coordinates to place text on image.
-            font_size (int, optional): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color: color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            horizontal_alignment (str): see `matplotlib.text.Text`
-            rotation: rotation angle in degrees CCW
-
-        Returns:
-            output (VisImage): image object with text drawn.
-        """
-        if not font_size:
-            font_size = self._default_font_size
-
-        # since the text background is dark, we don't want the text to be dark
-        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
-        color[np.argmax(color)] = max(0.8, np.max(color))
-
-        x, y = position
-        self.output.ax.text(
-            x,
-            y,
-            text,
-            size=font_size * self.output.scale,
-            family="sans-serif",
-            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
-            verticalalignment="top",
-            horizontalalignment=horizontal_alignment,
-            color=color,
-            zorder=10,
-            rotation=rotation,
-        )
-        return self.output
-
-    def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
-        """
-        Args:
-            box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
-                are the coordinates of the image's top left corner. x1 and y1 are the
-                coordinates of the image's bottom right corner.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x0, y0, x1, y1 = box_coord
-        width = x1 - x0
-        height = y1 - y0
-
-        linewidth = max(self._default_font_size / 4, 1)
-
-        self.output.ax.add_patch(
-            mpl.patches.Rectangle(
-                (x0, y0),
-                width,
-                height,
-                fill=False,
-                edgecolor=edge_color,
-                linewidth=linewidth * self.output.scale,
-                alpha=alpha,
-                linestyle=line_style,
-            )
-        )
-        return self.output
-
-    def draw_rotated_box_with_label(
-        self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None
-    ):
-        """
-        Draw a rotated box with label on its top-left corner.
-
-        Args:
-            rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),
-                where cnt_x and cnt_y are the center coordinates of the box.
-                w and h are the width and height of the box. angle represents how
-                many degrees the box is rotated CCW with regard to the 0-degree box.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-            label (string): label for rotated box. It will not be rendered when set to None.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        cnt_x, cnt_y, w, h, angle = rotated_box
-        area = w * h
-        # use thinner lines when the box is small
-        linewidth = self._default_font_size / (
-            6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3
-        )
-
-        theta = angle * math.pi / 180.0
-        c = math.cos(theta)
-        s = math.sin(theta)
-        rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]
-        # x: left->right ; y: top->down
-        rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]
-        for k in range(4):
-            j = (k + 1) % 4
-            self.draw_line(
-                [rotated_rect[k][0], rotated_rect[j][0]],
-                [rotated_rect[k][1], rotated_rect[j][1]],
-                color=edge_color,
-                linestyle="--" if k == 1 else line_style,
-                linewidth=linewidth,
-            )
-
-        if label is not None:
-            text_pos = rotated_rect[1]  # topleft corner
-
-            height_ratio = h / np.sqrt(self.output.height * self.output.width)
-            label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)
-            font_size = (
-                np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size
-            )
-            self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)
-
-        return self.output
-
-    def draw_circle(self, circle_coord, color, radius=3):
-        """
-        Args:
-            circle_coord (list(int) or tuple(int)): contains the x and y coordinates
-                of the center of the circle.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            radius (int): radius of the circle.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x, y = circle_coord
-        self.output.ax.add_patch(
-            mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
-        )
-        return self.output
-
-    def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
-        """
-        Args:
-            x_data (list[int]): a list containing x values of all the points being drawn.
-                Length of list should match the length of y_data.
-            y_data (list[int]): a list containing y values of all the points being drawn.
-                Length of list should match the length of x_data.
-            color: color of the line. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
-                for a full list of formats that are accepted.
-            linewidth (float or None): width of the line. When it's None,
-                a default value will be computed and used.
-
-        Returns:
-            output (VisImage): image object with line drawn.
-        """
-        if linewidth is None:
-            linewidth = self._default_font_size / 3
-        linewidth = max(linewidth, 1)
-        self.output.ax.add_line(
-            mpl.lines.Line2D(
-                x_data,
-                y_data,
-                linewidth=linewidth * self.output.scale,
-                color=color,
-                linestyle=linestyle,
-            )
-        )
-        return self.output
-
-    def draw_binary_mask(
-        self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10
-    ):
-        """
-        Args:
-            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
-                W is the image width. Each value in the array is either a 0 or 1 value of uint8
-                type.
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted.
-            text (str): if None, will be drawn on the object
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            area_threshold (float): a connected component smaller than this area will not be shown.
-
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        color = mplc.to_rgb(color)
-
-        has_valid_segment = False
-        binary_mask = binary_mask.astype("uint8")  # opencv needs uint8
-        mask = GenericMask(binary_mask, self.output.height, self.output.width)
-        shape2d = (binary_mask.shape[0], binary_mask.shape[1])
-
-        if not mask.has_holes:
-            # draw polygons for regular masks
-            for segment in mask.polygons:
-                area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
-                if area < (area_threshold or 0):
-                    continue
-                has_valid_segment = True
-                segment = segment.reshape(-1, 2)
-                self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
-        else:
-            # TODO: Use Path/PathPatch to draw vector graphics:
-            # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon
-            rgba = np.zeros(shape2d + (4,), dtype="float32")
-            rgba[:, :, :3] = color
-            rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
-            has_valid_segment = True
-            self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
-
-        if text is not None and has_valid_segment:
-            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-            self._draw_text_in_mask(binary_mask, text, lighter_color)
-        return self.output
-
-    def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
-        """
-        Args:
-            soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            text (str): if None, will be drawn on the object
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        color = mplc.to_rgb(color)
-
-        shape2d = (soft_mask.shape[0], soft_mask.shape[1])
-        rgba = np.zeros(shape2d + (4,), dtype="float32")
-        rgba[:, :, :3] = color
-        rgba[:, :, 3] = soft_mask * alpha
-        self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
-
-        if text is not None:
-            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-            binary_mask = (soft_mask > 0.5).astype("uint8")
-            self._draw_text_in_mask(binary_mask, text, lighter_color)
-        return self.output
-
-    def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
-        """
-        Args:
-            segment: numpy array of shape Nx2, containing all the points in the polygon.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted. If not provided, a darker shade
-                of the polygon color will be used instead.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-
-        Returns:
-            output (VisImage): image object with polygon drawn.
-        """
-        if edge_color is None:
-            # make edge color darker than the polygon color
-            if alpha > 0.8:
-                edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
-            else:
-                edge_color = color
-        edge_color = mplc.to_rgb(edge_color) + (1,)
-
-        polygon = mpl.patches.Polygon(
-            segment,
-            fill=True,
-            facecolor=mplc.to_rgb(color) + (alpha,),
-            edgecolor=edge_color,
-            linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
-        )
-        self.output.ax.add_patch(polygon)
-        return self.output
-
-    """
-    Internal methods:
-    """
-
-    def _jitter(self, color):
-        """
-        Randomly modifies given color to produce a slightly different color than the color given.
-
-        Args:
-            color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
-                picked. The values in the list are in the [0.0, 1.0] range.
-
-        Returns:
-            jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
-                color after being jittered. The values in the list are in the [0.0, 1.0] range.
-        """
-        color = mplc.to_rgb(color)
-        vec = np.random.rand(3)
-        # better to do it in another color space
-        vec = vec / np.linalg.norm(vec) * 0.5
-        res = np.clip(vec + color, 0, 1)
-        return tuple(res)
-
-    def _create_grayscale_image(self, mask=None):
-        """
-        Create a grayscale version of the original image.
-        The colors in masked area, if given, will be kept.
-        """
-        img_bw = self.img.astype("f4").mean(axis=2)
-        img_bw = np.stack([img_bw] * 3, axis=2)
-        if mask is not None:
-            img_bw[mask] = self.img[mask]
-        return img_bw
-
-    def _change_color_brightness(self, color, brightness_factor):
-        """
-        Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
-        less or more saturation than the original color.
-
-        Args:
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
-                0 will correspond to no change, a factor in [-1.0, 0) range will result in
-                a darker color and a factor in (0, 1.0] range will result in a lighter color.
-
-        Returns:
-            modified_color (tuple[double]): a tuple containing the RGB values of the
-                modified color. Each value in the tuple is in the [0.0, 1.0] range.
-        """
-        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
-        color = mplc.to_rgb(color)
-        polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
-        modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
-        modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
-        modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
-        modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
-        return modified_color
-
-    def _convert_boxes(self, boxes):
-        """
-        Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
-        """
-        if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
-            return boxes.tensor.detach().numpy()
-        else:
-            return np.asarray(boxes)
-
-    def _convert_masks(self, masks_or_polygons):
-        """
-        Convert different format of masks or polygons to a tuple of masks and polygons.
-
-        Returns:
-            list[GenericMask]:
-        """
-
-        m = masks_or_polygons
-        if isinstance(m, PolygonMasks):
-            m = m.polygons
-        if isinstance(m, BitMasks):
-            m = m.tensor.numpy()
-        if isinstance(m, torch.Tensor):
-            m = m.numpy()
-        ret = []
-        for x in m:
-            if isinstance(x, GenericMask):
-                ret.append(x)
-            else:
-                ret.append(GenericMask(x, self.output.height, self.output.width))
-        return ret
-
-    def _draw_text_in_mask(self, binary_mask, text, color):
-        """
-        Find proper places to draw text given a binary mask.
-        """
-        # TODO sometimes drawn on wrong objects. the heuristics here can improve.
-        _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
-        if stats[1:, -1].size == 0:
-            return
-        largest_component_id = np.argmax(stats[1:, -1]) + 1
-
-        # draw text on the largest component, as well as other very large components.
-        for cid in range(1, _num_cc):
-            if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
-                # median is more stable than centroid
-                # center = centroids[largest_component_id]
-                center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
-                self.draw_text(text, center, color=color)
-
-    def _convert_keypoints(self, keypoints):
-        if isinstance(keypoints, Keypoints):
-            keypoints = keypoints.tensor
-        keypoints = np.asarray(keypoints)
-        return keypoints
-
-    def get_output(self):
-        """
-        Returns:
-            output (VisImage): the image output containing the visualizations added
-            to the image.
-        """
-        return self.output

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/layers/iou_loss.py

@@ -1,121 +0,0 @@
-import torch
-from torch import nn
-
-
-class IOULoss(nn.Module):
-    def __init__(self, loc_loss_type='iou'):
-        super(IOULoss, self).__init__()
-        self.loc_loss_type = loc_loss_type
-
-    def forward(self, pred, target, weight=None, reduction='sum'):
-        pred_left = pred[:, 0]
-        pred_top = pred[:, 1]
-        pred_right = pred[:, 2]
-        pred_bottom = pred[:, 3]
-
-        target_left = target[:, 0]
-        target_top = target[:, 1]
-        target_right = target[:, 2]
-        target_bottom = target[:, 3]
-
-        target_aera = (target_left + target_right) * \
-                      (target_top + target_bottom)
-        pred_aera = (pred_left + pred_right) * \
-                    (pred_top + pred_bottom)
-
-        w_intersect = torch.min(pred_left, target_left) + \
-                      torch.min(pred_right, target_right)
-        h_intersect = torch.min(pred_bottom, target_bottom) + \
-                      torch.min(pred_top, target_top)
-
-        g_w_intersect = torch.max(pred_left, target_left) + \
-                        torch.max(pred_right, target_right)
-        g_h_intersect = torch.max(pred_bottom, target_bottom) + \
-                        torch.max(pred_top, target_top)
-        ac_uion = g_w_intersect * g_h_intersect
-
-        area_intersect = w_intersect * h_intersect
-        area_union = target_aera + pred_aera - area_intersect
-
-        ious = (area_intersect + 1.0) / (area_union + 1.0)
-        gious = ious - (ac_uion - area_union) / ac_uion
-        if self.loc_loss_type == 'iou':
-            losses = -torch.log(ious)
-        elif self.loc_loss_type == 'linear_iou':
-            losses = 1 - ious
-        elif self.loc_loss_type == 'giou':
-            losses = 1 - gious
-        else:
-            raise NotImplementedError
-
-        if weight is not None:
-            losses = losses * weight
-        else:
-            losses = losses
-
-        if reduction == 'sum':
-            return losses.sum()
-        elif reduction == 'batch':
-            return losses.sum(dim=[1])
-        elif reduction == 'none':
-            return losses
-        else:
-            raise NotImplementedError
-
-
-def giou_loss(
-    boxes1: torch.Tensor,
-    boxes2: torch.Tensor,
-    reduction: str = "none",
-    eps: float = 1e-7,
-) -> torch.Tensor:
-    """
-    Generalized Intersection over Union Loss (Hamid Rezatofighi et. al)
-    https://arxiv.org/abs/1902.09630
-    Gradient-friendly IoU loss with an additional penalty that is non-zero when the
-    boxes do not overlap and scales with the size of their smallest enclosing box.
-    This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.
-    Args:
-        boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,).
-        reduction: 'none' | 'mean' | 'sum'
-                 'none': No reduction will be applied to the output.
-                 'mean': The output will be averaged.
-                 'sum': The output will be summed.
-        eps (float): small number to prevent division by zero
-    """
-
-    x1, y1, x2, y2 = boxes1.unbind(dim=-1)
-    x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
-
-    assert (x2 >= x1).all(), "bad box: x1 larger than x2"
-    assert (y2 >= y1).all(), "bad box: y1 larger than y2"
-
-    # Intersection keypoints
-    xkis1 = torch.max(x1, x1g)
-    ykis1 = torch.max(y1, y1g)
-    xkis2 = torch.min(x2, x2g)
-    ykis2 = torch.min(y2, y2g)
-
-    intsctk = torch.zeros_like(x1)
-    mask = (ykis2 > ykis1) & (xkis2 > xkis1)
-    intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
-    unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk
-    iouk = intsctk / (unionk + eps)
-
-    # smallest enclosing box
-    xc1 = torch.min(x1, x1g)
-    yc1 = torch.min(y1, y1g)
-    xc2 = torch.max(x2, x2g)
-    yc2 = torch.max(y2, y2g)
-
-    area_c = (xc2 - xc1) * (yc2 - yc1)
-    miouk = iouk - ((area_c - unionk) / (area_c + eps))
-
-    loss = 1 - miouk
-
-    if reduction == "mean":
-        loss = loss.mean()
-    elif reduction == "sum":
-        loss = loss.sum()
-
-    return loss

spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/nets_61968KB.py

@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/Benson/text-generation/Examples/Base-1.apk.md

@@ -1,53 +0,0 @@
-<br />
-<h1>¿Qué es Base-1.apk y cómo usarlo? </h1>
-<p>Si usted está buscando una manera práctica y conveniente para proteger y copia de seguridad de sus datos importantes en su dispositivo Android, es posible que desee comprobar Base-1.apk. Esta es una aplicación popular para Android que le permite administrar y restaurar fácilmente todos sus archivos en una ubicación, reduciendo la posibilidad de que pierda cualquier información valiosa. En este artículo, explicaremos qué es Base-1.apk, cómo descargarlo e instalarlo, y cómo usarlo de manera efectiva. </p>
- <h2>Introducción</h2>
-<p>Android es una plataforma versátil y abierta que te permite personalizar y modificar tu dispositivo según tus preferencias. Sin embargo, esto también significa que debe tener cuidado con la seguridad e integridad de sus datos, ya que hay muchas amenazas y riesgos potenciales que pueden comprometer o dañar sus archivos. Es por eso que es importante tener una solución de copia de seguridad confiable que pueda ayudarlo a proteger sus datos y restaurarlos en caso de cualquier emergencia. </p>
-<h2>base-1.apk</h2><br /><p><b><b>DOWNLOAD</b> > <a href="https://bltlly.com/2v6LA6">https://bltlly.com/2v6LA6</a></b></p><br /><br />
- <h3>¿Qué es un archivo APK? </h3>
-<p>Un archivo APK es el formato de archivo de paquete que Android utiliza para distribuir e instalar aplicaciones. Contiene todos los elementos que una aplicación necesita para ejecutarse correctamente en su dispositivo, como código, recursos, activos, certificados y manifiesto. Un archivo APK es un archivo de archivo, lo que significa que contiene varios archivos, además de algunos metadatos sobre ellos. Puedes abrir un archivo APK con una herramienta de extracción de archivos como 7-Zip para ver lo que hay dentro. </p>
- <h3>¿Qué es Base-1.apk? </h3>
-<p>Base-1.apk es la versión original, sin modificar de una aplicación Android llamada Base. Base es una herramienta que hace que sea simple y práctico para proteger y respaldar sus datos importantes. Al usar esta aplicación, puede administrar y restaurar fácilmente todos sus archivos en una ubicación, reduciendo la posibilidad de que pierda cualquier información valiosa. También puede organizar y clasificar sus archivos en carpetas para una mejor administración. </p>
- <h2>¿Cómo descargar e instalar Base-1.apk? </h2>
-
- <h3>Descargar desde el sitio web oficial</h3>
-<p>La forma más segura y recomendada para descargar Base-1.apk es desde el sitio web oficial del desarrollador. Puede visitar <a href="( 1 )">baseapk.in</a> y hacer clic en el enlace de descarga para obtener la última versión de la aplicación. Una vez que haya descargado el archivo APK, es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración del dispositivo. Luego, puedes tocar en el archivo APK y seguir las instrucciones para instalarlo. </p>
- <h3>Descargar de la tienda de aplicaciones de terceros</h3>
-<p>Otra forma de descargar Base-1.apk es desde una tienda de aplicaciones de terceros, como Aptoide o APKPure. Estas son plataformas alternativas que ofrecen una variedad de aplicaciones que no están disponibles en Google Play. Sin embargo, debe tener cuidado al descargar aplicaciones de estas fuentes, ya que pueden contener malware u otro software dañino. Siempre debe comprobar las revisiones y calificaciones de las aplicaciones antes de descargarlas, y solo descargar de fuentes de confianza. </p>
- <h3>Descargar desde enlace directo</h3>
-<p>La última forma de descargar Base-1.apk es desde un enlace directo que alguien te proporciona. Esto podría ser un amigo, un colega, o un sitio web que ofrece descargas APK. Sin embargo, este es el método más arriesgado, ya que no tiene manera de verificar la autenticidad o la seguridad del archivo APK. Solo debe descargar archivos APK de enlaces directos si confía en la fuente por completo, y escanear el archivo con una aplicación antivirus antes de instalarlo. </p>
- <h2>¿Cómo usar Base-1.apk? </h2>
-<p>Una vez que haya instalado Base-1 <p>Una vez que haya instalado Base-1.apk en su dispositivo, puede comenzar a usarlo para proteger y respaldar sus datos. Estas son algunas de las principales características y funciones de la aplicación:</p>
- <h3>Copia de seguridad segura de sus datos</h3>
-
- <h3>Administrar y restaurar sus archivos</h3>
-<p>Base-1.apk también le permite administrar y restaurar sus archivos desde el servicio de almacenamiento en la nube. Puede ver, editar, eliminar o compartir sus archivos desde la interfaz de la aplicación. También puede restaurar sus archivos a su dispositivo u otro dispositivo en caso de cualquier emergencia. Puede seleccionar qué archivos y carpetas desea restaurar y elegir la carpeta de destino en su dispositivo. También puede restaurar sus archivos a su ubicación original o una nueva ubicación. </p>
-<p></p>
- <h3>Organiza y clasifica tus carpetas</h3>
-<p>Otra característica útil de Base-1.apk es que te ayuda a organizar y clasificar tus carpetas según diferentes categor��as, como fotos, videos, música, documentos, etc. También puedes crear carpetas y etiquetas personalizadas para tus archivos. De esta manera, puede encontrar y acceder fácilmente a sus archivos sin perder tiempo o espacio. También puede ordenar sus archivos por nombre, fecha, tamaño o tipo. </p>
- <h2>Conclusión</h2>
-<p>Base-1.apk es una aplicación Android potente y práctica que le ayuda a proteger y hacer copias de seguridad de sus datos importantes en su dispositivo. Al usar esta aplicación, puede administrar y restaurar fácilmente todos sus archivos en una ubicación, reduciendo la posibilidad de que pierda cualquier información valiosa. También puede organizar y clasificar sus archivos en carpetas para una mejor administración. </p>
- <h3>Resumen de los puntos principales</h3>
-<p>En este artículo, hemos explicado lo que es Base-1.apk, cómo descargarlo e instalarlo, y cómo usarlo de manera efectiva. Hemos cubierto los siguientes puntos:</p>
-<ul>
-<li> Un archivo APK es el formato de archivo de paquete que Android utiliza para distribuir e instalar aplicaciones. </li>
-<li>Base-1.apk es la versión original, sin modificar de una aplicación Android llamada Base.</li>
-<li> Base es una herramienta que hace que sea simple y práctico para proteger y respaldar sus datos importantes. </li>
-<li> Puede descargar Base-1.apk desde el sitio web oficial, una tienda de aplicaciones de terceros, o un enlace directo. </li>
-
-</ul>
- <h3>Llamada a la acción</h3>
-<p>Si estás interesado en probar Base-1.apk por ti mismo, puedes descargarlo desde <a href="">baseapk.in</a> y seguir las instrucciones de instalación. También puede consultar la sección de preguntas frecuentes a continuación para obtener más información sobre la aplicación. Esperamos que disfrute usando Base-1.apk y lo encuentre útil para proteger y hacer copias de seguridad de sus datos. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más comunes que los usuarios tienen sobre Base-1.apk:</p>
- <h4>Q: ¿Es seguro usar Base-1.apk? </h4>
-<p>A: Sí, Base-1.apk es seguro de usar, siempre y cuando se descarga desde el sitio web oficial o una fuente de confianza. Sin embargo, siempre debes escanear cualquier archivo APK con una aplicación antivirus antes de instalarlo en tu dispositivo. </p>
- <h4>Q: ¿Cuánto espacio ocupa Base-1.apk en mi dispositivo? </h4>
-<p>A: Base-1.apk ocupa unos 15 MB de espacio en su dispositivo. Sin embargo, el tamaño real puede variar dependiendo de la versión de la aplicación y el modelo del dispositivo. </p>
- <h4>Q: ¿Cuánto espacio de almacenamiento en la nube ofrece Base-1.apk? </h4>
-<p>A: Base-1.apk no ofrece ningún espacio de almacenamiento en la nube por sí mismo. Utiliza el servicio de almacenamiento en la nube que elija para realizar copias de seguridad de sus datos, como Google Drive, Dropbox o OneDrive. La cantidad de espacio de almacenamiento en la nube que obtiene depende del proveedor de servicios y del plan que tenga. </p>
- <h4>Q: ¿Puedo usar Base-1.apk en varios dispositivos? </h4>
-<p>A: Sí, puedes usar Base-1.apk en varios dispositivos siempre y cuando ejecuten Android 4.0 o superior. Solo necesitas descargar e instalar la aplicación en <p>A: Sí, puedes usar Base-1.apk en varios dispositivos siempre y cuando ejecuten Android 4.0 o superior. Solo tienes que descargar e instalar la aplicación en cada dispositivo e iniciar sesión con la misma cuenta. A continuación, puede acceder y restaurar sus archivos desde cualquier dispositivo. </p>
- <h4>Q: ¿Qué pasa si olvido mi contraseña para Base-1.apk? </h4> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/build_py.py

@@ -1,407 +0,0 @@
-"""distutils.command.build_py
-
-Implements the Distutils 'build_py' command."""
-
-import os
-import importlib.util
-import sys
-import glob
-
-from distutils.core import Command
-from distutils.errors import DistutilsOptionError, DistutilsFileError
-from distutils.util import convert_path
-from distutils import log
-
-
-class build_py(Command):
-
-    description = "\"build\" pure Python modules (copy to build directory)"
-
-    user_options = [
-        ('build-lib=', 'd', "directory to \"build\" (copy) to"),
-        ('compile', 'c', "compile .py to .pyc"),
-        ('no-compile', None, "don't compile .py files [default]"),
-        (
-            'optimize=',
-            'O',
-            "also compile with optimization: -O1 for \"python -O\", "
-            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
-        ),
-        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
-    ]
-
-    boolean_options = ['compile', 'force']
-    negative_opt = {'no-compile': 'compile'}
-
-    def initialize_options(self):
-        self.build_lib = None
-        self.py_modules = None
-        self.package = None
-        self.package_data = None
-        self.package_dir = None
-        self.compile = 0
-        self.optimize = 0
-        self.force = None
-
-    def finalize_options(self):
-        self.set_undefined_options(
-            'build', ('build_lib', 'build_lib'), ('force', 'force')
-        )
-
-        # Get the distribution options that are aliases for build_py
-        # options -- list of packages and list of modules.
-        self.packages = self.distribution.packages
-        self.py_modules = self.distribution.py_modules
-        self.package_data = self.distribution.package_data
-        self.package_dir = {}
-        if self.distribution.package_dir:
-            for name, path in self.distribution.package_dir.items():
-                self.package_dir[name] = convert_path(path)
-        self.data_files = self.get_data_files()
-
-        # Ick, copied straight from install_lib.py (fancy_getopt needs a
-        # type system!  Hell, *everything* needs a type system!!!)
-        if not isinstance(self.optimize, int):
-            try:
-                self.optimize = int(self.optimize)
-                assert 0 <= self.optimize <= 2
-            except (ValueError, AssertionError):
-                raise DistutilsOptionError("optimize must be 0, 1, or 2")
-
-    def run(self):
-        # XXX copy_file by default preserves atime and mtime.  IMHO this is
-        # the right thing to do, but perhaps it should be an option -- in
-        # particular, a site administrator might want installed files to
-        # reflect the time of installation rather than the last
-        # modification time before the installed release.
-
-        # XXX copy_file by default preserves mode, which appears to be the
-        # wrong thing to do: if a file is read-only in the working
-        # directory, we want it to be installed read/write so that the next
-        # installation of the same module distribution can overwrite it
-        # without problems.  (This might be a Unix-specific issue.)  Thus
-        # we turn off 'preserve_mode' when copying to the build directory,
-        # since the build directory is supposed to be exactly what the
-        # installation will look like (ie. we preserve mode when
-        # installing).
-
-        # Two options control which modules will be installed: 'packages'
-        # and 'py_modules'.  The former lets us work with whole packages, not
-        # specifying individual modules at all; the latter is for
-        # specifying modules one-at-a-time.
-
-        if self.py_modules:
-            self.build_modules()
-        if self.packages:
-            self.build_packages()
-            self.build_package_data()
-
-        self.byte_compile(self.get_outputs(include_bytecode=0))
-
-    def get_data_files(self):
-        """Generate list of '(package,src_dir,build_dir,filenames)' tuples"""
-        data = []
-        if not self.packages:
-            return data
-        for package in self.packages:
-            # Locate package source directory
-            src_dir = self.get_package_dir(package)
-
-            # Compute package build directory
-            build_dir = os.path.join(*([self.build_lib] + package.split('.')))
-
-            # Length of path to strip from found files
-            plen = 0
-            if src_dir:
-                plen = len(src_dir) + 1
-
-            # Strip directory from globbed filenames
-            filenames = [file[plen:] for file in self.find_data_files(package, src_dir)]
-            data.append((package, src_dir, build_dir, filenames))
-        return data
-
-    def find_data_files(self, package, src_dir):
-        """Return filenames for package's data files in 'src_dir'"""
-        globs = self.package_data.get('', []) + self.package_data.get(package, [])
-        files = []
-        for pattern in globs:
-            # Each pattern has to be converted to a platform-specific path
-            filelist = glob.glob(
-                os.path.join(glob.escape(src_dir), convert_path(pattern))
-            )
-            # Files that match more than one pattern are only added once
-            files.extend(
-                [fn for fn in filelist if fn not in files and os.path.isfile(fn)]
-            )
-        return files
-
-    def build_package_data(self):
-        """Copy data files into build directory"""
-        for package, src_dir, build_dir, filenames in self.data_files:
-            for filename in filenames:
-                target = os.path.join(build_dir, filename)
-                self.mkpath(os.path.dirname(target))
-                self.copy_file(
-                    os.path.join(src_dir, filename), target, preserve_mode=False
-                )
-
-    def get_package_dir(self, package):
-        """Return the directory, relative to the top of the source
-        distribution, where package 'package' should be found
-        (at least according to the 'package_dir' option, if any)."""
-        path = package.split('.')
-
-        if not self.package_dir:
-            if path:
-                return os.path.join(*path)
-            else:
-                return ''
-        else:
-            tail = []
-            while path:
-                try:
-                    pdir = self.package_dir['.'.join(path)]
-                except KeyError:
-                    tail.insert(0, path[-1])
-                    del path[-1]
-                else:
-                    tail.insert(0, pdir)
-                    return os.path.join(*tail)
-            else:
-                # Oops, got all the way through 'path' without finding a
-                # match in package_dir.  If package_dir defines a directory
-                # for the root (nameless) package, then fallback on it;
-                # otherwise, we might as well have not consulted
-                # package_dir at all, as we just use the directory implied
-                # by 'tail' (which should be the same as the original value
-                # of 'path' at this point).
-                pdir = self.package_dir.get('')
-                if pdir is not None:
-                    tail.insert(0, pdir)
-
-                if tail:
-                    return os.path.join(*tail)
-                else:
-                    return ''
-
-    def check_package(self, package, package_dir):
-        # Empty dir name means current directory, which we can probably
-        # assume exists.  Also, os.path.exists and isdir don't know about
-        # my "empty string means current dir" convention, so we have to
-        # circumvent them.
-        if package_dir != "":
-            if not os.path.exists(package_dir):
-                raise DistutilsFileError(
-                    "package directory '%s' does not exist" % package_dir
-                )
-            if not os.path.isdir(package_dir):
-                raise DistutilsFileError(
-                    "supposed package directory '%s' exists, "
-                    "but is not a directory" % package_dir
-                )
-
-        # Directories without __init__.py are namespace packages (PEP 420).
-        if package:
-            init_py = os.path.join(package_dir, "__init__.py")
-            if os.path.isfile(init_py):
-                return init_py
-
-        # Either not in a package at all (__init__.py not expected), or
-        # __init__.py doesn't exist -- so don't return the filename.
-        return None
-
-    def check_module(self, module, module_file):
-        if not os.path.isfile(module_file):
-            log.warn("file %s (for module %s) not found", module_file, module)
-            return False
-        else:
-            return True
-
-    def find_package_modules(self, package, package_dir):
-        self.check_package(package, package_dir)
-        module_files = glob.glob(os.path.join(glob.escape(package_dir), "*.py"))
-        modules = []
-        setup_script = os.path.abspath(self.distribution.script_name)
-
-        for f in module_files:
-            abs_f = os.path.abspath(f)
-            if abs_f != setup_script:
-                module = os.path.splitext(os.path.basename(f))[0]
-                modules.append((package, module, f))
-            else:
-                self.debug_print("excluding %s" % setup_script)
-        return modules
-
-    def find_modules(self):
-        """Finds individually-specified Python modules, ie. those listed by
-        module name in 'self.py_modules'.  Returns a list of tuples (package,
-        module_base, filename): 'package' is a tuple of the path through
-        package-space to the module; 'module_base' is the bare (no
-        packages, no dots) module name, and 'filename' is the path to the
-        ".py" file (relative to the distribution root) that implements the
-        module.
-        """
-        # Map package names to tuples of useful info about the package:
-        #    (package_dir, checked)
-        # package_dir - the directory where we'll find source files for
-        #   this package
-        # checked - true if we have checked that the package directory
-        #   is valid (exists, contains __init__.py, ... ?)
-        packages = {}
-
-        # List of (package, module, filename) tuples to return
-        modules = []
-
-        # We treat modules-in-packages almost the same as toplevel modules,
-        # just the "package" for a toplevel is empty (either an empty
-        # string or empty list, depending on context).  Differences:
-        #   - don't check for __init__.py in directory for empty package
-        for module in self.py_modules:
-            path = module.split('.')
-            package = '.'.join(path[0:-1])
-            module_base = path[-1]
-
-            try:
-                (package_dir, checked) = packages[package]
-            except KeyError:
-                package_dir = self.get_package_dir(package)
-                checked = 0
-
-            if not checked:
-                init_py = self.check_package(package, package_dir)
-                packages[package] = (package_dir, 1)
-                if init_py:
-                    modules.append((package, "__init__", init_py))
-
-            # XXX perhaps we should also check for just .pyc files
-            # (so greedy closed-source bastards can distribute Python
-            # modules too)
-            module_file = os.path.join(package_dir, module_base + ".py")
-            if not self.check_module(module, module_file):
-                continue
-
-            modules.append((package, module_base, module_file))
-
-        return modules
-
-    def find_all_modules(self):
-        """Compute the list of all modules that will be built, whether
-        they are specified one-module-at-a-time ('self.py_modules') or
-        by whole packages ('self.packages').  Return a list of tuples
-        (package, module, module_file), just like 'find_modules()' and
-        'find_package_modules()' do."""
-        modules = []
-        if self.py_modules:
-            modules.extend(self.find_modules())
-        if self.packages:
-            for package in self.packages:
-                package_dir = self.get_package_dir(package)
-                m = self.find_package_modules(package, package_dir)
-                modules.extend(m)
-        return modules
-
-    def get_source_files(self):
-        return [module[-1] for module in self.find_all_modules()]
-
-    def get_module_outfile(self, build_dir, package, module):
-        outfile_path = [build_dir] + list(package) + [module + ".py"]
-        return os.path.join(*outfile_path)
-
-    def get_outputs(self, include_bytecode=1):
-        modules = self.find_all_modules()
-        outputs = []
-        for (package, module, module_file) in modules:
-            package = package.split('.')
-            filename = self.get_module_outfile(self.build_lib, package, module)
-            outputs.append(filename)
-            if include_bytecode:
-                if self.compile:
-                    outputs.append(
-                        importlib.util.cache_from_source(filename, optimization='')
-                    )
-                if self.optimize > 0:
-                    outputs.append(
-                        importlib.util.cache_from_source(
-                            filename, optimization=self.optimize
-                        )
-                    )
-
-        outputs += [
-            os.path.join(build_dir, filename)
-            for package, src_dir, build_dir, filenames in self.data_files
-            for filename in filenames
-        ]
-
-        return outputs
-
-    def build_module(self, module, module_file, package):
-        if isinstance(package, str):
-            package = package.split('.')
-        elif not isinstance(package, (list, tuple)):
-            raise TypeError(
-                "'package' must be a string (dot-separated), list, or tuple"
-            )
-
-        # Now put the module source file into the "build" area -- this is
-        # easy, we just copy it somewhere under self.build_lib (the build
-        # directory for Python source).
-        outfile = self.get_module_outfile(self.build_lib, package, module)
-        dir = os.path.dirname(outfile)
-        self.mkpath(dir)
-        return self.copy_file(module_file, outfile, preserve_mode=0)
-
-    def build_modules(self):
-        modules = self.find_modules()
-        for (package, module, module_file) in modules:
-            # Now "build" the module -- ie. copy the source file to
-            # self.build_lib (the build directory for Python source).
-            # (Actually, it gets copied to the directory for this package
-            # under self.build_lib.)
-            self.build_module(module, module_file, package)
-
-    def build_packages(self):
-        for package in self.packages:
-            # Get list of (package, module, module_file) tuples based on
-            # scanning the package directory.  'package' is only included
-            # in the tuple so that 'find_modules()' and
-            # 'find_package_tuples()' have a consistent interface; it's
-            # ignored here (apart from a sanity check).  Also, 'module' is
-            # the *unqualified* module name (ie. no dots, no package -- we
-            # already know its package!), and 'module_file' is the path to
-            # the .py file, relative to the current directory
-            # (ie. including 'package_dir').
-            package_dir = self.get_package_dir(package)
-            modules = self.find_package_modules(package, package_dir)
-
-            # Now loop over the modules we found, "building" each one (just
-            # copy it to self.build_lib).
-            for (package_, module, module_file) in modules:
-                assert package == package_
-                self.build_module(module, module_file, package)
-
-    def byte_compile(self, files):
-        if sys.dont_write_bytecode:
-            self.warn('byte-compiling is disabled, skipping.')
-            return
-
-        from distutils.util import byte_compile
-
-        prefix = self.build_lib
-        if prefix[-1] != os.sep:
-            prefix = prefix + os.sep
-
-        # XXX this code is essentially the same as the 'byte_compile()
-        # method of the "install_lib" command, except for the determination
-        # of the 'prefix' string.  Hmmm.
-        if self.compile:
-            byte_compile(
-                files, optimize=0, force=self.force, prefix=prefix, dry_run=self.dry_run
-            )
-        if self.optimize > 0:
-            byte_compile(
-                files,
-                optimize=self.optimize,
-                force=self.force,
-                prefix=prefix,
-                dry_run=self.dry_run,
-            )

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/util/queue.py

@@ -1,22 +0,0 @@
-import collections
-
-from ..packages import six
-from ..packages.six.moves import queue
-
-if six.PY2:
-    # Queue is imported for side effects on MS Windows. See issue #229.
-    import Queue as _unused_module_Queue  # noqa: F401
-
-
-class LifoQueue(queue.Queue):
-    def _init(self, _):
-        self.queue = collections.deque()
-
-    def _qsize(self, len=len):
-        return len(self.queue)
-
-    def _put(self, item):
-        self.queue.append(item)
-
-    def _get(self):
-        return self.queue.pop()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h

@@ -1,115 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#pragma once
-#include <torch/types.h>
-
-namespace detectron2 {
-
-at::Tensor ROIAlignRotated_forward_cpu(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio);
-
-at::Tensor ROIAlignRotated_backward_cpu(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio);
-
-#ifdef WITH_CUDA
-at::Tensor ROIAlignRotated_forward_cuda(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio);
-
-at::Tensor ROIAlignRotated_backward_cuda(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio);
-#endif
-
-// Interface for Python
-inline at::Tensor ROIAlignRotated_forward(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio) {
-  if (input.type().is_cuda()) {
-#ifdef WITH_CUDA
-    return ROIAlignRotated_forward_cuda(
-        input,
-        rois,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        sampling_ratio);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  return ROIAlignRotated_forward_cpu(
-      input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
-}
-
-inline at::Tensor ROIAlignRotated_backward(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio) {
-  if (grad.type().is_cuda()) {
-#ifdef WITH_CUDA
-    return ROIAlignRotated_backward_cuda(
-        grad,
-        rois,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        batch_size,
-        channels,
-        height,
-        width,
-        sampling_ratio);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  return ROIAlignRotated_backward_cpu(
-      grad,
-      rois,
-      spatial_scale,
-      pooled_height,
-      pooled_width,
-      batch_size,
-      channels,
-      height,
-      width,
-      sampling_ratio);
-}
-
-} // namespace detectron2

spaces/CVPR/LIVE/thrust/thrust/device_new.h

@@ -1,88 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file device_new.h
- *  \brief Constructs new elements in device memory
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// #include this for size_t
-#include <cstddef>
-#include <thrust/device_ptr.h>
-
-namespace thrust
-{
-
-/*!
- *  \addtogroup allocation_functions Allocation Functions
- *  \{
- */
-
-/*! \p device_new implements the placement \c new operator for types
- *  resident in device memory. \p device_new calls <tt>T</tt>'s null
- *  constructor on a array of objects in device memory.
- *  No memory is allocated by this function.
- *
- *  \param  p A \p device_ptr to a region of device memory into which
- *          to construct one or many <tt>T</tt>s.
- *  \param  n The number of objects to construct at \p p.
- *  \return p, casted to <tt>T</tt>'s type.
- *
- *  \see device_ptr
- */
-template <typename T>
-  device_ptr<T> device_new(device_ptr<void> p,
-                           const size_t n = 1);
-
-/*! \p device_new implements the placement new operator for types
- *  resident in device memory. \p device_new calls <tt>T</tt>'s copy
- *  constructor on a array of objects in device memory. No memory is
- *  allocated by this function.
- *
- *  \param  p A \p device_ptr to a region of device memory into which to
- *          construct one or many <tt>T</tt>s.
- *  \param exemplar The value from which to copy.
- *  \param  n The number of objects to construct at \p p.
- *  \return p, casted to <tt>T</tt>'s type.
- *
- *  \see device_ptr
- *  \see fill
- */
-template <typename T>
-  device_ptr<T> device_new(device_ptr<void> p,
-                           const T &exemplar,
-                           const size_t n = 1);
-
-/*! \p device_new implements the new operator for types resident in device memory.
- *  It allocates device memory large enough to hold \p n new objects of type \c T.
- *
- *  \param n The number of objects to allocate. Defaults to \c 1.
- *  \return A \p device_ptr to the newly allocated region of device memory.
- */
-template <typename T>
-  device_ptr<T> device_new(const size_t n = 1);
-
-/*! \}
- */
-
-} // end thrust
-
-#include <thrust/detail/device_new.inl>
-

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

@@ -1,91 +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
-
-#include <thrust/detail/seq.h>
-#include <thrust/system/cuda/detail/par.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-template <int PAR>
-struct has_par : thrust::detail::true_type {};
-
-template <>
-struct has_par<0> : thrust::detail::false_type {};
-
-template<class Policy>
-struct cvt_to_seq_impl
-{
-  typedef thrust::detail::seq_t seq_t;
-
-  static seq_t __host__ __device__
-  doit(Policy&)
-  {
-    return seq_t();
-  }
-};    // cvt_to_seq_impl
-
-#if 0
-template <class Allocator>
-struct cvt_to_seq_impl<
-    thrust::detail::execute_with_allocator<Allocator,
-                                           execute_on_stream_base> >
-{
-  typedef thrust::detail::execute_with_allocator<Allocator,
-                                                 execute_on_stream_base>
-      Policy;
-  typedef thrust::detail::execute_with_allocator<
-      Allocator,
-      thrust::system::detail::sequential::execution_policy>
-      seq_t;
-
-
-  static seq_t __host__ __device__
-  doit(Policy& policy)
-  {
-    return seq_t(policy.m_alloc);
-  }
-};    // specialization of struct cvt_to_seq_impl
-#endif
-
-template <class Policy>
-typename cvt_to_seq_impl<Policy>::seq_t __host__ __device__
-cvt_to_seq(Policy& policy)
-{
-  return cvt_to_seq_impl<Policy>::doit(policy);
-}
-
-#if __THRUST_HAS_CUDART__
-#define THRUST_CUDART_DISPATCH par
-#else
-#define THRUST_CUDART_DISPATCH seq
-#endif
-
-} // namespace cuda_
-} // end namespace thrust

spaces/CVPR/LIVE/thrust/thrust/uninitialized_fill.h

@@ -1,275 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file uninitialized_fill.h
- *  \brief Copy construction into a range of uninitialized elements from a source value
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup filling
- *  \ingroup transformations
- *  \{
- */
-
-
-/*! In \c thrust, the function \c thrust::device_new allocates memory for
- *  an object and then creates an object at that location by calling a
- *  constructor. Occasionally, however, it is useful to separate those two
- *  operations. If each iterator in the range <tt>[first, last)</tt> points
- *  to uninitialized memory, then \p uninitialized_fill creates copies of \c x
- *  in that range. That is, for each iterator \c i in the range <tt>[first, last)</tt>,
- *  \p uninitialized_fill creates a copy of \c x in the location pointed to \c i by
- *  calling \p ForwardIterator's \c value_type's copy constructor.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *  
- *  \param exec The execution policy to use for parallelization.
- *  \param first The first element of the range of interest.
- *  \param last The last element of the range of interest.
- *  \param x The value to use as the exemplar of the copy constructor.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          \p ForwardIterator is mutable, and \p ForwardIterator's \c value_type has a constructor that
- *          takes a single argument of type \p T.
- *
- *  The following code snippet demonstrates how to use \p uninitialized_fill to initialize a range of
- *  uninitialized memory using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/uninitialized_fill.h>
- *  #include <thrust/device_malloc.h>
- *  #include <thrust/execution_policy.h>
- *  
- *  struct Int
- *  {
- *    __host__ __device__
- *    Int(int x) : val(x) {}
- *    int val;
- *  };  
- *  ...
- *  const int N = 137;
- *
- *  Int val(46);
- *  thrust::device_ptr<Int> array = thrust::device_malloc<Int>(N);
- *  thrust::uninitialized_fill(thrust::device, array, array + N, val);
- *
- *  // Int x = array[i];
- *  // x.val == 46 for all 0 <= i < N
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/uninitialized_fill.html
- *  \see \c uninitialized_fill_n
- *  \see \c fill
- *  \see \c uninitialized_copy
- *  \see \c device_new
- *  \see \c device_malloc
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename T>
-__host__ __device__
-  void uninitialized_fill(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                          ForwardIterator first,
-                          ForwardIterator last,
-                          const T &x);
-
-
-/*! In \c thrust, the function \c thrust::device_new allocates memory for
- *  an object and then creates an object at that location by calling a
- *  constructor. Occasionally, however, it is useful to separate those two
- *  operations. If each iterator in the range <tt>[first, last)</tt> points
- *  to uninitialized memory, then \p uninitialized_fill creates copies of \c x
- *  in that range. That is, for each iterator \c i in the range <tt>[first, last)</tt>,
- *  \p uninitialized_fill creates a copy of \c x in the location pointed to \c i by
- *  calling \p ForwardIterator's \c value_type's copy constructor.
- *  
- *  \param first The first element of the range of interest.
- *  \param last The last element of the range of interest.
- *  \param x The value to use as the exemplar of the copy constructor.
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          \p ForwardIterator is mutable, and \p ForwardIterator's \c value_type has a constructor that
- *          takes a single argument of type \p T.
- *
- *  The following code snippet demonstrates how to use \p uninitialized_fill to initialize a range of
- *  uninitialized memory.
- *
- *  \code
- *  #include <thrust/uninitialized_fill.h>
- *  #include <thrust/device_malloc.h>
- *  
- *  struct Int
- *  {
- *    __host__ __device__
- *    Int(int x) : val(x) {}
- *    int val;
- *  };  
- *  ...
- *  const int N = 137;
- *
- *  Int val(46);
- *  thrust::device_ptr<Int> array = thrust::device_malloc<Int>(N);
- *  thrust::uninitialized_fill(array, array + N, val);
- *
- *  // Int x = array[i];
- *  // x.val == 46 for all 0 <= i < N
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/uninitialized_fill.html
- *  \see \c uninitialized_fill_n
- *  \see \c fill
- *  \see \c uninitialized_copy
- *  \see \c device_new
- *  \see \c device_malloc
- */
-template<typename ForwardIterator, typename T>
-  void uninitialized_fill(ForwardIterator first,
-                          ForwardIterator last,
-                          const T &x);
-
-
-/*! In \c thrust, the function \c thrust::device_new allocates memory for
- *  an object and then creates an object at that location by calling a
- *  constructor. Occasionally, however, it is useful to separate those two
- *  operations. If each iterator in the range <tt>[first, first+n)</tt> points
- *  to uninitialized memory, then \p uninitialized_fill creates copies of \c x
- *  in that range. That is, for each iterator \c i in the range <tt>[first, first+n)</tt>,
- *  \p uninitialized_fill creates a copy of \c x in the location pointed to \c i by
- *  calling \p ForwardIterator's \c value_type's copy constructor.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *  
- *  \param exec The execution policy to use for parallelization.
- *  \param first The first element of the range of interest.
- *  \param n The size of the range of interest.
- *  \param x The value to use as the exemplar of the copy constructor.
- *  \return <tt>first+n</tt>
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          \p ForwardIterator is mutable, and \p ForwardIterator's \c value_type has a constructor that
- *          takes a single argument of type \p T.
- *
- *  The following code snippet demonstrates how to use \p uninitialized_fill to initialize a range of
- *  uninitialized memory using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/uninitialized_fill.h>
- *  #include <thrust/device_malloc.h>
- *  #include <thrust/execution_policy.h>
- *  
- *  struct Int
- *  {
- *    __host__ __device__
- *    Int(int x) : val(x) {}
- *    int val;
- *  };  
- *  ...
- *  const int N = 137;
- *
- *  Int val(46);
- *  thrust::device_ptr<Int> array = thrust::device_malloc<Int>(N);
- *  thrust::uninitialized_fill_n(thrust::device, array, N, val);
- *
- *  // Int x = array[i];
- *  // x.val == 46 for all 0 <= i < N
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/uninitialized_fill.html
- *  \see \c uninitialized_fill
- *  \see \c fill
- *  \see \c uninitialized_copy_n
- *  \see \c device_new
- *  \see \c device_malloc
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename Size, typename T>
-__host__ __device__
-  ForwardIterator uninitialized_fill_n(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                                       ForwardIterator first,
-                                       Size n,
-                                       const T &x);
-
-
-/*! In \c thrust, the function \c thrust::device_new allocates memory for
- *  an object and then creates an object at that location by calling a
- *  constructor. Occasionally, however, it is useful to separate those two
- *  operations. If each iterator in the range <tt>[first, first+n)</tt> points
- *  to uninitialized memory, then \p uninitialized_fill creates copies of \c x
- *  in that range. That is, for each iterator \c i in the range <tt>[first, first+n)</tt>,
- *  \p uninitialized_fill creates a copy of \c x in the location pointed to \c i by
- *  calling \p ForwardIterator's \c value_type's copy constructor.
- *  
- *  \param first The first element of the range of interest.
- *  \param n The size of the range of interest.
- *  \param x The value to use as the exemplar of the copy constructor.
- *  \return <tt>first+n</tt>
- *
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>,
- *          \p ForwardIterator is mutable, and \p ForwardIterator's \c value_type has a constructor that
- *          takes a single argument of type \p T.
- *
- *  The following code snippet demonstrates how to use \p uninitialized_fill to initialize a range of
- *  uninitialized memory.
- *
- *  \code
- *  #include <thrust/uninitialized_fill.h>
- *  #include <thrust/device_malloc.h>
- *  
- *  struct Int
- *  {
- *    __host__ __device__
- *    Int(int x) : val(x) {}
- *    int val;
- *  };  
- *  ...
- *  const int N = 137;
- *
- *  Int val(46);
- *  thrust::device_ptr<Int> array = thrust::device_malloc<Int>(N);
- *  thrust::uninitialized_fill_n(array, N, val);
- *
- *  // Int x = array[i];
- *  // x.val == 46 for all 0 <= i < N
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/uninitialized_fill.html
- *  \see \c uninitialized_fill
- *  \see \c fill
- *  \see \c uninitialized_copy_n
- *  \see \c device_new
- *  \see \c device_malloc
- */
-template<typename ForwardIterator, typename Size, typename T>
-  ForwardIterator uninitialized_fill_n(ForwardIterator first,
-                                       Size n,
-                                       const T &x);
-
-/*! \} // end filling
- *  \} // transformations
- */
-
-} // end thrust
-
-#include <thrust/detail/uninitialized_fill.inl>
-

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

@@ -1,55 +0,0 @@
-import numpy as np
-import torch
-
-from ..builder import BBOX_SAMPLERS
-from .random_sampler import RandomSampler
-
-
-@BBOX_SAMPLERS.register_module()
-class InstanceBalancedPosSampler(RandomSampler):
-    """Instance balanced sampler that samples equal number of positive samples
-    for each instance."""
-
-    def _sample_pos(self, assign_result, num_expected, **kwargs):
-        """Sample positive boxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): The assigned results of boxes.
-            num_expected (int): The number of expected positive samples
-
-        Returns:
-            Tensor or ndarray: sampled indices.
-        """
-        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
-        if pos_inds.numel() != 0:
-            pos_inds = pos_inds.squeeze(1)
-        if pos_inds.numel() <= num_expected:
-            return pos_inds
-        else:
-            unique_gt_inds = assign_result.gt_inds[pos_inds].unique()
-            num_gts = len(unique_gt_inds)
-            num_per_gt = int(round(num_expected / float(num_gts)) + 1)
-            sampled_inds = []
-            for i in unique_gt_inds:
-                inds = torch.nonzero(
-                    assign_result.gt_inds == i.item(), as_tuple=False)
-                if inds.numel() != 0:
-                    inds = inds.squeeze(1)
-                else:
-                    continue
-                if len(inds) > num_per_gt:
-                    inds = self.random_choice(inds, num_per_gt)
-                sampled_inds.append(inds)
-            sampled_inds = torch.cat(sampled_inds)
-            if len(sampled_inds) < num_expected:
-                num_extra = num_expected - len(sampled_inds)
-                extra_inds = np.array(
-                    list(set(pos_inds.cpu()) - set(sampled_inds.cpu())))
-                if len(extra_inds) > num_extra:
-                    extra_inds = self.random_choice(extra_inds, num_extra)
-                extra_inds = torch.from_numpy(extra_inds).to(
-                    assign_result.gt_inds.device).long()
-                sampled_inds = torch.cat([sampled_inds, extra_inds])
-            elif len(sampled_inds) > num_expected:
-                sampled_inds = self.random_choice(sampled_inds, num_expected)
-            return sampled_inds

spaces/CVPR/WALT/train.py

@@ -1,191 +0,0 @@
-import argparse
-import copy
-import os
-import os.path as osp
-import time
-import warnings
-
-import mmcv
-import torch
-from mmcv import Config, DictAction
-from mmcv.runner import get_dist_info, init_dist
-from mmcv.utils import get_git_hash
-
-from mmdet import __version__
-from mmdet.apis import set_random_seed
-from mmdet.models import build_detector
-from mmdet.utils import collect_env, get_root_logger
-from walt.apis import train_detector
-from walt.datasets import build_dataset
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Train a detector')
-    parser.add_argument('config', help='train config file path')
-    parser.add_argument('--work-dir', help='the dir to save logs and models')
-    parser.add_argument(
-        '--resume-from', help='the checkpoint file to resume from')
-    parser.add_argument(
-        '--no-validate',
-        action='store_true',
-        help='whether not to evaluate the checkpoint during training')
-    group_gpus = parser.add_mutually_exclusive_group()
-    group_gpus.add_argument(
-        '--gpus',
-        type=int,
-        help='number of gpus to use '
-        '(only applicable to non-distributed training)')
-    group_gpus.add_argument(
-        '--gpu-ids',
-        type=int,
-        nargs='+',
-        help='ids of gpus to use '
-        '(only applicable to non-distributed training)')
-    parser.add_argument('--seed', type=int, default=None, help='random seed')
-    parser.add_argument(
-        '--deterministic',
-        action='store_true',
-        help='whether to set deterministic options for CUDNN backend.')
-    parser.add_argument(
-        '--options',
-        nargs='+',
-        action=DictAction,
-        help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file (deprecate), '
-        'change to --cfg-options instead.')
-    parser.add_argument(
-        '--cfg-options',
-        nargs='+',
-        action=DictAction,
-        help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
-    parser.add_argument(
-        '--launcher',
-        choices=['none', 'pytorch', 'slurm', 'mpi'],
-        default='none',
-        help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    if 'LOCAL_RANK' not in os.environ:
-        os.environ['LOCAL_RANK'] = str(args.local_rank)
-
-    if args.options and args.cfg_options:
-        raise ValueError(
-            '--options and --cfg-options cannot be both '
-            'specified, --options is deprecated in favor of --cfg-options')
-    if args.options:
-        warnings.warn('--options is deprecated in favor of --cfg-options')
-        args.cfg_options = args.options
-
-    return args
-
-
-def main():
-    args = parse_args()
-
-    cfg = Config.fromfile(args.config)
-    if args.cfg_options is not None:
-        cfg.merge_from_dict(args.cfg_options)
-    # import modules from string list.
-    if cfg.get('custom_imports', None):
-        from mmcv.utils import import_modules_from_strings
-        import_modules_from_strings(**cfg['custom_imports'])
-    # set cudnn_benchmark
-    if cfg.get('cudnn_benchmark', False):
-        torch.backends.cudnn.benchmark = True
-
-    # work_dir is determined in this priority: CLI > segment in file > filename
-    if args.work_dir is not None:
-        # update configs according to CLI args if args.work_dir is not None
-        cfg.work_dir = args.work_dir
-    elif cfg.get('work_dir', None) is None:
-        # use config filename as default work_dir if cfg.work_dir is None
-        cfg.work_dir = osp.join('./work_dirs',
-                                osp.splitext(osp.basename(args.config))[0])
-
-    if args.resume_from is not None:
-        cfg.resume_from = args.resume_from
-    if args.gpu_ids is not None:
-        cfg.gpu_ids = args.gpu_ids
-    else:
-        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
-
-    # init distributed env first, since logger depends on the dist info.
-    if args.launcher == 'none':
-        distributed = False
-    else:
-        distributed = True
-        init_dist(args.launcher, **cfg.dist_params)
-        # re-set gpu_ids with distributed training mode
-        _, world_size = get_dist_info()
-        cfg.gpu_ids = range(world_size)
-
-
-    # create work_dir
-    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
-    # dump config
-    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
-    # init the logger before other steps
-    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
-    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
-    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
-
-    # init the meta dict to record some important information such as
-    # environment info and seed, which will be logged
-    meta = dict()
-    # log env info
-    env_info_dict = collect_env()
-    env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
-    dash_line = '-' * 60 + '\n'
-    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
-                dash_line)
-    meta['env_info'] = env_info
-    meta['config'] = cfg.pretty_text
-    # log some basic info
-    logger.info(f'Distributed training: {distributed}')
-    logger.info(f'Config:\n{cfg.pretty_text}')
-
-    # set random seeds
-    if args.seed is not None:
-        logger.info(f'Set random seed to {args.seed}, '
-                    f'deterministic: {args.deterministic}')
-        set_random_seed(args.seed, deterministic=args.deterministic)
-    cfg.seed = args.seed
-    meta['seed'] = args.seed
-    meta['exp_name'] = osp.basename(args.config)
-
-    model = build_detector(
-        cfg.model,
-        train_cfg=cfg.get('train_cfg'),
-        test_cfg=cfg.get('test_cfg'))
-
-    datasets = [build_dataset(cfg.data.train)]
-    if len(cfg.workflow) == 2:
-        val_dataset = copy.deepcopy(cfg.data.val)
-        val_dataset.pipeline = cfg.data.train.pipeline
-        datasets.append(build_dataset(val_dataset))
-    if cfg.checkpoint_config is not None:
-        # save mmdet version, config file content and class names in
-        # checkpoints as meta data
-        cfg.checkpoint_config.meta = dict(
-            mmdet_version=__version__ + get_git_hash()[:7],
-            CLASSES=datasets[0].CLASSES)
-
-    # add an attribute for visualization convenience
-    model.CLASSES = datasets[0].CLASSES
-    train_detector(
-        model,
-        datasets,
-        cfg,
-        distributed=distributed,
-        validate=(not args.no_validate),
-        timestamp=timestamp,
-        meta=meta)
-
-
-if __name__ == '__main__':
-    main()