Lack of support: A cracked version of a software may not have access to customer support or technical assistance from the developer, which means that you may not be able to get help if you encounter any problems cec2833e83Evermotion Archinteriors Vol. 44: A Review of the Latest Collection of Lobby Interior Scenes and HDR Maps
-Evermotion is a company that produces high-quality 3D models, scenes, textures and tutorials for architects, designers and 3D enthusiasts. Their main products are 3D models and scenes collections: Archmodels, Archinteriors and Archexteriors, HD cars and 3D people.
-Evermotion Archinteriors Vol. 44 Download File ——— https://byltly.com/2uKzMP
In this article, we will take a look at their latest release: Archinteriors Vol. 44, which includes 10 fully textured lobby interior scenes and 10 HDR maps. Each scene is ready to render with professional shaders and lighting and includes 2 cameras' setups. The collection also comes with 360° HDR maps (5000px) that can be used for realistic lighting and reflections.
-The scenes are designed for various types of lobbies, such as hotel, office, museum, airport, shopping mall and more. They feature modern and elegant furniture, reception desks, sculptures, plants, paintings and other details that create a realistic and inviting atmosphere. The scenes are prepared for V-ray 3.0 with 3dsmax 2011, but they can be easily converted to other formats and renderers.
-The collection is a great resource for anyone who needs to create stunning visualizations of lobby interiors, whether for personal or commercial projects. The scenes can be customized to suit different needs and preferences, or used as a base for further development. The HDR maps can also be applied to other scenes or models to enhance their realism.
-
-Archinteriors Vol. 44 is available for purchase on Evermotion's website[^1^] for $120. You can also download a free sample scene[^2^] to test the quality and compatibility of the product. If you are interested in other collections from Evermotion, you can browse their catalog[^3^] or subscribe to their newsletter to get updates on their latest releases and promotions.
How to Incorporate Archinteriors Vol. 44 into Your Lobby Design
-If you are looking for some inspiration and guidance on how to use Archinteriors Vol. 44 in your lobby design, here are some tips and ideas based on the latest interior design trends for 2023.
-
-Choose a color scheme that reflects the mood and purpose of your lobby. According to interior designers, earth tones are still popular in 2023, especially shades of brown, beige, taupe and cream. They create a warm and cozy atmosphere that welcomes visitors and guests. However, if you want to add some contrast and interest, you can also incorporate some pops of color, such as mauve, which is predicted to be the color of the year by Kathryn M. Ireland and Jake Arnold[^1^]. Mauve is a soft and romantic hue that can add some elegance and sophistication to your lobby.
-Use silver accents instead of gold or bronze. Silver is making a comeback in 2023, as it adds a touch of shine and texture to any space. Athena Calderone says that silver lends an everyday sophistication to any space[^1^]. You can use silver accessories, such as lamps, vases, trays, mirrors or frames, to complement your furniture and wall coverings. Silver also works well with earth tones and mauve, as it creates a harmonious and balanced look.
-Add some formal elements to your lobby design. After the dominance of the casual and relaxed mid-century modern style in the previous years, formal accents are now returning to the scene. Robert D. McKinley says that we are craving more sophistication after spending so much time in sweatpants during the pandemic[^1^]. You can add some formal elements to your lobby design by choosing classic furniture pieces, such as sofas, armchairs or tables, with curved lines, tufted details or carved legs. You can also use fabrics with rich textures and patterns, such as velvet, silk or brocade.
-Experiment with neo-brutalism. Brutalism is a style that emerged in the mid-20th century and is characterized by raw concrete structures, geometric shapes and minimal ornamentation. It is often associated with coldness and harshness, but in 2023, it is getting a makeover with warmer touches. You can experiment with neo-brutalism by using concrete floors or walls in your lobby design, but make sure to soften them with rugs, cushions or plants. You can also use geometric shapes in your furniture or accessories, but balance them with organic forms or natural materials.
-
-By following these tips and ideas, you can create a lobby design that is trendy but timeless, using Archinteriors Vol. 44 as your starting point.
7b8c122e87How to Download Fangirl by Rainbow Rowell for Free in PDF, EPUB and FB2 Formats
-If you are a fan of Rainbow Rowell's Fangirl , a novel about a college freshman who writes fanfiction about her favorite fictional character, Simon Snow, you might be wondering how to get a free copy of the book in different formats. Whether you want to read it on your e-reader, your phone or your computer, there are some ways to download Fangirl for free legally and safely.
-fangirl free ebook epub 17 DOWNLOAD »»» https://byltly.com/2uKwHU
One option is to use an online library service like Z-Library Project[^2^], which offers millions of books in various formats for free. You can search for Fangirl by title, author or ISBN and download it in PDF, EPUB or FB2 formats. You will need to create a free account and verify your email address before you can access the books.
-Another option is to use a website like Read Any Book[^1^], which allows you to read books online without downloading them. You can find Fangirl by Rainbow Rowell on their website and read it in your browser. You can also adjust the font size, color and background to suit your preferences. However, you will need an internet connection to read the book and you might encounter some ads on the website.
-A third option is to use a document sharing platform like Yumpu[^3^] [^4^], which hosts PDF files of various books and magazines. You can search for Fangirl by Rainbow Rowell and download it as a PDF file. You can also view it online or embed it on your own website. However, you should be aware that some of the files might be uploaded illegally and infringe on the author's rights.
-These are some of the ways to download Fangirl by Rainbow Rowell for free in different formats. However, if you enjoy the book and want to support the author, you should consider buying a copy from a reputable source or borrowing it from a library. Reading books is a great way to learn new things, expand your imagination and have fun!
-
-If you want to learn more about Fangirl by Rainbow Rowell, you can also check out some of the reviews, interviews and fanworks related to the book. For example, you can read what other readers thought about the book on Goodreads, Amazon or BookBub. You can also watch some videos of Rainbow Rowell talking about the book and her writing process on YouTube or listen to some podcasts featuring her on Spotify or Apple Podcasts.
-
-Moreover, if you are a fan of Simon Snow, the fictional character that Cath writes fanfiction about in Fangirl , you might be interested in reading Carry On and Wayward Son , two spin-off novels by Rainbow Rowell that tell the story of Simon and his friends in a magical world. You can also find fanfiction, fanart and other fanworks inspired by Simon Snow on websites like Archive of Our Own, Tumblr or DeviantArt.
-In conclusion, Fangirl by Rainbow Rowell is a wonderful book that explores the themes of fandom, family, love and identity. It is a book that many readers can relate to and enjoy. Whether you download it for free or buy it from a store, you will not regret reading this book and joining the Fangirl community.
7b8c122e87edilus is a software solution for modular structure that deals with structural calculations of buildings with its integrated fem solver to provide a unified experience of the steps of input, calculation, analysis of results and creation of detailed work plans and documents.
-acca software edilus v30.00sf is a handy application which can be used for performing structural analysis in order to build the design for determining their feasibility. it is a modular solution which will enable you to select any configuration from three application packages which includes concrete, masonry and steel. you can also downloadaqua design 5.4
-Acca Edilus V 25 00 Crack Download Zip ↔ https://imgfil.com/2uxX2q
acca software edilus v30.00sf allows you to perform structural calculations for new as well as existing building that feature any of the above mentioned prime materials with smart bim input. it will also let you generate an automatic drawing model along with the technical report of the plan which is ready for printing in no time. acca software edilus v30.00sf enables you to preview the calculation results on the bim objects directly in the design. you can preview the tensions and strains with just a single click. all in all acca software edilus v30.00sf is a handy application for performing structural analysis for building designs. you can also downloadbentley rm bridge advanced connect.
-youve already used windows explorer with acca software edilus v30.00sf x64 craacked software , youll immediately feel at home. the tool can take the following types of screen captures: capture a full-screen active window or part of it using the rectangular marquee tool. the advantage of this program is that it solves any problems that arise during the update process. two of the most common threats we can find on the internet are trojans and keyloggers. securing internet traffic over a local network is very important because many internet activities performed by users through the system are unreliable. deleting, formatting partitions, system crashes may you like to download iobit software updater crack.
899543212bCoin Master Card Download: How to Collect and Trade Cards in Coin Master
-Coin Master is a popular casual game that combines slot machine, village building, and social interaction. One of the features that makes this game fun and addictive is the card collection. In this article, we will explain what are Coin Master cards, how to get them, and how to trade them with other players. We will also share some tips and tricks to help you complete your card sets and advance in the game.
- What are Coin Master cards and why are they important?
-Coin Master cards are collectible items that you can find in chests or by spinning the wheel. There are different types of cards, each with a different rarity and value. Cards belong to different sets that correspond to different themes and villages. For example, there is a set called "Pets" that contains cards of various animals, such as a dog, a cat, a rabbit, etc.
-coin master card download Download Zip ⏩ https://urlin.us/2uSZyb
Cards are important because they can help you earn rewards and bonuses. By completing a card set, you can get free spins, coins, XP, or even pets. You can also use cards to unlock new villages and progress in the game. Moreover, cards can help you interact with other players and make new friends. You can trade cards with other players or join card trading groups on social media.
- Types of cards and how to get them
-There are four types of cards in Coin Master: wooden, golden, magical, and Joker. Wooden cards are the most common and have the lowest value. Golden cards are rare and have a higher value. Magical cards are very rare and have special effects. Joker cards are the most valuable and can be used as any card you want.
-You can get cards by opening chests or by spinning the wheel. There are different kinds of chests, such as wooden, golden, magical, or seasonal. The higher the quality of the chest, the more likely you are to get rare or magical cards. You can also get chests by completing events or missions in the game. Sometimes, you can also get free chests by watching ads or following Coin Master on social media.
-Another way to get cards is by spinning the wheel. You can win cards by landing on the card symbol or by getting three pigs in a row. You can also get free spins by inviting friends, watching ads, or following Coin Master on social media.
- How to trade cards with other players
-Trading cards with other players is a great way to complete your card sets and get rewards. You can trade cards with your friends or with strangers from around the world. To trade cards with your friends, you need to connect your game account to Facebook. Then, you can send or request cards from your friends list. You can also join a card trading group on Facebook or other platforms where you can find people who are looking for specific cards or who have extra cards to offer.
-coin master card collection download
-To trade cards with strangers, you need to use the trading feature in the game. You can access this feature by tapping on the menu icon on the top right corner of the screen and then selecting "Trade". You will see a list of available trades that other players have posted. You can filter the trades by card type, rarity, or set. You can also post your own trade by selecting the card you want to offer and the card you want to receive. You can only trade one card at a time and you can only trade wooden or golden cards. You cannot trade magical or Joker cards.
- Tips and tricks for collecting and trading cards in Coin Master
-Collecting and trading cards in Coin Master can be challenging but rewarding. Here are some tips and tricks to help you succeed:
- Join a card trading group on Facebook or other platforms
-One of the best ways to find people who have the cards you need or who need the cards you have is to join a card trading group on Facebook or other platforms. There are many groups dedicated to Coin Master card trading where you can post your requests or offers, chat with other players, and make deals. You can also find tips, tricks, and news about the game in these groups. To join a card trading group, you need to have a Facebook account or an account on the platform of your choice. You can search for "Coin Master card trading" or similar keywords and find a group that suits your needs. Some groups may have rules or requirements that you need to follow, such as being active, respectful, and honest. You may also need to answer some questions or verify your identity before joining.
- Use the Joker card wisely
-The Joker card is the most valuable and rarest card in Coin Master. It can be used as any card you want, which means you can complete any card set with it. However, you can only get one Joker card per season, which lasts for about a month. Therefore, you need to use it wisely and strategically. Here are some tips on how to use the Joker card:
-
-Save it for the last card of a set. Don't waste it on a common or easy-to-get card. Use it for the rarest or hardest-to-find card of a set that you are close to completing.
-Use it before moving to the next village. Once you move to a new village, the cards of the previous village become harder to get. Therefore, you should use your Joker card to complete any unfinished sets before moving on.
-Use it for a high-value set. Some sets have better rewards than others, such as more spins, coins, XP, or pets. You should use your Joker card for a set that gives you the most benefits.
-
- Complete card sets before moving to the next village
-Another tip for collecting and trading cards in Coin Master is to complete card sets before moving to the next village. As mentioned earlier, once you move to a new village, the cards of the previous village become harder to get. This means that you will have less chances of finding them in chests or by spinning the wheel. Moreover, you will have fewer people to trade with, as most players will be looking for cards of the current village. Therefore, you should try to complete as many card sets as possible before moving on. This will help you get more rewards and bonuses, as well as save you time and effort.
- Conclusion
-Coin Master card download is an exciting and rewarding feature of the game that allows you to collect and trade cards with other players. By following the tips and tricks we shared in this article, you can improve your chances of completing your card sets and advancing in the game. Remember to join a card trading group, use your Joker card wisely, and complete card sets before moving to the next village. Happy spinning!
- FAQs
-Here are some frequently asked questions about Coin Master card download:
-
-How many cards are there in Coin Master?
-There are over 300 cards in Coin Master, divided into 33 sets and 11 categories.
-How do I know which cards I need?
-You can check which cards you need by tapping on the menu icon on the top right corner of the screen and then selecting "Cards". You will see your card collection and the progress of each set. You can also tap on a specific card to see its details and value.
-How do I know which cards I have extra?
-You can check which cards you have extra by tapping on the menu icon on the top right corner of the screen and then selecting "Trade". You will see a list of your available trades and the number of cards you have for each one.
-Can I buy or sell cards with real money?
-No, you cannot buy or sell cards with real money in Coin Master. This is against the game's terms of service and may result in your account being banned or suspended.
-Can I trade cards with anyone?
-You can trade cards with anyone who is online and has posted a trade that matches yours. You can also trade cards with your friends who are connected to Facebook.
- 197e85843dDownload Garena Free Fire APK v1.38.2: A Guide for Beginners
-If you are looking for a thrilling and action-packed mobile game, you might want to try Garena Free Fire, one of the most popular battle royale games on Android and iOS devices. In this article, we will show you how to download Garena Free Fire APK v1.38.2, the latest version of the game, and give you some tips and tricks on how to play it.
-download garena free fire apk v1.38.2 Download File ⭐ https://urlin.us/2uT0qW
What is Garena Free Fire?
-Garena Free Fire is a mobile game that has quickly gained popularity since its release in 2017. The game is set in a fictional world where players are parachuted onto an island and must find weapons and gear to survive. The game offers a thrilling multiplayer experience where players fight to be the last ones standing on a remote island.
- A popular mobile battle royale game
-Garena Free Fire is a mobile game that belongs to the battle royale genre, which means that players are dropped into an arena where they have to fight each other until only one player or team remains alive. The game can be downloaded for free from the Google Play Store and the App Store, and offers in-app purchases for various items and features.
-The game has over 1 billion downloads on Google Play and has won several awards, such as the Best Popular Vote Game by Google Play in 2019, and the Most Competitive Game by Google Play in 2020.
- Features and modes of the game
-Garena Free Fire has many features and modes that make it an exciting and diverse game to play. Some of the key features of the game are:
-How to download garena free fire apk v1.38.2 on android
-
-The game has over 15 characters, each with their own unique skills and abilities that can help players in different situations.
-The game has two maps, Bermuda and Purgatory, each with different terrains, locations, and challenges.
-The game has over 15 game modes, including Team Deathmatch, Clash Squad, Big Head, Explosive Jump, Cold Steel, Rampage, and Pet Mania. However, modes other than Battle Royale, Clash Squad, and Lone Wolf are only available during special events.
-The game has realistic and smooth graphics, easy to use controls, and in-game voice chat that allow players to enjoy the optimum survival experience on mobile devices.
-
- How to download Garena Free Fire APK v1.38.2?
-If you want to download Garena Free Fire APK v1.38.2, which is the latest version of the game as of June 2023, you need to follow some simple steps. However, before you do that, you need to make sure that your device meets the requirements and compatibility of the game.
- Requirements and compatibility
-Garena Free Fire APK v1.38.2 requires Android 4.0.3 or higher to run smoothly on your device. The size of the APK file is about 500 MB, so you need to have enough storage space on your device before downloading it.
-The game is compatible with most Android devices, but some models may have problems running it due to hardware limitations or software issues. If you encounter any problems while playing the game, you can contact the customer service of Garena Free Fire through their official website or social media channels.
- Steps to download and install the APK file
-To download Garena Free Fire APK v1.38.2, you need to follow these steps:
-
-Go to [Garena Free Fire - Official Website] and click on the Download button. Alternatively, you can use this [direct link] to download the APK file.
-Once the download is complete, locate the APK file on your device and tap on it to install it. You may need to enable the installation of apps from unknown sources in your device settings.
-After the installation is done, launch the game and enjoy the latest features and updates of Garena Free Fire.
-
- How to play Garena Free Fire?
-Now that you have downloaded and installed Garena Free Fire APK v1.38.2, you might be wondering how to play the game and win more matches. Here are some tips and tricks for beginners that can help you improve your skills and have more fun in the game.
- Tips and tricks for beginners
-Garena Free Fire is a game that requires strategy, skill, and luck to survive and win. Here are some tips and tricks that can help you get started:
-
-Choose your character wisely. Each character has a unique skill that can give you an edge in different situations. For example, Kelly has increased sprinting speed, Alok can create a healing aura, and Chrono can create a force field that blocks damage.
-Loot fast and smart. As soon as you land on the island, you need to find weapons, armor, ammo, and other items that can help you survive. However, you also need to be careful of enemies who might be nearby. Try to loot in areas that are less crowded and have good cover.
-Use vehicles to move around. Vehicles are a great way to travel faster and escape from danger. However, they also make noise and attract attention, so use them wisely. You can also use vehicles as weapons by running over enemies or exploding them.
-Stay in the safe zone. The safe zone is the area where you will not take damage from the shrinking circle of fire. You need to keep an eye on the map and move towards the safe zone as soon as possible. However, be aware of enemies who might be camping or ambushing near the edge of the zone.
-Use cover and stealth. Cover is anything that can protect you from enemy fire, such as buildings, trees, rocks, or vehicles. Stealth is the ability to move without being seen or heard by enemies. You can use cover and stealth to hide, scout, snipe, or flank enemies.
-
- Reviews and ratings of the game
-Garena Free Fire has received mostly positive reviews and ratings from players and critics alike. The game has a 4.3 out of 5 stars rating on Google Play and a 4.6 out of 5 stars rating on App Store.
-Some of the pros of the game are:
-
-The game is free to play and offers a lot of content and variety.
-The game has smooth and realistic graphics and sound effects.
-The game has a large and active community of players and streamers.
-
-Some of the cons of the game are:
-
-The game can be laggy or buggy at times due to server issues or device compatibility.
-The game can be challenging or frustrating for new or casual players due to the high level of competition.
-The game can be addictive or harmful for mental health if played excessively or irresponsibly.
-
- Conclusion
-Garena Free Fire is a mobile game that offers a thrilling and action-packed survival experience for players who love battle royale games. The game can be downloaded for free from the Google Play Store or App Store, or by using the APK file provided in this article.
-If you want to enjoy the latest version of the game, which is Garena Free Fire APK v1.38.2, you need to follow some simple steps to download and install it on your device. You also need to learn some tips and tricks on how to play the game and win more matches.
-Garena Free Fire is a game that has many pros and cons, depending on your preferences and expectations. However, it is undeniable that it is one of the most popular and exciting mobile games in the world right now.
- FAQs
-Here are some frequently asked questions about Garena Free Fire:
- Q: Is Garena Free Fire safe to download?
-A: Yes, Garena Free Fire is safe to download as long as you use the official website or the trusted app stores. However, you should be careful of downloading the APK file from unknown or unverified sources, as they may contain malware or viruses that can harm your device or steal your data.
- Q: How to update Garena Free Fire?
-A: Garena Free Fire usually updates automatically when you launch the game, as long as you have a stable internet connection and enough storage space on your device. However, if you want to update the game manually, you can do so by following these steps:
-
-Go to the Google Play Store or App Store and search for Garena Free Fire.
-Tap on the Update button and wait for the download and installation to finish.
-Launch the game and enjoy the new features and improvements.
-
- Q: How to get diamonds in Garena Free Fire?
-A: Diamonds are the premium currency in Garena Free Fire that can be used to buy various items and features in the game, such as characters, skins, pets, crates, bundles, and more. There are several ways to get diamonds in Garena Free Fire, such as:
-
-Buying them with real money through the in-game store or third-party platforms.
-Earning them by completing tasks, surveys, offers, or watching ads through various apps or websites that partner with Garena Free Fire.
-Winning them by participating in events, tournaments, giveaways, or contests organized by Garena Free Fire or its affiliates.
-
- Q: How to get free fire redeem codes?
-A: Redeem codes are codes that can be used to get free rewards in Garena Free Fire, such as diamonds, coins, skins, crates, vouchers, and more. Redeem codes are usually given out by Garena Free Fire or its partners through their official social media channels, websites, or live streams. To get free fire redeem codes, you need to follow these steps:
-
-Find a valid and working redeem code from a reliable source. You can check the official social media pages of Garena Free Fire or its partners for the latest codes.
-Go to [Garena Free Fire - Redemption Center] and log in with your Facebook, Google, VK, Apple ID, Huawei ID, or Twitter account that is linked to your game account.
-Enter the redeem code in the box and click on Confirm.
-Check your in-game mail for the reward and claim it within 24 hours.
-
- Q: How to contact Garena Free Fire customer service?
-A: If you have any questions, issues, feedback, or suggestions regarding Garena Free Fire, you can contact their customer service through various channels, such as:
-
-Email: [freefire@garena.com]
-Website: [Garena Free Fire - Support]
-Facebook: [Garena Free Fire]
-Instagram: [@freefireth]
-Twitter: [@freefireth]
- 197e85843d
-
- {children}
-
-
-)
-AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
-
-const AlertDialogOverlay = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-
-))
-AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
-
-const AlertDialogHeader = ({
- className,
- ...props
-}: React.HTMLAttributes) => (
-
-)
-AlertDialogHeader.displayName = 'AlertDialogHeader'
-
-const AlertDialogFooter = ({
- className,
- ...props
-}: React.HTMLAttributes) => (
-
-)
-AlertDialogFooter.displayName = 'AlertDialogFooter'
-
-const AlertDialogTitle = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- '
-
-# request_address = 'http://11.29.21.161:80/generate'
-# request_address = 'http://0.0.0.0:7890/generate'
-LOGDIR = 'log'
-
-logger = build_logger("gradio_seed_llama", LOGDIR)
-headers = {"User-Agent": "SEED LLaMA Client"}
-
-no_change_btn = gr.Button.update()
-enable_btn = gr.Button.update(interactive=True)
-disable_btn = gr.Button.update(interactive=False)
-
-@dataclass
-class Arguments:
- server_port: Optional[int] = field(default=7860, metadata={"help": "network port"})
- server_name: Optional[str] = field(default='0.0.0.0', metadata={"help": "network address"})
- request_address: Optional[str] = field(default='http://127.0.0.1:7890/generate', metadata={"help": "request address"})
- model_type: Optional[str] = field(default='seed-llama-14b', metadata={"help": "choice: [seed-llama-8b, seed-llama-14b]"})
-
-parser = transformers.HfArgumentParser(Arguments)
-args, = parser.parse_args_into_dataclasses()
-
-if args.model_type == 'seed-llama-8b':
- conv_seed_llama = conv_seed_vicuna
-elif args.model_type == 'seed-llama-14b':
- conv_seed_llama = conv_seed_llama2
-else:
- raise ValueError
-
-
-def decode_image(encoded_image: str) -> Image:
- decoded_bytes = base64.b64decode(encoded_image.encode('utf-8'))
- # with io.BytesIO(decoded_bytes) as buffer:
- # image = Image.open(buffer)
- # return image
- buffer = io.BytesIO(decoded_bytes)
- image = Image.open(buffer)
- return image
-
-
-def encode_image(image: Image.Image, format: str = 'PNG') -> str:
- with io.BytesIO() as buffer:
- image.save(buffer, format=format)
- encoded_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
- return encoded_image
-
-
-def get_conv_log_filename():
- t = datetime.datetime.now()
- name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
- return name
-
-
-def get_conv_image_dir():
- name = os.path.join(LOGDIR, 'images')
- os.makedirs(name, exist_ok=True)
- return name
-
-
-def get_image_name(image, image_dir=None):
- buffer = io.BytesIO()
- image.save(buffer, format='PNG')
- image_bytes = buffer.getvalue()
- md5 = hashlib.md5(image_bytes).hexdigest()
-
- if image_dir is not None:
- image_name = os.path.join(image_dir, md5 + '.png')
- else:
- image_name = md5 + '.png'
-
- return image_name
-
-
-def resize_image(image, max_size=512):
- width, height = image.size
- aspect_ratio = float(width) / float(height)
-
- if width > height:
- new_width = max_size
- new_height = int(new_width / aspect_ratio)
- else:
- new_height = max_size
- new_width = int(new_height * aspect_ratio)
-
- resized_image = image.resize((new_width, new_height))
- return resized_image
-
-
-def center_crop_image(image, max_aspect_ratio=1.5):
- width, height = image.size
- aspect_ratio = max(width, height) / min(width, height)
-
- if aspect_ratio >= max_aspect_ratio:
- if width > height:
- new_width = int(height * max_aspect_ratio)
- left = (width - new_width) // 2
- right = (width + new_width) // 2
- top = 0
- bottom = height
- else:
- new_height = int(width * max_aspect_ratio)
- left = 0
- right = width
- top = (height - new_height) // 2
- bottom = (height + new_height) // 2
-
- cropped_image = image.crop((left, top, right, bottom))
- return cropped_image
- else:
- return image
-
-def vote_last_response(state, vote_type, request: gr.Request):
- with open(get_conv_log_filename(), "a") as fout:
- data = {
- "tstamp": round(time.time(), 4),
- "type": vote_type,
- "state": state.dict(),
- "ip": request.client.host,
- }
- fout.write(json.dumps(data) + "\n")
-
-
-def upvote_last_response(state, request: gr.Request):
- logger.info(f"upvote. ip: {request.client.host}")
- vote_last_response(state, "upvote", request)
- return (disable_btn, ) * 2
-
-
-def downvote_last_response(state, request: gr.Request):
- logger.info(f"downvote. ip: {request.client.host}")
- vote_last_response(state, "downvote", request)
- return (disable_btn, ) * 2
-
-
-def regenerate(dialog_state, request: gr.Request):
- logger.info(f"regenerate. ip: {request.client.host}")
- if dialog_state.messages[-1]['role'] == dialog_state.roles[1]:
- dialog_state.messages.pop()
- return (
- dialog_state,
- dialog_state.to_gradio_chatbot(),
- ) + (disable_btn, ) * 4
-
-
-def clear_history(request: gr.Request):
- logger.info(f"clear_history. ip: {request.client.host}")
- # state = None
- # return (state, [], "") + (disable_btn, ) * 5
- dialog_state = conv_seed_llama.copy()
- input_state = init_input_state()
- return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (disable_btn, ) * 4
-
-
-def init_input_state():
- return {'images': [], 'text': '', 'images_ids': []}
-
-
-def add_text(dialog_state, input_state, text, request: gr.Request):
- logger.info(f"add_text. ip: {request.client.host}.")
- # if len(input_state['text']) == 0:
- if text is None or len(text) == 0:
- # dialog_state.skip_next = True
- return (dialog_state, input_state, "", dialog_state.to_gradio_chatbot()) + (no_change_btn, ) * 4
- input_state['text'] += text
-
- # dialog_state.skip_next = False
-
- if len(dialog_state.messages) > 0 and dialog_state.messages[-1]['role'] == dialog_state.roles[0]:
- dialog_state.messages[-1]['message'] = input_state
- else:
- dialog_state.messages.append({'role': dialog_state.roles[0], 'message': input_state})
- print('add_text: ', dialog_state.to_gradio_chatbot())
-
- return (dialog_state, input_state, "", dialog_state.to_gradio_chatbot()) + (disable_btn, ) * 4
-
-
-def add_image(dialog_state, input_state, image, request: gr.Request):
- logger.info(f"add_image. ip: {request.client.host}.")
- if image is None:
- return (dialog_state, input_state, None, dialog_state.to_gradio_chatbot()) + (no_change_btn, ) * 4
-
- image = image.convert('RGB')
- image = resize_image(image, max_size=512)
- image = center_crop_image(image, max_aspect_ratio=1.3)
- image_dir = get_conv_image_dir()
- image_path = get_image_name(image=image, image_dir=image_dir)
- if not os.path.exists(image_path):
- image.save(image_path)
-
- input_state['images'].append(image_path)
- input_state['text'] += IMG_FLAG
- input_state['images_ids'].append(None)
-
- if len(dialog_state.messages) > 0 and dialog_state.messages[-1]['role'] == dialog_state.roles[0]:
- dialog_state.messages[-1]['message'] = input_state
- else:
- dialog_state.messages.append({'role': dialog_state.roles[0], 'message': input_state})
-
- print('add_image:', dialog_state)
-
- return (dialog_state, input_state, None, dialog_state.to_gradio_chatbot()) + (disable_btn, ) * 4
-
-
-def http_bot_test(dialog_state, input_state, temperature, top_p, max_new_tokens, num_beams, max_turns, force_image_gen, request: gr.Request):
- logger.info(f"http_bot. ip: {request.client.host}")
- output_state = {}
- output_state['text'] = 'This is test for frontend!'
- output_state['images'] = []
- if len(dialog_state.messages) > 0 and len(dialog_state.messages[-1]['message']['images']) != 0:
- image = random.choice(dialog_state.messages[-1]['message']['images'])
- output_state['images'].append(image)
- output_state['text'] += IMG_FLAG
-
- dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})
- input_state = init_input_state()
-
- print('http_bot: ', dialog_state.to_gradio_chatbot())
-
- return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (enable_btn, ) * 4
-
-
-def update_error_msg(chatbot, error_msg):
- if len(error_msg) > 0:
- info = '\n-------------\nSome errors occurred during response, please clear history and restart.\n' + '\n'.join(
- error_msg)
- chatbot[-1][-1] = chatbot[-1][-1] + info
-
- return chatbot
-
-
-def http_bot(dialog_state, input_state, temperature, top_p, max_new_tokens, num_beams, max_turns, force_image_gen, request: gr.Request):
- logger.info(f"http_bot. ip: {request.client.host}")
- print('input_state:', input_state)
-
- if len(dialog_state.messages) == 0 or dialog_state.messages[-1]['role'] != dialog_state.roles[0] or len(
- dialog_state.messages[-1]['message']['text'].strip(' ?.;!/')) == 0:
- # if len(input_state['text']) == 0:
- # dialog_state.skip_next = True
- return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (no_change_btn, ) * 4
-
- if len(dialog_state.messages) > max_turns * 2:
- output_state = init_input_state()
- output_state['text'] = 'Error: History exceeds maximum rounds, please clear history and restart.'
- dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})
- input_state = init_input_state()
- return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (disable_btn, ) * 3 + (enable_btn, )
-
- prompt = dialog_state.get_prompt()
- payload = {
- 'text': prompt['text'],
- 'temperature': float(temperature),
- 'top_p': float(top_p),
- 'max_new_tokens': int(max_new_tokens),
- 'num_beams': int(num_beams),
- 'images': prompt['images'],
- 'force_boi': force_image_gen,
- }
-
- print(
- 'request: ', {
- 'text': prompt['text'],
- 'temperature': float(temperature),
- 'top_p': float(top_p),
- 'max_new_tokens': int(max_new_tokens),
- 'num_beams': int(num_beams)
- })
- print('request_address', args.request_address)
- response = requests.request(method="POST", url=args.request_address, headers=headers, json=payload)
- results = response.json()
- print('response: ', {'text': results['text'], 'images_ids': results['images_ids'], 'error_msg': results['error_msg']})
-
- output_state = init_input_state()
- image_dir = get_conv_image_dir()
- output_state['text'] = results['text']
-
- for image_base64 in results['images']:
- if image_base64 == '':
- image_path = ''
- else:
- image = decode_image(image_base64)
- image = image.convert('RGB')
- image_path = get_image_name(image=image, image_dir=image_dir)
- if not os.path.exists(image_path):
- image.save(image_path)
- output_state['images'].append(image_path)
- output_state['images_ids'].append(None)
-
- dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})
- dialog_state.update_image_ids(results['images_ids'])
-
- vote_last_response(dialog_state, 'common', request)
- input_state = init_input_state()
- chatbot = update_error_msg(dialog_state.to_gradio_chatbot(), results['error_msg'])
- return (dialog_state, input_state, chatbot) + (enable_btn, ) * 4
-
-
-def load_demo(request: gr.Request):
- logger.info(f"load_demo. ip: {request.client.host}")
- dialog_state = conv_seed_llama.copy()
- input_state = init_input_state()
- return dialog_state, input_state
-
-
-title = ("""
-# SEED-LLaMA-14B (8bit)
-[[Project Page]](https://ailab-cvc.github.io/seed/seed_llama.html) [[Paper]](https://arxiv.org/pdf/2310.01218.pdf) [[Code]](https://github.com/AILab-CVC/SEED/tree/main)
-
-## Tips:
-* Check out the conversation examples (at the bottom) for inspiration.
-
-* You can adjust "Max History Rounds" to try a conversation with up to five rounds. For more turns, you can download our checkpoints from GitHub and deploy them locally for inference.
-
-* Our demo supports a mix of images and texts as input. You can freely upload an image or enter text, and then click on "Add Image/Text". You can repeat the former step multiple times, and click on "Submit" for model inference at last.
-
-* If you are not satisfied with the output, especially the generated image, you may click on "Regenerate" for another chance.
-
-* You can click "Force Image Generation" to compel the model to produce images when necessary. For example, our model might struggle to generate images when there is an excessive amount of text-only context.
-* SEED-LLaMA was trained with English-only data. It may process with other languages due to the inherent capabilities from LLaMA, but might not stable.
-""")
-
-css = """
-img {
- font-family: 'Helvetica';
- font-weight: 300;
- line-height: 2;
- text-align: center;
-
- width: auto;
- height: auto;
- display: block;
- position: relative;
-}
-
-img:before {
- content: " ";
- display: block;
-
- position: absolute;
- top: -10px;
- left: 0;
- height: calc(100% + 10px);
- width: 100%;
- background-color: rgb(230, 230, 230);
- border: 2px dotted rgb(200, 200, 200);
- border-radius: 5px;
-}
-
-img:after {
- content: " ";
- display: block;
- font-size: 16px;
- font-style: normal;
- font-family: FontAwesome;
- color: rgb(100, 100, 100);
-
- position: absolute;
- top: 5px;
- left: 0;
- width: 100%;
- text-align: center;
-}
-
-"""
-
-if __name__ == '__main__':
-
- examples_mix = [
- ['images/cat.jpg', 'Add sunglasses to the animal.'],
- ['images/eagle.jpg', 'Transform this image into cartoon style'],
- [None, 'Generate an image of dog on green grass.'],
- [None, 'Draw a painting of sunflowers in Van Gogh style.'],
- ['images/dogs_4.jpg', 'How many dogs in the image?'],
- ['images/spongebob.png', 'Who are they?'],
- ['images/star.jpg', 'Do you know this painting?'],
- ]
-
- examples_conv = [
- ['images/demo_example1.jpg'],
- ['images/demo_example2.jpg'],
- ['images/demo_example3.jpg'],
- ['images/demo_example7.jpg'],
- ['images/demo_example5.jpg'],
- ['images/demo_example6.jpg'],
- ]
-
- with gr.Blocks(css=css) as demo:
- gr.Markdown(title)
- dialog_state = gr.State()
- input_state = gr.State()
- with gr.Row():
- with gr.Column(scale=3):
- with gr.Row():
- image = gr.Image(type='pil', label='input_image')
- with gr.Row():
- text = gr.Textbox(lines=5,
- show_label=False,
- label='input_text',
- elem_id='textbox',
- placeholder="Enter text or add image, and press submit,").style(container=False)
- with gr.Row():
- add_image_btn = gr.Button("Add Image")
- add_text_btn = gr.Button("Add Text")
-
- submit_btn = gr.Button("Submit")
-
- with gr.Row():
- num_beams = gr.Slider(minimum=1, maximum=4, value=1, step=1, interactive=True, label="Num of Beams")
- max_new_tokens = gr.Slider(minimum=64,
- maximum=1024,
- value=256,
- step=64,
- interactive=True,
- label="Max New Tokens")
- temperature = gr.Slider(minimum=0.0,
- maximum=1.0,
- value=1.0,
- step=0.1,
- interactive=True,
- label="Temperature")
- top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.1, interactive=True, label="Top P")
- max_turns = gr.Slider(minimum=1, maximum=5, value=3, step=1, interactive=True, label="Max History Rounds")
- force_img_gen = gr.Radio(choices=[True, False], value=False, label='Force Image Generation')
-
- with gr.Column(scale=7):
- chatbot = gr.Chatbot(elem_id='chatbot', label="SEED LLaMA").style(height=700)
- with gr.Row():
- upvote_btn = gr.Button(value="👍 Upvote", interactive=False)
- downvote_btn = gr.Button(value="👎 Downvote", interactive=False)
- regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False)
- clear_btn = gr.Button(value="🗑️ Clear history", interactive=False)
-
- # with gr.Row():
- # gr.Examples(examples=examples_image, label='Image examples', inputs=[image])
- with gr.Row():
- # with gr.Column(scale=6):
- gr.Examples(examples=examples_mix, label='Input examples', inputs=[image, text])
- # with gr.Column(scale=0.4):
- # gr.Examples(examples=examples_text, inputs=[text])
-
-
- # with gr.Row():
- # gr.Examples(examples=examples_2, inputs=[image])
-
- with gr.Row():
- # gr.Gallery(value=[Image.open(e[0]) for e in examples_conv], show_label=True, label="Example Conversations", elem_id="gallery",height=1400, object_fit='contain').style(grid=[3], height='auto')
- gr.Gallery(value=[Image.open(e[0]) for e in examples_conv], show_label=True, label="Example Conversations", elem_id="gallery",height=1500, columns=[3], rows=[2])
-
- # Register listeners
- btn_list = [upvote_btn, downvote_btn, regenerate_btn, clear_btn]
- upvote_btn.click(upvote_last_response, [dialog_state], [upvote_btn, downvote_btn])
- downvote_btn.click(downvote_last_response, [dialog_state], [upvote_btn, downvote_btn])
- regenerate_btn.click(regenerate, [dialog_state], [dialog_state, chatbot] + btn_list).then(
- http_bot, [dialog_state, input_state, temperature, top_p, max_new_tokens, num_beams, max_turns, force_img_gen],
- [dialog_state, input_state, chatbot] + btn_list)
- add_image_btn.click(add_image, [dialog_state, input_state, image],
- [dialog_state, input_state, image, chatbot] + btn_list)
-
- add_text_btn.click(add_text, [dialog_state, input_state, text], [dialog_state, input_state, text, chatbot] + btn_list)
-
- submit_btn.click(
- add_image, [dialog_state, input_state, image], [dialog_state, input_state, image, chatbot] + btn_list).then(
- add_text, [dialog_state, input_state, text],
- [dialog_state, input_state, text, chatbot, upvote_btn, downvote_btn, regenerate_btn, clear_btn]).then(
- http_bot, [dialog_state, input_state, temperature, top_p, max_new_tokens, num_beams, max_turns, force_img_gen],
- [dialog_state, input_state, chatbot] + btn_list)
- clear_btn.click(clear_history, None, [dialog_state, input_state, chatbot] + btn_list)
-
- demo.load(load_demo, None, [dialog_state, input_state])
-
- demo.launch(server_name=args.server_name, server_port=args.server_port, enable_queue=True)
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov7_l_syncbn_fast_6x32b-100e_coco.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov7_l_syncbn_fast_6x32b-100e_coco.py
deleted file mode 100644
index 9a7a722e74a6b446c58e748d802511ae5fdab2d3..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov7_l_syncbn_fast_6x32b-100e_coco.py
+++ /dev/null
@@ -1,554 +0,0 @@
-_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
-
-data_root = './data-df2/'
-train_ann_file = 'annotations/train.json'
-train_data_prefix = 'smaller-dataset/'
-val_ann_file = 'annotations/val.json'
-val_data_prefix = 'smaller-dataset/'
-test_ann_file = 'annotations/test.json'
-test_data_prefix = 'smaller-dataset/'
-num_classes = 13
-train_batch_size_per_gpu = 32
-train_num_workers = 24
-persistent_workers = True
-
-vis_backends = [
- dict(type='LocalVisBackend'),
-]
-visualizer = dict(
- type='mmdet.DetLocalVisualizer',
- vis_backends=[
- dict(type='LocalVisBackend'),
- dict(type='WandbVisBackend'),
- # dict(type='TensorboardVisBackend')
- ],
- name='visualizer')
-log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
-log_level = 'INFO'
-load_from = None
-resume = False
-
-anchors = [
- [(12, 16), (19, 36), (40, 28)], # P3/8
- [(36, 75), (76, 55), (72, 146)], # P4/16
- [(142, 110), (192, 243), (459, 401)] # P5/32
-]
-
-base_lr = 0.01
-max_epochs = 100
-
-num_epoch_stage2 = 10 # The last 10 epochs switch evaluation interval
-val_interval_stage2 = 1
-
-model_test_cfg = dict(
- multi_label=True,
- nms_pre=30000,
- score_thr=0.001,
- nms=dict(type='nms', iou_threshold=0.65),
- max_per_img=300)
-
-img_scale = (640, 640)
-dataset_type = 'YOLOv5CocoDataset'
-metainfo = dict(
- classes=(
- 'short_sleeved_shirt', 'long_sleeved_shirt',
- 'short_sleeved_outwear', 'long_sleeved_outwear',
- 'vest', 'sling', 'shorts', 'trousers', 'skirt',
- 'short_sleeved_dress', 'long_sleeved_dress',
- 'vest_dress', 'sling_dress'),
- palette=[
- (255, 0, 0),
- (
- 255,
- 128,
- 0,
- ),
- (
- 255,
- 255,
- 0,
- ),
- (
- 128,
- 255,
- 0,
- ),
- (
- 0,
- 255,
- 0,
- ),
- (
- 0,
- 255,
- 128,
- ),
- (
- 0,
- 255,
- 255,
- ),
- (
- 0,
- 128,
- 255,
- ),
- (
- 0,
- 0,
- 255,
- ),
- (
- 127,
- 0,
- 255,
- ),
- (
- 255,
- 0,
- 255,
- ),
- (
- 255,
- 0,
- 127,
- ),
- (
- 128,
- 128,
- 128,
- ),
- ])
-
-val_batch_size_per_gpu = 1
-val_num_workers = 2
-batch_shapes_cfg = dict(
- type='BatchShapePolicy',
- batch_size=val_batch_size_per_gpu,
- img_size=img_scale[0],
- size_divisor=32,
- extra_pad_ratio=0.5)
-strides = [8, 16, 32] # Strides of multi-scale prior box
-num_det_layers = 3
-norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
-
-# Data augmentation
-max_translate_ratio = 0.2 # YOLOv5RandomAffine
-scaling_ratio_range = (0.1, 2.0) # YOLOv5RandomAffine
-mixup_prob = 0.15 # YOLOv5MixUp
-randchoice_mosaic_prob = [0.8, 0.2]
-mixup_alpha = 8.0 # YOLOv5MixUp
-mixup_beta = 8.0 # YOLOv5MixUp
-
-# -----train val related-----
-loss_cls_weight = 0.3
-loss_bbox_weight = 0.05
-loss_obj_weight = 0.7
-# BatchYOLOv7Assigner params
-simota_candidate_topk = 10
-simota_iou_weight = 3.0
-simota_cls_weight = 1.0
-prior_match_thr = 4. # Priori box matching threshold
-obj_level_weights = [4., 1.,
- 0.4] # The obj loss weights of the three output layers
-
-lr_factor = 0.1 # Learning rate scaling factor
-weight_decay = 0.0005
-save_epoch_intervals = 1
-max_keep_ckpts = 5
-
-env_cfg = dict(
- cudnn_benchmark=True,
- mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
- dist_cfg=dict(backend='nccl'))
-
-# ===============================Unmodified in most cases====================
-model = dict(
- type='YOLODetector',
- data_preprocessor=dict(
- type='YOLOv5DetDataPreprocessor',
- mean=[0., 0., 0.],
- std=[255., 255., 255.],
- bgr_to_rgb=True),
- backbone=dict(
- type='YOLOv7Backbone',
- arch='L',
- norm_cfg=norm_cfg,
- act_cfg=dict(type='SiLU', inplace=True)),
- neck=dict(
- type='YOLOv7PAFPN',
- block_cfg=dict(
- type='ELANBlock',
- middle_ratio=0.5,
- block_ratio=0.25,
- num_blocks=4,
- num_convs_in_block=1),
- upsample_feats_cat_first=False,
- in_channels=[512, 1024, 1024],
- # The real output channel will be multiplied by 2
- out_channels=[128, 256, 512],
- norm_cfg=norm_cfg,
- act_cfg=dict(type='SiLU', inplace=True)),
- bbox_head=dict(
- type='YOLOv7Head',
- head_module=dict(
- type='YOLOv7HeadModule',
- num_classes=num_classes,
- in_channels=[256, 512, 1024],
- featmap_strides=strides,
- num_base_priors=3),
- prior_generator=dict(
- type='mmdet.YOLOAnchorGenerator',
- base_sizes=anchors,
- strides=strides),
- # scaled based on number of detection layers
- loss_cls=dict(
- type='mmdet.CrossEntropyLoss',
- use_sigmoid=True,
- reduction='mean',
- loss_weight=loss_cls_weight *
- (num_classes / 80 * 3 / num_det_layers)),
- loss_bbox=dict(
- type='IoULoss',
- iou_mode='ciou',
- bbox_format='xyxy',
- reduction='mean',
- loss_weight=loss_bbox_weight * (3 / num_det_layers),
- return_iou=True),
- loss_obj=dict(
- type='mmdet.CrossEntropyLoss',
- use_sigmoid=True,
- reduction='mean',
- loss_weight=loss_obj_weight *
- ((img_scale[0] / 640)**2 * 3 / num_det_layers)),
- prior_match_thr=prior_match_thr,
- obj_level_weights=obj_level_weights,
- # BatchYOLOv7Assigner params
- simota_candidate_topk=simota_candidate_topk,
- simota_iou_weight=simota_iou_weight,
- simota_cls_weight=simota_cls_weight),
- test_cfg=model_test_cfg)
-
-pre_transform = [
- dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
- dict(type='LoadAnnotations', with_bbox=True)
-]
-
-mosiac4_pipeline = [
- dict(
- type='Mosaic',
- img_scale=img_scale,
- pad_val=114.0,
- pre_transform=pre_transform),
- dict(
- type='YOLOv5RandomAffine',
- max_rotate_degree=0.0,
- max_shear_degree=0.0,
- max_translate_ratio=max_translate_ratio, # note
- scaling_ratio_range=scaling_ratio_range, # note
- # img_scale is (width, height)
- border=(-img_scale[0] // 2, -img_scale[1] // 2),
- border_val=(114, 114, 114)),
-]
-
-mosiac9_pipeline = [
- dict(
- type='Mosaic9',
- img_scale=img_scale,
- pad_val=114.0,
- pre_transform=pre_transform),
- dict(
- type='YOLOv5RandomAffine',
- max_rotate_degree=0.0,
- max_shear_degree=0.0,
- max_translate_ratio=max_translate_ratio, # note
- scaling_ratio_range=scaling_ratio_range, # note
- # img_scale is (width, height)
- border=(-img_scale[0] // 2, -img_scale[1] // 2),
- border_val=(114, 114, 114)),
-]
-
-randchoice_mosaic_pipeline = dict(
- type='RandomChoice',
- transforms=[mosiac4_pipeline, mosiac9_pipeline],
- prob=randchoice_mosaic_prob)
-
-train_pipeline = [
- *pre_transform,
- randchoice_mosaic_pipeline,
- dict(
- type='YOLOv5MixUp',
- alpha=mixup_alpha, # note
- beta=mixup_beta, # note
- prob=mixup_prob,
- pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
- dict(type='YOLOv5HSVRandomAug'),
- dict(type='mmdet.RandomFlip', prob=0.5),
- dict(
- type='mmdet.PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
- 'flip_direction'))
-]
-
-test_pipeline = [
- dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
- dict(type='YOLOv5KeepRatioResize', scale=img_scale),
- dict(
- type='LetterResize',
- scale=img_scale,
- allow_scale_up=False,
- pad_val=dict(img=114)),
- dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
- dict(
- type='mmdet.PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor', 'pad_param'))
-]
-
-train_dataloader = dict(
- batch_size=train_batch_size_per_gpu,
- num_workers=train_num_workers,
- persistent_workers=persistent_workers,
- pin_memory=True,
- sampler=dict(type='DefaultSampler', shuffle=True),
- collate_fn=dict(type='yolov5_collate'), # FASTER
- dataset=dict(
- type='RepeatDataset',
- times=2,
- dataset=dict(
- type=dataset_type,
- metainfo=metainfo,
- data_root=data_root,
- ann_file=val_ann_file,
- data_prefix=dict(img=train_data_prefix),
- filter_cfg=dict(filter_empty_gt=False, min_size=32),
- pipeline=train_pipeline
- )
- )
-)
-val_dataloader = dict(
- dataset=dict(
- type=dataset_type,
- metainfo=metainfo,
- data_root=data_root,
- ann_file=val_ann_file,
- data_prefix=dict(img=val_data_prefix)))
-
-val_evaluator = dict(
- type='mmdet.CocoMetric',
- proposal_nums=(100, 1, 10),
- ann_file=data_root + val_ann_file,
- metric='bbox')
-
-test_dataloader = dict(
- dataset=dict(
- metainfo=metainfo,
- data_root=data_root,
- ann_file=test_ann_file,
- data_prefix=dict(img=test_data_prefix)))
-test_evaluator = dict(
- type='mmdet.CocoMetric',
- proposal_nums=(100, 1, 10),
- ann_file=data_root + test_ann_file,
- metric='bbox')
-
-train_cfg = dict(
- type='EpochBasedTrainLoop',
- max_epochs=max_epochs,
- val_interval=save_epoch_intervals,
- dynamic_intervals=[(max_epochs - num_epoch_stage2, val_interval_stage2)])
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
-
-param_scheduler = None
-optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(
- type='SGD',
- lr=base_lr,
- momentum=0.937,
- weight_decay=weight_decay,
- nesterov=True,
- batch_size_per_gpu=train_batch_size_per_gpu),
- constructor='YOLOv7OptimWrapperConstructor')
-
-# TO DO: change param_scheduler type to StepLR, refer to mobilenet
-default_scope = 'mmyolo'
-default_hooks = dict(
- timer=dict(type='IterTimerHook'),
- logger=dict(type='LoggerHook', interval=10),
- param_scheduler=dict(
- type='YOLOv5ParamSchedulerHook',
- scheduler_type='cosine',
- lr_factor=lr_factor, # note
- max_epochs=max_epochs),
- checkpoint=dict(
- type='CheckpointHook',
- save_param_scheduler=False,
- interval=save_epoch_intervals,
- save_best='auto',
- max_keep_ckpts=max_keep_ckpts),
- sampler_seed=dict(type='DistSamplerSeedHook'),
- visualization=dict(type='mmdet.DetVisualizationHook'))
-
-custom_hooks = [
- dict(
- type='EMAHook',
- ema_type='ExpMomentumEMA',
- momentum=0.001,
- update_buffers=True,
- strict_load=False,
- priority=49)
-]
-
-# ============================
-
-file_client_args = dict(backend='disk')
-_file_client_args = dict(backend='disk')
-tta_model = dict(
- type='mmdet.DetTTAModel',
- tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300))
-img_scales = [
- (
- 640,
- 640,
- ),
- (
- 320,
- 320,
- ),
- (
- 960,
- 960,
- ),
-]
-_multiscale_resize_transforms = [
- dict(
- type='Compose',
- transforms=[
- dict(type='YOLOv5KeepRatioResize', scale=(
- 640,
- 640,
- )),
- dict(
- type='LetterResize',
- scale=(
- 640,
- 640,
- ),
- allow_scale_up=False,
- pad_val=dict(img=114)),
- ]),
- dict(
- type='Compose',
- transforms=[
- dict(type='YOLOv5KeepRatioResize', scale=(
- 320,
- 320,
- )),
- dict(
- type='LetterResize',
- scale=(
- 320,
- 320,
- ),
- allow_scale_up=False,
- pad_val=dict(img=114)),
- ]),
- dict(
- type='Compose',
- transforms=[
- dict(type='YOLOv5KeepRatioResize', scale=(
- 960,
- 960,
- )),
- dict(
- type='LetterResize',
- scale=(
- 960,
- 960,
- ),
- allow_scale_up=False,
- pad_val=dict(img=114)),
- ]),
-]
-tta_pipeline = [
- dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
- dict(
- type='TestTimeAug',
- transforms=[
- [
- dict(
- type='Compose',
- transforms=[
- dict(type='YOLOv5KeepRatioResize', scale=(
- 640,
- 640,
- )),
- dict(
- type='LetterResize',
- scale=(
- 640,
- 640,
- ),
- allow_scale_up=False,
- pad_val=dict(img=114)),
- ]),
- dict(
- type='Compose',
- transforms=[
- dict(type='YOLOv5KeepRatioResize', scale=(
- 320,
- 320,
- )),
- dict(
- type='LetterResize',
- scale=(
- 320,
- 320,
- ),
- allow_scale_up=False,
- pad_val=dict(img=114)),
- ]),
- dict(
- type='Compose',
- transforms=[
- dict(type='YOLOv5KeepRatioResize', scale=(
- 960,
- 960,
- )),
- dict(
- type='LetterResize',
- scale=(
- 960,
- 960,
- ),
- allow_scale_up=False,
- pad_val=dict(img=114)),
- ]),
- ],
- [
- dict(type='mmdet.RandomFlip', prob=1.0),
- dict(type='mmdet.RandomFlip', prob=0.0),
- ],
- [
- dict(type='mmdet.LoadAnnotations', with_bbox=True),
- ],
- [
- dict(
- type='mmdet.PackDetInputs',
- meta_keys=(
- 'img_id',
- 'img_path',
- 'ori_shape',
- 'img_shape',
- 'scale_factor',
- 'pad_param',
- 'flip',
- 'flip_direction',
- )),
- ],
- ]),
-]
-
-launcher = 'none'
diff --git a/spaces/AchyuthGamer/text-to-speech-client/assets/index-77d0c996.js b/spaces/AchyuthGamer/text-to-speech-client/assets/index-77d0c996.js
deleted file mode 100644
index 37306ad9212a280562e02a99bb53b1e4f54bcef5..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/text-to-speech-client/assets/index-77d0c996.js
+++ /dev/null
@@ -1,40 +0,0 @@
-(function(){const n=document.createElement("link").relList;if(n&&n.supports&&n.supports("modulepreload"))return;for(const l of document.querySelectorAll('link[rel="modulepreload"]'))r(l);new MutationObserver(l=>{for(const u of l)if(u.type==="childList")for(const o of u.addedNodes)o.tagName==="LINK"&&o.rel==="modulepreload"&&r(o)}).observe(document,{childList:!0,subtree:!0});function t(l){const u={};return l.integrity&&(u.integrity=l.integrity),l.referrerPolicy&&(u.referrerPolicy=l.referrerPolicy),l.crossOrigin==="use-credentials"?u.credentials="include":l.crossOrigin==="anonymous"?u.credentials="omit":u.credentials="same-origin",u}function r(l){if(l.ep)return;l.ep=!0;const u=t(l);fetch(l.href,u)}})();function rc(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var Hi={exports:{}},el={},Wi={exports:{}},L={};/**
- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var Xt=Symbol.for("react.element"),lc=Symbol.for("react.portal"),uc=Symbol.for("react.fragment"),oc=Symbol.for("react.strict_mode"),ic=Symbol.for("react.profiler"),sc=Symbol.for("react.provider"),ac=Symbol.for("react.context"),cc=Symbol.for("react.forward_ref"),fc=Symbol.for("react.suspense"),dc=Symbol.for("react.memo"),pc=Symbol.for("react.lazy"),Mo=Symbol.iterator;function mc(e){return e===null||typeof e!="object"?null:(e=Mo&&e[Mo]||e["@@iterator"],typeof e=="function"?e:null)}var Qi={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},Ki=Object.assign,Yi={};function ut(e,n,t){this.props=e,this.context=n,this.refs=Yi,this.updater=t||Qi}ut.prototype.isReactComponent={};ut.prototype.setState=function(e,n){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,n,"setState")};ut.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function Xi(){}Xi.prototype=ut.prototype;function Uu(e,n,t){this.props=e,this.context=n,this.refs=Yi,this.updater=t||Qi}var $u=Uu.prototype=new Xi;$u.constructor=Uu;Ki($u,ut.prototype);$u.isPureReactComponent=!0;var Do=Array.isArray,Gi=Object.prototype.hasOwnProperty,Au={current:null},Zi={key:!0,ref:!0,__self:!0,__source:!0};function Ji(e,n,t){var r,l={},u=null,o=null;if(n!=null)for(r in n.ref!==void 0&&(o=n.ref),n.key!==void 0&&(u=""+n.key),n)Gi.call(n,r)&&!Zi.hasOwnProperty(r)&&(l[r]=n[r]);var i=arguments.length-2;if(i===1)l.children=t;else if(1>>1,G=x[W];if(0>>1;Wl(gl,z))gnl(er,gl)?(x[W]=er,x[gn]=z,W=gn):(x[W]=gl,x[yn]=z,W=yn);else if(gnl(er,z))x[W]=er,x[gn]=z,W=gn;else break e}}return P}function l(x,P){var z=x.sortIndex-P.sortIndex;return z!==0?z:x.id-P.id}if(typeof performance=="object"&&typeof performance.now=="function"){var u=performance;e.unstable_now=function(){return u.now()}}else{var o=Date,i=o.now();e.unstable_now=function(){return o.now()-i}}var s=[],c=[],h=1,m=null,p=3,w=!1,S=!1,g=!1,R=typeof setTimeout=="function"?setTimeout:null,f=typeof clearTimeout=="function"?clearTimeout:null,a=typeof setImmediate<"u"?setImmediate:null;typeof navigator<"u"&&navigator.scheduling!==void 0&&navigator.scheduling.isInputPending!==void 0&&navigator.scheduling.isInputPending.bind(navigator.scheduling);function d(x){for(var P=t(c);P!==null;){if(P.callback===null)r(c);else if(P.startTime<=x)r(c),P.sortIndex=P.expirationTime,n(s,P);else break;P=t(c)}}function v(x){if(g=!1,d(x),!S)if(t(s)!==null)S=!0,vl(E);else{var P=t(c);P!==null&&yl(v,P.startTime-x)}}function E(x,P){S=!1,g&&(g=!1,f(N),N=-1),w=!0;var z=p;try{for(d(P),m=t(s);m!==null&&(!(m.expirationTime>P)||x&&!Pe());){var W=m.callback;if(typeof W=="function"){m.callback=null,p=m.priorityLevel;var G=W(m.expirationTime<=P);P=e.unstable_now(),typeof G=="function"?m.callback=G:m===t(s)&&r(s),d(P)}else r(s);m=t(s)}if(m!==null)var bt=!0;else{var yn=t(c);yn!==null&&yl(v,yn.startTime-P),bt=!1}return bt}finally{m=null,p=z,w=!1}}var _=!1,C=null,N=-1,H=5,T=-1;function Pe(){return!(e.unstable_now()-Tx||125W?(x.sortIndex=z,n(c,x),t(s)===null&&x===t(c)&&(g?(f(N),N=-1):g=!0,yl(v,z-W))):(x.sortIndex=G,n(s,x),S||w||(S=!0,vl(E))),x},e.unstable_shouldYield=Pe,e.unstable_wrapCallback=function(x){var P=p;return function(){var z=p;p=P;try{return x.apply(this,arguments)}finally{p=z}}}})(ns);es.exports=ns;var Nc=es.exports;/**
- * @license React
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var ts=ae,ge=Nc;function y(e){for(var n="https://reactjs.org/docs/error-decoder.html?invariant="+e,t=1;t"u"||typeof window.document>"u"||typeof window.document.createElement>"u"),Kl=Object.prototype.hasOwnProperty,Pc=/^[:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD][:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD\-.0-9\u00B7\u0300-\u036F\u203F-\u2040]*$/,Fo={},Uo={};function zc(e){return Kl.call(Uo,e)?!0:Kl.call(Fo,e)?!1:Pc.test(e)?Uo[e]=!0:(Fo[e]=!0,!1)}function Lc(e,n,t,r){if(t!==null&&t.type===0)return!1;switch(typeof n){case"function":case"symbol":return!0;case"boolean":return r?!1:t!==null?!t.acceptsBooleans:(e=e.toLowerCase().slice(0,5),e!=="data-"&&e!=="aria-");default:return!1}}function Tc(e,n,t,r){if(n===null||typeof n>"u"||Lc(e,n,t,r))return!0;if(r)return!1;if(t!==null)switch(t.type){case 3:return!n;case 4:return n===!1;case 5:return isNaN(n);case 6:return isNaN(n)||1>n}return!1}function se(e,n,t,r,l,u,o){this.acceptsBooleans=n===2||n===3||n===4,this.attributeName=r,this.attributeNamespace=l,this.mustUseProperty=t,this.propertyName=e,this.type=n,this.sanitizeURL=u,this.removeEmptyString=o}var ee={};"children dangerouslySetInnerHTML defaultValue defaultChecked innerHTML suppressContentEditableWarning suppressHydrationWarning style".split(" ").forEach(function(e){ee[e]=new se(e,0,!1,e,null,!1,!1)});[["acceptCharset","accept-charset"],["className","class"],["htmlFor","for"],["httpEquiv","http-equiv"]].forEach(function(e){var n=e[0];ee[n]=new se(n,1,!1,e[1],null,!1,!1)});["contentEditable","draggable","spellCheck","value"].forEach(function(e){ee[e]=new se(e,2,!1,e.toLowerCase(),null,!1,!1)});["autoReverse","externalResourcesRequired","focusable","preserveAlpha"].forEach(function(e){ee[e]=new se(e,2,!1,e,null,!1,!1)});"allowFullScreen async autoFocus autoPlay controls default defer disabled disablePictureInPicture disableRemotePlayback formNoValidate hidden loop noModule noValidate open playsInline readOnly required reversed scoped seamless itemScope".split(" ").forEach(function(e){ee[e]=new se(e,3,!1,e.toLowerCase(),null,!1,!1)});["checked","multiple","muted","selected"].forEach(function(e){ee[e]=new se(e,3,!0,e,null,!1,!1)});["capture","download"].forEach(function(e){ee[e]=new se(e,4,!1,e,null,!1,!1)});["cols","rows","size","span"].forEach(function(e){ee[e]=new se(e,6,!1,e,null,!1,!1)});["rowSpan","start"].forEach(function(e){ee[e]=new se(e,5,!1,e.toLowerCase(),null,!1,!1)});var Bu=/[\-:]([a-z])/g;function Hu(e){return e[1].toUpperCase()}"accent-height alignment-baseline arabic-form baseline-shift cap-height clip-path clip-rule color-interpolation color-interpolation-filters color-profile color-rendering dominant-baseline enable-background fill-opacity fill-rule flood-color flood-opacity font-family font-size font-size-adjust font-stretch font-style font-variant font-weight glyph-name glyph-orientation-horizontal glyph-orientation-vertical horiz-adv-x horiz-origin-x image-rendering letter-spacing lighting-color marker-end marker-mid marker-start overline-position overline-thickness paint-order panose-1 pointer-events rendering-intent shape-rendering stop-color stop-opacity strikethrough-position strikethrough-thickness stroke-dasharray stroke-dashoffset stroke-linecap stroke-linejoin stroke-miterlimit stroke-opacity stroke-width text-anchor text-decoration text-rendering underline-position underline-thickness unicode-bidi unicode-range units-per-em v-alphabetic v-hanging v-ideographic v-mathematical vector-effect vert-adv-y vert-origin-x vert-origin-y word-spacing writing-mode xmlns:xlink x-height".split(" ").forEach(function(e){var n=e.replace(Bu,Hu);ee[n]=new se(n,1,!1,e,null,!1,!1)});"xlink:actuate xlink:arcrole xlink:role xlink:show xlink:title xlink:type".split(" ").forEach(function(e){var n=e.replace(Bu,Hu);ee[n]=new se(n,1,!1,e,"http://www.w3.org/1999/xlink",!1,!1)});["xml:base","xml:lang","xml:space"].forEach(function(e){var n=e.replace(Bu,Hu);ee[n]=new se(n,1,!1,e,"http://www.w3.org/XML/1998/namespace",!1,!1)});["tabIndex","crossOrigin"].forEach(function(e){ee[e]=new se(e,1,!1,e.toLowerCase(),null,!1,!1)});ee.xlinkHref=new se("xlinkHref",1,!1,"xlink:href","http://www.w3.org/1999/xlink",!0,!1);["src","href","action","formAction"].forEach(function(e){ee[e]=new se(e,1,!1,e.toLowerCase(),null,!0,!0)});function Wu(e,n,t,r){var l=ee.hasOwnProperty(n)?ee[n]:null;(l!==null?l.type!==0:r||!(2i||l[o]!==u[i]){var s=`
-`+l[o].replace(" at new "," at ");return e.displayName&&s.includes("")&&(s=s.replace("",e.displayName)),s}while(1<=o&&0<=i);break}}}finally{kl=!1,Error.prepareStackTrace=t}return(e=e?e.displayName||e.name:"")?gt(e):""}function Rc(e){switch(e.tag){case 5:return gt(e.type);case 16:return gt("Lazy");case 13:return gt("Suspense");case 19:return gt("SuspenseList");case 0:case 2:case 15:return e=El(e.type,!1),e;case 11:return e=El(e.type.render,!1),e;case 1:return e=El(e.type,!0),e;default:return""}}function Zl(e){if(e==null)return null;if(typeof e=="function")return e.displayName||e.name||null;if(typeof e=="string")return e;switch(e){case Dn:return"Fragment";case Mn:return"Portal";case Yl:return"Profiler";case Qu:return"StrictMode";case Xl:return"Suspense";case Gl:return"SuspenseList"}if(typeof e=="object")switch(e.$$typeof){case us:return(e.displayName||"Context")+".Consumer";case ls:return(e._context.displayName||"Context")+".Provider";case Ku:var n=e.render;return e=e.displayName,e||(e=n.displayName||n.name||"",e=e!==""?"ForwardRef("+e+")":"ForwardRef"),e;case Yu:return n=e.displayName||null,n!==null?n:Zl(e.type)||"Memo";case Je:n=e._payload,e=e._init;try{return Zl(e(n))}catch{}}return null}function Oc(e){var n=e.type;switch(e.tag){case 24:return"Cache";case 9:return(n.displayName||"Context")+".Consumer";case 10:return(n._context.displayName||"Context")+".Provider";case 18:return"DehydratedFragment";case 11:return e=n.render,e=e.displayName||e.name||"",n.displayName||(e!==""?"ForwardRef("+e+")":"ForwardRef");case 7:return"Fragment";case 5:return n;case 4:return"Portal";case 3:return"Root";case 6:return"Text";case 16:return Zl(n);case 8:return n===Qu?"StrictMode":"Mode";case 22:return"Offscreen";case 12:return"Profiler";case 21:return"Scope";case 13:return"Suspense";case 19:return"SuspenseList";case 25:return"TracingMarker";case 1:case 0:case 17:case 2:case 14:case 15:if(typeof n=="function")return n.displayName||n.name||null;if(typeof n=="string")return n}return null}function dn(e){switch(typeof e){case"boolean":case"number":case"string":case"undefined":return e;case"object":return e;default:return""}}function is(e){var n=e.type;return(e=e.nodeName)&&e.toLowerCase()==="input"&&(n==="checkbox"||n==="radio")}function jc(e){var n=is(e)?"checked":"value",t=Object.getOwnPropertyDescriptor(e.constructor.prototype,n),r=""+e[n];if(!e.hasOwnProperty(n)&&typeof t<"u"&&typeof t.get=="function"&&typeof t.set=="function"){var l=t.get,u=t.set;return Object.defineProperty(e,n,{configurable:!0,get:function(){return l.call(this)},set:function(o){r=""+o,u.call(this,o)}}),Object.defineProperty(e,n,{enumerable:t.enumerable}),{getValue:function(){return r},setValue:function(o){r=""+o},stopTracking:function(){e._valueTracker=null,delete e[n]}}}}function rr(e){e._valueTracker||(e._valueTracker=jc(e))}function ss(e){if(!e)return!1;var n=e._valueTracker;if(!n)return!0;var t=n.getValue(),r="";return e&&(r=is(e)?e.checked?"true":"false":e.value),e=r,e!==t?(n.setValue(e),!0):!1}function Tr(e){if(e=e||(typeof document<"u"?document:void 0),typeof e>"u")return null;try{return e.activeElement||e.body}catch{return e.body}}function Jl(e,n){var t=n.checked;return V({},n,{defaultChecked:void 0,defaultValue:void 0,value:void 0,checked:t??e._wrapperState.initialChecked})}function Ao(e,n){var t=n.defaultValue==null?"":n.defaultValue,r=n.checked!=null?n.checked:n.defaultChecked;t=dn(n.value!=null?n.value:t),e._wrapperState={initialChecked:r,initialValue:t,controlled:n.type==="checkbox"||n.type==="radio"?n.checked!=null:n.value!=null}}function as(e,n){n=n.checked,n!=null&&Wu(e,"checked",n,!1)}function ql(e,n){as(e,n);var t=dn(n.value),r=n.type;if(t!=null)r==="number"?(t===0&&e.value===""||e.value!=t)&&(e.value=""+t):e.value!==""+t&&(e.value=""+t);else if(r==="submit"||r==="reset"){e.removeAttribute("value");return}n.hasOwnProperty("value")?bl(e,n.type,t):n.hasOwnProperty("defaultValue")&&bl(e,n.type,dn(n.defaultValue)),n.checked==null&&n.defaultChecked!=null&&(e.defaultChecked=!!n.defaultChecked)}function Vo(e,n,t){if(n.hasOwnProperty("value")||n.hasOwnProperty("defaultValue")){var r=n.type;if(!(r!=="submit"&&r!=="reset"||n.value!==void 0&&n.value!==null))return;n=""+e._wrapperState.initialValue,t||n===e.value||(e.value=n),e.defaultValue=n}t=e.name,t!==""&&(e.name=""),e.defaultChecked=!!e._wrapperState.initialChecked,t!==""&&(e.name=t)}function bl(e,n,t){(n!=="number"||Tr(e.ownerDocument)!==e)&&(t==null?e.defaultValue=""+e._wrapperState.initialValue:e.defaultValue!==""+t&&(e.defaultValue=""+t))}var wt=Array.isArray;function Kn(e,n,t,r){if(e=e.options,n){n={};for(var l=0;l"+n.valueOf().toString()+"",n=lr.firstChild;e.firstChild;)e.removeChild(e.firstChild);for(;n.firstChild;)e.appendChild(n.firstChild)}});function Ot(e,n){if(n){var t=e.firstChild;if(t&&t===e.lastChild&&t.nodeType===3){t.nodeValue=n;return}}e.textContent=n}var Et={animationIterationCount:!0,aspectRatio:!0,borderImageOutset:!0,borderImageSlice:!0,borderImageWidth:!0,boxFlex:!0,boxFlexGroup:!0,boxOrdinalGroup:!0,columnCount:!0,columns:!0,flex:!0,flexGrow:!0,flexPositive:!0,flexShrink:!0,flexNegative:!0,flexOrder:!0,gridArea:!0,gridRow:!0,gridRowEnd:!0,gridRowSpan:!0,gridRowStart:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnSpan:!0,gridColumnStart:!0,fontWeight:!0,lineClamp:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,tabSize:!0,widows:!0,zIndex:!0,zoom:!0,fillOpacity:!0,floodOpacity:!0,stopOpacity:!0,strokeDasharray:!0,strokeDashoffset:!0,strokeMiterlimit:!0,strokeOpacity:!0,strokeWidth:!0},Mc=["Webkit","ms","Moz","O"];Object.keys(Et).forEach(function(e){Mc.forEach(function(n){n=n+e.charAt(0).toUpperCase()+e.substring(1),Et[n]=Et[e]})});function ps(e,n,t){return n==null||typeof n=="boolean"||n===""?"":t||typeof n!="number"||n===0||Et.hasOwnProperty(e)&&Et[e]?(""+n).trim():n+"px"}function ms(e,n){e=e.style;for(var t in n)if(n.hasOwnProperty(t)){var r=t.indexOf("--")===0,l=ps(t,n[t],r);t==="float"&&(t="cssFloat"),r?e.setProperty(t,l):e[t]=l}}var Dc=V({menuitem:!0},{area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0});function tu(e,n){if(n){if(Dc[e]&&(n.children!=null||n.dangerouslySetInnerHTML!=null))throw Error(y(137,e));if(n.dangerouslySetInnerHTML!=null){if(n.children!=null)throw Error(y(60));if(typeof n.dangerouslySetInnerHTML!="object"||!("__html"in n.dangerouslySetInnerHTML))throw Error(y(61))}if(n.style!=null&&typeof n.style!="object")throw Error(y(62))}}function ru(e,n){if(e.indexOf("-")===-1)return typeof n.is=="string";switch(e){case"annotation-xml":case"color-profile":case"font-face":case"font-face-src":case"font-face-uri":case"font-face-format":case"font-face-name":case"missing-glyph":return!1;default:return!0}}var lu=null;function Xu(e){return e=e.target||e.srcElement||window,e.correspondingUseElement&&(e=e.correspondingUseElement),e.nodeType===3?e.parentNode:e}var uu=null,Yn=null,Xn=null;function Wo(e){if(e=Jt(e)){if(typeof uu!="function")throw Error(y(280));var n=e.stateNode;n&&(n=ul(n),uu(e.stateNode,e.type,n))}}function hs(e){Yn?Xn?Xn.push(e):Xn=[e]:Yn=e}function vs(){if(Yn){var e=Yn,n=Xn;if(Xn=Yn=null,Wo(e),n)for(e=0;e>>=0,e===0?32:31-(Kc(e)/Yc|0)|0}var ur=64,or=4194304;function St(e){switch(e&-e){case 1:return 1;case 2:return 2;case 4:return 4;case 8:return 8;case 16:return 16;case 32:return 32;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return e&4194240;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return e&130023424;case 134217728:return 134217728;case 268435456:return 268435456;case 536870912:return 536870912;case 1073741824:return 1073741824;default:return e}}function Mr(e,n){var t=e.pendingLanes;if(t===0)return 0;var r=0,l=e.suspendedLanes,u=e.pingedLanes,o=t&268435455;if(o!==0){var i=o&~l;i!==0?r=St(i):(u&=o,u!==0&&(r=St(u)))}else o=t&~l,o!==0?r=St(o):u!==0&&(r=St(u));if(r===0)return 0;if(n!==0&&n!==r&&!(n&l)&&(l=r&-r,u=n&-n,l>=u||l===16&&(u&4194240)!==0))return n;if(r&4&&(r|=t&16),n=e.entangledLanes,n!==0)for(e=e.entanglements,n&=r;0t;t++)n.push(e);return n}function Gt(e,n,t){e.pendingLanes|=n,n!==536870912&&(e.suspendedLanes=0,e.pingedLanes=0),e=e.eventTimes,n=31-Oe(n),e[n]=t}function Jc(e,n){var t=e.pendingLanes&~n;e.pendingLanes=n,e.suspendedLanes=0,e.pingedLanes=0,e.expiredLanes&=n,e.mutableReadLanes&=n,e.entangledLanes&=n,n=e.entanglements;var r=e.eventTimes;for(e=e.expirationTimes;0=_t),bo=String.fromCharCode(32),ei=!1;function Is(e,n){switch(e){case"keyup":return Nf.indexOf(n.keyCode)!==-1;case"keydown":return n.keyCode!==229;case"keypress":case"mousedown":case"focusout":return!0;default:return!1}}function Fs(e){return e=e.detail,typeof e=="object"&&"data"in e?e.data:null}var In=!1;function zf(e,n){switch(e){case"compositionend":return Fs(n);case"keypress":return n.which!==32?null:(ei=!0,bo);case"textInput":return e=n.data,e===bo&&ei?null:e;default:return null}}function Lf(e,n){if(In)return e==="compositionend"||!to&&Is(e,n)?(e=Ms(),kr=bu=nn=null,In=!1,e):null;switch(e){case"paste":return null;case"keypress":if(!(n.ctrlKey||n.altKey||n.metaKey)||n.ctrlKey&&n.altKey){if(n.char&&1=n)return{node:t,offset:n-e};e=r}e:{for(;t;){if(t.nextSibling){t=t.nextSibling;break e}t=t.parentNode}t=void 0}t=li(t)}}function Vs(e,n){return e&&n?e===n?!0:e&&e.nodeType===3?!1:n&&n.nodeType===3?Vs(e,n.parentNode):"contains"in e?e.contains(n):e.compareDocumentPosition?!!(e.compareDocumentPosition(n)&16):!1:!1}function Bs(){for(var e=window,n=Tr();n instanceof e.HTMLIFrameElement;){try{var t=typeof n.contentWindow.location.href=="string"}catch{t=!1}if(t)e=n.contentWindow;else break;n=Tr(e.document)}return n}function ro(e){var n=e&&e.nodeName&&e.nodeName.toLowerCase();return n&&(n==="input"&&(e.type==="text"||e.type==="search"||e.type==="tel"||e.type==="url"||e.type==="password")||n==="textarea"||e.contentEditable==="true")}function Uf(e){var n=Bs(),t=e.focusedElem,r=e.selectionRange;if(n!==t&&t&&t.ownerDocument&&Vs(t.ownerDocument.documentElement,t)){if(r!==null&&ro(t)){if(n=r.start,e=r.end,e===void 0&&(e=n),"selectionStart"in t)t.selectionStart=n,t.selectionEnd=Math.min(e,t.value.length);else if(e=(n=t.ownerDocument||document)&&n.defaultView||window,e.getSelection){e=e.getSelection();var l=t.textContent.length,u=Math.min(r.start,l);r=r.end===void 0?u:Math.min(r.end,l),!e.extend&&u>r&&(l=r,r=u,u=l),l=ui(t,u);var o=ui(t,r);l&&o&&(e.rangeCount!==1||e.anchorNode!==l.node||e.anchorOffset!==l.offset||e.focusNode!==o.node||e.focusOffset!==o.offset)&&(n=n.createRange(),n.setStart(l.node,l.offset),e.removeAllRanges(),u>r?(e.addRange(n),e.extend(o.node,o.offset)):(n.setEnd(o.node,o.offset),e.addRange(n)))}}for(n=[],e=t;e=e.parentNode;)e.nodeType===1&&n.push({element:e,left:e.scrollLeft,top:e.scrollTop});for(typeof t.focus=="function"&&t.focus(),t=0;t=document.documentMode,Fn=null,fu=null,Nt=null,du=!1;function oi(e,n,t){var r=t.window===t?t.document:t.nodeType===9?t:t.ownerDocument;du||Fn==null||Fn!==Tr(r)||(r=Fn,"selectionStart"in r&&ro(r)?r={start:r.selectionStart,end:r.selectionEnd}:(r=(r.ownerDocument&&r.ownerDocument.defaultView||window).getSelection(),r={anchorNode:r.anchorNode,anchorOffset:r.anchorOffset,focusNode:r.focusNode,focusOffset:r.focusOffset}),Nt&&Ut(Nt,r)||(Nt=r,r=Fr(fu,"onSelect"),0An||(e.current=gu[An],gu[An]=null,An--)}function D(e,n){An++,gu[An]=e.current,e.current=n}var pn={},le=hn(pn),de=hn(!1),Nn=pn;function bn(e,n){var t=e.type.contextTypes;if(!t)return pn;var r=e.stateNode;if(r&&r.__reactInternalMemoizedUnmaskedChildContext===n)return r.__reactInternalMemoizedMaskedChildContext;var l={},u;for(u in t)l[u]=n[u];return r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=n,e.__reactInternalMemoizedMaskedChildContext=l),l}function pe(e){return e=e.childContextTypes,e!=null}function $r(){F(de),F(le)}function pi(e,n,t){if(le.current!==pn)throw Error(y(168));D(le,n),D(de,t)}function Js(e,n,t){var r=e.stateNode;if(n=n.childContextTypes,typeof r.getChildContext!="function")return t;r=r.getChildContext();for(var l in r)if(!(l in n))throw Error(y(108,Oc(e)||"Unknown",l));return V({},t,r)}function Ar(e){return e=(e=e.stateNode)&&e.__reactInternalMemoizedMergedChildContext||pn,Nn=le.current,D(le,e),D(de,de.current),!0}function mi(e,n,t){var r=e.stateNode;if(!r)throw Error(y(169));t?(e=Js(e,n,Nn),r.__reactInternalMemoizedMergedChildContext=e,F(de),F(le),D(le,e)):F(de),D(de,t)}var Ve=null,ol=!1,Il=!1;function qs(e){Ve===null?Ve=[e]:Ve.push(e)}function Zf(e){ol=!0,qs(e)}function vn(){if(!Il&&Ve!==null){Il=!0;var e=0,n=j;try{var t=Ve;for(j=1;e>=o,l-=o,Be=1<<32-Oe(n)+l|t<N?(H=C,C=null):H=C.sibling;var T=p(f,C,d[N],v);if(T===null){C===null&&(C=H);break}e&&C&&T.alternate===null&&n(f,C),a=u(T,a,N),_===null?E=T:_.sibling=T,_=T,C=H}if(N===d.length)return t(f,C),U&&wn(f,N),E;if(C===null){for(;NN?(H=C,C=null):H=C.sibling;var Pe=p(f,C,T.value,v);if(Pe===null){C===null&&(C=H);break}e&&C&&Pe.alternate===null&&n(f,C),a=u(Pe,a,N),_===null?E=Pe:_.sibling=Pe,_=Pe,C=H}if(T.done)return t(f,C),U&&wn(f,N),E;if(C===null){for(;!T.done;N++,T=d.next())T=m(f,T.value,v),T!==null&&(a=u(T,a,N),_===null?E=T:_.sibling=T,_=T);return U&&wn(f,N),E}for(C=r(f,C);!T.done;N++,T=d.next())T=w(C,f,N,T.value,v),T!==null&&(e&&T.alternate!==null&&C.delete(T.key===null?N:T.key),a=u(T,a,N),_===null?E=T:_.sibling=T,_=T);return e&&C.forEach(function(st){return n(f,st)}),U&&wn(f,N),E}function R(f,a,d,v){if(typeof d=="object"&&d!==null&&d.type===Dn&&d.key===null&&(d=d.props.children),typeof d=="object"&&d!==null){switch(d.$$typeof){case tr:e:{for(var E=d.key,_=a;_!==null;){if(_.key===E){if(E=d.type,E===Dn){if(_.tag===7){t(f,_.sibling),a=l(_,d.props.children),a.return=f,f=a;break e}}else if(_.elementType===E||typeof E=="object"&&E!==null&&E.$$typeof===Je&&ki(E)===_.type){t(f,_.sibling),a=l(_,d.props),a.ref=ht(f,_,d),a.return=f,f=a;break e}t(f,_);break}else n(f,_);_=_.sibling}d.type===Dn?(a=Cn(d.props.children,f.mode,v,d.key),a.return=f,f=a):(v=Lr(d.type,d.key,d.props,null,f.mode,v),v.ref=ht(f,a,d),v.return=f,f=v)}return o(f);case Mn:e:{for(_=d.key;a!==null;){if(a.key===_)if(a.tag===4&&a.stateNode.containerInfo===d.containerInfo&&a.stateNode.implementation===d.implementation){t(f,a.sibling),a=l(a,d.children||[]),a.return=f,f=a;break e}else{t(f,a);break}else n(f,a);a=a.sibling}a=Wl(d,f.mode,v),a.return=f,f=a}return o(f);case Je:return _=d._init,R(f,a,_(d._payload),v)}if(wt(d))return S(f,a,d,v);if(ct(d))return g(f,a,d,v);pr(f,d)}return typeof d=="string"&&d!==""||typeof d=="number"?(d=""+d,a!==null&&a.tag===6?(t(f,a.sibling),a=l(a,d),a.return=f,f=a):(t(f,a),a=Hl(d,f.mode,v),a.return=f,f=a),o(f)):t(f,a)}return R}var nt=oa(!0),ia=oa(!1),qt={},$e=hn(qt),Bt=hn(qt),Ht=hn(qt);function xn(e){if(e===qt)throw Error(y(174));return e}function po(e,n){switch(D(Ht,n),D(Bt,e),D($e,qt),e=n.nodeType,e){case 9:case 11:n=(n=n.documentElement)?n.namespaceURI:nu(null,"");break;default:e=e===8?n.parentNode:n,n=e.namespaceURI||null,e=e.tagName,n=nu(n,e)}F($e),D($e,n)}function tt(){F($e),F(Bt),F(Ht)}function sa(e){xn(Ht.current);var n=xn($e.current),t=nu(n,e.type);n!==t&&(D(Bt,e),D($e,t))}function mo(e){Bt.current===e&&(F($e),F(Bt))}var $=hn(0);function Kr(e){for(var n=e;n!==null;){if(n.tag===13){var t=n.memoizedState;if(t!==null&&(t=t.dehydrated,t===null||t.data==="$?"||t.data==="$!"))return n}else if(n.tag===19&&n.memoizedProps.revealOrder!==void 0){if(n.flags&128)return n}else if(n.child!==null){n.child.return=n,n=n.child;continue}if(n===e)break;for(;n.sibling===null;){if(n.return===null||n.return===e)return null;n=n.return}n.sibling.return=n.return,n=n.sibling}return null}var Fl=[];function ho(){for(var e=0;et?t:4,e(!0);var r=Ul.transition;Ul.transition={};try{e(!1),n()}finally{j=t,Ul.transition=r}}function _a(){return Ne().memoizedState}function ed(e,n,t){var r=cn(e);if(t={lane:r,action:t,hasEagerState:!1,eagerState:null,next:null},Ca(e))Na(n,t);else if(t=ta(e,n,t,r),t!==null){var l=oe();je(t,e,r,l),Pa(t,n,r)}}function nd(e,n,t){var r=cn(e),l={lane:r,action:t,hasEagerState:!1,eagerState:null,next:null};if(Ca(e))Na(n,l);else{var u=e.alternate;if(e.lanes===0&&(u===null||u.lanes===0)&&(u=n.lastRenderedReducer,u!==null))try{var o=n.lastRenderedState,i=u(o,t);if(l.hasEagerState=!0,l.eagerState=i,Me(i,o)){var s=n.interleaved;s===null?(l.next=l,co(n)):(l.next=s.next,s.next=l),n.interleaved=l;return}}catch{}finally{}t=ta(e,n,l,r),t!==null&&(l=oe(),je(t,e,r,l),Pa(t,n,r))}}function Ca(e){var n=e.alternate;return e===A||n!==null&&n===A}function Na(e,n){Pt=Yr=!0;var t=e.pending;t===null?n.next=n:(n.next=t.next,t.next=n),e.pending=n}function Pa(e,n,t){if(t&4194240){var r=n.lanes;r&=e.pendingLanes,t|=r,n.lanes=t,Zu(e,t)}}var Xr={readContext:Ce,useCallback:ne,useContext:ne,useEffect:ne,useImperativeHandle:ne,useInsertionEffect:ne,useLayoutEffect:ne,useMemo:ne,useReducer:ne,useRef:ne,useState:ne,useDebugValue:ne,useDeferredValue:ne,useTransition:ne,useMutableSource:ne,useSyncExternalStore:ne,useId:ne,unstable_isNewReconciler:!1},td={readContext:Ce,useCallback:function(e,n){return Ie().memoizedState=[e,n===void 0?null:n],e},useContext:Ce,useEffect:xi,useImperativeHandle:function(e,n,t){return t=t!=null?t.concat([e]):null,Cr(4194308,4,wa.bind(null,n,e),t)},useLayoutEffect:function(e,n){return Cr(4194308,4,e,n)},useInsertionEffect:function(e,n){return Cr(4,2,e,n)},useMemo:function(e,n){var t=Ie();return n=n===void 0?null:n,e=e(),t.memoizedState=[e,n],e},useReducer:function(e,n,t){var r=Ie();return n=t!==void 0?t(n):n,r.memoizedState=r.baseState=n,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:e,lastRenderedState:n},r.queue=e,e=e.dispatch=ed.bind(null,A,e),[r.memoizedState,e]},useRef:function(e){var n=Ie();return e={current:e},n.memoizedState=e},useState:Ei,useDebugValue:So,useDeferredValue:function(e){return Ie().memoizedState=e},useTransition:function(){var e=Ei(!1),n=e[0];return e=bf.bind(null,e[1]),Ie().memoizedState=e,[n,e]},useMutableSource:function(){},useSyncExternalStore:function(e,n,t){var r=A,l=Ie();if(U){if(t===void 0)throw Error(y(407));t=t()}else{if(t=n(),J===null)throw Error(y(349));zn&30||fa(r,n,t)}l.memoizedState=t;var u={value:t,getSnapshot:n};return l.queue=u,xi(pa.bind(null,r,u,e),[e]),r.flags|=2048,Kt(9,da.bind(null,r,u,t,n),void 0,null),t},useId:function(){var e=Ie(),n=J.identifierPrefix;if(U){var t=He,r=Be;t=(r&~(1<<32-Oe(r)-1)).toString(32)+t,n=":"+n+"R"+t,t=Wt++,0<\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=o.createElement(t,{is:r.is}):(e=o.createElement(t),t==="select"&&(o=e,r.multiple?o.multiple=!0:r.size&&(o.size=r.size))):e=o.createElementNS(e,t),e[Fe]=n,e[Vt]=r,Ia(e,n,!1,!1),n.stateNode=e;e:{switch(o=ru(t,r),t){case"dialog":I("cancel",e),I("close",e),l=r;break;case"iframe":case"object":case"embed":I("load",e),l=r;break;case"video":case"audio":for(l=0;llt&&(n.flags|=128,r=!0,vt(u,!1),n.lanes=4194304)}else{if(!r)if(e=Kr(o),e!==null){if(n.flags|=128,r=!0,t=e.updateQueue,t!==null&&(n.updateQueue=t,n.flags|=4),vt(u,!0),u.tail===null&&u.tailMode==="hidden"&&!o.alternate&&!U)return te(n),null}else 2*Q()-u.renderingStartTime>lt&&t!==1073741824&&(n.flags|=128,r=!0,vt(u,!1),n.lanes=4194304);u.isBackwards?(o.sibling=n.child,n.child=o):(t=u.last,t!==null?t.sibling=o:n.child=o,u.last=o)}return u.tail!==null?(n=u.tail,u.rendering=n,u.tail=n.sibling,u.renderingStartTime=Q(),n.sibling=null,t=$.current,D($,r?t&1|2:t&1),n):(te(n),null);case 22:case 23:return No(),r=n.memoizedState!==null,e!==null&&e.memoizedState!==null!==r&&(n.flags|=8192),r&&n.mode&1?he&1073741824&&(te(n),n.subtreeFlags&6&&(n.flags|=8192)):te(n),null;case 24:return null;case 25:return null}throw Error(y(156,n.tag))}function cd(e,n){switch(uo(n),n.tag){case 1:return pe(n.type)&&$r(),e=n.flags,e&65536?(n.flags=e&-65537|128,n):null;case 3:return tt(),F(de),F(le),ho(),e=n.flags,e&65536&&!(e&128)?(n.flags=e&-65537|128,n):null;case 5:return mo(n),null;case 13:if(F($),e=n.memoizedState,e!==null&&e.dehydrated!==null){if(n.alternate===null)throw Error(y(340));et()}return e=n.flags,e&65536?(n.flags=e&-65537|128,n):null;case 19:return F($),null;case 4:return tt(),null;case 10:return ao(n.type._context),null;case 22:case 23:return No(),null;case 24:return null;default:return null}}var hr=!1,re=!1,fd=typeof WeakSet=="function"?WeakSet:Set,k=null;function Wn(e,n){var t=e.ref;if(t!==null)if(typeof t=="function")try{t(null)}catch(r){B(e,n,r)}else t.current=null}function Tu(e,n,t){try{t()}catch(r){B(e,n,r)}}var Oi=!1;function dd(e,n){if(pu=Dr,e=Bs(),ro(e)){if("selectionStart"in e)var t={start:e.selectionStart,end:e.selectionEnd};else e:{t=(t=e.ownerDocument)&&t.defaultView||window;var r=t.getSelection&&t.getSelection();if(r&&r.rangeCount!==0){t=r.anchorNode;var l=r.anchorOffset,u=r.focusNode;r=r.focusOffset;try{t.nodeType,u.nodeType}catch{t=null;break e}var o=0,i=-1,s=-1,c=0,h=0,m=e,p=null;n:for(;;){for(var w;m!==t||l!==0&&m.nodeType!==3||(i=o+l),m!==u||r!==0&&m.nodeType!==3||(s=o+r),m.nodeType===3&&(o+=m.nodeValue.length),(w=m.firstChild)!==null;)p=m,m=w;for(;;){if(m===e)break n;if(p===t&&++c===l&&(i=o),p===u&&++h===r&&(s=o),(w=m.nextSibling)!==null)break;m=p,p=m.parentNode}m=w}t=i===-1||s===-1?null:{start:i,end:s}}else t=null}t=t||{start:0,end:0}}else t=null;for(mu={focusedElem:e,selectionRange:t},Dr=!1,k=n;k!==null;)if(n=k,e=n.child,(n.subtreeFlags&1028)!==0&&e!==null)e.return=n,k=e;else for(;k!==null;){n=k;try{var S=n.alternate;if(n.flags&1024)switch(n.tag){case 0:case 11:case 15:break;case 1:if(S!==null){var g=S.memoizedProps,R=S.memoizedState,f=n.stateNode,a=f.getSnapshotBeforeUpdate(n.elementType===n.type?g:Le(n.type,g),R);f.__reactInternalSnapshotBeforeUpdate=a}break;case 3:var d=n.stateNode.containerInfo;d.nodeType===1?d.textContent="":d.nodeType===9&&d.documentElement&&d.removeChild(d.documentElement);break;case 5:case 6:case 4:case 17:break;default:throw Error(y(163))}}catch(v){B(n,n.return,v)}if(e=n.sibling,e!==null){e.return=n.return,k=e;break}k=n.return}return S=Oi,Oi=!1,S}function zt(e,n,t){var r=n.updateQueue;if(r=r!==null?r.lastEffect:null,r!==null){var l=r=r.next;do{if((l.tag&e)===e){var u=l.destroy;l.destroy=void 0,u!==void 0&&Tu(n,t,u)}l=l.next}while(l!==r)}}function al(e,n){if(n=n.updateQueue,n=n!==null?n.lastEffect:null,n!==null){var t=n=n.next;do{if((t.tag&e)===e){var r=t.create;t.destroy=r()}t=t.next}while(t!==n)}}function Ru(e){var n=e.ref;if(n!==null){var t=e.stateNode;switch(e.tag){case 5:e=t;break;default:e=t}typeof n=="function"?n(e):n.current=e}}function $a(e){var n=e.alternate;n!==null&&(e.alternate=null,$a(n)),e.child=null,e.deletions=null,e.sibling=null,e.tag===5&&(n=e.stateNode,n!==null&&(delete n[Fe],delete n[Vt],delete n[yu],delete n[Xf],delete n[Gf])),e.stateNode=null,e.return=null,e.dependencies=null,e.memoizedProps=null,e.memoizedState=null,e.pendingProps=null,e.stateNode=null,e.updateQueue=null}function Aa(e){return e.tag===5||e.tag===3||e.tag===4}function ji(e){e:for(;;){for(;e.sibling===null;){if(e.return===null||Aa(e.return))return null;e=e.return}for(e.sibling.return=e.return,e=e.sibling;e.tag!==5&&e.tag!==6&&e.tag!==18;){if(e.flags&2||e.child===null||e.tag===4)continue e;e.child.return=e,e=e.child}if(!(e.flags&2))return e.stateNode}}function Ou(e,n,t){var r=e.tag;if(r===5||r===6)e=e.stateNode,n?t.nodeType===8?t.parentNode.insertBefore(e,n):t.insertBefore(e,n):(t.nodeType===8?(n=t.parentNode,n.insertBefore(e,t)):(n=t,n.appendChild(e)),t=t._reactRootContainer,t!=null||n.onclick!==null||(n.onclick=Ur));else if(r!==4&&(e=e.child,e!==null))for(Ou(e,n,t),e=e.sibling;e!==null;)Ou(e,n,t),e=e.sibling}function ju(e,n,t){var r=e.tag;if(r===5||r===6)e=e.stateNode,n?t.insertBefore(e,n):t.appendChild(e);else if(r!==4&&(e=e.child,e!==null))for(ju(e,n,t),e=e.sibling;e!==null;)ju(e,n,t),e=e.sibling}var q=null,Te=!1;function Ze(e,n,t){for(t=t.child;t!==null;)Va(e,n,t),t=t.sibling}function Va(e,n,t){if(Ue&&typeof Ue.onCommitFiberUnmount=="function")try{Ue.onCommitFiberUnmount(nl,t)}catch{}switch(t.tag){case 5:re||Wn(t,n);case 6:var r=q,l=Te;q=null,Ze(e,n,t),q=r,Te=l,q!==null&&(Te?(e=q,t=t.stateNode,e.nodeType===8?e.parentNode.removeChild(t):e.removeChild(t)):q.removeChild(t.stateNode));break;case 18:q!==null&&(Te?(e=q,t=t.stateNode,e.nodeType===8?Dl(e.parentNode,t):e.nodeType===1&&Dl(e,t),It(e)):Dl(q,t.stateNode));break;case 4:r=q,l=Te,q=t.stateNode.containerInfo,Te=!0,Ze(e,n,t),q=r,Te=l;break;case 0:case 11:case 14:case 15:if(!re&&(r=t.updateQueue,r!==null&&(r=r.lastEffect,r!==null))){l=r=r.next;do{var u=l,o=u.destroy;u=u.tag,o!==void 0&&(u&2||u&4)&&Tu(t,n,o),l=l.next}while(l!==r)}Ze(e,n,t);break;case 1:if(!re&&(Wn(t,n),r=t.stateNode,typeof r.componentWillUnmount=="function"))try{r.props=t.memoizedProps,r.state=t.memoizedState,r.componentWillUnmount()}catch(i){B(t,n,i)}Ze(e,n,t);break;case 21:Ze(e,n,t);break;case 22:t.mode&1?(re=(r=re)||t.memoizedState!==null,Ze(e,n,t),re=r):Ze(e,n,t);break;default:Ze(e,n,t)}}function Mi(e){var n=e.updateQueue;if(n!==null){e.updateQueue=null;var t=e.stateNode;t===null&&(t=e.stateNode=new fd),n.forEach(function(r){var l=kd.bind(null,e,r);t.has(r)||(t.add(r),r.then(l,l))})}}function ze(e,n){var t=n.deletions;if(t!==null)for(var r=0;rl&&(l=o),r&=~u}if(r=l,r=Q()-r,r=(120>r?120:480>r?480:1080>r?1080:1920>r?1920:3e3>r?3e3:4320>r?4320:1960*md(r/1960))-r,10e?16:e,tn===null)var r=!1;else{if(e=tn,tn=null,Jr=0,O&6)throw Error(y(331));var l=O;for(O|=4,k=e.current;k!==null;){var u=k,o=u.child;if(k.flags&16){var i=u.deletions;if(i!==null){for(var s=0;sQ()-_o?_n(e,0):xo|=t),me(e,n)}function Ga(e,n){n===0&&(e.mode&1?(n=or,or<<=1,!(or&130023424)&&(or=4194304)):n=1);var t=oe();e=Ye(e,n),e!==null&&(Gt(e,n,t),me(e,t))}function Sd(e){var n=e.memoizedState,t=0;n!==null&&(t=n.retryLane),Ga(e,t)}function kd(e,n){var t=0;switch(e.tag){case 13:var r=e.stateNode,l=e.memoizedState;l!==null&&(t=l.retryLane);break;case 19:r=e.stateNode;break;default:throw Error(y(314))}r!==null&&r.delete(n),Ga(e,t)}var Za;Za=function(e,n,t){if(e!==null)if(e.memoizedProps!==n.pendingProps||de.current)fe=!0;else{if(!(e.lanes&t)&&!(n.flags&128))return fe=!1,sd(e,n,t);fe=!!(e.flags&131072)}else fe=!1,U&&n.flags&1048576&&bs(n,Br,n.index);switch(n.lanes=0,n.tag){case 2:var r=n.type;Nr(e,n),e=n.pendingProps;var l=bn(n,le.current);Zn(n,t),l=yo(null,n,r,e,l,t);var u=go();return n.flags|=1,typeof l=="object"&&l!==null&&typeof l.render=="function"&&l.$$typeof===void 0?(n.tag=1,n.memoizedState=null,n.updateQueue=null,pe(r)?(u=!0,Ar(n)):u=!1,n.memoizedState=l.state!==null&&l.state!==void 0?l.state:null,fo(n),l.updater=il,n.stateNode=l,l._reactInternals=n,xu(n,r,e,t),n=Nu(null,n,r,!0,u,t)):(n.tag=0,U&&u&&lo(n),ue(null,n,l,t),n=n.child),n;case 16:r=n.elementType;e:{switch(Nr(e,n),e=n.pendingProps,l=r._init,r=l(r._payload),n.type=r,l=n.tag=xd(r),e=Le(r,e),l){case 0:n=Cu(null,n,r,e,t);break e;case 1:n=Li(null,n,r,e,t);break e;case 11:n=Pi(null,n,r,e,t);break e;case 14:n=zi(null,n,r,Le(r.type,e),t);break e}throw Error(y(306,r,""))}return n;case 0:return r=n.type,l=n.pendingProps,l=n.elementType===r?l:Le(r,l),Cu(e,n,r,l,t);case 1:return r=n.type,l=n.pendingProps,l=n.elementType===r?l:Le(r,l),Li(e,n,r,l,t);case 3:e:{if(ja(n),e===null)throw Error(y(387));r=n.pendingProps,u=n.memoizedState,l=u.element,ra(e,n),Qr(n,r,null,t);var o=n.memoizedState;if(r=o.element,u.isDehydrated)if(u={element:r,isDehydrated:!1,cache:o.cache,pendingSuspenseBoundaries:o.pendingSuspenseBoundaries,transitions:o.transitions},n.updateQueue.baseState=u,n.memoizedState=u,n.flags&256){l=rt(Error(y(423)),n),n=Ti(e,n,r,t,l);break e}else if(r!==l){l=rt(Error(y(424)),n),n=Ti(e,n,r,t,l);break e}else for(ve=on(n.stateNode.containerInfo.firstChild),ye=n,U=!0,Re=null,t=ia(n,null,r,t),n.child=t;t;)t.flags=t.flags&-3|4096,t=t.sibling;else{if(et(),r===l){n=Xe(e,n,t);break e}ue(e,n,r,t)}n=n.child}return n;case 5:return sa(n),e===null&&Su(n),r=n.type,l=n.pendingProps,u=e!==null?e.memoizedProps:null,o=l.children,hu(r,l)?o=null:u!==null&&hu(r,u)&&(n.flags|=32),Oa(e,n),ue(e,n,o,t),n.child;case 6:return e===null&&Su(n),null;case 13:return Ma(e,n,t);case 4:return po(n,n.stateNode.containerInfo),r=n.pendingProps,e===null?n.child=nt(n,null,r,t):ue(e,n,r,t),n.child;case 11:return r=n.type,l=n.pendingProps,l=n.elementType===r?l:Le(r,l),Pi(e,n,r,l,t);case 7:return ue(e,n,n.pendingProps,t),n.child;case 8:return ue(e,n,n.pendingProps.children,t),n.child;case 12:return ue(e,n,n.pendingProps.children,t),n.child;case 10:e:{if(r=n.type._context,l=n.pendingProps,u=n.memoizedProps,o=l.value,D(Hr,r._currentValue),r._currentValue=o,u!==null)if(Me(u.value,o)){if(u.children===l.children&&!de.current){n=Xe(e,n,t);break e}}else for(u=n.child,u!==null&&(u.return=n);u!==null;){var i=u.dependencies;if(i!==null){o=u.child;for(var s=i.firstContext;s!==null;){if(s.context===r){if(u.tag===1){s=We(-1,t&-t),s.tag=2;var c=u.updateQueue;if(c!==null){c=c.shared;var h=c.pending;h===null?s.next=s:(s.next=h.next,h.next=s),c.pending=s}}u.lanes|=t,s=u.alternate,s!==null&&(s.lanes|=t),ku(u.return,t,n),i.lanes|=t;break}s=s.next}}else if(u.tag===10)o=u.type===n.type?null:u.child;else if(u.tag===18){if(o=u.return,o===null)throw Error(y(341));o.lanes|=t,i=o.alternate,i!==null&&(i.lanes|=t),ku(o,t,n),o=u.sibling}else o=u.child;if(o!==null)o.return=u;else for(o=u;o!==null;){if(o===n){o=null;break}if(u=o.sibling,u!==null){u.return=o.return,o=u;break}o=o.return}u=o}ue(e,n,l.children,t),n=n.child}return n;case 9:return l=n.type,r=n.pendingProps.children,Zn(n,t),l=Ce(l),r=r(l),n.flags|=1,ue(e,n,r,t),n.child;case 14:return r=n.type,l=Le(r,n.pendingProps),l=Le(r.type,l),zi(e,n,r,l,t);case 15:return Ta(e,n,n.type,n.pendingProps,t);case 17:return r=n.type,l=n.pendingProps,l=n.elementType===r?l:Le(r,l),Nr(e,n),n.tag=1,pe(r)?(e=!0,Ar(n)):e=!1,Zn(n,t),ua(n,r,l),xu(n,r,l,t),Nu(null,n,r,!0,e,t);case 19:return Da(e,n,t);case 22:return Ra(e,n,t)}throw Error(y(156,n.tag))};function Ja(e,n){return xs(e,n)}function Ed(e,n,t,r){this.tag=e,this.key=t,this.sibling=this.child=this.return=this.stateNode=this.type=this.elementType=null,this.index=0,this.ref=null,this.pendingProps=n,this.dependencies=this.memoizedState=this.updateQueue=this.memoizedProps=null,this.mode=r,this.subtreeFlags=this.flags=0,this.deletions=null,this.childLanes=this.lanes=0,this.alternate=null}function xe(e,n,t,r){return new Ed(e,n,t,r)}function zo(e){return e=e.prototype,!(!e||!e.isReactComponent)}function xd(e){if(typeof e=="function")return zo(e)?1:0;if(e!=null){if(e=e.$$typeof,e===Ku)return 11;if(e===Yu)return 14}return 2}function fn(e,n){var t=e.alternate;return t===null?(t=xe(e.tag,n,e.key,e.mode),t.elementType=e.elementType,t.type=e.type,t.stateNode=e.stateNode,t.alternate=e,e.alternate=t):(t.pendingProps=n,t.type=e.type,t.flags=0,t.subtreeFlags=0,t.deletions=null),t.flags=e.flags&14680064,t.childLanes=e.childLanes,t.lanes=e.lanes,t.child=e.child,t.memoizedProps=e.memoizedProps,t.memoizedState=e.memoizedState,t.updateQueue=e.updateQueue,n=e.dependencies,t.dependencies=n===null?null:{lanes:n.lanes,firstContext:n.firstContext},t.sibling=e.sibling,t.index=e.index,t.ref=e.ref,t}function Lr(e,n,t,r,l,u){var o=2;if(r=e,typeof e=="function")zo(e)&&(o=1);else if(typeof e=="string")o=5;else e:switch(e){case Dn:return Cn(t.children,l,u,n);case Qu:o=8,l|=8;break;case Yl:return e=xe(12,t,n,l|2),e.elementType=Yl,e.lanes=u,e;case Xl:return e=xe(13,t,n,l),e.elementType=Xl,e.lanes=u,e;case Gl:return e=xe(19,t,n,l),e.elementType=Gl,e.lanes=u,e;case os:return fl(t,l,u,n);default:if(typeof e=="object"&&e!==null)switch(e.$$typeof){case ls:o=10;break e;case us:o=9;break e;case Ku:o=11;break e;case Yu:o=14;break e;case Je:o=16,r=null;break e}throw Error(y(130,e==null?e:typeof e,""))}return n=xe(o,t,n,l),n.elementType=e,n.type=r,n.lanes=u,n}function Cn(e,n,t,r){return e=xe(7,e,r,n),e.lanes=t,e}function fl(e,n,t,r){return e=xe(22,e,r,n),e.elementType=os,e.lanes=t,e.stateNode={isHidden:!1},e}function Hl(e,n,t){return e=xe(6,e,null,n),e.lanes=t,e}function Wl(e,n,t){return n=xe(4,e.children!==null?e.children:[],e.key,n),n.lanes=t,n.stateNode={containerInfo:e.containerInfo,pendingChildren:null,implementation:e.implementation},n}function _d(e,n,t,r,l){this.tag=n,this.containerInfo=e,this.finishedWork=this.pingCache=this.current=this.pendingChildren=null,this.timeoutHandle=-1,this.callbackNode=this.pendingContext=this.context=null,this.callbackPriority=0,this.eventTimes=_l(0),this.expirationTimes=_l(-1),this.entangledLanes=this.finishedLanes=this.mutableReadLanes=this.expiredLanes=this.pingedLanes=this.suspendedLanes=this.pendingLanes=0,this.entanglements=_l(0),this.identifierPrefix=r,this.onRecoverableError=l,this.mutableSourceEagerHydrationData=null}function Lo(e,n,t,r,l,u,o,i,s){return e=new _d(e,n,t,i,s),n===1?(n=1,u===!0&&(n|=8)):n=0,u=xe(3,null,null,n),e.current=u,u.stateNode=e,u.memoizedState={element:r,isDehydrated:t,cache:null,transitions:null,pendingSuspenseBoundaries:null},fo(u),e}function Cd(e,n,t){var r=3"u"||typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE!="function"))try{__REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE(nc)}catch(e){console.error(e)}}nc(),bi.exports=we;var Td=bi.exports,Bi=Td;Ql.createRoot=Bi.createRoot,Ql.hydrateRoot=Bi.hydrateRoot;function Rd({audioUrl:e,mimeType:n}){const t=ae.useRef(null),r=ae.useRef(null);return ae.useEffect(()=>{t.current&&r.current&&(r.current.src=e,t.current.load())},[e]),M.jsx("div",{className:"flex relative z-10 my-4 w-full",children:M.jsx("audio",{ref:t,controls:!0,className:"w-full h-14 rounded-lg bg-white shadow-xl shadow-black/5 ring-1 ring-slate-700/10",children:M.jsx("source",{ref:r,type:n})})})}function Od({text:e,percentage:n}){return n??(n=0),M.jsx("div",{className:"relative text-black bg-white rounded-lg text-left overflow-hidden",children:M.jsxs("div",{className:"px-2 w-[1%] h-full bg-blue-500 whitespace-nowrap",style:{width:`${n}%`},children:[e," (",`${n.toFixed(2)}%`,")"]})})}const jd={"US female 1":"cmu_us_slt_arctic-wav-arctic_a0001","US female 2":"cmu_us_clb_arctic-wav-arctic_a0001","US male 1":"cmu_us_bdl_arctic-wav-arctic_a0003","US male 2":"cmu_us_rms_arctic-wav-arctic_a0003","Canadian male":"cmu_us_jmk_arctic-wav-arctic_a0002","Scottish male":"cmu_us_awb_arctic-wav-arctic_b0002","Indian male":"cmu_us_ksp_arctic-wav-arctic_a0007"},Md="cmu_us_slt_arctic-wav-arctic_a0001",Dd=()=>{const[e,n]=ae.useState(null),[t,r]=ae.useState(!1),[l,u]=ae.useState([]),[o,i]=ae.useState("Text Here!"),[s,c]=ae.useState(Md),[h,m]=ae.useState(null),p=ae.useRef(null);ae.useEffect(()=>{p.current||(p.current=new Worker(new URL("/assets/worker-7f2d1abe.js",self.location),{type:"module"}));const g=R=>{switch(R.data.status){case"initiate":n(!1),u(a=>[...a,R.data]);break;case"progress":u(a=>a.map(d=>d.file===R.data.file?{...d,progress:R.data.progress}:d));break;case"done":u(a=>a.filter(d=>d.file!==R.data.file));break;case"ready":n(!0);break;case"complete":r(!1);const f=URL.createObjectURL(R.data.output);m(f);break}};return p.current.addEventListener("message",g),()=>p.current.removeEventListener("message",g)});const w=()=>{r(!0),p.current.postMessage({text:o,speaker_id:s})},S=e===!1;return M.jsxs("div",{className:"min-h-screen flex items-center justify-center bg-gray-100",children:[M.jsxs("div",{className:"absolute gap-1 z-50 top-0 left-0 w-full h-full transition-all px-8 flex flex-col justify-center text-center",style:{opacity:S?1:0,pointerEvents:S?"all":"none",background:"rgba(0, 0, 0, 0.9)",backdropFilter:"blur(8px)"},children:[S&&M.jsx("label",{className:"text-white text-xl p-3",children:"Loading models... (only run once)"}),l.map(g=>M.jsx("div",{children:M.jsx(Od,{text:`${g.name}/${g.file}`,percentage:g.progress})},`${g.name}/${g.file}`))]}),M.jsxs("div",{className:"bg-white p-8 rounded-lg shadow-lg w-full max-w-xl m-2",children:[M.jsx("h1",{className:"text-3xl font-semibold text-gray-800 mb-1 text-center",children:"Speechie - Your AI Voice Generator!"}),M.jsxs("h2",{className:"text-base font-medium text-gray-700 mb-2 text-center",children:["Made with Joy, Peace and Happiness!",M.jsx]}),M.jsxs("div",{className:"mb-4",children:[M.jsx("label",{htmlFor:"text",className:"block text-sm font-medium text-gray-600",children:"Text"}),M.jsx("textarea",{id:"text",className:"border border-gray-300 rounded-md p-2 w-full",rows:"4",placeholder:"Enter text here",value:o,onChange:g=>i(g.target.value)})]}),M.jsxs("div",{className:"mb-4",children:[M.jsx("label",{htmlFor:"speaker",className:"block text-sm font-medium text-gray-600",children:"Speaker"}),M.jsx("select",{id:"speaker",className:"border border-gray-300 rounded-md p-2 w-full",value:s,onChange:g=>c(g.target.value),children:Object.entries(jd).map(([g,R])=>M.jsx("option",{value:R,children:g},g))})]}),M.jsx("div",{className:"flex justify-center",children:M.jsx("button",{className:`${t?"bg-gray-400 cursor-not-allowed":"bg-blue-500 hover:bg-blue-600"} text-white rounded-md py-2 px-4`,onClick:w,disabled:t,children:t?"Generating...":"Generate"})}),h&&M.jsx(Rd,{audioUrl:h,mimeType:"audio/wav"})]})]})};Ql.createRoot(document.getElementById("root")).render(M.jsx(wc.StrictMode,{children:M.jsx(Dd,{})}));
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Make.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Make.d.ts
deleted file mode 100644
index 6c3c9a72a70abf8f29d6419a4018a266cf8d4026..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/Make.d.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-import Builders from './builders/Builders';
-export default Make;
-
-declare namespace Make {
- type BuilderType = Builders.BuilderType;
- type BuildersType = { [name: string]: BuilderType }
-}
-
-declare function Make(
- scene: Phaser.Scene,
- data: Object,
- view?: Object,
- styles?: Object,
- customBuilders?: Make.BuildersType
-): Phaser.GameObjects.GameObject;
\ No newline at end of file
diff --git a/spaces/AhmadHakami/Alzheimer_image_classification/README.md b/spaces/AhmadHakami/Alzheimer_image_classification/README.md
deleted file mode 100644
index 83d9c85fe709bc43d18aaa7ac6384201634a49f2..0000000000000000000000000000000000000000
--- a/spaces/AhmadHakami/Alzheimer_image_classification/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Alzheimer Image Classification
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AiPalsDev/Translate_It/app.py b/spaces/AiPalsDev/Translate_It/app.py
deleted file mode 100644
index dd92f4d72776d77ca302beb90ae9574c50ca26d1..0000000000000000000000000000000000000000
--- a/spaces/AiPalsDev/Translate_It/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from transformers import MarianMTModel, MarianTokenizer, GPT2LMHeadModel, GPT2Tokenizer, AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
- # Translation
-def translate(text, target_language):
- language_codes = {
- "Spanish": "es",
- "French (European)": "fr",
- "French (Canadian)": "fr",
- "Italian": "it",
- "Ukrainian": "uk",
- "Portuguese (Brazilian)": "pt_BR",
- "Portuguese (European)": "pt",
- "Russian": "ru",
- "Chinese": "zh",
- "Dutch": "nl",
- "German": "de",
- "Arabic": "ar",
- "Hebrew": "he",
- "Greek": "el"
- }
-
-
- # Text Generation
-def generate_text(prompt):
- text_gen = pipeline("text-generation", model="gpt2")
- generated_text = text_gen(prompt, max_length=max_length, do_sample=True)[0]["generated_text"]
- return generated_text
-
-
-
-
- # Text Classification
-def classify_text(text):
- classifier = pipeline("zero-shot-classification")
- result = classifier(text, labels.split(','))
- scores = result["scores"]
- predictions = result["labels"]
- sorted_predictions = [pred for _, pred in sorted(zip(scores, predictions), reverse=True)]
- return sorted_predictions
-
-
-
- # Sentiment Analysis
-def sentiment_analysis(text):
- model_name = "distilbert-base-uncased-finetuned-sst-2-english"
- tokenizer = AutoTokenizer.from_pretrained(model_name)
- model = AutoModelForSequenceClassification.from_pretrained(model_name)
- inputs = tokenizer(text, return_tensors="pt")
- outputs = model(**inputs)
- sentiment_scores = torch.softmax(outputs.logits, dim=1)
- sentiment = "positive" if sentiment_scores[0, 1] > sentiment_scores[0, 0] else "negative"
- return sentiment
-
- language_options = [
- "Spanish", "French (European)", "French (Canadian)", "Italian", "Ukrainian",
- "Portuguese (Brazilian)", "Portuguese (European)", "Russian", "Chinese",
- "Dutch", "German", "Arabic", "Hebrew", "Greek"
-]
-
- iface = gr.Interface(
- [translate, generate_text, classify_text, sentiment_analysis],
- inputs=[
- gr.inputs.Textbox(lines=5, label="Enter text to translate:"),
- gr.inputs.Dropdown(choices=language_options, label="Target Language"),
- gr.inputs.Textbox(lines=5, label="Enter text for text generation:"),
- gr.inputs.Textbox(lines=5, label="Enter text for text classification:"),
- gr.inputs.Textbox(lines=5, label="Enter text for sentiment analysis:"),
- ],
- outputs=[
- gr.outputs.Textbox(label="Translated Text"),
- gr.outputs.Textbox(label="Generated Text"),
- gr.outputs.Textbox(label="Classification Result"),
- gr.outputs.Textbox(label="Sentiment Result"),
- ],
-)
-
- iface.launch()
-
-
-
diff --git a/spaces/AlekseyKorshuk/gai-project/modules/common.py b/spaces/AlekseyKorshuk/gai-project/modules/common.py
deleted file mode 100644
index 69103d497a52c6ade2733c705c237d1dfbf02d1f..0000000000000000000000000000000000000000
--- a/spaces/AlekseyKorshuk/gai-project/modules/common.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import gradio as gr
-import config
-from modules import utils
-
-from modules.models import GuanacoModel, ChaiBot
-
-
-def render_section_separator(title):
- with gr.Row():
- gr.Markdown(f"## {title}")
-
-
-def render_model_selector():
- model_ids = list(config.MODELS.keys())
- model_tag = gr.Dropdown(
- model_ids,
- value=model_ids[0],
- allow_custom_values=False,
- label="Model ID",
- info="Select a model to chat with.",
- interactive=True
- )
- return model_tag
-
-
-def render_bot_profile(bot_config):
- with gr.Row():
- with gr.Column():
- bot_profile = _render_bot_profile_picture(bot_config)
- with gr.Column():
- bot_selector = _render_bot_selector()
- return bot_profile, bot_selector
-
-
-def _render_bot_profile_picture(bot_config):
- bot_profile = gr.HTML(utils.get_bot_picture_html(bot_config))
- return bot_profile
-
-
-def _render_bot_selector():
- bot_names = utils.get_bot_names()
- dropdown = gr.Dropdown(
- bot_names,
- value=config.DEFAULT_BOT_NAME,
- allow_custom_values=False,
- label="Bot Name",
- info="Select a bot to chat with.",
- )
- return dropdown
-
-
-def render_bot_config(bot_config):
- with gr.Accordion("Bot configuration:", open=False):
- bot_config_text = gr.Markdown(
- f"# Memory\n{bot_config.get('memory', '')}\n# Prompt\n{bot_config.get('prompt', '')}")
- return bot_config_text
-
-
-def render_chat_buttons():
- with gr.Row():
- msg = gr.Textbox(placeholder="Enter a message!")
- send = gr.Button("Send")
- regenerate = gr.Button("Regenerate")
- with gr.Row():
- clear = gr.Button("New conversation")
- return msg, send, regenerate, clear
-
-
-def get_convo_state(bot_config):
- bot = ChaiBot(bot_config)
- return gr.State(bot)
-
-
-def get_model_state(model_config):
- model = GuanacoModel(model_config)
- return gr.State(model)
diff --git a/spaces/Alican/pixera/util/html.py b/spaces/Alican/pixera/util/html.py
deleted file mode 100644
index cc3262a1eafda34842e4dbad47bb6ba72f0c5a68..0000000000000000000000000000000000000000
--- a/spaces/Alican/pixera/util/html.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import dominate
-from dominate.tags import meta, h3, table, tr, td, p, a, img, br
-import os
-
-
-class HTML:
- """This HTML class allows us to save images and write texts into a single HTML file.
-
- It consists of functions such as (add a text header to the HTML file),
- (add a row of images to the HTML file), and (save the HTML to the disk).
- It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API.
- """
-
- def __init__(self, web_dir, title, refresh=0):
- """Initialize the HTML classes
-
- Parameters:
- web_dir (str) -- a directory that stores the webpage. HTML file will be created at /index.html; images will be saved at 0:
- with self.doc.head:
- meta(http_equiv="refresh", content=str(refresh))
-
- def get_image_dir(self):
- """Return the directory that stores images"""
- return self.img_dir
-
- def add_header(self, text):
- """Insert a header to the HTML file
-
- Parameters:
- text (str) -- the header text
- """
- with self.doc:
- h3(text)
-
- def add_images(self, ims, txts, links, width=400):
- """add images to the HTML file
-
- Parameters:
- ims (str list) -- a list of image paths
- txts (str list) -- a list of image names shown on the website
- links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page
- """
- self.t = table(border=1, style="table-layout: fixed;") # Insert a table
- self.doc.add(self.t)
- with self.t:
- with tr():
- for im, txt, link in zip(ims, txts, links):
- with td(style="word-wrap: break-word;", halign="center", valign="top"):
- with p():
- with a(href=os.path.join('images', link)):
- img(style="width:%dpx" % width, src=os.path.join('images', im))
- br()
- p(txt)
-
- def save(self):
- """save the current content to the HMTL file"""
- html_file = '%s/index.html' % self.web_dir
- f = open(html_file, 'wt')
- f.write(self.doc.render())
- f.close()
-
-
-if __name__ == '__main__': # we show an example usage here.
- html = HTML('web/', 'test_html')
- html.add_header('hello world')
-
- ims, txts, links = [], [], []
- for n in range(4):
- ims.append('image_%d.png' % n)
- txts.append('text_%d' % n)
- links.append('image_%d.png' % n)
- html.add_images(ims, txts, links)
- html.save()
diff --git a/spaces/Alpaca233/SadTalker/src/audio2pose_models/audio2pose.py b/spaces/Alpaca233/SadTalker/src/audio2pose_models/audio2pose.py
deleted file mode 100644
index 2b8cd1427038460a7679260a424d2f01d2bcf2c5..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/audio2pose_models/audio2pose.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import torch
-from torch import nn
-from src.audio2pose_models.cvae import CVAE
-from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
-from src.audio2pose_models.audio_encoder import AudioEncoder
-
-class Audio2Pose(nn.Module):
- def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
- super().__init__()
- self.cfg = cfg
- self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
- self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
- self.device = device
-
- self.audio_encoder = AudioEncoder(wav2lip_checkpoint, device)
- self.audio_encoder.eval()
- for param in self.audio_encoder.parameters():
- param.requires_grad = False
-
- self.netG = CVAE(cfg)
- self.netD_motion = PoseSequenceDiscriminator(cfg)
-
-
- def forward(self, x):
-
- batch = {}
- coeff_gt = x['gt'].cuda().squeeze(0) #bs frame_len+1 73
- batch['pose_motion_gt'] = coeff_gt[:, 1:, 64:70] - coeff_gt[:, :1, 64:70] #bs frame_len 6
- batch['ref'] = coeff_gt[:, 0, 64:70] #bs 6
- batch['class'] = x['class'].squeeze(0).cuda() # bs
- indiv_mels= x['indiv_mels'].cuda().squeeze(0) # bs seq_len+1 80 16
-
- # forward
- audio_emb_list = []
- audio_emb = self.audio_encoder(indiv_mels[:, 1:, :, :].unsqueeze(2)) #bs seq_len 512
- batch['audio_emb'] = audio_emb
- batch = self.netG(batch)
-
- pose_motion_pred = batch['pose_motion_pred'] # bs frame_len 6
- pose_gt = coeff_gt[:, 1:, 64:70].clone() # bs frame_len 6
- pose_pred = coeff_gt[:, :1, 64:70] + pose_motion_pred # bs frame_len 6
-
- batch['pose_pred'] = pose_pred
- batch['pose_gt'] = pose_gt
-
- return batch
-
- def test(self, x):
-
- batch = {}
- ref = x['ref'] #bs 1 70
- batch['ref'] = x['ref'][:,0,-6:]
- batch['class'] = x['class']
- bs = ref.shape[0]
-
- indiv_mels= x['indiv_mels'] # bs T 1 80 16
- indiv_mels_use = indiv_mels[:, 1:] # we regard the ref as the first frame
- num_frames = x['num_frames']
- num_frames = int(num_frames) - 1
-
- #
- div = num_frames//self.seq_len
- re = num_frames%self.seq_len
- audio_emb_list = []
- pose_motion_pred_list = [torch.zeros(batch['ref'].unsqueeze(1).shape, dtype=batch['ref'].dtype,
- device=batch['ref'].device)]
-
- for i in range(div):
- z = torch.randn(bs, self.latent_dim).to(ref.device)
- batch['z'] = z
- audio_emb = self.audio_encoder(indiv_mels_use[:, i*self.seq_len:(i+1)*self.seq_len,:,:,:]) #bs seq_len 512
- batch['audio_emb'] = audio_emb
- batch = self.netG.test(batch)
- pose_motion_pred_list.append(batch['pose_motion_pred']) #list of bs seq_len 6
-
- if re != 0:
- z = torch.randn(bs, self.latent_dim).to(ref.device)
- batch['z'] = z
- audio_emb = self.audio_encoder(indiv_mels_use[:, -1*self.seq_len:,:,:,:]) #bs seq_len 512
- if audio_emb.shape[1] != self.seq_len:
- pad_dim = self.seq_len-audio_emb.shape[1]
- pad_audio_emb = audio_emb[:, :1].repeat(1, pad_dim, 1)
- audio_emb = torch.cat([pad_audio_emb, audio_emb], 1)
- batch['audio_emb'] = audio_emb
- batch = self.netG.test(batch)
- pose_motion_pred_list.append(batch['pose_motion_pred'][:,-1*re:,:])
-
- pose_motion_pred = torch.cat(pose_motion_pred_list, dim = 1)
- batch['pose_motion_pred'] = pose_motion_pred
-
- pose_pred = ref[:, :1, -6:] + pose_motion_pred # bs T 6
-
- batch['pose_pred'] = pose_pred
- return batch
diff --git a/spaces/Andy1621/uniformerv2_demo/app.py b/spaces/Andy1621/uniformerv2_demo/app.py
deleted file mode 100644
index 018e6b6005d44640e198db496fde0c1147c06f66..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformerv2_demo/app.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import os
-
-import torch
-import torch.nn as nn
-import numpy as np
-import torch.nn.functional as F
-import torchvision.transforms as T
-from PIL import Image
-from decord import VideoReader
-from decord import cpu
-from uniformerv2 import uniformerv2_b16
-from kinetics_class_index import kinetics_classnames
-from transforms import (
- GroupNormalize, GroupScale, GroupCenterCrop,
- Stack, ToTorchFormatTensor
-)
-
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-class Uniformerv2(nn.Module):
- def __init__(self, model):
- super().__init__()
- self.backbone = model
-
- def forward(self, x):
- return self.backbone(x)
-
-# Device on which to run the model
-# Set to cuda to load on GPU
-device = "cpu"
-model_path = hf_hub_download(repo_id="Andy1621/uniformerv2", filename="k400+k710_uniformerv2_b16_8x224.pyth")
-# Pick a pretrained model
-model = Uniformerv2(uniformerv2_b16(pretrained=False, t_size=8, no_lmhra=True, temporal_downsample=False))
-state_dict = torch.load(model_path, map_location='cpu')
-model.load_state_dict(state_dict)
-
-# Set to eval mode and move to desired device
-model = model.to(device)
-model = model.eval()
-
-# Create an id to label name mapping
-kinetics_id_to_classname = {}
-for k, v in kinetics_classnames.items():
- kinetics_id_to_classname[k] = v
-
-
-def get_index(num_frames, num_segments=8):
- seg_size = float(num_frames - 1) / num_segments
- start = int(seg_size / 2)
- offsets = np.array([
- start + int(np.round(seg_size * idx)) for idx in range(num_segments)
- ])
- return offsets
-
-
-def load_video(video_path):
- vr = VideoReader(video_path, ctx=cpu(0))
- num_frames = len(vr)
- frame_indices = get_index(num_frames, 8)
-
- # transform
- crop_size = 224
- scale_size = 256
- input_mean = [0.485, 0.456, 0.406]
- input_std = [0.229, 0.224, 0.225]
-
- transform = T.Compose([
- GroupScale(int(scale_size)),
- GroupCenterCrop(crop_size),
- Stack(),
- ToTorchFormatTensor(),
- GroupNormalize(input_mean, input_std)
- ])
-
- images_group = list()
- for frame_index in frame_indices:
- img = Image.fromarray(vr[frame_index].asnumpy())
- images_group.append(img)
- torch_imgs = transform(images_group)
- return torch_imgs
-
-
-def inference(video):
- vid = load_video(video)
-
- # The model expects inputs of shape: B x C x H x W
- TC, H, W = vid.shape
- inputs = vid.reshape(1, TC//3, 3, H, W).permute(0, 2, 1, 3, 4)
-
- prediction = model(inputs)
- prediction = F.softmax(prediction, dim=1).flatten()
-
- return {kinetics_id_to_classname[str(i)]: float(prediction[i]) for i in range(400)}
-
-
-def set_example_video(example: list) -> dict:
- return gr.Video.update(value=example[0])
-
-
-demo = gr.Blocks()
-with demo:
- gr.Markdown(
- """
- # UniFormerV2-B
- Gradio demo for UniFormerV2 : To use it, simply upload your video, or click one of the examples to load them. Read more at the links below.
- """
- )
-
- with gr.Box():
- with gr.Row():
- with gr.Column():
- with gr.Row():
- input_video = gr.Video(label='Input Video')
- with gr.Row():
- submit_button = gr.Button('Submit')
- with gr.Column():
- label = gr.Label(num_top_classes=5)
- with gr.Row():
- example_videos = gr.Dataset(components=[input_video], samples=[['hitting_baseball.mp4'], ['hoverboarding.mp4'], ['yoga.mp4']])
-
- gr.Markdown(
- """
- [Arxiv] UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer | Github Repo
- """
- )
-
- submit_button.click(fn=inference, inputs=input_video, outputs=label)
- example_videos.click(fn=set_example_video, inputs=example_videos, outputs=example_videos.components)
-
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/AnkitGaur2811/Image_Conversion_app_using_Opencv/app.py b/spaces/AnkitGaur2811/Image_Conversion_app_using_Opencv/app.py
deleted file mode 100644
index 6263cbd3d2699420d3687bfef3f1246da46ab9b6..0000000000000000000000000000000000000000
--- a/spaces/AnkitGaur2811/Image_Conversion_app_using_Opencv/app.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Image Conversion App .ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
- https://colab.research.google.com/drive/1GA79rJr1_J5bHa7sWxF1GtDqqaTznVia
-
-# ***Image Conversion from Simple image***
-> To
-1. Edge Image
-2. Pencile sketch
-3. Painting
-4. Cartoon image
-
-###Step 1:- Import All libraries
-Namely
-- Numpy (For storing Image)
-- Matplotlib (For Display image)
-- OpenCV (for Converting the image)
-"""
-
-# installing all imp libs to the machine
-#!pip install opencv_contrib_python
-
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import gradio as gr
-
-"""###Step 2:- Import and show the image
-for this we will be using opencv and matplotlib
-
-"""
-
-def read_image(Image):
- '''
- This Function is made to take image input from the user
- Input: Path of the Image
- Output: Image in RGB format
- '''
- img = cv2.imread(Image)
- img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) # As opencv read the image as BGR so we have to convert it to RGB
- plt.imshow(img)
- plt.show()
- return img
-
-#filename ="/content/to.jpeg"
-#Image= read_image(filename)
-
-"""### Step 3:- Converting the Image to respective types
-Making diffrent Function for each work
-
-####4. Cartoon Image
-"""
-
-# create Edge
-def edge_mask(Image, line_size, Blur_value):
- grey_img=cv2.cvtColor(Image,cv2.COLOR_RGB2GRAY)
- grey_blur= cv2.medianBlur(grey_img, Blur_value)
- edge_image= cv2.adaptiveThreshold(grey_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, line_size, Blur_value)
- return edge_image
-# line_size,Blur_value=7,7
-# edges= edge_mask(Image,line_size,Blur_value)
-# plt.imshow(edges, cmap="binary")
-# plt.show()
-
-#reduce Colour Palet
-def colour_quantization(Image, k):
- data=np.float32(Image).reshape(-1,3)
- critria= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 0.01)
- ret, label, center= cv2.kmeans(data,k, None, critria, 10, cv2.KMEANS_RANDOM_CENTERS)
- centers= np.uint8(center)
- result=centers[label.flatten()]
- result=result.reshape(Image.shape)
- return result
-# Paint= colour_quantization(Image,10)
-# plt.imshow(Paint)
-# plt.show()
-# cv2.imwrite("final1.jpg",Paint)
-
-def cartoon(Paint,edges):
- Blurred_img= cv2.bilateralFilter(Paint, d=8 ,sigmaColor=200, sigmaSpace=200)
- Final=cv2.bitwise_and(Blurred_img,Blurred_img,mask=edges)
- # plt.imshow(Final)
- # plt.show()
- # Final= cv2.cvtColor(Final, cv2.COLOR_RGB2BGR)
- # cv2.imwrite("Final.jpg", Final)
- return Final
-# cartoon()
-
-def Main_cartoon(Image,Line_size,Blur_value,Color_count):
- edge_mask_img= edge_mask(Image, Line_size, Blur_value)
- Paint_img=colour_quantization(Image, Color_count)
- cartoon_img= cartoon(Paint_img,edge_mask_img)
- # cartoon_img=cv2.cvtColor(cartoon_img, cv2.COLOR_RGB2BGR)
- return cartoon_img
-
-#x=Main_cartoon(Image,7,7,10)
-
-"""####3. Painting"""
-
-def Painting(Image, Colour_count):
- Painting=colour_quantization(Image,Colour_count)
- # Painting=cv2.cvtColor(Painting, cv2.COLOR_RGB2BGR)
- return Painting
-
-#y=Painting(Image,10)
-
-"""####2.Pencil Sketch"""
-
-def Pencil_sketch(Image):
- grey_img=cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
- invert_img=cv2.bitwise_not(grey_img)
- blur_img=cv2.GaussianBlur(invert_img, (111,111),0)
- invblur_img=cv2.bitwise_not(blur_img)
- sketch_img=cv2.divide(grey_img,invblur_img, scale=256.0)
- return sketch_img
-
-#z=Pencil_sketch(Image)
-
-"""####1.Edge Sketch"""
-
-def Edge_sketch(Image):
- edge_img = cv2.Canny(Image,100,200)
- edge_img = cv2.cvtColor(edge_img,cv2.COLOR_BGR2RGB)
- return edge_img
-
-#t=Edge_sketch(Image)
-
-"""###Step 4:- Testing"""
-
-#plt.imshow(x)
-#plt.show()
-#cv2.imwrite("Final.jpg", x)
-
-#plt.imshow(y)
-#plt.show()
-#cv2.imwrite("final1.jpg",y)
-
-#plt.imshow(z)
-#plt.show()
-#cv2.imwrite("final2.jpg",z)
-
-#plt.imshow(t)
-#plt.show()
-#cv2.imwrite("final3.jpg",t)
-
-"""### Step 5:- Gradio app and driver code
-
-#### Driver Code
-"""
-
-def Sketch_app(Image,Type,Color_count,Line_size,Blur_value):
- if Type == "Cartoon":
- Result = Main_cartoon(Image,Line_size,Blur_value,Color_count)
- elif Type == "Painting":
- Result = Painting(Image,Color_count)
- elif Type == "Pencil sketch":
- Result = Pencil_sketch(Image)
- else:
- Result = None
- return Result
-
-"""#### Gradio app
-
-##### Installing and Importing Gradio
-As Gradio is a 3rd party library we have to install it in Our Run time before Executing
-"""
-
-#!pip install gradio
-
-
-
-Image_conversion = gr.Interface(
- fn = Sketch_app,
- inputs=[
- gr.Image( tool="select",label="Image to Convert", show_label=True),
- gr.Dropdown( choices = ["Pencil sketch","Painting","Cartoon"],label="Type to convert", show_label=True),
- gr.Slider( minimum=5, maximum=20, value =10, step= 1,label="Number of colour to be used in photo ( use only in case of painting and cartoon)", show_label=True),
- gr.Slider( minimum=5, maximum=10, value =7, step= 2,label="Blurr effect to be used in photo ( use only in case of cartoon)", show_label=True),
- gr.Slider( minimum=5, maximum=10, value =7, step= 2,label="Thickness of edges to be used in photo ( use only in case of cartoon)", show_label=True)],
- outputs= "image",
- tittle = " Image Conversion App",
- description = """This is an image conversion app we take a regular photo and convert it into Cartoon, Painting, Pencil sketch. it is purely python based used
- Gradio(for interfaceing), OpenCV (For image conversion), Numpy(for storing the image), Matplotlib (for displaying the image) """,
- theme = "dark"
-)
-
-Image_conversion.launch(share=True)
-
diff --git a/spaces/Arielliu/just_talk/README.md b/spaces/Arielliu/just_talk/README.md
deleted file mode 100644
index 79d02b58dcc9352fa3b47c535180c6d64486c13a..0000000000000000000000000000000000000000
--- a/spaces/Arielliu/just_talk/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Just Talk
-emoji: 🏢
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ArtGAN/Diffusion-API/diffusion_webui/utils/data_utils.py b/spaces/ArtGAN/Diffusion-API/diffusion_webui/utils/data_utils.py
deleted file mode 100644
index c57719012aa6d1e73e144c84ca0aaddeac33a383..0000000000000000000000000000000000000000
--- a/spaces/ArtGAN/Diffusion-API/diffusion_webui/utils/data_utils.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from PIL import Image
-
-
-def image_grid(imgs, rows, cols):
- assert len(imgs) == rows * cols
-
- w, h = imgs[0].size
- grid = Image.new("RGB", size=(cols * w, rows * h))
-
- for i, img in enumerate(imgs):
- grid.paste(img, box=(i % cols * w, i // cols * h))
- return grid
diff --git a/spaces/Artrajz/vits-simple-api/bert_vits2/bert_vits2.py b/spaces/Artrajz/vits-simple-api/bert_vits2/bert_vits2.py
deleted file mode 100644
index 6a3d57871cb644cffa25b7481553e84af3428703..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/bert_vits2/bert_vits2.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import numpy as np
-import torch
-
-from bert_vits2 import commons
-from bert_vits2 import utils as bert_vits2_utils
-from bert_vits2.models import SynthesizerTrn
-from bert_vits2.text import *
-from bert_vits2.text.cleaner import clean_text
-from bert_vits2.utils import process_legacy_versions
-from contants import ModelType
-from utils import classify_language, get_hparams_from_file, lang_dict
-from utils.sentence import sentence_split_and_markup, cut
-
-
-class Bert_VITS2:
- def __init__(self, model, config, device=torch.device("cpu"), **kwargs):
- self.hps_ms = get_hparams_from_file(config) if isinstance(config, str) else config
- self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
- self.speakers = [item[0] for item in
- sorted(list(getattr(self.hps_ms.data, 'spk2id', {'0': 0}).items()), key=lambda x: x[1])]
- self.symbols = symbols
-
-
- # Compatible with legacy versions
- self.version = process_legacy_versions(self.hps_ms)
-
- if self.version in ["1.0", "1.0.0", "1.0.1"]:
- self.symbols = symbols_legacy
- self.hps_ms.model.n_layers_trans_flow = 3
-
-
- elif self.version in ["1.1.0-transition"]:
- self.hps_ms.model.n_layers_trans_flow = 3
-
- elif self.version in ["1.1", "1.1.0", "1.1.1"]:
- self.hps_ms.model.n_layers_trans_flow = 6
-
- key = f"{ModelType.BERT_VITS2.value}_v{self.version}" if self.version else ModelType.BERT_VITS2.value
- self.lang = lang_dict.get(key, ["unknown"])
- self.bert_handler = BertHandler(self.lang)
-
- self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
-
- self.net_g = SynthesizerTrn(
- len(self.symbols),
- self.hps_ms.data.filter_length // 2 + 1,
- self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
- n_speakers=self.hps_ms.data.n_speakers,
- symbols=self.symbols,
- **self.hps_ms.model).to(device)
- _ = self.net_g.eval()
- self.device = device
- self.load_model(model)
-
- def load_model(self, model):
- bert_vits2_utils.load_checkpoint(model, self.net_g, None, skip_optimizer=True, version=self.version)
-
- def get_speakers(self):
- return self.speakers
-
- @property
- def sampling_rate(self):
- return self.hps_ms.data.sampling_rate
-
- def get_text(self, text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, self._symbol_to_id)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = self.bert_handler.get_bert(norm_text, word2ph, language_str)
- del word2ph
- assert bert.shape[-1] == len(phone), phone
-
- if language_str == "zh":
- bert = bert
- ja_bert = torch.zeros(768, len(phone))
- elif language_str == "ja":
- ja_bert = bert
- bert = torch.zeros(1024, len(phone))
- else:
- bert = torch.zeros(1024, len(phone))
- ja_bert = torch.zeros(768, len(phone))
- assert bert.shape[-1] == len(
- phone
- ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
- return bert, ja_bert, phone, tone, language
-
- def infer(self, text, lang, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
- bert, ja_bert, phones, tones, lang_ids = self.get_text(text, lang, self.hps_ms)
- with torch.no_grad():
- x_tst = phones.to(self.device).unsqueeze(0)
- tones = tones.to(self.device).unsqueeze(0)
- lang_ids = lang_ids.to(self.device).unsqueeze(0)
- bert = bert.to(self.device).unsqueeze(0)
- ja_bert = ja_bert.to(self.device).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(self.device)
- speakers = torch.LongTensor([int(sid)]).to(self.device)
- audio = self.net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[
- 0][0, 0].data.cpu().float().numpy()
-
- torch.cuda.empty_cache()
- return audio
-
- def get_audio(self, voice, auto_break=False):
- text = voice.get("text", None)
- lang = voice.get("lang", "auto")
- sdp_ratio = voice.get("sdp_ratio", 0.2)
- noise_scale = voice.get("noise", 0.5)
- noise_scale_w = voice.get("noisew", 0.6)
- length_scale = voice.get("length", 1)
- sid = voice.get("id", 0)
- max = voice.get("max", 50)
- # sentence_list = sentence_split_and_markup(text, max, "ZH", ["zh"])
- if lang == "auto":
- lang = classify_language(text, target_languages=self.lang)
-
- sentence_list = cut(text, max)
- audios = []
- for sentence in sentence_list:
- audio = self.infer(sentence, lang, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid)
- audios.append(audio)
- audio = np.concatenate(audios)
- return audio
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/backports/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/backports/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/logging.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/logging.py
deleted file mode 100644
index e99c1b9d504d149ccabbeb33dae04dff786078ec..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/logging.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import sys
-import logging
-import distutils.log
-from . import monkey
-
-
-def _not_warning(record):
- return record.levelno < logging.WARNING
-
-
-def configure():
- """
- Configure logging to emit warning and above to stderr
- and everything else to stdout. This behavior is provided
- for compatibility with distutils.log but may change in
- the future.
- """
- err_handler = logging.StreamHandler()
- err_handler.setLevel(logging.WARNING)
- out_handler = logging.StreamHandler(sys.stdout)
- out_handler.addFilter(_not_warning)
- handlers = err_handler, out_handler
- logging.basicConfig(
- format="{message}", style='{', handlers=handlers, level=logging.DEBUG)
- if hasattr(distutils.log, 'Log'):
- monkey.patch_func(set_threshold, distutils.log, 'set_threshold')
- # For some reason `distutils.log` module is getting cached in `distutils.dist`
- # and then loaded again when patched,
- # implying: id(distutils.log) != id(distutils.dist.log).
- # Make sure the same module object is used everywhere:
- distutils.dist.log = distutils.log
-
-
-def set_threshold(level):
- logging.root.setLevel(level*10)
- return set_threshold.unpatched(level)
diff --git a/spaces/Atualli/yoloxTeste/yoloxdetect2/configs/yolox_nano.py b/spaces/Atualli/yoloxTeste/yoloxdetect2/configs/yolox_nano.py
deleted file mode 100644
index 8955dd2a7748c900cab7dca11adf877cd2cf5abd..0000000000000000000000000000000000000000
--- a/spaces/Atualli/yoloxTeste/yoloxdetect2/configs/yolox_nano.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-import torch.nn as nn
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
- def __init__(self):
- super(Exp, self).__init__()
- self.depth = 0.33
- self.width = 0.25
- self.input_size = (416, 416)
- self.random_size = (10, 20)
- self.mosaic_scale = (0.5, 1.5)
- self.test_size = (416, 416)
- self.mosaic_prob = 0.5
- self.enable_mixup = False
- self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-
- def get_model(self, sublinear=False):
-
- def init_yolo(M):
- for m in M.modules():
- if isinstance(m, nn.BatchNorm2d):
- m.eps = 1e-3
- m.momentum = 0.03
- if "model" not in self.__dict__:
- from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
- in_channels = [256, 512, 1024]
- # NANO model use depthwise = True, which is main difference.
- backbone = YOLOPAFPN(
- self.depth, self.width, in_channels=in_channels,
- act=self.act, depthwise=True,
- )
- head = YOLOXHead(
- self.num_classes, self.width, in_channels=in_channels,
- act=self.act, depthwise=True
- )
- self.model = YOLOX(backbone, head)
-
- self.model.apply(init_yolo)
- self.model.head.initialize_biases(1e-2)
- return self.model
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/register_coco.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/register_coco.py
deleted file mode 100644
index e564438d5bf016bcdbb65b4bbdc215d79f579f8a..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/register_coco.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .coco import register_coco_instances # noqa
-from .coco_panoptic import register_coco_panoptic_separated # noqa
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/structures/test_masks.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/structures/test_masks.py
deleted file mode 100644
index 7991eb0b35724f2f2f402d788a273d68b7cad5f2..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/structures/test_masks.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import unittest
-import torch
-
-from detectron2.structures.masks import BitMasks, PolygonMasks, polygons_to_bitmask
-
-
-class TestBitMask(unittest.TestCase):
- def test_get_bounding_box(self):
- masks = torch.tensor(
- [
- [
- [False, False, False, True],
- [False, False, True, True],
- [False, True, True, False],
- [False, True, True, False],
- ],
- [
- [False, False, False, False],
- [False, False, True, False],
- [False, True, True, False],
- [False, True, True, False],
- ],
- torch.zeros(4, 4),
- ]
- )
- bitmask = BitMasks(masks)
- box_true = torch.tensor([[1, 0, 4, 4], [1, 1, 3, 4], [0, 0, 0, 0]], dtype=torch.float32)
- box = bitmask.get_bounding_boxes()
- self.assertTrue(torch.all(box.tensor == box_true).item())
-
- for box in box_true:
- poly = box[[0, 1, 2, 1, 2, 3, 0, 3]].numpy()
- mask = polygons_to_bitmask([poly], 4, 4)
- reconstruct_box = BitMasks(mask[None, :, :]).get_bounding_boxes()[0].tensor
- self.assertTrue(torch.all(box == reconstruct_box).item())
-
- reconstruct_box = PolygonMasks([[poly]]).get_bounding_boxes()[0].tensor
- self.assertTrue(torch.all(box == reconstruct_box).item())
-
- def test_from_empty_polygons(self):
- masks = BitMasks.from_polygon_masks([], 100, 100)
- self.assertEqual(masks.tensor.shape, (0, 100, 100))
-
- def test_getitem(self):
- masks = BitMasks(torch.ones(3, 10, 10))
- self.assertEqual(masks[1].tensor.shape, (1, 10, 10))
- self.assertEqual(masks[1:3].tensor.shape, (2, 10, 10))
- self.assertEqual(masks[torch.tensor([True, False, False])].tensor.shape, (1, 10, 10))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/BasToTheMax/voicechange/README.md b/spaces/BasToTheMax/voicechange/README.md
deleted file mode 100644
index e965b7e3f3988bf69a8b3c5a79625fd689442352..0000000000000000000000000000000000000000
--- a/spaces/BasToTheMax/voicechange/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Voicechange
-emoji: 🏆
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.41.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/Anu Bsc 6th Sem Materiales Pdf.md b/spaces/Benson/text-generation/Examples/Anu Bsc 6th Sem Materiales Pdf.md
deleted file mode 100644
index 8fd9dad608a53ddc2a0b299ba0d9fb21589391e7..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Anu Bsc 6th Sem Materiales Pdf.md
+++ /dev/null
@@ -1,143 +0,0 @@
-
-Cómo descargar e instalar Paint 3D en Ubuntu
-Paint 3D es una herramienta potente y fácil de usar para crear impresionantes imágenes 3D y animaciones. Le permite dibujar, pintar, esculpir y transformar sus ideas en realidad con una variedad de herramientas y características. Tanto si quieres crear un logotipo, un meme, un póster o un personaje de juego, Paint 3D puede ayudarte a liberar tu creatividad.
-anu bsc 6th sem materiales pdf Download File ✦ https://bltlly.com/2v6KY3
En este artículo, te mostraremos cómo descargar e instalar Paint 3D en Ubuntu, una de las distribuciones de Linux más populares. También te presentaremos algunas de las increíbles características y beneficios de Paint 3D que harán que tu experiencia de arte digital sea más divertida y agradable.
- Descargar Paint 3D de la tienda Snap
-La forma más fácil de obtener Paint 3D en Ubuntu es utilizar el Snap Store, que es una tienda de aplicaciones para Linux que ofrece miles de aplicaciones para diversos fines. Puede acceder a Snap Store desde su navegador web o desde la aplicación Ubuntu Software de su ordenador. Para descargar Paint 3D desde Snap Store, sigue estos pasos:
-
-Abra su navegador web y vaya a la página Tienda rápida para Paint 3D .
-Haga clic en el botón verde Instalar y elija para abrir el enlace con Ubuntu Software.
-Alternativamente, puede copiar la línea de comandos que se muestra debajo del botón Instalar y pegarla en la ventana de su terminal. Presione Enter para ejecutar el comando.
-Espere a que se complete el proceso de descarga e instalación. Es posible que necesite introducir su contraseña o confirmar algunos permisos.
-
-
-Felicidades, has descargado e instalado con éxito Paint 3D en Ubuntu!
- Crear una unidad USB de arranque con Paint 3D
-Si desea utilizar Paint 3D en un equipo o dispositivo diferente que no tiene Ubuntu instalado, puede crear una unidad USB de arranque con el archivo de complemento Paint 3D. De esta manera, puede ejecutar Paint 3D desde la unidad USB sin afectar el sistema operativo existente o los datos en el ordenador o dispositivo. Para crear una unidad USB de arranque con Paint 3D, necesitará una herramienta como Rufus, que es una utilidad gratuita y de código abierto para crear unidades USB de arranque. También necesitará una unidad USB con al menos 4 GB de espacio de almacenamiento y el archivo de complemento Paint 3D, que puede descargar desde Snap Store. Para crear una unidad USB de arranque con Paint 3D, siga estos pasos:
-
-Descargar Rufus desde su sitio web oficial y ejecutarlo en su ordenador.
-Inserte su unidad USB en su computadora y selecciónela de la lista de dispositivos en Rufus.
-Seleccione la imagen ISO en el menú de selección de arranque y haga clic en el botón Seleccionar para buscar el archivo de complemento de Paint 3D que descargó de Snap Store. El archivo debe tener una extensión . snap.
-Elija GPT como el esquema de partición y FAT32 como el sistema de archivos.
-Haga clic en el botón Inicio para comenzar a crear la unidad USB de arranque con Paint 3D. Puede ver algunas advertencias o indicaciones durante el proceso. Siga las instrucciones en la pantalla y espere a que el proceso termine.
-Una vez que la unidad USB de arranque con Paint 3D esté lista, puede expulsarla de su computadora y usarla en cualquier otro equipo o dispositivo que admita el arranque desde USB.
-
-Nota: Para arrancar desde la unidad USB, es posible que necesite cambiar algunos ajustes en el BIOS o UEFI del equipo o dispositivo que desea usar. Consulte la documentación o soporte de su computadora o dispositivo para más detalles.
- Arranque desde la unidad USB e instalación de Paint 3D
-
-
-Inserte su unidad USB de arranque con Paint 3D en el ordenador o dispositivo que desea utilizar.
-Reinicie o encienda el ordenador o dispositivo y pulse la tecla correspondiente para entrar en el menú de arranque. La clave puede variar dependiendo del modelo de tu ordenador o dispositivo, pero suele ser F12, F9, F10, Esc o Del. Debería ver una lista de opciones de arranque en su pantalla.
-Seleccione su unidad USB de la lista de opciones de arranque y pulse Enter para arrancar desde ella. Debería ver una pantalla de bienvenida con algunas opciones.
-Seleccione Probar Ubuntu sin instalar desde la pantalla de bienvenida. Esto cargará Ubuntu desde su unidad USB sin afectar su sistema operativo o datos existentes.
-Una vez cargado Ubuntu, puede iniciar Paint 3D desde el menú de aplicaciones o desde la aplicación Ubuntu Software. Deberías ver la interfaz de Paint 3D en tu pantalla.
-Si desea instalar Paint 3D en Ubuntu de forma permanente, puede hacer clic en el icono Instalar Ubuntu en el escritorio y seguir las instrucciones en la pantalla. Tendrá que elegir una partición, un nombre de usuario, una contraseña y algunos otros ajustes. Tenga cuidado de no sobrescribir su sistema operativo o datos existentes a menos que lo desee.
-Una vez completada la instalación, puede reiniciar su computadora o dispositivo y disfrutar usando Paint 3D en Ubuntu.
-
-¡Eso es todo! ¡Ha arrancado con éxito desde la unidad USB e instalado Paint 3D en Ubuntu!
- Características y beneficios de Paint 3D
-Paint 3D no es solo un simple programa de pintura. Es una herramienta poderosa y versátil para crear increíbles imágenes y animaciones en 3D. Ofrece una gama de características y beneficios que harán que su experiencia de arte digital sea más divertida y agradable. Estas son algunas de las características y beneficios de Paint 3D que debes conocer:
- Creación de objetos 3D
-
-Para crear un objeto 3D en Paint 3D, puede usar uno de estos métodos:
-
-Utilice la biblioteca de modelos 3D: Paint 3D ofrece una biblioteca de modelos 3D prefabricados que puede utilizar en sus proyectos. Puede acceder a la biblioteca haciendo clic en el icono de modelos 3D en la barra de menú. Puede navegar a través de diferentes categorías, como animales, personas, edificios, vehículos y más. También puede buscar modelos específicos usando la barra de búsqueda. Para agregar un modelo a su lienzo, simplemente arrástrelo y suéltelo desde la biblioteca.
-Utilice la herramienta de formas 3D: Paint 3D también ofrece una herramienta para crear formas 3D básicas, como cubos, esferas, conos, cilindros y más. Puede acceder a la herramienta haciendo clic en el icono de formas 3D en la barra de menú. Para añadir una forma a tu lienzo, simplemente haz clic en ella y arrástrala al tamaño y posición deseados.
-Utilice la herramienta de texto: Paint 3D le permite agregar texto a su lienzo que se puede convertir en objetos 3D. Puede acceder a la herramienta haciendo clic en el icono de texto en la barra de menú. Para añadir texto a su lienzo, simplemente escriba el texto deseado y elija una fuente, tamaño, color y estilo. Para convertir su texto en un objeto 3D, simplemente haga clic en el botón Make 3D en la parte superior.
-Utilice la herramienta de pegatinas: Paint 3D también le permite agregar pegatinas a su lienzo que se pueden convertir en objetos 3D. Puede acceder a la herramienta haciendo clic en el icono de pegatinas en la barra de menú. Para añadir una pegatina a tu lienzo, simplemente elige una de la biblioteca o sube tu propia imagen. Para convertir su pegatina en un objeto 3D, simplemente haga clic en el botón Hacer 3D en la parte superior.
-
-Una vez que haya creado un objeto 3D, puede usar las herramientas en el panel derecho para ajustar sus propiedades, como color, opacidad, material, protuberancia y luz. También puede utilizar las herramientas de la barra superior para mover, rotar, escalar y editar el objeto. También puede usar la herramienta de selección mágica para cortar una parte del objeto o la herramienta de borrador para borrar una parte del objeto.
-
-
-Animación de la línea de tiempo
-Otra característica de Paint 3D es la capacidad de animar capas, objetos, efectos y filtros usando la línea de tiempo. Puede usar la línea de tiempo para crear y exportar proyectos animados, como GIF, videos o presentaciones de diapositivas. También puedes usar la línea de tiempo para previsualizar y editar tus animaciones.
-Para animar un proyecto en Paint 3D, puedes usar uno de estos métodos:
-
-Utilice la herramienta de animación de capas: Paint 3D ofrece una herramienta para animar capas, que son grupos de objetos que se pueden mover o transformar juntos. Puede acceder a la herramienta haciendo clic en el icono de animación de capas en la barra de menú. Para animar una capa, simplemente selecciónela desde el panel de capas y arrástrela a la línea de tiempo. A continuación, puede ajustar su posición, rotación y tamaño en diferentes puntos en el tiempo. También puede agregar efectos y filtros a la capa y animarlos.
-Utilice la herramienta de animación de objetos: Paint 3D también ofrece una herramienta para animar objetos individuales, que son elementos individuales que se pueden mover o transformar por separado. Puede acceder a la herramienta haciendo clic en el icono de animación de objetos en la barra de menú. Para animar un objeto, simplemente selecciónelo del lienzo y arrástrelo a la línea de tiempo. A continuación, puede ajustar su posición, rotación y tamaño en diferentes puntos en el tiempo. También puede agregar efectos y filtros al objeto y animarlos.
-
-
-Aquí hay algunos ejemplos de animaciones creadas con Paint 3D:
-Texturas realistas
-Otra característica de Paint 3D es la capacidad de aplicar texturas realistas a los objetos, como madera, metal, piedra o agua. Puede utilizar los filtros y efectos de imagen en Paint 3D para cambiar la apariencia y la sensación de los objetos y hacerlos parecer más naturales y realistas.
-Para aplicar una textura a un objeto en Paint 3D, puede usar uno de estos métodos:
-
-Usa la biblioteca de texturas: Paint 3D ofrece una biblioteca de texturas pre-hechas que puedes usar en tus proyectos. Puede acceder a la biblioteca haciendo clic en el icono de textura en la barra de menú. Puedes navegar a través de diferentes categorías, como orgánica, geométrica, artística y más. También puede buscar texturas específicas utilizando la barra de búsqueda. Para aplicar una textura a un objeto, simplemente arrástrelo y suéltelo desde la biblioteca al objeto.
-Utilice la herramienta de filtro de imagen: Paint 3D también ofrece una herramienta para aplicar filtros de imagen a los objetos, como sepia, escala de grises, invertir o pixelar. Puede acceder a la herramienta haciendo clic en el icono del filtro de imagen en la barra de menú. Para aplicar un filtro de imagen a un objeto, simplemente selecciónelo del lienzo y elija un filtro de la lista.
-Utilice la herramienta de efectos: Paint 3D también ofrece una herramienta para aplicar efectos a los objetos, como desenfoque, nitidez, relieve o brillo. Puede acceder a la herramienta haciendo clic en el icono de efecto en la barra de menú. Para aplicar un efecto a un objeto, simplemente selecciónelo del lienzo y elija un efecto de la lista.
-
-
-Aquí hay algunos ejemplos de texturas aplicadas a objetos con Paint 3D:
-Creador único de memes
-Otra característica de Paint 3D es la capacidad de crear memes únicos con efectos 3D. Puedes usar la herramienta de texto y las pegatinas de Paint 3D para añadir subtítulos e imágenes divertidas a tu lienzo y convertirlas en memes que harán reír a tus amigos.
-Para crear un meme en Paint 3D, puedes usar uno de estos métodos:
-
-Utilice la herramienta de texto: Paint 3D le permite agregar texto a su lienzo que se puede convertir en objetos 3D. Puede acceder a la herramienta haciendo clic en el icono de texto en la barra de menú. Para añadir texto a su lienzo, simplemente escriba el texto deseado y elija una fuente, tamaño, color y estilo. Para convertir su texto en un objeto 3D, simplemente haga clic en el botón Hacer 3D en la parte superior. A continuación, puede ajustar la posición, la rotación y el tamaño del texto para crear un efecto meme.
-Utilice la herramienta de pegatinas: Paint 3D también le permite agregar pegatinas a su lienzo que se pueden convertir en objetos 3D. Puede acceder a la herramienta haciendo clic en el icono de pegatinas en la barra de menú. Para añadir una pegatina a tu lienzo, simplemente elige una de la biblioteca o sube tu propia imagen. Para convertir su pegatina en un objeto 3D, simplemente haga clic en el botón Hacer 3D en la parte superior. A continuación, puede ajustar la posición, la rotación y el tamaño de la etiqueta para crear un efecto de meme.
-
-
-Aquí hay algunos ejemplos de memes creados con Paint 3D:
-3D Doodle
-Otra característica de Paint 3D es la capacidad de dibujar bocetos a mano alzada que se pueden convertir en objetos 3D. Puedes usar las herramientas de lápiz y pincel de Paint 3D para dibujar lo que quieras en tu lienzo y convertirlo en un garabato 3D. También puedes usar diferentes colores, tamaños y estilos para tus pinceladas y pinceladas.
-Para crear un doodle 3D en Paint 3D, puedes usar uno de estos métodos:
-
-Utilice la herramienta de lápiz: Paint 3D ofrece una herramienta de lápiz para dibujar líneas suaves y precisas en su lienzo. Puede acceder a la herramienta haciendo clic en el icono del lápiz en la barra de menú. Para dibujar con la herramienta del lápiz, simplemente haga clic y arrastre sobre su lienzo. Para convertir su lápiz en un objeto 3D, simplemente haga clic en el botón Hacer 3D en la parte superior. A continuación, puede ajustar la posición, la rotación y el tamaño del objeto 3D.
-Utilice la herramienta de pincel: Paint 3D ofrece una herramienta de pincel para dibujar trazos gruesos y texturizados en su lienzo. Puede acceder a la herramienta haciendo clic en el icono del pincel en la barra de menú. Para dibujar con la herramienta del pincel, simplemente haga clic y arrastre sobre su lienzo. Para convertir la pincelada en un objeto 3D, simplemente haga clic en el botón Hacer 3D en la parte superior. A continuación, puede ajustar la posición, la rotación y el tamaño del objeto 3D.
-
-
-Aquí hay algunos ejemplos de garabatos 3D creados con Paint 3D:
-Conclusión y preguntas frecuentes
-Paint 3D es una fantástica herramienta para crear impresionantes imágenes 3D y animaciones en Ubuntu. Ofrece una gama de características y beneficios que harán que su experiencia de arte digital sea más divertida y agradable. Puede crear y manipular objetos 3D, animar capas, objetos, efectos y filtros, aplicar texturas realistas, crear memes únicos y dibujar bocetos a mano alzada que se pueden convertir en objetos 3D. También puede descargar e instalar Paint 3D en Ubuntu fácilmente desde el Snap Store o desde una unidad USB de arranque.
-Si estás buscando una forma sencilla y potente de expresar tu creatividad en 3D, definitivamente deberías probar Paint 3D en Ubuntu. Te sorprenderá lo que puedes crear con esta increíble herramienta.
-Para obtener más información sobre Paint 3D y sus características, puede visitar su sitio web oficial tutoriales y https://bltlly.com/2v6M16
Aquaman 2 es una de las películas más esperadas de 2023, especialmente entre los fans de DC Comics y películas de superhéroes. La película promete ofrecer más acción submarina épica, aventura y drama, ya que Aquaman se enfrenta a una nueva amenaza de un antiguo poder que se ha desatado. La película también cuenta con uno de los superhéroes más populares de todos los tiempos, Batman, que une fuerzas con Aquaman para salvar la Atlántida y el mundo.
-¿Cómo descargar Aquaman 2 en tamil desde el sitio web de Filmyhit?
-Si usted es un fan de Aquaman 2 y quiere verlo en idioma tamil, es posible que se pregunte cómo descargarlo de Internet. Uno de los sitios web que afirma ofrecer la descarga gratuita de Aquaman 2 en Tamil es Filmyhit. Filmyhit es un sitio web torrent que sube las últimas películas y series web de forma gratuita. El sitio tiene películas en varios idiomas como Hindi, Inglés, Punjabi, etc. La gente sobre todo visita este sitio para descargar recién estrenado Bollywood, películas de Punjabi.
-Descargar Aquaman 2 en tamil desde el sitio web de Filmyhit no es una tarea difícil. Solo tiene que seguir estos sencillos pasos:
-
-Visite el sitio web de Filmyhit y busque Aquaman 2 en la barra de búsqueda. Verá algunos resultados relacionados basados en su consulta de búsqueda.
-Elija la calidad y el idioma de la película que desea descargar. Puede seleccionar desde 720p, 1080p o calidad HD, y desde el idioma tamil, hindi o inglés.
-
-
-¿Cuáles son los riesgos y beneficios de descargar Aquaman 2 desde el sitio web de Filmyhit?
-Descargar Aquaman 2 desde el sitio web de Filmyhit puede parecer una manera conveniente y fácil de ver su película favorita en idioma tamil. Sin embargo, hay algunos riesgos y beneficios asociados con ella que usted debe tener en cuenta antes de proceder.
-Algunos de los beneficios de descargar Aquaman 2 desde el sitio web de Filmyhit son:
-
-Puedes ver la película gratis sin pagar ninguna suscripción o tarifa de entrada.
- Puede ver la película en su propio tiempo y conveniencia sin esperar su lanzamiento teatral o en línea.
- Puede ver la película en su idioma y calidad preferidos sin comprometer su experiencia de visualización.
-
-Algunos de los riesgos de descargar Aquaman 2 desde el sitio web de Filmyhit son:
-
-
-Puede estar violando las leyes de derechos de autor y los derechos de propiedad intelectual de los cineastas y productores al descargar su contenido sin su permiso.
-Puede estar exponiendo su dispositivo y datos a malware, virus o hackers al visitar un sitio web no seguro e ilegal.
-Usted puede estar apoyando la piratería y perjudicando a la industria cinematográfica al privarlos de sus legítimos ingresos y reconocimiento.
-
-Preguntas frecuentes
-Aquí hay algunas preguntas y respuestas comunes sobre Aquaman 2 y Fil myhit.
-Q1: ¿Cuándo se estrenará Aquaman 2 en los cines?
-A1: Aquaman 2 está programado para ser lanzado el 20 de diciembre de 2023, en los Estados Unidos. La fecha de lanzamiento puede variar dependiendo del país y la región.
-Q2: ¿Quién es el reparto principal y el equipo de Aquaman 2?
-A2: Aquaman 2 está dirigida por James Wan y escrita por David Leslie Johnson-McGoldrick. El reparto principal incluye a Jason Momoa como Arthur Curry / Aquaman, Amber Heard como Mera, Patrick Wilson como King Orm, Yahya Abdul-Mateen II como Manta Negra, y Ben Affleck como Bruce Wayne / Batman.
-
-A3: Aquaman 2 sigue los eventos de la primera película, mientras Aquaman intenta equilibrar su vida como el rey de la Atlántida y el protector de los océanos. Se enfrenta a un nuevo enemigo de un antiguo poder que ha sido liberado, y se une a Batman para detenerlo.
-Q4: ¿Es Filmyhit un sitio web legal y seguro para descargar películas?
-A4: No, Filmyhit no es un sitio web legal y seguro para descargar películas. Es un sitio web torrent que sube contenido pirata sin el permiso de los creadores originales. También puede contener malware, virus o hackers que pueden dañar tu dispositivo y tus datos.
-Q5: ¿Cuáles son algunas alternativas legales y seguras al sitio web de Filmyhit?
-A5: Algunas alternativas legales y seguras al sitio web de Filmyhit son:
-
-Netflix: Un servicio de streaming en línea popular que ofrece una amplia gama de películas y programas en varios idiomas y géneros.
-Amazon Prime Video: Otro servicio de transmisión en línea que ofrece películas y programas exclusivos junto con otros beneficios como la entrega gratuita y la música.
-Disney+: una plataforma de streaming que incluye películas y programas de Disney, Marvel, Star Wars, Pixar y National Geographic.
-HBO Max: Un servicio de streaming que ofrece películas y programas de Warner Bros., DC Comics, Cartoon Network y más.
-YouTube: una plataforma para compartir vídeos que permite a los usuarios ver, cargar y compartir vídeos de forma gratuita o con una suscripción premium.
-
- Espero que haya disfrutado leyendo este artículo sobre Aquaman 2 película completa en Tamil descargar Filmyhit. Si tiene algún comentario o sugerencia, por favor hágamelo saber en la sección de comentarios a continuación. Gracias por su tiempo y atención.
64aa2da5cfCómo descargar ChessKid: La mejor aplicación de ajedrez para niños
-¿Quieres aprender y jugar ajedrez de una manera divertida y fácil? ¿Quieres unirte a una comunidad de ajedrez segura y amigable? ¿Quieres mejorar tus habilidades y conocimientos de ajedrez? Si respondiste sí a cualquiera de estas preguntas, entonces deberías descargar ChessKid, la mejor aplicación de ajedrez para niños.
-chesskid download Download ○ https://bltlly.com/2v6JZN
ChessKid es un sitio web y aplicación de ajedrez que está diseñado para niños de todas las edades y niveles de habilidad. Es 100% seguro y gratuito, y tiene todo lo necesario para aprender y disfrutar del juego de ajedrez. Ya seas principiante o experto, ChessKid te ayudará a convertirte en un mejor jugador de ajedrez.
-En este artículo, le mostraremos las características, beneficios y pasos para descargar ChessKid. Al final de este artículo, estarás listo para comenzar tu aventura de ajedrez con ChessKid.
- Características de ChessKid
-ChessKid tiene muchas características que lo convierten en la mejor aplicación de ajedrez para niños. Aquí están algunas de ellas:
- Aprenda ajedrez con lecciones, videos y rompecabezas divertidos e interactivos
-ChessKid tiene cientos de lecciones, videos y rompecabezas que te enseñan todo, desde las reglas básicas hasta las estrategias avanzadas. Puedes aprender de entrenadores divertidos y amigables, como FunMasterMike, que te explicarán todo de una manera clara y entretenida. También puede probar sus conocimientos con cuestionarios y desafíos.
-
- Juega al ajedrez online o offline con otros niños o contra el ordenador
-ChessKid te permite jugar al ajedrez con otros niños de todo el mundo en juegos en vivo o diarios. También puedes jugar contra el ordenador en diferentes niveles de dificultad. Puedes elegir entre diferentes modos, como ajedrez lento, ajedrez rápido, duelo de rompecabezas o bughouse. También puedes unirte a torneos y competir por premios y trofeos.
- Únete a una comunidad de ajedrez segura y amigable
-
- Beneficios de ChessKid
-ChessKid tiene muchos beneficios para los niños que quieren aprender y jugar al ajedrez. Estos son algunos de ellos:
- Mejora tus habilidades y conocimientos de ajedrez
-ChessKid te ayuda a mejorar tus habilidades y conocimientos de ajedrez proporcionándote contenido de alta calidad y comentarios. Puedes aprender nuevos conceptos, practicar tus tácticas, analizar tus juegos y seguir tu progreso. También puedes comparar tu puntuación con la de otros jugadores y ver cómo te clasificas entre ellos.
- Desarrolla tus habilidades de pensamiento crítico y resolución de problemas
-ChessKid te ayuda a desarrollar tus habilidades de pensamiento crítico y resolución de problemas al desafiarte con diferentes situaciones y escenarios. Tienes que pensar con anticipación, planificar tus movimientos, calcular las consecuencias y encontrar la mejor solución. También tienes que ser creativo, flexible y adaptable a las circunstancias cambiantes.
- Diviértete y disfruta del juego de ajedrez
-ChessKid te ayuda a divertirte y disfrutar del juego de ajedrez haciéndolo atractivo y entretenido. Puedes jugar con diferentes temas, animaciones, sonidos y avatares. También puedes explorar un mundo mágico de criaturas fantásticas en el.
Cómo descargar ChessKid
-Descargar ChessKid es fácil y rápido. Puede descargar ChessKid desde el sitio web oficial o la tienda de aplicaciones, dependiendo de su dispositivo. Estos son los pasos para descargar ChessKid:
- Descargar ChessKid desde el sitio web oficial o tienda de aplicaciones
-Si desea usar ChessKid en su computadora, puede descargarlo desde el sitio web oficial en https://www.chesskid.com/ . Simplemente haga clic en el botón "Descargar" y siga las instrucciones. También puede ver este video tutorial para más detalles: https://www.youtube.com/watch?v=ZmZmUxw6q4k .
-
- Crear una cuenta gratuita o actualizar a una membresía de oro
-Después de descargar ChessKid, necesitas crear una cuenta gratuita para comenzar a usarla. Puede crear una cuenta gratuita ingresando su nombre de usuario, contraseña, correo electrónico y correo electrónico de los padres. También puedes registrarte con tu cuenta de Facebook o Google. Usted tendrá que verificar su correo electrónico y obtener el permiso de los padres antes de que pueda acceder a todas las características de ChessKid.
-Si desea obtener más características y beneficios, puede actualizar a una membresía de oro. Una membresía de oro cuesta $49 por año y te da acceso ilimitado a todas las lecciones, videos, rompecabezas, juegos, torneos y recompensas. También puede crear varias cuentas para niños bajo una cuenta principal. Puede actualizar a una membresía de oro haciendo clic en el botón "Actualizar" en el sitio web o la aplicación.
- Empieza a aprender y jugar al ajedrez con ChessKid
-Una vez que haya creado su cuenta y verificado su correo electrónico, puede comenzar a aprender y jugar al ajedrez con ChessKid. Puede elegir entre diferentes secciones en el sitio web o la aplicación, como Aprender, Jugar, Puzzles, Videos, Clubes, Torneos y Más. También puede personalizar su perfil, configuración y preferencias. Puede explorar todas las características y opciones de ChessKid a su propio ritmo y nivel.
- Conclusión
-ChessKid es la mejor aplicación de ajedrez para niños que quieren aprender y jugar al ajedrez de una manera divertida y fácil. Tiene muchas características y beneficios que lo hacen atractivo y educativo. También es seguro y de uso gratuito. Para descargar ChessKid, solo tienes que seguir estos sencillos pasos:
-
-Descargar ChessKid desde el sitio web oficial o tienda de aplicaciones
-Crear una cuenta gratuita o actualizar a una membresía de oro
-Empieza a aprender y jugar al ajedrez con ChessKid
-
-Entonces, ¿qué estás esperando? Descarga ChessKid hoy y comienza tu aventura de ajedrez con ChessKid.
- Preguntas frecuentes
-Aquí hay algunas preguntas y respuestas comunes sobre ChessKid:
-
-A: Sí, ChessKid es 100% seguro para los niños. Tiene estrictas medidas de seguridad y políticas que protegen la privacidad y la seguridad de sus usuarios. Limita las opciones de chat y requiere el permiso de los padres para ciertas características. También monitorea y modera todos los contenidos y actividades de su plataforma.
- Q: ¿Es ChessKid realmente gratis para los niños?
-A: Sí, ChessKid es 100% gratis para los niños. Puede crear una cuenta gratuita y acceder a la mayoría de las funciones de ChessKid sin pagar nada. Sin embargo, si desea obtener más características y beneficios, puede actualizar a una membresía de oro por $ 49 por año.
- Q: ¿Cómo puedo contactar con el soporte de ChessKid?
-A: Si tiene alguna pregunta o problema con ChessKid, puede ponerse en contacto con el soporte de ChessKid enviándoles un correo electrónico a support@chesskid.com . También puede visitar su centro de ayuda en https://support.chesskid.com/hc/en-us para obtener más información y recursos.
- Q: ¿Cómo puedo eliminar mi cuenta de ChessKid?
-A: Si desea eliminar su cuenta de ChessKid, puede hacerlo iniciando sesión en su cuenta y yendo a la configuración de su perfil. También puedes enviar un correo electrónico al soporte de ChessKid a support@chesskid.com y pedirles que eliminen tu cuenta. Tenga en cuenta que la eliminación de su cuenta borrará todos sus datos y el progreso en ChessKid.
- P: ¿Cómo puedo obtener más estrellas, insignias, coronas y otras recompensas en ChessKid?
-A: Puedes obtener más estrellas, insignias, coronas y otras recompensas en ChessKid completando diferentes tareas y actividades. Por ejemplo, puedes conseguir estrellas resolviendo puzles, insignias viendo vídeos, coronas terminando clases y trofeos ganando torneos. También puedes obtener recompensas especiales participando en eventos y concursos.
64aa2da5cf"
-__version__ = "1.16.0"
-
-
-# Useful for very coarse version differentiation.
-PY2 = sys.version_info[0] == 2
-PY3 = sys.version_info[0] == 3
-PY34 = sys.version_info[0:2] >= (3, 4)
-
-if PY3:
- string_types = str,
- integer_types = int,
- class_types = type,
- text_type = str
- binary_type = bytes
-
- MAXSIZE = sys.maxsize
-else:
- string_types = basestring,
- integer_types = (int, long)
- class_types = (type, types.ClassType)
- text_type = unicode
- binary_type = str
-
- if sys.platform.startswith("java"):
- # Jython always uses 32 bits.
- MAXSIZE = int((1 << 31) - 1)
- else:
- # It's possible to have sizeof(long) != sizeof(Py_ssize_t).
- class X(object):
-
- def __len__(self):
- return 1 << 31
- try:
- len(X())
- except OverflowError:
- # 32-bit
- MAXSIZE = int((1 << 31) - 1)
- else:
- # 64-bit
- MAXSIZE = int((1 << 63) - 1)
- del X
-
-if PY34:
- from importlib.util import spec_from_loader
-else:
- spec_from_loader = None
-
-
-def _add_doc(func, doc):
- """Add documentation to a function."""
- func.__doc__ = doc
-
-
-def _import_module(name):
- """Import module, returning the module after the last dot."""
- __import__(name)
- return sys.modules[name]
-
-
-class _LazyDescr(object):
-
- def __init__(self, name):
- self.name = name
-
- def __get__(self, obj, tp):
- result = self._resolve()
- setattr(obj, self.name, result) # Invokes __set__.
- try:
- # This is a bit ugly, but it avoids running this again by
- # removing this descriptor.
- delattr(obj.__class__, self.name)
- except AttributeError:
- pass
- return result
-
-
-class MovedModule(_LazyDescr):
-
- def __init__(self, name, old, new=None):
- super(MovedModule, self).__init__(name)
- if PY3:
- if new is None:
- new = name
- self.mod = new
- else:
- self.mod = old
-
- def _resolve(self):
- return _import_module(self.mod)
-
- def __getattr__(self, attr):
- _module = self._resolve()
- value = getattr(_module, attr)
- setattr(self, attr, value)
- return value
-
-
-class _LazyModule(types.ModuleType):
-
- def __init__(self, name):
- super(_LazyModule, self).__init__(name)
- self.__doc__ = self.__class__.__doc__
-
- def __dir__(self):
- attrs = ["__doc__", "__name__"]
- attrs += [attr.name for attr in self._moved_attributes]
- return attrs
-
- # Subclasses should override this
- _moved_attributes = []
-
-
-class MovedAttribute(_LazyDescr):
-
- def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
- super(MovedAttribute, self).__init__(name)
- if PY3:
- if new_mod is None:
- new_mod = name
- self.mod = new_mod
- if new_attr is None:
- if old_attr is None:
- new_attr = name
- else:
- new_attr = old_attr
- self.attr = new_attr
- else:
- self.mod = old_mod
- if old_attr is None:
- old_attr = name
- self.attr = old_attr
-
- def _resolve(self):
- module = _import_module(self.mod)
- return getattr(module, self.attr)
-
-
-class _SixMetaPathImporter(object):
-
- """
- A meta path importer to import six.moves and its submodules.
-
- This class implements a PEP302 finder and loader. It should be compatible
- with Python 2.5 and all existing versions of Python3
- """
-
- def __init__(self, six_module_name):
- self.name = six_module_name
- self.known_modules = {}
-
- def _add_module(self, mod, *fullnames):
- for fullname in fullnames:
- self.known_modules[self.name + "." + fullname] = mod
-
- def _get_module(self, fullname):
- return self.known_modules[self.name + "." + fullname]
-
- def find_module(self, fullname, path=None):
- if fullname in self.known_modules:
- return self
- return None
-
- def find_spec(self, fullname, path, target=None):
- if fullname in self.known_modules:
- return spec_from_loader(fullname, self)
- return None
-
- def __get_module(self, fullname):
- try:
- return self.known_modules[fullname]
- except KeyError:
- raise ImportError("This loader does not know module " + fullname)
-
- def load_module(self, fullname):
- try:
- # in case of a reload
- return sys.modules[fullname]
- except KeyError:
- pass
- mod = self.__get_module(fullname)
- if isinstance(mod, MovedModule):
- mod = mod._resolve()
- else:
- mod.__loader__ = self
- sys.modules[fullname] = mod
- return mod
-
- def is_package(self, fullname):
- """
- Return true, if the named module is a package.
-
- We need this method to get correct spec objects with
- Python 3.4 (see PEP451)
- """
- return hasattr(self.__get_module(fullname), "__path__")
-
- def get_code(self, fullname):
- """Return None
-
- Required, if is_package is implemented"""
- self.__get_module(fullname) # eventually raises ImportError
- return None
- get_source = get_code # same as get_code
-
- def create_module(self, spec):
- return self.load_module(spec.name)
-
- def exec_module(self, module):
- pass
-
-_importer = _SixMetaPathImporter(__name__)
-
-
-class _MovedItems(_LazyModule):
-
- """Lazy loading of moved objects"""
- __path__ = [] # mark as package
-
-
-_moved_attributes = [
- MovedAttribute("cStringIO", "cStringIO", "io", "StringIO"),
- MovedAttribute("filter", "itertools", "builtins", "ifilter", "filter"),
- MovedAttribute("filterfalse", "itertools", "itertools", "ifilterfalse", "filterfalse"),
- MovedAttribute("input", "__builtin__", "builtins", "raw_input", "input"),
- MovedAttribute("intern", "__builtin__", "sys"),
- MovedAttribute("map", "itertools", "builtins", "imap", "map"),
- MovedAttribute("getcwd", "os", "os", "getcwdu", "getcwd"),
- MovedAttribute("getcwdb", "os", "os", "getcwd", "getcwdb"),
- MovedAttribute("getoutput", "commands", "subprocess"),
- MovedAttribute("range", "__builtin__", "builtins", "xrange", "range"),
- MovedAttribute("reload_module", "__builtin__", "importlib" if PY34 else "imp", "reload"),
- MovedAttribute("reduce", "__builtin__", "functools"),
- MovedAttribute("shlex_quote", "pipes", "shlex", "quote"),
- MovedAttribute("StringIO", "StringIO", "io"),
- MovedAttribute("UserDict", "UserDict", "collections"),
- MovedAttribute("UserList", "UserList", "collections"),
- MovedAttribute("UserString", "UserString", "collections"),
- MovedAttribute("xrange", "__builtin__", "builtins", "xrange", "range"),
- MovedAttribute("zip", "itertools", "builtins", "izip", "zip"),
- MovedAttribute("zip_longest", "itertools", "itertools", "izip_longest", "zip_longest"),
- MovedModule("builtins", "__builtin__"),
- MovedModule("configparser", "ConfigParser"),
- MovedModule("collections_abc", "collections", "collections.abc" if sys.version_info >= (3, 3) else "collections"),
- MovedModule("copyreg", "copy_reg"),
- MovedModule("dbm_gnu", "gdbm", "dbm.gnu"),
- MovedModule("dbm_ndbm", "dbm", "dbm.ndbm"),
- MovedModule("_dummy_thread", "dummy_thread", "_dummy_thread" if sys.version_info < (3, 9) else "_thread"),
- MovedModule("http_cookiejar", "cookielib", "http.cookiejar"),
- MovedModule("http_cookies", "Cookie", "http.cookies"),
- MovedModule("html_entities", "htmlentitydefs", "html.entities"),
- MovedModule("html_parser", "HTMLParser", "html.parser"),
- MovedModule("http_client", "httplib", "http.client"),
- MovedModule("email_mime_base", "email.MIMEBase", "email.mime.base"),
- MovedModule("email_mime_image", "email.MIMEImage", "email.mime.image"),
- MovedModule("email_mime_multipart", "email.MIMEMultipart", "email.mime.multipart"),
- MovedModule("email_mime_nonmultipart", "email.MIMENonMultipart", "email.mime.nonmultipart"),
- MovedModule("email_mime_text", "email.MIMEText", "email.mime.text"),
- MovedModule("BaseHTTPServer", "BaseHTTPServer", "http.server"),
- MovedModule("CGIHTTPServer", "CGIHTTPServer", "http.server"),
- MovedModule("SimpleHTTPServer", "SimpleHTTPServer", "http.server"),
- MovedModule("cPickle", "cPickle", "pickle"),
- MovedModule("queue", "Queue"),
- MovedModule("reprlib", "repr"),
- MovedModule("socketserver", "SocketServer"),
- MovedModule("_thread", "thread", "_thread"),
- MovedModule("tkinter", "Tkinter"),
- MovedModule("tkinter_dialog", "Dialog", "tkinter.dialog"),
- MovedModule("tkinter_filedialog", "FileDialog", "tkinter.filedialog"),
- MovedModule("tkinter_scrolledtext", "ScrolledText", "tkinter.scrolledtext"),
- MovedModule("tkinter_simpledialog", "SimpleDialog", "tkinter.simpledialog"),
- MovedModule("tkinter_tix", "Tix", "tkinter.tix"),
- MovedModule("tkinter_ttk", "ttk", "tkinter.ttk"),
- MovedModule("tkinter_constants", "Tkconstants", "tkinter.constants"),
- MovedModule("tkinter_dnd", "Tkdnd", "tkinter.dnd"),
- MovedModule("tkinter_colorchooser", "tkColorChooser",
- "tkinter.colorchooser"),
- MovedModule("tkinter_commondialog", "tkCommonDialog",
- "tkinter.commondialog"),
- MovedModule("tkinter_tkfiledialog", "tkFileDialog", "tkinter.filedialog"),
- MovedModule("tkinter_font", "tkFont", "tkinter.font"),
- MovedModule("tkinter_messagebox", "tkMessageBox", "tkinter.messagebox"),
- MovedModule("tkinter_tksimpledialog", "tkSimpleDialog",
- "tkinter.simpledialog"),
- MovedModule("urllib_parse", __name__ + ".moves.urllib_parse", "urllib.parse"),
- MovedModule("urllib_error", __name__ + ".moves.urllib_error", "urllib.error"),
- MovedModule("urllib", __name__ + ".moves.urllib", __name__ + ".moves.urllib"),
- MovedModule("urllib_robotparser", "robotparser", "urllib.robotparser"),
- MovedModule("xmlrpc_client", "xmlrpclib", "xmlrpc.client"),
- MovedModule("xmlrpc_server", "SimpleXMLRPCServer", "xmlrpc.server"),
-]
-# Add windows specific modules.
-if sys.platform == "win32":
- _moved_attributes += [
- MovedModule("winreg", "_winreg"),
- ]
-
-for attr in _moved_attributes:
- setattr(_MovedItems, attr.name, attr)
- if isinstance(attr, MovedModule):
- _importer._add_module(attr, "moves." + attr.name)
-del attr
-
-_MovedItems._moved_attributes = _moved_attributes
-
-moves = _MovedItems(__name__ + ".moves")
-_importer._add_module(moves, "moves")
-
-
-class Module_six_moves_urllib_parse(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_parse"""
-
-
-_urllib_parse_moved_attributes = [
- MovedAttribute("ParseResult", "urlparse", "urllib.parse"),
- MovedAttribute("SplitResult", "urlparse", "urllib.parse"),
- MovedAttribute("parse_qs", "urlparse", "urllib.parse"),
- MovedAttribute("parse_qsl", "urlparse", "urllib.parse"),
- MovedAttribute("urldefrag", "urlparse", "urllib.parse"),
- MovedAttribute("urljoin", "urlparse", "urllib.parse"),
- MovedAttribute("urlparse", "urlparse", "urllib.parse"),
- MovedAttribute("urlsplit", "urlparse", "urllib.parse"),
- MovedAttribute("urlunparse", "urlparse", "urllib.parse"),
- MovedAttribute("urlunsplit", "urlparse", "urllib.parse"),
- MovedAttribute("quote", "urllib", "urllib.parse"),
- MovedAttribute("quote_plus", "urllib", "urllib.parse"),
- MovedAttribute("unquote", "urllib", "urllib.parse"),
- MovedAttribute("unquote_plus", "urllib", "urllib.parse"),
- MovedAttribute("unquote_to_bytes", "urllib", "urllib.parse", "unquote", "unquote_to_bytes"),
- MovedAttribute("urlencode", "urllib", "urllib.parse"),
- MovedAttribute("splitquery", "urllib", "urllib.parse"),
- MovedAttribute("splittag", "urllib", "urllib.parse"),
- MovedAttribute("splituser", "urllib", "urllib.parse"),
- MovedAttribute("splitvalue", "urllib", "urllib.parse"),
- MovedAttribute("uses_fragment", "urlparse", "urllib.parse"),
- MovedAttribute("uses_netloc", "urlparse", "urllib.parse"),
- MovedAttribute("uses_params", "urlparse", "urllib.parse"),
- MovedAttribute("uses_query", "urlparse", "urllib.parse"),
- MovedAttribute("uses_relative", "urlparse", "urllib.parse"),
-]
-for attr in _urllib_parse_moved_attributes:
- setattr(Module_six_moves_urllib_parse, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_parse._moved_attributes = _urllib_parse_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_parse(__name__ + ".moves.urllib_parse"),
- "moves.urllib_parse", "moves.urllib.parse")
-
-
-class Module_six_moves_urllib_error(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_error"""
-
-
-_urllib_error_moved_attributes = [
- MovedAttribute("URLError", "urllib2", "urllib.error"),
- MovedAttribute("HTTPError", "urllib2", "urllib.error"),
- MovedAttribute("ContentTooShortError", "urllib", "urllib.error"),
-]
-for attr in _urllib_error_moved_attributes:
- setattr(Module_six_moves_urllib_error, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_error._moved_attributes = _urllib_error_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_error(__name__ + ".moves.urllib.error"),
- "moves.urllib_error", "moves.urllib.error")
-
-
-class Module_six_moves_urllib_request(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_request"""
-
-
-_urllib_request_moved_attributes = [
- MovedAttribute("urlopen", "urllib2", "urllib.request"),
- MovedAttribute("install_opener", "urllib2", "urllib.request"),
- MovedAttribute("build_opener", "urllib2", "urllib.request"),
- MovedAttribute("pathname2url", "urllib", "urllib.request"),
- MovedAttribute("url2pathname", "urllib", "urllib.request"),
- MovedAttribute("getproxies", "urllib", "urllib.request"),
- MovedAttribute("Request", "urllib2", "urllib.request"),
- MovedAttribute("OpenerDirector", "urllib2", "urllib.request"),
- MovedAttribute("HTTPDefaultErrorHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPRedirectHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPCookieProcessor", "urllib2", "urllib.request"),
- MovedAttribute("ProxyHandler", "urllib2", "urllib.request"),
- MovedAttribute("BaseHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPPasswordMgr", "urllib2", "urllib.request"),
- MovedAttribute("HTTPPasswordMgrWithDefaultRealm", "urllib2", "urllib.request"),
- MovedAttribute("AbstractBasicAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPBasicAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("ProxyBasicAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("AbstractDigestAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPDigestAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("ProxyDigestAuthHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPSHandler", "urllib2", "urllib.request"),
- MovedAttribute("FileHandler", "urllib2", "urllib.request"),
- MovedAttribute("FTPHandler", "urllib2", "urllib.request"),
- MovedAttribute("CacheFTPHandler", "urllib2", "urllib.request"),
- MovedAttribute("UnknownHandler", "urllib2", "urllib.request"),
- MovedAttribute("HTTPErrorProcessor", "urllib2", "urllib.request"),
- MovedAttribute("urlretrieve", "urllib", "urllib.request"),
- MovedAttribute("urlcleanup", "urllib", "urllib.request"),
- MovedAttribute("URLopener", "urllib", "urllib.request"),
- MovedAttribute("FancyURLopener", "urllib", "urllib.request"),
- MovedAttribute("proxy_bypass", "urllib", "urllib.request"),
- MovedAttribute("parse_http_list", "urllib2", "urllib.request"),
- MovedAttribute("parse_keqv_list", "urllib2", "urllib.request"),
-]
-for attr in _urllib_request_moved_attributes:
- setattr(Module_six_moves_urllib_request, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_request._moved_attributes = _urllib_request_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_request(__name__ + ".moves.urllib.request"),
- "moves.urllib_request", "moves.urllib.request")
-
-
-class Module_six_moves_urllib_response(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_response"""
-
-
-_urllib_response_moved_attributes = [
- MovedAttribute("addbase", "urllib", "urllib.response"),
- MovedAttribute("addclosehook", "urllib", "urllib.response"),
- MovedAttribute("addinfo", "urllib", "urllib.response"),
- MovedAttribute("addinfourl", "urllib", "urllib.response"),
-]
-for attr in _urllib_response_moved_attributes:
- setattr(Module_six_moves_urllib_response, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_response._moved_attributes = _urllib_response_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_response(__name__ + ".moves.urllib.response"),
- "moves.urllib_response", "moves.urllib.response")
-
-
-class Module_six_moves_urllib_robotparser(_LazyModule):
-
- """Lazy loading of moved objects in six.moves.urllib_robotparser"""
-
-
-_urllib_robotparser_moved_attributes = [
- MovedAttribute("RobotFileParser", "robotparser", "urllib.robotparser"),
-]
-for attr in _urllib_robotparser_moved_attributes:
- setattr(Module_six_moves_urllib_robotparser, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_robotparser._moved_attributes = _urllib_robotparser_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_robotparser(__name__ + ".moves.urllib.robotparser"),
- "moves.urllib_robotparser", "moves.urllib.robotparser")
-
-
-class Module_six_moves_urllib(types.ModuleType):
-
- """Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
- __path__ = [] # mark as package
- parse = _importer._get_module("moves.urllib_parse")
- error = _importer._get_module("moves.urllib_error")
- request = _importer._get_module("moves.urllib_request")
- response = _importer._get_module("moves.urllib_response")
- robotparser = _importer._get_module("moves.urllib_robotparser")
-
- def __dir__(self):
- return ['parse', 'error', 'request', 'response', 'robotparser']
-
-_importer._add_module(Module_six_moves_urllib(__name__ + ".moves.urllib"),
- "moves.urllib")
-
-
-def add_move(move):
- """Add an item to six.moves."""
- setattr(_MovedItems, move.name, move)
-
-
-def remove_move(name):
- """Remove item from six.moves."""
- try:
- delattr(_MovedItems, name)
- except AttributeError:
- try:
- del moves.__dict__[name]
- except KeyError:
- raise AttributeError("no such move, %r" % (name,))
-
-
-if PY3:
- _meth_func = "__func__"
- _meth_self = "__self__"
-
- _func_closure = "__closure__"
- _func_code = "__code__"
- _func_defaults = "__defaults__"
- _func_globals = "__globals__"
-else:
- _meth_func = "im_func"
- _meth_self = "im_self"
-
- _func_closure = "func_closure"
- _func_code = "func_code"
- _func_defaults = "func_defaults"
- _func_globals = "func_globals"
-
-
-try:
- advance_iterator = next
-except NameError:
- def advance_iterator(it):
- return it.next()
-next = advance_iterator
-
-
-try:
- callable = callable
-except NameError:
- def callable(obj):
- return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
-
-
-if PY3:
- def get_unbound_function(unbound):
- return unbound
-
- create_bound_method = types.MethodType
-
- def create_unbound_method(func, cls):
- return func
-
- Iterator = object
-else:
- def get_unbound_function(unbound):
- return unbound.im_func
-
- def create_bound_method(func, obj):
- return types.MethodType(func, obj, obj.__class__)
-
- def create_unbound_method(func, cls):
- return types.MethodType(func, None, cls)
-
- class Iterator(object):
-
- def next(self):
- return type(self).__next__(self)
-
- callable = callable
-_add_doc(get_unbound_function,
- """Get the function out of a possibly unbound function""")
-
-
-get_method_function = operator.attrgetter(_meth_func)
-get_method_self = operator.attrgetter(_meth_self)
-get_function_closure = operator.attrgetter(_func_closure)
-get_function_code = operator.attrgetter(_func_code)
-get_function_defaults = operator.attrgetter(_func_defaults)
-get_function_globals = operator.attrgetter(_func_globals)
-
-
-if PY3:
- def iterkeys(d, **kw):
- return iter(d.keys(**kw))
-
- def itervalues(d, **kw):
- return iter(d.values(**kw))
-
- def iteritems(d, **kw):
- return iter(d.items(**kw))
-
- def iterlists(d, **kw):
- return iter(d.lists(**kw))
-
- viewkeys = operator.methodcaller("keys")
-
- viewvalues = operator.methodcaller("values")
-
- viewitems = operator.methodcaller("items")
-else:
- def iterkeys(d, **kw):
- return d.iterkeys(**kw)
-
- def itervalues(d, **kw):
- return d.itervalues(**kw)
-
- def iteritems(d, **kw):
- return d.iteritems(**kw)
-
- def iterlists(d, **kw):
- return d.iterlists(**kw)
-
- viewkeys = operator.methodcaller("viewkeys")
-
- viewvalues = operator.methodcaller("viewvalues")
-
- viewitems = operator.methodcaller("viewitems")
-
-_add_doc(iterkeys, "Return an iterator over the keys of a dictionary.")
-_add_doc(itervalues, "Return an iterator over the values of a dictionary.")
-_add_doc(iteritems,
- "Return an iterator over the (key, value) pairs of a dictionary.")
-_add_doc(iterlists,
- "Return an iterator over the (key, [values]) pairs of a dictionary.")
-
-
-if PY3:
- def b(s):
- return s.encode("latin-1")
-
- def u(s):
- return s
- unichr = chr
- import struct
- int2byte = struct.Struct(">B").pack
- del struct
- byte2int = operator.itemgetter(0)
- indexbytes = operator.getitem
- iterbytes = iter
- import io
- StringIO = io.StringIO
- BytesIO = io.BytesIO
- del io
- _assertCountEqual = "assertCountEqual"
- if sys.version_info[1] <= 1:
- _assertRaisesRegex = "assertRaisesRegexp"
- _assertRegex = "assertRegexpMatches"
- _assertNotRegex = "assertNotRegexpMatches"
- else:
- _assertRaisesRegex = "assertRaisesRegex"
- _assertRegex = "assertRegex"
- _assertNotRegex = "assertNotRegex"
-else:
- def b(s):
- return s
- # Workaround for standalone backslash
-
- def u(s):
- return unicode(s.replace(r'\\', r'\\\\'), "unicode_escape")
- unichr = unichr
- int2byte = chr
-
- def byte2int(bs):
- return ord(bs[0])
-
- def indexbytes(buf, i):
- return ord(buf[i])
- iterbytes = functools.partial(itertools.imap, ord)
- import StringIO
- StringIO = BytesIO = StringIO.StringIO
- _assertCountEqual = "assertItemsEqual"
- _assertRaisesRegex = "assertRaisesRegexp"
- _assertRegex = "assertRegexpMatches"
- _assertNotRegex = "assertNotRegexpMatches"
-_add_doc(b, """Byte literal""")
-_add_doc(u, """Text literal""")
-
-
-def assertCountEqual(self, *args, **kwargs):
- return getattr(self, _assertCountEqual)(*args, **kwargs)
-
-
-def assertRaisesRegex(self, *args, **kwargs):
- return getattr(self, _assertRaisesRegex)(*args, **kwargs)
-
-
-def assertRegex(self, *args, **kwargs):
- return getattr(self, _assertRegex)(*args, **kwargs)
-
-
-def assertNotRegex(self, *args, **kwargs):
- return getattr(self, _assertNotRegex)(*args, **kwargs)
-
-
-if PY3:
- exec_ = getattr(moves.builtins, "exec")
-
- def reraise(tp, value, tb=None):
- try:
- if value is None:
- value = tp()
- if value.__traceback__ is not tb:
- raise value.with_traceback(tb)
- raise value
- finally:
- value = None
- tb = None
-
-else:
- def exec_(_code_, _globs_=None, _locs_=None):
- """Execute code in a namespace."""
- if _globs_ is None:
- frame = sys._getframe(1)
- _globs_ = frame.f_globals
- if _locs_ is None:
- _locs_ = frame.f_locals
- del frame
- elif _locs_ is None:
- _locs_ = _globs_
- exec("""exec _code_ in _globs_, _locs_""")
-
- exec_("""def reraise(tp, value, tb=None):
- try:
- raise tp, value, tb
- finally:
- tb = None
-""")
-
-
-if sys.version_info[:2] > (3,):
- exec_("""def raise_from(value, from_value):
- try:
- raise value from from_value
- finally:
- value = None
-""")
-else:
- def raise_from(value, from_value):
- raise value
-
-
-print_ = getattr(moves.builtins, "print", None)
-if print_ is None:
- def print_(*args, **kwargs):
- """The new-style print function for Python 2.4 and 2.5."""
- fp = kwargs.pop("file", sys.stdout)
- if fp is None:
- return
-
- def write(data):
- if not isinstance(data, basestring):
- data = str(data)
- # If the file has an encoding, encode unicode with it.
- if (isinstance(fp, file) and
- isinstance(data, unicode) and
- fp.encoding is not None):
- errors = getattr(fp, "errors", None)
- if errors is None:
- errors = "strict"
- data = data.encode(fp.encoding, errors)
- fp.write(data)
- want_unicode = False
- sep = kwargs.pop("sep", None)
- if sep is not None:
- if isinstance(sep, unicode):
- want_unicode = True
- elif not isinstance(sep, str):
- raise TypeError("sep must be None or a string")
- end = kwargs.pop("end", None)
- if end is not None:
- if isinstance(end, unicode):
- want_unicode = True
- elif not isinstance(end, str):
- raise TypeError("end must be None or a string")
- if kwargs:
- raise TypeError("invalid keyword arguments to print()")
- if not want_unicode:
- for arg in args:
- if isinstance(arg, unicode):
- want_unicode = True
- break
- if want_unicode:
- newline = unicode("\n")
- space = unicode(" ")
- else:
- newline = "\n"
- space = " "
- if sep is None:
- sep = space
- if end is None:
- end = newline
- for i, arg in enumerate(args):
- if i:
- write(sep)
- write(arg)
- write(end)
-if sys.version_info[:2] < (3, 3):
- _print = print_
-
- def print_(*args, **kwargs):
- fp = kwargs.get("file", sys.stdout)
- flush = kwargs.pop("flush", False)
- _print(*args, **kwargs)
- if flush and fp is not None:
- fp.flush()
-
-_add_doc(reraise, """Reraise an exception.""")
-
-if sys.version_info[0:2] < (3, 4):
- # This does exactly the same what the :func:`py3:functools.update_wrapper`
- # function does on Python versions after 3.2. It sets the ``__wrapped__``
- # attribute on ``wrapper`` object and it doesn't raise an error if any of
- # the attributes mentioned in ``assigned`` and ``updated`` are missing on
- # ``wrapped`` object.
- def _update_wrapper(wrapper, wrapped,
- assigned=functools.WRAPPER_ASSIGNMENTS,
- updated=functools.WRAPPER_UPDATES):
- for attr in assigned:
- try:
- value = getattr(wrapped, attr)
- except AttributeError:
- continue
- else:
- setattr(wrapper, attr, value)
- for attr in updated:
- getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
- wrapper.__wrapped__ = wrapped
- return wrapper
- _update_wrapper.__doc__ = functools.update_wrapper.__doc__
-
- def wraps(wrapped, assigned=functools.WRAPPER_ASSIGNMENTS,
- updated=functools.WRAPPER_UPDATES):
- return functools.partial(_update_wrapper, wrapped=wrapped,
- assigned=assigned, updated=updated)
- wraps.__doc__ = functools.wraps.__doc__
-
-else:
- wraps = functools.wraps
-
-
-def with_metaclass(meta, *bases):
- """Create a base class with a metaclass."""
- # This requires a bit of explanation: the basic idea is to make a dummy
- # metaclass for one level of class instantiation that replaces itself with
- # the actual metaclass.
- class metaclass(type):
-
- def __new__(cls, name, this_bases, d):
- if sys.version_info[:2] >= (3, 7):
- # This version introduced PEP 560 that requires a bit
- # of extra care (we mimic what is done by __build_class__).
- resolved_bases = types.resolve_bases(bases)
- if resolved_bases is not bases:
- d['__orig_bases__'] = bases
- else:
- resolved_bases = bases
- return meta(name, resolved_bases, d)
-
- @classmethod
- def __prepare__(cls, name, this_bases):
- return meta.__prepare__(name, bases)
- return type.__new__(metaclass, 'temporary_class', (), {})
-
-
-def add_metaclass(metaclass):
- """Class decorator for creating a class with a metaclass."""
- def wrapper(cls):
- orig_vars = cls.__dict__.copy()
- slots = orig_vars.get('__slots__')
- if slots is not None:
- if isinstance(slots, str):
- slots = [slots]
- for slots_var in slots:
- orig_vars.pop(slots_var)
- orig_vars.pop('__dict__', None)
- orig_vars.pop('__weakref__', None)
- if hasattr(cls, '__qualname__'):
- orig_vars['__qualname__'] = cls.__qualname__
- return metaclass(cls.__name__, cls.__bases__, orig_vars)
- return wrapper
-
-
-def ensure_binary(s, encoding='utf-8', errors='strict'):
- """Coerce **s** to six.binary_type.
-
- For Python 2:
- - `unicode` -> encoded to `str`
- - `str` -> `str`
-
- For Python 3:
- - `str` -> encoded to `bytes`
- - `bytes` -> `bytes`
- """
- if isinstance(s, binary_type):
- return s
- if isinstance(s, text_type):
- return s.encode(encoding, errors)
- raise TypeError("not expecting type '%s'" % type(s))
-
-
-def ensure_str(s, encoding='utf-8', errors='strict'):
- """Coerce *s* to `str`.
-
- For Python 2:
- - `unicode` -> encoded to `str`
- - `str` -> `str`
-
- For Python 3:
- - `str` -> `str`
- - `bytes` -> decoded to `str`
- """
- # Optimization: Fast return for the common case.
- if type(s) is str:
- return s
- if PY2 and isinstance(s, text_type):
- return s.encode(encoding, errors)
- elif PY3 and isinstance(s, binary_type):
- return s.decode(encoding, errors)
- elif not isinstance(s, (text_type, binary_type)):
- raise TypeError("not expecting type '%s'" % type(s))
- return s
-
-
-def ensure_text(s, encoding='utf-8', errors='strict'):
- """Coerce *s* to six.text_type.
-
- For Python 2:
- - `unicode` -> `unicode`
- - `str` -> `unicode`
-
- For Python 3:
- - `str` -> `str`
- - `bytes` -> decoded to `str`
- """
- if isinstance(s, binary_type):
- return s.decode(encoding, errors)
- elif isinstance(s, text_type):
- return s
- else:
- raise TypeError("not expecting type '%s'" % type(s))
-
-
-def python_2_unicode_compatible(klass):
- """
- A class decorator that defines __unicode__ and __str__ methods under Python 2.
- Under Python 3 it does nothing.
-
- To support Python 2 and 3 with a single code base, define a __str__ method
- returning text and apply this decorator to the class.
- """
- if PY2:
- if '__str__' not in klass.__dict__:
- raise ValueError("@python_2_unicode_compatible cannot be applied "
- "to %s because it doesn't define __str__()." %
- klass.__name__)
- klass.__unicode__ = klass.__str__
- klass.__str__ = lambda self: self.__unicode__().encode('utf-8')
- return klass
-
-
-# Complete the moves implementation.
-# This code is at the end of this module to speed up module loading.
-# Turn this module into a package.
-__path__ = [] # required for PEP 302 and PEP 451
-__package__ = __name__ # see PEP 366 @ReservedAssignment
-if globals().get("__spec__") is not None:
- __spec__.submodule_search_locations = [] # PEP 451 @UndefinedVariable
-# Remove other six meta path importers, since they cause problems. This can
-# happen if six is removed from sys.modules and then reloaded. (Setuptools does
-# this for some reason.)
-if sys.meta_path:
- for i, importer in enumerate(sys.meta_path):
- # Here's some real nastiness: Another "instance" of the six module might
- # be floating around. Therefore, we can't use isinstance() to check for
- # the six meta path importer, since the other six instance will have
- # inserted an importer with different class.
- if (type(importer).__name__ == "_SixMetaPathImporter" and
- importer.name == __name__):
- del sys.meta_path[i]
- break
- del i, importer
-# Finally, add the importer to the meta path import hook.
-sys.meta_path.append(_importer)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py
deleted file mode 100644
index 315fb9c8902c5e3f4dd8419ccdf7d85c6718096e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/provider.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import collections
-import math
-from typing import (
- TYPE_CHECKING,
- Dict,
- Iterable,
- Iterator,
- Mapping,
- Sequence,
- TypeVar,
- Union,
-)
-
-from pip._vendor.resolvelib.providers import AbstractProvider
-
-from .base import Candidate, Constraint, Requirement
-from .candidates import REQUIRES_PYTHON_IDENTIFIER
-from .factory import Factory
-
-if TYPE_CHECKING:
- from pip._vendor.resolvelib.providers import Preference
- from pip._vendor.resolvelib.resolvers import RequirementInformation
-
- PreferenceInformation = RequirementInformation[Requirement, Candidate]
-
- _ProviderBase = AbstractProvider[Requirement, Candidate, str]
-else:
- _ProviderBase = AbstractProvider
-
-# Notes on the relationship between the provider, the factory, and the
-# candidate and requirement classes.
-#
-# The provider is a direct implementation of the resolvelib class. Its role
-# is to deliver the API that resolvelib expects.
-#
-# Rather than work with completely abstract "requirement" and "candidate"
-# concepts as resolvelib does, pip has concrete classes implementing these two
-# ideas. The API of Requirement and Candidate objects are defined in the base
-# classes, but essentially map fairly directly to the equivalent provider
-# methods. In particular, `find_matches` and `is_satisfied_by` are
-# requirement methods, and `get_dependencies` is a candidate method.
-#
-# The factory is the interface to pip's internal mechanisms. It is stateless,
-# and is created by the resolver and held as a property of the provider. It is
-# responsible for creating Requirement and Candidate objects, and provides
-# services to those objects (access to pip's finder and preparer).
-
-
-D = TypeVar("D")
-V = TypeVar("V")
-
-
-def _get_with_identifier(
- mapping: Mapping[str, V],
- identifier: str,
- default: D,
-) -> Union[D, V]:
- """Get item from a package name lookup mapping with a resolver identifier.
-
- This extra logic is needed when the target mapping is keyed by package
- name, which cannot be directly looked up with an identifier (which may
- contain requested extras). Additional logic is added to also look up a value
- by "cleaning up" the extras from the identifier.
- """
- if identifier in mapping:
- return mapping[identifier]
- # HACK: Theoretically we should check whether this identifier is a valid
- # "NAME[EXTRAS]" format, and parse out the name part with packaging or
- # some regular expression. But since pip's resolver only spits out three
- # kinds of identifiers: normalized PEP 503 names, normalized names plus
- # extras, and Requires-Python, we can cheat a bit here.
- name, open_bracket, _ = identifier.partition("[")
- if open_bracket and name in mapping:
- return mapping[name]
- return default
-
-
-class PipProvider(_ProviderBase):
- """Pip's provider implementation for resolvelib.
-
- :params constraints: A mapping of constraints specified by the user. Keys
- are canonicalized project names.
- :params ignore_dependencies: Whether the user specified ``--no-deps``.
- :params upgrade_strategy: The user-specified upgrade strategy.
- :params user_requested: A set of canonicalized package names that the user
- supplied for pip to install/upgrade.
- """
-
- def __init__(
- self,
- factory: Factory,
- constraints: Dict[str, Constraint],
- ignore_dependencies: bool,
- upgrade_strategy: str,
- user_requested: Dict[str, int],
- ) -> None:
- self._factory = factory
- self._constraints = constraints
- self._ignore_dependencies = ignore_dependencies
- self._upgrade_strategy = upgrade_strategy
- self._user_requested = user_requested
- self._known_depths: Dict[str, float] = collections.defaultdict(lambda: math.inf)
-
- def identify(self, requirement_or_candidate: Union[Requirement, Candidate]) -> str:
- return requirement_or_candidate.name
-
- def get_preference(
- self,
- identifier: str,
- resolutions: Mapping[str, Candidate],
- candidates: Mapping[str, Iterator[Candidate]],
- information: Mapping[str, Iterable["PreferenceInformation"]],
- backtrack_causes: Sequence["PreferenceInformation"],
- ) -> "Preference":
- """Produce a sort key for given requirement based on preference.
-
- The lower the return value is, the more preferred this group of
- arguments is.
-
- Currently pip considers the following in order:
-
- * Prefer if any of the known requirements is "direct", e.g. points to an
- explicit URL.
- * If equal, prefer if any requirement is "pinned", i.e. contains
- operator ``===`` or ``==``.
- * If equal, calculate an approximate "depth" and resolve requirements
- closer to the user-specified requirements first. If the depth cannot
- by determined (eg: due to no matching parents), it is considered
- infinite.
- * Order user-specified requirements by the order they are specified.
- * If equal, prefers "non-free" requirements, i.e. contains at least one
- operator, such as ``>=`` or ``<``.
- * If equal, order alphabetically for consistency (helps debuggability).
- """
- try:
- next(iter(information[identifier]))
- except StopIteration:
- # There is no information for this identifier, so there's no known
- # candidates.
- has_information = False
- else:
- has_information = True
-
- if has_information:
- lookups = (r.get_candidate_lookup() for r, _ in information[identifier])
- candidate, ireqs = zip(*lookups)
- else:
- candidate, ireqs = None, ()
-
- operators = [
- specifier.operator
- for specifier_set in (ireq.specifier for ireq in ireqs if ireq)
- for specifier in specifier_set
- ]
-
- direct = candidate is not None
- pinned = any(op[:2] == "==" for op in operators)
- unfree = bool(operators)
-
- try:
- requested_order: Union[int, float] = self._user_requested[identifier]
- except KeyError:
- requested_order = math.inf
- if has_information:
- parent_depths = (
- self._known_depths[parent.name] if parent is not None else 0.0
- for _, parent in information[identifier]
- )
- inferred_depth = min(d for d in parent_depths) + 1.0
- else:
- inferred_depth = math.inf
- else:
- inferred_depth = 1.0
- self._known_depths[identifier] = inferred_depth
-
- requested_order = self._user_requested.get(identifier, math.inf)
-
- # Requires-Python has only one candidate and the check is basically
- # free, so we always do it first to avoid needless work if it fails.
- requires_python = identifier == REQUIRES_PYTHON_IDENTIFIER
-
- # Prefer the causes of backtracking on the assumption that the problem
- # resolving the dependency tree is related to the failures that caused
- # the backtracking
- backtrack_cause = self.is_backtrack_cause(identifier, backtrack_causes)
-
- return (
- not requires_python,
- not direct,
- not pinned,
- not backtrack_cause,
- inferred_depth,
- requested_order,
- not unfree,
- identifier,
- )
-
- def find_matches(
- self,
- identifier: str,
- requirements: Mapping[str, Iterator[Requirement]],
- incompatibilities: Mapping[str, Iterator[Candidate]],
- ) -> Iterable[Candidate]:
- def _eligible_for_upgrade(identifier: str) -> bool:
- """Are upgrades allowed for this project?
-
- This checks the upgrade strategy, and whether the project was one
- that the user specified in the command line, in order to decide
- whether we should upgrade if there's a newer version available.
-
- (Note that we don't need access to the `--upgrade` flag, because
- an upgrade strategy of "to-satisfy-only" means that `--upgrade`
- was not specified).
- """
- if self._upgrade_strategy == "eager":
- return True
- elif self._upgrade_strategy == "only-if-needed":
- user_order = _get_with_identifier(
- self._user_requested,
- identifier,
- default=None,
- )
- return user_order is not None
- return False
-
- constraint = _get_with_identifier(
- self._constraints,
- identifier,
- default=Constraint.empty(),
- )
- return self._factory.find_candidates(
- identifier=identifier,
- requirements=requirements,
- constraint=constraint,
- prefers_installed=(not _eligible_for_upgrade(identifier)),
- incompatibilities=incompatibilities,
- )
-
- def is_satisfied_by(self, requirement: Requirement, candidate: Candidate) -> bool:
- return requirement.is_satisfied_by(candidate)
-
- def get_dependencies(self, candidate: Candidate) -> Sequence[Requirement]:
- with_requires = not self._ignore_dependencies
- return [r for r in candidate.iter_dependencies(with_requires) if r is not None]
-
- @staticmethod
- def is_backtrack_cause(
- identifier: str, backtrack_causes: Sequence["PreferenceInformation"]
- ) -> bool:
- for backtrack_cause in backtrack_causes:
- if identifier == backtrack_cause.requirement.name:
- return True
- if backtrack_cause.parent and identifier == backtrack_cause.parent.name:
- return True
- return False
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/styled.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/styled.py
deleted file mode 100644
index 91cd0db31c14e30d4c1e2e9f36382b7a5e022870..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/styled.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from typing import TYPE_CHECKING
-
-from .measure import Measurement
-from .segment import Segment
-from .style import StyleType
-
-if TYPE_CHECKING:
- from .console import Console, ConsoleOptions, RenderResult, RenderableType
-
-
-class Styled:
- """Apply a style to a renderable.
-
- Args:
- renderable (RenderableType): Any renderable.
- style (StyleType): A style to apply across the entire renderable.
- """
-
- def __init__(self, renderable: "RenderableType", style: "StyleType") -> None:
- self.renderable = renderable
- self.style = style
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
- style = console.get_style(self.style)
- rendered_segments = console.render(self.renderable, options)
- segments = Segment.apply_style(rendered_segments, style)
- return segments
-
- def __rich_measure__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> Measurement:
- return Measurement.get(console, options, self.renderable)
-
-
-if __name__ == "__main__": # pragma: no cover
- from pip._vendor.rich import print
- from pip._vendor.rich.panel import Panel
-
- panel = Styled(Panel("hello"), "on blue")
- print(panel)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/connectionpool.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/connectionpool.py
deleted file mode 100644
index c23d736b186f50eb723eebbd6dfce281d91c2353..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/connectionpool.py
+++ /dev/null
@@ -1,1110 +0,0 @@
-from __future__ import absolute_import
-
-import errno
-import logging
-import re
-import socket
-import sys
-import warnings
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from .connection import (
- BaseSSLError,
- BrokenPipeError,
- DummyConnection,
- HTTPConnection,
- HTTPException,
- HTTPSConnection,
- VerifiedHTTPSConnection,
- port_by_scheme,
-)
-from .exceptions import (
- ClosedPoolError,
- EmptyPoolError,
- HeaderParsingError,
- HostChangedError,
- InsecureRequestWarning,
- LocationValueError,
- MaxRetryError,
- NewConnectionError,
- ProtocolError,
- ProxyError,
- ReadTimeoutError,
- SSLError,
- TimeoutError,
-)
-from .packages import six
-from .packages.six.moves import queue
-from .request import RequestMethods
-from .response import HTTPResponse
-from .util.connection import is_connection_dropped
-from .util.proxy import connection_requires_http_tunnel
-from .util.queue import LifoQueue
-from .util.request import set_file_position
-from .util.response import assert_header_parsing
-from .util.retry import Retry
-from .util.ssl_match_hostname import CertificateError
-from .util.timeout import Timeout
-from .util.url import Url, _encode_target
-from .util.url import _normalize_host as normalize_host
-from .util.url import get_host, parse_url
-
-xrange = six.moves.xrange
-
-log = logging.getLogger(__name__)
-
-_Default = object()
-
-
-# Pool objects
-class ConnectionPool(object):
- """
- Base class for all connection pools, such as
- :class:`.HTTPConnectionPool` and :class:`.HTTPSConnectionPool`.
-
- .. note::
- ConnectionPool.urlopen() does not normalize or percent-encode target URIs
- which is useful if your target server doesn't support percent-encoded
- target URIs.
- """
-
- scheme = None
- QueueCls = LifoQueue
-
- def __init__(self, host, port=None):
- if not host:
- raise LocationValueError("No host specified.")
-
- self.host = _normalize_host(host, scheme=self.scheme)
- self._proxy_host = host.lower()
- self.port = port
-
- def __str__(self):
- return "%s(host=%r, port=%r)" % (type(self).__name__, self.host, self.port)
-
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- self.close()
- # Return False to re-raise any potential exceptions
- return False
-
- def close(self):
- """
- Close all pooled connections and disable the pool.
- """
- pass
-
-
-# This is taken from http://hg.python.org/cpython/file/7aaba721ebc0/Lib/socket.py#l252
-_blocking_errnos = {errno.EAGAIN, errno.EWOULDBLOCK}
-
-
-class HTTPConnectionPool(ConnectionPool, RequestMethods):
- """
- Thread-safe connection pool for one host.
-
- :param host:
- Host used for this HTTP Connection (e.g. "localhost"), passed into
- :class:`http.client.HTTPConnection`.
-
- :param port:
- Port used for this HTTP Connection (None is equivalent to 80), passed
- into :class:`http.client.HTTPConnection`.
-
- :param strict:
- Causes BadStatusLine to be raised if the status line can't be parsed
- as a valid HTTP/1.0 or 1.1 status line, passed into
- :class:`http.client.HTTPConnection`.
-
- .. note::
- Only works in Python 2. This parameter is ignored in Python 3.
-
- :param timeout:
- Socket timeout in seconds for each individual connection. This can
- be a float or integer, which sets the timeout for the HTTP request,
- or an instance of :class:`urllib3.util.Timeout` which gives you more
- fine-grained control over request timeouts. After the constructor has
- been parsed, this is always a `urllib3.util.Timeout` object.
-
- :param maxsize:
- Number of connections to save that can be reused. More than 1 is useful
- in multithreaded situations. If ``block`` is set to False, more
- connections will be created but they will not be saved once they've
- been used.
-
- :param block:
- If set to True, no more than ``maxsize`` connections will be used at
- a time. When no free connections are available, the call will block
- until a connection has been released. This is a useful side effect for
- particular multithreaded situations where one does not want to use more
- than maxsize connections per host to prevent flooding.
-
- :param headers:
- Headers to include with all requests, unless other headers are given
- explicitly.
-
- :param retries:
- Retry configuration to use by default with requests in this pool.
-
- :param _proxy:
- Parsed proxy URL, should not be used directly, instead, see
- :class:`urllib3.ProxyManager`
-
- :param _proxy_headers:
- A dictionary with proxy headers, should not be used directly,
- instead, see :class:`urllib3.ProxyManager`
-
- :param \\**conn_kw:
- Additional parameters are used to create fresh :class:`urllib3.connection.HTTPConnection`,
- :class:`urllib3.connection.HTTPSConnection` instances.
- """
-
- scheme = "http"
- ConnectionCls = HTTPConnection
- ResponseCls = HTTPResponse
-
- def __init__(
- self,
- host,
- port=None,
- strict=False,
- timeout=Timeout.DEFAULT_TIMEOUT,
- maxsize=1,
- block=False,
- headers=None,
- retries=None,
- _proxy=None,
- _proxy_headers=None,
- _proxy_config=None,
- **conn_kw
- ):
- ConnectionPool.__init__(self, host, port)
- RequestMethods.__init__(self, headers)
-
- self.strict = strict
-
- if not isinstance(timeout, Timeout):
- timeout = Timeout.from_float(timeout)
-
- if retries is None:
- retries = Retry.DEFAULT
-
- self.timeout = timeout
- self.retries = retries
-
- self.pool = self.QueueCls(maxsize)
- self.block = block
-
- self.proxy = _proxy
- self.proxy_headers = _proxy_headers or {}
- self.proxy_config = _proxy_config
-
- # Fill the queue up so that doing get() on it will block properly
- for _ in xrange(maxsize):
- self.pool.put(None)
-
- # These are mostly for testing and debugging purposes.
- self.num_connections = 0
- self.num_requests = 0
- self.conn_kw = conn_kw
-
- if self.proxy:
- # Enable Nagle's algorithm for proxies, to avoid packet fragmentation.
- # We cannot know if the user has added default socket options, so we cannot replace the
- # list.
- self.conn_kw.setdefault("socket_options", [])
-
- self.conn_kw["proxy"] = self.proxy
- self.conn_kw["proxy_config"] = self.proxy_config
-
- def _new_conn(self):
- """
- Return a fresh :class:`HTTPConnection`.
- """
- self.num_connections += 1
- log.debug(
- "Starting new HTTP connection (%d): %s:%s",
- self.num_connections,
- self.host,
- self.port or "80",
- )
-
- conn = self.ConnectionCls(
- host=self.host,
- port=self.port,
- timeout=self.timeout.connect_timeout,
- strict=self.strict,
- **self.conn_kw
- )
- return conn
-
- def _get_conn(self, timeout=None):
- """
- Get a connection. Will return a pooled connection if one is available.
-
- If no connections are available and :prop:`.block` is ``False``, then a
- fresh connection is returned.
-
- :param timeout:
- Seconds to wait before giving up and raising
- :class:`urllib3.exceptions.EmptyPoolError` if the pool is empty and
- :prop:`.block` is ``True``.
- """
- conn = None
- try:
- conn = self.pool.get(block=self.block, timeout=timeout)
-
- except AttributeError: # self.pool is None
- raise ClosedPoolError(self, "Pool is closed.")
-
- except queue.Empty:
- if self.block:
- raise EmptyPoolError(
- self,
- "Pool reached maximum size and no more connections are allowed.",
- )
- pass # Oh well, we'll create a new connection then
-
- # If this is a persistent connection, check if it got disconnected
- if conn and is_connection_dropped(conn):
- log.debug("Resetting dropped connection: %s", self.host)
- conn.close()
- if getattr(conn, "auto_open", 1) == 0:
- # This is a proxied connection that has been mutated by
- # http.client._tunnel() and cannot be reused (since it would
- # attempt to bypass the proxy)
- conn = None
-
- return conn or self._new_conn()
-
- def _put_conn(self, conn):
- """
- Put a connection back into the pool.
-
- :param conn:
- Connection object for the current host and port as returned by
- :meth:`._new_conn` or :meth:`._get_conn`.
-
- If the pool is already full, the connection is closed and discarded
- because we exceeded maxsize. If connections are discarded frequently,
- then maxsize should be increased.
-
- If the pool is closed, then the connection will be closed and discarded.
- """
- try:
- self.pool.put(conn, block=False)
- return # Everything is dandy, done.
- except AttributeError:
- # self.pool is None.
- pass
- except queue.Full:
- # This should never happen if self.block == True
- log.warning(
- "Connection pool is full, discarding connection: %s. Connection pool size: %s",
- self.host,
- self.pool.qsize(),
- )
- # Connection never got put back into the pool, close it.
- if conn:
- conn.close()
-
- def _validate_conn(self, conn):
- """
- Called right before a request is made, after the socket is created.
- """
- pass
-
- def _prepare_proxy(self, conn):
- # Nothing to do for HTTP connections.
- pass
-
- def _get_timeout(self, timeout):
- """Helper that always returns a :class:`urllib3.util.Timeout`"""
- if timeout is _Default:
- return self.timeout.clone()
-
- if isinstance(timeout, Timeout):
- return timeout.clone()
- else:
- # User passed us an int/float. This is for backwards compatibility,
- # can be removed later
- return Timeout.from_float(timeout)
-
- def _raise_timeout(self, err, url, timeout_value):
- """Is the error actually a timeout? Will raise a ReadTimeout or pass"""
-
- if isinstance(err, SocketTimeout):
- raise ReadTimeoutError(
- self, url, "Read timed out. (read timeout=%s)" % timeout_value
- )
-
- # See the above comment about EAGAIN in Python 3. In Python 2 we have
- # to specifically catch it and throw the timeout error
- if hasattr(err, "errno") and err.errno in _blocking_errnos:
- raise ReadTimeoutError(
- self, url, "Read timed out. (read timeout=%s)" % timeout_value
- )
-
- # Catch possible read timeouts thrown as SSL errors. If not the
- # case, rethrow the original. We need to do this because of:
- # http://bugs.python.org/issue10272
- if "timed out" in str(err) or "did not complete (read)" in str(
- err
- ): # Python < 2.7.4
- raise ReadTimeoutError(
- self, url, "Read timed out. (read timeout=%s)" % timeout_value
- )
-
- def _make_request(
- self, conn, method, url, timeout=_Default, chunked=False, **httplib_request_kw
- ):
- """
- Perform a request on a given urllib connection object taken from our
- pool.
-
- :param conn:
- a connection from one of our connection pools
-
- :param timeout:
- Socket timeout in seconds for the request. This can be a
- float or integer, which will set the same timeout value for
- the socket connect and the socket read, or an instance of
- :class:`urllib3.util.Timeout`, which gives you more fine-grained
- control over your timeouts.
- """
- self.num_requests += 1
-
- timeout_obj = self._get_timeout(timeout)
- timeout_obj.start_connect()
- conn.timeout = Timeout.resolve_default_timeout(timeout_obj.connect_timeout)
-
- # Trigger any extra validation we need to do.
- try:
- self._validate_conn(conn)
- except (SocketTimeout, BaseSSLError) as e:
- # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
- self._raise_timeout(err=e, url=url, timeout_value=conn.timeout)
- raise
-
- # conn.request() calls http.client.*.request, not the method in
- # urllib3.request. It also calls makefile (recv) on the socket.
- try:
- if chunked:
- conn.request_chunked(method, url, **httplib_request_kw)
- else:
- conn.request(method, url, **httplib_request_kw)
-
- # We are swallowing BrokenPipeError (errno.EPIPE) since the server is
- # legitimately able to close the connection after sending a valid response.
- # With this behaviour, the received response is still readable.
- except BrokenPipeError:
- # Python 3
- pass
- except IOError as e:
- # Python 2 and macOS/Linux
- # EPIPE and ESHUTDOWN are BrokenPipeError on Python 2, and EPROTOTYPE is needed on macOS
- # https://erickt.github.io/blog/2014/11/19/adventures-in-debugging-a-potential-osx-kernel-bug/
- if e.errno not in {
- errno.EPIPE,
- errno.ESHUTDOWN,
- errno.EPROTOTYPE,
- }:
- raise
-
- # Reset the timeout for the recv() on the socket
- read_timeout = timeout_obj.read_timeout
-
- # App Engine doesn't have a sock attr
- if getattr(conn, "sock", None):
- # In Python 3 socket.py will catch EAGAIN and return None when you
- # try and read into the file pointer created by http.client, which
- # instead raises a BadStatusLine exception. Instead of catching
- # the exception and assuming all BadStatusLine exceptions are read
- # timeouts, check for a zero timeout before making the request.
- if read_timeout == 0:
- raise ReadTimeoutError(
- self, url, "Read timed out. (read timeout=%s)" % read_timeout
- )
- if read_timeout is Timeout.DEFAULT_TIMEOUT:
- conn.sock.settimeout(socket.getdefaulttimeout())
- else: # None or a value
- conn.sock.settimeout(read_timeout)
-
- # Receive the response from the server
- try:
- try:
- # Python 2.7, use buffering of HTTP responses
- httplib_response = conn.getresponse(buffering=True)
- except TypeError:
- # Python 3
- try:
- httplib_response = conn.getresponse()
- except BaseException as e:
- # Remove the TypeError from the exception chain in
- # Python 3 (including for exceptions like SystemExit).
- # Otherwise it looks like a bug in the code.
- six.raise_from(e, None)
- except (SocketTimeout, BaseSSLError, SocketError) as e:
- self._raise_timeout(err=e, url=url, timeout_value=read_timeout)
- raise
-
- # AppEngine doesn't have a version attr.
- http_version = getattr(conn, "_http_vsn_str", "HTTP/?")
- log.debug(
- '%s://%s:%s "%s %s %s" %s %s',
- self.scheme,
- self.host,
- self.port,
- method,
- url,
- http_version,
- httplib_response.status,
- httplib_response.length,
- )
-
- try:
- assert_header_parsing(httplib_response.msg)
- except (HeaderParsingError, TypeError) as hpe: # Platform-specific: Python 3
- log.warning(
- "Failed to parse headers (url=%s): %s",
- self._absolute_url(url),
- hpe,
- exc_info=True,
- )
-
- return httplib_response
-
- def _absolute_url(self, path):
- return Url(scheme=self.scheme, host=self.host, port=self.port, path=path).url
-
- def close(self):
- """
- Close all pooled connections and disable the pool.
- """
- if self.pool is None:
- return
- # Disable access to the pool
- old_pool, self.pool = self.pool, None
-
- try:
- while True:
- conn = old_pool.get(block=False)
- if conn:
- conn.close()
-
- except queue.Empty:
- pass # Done.
-
- def is_same_host(self, url):
- """
- Check if the given ``url`` is a member of the same host as this
- connection pool.
- """
- if url.startswith("/"):
- return True
-
- # TODO: Add optional support for socket.gethostbyname checking.
- scheme, host, port = get_host(url)
- if host is not None:
- host = _normalize_host(host, scheme=scheme)
-
- # Use explicit default port for comparison when none is given
- if self.port and not port:
- port = port_by_scheme.get(scheme)
- elif not self.port and port == port_by_scheme.get(scheme):
- port = None
-
- return (scheme, host, port) == (self.scheme, self.host, self.port)
-
- def urlopen(
- self,
- method,
- url,
- body=None,
- headers=None,
- retries=None,
- redirect=True,
- assert_same_host=True,
- timeout=_Default,
- pool_timeout=None,
- release_conn=None,
- chunked=False,
- body_pos=None,
- **response_kw
- ):
- """
- Get a connection from the pool and perform an HTTP request. This is the
- lowest level call for making a request, so you'll need to specify all
- the raw details.
-
- .. note::
-
- More commonly, it's appropriate to use a convenience method provided
- by :class:`.RequestMethods`, such as :meth:`request`.
-
- .. note::
-
- `release_conn` will only behave as expected if
- `preload_content=False` because we want to make
- `preload_content=False` the default behaviour someday soon without
- breaking backwards compatibility.
-
- :param method:
- HTTP request method (such as GET, POST, PUT, etc.)
-
- :param url:
- The URL to perform the request on.
-
- :param body:
- Data to send in the request body, either :class:`str`, :class:`bytes`,
- an iterable of :class:`str`/:class:`bytes`, or a file-like object.
-
- :param headers:
- Dictionary of custom headers to send, such as User-Agent,
- If-None-Match, etc. If None, pool headers are used. If provided,
- these headers completely replace any pool-specific headers.
-
- :param retries:
- Configure the number of retries to allow before raising a
- :class:`~urllib3.exceptions.MaxRetryError` exception.
-
- Pass ``None`` to retry until you receive a response. Pass a
- :class:`~urllib3.util.retry.Retry` object for fine-grained control
- over different types of retries.
- Pass an integer number to retry connection errors that many times,
- but no other types of errors. Pass zero to never retry.
-
- If ``False``, then retries are disabled and any exception is raised
- immediately. Also, instead of raising a MaxRetryError on redirects,
- the redirect response will be returned.
-
- :type retries: :class:`~urllib3.util.retry.Retry`, False, or an int.
-
- :param redirect:
- If True, automatically handle redirects (status codes 301, 302,
- 303, 307, 308). Each redirect counts as a retry. Disabling retries
- will disable redirect, too.
-
- :param assert_same_host:
- If ``True``, will make sure that the host of the pool requests is
- consistent else will raise HostChangedError. When ``False``, you can
- use the pool on an HTTP proxy and request foreign hosts.
-
- :param timeout:
- If specified, overrides the default timeout for this one
- request. It may be a float (in seconds) or an instance of
- :class:`urllib3.util.Timeout`.
-
- :param pool_timeout:
- If set and the pool is set to block=True, then this method will
- block for ``pool_timeout`` seconds and raise EmptyPoolError if no
- connection is available within the time period.
-
- :param release_conn:
- If False, then the urlopen call will not release the connection
- back into the pool once a response is received (but will release if
- you read the entire contents of the response such as when
- `preload_content=True`). This is useful if you're not preloading
- the response's content immediately. You will need to call
- ``r.release_conn()`` on the response ``r`` to return the connection
- back into the pool. If None, it takes the value of
- ``response_kw.get('preload_content', True)``.
-
- :param chunked:
- If True, urllib3 will send the body using chunked transfer
- encoding. Otherwise, urllib3 will send the body using the standard
- content-length form. Defaults to False.
-
- :param int body_pos:
- Position to seek to in file-like body in the event of a retry or
- redirect. Typically this won't need to be set because urllib3 will
- auto-populate the value when needed.
-
- :param \\**response_kw:
- Additional parameters are passed to
- :meth:`urllib3.response.HTTPResponse.from_httplib`
- """
-
- parsed_url = parse_url(url)
- destination_scheme = parsed_url.scheme
-
- if headers is None:
- headers = self.headers
-
- if not isinstance(retries, Retry):
- retries = Retry.from_int(retries, redirect=redirect, default=self.retries)
-
- if release_conn is None:
- release_conn = response_kw.get("preload_content", True)
-
- # Check host
- if assert_same_host and not self.is_same_host(url):
- raise HostChangedError(self, url, retries)
-
- # Ensure that the URL we're connecting to is properly encoded
- if url.startswith("/"):
- url = six.ensure_str(_encode_target(url))
- else:
- url = six.ensure_str(parsed_url.url)
-
- conn = None
-
- # Track whether `conn` needs to be released before
- # returning/raising/recursing. Update this variable if necessary, and
- # leave `release_conn` constant throughout the function. That way, if
- # the function recurses, the original value of `release_conn` will be
- # passed down into the recursive call, and its value will be respected.
- #
- # See issue #651 [1] for details.
- #
- # [1]
- release_this_conn = release_conn
-
- http_tunnel_required = connection_requires_http_tunnel(
- self.proxy, self.proxy_config, destination_scheme
- )
-
- # Merge the proxy headers. Only done when not using HTTP CONNECT. We
- # have to copy the headers dict so we can safely change it without those
- # changes being reflected in anyone else's copy.
- if not http_tunnel_required:
- headers = headers.copy()
- headers.update(self.proxy_headers)
-
- # Must keep the exception bound to a separate variable or else Python 3
- # complains about UnboundLocalError.
- err = None
-
- # Keep track of whether we cleanly exited the except block. This
- # ensures we do proper cleanup in finally.
- clean_exit = False
-
- # Rewind body position, if needed. Record current position
- # for future rewinds in the event of a redirect/retry.
- body_pos = set_file_position(body, body_pos)
-
- try:
- # Request a connection from the queue.
- timeout_obj = self._get_timeout(timeout)
- conn = self._get_conn(timeout=pool_timeout)
-
- conn.timeout = timeout_obj.connect_timeout
-
- is_new_proxy_conn = self.proxy is not None and not getattr(
- conn, "sock", None
- )
- if is_new_proxy_conn and http_tunnel_required:
- self._prepare_proxy(conn)
-
- # Make the request on the httplib connection object.
- httplib_response = self._make_request(
- conn,
- method,
- url,
- timeout=timeout_obj,
- body=body,
- headers=headers,
- chunked=chunked,
- )
-
- # If we're going to release the connection in ``finally:``, then
- # the response doesn't need to know about the connection. Otherwise
- # it will also try to release it and we'll have a double-release
- # mess.
- response_conn = conn if not release_conn else None
-
- # Pass method to Response for length checking
- response_kw["request_method"] = method
-
- # Import httplib's response into our own wrapper object
- response = self.ResponseCls.from_httplib(
- httplib_response,
- pool=self,
- connection=response_conn,
- retries=retries,
- **response_kw
- )
-
- # Everything went great!
- clean_exit = True
-
- except EmptyPoolError:
- # Didn't get a connection from the pool, no need to clean up
- clean_exit = True
- release_this_conn = False
- raise
-
- except (
- TimeoutError,
- HTTPException,
- SocketError,
- ProtocolError,
- BaseSSLError,
- SSLError,
- CertificateError,
- ) as e:
- # Discard the connection for these exceptions. It will be
- # replaced during the next _get_conn() call.
- clean_exit = False
-
- def _is_ssl_error_message_from_http_proxy(ssl_error):
- # We're trying to detect the message 'WRONG_VERSION_NUMBER' but
- # SSLErrors are kinda all over the place when it comes to the message,
- # so we try to cover our bases here!
- message = " ".join(re.split("[^a-z]", str(ssl_error).lower()))
- return (
- "wrong version number" in message or "unknown protocol" in message
- )
-
- # Try to detect a common user error with proxies which is to
- # set an HTTP proxy to be HTTPS when it should be 'http://'
- # (ie {'http': 'http://proxy', 'https': 'https://proxy'})
- # Instead we add a nice error message and point to a URL.
- if (
- isinstance(e, BaseSSLError)
- and self.proxy
- and _is_ssl_error_message_from_http_proxy(e)
- and conn.proxy
- and conn.proxy.scheme == "https"
- ):
- e = ProxyError(
- "Your proxy appears to only use HTTP and not HTTPS, "
- "try changing your proxy URL to be HTTP. See: "
- "https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html"
- "#https-proxy-error-http-proxy",
- SSLError(e),
- )
- elif isinstance(e, (BaseSSLError, CertificateError)):
- e = SSLError(e)
- elif isinstance(e, (SocketError, NewConnectionError)) and self.proxy:
- e = ProxyError("Cannot connect to proxy.", e)
- elif isinstance(e, (SocketError, HTTPException)):
- e = ProtocolError("Connection aborted.", e)
-
- retries = retries.increment(
- method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
- )
- retries.sleep()
-
- # Keep track of the error for the retry warning.
- err = e
-
- finally:
- if not clean_exit:
- # We hit some kind of exception, handled or otherwise. We need
- # to throw the connection away unless explicitly told not to.
- # Close the connection, set the variable to None, and make sure
- # we put the None back in the pool to avoid leaking it.
- conn = conn and conn.close()
- release_this_conn = True
-
- if release_this_conn:
- # Put the connection back to be reused. If the connection is
- # expired then it will be None, which will get replaced with a
- # fresh connection during _get_conn.
- self._put_conn(conn)
-
- if not conn:
- # Try again
- log.warning(
- "Retrying (%r) after connection broken by '%r': %s", retries, err, url
- )
- return self.urlopen(
- method,
- url,
- body,
- headers,
- retries,
- redirect,
- assert_same_host,
- timeout=timeout,
- pool_timeout=pool_timeout,
- release_conn=release_conn,
- chunked=chunked,
- body_pos=body_pos,
- **response_kw
- )
-
- # Handle redirect?
- redirect_location = redirect and response.get_redirect_location()
- if redirect_location:
- if response.status == 303:
- method = "GET"
-
- try:
- retries = retries.increment(method, url, response=response, _pool=self)
- except MaxRetryError:
- if retries.raise_on_redirect:
- response.drain_conn()
- raise
- return response
-
- response.drain_conn()
- retries.sleep_for_retry(response)
- log.debug("Redirecting %s -> %s", url, redirect_location)
- return self.urlopen(
- method,
- redirect_location,
- body,
- headers,
- retries=retries,
- redirect=redirect,
- assert_same_host=assert_same_host,
- timeout=timeout,
- pool_timeout=pool_timeout,
- release_conn=release_conn,
- chunked=chunked,
- body_pos=body_pos,
- **response_kw
- )
-
- # Check if we should retry the HTTP response.
- has_retry_after = bool(response.headers.get("Retry-After"))
- if retries.is_retry(method, response.status, has_retry_after):
- try:
- retries = retries.increment(method, url, response=response, _pool=self)
- except MaxRetryError:
- if retries.raise_on_status:
- response.drain_conn()
- raise
- return response
-
- response.drain_conn()
- retries.sleep(response)
- log.debug("Retry: %s", url)
- return self.urlopen(
- method,
- url,
- body,
- headers,
- retries=retries,
- redirect=redirect,
- assert_same_host=assert_same_host,
- timeout=timeout,
- pool_timeout=pool_timeout,
- release_conn=release_conn,
- chunked=chunked,
- body_pos=body_pos,
- **response_kw
- )
-
- return response
-
-
-class HTTPSConnectionPool(HTTPConnectionPool):
- """
- Same as :class:`.HTTPConnectionPool`, but HTTPS.
-
- :class:`.HTTPSConnection` uses one of ``assert_fingerprint``,
- ``assert_hostname`` and ``host`` in this order to verify connections.
- If ``assert_hostname`` is False, no verification is done.
-
- The ``key_file``, ``cert_file``, ``cert_reqs``, ``ca_certs``,
- ``ca_cert_dir``, ``ssl_version``, ``key_password`` are only used if :mod:`ssl`
- is available and are fed into :meth:`urllib3.util.ssl_wrap_socket` to upgrade
- the connection socket into an SSL socket.
- """
-
- scheme = "https"
- ConnectionCls = HTTPSConnection
-
- def __init__(
- self,
- host,
- port=None,
- strict=False,
- timeout=Timeout.DEFAULT_TIMEOUT,
- maxsize=1,
- block=False,
- headers=None,
- retries=None,
- _proxy=None,
- _proxy_headers=None,
- key_file=None,
- cert_file=None,
- cert_reqs=None,
- key_password=None,
- ca_certs=None,
- ssl_version=None,
- assert_hostname=None,
- assert_fingerprint=None,
- ca_cert_dir=None,
- **conn_kw
- ):
-
- HTTPConnectionPool.__init__(
- self,
- host,
- port,
- strict,
- timeout,
- maxsize,
- block,
- headers,
- retries,
- _proxy,
- _proxy_headers,
- **conn_kw
- )
-
- self.key_file = key_file
- self.cert_file = cert_file
- self.cert_reqs = cert_reqs
- self.key_password = key_password
- self.ca_certs = ca_certs
- self.ca_cert_dir = ca_cert_dir
- self.ssl_version = ssl_version
- self.assert_hostname = assert_hostname
- self.assert_fingerprint = assert_fingerprint
-
- def _prepare_conn(self, conn):
- """
- Prepare the ``connection`` for :meth:`urllib3.util.ssl_wrap_socket`
- and establish the tunnel if proxy is used.
- """
-
- if isinstance(conn, VerifiedHTTPSConnection):
- conn.set_cert(
- key_file=self.key_file,
- key_password=self.key_password,
- cert_file=self.cert_file,
- cert_reqs=self.cert_reqs,
- ca_certs=self.ca_certs,
- ca_cert_dir=self.ca_cert_dir,
- assert_hostname=self.assert_hostname,
- assert_fingerprint=self.assert_fingerprint,
- )
- conn.ssl_version = self.ssl_version
- return conn
-
- def _prepare_proxy(self, conn):
- """
- Establishes a tunnel connection through HTTP CONNECT.
-
- Tunnel connection is established early because otherwise httplib would
- improperly set Host: header to proxy's IP:port.
- """
-
- conn.set_tunnel(self._proxy_host, self.port, self.proxy_headers)
-
- if self.proxy.scheme == "https":
- conn.tls_in_tls_required = True
-
- conn.connect()
-
- def _new_conn(self):
- """
- Return a fresh :class:`http.client.HTTPSConnection`.
- """
- self.num_connections += 1
- log.debug(
- "Starting new HTTPS connection (%d): %s:%s",
- self.num_connections,
- self.host,
- self.port or "443",
- )
-
- if not self.ConnectionCls or self.ConnectionCls is DummyConnection:
- raise SSLError(
- "Can't connect to HTTPS URL because the SSL module is not available."
- )
-
- actual_host = self.host
- actual_port = self.port
- if self.proxy is not None:
- actual_host = self.proxy.host
- actual_port = self.proxy.port
-
- conn = self.ConnectionCls(
- host=actual_host,
- port=actual_port,
- timeout=self.timeout.connect_timeout,
- strict=self.strict,
- cert_file=self.cert_file,
- key_file=self.key_file,
- key_password=self.key_password,
- **self.conn_kw
- )
-
- return self._prepare_conn(conn)
-
- def _validate_conn(self, conn):
- """
- Called right before a request is made, after the socket is created.
- """
- super(HTTPSConnectionPool, self)._validate_conn(conn)
-
- # Force connect early to allow us to validate the connection.
- if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
- conn.connect()
-
- if not conn.is_verified:
- warnings.warn(
- (
- "Unverified HTTPS request is being made to host '%s'. "
- "Adding certificate verification is strongly advised. See: "
- "https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html"
- "#ssl-warnings" % conn.host
- ),
- InsecureRequestWarning,
- )
-
- if getattr(conn, "proxy_is_verified", None) is False:
- warnings.warn(
- (
- "Unverified HTTPS connection done to an HTTPS proxy. "
- "Adding certificate verification is strongly advised. See: "
- "https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html"
- "#ssl-warnings"
- ),
- InsecureRequestWarning,
- )
-
-
-def connection_from_url(url, **kw):
- """
- Given a url, return an :class:`.ConnectionPool` instance of its host.
-
- This is a shortcut for not having to parse out the scheme, host, and port
- of the url before creating an :class:`.ConnectionPool` instance.
-
- :param url:
- Absolute URL string that must include the scheme. Port is optional.
-
- :param \\**kw:
- Passes additional parameters to the constructor of the appropriate
- :class:`.ConnectionPool`. Useful for specifying things like
- timeout, maxsize, headers, etc.
-
- Example::
-
- >>> conn = connection_from_url('http://google.com/')
- >>> r = conn.request('GET', '/')
- """
- scheme, host, port = get_host(url)
- port = port or port_by_scheme.get(scheme, 80)
- if scheme == "https":
- return HTTPSConnectionPool(host, port=port, **kw)
- else:
- return HTTPConnectionPool(host, port=port, **kw)
-
-
-def _normalize_host(host, scheme):
- """
- Normalize hosts for comparisons and use with sockets.
- """
-
- host = normalize_host(host, scheme)
-
- # httplib doesn't like it when we include brackets in IPv6 addresses
- # Specifically, if we include brackets but also pass the port then
- # httplib crazily doubles up the square brackets on the Host header.
- # Instead, we need to make sure we never pass ``None`` as the port.
- # However, for backward compatibility reasons we can't actually
- # *assert* that. See http://bugs.python.org/issue28539
- if host.startswith("[") and host.endswith("]"):
- host = host[1:-1]
- return host
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/develop.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/develop.py
deleted file mode 100644
index 24fb0a7c81bc665844d5d307eee2d720079c039f..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/develop.py
+++ /dev/null
@@ -1,193 +0,0 @@
-from distutils.util import convert_path
-from distutils import log
-from distutils.errors import DistutilsError, DistutilsOptionError
-import os
-import glob
-import io
-
-import pkg_resources
-from setuptools.command.easy_install import easy_install
-from setuptools import namespaces
-import setuptools
-
-
-class develop(namespaces.DevelopInstaller, easy_install):
- """Set up package for development"""
-
- description = "install package in 'development mode'"
-
- user_options = easy_install.user_options + [
- ("uninstall", "u", "Uninstall this source package"),
- ("egg-path=", None, "Set the path to be used in the .egg-link file"),
- ]
-
- boolean_options = easy_install.boolean_options + ['uninstall']
-
- command_consumes_arguments = False # override base
-
- def run(self):
- if self.uninstall:
- self.multi_version = True
- self.uninstall_link()
- self.uninstall_namespaces()
- else:
- self.install_for_development()
- self.warn_deprecated_options()
-
- def initialize_options(self):
- self.uninstall = None
- self.egg_path = None
- easy_install.initialize_options(self)
- self.setup_path = None
- self.always_copy_from = '.' # always copy eggs installed in curdir
-
- def finalize_options(self):
- ei = self.get_finalized_command("egg_info")
- if ei.broken_egg_info:
- template = "Please rename %r to %r before using 'develop'"
- args = ei.egg_info, ei.broken_egg_info
- raise DistutilsError(template % args)
- self.args = [ei.egg_name]
-
- easy_install.finalize_options(self)
- self.expand_basedirs()
- self.expand_dirs()
- # pick up setup-dir .egg files only: no .egg-info
- self.package_index.scan(glob.glob('*.egg'))
-
- egg_link_fn = ei.egg_name + '.egg-link'
- self.egg_link = os.path.join(self.install_dir, egg_link_fn)
- self.egg_base = ei.egg_base
- if self.egg_path is None:
- self.egg_path = os.path.abspath(ei.egg_base)
-
- target = pkg_resources.normalize_path(self.egg_base)
- egg_path = pkg_resources.normalize_path(
- os.path.join(self.install_dir, self.egg_path)
- )
- if egg_path != target:
- raise DistutilsOptionError(
- "--egg-path must be a relative path from the install"
- " directory to " + target
- )
-
- # Make a distribution for the package's source
- self.dist = pkg_resources.Distribution(
- target,
- pkg_resources.PathMetadata(target, os.path.abspath(ei.egg_info)),
- project_name=ei.egg_name,
- )
-
- self.setup_path = self._resolve_setup_path(
- self.egg_base,
- self.install_dir,
- self.egg_path,
- )
-
- @staticmethod
- def _resolve_setup_path(egg_base, install_dir, egg_path):
- """
- Generate a path from egg_base back to '.' where the
- setup script resides and ensure that path points to the
- setup path from $install_dir/$egg_path.
- """
- path_to_setup = egg_base.replace(os.sep, '/').rstrip('/')
- if path_to_setup != os.curdir:
- path_to_setup = '../' * (path_to_setup.count('/') + 1)
- resolved = pkg_resources.normalize_path(
- os.path.join(install_dir, egg_path, path_to_setup)
- )
- if resolved != pkg_resources.normalize_path(os.curdir):
- raise DistutilsOptionError(
- "Can't get a consistent path to setup script from"
- " installation directory",
- resolved,
- pkg_resources.normalize_path(os.curdir),
- )
- return path_to_setup
-
- def install_for_development(self):
- self.run_command('egg_info')
-
- # Build extensions in-place
- self.reinitialize_command('build_ext', inplace=1)
- self.run_command('build_ext')
-
- if setuptools.bootstrap_install_from:
- self.easy_install(setuptools.bootstrap_install_from)
- setuptools.bootstrap_install_from = None
-
- self.install_namespaces()
-
- # create an .egg-link in the installation dir, pointing to our egg
- log.info("Creating %s (link to %s)", self.egg_link, self.egg_base)
- if not self.dry_run:
- with open(self.egg_link, "w") as f:
- f.write(self.egg_path + "\n" + self.setup_path)
- # postprocess the installed distro, fixing up .pth, installing scripts,
- # and handling requirements
- self.process_distribution(None, self.dist, not self.no_deps)
-
- def uninstall_link(self):
- if os.path.exists(self.egg_link):
- log.info("Removing %s (link to %s)", self.egg_link, self.egg_base)
- egg_link_file = open(self.egg_link)
- contents = [line.rstrip() for line in egg_link_file]
- egg_link_file.close()
- if contents not in ([self.egg_path], [self.egg_path, self.setup_path]):
- log.warn("Link points to %s: uninstall aborted", contents)
- return
- if not self.dry_run:
- os.unlink(self.egg_link)
- if not self.dry_run:
- self.update_pth(self.dist) # remove any .pth link to us
- if self.distribution.scripts:
- # XXX should also check for entry point scripts!
- log.warn("Note: you must uninstall or replace scripts manually!")
-
- def install_egg_scripts(self, dist):
- if dist is not self.dist:
- # Installing a dependency, so fall back to normal behavior
- return easy_install.install_egg_scripts(self, dist)
-
- # create wrapper scripts in the script dir, pointing to dist.scripts
-
- # new-style...
- self.install_wrapper_scripts(dist)
-
- # ...and old-style
- for script_name in self.distribution.scripts or []:
- script_path = os.path.abspath(convert_path(script_name))
- script_name = os.path.basename(script_path)
- with io.open(script_path) as strm:
- script_text = strm.read()
- self.install_script(dist, script_name, script_text, script_path)
-
- def install_wrapper_scripts(self, dist):
- dist = VersionlessRequirement(dist)
- return easy_install.install_wrapper_scripts(self, dist)
-
-
-class VersionlessRequirement:
- """
- Adapt a pkg_resources.Distribution to simply return the project
- name as the 'requirement' so that scripts will work across
- multiple versions.
-
- >>> from pkg_resources import Distribution
- >>> dist = Distribution(project_name='foo', version='1.0')
- >>> str(dist.as_requirement())
- 'foo==1.0'
- >>> adapted_dist = VersionlessRequirement(dist)
- >>> str(adapted_dist.as_requirement())
- 'foo'
- """
-
- def __init__(self, dist):
- self.__dist = dist
-
- def __getattr__(self, name):
- return getattr(self.__dist, name)
-
- def as_requirement(self):
- return self.project_name
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_apply_pyprojecttoml.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_apply_pyprojecttoml.py
deleted file mode 100644
index 8af556169cd6cce0282fce9ee09e6d6bcfc452c5..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_apply_pyprojecttoml.py
+++ /dev/null
@@ -1,377 +0,0 @@
-"""Translation layer between pyproject config and setuptools distribution and
-metadata objects.
-
-The distribution and metadata objects are modeled after (an old version of)
-core metadata, therefore configs in the format specified for ``pyproject.toml``
-need to be processed before being applied.
-
-**PRIVATE MODULE**: API reserved for setuptools internal usage only.
-"""
-import logging
-import os
-import warnings
-from collections.abc import Mapping
-from email.headerregistry import Address
-from functools import partial, reduce
-from itertools import chain
-from types import MappingProxyType
-from typing import (TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple,
- Type, Union)
-
-from setuptools._deprecation_warning import SetuptoolsDeprecationWarning
-
-if TYPE_CHECKING:
- from setuptools._importlib import metadata # noqa
- from setuptools.dist import Distribution # noqa
-
-EMPTY: Mapping = MappingProxyType({}) # Immutable dict-like
-_Path = Union[os.PathLike, str]
-_DictOrStr = Union[dict, str]
-_CorrespFn = Callable[["Distribution", Any, _Path], None]
-_Correspondence = Union[str, _CorrespFn]
-
-_logger = logging.getLogger(__name__)
-
-
-def apply(dist: "Distribution", config: dict, filename: _Path) -> "Distribution":
- """Apply configuration dict read with :func:`read_configuration`"""
-
- if not config:
- return dist # short-circuit unrelated pyproject.toml file
-
- root_dir = os.path.dirname(filename) or "."
-
- _apply_project_table(dist, config, root_dir)
- _apply_tool_table(dist, config, filename)
-
- current_directory = os.getcwd()
- os.chdir(root_dir)
- try:
- dist._finalize_requires()
- dist._finalize_license_files()
- finally:
- os.chdir(current_directory)
-
- return dist
-
-
-def _apply_project_table(dist: "Distribution", config: dict, root_dir: _Path):
- project_table = config.get("project", {}).copy()
- if not project_table:
- return # short-circuit
-
- _handle_missing_dynamic(dist, project_table)
- _unify_entry_points(project_table)
-
- for field, value in project_table.items():
- norm_key = json_compatible_key(field)
- corresp = PYPROJECT_CORRESPONDENCE.get(norm_key, norm_key)
- if callable(corresp):
- corresp(dist, value, root_dir)
- else:
- _set_config(dist, corresp, value)
-
-
-def _apply_tool_table(dist: "Distribution", config: dict, filename: _Path):
- tool_table = config.get("tool", {}).get("setuptools", {})
- if not tool_table:
- return # short-circuit
-
- for field, value in tool_table.items():
- norm_key = json_compatible_key(field)
-
- if norm_key in TOOL_TABLE_DEPRECATIONS:
- suggestion = TOOL_TABLE_DEPRECATIONS[norm_key]
- msg = f"The parameter `{norm_key}` is deprecated, {suggestion}"
- warnings.warn(msg, SetuptoolsDeprecationWarning)
-
- norm_key = TOOL_TABLE_RENAMES.get(norm_key, norm_key)
- _set_config(dist, norm_key, value)
-
- _copy_command_options(config, dist, filename)
-
-
-def _handle_missing_dynamic(dist: "Distribution", project_table: dict):
- """Be temporarily forgiving with ``dynamic`` fields not listed in ``dynamic``"""
- # TODO: Set fields back to `None` once the feature stabilizes
- dynamic = set(project_table.get("dynamic", []))
- for field, getter in _PREVIOUSLY_DEFINED.items():
- if not (field in project_table or field in dynamic):
- value = getter(dist)
- if value:
- msg = _WouldIgnoreField.message(field, value)
- warnings.warn(msg, _WouldIgnoreField)
-
-
-def json_compatible_key(key: str) -> str:
- """As defined in :pep:`566#json-compatible-metadata`"""
- return key.lower().replace("-", "_")
-
-
-def _set_config(dist: "Distribution", field: str, value: Any):
- setter = getattr(dist.metadata, f"set_{field}", None)
- if setter:
- setter(value)
- elif hasattr(dist.metadata, field) or field in SETUPTOOLS_PATCHES:
- setattr(dist.metadata, field, value)
- else:
- setattr(dist, field, value)
-
-
-_CONTENT_TYPES = {
- ".md": "text/markdown",
- ".rst": "text/x-rst",
- ".txt": "text/plain",
-}
-
-
-def _guess_content_type(file: str) -> Optional[str]:
- _, ext = os.path.splitext(file.lower())
- if not ext:
- return None
-
- if ext in _CONTENT_TYPES:
- return _CONTENT_TYPES[ext]
-
- valid = ", ".join(f"{k} ({v})" for k, v in _CONTENT_TYPES.items())
- msg = f"only the following file extensions are recognized: {valid}."
- raise ValueError(f"Undefined content type for {file}, {msg}")
-
-
-def _long_description(dist: "Distribution", val: _DictOrStr, root_dir: _Path):
- from setuptools.config import expand
-
- if isinstance(val, str):
- text = expand.read_files(val, root_dir)
- ctype = _guess_content_type(val)
- else:
- text = val.get("text") or expand.read_files(val.get("file", []), root_dir)
- ctype = val["content-type"]
-
- _set_config(dist, "long_description", text)
- if ctype:
- _set_config(dist, "long_description_content_type", ctype)
-
-
-def _license(dist: "Distribution", val: dict, root_dir: _Path):
- from setuptools.config import expand
-
- if "file" in val:
- _set_config(dist, "license", expand.read_files([val["file"]], root_dir))
- else:
- _set_config(dist, "license", val["text"])
-
-
-def _people(dist: "Distribution", val: List[dict], _root_dir: _Path, kind: str):
- field = []
- email_field = []
- for person in val:
- if "name" not in person:
- email_field.append(person["email"])
- elif "email" not in person:
- field.append(person["name"])
- else:
- addr = Address(display_name=person["name"], addr_spec=person["email"])
- email_field.append(str(addr))
-
- if field:
- _set_config(dist, kind, ", ".join(field))
- if email_field:
- _set_config(dist, f"{kind}_email", ", ".join(email_field))
-
-
-def _project_urls(dist: "Distribution", val: dict, _root_dir):
- _set_config(dist, "project_urls", val)
-
-
-def _python_requires(dist: "Distribution", val: dict, _root_dir):
- from setuptools.extern.packaging.specifiers import SpecifierSet
-
- _set_config(dist, "python_requires", SpecifierSet(val))
-
-
-def _dependencies(dist: "Distribution", val: list, _root_dir):
- if getattr(dist, "install_requires", []):
- msg = "`install_requires` overwritten in `pyproject.toml` (dependencies)"
- warnings.warn(msg)
- _set_config(dist, "install_requires", val)
-
-
-def _optional_dependencies(dist: "Distribution", val: dict, _root_dir):
- existing = getattr(dist, "extras_require", {})
- _set_config(dist, "extras_require", {**existing, **val})
-
-
-def _unify_entry_points(project_table: dict):
- project = project_table
- entry_points = project.pop("entry-points", project.pop("entry_points", {}))
- renaming = {"scripts": "console_scripts", "gui_scripts": "gui_scripts"}
- for key, value in list(project.items()): # eager to allow modifications
- norm_key = json_compatible_key(key)
- if norm_key in renaming and value:
- entry_points[renaming[norm_key]] = project.pop(key)
-
- if entry_points:
- project["entry-points"] = {
- name: [f"{k} = {v}" for k, v in group.items()]
- for name, group in entry_points.items()
- }
-
-
-def _copy_command_options(pyproject: dict, dist: "Distribution", filename: _Path):
- tool_table = pyproject.get("tool", {})
- cmdclass = tool_table.get("setuptools", {}).get("cmdclass", {})
- valid_options = _valid_command_options(cmdclass)
-
- cmd_opts = dist.command_options
- for cmd, config in pyproject.get("tool", {}).get("distutils", {}).items():
- cmd = json_compatible_key(cmd)
- valid = valid_options.get(cmd, set())
- cmd_opts.setdefault(cmd, {})
- for key, value in config.items():
- key = json_compatible_key(key)
- cmd_opts[cmd][key] = (str(filename), value)
- if key not in valid:
- # To avoid removing options that are specified dynamically we
- # just log a warn...
- _logger.warning(f"Command option {cmd}.{key} is not defined")
-
-
-def _valid_command_options(cmdclass: Mapping = EMPTY) -> Dict[str, Set[str]]:
- from .._importlib import metadata
- from setuptools.dist import Distribution
-
- valid_options = {"global": _normalise_cmd_options(Distribution.global_options)}
-
- unloaded_entry_points = metadata.entry_points(group='distutils.commands')
- loaded_entry_points = (_load_ep(ep) for ep in unloaded_entry_points)
- entry_points = (ep for ep in loaded_entry_points if ep)
- for cmd, cmd_class in chain(entry_points, cmdclass.items()):
- opts = valid_options.get(cmd, set())
- opts = opts | _normalise_cmd_options(getattr(cmd_class, "user_options", []))
- valid_options[cmd] = opts
-
- return valid_options
-
-
-def _load_ep(ep: "metadata.EntryPoint") -> Optional[Tuple[str, Type]]:
- # Ignore all the errors
- try:
- return (ep.name, ep.load())
- except Exception as ex:
- msg = f"{ex.__class__.__name__} while trying to load entry-point {ep.name}"
- _logger.warning(f"{msg}: {ex}")
- return None
-
-
-def _normalise_cmd_option_key(name: str) -> str:
- return json_compatible_key(name).strip("_=")
-
-
-def _normalise_cmd_options(desc: List[Tuple[str, Optional[str], str]]) -> Set[str]:
- return {_normalise_cmd_option_key(fancy_option[0]) for fancy_option in desc}
-
-
-def _attrgetter(attr):
- """
- Similar to ``operator.attrgetter`` but returns None if ``attr`` is not found
- >>> from types import SimpleNamespace
- >>> obj = SimpleNamespace(a=42, b=SimpleNamespace(c=13))
- >>> _attrgetter("a")(obj)
- 42
- >>> _attrgetter("b.c")(obj)
- 13
- >>> _attrgetter("d")(obj) is None
- True
- """
- return partial(reduce, lambda acc, x: getattr(acc, x, None), attr.split("."))
-
-
-def _some_attrgetter(*items):
- """
- Return the first "truth-y" attribute or None
- >>> from types import SimpleNamespace
- >>> obj = SimpleNamespace(a=42, b=SimpleNamespace(c=13))
- >>> _some_attrgetter("d", "a", "b.c")(obj)
- 42
- >>> _some_attrgetter("d", "e", "b.c", "a")(obj)
- 13
- >>> _some_attrgetter("d", "e", "f")(obj) is None
- True
- """
- def _acessor(obj):
- values = (_attrgetter(i)(obj) for i in items)
- return next((i for i in values if i is not None), None)
- return _acessor
-
-
-PYPROJECT_CORRESPONDENCE: Dict[str, _Correspondence] = {
- "readme": _long_description,
- "license": _license,
- "authors": partial(_people, kind="author"),
- "maintainers": partial(_people, kind="maintainer"),
- "urls": _project_urls,
- "dependencies": _dependencies,
- "optional_dependencies": _optional_dependencies,
- "requires_python": _python_requires,
-}
-
-TOOL_TABLE_RENAMES = {"script_files": "scripts"}
-TOOL_TABLE_DEPRECATIONS = {
- "namespace_packages": "consider using implicit namespaces instead (PEP 420)."
-}
-
-SETUPTOOLS_PATCHES = {"long_description_content_type", "project_urls",
- "provides_extras", "license_file", "license_files"}
-
-_PREVIOUSLY_DEFINED = {
- "name": _attrgetter("metadata.name"),
- "version": _attrgetter("metadata.version"),
- "description": _attrgetter("metadata.description"),
- "readme": _attrgetter("metadata.long_description"),
- "requires-python": _some_attrgetter("python_requires", "metadata.python_requires"),
- "license": _attrgetter("metadata.license"),
- "authors": _some_attrgetter("metadata.author", "metadata.author_email"),
- "maintainers": _some_attrgetter("metadata.maintainer", "metadata.maintainer_email"),
- "keywords": _attrgetter("metadata.keywords"),
- "classifiers": _attrgetter("metadata.classifiers"),
- "urls": _attrgetter("metadata.project_urls"),
- "entry-points": _attrgetter("entry_points"),
- "dependencies": _some_attrgetter("_orig_install_requires", "install_requires"),
- "optional-dependencies": _some_attrgetter("_orig_extras_require", "extras_require"),
-}
-
-
-class _WouldIgnoreField(UserWarning):
- """Inform users that ``pyproject.toml`` would overwrite previous metadata."""
-
- MESSAGE = """\
- {field!r} defined outside of `pyproject.toml` would be ignored.
- !!\n\n
- ##########################################################################
- # configuration would be ignored/result in error due to `pyproject.toml` #
- ##########################################################################
-
- The following seems to be defined outside of `pyproject.toml`:
-
- `{field} = {value!r}`
-
- According to the spec (see the link below), however, setuptools CANNOT
- consider this value unless {field!r} is listed as `dynamic`.
-
- https://packaging.python.org/en/latest/specifications/declaring-project-metadata/
-
- For the time being, `setuptools` will still consider the given value (as a
- **transitional** measure), but please note that future releases of setuptools will
- follow strictly the standard.
-
- To prevent this warning, you can list {field!r} under `dynamic` or alternatively
- remove the `[project]` table from your file and rely entirely on other means of
- configuration.
- \n\n!!
- """
-
- @classmethod
- def message(cls, field, value):
- from inspect import cleandoc
- return cleandoc(cls.MESSAGE.format(field=field, value=value))
diff --git a/spaces/Blessing/Asphalt-Pavement-Distresses-Detector/README.md b/spaces/Blessing/Asphalt-Pavement-Distresses-Detector/README.md
deleted file mode 100644
index ff93fe88e14b0163781a4865513052ed83005469..0000000000000000000000000000000000000000
--- a/spaces/Blessing/Asphalt-Pavement-Distresses-Detector/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Asphalt Pavement Distresses Detector
-emoji: 📊
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Bonosa2/movies/README.md b/spaces/Bonosa2/movies/README.md
deleted file mode 100644
index 170a5a360946334c56eb22702be6664966ac3f98..0000000000000000000000000000000000000000
--- a/spaces/Bonosa2/movies/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Movies
-emoji: 🐨
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/CONTRIBUTING.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/CONTRIBUTING.md
deleted file mode 100644
index 1c8736125121fec9e5842cf7353521d00b61ff68..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/CONTRIBUTING.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Contributing to grid-feats-vqa
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-1. Fork the repo and create your branch from `master`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
-
-Complete your CLA here:
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## Coding Style
-* 4 spaces for indentation rather than tabs
-
-## License
-By contributing to grid-feats-vqa, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/pybind11/include/pybind11/operators.h b/spaces/CVPR/LIVE/pybind11/include/pybind11/operators.h
deleted file mode 100644
index 086cb4cfd838797767dd3b3caa275bdb4348fc8e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/include/pybind11/operators.h
+++ /dev/null
@@ -1,173 +0,0 @@
-/*
- pybind11/operator.h: Metatemplates for operator overloading
-
- Copyright (c) 2016 Wenzel Jakob
-
- All rights reserved. Use of this source code is governed by a
- BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "pybind11.h"
-
-#if defined(__clang__) && !defined(__INTEL_COMPILER)
-# pragma clang diagnostic ignored "-Wunsequenced" // multiple unsequenced modifications to 'self' (when using def(py::self OP Type()))
-#elif defined(_MSC_VER)
-# pragma warning(push)
-# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
-#endif
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-/// Enumeration with all supported operator types
-enum op_id : int {
- op_add, op_sub, op_mul, op_div, op_mod, op_divmod, op_pow, op_lshift,
- op_rshift, op_and, op_xor, op_or, op_neg, op_pos, op_abs, op_invert,
- op_int, op_long, op_float, op_str, op_cmp, op_gt, op_ge, op_lt, op_le,
- op_eq, op_ne, op_iadd, op_isub, op_imul, op_idiv, op_imod, op_ilshift,
- op_irshift, op_iand, op_ixor, op_ior, op_complex, op_bool, op_nonzero,
- op_repr, op_truediv, op_itruediv, op_hash
-};
-
-enum op_type : int {
- op_l, /* base type on left */
- op_r, /* base type on right */
- op_u /* unary operator */
-};
-
-struct self_t { };
-static const self_t self = self_t();
-
-/// Type for an unused type slot
-struct undefined_t { };
-
-/// Don't warn about an unused variable
-inline self_t __self() { return self; }
-
-/// base template of operator implementations
-template struct op_impl { };
-
-/// Operator implementation generator
-template struct op_ {
- template void execute(Class &cl, const Extra&... extra) const {
- using Base = typename Class::type;
- using L_type = conditional_t::value, Base, L>;
- using R_type = conditional_t::value, Base, R>;
- using op = op_impl;
- cl.def(op::name(), &op::execute, is_operator(), extra...);
- #if PY_MAJOR_VERSION < 3
- if (id == op_truediv || id == op_itruediv)
- cl.def(id == op_itruediv ? "__idiv__" : ot == op_l ? "__div__" : "__rdiv__",
- &op::execute, is_operator(), extra...);
- #endif
- }
- template void execute_cast(Class &cl, const Extra&... extra) const {
- using Base = typename Class::type;
- using L_type = conditional_t::value, Base, L>;
- using R_type = conditional_t::value, Base, R>;
- using op = op_impl;
- cl.def(op::name(), &op::execute_cast, is_operator(), extra...);
- #if PY_MAJOR_VERSION < 3
- if (id == op_truediv || id == op_itruediv)
- cl.def(id == op_itruediv ? "__idiv__" : ot == op_l ? "__div__" : "__rdiv__",
- &op::execute, is_operator(), extra...);
- #endif
- }
-};
-
-#define PYBIND11_BINARY_OPERATOR(id, rid, op, expr) \
-template struct op_impl { \
- static char const* name() { return "__" #id "__"; } \
- static auto execute(const L &l, const R &r) -> decltype(expr) { return (expr); } \
- static B execute_cast(const L &l, const R &r) { return B(expr); } \
-}; \
-template struct op_impl { \
- static char const* name() { return "__" #rid "__"; } \
- static auto execute(const R &r, const L &l) -> decltype(expr) { return (expr); } \
- static B execute_cast(const R &r, const L &l) { return B(expr); } \
-}; \
-inline op_ op(const self_t &, const self_t &) { \
- return op_(); \
-} \
-template op_ op(const self_t &, const T &) { \
- return op_(); \
-} \
-template op_ op(const T &, const self_t &) { \
- return op_(); \
-}
-
-#define PYBIND11_INPLACE_OPERATOR(id, op, expr) \
-template struct op_impl { \
- static char const* name() { return "__" #id "__"; } \
- static auto execute(L &l, const R &r) -> decltype(expr) { return expr; } \
- static B execute_cast(L &l, const R &r) { return B(expr); } \
-}; \
-template op_ op(const self_t &, const T &) { \
- return op_(); \
-}
-
-#define PYBIND11_UNARY_OPERATOR(id, op, expr) \
-template struct op_impl { \
- static char const* name() { return "__" #id "__"; } \
- static auto execute(const L &l) -> decltype(expr) { return expr; } \
- static B execute_cast(const L &l) { return B(expr); } \
-}; \
-inline op_ op(const self_t &) { \
- return op_(); \
-}
-
-PYBIND11_BINARY_OPERATOR(sub, rsub, operator-, l - r)
-PYBIND11_BINARY_OPERATOR(add, radd, operator+, l + r)
-PYBIND11_BINARY_OPERATOR(mul, rmul, operator*, l * r)
-PYBIND11_BINARY_OPERATOR(truediv, rtruediv, operator/, l / r)
-PYBIND11_BINARY_OPERATOR(mod, rmod, operator%, l % r)
-PYBIND11_BINARY_OPERATOR(lshift, rlshift, operator<<, l << r)
-PYBIND11_BINARY_OPERATOR(rshift, rrshift, operator>>, l >> r)
-PYBIND11_BINARY_OPERATOR(and, rand, operator&, l & r)
-PYBIND11_BINARY_OPERATOR(xor, rxor, operator^, l ^ r)
-PYBIND11_BINARY_OPERATOR(eq, eq, operator==, l == r)
-PYBIND11_BINARY_OPERATOR(ne, ne, operator!=, l != r)
-PYBIND11_BINARY_OPERATOR(or, ror, operator|, l | r)
-PYBIND11_BINARY_OPERATOR(gt, lt, operator>, l > r)
-PYBIND11_BINARY_OPERATOR(ge, le, operator>=, l >= r)
-PYBIND11_BINARY_OPERATOR(lt, gt, operator<, l < r)
-PYBIND11_BINARY_OPERATOR(le, ge, operator<=, l <= r)
-//PYBIND11_BINARY_OPERATOR(pow, rpow, pow, std::pow(l, r))
-PYBIND11_INPLACE_OPERATOR(iadd, operator+=, l += r)
-PYBIND11_INPLACE_OPERATOR(isub, operator-=, l -= r)
-PYBIND11_INPLACE_OPERATOR(imul, operator*=, l *= r)
-PYBIND11_INPLACE_OPERATOR(itruediv, operator/=, l /= r)
-PYBIND11_INPLACE_OPERATOR(imod, operator%=, l %= r)
-PYBIND11_INPLACE_OPERATOR(ilshift, operator<<=, l <<= r)
-PYBIND11_INPLACE_OPERATOR(irshift, operator>>=, l >>= r)
-PYBIND11_INPLACE_OPERATOR(iand, operator&=, l &= r)
-PYBIND11_INPLACE_OPERATOR(ixor, operator^=, l ^= r)
-PYBIND11_INPLACE_OPERATOR(ior, operator|=, l |= r)
-PYBIND11_UNARY_OPERATOR(neg, operator-, -l)
-PYBIND11_UNARY_OPERATOR(pos, operator+, +l)
-// WARNING: This usage of `abs` should only be done for existing STL overloads.
-// Adding overloads directly in to the `std::` namespace is advised against:
-// https://en.cppreference.com/w/cpp/language/extending_std
-PYBIND11_UNARY_OPERATOR(abs, abs, std::abs(l))
-PYBIND11_UNARY_OPERATOR(hash, hash, std::hash()(l))
-PYBIND11_UNARY_OPERATOR(invert, operator~, (~l))
-PYBIND11_UNARY_OPERATOR(bool, operator!, !!l)
-PYBIND11_UNARY_OPERATOR(int, int_, (int) l)
-PYBIND11_UNARY_OPERATOR(float, float_, (double) l)
-
-#undef PYBIND11_BINARY_OPERATOR
-#undef PYBIND11_INPLACE_OPERATOR
-#undef PYBIND11_UNARY_OPERATOR
-PYBIND11_NAMESPACE_END(detail)
-
-using detail::self;
-// Add named operators so that they are accessible via `py::`.
-using detail::hash;
-
-PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
-
-#if defined(_MSC_VER)
-# pragma warning(pop)
-#endif
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/unique_by_key.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/unique_by_key.h
deleted file mode 100644
index 899ce02db7e0721520940bfcffba4332c59c0f61..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/unique_by_key.h
+++ /dev/null
@@ -1,116 +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 unique_by_key.h
- * \brief Sequential implementations of unique_by_key algorithms.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-__thrust_exec_check_disable__
-template
-__host__ __device__
- thrust::pair
- unique_by_key_copy(sequential::execution_policy &,
- InputIterator1 keys_first,
- InputIterator1 keys_last,
- InputIterator2 values_first,
- OutputIterator1 keys_output,
- OutputIterator2 values_output,
- BinaryPredicate binary_pred)
-{
- typedef typename thrust::iterator_traits::value_type InputKeyType;
- typedef typename thrust::iterator_traits::value_type OutputValueType;
-
- if(keys_first != keys_last)
- {
- InputKeyType temp_key = *keys_first;
- OutputValueType temp_value = *values_first;
-
- for(++keys_first, ++values_first;
- keys_first != keys_last;
- ++keys_first, ++values_first)
- {
- InputKeyType key = *keys_first;
- OutputValueType value = *values_first;
-
- if(!binary_pred(temp_key, key))
- {
- *keys_output = temp_key;
- *values_output = temp_value;
-
- ++keys_output;
- ++values_output;
-
- temp_key = key;
- temp_value = value;
- }
- }
-
- *keys_output = temp_key;
- *values_output = temp_value;
-
- ++keys_output;
- ++values_output;
- }
-
- return thrust::make_pair(keys_output, values_output);
-} // end unique_by_key_copy()
-
-
-template
-__host__ __device__
- thrust::pair
- unique_by_key(sequential::execution_policy &exec,
- ForwardIterator1 keys_first,
- ForwardIterator1 keys_last,
- ForwardIterator2 values_first,
- BinaryPredicate binary_pred)
-{
- // sequential unique_by_key_copy() permits in-situ operation
- return sequential::unique_by_key_copy(exec, keys_first, keys_last, values_first, keys_first, values_first, binary_pred);
-} // end unique_by_key()
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
diff --git a/spaces/CarlDennis/Lovelive-VITS-JPZH/utils.py b/spaces/CarlDennis/Lovelive-VITS-JPZH/utils.py
deleted file mode 100644
index e8383bbe6f4718bd25959b508fdd7791f6cb9b50..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/Lovelive-VITS-JPZH/utils.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import logging
-from json import loads
-from torch import load, FloatTensor
-from numpy import float32
-import librosa
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
-
-
-def load_checkpoint(checkpoint_path, model):
- checkpoint_dict = load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logging.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- pass
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logging.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return FloatTensor(audio.astype(float32))
diff --git a/spaces/Carlosito16/aitGPT/app.py b/spaces/Carlosito16/aitGPT/app.py
deleted file mode 100644
index 15f9e9b8e8c481f97cecc455e5a95aaffcb0e30a..0000000000000000000000000000000000000000
--- a/spaces/Carlosito16/aitGPT/app.py
+++ /dev/null
@@ -1,328 +0,0 @@
-#This version includes the memory and custom prompt, representing the final version
-
-import streamlit as st
-from streamlit_chat import message as st_message
-import pandas as pd
-import numpy as np
-import datetime
-import gspread
-import pickle
-import os
-import csv
-import json
-import torch
-from tqdm.auto import tqdm
-from transformers import BitsAndBytesConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, T5Tokenizer, AutoModel, T5ForConditionalGeneration
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-
-# from langchain.vectorstores import Chroma
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-
-
-from langchain import HuggingFacePipeline
-from langchain.chains import RetrievalQA
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferWindowMemory
-
-
-from langchain.chains import LLMChain
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
-
-
-
-
-
-prompt_template = """
-
-You are the chatbot and the face of Asian Institute of Technology (AIT). Your job is to give answers to prospective and current students about the school.
-Your job is to answer questions only and only related to the AIT. Anything unrelated should be responded with the fact that your main job is solely to provide assistance regarding AIT.
-MUST only use the following pieces of context to answer the question at the end. If the answers are not in the context or you are not sure of the answer, just say that you don't know, don't try to make up an answer.
-
-
-{context}
-Question: {question}
-
-When encountering abusive, offensive, or harmful language, such as fuck, bitch,etc, just politely ask the users to maintain appropriate behaviours.
-Always make sure to elaborate your response and use vibrant, positive tone to represent good branding of the school.
-Never answer with any unfinished response
-
-Answer:
-"""
-PROMPT = PromptTemplate(
- template=prompt_template, input_variables=["context", "question"]
-)
-chain_type_kwargs = {"prompt": PROMPT}
-
-
-st.set_page_config(
- page_title = 'aitGPT',
- page_icon = '✅')
-
-bitsandbyte_config = BitsAndBytesConfig(
- load_in_4bit=True,
- bnb_4bit_quant_type="nf4",
- bnb_4bit_compute_dtype=torch.float16)
-
-
-@st.cache_data
-def load_scraped_web_info():
- with open("ait-web-document", "rb") as fp:
- ait_web_documents = pickle.load(fp)
-
-
- text_splitter = RecursiveCharacterTextSplitter(
- # Set a really small chunk size, just to show.
- chunk_size = 500,
- chunk_overlap = 100,
- length_function = len,
- )
-
- chunked_text = text_splitter.create_documents([doc for doc in tqdm(ait_web_documents)])
-
-
-@st.cache_resource
-def load_embedding_model():
- embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base',
- model_kwargs = {'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu')})
- return embedding_model
-
-@st.cache_data
-def load_faiss_index():
- vector_database = FAISS.load_local("faiss_index_web_and_curri_new", embedding_model) #CHANGE THIS FAISS EMBEDDED KNOWLEDGE
- return vector_database
-
-@st.cache_resource
-def load_llm_model():
- #this one is for running with GPU
-
- model = T5ForConditionalGeneration.from_pretrained('lmsys/fastchat-t5-3b-v1.0',
- quantization_config = bitsandbyte_config,
- device_map= 'auto')
- tokenizer = AutoTokenizer.from_pretrained('lmsys/fastchat-t5-3b-v1.0')
-
-
- pipe = pipeline(
- task= 'text2text-generation', model=model, tokenizer=tokenizer, max_new_tokens=256, model_kwargs={"temperature":0,
- "repetition_penalty": 1.5}
- )
- llm = HuggingFacePipeline(pipeline=pipe)
-
- # llm = HuggingFacePipeline.from_model_id(model_id= 'lmsys/fastchat-t5-3b-v1.0',
- # task= 'text2text-generation',
- # model_kwargs={
- # # "device_map": "auto",
- # "max_length": 256, "temperature": 0,
- # "repetition_penalty": 1.5,
- # "quantization_config": bitsandbyte_config}) #add this quantization config
-
-
- # llm = HuggingFacePipeline.from_model_id(model_id= 'lmsys/fastchat-t5-3b-v1.0',
- # task= 'text2text-generation',
-
- # model_kwargs={ "max_length": 256, "temperature": 0,
- # "torch_dtype":torch.float32,
- # "repetition_penalty": 1.3})
- return llm
-
-
-@st.cache_resource
-def load_conversational_qa_memory_retriever():
-
- question_generator = LLMChain(llm=llm_model, prompt=CONDENSE_QUESTION_PROMPT)
- doc_chain = load_qa_chain(llm_model, chain_type="stuff", prompt = PROMPT)
- memory = ConversationBufferWindowMemory(k = 3, memory_key="chat_history", return_messages=True, output_key='answer')
-
-
-
- conversational_qa_memory_retriever = ConversationalRetrievalChain(
- retriever=vector_database.as_retriever(),
- question_generator=question_generator,
- combine_docs_chain=doc_chain,
- return_source_documents=True,
- memory = memory,
- get_chat_history=lambda h :h)
- return conversational_qa_memory_retriever, question_generator
-
-def load_retriever(llm, db):
- qa_retriever = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",
- retriever=db.as_retriever(),
- chain_type_kwargs= chain_type_kwargs)
-
- return qa_retriever
-
-def retrieve_document(query_input):
- related_doc = vector_database.similarity_search(query_input)
- return related_doc
-
-
-
-def retrieve_answer():
- prompt_answer= st.session_state.my_text_input
- answer = qa_retriever.run(prompt_answer)
- log = {"timestamp": datetime.datetime.now(),
- "question":st.session_state.my_text_input,
- "generated_answer": answer[6:],
- "rating":0 }
-
- st.session_state.history.append(log)
- # update_worksheet_qa()
- st.session_state.chat_history.append({"message": st.session_state.my_text_input, "is_user": True})
- st.session_state.chat_history.append({"message": answer[6:] , "is_user": False})
-
- st.session_state.my_text_input = ""
-
- return answer[6:] #this positional slicing helps remove " " at the beginning
-
-
-def new_retrieve_answer():
- prompt_answer= st.session_state.my_text_input + ". Try to be elaborate and informative in your answer."
- answer = conversational_qa_memory_retriever({"question": prompt_answer, })
- log = {"timestamp": datetime.datetime.now(),
- "question":st.session_state.my_text_input,
- "generated_answer": answer['answer'][6:],
- "rating":0 }
-
- print(f"condensed quesion : {question_generator.run({'chat_history': answer['chat_history'], 'question' : prompt_answer})}")
-
- print(answer["chat_history"])
- st.session_state.history.append(log)
- # update_worksheet_qa()
- st.session_state.chat_history.append({"message": st.session_state.my_text_input, "is_user": True})
- st.session_state.chat_history.append({"message": answer['answer'][6:] , "is_user": False})
-
- st.session_state.my_text_input = ""
-
- return answer['answer'][6:] #this positional slicing helps remove " " at the beginning
-
-# def update_score():
-# st.session_state.session_rating = st.session_state.rating
-
-
-def update_worksheet_qa():
- # st.session_state.session_rating = st.session_state.rating
- #This if helps validate the initiated rating, if 0, then the google sheet would not be updated
- #(edited) now even with the score of 0, we still want to store the log because some users do not give the score to complete the logging
- # if st.session_state.session_rating == 0:
- worksheet_qa.append_row([st.session_state.history[-1]['timestamp'].strftime(datetime_format),
- st.session_state.history[-1]['question'],
- st.session_state.history[-1]['generated_answer'],
- 0])
- # else:
- # worksheet_qa.append_row([st.session_state.history[-1]['timestamp'].strftime(datetime_format),
- # st.session_state.history[-1]['question'],
- # st.session_state.history[-1]['generated_answer'],
- # st.session_state.session_rating
- # ])
-
-def update_worksheet_comment():
- worksheet_comment.append_row([datetime.datetime.now().strftime(datetime_format),
- feedback_input])
- success_message = st.success('Feedback successfully submitted, thank you', icon="✅",
- )
- time.sleep(3)
- success_message.empty()
-
-
-def clean_chat_history():
- st.session_state.chat_history = []
- conversational_qa_memory_retriever.memory.chat_memory.clear() #add this to remove
-
-#--------------
-
-
-if "history" not in st.session_state: #this one is for the google sheet logging
- st.session_state.history = []
-
-
-if "chat_history" not in st.session_state: #this one is to pass previous messages into chat flow
- st.session_state.chat_history = []
-# if "session_rating" not in st.session_state:
-# st.session_state.session_rating = 0
-
-
-# credentials= json.loads(st.secrets['google_sheet_credential'])
-
-# service_account = gspread.service_account_from_dict(credentials)
-# workbook= service_account.open("aitGPT-qa-log")
-# worksheet_qa = workbook.worksheet("Sheet1")
-# worksheet_comment = workbook.worksheet("Sheet2")
-# datetime_format= "%Y-%m-%d %H:%M:%S"
-
-
-
-load_scraped_web_info()
-embedding_model = load_embedding_model()
-vector_database = load_faiss_index()
-llm_model = load_llm_model()
-qa_retriever = load_retriever(llm= llm_model, db= vector_database)
-conversational_qa_memory_retriever, question_generator = load_conversational_qa_memory_retriever()
-print("all load done")
-
-
-# Try adding this to set to clear the memory in each session
-if st.session_state.chat_history == []:
- conversational_qa_memory_retriever.memory.chat_memory.clear()
-#Addional things for Conversation flows
-
-
-
-
-
-
-st.write("# aitGPT 🤖 ")
-st.markdown("""
- #### The aitGPT project is a virtual assistant developed by the :green[Asian Institute of Technology] that contains a vast amount of information gathered from 205 AIT-related websites.
- The goal of this chatbot is to provide an alternative way for applicants and current students to access information about the institute, including admission procedures, campus facilities, and more.
- """)
-st.write(' ⚠️ Please expect to wait **~ 10 - 20 seconds per question** as thi app is running on CPU against 3-billion-parameter LLM')
-
-st.markdown("---")
-st.write(" ")
-st.write("""
- ### ❔ Ask a question
- """)
-
-
-for chat in st.session_state.chat_history:
- st_message(**chat)
-
-query_input = st.text_input(label= 'What would you like to know about AIT?' , key = 'my_text_input', on_change= new_retrieve_answer )
-# generate_button = st.button(label = 'Ask question!')
-
-# if generate_button:
-# answer = retrieve_answer(query_input)
-# log = {"timestamp": datetime.datetime.now(),
-# "question":query_input,
-# "generated_answer": answer,
-# "rating":0 }
-
-# st.session_state.history.append(log)
-# update_worksheet_qa()
-# st.session_state.chat_history.append({"message": query_input, "is_user": True})
-# st.session_state.chat_history.append({"message": answer, "is_user": False})
-
-# print(st.session_state.chat_history)
-
-
-clear_button = st.button("Start new convo",
- on_click=clean_chat_history)
-
-
-st.write(" ")
-st.write(" ")
-
-st.markdown("---")
-st.write("""
- ### 💌 Your voice matters
- """)
-
-feedback_input = st.text_area(label= 'please leave your feedback or any ideas to make this bot more knowledgeable and fun')
-feedback_button = st.button(label = 'Submit feedback!')
-
-if feedback_button:
- update_worksheet_comment()
\ No newline at end of file
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/acg_entrance/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/acg_entrance/__init__.py
deleted file mode 100644
index ead2405cc677132f304515a38a4b3bf6113cd91c..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/acg_entrance/__init__.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def acg_entrance(images: List[BuildImage], texts: List[str], args):
- text = texts[0] if texts else "走，跟我去二次元吧"
- frame = BuildImage.open(img_dir / "0.png")
- try:
- frame.draw_text(
- (30, 720, frame.width - 30, 810),
- text,
- max_fontsize=50,
- min_fontsize=25,
- fill="white",
- )
- except ValueError:
- raise TextOverLength(text)
-
- def make(img: BuildImage) -> BuildImage:
- img = img.convert("RGBA").resize((290, 410), keep_ratio=True)
- return frame.copy().paste(img, (190, 265), below=True)
-
- return make_jpg_or_gif(images[0], make)
-
-
-add_meme(
- "acg_entrance",
- acg_entrance,
- min_images=1,
- max_images=1,
- min_texts=0,
- max_texts=1,
- default_texts=["走，跟我去二次元吧"],
- keywords=["二次元入口"],
-)
diff --git a/spaces/CofAI/chat.v2/README.md b/spaces/CofAI/chat.v2/README.md
deleted file mode 100644
index cf115627c512a9f0a809659e87438cee2dc8f639..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.v2/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: BETA.Chat.CofAI
-emoji: 🗨☕💬
-colorFrom: indigo
-colorTo: green
-sdk: docker
-pinned: false
-duplicated_from: CofAI/chat.cofai
----
-
-✋ Приложение защищено авторским правом, копирование запрещено, но вы можете встроить это пространство!
\ No newline at end of file
diff --git a/spaces/CofAI/chat/g4f/Provider/__init__.py b/spaces/CofAI/chat/g4f/Provider/__init__.py
deleted file mode 100644
index 6ed51982755367e47c59199975be2c3539bfbee0..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/g4f/Provider/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from . import Provider
-from .Providers import (
- Aichat,
- Ails,
- AiService,
- Bard,
- Better,
- Bing,
- ChatFree,
- ChatgptAi,
- ChatgptLogin,
- ChatgptLogin,
- DeepAi,
- Easychat,
- Ezcht,
- Fakeopen,
- Forefront,
- GetGpt,
- Gravityengine,
- H2o,
- hteyun,
- Liaobots,
- Lockchat,
- Mishalsgpt,
- Phind,
- Theb,
- Vercel,
- Weuseing,
- Xiaor,
- Yqcloud,
- You,
- Zeabur,
- Wewordle
-)
-
-Palm = Bard
diff --git a/spaces/Crow34/Comicdraw/app.py b/spaces/Crow34/Comicdraw/app.py
deleted file mode 100644
index a82df332731f067826d3e1ef79fabceffb74d07e..0000000000000000000000000000000000000000
--- a/spaces/Crow34/Comicdraw/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/runwayml/stable-diffusion-v1-5").launch()
\ No newline at end of file
diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/dpm_solver/__init__.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/dpm_solver/__init__.py
deleted file mode 100644
index 7427f38c07530afbab79154ea8aaf88c4bf70a08..0000000000000000000000000000000000000000
--- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/dpm_solver/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/async_timeout/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/async_timeout/__init__.py
deleted file mode 100644
index 179d1b0a01bd896e9d1cb752a07be0f4422e0146..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/async_timeout/__init__.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import asyncio
-import enum
-import sys
-import warnings
-from types import TracebackType
-from typing import Any, Optional, Type
-
-
-if sys.version_info >= (3, 8):
- from typing import final
-else:
- from typing_extensions import final
-
-
-__version__ = "4.0.2"
-
-
-__all__ = ("timeout", "timeout_at", "Timeout")
-
-
-def timeout(delay: Optional[float]) -> "Timeout":
- """timeout context manager.
-
- Useful in cases when you want to apply timeout logic around block
- of code or in cases when asyncio.wait_for is not suitable. For example:
-
- >>> async with timeout(0.001):
- ... async with aiohttp.get('https://github.com') as r:
- ... await r.text()
-
-
- delay - value in seconds or None to disable timeout logic
- """
- loop = _get_running_loop()
- if delay is not None:
- deadline = loop.time() + delay # type: Optional[float]
- else:
- deadline = None
- return Timeout(deadline, loop)
-
-
-def timeout_at(deadline: Optional[float]) -> "Timeout":
- """Schedule the timeout at absolute time.
-
- deadline argument points on the time in the same clock system
- as loop.time().
-
- Please note: it is not POSIX time but a time with
- undefined starting base, e.g. the time of the system power on.
-
- >>> async with timeout_at(loop.time() + 10):
- ... async with aiohttp.get('https://github.com') as r:
- ... await r.text()
-
-
- """
- loop = _get_running_loop()
- return Timeout(deadline, loop)
-
-
-class _State(enum.Enum):
- INIT = "INIT"
- ENTER = "ENTER"
- TIMEOUT = "TIMEOUT"
- EXIT = "EXIT"
-
-
-@final
-class Timeout:
- # Internal class, please don't instantiate it directly
- # Use timeout() and timeout_at() public factories instead.
- #
- # Implementation note: `async with timeout()` is preferred
- # over `with timeout()`.
- # While technically the Timeout class implementation
- # doesn't need to be async at all,
- # the `async with` statement explicitly points that
- # the context manager should be used from async function context.
- #
- # This design allows to avoid many silly misusages.
- #
- # TimeoutError is raised immadiatelly when scheduled
- # if the deadline is passed.
- # The purpose is to time out as sson as possible
- # without waiting for the next await expression.
-
- __slots__ = ("_deadline", "_loop", "_state", "_timeout_handler")
-
- def __init__(
- self, deadline: Optional[float], loop: asyncio.AbstractEventLoop
- ) -> None:
- self._loop = loop
- self._state = _State.INIT
-
- self._timeout_handler = None # type: Optional[asyncio.Handle]
- if deadline is None:
- self._deadline = None # type: Optional[float]
- else:
- self.update(deadline)
-
- def __enter__(self) -> "Timeout":
- warnings.warn(
- "with timeout() is deprecated, use async with timeout() instead",
- DeprecationWarning,
- stacklevel=2,
- )
- self._do_enter()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> Optional[bool]:
- self._do_exit(exc_type)
- return None
-
- async def __aenter__(self) -> "Timeout":
- self._do_enter()
- return self
-
- async def __aexit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> Optional[bool]:
- self._do_exit(exc_type)
- return None
-
- @property
- def expired(self) -> bool:
- """Is timeout expired during execution?"""
- return self._state == _State.TIMEOUT
-
- @property
- def deadline(self) -> Optional[float]:
- return self._deadline
-
- def reject(self) -> None:
- """Reject scheduled timeout if any."""
- # cancel is maybe better name but
- # task.cancel() raises CancelledError in asyncio world.
- if self._state not in (_State.INIT, _State.ENTER):
- raise RuntimeError(f"invalid state {self._state.value}")
- self._reject()
-
- def _reject(self) -> None:
- if self._timeout_handler is not None:
- self._timeout_handler.cancel()
- self._timeout_handler = None
-
- def shift(self, delay: float) -> None:
- """Advance timeout on delay seconds.
-
- The delay can be negative.
-
- Raise RuntimeError if shift is called when deadline is not scheduled
- """
- deadline = self._deadline
- if deadline is None:
- raise RuntimeError("cannot shift timeout if deadline is not scheduled")
- self.update(deadline + delay)
-
- def update(self, deadline: float) -> None:
- """Set deadline to absolute value.
-
- deadline argument points on the time in the same clock system
- as loop.time().
-
- If new deadline is in the past the timeout is raised immediatelly.
-
- Please note: it is not POSIX time but a time with
- undefined starting base, e.g. the time of the system power on.
- """
- if self._state == _State.EXIT:
- raise RuntimeError("cannot reschedule after exit from context manager")
- if self._state == _State.TIMEOUT:
- raise RuntimeError("cannot reschedule expired timeout")
- if self._timeout_handler is not None:
- self._timeout_handler.cancel()
- self._deadline = deadline
- if self._state != _State.INIT:
- self._reschedule()
-
- def _reschedule(self) -> None:
- assert self._state == _State.ENTER
- deadline = self._deadline
- if deadline is None:
- return
-
- now = self._loop.time()
- if self._timeout_handler is not None:
- self._timeout_handler.cancel()
-
- task = _current_task(self._loop)
- if deadline <= now:
- self._timeout_handler = self._loop.call_soon(self._on_timeout, task)
- else:
- self._timeout_handler = self._loop.call_at(deadline, self._on_timeout, task)
-
- def _do_enter(self) -> None:
- if self._state != _State.INIT:
- raise RuntimeError(f"invalid state {self._state.value}")
- self._state = _State.ENTER
- self._reschedule()
-
- def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None:
- if exc_type is asyncio.CancelledError and self._state == _State.TIMEOUT:
- self._timeout_handler = None
- raise asyncio.TimeoutError
- # timeout has not expired
- self._state = _State.EXIT
- self._reject()
- return None
-
- def _on_timeout(self, task: "asyncio.Task[None]") -> None:
- task.cancel()
- self._state = _State.TIMEOUT
- # drop the reference early
- self._timeout_handler = None
-
-
-if sys.version_info >= (3, 7):
-
- def _current_task(loop: asyncio.AbstractEventLoop) -> "Optional[asyncio.Task[Any]]":
- return asyncio.current_task(loop=loop)
-
-else:
-
- def _current_task(loop: asyncio.AbstractEventLoop) -> "Optional[asyncio.Task[Any]]":
- return asyncio.Task.current_task(loop=loop)
-
-
-if sys.version_info >= (3, 7):
-
- def _get_running_loop() -> asyncio.AbstractEventLoop:
- return asyncio.get_running_loop()
-
-else:
-
- def _get_running_loop() -> asyncio.AbstractEventLoop:
- loop = asyncio.get_event_loop()
- if not loop.is_running():
- raise RuntimeError("no running event loop")
- return loop
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/relativedelta.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/relativedelta.py
deleted file mode 100644
index a9e85f7e6cd7488e6b2f4b249d5cf6af314c3859..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/relativedelta.py
+++ /dev/null
@@ -1,599 +0,0 @@
-# -*- coding: utf-8 -*-
-import datetime
-import calendar
-
-import operator
-from math import copysign
-
-from six import integer_types
-from warnings import warn
-
-from ._common import weekday
-
-MO, TU, WE, TH, FR, SA, SU = weekdays = tuple(weekday(x) for x in range(7))
-
-__all__ = ["relativedelta", "MO", "TU", "WE", "TH", "FR", "SA", "SU"]
-
-
-class relativedelta(object):
- """
- The relativedelta type is designed to be applied to an existing datetime and
- can replace specific components of that datetime, or represents an interval
- of time.
-
- It is based on the specification of the excellent work done by M.-A. Lemburg
- in his
- `mx.DateTime
-
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
-#include
-#include
-#elif defined (_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#ifndef NOMINMAX
-# define NOMINMAX
-#endif
-#include
-#include
-#endif
-
-// Used for debugging to print out beam tokens.
-struct ostream_beam_view {
- llama_context * ctx;
- llama_beam_view beam_view;
-};
-
-static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) {
- os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
- for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
- os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
- }
- return os << ')';
-}
-
-// Put here anything you want back in beam_search_callback().
-struct beam_search_callback_data {
- llama_context * ctx;
- std::vector response;
-};
-
-// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
-// For example, eob can be flagged due to maximum token length, stop words, etc.
-static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
- return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx);
-}
-
-// Function matching type llama_beam_search_callback_fn_t.
-// Custom callback example is called each time the beams lengths increase:
-// * Show progress by printing ',' following by number of convergent beam tokens if any.
-// * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
-// This is also called when the stop condition is met.
-// Collect tokens into std::vector response which is pointed to by callback_data.
-static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {
- auto& callback_data = *static_cast(callback_data_ptr);
- // Mark beams as EOS as needed.
- for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
- llama_beam_view& beam_view = beams_state.beam_views[i];
- if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {
- beam_view.eob = true;
- }
- }
- printf(","); // Show progress
- if (const size_t n = beams_state.common_prefix_length) {
- callback_data.response.resize(callback_data.response.size() + n);
- assert(0u < beams_state.n_beams);
- const llama_token * tokens = beams_state.beam_views[0].tokens;
- std::copy(tokens, tokens + n, callback_data.response.end() - n);
- printf("%zu", n);
- }
- fflush(stdout);
-#if 1 // DEBUG: print current beams for this iteration
- std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n";
- for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
- std::cout << "beams["< 3 )
- {
- params.prompt = argv[3];
- }
-
- if ( params.prompt.empty() )
- {
- params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n";
- }
-
- //---------------------------------
- // Init LLM :
- //---------------------------------
-
- llama_backend_init(params.numa);
-
- llama_model * model;
- llama_context * ctx;
-
- std::tie(model, ctx) = llama_init_from_gpt_params( params );
-
- if ( model == NULL )
- {
- fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
- return 1;
- }
-
- //---------------------------------
- // Tokenize the prompt :
- //---------------------------------
-
- std::vector tokens_list = llama_tokenize(ctx, params.prompt, true);
-
- const size_t max_context_size = llama_n_ctx( ctx );
- const size_t max_tokens_list_size = max_context_size - 4 ;
-
- if (tokens_list.size() > max_tokens_list_size)
- {
- fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" ,
- __func__ , tokens_list.size() , max_tokens_list_size );
- return 1;
- }
-
- fprintf( stderr, "\n\n" );
-
- // Print the tokens from the prompt :
-
- for( auto id : tokens_list )
- {
- std::cout << llama_token_to_piece(ctx, id);
- }
- std::cout << std::flush;
-
- int n_past = 0;
-
- if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0)))
- {
- fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
- return 1;
- }
- n_past += tokens_list.size();
-
- beam_search_callback_data callback_data{ctx, {}};
- size_t const beam_width = static_cast(params.n_beams);
- int const n_predict = 256;
- llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict);
-
- std::cout << "\n\n";
- for (llama_token const token_id : callback_data.response) {
- std::cout << llama_token_to_piece(ctx,token_id);
- }
- std::cout << std::endl;
-
- llama_free( ctx );
- llama_free_model( model );
-
- llama_backend_free();
-
- return 0;
-}
diff --git a/spaces/Intoval/privateChatGPT/modules/webui_locale.py b/spaces/Intoval/privateChatGPT/modules/webui_locale.py
deleted file mode 100644
index efcbb6ec66dc4c2fb9d98e51a2b3dccb7b5b2c70..0000000000000000000000000000000000000000
--- a/spaces/Intoval/privateChatGPT/modules/webui_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import locale
-import commentjson as json
-
-class I18nAuto:
- def __init__(self):
- if os.path.exists("config.json"):
- with open("config.json", "r", encoding='utf-8') as f:
- config = json.load(f)
- else:
- config = {}
- language = config.get("language", "auto") # 在这里输入你的 API 密钥
- language = os.environ.get("LANGUAGE", language)
- if language == "auto":
- language = locale.getdefaultlocale()[0] # get the language code of the system (ex. zh_CN)
- self.language_map = {}
- self.file_is_exists = os.path.isfile(f"./locale/{language}.json")
- if self.file_is_exists:
- with open(f"./locale/{language}.json", "r", encoding="utf-8") as f:
- self.language_map.update(json.load(f))
-
- def __call__(self, key):
- if self.file_is_exists and key in self.language_map:
- return self.language_map[key]
- else:
- return key
diff --git a/spaces/Izal887/rvc-hutao/vc_infer_pipeline.py b/spaces/Izal887/rvc-hutao/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/Izal887/rvc-hutao/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
- def __init__(self, tgt_sr, device, is_half):
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * x_query # 查询切点前后查询时间
- self.t_center = self.sr * x_center # 查询切点位置
- self.t_max = self.sr * x_max # 免查询时长阈值
- self.device = device
- self.is_half = is_half
-
- def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9, # layer 9
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0])
-
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
- _, I = index.search(npy, 1)
- npy = big_npy[I.squeeze()]
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- f0_file=None,
- ):
- if (
- file_big_npy != ""
- and file_index != ""
- and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- big_npy = np.load(file_big_npy)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- print("Feature retrieval library doesn't exist or ratio is 0")
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/JAWEE/stablediffusionapi-majicmixrealistic/app.py b/spaces/JAWEE/stablediffusionapi-majicmixrealistic/app.py
deleted file mode 100644
index 9ccefabd3c786c7a659e72fdfaf8735c4b7c9320..0000000000000000000000000000000000000000
--- a/spaces/JAWEE/stablediffusionapi-majicmixrealistic/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stablediffusionapi/majicmixrealistic").launch()
\ No newline at end of file
diff --git a/spaces/JacobLinCool/captcha-recognizer/src/app.py b/spaces/JacobLinCool/captcha-recognizer/src/app.py
deleted file mode 100644
index 26fd4457834d35b393d9e3e81667c4c77d3e1347..0000000000000000000000000000000000000000
--- a/spaces/JacobLinCool/captcha-recognizer/src/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-import numpy as np
-from preprocess import preprocess
-from solve import solve
-
-
-def run(img: np.ndarray) -> tuple[np.ndarray, str]:
- preprocessed = preprocess(img)
-
- solved = solve(preprocessed)
-
- return preprocessed, solved
-
-
-app = gr.Interface(
- fn=run,
- inputs=[
- gr.Image(label="captcha image", shape=(108, 30)),
- ],
- outputs=[
- gr.Image(label="preprocessed", shape=(108, 30)),
- gr.Textbox(label="solved"),
- ],
- allow_flagging="never",
-)
-
-app.queue().launch()
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/README.md b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/README.md
deleted file mode 100644
index ff085dc0ae3999343777cb3567ef46b1ada5bf25..0000000000000000000000000000000000000000
--- a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/README.md
+++ /dev/null
@@ -1,61 +0,0 @@
-
-### SPAGHETTI: Editing Implicit Shapes through Part Aware Generation
-
-
-
-
-### Installation
-
-```
-git clone https://github.com/amirhertz/spaghetti && cd spaghetti
-conda env create -f environment.yml
-conda activate spaghetti
-```
-
-Install [Pytorch](https://pytorch.org/). The installation during development and testing was pytorch==1.9.0 cudatoolkit=11.1
-
-
-### Demo
-- Download pre-trained models
-```
-python download_weights.py
-```
-- Run demo
-```
-python demo.py --model_name chairs_large --shape_dir samples
-```
-or
-```
-python demo.py --model_name airplanes --shape_dir samples
-```
-
-- User controls
- - Select shapes from the collection on bottom.
- - right click to select / deselect parts
- - Click the pencil button will toggle between selection / deselection.
- - Transform selected parts is similar to Blender short-keys.
- Pressing 'G' / 'R', will start translation / rotation mode. Toggle axis by pressing 'X' / 'Y' / 'Z'. Press 'Esc' to cancel transform.
- - Click the broom to reset.
-
-
-### Adding shapes to demo
-- From training data
-```
-python shape_inversion.py --model_name --source training --mesh_path --num_samples
-```
-- Random generation
-```
-python shape_inversion.py --model_name --source random --mesh_path --num_samples
-```
-- From existing watertight mesh:
-```
-python shape_inversion.py --model_name --mesh_path
-```
-For example, to add the provided sample chair to the exiting chairs in the demo:
-```
-python shape_inversion.py --model_name chairs_large --mesh_path ./assets/mesh/example.obj
-```
-
-### Training
-Coming soon.
-
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/occ_loss.py b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/occ_loss.py
deleted file mode 100644
index bb5bc0584b59a5e7a181993ef33edc7f91ed1873..0000000000000000000000000000000000000000
--- a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/occ_loss.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from ..custom_types import *
-
-
-def occupancy_bce(predict: T, winding_gt: T, ignore: Optional[T] = None, *args) -> T:
- if winding_gt.dtype is not torch.bool:
- winding_gt = winding_gt.gt(0)
- labels = winding_gt.flatten().float()
- predict = predict.flatten()
- if ignore is not None:
- ignore = (~ignore).flatten().float()
- loss = nnf.binary_cross_entropy_with_logits(predict, labels, weight=ignore)
- return loss
-
-
-def reg_z_loss(z: T) -> T:
- norms = z.norm(2, 1)
- loss = norms.mean()
- return loss
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/venv.sh b/spaces/Kangarroar/ApplioRVC-Inference/venv.sh
deleted file mode 100644
index aa230992e892292cb8aa5924ecdafc5758f14e95..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/venv.sh
+++ /dev/null
@@ -1 +0,0 @@
-python3.8 -m venv .venv
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/stft.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/stft.py
deleted file mode 100644
index 06b879e3cd810fc85b93fb4e3c118e38dfbce5f0..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/stft.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from typing import Optional
-from typing import Tuple
-from typing import Union
-
-import torch
-
-from .nets_utils import make_pad_mask
-
-
-class Stft(torch.nn.Module):
- def __init__(
- self,
- n_fft: int = 512,
- win_length: Union[int, None] = 512,
- hop_length: int = 128,
- center: bool = True,
- pad_mode: str = "reflect",
- normalized: bool = False,
- onesided: bool = True,
- kaldi_padding_mode=False,
- ):
- super().__init__()
- self.n_fft = n_fft
- if win_length is None:
- self.win_length = n_fft
- else:
- self.win_length = win_length
- self.hop_length = hop_length
- self.center = center
- self.pad_mode = pad_mode
- self.normalized = normalized
- self.onesided = onesided
- self.kaldi_padding_mode = kaldi_padding_mode
- if self.kaldi_padding_mode:
- self.win_length = 400
-
- def extra_repr(self):
- return (
- f"n_fft={self.n_fft}, "
- f"win_length={self.win_length}, "
- f"hop_length={self.hop_length}, "
- f"center={self.center}, "
- f"pad_mode={self.pad_mode}, "
- f"normalized={self.normalized}, "
- f"onesided={self.onesided}"
- )
-
- def forward(
- self, input: torch.Tensor, ilens: torch.Tensor = None
- ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
- """STFT forward function.
-
- Args:
- input: (Batch, Nsamples) or (Batch, Nsample, Channels)
- ilens: (Batch)
- Returns:
- output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
-
- """
- bs = input.size(0)
- if input.dim() == 3:
- multi_channel = True
- # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
- input = input.transpose(1, 2).reshape(-1, input.size(1))
- else:
- multi_channel = False
-
- # output: (Batch, Freq, Frames, 2=real_imag)
- # or (Batch, Channel, Freq, Frames, 2=real_imag)
- if not self.kaldi_padding_mode:
- output = torch.stft(
- input,
- n_fft=self.n_fft,
- win_length=self.win_length,
- hop_length=self.hop_length,
- center=self.center,
- pad_mode=self.pad_mode,
- normalized=self.normalized,
- onesided=self.onesided,
- return_complex=False
- )
- else:
- # NOTE(sx): Use Kaldi-fasion padding, maybe wrong
- num_pads = self.n_fft - self.win_length
- input = torch.nn.functional.pad(input, (num_pads, 0))
- output = torch.stft(
- input,
- n_fft=self.n_fft,
- win_length=self.win_length,
- hop_length=self.hop_length,
- center=False,
- pad_mode=self.pad_mode,
- normalized=self.normalized,
- onesided=self.onesided,
- return_complex=False
- )
-
- # output: (Batch, Freq, Frames, 2=real_imag)
- # -> (Batch, Frames, Freq, 2=real_imag)
- output = output.transpose(1, 2)
- if multi_channel:
- # output: (Batch * Channel, Frames, Freq, 2=real_imag)
- # -> (Batch, Frame, Channel, Freq, 2=real_imag)
- output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(
- 1, 2
- )
-
- if ilens is not None:
- if self.center:
- pad = self.win_length // 2
- ilens = ilens + 2 * pad
- olens = torch.div(ilens - self.win_length, self.hop_length, rounding_mode='floor') + 1
- # olens = ilens - self.win_length // self.hop_length + 1
- output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
- else:
- olens = None
-
- return output, olens
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/indicnlp/morph/__init__.py b/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/indicnlp/morph/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Kunal7/Gradio-Squats/process_frame.py b/spaces/Kunal7/Gradio-Squats/process_frame.py
deleted file mode 100644
index 0bf1990e789ca38e2c9a96290875b85be097666c..0000000000000000000000000000000000000000
--- a/spaces/Kunal7/Gradio-Squats/process_frame.py
+++ /dev/null
@@ -1,572 +0,0 @@
-import time
-import cv2
-import numpy as np
-from utils import find_angle, get_landmark_features, draw_text, draw_dotted_line
-
-
-class ProcessFrame:
- def __init__(self, thresholds, flip_frame = False):
-
- # Set if frame should be flipped or not.
- self.flip_frame = flip_frame
-
- # self.thresholds
- self.thresholds = thresholds
-
- # Font type.
- self.font = cv2.FONT_HERSHEY_SIMPLEX
-
- # line type
- self.linetype = cv2.LINE_AA
-
- # set radius to draw arc
- self.radius = 20
-
- # Colors in RGB format.
- self.COLORS = {
- 'blue' : (0, 127, 255),
- 'red' : (255, 50, 50),
- 'green' : (0, 255, 127),
- 'light_green': (100, 233, 127),
- 'yellow' : (255, 255, 0),
- 'magenta' : (255, 0, 255),
- 'white' : (255,255,255),
- 'cyan' : (0, 255, 255),
- 'light_blue' : (102, 204, 255)
- }
-
-
-
- # Dictionary to maintain the various landmark features.
- self.dict_features = {}
- self.left_features = {
- 'shoulder': 11,
- 'elbow' : 13,
- 'wrist' : 15,
- 'hip' : 23,
- 'knee' : 25,
- 'ankle' : 27,
- 'foot' : 31
- }
-
- self.right_features = {
- 'shoulder': 12,
- 'elbow' : 14,
- 'wrist' : 16,
- 'hip' : 24,
- 'knee' : 26,
- 'ankle' : 28,
- 'foot' : 32
- }
-
- self.dict_features['left'] = self.left_features
- self.dict_features['right'] = self.right_features
- self.dict_features['nose'] = 0
-
-
- # For tracking counters and sharing states in and out of callbacks.
- self.state_tracker = {
- 'state_seq': [],
-
- 'start_inactive_time': time.perf_counter(),
- 'start_inactive_time_front': time.perf_counter(),
- 'INACTIVE_TIME': 0.0,
- 'INACTIVE_TIME_FRONT': 0.0,
-
- # 0 --> Bend Backwards, 1 --> Bend Forward, 2 --> Keep shin straight, 3 --> Deep squat
- 'DISPLAY_TEXT' : np.full((4,), False),
- 'COUNT_FRAMES' : np.zeros((4,), dtype=np.int64),
-
- 'LOWER_HIPS': False,
-
- 'INCORRECT_POSTURE': False,
-
- 'prev_state': None,
- 'curr_state':None,
-
- 'SQUAT_COUNT': 0,
- 'IMPROPER_SQUAT':0
-
- }
-
- self.FEEDBACK_ID_MAP = {
- 0: ('BEND BACKWARDS', 215, (0, 153, 255)),
- 1: ('BEND FORWARD', 215, (0, 153, 255)),
- 2: ('KNEE FALLING OVER TOE', 170, (255, 80, 80)),
- 3: ('SQUAT TOO DEEP', 125, (255, 80, 80))
- }
-
-
-
-
- def _get_state(self, knee_angle):
-
- knee = None
-
- if self.thresholds['HIP_KNEE_VERT']['NORMAL'][0] <= knee_angle <= self.thresholds['HIP_KNEE_VERT']['NORMAL'][1]:
- knee = 1
- elif self.thresholds['HIP_KNEE_VERT']['TRANS'][0] <= knee_angle <= self.thresholds['HIP_KNEE_VERT']['TRANS'][1]:
- knee = 2
- elif self.thresholds['HIP_KNEE_VERT']['PASS'][0] <= knee_angle <= self.thresholds['HIP_KNEE_VERT']['PASS'][1]:
- knee = 3
-
- return f's{knee}' if knee else None
-
-
-
-
- def _update_state_sequence(self, state):
-
- if state == 's2':
- if (('s3' not in self.state_tracker['state_seq']) and (self.state_tracker['state_seq'].count('s2'))==0) or \
- (('s3' in self.state_tracker['state_seq']) and (self.state_tracker['state_seq'].count('s2')==1)):
- self.state_tracker['state_seq'].append(state)
-
-
- elif state == 's3':
- if (state not in self.state_tracker['state_seq']) and 's2' in self.state_tracker['state_seq']:
- self.state_tracker['state_seq'].append(state)
-
-
-
-
- def _show_feedback(self, frame, c_frame, dict_maps, lower_hips_disp):
-
-
- if lower_hips_disp:
- draw_text(
- frame,
- 'LOWER YOUR HIPS',
- pos=(30, 80),
- text_color=(0, 0, 0),
- font_scale=0.6,
- font_thickness=2,
- text_color_bg=(255, 255, 0)
- )
-
- for idx in np.where(c_frame)[0]:
- draw_text(
- frame,
- dict_maps[idx][0],
- pos=(30, dict_maps[idx][1]),
- text_color=(255, 255, 230),
- font_scale=0.6,
- font_thickness=2,
- text_color_bg=dict_maps[idx][2]
- )
-
- return frame
-
-
-
- def process(self, frame: np.array, pose):
- play_sound = None
-
-
- frame_height, frame_width, _ = frame.shape
-
- # Process the image.
- keypoints = pose.process(frame)
-
- if keypoints.pose_landmarks:
- ps_lm = keypoints.pose_landmarks
-
- nose_coord = get_landmark_features(ps_lm.landmark, self.dict_features, 'nose', frame_width, frame_height)
- left_shldr_coord, left_elbow_coord, left_wrist_coord, left_hip_coord, left_knee_coord, left_ankle_coord, left_foot_coord = \
- get_landmark_features(ps_lm.landmark, self.dict_features, 'left', frame_width, frame_height)
- right_shldr_coord, right_elbow_coord, right_wrist_coord, right_hip_coord, right_knee_coord, right_ankle_coord, right_foot_coord = \
- get_landmark_features(ps_lm.landmark, self.dict_features, 'right', frame_width, frame_height)
-
- offset_angle = find_angle(left_shldr_coord, right_shldr_coord, nose_coord)
-
- if offset_angle > self.thresholds['OFFSET_THRESH']:
-
- display_inactivity = False
-
- end_time = time.perf_counter()
- self.state_tracker['INACTIVE_TIME_FRONT'] += end_time - self.state_tracker['start_inactive_time_front']
- self.state_tracker['start_inactive_time_front'] = end_time
-
- if self.state_tracker['INACTIVE_TIME_FRONT'] >= self.thresholds['INACTIVE_THRESH']:
- self.state_tracker['SQUAT_COUNT'] = 0
- self.state_tracker['IMPROPER_SQUAT'] = 0
- display_inactivity = True
-
- cv2.circle(frame, nose_coord, 7, self.COLORS['white'], -1)
- cv2.circle(frame, left_shldr_coord, 7, self.COLORS['yellow'], -1)
- cv2.circle(frame, right_shldr_coord, 7, self.COLORS['magenta'], -1)
-
- if self.flip_frame:
- frame = cv2.flip(frame, 1)
-
- if display_inactivity:
- play_sound = 'reset_counters'
- self.state_tracker['INACTIVE_TIME_FRONT'] = 0.0
- self.state_tracker['start_inactive_time_front'] = time.perf_counter()
-
- draw_text(
- frame,
- 'CORRECT: '+str(self.state_tracker['SQUAT_COUNT']),
- pos=(int(frame_width*0.70), 30),
- text_color=(255, 255, 230),
- font_scale=0.6,
- text_color_bg=(18, 185, 0),
- font_thickness=2,
- overlay_image=True,
- overlay_type='correct'
- )
-
-
- draw_text(
- frame,
- 'INCORRECT: '+str(self.state_tracker['IMPROPER_SQUAT']),
- pos=(int(frame_width*0.70), 100),
- text_color=(255, 255, 230),
- font_scale=0.6,
- text_color_bg=(221, 0, 0),
- font_thickness=2,
- overlay_image=True,
- overlay_type='incorrect'
- )
-
-
- draw_text(
- frame,
- 'CAMERA NOT ALIGNED PROPERLY',
- pos=(30, frame_height-60),
- text_color=(255, 255, 230),
- font_scale=0.6,
- font_thickness=2,
- text_color_bg=(255, 153, 0),
- )
-
-
- draw_text(
- frame,
- 'OFFSET ANGLE: '+str(offset_angle),
- pos=(30, frame_height-30),
- text_color=(255, 255, 230),
- font_scale=0.6,
- font_thickness=2,
- text_color_bg=(255, 153, 0),
- )
-
- # Reset inactive times for side view.
- self.state_tracker['start_inactive_time'] = time.perf_counter()
- self.state_tracker['INACTIVE_TIME'] = 0.0
- self.state_tracker['prev_state'] = None
- self.state_tracker['curr_state'] = None
-
- # Camera is aligned properly.
- else:
-
- self.state_tracker['INACTIVE_TIME_FRONT'] = 0.0
- self.state_tracker['start_inactive_time_front'] = time.perf_counter()
-
-
- dist_l_sh_hip = abs(left_foot_coord[1]- left_shldr_coord[1])
- dist_r_sh_hip = abs(right_foot_coord[1] - right_shldr_coord)[1]
-
- shldr_coord = None
- elbow_coord = None
- wrist_coord = None
- hip_coord = None
- knee_coord = None
- ankle_coord = None
- foot_coord = None
-
- if dist_l_sh_hip > dist_r_sh_hip:
- shldr_coord = left_shldr_coord
- elbow_coord = left_elbow_coord
- wrist_coord = left_wrist_coord
- hip_coord = left_hip_coord
- knee_coord = left_knee_coord
- ankle_coord = left_ankle_coord
- foot_coord = left_foot_coord
-
- multiplier = -1
-
-
- else:
- shldr_coord = right_shldr_coord
- elbow_coord = right_elbow_coord
- wrist_coord = right_wrist_coord
- hip_coord = right_hip_coord
- knee_coord = right_knee_coord
- ankle_coord = right_ankle_coord
- foot_coord = right_foot_coord
-
- multiplier = 1
-
-
- # ------------------- Verical Angle calculation --------------
-
- hip_vertical_angle = find_angle(shldr_coord, np.array([hip_coord[0], 0]), hip_coord)
- cv2.ellipse(frame, hip_coord, (30, 30),
- angle = 0, startAngle = -90, endAngle = -90+multiplier*hip_vertical_angle,
- color = self.COLORS['white'], thickness = 3, lineType = self.linetype)
-
- draw_dotted_line(frame, hip_coord, start=hip_coord[1]-80, end=hip_coord[1]+20, line_color=self.COLORS['blue'])
-
-
-
-
- knee_vertical_angle = find_angle(hip_coord, np.array([knee_coord[0], 0]), knee_coord)
- cv2.ellipse(frame, knee_coord, (20, 20),
- angle = 0, startAngle = -90, endAngle = -90-multiplier*knee_vertical_angle,
- color = self.COLORS['white'], thickness = 3, lineType = self.linetype)
-
- draw_dotted_line(frame, knee_coord, start=knee_coord[1]-50, end=knee_coord[1]+20, line_color=self.COLORS['blue'])
-
-
-
- ankle_vertical_angle = find_angle(knee_coord, np.array([ankle_coord[0], 0]), ankle_coord)
- cv2.ellipse(frame, ankle_coord, (30, 30),
- angle = 0, startAngle = -90, endAngle = -90 + multiplier*ankle_vertical_angle,
- color = self.COLORS['white'], thickness = 3, lineType=self.linetype)
-
- draw_dotted_line(frame, ankle_coord, start=ankle_coord[1]-50, end=ankle_coord[1]+20, line_color=self.COLORS['blue'])
-
- # ------------------------------------------------------------
-
-
- # Join landmarks.
- cv2.line(frame, shldr_coord, elbow_coord, self.COLORS['light_blue'], 5, lineType=self.linetype)
- cv2.line(frame, wrist_coord, elbow_coord, self.COLORS['light_blue'], 5, lineType=self.linetype)
- cv2.line(frame, shldr_coord, hip_coord, self.COLORS['light_blue'], 5, lineType=self.linetype)
- cv2.line(frame, knee_coord, hip_coord, self.COLORS['light_blue'], 5, lineType=self.linetype)
- cv2.line(frame, ankle_coord, knee_coord,self.COLORS['light_blue'], 5, lineType=self.linetype)
- cv2.line(frame, ankle_coord, foot_coord, self.COLORS['light_blue'], 5, lineType=self.linetype)
-
- # Plot landmark points
- cv2.circle(frame, shldr_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
- cv2.circle(frame, elbow_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
- cv2.circle(frame, wrist_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
- cv2.circle(frame, hip_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
- cv2.circle(frame, knee_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
- cv2.circle(frame, ankle_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
- cv2.circle(frame, foot_coord, 7, self.COLORS['yellow'], -1, lineType=self.linetype)
-
-
-
- current_state = self._get_state(int(knee_vertical_angle))
- self.state_tracker['curr_state'] = current_state
- self._update_state_sequence(current_state)
-
-
-
- # -------------------------------------- COMPUTE COUNTERS --------------------------------------
-
- if current_state == 's1':
-
- if len(self.state_tracker['state_seq']) == 3 and not self.state_tracker['INCORRECT_POSTURE']:
- self.state_tracker['SQUAT_COUNT']+=1
- play_sound = str(self.state_tracker['SQUAT_COUNT'])
-
- elif 's2' in self.state_tracker['state_seq'] and len(self.state_tracker['state_seq'])==1:
- self.state_tracker['IMPROPER_SQUAT']+=1
- play_sound = 'incorrect'
-
- elif self.state_tracker['INCORRECT_POSTURE']:
- self.state_tracker['IMPROPER_SQUAT']+=1
- play_sound = 'incorrect'
-
-
- self.state_tracker['state_seq'] = []
- self.state_tracker['INCORRECT_POSTURE'] = False
-
-
- # ----------------------------------------------------------------------------------------------------
-
-
-
-
- # -------------------------------------- PERFORM FEEDBACK ACTIONS --------------------------------------
-
- else:
- if hip_vertical_angle > self.thresholds['HIP_THRESH'][1]:
- self.state_tracker['DISPLAY_TEXT'][0] = True
-
-
- elif hip_vertical_angle < self.thresholds['HIP_THRESH'][0] and \
- self.state_tracker['state_seq'].count('s2')==1:
- self.state_tracker['DISPLAY_TEXT'][1] = True
-
-
-
- if self.thresholds['KNEE_THRESH'][0] < knee_vertical_angle < self.thresholds['KNEE_THRESH'][1] and \
- self.state_tracker['state_seq'].count('s2')==1:
- self.state_tracker['LOWER_HIPS'] = True
-
-
- elif knee_vertical_angle > self.thresholds['KNEE_THRESH'][2]:
- self.state_tracker['DISPLAY_TEXT'][3] = True
- self.state_tracker['INCORRECT_POSTURE'] = True
-
-
- if (ankle_vertical_angle > self.thresholds['ANKLE_THRESH']):
- self.state_tracker['DISPLAY_TEXT'][2] = True
- self.state_tracker['INCORRECT_POSTURE'] = True
-
-
- # ----------------------------------------------------------------------------------------------------
-
-
-
-
- # ----------------------------------- COMPUTE INACTIVITY ---------------------------------------------
-
- display_inactivity = False
-
- if self.state_tracker['curr_state'] == self.state_tracker['prev_state']:
-
- end_time = time.perf_counter()
- self.state_tracker['INACTIVE_TIME'] += end_time - self.state_tracker['start_inactive_time']
- self.state_tracker['start_inactive_time'] = end_time
-
- if self.state_tracker['INACTIVE_TIME'] >= self.thresholds['INACTIVE_THRESH']:
- self.state_tracker['SQUAT_COUNT'] = 0
- self.state_tracker['IMPROPER_SQUAT'] = 0
- display_inactivity = True
-
-
- else:
-
- self.state_tracker['start_inactive_time'] = time.perf_counter()
- self.state_tracker['INACTIVE_TIME'] = 0.0
-
- # -------------------------------------------------------------------------------------------------------
-
-
-
- hip_text_coord_x = hip_coord[0] + 10
- knee_text_coord_x = knee_coord[0] + 15
- ankle_text_coord_x = ankle_coord[0] + 10
-
- if self.flip_frame:
- frame = cv2.flip(frame, 1)
- hip_text_coord_x = frame_width - hip_coord[0] + 10
- knee_text_coord_x = frame_width - knee_coord[0] + 15
- ankle_text_coord_x = frame_width - ankle_coord[0] + 10
-
-
-
- if 's3' in self.state_tracker['state_seq'] or current_state == 's1':
- self.state_tracker['LOWER_HIPS'] = False
-
- self.state_tracker['COUNT_FRAMES'][self.state_tracker['DISPLAY_TEXT']]+=1
-
- frame = self._show_feedback(frame, self.state_tracker['COUNT_FRAMES'], self.FEEDBACK_ID_MAP, self.state_tracker['LOWER_HIPS'])
-
-
-
- if display_inactivity:
-
- play_sound = 'reset_counters'
- self.state_tracker['start_inactive_time'] = time.perf_counter()
- self.state_tracker['INACTIVE_TIME'] = 0.0
-
-
- cv2.putText(frame, str(int(hip_vertical_angle)), (hip_text_coord_x, hip_coord[1]), self.font, 0.8, (187, 51, 255), 2, lineType=self.linetype)
- cv2.putText(frame, str(int(knee_vertical_angle)), (knee_text_coord_x, knee_coord[1]+10), self.font, 0.8, (187, 51, 255), 2, lineType=self.linetype)
- cv2.putText(frame, str(int(ankle_vertical_angle)), (ankle_text_coord_x, ankle_coord[1]), self.font, 0.8, (187, 51, 255), 2, lineType=self.linetype)
-
-
- draw_text(
- frame,
- 'CORRECT: '+str(self.state_tracker['SQUAT_COUNT']),
- pos=(int(frame_width*0.70), 30),
- text_color=(255, 255, 230),
- font_scale=0.6,
- text_color_bg=(18, 185, 0),
- font_thickness=2,
- overlay_image=True,
- overlay_type='correct'
- )
-
-
- draw_text(
- frame,
- 'INCORRECT: '+str(self.state_tracker['IMPROPER_SQUAT']),
- pos=(int(frame_width*0.70), 100),
- text_color=(255, 255, 230),
- font_scale=0.6,
- text_color_bg=(221, 0, 0),
- font_thickness=2,
- overlay_image=True,
- overlay_type='incorrect'
- )
-
-
- self.state_tracker['DISPLAY_TEXT'][self.state_tracker['COUNT_FRAMES'] > self.thresholds['CNT_FRAME_THRESH']] = False
- self.state_tracker['COUNT_FRAMES'][self.state_tracker['COUNT_FRAMES'] > self.thresholds['CNT_FRAME_THRESH']] = 0
- self.state_tracker['prev_state'] = current_state
-
-
-
-
- else:
-
- if self.flip_frame:
- frame = cv2.flip(frame, 1)
-
- end_time = time.perf_counter()
- self.state_tracker['INACTIVE_TIME'] += end_time - self.state_tracker['start_inactive_time']
-
- display_inactivity = False
-
- if self.state_tracker['INACTIVE_TIME'] >= self.thresholds['INACTIVE_THRESH']:
- self.state_tracker['SQUAT_COUNT'] = 0
- self.state_tracker['IMPROPER_SQUAT'] = 0
-
- display_inactivity = True
-
- self.state_tracker['start_inactive_time'] = end_time
-
- draw_text(
- frame,
- 'CORRECT: '+str(self.state_tracker['SQUAT_COUNT']),
- pos=(int(frame_width*0.70), 30),
- text_color=(255, 255, 230),
- font_scale=0.6,
- text_color_bg=(18, 185, 0),
- font_thickness=2,
- overlay_image=True,
- overlay_type='correct'
- )
-
-
- draw_text(
- frame,
- 'INCORRECT: '+str(self.state_tracker['IMPROPER_SQUAT']),
- pos=(int(frame_width*0.70), 100),
- text_color=(255, 255, 230),
- font_scale=0.6,
- text_color_bg=(221, 0, 0),
- font_thickness=2,
- overlay_image=True,
- overlay_type='incorrect'
- )
-
-
- if display_inactivity:
- play_sound = 'reset_counters'
- self.state_tracker['start_inactive_time'] = time.perf_counter()
- self.state_tracker['INACTIVE_TIME'] = 0.0
-
-
- # Reset all other state variables
-
- self.state_tracker['prev_state'] = None
- self.state_tracker['curr_state'] = None
- self.state_tracker['INACTIVE_TIME_FRONT'] = 0.0
- self.state_tracker['INCORRECT_POSTURE'] = False
- self.state_tracker['DISPLAY_TEXT'] = np.full((5,), False)
- self.state_tracker['COUNT_FRAMES'] = np.zeros((5,), dtype=np.int64)
- self.state_tracker['start_inactive_time_front'] = time.perf_counter()
-
-
-
- return frame, play_sound
-
-
diff --git a/spaces/KyanChen/FunSR/models/baselines/__init__.py b/spaces/KyanChen/FunSR/models/baselines/__init__.py
deleted file mode 100644
index 73c21e9f3894279b5ddaee031ebe59086576cfa2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/baselines/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .diinn import DIINN
-from .arbrcan import ArbRCAN
-from .aliif import ALIIF
-from .sadn import SADN
-from .OverNet import OverNet
-
-from .basis import *
-from .expansion import *
\ No newline at end of file
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/train/losses.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/train/losses.py
deleted file mode 100644
index b1b263e4c205e78ffe970f622ab6ff68f36d3b17..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/train/losses.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1 - dr) ** 2)
- g_loss = torch.mean(dg**2)
- loss += r_loss + g_loss
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1 - dg) ** 2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/Lbin123/Lbingo/src/lib/bots/bing/tts.ts b/spaces/Lbin123/Lbingo/src/lib/bots/bing/tts.ts
deleted file mode 100644
index cd10b7d1d7581bf9cf46ff6755fcca550c558c9b..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/lib/bots/bing/tts.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
- currentText = ''
- speakText = ''
- private controller = new AbortController()
- speaking = false
- get isSpeaking() {
- return this.speaking
- }
- finished = false
- constructor() {}
- abort = () => {
- this.controller.abort()
- }
-
- reset = () => {
- this.speaking = false
- this.finished = true
- this.currentText = ''
- this.speakText = ''
- this.abort()
- }
-
- speak = (text: string) => {
- if (!synth || text?.trim()?.length < 2) {
- return
- }
- this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
- this.finished = false
- this.loop()
- }
-
- private async doSpeek() {
- return new Promise((resolve) => {
- const endIndex = this.finished ? this.currentText.length :
- Math.max(
- this.currentText.lastIndexOf('。'),
- this.currentText.lastIndexOf('；'),
- this.currentText.lastIndexOf('、'),
- this.currentText.lastIndexOf('？'),
- this.currentText.lastIndexOf('\n')
- )
- const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
- if (startIndex >= endIndex) {
- return resolve(true)
- }
- const text = this.currentText.slice(startIndex, endIndex)
- this.speakText = text
- const utterThis = new SpeechSynthesisUtterance(text)
- this.controller.signal.onabort = () => {
- synth.cancel()
- this.finished = true
- resolve(false)
- }
-
- utterThis.onend = function (event) {
- resolve(true)
- }
-
- utterThis.onerror = function (event) {
- resolve(false)
- }
-
- const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
- utterThis.voice = voice
- synth.speak(utterThis)
- })
- }
-
- private async loop() {
- if (this.speaking) return
- this.speaking = true
- while(!this.finished) {
- await Promise.all([sleep(1000), this.doSpeek()])
- }
- this.speaking = false
- }
-}
diff --git a/spaces/Ld75/pyannote-voice-activity-detection/app.py b/spaces/Ld75/pyannote-voice-activity-detection/app.py
deleted file mode 100644
index 1abbfa274d9e6d5b801d21398e8d22d044ffbb6b..0000000000000000000000000000000000000000
--- a/spaces/Ld75/pyannote-voice-activity-detection/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/pyannote/voice-activity-detection").launch()
\ No newline at end of file
diff --git a/spaces/LightSY/W2L-TD/facelib/detection/align_trans.py b/spaces/LightSY/W2L-TD/facelib/detection/align_trans.py
deleted file mode 100644
index 07f1eb365462c2ec5bbac6d1854c786b6fd6be90..0000000000000000000000000000000000000000
--- a/spaces/LightSY/W2L-TD/facelib/detection/align_trans.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import cv2
-import numpy as np
-
-from .matlab_cp2tform import get_similarity_transform_for_cv2
-
-# reference facial points, a list of coordinates (x,y)
-REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
- [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
-
-DEFAULT_CROP_SIZE = (96, 112)
-
-
-class FaceWarpException(Exception):
-
- def __str__(self):
- return 'In File {}:{}'.format(__file__, super.__str__(self))
-
-
-def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
- """
- Function:
- ----------
- get reference 5 key points according to crop settings:
- 0. Set default crop_size:
- if default_square:
- crop_size = (112, 112)
- else:
- crop_size = (96, 112)
- 1. Pad the crop_size by inner_padding_factor in each side;
- 2. Resize crop_size into (output_size - outer_padding*2),
- pad into output_size with outer_padding;
- 3. Output reference_5point;
- Parameters:
- ----------
- @output_size: (w, h) or None
- size of aligned face image
- @inner_padding_factor: (w_factor, h_factor)
- padding factor for inner (w, h)
- @outer_padding: (w_pad, h_pad)
- each row is a pair of coordinates (x, y)
- @default_square: True or False
- if True:
- default crop_size = (112, 112)
- else:
- default crop_size = (96, 112);
- !!! make sure, if output_size is not None:
- (output_size - outer_padding)
- = some_scale * (default crop_size * (1.0 +
- inner_padding_factor))
- Returns:
- ----------
- @reference_5point: 5x2 np.array
- each row is a pair of transformed coordinates (x, y)
- """
-
- tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
- tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
-
- # 0) make the inner region a square
- if default_square:
- size_diff = max(tmp_crop_size) - tmp_crop_size
- tmp_5pts += size_diff / 2
- tmp_crop_size += size_diff
-
- if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
-
- return tmp_5pts
-
- if (inner_padding_factor == 0 and outer_padding == (0, 0)):
- if output_size is None:
- return tmp_5pts
- else:
- raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
-
- # check output size
- if not (0 <= inner_padding_factor <= 1.0):
- raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
-
- if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
- output_size = tmp_crop_size * \
- (1 + inner_padding_factor * 2).astype(np.int32)
- output_size += np.array(outer_padding)
- if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
- raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
-
- # 1) pad the inner region according inner_padding_factor
- if inner_padding_factor > 0:
- size_diff = tmp_crop_size * inner_padding_factor * 2
- tmp_5pts += size_diff / 2
- tmp_crop_size += np.round(size_diff).astype(np.int32)
-
- # 2) resize the padded inner region
- size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
-
- if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
- raise FaceWarpException('Must have (output_size - outer_padding)'
- '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
-
- scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
- tmp_5pts = tmp_5pts * scale_factor
- # size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
- # tmp_5pts = tmp_5pts + size_diff / 2
- tmp_crop_size = size_bf_outer_pad
-
- # 3) add outer_padding to make output_size
- reference_5point = tmp_5pts + np.array(outer_padding)
- tmp_crop_size = output_size
-
- return reference_5point
-
-
-def get_affine_transform_matrix(src_pts, dst_pts):
- """
- Function:
- ----------
- get affine transform matrix 'tfm' from src_pts to dst_pts
- Parameters:
- ----------
- @src_pts: Kx2 np.array
- source points matrix, each row is a pair of coordinates (x, y)
- @dst_pts: Kx2 np.array
- destination points matrix, each row is a pair of coordinates (x, y)
- Returns:
- ----------
- @tfm: 2x3 np.array
- transform matrix from src_pts to dst_pts
- """
-
- tfm = np.float32([[1, 0, 0], [0, 1, 0]])
- n_pts = src_pts.shape[0]
- ones = np.ones((n_pts, 1), src_pts.dtype)
- src_pts_ = np.hstack([src_pts, ones])
- dst_pts_ = np.hstack([dst_pts, ones])
-
- A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
-
- if rank == 3:
- tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
- elif rank == 2:
- tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
-
- return tfm
-
-
-def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
- """
- Function:
- ----------
- apply affine transform 'trans' to uv
- Parameters:
- ----------
- @src_img: 3x3 np.array
- input image
- @facial_pts: could be
- 1)a list of K coordinates (x,y)
- or
- 2) Kx2 or 2xK np.array
- each row or col is a pair of coordinates (x, y)
- @reference_pts: could be
- 1) a list of K coordinates (x,y)
- or
- 2) Kx2 or 2xK np.array
- each row or col is a pair of coordinates (x, y)
- or
- 3) None
- if None, use default reference facial points
- @crop_size: (w, h)
- output face image size
- @align_type: transform type, could be one of
- 1) 'similarity': use similarity transform
- 2) 'cv2_affine': use the first 3 points to do affine transform,
- by calling cv2.getAffineTransform()
- 3) 'affine': use all points to do affine transform
- Returns:
- ----------
- @face_img: output face image with size (w, h) = @crop_size
- """
-
- if reference_pts is None:
- if crop_size[0] == 96 and crop_size[1] == 112:
- reference_pts = REFERENCE_FACIAL_POINTS
- else:
- default_square = False
- inner_padding_factor = 0
- outer_padding = (0, 0)
- output_size = crop_size
-
- reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
- default_square)
-
- ref_pts = np.float32(reference_pts)
- ref_pts_shp = ref_pts.shape
- if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
- raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
-
- if ref_pts_shp[0] == 2:
- ref_pts = ref_pts.T
-
- src_pts = np.float32(facial_pts)
- src_pts_shp = src_pts.shape
- if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
- raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
-
- if src_pts_shp[0] == 2:
- src_pts = src_pts.T
-
- if src_pts.shape != ref_pts.shape:
- raise FaceWarpException('facial_pts and reference_pts must have the same shape')
-
- if align_type == 'cv2_affine':
- tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
- elif align_type == 'affine':
- tfm = get_affine_transform_matrix(src_pts, ref_pts)
- else:
- tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
-
- face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
-
- return face_img
diff --git a/spaces/LucasCodeBreak/MusicGen/tests/utils/__init__.py b/spaces/LucasCodeBreak/MusicGen/tests/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/LucasCodeBreak/MusicGen/tests/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/components/ui-lib.tsx b/spaces/LuxOAI/ChatGpt-Web/app/components/ui-lib.tsx
deleted file mode 100644
index c16f94a42b2d399a6dba1a708bd4f26dd5de2ab5..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/components/ui-lib.tsx
+++ /dev/null
@@ -1,246 +0,0 @@
-import styles from "./ui-lib.module.scss";
-import LoadingIcon from "../icons/three-dots.svg";
-import CloseIcon from "../icons/close.svg";
-import EyeIcon from "../icons/eye.svg";
-import EyeOffIcon from "../icons/eye-off.svg";
-
-import { createRoot } from "react-dom/client";
-import React, { HTMLProps, useEffect, useState } from "react";
-import { IconButton } from "./button";
-
-export function Popover(props: {
- children: JSX.Element;
- content: JSX.Element;
- open?: boolean;
- onClose?: () => void;
-}) {
- return (
-
- {props.children}
- {props.open && (
-
- )}
-
{props.children}
- );
-}
-
-export function ListItem(props: {
- title: string;
- subTitle?: string;
- children?: JSX.Element | JSX.Element[];
- icon?: JSX.Element;
- className?: string;
-}) {
- return (
-
-
- {props.icon &&
{props.icon}
}
-
-
{props.title}
- {props.subTitle && (
-
- {props.subTitle}
-
- )}
-
-
- {props.children}
-
- | JSX.Element
- | null
- | undefined;
-}) {
- return {props.children}
;
-}
-
-export function Loading() {
- return (
-
-
- );
-}
-
-interface ModalProps {
- title: string;
- children?: JSX.Element | JSX.Element[];
- actions?: JSX.Element[];
- onClose?: () => void;
-}
-export function Modal(props: ModalProps) {
- useEffect(() => {
- const onKeyDown = (e: KeyboardEvent) => {
- if (e.key === "Escape") {
- props.onClose?.();
- }
- };
-
- window.addEventListener("keydown", onKeyDown);
-
- return () => {
- window.removeEventListener("keydown", onKeyDown);
- };
- // eslint-disable-next-line react-hooks/exhaustive-deps
- }, []);
-
- return (
-
-
-
{props.title}
-
-
-
-
-
-
{props.children}
-
-
-
- {props.actions?.map((action, i) => (
-
- {action}
-
- ))}
-
-
-
-
- {props.content}
- {props.action && (
- {
- props.action?.onClick?.();
- props.onClose?.();
- }}
- className={styles["toast-action"]}
- >
- {props.action.text}
-
- )}
-
-
& {
- autoHeight?: boolean;
- rows?: number;
-};
-
-export function Input(props: InputProps) {
- return (
-
- );
-}
-
-export function PasswordInput(props: HTMLProps) {
- const [visible, setVisible] = useState(false);
-
- function changeVisibility() {
- setVisible(!visible);
- }
-
- return (
-
-
- );
-}
diff --git a/spaces/LyrithAkari/Bing/README.md b/spaces/LyrithAkari/Bing/README.md
deleted file mode 100644
index 66adfee92736d0dde9dc11188a4f996e1ae90e75..0000000000000000000000000000000000000000
--- a/spaces/LyrithAkari/Bing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bing
-emoji: ⚡
-colorFrom: gray
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/README.md b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/README.md
deleted file mode 100644
index 779983436c9727dd0d6301a1c857f2360245b51d..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Synchronized-BatchNorm-PyTorch
-
-**IMPORTANT: Please read the "Implementation details and highlights" section before use.**
-
-Synchronized Batch Normalization implementation in PyTorch.
-
-This module differs from the built-in PyTorch BatchNorm as the mean and
-standard-deviation are reduced across all devices during training.
-
-For example, when one uses `nn.DataParallel` to wrap the network during
-training, PyTorch's implementation normalize the tensor on each device using
-the statistics only on that device, which accelerated the computation and
-is also easy to implement, but the statistics might be inaccurate.
-Instead, in this synchronized version, the statistics will be computed
-over all training samples distributed on multiple devices.
-
-Note that, for one-GPU or CPU-only case, this module behaves exactly same
-as the built-in PyTorch implementation.
-
-This module is currently only a prototype version for research usages. As mentioned below,
-it has its limitations and may even suffer from some design problems. If you have any
-questions or suggestions, please feel free to
-[open an issue](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues) or
-[submit a pull request](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues).
-
-## Why Synchronized BatchNorm?
-
-Although the typical implementation of BatchNorm working on multiple devices (GPUs)
-is fast (with no communication overhead), it inevitably reduces the size of batch size,
-which potentially degenerates the performance. This is not a significant issue in some
-standard vision tasks such as ImageNet classification (as the batch size per device
-is usually large enough to obtain good statistics). However, it will hurt the performance
-in some tasks that the batch size is usually very small (e.g., 1 per GPU).
-
-For example, the importance of synchronized batch normalization in object detection has been recently proved with a
-an extensive analysis in the paper [MegDet: A Large Mini-Batch Object Detector](https://arxiv.org/abs/1711.07240).
-
-## Usage
-
-To use the Synchronized Batch Normalization, we add a data parallel replication callback. This introduces a slight
-difference with typical usage of the `nn.DataParallel`.
-
-Use it with a provided, customized data parallel wrapper:
-
-```python
-from sync_batchnorm import SynchronizedBatchNorm1d, DataParallelWithCallback
-
-sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-```
-
-Or, if you are using a customized data parallel module, you can use this library as a monkey patching.
-
-```python
-from torch.nn import DataParallel # or your customized DataParallel module
-from sync_batchnorm import SynchronizedBatchNorm1d, patch_replication_callback
-
-sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
-patch_replication_callback(sync_bn) # monkey-patching
-```
-
-You can use `convert_model` to convert your model to use Synchronized BatchNorm easily.
-
-```python
-import torch.nn as nn
-from torchvision import models
-from sync_batchnorm import convert_model
-# m is a standard pytorch model
-m = models.resnet18(True)
-m = nn.DataParallel(m)
-# after convert, m is using SyncBN
-m = convert_model(m)
-```
-
-See also `tests/test_sync_batchnorm.py` for numeric result comparison.
-
-## Implementation details and highlights
-
-If you are interested in how batch statistics are reduced and broadcasted among multiple devices, please take a look
-at the code with detailed comments. Here we only emphasize some highlights of the implementation:
-
-- This implementation is in pure-python. No C++ extra extension libs.
-- Easy to use as demonstrated above.
-- It uses unbiased variance to update the moving average, and use `sqrt(max(var, eps))` instead of `sqrt(var + eps)`.
-- The implementation requires that each module on different devices should invoke the `batchnorm` for exactly SAME
-amount of times in each forward pass. For example, you can not only call `batchnorm` on GPU0 but not on GPU1. The `#i
-(i = 1, 2, 3, ...)` calls of the `batchnorm` on each device will be viewed as a whole and the statistics will be reduced.
-This is tricky but is a good way to handle PyTorch's dynamic computation graph. Although sounds complicated, this
-will usually not be the issue for most of the models.
-
-## Known issues
-
-#### Runtime error on backward pass.
-
-Due to a [PyTorch Bug](https://github.com/pytorch/pytorch/issues/3883), using old PyTorch libraries will trigger an `RuntimeError` with messages like:
-
-```
-Assertion `pos >= 0 && pos < buffer.size()` failed.
-```
-
-This has already been solved in the newest PyTorch repo, which, unfortunately, has not been pushed to the official and anaconda binary release. Thus, you are required to build the PyTorch package from the source according to the
- instructions [here](https://github.com/pytorch/pytorch#from-source).
-
-#### Numeric error.
-
-Because this library does not fuse the normalization and statistics operations in C++ (nor CUDA), it is less
-numerically stable compared to the original PyTorch implementation. Detailed analysis can be found in
-`tests/test_sync_batchnorm.py`.
-
-## Authors and License:
-
-Copyright (c) 2018-, [Jiayuan Mao](https://vccy.xyz).
-
-**Contributors**: [Tete Xiao](https://tetexiao.com), [DTennant](https://github.com/DTennant).
-
-Distributed under **MIT License** (See LICENSE)
-
diff --git a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/losses.py b/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/losses.py
deleted file mode 100644
index f54cbaad0c849d3bbc83ae4dc2f5c4ea02a76b67..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive-Nijigasaku-Chat-iSTFT-GPT3/losses.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-from torch.nn import functional as F
-from stft_loss import MultiResolutionSTFTLoss
-
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1-dr)**2)
- g_loss = torch.mean(dg**2)
- loss += (r_loss + g_loss)
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1-dg)**2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
-
-def subband_stft_loss(h, y_mb, y_hat_mb):
- sub_stft_loss = MultiResolutionSTFTLoss(h.train.fft_sizes, h.train.hop_sizes, h.train.win_lengths)
- y_mb = y_mb.view(-1, y_mb.size(2))
- y_hat_mb = y_hat_mb.view(-1, y_hat_mb.size(2))
- sub_sc_loss, sub_mag_loss = sub_stft_loss(y_hat_mb[:, :y_mb.size(-1)], y_mb)
- return sub_sc_loss+sub_mag_loss
-
diff --git a/spaces/MaksTim/FirstTimi/app.py b/spaces/MaksTim/FirstTimi/app.py
deleted file mode 100644
index 9f513c886e1fa8eba9dd0237b24a1df33d962854..0000000000000000000000000000000000000000
--- a/spaces/MaksTim/FirstTimi/app.py
+++ /dev/null
@@ -1,2136 +0,0 @@
-ØÿàJFIFÿÛC
-
-
-
-
-
- $.' ",#(7),01444'9=82<.342ÿÛC
-
-
-
-
-2!!22222222222222222222222222222222222222222222222222ÿÂõI"ÿÄÿÄÿÚ
-ïI9vC çz¯AÏðè¹þEº.~ÙLH.ïÎÃ&ªn®~øñ£:k¾IT¼t=Ímtú¥@óßCóµ뗱rl-Òy¾l¯JÊtÙ¤H
-{0°|ïÑ|ígÏ&®9¯Ëttlή+¶՜ê¶rú}óGópÏn}4Ù}o΄f´×vxy Uhú¡y
-Íý&p,usn¯F¹çEbÏå£EµõmTZÀ焑ÅYÞåÈé9qOY̴ÜçÊøLt܉#ªçԷUȔתæç;NL
-˘é¹wCs9îdÖó캞ñö'b9æº\Rݷ#Øu£°â%Ûq<;=õgMϾcK ê9vUÐsá3Óq|´vÜyDuaY蹱:MvҹLIç£ÏG1õ-KÆtÙçµÁé¢ÄÛ\«®э4]OÏ.Lé¦xSҮ>VÞùêi9Q%µùÊÍVÕ+Uä½M3ö6«±Ä���Îés¯Ì{命þ½Íå:}G>sò÷ó"é§EvÕFô¬ç¦[cÕÞióNv¦굹õÝNù߳
-S(g÷55èã×
-FJìtãõÂËâú¯¦¬fÙN+ÚvãºtÛ
-CNj#»Ö ¶=!å¶&Fy÷óº+S^ñ¦[½_ËÜéÑ\c0îq{RëàߕOֆ}¼¶b®ÉhðöÖGv¶Õ:tk:«Vß+gËts×JnòUKÌà£V]ë>·+n3Dé{µóíÊË鬮ÍyÂ@>Cëþ.Z¯Úo¢yåKÙ]*荙ìחg¿ËÎøú\þNÕlLz3Nt3hEãuioa?)½qyta?%å3¦üÒïüçÐ[>ÕVÕåcééÃ^.xSÕå³=óhðöcl¡<º¯ӒÊçіO-¥
-m/bO¾ÇÓ&M¸µ¯·դòsª¬ý(©»/6=»+³»úÞ
-7åùÖÁNõá³Jt|æïRÈÙnY"¶ÕwµÏ&.͗ϗ9[ª'vs*tÃ=q¯í䧝ןVùQU´ת½Yº·r;uËUV×ќ¹©ê`èµx:|þ}0շSÉFWkvåÕ;*¶)²v×nhózxcXxòº{ìE^]äIeY´Q-uÏ'O»g^k©Î{F]ÜÁ ÷+LÑ5æY^ީ«BªFß)ˍº^s}³\b:Så]jÙEZ4é÷QÏó9èCrٝ+NtË|0ym5èîg[*QÖæu暪¶òÍ:íuöb96Uòäéù¤òóêͬ_}>yê#vxԖÞŷd¿ÉÏ1äid=·8¦['XÇЄӢMgúÓהeÓÌÇìWKqo¯
-&þìæßÑ]îò&6_¹u Å
-¬â¼[vf¼U\ë²~N º½[ó?½[%ȳfH²T]¶Y3ïÃ]èӚëeÖêåß³èͬs¬«Þ~»|®ÓL£W#©L¯öV×yïF-ڜeÓÂ
-yÔ[çß@«í
-l÷©8}ڈÉ-)püî®âãúb~z¿¥M~£½YÊԆ½OWU¥x9þ|Ï[ ªæô¹©çhâv2êÏíR¿²æsztÇÇ^ÛF[ g¿¦Ü[1¨<_~>NyiívFX%åzrw%Êêsm]Sϯüçӫì-®xzãFµVۜej¿I³ÜIÂÕV~T-Qx
-oK¨é������ïïyNmq¶«±¿>}b3Çl¦æ¥-Ý(dñϯ«ÓħF~
-}^¦O%î¦ëéõöWe@ù~fm&ë§tÆ/7&:¼9út
-.¾et;%è
-wGq(²s}&2N6GUvcGºs鎽zqÃ*u'Åö'µç#ȎÎëǗtiÅ;MBL²l¢õFØÎúnl¶ØOÔÙ]`H@F@
-oK²ÛXr䲺,ˣ2'¢£»Û3ڬ«¦nÍ9R°µ¯ª¨Ã: mhÆè£ÙFQ>½]îkñοkmVª=o-4×UFsØhWJn¶f:ì̍l¶ó_ÏNÈƤ_v
-ÍãíWé6sº8OòêgM~øzíùïæ^¹ë˷rÚsrmC§ÏN¾qëÙÎ×]pô¹ãWGªÏ|ôq\öʉ&Y6c׶¶«f º(¼Yb¸*°ô1mÁóٶ×1)y§;yíq§Lüª3¥þ(¯sJxtVï¤áv1ËU5Ùð3j˧UûyLHs-ÛKU̺Í*gÏ҅è'úÛj·L@硥BúlóÔäÑ"m¨Ӌ}iÍìmFf
-¬rôh!(Újàý
-Ú.
-I7²é=Ñæ§秞øPòú%u¬«NÏ"ùke¼½ò6kîü÷ÐóÒr«Ên¶Z}ðglóyZmèñ4F<+RS¦q?cmVéãÐ
-ç Òæ§æ5e±®e
-i¸,¶?\Å~Kq2ÙğÚþKEڝ¸:
-4筝³äۇL½yí=òcÞÏ;g;
-}Y´ÇÉû)¤+Õ*iõ×fdÖåV·eÊè«c_²ãÈë9¾)Ós²Ç
-S·(«¼áÝp¤ۋáÛpnOaÉ薁ÍésOñu:!琜#5êÛĆVú/yðÏiåéà½k£Ïzm£è>{èy£Ǿa\ÃÝ4MW]ͫ¡U{wår×Ký÷Üú=¥¾®Êì߄$Ïx©ʼnäí¢̖gÑ>÷JüOLsÑÞùúíoOæwùit¹zeuØí¯;^k&)ïð4Dí§7îC3W&ÜD§@úÍÿöeΟ5?7£&ªtƫªSM´]×ËLr®Vï%,ãéW¾{Ù7}Î}-eç±Á²^1R±ìȷØÂR²#gÄð²íççÓõöUn¡ !õÿiè`¿>[W-Y´³ØÙ]#}´ÆsÃ}SuG«íÃm´ótäÓãí¹îñ¶cZףÝxÇ>ë<¾ÿ7ìò*ìòeGÔ|¿ÒinÇ3§Í>VØ܅÷N9SNÎUºô¼ÖhÅ罹{(¯2ïp;¼×Óç;o<ô(¾¾lVWe¢¬Ús0îÂ[u7íŴãswógO¶¶«c0|×ü¤-Mõà³Êޭp¾ٛe1ɛÈlµyû5ññµq÷zº¯c
-s]^Ñ[mÕdМè3ÅFøR9ôw0My³è_ѷè鹗cÒç)òÂtºÊ=ÃÂol«ò+)Ïlô¸$ӁÔÉm5¿W>Î{}6ÓÑǎÊç1^mYMvâ-ºImŴâs:ÜÙ×쭮ÈÈ1ôÿ6ÒËÚë¾Öc)y3Ø۟ۯR^·?åîţ].ͧ-oe~ÂղÜÃLòm¬ùs¾_aùô'ˉ¶ÜõßNÎvØôjµl>oS=>/õJ~µ·YXË(ÏJÂȗººÒÑRè®Do£sm§Æ26C0Óæq¯̾Ã\°øg¡ێ¢xÀó¿EóѢ^O=ÒöT·ÊTGL¥åYvÇ]æ4ÕÑó·,óJVÃÉù1_¾ÂÙ÷}åéÆôusÝzfn éæÝÍæÞú.¬ºÂø¹Ý~n¯©5ùgԓò²ú~UõI|®¯ V>b?R´ü³êGϾJü«ê[å_T>WߩCå¼ú¡ò¯ª+£èö
-
-Ï}Î×Yιç´ç_µ RÏîÏe
-k§*,º]É<ܝ~VõQó^Yù(®bK©Èõ_¢÷4³¼¹z0ݿ%ïÑ|ïÑLu³.?`(Hõ)7ÐúO'*¥Mö¯kió·â»]¹¯%NSuht¸ý
-pÑÈéàè[oO&f찦è=][é¿'o=9öù¥·çÏÝæuãN²ÒsoÑäÖfº~.²Fn÷çÒ>_ê>Qy{_µÖÏkö-¯F:²Y̺;]/&¼=òéÅlòu1ÝNw÷כrÛNÌUd¾³ß<®¿
-é3ە~]±ö:s®~{õÓ0ò?]ñë{ìQ¥×ìZÈYiT=^Ù×8´(¿3zöܛ<®Êi)y¢):+Ãö5¤_7Ö|¯JiËn{v¨ÆΟ´¶ñ®Xc8#DrÒlkʼÇAÂí¼êpÛUף´¼Áz/QèU^c£æ[
-½Åáю=А
-Æýŭ/kó¯aҔgZp²ýÚp§e1ÕÚ3O;Âyå«á·4óӝI¦~ιÄôcì²Û-UzéюêmҮtç..¶;F#)ª«GÕØMÀ>Këy)ùôSnôy°¾Èòsé_¦÷-֬xÝlQ¯?6©Äáöp·?¢5Âm½Yb=ò~iD§
-;i³>r§+èêr>n_FîÏk²H§Êí=,¦êSUõ¢N%¹£é³-°-ÂNIRXô\өe áðþâ³â½ú¨§æO(³¼kóú;+?;ÐÛ})ó߮óL¾Jï§öÑó}Oo§ëùÿ~/óèG
-Þèù·ҩ¯Þ֘Sq¦@&L µ·@^¢$"HðõuÖr
-]Èw uN³:ÎG§YÈw#ӬäøuN³á×r´VÂ'y5qõtìúùqû]֮}UWÖY8 Õ9%v]@ð#m-ͯ±ÇNÊnÇr®R.WìLýõ¾_uÙK`CÃi½`¯ËÒÕ$]åBùQ¤É}]¨öþ~Sñzç &¾t>o¡ÎVúàB{ä|»óÓ6º4ã¢õÂ4¼ýl«9§ٜhu¹ûåKß/FqnºanX¥ö¾ÕmèÃõäGaƱ=W0t܈§2'UÅÚm£4v㧶çD鹒:.oMƑҟ6hѧĀÑçCíVÖ×àKS£¾KÆû95¦!î¹ù!~o}ßÛòY§W/f[i®líˣ³ÍèÇ3ß5ËÛóçµ?YmVހui{``¦V
-Ö
-þi§pó>W
-wÚ*Z)«X¦V
-úî:?UµVÀ5UÔÏYúslç¨ß¢ۗ"¹¬ººëqæõ܄Ö9´gן^ÎFÊi®»=Ç^==^oVÍmVYmVޠ<9Ë:²ãÜoZKî§ÓmnÊ@
-;£·ÏÕmU²QÑղ>òt{þvM"ò»ðËÈyiè¿ëأùo:å ²¹×|ú9=Lmî
-ð⬯¯mV£ë-ªÛÔóÉ#1m 9Ýt>~«j<]lvp×~Kªµţ«fËró[eWϢá]W¡(¢t]F=U ¤tgMº¾Â\Öçãërºqµ[¦_YmVޠÑçCçë²¾¾]<ó·ò>yïù»'v2·̷VóDè«5Ój;ü+Ö>{ä§~}±ΩÅçƻ6s:|Ýþrú¸&0[U½<ÿYmVހ<=®DI$DDI$DDI$DDNt
-ìî:?Ju$qôÑÏ¢¡¥wtù°®}lú±súUՇ,ɷJ~x^=èóîöâ÷ÆhÊ3·½NWC½´CäNëæúËj·J«h<<ª類P¹sØü6¹ú"úýèMХîR.BËWÆKK«kç¿lý¾l;£·ڴe·@rïͦpê LkªØV¾t9Dm£ØWd³/£« §Íxi[,÷U"ª¯¢·ҭy4S£¡ç¾s_äá×ÏõÕn¹>àk\ûMW&õèáèj»¿ó������íüúËÉÕ¬{5Ï2,uÞզ\n/«´îñĊv+˫-³¼ôåÕя¶Õm¢|î:9u㾳Îm¹zóîÏTW§VnS&ÚçòùûWèù·â0{äï[4ä¿--UëXڋ©¶¶éyçUm=X}eµ[¶ ϣ8çæZ¼¿£s\¹ýè¿'G{1£E3çÖ[pî×<^Eºµr÷[<[(Û(|ÿÑãåèûTM^F·kdz|=¶«mçtyÐàé˫öy'6ܨÊ=y÷ÁÖ×ÏÙZZ«ÔûÅíx¤9ûùÑ8gmPlÝu7Äïzå¿6kêçú»j·lFp
-us¯*ێ®¼ÛoK¡MØíô¹³&ó¡ÎèteÎGÌöÛ&µYé¹Ù2íGK?«jA¶b۶ÛU³çtyÐàhÍf[uG.üºôgê L.ÏÅ;7ð¬gÙÍÍÛKûM_ù{8N/讠4æÛY×ãÎkòáç½|ßYmVë3èÎ˪ʳèéa²èq4ÕZ&Ä-:tSWÑæu:rä#åvvù|a\êµwWLZ½°öÍgnìôçöڭ;£·Ïûäi§gÚìãéˋ¥Íè¨^ +²»fö2µíËÔÇlµígkß,ªÅýÔ
-G
-S+{ åÊyӌºù¾²ڭҀϣ8
-#5ù«Ó;ër%ä$ٚôW䪉ÓÔäõȣ{ø2z²ڭ½Fp
-6O¡®5ãÕÜGlq±ÄvÇ&®Øæuár=çz
-ó(í
-+´8®קÎ߆^W3[U³çtyÐùúª¶[³-wÂÏ9öäÇV¼¥äVÎ^{걶¿ÜÓóÝ쵳=ü(K3ݲöpZ©A+.Ëզº=ñˮnfýXymVß?¬¶«oPgќ¨!ͬÔʶ±Q&lvtüÇcM+íÍ~ù×äXi4 Y
-öLç*µu³ÝKKØe¹´ôeíµ[x;£·ÏÕmU²ڽOb¼Úy·³ӌÇ)ï{(Í
-YvÄï¿çûÙi*ìáˢsò^֟ Êíêù·õ>|ªтڭGÖ[U·¨3èιU϶zºWM[qLϝ¶]Ý
-w§gØ9í=umd|ËYQ:¯t¹ú¢<ÇvufZô5d×я¶Õm¢|î:?UµVÀO£˺t%a¯ÞöKW¢o÷]/«_+§Xs
-¾lçé¦~ÌG«åxë9{M-^?aшZj´úËj¶õ}À1Ue8ï;2Î'}üN±=·Zñïëò믲ū,ًn¸âóÏ3Õì|Mô쮑]r´ûEÔÄM®ًnØ{mVÚ'Îèó¡nÙVÙZ²_Òã۞½z=OÉZ96t²éHYGh®¹Ëїd#¥ïµDù^ùó{æÓ,çý]µ[z>àú.ϖò·4e»V
-fêèê=±«#¶ïçtm7Ï<®ÒE
-Ë̫^®wBY¨ՖlEvØ7釶Õm«>B|zΥ/ÊӫW欳|lç«~½ܝXk³=V!vMq9êÕ]¢uіÕӎ-²
-UëY,^÷"x.)}eè
-ú3s3ݚӓȞ·ÄWenêb¯ش½&þ/©l91F»[»¢±¶uâ£_¾+Fmymj^GiÙÐçto¶ÕlÖ`ãSßãRعöԝïRÒ*Z*öÂcl7Ë7µ¶Uá*äµjZ©i-ÑW©«§ËÒnÛïFjgќFZôÏMq'
-fMTm¸ìG]::¼¼áÄóÏ#¢~{ìåª_L)y<7ô¹9çöڭÌ#!RHE!RHE!RHE!ç ϢÀbÍ֊ܿ:£êW½AËu=)Õބ½çWÔ'çPrýéc¦9æ:cǪªÒ`åⴌ¿KòÿLe£UëM24YͰÝU5ÜëMsý7£ÚÃ#cy´®](®ï
-½³ҥ¾ýÖ3{|#
-ûcÝBáJáT4eÚ*Å+UéªñO¶¥0Å~Z*Z*Z*Z*Z*Z*Z*Z*Z*Z*Z*Z*Z+
-73ú^É#K?tt¨ðÐÇ#S¤֦9TZIOàx{æ4Ϭ®Ìîs´§B{?¥ì7MJbhe°¸tG3?j
-»fdÃÙðóÒôçÕҸãßÐôÛ®9¹{«5^ut¼O*çQæ]däÖsröã®^ÈËLê'z#n
-Ú*£`ÉW@eQSi³{ Öæê41Dގ¢É᱄ndµÓk/¦(X¬°ej#´`£¬0·
-&®å:¢ÐæÏxåéÖ2Óйn°åϢ9që
-jÖyìNn¦2hMWx{
-,3h®fm^ÄÕÇìsùl¶/*H¼UðU)ègАÿÚ
-!óÏ<óÏ<óÏ<óÏ<뭶ÇÏ<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<ä߯?<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<òòªGüóÏ<óÏ<óÏ<óÏ<ò<óÏ<óÏ<óϓÞóÏ<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ(U̫Ï<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<óÏ<ên'.o<óÏ<óÏ<óÏ<óÏ:ëî篻Ç3÷´(sÇ=÷^ñÿøóÝÏO±۹
-ã`(hÿðºÎXí,^
-dÖvóۭiØæ#¤-ª
-ã^¦3"þLSn{ÖóκX·p%
-ÚÍy#i±àÚf}Ùf²ÃÛÏ? ©N§֞£Näço ·ªÄïvEOÄ\å<ñ½' ÍúÚn³ؤ
-ÿÄrÞ Uüóék4¼¬?7cÎN DU#2ñìÒ7óÏ==÷-³Ç7Ç
-0ï8¢óóNêψPÖÓÏ<Óÿ<ÓL8ÃÏ<óÏ< _M÷éÛ×ÊÚÓß<ó?üÓÂ0`@<óÏ<òÔÜỵ¨úå½|óÎ<óÏ<óÏ<óÏ<óÏ=S{2¢̩BýtôóÎ5Őv\5Þ<óÏ(oqªÿһÔY*SÏ<óÏ<óÏ<óÏ8ãO<¯U±ý
-Ü®rïÖÌO<±lw SMՖ4òɫEeº
-Ù9½¼å<òË<óÏ×Ï<óÏ<óÏ<óÏ<óÎ0óÏ8ÃÏ8Ӎ6óÏ<¡¿=DóÏ<óÏ<óÆ9ü0Æ2j&4å <óÃ<óÏ<óÏ<óÏ<óÏî<£
-pXòCÙ4óÏDà=B@)óÏ:w úPò9ܼ9WÏ,°Ç<0Ç·嫇ñ|j¨ª¬`WÏ<"4û¸·
-J¤¶$߽_<àH9ԻÀïÿ_¡%ꅮ;=;Å|óÅ
-°rú«}
-¬¬
-º5·b|Kò![QóÏçÞ/C«ÄX°*ÂYfÌÉsóWÏ®
- æok-á&6WÏ<®á¾MÜdÿz@ú±»\´ù®ºc]_<ð
-îÜä÷ ±û@êÄψJ)
-u|óÁ8æ̯*lOMkã©05"çѨ4
-óÏg7Bð٥ij +HCÎ"&~û
-ÖÓÏ<ÑNR ì2٘Ѐóæþ¿¼©M~ÿþÏ<ñ²¦É]§¤d§cÏ
-óÏ<óÏ<óÏð¦GÑVºÁ8Ä
-/Bªezv
-` Ú
-jå+ÐxlǨȊlÓnA
-¼©@ac60æBRà?SÓç
-¹폜òÜlr:MMàØ+'å.²Vr ¨»<, 3íï×ò£ïJÿ¦'Ük¬ã¿ýÍ6ï4wmÚz
-V*sõ5Q·¬±ÝTí#ÖW¾׃,½6lϥ
-À̧V
- ?ØlÈ0ic¬ó¤DÖÐï±O?)©ue]§(±U½ãÖ+ýÓÆpzÎ"}ù{©`G±u:l#~}:F¸VFsÞTªç-Î=[üÐNı}ÄDÃeZ
-] ƣL·Iæ+)ãv2~´^(è:zþrk'S[j PV¡
-i¬Ðo>e}{ÄfV
-ç¶?8:{uK¸ ¹ñ·1ëð0°²YMGç§Ïÿ3®-a§³ay,$l
-óUG[;Moݜw´ÚG0ÌCÇ·ÚsJý&»ÿgÈÍv¶¹ç×ÖϦ®ñ&úMì{ǷÊ
-³´aÔÊÆ3Ìf?-F14çÜ2Ë8QÖ
-õ\78Æ&²á[(-JÌÕ(øÁÓÛ⺋(Ud#¿_ú}a·ÝqӕÜf£N¾QÏX¯ñ®É۞%6ùw
-ÏIW=6±>òçÿ´0¯L$Û2ÇÔӧd¸õ?¿ÆUiC¹zég÷ó4õE»ú=Ð`Bµ¦º÷Ehqü?Ï
-G¯TÆIÏ8àpxäg3_ÿƳäfg»:bjJªaÒxmªueÁÈäÍ+¨sÝ1ª;(%«GI~¢ъ©ë<ÝKryOOÖPV«MЂ?Ymî¤tD۫üh§*·ÇljÍL¹êz~Qu֨E#
-ÿèöë)kª!1»¶çÑ5\X1ÔüN²¡A¤9ÃzO¥ª&ڗõÄSÿ¯¤åû¡O\õù§´Ü8w½
-B¡´
-Ú+]?¼«P,%öÿ>mój
-±JÄQµ¥Ë[WsÀùÿ?)àÚ;)V{8Éà|=}9HªDZÆ@â;r&aæįú"Y÷¢õæbcݘZ3{Ã3[ZÅTb`õfgw÷GԲ¥~±tÊËtf2Oý¬cqþÐx#ýÇû¾
-J¶ì^+û¦&W¹0éÁïÃêh¾ZôcjBHcý ¡@
-8æ
-
-·W§5¶åèg<1ÔM&µ6ã·x+Pâ0
-¸Î[Ø}b4(®2Âkim;c±ï44Óë4=ßkoAÓêý6aÿ¾:Ç@˃Òmû=£Òit˧¬ ÿû1-àFÔ 1µé¶C¶}>Á×kXáBçå+9Y©ӭõi¢©©¤VýD#êô»¸ùD]ªÓÿ|uD^
-=8s-82ÄF¯]¼àßòá+|)ìK3?Ò86J<*»[j¹cóÂé¬ò¼ô|D
-mÀÜdþuDë9be½fµT̝@Èù¨|¶Χ¨¯ÃÔ!<õøÃ@7ù£®1O}bǫÝrs>ZSï*e{<°y® XçÀÜ
-DECøLã9s5º¬mṢÄo¬òr=
->&IÉYFº@ÂD##x®¥b2
->.Ð1uêW̴c?´ñFêpؚ
-kÔâôûØËN&ßï7&Øۗ4vùsGðÐÄnÓS«Ùî',gXî´äūugǠAÈuá\㙯O7Pªzû[2£Ho-Aê&¯Â×SẃýíhrmÀ2ßý¿ĪÄ'ìÎq
-ÊX×haò0õü ë-û$.%4<ÃÔþÓÝ_®ã
-Į¦QÉɔÑþ應◵w+!ãþæ§ÄŕíAf·R¾½ý·iÜ8þðãó#á8)ÇXÈOõü ë55¨Ö@á¢+&/ÒN
-`ñ'ónîÒín[mC2ߣ¥ø°ɔPw6\TݦÔÕn§¡'õïìðf¿PÞ`ZÇScy~kϤ×ë,ÖÖ
-Iø
-F~£0QÅa¼O1}b]]ØsOC360f ?Pu¶ZwäúbVECz
-TÙpH´È2IàM
-©k2qÛã+¥*Qͫé,¬0töéú¢Òj*ÔXï÷NqÎsÌ<ñ2zM;Öõî^0='øtZµSéÇÏI£½¯¨[9Çù¹cÌÏ0/¤8Êƶú,ädAhf6qÏí.ñæQmÇ$bd÷¬zÃðb V8í#X7××÷ébGQáڋqë4t.©{@év¶Q«ؾ_\~±o.pF=æGi¼)äï¥.¶A?
-?S*Mù-Æ&K¸ Éáy`Æ1
-/ÉIå4òÚl0!RÁ,hìcÓGmªV®¢m1@¨ïj¬;ÀPéÞ
-s`Çðí5vGnÏ>Î>ØL-õnéGS4úskA+хmǴ,b¾8«Bj#á%¥ ҨPsÖ[RØ0eUå@éÄZØ
-9Ïå^º²QqÏá\f2ÇI¢*¨ß8l+9Ç_0OÓ¢ÁÖéðÛâ#Z¨¥é(¼\»ÇHç'îdËu+YߜfSný@Mܐgv¡Éÿ]ێÑÛð¸Ìe±ôÿH»É,@Æp>r±c
-LÕÒ,§lðöXëЄǼ&ï¼Æ%eºÆõR¹zXòí܋
-Ìt*Ì}îsÚißqë×ð«2æZ<¬s̬s
-ÓÖÉ{ï57µ{»M%
-ä±9`F³KÀcP÷+`Íz}]]ºvõÿĥ+¯RQ:
-ãð«Fo(M_ûO©YQɌ9b>DÔi¬Ô;18
-ûü¿IZ]¢?"Êqó. 9i_b½©6ÜPSϳY§ÕÊO´«Ì(
-
-³iŭK7H¤0ȋÇ3Î`»GH
-<ÃZ%Àĵe
-Mî
-I¥ҥDºõ?
-Â
-)fîÄÔèõ¸ÚÞïq(¨R
-HAïTç
-q
-;{"=%¥Z»3§áIdÅ×ÐʹÁnpÜ&á7 ¸MÂnpÜ&á7«jw
-=Ûö̡]jPýqø
- «䈪ÌھjúFؠÚY¬¥8ÌoOCòÄêôêé~
-_ç¬Iµ}&ÅôAÛBuXO
-øVé4µz@1 ÀɚJ¸^³Qe®1ãöX¦ÀzÅAæ Ú =»ó¥³©2EÔ
-ViµÉoà9Dxª`~ºEPFL
-L×k±¬ÒrzÍ.Æ_|àN¤8Q6Ψ¶ÍÒúhº˕Èku
-§íýKIDÔjXàñùGÕއ¨3é÷ÿÒÚÖU¹ºÃâç´ÿP¿á+³Zã eºÊÆHýFÿé?Ôu C¬3u>Æé5§?¼í(]µ(ø{-ROC"í'çï0TàË+gl©Ïóøcz
-Iw¥'kÍ5û®,͂{CñÕÝCA(9O¯øêܿhfX¶p®ªê°ìêcªºq}
-OR ÌьS
-ó) %^aêfݣh&ïx(ý(C½f٦þû¤ñèçyÆb}Ñìu'Þ¬)]~bû¦*âà¬}Ê
-Z¸ïÿQt¶ù#ï iiÆ&Áõu
-HK2}aÇÄõÌd®߾% yÄ'I¥e
-ó*Ô]¶täMi¸ôÄղÈ3l£úkìnÄh$ÿ9
-T|½®ØlzNã«$Qpö?X}[å;M û%0}]HÁ$§·{5nnQ`<ÇÔåH%Åì¤ }!Xtnɸt½µf5
-ÎÔ,¡I¶S÷
-±ºMZn þs´ӶêûMBγ裰æ\[mS+,® #ùòöYÓۯm´ÊFg¬Võ%[IMg#Щ'&0
-2eD
-³Û2¡f%qÖÀG±©ɖ»Xyec
-=Ò2¯õÿԫ3ÃlÝNßOju¾qÝ7 9QD~ß|*§¬ӦûUDÀÄUmfÐzå¨="
-!X¬Ù6 ¶/Oct\¯¯m¹ç[²ݧ¿µ:CÄÛ l~±Ŋ3«ÃǷ]oq>Ã*ÝaoHݠú¼N'ħÄ
-±ºEéMCm#&\öÝïXxê
-ïqäËÓÖi4ÞRNO°¶:ÎIAõl_|ʫRA&q+$»YÏyµÅA4Ã
-ó15
-Ó5)VÌÒäM²éaË
-â+-rõ·
-û/Ò%Ã_¢µjò¬l,s~_Çß2ôUaXäUÏóÖQ¢µ¾ñ钡=â;OX¼}g_xƬ0ZquM×"î
-r[¬Û)XVjk¸³[W÷Ä{7(A4êêJ£#2±Ûi,r" ^r8ÄÜ}&ãé ÈÁêVÿoí
-~ïóõGP<¯óõZ÷i¸Óq������9$q°Ü÷ÔSõyx`0&ØkÌjý`©s
-XGh½?¯ÿ!1<:Ƭ·Cév±SÛþ¯EOyrÜzvéÚIÉÿôÿÄ>
-
-!1AQ"aq¡23@Áð #BR±ÑPáCSbñ0`ÿÚ?ö
-é¡ÏÈÖbæºÌ\Ô
-3"ÎÉñº3ÂÇûÀ-¡0|c=-õïBꚝ½]áÌîuL%í6RNږÞÖp×û=·êX3:'h
-h¢`Ëü¦¶ڪ¨ð0I wù¨e}\¦R-fÛûFÏH_P]'椓y{c.¶©íkCMÕl4±®»GïܨæÎCNDÿ`i±U1uÚ؇iÙ$ýpSÀè_»~¿º°]e.ñÃSaó*ÇØë¸ë®]Ãæ°Àýܝ¬¤ªxȘ_µQÒÒÄìOqwqURïZüQ
-b<±G
-ä6`[ëd(ç:7ö]FÓæ¨*«|XÆ'·%Úë©Ì
-^äÆ9ä5º,oÙâȱaÇB·OÊÙ(饔]ÉuÛRA$nÂñb6hRÑÏq=¶äq°]V^KªËËö]Z^KªËÉy
-Ú)Ü.û.¥=íöN§¶¸×Á9ÃlüB5
-òþÈlú£|ǵVêsØ:ø#E86-ó
-q'/¹Hmráªk§Ï#ÉQTJÑ`SXÊuD2P³nBtÍf;©Ù2USÇ0m³²qÄüeETöÛ]Ét²F©³0
-f¸`'뒙ÄÄû禪ZßdlʎFH.ÃuL{\\l@EÀ0´ùªXÃcrP
-L8µÎþ*W¹¯,¶.ãõ䢁î8Þ>Vú(܊ؔ£
-Î µÒââ°RÆ$-B"ÉڞæúÆ1Ϊvsã=¤¢Ä?@bÐAB'l³BÂøH¡Û%Iµ"؋YOH&8ØQ@.B¢c݈fLN+1·*Jybv¶Å6mÖöÙ&±Î6©É0XÁÍR&i<Ñ{qâhVPBؙº ÌÁÅDÛ0*q»zÂ;îR Æ拏¯¬U{Àa·vjIc9gsÂʞ'ÆîѺ«nüU$wuΈüгlUC.ANÔôìwá{¯ÝóM¤¦
-Þ杄¨lM)ݰ,¢¢ÖÏÁTÔ6 K´N9đpSlÁÚmü6ÑkZ؝é*§àö½ÖǓp!5ÍjsXém|îdb캛 uÞUhi»§õñN©lfÅ2FÈnp°GDEÊ-³UDnt.
-⺛˄|m{}
-*x#갋ݶlö9Rä×-ã%ÎêY£Ùګ\Ý;SÓBhåM.3¡VàãykEU,.i
-²M¬àx§½ïlÕCw: t!eÎG(ãi8
--pÂí
-d-ÂR©«ݳjT2Pãw©³Ä$fTÉwª¬ã$w(=c|S£¸ssTåp¶¥5µ9¼êAf©·"bô¸-õT®Å$cÝÉl÷Þ7ÜÞÅY§ÇØ&GQۿEZÆÁ0pЪ
-Ë~ÊÍli¸OôNÏÕÞäßLó[N·Þ0öÃæ¨htû³ÿÄÆZtO¦ìN¨Se®º¨ÄÝn|5+%s**6Óu¶0èN¶B!0ü´¶äS⑳ÆãÃ,
-§Îwºªp2
-j«-¸º§£½ID\nrL¥Àë!Cd)3×%N©ho5.AuDѺÇú?𥉒
-/
-¾9÷7˟½$MÂ[{÷¡V[ÚkF}ê¨Év⪽;W0uÓp'¯eù§2ù&:7˹Ä1rMÃ
-¬4nضp½KT¦îUÏ{ qb£7pE5·¬qÿ¨ýլ¤=®l¨]xìäè¿)XHvÓ>?r¶jcøgܥ¬|
-pãu
-[âk¶òFµäÞÈÖ<ðF¡Å[êÒà,CÕ8·
-Xw$+^-ÊK±ͭ#/fÔÕrK®^zaSOa:*\+ï/¨B̎e|ÊÙ-Å9÷¿r¾ #/{¬N£뚟laply
-j7ß4}/L-à`$ò>a>¦± 8ZɵmuEè¿u$uãw¿DúYK±Hn|n¡xU3ã Æôء´l0tp(>üT$ªM$Ç+©éÃg07P§Ùõ
-we¹*:
-2ÏN$}ȡS1r˫埲t/i± ».©Ú7Ì(ìÀ/Ì(Á Ô-Óí{"Ò5FFPp:+etâªi\/Ó¤)$Ånõ&Ï}5ˊÜi
-:óF¢c«Åo̭uAÄ
-]odýESÊD
-ÅÉ?Ò=1
-Ýo¢sZÓÿ&öSfìÞöUßjX-¢®HGg0T5QÌ܍x-¼ÞËrâkX¦PM-ÞђFdáew7Uw¢EÈiQÈüD@(ý £/âiUÓai[&«tÜz'ÉIwiáJæÄ;Eb$£Eke¸Á5aL-#¼~ôs¿vtué¤mî°**¯v<]§êêjhÝf×O{Ü
-ßc~
-6×øÇS·#u
-Üâ
-uUϑñ±Ì3¤³]Tlm-ù)bI,NVRìöEû!6®i
-Èp_cÈïI»2Äܪºn®@æ£ôÂ$y¤¸Ñh,g<´Ûޣ§§JÞÑUÔQ¶A!Å2§²ÝJ¦¡ûǸ¢è±o
-*cyG?½æ àۂ½4åÞäØî@¨Üæá
-ÑVEø®sHÈ÷!Pó`T²Æs[#±Úp¸*¥ v¡N{îvNö¿r¬âÝÞ÷_
-¦(݄F~^äÙi ks@´84öA ÙeEJ'¼£e·ldt°´Y
-¬D×àù*ڷ@öG¸Ø}f$m)ânm ÑÊ×ívhÓÇkXYK²*ËÉkr¿0¾Ǭýcù_cÖ~1ü¯±ë?GþQØõ_
-_cV|cùSÔ
-±ޝnþ~ﹳE˽ɡéULcÉyÕª("îō{d
-ÝÉÁTÍ{
-$ʉ§í9ß?LébsØ÷bnV%BæÉPÜL-Ϗù@`*·áâGE=L´îDZOÛo'G+¸²¦ۆ6ÆØÆCwõ¬`ûÑþ¡qÿl|TvGa·½7o½º3Íê ¾?ðڑÏ
-son_êþñ_êÿÆ>+ýBÿøÇŨÿø£ýDÿÐ>*]¿3ۅÍI+äv'¹³5w»æVª(CTHbkF|t͟LÝ~ÈkÎ8²?ºÙøà ß^
-ú¦ñFödnS6ÜñCj
-OkØCBWFnҌ
-$ðQÊÂq¾%u3U
-|xF¾˳½ɮMrpÏv绹4
-]L.ܕ,¡õEȯêVÀÂ
-᪳ª§i
-ÍA¦vDà0Õ[ãe qeHµ¼SZâðçä
-¼óÂƜCÙh^äØw²aº}c³ÁSï
-jjÓj¦ÕFAÉó[~@øcy_äç:Aª/Æ÷<°´åÁ5åí]ky>!m ('
-IíyE
-eØHì[Êʪ65µ²öZSbPu}Õ±iÄùªÊÛ2ÄG<6URÆúVsGNǘæfW:*¹Z\[I·/®JäÔtijT±9å̦SÆèÄ
-þj±ÒS;
-3ïUºµ6öZsk ä>Wj2
-íLLhpÌj©ålãnm·ÖÛKôaPÍ
-¶01þ§¸½ÅÎԨ
-ÍE Êú'ÚIî3ufËhUqicáoeÖ+XíÀÂâ:<&ÅlçԷ²Áv«
-ÀáÁf,P°»îT; è¸
-*WvÉ'¦zý·
-
-®£À=<% À!6º;i¿ùClÄ×dÓo5Q´#®/e#HD\!nÎ.7=, 8¢½Û"´LBÞ(Mϳ2w°½¢à{°;ÀîK¹,
-ä°;Àå˹,
-ä°;ÀîK¹"Ò>ì%¥Ú\'28۽c¬Õ)iy,ÓØ"rÒҋðutÖ=ÄÅGE)Ì蛳ÉñGg)(ån|>¹"/p®®áâÌJXÀÊÝBi Ü"s».·kTÐI°PPOÑE&¸ÈÒk°ØdV
-m͹å=͓j¢ÑæÝIÆíDöVê¯k5 ¹Õ\l%%|YO4QvFJ¿iî ;éÉ?lMPöþ[rQËXI©0@úÍlªÙêC±Øý|x/E|¬¤µÞÁû¢6(
-\·"P&٣¯²7PE0yÆí:6Öm3·MmϒÚU®«
-ÊֺÙô³V»«Ælyè©ö{höLíp<¼¯ïNpMtì³Eµö`È'ٵ®ªÇ÷D,Ue8؆ #¯²7P2ù²cp44pOu²Q³ ax¸
-il·ESºöu¹PìøãÎÕuhol#áôU=|°³
-øÿK3*EØTtÐÆýãZ/ලdTk>õM) ò,JÆáOii±A}ºU6ål÷öTͥ¯
-õ#&ïÕǟ×øBzép¶\Úܴ.aI
-³n_ëö*]Ãڢ«cf8_Åþ
-m§³Y>êb1èn/në¬`HKtèÚì|ú
-¾ÈÝBU;0DÑk)nj¼La-fU=<Òá|ìdkF|üy©KðٶVu~9dTÑáíp(
-³RCPkZúaÚÿꪆVÊ[0³M´*¢kci½¸Y@òøÁp±⪙xÉå#¯²7P¡`|¨©jݼï°0\òL.
-ݼXý{üò¹AÕ¹n\È7·²¿ùQ¿z·
-^ýW¾¥²31kï[J¼ÖÈÙÛ2_ÓѻÞáòùÙ@u 88§Û~ìtÌsA(ÓD5U6=n
-¢.³ÚÕÕcS01öR±uV'vdJÓJªê¬]UF¸ÐÝB¢Ì9ô?Ò=
-T|@±åõñUr6Iµ?Y墑ì øª*÷
-9ñ·»LÐ-tΑ°:{¸÷tI°fªx¯¥m*AOHÈã9üÇ;{ˬWn°#µ>è©_Ê:ýØG`*ƴ´sày'Õ6FÛ
-õ'¸*væ
-/$rc^Z[5T?©Î~íº*Ø!d
-sOhÛü¨¢$ÓtÕSÈ\0»P¬¦������Aë2èv§¢¢#MÔPz
-ø'SÌ iiº_wPGØDÙRV¶:W=ÿ梭§#»7¿ä(âdmÂÁ`¢ôÇEopG_»&6ªGd;yǒŖ^{¢ÏWë*ºp{mÕ5¦÷q¹Tµ/§sj¯sËݩÕYOëC} ¨â[ëNúG¥±
-1ï
-
-]p]®ëöUnk|Ͻ:투w±úÑCL٠|wÖÞZ-I,/q[D^WU¾µÁ~í8ü6«â}(ÞtÊXË2L¤
-7%Fàëä«Gã ܔ6ízÁpÚ7XªcæUGwCu
-֔2R
-8ô×=ì`,ÕÚûâm¯Ë>Zéޠv6â!8
-4hnaP8áÓítT:ï6G_»N? ¾ ±¾êZv=؉!4HÕDÒ ÉVúä4[F¹Ѐw#ÞsÉC
-cj²©õ®èn¡Dâ3W\)`ô°õÆ(A(b:§5ªìþ]0
-J{Z\x'stuû´ãð[àçáfE» ï$@f¤Þ{äq
-ªñøè
-\ë4â¬Aµ¬³ Î{OkD3ÍTúçt7P®AÉS¿c»%(»|:kßg´ˈ
-Ìh饄æTÒas®¶{¾¾],[eXü1ۚ
-$uû±mÆáÑ
-¤$3Uö§ýWڟõ_jÕ7iyª÷²cµږü©»D7Fy¯µ?è¾Ó?¡;hâ,óCiÓæ¥~7sèn¡³¤±ÁÏëùAUÈa6QCÅ=×wi4];EôR¸4f.¥sLJ,É$îâ3ۡTO2;*¾P÷á
-D×îÄр¹6½²F;jÝbÂãdÊxÎÆkäªE¤A¢È4dæ¶<º2RØr ÷ Ña+SúÃÐÝB*7av$%2ÆßzüvüÃêȂ2)ÎÂëO7?Eò%#ÃFbê|8®ђ
-tqvµv~àÒâÝJdbð
-x©¥1ÆI:èìN.æ:ýØGáy{.)µòSb¥¨ÚRRÍNq4¡j«Ö&Òᕞ
-Çð|UXCI®4ìv¨H&f ¬ª=aèn¡:Ç،¬-¶*®{Ûg¥û¨æe9"XûT´O·0Ng¢Þ
-Gµ¢Î£8Å
-£$Rb:_Z(!m8»³qXrÄìÀUS¼òèë÷`Ôæ7@ª
-¬
-J7¸
-в¹í³díàäz*ÏZFA0X(Þ\ë;vþÉM°VU>´ô7P bFÝxlÇh¤q 8h¥9¬Ù|Óhç§8¡7Fgÿ»
-ς¿ý¸mõõÅ:Yr
-±Â0Æ=è֝çÅUTc% Xtf끒Ð#¯ݧÕ9!¤
-HD 8U'%
-
-L±Ď*»քѐèc¯!!OC1ueUë]ÐÓbۧÓbf÷肩Ñw¨ê"y½ìSîàD¿3O\l¥©êB¡òߢÁÇ
-óúòMn3pÃ÷©á7Á=ã F_$ir²&³ÑW:*ñøÁ5½¤cÎ@älMOyyº×=~¹Ý1Ëab±#mU»ջÐÈÜÉÞÞ?ºòñ9ýw#]/;ÜouïVïVïBÈ;
-\Y½L?¾ ÷sð¯kÝ`<»PJâh+hzáà£
-mTÛÍÙñðçþ
-âPp/ÙIøl/<;ÕOch¸:[vy4Áøc¥ÅVz÷{Ck&hÂë³ëuêԺôÿ©uéÿR³̤òO7+®N±!U(7
-T+'uÉÿR5RbW\õ'¼¼âv¿þլ·×bþÉ%üÙ÷ñÂÃûտü¿ÿÄN
-!1AQ"2 34aqr#05BRS¡Cbc¢±$@PTD`sÁ%EÑádtðñÿÚ?ÿª^öÆÂ÷5%;mŎÑÆ玺/E¯Â?øT[̟Å|*>Èþ+áQö?̾dð³>Ìþ+á[ø`'ýÊM8x»>£1$NÄßúnÎKÅ8ÐYÅS3õ,%ÈÜgªag2ªØÙvꋋ¹¦Lø¼%=¨¯/²Ùk¸WîIý¶>pwºÔÞíäQ½9fLÌ!ÖUqÇø×{okg¡n§
-½ÿ@mo]îµ^ÿâxrMÇ3k 0¶ÊV6FYÊZ|'""ÑI+¹µSØí*r:ÿÐ_ç
-{MË?R¼agj
-ṳ´^ܺ©¬ÉÈ{';ãsÊÚ]V°ãs°áõVÀ}D]9٪/NÞÿ&òÒÍŹ|'QõTQG
-ÁÒÜÕ(ÅÓLmFtÿµö½֡÷(iÔ×qj,áÏ53Ä2¸9øt\kó#ÿ
-´qaöÑVd[ØÉ©4
-¯ÝÕEcu¶ҧõòmEgÇøõE½?Äí]îµcÄۨÝß]è¥
-x¸nµÇô£º
-k£ñÛ5qá·
-îæV6`+ñOè
-&ª`ÂÏYP|áL=ôÙô÷{Q¸áMϼ_+e¾v°
-JFèæXχªmâÆÜo³}TÑN8iB¼6bî{'Oõ"m¢~-©½ÿ@m¯OwºÕ!Õd
-c<óÊÈ`Çú¢ÅbÂ>êYnuEÚÝH.ßP
-;R~·ý¶Ç÷ç{Mîá>¤Ù2Ì&Ì9ÅpÌw½Kµá\bá>/Rlj¾´Zç·Ân©ÿ«@5?ôԠíݞuºzÐ.â猷ÚÆWaÉ
-#ÅuCíêWê;ðE_ÿÁ¡²a÷Z\ì¹
-ÖÉÙçóAÓþµÖÐ,# ÝaÓvÐ,# Vÿ"ºÅêX½Hm6Ú?Õ3lc62v×
-
-⳽;lµ¿
-ùvã]ûæ_
-7_̾þó!´Áýê¾gú¯ýê¦ÚÆùbG\Iu®~@[þå&Ù1vSùÂ}Ì\æ_
-°þeÝdðÜõ¦m¶ÈÌLì{Ú'íl#»
-/÷/í?òÇó¡¶ïÿ.mï/íoèç_³8y;k9¿ò®?îLÛæݔþeðð¿T6»KððæNÚX[~꛵ñ^Ð2øp}üËáÖ}ó/Ø2;xùsùlcÝÃÄÛUԫ+;%?,ík¯íÿL:þЏôÇó¦íÐæ߀2øl}üËá±ö
-ó'màÑèçó/íÿL:þÑôÇó¯íÿL:þÑôÇó¦màÿùr?ܾö_̛´ñFçðN^µðË
-ì❵XִàשB±¼,vËڛ_àEÞét���ØM>~òdØÿ2øT濙|)ü/æ_
-°?;þ
-u»1üëûB?ÓΙ·ÿåÏæ_
-ÿøçó'm ^,ú]3jxZ~ò~ÕÀ/?ûÛ--¿
-ùÃý9üÈÿÄ!¿òÇó¨åâDÇáñ
-¬^¥}öVN¦¾"29(%ª<'»{>IÍl³µ¬¾ªbH授1ŷ&ĔÙÜÎéÍqB252Qq¼V8Ùø'#®?UÄo
-ÃDj]¦=HMñ¹詠.å¬ïÕ0cdxc®Ìò\6âïd¥ïdÝַ;¥×Ñ5ìðâÍ>P\ÕÞíB aäü%®
-¹õrEýì\
-:,]Å`ë
-el!ñSÞV[oæãï
-àX[|ۃíà0:ÏxSSØ\úÓ.û;-Ï(ÙÞQ`yN¡bÐbÇԋx=Í/ÍQ±ñ±=R,ΐåt4ÑñÞ*qßÝNë9S·حS;R©^8iæu#0ºܷJܕ0þé¸?¢·DxÏŏ½Y·6++
-ìÑsq<ÉqÓÀõ¦R.êÖ6º«á{OÜSîÞ÷Õ*QݻyæÕÙÄÒTæل8h±>r̕î{
-°
-¬¥Ñ;1Ýä|7xOFìôXñ6á6\n°
-ö!vfôNü
-
-DÈÀî¨ÃÂ}¾©@&bc±ãxHê-BªʜfÈÌôM×ÑÝҥ
-c»®¾k§
-ÍÈЧüNro N©°wq¹)ߣR\¬Zâ˩áá62MÉ)ÍhG\ñ)©Û¼jíTk
-X{RÒ8¾W_+êׇnìÀO`xkc¶$àXƷ¢koó¡!Â1dùá)¯Á3]ЦÇÄì23OXOkñ² 03;YPD!
-t¾꼠ûВáb+ċÓå䯃ÚQqqÏESSÖÍ
-<ßä\lvNd||8MsU¶92uÝÏx*:²[ob.- X¹hõc*êêê?4ßg´i¤íVý,;Ŏð O"ýӪ·hü-Xò ÏÃ%F×tSN$h`äotØ$qÄÑv;p¾` ~+]BÆÃC#OfÀ)徧
-t*;«<Çޮ/î%9Ö ¹fQuΊ®{
-~õ&¡DâßG¡¦Ù5UÕ?Ƞô ª
-êdöï
-G
-¡©/7ýÈۣºk¹oñNG%gº<¹ââÄZò=±Ó_´KºÊZÉ$$4ىªºL"['¾
-`¹BâXGDØc· 0¶ÀYpY{á
-ÔoàLµ°7ðBíl
-·±T`mÆþ
-
-©¥pM 8wBª
-â³-è2
-X,,w¿[#îóûÖèQ·0ƅ3ìü8ouÆ;ø6àsëô²~I٨iÏf>õTq W3äÝõä
-lú
-½äke
-ɐÉjnQSÌ gïºQìQh£ño¡y±f Õ_çÇ/"Ò~å'w·xRêU9³Ó_Åe¾@rÍ Lj¬~¥Qù¦{>B¢¦aèz)¶$ó/
-
-EPØØÞ ¿2 ¤³:ôï÷gp.þèã벎`OÅ;4 7ÀWOªWÿT®
-þ©B'}TZæ·ÀJ¨©òe¬©é§k\×BüÔÔr8åN¡ª¾P=vJAÐ>üÙõMvq8iê]öT¬Gg°ÃuôEú¥:)Ð)ô³:kðͰ¦QUñ°X_RÁSû6eÑ
-JǴÂû
-¦lj¹0uqT;)£¾î#áämÃmþ੧ãG¯|-rRù
-1ߧ9±·RÈe~"²qºwT[Á Ü*ό9?#fÇñ+Y?Æ}¾D¡Zóeï@ë x±5\§è4L~«ªÀ8lñ*oDÜÓü§nüÚ}Äð࣐JÜMR)Ì.wäªçâ>ÃAäGáQø|>6H¹·1äAWÂf{zñ±ÿ½õò$ð©G{u%Gö>¯yªe|ÕMHo¦G¼J¦ôX½ÁþS·~m>ðLcO%¾y¼
-²¢ßÃíòY¢nMÜ< çèHÇÅ0$wJÂWZÖ]õ]õ]ýd)\4rì¯õ.Í \'¢SÇtÝM¾¢Ãô䪷<üÔ:*FÝY6i=Âó9ÖdmõðÄàæÆ*yxð6KZû+;»Õ:¦&µ®.ñiëM7Fd
-îQOwªuHmcaånÓ^×ß Ðةª)þãb}J:¥uìú'ÕC°¹ùóõ A$ÑÃln×@4opÂ9¦UC#°µùÿTú¨c$9َVF²o×åößÍßî
-¾%^ț%öL¥ÓB&
-KVLaÄ.¦¶ªêêågäO,mi(»o½Ùd£ïeomÎñ
-GÝ[Åãpõ(ˠ®,=ӢgÔˌ¶Þ(YEp±¶çݮ} ý£®=T0Ǵ
-,¾,`G¿-Y6à¶]TҰìÆár@Ú1Üþ̪yÁQÀZB®ÞÁJ_áâf¥!õtø.&ÝÇqÌâ
-j¥,¿ÝäH²¸nÑ8ù³»uPc|RېqUVæ9°_a²}©<ûª0>
-/¬¡óû£å¶ßÍßï)yZF«²¸ÊtAs|H&ÔËÅÛÕ6²7!+5qÕ}ûòEíў1������'m
-¦KÙáiuîSónæxµPhwHÉFÛ5Sú4^èù
-+
-Ù¨â˄XY 'ÆÉÐáëMkXÛ4=K¿Å3=rN¶&m.1¹øË]ÖˆËXr²dLÀƷØ¡CwÆÒ}a5ŠøÙ ³Ú=h5na썠ú
-¹Õ`n8FAaòÛsæãïÒÕsÜTÅ<âaÜÏçK
-kÇG°}W¡ÆåsìW¨n¸n¥qf?Y^~\)ܰ«³9¹ÞË @ðÇo[³Xˆ«Ïsb/yRGÂÍ2,MÅuÂHò)ý/píϛO¼u&¢ª|(&x·GÂcx,k+Ü âuS&ۀ걒/4ɤÆ.I
-Ròók¨Y QSµÿJþÄ\ìµAÖÞÎîjo6¢ð(|nZ"1ä7¢Cîkï
-؋}âF;Âðn/ÿ·>m>ðÞÍ(ê¢)stZî´Lü
-茥òÛ8xB»a{[ªốj
-wÒ¶KóD
-¹¨¤tNõ+ob°?ê¦ÀãÐ"ƴÚ÷ÝÉ<]¶V Y ¨Úæ8ݫԇ(KèûúygÂVÉq@Óá=¡T¸ʹY'ЎVÆ?²aÛë=ֺ;Cû~
-4AETØácVäU!ÑA
-2Ï.²¤¨50á!ÄYVö{eÕ5p©©6·ºѓºª& Å^õ=dÓGÅ0Aę]S$òkÚ49®Ó63G{ä¶ؾÎ>ðV¶æxFãÅ~Iºt
-{m{))ÞÏXQ1Ý7sÞfYPÓ
-FÖUtîd]ӈ!O5¯Ãrc&vM=TM
-1ÚÈ;¹sªsÅÎÉHÁ#ìxSX§¼É~+]QÄꄤ:÷Y;=
-÷ÝKèûúygB,ÃfDž¤O±4}èÒHÊzFÝÂ@÷©!=²WMLéüÙelé#áwø·
-O£±ÂmS0|ILéëˆ8
-yÍ6ƶtOÍx ã:ªºi¦¯n
-£s0¹ޥ-¡!
-´\0ìTѺ-âÎ
-9*ZP±¦/˸ᢖxé©Y¼uÉè!¦%ōZÇIE+.â2
-:w㇅JèÒ1=Å5%T¯l&Xåϻ¨)ÔóËTΎϕ¶k¨¥ìswÛ.¢©¥íÐãa1¦Qð+¥¬1Zþµٞi]cq¨ç!PÚxÁÔ4.Ï7àÇw䶷 ÿ¸)²ÜÏݮ8Ï<ÈÚÙ+¢y'Ó0éOaØïٯ`{ò$dT-k{¤¬
-ÁÁÜuÖҪÈ@3êUòQ7 (ÇÆ*º\}öj¶Iº&êI*êêéÇxmê_CÜÓü¥§²!-·³zÿھ}à¥n!ìÜÏPiñL¬ö¬`,|"®±òO´"٢,mº#5CZ:¦a>JöÅâ涄x*ÝÐî¤ó§ظf¥
-¦}`¥)óÑɴßãP
-âÍìnw;<1C>/õ*x¸%¶7c´)Ân§㷉Rú>àþàMSDüÅ~¯
-Sgq
-kņw·j"oîóy»ԟÑqMU<³Ϗº8xވ×Kð|aó¹¥ÄôU3ébsÉhº¨1ÔÄÁVëJãôtS妁w{¸yé*¢cæ2Ç)¶z¤ª2ÔJ
-8F^èÖLhorÙqZ¨¬ÕÆØM¢8õ=QհÃÆ0æl/*Écc#[-«V{KØÿ¢Á¦¡ñÒÎ
-ÈlA Üz9âÅ99â)
-_/DùoG÷°KÝkañ
-ªG%3LlÍáֲûÖÐí,ÂõÒù¾obgo³ÊÛ7x û¬EÙ&Ú6§I¨½5¯Öhº¦ٸ»ó:Í苣s°EM2Ï{1¸ÑWÀöU9Ør('»Cu þøÅNÑ
-²¤gxÄ?º¶á£@<½Ì9ÙZ÷w£õ'ÏníT¯`zä°3¢u¹R×Ùöhº.Äââu
-Ñ!÷
-ôòæó/÷J£©Ô,N1ö*¢§ÙNøCUGM¢
-~¯ÍÿzuQ³Hr˼ԎqH¥Ã3±~Õ÷ÌÓb.m[ CgC
- féÛU5h«d¯wt
-õ]ö¥Ä,öa²gpi$äx¶%
-|(ïYMNe 1[IUísë©Z×awzÅ2gT6jC°xZђu,ñÌù)¥kCós\9©h¤éîüX±êÞíB淴=à,ÆÛD)ÿ¾I1"Îf!IULPÔ4EÊã6ªèÎ*8øŋÇÍ6y'cêekyµ֨PZ(Ø\ܥâ;-Um3êaÆðÁ|ýjÐùc1¢ÖYm¹ô2µ mtr¹ÐIñ6ڣO
-aÚ{ÎuÚmá ·
-7=|¹ói÷êawû®䮉[.
-ÄøM§$÷´SHÊFádz¨j\é@¢ÜÖЌÉHïVh¿§J¯±°(µf9¢r[E¥Óc»䵠£vÞveG&ÛÈmþõ%L²jåmÍ:ª_DÜÓËÕ5¡ÂÐè9¡Ã×òe.
--,k% §«åvçͧÞ©9©3r)ËfԹ-ÆrÑ$pºâð´µk
-9û×xβcxvԦÎæ¼4h¸µʢ ù¿ «Ùex
-:6v\#ÄB}1ápDXÙ©Ì
-íêU/¢CîéþS·~m>ðÝàwo
- /ºâºɓÔI+ÀkB«Ù͚ïi8ÓãtOÂᚍî°M¦ÁÞx½ӝaÞ6MopÕ>Æí.7ibrɣUu/uóæ¯äYS· ²îW+Þ÷±DÜW¹B
-rpB7mB@àÓF3%åAèñ[L#ü§nüÚ}à£`-.vcyË$MÔñ¹£±FºôÂláâíÑqZÎJ°ÅRE}V8á:)ªÝ(
- /j£ô¶n¶élùð¾Š]xOÐoàCLöécêRì²û;?;ÏÃêUt½âÆà¦fÐQ¶âÎ)Îߧ$n©ý/p|N7aâb?º¤ÛA§¹
-!í_
-ôßΩviÀÃëĸ¦Ù5;náqi§þeðïÿüËáÃþù6ÛO,#ڟµ$)Ûïª]ªê°¼ªö¯f6b>Õý ?鿝húoçMÛ÷ÿþt6É6øæGl. ¿û¿â'ѿiú_çÿéø<鿝øÿ¥þeý¡6ô_çCoÜ_³2ø|ÿ¦þdí¾áÿ+üËûDïô¿ÏÿÒ;vÌì÷òo@ïngꡨ¡¢xpò6ïͧÞ
-#dbòüë~(4B3lÐÍÊ 8Q[Í%¯72¶0\cGo£ô¶yùý>
-qh¯sÉb<<³RIÙ·6¸[B@ùëä¢ók:ܟ¾èûU?£Eî-ïll/y³F¥Wí9*ÜZÂ[N¨ÉÕ
-Üt҇H<åuV-Rë8;Ö1É;ìÎZ®Í&2¬¡ðì7²¦á¢®c̝yä¸Oú«³eþäãÞÃk[%|.ç.Êü±ß0¶4_$ŸE¬«ãá×L?y
-ÚÐKàÜÇlÊِéï83öõ(¨iN¥d_Þ$gÂBk"5ø¤øÁڪ(/I5ÆsXôT®ÉÑ?DáBÔٟñÆâÇúÌÚ"¶<.°j:ïۿ6x(¼KôNføN-Ê:,{r<<Ôp°Ü
-EIFK;¯Ï֩iÄ%×rŒÚr4ÄÑÏ}7¤³گ¼CìñÚØr][F°©)`~o`(ACo6L!vz3¤c%N4ôç³èt£¥¥ÿe´"e=YcVº$YSú4^àòöåA³`n¹[{^ZÂÅ:úbîòOe¶dLxϚàÃK¡îú8QîÈsYYh+úÝ
-²(d2RÊ}©²:¨¤$殸ïd&d Åqª¨Õ3q$e²¶IÛBQQâ6÷îGiLÚÐ/Ðüa2º^,®³~4w}\fͅ¸Ü!§(|nÑ'¿¥öµÍÖ+¬GwÁ3eaï4¨%lð2Vèá}ÛwæÓï̞VYkú§
-O
-TO³Y8!
-*ËÕWÉÂcJ|îÌï§~
-;ց
-1¸¨ó)&lncOÒ]¶>ÓqĥªlMóvjãsÙ}:¦x]{áW§â`ÂnTU´îu]st*)¸{¦ɒӽu|°ñË7ꜰa[_ÓϺöªoEÜ]Søµr¿«dLá¶مê®óLÿ¼¦S}êH÷üâ¦óìö¿ú(´ÞÑSú;8;þØT>ûVÚu¶÷B/T¾¸?§áß>ÕYcCp
-"nòz®È{9~áË(`æ« k!î7BIh®u(ÁÑ=è© X¦¥°pì~J»ñÃ|OæV,EïMa(*3¬Ô6sHñ§P¼b±½Ý¹ªz¾«² ýPµ×^Àápx®°Д ¶¾Ð)§
-TÓ]ÝÓú¢ã{¯øwПïnۿ6xo`@]Hy(¦,È胚ý
-dÒ3+Ü#]P¦Ú/xÂ̽jû¬ïªP*é®ÇNÏbÄ=©ÎÉC暪üüÕÿ»uWy¦ÞCÓi¾õ?¤7þòÿTސÏkÿ¢JxEOèïOùÁßö¡ð¿ڶïÎ_í©}pO-þ}ªÊɳÇØ0¬n¬¶s¼ÞܕfQXóPÞF>)nÛ'U´!¹/ÇÁÝע:\Áb´ï¶T痛¸ÝF®Ë5;¦Ä[Ë5JƙZÓ̪̳9°´*hïsWõ¦X¼¢¬¤ÜsQӞÿ,:¨ûÓ²çïM̜:{S_ÿ©ÔHü»
-®#ãh5IÏrèl÷às>'³2Òÿ½ þöí»ói÷������·DÜßsEqR
-µ+Û!N^àÖóQÑRÐÂ7y%M7d2±szYUµ4O
-,>%I&:ob÷ûAæZªüüÕÿ»uWy¦ÞCÓi¾õ?¤7þòÿTސÏkÿ¢JxEOèïOùÁßö¡ð¿ڶçÎ?í©}pO,ñö«+,*ɭª3¨Ðä«ÃÎE
-Rº7Èÿ7g5øî¨!sùè¥eòU--À&´;&I óc6
-AC$}ìuÇ|,v·ö£´F
-<'#Tû7&Õ(=àêâ{ÄEÔخ[Q>ûVÚf*ûþè\" nxy4,ñö«+,+
-Å`Z×a£Ôö
-¼¡¤Ô|fBܔ»=;CN)ÆÍ`½ÊÚ7¨£·E¦üú§x¬iآ°smp!ºÄ\lãtÚöLdâۺ8úÕÃ
-î¸"ߨ
-ýIJ0È~ô*ÇøZI]¨ý ïêL
-7kϮáMÂ.ÄÇkÊé¸Dxxñ^&[Øðvx$mî1n¨´SIúBȴ±åȃbÈ%Øå¿t]IHèûtV
-ۋàVPÊè¹®×.çtùûܮe¼1Þænæ䰅*ní-ÑMÞø´ÕÚ^ù
-}áÍ={îlãæmÑ#s8,î¸
-´ñ0÷p®LÀæřBgְÆÚw8F8Ø;ÄZê3;õ[[ÓÚz¶2Ù^çäïj²²²Öî¦vT
-¨È67zñ|á������æÏmÏ%´lèEì
-jåº~
-ü@päy´¢Hý_ۮ¯\wÌ`÷Jٯ]sÍ77-ºÀ
-¯T°¹パæ»%@8ïuSÝs3PÔw®凊ÞèM³;²6Å8_¼4
-c۳ºÝwímÓùV
-8gSdsuՕ,Þ'ةé°73jº¾H¨ïi1=S¥å¾
-¨b0²ö_
-ÿ
-õ_
-
-õ_
-
-õ_
-ÿ
-õ_
-ÿ
-õ_
-ÿ
-õ_
-ÿ
-õ_
-ÿ
-õCksÁ|1ü/Õ|3ü/ÕTNê©ñ[3
-gQvX®ï8í}_ |E²²²£%)²âåk¦aÝ̪ãy֨@C.! >·âgʍå
-tTõLmoÅ>©ýã
-XØÝ\
-¯¹®¾yΨ§ihó[A´÷Ã~ú§øN56{?ePm;£ÃÞ<ÖÂôGûÞEVʎkº/è¤ÙõQ~ȟ[s\þÉÿfû'þU[ìùWo²åB:épfû'þUìùWo²åQSðûÎcé:)I?'å\~ÆOʸ2ÙIùWgþÍÿG@ÿ¦ø)â1ÂïY¸}ÿ*àMöOü«7Ù?ò®ßdÿʸ}ÿ*àMöOü«7Ù?ò®ßdÿʸ}ÿ*0ÍöOü¨SÎïØÉùT{*¦][ur¤ÙÐÒf;Ò}cò'ÄPCpÞâñ/5ÔÌu©ÛAÓ]Ñ¥UÒÌMùõAQ÷àϒ»ڤ´ ;ªÊ۬¬²¢u[ÕÍNNvì¥0Ô1óf!ÅI²˭ßvô2ýBçÚúªH-9"×´fi´CPsW[Ñï}"D³pyìt¥ñ¸;ÃÍch9CÞªè@öʲ
-¥ï4ªÊqٜn[26ö~ð䥈êÝЫÙޫ©_änlӜ1e¢Ô䯹È; Tu¡öküIЅUÓIñÁÂýÛrM¦Þ
-ðõ)Ò`5
-áIC_Ӝ^òã©ݰ}þ÷ùQñ+«¬J¢^ú¡wu箊¥ØÃoߺë
-ÍӴ!RK7 ²xOë)E¤u·±ØC²ÕX`=Uý
-Ùò ï`jÛQBÜ0S¼ª¡-@ïéõBs³Båav,<Ӛæ×ü?èo÷¼6$R`|ì
-訫¦lÍ "øwiª}SqD"{]ö)óG³äkO¬¢àn©E!³$k¨¨$tÿEö
-yLNֳctÉcø×[¡AÍsqꃃÚn?ÅÞ(uut÷YJq9PFx|C÷)$Ç#Ü䊎CäS©ÏËE.r;ڬ¬°«'äÓälʎ
-¶<ÔÛ8Ï!þá)Ôo§±uRÀÞ ú¥{©qvZõOi ×%°©_ïo«/mÆ?i²ØðPÍ©Âe¿Ó+c۶¦Æú»Ì
-øq^ÅRØÄôÄEÒN@dz¡´la´·Íä1[OF
-y5Røª?î[Æ
-^"×AXonÄÅƦn¯wsïMhcF/ñGÄ}ªêêêè³O7Ð&ÓÉ;¬ÆûT¡ÐQ?D+÷Z
-jéÊGwý×bqÉHÑseC6ÞڅUI³B
-ºqÉFЛÜx*<ÍNàèŏ%VÀ״
-,¸0â\0b
-ƦKcú;ýï"]E,ðêiTû2s,M!Ä[]ÎhsK\.
-m$, f4RSE#±9§C£á
-#m4- f9®
-0¼aÉþ/Zt¹kv·@£8ØÛÍ5¯øð
-7×þ,ñ������«ªpæE0ӀÀàÛõUuwc¡þòÑoaWXjOTY¶DärõNȟRl®i¸)äÊËñ
-]W5U i®
-*W[fºSjâKÄúE8ºW5§U-;£À ˒_Åã{ajØî½;½Gü¨ñö««ت:gVOð¡à¦´+YÕ|Ô±²a»S»®(fß_5ÌzÓ\÷¢3-ä
-
-¦~1ðٻè͜©à#f>¤Zàx
-LvjWbÊîs¿¢öfZQ+#¢Ï2îKcÁ!ýïò§xϷ}ÕÓg1Þڔ×|YÝ'CC·ÄûƯªͽÖ
-îEG@sSÈÈ]FÜò
-Y_3±9J· °w.ªæ!~®~5SÝôA°TöM0ÝCÛ¬£ WGæ¶f
-°ïچÐG¾5«=£?1KüåKRøäÁ;ä˖AvævGTaws'7_ X
-y'������³çÀ{oÑì4îð=æª+c§|l9¹ç@PTø,nÀ
-Ó*å<'â¿Uðm%4ý3¸m"æ6RÑÌ.ݤeA
-¿áo2£®¼h_ wüÔÒpa|_º}HeiÂmՍ¬î¹ÏCYė$nK\
-sT¬«DŽ[Ȩ+c¨V4eÒê¾ÈÈÚyújjèá 4¢Å0X]á¿5%cØ÷ÒÈö·W)k㎑5Êj¸â¥ãêӧvÎì
-x£±¹ösôãkâà×JÏÜ×ap=$
-ú]K±Ý6¡Hà×zÔrüxÊáoêWU¶xì︩ 'YÍûünÍ7؝³ "ê!{¶ýßR#
-§w%¢k5óyO¼ܦøjiTÇ
-«`~´pôÕl¶Ñâ:¼âߴpٲÉg.B>
-¸:YUÔ
-6E
-X ]£B§wjl.9ôsy(¥©â`{ò.F©ÕҊ`٣vtUuώfGQôºtóËU,Q>8Ä_R¨&|ôØä¶+â=¬&I*Β¦ªMeº:5>µñí>ÁÈ_¡)õÏøI²Ü;áq������Y3j;hÛ_ñM«t0Ô°Å>zØàgIkÈËUU,´øe臌s������ã+#³Þ_hi¡2àK͞l\évcgÏ>¶Tõì"N<¬kȲQÕK&Ïãc®ÅlNÒʞ±ý©:hækù´ZɒÖTK3#sÖ>؈F¶aGQ<&Ǣ¬¯|X²/=§|uìÖáH;¾ÕO;穛N
-
-ë?)¶¨ъÇyú¾èk|!|%õö*ªÏk4ä®AÉRÔ2¦1ouÝ%Dpó±M^\[ֹªzFÔÍlVSDaў[k¯_ȔdkOvêwq#¹å¸G#ÅÚÂBàÉõ¸O¿£Øz.
-P¡ø,qAS
-ÜóNqâJ
-nÕRְ¼G¢5
-úqS
-lu
-¡_ú>$ÿMI+£Â@éâæᓔ
-©3c±1 x[ÍRv¡Æ0`sx§'rN °ÈÌ@Í#±8òN p¦6_Ä{4ú.ÑXeÀ:ûU
-
-§¦áºÑUк¦²7ÜpgzÕM;¥²֍÷)ôFZû` (
-f¿
-ÅÏ'm5DO¬lNl¾7jMϊ»WaU1֚\.l!÷²'s%®Vl.n~¥ðqøâ,×cÅM-ES`¹kßvLTîÂ8ÚB¦¢
-qÇ<ò]QFÆw1²LväP§¨²älm
-¿uª´ª
-
-)»\»
-¦pç4Ï1¹蝳Ýظm Ê\÷kjÙ1^Ï
-=ª8d¦ Á[sꁪâÇv³ÿµSÌÞC¯ôz|ÑÎ2Òýñÿð×Fì/ikº&wÓ<óÕK[4¿JÃԚÒ÷XfS[T©q»ínK³ÅIÌ*ùbªHÁÞiÏ{
-æ±7¢A*iC²n©ëßO
-
-0{QÚ/'&rT͠øå{Ä~3}{[ñ~ͦøńy!µZ[ý³q¥/µ®¡ã :(hg©àYXªjfRŁyëòÄȁÀÛ\Üüå»O-22âÆÛ¹Þø!i{oÜ|¼°E8´±µþЎǡ?±·±Å|
-Cögóð-ٟÌSv=]qüÅ|Eögóð=ٟÌT{6'bdyûSèàYìºf˥c±5x§SÆ������õ£±(ìæ+à:²?¯è>Èþr¾¡·?¡±¨Gìæ(ìJû#ùø
-ìç+à*²?¯(~Ìþb¾¡û3ùÊøìÝùÊøìÝùÊøìÏç+àZû3ùøìÏæ(lj!û3ù#vBÛþ?õ¹6RNZÂà/eÓkÞ#pߟ^ֽ'7hö¾øMìlw±푘nø¢CEÜl=j]±GXËýоì¤ýÃð}¢ø~²ô_ÁöR~áø>ÆOÑ|?
-ØÉú/àû?Eðüc'è¾ìdýÃð}¢ø~²ô_Áö2~áø>ÆOÑ|?
-ÙIú/àû)?Eðüc'è¾ìdýÃð}¢ø~±ô_ÁöR~áø>ÆOÑ|?
-ØÉú/àû?Eðüc'è¾ìdýÛÔüã(v¥$æÂL'£²ù*§`¦{' Ôñ9øø(_¢uLÌsā¶$Tʃ#©ïu寒íÅt§^Jc#À$kGÒ{<åӺ>(¸n¦=
-ٶÕ
-ù)°ødÄwm1ýUr:*g=÷O
-Aΐ^9h¦̚ìÿÂM0·:©ã×Nìs
-(RDYÌ9¿Xn4S4
-bü×øQ®?ð£\áF¸ÿqÿãÿ
-5Çþkü(×ëÁbbýiґû8ÈꄗmøqÆ7·Ê[¬,\áF¸ÿqÿãÿ
-5Çþkü(×øQ¦Ê^àÑwU801Ì¡¶è©ø'µé~jyédtR?F?øLx{CnÈK'0óRÃ5;ó
-کY<çK7ª1mº´p#Áb÷fy&Ó: Ê;ÝvBc
-ãiÊ÷E!kæÚtQÃ/iâ¿
-0ØrQÃTÈ8?o}éÜÞ +j(¥íFi0æÛXrRÇ,ÔÎc°â¾Vèªb3C¦Îápçhü
-:þE%I®Ú7ÆZÍ
-±Eè&8hƭ¶ªJX ¢¼µÍ}ÊbbÆÛ
-ïÊ
-q£eÉÊöL¦2}(QçªÄr¸ÏõBÐ#ĪiG-Q¢8Hºt>$XCÈê-ä¶áÂÆÅ:óQak¹sL± éu%R½7ˢ»/I/'Ñp-Xcª4ïv&¼ä}*ֵÍÿ´äálùHԋ pB§®§äÇgs°Uu¦ ah³
-y<-ÑJð@h
-®~@6Es`átÊBë\èZ{Sqª4ÄÇ[g4Üe3&k§ÈæÌØÚ/p¤y
-º®ÂpåtøÍeUÃýàOÌk[ïEQ ÿºí6Òá´2PM¹SW
-ÜLQÙÇéÇÞZnìL
-ê/þS¶þn>ðÜ3Ð]2Àdäߢ5RÌÇ1àbϑ"o½¹ÌØ( ÆààrQS¶1¢ιsqw
-IƄ&Åk
-YbÛC
- Zâ½óÌ拢sÚëæÔ0ø¹Y`ÅT$¾@'߾ò;Ï6hEü0¤já,aÝT´ÍqÄ5O³(o¾V\xñã ò襦,ðC}7¢Ãî,袬¬N£°÷´XïMz9¹
-hæToÀë¨݉Z31µ9¢Æ×ÞïDÚU7¢Cî
-
-hzX"Ç\kH±bÅa~«sîõËT"ºFÑÏDøÙ ï´;Ú¢ÀX*Xå[q$w.CüNÛù¸ûÁIæaû÷sT±wuݠS¿֪Èä¯u
-$2
-0
-ÜxT´!þoºÿêÇFëðRy~������Îõ¢´U/»í¾ålú`~>OÑ é֑×kÍaïô0Ù
-FHýè?(dàÆöëU Э
-繇5utÓtuÝHÿ¢9µLߍ->Ûîw¢Gí*Ñ!÷
-ùNÛù¸ûÁIæaûÔfÎ
-,
-EHlq8ÖQÄe¬ӟÀcb9'ÔÔÜ°ßÝ*'Å#1Æ{§P~US
-8Ýãɩ¯¯¨ͭlM������£9ÿQ
-hû\Efö8°ê-Íñ&A\$%è´ß°È¡ZÛm¹މ´ªoDÜ M¢Æ¬Nê±;ªÄ¬Nê±;ªÄ¬Nê±;ªÄ¬Nê±;ªÄ¬Nê±;ªÄ¬Nê±;ªÄ®!Aàùoæãï'ïLo7(£\Õ[²ÙyËþ¨Y1ķ723 çêF))+¤Ç&¡<-6ÑRR=Ò¯!Ñ:F´kì]¤ֻ^iÅ|փ
-ÃïÞÜÞe©\dº×EDn9ª¿5'ӽ
-?iTމ¸<²l7ÿÇr;ößÍÇÞ
-O3ޠï¸
-hUÏÈÙî
-d©êÄ"1Þ栬{\û4
-Bà/¯tÂ×s=M@h/È.Ñ8úʖvºÏÄ}`ª¶(××-ìñoµÑÈo¤=ÄTͻ¾p§z$~ҩ½pyröó i²ã5qZ¸\P¸¡qBâÅ
-.(B@wqâÄ
-. \@¸qâÄ
-u÷´ÜnÛ7x)|Ì?z¥m¥·>käªMäò(ÏuË<9+sÁQýÕ{ªÞSûÓËq{dhá5س<+@Íú(ù6Ç ûwF²QìaäQS³b2ûêÔïDÚU7¢Cî,ë¾_8®®°:HÔ)IhÕɲµÍÅ|½k´B?hÔjlbғâ¤
-ºº7E Fy.pän÷ ¤¹·¶뫫 {ÛãÓvÛù¸ûÁIæaûÕ'yØù£¢:)|éò(3t}UPøìKJk9ÂÞe]´Ð:C|Jìªøݤn!{æªiߔЋñ\5ĩa³ yÌêUC¸tç؎»¡6pïe¨²|eE!øÄt\NO·TïDÚU7¢CîÎ+«£PؘZ÷٥A[Qá××eJ©°G|ÿE´(5NnvñGSÙÍðÝS·̛åÉx.vVZ
-îæáWWM»6¾±*ªªjóC/
-eM!îIj£h°OæÕ}íñ!º-
-í·óq÷x¼PëTzmtN9ã>E,¼ Úþ\Ә&n&¹pXgê[B gáÄÁܾjºâXbè
-vS=ֺl@ntÈÁ[R¤D9îB{Øz¬ÀÕdÿ6|o8¯áxrSç)oàèûJ¦ôH}ÁòSùӺG̻¢lSÖÎpwSìo¶{RÂآlP·Óâ(¶9èÌG&Z١
-iê¸Sè+Ïix^>xµø'#=UÓuMðÒå^[sjàSæt[إ<ÕCíw*ºyKËc9
-}keT½áÑI¨R
-.WWLñ!º-
-í·óq÷ÌÃ÷ª,ûÝQîõDY;Ä|)چ´éÉDÃÜì»Lb÷¸öTni%Ö!v¬,Sÿ¼Äæ`\I"u·%²˟Né$7Ï%µ;Ò5ÿvîKG)f5L.òL~E?DsC>ò«î¿ÖS½ҩ½p|G)£°O³9ßë¨j%~ÔñÊ,
-P¶NüµÂÕLÓ_7»¿«\¤Jy
-d®þ©»BÓ]̴xpØ*iL´ìº{pâMð履ltO
-nƋM݇¢`d 2öèGj(c¨rÙL/{¥<ôU:®®£Õ
-Ñhwm¿¼ã{U³>¤3̢{¤tOñ$BWÆcq6Fjøä'CaÍpãáxBqám׿(ÆWÔèIQ-Sþ,æíy&Ò҆Þʶ@`Îç]Ý7üª:¿uê¨ïJG+'z$~ҩ½p|O*7ۈó6S['
-j3
-뚥ٌ§ªt ߧ©E?¸`-Âyó[NÒDžä:ëcҺ+½Ù]Ms·
-*ý)(ß{ TláÆ
-LÑsñ(üI¾×ÝLì1^çÓ5Õ,ý橮|:)¶tÈＨøP´4䧔? ®®¢Õ
-Ñhwm¿¼¾b½Qs(ߑDwJÎ+gO¬Dû¥v[]¼bwâ°SÅ.vÑ0¸ÝÀ%ÇO*bÙ-Ì_£
-ãuóòé¼æ«ñz½?iTމ¸>J¨üyM~XKnA@rW²¢xák.ÍI,q»15°hï¸)6ÄcͰëL|µ5iyh®és½EãMð«çºê)¸gԛ/;®5ʸUñ>3Zc&
-s}
-ÚäûUÕÔ(nC»müÜ}à¤ó0}êüuþ-ܔùJ|¦¸±ÁÃPüù'z$~ҩ½p|_¤ïYG©OƛNéð
-¢éoáS±ì ¼ë»
-O HfÐT×PøÓ|*ùîwDë±àþõµ=¤.Ëo¤TÒp̃ïRmKdÌÕêkasýh §a5/̎í,|9þ;éÐÝvÛù¸ûÁIæaûÕ!Âüùè¹!¢ªËd²Gáujäkµ+·ÎCn©±IUwrÕ3tÂ劣ìÚ>õ wÅ疋D9®7O*etTÌ*^ñƽ?iTދ¸>J³Ò
-9÷UFSO?÷ÔsF®·Ôóñ~Jû®¢q| óS©üâo_5u3#Â\æ_ؙ\èŠ
-5uõ8ìµ
-PT>v±ýÒñqrCKKި:ܔûI¸Dtí¶£òéI·D2®©´CtZÛoæãï'ïQ¸³ ¬o?/ÿ×uñk
-»ä°úÂãSý°R;ÃAµµ*Êȡ§LÛF¹©q\X
-ïDÚU7¢CîôìQ<5÷)µ,Ôò¶G\+«è±碊3)ËNi´7SwÚU7M𣪺8ܣڜ8ØÎ
-ðvøΑ±´
-%MMAiq7nB÷ùQÇ~²±}ô¾7E¡ݶþn>ðRy~õ¥ßW5Nüq¹ªâËæwìöøF`ÌfßÁJÞjV`w7Éh»ÀL`AV¸ÚÍÕIly'z$~ҩ½p|w¥bºç¢ìĖà{N-ãs/~]áá
-ñúӋ9(á{ík
-ét!sqbxhn¶XÍòrÄzª_:àDæUÓZ^lÕÙݟğÁaè¬
-¢Âz-cȸa·±ÞÖâp°WÝIáCtZÛoæãï'ïAŧ%FëµávÙHÜ/òy"ÝILxWÄZïR,«oVkU«N®}˳¼ç,Åޡ¢©?ÞÐ-w7ɦf'ßw%Xû·s½?iTމ¸>J¿ÒϳtXpÜÉS<Ã=ØûÝz&½Í7w]ްkg;¢ÆK±üI$yuoâ¦Â,0»]Ԟ{îMð#©WQFÂöq;ôºí20HߦM±,xòó'=£ԁ+¢CswyÊ:7wx9[,t¦÷çÍ:ÁÝÓqÕ]QøÝvÛù¸ûÁIæaû÷@ëHÛxlµ*¶sò]¢ÿãp7xM(£ï<#]$û¼êí1ç-9·P£©V÷]÷VÛT?ך绗JË3s͚¤p»³»çz$~ҩ½p|Ð
-µÇ֣ÑÅ#|jÇ֬zEcÑgÑgÑgÑM+¦WYgЪ;ñ±!×9Deµ;ƐàëÝfV}
-ÏֻB³蠔Ç+
-îZ9#â6,ú*/6è´;¶ßÍÇÞ
-O3ߺ;ãP¼=Q6D±¬e}ð3(jnϦn|<ýhçÐÓÈnæ~
-¶6E0k
-%}Ç(V% 먛è -²
-ª\6Q's½?iTމ¸>IÌpBàÁ
-.\¸!pGEÁ!Á:0W.\¸!pBà®
-à.\Ác¶ø´;¶ßÍÇÞ
-O3߹®Ân¨äØ.Hª¸íÞò®G;&M98Z÷]D!hÙîJ¥s\÷
-Õ˵7ÜJÀ
-K\êÎÈäD£ԄMÍ÷;Ñ#öMèûäsWWWWWWXɳ\¬Jë7uut^
-5ÆËEÆB[«««« ìÐÝvÛù¸ûÁIæaû÷ÆKC5PI
-Y~2Ñ÷Ñ[â楌Æûo
-]ÔÛ?ÜnuqÝõ;ä9UU(§pÃ{òÀßKø¹)`nFߺ»Ux
-Ö*ªK2ÍÕKâÞïDÚU7¢CîǹòeÍ1ÂFb
-&קª·qϐLځ×â6Ý,¢ªV^Öêy}ÅGá\÷]=Ý7vjâ;fÑ5öWÝuôÓtZÛoæãï'ïÞ
-%K5¼K#hZßx¦ü@±ϙqSÄÙãÄ>äFXë¸n˪DEdL¿¬§Í=KÀ.×@EéÝ-5[È$f¤¬32Ò7>¨¦·éHÑ0Èû&´A°+Ën#]}RCã
-¿vÚ*KËÈw¢Gí*Ñ!÷
-ÉMç7KC42Ç7ÜQíGÇqժ {´¹İ÷B
-yãJÜíª}"xÅê)í|}×O
-/ݺà+î»°NÌZ§n+&µ¸nUS@¡äç`°9 )xñ!¦è´;¶ßÍÇÞ
-O3ßä5Ã
-Õ4ؙ× ó:òèú+Ý-ÃÉUS
-CTAi¶àƑª-X(馔ٍûÕ-
-)ûǼýǫ̃m¯U5Ðý·¨C
-ÆjGåa¹/uґ(ÈèPўäΊ¦FÛììV°ÓÈw¢Gí*Ñ!÷
-ÉOçÔNµï¦é _`§ìû
-VÕ@;´sj§Ëï䥕døîÕ=
-1ÇÞb®¹¦hÍ]<÷V1ÕS=¢âëï¥e<¸»Q³ºª!Îá0罚¡¦è´;¶ßÍÇÞ
-O3ßäÆHow[¨Äng2Î%Gî³uE(~cTæ; A1Ùب'0ÝUI÷´Bnx×ËSF&]TVÜå.墊ÂÍ.WêO;9¶@BøípAS?g÷©
-Èëo%މ´ªoDÜ%QçUÑu¬>õ¶ȠE¯|º¦9AiuQU>ª¡QüKrÄyª
-ýë`V©©ê)¼cX.ÖÌ F1(¤âêÜÝS4NñtüڙC1äEã[«¨êFdùñ7»w&øÕÔz¡¦è´;¶ßÍÇÞ Í.´\æn
-Âr>L/sKz¨enXsº2¼Jkè;T´Îfɒõ]҄=^~F|È
-´
-*BìܰÙÝQ¼L
-]â椂îkØlBÇmg{Ԧ$¿>@6 ±=÷Â4OôVYTމ¸>J§Ï}ۚÐÓ}ÛL¾(#_¾¨©¸t
-ÚÙTÔE³9ɁQV2º2àÛ¨G¢¬ÙÚÍMÝwÕ꠫Â8xm%óºc±¶üÂg3DïÞ^@ÃÉDî,W)÷Ǫlä@~FQ¢áθS)%v©Y`lok®TqGáN=⮘Ò÷i͍Á×7 ﷵ^èōÁix³Ç1¾
-7E¡ß]³jûLNÂ
-¾Õw¼Fùú¹JæKçNë6h£¸ÏËi±ºâ£Ýö!1ñc·PVãגínhï~
-´¼øFE²M¯÷§Jà~äô\N¢çˌbl¦³)ZÜ7vi§
-¹ÚgñÇõ\H>Çõ\H~Ëõ\X~Ëõ\H>Çõ\H>Çõ\H>Çõ\H>Çõ\H>Çõ\H>Çõ\H>Çõ\H>Çõ\H>Çõ\H>ÇõSÔqpX7u=%TÎø¨ßïh¨vP¦ïÊüoéËå+ý+îÜ.tXcqcH³ÏB¤l\BÖ?N¼ÐÂ/UxÑÂ|7ºkc
-G]¹,6G0
-Nås:émÔþt(ü)ç¾ïnëf¬¬¬à<áz¼jâþo?·©ZÃ5WTz4ÝÊá³ê7ð\6}Fþ
-ϨßÁpÙõø.>£ÃgÔoà¸lúü
-Q¿á³ê7ð\6}Fþ
-ϨßÁpÙõø.>£ÃgÔoà¸lúü
-Q¿á³ê7ð\6}Fþ
-ϨßÁpÙõø.>£ÃgÔoà¸lúü
-Q¿á³ê7ðA4~-´}/îWT¸&ë
-JPæÇabÝwq.,ñuvô+p[ÌxîÛS¸AuÚán¥óÊ?
-wµbA°ÝZÌÏU}Í̠âՐïZëê-µ
-7E¡ÿ-ª¢ãK
-°~ñB·GØ?yvÞ+°~ñ]÷ì¼Waýâ»ïhɵÞr]÷T7Þé%&ÏmÉÄW`JìVúEvԮÀޥvõìëØGR»êWbýâ»êWaJ
-HnC¿kVIᦉü3&¯è¢©3Iÿ¨IÂf®ÆTµ2ìù#*Ó;
-qºiÄÐîªBæ²ìn#Òû¦BÌG¼ˊ¢Híá=ðÍÅk³wÅ(¾°ùWÿuò0¬+
-°¬+
-°+n²²²²²²²²²²²²¶øü;öÆ{RÿÿsU5¤§ÇñѣR²®
-ý¡óû¡Wz)öꦌö¼Ä'oKæÕ(ô
-ÃrÀïj3F6ÉYÁpø¶2ἕû<
-Â#Æ
-BMgdávNâ_5;ämLø<6n'
-@SÇ
-f3&òE®ÂpàñËÅâç¾ä
-HÜNø½hÆØ*á
-Ü9ª !ªHÚçO(¸óºaÅIæ0²§ùF'c-ö,-x¡/âF}|H4CVèٓ,=£f ¿iktOÏNæ
-ë×ü;ü݅qò½;Yq³ð®)µєÜæ
-کgI/ڧîöfhúGR]6
-Ã~ҡuIXHãG¥ù¬;gçðgjÜBÉû?hWÊÎՆ8ÈXÌÀí郤/kÞÇpWfÁÏêL×÷ߍº>ù¨àdxµqv¥ÜУfCË
-оHDÑ#ßÍÙ50
-6â,â~
-:v±ø˜÷iwq66¸
-æn»,|ŝ¡ætícñ9ïÒî)´ícÞàM«y.ÄÏ
-'þ¥òF;È×Ü÷yrN¥kç
-½¼XN©Ա°
-·
-·±â7.sµsfgfàg>&¼°÷
-Çø
-Sä찌X²°@@¬:"ƝB·ɱ¶öÿÂឫW
-®\2¸epÊáÃ+W
-®\2¸epÊáÃ+W
-®\2¸epÊáÃ+Wփ@ÿ£������Ë[yÉ:Y*êxP¸¶(Ï}㟩MYánwF¶ëµÃٍ@uã
-á*lv»¬~²S×CNü/ÅùSk tOä5ºÜ(«¡Ay®:
-¶×Ü-MmK{Lk°j sê[$
-ö¹L¨dqàÕÈ[f.î¿ÖeCYò3 /l¶ß1¸µ×GQģÅ!ï3'ýËgTK5dî8Kq5½3NÚTíqq"àܔµpC²~C]
-òpÆ&»£®ӧk¬qßÝN¯âꎮX÷4ø¢ÒpãÈíNv²íC$H1øf%м1ÞÕG9t¼7ÆXë\gþ×D_TXÓk´*¸
-ô²DËäÈÃ@ÈbU®»4hr¢Xa¤°øÉ6¹Ü ËãÃ4:µIW´Xa
-
-ÔÓLÖ0ÅN-K`+ìٸÌÑ
-÷MÁÖF÷ÀÀǸÞëþc[
-áÉo×óQOS,£û¿
-?¤\T<í(d
-îJ¨XêÛU8Ü.jc&¨ªíGÃkCZu*
-I颊xÙñ£)õԍ¾ÓFF½¶sysMRúYÃîacy¦RIÙÄ}¾×¢;LrI ¨`e¬9<ö¢i{¯
-`¡A$}Ýïpx.ïd: Դ¦RËM£,[ãKM)|~¨cy
-
-9lÐ5PSMHØe$ZXÕlo·´ [
-áK
-o×Ây§qëe:lv#꣖X«ªË"â7¸®ÏVÁ ï¹ü@KqdЌÒ
-a¾4*gáFîÊìNuz|¯Ât¸ðã=1[/&Isy°ÿ1Å$6÷3þ
-ÃHê
-ð iÇÒoþBãÝNÈÏÅ=®3þ1_¹ÂÅeëj)
-üpÜ7°êud²SÓ¬%E
-÷Ø9Ec.'19������sª Û]¯Ãoò˫«§æç쵂#N&g
-LDw¬¥¤{x
-§païs
-iVj%{\\ËX
-=;¡¢0 Ï5Ø]Ù k_i¡Ñܑ¦ªhdF|[¯¡:x§|Ïe¤ÍÍUMMÂlWcOT´ædŌa ¨iM4Nv'»§§t4&F,óLxéà9CpxòÔ'Q¹½B5&S9ñpú榦ÙéÜðá!Ú£|2q¤¼X5£ BøâÆð'+òpUPº¢8À ¼8ª¨;Ca{N&¹vzäa©{00ß
-¥p*[<ÑV°v2ü9Ý#æZüu%ö»±YKM1u;âs1D-ÞN¢Q;¤¼IXd²^)ax±¸MÆD(PçÏ 7эuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuú/ÿÄ+!1AQaq ¡ð±ÁÑñ0@áP`ÿÚ?!ÿêO
-Ú0EùyhÃ(S±
-&eìDWÕ8&Èz
-ÝAbüùGä-Å-sµÛ6¸m´+ Ü
-u_Ú(vê#"Cå·#Ïÿ/ò7
-ï¯@¼âôg \(÷
-ÏÖT
-Úe#óÿüå&hÎó~%IAå'AUã;î
-\DZBNÅgôøùͼdUµ.PV¯d¢5¨c6pl£CX)è|D ; oy·Ûÿ«÷?ä@
-E>1üÿ±R?
-â(
-6íæl ²U
-`ÄY9æ0¯Z7¤'³öñ@±ØėG+§þÎçáA
-ã0hÙ״[¾hgÍÙÜæCP°& åW9Л,E~q,`ý¯íÿà1üÍ̩²{
-De}ðÊ`{E^kÚ1ÿg«P
-¹ælt&ç\ð®þófX-ZBå ]&¡¢²P3}"7ÌeÛö[İ
-&ÿÀ ¼ËDŽµðmOí
-'OeÌTã §<Ì~Î3Ä09ú?ü=®KvF$[^,²Qó`cN,ç9Mc.åÇHr²âӁô2£§Í|ùÿàQ°3úiý4¢ª±< DþMhü&zÒùUùâ .Þÿæ-6ù,n¿9ÕI~ϝ?iß_E7?>'+©_i\ßðÿï¿ñ-4xþÒùէ´ڿ=Aüþд?d½m1ý Ê?7´ÞK·õ çúOÃËí5ö×_´Öïùöóþ*
-M?=Aa,,9} ífëúA
-µõýcÝüûO?¤"J7¿Jùº
-ÏÆóÄŗçÄü¯ñ?+üKµóÄügñ?üOÎqù>#´ÿÐ͡W@Yf×í,
-Ý5ö
-¢���P8þ±ð¹øñ
-¿ÿÑ
-üIljùâ]büøºÉùêZÅþ$2ôÒcKðñ/º½Iøâ1OäöÅMoVzE¸+ÔÞä.f¥Υû¨²9æ/Kh¼þ)Lj*¼ñü_O}1>ìèxÏL£wZ7@Õ$`UNͤû訋
-U0Ókê
-¼ÍÎ#H æìè[·æuàdí1ÅLÖEØ/£S)¸î
-¾Í@%¢9
-|Cb Uêܹ¼éèT±NKgñ,b¬þ¹
-± ½7,|)òAc©³ÐÜT>@qh=ä3
-nÞé(9$)ÆÆfìbj3Õ:hæ!Y3'
-¿BÈáèæóe(·
-uÊ
--È#ÊOÀõñw(ÒÊÒÅÙcRéW*Ãï2AN½/ìZ¦tÆn ô6îR+£^f¸7Bù1°)p&mÔèØÔY굲ڪōú¥ÉÁÔrE+
-9%
-Xw.v§'2¯wGééúh*<$(/EÔ3qVa#J®`
-_ªÃI rç9h@ðT[i
-à\T@ åäèO#.9mÙ,ÄX̔&8§¼°¨@OÀuñÜ/°E5{éô§J`³¸BQ¹µ
-YÆb!)_(geT¸E-ÀX©Hº/(bðĴ7²S.æF¨bØÁs$¹+¸c9gäºôÛîDÌ
-åÓZ|ÕpÄ2WÇÓ3ïMbTß'aÏ<
-1
-òԿ±
-µéPÐbìó.ÑÅÜLÍÊnҘuJ
-#úÙø¾;eÔOi§È*éÏËa~øèrÊL2ë /1lÒçÄ
-»E£eÞâPH35Ýn^Ö
-j7~l¦iáÆó¶q,r
-T
-³QT8ÊY«T¼E.\zgó"\vM^jefä<Î#²Öú}äÔÍXµ>â ¼¯dµ1µÜ>:mw.\¸3CqÕL-µ¨¨×Phs,Ï*.&KcÅÛ
-å_\Á-²
-ʴxM)¥ÅËÕ
-ÄvFªt¢V
-˨ ^Wo£j
-?IØÕï5à"x±¾%mËdN-ÀÇR_ªèc¶`uÔÍg¶®ôå,,Å^eð&÷¡
-ÛaôýrdãUI!\L@K¦HÐôա°í1ôÌKzX¾DXTÞYUìCÔogÌ˿Q§PÀ}ÐܻÂ6ËPî';¤ƑKÈQ 8ö_Óã¯mú·k"6BkQÐé'Û\bùÀ`h¨!¯Tkó2a÷ºeÕDeؗͻë`=mҳ]÷c÷ÄÅl]
-ûÒ1ã!Ú1e{è®)Ì{%¤ãj
-¢ÂÌiAn[ôï 5}àÌ^,ù2`O
-¯z:~ä
-ÉÂJ*-!UÂh,SÔUP0À
-
-ʪ¥æmç²
-áÛÄæ
-«¸¡EÅÁG
-A6ۀ²SµÄùQFeÓqã'1QwämRîá\æDÉú
-/ÓãFÆ횅¢bÇsaÀb3¼Wlµ(y؎øUÔºïx[2@Ênb×5OGnñÀK
--ksbo¥Ö˜ì3J
-°ço ºz¼£¶ÞÓ4ËeKè ÝÎBe¸ÚØÜz6C>þS@*üÈXê9}hÙbÊÄäNüO`\+ÙÅ~ÇÂIÕ¼_hËWÞXȖ
-nIÃ,Ñès0-/¬¨z>Òurª˗É
-f"±T¸_io~ÜFhÃfçÚÿOLàç'
-÷¥×]*p1a~aÍÎ\U¹înv_[äfKhixh®ټMíj+QôåAFuF³h\ì:Mz«¯Þ"·l$=§xp.>QäVj>ÑÐ:#
-Ubî à̐Z`ó0L«ÄV
-º`j¤×pyìCX¢àxfÛϣ
-}%
-_(©5)Ô;9b;¢QdCåùO´á±KhºÖ5²ÇÁöréëå+Î#»u´|^6AÎÉv^Fb´q
-¹ùN¾4¿]Úc¬ |»ÇPf-N²íÊæ⋄mÛßéÄ+ß"±EÞ²0Ï F9`L9YÖ¦
-¼VWð¬Ëa«áW¹-휱OÓ
-D«¿`є´s(ÅÊ
-E
-ùí(=åT/ëjU)vA¼閍J+8f§í7&rÛé[¡¸})Tq±?PwðgílVýú¶
-0¸I¸#¶S)A¢Á)TO¶þà©b{Æ<¼J!±6*~RWșÓ/Atá
-Áë
-h舅¬ÝCĶT_!-Ð
-®åZº:æ$öKO¸¹ÚúÌWWâ)Ç(ӏ0²¢½!ý¢õvÇåð"£LʞAØDXäô"]VþFéíéðæÄ;cèm©'Z
-~¶9C-~¢jó,ECó
-àë0C¦K¥òñ£¿Ûå½?;ÄÌß2ém7 }ü÷¯3\5/®YðIyG
-cp7ì{ý=ƀcLË&qúLÕ[������ÑÿÊ7äk %|ó¢?¢AA¤PҖ2ÊZÙuæf;yÕlµ§àºøî
-ýELmκ
-4Õc
-1fù km·PÍ
-Yåì;.ð$1@hy\EtaùÆ8
-üը
-ÄȓX×äEô±^ðGB'É{p[Bì=ú,ÿ1´LASûÝDMÀlY} ¦2ðM {+ÔöÏöÂàJ#NÑ.}N¡ÙûÔdCw¶²Ƃû³3ØÐ1Þ*8÷}~б8/å
-XÏïgä:ÿ6*4¨ä¼#·´QtØ,M
-ÓܻÔsU5æ¡õÌJ;%I²G·põóØí~ЛAp¾¥1x;ëüvÀúz¸4»oÏ0\w(1ôsO»BêÒÊrֳA,óÁ
-sd"aÂbxç21±sD
-Ü
-[¦üë6Pڡ
-hs¹bϔ#¿@â&1mx¨[¯½NÆüàIþ4.Ä)ZQåÙq¹[ôQäBÖáÇ0¶@WÓ7
-ìTtá³ç8©ø
-¿ò¦þØQ·Ðr·©n£??ÓãÏڍê^D!fÉÂ텨®ÀzñǦu´° ¯óD©6¥ÇÒ\Tg$îQÇĻZ{ťsq DmRêî+ƽ9<õ-ù<ó-Wj6ÆRË*ùjW8¸¨gF´
-üư¥§Ta
-j%í6ÂñeÌZÃäy}5Z&ÕâÝSϧّ&Ô#À@¹@B ®àmÝ)¯«5²gaZlª©xèʉp°âtæ#Z'ÞÒ\Vi|̮ ܣ
-WPª{±³òý?Ò-qGÕ
-ßëâ߸ªfLXxªFçSG[¹mËʻ%±ÍÁí==<Ҙ8=bR¦o=æÙé\¾ȏ±ËMSNâ\Þg¡̾YBõ50ªýyDÈôm4#òý?ѿ-¬S˯hZ¸Ùk7νþ650èÊ)¨mä)&5åWäþ'ILÛöcY0u6¥)ÝWâôãzÕ9Â"KQ»¾ @KÍKÌ!/®*2ٓ¸
-©]ÄN.éñ³
-
->v@&}¨8f·:§¿Õ
-
-P)9tF-Tc§vW¢XÀ÷{-öYÄö9ÓÖ/ÈåÛR»DÓÎ^lI¹ó^!ªqSk%ʵùÊãtÏ'I22ÁL°{Së:D
-"-ԢVømøù̂îq̩IJØi.|âȫ¶?»jù9» zÜâX³K{C\µ:mºÔ7ÇÖ ÌäÂÛ»`>?Æu̬¿.m/ѬvðûÂdÙËYTâô1¾QFëO óMW ^©[ÕÆl7ôfGm×Gd¬Ô'۲*¶Áá¨ê¾4âò%Ü8Gö%F´©ÿ8¥c%(îâFǽ²zöD*ÜA큏f ),n4%È0oAA
-7Bóþ4ÐÞ?À҆Ò}çyçòqÒX_S ©RcϤÀyÔE^p0Í,%ø
-îq,kóRý?>ÓçúFK{
-þÑÓ?¥@@s2ý Ð_Ú%Ãñíãøöt?&hAïþeFÏQ¨£øñqxYJ;ünyÿ=¿¤^óÔ¿´íÿ/Æúý¦»=½öÏsá۪ÙÉ28Ãܣ]~qå¨#;),>«Më9¥ùý. ·¸kÑÁ6¥-|Jå7*´M=Êê
-½⍫uu8Ù
-þ¦Wã+8OÎõñÚq
-"ÑÃæÿɀ3qYxïW0«¢Ü*§ï:-&iºÏJe5`àT
--mq9G0>AÌĦ\ÀL©£k[1
-ãu¹±.¹8D¶Ö+OM21ßa<
-ƴ¡t³VjÞ6öL1\;>
-qÈÚc`XÀZî
-yâ´`Kç$q
-YHfo
-¡¯Fî
-ª\Âî^´ʢÐ
-JxT6Ä
-âg0cap³uP*³8&à5îS
-ªFbOÎõñÙ{ÏÃ3æ%mûQe݃,FĽÎĹÀ8GfLÓÔf¦ÆG3Yå
-"§F5QCÞbú¦~ôO:VÓڛ¯Õ
-²y[Tp@YS¸¨ñ -}Êü4¡©U:¨®b½VâiO]«×7aÏS
-ÖC6|ÌSf¥ûiDlâÝó< .,×CИ¬rBÎc«Çeê-âO/X_Q6\!<8a«0wf Ó)} hÚaÉÌî㨈Z¼Ü
-g ø^¾7aá{L!ºjmê¯NO3"3Æó
-£X«
-ÛPsÜæÙdiï/ã4Ț_AªC(WíPùK
-fÎà#;?ÚX¢l¥ܽЂH×2£+¸Ó6Ü9gaiÒ=·ë¶Qc
-¨ãҫð24°5è³m½-©wèÜàCÓ+¹³>åÜÓõlýçßþøÞ!úß\<âùïÑÒjÁ?ÓãúÊYÄu:á\±gÙ9¯D֘>Îfjäcá2cÝSvÖٶC¡df_$¥]CUYy8Xõ³ cÑ,¨ò*4
-/
-º Xå¡Xó0
-
-
-vGĴ³Ñæ6/¦fòퟂ÷c©}%U5¹
--<ìǤæVùg´{z)Aô|³í§ݧé?YöÿÞ}Ï韙â{¾²)������d®~?§Çõ¯a¥ôrEÊ9¨mߙc
-jbÝ%1&\çôÏÌñ
-=ßYûG×~LüÿOëïtÆ]ÇjW/UŴ}Ь+S(ß!B±±:]EB_LpqE¼@¨^¢ïpÀK¨ÆT¯rL¡a˹TçP
-³73¨[´Çô TCU¦âl¤f÷×ÑbÔÄϵUL¿ðH9{¸½õ
-ÖÐõH«Ķ
-?XøVÂ
-p/B¿õhÇ-FËôÛÿÄwطÖVYâp¢8
-ɨíÅÛ!÷º
-céBܦö£
-5¬¥)ýa²Uä4æ)´«gpú"ýÉhCé]¸[¿D¤ӌE!CSÊWOc17ªóQEPî´<1qÞ0âªÎÓ>7òYî躋tã߹Bï¾p¢=âᒃÛ
-ZWq¿¤2U
-
-åý¨'ø>¢z@k×qÌâ¥Gæ¸laxd{0½ݜ±ðÃlr{s)wÔ熱,ìVp
-:N®ó0(ó,©k(a-}òó¿(@µ8¯9>¾¼pyâ]^
-¯Q¬¹fZ³¦èJ~C3bª_;e"iD°cSÔqLúTàC]ÿlB³óHúACj2Ëöû¯ð¨ÁèAéò3¹²dJ@Kò}£kpà.ˎE(6äK#ܩR£°P¥»¦b
-îËSòKª,R¬Î9ÈÇÃS«z<Ê ¢¿
-¼¡6V_ÄS2·õяî }ôþÍJ/þý0~úü觧lcvç´
-ùß/^£룲o|ù¡٧÷)ýÊf٧öiýf٠ÿ£ê#åçí?"®ºÿÜ0A=@º%±OlË1Üx!f»ǏEù\=¦¿ê]nà fà ,¥ð^Ɉæ~ðâ¼#Þ:í»hÜËr+1Òôu.%Þðÿò»÷PPU\Yåû:£³
-i¢xqé4k78»ßÎ\RøJle¾uS+¶qääX@ffFa>R՚ê>ó
-ñ ²XóÊ vÃÿ/WÔ`ú¬âVÍá¹]ÆïRZÄ
-Ì4ݙwK¨#y
-T¨@mLxE
-Zo¦l%+(ìÄzș8éùŵi3Á~´%~zêڄ$([+>@X
-º*@ÑD¢öqqd®jºYúTò¨d©ã
-BäM'?í}Eô ÙԽx¥©ä%Êó3f$m:ș)Ì0̔kçà¹neúÃkë"\üËCçکµ¼+MÊȪn2®"·ë¼+*wQ1½²;õÚñg¦®Ñù
-¨)ÒmS)Ts-z·©c¶«éßÀ &«zÓ+O´çç
-Â˿Ó0¡§ûOê¡
-¯AÄæ]Ãۨh¤ë{
-rAn;
-¤;MK¡W3y+k2ìOÄn:¢cG屬n!ÓpA¥¾î-¬l¸ÓokzÛDf$Øi^T35rҭL<(¥ûÔÀZTÊұ*ýf±G
-
-¨ûoÞW6§öþ¶õOI_¢F¢üæûr9#ÃÎxaz²<Ë|â9å
-hED$á/k,¿JÔÌl SAÌV¸ëĴE-s>ðhPLÚéa< âùbMñ-ØڒTRå}}ë
- ·%/٘oöBZ{XJ]½GQJYfùÔ_,Y¹GY³ȏ2]TBDz EÀcÑD¼3
-WVϖ[Üb%9ÊƠ-Eæ
-¥¼ ªõÿÊ_U._©cԡêQcjæ\u*ûê<²bEBÆÉïō³rт¸æeS4¡ÛÅuWPOeó°koÊhÂÑ5Tÿk=Ñ0B\h÷5+ö9ú¡³ö
-Ã4x?å.,´
-6e'QM{×QW
-¢ÈSvpp°´I87ÒÝä^#i²1îqèÝÙ«©ê&Õ6g¼@9òUۙTù¹a¯6DG§õ#¶6-Be_PêV«ÊR÷^Ò:¥L};À
-?P©»J=iWóÿ}T@ÁP`&mЀ²ãù¤ÌËïÒwìô²©¿½7¨^%µ2nr-ùM=ÏpCZpFm £RQ
-ã²LC¤,Ô}e²B!G¯<'@¼GX5íì
- R²gzæ
-¼Ø6ÙÏ3 Áy¾£ÁÅÝ$âå»%%8éî!
-m\R`{8xg
-Àé0qìq[c
-×! d^¥wOÔæF£̓¢-¸gd}2WÄV PZ¾`¢Ñat{±»٫GT¼Lÿâ
-Düqè¹q=Rý%¶%"u,#AO2ísõâ~f;`á[^hU¤æs¾á)R½ÑLÃö3u¥z°Üó]
-Fsej
-«LA_ۙF«áy£Z���-¹N
-úà õlÂiï²vv¬¿ZîLFƤÊí]ܮw
-õnâ*úÌZá ;ó'YïÊ
-a,RÄá
-·®¥kN1â ÖðÂq;DÓqw¼xæs¼wS´Y⢲ÿd¾ãæ÷OÆ%UÛÎeÜÕr d¥ã½2RÓó+Í5Æ bC:¼X6&äXÍç۪·)vù'`Úô,ΏLjôcL¸2æØ!
-(3찇BٚK¤ÐÛ,é<·"ðÿØF
-# m¯¸ìê`JM¾^Êå¢hp¦áþ»Ś½ø©L=Åöþ#Eg]ÆdõyÑ5©ñü»ð®_kü¤_̿âP(kWÄA),
-
-
-´/{}OÃÍðÿoz~ßÞ~]ûÏÿx
-èÕÿ$Éï?
-ýåîÚÙQ#Þ5Ò4~hSRßÆó?4ýç柼ÐÃùs4æ
-/ËsñÏÞ~=ûÏǿyøçï?(ýçå¼ü³÷,üï3ðïÞ%Êó+ÇÙþ_ý¸"Ñ0).»Ê°Sºì^'Ô×?í? mTZ/ÞûÄ\?ãÌüå?/ùOËþSòüå?'ùOÉþSòüå?/ùOÉþSòü¯å?
-ùOÉþSòüå?'ùOËþSòüå?'ùOÉþSòsöaýá¯ø&������Æï-Ïq1é³͓
->k®B1Ø+ ïþN}| Ê{ÒݳD>PÈzõÍÊpÅWï
-¸$Ar|©²»AÔÙ3¥ Ó-ÕÑN¿/ÿ_þ
-¿üø2ÿðeÿàÌÿÌgGxÝçqΊs*Øû2¸
-øeÿàËÿ_eÿàËÿWÿ/ÿ_L÷m4_E§"eîUe<¿iÁÁ?à'Ô¸ðÀÔÌ;
-Tíöú`P¨ç������ÎÛ%OË83C¶y ]Ûï(|d²øæ
-r5J©-¾öÉ{e)udNèãé¨h'4ٟÅ]]>^W2=æí
-j¬ðO&e¿oĢ#8w2ïɉÊÔv°5EFh\Ëқ!,×5ÓW,`J«ð´ª4ƀ.vÅûJÔT£Hâ:ýåÉOÎÿÃW¿°EÝÿº慎çÊ)5EöÝ>Øl&exª
-mÛø¶1n
-Æ:N±¨ËE"O²0b;T*Ի{¨?2Ñ~k2å(lCԼWX\ÀJN ³àþåä#¼E°ÎPº ���`$åÿʞ qOa4ҝêpV_Rlk-Jסɲ$BB_éL˜G
-äìËGhªèævF]Ñ6{Ç5Ô0í)&çÊ;ôõµ¸1=
-S*«"jGlùGÃæq?7×Ʃ=C
-XÔ¬jy à}eóRîS²\»UÙè¹RF`R®ßkǥwаD±²q
-BÒOê
-@)¼ÅE
-k+Äa53pJØkxÿ÷ôøWXK¯©jYb;bXú*J-¨T:
-ô-Di}åÆÖs<¦àZR9»#¦¢*õ
-KùÊb@JõêrÖU¨¡RSÆY>̘ȫëãûIùÒô8b4¼óMUÊÌ-n~R¦OlvÆԊÑ
-Þ4~¤6¨Ø
-vXÆ27_Å(
-V¦z¶T¤G1÷Vú@VsAvỲP[Ä@º£é;TS^ÐkÊ$0+*©>#~O¯,©0¹^â1»¯i@½0
-*d)fbÂÕîd!²c1_l¿¤Èó^Y±³Æ^âǙÞwq7ʨ~ßÊâé
-Jªµ
-@Ê~Ç#Þo£ùÿ1µÊVÈ4,¨cF%°²
-«̴ö½Äj7ô!6)7ÔXnõ<©{?Ë> Cï
-µðÇ¢0ϴQÑ*ÖjgæYÝâbICrb^0sLh
-Ŀ*ãðëò}çO·
-Hw7µ/qս
-§@¬Ã}AM7s¿Áâ
-×,hÁAxKXfR¼ÿÜC¨¡Ðï¹@âèdA *)ÆV÷'
-LÆ(í£/ü$
-Q,5s±§¢s«
-þòØ@5DÍè]^!J[Ì5=Aý2¨FƠ
-¾pÒüɘ1,Äs\ù¢]~O¯üÉõJ3¢õ(Èe¹~ŷ1Ty
-îMÛÄȬpMB]Ê7+9Ä YYï5ÑäȎm
-NaZ
-FPUÌøùGMJd¯5±bq^ªüß_ùsàUe·I»z,óbbÔÑíîr«ºF÷q6×Ù
-×nD|7©î}&Uxú1H`1£gS|©ΚCqÜ
-LÕ=Jü_ùSûh³|¥î÷¡-Úǩ
-£§RÉ
-£Ë<·-Õ*òéKé{êԻ/à@@ò´õý
-|òÕL]£yî_{ÏqAîs"eÃíë¯Éõþ6±xW ó'<ÉæO2y̞dó'<ÉæO2y̞dó'<ÉæO2y̞dò <ÜÆ8~)ðØؼʈsA5ë{x&$9'ÔkFØø(pìÎs9ø@ÈLLJP÷
-!ô4 pMB ¯Z#ú*OiB`@ªÝzKò}|gsÛþ÷íß
-øíßÊÂ|ơF·´ºι h"sµ L,Qôñ
-Â9¶Vyô
-j ^ V»q
-Rs¹ǥ´Ìu-JP`:ô×äúøì§
-Ñ=ÉïOzx獞6xYãg¿=ÙîÆhé{Óܞç¡îz»=Ùîú 1ëFúÌD
-u2 øC9«eè°´°XÔ]¢ÎàYd"¯(5¶ë
-կX2«êå+A|ϼz]lI[Ù0ân~¹i
-¼чÓ_ëãÉ>ª¨-mí:ħ©ò#Ýï
-ßt9kÂ#¤õIì˗.ã5s̲ÚXæeIîð
-iGçÖ|X¯Dû¿[wl¤éàZ=s
-YòÌ9PÁñ^Xµ5ª¿bïP½·èi¬1xWr±ó2g"_Ë5¸Ùóé/Éõñ»õtý½JÌDÞHõÑÉ0²¶¯̥¤¨¹¹gAq1@
-®×QZeÁÃÒr}9%.
-%þ
-l2í¸KԶ伡-)CTAºÝz.\W:ü#iÈ!()\Ö'ðëSÃÙ
-ÖíÎ sºÏk;.¾TÇ$~É_å
-ê.²Tfûn`Mü½F*QÉԯUSN
-4ÞÒñk©Kò}|ný_җ<¸@92ÀL¼/Æå´ëÞ
-PÛvÎHSlÑ+Vù5«9Eb~\¾8ÑFS=°'V Äb4mJڮÙbqêgü"nX_hð<'Tûñ%¼ j
-¥º
-Ël³YjdéÙâ
-½>`Ôõ
-Ȑ+LÅÓ_±éµîÌ'~O¯ߪúQû4/fl͕Äó(
-<(n¿oi¯!z0±ʎ(¼Ô÷éúË.oJSº,ã
-ãȇ7٨·1
-p9:½YkðÝÎJjA|;b}ÇÁQ·Ü)À £h¥±Ýê:õ*Ø,ié2¢ÀU½ó°REGv³BüZeÀ|æO
-Íõµõ8Pü_¿Wô%r¸
-%ھÊ
-]"æÃ
-éÜdó+º
-:gVjøÐ*7| ºZpó6è.+ыÑp@¬ú[¹;Ë üL'ö5pÝV*´¦q¯Ä&ï
-=á&çâÒ]ÞVð8ԵIá¸óY·*Íæt*
-j¥"Ìt-p%cÝstõN>rSu2[ÜK»Ò_ëãwëô©ʪqçÄdÂÄg³7Cm©k°8Õò[1Ðx]9ä>èãôc_¡²í wÁ-ªizɽcï
-ïÔýó_N¹$tÇôAñ&¡Oþãg
-£M'Ý숑N*'@u»̟e3Á)÷ϊæÍ&0¯<à@ù>¾7~«èLÌÇÝÃp(@DízKÃ|¢Ä0AÆ)1g¸ôñ?N9=åÌà[Q۳á@¶¹ÔT`ػt[M}öØS_xI.2åÍ>s_O½ ¸Ê21[l»:»¹b@[ʄ´æ
-ÑóYxÁo-°Ñ*9:ËeñgFıJ÷M>üß_¿U^Ñ>A¡1u_¼L;4ê³5:EÔJìO3Ù?N9=ýŞÆ(>kCME´.¸¹Êý¡sE}¥H)B¢~՚ü"moqZL,ó4tøã=Bió-êdãD¨ã¿Á6#°TҦsSlX־*¨éâTáûIô×äúøÝú¿¤(©¥_H^ûªn(ÑðZÆÿ©(mÔ"·É?S|³ôbËßфñæÝÌ>TãÄçT¼Q{\ÌàyXUÍ%˚½æ¿ÛF .w;s¿rò·*Ì\Ny;=_u0ð\Dû%¸k{ßdÃ3*¾&|>ðzSe ç\=ù>¾7~¯è½¥«´O.Ø~| ²®¢îr%º3oóp®r-úDՉçõퟣ>¡èÅåòÔnMÍê ûz!彊eÎ{Bks:Küó_O¬[ñ4%¸8ø]¡¿¥ÌÅÎ>ñٱQ,Pþ`%ä*1FÚ'O3÷ÌÕòü>ҍ1L_¢¿'×ÆïÕý¸ùã
-02Z¥»¾}ã½97¯¿Zá
-|ã<ãgáñìÂKÔ~_Qô^ÆL!InÒÚ: AŮD±ÏýN½¼·H©^Òë
-ÀzLgOOGëÍ~1>Fu=¡Aæe¦zü8²wæܡ¸1Î<;7Sæ
-
-9p$À)?8çÁjÇ
-YZì½ £×_ëãwëÑN'ètºÔðGô»¾ÈúJìúJìúLyLåÍJÿ¡¨ßí
-vî¥v}&] ëFÙ_ðLx"¿àÝ «3æUDã»>»¹¯Â'öþnÖsí.CdpDëÈGÇÐEãöÏÖ,wí
-¼qSw½ªb
-nå®"}Ébx'°¥`&
-ÉTa=úëò}|nýs-K8'"xà'"x¤ñ>Ğ"
-`&fâ'"xâ%z%z%:'è5|b}«\LítÇ1W'Ö[Aß]ê
-1,Xc6´\u,d-
-öÊÀæí1õ\¹2}㽜O,>ºü_¿W*2ý²ý²ý²ýË÷/ܷlãf=N%»î_¤dÜ÷O|<¢h¥¨ªi¿WFdOaÙUS2õ¥0éãדԺ@W^3
-Oõoû6]Æ~|åÕÁi¨?7ieðz¥]Lî³ðkò}|nýU|âַ¡
-.!@G«ôuk¶}TKvi¬ìãô)~¶Ԫò*Ï0Çs)~?áú)ÍC¯&fv[ÆC\¥¨èT=6¦þ³Ðå´tʺØz4µðÊ4®®%Ç
-Ý/ßC@kt¨#uΤfÕ٦àKÆ|:ü_¿WöKßKÛkrÀgá
-ÜÜ2Òæ
-f3/z`ú¥ëÑ~Î\ÊFÓÜ6Ýn
-õ1ߏ'3s.\xM_áà[±ܳØ/ëÒR(ÿ® E3ÞEBök¶§×7¯]Læâ±Ä^,É<"Ï^ў^!À4@X¯_ëãwêþßS,§Ȧkۙ#Ë=I9< {þ7~®¾_AE«T^¬M¤;Qdù3Äi
-«*uÃï«qÅÅcÓ_SÐq1ÝΈÌéMÅͮ$%ç¨#ÔÎ5|"oâ% Á ´1,
-på0UÉÀK®½äD)&9fة°o¶ ]8äêaÑ
- [HÆ
-v§
-ÉåÆeù>¾7~®¾D»Djfĭ¯ÒQwç:1I¼§AØÔõ¤+"¯ѯ©.9'EC¬ÈÓ
- %Kê PcæYUϨî5|"~öÆvFæ+³Ì]Õê-jq¨{Y>ùã
-;ªhû^L6h÷òùʸûÎ÷%}àí_Îð
-2µ=ï ØW+x¥¼÷mõ¥Ýßu5¾o.»RÙSNgäúøÝú¯µ.
-(Àæê*'<í
-¸ƅJ9ÎװEiR52Ã[sM}OQÌ^= 8A¹[GI._£+¾Ê9/×зIùKY]½2ûyM»yõMÃ}æ^wĪSE;\±âX°¾&VÍ4·Qr@ÔæI@%#°Û
-¹ÜAuNZZCç#9eù¼߭ϲ&·.,
-æ uLD÷¢1°~ß¿U²\GYuW(]Qm
-C,¢íìÏdr¡Cyw/-6w>ԥú.
-æÜ
-RÏJ{L±*÷Ãqê;¿y«à¿7¤S{q>i[àÌ×$aCÛػ°ù&ø-qà2Bì^×,FU%X£Ĭ¾¾ZQmPæda6íÔ
-d¶¤¾8ëà
-å&[Ó/Áe&Z#1eùA¸Qñ»õtýεᗠöGgÈxeö:
-p¶z©YMÁ-cNåŇ¡õ¿Fä¢âBT¢ó)7p0FÉÌÄWÎ\¹«ï5|#¯áüYORçë51Â},
-_ßƵ+ÚfÈ*ûÓ
-c¹4ºÉr7¨öàÔ[jø«b«oÄuiw<.[j), QåpM»ó=çM<7«]ò-W}¸O\ÈzxùMpçJæ6-˻Yr©}bà4ý'k}7¨Ý^Yy¹ڿó¿[-%TküQ%â{³ññññó{
-Q=ÿ¤a½g¸7éÁ¿âÍWØ!E>µYá[f¿º¾o
-ɇ2¶ $¦
-L©6Ԥë,²®ñ7,º²æ±ÜËw,îé¹PÓuè#¦âԥÙsR˫ϢÒj]J]Y,º¼ÿ i}Rå%:êS©N¥:êx'x'W¯L¥ezêW©^¥zêW©^¥zêSÔäöúbÄÓðæÄc9ù
-§äúG£ Síbm4üà0/,ÜV1Ñ>(lENg¼g젖6±|Ãî&Ì1跷<±·ŋ¦7Qd3Ûå¶ãì@±,X/×Pi×7²ë=Ô̸.
-ëkTYÎ\MªgЧÉþ:f7
-éò+ælræYçî3M§
-ÚC³P5§Í-¢P£²¶5Jo}ûÀköLý*Ë#
-ÌÄ-v;]ÝÄîÃ|[Ë+9¨ëWÌ9֟¤À奪xßl÷»'w0¹)z5s³ÍBØäZ
-WÙ8EDX۲¦¡TR`_]ædyÿALEã¿ñ%îS©úá)z AæZÐ_SĈ´ܧGø·6ßüÆâ5âO1<ÄóÌO1<ÄóÌO1<ÄóÌO1<ÄóÌO1<ÄóÌO1<ÄóÌO1
-ñ©?øô,s|ût%ªAæ
-È>S³PÞ~ñcğYÌϜëcäzÆH1Z
-©wWQí d° LQ \Æ
-Îîã&®
-Â*ȗÞ«"Æ\6_ùdª¼â³"Zö^¢>DfT2â
-èî
-(¯oó%Xe?ø KH3ko}³ç·qfö)ÙÇ7®#Ýd+_C
-ÐWÌ~×P"ؠé¨V#ºnY
-®æxöÕyîh¿a}Þí²q]âø#RK_F3Ý-jQ³Çí0^@ÇMÏÍñ·P7Õp,
-
-ÚUBȵð<Ä׳R?n·ZNbb I7D2*KµP\zs»PԮ°Ʊ©M÷( æP¢Ð_q)å͒÷#ºT¢iqî@(ÕE,O_xgxÜZWùëF4õëfÏ
-£ÅVå덵(uæc@ϫ$HxÇ2ÙamçÞUÜ6x8÷²ÞðjV$
-7¿ç(¢VÆ8cçó_RÏÒC¨T©ºm´k(°.±³ÿgÂjHµ¯Ø|j¾ö²Vrw6¬耼
-Bp =Yó[\3´pfM×PpÉaêûD%säV¦OCo²U÷0[Tā K¥Ç µéìK± ۗcL¶e¥°`?7ÏÀê18÷;8®_+
-à.çO>bª"ÝÃÒ3ù¡*V"\fÀ&¼Ū®¸SÿD Β¬rOµFøô¥eØv»²
-K,´ycÌÔÿ¢00íÓ°+5¿s@tkÞæt«ݦT^)ñ
-¦søaØ楣> ÍF4¾㘱ż[äó
-¹ì±(YMy!!9ÂÑÁ¾rÐk
-0oUÈ«_5t kµs+Êã
-ø¬<¥:z
-9±}E<òv½ÿ
-EìçïK2|
-8Óöÿ譖^ºÿLQЩõ©Ð\
-ú?ÐæáV
-½Îê9ª큁Jµ 2çEa
-»^Ñʔ8/4+õ
-<;ZûCW:÷@K³³úJ0°±ßpûóö!¬¾,ÞóQaz
-á¨+/o3+¶|ñaMäÚh³D¤ÓêdñÜ~$RÕÃ÷*»T?#
-¥î<6ܚû?ù4ù§ÍØ
-kÔÌÉӤR`âFÜxl®³KD` m*»÷jTM@·F=JÀ>ï
-S2oADPd]fâül½©Tmc
-ÝÀ4Uþ>þ°Nmªäi¤F¾&~ã֙¬¶£ç\NwxÔÉY`i¬
-
-\K$¼
-8¨¹ÂþjjÀJù>ióO|Óæ4ù§Í>ióO|Óæ4ù§Í>ióO|Óæ4ù§Í>ióO|Óæ4ù§Í>ióO|Óæ4ù§Í>ióOÿÿÄ+!1AQaqð¡ ±ÁÑ0áñ@P`ÿÚ?ÿõ8
-°5CÌÚõºÿS{Esø/Q~¤?¨ä>`_â&ÀÿQ+Fê
-í.:ïM\уøÞDØøüîð/Àï-ߘYOÄ(%¶7£SUTJ#¸-ó6ÏXȩҨx*c¤h¼þýÌlSl.n3'SE§èøÿðòt®æ0x
->òÐKk§AÝLG%¥ê
-¡xfc*ï4óXª··ý¾2xÿðlxEzü¨¼:®¹Ó?ÚÁ±"ұ5ðxù³TÎa3^aówKõ,Â1Q*æ?üD6,z¿ò@J½0G,éfË3¹ð2öyrêUÙÍæЦÒÇå畆ªg Vf=Û=`¦°ì-_üda»ÐøðÂ@Ö¯;ý¡ÀF¿|çbYF\_ï!Sf]K9£OGñ9Z ÷O9©Kp-]<9©¨z"¥ᤪÅFÐÄÌD×ٟ´¤tʍ
-=Ãoþ1à5ÄéEûÖm;ý)±no$E
-àr×æXÍSªZÒí;#[õ¨Qîaq[£¯Ã(àþàµf
-eÖc»\~UÔ}éý_Îwò¿ü±9¬¢Ö{,¡®Í1¡S^mñÌ-¨SvչùѷB))+£>bÄnê*4B+Í÷i3>Ìãþil[Jÿø
-3 D,ÍÀJ0Úò9ÑÔá¥ÚZÝZ®õõ#`âÉa«pÀXweCϩo(eô8B7
- hþ«ÉçXQ:JYùjPٻõ|º\à Âäôl/]¥>çåëÔUÃö'ÐÈÏâ153}¿ü~rYG3xì¹±ßRX¤ÛSpQäÎãLû0¤i)r}¥d wM¼ê£W@"yku]Ë÷¢M«·àõÿð%QKðþ"@:'ü¨¯Iÿ?â%s¢¿øKÙï´µï������õÔZ{PF±o
-ó Í#¥¿$ï!â²×Xi³ Rªy§
-EÿèçêH'ΆúpQÝZ#%ðú.(òQ½áø©ºÇ
-K¥ä@¹-GårãϠ¸W2ÒAvºЧ¦3½¯ţÁÔj©Tø²£ç`
-è
-2ú6EZ|Q/Ò*lÇ!{Ñ7öɢX؋½-ßï̦w³tõ?â§ð¥£}à£p)e^ÿ.ãURSþÿÅJӗý~B\[îç@XJÑ6üb<5ÿB[ºuÚà
-+¬¡)[ ñìAçÝ0ô·ÊZ¦®´ǏÂ:Aã
-
-i#ïí¥PRñfÐ`)@8÷óaÂ
-Ý-¯[ù»¬½ÁøÊúÀjë;ÿ¸½>óÑJ@\°hJ;X¨rNÌ!ùp)vu)h a×Äqã#öô3pC@a`h^^#^±`&X&ïqélÄӘR+"öî+ ò²ÙJ
-IÍ;'\*bk0+ï3M»|`S
-q^ª¿x,ËÓr*ðK
-Ís§¯lgB¬Zªê®è+¹É2yçq¥@ÆsS%%HC+GíN
-@}¿
-ÞÅOE»ÉBH×xìâ9¡akeø «A±N üfYQòÔ(T
-Äʑã݂NظUa"¢Ö]õ̹R Åñ-m;Óm_³7ÀVçt"A]ræSQàÄ5jX.
-1
-2¬¹ÿ^Èö[swñØmFÛþ [[\ðl¿4Ä
-Þ/ùבn\½;®ãHf¬Ç]mʂ
-¨ïg>_@IA°
-]¢9øÁ5 Í&Υ8¿3ÎxGìîP«©ohç#ÌÙ ®ûЫñg%Ùqr±¥ÄÙZA$}»+
-¢´Ãe̗Hä¹LK
-Ø2áÛYl
-Jnbl3ٖæç:þ±¡Ï#ͰáUҌ?z,H9{Ã05
-°ö@HfF,JÛU0Z&Qï
-«{R8`1XéhêÜd²ޢgÖ."e¶SÎsp-[ خ´ÖõÙÉZ_Úd±·ÍĖÊDPzu®6Q^°wÔiÕÊ]J4&±éǡsa§H+.b\
-}ßõд#ÍsP
-P
-G
-z1ù'Yø®e3PZwnïqXóL¯)õ°râã0"wb)² Få& ¥¼î X4©³`býÉЊódW¼ø´Ý0@ÓÄGYÅJªuâÕùMO!0âAÓ]Ëؒ÷ف4póMf%b˞hÄEDd0S·ÚRҋcHo3Üi³ÒdMãG)n»¤N¡ß1ÊÖêG¸KÈE
-³Uúυëúþís©]"ÑÁ
-|ZLMîÛK¹¡^ƀt@Bªà¥UTÉ7ḵæ¶*6®ۍ£O喵üVà5+
-$8õü»×VÕE_P6_w.d
-±ï,tÂ}XѰrí©`
-ÀR4u[¹GRº1Üæ<~ÑF±85X{eë¡IVäSŢNPÕÔ©ÐüK²í`Ï,")¯¡gwA
-ÅÉÖñ~#£@ÂiX/¶èÞ_Hé
-&Þ/ÓlTEÌY٘ז="W@Ák御1`öB*ÜÌdd
-Qõ=ná
-Xb9>s¯뻐koaªBף©\B3i
--sƘPZ*ÐV=¡·©¬Ý^àØ)¸ìNUÞ"çZÁdE/ì¸Ä6Pýà]
-±ÅyGÞ;r!upƲ6ýÖ("è «MePù{`çÖ"£éÉû͔LJ؇F,Ì^jqLÏm»êeÐ<ÔÀò»`¯¸@lõú
-ISÛj:?
-¾ÁY¸°O!
-z}
-2åéô
-,£d
-ÚÑÄÝsOXyÊÆB¢hmP ¦t5
-CxØÉ3n7\¤QO¶9Ôe£6{GÇ'b,.ÜhËañýu)¿Û6ß"P
-ã²üMQlî r[Ú>¦"³ü@ÄCؖÄzL,¶ٌTaÈ@( Cj͊³ÚfØ
-v\¨Â iÖ;N}!
-³er5«,7eb±Ë~ú^#=TJ&XAÉÅôB;<ϖ
-³ê,*µ)[VÿÐ̣ûôXwÓp¸¢6B #B6bZJú
-riÈQµY.Ú5ò?1nWԇk+Ï,@Jc¨{ñÚ9©v¡ 1¶ób°®&j/Y&ýh|´{tӈû2@asUu
-Ú
-ԶüE®wöF8̑²×¤§Nѹ¹7ïÊ3*
-FriÍb+=3ǯã~·
-
-äĢ/M٤éí2+]>ÝÜñõ¼rÁÜm`áô»Ò4©EÙælϨÚ
-0¹¼Åkït#+kêí}¥þ.!³J#ßÊʠ5ç2ÏYÑ'%ā
-e)"pÔUj>ñ Ì5îÊÀó?´·¬´.ý
- օ"¢`䦬[5}æ°º{ç
-kªÁnSµ¨ªmg¡oÝ?2
-"ڽåó¨4¥§¬0V+]¿KdҍÝâZGX\X
-ÊÔRSQ±֪¥º_`º¦ZÂÖ[ïoú¢Ýú1ѨüE\
-lÍ_ɔÅ;\KÃ$¬0áG åm*jêmdXªTË0Z.RÌÖMÁSt7.'v©¸BÁ[ XzYisï*h ,ÁçqÊXÛðýu¾`EÌ"
-¶k~üËò&JukªÖõDRÿipàU§úH4:D]ڬX)¢¥¶T§÷\A
-mçïqH¦à
-»º*^(¾é¿h£ÙtÀûµ6yÙñíÔ×`,g>#
-XêHéâ°Tc©[ð@šÔoÖ51±¹¬MzJ)Ö
-:ÔlT¾åo{ã¬öÃZ:K"̩\,ZT£q±õcgK`(%?î^Àôa
-4EÊrÚ%²Xéç7¦t++_ôQ6~ä QtÅ8n?<^ý D+±×D6òi ñ£ïÌJY¾f ¿IyMÏR)üg\ `Ìø´WåìW BÍ2ÓÛf¦¡ó
-öu-¶±·L¼UrQL¬ UÇ2é]"µïö_8èÁýx÷ǢýÿÈsö.òBÍÇ,P5CG©N
-ÇY·pTàª1l-EJÕW@,L=ÃùKz¡KlÝ_
-?W8XZ]÷¸ýbZ»h§cä
-¿âg/ÇQK
-ǚÝTª¡e$»èxÎ&Y³Q¯Ö_m þ¢<±v¢
-P݊2
-pæրý¿0ðð{£
-5>
-VÈ(ÈûÍ~ÂKäɂÇizÃðLÎXz|3³ny«¯o£¶¿Ï
-
-Á1rŸ±3Å,ÙaæfԪ¶·ñ6»,9r«æ河1¸1gá¨oÕ}/è:s8ݪ./rNWE¬¿®£öC¸
-ÂlöE&Ø ÀCÓõ¢+Á
-®NÈީPäî5åeAÚù@k;ê\¢6vÔyRª
-â>3~µy@¦:*µ6ÀDuMÂø)eÚì7ܫDâßpm}«·Ĭ)Bÿ9lõÔ4.¦aê
-æBÇD¦ÝØ~P&_¼^8µI×ْ*@0sÙ.°kË+yKV
-®[$âÙzÚÖR|b7,ðíiâê/OgëWA"������z®¡º.KNL¢05ÜpÏÖBî6ÛbÈwùRØðxþì
-ªÌÏDÖV[è£(À4²}Ñ)ú\4à_¼¯ÕP_ b©bqºØ>Ö6÷-, ùã©HÅæfqPB?nïìEö¥AøaVæ+2a~3HNMÂÈv
-ûçp\D4þàQîÜax]:cp!zK#|é
-£ñ·ãûRØÚUr0«¨²
-<ÀÖÜW/¤¸Â,
-£õi`¼¶²çífQdñÍòþ\ '«
-wÒ1Wy¹3Azë*¥ÏlÞèÜS¸píí;
-Ó-wC¶C¸ÿ°ê
-ç걡@Ãp e/é½õ-¼/³´²%Â4g0s¸^tôǐtF8Éu¸ ¢uýfQRÁp=ýH+&`«ù=÷·»
-9öE ¹Åá4»»Î)������L¹ÝKlØø¨}Ä(ꕍòA`1w6J]US¿Ì&´'(ÌuºcSxÀ5Øg`ÄV¼{¡©+äep·ÃÓƺ²å+²Ú}aEjנ}¢¨ /AªçP¶f
-V
-÷TG"sþf;ÞaXèZ¶Åv\Ô(-A¤vY¸éÄus æ⡡PlÔ;÷PÀé3âeז¥Uàs9E������Sk@÷´̑ØÕyó
-̪
-]ÍBԭÜTÔ×qmÆ)ê><0ªó?ë#ìqڌ£76%8Fâ]¯\ RZK=¥ޣÎÓ_J"°7E&áÊ
-Êíª6¢¶°ÚëQýÒ3ê¹üD1ÍÈ>Ä"P»Ò sYX±RÅ`µ3Tßx¦oö¥ÕÇAÔvkI*]\½aݓQJÌR/Q¼çü ?3ûLî«àx~ﴽçU[g²WÚ4ZðîÏ2p @gróêQvڮ"Æ
-ĭÞ¢ I
-֪_̮íϣ`V®és U{æUe@V8,ºs4)»×ÁY
-ùË\ïQ²¨+(úÁªªw²è5ûBÁër[ 3z@ʄTP´֭£¹jª¯UsVwÀÿqDª¬XóQ
-¶IJVЕUUïüLt¬cTH¯[Ã5*¦¯gQ/mÕÔpË1àÈ fÍÔf؞§Ò&³tVVn#.)µÄî£î%ãÖ[zқ"¦yÍÌÒ
-lt/Ñ
-bâ>æÿl`m`÷µw
-÷˱gûJÁu/õÆJÑn5=S°ÿà@¨>Ñ
-§j֬ÝdGÚRƠ»
-z]{LÛ:
-X¡0LYdD¹
-ç
-bú¦F*ïØhîÍxaN»T¥'iJO,úÙ
-6÷4«;!ØrÔ(wmoæ;]-yý`Û3«>Úøñ
-©W䵏 {dQ8¤£ïÒ'M*ì`~Gª¶µWD3
-|ËûQ´(U6JÎUà`Ã-[¼IïØ%àÉZÚî·þ/î_:W¿Ò÷`T*ÛwRN=âꁶT¸U
-fêȤtxj'¨<ôò<}£U¤æ_$:¡qL¡Ú
-\ý}¦*e»z¨nÁdñP¤ϙ8ôÆ
-È#¿J\ë̾³ÚªÒt¾K)Ù>1@eÅ
-¿´$4äøw
-=Â
-B¡´L|à*V]ÁuÄcî\F¼[nppÅPƧÖ
-ZÐ^Z8Âåƙ4YWõ ðÛQ¢p×D£¥ÓwüG.¦¬_ønâVM)§ãõ0Ѷ\\C*ë¾X²¹ýfíe, 8Î:ÓibYFÕëpáÜ3ºCa½fÀ7ï0½WN¬¿Ԅ:ýØ/;kíæÝe
-GÄnÍ6°= -UËÃtym@WBØÂ+5'P*GR
-ǁc\-¯GD¥,Íe¯$ʮÝÇduߩ=¼nxåcKF5ëJn¼²ܝfl
-BZ¿1hNVq¯ÍA ʦå³(°={éZGTRêv:ÿËúéK+k2©íªÅvªT^ðcÖ$«b
-qÌ}&±ªoñ*v¥
-褜¢ÄġJWªñ*ËwÄÈ@ø[nâí)Ïrà[PÝ*ڢ!¥hH×
-
-{¯hثºT»v
-Ga®e8cÔàÅh/÷æ18PśÄG¡Ø"²/®Y{/¨ºatâѼàðGÃt.A-ó
-æ=)QÍ
-¡4昁QLjU¬n
-sMb:´YÍÜ*ðu(Ò+iD5s£ܧc¿Oi«åº?S³÷Aº"b%A
-Ëå°×J
-ÚÛ*Cì2ª
-Å0K(±=\Ð6ӏ¬«u¯AeúÂ\%È
-0MÈl`êí*ñl?mpM5Q¡Öze¢8 ¢^cnck&ôD±¶MÉ~¥Ô؊¼{Ê
-7
-¢ÙkQ¥xsÄàî6hæ`ÿ
-úώíÈY@
-çW2º
-«í¯UÌC Ⱦ
-=£[éN¥ð=æ:Cç>ñëÌÆùÁÊçpd
-¾å¹L¢Ø/cÄii¶z×uBwE߬'IåBq«XŨPñ©h¶ê g&):apÆŌ.öËÔlçɍ/P&
-Mº
-
-²
-¤-OÝËÌ0¥
-.]ýær«)ʽ1U¢0���:&O²Y â.äê8lKÖ=t[>Ó(´
-=bóís¬Sx¦×èíZ1|s÷Ì
-mûqÿe'tª¬ký
-Û-2n3%ìîT²ì§R¤+2Ömß%ttBÁ_Gs,ÍH+§ì`=ûº«La¾exÙñ+¬Ԯ¡d»Çës(6rXÜbUÅËÄYw1=ÌcZæ4ÅÔÏD^7薽A
-K炣@7PÀZ`+!Û{Ävþ¼2¤,N¡Hz PAM<ÿâßRۆþ
-kê»Nòü§l¹f¢è»*:ò®{y¥jóıºΥØD«ÜB._
-9Ár2;©Ô9
-î-j®àpAgeá~ )ÄÈaĵ4ژÐ
-0Æq5V2
-϶³ z¢àPXálüã»`ʘ
-«+W½ª ª¹¡ׁ4Ë\¡+²·¤3»îj%Ò$&¼é
-8Ôza*7WUò¼^b%ÊVl¼6;ØÕ}âX
-¯ġÑæ T0 Ä!TÆëÁýÂ-@Ö"ü֭V`
-°ò,5mÏT6Þ݅hÿäºøÙbº¹.
-Þ���°õê¬áÏ1>¡
-¦]ïÒUµVP4Zg§Ь®>Èå¤ӯ2ÇÇß7ë8%
-«l¹µV|C·AÍLîø_Ð
-¬30B` R lô'3!XQFȇm¬TCÆ
->Û#ÉÄ,jgOX .¯>±**Û+6®ªRÅì»íÿ«ÄÖ( ¨õÃóSëí±ç.߱YZ,1¿Ļ¿K8y&=³ôý8?ÂR!ÑGï>]Ý'½¾dÜ3jåªÊP\üu»`ÀØb ¾E=¥EÁyBwì7I©«r`DÛ%ò»-Ee6ÜÂÃKm'j>1áüNìÍýÉïú|l±,Âi*þ\&
-XëBy÷¸Yê£>°ú}[º ¹Uìz8®H7P×( âæ ¿H°1íîg<ìsÅÜRd
-µ»(ÔaÜíU9ꭟ°=,©y\<ĶXi!°1wÌjÛ,rö.üG;-ÄV
-þ·àhkH¡Ûÿª՚ðC
-^dµeþRÌñëÔØtûN¥*Ò¿O1Mís©`X(áÝLj5´õæ
-jWPF¨\tÔ45oGoèڃh5±dFíTÛm2°²]¯b@X±ÈÆ(ª¥xØ!qÞÂ1=á-¤Ñ@@{ĈXV²{þ¥q÷
-¥¸¶É`cSzðÈÌǧo͌t_¹Ç×æ»a*à8â
-p]¬RÅU¶ÈA' ìy
-Úa±ËåTÄÁ*¼Ê&ñ«õÌ
-'mF+Úv7@Ðñ̹ f/Ku\GpJS@¯x&#DU+#
-ÀB¸:P[
-x±àëb¦J[Ú
-[K-
-jµ3àõ¹ õ{L¼ðff©Ò'7g~<ܹñ[ÃU(ÉÛÐçí*c´ÜBÛ
-¬¡X´æ3^õ=³(uùý©)ªáÑ@T;`æиPRµrÊËFnâplÊèõ¶ڔ¤,ӅjWh.yh|@øZ%cCwû>&JHêÎ8©¨Ü߈×]UðôÁȀ<Í_3bã*Z¯µ¿lÀ֫
-
-6ÿû=ålTÑyüuð\µæ£7,Q[äc)¸NWbXúÅÖüu{#g·Ð_ÂË
-Àhª¬þ®Â4ß7ÔP}Ñ
-éVÑs·÷0 «Bõâ\ÈÁ:
-V(¹U
-j£f_¹pñ
-KíP<)j^¼va4°0(ÒcÁV.õÑu\&WýD¹Ã
-P[pvâµÅwS]óys¾.:ÛøcX
-UµÜRP@¯
-^ؒ;ª -c×L4PrÛu+bÒӉEÍõ¬æçhüÀ¢
-ãcxë1'©|Cì¸AÁæ#Utl
-ÉÁ&Q9¶ðúBIυK1Q¨iŒ͙ÃdPJK«¯´7zÚõÇ}1x'Ußt»"8õêK¶L^WŎ9Yx6d6
-Ü oo´q·ö°="Q¡©yv+$4§2tªuú4¯ä²í7I4ªÇ3¼
-B-±ÑQáôã
-}f8¶oÄ`½:`í(y4G+ýT¬¹ã
-ÐÍɁÞ%.愫eôw-GÇ?=Aí:} UÞç¢<·Vqòþ"qÍ
-¬ÓƼV9Ï:~ѶàûĢ²»QÛúòύ-BФÙL¥Ý\Ԉ¢;E2ÊHOàæ7ÑdVØZ±âT@5 ç(L¡ä7¢$«Y¨{®ê-µÐ9rx'¥æ
-.U4*¼L(ÄÍQĠ®¥¿Jµ:G£Y»±.K¥&ҝu3Èûr~Ҏʞåáql]X4l×æ+/kµÅk±Ks%½¿ÛèߒÂ3©J¹G{e±oûKû²ãp'j ªY 爞Q캔5, hÝBÅø t&Tï¸&a¡iøÿÚ|D~KâúOÄþC?7Û? ûOó
-
-ÿeÿ,ҸBCá"Ï [É
-'V«WS](¸S6`#ä>"¦MϢAL0ô
-ThLTº]ß1îuhín/HÁÕ°D¨-1ü±t÷sª
-àpêúêEÃg¨|Þê¶Ø@Íõ<0^õ,¥ùzB8jå_»X§ÔÚ`0ÍMÝîý>íù,Ի$¨s%æZ(Ä0Ï£¸±FòÛ¢¡>åYðóàD*¡^¤È'ÁÍyÃmJȍº̰BÄÜÕkÒ;ò=úq/é?úb
-Ë? ûAãJÀòìôaã=1É{!WædhÓÖùbp=Ê
-a«w÷ŦߛÖW
-b%d+
-6o6w73Áoü@
-EGî@¯穥çÄùøòFüNÃ'âj[çԬ÷뀶
-ɟ´¥òìü@ª^>õøÿðÿâ|?ø'þ'Áÿãý£ðÚ|ø|Ĥ¥ùõ.õþ¹uotôg������Häð8~|ÿÆÏé " <Á »Í#¸ iÿÅóíÉêÞ`[©BJæ/ðâ`pßcPzöRàÖýxöòÛ,.cÕjÀVæ
-`ǣS!
-\x|ÇPڱíú´·ÃáúF´P`²ÅsDðãS_é4û10`î@º+ÊÀüæ ø
-ơK¤or¡·A8o0ý®ÁáÿÕWÊÏÕü³Ée%¥¼ІÎ =rÆpe«Bùە É\LYS`ZØöJd^^eAP¡]G^¦Xê* |-xiMBÔ)6êhßÅJLí꼨°²{
-PnÓZÆ"¡sqr³4~K WVçî
-¿áõò¤$Y¯3®ª"àZZ·lxȋw~<<_+ñ©I¤Ë×_x¸þcL½âäU¼e÷¢2¯
-/jËÖ ExOvF
-ðµU0ãp¬4á|R+#ÐêøÇ2¾`w8yÊۦðØAsWµ<£/0컆TÅTM®F(7%3òé.3IJºD¬bÍÄM]#¨ ºVÚʄ`¸RYkKÌÃ@-klº_.
-Ó) U ºÁoLzC"
-ñ|×ÚS<ý°"خwxTX®>¨%9%s=£kM*°ßJ¡ì
-j2êWÈ
-:z°¿x¿µVRºJÓæ¬~«fҝjv»#¥ß]J¶iq)G®;¨ZZñ
-s`´Î:+Gýd|
-Ì4ÃÊy!å4ç³3%kY}ek*ªî,ytJcEú1Ahąl1ïà º\ûÊHè=ê65-cÄ(´¥³*¤XJòø,w´ôäâ¥ìëî㡨Ͱm7ȂRÿqxW-Cӂøy
-Ù͜9XD
-6ÙfxÁXWÅò¹¿ú0òú µ¸£327OHQ«ªÏtwD-UÜӗN³ëUX<íyyCÊ,±ù£%9XÔ0ªt×4³ӁþK¿V^å£Ì
-@MJD*_`̢à¼z£Jx¸2]¬Ãf̱ݾ F«7n·ì-¢¿ù_儒RUó6~QîK«þes &ecE4â)gm°Ê`IR¯Z;X4Æcmt§,Á""²³Ç
-Þßp5¥y|DLj@tÚ9
-ñ¾ِ2ý"J¶$¯Ö¾*Ë
-ՏVßxlX¸!kãJÔbÈ۪ËéE§uõPP4
-[võ?ÇæñWº®
-Aq¢P耀 ®9#®`ۖþ¨ÊXáeõ>ò¼rPYส`٬[ûÄ:EJ5
-a½bÆÃÄÓó#C¼K²ÍKõb
-Î|CÅJU±h¿#^øY
-ӐD½s*qD£[è/rÄèÐ
-ha٥Nqo!ªÒÏcÑL Ҝ£¸z%Vհ««eiñÝÐRÑm\êT
-»»êÄFRY yþ7²@µzþò|êe¨;g±Á
-cËÙëԭܾã`0BµÜJ¢햕 40£ÌÅk8·Äehlµê7\AÊy¦BÞ! ¥µÞ'pÀxê:
-¢(Í]Lª:DãÖUìù?¨¶o S¦;
-
-f
-pü³«óQ0ù½qøÓ%fîcà¥{o3¨ó_W½ͨq7suþ¢3a¶.}ÿQÕ*ů*ü¹Øaã uáW-ë$h- *üÃåU&×SrËg
-B×Ú)§ÛUC´
-!BDK
-ÒWô%Pç2öpY³q+Ò8¯kpZJ²U 7ìK,E¸â¦(´¨A¯.ñ:=På"Ã2_Uk;±騆-ém¶@½.¨h¹LC½%Æ&
-¡{_iE³ÛT;_î=Áça¥`¦`A5°)åQ³ú3C%Öüiÿ%,).ܡ´7Í0ið»3¸´.klðÆ X
-\MÁÂCt_,ð
-&E@ؗäÕÓäܧ7Pìû65qÎÑ%ÝõHЃ;®»
-ö^̋¬¦÷ïH-i àS3Æ5aOy¨1$ÇË
-1G>!3¹R°ÀBÁÐFáÂqõ9²ëÚU»·EâW0±s®ñÿ«a
-û¾`
-Y躲{°8Zrû7
-`9å`bUÎ {ed1í9i ¥±êqøéôî!¹L8S¬wK
-´¾(º³*£d8q¬ãCC3mÎO$m/·;)*½
-ܾâi´^ØS`æê&T QJ Â
-¢Û@q
-
-ÃÌâcĺ@Ú9¯©¢햦eÙPÞ
-±ýd
-¥¥ºÆuÔâ®ëïÿj¸_!ûÒ
-àõ*»+âÓk®¦\dAÔt(lþ¥Pv*¦àÂn¼Òéhüïõ¸æzvÞT¼pÂvQ.î#Ë]©4ÃKi
-)Ànó3
-ÎÀPùøجBʋ`1êSÀ82g[WöÛ|-Âz(¢Þí¤(¨sw®5ɻ06±iö²
-Âm§7ÔjÿGäæ¹&©!v¿£pÆ*Rނåsú8££GW«RçÌÞõaհ6֎f/þ®ºS7ë5]y_$nÜY y.¡=AûGlIî'?xªCUßÀþÈô]ݖyVú/QòÄ8ëëq»y¯悔Õ6j8v±ULR5²òFíÉ۴
-Ð~ÁfÙäxlç7ÞèÍñ]˩µU8e`0h ñ7_¤¼/Ú*¥B¤bèXrØAÌM
-I¥ݬâǫ³~©S¨ -ê |?Ì3ߕ¯*v
-I¦&K&RmúBÞº!¤0ÜAõ8AJ×nþÕÐ)PDg1]®E£Å6"Yâ^¶à1 :eô*îè&Ö[¾֣ØÍLIÏr
- Ä}ÈBÁZqÌ)*CTS/¯V)¤ÌJVTfß}íÀõƚqz»ú
-k
- ¹ò±Ca¢^æ3İ{¿hÓÞ?
-0:LŒµ'©xÅ\WoE,¿\hE*cDxçÁÓÔ3LÓ%u1,zR»õ¤ñ/ê¤i
-
-h/6Èiut%Pð@¥ý¦sªZÚûµèF]wYjëó(óKnwþ$²K+Zäʵîöê9¥CهÐ\,Õà1Dá j={ý¸
-¬WÞ9=WgZ@ÖÁ̿
-â¡㼜Eë(îoÔhêWÒ ¾%ñ
-Ðåêªq+£Ux>ñèÖ
-8/YîVÊ
-üfU0¯
-ÍÇÜкꊖÚV='tJáµé÷MO¹çø8ÿå9ôÿ¡v숀"XɁ@PP%Eo۟®MÇMw֗×üõ0REɸ¥«_¿ݑöaD[` ¸ 5ú²©E«=ãoAGÍ̈<ÕjÒᠯnJ)YHN^
-O¡0Ø+ÓÇPJn
-a0ÛLÃáHñ:usP ÐûdFÖ4¢ù§~BxýMº-üLSÃS³?E¢ØïR%ÓNüúïíXìE§î¡öc
-ð@M¯ÍûÐÿ¹ûÿ¹ûÿ¹ûôïXeü9 :þü?íÀ{àkpÿ¹ûÿ¹úÐÿ¹ûÿ¹úÿ±
-;»"6ò¢åz·³Ë?_¨B7Zå9 ÌÆ
-ÒT«kw逜Ð\3àã\æìÍÁ4_§ívá®Qà
-YE¯ÚctªÃêAû¤A#ÅμGd?à¶/±h4'f»¿Hı²'لÁxb;õÜ;¡²ÿÈQiþ åâo*åòÄÖ
-ļ÷VÈÆ&'º§ï>Qüϔ3åÌùGó>üÊ+_ôAMóþȸ8Ý}íƙ¬¤9ÜIQ°óC~PS.àë¹-_¼ùGó,ù?ûϔ3åÌùGó>QüÆÀZ1nàjbÊ_AÑ.NW~óÌÚ{-¯V= väõ5þ
-âáûÃ&6GcH{°Úm~S£#qhsÄ
-2pzܼöÛ8.µì1½ÛQ·HO<
-ÖÁiån%BنYÈÿ®
-
-A§,1F
-U¦{¸tKRJ
-ÄnwÙ
-ñG®ñ`Zºҗh¦4·ýB@ûäÎiE ÕBaµê U̮ìu)·ô´ï0Äk¯.=æ9·±PÕcÓxé¸"l¬3 Á(jlӒfüaÄn²À;'ún«/¦X½64ү`¸õiciH5ddõt]eÂW6·SÀ>õþ
-y>ªÝÓÚû¿ûi
-yð±c.ÑÄ'¶?9h~Oõ+åÂv®]ZÍi·¡Ð[Py¹ïêZaj[Ë ªNL_ÇæÉ{¢e5êÉ{$qx|ƽVÂuY¦Ï0pS\lu2êù4*èýæhvu\P¥fñ¾!³P× ¹LÑ÷0M|áÍ=$¢ ºy&
-qD"ΓàägåU3ÐØJnªØbñ¨-$²ö}® ¡ñ
-~ÑrÝTVÏÀþ¼tX[¼iG Ù(Æ6
-ϫhg¡4ÛèÕdÖw(
-耵#XioÖùRÝÀZ¬4ÌìmýËúÅÊuíwfÇáÍÍN!åxsIDÒ61ÍiûN\;j%JzH'
-´'c5V������Êí8ƃl½
-â`Üj\ˬõúÍü±P¸¯µV¬Ê^Eä¨÷ܤýï¼dâÕ]UÂn
-§ E̺èBÄÅù3̦âBíhÙcÈQë1MèÔM¼Á^ÅÊ(õÖtË!WK%BºôxNػi°a.
-i;qÈF)2ÚW@
-GF¢¡TîÈ]:ÕPxÕæaá½ß÷.®üOëúÿ3û}¯ºD1¡J²®³÷Ké±Ô,·
-dís5ׂh_Õhó8AVk·ÄÄ;7yªüÃ9 >ïxVՔ¿.
-LXb©P±]{¿Q>;´mÄàöyâmkPÝkíðî¦]Ùa9iº)Æz0X
-+LØ1¥,Z^W!"ļÌ$#Ä*L#[Áæ,
-XU®*
-CÈf#¨ö©iˡ-øË:Ê3äÁ"Ó"¥$)Ö[5NHª4«&Û(lȟ[§ó½乲ÛÖgÊõý`ëILÇ@ϫ.úõ
-S]5¥éXÒq-)³N5Blæhi®¬æ^Únۮ³)mdYo´Â8i³¶ggºè{à *kM1w°qs'¹^ënkR¢µ=ß/Ô撘ÈVݑöôSh·/>L±Ëz¯áï+lSQ`'¤MÃÅ ÕqÞw2UWÜås6Ìn
-·ÓÇPøÍ'.uñ,«
-ëædTfQÆង0¥c9ýÛ?ø}T
-ʽ[èM©Äàq
-¢¹
-¤B¡F½ßs¹W
-%Øã©cY]P½ܳ¦ç£P������â4ðn»"Sªéà`kÞffý ²5
-£"»¨)ɟÑñ=ϕëÿÉø®ÙøÃëu,³M×0AY2»bC¾Xh' =2í×2±嫨çÏ2ÏïcÀx¸`Þã3
-cÜh$iE'00¸n¥²0Ò:é ÁÔT¹z¹Ñ[Ü6=Ë
-a²?Dj¥áy¢®úüOsåzþ´ i}£÷)XoÚ5ºYt£·ó`¹ße,,ի½n_8)ø^Èkà.R
-¤_Yc¸t(
-¿Ì![)TnÉk5,)¤½:ÿÑñ]³ñ?ÕÎËÍubêíO1AÆðzõ;c¥Ù+Òd¼s,
-©]̂ÁÔ&Ò[m}#$¢hìÜ(a挥y @Tdî%*:
-@¯Fg ?tÑ
-â KÅJu!YkÄ
-½8ÝÌîXî=>§Ä÷>W¯ëüÏíÑ!È*tã[òé0sEc~/WFæHÒm÷nþò¥·u
-cÁ}ºñpC-7ljaË9'Þ9ó
-+UîþοôüWlù@²¨òË¥´àJ㉖ñ¥?¼ªln)ÑÊóÌj7ܯuåæo`â¶Ê&°Úf¬.Zè¥Óó-ÛsS¦"Odnq)Vf!csÔ!ÉùËWõÓ5ÌcÖçÚcÚ±轾§Ä÷>W¯ëK)Ô&Eö"È|b·´¬±
-j Th£
-¯u`¿ä÷ëä=~`Gõ'¥0=l=ý?Û?ø}""»Ùf¾ñÖ
-qSZBパ®%V{¹kqå0GÁ¦Âi®KS#uíÜÒté°Öü1
-ÅZUʶóÔDåuw#ٳaíb¡ªáK½±Ì@/BqÉÔ
-¢-#VàF#(
-A
-ÄGHú|osåzÿò~+¶~'ðúRKMÛËÐ9¤Ë2v/gÂ#*ն
-§wT@ñËÔ8YBǬîTMEP
-cÅòbªdpÞG¨(7))Þqæ
-ì9àE@ra³3R½Ãx§Rì-qpF/~²åjKpÆØÍß1F¢à!3ù9×ÿñ]³ñ?j42Â
-×ö
-Poø棐_ÈâÌ@ Fä`2
-P`¡n£¥Rð>Ñûj[³¾¹VãL9Ýé,âå¿3WZG$'¥6;C« Æe«P[JF·.¶Ä4)¤h0b¼2éf?'3äzÿò~+¶|§,®]8¼2¨ì/V`)X˜Ehêrܩ¹uQ&S±ÁË
-l£ðöpE;ǐôcõ0§ӆ
-n_y+Yï±ïcí
-¨·Bûqà³Õu/§y&O¦狗R
-pÀxõlÁFYµæ#¢ëF^¤XXÖ&̓"Üh(ÌøçÊõÿ(Þ5 ¹bܽÿUUUUUUUUUUUUQ5<Óܴþü"ô=»
-Á«xÔdԢQP?£©W,ûNCËí-Xòd_ò+ωdªnàÔ42Jìì
-ÎÎ K6§)ӴÌ=AÏl£æmTõ¡8º¨ñ
-
-[è ºÝÊݪnÕû@3âæ퓭øä_Oõ>
-¹ð=[?hí^×ñÿ¹lOSû}~+¶~'ðсÇ-þjW¨ mÊ9ÓI������°¦×»¥h¨&Ñ.
-ÃýÇ"u
-XàÑÔzºڹ`£
->çÏQÓ´ùîe4,a×Ä~
-Ó3 f´O¡ÆoÄsK°
-EÊÝÿLñاSÁöOÛ<_dó&øă<¿zOÙ|õ>Ñò ÜÓdítϯÓæ»f
-ÚÚCp^ÃÅÂWTTÎ+êÞÃ
-!ª
-þð_
-Ü|³%öAVBl§PN7@´¡,à
-À#\µ§HÎ@7ä7,¹¶=v31
-Áö~JUÊDÜT´8xËCy¸¥/µ~µÛ/zã8wi
-úÄé@ø³åzþµaʿ@¥oQ揨Ä0£É¤ÐH#I°\pq7¨}Dz9Ë>t¾íyÔÿhj©+ùÓ<
-ß
-w¼Gê>)®"faD¤¢:ºҩ]A,+¥L{jØ-.½¥ѫEÉX¹ªӛRt¬fgc^"Yz
-ÒeåmCE³-ù3÷£½¥RsÄé*UN%®z¸/ÐâYÅ(îíw/îϕëúözý2áôSe±4³[8.=¹ÜL²UÆׁ¸2PªíÊú¨Û
-õõwûʋ¦ý¡Ac3ú·ÄÑ<³Õ¥FºÝD-J
-×rè
-¶ú=q²
-GÅàa¥¾!l*^^OWò§ÈvÇX3Ʀ¶
-òÆðÁ|¸Iqi5GÜ,f^!
-¸ô ÅëÖ
-¥'҃¶Ìʯú¦2°ÊçÚ|3¶|¯_׳×貏Aôá{TʩrQÄd µ/§ gháÙ^¦Ym:SCî¯B
-·¬7Mº»
-õ"
-¼AnàèBYbÓèA5¨gCØ_
-åjaç+§÷}âSáìƺߺHyKw?Wò§ÍvÍB߈©Ax:³©-V<÷-+k'ÏÓ*T
-¿ÌÉÉ÷¶o.¡ÁÑlJ«×1bºôî
-4
-vǮ j5 }Äi!Lãl·
-*§ÚۈèX_aûÜ!ٝMF$· Jg2 Àñ{ðyIÏÕxwêu}°Øx<.Ã÷gÊõý{=~µzHeüKRDUnd¤ÈÍUJ÷ޔ8bñP¸àÂ"*+µ4@
-·BÕQÔ`¤µéúÌýË]¨Ôt:Èù±Wë3¼¶÷ÔVW Îô¨»kÒ^úÃe]ê7w
-Ò'
-¶Ùø@
-Ө qd0>õ_È>˿ÞaÓÈ}^!áD·Ùwæ¯ê«Û)ÜV¨H8 ÑõeùoQ)S@%Ô0í
-ӻôL¸oÉ4õÝÃûð¡
-B²¥½Øó6:ZV`õ4Ê>¥b8KYxó¾x0+·üϕëúözýh
-Å#¥ª;CbK½¦E5ö
-ü£.ãÒñ;C°ôâ J.©;ù3èxqêڿá/֨Ú
-÷@
-ëé7ݰùᯇV±
-QZ®"jRÙçÊT;MÁ¸#@Q×Z¶÷
-¥"Ñõ ú|×lƞOᤪò
-åd\^6u P90áõõQ̣©éC&]½ ]ºÃ¢GVÑM"ۨNk*WAÄ
--D¿öFødê½¢ü"
-m8ev°ܡ0Σ©_MÆôÁüÂ
-ÐFi)Êïߙ`)ÆÙò½^Ï_§~ ðF#ÜAAë2y¼ªÆCR®7!eþSÌ(
-D-¶Gɸ"iÕÝ?+bý#ÙRүÙ,öNwq¸ya隌mm§$
-i3î°
-)Ìy^ʙ æL/lªâá
-¦n"ÏTwx}_È>5Û!܉A,¡͠¶w
-\þé~abJ>ZջôȦ¸¹^iG¸ÚÐm®'Q÷Gp{ûÐF$¾ÝMB¹ð;`V ,°xº£SßvQë##[÷'9~óåzþ½¿Csíj8yXAÔƤ¥TG¥mèY'®3nvÁPà
-+keÏԂÜ]ê¿xã¸xjª6GKU#
-ÍÀZ:¢#;4·ÔUÀ%¶¡YAÚÄּ÷à|àÛ?øDÐk[a\4Rì֪zïþ§Ñýј« apN abI
-{½ãuÈÛÖ&ØõöÄ1^³0«ϼPâft±ë+ÏC*ʵ¥ÍâQæ\¯
-{'`V%)IÓÁæ|O_׳×èarñ6HÁÅ'¸µ]§sMû5ÑOtט·MæVj¥!,ÃÕ4~sl}åûà t Ϳ$͵!yzâ|ÔgGÇi µ˰OéÀ>¡»÷Õ=G#º×ÇGà
-ëÃõ?}>+¶|§¡mx3?f]?å1ÕÚàÇ£æ'Ô¬¹*!Ò
-vgQ¦ÏVú¸cMë*Ű3lp9ÎêVӛí5;|XúÔÔÞ
-ĸlz
-aڳg÷Üù^¿¯g¯әHü33&ÈģGÞr¡ڕ
-zí3^k2ÿy(÷0Z¦\¯TÌ°íÎxMñ÷wïô*ã ~%뫼úDUUTå]á¬o¶òKϲÑfiCگHÚPõÕÝõ?}>+¶|§V ÛGïdÀUß$ñ%ÎÍ*¥þm*Øu}L(Î
-òFÒëD¨JPxÇB±þééÑ?ªW¶ëTb0͂KîP#Cd,ÊM
-?G0³Vð*â¾ÓF8ÙF¸9läֆgòs>W¯ëÙëôæSàÏTh_9¥Ñ3B°õfW( ¼֣¯/k
-8)Ró!
-¿lyµYèÉï8#pl©ªc#çscfåûÃÊU}àn
-U¨-#D`!@½QUa¬¥?BN{pñ¶æ
-1ÐýOäOü!Æ.©'LԶ6æÿhi
-æ¬#³g麇¢¼C»j¶µͽ!r³àe(~÷
-1yGü ( Ø_ê¸üñAǎOG
-õÀD¬îÃÓô%êP£o̻
-ºÍxªÛ×í,ªÌ~NgÊõý{=~²¸gBÙGHΎtf½?£գÌíp>ª/ØW{kï¨ÛÌù\úBKäx®¡ϸöUB ¾uÃôü˘©E?+?Aóh
-7
-¥8ìحïÀöAÕó܃§4Ä
-þÙñænGJÈ9¸6ú½Ð_hãWÕüéñ]³ñ?ª&ß½¶ãÖ*,n[ÅÅE+ëu
-¡RníßT9`¨àñâ[u¯%î_¤äm=¢õicÌ
-k7ŤP`²"øêS¥j^ѕQ\Û.<6â`L¥Î
-#u>'¹ò½^Ï_£¨Ævه3$!éX«wW¢0V8ÃÅÀKh
-yèpçé´)ÍÃI¢9){x!Ïb6µÏÓT Ð0à Âc«Ü!¥7¹ò½^Ï_'É/â"å?hëñþtÿOø¯úÿ?æ%ÐOøЂ
-¡5
-Á:4K<Þ´OùDÿOøDÿOùÄÿJ¿¨óÝö¡é
-`ú~Aôø®ÙøÃéàB#ÒS.gz©L^§d¡Xc
-ÆV£2¹ú¬ù$&æd ֽ`çf¡üG
-YVÝ"%JaêÉlÐSöÞLK°eþfiwh"
-qE}¾òÔohŌ
-|=J#̙ð î=ÔôÂÕlu>'¹ò½^Ï_§1:¦9ޟ÷§÷oÞv0õuwr«uúÂ淦Û?ø}#WÃç'0Ë8¸k³,êË.¦OÖßgԶ
-9L©YRøǬ·»iÞVåϟd^ÿԱ,DìÖ#↩ڗD0ÕÇDD¡28ó
-þï§Òûòb0Ô<@¥´©¦ë©]
-U»lۛ§Ä÷>W¯ëÙëôIzxO\.K,+j\÷Äh3À,´2T
-(d_ô{,X|Bô,*.Z¶%°Û?ø}
-çPr˒Zº»âçKÄb¤
-»ÍMÍÔè&ú0¹BS(av,
-¨¤AWPóß&o8v1} wRfÛ
-}º"¥´ܩÖàyKÛELä
-EF¥Ïrkqʥ)ù»7í
-î!XÝïèê|Osåzþ½¿ZQá+PPudY¿ÆæCE¶g
->ð«@<-aáÓ-¡é$¿ápa(°ä¡ÌY¢}������ӈü¨4L}w¿ʌm¥iUXMKrk+,U¯Ñ1³úURæú
-sò§ÅvÏÄþ^HK´}%@$«1¡n¼Åxkf¨:Zn7@¹ÊJ?¨¼cË@^GÖT¥ÆYþbÛsÌRT±²3²¬hEdLâPãNOsۗ¢V&
-ºÌ+mw
-LºG0HºM¦,²±nùýÜù^¿¯g¯֔sIäÊÅR õm¤h¹l5ðHRYdÊnâ
-îñ\ËіޏpòS«ùJ¬?o¢û;ë©Eo(CفyYD¦9æKàf"¥Ðìj
-P¡¢¢odvA6_^ ñôº_ w? ú|WlüOáúsýDX÷`°òiú.|Ý×qx¨Aؗ·fë
-Å+¶2*Ö_
--á;f!þTB.¼Va.c¥+æ/E¤DSë׬¬ ·U[b}Ühs½0.xo
-D²¥\"~Òò4J/+þÒþջP.
-+oÅ'D¥JÈ[ãAýâ»WÃÌ1¯¢½ܺT
-èíñÀ&y+×õìõú^eÏÃq¡%ÛÜP«DsÍòñùD
-Y[ÖÉùEÀÒȋ§ÖpÆ
-@n7utÄbdagÄ7õ©K1>Q B
- ì@>ê9Ɗ>e ʀNÊÛW]¬:!pxú
-sò¢J>K0YU¥Ø=Bë-§
-5µ^VËCÙõiÎtÇG@»̦ØpÓrèеÀãiÏÒàÛaÄ
-±;á/
-*m*ê%ڀwo÷Cl X¸¥T]öåHÏÈ>¡]h'L
-u~¼?yù'£Gï7Gc2ѩ²oK×ÈdlÅ
-gÀ?ðæ|ùþcF/øs1ü¼.¶ïÙðkþ;[n}î²Éù¹ðìꞿÙ
-Ocû#Ãð<π3àÌø
-ó>üÌïo'ôæ/¬}ÃàägGÚãÔxÂò©ÛËöÿ
-Ï_§2µÏöÂHÄ:/C;¶½¥¥
-]á5à&YW±`-¸À
-ÛzøKDÐ)v'5Ü/έ#è¸Ú~Ì7ý ¨+Lys5«ÒF¬ÕýÒ8W)üäF"iu¶n#¥·ÚR)u,P²]Í}3¹ù
-é@R Ó´;[ø6þ'Ϳóoâ|Ûø6þ'Ϳóoâ|Ûø6þ'Ϳóoâ|Ûø6þ'Ϳóoâ|Ûø6þ'Ϳóoâ|Ûø6þ'Ϳóoâ|ÛøÚ=þ=¿U_s
-"WëПªçývÔ)voEÕòN
-¬Kþ Nºô<{K¾óR7OZÁ|KÄúwö±Eãù!87V êYÖ)O>³oQ\¶%VÿÙMTß&..MÕ
-?ê6FòXÙj³w&;·Ö~AÿÊÙëô?bjÄUWÒ+)
-±âi=%¿ЖÿZ|þ¿ú|ý¨(VG4KD `i#q¶Ìh«}Òպ¨¡h= Q½ ÿ ð]ÅÎì·ԍÀ\bZÞ/Bÿ¢Ë`ú úÜf¸!
-\*¹ñâiºÃ\Ë?
- 7cuá3
-¾·Iq@P( ¶ô[)趭«¢¢ÔÛ1
-Ùq"<ñâó
-ɅuÅTCu¸-ÖZ0g¯ߩ¨-ìniã3x^~Vî(@9`,y&S½PØ9?ðx)}ChÙiúÒI.ÏÐÛDdǐ¿ñ¤I$}j> ¨Xá9Ô7
-QWQÆ}>+¤U\cÜAw¼fÜöǤ
-ykHE§4'(¯ ÛCWQëÊU[ªqéZa¶Z\ñLuTë˵öóØq¡°§c̽ì«b}óqŜ;kÓYî¡#+v Ü 6Dô7 Ï!q^`mE](,#ٶçÐvsdSV¸pB hg£ªà
-"zñùl¿ü
-ƯøÝ@UYhÁoRàȽF´C zköó.r¡£UÜ1海ș
-¯Btº}T`t}Á%i
-«ÍëĽ³W¤¡t¼½CüHajÑÞ«ìZ¨äll±õezÇ@«͡uL®AlºµįaÀ`Ibºl
-䃦L
-#æ9+¶¥y£5t;Rz|Fj¥ëijïH6VÂÔ.Ýè4Q^aÔՄ¯¡2
-]E;.ÇE"^»m¶±¨«²Ô
-sAë49D+µnè±MµnV3MU§¸Àô+É\G¤óSUiM5ÉP*6ªӲùosÒMC ð\BMî»ñ¨ZLöSÿÉX̰¾?â 'LÇT¯IJ[#U¨¡K
-£µæl¶s
-`ê£èªYÍUþ`=×w_â*ðJ\ïÇÿ1#ö¾¶Gܟ=========================ø¼>nÝÿøúÒÇÃ2>J¤ùÿwýXÁ7)¦¢q؛,)Úí1UCSéÇøʭ"ë
-KH°u·T%¾ÐYe@gþ0òú~fAõuüñZñìʴBÍÎö«`¶!|Óq}ÍËíL_mýPãHxæN!çpìðÝĨ¶Çqmńɦë?¹ý°Yí@Ð(½
-AJ|K°"¶¥ö
-!jµ8åzÿ=w#AúÙuyÿÜtمìV\õUM¿Oo÷
-ÀÒh<úÄuBו<ÏD1X§7̲c²ðó)H¡7Ý_Ì5°\¢h)ø@gxE§´Xpb@¤ÑÍÁ$$,TáÚMedÎiûÅÃå9Xà
-ß6á*J)V÷|Ìc÷1ß'Öl{3àJ°-k^ï¢l=Á
-pj©©^]EÏÇìN³-;:EÃXñ
-7" ñ¼Ükæ®f³ڦaa¨y«ð¯âVÃK®?ÆÊ sâ8)گ°敏)MYS4Íj8®Ï2p¥ªÝ\lÇDµ(©uÝTä
-`º î-Ñ4Ð]DSAt^
-#8tÈìxhEààâ£GeÑ\JÐú¢Êêő 6ĸ7hJ
-V¹zp@7ªxcn:Heñś¾Gü%`/3ÁÊFFm7ÙÔWըÛEñI¨6¢·¸õ£`²!"DÂJڀ ñ̹»â.Wô
-ù
-h«Òx¨Ȝ(} ÆiJVT¬3]\ñbp Ò2e=f°`{î\îí±AXýæøK£gÍEUl
-AtÕ
-EH¼(}§#Ë������H'ÛüÈ"9=p'P63ۢb
-uGFd¡nËT0ͤЊEÅzFÇD´¦/
-'y¨¨q¬Ë
-JtÐĤBò2
-DwùÏhV3ÕïÍhôÃPøÍ]̬!4±B«Xw➖ð3 B¶Åõ
-*¢$ÊQuWßÿe҆í&Æ?ÖHB*Ù_úå6é_<3%xjúõëÅkõeUæ<Ø@
-·K!¬%åV°Īº óNòñé+²
-¬KªSoBª¼ê#jÜ@l:SøJy«PX¦F¿K4·£3>KÜðº¾Uè?ùÒþsuпCý@ªÒÑⱦ%Ú*Âð;ô¸Ëô½ÀPP"ü¬è:anӊú>î+
-È^ñq)[iå»ã¨>ù@~´g¸¥e¯þûÿT1VpÔ[Í%-À|¹e
-Eh¹v!\
-y¾ Dict:
- """Extract elements necessary to represent a prediction into a
- dictionary. It's better to contain only basic data elements such as
- strings and numbers in order to guarantee it's json-serializable.
-
- Args:
- data_sample (TextDetDataSample): The data sample to be converted.
-
- Returns:
- dict: The output dictionary.
- """
- result = {}
- pred_instances = data_sample.pred_instances
- if 'polygons' in pred_instances:
- result['polygons'] = self._array2list(pred_instances.polygons)
- if 'bboxes' in pred_instances:
- result['bboxes'] = self._array2list(pred_instances.bboxes)
- result['scores'] = self._array2list(pred_instances.scores)
- return result
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/engine/hooks/visualization_hook.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/engine/hooks/visualization_hook.py
deleted file mode 100644
index 2bbc6aaf490b1a1804afe54dd078a1f63224d391..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/engine/hooks/visualization_hook.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-from typing import Optional, Sequence, Union
-
-import mmcv
-import mmengine.fileio as fileio
-from mmengine.hooks import Hook
-from mmengine.runner import Runner
-from mmengine.visualization import Visualizer
-
-from mmocr.registry import HOOKS
-from mmocr.structures import TextDetDataSample, TextRecogDataSample
-
-
-# TODO Files with the same name will be overwritten for multi datasets
-@HOOKS.register_module()
-class VisualizationHook(Hook):
- """Detection Visualization Hook. Used to visualize validation and testing
- process prediction results.
-
- Args:
- enable (bool): Whether to enable this hook. Defaults to False.
- interval (int): The interval of visualization. Defaults to 50.
- score_thr (float): The threshold to visualize the bboxes
- and masks. It's only useful for text detection. Defaults to 0.3.
- show (bool): Whether to display the drawn image. Defaults to False.
- wait_time (float): The interval of show in seconds. Defaults
- to 0.
- backend_args (dict, optional): Instantiates the corresponding file
- backend. It may contain `backend` key to specify the file
- backend. If it contains, the file backend corresponding to this
- value will be used and initialized with the remaining values,
- otherwise the corresponding file backend will be selected
- based on the prefix of the file path. Defaults to None.
- """
-
- def __init__(
- self,
- enable: bool = False,
- interval: int = 50,
- score_thr: float = 0.3,
- show: bool = False,
- draw_pred: bool = False,
- draw_gt: bool = False,
- wait_time: float = 0.,
- backend_args: Optional[dict] = None,
- ) -> None:
- self._visualizer: Visualizer = Visualizer.get_current_instance()
- self.interval = interval
- self.score_thr = score_thr
- self.show = show
- self.draw_pred = draw_pred
- self.draw_gt = draw_gt
- self.wait_time = wait_time
- self.backend_args = backend_args
- self.enable = enable
-
- # TODO after MultiDatasetWrapper, rewrites this function and try to merge
- # with after_val_iter and after_test_iter
- def after_val_iter(self, runner: Runner, batch_idx: int,
- data_batch: Sequence[dict],
- outputs: Sequence[Union[TextDetDataSample,
- TextRecogDataSample]]) -> None:
- """Run after every ``self.interval`` validation iterations.
-
- Args:
- runner (:obj:`Runner`): The runner of the validation process.
- batch_idx (int): The index of the current batch in the val loop.
- data_batch (Sequence[dict]): Data from dataloader.
- outputs (Sequence[:obj:`TextDetDataSample` or
- :obj:`TextRecogDataSample`]): Outputs from model.
- """
- # TODO: data_batch does not include annotation information
- if self.enable is False:
- return
-
- # There is no guarantee that the same batch of images
- # is visualized for each evaluation.
- total_curr_iter = runner.iter + batch_idx
-
- # Visualize only the first data
- if total_curr_iter % self.interval == 0:
- for output in outputs:
- img_path = output.img_path
- img_bytes = fileio.get(
- img_path, backend_args=self.backend_args)
- img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
- self._visualizer.add_datasample(
- osp.splitext(osp.basename(img_path))[0],
- img,
- data_sample=output,
- draw_gt=self.draw_gt,
- draw_pred=self.draw_pred,
- show=self.show,
- wait_time=self.wait_time,
- pred_score_thr=self.score_thr,
- step=total_curr_iter)
-
- def after_test_iter(self, runner: Runner, batch_idx: int,
- data_batch: Sequence[dict],
- outputs: Sequence[Union[TextDetDataSample,
- TextRecogDataSample]]) -> None:
- """Run after every testing iterations.
-
- Args:
- runner (:obj:`Runner`): The runner of the testing process.
- batch_idx (int): The index of the current batch in the val loop.
- data_batch (Sequence[dict]): Data from dataloader.
- outputs (Sequence[:obj:`TextDetDataSample` or
- :obj:`TextRecogDataSample`]): Outputs from model.
- """
-
- if self.enable is False:
- return
-
- for output in outputs:
- img_path = output.img_path
- img_bytes = fileio.get(img_path, backend_args=self.backend_args)
- img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
-
- self._visualizer.add_datasample(
- osp.splitext(osp.basename(img_path))[0],
- img,
- data_sample=output,
- show=self.show,
- draw_gt=self.draw_gt,
- draw_pred=self.draw_pred,
- wait_time=self.wait_time,
- pred_score_thr=self.score_thr,
- step=batch_idx)
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/networks/README.md b/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/networks/README.md
deleted file mode 100644
index 42347373edc1e2999019c7259dda78bc58138ef2..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/modeling/networks/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Networks
-
-Networks are combinations of layers (and possibly other networks). They are sub-units of models that would not be trained alone. It
-encapsulates common network structures like a classification head
-or a transformer encoder into an easily handled object with a
-standardized configuration.
-
-* [`TransformerEncoder`](transformer_encoder.py) implements a bi-directional
-Transformer-based encoder as described in ["BERT: Pre-training of Deep
-Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805). It includes the embedding lookups,
-transformer layers and pooling layer.
-
-* [`AlbertTransformerEncoder`](albert_transformer_encoder.py) implements a
-Transformer-encoder described in the paper ["ALBERT: A Lite BERT for
-Self-supervised Learning of Language Representations]
-(https://arxiv.org/abs/1909.11942). Compared with [BERT](https://arxiv.org/abs/1810.04805), ALBERT refactorizes embedding parameters
-into two smaller matrices and shares parameters across layers.
-
-* [`Classification`](classification.py) contains a single hidden layer, and is
-intended for use as a classification or regression (if number of classes is set
-to 1) head.
-
-* [`TokenClassification`](token_classification.py) contains a single hidden
-layer, and is intended for use as a token classification head.
-
-* [`SpanLabeling`](span_labeling.py) implements a single-span labeler (that is, a prediction head that can predict one start and end index per batch item) based on a single dense hidden layer. It can be used in the SQuAD task.
-
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/configs.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/configs.py
deleted file mode 100644
index 41cfa6117cb49e00224becb87b129401562a9807..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/configs.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Common NHNet/Bert2Bert configuration."""
-
-from typing import List, Text
-
-import dataclasses
-
-from official.modeling.hyperparams import base_config
-
-
-@dataclasses.dataclass
-class BERT2BERTConfig(base_config.Config):
- """High-level configurations for BERT2BERT model.
-
- These include parameters that are not directly related to the experiment,
- e.g. encoder, decoder, prediction, training, etc.
- """
- vocab_size: int = 30522
- hidden_size: int = 768
- num_hidden_layers: int = 12
- num_attention_heads: int = 12
- intermediate_size: int = 3072
- hidden_act: str = "gelu"
- hidden_dropout_prob: float = 0.1
- attention_probs_dropout_prob: float = 0.1
- max_position_embeddings: int = 512
- type_vocab_size: int = 2
- initializer_range: float = 0.02
- decoder_intermediate_size: int = 3072
- num_decoder_attn_heads: int = 12
- num_decoder_layers: int = 12
-
- label_smoothing: float = 0.1
- learning_rate: float = 0.05
- learning_rate_warmup_steps: int = 20000
- optimizer: str = "Adam"
- adam_beta1: float = 0.9
- adam_beta2: float = 0.997
- adam_epsilon: float = 1e-09
-
- # predict params
- beam_size: int = 5
- alpha: float = 0.6
- initializer_gain: float = 1.0
- use_cache: bool = True
-
- # input params
- input_sharding: bool = False
- input_data_not_padded: bool = False
- pad_token_id: int = 0
- end_token_id: int = 102
- start_token_id: int = 101
-
-
-@dataclasses.dataclass
-class NHNetConfig(BERT2BERTConfig):
- """High-level configurations for NHNet model.
-
- These include parameters that are not directly related to the experiment,
- e.g. encoder, decoder, prediction, training, etc.
- """
- multi_channel_cross_attention: bool = True
- passage_list: List[Text] = dataclasses.field(
- default_factory=lambda: [chr(ord("b") + i) for i in range(5)])
-
- # Initialization method.
- # If init_from_bert2bert is false, we assume the checkpoint is from BERT
- # pretraining and only encoder and self-attention variables are initialized.
- init_from_bert2bert: bool = True
-
-
-UNITTEST_CONFIG = {
- "attention_probs_dropout_prob": 0.0,
- "hidden_act": "gelu",
- "hidden_dropout_prob": 0.0,
- "hidden_size": 16,
- "initializer_range": 0.02,
- "intermediate_size": 32,
- "max_position_embeddings": 128,
- "num_attention_heads": 2,
- "num_hidden_layers": 1,
- "type_vocab_size": 2,
- "vocab_size": 30522,
- "initializer_gain": 1.0,
- "decoder_intermediate_size": 32,
- "num_decoder_attn_heads": 2,
- "num_decoder_layers": 1,
- "use_cache": True,
- "input_data_not_padded": False,
- "pad_token_id": 0,
- "end_token_id": 102,
- "start_token_id": 101,
-}
diff --git a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/single_task/launch_tuning.sh b/spaces/NCTCMumbai/NCTC/models/research/brain_coder/single_task/launch_tuning.sh
deleted file mode 100644
index 97ce51b543e13d4b1c412656a93197b5b47373bb..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/single_task/launch_tuning.sh
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/bin/bash
-# Launches tuning jobs.
-# Modify this file to launch workers with your prefered cloud API.
-# The following implementation runs each worker as a subprocess on the local
-# machine.
-
-MODELS_DIR="/tmp/models"
-
-# Get command line options.
-OPTS=$(getopt -n "$0" -o "" --long "job_name:,config:,num_tuners:,num_workers_per_tuner:,num_ps_per_tuner:,max_npe:,num_repetitions:,stop_on_success:,fixed_hparams:,hparam_space_type:" -- "$@")
-if [ $? != 0 ] ; then echo "Failed parsing options." >&2 ; exit 1 ; fi
-
-eval set -- "$OPTS"
-
-JOB_NAME="" # Name of the process and the logs directory.
-CONFIG="" # Model and environment hparams.
-# NUM_TUNERS: Number of tuning jobs to launch. Each tuning job can train a
-# hparam combination. So more tuners means more hparams tried in parallel.
-NUM_TUNERS=1
-# NUM_WORKERS_PER_TUNER: Number of workers to launch for each tuning job. If
-# using neural networks, each worker will be 1 replica.
-NUM_WORKERS_PER_TUNER=1
-# NUM_PS_PER_TUNER: Number of parameter servers to launch for this tuning job.
-# Only set this if using neural networks. For 1 worker per tuner, no parameter
-# servers are needed. For more than 1 worker per tuner, at least 1 parameter
-# server per tuner is needed to store the global model for each tuner.
-NUM_PS_PER_TUNER=0
-# MAX_NPE: Maximum number of programs executed. Training will quit once this
-# threshold is reached. If 0, the threshold is infinite.
-MAX_NPE=0
-NUM_REPETITIONS=25 # How many times to run this experiment.
-STOP_ON_SUCCESS=true # Whether to halt training when a solution is found.
-# FIXED_HPARAMS: Hold hparams fixed in the grid search. This reduces the search
-# space.
-FIXED_HPARAMS=""
-# HPARAM_SPACE_TYPE: Specifies the hparam search space. See
-# `define_tuner_hparam_space` functions defined in pg_train.py and ga_train.py.
-HPARAM_SPACE_TYPE="pg"
-
-# Parse options into variables.
-while true; do
- case "$1" in
- --job_name ) JOB_NAME="$2"; shift; shift ;;
- --config ) CONFIG="$2"; shift; shift ;;
- --num_tuners ) NUM_TUNERS="$2"; shift; shift ;;
- --num_workers_per_tuner ) NUM_WORKERS_PER_TUNER="$2"; shift; shift ;;
- --num_ps_per_tuner ) NUM_PS_PER_TUNER="$2"; shift; shift ;;
- --max_npe ) MAX_NPE="$2"; shift; shift ;;
- --num_repetitions ) NUM_REPETITIONS="$2"; shift; shift ;;
- --stop_on_success ) STOP_ON_SUCCESS="$2"; shift; shift ;;
- --fixed_hparams ) FIXED_HPARAMS="$2"; shift; shift ;;
- --hparam_space_type ) HPARAM_SPACE_TYPE="$2"; shift; shift ;;
- -- ) shift; break ;;
- * ) break ;;
- esac
-done
-
-# Launch jobs.
-# TODO: multi-worker RL training
-
-LOGDIR="$MODELS_DIR/$JOB_NAME"
-mkdir -p $LOGDIR
-
-BIN_DIR="bazel-bin/single_task"
-for ((tuner=0;tuner "$LOGDIR/tuner_$tuner.task_$i.log" & # Run as subprocess
- echo "Launched tuner $tuner, task $i. Logs: $LOGDIR/tuner_$tuner.task_$i.log"
- done
-done
-
-# Use "pidof tune.par" to find jobs.
-# Kill with "pkill tune.par"
diff --git a/spaces/NKU-AMT/AMT/demo_vid.py b/spaces/NKU-AMT/AMT/demo_vid.py
deleted file mode 100644
index 5a29ab8d4b6b6374ad21afbc6b976694c857f923..0000000000000000000000000000000000000000
--- a/spaces/NKU-AMT/AMT/demo_vid.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import cv2
-import glob
-import torch
-import numpy as np
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-from networks.amts import Model as AMTS
-from networks.amtl import Model as AMTL
-from networks.amtg import Model as AMTG
-from utils import img2tensor, tensor2img, InputPadder
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model_dict = {
- 'AMT-S': AMTS, 'AMT-L': AMTL, 'AMT-G': AMTG
-}
-
-
-def vid2vid(model_type, video, iters):
- model = model_dict[model_type]()
- model.to(device)
- ckpt_path = hf_hub_download(repo_id='lalala125/AMT', filename=f'{model_type.lower()}.pth')
- ckpt = torch.load(ckpt_path, map_location=torch.device('cpu'))
- model.load_state_dict(ckpt['state_dict'])
- model.eval()
- vcap = cv2.VideoCapture(video)
- ori_frame_rate = vcap.get(cv2.CAP_PROP_FPS)
- inputs = []
- h = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))
- w = int(vcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- if device == 'cuda':
- anchor_resolution = 1024 * 512
- anchor_memory = 1500 * 1024**2
- anchor_memory_bias = 2500 * 1024**2
- vram_avail = torch.cuda.get_device_properties(device).total_memory
- else:
- # Do not resize in cpu mode
- anchor_resolution = 8192*8192
- anchor_memory = 1
- anchor_memory_bias = 0
- vram_avail = 1
-
- scale = anchor_resolution / (h * w) * np.sqrt((vram_avail - anchor_memory_bias) / anchor_memory)
- scale = 1 if scale > 1 else scale
- scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
- if scale < 1:
- print(f"Due to the limited VRAM, the video will be scaled by {scale:.2f}")
- padding = int(16 / scale)
- padder = InputPadder((h, w), padding)
- while True:
- ret, frame = vcap.read()
- if ret is False:
- break
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- frame_t = img2tensor(frame).to(device)
- frame_t = padder.pad(frame_t)
- inputs.append(frame_t)
- embt = torch.tensor(1/2).float().view(1, 1, 1, 1).to(device)
-
- for i in range(iters):
- print(f'Iter {i+1}. input_frames={len(inputs)} output_frames={2*len(inputs)-1}')
- outputs = [inputs[0]]
- for in_0, in_1 in zip(inputs[:-1], inputs[1:]):
- with torch.no_grad():
- imgt_pred = model(in_0, in_1, embt, scale_factor=scale, eval=True)['imgt_pred']
- imgt_pred = padder.unpad(imgt_pred)
- in_1 = padder.unpad(in_1)
- outputs += [imgt_pred, in_1]
- inputs = outputs
-
- out_path = 'results'
- size = outputs[0].shape[2:][::-1]
- writer = cv2.VideoWriter(f'{out_path}/demo_vfi.mp4',
- cv2.VideoWriter_fourcc(*'mp4v'),
- ori_frame_rate * 2 ** iters, size)
- for i, imgt_pred in enumerate(outputs):
- imgt_pred = tensor2img(imgt_pred)
- imgt_pred = cv2.cvtColor(imgt_pred, cv2.COLOR_RGB2BGR)
- writer.write(imgt_pred)
- writer.release()
- return 'results/demo_vfi.mp4'
-
-
-def demo_vid():
- with gr.Blocks() as demo:
- with gr.Row():
- gr.Markdown('## Video Demo')
- with gr.Row():
- gr.HTML(
- """
-
-
- Description: You can increase the frame rate of the video by 2 times, 4 times, or 8 times. (The video should be less than 10 seconds.)
-
-
- """)
-
- with gr.Row():
- with gr.Column():
- video = gr.Video(label='Video Input')
- with gr.Column():
- result = gr.Video(label="Generated Video")
- with gr.Accordion('Advanced options', open=False):
- ratio = gr.Slider(label='Multiple Ratio',
- minimum=1,
- maximum=4,
- value=2,
- step=1)
- model_type = gr.Radio(['AMT-S', 'AMT-L', 'AMT-G'],
- label='Model Select',
- value='AMT-S')
- run_button = gr.Button(label='Run')
- inputs = [
- model_type,
- video,
- ratio,
- ]
-
- gr.Examples(examples=glob.glob("examples/*.mp4"),
- inputs=video,
- label='Example videos (drag them to the input window)',
- run_on_click=False,
- )
-
- run_button.click(fn=vid2vid,
- inputs=inputs,
- outputs=result,)
- return demo
\ No newline at end of file
diff --git a/spaces/Nee001/bing0/src/components/turn-counter.tsx b/spaces/Nee001/bing0/src/components/turn-counter.tsx
deleted file mode 100644
index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/src/components/turn-counter.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
- throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
- if (!throttling) {
- return null
- }
-
- return (
-
-
- {throttling.numUserMessagesInConversation}
- 共
- {throttling.maxNumUserMessagesInConversation}
-
-
-
- {children}
-
-
-
- )
-}
diff --git a/spaces/OnabajoMonsurat/Brain_tumor_prediction/model.py b/spaces/OnabajoMonsurat/Brain_tumor_prediction/model.py
deleted file mode 100644
index 4ab5796482944bf1a06c682a197212d20355e5f0..0000000000000000000000000000000000000000
--- a/spaces/OnabajoMonsurat/Brain_tumor_prediction/model.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-import torchvision
-from torch import nn
-
-def create_model(num_classes:int=4,
- seed:int=42):
- # Create Effnet pretrained model
- weights= torchvision.models.EfficientNet_B0_Weights.DEFAULT
- transforms= weights.transforms()
- model= torchvision.models.efficientnet_b0(weights=weights)
-
- # Freeze all layers in the base model
- for param in model.parameters():
- param.requires_grad= False
-
- # Change the classifier layer
- torch.manual_seed(seed)
- model.classifier= nn.Sequential(
- nn.Dropout(p=0.2, inplace= True),
- nn.Linear(in_features= 1280, out_features= num_classes)
- )
- return model, transforms
diff --git a/spaces/Otter-AI/OtterHD-Demo/app.py b/spaces/Otter-AI/OtterHD-Demo/app.py
deleted file mode 100644
index ba8a7a14ac5fd843affeb57adae77af90c4e46b5..0000000000000000000000000000000000000000
--- a/spaces/Otter-AI/OtterHD-Demo/app.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import os
-import datetime
-import json
-import base64
-from PIL import Image
-import gradio as gr
-import hashlib
-import requests
-from utils import build_logger
-import io
-
-LOGDIR = "log"
-logger = build_logger("otter", LOGDIR)
-
-# no_change_btn = gr.Button.update()
-# enable_btn = gr.Button.update(interactive=True)
-# disable_btn = gr.Button.update(interactive=False)
-
-
-def decode_image(encoded_image: str) -> Image:
- decoded_bytes = base64.b64decode(encoded_image.encode("utf-8"))
- buffer = io.BytesIO(decoded_bytes)
- image = Image.open(buffer)
- return image
-
-
-def encode_image(image: Image.Image, format: str = "PNG") -> str:
- with io.BytesIO() as buffer:
- image.save(buffer, format=format)
- encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
- return encoded_image
-
-
-def get_conv_log_filename():
- t = datetime.datetime.now()
- name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
- return name
-
-
-def get_conv_image_dir():
- name = os.path.join(LOGDIR, "images")
- os.makedirs(name, exist_ok=True)
- return name
-
-
-def get_image_name(image, image_dir=None):
- buffer = io.BytesIO()
- image.save(buffer, format="PNG")
- image_bytes = buffer.getvalue()
- md5 = hashlib.md5(image_bytes).hexdigest()
-
- if image_dir is not None:
- image_name = os.path.join(image_dir, md5 + ".png")
- else:
- image_name = md5 + ".png"
-
- return image_name
-
-
-def resize_image(image, max_size):
- width, height = image.size
- aspect_ratio = float(width) / float(height)
-
- if width > height:
- new_width = max_size
- new_height = int(new_width / aspect_ratio)
- else:
- new_height = max_size
- new_width = int(new_height * aspect_ratio)
-
- resized_image = image.resize((new_width, new_height))
- return resized_image
-
-
-def http_bot(image_input, text_input, request: gr.Request):
- logger.info(f"http_bot. ip: {request.client.host}")
- print(f"Prompt request: {text_input}")
-
- base64_image_str = encode_image(image_input)
-
- payload = {
- "content": [
- {
- "prompt": text_input,
- "image": base64_image_str,
- }
- ],
- "token": "sk-OtterHD",
- }
-
- print(
- "request: ",
- {
- "prompt": text_input,
- "image": base64_image_str[:10],
- },
- )
-
- url = "https://ensures-picture-choices-labels.trycloudflare.com/app/otter"
- headers = {"Content-Type": "application/json"}
-
- response = requests.post(url, headers=headers, data=json.dumps(payload))
- results = response.json()
- print("response: ", {"result": results["result"]})
- return results["result"]
-
-
-title = """
-# OTTER-HD: A High-Resolution Multi-modality Model
-[[Otter Codebase]](https://github.com/Luodian/Otter) [[Paper]](https://arxiv.org/abs/2311.04219) [[Checkpoints & Benchmarks]](https://huggingface.co/Otter-AI)
-
-**OtterHD** is a multimodal fine-tuned from [Fuyu-8B](https://huggingface.co/adept/fuyu-8b) to facilitate a more fine-grained interpretation of high-resolution visual input *without a explicit vision encoder module*. All image patches are linear transformed and processed together with text tokens. This is a very innovative and elegant exploration. We are fascinated and paved in this way, we opensourced the finetune script for Fuyu-8B and improve training throughput by 4-5 times faster with [Flash-Attention-2](https://github.com/Dao-AILab/flash-attention).
-
-**Tips**:
-- Since high-res images are large that may cause the longer transmit time from HF Space to our backend server. Please be kinda patient for the response.
-- The model is currently mainly focus on high-res image resolution and need to be futher improved on (1) hallucination reduction (2) text formatting control and some more you can spot and suggest to us.
-- We are working on to finetune the model on LLaVA-1.5/LRV/LLaVAR data mixture and balance the detailed recognition and hallucination reduction. Stay tuned!
-- Please do not upload any NSFW images and ask relevant questions. We will ban the IP address if we found any inappropriate usage.
-"""
-
-css = """
- #mkd {
- height: 1000px;
- overflow: auto;
- border: 1px solid #ccc;
- }
-"""
-
-if __name__ == "__main__":
- with gr.Blocks(css=css) as demo:
- gr.Markdown(title)
- dialog_state = gr.State()
- input_state = gr.State()
- with gr.Tab("Ask a Question"):
- with gr.Row(equal_height=True):
- with gr.Column(scale=2):
- image_input = gr.Image(label="Upload a High-Res Image", type="pil")
- with gr.Column(scale=1):
- vqa_output = gr.Textbox(label="Output")
- text_input = gr.Textbox(label="Ask a Question")
-
- vqa_btn = gr.Button("Send It")
-
- gr.Examples(
- [
- [
- "./assets/IMG_00095.png",
- "How many camels are inside this image?",
- ],
- [
- "./assets/IMG_00057.png",
- "What's this image about?",
- ],
- [
- "./assets/IMG_00012.png",
- "How many apples are there?",
- ],
- [
- "./assets/./IMG_00012.png",
- "How many apples are there? Count them row by row.",
- ],
- [
- "./assets/IMG_00080.png",
- "What is this and where is it from?",
- ],
- [
- "./assets/IMG_00094.png",
- "What's important on this website?",
- ],
- ],
- inputs=[image_input, text_input],
- outputs=[vqa_output],
- fn=http_bot,
- label="Click on any Examples below👇",
- )
- vqa_btn.click(fn=http_bot, inputs=[image_input, text_input], outputs=vqa_output)
-
- demo.launch()
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/data/datasets/__init__.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/data/datasets/__init__.py
deleted file mode 100644
index bfb31c1afbb74c61680a58654d45a3f930658a1f..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/data/datasets/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from . import (
- register_ade20k_panoptic,
- register_cityscapes_panoptic,
- register_coco_panoptic_annos_semseg,
- register_ade20k_instance,
- register_coco_panoptic2instance,
- register_mapillary_vistas,
- register_mapillary_vistas_panoptic,
-)
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/accuracy.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/accuracy.py
deleted file mode 100644
index c0fd2e7e74a0f721c4a814c09d6e453e5956bb38..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/accuracy.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import torch.nn as nn
-
-
-def accuracy(pred, target, topk=1, thresh=None):
- """Calculate accuracy according to the prediction and target.
-
- Args:
- pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
- target (torch.Tensor): The target of each prediction, shape (N, , ...)
- topk (int | tuple[int], optional): If the predictions in ``topk``
- matches the target, the predictions will be regarded as
- correct ones. Defaults to 1.
- thresh (float, optional): If not None, predictions with scores under
- this threshold are considered incorrect. Default to None.
-
- Returns:
- float | tuple[float]: If the input ``topk`` is a single integer,
- the function will return a single float as accuracy. If
- ``topk`` is a tuple containing multiple integers, the
- function will return a tuple containing accuracies of
- each ``topk`` number.
- """
- assert isinstance(topk, (int, tuple))
- if isinstance(topk, int):
- topk = (topk, )
- return_single = True
- else:
- return_single = False
-
- maxk = max(topk)
- if pred.size(0) == 0:
- accu = [pred.new_tensor(0.) for i in range(len(topk))]
- return accu[0] if return_single else accu
- assert pred.ndim == target.ndim + 1
- assert pred.size(0) == target.size(0)
- assert maxk <= pred.size(1), \
- f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
- pred_value, pred_label = pred.topk(maxk, dim=1)
- # transpose to shape (maxk, N, ...)
- pred_label = pred_label.transpose(0, 1)
- correct = pred_label.eq(target.unsqueeze(0).expand_as(pred_label))
- if thresh is not None:
- # Only prediction values larger than thresh are counted as correct
- correct = correct & (pred_value > thresh).t()
- res = []
- for k in topk:
- correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
- res.append(correct_k.mul_(100.0 / target.numel()))
- return res[0] if return_single else res
-
-
-class Accuracy(nn.Module):
- """Accuracy calculation module."""
-
- def __init__(self, topk=(1, ), thresh=None):
- """Module to calculate the accuracy.
-
- Args:
- topk (tuple, optional): The criterion used to calculate the
- accuracy. Defaults to (1,).
- thresh (float, optional): If not None, predictions with scores
- under this threshold are considered incorrect. Default to None.
- """
- super().__init__()
- self.topk = topk
- self.thresh = thresh
-
- def forward(self, pred, target):
- """Forward function to calculate accuracy.
-
- Args:
- pred (torch.Tensor): Prediction of models.
- target (torch.Tensor): Target for each prediction.
-
- Returns:
- tuple[float]: The accuracies under different topk criterions.
- """
- return accuracy(pred, target, self.topk, self.thresh)
diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py b/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/PeepDaSlan9/meta-llama-Llama-2-70b-chat-hf/app.py b/spaces/PeepDaSlan9/meta-llama-Llama-2-70b-chat-hf/app.py
deleted file mode 100644
index a461703287a9bda9c93cfdfbb94d4c3cf90aaba9..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/meta-llama-Llama-2-70b-chat-hf/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/meta-llama/Llama-2-70b-chat-hf").launch()
\ No newline at end of file
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/swint_v2.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/swint_v2.py
deleted file mode 100644
index f1fe550c33f06880f67176cd03c918db06c25e6f..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/swint_v2.py
+++ /dev/null
@@ -1,734 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from einops import rearrange
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-class Mlp(nn.Module):
- """ Multilayer perceptron."""
-
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- """ Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """ Forward function.
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- """ Swin Transformer Block.
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=False):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- self.H = None
- self.W = None
-
- self.gamma = 1.0
- if layer_scale:
- self.gamma = nn.Parameter(
- 1e-4*torch.ones(dim), requires_grad=True
- )
-
-
- def forward(self, x, mask_matrix):
- """ Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- mask_matrix: Attention mask for cyclic shift.
- """
- B, L, C = x.shape
- H, W = self.H, self.W
- assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # pad feature maps to multiples of window size
- pad_l = pad_t = 0
- pad_r = (self.window_size - W % self.window_size) % self.window_size
- pad_b = (self.window_size - H % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- attn_mask = mask_matrix
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- x = x[:, :H, :W, :].contiguous()
-
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(self.gamma*x)
- x = x + self.drop_path(self.gamma*self.mlp(self.norm2(x)))
-
- return x
-
-
-class PatchMerging(nn.Module):
- """ Patch Merging Layer
- Args:
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x, H, W):
- """ Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
-
- x = x.view(B, H, W, C)
-
- # padding
- pad_input = (H % 2 == 1) or (W % 2 == 1)
- if pad_input:
- x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
- Args:
- dim (int): Number of feature channels
- depth (int): Depths of this stage.
- num_heads (int): Number of attention head.
- window_size (int): Local window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- dim,
- depth,
- num_heads,
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop=0.,
- attn_drop=0.,
- drop_path=0.,
- norm_layer=nn.LayerNorm,
- downsample=None,
- use_checkpoint=False,
- layer_scale=False):
- super().__init__()
- self.window_size = window_size
- self.shift_size = window_size // 2
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(
- dim=dim,
- num_heads=num_heads,
- window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop,
- attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer,
- layer_scale=layer_scale)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(patch_size=3, in_chans=dim, embed_dim=dim*2,
- stride=2, padding=1, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, H, W):
- """ Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
-
- # calculate attention mask for SW-MSA
- Hp = int(np.ceil(H / self.window_size)) * self.window_size
- Wp = int(np.ceil(W / self.window_size)) * self.window_size
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- for blk in self.blocks:
- blk.H, blk.W = H, W
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, attn_mask)
- else:
- x = blk(x, attn_mask)
- if self.downsample is not None:
- x_down = self.downsample(x, H, W)
- Wh, Ww = (H + 1) // 2, (W + 1) // 2
- return x, H, W, x_down, Wh, Ww
- else:
- return x, H, W, x, H, W
-
-
-# class PatchEmbed(nn.Module):
-# """ Image to Patch Embedding
-# Args:
-# patch_size (int): Patch token size. Default: 4.
-# in_chans (int): Number of input image channels. Default: 3.
-# embed_dim (int): Number of linear projection output channels. Default: 96.
-# norm_layer (nn.Module, optional): Normalization layer. Default: None
-# """
-#
-# def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-# super().__init__()
-# patch_size = to_2tuple(patch_size)
-# self.patch_size = patch_size
-#
-# self.in_chans = in_chans
-# self.embed_dim = embed_dim
-#
-# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-# if norm_layer is not None:
-# self.norm = norm_layer(embed_dim)
-# else:
-# self.norm = None
-#
-# def forward(self, x):
-# """Forward function."""
-# # padding
-# _, _, H, W = x.size()
-# if W % self.patch_size[1] != 0:
-# x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-# if H % self.patch_size[0] != 0:
-# x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-#
-# x = self.proj(x) # B C Wh Ww
-# if self.norm is not None:
-# Wh, Ww = x.size(2), x.size(3)
-# x = x.flatten(2).transpose(1, 2)
-# x = self.norm(x)
-# x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-#
-# return x
-
-
-class ConvEmbed(nn.Module):
- """ Image to Patch Embedding
- """
-
- def __init__(
- self,
- patch_size=7,
- in_chans=3,
- embed_dim=64,
- stride=4,
- padding=2,
- norm_layer=None
- ):
- super().__init__()
- self.patch_size = patch_size
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(
- in_chans, embed_dim,
- kernel_size=patch_size,
- stride=stride,
- padding=padding
- )
- self.norm = norm_layer(embed_dim) if norm_layer else None
-
- def forward(self, x, H=None, W=None):
- restore_hw = False
- if H is None and W is None and len(x.size()) == 4:
- _, _, H, W = x.size()
- if W % self.patch_size != 0:
- x = F.pad(x, (0, self.patch_size - W % self.patch_size))
- if H % self.patch_size != 0:
- x = F.pad(x, (0, 0, 0, self.patch_size - H % self.patch_size))
- restore_hw = True
-
- if len(x.size()) == 3:
- x = rearrange(
- x, 'b (h w) c -> b c h w',
- h=H,
- w=W
- )
- x = self.proj(x) # B C Wh Ww
- B, C, Wh, Ww = x.shape
- x = rearrange(x, 'b c h w -> b (h w) c')
- if self.norm:
- x = self.norm(x)
-
- if restore_hw:
- x = rearrange(
- x, 'b (h w) c -> b c h w',
- h=Wh,
- w=Ww
- )
-
- return x
-
-
-class SwinTransformer(nn.Module):
- """ Swin Transformer backbone.
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
- https://arxiv.org/pdf/2103.14030
- Args:
- pretrain_img_size (int): Input image size for training the pretrained model,
- used in absolute postion embedding. Default 224.
- patch_size (int | tuple(int)): Patch size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- depths (tuple[int]): Depths of each Swin Transformer stage.
- num_heads (tuple[int]): Number of attention head of each stage.
- window_size (int): Window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- drop_rate (float): Dropout rate.
- attn_drop_rate (float): Attention dropout rate. Default: 0.
- drop_path_rate (float): Stochastic depth rate. Default: 0.2.
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
- patch_norm (bool): If True, add normalization after patch embedding. Default: True.
- out_indices (Sequence[int]): Output from which stages.
- frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
- -1 means not freezing any parameters.
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- pretrain_img_size=224,
- patch_size=7,
- patch_padding=2,
- patch_stride=4,
- in_chans=3,
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2,
- norm_layer=nn.LayerNorm,
- ape=False,
- patch_norm=True,
- frozen_stages=-1,
- use_checkpoint=False,
- layer_scale=False,
- out_features=["stage2", "stage3", "stage4", "stage5"],
- out_norm=True,
- backbone_arch="SWINT-FPN-RETINANET"):
- super(SwinTransformer, self).__init__()
-
- print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint)
-
- self.pretrain_img_size = pretrain_img_size
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.frozen_stages = frozen_stages
-
- self.out_features = out_features
- self.out_norm = out_norm
-
- # split image into non-overlapping patches
- # self.patch_embed = PatchEmbed(
- # patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
- # norm_layer=norm_layer if self.patch_norm else None)
- self.patch_embed = ConvEmbed(
- patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, padding=patch_padding,
- norm_layer=norm_layer if self.patch_norm else None
- )
-
- # absolute position embedding
- if self.ape:
- pretrain_img_size = to_2tuple(pretrain_img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
- self.absolute_pos_embed = nn.Parameter(
- torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- self._out_feature_strides = {}
- self._out_feature_channels = {}
-
- # build layers
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = BasicLayer(
- dim=int(embed_dim * 2 ** i_layer),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
- norm_layer=norm_layer,
- downsample=ConvEmbed if (i_layer < self.num_layers - 1) else None,
- use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1,
- layer_scale=layer_scale)
- self.layers.append(layer)
-
- stage = f'stage{i_layer + 2}'
- if stage in self.out_features:
- self._out_feature_channels[stage] = embed_dim * 2 ** i_layer
- self._out_feature_strides[stage] = 4 * 2 ** i_layer
-
- num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
- self.num_features = num_features
-
- # add a norm layer for each output
- if self.out_norm:
- for i_layer in range(self.num_layers):
- stage = f'stage{i_layer + 2}'
- if stage in self.out_features:
- if i_layer == 0 and backbone_arch.endswith("RETINANET"):
- layer = nn.Identity()
- else:
- layer = norm_layer(num_features[i_layer])
- layer_name = f'norm{i_layer}'
- self.add_module(layer_name, layer)
-
- self._freeze_stages()
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- self.patch_embed.eval()
- for param in self.patch_embed.parameters():
- param.requires_grad = False
-
- if self.frozen_stages >= 1 and self.ape:
- self.absolute_pos_embed.requires_grad = False
-
- if self.frozen_stages >= 2:
- self.pos_drop.eval()
- for i in range(0, self.frozen_stages - 1):
- m = self.layers[i]
- m.eval()
- for param in m.parameters():
- param.requires_grad = False
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in backbone.
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
-
- def _init_weights(m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- self.apply(_init_weights)
-
- def forward(self, x):
- """Forward function."""
- x = self.patch_embed(x)
-
- Wh, Ww = x.size(2), x.size(3)
- if self.ape:
- # interpolate the position embedding to the corresponding size
- absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
- x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
- else:
- x = x.flatten(2).transpose(1, 2)
- x = self.pos_drop(x)
-
- outs = []
- for i in range(self.num_layers):
- layer = self.layers[i]
- x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
- name = f'stage{i + 2}'
- if name in self.out_features:
- if self.out_norm:
- norm_layer = getattr(self, f'norm{i}')
- x_out = norm_layer(x_out)
- out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
- outs.append(out)
-
- return outs
-
- def train(self, mode=True):
- """Convert the model into training mode while keep layers freezed."""
- super(SwinTransformer, self).train(mode)
- self._freeze_stages()
-
-
-def build_swint_backbone(cfg):
- """
- Create a SwinT instance from config.
-
- Returns:
- VoVNet: a :class:`VoVNet` instance.
- """
- return SwinTransformer(
- patch_size=7,
- patch_padding=2,
- patch_stride=4,
- in_chans=3,
- embed_dim=cfg.MODEL.SWINT.EMBED_DIM,
- depths=cfg.MODEL.SWINT.DEPTHS,
- num_heads=cfg.MODEL.SWINT.NUM_HEADS,
- window_size=cfg.MODEL.SWINT.WINDOW_SIZE,
- mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE,
- norm_layer=nn.LayerNorm,
- ape=cfg.MODEL.SWINT.APE,
- patch_norm=True,
- frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT,
- backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY,
- use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT,
- layer_scale=cfg.MODEL.SWINT.LAYER_SCALE,
- out_features=cfg.MODEL.BACKBONE.OUT_FEATURES,
- out_norm=cfg.MODEL.SWINT.OUT_NORM,
- )
\ No newline at end of file
diff --git a/spaces/Podtekatel/JoJo_Style_Transfer/app.py b/spaces/Podtekatel/JoJo_Style_Transfer/app.py
deleted file mode 100644
index 723b713301a09e9a89ce9d233933582e64ea2d4e..0000000000000000000000000000000000000000
--- a/spaces/Podtekatel/JoJo_Style_Transfer/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import logging
-import os
-
-import gradio as gr
-import numpy as np
-from PIL import Image
-from huggingface_hub import hf_hub_url, cached_download
-
-from inference.face_detector import StatRetinaFaceDetector
-from inference.model_pipeline import VSNetModelPipeline
-from inference.onnx_model import ONNXModel
-
-logging.basicConfig(
- format='%(asctime)s %(levelname)-8s %(message)s',
- level=logging.INFO,
- datefmt='%Y-%m-%d %H:%M:%S')
-
-MODEL_IMG_SIZE = 256
-usage_count = 35
-def load_model():
- REPO_ID = "Podtekatel/JJBAGAN"
- FILENAME = "198_jjba_8_k_2_099_ep.onnx"
-
- global model
- global pipeline
-
- model_path = cached_download(
- hf_hub_url(REPO_ID, FILENAME), use_auth_token=os.getenv('HF_TOKEN')
- )
- model = ONNXModel(model_path)
-
- pipeline = VSNetModelPipeline(model, StatRetinaFaceDetector(MODEL_IMG_SIZE), background_resize=1024, no_detected_resize=1024)
- return model
-
-load_model()
-
-def inference(img):
- img = np.array(img)
- out_img = pipeline(img)
- out_img = Image.fromarray(out_img)
-
- global usage_count
- usage_count += 1
- logging.info(f'Usage count is {usage_count}')
-
- return out_img
-
-
-title = "JJStyleTransfer"
-description = "Gradio Demo for JoJo Bizzare Adventures style transfer. To use it, simply upload your image, or click one of the examples to load them. Press ❤️ if you like this space!"
-article = "This is one of my successful experiments on style transfer. I've built my own pipeline, generator model and private dataset to train this model
-Since Stable Diffusion 2 uses OpenCLIP ViT-H model trained on LAION dataset compared to the OpenAI ViT-L of Stable Diffusion 1, the prompting style varies and the exact prompt is often hard to write.
-RunwayML Stable Diffusion 1.5 , then Interrogate the resulting image through CLIP Interrogator 2 to give you a Stable Diffusion 2 equivalent prompt.
-
-"""
-
-title="Prompt Extender 2"
-
-examples = [
- ["giant dragon flying in the sky"],
- ["peaceful village landscape"],
- ]
-
-demo = gr.Interface(fn=infer, inputs=input_prompt, outputs=[sd1_output,sd2_output], description = description, title = title, examples = examples)
-demo.queue(max_size=10,concurrency_count=20)
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/commands/download.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/commands/download.py
deleted file mode 100644
index 4132e0898831bc18faa313f103f55e7d5a1ea2b7..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/commands/download.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import logging
-import os
-from optparse import Values
-from typing import List
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.cmdoptions import make_target_python
-from pip._internal.cli.req_command import RequirementCommand, with_cleanup
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.operations.build.build_tracker import get_build_tracker
-from pip._internal.req.req_install import (
- LegacySetupPyOptionsCheckMode,
- check_legacy_setup_py_options,
-)
-from pip._internal.utils.misc import ensure_dir, normalize_path, write_output
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-class DownloadCommand(RequirementCommand):
- """
- Download packages from:
-
- - PyPI (and other indexes) using requirement specifiers.
- - VCS project urls.
- - Local project directories.
- - Local or remote source archives.
-
- pip also supports downloading from "requirements files", which provide
- an easy way to specify a whole environment to be downloaded.
- """
-
- usage = """
- %prog [options] [package-index-options] ...
- %prog [options] -r [package-index-options] ...
- %prog [options] ...
- %prog [options] ...
- %prog [options] ..."""
-
- def add_options(self) -> None:
- self.cmd_opts.add_option(cmdoptions.constraints())
- self.cmd_opts.add_option(cmdoptions.requirements())
- self.cmd_opts.add_option(cmdoptions.no_deps())
- self.cmd_opts.add_option(cmdoptions.global_options())
- self.cmd_opts.add_option(cmdoptions.no_binary())
- self.cmd_opts.add_option(cmdoptions.only_binary())
- self.cmd_opts.add_option(cmdoptions.prefer_binary())
- self.cmd_opts.add_option(cmdoptions.src())
- self.cmd_opts.add_option(cmdoptions.pre())
- self.cmd_opts.add_option(cmdoptions.require_hashes())
- self.cmd_opts.add_option(cmdoptions.progress_bar())
- self.cmd_opts.add_option(cmdoptions.no_build_isolation())
- self.cmd_opts.add_option(cmdoptions.use_pep517())
- self.cmd_opts.add_option(cmdoptions.no_use_pep517())
- self.cmd_opts.add_option(cmdoptions.check_build_deps())
- self.cmd_opts.add_option(cmdoptions.ignore_requires_python())
-
- self.cmd_opts.add_option(
- "-d",
- "--dest",
- "--destination-dir",
- "--destination-directory",
- dest="download_dir",
- metavar="dir",
- default=os.curdir,
- help="Download packages into .",
- )
-
- cmdoptions.add_target_python_options(self.cmd_opts)
-
- index_opts = cmdoptions.make_option_group(
- cmdoptions.index_group,
- self.parser,
- )
-
- self.parser.insert_option_group(0, index_opts)
- self.parser.insert_option_group(0, self.cmd_opts)
-
- @with_cleanup
- def run(self, options: Values, args: List[str]) -> int:
-
- options.ignore_installed = True
- # editable doesn't really make sense for `pip download`, but the bowels
- # of the RequirementSet code require that property.
- options.editables = []
-
- cmdoptions.check_dist_restriction(options)
-
- options.download_dir = normalize_path(options.download_dir)
- ensure_dir(options.download_dir)
-
- session = self.get_default_session(options)
-
- target_python = make_target_python(options)
- finder = self._build_package_finder(
- options=options,
- session=session,
- target_python=target_python,
- ignore_requires_python=options.ignore_requires_python,
- )
-
- build_tracker = self.enter_context(get_build_tracker())
-
- directory = TempDirectory(
- delete=not options.no_clean,
- kind="download",
- globally_managed=True,
- )
-
- reqs = self.get_requirements(args, options, finder, session)
- check_legacy_setup_py_options(
- options, reqs, LegacySetupPyOptionsCheckMode.DOWNLOAD
- )
-
- preparer = self.make_requirement_preparer(
- temp_build_dir=directory,
- options=options,
- build_tracker=build_tracker,
- session=session,
- finder=finder,
- download_dir=options.download_dir,
- use_user_site=False,
- verbosity=self.verbosity,
- )
-
- resolver = self.make_resolver(
- preparer=preparer,
- finder=finder,
- options=options,
- ignore_requires_python=options.ignore_requires_python,
- use_pep517=options.use_pep517,
- py_version_info=options.python_version,
- )
-
- self.trace_basic_info(finder)
-
- requirement_set = resolver.resolve(reqs, check_supported_wheels=True)
-
- downloaded: List[str] = []
- for req in requirement_set.requirements.values():
- if req.satisfied_by is None:
- assert req.name is not None
- preparer.save_linked_requirement(req)
- downloaded.append(req.name)
- if downloaded:
- write_output("Successfully downloaded %s", " ".join(downloaded))
-
- return SUCCESS
diff --git a/spaces/Realcat/image-matching-webui/third_party/lanet/config.py b/spaces/Realcat/image-matching-webui/third_party/lanet/config.py
deleted file mode 100644
index 84419d0a1f7199e8bec1afc7b046e674a629d886..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/lanet/config.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import argparse
-
-arg_lists = []
-parser = argparse.ArgumentParser(description="LANet")
-
-
-def str2bool(v):
- return v.lower() in ("true", "1")
-
-
-def add_argument_group(name):
- arg = parser.add_argument_group(name)
- arg_lists.append(arg)
- return arg
-
-
-# train data params
-traindata_arg = add_argument_group("Traindata Params")
-traindata_arg.add_argument("--train_txt", type=str, default="", help="Train set.")
-traindata_arg.add_argument(
- "--train_root", type=str, default="", help="Where the train images are."
-)
-traindata_arg.add_argument(
- "--batch_size", type=int, default=8, help="# of images in each batch of data"
-)
-traindata_arg.add_argument(
- "--num_workers",
- type=int,
- default=4,
- help="# of subprocesses to use for data loading",
-)
-traindata_arg.add_argument(
- "--pin_memory",
- type=str2bool,
- default=True,
- help="# of subprocesses to use for data loading",
-)
-traindata_arg.add_argument(
- "--shuffle",
- type=str2bool,
- default=True,
- help="Whether to shuffle the train and valid indices",
-)
-traindata_arg.add_argument("--image_shape", type=tuple, default=(240, 320), help="")
-traindata_arg.add_argument(
- "--jittering", type=tuple, default=(0.5, 0.5, 0.2, 0.05), help=""
-)
-
-# data storage
-storage_arg = add_argument_group("Storage")
-storage_arg.add_argument("--ckpt_name", type=str, default="PointModel", help="")
-
-# training params
-train_arg = add_argument_group("Training Params")
-train_arg.add_argument("--start_epoch", type=int, default=0, help="")
-train_arg.add_argument("--max_epoch", type=int, default=12, help="")
-train_arg.add_argument(
- "--init_lr", type=float, default=3e-4, help="Initial learning rate value."
-)
-train_arg.add_argument(
- "--lr_factor", type=float, default=0.5, help="Reduce learning rate value."
-)
-train_arg.add_argument(
- "--momentum", type=float, default=0.9, help="Nesterov momentum value."
-)
-train_arg.add_argument("--display", type=int, default=50, help="")
-
-# loss function params
-loss_arg = add_argument_group("Loss function Params")
-loss_arg.add_argument("--score_weight", type=float, default=1.0, help="")
-loss_arg.add_argument("--loc_weight", type=float, default=1.0, help="")
-loss_arg.add_argument("--desc_weight", type=float, default=4.0, help="")
-loss_arg.add_argument("--corres_weight", type=float, default=0.5, help="")
-loss_arg.add_argument("--corres_threshold", type=int, default=4.0, help="")
-
-# other params
-misc_arg = add_argument_group("Misc.")
-misc_arg.add_argument(
- "--use_gpu", type=str2bool, default=True, help="Whether to run on the GPU."
-)
-misc_arg.add_argument("--gpu", type=int, default=0, help="Which GPU to run on.")
-misc_arg.add_argument(
- "--seed", type=int, default=1001, help="Seed to ensure reproducibility."
-)
-misc_arg.add_argument(
- "--ckpt_dir",
- type=str,
- default="./checkpoints",
- help="Directory in which to save model checkpoints.",
-)
-
-
-def get_config():
- config, unparsed = parser.parse_known_args()
- return config, unparsed
diff --git a/spaces/Ritori/TTS_Yui/loss_scaler.py b/spaces/Ritori/TTS_Yui/loss_scaler.py
deleted file mode 100644
index 88cc9cf29d3ce122d7582020b73be4f4a5641fcd..0000000000000000000000000000000000000000
--- a/spaces/Ritori/TTS_Yui/loss_scaler.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-
-class LossScaler:
-
- def __init__(self, scale=1):
- self.cur_scale = scale
-
- # `params` is a list / generator of torch.Variable
- def has_overflow(self, params):
- return False
-
- # `x` is a torch.Tensor
- def _has_inf_or_nan(x):
- return False
-
- # `overflow` is boolean indicating whether we overflowed in gradient
- def update_scale(self, overflow):
- pass
-
- @property
- def loss_scale(self):
- return self.cur_scale
-
- def scale_gradient(self, module, grad_in, grad_out):
- return tuple(self.loss_scale * g for g in grad_in)
-
- def backward(self, loss):
- scaled_loss = loss*self.loss_scale
- scaled_loss.backward()
-
-class DynamicLossScaler:
-
- def __init__(self,
- init_scale=2**32,
- scale_factor=2.,
- scale_window=1000):
- self.cur_scale = init_scale
- self.cur_iter = 0
- self.last_overflow_iter = -1
- self.scale_factor = scale_factor
- self.scale_window = scale_window
-
- # `params` is a list / generator of torch.Variable
- def has_overflow(self, params):
-# return False
- for p in params:
- if p.grad is not None and DynamicLossScaler._has_inf_or_nan(p.grad.data):
- return True
-
- return False
-
- # `x` is a torch.Tensor
- def _has_inf_or_nan(x):
- cpu_sum = float(x.float().sum())
- if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
- return True
- return False
-
- # `overflow` is boolean indicating whether we overflowed in gradient
- def update_scale(self, overflow):
- if overflow:
- #self.cur_scale /= self.scale_factor
- self.cur_scale = max(self.cur_scale/self.scale_factor, 1)
- self.last_overflow_iter = self.cur_iter
- else:
- if (self.cur_iter - self.last_overflow_iter) % self.scale_window == 0:
- self.cur_scale *= self.scale_factor
-# self.cur_scale = 1
- self.cur_iter += 1
-
- @property
- def loss_scale(self):
- return self.cur_scale
-
- def scale_gradient(self, module, grad_in, grad_out):
- return tuple(self.loss_scale * g for g in grad_in)
-
- def backward(self, loss):
- scaled_loss = loss*self.loss_scale
- scaled_loss.backward()
-
-##############################################################
-# Example usage below here -- assuming it's in a separate file
-##############################################################
-if __name__ == "__main__":
- import torch
- from torch.autograd import Variable
- from dynamic_loss_scaler import DynamicLossScaler
-
- # N is batch size; D_in is input dimension;
- # H is hidden dimension; D_out is output dimension.
- N, D_in, H, D_out = 64, 1000, 100, 10
-
- # Create random Tensors to hold inputs and outputs, and wrap them in Variables.
- x = Variable(torch.randn(N, D_in), requires_grad=False)
- y = Variable(torch.randn(N, D_out), requires_grad=False)
-
- w1 = Variable(torch.randn(D_in, H), requires_grad=True)
- w2 = Variable(torch.randn(H, D_out), requires_grad=True)
- parameters = [w1, w2]
-
- learning_rate = 1e-6
- optimizer = torch.optim.SGD(parameters, lr=learning_rate)
- loss_scaler = DynamicLossScaler()
-
- for t in range(500):
- y_pred = x.mm(w1).clamp(min=0).mm(w2)
- loss = (y_pred - y).pow(2).sum() * loss_scaler.loss_scale
- print('Iter {} loss scale: {}'.format(t, loss_scaler.loss_scale))
- print('Iter {} scaled loss: {}'.format(t, loss.data[0]))
- print('Iter {} unscaled loss: {}'.format(t, loss.data[0] / loss_scaler.loss_scale))
-
- # Run backprop
- optimizer.zero_grad()
- loss.backward()
-
- # Check for overflow
- has_overflow = DynamicLossScaler.has_overflow(parameters)
-
- # If no overflow, unscale grad and update as usual
- if not has_overflow:
- for param in parameters:
- param.grad.data.mul_(1. / loss_scaler.loss_scale)
- optimizer.step()
- # Otherwise, don't do anything -- ie, skip iteration
- else:
- print('OVERFLOW!')
-
- # Update loss scale for next iteration
- loss_scaler.update_scale(has_overflow)
-
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py
deleted file mode 100644
index 615aa3ff703942b6c22b2d6e9642504dd3e41ebd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='CascadeEncoderDecoder',
- num_stages=2,
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=[
- dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- dict(
- type='OCRHead',
- in_channels=2048,
- in_index=3,
- channels=512,
- ocr_channels=256,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
- ],
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/Sadashiv/BERT-NER/README.md b/spaces/Sadashiv/BERT-NER/README.md
deleted file mode 100644
index 9657a1a8c15d98b0a9f8ce337d4650226b7d595f..0000000000000000000000000000000000000000
--- a/spaces/Sadashiv/BERT-NER/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
----
-title: BERT NER
-emoji: 🐨
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-# NER with Fine Tunned BERT
-
-## Application Screenshot
-
-
-## What is NER ?
-**Named Entity Recognition (NER)** is a task in natural language processing (NLP) that involves **identifying and classifying named entities in text.** Named entities are words or phrases that refer to specific objects or concepts, such as people, organizations, locations, and dates.
-
-## What is BERT ?
-
-
-BERT stands for Bidirectional Encoder Representations from Transformers. The name itself gives us several clues to what BERT is all about.
-
-BERT architecture consists of several Transformer encoders stacked together. Each Transformer encoder encapsulates two sub-layers: a self-attention layer and a feed-forward layer.
-
-### There are two different BERT models:
-- `BERT base` , which is a BERT model consists of 12 layers of Transformer encoder, 12 attention heads, 768 hidden size, and 110M parameters.
-
-- `BERT large` , which is a BERT model consists of 24 layers of Transformer encoder,16 attention heads, 1024 hidden size, and 340 parameters.
-
-### Reasons why BERT is a good choice for NER:
-1. **Contextual Information:** BERT leverages a transformer architecture, which enables it to consider the context of each word by looking at both its left and right context in a sentence. This contextual information is crucial for accurately identifying entities, especially when the same word can be an entity in one context but not in another.
-2. **Pre-trained Representations:** BERT is usually pre-trained on a large corpus of text, and the learned representations can be fine-tuned on downstream tasks like NER. Pre-training provides BERT with extensive linguistic knowledge, which often leads to improved performance on various NLP tasks, including NER.
-3. **Transfer Learning:** By using pre-trained BERT models, you can leverage knowledge learned from vast amounts of text data, even if you have limited labeled data for the specific NER task. This transfer learning ability is beneficial when working with limited annotated data.
-4. **State-of-the-Art Performance:** BERT has achieved state-of-the-art performance on a wide range of NLP benchmarks and competitions, including NER datasets. Its ability to handle long-range dependencies and capture subtle contextual information contributes to its success in NER.
-
-## Model
-### [BERT base model (uncased)](https://huggingface.co/bert-base-uncased)
-Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English.
-
-## Dataset
-### Dataset Summary
-The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
-
-The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2 tagging scheme, whereas the original dataset uses IOB1.
-[Dataset link](https://huggingface.co/datasets/conll2003)
-
-## Deployment
-The Huggingface Space's architecture is utilized for deploying the model. The code is stored in a GitHub repository and integrated into a CI/CD pipeline with the Huggingface Space. This allows the application to run smoothly and efficiently.
-
-[Application link on huggingface](https://huggingface.co/spaces/Sadashiv/BERT-NER)
-
-## Model Training
-The model training is conducted using a free session on Google Colab.
-
-[notebook link](notebook/Fine_tuning_BERT_NER.ipynb)
-
-## Fine Tuned Model
-The [fine-tuned](https://huggingface.co/Sadashiv/BERT-ner) BERT model is stored in Hugging Face's hub.
-
-## Addition Resources
-The Streamlit interface uses Streamlit's [st-annotated-text](https://github.com/tvst/st-annotated-text) package to highlight the recognized entity.
-
diff --git a/spaces/SalahZa/Tunisian-Speech-Recognition/app.py b/spaces/SalahZa/Tunisian-Speech-Recognition/app.py
deleted file mode 100644
index 63d9204b9027a927bbd511b307a623d8eae58120..0000000000000000000000000000000000000000
--- a/spaces/SalahZa/Tunisian-Speech-Recognition/app.py
+++ /dev/null
@@ -1,464 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import torch
-import logging
-import gradio as gr
-import speechbrain as sb
-from pathlib import Path
-import os
-import torchaudio
-from hyperpyyaml import load_hyperpyyaml
-from speechbrain.tokenizers.SentencePiece import SentencePiece
-from speechbrain.utils.data_utils import undo_padding
-from speechbrain.utils.distributed import run_on_main
-
-"""Recipe for training a sequence-to-sequence ASR system with CommonVoice.
-The system employs a wav2vec2 encoder and a CTC decoder.
-Decoding is performed with greedy decoding (will be extended to beam search).
-
-To run this recipe, do the following:
-> python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml
-
-With the default hyperparameters, the system employs a pretrained wav2vec2 encoder.
-The wav2vec2 model is pretrained following the model given in the hprams file.
-It may be dependent on the language.
-
-The neural network is trained with CTC on sub-word units estimated with
-Byte Pairwise Encoding (BPE).
-
-The experiment file is flexible enough to support a large variety of
-different systems. By properly changing the parameter files, you can try
-different encoders, decoders, tokens (e.g, characters instead of BPE),
-training languages (all CommonVoice languages), and many
-other possible variations.
-
-Authors
- * Titouan Parcollet 2021
-"""
-
-logger = logging.getLogger(__name__)
-
-
-# Define training procedure
-class ASR(sb.core.Brain):
- def compute_forward(self, batch, stage):
- """Forward computations from the waveform batches to the output probabilities."""
-
- batch = batch.to(self.device)
- wavs, wav_lens = batch.sig
- wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
- if stage == sb.Stage.TRAIN:
- if hasattr(self.hparams, "augmentation"):
- wavs = self.hparams.augmentation(wavs, wav_lens)
-
- # Forward pass
- feats = self.modules.wav2vec2(wavs, wav_lens)
- x = self.modules.enc(feats)
- logits = self.modules.ctc_lin(x)
- p_ctc = self.hparams.log_softmax(logits)
-
- return p_ctc, wav_lens
-
- def treat_wav(self,sig):
- feats = self.modules.wav2vec2(sig.to("cpu"), torch.tensor([1]).to("cpu"))
- feats = self.modules.enc(feats)
- logits = self.modules.ctc_lin(feats)
- p_ctc = self.hparams.log_softmax(logits)
- predicted_words =[]
- for logs in p_ctc:
- text = decoder.decode(logs.detach().cpu().numpy())
- predicted_words.append(text.split(" "))
- return " ".join(predicted_words[0])
-
- def compute_objectives(self, predictions, batch, stage):
- """Computes the loss (CTC) given predictions and targets."""
-
- p_ctc, wav_lens = predictions
-
- ids = batch.id
- tokens, tokens_lens = batch.tokens
-
- loss = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)
-
- if stage != sb.Stage.TRAIN:
- predicted_tokens = sb.decoders.ctc_greedy_decode(
- p_ctc, wav_lens, blank_id=self.hparams.blank_index
- )
- # Decode token terms to words
- if self.hparams.use_language_modelling:
- predicted_words = []
- for logs in p_ctc:
- text = decoder.decode(logs.detach().cpu().numpy())
- predicted_words.append(text.split(" "))
- else:
- predicted_words = [
- "".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
- for utt_seq in predicted_tokens
- ]
- # Convert indices to words
- target_words = [wrd.split(" ") for wrd in batch.wrd]
-
- self.wer_metric.append(ids, predicted_words, target_words)
- self.cer_metric.append(ids, predicted_words, target_words)
-
- return loss
-
- def fit_batch(self, batch):
- """Train the parameters given a single batch in input"""
- should_step = self.step % self.grad_accumulation_factor == 0
- # Managing automatic mixed precision
- # TOFIX: CTC fine-tuning currently is unstable
- # This is certainly due to CTC being done in fp16 instead of fp32
- if self.auto_mix_prec:
- with torch.cuda.amp.autocast():
- with self.no_sync():
- outputs = self.compute_forward(batch, sb.Stage.TRAIN)
- loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
- with self.no_sync(not should_step):
- self.scaler.scale(
- loss / self.grad_accumulation_factor
- ).backward()
- if should_step:
-
- if not self.hparams.wav2vec2.freeze:
- self.scaler.unscale_(self.wav2vec_optimizer)
- self.scaler.unscale_(self.model_optimizer)
- if self.check_gradients(loss):
- if not self.hparams.wav2vec2.freeze:
- if self.optimizer_step >= self.hparams.warmup_steps:
- self.scaler.step(self.wav2vec_optimizer)
- self.scaler.step(self.model_optimizer)
- self.scaler.update()
- self.zero_grad()
- self.optimizer_step += 1
- else:
- # This is mandatory because HF models have a weird behavior with DDP
- # on the forward pass
- with self.no_sync():
- outputs = self.compute_forward(batch, sb.Stage.TRAIN)
-
- loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
-
- with self.no_sync(not should_step):
- (loss / self.grad_accumulation_factor).backward()
- if should_step:
- if self.check_gradients(loss):
- if not self.hparams.wav2vec2.freeze:
- if self.optimizer_step >= self.hparams.warmup_steps:
- self.wav2vec_optimizer.step()
- self.model_optimizer.step()
- self.zero_grad()
- self.optimizer_step += 1
-
- self.on_fit_batch_end(batch, outputs, loss, should_step)
- return loss.detach().cpu()
-
- def evaluate_batch(self, batch, stage):
- """Computations needed for validation/test batches"""
- predictions = self.compute_forward(batch, stage=stage)
- with torch.no_grad():
- loss = self.compute_objectives(predictions, batch, stage=stage)
- return loss.detach()
-
- def on_stage_start(self, stage, epoch):
- """Gets called at the beginning of each epoch"""
- if stage != sb.Stage.TRAIN:
- self.cer_metric = self.hparams.cer_computer()
- self.wer_metric = self.hparams.error_rate_computer()
-
- def on_stage_end(self, stage, stage_loss, epoch):
- """Gets called at the end of an epoch."""
- # Compute/store important stats
- stage_stats = {"loss": stage_loss}
- if stage == sb.Stage.TRAIN:
- self.train_stats = stage_stats
- else:
- stage_stats["CER"] = self.cer_metric.summarize("error_rate")
- stage_stats["WER"] = self.wer_metric.summarize("error_rate")
-
- # Perform end-of-iteration things, like annealing, logging, etc.
- if stage == sb.Stage.VALID:
- old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
- stage_stats["loss"]
- )
- old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
- stage_stats["loss"]
- )
- sb.nnet.schedulers.update_learning_rate(
- self.model_optimizer, new_lr_model
- )
- if not self.hparams.wav2vec2.freeze:
- sb.nnet.schedulers.update_learning_rate(
- self.wav2vec_optimizer, new_lr_wav2vec
- )
- self.hparams.train_logger.log_stats(
- stats_meta={
- "epoch": epoch,
- "lr_model": old_lr_model,
- "lr_wav2vec": old_lr_wav2vec,
- },
- train_stats=self.train_stats,
- valid_stats=stage_stats,
- )
- self.checkpointer.save_and_keep_only(
- meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
- )
- elif stage == sb.Stage.TEST:
- self.hparams.train_logger.log_stats(
- stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
- test_stats=stage_stats,
- )
- with open(self.hparams.wer_file, "w") as w:
- self.wer_metric.write_stats(w)
-
- def init_optimizers(self):
- "Initializes the wav2vec2 optimizer and model optimizer"
-
- # If the wav2vec encoder is unfrozen, we create the optimizer
- if not self.hparams.wav2vec2.freeze:
- self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
- self.modules.wav2vec2.parameters()
- )
- if self.checkpointer is not None:
- self.checkpointer.add_recoverable(
- "wav2vec_opt", self.wav2vec_optimizer
- )
-
- self.model_optimizer = self.hparams.model_opt_class(
- self.hparams.model.parameters()
- )
-
- if self.checkpointer is not None:
- self.checkpointer.add_recoverable("modelopt", self.model_optimizer)
-
- def zero_grad(self, set_to_none=False):
- if not self.hparams.wav2vec2.freeze:
- self.wav2vec_optimizer.zero_grad(set_to_none)
- self.model_optimizer.zero_grad(set_to_none)
-
-
-# Define custom data procedure
-def dataio_prepare(hparams):
- """This function prepares the datasets to be used in the brain class.
- It also defines the data processing pipeline through user-defined functions."""
-
- # 1. Define datasets
- data_folder = hparams["data_folder"]
-
- train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
- csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
- )
-
- if hparams["sorting"] == "ascending":
- # we sort training data to speed up training and get better results.
- train_data = train_data.filtered_sorted(
- sort_key="duration",
- key_max_value={"duration": hparams["avoid_if_longer_than"]},
- )
- # when sorting do not shuffle in dataloader ! otherwise is pointless
- hparams["dataloader_options"]["shuffle"] = False
-
- elif hparams["sorting"] == "descending":
- train_data = train_data.filtered_sorted(
- sort_key="duration",
- reverse=True,
- key_max_value={"duration": hparams["avoid_if_longer_than"]},
- )
- # when sorting do not shuffle in dataloader ! otherwise is pointless
- hparams["dataloader_options"]["shuffle"] = False
-
- elif hparams["sorting"] == "random":
- pass
-
- else:
- raise NotImplementedError(
- "sorting must be random, ascending or descending"
- )
-
- valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
- csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
- )
- # We also sort the validation data so it is faster to validate
- valid_data = valid_data.filtered_sorted(sort_key="duration")
- test_datasets = {}
- for csv_file in hparams["test_csv"]:
- name = Path(csv_file).stem
- test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
- csv_path=csv_file, replacements={"data_root": data_folder}
- )
- test_datasets[name] = test_datasets[name].filtered_sorted(
- sort_key="duration"
- )
-
- datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]
-
-
- # 2. Define audio pipeline:
- @sb.utils.data_pipeline.takes("wav")
- @sb.utils.data_pipeline.provides("sig")
- def audio_pipeline(wav):
- info = torchaudio.info(wav)
- sig = sb.dataio.dataio.read_audio(wav)
- resampled = torchaudio.transforms.Resample(
- info.sample_rate, hparams["sample_rate"],
- )(sig)
- return resampled
-
- sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
- label_encoder = sb.dataio.encoder.CTCTextEncoder()
-
- # 3. Define text pipeline:
- @sb.utils.data_pipeline.takes("wrd")
- @sb.utils.data_pipeline.provides(
- "wrd", "char_list", "tokens_list", "tokens"
- )
- def text_pipeline(wrd):
- yield wrd
- char_list = list(wrd)
- yield char_list
- tokens_list = label_encoder.encode_sequence(char_list)
- yield tokens_list
- tokens = torch.LongTensor(tokens_list)
- yield tokens
-
- sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
- lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
- special_labels = {
- "blank_label": hparams["blank_index"],
- "unk_label": hparams["unk_index"]
- }
- label_encoder.load_or_create(
- path=lab_enc_file,
- from_didatasets=[train_data],
- output_key="char_list",
- special_labels=special_labels,
- sequence_input=True,
- )
-
- # 4. Set output:
- sb.dataio.dataset.set_output_keys(
- datasets, ["id", "sig", "wrd", "char_list", "tokens"],
- )
- return train_data, valid_data,test_datasets, label_encoder
-
-
-
-# Load hyperparameters file with command-line overrides
-hparams_file, run_opts, overrides = sb.parse_arguments(["train_semi.yaml"])
-with open(hparams_file) as fin:
- hparams = load_hyperpyyaml(fin, overrides)
-
-# If --distributed_launch then
-# create ddp_group with the right communication protocol
-sb.utils.distributed.ddp_init_group(run_opts)
-
-
-# Create experiment directory
-sb.create_experiment_directory(
- experiment_directory=hparams["output_folder"],
- hyperparams_to_save=hparams_file,
- overrides=overrides,
-)
-
-# Due to DDP, we do the preparation ONLY on the main python process
-# Defining tokenizer and loading it
-# Create the datasets objects as well as tokenization and encoding :-D
-label_encoder = sb.dataio.encoder.CTCTextEncoder()
-
-lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
-special_labels = {
- "blank_label": hparams["blank_index"],
- "unk_label": hparams["unk_index"]
-}
-label_encoder.load_or_create(
- path=lab_enc_file,
- from_didatasets=[[]],
- output_key="char_list",
- special_labels=special_labels,
- sequence_input=True,
-)
-
-from pyctcdecode import build_ctcdecoder
-ind2lab = label_encoder.ind2lab
-print(ind2lab)
-labels = [ind2lab[x] for x in range(len(ind2lab))]
-labels = [""] + labels[1:-1] + ["1"]
-# Replace the token with a blank character, needed for PyCTCdecode
-print(labels)
-decoder = build_ctcdecoder(
- labels,
- kenlm_model_path=hparams["ngram_lm_path"], # .arpa or .bin
- alpha=0.5, # Default by KenLM
- beta=1.0, # Default by KenLM
-)
-# Trainer initialization
-run_opts["device"] = "cpu"
-asr_brain = ASR(
- modules=hparams["modules"],
- hparams=hparams,
- run_opts=run_opts,
- checkpointer=hparams["checkpointer"],
-)
-
-# Adding objects to trainer.
-asr_brain.tokenizer = label_encoder
-asr_brain.checkpointer.recover_if_possible(device="cpu")
-asr_brain.modules.eval()
-description = """This is a speechbrain-based Automatic Speech Recognition (ASR) model for Tunisian arabic. It outputs Tunisian Arabic transcriptions written in Arabic characters.
-This model outputs transcriptions in arabic alphabet only and performs poorly with sentences containing foreign words. However if you do need code-switching in your transcripts, i.e. foreign outputs in latin alphabet, you would better use the code switched model, available in another space from the same author. (https://huggingface.co/SalahZa/Code_Switched_Tunisian_Speech_Recognition)
-
-Run is done on CPU to keep it free in this space. This leads to quite long running times on long sequences. If for your project or research, you want to transcribe long sequences, you would better use the model directly from its page, some instructions for inference on a test set have been provided there. (https://huggingface.co/SalahZa/Tunisian_Automatic_Speech_Recognition). If you need help, feel free to drop an email here : zaiemsalah@gmail.com
-
-Authors :
-* [Salah Zaiem](https://fr.linkedin.com/in/salah-zaiem)
-* [Ahmed Amine Ben Aballah](https://www.linkedin.com/in/aabenz/)
-* [Ata Kaboudi](https://www.linkedin.com/in/ata-kaboudi-63365b1a8)
-* [Amir Kanoun](https://tn.linkedin.com/in/ahmed-amir-kanoun)
-
-More in-depth details and insights are available in a released preprint. Please find the paper [here](https://arxiv.org/abs/2309.11327).
-If you use or refer to this model, please cite :
-
-```
-@misc{abdallah2023leveraging,
- title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition},
- author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem},
- year={2023},
- eprint={2309.11327},
- archivePrefix={arXiv},
- primaryClass={eess.AS}
-}
-
-
-"""
-title = "Tunisian Speech Recognition"
-
-def treat_wav_file(file_mic,file_upload ,asr=asr_brain, device="cpu") :
- if (file_mic is not None) and (file_upload is not None):
- warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
- wav = file_mic
- elif (file_mic is None) and (file_upload is None):
- return "ERROR: You have to either use the microphone or upload an audio file"
- elif file_mic is not None:
- wav = file_mic
- else:
- wav = file_upload
- info = torchaudio.info(wav)
- sr = info.sample_rate
- sig = sb.dataio.dataio.read_audio(wav)
- if len(sig.shape)>1 :
- sig = torch.mean(sig, dim=1)
- sig = torch.unsqueeze(sig, 0)
- tensor_wav = sig.to(device)
- resampled = torchaudio.functional.resample( tensor_wav, sr, 16000)
- sentence = asr.treat_wav(resampled)
- return sentence
-
-gr.Interface(
- title = title,
- description = description,
- fn=treat_wav_file,
- inputs=[gr.Audio(source="microphone", type='filepath', label = "record", optional = True),
- gr.Audio(source="upload", type='filepath', label="filein", optional=True)]
- ,outputs="text").launch()
-
-
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/matching.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/matching.py
deleted file mode 100644
index fa1bdf809a36808492a973368519a8a8f599f235..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/matching.py
+++ /dev/null
@@ -1,229 +0,0 @@
-# -----------------------------------------------------
-# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
-# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
-# -----------------------------------------------------
-
-import numpy as np
-import torch
-from scipy.optimize import linear_sum_assignment
-
-sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89])
-
-
-def candidate_reselect(bboxes, bboxes_scores, pose_preds):
- '''
- Grouping
- '''
- # Group same keypointns together
- kp_groups = grouping(bboxes, bboxes_scores, pose_preds)
-
- '''
- Re-select
- '''
-
- # Generate Matrix
- human_num = len(pose_preds.keys())
- costMatrix = []
- for k in range(17):
- kp_group = kp_groups[k]
- joint_num = len(kp_group.keys())
-
- costMatrix.append(np.zeros((human_num, joint_num)))
-
- group_size = {k: {} for k in range(17)}
-
- for n, person in pose_preds.items():
- h_id = n
- assert 0 <= h_id < human_num
-
- for k in range(17):
- g_id = person['group_id'][k]
- if g_id is not None:
- if g_id not in group_size[k].keys():
- group_size[k][g_id] = 0
-
- group_size[k][g_id] += 1
-
- g_id = int(g_id) - 1
- _, _, score = person[k][0]
- h_score = person['human_score']
-
- if score < 0.05:
- costMatrix[k][h_id][g_id] = 0
- else:
- costMatrix[k][h_id][g_id] = -(h_score * score)
-
- pose_preds = matching(pose_preds, costMatrix, kp_groups)
-
- # To JSON
- final_result = []
-
- for n, person in pose_preds.items():
-
- final_pose = torch.zeros(17, 2)
- final_score = torch.zeros(17, 1)
-
- max_score = 0
- mean_score = 0
-
- xmax, xmin = 0, 1e5
- ymax, ymin = 0, 1e5
-
- for k in range(17):
- assert len(person[k]) > 0
- x, y, s = person[k][0]
-
- xmax = max(xmax, x)
- xmin = min(xmin, x)
- ymax = max(ymax, y)
- ymin = min(ymin, y)
-
- final_pose[k][0] = x.item() - 0.3
- final_pose[k][1] = y.item() - 0.3
- final_score[k] = s.item()
- mean_score += (s.item() / 17)
- max_score = max(max_score, s.item())
-
- if torch.max(final_score).item() < 0.1:
- continue
-
- if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40):
- continue
-
- final_result.append({
- 'keypoints': final_pose,
- 'kp_score': final_score,
- 'proposal_score': mean_score + max_score + person['bbox_score']
- })
-
- return final_result
-
-
-def grouping(bboxes, bboxes_scores, pose_preds):
- kp_groups = {}
- for k in range(17):
- kp_groups[k] = {}
-
- ids = np.zeros(17)
-
- for n, person in pose_preds.items():
- pose_preds[n]['bbox'] = bboxes[n]
- pose_preds[n]['bbox_score'] = bboxes_scores[n]
- pose_preds[n]['group_id'] = {}
- s = 0
-
- for k in range(17):
- pose_preds[n]['group_id'][k] = None
- pose_preds[n][k] = np.array(pose_preds[n][k])
- assert len(pose_preds[n][k]) > 0
- s += pose_preds[n][k][0][-1]
-
- s = s / 17
-
- pose_preds[n]['human_score'] = s
-
- for k in range(17):
- latest_id = ids[k]
- kp_group = kp_groups[k]
-
- assert len(person[k]) > 0
- x0, y0, s0 = person[k][0]
- if s0 < 0.05:
- continue
-
- for g_id, g in kp_group.items():
- x_c, y_c = kp_group[g_id]['group_center']
-
- '''
- Get Average Box Size
- '''
- group_area = kp_group[g_id]['group_area']
- group_area = group_area[0] * group_area[1] / (group_area[2] ** 2)
-
- '''
- Groupingn Criterion
- '''
- # Joint Group
- dist = np.sqrt(
- ((x_c - x0) ** 2 + (y_c - y0) ** 2) / group_area)
-
- if dist <= 0.1 * sigmas[k]: # Small Distance
- if s0 >= 0.3:
- kp_group[g_id]['kp_list'][0] += x0 * s0
- kp_group[g_id]['kp_list'][1] += y0 * s0
- kp_group[g_id]['kp_list'][2] += s0
-
- kp_group[g_id]['group_area'][0] += (person['bbox'][2] - person['bbox'][0]) * person['human_score']
- kp_group[g_id]['group_area'][1] += (person['bbox'][3] - person['bbox'][1]) * person['human_score']
- kp_group[g_id]['group_area'][2] += person['human_score']
-
- x_c = kp_group[g_id]['kp_list'][0] / kp_group[g_id]['kp_list'][2]
- y_c = kp_group[g_id]['kp_list'][1] / kp_group[g_id]['kp_list'][2]
- kp_group[g_id]['group_center'] = (x_c, y_c)
-
- pose_preds[n]['group_id'][k] = g_id
- break
- else:
- # A new keypoint group
- latest_id += 1
- kp_group[latest_id] = {
- 'kp_list': None,
- 'group_center': person[k][0].copy()[:2],
- 'group_area': None
- }
-
- x, y, s = person[k][0]
- kp_group[latest_id]['kp_list'] = np.array((x * s, y * s, s))
-
- # Ref Area
- ref_width = person['bbox'][2] - person['bbox'][0]
- ref_height = person['bbox'][3] - person['bbox'][1]
- ref_score = person['human_score']
- kp_group[latest_id]['group_area'] = np.array((
- ref_width * ref_score, ref_height * ref_score, ref_score))
-
- pose_preds[n]['group_id'][k] = latest_id
- ids[k] = latest_id
- return kp_groups
-
-
-def matching(pose_preds, matrix, kp_groups):
- index = []
- for k in range(17):
- human_ind, joint_ind = linear_sum_assignment(matrix[k])
- # human_ind, joint_ind = greedy_matching(matrix[k])
-
- index.append(list(zip(human_ind, joint_ind)))
-
- for n, person in pose_preds.items():
- for k in range(17):
- g_id = person['group_id'][k]
- if g_id is not None:
- g_id = int(g_id) - 1
- h_id = n
-
- x, y, s = pose_preds[n][k][0]
- if ((h_id, g_id) not in index[k]) and len(pose_preds[n][k]) > 1:
- pose_preds[n][k] = np.delete(pose_preds[n][k], 0, 0)
- elif ((h_id, g_id) not in index[k]) and len(person[k]) == 1:
- x, y, _ = pose_preds[n][k][0]
- pose_preds[n][k][0] = (x, y, 1e-5)
- pass
- elif ((h_id, g_id) in index[k]):
- x, y = kp_groups[k][g_id + 1]['group_center']
- s = pose_preds[n][k][0][2]
- pose_preds[n][k][0] = (x, y, s)
-
- return pose_preds
-
-
-def greedy_matching(matrix):
- num_human, num_joint = matrix.shape
-
- if num_joint <= num_human or True:
- human_ind = np.argmin(matrix, axis=0)
- joint_ind = np.arange(num_joint)
- else:
- pass
-
- return human_ind.tolist(), joint_ind.tolist()
diff --git a/spaces/SeViLA/SeViLA/lavis/models/clip_models/model.py b/spaces/SeViLA/SeViLA/lavis/models/clip_models/model.py
deleted file mode 100644
index 8c3d5651848c6935e584abab1f9ecaad873b5392..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/clip_models/model.py
+++ /dev/null
@@ -1,1254 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-
- Based on https://github.com/mlfoundations/open_clip
-"""
-
-""" CLIP Model
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-
-import datetime
-import json
-import logging
-import os
-import re
-import time
-import warnings
-from collections import OrderedDict
-from copy import deepcopy
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Callable, List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from lavis.common.registry import registry
-from lavis.common.utils import get_abs_path
-from lavis.models.base_model import BaseModel
-from lavis.models.clip_models.clip_outputs import ClipOutput, ClipOutputFeatures
-from lavis.models.clip_models.timm_model import TimmModel
-from lavis.models.clip_models.transform import image_transform
-from lavis.models.clip_models.utils import freeze_batch_norm_2d
-from lavis.tasks.multimodal_classification import MultimodalClassificationTask
-from torch import nn
-
-from .pretrained import (
- download_pretrained,
- get_pretrained_url,
- list_pretrained_tag_models,
-)
-
-_MODEL_CONFIG_PATHS = [Path(__file__).parent.parent.parent / f"configs/models/clip/"]
-_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1):
- super().__init__()
-
- # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
- self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
-
- self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
-
- self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
- self.relu = nn.ReLU(inplace=True)
- self.downsample = None
- self.stride = stride
-
- if stride > 1 or inplanes != planes * Bottleneck.expansion:
- # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
- self.downsample = nn.Sequential(
- OrderedDict(
- [
- ("-1", nn.AvgPool2d(stride)),
- (
- "0",
- nn.Conv2d(
- inplanes,
- planes * self.expansion,
- 1,
- stride=1,
- bias=False,
- ),
- ),
- ("1", nn.BatchNorm2d(planes * self.expansion)),
- ]
- )
- )
-
- def forward(self, x: torch.Tensor):
- identity = x
-
- out = self.relu(self.bn1(self.conv1(x)))
- out = self.relu(self.bn2(self.conv2(out)))
- out = self.avgpool(out)
- out = self.bn3(self.conv3(out))
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
- return out
-
-
-class AttentionPool2d(nn.Module):
- def __init__(
- self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
- ):
- super().__init__()
- self.positional_embedding = nn.Parameter(
- torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
- )
- self.k_proj = nn.Linear(embed_dim, embed_dim)
- self.q_proj = nn.Linear(embed_dim, embed_dim)
- self.v_proj = nn.Linear(embed_dim, embed_dim)
- self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
- self.num_heads = num_heads
-
- def forward(self, x):
- x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(
- 2, 0, 1
- ) # NCHW -> (HW)NC
- x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
- x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
- x, _ = F.multi_head_attention_forward(
- query=x,
- key=x,
- value=x,
- embed_dim_to_check=x.shape[-1],
- num_heads=self.num_heads,
- q_proj_weight=self.q_proj.weight,
- k_proj_weight=self.k_proj.weight,
- v_proj_weight=self.v_proj.weight,
- in_proj_weight=None,
- in_proj_bias=torch.cat(
- [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
- ),
- bias_k=None,
- bias_v=None,
- add_zero_attn=False,
- dropout_p=0,
- out_proj_weight=self.c_proj.weight,
- out_proj_bias=self.c_proj.bias,
- use_separate_proj_weight=True,
- training=self.training,
- need_weights=False,
- )
-
- return x[0]
-
-
-class ModifiedResNet(nn.Module):
- """
- A ResNet class that is similar to torchvision's but contains the following changes:
- - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
- - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
- - The final pooling layer is a QKV attention instead of an average pool
- """
-
- def __init__(self, layers, output_dim, heads, image_size=224, width=64):
- super().__init__()
- self.output_dim = output_dim
- self.image_size = image_size
-
- # the 3-layer stem
- self.conv1 = nn.Conv2d(
- 3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
- )
- self.bn1 = nn.BatchNorm2d(width // 2)
- self.conv2 = nn.Conv2d(
- width // 2, width // 2, kernel_size=3, padding=1, bias=False
- )
- self.bn2 = nn.BatchNorm2d(width // 2)
- self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
- self.bn3 = nn.BatchNorm2d(width)
- self.avgpool = nn.AvgPool2d(2)
- self.relu = nn.ReLU(inplace=True)
-
- # residual layers
- self._inplanes = width # this is a *mutable* variable used during construction
- self.layer1 = self._make_layer(width, layers[0])
- self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
- self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
- self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
- embed_dim = width * 32 # the ResNet feature dimension
- self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
-
- self.init_parameters()
-
- def _make_layer(self, planes, blocks, stride=1):
- layers = [Bottleneck(self._inplanes, planes, stride)]
-
- self._inplanes = planes * Bottleneck.expansion
- for _ in range(1, blocks):
- layers.append(Bottleneck(self._inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def init_parameters(self):
- if self.attnpool is not None:
- std = self.attnpool.c_proj.in_features**-0.5
- nn.init.normal_(self.attnpool.q_proj.weight, std=std)
- nn.init.normal_(self.attnpool.k_proj.weight, std=std)
- nn.init.normal_(self.attnpool.v_proj.weight, std=std)
- nn.init.normal_(self.attnpool.c_proj.weight, std=std)
-
- for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
- for name, param in resnet_block.named_parameters():
- if name.endswith("bn3.weight"):
- nn.init.zeros_(param)
-
- def lock(self, unlocked_groups=0, freeze_bn_stats=False):
- assert (
- unlocked_groups == 0
- ), "partial locking not currently supported for this model"
- for param in self.parameters():
- param.requires_grad = False
- if freeze_bn_stats:
- freeze_batch_norm_2d(self)
-
- def stem(self, x):
- for conv, bn in [
- (self.conv1, self.bn1),
- (self.conv2, self.bn2),
- (self.conv3, self.bn3),
- ]:
- x = self.relu(bn(conv(x)))
- x = self.avgpool(x)
- return x
-
- def forward(self, x):
- x = self.stem(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- x = self.attnpool(x)
-
- return x
-
-
-class LayerNorm(nn.LayerNorm):
- """Subclass torch's LayerNorm to handle fp16."""
-
- def forward(self, x: torch.Tensor):
- orig_type = x.dtype
- x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
- return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
- # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
- def forward(self, x: torch.Tensor):
- return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
- def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU):
- super().__init__()
-
- self.attn = nn.MultiheadAttention(d_model, n_head)
- self.ln_1 = LayerNorm(d_model)
- self.mlp = nn.Sequential(
- OrderedDict(
- [
- ("c_fc", nn.Linear(d_model, d_model * 4)),
- ("gelu", act_layer()),
- ("c_proj", nn.Linear(d_model * 4, d_model)),
- ]
- )
- )
- self.ln_2 = LayerNorm(d_model)
-
- def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
- return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
-
- def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
- x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
- x = x + self.mlp(self.ln_2(x))
- return x
-
-
-class Transformer(nn.Module):
- def __init__(
- self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU
- ):
- super().__init__()
- self.width = width
- self.layers = layers
- self.resblocks = nn.ModuleList(
- [
- ResidualAttentionBlock(width, heads, act_layer=act_layer)
- for _ in range(layers)
- ]
- )
-
- def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
- for r in self.resblocks:
- x = r(x, attn_mask=attn_mask)
- return x
-
-
-class VisualTransformer(nn.Module):
- def __init__(
- self,
- image_size: int,
- patch_size: int,
- width: int,
- layers: int,
- heads: int,
- output_dim: int,
- act_layer: Callable = nn.GELU,
- ):
- super().__init__()
- self.image_size = image_size
- self.output_dim = output_dim
- self.conv1 = nn.Conv2d(
- in_channels=3,
- out_channels=width,
- kernel_size=patch_size,
- stride=patch_size,
- bias=False,
- )
-
- scale = width**-0.5
- self.class_embedding = nn.Parameter(scale * torch.randn(width))
- self.positional_embedding = nn.Parameter(
- scale * torch.randn((image_size // patch_size) ** 2 + 1, width)
- )
- self.ln_pre = LayerNorm(width)
-
- self.transformer = Transformer(width, layers, heads, act_layer=act_layer)
-
- self.ln_post = LayerNorm(width)
- self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
- def lock(self, unlocked_groups=0, freeze_bn_stats=False):
- assert (
- unlocked_groups == 0
- ), "partial locking not currently supported for this model"
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, x: torch.Tensor):
- x = self.conv1(x) # shape = [*, width, grid, grid]
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
- x = torch.cat(
- [
- self.class_embedding.to(x.dtype)
- + torch.zeros(
- x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
- ),
- x,
- ],
- dim=1,
- ) # shape = [*, grid ** 2 + 1, width]
- x = x + self.positional_embedding.to(x.dtype)
- x = self.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
-
- x = self.ln_post(x[:, 0, :])
-
- if self.proj is not None:
- x = x @ self.proj
-
- return x
-
-
-@dataclass
-class CLIPVisionCfg:
- layers: Union[Tuple[int, int, int, int], int] = 12
- width: int = 768
- patch_size: int = 16
- image_size: Union[Tuple[int, int], int] = 224
- timm_model_name: str = (
- None # a valid model name overrides layers, width, patch_size
- )
- timm_model_pretrained: bool = (
- False # use (imagenet) pretrained weights for named model
- )
- timm_pool: str = (
- "avg" # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
- )
- timm_proj: str = (
- "linear" # linear projection for timm model output ('linear', 'mlp', '')
- )
-
-
-@dataclass
-class CLIPTextCfg:
- context_length: int
- vocab_size: int
- width: int
- heads: int
- layers: int
-
-
-@registry.register_model("clip")
-@registry.register_model("clip_feature_extractor")
-class CLIP(BaseModel):
- PRETRAINED_MODEL_CONFIG_DICT = {
- "ViT-B-32": "configs/models/clip_vit_base32.yaml",
- "ViT-B-16": "configs/models/clip_vit_base16.yaml",
- "ViT-L-14": "configs/models/clip_vit_large14.yaml",
- "ViT-L-14-336": "configs/models/clip_vit_large14_336.yaml",
- "RN50": "configs/models/clip_resnet50.yaml",
- }
-
- def __init__(
- self,
- embed_dim: int,
- vision_cfg: CLIPVisionCfg,
- text_cfg: CLIPTextCfg,
- quick_gelu: bool = False,
- ):
- from .tokenizer import tokenize
-
- super().__init__()
-
- self.tokenizer = tokenize
- self._loss = None
-
- if isinstance(vision_cfg, dict):
- vision_cfg = CLIPVisionCfg(**vision_cfg)
- if isinstance(text_cfg, dict):
- text_cfg = CLIPTextCfg(**text_cfg)
-
- self.context_length = text_cfg.context_length
-
- # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
- # memory efficient in recent PyTorch releases (>= 1.10).
- # NOTE: timm models always use native GELU regardless of quick_gelu flag.
- act_layer = QuickGELU if quick_gelu else nn.GELU
-
- if vision_cfg.timm_model_name:
- self.visual = TimmModel(
- vision_cfg.timm_model_name,
- pretrained=vision_cfg.timm_model_pretrained,
- pool=vision_cfg.timm_pool,
- proj=vision_cfg.timm_proj,
- embed_dim=embed_dim,
- image_size=vision_cfg.image_size,
- )
- act_layer = (
- nn.GELU
- ) # so that text transformer doesn't use QuickGELU w/ timm models
- elif isinstance(vision_cfg.layers, (tuple, list)):
- vision_heads = vision_cfg.width * 32 // 64
- self.visual = ModifiedResNet(
- layers=vision_cfg.layers,
- output_dim=embed_dim,
- heads=vision_heads,
- image_size=vision_cfg.image_size,
- width=vision_cfg.width,
- )
- else:
- vision_heads = vision_cfg.width // 64
- self.visual = VisualTransformer(
- image_size=vision_cfg.image_size,
- patch_size=vision_cfg.patch_size,
- width=vision_cfg.width,
- layers=vision_cfg.layers,
- heads=vision_heads,
- output_dim=embed_dim,
- act_layer=act_layer,
- )
-
- self.transformer = Transformer(
- width=text_cfg.width,
- layers=text_cfg.layers,
- heads=text_cfg.heads,
- act_layer=act_layer,
- )
-
- self.vocab_size = text_cfg.vocab_size
- self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
- self.positional_embedding = nn.Parameter(
- torch.empty(self.context_length, text_cfg.width)
- )
- self.ln_final = LayerNorm(text_cfg.width)
-
- self.text_projection = nn.Parameter(torch.empty(text_cfg.width, embed_dim))
- self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
- self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False)
-
- self.prompt_templates = openai_imagenet_template
- self.classifier = None
-
- self.init_parameters()
-
- @property
- def loss(self):
- if self._loss is None:
- from lavis.models.clip_models.loss import ClipLoss
- from torch import distributed as dist
-
- self._loss = ClipLoss(
- world_size=dist.get_world_size(),
- rank=dist.get_rank(),
- local_loss=False,
- gather_with_grad=False,
- use_horovod=False,
- )
-
- return self._loss
-
- def init_parameters(self):
- nn.init.normal_(self.token_embedding.weight, std=0.02)
- nn.init.normal_(self.positional_embedding, std=0.01)
- nn.init.constant_(self.logit_scale, np.log(1 / 0.07))
-
- if hasattr(self.visual, "init_parameters"):
- self.visual.init_parameters()
-
- proj_std = (self.transformer.width**-0.5) * (
- (2 * self.transformer.layers) ** -0.5
- )
- attn_std = self.transformer.width**-0.5
- fc_std = (2 * self.transformer.width) ** -0.5
- for block in self.transformer.resblocks:
- nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
- nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
- nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
- nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
- if self.text_projection is not None:
- nn.init.normal_(self.text_projection, std=self.transformer.width**-0.5)
-
- def build_attention_mask(self):
- # lazily create causal attention mask, with full attention between the vision tokens
- # pytorch uses additive attention mask; fill with -inf
- mask = torch.empty(self.context_length, self.context_length)
- mask.fill_(float("-inf"))
- mask.triu_(1) # zero out the lower diagonal
- return mask
-
- def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
- # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
- self.visual.lock(
- unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats
- )
-
- def encode_image(self, image):
- return self.visual(image)
-
- def encode_text(self, text):
- x = self.token_embedding(text) # [batch_size, n_ctx, d_model]
-
- x = x + self.positional_embedding
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x, attn_mask=self.attn_mask)
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.ln_final(x)
-
- # x.shape = [batch_size, n_ctx, transformer.width]
- # take features from the eot embedding (eot_token is the highest number in each sequence)
- x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
- return x
-
- # def forward(self, image, text):
- def forward(self, samples):
- image = samples.get("image")
- text = samples.get("text_input")
-
- if text is not None:
- text = self.tokenizer(text).to(self.device)
-
- if image is None:
- return self.encode_text(text)
- elif text is None:
- return self.encode_image(image)
- image_embeds = self.encode_image(image)
- image_features = F.normalize(image_embeds, dim=-1)
-
- text_embeds = self.encode_text(text)
- text_features = F.normalize(text_embeds, dim=-1)
-
- loss = self.loss(image_features, text_features, self.logit_scale.exp())
-
- # return image_features, text_features, self.logit_scale.exp()
- # return {"loss": loss}
- return ClipOutput(
- intermediate_output=ClipOutputFeatures(
- image_embeds=image_embeds,
- image_embeds_proj=image_features,
- text_embeds=text_embeds,
- text_embeds_proj=text_features,
- ),
- loss=loss,
- logit_scale_exp=self.logit_scale.exp(),
- )
-
- def extract_features(self, samples):
- """
- Extract features from the model for samples.
-
- Keys allowed are "image" and "text_input" in samples.
- If either key is missing, the corresponding features are not extracted.
-
- Args:
- samples: dict of samples to extract features from.
-
- Returns:
- ClipOutputFeatures object with features for the samples.
- """
- image = samples.get("image")
- text = samples.get("text_input")
-
- if text is not None:
- text = self.tokenizer(text).to(self.device)
-
- if image is None:
- return self.encode_text(text)
- elif text is None:
- return self.encode_image(image)
-
- image_embeds = self.encode_image(image)
- image_features = F.normalize(image_embeds, dim=-1)
-
- text_embeds = self.encode_text(text)
- text_features = F.normalize(text_embeds, dim=-1)
-
- return ClipOutputFeatures(
- image_embeds=image_embeds,
- image_embeds_proj=image_features,
- text_embeds=text_embeds,
- text_embeds_proj=text_features,
- )
-
- def predict(self, samples):
- image = samples["image"]
- targets = samples["label"]
-
- image_features = self.encode_image(image)
- image_features = F.normalize(image_features, dim=-1)
-
- logits = 100.0 * image_features @ self.classifier
-
- return {"predictions": logits, "targets": targets}
-
- def before_evaluation(self, dataset, task_type, **kwargs):
- if task_type == MultimodalClassificationTask:
- self.classifier = self.zero_shot_classifier(
- classnames=dataset.classnames,
- templates=self.prompt_templates,
- )
-
- def zero_shot_classifier(self, classnames, templates):
- with torch.no_grad():
- zeroshot_weights = []
- for classname in classnames:
- texts = [
- template(classname) for template in templates
- ] # format with class
- texts = self.tokenizer(texts).to(self.device) # tokenize
-
- class_embeddings = self.encode_text(texts)
- class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0)
- class_embedding /= class_embedding.norm()
- zeroshot_weights.append(class_embedding)
- zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(self.device)
- return zeroshot_weights
-
- @classmethod
- def default_config_path(cls, model_type="base"):
- model_type = "ViT-B-32" if model_type == "base" else model_type
-
- assert (
- model_type in cls.PRETRAINED_MODEL_CONFIG_DICT
- ), "Unknown model type {}. \n Available types: {}".format(
- model_type, cls.PRETRAINED_MODEL_CONFIG_DICT.keys()
- )
- return get_abs_path(cls.PRETRAINED_MODEL_CONFIG_DICT[model_type])
-
- @classmethod
- def from_config(cls, cfg=None):
- model_name = cfg.model_type
- pretrained = cfg.pretrained
-
- precision = cfg.get("precision", "fp32")
-
- return create_model(
- model_name=model_name, pretrained=pretrained, precision=precision
- )
-
- def zero_shot_predict(self, image_path, categories):
- assert isinstance(
- categories, list
- ), f"categories must be a list, got {type(categories)}."
- assert os.path.exists(image_path), f"File {image_path} does not exist."
-
- from lavis.processors.clip_processors import ClipImageEvalProcessor
- from PIL import Image
-
- image_preprocess = ClipImageEvalProcessor()
- image = image_preprocess(Image.open(image_path)).unsqueeze(0)
-
- text = self.tokenizer(categories)
-
- with torch.no_grad():
- image_features = self.encode_image(image)
- text_features = self.encode_text(text)
- image_features /= image_features.norm(dim=-1, keepdim=True)
- text_features /= text_features.norm(dim=-1, keepdim=True)
-
- text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
-
- print("Label probs:", text_probs) # prints: [[1., 0., 0.]]
-
- def compute_sim_matrix(self, data_loader, **kwargs):
- logging.info("Computing features for evaluation...")
- start_time = time.time()
-
- texts = data_loader.dataset.text
- num_text = len(texts)
- text_bs = 256
- text_features = []
-
- for i in range(0, num_text, text_bs):
-
- text = texts[i : min(num_text, i + text_bs)]
- text_input = self.tokenizer(text).to(self.device)
-
- text_feat = self.encode_text(text_input)
- text_feat = F.normalize(text_feat, dim=-1)
-
- text_features.append(text_feat)
-
- text_features = torch.cat(text_features, dim=0)
-
- image_features = []
- for samples in data_loader:
- image = samples["image"]
-
- image = image.to(self.device)
- image_feat = self.encode_image(image)
- image_feat = F.normalize(image_feat, dim=-1)
-
- image_features.append(image_feat)
-
- image_features = torch.cat(image_features, dim=0)
-
- sims_matrix_i2t = image_features @ text_features.t()
- sims_matrix_t2i = sims_matrix_i2t.t()
-
- total_time = time.time() - start_time
- total_time_str = str(datetime.timedelta(seconds=int(total_time)))
- logging.info("Evaluation time {}".format(total_time_str))
-
- return sims_matrix_i2t.cpu().numpy(), sims_matrix_t2i.cpu().numpy()
-
-
-def convert_weights_to_fp16(model: nn.Module):
- """Convert applicable model parameters to fp16"""
-
- def _convert_weights_to_fp16(l):
- if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
- l.weight.data = l.weight.data.half()
- if l.bias is not None:
- l.bias.data = l.bias.data.half()
-
- if isinstance(l, nn.MultiheadAttention):
- for attr in [
- *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
- "in_proj_bias",
- "bias_k",
- "bias_v",
- ]:
- tensor = getattr(l, attr)
- if tensor is not None:
- tensor.data = tensor.data.half()
-
- for name in ["text_projection", "proj"]:
- if hasattr(l, name):
- attr = getattr(l, name)
- if attr is not None:
- attr.data = attr.data.half()
-
- model.apply(_convert_weights_to_fp16)
-
-
-def build_model_from_openai_state_dict(state_dict: dict):
- vit = "visual.proj" in state_dict
-
- if vit:
- vision_width = state_dict["visual.conv1.weight"].shape[0]
- vision_layers = len(
- [
- k
- for k in state_dict.keys()
- if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
- ]
- )
- vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
- grid_size = round(
- (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5
- )
- image_size = vision_patch_size * grid_size
- else:
- counts: list = [
- len(
- set(
- k.split(".")[2]
- for k in state_dict
- if k.startswith(f"visual.layer{b}")
- )
- )
- for b in [1, 2, 3, 4]
- ]
- vision_layers = tuple(counts)
- vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
- output_width = round(
- (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5
- )
- vision_patch_size = None
- assert (
- output_width**2 + 1
- == state_dict["visual.attnpool.positional_embedding"].shape[0]
- )
- image_size = output_width * 32
-
- embed_dim = state_dict["text_projection"].shape[1]
- context_length = state_dict["positional_embedding"].shape[0]
- vocab_size = state_dict["token_embedding.weight"].shape[0]
- transformer_width = state_dict["ln_final.weight"].shape[0]
- transformer_heads = transformer_width // 64
- transformer_layers = len(
- set(
- k.split(".")[2]
- for k in state_dict
- if k.startswith(f"transformer.resblocks")
- )
- )
-
- vision_cfg = CLIPVisionCfg(
- layers=vision_layers,
- width=vision_width,
- patch_size=vision_patch_size,
- image_size=image_size,
- )
- text_cfg = CLIPTextCfg(
- context_length=context_length,
- vocab_size=vocab_size,
- width=transformer_width,
- heads=transformer_heads,
- layers=transformer_layers,
- )
- model = CLIP(
- embed_dim,
- vision_cfg=vision_cfg,
- text_cfg=text_cfg,
- quick_gelu=True, # OpenAI models were trained with QuickGELU
- )
-
- for key in ["input_resolution", "context_length", "vocab_size"]:
- state_dict.pop(key, None)
-
- convert_weights_to_fp16(model)
- model.load_state_dict(state_dict)
- return model.eval()
-
-
-def trace_model(model, batch_size=256, device=torch.device("cpu")):
- model.eval()
- image_size = model.visual.image_size
- example_images = torch.ones((batch_size, 3, image_size, image_size), device=device)
- example_text = torch.zeros(
- (batch_size, model.context_length), dtype=torch.int, device=device
- )
- model = torch.jit.trace_module(
- model,
- inputs=dict(
- forward=(example_images, example_text),
- encode_text=(example_text,),
- encode_image=(example_images,),
- ),
- )
- model.visual.image_size = image_size
- return
-
-
-def _natural_key(string_):
- return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())]
-
-
-def _rescan_model_configs():
- global _MODEL_CONFIGS
-
- config_ext = (".json",)
- config_files = []
- for config_path in _MODEL_CONFIG_PATHS:
- if config_path.is_file() and config_path.suffix in config_ext:
- config_files.append(config_path)
- elif config_path.is_dir():
- for ext in config_ext:
- config_files.extend(config_path.glob(f"*{ext}"))
-
- for cf in config_files:
- with open(cf, "r") as f:
- model_cfg = json.load(f)
- if all(a in model_cfg for a in ("embed_dim", "vision_cfg", "text_cfg")):
- _MODEL_CONFIGS[cf.stem] = model_cfg
-
- _MODEL_CONFIGS = {
- k: v
- for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))
- }
-
-
-_rescan_model_configs() # initial populate of model config registry
-
-
-def load_state_dict(checkpoint_path: str, map_location="cpu"):
- checkpoint = torch.load(checkpoint_path, map_location=map_location)
- if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
- state_dict = checkpoint["state_dict"]
- else:
- state_dict = checkpoint
- if next(iter(state_dict.items()))[0].startswith("module"):
- state_dict = {k[7:]: v for k, v in state_dict.items()}
- return state_dict
-
-
-def create_model(
- model_name: str,
- pretrained: str = "",
- precision: str = "fp32",
- device: torch.device = torch.device("cpu"),
- jit: bool = False,
- force_quick_gelu: bool = False,
- pretrained_image: bool = False,
-):
- model_name = model_name.replace(
- "/", "-"
- ) # for callers using old naming with / in ViT names
-
- if pretrained.lower() == "openai":
- logging.info(f"Loading pretrained {model_name} from OpenAI.")
- model = load_openai_model(model_name, device=device, jit=jit)
- # See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372
- if precision == "amp" or precision == "fp32":
- model = model.float()
- else:
- logging.info(f"No pretrained weights loaded for {model_name} model.")
- if model_name in _MODEL_CONFIGS:
- logging.info(f"Loading {model_name} model config.")
- model_cfg = deepcopy(_MODEL_CONFIGS[model_name])
- else:
- logging.error(
- f"Model config for {model_name} not found; available models {list_models()}."
- )
- raise RuntimeError(f"Model config for {model_name} not found.")
-
- if force_quick_gelu:
- # override for use of QuickGELU on non-OpenAI transformer models
- model_cfg["quick_gelu"] = True
-
- if pretrained_image:
- if "timm_model_name" in model_cfg.get("vision_cfg", {}):
- # pretrained weight loading for timm models set via vision_cfg
- model_cfg["vision_cfg"]["timm_model_pretrained"] = True
- else:
- assert (
- False
- ), "pretrained image towers currently only supported for timm models"
-
- model = CLIP(**model_cfg)
-
- if pretrained:
- checkpoint_path = ""
- url = get_pretrained_url(model_name, pretrained)
- if url:
- checkpoint_path = download_pretrained(url)
- elif os.path.exists(pretrained):
- checkpoint_path = pretrained
-
- if checkpoint_path:
- logging.info(f"Loading pretrained {model_name} weights ({pretrained}).")
- model.load_state_dict(load_state_dict(checkpoint_path))
- else:
- logging.warning(
- f"Pretrained weights ({pretrained}) not found for model {model_name}."
- )
- raise RuntimeError(
- f"Pretrained weights ({pretrained}) not found for model {model_name}."
- )
-
- model.to(device=device)
- if precision == "fp16":
- assert device.type != "cpu"
- convert_weights_to_fp16(model)
-
- if jit:
- model = torch.jit.script(model)
-
- return model
-
-
-def create_model_and_transforms(
- model_name: str,
- pretrained: str = "",
- precision: str = "fp32",
- device: torch.device = torch.device("cpu"),
- jit: bool = False,
- force_quick_gelu: bool = False,
- pretrained_image: bool = False,
-):
- model = create_model(
- model_name,
- pretrained,
- precision,
- device,
- jit,
- force_quick_gelu=force_quick_gelu,
- pretrained_image=pretrained_image,
- )
- preprocess_train = image_transform(model.visual.image_size, is_train=True)
- preprocess_val = image_transform(model.visual.image_size, is_train=False)
- return model, preprocess_train, preprocess_val
-
-
-def list_models():
- """enumerate available model architectures based on config files"""
- return list(_MODEL_CONFIGS.keys())
-
-
-def add_model_config(path):
- """add model config path or file and update registry"""
- if not isinstance(path, Path):
- path = Path(path)
- _MODEL_CONFIG_PATHS.append(path)
- _rescan_model_configs()
-
-
-def list_openai_models() -> List[str]:
- """Returns the names of available CLIP models"""
- return list_pretrained_tag_models("openai")
-
-
-def load_openai_model(
- name: str,
- device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
- jit=True,
-):
- """Load a CLIP model
- Parameters
- ----------
- name : str
- A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
- device : Union[str, torch.device]
- The device to put the loaded model
- jit : bool
- Whether to load the optimized JIT model (default) or more hackable non-JIT model.
- Returns
- -------
- model : torch.nn.Module
- The CLIP model
- preprocess : Callable[[PIL.Image], torch.Tensor]
- A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
- """
- if get_pretrained_url(name, "openai"):
- model_path = download_pretrained(get_pretrained_url(name, "openai"))
- elif os.path.isfile(name):
- model_path = name
- else:
- raise RuntimeError(
- f"Model {name} not found; available models = {list_openai_models()}"
- )
-
- try:
- # loading JIT archive
- model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
- state_dict = None
- except RuntimeError:
- # loading saved state dict
- if jit:
- warnings.warn(
- f"File {model_path} is not a JIT archive. Loading as a state dict instead"
- )
- jit = False
- state_dict = torch.load(model_path, map_location="cpu")
-
- if not jit:
- try:
- model = build_model_from_openai_state_dict(
- state_dict or model.state_dict()
- ).to(device)
- except KeyError:
- sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
- model = build_model_from_openai_state_dict(sd).to(device)
-
- if str(device) == "cpu":
- model.float()
- return model
-
- # patch the device names
- device_holder = torch.jit.trace(
- lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]
- )
- device_node = [
- n
- for n in device_holder.graph.findAllNodes("prim::Constant")
- if "Device" in repr(n)
- ][-1]
-
- def patch_device(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("prim::Constant"):
- if "value" in node.attributeNames() and str(node["value"]).startswith(
- "cuda"
- ):
- node.copyAttributes(device_node)
-
- model.apply(patch_device)
- patch_device(model.encode_image)
- patch_device(model.encode_text)
-
- # patch dtype to float32 on CPU
- if str(device) == "cpu":
- float_holder = torch.jit.trace(
- lambda: torch.ones([]).float(), example_inputs=[]
- )
- float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
- float_node = float_input.node()
-
- def patch_float(module):
- try:
- graphs = [module.graph] if hasattr(module, "graph") else []
- except RuntimeError:
- graphs = []
-
- if hasattr(module, "forward1"):
- graphs.append(module.forward1.graph)
-
- for graph in graphs:
- for node in graph.findAllNodes("aten::to"):
- inputs = list(node.inputs())
- for i in [
- 1,
- 2,
- ]: # dtype can be the second or third argument to aten::to()
- if inputs[i].node()["value"] == 5:
- inputs[i].node().copyAttributes(float_node)
-
- model.apply(patch_float)
- patch_float(model.encode_image)
- patch_float(model.encode_text)
- model.float()
-
- # ensure image_size attr available at consistent location for both jit and non-jit
- model.visual.image_size = model.input_resolution.item()
- return model
-
-
-openai_imagenet_template = [
- lambda c: f"a bad photo of a {c}.",
- lambda c: f"a photo of many {c}.",
- lambda c: f"a sculpture of a {c}.",
- lambda c: f"a photo of the hard to see {c}.",
- lambda c: f"a low resolution photo of the {c}.",
- lambda c: f"a rendering of a {c}.",
- lambda c: f"graffiti of a {c}.",
- lambda c: f"a bad photo of the {c}.",
- lambda c: f"a cropped photo of the {c}.",
- lambda c: f"a tattoo of a {c}.",
- lambda c: f"the embroidered {c}.",
- lambda c: f"a photo of a hard to see {c}.",
- lambda c: f"a bright photo of a {c}.",
- lambda c: f"a photo of a clean {c}.",
- lambda c: f"a photo of a dirty {c}.",
- lambda c: f"a dark photo of the {c}.",
- lambda c: f"a drawing of a {c}.",
- lambda c: f"a photo of my {c}.",
- lambda c: f"the plastic {c}.",
- lambda c: f"a photo of the cool {c}.",
- lambda c: f"a close-up photo of a {c}.",
- lambda c: f"a black and white photo of the {c}.",
- lambda c: f"a painting of the {c}.",
- lambda c: f"a painting of a {c}.",
- lambda c: f"a pixelated photo of the {c}.",
- lambda c: f"a sculpture of the {c}.",
- lambda c: f"a bright photo of the {c}.",
- lambda c: f"a cropped photo of a {c}.",
- lambda c: f"a plastic {c}.",
- lambda c: f"a photo of the dirty {c}.",
- lambda c: f"a jpeg corrupted photo of a {c}.",
- lambda c: f"a blurry photo of the {c}.",
- lambda c: f"a photo of the {c}.",
- lambda c: f"a good photo of the {c}.",
- lambda c: f"a rendering of the {c}.",
- lambda c: f"a {c} in a video game.",
- lambda c: f"a photo of one {c}.",
- lambda c: f"a doodle of a {c}.",
- lambda c: f"a close-up photo of the {c}.",
- lambda c: f"a photo of a {c}.",
- lambda c: f"the origami {c}.",
- lambda c: f"the {c} in a video game.",
- lambda c: f"a sketch of a {c}.",
- lambda c: f"a doodle of the {c}.",
- lambda c: f"a origami {c}.",
- lambda c: f"a low resolution photo of a {c}.",
- lambda c: f"the toy {c}.",
- lambda c: f"a rendition of the {c}.",
- lambda c: f"a photo of the clean {c}.",
- lambda c: f"a photo of a large {c}.",
- lambda c: f"a rendition of a {c}.",
- lambda c: f"a photo of a nice {c}.",
- lambda c: f"a photo of a weird {c}.",
- lambda c: f"a blurry photo of a {c}.",
- lambda c: f"a cartoon {c}.",
- lambda c: f"art of a {c}.",
- lambda c: f"a sketch of the {c}.",
- lambda c: f"a embroidered {c}.",
- lambda c: f"a pixelated photo of a {c}.",
- lambda c: f"itap of the {c}.",
- lambda c: f"a jpeg corrupted photo of the {c}.",
- lambda c: f"a good photo of a {c}.",
- lambda c: f"a plushie {c}.",
- lambda c: f"a photo of the nice {c}.",
- lambda c: f"a photo of the small {c}.",
- lambda c: f"a photo of the weird {c}.",
- lambda c: f"the cartoon {c}.",
- lambda c: f"art of the {c}.",
- lambda c: f"a drawing of the {c}.",
- lambda c: f"a photo of the large {c}.",
- lambda c: f"a black and white photo of a {c}.",
- lambda c: f"the plushie {c}.",
- lambda c: f"a dark photo of a {c}.",
- lambda c: f"itap of a {c}.",
- lambda c: f"graffiti of the {c}.",
- lambda c: f"a toy {c}.",
- lambda c: f"itap of my {c}.",
- lambda c: f"a photo of a cool {c}.",
- lambda c: f"a photo of a small {c}.",
- lambda c: f"a tattoo of the {c}.",
-]
diff --git a/spaces/ShieldX/Llama2CSV/README.md b/spaces/ShieldX/Llama2CSV/README.md
deleted file mode 100644
index 537b26c8b4e2363d95e67009253980c207b3340c..0000000000000000000000000000000000000000
--- a/spaces/ShieldX/Llama2CSV/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Llama2CSV
-emoji: ⚡
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: llama2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/StarbucksCN/starbucks_doc/core/test_lifecycle.py b/spaces/StarbucksCN/starbucks_doc/core/test_lifecycle.py
deleted file mode 100644
index e0239bc37c20fd9a12537025ebebba179c3f8fd4..0000000000000000000000000000000000000000
--- a/spaces/StarbucksCN/starbucks_doc/core/test_lifecycle.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from unittest import TestCase
-
-from core.lifecycle import Lifecycle
-
-
-class SubLifecycle(Lifecycle):
- def __init__(self) -> None:
- super().__init__()
- self.init_counter = 0
-
- def do_init(self) -> None:
- self.init_counter += 1
-
- def do_start(self) -> None:
- self.init_counter += 1
-
- def do_stop(self) -> None:
- self.init_counter += 1
-
- def do_dispose(self) -> None:
- self.init_counter += 1
-
-
-class TestLifecycle(TestCase):
- def test_initialize(self) -> None:
- ls = SubLifecycle()
- ls.initialize()
- ls.logger.info(ls.lifecycle_state.get_phase())
- ls.start()
- ls.logger.info(ls.lifecycle_state.get_phase())
- ls.stop()
- ls.logger.info(ls.lifecycle_state.get_phase())
- ls.dispose()
- ls.logger.info(ls.lifecycle_state.get_phase())
-
- def test_start(self) -> None:
- self.fail()
-
- def test_stop(self) -> None:
- self.fail()
-
- def test_dispose(self) -> None:
- self.fail()
-
- def test_do_init(self) -> None:
- self.fail()
-
- def test_do_start(self) -> None:
- self.fail()
-
- def test_do_stop(self) -> None:
- self.fail()
-
- def test_do_dispose(self) -> None:
- self.fail()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/ipapp.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/ipapp.py
deleted file mode 100644
index 6280bce3b200abdda68615f1a27cd7bfa0f3e3a8..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/ipapp.py
+++ /dev/null
@@ -1,343 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-"""
-The :class:`~traitlets.config.application.Application` object for the command
-line :command:`ipython` program.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import logging
-import os
-import sys
-import warnings
-
-from traitlets.config.loader import Config
-from traitlets.config.application import boolean_flag, catch_config_error
-from IPython.core import release
-from IPython.core import usage
-from IPython.core.completer import IPCompleter
-from IPython.core.crashhandler import CrashHandler
-from IPython.core.formatters import PlainTextFormatter
-from IPython.core.history import HistoryManager
-from IPython.core.application import (
- ProfileDir, BaseIPythonApplication, base_flags, base_aliases
-)
-from IPython.core.magic import MagicsManager
-from IPython.core.magics import (
- ScriptMagics, LoggingMagics
-)
-from IPython.core.shellapp import (
- InteractiveShellApp, shell_flags, shell_aliases
-)
-from IPython.extensions.storemagic import StoreMagics
-from .interactiveshell import TerminalInteractiveShell
-from IPython.paths import get_ipython_dir
-from traitlets import (
- Bool, List, default, observe, Type
-)
-
-#-----------------------------------------------------------------------------
-# Globals, utilities and helpers
-#-----------------------------------------------------------------------------
-
-_examples = """
-ipython --matplotlib # enable matplotlib integration
-ipython --matplotlib=qt # enable matplotlib integration with qt4 backend
-
-ipython --log-level=DEBUG # set logging to DEBUG
-ipython --profile=foo # start with profile foo
-
-ipython profile create foo # create profile foo w/ default config files
-ipython help profile # show the help for the profile subcmd
-
-ipython locate # print the path to the IPython directory
-ipython locate profile foo # print the path to the directory for profile `foo`
-"""
-
-#-----------------------------------------------------------------------------
-# Crash handler for this application
-#-----------------------------------------------------------------------------
-
-class IPAppCrashHandler(CrashHandler):
- """sys.excepthook for IPython itself, leaves a detailed report on disk."""
-
- def __init__(self, app):
- contact_name = release.author
- contact_email = release.author_email
- bug_tracker = 'https://github.com/ipython/ipython/issues'
- super(IPAppCrashHandler,self).__init__(
- app, contact_name, contact_email, bug_tracker
- )
-
- def make_report(self,traceback):
- """Return a string containing a crash report."""
-
- sec_sep = self.section_sep
- # Start with parent report
- report = [super(IPAppCrashHandler, self).make_report(traceback)]
- # Add interactive-specific info we may have
- rpt_add = report.append
- try:
- rpt_add(sec_sep+"History of session input:")
- for line in self.app.shell.user_ns['_ih']:
- rpt_add(line)
- rpt_add('\n*** Last line of input (may not be in above history):\n')
- rpt_add(self.app.shell._last_input_line+'\n')
- except:
- pass
-
- return ''.join(report)
-
-#-----------------------------------------------------------------------------
-# Aliases and Flags
-#-----------------------------------------------------------------------------
-flags = dict(base_flags)
-flags.update(shell_flags)
-frontend_flags = {}
-addflag = lambda *args: frontend_flags.update(boolean_flag(*args))
-addflag('autoedit-syntax', 'TerminalInteractiveShell.autoedit_syntax',
- 'Turn on auto editing of files with syntax errors.',
- 'Turn off auto editing of files with syntax errors.'
-)
-addflag('simple-prompt', 'TerminalInteractiveShell.simple_prompt',
- "Force simple minimal prompt using `raw_input`",
- "Use a rich interactive prompt with prompt_toolkit",
-)
-
-addflag('banner', 'TerminalIPythonApp.display_banner',
- "Display a banner upon starting IPython.",
- "Don't display a banner upon starting IPython."
-)
-addflag('confirm-exit', 'TerminalInteractiveShell.confirm_exit',
- """Set to confirm when you try to exit IPython with an EOF (Control-D
- in Unix, Control-Z/Enter in Windows). By typing 'exit' or 'quit',
- you can force a direct exit without any confirmation.""",
- "Don't prompt the user when exiting."
-)
-addflag('term-title', 'TerminalInteractiveShell.term_title',
- "Enable auto setting the terminal title.",
- "Disable auto setting the terminal title."
-)
-classic_config = Config()
-classic_config.InteractiveShell.cache_size = 0
-classic_config.PlainTextFormatter.pprint = False
-classic_config.TerminalInteractiveShell.prompts_class='IPython.terminal.prompts.ClassicPrompts'
-classic_config.InteractiveShell.separate_in = ''
-classic_config.InteractiveShell.separate_out = ''
-classic_config.InteractiveShell.separate_out2 = ''
-classic_config.InteractiveShell.colors = 'NoColor'
-classic_config.InteractiveShell.xmode = 'Plain'
-
-frontend_flags['classic']=(
- classic_config,
- "Gives IPython a similar feel to the classic Python prompt."
-)
-# # log doesn't make so much sense this way anymore
-# paa('--log','-l',
-# action='store_true', dest='InteractiveShell.logstart',
-# help="Start logging to the default log file (./ipython_log.py).")
-#
-# # quick is harder to implement
-frontend_flags['quick']=(
- {'TerminalIPythonApp' : {'quick' : True}},
- "Enable quick startup with no config files."
-)
-
-frontend_flags['i'] = (
- {'TerminalIPythonApp' : {'force_interact' : True}},
- """If running code from the command line, become interactive afterwards.
- It is often useful to follow this with `--` to treat remaining flags as
- script arguments.
- """
-)
-flags.update(frontend_flags)
-
-aliases = dict(base_aliases)
-aliases.update(shell_aliases) # type: ignore[arg-type]
-
-#-----------------------------------------------------------------------------
-# Main classes and functions
-#-----------------------------------------------------------------------------
-
-
-class LocateIPythonApp(BaseIPythonApplication):
- description = """print the path to the IPython dir"""
- subcommands = dict(
- profile=('IPython.core.profileapp.ProfileLocate',
- "print the path to an IPython profile directory",
- ),
- )
- def start(self):
- if self.subapp is not None:
- return self.subapp.start()
- else:
- print(self.ipython_dir)
-
-
-class TerminalIPythonApp(BaseIPythonApplication, InteractiveShellApp):
- name = u'ipython'
- description = usage.cl_usage
- crash_handler_class = IPAppCrashHandler # typing: ignore[assignment]
- examples = _examples
-
- flags = flags
- aliases = aliases
- classes = List()
-
- interactive_shell_class = Type(
- klass=object, # use default_value otherwise which only allow subclasses.
- default_value=TerminalInteractiveShell,
- help="Class to use to instantiate the TerminalInteractiveShell object. Useful for custom Frontends"
- ).tag(config=True)
-
- @default('classes')
- def _classes_default(self):
- """This has to be in a method, for TerminalIPythonApp to be available."""
- return [
- InteractiveShellApp, # ShellApp comes before TerminalApp, because
- self.__class__, # it will also affect subclasses (e.g. QtConsole)
- TerminalInteractiveShell,
- HistoryManager,
- MagicsManager,
- ProfileDir,
- PlainTextFormatter,
- IPCompleter,
- ScriptMagics,
- LoggingMagics,
- StoreMagics,
- ]
-
- subcommands = dict(
- profile = ("IPython.core.profileapp.ProfileApp",
- "Create and manage IPython profiles."
- ),
- kernel = ("ipykernel.kernelapp.IPKernelApp",
- "Start a kernel without an attached frontend."
- ),
- locate=('IPython.terminal.ipapp.LocateIPythonApp',
- LocateIPythonApp.description
- ),
- history=('IPython.core.historyapp.HistoryApp',
- "Manage the IPython history database."
- ),
- )
-
-
- # *do* autocreate requested profile, but don't create the config file.
- auto_create=Bool(True)
- # configurables
- quick = Bool(False,
- help="""Start IPython quickly by skipping the loading of config files."""
- ).tag(config=True)
- @observe('quick')
- def _quick_changed(self, change):
- if change['new']:
- self.load_config_file = lambda *a, **kw: None
-
- display_banner = Bool(True,
- help="Whether to display a banner upon starting IPython."
- ).tag(config=True)
-
- # if there is code of files to run from the cmd line, don't interact
- # unless the --i flag (App.force_interact) is true.
- force_interact = Bool(False,
- help="""If a command or file is given via the command-line,
- e.g. 'ipython foo.py', start an interactive shell after executing the
- file or command."""
- ).tag(config=True)
- @observe('force_interact')
- def _force_interact_changed(self, change):
- if change['new']:
- self.interact = True
-
- @observe('file_to_run', 'code_to_run', 'module_to_run')
- def _file_to_run_changed(self, change):
- new = change['new']
- if new:
- self.something_to_run = True
- if new and not self.force_interact:
- self.interact = False
-
- # internal, not-configurable
- something_to_run=Bool(False)
-
- @catch_config_error
- def initialize(self, argv=None):
- """Do actions after construct, but before starting the app."""
- super(TerminalIPythonApp, self).initialize(argv)
- if self.subapp is not None:
- # don't bother initializing further, starting subapp
- return
- # print self.extra_args
- if self.extra_args and not self.something_to_run:
- self.file_to_run = self.extra_args[0]
- self.init_path()
- # create the shell
- self.init_shell()
- # and draw the banner
- self.init_banner()
- # Now a variety of things that happen after the banner is printed.
- self.init_gui_pylab()
- self.init_extensions()
- self.init_code()
-
- def init_shell(self):
- """initialize the InteractiveShell instance"""
- # Create an InteractiveShell instance.
- # shell.display_banner should always be False for the terminal
- # based app, because we call shell.show_banner() by hand below
- # so the banner shows *before* all extension loading stuff.
- self.shell = self.interactive_shell_class.instance(parent=self,
- profile_dir=self.profile_dir,
- ipython_dir=self.ipython_dir, user_ns=self.user_ns)
- self.shell.configurables.append(self)
-
- def init_banner(self):
- """optionally display the banner"""
- if self.display_banner and self.interact:
- self.shell.show_banner()
- # Make sure there is a space below the banner.
- if self.log_level <= logging.INFO: print()
-
- def _pylab_changed(self, name, old, new):
- """Replace --pylab='inline' with --pylab='auto'"""
- if new == 'inline':
- warnings.warn("'inline' not available as pylab backend, "
- "using 'auto' instead.")
- self.pylab = 'auto'
-
- def start(self):
- if self.subapp is not None:
- return self.subapp.start()
- # perform any prexec steps:
- if self.interact:
- self.log.debug("Starting IPython's mainloop...")
- self.shell.mainloop()
- else:
- self.log.debug("IPython not interactive...")
- self.shell.restore_term_title()
- if not self.shell.last_execution_succeeded:
- sys.exit(1)
-
-def load_default_config(ipython_dir=None):
- """Load the default config file from the default ipython_dir.
-
- This is useful for embedded shells.
- """
- if ipython_dir is None:
- ipython_dir = get_ipython_dir()
-
- profile_dir = os.path.join(ipython_dir, 'profile_default')
- app = TerminalIPythonApp()
- app.config_file_paths.append(profile_dir)
- app.load_config_file()
- return app.config
-
-launch_new_instance = TerminalIPythonApp.launch_instance
-
-
-if __name__ == '__main__':
- launch_new_instance()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_sitecustomize/sitecustomize.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_sitecustomize/sitecustomize.py
deleted file mode 100644
index 8b51eba7848825b05764b62c399390425a07956f..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydev_sitecustomize/sitecustomize.py
+++ /dev/null
@@ -1,237 +0,0 @@
-'''
- This module will:
- - change the input() and raw_input() commands to change \r\n or \r into \n
- - execute the user site customize -- if available
- - change raw_input() and input() to also remove any trailing \r
-
- Up to PyDev 3.4 it also was setting the default encoding, but it was removed because of differences when
- running from a shell (i.e.: now we just set the PYTHONIOENCODING related to that -- which is properly
- treated on Py 2.7 onwards).
-'''
-DEBUG = 0 #0 or 1 because of jython
-
-import sys
-encoding = None
-
-IS_PYTHON_3_ONWARDS = 0
-
-try:
- IS_PYTHON_3_ONWARDS = sys.version_info[0] >= 3
-except:
- #That's OK, not all versions of python have sys.version_info
- if DEBUG:
- import traceback;traceback.print_exc() #@Reimport
-
-#-----------------------------------------------------------------------------------------------------------------------
-#Line buffering
-if IS_PYTHON_3_ONWARDS:
- #Python 3 has a bug (http://bugs.python.org/issue4705) in which -u doesn't properly make output/input unbuffered
- #so, we need to enable that ourselves here.
- try:
- sys.stdout._line_buffering = True
- except:
- pass
- try:
- sys.stderr._line_buffering = True
- except:
- pass
- try:
- sys.stdin._line_buffering = True
- except:
- pass
-
-
-try:
- import org.python.core.PyDictionary #@UnresolvedImport @UnusedImport -- just to check if it could be valid
- def dict_contains(d, key):
- return d.has_key(key)
-except:
- try:
- #Py3k does not have has_key anymore, and older versions don't have __contains__
- dict_contains = dict.__contains__
- except:
- try:
- dict_contains = dict.has_key
- except NameError:
- def dict_contains(d, key):
- return d.has_key(key)
-
-def install_breakpointhook():
- def custom_sitecustomize_breakpointhook(*args, **kwargs):
- import os
- hookname = os.getenv('PYTHONBREAKPOINT')
- if (
- hookname is not None
- and len(hookname) > 0
- and hasattr(sys, '__breakpointhook__')
- and sys.__breakpointhook__ != custom_sitecustomize_breakpointhook
- ):
- sys.__breakpointhook__(*args, **kwargs)
- else:
- sys.path.append(os.path.dirname(os.path.dirname(__file__)))
- import pydevd
- kwargs.setdefault('stop_at_frame', sys._getframe().f_back)
- pydevd.settrace(*args, **kwargs)
-
- if sys.version_info[0:2] >= (3, 7):
- # There are some choices on how to provide the breakpoint hook. Namely, we can provide a
- # PYTHONBREAKPOINT which provides the import path for a method to be executed or we
- # can override sys.breakpointhook.
- # pydevd overrides sys.breakpointhook instead of providing an environment variable because
- # it's possible that the debugger starts the user program but is not available in the
- # PYTHONPATH (and would thus fail to be imported if PYTHONBREAKPOINT was set to pydevd.settrace).
- # Note that the implementation still takes PYTHONBREAKPOINT in account (so, if it was provided
- # by someone else, it'd still work).
- sys.breakpointhook = custom_sitecustomize_breakpointhook
- else:
- if sys.version_info[0] >= 3:
- import builtins as __builtin__ # Py3
- else:
- import __builtin__
-
- # In older versions, breakpoint() isn't really available, so, install the hook directly
- # in the builtins.
- __builtin__.breakpoint = custom_sitecustomize_breakpointhook
- sys.__breakpointhook__ = custom_sitecustomize_breakpointhook
-
-# Install the breakpoint hook at import time.
-install_breakpointhook()
-
-#-----------------------------------------------------------------------------------------------------------------------
-#now that we've finished the needed pydev sitecustomize, let's run the default one (if available)
-
-#Ok, some weirdness going on in Python 3k: when removing this module from the sys.module to import the 'real'
-#sitecustomize, all the variables in this scope become None (as if it was garbage-collected), so, the the reference
-#below is now being kept to create a cyclic reference so that it neven dies)
-__pydev_sitecustomize_module__ = sys.modules.get('sitecustomize') #A ref to this module
-
-
-#remove the pydev site customize (and the pythonpath for it)
-paths_removed = []
-try:
- for c in sys.path[:]:
- #Pydev controls the whole classpath in Jython already, so, we don't want a a duplicate for
- #what we've already added there (this is needed to support Jython 2.5b1 onwards -- otherwise, as
- #we added the sitecustomize to the pythonpath and to the classpath, we'd have to remove it from the
- #classpath too -- and I don't think there's a way to do that... or not?)
- if c.find('pydev_sitecustomize') != -1 or c == '__classpath__' or c == '__pyclasspath__' or \
- c == '__classpath__/' or c == '__pyclasspath__/' or c == '__classpath__\\' or c == '__pyclasspath__\\':
- sys.path.remove(c)
- if c.find('pydev_sitecustomize') == -1:
- #We'll re-add any paths removed but the pydev_sitecustomize we added from pydev.
- paths_removed.append(c)
-
- if dict_contains(sys.modules, 'sitecustomize'):
- del sys.modules['sitecustomize'] #this module
-except:
- #print the error... should never happen (so, always show, and not only on debug)!
- import traceback;traceback.print_exc() #@Reimport
-else:
- #Now, execute the default sitecustomize
- try:
- import sitecustomize #@UnusedImport
- sitecustomize.__pydev_sitecustomize_module__ = __pydev_sitecustomize_module__
- except:
- pass
-
- if not dict_contains(sys.modules, 'sitecustomize'):
- #If there was no sitecustomize, re-add the pydev sitecustomize (pypy gives a KeyError if it's not there)
- sys.modules['sitecustomize'] = __pydev_sitecustomize_module__
-
- try:
- if paths_removed:
- if sys is None:
- import sys
- if sys is not None:
- #And after executing the default sitecustomize, restore the paths (if we didn't remove it before,
- #the import sitecustomize would recurse).
- sys.path.extend(paths_removed)
- except:
- #print the error... should never happen (so, always show, and not only on debug)!
- import traceback;traceback.print_exc() #@Reimport
-
-
-
-
-if sys.version_info[0] < 3:
- try:
- #Redefine input and raw_input only after the original sitecustomize was executed
- #(because otherwise, the original raw_input and input would still not be defined)
- import __builtin__
- original_raw_input = __builtin__.raw_input
- original_input = __builtin__.input
-
-
- def raw_input(prompt=''):
- #the original raw_input would only remove a trailing \n, so, at
- #this point if we had a \r\n the \r would remain (which is valid for eclipse)
- #so, let's remove the remaining \r which python didn't expect.
- ret = original_raw_input(prompt)
-
- if ret.endswith('\r'):
- return ret[:-1]
-
- return ret
- raw_input.__doc__ = original_raw_input.__doc__
-
- def input(prompt=''):
- #input must also be rebinded for using the new raw_input defined
- return eval(raw_input(prompt))
- input.__doc__ = original_input.__doc__
-
-
- __builtin__.raw_input = raw_input
- __builtin__.input = input
-
- except:
- #Don't report errors at this stage
- if DEBUG:
- import traceback;traceback.print_exc() #@Reimport
-
-else:
- try:
- import builtins #Python 3.0 does not have the __builtin__ module @UnresolvedImport
- original_input = builtins.input
- def input(prompt=''):
- #the original input would only remove a trailing \n, so, at
- #this point if we had a \r\n the \r would remain (which is valid for eclipse)
- #so, let's remove the remaining \r which python didn't expect.
- ret = original_input(prompt)
-
- if ret.endswith('\r'):
- return ret[:-1]
-
- return ret
- input.__doc__ = original_input.__doc__
- builtins.input = input
- except:
- #Don't report errors at this stage
- if DEBUG:
- import traceback;traceback.print_exc() #@Reimport
-
-
-
-try:
- #The original getpass doesn't work from the eclipse console, so, let's put a replacement
- #here (note that it'll not go into echo mode in the console, so, what' the user writes
- #will actually be seen)
- #Note: same thing from the fix_getpass module -- but we don't want to import it in this
- #custom sitecustomize.
- def fix_get_pass():
- try:
- import getpass
- except ImportError:
- return #If we can't import it, we can't fix it
- import warnings
- fallback = getattr(getpass, 'fallback_getpass', None) # >= 2.6
- if not fallback:
- fallback = getpass.default_getpass # <= 2.5
- getpass.getpass = fallback
- if hasattr(getpass, 'GetPassWarning'):
- warnings.simplefilter("ignore", category=getpass.GetPassWarning)
- fix_get_pass()
-
-except:
- #Don't report errors at this stage
- if DEBUG:
- import traceback;traceback.print_exc() #@Reimport
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/visualization/image.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/visualization/image.py
deleted file mode 100644
index 61a56c75b67f593c298408462c63c0468be8e276..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/visualization/image.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-
-from annotator.uniformer.mmcv.image import imread, imwrite
-from .color import color_val
-
-
-def imshow(img, win_name='', wait_time=0):
- """Show an image.
-
- Args:
- img (str or ndarray): The image to be displayed.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- """
- cv2.imshow(win_name, imread(img))
- if wait_time == 0: # prevent from hanging if windows was closed
- while True:
- ret = cv2.waitKey(1)
-
- closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1
- # if user closed window or if some key pressed
- if closed or ret != -1:
- break
- else:
- ret = cv2.waitKey(wait_time)
-
-
-def imshow_bboxes(img,
- bboxes,
- colors='green',
- top_k=-1,
- thickness=1,
- show=True,
- win_name='',
- wait_time=0,
- out_file=None):
- """Draw bboxes on an image.
-
- Args:
- img (str or ndarray): The image to be displayed.
- bboxes (list or ndarray): A list of ndarray of shape (k, 4).
- colors (list[str or tuple or Color]): A list of colors.
- top_k (int): Plot the first k bboxes only if set positive.
- thickness (int): Thickness of lines.
- show (bool): Whether to show the image.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- out_file (str, optional): The filename to write the image.
-
- Returns:
- ndarray: The image with bboxes drawn on it.
- """
- img = imread(img)
- img = np.ascontiguousarray(img)
-
- if isinstance(bboxes, np.ndarray):
- bboxes = [bboxes]
- if not isinstance(colors, list):
- colors = [colors for _ in range(len(bboxes))]
- colors = [color_val(c) for c in colors]
- assert len(bboxes) == len(colors)
-
- for i, _bboxes in enumerate(bboxes):
- _bboxes = _bboxes.astype(np.int32)
- if top_k <= 0:
- _top_k = _bboxes.shape[0]
- else:
- _top_k = min(top_k, _bboxes.shape[0])
- for j in range(_top_k):
- left_top = (_bboxes[j, 0], _bboxes[j, 1])
- right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
- cv2.rectangle(
- img, left_top, right_bottom, colors[i], thickness=thickness)
-
- if show:
- imshow(img, win_name, wait_time)
- if out_file is not None:
- imwrite(img, out_file)
- return img
-
-
-def imshow_det_bboxes(img,
- bboxes,
- labels,
- class_names=None,
- score_thr=0,
- bbox_color='green',
- text_color='green',
- thickness=1,
- font_scale=0.5,
- show=True,
- win_name='',
- wait_time=0,
- out_file=None):
- """Draw bboxes and class labels (with scores) on an image.
-
- Args:
- img (str or ndarray): The image to be displayed.
- bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
- (n, 5).
- labels (ndarray): Labels of bboxes.
- class_names (list[str]): Names of each classes.
- score_thr (float): Minimum score of bboxes to be shown.
- bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
- text_color (str or tuple or :obj:`Color`): Color of texts.
- thickness (int): Thickness of lines.
- font_scale (float): Font scales of texts.
- show (bool): Whether to show the image.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- out_file (str or None): The filename to write the image.
-
- Returns:
- ndarray: The image with bboxes drawn on it.
- """
- assert bboxes.ndim == 2
- assert labels.ndim == 1
- assert bboxes.shape[0] == labels.shape[0]
- assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
- img = imread(img)
- img = np.ascontiguousarray(img)
-
- if score_thr > 0:
- assert bboxes.shape[1] == 5
- scores = bboxes[:, -1]
- inds = scores > score_thr
- bboxes = bboxes[inds, :]
- labels = labels[inds]
-
- bbox_color = color_val(bbox_color)
- text_color = color_val(text_color)
-
- for bbox, label in zip(bboxes, labels):
- bbox_int = bbox.astype(np.int32)
- left_top = (bbox_int[0], bbox_int[1])
- right_bottom = (bbox_int[2], bbox_int[3])
- cv2.rectangle(
- img, left_top, right_bottom, bbox_color, thickness=thickness)
- label_text = class_names[
- label] if class_names is not None else f'cls {label}'
- if len(bbox) > 4:
- label_text += f'|{bbox[-1]:.02f}'
- cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
- cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
-
- if show:
- imshow(img, win_name, wait_time)
- if out_file is not None:
- imwrite(img, out_file)
- return img
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/apis/inference.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/apis/inference.py
deleted file mode 100644
index 90bc1c0c68525734bd6793f07c15fe97d3c8342c..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/apis/inference.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import matplotlib.pyplot as plt
-import annotator.uniformer.mmcv as mmcv
-import torch
-from annotator.uniformer.mmcv.parallel import collate, scatter
-from annotator.uniformer.mmcv.runner import load_checkpoint
-
-from annotator.uniformer.mmseg.datasets.pipelines import Compose
-from annotator.uniformer.mmseg.models import build_segmentor
-
-
-def init_segmentor(config, checkpoint=None, device='cuda:0'):
- """Initialize a segmentor from config file.
-
- Args:
- config (str or :obj:`mmcv.Config`): Config file path or the config
- object.
- checkpoint (str, optional): Checkpoint path. If left as None, the model
- will not load any weights.
- device (str, optional) CPU/CUDA device option. Default 'cuda:0'.
- Use 'cpu' for loading model on CPU.
- Returns:
- nn.Module: The constructed segmentor.
- """
- if isinstance(config, str):
- config = mmcv.Config.fromfile(config)
- elif not isinstance(config, mmcv.Config):
- raise TypeError('config must be a filename or Config object, '
- 'but got {}'.format(type(config)))
- config.model.pretrained = None
- config.model.train_cfg = None
- model = build_segmentor(config.model, test_cfg=config.get('test_cfg'))
- if checkpoint is not None:
- checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
- model.CLASSES = checkpoint['meta']['CLASSES']
- model.PALETTE = checkpoint['meta']['PALETTE']
- model.cfg = config # save the config in the model for convenience
- model.to(device)
- model.eval()
- return model
-
-
-class LoadImage:
- """A simple pipeline to load image."""
-
- def __call__(self, results):
- """Call function to load images into results.
-
- Args:
- results (dict): A result dict contains the file name
- of the image to be read.
-
- Returns:
- dict: ``results`` will be returned containing loaded image.
- """
-
- if isinstance(results['img'], str):
- results['filename'] = results['img']
- results['ori_filename'] = results['img']
- else:
- results['filename'] = None
- results['ori_filename'] = None
- img = mmcv.imread(results['img'])
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
- return results
-
-
-def inference_segmentor(model, img):
- """Inference image(s) with the segmentor.
-
- Args:
- model (nn.Module): The loaded segmentor.
- imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
- images.
-
- Returns:
- (list[Tensor]): The segmentation result.
- """
- cfg = model.cfg
- device = next(model.parameters()).device # model device
- # build the data pipeline
- test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
- test_pipeline = Compose(test_pipeline)
- # prepare data
- data = dict(img=img)
- data = test_pipeline(data)
- data = collate([data], samples_per_gpu=1)
- if next(model.parameters()).is_cuda:
- # scatter to specified GPU
- data = scatter(data, [device])[0]
- else:
- data['img_metas'] = [i.data[0] for i in data['img_metas']]
-
- # forward the model
- with torch.no_grad():
- result = model(return_loss=False, rescale=True, **data)
- return result
-
-
-def show_result_pyplot(model,
- img,
- result,
- palette=None,
- fig_size=(15, 10),
- opacity=0.5,
- title='',
- block=True):
- """Visualize the segmentation results on the image.
-
- Args:
- model (nn.Module): The loaded segmentor.
- img (str or np.ndarray): Image filename or loaded image.
- result (list): The segmentation result.
- palette (list[list[int]]] | None): The palette of segmentation
- map. If None is given, random palette will be generated.
- Default: None
- fig_size (tuple): Figure size of the pyplot figure.
- opacity(float): Opacity of painted segmentation map.
- Default 0.5.
- Must be in (0, 1] range.
- title (str): The title of pyplot figure.
- Default is ''.
- block (bool): Whether to block the pyplot figure.
- Default is True.
- """
- if hasattr(model, 'module'):
- model = model.module
- img = model.show_result(
- img, result, palette=palette, show=False, opacity=opacity)
- # plt.figure(figsize=fig_size)
- # plt.imshow(mmcv.bgr2rgb(img))
- # plt.title(title)
- # plt.tight_layout()
- # plt.show(block=block)
- return mmcv.bgr2rgb(img)
diff --git a/spaces/TRI-ML/risk_biased_prediction/scripts/scripts_utils/plotly_interface.py b/spaces/TRI-ML/risk_biased_prediction/scripts/scripts_utils/plotly_interface.py
deleted file mode 100644
index 5eb48ce596116958f991c60fbe07d9227d0f122d..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/scripts/scripts_utils/plotly_interface.py
+++ /dev/null
@@ -1,407 +0,0 @@
-from datasets import load_dataset, Dataset
-import fire
-from functools import partial, update_wrapper
-import numpy
-import os
-from typing import Dict, Iterable, Tuple
-import sys
-import time
-import torch
-
-import gradio as gr
-from huggingface_hub import hf_hub_download
-from mmcv import Config
-import plotly.graph_objects as go
-from torch.utils.data.dataloader import DataLoader
-
-from risk_biased.utils.load_model import get_predictor
-from risk_biased.utils.torch_utils import load_weights
-from risk_biased.utils.waymo_dataloader import WaymoDataloaders
-from risk_biased.predictors.biased_predictor import (
- LitTrajectoryPredictor,
-)
-
-
-def to_numpy(**kwargs):
- dic_outputs = {}
- for k, v in kwargs.items():
- dic_outputs[k] = v.detach().cpu().numpy()
- return dic_outputs
-
-
-def get_scatter_data(x, mask_x, name, **kwargs):
- return [
- go.Scatter(
- x=x[k, mask_x[k], 0],
- y=x[k, mask_x[k], 1],
- showlegend=k == 0,
- name=name,
- **kwargs,
- )
- for k in range(x.shape[0])
- ]
-
-
-def configuration_paths() -> Iterable[os.PathLike]:
- working_dir = os.path.dirname(os.path.realpath(__file__))
- return [
- os.path.join(
- working_dir,
- "../../risk_biased/config",
- config_file,
- )
- for config_file in ("learning_config.py", "waymo_config.py")
- ]
-
-
-def load_item(index: int, dataset: Dataset, device: str = "cpu") -> Tuple:
- x = torch.from_numpy(numpy.array(dataset[index]["x"]).astype(numpy.float32)).to(device)
- mask_x = torch.from_numpy(numpy.array(dataset[index]["mask_x"]).astype(numpy.bool_)).to(device)
- y = torch.from_numpy(numpy.array(dataset[index]["y"]).astype(numpy.float32)).to(device)
- mask_y = torch.from_numpy(numpy.array(dataset[index]["mask_y"]).astype(numpy.bool_)).to(device)
- mask_loss = torch.from_numpy( numpy.array(dataset[index]["mask_loss"]).astype(numpy.bool_)).to(device)
- map_data = torch.from_numpy(numpy.array(dataset[index]["map_data"]).astype(numpy.float32)).to(device)
- mask_map = torch.from_numpy(numpy.array(dataset[index]["mask_map"]).astype(numpy.bool_)).to(device)
- offset = torch.from_numpy(numpy.array(dataset[index]["offset"]).astype(numpy.float32)).to(device)
- x_ego = torch.from_numpy(numpy.array(dataset[index]["x_ego"]).astype(numpy.float32)).to(device)
- y_ego = torch.from_numpy(numpy.array(dataset[index]["y_ego"]).astype(numpy.float32)).to(device)
-
- return (x, mask_x, map_data, mask_map, offset, x_ego, y_ego), y, mask_y, mask_loss
-
-
-def build_data(
- predictor: LitTrajectoryPredictor,
- dataset: Dataset,
- index: int,
- risk_level: float,
- n_samples: int,
-) -> Dict[str, go.Scatter]:
- assert n_samples >= 1
-
- batch, y, mask_y, mask_loss = load_item(index, dataset, predictor.device)
- predictions = predictor.predict_step(
- batch=batch,
- risk_level=risk_level,
- n_samples=n_samples,
- )
-
- offset = batch[4]
- y = predictor._unnormalize_trajectory(y, offset)
- x = predictor._unnormalize_trajectory(batch[0], offset)
- numpy_data = to_numpy(
- predictions=predictions,
- y=y,
- mask_y=mask_y,
- x=x,
- mask_x=batch[1],
- map_data=batch[2],
- mask_map=batch[3],
- mask_pred=mask_loss,
- )
-
- x = numpy_data["x"][0]
- mask_x = numpy_data["mask_x"][0]
- y = numpy_data["y"][0]
- mask_y = numpy_data["mask_y"][0]
- pred = numpy_data["predictions"][0]
- mask_pred = numpy_data["mask_pred"][0]
- map_data = numpy_data["map_data"][0]
- mask_map = numpy_data["mask_map"][0]
-
- marker_size = 12
-
- data_x = get_scatter_data(
- x,
- mask_x,
- mode="lines",
- line=dict(width=2, color="black"),
- name="Past",
- )
- ego_present = get_scatter_data(
- x=x[0:1, -1:],
- mask_x=mask_x[0:1, -1:],
- mode="markers",
- marker=dict(color="blue", size=marker_size, opacity=0.5),
- name="Ego",
- )
- agent_present = get_scatter_data(
- x=x[1:2, -1:],
- mask_x=mask_x[1:2, -1:],
- mode="markers",
- marker=dict(color="green", size=marker_size, opacity=0.5),
- name="Agent",
- )
-
- data_y = get_scatter_data(
- y,
- mask_y,
- mode="lines",
- line=dict(width=2, color="green"),
- name="Ground truth",
- )
- data_map = get_scatter_data(
- map_data,
- mask_map,
- mode="lines",
- line=dict(width=15, color="gray"),
- opacity=0.3,
- name="Centerline",
- )
- data_pred = []
- forecasts_end = []
- for i in range(n_samples):
- cur_data_pred = get_scatter_data(
- pred[:, i],
- mask_pred,
- mode="lines",
- line=dict(width=2, color="red"),
- name="Forecast",
- )
- data_pred += cur_data_pred
-
- forecast_end = get_scatter_data(
- pred[:, i, -1:],
- mask_pred[:, -1:],
- mode="markers",
- marker=dict(color="red", size=marker_size/2, opacity=0.5, symbol="x"),
- name="Forecast end",
- )
- forecasts_end += forecast_end
-
- static_data = data_map + data_x + data_y + data_pred + ego_present + agent_present + forecasts_end
-
- animation_opacity = 0.5
- frames_x = [
- go.Frame(
- data=[
- go.Scatter(
- x=x[mask_x[:, k], k, 0],
- y=x[mask_x[:, k], k, 1],
- mode="markers",
- opacity=animation_opacity,
- marker=dict(color="black", size=marker_size),
- showlegend=False,
- ),
- go.Scatter(
- x=x[0:1, k, 0],
- y=x[0:1, k, 1],
- mode="markers",
- opacity=animation_opacity,
- marker=dict(color="blue", size=marker_size),
- showlegend=False,
- ),
- ]
- )
- for k in range(x.shape[1])
- ]
-
- frames_y_pred = []
- for k in range(y.shape[1]):
- cur_gt_agent_data = go.Scatter(
- x=y[1:2][mask_y[1:2, k], k, 0],
- y=y[1:2][mask_y[1:2, k], k, 1],
- mode="markers",
- opacity=animation_opacity,
- marker=dict(color="green", size=marker_size),
- )
- cur_gt_future_data = go.Scatter(
- x=y[2:][mask_y[2:, k], k, 0],
- y=y[2:][mask_y[2:, k], k, 1],
- mode="markers",
- opacity=animation_opacity,
- marker=dict(color="black", size=marker_size),
- )
- cur_pred_data = []
- for i in range(n_samples):
- cur_pred_data.append(
- go.Scatter(
- x=pred[mask_pred[:, k], i, k, 0],
- y=pred[mask_pred[:, k], i, k, 1],
- mode="markers",
- opacity=animation_opacity,
- marker=dict(color="red", size=marker_size),
- showlegend=False,
- )
- )
- cur_ego_data = go.Scatter(
- x=y[0:1, k, 0],
- y=y[0:1, k, 1],
- mode="markers",
- opacity=animation_opacity,
- marker=dict(color="blue", size=marker_size),
- )
- cur_data = [cur_gt_agent_data, cur_gt_future_data, *cur_pred_data, cur_ego_data]
- frame = go.Frame(data=cur_data)
- frames_y_pred.append(frame)
-
-
- return {"frames": frames_x + frames_y_pred, "data": static_data}
-
-
-def prediction_plot(
- predictor: LitTrajectoryPredictor,
- dataset: Dataset,
- index: int,
- risk_level: float,
- n_samples: int = 1,
- use_biaser: bool = True,
-) -> go.Figure:
- range_radius = 80
- if use_biaser:
- risk_level = float(risk_level)
- else:
- risk_level = None
- layout = go.Layout(
- xaxis=dict(
- range=[-0.5*range_radius, 1.5*range_radius],
- autorange=False,
- zeroline=False,
- ),
- yaxis=dict(
- range=[-range_radius, range_radius],
- autorange=False,
- zeroline=False,
- ),
- title_text="Road Scene",
- hovermode="closest",
- width=800,
- height=600,
- updatemenus=[
- dict(
- type="buttons",
- buttons=[
- dict(
- label="Play",
- method="animate",
- args=[
- None,
- dict(
- frame=dict(duration=100, redraw=False),
- mode="immediate",
- fromcurrent=True,
- ),
- ],
- ),
- dict(
- label="Pause",
- method="animate",
- args=[[None], {"frame": {"duration": 0, "redraw": False},
- "mode": "immediate",
- "transition": {"duration": 0}}],
- )
- ],
- )
- ],
- )
-
- fig = go.Figure(
- **build_data(predictor, dataset, index, risk_level, n_samples),
- layout=layout,
- )
- fig.update_geos(projection_type="equirectangular", visible=True, resolution=110)
-
- return fig
-
-def get_figure(
- predictor: LitTrajectoryPredictor,
- dataset: Dataset,
- index: int,
- risk_level: float,
- n_samples: int,
-) -> go.Figure:
-
- fig = prediction_plot(
- predictor, dataset, index, risk_level, n_samples, use_biaser=True
- )
- fig.update_layout()
-
- return fig
-
-def update_figure(
- predictor: LitTrajectoryPredictor,
- dataset: Dataset,
- index: int,
- risk_level: float,
- n_samples: int,
- image = None
-) -> go.Figure:
-
- fig = prediction_plot(
- predictor, dataset, index, risk_level, n_samples, use_biaser=True
- )
- fig.update_layout()
-
- return fig
-
-def load_predictor_from_hf(model_source: str = "TRI-ML/risk_biased_model", config_name: str="learning_config.py", checkpoint_name: str = "last.ckpt", device: str = "cpu") -> Tuple[LitTrajectoryPredictor, Dataset]:
- config_file = hf_hub_download(model_source, filename=config_name, use_auth_token=os.getenv('SECRET_AUTH_TOKEN'))
- ckpt = torch.load(hf_hub_download(model_source, filename=checkpoint_name, use_auth_token=os.getenv('SECRET_AUTH_TOKEN')), map_location="cpu")
- cfg = Config.fromfile(config_file)
- predictor = get_predictor(cfg, WaymoDataloaders.unnormalize_trajectory)
- predictor = load_weights(predictor, ckpt)
- predictor.eval()
- predictor = predictor.to(device)
-
- return predictor
-
-def load_dataset_from_hf(data_source: str = "jmercat/risk_biased_dataset") -> Dataset:
- dataset = load_dataset(data_source, split="test")
- return dataset
-
-def main(load_from=None, cfg_path=None):
- # Define the device to use
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- print("Getting dataset")
- dataset = load_dataset_from_hf()
-
- if load_from is not None:
- cfg = Config.fromfile(cfg_path)
- predictor = get_predictor(cfg, WaymoDataloaders.unnormalize_trajectory)
- predictor = load_weights(predictor, torch.load(load_from, map_location="cpu"))
- else:
- print("Getting model.")
- predictor = load_predictor_from_hf(device=device)
-
- ui_update_fn = partial(update_figure, predictor, dataset)
-
- # Do the same thing as above but using the gradio blocks API
- with gr.Blocks() as interface:
-
- gr.Markdown(
- """
- # Risk-Aware Prediction
-
- Make predictions for the green agent with a risk-seeking bias towards the ego vehicle in blue.
- The risk level is a value between 0 and 1, where 0 is not risk-seeking and 1 is the most risk-seeking.
- Once the sliders are set, click the "Run" button to see the predictions.
- The play button will animate the prediction over time (it is slow especially with many samples).
-
- For more information, see the paper [RAP: Risk-Aware Prediction for Robust Planning](https://arxiv.org/abs/2210.01368) published at [CoRL 2022](https://corl2022.org/).
- """)
- initial_index = 27
- initial_n_samples = 10
- image = gr.Plot(get_figure(predictor, dataset, initial_index, 0, initial_n_samples))
- interface.queue()
- index = gr.Slider(
- minimum=0,
- maximum=len(dataset)-1,
- step=1,
- value=initial_index,
- label="Index",
- )
- risk_level = gr.Slider(minimum=0, maximum=1, step=0.01, label="Risk")
- n_samples = gr.Slider(minimum=1, maximum=20, step=1, value=initial_n_samples, label="Number of prediction samples")
- button = gr.Button(label="Run")
-
- # Removed the interactive plot because it was running on the first change and all changes made during computation were ignored
- # This caused the plot to be out of sync with the sliders
- # index.change(ui_update_fn, inputs=[index, risk_level, n_samples, image], outputs=image)
- # risk_level.change(ui_update_fn, inputs=[index, risk_level, n_samples, image], outputs=image)
- # n_samples.change(ui_update_fn, inputs=[index, risk_level, n_samples, image], outputs=image)
- button.click(ui_update_fn, inputs=[index, risk_level, n_samples, image], outputs=image)
-
- interface.launch(debug=False)
-
-
-if __name__ == "__main__":
- fire.Fire(main)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/setuptools_build.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/setuptools_build.py
deleted file mode 100644
index 96d1b2460670e20ac92a5ade7a74b7ab1cba71d8..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/setuptools_build.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import sys
-import textwrap
-from typing import List, Optional, Sequence
-
-# Shim to wrap setup.py invocation with setuptools
-# Note that __file__ is handled via two {!r} *and* %r, to ensure that paths on
-# Windows are correctly handled (it should be "C:\\Users" not "C:\Users").
-_SETUPTOOLS_SHIM = textwrap.dedent(
- """
- exec(compile('''
- # This is -- a caller that pip uses to run setup.py
- #
- # - It imports setuptools before invoking setup.py, to enable projects that directly
- # import from `distutils.core` to work with newer packaging standards.
- # - It provides a clear error message when setuptools is not installed.
- # - It sets `sys.argv[0]` to the underlying `setup.py`, when invoking `setup.py` so
- # setuptools doesn't think the script is `-c`. This avoids the following warning:
- # manifest_maker: standard file '-c' not found".
- # - It generates a shim setup.py, for handling setup.cfg-only projects.
- import os, sys, tokenize
-
- try:
- import setuptools
- except ImportError as error:
- print(
- "ERROR: Can not execute `setup.py` since setuptools is not available in "
- "the build environment.",
- file=sys.stderr,
- )
- sys.exit(1)
-
- __file__ = %r
- sys.argv[0] = __file__
-
- if os.path.exists(__file__):
- filename = __file__
- with tokenize.open(__file__) as f:
- setup_py_code = f.read()
- else:
- filename = ""
- setup_py_code = "from setuptools import setup; setup()"
-
- exec(compile(setup_py_code, filename, "exec"))
- ''' % ({!r},), "", "exec"))
- """
-).rstrip()
-
-
-def make_setuptools_shim_args(
- setup_py_path: str,
- global_options: Optional[Sequence[str]] = None,
- no_user_config: bool = False,
- unbuffered_output: bool = False,
-) -> List[str]:
- """
- Get setuptools command arguments with shim wrapped setup file invocation.
-
- :param setup_py_path: The path to setup.py to be wrapped.
- :param global_options: Additional global options.
- :param no_user_config: If True, disables personal user configuration.
- :param unbuffered_output: If True, adds the unbuffered switch to the
- argument list.
- """
- args = [sys.executable]
- if unbuffered_output:
- args += ["-u"]
- args += ["-c", _SETUPTOOLS_SHIM.format(setup_py_path)]
- if global_options:
- args += global_options
- if no_user_config:
- args += ["--no-user-cfg"]
- return args
-
-
-def make_setuptools_bdist_wheel_args(
- setup_py_path: str,
- global_options: Sequence[str],
- build_options: Sequence[str],
- destination_dir: str,
-) -> List[str]:
- # NOTE: Eventually, we'd want to also -S to the flags here, when we're
- # isolating. Currently, it breaks Python in virtualenvs, because it
- # relies on site.py to find parts of the standard library outside the
- # virtualenv.
- args = make_setuptools_shim_args(
- setup_py_path, global_options=global_options, unbuffered_output=True
- )
- args += ["bdist_wheel", "-d", destination_dir]
- args += build_options
- return args
-
-
-def make_setuptools_clean_args(
- setup_py_path: str,
- global_options: Sequence[str],
-) -> List[str]:
- args = make_setuptools_shim_args(
- setup_py_path, global_options=global_options, unbuffered_output=True
- )
- args += ["clean", "--all"]
- return args
-
-
-def make_setuptools_develop_args(
- setup_py_path: str,
- *,
- global_options: Sequence[str],
- no_user_config: bool,
- prefix: Optional[str],
- home: Optional[str],
- use_user_site: bool,
-) -> List[str]:
- assert not (use_user_site and prefix)
-
- args = make_setuptools_shim_args(
- setup_py_path,
- global_options=global_options,
- no_user_config=no_user_config,
- )
-
- args += ["develop", "--no-deps"]
-
- if prefix:
- args += ["--prefix", prefix]
- if home is not None:
- args += ["--install-dir", home]
-
- if use_user_site:
- args += ["--user", "--prefix="]
-
- return args
-
-
-def make_setuptools_egg_info_args(
- setup_py_path: str,
- egg_info_dir: Optional[str],
- no_user_config: bool,
-) -> List[str]:
- args = make_setuptools_shim_args(setup_py_path, no_user_config=no_user_config)
-
- args += ["egg_info"]
-
- if egg_info_dir:
- args += ["--egg-base", egg_info_dir]
-
- return args
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/ssltransport.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/ssltransport.py
deleted file mode 100644
index 4a7105d17916a7237f3df6e59d65ca82375f8803..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/ssltransport.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import io
-import socket
-import ssl
-
-from ..exceptions import ProxySchemeUnsupported
-from ..packages import six
-
-SSL_BLOCKSIZE = 16384
-
-
-class SSLTransport:
- """
- The SSLTransport wraps an existing socket and establishes an SSL connection.
-
- Contrary to Python's implementation of SSLSocket, it allows you to chain
- multiple TLS connections together. It's particularly useful if you need to
- implement TLS within TLS.
-
- The class supports most of the socket API operations.
- """
-
- @staticmethod
- def _validate_ssl_context_for_tls_in_tls(ssl_context):
- """
- Raises a ProxySchemeUnsupported if the provided ssl_context can't be used
- for TLS in TLS.
-
- The only requirement is that the ssl_context provides the 'wrap_bio'
- methods.
- """
-
- if not hasattr(ssl_context, "wrap_bio"):
- if six.PY2:
- raise ProxySchemeUnsupported(
- "TLS in TLS requires SSLContext.wrap_bio() which isn't "
- "supported on Python 2"
- )
- else:
- raise ProxySchemeUnsupported(
- "TLS in TLS requires SSLContext.wrap_bio() which isn't "
- "available on non-native SSLContext"
- )
-
- def __init__(
- self, socket, ssl_context, server_hostname=None, suppress_ragged_eofs=True
- ):
- """
- Create an SSLTransport around socket using the provided ssl_context.
- """
- self.incoming = ssl.MemoryBIO()
- self.outgoing = ssl.MemoryBIO()
-
- self.suppress_ragged_eofs = suppress_ragged_eofs
- self.socket = socket
-
- self.sslobj = ssl_context.wrap_bio(
- self.incoming, self.outgoing, server_hostname=server_hostname
- )
-
- # Perform initial handshake.
- self._ssl_io_loop(self.sslobj.do_handshake)
-
- def __enter__(self):
- return self
-
- def __exit__(self, *_):
- self.close()
-
- def fileno(self):
- return self.socket.fileno()
-
- def read(self, len=1024, buffer=None):
- return self._wrap_ssl_read(len, buffer)
-
- def recv(self, len=1024, flags=0):
- if flags != 0:
- raise ValueError("non-zero flags not allowed in calls to recv")
- return self._wrap_ssl_read(len)
-
- def recv_into(self, buffer, nbytes=None, flags=0):
- if flags != 0:
- raise ValueError("non-zero flags not allowed in calls to recv_into")
- if buffer and (nbytes is None):
- nbytes = len(buffer)
- elif nbytes is None:
- nbytes = 1024
- return self.read(nbytes, buffer)
-
- def sendall(self, data, flags=0):
- if flags != 0:
- raise ValueError("non-zero flags not allowed in calls to sendall")
- count = 0
- with memoryview(data) as view, view.cast("B") as byte_view:
- amount = len(byte_view)
- while count < amount:
- v = self.send(byte_view[count:])
- count += v
-
- def send(self, data, flags=0):
- if flags != 0:
- raise ValueError("non-zero flags not allowed in calls to send")
- response = self._ssl_io_loop(self.sslobj.write, data)
- return response
-
- def makefile(
- self, mode="r", buffering=None, encoding=None, errors=None, newline=None
- ):
- """
- Python's httpclient uses makefile and buffered io when reading HTTP
- messages and we need to support it.
-
- This is unfortunately a copy and paste of socket.py makefile with small
- changes to point to the socket directly.
- """
- if not set(mode) <= {"r", "w", "b"}:
- raise ValueError("invalid mode %r (only r, w, b allowed)" % (mode,))
-
- writing = "w" in mode
- reading = "r" in mode or not writing
- assert reading or writing
- binary = "b" in mode
- rawmode = ""
- if reading:
- rawmode += "r"
- if writing:
- rawmode += "w"
- raw = socket.SocketIO(self, rawmode)
- self.socket._io_refs += 1
- if buffering is None:
- buffering = -1
- if buffering < 0:
- buffering = io.DEFAULT_BUFFER_SIZE
- if buffering == 0:
- if not binary:
- raise ValueError("unbuffered streams must be binary")
- return raw
- if reading and writing:
- buffer = io.BufferedRWPair(raw, raw, buffering)
- elif reading:
- buffer = io.BufferedReader(raw, buffering)
- else:
- assert writing
- buffer = io.BufferedWriter(raw, buffering)
- if binary:
- return buffer
- text = io.TextIOWrapper(buffer, encoding, errors, newline)
- text.mode = mode
- return text
-
- def unwrap(self):
- self._ssl_io_loop(self.sslobj.unwrap)
-
- def close(self):
- self.socket.close()
-
- def getpeercert(self, binary_form=False):
- return self.sslobj.getpeercert(binary_form)
-
- def version(self):
- return self.sslobj.version()
-
- def cipher(self):
- return self.sslobj.cipher()
-
- def selected_alpn_protocol(self):
- return self.sslobj.selected_alpn_protocol()
-
- def selected_npn_protocol(self):
- return self.sslobj.selected_npn_protocol()
-
- def shared_ciphers(self):
- return self.sslobj.shared_ciphers()
-
- def compression(self):
- return self.sslobj.compression()
-
- def settimeout(self, value):
- self.socket.settimeout(value)
-
- def gettimeout(self):
- return self.socket.gettimeout()
-
- def _decref_socketios(self):
- self.socket._decref_socketios()
-
- def _wrap_ssl_read(self, len, buffer=None):
- try:
- return self._ssl_io_loop(self.sslobj.read, len, buffer)
- except ssl.SSLError as e:
- if e.errno == ssl.SSL_ERROR_EOF and self.suppress_ragged_eofs:
- return 0 # eof, return 0.
- else:
- raise
-
- def _ssl_io_loop(self, func, *args):
- """Performs an I/O loop between incoming/outgoing and the socket."""
- should_loop = True
- ret = None
-
- while should_loop:
- errno = None
- try:
- ret = func(*args)
- except ssl.SSLError as e:
- if e.errno not in (ssl.SSL_ERROR_WANT_READ, ssl.SSL_ERROR_WANT_WRITE):
- # WANT_READ, and WANT_WRITE are expected, others are not.
- raise e
- errno = e.errno
-
- buf = self.outgoing.read()
- self.socket.sendall(buf)
-
- if errno is None:
- should_loop = False
- elif errno == ssl.SSL_ERROR_WANT_READ:
- buf = self.socket.recv(SSL_BLOCKSIZE)
- if buf:
- self.incoming.write(buf)
- else:
- self.incoming.write_eof()
- return ret
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py
deleted file mode 100644
index a3dcf8be42a39c6e5f6e76e3ab23adeccb33085d..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.meta_arch import GeneralizedRCNN
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
-from detectron2.modeling.backbone import BasicStem, BottleneckBlock, ResNet
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.modeling.proposal_generator import RPN, StandardRPNHead
-from detectron2.modeling.roi_heads import (
- FastRCNNOutputLayers,
- MaskRCNNConvUpsampleHead,
- Res5ROIHeads,
-)
-
-model = L(GeneralizedRCNN)(
- backbone=L(ResNet)(
- stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
- stages=L(ResNet.make_default_stages)(
- depth=50,
- stride_in_1x1=True,
- norm="FrozenBN",
- ),
- out_features=["res4"],
- ),
- proposal_generator=L(RPN)(
- in_features=["res4"],
- head=L(StandardRPNHead)(in_channels=1024, num_anchors=15),
- anchor_generator=L(DefaultAnchorGenerator)(
- sizes=[[32, 64, 128, 256, 512]],
- aspect_ratios=[0.5, 1.0, 2.0],
- strides=[16],
- offset=0.0,
- ),
- anchor_matcher=L(Matcher)(
- thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True
- ),
- box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
- batch_size_per_image=256,
- positive_fraction=0.5,
- pre_nms_topk=(12000, 6000),
- post_nms_topk=(2000, 1000),
- nms_thresh=0.7,
- ),
- roi_heads=L(Res5ROIHeads)(
- num_classes=80,
- batch_size_per_image=512,
- positive_fraction=0.25,
- proposal_matcher=L(Matcher)(
- thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
- ),
- in_features=["res4"],
- pooler=L(ROIPooler)(
- output_size=14,
- scales=(1.0 / 16,),
- sampling_ratio=0,
- pooler_type="ROIAlignV2",
- ),
- res5=L(ResNet.make_stage)(
- block_class=BottleneckBlock,
- num_blocks=3,
- stride_per_block=[2, 1, 1],
- in_channels=1024,
- bottleneck_channels=512,
- out_channels=2048,
- norm="FrozenBN",
- stride_in_1x1=True,
- ),
- box_predictor=L(FastRCNNOutputLayers)(
- input_shape=L(ShapeSpec)(channels="${...res5.out_channels}", height=1, width=1),
- test_score_thresh=0.05,
- box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
- num_classes="${..num_classes}",
- ),
- mask_head=L(MaskRCNNConvUpsampleHead)(
- input_shape=L(ShapeSpec)(
- channels="${...res5.out_channels}",
- width="${...pooler.output_size}",
- height="${...pooler.output_size}",
- ),
- num_classes="${..num_classes}",
- conv_dims=[256],
- ),
- ),
- pixel_mean=[103.530, 116.280, 123.675],
- pixel_std=[1.0, 1.0, 1.0],
- input_format="BGR",
-)
diff --git a/spaces/Uday-07/testing/app.py b/spaces/Uday-07/testing/app.py
deleted file mode 100644
index 57fdd24e448230c5679b2027b6ddb874fc9956f7..0000000000000000000000000000000000000000
--- a/spaces/Uday-07/testing/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_images']
-
-# %% app.ipynb 4
-from fastai.vision.all import *
-import gradio as gr
-def is_cat(x): return x[0].isupper()
-
-# %% app.ipynb 7
-learn = load_learner('model.pkl')
-#we froze the learner in the model and brought it here using the pickle file
-
-# %% app.ipynb 9
-#to create a gradio interface
-categories = {'Dog','Cat'}
-def classify_images(img):
- pred,idx,probs = learn.predict(img)
- return dict(zip(categories,map(float,1-probs))) #annoying thing about gradio is it does not support numpy arrays and tensors so were converting it to float
-
-
-# %% app.ipynb 11
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = ['dog.jpg','cat.jpg']
-intf = gr.Interface(fn=classify_images,inputs=image,outputs=label,examples=examples)
-intf.launch(inline=False)
diff --git a/spaces/Uday007/Insurance-Predictor/app.py b/spaces/Uday007/Insurance-Predictor/app.py
deleted file mode 100644
index 83694fb7c15125c60b2d923ac95b059f2b9f9141..0000000000000000000000000000000000000000
--- a/spaces/Uday007/Insurance-Predictor/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from pickle import FALSE
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-# from traitlets import default
-
-def predict_expenses(
- age,sex,bmi,
- children,smoker,region
- ):
-
- # Load the model
- model=load("insurancepredict.jb")
-
- # Create dict array from parameters
- data={
- 'age':[age],
- 'sex':[sex],
- 'bmi':[bmi],
- 'children':[children],
- 'smoker':[smoker],
- 'region':[region],
- }
-
- xinp=pd.DataFrame(data)
- # print(xinp)
-
- expenses=model.predict(xinp)
- return expenses[0]
-
-ui=gr.Interface(
- fn=predict_expenses,
- inputs=[
- gr.inputs.Textbox(placeholder="Age",default=20,numeric=True,label="AGE"),
- gr.Radio(['male','female'],label="GENDER"),
- gr.inputs.Textbox(placeholder="BMI",default=25,numeric=True,label="BMI"),
- gr.inputs.Textbox(placeholder="Childrens",default=2,label="CHILDRENS"),
- gr.Radio(['yes','no'],label="SMOKER"),
- gr.Dropdown(['southwest','southeast','northwest','northeast'],label="REGION"),
-
- ],
- title="INSURANCE PREDICTOR",
-
- outputs="text",
-
- examples=[[19,"female",27.9,0,"yes","southwest",16884.92],
- [61,"male",36.3,1,"yes","southwest",47403.88]],
-
- # theme="darkdefault",
-
- css= """body {body-color: blue}"""
-
-)
-
-if __name__=="__main__":
- ui.launch()
-
-
diff --git a/spaces/UjjwalVIT/Text_analysis_and_metadata_app/stanfordmodel/ner-gui.bat b/spaces/UjjwalVIT/Text_analysis_and_metadata_app/stanfordmodel/ner-gui.bat
deleted file mode 100644
index 097cf485ce77c399386509b9ca366238ef86c235..0000000000000000000000000000000000000000
--- a/spaces/UjjwalVIT/Text_analysis_and_metadata_app/stanfordmodel/ner-gui.bat
+++ /dev/null
@@ -1 +0,0 @@
-java -mx1500m -cp "stanford-ner.jar;lib/*" edu.stanford.nlp.ie.crf.NERGUI
diff --git a/spaces/Vgi/nu-dialogue-sfc2022-stable-diffusion/app.py b/spaces/Vgi/nu-dialogue-sfc2022-stable-diffusion/app.py
deleted file mode 100644
index 3f34f6aea68660f196ac638b579ec413fe4cea68..0000000000000000000000000000000000000000
--- a/spaces/Vgi/nu-dialogue-sfc2022-stable-diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/nu-dialogue/sfc2022-stable-diffusion").launch()
\ No newline at end of file
diff --git a/spaces/VideoCrafter/VideoCrafter/lvdm/ema.py b/spaces/VideoCrafter/VideoCrafter/lvdm/ema.py
deleted file mode 100644
index c8c75af43565f6e140287644aaaefa97dd6e67c5..0000000000000000000000000000000000000000
--- a/spaces/VideoCrafter/VideoCrafter/lvdm/ema.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
- def __init__(self, model, decay=0.9999, use_num_upates=True):
- super().__init__()
- if decay < 0.0 or decay > 1.0:
- raise ValueError('Decay must be between 0 and 1')
-
- self.m_name2s_name = {}
- self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
- self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
- else torch.tensor(-1,dtype=torch.int))
-
- for name, p in model.named_parameters():
- if p.requires_grad:
- #remove as '.'-character is not allowed in buffers
- s_name = name.replace('.','')
- self.m_name2s_name.update({name:s_name})
- self.register_buffer(s_name,p.clone().detach().data)
-
- self.collected_params = []
-
- def forward(self,model):
- decay = self.decay
-
- if self.num_updates >= 0:
- self.num_updates += 1
- decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
-
- one_minus_decay = 1.0 - decay
-
- with torch.no_grad():
- m_param = dict(model.named_parameters())
- shadow_params = dict(self.named_buffers())
-
- for key in m_param:
- if m_param[key].requires_grad:
- sname = self.m_name2s_name[key]
- shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
- shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
- else:
- assert not key in self.m_name2s_name
-
- def copy_to(self, model):
- m_param = dict(model.named_parameters())
- shadow_params = dict(self.named_buffers())
- for key in m_param:
- if m_param[key].requires_grad:
- m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
- else:
- assert not key in self.m_name2s_name
-
- def store(self, parameters):
- """
- Save the current parameters for restoring later.
- Args:
- parameters: Iterable of `torch.nn.Parameter`; the parameters to be
- temporarily stored.
- """
- self.collected_params = [param.clone() for param in parameters]
-
- def restore(self, parameters):
- """
- Restore the parameters stored with the `store` method.
- Useful to validate the model with EMA parameters without affecting the
- original optimization process. Store the parameters before the
- `copy_to` method. After validation (or model saving), use this to
- restore the former parameters.
- Args:
- parameters: Iterable of `torch.nn.Parameter`; the parameters to be
- updated with the stored parameters.
- """
- for c_param, param in zip(self.collected_params, parameters):
- param.data.copy_(c_param.data)
diff --git a/spaces/WhyLIM/ChatGPT-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/WhyLIM/ChatGPT-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/WhyLIM/ChatGPT-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include
-
-#include
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
- static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
- static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
- // stolen from IImageWrapper.h
- enum ERGBFormatJPG
- {
- Invalid = -1,
- RGBA = 0,
- BGRA = 1,
- Gray = 2,
- };
- static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
- // DCT coefficients are stored in this sequence.
- static int g_ZAG[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
- enum JPEG_MARKER
- {
- M_SOF0 = 0xC0, M_SOF1 = 0xC1, M_SOF2 = 0xC2, M_SOF3 = 0xC3, M_SOF5 = 0xC5, M_SOF6 = 0xC6, M_SOF7 = 0xC7, M_JPG = 0xC8,
- M_SOF9 = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT = 0xC4, M_DAC = 0xCC,
- M_RST0 = 0xD0, M_RST1 = 0xD1, M_RST2 = 0xD2, M_RST3 = 0xD3, M_RST4 = 0xD4, M_RST5 = 0xD5, M_RST6 = 0xD6, M_RST7 = 0xD7,
- M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_DNL = 0xDC, M_DRI = 0xDD, M_DHP = 0xDE, M_EXP = 0xDF,
- M_APP0 = 0xE0, M_APP15 = 0xEF, M_JPG0 = 0xF0, M_JPG13 = 0xFD, M_COM = 0xFE, M_TEM = 0x01, M_ERROR = 0x100, RST0 = 0xD0
- };
-
- enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS 13
-#define PASS1_BITS 2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336 ((int32)2446) /* FIX(0.298631336) */
-#define FIX_0_390180644 ((int32)3196) /* FIX(0.390180644) */
-#define FIX_0_541196100 ((int32)4433) /* FIX(0.541196100) */
-#define FIX_0_765366865 ((int32)6270) /* FIX(0.765366865) */
-#define FIX_0_899976223 ((int32)7373) /* FIX(0.899976223) */
-#define FIX_1_175875602 ((int32)9633) /* FIX(1.175875602) */
-#define FIX_1_501321110 ((int32)12299) /* FIX(1.501321110) */
-#define FIX_1_847759065 ((int32)15137) /* FIX(1.847759065) */
-#define FIX_1_961570560 ((int32)16069) /* FIX(1.961570560) */
-#define FIX_2_053119869 ((int32)16819) /* FIX(2.053119869) */
-#define FIX_2_562915447 ((int32)20995) /* FIX(2.562915447) */
-#define FIX_3_072711026 ((int32)25172) /* FIX(3.072711026) */
-
-#define DESCALE(x,n) (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n) (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst) ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
- // Compiler creates a fast path 1D IDCT for X non-zero columns
- template
- struct Row
- {
- static void idct(int* pTemp, const jpgd_block_t* pSrc)
- {
- // ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
- const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
- const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
- const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
- const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
- const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
- const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
- const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
- const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
- const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
- const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
- const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
- const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
- const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
- const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
- const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
- const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
- const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
- const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
- pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
- pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
- pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
- pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
- pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
- pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
- pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
- pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
- }
- };
-
- template <>
- struct Row<0>
- {
- static void idct(int* pTemp, const jpgd_block_t* pSrc)
- {
-#ifdef _MSC_VER
- pTemp; pSrc;
-#endif
- }
- };
-
- template <>
- struct Row<1>
- {
- static void idct(int* pTemp, const jpgd_block_t* pSrc)
- {
- const int dcval = (pSrc[0] << PASS1_BITS);
-
- pTemp[0] = dcval;
- pTemp[1] = dcval;
- pTemp[2] = dcval;
- pTemp[3] = dcval;
- pTemp[4] = dcval;
- pTemp[5] = dcval;
- pTemp[6] = dcval;
- pTemp[7] = dcval;
- }
- };
-
- // Compiler creates a fast path 1D IDCT for X non-zero rows
- template
- struct Col
- {
- static void idct(uint8* pDst_ptr, const int* pTemp)
- {
- // ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
- const int z2 = ACCESS_ROW(2);
- const int z3 = ACCESS_ROW(6);
-
- const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
- const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
- const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
- const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
- const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
- const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
- const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
- const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
- const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
- const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
- const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
- const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
- const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
- const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
- const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
- const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
- const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
- int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*0] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*7] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*1] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*6] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*2] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*5] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*3] = (uint8)CLAMP(i);
-
- i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
- pDst_ptr[8*4] = (uint8)CLAMP(i);
- }
- };
-
- template <>
- struct Col<1>
- {
- static void idct(uint8* pDst_ptr, const int* pTemp)
- {
- int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
- const uint8 dcval_clamped = (uint8)CLAMP(dcval);
- pDst_ptr[0*8] = dcval_clamped;
- pDst_ptr[1*8] = dcval_clamped;
- pDst_ptr[2*8] = dcval_clamped;
- pDst_ptr[3*8] = dcval_clamped;
- pDst_ptr[4*8] = dcval_clamped;
- pDst_ptr[5*8] = dcval_clamped;
- pDst_ptr[6*8] = dcval_clamped;
- pDst_ptr[7*8] = dcval_clamped;
- }
- };
-
- static const uint8 s_idct_row_table[] =
- {
- 1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
- 4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
- 6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
- 6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
- 8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
- 8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
- 8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
- 8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
- };
-
- static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
- void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
- {
- JPGD_ASSERT(block_max_zag >= 1);
- JPGD_ASSERT(block_max_zag <= 64);
-
- if (block_max_zag == 1)
- {
- int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
- k = CLAMP(k);
- k = k | (k<<8);
- k = k | (k<<16);
-
- for (int i = 8; i > 0; i--)
- {
- *(int*)&pDst_ptr[0] = k;
- *(int*)&pDst_ptr[4] = k;
- pDst_ptr += 8;
- }
- return;
- }
-
- int temp[64];
-
- const jpgd_block_t* pSrc = pSrc_ptr;
- int* pTemp = temp;
-
- const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
- int i;
- for (i = 8; i > 0; i--, pRow_tab++)
- {
- switch (*pRow_tab)
- {
- case 0: Row<0>::idct(pTemp, pSrc); break;
- case 1: Row<1>::idct(pTemp, pSrc); break;
- case 2: Row<2>::idct(pTemp, pSrc); break;
- case 3: Row<3>::idct(pTemp, pSrc); break;
- case 4: Row<4>::idct(pTemp, pSrc); break;
- case 5: Row<5>::idct(pTemp, pSrc); break;
- case 6: Row<6>::idct(pTemp, pSrc); break;
- case 7: Row<7>::idct(pTemp, pSrc); break;
- case 8: Row<8>::idct(pTemp, pSrc); break;
- }
-
- pSrc += 8;
- pTemp += 8;
- }
-
- pTemp = temp;
-
- const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
- for (i = 8; i > 0; i--)
- {
- switch (nonzero_rows)
- {
- case 1: Col<1>::idct(pDst_ptr, pTemp); break;
- case 2: Col<2>::idct(pDst_ptr, pTemp); break;
- case 3: Col<3>::idct(pDst_ptr, pTemp); break;
- case 4: Col<4>::idct(pDst_ptr, pTemp); break;
- case 5: Col<5>::idct(pDst_ptr, pTemp); break;
- case 6: Col<6>::idct(pDst_ptr, pTemp); break;
- case 7: Col<7>::idct(pDst_ptr, pTemp); break;
- case 8: Col<8>::idct(pDst_ptr, pTemp); break;
- }
-
- pTemp++;
- pDst_ptr++;
- }
- }
-
- void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
- {
- int temp[64];
- int* pTemp = temp;
- const jpgd_block_t* pSrc = pSrc_ptr;
-
- for (int i = 4; i > 0; i--)
- {
- Row<4>::idct(pTemp, pSrc);
- pSrc += 8;
- pTemp += 8;
- }
-
- pTemp = temp;
- for (int i = 8; i > 0; i--)
- {
- Col<4>::idct(pDst_ptr, pTemp);
- pTemp++;
- pDst_ptr++;
- }
- }
-
- // Retrieve one character from the input stream.
- inline uint jpeg_decoder::get_char()
- {
- // Any bytes remaining in buffer?
- if (!m_in_buf_left)
- {
- // Try to get more bytes.
- prep_in_buffer();
- // Still nothing to get?
- if (!m_in_buf_left)
- {
- // Pad the end of the stream with 0xFF 0xD9 (EOI marker)
- int t = m_tem_flag;
- m_tem_flag ^= 1;
- if (t)
- return 0xD9;
- else
- return 0xFF;
- }
- }
-
- uint c = *m_pIn_buf_ofs++;
- m_in_buf_left--;
-
- return c;
- }
-
- // Same as previous method, except can indicate if the character is a pad character or not.
- inline uint jpeg_decoder::get_char(bool *pPadding_flag)
- {
- if (!m_in_buf_left)
- {
- prep_in_buffer();
- if (!m_in_buf_left)
- {
- *pPadding_flag = true;
- int t = m_tem_flag;
- m_tem_flag ^= 1;
- if (t)
- return 0xD9;
- else
- return 0xFF;
- }
- }
-
- *pPadding_flag = false;
-
- uint c = *m_pIn_buf_ofs++;
- m_in_buf_left--;
-
- return c;
- }
-
- // Inserts a previously retrieved character back into the input buffer.
- inline void jpeg_decoder::stuff_char(uint8 q)
- {
- *(--m_pIn_buf_ofs) = q;
- m_in_buf_left++;
- }
-
- // Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
- inline uint8 jpeg_decoder::get_octet()
- {
- bool padding_flag;
- int c = get_char(&padding_flag);
-
- if (c == 0xFF)
- {
- if (padding_flag)
- return 0xFF;
-
- c = get_char(&padding_flag);
- if (padding_flag)
- {
- stuff_char(0xFF);
- return 0xFF;
- }
-
- if (c == 0x00)
- return 0xFF;
- else
- {
- stuff_char(static_cast(c));
- stuff_char(0xFF);
- return 0xFF;
- }
- }
-
- return static_cast(c);
- }
-
- // Retrieves a variable number of bits from the input stream. Does not recognize markers.
- inline uint jpeg_decoder::get_bits(int num_bits)
- {
- if (!num_bits)
- return 0;
-
- uint i = m_bit_buf >> (32 - num_bits);
-
- if ((m_bits_left -= num_bits) <= 0)
- {
- m_bit_buf <<= (num_bits += m_bits_left);
-
- uint c1 = get_char();
- uint c2 = get_char();
- m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
- m_bit_buf <<= -m_bits_left;
-
- m_bits_left += 16;
-
- JPGD_ASSERT(m_bits_left >= 0);
- }
- else
- m_bit_buf <<= num_bits;
-
- return i;
- }
-
- // Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
- inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
- {
- if (!num_bits)
- return 0;
-
- uint i = m_bit_buf >> (32 - num_bits);
-
- if ((m_bits_left -= num_bits) <= 0)
- {
- m_bit_buf <<= (num_bits += m_bits_left);
-
- if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
- {
- uint c1 = get_octet();
- uint c2 = get_octet();
- m_bit_buf |= (c1 << 8) | c2;
- }
- else
- {
- m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
- m_in_buf_left -= 2;
- m_pIn_buf_ofs += 2;
- }
-
- m_bit_buf <<= -m_bits_left;
-
- m_bits_left += 16;
-
- JPGD_ASSERT(m_bits_left >= 0);
- }
- else
- m_bit_buf <<= num_bits;
-
- return i;
- }
-
- // Decodes a Huffman encoded symbol.
- inline int jpeg_decoder::huff_decode(huff_tables *pH)
- {
- int symbol;
-
- // Check first 8-bits: do we have a complete symbol?
- if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
- {
- // Decode more bits, use a tree traversal to find symbol.
- int ofs = 23;
- do
- {
- symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
- ofs--;
- } while (symbol < 0);
-
- get_bits_no_markers(8 + (23 - ofs));
- }
- else
- get_bits_no_markers(pH->code_size[symbol]);
-
- return symbol;
- }
-
- // Decodes a Huffman encoded symbol.
- inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
- {
- int symbol;
-
- // Check first 8-bits: do we have a complete symbol?
- if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
- {
- // Use a tree traversal to find symbol.
- int ofs = 23;
- do
- {
- symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
- ofs--;
- } while (symbol < 0);
-
- get_bits_no_markers(8 + (23 - ofs));
-
- extra_bits = get_bits_no_markers(symbol & 0xF);
- }
- else
- {
- JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
- if (symbol & 0x8000)
- {
- get_bits_no_markers((symbol >> 8) & 31);
- extra_bits = symbol >> 16;
- }
- else
- {
- int code_size = (symbol >> 8) & 31;
- int num_extra_bits = symbol & 0xF;
- int bits = code_size + num_extra_bits;
- if (bits <= (m_bits_left + 16))
- extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
- else
- {
- get_bits_no_markers(code_size);
- extra_bits = get_bits_no_markers(num_extra_bits);
- }
- }
-
- symbol &= 0xFF;
- }
-
- return symbol;
- }
-
- // Tables and macro used to fully decode the DPCM differences.
- static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
- static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
- static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
- // Clamps a value between 0-255.
- inline uint8 jpeg_decoder::clamp(int i)
- {
- if (static_cast(i) > 255)
- i = (((~i) >> 31) & 0xFF);
-
- return static_cast(i);
- }
-
- namespace DCT_Upsample
- {
- struct Matrix44
- {
- typedef int Element_Type;
- enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
- Element_Type v[NUM_ROWS][NUM_COLS];
-
- inline int rows() const { return NUM_ROWS; }
- inline int cols() const { return NUM_COLS; }
-
- inline const Element_Type & at(int r, int c) const { return v[r][c]; }
- inline Element_Type & at(int r, int c) { return v[r][c]; }
-
- inline Matrix44() { }
-
- inline Matrix44& operator += (const Matrix44& a)
- {
- for (int r = 0; r < NUM_ROWS; r++)
- {
- at(r, 0) += a.at(r, 0);
- at(r, 1) += a.at(r, 1);
- at(r, 2) += a.at(r, 2);
- at(r, 3) += a.at(r, 3);
- }
- return *this;
- }
-
- inline Matrix44& operator -= (const Matrix44& a)
- {
- for (int r = 0; r < NUM_ROWS; r++)
- {
- at(r, 0) -= a.at(r, 0);
- at(r, 1) -= a.at(r, 1);
- at(r, 2) -= a.at(r, 2);
- at(r, 3) -= a.at(r, 3);
- }
- return *this;
- }
-
- friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
- {
- Matrix44 ret;
- for (int r = 0; r < NUM_ROWS; r++)
- {
- ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
- ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
- ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
- ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
- }
- return ret;
- }
-
- friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
- {
- Matrix44 ret;
- for (int r = 0; r < NUM_ROWS; r++)
- {
- ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
- ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
- ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
- ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
- }
- return ret;
- }
-
- static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
- {
- for (int r = 0; r < 4; r++)
- {
- pDst[0*8 + r] = static_cast(a.at(r, 0) + b.at(r, 0));
- pDst[1*8 + r] = static_cast(a.at(r, 1) + b.at(r, 1));
- pDst[2*8 + r] = static_cast(a.at(r, 2) + b.at(r, 2));
- pDst[3*8 + r] = static_cast(a.at(r, 3) + b.at(r, 3));
- }
- }
-
- static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
- {
- for (int r = 0; r < 4; r++)
- {
- pDst[0*8 + r] = static_cast(a.at(r, 0) - b.at(r, 0));
- pDst[1*8 + r] = static_cast(a.at(r, 1) - b.at(r, 1));
- pDst[2*8 + r] = static_cast(a.at(r, 2) - b.at(r, 2));
- pDst[3*8 + r] = static_cast(a.at(r, 3) - b.at(r, 3));
- }
- }
- };
-
- const int FRACT_BITS = 10;
- const int SCALE = 1 << FRACT_BITS;
-
- typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
- // Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
- // NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
- template
- struct P_Q
- {
- static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
- {
- // 4x8 = 4x8 times 8x8, matrix 0 is constant
- const Temp_Type X000 = AT(0, 0);
- const Temp_Type X001 = AT(0, 1);
- const Temp_Type X002 = AT(0, 2);
- const Temp_Type X003 = AT(0, 3);
- const Temp_Type X004 = AT(0, 4);
- const Temp_Type X005 = AT(0, 5);
- const Temp_Type X006 = AT(0, 6);
- const Temp_Type X007 = AT(0, 7);
- const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
- const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
- const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
- const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
- const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
- const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
- const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
- const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
- const Temp_Type X020 = AT(4, 0);
- const Temp_Type X021 = AT(4, 1);
- const Temp_Type X022 = AT(4, 2);
- const Temp_Type X023 = AT(4, 3);
- const Temp_Type X024 = AT(4, 4);
- const Temp_Type X025 = AT(4, 5);
- const Temp_Type X026 = AT(4, 6);
- const Temp_Type X027 = AT(4, 7);
- const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
- const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
- const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
- const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
- const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
- const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
- const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
- const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- P.at(0, 0) = X000;
- P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
- P.at(0, 2) = X004;
- P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
- P.at(1, 0) = X010;
- P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
- P.at(1, 2) = X014;
- P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
- P.at(2, 0) = X020;
- P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
- P.at(2, 2) = X024;
- P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
- P.at(3, 0) = X030;
- P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
- P.at(3, 2) = X034;
- P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
- // 40 muls 24 adds
-
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
- Q.at(0, 1) = X002;
- Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
- Q.at(0, 3) = X006;
- Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
- Q.at(1, 1) = X012;
- Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
- Q.at(1, 3) = X016;
- Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
- Q.at(2, 1) = X022;
- Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
- Q.at(2, 3) = X026;
- Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
- Q.at(3, 1) = X032;
- Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
- Q.at(3, 3) = X036;
- // 40 muls 24 adds
- }
- };
-
- template
- struct R_S
- {
- static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
- {
- // 4x8 = 4x8 times 8x8, matrix 0 is constant
- const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
- const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
- const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
- const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
- const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
- const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
- const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
- const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
- const Temp_Type X110 = AT(2, 0);
- const Temp_Type X111 = AT(2, 1);
- const Temp_Type X112 = AT(2, 2);
- const Temp_Type X113 = AT(2, 3);
- const Temp_Type X114 = AT(2, 4);
- const Temp_Type X115 = AT(2, 5);
- const Temp_Type X116 = AT(2, 6);
- const Temp_Type X117 = AT(2, 7);
- const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
- const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
- const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
- const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
- const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
- const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
- const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
- const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
- const Temp_Type X130 = AT(6, 0);
- const Temp_Type X131 = AT(6, 1);
- const Temp_Type X132 = AT(6, 2);
- const Temp_Type X133 = AT(6, 3);
- const Temp_Type X134 = AT(6, 4);
- const Temp_Type X135 = AT(6, 5);
- const Temp_Type X136 = AT(6, 6);
- const Temp_Type X137 = AT(6, 7);
- // 80 muls 48 adds
-
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- R.at(0, 0) = X100;
- R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
- R.at(0, 2) = X104;
- R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
- R.at(1, 0) = X110;
- R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
- R.at(1, 2) = X114;
- R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
- R.at(2, 0) = X120;
- R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
- R.at(2, 2) = X124;
- R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
- R.at(3, 0) = X130;
- R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
- R.at(3, 2) = X134;
- R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
- // 40 muls 24 adds
- // 4x4 = 4x8 times 8x4, matrix 1 is constant
- S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
- S.at(0, 1) = X102;
- S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
- S.at(0, 3) = X106;
- S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
- S.at(1, 1) = X112;
- S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
- S.at(1, 3) = X116;
- S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
- S.at(2, 1) = X122;
- S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
- S.at(2, 3) = X126;
- S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
- S.at(3, 1) = X132;
- S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
- S.at(3, 3) = X136;
- // 40 muls 24 adds
- }
- };
- } // end namespace DCT_Upsample
-
- // Unconditionally frees all allocated m_blocks.
- void jpeg_decoder::free_all_blocks()
- {
- m_pStream = NULL;
- for (mem_block *b = m_pMem_blocks; b; )
- {
- mem_block *n = b->m_pNext;
- jpgd_free(b);
- b = n;
- }
- m_pMem_blocks = NULL;
- }
-
- // This method handles all errors.
- // It could easily be changed to use C++ exceptions.
- void jpeg_decoder::stop_decoding(jpgd_status status)
- {
- m_error_code = status;
- free_all_blocks();
- longjmp(m_jmp_state, status);
-
- // we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
- // that this function doesn't return, otherwise we get this error:
- //
- // error : function declared 'noreturn' should not return
- exit(1);
- }
-
- void *jpeg_decoder::alloc(size_t nSize, bool zero)
- {
- nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
- char *rv = NULL;
- for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
- {
- if ((b->m_used_count + nSize) <= b->m_size)
- {
- rv = b->m_data + b->m_used_count;
- b->m_used_count += nSize;
- break;
- }
- }
- if (!rv)
- {
- int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
- mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
- if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
- b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
- b->m_used_count = nSize;
- b->m_size = capacity;
- rv = b->m_data;
- }
- if (zero) memset(rv, 0, nSize);
- return rv;
- }
-
- void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
- {
- uint8 *pD = (uint8*)p;
- const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
- while (n)
- {
- pD[0] = l; pD[1] = h; pD += 2;
- n--;
- }
- }
-
- // Refill the input buffer.
- // This method will sit in a loop until (A) the buffer is full or (B)
- // the stream's read() method reports and end of file condition.
- void jpeg_decoder::prep_in_buffer()
- {
- m_in_buf_left = 0;
- m_pIn_buf_ofs = m_in_buf;
-
- if (m_eof_flag)
- return;
-
- do
- {
- int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
- if (bytes_read == -1)
- stop_decoding(JPGD_STREAM_READ);
-
- m_in_buf_left += bytes_read;
- } while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
- m_total_bytes_read += m_in_buf_left;
-
- // Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
- // (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
- word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
- }
-
- // Read a Huffman code table.
- void jpeg_decoder::read_dht_marker()
- {
- int i, index, count;
- uint8 huff_num[17];
- uint8 huff_val[256];
-
- uint num_left = get_bits(16);
-
- if (num_left < 2)
- stop_decoding(JPGD_BAD_DHT_MARKER);
-
- num_left -= 2;
-
- while (num_left)
- {
- index = get_bits(8);
-
- huff_num[0] = 0;
-
- count = 0;
-
- for (i = 1; i <= 16; i++)
- {
- huff_num[i] = static_cast(get_bits(8));
- count += huff_num[i];
- }
-
- if (count > 255)
- stop_decoding(JPGD_BAD_DHT_COUNTS);
-
- for (i = 0; i < count; i++)
- huff_val[i] = static_cast(get_bits(8));
-
- i = 1 + 16 + count;
-
- if (num_left < (uint)i)
- stop_decoding(JPGD_BAD_DHT_MARKER);
-
- num_left -= i;
-
- if ((index & 0x10) > 0x10)
- stop_decoding(JPGD_BAD_DHT_INDEX);
-
- index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
- if (index >= JPGD_MAX_HUFF_TABLES)
- stop_decoding(JPGD_BAD_DHT_INDEX);
-
- if (!m_huff_num[index])
- m_huff_num[index] = (uint8 *)alloc(17);
-
- if (!m_huff_val[index])
- m_huff_val[index] = (uint8 *)alloc(256);
-
- m_huff_ac[index] = (index & 0x10) != 0;
- memcpy(m_huff_num[index], huff_num, 17);
- memcpy(m_huff_val[index], huff_val, 256);
- }
- }
-
- // Read a quantization table.
- void jpeg_decoder::read_dqt_marker()
- {
- int n, i, prec;
- uint num_left;
- uint temp;
-
- num_left = get_bits(16);
-
- if (num_left < 2)
- stop_decoding(JPGD_BAD_DQT_MARKER);
-
- num_left -= 2;
-
- while (num_left)
- {
- n = get_bits(8);
- prec = n >> 4;
- n &= 0x0F;
-
- if (n >= JPGD_MAX_QUANT_TABLES)
- stop_decoding(JPGD_BAD_DQT_TABLE);
-
- if (!m_quant[n])
- m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
- // read quantization entries, in zag order
- for (i = 0; i < 64; i++)
- {
- temp = get_bits(8);
-
- if (prec)
- temp = (temp << 8) + get_bits(8);
-
- m_quant[n][i] = static_cast(temp);
- }
-
- i = 64 + 1;
-
- if (prec)
- i += 64;
-
- if (num_left < (uint)i)
- stop_decoding(JPGD_BAD_DQT_LENGTH);
-
- num_left -= i;
- }
- }
-
- // Read the start of frame (SOF) marker.
- void jpeg_decoder::read_sof_marker()
- {
- int i;
- uint num_left;
-
- num_left = get_bits(16);
-
- if (get_bits(8) != 8) /* precision: sorry, only 8-bit precision is supported right now */
- stop_decoding(JPGD_BAD_PRECISION);
-
- m_image_y_size = get_bits(16);
-
- if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
- stop_decoding(JPGD_BAD_HEIGHT);
-
- m_image_x_size = get_bits(16);
-
- if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
- stop_decoding(JPGD_BAD_WIDTH);
-
- m_comps_in_frame = get_bits(8);
-
- if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
- stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
- if (num_left != (uint)(m_comps_in_frame * 3 + 8))
- stop_decoding(JPGD_BAD_SOF_LENGTH);
-
- for (i = 0; i < m_comps_in_frame; i++)
- {
- m_comp_ident[i] = get_bits(8);
- m_comp_h_samp[i] = get_bits(4);
- m_comp_v_samp[i] = get_bits(4);
- m_comp_quant[i] = get_bits(8);
- }
- }
-
- // Used to skip unrecognized markers.
- void jpeg_decoder::skip_variable_marker()
- {
- uint num_left;
-
- num_left = get_bits(16);
-
- if (num_left < 2)
- stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
- num_left -= 2;
-
- while (num_left)
- {
- get_bits(8);
- num_left--;
- }
- }
-
- // Read a define restart interval (DRI) marker.
- void jpeg_decoder::read_dri_marker()
- {
- if (get_bits(16) != 4)
- stop_decoding(JPGD_BAD_DRI_LENGTH);
-
- m_restart_interval = get_bits(16);
- }
-
- // Read a start of scan (SOS) marker.
- void jpeg_decoder::read_sos_marker()
- {
- uint num_left;
- int i, ci, n, c, cc;
-
- num_left = get_bits(16);
-
- n = get_bits(8);
-
- m_comps_in_scan = n;
-
- num_left -= 3;
-
- if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
- stop_decoding(JPGD_BAD_SOS_LENGTH);
-
- for (i = 0; i < n; i++)
- {
- cc = get_bits(8);
- c = get_bits(8);
- num_left -= 2;
-
- for (ci = 0; ci < m_comps_in_frame; ci++)
- if (cc == m_comp_ident[ci])
- break;
-
- if (ci >= m_comps_in_frame)
- stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
- m_comp_list[i] = ci;
- m_comp_dc_tab[ci] = (c >> 4) & 15;
- m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
- }
-
- m_spectral_start = get_bits(8);
- m_spectral_end = get_bits(8);
- m_successive_high = get_bits(4);
- m_successive_low = get_bits(4);
-
- if (!m_progressive_flag)
- {
- m_spectral_start = 0;
- m_spectral_end = 63;
- }
-
- num_left -= 3;
-
- while (num_left) /* read past whatever is num_left */
- {
- get_bits(8);
- num_left--;
- }
- }
-
- // Finds the next marker.
- int jpeg_decoder::next_marker()
- {
- uint c, bytes;
-
- bytes = 0;
-
- do
- {
- do
- {
- bytes++;
- c = get_bits(8);
- } while (c != 0xFF);
-
- do
- {
- c = get_bits(8);
- } while (c == 0xFF);
-
- } while (c == 0);
-
- // If bytes > 0 here, there where extra bytes before the marker (not good).
-
- return c;
- }
-
- // Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
- // encountered.
- int jpeg_decoder::process_markers()
- {
- int c;
-
- for ( ; ; )
- {
- c = next_marker();
-
- switch (c)
- {
- case M_SOF0:
- case M_SOF1:
- case M_SOF2:
- case M_SOF3:
- case M_SOF5:
- case M_SOF6:
- case M_SOF7:
- // case M_JPG:
- case M_SOF9:
- case M_SOF10:
- case M_SOF11:
- case M_SOF13:
- case M_SOF14:
- case M_SOF15:
- case M_SOI:
- case M_EOI:
- case M_SOS:
- {
- return c;
- }
- case M_DHT:
- {
- read_dht_marker();
- break;
- }
- // No arithmitic support - dumb patents!
- case M_DAC:
- {
- stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
- break;
- }
- case M_DQT:
- {
- read_dqt_marker();
- break;
- }
- case M_DRI:
- {
- read_dri_marker();
- break;
- }
- //case M_APP0: /* no need to read the JFIF marker */
-
- case M_JPG:
- case M_RST0: /* no parameters */
- case M_RST1:
- case M_RST2:
- case M_RST3:
- case M_RST4:
- case M_RST5:
- case M_RST6:
- case M_RST7:
- case M_TEM:
- {
- stop_decoding(JPGD_UNEXPECTED_MARKER);
- break;
- }
- default: /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
- {
- skip_variable_marker();
- break;
- }
- }
- }
- }
-
- // Finds the start of image (SOI) marker.
- // This code is rather defensive: it only checks the first 512 bytes to avoid
- // false positives.
- void jpeg_decoder::locate_soi_marker()
- {
- uint lastchar, thischar;
- uint bytesleft;
-
- lastchar = get_bits(8);
-
- thischar = get_bits(8);
-
- /* ok if it's a normal JPEG file without a special header */
-
- if ((lastchar == 0xFF) && (thischar == M_SOI))
- return;
-
- bytesleft = 4096; //512;
-
- for ( ; ; )
- {
- if (--bytesleft == 0)
- stop_decoding(JPGD_NOT_JPEG);
-
- lastchar = thischar;
-
- thischar = get_bits(8);
-
- if (lastchar == 0xFF)
- {
- if (thischar == M_SOI)
- break;
- else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
- stop_decoding(JPGD_NOT_JPEG);
- }
- }
-
- // Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
- thischar = (m_bit_buf >> 24) & 0xFF;
-
- if (thischar != 0xFF)
- stop_decoding(JPGD_NOT_JPEG);
- }
-
- // Find a start of frame (SOF) marker.
- void jpeg_decoder::locate_sof_marker()
- {
- locate_soi_marker();
-
- int c = process_markers();
-
- switch (c)
- {
- case M_SOF2:
- m_progressive_flag = JPGD_TRUE;
- case M_SOF0: /* baseline DCT */
- case M_SOF1: /* extended sequential DCT */
- {
- read_sof_marker();
- break;
- }
- case M_SOF9: /* Arithmitic coding */
- {
- stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
- break;
- }
- default:
- {
- stop_decoding(JPGD_UNSUPPORTED_MARKER);
- break;
- }
- }
- }
-
- // Find a start of scan (SOS) marker.
- int jpeg_decoder::locate_sos_marker()
- {
- int c;
-
- c = process_markers();
-
- if (c == M_EOI)
- return JPGD_FALSE;
- else if (c != M_SOS)
- stop_decoding(JPGD_UNEXPECTED_MARKER);
-
- read_sos_marker();
-
- return JPGD_TRUE;
- }
-
- // Reset everything to default/uninitialized state.
- void jpeg_decoder::init(jpeg_decoder_stream *pStream)
- {
- m_pMem_blocks = NULL;
- m_error_code = JPGD_SUCCESS;
- m_ready_flag = false;
- m_image_x_size = m_image_y_size = 0;
- m_pStream = pStream;
- m_progressive_flag = JPGD_FALSE;
-
- memset(m_huff_ac, 0, sizeof(m_huff_ac));
- memset(m_huff_num, 0, sizeof(m_huff_num));
- memset(m_huff_val, 0, sizeof(m_huff_val));
- memset(m_quant, 0, sizeof(m_quant));
-
- m_scan_type = 0;
- m_comps_in_frame = 0;
-
- memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
- memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
- memset(m_comp_quant, 0, sizeof(m_comp_quant));
- memset(m_comp_ident, 0, sizeof(m_comp_ident));
- memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
- memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
- m_comps_in_scan = 0;
- memset(m_comp_list, 0, sizeof(m_comp_list));
- memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
- memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
- m_spectral_start = 0;
- m_spectral_end = 0;
- m_successive_low = 0;
- m_successive_high = 0;
- m_max_mcu_x_size = 0;
- m_max_mcu_y_size = 0;
- m_blocks_per_mcu = 0;
- m_max_blocks_per_row = 0;
- m_mcus_per_row = 0;
- m_mcus_per_col = 0;
- m_expanded_blocks_per_component = 0;
- m_expanded_blocks_per_mcu = 0;
- m_expanded_blocks_per_row = 0;
- m_freq_domain_chroma_upsample = false;
-
- memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
- m_total_lines_left = 0;
- m_mcu_lines_left = 0;
- m_real_dest_bytes_per_scan_line = 0;
- m_dest_bytes_per_scan_line = 0;
- m_dest_bytes_per_pixel = 0;
-
- memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
- memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
- memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
- memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
- m_eob_run = 0;
-
- memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
- m_pIn_buf_ofs = m_in_buf;
- m_in_buf_left = 0;
- m_eof_flag = false;
- m_tem_flag = 0;
-
- memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
- memset(m_in_buf, 0, sizeof(m_in_buf));
- memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
- m_restart_interval = 0;
- m_restarts_left = 0;
- m_next_restart_num = 0;
-
- m_max_mcus_per_row = 0;
- m_max_blocks_per_mcu = 0;
- m_max_mcus_per_col = 0;
-
- memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
- m_pMCU_coefficients = NULL;
- m_pSample_buf = NULL;
-
- m_total_bytes_read = 0;
-
- m_pScan_line_0 = NULL;
- m_pScan_line_1 = NULL;
-
- // Ready the input buffer.
- prep_in_buffer();
-
- // Prime the bit buffer.
- m_bits_left = 16;
- m_bit_buf = 0;
-
- get_bits(16);
- get_bits(16);
-
- for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
- m_mcu_block_max_zag[i] = 64;
- }
-
-#define SCALEBITS 16
-#define ONE_HALF ((int) 1 << (SCALEBITS-1))
-#define FIX(x) ((int) ((x) * (1L<> SCALEBITS;
- m_cbb[i] = ( FIX(1.77200f) * k + ONE_HALF) >> SCALEBITS;
- m_crg[i] = (-FIX(0.71414f)) * k;
- m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
- }
- }
-
- // This method throws back into the stream any bytes that where read
- // into the bit buffer during initial marker scanning.
- void jpeg_decoder::fix_in_buffer()
- {
- // In case any 0xFF's where pulled into the buffer during marker scanning.
- JPGD_ASSERT((m_bits_left & 7) == 0);
-
- if (m_bits_left == 16)
- stuff_char( (uint8)(m_bit_buf & 0xFF));
-
- if (m_bits_left >= 8)
- stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
- stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
- stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
- m_bits_left = 16;
- get_bits_no_markers(16);
- get_bits_no_markers(16);
- }
-
- void jpeg_decoder::transform_mcu(int mcu_row)
- {
- jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
- uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
- for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
- {
- idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
- pSrc_ptr += 64;
- pDst_ptr += 64;
- }
- }
-
- static const uint8 s_max_rc[64] =
- {
- 17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
- 102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
- 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
- 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
- };
-
- void jpeg_decoder::transform_mcu_expand(int mcu_row)
- {
- jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
- uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
- // Y IDCT
- int mcu_block;
- for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
- {
- idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
- pSrc_ptr += 64;
- pDst_ptr += 64;
- }
-
- // Chroma IDCT, with upsampling
- jpgd_block_t temp_block[64];
-
- for (int i = 0; i < 2; i++)
- {
- DCT_Upsample::Matrix44 P, Q, R, S;
-
- JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
- JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
- switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
- {
- case 1*16+1:
- DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
- break;
- case 1*16+2:
- DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
- break;
- case 2*16+2:
- DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
- break;
- case 3*16+2:
- DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
- break;
- case 3*16+3:
- DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
- break;
- case 3*16+4:
- DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
- break;
- case 4*16+4:
- DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
- break;
- case 5*16+4:
- DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
- break;
- case 5*16+5:
- DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
- break;
- case 5*16+6:
- DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
- break;
- case 6*16+6:
- DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
- break;
- case 7*16+6:
- DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
- break;
- case 7*16+7:
- DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
- break;
- case 7*16+8:
- DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
- break;
- case 8*16+8:
- DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
- DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
- break;
- default:
- JPGD_ASSERT(false);
- }
-
- DCT_Upsample::Matrix44 a(P + Q); P -= Q;
- DCT_Upsample::Matrix44& b = P;
- DCT_Upsample::Matrix44 c(R + S); R -= S;
- DCT_Upsample::Matrix44& d = R;
-
- DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
- idct_4x4(temp_block, pDst_ptr);
- pDst_ptr += 64;
-
- pSrc_ptr += 64;
- }
- }
-
- // Loads and dequantizes the next row of (already decoded) coefficients.
- // Progressive images only.
- void jpeg_decoder::load_next_row()
- {
- int i;
- jpgd_block_t *p;
- jpgd_quant_t *q;
- int mcu_row, mcu_block, row_block = 0;
- int component_num, component_id;
- int block_x_mcu[JPGD_MAX_COMPONENTS];
-
- memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
- for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
- {
- int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
- for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
- {
- component_id = m_mcu_org[mcu_block];
- q = m_quant[m_comp_quant[component_id]];
-
- p = m_pMCU_coefficients + 64 * mcu_block;
-
- jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
- jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
- p[0] = pDC[0];
- memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
- for (i = 63; i > 0; i--)
- if (p[g_ZAG[i]])
- break;
-
- m_mcu_block_max_zag[mcu_block] = i + 1;
-
- for ( ; i >= 0; i--)
- if (p[g_ZAG[i]])
- p[g_ZAG[i]] = static_cast(p[g_ZAG[i]] * q[i]);
-
- row_block++;
-
- if (m_comps_in_scan == 1)
- block_x_mcu[component_id]++;
- else
- {
- if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
- {
- block_x_mcu_ofs = 0;
-
- if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
- {
- block_y_mcu_ofs = 0;
-
- block_x_mcu[component_id] += m_comp_h_samp[component_id];
- }
- }
- }
- }
-
- if (m_freq_domain_chroma_upsample)
- transform_mcu_expand(mcu_row);
- else
- transform_mcu(mcu_row);
- }
-
- if (m_comps_in_scan == 1)
- m_block_y_mcu[m_comp_list[0]]++;
- else
- {
- for (component_num = 0; component_num < m_comps_in_scan; component_num++)
- {
- component_id = m_comp_list[component_num];
-
- m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
- }
- }
- }
-
- // Restart interval processing.
- void jpeg_decoder::process_restart()
- {
- int i;
- int c = 0;
-
- // Align to a byte boundry
- // FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
- //get_bits_no_markers(m_bits_left & 7);
-
- // Let's scan a little bit to find the marker, but not _too_ far.
- // 1536 is a "fudge factor" that determines how much to scan.
- for (i = 1536; i > 0; i--)
- if (get_char() == 0xFF)
- break;
-
- if (i == 0)
- stop_decoding(JPGD_BAD_RESTART_MARKER);
-
- for ( ; i > 0; i--)
- if ((c = get_char()) != 0xFF)
- break;
-
- if (i == 0)
- stop_decoding(JPGD_BAD_RESTART_MARKER);
-
- // Is it the expected marker? If not, something bad happened.
- if (c != (m_next_restart_num + M_RST0))
- stop_decoding(JPGD_BAD_RESTART_MARKER);
-
- // Reset each component's DC prediction values.
- memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
- m_eob_run = 0;
-
- m_restarts_left = m_restart_interval;
-
- m_next_restart_num = (m_next_restart_num + 1) & 7;
-
- // Get the bit buffer going again...
-
- m_bits_left = 16;
- get_bits_no_markers(16);
- get_bits_no_markers(16);
- }
-
- static inline int dequantize_ac(int c, int q) { c *= q; return c; }
-
- // Decodes and dequantizes the next row of coefficients.
- void jpeg_decoder::decode_next_row()
- {
- int row_block = 0;
-
- for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
- {
- if ((m_restart_interval) && (m_restarts_left == 0))
- process_restart();
-
- jpgd_block_t* p = m_pMCU_coefficients;
- for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
- {
- int component_id = m_mcu_org[mcu_block];
- jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
- int r, s;
- s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
- s = HUFF_EXTEND(r, s);
-
- m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
- p[0] = static_cast(s * q[0]);
-
- int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
- huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
- int k;
- for (k = 1; k < 64; k++)
- {
- int extra_bits;
- s = huff_decode(pH, extra_bits);
-
- r = s >> 4;
- s &= 15;
-
- if (s)
- {
- if (r)
- {
- if ((k + r) > 63)
- stop_decoding(JPGD_DECODE_ERROR);
-
- if (k < prev_num_set)
- {
- int n = JPGD_MIN(r, prev_num_set - k);
- int kt = k;
- while (n--)
- p[g_ZAG[kt++]] = 0;
- }
-
- k += r;
- }
-
- s = HUFF_EXTEND(extra_bits, s);
-
- JPGD_ASSERT(k < 64);
-
- p[g_ZAG[k]] = static_cast(dequantize_ac(s, q[k])); //s * q[k];
- }
- else
- {
- if (r == 15)
- {
- if ((k + 16) > 64)
- stop_decoding(JPGD_DECODE_ERROR);
-
- if (k < prev_num_set)
- {
- int n = JPGD_MIN(16, prev_num_set - k);
- int kt = k;
- while (n--)
- {
- JPGD_ASSERT(kt <= 63);
- p[g_ZAG[kt++]] = 0;
- }
- }
-
- k += 16 - 1; // - 1 because the loop counter is k
- // BEGIN EPIC MOD
- JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
- // END EPIC MOD
- }
- else
- break;
- }
- }
-
- if (k < prev_num_set)
- {
- int kt = k;
- while (kt < prev_num_set)
- p[g_ZAG[kt++]] = 0;
- }
-
- m_mcu_block_max_zag[mcu_block] = k;
-
- row_block++;
- }
-
- if (m_freq_domain_chroma_upsample)
- transform_mcu_expand(mcu_row);
- else
- transform_mcu(mcu_row);
-
- m_restarts_left--;
- }
- }
-
- // YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
- void jpeg_decoder::H1V1Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d = m_pScan_line_0;
- uint8 *s = m_pSample_buf + row * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int j = 0; j < 8; j++)
- {
- int y = s[j];
- int cb = s[64+j];
- int cr = s[128+j];
-
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d[0] = clamp(y + m_cbb[cb]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_crr[cr]);
- d[3] = 255;
- }
- else
- {
- d[0] = clamp(y + m_crr[cr]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_cbb[cb]);
- d[3] = 255;
- }
- d += 4;
- }
-
- s += 64*3;
- }
- }
-
- // YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
- void jpeg_decoder::H2V1Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d0 = m_pScan_line_0;
- uint8 *y = m_pSample_buf + row * 8;
- uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int l = 0; l < 2; l++)
- {
- for (int j = 0; j < 4; j++)
- {
- int cb = c[0];
- int cr = c[64];
-
- int rc = m_crr[cr];
- int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
- int bc = m_cbb[cb];
-
- int yy = y[j<<1];
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d0[0] = clamp(yy+bc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+rc);
- d0[3] = 255;
- yy = y[(j<<1)+1];
- d0[4] = clamp(yy+bc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+rc);
- d0[7] = 255;
- }
- else
- {
- d0[0] = clamp(yy+rc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+bc);
- d0[3] = 255;
- yy = y[(j<<1)+1];
- d0[4] = clamp(yy+rc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+bc);
- d0[7] = 255;
- }
-
- d0 += 8;
-
- c++;
- }
- y += 64;
- }
-
- y += 64*4 - 64*2;
- c += 64*4 - 8;
- }
- }
-
- // YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
- void jpeg_decoder::H1V2Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d0 = m_pScan_line_0;
- uint8 *d1 = m_pScan_line_1;
- uint8 *y;
- uint8 *c;
-
- if (row < 8)
- y = m_pSample_buf + row * 8;
- else
- y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
- c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int j = 0; j < 8; j++)
- {
- int cb = c[0+j];
- int cr = c[64+j];
-
- int rc = m_crr[cr];
- int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
- int bc = m_cbb[cb];
-
- int yy = y[j];
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d0[0] = clamp(yy+bc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+rc);
- d0[3] = 255;
- yy = y[8+j];
- d1[0] = clamp(yy+bc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+rc);
- d1[3] = 255;
- }
- else
- {
- d0[0] = clamp(yy+rc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+bc);
- d0[3] = 255;
- yy = y[8+j];
- d1[0] = clamp(yy+rc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+bc);
- d1[3] = 255;
- }
-
- d0 += 4;
- d1 += 4;
- }
-
- y += 64*4;
- c += 64*4;
- }
- }
-
- // YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
- void jpeg_decoder::H2V2Convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d0 = m_pScan_line_0;
- uint8 *d1 = m_pScan_line_1;
- uint8 *y;
- uint8 *c;
-
- if (row < 8)
- y = m_pSample_buf + row * 8;
- else
- y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
- c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int l = 0; l < 2; l++)
- {
- for (int j = 0; j < 8; j += 2)
- {
- int cb = c[0];
- int cr = c[64];
-
- int rc = m_crr[cr];
- int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
- int bc = m_cbb[cb];
-
- int yy = y[j];
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d0[0] = clamp(yy+bc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+rc);
- d0[3] = 255;
- yy = y[j+1];
- d0[4] = clamp(yy+bc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+rc);
- d0[7] = 255;
- yy = y[j+8];
- d1[0] = clamp(yy+bc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+rc);
- d1[3] = 255;
- yy = y[j+8+1];
- d1[4] = clamp(yy+bc);
- d1[5] = clamp(yy+gc);
- d1[6] = clamp(yy+rc);
- d1[7] = 255;
- }
- else
- {
- d0[0] = clamp(yy+rc);
- d0[1] = clamp(yy+gc);
- d0[2] = clamp(yy+bc);
- d0[3] = 255;
- yy = y[j+1];
- d0[4] = clamp(yy+rc);
- d0[5] = clamp(yy+gc);
- d0[6] = clamp(yy+bc);
- d0[7] = 255;
- yy = y[j+8];
- d1[0] = clamp(yy+rc);
- d1[1] = clamp(yy+gc);
- d1[2] = clamp(yy+bc);
- d1[3] = 255;
- yy = y[j+8+1];
- d1[4] = clamp(yy+rc);
- d1[5] = clamp(yy+gc);
- d1[6] = clamp(yy+bc);
- d1[7] = 255;
- }
-
- d0 += 8;
- d1 += 8;
-
- c++;
- }
- y += 64;
- }
-
- y += 64*6 - 64*2;
- c += 64*6 - 8;
- }
- }
-
- // Y (1 block per MCU) to 8-bit grayscale
- void jpeg_decoder::gray_convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
- uint8 *d = m_pScan_line_0;
- uint8 *s = m_pSample_buf + row * 8;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- *(uint *)d = *(uint *)s;
- *(uint *)(&d[4]) = *(uint *)(&s[4]);
-
- s += 64;
- d += 8;
- }
- }
-
- void jpeg_decoder::expanded_convert()
- {
- int row = m_max_mcu_y_size - m_mcu_lines_left;
-
- uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
- uint8* d = m_pScan_line_0;
-
- for (int i = m_max_mcus_per_row; i > 0; i--)
- {
- for (int k = 0; k < m_max_mcu_x_size; k += 8)
- {
- const int Y_ofs = k * 8;
- const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
- const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
- for (int j = 0; j < 8; j++)
- {
- int y = Py[Y_ofs + j];
- int cb = Py[Cb_ofs + j];
- int cr = Py[Cr_ofs + j];
-
- if (jpg_format == ERGBFormatJPG::BGRA)
- {
- d[0] = clamp(y + m_cbb[cb]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_crr[cr]);
- d[3] = 255;
- }
- else
- {
- d[0] = clamp(y + m_crr[cr]);
- d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
- d[2] = clamp(y + m_cbb[cb]);
- d[3] = 255;
- }
-
- d += 4;
- }
- }
-
- Py += 64 * m_expanded_blocks_per_mcu;
- }
- }
-
- // Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
- void jpeg_decoder::find_eoi()
- {
- if (!m_progressive_flag)
- {
- // Attempt to read the EOI marker.
- //get_bits_no_markers(m_bits_left & 7);
-
- // Prime the bit buffer
- m_bits_left = 16;
- get_bits(16);
- get_bits(16);
-
- // The next marker _should_ be EOI
- process_markers();
- }
-
- m_total_bytes_read -= m_in_buf_left;
- }
-
- int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
- {
- if ((m_error_code) || (!m_ready_flag))
- return JPGD_FAILED;
-
- if (m_total_lines_left == 0)
- return JPGD_DONE;
-
- if (m_mcu_lines_left == 0)
- {
- if (setjmp(m_jmp_state))
- return JPGD_FAILED;
-
- if (m_progressive_flag)
- load_next_row();
- else
- decode_next_row();
-
- // Find the EOI marker if that was the last row.
- if (m_total_lines_left <= m_max_mcu_y_size)
- find_eoi();
-
- m_mcu_lines_left = m_max_mcu_y_size;
- }
-
- if (m_freq_domain_chroma_upsample)
- {
- expanded_convert();
- *pScan_line = m_pScan_line_0;
- }
- else
- {
- switch (m_scan_type)
- {
- case JPGD_YH2V2:
- {
- if ((m_mcu_lines_left & 1) == 0)
- {
- H2V2Convert();
- *pScan_line = m_pScan_line_0;
- }
- else
- *pScan_line = m_pScan_line_1;
-
- break;
- }
- case JPGD_YH2V1:
- {
- H2V1Convert();
- *pScan_line = m_pScan_line_0;
- break;
- }
- case JPGD_YH1V2:
- {
- if ((m_mcu_lines_left & 1) == 0)
- {
- H1V2Convert();
- *pScan_line = m_pScan_line_0;
- }
- else
- *pScan_line = m_pScan_line_1;
-
- break;
- }
- case JPGD_YH1V1:
- {
- H1V1Convert();
- *pScan_line = m_pScan_line_0;
- break;
- }
- case JPGD_GRAYSCALE:
- {
- gray_convert();
- *pScan_line = m_pScan_line_0;
-
- break;
- }
- }
- }
-
- *pScan_line_len = m_real_dest_bytes_per_scan_line;
-
- m_mcu_lines_left--;
- m_total_lines_left--;
-
- return JPGD_SUCCESS;
- }
-
- // Creates the tables needed for efficient Huffman decoding.
- void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
- {
- int p, i, l, si;
- uint8 huffsize[257];
- uint huffcode[257];
- uint code;
- uint subtree;
- int code_size;
- int lastp;
- int nextfreeentry;
- int currententry;
-
- pH->ac_table = m_huff_ac[index] != 0;
-
- p = 0;
-
- for (l = 1; l <= 16; l++)
- {
- for (i = 1; i <= m_huff_num[index][l]; i++)
- huffsize[p++] = static_cast(l);
- }
-
- huffsize[p] = 0;
-
- lastp = p;
-
- code = 0;
- si = huffsize[0];
- p = 0;
-
- while (huffsize[p])
- {
- while (huffsize[p] == si)
- {
- huffcode[p++] = code;
- code++;
- }
-
- code <<= 1;
- si++;
- }
-
- memset(pH->look_up, 0, sizeof(pH->look_up));
- memset(pH->look_up2, 0, sizeof(pH->look_up2));
- memset(pH->tree, 0, sizeof(pH->tree));
- memset(pH->code_size, 0, sizeof(pH->code_size));
-
- nextfreeentry = -1;
-
- p = 0;
-
- while (p < lastp)
- {
- i = m_huff_val[index][p];
- code = huffcode[p];
- code_size = huffsize[p];
-
- pH->code_size[i] = static_cast(code_size);
-
- if (code_size <= 8)
- {
- code <<= (8 - code_size);
-
- for (l = 1 << (8 - code_size); l > 0; l--)
- {
- JPGD_ASSERT(i < 256);
-
- pH->look_up[code] = i;
-
- bool has_extrabits = false;
- int extra_bits = 0;
- int num_extra_bits = i & 15;
-
- int bits_to_fetch = code_size;
- if (num_extra_bits)
- {
- int total_codesize = code_size + num_extra_bits;
- if (total_codesize <= 8)
- {
- has_extrabits = true;
- extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
- JPGD_ASSERT(extra_bits <= 0x7FFF);
- bits_to_fetch += num_extra_bits;
- }
- }
-
- if (!has_extrabits)
- pH->look_up2[code] = i | (bits_to_fetch << 8);
- else
- pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
- code++;
- }
- }
- else
- {
- subtree = (code >> (code_size - 8)) & 0xFF;
-
- currententry = pH->look_up[subtree];
-
- if (currententry == 0)
- {
- pH->look_up[subtree] = currententry = nextfreeentry;
- pH->look_up2[subtree] = currententry = nextfreeentry;
-
- nextfreeentry -= 2;
- }
-
- code <<= (16 - (code_size - 8));
-
- for (l = code_size; l > 9; l--)
- {
- if ((code & 0x8000) == 0)
- currententry--;
-
- if (pH->tree[-currententry - 1] == 0)
- {
- pH->tree[-currententry - 1] = nextfreeentry;
-
- currententry = nextfreeentry;
-
- nextfreeentry -= 2;
- }
- else
- currententry = pH->tree[-currententry - 1];
-
- code <<= 1;
- }
-
- if ((code & 0x8000) == 0)
- currententry--;
-
- pH->tree[-currententry - 1] = i;
- }
-
- p++;
- }
- }
-
- // Verifies the quantization tables needed for this scan are available.
- void jpeg_decoder::check_quant_tables()
- {
- for (int i = 0; i < m_comps_in_scan; i++)
- if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
- stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
- }
-
- // Verifies that all the Huffman tables needed for this scan are available.
- void jpeg_decoder::check_huff_tables()
- {
- for (int i = 0; i < m_comps_in_scan; i++)
- {
- if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
- stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
- if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
- stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
- }
-
- for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
- if (m_huff_num[i])
- {
- if (!m_pHuff_tabs[i])
- m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
- make_huff_table(i, m_pHuff_tabs[i]);
- }
- }
-
- // Determines the component order inside each MCU.
- // Also calcs how many MCU's are on each row, etc.
- void jpeg_decoder::calc_mcu_block_order()
- {
- int component_num, component_id;
- int max_h_samp = 0, max_v_samp = 0;
-
- for (component_id = 0; component_id < m_comps_in_frame; component_id++)
- {
- if (m_comp_h_samp[component_id] > max_h_samp)
- max_h_samp = m_comp_h_samp[component_id];
-
- if (m_comp_v_samp[component_id] > max_v_samp)
- max_v_samp = m_comp_v_samp[component_id];
- }
-
- for (component_id = 0; component_id < m_comps_in_frame; component_id++)
- {
- m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
- m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
- }
-
- if (m_comps_in_scan == 1)
- {
- m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
- m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
- }
- else
- {
- m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
- m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
- }
-
- if (m_comps_in_scan == 1)
- {
- m_mcu_org[0] = m_comp_list[0];
-
- m_blocks_per_mcu = 1;
- }
- else
- {
- m_blocks_per_mcu = 0;
-
- for (component_num = 0; component_num < m_comps_in_scan; component_num++)
- {
- int num_blocks;
-
- component_id = m_comp_list[component_num];
-
- num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
- while (num_blocks--)
- m_mcu_org[m_blocks_per_mcu++] = component_id;
- }
- }
- }
-
- // Starts a new scan.
- int jpeg_decoder::init_scan()
- {
- if (!locate_sos_marker())
- return JPGD_FALSE;
-
- calc_mcu_block_order();
-
- check_huff_tables();
-
- check_quant_tables();
-
- memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
- m_eob_run = 0;
-
- if (m_restart_interval)
- {
- m_restarts_left = m_restart_interval;
- m_next_restart_num = 0;
- }
-
- fix_in_buffer();
-
- return JPGD_TRUE;
- }
-
- // Starts a frame. Determines if the number of components or sampling factors
- // are supported.
- void jpeg_decoder::init_frame()
- {
- int i;
-
- if (m_comps_in_frame == 1)
- {
- if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
- stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
- m_scan_type = JPGD_GRAYSCALE;
- m_max_blocks_per_mcu = 1;
- m_max_mcu_x_size = 8;
- m_max_mcu_y_size = 8;
- }
- else if (m_comps_in_frame == 3)
- {
- if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
- ((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
- stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
- if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
- {
- m_scan_type = JPGD_YH1V1;
-
- m_max_blocks_per_mcu = 3;
- m_max_mcu_x_size = 8;
- m_max_mcu_y_size = 8;
- }
- else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
- {
- m_scan_type = JPGD_YH2V1;
- m_max_blocks_per_mcu = 4;
- m_max_mcu_x_size = 16;
- m_max_mcu_y_size = 8;
- }
- else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
- {
- m_scan_type = JPGD_YH1V2;
- m_max_blocks_per_mcu = 4;
- m_max_mcu_x_size = 8;
- m_max_mcu_y_size = 16;
- }
- else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
- {
- m_scan_type = JPGD_YH2V2;
- m_max_blocks_per_mcu = 6;
- m_max_mcu_x_size = 16;
- m_max_mcu_y_size = 16;
- }
- else
- stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
- }
- else
- stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
- m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
- m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
- // These values are for the *destination* pixels: after conversion.
- if (m_scan_type == JPGD_GRAYSCALE)
- m_dest_bytes_per_pixel = 1;
- else
- m_dest_bytes_per_pixel = 4;
-
- m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
- m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
- // Initialize two scan line buffers.
- m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
- if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
- m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
- m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
- // Should never happen
- if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
- stop_decoding(JPGD_ASSERTION_ERROR);
-
- // Allocate the coefficient buffer, enough for one MCU
- m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
- for (i = 0; i < m_max_blocks_per_mcu; i++)
- m_mcu_block_max_zag[i] = 64;
-
- m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
- m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
- m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
- // Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
- m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
- m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
- if (m_freq_domain_chroma_upsample)
- m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
- else
- m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
- m_total_lines_left = m_image_y_size;
-
- m_mcu_lines_left = 0;
-
- create_look_ups();
- }
-
- // The coeff_buf series of methods originally stored the coefficients
- // into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
- // was used to make this process more efficient. Now, we can store the entire
- // thing in RAM.
- jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
- {
- coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
- cb->block_num_x = block_num_x;
- cb->block_num_y = block_num_y;
- cb->block_len_x = block_len_x;
- cb->block_len_y = block_len_y;
- cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
- cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
- return cb;
- }
-
- inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
- {
- JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
- return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
- }
-
- // The following methods decode the various types of m_blocks encountered
- // in progressively encoded images.
- void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- int s, r;
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
- if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
- {
- r = pD->get_bits_no_markers(s);
- s = HUFF_EXTEND(r, s);
- }
-
- pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
- p[0] = static_cast(s << pD->m_successive_low);
- }
-
- void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- if (pD->get_bits_no_markers(1))
- {
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
- p[0] |= (1 << pD->m_successive_low);
- }
- }
-
- void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- int k, s, r;
-
- if (pD->m_eob_run)
- {
- pD->m_eob_run--;
- return;
- }
-
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
- for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
- {
- s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
- r = s >> 4;
- s &= 15;
-
- if (s)
- {
- if ((k += r) > 63)
- pD->stop_decoding(JPGD_DECODE_ERROR);
-
- r = pD->get_bits_no_markers(s);
- s = HUFF_EXTEND(r, s);
-
- p[g_ZAG[k]] = static_cast(s << pD->m_successive_low);
- }
- else
- {
- if (r == 15)
- {
- if ((k += 15) > 63)
- pD->stop_decoding(JPGD_DECODE_ERROR);
- }
- else
- {
- pD->m_eob_run = 1 << r;
-
- if (r)
- pD->m_eob_run += pD->get_bits_no_markers(r);
-
- pD->m_eob_run--;
-
- break;
- }
- }
- }
- }
-
- void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
- {
- int s, k, r;
- int p1 = 1 << pD->m_successive_low;
- int m1 = (-1) << pD->m_successive_low;
- jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
- k = pD->m_spectral_start;
-
- if (pD->m_eob_run == 0)
- {
- for ( ; k <= pD->m_spectral_end; k++)
- {
- s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
- r = s >> 4;
- s &= 15;
-
- if (s)
- {
- if (s != 1)
- pD->stop_decoding(JPGD_DECODE_ERROR);
-
- if (pD->get_bits_no_markers(1))
- s = p1;
- else
- s = m1;
- }
- else
- {
- if (r != 15)
- {
- pD->m_eob_run = 1 << r;
-
- if (r)
- pD->m_eob_run += pD->get_bits_no_markers(r);
-
- break;
- }
- }
-
- do
- {
- // BEGIN EPIC MOD
- JPGD_ASSERT(k < 64);
- // END EPIC MOD
-
- jpgd_block_t *this_coef = p + g_ZAG[k];
-
- if (*this_coef != 0)
- {
- if (pD->get_bits_no_markers(1))
- {
- if ((*this_coef & p1) == 0)
- {
- if (*this_coef >= 0)
- *this_coef = static_cast(*this_coef + p1);
- else
- *this_coef = static_cast(*this_coef + m1);
- }
- }
- }
- else
- {
- if (--r < 0)
- break;
- }
-
- k++;
-
- } while (k <= pD->m_spectral_end);
-
- if ((s) && (k < 64))
- {
- p[g_ZAG[k]] = static_cast(s);
- }
- }
- }
-
- if (pD->m_eob_run > 0)
- {
- for ( ; k <= pD->m_spectral_end; k++)
- {
- // BEGIN EPIC MOD
- JPGD_ASSERT(k < 64);
- // END EPIC MOD
-
- jpgd_block_t *this_coef = p + g_ZAG[k];
-
- if (*this_coef != 0)
- {
- if (pD->get_bits_no_markers(1))
- {
- if ((*this_coef & p1) == 0)
- {
- if (*this_coef >= 0)
- *this_coef = static_cast(*this_coef + p1);
- else
- *this_coef = static_cast(*this_coef + m1);
- }
- }
- }
- }
-
- pD->m_eob_run--;
- }
- }
-
- // Decode a scan in a progressively encoded image.
- void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
- {
- int mcu_row, mcu_col, mcu_block;
- int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
- memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
- for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
- {
- int component_num, component_id;
-
- memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
- for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
- {
- int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
- if ((m_restart_interval) && (m_restarts_left == 0))
- process_restart();
-
- for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
- {
- component_id = m_mcu_org[mcu_block];
-
- decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
- if (m_comps_in_scan == 1)
- block_x_mcu[component_id]++;
- else
- {
- if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
- {
- block_x_mcu_ofs = 0;
-
- if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
- {
- block_y_mcu_ofs = 0;
- block_x_mcu[component_id] += m_comp_h_samp[component_id];
- }
- }
- }
- }
-
- m_restarts_left--;
- }
-
- if (m_comps_in_scan == 1)
- m_block_y_mcu[m_comp_list[0]]++;
- else
- {
- for (component_num = 0; component_num < m_comps_in_scan; component_num++)
- {
- component_id = m_comp_list[component_num];
- m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
- }
- }
- }
- }
-
- // Decode a progressively encoded image.
- void jpeg_decoder::init_progressive()
- {
- int i;
-
- if (m_comps_in_frame == 4)
- stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
- // Allocate the coefficient buffers.
- for (i = 0; i < m_comps_in_frame; i++)
- {
- m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
- m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
- }
-
- for ( ; ; )
- {
- int dc_only_scan, refinement_scan;
- pDecode_block_func decode_block_func;
-
- if (!init_scan())
- break;
-
- dc_only_scan = (m_spectral_start == 0);
- refinement_scan = (m_successive_high != 0);
-
- if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
- stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
- if (dc_only_scan)
- {
- if (m_spectral_end)
- stop_decoding(JPGD_BAD_SOS_SPECTRAL);
- }
- else if (m_comps_in_scan != 1) /* AC scans can only contain one component */
- stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
- if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
- stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
- if (dc_only_scan)
- {
- if (refinement_scan)
- decode_block_func = decode_block_dc_refine;
- else
- decode_block_func = decode_block_dc_first;
- }
- else
- {
- if (refinement_scan)
- decode_block_func = decode_block_ac_refine;
- else
- decode_block_func = decode_block_ac_first;
- }
-
- decode_scan(decode_block_func);
-
- m_bits_left = 16;
- get_bits(16);
- get_bits(16);
- }
-
- m_comps_in_scan = m_comps_in_frame;
-
- for (i = 0; i < m_comps_in_frame; i++)
- m_comp_list[i] = i;
-
- calc_mcu_block_order();
- }
-
- void jpeg_decoder::init_sequential()
- {
- if (!init_scan())
- stop_decoding(JPGD_UNEXPECTED_MARKER);
- }
-
- void jpeg_decoder::decode_start()
- {
- init_frame();
-
- if (m_progressive_flag)
- init_progressive();
- else
- init_sequential();
- }
-
- void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
- {
- init(pStream);
- locate_sof_marker();
- }
-
- jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
- {
- if (setjmp(m_jmp_state))
- return;
- decode_init(pStream);
- }
-
- int jpeg_decoder::begin_decoding()
- {
- if (m_ready_flag)
- return JPGD_SUCCESS;
-
- if (m_error_code)
- return JPGD_FAILED;
-
- if (setjmp(m_jmp_state))
- return JPGD_FAILED;
-
- decode_start();
-
- m_ready_flag = true;
-
- return JPGD_SUCCESS;
- }
-
- jpeg_decoder::~jpeg_decoder()
- {
- free_all_blocks();
- }
-
- jpeg_decoder_file_stream::jpeg_decoder_file_stream()
- {
- m_pFile = NULL;
- m_eof_flag = false;
- m_error_flag = false;
- }
-
- void jpeg_decoder_file_stream::close()
- {
- if (m_pFile)
- {
- fclose(m_pFile);
- m_pFile = NULL;
- }
-
- m_eof_flag = false;
- m_error_flag = false;
- }
-
- jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
- {
- close();
- }
-
- bool jpeg_decoder_file_stream::open(const char *Pfilename)
- {
- close();
-
- m_eof_flag = false;
- m_error_flag = false;
-
-#if defined(_MSC_VER)
- m_pFile = NULL;
- fopen_s(&m_pFile, Pfilename, "rb");
-#else
- m_pFile = fopen(Pfilename, "rb");
-#endif
- return m_pFile != NULL;
- }
-
- int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
- {
- if (!m_pFile)
- return -1;
-
- if (m_eof_flag)
- {
- *pEOF_flag = true;
- return 0;
- }
-
- if (m_error_flag)
- return -1;
-
- int bytes_read = static_cast(fread(pBuf, 1, max_bytes_to_read, m_pFile));
- if (bytes_read < max_bytes_to_read)
- {
- if (ferror(m_pFile))
- {
- m_error_flag = true;
- return -1;
- }
-
- m_eof_flag = true;
- *pEOF_flag = true;
- }
-
- return bytes_read;
- }
-
- bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
- {
- close();
- m_pSrc_data = pSrc_data;
- m_ofs = 0;
- m_size = size;
- return true;
- }
-
- int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
- {
- *pEOF_flag = false;
-
- if (!m_pSrc_data)
- return -1;
-
- uint bytes_remaining = m_size - m_ofs;
- if ((uint)max_bytes_to_read > bytes_remaining)
- {
- max_bytes_to_read = bytes_remaining;
- *pEOF_flag = true;
- }
-
- memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
- m_ofs += max_bytes_to_read;
-
- return max_bytes_to_read;
- }
-
- unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
- {
- if (!actual_comps)
- return NULL;
- *actual_comps = 0;
-
- if ((!pStream) || (!width) || (!height) || (!req_comps))
- return NULL;
-
- if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
- return NULL;
-
- jpeg_decoder decoder(pStream);
- if (decoder.get_error_code() != JPGD_SUCCESS)
- return NULL;
-
- const int image_width = decoder.get_width(), image_height = decoder.get_height();
- *width = image_width;
- *height = image_height;
- *actual_comps = decoder.get_num_components();
-
- if (decoder.begin_decoding() != JPGD_SUCCESS)
- return NULL;
-
- const int dst_bpl = image_width * req_comps;
-
- uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
- if (!pImage_data)
- return NULL;
-
- for (int y = 0; y < image_height; y++)
- {
- const uint8* pScan_line = 0;
- uint scan_line_len;
- if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
- {
- jpgd_free(pImage_data);
- return NULL;
- }
-
- uint8 *pDst = pImage_data + y * dst_bpl;
-
- if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
- ((req_comps == 1) && (decoder.get_num_components() == 1)))
- {
- memcpy(pDst, pScan_line, dst_bpl);
- }
- else if (decoder.get_num_components() == 1)
- {
- if (req_comps == 3)
- {
- for (int x = 0; x < image_width; x++)
- {
- uint8 luma = pScan_line[x];
- pDst[0] = luma;
- pDst[1] = luma;
- pDst[2] = luma;
- pDst += 3;
- }
- }
- else
- {
- for (int x = 0; x < image_width; x++)
- {
- uint8 luma = pScan_line[x];
- pDst[0] = luma;
- pDst[1] = luma;
- pDst[2] = luma;
- pDst[3] = 255;
- pDst += 4;
- }
- }
- }
- else if (decoder.get_num_components() == 3)
- {
- if (req_comps == 1)
- {
- const int YR = 19595, YG = 38470, YB = 7471;
- for (int x = 0; x < image_width; x++)
- {
- int r = pScan_line[x*4+0];
- int g = pScan_line[x*4+1];
- int b = pScan_line[x*4+2];
- *pDst++ = static_cast((r * YR + g * YG + b * YB + 32768) >> 16);
- }
- }
- else
- {
- for (int x = 0; x < image_width; x++)
- {
- pDst[0] = pScan_line[x*4+0];
- pDst[1] = pScan_line[x*4+1];
- pDst[2] = pScan_line[x*4+2];
- pDst += 3;
- }
- }
- }
- }
-
- return pImage_data;
- }
-
-// BEGIN EPIC MOD
- unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
- {
- jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
- jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
- return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
- }
-
- unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
- {
- jpgd::jpeg_decoder_file_stream file_stream;
- if (!file_stream.open(pSrc_filename))
- return NULL;
- return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
- }
-
-} // namespace jpgd
diff --git a/spaces/XCLiu/InstaFlow/README.md b/spaces/XCLiu/InstaFlow/README.md
deleted file mode 100644
index 8d71ad5c64b8230d2078e85607709a44e839af6e..0000000000000000000000000000000000000000
--- a/spaces/XCLiu/InstaFlow/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: InstaFlow
-emoji: 🐨
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XuebaoDingZhen/YOLOv50.0.1/data/scripts/get_coco.sh b/spaces/XuebaoDingZhen/YOLOv50.0.1/data/scripts/get_coco.sh
deleted file mode 100644
index 0bb276140b075a61cf57b7c1f19717477812ea9b..0000000000000000000000000000000000000000
--- a/spaces/XuebaoDingZhen/YOLOv50.0.1/data/scripts/get_coco.sh
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-# Download COCO 2017 dataset http://cocodataset.org
-# Example usage: bash data/scripts/get_coco.sh
-# parent
-# ├── yolov5
-# └── datasets
-# └── coco ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
-if [ "$#" -gt 0 ]; then
- for opt in "$@"; do
- case "${opt}" in
- --train) train=true ;;
- --val) val=true ;;
- --test) test=true ;;
- --segments) segments=true ;;
- esac
- done
-else
- train=true
- val=true
- test=false
- segments=false
-fi
-
-# Download/unzip labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-if [ "$segments" == "true" ]; then
- f='coco2017labels-segments.zip' # 168 MB
-else
- f='coco2017labels.zip' # 46 MB
-fi
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-
-# Download/unzip images
-d='../datasets/coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-if [ "$train" == "true" ]; then
- f='train2017.zip' # 19G, 118k images
- echo 'Downloading' $url$f '...'
- curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$val" == "true" ]; then
- f='val2017.zip' # 1G, 5k images
- echo 'Downloading' $url$f '...'
- curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$test" == "true" ]; then
- f='test2017.zip' # 7G, 41k images (optional)
- echo 'Downloading' $url$f '...'
- curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-wait # finish background tasks
diff --git a/spaces/YueMafighting/mmpose-estimation/configs/faster_rcnn_r50_fpn_1x_coco.py b/spaces/YueMafighting/mmpose-estimation/configs/faster_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index c0d22186965c0f944f5e16f01fa639ff80b90f35..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/mmpose-estimation/configs/faster_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,228 +0,0 @@
-model = dict(
- type='FasterRCNN',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=5),
- rpn_head=dict(
- type='RPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0.0, 0.0, 0.0, 0.0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- roi_head=dict(
- type='StandardRoIHead',
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- bbox_head=dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0.0, 0.0, 0.0, 0.0],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
- train_cfg=dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=2000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- pos_weight=-1,
- debug=False)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=1000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100)))
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type='CocoDataset',
- ann_file='data/coco/annotations/instances_train2017.json',
- img_prefix='data/coco/train2017/',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
- ]),
- val=dict(
- type='CocoDataset',
- ann_file='data/coco/annotations/instances_val2017.json',
- img_prefix='data/coco/val2017/',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
- ]),
- test=dict(
- type='CocoDataset',
- ann_file='data/coco/annotations/instances_val2017.json',
- img_prefix='data/coco/val2017/',
- pipeline=[
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(
- type='Normalize',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
- ]))
-evaluation = dict(interval=1, metric='bbox')
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-custom_hooks = [dict(type='NumClassCheckHook')]
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-opencv_num_threads = 0
-mp_start_method = 'fork'
-auto_scale_lr = dict(enable=False, base_batch_size=16)
diff --git a/spaces/Yuliang/ECON/lib/pymafx/models/transformers/__init__.py b/spaces/Yuliang/ECON/lib/pymafx/models/transformers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/YumingYuan/Latex_OCR/README.md b/spaces/YumingYuan/Latex_OCR/README.md
deleted file mode 100644
index cefa877a509b72bef056c86f4661cc0462949142..0000000000000000000000000000000000000000
--- a/spaces/YumingYuan/Latex_OCR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Latex OCR
-emoji: 🐢
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Zakia/cat_or_dog_predictor/app.py b/spaces/Zakia/cat_or_dog_predictor/app.py
deleted file mode 100644
index 5ccca5ad19a3e9256eede00c0f820d7dd30ab1dd..0000000000000000000000000000000000000000
--- a/spaces/Zakia/cat_or_dog_predictor/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Bismillahir Rahmaanir Raheem
-# Almadadh Ya Gause Radi Allahu Ta'alah Anh - Ameen
-
-
-from fastai.vision.all import *
-import gradio as gr
-
-
-def is_cat(x):
- return x[0].isupper()
-
-
-
-# load the trained fast ai model for predictions
-learn = load_learner('model.pkl')
-
-
-# define the function to call
-categories = ('Dog', 'Cat')
-
-def predict(img):
- pred, idx, probs = learn.predict(img)
- return dict(zip(categories, map(float, probs)))
-
-
-title = "Cat or Dog Predictor"
-
-description = "A cat or dog predictor model trained on the Pets dataset using ResNet18 via fast.ai ."
-
-
-article = "Cat or Dog Predictor. Zakia Salod. 2022.
"
-
-
-image = gr.inputs.Image(shape=(512, 512))
-label = gr.outputs.Label()
-examples = [
- ['cat1.jpg'],
- ['dog1.jpg'],
- ['cat2.jpg'],
- ['dog2.jpg'],
- ['cat3.jpg'],
- ['dog3.jpg'],
- ['cat4.jpg'],
- ['dog4.jpg'],
- ]
-interpretation = 'default'
-enable_queue = True
-
-
-
-
-iface = gr.Interface(
- fn=predict,
- title=title,
- description=description,
- article=article,
- inputs=image,
- outputs=label,
- theme="default",
- examples=examples,
- interpretation=interpretation,
- enable_queue=enable_queue
-)
-
-
-
-iface.launch(inline=False)
diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v3/app.py b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v3/app.py
deleted file mode 100644
index 2687ba266cf9d3dcb5a60ab273f296c0862a90d8..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v3/app.py
+++ /dev/null
@@ -1,461 +0,0 @@
-# Gradio YOLOv5 Det v0.3
-# author: Zeng Yifu（曾逸夫）
-# creation time: 2022-05-09
-# email: zyfiy1314@163.com
-# project homepage: https://gitee.com/CV_Lab/gradio_yolov5_det
-
-
-import argparse
-import csv
-import json
-import sys
-from collections import Counter
-from pathlib import Path
-import pandas as pd
-
-import gradio as gr
-import torch
-import yaml
-from PIL import Image, ImageDraw, ImageFont
-
-from util.fonts_opt import is_fonts
-from util.pdf_opt import pdf_generate
-
-ROOT_PATH = sys.path[0] # root directory
-
-# model path
-model_path = "ultralytics/yolov5"
-
-# Gradio YOLOv5 Det version
-GYD_VERSION = "Gradio YOLOv5 Det v0.3"
-
-# model name temporary variable
-model_name_tmp = ""
-
-# Device temporary variables
-device_tmp = ""
-
-# File extension
-suffix_list = [".csv", ".yaml"]
-
-# font size
-FONTSIZE = 25
-
-# object style
-obj_style = ["Small Object", "Medium Object", "Large Object"]
-
-
-def parse_args(known=False):
- parser = argparse.ArgumentParser(description="Gradio YOLOv5 Det v0.3")
- parser.add_argument("--source", "-src", default="upload", type=str, help="input source")
- parser.add_argument("--img_tool", "-it", default="editor", type=str, help="input image tool")
- parser.add_argument("--model_name", "-mn", default="yolov5s", type=str, help="model name")
- parser.add_argument(
- "--model_cfg",
- "-mc",
- default="./model_config/model_name_p5_p6_all.yaml",
- type=str,
- help="model config",
- )
- parser.add_argument(
- "--cls_name",
- "-cls",
- default="./cls_name/cls_name_en.yaml",
- type=str,
- help="cls name",
- )
- parser.add_argument(
- "--nms_conf",
- "-conf",
- default=0.5,
- type=float,
- help="model NMS confidence threshold",
- )
- parser.add_argument("--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold")
- parser.add_argument(
- "--device",
- "-dev",
- default="cpu",
- type=str,
- help="cuda or cpu",
- )
- parser.add_argument("--inference_size", "-isz", default=640, type=int, help="model inference size")
- parser.add_argument("--max_detnum", "-mdn", default=50, type=float, help="model max det num")
- parser.add_argument("--slider_step", "-ss", default=0.05, type=float, help="slider step")
- parser.add_argument(
- "--is_login",
- "-isl",
- action="store_true",
- default=False,
- help="is login",
- )
- parser.add_argument('--usr_pwd',
- "-up",
- nargs='+',
- type=str,
- default=["admin", "admin"],
- help="user & password for login")
- parser.add_argument(
- "--is_share",
- "-is",
- action="store_true",
- default=False,
- help="is login",
- )
-
- args = parser.parse_known_args()[0] if known else parser.parse_args()
- return args
-
-
-# yaml file parsing
-def yaml_parse(file_path):
- return yaml.safe_load(open(file_path, encoding="utf-8").read())
-
-
-# yaml csv file parsing
-def yaml_csv(file_path, file_tag):
- file_suffix = Path(file_path).suffix
- if file_suffix == suffix_list[0]:
- # model name
- file_names = [i[0] for i in list(csv.reader(open(file_path)))] # csv version
- elif file_suffix == suffix_list[1]:
- # model name
- file_names = yaml_parse(file_path).get(file_tag) # yaml version
- else:
- print(f"{file_path} is not in the correct format! Program exits!")
- sys.exit()
-
- return file_names
-
-
-# model loading
-def model_loading(model_name, device):
-
- # load model
- model = torch.hub.load(
- model_path, model_name, force_reload=True, device=device, _verbose=False
- )
-
- return model
-
-
-# check information
-def export_json(results, img_size):
-
- return [[{
- "ID": i,
- "CLASS": int(result[i][5]),
- "CLASS_NAME": model_cls_name_cp[int(result[i][5])],
- "BOUNDING_BOX": {
- "XMIN": round(result[i][:4].tolist()[0], 6),
- "YMIN": round(result[i][:4].tolist()[1], 6),
- "XMAX": round(result[i][:4].tolist()[2], 6),
- "YMAX": round(result[i][:4].tolist()[3], 6),},
- "CONF": round(float(result[i][4]), 2),
- "FPS": round(1000 / float(results.t[1]), 2),
- "IMG_WIDTH": img_size[0],
- "IMG_HEIGHT": img_size[1],} for i in range(len(result))] for result in results.xyxyn]
-
-
-# frame conversion
-def pil_draw(img, countdown_msg, textFont, xyxy, font_size, opt):
-
- img_pil = ImageDraw.Draw(img)
-
- img_pil.rectangle(xyxy, fill=None, outline="green") # bounding box
-
- if "label" in opt:
- text_w, text_h = textFont.getsize(countdown_msg) # Label size
- img_pil.rectangle(
- (xyxy[0], xyxy[1], xyxy[0] + text_w, xyxy[1] + text_h),
- fill="green",
- outline="green",
- ) # label background
- img_pil.multiline_text(
- (xyxy[0], xyxy[1]),
- countdown_msg,
- fill=(205, 250, 255),
- font=textFont,
- align="center",
- )
-
- return img
-
-
-# YOLOv5 image detection function
-def yolo_det(img, device, model_name, infer_size, conf, iou, max_num, model_cls, opt):
-
- global model, model_name_tmp, device_tmp
-
- # object size num
- s_obj, m_obj, l_obj = 0, 0, 0
- # object area list
- area_obj_all = []
- # cls num stat
- cls_det_stat = []
-
- if model_name_tmp != model_name:
- # Model judgment to avoid repeated loading
- model_name_tmp = model_name
- model = model_loading(model_name_tmp, device)
- elif device_tmp != device:
- device_tmp = device
- model = model_loading(model_name_tmp, device)
-
- # -------------Model tuning -------------
- model.conf = conf # NMS confidence threshold
- model.iou = iou # NMS IoU threshold
- model.max_det = int(max_num) # Maximum number of detection frames
- model.classes = model_cls # model classes
-
- img_size = img.size # frame size
-
- results = model(img, size=infer_size) # detection
-
- # Data Frame
- dataframe = results.pandas().xyxy[0].round(2)
-
- # ----------------Load fonts----------------
- yaml_index = cls_name.index(".yaml")
- cls_name_lang = cls_name[yaml_index - 2:yaml_index]
-
- if cls_name_lang == "zh":
- # Chinese
- textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE)
- elif cls_name_lang in ["en", "ru", "es", "ar"]:
- # English, Russian, Spanish, Arabic
- textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"), size=FONTSIZE)
- elif cls_name_lang == "ko":
- # Korean
- textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/malgun.ttf"), size=FONTSIZE)
-
- for result in results.xyxyn:
- for i in range(len(result)):
- id = int(i) # instance ID
- obj_cls_index = int(result[i][5]) # category index
- obj_cls = model_cls_name_cp[obj_cls_index] # category
- cls_det_stat.append(obj_cls)
-
- # ------------ border coordinates ------------
- x0 = float(result[i][:4].tolist()[0])
- y0 = float(result[i][:4].tolist()[1])
- x1 = float(result[i][:4].tolist()[2])
- y1 = float(result[i][:4].tolist()[3])
-
- # ------------ Actual coordinates of the border ------------
- x0 = int(img_size[0] * x0)
- y0 = int(img_size[1] * y0)
- x1 = int(img_size[0] * x1)
- y1 = int(img_size[1] * y1)
-
- conf = float(result[i][4]) # confidence
- # fps = f"{(1000 / float(results.t[1])):.2f}" # FPS
-
- det_img = pil_draw(
- img,
- f"{id}-{obj_cls}:{conf:.2f}",
- textFont,
- [x0, y0, x1, y1],
- FONTSIZE,
- opt,
- )
-
- # ----------add object size----------
- w_obj = x1 - x0
- h_obj = y1 - y0
- area_obj = w_obj * h_obj
- area_obj_all.append(area_obj)
-
- # ------------JSON generate------------
- det_json = export_json(results, img.size)[0] # Detection information
- det_json_format = json.dumps(det_json, sort_keys=False, indent=4, separators=(",", ":"), ensure_ascii=False) # JSON formatting
-
- if "json" not in opt:
- det_json = None
-
- # -------PDF generate-------
- report = "./Det_Report.pdf"
- if "pdf" in opt:
- pdf_generate(f"{det_json_format}", report, GYD_VERSION)
- else:
- report = None
-
- # --------------object size compute--------------
- for i in range(len(area_obj_all)):
- if (0 < area_obj_all[i] <= 32 ** 2):
- s_obj = s_obj + 1
- elif (32 ** 2 < area_obj_all[i] <= 96 ** 2):
- m_obj = m_obj + 1
- elif (area_obj_all[i] > 96 ** 2):
- l_obj = l_obj + 1
-
- sml_obj_total = s_obj + m_obj + l_obj
-
- objSize_dict = {obj_style[i]: [s_obj, m_obj, l_obj][i] / sml_obj_total for i in range(3)}
-
- # ------------cls stat------------
- clsRatio_dict = {}
- clsDet_dict = Counter(cls_det_stat)
- clsDet_dict_sum = sum(clsDet_dict.values())
-
- for k, v in clsDet_dict.items():
- clsRatio_dict[k] = v / clsDet_dict_sum
-
-
- return det_img, objSize_dict, clsRatio_dict, det_json, report, dataframe
-
-
-def main(args):
- gr.close_all()
-
- global model, model_cls_name_cp, cls_name
-
- source = args.source
- img_tool = args.img_tool
- nms_conf = args.nms_conf
- nms_iou = args.nms_iou
- model_name = args.model_name
- model_cfg = args.model_cfg
- cls_name = args.cls_name
- device = args.device
- inference_size = args.inference_size
- max_detnum = args.max_detnum
- slider_step = args.slider_step
- is_login = args.is_login
- usr_pwd = args.usr_pwd
- is_share = args.is_share
-
- is_fonts(f"{ROOT_PATH}/fonts") # Check font files
-
- # model loading
- model = model_loading(model_name, device)
-
- model_names = yaml_csv(model_cfg, "model_names") # model names
- model_cls_name = yaml_csv(cls_name, "model_cls_name") # class name
-
- model_cls_name_cp = model_cls_name.copy() # class name
-
- # ------------------- Input Components -------------------
- inputs_img = gr.Image(image_mode="RGB", source=source, tool=img_tool, type="pil", label="original image")
- inputs_device = gr.Radio(choices=["cuda:0", "cpu"], value=device, label="device")
- inputs_model = gr.Dropdown(choices=model_names, value=model_name, type="value", label="model")
- inputs_size = gr.Radio(choices=[320, 640, 1280], value=inference_size, label="inference size")
- input_conf = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="confidence threshold")
- inputs_iou = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
- inputs_maxnum = gr.Number(value=max_detnum, label="Maximum number of detections")
- inputs_clsName = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
- inputs_opt = gr.CheckboxGroup(choices=["label", "pdf", "json"],
- value=["label", "pdf"],
- type="value",
- label="operate")
-
- # Input parameters
- inputs = [
- inputs_img, # input image
- inputs_device, # device
- inputs_model, # model
- inputs_size, # inference size
- input_conf, # confidence threshold
- inputs_iou, # IoU threshold
- inputs_maxnum, # maximum number of detections
- inputs_clsName, # category
- inputs_opt, # detect operations
- ]
-
- # Output parameters
- outputs_img = gr.Image(type="pil", label="Detection image")
- outputs_json = gr.JSON(label="Detection information")
- outputs_pdf = gr.File(label="Download test report")
- outputs_df = gr.Dataframe(max_rows=5, overflow_row_behaviour="paginate", type="pandas", label="List of detection information")
- outputs_objSize = gr.Label(label="Object size ratio statistics")
- outputs_clsSize = gr.Label(label="Category detection proportion statistics")
-
- outputs = [outputs_img, outputs_objSize, outputs_clsSize, outputs_json, outputs_pdf, outputs_df]
-
- # title
- title = "Gradio YOLOv5 Det v0.3"
-
- # describe
- description = "Author: 曾逸夫（Zeng Yifu）, Project Address: https://gitee.com/CV_Lab/gradio_yolov5_det, Github: https://github.com/Zengyf-CVer, thanks to [Gradio](https://github.com/gradio-app/gradio) & [YOLOv5](https://github.com/ultralytics/yolov5)"
- # article="https://gitee.com/CV_Lab/gradio_yolov5_det"
-
- # example image
- examples = [
- [
- "./img_example/bus.jpg",
- "cpu",
- "yolov5s",
- 640,
- 0.6,
- 0.5,
- 10,
- ["person", "bus"],
- ["label", "pdf"],],
- [
- "./img_example/giraffe.jpg",
- "cpu",
- "yolov5l",
- 320,
- 0.5,
- 0.45,
- 12,
- ["giraffe"],
- ["label", "pdf"],],
- [
- "./img_example/zidane.jpg",
- "cpu",
- "yolov5m",
- 640,
- 0.6,
- 0.5,
- 15,
- ["person", "tie"],
- ["pdf", "json"],],
- [
- "./img_example/Millenial-at-work.jpg",
- "cpu",
- "yolov5s6",
- 1280,
- 0.5,
- 0.5,
- 20,
- ["person", "chair", "cup", "laptop"],
- ["label", "pdf"],],]
-
- # interface
- gyd = gr.Interface(
- fn=yolo_det,
- inputs=inputs,
- outputs=outputs,
- title=title,
- description=description,
- # article=article,
- # examples=examples,
- # theme="seafoam",
- # flagging_dir="run", # output directory
- )
-
- if not is_login:
- gyd.launch(
- inbrowser=True, # Automatically open default browser
- show_tips=True, # Automatically display the latest features of gradio
- share=is_share, # Project sharing, other devices can access
- favicon_path="./icon/logo.ico", # web icon
- show_error=True, # Display error message in browser console
- quiet=True, # Suppress most print statements
- )
- else:
- gyd.launch(
- inbrowser=True, # Automatically open default browser
- show_tips=True, # Automatically display the latest features of gradio
- auth=usr_pwd, # login interface
- share=is_share, # Project sharing, other devices can access
- favicon_path="./icon/logo.ico", # web icon
- show_error=True, # Display error message in browser console
- quiet=True, # Suppress most print statements
- )
-
-
-if __name__ == "__main__":
- args = parse_args()
- main(args)
\ No newline at end of file
diff --git a/spaces/a-v-bely/russian-task-generator/utilities_cookies/build/static/js/main.666d1b34.chunk.js b/spaces/a-v-bely/russian-task-generator/utilities_cookies/build/static/js/main.666d1b34.chunk.js
deleted file mode 100644
index c9641dd90cef4d6d87b76c5e0113ff5903fde4c6..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/russian-task-generator/utilities_cookies/build/static/js/main.666d1b34.chunk.js
+++ /dev/null
@@ -1,2 +0,0 @@
-(this.webpackJsonpstreamlit_cookie_manager=this.webpackJsonpstreamlit_cookie_manager||[]).push([[0],{5:function(e,n,o){"use strict";o.r(n);var a=o(2),t=(window.parent||window).document,c=null;a.a.events.addEventListener(a.a.RENDER_EVENT,(function(e){var n,o=e.detail;n=o.args.queue,Object.keys(n).forEach((function(e){var o=n[e];if(null===o.value)t.cookie="".concat(encodeURIComponent(e),"=; max-age=0; path=").concat(encodeURIComponent(o.path));else{var a=new Date(o.expires_at);t.cookie="".concat(encodeURIComponent(e),"=").concat(encodeURIComponent(o.value),";")+" expires=".concat(a.toUTCString(),";")+" path=".concat(encodeURIComponent(o.path),";")}}));var i=t.cookie;c===i||o.args.saveOnly||(a.a.setComponentValue(i),c=i)})),a.a.setComponentReady(),a.a.setFrameHeight(0)}},[[5,1,2]]]);
-//# sourceMappingURL=main.666d1b34.chunk.js.map
\ No newline at end of file
diff --git a/spaces/abdvl/datahub_qa_bot/docs/api/graphql/querying-entities.md b/spaces/abdvl/datahub_qa_bot/docs/api/graphql/querying-entities.md
deleted file mode 100644
index 9dd05b4cac60e669e0e8728b7271951371450077..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/api/graphql/querying-entities.md
+++ /dev/null
@@ -1,551 +0,0 @@
-# Working with Metadata Entities
-
-Learn how to find, retrieve & update entities comprising your Metadata Graph programmatically.
-
-## Reading an Entity: Queries
-
-DataHub provides the following GraphQL queries for retrieving entities in your Metadata Graph.
-
-### Getting a Metadata Entity
-
-To retrieve a Metadata Entity by primary key (urn), simply use the `(urn: String!)` GraphQL Query.
-
-For example, to retrieve a `dataset` entity, you can issue the following GraphQL Query:
-
-*As GraphQL*
-
-```graphql
-{
- dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:kafka,SampleKafkaDataset,PROD)") {
- urn
- properties {
- name
- }
- }
-}
-```
-
-*As CURL*
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer ' \
---header 'Content-Type: application/json' \
---data-raw '{ "query":"{ dataset(urn: \"urn:li:dataset:(urn:li:dataPlatform:kafka,SampleKafkaDataset,PROD)\") { urn properties { name } } }", "variables":{}}'
-```
-
-In the following examples, we'll look at how to fetch specific types of metadata for an asset.
-
-#### Querying for Owners of an entity
-
-As GraphQL:
-
-```graphql
-query {
- dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)") {
- ownership {
- owners {
- owner {
- ... on CorpUser {
- urn
- type
- }
- ... on CorpGroup {
- urn
- type
- }
- }
- }
- }
- }
-}
-```
-
-#### Querying for Tags of an asset
-
-As GraphQL:
-
-```graphql
-query {
- dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)") {
- tags {
- tags {
- tag {
- name
- }
- }
- }
- }
-}
-```
-
-#### Querying for Domain of an asset
-
-As GraphQL:
-
-```graphql
-query {
- dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)") {
- domain {
- domain {
- urn
- }
- }
- }
-}
-```
-
-#### Querying for Glossary Terms of an asset
-
-As GraphQL:
-
-```graphql
-query {
- dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)") {
- glossaryTerms {
- terms {
- term {
- urn
- }
- }
- }
- }
-}
-```
-
-#### Querying for Deprecation of an asset
-
-As GraphQL:
-
-```graphql
-query {
- dataset(urn: "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)") {
- deprecation {
- deprecated
- decommissionTime
- }
- }
-}
-```
-
-#### Relevant Queries
-
-- [dataset](../../../graphql/queries.md#dataset)
-- [container](../../../graphql/queries.md#container)
-- [dashboard](../../../graphql/queries.md#dashboard)
-- [chart](../../../graphql/queries.md#chart)
-- [dataFlow](../../../graphql/queries.md#dataflow)
-- [dataJob](../../../graphql/queries.md#datajob)
-- [domain](../../../graphql/queries.md#domain)
-- [glossaryTerm](../../../graphql/queries.md#glossaryterm)
-- [glossaryNode](../../../graphql/queries.md#glossarynode)
-- [tag](../../../graphql/queries.md#tag)
-- [notebook](../../../graphql/queries.md#notebook)
-- [corpUser](../../../graphql/queries.md#corpuser)
-- [corpGroup](../../../graphql/queries.md#corpgroup)
-
-
-### Searching for a Metadata Entity
-
-To perform full-text search against an Entity of a particular type, use the `search(input: SearchInput!)` GraphQL Query.
-
-As GraphQL:
-
-```graphql
-{
- search(input: { type: DATASET, query: "my sql dataset", start: 0, count: 10 }) {
- start
- count
- total
- searchResults {
- entity {
- urn
- type
- ...on Dataset {
- name
- }
- }
- }
- }
-}
-```
-
-As CURL:
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer ' \
---header 'Content-Type: application/json' \
---data-raw '{ "query":"{ search(input: { type: DATASET, query: \"my sql dataset\", start: 0, count: 10 }) { start count total searchResults { entity { urn type ...on Dataset { name } } } } }", "variables":{}}'
-```
-
-> **Note** that by default Elasticsearch only allows pagination through 10,000 entities via the search API.
-> If you need to paginate through more, you can change the default value for the `index.max_result_window` setting in Elasticsearch,
-> or using the [scroll API](https://www.elastic.co/guide/en/elasticsearch/reference/current/scroll-api.html) to read from the index directly.
-
-#### Relevant Queries
-
-- [search](../../../graphql/queries.md#search)
-- [searchAcrossEntities](../../../graphql/queries.md#searchacrossentities)
-- [searchAcrossLineage](../../../graphql/queries.md#searchacrosslineage)
-- [browse](../../../graphql/queries.md#browse)
-- [browsePaths](../../../graphql/queries.md#browsepaths)
-
-
-## Modifying an Entity: Mutations
-
-### Authorization
-
-Mutations which change Entity metadata are subject to [DataHub Access Policies](../../authorization/policies.md). This means that DataHub's server
-will check whether the requesting actor is authorized to perform the action.
-
-### Updating a Metadata Entity
-
-To update an existing Metadata Entity, simply use the `update(urn: String!, input: EntityUpdateInput!)` GraphQL Query.
-
-For example, to update a Dashboard entity, you can issue the following GraphQL mutation:
-
-*As GraphQL*
-
-```graphql
-mutation updateDashboard {
- updateDashboard(
- urn: "urn:li:dashboard:(looker,baz)",
- input: {
- editableProperties: {
- description: "My new desription"
- }
- }
- ) {
- urn
- }
-}
-```
-
-*As CURL*
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer ' \
---header 'Content-Type: application/json' \
---data-raw '{ "query": "mutation updateDashboard { updateDashboard(urn:\"urn:li:dashboard:(looker,baz)\", input: { editableProperties: { description: \"My new desription\" } } ) { urn } }", "variables":{}}'
-```
-
-**Be careful**: these APIs allow you to make significant changes to a Metadata Entity, often including
-updating the entire set of Owners & Tags.
-
-#### Relevant Mutations
-
-- [updateDataset](../../../graphql/mutations.md#updatedataset)
-- [updateChart](../../../graphql/mutations.md#updatechart)
-- [updateDashboard](../../../graphql/mutations.md#updatedashboard)
-- [updateDataFlow](../../../graphql/mutations.md#updatedataFlow)
-- [updateDataJob](../../../graphql/mutations.md#updatedataJob)
-- [updateNotebook](../../../graphql/mutations.md#updatenotebook)
-
-
-### Adding & Removing Tags
-
-To attach Tags to a Metadata Entity, you can use the `addTags` or `batchAddTags` mutations.
-To remove them, you can use the `removeTag` or `batchRemoveTags` mutations.
-
-For example, to add a Tag a Pipeline entity, you can issue the following GraphQL mutation:
-
-*As GraphQL*
-
-```graphql
-mutation addTags {
- addTags(input: { tagUrns: ["urn:li:tag:NewTag"], resourceUrn: "urn:li:dataFlow:(airflow,dag_abc,PROD)" })
-}
-```
-
-*As CURL*
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer ' \
---header 'Content-Type: application/json' \
---data-raw '{ "query": "mutation addTags { addTags(input: { tagUrns: [\"urn:li:tag:NewTag\"], resourceUrn: \"urn:li:dataFlow:(airflow,dag_abc,PROD)\" }) }", "variables":{}}'
-```
-
-> **Pro-Tip**! You can also add or remove Tags from Dataset Schema Fields (or *Columns*) by
-> providing 2 additional fields in your Query input:
->
-> - subResourceType
-> - subResource
->
-> Where `subResourceType` is set to `DATASET_FIELD` and `subResource` is the field path of the column
-> to change.
-
-#### Relevant Mutations
-
-- [addTags](../../../graphql/mutations.md#addtags)
-- [batchAddTags](../../../graphql/mutations.md#batchaddtags)
-- [removeTag](../../../graphql/mutations.md#removetag)
-- [batchRemoveTags](../../../graphql/mutations.md#batchremovetags)
-
-
-### Adding & Removing Glossary Terms
-
-To attach Glossary Terms to a Metadata Entity, you can use the `addTerms` or `batchAddTerms` mutations.
-To remove them, you can use the `removeTerm` or `batchRemoveTerms` mutations.
-
-For example, to add a Glossary Term a Pipeline entity, you could issue the following GraphQL mutation:
-
-*As GraphQL*
-
-```graphql
-mutation addTerms {
- addTerms(input: { termUrns: ["urn:li:glossaryTerm:NewTerm"], resourceUrn: "urn:li:dataFlow:(airflow,dag_abc,PROD)" })
-}
-```
-
-*As CURL*
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer ' \
---header 'Content-Type: application/json' \
---data-raw '{ "query": "mutation addTerms { addTerms(input: { termUrns: [\"urn:li:glossaryTerm:NewTerm\"], resourceUrn: \"urn:li:dataFlow:(airflow,dag_abc,PROD)\" }) }", "variables":{}}'
-```
-
-> **Pro-Tip**! You can also add or remove Glossary Terms from Dataset Schema Fields (or *Columns*) by
-> providing 2 additional fields in your Query input:
->
-> - subResourceType
-> - subResource
->
-> Where `subResourceType` is set to `DATASET_FIELD` and `subResource` is the field path of the column
-> to change.
-
-#### Relevant Mutations
-
-- [addTerms](../../../graphql/mutations.md#addterms)
-- [batchAddTerms](../../../graphql/mutations.md#batchaddterms)
-- [removeTerm](../../../graphql/mutations.md#removeterm)
-- [batchRemoveTerms](../../../graphql/mutations.md#batchremoveterms)
-
-
-### Adding & Removing Domain
-
-To add an entity to a Domain, you can use the `setDomain` and `batchSetDomain` mutations.
-To remove entities from a Domain, you can use the `unsetDomain` mutation or the `batchSetDomain` mutation.
-
-For example, to add a Pipeline entity to the "Marketing" Domain, you can issue the following GraphQL mutation:
-
-*As GraphQL*
-
-```graphql
-mutation setDomain {
- setDomain(domainUrn: "urn:li:domain:Marketing", entityUrn: "urn:li:dataFlow:(airflow,dag_abc,PROD)")
-}
-```
-
-*As CURL*
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer ' \
---header 'Content-Type: application/json' \
---data-raw '{ "query": "mutation setDomain { setDomain(domainUrn: \"urn:li:domain:Marketing\", entityUrn: \"urn:li:dataFlow:(airflow,dag_abc,PROD)\") }", "variables":{}}'
-```
-
-#### Relevant Mutations
-
-- [setDomain](../../../graphql/mutations.md#setdomain)
-- [batchSetDomain](../../../graphql/mutations.md#batchsetdomain)
-- [unsetDomain](../../../graphql/mutations.md#unsetdomain)
-
-
-### Adding & Removing Owners
-
-To attach Owners to a Metadata Entity, you can use the `addOwners` or `batchAddOwners` mutations.
-To remove them, you can use the `removeOwner` or `batchRemoveOwners` mutations.
-
-For example, to add an Owner a Pipeline entity, you can issue the following GraphQL mutation:
-
-*As GraphQL*
-
-```graphql
-mutation addOwners {
- addOwners(input: { owners: [ { ownerUrn: "urn:li:corpuser:datahub", ownerEntityType: CORP_USER, type: TECHNICAL_OWNER } ], resourceUrn: "urn:li:dataFlow:(airflow,dag_abc,PROD)" })
-}
-```
-
-*As CURL*
-
-```curl
-curl --location --request POST 'http://localhost:8080/api/graphql' \
---header 'Authorization: Bearer 
-
-
-
- 
-
-
-
-
-
-
- 
-
-
-
- 
-
-
-
- 
-